Investigating the Impact of First-Line Anti-Tuberculosis Drugs Encapsulated in a Eugenol-Based Nanoemulsion on Human Serum Albumin.

BACKGROUND: Eugenol exhibits broad-spectrum antibacterial and anti-inflammatory properties. However, cytotoxicity at high concentrations limits the full utilization of eugenol-based drug complexes. Formulations of multidrug-loaded eugenol-based nanoemulsions have reduced cytotoxicity; however, it remains crucial to understand how these eugenol complexes interact with primary human carrier proteins to design and develop therapeutic alternatives. Consequently, this study primarily aims to investigate the impact on Human Serum Albumin (HSA) when it interacts with eugenol-based complexes loaded with first-line anti-tuberculosis drugs. METHODS: This study used various spectroscopic such as UV-visible spectroscopy, Fluorescence spectroscopy, Fourier-transform infrared spectroscopy and computational methods such as molecular docking and 100 ns molecular simulation to understand the impact of eugenol-based first-line anti-tuberculosis drug-loaded nanoemulsions on HSA structure. RESULTS: The binding of the HSA protein and eugenol-based complexes was studied using UV-visible spectroscopic analysis. Minor changes in the fluorophores of the protein further confirmed binding upon interaction with the complexes. The Fourier-transform infrared spectra showed no significant changes in protein structure upon interaction with eugenol-based multidrug-loaded nanoemulsions, suggesting that this complex is safe for internal administration. Unlike eugenol or first-line anti-tuberculosis alone, molecular docking revealed the strength of the binding interactions between the complexes and the protein through hydrogen bonds. The docked complexes were subjected to a 100 ns molecular dynamics simulation, which strongly supported the conclusion that the structure and stability of the protein were not compromised by the interaction. CONCLUSIONS: From the results we could comprehend that the eugenol (EUG)-drug complex showed greater stability in HSA protein structure when compared to HSA interacting with isoniazid (INH), rifampicin (RIF), pyrazinamide (PYR), or ethambutol (ETH) alone or with EUG alone. Thus, inferring the potential of EUG-based drug-loaded formulations for a safer and efficient therapeutic use.

How Precise are Nanomedicines in Overcoming the Blood-Brain Barrier? A Comprehensive Review of the Literature.

New nanotechnology strategies for enhancing drug delivery in brain disorders have recently received increasing attention from drug designers. The treatment of neurological conditions, including brain tumors, stroke, Parkinson's Disease (PD), and Alzheimer's disease (AD), may be greatly influenced by nanotechnology. Numerous studies on neurodegeneration have demonstrated the effective application of nanomaterials in the treatment of brain illnesses. Nanocarriers (NCs) have made it easier to deliver drugs precisely to where they are needed. Thus, the most effective use of nanomaterials is in the treatment of various brain diseases, as this amplifies the overall impact of medication and emphasizes the significance of nanotherapeutics through gene therapy, enzyme replacement therapy, and blood-barrier mechanisms. Recent advances in nanotechnology have led to the development of multifunctional nanotherapeutic agents, a promising treatment for brain disorders. This novel method reduces the side effects and improves treatment outcomes. This review critically assesses efficient nano-based systems in light of obstacles and outstanding achievements. Nanocarriers that transfer medications across the blood-brain barrier and nano-assisted therapies, including nano-immunotherapy, nano-gene therapy, nano enzyme replacement therapy, scaffolds, and 3D to 6D printing, have been widely explored for the treatment of brain disorders. This study aimed to evaluate existing literature regarding the use of nanotechnology in the development of drug delivery systems that can penetrate the blood-brain barrier (BBB) and deliver therapeutic agents to treat various brain disorders.

Comparative Analysis of Machine Learning Algorithms Evaluating the Single Nucleotide Polymorphisms of Metabolizing Enzymes with Clinical Outcomes Following Intravenous Paracetamol in Preterm Neonates with Patent Ductus Arteriosus.

AIMS: Pharmacogenomics has been identified to play a crucial role in determining drug response. The present study aimed to identify significant genetic predictor variables influencing the therapeutic effect of paracetamol for new indications in preterm neonates. BACKGROUND: Paracetamol has recently been preferred as a first-line drug for managing Patent Ductus Arteriosus (PDA) in preterm neonates. Single Nucleotide Polymorphisms (SNPs) in CYP1A2, CYP2A6, CYP2D6, CYP2E1, and CYP3A4 have been observed to influence the therapeutic concentrations of paracetamol. OBJECTIVES: The purpose of this study was to evaluate various Machine Learning Algorithms (MLAs) and bioinformatics tools for identifying the key genotype predictor of therapeutic outcomes following paracetamol administration in neonates with PDA. METHODS: Preterm neonates with hemodynamically significant PDA were recruited in this prospective, observational study. The following SNPs were evaluated: CYP2E1*5B, CYP2E1*2, CYP3A4*1B, CYP3A4*2, CYP3A4*3, CYP3A5*3, CYP3A5*7, CYP3A5*11, CYP1A2*1C, CYP1A2*1K, CYP1A2*3, CYP1A2*4, CYP1A2*6, and CYP2D6*10. Amongst the MLAs, Artificial Neural Network (ANN), C5.0 algorithm, Classification and Regression Tree analysis (CART), discriminant analysis, and logistic regression were evaluated for successful closure of PDA. Generalized linear regression, ANN, CART, and linear regression were used to evaluate maximum serum acetaminophen concentrations. A two-step cluster analysis was carried out for both outcomes. Area Under the Curve (AUC) and Relative Error (RE) were used as the accuracy estimates. Stability analysis was carried out using in silico tools, and Molecular Docking Studies (MDS) were carried out for the above-mentioned enzymes. RESULTS: Two-step cluster analyses have revealed CYP2D6*10 and CYP1A2*1C to be the key predictors of the successful closure of PDA and the maximum serum paracetamol concentrations in neonates. The ANN was observed with the maximum accuracy (AUC = 0.53) for predicting the successful closure of PDA with CYP2D6*10 as the most important predictor. Similarly, ANN was observed with the least RE (1.08) in predicting maximum serum paracetamol concentrations, with CYP2D6*10 as the most important predictor. Further MDS confirmed the conformational changes for P34A and P34S compared to the wildtype structure of CYP2D6 protein for stability, flexibility, compactness, hydrogen bond analysis, and the binding affinity when interacting with paracetamol, respectively. The alterations in enzyme activity of the mutant CYP2D6 were computed from the molecular simulation results. CONCLUSION: We have identified CYP2D6*10 and CYP1A2*1C polymorphisms to significantly predict the therapeutic outcomes following the administration of paracetamol in preterm neonates with PDA. Prospective studies are required for confirmation of the findings in the vulnerable population.

Microplastic interactions in the agroecosystems: methodological advances and limitations in quantifying microplastics from agricultural soil.

The instantaneous growth of the world population is intensifying the pressure on the agricultural sector. On the other hand, the critical climate changes and increasing load of pollutants in the soil are imposing formidable challenges on agroecosystems, affecting productivity and quality of the crops. Microplastics are among the most prevalent pollutants that have already invaded all terrestrial and aquatic zones. The increasing microplastic concentration in soil critically impacts crop plants growth and yield. The current review elaborates on the behaviors of microplastics in soil and their impact on soil quality and plant growth. The study shows that microplastics alter the soil's biophysical properties, including water-holding capacity, bulk density, aeration, texture, and microbial composition. In addition, microplastics interact with multiple pollutants, such as polyaromatic hydrocarbons and heavy metals, making them more bioavailable to crop plants. The study also provides a detailed insight into the current techniques available for the isolation and identification of soil microplastics, providing solutions to some of the critical challenges faced and highlighting the research gaps. In our study, we have taken a holistic, comprehensive approach by analysing and comparing various interconnected aspects to provide a deeper understanding of all research perspectives on microplastics in agroecosystems.

Mechanistic Insights into the Binding of Boar Salivary Pheromones and Putative Molecule with Receptor Proteins: A Comparative Computational Approach.

Precise estrus detection in sows is pivotal in increasing the productivity within the pork industry. Sows in estrus exhibit exclusive behaviors when exposed to either a live boar or the steroid pheromones androstenone and androstenol. Recently, a study employing solid-phase microextraction-gas chromatography-mass spectrometry has identified a novel salivary molecule in boars, known as quinoline. This finding has intriguing implications as a synthetic mixture of androstenone, androstenol, and quinoline induces estrus behaviors in sows. Nevertheless, the precise pheromonal characteristics of quinoline remain elusive. In this study, we validate and compare the binding efficiency of androstenone, androstenol, and quinoline with porcine olfactory receptor proteins (odorant-binding protein [OBP], pheromaxein, salivary lipocalin [SAL], and Von Ebner's gland protein [VEGP]) using molecular docking and molecular dynamics simulations. All protein-ligand complexes demonstrated stability, as evidenced by the root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA), and hydrogen-bond (H-bond) plots. Furthermore, quinoline displayed higher binding efficiency with OBP, measured at -85.456 ± 8.268 kJ/mol, compared to androstenone and androstenol, as determined by molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) calculations. Conversely, quinoline exhibited a lower binding efficacy when interacting with SAL, pheromaxein, and VEGP compared to androstenone and androstenol. These findings, in part, suggest the binding possibility of quinoline with carrier proteins and warrant further investigation to support the role of quinoline in porcine chemical communication.

Identification of Potential Inhibitors Targeting GTPase-Kirsten RAt Sarcoma Virus (K-Ras) Driven Cancers via E-Pharmacophore-Based Virtual Screening and Drug Repurposing Approach.

BACKGROUND: Mutations in the K-Ras gene are among the most frequent genetic alterations in various cancers, and inhibiting RAS signaling has shown promising results in treating solid tumors. However, finding effective drugs that can bind to the RAS protein remains challenging. This drove us to explore new compounds that could inhibit tumor growth, particularly in cancers that harbor K-Ras mutations. METHODS: Our study used bioinformatic techniques such as E-pharmacophore virtual screening, molecular simulation, principal component analysis (PCA), extra precision (XP) docking, and ADMET analyses to identify potential inhibitors for K-Ras mutants G12C and G12D. RESULTS: In our study, we discovered that inhibitors such as afatinib, osimertinib, and hydroxychloroquine strongly inhibit the G12C mutant. Similarly, hydroxyzine, zuclopenthixol, fluphenazine, and doxapram were potent inhibitors for the G12D mutant. Notably, all six of these molecules exhibit a high binding affinity for the H95 cryptic groove present in the mutant structure. These molecules exhibited a unique affinity mechanism at the molecular level, which was further enhanced by hydrophobic interactions. Molecular simulations and PCA revealed the formation of stable complexes within switch regions I and II. This was particularly evident in three complexes: G12C-osimertinib, G12D-fluphenazine, and G12D-zuclopenthixol. Despite the dynamic nature of switches I and II in K-Ras, the interaction of inhibitors remained stable. According to QikProp results, the properties and descriptors of the selected molecules fell within an acceptable range compared to sotorasib. CONCLUSIONS: We have successfully identified potential inhibitors of the K-Ras protein, laying the groundwork for the development of targeted therapies for cancers driven by K-Ras mutations.

Evaluation of Supervised Machine Learning Algorithms and Computational Structural Validation of Single Nucleotide Polymorphisms Related to Acute Liver Injury with Paracetamol.

AIMS: To identify single nucleotide polymorphisms (SNPs) of paracetamol-metabolizing enzymes that can predict acute liver injury. BACKGROUND: Paracetamol is a commonly administered analgesic/antipyretic in critically ill and chronic renal failure patients and several SNPs influence the therapeutic and toxic effects. OBJECTIVE: To evaluate the role of machine learning algorithms (MLAs) and bioinformatics tools to delineate the predictor SNPs as well as to understand their molecular dynamics. METHODS: A cross-sectional study was undertaken by recruiting critically ill patients with chronic renal failure and administering intravenous paracetamol as a standard of care. Serum concentrations of paracetamol and the principal metabolites were estimated. Following SNPs were evaluated: CYP2E1*2, CYP2E1_-1295G>C, CYP2D6*10, CYP3A4*1B, CYP3A4*2, CYP1A2*1K, CYP1A2*6, CYP3A4*3, and CYP3A5*7. MLAs were used to identify the predictor genetic variable for acute liver failure. Bioinformatics tools such as Predict SNP2 and molecular docking (MD) were undertaken to evaluate the impact of the above SNPs with binding affinity to paracetamol. RESULTS: CYP2E1*2 and CYP1A2*1C genotypes were identified by MLAs to significantly predict hepatotoxicity. The predictSNP2 revealed that CYP1A2*3 was highly deleterious in all the tools. MD revealed binding energy of -5.5 Kcal/mol, -6.9 Kcal/mol, and -6.8 Kcal/mol for CYP1A2, CYP1A2*3, and CYP1A2*6 against paracetamol. MD simulations revealed that CYP1A2*3 and CYP1A2*6 missense variants in CYP1A2 affect the binding ability with paracetamol. In-silico techniques found that CYP1A2*2 and CYP1A2*6 are highly harmful. MD simulations revealed CYP3A4*2 (A>G) had decreased binding energy with paracetamol than CYP3A4, and CYP3A4*2(A>T) and CYP3A4*3 both have greater binding energy with paracetamol. CONCLUSION: Polymorphisms in CYP2E1, CYP1A2, CYP3A4, and CYP3A5 significantly influence paracetamol's clinical outcomes or binding affinity. Robust clinical studies are needed to identify these polymorphisms' clinical impact on the pharmacokinetics or pharmacodynamics of paracetamol.

Airway and Oral microbiome profiling of SARS-CoV-2 infected asthma and non-asthma cases revealing alterations-A pulmonary microbial investigation.

New evidence strongly discloses the pathogenesis of host-associated microbiomes in respiratory diseases. The microbiome dysbiosis modulates the lung's behavior and deteriorates the respiratory system's effective functioning. Several exogenous and environmental factors influence the development of asthma and chronic lung disease. The relationship between asthma and microbes is reasonably understood and yet to be investigated for more substantiation. The comorbidities such as SARS-CoV-2 further exacerbate the health condition of the asthma-affected individuals. This study examines the raw 16S rRNA sequencing data collected from the saliva and nasopharyngeal regions of pre-existing asthma (23) and non-asthma patients (82) infected by SARS-CoV-2 acquired from the public database. The experiment is designed in a two-fold pattern, analyzing the associativity between the samples collected from the saliva and nasopharyngeal regions. Later, investigates the microbial pathogenesis, its role in exacerbations of respiratory disease, and deciphering the diagnostic biomarkers of the target condition. LEfSE analysis identified that Actinobacteriota and Pseudomonadota are enriched in the SARS-CoV-2-non-asthma group and SARS-CoV-2 asthma group of the salivary microbiome, respectively. Random forest algorithm is trained with amplicon sequence variants (ASVs) attained better classification accuracy, ROC scores on nasal (84% and 87%) and saliva datasets (93% and 97.5%). Rothia mucilaginosa is less abundant, and Corynebacterium tuberculostearicum showed higher abundance in the SARS-CoV-2 asthma group. The increase in Streptococcus at the genus level in the SARS-CoV-2-asthma group is evidence of discriminating the subgroups.

Computational Structural Validation of CYP2C9 Mutations and Evaluation of Machine Learning Algorithms in Predicting the Therapeutic Outcomes of Warfarin.

AIM: The study aimed to identify the key pharmacogenetic variable influencing the therapeutic outcomes of warfarin using machine learning algorithms and bioinformatics tools. BACKGROUND: Warfarin, a commonly used anticoagulant drug, is influenced by cytochrome P450 (CYP) enzymes, particularly CYP2C9. MLAs have been identified to have great potential in personalized therapy. OBJECTIVE: The purpose of the study was to evaluate MLAs in predicting the critical outcomes of warfarin therapy and validate the key predictor genotyping variable using bioinformatics tools. METHODS: An observational study was conducted on adults receiving warfarin. Allele discrimination method was used for estimating the single nucleotide polymorphisms (SNPs) in CYP2C9, VKORC1, and CYP4F2. MLAs were used for identifying the significant genetic and clinical variables in predicting the poor anticoagulation status (ACS) and stable warfarin dose. Advanced computational methods (SNPs' deleteriousness and impact on protein destabilization, molecular dockings, and 200 ns molecular dynamics simulations) were employed for examining the influence of CYP2C9 SNPs on structure and function. RESULTS: Machine learning algorithms revealed CYP2C9 to be the most important predictor for both outcomes compared to the classical methods. Computational validation confirmed the altered structural activity, stability, and impaired functions of protein products of CYP2C9 SNPs. Molecular docking and dynamics simulations revealed significant conformational changes with mutations R144C and I359L in CYP2C9. CONCLUSION: We evaluated various MLAs in predicting the critical outcome measures associated with warfarin and observed CYP2C9 as the most critical predictor variable. The results of our study provide insight into the molecular basis of warfarin and the CYP2C9 gene. A prospective study validating the MLAs is urgently needed.

Machine learning in photosynthesis: Prospects on sustainable crop development.

Improving photosynthesis is a promising avenue to increase food security. Studying photosynthetic traits with the aim to improve efficiency has been one of many strategies to increase crop yield but analyzing large data sets presents an ongoing challenge. Machine learning (ML) represents a ubiquitous tool that can provide a more elaborate data analysis. Here we review the application of ML in various domains of photosynthetic research, as well as in photosynthetic pigment studies. We highlight how correlating hyperspectral data with photosynthetic parameters to improve crop yield could be achieved through various ML algorithms. We also propose strategies to employ ML in promoting photosynthetic pigment research for furthering crop yield.

Machine learning algorithms and computational validation of single-nucleotide polymorphisms of antioxidant enzymes and oxidative stress markers in neonates.

Aim: To evaluate machine learning algorithms (MLAs) for predicting factors (oxidative stress biomarkers [OSBs] and single-nucleotide polymorphism of the antioxidant enzymes) for respiratory distress syndrome (RDS) and significant alterations in the liver functions (SALVs). Materials & methods: MLAs were applied for predicting the RDS and SALV (with OSB and single-nucleotide polymorphisms in the antioxidant enzymes) with area under the curve (AUC) as the accuracy measure. Results: The C5.0 algorithm best predicted SALV (AUC: 0.63) with catalase as the most important predictor. Bayesian network best predicted RDS (AUC: 0.6) and ENOS1 was the most important predictor. Conclusion: MLAs carry great potential in identifying the potential genetic and OSBs in neonatal RDS and SALV. Validation in prospective studies is needed urgently.

Childbirth usually occurs around 37 weeks of pregnancy. A newborn that is born before this gestational period is referred to as preterm neonate that in many aspects may not have optimum organ functions, in particular, the ability of respiration by lung. This is referred to as respiratory distress syndrome. Respiratory distress syndrome is most often characterized with an imbalance in the molecules that prevent oxidative damage to the cellular molecules (called antioxidants) and those that cause damage (called pro-oxidants). When the balance shifts more to pro-oxidants, it is referred to as oxidative stress. Antioxidant enzymes are key elements for providing appropriate antioxidants in the body. The present study evaluated the role of artificial intelligence (machine learning algorithms in particular) in delineating the role of genetic and oxidative stress biomarkers with oxidative stress in preterm neonates with respiratory distress syndrome. We observed that mutations in certain antioxidant enzymes are associated with respiratory distress syndrome and abnormal liver functions.

Unraveling the Dysbiosis of Vaginal Microbiome to Understand Cervical Cancer Disease Etiology-An Explainable AI Approach.

Microbial Dysbiosis is associated with the etiology and pathogenesis of diseases. The studies on the vaginal microbiome in cervical cancer are essential to discern the cause and effect of the condition. The present study characterizes the microbial pathogenesis involved in developing cervical cancer. Relative species abundance assessment identified Firmicutes, Actinobacteria, and Proteobacteria dominating the phylum level. A significant increase in Lactobacillus iners and Prevotella timonensis at the species level revealed its pathogenic influence on cervical cancer progression. The diversity, richness, and dominance analysis divulges a substantial decline in cervical cancer compared to control samples. The ß diversity index proves the homogeneity in the subgroups' microbial composition. The association between enriched Lactobacillus iners at the species level, Lactobacillus, Pseudomonas, and Enterococcus genera with cervical cancer is identified by Linear discriminant analysis Effect Size (LEfSe) prediction. The functional enrichment corroborates the microbial disease association with pathogenic infections such as aerobic vaginitis, bacterial vaginosis, and chlamydia. The dataset is trained and validated with repeated k-fold cross-validation technique using a random forest algorithm to determine the discriminative pattern from the samples. SHapley Additive exPlanations (SHAP), a game theoretic approach, is employed to analyze the results predicted by the model. Interestingly, SHAP identified that the increase in Ralstonia has a higher probability of predicting the sample as cervical cancer. New evidential microbiomes identified in the experiment confirm the presence of pathogenic microbiomes in cervical cancer vaginal samples and their mutuality with microbial imbalance.

Integrated gene network analysis sheds light on understanding the progression of Osteosarcoma.

Introduction: Osteosarcoma is a rare disorder among cancer, but the most frequently occurring among sarcomas in children and adolescents. It has been reported to possess the relapsing capability as well as accompanying collateral adverse effects which hinder the development process of an effective treatment plan. Using networks of omics data to identify cancer biomarkers could revolutionize the field in understanding the cancer. Cancer biomarkers and the molecular mechanisms behind it can both be understood by studying the biological networks underpinning the etiology of the disease. Methods: In our study, we aimed to highlight the hub genes involved in gene-gene interaction network to understand their interaction and how they affect the various biological processes and signaling pathways involved in Osteosarcoma. Gene interaction network provides a comprehensive overview of functional gene analysis by providing insight into how genes cooperatively interact to elicit a response. Because gene interaction networks serve as a nexus to many biological problems, their employment of it to identify the hub genes that can serve as potential biomarkers remain widely unexplored. A dynamic framework provides a clear understanding of biological complexity and a pathway from the gene level to interaction networks. Results: Our study revealed various hub genes viz. TP53, CCND1, CDK4, STAT3, and VEGFA by analyzing various topological parameters of the network, such as highest number of interactions, average shortest path length, high cluster density, etc. Their involvement in key signaling pathways, such as the FOXM1 transcription factor network, FAK-mediated signaling events, and the ATM pathway, makes them significant candidates for studying the disease. The study also highlighted significant enrichment in GO terms (Biological Processes, Molecular Function, and Cellular Processes), such as cell cycle signal transduction, cell communication, kinase binding, transcription factor activity, nucleoplasm, PML body, nuclear body, etc. Conclusion: To develop better therapeutics, a specific approach toward the disease targeting the hub genes involved in various signaling pathways must have opted to unravel the complexity of the disease. Our study has highlighted the candidate hub genes viz. TP53, CCND1 CDK4, STAT3, VEGFA. Their involvement in the major signaling pathways of Osteosarcoma makes them potential candidates to be targeted for drug development. The highly enriched signaling pathways include FOXM1 transcription pathway, ATM signal-ling pathway, FAK mediated signaling events, Arf6 signaling events, mTOR signaling pathway, and Integrin family cell surface interactions. Targeting the hub genes and their associated functional partners which we have reported in our studies may be efficacious in developing novel therapeutic targets.

The impact of human papilloma virus on human reproductive health and the effect on male infertility: An updated review.

It is believed that human papilloma virus infection (HPV), which is caused by the DNA virus, is the most prominent factor contributing to sexually transmitted disease (STD) in the world, with males having a prevalence rate of 3.5%-45% while that women are 2%-44%. Infertility is a rising problem on a global basis, affecting anywhere from 10% to 30% of couples who have reached reproductive age. This study aims to investigate the existing research on HPV, its connection to male infertility, and how it could be a helpful tool for medical professionals managing HPV in the context of reproductive health care. Infection with HPV has been identified as a risk factor for several spontaneous abortions; however, there is a lack of evidence on how HPV influences individuals undergoing assisted reproductive technology (ART) in terms of live births. The significance of the immune response to HPV-infected male reproductive system cells and its effect on embryos, as well as the oxidative stress generated by high-risk HPV DNA damage and genomic instability, is discussed in this review. Further, the association between male individuals infected with HPV and asthenozoospermia should provide a compelling case for vaccinating young people against HPV.

Non-coding RNAs in human health and disease: potential function as biomarkers and therapeutic targets.

Human diseases have been a critical threat from the beginning of human history. Knowing the origin, course of action and treatment of any disease state is essential. A microscopic approach to the molecular field is a more coherent and accurate way to explore the mechanism, progression, and therapy with the introduction and evolution of technology than a macroscopic approach. Non-coding RNAs (ncRNAs) play increasingly important roles in detecting, developing, and treating all abnormalities related to physiology, pathology, genetics, epigenetics, cancer, and developmental diseases. Noncoding RNAs are becoming increasingly crucial as powerful, multipurpose regulators of all biological processes. Parallel to this, a rising amount of scientific information has revealed links between abnormal noncoding RNA expression and human disorders. Numerous non-coding transcripts with unknown functions have been found in addition to advancements in RNA-sequencing methods. Non-coding linear RNAs come in a variety of forms, including circular RNAs with a continuous closed loop (circRNA), long non-coding RNAs (lncRNA), and microRNAs (miRNA). This comprises specific information on their biogenesis, mode of action, physiological function, and significance concerning disease (such as cancer or cardiovascular diseases and others). This study review focuses on non-coding RNA as specific biomarkers and novel therapeutic targets.

The pathophysiological and immunological background of the monkeypox virus infection: An update.

In addition to the COVID-19 waves, the globe is facing global monkeypox (MPX) outbreak. MPX is an uncommon zoonotic infection characterized by symptoms similar to smallpox. It is caused by the monkeypox virus (MPXV), a double-stranded DNA virus that belongs to the genus Orthopoxvirus (OPXV). MPXV, which causes human disease, has been confined to Africa for many years, with only a few isolated cases in other areas. Outside of Africa, the continuing MPXV outbreak in multiple countries in 2022 is the greatest in recorded history. The current outbreak, with over 10 000 confirmed cases in over 50 countries between May and July 2022, demonstrates that MPXV may travel rapidly among humans and pose a danger to human health worldwide. The rapid spread of such outbreaks in recent times has elevated MPX to the status of a rising zoonotic disease with significant epidemic potential. While the MPXV is not as deadly or contagious as the variola virus that causes smallpox, it poses a threat because it could evolve into a more potent human pathogen. This review assesses the potential threat to the human population and provides a brief overview of what is currently known about this reemerging virus. By analyzing the biological effects of MPXV on human health, its shifting epidemiological footprint, and currently available therapeutic options, this review has presented the most recent insights into the biology of the virus. This study also clarifies the key potential causes that could be to blame for the present MPX outbreak and draw attention to major research questions and promising new avenues for combating the current MPX epidemic.

An integrated investigation of structural and pathway alteration caused by PIK3CA and TP53 mutations identified in cfDNA of metastatic breast cancer.

In peripheral blood, cell-free DNA (cfDNA) contains circulating tumor DNA (ctDNA), which indicates molecular abnormalities in metastatic breast tumor tissue. The sequencing of cfDNA of Metastatic Breast Cancer (MBC) patients allows assessment of therapy response and noninvasive treatment. In the proposed study, clinically significant alterations in PIK3CA and TP53 genes associated with MBC resulting in a missense substitution of His1047Arg and Arg282Trp from an next-generation sequencing-based multi-gene panel were reported in a cfDNA of a patient with MBC. To investigate the impact of the reported mutation, we used molecular docking, molecular dynamics simulation, network analysis, and pathway analysis. Molecular Docking analysis determined the distinct binding pattern revealing H1047R-ATP complex has a higher number of Hydrogen bonds (H-bonds) and binding affinity with a slight difference compared to the PIK3CA-ATP complex. Following, molecular dynamics simulation for 200 ns, of which H1047R-ATP complex resulted in the instability of PIK3CA. Similarly, for TP53 mutant R282W, the zinc-free state (apo) and zinc-bounded (holo) complexes were investigated for conformational change between apo and holo complexes, of which the holo complex mutant R282W was unstable. To validate the conformational change of PIK3CA and TP53, 80% mutation of H1047R in the kinase domain of p110α expressed ubiquitously in PIK3CA protein that alters PI3K pathway, while R282W mutation in DNA binding helix (H2) region of P53 protein inhibits the transcription factor in P53 pathway causing MBC. According to our findings, the extrinsic (hypoxia, oxidative stress, and acidosis); intrinsic factors (MYC amplification) in PIK3CA and TP53 mutations will provide potential insights for developing novel therapeutic methods for MBC therapy.

Effects of the pathological E200K mutation on human prion protein: A computational screening and molecular dynamics approach.

The human prion protein gene (PRNP) is mapped to the short arm of chromosome 20 (20pter-12). Prion disease is associated with mutations in the prion protein-encoding gene sequence. Earlier studies found that the mutation G127V in the PRNP increases protein stability. In contrast, the mutation E200K, which has the highest mutation rate in the prion protein, causes Creutzfeldt-Jakob disease (CJD) in humans and induces protein aggregation. We aimed to identify the structural mechanisms of E200k and G127V mutations causing CJD. We used a variety of bioinformatic algorithms, including SIFT, PolyPhen, I-Mutant, PhD-SNP, and SNP& GO, to predict the association of the E200K mutation with prion disease. MD simulation is performed, and graphs for root mean square deviation, root mean square fluctuation, radius of gyration, DSSP, principal component analysis, porcupine, and free energy landscape are generated to confirm and prove the stability of the wild-type and mutant protein structures. The protein is analyzed for aggregation, and the results indicate more fluctuations in the protein structure during the simulation owing to the E200K mutation; however, the G127V mutation makes the protein structure stable against aggregation during the simulation.

Unraveling the Structural Changes in the DNA-Binding Region of Tumor Protein p53 (TP53) upon Hotspot Mutation p53 Arg248 by Comparative Computational Approach.

The vital tissue homeostasis regulator p53 forms a tetramer when it binds to DNA and regulates the genes that mediate essential biological processes such as cell-cycle arrest, senescence, DNA repair, and apoptosis. Missense mutations in the core DNA-binding domain (109-292) simultaneously cause the loss of p53 tumor suppressor function and accumulation of the mutant p53 proteins that are carcinogenic. The most common p53 hotspot mutation at codon 248 in the DNA-binding region, where arginine (R) is substituted by tryptophan (W), glycine (G), leucine (L), proline (P), and glutamine (Q), is reported in various cancers. However, it is unclear how the p53 Arg248 mutation with distinct amino acid substitution affects the structure, function, and DNA binding affinity. Here, we characterized the pathogenicity and protein stability of p53 hotspot mutations at codon 248 using computational tools PredictSNP, Align GVGD, HOPE, ConSurf, and iStable. We found R248W, R248G, and R248P mutations highly deleterious and destabilizing. Further, we subjected all five R248 mutant-p53-DNA and wt-p53-DNA complexes to molecular dynamics simulation to investigate the structural stability and DNA binding affinity. From the MD simulation analysis, we observed increased RMSD, RMSF, and Rg values and decreased protein-DNA intermolecular hydrogen bonds in the R248-p53-DNA than the wt-p53-DNA complexes. Likewise, due to high SASA values, we observed the shrinkage of proteins in R248W, R248G, and R248P mutant-p53-DNA complexes. Compared to other mutant p53-DNA complexes, the R248W, R248G, and R248P mutant-p53-DNA complexes showed more structural alteration. MM-PBSA analysis showed decreased binding energies with DNA in all five R248-p53-DNA mutants than the wt-p53-DNA complexes. Henceforth, we conclude that the amino acid substitution of Arginine with the other five amino acids at codon 248 reduces the p53 protein's affinity for DNA and may disrupt cell division, resulting in a gain of p53 function. The proposed study influences the development of rationally designed molecular-targeted treatments that improve p53-based therapeutic outcomes in cancer.

Molecular Crosstalk between the Immunological Mechanism of the Tumor Microenvironment and Epithelial-Mesenchymal Transition in Oral Cancer.

Oral cancer is a significant non-communicable disease affecting both emergent nations and developed countries. Squamous cell carcinoma of the head and neck represent the eight major familiar cancer types worldwide, accounting for more than 350,000 established cases every year. Oral cancer is one of the most exigent tumors to control and treat. The survival rate of oral cancer is poor due to local invasion along with recurrent lymph node metastasis. The tumor microenvironment contains a different population of cells, such as fibroblasts associated with cancer, immune-infiltrating cells, and other extracellular matrix non-components. Metastasis in a primary site is mainly due to multifaceted progression known as epithelial-to-mesenchymal transition (EMT). For the period of EMT, epithelial cells acquire mesenchymal cell functional and structural characteristics, which lead to cell migration enhancement and promotion of the dissemination of tumor cells. The present review links the tumor microenvironment and the role of EMT in inflammation, transcriptional factors, receptor involvement, microRNA, and other signaling events. It would, in turn, help to better understand the mechanism behind the tumor microenvironment and EMT during oral cancer.