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Electroencephalogram (EEG) alpha asymmetry has become a pivotal area of research for understanding functional hemispheric differences in neuroscience. To the best of our knowledge, the relationship between frontal alpha asymmetry (FAA) and the depth of meditation has yet to be thoroughly examined. To address this gap, the present cross-sectional study was conducted to explore the meditative states of long-term meditators and non-meditators. This study examined 26 long-term heartfulness meditation practitioners (LTM) and 33 non-meditators (NM), aged 30 to 45 years. Frontal EEG activity was employed to assess frontal alpha asymmetry (FAA), while self-reported measures, including the Meditation Depth Questionnaire (MEDEQ) and the Visual Analogue Scale (VAS), were used to evaluate the depth of meditation. The results demonstrated significant differences in self-reported meditation depth between the long-term meditators and non-meditators, as shown through MEDEQ and VAS assessments. Notably, the FAA findings exhibited distinct interaction effects that highlight variations between the two groups. Furthermore, a positive correlation was established between FAA and the depth of meditation, supporting the notion that EEG patterns are reflective of self-reported meditative experiences. The findings suggest that heartfulness meditation may modulate FAA patterns in practitioners, which could be linked to enhanced emotional balance.

[This corrects the article DOI: 10.3389/fnhum.2025.1576642.].

Introduction The recent development of robust indices to quantify biological aging, along with the dynamic epidemiological transitions of population aging generate the unmet need to examine the extent up to which potential interventions can delay, halt or temporarily modulate aging trajectories. Methods and analysis The study is a two-armed, open label randomised controlled trial. We aim to recruit 166 subjects, aged 60–75 years from the residential communities and old age clubs in Bangalore city, India, who will undergo randomisation into intervention or control arms (1:1). Intervention will include yoga sessions tailored for the older adults, 1 h per day for 5 days a week, spread for 12 months. Data would be collected at the baseline, 26 th week and 52 nd week. The primary outcome of the study is estimation in biological age with yoga practice. The secondary outcomes will include cardinal mechanistic indicators of aging- telomere length, interleukin-6 (IL-6), tumor necrosis factor receptor II (TNF-RII), high sensitivity c-reactive protein (hsCRP)], insulin signaling [insulin and IGF1], renal function [cystatin], senescence [growth differentiating factor 15 (GDF-15)] and cardiovascular function [N-terminal B-type natriuretic peptides (NT-proBNP)] . Analyses will be by intention-to-treat model. Ethics & dissemination The study is approved by the Institutional Ethics Committee of Swami Vivekananda Yoga Anusandhana Samsthana University, Bangalore (ID:RES/IEC-SVYASA/242/2022). Written informed consent will be obtained from each participant prior to inclusion. Trial registration number CTRI/2022/07/044442.

Yoga-based clinical research has shown considerable promise in varied ageing-related health outcomes in older adults. However, robust frameworks have yet to be used in intervention research to endorse yoga as a healthy ageing intervention to test the multidimensional construct of healthy ageing. This was an assessor-masked, randomized controlled trial conducted among 258 sedentary, community-dwelling older adults aged 60–80 years, randomly allocated to 26-week yoga-based intervention (YBI) ( n  = 132) or waitlist control (WLC) ( n  = 126). The effectiveness of YBI was assessed through two separate global statistical tests, generalized estimating equations and rank sum-based test, against a comprehensive healthy aging panel comprised of ten markers representing the domains of physiological and metabolic, cognitive, physical capability, psychological, and social well-being. The secondary outcomes were individual primary marker scores, Klotho, inflammatory markers, and auxiliary blood markers. We could establish the healthy aging effect of the 26-week YBI over WLC using two models of global statistical test (GEE, β  = 0.29; 95% CI = 0.20 to 0.38, p  < 0.001), and rank sum-based test ( β  = 0.28, 95% CI = 0.19 to 0.36, p  < 0.001). There were also significant improvements in direction of benefit at individual levels of all the aging markers. Exploratory evaluation with adopted indices from contemporary clinical trials also validated the potential of YBI for healthy aging; HATICE adapted composite score (mean difference =  − 0.18; 95% CI =  − 0.26 to − 0.09, p  < 0.001) and healthy ageing index (mean difference =  − 0.33; 95% CI =  − 0.63 to − 0.02, p  = 0.03). The global effect of YBI across multiple ageing-related outcomes provides a proof of concept for further large-scale validation. The findings hold a great translational value given the accelerated pace of population aging across the globe. Trial registration: CTRI/2021/02/031373.

Extramedullary disease (EMD) is an aggressive and treatment-resistant manifestation of multiple myeloma with limited therapeutic options, particularly in heavily pretreated patients. We conducted a retrospective study to evaluate the efficacy and safety of concurrent immune checkpoint inhibitors (ICIs) and radiation therapy (RT) in patients with EMD treated at Hackensack University Medical Center and John Theurer Cancer Center between January 2016 and May 2025. Patients were included if they had confirmed EMD and received nivolumab or pembrolizumab with concurrent RT. A total of 21 patients were included, representing a high-risk cohort with a median of 6 prior lines of therapy (range 2-13), including 47.6% triple-class refractory and 19.0% penta-refractory disease. The overall response rate (ORR) was 47.6%, with a clinical benefit rate of 57.1%. Despite these responses, median progression-free survival (PFS) and overall survival (OS) were 4 and 12 months, respectively. Notably, two patients achieved complete responses with nivolumab and RT early in their treatment course following cellular therapy and remain disease-free at last follow-up. The combination of ICIs and RT was generally well-tolerated, with manageable immune-related adverse events and no treatment-related deaths. These findings suggest that concurrent ICI and RT may provide a signal of treatment responses in a subset of patients with advanced EMD, although durability remains limited. Further prospective studies are warranted to further define the role of this combination and identify patients most likely to benefit.

Spinal cord injury (SCI) is the leading cause of motor and sensory abnormalities due to damage caused to any part of the spinal cord resulting from trauma, disease, or degeneration. Most of the disability caused will be irreversible with various systemic manifestations. Hence, management of SCI focuses on minimising disability, diminishing limitations due to impairment, and improving quality of life, emotional, and psychological aspects. This review is aimed at describing Ayurveda, Yoga, and Acupuncture therapies in the management of SCI as individual and integrated approaches for alleviating the symptom score in patients with SCI. The data was collected from six databases, including PubMed Central, the Cochrane Library, Google Scholar, Scopus, MEDLINE, and Grey Literature. The subjects in these studies were between the ages group 21–70 years and had been previously diagnosed with SCI and its clinical presentation. The interventions used in the selected studies incorporate Ayurveda (medicinal system of longevity) herbal medications, Panchakarma (five methods) treatment, diet, and yoga (mind-body medicine) therapy. Full-text publications in English, and research designs such as randomised controlled trials, case studies, review articles and cohort studies were included. Letter to the editor, study protocol, animal trials, and in vitro studies were excluded. 216 records were identified using keywords such as spinal cord injury, Äyurveda, Acupuncture, païca karma, rehabilitation, and yoga. After applying inclusion and exclusion criteria, 28 articles were selected for synthesis, which contain 12 case studies, 12 literature review articles, 2 randomised controlled trials, 1 cohort study, and 1 meta-analysis. The integration of Ayurveda management, including Panchakarmatherapy and Ayurveda medications, with other alternative therapies like Acupuncture, Yoga, and Rehabilitation improved muscle strength, quality of life, range of motion, and neuronal function, and reduced depression, stress, and pain with symptom scores.

Purpose: Executive function, attention, and memory are an important indicator of cognitive health in children. In this study, we analyze the effect of yoga and physical exercise on executive functioning, attention, and memory. Methods: In this prospective two-armed randomized controlled trial, around 802 students from ten schools across four districts were randomized to receive daily 1 h yoga training (n = 411) or physical exercise (n = 391) for 2 months. Executive function, attention, and memory were studied using Trail Making Test (TMT). Yoga (n = 377) and physical exercise (n = 371) students contributed data to the analyses. The data were analyzed using intention-to-treat approach using Student's t-test. Results: There was a significant increase in numerical TMT (TMTN) values within yoga (t = −2.17; P < 0.03) and physical activity (PA) (t = −3.37; P < 0.001) groups following interventional period. However, there was no significant change in TMTN between yoga and PA groups (t = 0.44; P = 0.66). There was a significant increase in alphabetical TMT (TMTA) values within yoga (t = 6.21; P < 0.00) and PA groups (t = 1.19; P < 0.234) following interventional period. However, there was no significant change in TMTA between yoga and PA groups (t = 3.46; P = 0.001). Conclusion: The results suggest that yoga improves executive function, attention, and working memory as effectively as physical exercise intervention in adolescent schoolchildren.

IntroductionThe conceptualisation of healthy ageing phenotype (HAP) and the availability of a tentative panel for HAP biomarkers raise the need to test the efficacy of potential interventions to promote health in older adults. This study protocol reports the methodology for a 24-week programme to explore the holistic influence of the yoga-based intervention on the (bio)markers of HAP.Methods and analysisThe study is a two-armed, randomised waitlist controlled trial with blinded outcome assessors and multiple primary outcomes. We aim to recruit 250 subjects, aged 60–80 years from the residential communities and old age clubs in Bangalore city, India, who will undergo randomisation into intervention or control arms (1:1). The intervention will include a yoga-based programme tailored for the older adults, 1 hour per day for 6 days a week, spread for 24 weeks. Data would be collected at the baseline and post-intervention, the 24th week. The multiple primary outcomes of the study are the (bio)markers of HAP: glycated haemoglobin, low-density lipoprotein cholesterol (LDL-C), systolic blood pressure, and forced expiratory volume in 1 s for physiological and metabolic health; Digit Symbol Substitution Test, Trail Making Tests A and B for cognition; hand grip strength and gait speed for physical capability; loneliness for social well-being and WHO Quality of Life Instrument-Short Form for quality of life. The secondary outcomes include inflammatory markers, tumour necrosis factor-alpha receptor II, C reactive protein, interleukin 6 and serum Klotho levels. Analyses will be by intention-to-treat and the holistic impact of yoga on HAP will be assessed using global statistical test.Ethics and disseminationThe study is approved by the Institutional Ethics Committee of Swami Vivekananda Yoga Anusandhana Samsthana University, Bangalore (ID: RES/IEC-SVYASA/143/2019). Written informed consent will be obtained from each participant prior to inclusion. Results will be available through research articles and conferences.Trial registration numberCTRI/2021/02/031373.

Timely identification of smoking-related lung abnormalities is essential for effective clinical intervention, as smoking is a significant risk factor for a variety of pulmonary diseases. This work proposes a deep learning-based methodology to categorize an individual as a smoker or non-smoker based on chest X-rays (CXR). Utilizing the capabilities of Convolutional Neural Networks (CNNs), the proposed model examined the efficacy of a baseline CNN architecture and a transfer learning model derived from VGG16. A curated dataset of 5856 labeled chest X-ray images was utilized, and random shuffling and augmentation were used to oversample the dataset to address the data imbalance issue and then preprocess to train and assess both models. The baseline CNN achieved a classification AUC score of 95.15%, illustrating the efficacy of end-to-end learning in retrieving radiographic characteristics associated with smoking. The VGG16-based model, fine-tuned for classification, significantly surpassed the baseline with an accuracy of 98.74%, demonstrating that deep feature representations acquired from extensive datasets can be efficiently used for medical imaging tasks. These results highlight the feasibility of using CNNs for automated, noninvasive screening of smoking history from CXR images. The proposed method can assist in making correct clinical decisions, particularly where patient history is incomplete or unreliable. Furthermore, this approach has potential applications in public health surveillance and early risk assessment, especially in resource-limited settings like primary healthcare centers in rural areas.Introduces one of the first deep learning algorithms for classifying chest X-ray images by smoking status, employing both a baseline CNN and a pre-trained VGG16 model.Using a lightweight baseline CNN and a transfer-learning based VGG16 model fine-tuned on CXR data, the study performs smoker versus non-smoker classification and compares their performance using AUC, precision, recall, and F1-score.Grad-CAM images are used to highlight lung regions that influence predictions, demonstrating the ability of deep learning models to identify early smoking-associated structural alterations, allowing for risk assessment even when there is no visible disease.