Objective To determine the prognostic biomarkers and new therapeutic targets of the lung adenocarcinoma (LUAD), based on which to establish a prediction model for the survival of LUAD patients. Methods An integrative analysis was conducted on gene expression and clinicopathologic data of LUAD, which were obtained from the UCSC database. Subsequently, various methods, including screening of differentially expressed genes (DEGs), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Set Enrichment Analysis (GSEA), were employed to analyze the data. Cox regression and least absolute shrinkage and selection operator (LASSO) regression were used to establish an assessment model. Based on this model, we constructed a nomogram to predict the probable survival of LUAD patients at different time points (1-year, 2-year, 3-year, 5-year, and 10-year). Finally, we evaluated the predictive ability of our model using Kaplan-Meier survival curves, receiver operating characteristic (ROC) curves, and time-dependent ROC curves. The validation group further verified the prognostic value of the model. Results The different-grade pathological subtypes' DEGs were mainly enriched in biological processes such as metabolism of xenobiotics by cytochrome P450, natural killer cell-mediated cytotoxicity, antigen processing and presentation, and regulation of enzyme activity, which were closely related to tumor development. Through Cox regression and LASSO regression, we constructed a reliable prediction model consisting of a five-gene panel (MELTF, MAGEA1, FGF19, DKK4, C14ORF105). The model demonstrated excellent specificity and sensitivity in ROC curves, with an area under the curve (AUC) of 0.675. The time-dependent ROC analysis revealed AUC values of 0.893, 0.713, and 0.632 for 1-year, 3-year, and 5-year survival, respectively. The advantage of the model was also verified in the validation group. Additionally, we developed a nomogram that accurately predicted survival, as demonstrated by calibration curves and C-index. Conclusion We have developed a prognostic prediction model for LUAD consisting of five genes. This novel approach offers clinical practitioners a personalized tool for making informed decisions regarding the prognosis of their patients.

Objective To construct a machine learning prediction model for postoperative liver injury in patients with non-liver surgery based on preoperative and intraoperative medication indicators.Methods A case-control study was conducted on 315 patients with liver injury after non-liver surgery selected from the databases developed by 3 large general hospitals from January 2014 to September 2022.With the positive/negative ratio of 1 ∶3,928 cases in corresponding period with non-liver surgery and without liver injury were randomly matched as negative control cases.These 1243 patients were randomly divided into the modeling group(n=869)and the validation group(n=374)in a ratio of 7∶3 using the R language setting code.Preoperative clinical indicators(basic information,medical history,relevant scale score,surgical information and results of laboratory tests)and intraoperative medication were used to construct the prediction model for liver injury after non-liver surgery based on 4 machine learning algorithms,k-nearest neighbor(KNN),support vector machine linear(SVM),logic regression(LR)and extreme gradient boosting(XGBoost).In the validation group,receiver operating characteristic(ROC)curve,precision-recall curve(P-R),decision curve analysis(DCA)curve,Kappa value,sensitivity,specificity,Brier score,and F1 score were applied to evaluate the efficacy of model.Results The model established by 4 machine learning algorithms to predict postoperative liver injury after non-liver surgery was optimal using the XGBoost algorithm.The area under the receiver operating characteristic curve(AUROC)was 0.916(95%CI:0.883～0.949),area under the precision-recall curve(AUPRC)was 0.841,Brier score was 0.097,and sensitivity and specificity was 78.95%and 87.10%,respectively.Conclusion The postoperative liver injury prediction model for non-liver surgery based on the XGBoost algorithm has effective prediction for the occurrence of postoperative liver injury.

Cryoablation therapy with explicit anti-tumor mechanisms and histopathological manifestations has a long history.A large number of clinical practice has shown that cryoablation therapy is safe and effective,making it an ideal tumor treatment method in theory.Previously,its efficacy and clinical application were constrained by the limitations of refrigerants and refrigeration equipment.With the development of the new generation of cryoablation equipment represented by argon helium knives,significant progress has been made in refrigeration efficien-cy,ablation range,and precise temperature measurement,greatly promoting the progression of tumor cryoablation technology.This consensus systematically summarizes the mechanism of cryoablation technology,indications for oral mucosal melanoma(OMM)cryotherapy,clinical treatment process,adverse reactions and management,cryotherapy combination therapy,etc.,aiming to provide reference for carrying out the standardized cryoablation therapy of OMM.

Temporomandibular joint (TMJ) diseases are common clinical conditions. The number of patients with TMJ diseases is large, and the etiology, epidemiology, disease spectrum, and treatment of the disease remain controversial and unknown. To understand and master the current situation of the occurrence, development and prevention of TMJ diseases, as well as to identify the patterns in etiology, incidence, drug sensitivity, and prognosis is crucial for alleviating patients′suffering.This will facilitate in-depth medical research, effective disease prevention measures, and the formulation of corresponding health policies. Cohort construction and research has an irreplaceable role in precise disease prevention and significant improvement in diagnosis and treatment levels. Large-scale cohort studies are needed to explore the relationship between potential risk factors and outcomes of TMJ diseases, and to observe disease prognoses through long-term follw-ups. The consensus aims to establish a standard conceptual frame work for a cohort study on patients with TMJ disease while providing ideas for cohort data standards to this condition. TMJ disease cohort data consists of both common data standards applicable to all specific disease cohorts as well as disease-specific data standards. Common data were available for each specific disease cohort. By integrating different cohort research resources, standard problems or study variables can be unified. Long-term follow-up can be performed using consistent definitions and criteria across different projects for better core data collection. It is hoped that this consensus will be facilitate the development cohort studies of TMJ diseases.

Objective: To understand the prevalence of Lying Flat behaviors and its association with depressive symptoms among Chinese college students, so as to provide a scientific basis for promoting the physical and mental health development of adolescents. Methods: From July to October 2023, three universities were selected through convenient sampling from Jiangxi Province, Liaoning Province and Beijing City, respectively. Selfdesigned questionnaire links were distributed on campus to collect basic information and Lying Flat behaviors among college students, and the Patient Health Questionnaire-9 (PHQ-9) was utilized to screen for students with depressive symptoms. Finally, a total of 4 225 valid questionnaires were obtained. Chisquare was used to compare of report rates of Lying Flat behaviors across different demographic characteristics. Ordered Logistic regression analysis was used to explore the association between Lying Flat behaviors and depressive symptoms, with Z test used to assess variations in the strength of associations. Results: The reporting rates of academic, life, and social Lying Flat were 32.7%, 17.8% and 17.5%, respectively. And 6.7% of the participants were found of all three Lying Flat behaviors simultaneously.Among college students with three Lying Flat behaviors, the constituent ratios of no, mild, moderate and above depressive symptoms were 9.9%, 30.5% and 59.6%, respectively. Additionally, college students who had three Lying Flat behaviors were more likely to show mild, moderate and above depressive symptoms [OR(95%CI)=2.49(1.60-3.87), 7.69(5.01-11.79), P<0.01]. Conclusions Academic Lying Flat behavior is most prevalent among college students. Academic, life and social Lying Flat behaviors are all significantly positively correlated with depressive symptoms. Attention should be paid to the Lying Flat behaviors and college students psychological health conditions to promote their physical and mental health development.

Background Formaldehyde is a toxic gas. Automatic iodine analyzer is a high-throughput automatic instrument for measuring water and urine iodine, but at present, there is a lack of its application in the determination for air formaldehyde. Objective To develop a high-throughput method for air formaldehyde determination with an automated iodine analyzer. Methods The formaldehyde in the air was collected with an atmospheric sampler, and 1.0 mL of working curve solution, standard solution, and sample solution were loaded to an automated iodine analyzer. Solution resting time and instrumental parameters, including detection wavelength, sample needle depth, and regent₂ (R₂) reaction time were optimized. Finally, the method was validated by precision, detection and quantification limits, and spiked recovery. Four different concentrations of standard substance solutions and 10 air samples were collected for comparison between manual measurement method and high-throughput fully automated detection method. Results The final optimized conditions in this study were: 2 h and above resting time for prepared solution, and the optimal parameters of the instrument work were: detection wavelength at 660 nm, sample needle depth at 2800, and R₂ reaction time at 900 s. Under the optimized conditions the linear equation of the method was optical density(D) =0.9787 × formaldehyde concentration (C) − 0.0364 and the correlation coefficient was 1.0000. The limits of detection (LOD) and quantification (LOQ) were 0.015 μg·mL⁻¹ and 0.052 μg·mL⁻¹ , respectively. The relative standard deviations (RSDs) of the intra-day precision experiments were 1.35% and 1.18% for the two different concentrations of standard sample solutions. The RSDs of the inter-day precision experiments were 1.82% and 1.50%, respectively. The recoveries ranged from 96.8% to 107.4%. The results produced by spectrophotometry and automated detection method in the comparison study were identical, and the measurements of selected four concentrations of standard solutions were within the uncertainty range. Conclusion The method established in this study features small solvent dosage, fully automated high throughput, comparable sensitivity to the national standard method, high accuracy, and inter-day and intra-day precision with a recovery rate above 96%. It can provide strong technical support for the determination of air formaldehyde.

In view of the education and teaching problems in the course of Pediatrics about the construction of students' knowledge system and the cultivation of high-order abilities, on the basis of integrated organ and system courses, with the learner-centered and goal-oriented teaching concept seeking continuous improvement, we have developed a clinical problem-centered integrated teaching model in line with the learning law of medical students, innovatively integrating theoretical learning and clinical practice, and featuring the normalization of hybrid teaching and diversified teaching activities and evaluations. The curriculum innovation efforts substantially improve student satisfaction with teaching, and achieve good teaching effects. It has been actively promoted in the western pediatrics alliance of China, receiving extensive attention and positive feedback.

Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in tertiary hospitals in major regions of China in 2022.Methods Clinical isolates from 58 hospitals in China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2022 Clinical &Laboratory Standards Institute(CLSI)breakpoints.Results A total of 318 013 clinical isolates were collected from January 1,2022 to December 31,2022,of which 29.5％were gram-positive and 70.5％were gram-negative.The prevalence of methicillin-resistant strains in Staphylococcus aureus,Staphylococcus epidermidis and other coagulase-negative Staphylococcus species(excluding Staphylococcus pseudintermedius and Staphylococcus schleiferi)was 28.3％,76.7％and 77.9％,respectively.Overall,94.0％of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 90.8％of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis showed significantly lower resistance rates to most antimicrobial agents tested than Enterococcus faecium.A few vancomycin-resistant strains were identified in both E.faecalis and E.faecium.The prevalence of penicillin-susceptible Streptococcus pneumoniae was 94.2％in the isolates from children and 95.7％in the isolates from adults.The resistance rate to carbapenems was lower than 13.1％in most Enterobacterales species except for Klebsiella,21.7％-23.1％of which were resistant to carbapenems.Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.1％to 13.3％.The prevalence of meropenem-resistant strains decreased from 23.5％in 2019 to 18.0％in 2022 in Pseudomonas aeruginosa,and decreased from 79.0％in 2019 to 72.5％in 2022 in Acinetobacter baumannii.Conclusions The resistance of clinical isolates to the commonly used antimicrobial agents is still increasing in tertiary hospitals.However,the prevalence of important carbapenem-resistant organisms such as carbapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a downward trend in recent years.This finding suggests that the strategy of combining antimicrobial resistance surveillance with multidisciplinary concerted action works well in curbing the spread of resistant bacteria.

Objective To investigate the distribution and antimicrobial resistance profiles of the common pathogens isolated from urine from 2015 to 2021 in the CHINET Antimicrobial Resistance Surveillance Program.Methods The bacterial strains were isolated from urine and identified routinely in 51 hospitals across China in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Antimicrobial susceptibility was determined by Kirby-Bauer method,automatic microbiological analysis system and E-test according to the unified protocol.Results A total of 261 893 nonduplicate strains were isolated from urine specimen from 2015 to 2021,of which gram-positive bacteria accounted for 23.8％(62 219/261 893),and gram-negative bacteria 76.2％(199 674/261 893).The most common species were E.coli(46.7％),E.faecium(10.4％),K.pneumoniae(9.8％),E.faecalis(8.7％),P.mirabilis(3.5％),P.aeruginosa(3.4％),SS.agalactiae(2.6％),and E.cloacae(2.1％).The strains were more frequently isolated from inpatients versus outpatients and emergency patients,from females versus males,and from adults versus children.The prevalence of ESBLs-producing strains in E.coli,K.pneumoniae and P.mirabilis was 53.2％,52.8％and 37.0％,respectively.The prevalence of carbapenem-resistant strains in E.coli,K.pneumoniae,P.aeruginosa and A.baumannii was 1.7％,18.5％,16.4％,and 40.3％,respectively.Lower than 10％of the E.faecalis isolates were resistant to ampicillin,nitrofurantoin,linezolid,vancomycin,teicoplanin and fosfomycin.More than 90％of the E.faecium isolates were ressitant to ampicillin,levofloxacin and erythromycin.The percentage of strains resistant to vancomycin,linezolid or teicoplanin was＜2％.The E.coli,K.pneumoniae,P.aeruginosa and A.baumannii strains isolated from ICU inpatients showed significantly higher resistance rates than the corresponding strains isolated from outpatients and non-ICU inpatients.Conclusions E.coli,Enterococcus and K.pneumoniae are the most common pathogens in urinary tract infection.The bacterial species and antimicrobial resistance of urinary isolates vary with different populations.More attention should be paid to antimicrobial resistance surveillance and reduce the irrational use of antimicrobial agents.

Objective To understand the distribution and changing resistance profiles of clinical isolates of Enterococcus in hospitals across China from 2015 to 2021.Methods Antimicrobial susceptibility testing was conducted for the clinical isolates of Enterococcus according to the unified protocol of CHINET program by automated systems,Kirby-Bauer method,or E-test strip.The results were interpreted according to the Clinical & Laboratory Standards Institute(CLSI)breakpoints in 2021.WHONET 5.6 software was used for statistical analysis.Results A total of 124 565 strains of Enterococcus were isolated during the 7-year period,mainly including Enterococcus faecalis(50.7％)and Enterococcus faecalis(41.5％).The strains were mainly isolated from urinary tract specimens(46.9％±2.6％),and primarily from the patients in the department of internal medicine,surgery and ICU.E.faecium and E.faecalis strains showed low level resistance rate to vancomycin,teicoplanin and linezolid(≤3.6％).The prevalence of vancomycin-resistant E.faecalis and E.faecium was 0.1％and 1.3％,respectively.The prevalence of linezolid-resistant E.faecalis increased from 0.7％in 2015 to 3.4％in 2021,while the prevalence of linezolid-resistant E.faecium was 0.3％.Conclusions The clinical isolates of Enterococcus were still highly susceptible to vancomycin,teicoplanin,and linezolid,evidenced by a low resistance rate.However,the prevalence of linezolid-resistant E.faecalis was increasing during the 7-year period.It is necessary to strengthen antimicrobial resistance surveillance to effectively identify the emergence of antibiotic-resistant bacteria and curb the spread of resistant pathogens.