Association of sleep quality with physical and psychological health indicators in overweight and obese rural Indians.

Objective: To measure the association of sleep quality with physical (i.e., grip strength, functional mobility, balance) and psychological (depression, anxiety) health indicators in an overweight/obese population. Methods: Baseline data of 2337 participants (1382 overweight/obese and 955 normal weight) from an aging cohort in rural southern India (CBR-SANSCOG) was analyzed retrospectively. Assessment tools included the Pittsburgh Sleep Quality Index (PSQI) for sleep quality, dynamometry for Hand Grip Strength (HGS), Timed Up-and-Go (TUG) for functional mobility, Chair Stand Test (CST) for lower limb strength, Geriatric Depression scale (GDS-30) for depressive symptoms and Generalized Anxiety Disorder scale (GAD-7) for anxiety symptoms. Linear regression models, adjusted for known confounders, were used to examine the association of sleep quality with the health parameters in overweight/obese and normal-weight groups. Results: In the fully adjusted model, higher global PSQI score was associated with higher TUG time (ß = 0.06, 95 % CI: 0.004,0.12), higher scores on GDS (ß = 1.08, 95 % CI: 0.96,1.20) and GAD (ß = 0.71, 95 % CI: 0.62,0.79), and lower scores on CST (ß = -0.12, 95 % CI: -0.19,-0.06) in overweight/obese individuals. The sleep disturbance sub-component of PSQI was associated with most of the physical (TUG, CST) and psychological (GDS and GAD) health indicators. Sleep duration and use of sleep medication showed no significant association with any of the health indicators. Conclusion: The concurrent presence of poor sleep quality and overweight/obesity could worsen physical and psychological health in middle-aged and older adults. We highlight the importance of early detection and timely management of sleep problems in this population to reduce physical and psychological morbidities.

Bridging, Mapping, and Addressing Research Gaps in Health Sciences: The Naqvi-Gabr Research Gap Framework.

Innovations pertaining to the ever-evolving needs of the medical and healthcare sciences remain constant. This creates a gap between the rationalized needs of the study and the proposed research question. However, classifying, identifying, and addressing these research gaps require a systematic and precise structured map. Using the Medical Subject Heading (MeSH) terms "Research Gaps" AND "Healthcare" AND "Framework" in MEDLINE, Scopus, and CINAHL databases with the filters yielded no relevant literature. Therefore, this review aims to fill this practical and clinical knowledge gap by developing the Naqvi-Gabr Research Gap Framework through critical synthesis based on extensive research on medical and healthcare research gaps. Fourteen research gaps are distributed for allocation as per the healthcare delivery system approach: developing new treatments or prevention strategies, improving diagnostic tools and techniques, addressing health disparities, and improving access to healthcare services. This structured framework determines the strategic mapping of research gaps corresponding to the nature of the research. The identification and classification of the appropriate research gap led to precise and concise conclusions corresponding to the research process proposed in this study. Hence, the Naqvi-Gabr Research Gap Framework is a valuable tool for determining the potential application of gaps by researchers, policymakers, and other stakeholders with a productive address.

Unlocking The Mysteries of DNA Adducts with Artificial Intelligence.

Cellular genome is considered a dynamic blueprint of a cell since it encodes genetic information that gets temporally altered due to various endogenous and exogenous insults. Largely, the extent of genomic dynamicity is controlled by the trade-off between DNA repair processes and the genotoxic potential of the causative agent (genotoxins or potential carcinogens). A subset of genotoxins form DNA adducts by covalently binding to the cellular DNA, triggering structural or functional changes that lead to significant alterations in cellular processes via genetic (e. g., mutations) or non-genetic (e. g., epigenome) routes. Identification, quantification, and characterization of DNA adducts are indispensable for their comprehensive understanding and could expedite the ongoing efforts in predicting carcinogenicity and their mode of action. In this review, we elaborate on using Artificial Intelligence (AI)-based modeling in adducts biology and present multiple computational strategies to gain advancements in decoding DNA adducts. The proposed AI-based strategies encompass predictive modeling for adduct formation via metabolic activation, novel adducts' identification, prediction of biochemical routes for adduct formation, adducts' half-life predictions within biological ecosystems, and, establishing methods to predict the link between adducts chemistry and its location within the genomic DNA. In summary, we discuss some futuristic AI-based approaches in DNA adduct biology.

A protocol for the development of PhyCaRe: An extension of the CARE guideline for physiotherapy using the Delphi method.

Background: Case reports are one of the important forms of documentation and publication of clinical physiotherapy presenting the first line of evidence in scientific literature. In order to provide a systematic and precise structure for reporting and presenting cases, the CARE guidelines were established in 2013. However, these guidelines present limitations as while reporting require items of specific specialties following the checklist. Authors from different specialities have developed CARE extensions specifying the characteristic features of corresponding fields, however, an extension dealing with physiotherapy assessment and line of management in the CARE guidelines is proposed as PhyCaRe. Method: After consulting with the advisors, a draft will be prepared of the specific elements that should be included in the PhyCaRe using web Delphi methodology considering CARE statement as the source and SurveyMonkey will be used to undertake the web Delphi questionnaire. The web Delphi methodology will be assumed for three rounds and will be open to physiotherapists and others with substantial experience in reviewing case reports. Subsequently, an online consensus meeting, pilot testing, and submission of the CARE extension for physiotherapy will be conducted for publication. Dissemination: The 2010 "Guidance for Developers of Health Research Reporting" and instructions from the EQUATOR Network will be followed in the preparation of PhyCaRe guidelines. The guidelines will be propagated at different platforms and journals will be requested to adopt the guidelines. Registration: The reporting guideline under development is prospectively registered on the EQUATOR Network website on PhyCaRe - Reporting guideline for physiotherapy case reports.

Inhibition of EphA3 Expression in Tumour Stromal Cells Suppresses Tumour Growth and Progression.

Tumour progression relies on interactions with untransformed cells in the tumour microenvironment (TME), including cancer-associated fibroblasts (CAFs), which promote blood supply, tumour progression, and immune evasion. Eph receptor tyrosine kinases are cell guidance receptors that are most active during development but re-emerge in cancer and are recognised drug targets. EphA3 is overexpressed in a wide range of tumour types, and we previously found expression particularly in stromal and vascular tissues of the TME. To investigate its role in the TME, we generated transgenic mice with inducible shRNA-mediated knockdown of EphA3 expression. EphA3 knockdown was confirmed in aortic mesenchymal stem cells (MSCs), which displayed reduced angiogenic capacity. In mice with syngeneic lung tumours, EphA3 knockdown reduced vasculature and CAF/MSC-like cells in tumours, and inhibited tumour growth, which was confirmed also in a melanoma model. Single cell RNA sequencing analysis of multiple human tumour types confirmed EphA3 expression in CAFs, including in breast cancer, where EphA3 was particularly prominent in perivascular- and myofibroblast-like CAFs. Our results thus indicate expression of the cell guidance receptor EphA3 in distinct CAF subpopulations is important in supporting tumour angiogenesis and tumour growth, highlighting its potential as a therapeutic target.

Coordination of floral and fiber development in cotton (Gossypium) by hormone- and flavonoid-signalling associated regulatory miRNAs.

Various plant development activities and stress responses are tightly regulated by various microRNAs (miRNA) and their target genes, or transcription factors in a spatiotemporal manner. Here, to exemplify how flowering-associated regulatory miRNAs synchronize their expression dynamics during floral and fiber development in cotton, constitutive expression diminution transgenic lines of auxin-signaling regulatory Gh-miR167 (35S-MIM167) were developed through target mimicry approach. 'Moderate' (58% to 80%)- and 'high' (> 80%)-Gh-miR167 diminution mimic lines showed dosage-dependent developmental deformities in anther development, pollen maturation, and fruit (= boll) formation. Cross pollination of 'moderate' 35S-MIM167 mimic lines with wild type (WT) plant partially restored boll formation and emergence of fiber initials on the ovule surface. Gh-miR167 diminution favored organ-specific transcription biases in miR159, miR166 as well as miR160, miR164, and miR172 along with their target genes during anther and petal development, respectively. Similarly, accumulative effect of percent Gh-miR167 diminution, cross regulation of its target ARF6/8 genes, and temporal mis-expression of hormone signaling- and flavonoid biosynthesis-associated regulatory miRNAs at early fiber initiation stage caused irregular fiber formation. Spatial and temporal transcription proportions of regulatory miRNAs were also found crucial for the execution of hormone- and flavonoid-dependent progression of floral and fiber development. These observations discover how assorted regulatory genetic circuits get organized in response to Gh-miR167 diminution and converge upon ensuing episodes of floral and fiber development in cotton.

Eph Receptors in Cancer.

Eph receptor tyrosine kinases play critical functions during development, in the formation of tissue and organ borders, and the vascular and neural systems. Uniquely among tyrosine kinases, their activities are controlled by binding to membrane-bound ligands, called ephrins. Ephs and ephrins generally have a low expression in adults, functioning mainly in tissue homeostasis and plasticity, but are often overexpressed in cancers, where they are especially associated with undifferentiated or progenitor cells, and with tumour development, vasculature, and invasion. Mutations in Eph receptors also occur in various tumour types and are suspected to promote tumourigenesis. Ephs and ephrins have the capacity to operate as both tumour promoters and tumour suppressors, depending on the circumstances. They have been demonstrated to impact tumour cell proliferation, migration, and invasion in vitro, as well as tumour development, angiogenesis, and metastases in vivo, making them potential therapeutic targets. However, successful development of therapies will require detailed understanding of the opposing roles of Ephs in various cancers. In this review, we discuss the variations in Eph expression and functions in a variety of malignancies. We also describe the multiple strategies that are currently available to target them in tumours, including preclinical and clinical development.

Performance analysis of U-Net with hybrid loss for foreground detection.

With the latest developments in deep neural networks, the convolutional neural network (CNN) has made considerable progress in the area of foreground detection. However, the top-rank background subtraction algorithms for foreground detection still have many shortcomings. It is challenging to extract the true foreground against complex background. To tackle the bottleneck, we propose a hybrid loss-assisted U-Net framework for foreground detection. A proposed deep learning model integrates transfer learning and hybrid loss for better feature representation and faster model convergence. The core idea is to incorporate reference background image and change detection mask in the learning network. Furthermore, we empirically investigate the potential of hybrid loss over single loss function. The advantages of two significant loss functions are combined to tackle the class imbalance problem in foreground detection. The proposed technique demonstrates its effectiveness on standard datasets and performs better than the top-rank methods in challenging environment. Moreover, experiments on unseen videos also confirm the efficacy of proposed method.

Osteoporosis diagnosis in knee X-rays by transfer learning based on convolution neural network.

Osteoporosis degrades the quality of bones and is the primary cause of fractures in the elderly and women after menopause. The high diagnostic and treatment costs urge the researchers to find a cost-effective diagnostic system to diagnose osteoporosis in the early stages. X-ray imaging is the cheapest and most common imaging technique to detect bone pathologies butmanual interpretation of x-rays for osteoporosis is difficult and extraction of required features and selection of high-performance classifiers is a very challenging task. Deep learning systems have gained the popularity in image analysis field over the last few decades. This paper proposes a convolution neural network (CNN) based approach to detect osteoporosis from x-rays. In our study, we have used the transfer learning of deep learning-based CNNs namely AlexNet, VggNet-16, ResNet, and VggNet -19 to classify the x-ray images of knee joints into normal, osteopenia, and osteoporosis disease groups. The main objectives of the current study are: (i) to present a dataset of 381 knee x-rays medically validated by the T-scores obtained from the Quantitative Ultrasound System, and (ii) to propose a deep learning approach using transfer learning to classify different stages of the disease. The performance of these classifiers is compared and the best accuracy of 91.1% is achieved by pretrained Alexnet architecture on the presented dataset with an error rate of 0.09 and validation loss of 0.54 as compared to the accuracy of 79%, an error rate of 0.21, and validation loss of 0.544 when pretrained network was not used.. The results of the study suggest that a deep learning system with transfer learning can help clinicians to detect osteoporosis in its early stages hence reducing the risk of fractures.

Physical Therapy Interventions of an Electrical Burn Injury-Afflicted Patient: A Case Report.

Electrical injuries are uncommon but not completely rare. It is most prevalent in the male population, although females are also affected in the workplace or household-related activities. These injuries usually occur in situations where proper precautions are not taken by the individual and also appropriate safety drills and education for personnel are not carried out. Electrical burns affecting children are very rare, but when they do occur, it is usually due to accidental contact with exposed electrical sources. In this patient, there were severe levels of secondary complications following the burn injury. The patient developed blood infections and also was hampered in doing a variety of activities of daily living. The patient was diagnosed with 45%-50% body surface area (BSA) covered with burns, which suggests its severe nature. Treatment focuses on preventing wound infection, managing the excruciating amount of pain, preventing complications of immobility, promoting mobility as much as the patient can, and also educating the patient and the family members.

Rehabilitation of a Patient after a Transtibial Amputation: A Case Report.

Amputation is the surgical removal of a body part or limb caused by excessive tightness, pathological conditions, or a surgical procedure on an extremity. Transtibial amputation involves removing the feet, ankle, distal part of the tibia, and fibula with the surrounding structures like connective tissues and other structures surrounding the distal part of the bone. This type of surgery has a high rate of contraindications but has adequate indications; it maintains a therapeutic technique with a high clinical price and, in general, lifesaving value. The primary objectives of rehabilitation are to improve normal and affected extremity power, patient mobility, aerobic capacity, coordination and balance, and independence in performing activities of daily living. In this case, the patient was a 50-year-old man who complained of pain in his left lower limb and had an ulcer on his left foot. Initially, it was small in size when noticed by the patient but gradually progressed to a large wound of 7x6cm over the medial side, with foul-smelling discharge, associated with blackish discoloration. Walking aggravated foot pain, which was relieved by standing still, medications, and rest. On investigation, the patient was diagnosed with gangrene in the left lower limb and referred for rehabilitation to the physiotherapy department after surgery. This case study provides information on the recovery of a patient with the help of prosthetic devices.

A Systematic Review of Randomized Controlled Trials on Virtual Reality Application in Pediatric Patients.

Virtual reality is a novel approach for distracting and alleviating anxiety, pain, and other complications during medical procedures, and it can be more effective than conventional methods. In virtual reality, the patient is completely immersed in the virtual environment, which is used to make patients feel more comfortable and can provide a positive prognosis. The data were searched by using the Boolean operator "AND" between the search phrases "Virtual reality," and "Pediatrics" and the relevant literature was extracted. The inclusion criteria were the free full text, randomized controlled trials, studies between 2016 and 2022 and pediatric patients. This systematic review was conducted to compare randomized controlled trials of virtual reality applications in pediatric patients in different clinical settings. Of the included 15 randomized controlled trials, 12 studies were on pain and anxiety, two on brain injury and cerebral palsy, and one on awareness among asthmatic patients. This review concluded that virtual reality exposure has a beneficial effect on pediatric patients in reducing pain and anxiety, improving muscle strength and dexterity, and awareness among asthmatic patients.

Artificial intelligence uncovers carcinogenic human metabolites.

The genome of a eukaryotic cell is often vulnerable to both intrinsic and extrinsic threats owing to its constant exposure to a myriad of heterogeneous compounds. Despite the availability of innate DNA damage responses, some genomic lesions trigger malignant transformation of cells. Accurate prediction of carcinogens is an ever-challenging task owing to the limited information about bona fide (non-)carcinogens. We developed Metabokiller, an ensemble classifier that accurately recognizes carcinogens by quantitatively assessing their electrophilicity, their potential to induce proliferation, oxidative stress, genomic instability, epigenome alterations, and anti-apoptotic response. Concomitant with the carcinogenicity prediction, Metabokiller is fully interpretable and outperforms existing best-practice methods for carcinogenicity prediction. Metabokiller unraveled potential carcinogenic human metabolites. To cross-validate Metabokiller predictions, we performed multiple functional assays using Saccharomyces cerevisiae and human cells with two Metabokiller-flagged human metabolites, namely 4-nitrocatechol and 3,4-dihydroxyphenylacetic acid, and observed high synergy between Metabokiller predictions and experimental validations.

deepGraphh: AI-driven web service for graph-based quantitative structure-activity relationship analysis.

Artificial intelligence (AI)-based computational techniques allow rapid exploration of the chemical space. However, representation of the compounds into computational-compatible and detailed features is one of the crucial steps for quantitative structure-activity relationship (QSAR) analysis. Recently, graph-based methods are emerging as a powerful alternative to chemistry-restricted fingerprints or descriptors for modeling. Although graph-based modeling offers multiple advantages, its implementation demands in-depth domain knowledge and programming skills. Here we introduce deepGraphh, an end-to-end web service featuring a conglomerate of established graph-based methods for model generation for classification or regression tasks. The graphical user interface of deepGraphh supports highly configurable parameter support for model parameter tuning, model generation, cross-validation and testing of the user-supplied query molecules. deepGraphh supports four widely adopted methods for QSAR analysis, namely, graph convolution network, graph attention network, directed acyclic graph and Attentive FP. Comparative analysis revealed that deepGraphh supported methods are comparable to the descriptors-based machine learning techniques. Finally, we used deepGraphh models to predict the blood-brain barrier permeability of human and microbiome-generated metabolites. In summary, deepGraphh offers a one-stop web service for graph-based methods for chemoinformatics.

A research protocol on leap motion tracking device: A novel intervention method in distal radial fracture rehabilitation.

INTRODUCTION: Physiotherapeutic rehabilitation are used to optimize functional recovery following a distal radial fracture (DRF). Being most common upper limb fracture in all age groups, the DRF peaks in young men and in post-menopausal women with incidence ratio of 1:4. Leap motion control based rehabilitation of patients with DRF is limited. This research aims to assess the efficacy of leap motion control based rehabilitation in patients with DRF. METHODS: In an randomized parallel group trial, subjects (n = 40) with DRF will be recruited. The participants will be enrolled into either experimental or control group with 1:1 allocation ratio. Following the primary assessment and allocation, the participants in experimental group will receive both leap motion control and conventional therapy over a period of six weeks. Participants in conventional group would undergo only conventional therapy. The primary outcome measures will be Disabilities of the Arm, Shoulder, and Hand (DASH) questionnaire and Universal goniometer however the grip strength and Visual Analog Scale (VAS) will be used as secondary outcome measures. PURPOSE OF THE STUDY: The findings of this trial will examine the impact of leap motion control in DRF patients with conventional therapy on improving the functional activity, range of motion (ROM), grip strength and pain. EXPECTED CLINICAL IMPLICATIONS: To conclude, this research seeks to examine the rapid and long term effects of leap motion control in DRF patients. The study findings would help prospective patients with DRF, which may include a newly designed approach of rehabilitation.

Effect of virtual reality and haptic feedback on upper extremity function and functional independence in children with hemiplegic cerebral palsy: a research protocol.

Hemiplegic cerebral palsy (CP) is a subcategory of CP which is characterized by sensory motor deficits primarily on one side of the body that adversely affects functionality. Virtual reality (VR) systems have been advanced in the recent past for the use in rehabilitation of patients with neurological conditions. Virtual reality has an inherent motivational component that provides the much-needed compliance for active participation by children. The rationale of the proposed study is to investigate the effect of VR and haptic feedback for improvement of upper extremity function of children with hemiplegic cerebral palsy. This comparative experimental study will be recruiting 36 children with hemiplegic CP and will be treating them by VR and haptic feedback along with conventional physiotherapy in group A and by conventional physiotherapy only in group B. The children will undergo the treatment for six weeks (five days/week) with each session extending for 60 minutes/day. The primary outcome measures including `nine-hole peg test´ (9HPT) and `box and block test´ (BBT) will assess the manual dexterity and secondary outcome measures including `ABILHAND-kids´ and `WeeFIM (self-care)´ will assess the functional independence that are hypothesized to be gained by haptic enhanced VR intervention when combined with the conventional therapy.

A Novel Method for Muscle Elongation in Myalgia with Naqvi's-Dynamic Electrical Therapy Approach (DELTA)©: The First-Ever Case Report.

Lifestyle-related neck and shoulder pain can be attributed to trapezius myalgia (TM) in a significant number of cases. Apart from pain, manifestations of TM include tightness of the trapezius muscle, especially in the upper fibres. Naqvi's-Dynamic Electrical Therapy Approach (Naqvi's-DELTA)© is a novel electrotherapeutic intervention based on the principle of myofibril elongation obtained by interference of poled vector current that is moved along the length of muscle fibres. A 22-year-old male approached the physiotherapy outpatient department (OPD) with the chief complaint of persistent neck pain and stiffness for three years that adversely affected his activities of daily living (ADLs). The pain was evaluated using a visual analog scale (VAS), restrictions in the range of motion (ROM) were determined by the cervical range of motion (CROM) device, and limitations in ADLs were assessed by neck disability index (NDI). Naqvi's-DELTA© was administered once a day for seven days, with each session lasting for 15 minutes. After the intervention, an evident beneficial effect was noted in all outcomes measures suggesting that this novel method was effective in decreasing pain, stiffness, and limitations in ADLs. Further investigation to explore this method for myalgia management is warranted.