Nutritional supplementation to prevent tuberculosis incidence in household contacts of patients with pulmonary tuberculosis in India (RATIONS): a field-based, open-label, cluster-randomised, controlled trial.

BACKGROUND: In India, tuberculosis and undernutrition are syndemics with a high burden of tuberculosis coexisting with a high burden of undernutrition in patients and in the population. The aim of this study was to determine the effect of nutritional supplementation on tuberculosis incidence in household contacts of adults with microbiologically confirmed pulmonary tuberculosis. METHODS: In this field-based, open-label, cluster-randomised controlled trial, we enrolled household contacts of 2800 patients with microbiologically confirmed pulmonary tuberculosis across 28 tuberculosis units of the National Tuberculosis Elimination Programme in four districts of Jharkhand, India. The tuberculosis units were randomly allocated 1:1 by block randomisation to the control group or the intervention group, by a statistician using computer-generated random numbers. Although microbiologically confirmed pulmonary tuberculosis patients in both groups received food rations (1200 kcal, 52 grams of protein per day with micronutrients) for 6 months, only household contacts in the intervention group received monthly food rations and micronutrients (750 kcal, 23 grams of protein per day with micronutrients). After screening all household contacts for co-prevalent tuberculosis at baseline, all participants were followed up actively until July 31, 2022, for the primary outcome of incident tuberculosis (all forms). The ascertainment of the outcome was by independent medical staff in health services. We used Cox proportional hazards model and Poisson regression via the generalised estimating equation approach to estimate unadjusted hazard ratios, adjusted hazard ratios (aHRs), and incidence rate ratios (IRRs). This study is registered with CTRI-India, CTRI/2019/08/020490. FINDINGS: Between Aug 16, 2019, and Jan 31, 2021, there were 10 345 household contacts, of whom 5328 (94·8%) of 5621 household contacts in the intervention group and 4283 (90·7%) of 4724 household contacts in the control group completed the primary outcome assessment. Almost two-thirds of the population belonged to Indigenous communities (eg, Santhals, Ho, Munda, Oraon, and Bhumij) and 34% (3543 of 10 345) had undernutrition. We detected 31 (0·3%) of 10 345 household contact patients with co-prevalent tuberculosis disease in both groups at baseline and 218 (2·1%) people were diagnosed with incident tuberculosis (all forms) over 21 869 person-years of follow-up, with 122 of 218 incident cases in the control group (2·6% [122 of 4712 contacts at risk], 95% CI 2·2-3·1; incidence rate 1·27 per 100 person-years) and 96 incident cases in the intervention group (1·7% [96 of 5602], 1·4-2·1; 0·78 per 100 person-years), of whom 152 (69·7%) of 218 were patients with microbiologically confirmed pulmonary tuberculosis. Tuberculosis incidence (all forms) in the intervention group had an adjusted IRR of 0·61 (95% CI 0·43-0·85; aHR 0·59 [0·42-0·83]), with an even greater decline in incidence of microbiologically confirmed pulmonary tuberculosis (0·52 [0·35-0·79]; 0·51 [0·34-0·78]). This translates into a relative reduction of tuberculosis incidence of 39% (all forms) to 48% (microbiologically confirmed pulmonary tuberculosis) in the intervention group. An estimated 30 households (111 household contacts) would need to be provided nutritional supplementation to prevent one incident tuberculosis. INTERPRETATION: To our knowledge, this is the first randomised trial looking at the effect of nutritional support on tuberculosis incidence in household contacts, whereby the nutritional intervention was associated with substantial (39-48%) reduction in tuberculosis incidence in the household during 2 years of follow-up. This biosocial intervention can accelerate reduction in tuberculosis incidence in countries or communities with a tuberculosis and undernutrition syndemic. FUNDING: Indian Council of Medical Research-India TB Research Consortium.

Seasonal variations of leaf ecophysiological traits and strategies of co-occurring evergreen and deciduous trees in white oak forest in the central Himalaya.

The present study investigates the seasonal variations in leaf ecophysiological traits and strategies employed by co-occurring evergreen and deciduous tree species within a white oak forest (Quercus leucotrichophora A. Camus) ecosystem in the central Himalaya. Seasonal variations in physiological, morphological, and chemical traits were observed from leaf initiation until senescence in co-occurring deciduous and evergreen tree species. We compared various parameters, including net photosynthetic capacity (Aarea and Amass), leaf stomatal conductance (gswarea and gswmass), transpiration rate (Earea and Emass), specific leaf area (SLA), mid-day water potential (Ψmd), leaf nitrogen (N) and phosphorus (P) concentration, leaf total chlorophyll concentration, photosynthetic nitrogen- and phosphorus-use efficiency (PNUE and PPUE), and water use efficiency (WUE) across four evergreen and four deciduous tree species. Our findings reveal that evergreen and deciduous trees exhibit divergent strategies in coping with seasonal changes, which are crucial for their survival and growth. Deciduous trees consistently exhibited significantly higher photosynthetic rates, transpiration rates, mass-based N and P concentrations (Nmass and Pmass), mass-based chlorophyll concentration (Chlmass), SLA, and leaf Ψmd, while maintaining lower leaf structural investments throughout the year compared to evergreen trees. These findings indicate that deciduous trees achieve greater assimilation rates per unit mass and higher nutrient-use efficiency. Physiological, morphological, and leaf N and P concentrations were higher in the summer (fully expanded leaf) than in the fall (senesced leaf). These insights provide valuable contributions to our understanding of tree species coexistence and their ecological roles in temperate forest ecosystems, with implications for forest management and conservation in the Himalayan region.

Topography-mediated light environment regulates intra-specific seasonal and diurnal patterns of photosynthetic plasticity and plant ecophysiological adaptation strategies.

Due to substantial topographic variations in the Himalaya, incident solar radiation in the forest canopy is highly unequal. This results in significant environmental differences at finer scales and may lead to considerable differences in photosynthetic productivity in montane forests. Therefore, local-scale ecophysiological investigations, may be more effective and instructive than landscape-level inventories and models. We investigated leaf ecophysiological differences and related adaptations between two Quercus semecarpifolia forests in aspect-mediated, significantly varying light regimes in the same mountain catchment. Seasonal and diurnal rates of photosynthesis (A) were significantly higher in south aspect (S) than the north (N). Although temperature was a key contributor to seasonal fluctuations in photosynthetic physiology, photoperiod significantly determined intraspecific variations in seasonal and diurnal plasticity of leaf ecophysiological traits between the two topography-mediated light environments. The regression model for A as a function of stomatal conductivity (gsw) explained the critical role of gsw in triggering photosynthetic plasticity as an adaptive function against varying environmental stresses due to seasonal solar differences. We also examined, modifications in chlorophyll content between the two light regimes across seasons to determine the chlorophyll adaptation strategy. The N aspect had higher leaf chl a, b, and chl a + b and a lower chl-allocation ratio (a/b) than S, which helped to optimize the required light reception in the photoreaction centers for improved photosynthetic performance. The leaf light response curves for A and gsw were observed against varying incident photosynthetic photon flux densities (0-2000 mol.m2 s-1 PPFD) for both aspects. We found that the same species developed significantly distinct light response strategies and photosynthetic capacities in S than in N for the given magnitudes of PPFD. Such acquired ecophysiological adaptations owing to varying light environments may provide significant clues for understanding the impact of future climate change on Himalayan tree species.

Implementation of a tuberculosis elimination project, India 2018-2019.

Objective: To describe the changes in tuberculosis case notifications by the private sector after implementation of the Joint Effort for Elimination of Tuberculosis project in India in 2018. Methods: We retrieved data from the project recorded in India's national tuberculosis surveillance system. We analysed data on 95 project districts in six states (Andhra Pradesh, Himachal Pradesh, Karnataka, Punjab including Chandigarh, Telangana and West Bengal) to assess changes in the number of tuberculosis notifications, private provider notifiers and microbiological confirmations of cases from 2017 (baseline) to 2019. We compared case notification rates in districts where the project was implemented with the rates in districts where it was not. Findings: From 2017 to 2019, tuberculosis notifications increased by 138.1% (from 44 695 to 106 404), and case notification rates more than doubled from 20 to 44 per 100 000 population. The number of private notifiers increased by over threefold, from 2912 to 9525, during this period. The number of microbiologically confirmed pulmonary and extra-pulmonary tuberculosis cases notified increased by more than two times (from 10 780 to 25 384) and nearly three times (from 1477 to 4096), respectively. The districts where the project was implemented showed a 150.3% increase in case notification rates per 100 000 population from 2017 to 2019 (from 16.8 to 41.9) while in non-project districts, this increase was only 89.8% (from 6.1 to 11.6). Conclusion: The substantial increase in tuberculosis notifications demonstrate the value of the project in engaging the private sector. Scaling up these interventions is important to consolidate and extend these gains towards tuberculosis elimination.

Retaining Patients with Drug-Resistant Tuberculosis on Treatment During the COVID-19 Pandemic - Dharavi, Mumbai, India, 2020-2022.

Mumbai, India's second largest city, has one of the highest prevalences of drug-resistant tuberculosis* (DRTB) in the world. Treatment for DRTB takes longer and is more complicated than treatment for drug-susceptible tuberculosis (TB). Approximately 300 persons receive a new DRTB diagnosis each year in Mumbai's Dharavi slum†; historically, fewer than one half of these patients complete DRTB treatment. As nationwide restrictions to mitigate the COVID-19 pandemic were implemented, a program to facilitate uninterrupted DRTB care for patients receiving treatment was also implemented. A comprehensive tool and risk assessment provided support to DRTB patients and linked those who relocated outside of Dharavi during the pandemic to DRTB care at their destination. During May 2020-September 2022, a total of 973 persons received DRTB treatment in Dharavi, including 255 (26%) who relocated during treatment. Overall, 25 (3%) DRTB patients were lost to follow-up, a rate substantially lower than the rate before the pandemic (18%). Proactive planning and implementation of simple tools retained patients on treatment during periods of travel restrictions and relocations, improving programmatic outcomes. This approach might aid public health programs serving migrant populations or patients receiving treatment for DRTB during public health emergencies.

Machine Learning Models for Predicting Molecular UV-Vis Spectra with Quantum Mechanical Properties.

Accurate understanding of ultraviolet-visible (UV-vis) spectra is critical for the high-throughput synthesis of compounds for drug discovery. Experimentally determining UV-vis spectra can become expensive when dealing with a large quantity of novel compounds. This provides us an opportunity to drive computational advances in molecular property predictions using quantum mechanics and machine learning methods. In this work, we use both quantum mechanically (QM) predicted and experimentally measured UV-vis spectra as input to devise four different machine learning architectures, UVvis-SchNet, UVvis-DTNN, UVvis-Transformer, and UVvis-MPNN, and assess the performance of each method. We find that the UVvis-MPNN model outperforms the other models when using optimized 3D coordinates and QM predicted spectra as input features. This model has the highest performance for predicting UV-vis spectra with a training RMSE of 0.06 and validation RMSE of 0.08. Most importantly, our model can be used for the challenging task of predicting differences in the UV-vis spectral signatures of regioisomers.

AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2.

Direct-acting antivirals for the treatment of the COVID-19 pandemic caused by the SARS-CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of new variants, automated experimentation, and active learning based fast workflows for antiviral lead discovery remain critical to our ability to address the pandemic's evolution in a timely manner. While several such pipelines have been introduced to discover candidates with noncovalent interactions with the main protease (Mpro), here we developed a closed-loop artificial intelligence pipeline to design electrophilic warhead-based covalent candidates. This work introduces a deep learning-assisted automated computational workflow to introduce linkers and an electrophilic "warhead" to design covalent candidates and incorporates cutting-edge experimental techniques for validation. Using this process, promising candidates in the library were screened, and several potential hits were identified and tested experimentally using native mass spectrometry and fluorescence resonance energy transfer (FRET)-based screening assays. We identified four chloroacetamide-based covalent inhibitors of Mpro with micromolar affinities (KI of 5.27 µM) using our pipeline. Experimentally resolved binding modes for each compound were determined using room-temperature X-ray crystallography, which is consistent with the predicted poses. The induced conformational changes based on molecular dynamics simulations further suggest that the dynamics may be an important factor to further improve selectivity, thereby effectively lowering KI and reducing toxicity. These results demonstrate the utility of our modular and data-driven approach for potent and selective covalent inhibitor discovery and provide a platform to apply it to other emerging targets.

The potential impact of vaccination on tuberculosis burden in India: A modelling analysis.

Background & objectives: Vaccination will play an important role in meeting the end tuberculosis (TB) goals. While certain vaccine candidates in advanced stages of clinical trials raise hope for the future availability of new tools, in the immediate term, there is also increasing interest in Bacille Calmette-Guérin revaccination among adults and adolescents as a potential strategy. Here, we sought to estimate the potential epidemiological impact of TB vaccination in India. Methods: We developed a deterministic, age-structured, compartmental model of TB in India. Data from the recent national prevalence survey was used to inform epidemiological burden while also incorporating a vulnerable population who may be prioritized for vaccination, the latter consistent with the burden of undernutrition. Using this framework, the potential impact on incidence and mortality of a vaccine with 50 per cent efficacy was estimated, if rolled out in 2023 to cover 50 per cent of the unvaccinated each year. Simulated impacts were compared for disease- vs. infection-preventing vaccines, as well as when prioritizing vulnerable groups (those with undernutrition) rather than the general population. A sensitivity analyses were also conducted with respect to the duration, and efficacy, of vaccine immunity. Results: When rolled out in the general population, an infection-preventing vaccine would avert 12 per cent (95% Bayesian credible intervals (Crl): 4.3-28%) of cumulative TB incidence between 2023 and 2030, while a disease-preventing vaccine would avert 29 per cent (95% Crl: 24-34%). Although the vulnerable population accounts for only around 16 per cent of India's population, prioritizing this group for vaccination would achieve almost half the impact of rollout in the general population, in the example of an infection-preventing vaccine. Sensitivity analysis also highlights the importance of the duration and efficacy of vaccine-induced immunity. Interpretation & conclusions: These results highlight how even a vaccine with moderate effectiveness (50%) could achieve substantial reductions in TB burden in India, especially when prioritized for the most vulnerable.

Markov State Modeling Analysis Captures Changes in the Temperature-Sensitive N-Terminal and ß-Turn Regions of the p53 DNA-Binding Domain.

The transcription factor p53 is one of the most widely studied cancer proteins. Its temperature-sensitive nature suggests reduction in functionality at physiological temperatures. Temperature-induced conformational variations and their impact on its functional ability still remain unexplored. A total of 20.8 µs molecular dynamics simulations of wildtype p53 in the apo and the DNA-bound states have been performed at 300 K and 310 K. Further, Markov State Modeling (MSM) analyses were performed, considering Cα-Cα distances as reaction coordinates. Filtering of these distances based on correlation with the time-independent components (tICs) resulted in 16 and 32 distances for apo and DNA-bound systems, respectively. Individual MSM analyses using these filtered distances were performed for both p53 systems. These Cα-Cα residue pairs belonged to the N-terminal, S6/7 ß-turn, loop L2, loop L3, and hydrophobic core residues. At physiological temperatures, apo-p53 exhibits exposure of its hydrophobic core, where the temperature-sensitive hotspot residues were also located. This exposure was the result of the S6/7 ß-turn and N-terminal moving apart. In the DNA-bound p53 system, loop L1 attains an open conformation at physiological temperatures, which weakens the DNA binding. It is already known that p53 mutants that lack DNA binding also tend to show similar conformational variations. The S6/7 ß-turn along with the already known functionally important loop L2 may pose as regions to be targeted to overcome the loss in binding of temperature-sensitive wildtype p53. Rescue strategies directed toward these temperature-sensitive regions may be useful to recuperate its strong binding at physiological temperatures.

Evaluation of crotamine based probes as intracellular targeted contrast agents for magnetic resonance imaging.

Crotamine is a lysine and cysteine rich 42 amino acids long bio-active polypeptide, isolated from the venom of a South American rattlesnake, that can also be used as cell penetrating peptide. A facile synthetic scheme for coupling cargo molecules like fluorophores (carboxyfluorescein) or MRI probes (Gd-DO3A-based macrocycle) is presented. The toxicity, cellular internalization and steady-state accumulation after long-term incubation for 18 h, as well as magnetic resonance relaxivities and cellular relaxation rates of crotamine based probes were evaluated and compared to its shorter synthetic fragment CyLoP-1. The longitudinal relaxivity (r1) of the conjugates of CyLoP-1 and crotamine is significantly lower in medium than in water indicating to the lower contrast enhancement efficacy of DO3A-based probes in biological samples. Carboxyfluorescein labeled crotamine did not exhibit toxicity up to a concentration of 2.5 µM. CyLoP-1 accumulated about four times better within the cells compared to crotamine. Fluorescence microscopy suggests different predominant uptake mechanisms for crotamine and CyLoP-1 in 3T3 cells. While crotamine is predominantly localized in vesicular structures (most likely endosomes and lysosomes) within the cell, CyLoP-1 is mainly homogeneously distributed in the cytosol. The cellular relaxation rate (R1, cell) of the crotamine based probe was not significantly increased whereas the corresponding CyLoP-1-derivative showed a slightly elevated R1, cell. This study indicates the potential of crotamine and in particular the shorter fragment CyLoP-1 to be useful for an efficient transmembrane delivery of agents directed to intracellular (cytosolic) targets. However, the applicability of the conjugates synthesized here as contrast agents in MR imaging is limited. Further improvement is needed to prepare more efficient probes for MRI applications, i.e., by replacing the DO3A- with a DOTA-based chelate.

MC1R diversity and its role in skin pigmentation variation in West Maharashtra, India.

OBJECTIVES: MC1R polymorphisms have been reported to be under a selective constraint in populations inhabiting high UVR regions such as Africans; however, these patterns are not consistent. Here we analyze the MC1R gene in West Maharashtra, India to see if sequence diversity corresponds to their diverse pigmentary profiles and if MC1R is constrained in dark skinned tribal as compared to lighter skinned caste populations. METHODS: A 2648 bp region of this gene was sequenced in 102 individuals and the data was compared for π, Ï´ diversity indices. Tajima's D was assessed for signatures of purifying selection and MC1R variants were associated with MI measures using the additive, dominant, and recessive models. Pairwise FST was tested among study populations and between study populations and 1000 Genomes regional samples. RESULTS: MC1R diversity was not uniquely patterned among castes and tribes. Non-synonymous variants rs2228479A, rs1805007_T, and rs885479_A showed low variability in these populations. Selection tests did not indicate any constraint on MC1R and pairwise FST were also low among the study populations (-0.0163 to 0.06112). The SNP rs3212359 was significantly associated with MI measures when tested using different association models. CONCLUSIONS: We do not find evidence of a selective constraint on MC1R. The presence of a large number of unique haplotypes and low FST values at this locus suggests that MC1R polymorphisms may not be influencing pigmentation variation among castes and tribes in this region. Observed associations between rs3212359 and MI measures need to be validated through studies on larger samples and in-vitro functional studies.

Artificial Intelligence for Autonomous Molecular Design: A Perspective.

Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.

An insight into the inhibitory mechanism of phytochemicals and FDA-approved drugs on the ACE2-Spike complex of SARS-CoV-2 using computational methods.

The S-glycoprotein (Spike) of the SARS-CoV-2 forms a complex with the human transmembrane protein angiotensin-converting enzyme 2 (ACE2) during infection. It forms the first line of contact with the human cell. The FDA-approved drugs and phytochemicals from Indian medicinal plants were explored. Molecular docking and simulations of these molecules targeting the ACE2-Spike complex were performed. Rutin DAB10 and Swertiapuniside were obtained as the top-scored drugs as per the docking protocol. The MD simulations of ligand-free, Rutin DAB10-bound, and Swertiapuniside-bound ACE2-Spike complex revealed abrogation of the hydrogen bonding network between the two proteins. The principal component and dynamic cross-correlation analysis pointed out conformational changes in both the proteins unique to the ligand-bound systems. The interface residues, His34, and Lys353 from ACE2 and Arg403, and Tyr495 from the Spike protein formed significant strong interactions with the ligand molecules, inferring the inhibition of ACE2-Spike complex. Few novel interactions specific to Rutin-DAB10 and Swertiapuniside were also identified. The conformational flexibility of the drug-binding pocket was captured using the RMSD-based clustering of the ligand-free simulations. Ensemble docking was performed wherein the FDA-approved database and phytochemical dataset were docked on each of the cluster representatives of the ACE2-Spike. The phytochemicals identified belonged to Withania somnifera, Swertia chirayita, Tinospora cordifolia and Rutin DAB10, fulvestrant, elbasvir from FDA. Supplementary Information: The online version contains supplementary material available at 10.1007/s11696-021-01680-1.

Controlled shape morphing of solvent free thermoresponsive soft actuators.

High performance thermoresponsive soft, controllable and reversible actuators are highly desirable for diverse applications. The practical implementation of the existing poly(N-isopropylacrylamide) (pNipam) based soft thermoresponsive actuators faces serious limitations due to their functional requirement of proximal bulk solvent medium. In this work, addressing this issue, we report the development of a bilayer based actuator composed of a solvent responsive biodegradable polymer and temperature responsive pNipam. The designed bilayer is capable of achieving reversible and irreversible actuation as needed when exposed to a physiological range of body temperature, without any solvent bath around. The solvent or water supplied by the pNipam layer at its lower critical solution temperature (LCST) builds a concentration gradient across the thickness of the polymer layer. The concentration gradient results in a strain gradient, causing an out-of-plane folding of the bilayer. The underlying coupled diffusion-deformation interaction during folding and unfolding is incorporated in the reported finite element model, capable of predicting actuation characteristics under different initial conditions. The combined experimental and modelling effort in this work highlights the possibility of engineering 2-dimensional films into complex 3-dimensional shapes, which could have potential applications in soft machines and robotics.

TANGO: A high through-put conformation generation and semiempirical method-based optimization tool for ligand molecules.

Lead optimization is one of the crucial steps in the drug discovery pipeline. After identifying the lead molecule and obtaining its 2D geometry, understanding the best conformation it would attain in 3D still remains one of the most challenging steps in drug discovery. There have been multiple methods and algorithms that are directed toward achieving best conformation for the lead molecules. TANGO focuses on conformation generation and its optimization using semiempirical energy calculations. The conformation generation is based on torsion angle rotation of the exocyclic bonds. The energy calculations are performed using MOPAC. The unique feature of this tool lies in the implementation of Message Passing Interface (MPI) for conformation generation and semiempirical-based optimization. A well-defined architecture handling the input and output generation has been used. The master and slave approach to handle operations involved in torsion angle rotation and energy calculations has helped in load balancing the process of conformation generation. The benchmarking results suggest that TANGO scales significantly well across eight nodes with each node utilizing 16 cores. This tool may prove to very useful in high throughput generation of semiempirically optimized small molecule conformations. The use of semiempirical methods for optimization generates a conformational ensemble thereby helping to obtain stable and alternate stable conformers for a given ligand molecule. © 2018 Wiley Periodicals, Inc.

Acetylcholinesterase and Aß Aggregation Inhibition by Heterometallic Ruthenium(II)-Platinum(II) Polypyridyl Complexes.

Two heteronuclear ruthenium(II)-platinum(II) complexes [Ru(bpy)2(BPIMBp)PtCl2]2+ (3) and [Ru(phen)2(BPIMBp)PtCl2]2+ (4), where bpy = 2,2'-bipyridine, phen = 1,10-phenanthroline, and BPIMBp = 1,4'-bis[(2-pyridin-2-yl)-1H-imidazol-1-ylmethyl]-1,1'-biphenyl, have been designed and synthesized from their mononuclear precursors [Ru(bpy)2(BPIMBp)]2+ (1) and [Ru(phen)2(BPIMBp)]2+ (2) as multitarget molecules for Alzheimer's disease (AD). The inclusion of the cis-PtCl2 moiety facilitates the covalent interaction of Ru(II) polypyridyl complexes with amyloid ß (Aß) peptide. These multifunctional complexes act as inhibitors of acetylcholinesterase (AChE), Aß aggregation, and Cu-induced oxidative stress and protect neuronal cells against Aß-toxicity. The study highlights the design of metal based anti-Alzheimer's disease (AD) systems.

Shrinking Self-Interaction Errors with the Fermi-Löwdin Orbital Self-Interaction-Corrected Density Functional Approximation.

The self-interaction error (SIE) is one of the major drawbacks of practical exchange-correlation functionals for Kohn-Sham density functional theory. Despite this, the use of methods that explicitly remove SIE from approximate density functionals is scarce in the literature due to their relatively high computational cost and lack of consistent improvement over standard modern functionals. In this article we assess the performance of a novel approach recently proposed by Pederson, Ruzsinszky, and Perdew [ J. Chem. Phys. 2014, 140, 121103] for performing self-interaction free calculations in density functional theory based on Fermi orbitals. To this end, we employ test sets consisting of reaction energies that are considered particularly sensitive to SIE. We found that the parameter-free Fermi-Löwdin orbital self-interaction correction method combined with the standard local spin density approximation (LSDA) and Perdew-Burke-Ernzerhof (PBE) functionals gives a much better estimate of reaction energies compared to their parent LSDA and PBE functionals for most of the reactions in these two sets. They also perform on par with the global PBE0 and range-separated LC-ωPBE hybrids, which partially eliminate the SIE by including Hartree-Fock exchange. This shows the potential of the Fermi-Löwdin orbital self-interaction correction (FLOSIC) method for practical density functional calculations without SIE.

Fermi-Löwdin orbital self-interaction correction to magnetic exchange couplings.

We analyze the effect of removing self-interaction error on magnetic exchange couplings using the Fermi-Löwdin orbital self-interaction correction (FLOSIC) method in the framework of density functional theory (DFT). We compare magnetic exchange couplings obtained from self-interaction-free FLOSIC calculations with the local spin density approximation (LSDA) with several widely used DFT realizations and wave function based methods. To this end, we employ the linear H-He-H model system, six organic radical molecules, and [Cu2Cl6]2- as representatives of different types of magnetic interactions. We show that the simple self-interaction-free version of LSDA improves calculated couplings with respect to LSDA in all cases, even though the nature of the exchange interaction varies across the test set, and in most cases, it yields results comparable to modern hybrids and range-separated approximate functionals.

Identifying signatures of positive selection in pigmentation genes in two South Asian populations.

OBJECTIVES: Skin pigmentation is a polygenic trait showing wide phenotypic variations among global populations. While numerous pigmentation genes have been identified to be under positive selection among European and East populations, genes contributing to phenotypic variation in skin pigmentation within and among South Asian populations are still poorly understood. The present study uses data from the Phase 3 of the 1000 genomes project focusing on two South Asian populations-GIH (Gujarati Indian from Houston, Texas) and ITU (Indian Telugu from UK), so as to decode the genetic architecture involved in adaptation to ultraviolet radiation in South Asian populations. METHODS: Statistical tests included were (1) tests to identify deviations of the Site Frequency Spectrum (SFS) from neutral expectations (Tajima's D, Fay and Wu's H and Fu and Li's D* and F*), (2) tests focused on the identification of high-frequency haplotypes with extended linkage disequilibrium (iHS and Rsb), and (3) tests based on genetic differentiation between populations (LSBL). RESULTS: Twenty-two pigmentation genes fall in the top 1% for at least one statistic in the GIH population, 5 of which (LYST, OCA2, SLC24A5, SLC45A2, and TYR) have been previously associated with normal variation in skin, hair, or eye color. In comparison, 17 genes fall in the top 1% for at least one statistic in the ITU population. Twelve loci which are identified as outliers in the ITU scan were also identified in the GIH population. CONCLUSIONS: These results suggest that selection may have affected these loci broadly across the region.

Auto-regulation of Slug mediates its activity during epithelial to mesenchymal transition.

Slug, a five C2H2 zinc finger (ZF) motif transcription factor mediates cell migration in development, adult tissue repair and regeneration, as well as during tumor metastases through epithelial to mesenchymal transition. At the molecular level, this involves interactions with E-box (CACC/GGTG) consensus elements within target gene promoters to achieve transcriptional repression. However, precise elucidation of events involved in this DNA recognition and binding of specific promoters to regulate target genes have not been achieved. In the present study, we show that besides transcriptional repression, Slug can also directly activate its own expression by preferential binding to specific E-box elements in the distal binding region of its promoter. Our findings suggest that while the first ZF does not contribute to the transcription-associated functions of Slug, all the remaining four ZFs are involved in regulating the expression of target genes with ZF3 and ZF4 being more crucial than ZF2 or ZF5. We also report that recognition and binding preferences of ZFs are defined through intrinsic differences in the E-box core base pairs and/or flanking sequences, with the S2 E-box element being most critical during autoregulation. However, specific target E-box recognition and binding are also defined by the cellular context, which implies that in silico and/or biochemical DNA binding preferences may not necessarily be able to accurately predict in situ events. Our studies thus constitute a novel understanding of transcriptional regulation.