Objective:To quantify thoracic and abdominal movements during breathing using inertial measurement units (IMUs) and to build a machine learning model which identifies the abdominal breathing (AB) pattern.Methods:Ten rehabilitation therapists formed the study′s professional group, while 15 patients receiving AB training comprised the validation group. Two synchronized IMUs were applied to capture breathing motions during natural breathing (NB), deep breathing (DB) and AB. Six kinematic features were extracted from each respiratory cycle, and inter-group and inter-pattern differences were analyzed. Correlation analysis was also performed with manually measured changes in thoracic and abdominal circumferences. A support vector classification model for AB pattern detection was then developed using data from the professional and validation groups.Results:A total of 1113 respiratory cycles were extracted and analyzed. The breathing pattern significantly influenced all of the kinematic features studied (0.21≤partial η 2≤0.65, all P≤0.001). The ranges of the angles in medial-lateral axis of the IMUs showed strong correlation with the changes in abdominal and thoracic circumferences (ρ1=0.928, ρ2=0.807, P≤0.001 in both cases). A greater range of abdominal angles was found during AB compared to the other patterns. The best of the models achieved an F1 score of 0.970 (sensitivity: 0.983, specificity: 0.980) in validation. Conclusions:AB generates the greatest abdominal movement. Combining IMUs and machine learning can provide real-time quantification of chest movement and accurate detection of AB during breathing training.

Objective: To identify ferroptosis-related genes associated with gastric cancer prognosis and investigate their potential molecular functions. Methods: Gene expression profiles and clinical information of gastric cancer tissues and adjacent normal tissues were obtained from TCGA database. Differential expression analysis of ferroptosis-related genes was performed using the "DESeq2" package in R software. Key genes were identified and a prognostic model for gastric cancer was constructed through Cox regression analysis based on the LASSO algorithm. Patients were stratified into high-risk and low-risk groups according to the median risk score. The accuracy of the model was evaluated using Kaplan-Meier survival analysis and ROC curve analysis. Immune cell infiltration in gastric cancer patients was assessed with the "CIBERSORT" package. The mRNA expression of differentially expressed genes(DEGs) with prognostic significance was examined in both gastric cancer and adjacent normal tissue samples. In vitro experiments were conducted to validate the impact of hydroxycarboxylic acid receptor 1(HCAR1) on the malignant biological behavior of gastric cancer. Results: Based on ferroptosis-related genes from the TCGA database, a novel prognostic model was constructed. It demonstrated robust predictive power for survival in both training and validation cohorts. RT-qPCR analysis of 8 pairs of gastric cancer and normal tissues revealed that the expression patterns of 6 prognostic DEGs in cancer tissues were consistent with those predicted by the model. In vitro experiments confirmed that downregulation of the key gene HCAR1 could inhibit the proliferation, invasion, and metastasis of gastric cancer cells. Conclusion The ferroptosis-related gene based prognostic model exhibits robust predictive capability, allowing for accurate determination of prognosis and survival in individuals with gastric cancer.

Objective To analyze the predictive value of spermatic vein diameter in diagnosing occult oblique inguinal hernia in children.Methods A total of 93 children of oblique inguinal hernia were admitted to Hangzhou Linping District Maternal &Child Health Care Hospital from August 2021 to August 2023 as research objects,including 15 with 30 sides of bilateral dominant hernia and 78 with 78 sides of unilateral dominant hernia.Among them,21 with 21 sides of contralateral recessive hernia were found by laparoscopic exploration in unilateral dominant hernia.The 108 sides of dominant hernia were used as dominant hernia group and 21 sides of occult hernia group as recessive hernia group.According to different ages,the patients were divided into 1 to 3 years old group 60 cases,4 to 6 years old group 33 cases;Children with oblique inguinal hernia were divided into＜2 years group(68 cases)and ≥2 years group(25 cases).The color ultrasound diagnostic instrument uses a 7.5MHz linear transducer to measure the diameter of the spermatic vein in the inguinal region of the child using blood flow imaging with a signal displayed at 2mm/s.Receiver operating characteristic(ROC)curve analysis of the predictive value of spermatic vein diameter for the diagnosis of occult inguinal hernia in children.Results Compared with occult hernia group,diameter of spermatic cord was increased in dominant hernia group(P＜0.05).Compared with overt hernia group,diameter of spermatic cord in＜2 year group and ≥2 year group was smaller,and difference was statistically significant(P＜0.05).In prediction of disease diagnosis with different age and course of disease,sensitivity of 1 to 3 years old was lower than that of 4 to 6 years old,and specificity was higher.The sensitivity of ≥2 years group was lower than that of＜2 years group.The specificity was higher and difference was statistically significant(P＜0.05).Conclusion diameter of spermatic vein can be used to predict the diagnosis of occult oblique inguinal hernia in children with high sensitivity and specificity.

ObjectiveTo investigate the clinical efficacy and potential mechanism of Shengmai Jiuxin decoction in the treatment of acute decompensated heart failure （ADHF） with the traditional Chinese medicine （TCM） pattern of Qi and Yin deficiency， Yang deficiency， and blood stasis. MethodsA total of 68 patients diagnosed with ADHF of Qi and Yin deficiency， Yang deficiency， and blood stasis type were randomly assigned to an observation group （34 cases） and a control group （34 cases）. Both groups received conventional Western medical treatment， while the observation group was additionally administered Shengmai Jiuxin decoction. Parameters compared before and after treatment included： TCM syndrome score， TCM syndrome efficacy， New York Heart Association （NYHA） functional classification， left ventricular ejection fraction （LVEF）， N-terminal pro-B-type natriuretic peptide （NT-proBNP）， six-minute walk distance （6MWD）， hypoxia-inducible factor-1 alpha （HIF-1α）， vascular endothelial growth factor A （VEGF-A）， Caspase-3， and the number of rehospitalizations for heart failure within one month after discharge. ResultsThere were no significant differences in sex， age， vital signs， or underlying diseases between the two groups. Compared with baseline， both groups exhibited significant reductions in TCM syndrome scores， NT-proBNP， and HIF-1α levels （P<0.01）， as well as significant increases in 6MWD， LVEF， VEGF-A， and Caspase-3 levels （P<0.05， P<0.01）. After treatment， the observation group showed significantly greater reductions in TCM syndrome score， NT-proBNP， HIF-1α， and Caspase-3 levels compared with the control group （P<0.05） and significantly greater increases in 6MWD， TCM syndrome efficacy， and VEGF-A levels （P<0.05）. No significant differences were observed between the groups in NYHA functional classification， LVEF， or the number of rehospitalizations for heart failure within one month after discharge. No drug-related adverse events were reported in either group during the treatment period. ConclusionShengmai Jiuxin decoction can improve cardiac function and clinical symptoms in patients with ADHF of Qi and Yin deficiency， Yang deficiency， and blood stasis type. Its mechanisms may be related to the regulation of the HIF-1 signaling pathway by modulating targets such as HIF-1α， VEGF-A， and Caspase-3.

ObjectiveTo investigate the clinical efficacy and potential mechanism of Shengmai Jiuxin decoction in the treatment of acute decompensated heart failure （ADHF） with the traditional Chinese medicine （TCM） pattern of Qi and Yin deficiency， Yang deficiency， and blood stasis. MethodsA total of 68 patients diagnosed with ADHF of Qi and Yin deficiency， Yang deficiency， and blood stasis type were randomly assigned to an observation group （34 cases） and a control group （34 cases）. Both groups received conventional Western medical treatment， while the observation group was additionally administered Shengmai Jiuxin decoction. Parameters compared before and after treatment included： TCM syndrome score， TCM syndrome efficacy， New York Heart Association （NYHA） functional classification， left ventricular ejection fraction （LVEF）， N-terminal pro-B-type natriuretic peptide （NT-proBNP）， six-minute walk distance （6MWD）， hypoxia-inducible factor-1 alpha （HIF-1α）， vascular endothelial growth factor A （VEGF-A）， Caspase-3， and the number of rehospitalizations for heart failure within one month after discharge. ResultsThere were no significant differences in sex， age， vital signs， or underlying diseases between the two groups. Compared with baseline， both groups exhibited significant reductions in TCM syndrome scores， NT-proBNP， and HIF-1α levels （P<0.01）， as well as significant increases in 6MWD， LVEF， VEGF-A， and Caspase-3 levels （P<0.05， P<0.01）. After treatment， the observation group showed significantly greater reductions in TCM syndrome score， NT-proBNP， HIF-1α， and Caspase-3 levels compared with the control group （P<0.05） and significantly greater increases in 6MWD， TCM syndrome efficacy， and VEGF-A levels （P<0.05）. No significant differences were observed between the groups in NYHA functional classification， LVEF， or the number of rehospitalizations for heart failure within one month after discharge. No drug-related adverse events were reported in either group during the treatment period. ConclusionShengmai Jiuxin decoction can improve cardiac function and clinical symptoms in patients with ADHF of Qi and Yin deficiency， Yang deficiency， and blood stasis type. Its mechanisms may be related to the regulation of the HIF-1 signaling pathway by modulating targets such as HIF-1α， VEGF-A， and Caspase-3.

Objective:To construct a prediction model for bowel preparation failure in patients undergoing colonoscopy.Methods:A total of 378 inpatients from the Department of Gastroenterology who underwent colonoscopy at the Fifth Affiliated Hospital of Sun Yat-sen University from August 2021 to May 2022 were selected by convenience sampling. The quality of bowel preparation was evaluated using the Boston Bowel Preparation Scale (BBPS). Baseline demographic information, relevant clinical data, and laboratory test results after admission were collected. Logistic regression analysis was performed to identify factors influencing bowel preparation failure and to construct the prediction model. The discriminative ability, calibration, and clinical utility of the model were evaluated using receiver operating characteristic (ROC) curve, nomogram calibration curve, and decision curve analysis.Results:According to BBPS scores, 199 of the 378 patients experienced bowel preparation failure. Logistic regression analysis showed that a history of abdominal surgery, dyslipidemia, hypertension, liver cirrhosis, and colonoscopy timing were independent predictors of bowel preparation failure ( P<0.05). The area under the ROC curve of the constructed model was 0.835 (95% confidence interval: 0.759-0.874). The calibration curve indicated that the deviation between predicted and actual outcomes was small. The decision curve analysis showed favorable clinical utility within a threshold probability range of 0.18-0.85. Conclusions:The prediction model for bowel preparation failure constructed in this study demonstrated good discrimination, calibration, and clinical utility. The model can effectively predict the quality of bowel preparation in patients undergoing colonoscopy, provide scientific support for clinical decision-making, and help optimize bowel preparation regimens, thereby improving the success rate and safety of colonoscopy.

Objective:To construct a prediction model for bowel preparation failure in patients undergoing colonoscopy.Methods:A total of 378 inpatients from the Department of Gastroenterology who underwent colonoscopy at the Fifth Affiliated Hospital of Sun Yat-sen University from August 2021 to May 2022 were selected by convenience sampling. The quality of bowel preparation was evaluated using the Boston Bowel Preparation Scale (BBPS). Baseline demographic information, relevant clinical data, and laboratory test results after admission were collected. Logistic regression analysis was performed to identify factors influencing bowel preparation failure and to construct the prediction model. The discriminative ability, calibration, and clinical utility of the model were evaluated using receiver operating characteristic (ROC) curve, nomogram calibration curve, and decision curve analysis.Results:According to BBPS scores, 199 of the 378 patients experienced bowel preparation failure. Logistic regression analysis showed that a history of abdominal surgery, dyslipidemia, hypertension, liver cirrhosis, and colonoscopy timing were independent predictors of bowel preparation failure ( P<0.05). The area under the ROC curve of the constructed model was 0.835 (95% confidence interval: 0.759-0.874). The calibration curve indicated that the deviation between predicted and actual outcomes was small. The decision curve analysis showed favorable clinical utility within a threshold probability range of 0.18-0.85. Conclusions:The prediction model for bowel preparation failure constructed in this study demonstrated good discrimination, calibration, and clinical utility. The model can effectively predict the quality of bowel preparation in patients undergoing colonoscopy, provide scientific support for clinical decision-making, and help optimize bowel preparation regimens, thereby improving the success rate and safety of colonoscopy.

Objective To analyze the predictive value of spermatic vein diameter in diagnosing occult oblique inguinal hernia in children.Methods A total of 93 children of oblique inguinal hernia were admitted to Hangzhou Linping District Maternal &Child Health Care Hospital from August 2021 to August 2023 as research objects,including 15 with 30 sides of bilateral dominant hernia and 78 with 78 sides of unilateral dominant hernia.Among them,21 with 21 sides of contralateral recessive hernia were found by laparoscopic exploration in unilateral dominant hernia.The 108 sides of dominant hernia were used as dominant hernia group and 21 sides of occult hernia group as recessive hernia group.According to different ages,the patients were divided into 1 to 3 years old group 60 cases,4 to 6 years old group 33 cases;Children with oblique inguinal hernia were divided into＜2 years group(68 cases)and ≥2 years group(25 cases).The color ultrasound diagnostic instrument uses a 7.5MHz linear transducer to measure the diameter of the spermatic vein in the inguinal region of the child using blood flow imaging with a signal displayed at 2mm/s.Receiver operating characteristic(ROC)curve analysis of the predictive value of spermatic vein diameter for the diagnosis of occult inguinal hernia in children.Results Compared with occult hernia group,diameter of spermatic cord was increased in dominant hernia group(P＜0.05).Compared with overt hernia group,diameter of spermatic cord in＜2 year group and ≥2 year group was smaller,and difference was statistically significant(P＜0.05).In prediction of disease diagnosis with different age and course of disease,sensitivity of 1 to 3 years old was lower than that of 4 to 6 years old,and specificity was higher.The sensitivity of ≥2 years group was lower than that of＜2 years group.The specificity was higher and difference was statistically significant(P＜0.05).Conclusion diameter of spermatic vein can be used to predict the diagnosis of occult oblique inguinal hernia in children with high sensitivity and specificity.

Objective:To quantify thoracic and abdominal movements during breathing using inertial measurement units (IMUs) and to build a machine learning model which identifies the abdominal breathing (AB) pattern.Methods:Ten rehabilitation therapists formed the study′s professional group, while 15 patients receiving AB training comprised the validation group. Two synchronized IMUs were applied to capture breathing motions during natural breathing (NB), deep breathing (DB) and AB. Six kinematic features were extracted from each respiratory cycle, and inter-group and inter-pattern differences were analyzed. Correlation analysis was also performed with manually measured changes in thoracic and abdominal circumferences. A support vector classification model for AB pattern detection was then developed using data from the professional and validation groups.Results:A total of 1113 respiratory cycles were extracted and analyzed. The breathing pattern significantly influenced all of the kinematic features studied (0.21≤partial η 2≤0.65, all P≤0.001). The ranges of the angles in medial-lateral axis of the IMUs showed strong correlation with the changes in abdominal and thoracic circumferences (ρ1=0.928, ρ2=0.807, P≤0.001 in both cases). A greater range of abdominal angles was found during AB compared to the other patterns. The best of the models achieved an F1 score of 0.970 (sensitivity: 0.983, specificity: 0.980) in validation. Conclusions:AB generates the greatest abdominal movement. Combining IMUs and machine learning can provide real-time quantification of chest movement and accurate detection of AB during breathing training.

Objective To establish an artificial intelligence (AI)-assisted platform for detection of parasite eggs, and to evaluate its detection efficiency and accuracy, so as to provide technical supports for elimination of parasitic diseases. Methods A total of 1 003 slides of Enterobius vermicularis, horkworm, Trichuris trichiura, Clonorchis sinensis, Taenia, Ascaris lumbricoides, Schistosoma japonicum, Paragonimus westermani and Fasciolopsis buski eggs were collected, and converted into digital images with an automatated scanning microscope to create a dataset. Based on the Object Detection platform on the Baidu Easy DL model, an AI-assisted platform for detection of parasite eggs was created through procedures of uploading, labeling, training, evaluation and optimization. Then, 70% of the datasets were randomly selected for model training, and the precision, recall and average accuracy were calculated to evaluate the effectiveness of platform for recognition of parasite eggs. In addition, the platform was deployed on the computer and smart phone terminals for use. Results An AI-assisted platform for detection of parasite eggs was successfully created. If the platform was deployed using the public cloud application programming interface (API), the average accuracy, precision and recall of the platform were 93.42%, 92.55% and 89.32% for recognition of parasite eggs. If the platform was deployed using the offline software development kit (SDK), the average accuracy, precision and recall of the platform were 92.97%, 94.78% and 87.63% for recognition of parasite eggs. In addition, the precision of the platform was 97.00% and 96.23% for identification of Taenia and C. sinensis eggs, respectively. Conclusions The AI-assisted platform for detection of parasite eggs has been successfully created, which is high in the accuracy for recognition of parasite eggs and convenient in use. This platform may provide a powerful technical support for parasitic disease diagnosis.