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Lupine Publishers | Optimization of Chitosan+Activated Carbon Nanocomposite. DFT Study

Abstract

First, the minimum energy (geometry optimization DFT-DMol3) is obtained among C48 optimized ring carbon-system, and one non-optimized chitosan copolymer unit. Second, C24 and C9 optimized rings, each one interacting with an optimized chitosan copolymer unit (Ch). With the aim to investigate structural properties, the first case is optimized by applying smearing; and the second without smearing. Two parallel hypothetical carbon chains of 12 carbon atoms, symmetrically arranged are optimized in C24 carbyne ring; and one hypothetical 5 carbon-chain parallel to another 4 carbon-chain end optimized in a cumulene C9-ring. These carbon-ring structures here defined as activated carbons (AC), correspond to big pore size diameter obtained without chemical agent acting on them. Single point calculations are to build potential energy surfaces with GGA-PW91 functional to deal with exchange correlation energies for unrestricted spin, all-electron with dnd basis set. Only in the first case, orbital occupation is optimized with diverse smearing values. To determine structure stability, the minimum energy criterion is applied on AC+Ch nanocomposite. To generate fractional occupation, virtual orbitals are formed in this occupation space, whether homo-lumo gap is small and there is certain density near Fermi level. This fractional occupation pattern depends on the temperature. It must be noticed that when AC and Ch are solids, there is no adsorption; however, by applying smearing it was possible to find potential energy surfaces with a high equilibrium energy indicating glass phase transition in Chitosan due to the chemisorption given at the minimum of energy. AC+Ch molecular complex nanocomposite is expected to be applied not only in medicine but also in high technology.

Introduction

With the aim to figure out a molecular complex formed through the interaction between a system of 48 carbons arranged in planar way and a copolymer unit of chitosan, potential energy surfaces were built [1,2] using single point step by step calculations. The problem is studied considering that a molecular complex is obtained by changing smearing value according to the energy value convergence. Considering that electrons occupy orbitals with the lowest energies and with an integral occupation number in calculations of density functionals, a smearing change indicates a fractional occupation in virtual orbitals within this space of occupation. The smearing calculations correspond to the explicit inclusion of the fractional occupation numbers of the DFT calculations, requiring an additional term to achieve a functional energy from variation theory [35]. The contribution of this term to the density functional force exactly cancels the correction term as a function of the change in the occupation number. For occupation numbers satisfying a Fermi distribution, the variation total-energy functional is identical in form to the grand potential [3-6]. From the grand canonical distribution or Gibbs distribution, the normalized probability distribution of finding the system in a state with n particles and energy 𝐸𝑛𝑟 [7], the Z grand partition function of the system, and the number of particles remains according to the Fermi energy ℰf =μ(T,V,n). When T = 0 the fermion gas is in the state of minimum energy in which the particles occupy the n states of 𝜓𝑖 of lower energy, since the exclusion principle of Pauli does not allow more than one particle in each state. Therefore, the Fermi function 𝑓(ℰ) gives the probability that certain states of available electron energy are occupied at a given temperature.

Other options for the shape of the occupancy numbers result from the different associated functional with finite temperature to DFT but without physical meaning, such as the temperature or the entropy associated with this term [3]. These terms, although numerically small must be included in the practical calculations that allow numbers of fractional occupation [3,8]. To consider the scope of smearing, it is known that electrons occupy orbitals with the lowest energies, and occupancy numbers are integers; nonetheless, there is a need for a fractional occupation in virtual orbitals within this space of occupation. We apply this when the HOMO-LUMO gap is small and there is especially a significant density near of Fermi level [9], thus in order to obtain the fractional occupation a kT term is implemented. This fractional occupation pattern depends on the temperature. The systems C48 carbinoid, C24 carbyne-ring, and C9 cumulene-ring (almost-planar) are arrangements obtained through DFT geometry optimization of two hypothetical parallel zigzag linear carbon chains. We consider these systems as carbon physically activated, due to the pore size diameter, and since no activating chemical agent has been applied. Carbyne is known as linear carbons alternating single and triple bonds (-C≡C-) n or with double bonds (=C=C=)n (cumulene) [10]. Polyyne is known as a allotrope carbon having H(-C≡C-) nH chemical structure repeating chain, with alternating single and triple bonds [11] and hydrogen at every extremity, corresponding to hydrogenated linear carbon chain as any member of the polyyne family HC2nH [12] with sp hybridization atoms. It is known that polyyne, carbyne and carbinoid have been actually synthesized as documented by Cataldo [13]. Bond length alternation (BLA) of carbyne pattern is retained in the rings having an even number of atoms [10]. Additional care must be taken with carbyne rings since the Jahn-Teller distortion (the counterpart of Peierls instability in non-linear molecules) is different in the C4N and C4N+2 families of rings [14-16]. There is a great variety of applications of activated carbon as an adsorbent material, and it has been used in areas related to the energy, and the environment, generating materials with a high-energy storage capacity [17].

Chitin is, after cellulose, the most abundant biopolymer in nature. When the degree of deacetylation of chitin reaches about 50% (depending on the origin of the polymer), it becomes soluble in aqueous acidic media and is called chitosan [18]. Chitosan is applied to remediation of heavy metals in drinking water and other contaminants by adsorption. The affinity of chitosan with heavy metals makes the bisorption process stable and advantageous, being only by the alginates present in brown algae matched [19]. The glass transition temperature of chitosan is 203°C (476.15 K) according to Sakurai et al. [20], 225°C (498.15 K) according to Kadokawa [21], and 280°C (553.15 K) according to Cardona-Trujillo [22]. One can differentiate specific reactions involving the -NH2 group at nonspecific reactions of -OH groups. This is important to difference between chitosan and cellulose, where three -OH groups of nearly equal reactivity are available [23,24]. In industrial applications, several solids having pores close to molecular dimensions (micropores < 20 Å) are used as selective adsorbents because of the physicochemical specificity they display towards certain molecules in contrast to the mesoporous substrates (20-500 Å) and macropores (> 500 Å). Adsorbents with these selective properties include activated carbon among others [25]. Chitosan-based highly activated carbons have also application for hydrogen storage [26]. In principle, electronic structure of diatomic molecules has been built through the overlapping knowledge of the interacting atomic orbitals [27]. In this case, the orbitals correspond to bonding (σg, πg) and antibonding (σu, πu) orbitals of hydrogen, carbon, nitrogen and oxygen diatomic molecules, whose H2, C2, N2, and O2 groundstate electronic configurations are and with 2, 8, 10 and 12 valence electrons, respectively. Actually, the reactivity sites in a molecule correspond to the highest occupied molecular orbitals (HOMO) and lowest unoccupied molecular orbitals (LUMO). HOMO as base (donor), and LUMO as acid (acceptor) are particularly important MOs to predict reactivity in many types of reaction [28,29]. Activated carbon and chitosan have been independently applied as sorption materials to increase environmental quality standards. Then, we expect AC-Ch nanocomposite to have a powerful handleable adsorption property of pollutants that can be applied not only in wastewater treatment, but also in medicine against intoxication, in batteries to increase storage capacity, in electrodes of fuel cells, and in more possible applications, according to the pore size distribution to be generated on this new material.

Methodology

The interaction between an activated carbon molecule (AC) and a unit of the chitosan copolymer (Ch) is studied by means of DFTDMol3 [30-32]. The AC system is a hypothetical model of two parallel linear chains of 24 carbons each one geometrically optimized using DFT, converging into a plane molecular carbon system. In this system six nodes were formed allowing 7 interconnected rings of different bond lengths and sizes: 2 of 6 carbons, 4 of 8 carbons and one of 16 carbons. By summing these quantities gives 54 carbons since the carbons are in the nodes double counted. When subtracted they are the 48 carbons of the AC system. This system has a length of 28.4Å comparable to that of the chitosan copolymer unit (Ch). The reactants are AC + Ch corresponding to C48 + C14H24N2O9.

Single point potential energy curves were constructed [1,2] by using smearing. The following conditions to find AC+Ch (Activated Carbon+Chitosan) interaction energy are: functional GGA-PW91 [31,33-36], unrestricted spin, dnd bases, and orbital occupation with various smearing values. Considering that we obtained a solution for the energy value convergence, the interaction by changing the smearing value was studied. Since electrons occupy orbitals with lower energies and integral occupation numbers in calculations of density functional, a smearing change indicates fractional occupation and virtual orbital within this occupation space [19]. When generating a fractional occupation, virtual orbitals are in this occupation space generated, if the HOMO-LUMO gap is small, and there is certain density near the Fermi level [1], then it is implemented the fractional occupation term kT. This pattern of fractional occupation depends on temperature. Covalent connectivity calculations [37] according to DMol3 on no-bonding to s- and f-shell scheme, bond type, and converting representation to Kekulé, for bond length tolerances from 0.6 to 1.15 Ǻ were accomplished in this molecular complex mostly composed of carbon. Area calculations have been carried out by inserting triangles in each amorphous carbon ring and using the

Heron formula: where P=(a+b+c)/2 is the perimeter of a triangle of a, b, c sides; while the pore size diameter (PSD) is calculated as an approximation to the circle area. Periodic systems can be constructed using amorphous builder of BIOVIA Materials Studio, these are useful to calculate Radial Distribution Functions and the area under the curve on a significant interval.

Results

Chitosan Optimized by Applying Smearing

The default smearing value of 0.005Ha corresponds to T=1578.87 K and P=224.806 atm. We now exhibit electron smearing behavior using the known Fermi-Dirac statistic [38]. Facing two hydrogen atoms and using geometry optimization calculations, we built energy as a function of smearing value. Figure 1 shows the total energy variation when the system is optimized with respect to smearing value [39] (Figure 1). The fractional occupational pattern depends on the temperature, and this is derived from the energy change of Fermi distribution [6] as: 𝛿𝐸 = 𝑇𝑘; where k is Boltzmann constant. Considering a model in which the electrons are free and given that clouds of electrons are being a Fermi gas considered. The pressure is: 2/3 δE/δV [38]. From the latter two previous equations, temperature and pressure change is observed in Table 1 given the 𝛿𝐸 smearing energy. The planar molecular hypothetical system of 48 carbons is built by applying geometry optimization at two linear chains of 24 carbons as shown in Figure 2a, and the chitosan copolymer molecular system is built without applying geometry optimization, as observed in Figure 2b. Approaching enough these two molecular systems we studied a new molecular complex at different smearing values. The molecular model of carbon is symmetrically arranged in planar geometry, and it is physically activated through geometry optimization. We called activated carbon (AC) to the resulting planar carbon system. The length of this planar system is comparable to that one of chitosan (Ch). Each six-carbon ring has an area 4.34 Å2, each eight-carbon ring along with this has an area 8.74 Å2, each eight-carbon ring along with the sixteen-carbon ring has an area 8.55 Å2, and the sixteencarbon ring has an area 27.32 Å2. Considering each one of this area as circle areas the pore size diameter distribution is from 2.35 Å to 5.9 Å, which correspond to micropore size distribution of this carbon system. When considering the whole area of this system for calculating the pore size diameter 9.48 Å [40,41]. Chitosan is very well known to be macropore size [42] (Figure 2

Searching for a new molecular complex, Figure 3 exhibits the potential energy curve of the interaction between AC and Ch having equilibrium at (1.6Å, -1089Kcal/mol). In this case chitosan was not geometrically optimized in order to build the potential energy curve observed in Figures 3b & 3c. It was really easy to build this curve using smearing energy 0.05 Ha for every single point calculated, and hard to build it at 0.03 Ha. We also tried lower values than this, and we obtained poor or none results (Figure 3). After applying geometry optimization at smearing 0.05 Ha, and subsequently at 0.03 Ha. The smearing at 0.02 Ha is shown in Figure 4a. Then, we built the potential energy curve as shown in Figure 4b in step by step single point calculations for AC + Ch face to face interaction, when 2.264 Å is the separation between their corresponding centers of mass. The latter has a potential well depth of 165 Kcal/ mol at a distance of 2.2 Å, meaning formation of a new molecular complex at an adsorption energy greater than 20 kcal/mol in the chemisorption range [43] (Figure 4). Covalent connectivity [37] to the resulting system in Figure 4a was applied under the conditions previously mentioned in methodology, and the molecular complex observed in Figure 5 is obtained. In this complex the reactants and products are C48 + C14H24N2O9 and C49H3O3 + CH2 + C4H6O2 + CH3NO + C2H2O + CH2O + C2H2 + CHNO + CH3, respectively. Carbon bonds are single, double, and triple, as an example the C12 ring has eight double bonds, one triple bond, and three single bonds, where all the carbon valence electrons are shared. Furthermore, C8 and C16 rings have double bonds in one side of the ring, and single and triple bonds in the other side; and C6 ring has four double bonds and two single bonds. This whole carbon system has been activated by chitosan, and double bonds, and single and triple bonds are the representative characteristics of carbine-type molecules (Figure 5).

It must be noticed that geometry optimization of this whole system provides a lowest unoccupied molecular orbital (LUMO - electron acceptor) receiving an electron pair from the highest occupied molecular orbital (HOMO - electron donor). The donor HOMO from the base and the acceptor LUMO from the acid, combine with a molecular orbital bonding, which in our case corresponds to the orbitals 242-HOMO for E=-0.18317 Ha and 243-LUMO for E=- 0.17786, for a Fermi energy of -3136.28 Ha with A as irreducible representation of symmetry C1. The total orbitals number is 274. The orbital occupation is 202 A (2) plus 78 electrons in 65 orbitals, for a total number of 482 active electrons and binding energy of -22.997 Ha, at 2 steps. However, in order to get HOMO and LUMO drawn in this model, we run an energy calculation. Then, this molecular complex as seen in Figures 6a & 6b has HOMO-484 with E=-0.16398 Ha, LUMO-485 E=-0.16196 Ha, and Fermi energy Ef = -3161.44 Ha, for the reactivity sites with 482 active electrons. The total number of valence orbitals is 1070. The orbital occupation is 206 A (1) alpha and 206 A (1) beta, and 35.00 alpha electrons in 62 orbitals plus 35 beta electrons in 62 orbitals. HOMO as base-donor, and LUMO as acid-acceptor are the MOs locating possible reactivity in this reaction. An acid-acceptor can receive an electron pair in its lowest unoccupied molecular orbital from the base-donor highest occupied molecular orbital. That is to say, the HOMO from the base and the LUMO from the acid combine with a bonding molecular orbital in the ground state see Figure 6c.

After applying covalent connectivity [37] to the resulting system in Figure 6, we again applied geometry optimization for smearing 0.02Ha, and we obtain different molecular orbitals in the results, as shown in Figure 7. This molecular complex as seen in Figure 7 has HOMO-482 with E=-0.17650 Ha, LUMO-483 E=0.16060 Ha, and Fermi energy Ef = -3162.004 Ha, for the reactivity sites with 482 active electrons. The orbital occupation is 204 A (1) alpha and 204 A (1) beta, and 37.00 alpha electrons in 62 orbitals plus 37 beta electrons in 62 orbitals. The molecular complex observed in Figure 7 has the same products previously mentioned. It must be noticed that the lowest unoccupied molecular orbitals (LUMO-acceptor) only draw orbitals in the CH3 product, the rest of the molecular orbitals correspond to the highest occupied molecular orbitals (HOMOdonor) complex. Then, this is a very stable molecular system only allowing reactivity through the methyl radical CH3 (Figure 7) The potential energy curve in Figure 3b is very near to physisorption; however, smearing energy in this case corresponds with a very high temperature, which actually occurrs little inside sun surface. In this work, we gradually get down smearing energy searching until reaching the glass transition temperature of chitosan. The smearing energy value 0.02 Ha corresponds with temperature 6315.49 K according to Table 1, and it is still too high; however, is this way we have been achieving geometry optimization to reach right smearing values according to experimental measurements. After successful convergence in geometry optimizations at 0.01, 0.007, 0.005, 0.003, and 0.002 smearing energies, the convergence at smearing energy 0.0017 Ha has been unsuccessful after more than 10000 SCF iterations for an oscillating energy with an energy tolerance of 0.00002 Ha. After these calculations, we continued rising the smearing energy until 0.00175, and after more than 5000 SCF, convergence is successfully accomplished. The temperature 552.6 K reached for smearing at 0.00175 agrees with glass transition temperature range [498.15K, 553.15K] of chitosan, according to experimental measurements [20-22].

Figure 8 illustrates the final stage of the molecular complex formed. We can observe that while C48 has been deformed mainly in its planarity, the chitosan ended broken in the two initial groups of each polymer, also apparently divided in several smaller molecules. This fact is very well known experimentally, because one bonding solution (epichlorhydrine, glutaraldehyde, or EGDE -ethylene glycol glycidyl ether-) is commonly used to keep chitosan copolymer cross-linked for enhancing the resistance of sorbent beads against acid, alkali, or chemicals [19]. The products observed by applying covalent connectivity (under the bonding scheme for no bonding to s- and f- shell, covalent connectivity and bond type, and converting representation to Kekulé) are the following: C51H7NO4 + C4H6O2 + C2H2O + C2H2 + CH3O + CHNO + CH3. As it can be seen part of each polymer remain bonded to the AC system (Figure 8). Then, at smearing 0.00175 Ha we mostly obtain highest occupied molecular orbitals for the molecular complex observed in Figure 9. This output exhibits the orbitals a) HOMO-482 with an eigenvalue of -0.17013 Ha, b) LUMO-483 with an eigenvalue of -0.16923 Ha, and c) HOMOLUMO. The Fermi energy is Ef = 3162.0047053 Ha, for the reactivity sites with 482 active electrons. The orbital occupation is 238 A (1) alpha and 239 A (1) beta, and 2.96 alpha electrons in 5 orbitals plus 2.04 beta electrons in 4 orbitals (Figure 9)

Chitosan Optimized Without Smearing

First of all, the C24 carbyne-type ring alternating single and triple bonds is obtained by applying connectivity [37] and bond type to a C24 carbon ring which is the output of the input shown in Figure 10a corresponding to the geometry optimization of two hypothetical C12-carbon chains (Figure 10b). Then, Figure 10c exhibits an alternating single and triple bonds C24-ring. Second, applying clean of BIOVIA Materials Studio on chitosan copolymer molecule designed in Figure 2b, we obtain the input of a chitosan copolymer molecule as in Figure10d, and the Output exhibiting geometry optimization of the previous molecule is shown in Figure 10e. As we can observe, in this case chitosan remained complete. We made this, after suspecting that the initial bonds lengths and angles were not right in our design of chitosan, because broken chitosan is not a satisfactory result. Then, mixing the optimized C24 and Ch systems as shown in Figure 10f in the Input of a C24-ring surrounding a chitosan copolymer molecule, and after applying geometry optimization we obtain the Output of the previous CA-Ch nanocomposite see Figure 10g. Finally, we applied bonding scheme criteria as in Figure 10h.The nanocomposite in Figure 10h is a good example of the possibility of modifying the pore size distribution of chitosan when it is embedded into activated carbon. Here we consider INPUT and OUTPUT for applying geometry optimization on activated carbon and chitosan C14H24N2O9 system after each part has been previously optimized, and we also applied bond criteria for connectivity, bond type and kekulé representation. The C24-ring is carbyne type, and the chitosan copolymer molecule has been optimized in three dimensions in this case. The position of C24- ring surrounding a chitosan copolymer molecule has been only proposed.

From the interaction through geometry optimization of two linear carbon chains of four and five carbon atoms as in Figure 11a, cumulene C9-ring shown in Figure 11b is obtained. This is a clear evidence of Jahn-Teller effect, because we observe double bond lengths alternating long/short with a difference among .02 and .03 Å, and the angles in this non-planar (Figure 11b) cumulene molecule are also different. The expected angles in a planar symmetrical molecule should be the same according to a well-defined symmetry. We considered the interaction of chitosan with another almost planar carbon ring of nine carbon atoms, now one in front to the other as in Figure 11c. Then, in Figure 11d there is another example about building pore size distribution among chitosan and activated carbon. In this case, we consider INPUT and OUTPUT for geometry optimization of cumulene C9-ring and chitosan C14H24N2O9, each one previously optimized by applying geometry optimization to the whole system, and also considering the bond criteria for connectivity, bond type and Kekulé representation as shown in Figure 11e. The cumulene C9-ring and chitosan copolymer molecule have been optimized in three dimensions, and we clearly observe the cumulene passing from face to face to almost T-shape orientation taking three hydrogen atoms from chitosan. The input position of cumulene C9 ring face to face with chitosan in that precise location has been proposed, and the result has been excellent.

Discussion

We consider each carbon ring as physically activated through geometry optimization, due to pore size diameter remains in the average size compared against experimental measurements [41]. The C48 optimized ring carbon-system and one non-optimized chitosan copolymer unit has been studied considering the result after geometry optimization, as a molecular complex obtained when smearing value changes for converging energy values. Different elongation among single and triple carbon bonds in the carbyne-type are due to Jahn-Teller effect [14]. Then, C24 carbynering when we optimize two carbon chains at 3.074 Å of separation distance, is due to the Jahn-Teller effect. The Jahn-Teller effect is also present in C48 carbinoid -ring for their C8- and C4- carbinoid -rings. Carbon rings C4N (N<~8) exhibit a substantial first-order Jahn-Teller distortion that leads to long/short (single/triple) bond alternation decreasing with increasing N [14]. Whether we want to draw HOMO-LUMO orbitals, it is necessary to ask for orbitals in the geometry optimization as input data. At this work, for smearing energy 0.02 Ha we found different HOMO LUMO orbital numbers among the initial system in Figure 5 without asking for orbitals in the geometry optimization calculation, and its output asking for orbitals in a new energy calculation shown in Figure 6. Again after practicing connectivity, bond type, and Kekulé representation at smearing energy 0.02 Ha, we asked for orbitals, and we found in Figure 7 a small change at the orbital numbers previously obtained, and the corresponding energies were little different to the previous ones. We infer that bonding type change produced the differences, and the correct values correspond to the correct bonding type in the new molecular complex system formed.

The strongly dependence on smearing means very closely spaced energy levels (high degeneracy) near Fermi level. When there is a degenerate electron state, any symmetrical position of the nuclei (except when they are collinear) is unstable. As a result of this instability, the nuclei move in such a way that the symmetry of their configuration is destroyed, the degeneracy of the term is being completely removed [44,45]. High degeneracy indicates a high symmetry of the molecule, then the system tends to be distorted, in such way that when moving, the occupied levels are down and the unoccupied ones are up [46]. When levels are very densely spaced, convergence is hard to reach, since very small changes will occupy completely different states, and we get oscillations. These can be damped by smearing out the occupancy over more states, so that we turn off the binary occupancy of the states. We get down smearing width to glass transition temperature by decreasing the smearing parameter in steps to gradually stabilize our molecular complex system at the right temperature.

We initially observe distortion of chitosan system, and then its possible breaking in some products. This is partially in agreement with the results presented by Chigo et al. [46] in a study of the interaction among graphene-chitosan for a relaxed system doped with boron, in which they consider the interaction of pristine graphene with the monomer of chitosan (G + MCh:C6H13O5N) in different configurations, whereas we consider a chitosan copolymer molecule: C14H24N2O9 in only one orientation. While Chigo et al. [46] found a perpendicular chitosan, molecule linked to a carbon nanotube system, we obtained a cumulene carbon ring almost perpendicularly linked to a chitosan copolymer molecule.

Conclusion

We found one mechanism to figure out an optimized big molecular complex system by using DFT geometry optimization. This mechanism is based on smearing calculations, and on decrements of smearing energy in the molecular complex system until reaching the glass transition temperature of one of the components, which in this case correspond to the chitosan copolymer molecule. In order to get a molecular complex system AC + Ch, it is needed a high temperature among them at least to the phase transition temperature of either AC or Ch, because when they are solids there is only a heterogeneous mixture at room temperature. The use of smearing allows to reach high temperatures because according to Table 1 temperature increases as the smearing energy increases. We observed that the use of smearing to optimize a molecule as complex as the chitosan causes this to be fractionated, nevertheless when putting it in a plate of coal we obtained the glass transition temperature of the chitosan reported experimentally. The potential well depth providing chemisorption indicates existence of phase transition in one of our two molecular systems. This phase change is attributed to chitosan, due to carbon is more stable, and because we reach glass transition temperature of chitosan when dealing with the whole molecular complex system. In addition, when applying covalent connectivity, the activated carbon is the most stable molecular system keeping its molecular structure. According to HOMO and LUMO in Figures 6 -9, the sites with the greatest reactivity correspond to double and triple bonds. Besides, Figure 9 exhibits one amine functional group linked to the carbon system now C51 carbon molecular complex formed with a particular pore size distribution. Considering that after geometry optimization physisorption provides bonding in two parts of the chitosan molecule, this is an indication of a more environmental linking than that caused by cross-linking solutions, because cross-linking solutions might be toxic in medicine applications. The first chitosan molecule used, and optimized using smearing resulted to be unstable, because finished broken in several products. The second chitosan molecule used, and optimized without smearing, or with a very small smearing value resulted to be very stable, on which we were able to add activated carbon and to obtain good results. We have been able to optimize chitosan and add activated carbon, and we have observed the change in pore size distribution, even though we are missing its calculation, to assign the type of material obtained (micropore, mesopore, or macropore). We are working on it.

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mastersofdisassters · 4 years ago

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1. e4 { [%eval 0.25] [%clk 0:10:00] } 1... e5 { [%eval 0.21] [%clk 0:10:00] } 2. Bc4 { [%eval 0.0] [%clk 0:10:03] } 2... Nf6 { [%eval 0.0] [%clk 0:10:02] } { C24 Bishop's Opening: Berlin Defense } 3. d3 { [%eval 0.0] [%clk 0:10:06] } 3... h6 { [%eval 0.4] [%clk 0:10:04] } 4. Nf3 { [%eval 0.4] [%clk 0:10:09] } 4... d6 { [%eval 0.48] [%clk 0:10:04] } 5. O-O { [%eval 0.65] [%clk 0:10:12] } 5... Bg4 { [%eval 0.58] [%clk 0:10:06] } 6. h3 { [%eval 0.63] [%clk 0:10:13] } 6... Bd7 { [%eval 0.71] [%clk 0:10:05] } 7. a3 { [%eval 0.59] [%clk 0:10:12] } 7... Qc8?! { (0.59 → 1.12) Inaccuracy. Nc6 was best. } { [%eval 1.12] [%clk 0:10:08] } (7... Nc6 8. Nc3) 8. Nh4? { (1.12 → -0.10) Mistake. Re1 was best. } { [%eval -0.1] [%clk 0:10:00] } (8. Re1 Nc6) 8... Bxh3?! { (-0.10 → 0.86) Inaccuracy. g5 was best. } { [%eval 0.86] [%clk 0:10:07] } (8... g5 9. Qf3) 9. gxh3 { [%eval 0.79] [%clk 0:10:02] } 9... Qxh3 { [%eval 0.66] [%clk 0:10:10] } 10. Ng2?? { (0.66 → -3.39) Blunder. Nf3 was best. } { [%eval -3.39] [%clk 0:10:04] } (10. Nf3 Ng4) 10... Ng4 { [%eval -3.47] [%clk 0:10:11] } 11. f3?? { (-3.47 → Mate in 1) Checkmate is now unavoidable. Re1 was best. } { [%eval #-1] [%clk 0:09:55] } (11. Re1) 11... Qh2# { [%clk 0:10:06] } { Black wins by checkmate. } 0-1

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lupine-publishers-aoics · 4 years ago

Text

Lupine Publishers| Optimization of Chitosan+Activated Carbon Nanocomposite. DFT Study

Lupine Publishers | An archive of organic and inorganic chemical sciences

Abstract

Introduction

Methodology

Results

Chitosan Optimized by Applying Smearing

Chitosan Optimized Without Smearing

Discussion

Conclusion

https://lupinepublishers.com/chemistry-journal/pdf/AOICS.MS.ID.000175.pdf

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jambear12 · 7 years ago

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[Event "Rated Classical game"] [Site " "2018.03.08"] [Round "-"] [White "shaltill40"] [Black "Poseidon12"] [Result "0-1"] [UTCDate "2018.03.08"] [UTCTime "18:47:34"] [WhiteElo "1343"] [BlackElo "1364"] [WhiteRatingDiff "-19"] [BlackRatingDiff "+10"] [Variant "Standard"] [TimeControl "900+15"] [ECO "C24"] [Opening "Bishop's Opening: Berlin Defense"] [Termination "Time forfeit"] [Annotator "lichess.org"] 1. e4 { [%clk 0:15:00] } e5 { [%clk 0:15:00] } 2. Bc4 { [%clk 0:15:09] } Nf6 { [%clk 0:15:12] } { C24 Bishop's Opening: Berlin Defense } 3. d3 { [%clk 0:15:22] } Bc5 { [%clk 0:15:24] } 4. h3 { [%clk 0:15:34] } O-O { [%clk 0:15:37] } 5. Nf3 { [%clk 0:15:46] } d6 { [%clk 0:15:49] } 6. Nh4 { [%clk 0:15:39] } g6 { [%clk 0:15:56] } 7. Qf3 { [%clk 0:15:49] } Be6 { [%clk 0:15:52] } 8. Bxe6 { [%clk 0:15:55] } fxe6 { [%clk 0:16:05] } 9. Bh6 { [%clk 0:15:58] } Rf7 { [%clk 0:16:12] } 10. Nc3 { [%clk 0:16:01] } Nc6 { [%clk 0:16:11] } 11. Bg5 { [%clk 0:15:55] } Nd4 { [%clk 0:16:19] } 12. Qd1 { [%clk 0:16:06] } Qf8 { [%clk 0:16:16] } 13. a3 { [%clk 0:15:54] } Qg7 { [%clk 0:15:52] } 14. Qd2 { [%clk 0:15:55] } h6 { [%clk 0:15:59] } 15. Bxh6 { [%clk 0:16:06] } Qh7 { [%clk 0:16:02] } 16. b4 { [%clk 0:16:01] } Bb6 { [%clk 0:16:10] } 17. Na4 { [%clk 0:16:07] } c5 { [%clk 0:16:18] } 18. Nxb6 { [%clk 0:15:41] } axb6 { [%clk 0:16:32] } 19. g4 { [%clk 0:15:36] } Rd8 { [%clk 0:16:01] } 20. O-O-O { [%clk 0:14:18] } d5 { [%clk 0:16:11] } 21. bxc5 { [%clk 0:14:20] } dxe4 { [%clk 0:15:54] } 22. dxe4 { [%clk 0:14:16] } bxc5 { [%clk 0:16:05] } 23. Qa5 { [%clk 0:14:26] } Qxh6+ { [%clk 0:15:33] } 24. Kb1 { [%clk 0:14:13] } Rfd7 { [%clk 0:15:03] } 25. Ng2 { [%clk 0:13:42] } Nb3 { [%clk 0:14:57] } 26. Qc3 { [%clk 0:13:29] } Rxd1+ { [%clk 0:14:40] } 27. Kb2 { [%clk 0:12:25] } Nxe4 { [%clk 0:14:18] } { White left the game. } 0-1

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nerdinmadrid · 4 years ago

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Armengot vs Schwammkopf

[Event "Rated Rapid game"] [Site "https://lichess.org/G5FV8g5n"] [Date "2020.12.06"] [White "armengot"] [Black "Schwammkopf"] [Result "1-0"] [UTCDate "2020.12.06"] [UTCTime "18:35:40"] [WhiteElo "1360"] [BlackElo "1362"] [WhiteRatingDiff "+6"] [BlackRatingDiff "-6"] [Variant "Standard"] [TimeControl "900+10"] [ECO "C24"] [Opening "Bishop's Opening: Berlin Defense"] [Termination "Normal"] [Annotator "lichess.org"] 1. e4 e5 2. Bc4 Nf6 { C24 Bishop's Opening: Berlin Defense } 3. d3 Bc5 4. Nf3 Ng4 5. Bxf7+ Kxf7 6. Ng5+ Kg8 7. Qxg4 d6 8. Qg3 h6 9. Nf3 Nd7 10. Nh4 Nf8 11. Nf5 Bxf5 12. exf5 c6 13. Qf3 Nd7 14. g4 Qe7 15. Nc3 d5 16. Na4 Bb6 17. Nxb6 axb6 18. Rg1 e4 19. dxe4 dxe4 20. Qb3+ Qf7 21. Be3 Qxb3 22. cxb3 Ne5 23. g5 hxg5 24. Rxg5 Nd3+ 25. Kd2 Nxb2 26. Bxb6 Rxh2 27. Ke3 Rh4 28. f6 Rh7 29. Bd4 Rd8 30. Bxb2 { Black resigns. } 1-0

#chess #lichess

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blogrevolve2020 · 4 years ago

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The Winners of the 2009-20 SBB Grant Program

Chicago-based investment firm SBB Research Group applauds the August 5th finalists of its own month-long grant program aiding impactful non-profit organizations during the 2009-20 pandemic season. This year's awardees (listed below in alphabetical order) offer a variety of solutions to the greater Chicago area: C24/7 (Chicago), the Center for Social Inclusion (wrigleyville, IL), Illinois AIDS Society (Champaign), and National AIDS Memorial Foundation (Chicago). Other members of this SBB Research team are also responsible for providing services for other members of the Chicagoland area.

The middle for Social Inclusion is known as a community source for various social service providers, including social workers, counselors, lawyers, etc.. Its program provides free assistance to people with HIV and AIDS and their families that are at risk of losing job or facing discrimination from businesses SBB Research Group. The Center also works to reduce poverty through access to financial aid.

The HIV/AIDS App of SBB Research provides financial aid to people with HIV that are employed in Champaign and different regions of the state. Funding is available for somebody to help cover his or her HIV medical bills, as well as help pay for housing, transportation, and food. An individual might also be eligible to receive other benefits such as free clothes, or food vouchers for food pantries.

The middle for Social Inclusion's HIV/AIDS Outreach Team provides outreach programs to your local healthcare provider that offers care for people with HIV. The outreach team, called the"Social Impact Team," has worked with this health care provider to execute a multifaceted, strategic approach to AIDS education and outreach. Through such efforts, the medical care provider provides education to its customers about the disease, its prevention, its symptoms, and HIV therapy. The program also offers free screenings, free counselling and referral services, and free food vouchers to the food pantry of the neighborhood gym. As a result of this work, the social support supplier is able to serve an increased variety of people, making it simpler for them to provide improved service to their clientele.

The Illinois AIDS Society is a nonprofit company whose main mission is to enhance the quality of life of individuals living with HIV and AIDS. It works to increase awareness, encourage, and tools for people living with the illness. The AIDS Society also works to enhance the standard of life by helping to avoid illness and increase awareness about the disease among vulnerable communities.

The AIDS Society is located in Champaign, IL and serves families and individuals living with HIV or AIDS, the elderly, children, and people with disabilities. The organization is committed to improving the lives of those it serves through educational programs, HIV/AIDS prevention, public awareness, fundraising, and advocacy, and support solutions.

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shereen-hasan · 4 years ago

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The Relationship Between Feeling Scale And Relations With Parents

Below is my python code showing the frequency distribution concerning single young adults (age 16-21). Variables where chosen depending on questions that could(not) indicate depression in addition to questions indicating relations with parents.

The results shows that there is roughly no relationship between depression and relations with parents.

Note:

1. There are two files imported: the first is imported for sub1; the second is imported for sub3.

2. Values the variables can take and information about missing data are showed in the comments before each related section among the code.

Code:

import pandas as pd import numpy as np # any additional libraries would be imported here #Call in data csv file data = pd.read_csv('SUB2.csv', low_memory=False) print (len(data)) #number of observations (rows) print (len(data.columns)) # number of variables (columns) #setting variables you will be working with to numeric data['H1GI1Y'] = pd.to_numeric(data['H1GI1Y']) data['H1GI15'] = pd.to_numeric(data['H1GI15']) #subset data to young single adults age 16 to 21 sub1 = data[(data['H1GI1Y']>=74) & (data['H1GI1Y']<=79)& (data['H1GI15']==0)] #make a copy of my new subsetted data sub2 = sub1.copy() # frequency distritions on new sub2 data frame print ('counts for H1GI1Y') c1 = sub2['H1GI1Y'].value_counts(sort=False, dropna=True) print(c1) print ('percentages for H1GI1Y') p1 = sub2['H1GI1Y'].value_counts(sort=False, normalize=True, dropna=True) print (p1*100) print ('counts for H1GI15') c2 = sub2['H1GI15'].value_counts(sort=False, dropna=True) print(c2) print ('percentages for H1GI15') p2 = sub2['H1GI15'].value_counts(sort=False, normalize=True, dropna=True) print (p2*100) #Frequency distribution of feeling scale section questions #0 never or rarely #1 sometimes #2 a lot of the time #3 most of the time or all of the time #6 refused #8 don’t know #Call in data csv file sub3 = pd.read_csv('SUB4.csv', low_memory=False) #Replacing 6(refused) & 8(don't know) with NaN sub3= sub3.replace([6,8], np.NaN) #Q1 You were bothered by things that usually don’t bother you (indicates depression) print ('counts for H1FS1') c3 = sub3['H1FS1'].value_counts(sort=False, dropna=True) print(c3) print ('percentages H1FS1') p3 = sub3['H1FS1'].value_counts(sort=False, normalize=True, dropna=True) print (p3*100) #Q2 You didn’t feel like eating, your appetite was poor (indicates depression) print ('counts for H1FS2') c4 = sub3['H1FS2'].value_counts(sort=False, dropna=True) print(c4) print ('percentages H1FS2') p4 = sub3['H1FS2'].value_counts(sort=False, normalize=True, dropna=True) print (p4*100) #Q3 You felt that you could not shake off the blues, even with help from your family and your friends (indicates depression) print ('counts for H1FS3') c5 = sub3['H1FS3'].value_counts(sort=False, dropna=True) print(c5) print ('percentages H1FS3') p5 = sub3['H1FS3'].value_counts(sort=False, normalize=True, dropna=True) print (p5*100) #Q4 You felt that you were just as good as other people.(indicates depression) print ('counts for H1FS4') c6 = sub3['H1FS4'].value_counts(sort=False, dropna=True) print(c6) print ('percentages H1FS4') p6 = sub3['H1FS4'].value_counts(sort=False, normalize=True, dropna=True) print (p6*100) #Q5 You had trouble keeping your mind on what you were doing(indicates depression) print ('counts for H1FS5') c7 = sub3['H1FS5'].value_counts(sort=False, dropna=True) print(c7) print ('percentages H1FS5') p7 = sub3['H1FS5'].value_counts(sort=False, normalize=True, dropna=True) print (p7*100) #Q6 You felt depressed( indicates depression) print ('counts for H1FS6') c8 = sub3['H1FS6'].value_counts(sort=False, dropna=True) print(c8) print ('percentages H1FS6') p8 = sub3['H1FS6'].value_counts(sort=False, normalize=True, dropna=True) print (p8*100) #Q7 You felt that you were too tired to do things (indicates depression) print ('counts for H1FS7') c9 = sub3['H1FS7'].value_counts(sort=False, dropna=True) print(c9) print ('percentages H1FS7') p9 = sub3['H1FS7'].value_counts(sort=False, normalize=True, dropna=True) print (p9*100) #Q8 You felt hopeful about the future (negation indicates a depression symptom) print ('counts for H1FS8') c10 = sub3['H1FS8'].value_counts(sort=False, dropna=True) print(c10) print ('percentages H1FS8') p10 = sub3['H1FS8'].value_counts(sort=False, normalize=True, dropna=True) print (p10*100) #Q9 You thought your life had been a failure (indicates depression) print ('counts for H1FS9') c11 = sub3['H1FS9'].value_counts(sort=False, dropna=True) print(c11) print ('percentages H1FS9') p11 = sub3['H1FS9'].value_counts(sort=False, normalize=True, dropna=True) print (p11*100) #Q10 You felt fearful (may be an anxiety symptom) print ('counts for H1FS10') c12 = sub3['H1FS10'].value_counts(sort=False, dropna=True) print(c12) print ('percentages H1FS10') p12 = sub3['H1FS10'].value_counts(sort=False, normalize=True, dropna=True) print (p12*100) #Q11 You were happy (negation indicates a depression symptom) print ('counts for H1FS11') c13 = sub3['H1FS11'].value_counts(sort=False, dropna=True) print(c13) print ('percentages H1FS11') p13 = sub3['H1FS11'].value_counts(sort=False, normalize=True, dropna=True) print (p13*100) #Q12 You talked less than usual. print ('counts for H1FS12') c14 = sub3['H1FS12'].value_counts(sort=False, dropna=True) print(c14) print ('percentages H1FS12') p14 = sub3['H1FS12'].value_counts(sort=False, normalize=True, dropna=True) print (p14*100) #Q13 You felt lonely ( indicates depression) print ('counts for H1FS13') c15 = sub3['H1FS13'].value_counts(sort=False, dropna=True) print(c15) print ('percentages H1FS13') p15 = sub3['H1FS13'].value_counts(sort=False, normalize=True, dropna=True) print (p15*100) #Q14 People were unfriendly to you print ('counts for H1FS14') c16 = sub3['H1FS14'].value_counts(sort=False, dropna=True) print(c16) print ('percentages H1FS14') p16 = sub3['H1FS14'].value_counts(sort=False, normalize=True, dropna=True) print (p16*100) #Q15 You enjoyed life (negation indicates a depression symptom) print ('counts for H1FS15') c17 = sub3['H1FS15'].value_counts(sort=False, dropna=True) print(c17) print ('percentages H1FS15') p17 = sub3['H1FS15'].value_counts(sort=False, normalize=True, dropna=True) print (p17*100) #Q16 You felt sad ( indicates depression) print ('counts for H1FS16') c18 = sub3['H1FS16'].value_counts(sort=False, dropna=True) print(c18) print ('percentages H1FS16') p18 = sub3['H1FS16'].value_counts(sort=False, normalize=True, dropna=True) print (p18*100) #Q17 You felt that people disliked you (depression symptom--Lack of self confidence) print ('counts for H1FS17') c19 = sub3['H1FS17'].value_counts(sort=False, dropna=True) print(c19) print ('percentages H1FS17') p19 = sub3['H1FS17'].value_counts(sort=False, normalize=True, dropna=True) print (p19*100) #Q18 It was hard to get started doing things (depression symptom) print ('counts for H1FS18') c20 = sub3['H1FS18'].value_counts(sort=False, dropna=True) print(c20) print ('percentages H1FS18') p20 = sub3['H1FS18'].value_counts(sort=False, normalize=True, dropna=True) print (p20*100) #Q19 You felt life was not worth living (depression symptom) print ('counts for H1FS19') c21 = sub3['H1FS19'].value_counts(sort=False, dropna=True) print(c21) print ('percentages H1FS19') p21 = sub3['H1FS19'].value_counts(sort=False, normalize=True, dropna=True) print (p21*100) #Frequency distribution for those with one or more depression symptom sub4=sub3[(sub3['H1FS1'] ==3) | (sub3['H1FS2'] ==3) | (sub3['H1FS3'] ==3) | (sub3['H1FS4'] ==3) | (sub3['H1FS5'] ==3) | (sub3['H1FS6'] ==3) | (sub3['H1FS7'] ==3) | (sub3['H1FS8'] == 0) | (sub3['H1FS9'] ==3) | (sub3['H1FS10'] ==3) | (sub3['H1FS11'] == 0) | (sub3['H1FS12'] ==3) | (sub3['H1FS13'] ==3) | (sub3['H1FS14'] ==3) | (sub3['H1FS15'] == 0) | (sub3['H1FS16'] ==3) | (sub3['H1FS17'] ==3) | (sub3['H1FS18'] ==3) | (sub3['H1FS19'] ==3) ] #Frequency distribution showing the effect of Family relationship on feeling scale print('Frequency distribution showing the effect of Family relationship on feeling scale') # 1 may indicate good relationship while 0 indicates the opposite #Replacing 6 (refused), 7 (legitimate skip), 8 (don’t know) and 9 (not applicable) with NaN sub4= sub4.replace([6,7,8,9], np.nan) sub5=sub4.copy() #On how many of the past 7 days was at least one of your parents in the room with you while you ate your evening meal? #Assume from 4 to 7 means yes (=1) #Assume from 0 to 3 means no (=0) sub5['H1WP8']= sub5['H1WP8'].replace([4,5,6,7], 1) sub5['H1WP8']= sub5['H1WP8'].replace([0,1,2,3], 0) #Replacing 96 (refused), 97 (legitimate skip) and 98 (don’t know) with NaN sub5['H1WP8']= sub5['H1WP8'].replace([96,97,98], np.nan) print ('counts for H1WP8') c22 = sub5['H1WP8'].value_counts(sort=False, dropna=True) print(c22) print ('percentages H1WP8') p22 = sub5['H1WP8'].value_counts(sort=False, normalize=True, dropna=True) print (p22*100) # Did you have a talk with your mother about a personal problem you were having print ('counts for H1WP17F') c23 = sub5['H1WP17F'].value_counts(sort=False, dropna=True) print(c23) print ('percentages H1WP17F') p23 = sub5['H1WP17F'].value_counts(sort=False, normalize=True, dropna=True) print (p23*100) # Did you have a talk with your father about a personal problem you were having print ('counts for H1WP18F') c24 = sub5['H1WP18F'].value_counts(sort=False, dropna=True) print(c24) print ('percentages H1WP18F') p24 = sub5['H1WP18F'].value_counts(sort=False, normalize=True, dropna=True) print (p24*100) #Did you talk with your mother about other things you’re doing in school print ('counts for H1WP17J') c25 = sub5['H1WP17J'].value_counts(sort=False, dropna=True) print(c25) print ('percentages H1WP17J') p25 = sub5['H1WP17J'].value_counts(sort=False, normalize=True, dropna=True) print (p25*100) #Did you talk with your father about other things you’re doing in school print ('counts for H1WP18J') c26 = sub5['H1WP18J'].value_counts(sort=False, dropna=True) print(c26) print ('percentages H1WP18J') p26 = sub5['H1WP18J'].value_counts(sort=False, normalize=True, dropna=True) print (p26*100) #gone shopping with mother? print ('counts for H1WP17A') c27 = sub5['H1WP17A'].value_counts(sort=False, dropna=True) print(c27) print ('percentages H1WP17A') p27 = sub5['H1WP17A'].value_counts(sort=False, normalize=True, dropna=True) print (p27*100) #played a sport with mother? print ('counts for H1WP17B') c28 = sub5['H1WP17B'].value_counts(sort=False, dropna=True) print(c28) print ('percentages H1WP17B') p28 = sub5['H1WP17B'].value_counts(sort=False, normalize=True, dropna=True) print (p28*100) #worked on a project for school with mother? print ('counts for H1WP17I') c29 = sub5['H1WP17I'].value_counts(sort=False, dropna=True) print(c29) print ('percentages H1WP17I') p29 = sub5['H1WP17I'].value_counts(sort=False, normalize=True, dropna=True) print (p29*100) #gone shopping with father? print ('counts for H1WP18A') c30 = sub5['H1WP18A'].value_counts(sort=False, dropna=True) print(c30) print ('percentages H1WP18A') p30 = sub5['H1WP18A'].value_counts(sort=False, normalize=True, dropna=True) print (p30*100) #played a sport with father? print ('counts for H1WP18B') c31 = sub5['H1WP18B'].value_counts(sort=False, dropna=True) print(c31) print ('percentages H1WP18B') p31 = sub5['H1WP18B'].value_counts(sort=False, normalize=True, dropna=True) print (p31*100) #worked on a project for school with father? print ('counts for H1WP18I') c32 = sub5['H1WP18I'].value_counts(sort=False, dropna=True) print(c32) print ('percentages H1WP18I') p32 = sub5['H1WP18I'].value_counts(sort=False, normalize=True, dropna=True) print (p32*100) # Frequency distribution of those with no potential depression symptoms sub6=sub3[(sub3['H1FS1'] ==0) | (sub3['H1FS2'] ==0) | (sub3['H1FS3'] ==0) | (sub3['H1FS4'] ==0) | (sub3['H1FS5'] ==0) | (sub3['H1FS6'] ==0) | (sub3['H1FS7'] ==0) | (sub3['H1FS8'] == 3) | (sub3['H1FS9'] ==0) | (sub3['H1FS10'] ==0) | (sub3['H1FS11'] == 3) | (sub3['H1FS12'] ==0) | (sub3['H1FS13'] ==0) | (sub3['H1FS14'] ==0) | (sub3['H1FS15'] == 3) | (sub3['H1FS16'] ==0) | (sub3['H1FS17'] ==0) | (sub3['H1FS18'] ==0) | (sub3['H1FS19'] ==0) ] #Replacing 6 (refused), 7 (legitimate skip), 8 (don’t know) and 9 (not applicable) with NaN sub6= sub6.replace([6,7,8,9], np.nan) sub7=sub6.copy() print('Frequency distribution of those with no potential depression symptoms') #On how many of the past 7 days was at least one of your parents in the room with you while you ate your evening meal? #Assume from 4 to 7 means yes (=1) #Assume from 0 to 3 means no (=0) sub7['H1WP8']= sub7['H1WP8'].replace([4,5,6,7], 1) sub7['H1WP8']= sub7['H1WP8'].replace([0,1,2,3], 0) #Replacing 96 (refused), 97 (legitimate skip) and 98 (don’t know) with NaN sub7['H1WP8']= sub7['H1WP8'].replace([96,97,98], np.nan) print ('counts for H1WP8') c33 = sub7['H1WP8'].value_counts(sort=False, dropna=True) print(c33) print ('percentages H1WP8') p33 = sub7['H1WP8'].value_counts(sort=False, normalize=True, dropna=True) print (p33*100) # Did you have a talk with your mother about a personal problem you were having print ('counts for H1WP17F') c34 = sub7['H1WP17F'].value_counts(sort=False, dropna=True) print(c34) print ('percentages H1WP17F') p34 = sub7['H1WP17F'].value_counts(sort=False, normalize=True, dropna=True) print (p34*100) # Did you have a talk with your father about a personal problem you were having print ('counts for H1WP18F') c35 = sub7['H1WP18F'].value_counts(sort=False, dropna=True) print(c35) print ('percentages H1WP18F') p35 = sub7['H1WP18F'].value_counts(sort=False, normalize=True, dropna=True) print (p35*100) #Did you talk with your mother about other things you’re doing in school print ('counts for H1WP17J') c36 = sub7['H1WP17J'].value_counts(sort=False, dropna=True) print(c36) print ('percentages H1WP17J') p36 = sub7['H1WP17J'].value_counts(sort=False, normalize=True, dropna=True) print (p36*100) #Did you talk with your father about other things you’re doing in school print ('counts for H1WP18J') c37 = sub7['H1WP18J'].value_counts(sort=False, dropna=True) print(c37) print ('percentages H1WP18J') p37 = sub7['H1WP18J'].value_counts(sort=False, normalize=True, dropna=True) print (p37*100) #gone shopping with mother? print ('counts for H1WP17A') c38 = sub7['H1WP17A'].value_counts(sort=False, dropna=True) print(c38) print ('percentages H1WP17A') p38 = sub7['H1WP17A'].value_counts(sort=False, normalize=True, dropna=True) print (p38*100) #played a sport with mother? print ('counts for H1WP17B') c39 = sub7['H1WP17B'].value_counts(sort=False, dropna=True) print(c39) print ('percentages H1WP17B') p39 = sub7['H1WP17B'].value_counts(sort=False, normalize=True, dropna=True) print (p39*100) #worked on a project for school with mother? print ('counts for H1WP17I') c40 = sub7['H1WP17I'].value_counts(sort=False, dropna=True) print(c40) print ('percentages H1WP17I') p40 = sub7['H1WP17I'].value_counts(sort=False, normalize=True, dropna=True) print (p40*100) #gone shopping with father? print ('counts for H1WP18A') c41 = sub7['H1WP18A'].value_counts(sort=False, dropna=True) print(c41) print ('percentages H1WP18A') p41 = sub7['H1WP18A'].value_counts(sort=False, normalize=True, dropna=True) print (p41*100) #played a sport with father? print ('counts for H1WP18B') c42 = sub7['H1WP18B'].value_counts(sort=False, dropna=True) print(c42) print ('percentages H1WP18B') p42 = sub7['H1WP18B'].value_counts(sort=False, normalize=True, dropna=True) print (p42*100) #worked on a project for school with father? print ('counts for H1WP18I') c43 = sub7['H1WP18I'].value_counts(sort=False, dropna=True) print(c43) print ('percentages H1WP18I') p43 = sub7['H1WP18I'].value_counts(sort=False, normalize=True, dropna=True) print (p43*100) #upper-case all DataFrame column names - place afer code for loading data above data.columns = map(str.upper, data.columns) # bug fix for display formats to avoid run time errors - put after code for loading data above pd.set_option('display.float_format', lambda x:'%f'%x)

Output:

6504 2 counts for H1GI1Y 74 17 76 376 78 1165 75 43 77 1133 79 1159 Name: H1GI1Y, dtype: int64 percentages for H1GI1Y 74 0.436681 76 9.658361 78 29.925507 75 1.104547 77 29.103519 79 29.771385 Name: H1GI1Y, dtype: float64 counts for H1GI15 0 3893 Name: H1GI15, dtype: int64 percentages for H1GI15 0 100.000000 Name: H1GI15, dtype: float64 counts for H1FS1 1.000000 2068 0.000000 3913 2.000000 385 3.000000 116 Name: H1FS1, dtype: int64 percentages H1FS1 1.000000 31.903733 0.000000 60.367171 2.000000 5.939525 3.000000 1.789571 Name: H1FS1, dtype: float64 counts for H1FS2 0.000000 4192 1.000000 1744 2.000000 410 3.000000 141 Name: H1FS2, dtype: int64 percentages H1FS2 0.000000 64.621551 1.000000 26.884538 2.000000 6.320333 3.000000 2.173578 Name: H1FS2, dtype: float64 counts for H1FS3 1.000000 1296 0.000000 4668 2.000000 372 3.000000 144 Name: H1FS3, dtype: int64 percentages H1FS3 1.000000 20.000000 0.000000 72.037037 2.000000 5.740741 3.000000 2.222222 Name: H1FS3, dtype: float64 counts for H1FS4 3.000000 2345 2.000000 2070 0.000000 715 1.000000 1353 Name: H1FS4, dtype: int64 percentages H1FS4 3.000000 36.171526 2.000000 31.929662 0.000000 11.028845 1.000000 20.869968 Name: H1FS4, dtype: float64 counts for H1FS5 1.000000 2768 3.000000 277 0.000000 2624 2.000000 816 Name: H1FS5, dtype: int64 percentages H1FS5 1.000000 42.683115 3.000000 4.271396 0.000000 40.462606 2.000000 12.582884 Name: H1FS5, dtype: float64 counts for H1FS6 1.000000 1853 0.000000 3994 2.000000 444 3.000000 193 Name: H1FS6, dtype: int64 percentages H1FS6 1.000000 28.578038 0.000000 61.597779 2.000000 6.847625 3.000000 2.976558 Name: H1FS6, dtype: float64 counts for H1FS7 1.000000 2934 0.000000 2755 2.000000 630 3.000000 168 Name: H1FS7, dtype: int64 percentages H1FS7 1.000000 45.228919 0.000000 42.469554 2.000000 9.711731 3.000000 2.589795 Name: H1FS7, dtype: float64 counts for H1FS8 3.000000 2003 2.000000 2185 1.000000 1567 0.000000 720 Name: H1FS8, dtype: int64 percentages H1FS8 3.000000 30.934363 2.000000 33.745174 1.000000 24.200772 0.000000 11.119691 Name: H1FS8, dtype: float64 counts for H1FS9 1.000000 782 0.000000 5451 3.000000 80 2.000000 164 Name: H1FS9, dtype: int64 percentages H1FS9 1.000000 12.073491 0.000000 84.159333 3.000000 1.235140 2.000000 2.532036 Name: H1FS9, dtype: float64 counts for H1FS10 0.000000 4714 1.000000 1545 2.000000 163 3.000000 65 Name: H1FS10, dtype: int64 percentages H1FS10 0.000000 72.668414 1.000000 23.816864 2.000000 2.512718 3.000000 1.002004 Name: H1FS10, dtype: float64 counts for H1FS11 3.000000 2397 2.000000 2690 0.000000 172 1.000000 1230 Name: H1FS11, dtype: int64 percentages H1FS11 3.000000 36.939436 2.000000 41.454770 0.000000 2.650640 1.000000 18.955155 Name: H1FS11, dtype: float64 counts for H1FS12 1.000000 2206 0.000000 3642 2.000000 476 3.000000 161 Name: H1FS12, dtype: int64 percentages H1FS12 1.000000 34.016962 0.000000 56.160370 2.000000 7.340015 3.000000 2.482652 Name: H1FS12, dtype: float64 counts for H1FS13 1.000000 1787 0.000000 4157 2.000000 401 3.000000 140 Name: H1FS13, dtype: int64 percentages H1FS13 1.000000 27.555898 0.000000 64.101773 2.000000 6.183500 3.000000 2.158828 Name: H1FS13, dtype: float64 counts for H1FS14 0.000000 4307 1.000000 1839 2.000000 256 3.000000 87 Name: H1FS14, dtype: int64 percentages H1FS14 0.000000 66.373863 1.000000 28.340268 2.000000 3.945138 3.000000 1.340730 Name: H1FS14, dtype: float64 counts for H1FS15 3.000000 3141 1.000000 1043 2.000000 2047 0.000000 255 Name: H1FS15, dtype: int64 percentages H1FS15 3.000000 48.427382 1.000000 16.080789 2.000000 31.560284 0.000000 3.931545 Name: H1FS15, dtype: float64 counts for H1FS16 1.000000 2629 0.000000 3405 2.000000 336 3.000000 120 Name: H1FS16, dtype: int64 percentages H1FS16 1.000000 40.508475 0.000000 52.465331 2.000000 5.177196 3.000000 1.848998 Name: H1FS16, dtype: float64 counts for H1FS17 0.000000 4246 1.000000 1859 3.000000 105 2.000000 276 Name: H1FS17, dtype: int64 percentages H1FS17 0.000000 65.464076 1.000000 28.661733 3.000000 1.618871 2.000000 4.255319 Name: H1FS17, dtype: float64 counts for H1FS18 0.000000 3124 1.000000 2814 2.000000 462 3.000000 84 Name: H1FS18, dtype: int64 percentages H1FS18 0.000000 48.180136 1.000000 43.399136 2.000000 7.125231 3.000000 1.295497 Name: H1FS18, dtype: float64 counts for H1FS19 1.000000 545 0.000000 5728 2.000000 149 3.000000 63 Name: H1FS19, dtype: int64 percentages H1FS19 1.000000 8.404009 0.000000 88.326908 2.000000 2.297610 3.000000 0.971473 Name: H1FS19, dtype: float64 Frequency distribution showing the effect of Family relationship on feeling scale counts for H1WP8 0.000000 1884 Name: H1WP8, dtype: int64 percentages H1WP8 0.000000 100.000000 Name: H1WP8, dtype: float64 counts for H1WP17F 1.000000 1354 0.000000 2037 Name: H1WP17F, dtype: int64 percentages H1WP17F 1.000000 39.929224 0.000000 60.070776 Name: H1WP17F, dtype: float64 counts for H1WP18F 0.000000 1934 1.000000 470 Name: H1WP18F, dtype: int64 percentages H1WP18F 0.000000 80.449251 1.000000 19.550749 Name: H1WP18F, dtype: float64 counts for H1WP17J 1.000000 1750 0.000000 1641 Name: H1WP17J, dtype: int64 percentages H1WP17J 1.000000 51.607196 0.000000 48.392804 Name: H1WP17J, dtype: float64 counts for H1WP18J 0.000000 1350 1.000000 1054 Name: H1WP18J, dtype: int64 percentages H1WP18J 0.000000 56.156406 1.000000 43.843594 Name: H1WP18J, dtype: float64 counts for H1WP17A 1.000000 2418 0.000000 973 Name: H1WP17A, dtype: int64 percentages H1WP17A 1.000000 71.306399 0.000000 28.693601 Name: H1WP17A, dtype: float64 counts for H1WP17B 0.000000 3080 1.000000 311 Name: H1WP17B, dtype: int64 percentages H1WP17B 0.000000 90.828664 1.000000 9.171336 Name: H1WP17B, dtype: float64 counts for H1WP17I 0.000000 2917 1.000000 474 Name: H1WP17I, dtype: int64 percentages H1WP17I 0.000000 86.021822 1.000000 13.978178 Name: H1WP17I, dtype: float64 counts for H1WP18A 0.000000 1758 1.000000 646 Name: H1WP18A, dtype: int64 percentages H1WP18A 0.000000 73.128120 1.000000 26.871880 Name: H1WP18A, dtype: float64 counts for H1WP18B 0.000000 1682 1.000000 722 Name: H1WP18B, dtype: int64 percentages H1WP18B 0.000000 69.966722 1.000000 30.033278 Name: H1WP18B, dtype: float64 counts for H1WP18I 0.000000 2140 1.000000 264 Name: H1WP18I, dtype: int64 percentages H1WP18I 0.000000 89.018303 1.000000 10.981697 Name: H1WP18I, dtype: float64 Frequency distribution of those with no potential depression symptoms counts for H1WP8 0.000000 3363 Name: H1WP8, dtype: int64 percentages H1WP8 0.000000 100.000000 Name: H1WP8, dtype: float64 counts for H1WP17F 1.000000 2348 0.000000 3729 Name: H1WP17F, dtype: int64 percentages H1WP17F 1.000000 38.637486 0.000000 61.362514 Name: H1WP17F, dtype: float64 counts for H1WP18F 0.000000 3645 1.000000 866 Name: H1WP18F, dtype: int64 percentages H1WP18F 0.000000 80.802483 1.000000 19.197517 Name: H1WP18F, dtype: float64 counts for H1WP17J 1.000000 3160 0.000000 2917 Name: H1WP17J, dtype: int64 percentages H1WP17J 1.000000 51.999342 0.000000 48.000658 Name: H1WP17J, dtype: float64 counts for H1WP18J 0.000000 2534 1.000000 1977 Name: H1WP18J, dtype: int64 percentages H1WP18J 0.000000 56.173797 1.000000 43.826203 Name: H1WP18J, dtype: float64 counts for H1WP17A 1.000000 4429 0.000000 1648 Name: H1WP17A, dtype: int64 percentages H1WP17A 1.000000 72.881356 0.000000 27.118644 Name: H1WP17A, dtype: float64 counts for H1WP17B 0.000000 5537 1.000000 540 Name: H1WP17B, dtype: int64 percentages H1WP17B 0.000000 91.114037 1.000000 8.885963 Name: H1WP17B, dtype: float64 counts for H1WP17I 0.000000 5272 1.000000 805 Name: H1WP17I, dtype: int64 percentages H1WP17I 0.000000 86.753332 1.000000 13.246668 Name: H1WP17I, dtype: float64 counts for H1WP18A 0.000000 3316 1.000000 1195 Name: H1WP18A, dtype: int64 percentages H1WP18A 0.000000 73.509200 1.000000 26.490800 Name: H1WP18A, dtype: float64 counts for H1WP18B 0.000000 3142 1.000000 1369 Name: H1WP18B, dtype: int64 percentages H1WP18B 0.000000 69.651962 1.000000 30.348038 Name: H1WP18B, dtype: float64 counts for H1WP18I 0.000000 4004 1.000000 507 Name: H1WP18I, dtype: int64 percentages H1WP18I 0.000000 88.760807 1.000000 11.239193 Name: H1WP18I, dtype: float64

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juniperpublisherscasestudies · 5 years ago

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Juniper Publishers- Open Access Journal of Case Studies

Juniper Publishers-Open Access Journal of Case Studies

Authored by Elena Kochova

Abstract

X-Linked adrenoleukodystrophy (ALD) is a rare neurodegenerative disorder with a wide clinical spectrum. It is characterized by progressive cerebral demyelination, spinal cord axonal degeneration and adrenal and testicular insufficiency. ALD is caused by mutations in the ABCD1 gene, involved in the metabolism of fatty acids. The main biochemical feature is elevated plasma and tissue levels of very long-chain fatty acids and the diagnosis relies on their measurement in plasma along with MRI studies. Allogeneic bone marrow transplantation is the preferred treatment in patients with the severe, cerebral form of ALD, offering a halt in disease progression, but only when instituted in the early stages of cerebral demyelization. The aim of this review is to summarize the present understanding of ALD, as well as to overview the advances in the diagnostics and treatment.

Keywords: Adrenoleukodystrophy, X-linked, Hematopoietic stem cell transplantation, ABCD1

Abbreviations: ABC: Adenosine Triphosphate - Binding Cassette; ALD: Adrenoleukodystrophy; ALDP: Adrenoleukodystrophy Protein; ALDRP: Adrenoleukodystrophy Related Protein; AMN: Adrenomyeloneuropathy; Cr: Creatine; Ch: Choline; HSCT: Hematopoietic Stem Cell Therapy; MR: Magnetic Resonance; MRI: Magnetic Resonance Imaging; NAA: N-Acetylaspartate; VLCFA: Very Long Chain Fatty Acids

Introduction

Adrenoleukodystrophy (ALD) is an X-linked inherited disorder that affects the central nervous system, peripheral nerves, adrenal cortex and testes. Also known as Schilder’s disease and sudanophillic leukodystrophy, ALD is a peroxisomal metabolic storage disease caused by mutations in the ABCD1 gene, involved in the degradation of very long-chain fatty acids (VLCFA). The consequent accumulation of VLCFA leads to progressive central demyelination and adrenal insufficiency.

The first case of X-linked ALD (X-ALD) was likely described in 1910 [1]. Siemerling and Creutzfeldt in 1923 described it as “bronzed sclerosing encephalomyelitis” [2]; another synonym for ALD is Siemerling-Creutzfeldt disease. Biaw [3] in 1970 assigned the term adrenoleukodystrophy [3]. Since then, significant advances have been made in elucidating the pathophysiological mechanisms of the disease, as well as in the diagnostic and therapeutic approaches.

X-ALD is a rare disease, with an overall estimated incidence of 1:17,000, including symptomatic female heterozygotecarriers [4]. This makes it the most common of the leukodystrophies. The clinical presentation of ALD is strikingly diverse, from rapidly progressive, fatal neurological involvement in young children, to slowly progressive adrenomyeloneuropathy in older children or adults. The symptoms of ALD overlap with the other numerous leukodystrophies which makes the diagnosis of ALD challenging.

Genetics and Pathophysiology

The breakthrough in the ALD field came in 1993 when mutations in the ABCD1 gene were identified in patients with ALD [5]. The gene is mapped to Xq28 and encodes the adrenoleukodystrophy protein (ALDP), one of four members of the peroxisomal subfamily of ATP-binding cassette (ABC) proteins. ALDP is involved in the transport of very long-chain fatty acids (VLCFA) from the cytoplasm into the peroxisomes, where the oxidation and degradation of VLCFA takes place.

A gene that is closely related to ABCD1 has been mapped to chromosome 12q11. This gene is referred to as ALDR or ABC2. Its gene product, referred to as ALDRP, has 66% homology to ALDP [6], is also localized to the peroxisomal membrane and can correct the defect in very long chain fatty acid metabolism in cultured fibroblasts of X-ALD patients. The relationship between ALDP and ALDRP is still unclear, but it has been proposed that variations in the expression of ALDRP may account for the wide range of phenotypic expression that is a specific feature of X-ALD. Also, these similarities between ALDP and ALDRP have led to the hypothesis that over expression of ALDRP might be used as a therapeutic strategy.

The inactivating mutations in the adrenoleukodystrophy gene, most of which are unique to particular families, have been defined in more than 400 families. In general, there is no correlation between the nature of the mutation and the phenotype. In one family the same mutation was associated with 5 widely different phenotypes [7]. De novo mutation of ABCD1 occurs in less than 8% of ALD patients [8].

The defect in the ALDP results in accumulation of VLCFA, which in turn produces an intense inflammatory response in the white matter of the central and peripheral nervous system. The VLCFA accumulation itself is thought to be responsible for the adrenal and testicular involvement. The inflammatory mechanism that leads to demyelination is not completely understood, but thought to result from activation of brain macrophages and astrocytes bearing CD1 molecules that recognize lipid antigens which are abnormally acylated by the excessive VLCFA. The precise mechanism by which the accumulation of VLCFA leads to the inflammatory demyelination that is the cause of the neurologic disability is still unclear.

Clinical Presentation

Most male X-ALD patients develop adrenocortical insufficiency in childhood and progressive myelopathy and peripheral neuropathy in adulthood. A subset of male patients, however, develops a fatal cerebral demyelinating disease, cerebral ALD. Female carriers can also develop progressive myelopathy and peripheral neuropathy, but generally at a later age than males. They only very rarely develop adrenocortical insufficiency or cerebral ALD. The three major disease categories are:

The severe, cerebral demyelinating form (cerebral childhood form) - appearing in mid-childhood (4-8 years);

Spinal cord demyelination and axonal degeneration (adrenomyelopathy, AMN) - occurring in men in their 20s or later and older women.

Impaired adrenal gland function (Addison’s disease or Addison-like phenotype). AMN is the most frequent form, affecting 60% of affected males and 50% of female carriers [4].

The cerebral childhood form occurs in approximately 35% of ALD patients. The mean age of onset is 7 years, with the earliest onset noted at around 3 years. It develops in three phases:

A.An asymptomatic latent phase, when there are no clinical signs, but MRI changes of demyelination are present (the first lesions can be evident at around 4 years of age);

C.A terminal phase with significant motor, sensory and cognitive sequellae, which leads to coma and death.

The symptoms depend on the topography of the demyelienation. The parieto-occipital forms are the most frequent [4], the initial symptoms consisting of cognitive visuomotor and visuospatial abnormalities and immediate memory deficits. Behavioral changes ensue, such as hyperactivity or attention deficit and emotional troubles, along with sensory disorders (visual field amputation, diminishing visual acuity, impaired auditory discrimination) and motor disorders (pyramidal syndrome of the lower extremities, gait abnormalities, hemiparesis, cerebellar ataxia). Sometimes seizures also occur. Once the symptoms appear, they progress rapidly and lead to an almost absolute loss of all cognitive functions, tetraplegy and blindness, and a vegetative state ensues usually within 3 years.

The frontal form presents with a frontal syndrome and hemiparesis. Sometimes adrenal insufficiency is the first sign of the disease, appearing well in advance of the neurological symptoms. The cerebral demyelinating form of ALD can also occur at a later age or in adulthood, when it assumes a progressive course similar to the childhood form.

Adrenomyeloneuropathy (AMN) is the more frequent form of ALD. The symptoms appear at an age of 20-30 in men and 40-50 in half of the female carriers and typically consist of spastic paraparesis with disturbed gait as a result of posterior spinal cord degeneration and urinary problems such as dysuria and urgency. Sometimes clinical signs of peripheral neuropathy (demyelinating, axonal, or both) can be seen. The symptoms are progressive and lead to a significant motor disability, but the progression varies and can extend over 20 years, without remission.

Adrenal insufficiency may be diagnosed after the appearance of neurological symptoms or decades in advance. Testicular dysfunction usually occurs late in the course of the disease. AMN in female carriers assumes a less severe form; the peripheral neuropathy is rarer, but neurogenic pain is a more frequent and severe feature. A certain percentage of men diagnosed with AMN will later develop the cerebral form. There is no biological marker that can be used for predicting the evolution of the disease on an individual level, but in general the earlier the onset, the faster the progression [4].

A significant proportion of the patients with ALD will develop adrenal insufficiency at some point [9], which affects primarily the glucocorticoid, followed by the mineralocorticoid function. ALD is the most frequent cause of adrenal insufficiency in males over the age of 4, and the second most frequent cause of adrenal insufficiency in adults [9]. It assumes the usual clinical presentation - melanodermy, followed by adrenal crisis with its usual clinical presentation. It can be the only sign of ALD for decades before the appearance of neurological signs.

Diagnostic Workup

The diagnosis of ALD is primarily based on biochemical and MRI studies. The biochemical signature of ALD is elevated plasma VLCFA levels, present in all affected males. Three parameters are analyzed: the concentration of C26:0, the ratio of C24:0 to C22:0 and the ratio of C26:0 to C22:0. All three parameters are usually elevated. With methodological advances, false positives and false negatives in males are exceptionally rare. The abnormality is present at birth and remains relatively constant throughout life. In contrast, only a proportion of female carriers have elevated plasma VLCFA; therefore targeted mutation analysis is the most effective means for carrier detection. MRI diagnostics are of great value, most frequently showing signs of symmetrical demyelination, with contrast accumulation at the edge of the lesions.

In the childhood cerebral form, MRI of the brain shows signs of demyelination with hyposignal in the T1 sequence and hypersignal in the T2 and FLAIR sequence, which allows for localization of the lesions and evaluation of the inflammatory character of the lesions based on the gadolinium uptake. Cortical atrophy can be seen in the later stages [4]. Studies have shown that the degree of MRI abnormality as assessed by the Loes scoring system [10], when coupled with age, aids in predicting the future course and in selecting patients who are candidates for bone marrow transplantation [11].

Newer modalities like MRI spectroscopy have provided new inputs into the disease.Proton MR spectroscopy is useful for determining the early signs of disease in patients even when MRI is still normal. MR spectroscopy shows abnormal metabolite ratios in the areas of abnormal T2 signal, but also in normal-appearing brain regions, including a decrease in N-acetylaspartate (NAA)/Creatine (Cr) and NAA/Choline (Ch) and an increase in Ch/Cr [12]. Lipids-lactate peaks are also valuable markers for the demonstration of the presence and progression of lesions. The metabolic ratio alterations seem to be proportionate to the severity of the ALD phenotype. Interestingly, higher VLCFA levels are associated with a lower NAA/Cr ratio [13].

The spectroscopic changes are not disease-specific, but are a sensitive indicator of disease progression [14] and can be useful in the evaluation of therapeutic interventions [15]. In AMN, cerebral and spinal MRI shows no changes in the early stages of the disease. The lesions, namely progressive atrophy of the spinal cord, appear in the later stages, but never show gadolinium uptake. Spectroscopy reveals axonopathy in the morphologically normal cerebral white matter, with reduced NAA/Cr and NAA/ Ch, most prominent in the internal capsule and parieto-occipital white matter [16].

Although MR abnormalities are rare in heterozygous women, even when symptomatic [17], the spectroscopy shows axonal abnormalities, which may be indicative of the distal axonopathy that represents the principal neuropathological change in AMN [18]. Wilken and colleagues examined the prognostic significance of MR spectroscopy for patients who received bone marrow transplants [15]. They found an association between outcome and the N-acetylaspartate levels in affected brain white matter. A high level was associated with a positive outcome, whereas low levels had a negative predictive value, as did increased levels of choline-containing compounds. Abnormal NAA/Ch ratios in the regions adjacent to the MRI lesions are a negative predictor for progression [14].

Genetic testing for ABCD1 mutations is useful in the identification of female carriers, as they may have normal VLCFA levels. This investigation should be performed in all females who are at risk of being a carrier for ALD, but is only possible when the mutation in the family is known. Prenatal diagnosis is important for the prevention of the disease, and is performed by measuring VLCFA levels in cultured amniocytes and ABCD1 mutational analysis in chorionic villus samples.

Therapeutic Options

The application of immunomodulatory and immunosuppressive drugs has failed to prevent progression of cerebral neuroinflammation. Initially proposed treatments were Lorenzo’s oil and statin therapy as well as VLCFA intake restriction, but clinically relevant benefit from such treatments has not been proven and they remain controversial. The preferred treatment option for preadolescent patients in the early stages of childhood cerebral ALD is allogeneic hematopoietic stem cell transplantation (HSCT).

This therapy was first described in 1990 [19] and several studies have shown promising results, when the treatment is performed early in the course of cerebral involvement and a human antigen-matched donor is available. The procedure can stop the progression of demyelination and stabilize the neurological symptoms. The outcome is significantly better when there are no neurological deficits and the MRI Loes severity score is less than 9 at the time of treatment. Despite the success of HSCT reports, numerous factors complicate the widespread usage of this treatment for ALD patients. The procedure should not be performed in patients with advanced disease, as the treatment is unable to stabilize neurological involvement in patients with advanced ALD.

It has been shown that MR spectroscopy might be predictive of clinical outcome after HSCT and may be further substantiated by using additional new imaging approaches, such as diffusion tensor imaging and magnetization transfer MRI [20]. While disease progression of patients before HSCT is mainly characterized by a further increase of elevated cholinecontaining compounds as an indicator of active demyelination, a positive outcome after HSCT is found to be correlated with high NAA levels in affected white matter before HSCT.

The positive effects of HSCT when instituted at an early stage of the disease may validate the efforts to institute newborn screening programs [21]. The benefit of HSCT was also shown for the first time in a patient with adult-onset ALD, with only mild symptoms remaining 2 years post-treatment [22]. The overall transplant-related mortality in HSCT patients is relatively high and non-myeloablative HSCT is being introduced as an alternative to myeloablative HSCT, with promising outcomes. For patients where a matched donor is unavalaible, umbilical cord blood stem cells are an alternative.

4-Phenylbutyrate (4-PB) has attracted interest as a potential therapeutic option for ALD. It has shown to improve the capacity of cultured ALD-cells to metabolize VLCFA. In the mouse model, it was shown that 4-PB reduces VLCFA levels in the brain and adrenal gland. 4-PB has the effect of increasing the expression of ALDRP, which, as noted previously, may be able to substitute for the function of ALDP. Its clinical effects have not yet been evaluated. ABCD2, the gene encoding ALDRP, when over expressed in cultured human fibroblast cell lines from ALD patients can normalize peroxisomal β-oxidation and prevent accumulation of VLCFA. Thus, the pharmacological induction of ABCD2 should be able to compensate for the lack of functional ABCD1 and is a potential attractive therapeutic target. Pujol and coworkers have shown that valproic acid induces expression of ABCD2 in human ALD fibroblasts. Other potential therapeutic strategies such as antioxidant therapy and neuroprotective therapy with insulin-like growth factor and neurotrophin-3 are being experimented upon. Preliminary work using lentiviralbased gene therapy in two young ALD patients shows short-term neurological benefits similar to HSCT [23].

It is now clear that early diagnosis is perhaps the single most important factor in treating ALD patients as there is no effective treatment for patients experiencing severe neurological symptoms. Efforts to add newborn screening for ALD and other peroxisomal disorders are moving forward; ALD screening has been initialized in the Netherlands as of 2015 and the USA since 2016. As in many fields, advances in gene therapy have the potential to revolutionize the treatment of ALD.

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