The unavailability of accurate and reliable methods for early ovarian cancer detection represents a major gap in ovarian cancer diagnosis and management.The emergence and recent integration of machine learning with ca...The unavailability of accurate and reliable methods for early ovarian cancer detection represents a major gap in ovarian cancer diagnosis and management.The emergence and recent integration of machine learning with cancer diagnostic techniques,particularly biomarker-based blood tests,have the potential to improve the selectivity and sensitivity of ovarian cancer detection substantially.Herein,we leverage a series of machine learning and statistical approaches to analyze clinically relevant data sets of more than 300 patients with ovarian tumors and 47 blood-obtained features to distinguish between cancerous and benign tumors.We found that HE4,CA125,menopausal status,and age were some of the most important features distinguishing cancerous from benign ovarian tumors in all patient populations.Age was noted to be a critical feature with cancer discriminatory power only in premenopausal patients but less so in postmenopausal patients.Systematic consideration of patient menopausal status,types of machine learning algorithms,and number of clinical features is necessary prior to ovarian cancer screening to yield more accurate and reliable diagnostic results.Overall,this study provides deeper insight into the use of machine learning,feature selection,and other relevant quantitative approaches to advance ovarian cancer diagnosis to improve patient outcomes.展开更多
There are now numerous emerging flexible and wearable sensing technologies that can perform a myriad of physical and physiological measurements.Rapid advances in developing and implementing such sensors in the last se...There are now numerous emerging flexible and wearable sensing technologies that can perform a myriad of physical and physiological measurements.Rapid advances in developing and implementing such sensors in the last several years have demonstrated the growing significance and potential utility of this unique class of sensing platforms.Applications include wearable consumer electronics,soft robotics,medical prosthetics,electronic skin,and health monitoring.In this review,we provide a state-ofthe-art overview of the emerging flexible and wearable sensing platforms for healthcare and biomedical applications.We first introduce the selection of flexible and stretchable materials and the fabrication of sensors based on these materials.We then compare the different solid-state and liquid-state physical sensing platforms and examine the mechanical deformation-based working mechanisms of these sensors.We also highlight some of the exciting applications of flexible and wearable physical sensors in emerging healthcare and biomedical applications,in particular for artificial electronic skins,physiological health monitoring and assessment,and therapeutic and drug delivery.Finally,we conclude this review by offering some insight into the challenges and opportunities facing this field.展开更多
CONSPECTUS:Photodynamic therapy has been actively explored recently to combat various physiological disorders and diseases,including bacterial infections,inflammation,and cancer.As a noninvasive modality with high spa...CONSPECTUS:Photodynamic therapy has been actively explored recently to combat various physiological disorders and diseases,including bacterial infections,inflammation,and cancer.As a noninvasive modality with high spatiotemporal selectivity,photodynamic therapy leverages photosensitizers,light,and reactive oxygen species to induce cytotoxicity and cell death.Specifically,upon light irradiation,photosensitizers harvest the incident light energy to generate highly reactive singlet oxygen species through photochemical reactions to disrupt the integrity of certain cellular components of the target cells.展开更多
文摘The unavailability of accurate and reliable methods for early ovarian cancer detection represents a major gap in ovarian cancer diagnosis and management.The emergence and recent integration of machine learning with cancer diagnostic techniques,particularly biomarker-based blood tests,have the potential to improve the selectivity and sensitivity of ovarian cancer detection substantially.Herein,we leverage a series of machine learning and statistical approaches to analyze clinically relevant data sets of more than 300 patients with ovarian tumors and 47 blood-obtained features to distinguish between cancerous and benign tumors.We found that HE4,CA125,menopausal status,and age were some of the most important features distinguishing cancerous from benign ovarian tumors in all patient populations.Age was noted to be a critical feature with cancer discriminatory power only in premenopausal patients but less so in postmenopausal patients.Systematic consideration of patient menopausal status,types of machine learning algorithms,and number of clinical features is necessary prior to ovarian cancer screening to yield more accurate and reliable diagnostic results.Overall,this study provides deeper insight into the use of machine learning,feature selection,and other relevant quantitative approaches to advance ovarian cancer diagnosis to improve patient outcomes.
文摘There are now numerous emerging flexible and wearable sensing technologies that can perform a myriad of physical and physiological measurements.Rapid advances in developing and implementing such sensors in the last several years have demonstrated the growing significance and potential utility of this unique class of sensing platforms.Applications include wearable consumer electronics,soft robotics,medical prosthetics,electronic skin,and health monitoring.In this review,we provide a state-ofthe-art overview of the emerging flexible and wearable sensing platforms for healthcare and biomedical applications.We first introduce the selection of flexible and stretchable materials and the fabrication of sensors based on these materials.We then compare the different solid-state and liquid-state physical sensing platforms and examine the mechanical deformation-based working mechanisms of these sensors.We also highlight some of the exciting applications of flexible and wearable physical sensors in emerging healthcare and biomedical applications,in particular for artificial electronic skins,physiological health monitoring and assessment,and therapeutic and drug delivery.Finally,we conclude this review by offering some insight into the challenges and opportunities facing this field.
基金the Singapore NRF Competitive Research Program(R279-000-483-281)an NRF investigatorship(R279-000-444-281)the National University of Singapore(R279-000-482-133)for financial support.
文摘CONSPECTUS:Photodynamic therapy has been actively explored recently to combat various physiological disorders and diseases,including bacterial infections,inflammation,and cancer.As a noninvasive modality with high spatiotemporal selectivity,photodynamic therapy leverages photosensitizers,light,and reactive oxygen species to induce cytotoxicity and cell death.Specifically,upon light irradiation,photosensitizers harvest the incident light energy to generate highly reactive singlet oxygen species through photochemical reactions to disrupt the integrity of certain cellular components of the target cells.
