Background:Ashwagandha (Withania somnifera (L.) Dunal) is a herb traditionally used to reduce stress and enhance wellbeing. The aim of this study was to investigate its anxiolytic effects on adults with self-reported high stress and to examine potential mechanisms associated with its therapeutic effects.Methods:In this 60-day, randomized, double-blind, placebo-controlled study the stress-relieving and pharmacological activity of an ashwagandha extract was investigated in stressed, healthy adults. Sixty adults were randomly allocated to take either a placebo or 240 mg of a standardized ashwagandha extract (Shoden) once daily. Outcomes were measured using the Hamilton Anxiety Rating Scale (HAM-A), Depression, Anxiety, and Stress Scale -21 (DASS-21), and hormonal changes in cortisol, dehydroepiandrosterone-sulphate (DHEA-S), and testosterone.Results:All participants completed the trial with no adverse events reported. In comparison with the placebo, ashwagandha supplementation was associated with a statistically significant reduction in the HAM-A (P = .040) and a near-significant reduction in the DASS-21 (P = .096). Ashwagandha intake was also associated with greater reductions in morning cortisol (P < .001), and DHEA-S (P = .004) compared with the placebo. Testosterone levels increased in males (P = .038) but not females (P = .989) over time, although this change was not statistically significant compared with the placebo (P = .158).Conclusions:These findings suggest that ashwagandha's stress-relieving effects may occur via its moderating effect on the hypothalamus-pituitary-adrenal axis. However, further investigation utilizing larger sample sizes, diverse clinical and cultural populations, and varying treatment dosages are needed to substantiate these findings.Trial registration:Clinical Trials Registry—India (CTRI registration number: CTRI/2017/08/009449; date of registration 22/08/2017)

Physicians need to be aware about the harmful effects of biomass smoke exposure and ensure early diagnosis and appropriate management to reduce the disease burden. More research needs to be done to study health effects due to biomass smoke exposure better. Reducing the exposure to biomass smoke through proper home ventilation, home design and, if possible, change of biomass to cleaner fuels is strongly recommended in order to reduce biomass smoke-induced mortality and morbidity.

Background Metformin is the first-line treatment for type 2 diabetes mellitus (T2DM), but many patients either cannot tolerate it or cannot achieve glycemic control with metformin alone, so treatment with other glucose-lowering agents in combination with metformin is frequently required. Remogliflozin etabonate, a novel agent, is an orally bioavailable prodrug of remogliflozin, which is a potent and selective sodium-glucose co-transporter-2 inhibitor. Objective Our objective was to evaluate the efficacy and safety of remogliflozin etabonate compared with dapagliflozin in subjects with T2DM in whom a stable dose of metformin as monotherapy was providing inadequate glycemic control. Methods A 24-week randomized, double-blind, double-dummy, active-controlled, three-arm, parallel-group, multicenter, phase III study was conducted in India. Patients aged ≥ 18 and ≤ 65 years diagnosed with T2DM, receiving metformin ≥ 1500 mg/day, and with glycated hemoglobin (HbA1c) levels ≥ 7 to ≤ 10% at screening were randomized into three groups. Every patient received metformin ≥ 1500 mg and either remogliflozin etabonate 100 mg twice daily (BID) (group 1, n = 225) or remogliflozin etabonate 250 mg BID (group 2, n = 241) or dapagliflozin 10 mg once daily (QD) in the morning and placebo QD in the evening (group 3, n = 146). The patients were followed-up at weeks 1 and 4 and at 4-week intervals thereafter until week 24. The endpoints included mean change in HbA1c (primary endpoint, noninferiority margin = 0.35), fasting plasma glucose (FPG), postprandial plasma glucose (PPG), bodyweight, blood pressure, and fasting lipids. Treatmentemergent adverse events (TEAEs), safety laboratory values, electrocardiogram, and vital signs were evaluated. Results Of 612 randomized patients, 167 (group 1), 175 (group 2), and 103 (group 3) patients with comparable baseline characteristics completed the study. Mean change ± standard error (SE) in HbA1c from baseline to week 24 was − 0.72 ± 0.09, − 0.77 ± 0.09, and − 0.58 ± 0.12% in groups 1, 2, and 3, respectively. The difference in mean HbA1c of group 1 versus group 3 (− 0.14%, 90% confidence interval [CI] − 0.38 to 0.10) and group 2 versus group 3 (− 0.19%; 90% CI − 0.42 to 0.05) was noninferior to that in group 3 (p < 0.001). No significant difference was found between group 1 or group 2 and group 3 in change in FPG, PPG, and bodyweight. The overall incidence of TEAEs was comparable across study groups (group 1 = 32.6%, group 2 = 34.4%, group 3 = 29.5%), including adverse events (AEs) of special interest (hypoglycemic events, urinary tract infection, genital fungal infection). Most TEAEs were mild to moderate in intensity, and no severe AEs were reported. Conclusion This study demonstrated the noninferiority of remogliflozin etabonate 100 and 250 mg compared with dapagliflozin, from the first analysis of an initial 612 patients. Remogliflozin etabonate therefore may be considered an effective and well-tolerated alternative treatment option for glycemic control in T2DM. Trial Registration CTRI/2017/07...

Peak flow meter with questionnaire and mini-spirometer are considered as alternative tools to spirometry for screening of asthma and chronic obstructive pulmonary disease. However, the accuracy of these tools together, in clinical settings for disease diagnosis, has not been studied. Two hundred consecutive patients with respiratory complaints answered a short symptom questionnaire and performed peak expiratory flow measurements, standard spirometry with Koko spirometer and mini-spirometry (COPD-6). Spirometry was repeated after bronchodilation. Physician made a final diagnosis of asthma, chronic obstructive pulmonary disease and others. One eighty nine patients (78 females) with age 51 ± 17 years with asthma (115), chronic obstructive pulmonary disease (33) and others (41) completed the study. “Breathlessness > 6months” and “cough > 6months” were important symptoms to detect obstructive airways disease. “Asymptomatic period > 2 weeks” had the best sensitivity (Sn) and specificity (Sp) to differentiate asthma and chronic obstructive pulmonary disease. A peak expiratory flow of < 80% predicted was the best cut-off to detect airflow limitation (Sn 90%, Sp 50%). Respiratory symptoms with PEF < 80% predicted, had Sn 84 and Sp 93% to detect OAD. COPD-6 device under-estimated FEV1 by 13 mL (95% CI: −212, 185). At a cut-off of 0.75, the FEV1/FEV6 had the best accuracy (Sn 80%, Sp 86%) to detect airflow limitation. Peak flow meter with few symptom questions can be effectively used in clinical practice for objective detection of asthma and chronic obstructive pulmonary disease, in the absence of good quality spirometry. Mini-spirometers are useful in detection of obstructive airways diseases but FEV1 measured is inaccurate.

The analysis of lung sounds, collected through auscultation, is a fundamental component of pulmonary disease diagnostics for primary care and general patient monitoring for telemedicine. Despite advances in computation and algorithms, the goal of automated lung sound identification and classification has remained elusive. Over the past 40 years, published work in this field has demonstrated only limited success in identifying lung sounds, with most published studies using only a small numbers of patients (typically N<;20) and usually limited to a single type of lung sound. Larger research studies have also been impeded by the challenge of labeling large volumes of data, which is extremely labor-intensive. In this paper, we present the development of a semi-supervised deep learning algorithm for automatically classify lung sounds from a relatively large number of patients (N=284). Focusing on the two most common lung sounds, wheeze and crackle, we present results from 11,627 sound files recorded from 11 different auscultation locations on these 284 patients with pulmonary disease. 890 of these sound files were labeled to evaluate the model, which is significantly larger than previously published studies. Data was collected with a custom mobile phone application and a low-cost (US$30) electronic stethoscope. On this data set, our algorithm achieves ROC curves with AUCs of 0.86 for wheeze and 0.74 for crackle. Most importantly, this study demonstrates how semi-supervised deep learning can be used with larger data sets without requiring extensive labeling of data.