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British Journal of Medical and Health Research

British Journal of Medical and Health Research

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📢 Latest Update: 🔔 Call for Papers 2026 | BJMHR Now Accepting Manuscripts for September 2026 Issue | Peer-Reviewed | Open Access | Fast Review in 5–7 Days | Submit Now

📢 Latest Update: 🔔 Call for Papers 2026 | BJMHR Now Accepting Manuscripts for September 2026 Issue | Peer-Reviewed | Open Access | Fast Review in 5–7 Days | Submit Now

Volume 13, Issue 11 - 2026 (November 2026 Issue 11)

Volume 13 Issue 11 Cover

Issue Details:

Volume 13 Issue 11
Published:Nov 2, 2026

Editorial: November 2026 Issue 11

Welcome to the 2026 issue of British Journal of Medical and Health Research. This issue showcases the remarkable breadth and depth of contemporary research across multiple disciplines. From cutting-edge applications of machine learning in climate science to the revolutionary potential of quantum computing in drug discovery, our featured articles demonstrate the power of interdisciplinary collaboration in addressing global challenges.

We are particularly excited to present research that bridges traditional academic boundaries, reflecting our journal's commitment to fostering innovation through cross-disciplinary dialogue. The integration of artificial intelligence with environmental science, the application of blockchain technology to supply chain management, and the convergence of urban planning with smart city technologies exemplify the transformative potential of collaborative research.

As we continue to navigate an era of rapid technological advancement and global challenges, the research presented in this issue offers both insights and solutions that will shape our future. We thank our authors, reviewers, and editorial board members for their continued dedication to advancing knowledge and promoting scientific excellence.

Dr J S Patel
Editor in Chief
British Journal of Medical and Health Research

Articles in This Issue

Showing 6 of 6 articles
Survey-based researchID: BJMHR11130001Pages 1-11Open AccessOpen Access

Clinical Patterns and Management of Traumatic Spinal Injuries at a Tertiary Referral Hospital in Western Kenya: A Prospective Study

Biko Opidi, Kibor Lelei, Barry Ayumba

Background: Traumatic spinal injuries (TSI) are a significant cause of morbidity, disability, and socioeconomic burden, particularly in low- and middle-income countries where access to specialized care and rehabilitation remains limited. This study evaluated theclinical presentation, injury patterns, and management ofTSI at Moi Teaching and Referral Hospital (MTRH), Eldoret, Kenya. A prospective descriptive study was conducted among 28 patients with TSI recruited Methods: through consecutive sampling. Data collected included demographics, injury mechanisms, anatomical levels, neurological severity, associated injuries, treatment modalities, complications, and functional outcomes. Neurological assessments was performed using the ASIA Impairment Scale. Results : The mean age was 37.9 years (SD 12.4), with males comprising 85.7% of participants. Farmers (25%) and casual labourers (17.9%) were the most represented occupational groups. Falls were the leading mechanism of injury, while the lumbar spine was the most frequently affected region. Most patients (89.3%) had incomplete or no neurological impairment (ASIA D/E). Surgical intervention was performed in 67.9% of patients, with a mean time to surgery of 3.8days (SD3.22). The mean hospitalstay was 12.8days (SD10.9). Conclusion: TSI predominantly affected economically active males, with falls being the leading cause. Strengthening occupational injury prevention, prehospital spinal care, timely referral, and comprehensive rehabilitation services may improve patient management in resource-limited settings. Keywords: Traumatic spinal injuries; Spinal trauma; Neurological impairment; Surgical management;Kenya.

Clinical PatternManagementTraumatic Spine injuriesTertiary hospitalProspective study
70,055 views
21,160 downloads

Contributors:

 Biko Opidi
,
 Kibor Lelei
,
 Barry Ayumba
Survey-based researchID: BJMHR11130002Pages 12-24Open AccessOpen Access

ARTIFICIAL INTELLEGENCE IN ORTHODONTICS- A COMPREHENSIVE CLINICAL PERSPECTIVE

Dr. AMEER JAFFAR, Dr. Manas G, Dr. Jithesh Kumar K, Dr. Panjami Marish

Artificial intelligence (AI) is increasingly influencing modern orthodontic practice by facilitating more precise diagnosis, individualized treatment planning, and improved prediction of treatment outcomes. This review explores the clinical applications of AI in orthodontics, focusing on the contributions of machine learning and deep learning to contemporary diagnostic and therapeutic procedures. AI-driven technologies enable automated interpretation of radiographs, facial photographs, and digital dental models, supporting cephalometric analysis, cervical vertebral maturation assessment, and anatomical landmark identification. Their applications also encompass extraction decision-making, bracket positioning, fixed appliance therapy, clear aligner treatment, and orthognathic surgical planning. In clear aligner therapy, AI facilitates virtual treatment simulation, prediction of tooth movement, optimization of aligner staging, digital fabrication, and remote monitoring of treatment progress. Furthermore, predictive algorithms may assist in evaluating treatment complexity and anticipating postoperative soft tissue changes. Despite these advances, clinical implementation remains constrained by limitations in training data quality, algorithmic bias, generalizability, data security, regulatory frameworks, and implementation costs. Establishing the reliability of AI-based systems requires standardized assessment protocols and robust validation across diverse patient populations. Consequently, AI should currently be integrated as an adjunct to, rather than a substitute for, the clinical judgment and expertise of orthodontists. With continued technological development, rigorous clinical evaluation, and appropriate ethical safeguards, AI has the potential to strengthen evidence-based decision-making and advance efficient, predictable, and patient-centered orthodontic care.

Artificial IntelligenceOrthodonticsMachine LearningDeep LearningDigital DentistryTreatment Planning.
70,386 views
21,184 downloads

Contributors:

 Dr. AMEER JAFFAR
,
 Dr. Manas G
,
 Dr. Jithesh Kumar K
,
 Dr. Panjami Marish
Clinical trialsID: BJMHR11130003Pages 25-34Open AccessOpen Access

Biologic and Targeted Synthetic DMARDs Combined with Exercise Therapy in Rheumatoid Arthritis: A Scoping Review of Synergistic Effects and Functional Outcomes

Dr Shaik Abdul munaf, Dr. Muhammed siraj, Gnana karthikeyan.M

Background: Rheumatoid arthritis is a long-term illness where medicines that change the course of the disease lower inflammation and stop damage from happening while exercise helps with being out of shape, weak muscles, trouble moving and heart problems. It is not clear if medicines that work on the system and exercise together have better results than either one on its own. Methods: This paper uses a way of looking at all the information called a scoping review. It brings together guidelines and published information about medicines, exercise and studies that look at both together. The draft does not say that a full search of all information has been done; the final people who write the paper should do the search check the information and record what they find before they submit it. Results: There is evidence that exercise works with regular care for rheumatoid arthritis especially for heart fitness, muscle strength, how well they can move and parts of feeling good. A study that looked at exercise with medicines found results on how active the disease was: some studies said the combination was better others showed no big difference and the way the studies were done stopped clear conclusions about a good combination. There is not information that is split up by the type of medicine. Exercise should usually go with not take the place of, medicine that tries to get the disease under control. The idea that exercise and the newer medicines work better together needs studies with clear plans for checking this standard ways to plan the exercise and important results that matter to people.

activity; rehabilitation; ability to function; inflammatory arthritis; results of treatmentRheumatoid arthritisweak muscles
70,166 views
21,047 downloads

Contributors:

 Dr Shaik Abdul munaf
,
 Dr. Muhammed siraj
,
 Gnana karthikeyan.M
Survey-based researchID: BJMHR11130004Pages 35-59Open AccessOpen Access

Forecasting Health Risks from Air Pollution using an Advanced Hybrid Time Series Approach

Ms. Pranjali Subhash Sonone, Dr. Abhay Kamlakar Khamborkar

Ambient air pollution is a major driver of respiratory, cardiovascular, and metabolic disease in India, yet frameworks that jointly address forecasting, prediction, uncertainty, and interpretability remain scarce in low- and middle-income countries (LMICs). We propose the Hybrid Explainable Air–Health Learning (HEAL) framework, a four-stage pipeline combining (i) Prophet–LSTM pollutant forecasting, (ii) ensemble machine learning with the Synthetic Minority Over-sampling Technique (SMOTE), (iii) Bayesian calibration, and (iv) Explainable Boosting Machines (EBM). Data from the Longitudinal Ageing Study in India (LASI; n = 43,267) were linked with Central Pollution Control Board air quality records to predict six outcomes: COPD, asthma, cardiovascular disease, stroke, type 2 diabetes, and lung cancer (prevalence 0.9–6.8%). The hybrid forecaster achieved the lowest error (RMSE 13.2 vs. 14.8 for LSTM and 16.5 for Prophet). HEAL outperformed baseline models for all six outcomes (AUC 0.58–0.64), and SMOTE improved minority-class recall by 11–18 percentage points, with the largest gains for the rarest diseases. Bayesian calibration yielded narrow 95% credible intervals, and bootstrap sensitivity analysis (±20% pollutant perturbation) confirmed stable associations. Age was the dominant predictor, while O3 and NO2 were most influential for respiratory outcomes and particulate matter for cardiometabolic outcomes. HEAL offers an accurate, interpretable, and uncertainty-aware approach to multi-disease environmental health surveillance in LMIC settings. The specific objectives of this study are to forecast ambient air pollutant concentrations using a hybrid time-series approach combining Prophet and Long Short-Term Memory (LSTM) models; to improve prediction of multiple pollution-related health outcomes using ensemble machine learning with the Synthetic Minority Over-sampling Technique (SMOTE); to quantify predictive uncertainty through Bayesian calibration by estimating credible intervals for individual-level risk predictions; and to enhance interpretability using Explainable Boosting Machines (EBM) to identify disease-specific pollutant effects and interaction patterns. The Longitudinal Ageing Study in India (LASI) is a household survey of the elderly, which is nationally representative and organized by the International Institute of Population Sciences (IIPS), Mumbai [14]. The basis year (2017-2018) itself comprised 43,267 people aged 45 years and above and represented all 28 states and 8 union territories in India. The LASI dataset gives self-reported physician-diagnosed chronic conditions, such as asthma, chronic obstructive pulmonary disease (COPD), chronic bronchitis, lung cancer, cardiovascular disease, diabetes, stroke, and tuberculosis.

Air pollutiondisease predictionhybrid modelspublic health informatics
70,428 views
21,139 downloads

Contributors:

 Ms. Pranjali Subhash Sonone
,
 Dr. Abhay Kamlakar Khamborkar
Systematic reviewsID: BJMHR11130005Pages 60-74Open AccessOpen Access

Zika Virus, A Tool to Fight Against Glioblastoma

Sankhajit Bhattacharjee

Glioblastoma multiforme (GBM) is a deadly human cancer. It occurs in the brain cells or in spinal cord. GBM stem cells are reported to be resistant to treatment and reappearance is unpreventable. GBM stem cells are the target of Zika virus (ZIKV) which is known to cause impaired brain development leading to microcephaly in human embryos. The virus ensures the survival of mice having gliomas. ZIKV has been reported as an oncolytic virus targeting GBM cells selectively. Here, we will discuss the immunological aspect of protection against GBM mediated by ZIKV. When ZIKV was introduced into the brain, the recruitment of CD8+ T and myeloid cells to the tumor microenvironment was augmented. CD8+ T cells were needed for ZIKV-mediated tumor clearance. Here we will also discuss the role of cellular gasdermin D (GSDMD) in efficient killing of a human GBM cell line that was promoted by ZIKV infection. The ZIKV protease cleaves specifically human GSDMD for activating caspase-independent pyroptosis thus damaging, both the naive neighboring as well as viral protease-harboring cells.

Glioblastoma multiforme (GBM)Glioblastoma Stem Cells (GSC)Zika virus (ZIKV)Gasdermin D (GSDMD)SRY (sex determining region Y)-box 2 (SOX2).
70,412 views
21,133 downloads

Contributors:

 Sankhajit Bhattacharjee
Clinical trialsID: BJMHR11130006Pages 75-92Open AccessOpen Access

Morphometric Measurement of the Anterior Cruciate Ligament (ACL) In the Kenyan Population

Sammy Nyariki, Barry Ayumba, Paul Makokha

Morphometric data also guides surgical techniques and tunnel placement. The ACL inserts onto relatively broad, variably shaped footprints on the femur and tibia, which cannot always be reproduced by a single circular tunnel . Precise knowledge of footprint size and shape allows surgeons to determine whether single-bundle or double-bundle reconstruction best restores the patient’s anatomy. Another critical role of ACL morphometry is in biomechanical outcomes and joint stability. A close match between graft and native ACL dimensions improves anterior-posterior stability and rotational control, both of which are essential for return to high-level activities . When tunnel placement fails to align with the native fiber orientation, abnormal graft tensioning and altered kinematics can result, predisposing to failure or early degenerative changes . Hence, morphometric insights translate directly to surgical accuracy, graft longevity, and patient functional outcomes.

Anterior cruciate ligamentMorphometryKnee jointCadaveric study
70,865 views
21,318 downloads

Contributors:

 Sammy Nyariki
,
 Barry Ayumba
,
 Paul Makokha

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