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Neurosyphilis presents significant diagnostic and therapeutic challenges due to its heterogeneous clinical manifestations, absence of a gold-standard diagnostic criterion, and variable treatment responses. This study aims to identify clinically homogeneous subtypes of suspected neurosyphilis patients and develop a machine learning-based subtyping model to support clinical decision-making. Data from 451 suspected neurosyphilis patients were retrospectively collected from West China Hospital of Sichuan University. Patients were divided into a model development cohort (n=369) and an external validation cohort (n=82) by time. Latent class analysis (LCA) was performed to identify subtypes, with the optimal class number determined by model fit indicators. Key predictive variables were selected using LASSO regression and Boruta algorithm. Six machine learning algorithms were employed to build LCA subtype prediction models. Feature importance was interpreted via SHAP analysis, and model generalizability was assessed using the external cohort. LCA classified patients into three homogeneous subtypes: \"typical neurosyphilis\" (43.7%; predominantly male, high serum TRUST titer, significant CSF abnormalities, and robust intrathecal immune activation), \"atypical neurosyphilis\" (17.9%; absence of elevated CSF protein, mild intrathecal IgG synthesis), \"non-neurosyphilis\" (38.5%; normal CSF parameters). Six variables (age, serum TRUST titer, CSF protein, CSF nucleated cells, IgG index, CSF TTs) were used for model construction. The XGBoost model demonstrated optimal performance, achieving an AUC of 0.966 (accuracy: 87.3%) on the internal test set and 0.970 (accuracy: 91.5%) on the external validation set. Key predictors included CSF nucleated cells, CSF TTs, and IgG index. This study defines three clinically meaningful latent subtypes of neurosyphilis. The developed XGBoost model effectively discriminates between these subtypes of neurosyphilis and non-neurosyphilis in clinical settings, facilitating timely diagnosis and treatment.

The fourth-generation human immunodeficiency virus (HIV) serology assay, which simultaneously detects the HIV-1 p24 antigen and HIV-1 antibodies, is available either in a combined format or as dual tests that differentiate between the p24 antigen and antibodies. Divergent detection methodologies require distinct confirmatory testing algorithms, which significantly impact the time to HIV infection. In this report, we present three cases where the HIV-1 p24 antigen tested reactive, while the HIV-1 antibody remained non-reactive in a dual testing scenario-despite both the combined test and the colloidal gold immunochromatographic assay (GICA) for HIV-1 antibodies yielding reactive results. Upon further analysis of subsequent laboratory procedures, we observed that due to the application of various complementary tests, the assay with high antibody sensitivity such as the GICA paradoxically resulted in a prolonged time to diagnosis, extending the diagnostic window for patients from 5 days to 11 days. Our findings underscore the importance of prioritizing HIV-1 RNA testing in cases of discordant results between combined antigen/antibody testing, dual testing, and stand-alone antibody testing, particularly for patients who have not received pre-exposure or post-exposure prophylaxis.

Acute HIV infection (AHI), i.e., the early stage of HIV infection, plays an important role in immune system failure and HIV transmission, but most AHI patients are missed due to their non-specific symptoms. To facilitate the identification of patients with high AHI risk and reduction of missed diagnosis, we characterized 61 AHI patients in a Southwest China hospital with 4300 beds; specifically, we characterized their general clinical characteristics, evolution in results of a novel HIV screening assay called Elecsys® HIV Duo, and by programming, we analyzed the ability of all routine laboratory tests (e.g., routine blood analysis) to identify AHI patients. Among 61 AHI patients, 85.2% were male and the median age was 42 (interquartile range, 25–62) years. A total of 61.9% of patients visit the emergency department first during AHI. Clinical presentation of AHI patients included fever, fatigue, chills, rash, and various respiratory, digestive, and nervous system symptoms. Two of three results from Elecsys® HIV Duo show clear evolution trends: HIV P24 antigen decreased while HIV antibody increased in consecutive samples of nearly all patients. High fluorescence lymphocytes have a very high positive likelihood ratio (LR+) of 10.33 and a relatively high “rate of out-of-range tests” of 56.8% (21 in 37 patients who received this test had a result outside the reference range). In addition, we identified more than ten tests with LR+ greater than two. In summary, the emergency department is important for AHI screening. The evolution of HIV P24 Ag and HIV Ab and those laboratory tests with a high “rate of out-of-range tests” or high LR+ may aid the AHI identification and missed diagnosis reduction.

Background COVID-19 (coronavirus disease 2019) has caused a major epidemic worldwide; however, much is yet to be known about the epidemiology and evolution of the virus partly due to the scarcity of full-length SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) genomes reported. One reason is that the challenges underneath sequencing SARS-CoV-2 directly from clinical samples have not been completely tackled, i.e., sequencing samples with low viral load often results in insufficient viral reads for analyses. Methods We applied a novel multiplex PCR amplicon (amplicon)-based and hybrid capture (capture)-based sequencing, as well as ultra-high-throughput metatranscriptomic (meta) sequencing in retrieving complete genomes, inter-individual and intra-individual variations of SARS-CoV-2 from serials dilutions of a cultured isolate, and eight clinical samples covering a range of sample types and viral loads. We also examined and compared the sensitivity, accuracy, and other characteristics of these approaches in a comprehensive manner. Results We demonstrated that both amplicon and capture methods efficiently enriched SARS-CoV-2 content from clinical samples, while the enrichment efficiency of amplicon outran that of capture in more challenging samples. We found that capture was not as accurate as meta and amplicon in identifying between-sample variations, whereas amplicon method was not as accurate as the other two in investigating within-sample variations, suggesting amplicon sequencing was not suitable for studying virus-host interactions and viral transmission that heavily rely on intra-host dynamics. We illustrated that meta uncovered rich genetic information in the clinical samples besides SARS-CoV-2, providing references for clinical diagnostics and therapeutics. Taken all factors above and cost-effectiveness into consideration, we proposed guidance for how to choose sequencing strategy for SARS-CoV-2 under different situations. Conclusions This is, to the best of our knowledge, the first work systematically investigating inter- and intra-individual variations of SARS-CoV-2 using amplicon- and capture-based whole-genome sequencing, as well as the first comparative study among multiple approaches. Our work offers practical solutions for genome sequencing and analyses of SARS-CoV-2 and other emerging viruses.

Population aging and widespread sedentary lifestyles have increased the prevalence of chronic non-communicable diseases, many of which are linked to progressive disruptions of cellular homeostasis. Autophagy, a conserved cellular degradation and recycling pathway, plays a central role in maintaining metabolic flexibility, proteostasis, and organ function. However, aging and physical inactivity impair autophagic regulation, thereby contributing to the development of sarcopenia, cardiovascular diseases, metabolic disorders, and neurodegenerative diseases. Physical exercise is a non-pharmacological intervention that can restore autophagic activity and confer systemic health benefits in multiple preclinical and clinical contexts. Increasing evidence indicates that these benefits are mediated not only by local tissue adaptations but also by complex inter-organ communication. Central to this process are exercise-induced bioactive factors, collectively termed exerkines, including myokines, cardiokines, adipokines, hepatokines, osteokines, and circulating miRNAs. Rather than acting independently, exerkines form an integrated signaling network that fine-tunes autophagic flux across multiple tissues. Exerkine-mediated regulation of autophagy involves key pathways such as AMPK/mTOR, FoxO, SIRT1, ULK1, and TFEB, thereby coordinating energy metabolism, mitochondrial quality control, inflammation, and protein turnover in skeletal muscle, heart, liver, adipose tissue, bone, and the central nervous system. This review summarizes current evidence on representative exerkines and their roles in autophagy-dependent inter-organ crosstalk, highlighting the exercise–exerkine–autophagy axis as a promising target for preventing and managing chronic diseases.

COVID-19 has caused a major epidemic worldwide, however, much is yet to be known about the epidemiology and evolution of the virus. One reason is that the challenges underneath sequencing HCoV-19 directly from clinical samples have not been completely tackled. Here we illustrate the application of amplicon and hybrid capture (capture)-based sequencing, as well as ultra-high-throughput metatranscriptomic (meta) sequencing in retrieving complete genomes, inter-individual and intra-individual variations of HCoV-19 from clinical samples covering a range of sample types and viral load. We also examine and compare the bias, sensitivity, accuracy, and other characteristics of these approaches in a comprehensive manner. This is, to date, the first work systematically implements amplicon and capture approaches in sequencing HCoV-19, as well as the first comparative study across methods. Our work offers practical solutions for genome sequencing and analyses of HCoV-19 and other emerging viruses.