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Klebsiella pneumoniae is an opportunistic pathogen causing diseases ranging from gastrointestinal disorders to severe liver abscesses. While clinical isolates of K. pneumoniae have been extensively studied, less is known about asymptomatic variants colonizing the human gut across diverse populations. Developments in genome-resolved metagenomics have offered unprecedented access to metagenome-assembled genomes (MAGs), expanding the known bacterial diversity within the gut microbiome. Here we analysed 656 human gut-derived K. pneumoniae genomes (317 MAGs, 339 isolates) from 29 countries to investigate the population structure and genomic landscape of gut-associated lineages. Over 60% of MAGs were found to belong to new sequence types, highlighting a large uncharacterized diversity of K. pneumoniae missing among sequenced clinical isolates. In particular, integrating MAGs nearly doubled gut-associated K. pneumoniae phylogenetic diversity, and uncovered 86 MAGs with >0.5% genomic distance compared to 20,792 Klebsiella isolate genomes from various sources. Pan-genome analyses identified 214 genes exclusively detected among MAGs, with 107 predicted to encode putative virulence factors. Notably, combining MAGs and isolates revealed genomic signatures linked to health and disease and more accurately classified disease and carriage states compared to isolates alone. These findings showcase the value of metagenomics to understand pathogen evolution and diversity with implications for public health surveillance strategies. Klebsiella pneumoniae is a common opportunistic gut pathogen. Here, the authors showcase the value of metagenomics in offering a broader view of the diversity of K. pneumoniae found in the human gut across different regions and health states.

Background Resistance remains a major barrier to targeted cancer therapies. Axitinib, a VEGF receptor inhibitor with anti-angiogenic activity, is effective in several cancers but shows heterogeneous patient responses, reflecting context-specific molecular adaptations. A comprehensive multi-omics approach is needed to define these mechanisms and uncover compensatory survival pathways limiting Axitinib efficacy. Methodology We conducted a high-throughput analysis of ~ 1000 pan-cancer cell lines treated with 44 FDA-approved targeted drugs. Basal transcriptomic (~ 36,000 transcripts) and proteomic (~ 9000 proteins) profiles were integrated to predict cell-line-specific drug response using a multi-classifier machine learning framework. Multiple models, including ensemble, linear, and kernel-based classifiers, were trained per drug and evaluated via fivefold cross-validation. Axitinib, the best-predictive drug, was further analyzed using explainable AI (LIME) to identify resistance-driving features for each cell line. Resistant cell lines were clustered using agglomerative hierarchical clustering based on LIME-identified features and highly correlated partners. Optimal clusters were determined via silhouette scoring. Enrichment analysis, pathway annotation, and literature mining were used to uncover cluster-specific resistance mechanisms. Results Axitinib achieved the highest predictive accuracy across all 44 drugs. The machine learning pipeline reliably classified cell lines as resistant or sensitive from basal transcriptomic and proteomic data. LIME identified key resistance-driving features at the individual cell line level. Clustering based on these features revealed two resistance subtypes shaped by tissue origin and survival constraints. In blood-derived cancers, resistance involves purine metabolism rewiring and alternative growth factor signaling to sustain proliferation. In solid tumors, resistance reflected adaptation to hypoxia, including ECM remodeling, mechanosensing, EMT, immune evasion, and senescence-induced paracrine signaling. Conclusion Axitinib resistance emerges through tissue- and context-specific adaptations. Multi-omics profiling with explainable machine learning reveals distinct survival strategies, underscoring the need for precision re-sensitization approaches tailored to tumor context. Graphical Abstract Highlights A new blueprint for understanding therapeutic failure. This study provides a scalable, multi-omics framework that decodes how different cancer types evolve unique solutions to the same drug, offering a new approach to anticipating and overcoming therapeutic resistance. Resistance is driven by pre-existing expression, not target mutation. We found that resistance to Axitinib is primarily governed by pre-existing gene and protein expression patterns, rather than by mutations in the drug’s direct targets (VEGF receptors). Explainable AI reveals two divergent resistance strategies. Our machine learning model derived features uncovered that resistant cancers employ two distinct, lineage-specific strategies: solid tumors adapt by remodeling their microenvironment and evading immune attack, while blood cancers rewire their metabolism and use alternative growth factors to survive.

“Women are bitchy but men are sarcastic”, such comments reveal the relationship between gender and sarcasm. Automatic gender identification can play a crucial role in services that depend on data about a user’s background. Although for some social media users the gender of a user is typically unavailable due to privacy and anonymity. Based on the notion that male and female users may express their thoughts and sentiments differently in their posts, social media accounts can be examined using their posts (text) in order to automatically identify the gender of an anonymous user. In the current work, efforts are made in analyzing the effects of emotion and sarcasm intended by the users in their tweets for predicting gender. Sarcasm + emotion aided gender prediction systems are developed using different machine learning and neural network-based architectures. In our developed model, tweet features are extracted by using pre-trained GloVe embeddings. The sarcasm intensity is concatenated with the corresponding tweet representation and at last classification layer is used to predict the gender labels. For the experimentation purpose, the PAN-2018 dataset has been used. We have also shown the effect of utilizing emotion, and sarcasm information over gender prediction using different models

Upregulation of stress chaperone Mortalin has been closely linked to the malignant transformation of cells, tumorigenesis, the progression of tumors to highly aggressive stages, metastasis, drug resistance, and relapse. Various in vitro and in vivo assays have provided evidence of the critical role of Mortalin upregulation in promoting cancer cell characteristics, including proliferation, migration, invasion, and the inhibition of apoptosis, a consistent feature of most cancers. Given its critical role in several steps in oncogenesis and multi-modes of action, Mortalin presents a promising target for cancer therapy. Consequently, Mortalin inhibitors are emerging as potential anti-cancer drugs. In this review, we discuss various inhibitors of Mortalin (peptides, small RNAs, natural and synthetic compounds, and antibodies), elucidating their anti-cancer potentials.

Lung cancer is one of the most invasive cancers affecting over a million of the population. Non-small cell lung cancer (NSCLC) constitutes up to 85% of all lung cancer cases, and therefore, it is essential to identify predictive biomarkers of NSCLC for therapeutic purposes. Here we use a network theoretical approach to investigate the complex behavior of the NSCLC gene-regulatory interactions. We have used eight NSCLC microarray datasets GSE19188, GSE118370, GSE10072, GSE101929, GSE7670, GSE33532, GSE31547, and GSE31210 and meta-analyzed them to find differentially expressed genes (DEGs) and further constructed a protein–protein interaction (PPI) network. We analyzed its topological properties and identified significant modules of the PPI network using cytoscape network analyzer and MCODE plug-in. From the PPI network, top ten genes of each of the six topological properties like closeness centrality, maximal clique centrality (MCC), Maximum Neighborhood Component (MNC), radiality, EPC (Edge Percolated Component) and bottleneck were considered for key regulator identification. We further compared them with top ten hub genes (those with the highest degrees) to find key regulator (KR) genes. We found that two genes, CDK1 and HSP90AA1, were common in the analysis suggesting a significant regulatory role of CDK1 and HSP90AA1 in non-small cell lung cancer. Our study using a network theoretical approach, as a summary, suggests CDK1 and HSP90AA1 as key regulator genes in complex NSCLC network.

Klebsiella pneumoniae is an opportunistic pathogen causing diseases ranging from gastrointestinal disorders to severe liver abscesses. While clinical isolates of K. pneumoniae have been extensively studied, less is known about asymptomatic variants colonizing the human gut across diverse populations. Genome-resolved metagenomics has offered unprecedented access to metagenome-assembled genomes (MAGs) from diverse host states and geographical locations, opening opportunities to explore health-associated microbial features. Here we analysed 662 human gut-derived K. pneumoniae genomes (319 MAGs, 343 isolates) from 29 countries to investigate the population structure and genomic diversity of K. pneumoniae in carriage and disease. Only 9% of sequence types were found to be shared between healthy and disease states, highlighting distinct diversity across health conditions. Integrating MAGs nearly doubled gut-associated K. pneumoniae phylogenetic diversity, and uncovered 86 lineages without representation among >20,000 Klebsiella isolate genomes from various sources. Genomic signatures linked to pathogenicity and carriage included those involved in antibiotic resistance, iron regulation, restriction modification systems and polysaccharide biosynthesis. Notably, machine learning models integrating MAGs and isolates more accurately classified disease and carriage states compared to isolates alone. These findings showcase the value of metagenomics to understand pathogen evolution with implications for public health surveillance strategies.

Lung cancer is one of the most invasive cancer affecting over a million of population. Non-small cell lung cancer constitutes up to 85% of all lung cancer cases. Therefore, it is important to identify prognostic biomarkers of NSCLC for therapeutic purpose. The complex behaviour of the NSCLC gene-regulatory network interaction is investigated using a network theoretical approach. We used eight NSCLC microarray datasets GSE19188, GSE118370, GSE10072, GSE101929, GSE7670, GSE33532, GSE31547, GSE31210 and meta analyse them to find differentially expressed genes (DEGs), construct protein-protein interaction (PPI) network, analysed its topological properties, significant modules using network analyser with MCODE, construct a PPI-MCODE network using the genes of the significant modules. We used topological properties such as Maximal Clique Centrality (MCC) and bottleneck from the PPI-MCODE network. We compare them with hub genes (those with highest degrees) to find key regulator (KR) gene. This result is also validated by finding of common genes among top twenty hub genes, genes with highest betweenness, closeness centrality and eigenvector values. It was found that two genes, CDK1 and HSP90AA1 were common in PPI-MCODE combined analysis, and it was also found that CDK1, HSP90AA1 and HSPA8 were common among hub and bottle neck properties and suggesting significant regulatory role of CDK1 in non-small cell lung cancer. After validation, the common genes among top twenty hubs and centrality values like Betweenness Centrality, Closeness Centrality and eigen vector properties, CDK1 again appeared as the common gene. Our study as a summary suggested CDK1 as key regulator gene in complex NSCLC network interaction using network theoretical approach and described the complex topological properties of the network. Competing Interest Statement The authors have declared no competing interest.

This review article provides an overview of using artificial intelligence (AI) in radiology. It discusses the advantages and limitations of ChatGPT, a large language model, for medical imaging diagnosis. ChatGPT has shown great promise in improving the accuracy and efficiency of radiological diagnoses by reducing interpretation variability and errors and improving workflow efficiency. However, there are also limitations, including the need for high-quality training data, ethical considerations, and further research and development to improve its performance and usability. Despite these challenges, ChatGPT has the potential to significantly impact radiology and medical imaging diagnosis. The review article highlights the need for continued research and development, coupled with ethical and regulatory considerations, to ensure that ChatGPT is used to its full potential in improving radiological diagnoses and patient care.

Intimate partner violence (IPV) is a significant public health issue that affects many women, including pregnant women. The aim of this comprehensive review is to examine the prevalence of IPV during pregnancy and its adverse effects on maternal and fetal health. IPV during pregnancy can take various forms, including physical, sexual, emotional, and financial abuse. The consequences of IPV during pregnancy can be severe, with adverse effects on maternal and fetal health including an increased risk of preterm birth, low birth weight (LBW), fetal injury, maternal depression, anxiety, post-traumatic stress disorder (PTSD), and even maternal death. Identifying women experiencing IPV during pregnancy and providing appropriate support and care can help mitigate the adverse effects on maternal and fetal health. The review also discusses various interventions and strategies that can be used to prevent IPV during pregnancy, such as screening and counseling for IPV, training healthcare providers to identify and manage IPV during pregnancy, and providing resources and support for women who experience IPV. Overall, the review highlights the need for increased awareness, research, and resources to prevent and address IPV during pregnancy and to promote the health and well-being of women and their infants.

Hepatitis E virus (HEV) is a leading cause of acute viral hepatitis. Our earlier study reported the presence of an 87-nucleotide-long internal ribosome entry-site-like element (IRESl) in the genotype 1 (g1)-HEV genome, which mediated cap-independent translation of the viral ORF4 protein. RNA-protein interactome analysis of the HEV IRESl revealed its association with multiple host proteins, including translation regulatory proteins, which controlled its function. Role of HEV-encoded proteins in modulating the viral IRESl activity remains unknown. In the present study, we investigated the role of viral proteins in modulating the activity of the HEV IRESl element. Luciferase-reporter assay using a bicistronic vector revealed the ability of viral Helicase and MeT-Y-domain proteins in upregulating the HEV IRESl activity. Further studies confirmed direct interaction of the viral Helicase and MeT-Y-domain with the HEV IRESl. Collectively, these findings unravel the positive role of HEV Helicase and MeT-Y-domain proteins in modulating the viral IRESl activity.