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Increasing water pollution and the need for clean and safe water have urged for the emergence of novel technologies. Microorganisms play a vital role in degrading pollution and maintaining the ecological stability. This study brings to limelight some of the technologies in wastewater treatment that employ microorganisms. Biological aerobic and anaerobic treatments, green technologies involving microalgae, membrane bioreactors and sequencing batch reactors, bioflocculant technologies and biofilm technologies are elaborated with their advantages and applications. The implications provide further avenues towards sustainable life and environment.

Objectives: Body mass index (BMI) is a traditional measurement practice that divides a person’s weight by their height to find out whether they have a healthy range of weight. The objective of this study is to understand the relation between BMI and prevalence of diabetes mellitus, hypertension, and dyslipidemia. Material and Methods: BMI distribution among these patients was compared with and without hypertension and dyslipidemia. The improper of secretion of insulin leads to changes in metabolism of lipids, proteins, and carbohydrates also characterized hyperglycemia generally considered as diabetes mellitus (DM). Results: The excessive formation of free radicals induces oxidative stress, and the antioxidant defense system becomes weaken and hence arises diabetic disorder and overproduction of malondialdehyde (MDA). In cells, the polyunsaturated fatty acid peroxidation is considered as the final product and marker of oxidative stress. Conclusion: In this study, significantly increased BMI is observed in diabetic victims in comparison with control group. A significant positive correlation was noticed between glycated hemoglobin, cholesterol, and MDA. In conclusion, for the prevention and management of vascular complication in type 2 diabetes mellitus, maintaining normal levels of MDA and body weight are very important. Further large-scale studies are required to confirm it.

The classification of ultrasound scan images is important in monitoring the development of prenatal and maternal structures. This paper proposes a big data classification system for ultrasound Doppler scan images that combines the residual of maximally stable extreme regions and speeded up robust features (SURF) with a decision tree classifier. The algorithm first preprocesses the ultrasound scan images before detecting the maximally stable extremal regions (MSER). A few essential regions are chosen from the MSER regions, along with the residual region that provides the best Region of Interest (ROI). SURF features points that best represent the region are detected using the gradient of the estimated cumulative region of interest. To extract the feature from the pixels that surround the SURF feature points, the Triangular Vertex Transform (TVT) transform is used. A decision tree classifier is used to train the extracted TVT features. The proposed ultrasound scan image classification system is validated using performance parameters such as accuracy, specificity, precision, sensitivity, and F1 score. For validation, a large dataset of 12,400 scan images collected from 1792 patients is used. The proposed method has an F1score of 94.12%, sensitivity, specificity, precision, and accuracy of 93.57%, 93.57%, and 97.96%, respectively. The evaluation results show that the proposed algorithm for classifying Doppler scan images is better than other algorithms that have been used in the past.

This study aims at evaluating the relationship between visual fields by HFA and retinal nerve fibre layer thickness and ONH parameters by OCT in assessing optic nerve head damage in patients with primary open-angle glaucoma. A total of 87 glaucomatous eyes were included. All subjects underwent complete ophthalmic examination, gonioscopy, visual field examination by Humphrey’s automated visual field analyzer and RNFL thickness and ONH parameters assessment by SD-OCT. Glaucomatous eyes were graded according to Hodapp, Parrish, and Anderson’s classification. Quadrant wise RNFL thickness and average thickness were recorded. Statistical analysis was done using Epidemiological Information Package (EPI) version 7.2. : Average and vertical CD ratio was found to be the most important ONH parameter to discriminate early from moderate glaucoma and for assessing progression from moderate to severe glaucoma. All ONH parameters except the disc area were significant. A significant correlation was detected between mean deviation, pattern standard deviation, and average RNFL thickness. Average RNFL thickness was the best parameter to discriminate early from moderate, and moderate from severe glaucoma. The sectoral RNFL thicknesses also showed a gradual decline and were statistically significant in all sectors in moderate and severe glaucoma and only in inferior and superior sectors in mild and moderate glaucoma. In the present study, average RNFL thickness had a good diagnostic value in the diagnosis of glaucoma and for grading of glaucoma according to its severity. The structural changes on OCT correlated well with functional damage shown on visual fields.

As medical image processing research has progressed, image fusion has emerged as a realistic solution, automatically extracting relevant data from many images before fusing them into a single, unified image. Medical imaging techniques, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), etc., play a crucial role in the diagnosis and classification of brain tumors (BT). A single imaging technique is not sufficient for correct diagnosis of the disease. In case the scans are ambiguous, it can lead doctors to incorrect diagnoses, which can be unsafe to the patient. The solution to this problem is fusing images from different scans containing complementary information to generate accurate images with minimum uncertainty. This research presents a novel method for the automated identification and classification of brain tumors using multi-modal deep learning (AMDL-BTDC). The proposed AMDL-BTDC model initially performs image pre-processing using bilateral filtering (BF) technique. Next, feature vectors are generated using a pair of pre-trained deep learning models called EfficientNet and SqueezeNet. Slime Mold Algorithm is used to acquire the DL models’ optimal hyperparameter settings (SMA). In the end, an autoencoder (AE) model is used for BT classification once features have been fused. The suggested model’s superior performance over other techniques under diverse measures was validated by extensive testing on the benchmark medical imaging dataset.

Polycystic ovarian syndrome is a common endocrine disorder predominantly affecting women of reproductive age group. This is one of the major causes for infertility. The objective of this article is to conduct a review of the Research articles indexed in PUBMED and GOOGLE SCHOLAR regarding the effectiveness of Homeopathic treatment in PCOS. This review is done to analyse the research work done by various Homoeopathic colleges till date in the field of PCOS and future research required to be undertaken in this less explored area. Analysis of these studies unveil the management strategy for PCOS through homoeopathy. These includes publications of various Homoeopathic medical colleges from 1990-2022 published in Indian journal of homoeopathy, International Journal of health sciences and research, Materia Novum- The journal of homoeopathy. The publications pertaining to the clinical research work were included in the review. All publications are categorized as observational studies, clinical studies and case reports. Out of 12 publications searched, 4 were included in this article. Observational case studies and case series shows the improvement in the clinical symptoms, USG findings and in the quality of life of the patient. Therefore, these studies should be given importance in further homoeopathic research and should be authenticated with the pragmatic trials.

The endophytic microorganisms are believed to be an important bio-resource for modern agriculture because of their beneficial effects on plant growth promotion, biocontrol, stress tolerance, and diseases resistance. This study was focused to know the beneficial effect of endophytic bacterium (FRR2) isolated from the roots of Ficus religiosa L. on Amaranthus campestris L. and their tolerance ability against salinity and heavy metals. The strain FRR2 was recognized as Cellulosimicrobium sp. by 16s rRNA sequencing and phylogenetic study. The bacterial isolate FRR2 showed salt (at 150 mM NaCl) and metal (at 150 µM CuSO4 and 100 µM ZnSO4) tolerance ability and significantly higher growth rate of Amaranthus campestris in a green leafy vegetable might be due to the nitrogen fixation, indole acetic acid production, amylase and protease activities. In addition, the endophyte FRR2 application slightly increased the antioxidants activity than their controls. The results of this study revealed that Cellulosimicrobium sp. strain FRR2 would be an effective endophyte to increase the growth of green leafy vegetables.

Introduction: To assess the potential of galectin-3 and growth differentiation factor-15 (GDF-15) biomarkers for the early detection of diabetic kidney disease (DKD). Methodology: This was a cross-sectional study conducted over a period of 1.2 years. Patients were stratified based on estimated glomerular filtration rate (eGFR) and albuminuria level. The receiver operating characteristic (ROC) curve was plotted to assess the diagnostic potential of biomarkers. Conclusion: In DKD patients the galectin-3 and GDF-15 levels were inversely related to the eGFR which was further confirmed by the ROC curve demonstrating the potential of galectin-3 and GDF-15 as a biomarker.