Understanding College Students’ Phone Call Behaviors Towards a Sustainable Mobile Health and Well-Being Solution

During the transition from high school to on-campus college life, students leave home and start facing enormous life changes, including meeting new people, taking on more responsibilities, being away from the family, and dealing with academic challenges. These changes lead to an elevation of stress and anxiety, affecting students’ health and well-being. With the help of smartphones and their rich collection of sensors, we can continuously moni tor various factors that affect students’ behavioral patterns, such as communication behaviors associated with their health, well-being, and academic success. In this work, we try to assess college students’ communication patterns (in terms of phone call duration and frequency) that vary across various geographical contexts (e.g., dormitories, class buildings, dining halls) during different times (e.g., epochs of a day, days of a week) using visualization techniques. The findings from this work will help foster the design and delivery of smartphone-based health interventions, thus helping the students adapt to the changes in life.Durante la transición de la escuela secundaria a la vida universitaria en el campus, un estudiante deja su casa y empieza a enfrentarse a enormes cambios en su vida, como cono cer gente nueva, mayores responsabilidades, estar lejos de la familia y retos académicos. Estos cambios provocan un aumento del estrés y la ansiedad, lo que afecta a la salud y el bienestar del estudiante. Con la ayuda de los smartphones y su enriquecida colección de sensores, podemos monitorizar continuamente varios factores que afectan a los patrones de comportamiento de los estudiantes, como las conductas de comunicación asociadas a su salud, bienestar y éxito académico. En este trabajo tratamos de evaluar los patrones de comunicación de los estudian tes universitarios (en términos de duración y frecuencia de las llamadas telefónicas) que varían a través de varios contextos geográficos (por ejemplo, dormitorios, clases, comedores) durante diferentes momentos (por ejemplo, épocas de un día, días de una semana) utilizando técnicas de visualización. Los resultados de este trabajo ayudarán a fomentar el diseño y la realización de intervenciones sanitarias basadas en los teléfonos inteligentes; de este modo, se ayudará a los estudiantes a adaptarse a los distintos cambios en sus vidas

Environment Knowledge-Driven Generic Models to Detect Coughs From Audio Recordings

Goal: Millions of people are dying due to res- piratory diseases, such as COVID-19 and asthma, which are often characterized by some common symptoms, including coughing. Therefore, objective reporting of cough symp- toms utilizing environment-adaptive machine-learning models with microphone sensing can directly contribute to respiratory disease diagnosis and patient care. Methods: In this work, we present three generic modeling approaches – unguided, semi-guided, and guided approaches consid- ering three potential scenarios, i.e., when a user has no prior knowledge, some knowledge, and detailed knowledge about the environments, respectively. Results: From detailed analysis with three datasets, we find that guided models are up to 28% more accurate than the unguided models. We find reasonable performance when assessing the applicability of our models using three additional datasets, including two open-sourced cough datasets. Con- clusions: Though guided models outperform other models, they require a better understanding of the environment

Leveraging Spatio-Temporal Data Science Techniques on Non-Stop Smart Sensing to Improve Health and Well-Being

When high school students leave their homes for a college education, the students oftenface enormous changes and challenges in life, such as meeting new people, more responsibilitiesin life, and being away from family and their comfort zones. These sudden changes often lead to anelevation of stress and anxiety, which can affect a student’s health and well-being. Researchers areincreasingly relying on smartphones to monitor individuals (such as college students) continuouslyto identify various factors that can affect students’ behavioral patterns (such as communicationbehaviors) that may be associated with their health, well-being, and academic success. In thiswork, we use different visualizations and statistical techniques to find various geographical placesand temporal factors that affect students’ communication patterns (in terms of phone call durationand frequency) to foster the design and delivery of future smartphone-based health interventions;thereby, potentially helping students adjust to college life. From our detailed analysis of an 18-month dataset collected from a cohort of 464 freshmen, we obtain insights on communicationpattern variations during different temporal contexts, e.g., epochs of a day, days of a week, theparts of a semester, social events, and in various geographical contexts (i.e., places of interest).Finally, we also obtain a negative correlation of −0.29 between physical activity and phone callduration, which can help provide guided feedback to improve future health behaviors

Environment Knowledge-Driven Generic Models to Detect Coughs From Audio Recordings

Goal: Millions of people are dying due to respiratory diseases, such as COVID-19 and asthma, which are often characterized by some common symptoms, including coughing. Therefore, objective reporting of cough symptoms utilizing environment-adaptive machine-learning models with microphone sensing can directly contribute to respiratory disease diagnosis and patient care. Methods: In this work, we present three generic modeling approaches – unguided, semi-guided, and guided approaches considering three potential scenarios, i.e., when a user has no prior knowledge, some knowledge, and detailed knowledge about the environments, respectively. Results: From detailed analysis with three datasets, we find that guided models are up to 28% more accurate than the unguided models. We find reasonable performance when assessing the applicability of our models using three additional datasets, including two open-sourced cough datasets. Conclusions: Though guided models outperform other models, they require a better understanding of the environment

Comprensión de los comportamientos de los estudiantes universitarios en materia de llamadas telefónicas para una solución sostenible de salud y bienestar móvil

During the transition from high school to on-campus college life, students leave home and start facing enormous life changes, including meeting new people, taking on more responsibilities, being away from the family, and dealing with academic challenges. These changes lead to an elevation of stress and anxiety, affecting students’ health and well-being. With the help of smartphones and their rich collection of sensors, we can continuously moni tor various factors that affect students’ behavioral patterns, such as communication behaviors associated with their health, well-being, and academic success. In this work, we try to assess college students’ communication patterns (in terms of phone call duration and frequency) that vary across various geographical contexts (e.g., dormitories, class buildings, dining halls) during different times (e.g., epochs of a day, days of a week) using visualization techniques. The findings from this work will help foster the design and delivery of smartphone-based health interventions, thus helping the students adapt to the changes in life.Durante la transición de la escuela secundaria a la vida universitaria en el campus, un estudiante deja su casa y empieza a enfrentarse a enormes cambios en su vida, como cono cer gente nueva, mayores responsabilidades, estar lejos de la familia y retos académicos. Estos cambios provocan un aumento del estrés y la ansiedad, lo que afecta a la salud y el bienestar del estudiante. Con la ayuda de los smartphones y su enriquecida colección de sensores, podemos monitorizar continuamente varios factores que afectan a los patrones de comportamiento de los estudiantes, como las conductas de comunicación asociadas a su salud, bienestar y éxito académico. En este trabajo tratamos de evaluar los patrones de comunicación de los estudian tes universitarios (en términos de duración y frecuencia de las llamadas telefónicas) que varían a través de varios contextos geográficos (por ejemplo, dormitorios, clases, comedores) durante diferentes momentos (por ejemplo, épocas de un día, días de una semana) utilizando técnicas de visualización. Los resultados de este trabajo ayudarán a fomentar el diseño y la realización de intervenciones sanitarias basadas en los teléfonos inteligentes; de este modo, se ayudará a los estudiantes a adaptarse a los distintos cambios en sus vidas

Environment Knowledge-Driven Generic Models to Detect Coughs from Audio Recordings

Goal: Millions of people are dying due to respiratory diseases, such as COVID-19 and asthma, which are often characterized by some common symptoms, including coughing. Therefore, objective reporting of cough symptoms utilizing environment-adaptive machine-learning models with microphone sensing can directly contribute to respiratory disease diagnosis and patient care. Methods: In this work, we present three generic modeling approaches - unguided, semi-guided, and guided approaches considering three potential scenarios, i.e., when a user has no prior knowledge, some knowledge, and detailed knowledge about the environments, respectively. Results: From detailed analysis with three datasets, we find that guided models are up to 28% more accurate than the unguided models. We find reasonable performance when assessing the applicability of our models using three additional datasets, including two open-sourced cough datasets. Conclusions: Though guided models outperform other models, they require a better understanding of the environment

Gut–Kidney Axis on Chip for Studying Effects of Antibiotics on Risk of Hemolytic Uremic Syndrome by Shiga Toxin-Producing Escherichia coli

Shiga toxin-producing Escherichia coli (STEC) infects humans by colonizing the large intestine, and causes kidney damage by secreting Shiga toxins (Stxs). The increased secretion of Shiga toxin 2 (Stx2) by some antibiotics, such as ciprofloxacin (CIP), increases the risk of hemolytic–uremic syndrome (HUS), which can be life-threatening. However, previous studies evaluating this relationship have been conflicting, owing to the low frequency of EHEC infection, very small number of patients, and lack of an appropriate animal model. In this study, we developed gut–kidney axis (GKA) on chip for co-culturing gut (Caco-2) and kidney (HKC-8) cells, and observed both STEC O157:H7 (O157) infection and Stx intoxication in the gut and kidney cells on the chip, respectively. Without any antibiotic treatment, O157 killed both gut and kidney cells in GKA on the chip. CIP treatment reduced O157 infection in the gut cells, but increased Stx2-induced damage in the kidney cells, whereas the gentamycin treatment reduced both O157 infection in the gut cells and Stx2-induced damage in the kidney cells. This is the first report to recapitulate a clinically relevant situation, i.e., that CIP treatment causes more damage than gentamicin treatment. These results suggest that GKA on chip is very useful for simultaneous observation of O157 infections and Stx2 poisoning in gut and kidney cells, making it suitable for studying the effects of antibiotics on the risk of HUS

Gut–Kidney Axis on Chip for Studying Effects of Antibiotics on Risk of Hemolytic Uremic Syndrome by Shiga Toxin-Producing <i>Escherichia coli</i>

Shiga toxin-producing Escherichia coli (STEC) infects humans by colonizing the large intestine, and causes kidney damage by secreting Shiga toxins (Stxs). The increased secretion of Shiga toxin 2 (Stx2) by some antibiotics, such as ciprofloxacin (CIP), increases the risk of hemolytic–uremic syndrome (HUS), which can be life-threatening. However, previous studies evaluating this relationship have been conflicting, owing to the low frequency of EHEC infection, very small number of patients, and lack of an appropriate animal model. In this study, we developed gut–kidney axis (GKA) on chip for co-culturing gut (Caco-2) and kidney (HKC-8) cells, and observed both STEC O157:H7 (O157) infection and Stx intoxication in the gut and kidney cells on the chip, respectively. Without any antibiotic treatment, O157 killed both gut and kidney cells in GKA on the chip. CIP treatment reduced O157 infection in the gut cells, but increased Stx2-induced damage in the kidney cells, whereas the gentamycin treatment reduced both O157 infection in the gut cells and Stx2-induced damage in the kidney cells. This is the first report to recapitulate a clinically relevant situation, i.e., that CIP treatment causes more damage than gentamicin treatment. These results suggest that GKA on chip is very useful for simultaneous observation of O157 infections and Stx2 poisoning in gut and kidney cells, making it suitable for studying the effects of antibiotics on the risk of HUS

Advances in methylation analysis of liquid biopsy in early cancer detection of colorectal and lung cancer

Abstract Methylation patterns in cell-free DNA (cfDNA) have emerged as a promising genomic feature for detecting the presence of cancer and determining its origin. The purpose of this study was to evaluate the diagnostic performance of methylation-sensitive restriction enzyme digestion followed by sequencing (MRE-Seq) using cfDNA, and to investigate the cancer signal origin (CSO) of the cancer using a deep neural network (DNN) analyses for liquid biopsy of colorectal and lung cancer. We developed a selective MRE-Seq method with DNN learning-based prediction model using demethylated-sequence-depth patterns from 63,266 CpG sites using SacII enzyme digestion. A total of 191 patients with stage I–IV cancers (95 lung cancers and 96 colorectal cancers) and 126 noncancer participants were enrolled in this study. Our study showed an area under the receiver operating characteristic curve (AUC) of 0.978 with a sensitivity of 78.1% for colorectal cancer, and an AUC of 0.956 with a sensitivity of 66.3% for lung cancer, both at a specificity of 99.2%. For colorectal cancer, sensitivities for stages I–IV ranged from 76.2 to 83.3% while for lung cancer, sensitivities for stages I–IV ranged from 44.4 to 78.9%, both again at a specificity of 99.2%. The CSO model's true-positive rates were 94.4% and 89.9% for colorectal and lung cancers, respectively. The MRE-Seq was found to be a useful method for detecting global hypomethylation patterns in liquid biopsy samples and accurately diagnosing colorectal and lung cancers, as well as determining CSO of the cancer using DNN analysis. Trial registration: This trial was registered at ClinicalTrials.gov (registration number: NCT 04253509) for lung cancer on 5 February 2020, https://clinicaltrials.gov/ct2/show/NCT04253509 . Colorectal cancer samples were retrospectively registered at CRIS (Clinical Research Information Service, registration number: KCT0008037) on 23 December 2022, https://cris.nih.go.kr , https://who.init/ictrp . Healthy control samples were retrospectively registered