Transfer Learning Fusion Approaches for Colorectal Cancer Histopathological Image Analysis.

It is well-known that accurate classification of histopathological images is essential for effective diagnosis of colorectal cancer. Our study presents three attention-based decision fusion models that combine pre-trained CNNs (Inception V3, Xception, and MobileNet) with a spatial attention mechanism to enhance feature extraction and focus on critical image regions. A key innovation is the attention-driven fusion strategy at the decision level, where model predictions are weighted by relevance and confidence to improve classification performance.

In-line Raman-based quantification of the anhydrous content in a fully integrated continuous powder-to-granule line.

This research is based on a fully integrated continuous twin-screw wet granulation line from powder feeding to dried granules. One of its crucial steps is continuous drying, which can influence the crystal form and thus, the quality of the granules. Therefore, the main objective of this research was to develop a Raman spectroscopic method for monitoring the crystal form transition in-line and real-time during the continuous process while applying different drying temperatures.

Expansion of applicability of multiple scan average dose measurements for CT dosimetry.

The CT dose index (CTDI) can be directly measured only for axial scan protocols; however, these days, helical protocols are applied in most cases, and sometimes axial protocols are not even available. The multiple scan average dose (MSAD) method can address this issue, but it is not widely adopted. This article aims to support the appropriateness of the MSAD method first by showing that it is measurable with the CTDI tools, and second by presenting a simple rule for scan pitch to avoid the main uncertainty of the measurement.

Frequency-spatial feature fusion via a hierarchical framework for diabetic retinopathy classification in low-quality fundus images.

Low-quality fundus images pose significant challenges for diabetic retinopathy (DR) classification due to noise, blurred boundaries, and the loss of high-frequency details, which hinder both global contextual understanding and local fine-grained feature extraction. To address these limitations, this work proposes a Hierarchical Frequency-Spatial Feature Fusion Network (HFSF-Net) that effectively integrates frequency-domain and spatial-domain information for robust DR classification. Initially, this paper introduces the Spatial Prior-Aware Transformer (SPAT) Block, which incorporates spatial prior knowledge to direct the attention distribution, enabling precise localization of the complex distribution of lesion regions in low-quality fundus images.

Healthcare fraud in the United States of America and Ukraine: A comparative legal research.

Objective: Aim: To conduct a comparative legal study of healthcare fraud in the United States of America and Ukraine.

Patients And Methods: Materials and Methods: This study is based on the analysis of the US federal regulatory legislation (False Claims Act, Anti-Kickback Statute, Stark Law); data from the Fraud Section of the US Department of Justice for the last five years; case law in Ukraine (more than 30 court verdicts were analysed); data from the National Health Service of Ukraine. Dialectical, hermeneutic, comparative, analytical, synthetic, and systems analysis research methods were used.

Shaping the Visual Mismatch Response: The Influence of Exogenous Components and Stimulus Placement.

This study investigated the relationship between the pattern-specific event-related potential (ERP) components (C1, C2, C3) and the component specific to automatic change detection in the visual modality (visual mismatch response, vMMR) and the influence of stimulus placement on the latter. These insights would help to understand whether the emergence of vMMR is due to the modulation of exogenous activity or a specific deviant-related activity and advance the methodology of investigating vMMR. In two experiments, we presented checkerboard patterns in passive oddball sequences consisting of frequent (standard) and rare (deviant) events to examine vMMR as a function of the stimulated half-fields.

Limitations of glycated hemoglobin and emerging biomarkers for diabetes care after bariatric surgery.

Bariatric surgery significantly improves glycemic control and can lead to type 2 diabetes remission. However, the reliability of glycated hemoglobin (HbA1c) as a type 2 diabetes biomarker post-surgery can be confounded by conditions such as anemia and gastrointestinal complications. Hence, we explored the use of alternative biomarkers such as glycated albumin (GA), 1,5-anhydroglucitol (1,5-AG), and insulin-like growth factor binding protein-1 (IGFBP-1) to monitor glycemic control more effectively in post-bariatric surgery patients.

Activating Ferroelectric-Magnetic Synergistic Effects at Cathode-Electrolyte Interfaces Toward Superfast and Stable Sodium Storage.

Layered oxides are promising cathode candidates for sodium-ion batteries due to their high energy density. However, the rate and cycling performances are hindered by severe interfacial side reactions and sluggish kinetics. Using NaNiMnO (NM) as a model material, ferroelectric-magnetic synergistic effects are activated at the NM-electrolyte interfaces via constructing a multiferroic layer on the NM surface, significantly realizing the superfast and stable sodium storage.

A cross-sectional molecular epidemiological study of biofilm-producing methicillin-resistant Staphylococcus aureus.

There is growing concern regarding biofilm-producing methicillin-resistant Staphylococcus aureus (MRSA) due to the sudden rise in infection rates and associated morbidity and mortality. Therefore, epidemiological studies, including molecular typing and correlation analysis, are essential for understanding this pathogen. This cross-sectional study investigated epidemiological factors and correlations in MRSA isolates.

Impact of metal oxides on thermal response of zirconia coated diesel engines fueled by Momordica biodiesel machine learning insights.

Biodiesel presents a favourable economic outlook and environmental benefits, yet it faces limitations such as diminished calorific value and suboptimal combustion characteristics. Recent research focuses on enhancing biodiesel performance using nanoparticles and thermal barrier coatings. This study investigates non-edible biodiesel from Momordica seed oil, tested on a single-cylinder diesel engine.

Breaking the heavy-atom paradigm: weak-donor-engineered triplet harvesting in BODIPY photosensitizers for immunogenic pyroptosis therapy.

Boron-dipyrromethene (BODIPY)-based dyes emerge as promising agents for phototherapy; however, traditional methods to enhance spin-orbit coupling (SOC) through halogenation introduce dark toxicity and limit therapeutic applications. Here, we present a thiophene-bridged BODIPY functionalized scaffold with carbazole-benzothiophene (Cbz-Bth) substituents at the 2,6-positions. This design employs a weak yet semi-rigid donor to destabilize charge-transfer (CT) states, enabling T-mediated spin-orbit charge-transfer intersystem crossing (SOCT-ISC).

A Skin-Like Transparent, Low-Hysteresis, and Highly Conductive Ionogel for Human Motion Monitoring and Deep-Learning Assisted Human-Machine Interface.

Human skin functions not only as a barrier but also as a sensitive interface responding to environmental stimuli. Skin-attachable conductive materials are gaining increasingly attention in the field of on-skin electronics for applications in health monitoring and human-machine interfaces (HMIs). However, achieving both low modulus and low hysteresis in skin-attachable conductive materials remains challenging.

Wink or blush? Pupil-linked phasic arousal signals both change and uncertainty during assessment of changing environmental regularities.

One main cornerstone of adaptive behavior is belief updating, whereby new and unexpected observations lead to the updating of learned associations between events, behaviors and outcomes. This process necessitates the detection of changed environmental contingencies which in turn leads to uncertainty about the environmental regularities. Change and uncertainty are thus inherently linked, and both constructs have been linked to pupil size changes, which might reflect activity in neural networks underlying belief updating.

Navigating protein landscapes with a machine-learned transferable coarse-grained model.

The most popular and universally predictive protein simulation models employ all-atom molecular dynamics, but they come at extreme computational cost. The development of a universal, computationally efficient coarse-grained (CG) model with similar prediction performance has been a long-standing challenge. By combining recent deep-learning methods with a large and diverse training set of all-atom protein simulations, we here develop a bottom-up CG force field with chemical transferability, which can be used for extrapolative molecular dynamics on new sequences not used during model parameterization.

Bio-inspired hydrogels comprising organic and inorganic components association explored as Bingham precursor solution for extending direct ink writing technique in 3D printing.

This study is basically, the development of Bingham precursor hydrogel-based solution for direct ink writing (DIW) for the development of 3D printed hydrogels. The hydrogel is used as a precursor solution for its promising application in artificial human organ development, flexible, and wearable strain sensor by DIW method. The development of Bingham fluid with controlled and improved fluidity, enhancing the mechanical property of extruded materials, stability, and conductivity remains challenging.

Vision-language models for automated video analysis and documentation in laparoscopic surgery: a proof-of-concept study.

Background: The ongoing shortage of medical personnel highlights the urgent need to automate clinical documentation and reduce administrative burden. Large Vision-Language Models (VLMs) offer promising potential for supporting surgical documentation and intraoperative analysis.

Methods: We conducted an observational, comparative performance study of two general-purpose VLMs-GPT-4o (OpenAI) and Gemini-1.

Advances in nanotechnology for colorectal cancer: a smart targeting and theranostics approach.

Colorectal cancer remains one of the most common and deadly malignancies globally, with high mortality rates, particularly in advanced stages. Conventional therapies such as surgery, chemotherapy, and radiotherapy, though beneficial, often suffer from drawbacks including systemic toxicity, multidrug resistance, and poor tumor selectivity. Recent progress in nanotechnology has introduced innovative theranostic strategies that integrate diagnosis and treatment using nanoparticle-based platforms.

Network Analysis of the Organic Chemistry in Patents, Literature, and Pharmaceutical Industry.

Chemical reactions can be connected in large networks such as knowledge graphs. In this way, prior work has been able to draw meaningful conclusions about the properties and structures involved in organic chemistry reactions. However, the research has focused on public sources of organic synthesis that might lack the intricate details of the synthetic routes used in in-house drug discovery.

Beyond traditional models: Jaya-optimized ensembles for accurate heart disease prediction.

Introduction: Heart Disease (HD) stands as the foremost reason for mortality all over the world for both men and women. Millions of people are affected worldwide every year, resulting in numerous fatalities. Timely and precise detection is essential for enhancing patient survival rates and potentially preventing further complications.

Extending the Projection-Based Embedding Technique to Open-Shell Systems Using the Huzinaga Equation.

In this work, we present an approach for the embedding of wave function theory (WFT) and density functional theory (DFT) methods in a lower-level density functional approximation using the projection-based embedding (PbE) technique for open-shell systems. Our method is based on the Huzinaga equation, which is implemented in both spin-restricted and spin-unrestricted forms. While the unrestricted PbE approach has been previously reported in the literature and follows naturally from the theory for closed-shell systems, the restricted formulation required the development of a new theory, building on earlier work by Roothaan (, , , 179) as well as Shaik and Filatov (, , , 429).

Intensive luteal phase support in hormone replacement and modified natural cycle frozen embryo transfers in ovulatory patients: A propensity score-matched study.

Background: The optimal endometrial preparation protocol for frozen embryo transfer (FET) remains controversial, with different cycle regimens and luteal phase support strategies across studies yielding conflicting results. This study aimed to compare the pregnancy outcomes of modified natural cycles (mNC) versus hormone replacement therapy (HRT) cycles, both with intensive luteal support using vaginal micronized progesterone and oral dydrogesterone.

Methods: This retrospective cohort study included 2365 FET cycles (1892 HRT and 473 mNC) in ovulatory women.

Analysis of anti-tumor active components and preparation of self-assembled polymeric micelles in seeds.

Objective: Seeds of contain effective anti-tumor substances. In this study, the main anti-tumor active compound was screened from seeds, which was combined with TPGS and mPEG-PCL to make micelles, improve its solubility and anti-tumor activity, and reduce the toxic side effects.

Methods: Organic solvent extraction, silica gel column chromatography, thin layer chromatography and other separation and purification techniques were used, combined with MTT method, to screen out the anti-tumor active fraction F5 (BE) from the ethanol extract of seeds, and then the main compound of BE, 20-hydroxyecdyone (20-HE), was obtained.

3D-Bioprinted Scaffolds with Precise Factor Delivery for Spinal Cord Injury Repair.

Spinal cord injury (SCI) remains a major global health challenge, often resulting in the permanent loss of motor and sensory functions. To address this issue, we developed a 3D-bioprinted precision location scaffold to promote SCI repair. The scaffold was designed to incorporate region-specific growth factors tailored to the distinct functions of gray and white matter in the spinal cord.

Continuous-flow synthesis of 1,4,6,7-tetrahydro-5-[1,2,3]triazolo[4,5-]pyridines towards key intermediates of clinical candidates JNJ-54175446 and zanvipixant (JNJ-55308942) with antidepressant activity.

1,4,6,7-Tetrahydro-5-[1,2,3]triazolo[4,5-]pyridines are potent antagonists of the P2X7 receptor, a key regulator of neuroinflammation associated with depression. Two clinical candidates, JNJ-54175446 and JNJ-55308942 were developed based on this scaffold, together with a stereoselective synthesis involving a one-pot [3 + 2] cycloaddition/Cope-elimination cascade. Besides challenging regioselectivity, this route raises safety concerns, as both steps involve hazardous reagents.

A hybrid variational mode decomposition framework for enhanced cardiac output estimation using impedance cardiography.

Accurate cardiac output (CO) estimation from impedance cardiography (ICG) signals is vital for non-invasive monitoring of cardiovascular disorders, including heart failure, arrhythmias, and hemodynamic instability. However, the reliability of ICG-based assessment is often limited by noise artifacts that obscure clinically relevant features. This study introduces a novel three-stage denoising framework integrating Variational Mode Decomposition (VMD), Non-Local Means (NLM), and Discrete Wavelet Transform (DWT) to enhance ICG signal quality for robust CO estimation.