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The causes of impaired skeletal muscle mass and strength during aging are well-studied in healthy populations. Less is known on pathological age-related muscle wasting and weakness termed sarcopenia, which directly impacts physical autonomy and survival. Here, we compare genome-wide transcriptional changes of sarcopenia versus age-matched controls in muscle biopsies from 119 older men from Singapore, Hertfordshire UK and Jamaica. Individuals with sarcopenia reproducibly demonstrate a prominent transcriptional signature of mitochondrial bioenergetic dysfunction in skeletal muscle, with low PGC-1α/ERRα signalling, and downregulation of oxidative phosphorylation and mitochondrial proteostasis genes. These changes translate functionally into fewer mitochondria, reduced mitochondrial respiratory complex expression and activity, and low NAD + levels through perturbed NAD + biosynthesis and salvage in sarcopenic muscle. We provide an integrated molecular profile of human sarcopenia across ethnicities, demonstrating a fundamental role of altered mitochondrial metabolism in the pathological loss of skeletal muscle mass and function in older people. Sarcopenia is the loss of muscle mass and strength associated with physical disability during ageing. Here, the authors analyse muscle biopsies from 119 patients with sarcopenia and age-matched controls of different ethnic groups and find transcriptional signatures indicating mitochondrial dysfunction, associated with reduced mitochondria numbers and lower NAD +  levels in older individuals with sarcopenia.

Virtual technology is a computer simulation system based on new technology and visual technology to realize the creation and experience of virtual world. It belongs to the use of information technology computer system function for environmental simulation, and it uses the combination of multi-information fusion and 3D dynamic visual interaction and physical behavioral experience to build virtual and real scenes to make the entire environment reality. It lets users feel the real environment and interact with them. At present, the application of visual simulation system for virtual reality technology has been gradually popularized. This article first introduces the development of virtual reality technology, and briefly describes the specific characteristics of virtual display technology, and finally puts forward some countermeasures for the application of virtual display technology in the visual simulation system.

Dual polarization is a typical operational mode of polarimetric synthetic aperture radar (SAR). However, few studies have considered the scattering mechanism extraction of dual-polarization SARs. A modified Cloude-Pottier decomposition is proposed to investigate the performance of the scattering mechanism extraction of dual-polarization SARs. It is theoretically demonstrated that only HH-VV SAR can discriminate the three canonical scattering mechanisms from an isotropic surface, horizontal dipole, and isotropic dihedral. Various experiments are conducted using 21 scenes from real datasets acquired by AIRSAR, Convair-580 SAR, EMISAR, E-SAR, Pi-SAR, and RADARSAT-2. Division of the dual-polarization H-α plane is experimentally obtained. The lack of cross-polarization induces the diffusion of scattering mechanisms and their overlap in the HH-VV H-α plane. However, the performance of HH-VV SAR for extracting scattering mechanisms is acceptable. Thus, HH-VV SAR is a suitable alternative to full-polarization SAR in certain cases. Meanwhile, the extraction performance of the other two dual-polarization SARs is badly degraded due to the lack of co-polarization. Therefore, HH-HV and HV-VV SARs cannot effectively extract the scattering mechanisms in the H-α plane.

There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI systems utilizing clinical narratives. However, there are few clinical language models, the largest of which trained in the clinical domain is comparatively small at 110 million parameters (compared with billions of parameters in the general domain). It is not clear how large clinical language models with billions of parameters can help medical AI systems utilize unstructured EHRs. In this study, we develop from scratch a large clinical language model—GatorTron—using >90 billion words of text (including >82 billion words of de-identified clinical text) and systematically evaluate it on five clinical NLP tasks including clinical concept extraction, medical relation extraction, semantic textual similarity, natural language inference (NLI), and medical question answering (MQA). We examine how (1) scaling up the number of parameters and (2) scaling up the size of the training data could benefit these NLP tasks. GatorTron models scale up the clinical language model from 110 million to 8.9 billion parameters and improve five clinical NLP tasks (e.g., 9.6% and 9.5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery. The GatorTron models are publicly available at: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/gatortron_og .

Efficient proteostasis and immune evasion are both critical for tumor progression. The chaperonin TRiC/CCT complex, which mediates the folding of cytoskeletal and signaling proteins, has been associated with oncogenesis; however, the specific role of its subunit CCT4 in tumor-immune interactions remain unclear. To address this gap, we integrated transcriptomics, proteomics, genomics, epigenetics and immunogenomics data. A comprehensive pan-cancer analysis was conducted (including the expression patterns, clinical relevance, prognosis value, immune infiltration of pan-cancer). Then an in-depth analysis of lung adenocarcinoma (LUAD) was carried out through enrichment analysis and single-cell RNA sequencing, and verified through cell experiments. CCT4 was found to be aberrantly upregulated across a majority of tumor types, particularly in LUAD, where elevated expression was associated with advanced stage and inferior survival outcomes. High CCT4 levels were linked to reduced immune cell infiltration and diminished anti-tumor immune signaling, specifically manifested as increased Th2 cell infiltration and decreased Th1 and CD8 T-cell signatures. Single-cell analyses revealed coordinated overexpression of all CCT subunits in tumor epithelial cells, supporting a global TRiC activation. However, CCT4 was preferentially enriched within highly proliferative subclusters, suggesting partial subunit-specific regulation. CCT4 knockdown suppressed LUAD cell proliferation, migration, and invasion . CCT4 links enhanced proteostasis with immune evasion in LUAD, acting partly through TRiC complex activity and possibly through independent nuclear functions. These findings refine the understanding of how proteostatic machinery contributes to immune modulation in cancer and highlight CCT4 as a potential molecular node bridging tumor growth and immune suppression.

Conventional therapeutic interventions for rheumatoid arthritis (RA) are frequently induced immunosuppressive side effects. The present study explores the therapeutic potential of Cyclovirobuxine D (CycD), a triterpenoid alkaloid isolated from Buxus microphylla, in a collagen-induced arthritis (CIA) mouse model or in vitro. The CIA model was treated with varying doses of CycD or methotrexate. The biomarkers of inflammatory factors related to macrophage polarization and oxidative stress in serum or synovial tissue was conducted. The RNA sequencing (RNA-seq) was employed to identify key post-regulatory genes in CycD treatment. The pathway was investigated using PI3K activators and inhibitors, both in vivo and vitro. Molecular docking, Network Pharmacology, Drug Affinity Responsive Target Stability (DARTS) and Cellular Thermal Shift Assay (CETSA) were performed to validate. The administration of CycD resulted in a substantial alleviation arthritis severity, joint pathology, and levels of biomarkers associated with macrophage polarization and oxidative stress in CIA mice. In contrast to methotrexate, CycD exhibited minimal adverse effects on the spleen and thymus. RNA-seq and immunofluorescent staining has showed that the biological function of CycD is predominantly characterized by its capacity to suppress the PI3K-AKT pathway in M1 macrophages. The activation of PI3K was found to counteract the therapeutic benefits of CycD in both CIA mice, RAW264.7 and THP-1 cells. Molecular docking, Network Pharmacology, DARTS and CETSA confirmed that CycD competes with PI3K for binding in M1-type macrophages. CycD alleviates collagen-induced arthritis by inhibiting the PI3K-AKT pathway in macrophages. Graphical Abstract Cyclovirobuxine D primarily attenuates symptoms and pathological damage in collagen-induced arthritis in mice by inhibiting the PI3K-AKT in macrophages through competitive binding with PI3K, thereby suppressing inflammation and oxidative stress. Cyclovirobuxine D demonstrates a lower incidence of immunosuppressive side effects, underscoring its promise as a potential therapeutic agent for the management of rheumatoid arthritis.

There are enormous enthusiasm and concerns in applying large language models (LLMs) to healthcare. Yet current assumptions are based on general-purpose LLMs such as ChatGPT, which are not developed for medical use. This study develops a generative clinical LLM, GatorTronGPT, using 277 billion words of text including (1) 82 billion words of clinical text from 126 clinical departments and approximately 2 million patients at the University of Florida Health and (2) 195 billion words of diverse general English text. We train GatorTronGPT using a GPT-3 architecture with up to 20 billion parameters and evaluate its utility for biomedical natural language processing (NLP) and healthcare text generation. GatorTronGPT improves biomedical natural language processing. We apply GatorTronGPT to generate 20 billion words of synthetic text. Synthetic NLP models trained using synthetic text generated by GatorTronGPT outperform models trained using real-world clinical text. Physicians’ Turing test using 1 (worst) to 9 (best) scale shows that there are no significant differences in linguistic readability (p = 0.22; 6.57 of GatorTronGPT compared with 6.93 of human) and clinical relevance (p = 0.91; 7.0 of GatorTronGPT compared with 6.97 of human) and that physicians cannot differentiate them (p < 0.001). This study provides insights into the opportunities and challenges of LLMs for medical research and healthcare.

In traditional pharmaceutics, drug–crystalline nanoparticles and drug–polymer composites are frequently explored for their ability to modify drug release profiles. In this study, a novel sort of hybrid with a coating of acyclovir crystalline nanoparticles on acyclovir-polyacrylonitrile composites was fabricated using modified, coaxial electrospinning processes. The developed acyclovir-polyacrylonitrile at the acyclovir nanohybrids was loaded with various amounts of acyclovir, which could be realized simply by adjusting the sheath fluid flow rates. Compared with the electrospun composite nanofibers from a single-fluid blending process, the nanohybrids showed advantages of modifying the acyclovir release profiles in the following aspects: (1) the initial release amount was more accurately and intentionally controlled; (2) the later sustained release was nearer to a zero-order kinetic process; and (3) the release amounts at different stages could be easily allocated by the sheath fluid flow rate. X-ray diffraction results verified that the acyclovir nanoparticles were in a crystalline state, and Fourier-transform infrared spectra verified that the drug acyclovir and the polymer polyacrylonitrile had a good compatibility. The protocols reported here could pave the way for developing new types of functional nanostructures.

Currently, endoscopic treatment for small gastrointestinal stromal tumors (GIST) has been widely accepted. However, for tumors larger than 5 cm, endoscopic treatment has not been recognized by national guidelines as the standard therapy due to concerns about safety and adverse tumor outcomes. Therefore, this study compares the long-term survival outcomes of endoscopic treatment and surgical treatment for GIST in the range of 5–10 cm. We selected patients with GIST from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. Kaplan–Meier analysis and the log-rank test were employed to compare the long-term survival outcomes between endoscopic treatment and surgical treatment. A multivariate Cox proportional hazards model was used for analysis to identify risk factors influencing patient prognosis. To balance baseline data, we performed 1:1 propensity score matching (PSM). A total of 1223 GIST patients were included, with 144 patients (11.8%) received endoscopic treatment and 1079 patients (88.2%) received surgical treatment. Before PSM, there was no significant difference in the long-term survival rates between the two groups [5-year OS (86.5% vs. 83.5%, P  = 0.42), 10-year OS (70.4% vs. 66.7%, P  = 0.42)]. After adjusting for covariates, we found that the overall survival (HR = 1.26, 95% CI 0.89–1.77, P  = 0.19) and cancer-specific survival (HR = 1.69, 95% CI 0.99–2.89, P  = 0.053) risks were comparable between the endoscopic treatment group and the surgical treatment group. In the analysis after PSM, there was no significant difference between the endoscopic treatment group and the surgical treatment group. Our study found that for GIST patients with tumor sizes between 5 and 10 cm, the long-term OS and CSS outcomes were similar between the endoscopic treatment group and the surgical treatment group.

In recent years, viral co-infections, particularly with respiratory viruses, have resulted in more complex symptoms, a greater disease burden, and increased challenges in clinical decision-making. These complexities underscore the urgent need for improved diagnostic tools in the managing acute respiratory infections. To address the limitations of conventional qPCR and current POCT methodologies, we developed a passively driven microfluidic chip capable of rapidly screening multiple respiratory viruses. This platform is particularly suited for the point-of-care diagnosis of viral co-infections. Our device integrates nucleic acid amplification and CRISPR-based detection within a single, passively operated system. By utilizing a rapid, 10-minute sample preparation protocol and a 35-minute on-chip assay, this platform enables the multiplex detection of influenza A/B, human parainfluenza virus, and SARS-CoV-2. The total assay time from sample to answer is approximately 45 min, with equipment requirements minimized to a heating block. The assay demonstrated a detection sensitivity of about 10 copies/µL for viral RNA in dilution series experiments. The sensitivity of the assay was 98.44% (95% CI: 91.6%–99.96%), and the specificity was 100% (95% CI: 79.4%–100%). The system combines CRISPR-Cas12a-mediated sensing with reverse transcription recombinase polymerase amplification (RPA) for highly specific nucleic acid detection. The chip design utilizes capillary action and gravity-driven flow for autonomous fluid control, while lyophilized reagent preloading ensures storage stability and minimizes user intervention.