Ultrasonic Technologies (United States)

Background and aimDeep learning has improved medical image analysis but often produces opaque decisions and correlation-driven predictions that may diverge from clinical reasoning. We hypothesize that a physiology-informed hybrid framework, which explicitly models placenta–pulmonary interactions and integrates multimodal data, could provide interpretable and reliable guidance for assessing fetal lung maturity (FLM) and optimizing antenatal glucocorticoids (GCs).Materials and methodsIn a prospective cohort study involving 320 pregnancies—including 160 with hypertensive disorders of pregnancy (HDP)—each with weekly acquisitions from 28 to 36 weeks, we combined 2D/3D ultrasound, shear-wave elastography, Doppler, and maternal plasma metabolomics. A biophysical placenta–pulmonary coupling model used the umbilical artery pulsatility index (PI) and a metabolomic hypoxia–steroid score to represent placental reserve, while backscatter integrals and elastography were used to characterize fetal lung properties. Constrained by this model, a dual-branch network was developed: (i) a cross-modal attention Transformer with self-supervised contrastive learning to generate unsupervised FLM stages from fused representations and (ii) a spatiotemporal convolution–LSTM network to predict individualized GC dosing and the optimal administration window. A composite loss penalized both projected respiratory distress syndrome (RDS) risk and the biomarker-derived neurotoxicity index.ResultsThe cross-modal representations clustered into four distinct maturity stages matching biochemical benchmarks, with an inter-stage silhouette score of 0.72. A downstream classifier achieved 92.3% accuracy in discriminating early from late maturity. The dosing branch predicted the GC dose within ±0.5 mg of clinically prescribed regimens and reduced projected RDS risk by 27% compared to standard dosing, while maintaining the biomarker-derived neurotoxicity index below the prespecified threshold.ConclusionA mechanism-guided, multimodal AI framework constrained by placenta–pulmonary physiology transforms imaging features into traceable decision pathways that align with clinical cognition. This interpretable framework may enable non-invasive FLM staging and individualized GC therapy, providing hypothesis-generating decision support that warrants external validation and prospective trials.

Abstract Background The world's biggest killer is ischemic heart disease, responsible for 16% of the world's total deaths. Ischemic heart disease is the leading cause of death in Egypt, responsible for 21% of all-cause mortality. Ischemic heart disease is the leading cause of heart failure in Egypt, ranging between 40.9% to 72.5% in different regions in Egypt. Late presentation ST elevation acute myocardial infarction is defined as patients who are diagnosed as ST elevation MI > 12-48 hours after the onset of symptoms. Late presentation ST elevation acute myocardial infarction may lead to myocardial necrosis and scarring due to irreversible damage of cardiac myocytes with subsequent development of heart failure with reduced EF% (HFrEF). There are several methods to assess myocardial viability, including dobutamine stress echocardiography, speckle tracking, Single Photon Emission Computed Tomography (SPECT), Positron emission Tomography (PET) and Cardiac Magnetic Resonance (CMR). The advantage of STE is the possibility to use regional strain curves to quantify the intraventricular synchrony and synergy of myocardial contraction. Early detection of ischemic injury and evaluation of myocardial viability after myocardial infarction (MI) can prevent serious complications, such as ventricular remodeling with development and progressive worsening of heart failure, arrhythmias, and sudden cardiac death. Aim and objectives To formulate cutoff regional strain values between scarred and viable myocardium by speckle tracking validated by SPECT in patients with late presentation MI to predict how far the patient can benefit before proceeding into revascularization. Methods A study was conducted on 40 individuals who presented to the cardiology unit in RIO (Research Institute of Ophthalmology) with late presentation STEMI. Patients were subjected to: - Full medical history, complete clinical examination, standard 12 leads resting ECG, conventional resting transthoracic echocardiography study, automated functional imaging (Speckle tracking) echocardiography (offline) study of left ventricle and Single Photon Emission Computed Tomography Study (SPECT). Result Ischemic segments in SPECT were correlated with regional strain value -9.84±1.323%. Mixed scar and ischemic tissue in SPECT were correlated with the regional strain value -7.45±1.323%. The predominant scar in mixed ischemia and scar tissue in SPECT was correlated with the regional strain value -3.88±1.711%. Totally scarred myocardial segments with aneurysmal wall in SPECT were correlated with regional strain values > 0%. Normal myocardial tissues in SPECT were correlated with regional strain values of 16.61 ±3.942%. Conclusion Speckle tracking echocardiography is a new, reliable, bedside and relatively inexpensive modality for the detection of myocardial ischemia and scar tissue after acute myocardial infarction.

Accurate classification of carotid plaques is critical to assessing the risk of cardiovascular disease. However, this task remains challenging due to several factors: temporal discontinuity caused by probe motion, the small size of plaques combined with interference from surrounding tissue, and the limited availability of annotated data, which often leads to overfitting in deep learning models. To address these challenges, this study introduces a structured self-distillation framework, named DualDistill, designed to improve classification accuracy and generalization performance in analyzing ultrasound videos of carotid plaques. DualDistill incorporates two novel strategies to address the identified challenges. First, an intra-frame relationship-guided strategy is proposed to capture long-term temporal dependencies, effectively addressing temporal discontinuity. Second, a spatial-temporal attention-guided strategy is developed to reduce the impact of irrelevant features and noise by emphasizing relevant regions within both spatial and temporal dimensions. These strategies jointly act as supervisory signals within the self-distillation process, guiding the student layers to better align with the critical features identified by the teacher layers. Besides, the self-distillation process acts as an implicit regularization mechanism, which decreases overfitting in limited datasets. DualDistill is designed as a plug-and-play framework, enabling seamless integration with various existing models. Extensive experiments were conducted on 317 carotid plaque ultrasound videos collected from a collaborating hospital. The proposed framework demonstrated its versatility and effectiveness. It achieved consistent improvements in classification accuracy across 13 representative models. Specifically, the average accuracy improvement is 2.97%, with the maximum improvement reaching 4.74% on 3D ResNet50. These results highlight the robustness and generalizability of DualDistill. It shows strong potential for reliable cardiovascular risk assessment through automated carotid plaque classification.

High-temperature baking is a typical method to remove organic matter from diatoms, but it's not suitable for bio-silica because of the high crystallinity. This study provides a method using the VAUS™ to remove organic matter from diatoms more quickly and biocompatibly. Organic matter was removed by using VAUS™, while NaOCl was utilized to remove the organic matter from the silicate frustule. The optimal frequency for organic matter removal was investigated to domestically produced M. nummuloides. The removal efficiency of TOC/TN was calculated and analyzed. The C and Si elements were analyzed in EDS, while visual confirmation of organic matter removal was analyzed by using XRD. TOC RE% at a frequency of 35kHz exhibited the highest value, indicating a statistically significant difference. XRD analysis demonstrated that the organic matter was almost removed using NaOCl compared to the high-temperature baked M. nummuloides. In the EDS analysis, there were significant differences in the C and Si elements with respect to frequency. This is very similar to the values from the positive control group, high-temperature baked M. nummuloides. This new procedure of applying periodic negative pressure to NaOCl pre-treatment is considered to be an effective method of chemically removing organic matter from diatoms.

The heat transfer rate of magnetocaloric regenerators is a topic of extensive research and the cyclability of these regenerators is critical to the operation of systems with a high coefficient of performance (e.g., potentially >22, significantly higher than typical vapor compression cooling technologies). To enable a high operating frequency that will result in a high specific cooling power, the heat transfer fluid should have high thermal conductivity and lower specific heat, i.e., higher thermal diffusivity. Eutectic metal alloys possess these qualities, such as gallium–indium–tin (Galinstan), whose thermal diffusivity has been found to be approximately an order of magnitude higher than water. For this study, the effects of eutectic liquid Galinstan exposure on the phase stability of LaFe13−x−yMnxSiyH1.6 magnetocaloric powders in an active magnetic regenerator device were investigated. The powders were characterized before and after exposure to Galinstan using X-ray diffraction, in which the phases were determined using the Rietveld refinement technique and X-ray fluorescence. It was found that after Galinstan exposure, hydrogen containing phases were present in the powder, suggesting that the hydrogen was lost from the magnetocaloric phase. The magnetocaloric phase degradation indicates that the powder was incompatible with the Galinstan metal in an environment with moisture.

Even though numerical simulation of two-phase flows in microchannels has been attempted by many investigators, most efforts seem to have failed in correctly capturing the flow physics, especially the slug-flow regime characteristics. The presence of a thin liquid film in the order of 10 μm around the bubble (sometimes called gas pocket or gas slug) may be a contributing factor to the above difficulty. Typically, liquid films have a significant effect on the flow field. Thus, there is a strong motivation to employ numerical simulation methods to avoid some of the experimental difficulties. In this paper, the characteristics of two-phase slug flows in microchannels are calculated with the help of the volume-of-fluid (VOF) method. Formation of the slugs for different superficial velocities, capillary numbers, and gas volume fractions are investigated. The minimum mesh resolution required to capture the liquid film surrounding the gas bubble is reported employing a dynamic mesh adaptation methodology with interface tracking. Results are shown to be in good agreement with experimental data and empirical correlations.

Devido ao avanço tecnológico, às novas dinâmicas do mundo moderno, o acesso à informação em tempo real, dentre outros fatores, percebe-se uma mudança no perfil do estudante e uma necessidade de adequação do ensino para o atendimento desta demanda. Diante deste cenário, este artigo tem como objetivo verificar se a utilização dos elementos da Gamificação no setor educacional pode proporcionar aumento do engajamento e da motivação do aluno, que são desafios atualmente encarados por este setor. Para tal, foi realizada, primeiramente, uma exploração conceitual do termo Jogo e suas funções para, em seguida, entender o conceito de Gamificação, através de uma revisão bibliográfica. Também foram selecionados trabalhos que exploram a utilização do tema na educação e fazem uma análise crítica do assunto.