Horizontal sub surface flow Constructed Wetlands coupled with tubesettler for hospital wastewater treatment.

Hospital wastewater are a lurking threat to environment and human health security for any given moment of time owing to its complexity and high vulnerability to cause disease outbreak. Though there are a number of treatment process for wastewater., there is a high need for employing cost-efficient and sustainable method of treatment. Hence a pilot scale horizontal surface flow Constructed Wetland (HSFCW) coupled with Tubesettler was installed at New Delhi, India (February to may 2019). This study reports comparative pollutants removal from hospital wastewater using Constructed Wetlands and associated tubesettler dosed with Hospital wastewater. A pilot scale CW system was used for treating 10m3/day of hospital wastewater. The system was tested for 3 Months to evaluate its performance for removing pollutants from the wastewater. The HSFCW coupled with tubesettler achieved over all removal efficiency of 94% (COD), MLSS (97%), TSS (98%), BOD5 (96%), Phosphate (79%). However, process of nitrification was not observed and accumulation of Nitrate up to 197% was observed. The study concluded that it may be due to the presence of pharmaceuticals and other elements present in hospital wastewater. This conclusion was based on the fact that Alkalinity increased by 52% in effluent and pH value also exhibited an average increase of 12%. Further research studies are required to investigate effect of pharmaceutical originating from hospital on treatment efficiency, to incorporate anaerobic setup to complete denitrification-nitrification process and also to determine efficiency of thermophilic, mesophilic, and psychrophilic bacteria with respect to climate and temperature.

Peritraumatic distress in Southern Indian inhabitants during second wave COVID-19 pandemic: A community-level survey.

Introduction: As coronavirus disease 2019 (COVID-19) is a new disease which has ruining and dismantling the harmony of people internationally, its development and spread, creates turmoil, nervousness and dread among worldwide. Aim: To evaluate seriousness of peritraumatic problems among southern Indian inhabitants during the COVID-19 pandemic. Methodology: A referral sampling technique was continued until a sufficient sample size was reached, while this self-administered survey catches insights about depression, fears, psychological change, evasion cognitive change, and collectively quantifies the stress on a scale of 0-60. Expressive factual examinations were utilized to sum up clear cut information and inferential measurable investigations included Chi-square tests and Pearson's correlations were done. Results: The study included only those participants who understood English and had access to the internet. Inconsequential outcomes were found among gender in which both male and female members frequently showed apprehension and nervousness about COVID-19. 44.8% tried not to watch the news on COVID-19 as they were excessively frightened and make frenzy to family members. 90.9% of participants with lower education levels were more stressed over the thought of getting COVID-19 when showing manifestations related with the novel corona virus, which was statistically significant. Conclusion: The current assessment shows that almost all the study participants have felt restless, uncomfortable and terrified of watching the news whilst stressed over appearances related with COVID-19 due to their greater access to information.

Comparing The Functional Outcome Of Different Dose Regimes Of Succinylcholine When Used For Rapid Induction And Intubation.

BACKGROUND: This study was conducted to compare outcomes of different doses of succinylcholine, in terms of intubation condition, onset of action, duration of action and abdominal fasciculation. METHODS: Thus, randomized control trial was conducted in the department of anaesthesia and ICU, Nishter Hospital Multan from April 2016 to November 2016. A total number of 60 patients with ASA status I and II were enrolled. All patients were divided into three groups by lottery method. Data was entered and analysed by computer software SPSS version 23.2. Descriptive variables like age and onset of action were presented as mean and SD and continues statistics like gender, abdominal fasciculation and incubation condition were presented as frequency and percentages. Chi square test and one-way ANOVA was applied to see effect modification and significance of results. The p-value 0.05 was considered as significant. RESULTS: A Total number of 60 patients included in this study and all were female. The mean age of the patients was 28.15±4.5 years. The main outcome variables of this study were the fasciculation, satisfactory intubation, onset time (seconds) and duration of action (in minutes). In group (A) 1mg, abdominal fasciculation was found 80%, 85% and 75% in group A, B, C respectively. It was also observed that satisfactory intubation was found 90%, 80% and 30% in three groups respectively. The mean onset time was 50.95±4.6, 70.7±5.66 and 94.15±8.73 seconds in three groups respectively. Similarly, the mean duration of action was 16.1±3.76, 13.55±3.01 and 8±2.05 minutes respectively. CONCLUSIONS: Results of our clinical trial suggest that succinylcholine in low doses shorter duration of action and low rate of abdominal fasciculation which is desirable for rapid induction but onset of action is prolonged and intubation conditions were not satisfactory predominantly. So, we concluded that low doses of succnylcholine are not so much beneficial that I can replace full doses of succinylcholine when used for rapid induction and intubation.

Comparative analysis of classifiers for developing an adaptive computer-assisted EEG analysis system for diagnosing epilepsy.

Computer-assisted analysis of electroencephalogram (EEG) has a tremendous potential to assist clinicians during the diagnosis of epilepsy. These systems are trained to classify the EEG based on the ground truth provided by the neurologists. So, there should be a mechanism in these systems, using which a system's incorrect markings can be mentioned and the system should improve its classification by learning from them. We have developed a simple mechanism for neurologists to improve classification rate while encountering any false classification. This system is based on taking discrete wavelet transform (DWT) of the signals epochs which are then reduced using principal component analysis, and then they are fed into a classifier. After discussing our approach, we have shown the classification performance of three types of classifiers: support vector machine (SVM), quadratic discriminant analysis, and artificial neural network. We found SVM to be the best working classifier. Our work exhibits the importance and viability of a self-improving and user adapting computer-assisted EEG analysis system for diagnosing epilepsy which processes each channel exclusive to each other, along with the performance comparison of different machine learning techniques in the suggested system.