Assessment of arsenic exposure in the population of Sabalpur village of Saran District of Bihar with mitigation approach.

Arsenic poisoning through groundwater is the world's greatest normal groundwater catastrophe which got an immense effect on worldwide general wellbeing. India is confronting the outcomes of arsenic poisoning in the zone of Ganga Brahmaputra alluvial plains. In Bihar, out of 38 districts, 18 districts are exceptionally influenced with groundwater arsenic defilement. In the present study, we have assessed the current situation of arsenic exposure in Sabalpur village of Saran district of Bihar after reporting of breast, renal, skin and thyroid cancer cases from this village along with typical symptoms of arsenicosis. Such cancer patients were identified at our institute and were taken for the study. The present investigation deals with the quantification of arsenic in groundwater, hair and nail samples of subjects as well as the survey of entire village to know the overall health status of the village people. A total of n=128 household handpump water samples as well as n=128 human hair and nail samples were collected from over n=520 households. Using the graphite furnace atomic absorption spectrophotometer (GF-AAS), all the samples were analysed. The investigation resulted that the 61% of the analysed samples particularly the groundwater had the arsenic levels more than the permissible limit of WHO (> 10 µg/L) with 244.20 µg/L as the highest arsenic contamination in one of the handpump water sample. The exposure effect of hair sample was worst as 88% of all the collected samples were having high arsenic levels more than the permissible limit (> 0.2 mg/Kg). In case of nail samples, 92% of the samples were having high arsenic concentration more than the permissible limit (> 0.5 mg/Kg). The health survey study revealed high magnitude of disease burden in the exposed population with symptoms such as asthma, anaemia, hepatomegaly, diabetes, cardiac problem, skin fungal infections, breathlessness and mental disability. Few cancer cases of renal, skin, breast and cervix were also found among the exposed population of this village. The percentage of cancer cases in this village was 0.94% that was low, but it would be an aggravated situation in the near future if people will continue drinking arsenic-contaminated water. Therefore, a mitigation intervention was carried out in March 2020 by installing an arsenic filter plant. The health situation in the village in the present scenario is hope to improve in the coming years. However, motivation and awareness among the village population are still required.

Distribution and Geochemical Controls of Arsenic and Uranium in Groundwater-Derived Drinking Water in Bihar, India.

Chronic exposure to groundwater containing elevated concentrations of geogenic contaminants such as arsenic (As) and uranium (U) can lead to detrimental health impacts. In this study, we have undertaken a groundwater survey of representative sites across all districts of the State of Bihar, in the Middle Gangetic Plain of north-eastern India. The aim is to characterize the inorganic major and trace element aqueous geochemistry in groundwater sources widely used for drinking in Bihar, with a particular focus on the spatial distribution and associated geochemical controls on groundwater As and U. Concentrations of As and U are highly heterogeneous across Bihar, exceeding (provisional) guideline values in ~16% and 7% of samples (n = 273), respectively. The strongly inverse correlation between As and U is consistent with the contrasting redox controls on As and U mobility. High As is associated with Fe, Mn, lower Eh and is depth-dependent; in contrast, high U is associated with HCO3-, NO3- and higher Eh. The improved understanding of the distribution and geochemical controls on As and U in Bihar has important implications on remediation priorities and selection, and may contribute to informing further monitoring and/or representative characterization efforts in Bihar and elsewhere in India.

InstaBP: Cuff-less Blood Pressure Monitoring on Smartphone using Single PPG Sensor.

Cuff-less Blood Pressure (BP) monitoring has gained interest of the research community in recent years, due to its importance in continuous and non-invasive monitoring of BP for early detection of hypertension, thereby reducing mortality. Several approaches that involve photoplethysmography (PPG) and Pulse Transit Time (PTT) have been explored with promising results; however the requirement of two sensors makes them obtrusive for continuous use. Single PPG sensor approaches using machine learning have also been attempted, but there are certain deficiencies in these methods as they go for a one-size-fits-all approach. In this work, we develop an ensemble of BP prediction models based on demographic and physiological partitioning. Also, we incorporate a set of unique PPG features into our models, which results in test accuracies of 5 mmHg Mean Absolute Error (MAE) for Diastolic BP, and 6.9 mmHg MAE for Systolic BP. Given our marked improvement over ubiquitous models (18% for Diastolic BP and 11.5% for Systolic BP), this approach opens up avenues where single PPG sensor based methods can predict BP with a high degree of accuracy. This is a big step towards developing continuous BP monitoring systems, and can help in better management of cardiac health.

Cost of Immediate Surgery Versus Non-operative Treatment for Trigger Finger in Diabetic Patients.

PURPOSE: As health care costs continue to rise, providers must increasingly identify and implement cost-effective practice measures without sacrificing quality of care. Corticosteroid injections are an established treatment for trigger finger; however, numerous clinical trials have documented the limited efficacy of these injections in the diabetic population. Furthermore, the most cost-effective treatment strategy for diabetic trigger finger has not been determined. The purpose of this study was to perform a decision analysis to identify the least costly strategy for effective treatment of diabetic trigger finger using existing evidence in the literature. METHODS: Four treatment strategies for diabetic trigger finger were identified: (1) 1 steroid injection followed by surgical release, (2) 2 steroid injections followed by surgical release, (3) immediate surgical release in the operating room, and (4) immediate surgical release in the clinic. A literature review was conducted to determine success rates of the different treatment strategies. Costing analysis was performed using our institutional reimbursement from Medicare. One-way sensitivity and threshold analysis was utilized to determine the least costly treatment strategy. RESULTS: The least costly treatment strategy was immediate surgical release in the clinic. In patients with insulin-dependent diabetes mellitus, this strategy results in a 32% and a 39% cost reduction when compared with treatment with 1 or 2 corticosteroid injections, respectively. For 1 or 2 corticosteroid injections to be the most cost-effective strategy, injection failure rates would need to be less than 36% and 34%, respectively. The overall cost of care for immediate surgical release in the clinic was $642. CONCLUSIONS: Diabetic trigger finger is a common problem faced by hand surgeons, with a variety of acceptable treatment algorithms. Management of diabetic trigger finger with immediate surgical release in the clinic is the most cost-effective treatment strategy, assuming a corticosteroid injection failure rate of at least 34%. TYPE OF STUDY/LEVEL OF EVIDENCE: Economic/decision III.

Cuff-less PPG based continuous blood pressure monitoring: a smartphone based approach.

Cuff-less estimation of systolic (SBP) and diastolic (DBP) blood pressure is an efficient approach for non-invasive and continuous monitoring of an individual's vitals. Although pulse transit time (PTT) based approaches have been successful in estimating the systolic and diastolic blood pressures to a reasonable degree of accuracy, there is still scope for improvement in terms of accuracies. Moreover, PTT approach requires data from sensors placed at two different locations along with individual calibration of physiological parameters for deriving correct estimation of systolic and diastolic blood pressure (BP) and hence is not suitable for smartphone deployment. Heart Rate Variability is one of the extensively used non-invasive parameters to assess cardiovascular autonomic nervous system and is known to be associated with SBP and DBP indirectly. In this work, we propose a novel method to extract a comprehensive set of features by combining PPG signal based and Heart Rate Variability (HRV) related features using a single PPG sensor. Further, these features are fed into a DBP feedback based combinatorial neural network model to arrive at a common weighted average output of DBP and subsequently SBP. Our results show that using this current approach, an accuracy of ±6.8 mmHg for SBP and ±4.7 mmHg for DBP is achievable on 1,750,000 pulses extracted from a public database (comprising 3000 people). Since most of the smartphones are now equipped with PPG sensor, a mobile based cuff-less BP estimation will enable the user to monitor their BP as a vital parameter on demand. This will open new avenues towards development of pervasive and continuous BP monitoring systems leading to an early detection and prevention of cardiovascular diseases.

StayFit: a wearable application for Gym based power training.

Comprehensive fitness training involves both cardiorespiratory and power components. Often power/muscle strength training is confused with cardiorespiratory endurance training. However, each of them target different physiological aspects of fitness. Although, wearable based fitness trackers designed towards cardiorespiratory endurance training are available in the market, a dedicated wearable based fitness application designed for power training/tracking is still not readily available to fitness enthusiasts. With growing usage of wearable technology to manage and track personal health, it is imperative to develop health/fitness applications for wearables. A wearable based application for power training will allow the user to track build-up of muscle strength unobtrusively over a period of time. This work provides a framework and design for automatic detection, counting repetitions of strength training Gym exercises (covering all the major muscle groups), estimate personalized calories spent in each session and track power on a standalone Gear watch (both analysis and display including User Experience(UX) design). Our proposed method detects activity with ~96% sensitivity and ~96% specificity on an average and count repetitions with an overall accuracy of >95% using motion sensor data (accelerometer, gyroscope) for eight major Gym exercises. Additionally, using heart rate sensor data we have provided a mechanism to individually track the power/muscle strength of a person. This work will give further impetus towards developing wearable based systems for personalized fitness tracking and training. This will also give an option for wearable users to address both the crucial aspects of fitness (cardiorespiratory and muscle strength).