Interaction of eugenol-based anti-tuberculosis nanoemulsion with bovine serum albumin: A spectroscopic study including Rifampicin, Isoniazid, Pyrazinamide, and Ethambutol.

Tuberculosis (TB), a deadly infectious disease, is primarily caused by the bacterium Mycobacterium tuberculosis. The misuse of antibiotics has led to the development of drug resistance, prompting researchers to explore new technologies to combat multidrug-resistant Tuberculosis (MDR TB). Phospholipid-based nanotherapeutics, such as nanoemulsions, are gaining traction as they enhance drug solubility, stability, and bioavailability. Our study focuses on the interaction between Bovine Serum Albumin (BSA) and a drug-loaded nanoemulsion based on Eugenol. This nanoemulsion incorporates Eugenol, Clove, cinnamon oil, and first-line anti-tuberculosis drugs like Rifampicin, Isoniazid, Pyrazinamide, and Ethambutol. The primary objective is to assess the biosafety profile of the nanoemulsion upon interaction with BSA. We employed Fluorescence, UV-visible, and Fourier Transform Infrared Spectroscopy (FTIR) to analyze this interaction. UV-visible spectroscopy detected changes in hydrophobicity due to structural alterations in BSA near the tryptophan residue, leading to the formation of ground-state complexes. Fluorescence spectroscopy demonstrated that the nanoemulsion effectively quenched fluorescence originating from tryptophan and tyrosine residues. Studies using synchronous and three-dimensional spectroscopy point to a potential modification of the aromatic environment of BSA by the nanoemulsion. Resonance light scattering spectra indicated the formation of large aggregates due to the interaction with the nanoemulsion. The second derivative FTIR spectra showed an increase in the magnitude of secondary structure bands, suggesting a conformational shift. This research has significant pharmacological implications for developing safer, more targeted drug delivery systems. The information obtained from the interaction of the nanoemulsion with the blood carrier protein is vital for the future development of superior carriers with minimal adverse effects on patients. It is crucial to remember that conformational changes brought on by drug-ligand complexes attaching to carrier proteins may have negative consequences. Therefore, this study enhances the in vitro evaluation of potential adverse effects of the nanoemulsion on serum proteins.

Surface functionalization, particle size and pharmaceutical co-contaminant dependent impact of nanoplastics on marine crustacean - Artemia salina.

Despite a significant amount of research on micronanoplastics (MNPs), there is still a gap in our understanding of their function as transporters of other environmental pollutants (known as the Trojan horse effect) and the combined effects of ingestion, bioaccumulation, and toxicity to organisms. This study examined the individual effects of polystyrene nanoplastics (PSNPs) with various surface functionalizations (plain (PS), carboxylated (PS-COOH), and aminated (PS-NH2)), particle sizes (100 nm and 500 nm), and a pharmaceutical co-contaminant (metformin hydrochloride (MH), an anti-diabetic drug) on the marine crustacean - Artemia salina. The study specifically aimed to determine if MH alters the detrimental effects of PSNPs on A. salina. The potential toxicity of these emerging pollutants was assessed by examining mortality, hatching rate, morphological changes, and biochemical changes. Smaller nanoparticles had a more significant impact than larger ones, and PS-NH2 was more harmful than PS and PS-COOH. Exposure to the nanoparticle complex with MH resulted in a decrease in hatching rate, an increase in mortality, developmental abnormalities, an increase in reactive oxygen species, catalase, and lipid peroxidase, and a decrease in total protein and superoxide dismutase, indicating a synergistic effect. There were no significant differences between the complex and the individual nanoparticles. However, accumulating these particles in organisms could contaminate the food chain. These results highlight the potential environmental risks associated with the simultaneous exposure of aquatic species to plastics, particularly smaller PS, aminated PS, and pharmaceutical complex PS.

Deciphering the Impact of Rare Missense Variants in EGFR-TKI-Resistant Non-Small-Cell Lung Cancer through Whole Exome Sequencing: A Computational Approach.

Targeted therapy revolutionizes the treatment of non-small-cell lung cancer (NSCLC), harboring molecular change. Epidermal growth factor receptor(EGFR) mutations play a crucial role in the development of NSCLC, serving as a pivotal factor in its pathogenesis. We elucidated the mechanisms of resistance and potential therapeutic strategies in NSCLC resistant to the EGFR-tyrosine kinase inhibitor (EGFR-TKI). This is achieved by identifying rare missense variants through whole exome sequencing (WES). The goal is to enhance our understanding, identify biomarkers, and lay the groundwork for targeted interventions, thereby offering hope for an improved NSCLC treatment landscape. We conducted WES analysis on 16 NSCLC samples with EGFR-TKI-resistant NSCLC obtained from SRA-NCBI (PRJEB50602) to reveal genomic profiles within the EGFR-TKI. Our findings showed that 48% of the variants were missense, and after filtering with the Ensembl variant effect predictor, 53 rare missense variants in 23 genes were identified as highly deleterious. Further examination using pathogenic tools like PredictSNP revealed 12 deleterious rare missense variants in 7 genes: ZNF717, PSPH, ESRRA, SEMA3G, PTPN7, CAVIN4, and MYBBP1A. Molecular dynamics simulation (MDS) suggested that the L385P variant alters the structural flexibility of ESRRA, potentially leading to unfolding of ERRα proteins. This could impact their function and alter ERRα expression. These insights from MDS enhance our understanding of the structural and dynamic consequences of the L385P ESRRA variant and provide valuable implications for subsequent therapeutic considerations and targeted interventions.

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.

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.

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.

Emerging strategies and therapeutic innovations for combating drug resistance in Staphylococcus aureus strains: A comprehensive review.

In recent years, antibiotic therapy has encountered significant challenges due to the rapid emergence of multidrug resistance among bacteria responsible for life-threatening illnesses, creating uncertainty about the future management of infectious diseases. The escalation of antimicrobial resistance in the post-COVID era compared to the pre-COVID era has raised global concern. The prevalence of nosocomial-related infections, especially outbreaks of drug-resistant strains of Staphylococcus aureus, have been reported worldwide, with India being a notable hotspot for such occurrences. Various virulence factors and mutations characterize nosocomial infections involving S. aureus. The lack of proper alternative treatments leading to increased drug resistance emphasizes the need to investigate and examine recent research to combat future pandemics. In the current genomics era, the application of advanced technologies such as next-generation sequencing (NGS), machine learning (ML), and quantum computing (QC) for genomic analysis and resistance prediction has significantly increased the pace of diagnosing drug-resistant pathogens and insights into genetic intricacies. Despite prompt diagnosis, the elimination of drug-resistant infections remains unattainable in the absence of effective alternative therapies. Researchers are exploring various alternative therapeutic approaches, including phage therapy, antimicrobial peptides, photodynamic therapy, vaccines, host-directed therapies, and more. The proposed review mainly focuses on the resistance journey of S. aureus over the past decade, detailing its resistance mechanisms, prevalence in the subcontinent, innovations in rapid diagnosis of the drug-resistant strains, including the applicants of NGS and ML application along with QC, it helps to design alternative novel therapeutics approaches against S. aureus infection.

In silico network pharmacology study on Glycyrrhiza glabra: Analyzing the immune-boosting phytochemical properties of Siddha medicinal plant against COVID-19.

Immunosenescence is a pertinent factor in the mortality rate caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The changes in the immune system are strongly associated with age and provoke the deterioration of the individual's health. Traditional medical practices in ancient India effectively deal with COVID-19 by boosting natural immunity through medicinal plants. The anti-inflammatory and antiviral properties of Glycyrrhiza glabra are potent in fighting against COVID-19 and promote immunity boost against the severity of the infection. Athimadhura Chooranam, a polyherbal formulation containing Glycyrrhiza glabra as the main ingredient, is recommended as an antiviral Siddha herb by the Ministry of AYUSH. This paper is intended to identify the phytoconstituents of Glycyrrhiza glabra that are actively involved in preventing individuals from COVID-19 transmission. The modulated pathways, enrichment study, and drug-likeness are calculated from the target proteins of the phytoconstituents at the pharmacological activity (Pa) of more than 0.7. Liquiritigenin and Isoliquiritin, the natural compounds in Glycyrrhiza glabra, belong to the flavonoid class and exhibit ameliorative effects against COVID-19. The latter compound displays a higher protein interaction to a maximum of six, out of which HMOX1, PLAU, and PGR are top-hub genes. ADMET screening further confirms the significance of the abovementioned components containing better drug-likeness. The molecular docking and molecular dynamics method identified liquiritigenin as a possible lead molecule capable of inhibiting the activity of the major protease protein of SARS-CoV-2. The findings emphasize the importance of in silico network pharmacological assessments in delivering cost-effective, time-bound clinical drugs.

In silico network pharmacology analysis and molecular docking validation of Swasa Kudori tablet for screening druggable phytoconstituents of asthma.

Traditional medicines are impactful in treating a cluster of respiratory-related illnesses. This paper demonstrates screening active, druggable phytoconstituents from a classical Siddha-based poly-herbal formulation called Swasa Kudori Tablet to treat asthma. The phytoconstituents of Swasa Kudori are identified as Calotropis gigantea, Piper nigrum, and (Co-drug) Abies webbiana. Active chemical compounds are extracted with the Chemical Entities of Biological Interest (ChEBI) database. The gene targets of each compound are identified based on the pharmacological activity using the DIGEP-Pred database. Thirty-two genes showing Pa> 0.7 is screened, and the target markers are selected after performing gene overlap evaluation with the asthma genes reported in GeneCards and DisGeNET database. Ten markers are identified, such as ADIPOQ, CASP8, CAT, CCL2, CD86, FKBP5, HMOX1, NFE2L2, TIMP1, VDR, in common, listed as molecular targets. Pharmacokinetic assessment (ADME) revealed five natural drug compounds 2-5-7-trihydroxy-2-(4-hydroxyphenyl)-2,3-dihydro-4H-chromen-4-one, (+)-catechin-3'-methyl ether, futoenone, 5-hydroxy-4',7-dimethoxyflavanone, and pinocembrin showing better druggability. Further screening delineates the target (HMOX1) and drug (pinocembrin) for molecular docking evaluation. When docked with HO-1, Pinocembrin showed a binding affinity of -8.0 kcal/mol. MD simulation studies substantiate the docking studies as HO-1 in complex with pinocembrin remains stable in the simulated trajectory. The current findings exhibit the significance of traditional medicines as potential drug candidates against asthma.

Assessment of polystyrene nano plastics effect on human salivary α-amylase structural alteration: Insights from an in vitro and in silico study.

The study found that the enzyme activity of human salivary α-amylase (α-AHS) was competitively inhibited by nanoplastic polystyrene (PS-NPs), with a half-inhibitory concentration (IC50) of 92 µg/mL, while the maximum reaction rate (Vmax) remained unchanged at 909 µg/mL•min. An increase in the concentration of PS-NPs led to a quenching of α-AHS fluorescence with a slight red shift, indicating a static mechanism. The binding constant (Ka) and quenching constant (Kq) were calculated to be 2.92 × 1011 M-1 and 1.078 × 1019 M-1• S-1 respectively, with a hill coefficient (n) close to one and an apparent binding equilibrium constant (KA) of 1.54 × 1011 M-1. Molecular docking results suggested that the interaction between α-AHS and PS-NPs involved π-anion interactions between the active site Asp197, Asp300 residues, and van der Waals force interactions affecting the Tyr, Trp, and other residues. Fourier transform infrared (FT-IR) and circular dichroism (CD) analyses revealed conformational changes in α-AHS, including a loss of secondary structure α-helix and ß-sheet. The study concludes that the interaction between α-AHS and PS-NPs leads to structural and functional changes in α-AHS, potentially impacting human health. This research provides a foundation for further toxicological analysis of MPs/NPs in the human digestive system.

Advancements and hurdles in the development of a vaccine for triple-negative breast cancer: A comprehensive review of multi-omics and immunomics strategies.

Triple-Negative Breast Cancer (TNBC) presents a significant challenge in oncology due to its aggressive behavior and limited therapeutic options. This review explores the potential of immunotherapy, particularly vaccine-based approaches, in addressing TNBC. It delves into the role of immunoinformatics in creating effective vaccines against TNBC. The review first underscores the distinct attributes of TNBC and the importance of tumor antigens in vaccine development. It then elaborates on antigen detection techniques such as exome sequencing, HLA typing, and RNA sequencing, which are instrumental in identifying TNBC-specific antigens and selecting vaccine candidates. The discussion then shifts to the in-silico vaccine development process, encompassing antigen selection, epitope prediction, and rational vaccine design. This process merges computational simulations with immunological insights. The role of Artificial Intelligence (AI) in expediting the prediction of antigens and epitopes is also emphasized. The review concludes by encapsulating how Immunoinformatics can augment the design of TNBC vaccines, integrating tumor antigens, advanced detection methods, in-silico strategies, and AI-driven insights to advance TNBC immunotherapy. This could potentially pave the way for more targeted and efficacious treatments.

Genomic insights and advanced machine learning: characterizing autism spectrum disorder biomarkers and genetic interactions.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by altered brain connectivity and function. In this study, we employed advanced bioinformatics and explainable AI to analyze gene expression associated with ASD, using data from five GEO datasets. Among 351 neurotypical controls and 358 individuals with autism, we identified 3,339 Differentially Expressed Genes (DEGs) with an adjusted p-value (≤ 0.05). A subsequent meta-analysis pinpointed 342 DEGs (adjusted p-value ≤ 0.001), including 19 upregulated and 10 down-regulated genes across all datasets. Shared genes, pathogenic single nucleotide polymorphisms (SNPs), chromosomal positions, and their impact on biological pathways were examined. We identified potential biomarkers (HOXB3, NR2F2, MAPK8IP3, PIGT, SEMA4D, and SSH1) through text mining, meriting further investigation. Additionally, we shed light on the roles of RPS4Y1 and KDM5D genes in neurogenesis and neurodevelopment. Our analysis detected 1,286 SNPs linked to ASD-related conditions, of which 14 high-risk SNPs were located on chromosomes 10 and X. We highlighted potential missense SNPs associated with FGFR inhibitors, suggesting that it may serve as a promising biomarker for responsiveness to targeted therapies. Our explainable AI model identified the MID2 gene as a potential ASD biomarker. This research unveils vital genes and potential biomarkers, providing a foundation for novel gene discovery in complex diseases.

Rational design of a multivalent vaccine targeting arthropod-borne viruses using reverse vaccinology strategies.

Viruses transmitted by arthropods, such as Dengue, Zika, and Chikungunya, represent substantial worldwide health threats, particularly in countries like India. The lack of approved vaccines and effective antiviral therapies calls for developing innovative strategies to tackle these arboviruses. In this study, we employed immunoinformatics methodologies, incorporating reverse vaccinology, to design a multivalent vaccine targeting the predominant arboviruses. Epitopes of B and T cells were recognized within the non-structural proteins of Dengue, Zika, and Chikungunya viruses. The predicted epitopes were enhanced with adjuvants ß-defensin and RS-09 to boost the vaccine's immunogenicity. Sixteen distinct vaccine candidates were constructed, each incorporating epitopes from all three viruses. FUVAC-11 emerged as the most promising vaccine candidate through molecular docking and molecular dynamics simulations, demonstrating favorable binding interactions and stability. Its effectiveness was further evaluated using computational immunological studies confirming strong immune responses. The in silico cloning performed using the pET-28a(+) plasmid facilitates the future experimental implementation of this vaccine candidate, paving the way for potential advancements in combating these significant arboviral threats. However, further in vitro and in vivo studies are warranted to confirm the results obtained in this computational study, which highlights the effectiveness of immunoinformatics and reverse vaccinology in creating vaccines against major Arboviruses, offering a promising model for developing vaccines for other vector-borne diseases and enhancing global health security.

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.

Baseline Perceptions of Women With Gestational Diabetes Mellitus and Health Care Professionals About Digital Gestational Diabetes Mellitus Self-Management Health Care Technologies: Interview Study Among Patients and Health Care Professionals.

BACKGROUND: Gestational diabetes mellitus (GDM) is a significant medical complication of pregnancy that requires close monitoring by a multidisciplinary health care team. The growing sophistication of mobile health (mHealth) technology could play a significant supporting role for women with GDM and health professionals (HPs) regarding GDM management. OBJECTIVE: This study included 2 phases. The aim of phase 1 was to explore the perceptions of HPs and women with GDM regarding the use of mHealth for GDM self-management and to identify their needs from these technologies. The aim of phase 2 was to explore the perceptions of women with GDM about their experiences with a state-of-the-art app for managing GDM that was offered to them during the COVID-19 lockdown. This phase aimed to understand the impact that COVID-19 has had on women's perceptions about using technology to manage their GDM. By combining both phases, the overall aim was to establish how perceptions about GDM self-management technology have changed owing to the pandemic restrictions and experience of using such technology. METHODS: In total, 26 semistructured interviews were conducted in 2 phases. In phase 1, overall, 62% (16/26) of the participants, including 44% (7/16) of HPs, 50% (8/16) of women with GDM, and 6% (1/16) of women in the postpartum period with GDM history participated in the interviews. In phase 2, overall, 38% (10/26) of women with GDM participated in the interviews. NVivo (QSR International) was used to extract qualitative data, which were subjected to thematic analysis. RESULTS: Phase 1 identified 3 themes from the interviews with women with GDM: fitting with women's lifestyle constraints, technology's design not meeting women's needs, and optimizing the technology's design to meet women's needs. Overall, 3 themes were derived from the interviews with HPs: optimizing the technology's design to improve the quality of care, technology to support women's independence, and limitations in the care system and facilities. Analysis of phase-2 interviews identified 2 further themes: enhancing the information and functionalities and optimizing the interface design. In both phases, participants emphasized a simple and user-friendly interface design as the predominant positive influence on their use of technology for GDM management. CONCLUSIONS: The combined findings underlined similar points. Poor usability, data visualization limitations, lack of personalization, limited information, and lack of communication facilities were the prime issues of current GDM self-management mHealth technology that need to be addressed. The analysis also revealed how women with GDM should play a vital role in gathering the requirements for GDM self-management technology; some needs were identified from in-depth discussion with women with GDM that would be missed without their involvement.

Impact of COVID-19 on gestational diabetes pregnancy outcomes in the UK: A multicentre retrospective cohort study.

OBJECTIVE: To determine the impact of implementing emergency care pathway(s) for screening, diagnosing and managing women with gestational diabetes (GDM) during COVID-19. DESIGN: Retrospective multicentre cohort. SETTING: Nine National Health Service (NHS) Hospital Trusts/Health boards in England and Scotland. POPULATION: 4915 women with GDM pre-pandemic (1 April 2018 to 31 March 2020), and 3467 women with GDM during the pandemic (1 May 2020 to 31 March 2021). METHODS: We examined clinical outcomes for women with GDM prior to and during the pandemic following changes in screening methods, diagnostic testing, glucose thresholds and introduction of virtual care for monitoring of antenatal glycaemia. MAIN OUTCOME MEASURES: Intervention at birth, perinatal mortality, large-for-gestational-age infants and neonatal unit admission. RESULTS: The new diagnostic criteria more often identified GDM women who were multiparous, had higher body mass index (BMI) and greater deprivation, and less frequently had previous GDM (all p < 0.05). During COVID, these women had no differences in the key outcome measures. Of the women, 3% were identified with pre-existing diabetes at antenatal booking. Where OGTT continued during COVID, but virtual care was introduced, outcomes were also similar pre- and during the pandemic. CONCLUSIONS: Using HbA1c and fasting glucose identified a higher risk GDM population during the pandemic but this had minimal impact on pregnancy outcomes. The high prevalence of undiagnosed pre-existing diabetes suggests that women with GDM risk factors should be offered HbA1c screening in early pregnancy.

Exploring natural products library to identify potential inhibitors targeting isoniazid-resistant tuberculosis.

Mycobacterium tuberculosis (MTB) causing tuberculosis (TB) infection is a leading source of illness and death in developing nations, and the emergence of drug-resistant TB remains a significant global threat and a challenge in treating the disease. Mutations in the inhA and katG genes are connected to the principal molecular mechanism of isoniazid (INH) resistance, and continuous treatment of INH for more than a decade led to the evolution of INH resistant-TB (inhR-TB). Structure-based drug discovery approaches on traditional natural compounds are the contemporary source to identify significant lead molecules. This work focuses on discovering effective small compounds from natural compound libraries and applying pharmacophore-based virtual screening to filter out the molecules. The best-identified hit complexes were used for molecular dynamics simulations (MDS) to observe their stability and compactness. A three-dimensional e-pharmacophore hypothesis and screening generated 62 hits based on phase fitness scores from the pharmacophore-based virtual screening. Molecular docking experiments in Maestro's GLIDE module indicated that ZINC000002383126 and ASN22022 may be potential inhibitors of inhA and katG (native, inhA mutants S94A, Y158A, Y158F and Y158S and D137S, Y229F, S315T, W321F, and R418L mutants of katG). In addition, MDS analysis indicated that the native and mutant docked complexes of inhA and katG had good stability and remained compact in the binding pocket of the targets. In vitro studies can further validate the compounds that can act as INH competitive inhibitors.Communicated by Ramaswamy H. Sarma.

Comparative Atomistic Insights on Apo and ATP-I1171N/S/T in Nonsmall-Cell Lung Cancer.

Anaplastic lymphoma kinase (ALK) rearrangements occur in about 5% of nonsmall cell lung cancer (NSCLC) patients. Despite being first recognized as EML4-ALK, fusions with several additional genes have been identified, all of which cause constitutive activation of the ALK kinase and subsequently lead to tumor development. ALK inhibitors first-line crizotinib, second-line ceritinib, and alectinib are effective against NSCLC patients with these rearrangements. Patients progressing on crizotinib had various mutations in the ALK kinase domain. ALK fusion proteins are activated by oligomerization through the fusion partner, which leads to the autophosphorylation of the kinase's domain and consequent downstream activation. The proposed computational study focuses on understanding the activation mechanism of ALK and ATP binding of wild-type (WT) and I1171N/S/T mutations. We analyzed the conformational change of ALK I1171N/S/T mutations and ATP binding using molecular docking and molecular dynamics simulation approaches. According to principal component analysis and free energy landscape, it is clear that I1171N/S/T mutations in Apo and ATP showed different energy minima/unstable structures compared to WT-Apo. The results revealed that I1171N/S/T mutations and ATP binding significantly supported a change toward an active-state conformation, whereas WT-Apo remained inactive. We demonstrated that I1171N/S/T mutations are persistent in an active state and independent of ATP. The I1171S/T mutations showed greater intermolecular H-bonds with ATP than WT-ATP. The molecular mechanics Poisson-Boltzmann surface area analysis revealed that the I1171N/S/T mutation binding energy was similar to that of WT-ATP. This study shows that I1171N/S/T can form stable bonds with ATP and may contribute to a constitutively active kinase. Based on the Y1278-C1097 H-bond and E1167-K1150 salt bridge interaction, I1171N strongly promotes the constitutively active kinase independent of ATP. This structural mechanism study will aid in understanding the oncogenic activity of ALK and the basis for improving the ALK inhibitors.

Whole genome analysis of Rhizopus species causing rhino-cerebral mucormycosis during the COVID-19 pandemic.

Introduction: Mucormycosis is an acute invasive fungal disease (IFD) seen mainly in immunocompromised hosts and in patients with uncontrolled diabetes. The incidence of mucormycosis increased exponentially in India during the SARS-CoV-2 (henceforth COVID-19) pandemic. Since there was a lack of data on molecular epidemiology of Mucorales causing IFD during and after the COVID-19 pandemic, whole genome analysis of the Rhizopus spp. isolated during this period was studied along with the detection of mutations that are associated with antifungal drug resistance. Materials and methods: A total of 50 isolates of Rhizopus spp. were included in this prospective study, which included 28 from patients with active COVID-19 disease, 9 from patients during the recovery phase, and 13 isolates from COVID-19-negative patients. Whole genome sequencing (WGS) was performed for the isolates, and the de novo assembly was done with the Spades assembler. Species identification was done by extracting the ITS gene sequence from each isolate followed by searching Nucleotide BLAST. The phylogenetic trees were made with extracted ITS gene sequences and 12 eukaryotic core marker gene sequences, respectively, to assess the genetic distance between our isolates. Mutations associated with intrinsic drug resistance to fluconazole and voriconazole were analyzed. Results: All 50 patients presented to the hospital with acute fungal rhinosinusitis. These patients had a mean HbA1c of 11.2%, and a serum ferritin of 546.8 ng/mL. Twenty-five patients had received steroids. By WGS analysis, 62% of the Rhizopus species were identified as R. delemar. Bayesian analysis of population structure (BAPS) clustering categorized these isolates into five different groups, of which 28 belong to group 3, 9 to group 5, and 8 to group 1. Mutational analysis revealed that in the CYP51A gene, 50% of our isolates had frameshift mutations along with 7 synonymous mutations and 46% had only synonymous mutations, whereas in the CYP51B gene, 68% had only synonymous mutations and 26% did not have any mutations. Conclusion: WGS analysis of Mucorales identified during and after the COVID-19 pandemic gives insight into the molecular epidemiology of these isolates in our community and establishes newer mechanisms for intrinsic azole resistance.