Comparison of Growth of School Children in Mymensingh City Area, Bangladesh Using the 2000 CDC Standards and 2007 WHO Standards

Growth assessment is an essential component of child health surveillance. The most influential tool in the growth assessment is the growth chart. Growth parameters of children are usually interpreted in relation to international standards like the NCHS/1977, CDC/2000 and WHO/2007 growth charts. This comparative study was carried out in the Department of Paediatrics, Mymensingh Medical College, Mymensingh, Bangladesh from November 2014 to October 2015 to compare the growth parameters of children using 2000 CDC standards and 2007 WHO standards. A total of five hundred healthy school children, aged 6 to 10 years were randomly selected proportionately from each class of five government primary schools from Mymensingh city area. Children with physical deformities and acutely ill and those with chronic debilitating diseases were excluded from the study. The mean and median weight, height and BMI were calculated for each age and sex. The median weight, height and BMI were then standardized by converting them to Z-scores (SD) for comparing against known reference values of 2000 CDC and 2007 WHO charts. Among 500 children, 252(50.4%) were boys and 248(49.6%) were girls. The mean age was found 8.0±1.42 years in boys and 8.04±1.42 years in girls. The mean weight was found 24.4±6.36kg in boys and 24.08±6.35kg in girls. The mean height was 125.90±10.16cm in boys and 125.38±10.74cm in girls. The mean weight, height and BMI of boys and girls were found to be lower in all age groups with compared to 2000 CDC standard and 2007 WHO standard but were closer to the WHO standards compared to the CDC standards. According to CDC and WHO in the study it was observed that underweight was found 16.3% and 14.7%, stunting 7.1% and 6.7%, overweight 6.7% and 8.7%, and obese 2.8% and 4.0% respectively in male subjects. On the other hand, in female subjects it was observed that according to CDC chart and WHO chart underweight was found 19.4% and 13.3%, stunting 6.0% and 4.8%, overweight 7.3% and 8.1% and obese 2.0% and 2.0% respectively. The findings in this study imply that growth pattern of Bangladeshi school children is closer with WHO standards and wider from CDC standards.

Prevalence of Hepatitis B Virus Infection among Children with Liver Disease Admitted In Mymensingh Medical College Hospital

Hepatitis B virus infection is an important cause of liver disease. Hepatitis B Virus may present with varying degree of severity. In older children, 5-10% cases leading to chronic liver disease, cirrhosis and hepatocellular carcinoma. This descriptive cross sectional study was done to assess the prevalence of Hepatitis B Virus infection among hospitalized children with liver disease in pediatric department of Mymensingh Medical College Hospital, Bangladesh from December 2015 to October 2016. All the children of both sexes having age between six months to twelve years admitted in the pediatric ward with acute or chronic liver disease were included in this study by purposive sampling. A written consent was obtained from legal guardian of children before inclusion. Ethical clearance was obtained from competent authority. A detailed history was taken from parents in each case according to pre-designed questionnaire about demography of the patients and the risk factors of the liver disease. A thorough clinical examination and available relevant investigations like serological testing for HBV infection was done in all patients. We had figure out the seropositivity of HBV among patients having liver disease by doing HBsAg and Anti-HBc IgM. Progress of the patient was monitored by daily clinical examinations and by investigating HBsAg and Anti-HBc IgM. Finally data analysis was done by SPSS version 21.0. Among total 100 patients most (44%) patients were in 7-10 years old and most (62%) of the participants were male. Acute liver disease was 58% cases and chronic liver disease was 42% cases. HBsAg was positive in 1 case among acute liver disease and 5 cases among chronic cases. Total 6 (six) patients were found positive for HBsAg. Anti HBc IgM was positive in 4 patients among acute liver disease. Among these Anti HBc IgM positive (4) patients only one had both HBsAg and Anti HBc IgM positive. So, four patients were confirming suffered from acute viral hepatitis because they had anti HBc IgM positive. On the contrary 5 patients suffered from chronic hepatitis by hepatitis B because they were only HBsAg positive. So, in this study 9 patients (9%) were confirming suffered from HBV infection. Possible transmission factors of hepatitis B were history of (H/O) blood transfusion/trauma/parenteral injection, H/O umbilical sepsis, H/O maternal illness/infection during pregnancy. HBV still is a major cause of morbidity. All the children with liver disease should be routinely tested for HBV.

Treacher Collins Syndrome: A Case Report

Treacher collins syndrome (TCS) or Franceschetti syndrome is an autosomal dominant inherited disorder with variable expressivity. It affects mainly craniofacial structure that derives from 1st and 2nd branchial arches approximately between the 20th day and 12th week of intrauterine life. This syndrome has different clinical types. Most common features are antimongoloid slanting of the palpebral fissures, hypoplasia of zygoma, maxilla & mandible with various eye and ear abnormalities. Here we present a case of an 11 days old female neonate, who was ill looking, dyspnoeic having significant facial profile, multiple congenital anomalies and dolicocephaly; admitted in the department of Neonatology, Mymensingh Medical College Hospital (MMCH), Mymensingh, Bangladesh on 7th August 2020. After taking all the diagnostic assistance of the multidisciplinary approach mainly on the basis of clinical features and radiology we diagnosed the case as TCS. We managed the patient by maintaining temperature, giving nutritional support and injectable antibiotic, took consultation from Otolaryngology department then we discharged the baby with proper counseling, advised regarding further follow up and to consult with paediatric surgeon and cardiologist.

Clinical Profile and Immediate Outcome of Children Admitted With Acute Glomerulonephritis in Pediatrics Department of A Tertiary Level Hospital

Acute post-streptococcal glomerulonephritis (APSGN) is the commonest cause of acute glomerulonephritis (AGN), which usually present with gross hematuria, mild edema, oliguria, hypertension and varying degree of renal insufficiency. It is more common among the population of school going age where poverty, overcrowding and poor hygienic conditions are prevailing. This cross sectional observational study was aimed to know the socio-demographic variables, clinical profile and immediate outcome of AGN in hospitalized children and was conducted in the Pediatric department of Mymensingh Medical College Hospital (MMCH), Mymensingh, Bangladesh from November 2014 to April 2015. A detailed history was taken from the parents in each case with a written questionnaire. A written consent was also taken from the guardian of the including patients and also permission was taken from the ethical committee of MMCH. Thorough clinical examination and available relevant investigations were done in all patients. Progresses of the patient were monitored by daily clinical examinations and also by investigations. Data were analyzed by statistical package for social science (SPSS) windows version 18. Results were verified by doing standard test for significance. Among total 60 cases male was 58.3% & female was 41.7%. The common age group of presentation was between 7-12 years (73%), peak age of incidence was 7-9 years. Most of them came from low socioeconomic status (83.3%), 63.3% from rural area with average 5-6 member's family size. Most of the parents were illiterate. History of (H/O) skin infection was present in 35(58.3%) patients, 15(25%) had H/O sore throat, 15% did not give any H/O infection before presentation. Average duration of gap between infection and appearance of clinical feature was 7-14 days in 73.40%and 15-21 days was in 45.7% in case of sore throat & skin infection respectively. Almost all (95%) patients presented with puffiness of face, others presented with scanty micturition, gross hematuria, respiratory distress, fever, convulsion and altered sensorium. Edema (75%), hypertension (88.3%), pallor (38%), tachypnea (25%), tachycardia (26.7%) were the important clinical findings. Microscopic hematuria was present among 96.66% patients; low complement level was found in 85% cases. There is significant association between low socioeconomic statuses with more hospital stay. Only one patient died due to heart failure and 98.3% patient had complete recovery. Results of this study conclude that most of the patients came from rural illiterate family with low socioeconomic background. Skin infection is the commonest cause of acute glomerulonephritis. Edema, scanty micturation, hematuria and hypertension are the common mode of presentation. Heart failure and hypertensive encephalopathy are the common complication of AGN. Immediate prognosis of AGN was excellent.

Antibacterial Activities of Clove (Syzygium aromaticum) Extracts Against Three Food Borne Pathogens: Staphylococcus aureus, Escherichia coli and Pseudomonas aeruginosa

Evaluation of the in vitro antibacterial activity of aqueous and ethanolic extracts isolated from Clove (Syzygium aromaticum) buds against three food borne pathogens, gram-positive Staphylococcus aureus and gram-negative Escherichia coli & Pseudomonas aeruginosa. This interventional study was carried out during the period of July 2018 to June 2019 in the Department of Pharmacology and Therapeutics with the collaboration of Department of Microbiology, Mymensingh Medical College, Mymensingh, Bangladesh. The antibacterial activity was tested at different concentrations of both extracts of spice by using disc diffusion & broth dilution method. The extracts were prepared by using solvents aqueous & ethanol. The test microorganisms were also tested for their activity against a standard antibiotic Gentamicin (80mg) by broth dilution method and the result was compared with that of Aqueous and Ethanolic extracts. Aqueous and ethanolic extracts of clove had inhibitory activity against the test bacteria. Among different concentrations of the ACE, 500μg/ml & above concentration showed inhibitory effect against Staphylococcus aureus & Escherichia coli and 700μg/ml & above concentration showed inhibitory effect against Pseudomonas aeruginosa. In case of ECE, 500μg/ml & above concentration showed inhibitory effect against aforesaid bacteria. In disc diffusion method, S. aureus was found to be most susceptible to ACE (30.5mm) & Pseudomonas aeruginosa was found to be most susceptible to ECE (38mm). Minimum inhibitory concentrations (MIC) of ECE were lower than ACE for the test bacteria except Staphylococcus aureus where MICs of ACE & ECE were the same. This result was also compared against a standard antibiotic Gentamicin where the MICs of Gentamicin were lower in comparison to MICs of ACE & ECE. The present study showed that aqueous and ethanolic extracts of Clove demonstrated antibacterial effects against food borne pathogens.

Chronodes: Interactive Multifocus Exploration of Event Sequences

The advent of mobile health (mHealth) technologies challenges the capabilities of current visualizations, interactive tools, and algorithms. We present Chronodes, an interactive system that unifies data mining and human-centric visualization techniques to support explorative analysis of longitudinal mHealth data. Chronodes extracts and visualizes frequent event sequences that reveal chronological patterns across multiple participant timelines of mHealth data. It then combines novel interaction and visualization techniques to enable multifocus event sequence analysis, which allows health researchers to interactively define, explore, and compare groups of participant behaviors using event sequence combinations. Through summarizing insights gained from a pilot study with 20 behavioral and biomedical health experts, we discuss Chronodes's efficacy and potential impact in the mHealth domain. Ultimately, we outline important open challenges in mHealth, and offer recommendations and design guidelines for future research.

mHealth Visual Discovery Dashboard

We present Discovery Dashboard, a visual analytics system for exploring large volumes of time series data from mobile medical field studies. Discovery Dashboard offers interactive exploration tools and a data mining motif discovery algorithm to help researchers formulate hypotheses, discover trends and patterns, and ultimately gain a deeper understanding of their data. Discovery Dashboard emphasizes user freedom and flexibility during the data exploration process and enables researchers to do things previously challenging or impossible to do - in the web-browser and in real time. We demonstrate our system visualizing data from a mobile sensor study conducted at the University of Minnesota that included 52 participants who were trying to quit smoking.

Finding Significant Stress Episodes in a Discontinuous Time Series of Rapidly Varying Mobile Sensor Data

Management of daily stress can be greatly improved by delivering sensor-triggered just-in-time interventions (JITIs) on mobile devices. The success of such JITIs critically depends on being able to mine the time series of noisy sensor data to find the most opportune moments. In this paper, we propose a time series pattern mining method to detect significant stress episodes in a time series of discontinuous and rapidly varying stress data. We apply our model to 4 weeks of physiological, GPS, and activity data collected from 38 users in their natural environment to discover patterns of stress in real-life. We find that the duration of a prior stress episode predicts the duration of the next stress episode and stress in mornings and evenings is lower than during the day. We then analyze the relationship between stress and objectively rated disorder in the surrounding neighborhood and develop a model to predict stressful episodes.

Visualization of Time-Series Sensor Data to Inform the Design of Just-In-Time Adaptive Stress Interventions

We investigate needs, challenges, and opportunities in visualizing time-series sensor data on stress to inform the design of just-in-time adaptive interventions (JITAIs). We identify seven key challenges: massive volume and variety of data, complexity in identifying stressors, scalability of space, multifaceted relationship between stress and time, a need for representation at multiple granularities, interperson variability, and limited understanding of JITAI design requirements due to its novelty. We propose four new visualizations based on one million minutes of sensor data (n=70). We evaluate our visualizations with stress researchers (n=6) to gain first insights into its usability and usefulness in JITAI design. Our results indicate that spatio-temporal visualizations help identify and explain between- and within-person variability in stress patterns and contextual visualizations enable decisions regarding the timing, content, and modality of intervention. Interestingly, a granular representation is considered informative but noise-prone; an abstract representation is the preferred starting point for designing JITAIs.

Center of excellence for mobile sensor data-to-knowledge (MD2K)

Mobile sensor data-to-knowledge (MD2K) was chosen as one of 11 Big Data Centers of Excellence by the National Institutes of Health, as part of its Big Data-to-Knowledge initiative. MD2K is developing innovative tools to streamline the collection, integration, management, visualization, analysis, and interpretation of health data generated by mobile and wearable sensors. The goal of the big data solutions being developed by MD2K is to reliably quantify physical, biological, behavioral, social, and environmental factors that contribute to health and disease risk. The research conducted by MD2K is targeted at improving health through early detection of adverse health events and by facilitating prevention. MD2K will make its tools, software, and training materials widely available and will also organize workshops and seminars to encourage their use by researchers and clinicians.

Estimating Drivers' Stress from GPS Traces

Driving is known to be a daily stressor. Measurement of driver's stress in real-time can enable better stress management by increasing self-awareness. Recent advances in sensing technology has made it feasible to continuously assess driver's stress in real-time, but it requires equipping the driver with these sensors and/or instrumenting the car. In this paper, we present "GStress", a model to estimate driver's stress using only smartphone GPS traces. The GStress model is developed and evaluated from data collected in a mobile health user study where 10 participants wore physiological sensors for 7 days ( for an average of 10.45 hours/day) in their natural environment. Each participant engaged in 10 or more driving episodes, resulting in a total of 37 hours of driving data. We find that major driving events such as stops, turns, and braking increase stress of the driver. We quantify their impact on stress and thus construct our GStress model by training a Generalized Linear Mixed Model (GLMM) on our data. We evaluate the applicability of GStress in predicting stress from GPS traces, and obtain a correlation of 0.72. By obviating any burden on the driver or the car, we believe, GStress can make driver's stress assessment ubiquitous.

Assessing the Availability of Users to Engage in Just-in-Time Intervention in the Natural Environment

Wearable wireless sensors for health monitoring are enabling the design and delivery of just-in-time interventions (JITI). Critical to the success of JITI is to time its delivery so that the user is available to be engaged. We take a first step in modeling users' availability by analyzing 2,064 hours of physiological sensor data and 2,717 self-reports collected from 30 participants in a week-long field study. We use delay in responding to a prompt to objectively measure availability. We compute 99 features and identify 30 as most discriminating to train a machine learning model for predicting availability. We find that location, affect, activity type, stress, time, and day of the week, play significant roles in predicting availability. We find that users are least available at work and during driving, and most available when walking outside. Our model finally achieves an accuracy of 74.7% in 10-fold cross-validation and 77.9% with leave-one-subject-out.

Are We There Yet? Feasibility of Continuous Stress Assessment via Wireless Physiological Sensors

Stress can lead to headaches and fatigue, precipitate addictive behaviors (e.g., smoking, alcohol and drug use), and lead to cardiovascular diseases and cancer. Continuous assessment of stress from sensors can be used for timely delivery of a variety of interventions to reduce or avoid stress. We investigate the feasibility of continuous stress measurement via two field studies using wireless physiological sensors - a four-week study with illicit drug users (n = 40), and a one-week study with daily smokers and social drinkers (n = 30). We find that 11+ hours/day of usable data can be obtained in a 4-week study. Significant learning effect is observed after the first week and data yield is seen to be increasing over time even in the fourth week. We propose a framework to analyze sensor data yield and find that losses in wireless channel is negligible; the main hurdle in further improving data yield is the attachment constraint. We show the feasibility of measuring stress minutes preceding events of interest and observe the sensor-derived stress to be rising prior to self-reported stress and smoking events.