ObjectiveTo establish a highly specific and sensitive triplex TaqMan quantitative PCR method for the detection of mouse hepatitis virus (MHV), mouse parvovirus (MPV), and reovirus type 3 (Reo-3) in laboratory mice. MethodsRecombinant plasmid standards were constructed using conserved genomic regions of the three target viruses. Specific primers and probes were designed, and a triplex TaqMan quantitative PCR (qPCR) system was optimized through preliminary experiments. Sensitivity was evaluated using 10-fold serial dilutions (10¹-10⁷ copies/μL). Intra- and inter-assay repeatability were assessed via reproducibility experiments. ResultsA strong linear correlation (R ²>0.99) was observed between copy number and Ct value at template concentrations of 10²-10⁷ copies/μL. Intra-assay and inter-assay coefficients of variation (CV) were both below 5%. No cross-reactivity was observed with mouse cytomegalovirus (MCMV), vesicular stomatitis virus (VSV), Sendai virus (SeV), or pneumonia virus of mice (PVM). ConclusionThe triplex TaqMan quantitative PCR method established in this study has high sensitivity, good repeatability, and strong specificity. It enables the rapid, specific, and sensitive detection of MHV, MPV, and Reo-3, allows the simultaneous detection of multiple pathogens in a single tube, and can be applied to the detection of clinical samples from laboratory animals and to epidemiological investigations, thereby providing effective scientific and technological support for the prevention and control of viral infection and the interruption of transmission.

Objective To investigate the predictive value of combined radiomic features derived from chest CT scans with clinical characteristics for epidermal growth factor receptor(EGFR)gene mutations in non-small cell lung cancer(NSCLC).Methods A multi-center case-control study was conducted on the clinical data and CT images of 1 070 NSCLC patients from the radiology departments of the 3 medical institutions between January 2013 and October 2023.The 719 NSCLC patients from the First Affiliated Hospital of Army Medical University were randomly divided into a training set and an internal validation set in a ratio of 7∶3;The 173 patients in the Eastern Theatre General Hospital and the 178 patients in Army Medical Centre of PLA were assigned into the external validation set 1 and 2,respectively.Least absolute shrinkage and selection operator(LASSO)regression was employed to identify the optimal radiomic features,which were subsequently used to construct a radiomics model.Univariate and multivariate logistic regression analyses were applied to identify clinical features associated with EGFR mutation,thereby developing a clinical model.The radiomic and clinical features were subsequently combined to develop a comprehensive model.All the 3 classification models were built using random forest(RF)machine learning.The area under curve(AUC),accuracy,sensitivity and specificity were utilized to evaluate the predictive performance of the models.Calibration curve was plotted to assess the goodness of fit of the comprehensive model,while decision curve analysis was performed to assess the clinical utility of the model.Results The AUC value of the radiomics model was 0.762 4(95%CI:0.692 4～0.825 1),0.745 4(95%CI:0.671 1～0.814 3),and 0.724 7(95%CI:0.639 7～0.801 6),respectively,in the internal validation set,external validation set 1,and external validation set 2;The AUC value of the clinical prediction model was 0.691 7(95%CI:0.627 9～0.757 6),0.652 5(95%CI:0.576 7～0.729 1),and 0.779 2(95%CI:0.712 5～0.847 3),respectively in the above sets in turn;The comprehensive model constructed based on clinical features and radiomic features showed the best predictive efficacy,with an AUC value of 0.818 0(95%CI:0.757 7～0.874 3),0.782 4(95%CI:0.703 1～0.848 2),and 0.796 6(95%CI:0.718 1～0.868 6),respectively in the above sets.Calibration curve analysis indicated that the comprehensive model had a good fit,while decision curve analysis revealed that the model provided a favorable net benefit.Conclusion Our comprehensive model constructed based on chest CT radiomic features and clinical characteristics shows superior predictive performance for EGFR gene mutations in NSCLC across multiple center datasets,which may be helpful for clinical decision-making for treatment strategies.

Objective To evaluate the predictive value of deep learning features from chest CT images combined with clinical and radiomics features for epidermal growth factor receptor(EGFR)mutations in non-small cell lung cancer(NSCLC).Methods This case-control study retrospectively analyzed clinical and imaging data of 1 070 NSCLC patients from radiology departments at three hospitals(January 2013 to October 2023).Patients were divided into:a training set(n=502)and internal validation set(n=217)via 7∶3 randomization of 719 cases from the First Affiliated Hospital of Army Medical University;external validation set 1(n=173)from General Hospital of Eastern Theater Command;external validation set 2(n=178)from Daping Hospital of Army Medical University.Deep learning features were extracted using a 2.5D convolutional neural network(CNN)with ResNet101 backbone,radiomics features were derived from CT images,and clinical risk factors were identified to construct models.An integrated model combined deep learning,clinical,and radiomics features.All four models were developed using random forest(RF)classifiers.Calibration curves assessed goodness-of-fit,and decision curve analysis(DCA)evaluated clinical utility.Results The deep learning model achieved AUCs of 0.833 7(95%CI:0.770 6～0.884 7),0.815 1(0.741 6～0.882 8),and 0.810 1(0.745 2～0.873 6)in the internal and two external validation sets,respectively.Clinical models yielded AUCs of 0.731 0(0.660 2～0.802 1),0.746 0(0.666 4～0.824 9),and 0.813 4(0.743 1～0.883 6);radiomics models showed AUCs of 0.762 4(0.692 4～0.825 1),0.745 4(0.671 1～0.814 3),and 0.724 7(0.639 7～0.801 6).The integrated model demonstrated optimal performance with AUCs of 0.905 5(0.857 0～0.945 4),0.832 7(0.763 3～0.896 4),and 0.889 0(0.834 4～0.934 3).DCA indicated significant net benefit for EGFR prediction at threshold probabilities of 0.15～0.85 using the integrated model.Conclusion Deep learning features from CT images effectively predict EGFR mutation status in NSCLC.The integrated model combining deep learning,clinical,and radiomics features further enhances predictive performance.

In recent years, sleep problems among nurses have become a prominent social focus, and sleep disorders are widespread among nurses. ICU nurses are more prone to problems such as decreased sleep quality, shorter sleep duration, and daytime sleepiness than nurses in other departments due to special and complex work environments. Sleep disorders seriously affect the physical and mental health of ICU nurses and the quality of nursing care. This paper reviews the influencing factors, interventions, and assessment tools for sleep disorders among ICU nurses in order to inform the development of effective interventions.

In recent years, sleep problems among nurses have become a prominent social focus, and sleep disorders are widespread among nurses. ICU nurses are more prone to problems such as decreased sleep quality, shorter sleep duration, and daytime sleepiness than nurses in other departments due to special and complex work environments. Sleep disorders seriously affect the physical and mental health of ICU nurses and the quality of nursing care. This paper reviews the influencing factors, interventions, and assessment tools for sleep disorders among ICU nurses in order to inform the development of effective interventions.

This paper summarized the clinical experience of Professor LIU Shenlin in diagnosing and treating gastric cancer with the thinking of "prescription according to tendency". In this paper， the thinking of diagnosis and treatment for gastric cancer and medication skills were summarized into four dimensions： power， energy， chronology and situation. The diagnosis emphasizes the tendency of qi power， considering that the onset of gastric cancer primarily stems from the disorder of qi movement in zang-fu organs， and emphasizing the importance of regulating the liver and spleen qi to intercept the tendency of disease. It points out that dampness-phlegm-stasis-toxin is a crucial link leading to the metabolic imbalance of body energy， and Professor LIU adepts at using methods such as breaking up blood and expelling stasis， and clearing heat and resolving toxins to block the pathological chain reaction caused by energy imbalance and to restore the homeostasis of the organism. In the treatment process， according to the characteristics of gastric cancer staging and chronological evolution， we will explore the changes of the exuberance and weakness of healthy qi and pathogenic qi in the context of the chronologic tendency， and adjust the dosage of attacking and tonic medicines in different stages of the disease in order to balance and restore the body. The "situation" is in line with the state of consumptive disease in advanced gastric cancer， Professor LIU skillfully uses large doses of Huangqi （Astragalus mongholicus） with flexible combination of medicinals to replenish deficiencies， invigorate qi， and regulate blood vessels.

Objective To observe the application effect of magnetic resonance imaging technology in evaluating the brain structure and function of patients with type 2 diabetic painful neuropathy (PDN). Methods Forty patients with type 2 diabetes mellitus hospitalized in our hospital were selected as the study objects, and were divided into diabetes mellitus (DM) group (n=12), peripheral neuropathy (DPN) group (n=14) and PDN group (n=14). General clinical biochemical indexes of three groups were analyzed. The structural brain and function of brain area in three groups were compared. Results Age, duration of diabetes, systolic blood pressure, diastolic blood pressure, fasting blood glucose (FBG), glycosylated hemoglobin (HbA1c), free fatty acid (FFA), albumin (ALB), creatinine (Cr), uric acid (UA), estimated glomerular filtration rate (eGFR), cystatin C (Cys-C), total cholesterol (TC), triglyceride (TG) of the three groups were compared, high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), triiodothyronine (T3), thyroxine (T4), thyroid stimulating hormone (TSH), thyroglobulin antibody (TGAb), thyroid peroxidase antibody (TPO-Ab) and serum calcium (Ca) in the three groups showed no significant differences (P > 0.05). Compared with the DM group, the gray matter volume (GMV) in the DPN group was significantly decreased (P < 0.05). Compared with the DM group, amplitude of low frequency fluctuation (ALFF) and fractional amplitude of low frequency fluctuation (fALFF) of left medial superiorfrontal gyrus in the PDN group were significantly decreased (P < 0.05). Conclusion Abnormal GMV in the left angular gyrus in DPN patients may be associated with a higher risk of concomitant cognitive impairment. The decrease of ALFF in the right cerebellar vermis and fALFF in the left medial superior frontal gyrus in PDN patients may be related to the pathogenesis of pain.

Increasing evidences suggest the important role of calcium homeostasis in hallmarks of cancer, but its function and regulatory network in metastasis remain unclear. A comprehensive investigation of key regulators in cancer metastasis is urgently needed. Transcriptome sequencing (RNA-seq) of primary esophageal squamous cell carcinoma (ESCC) and matched metastatic tissues and a series of gain/loss-of-function experiments identified potassium channel tetramerization domain containing 4 (KCTD4) as a driver of cancer metastasis. KCTD4 expression was found upregulated in metastatic ESCC. High KCTD4 expression is associated with poor prognosis in patients with ESCC and contributes to cancer metastasis in vitro and in vivo. Mechanistically, KCTD4 binds to CLIC1 and disrupts its dimerization, thus increasing intracellular Ca²⁺ level to enhance NFATc1-dependent fibronectin transcription. KCTD4-induced fibronectin secretion activates fibroblasts in a paracrine manner, which in turn promotes cancer cell invasion via MMP24 signaling as positive feedback. Furthermore, a lead compound K279-0738 significantly suppresses cancer metastasis by targeting the KCTD4‒CLIC1 interaction, providing a potential therapeutic strategy. Taken together, our study not only uncovers KCTD4 as a regulator of calcium homeostasis, but also reveals KCTD4/CLIC1-Ca²⁺-NFATc1-fibronectin signaling as a novel mechanism of cancer metastasis. These findings validate KCTD4 as a potential prognostic biomarker and therapeutic target for ESCC.

OBJECTIVE To explore the intestinal absorption characteristics of saikosaponins. METHODS Based on everted intestinal sac model， using accumulative absorption amount （Q） and absorption rate constant （Ka） as indexes， UHPLC-MS/MS technique as a method， the absorption of saikosaponin A， B2， C， D and F from total saponins of Bupleurum chinense （8 g/mL， by crude drug） in the duodenum， jejunum and ileum was detected. RESULTS The correlation coefficients （r） of the regression equations for the absorption of saikosaponins A， B2， C and F in the duodenum， jejunum and ileum were all higher than 0.95， while the r of saikosaponin D in the above intestinal segments was lower than 0.95； compared with the absorption of the same composition in the duodenum， the Q and Ka of saikosaponin A and C circulating in jejunum and ileum for 120 min， as well as the Q and Ka of saikosaponin F circulating in the ileum for 120 min were significantly decreased （P＜0.05）. CONCLUSIONS Saikosaponin A and the other 4 saikosaponins are all absorbed in the duodenum， jejunum and ileum； among them， saikosaponin A， B2， C and F are linearly absorbed， which conforms to the zero-order absorption characteristics， but saikosaponin D shows non- linear absorption.

Objective : To explore the association of time in range(TIR) and glucose management indicator ( GMI) with the risk of type 2 diabetic nephropathy (DN) ． Methods : The clinical data of 215 patients with type 2 diabetes mellitus (T2DM) were collected and analyzed．According to the results of estimated glomerular filtration rate (eGFR) and urinary albumin to creatinine ratio( UACR) ，they were divided into 117 patients with T2DM and 98 patients with DN．The clinical data，biochemical indicators and continuous glucose monitoring ( CGM) indicators of the two groups were compared．Logistic regression was used to analyze the influencing factors of DN risk．The predictive value of TIR and GMI on the risk of DN was evaluated by receiver operating characteristic (ROC) curve. Results: There were significant differences in age，duration of diabetes，systolic blood pressure，glycosylated hemoglobin ( HbA1c) ，fasting plasma glucose (FPG) ，2 hour postprandial plasma glucose (2hPG) ，creatinine( Cr) ，UACR, eGFR between the two groups(P＜0. 05) ．There were statistically significant differences between the two groups in the CGM indexes of GMI，mean absolute difference of mean of daily differences ( MODD) ，glucose above target range time(TAR) and TIR(P＜0. 05) ．The results of logistic regression analysis showed that TIR was a protective factor of DN．In the ROC curve analysis of TIR prediction DN，the area under the ROC curve was 0. 718 (95% CI = 0. 648 ～0. 789，P＜0. 001) ，and the Yoden index was 0. 38．At this time，the sensitivity was 66. 7% ，and the specificity was 71. 3%．In the ROC curve analysis of GMI prediction DN，the area under the ROC curve was 0. 701 (95% CI = 0. 629 ～0. 774，P＜0. 001) ，and the Yoden index was 0. 368．At this time，the sensitivity was 63. 3% ， and the specificity was 73. 5%． Conclusion Specifically，lower TIR and higher GMI increase the risk of DN.