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Antimicrobial resistance (AMR) is a significant public health threat. With the rise of affordable whole genome sequencing, in silico approaches to assessing AMR gene content can be used to detect known resistance mechanisms and potentially identify novel mechanisms. To enable accurate assessment of AMR gene content, as part of a multi-agency collaboration, NCBI developed a comprehensive AMR gene database, the Bacterial Antimicrobial Resistance Reference Gene Database and the AMR gene detection tool AMRFinder. Here, we describe the expansion of the Reference Gene Database, now called the Reference Gene Catalog, to include putative acid, biocide, metal, stress resistance genes, in addition to virulence genes and species-specific point mutations. Genes and point mutations are classified by broad functions, as well as more detailed functions. As we have expanded both the functional repertoire of identified genes and functionality, NCBI released a new version of AMRFinder, known as AMRFinderPlus. This new tool allows users the option to utilize only the core set of AMR elements, or include stress response and virulence genes, too. AMRFinderPlus can detect acquired genes and point mutations in both protein and nucleotide sequence. In addition, the evidence used to identify the gene has been expanded to include whether nucleotide or protein sequence was used, its location in the contig, and presence of an internal stop codon. These database improvements and functional expansions will enable increased precision in identifying AMR genes, linking AMR genotypes and phenotypes, and determining possible relationships between AMR, virulence, and stress response.

The ongoing generation of prodigious amounts of genomic sequence data from myriad vertebrates is providing unparalleled opportunities for establishing definitive phylogenetic relationships among species. The size and complexities of such comparative sequence data sets not only allow smaller and more difficult branches to be resolved but also present unique challenges, including large computational requirements and the negative consequences of systematic biases. To explore these issues and to clarify the phylogenetic relationships among mammals, we have analyzed a large data set of over 60 megabase pairs (Mb) of high-quality genomic sequence, which we generated from 41 mammals and 3 other vertebrates. All sequences are orthologous to a 1.9-Mb region of the human genome that encompasses the cystic fibrosis transmembrane conductance regulator gene (CFTR). To understand the characteristics and challenges associated with phylogenetic analyses of such a large data set, we partitioned the sequence data in several ways and utilized maximum likelihood, maximum parsimony, and Neighbor-Joining algorithms, implemented in parallel on Linux clusters. These studies yielded well-supported phylogenetic trees, largely confirming other recent molecular phylogenetic analyses. Our results provide support for rooting the placental mammal tree between Atlantogenata (Xenarthra and Afrotheria) and Boreoeutheria (Euarchontoglires and Laurasiatheria), illustrate the difficulty in resolving some branches even with large amounts of data (e.g., in the case of Laurasiatheria), and demonstrate the valuable role that very large comparative sequence data sets can play in refining our understanding of the evolutionary relationships of vertebrates. [PUBLICATION ABSTRACT]

Background The transcription factor SOX10 is essential for all stages of Schwann cell development including myelination. SOX10 cooperates with other transcription factors to activate the expression of key myelin genes in Schwann cells and is therefore a context-dependent, pro-myelination transcription factor. As such, the identification of genes regulated by SOX10 will provide insight into Schwann cell biology and related diseases. While genome-wide studies have successfully revealed SOX10 target genes, these efforts mainly focused on myelinating stages of Schwann cell development. We propose that less-biased approaches will reveal novel functions of SOX10 outside of myelination. Results We developed a stringent, computational-based screen for genome-wide identification of SOX10 response elements. Experimental validation of a pilot set of predicted binding sites in multiple systems revealed that SOX10 directly regulates a previously unreported alternative promoter at SOX6 , which encodes a transcription factor that inhibits glial cell differentiation. We further explored the utility of our computational approach by combining it with DNase-seq analysis in cultured Schwann cells and previously published SOX10 ChIP-seq data from rat sciatic nerve. Remarkably, this analysis enriched for genomic segments that map to loci involved in the negative regulation of gliogenesis including SOX5 , SOX6 , NOTCH1 , HMGA2 , HES1 , MYCN , ID4 , and ID2 . Functional studies in Schwann cells revealed that: (1) all eight loci are expressed prior to myelination and down-regulated subsequent to myelination; (2) seven of the eight loci harbor validated SOX10 binding sites; and (3) seven of the eight loci are down-regulated upon repressing SOX10 function. Conclusions Our computational strategy revealed a putative novel function for SOX10 in Schwann cells, which suggests a model where SOX10 activates the expression of genes that inhibit myelination during non-myelinating stages of Schwann cell development. Importantly, the computational and functional datasets we present here will be valuable for the study of transcriptional regulation, SOX protein function, and glial cell biology.

Antimicrobial resistance (AMR) is a major public health problem that requires publicly available tools for rapid analysis. To identify acquired AMR genes in whole genome sequences, the National Center for Biotechnology Information (NCBI) has produced a high-quality, curated, AMR gene reference database consisting of up-to-date protein and gene nomenclature, a set of hidden Markov models (HMMs), and a curated protein family hierarchy. Currently, the Bacterial Antimicrobial Resistance Reference Gene Database contains 4,579 antimicrobial resistance gene proteins and more than 560 HMMs. Here, we describe AMRFinder, a tool that uses this reference dataset to identify AMR genes. To assess the predictive ability of AMRFinder, we measured the consistency between predicted AMR genotypes from AMRFinder against resistance phenotypes of 6,242 isolates from the National Antimicrobial Resistance Monitoring System (NARMS). This included 5,425 Salmonella enterica, 770 Campylobacter spp., and 47 Escherichia coli phenotypically tested against various antimicrobial agents. Of 87,679 susceptibility tests performed, 98.4% were consistent with predictions. To assess the accuracy of AMRFinder, we compared its gene symbol output with that of a 2017 version of ResFinder, another publicly available resistance gene database. Most gene calls were identical, but there were 1,229 gene symbol differences between them, with differences due to both algorithmic differences and database composition. AMRFinder missed 16 loci that Resfinder found, while Resfinder missed 1,147 loci AMRFinder identified. Two missing drug classes from the 2017 version of ResFinder contributed 81% of missed loci. Based on these results, AMRFinder appears to be a highly accurate AMR gene detection system.

The ever-accelerating production of genome sequence from numerous species is providing new opportunities to examine evolution and evolutionary processes. My thesis work aimed to explore applications of comparative genome sequence datasets. As a first step we developed a computational method (ExactPlus) that takes advantage of the experiments conducted by natural selection to identify conserved non-coding sequences. This method proved comparable to several other methods of identifying conserved non-coding sequences, and was successfully applied to identify candidates for functional assays of gene-regulatory potential. We next explored the utility of large comparative genome sequence datasets for inferring the phylogenetic relationships among mammals. The large amount of data allowed high-confidence inferences to be made, even for difficult to resolve taxa (such as Atlantogenata, Glires, and Theria), however for this to be successful, we had to carefully control for sources of bias, such as base composition and alignment error. We found a remarkable level of heterogeneity in tree support among regions. To better understand these patterns, we developed a sliding window-based approach (PartFinder) to identify the boundaries of congruent blocks, and validated this method by using it to examine the genetic relationships among human, chimpanzee, and gorilla. In aggregate, this body of work demonstrates the utility and promise of comparative genome sequence datasets when combined with evolutionary and genomic techniques.

This study reports the green synthesis of ZnFe₂O₄ nanoparticles using a papaya extract by combustion method with papaya leaf extract as a fuel. Structural, physical and chemical properties of the nanoparticles were characterized using advanced spectroscopic and analytical techniques, such as X-ray powder diffraction (XRD), energy dispersive X-ray spectroscopy (EDX) and mapping, scanning electron microscopy (SEM), UV–Vis absorbance spectroscopy (UV–Vis), transmission electron microscopy (TEM) and dynamic light scattering (DLS). Development of eco-friendly electrochemical sensor for detection of AC drug. Electrochemical assessment of drug was done using bare GCE and drop casting GCE/ZnFe₂O₄. The electrochemical study revealed that ZnFe₂O₄ nanoparticles facilitated electron transfer, resulting in enhanced redox peak currents and reduced peak potentials. The GCE/ZnFe₂O₄ sensor exhibited a low detection limit of 0.5274 µM and a broad linear response range from 0.1 to 40 µM. In real sample analysis, the sensor demonstrated good recovery rates, indicating its accuracy in detecting and quantifying AC in pharmaceutical samples, such as Zerodol P and Dolo drops. Additionally, the sensor displayed acceptable reproducibility, stability, selectivity, sensitivity and reliability towards AC. The study underscores the effectiveness of ZnFe₂O₄ nanoparticles in enhancing the electrochemical performance of sensors, contributing to advancements in sensor technology for pharmaceutical analysis.

Lung adenocarcinomas (LUADs) display a broad histological spectrum from low-grade lepidic tumors through to mid-grade acinar and papillary and high-grade solid, cribriform and micropapillary tumors. How morphology reflects tumor evolution and disease progression is poorly understood. Whole-exome sequencing data generated from 805 primary tumor regions and 121 paired metastatic samples across 248 LUADs from the TRACERx 421 cohort, together with RNA-sequencing data from 463 primary tumor regions, were integrated with detailed whole-tumor and regional histopathological analysis. Tumors with predominantly high-grade patterns showed increased chromosomal complexity, with higher burden of loss of heterozygosity and subclonal somatic copy number alterations. Individual regions in predominantly high-grade pattern tumors exhibited higher proliferation and lower clonal diversity, potentially reflecting large recent subclonal expansions. Co-occurrence of truncal loss of chromosomes 3p and 3q was enriched in predominantly low-/mid-grade tumors, while purely undifferentiated solid-pattern tumors had a higher frequency of truncal arm or focal 3q gains and SMARCA4 gene alterations compared with mixed-pattern tumors with a solid component, suggesting distinct evolutionary trajectories. Clonal evolution analysis revealed that tumors tend to evolve toward higher-grade patterns. The presence of micropapillary pattern and ‘tumor spread through air spaces’ were associated with intrathoracic recurrence, in contrast to the presence of solid/cribriform patterns, necrosis and preoperative circulating tumor DNA detection, which were associated with extra-thoracic recurrence. These data provide insights into the relationship between LUAD morphology, the underlying evolutionary genomic landscape, and clinical and anatomical relapse risk. Analyses of the TRACERx study unveil the relationship between tissue morphology, the underlying evolutionary genomic landscape, and clinical and anatomical relapse risk of lung adenocarcinomas.

Pyoderma gangrenosum (PG) is a challenging cutaneous manifestation associated with Dubowitz syndrome, a rare genetic disorder characterized by multiple congenital anomalies, developmental delay, and distinctive facial features. This review article aims to provide a comprehensive overview of the association between Dubowitz syndrome and pyoderma gangrenosum, emphasizing the clinical presentation, challenges in diagnosis and management, and potential underlying mechanisms. A comprehensive literature search was conducted to gather relevant studies, and inclusion and exclusion criteria were applied to select appropriate articles. The association between Dubowitz syndrome and pyoderma gangrenosum has been documented in reported cases and studies. Clinical characteristics of Pyoderma gangrenosum in Dubowitz syndrome include painful necrotic ulcers with undermined borders. Diagnosing pyoderma gangrenosum in the context of Dubowitz syndrome can be challenging due to the overlapping clinical features and complexities associated with the syndrome. Managing pyoderma gangrenosum involves a multidisciplinary approach, with general principles of wound care, systemic therapy, and pain management. Specific considerations for treating pyoderma gangrenosum in Dubowitz syndrome include collaboration among specialists and careful monitoring. Future directions for management include further research to understand the underlying mechanisms and develop targeted therapies. Recognizing and addressing pyoderma gangrenosum in Dubowitz syndrome is crucial for optimal patient care. This review enhances awareness among healthcare professionals and provides insights for improving diagnosis, management, and treatment outcomes for individuals with this challenging combination of conditions.

Ensuring reliability of Large Language Models (LLMs) in clinical tasks is crucial. Our study assesses two state-of-the-art LLMs (ChatGPT and LlaMA-2) for extracting clinical information, focusing on cognitive tests like MMSE and CDR. Our data consisted of 135,307 clinical notes (Jan 12th, 2010 to May 24th, 2023) mentioning MMSE, CDR, or MoCA. After applying inclusion criteria 34,465 notes remained, of which 765 underwent ChatGPT (GPT-4) and LlaMA-2, and 22 experts reviewed the responses. ChatGPT successfully extracted MMSE and CDR instances with dates from 742 notes. We used 20 notes for fine-tuning and training the reviewers. The remaining 722 were assigned to reviewers, with 309 each assigned to two reviewers simultaneously. Inter-rater-agreement (Fleiss’ Kappa), precision, recall, true/false negative rates, and accuracy were calculated. Our study follows TRIPOD reporting guidelines for model validation. For MMSE information extraction, ChatGPT (vs. LlaMA-2) achieved accuracy of 83% (vs. 66.4%), sensitivity of 89.7% (vs. 69.9%), true-negative rates of 96% (vs 60.0%), and precision of 82.7% (vs 62.2%). For CDR the results were lower overall, with accuracy of 87.1% (vs. 74.5%), sensitivity of 84.3% (vs. 39.7%), true-negative rates of 99.8% (98.4%), and precision of 48.3% (vs. 16.1%). We qualitatively evaluated the MMSE errors of ChatGPT and LlaMA-2 on double-reviewed notes. LlaMA-2 errors included 27 cases of total hallucination, 19 cases of reporting other scores instead of MMSE, 25 missed scores, and 23 cases of reporting only the wrong date. In comparison, ChatGPT’s errors included only 3 cases of total hallucination, 17 cases of wrong test reported instead of MMSE, and 19 cases of reporting a wrong date. In this diagnostic/prognostic study of ChatGPT and LlaMA-2 for extracting cognitive exam dates and scores from clinical notes, ChatGPT exhibited high accuracy, with better performance compared to LlaMA-2. The use of LLMs could benefit dementia research and clinical care, by identifying eligible patients for treatments initialization or clinical trial enrollments. Rigorous evaluation of LLMs is crucial to understanding their capabilities and limitations.

Background Large language models (LLMs) provide powerful natural language processing capabilities in medical and clinical tasks. Evaluating LLM performance is crucial due to potential false results. In this study, we assessed ChatGPT and Llama2, two state‐of‐the‐art LLMs, in extracting information from clinical notes, focusing on cognitive tests, specifically the Mini Mental State Exam (MMSE) and Cognitive Dementia Rating (CDR). Method We compiled a dataset consisting of 765 clinical notes mentioning MMSE and CDR. 22 medically trained experts provided the ground truth. ChatGPT (GPT‐4, version “2023‐03‐15‐preview”) and Llama2 (“Llama‐2‐70b‐chat”) were used to extract MMSE and CDR instances with corresponding dates. Inference was successful for 742 notes. We used 20 notes for fine‐tuning and training the reviewers. The remaining 722 were assigned to reviewers, with 309 assigned to two reviewers simultaneously. Precision, sensitivity, true/false negative rates, and accuracy were calculated. For double‐reviewed notes, we qualitatively assessed the errors. Result The patient and note characteristics can be found in Table 1. For MMSE information extraction, ChatGPT (vs. Llama2) achieved accuracy of 83% (vs. 66.4%), sensitivity of 89.7% (vs. 69.9%), true‐negative rates of 96% (vs 60.0%), and precision of 82.7% (vs 62.2%). For CDR the results were lower overall, with accuracy of 87.1% (vs. 74.5%), sensitivity of 84.3% (vs. 39.7%), true‐negative rates of 99.8% (98.4%), and precision of 48.3% (vs. 16.1%). We qualitatively evaluated the MMSE errors of ChatGPT and Llama2 on double‐reviewed notes. Llama2 errors included 27 cases of total hallucination, 19 cases where other scores were reported instead of MMSE, 25 missed scores, and 23 cases where the wrong date was reported for the right score. In comparison, ChatGPT’s errors included only 3 cases of total hallucination, 17 cases of wrong test reported instead of MMSE, and 19 cases of reporting a wrong date. Conclusion ChatGPT exhibited high accuracy in extracting MMSE scores and dates, with better performance compared to Llama2. The use of LLMs could benefit dementia research and clinical care, by identifying eligible patients for treatments initialization or clinical trial enrollments. Rigorous evaluation of LLMs is crucial to understanding their capabilities and limitations.