Objective:To investigate the feasibility of deep learning based on computed tomography(CT)radiomics in improving diagnostic accuracy for pulmonary nodules.Methods:A total of 500 patients with pulmonary nodules who admitted to our hospital from January 2023 to January 2024 were selected as study subjects,and they were randomly divided into a training set(350 patients)and a test set(150 patients)as 7:3 ratio.All patients underwent CT examination,and pathological diagnosis was used as gold standard to record pulmonary nodules that were judged by clinical judgment.The radiomics features were screened from the CT images of the patients,and these features were used to construct multiple machine learning models.The predictive value of different models in diagnosing pulmonary nodules was analyzed through confusion matrices and receiver operating characteristic(ROC)curve.Results:A total of 1,594 radiomics features,including 1,195 texture features(74.97%)that was the largest ratio,334 first-order histograms(20.95%),and 65 second-order histograms(4.08%),were extracted in this study.After least absolute shrinkage and selection operator(LASSO)regression analysis and ten-fold cross-validation processing,a total of six radiomics features were screened out.The screened radiomics features were incorporated respectively into four assembled models with machine learning,including ResNet50,DenseNet121,Inception_V3 and VGG19.The constructed models were evaluated respectively using the training set and the test set.The results showed that the assembled model had the highest accuracies in both training set and the test set(96.57%and 95.33%),which area under curve(AUC)values were 0.934 and 0.923,and specificities were 81.64%and 80.52%,and sensitivities were 90.25%and 88.71%,respectively.The results of consistency test indicated that the assembled model had the best classification consistency(Kappa=0.856,P<0.001)in the constructed diagnostic model for pulmonary nodule,which was the best-performing model.Conclusion:The deep learning technique based on CT radiomics has a certain feasibility in improving the diagnostic accuracy for pulmonary nodules,and the machine learning model that is included in this study has favorable predictive value in diagnosing pulmonary nodules.In them,the assembled model that is constructed on the basis of ResNet50,DenseNet121,Inception_V3,and VGG19 has better classification ability.

Objective:To explore the association between A Body Shape Index (ABSI) and sarcopenia in Chinese older adults.Methods:Totally 1 728 participants in waves 1 and 3 of the China Health and Retirement Longitudinal Survey (CHARLS) were enrolled in this cohort-based cross-sectional analysis. ABSI was calculated by using anthropometric measurements from CHARLS. According to the Asian Working Group for Sarcopenia (AWGS) 2019 consensus update on sarcopenia, low muscle strength and low muscle mass were used as the diagnostic criteria for sarcopenia. Logistic regression and restricted cubic spline (RCS) were used to assess the relationship between ABSI and sarcopenia, and subgroup and interaction analyses were conducted to evaluate the influence of other factors on this association.Results:High ABSI showed a significant association with the risk of sarcopenia among older adults in China, and RCS analysis demonstrated a positive linear correlation between ABSI and sarcopenia. Additionally, the relationship of ABSI with sarcopenia was significantly modified by educational level and depressive status.Conclusion:ABSI, a novel anthropometric measure, serves as an effective threshold for assessing sarcopenia risk in the Chinese older population, providing a basis for developing clinical interventions and public health strategies for the prevention and early identification of sarcopenia.

AIM:To investigate the effect of Jianpi-Huayu decoction(JPHYD)combined with gemcitabine(GEM)on ferroptosis of pancreatic cancer PANC-1 cells and its mechanism.METHODS:PANC-1 cells were cultured in vitro,and CCK8 method was used to detect the cell viability after different concentrations of JPHYD and GEM,and ap-propriate concentrations were selected for follow-up experiments.EDU assay and clonogenesis assay were used to detect cell proliferation.The apoptosis rate was detected by flow cytometry.Intracellular reactive oxygen species(ROS)were de-tected by DCFH-DA fluorescent probe and lipid peroxidation was detected by BODIPY 581/591C11 staining.The contents of glutathione(GSH),ferrous ion(Fe2+)and malondialdehyde(MDA)in the cells were detected by the kit.The mRNA levels and protein expression levels of Nrf2,HO-1,SLC7A11,GPX4,TFR1 and ACSL4 were detected by RT-qPCR and Western blot.RESULTS:Compared with the control group,the cell viability of PANC-1 treated with JPHYD and GEM was significantly decreased in a concentration-dependent manner.And the combined use of the two can significantly im-prove the cytotoxic effect of GEM and have a synergistic effect;Compared with control group,JPHYD group,GEM group and JPHYD+GEM group can significantly reduce EDU positive efficiency,colony formation numbers and promote cell apoptosis,and the combined group has the most obvious effect.After adding JPHYD+GEM into the cells,the cells be-came rounded and the cell viability decreased.The addition of ferrostatin-1(Fer-1),an inhibitor of ferroptosis,had no significant effect on cell morphology and viability,and the co-treatment with JPHYD+GEM and Fer-1 could reverse the ef-fects of JPHYD+GEM on cell morphology and viability.Compared with control group and GEM group,JPHYD+GEM group can significantly increase the levels of intracellular reactive oxygen species(ROS)and lipid peroxidation,increase the levels of Fe2+and MDA,decrease the levels of GSH,further promote lipid peroxidation and induce ferroptosis.JPHYD+GEM also significantly down-regulated the mRNA and protein expressions of Nrf2,HO-1,SLC7A11 and GPX4,and up-regulated the mRNA and protein expressions of TFR1 and ACSL4.The addition of Fer-1 significantly reversed the activation of iron death in the combined treatment group and reversed its efficacy,and the difference was statistically signif-icant.CONCLUSION:Jianpi Huayu decoction and gemcitabine may induce ferroptosis of PANC-1 cells by inhibiting Nrf2/SLC7A11/GPX4 axis in vitro,thus playing a synergistic anticancer role.

Objective To analyze the factors affecting the prevalence of hyperuricemia(HUA)in an island troop.Methods A total of 1 113 soldiers stationed on an island from December 2021 to December 2022 were selected as research objects by cluster sampling.Their lifestyle and health information were collected.Physical examination and laboratory detection were conducted.Multivariate logistic regression was used to analyze the influencing factors of HUA.Results The prevalence rate of HUA was 21.02%(234/1 113).There were significant differences in the body mass index(BMI),waist-to-hip ratio,triglyceride,alanine aminotransferase,and creatinine between the soldiers with hyperuricemia and the soldiers with normal blood uric acid(P<0.05).Multivariate logistic regression analysis showed that BMI≥24(OR=1.49,95%CI:1.09-2.05),abnormal liver function(OR=2.26,95%CI:1.31-3.92),and dyslipidemia(OR=1.46,95%CI:1.01-2.12)were positively correlated with hyperuricemia;age>30 years old(OR=0.59,95%CI:0.37-0.93)and exercise time>1 h per week(OR=0.46,95%CI:0.22-0.97)were negatively correlated with HUA.Conclusion The prevalence rate of hyperuricemia is at a high level in an island troop.BMI≥24,age≤30 years old,exercise time≤1 h per week,abnormal liver function,and dyslipidemia are the risk factors for HUA.Prevention and control measures should be taken as early as possible for the soldiers with these risk factors.

Objective:To investigate the feasibility of deep learning based on computed tomography(CT)radiomics in improving diagnostic accuracy for pulmonary nodules.Methods:A total of 500 patients with pulmonary nodules who admitted to our hospital from January 2023 to January 2024 were selected as study subjects,and they were randomly divided into a training set(350 patients)and a test set(150 patients)as 7:3 ratio.All patients underwent CT examination,and pathological diagnosis was used as gold standard to record pulmonary nodules that were judged by clinical judgment.The radiomics features were screened from the CT images of the patients,and these features were used to construct multiple machine learning models.The predictive value of different models in diagnosing pulmonary nodules was analyzed through confusion matrices and receiver operating characteristic(ROC)curve.Results:A total of 1,594 radiomics features,including 1,195 texture features(74.97%)that was the largest ratio,334 first-order histograms(20.95%),and 65 second-order histograms(4.08%),were extracted in this study.After least absolute shrinkage and selection operator(LASSO)regression analysis and ten-fold cross-validation processing,a total of six radiomics features were screened out.The screened radiomics features were incorporated respectively into four assembled models with machine learning,including ResNet50,DenseNet121,Inception_V3 and VGG19.The constructed models were evaluated respectively using the training set and the test set.The results showed that the assembled model had the highest accuracies in both training set and the test set(96.57%and 95.33%),which area under curve(AUC)values were 0.934 and 0.923,and specificities were 81.64%and 80.52%,and sensitivities were 90.25%and 88.71%,respectively.The results of consistency test indicated that the assembled model had the best classification consistency(Kappa=0.856,P<0.001)in the constructed diagnostic model for pulmonary nodule,which was the best-performing model.Conclusion:The deep learning technique based on CT radiomics has a certain feasibility in improving the diagnostic accuracy for pulmonary nodules,and the machine learning model that is included in this study has favorable predictive value in diagnosing pulmonary nodules.In them,the assembled model that is constructed on the basis of ResNet50,DenseNet121,Inception_V3,and VGG19 has better classification ability.

Objective To explore the relationship of changes in serum neuron-specific enolase(NSE),neuropeptide Y(NPY)and hypocretin(Hcrt)levels with sleep disorders in the elderly stroke patients.Methods A total of 105 elderly stroke patients admitted in our department from February 2021 to February 2024 were retrospectively enrolled in this study.According to the re-sults of Pittsburgh sleep quality index(PSQI)evaluation,they were divided into sleep disorder group(64 cases)and non-sleep disorder group(41 cases).The serum NSE,NPY and Hcrt levels at admission were measured,their sleep status was evaluated with PSQI questionnaire,and the levels and clinical data were compared between groups.Multivariate logistic regression analysis was applied to analyze the influencing factors of sleep disorders in elderly stroke patients,and ROC curve analysis was performed.Results The NSE level was significantly higher,and the le-vels of NPY and Hcrt were obviously lower in the sleep disorder group than the non-sleep disor-der group(P＜0.01).Multivariate logistic regression analysis suggested that NSE,NPY,Hcrt,age,NIHSS score and history of stroke were independent influencing factor for sleep disorders in elderly stroke patients(P＜0.05,P＜0.01).ROC curve analysis indicated that the AUC value of NSE,NPY and Hcrt in predicting the occurrence of sleep disorders was 0.797,0.754 and 0.915,re-spectively.Conclusion Serum NSE,NPY and Hcrt are closely associated with sleep disorders in elderly stroke patients,and are of important value in predicting the occurrence of sleep disorders.

Objective:To compare the differences in specimen results between the intelligent robotic phlebotomy group and the manual venipuncture group, and to evaluate the clinical applicability of the autonomous blood collection system.Methods:From January 20 to October 28, 2022, 154 volunteers at Zhongshan Hospital, Fudan University underwent paired blood collections (robotic and manual) within 5 minutes. The collected samples were analyzed for: hemolysis index (HI), alanine transaminase (ALT), aspartate transaminase (AST), L-γ-glutamyltransferase (γ-GT), lactate dehydrogenase (LDH), urea nitrogen (UREA), creatinine (CRE), uric acid (UA), glucose (GLU), total cholesterol (TC), triglyceride (TG), natrium (Na), kalium (K), chlorine (Cl), creatine kinase (CK), CK-MB, CK-MM, and neuron-specific enolase (NSE). Statistical analyses used t-tests and Wilcoxon signed-rank tests.Results:The results of two different blood collection methods revealed that the HI values of 154 specimens in the intelligent robot blood collection group were all less than 20SI, while 7 specimens (4.54%) in the manual blood collection group had HI values exceeding 20SI; In the comparison of 17 biochemical and immunological markers, there were statistically significant differences between groups in 8 items including γ-GT[20.00(15.00, 37.75)U/L vs. 19.00 (14.00, 36.25)U/L, Z=2.497, P<0.05], LDH[165.5 (147.0, 183.0)U/L vs. 173.0 (155.0, 193.0)U/L, Z=8.629, P<0.05], TC[(5.002±0.856)mmol/L vs.(5.031±0.870) mmol/L, t=-3.006, P<0.05], K[4.1 (4.0, 4.3)mmol/L vs. 4.3 (4.1, 4.4)mmol/L, Z=5.592, P<0.05], CK[97.00 (73.00, 133.00)U/L vs. 99.00 (74.75, 136.25)U/L, Z=3.490, P<0.05], CK-MB[13 (11, 15)U/L vs. 14 (12, 16)U/L, Z=6.581, P<0.05], CK-MM[84.00 (60.00, 119.00)U/L vs. 83.50 (58.75, 118.00)U/L, Z=3.790, P<0.05], and NSE[10.600 (9.500, 11.700)ng/ml vs. 11.950 (10.475, 13.725)ng/ml, Z=8.151, P<0.05]. Conclusions:In the collection of serum samples, intelligent blood collection robots can achieve standardization and normalization of specimen collection volume and mixing in the pre-analysis stage. The hemolysis related indicators of the collected specimens are lower than those of the manual collection group, and can be used for the collection of clinical serological specimens.

The key link to colorectal cancer progression is that epigenetic derangements promote reprogramming of glucose metabolism.Based on many studies on the relationship between spleen being the origin of acquired constitution and epigenetics,spleen qi dispersing essence and glucose metabolism,combined with the extensive experience of traditional Chinese medicine in the treatment of colorectal cancer and the accumulation of researches on glucose metabolism,it is believed that the"epigenetic-reprogramming of glucose metabolism"pathology of colorectal cancer is the microscopic essence of the key pathogenesis of"dysfunction of spleen transforming essences".The"promoting spleen and dispersing essences"method means that"promoting spleen"to reverse epigenetic derangements and"dispersing essences"to regulate reprogramming of glucose metabolism was put forward to treat colorectal cancer.Look forward to enrich the scientific connotation of the pathogenesis of"dysfunction of spleen transforming essences",new ideas and potential empirical basis for traditional Chinese medicine intervention in colorectal cancer were provided.

The key link to colorectal cancer progression is that epigenetic derangements promote reprogramming of glucose metabolism.Based on many studies on the relationship between spleen being the origin of acquired constitution and epigenetics,spleen qi dispersing essence and glucose metabolism,combined with the extensive experience of traditional Chinese medicine in the treatment of colorectal cancer and the accumulation of researches on glucose metabolism,it is believed that the"epigenetic-reprogramming of glucose metabolism"pathology of colorectal cancer is the microscopic essence of the key pathogenesis of"dysfunction of spleen transforming essences".The"promoting spleen and dispersing essences"method means that"promoting spleen"to reverse epigenetic derangements and"dispersing essences"to regulate reprogramming of glucose metabolism was put forward to treat colorectal cancer.Look forward to enrich the scientific connotation of the pathogenesis of"dysfunction of spleen transforming essences",new ideas and potential empirical basis for traditional Chinese medicine intervention in colorectal cancer were provided.

Objective:To explore the association between A Body Shape Index (ABSI) and sarcopenia in Chinese older adults.Methods:Totally 1 728 participants in waves 1 and 3 of the China Health and Retirement Longitudinal Survey (CHARLS) were enrolled in this cohort-based cross-sectional analysis. ABSI was calculated by using anthropometric measurements from CHARLS. According to the Asian Working Group for Sarcopenia (AWGS) 2019 consensus update on sarcopenia, low muscle strength and low muscle mass were used as the diagnostic criteria for sarcopenia. Logistic regression and restricted cubic spline (RCS) were used to assess the relationship between ABSI and sarcopenia, and subgroup and interaction analyses were conducted to evaluate the influence of other factors on this association.Results:High ABSI showed a significant association with the risk of sarcopenia among older adults in China, and RCS analysis demonstrated a positive linear correlation between ABSI and sarcopenia. Additionally, the relationship of ABSI with sarcopenia was significantly modified by educational level and depressive status.Conclusion:ABSI, a novel anthropometric measure, serves as an effective threshold for assessing sarcopenia risk in the Chinese older population, providing a basis for developing clinical interventions and public health strategies for the prevention and early identification of sarcopenia.