Pub Date : 2024-09-19DOI: 10.1080/07853890.2024.2405077
Kyung Mee Park,Sang Eun Lee,Changhee Lee,Hyun Duck Hwang,Do Hoon Yoon,Eunchae Choi,Eun Lee
OBJECTIVEWe aimed to improve the performance of sleep prediction algorithms by increasing the data amount, adding variables reflecting psychological state, and adjusting the data length.MATERIALS AND METHODSWe used ActiGraph GT3X+® and Galaxy Watch Active2™ to collect physical activity and light exposure data. We collected heart rate variability (HRV) data with the Galaxy Watch. We evaluated the performance of sleep prediction algorithms based on different data sources (wearable devices only, sleep diary only, or both), data lengths (1, 2, or 3 days), and analysis methods. We defined the target outcome, 'good sleep', as ≥90% sleep efficiency.RESULTSAmong 278 participants who denied having sleep disturbance, we used data including 2136 total days and nights from 230 participants. The performance of the sleep prediction algorithms improved with an increased amount of data and added HRV data. The model with the best performance was the extreme gradient boosting model; XGBoost, using both sources combined data with HRV, and 2-day data (accuracy=.85, area under the curve =.80).CONCLUSIONSThe results show that the performance of the sleep prediction models improved by increasing the data amount and adding HRV data. Further studies targeting insomnia patients and applied researches on non-pharmacological insomnia treatment are needed.
{"title":"Predicting sleep based on physical activity, light exposure, and Heart rate variability data using wearable devices.","authors":"Kyung Mee Park,Sang Eun Lee,Changhee Lee,Hyun Duck Hwang,Do Hoon Yoon,Eunchae Choi,Eun Lee","doi":"10.1080/07853890.2024.2405077","DOIUrl":"https://doi.org/10.1080/07853890.2024.2405077","url":null,"abstract":"OBJECTIVEWe aimed to improve the performance of sleep prediction algorithms by increasing the data amount, adding variables reflecting psychological state, and adjusting the data length.MATERIALS AND METHODSWe used ActiGraph GT3X+® and Galaxy Watch Active2™ to collect physical activity and light exposure data. We collected heart rate variability (HRV) data with the Galaxy Watch. We evaluated the performance of sleep prediction algorithms based on different data sources (wearable devices only, sleep diary only, or both), data lengths (1, 2, or 3 days), and analysis methods. We defined the target outcome, 'good sleep', as ≥90% sleep efficiency.RESULTSAmong 278 participants who denied having sleep disturbance, we used data including 2136 total days and nights from 230 participants. The performance of the sleep prediction algorithms improved with an increased amount of data and added HRV data. The model with the best performance was the extreme gradient boosting model; XGBoost, using both sources combined data with HRV, and 2-day data (accuracy=.85, area under the curve =.80).CONCLUSIONSThe results show that the performance of the sleep prediction models improved by increasing the data amount and adding HRV data. Further studies targeting insomnia patients and applied researches on non-pharmacological insomnia treatment are needed.","PeriodicalId":8371,"journal":{"name":"Annals of medicine","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19DOI: 10.1080/07853890.2024.2401112
Zheng Cao,Xuejun Jiang,Yiyu He,Xiaoxin Zheng
The findings of the last decade suggest a complex link between inflammatory cells, coagulation, and the activation of platelets and their synergistic interaction to promote venous thrombosis. Inflammation is present throughout the process of venous thrombosis, and various metabolic pathways of erythrocytes, endothelial cells, and immune cells involved in venous thrombosis, including glucose metabolism, lipid metabolism, homocysteine metabolism, and oxidative stress, are associated with inflammation. While the metabolic microenvironment has been identified as a marker of malignancy, recent studies have revealed that for cancer thrombosis, alterations in the metabolic microenvironment appear to also be a potential risk. In this review, we discuss how the synergy between metabolism and thrombosis drives thrombotic disease. We also explore the great potential of anti-inflammatory strategies targeting venous thrombosis and the complex link between anti-inflammation and metabolism. Furthermore, we suggest how we can use our existing knowledge to reduce the risk of venous thrombosis.
{"title":"Metabolic landscape in venous thrombosis: insights into molecular biology and therapeutic implications.","authors":"Zheng Cao,Xuejun Jiang,Yiyu He,Xiaoxin Zheng","doi":"10.1080/07853890.2024.2401112","DOIUrl":"https://doi.org/10.1080/07853890.2024.2401112","url":null,"abstract":"The findings of the last decade suggest a complex link between inflammatory cells, coagulation, and the activation of platelets and their synergistic interaction to promote venous thrombosis. Inflammation is present throughout the process of venous thrombosis, and various metabolic pathways of erythrocytes, endothelial cells, and immune cells involved in venous thrombosis, including glucose metabolism, lipid metabolism, homocysteine metabolism, and oxidative stress, are associated with inflammation. While the metabolic microenvironment has been identified as a marker of malignancy, recent studies have revealed that for cancer thrombosis, alterations in the metabolic microenvironment appear to also be a potential risk. In this review, we discuss how the synergy between metabolism and thrombosis drives thrombotic disease. We also explore the great potential of anti-inflammatory strategies targeting venous thrombosis and the complex link between anti-inflammation and metabolism. Furthermore, we suggest how we can use our existing knowledge to reduce the risk of venous thrombosis.","PeriodicalId":8371,"journal":{"name":"Annals of medicine","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19DOI: 10.1080/07853890.2024.2405075
Long Jiang,Yang Zhou,Wang Miao,Hongda Zhu,Ningyuan Zou,Yu Tian,Hanbo Pan,Weiqiu Jin,Jia Huang,Qingquan Luo
INTRODUCTIONArtificial intelligence (AI) shows promise for evaluating solitary pulmonary nodules (SPNs) on computed tomography (CT). Accurately determining cancer invasiveness can guide treatment. We aimed to investigate quantitative CT parameters for invasiveness prediction.METHODSPatients with stage 0-IB NSCLC after surgical resection were retrospectively analysed. Preoperative CTs were evaluated with specialized software for nodule segmentation and CT quantification. Pathology was the reference for invasiveness. Univariate and multivariate logistic regression assessed predictors of high-risk SPN.RESULTSThree hundred and fifty-five SPN were included. On multivariate analysis, CT value mean and nodule type (ground glass opacity vs. solid) were independent predictors of high-risk SPN. The area under the curve (AUC) was 0.811 for identifying high-risk nodules.CONCLUSIONSQuantitative CT measures and nodule type correlated with invasiveness. Software-based CT assessment shows potential for noninvasive prediction to guide extent of resection. Further prospective validation is needed, including comparison with benign nodules.
{"title":"Artificial intelligence-assisted quantitative CT parameters in predicting the degree of risk of solitary pulmonary nodules.","authors":"Long Jiang,Yang Zhou,Wang Miao,Hongda Zhu,Ningyuan Zou,Yu Tian,Hanbo Pan,Weiqiu Jin,Jia Huang,Qingquan Luo","doi":"10.1080/07853890.2024.2405075","DOIUrl":"https://doi.org/10.1080/07853890.2024.2405075","url":null,"abstract":"INTRODUCTIONArtificial intelligence (AI) shows promise for evaluating solitary pulmonary nodules (SPNs) on computed tomography (CT). Accurately determining cancer invasiveness can guide treatment. We aimed to investigate quantitative CT parameters for invasiveness prediction.METHODSPatients with stage 0-IB NSCLC after surgical resection were retrospectively analysed. Preoperative CTs were evaluated with specialized software for nodule segmentation and CT quantification. Pathology was the reference for invasiveness. Univariate and multivariate logistic regression assessed predictors of high-risk SPN.RESULTSThree hundred and fifty-five SPN were included. On multivariate analysis, CT value mean and nodule type (ground glass opacity vs. solid) were independent predictors of high-risk SPN. The area under the curve (AUC) was 0.811 for identifying high-risk nodules.CONCLUSIONSQuantitative CT measures and nodule type correlated with invasiveness. Software-based CT assessment shows potential for noninvasive prediction to guide extent of resection. Further prospective validation is needed, including comparison with benign nodules.","PeriodicalId":8371,"journal":{"name":"Annals of medicine","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-18DOI: 10.1080/07853890.2024.2403721
Zhanbin Li,Zhenyu Yao,Qiaoran Liu
OBJECTIVESThe relationship between serum calcium and occurrence of MHO (metabolically healthy obesity) and MUNO (metabolically unhealthy non-obesity) remains unclear, and distinguishing these two phenotypes is difficult within primary healthcare units. This study explores that relationship.METHODSThis survey included 28590 adults from the National Health and Nutrition Examination Survey (NHANES) 2001-2018. Obesity phenotypes were categorized based on BMI and presence or absence of metabolic syndrome components. Weighted multivariate logistic regression analyses were used to assess the association between serum calcium levels and the obesity phenotype. Restricted cubic spline analysis characterized dose-response relationships, and stratified analyses explored these relationships across sociodemographic and lifestyle factors.RESULTSThe overall prevalence of MHO and MUNO were 2.6% and 46.6%, respectively. After adjusting for covariates, serum calcium exhibited a negative association with MHO [OR (95%): 0.49 (0.36,0.67), p < 0.001], while exhibiting a positive association with MUNO [OR (95%): 1.48 (1.26,1.84), p < 0.001]. Additionally, we found a non-linear association between serum calcium levels and the incidences of MHO and MUNO. Stratified analyses demonstrated a strong negative correlation between serum calcium levels and MHO occurrence across various subgroups. There was no significant interaction between calcium and stratified variables except sex; the association between calcium and the occurrence of MHO was remarkable in female patients. Meanwhile, the predictive ability of serum calcium level for the occurrence of MUNO among all patients was consistent across various subgroups. There was a significant interaction between calcium level and stratified variables based on age, sex, race, and smoking status; the association was remarkable in older (≥ 40 years old), white, none or less smoking, and female patients.CONCLUSIONSA significant correlation was identified between serum calcium levels and MHO or MUNO. The findings suggest that serum calcium levels may serve as an indicator for more accurate assessment and diagnosis of MUNO and MHO, especially among individuals with abdominal obesity.
{"title":"Association of serum calcium and metabolically healthy obese in US adults: a cross-sectional study.","authors":"Zhanbin Li,Zhenyu Yao,Qiaoran Liu","doi":"10.1080/07853890.2024.2403721","DOIUrl":"https://doi.org/10.1080/07853890.2024.2403721","url":null,"abstract":"OBJECTIVESThe relationship between serum calcium and occurrence of MHO (metabolically healthy obesity) and MUNO (metabolically unhealthy non-obesity) remains unclear, and distinguishing these two phenotypes is difficult within primary healthcare units. This study explores that relationship.METHODSThis survey included 28590 adults from the National Health and Nutrition Examination Survey (NHANES) 2001-2018. Obesity phenotypes were categorized based on BMI and presence or absence of metabolic syndrome components. Weighted multivariate logistic regression analyses were used to assess the association between serum calcium levels and the obesity phenotype. Restricted cubic spline analysis characterized dose-response relationships, and stratified analyses explored these relationships across sociodemographic and lifestyle factors.RESULTSThe overall prevalence of MHO and MUNO were 2.6% and 46.6%, respectively. After adjusting for covariates, serum calcium exhibited a negative association with MHO [OR (95%): 0.49 (0.36,0.67), p < 0.001], while exhibiting a positive association with MUNO [OR (95%): 1.48 (1.26,1.84), p < 0.001]. Additionally, we found a non-linear association between serum calcium levels and the incidences of MHO and MUNO. Stratified analyses demonstrated a strong negative correlation between serum calcium levels and MHO occurrence across various subgroups. There was no significant interaction between calcium and stratified variables except sex; the association between calcium and the occurrence of MHO was remarkable in female patients. Meanwhile, the predictive ability of serum calcium level for the occurrence of MUNO among all patients was consistent across various subgroups. There was a significant interaction between calcium level and stratified variables based on age, sex, race, and smoking status; the association was remarkable in older (≥ 40 years old), white, none or less smoking, and female patients.CONCLUSIONSA significant correlation was identified between serum calcium levels and MHO or MUNO. The findings suggest that serum calcium levels may serve as an indicator for more accurate assessment and diagnosis of MUNO and MHO, especially among individuals with abdominal obesity.","PeriodicalId":8371,"journal":{"name":"Annals of medicine","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
PURPOSEPatients with bacterial, fungal, and viral community-acquired pneumonia (CAP) were studied to determine their metabolic profiles.METHODSLoop-mediated isothermal amplification technology and nucleic acid sequence-dependent amplification combined with microfluidic chip technology were applied to screen multiple pathogens from respiratory tract samples. Eighteen patients with single bacterial infection (B-CAP), fifteen with single virus infection (V-CAP), twenty with single fungal infection (F-CAP), and twenty controls were enrolled. UHPLC-MS/MS analysis of untargeted serum samples for metabolic profiles. Multiple linear regression and Spearman's rank correlation analysis were used to determine associations between metabolites and clinical parameters. The sensitivity and specificity of the screened metabolites were also examined, along with their area under the curve.RESULTSThe metabolic signatures of patients with CAP infected by bacteria, viruses, and fungi were markedly different from those of controls. The abundances of 45, 56, and 79 metabolites were significantly unbalanced. Among these differential metabolites, 11, 13, and 29 were unique to the B-CAP, V-CAP, and F-CAP groups, respectively. Bacterial infections were the only known causes of disturbances in the pentose and glucuronate and aldarate and ascorbate metabolism interconversions metabolic pathway.CONCLUSIONSSerum metabolomic techniques based on UHPLC-MS/MS may identify differences between individuals with CAP who have been infected by various pathogens, and they can also build a metabolite signature for early detection of the origin of infection and prompt care.
{"title":"Serum metabolomics profile identifies patients with community-acquired pneumonia infected by bacteria, fungi, and viruses.","authors":"Li Chen,Jianbo Xue,Yukun He,Lili Zhao,Ying Zhang,Lu Yin,Shining Fu,Wenyi Yu,Xinqian Ma,Yu Wang,Yanfen Tang,Zhancheng Gao","doi":"10.1080/07853890.2024.2399320","DOIUrl":"https://doi.org/10.1080/07853890.2024.2399320","url":null,"abstract":"PURPOSEPatients with bacterial, fungal, and viral community-acquired pneumonia (CAP) were studied to determine their metabolic profiles.METHODSLoop-mediated isothermal amplification technology and nucleic acid sequence-dependent amplification combined with microfluidic chip technology were applied to screen multiple pathogens from respiratory tract samples. Eighteen patients with single bacterial infection (B-CAP), fifteen with single virus infection (V-CAP), twenty with single fungal infection (F-CAP), and twenty controls were enrolled. UHPLC-MS/MS analysis of untargeted serum samples for metabolic profiles. Multiple linear regression and Spearman's rank correlation analysis were used to determine associations between metabolites and clinical parameters. The sensitivity and specificity of the screened metabolites were also examined, along with their area under the curve.RESULTSThe metabolic signatures of patients with CAP infected by bacteria, viruses, and fungi were markedly different from those of controls. The abundances of 45, 56, and 79 metabolites were significantly unbalanced. Among these differential metabolites, 11, 13, and 29 were unique to the B-CAP, V-CAP, and F-CAP groups, respectively. Bacterial infections were the only known causes of disturbances in the pentose and glucuronate and aldarate and ascorbate metabolism interconversions metabolic pathway.CONCLUSIONSSerum metabolomic techniques based on UHPLC-MS/MS may identify differences between individuals with CAP who have been infected by various pathogens, and they can also build a metabolite signature for early detection of the origin of infection and prompt care.","PeriodicalId":8371,"journal":{"name":"Annals of medicine","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTIONTraffic-related air and noise pollution are important public health issues. The aim of this study was to estimate their effects on allergic/respiratory outcomes in adult and elderly subjects.MATERIALS AND METHODSSix hundred and forty-five subjects living in Pisa (Tuscany, Italy) were investigated through a questionnaire on allergic/respiratory symptoms and diseases. Traffic-related air pollution and noise exposures were assessed at residential address by questionnaire, modelled annual mean NO2 concentrations (1 km and 200 m resolution), and noise level over a 24-h period (Lden). Exposure effects were assessed through logistic regression models stratified by age group (18-64 years, ≥65 years), and adjusted for sex, educational level, occupational exposure, and smoking habits.RESULTS63.6% of the subjects reported traffic exposure near home. Mean exposure levels were: 28.24 (±3.26 SD) and 27.23 (±3.16 SD) µg/m3 for NO2 at 200 m and 1 km of resolution, respectively; 57.79 dB(A) (±6.12 SD) for Lden. Exposure to vehicular traffic (by questionnaire) and to high noise levels [Lden ≥ 60 dB(A)] were significantly associated with higher odds of allergic rhinitis (OR 2.01, 95%CI 1.09-3.70, and OR 1.99, 95%CI 1.18-3.36, respectively) and borderline with rhino-conjunctivitis (OR 2.20, 95%CI 0.95-5.10, and OR 1.76, 95%CI 0.91-3.42, respectively) only in the elderly. No significant result emerged for NO2.CONCLUSIONSOur findings highlighted the need to better assess the effect of traffic-related exposure in the elderly, considering the increasing trend in the future global population's ageing.
{"title":"Effects of traffic-related air and noise pollution exposure on allergic diseases in the elderly: an observational study.","authors":"Sofia Tagliaferro,Federico Pirona,Salvatore Fasola,Ilaria Stanisci,Giuseppe Sarno,Sandra Baldacci,Claudio Gariazzo,Gaetano Licitra,Antonino Moro,Camillo Silibello,Massimo Stafoggia,Giovanni Viegi,Sara Maio,,,","doi":"10.1080/07853890.2024.2398193","DOIUrl":"https://doi.org/10.1080/07853890.2024.2398193","url":null,"abstract":"INTRODUCTIONTraffic-related air and noise pollution are important public health issues. The aim of this study was to estimate their effects on allergic/respiratory outcomes in adult and elderly subjects.MATERIALS AND METHODSSix hundred and forty-five subjects living in Pisa (Tuscany, Italy) were investigated through a questionnaire on allergic/respiratory symptoms and diseases. Traffic-related air pollution and noise exposures were assessed at residential address by questionnaire, modelled annual mean NO2 concentrations (1 km and 200 m resolution), and noise level over a 24-h period (Lden). Exposure effects were assessed through logistic regression models stratified by age group (18-64 years, ≥65 years), and adjusted for sex, educational level, occupational exposure, and smoking habits.RESULTS63.6% of the subjects reported traffic exposure near home. Mean exposure levels were: 28.24 (±3.26 SD) and 27.23 (±3.16 SD) µg/m3 for NO2 at 200 m and 1 km of resolution, respectively; 57.79 dB(A) (±6.12 SD) for Lden. Exposure to vehicular traffic (by questionnaire) and to high noise levels [Lden ≥ 60 dB(A)] were significantly associated with higher odds of allergic rhinitis (OR 2.01, 95%CI 1.09-3.70, and OR 1.99, 95%CI 1.18-3.36, respectively) and borderline with rhino-conjunctivitis (OR 2.20, 95%CI 0.95-5.10, and OR 1.76, 95%CI 0.91-3.42, respectively) only in the elderly. No significant result emerged for NO2.CONCLUSIONSOur findings highlighted the need to better assess the effect of traffic-related exposure in the elderly, considering the increasing trend in the future global population's ageing.","PeriodicalId":8371,"journal":{"name":"Annals of medicine","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
28–55% of chronic hepatitis B (CHB) patients belong to the grey zone (GZ). By analyzing the pathological characteristics of the liver of patients in the GZ, this study clarified whether the patient...
{"title":"Necessity of antiviral treatment for patients with chronic hepatitis B in the grey zone based on liver pathology analysis","authors":"Jianna Zhang, Sijie Yu, Kailu Zhu, Shibo Li, Yu Huang","doi":"10.1080/07853890.2024.2399757","DOIUrl":"https://doi.org/10.1080/07853890.2024.2399757","url":null,"abstract":"28–55% of chronic hepatitis B (CHB) patients belong to the grey zone (GZ). By analyzing the pathological characteristics of the liver of patients in the GZ, this study clarified whether the patient...","PeriodicalId":8371,"journal":{"name":"Annals of medicine","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-16DOI: 10.1080/07853890.2024.2404186
Weiqing Tian, Lan Zhang, Yongjun Wang, Ligong Lin, Wei Jiang, Guangming Dai, Bo Feng
Diabetic foot ulcers (DFUs) are a common and serious complication of diabetes, often leading to amputation and decreased quality of life. Current treatment methods have limited success rates, highl...
{"title":"Tibial transverse transport promotes wound healing in diabetic foot ulcers by stimulating endothelial progenitor cell mobilization and homing mediated neovascularization","authors":"Weiqing Tian, Lan Zhang, Yongjun Wang, Ligong Lin, Wei Jiang, Guangming Dai, Bo Feng","doi":"10.1080/07853890.2024.2404186","DOIUrl":"https://doi.org/10.1080/07853890.2024.2404186","url":null,"abstract":"Diabetic foot ulcers (DFUs) are a common and serious complication of diabetes, often leading to amputation and decreased quality of life. Current treatment methods have limited success rates, highl...","PeriodicalId":8371,"journal":{"name":"Annals of medicine","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
OBJECTIVETo evaluate the effectiveness of a machine learning based on computed tomography (CT) radiomics to distinguish nontuberculous mycobacterial pulmonary disease (NTM-PD) from pulmonary tuberculosis (PTB).METHODSIn this retrospective analysis, medical records of 99 individuals afflicted with NTM-PD and 285 individuals with PTB in Zhejiang Chinese and Western Medicine Integrated Hospital were examined. Random numbers generated by a computer were utilized to stratify the study cohort, with 80% designated as the training cohort and 20% as the validation cohort. A total of 2153 radiomics features were extracted using Python (Pyradiomics package) to analyse the CT characteristics of the large disease areas. The identification of significant factors was conducted through the least absolute shrinkage and selection operator (LASSO) regression. The following four supervised learning classifier models were developed: random forest (RF), support vector machine (SVM), logistic regression (LR), and extreme gradient boosting (XGBoost). For assessment and comparison of the predictive performance among these models, receiver-operating characteristic (ROC) curves and the areas under the ROC curves (AUCs) were employed.RESULTSThe Student's t-test, Levene test, and LASSO algorithm collectively selected 23 optimal features. ROC analysis was then conducted, with the respective AUC values of the XGBoost, LR, SVM, and RF models recorded to be 1, 0.9044, 0.8868, and 0.7982 in the training cohort. In the validation cohort, the respective AUC values of the XGBoost, LR, SVM, and RF models were 0.8358, 0.8085, 0.87739, and 0.7759. The DeLong test results noted the lack of remarkable variation across the models.CONCLUSIONThe CT radiomics features can help distinguish between NTM-PD and PTB. Among the four classifiers, SVM showed a stable performance in effectively identifying these two diseases.
{"title":"A retrospective study differentiating nontuberculous mycobacterial pulmonary disease from pulmonary tuberculosis on computed tomography using radiomics and machine learning algorithms.","authors":"Lihong Zhou,Yiwen Wang,Wenchao Zhu,Yafang Zhao,Yihang Yu,Qin Hu,Wenke Yu","doi":"10.1080/07853890.2024.2401613","DOIUrl":"https://doi.org/10.1080/07853890.2024.2401613","url":null,"abstract":"OBJECTIVETo evaluate the effectiveness of a machine learning based on computed tomography (CT) radiomics to distinguish nontuberculous mycobacterial pulmonary disease (NTM-PD) from pulmonary tuberculosis (PTB).METHODSIn this retrospective analysis, medical records of 99 individuals afflicted with NTM-PD and 285 individuals with PTB in Zhejiang Chinese and Western Medicine Integrated Hospital were examined. Random numbers generated by a computer were utilized to stratify the study cohort, with 80% designated as the training cohort and 20% as the validation cohort. A total of 2153 radiomics features were extracted using Python (Pyradiomics package) to analyse the CT characteristics of the large disease areas. The identification of significant factors was conducted through the least absolute shrinkage and selection operator (LASSO) regression. The following four supervised learning classifier models were developed: random forest (RF), support vector machine (SVM), logistic regression (LR), and extreme gradient boosting (XGBoost). For assessment and comparison of the predictive performance among these models, receiver-operating characteristic (ROC) curves and the areas under the ROC curves (AUCs) were employed.RESULTSThe Student's t-test, Levene test, and LASSO algorithm collectively selected 23 optimal features. ROC analysis was then conducted, with the respective AUC values of the XGBoost, LR, SVM, and RF models recorded to be 1, 0.9044, 0.8868, and 0.7982 in the training cohort. In the validation cohort, the respective AUC values of the XGBoost, LR, SVM, and RF models were 0.8358, 0.8085, 0.87739, and 0.7759. The DeLong test results noted the lack of remarkable variation across the models.CONCLUSIONThe CT radiomics features can help distinguish between NTM-PD and PTB. Among the four classifiers, SVM showed a stable performance in effectively identifying these two diseases.","PeriodicalId":8371,"journal":{"name":"Annals of medicine","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To explore the mechanism underlying the therapeutic effect of Bufei Yishen Formula III combined with exercise rehabilitation (ECC-BYF III + ER) on chronic obstructive pulmonary disease (COPD) and f...
{"title":"Mechanism underlying the therapeutic effects of effective component compatibility of Bufei Yishen formula III combined with exercise rehabilitation on chronic obstructive pulmonary disease","authors":"Lidong Huang, Qingzhou Guan, Ruilong Lu, Zhenzhen Zhang, Chunlei Liu, Yange Tian, Jiansheng Li","doi":"10.1080/07853890.2024.2403729","DOIUrl":"https://doi.org/10.1080/07853890.2024.2403729","url":null,"abstract":"To explore the mechanism underlying the therapeutic effect of Bufei Yishen Formula III combined with exercise rehabilitation (ECC-BYF III + ER) on chronic obstructive pulmonary disease (COPD) and f...","PeriodicalId":8371,"journal":{"name":"Annals of medicine","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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