Female cancers, which include breast and gynaecological cancers, represent a significant global health burden for women. Despite advancements in research pertinent to unearthing crucial pathological characteristics of these cancers, challenges persist in discovering potential therapeutic strategies. This is further exacerbated by economic burdens associated with de novo drug discovery and clinical intricacies such as development of drug resistance and metastasis. Drug repurposing, an innovative approach leveraging existing FDA-approved drugs for new indications, presents a promising avenue to expedite therapeutic development. Computational techniques, including virtual screening and analysis of drug-target-disease relationships, enable the identification of potential candidate drugs. Integration of diverse data types, such as omics and clinical information, enhances the precision and efficacy of drug repurposing strategies. Experimental approaches, including high-throughput screening assays, in vitro, and in vivo models, complement computational methods, facilitating the validation of repurposed drugs. This review highlights various target mining strategies based on analysis of differential gene expression, weighted gene co-expression, protein-protein interaction network, and host-pathogen interaction, among others. To unearth drug candidates, the technicalities of leveraging information from databases such as DrugBank, STITCH, LINCS, and ChEMBL, among others are discussed. Further in silico validation techniques encompassing molecular docking, pharmacophore modelling, molecular dynamic simulations, and ADMET analysis are elaborated. Overall, this review delves into the exploration of individual case studies to offer a wide perspective of the ever-evolving field of drug repurposing, emphasizing the multifaceted approaches and methodologies employed for the same to confront female cancers.

Plasma extracellular vesicle (EV) number and composition are altered following myocardial infarction (MI), but to properly understand the significance of these changes it is essential to appreciate how the different isolation methods affect EV characteristics, proteome and sphingolipidome. Here, we compared plasma EV isolated from platelet-poor plasma from four healthy donors and six MI patients at presentation and 1-month post-MI using ultracentrifugation (UC), polyethylene glycol precipitation, acoustic trapping, size-exclusion chromatography (SEC) and immunoaffinity capture. The isolated EV were evaluated by Nanoparticle Tracking Analysis (NTA), Western blot, transmission electron microscopy (TEM), an EV-protein array, untargeted proteomics (LC-MS/MS) and targeted sphingolipidomics (LC-MS/MS). The application of the five different plasma EV isolation methods in patients presenting with MI showed that the choice of plasma EV isolation method influenced the ability to distinguish elevations in plasma EV concentration following MI, enrichment of EV-cargo (EV-proteins and sphingolipidomics) and associations with the size of the infarct determined by cardiac magnetic resonance imaging 6 months post-MI. Despite the selection bias imposed by each method, a core of EV-associated proteins and lipids was detectable using all approaches. However, this study highlights how each isolation method comes with its own idiosyncrasies and makes the comparison of data acquired by different techniques in clinical studies problematic.

Patients with chronic kidney disease (CKD) have increased oxidative stress and chronic inflammation, which may escalate the production of advanced glycation end-products (AGEs). High soluble receptor for AGE (sRAGE) and low estimated glomerular filtration rate (eGFR) levels are associated with CKD and aging. We evaluated whether eGFR calculated from creatinine and cystatin C share pleiotropic genetic factors with sRAGE. We employed whole-genome sequencing and correlated meta-analyses on combined genome-wide association study (GWAS) p-values in 4182 individuals (age range: 24-110) from the Long Life Family Study (LLFS). We also conducted transcriptome-wide association studies (TWAS) on whole blood in a subset of 1209 individuals. We identified 59 pleiotropic GWAS loci (p < 5 × 10-8) and 17 TWAS genes (Bonferroni-p < 2.73 × 10-6) for eGFR traits and sRAGE. TWAS genes, LSP1 and MIR23AHG, were associated with eGFR and sRAGE located within GWAS loci, lncRNA-KCNQ1OT1 and CACNA1A/CCDC130, respectively. GWAS variants were eQTLs in the kidney glomeruli and tubules, and GWAS genes predicted kidney carcinoma. TWAS genes harbored eQTLs in the kidney, predicted kidney carcinoma, and connected enhancer-promoter variants with kidney function-related phenotypes at p < 5 × 10-8. Additionally, higher allele frequencies of protective variants for eGFR traits were detected in LLFS than in ALFA-Europeans and TOPMed, suggesting better kidney function in healthy-aging LLFS than in general populations. Integrating genomic annotation and transcriptional gene activity revealed the enrichment of genetic elements in kidney function and aging-related processes. The identified pleiotropic loci and gene expressions for eGFR and sRAGE suggest their underlying shared genetic effects and highlight their roles in kidney- and aging-related signaling pathways.

BACKGROUND: Respiratory viruses significantly impact global morbidity and mortality, causing more disease in humans than any other infectious agent. Beyond pathogens, various viruses and bacteria colonize the respiratory tract without causing disease, potentially influencing respiratory diseases\' pathogenesis. Nevertheless, our understanding of respiratory microbiota is limited by technical constraints, predominantly focusing on bacteria and neglecting crucial populations like viruses. Despite recent efforts to improve our understanding of viral diversity in the human body, our knowledge of viral diversity associated with the human respiratory tract remains limited.
METHODS: Following a comprehensive search in bibliographic and sequencing data repositories using keyword terms, we retrieved shotgun metagenomic data from public repositories (n = 85). After manual curation, sequencing data files from 43 studies were analyzed using EVEREST (pipEline for Viral assEmbly and chaRactEriSaTion). Complete and high-quality contigs were further assessed for genomic and taxonomic characterization.
RESULTS: Viral contigs were obtained from 194 out of the 868 FASTQ files processed through EVEREST. Of the 1842 contigs that were quality assessed, 8% (n = 146) were classified as complete/high-quality genomes. Most of the identified viral contigs were taxonomically classified as bacteriophages, with taxonomic resolution ranging from the superkingdom level down to the species level. Captured contigs were spread across 25 putative families and varied between RNA and DNA viruses, including previously uncharacterized viral genomes. Of note, airway samples also contained virus(es) characteristic of the human gastrointestinal tract, which have not been previously described as part of the lung virome. Additionally, by performing a meta-analysis of the integrated datasets, ecological trends within viral populations linked to human disease states and their biogeographical distribution along the respiratory tract were observed.
CONCLUSIONS: By leveraging publicly available repositories of shotgun metagenomic data, the present study provides new insights into viral genomes associated with specimens from the human respiratory tract across different disease spectra. Further studies are required to validate our findings and evaluate the potential impact of these viral communities on respiratory tract physiology.

The development of the textile industry has negative effects on the natural environment. Cotton cultivation, dyeing fabrics, washing, and finishing require a lot of water and energy and use many chemicals. One of the most dangerous pollutants generated by the textile industry is dyes. Most of them are characterized by a complex chemical structure and an unfavorable impact on the environment. Especially azo dyes, whose decomposition by bacteria may lead to the formation of carcinogenic aromatic amines and raise a lot of concern. Using the metabolic potential of microorganisms that biodegrade dyes seems to be a promising solution for their elimination from contaminated environments. The development of omics sciences such as genomics, transcriptomics, proteomics, and metabolomics has allowed for a comprehensive approach to the processes occurring in cells. Especially multi-omics, which combines data from different biomolecular levels, providing an integrative understanding of the whole biodegradation process. Thanks to this, it is possible to elucidate the molecular basis of the mechanisms of dye biodegradation and to develop effective methods of bioremediation of dye-contaminated environments.

Macrobrachium rosenbergii is an essential species for freshwater economic aquaculture in China, but in the larval process, their salinity requirement is high, which leads to salinity stress in the water. In order to elucidate the mechanisms regulating the response of M. rosenbergii to acute low-salinity exposure, we conducted a comprehensive study of the response of M. rosenbergii exposed to different salinities\' (0‱, 6‱, and 12‱) data for 120 h. The activities of catalase, superoxide dismutase, and glutathione peroxidase were found to be significantly inhibited in the hepatopancreas and muscle following low-salinity exposure, resulting in oxidative damage and immune deficits in M. rosenbergii. Differential gene enrichment in transcriptomics indicated that low-salinity stress induced metabolic differences and immune and inflammatory dysfunction in M. rosenbergii. The differential expressions of MIH, JHEH, and EcR genes indicated the inhibition of growth, development, and molting ability of M. rosenbergii. At the proteomic level, low salinity induced metabolic differences and affected biological and cellular regulation, as well as the immune response. Tyramine, trans-1,2-Cyclohexanediol, sorbitol, acetylcholine chloride, and chloroquine were screened by metabolomics as differential metabolic markers. In addition, combined multi-omics analysis revealed that metabolite chloroquine was highly correlated with low-salt stress.

Aging is a complex and time-dependent decline in physiological function that affects most organisms, leading to increased risk of age-related diseases. Investigating the molecular underpinnings of aging is crucial to identify geroprotectors, precisely quantify biological age, and propose healthy longevity approaches. This review explores pathways that are currently being investigated as intervention targets and aging biomarkers spanning molecular, cellular, and systemic dimensions. Interventions that target these hallmarks may ameliorate the aging process, with some progressing to clinical trials. Biomarkers of these hallmarks are used to estimate biological aging and risk of aging-associated disease. Utilizing aging biomarkers, biological aging clocks can be constructed that predict a state of abnormal aging, age-related diseases, and increased mortality. Biological age estimation can therefore provide the basis for a fine-grained risk stratification by predicting all-cause mortality well ahead of the onset of specific diseases, thus offering a window for intervention. Yet, despite technological advancements, challenges persist due to individual variability and the dynamic nature of these biomarkers. Addressing this requires longitudinal studies for robust biomarker identification. Overall, utilizing the hallmarks of aging to discover new drug targets and develop new biomarkers opens new frontiers in medicine. Prospects involve multi-omics integration, machine learning, and personalized approaches for targeted interventions, promising a healthier aging population.

Delirium presents a significant clinical challenge, primarily due to its profound impact on patient outcomes and the limitations of the current diagnostic methods, which are largely subjective. During the COVID-19 pandemic, this challenge was intensified as the frequency of delirium assessments decreased in Intensive Care Units (ICUs), even as the prevalence of delirium among critically ill patients increased. The present study evaluated how the serum molecular fingerprint, as acquired by Fourier-Transform InfraRed (FTIR) spectroscopy, can enable the development of predictive models for delirium. A preliminary univariate analysis of serum FTIR spectra indicated significantly different bands between 26 ICU patients with delirium and 26 patients without, all of whom were admitted with COVID-19. However, these bands resulted in a poorly performing Naïve-Bayes predictive model. Considering the use of a Fast-Correlation-Based Filter for feature selection, it was possible to define a new set of spectral bands with a wider coverage of molecular functional groups. These bands ensured an excellent Naïve-Bayes predictive model, with an AUC, a sensitivity, and a specificity all exceeding 0.92. These spectral bands, acquired through a minimally invasive analysis and obtained rapidly, economically, and in a high-throughput mode, therefore offer significant potential for managing delirium in critically ill patients.

Omics approaches have significantly contributed to our understanding of several aspects of chicken reproduction. This review paper gives an overview of the use of omics technologies such as genomics, transcriptomics, proteomics, and metabolomics to elucidate the mechanisms of chicken reproduction. Genomics has transformed the study of chicken reproduction by allowing the examination of the full genetic makeup of chickens, resulting in the discovery of genes associated with reproductive features and disorders. Transcriptomics has provided insights into the gene expression patterns and regulatory mechanisms involved in reproductive processes, allowing for a better knowledge of developmental stages and hormone regulation. Furthermore, proteomics has made it easier to identify and quantify the proteins involved in reproductive physiology to better understand the molecular mechanisms driving fertility, embryonic development, and egg quality. Metabolomics has emerged as a useful technique for understanding the metabolic pathways and biomarkers linked to reproductive performance, providing vital insights for enhancing breeding tactics and reproductive health. The integration of omics data has resulted in the identification of critical molecular pathways and biomarkers linked with chicken reproductive features, providing the opportunity for targeted genetic selection and improved reproductive management approaches. Furthermore, omics technologies have helped to create biomarkers for fertility and embryonic viability, providing the poultry sector with tools for effective breeding and reproductive health management. Finally, omics technologies have greatly improved our understanding of chicken reproduction by revealing the molecular complexities that underpin reproductive processes.

Traditional methodologies often fall short in addressing the complexity of biological systems. In this regard, system biology omics have brought invaluable tools for conducting comprehensive analysis. Current sequencing capabilities have revolutionized genetics and genomics studies, as well as the characterization of transcriptional profiling and dynamics of several species and sample types. Biological systems experience complex biochemical processes involving thousands of molecules. These processes occur at different levels that can be studied using mass spectrometry-based (MS-based) analysis, enabling high-throughput proteomics, glycoproteomics, glycomics, metabolomics, and lipidomics analysis. Here, we present the most up-to-date techniques utilized in the completion of omics analysis. Additionally, we include some interesting examples of the applicability of multi omics to a variety of biological systems.