Bronchiolar adenoma (BA) is a rare type of lung tumor characterized by bilayered epithelial cells having a continuous basal layer and a luminal layer. It resembles mucinous adenocarcinoma (MA) on frozen section, with difficulty in distinguishing the basal layer. Immunohistochemistry is the best choice for verifying the diagnosis. This study aimed to comprehensively characterize three cases of BA-combined carcinoma using clinical, histopathological, and genetic features. BA and carcinoma sections were subjected to next-generation sequencing, respectively. It was hypothesized that while different mutation forms matched different regions, BA and lung adenocarcinoma shared the same gene mutation when they co-occurred in the same location. BA with extensive carcinoma is extremely rare and presents diagnostic challenges due to its overlap with conditions such as MA. Because of its distinctive morphological characteristics, BA may be regarded as a low-grade malignancy, particularly during a confusing evaluation. A multifaceted examination of clinical, radiological, immunohistochemical, and genetic data is necessary for an accurate diagnosis.

Objective:Non-targeted metabolomics based on ultra-performance liquid chromatography mass spectrometry(UPLC-MS)platform was used to study the metabolic characteristics of multiple myeloma(MM)patients,and explore potential new metabolic mechanisms affecting the occurrence and development of MM.Methods:The study enrolled 42 MM patients,including 21 newly diagnosed(ND)patients and 21 relapsed patients(RP),and 21 age-sex matched healthy controls(HC)as subjects.UPLC-MS analysis platform was used to detect small molecule metabolites in serum of the subjects.Principal component analysis(PCA),orthogonal partial least-squared discriminant analysis(OPLS-DA)and 200 random permutations were used to analyze the differences of metabolic profiles among groups.Identification of differential metabolites was completed in the Human Metabolome Database(HMDB)and abnormal metabolic pathway analysis was performed using the KEGG database.Results:The metabolic profiles of MM patients and healthy controls were significantly separated in both PCA and OPLS-DA models,while the metabolic profiles of newly diagnosed and relapsed MM patients were significantly separated only in OPLS-DA model.In ND vs HC,RP vs HC and RP vs ND cohorts,19,24 and 18 differential metabolites were identified respectively,mainly sphingolipids,glycerophospholipids and fatty acyl amino acids.The metabolic pathway abnormalities in newly diagnosed and relapsed MM patients were mainly manifested in sphingolipid metabolism and glycerophospholipid metabolism compared to healthy controls.Compared with newly diagnosed MM patients,relapsed MM patients were mainly manifested in sphingolipid metabolism and ether-lipid metabolism.Conclusion:The metabolic profiles of MM patients are significantly different from those of healthy people.Relapsed MM patients and newly diagnosed MM patients have similar metabolic profiles,but there are still some differences.Glycerophospholipid metabolism,sphingolipid meta-bolism and ether-lipid metabolism may play important biological roles in the occurrence and development of MM.

Objectives:To explore the predictive value of residual Murray's law-based quantitative flow ratio(μQFR)on long-term vessel-oriented composite endpoints(VoCE).Methods:This retrospective study included 3 510 patients from the FAVOR Ⅲ China trial.Offline residual μQFR analysis was performed on all vessels(diameter≥2.5 mm)with 50%-90%stenotic lesions.Patients were stratified into high-,intermediate-,and low-risk groups based on residual μQFR tertiles.The primary endpoint was 3-year VoCE,defined as a composite of cardiac death related to the target vessel,target vessel-related spontaneous myocardial infarction,and ischemia-driven target vessel revascularization.Results:Offline analysis was performed on 5 256 vessels from 3 510 patients.The mean residual μQFR was 0.92±0.75.The high-risk group(residual μQFR≤0.91)with 1 554 patients(1 958 vessels);the intermediate-risk group(residual μQFR 0.92-0.96)with 1 211 patients(1 906 vessels);and the low-risk group(residual μQFR>0.96)with 745 patients(1 392 vessels).Over 3-year follow-up,VoCE occurred in 227 vessels(4.3%).The 3-year VoCE incidence was significantly higher in the high-risk group compared to the intermediate-and low-risk groups(6.2%vs.4.1%vs.2.5%,log-rank P<0.001),primarily driven by ischemia-driven target vessel revascularization(5.0%vs.3.0%vs.1.6%,log-rank P<0.001).Hypertension(OR=0.83,95%CI:0.72-0.96),hypercholesterolemia(OR=0.84,95%CI:0.73-0.97),bifurcation lesions(OR=0.72,95%CI:0.63-0.83),moderate/severe calcification(OR=0.70,95%CI:0.57-0.84),and tandem lesions(OR=0.59,95%CI:0.47-0.75)were independent predictors of lower residual μQFR values.Conclusions:Lower residual μQFR is significantly associated with increased VoCE risk during the 3-year follow up period.

This study was aimed at establishing a method of ultra-fast quantitative PCR for Brucella detection.We used an exogenous recombinant plasmid as the internal reference and targeted the T4SS secretion system,an important Brucella viru-lence factor,to design specific primers and probes.The sensitivity,specificity,and repeatability of this method were evaluated,and a standard curve was constructed.The coincidence rate of detection findings with this method versus quantitative PCR was determined.This method markedly decreased the detection time to only 10 minutes.The standard curve demonstrated a good linear relationship(Y=-3.410 7x+38.357,R2=0.998 5)with a low minimum detection limit of 10 copies/μL.The method exhibited good specificity and did not specifically amplify several common clinical bacteria other than Brucella.The de-tection of three concentrations of positive plasmids yielded coefficients of variation(CVs)of 0.20％to 0.91％,thus demonstra-ting the method's excellent repeatability.Furthermore,140 clinical samples were analyzed concurrently with the fluorescence PCR method,which yielded a 100％compliance rate and consistent results.Our findings indicated that the Brucella ultra-fast quantitative PCR was ultrafast;had high sensitivity,high specificity,and good specificity;and can be used for the clinical de-tection of Brucella and emergency investigation of epidemics.Therefore,this method is valuable for the early diagnosis of Bru-cella.

Objective:Non-targeted metabolomics based on ultra-performance liquid chromatography mass spectrometry(UPLC-MS)platform was used to study the metabolic characteristics of multiple myeloma(MM)patients,and explore potential new metabolic mechanisms affecting the occurrence and development of MM.Methods:The study enrolled 42 MM patients,including 21 newly diagnosed(ND)patients and 21 relapsed patients(RP),and 21 age-sex matched healthy controls(HC)as subjects.UPLC-MS analysis platform was used to detect small molecule metabolites in serum of the subjects.Principal component analysis(PCA),orthogonal partial least-squared discriminant analysis(OPLS-DA)and 200 random permutations were used to analyze the differences of metabolic profiles among groups.Identification of differential metabolites was completed in the Human Metabolome Database(HMDB)and abnormal metabolic pathway analysis was performed using the KEGG database.Results:The metabolic profiles of MM patients and healthy controls were significantly separated in both PCA and OPLS-DA models,while the metabolic profiles of newly diagnosed and relapsed MM patients were significantly separated only in OPLS-DA model.In ND vs HC,RP vs HC and RP vs ND cohorts,19,24 and 18 differential metabolites were identified respectively,mainly sphingolipids,glycerophospholipids and fatty acyl amino acids.The metabolic pathway abnormalities in newly diagnosed and relapsed MM patients were mainly manifested in sphingolipid metabolism and glycerophospholipid metabolism compared to healthy controls.Compared with newly diagnosed MM patients,relapsed MM patients were mainly manifested in sphingolipid metabolism and ether-lipid metabolism.Conclusion:The metabolic profiles of MM patients are significantly different from those of healthy people.Relapsed MM patients and newly diagnosed MM patients have similar metabolic profiles,but there are still some differences.Glycerophospholipid metabolism,sphingolipid meta-bolism and ether-lipid metabolism may play important biological roles in the occurrence and development of MM.

Objectives:To explore the predictive value of residual Murray's law-based quantitative flow ratio(μQFR)on long-term vessel-oriented composite endpoints(VoCE).Methods:This retrospective study included 3 510 patients from the FAVOR Ⅲ China trial.Offline residual μQFR analysis was performed on all vessels(diameter≥2.5 mm)with 50%-90%stenotic lesions.Patients were stratified into high-,intermediate-,and low-risk groups based on residual μQFR tertiles.The primary endpoint was 3-year VoCE,defined as a composite of cardiac death related to the target vessel,target vessel-related spontaneous myocardial infarction,and ischemia-driven target vessel revascularization.Results:Offline analysis was performed on 5 256 vessels from 3 510 patients.The mean residual μQFR was 0.92±0.75.The high-risk group(residual μQFR≤0.91)with 1 554 patients(1 958 vessels);the intermediate-risk group(residual μQFR 0.92-0.96)with 1 211 patients(1 906 vessels);and the low-risk group(residual μQFR>0.96)with 745 patients(1 392 vessels).Over 3-year follow-up,VoCE occurred in 227 vessels(4.3%).The 3-year VoCE incidence was significantly higher in the high-risk group compared to the intermediate-and low-risk groups(6.2%vs.4.1%vs.2.5%,log-rank P<0.001),primarily driven by ischemia-driven target vessel revascularization(5.0%vs.3.0%vs.1.6%,log-rank P<0.001).Hypertension(OR=0.83,95%CI:0.72-0.96),hypercholesterolemia(OR=0.84,95%CI:0.73-0.97),bifurcation lesions(OR=0.72,95%CI:0.63-0.83),moderate/severe calcification(OR=0.70,95%CI:0.57-0.84),and tandem lesions(OR=0.59,95%CI:0.47-0.75)were independent predictors of lower residual μQFR values.Conclusions:Lower residual μQFR is significantly associated with increased VoCE risk during the 3-year follow up period.

With the latest development of information and communication technology, mobile applications for health care have attracted much attention to help cancer patients better manage their diseases. Digital therapeutics is an evidence-based intervention driven by high-quality software programs that prevent, manage, or treat medical disorders or diseases. This paper summarizes the origin, concept and characteristics of digital therapeutics and the application form, effect, challenge and opportunity of symptom management in cancer patients, in order to provide reference for the scientific and efficient application of Digital therapeutics to cancer patients.

Objective To propose a dynamic early warning model based on machine learning methods and validate its predi-ctive efficacy so as to achieve precise assessment and early warning of mortality risk in patients with traumatic hypothermia.Methods Firstly,a total of 480 patients who met inclusion criteria were retrospectively selected from the eICU database and randomly divided into training and test sets at an 8∶2 ratio.Secondly,physiological parameters were extracted from these patients,and five machine learning algorithms including XGBoost,AdaBoost,LightGBM,logistic regression(LR)and random forest(RF)were employed respectively to develop dynamic mortality risk warning models for traumatic hypothermia patients,utilizing a 1-hour observation window.Thirdly,receiver operating characteristic curves(ROC)were plotted using the test set data and the effects of different warning windows on the model performance were analyzed by calculating the AUC.Finally,the interpretability of the models was analyzed using the SHapley Additive exPlanations(SHAP)algorithm to elucidate the contribution of each feature to predictive performance.Results The optimal warning window for the dynamic warning model constructed using the eICU database was 12 hours,and in case of 12-hour warning window the logistic regression model achieved the highest AUC of 0.935 and showed optimal predictive performance.The results of the interpretability analysis by the SHAP algorithm showed that body temperature was the feature that had the greatest impact on the model results,and its reduction was positively correlated with the increased risk of death.Conclusion The machine learning-based dynamic warning model for mortality risk in traumatic hypothermia patients enables real-time dynamic risk assessment,providing robust support for clinicians to identify the patient's condition changes at an early stage and references for the adjustment of clinical treatment programs.[Chinese Medical Equipment Journal,2025,46(3):9-14]

Objective To investigate the learning curve of real-life vaginal delivery,including its difficult steps and influencing factors,to optimize the future training of vaginal delivery for residents in department of obstetrics and gynecology.Methods From 25 Sep 2020 to 12 Mar 2022,OBGYN residents without previous experiences in vaginal delivery were prospectively enrolled in Obstetrics and Gynecology Hospital,Fudan University.Residents performed normal vaginal delivery under the supervision of senior obstetricians and midwives.The performance score(PS)of vaginal delivery and its 9 steps were evaluated via a questionnaire fulfilled by the supervisor once each delivery was finished.Logistic regression models were performed for univariate and multivariate analyses to evaluate the factors that might be correlated with the PS.Results Eventually,233 deliveries performed by 60 residents were analyzed.Results showed that more than 10 deliveries were needed for 70%of residents to obtain minimal competence of vaginal delivery.Perineal protection,delivery of the fetal head,delivery of the fetal shoulders and repair of episiotomy or laceration were found to be the most difficult steps,which required more practices to achieve minimal competence level.Univariate analyses showed the delivery experience,the times of observation/simulation/training,and humanistic care skills might influence the total PS(P<0.05).However,only delivery experience(OR=1.43,95%CI:1.22-1.67)and the times of observation(OR=1.02,95%CI:1.00-1.04)were found to be independently correlated with the total PS in multivariate analyses.Conclusion More than 10 real-life practices were required to achieve the minimal competence of normal vaginal delivery.Enhancing the training on the four difficult steps of vaginal delivery might improve the learning efficiency when delivery opportunities are limited.

This study was aimed at establishing a method of ultra-fast quantitative PCR for Brucella detection.We used an exogenous recombinant plasmid as the internal reference and targeted the T4SS secretion system,an important Brucella viru-lence factor,to design specific primers and probes.The sensitivity,specificity,and repeatability of this method were evaluated,and a standard curve was constructed.The coincidence rate of detection findings with this method versus quantitative PCR was determined.This method markedly decreased the detection time to only 10 minutes.The standard curve demonstrated a good linear relationship(Y=-3.410 7x+38.357,R2=0.998 5)with a low minimum detection limit of 10 copies/μL.The method exhibited good specificity and did not specifically amplify several common clinical bacteria other than Brucella.The de-tection of three concentrations of positive plasmids yielded coefficients of variation(CVs)of 0.20％to 0.91％,thus demonstra-ting the method's excellent repeatability.Furthermore,140 clinical samples were analyzed concurrently with the fluorescence PCR method,which yielded a 100％compliance rate and consistent results.Our findings indicated that the Brucella ultra-fast quantitative PCR was ultrafast;had high sensitivity,high specificity,and good specificity;and can be used for the clinical de-tection of Brucella and emergency investigation of epidemics.Therefore,this method is valuable for the early diagnosis of Bru-cella.