Impact of porcine skin processing on in vitro permeation testing: implications for reliable evaluation of topical metformin delivery.

IML-UNet: A brain-inspired spatiotemporal collaborative encoding method for medical image sequence registration.

Synthesis of coronary 4D CT Image by denoising diffusion probabilistic model.

Cross-reactive molecularly imprinted electrochemical sensor arrays with adaptive deep learning for multiplexed PFAS quantification.

The ventromedial hypothalamic nucleus responds differentially to genistein exposure during development in male and female rats in the long term.

Heparin, an essential anticoagulant in clinical practice, is susceptible to contamination with chondroitin sulfate (CS) and dermatan sulfate (DS). Ensuring the reliable quantification of these polysaccharides in crude heparins is critical for product safety and for monitoring the purification process, yet remains challenging due to their structural similarity and heterogeneous composition. ¹H NMR and Independent Component Analysis (ICA) were used to resolve the overlapping acetyl signals of CS and DS. Samples of crude heparin and synthetic mixtures were measured on a 500 MHz high-field NMR and an 80 MHz benchtop instrument. The ICA yielded models with root mean square errors (RMSEs) ≤ 1.7 % w/w (500 MHz) and ≤ 2.1 % w/w (80 MHz) for the synthetic mixtures (concentration range of 3-19 % w/w for CS and 3-40 % w/w for DS). The accuracy of the ICA method was confirmed by the conventional enzymatic digestion method for crude heparin samples. While the 500 MHz model completely conformed with the results of the enzymatic method, the 80 MHz benchtop system is best suited for screening applications where impurity levels are from moderate to high. The ICA-NMR workflow surpasses the traditional enzymatic assay in speed and simplicity, and opens the way towards the development of an automated and validated on‑site heparin quality control method.

Toward protocol simplification: Deep learning-based image synthesis in three-phase CT urography.

Evaluation and application of a chlorobenzimidazole bonded stationary phase for polyphenols separation in supercritical fluid chromatography.

RFDNet: Robust Frequency-Based Denoising Network for 3D Ultrasound Vascular Imaging Using a Row-Column Addressed Array.

A specific integrin αPS3βPS1 heterodimer is required for hemocyte phagocytosis in Chinese mitten crab, Eriocheir sinensis.

Three-dimensional X-ray histology offers a non-invasive alternative to conventional 2D histology, enabling volumetric imaging of biological tissues without physical sectioning or chemical staining. However, the intrinsic greyscale contrast of X-ray tomography limits its biochemical specificity compared with traditional histological stains. In this study, we extend deep-learning-based virtual staining to the X-ray domain via cross-modality image translation to generate artificially stained slices directly from synchrotron radiation microtomography (µCT) scans. Using over 50 co-registered pairs of µCT and toluidine blue-stained histology from bone-implant samples, we trained a modified CycleGAN network tailored for limited paired data. Whole-slide histology images were downsampled to the CT voxel size, with on-the-fly data augmentation for patch-based training. The model incorporates pixelwise supervision and greyscale consistency losses, enabling histologically realistic colour outputs while preserving structural detail. Results outperformed Pix2Pix and standard CycleGAN baselines across metrics of structural similarity, perceptual fidelity, and peak signal-to-noise ratio. Once trained, the model can be applied to full µCT volumes to produce virtually stained 3D datasets that enhance interpretability without additional sample preparation. This work introduces virtual staining to 3D X-ray imaging, which may provide a scalable route for chemically informative, label-free tissue characterization in biomedical research.

The development of both thermally activated delayed fluorescence (TADF) emitters and host materials plays a crucial role in achieving high quantum efficiency in organic light-emitting diode (OLED) devices. While numerous host materials have been designed and used for TADF emitters, the structure-property relationship of hosts and their influence on the luminescence properties of TADF emitters remain relatively unexplored. In this work, the structure-property relationship of 13 host materials and their influence on the luminescence properties of the classical TADF molecule 4CzIPN are investigated. Based on molecular dynamics (MD) simulations, the doped host-guest systems at different time snapshots are selected for computational and statistical averaging analyses. Our results reveal that mCP, a commonly used host, weakens Dexter energy transfer (DET), suppresses structural variation, and facilitates high color purity in the emission of 4CzIPN. In contrast, CBP can enhance the oscillator strength and spin-orbit coupling (SOC) of 4CzIPN in the doped film. Host material mCP-OMe, which shares structural similarities with mCP, combines the advantages of both mCP and CBP. 4CzIPN in mCP-OMe:4CzIPN films exhibits reduced non-radiative loss, higher oscillator strength, and enhanced up-conversion. The results obtained through cluster analysis of the simulated structures agree well with the statistical average results. This study provides a theoretical foundation for selecting and designing more efficient host materials for 4CzIPN.

This study evaluated the efficacy of optimized deep learning architectures using a portable fluorescence imaging device specifically for the in-situ detection of fecal contamination on chicken eggshells to enhance food safety. The research utilized a Contamination and Sanitization Inspection device to establish a comprehensive dataset of fluorescence images, leveraging the spectral characteristics of fecal matter which emits fluorescence in the 600 to 720 nm range. Based on this fluorescence image data set, the study developed high performance models to identify fecal residues across both brown and white eggshells. Experimental results demonstrated that the fluorescence signals of fecal contaminants remain highly stable under ambient lighting, with both the primary mode utilizing 405 nm excitation and the enhance mode utilizing 365 nm excitation achieving Structural Similarity Index Measure (SSIM) values consistently exceeding 0.9200. These metrics confirm that the intrinsic high contrast of fluorescence imaging maintains structural integrity without the need for strict darkroom environments. Through the evaluation of nine distinct neural networks, it was found that the 365 nm excitation effectively suppressed background interference on brown eggs, allowing the lightweight MobileNet architecture to detect fecal contamination with an accuracy of 0.9000. For white eggshells, the 405 nm excitation coupled with the ViT Base 384 model yielded a peak accuracy of 0.9333 in identifying minute fecal traces. The reliability of the detection was further validated through Explainable AI frameworks which confirmed that the classification logic was consistently based on actual contaminated regions marked by fecal residues. These findings provide a robust methodology for leveraging handheld portable fluorescence technology to establish objective standards for detecting fecal contamination in the poultry industry.

Cheminformatics analysis, machine learning-based QSAR model building and virtual screening: identification of dual ROCK1 and TGFBR1 inhibitors for the treatment of cancer metastasis.

A drug designed for a specific target often interacts with multiple targets, either unintentionally or as part of its intended mechanism of action. This has been called pharmacological pleiotropy or polypharmacology. There are key endogenous ligands such as ATP, GABA and glutamate that act on various proteins in humans. Furthermore, several drugs act on multiple proteins without apparent structural similarity. G protein-coupled receptors (GPCRs) and protein kinases are among the most important families of drug targets. The aim of this review analysis is to identify drugs with dual actions on GPCRs and kinases and clarify what is known about these actions. Data searches for ligands with affinity for both kinases and GPCRs were conducted in the Drugbank and Pharos databases. Physiochemical properties of selected compounds were identified using the rdMolDescriptors module from RDKit. A detailed literature search was conducted in search engines such as Google Scholar and Medline, to identify pleiotropic compounds with both GPCR and kinase affinity. Thirty-four compounds were identified to interact with proteins within both the kinase and GPCR protein families. Notable examples included the drugs loratadine, terfenadine, clozapine, thioridazine, aripiprazole, fluspirilene, sorafenib, dasatinib and fasudil. Drug pleiotropy among GPCRs and kinases is a occurring phenomenon. Structural factors that may contribute to pleiotropy include chemical similarity to endogenous ligands, lipophilicity and planarity of chemical structure, which may guide the development of drugs with intentional multi-target effects. Although pleiotropy presents challenges in ensuring selectivity, it also creates opportunities for innovative therapeutic strategies if strategically applied.

Reconstructing 3D volumes from 2D freehand ultrasound (US) is a challenging task. During reconstruction, the ensuing overlap between sweeps can cause multiple pixels to be assigned to the same voxel, so the accurate alignment of these sweeps is critical. In recent years, implicit representation methods, such as neural radiance fields (NeRFs), have been utilized for modeling the 3D scene as a continuous volumetric function learned from 2D B-mode images. However, NeRF-based methods are also highly sensitive to errors in camera or transducer poses, a challenge that is particularly pronounced in freehand US with misregistration between overlapping sweeps, leading to severe reconstruction artifacts. To address this challenge, we propose geometric aware US NeRF (GAU-NeRF) by introducing a gradient reweighting strategy to reduce gradient fluctuations from noisy poses during early training iterations and to stabilize the optimization process. GAU-NeRF results in improved accurate pose refinement and improved reconstruction quality. Our method significantly outperforms existing baseline models on both simulated andin vivoUS datasets, achieving substantial gains across multiple metrics, including up to 132% increase in the peak signal-to-noise ratio, 133% improvement in the structural similarity index measure, and 350% reduction in the learned perceptual image patch similarity.

Medical images serve as the core basis for precision diagnosis and treatment, yet their scarcity severely hampers the advancement of intelligent medical image analysis. Data augmentation for medical images represents a key pathway to overcoming this data bottleneck. However, existing methods primarily focus on global image transformations and exhibiting limited control over local regionaldetails. In order to enhance image diversity, this paper proposes a text- and mask-guided local augmentation method for medicalimages. Aiming at the problem of insufficient diversity of medical synthesized images, this paper designs a text- and mask-guided local augmentation method for medical images (MILA-TMGDiff). This method first employs a pre-trained MedSAM model to segment target regions within input medical images, yielding precise masks. Subsequently, text prompts with semantic relevance and task-specificity are designed for different types of medical imaging data. Finally, the mask and text prompts are jointly input as local guidance conditions into a diffusion generative model. By applying controlled perturbations to the local noise distribution, fine-grained generation control over specific anatomical regions is achieved, ultimately producing synthetic medical images of high quality in both visual realism anddiversity. The method in this paper has been tested on x-ray, MRI, and CT images for local augmentation experiments, and the quantitative analysis results show that the local structural similarity of the images generated by this paper in the Mask region exhibits a significant change: a reduction of 97.9%, 103.3%, and 42.2% on chest x-ray, pelvic CT, and brain CT data, respectively. This phenomenon confirms that the local feature enhancement mechanism proposed in this paper can effectively modulate the distribution of structural features in the Mask region while maintaining the global textureconsistency. This provides a new technical pathway for controlled data augmentation in medical imaging, helping to advance the development of intelligent medical image analysis and laying the foundation for future research on fine-grained medical imagegeneration.

Positron Emission Tomography (PET) diagnostic precision is often compromised by low spatial resolution. Deep learning restoration models tend to sacrifice quantitative accuracy for visual sharpness, and most are trained on a single fixed degradation profile, limiting generalization across scanners. This paper presents a metabolically faithful 3D restoration framework pairing a volumetric extension of SwinFIR with two innovations: (1) a composite metabolic-aware loss enforcing structural, distributional, and frequency-domain agreement with the ground truth, and (2) a stochastic degradation augmentation strategy that randomizes point spread function parameters, voxel sampling, and counting noise during training, exposing the model to a distribution of simulated scanner-like degradations rather than a single fixed simulation. Evaluated on NeuroEXPLORER data, the proposed method outperforms baselines with a Structural Similarity Index Measure (SSIM) of 0.843, Peak Signal to Noise Ratio (PSNR) of 27.08 dB, and Normalized Root Mean Squared Error (NRMSE) of 0.117, while maintaining metabolic fidelity (Concordance Correlation Coefficient (CCC) 0.948, Wasserstein distance 0.018). Ablation experiments confirm that stochastic degradation augmentation improves robustness over fixed-profile training. The framework recovers anatomical detail with only small but measurable regional SUVR biases (≤3.3%) in selected cortical regions, which are symmetric across diagnostic groups.

The pursuit of pharmacological enhancement in sport has evolved from the widespread use of anabolic-androgenic steroids (AAS) to novel agents such as peptides and peptide analogues. Marketed as more selective and ostensibly safer alternatives, peptides-including growth hormone secretagogues (e.g., Ipamorelin), growth hormone-releasing hormone analogues (e.g., CJC-1295, Sermorelin), and synthetic fragments (e.g., Frag 176-191, KPV)-are promoted for muscle growth, fat metabolism, recovery, and anti-inflammatory effects. Their pharmacological profiles, including enhanced stability and receptor selectivity, have made them attractive in both medical research and bodybuilding communities. Despite their growing popularity, the clinical evidence supporting peptide use in sport is limited. Most published studies examine therapeutic applications under controlled dosing regimens, not the supraphysiological or combined protocols common in bodybuilding. Emerging data highlight potential risks: cardiovascular strain, insulin resistance, dyslipidemia, and psychiatric instability. The largely unregulated supply chain exacerbates these dangers, as products are often mislabeled or contaminated. Regulatory bodies such as the World Anti-Doping Agency (WADA) have responded by expanding detection technologies, yet analytical challenges remain due to peptides' structural similarity to endogenous hormones and short half-lives. Beyond elite sport, the extent of peptide use in the general population is unknown. Anecdotal reports and widespread promotion on social media suggest growing uptake among recreational gym-goers, including younger individuals, but prevalence studies are lacking. This represents a critical gap in current knowledge. In conclusion, peptides represent a new phase in performance enhancement but remain experimental substances with poorly defined long-term risks. Until longitudinal data clarify their safety and prevalence, peptide use in both competitive and recreational settings should be considered high-risk and ethically problematic.

Retail pork markets as underrecognized hubs of tigecycline resistance: Genomic epidemiology of tet(X4)-harboring Escherichia coli and global ST195 dissemination.