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.

Background: Dysphagia affects more than half of older adults with dementia and is associated with a 10-fold increase in mortality. The development of accessible, objective, and reliable screening tools is crucial for early detection and management. Objective: This systematic scoping review aimed to (1) examine the current state of the art in artificial intelligence (AI) and sensor-based technologies for dysphagia screening, (2) evaluate the performance of these AI-based screening tools, and (3) assess the methodological quality and rigor of studies on AI-based dysphagia screening tools. Methods: We conducted a systematic literature search across CINAHL, Embase, PubMed, and Web of Science from inception to July 4, 2024, following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. In total, 2 independent researchers conducted the search, screening, and data extraction. Eligibility criteria included original studies using sensor-based instruments with AI to identify individuals with dysphagia or unsafe swallow events. We excluded studies on pediatric, infant, or postextubation dysphagia, as well as those using non–sensor-based assessments or diagnostic tools. We used a modified Quality Assessment of Diagnostic Accuracy Studies–2 tool to assess methodological quality, adding a “model” domain for AI-specific evaluation. Data were synthesized narratively. Results: This review included 24 studies involving 2979 participants (1717 with dysphagia and 1262 controls). In total, 75% (18/24) of the studies focused solely on per-individual classification rather than per–swallow event classification. Acoustic (13/24, 54%) and vibratory (9/24, 38%) signals were the primary modality sources. In total, 25% (6/24) of the studies used multimodal approaches, whereas 75% (18/24) used a single modality. Support vector machine was the most common AI model (15/24, 62%), with deep learning approaches emerging in recent years (3/24, 12%). Performance varied widely—accuracy ranged from 71.2% to 99%, area under the receiver operating characteristic curve ranged from 0.77 to 0.977, and sensitivity ranged from 63.6% to 100%. Multimodal systems generally outperformed unimodal systems. The methodological quality assessment revealed a risk of bias, particularly in patient selection (unclear in 18/24, 75% of the studies), index test (unclear in 23/24, 96% of the studies), and modeling (high risk in 13/24, 54% of the studies). Notably, no studies conducted external validation or domain adaptation testing, raising concerns about real-world applicability. Conclusions: This review provides a comprehensive overview of technological advancements in AI and sensor-based dysphagia screening. While these developments show promise for continuous long-term tele-swallowing assessments, significant methodological limitations were identified. Future studies can explore how each modality can target specific anatomical regions and manifestations of dysphagia. This detailed understanding of how different modalities address various aspects of dysphagia can significantly benefit multimodal systems, enabling them to better handle the multifaceted nature of dysphagia conditions.

Early diagnosis of Alzheimer’s disease (AD) remains challenging due to the extremely low concentration of relevant biomarkers and the limited sensitivity of conventional detection techniques. In this study, we present a carbon nanotube field-effect transistor (CNT-FET) immunosensor for label-free detection of phosphorylated tau at threonine 217 (p-tau217). The device employs a Y₂O₃/HfO₂ dielectric layer and gold nanoparticles (AuNPs) to improve biofunctionalization, with anti-p-tau217 antibodies immobilized on the CNT channels. In phosphate-buffered saline (PBS), the sensor exhibited a linear response over a concentration range of 3 fM to 30 pM (R² = 0.973) and achieved a limit of detection (LOD) of 1.66 fM. The device demonstrated high selectivity, with a normalized signal response (NSR) for p-tau217 that was 5–6 times higher than for human serum albumin (HSA) and p-tau231, even at 1000-fold higher concentrations of these interferents. The sensor exhibited reproducibility with a relative standard deviation (RSD) of 4.8% (n = 9) and storage stability with only a 10% decrease in signal after 7 days at 4 °C. Mechanistic analysis indicated that the net positive charge and structural flexibility of the p-tau217 peptide led to a reduction in drain current upon binding, consistent with electrostatic gating effects in p-type CNT-FETs. Current limitations include the absence of standardized p-tau217 reference materials. Future work will focus on validation with clinical samples. This CNT-FET platform enables rapid, minimally invasive detection of p-tau217 and holds strong potential for integration into clinical workflows to facilitate early AD diagnosis.

Background: Hallux valgus is a common foot deformity that may alter plantar pressure distribution. This study aimed to determine whether hallux valgus consistently reduces medial forefoot (hallux and medial metatarsal) load and impulse through a meta‐analysis of plantar pressure data and increases central metatarsal loading.Methods: A systematic review and meta‐analysis were conducted following PRISMA guidelines. PubMed, Web of Science, Scopus, CINAHL, and Embase were searched for studies comparing plantar pressure distribution between hallux valgus and non‐hallux valgus feet. Random‐effects meta‐analyses were performed using standardized mean differences (SMD) for load (force and pressure) and impulse (force and pressure‐time integrals) measures in hallux, medial metatarsal, and central metatarsal regions. Risk of bias was assessed using the JBI critical appraisal checklist for analytical cross‐sectional studies.Results: Twenty‐one studies (3911 participants and 6407 feet) were included. In‐shoe measurements showed significant reductions in hallux loading (force SMD: −0.78 [95% CI: −1.08, −0.49]; force‐time integral SMD: −0.39 [−0.71, −0.07]). Platform‐based measurements yielded inconsistent results. No conclusive evidence was found for reduced medial metatarsal loading or increased central metatarsal loading. Measurement modality significantly influenced results, with platform‐based systems generally showing higher heterogeneity than in‐shoe sensors. Nine (43%) and 12 studies (57%) had a high risk of bias on statistical analysis and confounder management, respectively.Conclusions: Hallux valgus is associated with reduced hallux loading in in‐shoe measurements, but evidence for load redistribution to other forefoot regions is inconclusive. The choice of measurement modality significantly impacts results, highlighting the need for standardized assessment protocols in hallux valgus research.Trial Registration: This study was registered in PROSPERO with the reference number CRD42024574195

BACKGROUND Dysphagia affects more than half of older adults with dementia and is associated with a 10-fold increase in mortality. The development of accessible, objective, and reliable screening tools is crucial for early detection and management. OBJECTIVE This systematic scoping review aimed to (1) examine the current state of the art in artificial intelligence (AI) and sensor-based technologies for dysphagia screening, (2) evaluate the performance of these AI-based screening tools, and (3) assess the methodological quality and rigor of studies on AI-based dysphagia screening tools. METHODS We conducted a systematic literature search across CINAHL, Embase, PubMed, and Web of Science from inception to July 4, 2024, following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. In total, 2 independent researchers conducted the search, screening, and data extraction. Eligibility criteria included original studies using sensor-based instruments with AI to identify individuals with dysphagia or unsafe swallow events. We excluded studies on pediatric, infant, or postextubation dysphagia, as well as those using non–sensor-based assessments or diagnostic tools. We used a modified Quality Assessment of Diagnostic Accuracy Studies–2 tool to assess methodological quality, adding a “model” domain for AI-specific evaluation. Data were synthesized narratively. RESULTS This review included 24 studies involving 2979 participants (1717 with dysphagia and 1262 controls). In total, 75% (18/24) of the studies focused solely on per-individual classification rather than per–swallow event classification. Acoustic (13/24, 54%) and vibratory (9/24, 38%) signals were the primary modality sources. In total, 25% (6/24) of the studies used multimodal approaches, whereas 75% (18/24) used a single modality. Support vector machine was the most common AI model (15/24, 62%), with deep learning approaches emerging in recent years (3/24, 12%). Performance varied widely—accuracy ranged from 71.2% to 99%, area under the receiver operating characteristic curve ranged from 0.77 to 0.977, and sensitivity ranged from 63.6% to 100%. Multimodal systems generally outperformed unimodal systems. The methodological quality assessment revealed a risk of bias, particularly in patient selection (unclear in 18/24, 75% of the studies), index test (unclear in 23/24, 96% of the studies), and modeling (high risk in 13/24, 54% of the studies). Notably, no studies conducted external validation or domain adaptation testing, raising concerns about real-world applicability. CONCLUSIONS This review provides a comprehensive overview of technological advancements in AI and sensor-based dysphagia screening. While these developments show promise for continuous long-term tele-swallowing assessments, significant methodological limitations were identified. Future studies can explore how each modality can target specific anatomical regions and manifestations of dysphagia. This detailed understanding of how different modalities address various aspects of dysphagia can significantly benefit multimodal systems, enabling them to better handle the multifaceted nature of dysphagia conditions.

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.