Materiovigilance is a crucial component of health-care policy designed to ensure patient safety by monitoring and addressing safety issues associated with medical devices. However, traditional systems encounter challenges related to timely reporting, standardization, and the detection of adverse events. Artificial intelligence (AI) has the potential to transform materiovigilance by improving data processing, real-time monitoring, and predictive analytics. This review explores the potential of AI in strengthening medical device safety, highlighting its benefits in enhancing patient safety, personalizing medical devices, and streamlining regulatory reporting. AI-powered systems can detect adverse events, predict patient deterioration, and provide personalized treatment plans, ultimately improving patient outcomes. Furthermore, AI enables the analysis of large and complex datasets, facilitating proactive decision-making and the early identification of emerging risks associated with medical devices. By automating routine tasks and improving accuracy, AI can significantly reduce the administrative burden on health-care professionals. In addition, AI can enhance post-market surveillance by identifying trends and anomalies in real time, thereby accelerating corrective actions. However, ethical and regulatory considerations, such as algorithmic biases, data privacy, and accountability, must be addressed to ensure the responsible development and implementation of AI in materiovigilance. Establishing robust regulatory frameworks, fostering transparency, and promoting interdisciplinary collaboration are essential to overcoming these challenges and fully realizing AI’s potential in health care.

The field of modern surgery has undergone significant transformation with the integration of robotics, which offers unprecedented precision, reduced invasiveness, and improved surgical outcomes. Robotic-assisted surgery has gained popularity across various specialties, including neurosurgery, orthopedics, urology, and cardiac surgery, with systems such as the da Vinci Surgical System serving as a key example. These robotic platforms enhance surgical performance by providing greater control, three-dimensional visualization, and improved dexterity, which collectively reduce operating fatigue, minimize human error, and shorten patient recovery times. Despite these advancements, challenges remain, including high operational costs, the need for specialized training, and the limitations of robotic systems in handling complex or unforeseen situations. This review explores the current state of robotic applications in surgery, addressing both their potential and their limitations. It also discusses future developments, particularly the role of enhanced sensory feedback, machine learning, and artificial intelligence in advancing robotic surgery. While robotic technologies hold the promise of improving patient outcomes, reducing complications, and increasing accessibility, ethical, financial, and technological challenges still need to be addressed. As robotic technologies continue to evolve, they have the potential to reshape the landscape of essential procedures and surgeries.

It is well established that there is an elevated overdose risk with benzodiazepine (BZD) use during opioid agonist treatment (OAT). However, further studies regarding other aspects of how BZDs influence OAT are necessary. This review summarizes the literature on concurrent BZD use with medications for opioid use disorder (MOUD) and how they affect treatment retention in OAT. EMBASE (Ovid), PubMed, and Google Scholar database search tools were used to search for studies that examined the effect of concurrent BZD and MOUD on treatment retention in OAT. Studies published up to January 30th, 2024, were included with no restriction applied other than English language. The criteria for included literature were the presence of both BZD and at least one MOUD as a variable and treatment retention or MOUD adherence as an outcome. Fourteen articles met the criteria for review: eleven retrospective studies and three observational studies. Methadone was utilized in seven studies, buprenorphine in five, naltrexone in one, and suboxone (buprenorphine + naloxone) in one study. The included studies indicated that when BZDs are taken as prescribed and for shorter periods in conjunction with OAT, subjects are retained in their MOUD programs just as well as patients who do not take BZDs. Any potential benefits of increased treatment retention must be balanced against potential harmful effects of BZD use, such as drug overdose and addiction. Further studies must be performed to validate the results of treatment retention and to evaluate other factors that might affect OAT.

Precision medicine in oncology is an evolving therapeutic approach that leverages genetic, clinical, and biomarker data to tailor treatments to individual patients. This review explores the three core pillars of modern precision oncology: targeted therapy, immunotherapy, and the integration of artificial intelligence (AI) into clinical practice. Targeted therapies, including monoclonal antibodies and antibody-drug conjugates, selectively inhibit molecular pathways involved in tumor growth. While conventional chemotherapy remains the backbone of treatment and has improved remission rates, its cytotoxic nature limits broader applicability and increases the risk of comorbidities. Immunotherapies, particularly immune checkpoint inhibitors and chimeric antigen receptor T-cell therapies, have transformed treatment for hematologic malignancies and are now being adapted for solid tumors such as colorectal, pancreatic, and hepatocellular carcinomas through novel combination regimens. This review also highlights the therapeutic potential of modulating the tumor microenvironment and introduces emerging modalities such as neoantigen vaccines and microRNA-based therapies. Furthermore, we outline the expanding role of AI in enhancing cancer diagnosis, drug development, and clinical decision-making. By integrating computational tools with molecular therapies, precision medicine rapidly advances toward individualized data-driven care. This review provides an overview of established therapies in the current clinical practice, novel regimens, and emerging AI technologies. Despite ongoing challenges, such as resistance and toxicity, precision medicine demonstrates significant promise in improving oncologic outcomes and transforming cancer care.

Given the rapid growth of the pharmaceutical industry and the increasing use of medication, ecopharmacovigilance (EPV) has become an effective approach for managing and reducing the environmental impact of pharmaceuticals. EPV addresses and explains the undesired environmental effects of pharmaceutical use. This study aimed to evaluate the knowledge, perceptions, and practices related to the disposal of unused and expired medications among undergraduate medical students in Bangladesh. A questionnaire-based cross-sectional study was conducted over a period of 3 months, from August to October 2024, at Armed Forces Medical College, Mugda Medical College and Hospital, and Shahabuddin Medical College in Dhaka. A total of 300 3rd- and 4th-year medical students from these medical colleges in Bangladesh completed a self-administered questionnaire. In this study, knowledge of EPV was found to be only 27%. Overall, medical students’ perceptions of environmental medication contamination and EPV were encouraging. Among those interviewed, 54.3% “strongly agreed” that leftover medications could have a negative impact on the environment. Despite knowledge of the environmental risks posed by pharmaceuticals, the common practice of storing medications at home until they expire and then discarding them persists. This highlights both a lack of knowledge and the absence of safe disposal procedures. Thus, greater efforts are needed to improve medical students’ knowledge of EPV. Respondents also expressed a preference for evidence-based and environmentally friendly methods for disposing of unwanted medications.

Gram-negative bacterial infections pose a serious public health challenge due to their high global mortality rates and potential to cause severe complications. Antibiotics - one of the most impactful medical innovations of the 20th century - remain vital in treating life-threatening bacterial infections. However, the increasing prevalence of antibiotic resistance has made it progressively harder to treat Gram-negative bacterial infections effectively. Therefore, nanoparticles have gained attention as a promising alternative treatment owing to their targeted antibacterial properties. Among the various synthesis methods, green synthesis is considered one of the most effective approaches for nanoparticle production. In this study, silver nanoparticles were synthesized using a green approach that utilized silver nitrate salt and an extract derived from carpenter bee wings (CBWs). The synthesized nanoparticles were characterized using spectroscopic techniques and scanning electron microscopy. Their antibacterial activity was tested against two pathogenic Gram-negative bacteria using the broth dilution method. Furthermore, whole genome sequencing was conducted to assess the mutagenic effects of the biosynthesized silver nanoparticles on the two bacterial strains. The results demonstrated that the green-synthesized silver nanoparticles exhibit notable antibacterial activity, likely through electrostatic interactions that promote cell binding and induce significant morphological alterations. Genomic analysis revealed mutations associated with efflux pump regulation, neutralization, transport, energy metabolism, cell division, biosynthetic pathways, adaptation, and invasion in the tested strains. These findings demonstrate the potential of CBWs as a novel biological resource for the green synthesis of silver nanoparticles with antibacterial properties. However, the study also raises concerns regarding the potential for bacteria to develop resistance to nanoparticles over time.

Internal fixation (IF) surgery has been promoted with the combination of robotic technology, promising increased accuracy and improved patient prognosis. This study examined the effect of IF surgery using Tianji orthopedic robot on patient satisfaction and quality of life (QoL) over a longitudinal follow-up time. A cohort of 387 patients undergoing IF guided by Tianji orthopedic robot surgery was followed from the pre-surgery phase through 12 months post-surgery. Patient-reported outcome measures, including the Oswestry Disability Index (ODI) and the Short Form Health Survey (SF-36), were administered at baseline, 6 months, and 12 months. In addition, the Newcastle Satisfaction with Nursing Care Scale (NSNCS) was also used to assess patient satisfaction. Data were analyzed using repeated measures analysis of variance. However, only 338 (87.33%) patients who underwent robot-assisted surgery completed the survey. A total of 214 (63.31%) females and 124 (36.68%) males, with an age of 63.76 ± 14 years, were included in the study. The study indicated significant progress in patient satisfaction and QoL. The mean ODI score decreased from 79.1 ± 4.76 pre-surgery to 46.2 ± 6.09 at 12 months (p<0.001), compared to the SF-36 score from 43.5 ± 4.20 to 84 ± 4.8 (p<0.05). Moreover, the NSNCS scores of 86 ± 4.32 reflected high satisfaction levels, indicating that participants were satisfied with their surgical outcomes at the 12-month follow-up. The Tianji orthopedic robot significantly improves patient satisfaction and QoL over a year. These findings confirm the significance of robotic technology and surgical procedures and highlight the essential role of nurses in using telehealth for continuous follow-up care.

Pre-eclampsia is a complicated hypertensive pregnancy condition that has a major effect on the health of both the mother and the fetus and puts the affected women at risk for long-term cardiovascular (CV) problems. Despite advances in understanding its etiology, early detection of pre-eclampsia and its associated CV risks remains challenging. This mini-review emphasizes the critical role of biomarkers and advanced diagnostic techniques in addressing this gap. Emerging biomarkers, including angiogenic factors (soluble fms-like tyrosine kinase-1/placental growth factor ratio), metabolic and lipidomics markers, inflammatory cytokines, and exosomal components, provide promising pathways for early identification and risk stratification. Diagnostic techniques can be further improved by classifying these biomarkers according to their capacity to predict long-term CV risks. Technological advancements, such as omics platforms, molecular imaging, wearable health devices, and artificial intelligence (AI) and machine learning, further improve real-time detection and personalized management of pre-eclampsia. By focusing on biomarker-centric predictors of CV risks, this review highlights the integration of multi-biomarker panels and AI-driven algorithms to optimize risk prediction. The transition from association to action is explored, with an emphasis on translating knowledge into effective prevention strategies and improved risk assessment protocols. Structured postpartum follow-up is advocated to monitor and mitigate long-term CV risks in pre-eclamptic women. Practical applications, including targeted interventions and personalized risk management strategies, are discussed. By bridging cutting-edge research and clinical practice, this review aims to enhance maternal health outcomes and advance preventative measures for CV diseases in women with a history of pre-eclampsia.

Persistent smell and taste disorders following COVID-19 vaccination are rare adverse effects. Herein, we reported three cases in which patients developed smell and taste disorders 9 - 20 days after receiving their second dose of the AstraZeneca/Oxford COVID-19 vaccine in 2021. These symptoms persisted for 1 - 3 years. All patients underwent nasal endoscopy, imaging of the nasal and olfactory structures, as well as Sniffin’ Odor along with flavor and taste identification tests. Case 1 was a 37-year-old male who presented in December 2022 with persistent dysgeusia for 18 months. Case 2 was a 40-year-old male who presented in February 2023 with persistent anosmia and parosmia for 20 months. Case 3 was a 48-year-old male who presented in August 2024 with persistent hyposmia for 3 years. These persistent disorders may be due to immune responses triggered by the vaccine, potentially affecting the olfactory neuroepithelium. Recognition and reporting of such adverse effects are important to acknowledge among physicians and for future studies and treatment trials targeting related disorders.

Cholesteatoma presents a significant clinical challenge with limited pharmaceutical treatment options. This paper aims to explore the potential of drug repurposing for managing cholesteatoma through advanced bioinformatics analysis of high-throughput genetic data. For this purpose, we conducted a systematic search of published high-throughput genetic studies related to cholesteatoma and used functional and literature enrichment analysis of multiple sets, a validated web-based bioinformatics platform, for analysis. We employed common pathway enrichment analysis and cross-referenced data from DrugBank and gene list automatically derived for you drug annotations to identify potential medical treatments. Our analysis covered eight high-throughput genetic studies, with extended gene lists available for five of them. Enrichment analysis identified common pathways, including matrix metalloproteinases, interleukins, apoptosis, and the phosphoinositide 3-kinase-AKT pathway, shedding light on both expected and less-studied aspects of cholesteatoma pathogenesis. In addition, the analysis proposed several medications, including anti-tumor necrosis factor-α (TNF-α), zinc, and marimastat, as potential treatments. In conclusion, drug repurposing is a potential cost-effective approach to address the unmet medical need for cholesteatoma management. The identified medications, especially anti-TNFα and zinc, offer promising options. Given the limited research funding in this field, this bioinformatic approach holds great promise, highlighting specific molecular pathways that hold the greatest potential to be implicated in the pathogenesis of cholesteatoma and offering a faster route for future trials to reduce cholesteatoma recurrence.