Next-generation hybrid technologies for the treatment of pharmaceutical industry effluents.

Water and industries are intangible units of the globe that are always set to meet the population's demand. The global population depends on one-third of freshwater increasing the demand. The increase in population along with urbanization has polluted the fresh water resources. The pharmaceutical industry is marked as an emerging contaminant of water pollution. The most common type of pharmaceutical drugs that are detected in the environment includes antibiotics, analgesics, NSAIDs, and pain-relieving drugs. These drugs alter the food chain of the organisms causing chaos mainly in the marine ecosystem. Pharmaceutical drugs are found only in shallow amounts (ng/mg) they have a huge impact on the living system. The consumption of water contaminated with pharmaceutical ingredients can disrupt reproduction, hormonal imbalance, cancer, and respiratory problems. Various methods are used to remove these chemicals from the environment. In this review, we mainly focused on the emerging hybrid technologies and their significance in the effective treatment of pharmaceutical wastewater. This review paper primarily elaborates on the merits and demerits of existing conventional technologies helpful in developing integrated technologies for the modern era of pharmaceutical effluent treatment. This review paper further in detail discusses the various strategies of eco-friendly bioremediation techniques namely biostimulation, bioaugmentation, bacterial degradation, mycoremediation, phytoremediation, and others for the ultimate removal of pharmaceutical contaminants in wastewater. The review makes clear that targeted and hybrid solutions are what the world will require in the future to get rid of these pharmacological prints.

Internet of Things Enabled Financial Crisis Prediction in Enterprises Using Optimal Feature Subset Selection-Based Classification Model.

At present time, an effective tool becomes essential to forecast business failure as well as financial crisis on small- to medium-sized enterprises. This article presents a new optimal feature selection (FS)-based classification model for financial crisis prediction (FCP). The proposed FCP method involves data acquisition, preprocessing, FS, and classification. Initially, the financial data of the enterprises are collected by the use of the internet of things devices, such as smartphones and laptops. Then, the pigeon-inspired optimization (PIO)-based FS technique is applied to choose an optimal set of features. Afterward, the extreme gradient boosting (XGB)-based classification optimized by the Jaya optimization (JO) algorithm called JO-XGB is employed to classify the financial data. The application of the JO algorithm helps to tune the parameters of the XGB model. A detailed experimental validation process takes place to ensure the performance of the presented PIO-JO-XGBoost model. The obtained simulation results verified the effectiveness of the presented model over the compared methods.

Computational Intelligence-Based Financial Crisis Prediction Model Using Feature Subset Selection with Optimal Deep Belief Network.

At present times, financial decisions are mainly based on the classifier technique, which is utilized to allocate a collection of observations into fixed groups. A diverse set of data classifier approaches were presented for forecasting the financial crisis of an institution using the past data. An essential process toward the design of a precise financial crisis prediction (FCP) approach comprises the choice of proper variables (features) that are related to the issues at hand. This is termed as a feature selection (FS) issue that assists to improvise the classifier results. Besides, computational intelligence techniques can be used as a classification model to determine the financial crisis of an organization. In this view, this article introduces a new FS using elephant herd optimization (EHO) with modified water wave optimization (MWWO) algorithm-based deep belief network (DBN) for FCP. The EHO algorithm is applied as a feature selector, and MWWO-DBN is utilized for the classification process. The application of the MWWO algorithm helps to tune the parameters of the DBN model, and the choice of optimal feature subset from the EHO algorithm leads to enhanced classification performance. The experimental results of the proposed model are tested against three benchmark data sets, namely AnalcatData, German Credit, and Australian Credit. The obtained simulation results indicated the superior performance of the proposed model by attaining maximum classification performance.

Dataset on the assessment of water quality of surface water in Kalingarayan Canal for heavy metal pollution, Tamil Nadu.

This data article aimed to investigate the quality of surface water in Kalingarayan Canal for heavy metal pollution, Tamil Nadu. Eight heavy metals like Fe, Cu, Mn, Cr, Zn, Cd, Pb, and Ni were analyzed in the water, for a period of three years, spanning the time frame between January 2014 to December 2016. Eight stations were selected along the Kalingarayan Canal, and water samples were collected on a monthly basis from these stations. The pH of the samples was in the alkaline state (6.88-8.90), whereas conductance was in the range of 394-4276 µs/cm. The average concentration of heavy metals in the surface water ranges from 0.040 to 10.75, 0.030 to 0.890, 0.02 to 0.91, 0.00 to 1.96, 0.00 to 0.01, 0.00 to 0.053, 0.01 to 0.12 and 0.110 to 3.40 mg/L for the metals Fe, Mn, Zn, Cu, Cd, Ni, Pb and Cr respectively. The dominance of various heavy metals in the surface water follows the sequence: Fe > Cr > Cu > Zn > Mn > Pb > Ni > Cd respectively. The canal is affected by anthropogenic activities and industrialization in terms of heavy metals.

Preclinical assessment of utility of M6S for multimodal acute and chronic pain treatment in diabetic neuropathy.

AIMS: Previous reports from our laboratory have established that morphine-6-O-sulfate (M6S) is a mixed µ/δ opioid receptor (OR) agonist and a potential improved alternative to morphine for treatment of chronic multimodal pain in non-diabetic rats. This study extends the antinociceptive effects of M6S and morphine in STZ-induced diabetic rats. MATERIALS AND METHODS: Effects of morphine and M6S were studied across a range of pain modalities, using hot plate threshold (HPT), pinprick sensitivity threshold (PST) and paw pressure threshold (PPT) tests. KEY FINDINGS: Acutely, M6S was 3- to 5-fold more potent and 2- to 3-fold more efficacious than morphine in HPT and PST tests. No differences in analgesic drug potency/efficacy were detected in the PPT test. After 7-9days of chronic treatment, tolerance developed to the antinociceptive effects of morphine, but not to M6S, in all three pain tests. Furthermore, morphine-tolerant rats were not cross-tolerant to M6S. The selective δ-OR antagonist, naltrindole, blocked M6S-induced antinociception by 62±3% in the HPT test, 93±5% in the PST test, and 30±17% in the PPT test when examined acutely. SIGNIFICANCE: These studies provide additional confirmation for the mixed µ/δ activity of M6S and demonstrate potential improved clinical utility for dual µ/δ agonists relative to morphine in treatment of diabetic neuropathy across multiple pain domains.

Should air-oxygen replace nitrous oxide-oxygen in general anaesthesia?

The use of compressed air-oxygen mixtures to replace nitrous oxide-oxygen in general anaesthesia was investigated in 378 patients. There were neither prolongations of recovery time nor instances of awareness under anaesthesia. The cost of general anaesthesia using compressed air-oxygen was about half that for nitrous oxide-oxygen mixtures.

Preoperative risk assessment in cardiac surgery: comparison of predicted and observed results.

In the present climate of quality-assurance policies, rigorous requirements for informed consent, and a constantly changing patient population, a system of preoperative risk assignment and postoperative correlation was developed to monitor and evaluate surgical performance. Patients were categorized by operation, priority (emergent, urgent, elective), New York Heart Association Functional Class, and risk. Risk was assigned before operation using data from the Coronary Artery Surgery Study (CASS) and the recent literature. Data were collected by a full-time data manager and were stored and analyzed by computer. From January 1, 1984, to July 1, 1985, 1,303 patients underwent operation for acquired disease. This group included 913 patients undergoing isolated primary coronary artery bypass grafting (CABG). The comparison of predicted and observed results showed: (Table: see text). For patients undergoing isolated primary CABG, the elective group had an operative mortality of 0.6% (2/329); the urgent group, 1.1% (5/450); and the emergent group, 5.2% (7/134). Preoperative risk assignment is an effective method of quality assurance. Female sex and age older than 60 years, which predicted an operative mortality of 2 to 5% in the CASS study and other recent series, did not predict a similar risk in our series.