A context-driven approach through stakeholder engagement to introduce a digital emergency obstetric and newborn care register into routine obstetric health care services in Bangladesh.

Background: Emergency obstetric and newborn care (EmONC) in Bangladesh focusses on maternal health, whereby it addresses childbirth and postpartum complications to ensure women's health and well-being. It was transitioned to a digital platform to overcome challenges with the paper-based EmONC register and we conducted implementation research to assess the outcome. Here we outline the stakeholder engagement process integral to the implementation research process. Methods: We adopted a four-step stakeholder engagement model based on the identification, sensitisation, involvement, and engagement of stakeholders. The approach was informed by previous experience, desk reviews, and expert consultations to ensure comprehensive engagement with stakeholders at multiple levels. Led by the Maternal Health Programme of the Government of Bangladesh, we involved high-power and high-interest stakeholders in developing a joint action plan for digitisation of the paper-based EmONC register. Finally, we demonstrated this digital EmONC register in real-life settings to stakeholders at different levels. Results: The successful demonstration process fostered government ownership and collaboration with multiple stakeholders, while laying the foundation for scalability and sustainability. Nevertheless, our experience highlighted that the stakeholder engagement process is context-driven, time-consuming, resource-intensive, iterative, and dynamic, and it requires involving stakeholders with varied expertise. Effective strategic planning, facilitation, and the allocation of sufficient time and resources are essential components for successful stakeholder engagement. Conclusions: Our experience demonstrates the potential of adopting the 'identification, sensitisation, involvement, and engagement' stakeholder engagement model. Success in implementing this model in diverse settings depends on leveraging knowledge gained during implementation, maintaining robust communication with stakeholders, and harnessing the patience and determination of the facilitating organisation.

A Decision-Aware Ambient Assisted Living System with IoT Embedded Device for In-Home Monitoring of Older Adults.

Older adults' independent life is compromised due to various problems, such as memory impairments and decision-making difficulties. This work initially proposes an integrated conceptual model for assisted living systems capable of providing helping means for older adults with mild memory impairments and their caregivers. The proposed model has four main components: (1) an indoor location and heading measurement unit in the local fog layer, (2) an augmented reality (AR) application to make interactions with the user, (3) an IoT-based fuzzy decision-making system to handle the direct and environmental interactions with the user, and (4) a user interface for caregivers to monitor the situation in real time and send reminders once required. Then, a preliminary proof-of-concept implementation is performed to evaluate the suggested mode's feasibility. Functional experiments are carried out based on various factual scenarios, which validate the effectiveness of the proposed approach. The accuracy and response time of the proposed proof-of-concept system are further examined. The results suggest that implementing such a system is feasible and has the potential to promote assisted living. The suggested system has the potential to promote scalable and customizable assisted living systems to reduce the challenges of independent living for older adults.

Perception and experiences regarding COVID-19 pandemic among urban young adults in Bangladesh: a mixed-method study.

Aim: This study aimed at exploring the perception and experiences with regard to the COVID-19 pandemic among Bangladeshi urban young adults. Subject and methods: Using a mixed-method approach, an online cross-sectional survey among 315 participants and in-depth interviews (IDI) among 20 young adults were conducted from May 1 to May 25, 2020. Descriptive statistics and chi-square tests were performed for quantitative data, along with the thematic analysis for qualitative data. Results: The mean (± SD) age of the participants was 26.54 (± 3.05), and the majority were male (54.9%). About 81.6% of the participants reported COVID-19 as a viral disease, transmitted through droplets of sneezing and coughing, and close contact with another person (90.8%). Nearly 40% of participants reported news channels as a reliable source of information for COVID-19. Participants who were male were less likely to be aware than females in terms of mode of transmission of COVID-19 such as going outside of the home (82.7% male vs 90.8% female; p < 0.05). Male participants thought they were perfectly healthy and more reluctant to agree with maintaining social distance compared to female participants (72.8% male vs 90.1% female; p < 0.001). Participant's satisfaction level with services provided by the government was also significantly different and higher among females than male participants (39.9% male vs 53.5% female; p < 0.05). The majority of the participants reported suffering due to financial uncertainty, psychological distress, and inadequate health facilities. Dissatisfaction was reported with the existing health services as creating several misconceptions, lacking testing facilities, and debasement by the health professionals. Conclusion: This study found a better perception regarding COVID-19 among the young adults, but they had poor preventive practices. Health education intervention with the rapid response should be implemented targeting this vulnerable group to improve their preventive practices.

Knowledge, attitudes, and practices related to the COVID-19 pandemic among Bangladeshi youth: a web-based cross-sectional analysis.

Aim: This study aimed to assess knowledge, attitudes, and practices (KAP) toward COVID-19 among youth in Bangladesh. Subject and methods: A cross-sectional survey was conducted from 5 May to 25 May 2020. People aged between 18 and 35 years were approached via social media to complete an online questionnaire that consisted of socio-demographic information and KAP toward COVID-19. Descriptive statistics, t-tests, one-way analysis of variance (ANOVA), and logistic regression analyses were conducted. Results: Out of 707 survey participants, 57.1% were male, the majority were students (60.3%), aged 24-29 years (61.5%), having a bachelor's degree (57%), having family income 25,000-50,000 BDT (40.5%) and living in urban areas (64.4%). Participants gathered information on COVID-19 mostly through social media (70.4%). Overall, 61.2% had adequate knowledge with 78.9% having a positive attitudes toward COVID-19 and only 51.6% had good practices. Most (86.8%) of the participants were confident that COVID-19 will be successfully controlled and Bangladesh was handling the COVID-19 health crisis well (84.2%). Only 75.2% of participants always washed their hands with soap or hand-sanitizer, and 70.6% wore a mask when going outside the home. Factors associated with adequate knowledge were being female, having a master's degree and above, and living in an urban area (p < 0.05). Participants having adequate knowledge of COVID-19 had higher likelihood of positive attitudes (OR: 6.41, 95% CI = 2.34-25.56, p < 0.001) and good practices (OR: 8.93, 95% CI = 3.92-38.42, p < 0.001). Conclusion: The findings highlight the need for tailored education programs for COVID-19 which incorporates consideration of associated factors to improve the level of public knowledge, attitudes, and practices.

Correction to: Knowledge, attitudes, and practices related to the COVID-19 pandemic among Bangladeshi youth: a web-based cross-sectional analysis.

[This corrects the article DOI: 10.1007/s10389-020-01432-7.].

Perception regarding health and barriers to seeking healthcare services among rural rickshaw pullers in Bangladesh: A qualitative exploration.

Background: Understanding health in daily life can vary from person to person. The concept of health arises from the perspective of an individual's experience. People face several kinds of barriers while seeking healthcare services, where rickshaw pullers are one of the most vulnerable groups to meet their basic health needs. This study aimed to investigate Bangladeshi rural rickshaw pullers' perception regarding health and what obstacles they face while seeking healthcare services. Methods: This study followed a qualitative approach conducted in-depth interviews involving 20 rickshaw pullers in rural Bangladesh from 4th to 15th December 2020. Participants were selected through purposive and snowball sampling techniques. The verbatim transcription was performed, and the thematic analysis was done through manual coding and NVivo version 12. Results: According to the study's findings, participants' perception regarding health were mainly based on physical, nutritional, and social points of view. The financial hardship to convey medical costs, long waiting time in receiving healthcare services, social class inequality, low trustworthiness on diagnostic services, and mastery of broker in the hospital setting were acknowledged as prevailing barriers to seeking healthcare services. Conclusion: Several health perceptions existed among the rural rickshaw pullers. They faced different kinds of barriers while seeking healthcare services, and those obstacles made them hopeless and worried about getting quality healthcare services. Concerned authorities, including government and private organizations, should take effective strategies to ensure that healthcare services are available, reliable, and affordable.

Factors associated with Facebook addiction among university students amid the COVID-19 pandemic: Findings from an online cross-sectional survey.

BACKGROUND: Facebook addiction (FA) has been suggested as a potential behavioral addiction. There is a severe lack of research evidence regarding the Facebook addiction behavior among university students during the ongoing COVID-19 pandemic. The aim of this study was to determine factors associated with Facebook addiction among Bangladeshi university students. METHODS: A cross-sectional online survey was conducted among 2,161 Bangladeshi university students during the COVID-19 pandemic from June 2021 to September 2021. A well fitted regression model in R programming language was used for this study. RESULTS: Female respondents and those whose family monthly income was <25,000 BDT were more addicted to Facebook than other respondents. Respondents who lost a family member or a relative to COVID-19, engaged in physical activities (exercise) during the pandemic, used Facebook for work purposes or used Facebook to relieve daily stress were more addicted to Facebook. CONCLUSION: Overuse of social media is problematic as it can trigger several mental health symptoms, especially among students. Adequate and effective interventions are required to educate students about the dangers of Facebook addiction and to provide an alternative, healthy options.

Factors associated with suicidal behavior among university students in Bangladesh after one year of COVID-19 pandemic.

BACKGROUND: The COVID-19 outbreak spillovers mental health burden where suicide is a common psychological public health issue that affects people all over the world. This study aimed to explore the factors associated with suicidal behavior among university students in Bangladesh after one year of the COVID-19 outbreak. METHODS: An online cross-sectional survey was conducted among 2100 Bangladeshi university students aged ≥18 years from April 29 to May 15, 2021. The survey questionnaire contained socio-demographic information, COVID-19 related physical and psychosocial factors (CRPPF), preventive response to psychological stress, and the Suicidal Behaviors Questionnaire-Revised (SBQ-R) scale. Descriptive statistics along with logistic regression were performed for statistical analysis. RESULTS: About 47.90% of the students were at risk of suicidal behavior, and female students were very likely to be at risk of suicidal behavior than their male counterparts (AOR = 2.28; 95% CI: 1.86 to 2.81). Keeping distance from friends or family (AOR = 1.66; 95% CI: 1.34 to 2.04), having relationship problems (AOR = 2.20; 95% CI: 1.79 to 2.70), feeling own selves as burden to families (AOR = 2.50; 95% CI: 2.02 to 3.11), and being stressed of lockdown (AOR = 1.56; 95% CI: 1.19 to 2.03) were highlighted as some of the significant factors associated with increased risk of suicidal behavior. CONCLUSION: University students were exposed to several factors that impose the risk of developing suicidal behavior. Concerned authorities should design & implement appropriate strategies for ensuring suicidal prevention besides their mental well-being.

Understanding the determinants of COVID-19 vaccination intention and willingness to pay: findings from a population-based survey in Bangladesh.

BACKGROUND: Several coronavirus disease (COVID-19) vaccines have already been authorized and distributed in different countries all over the world, including Bangladesh. Understanding public acceptance of such a novel vaccine is vital, but little is known about the topic. OBJECTIVES: This study aimed to investigate the determinants of intention to receive a COVID-19 vaccine and willingness to pay (WTP) among people in Bangladesh. METHODS: An anonymous and online-based survey of Bangladeshi people (mean age = 29.96 ± 9.15 years; age range = 18-60 years) was conducted using a self-reported questionnaire consisting of socio-demographics, COVID-19 experience, and vaccination-related information as well as the health belief model (HBM). Multivariable logistic regression was performed to determine the factors influencing COVID-19 vaccination intent and WTP. RESULTS: Of the 894 participants, 38.5% reported a definite intention to receive a COVID-19 vaccine, whereas 27% had a probable intention, and among this intent group, 42.8% wanted to get vaccinated as soon as possible. Older age, feeling optimistic about the effectiveness of COVID-19 vaccination, believing that vaccination decreases worries and risk of COVID-19 infection, and being less concerned about side effects and safety of COVID-19 vaccination under the HBM construct were found to be significant factors in COVID-19 vaccination intention. Most of the participants (72.9%) were willing to pay for a COVID-19 vaccine, with a median (interquartile range [IQR]) amount of BDT 400/US$ 4.72 (IQR; BDT 200-600/US$ 2.36-7.07) per dose. Factors associated with higher WTP were younger age, being male, having higher education, residing in an urban area, having good self-rated health status, positivity towards COVID-19 vaccination's effectiveness, and being worried about the likelihood of getting infected with COVID-19. Participants who were COVID-19 vaccination intent preferred an imported vaccine over a domestically-made vaccine (22.9% vs. 14.8%), while 28.2% preferred a routine immunization schedule. CONCLUSION: The findings indicate a considerable proportion of Bangladeshi people intended to get vaccinated and had WTP for the COVID-19 vaccine. However, urgent education and awareness programs are warranted to alleviate public skepticism regarding the COVID-19 vaccination.

Management of cardiorespiratory function of rabbits by a customized chest drain: An experimental study.

OBJECTIVE: This study aimed to salvage the study population from the fatality that occurs due to iatrogenic injury to the thoracic cavity's pleural membrane. MATERIALS AND METHODS: An experimental study of temporomandibular joint arthroplasty with costochondral graft was carried out on 72 healthy 'Oryctolagus cuniculus' species of male rabbits. The rabbits were distributed into two age groups: growing (3-4 months) and adult (12-18 months). All the procedures were carried out under general anesthesia with xylazine hydrochloride and ketamine hydrochloride after calculating the doses, maintained by halothane and O2 inhalations. Out of 72 rabbits, 33 rabbits had accidental perforation of the pleural membrane observed that required a chest drain. RESULTS: In this study, 21 (63.64%) rabbits received chest drain and salvaged. The rest of the rabbits (n = 12; 36.36%) that did not receive any chest drain and died. Most of the rabbits (n = 17; 81%) were under the growing group, weighing less than 2 kg and four (19%) were adult rabbits. CONCLUSION: This manual chest drain is life-saving for rabbits. It is a new addition to the advancement of thoracic surgery on animals. It is cost-effective and safe. The developed customized drainage system may make it easier to harvest the costochondral graft-related experiments.

Psychological states of Bangladeshi people four months after the COVID-19 pandemic: An online survey.

BACKGROUND: The COVID-19 pandemic essentially imposes psychological effects on people. As the pandemic progresses, people experience psychological trauma gradually, which can change over time. The present study aimed to assess the prevalence of depression, anxiety, and stress among Bangladeshi people four months after the COVID-19 outbreak. METHODS: An online cross-sectional survey was conducted among Bangladeshi citizens aged ≥18 years from June 1 to June 10, 2020. The participants completed an online questionnaire examining socio-demographic variables and COVID-19 related factors, along with the Depression Anxiety and Stress Scale 21. A total of 1146 respondents have been included in the study. Data were analyzed using the Statistical Package for Social Sciences, IBM Statistics version 22.0. RESULTS: The prevalence of moderate to the extremely severe levels of depression, anxiety, and stress was 47.2%, 46.0%, and 32.5%, respectively, with no significant gender differences. The prevalence of anxiety and stress was significantly higher in participants aged >30 than in participants aged 18-30 years. Daily follow up COVID-19 related news, having COVID-19 symptoms so far, having contact (direct or indirect) with COVID-19 infected person, and fear of infection were significantly associated with depression, anxiety, and stress. CONCLUSIONS: Sizable proportions of participants had depression, anxiety and stress four months after the COVID-19 outbreak in Bangladesh. The findings of this study underscores the need for strategies aimed at reducing these psychological sufferings in Bangladeshi people in the context of COVID-19.

Global Burden of Head and Neck Cancer.

Head and neck cancer is increasing globally owing to rising rates of tobacco use and human papillomavirus infection. Today, cancer is the leading cause of death and disabilities in developed countries and the second leading cause of death in countries with developing economies. Understanding the global landscape of head and neck cancer will empower oral and maxillofacial surgeons to play a critical role among patients and societal education regarding the importance of addressing modifiable risk factors and continuing to play an important role in the diagnosis and management of head and neck cancer.

Modeling and Forecasting of Energy Demands for Household Applications.

Energy use is on the rise due to an increase in the number of households and general consumptions. It is important to estimate and forecast the number of houses and the resultant energy consumptions to address the effective and efficient use of energy in future planning. In this paper, the number of houses in Brunei Darussalam is estimated by using Spline interpolation and forecasted by using two methods, namely an autoregressive integrated moving average (ARIMA) model and nonlinear autoregressive (NAR) neural network. The NAR model is more accurate in forecasting the number of houses as compared to the ARIMA model. The energy required for water heating and other appliances is investigated and are found to be 21.74% and 78.26% of the total energy used, respectively. Through analysis, it is demonstrated that 9 m2 solar heater and 90 m2 of solar panel can meet these energy requirements.

Interpretability and Class Imbalance in Prediction Models for Pain Volatility in Manage My Pain App Users: Analysis Using Feature Selection and Majority Voting Methods.

BACKGROUND: Pain volatility is an important factor in chronic pain experience and adaptation. Previously, we employed machine-learning methods to define and predict pain volatility levels from users of the Manage My Pain app. Reducing the number of features is important to help increase interpretability of such prediction models. Prediction results also need to be consolidated from multiple random subsamples to address the class imbalance issue. OBJECTIVE: This study aimed to: (1) increase the interpretability of previously developed pain volatility models by identifying the most important features that distinguish high from low volatility users; and (2) consolidate prediction results from models derived from multiple random subsamples while addressing the class imbalance issue. METHODS: A total of 132 features were extracted from the first month of app use to develop machine learning-based models for predicting pain volatility at the sixth month of app use. Three feature selection methods were applied to identify features that were significantly better predictors than other members of the large features set used for developing the prediction models: (1) Gini impurity criterion; (2) information gain criterion; and (3) Boruta. We then combined the three groups of important features determined by these algorithms to produce the final list of important features. Three machine learning methods were then employed to conduct prediction experiments using the selected important features: (1) logistic regression with ridge estimators; (2) logistic regression with least absolute shrinkage and selection operator; and (3) random forests. Multiple random under-sampling of the majority class was conducted to address class imbalance in the dataset. Subsequently, a majority voting approach was employed to consolidate prediction results from these multiple subsamples. The total number of users included in this study was 879, with a total number of 391,255 pain records. RESULTS: A threshold of 1.6 was established using clustering methods to differentiate between 2 classes: low volatility (n=694) and high volatility (n=185). The overall prediction accuracy is approximately 70% for both random forests and logistic regression models when using 132 features. Overall, 9 important features were identified using 3 feature selection methods. Of these 9 features, 2 are from the app use category and the other 7 are related to pain statistics. After consolidating models that were developed using random subsamples by majority voting, logistic regression models performed equally well using 132 or 9 features. Random forests performed better than logistic regression methods in predicting the high volatility class. The consolidated accuracy of random forests does not drop significantly (601/879; 68.4% vs 618/879; 70.3%) when only 9 important features are included in the prediction model. CONCLUSIONS: We employed feature selection methods to identify important features in predicting future pain volatility. To address class imbalance, we consolidated models that were developed using multiple random subsamples by majority voting. Reducing the number of features did not result in a significant decrease in the consolidated prediction accuracy.

Two-stage thermophilic anaerobic co-digestion of corn stover and cattle manure to enhance biomethane production.

Two-stage thermophilic anaerobic co-digestion of cattle manure and corn stover was conducted to increase biomethane production. The first stage pre-digestion of corn stover was studied based on the following treatment variables: corn stover to liquid fraction of digestate (CS:LFD) ratio (1:7, 1:10, 1:13, 1:14), digestion temperature (55 °C, 60 °C) and digestion time (3, 7, 14 days). The reduction in lignin, cellulose and hemicellulose (LCH) was between 3.97% and 11.98%, which increased the biodegradability of corn stover. Corn stover pre-digested with a CS:LFD ratio of 1:10 at 55 °C for a period of 3 and 7 days was subjected to anaerobic co-digestion with cattle manure. The highest biomethane yield was observed on day 21 with a value of 357.41 mL/g volatile solids (VS) for untreated corn stover, 446.84 mL/g VS for corn stover pre-digested for 3 days and 518.58 mL/g VS for corn stover pre-digested for 7 days with LFD. The VS conversion efficiency for co-digestion of cattle manure with untreated corn stover, corn stover pre-digested for 3 days and 7 days was 42.8%, 43.3% and 51.8%, respectively, on day 21, which was higher than that (34.0%) of cattle manure only.

Defining and Predicting Pain Volatility in Users of the Manage My Pain App: Analysis Using Data Mining and Machine Learning Methods.

BACKGROUND: Measuring and predicting pain volatility (fluctuation or variability in pain scores over time) can help improve pain management. Perceptions of pain and its consequent disabling effects are often heightened under the conditions of greater uncertainty and unpredictability associated with pain volatility. OBJECTIVE: This study aimed to use data mining and machine learning methods to (1) define a new measure of pain volatility and (2) predict future pain volatility levels from users of the pain management app, Manage My Pain, based on demographic, clinical, and app use features. METHODS: Pain volatility was defined as the mean of absolute changes between 2 consecutive self-reported pain severity scores within the observation periods. The k-means clustering algorithm was applied to users' pain volatility scores at the first and sixth month of app use to establish a threshold discriminating low from high volatility classes. Subsequently, we extracted 130 demographic, clinical, and app usage features from the first month of app use to predict these 2 volatility classes at the sixth month of app use. Prediction models were developed using 4 methods: (1) logistic regression with ridge estimators; (2) logistic regression with Least Absolute Shrinkage and Selection Operator; (3) Random Forests; and (4) Support Vector Machines. Overall prediction accuracy and accuracy for both classes were calculated to compare the performance of the prediction models. Training and testing were conducted using 5-fold cross validation. A class imbalance issue was addressed using a random subsampling of the training dataset. Users with at least five pain records in both the predictor and outcome periods (N=782 users) are included in the analysis. RESULTS: k-means clustering algorithm was applied to pain volatility scores to establish a threshold of 1.6 to differentiate between low and high volatility classes. After validating the threshold using random subsamples, 2 classes were created: low volatility (n=611) and high volatility (n=171). In this class-imbalanced dataset, all 4 prediction models achieved 78.1% (611/782) to 79.0% (618/782) in overall accuracy. However, all models have a prediction accuracy of less than 18.7% (32/171) for the high volatility class. After addressing the class imbalance issue using random subsampling, results improved across all models for the high volatility class to greater than 59.6% (102/171). The prediction model based on Random Forests performs the best as it consistently achieves approximately 70% accuracy for both classes across 3 random subsamples. CONCLUSIONS: We propose a novel method for measuring pain volatility. Cluster analysis was applied to divide users into subsets of low and high volatility classes. These classes were then predicted at the sixth month of app use with an acceptable degree of accuracy using machine learning methods based on the features extracted from demographic, clinical, and app use information from the first month.

A Review of the Mechanism of Antagonism of N-methyl-D-aspartate Receptor by Ketamine in Treatment-resistant Depression.

The biochemical processes involved in depression go beyond serotonin, norepinephrine, and dopamine. The N-methyl-D-aspartate (NMDA) receptor has a major role in the neurophysiology of depression. Ketamine, one of the prototypical NMDA antagonists, works rapidly in controlling depressive symptoms, including acutely suicidal behavior, by just a single injection. Ketamine may rapidly increase the glutamate levels and lead to structural neuronal changes. Increased neuronal dendritic growth may contribute to synaptogenesis and an increase in brain-derived neurotrophic factor (BDNF). Activation of the mechanistic target of rapamycin (mTOR), as well as increased levels of BDNF, may increase long-term potentiation and result in an improvement in the symptoms of depression. The mechanisms of ketamine's proposed effect as an off-label treatment for resistant depression are outlined in this paper.

Patterns of User Engagement With the Mobile App, Manage My Pain: Results of a Data Mining Investigation.

BACKGROUND: Pain is one of the most prevalent health-related concerns and is among the top 3 most common reasons for seeking medical help. Scientific publications of data collected from pain tracking and monitoring apps are important to help consumers and healthcare professionals select the right app for their use. OBJECTIVE: The main objectives of this paper were to (1) discover user engagement patterns of the pain management app, Manage My Pain, using data mining methods; and (2) identify the association between several attributes characterizing individual users and their levels of engagement. METHODS: User engagement was defined by 2 key features of the app: longevity (number of days between the first and last pain record) and number of records. Users were divided into 5 user engagement clusters employing the k-means clustering algorithm. Each cluster was characterized by 6 attributes: gender, age, number of pain conditions, number of medications, pain severity, and opioid use. Z tests and chi-square tests were used for analyzing categorical attributes. Effects of gender and cluster on numerical attributes were analyzed using 2-way analysis of variances (ANOVAs) followed up by pairwise comparisons using Tukey honest significant difference (HSD). RESULTS: The clustering process produced 5 clusters representing different levels of user engagement. The proportion of males and females was significantly different in 4 of the 5 clusters (all P ≤.03). The proportion of males was higher than females in users with relatively high longevity. Mean ages of users in 2 clusters with high longevity were higher than users from other 3 clusters (all P <.001). Overall, males were significantly older than females (P <.001). Across clusters, females reported more pain conditions than males (all P <.001). Users from highly engaged clusters reported taking more medication than less engaged users (all P <.001). Females reported taking a greater number of medications than males (P =.04). In 4 of 5 clusters, the percentage of males taking an opioid was significantly greater (all P ≤.05) than that of females. The proportion of males with mild pain was significantly higher than that of females in 3 clusters (all P ≤.008). CONCLUSIONS: Although most users of the app reported being female, male users were more likely to be highly engaged in the app. Users in the most engaged clusters self-reported a higher number of pain conditions, a higher number of current medications, and a higher incidence of opioid usage. The high engagement by males in these clusters does not appear to be driven by pain severity which may, in part, be the case for females. Use of a mobile pain app may be relatively more attractive to highly-engaged males than highly-engaged females, and to those with relatively more complex chronic pain problems.

Effectiveness of Preoperative Lymphoscintigraphy for the Detection of Cervical Lymph Node Metastasis in Patients with Oral Squamous Cell Carcinoma.

BACKGROUND: Oral squamous cell carcinoma (OSCC) is one of the most common malignant tumor. OSCC is the malignancy of squamous epithelium of oral cavity, which is the sixth most common malignancy reported worldwide and one with highest mortality rate among all malignancies. AIMS: The aims of this study is to assess the diagnostic performance of lymphoscintigraphy (LSG) for the detection of cervical lymph node metastasis in patients with OSCC. MATERIALS AND METHODS: This was a prospective study done in Oral and Maxillofacial Surgery Department and National Institute of Nuclear Medicine and Allied Science, Bangabandhu Sheikh Mujib Medical University during July 2015-June 2016. Thirty-six patients with OSCC were included in this study. Radioisotope technique was used in the detection of cervical metastases in patients with histologically proven OSCC. Patients were assessed by LSG after diagnosis of OSCC, and then, it was compared with postoperative histopathology report. RESULTS: Lymphoscintigraphically out of 36 patients, 23 had lymphatic channel obstruction where histologically 20 patients had lymph node metastasis. There were 20 true positive cases, 13 true negative cases, and 3 false positive cases but no false negative case was found. The test of validity result reveals that sensitivity 100.0%, specificity 81.25%, accuracy 96.66%, positive predictive value 86.96%, and negative predictive value 100.0%. CONCLUSION: LSG for the detection of cervical lymph node metastasis has an important role for the management of OSCC. It is also cost-effective and decreases the morbidity.

Utilizing ECG-Based Heartbeat Classification for Hypertrophic Cardiomyopathy Identification.

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is (potentially fatally) obstructed. A test based on electrocardiograms (ECG) that record the heart electrical activity can help in early detection of HCM patients. This paper presents a cardiovascular-patient classifier we developed to identify HCM patients using standard 10-second, 12-lead ECG signals. Patients are classified as having HCM if the majority of their recorded heartbeats are recognized as characteristic of HCM. Thus, the classifier's underlying task is to recognize individual heartbeats segmented from 12-lead ECG signals as HCM beats, where heartbeats from non-HCM cardiovascular patients are used as controls. We extracted 504 morphological and temporal features­both commonly used and newly-developed ones­from ECG signals for heartbeat classification. To assess classification performance, we trained and tested a random forest classifier and a support vector machine classifier using 5-fold cross validation. We also compared the performance of these two classifiers to that obtained by a logistic regression classifier, and the first two methods performed better than logistic regression. The patient-classification precision of random forests and of support vector machine classifiers is close to 0.85. Recall (sensitivity) and specificity are approximately 0.90. We also conducted feature selection experiments by gradually removing the least informative features; the results show that a relatively small subset of 264 highly informative features can achieve performance measures comparable to those achieved by using the complete set of features.