Accepted

Accumulated evidence garnered in the last few decades has highlighted the role of yoga in health and disease. The overwhelming mortality and morbidity mediated by noncommunicable epidemics such as heart disease and cancer have fostered a search for mechanisms to attenuate them. Despite overwhelming success in acute care, the efficacy of modern medicines has been limited on this front. Yoga is one of the integrative therapies that has come to light as having a substantial role in preventing and mitigating such disorders. It thus seems trite to analyze and discuss the research advancements in yoga for 2020. The present review attempts to distill recent research highlights from voluminous literature generated in 2020. This review was conducted on the articles published or assigned to an issue in 2020. The authors searched the PubMed database for clinical studies published in the English language, using yoga (including meditation) as the intervention, and having an adequate description of the intervention. Then, they extracted data from each study into a standardized Google sheet. A total of 1149 citations were retrieved in the initial search. Of these, 46 studies met eligibility criteria and were finally included. The studies were predominantly on mental health and neuropsychology, addressing various issues such as anxiety, postural balance, migraine, academic performance, and childhood neglect. Anxiety, stress, and depression were other common denominators. Eight studies were on cardiorespiratory systems, including exercise capacity, cardiac rehabilitation, myocardial infarction, and hypertension. Three studies were on diabetes, evaluating the effect of yoga. Five studies focused on cognition, health status, and autonomic regulation and few others included cancers, infertility, ulcerative colitis, urinary incontinence, restless leg syndrome, rheumatoid arthritis, chronic pain, and metabolic syndrome. Finally, most studies were on noncommunicable diseases with one exception, human immunodeficiency virus; two randomized controlled trials were dedicated to it. Yoga has been studied under a wide variety of clinicopathological conditions in the year 2020. This landscape review intends to provide an idea of the role of yoga in various clinical conditions and its future therapeutic implications.

The early identification of factors that increase risk of poor recovery from acute low back pain (LBP) is critical to prevent the transition to chronicity. Although most studies of risk factors for poor outcome in LBP tend to investigate the condition once it is already persistent, there is evidence to suggest that this differs from risk factors measured during the early-acute stage. This study aimed to identify early risk factors for poor outcome in the short- and long-term in individuals with acute LBP, and to compare this with factors identified at 3 months in the same cohort.

Nitric oxide (NO) levels in brain nuclei, such as the hippocampus and brainstem, are involved in morphine analgesia, but the relationship between the dorsal hippocampus (dH) and the dorsolateral periaqueductal gray matter (dlPAG) needs to be clarified, which is our goal.

Pain is an integrative phenomenon coupled with dynamic interactions between sensory and contextual processes in the brain, often associated with detectable neurophysiological changes. Recent advances in brain activity recording tools and machine learning technologies have intrigued research and development of neurocomputing techniques for objective and neurophysiology-based pain detection. This paper proposes a pain detection framework based on Electroencephalogram (EEG) and deep convolutional neural networks (CNN). The feasibility of CNN is investigated for distinguishing induced pain state from resting state in the recruitment of 10 chronic back pain patients. The experimental study recorded EEG signals in two phases: 1. movement stimulation (MS), where induces back pain by executing predefined movement tasks; 2. video stimulation (VS), where induces back pain perception by watching a set of video clips. A multi-layer CNN classifies the EEG segments during the resting state and the pain state. The novel approach offers high and robust performance and hence is significant in building a powerful pain detection algorithm. The area under the receiver operating characteristic curve (AUC) of our approach is 0.83±0.09 and 0.81±0.15, in MS and VS, respectively, higher than the state-of-the-art approaches. The sub-brain-areas are also analyzed, to examine distinct brain topographies relevant for pain detection. The results indicate that MS-induced pain tends to evoke a generalized brain area, while the evoked area is relatively partial under VS-induced pain. This work may provide a new solution for researchers and clinical practitioners on pain detection.