Patna Women's College

The clinical course of primary infections is frequently complicated by coinfections and opportunistic pathogens, which have a substantial impact on disease outcomes. Multiple pathogens can worsen illness severity, make identification more difficult, and reduce the effectiveness of treatment. Advanced molecular methods, such as polymerase chain reaction (PCR) and next-generation sequencing (NGS), are necessary for accurate diagnosis of co-infections because they can identify many pathogens at once and offer detailed insights into the microbial ecology. Treatment plans must be customized to target the particular infections after they have been discovered, taking into account the patient's immunological condition as well as any possible drug-drug interactions. Opportunistic infections, caused by pathogens that exploit weakened immune defenses, further complicate treatment. These infections often require a combination of antimicrobial therapies and immunomodulatory treatments to restore immune function and control pathogen proliferation. The interplay between co-infections and opportunistic pathogens necessitates a multidisciplinary approach, involving infectious disease specialists, microbiologists, and immunologists, to optimize patient outcomes. Advances in personalized medicine, including the use of biomarker-driven therapies and immune profiling, are showing promise in improving the management of co-infections. Early detection and targeted treatment are critical in preventing the progression of disease and reducing mortality rates. Future research should focus on understanding the mechanisms of pathogen interaction within the host and developing novel therapeutic strategies to mitigate the impact of co-infections and opportunistic pathogens on disease outcomes.

Probiotic microorganisms are frequently utilized in pharmaceutical and food formulations due to their beneficial effects on human health and gut microbiota. However, the unforeseen emergence of resistance to antibiotics among such probiotic microbes invites attention for the careful evaluation of the safety and therapeutic applicability of these probiotic microbes. The present study investigated the antibiogram profile of such isolates procured from locally available pharmaceutical probiotic formulations. Antibiotic susceptibility test revealed considerable variation among the isolates six. All six isolates exhibited complete sensitivity towards gentamicin and showed predominant sensitivity to amikacin, vancomycin, ampicillin-sulbactam, chloramphenicol, amoxiclav, and ampicillin. Intermediate resistance was recorded against ciprofloxacin, tetracycline, nalidixic acid, azithromycin, and kanamycin. Certain isolates revealed resistance to clindamycin, ceftazidime, erythromycin, and penicillin. The study highlights that probiotic strains possessing intrinsic/non-transferable resistance may provide advantages during concurrent antibiotic therapy by maintaining gut microbial balance. The occurrence of multidrug resistance, particularly on mobile genetic elements, among some isolates raises biosafety concerns regarding the possible dissemination of resistance. Besides such resistant microbes may, sometimes, compromise the efficacy of therapeutic antibiotics. Overall, this investigation emphasizes that antibiotic resistance profiling should be considered a critical criterion in the development of probiotic formulations. A comprehensive evaluation of resistance mechanisms, transmissibility, and biosafety parameters will ensure the safe, effective, and sustainable utilization of probiotics in food, pharmaceutical and clinical setups Int. J. Appl. Sci. Biotechnol. Vol 14(2): 65-77.

Introduction: Tuberculosis (TB) continues to be a significant public health challenge in India, with alarming prevalence rates and unique socio-demographic risk factors. Resistance to Rifampicin (RIF), is caused by mutations in the rpoB gene of the causative agent. It is usually situated in a region at the 507-533 amino acid residues (81 bp) within the rpoB gene, known as the Rifampicin Resistance Determining Region (RRDR). Aim: To assess the prevalence of Rifampicin Resistance (RR) and identify mutations in the RRDR of the rpoB gene. Materials and Methods: This cross-sectional study was conducted at ESIC Medical College, Patna, in collaboration with the NABL-accredited laboratory associated with Tertiary care Hospitals, Patna, Bihar, India from July 2024 to January 2025. A total of 502 clinical samples from suspected cases of TB were analysed by GeneXpert MTB/RIF testing. Deoxyribonucleic Acid (DNA) extraction was done only for the MTB complex with RR followed by automated DNA sequencing. Data obtained from these 68 RR-MTB clinical samples were enrolled in this study. Statistical analysis was performed using Statistical Package for the Social Sciences (SPSS) software with the association of multivariate logistic regression with p-value <0.05. Results: In this study, 46.57% (68/146) of all MTB-positive patients were RR. The highest alteration was at the Ser/Leu substitution at codon 531 present in 45.6% (31/68) isolates, followed by His/Tyr substitution at codon 526 in 25% (17/68), mutation at codon 516 in 16.17% (11/68) isolates and mutations at codon 511 demonstrated in 13.23% (9/68) isolates. Conclusion: Mutations within the RRDR of the rpoB gene, particularly at codons 531 and 526, were identified as the predominant drivers of RR among pulmonary TB patients in Eastern India. The study highlights the importance of integrating rapid molecular diagnostics with socio-demographic risk assessment to enable early detection, guide individualised treatment, and strengthen regional TB control strategies.

Connecting physical items to the Internet (IoT) enables the development of intelligent systems and cutting-edge technologies like the intelligent transportation system (ITS). IoT solutions greatly aid the development of the worldwide IoT in Intelligent Transportation Systems. IoT-based vehicle connectivity ushers in a new era of communication that will eventually lead to ITS. IoT combines the storage, processing, and computing of sensor data with data analytics to efficiently manage the traffic system. Automating railroads, roads, airports, and ships thanks to IoT-based intelligent transportation systems (IoT-ITS) improves customer perceptions of how things are moved, tracked, and delivered. This paper presents an intelligent accident detection, prevention, and alert system that use the Internet of Things and artificial intelligence sensors. Through this proposed model, we cannot only detect the accident and send it to the server but also create a google API map that can show the accident-prone areas to the registered user and send them messages or alarms to slow down their speed. This will help users to identify accident-prone areas and reduce road accidents.

The Emden-Fowler (EF) equations are fundamental in modeling complex phenomena across astrophysics, quantum mechanics, and nonlinear science disciplines. This work proposes an efficient computational approach for solving the third-order EF equation with suitable boundary conditions. We transform the differential equation into its integral counterpart to manage the singularity at the origin. The solution is then approximated using a Bernstein polynomial-based collocation technique, with Chebyshev points selected to optimize convergence and stability. This strategy leads to a nonlinear system of equations, which is effectively handled using Newton's iterative method. We provide a theoretical analysis of the existence and uniqueness of solutions within this framework and establish error estimates for the proposed numerical scheme. Numerical experiments, including comparisons with established techniques, demonstrate that our approach delivers high accuracy and computational efficiency for benchmark problems. These results highlight the method's potential for reliably addressing singular and nonlinear boundary value problems in scientific modeling.