Objective: To explain process of developing basic veterinary clinical performance guidelines, based on frequently observable clinical manifestations, thereby supporting competencybased veterinary education in Korea. Methods: A structured review of learning outcomes established by Korean Association of Veterinary Medical Colleges (KAVMC) was conducted by a planning committee including veterinary educators, practitioners, and advisory members. Owner-oriented descriptions were used to frame each performance task, and each was mapped to corresponding learning outcomes. These tasks were aligned with learning outcomes recommended by the KAVMC to support the development of communication, clinical reasoning, and performance-related competencies among veterinary students, thereby enhancing day-one clinical readiness. Results: In total, 63 clinical manifestations for a guidebook format that can be used for clinical education were identified and categorized by organ systems that are described in language understandable to animal owners. Conclusions and Relevance: The basic veterinary clinical performance guidelines based on common clinical manifestations would serve as a vital component in veterinary education to reinforce core graduation competencies.

Multidrug-resistant (MDR) bacterial infections have emerged as a serious challenge of global public health crisis. The overuse and misuse of conventional antibiotics have dramatically accelerated the emergence, evolution and worldwide spread of drug-resistant bacterial strains, necessitating urgent exploration of novel antibacterial strategies. Bacteriophages serve as natural bacterial predators offering distinct advantages including high host specificity, autonomous self-replication capabilities and cost-effective large-scale production. However, wild-type phages present significant clinical limitations due to their narrow host ranges, susceptibility to rapid immune clearance and poor penetration of bacterial biofilms, which severely restrict their therapeutic applications. The convergence of synthetic biology, nanotechnology and advanced gene editing technologies has accelerated the development of engineered bacteriophage platforms, providing programmable, scalable and clinically translatable pathways to overcome these inherent biological constraints. Here, we systematically delineate four fundamental strategies for engineered bacteriophage development. Chemical modification utilizes reactive functional groups such as amino, carboxyl and thiol moieties on capsid proteins through esterification, amidation or click chemistry reactions to achieve precise drug conjugation and surface functionalization. In vivo editing encompasses ultraviolet or chemical mutagenesis for random mutation induction, homologous recombination for targeted genetic alterations, recombineering methodologies including electroporation-mediated bacteriophage recombination engineering, and CRISPR-Cas systems for precise genome editing to enable exact genetic reconstruction and host range reprogramming. In vitro synthesis leverages genome engineering platforms where intact phage genomes are transferred into yeast or host bacteria to facilitate highly efficient homologous recombination, enabling large DNA fragment assembly and cross-gene host range expansion without bacterial toxicity constraints. Directed evolution combines artificial selection through mutation library screening with rational design approaches involving chimeric receptor binding protein construction or site-specific mutagenesis, effectively balancing the discovery of unknown adaptive pathways with targeted host specificity modification. Moreover, we comprehensively discuss therapeutic applications across diverse clinical scenarios. Engineered bacteriophage effectively disrupt bacterial biofilms through sophisticated functionalized delivery platforms including nanozyme-conjugated phages, phage-liposome nanoconjugates and bio-responsive hydrogels, demonstrating significantly enhanced bactericidal efficiency compared to unmodified free phages. These bioengineered vectors attenuate bacterial virulence and resensitize pathogens to antibiotics by delivering CRISPR-Cas systems or base editors to disrupt critical virulence factors such as pili, capsule synthesis machineries and quorum sensing systems, or by inactivating antibiotic resistance determinants including beta-lactamase genes. As an intelligent nanomedicine delivery platform, engineered bacteriophage enable precise pathogen elimination an through photocatalytic reactive oxygen species generation, immunomodulatory interventions, or controlled release of antibacterial drugs. Furthermore, oral administration of engineered bacteriophage facilitates microbiota modulation, which selectively eliminate intestinal pathogens while preserve beneficial commensal microbiota, thereby restoring microbial community balance and preventing complications associated with dysbiosis. Finally, we critically analyze persistent challenges including host strain matching complexity, evolution of bacterial resistance mechanisms, pharmacokinetic optimization requirements, optimal administration route selection, large-scale production quality control standards and clinical dosing determination protocols. Through multidisciplinary integration of synthetic biology, infectious disease medicine and immunology, future translational medicine studies of bacteriophage should establish comprehensive technical platforms encompassing rapid phage screening, intelligent rational design, rigorous in vivo evaluation and standardized clinical validation processes, ultimately advancing engineered bacteriophage from laboratory innovations to clinically approved therapeutics for effectively combating MDR bacterial infections.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

OBJECTIVE To compare the clinical efficacy and safety of remimazolam and propofol in general anesthesia induction and maintenance for elderly patients undergoing thoracoscopic lobectomy. METHODS A total of 86 elderly lung cancer patients who underwent thoracoscopic lobectomy at Chongqing University Three Gorges Hospital from February to July 2024 were selected and divided into the propofol group and the remimazolam group according to the randomized numerical table method， with 43 cases in each group. During anesthesia induction， patients in the propofol group and the remimazolam group were intravenously administered 2 mg/kg of Propofol medium- and long-chain fat emulsion injection or 0.25 mg/kg of Remimazolam tosilate for injection， respectively； during anesthesia maintenance， the two groups received intravenous infusion of 6-10 mg/（kg·h） of Propofol medium- and long- chain fat emulsion injection or 1-3 mg/（kg·h） of Remimazolam tosilate for injection， respectively. The anesthesia effects， anesthesia-related indicators， intraoperative opioid and muscle relaxant dosages， Ramsay sedation score， numerical rating scale （NRS） score， and hemodynamic parameters were compared between the two groups， and the occurrence of adverse drug reactions was recorded. RESULTS A total of 41 patients in the propofol group and 43 patients in the remimazolam group completed the trial. The proportion of patients with grade Ⅰ anesthesia effect in the remimazolam group was significantly higher than that in the propofol group， while the proportion of patients with grade Ⅱ anesthesia effect was significantly lower than that in the propofol group （P＜0.05）. In this group， the disappearance time of eyelash reflex， the time taken for the bispectral index to drop to 60， and the Ramsay sedation scores （2 and 6 hours after operation） were all significantly prolonged or increased， while the recovery time， NRS scores （2 and 6 hours after operation）， and the incidence of intraoperative hypotension were all significantly shortened or reduced； moreover， the improvements of the above sedation/NRS scores exhibited a time-dependent pattern within 2 to 24 hours after operation （P＜0.05）. Compared with before anesthesia induction （T0）， the heart rate [except at 2 min after medication （T1）， 60 min after anesthesia （T4）， and at the end of surgery （T5） in the remimazolam group] and mean arterial pressure [except at T1 in the remimazolam group] of patients in both groups significantly decreased at T1， 5 min after medication （T2）， at the start of surgery （T3）， T4， and T5 （P＜0.05）. Meanwhile， regional cerebral oxygen saturation significantly increased in both groups. Furthermore， the heart rate and mean arterial pressure of patients in the remimazolam group were significantly higher than those in the propofol group at T1， T2 and T4 （P＜0.05）. No statistically significant differences were observed between the two groups in terms of postanesthesia care unit stay time， dosage of opioids and muscle relaxants， regional cerebral oxygen saturation， or peripheral oxygen saturation at various time points （P＞0.05）. CONCLUSIONS Compared to propofol， remimazolam demonstrates superior anesthesia effects when used for the induction and maintenance of general anesthesia in elderly patients undergoing thoracoscopic lobectomy. It not only provides more stable intraoperative hemodynamics and shortens the postoperative recovery time but also effectively reduces the incidence of intraoperative hypotension.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

AIM:To investigate the impact of es-ketamine-mediated opioid-free anesthesia(OFA)on postoperative gastrointestinal function in patients undergoing laparoscopic distal gastrectomy for gas-tric cancer.METHODS:A total of 150 pa-tients,scheduled for elective laparoscopic distal gas-trectomy for gastric cancer and meeting the inclu-sion and exclusion criteria,were randomly assigned to either the OFA group or the opioid-based anes-thesia(OBA)group using a random number ta-ble,with 75 patients in each group.The OFA group was administered an anesthesia regimen pri-marily consisting of esketamine,while the OBA group received conventional opioid anesthesia,pri-marily consisting of sufentanil and remifentanil.The primary outcome measure was postoperative flatus time,defined as the interval from the end of sur-gery to the first passage of gas.RESULTS:The OFA group exhibited a shorter postoperative flatus time compared to the OBA group(P＜0.01).Intraopera-tive blood loss and norepinephrine consumption were significantly less in the OFA group compared to the OBA group(P＜0.05);the postoperative HADS-D score was better in the OFA group than in the OBA group,and both the OFA and OBA groups showed significantly lower postoperative HADS-A and HADS-D scores compared to their preoperative levels(P＜0.05);the incidence rate of abdominal distension was significantly lower in the OFA group compared to the OBA group(P＜0.05).CONCLUSION:The use of esketamine-mediated opioid-free anesthesia can expedite gastrointestinal function recovery,reduce hospital stay duration,and decrease postoperative adverse reactions in patients undergoing laparo-scopic distal gastrectomy for gastric cancer.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective:To construct an evaluation indicator system for accessories of patient monitor,so as to provide a basis for clinical management,equipment procurement,and technical improvement for accessories of medical monitor.Methods:The initial selection indicators of corresponding accessories of three types of monitoring of medical monitors,including electrocardiogram(ECG),blood oxygen saturation(SpO2)and non-invasive blood pressure(NIBP),were determined through literature research,expert consultation,and actual investigation.The Delphi method was adopted to conduct two rounds of questionnaire consultation for experts from clinical medicine,biomedical engineering and other fields in medical institutions included Sichuan Provincial People's Hospital,The Affiliated Hospital of Southwest Medical University and Medical Institute of Chengdu Institute of Metrology Verification and Testing.The evaluation indicators were screened and optimized,and the Analytic Hierarchy Process(AHP)was used to calculate the weights of each indicator.The consistency test was conducted to verify the rationality of the evaluation indicator system.Results:The evaluation indicator system for accessories of medical monitor included three first-level indicators:clinical value,cost value,and management value.The number of second-level indicators about ECG,SpO2 and NIBP of evaluation indicator system were respectively 10,9 and 8,and the number of third-level indicators of that were respectively 20,19,and 14.In the first-level indicators,the clinical value had the highest weight,with 72.49%for ECG,70.88%for SpO2 and 70.32%for NIBP.In the second-level indicators,the accuracy(28.70%for ECG,38.13%for SpO2 and 43.03%for NIBP)and safety(27.47%for ECG,26.48%for SpO2 and 23.06%for NIBP)were the core indicators.The weights of cost value and management value were between 14.62%and 17.41%,and between 12.27%and 12.89%,respectively.Conclusion:The evaluation indicator system for accessories of medical monitor integrates multi-dimensional expert opinions and quantitative analysis,highlights the priority principle for clinical performance.It can provide theoretical support for optimizing selection about accessory for medical institutions,and improving quality of monitoring,and promoting standardized management in the industry.

Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1％(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5％,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3％(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1％)were isolated from sputum and respiratory secretions,followed by urine(10.7％)and blood samples(8.1％).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1％.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4％in the patients＜18 years old to 20.6％in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.