Tuberculosis (TB) remains a leading cause of morbidity and mortality worldwide, necessitating the development of more effective vaccines. Nanoparticle-based adjuvants represent a promising approach to enhancing tuberculosis vaccine efficacy. This review focuses on the advantages of nanoparticulate-loaded vaccines, emphasizing their ability to improve antigen delivery, safety, and immunogenicity. We discuss the various types of nanoparticles and their unique physicochemical properties that contribute to improved antigen delivery and sustained immune activation. Additionally, we highlight the advantages of nanoparticle-based adjuvants in inducing strong cellular and humoral immunity, enhancing vaccine stability, and reducing adverse effects. Finally, we address current challenges and future perspectives in the application of these novel adjuvants, emphasizing their potential to transform TB vaccine strategies and ultimately contribute to better global health outcomes.

Peritoneal dialysis (PD) is a vital treatment for end-stage renal disease patients, but its efficacy is often compromised by complications such as infections and peritoneal fibrosis. Biological field-effect transistors (BioFETs) present a promising solution for rapid, sensitive, and non-invasive detection of indicators and biomarkers associated with these complications, potentially enabling early intervention. However, BioFETs are yet to be adopted for PD monitoring. This review presents a forward-looking analysis of the capacity and potential integration of BioFETs into PD management systems, highlighting their capacity to monitor both routine indicators of dialysis efficiency and metabolic status, as well as specific biomarkers for complications such as inflammation and fibrosis. We examine the challenges in adapting BioFETs for PD applications, focusing on key areas for improvement, including sensitivity, specificity, stability, reusability, and clinical integration. Furthermore, we discuss various approaches to address these challenges, which are crucial for developing point-of-care (PoC) and multiplexed wearable devices. These advancements could facilitate continuous, precise, and user-friendly monitoring, potentially revolutionizing PD complication management and enhancing patient care.

Thyroid-related diseases, particularly thyroid cancer, are rising globally, emphasizing the critical need for the early detection and accurate screening of thyroid nodules. Ultrasound imaging has inherent limitations—high noise, low contrast, and blurred boundaries—that make manual interpretation subjective and error-prone. To address these challenges, YOLO-Thyroid, an improved model for the automatic detection of thyroid nodules in ultrasound images, is presented herein. Building upon the YOLOv8 architecture, YOLO-Thyroid introduces the C2fA module—an extension of C2f that incorporates Coordinate Attention (CA)—to enhance feature extraction. Additionally, loss functions were incorporated, including class-weighted binary cross-entropy to alleviate class imbalance and SCYLLA-IoU (SIoU) to improve localization accuracy during boundary regression. A publicly available thyroid ultrasound image dataset was optimized using format conversion and data augmentation. The experimental results demonstrate that YOLO-Thyroid outperforms mainstream object detection models across multiple metrics, achieving a higher detection precision of 54%. The recall, calculated based on the detection of nodules containing at least one feature suspected of being malignant, reaches 58.2%, while the model maintains a lightweight structure. The proposed method significantly advances ultrasound nodule detection, providing an effective and practical solution for enhancing diagnostic accuracy in medical imaging.

Natural disasters create complex environments where effective human detection is both critical and challenging, especially when individuals are partially occluded. While recent advancements in computer vision have improved detection capabilities, there remains a significant need for efficient solutions that can enhance search-and-rescue (SAR) operations in resource-constrained disaster scenarios. This study modified the original DINO (Detection Transformer with Improved Denoising Anchor Boxes) model and introduced the visibility-enhanced DINO (VE-DINO) model, designed for robust human detection in occlusion-heavy environments, with potential integration into SAR system. VE-DINO enhances detection accuracy by incorporating body part key point information and employing a specialized loss function. The model was trained and validated using the COCO2017 dataset, with additional external testing conducted on the Disaster Occlusion Detection Dataset (DODD), which we developed by meticulously compiling relevant images from existing public datasets to represent occlusion scenarios in disaster contexts. The VE-DINO achieved an average precision of 0.615 at IoU 0.50:0.90 on all bounding boxes, outperforming the original DINO model (0.491) in the testing set. The external testing of VE-DINO achieved an average precision of 0.500. An ablation study was conducted and demonstrated the robustness of the model subject when confronted with varying degrees of body occlusion. Furthermore, to illustrate the practicality, we conducted a case study demonstrating the usability of the model when integrated into an unmanned aerial vehicle (UAV)-based SAR system, showcasing its potential in real-world scenarios.

Background: Swallowing is a complex biomechanical process, and its impairment (dysphagia) poses major health risks for older adults. Current diagnostic methods such as videofluoroscopic swallowing (VFSS) and fiberoptic endoscopic evaluation of swallowing (FEES) are effective but invasive, resource-intensive, and unsuitable for continuous monitoring. This study proposes a novel end-to-end RGB–D framework for automated swallowing event localization in continuous video streams. Methods: The framework enhances the AdaTAD backbone through three key innovations: (i) finding the optimal strategy to integrate depth information to capture subtle neck movements, (ii) examining the best adapter design for efficient temporal feature adaptation, and (iii) introducing a Kolmogorov–Arnold Network (KAN) decoder that leverages Chebyshev polynomials for non-linear temporal modeling. Evaluation on a proprietary swallowing dataset comprising 641 clips and 3153 annotated events demonstrated the effectiveness of the proposed framework. We analysed and compared the modification strategy across designs of adapters, decoders, input channel combinations, regression methods, and patch embedding techniques. Results: The optimized configuration (VideoMAE + GRNConvNeXtAdapter + KAN + RGD + boundary regression + sinusoidal embedding) achieved an average mAP of 83.25%, significantly surpassing the baseline I3D + RGB + MLP model (61.55%). Ablation studies further confirmed that each architectural component contributed incrementally to the overall improvement. Conclusions: These results establish the feasibility of accurate, non-invasive, and automated swallowing event localization using depth-augmented video. The proposed framework paves the way for practical dysphagia screening and long-term monitoring in clinical and home-care environments.

Alzheimer’s disease (AD) diagnostic workflows are undergoing a paradigm shift from symptom-centric cognitive assessment to biomarker-guided pathological stratification, fuelling an urgent need for minimally invasive analytical tools to quantify ultralow-concentration pathological markers within peripheral biofluids. Biological field-effect transistors (Bio-FETs) stand out as competitive label-free transducers for ultrasensitive biomolecular detection; nevertheless, persistent drawbacks including unstable solid-liquid interfaces, severe signal drift, and limited multiplexing capacity impede their real-world clinical deployment. This review first outlines high-specificity circulating AD biomarkers and elaborates the fundamental transduction principles of Bio-FET sensors, before systematically dissecting two complementary hardware–software strategies to mitigate intrinsic device defects: microfluidic chip integration and artificial intelligence (AI)-assisted data processing. Microfluidic engineering standardizes pre-analytical sample manipulation, precisely regulates on-chip biochemical reactions, and facilitates monolithic sensor integration, whereas AI algorithms streamline microchannel layout optimization, refine biomolecular signal deconvolution, suppress electronic noise, and unify device quality management. Jointly, the combinatorial microfluidic-AI framework creates a clinically feasible route for decentralized, multiplexed, high-fidelity liquid biopsy screening of early Alzheimer’s disease.