One patient with kidney stones was prescribed Paishi granules and diclofenac sodium sustained-release tablets for pain relief in the outpatient setting.That evening,the patient took 1 sachet of Paishi granules and 1 diclofenac sodium sustained-release tablet together.The patient subsequently developed a generalized rash with itching.Liver function indexes of alanine aminotransferase(ALT),aspartate aminotransferase(AST),total bilirubin(T-BiL),direct bilirubin(D-BiL),and alkaline phosphatase(ALP)were all 2 to 6 times higher than the upper limit of normal.After symptomatico treatment,the patients systemic rash had subsided,and the liver function indicators have returned to normal.

Objective:To explore the impact of adolescent mental health status on smartphone addiction, and construct a predictive model for smartphone addiction based on the eXtreme Gradient Boosting(XGBoost) algorithm and multivariate Logistic regression.Methods:In April 2023, a cross-sectional survey was conducted among 14 666 adolescents.All participants were systematically evaluated using a self-developed general information questionnaire, the middle school student mental health scale(MSSMHS), the adolescents self-harm scale(ASHS), the interaction anxiousness scale(IAS), the mobile phone addiction index(MPAI), the middle school students shame scale(MSSS), the UCLA loneliness scale(UCLA-LS), the multidimensional peer victimization scale(MPVS), and the basic psychological needs scale(BPNS).R software version 4.3.2 was used for data analysis. Participants were randomly divided into training set and validation set at the ratio of 7∶3.The XGBoost model and multivariate logistic regression model were constructed to predict the risk of smartphone addiction, and a nomogram was plotted.Model performance was evaluated using the Hosmer-Lemeshow test, area under the curve(AUC), and accuracy(ACC).Results:(1) A total of 14 036 high school students were included in the study, with 5 069(36.1%) exhibited smartphone addiction.The training set comprised 9 826 students, with 3 549(36.1%) being smartphone addicts.The validation set included 4 210 students, with 1 520(36.1%) being smartphone addicts.(2) The XGBoost model identified shame-proneness and social anxiety as the two main predictors of smartphone addiction.(3) Multivariate Logistic regression analysis revealed that anxiety( B=0.328, OR(95% CI)=1.39(1.07-1.81), P=0.015), interpersonal sensitivity( B=0.311, OR(95% CI)=1.36(1.05-1.77), P=0.018), learning pressure( B=0.606, OR(95% CI)=1.83(1.46-2.31), P<0.001), mood swings( B=0.775, OR(95% CI)=2.17(1.70-2.78), P<0.001), social anxiety( B=0.024, OR(95% CI)=1.02(1.01-1.04), P<0.001), shame-proneness( B=0.049, OR(95% CI)=1.05(1.04-1.06), P<0.001), and peer victimization( B=0.037, OR(95% CI)=1.04(1.02-1.06), P<0.001) were significant predictors of smartphone addiction.(4) The ACC and AUC values of the XGBoost model were 0.890 and 0.929 in the training set, and 0.865 and 0.864 in the validation set, respectively.The multivariate Logistic regression model achieved ACC and AUC values of 0.870 and 0.854 in the training set, and 0.867 and 0.859 in the validation set, respectively. Conclusion:Anxiety, interpersonal sensitivity, learning pressure, mood swings, social anxiety, shame-proneness, and peer victimization are identified risk predictors of smartphone addiction in high school adolescents.

In order to effectively prevent and control the occurrence of catheter-associated urinary tract infection and ensure the safety of both patients and medical personnel,the National Health Commission of the People's Re-public of China officially released the recommended health industry standard"Standard for prevention and control of catheter-associated urinary tract infection"(WS/T862-2025)in Aug.2025.This paper provides an interpreta-tion of the standard,covering its drafting background,basis and content,to assist relevant medical personnel in healthcare institutions in enhancing their understanding and recognition of the standard,and to further promote its implementation and enforcement.

Objective:To evaluate the validity and reliability of the Simplified Chinese version of the Beck Cognitive Insight Scale(SC-BCIS).Methods:Totally 188 patients with schizophrenia meeting the Diagnostic and Statistical Manual of Mental Disorders,Fifth Edition(DSM-5)diagnostic criteria were selected for SC-BCIS and the Insight and Treatment Attitudes Questionnaire(ITAQ)assessment.Thirty-eight patients were selected for retest-ing within 4 weeks.The item analysis was conducted using the Spearman correlation method,and the reliability of the scale was tested with Cronbach's α coefficient and ICC coefficient.The structural validity of the scale was ex-amined through principal component analysis and exploratory factor analysis.Results:The correlation coefficients between the 15 item scores and the total score of the SC-BCIS all met the screening criteria.The Cronbach's α coef-ficient of the scale was 0.69,the test-retest ICC value was 0.82,the ICC coefficient between the SC-BCIS and ITAQ scales was 0.83,and the scale had a two-factor structure,with a cumulative contribution rate of 42.4％for the two factors.Conclusion:The Simplified Chinese version of the Beck Cognitive Insight Scale(SC-BCIS)has good validity and reliability in measuring cognitive insight in patients with schizophrenia.

With the advancement of China's healthcare reform,enhancing the capacity of primary healthcare services has become a pivotal task.Colorectal cancer,one of the most prevalent malignancies in China,highlights the critical importance of early screening and diagnosis to improve patient survival rates.This study,guided by the principles of Party building and Xi Jinping Thought on Socialism with Chinese Characteristics,examines the implementation and outcomes of a rural outreach program focused on colorectal cancer screening and diagnostic technologies.By promoting the dissemination of colorectal cancer screening initiatives,the paper aims to provide empirical evidence to support the deepening of primary-care services,foster high-quality ad-vancement of grassroots health services,and align with the national Healthy China Initiative,thereby more effectively safeguarding population health.

Here reports a rare case of severe pneumonia with pulmonary abscess and empyema caused by Trophe-ryma whipplei(TW)infection.TW infection was confirmed by clinical manifestations as well as polymerase chain reaction(PCR)of bronchoalveolar lavage fluid(BALF)and pleural effusion.After treatment with meropenem(fo-llowed by sequential ceftriaxone)combined with compound sulfamethoxazole,the patient improved and was dis-charged from hospital.Combined with literature review,this study explores the clinical manifestations,laboratory examinations,and treatment scheme of TW infection,aiming to improve the awareness and vigilance of clinicians on TW infection,conduct targeted anti-infection therapy as early as possible,and reduce the mortality of patients.

Objectives:To analyze whether there is a causal relationship between plasma proteins and the risk of coronary atherosclerosis(CAS)based on a two-sample Mendelian randomization(MR)analysis and to identify potential therapeutic targets for CAS.Methods:Single nucleotide polymorphisms(SNP)associated with plasma proteins from the UK Biobank Pharmacoproteomics Program(UKB-PPP)database were used as instrumental variables and outcome data were obtained from genome-wide association study databases.The Wald ratio method and inverse variance weighted(IVW)method in two-sample MR were employed as the primary approaches to assess the causal relationship between plasma proteins and CAS.Colocalization analysis was conducted as a sensitivity analysis to ensure the robustness of the MR findings.Results:A total of 132 plasma proteins were found to have causal associations with an increased risk of CAS.Colocalization analysis revealed that 12 plasma proteins shared genetic variants with CAS.Among them,Proprotein convertase subtilise/kexin type 9(PCSK9)(OR=1.23,95%CI:1.15-1.32,P<0.01)and neurocan(NCAN)(OR=1.35,95%CI:1.21-1.52,P<0.01)exhibited posterior probability of hypothesis4(PPH4)values<0.80 in the colocalization analysis,indicating strong support for colocalization and suggesting their potential as primary plasma protein drug targets for CAS.Conclusions:PCSK9 is associated with an increased risk of CAS and is confirmed as a therapeutic target for CAS.NCAN emerges as another potential therapeutic target for CAS.

Objective To explore the value of 3D and 2D radiomics features models based on multiparameter MRI in predicting Ki-67 expression(with 14%and 20%as the critical values,respectively)in breast cancer.Methods The MRI images of 147 patients with pathologically confirmed Luminal breast cancer were analyzed retrospectively.The patients were randomly divided into training set and test set according to the ratio of 7︰3.The 3D and 2D radiomics features of intratumor and peritumor were extracted from diffusion weighted imaging(DWI),dynamic contrast enhancement(DCE)mask(S0)and DCE phase 3(S3)images.Then the models were constructed by multiple pipeline combinations of three feature normalization methods,two feature dimensionality reduction methods,four feature selection methods,and ten classifiers.The receiver operating characteristic(ROC)curve and the area under the curve(AUC)were used to evaluate the prediction performance of the models in order to select the best 3D and 2D single parame-ter(DWI,S0,S3)and multiparameter combination(S0+S3,S0+DWI,S3+DWI,S0+S3+DWI)models.Finally,the differ-ences between the models were compared by De Long test.Results With 14%as the critical value,the AUC of 3D and 2D models in the training set were 0.726-0.824 and 0.707-0.835,respectively,and those in the test set were 0.724-0.82 and 0.701-0.805.With 20%as the critical value,the AUC of 3D and 2D models in the training set were 0.743-0.868 and 0.793-0.881,respectively,and those in the test set were 0.738-0.853 and 0.743-0.814.There was no significant statistical difference between 3D and 2D models with the same parameter in the two critical values standards.Conclusion The multiparameter MRI-based radiomics models can bet-ter predict the expression of Ki-67 in breast cancer,and the models based on intratumor and peritumor 3D and 2D features have the same prediction efficiency.

Objective:To explore the impact of adolescent mental health status on smartphone addiction, and construct a predictive model for smartphone addiction based on the eXtreme Gradient Boosting(XGBoost) algorithm and multivariate Logistic regression.Methods:In April 2023, a cross-sectional survey was conducted among 14 666 adolescents.All participants were systematically evaluated using a self-developed general information questionnaire, the middle school student mental health scale(MSSMHS), the adolescents self-harm scale(ASHS), the interaction anxiousness scale(IAS), the mobile phone addiction index(MPAI), the middle school students shame scale(MSSS), the UCLA loneliness scale(UCLA-LS), the multidimensional peer victimization scale(MPVS), and the basic psychological needs scale(BPNS).R software version 4.3.2 was used for data analysis. Participants were randomly divided into training set and validation set at the ratio of 7∶3.The XGBoost model and multivariate logistic regression model were constructed to predict the risk of smartphone addiction, and a nomogram was plotted.Model performance was evaluated using the Hosmer-Lemeshow test, area under the curve(AUC), and accuracy(ACC).Results:(1) A total of 14 036 high school students were included in the study, with 5 069(36.1%) exhibited smartphone addiction.The training set comprised 9 826 students, with 3 549(36.1%) being smartphone addicts.The validation set included 4 210 students, with 1 520(36.1%) being smartphone addicts.(2) The XGBoost model identified shame-proneness and social anxiety as the two main predictors of smartphone addiction.(3) Multivariate Logistic regression analysis revealed that anxiety( B=0.328, OR(95% CI)=1.39(1.07-1.81), P=0.015), interpersonal sensitivity( B=0.311, OR(95% CI)=1.36(1.05-1.77), P=0.018), learning pressure( B=0.606, OR(95% CI)=1.83(1.46-2.31), P<0.001), mood swings( B=0.775, OR(95% CI)=2.17(1.70-2.78), P<0.001), social anxiety( B=0.024, OR(95% CI)=1.02(1.01-1.04), P<0.001), shame-proneness( B=0.049, OR(95% CI)=1.05(1.04-1.06), P<0.001), and peer victimization( B=0.037, OR(95% CI)=1.04(1.02-1.06), P<0.001) were significant predictors of smartphone addiction.(4) The ACC and AUC values of the XGBoost model were 0.890 and 0.929 in the training set, and 0.865 and 0.864 in the validation set, respectively.The multivariate Logistic regression model achieved ACC and AUC values of 0.870 and 0.854 in the training set, and 0.867 and 0.859 in the validation set, respectively. Conclusion:Anxiety, interpersonal sensitivity, learning pressure, mood swings, social anxiety, shame-proneness, and peer victimization are identified risk predictors of smartphone addiction in high school adolescents.

Objective:In alignment with the practical teaching objectives of Diseases of the Locomotor System and the competency requirements for graduates of military medical universities, this study conducted a multidimensional modification of the traditional objective structured clinical examination (OSCE) to provide a novel training mode that enhances the effectiveness of practical teaching of this course.Methods:The traditional OSCE was modified from the four dimensions of integration, individuality, immersion, and intelligence. The first three stations were set as "diagnosis" and the last three stations were set as "operation" to reflect the "integration" of diagnosis and treatment. The last station of "operation" was set as "medical cares of combat and training injuries" to reflect "individuality" of military medical training. The method of situation creation was used to reflect "immersion", and the application of intelligent terminals reflected "intelligence". The study involved 50 trainees who were assigned to either a modified OSCE group or a traditional teaching group through a random drawing process. The teaching effectiveness was assessed through evaluation of theoretical knowledge, practical skills, and participant satisfaction. The statistical analysis was conducted by SPSS 22.0, with parametric data assessed by t-tests and non-parametric data assessed by chi-square tests. Results:Before entering the department, there were no statistically significant differences in theoretical ( P=0.832) and practical ( P=0.513) scores between the two groups of trainees. However, after the internship, the modified OSCE group demonstrated significantly enhanced scores compared to the traditional teaching group, both in theory assessment [(93.88±1.92) vs. (90.76±2.85), P=0.001] and skill assessment [(94.32±1.25) vs. (91.68±2.82), P=0.001]. Additionally, the self-assessment of clinical capability improvement by the modified OSCE group was markedly higher across all dimensions than the traditional teaching group ( P=0.001). Furthermore, the evaluation conducted by basic combat unit on the job competency of graduated trainees indicated that the modified OSCE group outperformed the traditional teaching group in basic clinical diagnosis and treatment [(4.72±0.46) vs. (3.44±0.71), P=0.001], emergency management of combat and training injuries [(4.72±0.46) vs. (3.52±0.71), P=0.001], application of information technology [(4.44±0.71) vs. (3.91±0.80), P=0.029], basic military qualities [(4.40±0.71) vs. (3.92±0.91), P=0.043], mental health and resilience [(4.36±0.70) vs. (3.68±0.85), P=0.003], and the capacity for continuous learning [(4.64±0.70) vs. (3.83±0.76), P=0.001]. Conclusions:The modified OSCE teaching mode can better meet the practical teaching requirements for Diseases of the Locomotor System in the military medical university and thus holds promise for further application.