Objective To explore the effectiveness of the generative large language model MedGo in nursing decision-making for elderly patients with multimorbidity. Methods A quasi-randomized controlled trial study was conducted involving 6 junior nurses, 6 senior nurses and the MedGo model from January 1, 2025 to March 31, 2025 at the Emergency Internal Medicine Ward of Shanghai East Hospital Affiliated to Tongji University. Clinical data of 120 elderly patients with multimorbidity were analyzed to compare the performance of the three groups in four tasks (nursing diagnosis assessment, nursing intervention formulation, complication identification, and complication prevention) from three evaluation dimensions: decision-making time consumption, decision accuracy, and decision-making quality. Results In terms of decision-making time, the senior nurse group completed all four tasks faster than the junior nurse group (P＜0.01), and the MedGo group completed all four tasks faster than the junior nurse group (P＜0.001) and the senior nurse group (P＜0.001). In terms of decision-making accuracy, senior nurse group scored higher than junior nurse group in all four tasks (P＜0.001), while the MedGo group outperformed the senior nurse group only in complication identification (P＜0.001). In terms of decision-making quality, the MedGo group scored higher than junior nurse group (P＜0.001) and senior nurse group (P＜0.001) in all four tasks. Conclusions The MedGo model demonstrates advantages of high efficiency, accuracy, and quality in nursing decision-making for elderly patients with multimorbidity; senior nurses outperform junior nurses in decision-making, providing diverse references for clinical nursing decision-making.

In recent years， the incidence of depression among adolescents has increased significantly， becoming a serious global public health problem. There is a close relationship between adolescent depression and lipid metabolism abnormalities. However， the mechanisms underlying this association have not been fully elucidated. This article provides a review of the mechanisms and interventions linking adolescent depression and lipid metabolism abnormalities， aiming to offer new ideas for interventions in adolescent depression. Using computerized searches of the China National Knowledge Infrastructure （CNKI）， Wanfang Data， PubMed， and Web of Science， studies on the mechanisms and interventions related to adolescent depression and lipid metabolism were collected， with the search period extending up to October 10， 2025. Two researchers independently conducted literature screening and extracted basic information from the included studies. A total of 12 original studies were included. The results indicate that adolescent depression is associated with certain lipid metabolites. Neurobiological factors， immune inflammation， oxidative stress， and gut microbiota imbalance play important roles in this association. In addition， lipid metabolites such as omega-3 polyunsaturated fatty acids （ω-3 PUFAs） have a significant ameliorating effect on adolescent depression. ［Funded by Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties （number， SZGSP013）； Shenzhen Science and Technology Program （number， 20210617155253001）］

ObjectiveTo investigate the effect of repetitive peripheral magnetic stimulation (rPMS) combined with upper limb intelligent robotic training on muscle tension, motor function and cortical excitability in children with unilateral spastic cerebral palsy (USCP). MethodsFrom March, 2023 to December, 2024, 90 children with USCP admitted to Children's Hospital of Soochow University were selected and randomly divided into control group (n = 30), rPMS group (n = 30) and combined group (n = 30). The control group received conventional occupational therapy. The rPMS group received rPMS intervention followed by conventional occupational therapy. The combined group received rPMS followed by upper limb intelligent robot training, for four weeks. Before and after treatment, muscle tension of biceps brachii was assessed using the modified Ashworth Scale (MAS); upper limb motor function was evaluated using the Fugl-Meyer Assessment-Upper Extremity (FMA-UE) and upper limb intelligent parameters; and cortical excitability was measured using transcranial magnetic stimulation (TMS), including resting motor threshold (RMT) and motor-evoked potential (MEP) amplitude of the affected hemisphere. ResultsAfter treatment, MAS grades improved in all groups (|Z| > 3.523, P < 0.001), and the improvement in the combined group was superior to that in the control group (P < 0.05). Significant intra-group (F > 65.21, P < 0.001), inter-group (F > 17.94, P < 0.001) and interaction effects (F > 5.36, P < 0.01) were observed in FMA-UE scores, upper limb intelligent parameters and TMS parameters. Post Hoc analysis showed that the combined group demonstrated significantly greater improvements in FMA-UE scores, upper limb intelligent parameters, and TMS parameters compared with both the control and rPMS groups (all P < 0.01). Except for FMA-UE scores, the rPMS group showed significantly greater improvements than the control group in upper limb intelligent parameters (mechanical feedback, trajectory, and range of motion) and TMS parameters (RMT and MEP amplitude) (P < 0.05). ConclusionrPMS combined with upper limb intelligent robotic training can reduce upper limb muscle tension, improve motor function, and enhance cortical excitability in children with USCP.

ObjectiveThis paper aims to observe the syndrome improvement and negative antigen conversion rate of Qingxuan Daozhi formula in the treatment of influenza in children （heat accumulation in the lung and stomach syndrome）. MethodsThrough a multi-center randomized controlled methodology design，confirmed influenza cases were collected from October 2022 to April 2023 in the pediatrics department of eight hospitals，such as Dongfang Hospital of Beijing University of Chinese Medicine. A total of 180 children with influenza and heat accumulation in the lung and stomach syndrome conforming to the standard were recruited through the clinic. The sick children meeting the inclusion criteria were randomly divided into groups by a block-randomized method. The children in the experimental group were treated with Qingxuan Daozhi formula for five days，and those in the control group were treated with Oseltamivir Phosphate Granules for five days. The primary efficacy indicator was the negative conversion rate of influenza antigen detection. Secondary efficacy indicators were the Canadian acute respiratory illness and flu scale （CARIFS） and the incidence of complications，severe cases， and critical cases. Follow-up observation was conducted on the day of enrollment，48 hours after medication，72 hours after medication， and （6+1） d after medication. ResultsOne hundred and eighty participants were randomly assigned to the experimental group （90 cases） or the control group （90 cases）. All participants were followed up during the study. Comparison of influenza antigen detection results in the primary efficacy indicators showed that the average time of negative influenza antigen conversion in the experimental group was （5.29±1.25） d，and that in the control group was （5.40±1.68） d，without a statistically significant difference. After five days of intervention，52 cases in the experimental group and 51 cases in the control group converted to negative，without a statistically significant difference. CARIFS score results in the secondary efficacy indicators showed that during 72 hours after intervention，there were statistically significant differences between the experimental group and the control group in three dimensions， including headache，muscle soreness， and the need for extra care （P<0.05）. On the （6+1） days after the intervention，the differences in both the experimental group and the control group were statistically significant in 10 dimensions， including sore throat，bad sleep，uncomfortable feeling，poor spirit and fatigue，crying more than usual，the need for extra care，symptom，function，influence on parents，and total score （P<0.05）. The comparison results within the group in the dimensional scores of symptom， function， and influence on parents，as well as the CARIFS total score showed that with the delay of follow-up time，scores of both groups decreased significantly，with a statistically significant difference （P<0.01）. Inter-group comparison results showed that the mean score of the experimental group was higher than that of the control group at the time of enrollment. With the progress of intervention，the score of the experimental group was significantly decreased compared with that of the control group. At the end of follow-up，the mean score of the experimental group was lower than that of the control group，with no statistically significant difference. In terms of the incidence of complications，severe cases， and critical cases， there were no complications，severe cases， and critical cases in the two groups，without a statistically significant difference. ConclusionThe symptom improvement effect and negative antigen conversion rate of Qingxuan Daozhi formula in the treatment of influenza in children （heat accumulation in the lung and stomach syndrome） are not inferior to Oseltamivir Phosphate granules， and children's acceptance is better. It can be more widely used in clinical treatment of influenza in children （heat accumulation in the lung and stomach syndrome）.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

Background: and Purpose Live cell-based assays (LCBA) are increasingly used for serological antibody detection due to their ability to preserve antigen conformation, offering moderately higher sensitivity than fixed cell-based assays. However, the clinical necessity of prioritizing LCBA for the detection of aquaporin-4 immunoglobulin G (AQP4-IgG) in neuromyelitis optica spectrum disorder (NMOSD) remains unclear, especially when compared to its established role in diagnosing myelin oligodendrocyte glycoprotein antibody-associated disease. Methods: We compared the performance of live cell-based assays using immunofluorescence (LCBA-IF) and fixed cell-based assays using immunofluorescence (FCBA-IF) in detecting AQP4-IgG in 90 cases of NMOSD meeting 2015 International Panel for Neuromyelitis Optica Diagnosis criteria, alongside 40 controls. Additionally, we further investigated the relationship between AQP4-IgG titers as measured by LCBA-IF and FCBA-IF and clinical parameters in NMOSD patients. Results: Results showed 96.9% agreement between LCBA-IF and FCBA-IF (Cohen’s κ=0.935), with a strong Spearman correlation (0.977, p<0.001). Both methods demonstrated 100% specificity, with LCBA-IF showing slightly higher sensitivity compared to FCBA-IF. Within LCBAIF-tested groups, statistically significant differences in annualized relapse rates were observed across all pairwise comparisons (low-titer vs. moderate-titer, low-titer vs. high-titer, and moderate-titer vs. high-titer; all p<0.050). However, this association reached statistical significance in some FCBA-IF-tested groups. Conclusions Overall, there is a strong concordance between LCBA-IF and FCBA-IF in detecting AQP4-IgG, where LCBA-IF shows slightly higher sensitivity. Furthermore, there is a potential link between elevated AQP4-IgG titers and an increased risk of relapse, and this correlation may appear more pronounced when using LCBA-IF.

ObjectiveIdentification and analysis, animal regression test, and drug susceptibility study were conducted on a pathogenic strain causing diarrhea in cynomolgus monkeys, aiming to provide a practical basis for the clinical treatment of Citrobacter freundii. MethodsFresh fecal samples were collected from nine diarrheal cynomolgus monkeys and streaked on Salmonella-Shigella (SS) medium, LB medium, and Columbia blood agar medium, and incubated at 37 ℃ for 24 h. Subsequently, the isolated strain was identified by colony morphology observation, Gram staining, biochemical tests and 16S rRNA gene sequencing, and PCR was used to detect its major virulence genes. After propagation, the isolate was intragastrically administered to healthy cynomolgus monkeys and C57BL/6 mice for animal regression tests to evaluate its pathogenicity. Finally, the disk-diffusion method was used to detect the antimicrobial susceptibility of the isolate. ResultsA single bacterial strain, designated MF071743, was isolated from 9 fecal samples collected from diarrheal cynomolgus monkeys. The isolate formed pink, smooth, round colonies on SS medium; translucent, smooth, moist, shiny colonies with neat edges on LB medium; and smooth, moist, off-white colonies on Columbia blood agar medium. Gram staining revealed that the isolate was a Gram-negative, spore-free short bacillus. Biochemical tests showed that the isolate was positive for motility test, mannitol test, hydrogen sulfide test, methyl red test, citrate utilization test, gas production from glucose test, raffinose test, sorbitol test, and D-xylose test, but negative for phenylalanine test, gluconate test, indole test, Voges Proskauer (VP) test, urease test, lysine test, ornithine test, and adonitol test. The results of 16S rRNA gene sequencing showed that the gene sequence similarity between strain MF071743 and Citrobacter freundii was 99.0%. PCR results showed that this strain carries genes encoding the urease accessory proteins UreD, UreE, and UreF. Animal intragastric administration assays demonstrated that the strain caused loose stools in cynomolgus monkeys, and all C57BL/6 mice died within 72 h. Drug susceptibility test results indicated that the isolate was sensitive to 16 antibiotics, including ceftriaxone, amikacin, gentamicin, ceftazidime, and levofloxacin, while it exhibited resistance to 5 antibiotics, including ampicillin, cefazolin, vancomycin, erythromycin, and cephalexin. ConclusionA strain of Citrobacter freundii with certain drug resistance was isolated from the feces of diarrheal cynomolgus monkeys. The results of this study provide a reference for the diagnosis and treatment of gastrointestinal diseases in captive laboratory monkeys.

Effective symptom management can alleviate the physical and psychological distress experienced by patients with cancer, improve quality of life, and contribute to treatment adherence and improve clinical outcomes. However, most existing guidelines are developed for healthcare professionals, and patients and the public have limited access to standardized and comprehensible guidance on symptom management. To address this gap and to facilitate effective communication and shared decision-making, this study proposes the development of a patient version of the cancer symptom management guideline. The development process will adhere to the methodological framework recommended by the Guidelines International Network and the World Health Organization. The GRADE approach will be employed to assess the certainty of evidence and to formulate recommendations. In addition, the process will be informed by the Appraisal of Guidelines for Research and Evaluation Ⅱ (AGREE Ⅱ) instrument and the Reporting Items for Practice Guidelines in Healthcare-Public or Patient Versions of Guidelines (RIGHT-PVG). This protocol outlines the establishment of the guideline working group, the identification and prioritization of key questions, evidence retrieval and appraisal, and the formulation of recommendations, with the aim of ensuring methodological rigor and transparency in the development of the patient guideline and providing methodological reference for similar guideline initiatives.

OBJECTIVE To explore the mechanism of the active ingredient compatibility（quercetin， quercitrin and kaempferol at a mass ratio of 2∶9∶3）of Dimocarpus longan Lour. leaves（abbreviated as CDL） on ameliorating glucose and lipid metabolism disorders in type 2 diabetes mellitus （T2DM） rats. METHODS SD rats were randomly divided into blank control group， model group， metformin hydrochloride group （100 mg/kg）， and CDL high-， medium- and low-dose groups （280， 140， 75 mg/kg）， with 10 rats in each group. Rats in the blank control group were fed with standard chow， while rats in the other groups were given high-sugar and high-fat diet combined with intraperitoneal injection of streptozotocin to establish the T2DM rat model. After successful modeling， rats in each administration group were given corresponding drug solution， and rats in the blank control group and model group were intragastrically administered with equal volume of pure water， once a day， for consecutive 4 weeks. Fasting blood glucose （FBG） was detected at fixed time every week. The curves of oral glucose tolerance test （OGTT） and intraperitoneal insulin tolerance test （IPITT） were plotted， and the area under curve （AUC） was calculated. The pancreatic islet function indexes [fasting insulin （FINS）， homeostasis model assessment of insulin resistance （HOMA-IR）， insulin sensitivity index （ISI）]，blood lipid indexes [total cholesterol （TC）， triglyceride （TG）， low-density lipoprotein cholesterol （LDL-C）， high-density lipoprotein cholesterol （HDL-C）] and hepatic glycogen content were determined. The pathological morphological changes of liver and pancreatic tissues were observed. The protein and mRNA expression levels of molecules related to phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt） signaling pathway in liver tissues were detected. RESULTS Compared with the blank control group， the FBG， AUC of IPITT curve， AUC of OGTT curve， HOMA-IR， the levels of FINS， TC， TG and LDL-C， as well as the protein and mRNA expression of phosphatase and tensin homolog， forkhead box protein O1 and glycogen synthase kinase-3β in liver tissues were significantly increased in the model group （ P ＜0.05）. ISI， the levels of HDL-C and hepatic glycogen content， along with the protein and mRNA expression of PI3K， insulin receptor substrate-1， Akt and protein expression of phosphorylated Akt in liver tissues were markedly decreased （ P ＜0.05）. In model group rats， the arrangement of hepatocytes was irregular， the overall structure of pancreatic lobules was disordered， and a large number of inflammatory cell infiltration was observed. Compared with the model group， most of the above quantitative indexes were significantly reversed in the CDL high-dose group （ P ＜0.05）， and the pathological lesions of liver and pancreas were obviously alleviated. CONCLUSIONS CDL can regulate glucose and lipid metabolism disorders， elevate insulin sensitivity and relieve insulin resistance in T2DM rats. Its mechanism may be related to the activation of the PI3K/Akt signaling pathway.

ObjectiveThis paper aims to clarify the material basis of Gualou Niubangtang and establish a quantitative analysis method for its main constituents， providing a reference for the overall quality control of this preparation. MethodsThe constituents in the formula were systematically characterized based on ultra-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry （UPLC-Q-TOF-MS/MS）. Identification was performed by matching with the UNIFI 9.6 software and utilizing database platforms such as PubChem， ChemicalBook， and ChemSpider， combined with relevant literature reports. A quantitative analysis method for the seven main constituents in Gualou Niubangtang was established by using high performance liquid chromatography （HPLC）. ResultsUPLC-Q-TOF-MS/MS analysis identified 155 constituents， including 69 flavonoids， 36 terpenoids， 23 phenylpropanoids， 8 phenylethanoid glycosides， and 19 other types of constituents. In the established quantitative analysis method， the seven main constituents showed good linearity within their respective linear ranges. The precision， repeatability， stability， and spike recovery all met the required standards. The results showed that the content ranges of geniposide， liquiritin， hesperidin， arctiin， baicalin， oroxylin A-7-O-β-D-glucuronide， and wogonoside in 15 batches of Gualou Niubangtang were 13.67-21.25， 1.20-7.64， 5.45-7.45， 22.97-33.51， 29.95-39.07， 2.58-4.80， and 6.56-9.31 mg·g^-1， respectively. ConclusionThis study successfully characterizes and attributes multi-category constituents in Gualou Niubangtang， clarifying that its material basis is primarily composed of flavonoids， terpenoids， phenylethanoid glycosides， and phenylpropanoids. Furthermore， it enables the quantification of seven constituents within the formula. This work lays a foundation for research on the quality control， action mechanism， and clinical application of this formula.