Pub Date : 2024-02-26DOI: 10.1097/nr9.0000000000000046
Quanming Peng, Jun Li, Lutong Zheng, Liping Guo
This article aimed to offer insights into patients’ expectations regarding the traits of physicians, with the goal of helping physicians gain a better understanding of patient needs and provide better care. This study used a Python crawler script to collect patients’ comments from haodf.com, a major online consultation platform in China, to examine the expected character traits of physicians by patients. A total of 83,315 comments were obtained. We selected positive comments from patients, performed text segmentation using Jieba, and utilized the TextRank algorithm to identify high-ranking words based on the Index of Relative Importance (IRI) within these comments. To make the findings comprehensible and practical for physicians and medical educators, we utilized a word cloud to visualize the results. We classified the high-ranking words into four dimensions—professional competence, communication attitude, communication ability, and character traits—based on the categorization of positive physician qualities found in relevant literature. Key findings from the study included: (1) The top 23 high ranking words for traits of good physicians (in descending order) were: patient, meticulous, proficient, precise, kind, moderate, successful, gentle, rigorous, explicit, clear, effective, humorous, sincere, skilled, kindhearted, modest, awesome, practical (and not flashy), unhurried, experienced, clean, and excellent; (2) Patients placed the highest value on the professional competence of physicians, followed by their communication attitude, communication ability, and character traits; (3) Despite the highest IRI score for professional competence, it was exceeded by the combined scores of communication attitude and communication ability. This underscored the significance of effective communication in medical encounters. Drawing from these findings, recommendations are proposed for physicians and medical educators to enhance the quality of medical encounters. These suggestions include implementing Narrative Medicine training to improve communication awareness and skills as well as encouraging lifelong continuing medical education to maintain professional competence among practitioners. This study contributes to the establishment of positive physician-patient relationships in both telemedicine and face-to-face medical interactions.
{"title":"Perceptions of good physicians in patients’ online consultations: Evidence from a Chinese platform","authors":"Quanming Peng, Jun Li, Lutong Zheng, Liping Guo","doi":"10.1097/nr9.0000000000000046","DOIUrl":"https://doi.org/10.1097/nr9.0000000000000046","url":null,"abstract":"\u0000 \u0000 \u0000 This article aimed to offer insights into patients’ expectations regarding the traits of physicians, with the goal of helping physicians gain a better understanding of patient needs and provide better care.\u0000 \u0000 \u0000 \u0000 This study used a Python crawler script to collect patients’ comments from haodf.com, a major online consultation platform in China, to examine the expected character traits of physicians by patients. A total of 83,315 comments were obtained. We selected positive comments from patients, performed text segmentation using Jieba, and utilized the TextRank algorithm to identify high-ranking words based on the Index of Relative Importance (IRI) within these comments. To make the findings comprehensible and practical for physicians and medical educators, we utilized a word cloud to visualize the results. We classified the high-ranking words into four dimensions—professional competence, communication attitude, communication ability, and character traits—based on the categorization of positive physician qualities found in relevant literature.\u0000 \u0000 \u0000 \u0000 Key findings from the study included: (1) The top 23 high ranking words for traits of good physicians (in descending order) were: patient, meticulous, proficient, precise, kind, moderate, successful, gentle, rigorous, explicit, clear, effective, humorous, sincere, skilled, kindhearted, modest, awesome, practical (and not flashy), unhurried, experienced, clean, and excellent; (2) Patients placed the highest value on the professional competence of physicians, followed by their communication attitude, communication ability, and character traits; (3) Despite the highest IRI score for professional competence, it was exceeded by the combined scores of communication attitude and communication ability. This underscored the significance of effective communication in medical encounters.\u0000 \u0000 \u0000 \u0000 Drawing from these findings, recommendations are proposed for physicians and medical educators to enhance the quality of medical encounters. These suggestions include implementing Narrative Medicine training to improve communication awareness and skills as well as encouraging lifelong continuing medical education to maintain professional competence among practitioners. This study contributes to the establishment of positive physician-patient relationships in both telemedicine and face-to-face medical interactions.\u0000","PeriodicalId":73407,"journal":{"name":"Interdisciplinary nursing research","volume":"226 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140428328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Older adults exhibit high desire for Active and Healthy Aging (AHA) without physical or mental dysfunction, particularly those living independently in senior facilities. Preserving or improving cognitive function and minimizing fall risks are essential for older adults to live a happy and active lifestyle. The purpose of this pilot study was to examine the feasibility, safety, and preliminary effectiveness of the innovative Digitalized Community-based Square-Stepping Exercise Program (DC-SSEP) in improving cognitive and physical function among older adults residing in senior facilities. Guided by the Health Promotion Model and Social Cognitive Theory, this pilot study used a quasi-experiment design with one intervention group. A total of 17 older adults recruited from a senior facility in Southern Texas participated in 40 sessions of DC-SSEP over 20 weeks. Cognitive function was measured using the latest version (8.1) of MoCA and the balance function focusing on balance and functional mobility was measured using Berg’s Balance Scale and Time to Up and Go. Most participants were non-Hispanic White women. The DC-SSEP was a feasible and safe exercise program for older adults; and the results showed the preliminary effectiveness of the DC-SSEP in improving cognitive and balance function (P<0.01) among older adults, especially among older adults living in senior facilities. This pilot study is distinctive as it is among the first to evaluate the multi-layered impacts of DC-SSEP using IoT technology and integrated operating software in the U.S. Despite the small sample size and homogeneity of participants, this pilot study suggests multiple valuable directions for future research using DC-SSEP.
{"title":"The impact of digitalized community-based square-stepping exercise program (DC-SSEP) on cognitive and balance functions among older adults living in senior facilities: A pilot study","authors":"K. Lee, Mikaela Boham, Meng Zhao, YoungHee Ro, Xiaomei Cong, Yuxia Huang","doi":"10.1097/nr9.0000000000000053","DOIUrl":"https://doi.org/10.1097/nr9.0000000000000053","url":null,"abstract":"\u0000 \u0000 \u0000 Older adults exhibit high desire for Active and Healthy Aging (AHA) without physical or mental dysfunction, particularly those living independently in senior facilities. Preserving or improving cognitive function and minimizing fall risks are essential for older adults to live a happy and active lifestyle. The purpose of this pilot study was to examine the feasibility, safety, and preliminary effectiveness of the innovative Digitalized Community-based Square-Stepping Exercise Program (DC-SSEP) in improving cognitive and physical function among older adults residing in senior facilities.\u0000 \u0000 \u0000 \u0000 Guided by the Health Promotion Model and Social Cognitive Theory, this pilot study used a quasi-experiment design with one intervention group. A total of 17 older adults recruited from a senior facility in Southern Texas participated in 40 sessions of DC-SSEP over 20 weeks. Cognitive function was measured using the latest version (8.1) of MoCA and the balance function focusing on balance and functional mobility was measured using Berg’s Balance Scale and Time to Up and Go.\u0000 \u0000 \u0000 \u0000 Most participants were non-Hispanic White women. The DC-SSEP was a feasible and safe exercise program for older adults; and the results showed the preliminary effectiveness of the DC-SSEP in improving cognitive and balance function (P<0.01) among older adults, especially among older adults living in senior facilities.\u0000 \u0000 \u0000 \u0000 This pilot study is distinctive as it is among the first to evaluate the multi-layered impacts of DC-SSEP using IoT technology and integrated operating software in the U.S. Despite the small sample size and homogeneity of participants, this pilot study suggests multiple valuable directions for future research using DC-SSEP.\u0000","PeriodicalId":73407,"journal":{"name":"Interdisciplinary nursing research","volume":"173 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140454403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-16DOI: 10.1097/nr9.0000000000000052
Yuhan Zhang, Longxiang Luo, Xiuli Wang
Robotic solutions designed to cater to the needs of the elderly, commonly known as eldercare robots or nursing robots, hold the promise of delivering intelligent elderly care, alleviating societal caregiving pressures, and reducing financial burdens on nations. Scholars across various disciplines have delved into the realm of eldercare robotics from different perspectives. Four types of robots at the design level are currently used in the elderly care industry: anthropomorphic, zoomorphic, cartoon, and mechanical-functional. They can play such roles as assistants, companions, and even surrogate pets, providing support for the elderly’s daily routines, cognitive enhancement, and emotional well-being. Acceptance of eldercare robots hinges on three key factors: technical attributes, user-specific characteristics, and the surrounding social environment. The utilization of eldercare robots has the potential to positively impact various aspects of the elderly population, such as their physiological health, cognitive abilities, psychological well-being, and social interactions. However, it can also lead to social isolation, reduced autonomy, increased sense of objectification, blurred responsibility attribution, and tendencies towards deceptive and childish behavior. Additionally, eldercare robots also influence healthcare professionals in terms of workload, working conditions, job satisfaction and sense of purpose, both positively and negatively. This paper examines these research findings within the context of theories in communication, technology ethics, and medical ethics, shedding light on the multifaceted landscape of eldercare robotics.
{"title":"Aging with robots: a brief review on eldercare automation","authors":"Yuhan Zhang, Longxiang Luo, Xiuli Wang","doi":"10.1097/nr9.0000000000000052","DOIUrl":"https://doi.org/10.1097/nr9.0000000000000052","url":null,"abstract":"\u0000 Robotic solutions designed to cater to the needs of the elderly, commonly known as eldercare robots or nursing robots, hold the promise of delivering intelligent elderly care, alleviating societal caregiving pressures, and reducing financial burdens on nations. Scholars across various disciplines have delved into the realm of eldercare robotics from different perspectives. Four types of robots at the design level are currently used in the elderly care industry: anthropomorphic, zoomorphic, cartoon, and mechanical-functional. They can play such roles as assistants, companions, and even surrogate pets, providing support for the elderly’s daily routines, cognitive enhancement, and emotional well-being. Acceptance of eldercare robots hinges on three key factors: technical attributes, user-specific characteristics, and the surrounding social environment. The utilization of eldercare robots has the potential to positively impact various aspects of the elderly population, such as their physiological health, cognitive abilities, psychological well-being, and social interactions. However, it can also lead to social isolation, reduced autonomy, increased sense of objectification, blurred responsibility attribution, and tendencies towards deceptive and childish behavior. Additionally, eldercare robots also influence healthcare professionals in terms of workload, working conditions, job satisfaction and sense of purpose, both positively and negatively. This paper examines these research findings within the context of theories in communication, technology ethics, and medical ethics, shedding light on the multifaceted landscape of eldercare robotics.","PeriodicalId":73407,"journal":{"name":"Interdisciplinary nursing research","volume":"446 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140453923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To construct and validate a prediction model based on machine learning algorithms for early recurrence and metastasis in patients with colorectal cancer after surgery. This study employed a prospective cohort design. A total of 498 postoperative patients with colorectal cancer, treated at an affiliated hospital of Qingdao University, were recruited using convenience sampling from June to December 2021. Data were collected during outpatient visits and hospitalizations. The risk factors for early recurrence and metastasis of colorectal cancer were determined through multivariate logistic regression analysis in SPSS 26.0 software. Using Python 3.7.0 software, four machine learning algorithms (logistic regression, Support Vector Machine, XGBoost, and LightGBM) were used to develop and validate prediction models for early recurrence and metastasis of colorectal cancer after surgery. Of the 498 patients, 51 (10.24%) had early recurrence and metastasis. Multivariate logistic regression analysis showed that personal traits (family history of cancer, histological type, degree of tumor differentiation, number of positive lymph nodes, and T stage), behaviour and/or lifestyle (intake of refined grains, whole grains, fish, shrimp, crab, and nuts, as well as resilience), and interpersonal networks (social support) were all associated with early recurrence and metastasis of colorectal cancer (P<0.05). The logistic regression prediction model showed the best prediction performance out of the four models, with an accuracy rate of 0.920, specificity of 0.982, F1 of 0.495, AUC of 0.867, Kappa of 0.056, and Brier score of 0.067. Our findings suggest that a prediction model based on logistic regression could accurately and scientifically predict which patients are likely to experience early recurrence and metastasis, helping to lessen the burden for both patients and the healthcare system.
{"title":"Predictive models based on machine learning for early recurrence and metastasis in postoperative patients with colorectal cancer","authors":"Qian Dong, Minghui Mo, Xia Huang, Xia Sun, Peipei Jia, Ting Wang, Cuiping Liu","doi":"10.1097/nr9.0000000000000051","DOIUrl":"https://doi.org/10.1097/nr9.0000000000000051","url":null,"abstract":"\u0000 \u0000 \u0000 To construct and validate a prediction model based on machine learning algorithms for early recurrence and metastasis in patients with colorectal cancer after surgery.\u0000 \u0000 \u0000 \u0000 This study employed a prospective cohort design. A total of 498 postoperative patients with colorectal cancer, treated at an affiliated hospital of Qingdao University, were recruited using convenience sampling from June to December 2021. Data were collected during outpatient visits and hospitalizations. The risk factors for early recurrence and metastasis of colorectal cancer were determined through multivariate logistic regression analysis in SPSS 26.0 software. Using Python 3.7.0 software, four machine learning algorithms (logistic regression, Support Vector Machine, XGBoost, and LightGBM) were used to develop and validate prediction models for early recurrence and metastasis of colorectal cancer after surgery.\u0000 \u0000 \u0000 \u0000 Of the 498 patients, 51 (10.24%) had early recurrence and metastasis. Multivariate logistic regression analysis showed that personal traits (family history of cancer, histological type, degree of tumor differentiation, number of positive lymph nodes, and T stage), behaviour and/or lifestyle (intake of refined grains, whole grains, fish, shrimp, crab, and nuts, as well as resilience), and interpersonal networks (social support) were all associated with early recurrence and metastasis of colorectal cancer (P<0.05). The logistic regression prediction model showed the best prediction performance out of the four models, with an accuracy rate of 0.920, specificity of 0.982, F1 of 0.495, AUC of 0.867, Kappa of 0.056, and Brier score of 0.067.\u0000 \u0000 \u0000 \u0000 Our findings suggest that a prediction model based on logistic regression could accurately and scientifically predict which patients are likely to experience early recurrence and metastasis, helping to lessen the burden for both patients and the healthcare system.\u0000","PeriodicalId":73407,"journal":{"name":"Interdisciplinary nursing research","volume":"40 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139798501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To construct and validate a prediction model based on machine learning algorithms for early recurrence and metastasis in patients with colorectal cancer after surgery. This study employed a prospective cohort design. A total of 498 postoperative patients with colorectal cancer, treated at an affiliated hospital of Qingdao University, were recruited using convenience sampling from June to December 2021. Data were collected during outpatient visits and hospitalizations. The risk factors for early recurrence and metastasis of colorectal cancer were determined through multivariate logistic regression analysis in SPSS 26.0 software. Using Python 3.7.0 software, four machine learning algorithms (logistic regression, Support Vector Machine, XGBoost, and LightGBM) were used to develop and validate prediction models for early recurrence and metastasis of colorectal cancer after surgery. Of the 498 patients, 51 (10.24%) had early recurrence and metastasis. Multivariate logistic regression analysis showed that personal traits (family history of cancer, histological type, degree of tumor differentiation, number of positive lymph nodes, and T stage), behaviour and/or lifestyle (intake of refined grains, whole grains, fish, shrimp, crab, and nuts, as well as resilience), and interpersonal networks (social support) were all associated with early recurrence and metastasis of colorectal cancer (P<0.05). The logistic regression prediction model showed the best prediction performance out of the four models, with an accuracy rate of 0.920, specificity of 0.982, F1 of 0.495, AUC of 0.867, Kappa of 0.056, and Brier score of 0.067. Our findings suggest that a prediction model based on logistic regression could accurately and scientifically predict which patients are likely to experience early recurrence and metastasis, helping to lessen the burden for both patients and the healthcare system.
{"title":"Predictive models based on machine learning for early recurrence and metastasis in postoperative patients with colorectal cancer","authors":"Qian Dong, Minghui Mo, Xia Huang, Xia Sun, Peipei Jia, Ting Wang, Cuiping Liu","doi":"10.1097/nr9.0000000000000051","DOIUrl":"https://doi.org/10.1097/nr9.0000000000000051","url":null,"abstract":"\u0000 \u0000 \u0000 To construct and validate a prediction model based on machine learning algorithms for early recurrence and metastasis in patients with colorectal cancer after surgery.\u0000 \u0000 \u0000 \u0000 This study employed a prospective cohort design. A total of 498 postoperative patients with colorectal cancer, treated at an affiliated hospital of Qingdao University, were recruited using convenience sampling from June to December 2021. Data were collected during outpatient visits and hospitalizations. The risk factors for early recurrence and metastasis of colorectal cancer were determined through multivariate logistic regression analysis in SPSS 26.0 software. Using Python 3.7.0 software, four machine learning algorithms (logistic regression, Support Vector Machine, XGBoost, and LightGBM) were used to develop and validate prediction models for early recurrence and metastasis of colorectal cancer after surgery.\u0000 \u0000 \u0000 \u0000 Of the 498 patients, 51 (10.24%) had early recurrence and metastasis. Multivariate logistic regression analysis showed that personal traits (family history of cancer, histological type, degree of tumor differentiation, number of positive lymph nodes, and T stage), behaviour and/or lifestyle (intake of refined grains, whole grains, fish, shrimp, crab, and nuts, as well as resilience), and interpersonal networks (social support) were all associated with early recurrence and metastasis of colorectal cancer (P<0.05). The logistic regression prediction model showed the best prediction performance out of the four models, with an accuracy rate of 0.920, specificity of 0.982, F1 of 0.495, AUC of 0.867, Kappa of 0.056, and Brier score of 0.067.\u0000 \u0000 \u0000 \u0000 Our findings suggest that a prediction model based on logistic regression could accurately and scientifically predict which patients are likely to experience early recurrence and metastasis, helping to lessen the burden for both patients and the healthcare system.\u0000","PeriodicalId":73407,"journal":{"name":"Interdisciplinary nursing research","volume":"1 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139858093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-05DOI: 10.1097/nr9.0000000000000050
Yufeng Deng, Ying Wu
Postoperative atrial fibrillation (POAF) is a common complication of coronary artery bypass grafting (CABG) surgery, and contributes significantly to morbidity, mortality, and rising healthcare costs. Although the underlying mechanisms for POAF are not completely understood, surgery-related inflammation, often in the presence of pre-existing factors, renders the atria susceptible to the induction and persistence of POAF. Notably, interleukin-6 (IL-6), a primary cytokine of the inflammatory cascade, has been identified as one of the principal molecular components of POAF pathogenesis. Atrial fibrosis may also be a key mechanistic link by which inflammation contributes to POAF. Recently, it has been shown that atrial fibrosis, in combination with the presence of an electrophysiological substrate capable of maintaining atrial fibrillation (AF), also promotes arrhythmia, suggesting that POAF shares proarrhythmic mechanisms with other types of AF. In this review, the impact of inflammation and the particular role of IL-6, on the structural and electrical changes that promote to the development of POAF is summarized.
{"title":"Postoperative atrial fibrillation following coronary artery bypass grafting surgery: role of IL-6 from structural to electrical remodeling","authors":"Yufeng Deng, Ying Wu","doi":"10.1097/nr9.0000000000000050","DOIUrl":"https://doi.org/10.1097/nr9.0000000000000050","url":null,"abstract":"\u0000 Postoperative atrial fibrillation (POAF) is a common complication of coronary artery bypass grafting (CABG) surgery, and contributes significantly to morbidity, mortality, and rising healthcare costs. Although the underlying mechanisms for POAF are not completely understood, surgery-related inflammation, often in the presence of pre-existing factors, renders the atria susceptible to the induction and persistence of POAF. Notably, interleukin-6 (IL-6), a primary cytokine of the inflammatory cascade, has been identified as one of the principal molecular components of POAF pathogenesis. Atrial fibrosis may also be a key mechanistic link by which inflammation contributes to POAF. Recently, it has been shown that atrial fibrosis, in combination with the presence of an electrophysiological substrate capable of maintaining atrial fibrillation (AF), also promotes arrhythmia, suggesting that POAF shares proarrhythmic mechanisms with other types of AF. In this review, the impact of inflammation and the particular role of IL-6, on the structural and electrical changes that promote to the development of POAF is summarized.","PeriodicalId":73407,"journal":{"name":"Interdisciplinary nursing research","volume":"31 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139865094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-05DOI: 10.1097/nr9.0000000000000048
Qingbo Fang, Tianlai Qiu, Yanqun Liu
Previous studies have reported an association between depression with gut microbiota and residential greenness exposure. The aim of our study was to explore whether gut microbiota and residential greenness co-exposure contributed to maternal prenatal depression. We collected demographic information, stool samples, and exposure to residential greenness from 75 pregnant women in the third trimester. Participants were divided into prenatal depression group and control group according to the score of Edinburgh Postnatal Depression Scale (EPDS). Gut microbiota was analyzed using 16S rRNA V3/V4 gene sequence. Residential greenness [normalized difference vegetation index (NDVI)] during pregnancy was calculated using database of National Science and Technology Infrastructure of China. There were significant differences between gut microbial composition in two groups. Phylum Patescibacteria (OR=5.34*e4, 95% CI: 1.48 - 1.92*e9, P-value=0.042) and greenness exposure (OR=0.15, 95% CI: 0.04 - 0.63, P-value=0.010) significantly contributed to prenatal depression, which indicated the protective effects of greenness exposure to prenatal depression. And Adlercreutzia (OR=1.44*e4, 95% CI: 2.70 – 7.70*e9, P-value=0.032) and greenness exposure (OR=0.39, 95% CI: 0.21 – 0.73, P-value=0.003) also significantly contributed to prenatal depression. Our study highlights that gut microbiota and greenness co-exposure during pregnancy contributed to maternal prenatal depression. Further research is needed to explore the mechanisms contributing to the co-exposure of gut microbiota and greenness associated with depression in pregnant women.
{"title":"Gut microbiota and greenness co-exposure contributed to maternal prenatal depression","authors":"Qingbo Fang, Tianlai Qiu, Yanqun Liu","doi":"10.1097/nr9.0000000000000048","DOIUrl":"https://doi.org/10.1097/nr9.0000000000000048","url":null,"abstract":"\u0000 \u0000 \u0000 Previous studies have reported an association between depression with gut microbiota and residential greenness exposure. The aim of our study was to explore whether gut microbiota and residential greenness co-exposure contributed to maternal prenatal depression.\u0000 \u0000 \u0000 \u0000 We collected demographic information, stool samples, and exposure to residential greenness from 75 pregnant women in the third trimester. Participants were divided into prenatal depression group and control group according to the score of Edinburgh Postnatal Depression Scale (EPDS). Gut microbiota was analyzed using 16S rRNA V3/V4 gene sequence. Residential greenness [normalized difference vegetation index (NDVI)] during pregnancy was calculated using database of National Science and Technology Infrastructure of China.\u0000 \u0000 \u0000 \u0000 There were significant differences between gut microbial composition in two groups. Phylum Patescibacteria (OR=5.34*e4, 95% CI: 1.48 - 1.92*e9, P-value=0.042) and greenness exposure (OR=0.15, 95% CI: 0.04 - 0.63, P-value=0.010) significantly contributed to prenatal depression, which indicated the protective effects of greenness exposure to prenatal depression. And Adlercreutzia (OR=1.44*e4, 95% CI: 2.70 – 7.70*e9, P-value=0.032) and greenness exposure (OR=0.39, 95% CI: 0.21 – 0.73, P-value=0.003) also significantly contributed to prenatal depression.\u0000 \u0000 \u0000 \u0000 Our study highlights that gut microbiota and greenness co-exposure during pregnancy contributed to maternal prenatal depression. Further research is needed to explore the mechanisms contributing to the co-exposure of gut microbiota and greenness associated with depression in pregnant women.\u0000","PeriodicalId":73407,"journal":{"name":"Interdisciplinary nursing research","volume":"4 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139803905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-05DOI: 10.1097/nr9.0000000000000050
Yufeng Deng, Ying Wu
Postoperative atrial fibrillation (POAF) is a common complication of coronary artery bypass grafting (CABG) surgery, and contributes significantly to morbidity, mortality, and rising healthcare costs. Although the underlying mechanisms for POAF are not completely understood, surgery-related inflammation, often in the presence of pre-existing factors, renders the atria susceptible to the induction and persistence of POAF. Notably, interleukin-6 (IL-6), a primary cytokine of the inflammatory cascade, has been identified as one of the principal molecular components of POAF pathogenesis. Atrial fibrosis may also be a key mechanistic link by which inflammation contributes to POAF. Recently, it has been shown that atrial fibrosis, in combination with the presence of an electrophysiological substrate capable of maintaining atrial fibrillation (AF), also promotes arrhythmia, suggesting that POAF shares proarrhythmic mechanisms with other types of AF. In this review, the impact of inflammation and the particular role of IL-6, on the structural and electrical changes that promote to the development of POAF is summarized.
{"title":"Postoperative atrial fibrillation following coronary artery bypass grafting surgery: role of IL-6 from structural to electrical remodeling","authors":"Yufeng Deng, Ying Wu","doi":"10.1097/nr9.0000000000000050","DOIUrl":"https://doi.org/10.1097/nr9.0000000000000050","url":null,"abstract":"\u0000 Postoperative atrial fibrillation (POAF) is a common complication of coronary artery bypass grafting (CABG) surgery, and contributes significantly to morbidity, mortality, and rising healthcare costs. Although the underlying mechanisms for POAF are not completely understood, surgery-related inflammation, often in the presence of pre-existing factors, renders the atria susceptible to the induction and persistence of POAF. Notably, interleukin-6 (IL-6), a primary cytokine of the inflammatory cascade, has been identified as one of the principal molecular components of POAF pathogenesis. Atrial fibrosis may also be a key mechanistic link by which inflammation contributes to POAF. Recently, it has been shown that atrial fibrosis, in combination with the presence of an electrophysiological substrate capable of maintaining atrial fibrillation (AF), also promotes arrhythmia, suggesting that POAF shares proarrhythmic mechanisms with other types of AF. In this review, the impact of inflammation and the particular role of IL-6, on the structural and electrical changes that promote to the development of POAF is summarized.","PeriodicalId":73407,"journal":{"name":"Interdisciplinary nursing research","volume":"23 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139805184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-05DOI: 10.1097/nr9.0000000000000048
Qingbo Fang, Tianlai Qiu, Yanqun Liu
Previous studies have reported an association between depression with gut microbiota and residential greenness exposure. The aim of our study was to explore whether gut microbiota and residential greenness co-exposure contributed to maternal prenatal depression. We collected demographic information, stool samples, and exposure to residential greenness from 75 pregnant women in the third trimester. Participants were divided into prenatal depression group and control group according to the score of Edinburgh Postnatal Depression Scale (EPDS). Gut microbiota was analyzed using 16S rRNA V3/V4 gene sequence. Residential greenness [normalized difference vegetation index (NDVI)] during pregnancy was calculated using database of National Science and Technology Infrastructure of China. There were significant differences between gut microbial composition in two groups. Phylum Patescibacteria (OR=5.34*e4, 95% CI: 1.48 - 1.92*e9, P-value=0.042) and greenness exposure (OR=0.15, 95% CI: 0.04 - 0.63, P-value=0.010) significantly contributed to prenatal depression, which indicated the protective effects of greenness exposure to prenatal depression. And Adlercreutzia (OR=1.44*e4, 95% CI: 2.70 – 7.70*e9, P-value=0.032) and greenness exposure (OR=0.39, 95% CI: 0.21 – 0.73, P-value=0.003) also significantly contributed to prenatal depression. Our study highlights that gut microbiota and greenness co-exposure during pregnancy contributed to maternal prenatal depression. Further research is needed to explore the mechanisms contributing to the co-exposure of gut microbiota and greenness associated with depression in pregnant women.
{"title":"Gut microbiota and greenness co-exposure contributed to maternal prenatal depression","authors":"Qingbo Fang, Tianlai Qiu, Yanqun Liu","doi":"10.1097/nr9.0000000000000048","DOIUrl":"https://doi.org/10.1097/nr9.0000000000000048","url":null,"abstract":"\u0000 \u0000 \u0000 Previous studies have reported an association between depression with gut microbiota and residential greenness exposure. The aim of our study was to explore whether gut microbiota and residential greenness co-exposure contributed to maternal prenatal depression.\u0000 \u0000 \u0000 \u0000 We collected demographic information, stool samples, and exposure to residential greenness from 75 pregnant women in the third trimester. Participants were divided into prenatal depression group and control group according to the score of Edinburgh Postnatal Depression Scale (EPDS). Gut microbiota was analyzed using 16S rRNA V3/V4 gene sequence. Residential greenness [normalized difference vegetation index (NDVI)] during pregnancy was calculated using database of National Science and Technology Infrastructure of China.\u0000 \u0000 \u0000 \u0000 There were significant differences between gut microbial composition in two groups. Phylum Patescibacteria (OR=5.34*e4, 95% CI: 1.48 - 1.92*e9, P-value=0.042) and greenness exposure (OR=0.15, 95% CI: 0.04 - 0.63, P-value=0.010) significantly contributed to prenatal depression, which indicated the protective effects of greenness exposure to prenatal depression. And Adlercreutzia (OR=1.44*e4, 95% CI: 2.70 – 7.70*e9, P-value=0.032) and greenness exposure (OR=0.39, 95% CI: 0.21 – 0.73, P-value=0.003) also significantly contributed to prenatal depression.\u0000 \u0000 \u0000 \u0000 Our study highlights that gut microbiota and greenness co-exposure during pregnancy contributed to maternal prenatal depression. Further research is needed to explore the mechanisms contributing to the co-exposure of gut microbiota and greenness associated with depression in pregnant women.\u0000","PeriodicalId":73407,"journal":{"name":"Interdisciplinary nursing research","volume":"46 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139864066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Patients undergoing surgery are at high risk of developing venous thromboembolism (VTE). This study aimed to determine the predictive value of risk factors for VTE in surgical patients and to develop a prediction model by integrating independent predictors. A total of 1,111 patients who underwent surgery at clinical departments in a tertiary general hospital were recruited between May and July 2021. Clinical data including patient-related, surgery-related, and laboratory parameters were extracted from the hospital information system and electronic medical records. A VTE prediction model incorporating ten risk variables was constructed using artificial neural networks (ANNs). Ten independent factors (X1: age, X2: alcohol consumption, X3: hypertension, X4: bleeding, X5: blood transfusions, X6: general anesthesia, X7: intrathecal anesthesia, X8: D-dimer, X9: C-reactive protein, and X10: lymphocyte percentage) were identified as associated with an increased risk of VTE. Ten-fold cross validation results showed that the ANN model was capable of predicting VTE in surgical patients, with an area under the curve (AUC) of 0.89, a Brier score of 0.01, an accuracy of 0.96, and a F1 score of 0.92. The ANN model slightly outperformed the logistic regression model and the Caprini model, but a DeLong test showed that the statistical difference in the AUCs of the ANN and logistic regression models was insignificant (P>0.05). Ten statistical indicators relevant to VTE risk prediction for surgical patients were identified, and ANN and logistic regression both showed promising results as decision-supporting tools for VTE prediction.
{"title":"Risk factor analysis and prediction model construction for surgical patients with venous thromboembolism: a prospective study","authors":"Shucheng Pan, Lifang Bian, Huafang Luo, Aaron Conway, Wenbo Qiao, Topatana Win, Wei Wang","doi":"10.1097/nr9.0000000000000047","DOIUrl":"https://doi.org/10.1097/nr9.0000000000000047","url":null,"abstract":"\u0000 \u0000 \u0000 Patients undergoing surgery are at high risk of developing venous thromboembolism (VTE). This study aimed to determine the predictive value of risk factors for VTE in surgical patients and to develop a prediction model by integrating independent predictors.\u0000 \u0000 \u0000 \u0000 A total of 1,111 patients who underwent surgery at clinical departments in a tertiary general hospital were recruited between May and July 2021. Clinical data including patient-related, surgery-related, and laboratory parameters were extracted from the hospital information system and electronic medical records. A VTE prediction model incorporating ten risk variables was constructed using artificial neural networks (ANNs).\u0000 \u0000 \u0000 \u0000 Ten independent factors (X1: age, X2: alcohol consumption, X3: hypertension, X4: bleeding, X5: blood transfusions, X6: general anesthesia, X7: intrathecal anesthesia, X8: D-dimer, X9: C-reactive protein, and X10: lymphocyte percentage) were identified as associated with an increased risk of VTE. Ten-fold cross validation results showed that the ANN model was capable of predicting VTE in surgical patients, with an area under the curve (AUC) of 0.89, a Brier score of 0.01, an accuracy of 0.96, and a F1 score of 0.92. The ANN model slightly outperformed the logistic regression model and the Caprini model, but a DeLong test showed that the statistical difference in the AUCs of the ANN and logistic regression models was insignificant (P>0.05).\u0000 \u0000 \u0000 \u0000 Ten statistical indicators relevant to VTE risk prediction for surgical patients were identified, and ANN and logistic regression both showed promising results as decision-supporting tools for VTE prediction.\u0000","PeriodicalId":73407,"journal":{"name":"Interdisciplinary nursing research","volume":"90 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140484836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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