J Community Hosp Intern Med Perspect. 2025 Jul 3;15(4):66-68. doi: 10.55729/2000-9666.1497. eCollection 2025.

ABSTRACT

Familial hypocalciuric hypercalcemia (FHH) is a rare autosomal dominant condition caused by mutations in the calcium-sensing receptor gene (CASR), leading to asymptomatic hypercalcemia. Here, we report a case of hypercalcemia in a patient with acute pancreatitis, subsequently diagnosed with FHH. A 41-year-old male presented with abdominal pain and elevated pancreatic enzymes. Imaging revealed changes consistent with acute pancreatitis. Surprisingly, serum calcium was elevated, which is uncommon in acute pancreatitis. Further work-up demonstrated normal parathyroid hormone (PTH), PTH-related peptide (PTHrp), and vitamin D levels. A 24-h urinary calcium excretion of 24 mg/24 h and a calcium to creatinine clearance ratio (CCCR) of 0.002 confirmed the diagnosis of FHH. This condition is typically asymptomatic, with few complications, and is managed conservatively with patient education and genetic counselling.

PMID:40757212 | PMC:PMC12315876 | DOI:10.55729/2000-9666.1497

Respir Med Case Rep. 2025 Jul 24;57:102264. doi: 10.1016/j.rmcr.2025.102264. eCollection 2025.

ABSTRACT

BACKGROUND: The missense CFTR variant I1234V (c.3700A > G) produces class II protein-folding defects and is prevalent in the Middle East, yet clinical evidence for elexacaftor/tezacaftor/ivacaftor (ETI) in homozygous carriers is sparse. We prospectively evaluated ETI efficacy and safety in two paediatric siblings with homozygous I1234V cystic fibrosis (CF).

METHODS: A single-centre, prospective case series was undertaken at King Fahad Medical City. Baseline assessments included spirometry, body-mass index (BMI), sputum microbiology, liver biochemistry and high-resolution chest CT. ETI was initiated according to weight-based dosing and patients were reviewed at 6 and 8 months. Primary outcomes were change in percent-predicted forced expiratory volume in 1 second (ppFEV1) and BMI; secondary outcomes were Pseudomonas aeruginosa status, radiological changes and adverse events.

RESULTS: After eight months of ETI, ppFEV1 increased from 36 % to 46 % in the 11-year-old girl and from 57 % to 73 % in the 9-year-old boy. Corresponding BMI rose from 11.71 kg/m2 (z = -4.37) to 15.48 kg/m2 (z = -1.22) and from 12.69 kg/m2 (z = -3.12) to 15.74 kg/m2 (z = -0.55), respectively. Chronic P. aeruginosa was eradicated in both patients. Chest CT demonstrated reduced mucus plugging and peribronchial wall thickening with partial regression of cystic bronchiectasis.

CONCLUSIONS: ETI produced clinically meaningful improvements in lung function, nutritional status, microbiological clearance and radiological appearance in two children homozygous for the rare I1234V mutation. These real-world findings support extending ETI access to patients with rare class II CFTR variants and justify larger multicentre studies to confirm efficacy and long-term safety.

PMID:40755839 | PMC:PMC12318305 | DOI:10.1016/j.rmcr.2025.102264

Cureus. 2025 Jul 4;17(7):e87264. doi: 10.7759/cureus.87264. eCollection 2025 Jul.

ABSTRACT

Hypereosinophilic syndrome (HES) is a rare condition characterized by persistent eosinophilia (eosinophil count ≥1.5 × 109/L) and end-organ damage in the absence of an identifiable cause. Cardiac involvement is common and may lead to life-threatening complications. Cystic fibrosis (CF) is a chronic multisystem disease predominantly associated with neutrophilic inflammation, and eosinophilic disorders are less often reported in this population. A 32-year-old woman with CF, complicated by CF-related diabetes and pancreatic insufficiency, presented with chest pain and peripheral eosinophilia (3.2 × 10⁹/L); infectious, autoimmune, and allergic evaluations were negative. Imaging revealed perimyocarditis, and systemic corticosteroids were initially effective but discontinued due to cushingoid side effects and anasarca. She subsequently experienced a recurrence of chest pain accompanied by eosinophilia (1.7 × 10⁹/L), and a diagnosis of idiopathic HES was made based on persistent eosinophilia, cardiac involvement, and exclusion of secondary causes. She responded favorably to monthly subcutaneous mepolizumab, a monoclonal antibody that prevents interleukin-5 (IL-5) from binding to its receptor, thereby inhibiting the recruitment and activation of eosinophils, with resolution of eosinophilia and improvement in symptoms. This case underscores the importance of considering HES in CF patients presenting with unexplained eosinophilia and extrapulmonary symptoms. It also illustrates the efficacy of targeted biologic therapy in managing idiopathic HES when corticosteroids are poorly tolerated.

PMID:40755600 | PMC:PMC12318349 | DOI:10.7759/cureus.87264

J Imaging Inform Med. 2025 Aug 4. doi: 10.1007/s10278-025-01623-2. Online ahead of print.

ABSTRACT

Hand-wrist radiographs are used in bone age prediction. Computer-assisted clinical decision support systems offer solutions to the limitations of the radiographic bone age assessment methods. In this study, a multi-output prediction model was designed to predict bone age and gender using digital hand-wrist radiographs. The InceptionV3 architecture was used as the backbone, and the model was trained and tested using the open-access dataset of 2017 RSNA Pediatric Bone Age Challenge. A total of 14,048 samples were divided to training, validation, and testing subsets with the ratio of 7:2:1, and additional specialized convolutional neural network layers were implemented for robust feature management, such as Squeeze-and-Excitation block. The proposed model achieved a mean squared error of approximately 25 and a mean absolute error of 3.1 for predicting bone age. In gender classification, an accuracy of 95% and an area under the curve of 97% were achieved. The intra-class correlation coefficient for the continuous bone age predictions was found to be 0.997, while the Cohen's κ coefficient for the gender predictions was found to be 0.898 ( p < 0.001). The proposed model aims to increase model efficiency by identifying common and discrete features. Based on the results, the proposed algorithm is promising; however, the mid-high-end hardware requirement may be a limitation for its use on local machines in the clinic. The future studies may consider increasing the dataset and simplification of the algorithms.

PMID:40758204 | DOI:10.1007/s10278-025-01623-2

J Chem Inf Model. 2025 Aug 4. doi: 10.1021/acs.jcim.5c01171. Online ahead of print.

ABSTRACT

Drug-drug interactions (DDIs) present significant challenges within clinical pharmacology, as they can impact therapeutic outcomes, especially given the growing prevalence of polypharmacy. Traditional methods for the clinical validation of DDIs typically exhibit inefficiency and high cost, underscoring the necessity for more advanced computational methodologies. Although deep learning-based methods have improved DDI prediction performance, current approaches often face challenges in extracting and integrating multidimensional molecular features and capturing molecular reaction patterns. To overcome these limitations, we propose a Multidimensional Joint and Cross-learning (MDJCL) model that effectively integrates 1D, 2D, and 3D molecular features of drugs. A cross-attention fusion module aggregates multidimensional features while minimizing information loss, and a molecular-pair reaction module pinpoints potential interaction sites. Experimental results on benchmark data sets demonstrate that MDJCL consistently outperforms state-of-the-art models. Ablation studies reveal that each module contributes distinctively to the overall enhancement of evaluation metrics. These results validate the effectiveness of multidimensional feature integration and cross learning mechanisms in enhancing DDI prediction, offering a reliable tool for clinical decision-making and precision medicine.

PMID:40758117 | DOI:10.1021/acs.jcim.5c01171

Hum Brain Mapp. 2025 Aug 1;46(11):e70310. doi: 10.1002/hbm.70310.

ABSTRACT

Alzheimer's disease (AD) is accompanied by alterations in retinal vascular density (VD), but the mechanisms remain unclear. This study investigated the relationship among cerebral amyloid-β (Aβ) deposition, VD, and cognitive decline. We enrolled 92 participants, including 47 AD patients and 45 healthy control (HC) participants. VD across retinal subregions was quantified using deep learning-based fundus photography, and cerebral Aβ deposition was measured with 18F-florbetapir (18F-AV45) PET/MRI. Using the minimum bounding circle of the optic disc as the diameter (papilla-diameter, PD), VD (total, 0.5-1.0 PD, 1.0-1.5 PD, 1.5-2.0 PD, 2.0-2.5 PD) was calculated. Standardized uptake value ratio (SUVR) for Aβ deposition was computed for global and regional cortical areas, using the cerebellar cortex as the reference region. Cognitive performance was assessed with the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Pearson correlation, multiple linear regression, and mediation analyses were used to explore Aβ deposition, VD, and cognition. AD patients exhibited significantly lower VD in all subregions compared to HC (p < 0.05). Reduced VD correlated with higher SUVR in the global cortex and a decline in cognitive abilities (p < 0.05). Mediation analysis indicated that VD influenced MMSE and MoCA through SUVR in the global cortex, with the most pronounced effects observed in the 1.0-1.5 PD range. Retinal VD is associated with cognitive decline, a relationship primarily mediated by cerebral Aβ deposition measured via 18F-AV45 PET. These findings highlight the potential of retinal VD as a biomarker for early detection in AD.

PMID:40757876 | DOI:10.1002/hbm.70310

Mutagenesis. 2025 Aug 4:geaf014. doi: 10.1093/mutage/geaf014. Online ahead of print.

ABSTRACT

The mutagenicity of chemical compounds is a key consideration in toxicology, drug development, and environmental safety. Traditional methods such as the Ames test, while reliable, are time-intensive and costly. With advances in imaging and machine learning, high-content assays like Cell Painting offer new opportunities for predictive toxicology. Cell Painting captures extensive morphological features of cells, which can correlate with chemical bioactivity. In this study, we leveraged Cell Painting data to develop machine learning models for predicting mutagenicity and compared their performance with structure-based models. We used two datasets: a Broad Institute dataset containing profiles of over 30,000 molecules and a US-EPA dataset with images of 1,200 chemicals tested at multiple concentrations. By integrating these datasets, we aimed to improve the robustness of our models. Among three algorithms tested - Random Forest, Support Vector Machine, and Extreme Gradient Boosting - the third showed the best performance for both datasets. Notably, selecting the most relevant concentration per compound, the Phenotypic Altering Concentration, significantly improved prediction accuracy. Our models outperformed traditional QSAR tools such as VEGA and the CompTox Dashboard for the majority of compounds, demonstrating the utility of Cell Painting features. The Cell Painting-based models revealed morphological changes related to DNA/RNA and ER perturbation, especially in mitochondria and nuclei, aligning with mutagenicity mechanisms. Despite this, certain compounds remained challenging to predict due to inherent dataset limitations and inter-laboratory variability in Cell Painting technology. The findings highlight the potential of Cell Painting in mutagenicity prediction, offering a complementary perspective to chemical structure-based models. Future work could involve harmonizing Cell Painting methodologies across datasets and exploring deep learning techniques to enhance predictive accuracy. Ultimately, integrating Cell Painting data with QSAR descriptors in hybrid models may unlock novel insights into chemical mutagenicity.

PMID:40757573 | DOI:10.1093/mutage/geaf014

J Chem Inf Model. 2025 Aug 4. doi: 10.1021/acs.jcim.5c01148. Online ahead of print.

ABSTRACT

Carbon capture through physical solvents reduces energy consumption and lowers environmental impact compared with conventional chemical absorption methods. Typical properties for solvent screening are solubility and selectivity. However, they require accurate prediction of vapor-liquid equilibrium (VLE), which remains a critical challenge due to the lack of enough available experimental data. This could be supplemented by in silico data prediction, provided that current prediction models are improved as this paper intends. When modeling physical solvents, a challenge arises due to the dominant role of nonbonding interactions and molecular geometry. For this purpose, a machine learning pipeline is developed using VLE results obtained from the quantum chemical-based thermodynamic model COnductor-like Screening MOdel for Real Solvents (COSMO-RS) and experimental data. A directed message passing neural network (D-MPNN) architecture is employed, leveraging molecular representations, additional features, and transfer learning to refine predictions. Two models, solubility and selectivity, are pretrained over 30,000 COSMO-RS simulated data points and fine-tuned with experimental VLE data sets for CO2 and common gas impurities (H2S, CH4, N2, and H2), respectively. The models' accuracy is significantly improved over that of COSMO alone by correcting bias in total pressure predictions. Experimental trends are successfully reproduced in the test data, confirming the physical consistency of the models. Sensitivity analysis confirms that molecular features have the highest impact on estimations, while the scaling effect of additional features is essential for accuracy. These results demonstrate the potential of the proposed methodology to systematically screen and optimize an extensive range of physical solvents on the basis of their chemical structure for carbon capture applications, reducing the reliance on costly and time-consuming experimental measurements.

PMID:40757514 | DOI:10.1021/acs.jcim.5c01148

Front Med (Lausanne). 2025 Jul 18;12:1628311. doi: 10.3389/fmed.2025.1628311. eCollection 2025.

ABSTRACT

Dry eye disease (DED) is a multifactorial ocular surface disorder characterized by ocular discomfort, visual disturbances, and potential structural damage. The heterogeneous etiology and symptomatology of DED pose significant challenges for accurate diagnosis and effective treatment. In recent years, artificial intelligence (AI), particularly deep learning (DL), has shown substantial promise in improving the objectivity and efficiency of DED assessment. This review provides a comprehensive synthesis of AI-assisted techniques for the quantification of key DED biomarkers, including tear film stability [e.g., tear meniscus height (TMH) and tear film break-up time (TBUT)], meibomian gland morphology, and corneal epithelial damage. We discuss how these technologies enhance diagnostic accuracy, standardize evaluation, and support personalized treatment. Collectively, these advancements underscore the transformative potential of AI in reshaping DED diagnostics and management.

PMID:40757197 | PMC:PMC12313631 | DOI:10.3389/fmed.2025.1628311

Front Med (Lausanne). 2025 Jul 18;12:1617537. doi: 10.3389/fmed.2025.1617537. eCollection 2025.

ABSTRACT

With the rise of generative artificial intelligence (AI) technology, AI has played a significant role in ophthalmology clinical applications, and AI-generated content (AIGC) has shown great potential in ophthalmology education. Specifically, AIGC plays an important role in lesson plan generation, simulated cases, and disease diagnosis, but its application also faces challenges related to the invasion of patient privacy and the accuracy of generated content. To better enable AIGC and promote the development of ophthalmology education, this article provides an overview of AI and ophthalmology and the application, challenges, and development prospects of AIGC in ophthalmology education. References for related research as well as practice are also provided.

PMID:40757196 | PMC:PMC12313564 | DOI:10.3389/fmed.2025.1617537

Front Neurol. 2025 Jul 18;16:1609594. doi: 10.3389/fneur.2025.1609594. eCollection 2025.

ABSTRACT

INTRODUCTION: Glioma is the most common primary malignant tumor of the central nervous system. The mutation status of isocitrate dehydrogenase (IDH) and the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter are key biomarkers for glioma diagnosis and prognosis. Accurate, non-invasive prediction of these biomarkers using MRI is of significant clinical value.

MATERIALS AND METHODS: We proposed a novel multitask deep learning framework based on Coordinate Attention-EfficientNetV2 (CA-EfficientNetV2) to simultaneously predict IDH mutation and MGMT promoter methylation status based on MRI data. Initially, unlabeled MR images were annotated using K-means clustering to generate pseudolabels, which were subsequently refined using a Vision Transformer (ViT) network to improve labeling accuracy. Then, the Fruit Fly Optimization Algorithm (FOA) was employed to assign optimal weights to the pseudolabeled data. The CA-EfficientNetV2 model, integrated with a coordinate attention mechanism, was constructed. The multitask framework comprised three independent subnetworks: T2-net (based on T2-weighted imaging), T1C-net (based on contrast-enhanced T1-weighted imaging), and TU-net (based on the fusion of T2WI and T1CWI).

RESULTS: The proposed framework demonstrated high performance in predicting both IDH mutation and MGMT promoter methylation status. Among the three subnetworks, TU-net achieved the best results, with accuracies of 0.9598 for IDH and 0.9269 for MGMT, and AUCs of 0.9930 and 0.9584, respectively. Comparative analysis showed that our proposed model outperformed other convolutional neural network (CNN) - based approaches.

CONCLUSION: The CA-EfficientNetV2-based multitask framework offers a robust, non-invasive method for preoperative prediction of glioma molecular markers. This approach holds strong potential to support clinical decision-making and personalized treatment planning in glioma management.

PMID:40757022 | PMC:PMC12313511 | DOI:10.3389/fneur.2025.1609594

J Maxillofac Oral Surg. 2025 Aug;24(4):1151-1178. doi: 10.1007/s12663-025-02664-4. Epub 2025 Jul 2.

ABSTRACT

PURPOSE: This review highlights the potential of artificial intelligence algorithms, including machine learning (ML) and deep learning (DL), in improving the diagnosis and management of oral and maxillofacial diseases through advanced imaging techniques such as computerized tomography (CT) and cone-beam computed tomography (CBCT).

METHODS: The current review was conducted on the basis of ISI Web of Science, PubMed, Scopus, and Google Scholar (2010-2024) using keywords related to radiography, MRI, CT, CBCT, ML, DL, and maxillofacial pathology, with a focus on clinical applications.

RESULTS: The DL algorithms for detecting vertical root fractures achieved a diagnostic accuracy of 89.0% for premolars, with a sensitivity of 84.0% and specificity of 94.0%. It demonstrated an accuracy of 93% and a specificity of 88% in evaluating CBCT images. The GoogLeNet Inception v3 architecture achieved an AUC of 0.914, sensitivity of 96.1%, and specificity of 77.1% for CBCT, outperforming the panoramic radiograph, which had an AUC of 0.847, sensitivity of 88.2%, and specificity of 77.0%. CBCT demonstrated higher diagnostic accuracy (91.4%) than panoramic images (84.6%), with odontogenic cystic lesions exhibiting the highest accuracy. The U-Net-based DL algorithm achieves recall, precision, and F1 scores of 0.742, 0.942, and 0.831 for metastatic lymph nodes, and 0.782, 0.990, and 0.874 for nonmetastatic lymph nodes, respectively.

CONCLUSION: This study highlights the superior anatomical detail of CBCT, making it more reliable for diagnosing oral and dentomaxillofacial disorders. DL algorithms demonstrate high accuracy and sensitivity in diagnosing dental and odontogenic disorders and often outperform radiologists.

PMID:40756906 | PMC:PMC12316632 | DOI:10.1007/s12663-025-02664-4

J R Soc N Z. 2025 May 14;55(6):2082-2102. doi: 10.1080/03036758.2025.2500998. eCollection 2025.

ABSTRACT

New Zealand is considered to be the 'seabird capital' of the world. As part of the harvesting process, some commercial fishers accidentally bycatch seabirds during fishing operations, which can result in accidental deaths and injuries. The accidental bycatch is impacting the long-term sustainability of New Zealand seabird populations. To address this, we developed a YOLO model that can be used to automatically detect seabirds that interact with the fishing vessels. The model development process involved gathering, annotating and preprocessing a new image dataset, conducting transfer learning across YOLO benchmark models, and performing hyperparameter tuning on the top YOLO models to further improve the model's performance. We evaluate the performance and effectiveness of our developed model under diverse data conditions, with it achieving a mAP@50 score of 0.9926 and a mAP@50-95 score of 0.9147 on the test data. The results demonstrate that the developed model performs effectively in unconstrained real-world marine scenarios, addressing the limitations of previous models primarily evaluated in controlled settings. This automation could help to reduce or even eliminate manual inspection of footages by reviewers and will help to quantify seabird interactions with commercial fishing vessels. Our contributions represent a significant first step in automated seabird detection, mitigating the gap between constrained and unconstrained real-world maritime scenarios.

PMID:40756846 | PMC:PMC12315183 | DOI:10.1080/03036758.2025.2500998

Cureus. 2025 Jul 4;17(7):e87264. doi: 10.7759/cureus.87264. eCollection 2025 Jul.

ABSTRACT

Hypereosinophilic syndrome (HES) is a rare condition characterized by persistent eosinophilia (eosinophil count ≥1.5 × 109/L) and end-organ damage in the absence of an identifiable cause. Cardiac involvement is common and may lead to life-threatening complications. Cystic fibrosis (CF) is a chronic multisystem disease predominantly associated with neutrophilic inflammation, and eosinophilic disorders are less often reported in this population. A 32-year-old woman with CF, complicated by CF-related diabetes and pancreatic insufficiency, presented with chest pain and peripheral eosinophilia (3.2 × 10⁹/L); infectious, autoimmune, and allergic evaluations were negative. Imaging revealed perimyocarditis, and systemic corticosteroids were initially effective but discontinued due to cushingoid side effects and anasarca. She subsequently experienced a recurrence of chest pain accompanied by eosinophilia (1.7 × 10⁹/L), and a diagnosis of idiopathic HES was made based on persistent eosinophilia, cardiac involvement, and exclusion of secondary causes. She responded favorably to monthly subcutaneous mepolizumab, a monoclonal antibody that prevents interleukin-5 (IL-5) from binding to its receptor, thereby inhibiting the recruitment and activation of eosinophils, with resolution of eosinophilia and improvement in symptoms. This case underscores the importance of considering HES in CF patients presenting with unexplained eosinophilia and extrapulmonary symptoms. It also illustrates the efficacy of targeted biologic therapy in managing idiopathic HES when corticosteroids are poorly tolerated.

PMID:40755600 | PMC:PMC12318349 | DOI:10.7759/cureus.87264

Nanoscale. 2025 Aug 4. doi: 10.1039/d5nr02062j. Online ahead of print.

ABSTRACT

Cortisol plays a central role in maintaining physiological homeostasis, and both cortisol excess and deficiency are associated with life-threatening conditions. Accurate diagnosis, adequate treatment and monitoring of disorders of cortisol secretion are essential for good health, normal growth and development. Although commercially available lateral flow immunoassay (LFI) strips can be used to measure cortisol, they have limitations, especially low sensitivity and limited quantitative performance, inhibiting their use in clinical settings. Here, we present a novel LFI platform integrated with surface-enhanced Raman scattering (SERS), employing precisely size-controlled gold nanoparticle clusters functionalised with Raman reporter molecules to overcome these limitations. The approach achieves exceptional sensitivity, covering the relevant therapeutic range in humans, with a limit of detection (LOD) for cortisol of 0.014 pg mL-1, which is >500 times more sensitive than conventional LFI strips. The platform also showed high specificity for cortisol. The diagnostic potential was confirmed by testing with human saliva samples (n = 28), cross-validated with UPLC-MS/MS, showing excellent correlation (R2 = 0.9977). Bland-Altman analysis demonstrated strong agreement, with all samples falling within the 95% limits and yielding a mean bias of -3.5% ± 13.2% relative to UPLC-MS/MS. Given its sensitivity, specificity and simplicity, this LFI-SERS platform offers strong potential for clinical translation to enable convenient cortisol monitoring.

PMID:40758280 | DOI:10.1039/d5nr02062j

Mol Biol Rep. 2025 Aug 4;52(1):789. doi: 10.1007/s11033-025-10874-9.

ABSTRACT

BACKGROUND: Sanfilippo syndrome type B results from NAGLU mutations which cause progressive cognitive impairments and central nervous system degeneration. A 10-year-old boy presented with developmental regression, brain atrophy, intellectual disability, attention-deficit/hyperactivity disorder, and restlessness. His parents were non-consanguineous and asymptomatic.

METHODS: Whole-exome sequencing (WES) was performed, and variants were confirmed by Sanger sequencing. Downstream analyses integrated protein-protein interaction (PPI), gene-microRNA interaction (GMI), and drug-disease association (DDA) networks using STRING, NetworkAnalyst, and Enrichr.

RESULTS: Two missense variants were identified including rs1358994052 (NAGLU:c.874G > A; p.Gly292Arg) and rs768918822 (NAGLU:c.1004 A > G; p.Tyr335Cys [Y335C]), classified as pathogenic and likely pathogenic, respectively, by ACMG guidelines. Both variants localize to regulatory elements. The compound heterozygote network exhibited increased PPI connectivity and the absence of hsa-miR-27a-3p in GMI analysis. DDA highlighted carcinogenesis as the top-ranked term in the compound heterozygote network, contrasting with leukemia associations in homozygous contexts.

CONCLUSION: Compound heterozygous regulatory variants in NAGLU underlie diverse biochemical and neurodevelopmental phenotypes beyond enzymatic deficiency, emphasizing the value of integrative WES and systems biology approaches to refine pathogenicity assessments and guide targeted functional validation.

PMID:40758221 | DOI:10.1007/s11033-025-10874-9

Phys Rev Lett. 2025 Jul 18;135(3):038401. doi: 10.1103/jfm6-8h9s.

ABSTRACT

Microphase separation is common in active biological systems as exemplified by the separation of RNA- and DNA-rich phases in the cell nucleus driven by the transcriptional activity of polymerase enzymes acting similarly to amphiphiles in a microemulsion. Here we propose an analytically tractable model of an active microemulsion to investigate how the activity affects its structure and relaxation dynamics. Continuum theory derived from a lattice model exhibits two distinct regimes of the relaxation dynamics and is linked to the broken detailed balance due to intermittent activity of the amphiphiles.

PMID:40758004 | DOI:10.1103/jfm6-8h9s

Biotechnol J. 2025 Aug;20(8):e70091. doi: 10.1002/biot.70091.

ABSTRACT

Caffeic acid, a high-value natural phenolic compound synthesized through plant metabolism, plays a critical role in producing phenylpropanoid derivatives and serves as a direct precursor to several key phenolic acids. As a food additive and medicine, caffeic acid has garnered significant attention for its potential in various applications. Recent advances in synthetic biology and metabolic engineering have enabled its biosynthesis via microbial cell factories. This review summarizes five strategies for optimizing caffeic acid production: caffeic acid biosynthetic pathway, modification of metabolic pathway, systems biology and synthetic biology, cofactor engineering, and modular co-culture. However, caffeic acid production via microbial chassis faces bottlenecks such as limited precursor availability for biosynthesis, toxicity from metabolic intermediates, inefficient cofactor utilization, and over-reliance on conventional host microorganisms. Breaking through these bottlenecks by integrating the five strategies outlined is expected to further increase caffeic acid production.

PMID:40757780 | DOI:10.1002/biot.70091

CPT Pharmacometrics Syst Pharmacol. 2025 Aug 4. doi: 10.1002/psp4.70096. Online ahead of print.

ABSTRACT

Drug development in pediatric rare diseases is complicated by practical and ethical constraints on clinical trial design, stemming from small, highly heterogeneous, and vulnerable patient populations. Virtual patients (VPs) created with machine-learning (ML), mechanistically driven computational approaches, or hybrids thereof, have the potential to expedite and maximize the impact of trials. We discuss the potential of VPs to transform the efficiency and impact of clinical trials in pediatric rare diseases, based on adult and pediatric examples.

PMID:40757668 | DOI:10.1002/psp4.70096

Physiol Mol Biol Plants. 2025 Jun;31(6):863-876. doi: 10.1007/s12298-025-01621-2. Epub 2025 Jul 17.

ABSTRACT

Saffron (Crocus sativus L.) is a sterile triploid medicinal plant and is the world's most expensive cultivated herb. Its dried red stigmas accumulate important carotenoids, which produce apocarotenoids after oxidative cleavage. Saffron produces important apocarotenoids, crocin, picrocrocin and safranal, that provide color, flavor and aroma to it. To understand the expression pattern and stage specificity of apocarotenoid biosynthesis genes, we performed RNA sequencing at six different stages of stigma development (yellow, orange, red, two days before anthesis, at the day of anthesis and two days after anthesis) using Illumina platform. Differential expression analysis revealed preferential/specific expression of many genes at the different stages of stigma development. Functional annotation identified many genes encoding enzymes involved in different steps of apocarotenoid biosynthesis pathways expressed preferentially at red and later stages of stigma development. In addition, gene ontology enrichment analysis revealed several genes involved in primary/secondary metabolic processes and reproductive development pathways, exhibiting higher transcript abundance at the later stages of stigma development. Overall, the data and results presented in this study can serve as a rich resource for understanding the apocarotenoid biosynthesis in C. sativus during stigma development.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12298-025-01621-2.

PMID:40756438 | PMC:PMC12314296 | DOI:10.1007/s12298-025-01621-2