OBJECTIVE To compare the predictive accuracy and clinical applicability of four vancomycin individualized dosing tools （SmartDose， ClinCalc， Gulou， Pharmado） and provide a basis for rational clinical medication use. METHODS A retrospective cohort study was conducted， enrolling 479 adult patients who received vancomycin therapy and underwent steady-state trough concentration monitoring in Zhongshan Hospital， Fudan University （Xiamen Branch） from January 1， 2022， to June 30， 2024. The predictive accuracy of each tool was evaluated using indicators， such as mean error （ME）, mean absolute error （MAE）， mean percentage error （MPE）， mean absolute percentage error （MAPE）， the proportion of patients with an absolute percentage error （APE） of less than 30%， the 95% limits of agreement， and the overall relative percentage difference between predicted and measured values. Using indicators such as accessibility， patient management， and recommendation of multiple treatment options， the clinical panxso@163.com applicability of the tools for all patients was evaluated； using the discrepancy in accuracy between the predicted and actual measured blood drug concentrations as an indicator， the clinical applicability was assessed for patients in different renal function subgroups （hyperfunction， normal， mild impairment， moderate impairment， and severe impairment）. RESULTS In terms of accuracy， SmartDose demonstrated the best overall performance with an MAPE of 46.40% and a proportion of APE ＜30% （46.56%）. Bland-Altman analysis indicated that SmartDose had the smallest overall relative percentage difference （－7.25%）， although the 95% limits of agreement were broad for all tools， with differences between the upper and lower limits exceeding 200%. In terms of applicability， all four dosing tools were freely accessible and demonstrated good availability； SmartDose and Pharmado provided the most comprehensive solutions， offering features such as patient management， multiple regimen recommendations， and drug concentration-time curve plotting. Stratified analysis based on renal function revealed that Pharmado showed optimal prediction for hyperfiltration patients （mean difference： 0.11 mg/L）. SmartDose and ClinCalc showed relatively better performance in normal and mild renal impaiment （mean difference： 0.37， 0.51 mg/L and －1.13， －1.33 mg/L，respectively）. SmartDose performed best in moderate renal impairment （mean difference： －2.60 mg/L）. Pharmado and Gulou had smaller prediction biases in severe renal impairment （mean differences： 1.52 mg/L and －0.23 mg/L， respectively）. CONCLUSIONS The four individualized dosing tools demonstrated limited accuracy in the initial prediction of vancomycin concentrations. Among them， SmartDose demonstrates the highest overall prediction accuracy and possesses comprehensive clinical management features. It is recommended that Pharmado be preferred for patients with renal hyperfiltration； SmartDose or ClinCalc can be used for patients with normal or mildly impaired renal function； SmartDose is recommended for patients with moderately impaired renal function； Pharmado or Gulou may be considered for patients with severely impaired renal function.

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.
Humans ; Large Language Models ; Tomography, X-Ray Computed

Lung cancer is the leading cause of cancer-related deaths worldwide. Radiation pneumonitis is a major complication in lung cancer radiotherapy. Four-dimensional computed tomography (4DCT) imaging provides dynamic ventilation information, which is valuable for lung function assessment and radiation pneumonitis prevention. Many methods have been developed to calculate lung ventilation from 4DCT, but a systematic comparison is lacking. Prediction of radiation pneumonitis using 4DCT-based ventilation is still in an early stage, and no comprehensive review exists. This paper presented the first systematic comparison of functional lung ventilation algorithms based on 4DCT over the past 15 years, highlighting their clinical value and limitations. It then reviewed multimodal approaches combining 4DCT ventilation imaging, dose metrics, and clinical data for radiation pneumonitis prediction. Finally, it summarized current research and future directions of 4DCT in lung cancer radiotherapy, offering insights for clinical practice and further studies.
Humans ; Lung Neoplasms/diagnostic imaging* ; Four-Dimensional Computed Tomography/methods* ; Radiation Pneumonitis/etiology* ; Algorithms ; Lung/radiation effects* ; Pulmonary Ventilation

Most current medical image segmentation models are primarily built upon the U-shaped network (U-Net) architecture, which has certain limitations in capturing both global contextual information and fine-grained details. To address this issue, this paper proposes a novel U-shaped network model, termed the Multi-View U-Net (MUNet), which integrates self-attention and multi-view attention mechanisms. Specifically, a newly designed multi-view attention module is introduced to aggregate semantic features from different perspectives, thereby enhancing the representation of fine details in images. Additionally, the MUNet model leverages a self-attention encoding block to extract global image features, and by fusing global and local features, it improves segmentation performance. Experimental results demonstrate that the proposed model achieves superior segmentation performance in coronary artery image segmentation tasks, significantly outperforming existing models. By incorporating self-attention and multi-view attention mechanisms, this study provides a novel and efficient modeling approach for medical image segmentation, contributing to the advancement of intelligent medical image analysis.
Humans ; Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Algorithms ; Attention ; Coronary Vessels/diagnostic imaging* ; Diagnostic Imaging/methods*

At present, research on the efficacy of local physical cooling devices is mainly based on clinical observation, but there is relatively little research on evaluating the effectiveness of local cold therapy cooling and the penetration depth. This study is based on the research of the structure and morphology of local muscle tissue in the human body, as well as the heat transfer characteristics and mechanisms of the human body. A simulation phantom of human muscle tissue under temperature cycling was created, and the differences in evaluating the effectiveness of local cold therapy between the human body and the simulation phantom were compared. This provides a new evaluation method for evaluating the cooling effectiveness of local physical cooling equipment.
Humans ; Phantoms, Imaging ; Hypothermia, Induced/methods*

OBJECTIVE: This study reports the first imported case of Lassa fever (LF) in China. Laboratory detection and molecular epidemiological analysis of the Lassa virus (LASV) from this case offer valuable insights for the prevention and control of LF. METHODS: Samples of cerebrospinal fluid (CSF), blood, urine, saliva, and environmental materials were collected from the patient and their close contacts for LASV nucleotide detection. Whole-genome sequencing was performed on positive samples to analyze the genetic characteristics of the virus. RESULTS: LASV was detected in the patient's CSF, blood, and urine, while all samples from close contacts and the environment tested negative. The virus belongs to the lineage IV strain and shares the highest homology with strains from Sierra Leone. The variability in the glycoprotein complex (GPC) among different strains ranged from 3.9% to 15.1%, higher than previously reported for the seven known lineages. Amino acid mutation analysis revealed multiple mutations within the GPC immunogenic epitopes, increasing strain diversity and potentially impacting immune response. CONCLUSION The case was confirmed through nucleotide detection, with no evidence of secondary transmission or viral spread. The LASV strain identified belongs to lineage IV, with broader GPC variability than previously reported. Mutations in the immune-related sites of GPC may affect immune responses, necessitating heightened vigilance regarding the virus.
Humans ; China/epidemiology* ; Genome, Viral ; Lassa Fever/virology* ; Lassa virus/classification* ; Molecular Epidemiology ; Phylogeny

OBJECTIVE: Coronavirus disease 2019 (COVID-19) can result in fatigue and post-exertional malaise; however, whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection exacerbates lower urinary tract symptoms (LUTS) is unclear. This study investigated the association between prenatal SARS-CoV-2 infection and postpartum LUTS. METHODS: A multicenter, retrospective cohort study was conducted at two tertiary hospitals in China from November 1, 2022, to November 1, 2023. Participants were classified into infected and uninfected groups based on SARS-CoV-2 antigen results. LUTS prevalence and severity were assessed using self-reported symptoms and the Incontinence Impact Questionnaire-Short Form (IIQ-7). Pelvic floor muscle activity was measured using electromyography following the Glazer protocol. Group comparisons were performed to evaluate the association of SARS-CoV-2 infection with LUTS and electromyography parameters, with stratified analyses conducted using SPSS version 26.0. RESULTS: Among 3,652 participants (681 infected, 2,971 uninfected), no significant differences in LUTS prevalence or IIQ-7 scores were observed. However, SARS-CoV-2 infection was an independent factor influencing the electromyographic activity of the pelvic floor muscles (mean tonic contraction amplitudes), regardless of delivery mode ( P = 0.001). CONCLUSION Prenatal SARS-CoV-2 infection was not significantly associated with an increased risk of postpartum LUTS but independently altered pelvic floor muscle electromyographic activity, suggesting potential neuromuscular effects.
Humans ; Female ; COVID-19/epidemiology* ; Retrospective Studies ; Adult ; Pregnancy ; Lower Urinary Tract Symptoms/virology* ; Postpartum Period ; Pregnancy Complications, Infectious/virology* ; China/epidemiology* ; Electromyography ; SARS-CoV-2/physiology* ; Pelvic Floor/physiopathology* ; Prevalence

ObjectiveTo analyze the evolutionary characteristics and genetic variations of the HA (hemagglutinin) and NA (neuraminidase) genes of influenza A(H1N1) viruses in Shanghai during 2024, to investigate their transmission patterns, and to evaluate their potential impact on vaccine effectiveness. MethodsFrom January to October 2024, throat swab specimens were collected from influenza like illness (ILI) patients at 4 hospitals in Shanghai. Real-time fluorescence ploymerase chain reaction (RT-PCR) was used for virus detection and isolation of H1N1 influenza viruses. Forty influenza A(H1N1) virus strains were sequenced using Illumina NovaSeq 6000 platform, followed by phylogenetic analyses, genetic distance analysis, and amino acid variation analyses of HA and NA genes. ResultsPhylogenetic tree of the HA and NA genes revealed that the 40 influenza A(H1N1) virus strains circulating in Shanghai in 2024 exhibited no significant geographic clustering, with a broad origin of strains and complex transmission chains. Genetic distance analyses demonstrated that the average intra-group genetic distances of HA and NA genes among the Shanghai strains were 0.005 1±0.000 6 and 0.004 6±0.000 6, respectively, which were comparable to or higher than those observed in global surveillance strains. Both HA and NA genes displayed frequent mutations. Compared to the 2023‒2024 and 2024‒2025 Northern Hemisphere A(H1N1) vaccine strains (WHO-recommended), the HA proteins of 40 Shanghai strains exhibited amino acid substitutions at positions 120, 137, 142, 169, 216, 223, 260, 277, 356 and 451, with critical mutations at positions 137 and 142 located within the Ca2 antigenic determinant. Furthermore, mutations in the NA protein were observed at positions 13, 50, 200, 257, 264, 339 and 382. ConclusionThe genetic background of the 2024 Shanghai influenza A(H1N1) virus strains is complex and diverse, and antigenic variation may affect vaccine effectiveness. Therefore, it is recommended to enhance genomic surveillance of influenza viruses, evaluate vaccine suitability, and implement more targeted prevention and control strategies against imported influenza viruses.

ObjectiveTo understand the distribution of enrofloxacin (ENR) residues in freshwater fish, to evaluate the dietary exposure risk to ENR for consumers through the consumption of different freshwater fish in Shanghai, and to provide a reference for controlling antibiotic residues in freshwater fish. MethodsGrass carp, Wuchang bream, pond loach, and Asian swamp eels were purchased from the markets in Shanghai. After being fed with ENR, the fish were divided into 42 batches according to their species and weight, and thereafter ENR residues in the muscles and skin of the fish were measured. In addition, a total of 44 batches of Wuchang bream, pond loach, Asian swamp eels were purchased from the markets, and the ENR residues in the muscles with or without the fish skin were measured, and the exposure risk was evaluated. ResultsThe average residues of ENR in skin of the freshwater fish after being fed with drugs in the 42 groups were higher than those in muscles (M=659.38 μg·kg^-1, M=460.83 μg·kg^-1; z=-2.212, P=0.027). The over-standard rates of ENR residues in the muscles with or without skin 44 batches of freshwater fish of sold in Shanghai were 36.36% and 29.55%, respectively. The median exposure, P₉₅ exposure, and maximum exposure to ENR through the consumption of the muscles with the skin for adults and children in Shanghai were higher than those through the consumption of muscles without the skin. For children, the margin of safety (MOS) for the max exposure to ENR by consuming the muscles with the skin was more than 1, while the MOS was less than 1 in all other cases for both children and adults. ConclusionThe ENR residues in the skin of freshwater fish are generally higher than those in the muscles. The risk of ENR residues in freshwater fish sold in Shanghai is within a controllable range. However, there might be a certain risk of acute exposure to ENR for children by consuming muscles with the skin of freshwater fish.

Objective To investigate associations between three-dimensional(3D)morphology of cervical vertebrae and skeletal mat-uration by cone-beam computed tomography(CBCT)and establish corresponding regression models for quantitatively evaluating cervical vertebral maturation(CVM).Methods The analyzed sample consisted of 358 CBCT images(175 male,183 female),of which 277 images were randomly selected as the model development group and 81 as the performance test group.Twenty-one 3D morphological pa-rameters were defined and measured,incorporating all parts of the cervical vertebrae,including the cervical vertebral bodies,transverse processes,spinous processes,pedicles,lamina,and articular processes.The cervical vertebral maturation index(CVMI)was determined by experienced orthodontists as reference standard.Spearman's rank correlation coefficient and multivariable stepwise regression analysis were used to identify the associations and build regression models.The performance test group was employed to ex-amine each model's reliability.Paired-samples Wilcoxon signed-rank test compared the CVMI of the model prediction with the reference standard.Results Three-dimensional morphological changes in various parts of the cervical vertebrae correlated with CVMI(P＜0.05).Six 3D morphometric parameters were each recognized for male and female models,three of which were identical.The adjusted R2 was 0.899 for males and 0.902 for females,with corresponding accuracies of 85.0%and 85.4%,respectively.These models showed no difference as compared with the reference standard(P＞0.05).Con-clusion New associations were found between 3D morphology of cer-vical vertebrae and skeletal maturation.The 3D-driven morphometric CVM assessment method and corresponding regression models exhibited good credibility and high consistency with experts.