Healthcare (Basel). 2025 Apr 17;13(8):923. doi: 10.3390/healthcare13080923.

ABSTRACT

Pediatric gastroenterology is entering a pivotal phase marked by significant challenges and emerging opportunities in treating conditions like celiac disease (CeD), eosinophilic esophagitis (EoE), inflammatory bowel disease (IBD), and autoimmune hepatitis (AIH) pose significant clinical hurdles, but new therapeutic avenues are emerging. Advances in precision medicine, particularly proteomics, are reshaping care by tailoring treatments to individual patient characteristics. For CeD, therapies like gluten-degrading enzymes (latiglutenase, Kuma030) and zonulin inhibitors (larazotide acetate) show promise, though clinical outcomes are inconsistent. Immunotherapy and microbiota modulation, including probiotics and fecal microbiota transplantation (FMT), are also under exploration, with potential benefits in symptom management. Transglutaminase 2 inhibitors like ZED-1227 could help prevent gluten-induced damage. Monoclonal antibodies targeting immune pathways, such as AMG 714 and larazotide acetate, require further validation in pediatric populations. In EoE, biologics like dupilumab, cendakimab, dectrekumab (IL-13 inhibitors), and mepolizumab, reslizumab, and benralizumab (IL-5/IL-5R inhibitors) show varying efficacy, while thymic stromal lymphopoietin (TSLP) inhibitors like tezepelumab are also being investigated. These therapies require more pediatric-specific research to optimize their use. For IBD, biologics like vedolizumab, ustekinumab, and risankizumab, as well as small molecules like tofacitinib, etrasimod, and upadacitinib, are emerging treatments. New medications for individuals with refractory or steroid-dependent AIH have been explored. Personalized therapy, integrating precision medicine, therapeutic drug monitoring, and lifestyle changes, is increasingly guiding pediatric IBD management. This narrative review explores recent breakthroughs in treating CeD, EoE, IBD, and AIH, with a focus on pediatric studies when available, and discusses the growing role of proteomics in advancing personalized gastroenterological care.

PMID:40281872 | DOI:10.3390/healthcare13080923

Int J Tuberc Lung Dis. 2025 May 25;29(5):199-201. doi: 10.5588/ijtld.25.0157.

ABSTRACT

Until relatively recently, bronchiectasis (not due to cystic fibrosis) was considered an orphan disease, lacking clinical and commercial interest, and was rarely diagnosed. Since the 2000s, several working groups have emerged in Europe and the US - with the first register for bronchiectasis launching in Spain - and these have demonstrated the impact bronchiectasis has on health. Today, bronchiectasis is considered the third most common chronic inflammatory disease of the airways, after COPD and asthma, and represents a significant economic burden. We make the case for further characterization of these registries to better understand the heterogeneous epidemiology of bronchiectasis.

PMID:40281677 | DOI:10.5588/ijtld.25.0157

Jpn J Radiol. 2025 Apr 26. doi: 10.1007/s11604-025-01790-w. Online ahead of print.

ABSTRACT

PURPOSE: This study aimed to evaluate the efficacy of deep learning-based reconstruction (DLR) in improving pancreatic diffusion-weighted imaging (DWI) quality.

MATERIALS AND METHODS: In total, 117 patients (mean age of 68.0 ± 12.9 years) suspected of pancreatic diseases underwent magnetic resonance imaging (MRI) between July and December 2023. MRI sequences included respiratory-gated conventional diffusion-weighted images (RGC-DWIs), respiratory-gated diffusion-weighted images with deep learning-based reconstruction (DLR) (RGDLR-DWIs), and breath-hold diffusion-weighted images with DLR (BHDLR-DWIs) (short TE and long TE equal to other DWIs) at a 3 T MR system. Among these patients, 27 had solid lesions. Two radiologists qualitatively assessed pancreatic shape, main pancreatic duct (MPD) visualization, and solid lesion conspicuity using a 5-point scale. Quantitative analysis included apparent diffusion coefficient (ADC) values for pancreatic parenchyma and solid lesions, signal-to-noise ratio (SNR), pancreas-to-muscle signal-intensity ratio (PM-SIR) and lesion-to-pancreas signal-intensity ratio (LP-SIR). Differences among DWI sequences were analyzed using Friedman's and Bonferroni's tests.

RESULTS: Qualitatively, BHDLR-DWIs (short TE) had the highest scores for pancreatic shape and MPD but lowest for solid lesions visibility, whereas RGDLR-DWIs had the highest score for solid lesions. Quantitatively, BHDLR-DWIs (short TE) had the lowest ADC values for pancreatic parenchyma and solid lesions, with the highest PM-SIR. There was no significant difference between BHDLR-DWIs (short TE) and RGDLR-DWIs for solid lesion ADC values. RGC-DWIs had the highest SNR, though differences from RGDLR-DWIs and BHDLR-DWIs (short TE) were not significant. Although LP-SIR in RGDLR-DWIs were the lowest, the difference was not significant.

CONCLUSION: BHDLR-DWIs (short TE) provided the best pancreatic morphology image quality, whereas RGDLR-DWIs were superior for solid lesion detection.

PMID:40285832 | DOI:10.1007/s11604-025-01790-w

Abdom Radiol (NY). 2025 Apr 26. doi: 10.1007/s00261-025-04949-1. Online ahead of print.

ABSTRACT

PURPOSE: To establish and validate a model based on deep learning (DL), integrating radiomic features with relevant clinical features to generate nomogram, for predicting preoperative serosal invasion in gastric cancer (GC).

METHODS: This retrospective study included 335 patients from dual centers. T staging (T1-3 or T4) was used to assess serosal invasion. Radiomic features were extracted from primary GC lesions in the venous phase CT, and DL features from 8 transfer learning models were combined to create the Hand-crafted Radiomics and Deep Learning Radiomics (HCR-DLR) model. The Clinical (CL) model was built using clinical features, and both were combined into the Clinical and Radiomics Combined (CRC) model. In total, 15 predictive models were developed using 5 machine learning algorithms. The best-performing models were visualized as nomograms.

RESULTS: The total of 14 radiomic features, 13 DL features, and 2 clinical features were considered valuable through dimensionality reduction and selection. Among the constructed models: CRC model (AUC, training cohort: 0.9212; internal test cohort: 0.8743; external test cohort: 0.8853) than HCR-DLR model (AUC, training cohort: 0.8607; internal test cohort: 0.8543; external test cohort: 0.8824) and CL model (AUC, training cohort: 0.7632; internal test cohort: 0.7219; external test cohort: 0.7294) showed better performance. A nomogram based on the logistic CL model was drawn to facilitate the usage and showed its excellent predictive performance.

CONCLUSION: The predictive performance of the CRC Model, which integrates clinical features, radiomic features, and DL features, exhibits robust predictive capability and can serve as a simple, non-invasive, and practical tool for predicting the serosal invasion status of GC.

PMID:40285792 | DOI:10.1007/s00261-025-04949-1

Chem Res Toxicol. 2025 Apr 26. doi: 10.1021/acs.chemrestox.4c00560. Online ahead of print.

ABSTRACT

Transthyretin (TTR) plays a vital role in thyroid hormone transport and homeostasis in both the blood and target tissues. Interactions between exogenous compounds and TTR can disrupt the function of the endocrine system, potentially causing toxicity. In the Tox24 challenge, we leveraged the data set provided by the organizers to develop a deep learning-based consensus model, integrating sPhysNet, KANO, and GGAP-CPI for predicting TTR binding affinity. Each model utilized distinct levels of molecular information, including 2D topology, 3D geometry, and protein-ligand interactions. Our consensus model achieved favorable performance on the blind test set, yielding an RMSE of 20.8 and ranking fifth among all submissions. Following the release of the blind test set, we incorporated the leaderboard test set into our training data, further reducing the RMSE to 20.6 in an offlineretrospective study. These results demonstrate that combining three regression models across different modalities significantly enhances the predictive accuracy. Furthermore, we employ the standard deviation of the consensus model's ensemble outputs as an uncertainty estimate. Our analysis reveals that both the RMSE and interval error of predictions increase with rising uncertainty, indicating that the uncertainty can serve as a useful measure of prediction confidence. We believe that this consensus model can be a valuable resource for identifying potential TTR binders and predicting their binding affinity in silico. The source code for data preparation, model training, and prediction can be accessed at https://github.com/xiaolinpan/tox24_challenge_submission_yingkai_lab.

PMID:40285676 | DOI:10.1021/acs.chemrestox.4c00560

Adv Mater. 2025 Apr 26:e2419096. doi: 10.1002/adma.202419096. Online ahead of print.

ABSTRACT

Shear-stiffening materials, renowned for their rate-dependent behavior, hold immense potential for impact-resistant applications but are often constrained by limited load-bearing capacity under extreme conditions. In this study, a novel hybrid additive manufacturing strategy that successfully achieves anisotropic structural design of shear-stiffening materials is proposed. In this strategy, fused deposition modeling (FDM) is synergistically combined with direct ink writing (DIW) to fabricate lattice-structured soft-hard phase elastomer composites (TPR-SSE composites) with enhanced mechanical properties. Through quasistatic characterization and dynamic impact experiments, complemented by noncontact optical measurement and finite element simulation, the mechanical enhancement mechanisms imparted by the lattice architecture are systematically uncovered. The resulting composites exhibit exceptional load-bearing capacity under quasistatic conditions and superior energy dissipation under dynamic impacts, making them ideal for advanced protective systems. Building on this, smart sports shoes featuring a deep-learning-based smart sensing module that integrates structural customizability, buffering capacity, and gait recognition, are developed. This work provides a transformative structure design approach to shear-stiffening materials systems, paving the way for next-generation intelligent wearable protection applications.

PMID:40285578 | DOI:10.1002/adma.202419096

Adv Sci (Weinh). 2025 Apr 26:e2417682. doi: 10.1002/advs.202417682. Online ahead of print.

ABSTRACT

The increasing demand for automation in livestock farming scenarios highlights the need for effective noncontact measurement methods. The current methods typically require either fixed postures and specific positions of the target animals or high computational demands, making them difficult to implement in practical situations. In this study, a novel dual-network framework is presented that extracts accurate contour information instead of segmented images from unconstrained pigs and then directly employs this information to obtain precise liveweight estimates. The experimental results demonstrate that the developed framework achieves high accuracy, providing liveweight estimates with an R2 value of 0.993. When contour information is used directly to estimate the liveweight, the real-time performance of the framework can reach 1131.6 FPS. This achievement sets a new benchmark for accuracy and efficiency in non-contact liveweight estimation. Moreover, the framework holds significant practical value, equipping farmers with a robust and scalable tool for precision livestock management in dynamic, real-world farming environments. Additionally, the Liveweight and Instance Segmentation Annotation of Pigs dataset is introduced as a comprehensive resource designed to support further advancements and validation in this field.

PMID:40285549 | DOI:10.1002/advs.202417682

Brief Bioinform. 2025 Mar 4;26(2):bbaf186. doi: 10.1093/bib/bbaf186.

ABSTRACT

Deep learning (DL) methods have drastically advanced structure-based drug discovery by directly predicting protein structures from sequences. Recently, these methods have become increasingly accurate in predicting complexes formed by multiple protein chains. We evaluated these advancements to predict and accurately model the largest receptor family and its cognate peptide hormones. We benchmarked DL tools, including AlphaFold 2.3 (AF2), AlphaFold 3 (AF3), Chai-1, NeuralPLexer, RoseTTAFold-AllAtom, Peptriever, ESMFold, and D-SCRIPT, to predict interactions between G protein-coupled receptors (GPCRs) and their endogenous peptide ligands. Our results showed that structure-aware models outperformed language models in peptide binding classification, with the top-performing model achieving an area under the curve of 0.86 on a benchmark set of 124 ligands and 1240 decoys. Rescoring predicted structures on local interactions further improved the principal ligand discovery among decoy peptides, whereas DL-based approaches did not. We explored a competitive tournament approach for modeling multiple peptides simultaneously on a single GPCR, which accelerates the performance but reduces true-positive recovery. When evaluating the binding poses of 67 recent complexes, AF2 reproduced the correct binding modes in nearly all cases (94%), surpassing those of both AF3 and Chai-1. Confidence scores correlate with structural binding mode accuracy, which provides a guide for interpreting interface predictions. These results demonstrated that DL models can reliably rediscover peptide binders, aid peptide drug discovery, and guide the selection of optimal tools for GPCR-targeted therapies. To this end, we provided a practical guide for selecting the best models for specific applications and an independent benchmarking set for future model evaluation.

PMID:40285358 | DOI:10.1093/bib/bbaf186

Sensors (Basel). 2025 Apr 21;25(8):2623. doi: 10.3390/s25082623.

ABSTRACT

Automated storage and retrieval systems (AS/RS) play a crucial role in modern logistics, yet effectively monitoring cargo stacking patterns remains challenging. While computer vision and deep learning offer promising solutions, existing methods struggle to balance detection accuracy, computational efficiency, and environmental adaptability. This paper proposes a novel machine vision-based detection algorithm that integrates a pallet surface object detection network (STEGNet) with a box edge detection algorithm. STEGNet's core innovation is the Efficient Gated Pyramid Feature Network (EG-FPN), which integrates a Gated Feature Fusion module and a Lightweight Attention Mechanism to optimize feature extraction and fusion. In addition, we introduce a geometric constraint method for box edge detection and employ a Perspective-n-Point (PnP)-based 2D-to-3D transformation approach for precise pose estimation. Experimental results show that STEGNet achieves 93.49% mAP on our proposed GY Warehouse Box View 4-Dimension (GY-WSBW-4D) dataset and 83.2% mAP on the WSGID-B dataset, surpassing existing benchmarks. The lightweight variant maintains competitive accuracy while reducing the model size by 34% and increasing the inference speed by 68%. In practical applications, the system achieves pose estimation with a Mean Absolute Error within 4 cm and a Rotation Angle Error below 2°, demonstrating robust performance in complex warehouse environments. This research provides a reliable solution for automated cargo stack monitoring in modern logistics systems.

PMID:40285312 | DOI:10.3390/s25082623

Sensors (Basel). 2025 Apr 20;25(8):2615. doi: 10.3390/s25082615.

ABSTRACT

During image collection, images are often polluted by noise because of imaging conditions and equipment limitations. Images are also disturbed by external noise during compression and transmission, which adversely affects consequent processing, like image segmentation, target recognition, and text detection. A two-dimensional amplitude image is one of the most common image categories, which is widely used in people's daily life and work. Research on this kind of image-denoising algorithm is a hotspot in the field of image denoising. Conventional denoising methods mainly use the nonlocal self-similarity of images and sparser representatives in the converted domain for image denoising. In particular, the three-dimensional block matching filtering (BM3D) algorithm not only effectively removes the image noise but also better retains the detailed information in the image. As artificial intelligence develops, the deep learning-based image-denoising method has become an important research direction. This review provides a general overview and comparison of traditional image-denoising methods and deep neural network-based image-denoising methods. First, the essential framework of classic traditional denoising and deep neural network denoising approaches is presented, and the denoising approaches are classified and summarized. Then, existing denoising methods are compared with quantitative and qualitative analyses on a public denoising dataset. Finally, we point out some potential challenges and directions for future research in the field of image denoising. This review can help researchers clearly understand the differences between various image-denoising algorithms, which not only helps them to choose suitable algorithms or improve and innovate on this basis but also provides research ideas and directions for subsequent research in this field.

PMID:40285303 | DOI:10.3390/s25082615

Sensors (Basel). 2025 Apr 20;25(8):2608. doi: 10.3390/s25082608.

ABSTRACT

Deep learning has been widely applied in building segmentation from high-resolution remote sensing (HRS) images. However, HRS images suffer from insufficient complementary representation of target points in terms of capturing details and global information. To this end, we propose a novel building segmentation model for HRS images, termed C_ASegformer. Specifically, we design a Deep Layered Enhanced Fusion (DLEF) module to integrate hierarchical information from different receptive fields, thereby enhancing the feature representation capability of HRS information from global to detailed levels. Additionally, we introduce a Triplet Attention (TA) Module, which establishes dependency relationships between buildings and the environment through multi-directional rotation operations and residual transformations. Furthermore, we propose a Multi-Level Dilated Connection (MDC) Module to efficiently capture contextual relationships across different scales at a low computational cost. We conduct comparative experiments with several state-of-the-art models on three datasets, including the Massachusetts dataset, the INRIA dataset, and the WHU dataset. On the Massachusetts dataset, C_ASegformer achieves 95.42%, 85.69%, and 75.46% for OA, F1score, and mIoU, respectively. C_ASegformer shows more accurate performance, demonstrating the validity and sophistication of the model.

PMID:40285301 | DOI:10.3390/s25082608

Sensors (Basel). 2025 Apr 20;25(8):2603. doi: 10.3390/s25082603.

ABSTRACT

Challenges such as perspective distortion, irregular reading regions, and complex backgrounds in natural scenes hinder the accuracy and efficiency of automatic meter reading systems. Current mainstream approaches predominantly utilize object-detection-based methods without optimizing for text characteristics, while enhancements in detection robustness under complex backgrounds typically focus on data preprocessing rather than model architecture. To address these limitations, a novel end-to-end framework, i.e., EDPNet (Efficient DB and PARSeq Network), is proposed to integrate efficient boundary detection and text recognition. EDPNet comprises two key components, EDNet for detection and EPNet for recognition, where EDNet employs EfficientNetV2-s as its backbone with the Multi-Scale KeyDrop Attention (MSKA) and Efficient Multi-scale Attention (EMA) mechanisms to address perspective distortion and complex background challenges, respectively. During the recognition stage, EPNet integrates a DropKey Attention module into the PARSeq encoder, enhancing the recognition of irregular readings while effectively mitigating overfitting. Experimental evaluations show that EDNet achieves an F1-score of 0.997988, outperforming DBNet++ (ResNet50) by 0.61%. In challenging scenarios, EDPNet surpasses state-of-the-art methods by 0.7~1.9% while reducing parameters by 20.03%. EPNet achieves 90.0% recognition accuracy, exceeding the current best performance by 0.2%. The proposed framework delivers superior accuracy and robustness in challenging conditions while remaining lightweight.

PMID:40285298 | DOI:10.3390/s25082603

Molecules. 2025 Mar 21;30(7):1407. doi: 10.3390/molecules30071407.

ABSTRACT

Fennel waste is rich in compounds that may have beneficial effects on human health. For this reason, the most abundant metabolites in fennel were isolated as the following: quercetin-3-O-glucoside, quinic acid, 1,5-dicaffeoylquinic acid, kaempferol-3-O-glucuronide, and quercetin-3-O-glucuronide. After inducing inflammation in human bronchial epithelial cells by stimulating them with IL-1β, the cells were treated with the specialized Foeniculum vulgare metabolites at different concentrations to assess their anti-inflammatory effect. Eicosanoids, fatty acids, and sphingolipids were extracted from the cell medium and quantified by UPLC-ESI-QTRAP-MS/MS analysis. The anti-inflammatory activity of the metabolites isolated from fennel waste was demonstrated. They were able to alleviate the inflammatory state in human bronchial epithelium by modulating the metabolic expression of both pro- and anti-inflammatory eicosanoids, fatty acids, and sphingolipids. These findings suggest the potential use of fennel waste in the production of dietary supplements to alleviate the symptoms of chronic inflammatory diseases like asthma, chronic obstructive pulmonary disease (COPD), and idiopathic pulmonary fibrosis (IPF), where the continuous use of antiphlogistics may have significant side effects.

PMID:40286023 | DOI:10.3390/molecules30071407

Life (Basel). 2025 Mar 21;15(4):516. doi: 10.3390/life15040516.

ABSTRACT

There is increasing evidence supporting the use of morphofunctional assessment (MFA) as a tool for clinical characterization and decision-making in malnourished patients. MFA enables the diagnosis of malnutrition, sarcopenia, obesity, and cachexia, leading to a novel phenotype-based classification of nutritional disorders. Bioelectrical impedance analysis (BIVA), nutritional ultrasound® (NU) and computed tomography (CT) are included, along with functional tests like the Timed Up and Go test (TUG). Myoesteatosis, detectable via CT, can occur independently from nutritional phenotypes and has been identified as a significant mortality predictor in idiophatic pulmonary fibrosis (IPF). Our aim is to analyze the prevalence and overlap of nutritional phenotypes in IPF and evaluate the prognostic value of myoesteatosis. Our bicenter cross-sectional study included 82 IPF patients (84.1% male and with a medium age of 71.1 ± 7.35 years). MFA was performed using BIVA, NU, CT at the T12 level (CT-T12), the handgrip strength (HGS) test, and the TUG. CT-T12 BC parameters were analyzed using FocusedON® software, while statistical analyses were conducted with JAMOVI version 2.3.22. All four major nutritional phenotypes were represented in our cohort, with significant overlap. A total of 80.5% met the GLIM criteria for malnutrition, 14.6% had cachexia, 17% were sarcopenic, and 28% were obese. Of the obese patients, 70% were also malnourished, while 100% of sarcopenic obese patients (5.9% of total) had malnutrition. A total of 55% of sarcopenic patients with available CT also had myosteatosis, suggesting muscle quality deterioration as a potential driver of functional impairment. The presence of myosteatosis > 15% in T12-CT was an independent predictor of 12-month mortality (HR = 3.13; 95% CI: 1.01-9.70; p = 0.049), with survival rates of 78.1% vs. 96.6% in patients with vs. without myosteatosis, respectively. To conclude, this study underscores the relevance of MFA in the nutritional characterization of patients with IPF, demonstrating its potential to identify specific phenotypes associated with malnutrition, functional impairment, and the presence of myoesteatosis, thereby providing a valuable tool for clinical decision-making.

PMID:40283071 | DOI:10.3390/life15040516

Medicina (Kaunas). 2025 Mar 26;61(4):599. doi: 10.3390/medicina61040599.

ABSTRACT

Usual Interstitial Pneumonia (UIP) is the most severe radiological/histological pattern of Interstitial Lung Disease (ILD). It is typical of Idiopathic Pulmonary Fibrosis (IPF), but is also frequently described in Autoimmune Rheumatic Diseases (ARDs), sharing with IPF common risk factors, genetic backgrounds, and in some cases, disease progression and prognosis. Following the results of the PANTHER study, immunosuppressive drugs are now not recommended for the treatment of IPF; however, their use for the treatment of UIP secondary to ARDs is still under debate. The aim of this review is to summarize existing knowledge on the clinical presentation of autoimmune UIP and its treatment with immunosuppressive drugs. We searched PubMed for English language clinical trials and studies on treatment of ARDs-ILD, looking for specific treatments of UIP-ARDs. The available clinical trials rarely stratify patients by ILD pattern, and clinical studies generally lack a comparison with a placebo group. In Systemic Sclerosis, UIP patients showed a non-significant trend of worsening under immunosuppression. On the contrary, in Interstitial Pneumonia with Autoimmune Features and, above all, Rheumatoid Arthritis, immunosuppressive treatment produced promising results in the management of UIP patients. In conclusion, the current evidence about the immunosuppressive treatment of UIP-ARDs is limited and conflicting. There is an urgent need to adequately assess this topic with specific clinical trials, as has already been performed for IPF. The possibility should be considered that different ARDs can respond differently to immunosuppression. Finally, a wider use of histological samples could produce valuable information from a diagnostic, therapeutic, and research point of view.

PMID:40282891 | DOI:10.3390/medicina61040599

Proteomics. 2025 Apr 26:e202400417. doi: 10.1002/pmic.202400417. Online ahead of print.

ABSTRACT

The accurate construction of computational models in systems biology heavily relies on the availability of quantitative proteomics data, specifically, absolute protein abundances. However, the complex nature of proteomics data analysis necessitates specialised expertise, making the integration of this data into models challenging. Therefore, the development of software tools that ease the analysis of proteomics data and bridge between disciplines is crucial for advancing the field of systems biology. We developed an open access Python-based software tool available either as downloadable library or as web-based graphical user interface (GUI). The pipeline simplifies the extraction and calculation of protein abundances from unprocessed proteomics data, accommodating a range of experimental approaches based on label-free quantification. Our tool was conceived as a versatile and robust pipeline designed to ease and simplify data analysis, thereby improving reproducibility between researchers and institutions. Moreover, the robust modular structure of Alpaca allows its integration with other software tools.

PMID:40285550 | DOI:10.1002/pmic.202400417

Vet Sci. 2025 Apr 9;12(4):347. doi: 10.3390/vetsci12040347.

ABSTRACT

Milk fever, or periparturient hypocalcemia, in dairy cows has traditionally been addressed as an acute calcium deficiency, leading to interventions like supplementation and adjustments in dietary cation-anion balance. Although these measures have improved clinical outcomes, milk fever remains a widespread and economically significant issue for the dairy industry. Emerging findings demonstrate that a narrow emphasis on blood calcium concentration overlooks the complex interactions of immune, endocrine, and metabolic pathways. Inflammatory mediators and bacterial endotoxins can compromise hormone-driven calcium regulation and induce compensatory calcium sequestration, thereby worsening both clinical and subclinical hypocalcemia. Recent insights from systems biology illustrate that milk fever arises from nonlinear interactions among various physiological networks, rather than a single deficiency. Consequently, this review contends that a holistic strategy including integrating nutrition, immunology, microbiology, genetics, and endocrinology is vital for comprehensive management and prevention of milk fever. By embracing a multidisciplinary perspective, producers and veterinarians can develop more robust, customized solutions that not only safeguard animal well-being but also bolster profitability. Such an approach promises to meet the evolving demands of modern dairy operations by reducing disease prevalence and enhancing overall productivity. Tackling milk fever through integrated methods may unlock possibilities for improved herd health and sustainable dairy farming.

PMID:40284849 | DOI:10.3390/vetsci12040347

Polymers (Basel). 2025 Apr 18;17(8):1101. doi: 10.3390/polym17081101.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers worldwide due to its resistance to conventional therapies that is attributed to its dense and acidic tumor microenvironment. Chemotherapy based on gemcitabine usually lacks efficacy due to poor drug penetration and the metabolic characteristics of the cells adapted to grow at a more acidic pHe, thus presenting a more aggressive phenotype. In this context, photodynamic therapy (PDT) offers a promising alternative since it generally does not suffer from the same patterns of cross-resistance observed with chemotherapy drugs. In the present work, a novel bromine-substituted heptamethine-cyanine dye (BrCY7) was synthesized, loaded into PEG-PLGA NPs, and tested on the pancreatic ductal adenocarcinoma cell line cultured under physiological (PANC-1 CT) and acidic (PANC-1 pH selected) conditions, which promotes the selection of a more aggressive phenotype. The cytotoxicity of BrCY7-PEG-PLGA is dose-dependent, with an IC50 of 2.15 µM in PANC-1 CT and 2.87 µM in PANC-1 pH selected. Notably, BrCY7-PEG-PLGA demonstrated a phototoxic effect against PANC-1 pH selected cells but not on PANC-1 CT, which makes these findings particularly relevant since PANC-1 pH selected cells are more resistant to gemcitabine as compared with PANC-1 CT cells.

PMID:40284366 | DOI:10.3390/polym17081101

Plants (Basel). 2025 Apr 20;14(8):1256. doi: 10.3390/plants14081256.

ABSTRACT

Castor bean (Ricinus communis L.) can tolerate long periods of dehydration, allowing the investigation of gene circuits involved in drought tolerance. Genes from gibberellins, jasmonates, and auxin signaling are important for crosstalk in the developmental and environmental adaptation process to drought conditions. However, the genes related to these signals, as well as their transcription profiles under drought, remain poorly characterized in the castor bean. In the present work, genes from gibberellins, jasmonates, and auxin signaling were identified and molecularly characterized. These analyses allowed us to identify genes encoding receptors, inhibitory proteins, and transcription factors from each signaling pathway in the castor bean genome. Chromosomal distribution, gene structure, evolutionary relationships, and conserved motif analyses were performed. Expression analysis through RNA-seq and RT-qPCR revealed that gibberellins, jasmonates, and auxin signaling were modulated at multiple levels under drought, with notable changes in specific genes. The gibberellin receptor RcGID1c was downregulated in response to drought, and RcDELLA3 was strongly repressed, whereas its homologues were not, reinforcing the suggestion of a nuanced regulation of gibberellin signaling during drought. Considering jasmonate signaling, the downregulation of the transcription factor RcMYC2 aligned with the drought tolerance observed in mutants lacking this gene. Altogether, these analyses have provided insights into hormone signaling in the castor bean, unveiling transcriptional responses that enhance our understanding of high drought tolerance in this plant. This knowledge opens avenues for identifying potential candidate genes suitable for genetic manipulation in biotechnological approaches.

PMID:40284144 | DOI:10.3390/plants14081256

Biology (Basel). 2025 Apr 16;14(4):426. doi: 10.3390/biology14040426.

ABSTRACT

Runs of homozygosity (ROH) are key elements of the genetic structure of populations, reflecting inbreeding levels, selection history, and potential associations with phenotypic traits. This study proposes a novel approach to ROH analysis through visualization and classification using convolutional neural networks (CNNs). Genetic data from Large White (n = 568) and Duroc (n = 600) pigs were used to construct ROH maps, where each homozygous segment was classified by length and visualized as a color-coded image. The analysis was conducted in two stages: (1) classification of animals by breed based on ROH maps and (2) identification of the presence or absence of a phenotypic trait (limb defects). Genotyping was performed using the GeneSeek® GGP SNP80x1_XT chip (Illumina Inc., San Diego, CA, USA), and ROH segments were identified using the software tool PLINK v1.9. To visualize individual maps, we utilized a modified function from the HandyCNV package. The results showed that the CNN model achieved 100% accuracy, sensitivity, and specificity in classifying pig breeds based on ROH maps. When analyzing the binary trait (presence or absence of limb defects), the model demonstrated an accuracy of 78.57%. Despite the moderate accuracy in predicting the phenotypic trait, the high negative predictive value (84.62%) indicates the model's reliability in identifying healthy animals. This method can be applied not only in animal breeding research but also in medicine to study the association between ROH and hereditary diseases. Future plans include expanding the method to other types of genetic data and developing mechanisms to improve the interpretability of deep learning models.

PMID:40282291 | DOI:10.3390/biology14040426