Browse Articles

Design and validation of a novel multiple sites signal acquisition and analysis system based on pressure stimulation for human cardiovascular information

Abstract Cardiovascular diseases (CVDs) pose a significant threat to human health and place considerable strain on healthcare systems. Therefore, it is crucial to maximize the acquisition of cardiovascular information (CVI) through non-invasive methods to enhance early screening, diagnosis, and evaluation of CVDs. Numerous studies have demonstrated that obtaining more CVI by simultaneously acquiring multi-site signals and applying pressure stimulation at specific sites, such as blood pressure measurement, is an effective approach. Based on this evidence, we proposed a novel signal acquisition-and-analysis system to gather comprehensive CVI through a combination of a non-pressure and six pressure-stimulation sub-processes. This system involves the novelty of applying slowly gradual decrease, personalized maximum-pulse amplitude, and blocking blood-flow pressure to six cuffs placed on both arms, wrists, and ankles in a predetermined time sequence. During each sub-process, the system has newly integrated the multi-site simultaneous collection of 27-channel non-invasive signals, including electrocardiogram, heart sound, lung sound, photoplethysmographic-and-pressure pulse. To ensure measurement accuracy, three types of verification-and-calibration instruments were employed. Our results demonstrate that the system can achieve simultaneous acquisition of 27-channel signals during each sub-process, yielding both novel and traditional cardiovascular parameters with high accuracy and good stability. Furthermore, the results suggest that the system can facilitate in-depth research into the relationships between collected signals and CVDs, provide rich raw data for cardiovascular health assessment and disease prediction models based on machine learning algorithms, and offer a new non-invasive method for early diagnosis, evaluation, and prediction of CVDs.

Cell tropism of adeno-associated viruses within the mouse inner ear in vivo: from embryonic to adult stages

Abstract Adeno-associated virus (AAV)-based gene therapy is emerging as a promising treatment for deafness and vestibular deficits, due to the variety of available serotypes that offer a large range of cell targeting capabilities. Nevertheless, the tropism of these AAV serotypes for sensory inner ear cells varies greatly as the cochlea matures, presenting a significant burden for successful preclinical trials. Therefore, identifying serotypes with strong tropism for cochlear and vestibular hair cells during key stages of development in mouse inner ear, the most widely used preclinical model, is essential for advancing clinical applications. We conducted a comparative analysis of the cellular tropism and hair-cell transduction rates of four AAV serotypes in the cochlea and vestibular organs during maturation. We used AAV2, AAV8, AAV9-PHP.eB, and Anc80L65 at the embryonic, neonatal, and adult stages. Our results indicate that the cell transduction rate of these four serotypes varies with age. Notably outer hair cells were mostly targeted during the embryonic stage, inner hair cells were primarily transduced principally at the mature stage, and vestibular hair cells were the most permissive at the neonatal stage. These results provide new insights for preclinical gene therapy studies for the inner ear with potential implications for therapeutic outcomes.

Automated learning of glaucomatous visual fields from OCT images using a comprehensive, segmentation-free 3D convolutional neural network model

Abstract A segmentation-free 3D Convolutional Neural Network (3DCNN) model was adopted to estimate Visual Field (VF) in glaucoma cases using Optical Coherence Tomography (OCT) images. This study, conducted at a university hospital, included 6335 participants (12,325 eyes). Two models were trained, one on the Glaucoma-Specific Training Group (GTG) and one on the Comprehensive Training Group (CTG) that included various ocular conditions without manual preselection. The CTG showed significantly better performance than the GTG in estimating VF thresholds and Mean Deviation (MD) for both Humphrey Field Analyzer (HFA) 24-2 and HFA10-2 test patterns (p < 0.001). Strong correlations were observed between the estimated and actual VF thresholds for HFA24-2 (Pearson’s r: 0.878) and HFA10-2 (r: 0.903), as well as MD for HFA24-2 (r: 0.911) and HFA10-2 (r: 0.944) in the CTG. The CTG demonstrated lower estimation errors than the GTG and smaller errors in severe cases. The model’s performance remained relatively stable even in advanced glaucoma cases. The model’s ability to learn from a comprehensive dataset without human annotation highlights its potential for large-scale training in the future, potentially improving glaucoma assessment and monitoring in clinical practice. Further validation in external datasets and exploration in different clinical settings are warranted.

Transformer-inspired training principles based breast cancer prediction: combining EfficientNetB0 and ResNet50

A method for quantifying and automatic grading of musculoskeletal ultrasound superb microvascular imaging based on dynamic analysis of optical flow model

AI analysis for ejection fraction estimation from 12-lead ECG

Heavy metal adsorption efficiency prediction using biochar properties: a comparative analysis for ensemble machine learning models

Optimizing power network expansion with pumped hydro energy storage using a multi-objective enhanced spider wasp optimizer approach

What role does social class play in the impact of social environment on residents’ health

Maintenance therapy with Azacitidine for patients with myeloid malignancies after allogeneic hematopoietic stem cell transplantation

A robust pressure drop prediction model in vertical multiphase flow: a machine learning approach

Transcriptomic profiling of severe and critical COVID-19 patients reveals alterations in expression, splicing and polyadenylation

Caerin 1.1 and 1.9 peptides induce acute caspase 3/GSDME-mediated pyroptosis in epithelial cancer cells

Numerical and experimental study on cavitation and noise characteristics of electronic expansion valve

Exposure to air pollutants contributes to increased rate of neovascular age-related macular degeneration in Israel

Age-related macular degeneration (AMD) is a multi-factorial degenerative disease of the retina and the leading cause for vision loss in the developed world. Air pollution is considered the greatest environmental threat to public health globally. Accumulating evidence indicates that air pollution may be a modifiable risk factor for chronic eye diseases of the lens and retina, including AMD. We examined the concentration of seven air pollution particles and their influence on the prevalence of neovascular AMD in Israel. Records of patients with AMD between 2016 and 2019 were crossed with their residential areas and correlated with pollution data. AMD rates were correlated with 5 types of gas: nitrogen dioxide (NO2), nitrogen oxide (NO), carbon monoxide (CO), ozone (O3), sulphur dioxide (SO2), and particulate matter - PM2.5 and PM10. A total of 93 localities across Israel were included in the analysis. AMD rates were higher in localities with greater air pollution. NO2, NOx, and PM2.5 were positively correlated with AMD rates, while O3 was negatively correlated with AMD rates. However, analysis of the effect of all air pollutant particles combined, showed a complex and highly non-linear effect on AMD rate, with the strongest non-linearity observed for carbon monoxide. NO2, NOx, and PM2.5 contribute to higher rate of AMD in Israel while O3 seems to have a protective role (probably due to ultraviolet filtering) on AMD rates. The interaction between air pollutants and AMD seems to be complex and non-linear and should be further studied.

A laypeople’s typology of cosmetic surgeries using cluster analysis

Protein hydrolysates from fish wastes: nutritional characteristics and its inclusion in diets for Octopus maya

The utilization of fish waste protein as an alternative to crab and squid protein presents an important alternative for octopus fattening. During this study, nutritional characteristics of fish protein hydrolysate (FPH) and its inclusion in prepared diets were evaluated on growth performance and enzyme activity of digestive gland of O. maya juveniles. FPH were prepared using fish waste and their nutritional properties were evaluated. Four diets with different levels of FPH (0%, 10%, 15%, and 20%) in substitution for crab meals were fed to octopuses (mean body weight 100 mg) individually distributed for 70 days. Regarding yield, at the end of the hydrolysis period (day 15) the FPH fraction constitutes 67% of the total silage (dried powder). Small peptides were recorded in FPH (< 2.12 DA). Altogether, 17 amino acids were identified on FPH, encompassing nine essential amino acids (EAAs; 182 mg g-1) and eight non-essential amino acids (NEAAs; 427 mg g-1). Also, the free amino acids (FAAs) content was 8.3% of the total amino acids content with the predominance of taurine. Octopuses fed with FPH15 had the highest weight gain (3.06 g), SGR (4.76% day-1), and survival (90%) compared to FPH0. Total alkaline protease activity of octopuses digestive gland was lower in FPH20 (3550 U mg of protein−1) than in the control (5277 U mg of protein−1). Incorporating protein hydrolysate derived from fish waste into prepared diet may offer unique advantages in promoting optimal growth and general physiological well-being for O. maya.

Bacopa monnieri phytochemicals as promising BACE1 inhibitors for Alzheimer’s disease therapy

Functional perspectives in mental jigsaw puzzles: Insights from eye-tracking, questionnaire, and behavioral data

This study investigated cognitive strategies in mental jigsaw puzzles, integrating mental rotation and translation with a focus on directionality and detour arguments. Unlike object mental rotation tasks, these puzzles introduced physical constraints, revealing systematic directional tendencies in both eye movements and subjective reports. Specifically, smaller protruding objects were consistently directed toward larger indented objects. This was accompanied by longer completion times and reduced linearity, paralleling strategies used in physical puzzle-solving. Behavioral asymmetries observed in the puzzles unexpectedly mirrored those found in object mental rotation tasks. While controlling for mental motion directions showed comparable completion times at 300° between tasks, the study did not fully clarify the role of detours, indicating the need for further research.

Unsupervised machine learning analysis of optical coherence tomography radiomics features for predicting treatment outcomes in diabetic macular edema

Abstract This study aimed to identify distinct clusters of diabetic macular edema (DME) patients with differential anti-vascular endothelial growth factor (VEGF) treatment outcomes using an unsupervised machine learning (ML) approach based on radiomic features extracted from pre-treatment optical coherence tomography (OCT) images. Retrospective data from 234 eyes with DME treated with three anti-VEGF therapies between January 2020 and March 2024 were collected from two clinical centers. Radiomic analysis was conducted on pre-treatment OCT images. Following principal component analysis (PCA) for dimensionality reduction, two unsupervised clustering methods (K-means and hierarchical clustering) were applied. Baseline characteristics and treatment outcomes were compared across clusters to assess clustering efficacy. Feature selection employed a three-stage pipeline: exclusion of collinear features (Pearson’s r > 0.8); sequential filtering through ANOVA (P < 0.05) and Boruta algorithm (500 iterations); multivariate stepwise regression (entry criteria: univariate P < 0.1) to identify outcome-associated predictors. From 1165 extracted radiomic features, four distinct DME clusters were identified. Cluster 4 exhibited a significantly lower incidence of residual/recurrent DME (RDME) (34.29%) compared to Clusters 1–3 (P = 0.003, P = 0.005 and P = 0.002, respectively). This cluster also demonstrated the highest proportion of eyes (71.43%) with best-corrected visual acuity (BCVA) exceeding 20/63 (P = 0.003, P = 0.005 and P = 0.002, respectively). Multivariate analysis identified logarithm_gldm_DependenceVariance as an independent risk factor for RDME (OR 1.75, 95% CI 1.28–2.40; P < 0.001), while Wavelet-LH_Firstorder_Mean correlated with worse visual outcomes (OR 8.76, 95% CI 1.22–62.84; P = 0.031). Unsupervised ML leveraging pre-treatment OCT radiomics successfully stratifies DME eyes into clinically distinct subgroups with divergent therapeutic responses. These quantitative features may serve as non-invasive biomarkers for personalized outcome prediction and retinal pathology assessment.