Background and purpose:Approximately 30%of patients with metastatic colorectal cancer(CRC)develops pulmonary metastasis,yet less than 10%are eligible for surgical resection.Radiofrequency ablation(RFA)serves as an alternative therapy for non-surgical candidates,but the relationship between its efficacy and tumor diameter remains controversial.This study aimed to investigate the impact of tumor size on survival outcomes and local progression risk in CRC patients with pulmonary metastasis after RFA,and to validate the clinical utility of a 3 cm threshold for prognosis.Methods:This retrospective study included CRC patients with pulmonary metastasis who underwent RFA at Fudan University Shanghai Cancer Center between January 2016 and December 2024.Patients were stratified into two groups based on maximum lesion diameter:≤3 cm(Small group)and 3-5 cm(Large group).Patient inclusion criteria:⑴ pathologically confirmed lung metastases originating from CRC,with metastases limited to the lungs or extra-pulmonary metastatic lesions having been radically treated;⑵ maximum lesion diameter<5 cm;⑶complete clinical data available;⑷ complete imaging data available,including computed tomography(CT)images during ablation and contrast-enhanced CT images during postoperative follow-up;⑸ follow-up time of at least＞6 months after RFA;⑹ technical complete ablation;⑺ fewer than 3 pulmonary metastatic lesions.Exclusion criteria:⑴ target lesions previously treated with local therapies such as RFA or radiotherapy;⑵ patients unable to tolerate RFA;⑶ patients with follow-up time＜6 months after RFA.Three senior interventional physicians performed percutaneous RFA under guidance of a 64-slice spiral CT scanner.Chest contrast-enhanced CT scans obtained 1 month after RFA were used as the baseline,followed by contrast-enhanced CT scans every 3 months for 1 year,then every 6 months for subsequent follow-up.This study was approved by the medical ethics committee of Fudan University Shanghai Cancer Center(ethical approval number:2108241-11).Primary endpoints included overall survival(OS),progression-free survival(PFS),and local tumor progression(LTP).Kaplan-Meier analysis and multivariate COX regression were employed to evaluate the independent prognostic value of tumor size.Results:A total of 134 patients who met the inclusion criteria were ultimately enrolled,including 77 in the Small group and 57 in the Large group.With a median follow-up of 35 months,the≤3 cm group demonstrated superior 1-,3-,and 5-year OS rates(100.0%,95.1%,74.2%)compared to the 3-5 cm group(94.7%,36.8%,27.0%,P＜0.0001),and the≤3 cm group demonstrated superior 1-,3-,and 5-year PFS rates(90.9%,34.4%,23.3%)compared to the 3-5 cm group(13.8%,0.0%,0.0%,P＜0.000 1).The≤3 cm group also exhibited significantly lower 1-,3-,and 5-year LTP rates(0.0%,19.7%,33.6%)compared to the 3-5 cm group(46.0%,75.5%,75.5%,P＜0.000 1).Multivariable analysis identified tumor diameter＞3 cm as an independent predictor of worse OS[hazard ratio(HR)=6.49,95%CI:3.18-13.24,P＜0.001],while elevated preoperative carcinoembryonic antigen(CEA)(≥5 ng/mL)correlated with shorter OS(HR=1.82,P=0.033).Conclusion:CRC patients with pulmonary metastasis and tumor diameters of 3-5 cm exhibited significantly inferior survival outcomes after RFA compared to the≤3 cm group.A tumor diameter of 3 cm can serve as a critical threshold for selecting RFA indications,and combining preoperative CEA levels can optimize patient stratification.

Coronary heart disease with angina pectoris,characterized by myocardial ischemic injury as its fundamental pathological mechanism,represents a prevalent cardiovascular condition.The"State-Target Differential Diagnosis and Treatment"presents a holistic regulatory framework,integrating macroscopic state regulation with microscopic targeting.Guided by this approach,the pathological evolution of coronary heart disease is examined through four dimensions:"state-target-cause-effect."The"cause"encompasses both the pathogenesis and etiological factors of traditional Chinese and Western medicine.The"effect"manifests as adverse cardiovascular events,including myocardial infarction and heart failure.The"state"delineates the progressive development pattern of qi to blood to deficiency,beginning with qi stagnation and cold congealment in the initial stage,followed by blood stasis and phlegm obstruction in the intermediate stage,and culminating in qi-blood deficiency in the advanced stage.The"target"encompasses multi-level therapeutic interventions addressing both symptomatic manifestations and clinical indicators.Building on this theoretical foundation,this research focuses on the pattern of qi sinking and blood stasis commonly observed in late-stage angina,systematically elucidating its state regulation and targeting therapeutic strategies.Using the clinical empirical formula Shengxian Quyu Decoction as the baseline state prescription,an in-depth investigation was conducted to determine optimal combination patterns of symptom-and biomarker-targeted medications.This study aims to establish a modernized differential treatment system for angina pectoris with the pattern of qi sinking and blood stasis,providing novel research perspectives and theoretical foundations for enhancing clinical efficacy and reducing the risk of cardiovascular events.

Objective This study aims to explore the diagnostic value of medical indicators in identifying diuretic resistance(DR)among patients with heart failure(HF)using machine learning methods such as Least Absolute Shrinkage and Selection Operator(LASSO)regression.An early recognition model for diuretic resistance in heart failure patients is constructed to provide clinicians with timely intervention opportunities and reduce hospitalization duration.Methods 267 heart failure patients from Tianjin Chest Hospital were included in the study.Standard urine output and DR status were employed as dependent factors,whereas 64 clinical indicators were gathered as independent variables.Depending on the kind of dependent variable,two modeling strategies were used.The first method used LASSO regression to fit the model,and 10-fold cross-validation was used to find the ideal regularization parameter.Standard urine output was used to predict diuretic resistance in the test set.Root mean square error(RMSE),coefficient of determination(R2),and classification accuracy were employed to assess the model's performance.In the second method,the most pertinent subset of features in relation to DR status was selected utilizing the max-relevance and min-redundancy(mRMR)algorithm.After that,three machine learning classifiers were used for classification:support vector machines(SVM),random forests(RF),and logistic regression(LR).Accuracy,precision,recall,area under the receiver operating characteristic curve(AUC),and F1-score were used to evaluate the model's performance.Results The optimal LASSO parameter was 0.0161.On the test set,the RMSE was 0.3489,the R2 was 0.7200,and the classification accuracy was 91.36%.The AUCs for mRMR+LR,mRMR+RF,and mRMR+SVM were 0.94,0.89,and 0.95,respectively.Their accuracies were 0.86,0.77,and 0.90;precisions were 0.87,0.80,and 0.90;recalls were 0.86,0.75,and 0.90;and F1-scores were 0.86,0.75,and 0.90,respectively.In the validation set,the LASSO regression model outperformed the other machine learning methods in predictive performance.Conclusion Diuretic resistance in heart failure patients was successfully detected by both the LASSO regression model and mRMR-based machine learning classifiers.The LASSO regression model performed better than the mRMR-based classifiers,providing more accurate scientific evidence for early clinical intervention.

Objective: To establish and validate a whole blood (WB) supply model, thereby providing practical experience for the clinical application of WB in domestic trauma emergency care and informing the development of a wartime blood supply system for the military. Methods: A “10×24” WB supply model was established by formulating blood collection protocols, storage standards, and transfusion criteria. Multiple WB samples were tested under specific storage conditions to assess key indicators at different time points, including red blood cell (RBC), white blood cell (WBC), and platelet counts, hemoglobin concentration, coagulation parameters (PT, APTT, TT, FIB), coagulation factor activity, thromboelastography (TEG) parameters, and electrolyte levels. Additionally, clinical data from hemorrhagic patients who met the criteria for WB transfusion and were admitted between March and July 2024 were analyzed to evaluate WB transfusion volume. Results: RBC counts and hemoglobin levels remained stable in WB stored at 4℃ for up to 10 days. However, platelet counts and coagulation function (PT, APTT) significantly declined with prolonged storage, while potassium levels increased. From March to July 2024, the model was successfully applied to 23 patients with acute hemorrhage, with a median WB transfusion volume of 543 mL. A detailed case study of a severe traumatic hemorrhagic shock patient was reported, who was successfully treated with 5.5 units of refrigerated WB combined with component blood. Conclusion: The “10×24” WB supply model demonstrated acceptable changes in critical quality parameters under strict management and a 10-day rotation cycle. This model effectively supports the treatment of acute hemorrhage and holds promise for integration into the future wartime blood supply system of the military.

Objective This study aims to explore the diagnostic value of medical indicators in identifying diuretic resistance(DR)among patients with heart failure(HF)using machine learning methods such as Least Absolute Shrinkage and Selection Operator(LASSO)regression.An early recognition model for diuretic resistance in heart failure patients is constructed to provide clinicians with timely intervention opportunities and reduce hospitalization duration.Methods 267 heart failure patients from Tianjin Chest Hospital were included in the study.Standard urine output and DR status were employed as dependent factors,whereas 64 clinical indicators were gathered as independent variables.Depending on the kind of dependent variable,two modeling strategies were used.The first method used LASSO regression to fit the model,and 10-fold cross-validation was used to find the ideal regularization parameter.Standard urine output was used to predict diuretic resistance in the test set.Root mean square error(RMSE),coefficient of determination(R2),and classification accuracy were employed to assess the model's performance.In the second method,the most pertinent subset of features in relation to DR status was selected utilizing the max-relevance and min-redundancy(mRMR)algorithm.After that,three machine learning classifiers were used for classification:support vector machines(SVM),random forests(RF),and logistic regression(LR).Accuracy,precision,recall,area under the receiver operating characteristic curve(AUC),and F1-score were used to evaluate the model's performance.Results The optimal LASSO parameter was 0.0161.On the test set,the RMSE was 0.3489,the R2 was 0.7200,and the classification accuracy was 91.36%.The AUCs for mRMR+LR,mRMR+RF,and mRMR+SVM were 0.94,0.89,and 0.95,respectively.Their accuracies were 0.86,0.77,and 0.90;precisions were 0.87,0.80,and 0.90;recalls were 0.86,0.75,and 0.90;and F1-scores were 0.86,0.75,and 0.90,respectively.In the validation set,the LASSO regression model outperformed the other machine learning methods in predictive performance.Conclusion Diuretic resistance in heart failure patients was successfully detected by both the LASSO regression model and mRMR-based machine learning classifiers.The LASSO regression model performed better than the mRMR-based classifiers,providing more accurate scientific evidence for early clinical intervention.

Background and purpose:Approximately 30%of patients with metastatic colorectal cancer(CRC)develops pulmonary metastasis,yet less than 10%are eligible for surgical resection.Radiofrequency ablation(RFA)serves as an alternative therapy for non-surgical candidates,but the relationship between its efficacy and tumor diameter remains controversial.This study aimed to investigate the impact of tumor size on survival outcomes and local progression risk in CRC patients with pulmonary metastasis after RFA,and to validate the clinical utility of a 3 cm threshold for prognosis.Methods:This retrospective study included CRC patients with pulmonary metastasis who underwent RFA at Fudan University Shanghai Cancer Center between January 2016 and December 2024.Patients were stratified into two groups based on maximum lesion diameter:≤3 cm(Small group)and 3-5 cm(Large group).Patient inclusion criteria:⑴ pathologically confirmed lung metastases originating from CRC,with metastases limited to the lungs or extra-pulmonary metastatic lesions having been radically treated;⑵ maximum lesion diameter<5 cm;⑶complete clinical data available;⑷ complete imaging data available,including computed tomography(CT)images during ablation and contrast-enhanced CT images during postoperative follow-up;⑸ follow-up time of at least＞6 months after RFA;⑹ technical complete ablation;⑺ fewer than 3 pulmonary metastatic lesions.Exclusion criteria:⑴ target lesions previously treated with local therapies such as RFA or radiotherapy;⑵ patients unable to tolerate RFA;⑶ patients with follow-up time＜6 months after RFA.Three senior interventional physicians performed percutaneous RFA under guidance of a 64-slice spiral CT scanner.Chest contrast-enhanced CT scans obtained 1 month after RFA were used as the baseline,followed by contrast-enhanced CT scans every 3 months for 1 year,then every 6 months for subsequent follow-up.This study was approved by the medical ethics committee of Fudan University Shanghai Cancer Center(ethical approval number:2108241-11).Primary endpoints included overall survival(OS),progression-free survival(PFS),and local tumor progression(LTP).Kaplan-Meier analysis and multivariate COX regression were employed to evaluate the independent prognostic value of tumor size.Results:A total of 134 patients who met the inclusion criteria were ultimately enrolled,including 77 in the Small group and 57 in the Large group.With a median follow-up of 35 months,the≤3 cm group demonstrated superior 1-,3-,and 5-year OS rates(100.0%,95.1%,74.2%)compared to the 3-5 cm group(94.7%,36.8%,27.0%,P＜0.0001),and the≤3 cm group demonstrated superior 1-,3-,and 5-year PFS rates(90.9%,34.4%,23.3%)compared to the 3-5 cm group(13.8%,0.0%,0.0%,P＜0.000 1).The≤3 cm group also exhibited significantly lower 1-,3-,and 5-year LTP rates(0.0%,19.7%,33.6%)compared to the 3-5 cm group(46.0%,75.5%,75.5%,P＜0.000 1).Multivariable analysis identified tumor diameter＞3 cm as an independent predictor of worse OS[hazard ratio(HR)=6.49,95%CI:3.18-13.24,P＜0.001],while elevated preoperative carcinoembryonic antigen(CEA)(≥5 ng/mL)correlated with shorter OS(HR=1.82,P=0.033).Conclusion:CRC patients with pulmonary metastasis and tumor diameters of 3-5 cm exhibited significantly inferior survival outcomes after RFA compared to the≤3 cm group.A tumor diameter of 3 cm can serve as a critical threshold for selecting RFA indications,and combining preoperative CEA levels can optimize patient stratification.

Coronary heart disease with angina pectoris,characterized by myocardial ischemic injury as its fundamental pathological mechanism,represents a prevalent cardiovascular condition.The"State-Target Differential Diagnosis and Treatment"presents a holistic regulatory framework,integrating macroscopic state regulation with microscopic targeting.Guided by this approach,the pathological evolution of coronary heart disease is examined through four dimensions:"state-target-cause-effect."The"cause"encompasses both the pathogenesis and etiological factors of traditional Chinese and Western medicine.The"effect"manifests as adverse cardiovascular events,including myocardial infarction and heart failure.The"state"delineates the progressive development pattern of qi to blood to deficiency,beginning with qi stagnation and cold congealment in the initial stage,followed by blood stasis and phlegm obstruction in the intermediate stage,and culminating in qi-blood deficiency in the advanced stage.The"target"encompasses multi-level therapeutic interventions addressing both symptomatic manifestations and clinical indicators.Building on this theoretical foundation,this research focuses on the pattern of qi sinking and blood stasis commonly observed in late-stage angina,systematically elucidating its state regulation and targeting therapeutic strategies.Using the clinical empirical formula Shengxian Quyu Decoction as the baseline state prescription,an in-depth investigation was conducted to determine optimal combination patterns of symptom-and biomarker-targeted medications.This study aims to establish a modernized differential treatment system for angina pectoris with the pattern of qi sinking and blood stasis,providing novel research perspectives and theoretical foundations for enhancing clinical efficacy and reducing the risk of cardiovascular events.

Background and Purpose:The early prediction of local tumor progression-free survival(LTPFS)after radiofrequency ablation(RFA)for colorectal cancer(CRC)lung metastases has significant clinical importance.The application of radiomics in the prediction of tumor prognosis has been explored.This study aimed to construct a radiomics-based nomogram for predicting LTPFS after RFA in CRC patients with lung metastases.Methods:This study retrospectively analyzed 172 CRC patients with 401 lung metastases admitted to Department of Interventional Radiology,Fudan University Shanghai Cancer Center from August 2016 to January 2019.This study was reviewed by the medical ethics committee of Fudan University Shanghai Cancer Center(ethics number:2402291-24).After augmentation of pre-ablation and immediate post-ablation computed tomography(CT)images,the target metastases and ablation regions were segmented manually to extract the radiomic features.Maximum relevance and minimum redundancy algorithm(MRMRA)and least absolute shrinkage and selection operator(LASSO)regression models were applied for feature selection.The clinical model,the radiomics model,and the fusion model were constructed based on the selected radiomic features and clinical variables screened by the multivariate analysis.The Harrell concordance index(C-index)and area under receiver operating characteristic(ROC)curves(AUC)were calculated to evaluate the prediction performance.Finally,the corresponding nomogram of the best model was drawn.Results:Among all the lung metastases,102(25.4%)had final recurrence,and 299(74.6%)had complete response(CR).The median follow-up time was 21 months(95%CI:19.466-22.534),and the LTPFS rates at 1,2,and 3 years after RFA were 76.5%(95%CI:72.0-80.4),72.1%(95%CI:66.6-76.9)and 69.9%(95%CI:64.0-75.1).In both the training and test dataset,the fusion model based on the final 12 radiomic features through the LASSO regression and 4 clinical variables screened by multivariate analysis achieved the highest AUC values for LTPFS,with C-index values of 0.890(95%CI:0.854-0.927)and 0.843(95%CI:0.768-0.916),respectively.Conclusion:The fusion model based on radiomic features and clinical variables is feasible for predicting LTPFS after RFA of CRC patients with lung metastases,whose performance is superior to the single radiomic and clinical model.At the same time,the nomogram of the fusion model can intuitively predict the prognosis of CRC patients with lung metastases after RFA,thus assisting clinicians in developing individualized follow-up review plans for patients and adjusting treatment strategies flexibly.

A high-level performance management system is indispensable for supporting the high-quality development of public hospitals.Under the background of comprehensive overhaul in the remuneration mechanisms within public hospitals and promotion of the contemporary management system of public hospitals,an increasing number of these hospitals are embracing per-formance-driven reform.Through a systematic analysis of the current issues in the remuneration system reform of public hospitals,this paper conducts a series of reform measures implemented within these public hospital to underscore the beneficial impact of ef-fective performance appraisal andremuneration incentives on the morale of medical staff and the sustainable cultivation of talent.This paper seeks to offer insights into enhancing remuneration system reforms and fostering high-quality development in public hospitals in the contemporary era.

ObjectiveTo explore the relationship between the occupational noise exposure and arteriosclerosis in mechanical manufacturing workers. Methods A total of 453 employees of a machinery manufacturing enterprise were selected as the study subjects using the judgment sampling method. The noise exposure levels in their workplaces were measured, and their cumulative noise exposure (CNE) was assessed based on the type of job-noise exposure matrix and occupational hazard exposure history. Pure-tone audiometry was performed on the research subjects, and their brachial-ankle pulse wave velocity (baPWV) was measured. Results The CNE was (91±11) dB(A) per year and the median baPWV was 1 278.0 cm/s in the research subjects. The results of the generalized linear regression model analysis showed that for every one dB(A) per year increase in CNE, the baPWV of the general population increased by 0.20% ［95% confidence interval (CI) 0.10%-0.30%, P<0.01］, with an increase of 0.17% in males (95%CI 0.06%-0.28%, P<0.01) and 0.28% in females (95%CI 0.07%-0.49%, P<0.01). Using the hearing loss as an outcome indicator for high intensity noise exposure, the results showed that baPWV increased by 7.04% (95%CI 2.42%-11.87%, P<0.01) in individuals with bilateral hearing loss, and by 9.84% and 6.53% (95%CI 3.07%-17.07% and 2.13%-11.11%, all P<0.01) in individuals with elevated high-frequency hearing thresholds in both ears and in either ear, respectively. There was no significant association in elevated speech-frequency hearing thresholds and arteriosclerosis (P>0.05). Conclusion Occupational noise exposure may increase the risk of arteriosclerosis.