Pattern of Medical Device Adverse Events in a Tertiary Care Hospital in Northern India: An Ambispective Study.

BACKGROUND: Knowledge about the pattern of adverse events caused by medical devices (MDs) is limited in India. We aimed to assess the pattern of MD adverse events (MDAEs) in a tertiary hospital in Northern India. MATERIALS AND METHODS: This descriptive study was conducted ambispectively at various clinical departments of PGIMER, Chandigarh. We followed the guidelines edged by the Materiovigilance Program of India (MvPI) to conduct this study. The prospective study (PS) was done from January to December 2020, with a concurrent retrospective study (RS) proceeding to 3 years to learn more about the reporting culture, demographics, notification status, risk class of defective devices, and the type of adverse events. RESULTS: We received 224 MDAE in the PS and identified 413 MDAE in the RS. Reporting of adverse events to the national MvPI was negligible in the RS. In the PS, nurses reported the majority of MDAEs (65%), followed by doctors (30%). The occurrence of MDAE was higher in males (PS; 52%, RS; 57%) and age groups between 21 and 30 years (PS; 19.1%, RS; 23.2%) in both studies. MDAEs were frequent in low- to moderate-risk devices (class B: 66%) in the PS, while it was documented only for high-risk devices (class C: 51% and class D: 49%) in the RS. Most of the serious adverse events (SAEs) were reported among moderate to high-risk devices, and an increased frequency of SAE (60.4%) was observed among nonnotified MDs. The overall incidence of near-miss events was 14%. CONCLUSION: Knowledge of MDAEs and reporting of defective devices to regulatory authorities is essential to prevent further incidence. Adverse events caused by MDs are ubiquitous irrespective of their risk classification, notification status, and patient demographic factors. Accelerated reporting of MDAE by all cadre of healthcare professionals is urgently required to safeguard the health of Indians.

Application of Monte Carlo simulation for carcinogenic and non-carcinogenic risks assessment through multi-exposure pathways of heavy metals of river water and sediment, India.

Heavy metal contamination has severe detrimental impacts on the entire river ecosystem's quality and causes potential risks to human health. An integrated approach comprising deterministic and probabilistic (Monte Carlo simulation) models with sensitivity analysis was adopted to determine heavy metals' chronic daily intake (CDI) and their associated health risks from the riverine ecosystem. Both carcinogenic and non-carcinogenic risks of water and sediment were estimated through multi-exposure pathways. The analytical results indicated that the concentration patterns of heavy metals in sediment (Fe > Mn > Sr > Zn > Cr > Cu > Cd) were slightly different and higher than in water (Fe > Zn > Cr > Sr > Mn > Cu > Cd). The potential carcinogenic risks of Cr and Cd in sediment (5.06E-02, 5.98E-04) were significantly (p < 0.05) higher than in water (9.08E-04, 8.97E-05). Moreover, 95th percentile values of total cancer risk (TCR) for sediment (1.80E-02, 3.37E-02) were about 22 and 143 times higher than those of water (8.10E-04, 2.36E-04) for adults and children, respectively. The analysis of non-carcinogenic risk revealed a significantly higher overall hazard index (OHI) for both sediment (adults: 1.26E+02, children: 1.11E+03) and water (adults: 3.26E+00, children: 9.85E+00) than the USEPA guidelines (OHI ≤ 1). The sensitivity analysis identified that the concentration of heavy metals was the most influencing input factor in health risk assessment. Based on the reasonable maximum exposure estimate (RME), the study will be advantageous for researchers, scientists, policymakers, and regulatory authorities to predict and manage human health risks.

Hydrochemical investigation of groundwater in a trans-Himalayan region of Ladakh, India, using geochemical modelling and entropy technique.

Evaluating the hydrogeochemistry and groundwater quality status is vital to understand the sources and extent of groundwater contamination. Chemometric analysis, geochemical modelling and entropy technique were explored to delineate the hydrogeochemistry of groundwater in the trans-Himalayan region. Analysis of hydrochemical facies revealed that 57.14, 39.29, and 3.57% of samples were Ca-Mg-HCO3-, Ca-Mg-Cl- and Mg-HCO3- water types, respectively. Gibbs diagrams illustrate the effects of the dissolution of carbonates and silicates during weathering on groundwater hydrogeochemistry. The PHREEQC modelling depicted that most of the secondary minerals are supersaturated except for halite, sylvite, and magnetite which are undersaturated and in equilibrium with nature. Multivariate statistical techniques, including principal component analysis, were applied for source apportionment indicating that the hydrochemistry of the groundwater was mainly controlled by geogenic sources (rock-water interaction) along with secondary pollution through increased anthropogenic sources. Heavy metal accumulation in groundwater depicted the order of Cd > Cr > Mn > Fe > Cu > Ni > Zn. EWQI analysis revealed that none of the samples fell into excellent and good categories. In total, 92.86% of groundwater samples were in an average category while the rest of the samples (7.14%) were unfit for drinking. This study will provide baseline data and a scientific framework which can be used in source apportionment studies, predictive modelling and efficient management of water resources.

Optimisation of process parameters of a thermal digester for the rapid conversion of food waste into value-added soil conditioner.

A novel thermal digester for converting food waste (FW) into nutrient-rich soil conditioner was designed and explored. The process variables, that is, temperature, the volume of the digestion chamber and the rotational speed of the digester were optimised using response surface methodology (RSM). The study revealed that the digester temperature of 150°C and rotational speed of 40 RPM required minimum time (180 minutes) for attaining the equilibrium moisture with a minimum energy consumption of 0.218 kWh kg-1. The process resulted in 80 ± 2.5% reduction in total volume of the FW. Detailed characterisation revealed that the end product was comparable to the organic fertiliser as per the Fertiliser Association of India norms. The digestion helps in breakdown of cellulose content of FW into hemicellulose which supports formation of primary and secondary walls, seed storage carbohydrates, and facilitates plant growth. 1H-Nuclear magnetic resonance (1H-NMR) spectra of the end product revealed mineralisation of organics during digestion. Decrease in ultraviolet (UV) absorbance value at 280 nm also revealed the humification of the end product. X-ray diffraction (XRD) analysis disclosed extremely low crystallinity and non-recalcitrant nature of the end product. A low humification index value (HI-3.43), high fertilising index (FI-4.8), and clean index (CI-5.0) revealed that the end product could safely be utilised as an organic fertiliser. The cost-benefit analysis revealed that thermal digestion technique is profitable and economically viable with benefit-cost ratio (BCR) of 1.35. The study offers a unique approach for the rapid and hassle-free production of value-added soil conditioner from FW.

Investigating the dynamics of ammonia volatilisation and the role of additives in thermal digestion of food waste.

Averting nutrient volatilisation in thermal treatment of organic waste is a challenging task. The dynamics of ammonia volatilisation and the role of additives in preventing the losses of nutrients in thermal digestion of food waste (FW) were explored. The experimental trials were performed in a convective dehydrator at different combinations of temperatures and airflow velocities. The study dictated that ammonia volatilisation rate increased with increase in temperature and airflow velocity. The losses reached to its peak during the initial drying period and then gradually declined in the falling rate period. An artificial intelligence-based random forest model was explored to precisely predict the ammonia losses during the drying process. The SEM-EDX images confirmed enhanced N (2.25%) in the alum treated end product compared to blank (N - 1.8%) and thus reveals alum induced mineralization of nutrients. Higher intensities of the N containing compounds peaks observed in FTIR spectra also supported the mineralization of nitrogen. XRD analysis indicated formation of stable ammonium compounds in the sample digested with alum. Cost benefit analysis of the alum aided digestion revealed that it enhances the nutrient retention and overall cost of N in the end product by ₹626/tonne. The study revealed high potential of alum in reducing the ammonia volatilisation and enhancing the agronomical value of nutrients in the thermal digestion process.

Seasonal variation and Incidence of rupture of intracranial aneurysm : A prospective study and Literature review.

OBJECTIVES: Some studies have found an association of incidence of aneurysmal Sub arachnoid hemorrhage (aSAH) seasonal variations and weather patterns but others have refuted this. With conflicting reports in the literature, we tried to find out whether climatic conditions influence the incidence of aSAH. PATIENTS AND METHODS: This was a prospective single centre study involving patients with aSAH operated in a tertiary care hospital over one calendar year. Meteorological parameters like temperature, barometric pressure, humidity and sunshine hours were noted for 2 consecutive days prior to the ictus and on the day of ictus. RESULTS: 392 patients of aSAH who underwent clipping were enrolled. There was no significant difference in the incidence of aSAH across various seasons (p > 0.05). Pre ictus fall in temperature lead to a surge in number of cases. 241 patients (61.5%) reported were from geographical areas which had experienced a fall in temperature over preceding 2 days, with a mean fall in temperature of 1.1(SD 2.1) degree celsius (p less then 0.05). The incidence of aSAH patients in low sunshine hour seasons (1.13 patients/day) was significantly more than that in higher sunshine hour seasons (0.9 patients/day) (p less than 0.05 ). CONCLUSIONS: Seasonal variation had no direct bearing on the incidence of aSAH. Pre ictus fall in temperature lead to a rise in number of cases. Also, higher incidence of aneurysmal subarachnoid haemorrhage was seen in lower sunshine hour seasons.

Performance study of fly-ash-derived coagulant in removing natural organic matter from drinking water: synthesis, characterization, and modelling.

This study is an attempt to develop a liquid coagulant using fly ash (FAC) for removing natural organic matter (NOM) from drinking water systems. Acid-alkali leaching and polymerization technique was used for developing FAC. Characterization of FAC was performed using Fourier-transform infrared spectroscopy (FESEM), field emission scanning electron microscopy (FTIR), and X-ray diffraction (XRD) to assess the surface morphology and functional groups present. FE SEM analysis revealed uneven, coarse, and irregular structure with numerous pores, an indicative of their high adsorption capacity. XRD study revealed that Al, Fe, and Si are the major constituent group of FAC. FAC demonstrated excellent potential in removing THMs precursors: dissolved organic carbon (84.46%), UV254 (90.57%), and turbidity (96.85%) from the drinking water systems. Charge neutralization followed by adsorption is the main mechanism behind NOM removal. Moreover, FAC also showed good capability in minimizing the reactivity of NOM (ASI-72.86%) towards THM formation. FAC proved to be a good alternative for conventional coagulant used in drinking water treatment and can be effectively used for reducing NOM content of raw water which leads to the formation of THMs on chlorination.

Non-invasive assessment of cerebral microvascular changes for predicting postoperative cerebral hyperperfusion after surgical revascularisation for moyamoya disease: an arterial spin labelling MRI study.

PURPOSE: Cerebral hyperperfusion syndrome (CHPS) can result after anastomotic surgery as the reperfusion is established in chronically ischemic cerebral territories in patients of moyamoya disease (MMD). In this study, we have evaluated the feasibility of arterial spin labelling (ASL) perfusion MRI to predict cerebral hyperperfusion syndrome based on changes of cerebral blood flow (CBF) after revascularisation surgery in patients of MMD. METHODS: Our prospective study included 25 patients with MMD who underwent superficial temporal artery-middle cerebral artery (STA-MCA) bypass with or without dural/muscle synangiosis. ASL MRI was performed before and 1-7 days after surgery. On the side planned for operation, 5-mm ROI circle was drawn on the predetermined regions in frontal lobe, temporal lobe, parietal lobe and basal ganglia in proximal and distal territories of MCA to calculate ipsilateral CBF values (CBFi). An attempt was made to select the same location on contralateral side (non-operative) (CBFc) for each measurement for calculation of hemispheric normalised CBF (nCBFh) ratios. To adjust for inter individual variation among MR imagers and CBF, additional regions of interest were drawn within the cerebellum (CBFcbl) for cerebellar CBF normalised ratios (nCBFCbl). RESULTS: Of the 25 patients (26 operated hemispheres), 5 patients showed significant immediate postoperative symptoms suggestive of CHPS. Based on our findings, sensitivity and specificity of ASL perfusion to detect CHPS were evaluated. ASL was found to have 47-100% sensitivity and 45-88% specificity to detect CHPS. We have tried to calculate the prevalence of CHPS in postoperative patients of moyamoya disease, which in our study ranged from 6.83 to 40.70%. CONCLUSION: Based on our results, we concluded that ASL perfusion is an appropriate alternative to standard nuclear medicine studies to monitor the changes in perfusion after STA-MCA bypass surgery in moyamoya patients. ASL MR perfusion can be used to identify changes in cerebral blood flow (CBF) for early detection of cerebral hyperperfusion syndrome in patients with otherwise normal conventional MRI sequences with very high sensitivity but moderate specificity.

Head injury care in a low- and middle-income country tertiary trauma center: epidemiology, systemic lacunae, and possible leads.

BACKGROUND: Although head injury (HI) from low- and middle-income countries (LMIC) heavily contributes to the global disease burden, studies are disproportionately less from this part of the world. Knowing the different epidemiological characteristics from high-income nations can target appropriate prevention strategies. This study aims to provide a comprehensive overview of the clinico-epidemiological data of HI patients, focusing on the existing challenges with possible solutions from a developing nation's perspective. METHODS: This is a prospective, registry-based, observational study of HI in an Indian tertiary trauma-care center over 4 years. Various clinico-epidemiological parameters, risk factors, and imaging spectrum were analyzed in a multivariate model to identify the challenges faced by LMIC and discuss pragmatic solutions. RESULTS: The study included a large-volume cohort of 14,888 patients. Notably, half of these patients belonged to mild HI, despite most were referred (90.3%) cases. Only one-third (30.8%) had severe HI. Less than a third reached us within 6 h of injury. Road traffic accidents (RTA) accounted for most injuries (61.1%), especially in the young (70.9%). Higher age, males, RTA, helmet non-usage, drunken driving, systemic injuries, and specific imaging features had an independent association with injury severity. CONCLUSIONS: The study represents the much-needed, large-volume, epidemiological profile of HI from an LMIC, highlighting the suboptimal utilization of peripheral healthcare systems. Strengthening and integrating these facilities with the tertiary centers in a hub and enhanced spoke model, task sharing design, and efficient back-referrals promise effective neurotrauma care while avoiding overburden in the tertiary centers. Better implementation of road safety laws also has the potential to reduce the burden of HI.

Analysis of 24 cases of epithelioid glioblastoma: Experience from a tertiary centre of North India.

BACKGROUND: Epithelioid glioblastoma (eGB) is a recently recognized and a rare variant of glioblastoma. This study aimed to describe the clinical, histological and immunohistochemical spectrum and outcome of eGB from a tertiary care hospital in north India. MATERIALS AND METHODS: Twenty four cases of eGB diagnosed over past 10 years were reviewed with detailed morphological and immunohistochemical analysis (GFAP, EMA, Vimentin, Myogenin, INI-1, Cytokeratin, Synaptophysin, CD99, S100, MelanA, IDH1, ATRX, p16, EZH2, Ki-67, and BRAF V600E mutant antibody). RESULT: The mean age was 29.9 years (3-54 years), with equal male and female patients. All had supratentorial tumor. All cases showed epithelioid cells in sheets; however, focal spindling (7 cases, 29.2%), grouping/nesting (6 cases, 25%) and papillary configuration (5 cases, 20.8%) were also noted. All showed microvascular proliferation (MVP) and all except one demonstrated areas of necrosis. INI1 was retained in all cases, while 2 showed patchy loss. EZH2 overexpression (>25%) was observed in 4 cases, while 5 cases showed loss of p16 expression. BRAF V600E mutant protein expression was seen in 12/23 (52.2%) cases. Outcome was available in 8 cases, out of which 6 (75%) experienced recurrence. The median survival was 25.5 months. Cases with tumor infiltrating lymphocytes had a better outcome. CONCLUSION: eGB is a distinct variant of glioblastoma which has predilection towards younger age group. It shows high percentage of BRAF V600E mutation and a subset of it shows longer survival. Cases with presence of tumor infiltrating lymphocytes are associated with better outcome.

COVID-19: changing patterns among neurosurgical patients from North India, efficacy of repeat testing, and inpatient prevalence.

OBJECTIVE: COVID-19 has affected surgical practice globally. Treating neurosurgical patients with the restrictions imposed by the pandemic is challenging in institutions with shared patient areas. The present study was performed to assess the changing patterns of neurosurgical cases, the efficacy of repeated testing before surgery, and the prevalence of COVID-19 in asymptomatic neurosurgical inpatients. METHODS: Cases of non-trauma-related neurosurgical patients treated at the Postgraduate Institute of Medical Education and Research (PGIMER) before and during the COVID-19 pandemic were reviewed. During the pandemic, all patients underwent a nasopharyngeal swab reverse transcription-polymerase chain reaction test to detect COVID-19 at admission. Patients who needed immediate intervention were surgically treated following a single COVID-19 test, while stable patients who initially tested negative for COVID-19 were subjected to repeated testing at least 5 days after the first test and within 48 hours prior to the planned surgery. The COVID-19 positivity rate was compared with the local period prevalence. The number of patients who tested positive at the second test, following a negative first test, was used to determine the probable number of people who could have become infected during the surgical procedure without second testing. RESULTS: Of the total 1769 non-trauma-related neurosurgical patients included in this study, a mean of 337.2 patients underwent surgery per month before COVID-19, while a mean of 184.2 patients (54.6% of pre-COVID-19 capacity) underwent surgery per month during the pandemic period, when COVID-19 cases were on the rise in India. There was a significant increase in the proportion of patients undergoing surgery for a ruptured aneurysm, stroke, hydrocephalus, and cerebellar tumors, while the number of patients seeking surgery for chronic benign diseases declined. At the first COVID-19 test, 4 patients (0.48%) tested were found to have the disease, a proportion 3.7 times greater than that found in the local community. An additional 5 patients tested positive at the time of the second COVID-19 test, resulting in an overall inpatient period prevalence of 1%, in contrast to a 0.2% national cumulative caseload. It is possible that COVID-19 was prevented in approximately 67.4 people every month by using double testing. CONCLUSIONS: COVID-19 has changed the pattern of neurosurgical procedures, with acute cases dominating the practice. Despite the fact that the pandemic has not yet reached its peak in India, COVID-19 has been detected 3.7 times more often in asymptomatic neurosurgical inpatients than in the local community, even with single testing. Double testing displays an incremental value by disclosing COVID-19 overall in 1 in 100 inpatients and thus averting its spread through neurosurgical services.

Comparative investigation of coal- and oil-fired boilers based on emission factors, ozone and secondary organic aerosol formation potentials of VOCs.

Volatile organic compounds (VOCs) are the important precursors of the tropospheric ozone (O3) and secondary organic aerosols (SOA), both of which are known to harm human health and disrupt the earth's climate system. In this study, VOC emission factors, O3 and SOA formation potentials were estimated for two types of industrial boilers: coal-fired boilers (n = 3) and oil-fired boilers (n = 3). Results showed that ∑VOCs concentrations were more than nine times higher for oil-fired boilers compared to those for coal-fired boilers. Emission factors of ∑VOCs were found to be higher for oil-fired boilers (9.26-32.83 mg-VOC/kg) than for coal-fired boilers (1.57-4.13 mg-VOC/kg). Alkanes and aromatics were obtained as the most abundant groups in coal-fired boilers, while oxygenated organics and aromatics were the most contributing groups in oil-fired boilers. Benzene, n-hexane and o-ethyl toluene were the abundant VOC species in coal-fired boiler emissions, whereas toluene was the most abundant VOC species emitted from oil-fired boilers. O3 and SOA formation potentials were found 12 and 18 times, respectively, higher for oil-fired than for coal-fired boilers. Total OFP ranged from 3.99 to 11.39 mg-O3/kg for coal-fired boilers. For oil-fired boilers, total OFP ranged from 36.16 to 131.93 mg-O3/kg. Moreover, total secondary organic aerosol potential (SOAP) ranged from 65.4 to 122.5 mg-SOA/kg and 779.9 to 2252.5 mg-SOA/kg for the coal-fired and oil-fired boilers, respectively.

Dependence of urban air pollutants on morning/evening peak hours and seasons.

Traffic emission is a major source of air pollution in urban cities of developing world. This paper shows dependence of traffic-related air pollutants in urban cities on morning/evening peak hours and winter/summer seasons. This research also shows the meteorological impact, such as temperature (T), relative humidity (RH), and wind speed (WS), on traffic-related air pollutants in urban cites. Based on the research output, the elevated level of PM concentration was observed between 1.8 and 6.7 times at all nearby roadway locations compared with background (IIT [ISM] campus). We have found 2.3, 2.4, 2.6 (morning) and 2.0, 2.1, and 2.1 (evening) times higher average PM10, PM2.5, and PM1 concentrations, respectively, in the winter than summer monitoring periods across all locations, due to the stable boundary layer, lower mixing height, and lower friction velocity. It is indicated that urban meteorology plays a crucial role in increasing or decreasing exposed pollutant concentrations in various microenvironments. The analysis of PM2.5/PM10 ratios was lower during whole campaign due to higher contribution of coarser particles generated by vehicles. During winter and summer seasons, 0.57 and 0.33 was observed, respectively. It is indicated that 57% and 33% of PM10 makes up PM2.5 particle, respectively. PM concentrations have showed a negative linear relationship with T and WS and positive relationship with RH in winter/summer seasons. Therefore, traffic and meteorology play a big role to increase or decrease in traffic-related air pollutants in urban air quality.

Comparative evaluation of H&H and WFNS grading scales with modified H&H (sans systemic disease): A study on 1000 patients with subarachnoid hemorrhage.

The comparative studies on grading in subarachnoid hemorrhage (SAH) had several limitations such as the unclear grading of Glasgow Coma Scale 15 with neurological deficits in World Federation of Neurosurgical Societies (WFNS), and the inclusion of systemic disease in Hunt and Hess (H&H) scales. Their differential incremental impacts and optimum cut-off values for unfavourable outcome are unsettled. This is a prospective comparison of prognostic impacts of grading schemes to address these issues. SAH patients were assessed using WFNS, H&H (including systemic disease), modified H&H (sans systemic disease) and followed up with Glasgow Outcome Score (GOS) at 3 months. Their performance characteristics were analysed as incremental ordinal variables and different grading scale dichotomies using rank-order correlation, sensitivity, specificity, positive predictive value, negative predictive value, Youden's J and multivariate analyses. A total of 1016 patients were studied. As univariate incremental variable, H&H sans systemic disease had the best negative rank-order correlation coefficient (-0.453) with respect to lower GOS (p < 0.001). As univariate dichotomized category, WFNS grades 3-5 had the best performance index of 0.39 to suggest unfavourable GOS with a specificity of 89% and sensitivity of 51%. In multivariate incremental analysis, H&H sans systemic disease had the greatest adjusted incremental impact of 0.72 (95% confidence interval (CI) 0.54-0.91) against a lower GOS as compared to 0.6 (95% CI 0.45-0.74) and 0.55 (95% CI 0.42-0.68) for H&H and WFNS grades, respectively. In multivariate categorical analysis, H&H grades 4-5 sans systemic disease had the greatest impact on unfavourable GOS with an adjusted odds ratio of 6.06 (95% CI 3.94-9.32). To conclude, H&H grading sans systemic disease had the greatest impact on unfavourable GOS. Though systemic disease is an important prognostic factor, it should be considered distinctly from grading. Appropriate cut-off values suggesting unfavourable outcome for H&H and WFNS were 4-5 and 3-5, respectively, indicating the importance of neurological deficits in addition to level of consciousness.

Evaluation of chromium removal efficiency at varying operating conditions of a novel bioelectrochemical system.

The assessment of chromium removal efficiency (CRE) was studied using experimental trials with variations in operating conditions for evaluating performance efficiency of the Bioelectrochemical system (BES). During the study, maximum CRE of 88.36 ± 8.16% was obtained at cathode pH 2 ± 0.1 from the varied cathode pH (pH 1, 2, 3, 4, 5, 6, 7). With varying reactor temperatures from 25 to 45 °C (25, 30, 35, 40, 45 °C), BES resulted in maximum CRE of 85.93 ± 9.62% at 40 °C. CRE increased from 78.50 ± 5.24 to 88.66 ± 8.40% with an increase in substrate concentration from 500 to 2000 mg/L chemical oxygen demand (COD). CRE decreased from 95.9 ± 2.9 to 89.56 ± 1.74% with an increase in initial chromium concentration from 10 to 100 mg/L. Correlation analysis revealed a positive correlation with COD reduction, COD, and temperature, whereas a negative correlation was observed for pH and initial chromium concentration.

SECURE INTERNET OF THINGS-BASED CLOUD FRAMEWORK TO CONTROL ZIKA VIRUS OUTBREAK.

OBJECTIVES: Zika virus (ZikaV) is currently one of the most important emerging viruses in the world which has caused outbreaks and epidemics and has also been associated with severe clinical manifestations and congenital malformations. Traditional approaches to combat the ZikaV outbreak are not effective for detection and control. The aim of this study is to propose a cloud-based system to prevent and control the spread of Zika virus disease using integration of mobile phones and Internet of Things (IoT). METHODS: A Naive Bayesian Network (NBN) is used to diagnose the possibly infected users, and Google Maps Web service is used to provide the geographic positioning system (GPS)-based risk assessment to prevent the outbreak. It is used to represent each ZikaV infected user, mosquito-dense sites, and breeding sites on the Google map that helps the government healthcare authorities to control such risk-prone areas effectively and efficiently. RESULTS: The performance and accuracy of the proposed system are evaluated using dataset for 2 million users. Our system provides high accuracy for initial diagnosis of different users according to their symptoms and appropriate GPS-based risk assessment. CONCLUSIONS: The cloud-based proposed system contributed to the accurate NBN-based classification of infected users and accurate identification of risk-prone areas using Google Maps.

The missing falx: a potential surgical pitfall during interhemispheric transcallosal approach.

One of the standard surgical approaches to lateral ventricular tumors involves interhemispheric dissection along the falx cerebri. The falx cerebri thus provides an important landmark in guiding the surgeon to the midline. Unrecognized absence of falx may lead to surgical complications. A child with tuberous sclerosis presented with refractory epilepsy and a lateral ventricular tumor. The tumor was excised by an interhemispheric trans-callosal approach. The midline falx cerebri was absent anteriorly, rendering the identification of midline difficult. The embryology of a deficient falx and its surgical implications are discussed in this short report.

Stabilizing l1-norm prediction models by supervised feature grouping.

Emerging Electronic Medical Records (EMRs) have reformed the modern healthcare. These records have great potential to be used for building clinical prediction models. However, a problem in using them is their high dimensionality. Since a lot of information may not be relevant for prediction, the underlying complexity of the prediction models may not be high. A popular way to deal with this problem is to employ feature selection. Lasso and l1-norm based feature selection methods have shown promising results. But, in presence of correlated features, these methods select features that change considerably with small changes in data. This prevents clinicians to obtain a stable feature set, which is crucial for clinical decision making. Grouping correlated variables together can improve the stability of feature selection, however, such grouping is usually not known and needs to be estimated for optimal performance. Addressing this problem, we propose a new model that can simultaneously learn the grouping of correlated features and perform stable feature selection. We formulate the model as a constrained optimization problem and provide an efficient solution with guaranteed convergence. Our experiments with both synthetic and real-world datasets show that the proposed model is significantly more stable than Lasso and many existing state-of-the-art shrinkage and classification methods. We further show that in terms of prediction performance, the proposed method consistently outperforms Lasso and other baselines. Our model can be used for selecting stable risk factors for a variety of healthcare problems, so it can assist clinicians toward accurate decision making.