Upconverting Nanoparticle-based Enhanced Luminescence Lateral-Flow Assay for Urinary Biomarker Monitoring.

Development of efficient portable sensors for accurately detecting biomarkers is crucial for early disease diagnosis, yet remains a significant challenge. To address this need, we introduce the enhanced luminescence lateral-flow assay, which leverages highly luminescent upconverting nanoparticles (UCNPs) alongside a portable reader and a smartphone app. The sensor's efficiency and versatility were shown for kidney health monitoring as a proof of concept. We engineered Er3+- and Tm3+-doped UCNPs coated with multiple layers, including an undoped inert matrix shell, a mesoporous silica shell, and an outer layer of gold (UCNP@mSiO2@Au). These coatings synergistically enhance emission by over 40-fold and facilitate biomolecule conjugation, rendering UCNP@mSiO2@Au easy to use and suitable for a broad range of bioapplications. Employing these optimized nanoparticles in lateral-flow assays, we successfully detected two acute kidney injury-related biomarkers─kidney injury molecule-1 (KIM-1) and neutrophil gelatinase-associated lipocalin (NGAL)─in urine samples. Using our sensor platform, KIM-1 and NGAL can be accurately detected and quantified within the range of 0.1 to 20 ng/mL, boasting impressively low limits of detection at 0.28 and 0.23 ng/mL, respectively. Validating our approach, we analyzed clinical urine samples, achieving biomarker concentrations that closely correlated with results obtained via ELISA. Importantly, our system enables biomarker quantification in less than 15 min, underscoring the performance of our novel UCNP-based approach and its potential as reliable, rapid, and user-friendly diagnostics.

Survival analysis on subchondral bone length for total knee replacement.

OBJECTIVE: Use subchondral bone length (SBL), a new MRI-derived measure that reflects the extent of cartilage loss and bone flattening, to predict the risk of progression to total knee replacement (TKR). METHODS: We employed baseline MRI data from the Osteoarthritis Initiative (OAI), focusing on 760 men and 1214 women with bone marrow lesions (BMLs) and joint space narrowing (JSN) scores, to predict the progression to TKR. To minimize bias from analyzing both knees of a participant, only the knee with a higher Kellgren-Lawrence (KL) grade was considered, given its greater potential need for TKR. We utilized the Kaplan-Meier survival curves and Cox proportional hazards models, incorporating raw and normalized values of SBL, JSN, and BML as predictors. The study included subgroup analyses for different demographics and clinical characteristics, using models for raw and normalized SBL (merged, femoral, tibial), BML (merged, femoral, tibial), and JSN (medial and lateral compartments). Model performance was evaluated using the time-dependent area under the curve (AUC), Brier score, and Concordance index to gauge accuracy, calibration, and discriminatory power. Knee joint and region-level analyses were conducted to determine the effectiveness of SBL, JSN, and BML in predicting TKR risk. RESULTS: The SBL model, incorporating data from both the femur and tibia, demonstrated a predictive capacity for TKR that closely matched the performance of the BML score and the JSN grade. The Concordance index of the SBL model was 0.764, closely mirroring the BML's 0.759 and slightly below JSN's 0.788. The Brier score for the SBL model stood at 0.069, showing comparability with BML's 0.073 and a minor difference from JSN's 0.067. Regarding the AUC, the SBL model achieved 0.803, nearly identical to BML's 0.802 and slightly lower than JSN's 0.827. CONCLUSION: SBL's capacity to predict the risk of progression to TKR highlights its potential as an effective imaging biomarker for knee osteoarthritis.

Deep Learning-Based Subtask Segmentation of Timed Up-and-Go Test Using RGB-D Cameras.

The timed up-and-go (TUG) test is an efficient way to evaluate an individual's basic functional mobility, such as standing up, walking, turning around, and sitting back. The total completion time of the TUG test is a metric indicating an individual's overall mobility. Moreover, the fine-grained consumption time of the individual subtasks in the TUG test may provide important clinical information, such as elapsed time and speed of each TUG subtask, which may not only assist professionals in clinical interventions but also distinguish the functional recovery of patients. To perform more accurate, efficient, robust, and objective tests, this paper proposes a novel deep learning-based subtask segmentation of the TUG test using a dilated temporal convolutional network with a single RGB-D camera. Evaluation with three different subject groups (healthy young, healthy adult, stroke patients) showed that the proposed method demonstrated better generality and achieved a significantly higher and more robust performance (healthy young = 95.458%, healthy adult = 94.525%, stroke = 93.578%) than the existing rule-based and artificial neural network-based subtask segmentation methods. Additionally, the results indicated that the input from the pelvis alone achieved the best accuracy among many other single inputs or combinations of inputs, which allows a real-time inference (approximately 15 Hz) in edge devices, such as smartphones.

Smartphone-Based SARS-CoV-2 and Variants Detection System using Colorimetric DNAzyme Reaction Triggered by Loop-Mediated Isothermal Amplification (LAMP) with Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR).

Coronavirus disease (COVID-19) has affected people for over two years. Moreover, the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants has raised concerns regarding its accurate diagnosis. Here, we report a colorimetric DNAzyme reaction triggered by loop-mediated isothermal amplification (LAMP) with clustered regularly interspaced short palindromic repeats (CRISPR), referred to as DAMPR assay for detecting SARS-CoV-2 and variants genes with attomolar sensitivity within an hour. The CRISPR-associated protein 9 (Cas9) system eliminated false-positive signals of LAMP products, improving the accuracy of DAMPR assay. Further, we fabricated a portable DAMPR assay system using a three-dimensional printing technique and developed a machine learning (ML)-based smartphone application to routinely check diagnostic results of SARS-CoV-2 and variants. Among blind tests of 136 clinical samples, the proposed system successfully diagnosed COVID-19 patients with a clinical sensitivity and specificity of 100% each. More importantly, the D614G (variant-common), T478K (delta-specific), and A67V (omicron-specific) mutations of the SARS-CoV-2 S gene were detected selectively, enabling the diagnosis of 70 SARS-CoV-2 delta or omicron variant patients. The DAMPR assay system is expected to be employed for on-site, rapid, accurate detection of SARS-CoV-2 and its variants gene and employed in the diagnosis of various infectious diseases.

Midfoot and ankle movement coordination during heel rise is disrupted in people with diabetes and peripheral neuropathy.

BACKGROUND: A heel rise task can be used to evaluate midfoot and ankle movement dysfunction in people with diabetes mellitus and peripheral neuropathy. Quantifying movement coordination during heel rise is important to better understand potentially detrimental movement strategies in people with foot pathologies; however, coordination and the impact of limited excursion on coordination is not well-understood in people with diabetes. METHODS: Sixty patients with diabetes mellitus and peripheral neuropathy, and 22 older and 25 younger controls performed single-limb heel rise task. Midfoot (forefoot relative to hindfoot) sagittal and ankle (hindfoot relative to shank) sagittal and frontal kinematics were measured and normalized to time (0 to 100%). Cross-correlation coefficients were calculated across individuals in each group. A graphical illustration was used to interpret the relationship of midfoot and ankle excursion and cross-correlation coefficient during heel rise. FINDINGS: People with diabetes mellitus and peripheral neuropathy showed significantly lower midfoot and ankle cross-correlation coefficients during heel rise compared to older controls (p = 0.003-0.007). There was no difference in the midfoot and ankle cross-correlation coefficients during heel rise for the older and younger controls (p = 0.059-0.425). The graphic data illustrated a trend of greater excursion of two joints and a higher cross-correlation coefficient. Some individuals with lower excursion showed a high cross-correlation coefficient. INTERPRETATION: Foot pathologies, but not aging, impairs midfoot and ankle movement coordination during heel rise task. Investigating both movement coordination as well as joint excursion would better inform and characterize the dynamic movements of midfoot and ankle during heel rise task.

Deep-Learning-Based Emergency Stop Prediction for Robotic Lower-Limb Rehabilitation Training Systems.

Robotic lower-limb rehabilitation training is a better alternative for the physical training efforts of a therapist due to advantages, such as intensive repetitive motions, economical therapy, and quantitative assessment of the level of motor recovery through the measurement of force and movement patterns. However, in actual robotic rehabilitation training, emergency stops occur frequently to prevent injury to patients. However, frequent stopping is a waste of time and resources of both therapists and patients. Therefore, early detection of emergency stops in real-time is essential to take appropriate actions. In this paper, we propose a novel deep-learning-based technique for detecting emergency stops as early as possible. First, a bidirectional long short-term memory prediction model was trained using only the normal joint data collected from a real robotic training system. Next, a real-time threshold-based algorithm was developed with cumulative error. The experimental results revealed a precision of 0.94, recall of 0.93, and F1 score of 0.93. Additionally, it was observed that the prediction model was robust for variations in measurement noise.