CHIVID: A Rapid Deployment of Community and Home Isolation During COVID-19 Pandemics.

BACKGROUND: CHIVID is a telemedicine solution developed under tight time constraints that assists Thai healthcare practitioners in monitoring non-severe COVID-19 patients in isolation programs during crises. It assesses patient health and notifies healthcare practitioners of high-risk scenarios through a chatbot. The system was designed to integrate with the famous Thai messaging app LINE, reducing development time and enhancing user-friendliness, and the system allowed patients to upload a pulse oximeter image automatically processed by the PACMAN function to extract oxygen saturation and heart rate values to reduce patient input errors. METHODS: This article describes the proposed system and presents a mixed-methods study that evaluated the system's performance by collecting survey responses from 70 healthcare practitioners and analyzing 14,817 patient records. RESULTS: Approximately 71.4% of healthcare practitioners use the system more than twice daily, with the majority managing 1-10 patients, while 11.4% handle over 101 patients. The progress note is a function that healthcare practitioners most frequently use and are satisfied with. Regarding patient data, 58.9%(8,724/14,817) are male, and 49.7%(7,367/14,817) within the 18 to 34 age range. The average length of isolation was 7.6 days, and patients submitted progress notes twice daily on average. Notably, individuals aged 18 to 34 demonstrated the highest utilization rates for the PACMAN function. Furthermore, most patients, totaling over 95.52%(14,153/14,817), were discharged normally. CONCLUSION: The findings indicate that CHIVID could be one of the telemedicine solutions for hospitals with patient overflow and healthcare practitioners unfamiliar with telemedicine technology to improve patient care during a critical crisis. Clinical and Translational Impact Statement- CHIVID's success arises from seamlessly integrating telemedicine into third-party application within a limited timeframe and effectively using clinical decision support systems to address challenges during the COVID-19 crisis.

A machine learning-based prediction model of pelvic lymph node metastasis in women with early-stage cervical cancer.

OBJECTIVE: To develop a novel machine learning-based preoperative prediction model for pelvic lymph node metastasis (PLNM) in early-stage cervical cancer by combining the clinical findings and preoperative computerized tomography (CT) of the whole abdomen and pelvis. METHODS: Patients diagnosed with International Federation of Gynecology and Obstetrics stage IA2-IIA1 squamous cell carcinoma, adenocarcinoma, and adenosquamous carcinoma of the cervix who had primary radical surgery with bilateral pelvic lymphadenectomy from January 1, 2003 to December 31, 2020, were included. Seven supervised machine learning algorithms, including logistic regression, random forest, support vector machine, adaptive boosting, gradient boosting, extreme gradient boosting, and category boosting, were used to evaluate the risk of PLNM. RESULTS: PLNM was found in 199 (23.9%) of 832 patients included. Younger age, larger tumor size, higher stage, no prior conization, tumor appearance, adenosquamous histology, and vaginal metastasis as well as the CT findings of larger tumor size, parametrial metastasis, pelvic lymph node enlargement, and vaginal metastasis, were significantly associated with PLNM. The models' predictive performance, including accuracy (89.1%-90.6%), area under the receiver operating characteristics curve (86.9%-91.0%), sensitivity (77.4%-82.4%), specificity (92.1%-94.3%), positive predictive value (77.0%-81.7%), and negative predictive value (93.0%-94.4%), appeared satisfactory and comparable among all the algorithms. After optimizing the model's decision threshold to enhance the sensitivity to at least 95%, the 'highly sensitive' model was obtained with a 2.5%-4.4% false-negative rate of PLNM prediction. CONCLUSION: We developed prediction models for PLNM in early-stage cervical cancer with promising prediction performance in our setting. Further external validation in other populations is needed with potential clinical applications.

Association between air quality index and effects on emergency department visits for acute respiratory and cardiovascular diseases.

BACKGROUND AND OBJECTIVE: Several studies suggest that air pollution, particularly PM2.5, increases morbidity and mortality, Emergency Department (ED) visits, and hospitalizations for acute respiratory and cardiovascular diseases. However, no prior study in Southeastern Asia (SEA) has examined the effects of air pollutants on ED visits and health outcomes. This study focused on the association of the Air Quality Index (AQI) of PM2.5 and other pollutants' effects on ED visits, hospitalization, and unexpected deaths due to acute respiratory disease, acute coronary syndrome (ACS), acute heart failure (AHF), and stroke. METHODS: We conducted a retrospective study with daily data from ED visits between 2018 and 2019 at Maharaj Nakorn Chiang Mai Hospital, Chiang Mai, Thailand. The AQI of air pollution data was collected from outdoor air quality from the Smoke Haze Integrated Research Unit and the Air Quality Index Visual Map. A distributed lag, non-linear and quasi-Poisson models were used to explore the relationship between air quality parameters and ED visits for each disease. RESULTS: 3,540 ED visits were recorded during the study period. The mean daily AQI of PM2.5 was 89.0 ± 40.2. We observed associations between AQI of PM2.5 and the ED visits due to ACS on the following day (RR = 1.023, 95% confidence interval [CI]: 1.002-1.044) and two days after exposure (RR = 1.026, 95% CI: 1.005-1.047). Also, subgroup analysis revealed the association between AQI of PM2.5 and the ED visits due to pneumonia on the current day (RR = 1.071, 95% CI: 1.025-1.118) and on the following day after exposure (RR = 1.024, 95% CI: 1.003-1.046). AQI of PM2.5 associated with increased mortality resulted from ACS on lag day 3 (OR = 1.36, 95% CI: 1.08-1.73). The AQI of PM10 is also associated with increased ED visits due to COPD/asthma and increased hospitalization in AHF. In addition, the AQI of O3 and AQI of NO2 is associated with increased ICU admissions and mortality in AHF. CONCLUSION: Short-term PM2.5 exposure escalates ED visits for ACS and pneumonia. PM10's AQI associates with COPD/asthma ED visits and AHF hospitalizations. AQI of O3 and NO2's link to increased ICU admissions and AHF mortality. Urgent action against air pollution is vital to safeguard public health.

EEGANet: Removal of Ocular Artifacts From the EEG Signal Using Generative Adversarial Networks.

The elimination of ocular artifacts is critical in analyzing electroencephalography (EEG) data for various brain-computer interface (BCI) applications. Despite numerous promising solutions, electrooculography (EOG) recording or an eye-blink detection algorithm is required for the majority of artifact removal algorithms. This reliance can hinder the model's implementation in real-world applications. This paper proposes EEGANet, a framework based on generative adversarial networks (GANs), to address this issue as a data-driven assistive tool for ocular artifacts removal (source code is available at https://github.com/IoBT-VISTEC/EEGANet). After the model was trained, the removal of ocular artifacts could be applied calibration-free without relying on the EOG channels or the eye blink detection algorithms. First, we tested EEGANet's ability to generate multi-channel EEG signals, artifacts removal performance, and robustness using the EEG eye artifact dataset, which contains a significant degree of data fluctuation. According to the results, EEGANet is comparable to state-of-the-art approaches that utilize EOG channels for artifact removal. Moreover, we demonstrated the effectiveness of EEGANet in BCI applications utilizing two distinct datasets under inter-day and subject-independent schemes. Despite the absence of EOG signals, the classification performance of the signals processed by EEGANet is equivalent to that of traditional baseline methods. This study demonstrates the potential for further use of GANs as a data-driven artifact removal technique for any multivariate time-series bio-signal, which might be a valuable step towards building next-generation healthcare technology.

Helpful family climate moderates the relationship between perceived family support of ADHD symptoms and depression: a conditional process model.

BACKGROUND: Symptoms of attention deficit hyperactivity disorder (ADHD) are commonly comorbid with depression This study aimed to examine the relationship between ADHD symptoms and depression through perceived family support and to explore whether the magnitude of the relationship depended on the type of family climate of medical students. METHODS: This cross-sectional study was conducted among 124 first year medical students in Thailand. Participants completed questionnaires on ADHD symptoms, depression, perceived family support, and 9 types of family climate. The questionnaires included the Adult ADHD Self-Report Scale Screener, Patient Health Questionnaire-9, and revised Thai Multidimensional Scale of Perceived Social Support. Mediational analysis was adopted to examine the mediating role of perceived family support in the relationship between ADHD symptoms and depression, while moderation analysis was applied to examine the extent of the relationship depending on family climate. RESULTS: The relationship between ADHD symptoms and depression was moderate. Perceived family support partially mediated this relationship after controlling for age and sex. Among the types of family climate, only helpful family climate was a significant moderator of perceived family support and depression. The moderated mediation model increased the variance in depression from 17% by the mediation model to 21%. However, follow-up conditional mediational analysis showed that the indirect effect of ADHD symptoms on depression via perceived family support was not significant and that this effect did not vary linearly as a function of helpful family climate. CONCLUSION: The findings of the study revealed that poor family support might be one risk of developing depression in the context of ADHD symptoms. Further study on providing intervention concerning family support among those with ADHD symptoms should be warranted. In addition, a study on helpful family climate in a larger sample size, in other populations, and in a longitudinal fashion for a more robust conclusion is encouraged.

Impact of Perceived Social Support on the Relationship between ADHD and Depressive Symptoms among First Year Medical Students: A Structural Equation Model Approach.

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is associated with depression among college students, while perceived social support is also associated with depression, especially among young adults. This study aimed to examine to what extent perceived social support mediated the relationship between ADHD symptoms and depressive symptoms. METHODS: In total, 124 first year medical students completed the Adult ADHD Self-Report Scale Screener (ASRS), the Patient Health questionnaire-9 and the revised Thai Multidimensional scale of perceived social support reflecting ADHD symptoms, depressive symptoms, and perceive social support, i.e., family members, friends and other significant people, respectively. Structural equation modeling was used to investigate the hypothesized mediation model. RESULTS: ADHD symptoms exhibited a significant indirect effect on depressive symptoms via perceived social support. ADHD symptoms initially had a direct effect on depression; thereafter, it reduced to a non-significance effect after perceived social support was added. The total variance explained by this model was 35.2%. The mediation model with family support as a mediator showed the highest effect size. CONCLUSIONS: The study highlighted the importance of perceived social support, particularly family support, on depressive symptoms among young medical students experiencing ADHD symptoms. The model suggests promising relationships for further research on ADHD-related depression and potential treatment in the future.

Potential Applications of Mobile and Wearable Devices for Psychological Support During the COVID-19 Pandemic: A Review.

The coronavirus disease 19 (COVID-19) pandemic that has been raging in 2020 does affect not only the physical state but also the mental health of the general population, particularly, that of the healthcare workers. Given the unprecedented large-scale impacts of the COVID-19 pandemic, digital technology has gained momentum as invaluable social interaction and health tracking tools in this time of great turmoil, in part due to the imposed state-wide mobilization limitations to mitigate the risk of infection that might arise from in-person socialization or hospitalization. Over the last five years, there has been a notable increase in the demand and usage of mobile and wearable devices as well as their adoption in studies of mental fitness. The purposes of this scoping review are to summarize evidence on the sweeping impact of COVID-19 on mental health as well as to evaluate the merits of the devices for remote psychological support. We conclude that the COVID-19 pandemic has inflicted a significant toll on the mental health of the population, leading to an upsurge in reports of pathological stress, depression, anxiety, and insomnia. It is also clear that mobile and wearable devices (e.g., smartwatches and fitness trackers) are well placed for identifying and targeting individuals with these psychological burdens in need of intervention. However, we found that most of the previous studies used research-grade wearable devices that are difficult to afford for the normal consumer due to their high cost. Thus, the possibility of replacing the research-grade wearable devices with the current smartwatch is also discussed.

Comparative effects of sex hormone deprivation on the brain of insulin-resistant rats.

Obese-insulin resistance following chronic high-fat diet consumption led to cognitive decline through several mechanisms. Moreover, sex hormone deprivation, including estrogen and testosterone, could be a causative factor in inducing cognitive decline. However, comparative studies on the effects of hormone-deprivation on the brain are still lacking. Adult Wistar rats from both genders were conducted sham operations or orchiectomies/ovariectomies and given a normal diet or high-fat diet for 4, 8, and 12 weeks. Blood was collected to determine the metabolic parameters. At the end of the experiments, rats were decapitated and their brains were collected to determine brain mitochondrial function, brain oxidative stress, hippocampal plasticity, insulin-induced long-term depression, dendritic spine density, and cognition. We found that male and female rats fed a high-fat diet developed obese-insulin resistance by week 8 and brain defects via elevated brain oxidative stress, brain mitochondrial dysfunction, impaired insulin-induced long-term depression, hippocampal dysplasticity, reduced dendritic spine density, and cognitive decline by week 12. In normal diet-fed rats, estrogen-deprivation, not testosterone-deprivation, induced obese-insulin resistance, oxidative stress, brain mitochondrial dysfunction, impaired insulin-induced long-term depression, hippocampal dysplasticity, and reduced dendritic spine density. In high-fat-diet-fed rats, estrogen deprivation, not testosterone-deprivation, accelerated and aggravated obese-insulin resistance and brain defects at week 8. In conclusion, estrogen deprivation aggravates brain dysfunction more than testosterone deprivation through increased oxidative stress, brain mitochondrial dysfunction, impaired insulin-induced long-term depression, and dendritic spine reduction. These findings may explain clinical reports which show more severe cognitive decline in aging females than males with obese-insulin resistance.

Pretreatment With PCSK9 Inhibitor Protects the Brain Against Cardiac Ischemia/Reperfusion Injury Through a Reduction of Neuronal Inflammation and Amyloid Beta Aggregation.

Background Cardiac ischemic/reperfusion (I/R) injury leads to brain damage. A new antihyperlipidemic drug is aimed at inhibiting PCSK 9 (proprotein convertase subtilisin/kexin type 9), a molecule first identified in a neuronal apoptosis paradigm. Thus, the PCSK 9 inhibitor ( PCSK 9i) may play a role in neuronal recovery following cardiac I/R insults. We hypothesize that PCSK 9i attenuates brain damage caused by cardiac I/R via diminishing microglial/astrocytic hyperactivation, ß-amyloid aggregation, and loss of dendritic spine. Methods and Results Adult male rats were divided into 7 groups: (1) control (n=4); (2) PCSK 9i without cardiac I/R (n=4); (3) sham (n=4); and cardiac I/R (n=40). Cardiac I/R rats were divided into 4 subgroups (n=10/subgroup): (1) vehicle; (2) PCSK 9i (10 µg/kg, IV) before ischemia; (3) PCSK 9i during ischemia; and (4) PCSK 9i at the onset of reperfusion. At the end of cardiac I/R protocol, brains were removed to determine microglial and astrocytic activities, ß-amyloid aggravation, and dendritic spine density. The cardiac I/R led to the activation of the brain's innate immunity resulting in increasing Iba1+ microglia, GFAP + astrocytes, and CD 11b+/ CD 45+high cell numbers. However, CD 11b+/ CD 45+low cell numbers were decreased following cardiac I/R. In addition, cardiac I/R led to reduced dendritic spine density, and increased ß-amyloid aggregation. Only the administration of PCSK 9i before ischemia effectively attenuated these deleterious effects on the brain following cardiac I/R. PCSK 9i administration under the physiologic condition did not affect the aforementioned parameters. Conclusions Cardiac I/R injury activated microglial activity in the brain, leading to brain damage. Only the pretreatment with PCSK 9i prevented dendritic spine loss via reduction of microglial activation and Aß aggregation.