Approximately 450,000 Veterans are living with Alzheimer disease and related dementias (ADRD), and the high prevalence of ADRD represents a major public health challenge for the Veterans Health Administration. While advancing age and genetic predisposition are well-established ADRD risk factors, growing evidence suggests that additional modifiable factors may also play an important role. This study leveraged data from the VA Million Veteran Program (MVP) to (1) estimate 10-year incidence of ADRD and (2) evaluate associations between a broad range of individual-level risk and resilience factors and incident ADRD in a large, nationally representative sample of Veterans. This retrospective cohort study included Veterans aged ≥65 years at MVP enrollment who completed the MVP Baseline Survey and had VA electronic health record (EHR) data available. Individual-level variables including sociodemographic factors, military-specific characteristics, military environmental exposures (MEEs), health conditions, and health behaviors were characterized using MVP Baseline Survey data and supplemented with EHR data as available. The primary outcome was ADRD, which was determined using a validated algorithm based on International Classification of Diseases diagnosis codes extracted from the EHR. Associations between each risk/resilience factor and incident ADRD were examined using separate Cox regression models adjusted for age, sex, and education. The sample included 245,949 Veterans (age: mean 73.16, SD 6.84 years; 2.59% female). Approximately 4.56% (n = 11,216) of the sample developed ADRD over 10 years. History of traumatic brain injury (TBI; hazard ratio [HR] 2.96, 95% CI 2.76-3.17), depression (HR 2.93, 95% CI 2.82-3.04), and alcohol use disorder (AUD; HR 2.35, 95% CI 2.19-2.53) were the health factors most strongly associated with ADRD. ADRD risk was also elevated among Veterans with a history of exposure to Agent Orange (HR 1.09, 95% CI 1.03-1.14), chemical/biological warfare agents (HR 1.31, 95% CI 1.23-1.39), and pyridostigmine bromide tablets (HR 1.67, 95% CI 1.44-1.93). Findings identified TBI, depression, AUD, and MEEs as key variables associated with ADRD in Veterans. These factors may represent important targets for prevention and intervention efforts aimed at improving the long-term health of aging Veterans. Additional work is needed to clarify the mechanisms through which these factors influence ADRD risk and to establish whether observed associations are causal.

Ultra-large Stokes-shifted NIR fluorescent probe for diagnosis of APAP-induced liver injury and its applications in food and bioimaging.

Early diagnosis of Alzheimer's disease from functional rs-fMRI images based on deep learning networks and transfer learning approach.

Multimodal MRI–EEG fusion for brain–computer interface applications using a lightweight CNN and attention in offline Parkinson’s disease diagnosis

Multimodal adaptive subspace learning for Parkinson’s disease diagnosis and prediction

Development and validation of data-driven, decision tree-based algorithms for identifying Behçet's disease in claims data.

A research on applying the diffusion model algorithm for Infrared and Raman spectroscopy data augmentation to improve the accuracy of diseases.

Alarm symptoms at coeliac disease (CeD) diagnosis predict a more severe disease presentation, but the long-term implications remain unclear. We studied the prevalence of alarm symptoms at diagnosis and their association with outcomes. A mixed-method cohort study combined retrospective medical record review with data collection through patient interviews and blood sampling from 814 adult patients with CeD after a median of 9.7 years on a gluten-free diet (GFD). Validated questionnaires assessed symptoms and quality of life. Alarm symptoms included anaemia, weight loss, dysphagia, vomiting, melaena, and rectal bleeding. Patients were grouped by the presence or absence of alarm symptoms. 45% of the patients presented with alarm symptoms, primarily (95%) anaemia and weight loss. These patients were significantly more often female (83 vs. 71%; P < 0.001), had more severe clinical presentation ( P < 0.001; reported severe symptoms 41 vs. 2%) and more advanced mucosal damage ( P < 0.001; subtotal or total villous atrophy 72 vs. 57%) than those without these symptoms. On GFD, these patients experienced fewer persistent symptoms (asymptomatic 71 vs. 79%; P = 0.035) but more often had osteopenia/osteoporosis (15 vs. 9%; P = 0.008). The groups did not differ in the strictness of GFD, positivity of CeD autoantibodies, quality of life, fractures, or other comorbidities. Alarm symptoms were common at CeD diagnosis. After 9.7 years on a GFD, patients with alarm symptoms had a higher incidence of osteopenia/osteoporosis, but generally did not demonstrate poorer long-term outcomes compared to those without alarm symptoms.

ER-Aβ dual-targeting NIRF/photoacoustic probe reveals Aβ-induced ER-stress via real-time viscosity imaging for early Alzheimer's disease diagnosis

Lipid droplet-specific fluorescent biosensor based on triphenylamine derivatives for the diagnosis of non-alcoholic fatty liver disease (NAFLD) at varying stages.

Pericardial disease spans a wide spectrum from small effusions to life-threatening tamponade or constriction. Transthoracic echocardiography (TTE) is the main diagnostic tool, but its interpretation is limited by operator dependence and incomplete functional assessment. Existing deep learning (DL) models focus mainly on effusion detection, lacking broader evaluation. We developed a DL-based framework that performs sequential assessment of pericardial disease: (i) morphological features, including effusion amount (normal/small/moderate/large) and pericardial thickening/adhesion (yes/no), from five B-mode views, and (ii) haemodynamic significance (yes/no), incorporating Doppler and inferior vena cava measurements. The developmental dataset comprises 2253 TTEs from multiple Korean institutions (225 for internal testing), and the independent external test set consists of 274 TTEs. In the internal test set, diagnostic accuracy was 81.8-97.3% for effusion, 91.6% for thickening/adhesion, and 86.2% for haemodynamic significance. External test set accuracy was 80.3-94.2%, 94.5%, and 85.5%, respectively. Area under the receiver operating curves for the three tasks were 0.92-0.99, 0.90, and 0.79 internally, and 0.95-0.98, 0.85, and 0.76 externally. Sensitivity for thickening/adhesion and haemodynamic significance improved from 66.7% to 77.3%, and 68.8% to 80.8%, respectively, when poor image quality were excluded. Similar performance gains were observed in subgroups with complete target views and a higher number of available video clips. This study presents the first DL-based TTE model for broader pericardial disease evaluation, integrating morphological with supportive functional assessments. The proposed framework demonstrated strong generalizability and aligned with the real-world diagnostic workflow. However, caution is warranted when interpreting results under suboptimal imaging conditions.

The accurate differentiation between early-stage hepatocellular carcinoma (HCC) and acute liver injury (ALI) remains a critical yet unresolved challenge in clinical practice, as these conditions share overlapping symptoms and biomarkers, often leading to misdiagnosis with conventional techniques. Although fluorescent probes offer potential for high-sensitivity imaging, existing designs typically detect only a single parameter and lack subcellular targeting precision. Consequently, a methodology that simultaneously visualizes multiple pathology-specific biomarkers within a key organelle for precise discrimination is urgently needed. Here, we present a lipid droplet-targeted, dual-channel fluorescent probe, TPA-DCN-TPE, which independently monitors microenvironmental viscosity and hypochlorous acid (HOCl). This probe exhibits a remarkable 1532-fold fluorescence enhancement in response to viscosity (red channel) and a 363-fold turn-on response to HOCl (green channel). Crucially, we discovered a distinct red-to-green signal ratio that clearly discriminates HCC (high ratio) from ALI (low ratio) in vivo, ex vivo, and in tissue sections. Our work establishes a quantifiable optical criterion for differentiating these liver pathologies, thereby addressing a major diagnostic gap. We anticipate this dual-parameter imaging strategy will advance the precision diagnosis of liver diseases and provide a versatile platform for studying organelle-specific microenvironments in other metabolic and inflammatory disorders.

Ultrasensitive extracellular vesicles-associated amyloid-β1-42 oligomers analytical platform for early diagnosis of Alzheimer's disease.

Artificial Intelligence in Diagnostic Oral and Maxillofacial Imaging, Surgical Applications, and Teledentistry.

Corneal staining: Beyond the grade.

SWaRaA: A multi-modal deep learning framework for the diagnosis and classification of respiratory diseases using medical acoustic representations

Sputum proteomics and phosphoproteomics for improving chronic obstructive pulmonary disease knowledge.

Engineered self-driven intelligent nanomachine induced by target-mediated knock-on effect to determine attomolar nucleic acids.

An ESIPT-based fluorescent probe for visualizing pH dynamics during oxidative stress in living system.

The early identification of Parkinson's disease (PD) prior to the emergence of motor symptoms is paramount for effective treatment and mitigation of disease progression. Moreover, early predictions and assessments of disease progression in certain patients are critical for timely clinical interventions. This study investigates the neuroanatomical alterations in various brain regions during the initial stages and progression of PD, to explore the potential of quantitative regional metrics as candidate imaging markers for early diagnosis and disease progression. We enrolled 31 PD patients and 25 healthy controls (HCs), categorizing PD patients into early-stage Parkinson's (ESP) (n = 22) and advanced-stage Parkinson's (ASP)(n = 9) based on the Hoehn and Yahr grading. The study employed 3D T1BRAVO and synthetic MRI for data acquisition, followed by voxel-based morphometry (VBM) and extraction of T1, T2, and proton density (PrD) values. Comparative analysis of brain volume and regional relaxation metrics was performed among the groups. A classification model based on regions showing significant group differences was evaluated using internal cross-validation. Significant variations were identified in specific brain regions when comparing the ESP group with HCs, particularly in the right Calcarine_T1GM and left Cuneus_T1GM regions. Additionally, notable differences were discerned between the ESP group and the ASP group, specifically in the left Putamen_T1GM, left ParaHippocampal_T1WM, Precentral_T2WM, left ParaHippocampal_T2WM, Anterior Cingulate Cortex (ACC)_T2WM, and left Putamen_PDGM regions. Scatter plot analysis revealed a strong correlation between these brain regions (with the exception of left ParaHippocampal_T2WM and left Putamen_PDGM) and both the Hoehn and Yahr (H&Y) and Movement Disorder Society (MDS) scores. Under internal cross-validation, T1-based gray-matter regional metrics demonstrated the most stable discriminative performance among the evaluated modalities. Cross-validated classification performance was moderate, particularly for distinguishing ESP from ASP, indicating limited but potentially informative progression-related signals. Synthetic MRI-derived regional relaxometry reveals stage-related brain alterations in PD. T1-based gray-matter metrics show relatively robust performance under internal validation and may serve as candidate imaging markers associated with early disease-related changes and progression in Parkinson's disease. However, all classification results should be regarded as exploratory and warrant further validation in larger, independent cohorts.