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British Journal of Medical and Health Research

📢 Latest Update: Call for Papers: Special Issue on Medical and Health Research – Submit to British Journal (BJMHR) by March 31, 2026

📢 Latest Update: Call for Papers: Special Issue on Medical and Health Research – Submit to British Journal (BJMHR) by March 31, 2026

Volume 10, Issue 6 - 2023 (June 2023 Issue 6)

Volume 10 Issue 6 Cover

Issue Details:

Volume 10 Issue 6
Published:Invalid Date

Editorial: June 2023 Issue 6

Welcome to the 2023 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 Hemangi J Patel
Editor-in-Chief
British Journal of Medical and Health Research

Articles in This Issue

Showing 3 of 3 articles
Research PaperID: BJMHR1006001

In Silico computational screening of Amurthathi Chooranam - Siddha Poly herbal formulation for management of Urolithiasis against target Tamm – Horsfall protein

Ramani Mani, Kabilan Natarajan, Kanakavalli kadarkarai

Urolithiasis plagues are common urinary disease in worldwide. Siddha medicine is one of the traditional systems of medicines practiced in the southern India. Amurthathi Chooranam is a classical Siddha Poly herbal formulation which was analyzed by molecular docking for Urolithiasis. The present study is aimed to accomplish the In Silico computational binding of phytocomponents of Amurthathi Chooranam with the core amino acids (CYS 527, PRO 528, HIS 529, GLY 534, ARG 583, THR 585, ARG 586) of the target protein Tamm–Horsfall protein (PDB) - 4WRN which is involved in calcium oxalate crystallization for management of urolithiasis. Docking calculations were carried out for retrieved phytocomponents against target protein Tamm–Horsfall protein. Essential hydrogen atoms, Kollman united atom type charges, and solvation parameters were added with the aid of AutoDock tools. Docking simulations were performed using the Lamarckian genetic algorithm (LGA) and the Solis & Wets local search method. Initial position, orientation and torsions of the ligand molecules were set randomly.Total 9 bioactive lead compounds were retrieved from the herbs present in Amurthathi Chooranam such as Alpha-phellandrene, Elemene, Elemicin, Tinosporide, Nerolidol, Eugenol, Quercetin, Syringic Acid and Morphine possess maximum of 3-4 interactions with the core active amino acid residues present on the target protein Tamm–Horsfall protein. Based on the results it was concluded that the above mentioned compounds exerts anti-urolithiasis activity by preventing calcium oxalate crystallization which inhibit the target Tamm–Horsfall protein for management of urolithiasis. Keywords: Molecular Docking, Amurthathi Chooranam, Antiurolithiatic activity, Tamm–Horsfall protein

Molecular DockingAmurthathi ChooranamAntiurolithiatic activityTamm–Horsfall protein
56,166 views
16,906 downloads

Contributors:

 Ramani Mani
,
 Kabilan Natarajan
,
 Kanakavalli kadarkarai
Research PaperID: BJMHR1006002

CHANGING PARADIGMS IN CRITICAL CARE: VARIATION IN OROPHARYNGEAL MICROBIOTA AS A DECIDING FACTOR TO ANTIBIOTIC RESISTANCE IN ICU PATIENTS

Isha Katyal, Nitin R. Ankle, Prashant H. Patil, Sachin K. Damam

Background: Oropharyngeal colonization by pathogenic organisms especially in the initial crucial days of ICU admission, is the main cause of predisposition to nosocomial infections. Hence, identifying bacterial flora, in respect to hospitalization will help in further management to prevent morbidity and mortality of these patients. Objective: Determining changes in bacterial flora in oropharynx of patients with nasogastric tube, admitted in ICU. Methods: One year prospective observational study including 40 patients between 18 and 70 years, Group A 20 cases- with nasogastric tube in situ, Group B 20 controls including those without tube. Oropharyngeal swab taken on Day 0 and 7 of ICU admission respectively. Results: There was nearly equal distribution of gender, with 19 females and 21 males, majority in the age group of 35-74 years. Most of patients had no growth on day 0 (35% cases) but as the day of ICU admission progressed, there was increased growth of Gram negative bacteria like Pseudomonas aeruginosa (20%), Klebsiella pneumonia (25%) especially in cases group. Conclusion: The progressive increase of pathogenic organism and the variation to gram negative bacteria further confirmed predispositions to pathogen carriage at these sites and the subsequent risk of infection within the crucial 48 hours. Hence, need for consistent oral cleansing procedures, maintaining good oral hygiene in these prolonged tube-fed patients will help prevention of nosocomial infections.

Nasogastric tubeOropharyngeal floraICUbacteriagrowth
56,626 views
16,973 downloads

Contributors:

 Isha Katyal
,
 Nitin R. Ankle
,
 Prashant H. Patil
,
 Sachin K. Damam
Research PaperID: BJMHR1006004

MACHINE LEARNING APPLICATIONS IN HEALTHCARE: THE STATE OF KNOWLEDGE AND FUTURE DIRECTIONS

Mrinmoy Roy, Sarwar J. Minar, Porarthi Dhar, A T M Omor Faruq

Detection of easily missed hidden patterns with fast processing power makes machine learning (ML) indispensable to today’s healthcare system. Though many ML applications have already been discovered and many are still under investigation, only a few have been adopted by current healthcare systems. As a result, there exists an enormous opportunity in healthcare system for ML but distributed information, scarcity of properly arranged and easily explainable documentation in related sector are major impede which are making ML applications difficult to healthcare professionals. This study aimed to gather ML applications in different areas of healthcare concisely and more effectively so that necessary information can be accessed immediately with relevant references. We divided our study into five major groups: community level work, risk management/ preventive care, healthcare operation management, remote care, and early detection. Dividing these groups into subgroups, we provided relevant references with description in tabular form for quick access. Our objective is to inform people about ML applicability in healthcare industry, reduce the knowledge gap of clinicians about the ML applications and motivate healthcare professionals towards more machine learning based healthcare system.

Machine LearningHealthcareCommunity HealthTelemedicineAHC Screening
56,603 views
17,026 downloads

Contributors:

 Mrinmoy Roy
,
 Sarwar J. Minar
,
 Porarthi Dhar
,
 A T M Omor Faruq