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.

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.

Determination of potential combination of non-ß-lactam, ß-lactam, and ß-lactamase inhibitors/ß-lactam enhancer against class D oxacillinases producing Acinetobacter baumannii: Evidence from in-vitro, molecular docking and dynamics simulation.

Carbapenem-resistant Acinetobacter baumannii, a predominant nosocomial pathogen in hospitals of intensive care units, is associated with bacteremia and ventilator-associated pneumonia with a high-risk mortality rate. To increase the effectiveness of the ß-lactam (BL) antibiotics, the use of ß-lactamase inhibitors (BLI) acts as a booster when given in combination with BL antibiotics. To this aspect, we selected BL antibiotics of cefiderocol, cefepime, non-BL antibiotic eravacycline, BLI of durlobactam, avibactam, and a ß-lactam enhancer (BLE) of zidebactam. To prove our hypothesis, we determined the minimum inhibitory concentration (MIC) of various BL or non-BL/BLI or BLE combinations using broth microdilution method followed by in silico analysis of molecular docking, molecular dynamics (MD) simulation, and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) identifies the potential combination. In MIC testing, eravacycline, cefepime/zidebactam, cefiderocol/zidebactam, and eravacycline in combination with zidebactam or durlobactam were found to be effective against oxacillinases (OXAs) (OXA-23/24/58 like) expressing A. baumannii isolates. The docking results of the selected ligands toward OXA-23, OXA-24, and OXA-58 had an excellent binding score ranging from -5.8 to -9.3 kcal/mol. Further, the docked complexes were subjected and evaluated using gromacs for molecular dynamics simulation of 50 ns toward selected class D OXAs. The binding energies obtained from MM-PBSA shed light on the binding efficiencies of each non-BL, BL, and BLI/BLE, thereby helping us to propose the drug combinations. Based on the MD trajectories scoring acquired, we propose using eravacycline, cefepime/zidebactam, cefiderocol/zidebactam, and eravacycline in combination with durlobactam or zidebactam would be promising for treating OXA-23, OXA-24, and OXA-58 like expressing A. baumannii infections.

Structural immunoinformatics approach for rational design of a multi-epitope vaccine against triple negative breast cancer.

TNBC is a highly malignant breast cancer known for its aggressive behavior affecting young female adults. The standard treatment for TNBC includes surgery, chemotherapy, and radiotherapy, which often have significant side effects. Therefore, novel preventive methods are required to combat TNBC effectively. In this study, we utilized immunoinformatics to construct an in-silico vaccine against TNBC using the TRIM25 molecule via the reverse vaccinology method. Four vaccines were designed by generating T and B-cell epitopes linked with four different linkers. The modeled vaccine was docked and the results showed that vaccine-3 exhibited the highest affinity with the immune receptors. The molecular dynamics results revealed that the binding affinity and stability of Vaccine-3 were greater than those of Vaccine 2 complexes. This study has great potential preventive measures for TNBC, and further research is warranted to evaluate its efficacy in preclinical settings. This study presents an innovative preventive strategy for triple-negative breast cancer (TNBC) through immunoinformatics and reverse vaccinology to develop an in-silico vaccine. Leveraging these innovative techniques offers a novel avenue for combating the complex challenges associated with TNBC. This approach demonstrates considerable potential as a significant breakthrough in preventive measures for this particularly aggressive and malignant form of breast cancer.

Exome sequence analysis of rare frequency variants in Late-Onset Alzheimer Disease.

Alzheimer disease (AD) is a leading cause of dementia in elderly patients who continue to live between 3 and 11 years of diagnosis. A steep rise in AD incidents is observed in the elderly population in East-Asian countries. The disease progresses through several changes, including memory loss, behavioural issues, and cognitive impairment. The etiology of AD is hard to determine because of its complex nature. The whole exome sequences of late-onset AD (LOAD) patients of Korean origin are investigated to identify rare genetic variants that may influence the complex disorder. Computational annotation was performed to assess the function of candidate variants in LOAD. The in silico pathogenicity prediction tools such as SIFT, Polyphen-2, Mutation Taster, CADD, LRT, PROVEAN, DANN, VEST3, fathmm-MKL, GERP + + , SiPhy, phastCons, and phyloP identified around 17 genes harbouring deleterious variants. The variants in the ALDH3A2 and RAD54B genes were pathogenic, while in 15 other genes were predicted to be variants of unknown significance. These variants can be potential risk candidates contributing to AD. In silico computational techniques such as molecular docking, molecular dynamic simulation and steered molecular dynamics were carried out to understand the structural insights of RAD54B with ATP. The simulation of mutant (T459N) RAD54B with ATP revealed reduced binding strength of ATP at its binding site. In addition, lower binding free energy was observed when compared to the wild-type RAD54B. Our study shows that the identified uncommon variants are linked to AD and could be probable predisposing genetic factors of LOAD.

Therapeutic and diagnostic applications of exosomal circRNAs in breast cancer.

Circular RNAs (circRNAs) are regulatory elements that are involved in orchestrating gene expression and protein functions and are implicated in various biological processes including cancer. Notably, breast cancer has a significant mortality rate and is one of the most common malignancies in women. CircRNAs have been demonstrated to contribute to the pathogenesis of breast cancer including its initiation, progression, metastasis, and resistance to drugs. By acting as miRNA sponges, circRNAs can indirectly influence gene expression by disrupting miRNA regulation of their target genes, ultimately altering the course of cancer development and progression. Additionally, circRNAs can interact with proteins and modulate their functions including signaling pathways involved in the initiation and development of cancer. Recently, circRNAs can encode peptides that play a role in the pathophysiology of breast cancer and other diseases and their potential as diagnostic biomarkers and therapeutic targets for various cancers including breast cancer. CircRNAs possess biomarkers that differentiate, such as stability, specificity, and sensitivity, and can be detected in several biological specimens such as blood, saliva, and urine. Moreover, circRNAs play an important role in various cellular processes including cell proliferation, differentiation, and apoptosis, all of which are integral factors in the development and progression of cancer. This review synthesizes the functions of circRNAs in breast cancer, scrutinizing their contributions to the onset and evolution of the disease through their interactions with exosomes and cancer-related intracellular pathways. It also delves into the potential use of circRNA as a biomarker and therapeutic target against breast cancer. It discusses various databases and online tools that offer crucial circRNA information and regulatory networks. Lastly, the challenges and prospects of utilizing circRNAs in clinical settings associated with breast cancer are explored.

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.

Molecular docking and dynamic approach to screen the drug candidate against the Imipenem-resistant CarO porin in Acinetobacter baumannii.

The multidrug-resistant Acinetobacter baumannii is an emerging nosocomial pathogen in the healthcare sector. Intrinsic resistance in A. baumannii is a significant problem framing a perfect treatment regimen. Also, this organism showed more resistance towards the carbapenem antibiotics, especially for imipenem and meropenem. The development of carbapenem-resistant Acinetobacter baumannii is mainly due to the alteration or loss of the porin region in the outer membrane. The most well-known porin in Acinetobacter baumannii is CarO (carbapenem-associated outer membrane protein). The CarO protein, which functions as a porin channel for carbapenem inflow, may contribute to carbapenem resistance. The current study identifies a potent drug candidate with a better binding affinity to the carbapenem-resistant outer membrane protein. We investigated the specificity of carbapenems such as imipenem, meropenem, ertapenem, biapenem, doripenem, and fluoroquinolone drugs such as sitafloxacin against the imipenem-resistant CarO protein was demonstrated using the computational approaches molecular docking and dynamic simulation for 50 ns. As a result, the high to low enzyme-ligand complex's binding affinity exhibited a greater binding affinity for ertapenem -7.76 kcal·mol-1 and sitafloxacin -7.75 kcal·mol-1 than biapenem, doripenem, meropenem, and imipenem. The molecular dynamic simulation and the MMPBSA analysis depicted ertapenem -55.431±25.908 kJ/mol and sitafloxacin -47.154 ± 11.052 kJ/mol with better binding affinity and more stability against the imipenem resistant CarO protein when it compared to other antibiotics.

Phage-based therapy against biofilm producers in gram-negative ESKAPE pathogens.

Persistent antibiotic use results in the rise of antimicrobial resistance with limited or no choice for multidrug-resistant (MDR) and extensively drug resistant (XDR) bacteria. This necessitates a need for alternative therapy to effectively combat clinical pathogens that are resistant to last resort antibiotics. The study investigates hospital sewage as a potential source of bacteriophages to control resistant bacterial pathogens. Eighty-one samples were screened for phages against selected clinical pathogens. Totally, 10 phages were isolated against A. baumannii, 5 phages against K. pneumoniae, and 16 phages were obtained against P. aeruginosa. The novel phages were observed to be strain-specific with complete bacterial growth inhibition of up to 6 h as monotherapy without antibiotics. Phage plus colistin combinations reduced the minimum-biofilm eradication concentration of colistin up to 16 folds. Notably, a cocktail of phages exhibited maximum efficacy with complete killing at 0.5-1 µg/ml colistin concentrations. Thus, phages specific to clinical strains have a higher edge in treating nosocomial pathogens with their proven anti-biofilm efficacy. In addition, analysis of phage genomes revealed close phylogenetic relations with phages reported from Europe, China, and other neighbouring countries. This study serves as a reference and can be extended to other antibiotics and phage types to assess optimum synergistic combinations to combat various drug resistant pathogens in the ongoing AMR crisis.

Towards computational solutions for precision medicine based big data healthcare system using deep learning models: A review.

The emergence of large-scale human genome projects, advances in DNA sequencing technologies, and the massive volume of electronic medical records [EMR] shift the transformation of healthcare research into the next paradigm, namely 'Precision Medicine.' This new clinical system model uses patients' genomic profiles and disparate healthcare data sources to a greater extent and provides personalized deliverables. As an advanced analytical technique, deep learning models significantly impact precision medicine because they can process voluminous amounts of diversified data with improved accuracy. Two salient features of deep learning models, namely processing a massive volume of multi-model data at multiple levels of abstraction and the ability to identify inherent features from the input data on their own, attract the implication of deep learning techniques in precision medicine research. The proposed review highlights the importance of deep learning-based analytical models in handling diversified and disparate big data sources of precision medicine. To augment further, state-of-the-art precision medicine research based on the taxonomy of deep learning models has been reviewed along with their research outcomes. The diversified data inputs used in research attempts, their applications, benchmarking data repositories, and usage of various evaluation measures for accuracy estimations are highlighted in this review. This review also brings out some promising analytical avenues of precision medicine research that give directions for future exploration.