Objective To study the variations in lower limb muscle strength and gait after long-distance load marching in soldiers and to investigate the effects of muscle fatigue of the lower limbs on the walking and running gait.Methods Fifteen male soldiers completed a 15 km march with a 17.5 kg load carriage.The isometric muscle strength of the hip,knee,and ankle joints under flexion and extension were tested.The walking and running gait parameters were collected with a Vicon motion capture system and AMTI force platform.Results After load marching,the peak moment of the hip decreased significantly(P＜0.05),the mean power of the right knee decreased significantly(P＜0.05),and the relative peak moment and mean power of the ankle plantar flexion decreased significantly(P＜0.05).During walking,the left hip flexion,knee flexion and extension,ankle dorsiflexion,and inversion angles(P＜0.05)decreased significantly in conjunction with a significant increase in the knee inversion angle(P＜0.05).Additionally,the moments of left hip flexion,knee flexion,ankle plantar flexion,and bilateral knee inversion(P＜0.05)decreased significantly,coupled with a significant increase in the moments of right hip extension and vertical ground reaction forces(P＜0.05).During running,the step frequency decreased significantly(P＜0.01),and the left step width increased(P＜0.05).Both left and right ankle dorsiflexion angles decreased significantly(P＜0.05),whereas the knee adduction angle increased significantly(P＜0.05).Additionally,the moment of bilateral hip flexion,left knee flexion,right ankle dorsiflexion,and knee adduction decreased significantly(P＜0.05),and the ground reaction force on the right side increased significantly(P＜0.05).Conclusions The lower limb muscle strength decreased after long-distance marching.This was particularly so in the hip joint.The hip drive mechanism was used in the walking gait after marching.However,knee hyperextension and valgus occurred owing to the decreased muscle strength.The ankle joint was used more frequently during running.This increased the risk of ankle injury.It is necessary to strengthen the hip and hamstring muscle groups to optimize the gait,increase the load capacity,and prevent injury.

Objective To investigate the value of CT radiomics for predicting effect of neoadjuvant chemotherapy(NACT)for locally advanced gastric cancer(LAGC).Methods Totally 325 LAGC patients who received NACT were retrospectively enrolled,among them 247 were taken as training set,while the rest 78 were taken as validation set.Tumor regression scale(TRG)was evaluated according to postoperation pathology after NACT,and the efficacy of NACT was evaluated.Univariate logistic regression was used to analyze and screen clinical predictors of effect of NACT,and clinical model was constructed.Radiomics features were extracted based on venous phase enhanced CT pre-and post-NACT,and Delta radiomics features(i.e.the ratio of the difference of pre-and post-NACT radiomics features and pre-NACT radiomics features)were calculated.The best features were screened based on pre-NACT,post-NACT and Delta radiomics features to construct radiomics labels,the optimal label was screened and used to construct combined model through combining clinical model.Receiver operating characteristic(ROC)curve was plotted,and the area under the curve(AUC)was calculated to evaluate predicting efficiency of the above models.Decision curve analysis(DCA)was performed to explore the clinical value of each model.Results In training set,significant effect was found in 67 cases,but not in 180 cases,while in validation set,significant effect was found in 18 cases but not in 60 cases.Borrmann classification of LAGC before NACT was the clinical predictor(P=0.031),and clinical model was constructed,which had AUC of 0.577 and 0.520 in training and validation sets,respectively.Based on pre-NACT,post-NACT and Delta radiomics features,19,14 and 17 best features were selected,and AUC of the established radiomics labels of Pre-Rad,Post-Rad and Delta-Rad in training set was 0.672,0.796 and 0.789,while in validation set was 0.558,0.805 and 0.666,respectively.Post-Rad was the optimal label,which was used to construct combined model.AUC of the obtained combined model in training and validation sets was 0.824 and 0.818,respectively,both higher than that of clinical model(both P＜0.001)but not different with that of Post-Rad(both P＞0.05).Taken 0.4 to 0.7 as the threshold,the combined model had higher clinical net benefit than the other two.Conclusion Venous CT radiomics could effectively predict effect of NACT for LAGC.Combining with clinical features could improve its predictive efficacy.

Objective:To investigate the correlation of color Doppler flow imaging (CDFI) and shear wave elastography (SWE) with pathological classification and prognosis of breast cancer patients.Methods:A total of 87 patients (103 lesions) with breast cancer admitted to Shanxi Maternal and Child Health Care Hospital and the Second Hospital of Shanxi Medical University From May. 2021 to Mar. 2024 were retrospectively included. All patients underwent CDFI and SWE examinations before surgery. The pathological characteristics and molecular typing of each lesion were recorded, and the correlation of CDFI and SWE examination parameters with molecular typing of breast cancer was evaluated. Patients were followed up for 1 year, and the predictive value of CDFI and SWE parameters in lymph node metastasis was analyzed by receiver operating characteristic curve (ROC) .Results:There were no statistically significant differences in the pulse index (PI) , resistance index (RI) , maximum lesion elastic modulus (E max) , and the ratio between the elastic value at the hardest lesion and the elastic value of adipose tissue (E ratio) among patients with different pathological types ( F=0.64, 0.13, 0.81, 2.84, P>0.05) . There were no statistically significant differences in PI and RI values among patients with different tumor sizes ( F=2.99, 1.81, P>0.05) , and statistically significant differences in E max and E ratio among patients with different tumor sizes ( F=6.42, 34.31, P<0.05) . The differences among different molecular types PI, RI, E max, and E ratio were statistically significant ( F=406.59, 245.23, 206.30, 204.36, P<0.05) , and Luminal B type PI, RI, E max, and E ratio were the highest, followed by HER2-positive, triple-negative, and Luminal A type, with statistically significant differences ( P<0.05) . PI, RI, E max and E ratio in patients with positive lymph node metastasis were higher than those in patients with negative lymph node metastasis ( t=4.99, 3.04, 2.70, 3.13, all P<0.05) . ROC results showed that the area under the curve (AUC) of PI, RI, E max and E ratio for predicting lymph node metastasis of breast cancer were 0.654, 0.704, 0.664 and 0.696, respectively. The sensitivity to predict lymph node metastasis of breast cancer was 74.19%, 54.84%, 51.61%, 64.52, and the specificity was 54.17%, 79.17%, 79.17%, 70.83% (all P<0.05) . Conclusions:The correlation of CDFI and SWE examination parameters are correlated with the molecular classification of breast cancer, and the prediction of lymph node metastasis of breast cancer is good.

Objective:To investigate the correlation of color Doppler flow imaging (CDFI) and shear wave elastography (SWE) with pathological classification and prognosis of breast cancer patients.Methods:A total of 87 patients (103 lesions) with breast cancer admitted to Shanxi Maternal and Child Health Care Hospital and the Second Hospital of Shanxi Medical University From May. 2021 to Mar. 2024 were retrospectively included. All patients underwent CDFI and SWE examinations before surgery. The pathological characteristics and molecular typing of each lesion were recorded, and the correlation of CDFI and SWE examination parameters with molecular typing of breast cancer was evaluated. Patients were followed up for 1 year, and the predictive value of CDFI and SWE parameters in lymph node metastasis was analyzed by receiver operating characteristic curve (ROC) .Results:There were no statistically significant differences in the pulse index (PI) , resistance index (RI) , maximum lesion elastic modulus (E max) , and the ratio between the elastic value at the hardest lesion and the elastic value of adipose tissue (E ratio) among patients with different pathological types ( F=0.64, 0.13, 0.81, 2.84, P>0.05) . There were no statistically significant differences in PI and RI values among patients with different tumor sizes ( F=2.99, 1.81, P>0.05) , and statistically significant differences in E max and E ratio among patients with different tumor sizes ( F=6.42, 34.31, P<0.05) . The differences among different molecular types PI, RI, E max, and E ratio were statistically significant ( F=406.59, 245.23, 206.30, 204.36, P<0.05) , and Luminal B type PI, RI, E max, and E ratio were the highest, followed by HER2-positive, triple-negative, and Luminal A type, with statistically significant differences ( P<0.05) . PI, RI, E max and E ratio in patients with positive lymph node metastasis were higher than those in patients with negative lymph node metastasis ( t=4.99, 3.04, 2.70, 3.13, all P<0.05) . ROC results showed that the area under the curve (AUC) of PI, RI, E max and E ratio for predicting lymph node metastasis of breast cancer were 0.654, 0.704, 0.664 and 0.696, respectively. The sensitivity to predict lymph node metastasis of breast cancer was 74.19%, 54.84%, 51.61%, 64.52, and the specificity was 54.17%, 79.17%, 79.17%, 70.83% (all P<0.05) . Conclusions:The correlation of CDFI and SWE examination parameters are correlated with the molecular classification of breast cancer, and the prediction of lymph node metastasis of breast cancer is good.

Background and Aims:Coactivator-associated arginine methyltransferase 1(CARM1)is involved in the regulation of RNA and protein processing.Recent studies have revealed its crucial role in tumorigenesis and cancer progression.However,the correlation between CARM1 expression,pan-cancer prognosis,and the immune microenvironment remains unclear.This study was conducted to investigate the relationship between CARM1 expression and pan-cancer prognosis,immune microenvironment,and its potential biological mechanisms using bioinformatics approaches.Methods:Using the TCGA,GTEx,and HPA databases,the expression pattern of CARM1 in various cancers and its prognostic relevance were analyzed.The correlation between CARM1 expression and the tumor microenvironment,as well as immune checkpoint genes,were analyzed using TCGA data.The PPI network of CARM1-interacting genes was constructed using the STRING database.GSEA was performed to investigate the potential biological mechanisms of CARM1 in pan-cancer.Results:Compared to normal tissues,CARM1 was upregulated in most tumor tissues,and its expression was associated with prognosis in multiple cancers.CARM1 expression was correlated with immune cell infiltration and immune checkpoint gene expression.Enrichment analysis suggested that CARM1 might alter the biological function of urinary system tumors by affecting ribosome biogenesis.Additionally,it may influence the biological functions of digestive system tumors by modulating arginine biosynthesis,DNA replication,and the cell cycle.Conclusion:CARM1 is aberrantly expressed in tumor tissues and may serve as a prognostic biomarker for multiple cancers.It is associated with the tumor microenvironment and immune checkpoint gene expression in pan-cancer and may contribute to tumorigenesis through multiple biological pathways,making it a potential therapeutic target for pan-cancer treatment.

Background and Aims:Coactivator-associated arginine methyltransferase 1(CARM1)is involved in the regulation of RNA and protein processing.Recent studies have revealed its crucial role in tumorigenesis and cancer progression.However,the correlation between CARM1 expression,pan-cancer prognosis,and the immune microenvironment remains unclear.This study was conducted to investigate the relationship between CARM1 expression and pan-cancer prognosis,immune microenvironment,and its potential biological mechanisms using bioinformatics approaches.Methods:Using the TCGA,GTEx,and HPA databases,the expression pattern of CARM1 in various cancers and its prognostic relevance were analyzed.The correlation between CARM1 expression and the tumor microenvironment,as well as immune checkpoint genes,were analyzed using TCGA data.The PPI network of CARM1-interacting genes was constructed using the STRING database.GSEA was performed to investigate the potential biological mechanisms of CARM1 in pan-cancer.Results:Compared to normal tissues,CARM1 was upregulated in most tumor tissues,and its expression was associated with prognosis in multiple cancers.CARM1 expression was correlated with immune cell infiltration and immune checkpoint gene expression.Enrichment analysis suggested that CARM1 might alter the biological function of urinary system tumors by affecting ribosome biogenesis.Additionally,it may influence the biological functions of digestive system tumors by modulating arginine biosynthesis,DNA replication,and the cell cycle.Conclusion:CARM1 is aberrantly expressed in tumor tissues and may serve as a prognostic biomarker for multiple cancers.It is associated with the tumor microenvironment and immune checkpoint gene expression in pan-cancer and may contribute to tumorigenesis through multiple biological pathways,making it a potential therapeutic target for pan-cancer treatment.

Objective To study the variations in lower limb muscle strength and gait after long-distance load marching in soldiers and to investigate the effects of muscle fatigue of the lower limbs on the walking and running gait.Methods Fifteen male soldiers completed a 15 km march with a 17.5 kg load carriage.The isometric muscle strength of the hip,knee,and ankle joints under flexion and extension were tested.The walking and running gait parameters were collected with a Vicon motion capture system and AMTI force platform.Results After load marching,the peak moment of the hip decreased significantly(P＜0.05),the mean power of the right knee decreased significantly(P＜0.05),and the relative peak moment and mean power of the ankle plantar flexion decreased significantly(P＜0.05).During walking,the left hip flexion,knee flexion and extension,ankle dorsiflexion,and inversion angles(P＜0.05)decreased significantly in conjunction with a significant increase in the knee inversion angle(P＜0.05).Additionally,the moments of left hip flexion,knee flexion,ankle plantar flexion,and bilateral knee inversion(P＜0.05)decreased significantly,coupled with a significant increase in the moments of right hip extension and vertical ground reaction forces(P＜0.05).During running,the step frequency decreased significantly(P＜0.01),and the left step width increased(P＜0.05).Both left and right ankle dorsiflexion angles decreased significantly(P＜0.05),whereas the knee adduction angle increased significantly(P＜0.05).Additionally,the moment of bilateral hip flexion,left knee flexion,right ankle dorsiflexion,and knee adduction decreased significantly(P＜0.05),and the ground reaction force on the right side increased significantly(P＜0.05).Conclusions The lower limb muscle strength decreased after long-distance marching.This was particularly so in the hip joint.The hip drive mechanism was used in the walking gait after marching.However,knee hyperextension and valgus occurred owing to the decreased muscle strength.The ankle joint was used more frequently during running.This increased the risk of ankle injury.It is necessary to strengthen the hip and hamstring muscle groups to optimize the gait,increase the load capacity,and prevent injury.

Objective To investigate the value of CT radiomics for predicting effect of neoadjuvant chemotherapy(NACT)for locally advanced gastric cancer(LAGC).Methods Totally 325 LAGC patients who received NACT were retrospectively enrolled,among them 247 were taken as training set,while the rest 78 were taken as validation set.Tumor regression scale(TRG)was evaluated according to postoperation pathology after NACT,and the efficacy of NACT was evaluated.Univariate logistic regression was used to analyze and screen clinical predictors of effect of NACT,and clinical model was constructed.Radiomics features were extracted based on venous phase enhanced CT pre-and post-NACT,and Delta radiomics features(i.e.the ratio of the difference of pre-and post-NACT radiomics features and pre-NACT radiomics features)were calculated.The best features were screened based on pre-NACT,post-NACT and Delta radiomics features to construct radiomics labels,the optimal label was screened and used to construct combined model through combining clinical model.Receiver operating characteristic(ROC)curve was plotted,and the area under the curve(AUC)was calculated to evaluate predicting efficiency of the above models.Decision curve analysis(DCA)was performed to explore the clinical value of each model.Results In training set,significant effect was found in 67 cases,but not in 180 cases,while in validation set,significant effect was found in 18 cases but not in 60 cases.Borrmann classification of LAGC before NACT was the clinical predictor(P=0.031),and clinical model was constructed,which had AUC of 0.577 and 0.520 in training and validation sets,respectively.Based on pre-NACT,post-NACT and Delta radiomics features,19,14 and 17 best features were selected,and AUC of the established radiomics labels of Pre-Rad,Post-Rad and Delta-Rad in training set was 0.672,0.796 and 0.789,while in validation set was 0.558,0.805 and 0.666,respectively.Post-Rad was the optimal label,which was used to construct combined model.AUC of the obtained combined model in training and validation sets was 0.824 and 0.818,respectively,both higher than that of clinical model(both P＜0.001)but not different with that of Post-Rad(both P＞0.05).Taken 0.4 to 0.7 as the threshold,the combined model had higher clinical net benefit than the other two.Conclusion Venous CT radiomics could effectively predict effect of NACT for LAGC.Combining with clinical features could improve its predictive efficacy.

Objective To observe the efficacy of gray-scale ultrasound-based radiomics for differentiating peripheral pulmonary adenocarcinoma and squamous cell carcinoma.Methods Data of 88 patients with single peripheral lung adenocarcinoma and 58 patients with single peripheral lung squamous cell carcinoma proved pathologically with puncture biopsy and clearly visualized with lung ultrasound were retrospectively analyzed.The patients were divided into training set(n=103)and test set(n=43)at the ratio of 7:3.Based on gray-scale ultrasound of training set,radiomics features associated with differential diagnosis of peripheral lung adenocarcinoma and lung squamous cell carcinoma were extracted and screened.Using 4 different classifiers,including support vector machine(SVM),linear discriminant analysis(LDA),logistic regression(LR)and the least absolute shrinkage and selection operator combined with logistic regression(LASSO-LR),4 corresponding radiomics models were obtained,and the relative best models were selected according to their performances under 10-fold cross validation.The receiver operating characteristic curves were drawn,the areas under the curve(AUC)were calculated to evaluate the differentiating efficacy of each model,and DeLong test was used for the comparison.The differentiating accuracy of models were obtained under the best cutoff value with the maximum Youden index.Results The AUC of SVM,LDA,LR and LASSO-LR radiomics models for differentiating peripheral lung adenocarcinoma and lung squamous carcinoma in test set was 0.864,0.867,0.880 and 0.844,respectively,and no significant difference was found among 4 models(all P＞0.05).Under the best cutoff value of each model,the corresponding accuracy of SVM,LDA,LR and LASSO-LR radiomics models for differentiating peripheral lung adenocarcinoma and lung squamous cell carcinoma was 86.05％,83.72％,88.37％and 86.05％,respectively.Conclusion Radiomics models based on gray-scale ultrasound could be used to differentiate peripheral lung adenocarcinoma and lung squamous cell carcinoma.

OBJECTIVE: We aimed to understand the willingness and barriers to the acceptance of tuberculosis (TB) preventive treatment (TPT) among people with latent TB infection (LTBI) in China. METHODS: A multicenter cross-sectional study was conducted from May 18, 2023 to December 31, 2023 across 10 counties in China. According to a national technical guide, we included healthcare workers, students, teachers, and others occupations aged 15-65 years as our research participants. RESULTS: Overall, 17.0% (183/1,077) of participants accepted TPT. There were statistically significant differences in the acceptance rate of TPT among different sexes, ages, educational levels, and occupations ( P < 0.05). The main barriers to TPT acceptance were misconceptions that it had uncertain effects on prevention (57.8%, 517/894), and concerns about side effects (32.7%, 292/894). CONCLUSION An enhanced and comprehensive understanding of LTBI and TPT among people with LTBI is vital to further expand TPT in China. Moreover, targeted policies need to be developed to address barriers faced by different groups of people.
Humans ; China/epidemiology* ; Adult ; Male ; Female ; Cross-Sectional Studies ; Middle Aged ; Young Adult ; Adolescent ; Aged ; Latent Tuberculosis/prevention & control* ; Patient Acceptance of Health Care ; Tuberculosis/prevention & control* ; Antitubercular Agents/therapeutic use* ; Health Knowledge, Attitudes, Practice