The novelty and the essential purpose of this research is the preparation of new anti-inflammatory iron complexes in water green solvent using critical micelle concentration of anionic surface active agent (SAA). Three new anti-inflammatory iron complexes have been prepared. Thiophene-electron (es) donor (D) Schiff base (2-(2-OH-benzylidene)-amino)-4, 5, 6, 7-tetrah ydrobenzo[b] thiophene-3-carbonitrile) has been prepared. Molecular structures of all samples were confirmed based on CNH analysis, 1H NMR and 13C NMR spectra. The molecular structure of Schiff base is further confirmed by computational chemistry using the DFT-B3LYP method, 6-31G (d) basis set. Observed and simulated 1H NMR, UV-Vis. IR/Raman spectra confirmed the molecular structure of D. This Schiff base is intercalated to ferric chloride (FeCl3) giving pure iron charge transfer complex (CTCs). In vitro and kinetic studies confirmed Fe-CTC complexes had (concentration-dependent) potent antimicrobial-, good anti-inflammatory activities. Free radical scavenging activity nitrous oxide (NO.) of Fe (III)CTCs is attributed to geometry Fe(III) ions as distorted octahedral (either monoclinic or triclinic single crystals) via functional groups (-C]N-O, NH2). Elemental analysis and EDS spectra confirmed strong binding between iron and hetero atoms (N, S, O) of D molecules.

Dialysis membranes are not hemocompatible with human blood, as the patients are suffering from the blood-membrane interactions\' side effects. Zwitterionic structures have shown improved hemocompatibility; however, their complicated synthesis hinders their commercialization. The goal of the study is to achieve fast functionalization for carboxybetaine and sulfobetaine zwitterionic immobilization on PES membranes while comparing the stability and the targeted hemocompatibility. The chemical modification approach is based on an aminolysis reaction. Characterization, computational simulations, and clinical analysis were conducted to study the modified membranes. Atomic force microscopy (AFM) patterns showed a lower mean roughness for carboxybetaine-modified (6.3 nm) and sulfobetaine-modified (7.7 nm) membranes compared to the neat membrane (52.61 nm). The pore size of the membranes was reduced from values above 50 nm for the neat PES to values between 2 and 50 nm for zwitterionized membranes, using Brunauer-Emmett-Teller (BET) analysis. More hydrophilic surfaces led to a growth equilibrium water content (EWC) of nearly 6% for carboxybetaine and 10% for sulfobetaine-modified membranes. Differential scanning calorimetry (DSC) measurements were 12% and 16% stable water for carboxybetaine- and sulfobetaine-modified membranes, respectively. Sulfobetaine membranes showed better compatibility with blood with respect to C5a, IL-1a, and IL-6 biomarkers. Aminolysis-based zwitterionization was found to be suitable for the improvement of hemodialysis membranes. The approach introduced in this paper could be used to modify the current dialysis membranes with minimal change in the production facilities.

Next-generation pathogenicity predictors are designed to identify pathogenic mutations in genetic disorders but are increasingly used to detect driver mutations in cancer. Despite this, their suitability for cancer is not fully established. Here we have assessed the effectiveness of next-generation pathogenicity predictors when applied to cancer by using a comprehensive experimental benchmark of cancer driver and neutral mutations. Our findings indicate that state-of-the-art methods AlphaMissense and VARITY demonstrate commendable performance despite generally underperforming compared to cancer-specific methods. This is notable considering that these methods do not explicitly incorporate cancer-related data in their training and have made concerted efforts to prevent data leakage from the human-curated training and test sets. Nevertheless, it should be mentioned that a significant limitation of using pathogenicity predictors for cancer arises from their inability to detect cancer potential driver mutations specific for a particular cancer type.

To be viable therapeutics, antibodies must be tolerated by the human immune system. Rational approaches to reduce the risk of unwanted immunogenicity involve maximizing the \'humanness\' of the candidate drug. However, despite the emergence of new discovery technologies, many of which start from entirely human gene fragments, most antibody therapeutics continue to be derived from non-human sources with concomitant humanization to increase their human compatibility. Early experimental humanization strategies that focus on CDR loop grafting onto human frameworks have been critical to the dominance of this discovery route but do not consider the context of each antibody sequence, impacting their success rate. Other challenges include the simultaneous optimization of other drug-like properties alongside humanness and the humanization of fundamentally non-human modalities such as nanobodies. Significant efforts have been made to develop in silico methodologies able to address these issues, most recently incorporating machine learning techniques. Here, we outline these recent advancements in antibody and nanobody humanization, focusing on computational strategies that make use of the increasing volume of sequence and structural data available and the validation of these tools. We highlight that structural distinctions between antibodies and nanobodies make the application of antibody-focused in silico tools to nanobody humanization non-trivial. Furthermore, we discuss the effects of humanizing mutations on other essential drug-like properties such as binding affinity and developability, and methods that aim to tackle this multi-parameter optimization problem.

Multiple sclerosis (MS), an intricate neurological disorder, continues to challenge our understanding of the pivotal interplay between the immune system and the central nervous system (CNS). This condition arises from the immune system\'s misdirected attack on nerve fiber protection, known as myelin sheath, alongside nerve fibers themselves. This enigmatic condition, characterized by demyelination and varied clinical manifestations, prompts exploration into its multifaceted etiology and potential therapeutic avenues. Research has revealed a potential connection between Epstein Barr virus (EBV), specifically Epstein Barr Nuclear Antigen 1 (EBNA-1), and MS. The immune response to EBNA-1 antigen triggers the production of anti-EBNA-1 molecules, including IgG that identify a similar amino acid sequence to EBNA-1 in myelin, inadvertently targeting myelin sheath and contributing to MS progression. Currently, no treatment exists for EBNA-1-induced MS apart from symptom management. Addressing this, a novel potential therapeutic avenue utilizing small interference RNAs (siRNA) has been designed. By targeting the conserved EBNA-1 gene sequences in EBV types 1 and 2, five potential siRNAs were identified in our analysis. Thorough evaluations encompassing off-target binding, thermodynamics and secondary structure elucidation, efficacy prediction, siRNA-mRNA sequence binding affinity exploration, melting temperature, and docking of siRNAs with human argonaute protein 2 (AGO2) were conducted to elucidate the siRNAs efficiency. These designed siRNA molecules harnessed promising silencing activity in the EBNA-1 gene encoding the EBNA-1 antigen protein and thus have the potential to mitigate the severity of this dangerous virus.

The drug discovery and development (DDD) process greatly relies on the data available in various forms to generate hypotheses for novel drug design. The complex and heterogeneous nature of biological data makes it difficult to utilize or gather meaningful information as such. Computational biology techniques have provided us with opportunities to better understand biological systems through refining and organizing large amounts of data into actionable and systematic purviews. The drug repurposing approach has been utilized to overcome the expansive time periods and costs associated with traditional drug development. It deals with discovering new uses of already approved drugs that have an established safety and efficacy profile, thereby, requiring them to go through fewer development phases. Thus, drug repurposing through computational biology provides a systematic approach to drug development and overcomes the constraints of traditional processes. The current chapter covers the basics, approaches and tools of computational biology that can be employed to effectively develop repurposing profile of already approved drug molecules.

Devaluation of alcohol leads to reductions in alcohol choice and consumption; however, the cognitive mechanisms that underpin this relationship are not well-understood. In this study we applied a computational model of value-based decision-making (VBDM) to decisions made about alcohol and alcohol-unrelated cues following experimental manipulation of alcohol value.
Using a pre-registered within-subject design, thirty-six regular alcohol consumers (≥14 UK units per week) completed a two-alternative forced choice task where they chose between two alcohol images (in one block) or two soft drink images (in a different block) after watching videos that emphasised the positive (alcohol value), and separately, the negative (alcohol devalue) consequences of alcohol. On each block, participants pressed a key to select the image depicting the drink they would rather consume. A drift-diffusion model (DDM) was fitted to reaction time and choice data to estimate evidence accumulation (EA) processes and response thresholds during the different blocks in each experimental condition.
In the alcohol devalue condition, soft drink EA rates were significantly higher compared to alcohol EA rates (p = 0.04, d = 0.31), and compared to soft drink EA rates in the alcohol value condition (p = 0.01, d = 0.38). However, EA rates for alcoholic drinks and response thresholds (for either drink type) were unaffected by the experimental manipulation.
In line with behavioural economic models of addiction that emphasise the important role of alternative reinforcement, experimentally manipulating alcohol value is associated with changes in the internal cognitive processes that precede soft drink choice.

OBJECTIVE: This study was undertaken to validate a set of candidate biomarkers of seizure susceptibility in a retrospective, multisite case-control study, and to determine the robustness of these biomarkers derived from routinely collected electroencephalography (EEG) within a large cohort (both epilepsy and common alternative conditions such as nonepileptic attack disorder).
METHODS: The database consisted of 814 EEG recordings from 648 subjects, collected from eight National Health Service sites across the UK. Clinically noncontributory EEG recordings were identified by an experienced clinical scientist (N = 281; 152 alternative conditions, 129 epilepsy). Eight computational markers (spectral [n = 2], network-based [n = 4], and model-based [n = 2]) were calculated within each recording. Ensemble-based classifiers were developed using a two-tier cross-validation approach. We used standard regression methods to assess whether potential confounding variables (e.g., age, gender, treatment status, comorbidity) impacted model performance.
RESULTS: We found levels of balanced accuracy of 68% across the cohort with clinically noncontributory normal EEGs (sensitivity =61%, specificity =75%, positive predictive value =55%, negative predictive value =79%, diagnostic odds ratio =4.64, area under receiver operated characteristics curve =.72). Group level analysis found no evidence suggesting any of the potential confounding variables significantly impacted the overall performance.
CONCLUSIONS: These results provide evidence that the set of biomarkers could provide additional value to clinical decision-making, providing the foundation for a decision support tool that could reduce diagnostic delay and misdiagnosis rates. Future work should therefore assess the change in diagnostic yield and time to diagnosis when utilizing these biomarkers in carefully designed prospective studies.

So far, the cellular origin of glioblastoma (GBM) needs to be determined, with prevalent theories suggesting emergence from transformed endogenous stem cells. Adult neurogenesis primarily occurs in two brain regions: the subventricular zone (SVZ) and the subgranular zone (SGZ) of the hippocampal dentate gyrus. Whether the proximity of GBM to these neurogenic niches affects patient outcome remains uncertain. Previous studies often rely on subjective assessments, limiting the reliability of those results. In this study, we assessed the impact of GBM\'s relationship with the cortex, SVZ and SGZ on clinical variables using fully automated segmentation methods. In 177 glioblastoma patients, we calculated optimal cutpoints of minimal distances to the SVZ and SGZ to distinguish poor from favorable survival. The impact of tumor contact with neurogenic zones on clinical parameters, such as overall survival, multifocality, MGMT promotor methylation, Ki-67 and KPS score was also examined by multivariable regression analysis, chi-square test and Mann-Whitney-U. The analysis confirmed shorter survival in tumors contacting the SVZ with an optimal cutpoint of 14 mm distance to the SVZ, separating poor from more favorable survival. In contrast, tumor contact with the SGZ did not negatively affect survival. We did not find significant correlations with multifocality or MGMT promotor methylation in tumors contacting the SVZ, as previous studies discussed. These findings suggest that the spatial relationship between GBM and neurogenic niches needs to be assessed differently. Objective measurements disprove prior assumptions, warranting further research on this topic.

The current study aimed to investigate the influence of taxifolin on depression symptoms alleviation in Male Sprague-Dawley rats by targeting underlying pathways of depression. Molecular docking analyses were conducted to validate taxifolin\'s binding affinities against various targets. In silico analysis of taxifolin revealed various aspects of post docking interactions with different protein targets. Depression was induced in rats via intraperitoneal injection of Lipopolysaccharide (LPS; 500 μ g/Kg) for 14 alternative days. Rats (n = 6/group) were randomly assigned to four groups: (i) Saline/Control, (ii) Disease (LPS 500 μg/kg), (iii) Standard (fluoxetine 20 mg/kg), and (iv) Treatment (taxifolin 20 mg/kg). At the end of the in vivo study, brain samples were used for biochemical and morphological analysis. Taxifolin exhibited neuroprotective effects, as evidenced by behavioral studies, antioxidant analysis, histopathological examination, immunohistochemistry, ELISA and RT PCR, indicating an increase number of surviving neurons, normalization of cell size and shape, and reduction in vacuolization. Taxifolin also decreased inflammatory markers such as TNF-α, NF-κb, IL-6 and COX-2, while significantly upregulating and activating the protective PPAR-γ pathway, through which it reduces the oxidative stress, neuroinflammation, neurodegeneration, thereby ameliorating depression symptoms in experimental rat model of depression. Our finding suggests that taxifolin act as neuroprotective agent partially mediated through PPAR-γ pathway.