Isolation and characterization of arsenite oxidizing Pseudomonas lubricans and its potential use in bioremediation of wastewater

A bacterium, Pseudomonas lubricans, isolated from heavy metal laden industrial wastewater, has been shown to tolerate multiple heavy metals suggesting its importance in bioremediation of industrial effluents. P. lubricans tolerated As(III) up to 3 mg ml-1, Cu2+ up to 0.7 mg ml-1, Hg2+ up to 0.4 mg ml-1, Ni2+ up to 0.4 mg ml-1 and Cr6+ up to 0.5 mg ml-1. P. lubricans showed optimum growth at pH 7 while optimum temperature for growth was 30°C. P. lubricans could oxidize As(III) 42% (42 μg mg-1 of protein), 78% (78 μg mg-1 of protein) and 95% (95 μg mg-1 of protein) from the medium after 24, 48 and 72 h of incubation at optimal conditions, respectively. The arsenite oxidizing ability shown by P. lubricans indicates its potential application in biological treatment of wastewaters contaminated with arsenic

Photoelectrochemical properties of mesoporous NiOx deposited on technical FTO via nanopowder sintering in conventional and plasma atmospheres

Nanoporous nickel oxide (NiO x ) has been deposited with two different procedures of sintering (CS and RDS). Both samples display solid state oxidation at about 3.1 V vs Li+/Li. Upon sensitization of CS/RDS NiO x with erythrosine b (ERY), nickel oxide oxidation occurs at the same potential. Impedance spectroscopy revealed a higher charge transfer resistance for ERY-sensitized RDS NiO x with respect to sensitized CS NiO x . This was due to the chemisorption of a larger amount of ERY on RDS with respect to CS NiO x . Upon illumination the photoinduced charge transfer between ERY layer and NiO x could be observed only with oxidized CS. Photoelectrochemical effects of sensitized RDS NiO x were evidenced upon oxide reduction. With the addition of iodine RDS NiOx electrodes could give the reduction iodine → iodide in addition to the reduction of RDS NiO x . p-type dye sensitized solar cells were assembled with RDS NiO x photocathodes sensitized either by ERY or Fast Green. Resulting overall efficiencies ranged between 0.02 and 0.04 % upon irradiation with solar spectrum simulator (Iin : 0.1 W cm −2 )

Failure of Guideline Adherence for Intervention in Patients With Severe Mitral Regurgitation

ObjectivesThis study sought to evaluate the incidence with which adult patients with significant mitral regurgitation (MR) do not undergo surgical intervention despite guideline recommendations, and the associated considerations resulting in no intervention.BackgroundDespite the existence of accepted guidelines, many patients with severe symptomatic heart valve disease might not undergo intervention.MethodsAt a single large tertiary medical center, patients were retrospectively identified who had moderate-to-severe or severe MR on echocardiographic imaging during 2005. Clinical data were reviewed to determine indications for intervention and whether surgery was performed.ResultsDuring 2005, 300 patients were identified with significant MR, including 188 with functional MR and 112 with organic MR. Mitral surgery was performed in 30 of 188 patients with functional MR, mostly to treat heart failure or during another cardiac surgical procedure. Mitral surgery was performed in 59 (53%) of 112 patients with organic MR. Among unoperated patients with organic MR, common reasons included stable left ventricular size or function, absence of symptoms, and prohibitive comorbidities. Using American College of Cardiology/American Heart Association guidelines, 1 or more indication for intervention was present in 39 (74%) of 53 unoperated patients. Perioperative mortality risk was not higher for patients who did not undergo surgery (median 1.2%, interquartile range [IQR] 0.4% to 3.3%) than for those who did (median 1.1%, IQR 0.6% to 5.3%; p = 0.71). During follow-up, there were 12 cardiac and 2 unexplained deaths.ConclusionsAmong patients with severe organic MR, surgical intervention occurred in approximately one-half. However, accepted guideline indications for intervention were present in the majority of unoperated patients. Objectively assessed operative risk was not prohibitive in many unoperated patients

Smoking prevalence, knowledge and attitudes among medical students in Karachi, Pakistan

A survey of smoking prevalence and attitudes was made among medical students randomly selected from classes at the Aga Khan University, Karachi, Pakistan. Of 271 respondents, 14.4% were current smokers (22.0% male and 3.8% females) and 3.3% ex-smokers. A majority of students recognized the dangers associated with active as well as passive smoking although only 55% of current smokers planned to quit in the near future. Most smokers (96%) believed that they as well as other health professionals needed training on smoking cessation and 95% of all students believed that doctors should play a role model in smoking cessation by not smoking themselves. Specific training and counselling should be a part of the required curriculum at medical schools

Anorexic behaviour and attitudes among female medical and nursing students at a private university hospital

Objective: To study the anorexic behaviour and attitude among female medical and nursing students in a tertiary care hospital.SUBJECTS AND Methods: A cross-sectional survey to determine the proportion of anorexic behaviour among female medical and nursing students at The Aga Khan University Hospital Karachi was conducted. A multistaged sampling technique was utilized in which our study population was first divided according to field of enrollment (medical or nursing school) and then stratified by class. Within each stratum, we used convenience sampling due to time and availability constraints. We utilized a modified Eating Attitudes Test (EAT-26) to collect data. The Eating Attitude Test is probably the most widely used standardized measure of symptoms characteristic of eating disorders, including anorexia nervosa.Results: A total of 180 female students were interviewed. Ninety-four (52.2%) were medical students and 86 (47.8%) were nursing students. Among the 94 medical students, 26 (27.7%) were from first year, 14 (14.9%) from second year, 15 (16.0%) from third year, 20 (21.3%) from fourth year and 19 (20.2%) from the final year of medical college. Among the 86 nursing students 34 (39.5%) were from first year, 23 (26.7%) from second year, 27 (31.4%) from third year and 2 (2.4%) from fourth year. The proportion of anorexic behaviour among medical students was 8.0%, 7.1% and 20.0% in first, third and fourth years respectively. No individuals with anorexic behaviour were found in second and fifth years. The total proportion of anorexic behaviour among female health care students was 21.7%.CONCLUSION: Results showed a 21.7% prevalence of anorexic behaviour, a figure much higher than that reported in similar studies conducted in Asia. We also found that the proportion was much higher among female nursing students as compared to female medical students. A previous visit to a psychiatrist for reasons other than eating disorders was found to be associated with anorexic behaviour

Effect logs of double diffusion on MHD Prandtl nano fluid adjacent to stretching surface by way of numerical approach

AbstractThe current communication is carried to contemplate the unique and novel characteristics of nanofluids by constructing formulation of Prandtl fluid model. The fascinating aspects of thermo diffusion effects are also accounted in this communication. Mathematical modelling is performed by employing boundary layer approach. Afterwards, similarity variables are selected to convert dimensional non-linear system into dimensionless expressions. The solution of governing dimensionless problem is executed by shooting method (SM). Graphical evaluation is displayed to depict the intrinsic behavior of embedded parameters on dimensionless velocity, temperature, solutal concentration and nanoparticle concentration profiles. Furthermore, the numerical variation for skin friction coefficient, local Nusselt number, Sherwood number and nano Sherwood number is scrutinized through tables. The assurance of current analysis is affirmed by developing comparison with previous findings available in literature, which sets a benchmark for implementation of computational approach. It is inferred from the computation that concentration profile increases whereas Sherwood number decreases for progressive values of Dufour solutal number

Activities of daily life recognition using process representation modelling to support intention analysis

Purpose – This paper aims to focus on applying a range of traditional classification- and semantic reasoning-based techniques to recognise activities of daily life (ADLs). ADL recognition plays an important role in tracking functional decline among elderly people who suffer from Alzheimer’s disease. Accurate recognition enables smart environments to support and assist the elderly to lead an independent life for as long as possible. However, the ability to represent the complex structure of an ADL in a flexible manner remains a challenge. Design/methodology/approach – This paper presents an ADL recognition approach, which uses a hierarchical structure for the representation and modelling of the activities, its associated tasks and their relationships. This study describes an approach in constructing ADLs based on a task-specific and intention-oriented plan representation language called Asbru. The proposed method is particularly flexible and adaptable for caregivers to be able to model daily schedules for Alzheimer’s patients. Findings – A proof of concept prototype evaluation has been conducted for the validation of the proposed ADL recognition engine, which has comparable recognition results with existing ADL recognition approaches. Originality/value – The work presented in this paper is novel, as the developed ADL recognition approach takes into account all relationships and dependencies within the modelled ADLs. This is very useful when conducting activity recognition with very limited features

Wireless E-Nose Sensors to Detect Volatile Organic Gases through Multivariate Analysis

Gas sensors are critical components when adhering to health safety and environmental policies in various manufacturing industries, such as the petroleum and oil industry; scent and makeup production; food and beverage manufacturing; chemical engineering; pollution monitoring. In recent times, gas sensors have been introduced to medical diagnostics, bioprocesses, and plant disease diagnosis processes. There could be an adverse impact on human health due to the mixture of various gases (e.g., acetone (A), ethanol (E), propane (P)) that vent out from industrial areas. Therefore, it is important to accurately detect and differentiate such gases. Towards this goal, this paper presents a novel electronic nose (e-nose) detection method to classify various explosive gases. To detect explosive gases, metal oxide semiconductor (MOS) sensors are used as reliable tools to detect such volatile gases. The data received from MOS sensors are processed through a multivariate analysis technique to classify different categories of gases. Multivariate analysis was done using three variants—differential, relative, and fractional analyses—in principal components analysis (PCA). The MOS sensors also have three different designs: loading design, notch design, and Bi design. The proposed MOS sensor-based e-nose accurately detects and classifies three different gases, which indicates the reliability and practicality of the developed system. The developed system enables discrimination of these gases from the mixture. Based on the results from the proposed system, authorities can take preventive measures to deal with these gases to avoid their potential adverse impacts on employee health

Exploring the Impact of Preprocessing Techniques on Retinal Blood Vessel Segmentation Using a Study Group Learning Scheme

The segmentation of retinal vessels in retinal images is vital for automated diagnosis of retinal diseases. This is a challenging task because it requires accurate manual labeling of the vessels by expert clinicians and the detection of tiny vessels is difficult due to limited samples, low contrast, and noise. In this study, we explore the use of preprocessing techniques such as contrast-limited adaptive histogram equalization (CLAHE), grad-cam analysis and min-max contrast stretching to improve the performance of a study-group learning (SGL) segmentation model. We evaluate the impact of these preprocessing techniques on the accuracy, sensitivity, specificity, AUC, IoU, and Dice scores using four publicly available datasets, DRIVE, CHASE, HRF and IOSTAR. Our findings indicate that the utilization of the Min-Max technique resulted in a notable enhancement in the accuracy of both the DRIVE and CHASE datasets, with an approximate increase of 3% and 2% respectively. Conversely, the impact of the CLAHE method was discernible solely in the DRIVE dataset, demonstrating an improvement in accuracy of 1%. In addition, our results demonstrated superior accuracy performance for both the DRIVE and CHASE datasets compared to the findings of the reviewed studies. The GitHub repo for this project is available at Link

An Internet of Things based bed-egress alerting paradigm using wearable sensors in elderly care environment

The lack of healthcare staff and increasing proportions of elderly population is alarming. The traditional means to look after elderly has resulted in 255,000 reported falls (only within UK). This not only resulted in extensive aftercare needs and surgeries (summing up to £4.4 billion) but also in added suffering and increased mortality. In such circumstances, the technology can greatly assist by offering automated solutions for the problem at hand. The proposed work offers an Internet of things (IoT) based patient bed-exit monitoring system in clinical settings, capable of generating a timely response to alert the healthcare workers and elderly by analyzing the wireless data streams, acquired through wearable sensors. This work analyzes two different datasets obtained from divergent families of sensing technologies, i.e., smartphone-based accelerometer and radio frequency identification (RFID) based accelerometer. The findings of the proposed system show good efficacy in monitoring the bed-exit and discriminate other ambulating activities. Furthermore, the proposed work manages to keep the average end-to-end system delay (i.e., communications of sensed data to Data Sink (DS)/Control Center (CC) + machine-based feature extraction and class identification + feedback communications to a relevant healthcare worker/elderly) below 1 10 th of a second