Omega-3 fatty acids (omega-3 FAs) are essential fatty acids with diverse biological effects in human health and disease. Reduced cardiovascular morbidity and mortality is a well-established benefit of their intake. Dietary supplementation may also benefit patients with dyslipidaemia, atherosclerosis, hypertension, diabetes mellitus, metabolic syndrome, obesity, inflammatory diseases, neurological/ neuropsychiatric disorders and eye diseases. Consumption of omega-3 FAs during pregnancy reduces the risk of premature birth and improves intellectual development of the fetus. Fish, fish oils and some vegetable oils are rich sources of omega-3 FAs. According to the UK Scientific Advisory Committee on Nutrition guidelines (2004), a healthy adult should consume a minimum of two portions of fish a week to obtain the health benefit. This review outlines the health implications, dietary sources, deficiency states and recommended allowances of omega-3 FAs in relation to human nutrition.

BackgroundRadical cure of Plasmodium vivax malaria requires treatment with a blood schizonticide and a hypnozoitocide (primaquine) to eradicate the dormant liver stages. There has been uncertainty about the operational effectiveness and optimum dosing of the currently recommended 14-day primaquine (PQ) course.MethodsA two centre, randomized, open-label, two arm study was conducted in South India. Patients were randomized to receive either high dose (0.5 mg base/kg body weight) or conventional dose (0.25 mg/kg) PQ for 14 days. Plasma concentrations of PQ and carboxyprimaquine (CPQ) on the 7th day of treatment were measured by reverse phase high performance liquid chromatography. Study subjects were followed up for 6 months. Recurrent infections were genotyped using capillary fragment length polymorphism of two PCR-amplified microsatellite markers (MS07 and MS 10).ResultsFifty patients were enrolled. Baseline characteristics and laboratory features did not differ significantly between the groups. Mean age of the study population was 42 ± 16.0 years. Recurrences 80–105 days later occurred in 4 (8%) patients, two in each the groups. All recurrences had the same microsatellite genotype as that causing the index infection suggesting all were relapses. One relapse was associated with low CPQ concentrations suggesting poor adherence.ConclusionsThis small pilot trial supports the effectiveness of the currently recommended lower dose (0.25 mg/kg/day) 14 day PQ regimen for the radical cure of vivax malaria in South India.Trial registration Clinical Trials Registry-India, CTRI/2017/03/007999. Registered 3 March 2017, http://ctri.nic.in/Clinicaltrials/regtrial.php?modid=1&compid=19&EncHid=82755.86366.

SUMMARYHypoparathyroidism is an uncommon endocrine deficiency characterised by low serum calcium, absent or inappropriately low parathyroid hormone and normal or high serum phosphorus levels. Parathyroid hormone is essential for calcium homoeostasis. Pregnancy and lactation are known for increased calcium requirement. They cause calcium stress as well as alter its metabolism. Hence, many abnormalities are expected in hypoparathyroidism during pregnancy and lactation. We report a case of pregnancy in postsurgical hypoparathyroidism, which is rarely encountered in antenatal clinics. We describe our clinical, biochemical and therapeutic experience of pregnancy and lactation in this patient with hypoparathyroidism.

The coronavirus pandemic emerged in early 2020 and turned out to be deadly, killing a vast number of people all around the world. Fortunately, vaccines have been discovered, and they seem effectual in controlling the severe prognosis induced by the virus. The reverse transcription-polymerase chain reaction (RT-PCR) test is the current golden standard for diagnosing different infectious diseases, including COVID-19; however, it is not always accurate. Therefore, it is extremely crucial to find an alternative diagnosis method which can support the results of the standard RT-PCR test. Hence, a decision support system has been proposed in this study that uses machine learning and deep learning techniques to predict the COVID-19 diagnosis of a patient using clinical, demographic and blood markers. The patient data used in this research were collected from two Manipal hospitals in India and a custom-made, stacked, multi-level ensemble classifier has been used to predict the COVID-19 diagnosis. Deep learning techniques such as deep neural networks (DNN) and one-dimensional convolutional networks (1D-CNN) have also been utilized. Further, explainable artificial techniques (XAI) such as Shapley additive values (SHAP), ELI5, local interpretable model explainer (LIME), and QLattice have been used to make the models more precise and understandable. Among all of the algorithms, the multi-level stacked model obtained an excellent accuracy of 96%. The precision, recall, f1-score and AUC obtained were 94%, 95%, 94% and 98% respectively. The models can be used as a decision support system for the initial screening of coronavirus patients and can also help ease the existing burden on medical infrastructure.