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Alzheimer's disease (AD) stands as the predominant contributor to dementia cases. The ongoing developments in our understanding of its pathogenesis have sparked the interest of researchers, driving them to explore innovative treatment approaches. Existing therapies incorporating cholinesterase inhibitors and/or NMDA antagonists have shown limited improvement in alleviating symptoms. This, in turn, highlights the urgency for the pursuit of more effective therapeutic options. Given the annual rise in the number of individuals affected by dementia, it is imperative to allocate resources and efforts towards the exploration of novel therapeutic options. This review aims to provide a comprehensive overview of the AD-related hypotheses, along with the computational approaches employed in research within each hypothesis. In this comprehensive review, the authors shed light on using various computational tools, including diverse case studies, in the pursuit of finding efficacious treatments for AD. The development of more sophisticated diagnostic techniques is crucial, enabling early detection and intervention in the battle against this challenging condition. The potential treatments investigated in this analysis are poised to assume ever more significant functions in both preventing and treating AD, ultimately enhancing the management of the condition and the overall well-being of individuals affected by AD.

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Cellular DNA is constantly exposed to endogenous or exogenous factors that can induce lesions. Several types of lesions have been described that can result from UV/ionizing irradiations, oxidative stress, or free radicals, among others. In order to overcome the deleterious effects of such damages, i.e., mutagenicity or cytotoxicity, cells possess a highly complex DNA repair machinery, involving repair enzymes targeting specific types of lesions through dedicated cellular pathways. In addition, DNA is highly compacted in the nucleus, the first level of compaction consisting of ~ 147 DNA base pairs wrapped around a core of histones, the so-called nucleosome core particle. In this complex environment, the DNA structure is highly constrained, and fine-tuned mechanisms involving remodeling processes are required to expose the DNA to repair enzymes and to facilitate the damage removal. However, these nucleosome-specific mechanisms remain poorly understood, and computational methods emerged only recently as powerful tools to investigate DNA damages in such complex systems as the nucleosome. In this mini-review, we summarize the latest advances brought out by computational approaches in the field, opening new exciting perspectives for the study of DNA damage and repair in the nucleosome context.

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PPIs, or protein-protein interactions, are essential for many biological processes. According to the findings, abnormal PPIs have been linked to several diseases, such as cancer and infectious and neurological disorders. Consequently, focusing on PPIs is a path toward disease treatment and a crucial tool for producing novel medications. Many methods exist to investigate PPIs, including low- and high-throughput studies. Since many PPIs have been discovered using in vitro and in vivo experimental approaches, the use of computational methods to predict PPIs has grown due to the expanding scale of PPI data and the intrinsic complexity of interacting mechanisms. Recognizing PPI networks offers a systematic means of predicting protein functions, and pathways that are included. These investigations can help uncover the underlying molecular mechanisms of complex phenotypes and clarify the biological processes related to health and diseases. Therefore, our goal in this study is to provide an overview of the latest and most popular approaches for investigating PPIs. We also overview some important clinical approaches based on the PPIs and how these interactions can be targeted.

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Rheumatoid Arthritis (RA) is a chronic, symmetrical inflammatory autoimmune disorder characterized by painful, swollen synovitis and joint erosions, which can cause damage to bone and cartilage and be associated with progressive disability. Despite expanded treatment options, some patients still experience inadequate response or intolerable adverse effects. Consequently, the treatment options for RA remain quite limited. The enzyme AKT1 is crucial in designing drugs for various human diseases, supporting cellular functions like proliferation, survival, metabolism, and angiogenesis in both normal and malignant cells. Therefore, AKT serine/threonine kinase 1 is considered crucial for targeting therapeutic strategies aimed at mitigating RA mechanisms. In this context, directing efforts toward AKT1 represents an innovative approach to developing new anti-arthritis medications. The primary objective of this research is to prioritize AKT1 inhibitors using computational techniques such as molecular modeling and dynamics simulation (MDS) and shape-based virtual screening (SBVS). A combined SBVS approach was employed to predict potent inhibitors against AKT1 by screening a pool of compounds sourced from the ChemDiv and IMPPAT databases. From the SBVS results, only the top three compounds, ChemDiv_7266, ChemDiv_2796, and ChemDiv_9468, were subjected to stability analysis based on their high binding affinity and favorable ADME/Tox properties. The SBVS findings have revealed that critical residues, including Glu17, Gly37, Glu85, and Arg273, significantly contribute to the successful binding of the highest-ranked lead compounds at the active site of AKT1. This insight helps to understand the specific binding mechanism of these leads in inhibiting RA, facilitating the rational design of more effective therapeutic agents.

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The outbreak of SARS-CoV-2, also known as the COVID-19 pandemic, is still a critical risk factor for both human life and the global economy. Although, several promising therapies have been introduced in the literature to inhibit SARS-CoV-2, most of them are synthetic drugs that may have some adverse effects on the human body. Therefore, the main objective of this study was to carry out an in-silico investigation into the medicinal properties of Petiveria alliacea L. (P. alliacea L.)-mediated phytocompounds for the treatment of SARS-CoV-2 infections since phytochemicals have fewer adverse effects compared to synthetic drugs. To explore potential phytocompounds from P. alliacea L. as candidate drug molecules, we selected the infection-causing main protease (Mpro) of SARS-CoV-2 as the receptor protein. The molecular docking analysis of these receptor proteins with the different phytocompounds of P. alliacea L. was performed using AutoDock Vina. Then, we selected the three top-ranked phytocompounds (myricitrin, engeletin, and astilbin) as the candidate drug molecules based on their highest binding affinity scores of -8.9, -8.7 and -8.3 (Kcal/mol), respectively. Then, a 100 ns molecular dynamics (MD) simulation study was performed for their complexes with Mpro using YASARA software, computed RMSD, RMSF, PCA, DCCM, MM/PBSA, and free energy landscape (FEL), and found their almost stable binding performance. In addition, biological activity, ADME/T, DFT, and drug-likeness analyses exhibited the suitable pharmacokinetics properties of the selected phytocompounds. Therefore, the results of this study might be a useful resource for formulating a safe treatment plan for SARS-CoV-2 infections after experimental validation in wet-lab and clinical trials.

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This review discusses the potential of liposomes as drug delivery systems for antimalarial therapies. Malaria continues to be a significant cause of mortality and morbidity, particularly among children and pregnant women. Drug resistance due to patient non-compliance and troublesome side effects remains a significant challenge in antimalarial treatment. Liposomes, as targeted and efficient drug carriers, have garnered attention owing to their ability to address these issues. Liposomes encapsulate hydrophilic and/or hydrophobic drugs, thus providing comprehensive and suitable therapeutic drug delivery. Moreover, the potential of passive and active drug delivery enables drug concentration in specific target tissues while reducing adverse effects. However, successful liposome formulation is influenced by various factors, including drug physicochemical characteristics and physiological barriers encountered during drug delivery. To overcome these challenges, researchers have explored modifications in liposome nanocarriers to achieve efficient drug loading, controlled release, and system stability. Computational approaches have also been adopted to predict liposome system stability, membrane integrity, and drug-liposome interactions, improving formulation development efficiency. By leveraging computational methods, optimizing liposomal drug delivery systems holds promise for enhancing treatment efficacy and minimizing side effects in malaria therapy. This review consolidates the current understanding and highlights the potential of liposome strategies against malaria.

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Recent evolution in drug repurposing has brought new anticipation, especially in the conflict against neurodegenerative diseases (NDDs). The traditional approach to developing novel drugs for these complex disorders is laborious, time-consuming, and often abortive. However, drug reprofiling which is the implementation of illuminating novel therapeutic applications of existing approved drugs, has shown potential as a promising strategy to accelerate the hunt for therapeutics. The advancement of computational approaches and artificial intelligence has expedited drug repurposing. These progressive technologies have enabled scientists to analyse extensive datasets and predict potential drug-disease interactions. By prospecting into the existing pharmacological knowledge, scientists can recognise potential therapeutic candidates for reprofiling, saving precious time and resources. Preclinical models have also played a pivotal role in this field, confirming the effectiveness and mechanisms of action of repurposed drugs. Several studies have occurred in recent years, including the discovery of available drugs that demonstrate significant protective effects in NDDs, relieve debilitating symptoms, or slow down the progression of the disease. These findings highlight the potential of repurposed drugs to change the landscape of NDD treatment. Here, we present an overview of recent developments and major advances in drug repurposing intending to provide an in-depth analysis of traditional drug discovery and the strategies, approaches and technologies that have contributed to drug repositioning. In addition, this chapter attempts to highlight successful case studies of drug repositioning in various therapeutic areas related to NDDs and explore the clinical trials, challenges and limitations faced by researchers in the field. Finally, the importance of drug repositioning in drug discovery and development and its potential to address discontented medical needs is also highlighted.

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Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic joint pain and inflammation, loss of mobility, which affects the quality of life. The etiology of RA is unknown, and there is no isolated cause for the development of this illness. Synovial inflammation, autoantibody generation and bone degradation leading to deformity are classic characteristics of RA. The search for a non-invasive biomarker is crucial for helping patients receive standard therapies leading to faster treatments, as diagnosis depends on invasive biopsies. Therefore, in the present investigation, using transcriptomics and proteomic approaches, potential genes involved in crucial networks in RA were identified. Gene expression datasets of two tissue types i.e. whole blood and synovial tissue were retrieved from the GEO database and used for transcriptomic analyses. Using SWATH-MS analysis, differentially expressed proteins were identified from collected saliva of RA patients. Through bioinformatics analysis, S100A8, also known as Calprotectin (a complex of S100A8/A9) or Calgranulin A, was found to be common among all tissue types. S100A8 was then further quantified in saliva of healthy volunteers and RA patients, where it was found that the protein's level in healthy controls was lowered when compared to RA patients. Through in-silico characterization, potential plant-based inhibitors of S100A8 were also identified, to reduce its pro-inflammatory effects. Thus, it may be important in the pathophysiology of RA and, in the future, might help guide targeted treatment. as well as act as a non-invasive diagnostic biomarker platform.

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Cancer persists as a global challenge necessitating continual innovation in treatment strategies. Despite significant advancements in comprehending the disease, cancer remains a leading cause of mortality worldwide, exerting substantial economic burdens on healthcare systems and societies. The emergence of drug resistance further complicates therapeutic efficacy, underscoring the urgent need for alternative approaches. Drug repurposing, characterized by the utilization of existing drugs for novel clinical applications, emerges as a promising avenue for addressing these challenges. Repurposed drugs, comprising FDA-approved (in other disease indications), generic, off-patent, and failed medications, offer distinct advantages including established safety profiles, cost-effectiveness, and expedited development timelines compared to novel drug discovery processes. Various methodologies, such as knowledge-based analyses, drug-centric strategies, and computational approaches, play pivotal roles in identifying potential candidates for repurposing. However, despite the promise of repurposed drugs, drug repositioning confronts formidable obstacles. Patenting issues, financial constraints associated with conducting extensive clinical trials, and the necessity for combination therapies to overcome the limitations of monotherapy pose significant challenges. This review provides an in-depth exploration of drug repurposing, covering a diverse array of approaches including experimental, re-engineering protein, nanotechnology, and computational methods. Each of these avenues presents distinct opportunities and obstacles in the pursuit of identifying novel clinical uses for established drugs. By examining the multifaceted landscape of drug repurposing, this review aims to offer comprehensive insights into its potential to transform cancer therapeutics.

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The utilization of evolutive models and algorithms for predicting the evolution of hepatic steatosis holds immense potential benefits. These computational approaches enable the analysis of complex datasets, capturing temporal dynamics and providing personalized prognostic insights. By optimizing intervention planning and identifying critical transition points, they promise to revolutionize our approach to understanding and managing hepatic steatosis progression, ultimately leading to enhanced patient care and outcomes in clinical settings. This paradigm shift towards a more dynamic, personalized, and comprehensive approach to hepatic steatosis progression signifies a significant advancement in healthcare. The application of evolutive models and algorithms allows for a nuanced characterization of disease trajectories, facilitating tailored interventions and optimizing clinical decision-making. Furthermore, these computational tools offer a framework for integrating diverse data sources, creating a more holistic understanding of hepatic steatosis progression. In summary, the potential benefits encompass the ability to analyze complex datasets, capture temporal dynamics, provide personalized prognostic insights, optimize intervention planning, identify critical transition points, and integrate diverse data sources. The application of evolutive models and algorithms has the potential to revolutionize our understanding and management of hepatic steatosis, ultimately leading to improved patient outcomes in clinical settings.

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Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a highly contagious and dangerous virus that caused the global COVID-19 pandemic in early 2020. It primarily affects the respiratory system, leading to severe illness and high rates of mortality worldwide. The virus enters the body by binding to a receptor called ACE2, which is present in specific cells of the lungs known as type 2 alveolar epithelial cells. Numerous studies have investigated the consequences of SARS-CoV-2 infection, revealing various impacts on the body. This review provides an overview of SARS-CoV-2, including its structure and how it infects cells. It also examines the different variants of concern, such as Alpha, Beta, Gamma, Delta, and the more recent Omicron variant, discussing their characteristics and the level of damage they cause. The usage of drugs to treat COVID-19 is another aspect that has been covered and compares the effectiveness and use of antiviral drugs in the treatment and its potential benefits in COVID-19 treatment. Furthermore, this review explores the consequences and abnormalities associated with SARS-CoV-2 infection, including its impact on various organs and systems in the body. And also discussing the different COVID-19 vaccines available and their effectiveness in preventing infection and reducing the severity of illness. The current review ensures the recent update of the COVID research with expert's knowledge, collection of numerous data from reliable sources and methodologies as well as update of findings based on reviews. This review also provided clear contextual explanations to aid the interpretation and application of the results. The main motto and limitation of this manuscript are to address the computational methods of drug discovery against the rapidly evolving SARS-CoV-2 virus, which has been discussed. Additionally, current computational approaches which are cost effective and can able to predict the therapeutic agents for the treatment against the virus have also been discussed.

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Seizure events can manifest as transient disruptions in the control of movements which may be organized in distinct behavioral sequences, accompanied or not by other observable features such as altered facial expressions. The analysis of these clinical signs, referred to as semiology, is subject to observer variations when specialists evaluate video-recorded events in the clinical setting. To enhance the accuracy and consistency of evaluations, computer-aided video analysis of seizures has emerged as a natural avenue. In the field of medical applications, deep learning and computer vision approaches have driven substantial advancements. Historically, these approaches have been used for disease detection, classification, and prediction using diagnostic data; however, there has been limited exploration of their application in evaluating video-based motion detection in the clinical epileptology setting. While vision-based technologies do not aim to replace clinical expertise, they can significantly contribute to medical decision-making and patient care by providing quantitative evidence and decision support. Behavior monitoring tools offer several advantages such as providing objective information, detecting challenging-to-observe events, reducing documentation efforts, and extending assessment capabilities to areas with limited expertise. The main applications of these could be (1) improved seizure detection methods; (2) refined semiology analysis for predicting seizure type and cerebral localization. In this paper, we detail the foundation technologies used in vision-based systems in the analysis of seizure videos, highlighting their success in semiology detection and analysis, focusing on work published in the last 7 years. We systematically present these methods and indicate how the adoption of deep learning for the analysis of video recordings of seizures could be approached. Additionally, we illustrate how existing technologies can be interconnected through an integrated system for video-based semiology analysis. Each module can be customized and improved by adapting more accurate and robust deep learning approaches as these evolve. Finally, we discuss challenges and research directions for future studies.

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Opioid Use Disorder (OUD) is a chronic and relapsing condition characterized by the misuse of opioid drugs, causing significant morbidity and mortality in the United States. Existing medications for OUD are limited, and there is an immediate need to discover treatments with enhanced safety and efficacy. Drug repurposing aims to find new indications for existing medications, offering a time-saving and cost-efficient alternative strategy to traditional drug discovery. Computational approaches have been developed to further facilitate the drug repurposing process. In this paper, we reviewed state-of-the-art data-driven computational drug repurposing approaches for OUD and discussed their advantages and potential challenges. We also highlighted promising repurposed candidate drugs for OUD that were identified by computational drug repurposing techniques and reviewed studies supporting their potential mechanisms of action in treating OUD.

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The development of new coatings based on a biopolymer, epichlorohydrin-modified alginate, and alginate-epichlorohydrin-SrTiO3 nanocomposites incorporating SrTiO3 (STO) nanoparticles in the alginate (Alg) matrix (Alg-Ep-STO), has been addressed in this study. Various characterization techniques were employed to analyze the prepared compounds, including X-ray diffraction spectroscopy (XRD), Fourier-transform infrared spectroscopy (FTIR), as well as surface analysis methods such as Scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDX). Furthermore, electrochemical impedance spectroscopy (EIS) and potentiodynamic polarisation (PDP) methods were used to evaluate corrosion inhibition and protection durability. The results demonstrate that the incorporation of STO nanoparticles into the alginate matrix with epichlorohydrin significantly improved the metal's resistance to corrosion. The experimental findings received reinforcement from various computational methods, including density functional theory (DFT), Molecular Dynamics (MD) and Monte Carlo (MC) simulations, which were employed to investigate the interactions between the Alg-Ep-STO nanocomposite and the copper surface. The computational outcomes revealed that the Alg-Ep-STO nanocomposite exhibits robust adhesion to the copper surface, maintaining a flat orientation, with its alignment being notably influenced by the presence of STO nanoparticles.

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Modeling longitudinal and survival data jointly offers many advantages such as addressing measurement error and missing data in the longitudinal processes, understanding and quantifying the association between the longitudinal markers and the survival events, and predicting the risk of events based on the longitudinal markers. A joint model involves multiple submodels (one for each longitudinal/survival outcome) usually linked together through correlated or shared random effects. Their estimation is computationally expensive (particularly due to a multidimensional integration of the likelihood over the random effects distribution) so that inference methods become rapidly intractable, and restricts applications of joint models to a small number of longitudinal markers and/or random effects. We introduce a Bayesian approximation based on the integrated nested Laplace approximation algorithm implemented in the R package R-INLA to alleviate the computational burden and allow the estimation of multivariate joint models with fewer restrictions. Our simulation studies show that R-INLA substantially reduces the computation time and the variability of the parameter estimates compared with alternative estimation strategies. We further apply the methodology to analyze five longitudinal markers (3 continuous, 1 count, 1 binary, and 16 random effects) and competing risks of death and transplantation in a clinical trial on primary biliary cholangitis. R-INLA provides a fast and reliable inference technique for applying joint models to the complex multivariate data encountered in health research.

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Macromolecules i.e., carbohydrate derivatives are crucial to biochemical and medical research. Herein, we designed and synthesized eight methyl α-D-glucopyranoside (MGP) derivatives (2-8) in good yields following the regioselective direct acylation method. The structural configurations of the synthesized MGP derivatives were analyzed and verified using multiple physicochemical and spectroscopic techniques. Antimicrobial experiments revealed that almost all derivatives demonstrated noticeable antifungal and antibacterial efficacy. The synthesized derivatives showed minimum inhibitory concentration (MIC) values ranging from 0.75 µg/mL to 1.50 µg/mL and minimum bactericidal concentrations (MBCs) ranging from 8.00 µg/mL to 16.00 µg/mL. Compound 6 inhibited Ehrlich ascites carcinoma (EAC) cell proliferation by 10.36% with an IC50 of 2602.23 µg/mL in the MTT colorimetric assay. The obtained results were further rationalized by docking analysis of the synthesized derivatives against 4URO and 4XE3 receptors to explore the binding affinities and nonbonding interactions of MGP derivatives with target proteins. Compound 6 demonstrated the potential to bind with the target with the highest binding energy. In a stimulating environment, a molecular dynamics study showed that MGP derivatives have a stable conformation and binding pattern. The MGP derivatives were examined using POM (Petra/Osiris/Molinspiration) bioinformatics, and as a result, these derivatives showed good toxicity, bioavailability, and pharmacokinetics. Various antifungal/antiviral pharmacophore (Oδ-, O'δ-) sites were identified by using POM investigations, and compound 6 was further tested against other pathogenic fungi and viruses, such as Micron and Delta mutants of SARS-CoV-2.

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The study reported the antimicrobial efficacy of chemically characterized Coleus aromaticus essential oil (CEO) against food-borne bacteria, molds (Aspergillus flavus), aflatoxin B1 (AFB1) and explored its mechanism of action using biochemical and molecular simulation approaches. The chemical profile of CEO was explored by Gas chromatography-mass spectrometry (GC-MS) analysis, which revealed thymol (46.0%) as the major compound. The minimum inhibitory concentration values of CEO for bacterial species Escherichia coli, Salmonella enterica, Bacillus cereus, and Shigella flexneri was found to be 0.9 µl/ml, 0.7 µl/ml, 0.16 µl/ml, and 0.12 µl/ml respectively. The MIC value for A. flavus and AFB1 contamination was 0.6 µl/ml. The DPPH radical scavenging activity of CEO was recorded with IC50 0.32 µl/ml. Biochemical and computational approaches (docking and dynamics simulation) have been performed to explore the multi-faceted antimicrobial inhibitory effects of CEO at the molecular level, which shows the impairment in membrane functioning, leakage of cellular contents, release of 260-nm absorbing materials, antioxidative defense, carbon catabolism and vital genes (7AP3, Nor1, Omt1, and Vbs). The findings indicated that CEO could be used as natural antimicrobial agents against food-spoilage bacteria, A. flavus and AFB1 contamination to extend the shelf-life of food product and prevention of food-borne diseases.

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Neurodegenerative disorders are characterized by a gradual but irreversible loss of neurological function. The ability to detect and treat these conditions successfully is crucial for ensuring the best possible quality of life for people who suffer from them. The development of effective new methods for managing and treating neurodegenerative illnesses has been made possible by recent developments in computer technology. In this overview, we take a look at the prospects for applying computational approaches, such as drug design, AI, ML, and DL, to the treatment of neurodegenerative diseases. To review the current state of the field, this article discusses the potential of computational methods for early disease detection, quantifying disease progression, and understanding the underlying biological mechanisms of neurodegenerative diseases, as well as the challenges associated with these approaches and potential future directions. Moreover, it delves into the creation of computational models for the individualization of care for neurodegenerative diseases. The article concludes with suggestions for future studies and clinical applications, highlighting the advantages and disadvantages of using computational techniques in the treatment of neurodegenerative diseases.

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Keloids are fibroproliferative disorders described by excessive growth of fibrotic tissue, which also invades adjacent areas (beyond the original wound borders). Since these disorders are specific to humans (no other animal species naturally develop keloid-like tissue), experimental in vivo/in vitro research has not led to significant advances in this field. One possible approach could be to combine in vitro human models with calibrated in silico mathematical approaches (i.e., models and simulations) to generate new testable biological hypotheses related to biological mechanisms and improved treatments. Because these combined approaches do not really exist for keloid disorders, in this brief review we start by summarising the biology of these disorders, then present various types of mathematical and computational approaches used for related disorders (i.e., wound healing and solid tumours), followed by a discussion of the very few mathematical and computational models published so far to study various inflammatory and mechanical aspects of keloids. We conclude this review by discussing some open problems and mathematical opportunities offered in the context of keloid disorders by such combined in vitro/in silico approaches, and the need for multi-disciplinary research to enable clinical progress.

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Brain tumor segmentation from magnetic resonance imaging (MRI) scans is critical for the diagnosis, treatment planning, and monitoring of therapeutic outcomes. Thus, this research introduces a novel hybrid approach that combines handcrafted features with convolutional neural networks (CNNs) to enhance the performance of brain tumor segmentation. In this study, handcrafted features were extracted from MRI scans that included intensity-based, texture-based, and shape-based features. In parallel, a unique CNN architecture was developed and trained to detect the features from the data automatically. The proposed hybrid method was combined with the handcrafted features and the features identified by CNN in different pathways to a new CNN. In this study, the Brain Tumor Segmentation (BraTS) challenge dataset was used to measure the performance using a variety of assessment measures, for instance, segmentation accuracy, dice score, sensitivity, and specificity. The achieved results showed that our proposed approach outperformed the traditional handcrafted feature-based and individual CNN-based methods used for brain tumor segmentation. In addition, the incorporation of handcrafted features enhanced the performance of CNN, yielding a more robust and generalizable solution. This research has significant potential for real-world clinical applications where precise and efficient brain tumor segmentation is essential. Future research directions include investigating alternative feature fusion techniques and incorporating additional imaging modalities to further improve the proposed method's performance.