Research Insights & Reviews: Medical Laboratory Technology

Enhancing context-aware SARS disorder management: a proposed multi-agent simulation framework with machine learning and bio-sensor data integration

Mon, 31 Aug 2026 00:00:00 +0000

In this work, SARS disorder denotes a generic acute severe respiratory distress condition characterized by abnormal respiratory rate, oxygen saturation, fever, and cardiovascular stress indicators, and does not represent a COVID-19 diagnostic system. Our research aims at analyzing a context-aware SARS disorder management system through the implementation of a multi-agent simulation framework using the NetLogo setting. The system relies on the use of interacting agents as well as non-monotonic, context-sensitive reasoning to reduce uncertainty and deal with the possible inconsistencies that happen due to biosensor recordings. A knowledge-based inference component is the combination of physiological sensor outputs and domain specific contextual data to assist in making informed decisions. The research involved the use of several machine-learning classifiers, that is, Naïve Bayes, Multinomial Naïve Bayes, Decision Table, Logistic Regression, and Sequential Minimal Optimization (SMO) so as to evaluate their appropriateness in being incorporated into the developed structure. To measure the system performance, standard evaluation measures were used such as True Positive (TP), False Positive (FP), Precision, Recall, F-Measure, Matthews Correlation Coefficient (MCC), Receiver Operating Characteristic (ROC) curve, and the Precision-Recall curve (PRC). The framework includes a list of physiological, environmental, and contextual variables, such as electrocardiographic parameters, heart-rate parameters, blood-pressure parameters, arterial oxygen saturation parameters, core body temperature, room temperature, the past history of the patient, and parameters that relate to alerts. The classification task is to produce probabilistic forecasts that help to define whether a patient should be alerted or clinical staff members informed in order to facilitate context-specific healthcare response.

Exploring the treatment of osteomyelitis based on nanozyme-based antibacterial strategies

Junzhen Wu, Shuo Duan, Shuaiwei Li, Yisi Wang, Minglei Zhang — Mon, 31 Aug 2026 00:00:00 +0000

Osteomyelitis is a severe bone infection caused by bacterial invasion, posing significant therapeutic challenges due to biofilm formation and antibiotic resistance. In recent years, nanozymes—a class of functional nanomaterials with enzyme-like catalytic activities—have shown considerable potential in antimicrobial applications owing to their high stability, tunable catalytic activity, and low tendency to induce resistance, positioning them as promising alternatives to conventional antibiotics. This review systematically examines the therapeutic prospects of nanozyme-based delivery systems for treating osteomyelitis. We detail the antibacterial mechanisms of nanozymes exhibiting various enzyme-mimicking activities—primarily peroxidase-, oxidase-, haloperoxidase-, and hydrolase-like functionalities—such as generating reactive oxygen species (ROS) by catalyzing endogenous hydrogen peroxide and physically degrading biofilm components. This review aims to provide a theoretical foundation and novel insights for developing efficient and safe nanozyme-based antimicrobial agents against osteomyelitis.

Mamba-enhanced codebook learning with anatomical constraints for liver and tumor segmentation in 3D CT volumes

Yanfei Teng, Xiang Li, Zhenpeng Chen, Shunlin Guo — Mon, 18 May 2026 00:00:00 +0000

Precise delineation of the liver and its tumors in 3D CT scans plays a vital role in clinical diagnosis and therapeutic planning. However, current deep learning approaches frequently struggle with tumor heterogeneity, varying lesion sizes, and ambiguous boundaries, which can limit their effectiveness. To address these issues, we propose an end-to-end hierarchical network that effectively integrates multi-scale context modeling, global relational learning, and structured feature representation. First, a multi-scale texture encoder is designed to capture tumor characteristics across different spatial resolutions. To model long-range dependencies across slices, we introduce a global relational representation module built upon the emerging Mamba architecture, enabling efﬁcient and directional context aggregation in 3D volumes. Second, to enhance feature compactness and stability, we propose a learnable codebook module that quantizes high-dimensional features into a ﬁnite set of semantic prototypes, promoting discriminative representation learning while suppressing redundancy. Furthermore, anatomical prior knowledge—speciﬁcally, the spatial constraint that tumors must reside within the liver—is incorporated via an inclusion loss, which explicitly regularizes the segmentation outputs. Comprehensive experiments on the public LiTS dataset show that our method attains state-of-the-art results, surpassing existing methods in Dice score, volumetric overlap error (VOE), and boundary metrics (ASD and 95HD). Ablation analyses conﬁrm the individual contribution of each module, demonstrating the architecture’s effectiveness for accurate and reliable liver and tumor segmentation.

Characterization of endovascular rescue of massive gastroduodenal artery hemorrhage following upper gastrointestinal surgery

Mon, 31 Aug 2026 00:00:00 +0000

Background: Postoperative hemorrhage from the gastroduodenal artery (GDA) is a rare but life-threatening complication following upper gastrointestinal surgery. Its management is particularly challenging due to the frequent co-occurrence of hypovolemic shock, complex intra-abdominal infection, and malnutrition, which collectively contribute to high morbidity and mortality.

Methods: We conducted a retrospective analysis of patients who underwent either gastrectomy or pancreaticoduodenectomy and subsequently developed delayed massive GDA bleeding between January 2021 and June 2025 in the Department of General Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine.

Results: Six patients was included in this study, of whom five had undergone radical D2 lymphadenectomy and developed duodenal stump leakage or anastomotic leakage and one had undergone pancreaticoduodenectomy. All cases experienced massive postoperative arterial hemorrhage. Emergent angiographic arterial embolization was successfully performed in all cases, achieving effective hemostasis with satisfactory outcomes. Specifically, one patient required four sequential interventions, one required three, one required two, and the remaining three achieved hemostasis after a single emergent procedure. Notably, acute liver function abnormalities were observed in two cases following embolization of the common hepatic artery.

Conclusion: Massive postoperative GDA hemorrhage is a life-threatening complication after abdominal surgery. Emergent arterial embolization proves to be a rapid, minimally invasive, effective, and safe therapeutic option for this critical condition.

Dental devices and antimicrobial resistance: challenges, innovations, and regulatory compliances

Gargi Singh, Manav Singh, Anjanasree Murali, Sanyam Gandhi, Om V. Singh — Mon, 31 Aug 2026 00:00:00 +0000

Antimicrobial resistance (AMR) is an escalating threat to infection control in dentistry, where dense multispecies biofilms and frequent device-tissue contact can select for and disseminate antibiotic resistance genes (ARGs). The oral cavity functions as a reservoir and exchange network for ARGs (the oral resistome), enabling multidrug-resistant (MDR) infections and treatment failure, particularly on dental implants, restorative materials, and dental unit waterlines when reprocessing or maintenance is inadequate. Anti-biofilm surface engineering, including antimicrobial peptide-functionalized coatings, silver-based nanostructures, and nitric oxide-releasing platforms, offers nonantibiotic approaches to reduce adhesion and disrupt early biofilm development. However, translation requires addressing durability, long-term biocompatibility and toxicology (e.g., ion release), the potential for resistance or tolerance under sublethal exposure, and clinically meaningful validation in relevant multispecies models and human studies. Regulatory expectations (e.g., FDA quality system requirements under 21 CFR Part 820 and international standards ISO 13485, ISO 14971, and ISO 10993-1) frame risk management, antimicrobial performance claims, sterilization/reprocessing validation, and postmarket surveillance. Aligning antimicrobial stewardship with validated device design, sterilization, and monitoring strategies is essential to mitigate AMR proliferation and improve long-term clinical outcomes. This article is a comprehensive narrative review focusing on AMR in dental devices, materials innovation, and regulatory frameworks.