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Abstract Large language models (LLMs) such as ChatGPT, Claude, Llama, and Qwen are emerging as transformative technologies for the diagnosis and treatment of various diseases. With their exceptional long-context reasoning capabilities, LLMs are proficient in clinically relevant tasks, particularly in medical text analysis and interactive dialogue. They can enhance diagnostic accuracy by processing vast amounts of patient data and medical literature and have demonstrated their utility in diagnosing common diseases and facilitating the identification of rare diseases by recognizing subtle patterns in symptoms and test results. Building on their image-recognition abilities, multimodal LLMs (MLLMs) show promising potential for diagnosis based on radiography, chest computed tomography (CT), electrocardiography (ECG), and common pathological images. These models can also assist in treatment planning by suggesting evidence-based interventions and improving clinical decision support systems through integrated analysis of patient records. Despite these promising developments, significant challenges persist regarding the use of LLMs in medicine, including concerns regarding algorithmic bias, the potential for hallucinations, and the need for rigorous clinical validation. Ethical considerations also underscore the importance of maintaining the function of supervision in clinical practice. This paper highlights the rapid advancements in research on the diagnostic and therapeutic applications of LLMs across different medical disciplines and emphasizes the importance of policymaking, ethical supervision, and multidisciplinary collaboration in promoting more effective and safer clinical applications of LLMs. Future directions include the integration of proprietary clinical knowledge, the investigation of open-source and customized models, and the evaluation of real-time effects in clinical diagnosis and treatment practices.

Background It is worrisome that several pollutants can enhance the abundance of antibiotic resistance genes (ARGs) in the environment, including agricultural fungicides. As an important bioindicator for environmental risk assessment, earthworm is still a neglected focus that the effects of the fungicide carbendazim (CBD) residues on the gut microbiome and resistome are largely unknown. In this study, Eisenia fetida was selected to investigate the effects of CBD in the soil-earthworm systems using shotgun metagenomics and qPCR methods. Results CBD could significantly perturb bacterial community and enrich specific bacteria mainly belonging to the phylum Actinobacteria. More importantly, CBD could serve as a co-selective agent to elevate the abundance and diversity of ARGs, particularly for some specific types (e.g., multidrug, glycopeptide, tetracycline, and rifamycin resistance genes) in the earthworm gut. Additionally, host tracking analysis suggested that ARGs were mainly carried in some genera of the phyla Actinobacteria and Proteobacteria. Meanwhile, the level of ARGs was positively relevant to the abundance of mobile genetic elements (MGEs) and some representative co-occurrence patterns of ARGs and MGEs (e.g., cmx -transposase and sul1 -integrase) were further found on the metagenome-assembled contigs in the CBD treatments. Conclusions It can be concluded that the enhancement effect of CBD on the resistome in the earthworm gut may be attributed to its stress on the gut microbiome and facilitation on the ARGs dissemination mediated by MGEs, which may provide a novel insight into the neglected ecotoxicological risk of the widely used agrochemicals on the gut resistome of earthworm dwelling in soil. 9FRi6Qq4wsD9g5Mb-6DnXW Video abstract Graphical Abstract

To evaluate the diagnostic accuracy of matrix-assisted laser desorption ionization time-of-flight mass spectrometry based on nucleotide (nucleotide MALDI-TOF MS) on bronchoalveolar lavage fluid (BALF) from suspected pulmonary tuberculosis (PTB) patients. A retrospective study was conducted on suspected PTB patients (total of 960) admitted to Chongqing Public Health Medical Center between May 2021 and January 2022. The sensitivity, specificity, positive predictive value, negative predictive value (NPV) and area under the curve values of nucleotide MALDI-TOF MS as well as smear microscopy, Mycobacterium Growth Indicator Tube 960 culture (MGIT culture), and Xpert MTB/RIF were calculated and compared. Total of 343 presumed PTB cases were enrolled. Overall, using the clinical diagnosis as reference, the sensitivity and NPV of nucleotide MALDI-TOF MS was 71.5% and 43.1%, respectively, significantly higher than smear microscopy (22.6%, 23.2%), MGIT culture (40.6%, 18.9%), Xpert MTB/RIF (40.8%, 27.9%). Furthermore, nucleotide MALDI-TOF MS also outperformed over Xpert MTB/RIF and MGIT culture on smear-negative BALFs. Approximately 50% and 30% of patients benefited from nucleotide MALDI-TOF MS compared with smear and MGIT culture or Xpert MTB/RIF, respectively. This study demonstrated that the analysis of BALF with nucleotide MALDI-TOF MS provided an accurate and promising tool for the early diagnosis of PTB.

Background To investigate the prevalence and molecular characterization of bedaquiline resistance among MDR-TB isolates collected from Chongqing, China. Methods A total of 205 MDR-TB isolates were collected from Chongqing Tuberculosis Control Institute between March 2019 and June 2020. The MICs of BDQ were determined by microplate alamarblue assay. All strains were genotyped by melting curve spoligotyping, and were subjected to WGS. Results Among the 205 MDR isolates, the resistance rate of BDQ was 4.4% (9/205). The 55 (26.8%) were from male patients and 50 (24.4%) were new cases. Furthermore, 81 (39.5%) of these patients exhibited lung cavitation, 13 (6.3%) patients afflicted with diabetes mellitus, and 170 (82.9%) isolates belonged to Beijing family. However, the distribution of BDQ resistant isolates showed no significant difference among these characteristics. Of the 86 OFX resistant isolates, 8 isolates were XDR (9.3%, 8/86). Six BDQ resistant isolates (66.7%, 6/9) and two BDQ susceptible isolates (1.0%, 2/196) carried mutations in Rv0 678. A total of 4 mutations types were identified in BDQ resistant isolates, including mutation in A152G (50%, 3/6), T56C (16.7%, 1/6), GA492 insertion (16.7%, 1/6), and A274 insertion (16.7%, 1/6). BDQ showed excellent activity against MDR-TB in Chongqing. Conclusions BDQ showed excellent activity against MDR-TB in Chongqing. The resistance rate of BDQ was not related to demographic and clinical characteristics. Mutations in Rv 0678 gene were the major mechanism to BDQ resistance, with A152G as the most common mutation type. WGS has a good popularize value and application prospect in the rapid detection of BDQ resistance.

Background Asthma exacerbation (AE) is a significant clinical problem during pregnancy. This study aimed to identify maternal and perinatal outcomes associated with AE during pregnancy. Methods We conducted a retrospective cohort study using the Peking University Third Hospital database from January 1, 2013 to December 31, 2020. We compared the clinical characteristics and maternal, perinatal and offspring outcomes of asthma with and without exacerbations among women who delivered during this period. The primary outcome was hypertensive disorders of pregnancy (HDP). Univariable and multivariable logistic regression analyses were used to analyze the clinical characteristics of AE during pregnancy and the association between AE and adverse maternal and perinatal outcomes. Results The prevalence of asthma during pregnancy increased from 0.52% in 2013 to 0.98% in 2020. Of the 220 patients with asthma during pregnancy included in the study, 105 experienced AE during pregnancy: 62.9% ( n  = 66) had mild-to-moderate AE and 37.1% ( n  = 39) had severe AE. Pregnant women with allergic rhinitis have a higher risk of AE during pregnancy. Women who experienced AE were more at risk for hypertensive disorders of pregnancy than women who did not experience any exacerbation (12.4%vs3.5%, p  < 0.05). Conclusions The prevalence of asthma among pregnant women in China is on the rise. There is a notable correlation between pregnant women who suffer from allergic rhinitis and an elevated risk of AE during pregnancy. Studies have shown that AE during pregnancy are associated with an increased risk of hypertensive disorders of pregnancy.

Sustainable development in cities has gained popularity due to the emergence of numerous urban challenges in harsh environments. Selecting an accurate turbulence model in CFD is crucial for assessing the outdoor environment. Among the widely used microclimate simulation tools, ENVI-met stands out for its convenience and its proven effectiveness in urban microclimate studies. Elevated design, often referred to as ‘lifted up design,’ is standard in architectural practice, serving both as recreational spaces and corridors, potentially improving thermal comfort. To ensure reliable microclimate modeling, assessments in such areas should be validated against empirical data. This study compares the microclimatic conditions in open space beneath an elevated building using ENVI-met with on-site meteorological data collected in Xi’an, China, across three days with varying weather conditions. The results show that ENVI-met can reasonably reproduce air temperature (R2 = 0.80–0.96, RMSE = 0.67–1.42 °C), relative humidity (R2 = 0.85–0.99, RMSE = 2.83–9.32%), and mean radiant temperature (R2 = 0.87–0.90, RMSE = 4.11–7.23 °C) under different conditions, though some deviations exist—especially with diffuse radiation, which ENVI-met tends to underestimate beneath elevated structures. Despite these discrepancies, the model performance was evaluated by comparing field measurements with ENVI-met outputs, and the results indicate that ENVI-met can provide useful insights for simulating microclimate conditions in open spaces beneath elevated buildings under different weather conditions.

Background Hyperprolactinemia frequently occurs during antipsychotic treatment but is also observed in antipsychotic-naïve schizophrenia patients. The relationship between prolactin (PRL) and treatment resistance remains underexplored in real-world populations. Objective To characterize age-/sex-stratified PRL distribution in first-hospitalized schizophrenia spectrum disorder (SSD) patients and examine associations with treatment resistance proxies (clozapine use/rehospitalization). Methods We analyzed 4,103 first-hospitalized SSD patients using real-world data. PRL levels were stratified by age/sex. Binary logistic regression evaluated PRL-clozapine/rehospitalization associations with confounder adjustment. ROC analysis assessed PRL’s discriminative capacity for clozapine use. Results Elevated PRL prevalence was highest in females aged 18–45 (71.5%). PRL negatively correlated with clozapine use in males ( P  < 0.001), with lower median levels in users vs. non-users (374.40 vs. 529.10 mIU/L, P  < 0.001). This persisted in males aged 18–45 (434.90 vs. 558.60 mIU/L) and ≥ 55 years (308.60 vs. 583.30 mIU/L) (both P  < 0.01). In males ≥ 55, each 1 mIU/L PRL increase reduced clozapine use probability by 0.2% (aOR = 0.998, 95%CI:0.997-1.000). ROC analysis showed moderate discriminative capacity (AUC = 0.72, sensitivity 78.6%, specificity 39.4%). No significant associations occurred in females or for rehospitalization. Conclusion The PRL demonstrates inverse, age-specific associations with clozapine use in male SSD patients, suggesting potential as a stratification biomarker for treatment resistance. Highlights First report on hyperprolactinemia prevalence and predictive role of PRL in a large real-world cohort of first-hospitalized SSD patients (N=4,103). Elevated prolactin levels may serve as a marker associated with clozapine use in males aged ≥55 years with SSD. Moderate discriminative capacity (AUC=0.721) for treatment resistance proxies supports PRL as stratified biomarker.

Background The human brain is a dynamic neural system with time-varying functional connectivity (FC) strengths between brain regions. Evidence indicates that adolescents with major depressive disorder (MDD) exhibit decreased average FC strength in cognitive tasks. Nevertheless, research focused on dynamic FC analysis in this population remains limited. This study aims to identify cognitive task-related dynamic FC features as valuable biomarkers to characterize clinical symptoms in adolescents with MDD. Methods A total of 83 adolescents with MDD and 78 age/sex-matched healthy controls (HCs) were recruited. We utilized functional near-infrared spectroscopy (fNIRS) to record brain functional data from participants while they performed the verbal fluency task (VFT). An analytical framework for fNIRS data was proposed, in which the average FC strength values over the entire VFT duration and the principal components (PCs) of dynamic (time-varying) FC strength values were extracted as static and dynamic FC features, respectively. A random forest model was built to distinguish adolescents with MDD from HCs. Statistical analyses of the FC features were conducted to identify between-group differences, as well as their relationships with clinical symptoms in adolescents with MDD. Results The random forest model achieved an accuracy of 86.32% (95% confidence interval: 83.75%-89.38%) for distinguishing adolescents with MDD from HCs. Significant between-group differences emerged in several FC features (false discovery rate-corrected q  < 0.05). For adolescents with MDD, the average FC strength value in the right dorsolateral prefrontal cortex (DLPFC) ~ right medial prefrontal cortex (mPFC) pathway was a significant predictor of depressive and anxious symptoms; the 3rd PC of dynamic FC strength values in the left DLPFC ~ left temporal lobe (TL) pathway and the 5th PC of dynamic FC strength values in the right mPFC ~ right TL pathway were significant predictors of anhedonic symptoms. Conclusions VFT-related static and dynamic FC features in specific brain pathways are potential biomarkers for characterizing clinical symptoms in adolescents with MDD. The developed random forest model holds promise as a diagnostic tool for MDD in adolescents. Clinical trial number Not applicable.