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

Due to the limited reliability of traditional self-completed questionnaire, the accuracy of measurement of physical behaviors (physical activity, sedentary behavior and sleep) is not high. With the development of technology, wearable devices (e.g. accelerometer) can be used for more accurate measurement of physical behaviors and have great application potential in large-scale research. However, the data of objective measurement of physical behaviors from large-scale cohort research in Asian populations is still limited. Between August 2020 and December 2021, the 3 rd resurvey of China Kadoorie Biobank (CKB) project used Axivity AX3 wrist triaxial accelerometer to collect the data of participants' daily activity and sleep status. A total of 20 370 participants from 10 study areas were included in the study, in whom 65.2% were women, and the age was (65.4±9.1) years. The participants' physical activity level varied greatly in different study areas. The objective measurement of participants' physical behaviors in CKB project has provided valuable resources for the description of 24-hour patterns of physical behaviors and evaluation of the health effect of physical activity, sedentary behavior and sleep as well as their association with diseases in the elderly in China.

Objective:To describe the distribution of lipoprotein (a) [Lp(a)] levels in non-arteriosclerotic cardiovascular disease (ASCVD) population in China and explore its influencing factors.Methods:This study was based on a nested case-control study in the CKB study measured plasma biomarkers. Lp(a) levels was measured using a polyclonal antibody-based turbidimetric assay certified by the reference laboratory and ≥75.0 nmol/L defined as high Lp(a). Multiple logistic regression model was used to examine the factors related to Lp(a) levels.Results:Among the 5 870 non-ASCVD population included in the analysis, Lp(a) levels showed a right-skewed distribution, with a M ( Q1, Q3) of 17.5 (8.8, 43.5) nmol/L. The multiple logistic regression analysis found that female was associated with high Lp(a) ( OR=1.23, 95% CI: 1.05-1.43). The risk of increased Lp(a) levels in subjects with abdominal obesity was significantly reduced ( OR=0.68, 95% CI: 0.52-0.89). As TC, LDL-C, apolipoprotein A1(Apo A1), and apolipoprotein B(Apo B) levels increased, the risk of high Lp(a) increased, with OR (95% CI) for each elevated group was 2.40 (1.76-3.24), 2.68 (1.36-4.93), 1.29 (1.03-1.61), and 1.65 (1.27-2.13), respectively. The risk of high Lp(a) was reduced in the HDL-C lowering group with an OR (95% CI) of 0.76 (0.61-0.94). In contrast, an increase in TG levels and the ratio of Apo A1/Apo B(Apo A1/B) was negatively correlated with the risk of high Lp(a), with OR (95% CI) of 0.73 (0.60-0.89) for elevated triglyceride group, and OR (95% CI) of 0.60 (0.50-0.72) for the Apo A1/B ratio increase group (linear trend test P≤0.001 except for Apo A1). However, no correlation was found between Lp(a) levels and lifestyle factors such as diet, smoking, and physical activity. Conclusions:Lp(a) levels were associated with sex and abdominal obesity, but less with lifestyle behaviors.

Objective:To investigate the acute gastrointestinal injury (AGI) in patients with extracorporeal membrane oxygenation (ECMO) at the early stage of operation and its influencing factors.Methods:A total of 70 patients with ECMO who were hospitalized in the Emergency Care Unit of Guangxi Zhuang Autonomous Region People's Hospital from September 2020 to December 2021 were retrospectively analyzed, and a total of 70 patients with ECMO who were hospitalized in the emergency care unit of Guangxi Zhuang Autonomous Region People's Hospital from September 2020 to December 2021 were retrospectively analyzed. According to the 2012 guidelines of the European Society of Intensive Care Medicine on the classification of acute gastrointestinal injury in critically ill patients, the patients were divided into AGI group and non-AGI group. The incidence of acute gastrointestinal injury in the early stage was statistically analyzed, and the results of blood gas analysis during ECMO loading and ECMO parameters, hemodynamic indexes and biochemical indexes after ECMO transfer were statistically analyzed. To explore the influencing factors and independent risk factors of AGI in the early stage. In addition, 70 patients were divided into successful group and non-successful group according to whether they were successfully withdrawn. The occurrence of acute gastrointestinal injury between the two groups was compared, and the effect of acute gastrointestinal injury on ECMO patients was analyzed.Results:Among the 70 ECMO patients, the incidence of early AGI was 71.43% (50 cases), and the components of AGI Ⅰ, Ⅱ, Ⅲ and Ⅳ were 18.57% (13 cases), 41.43% (29 cases), 11.43% (8 cases) and 0% (0 cases), respectively. ① Univariate analysis showed that systolic blood pressure, diastolic blood pressure, mean arterial pressure (MAP), vasoactive drug index (VIS), pH, lactic acid and BMI were significantly different between AGI group and non-AGI group when ECMO was used ( P < 0.05). Logistic binary regression analysis showed that BMI was an independent risk factor for early AGI in ECMO patients (ROC area 0.657, 95% confidence interval 0.522-0.791 ( P < 0.05), and Yoden index 0.15). (3) The AGI composition ratio of the unsuccessful group was higher than that of the unsuccessful group ( P < 0.05). Conclusions:Patients with ECMO have a high incidence of AGI in the early stage, mainly occurring in grade I and Ⅱ. Systolic blood pressure, diastolic blood pressure, MAP, VIS, pH, lactic acid and BMI when ECMO is put on are influential factors for the early development of AGI in ECMO patients, among which BMI is an independent risk factor for the early development of AGI in ECMO patients. The occurrence of AGI reduces the probability of successful withdrawal in ECMO patients.