World J Urol. 2025 Jan 10;43(1):65. doi: 10.1007/s00345-025-05440-8.

ABSTRACT

PURPOSE: To develop a deep learning (DL) model based on primary tumor tissue to predict the lymph node metastasis (LNM) status of muscle invasive bladder cancer (MIBC), while validating the prognostic value of the predicted aiN score in MIBC patients.

METHODS: A total of 323 patients from The Cancer Genome Atlas (TCGA) were used as the training and internal validation set, with image features extracted using a visual encoder called UNI. We investigated the ability to predict LNM status while assessing the prognostic value of aiN score. External validation was conducted on 139 patients from Renmin Hospital of Wuhan University (RHWU; Wuhan, China).

RESULTS: The DL model achieved area under the receiver operating characteristic curves of 0.79 (95% confidence interval [CI], 0.69-0.88) in the internal validation set for predicting LNM status, and 0.72 (95% CI, 0.68-0.75) in the external validation set. In multivariable Cox analysis, the model-predicted aiN score emerged as an independent predictor of survival for MIBC patients, with a hazard ratio of 1.608 (95% CI, 1.128-2.291; p = 0.008) in the TCGA cohort and 2.746 (95% CI, 1.486-5.076; p < 0.001) in the RHWU cohort. Additionally, the aiN score maintained prognostic value across different subgroups.

CONCLUSION: In this study, DL-based image analysis showed promising results by directly extracting relevant prognostic information from H&E-stained histology to predict the LNM status of MIBC patients. It might be used for personalized management of MIBC patients following prospective validation in the future.

PMID:39792275 | DOI:10.1007/s00345-025-05440-8

Eur Radiol. 2025 Jan 10. doi: 10.1007/s00330-024-11317-y. Online ahead of print.

ABSTRACT

OBJECTIVES: To evaluate the image quality and lung nodule detectability of ultralow-dose CT (ULDCT) with adaptive statistical iterative reconstruction-V (ASiR-V) post-processed using a deep learning image reconstruction (DLIR)-based image domain compared to low-dose CT (LDCT) and ULDCT without DLIR.

MATERIALS AND METHODS: A total of 210 patients undergoing lung cancer screening underwent LDCT (mean ± SD, 0.81 ± 0.28 mSv) and ULDCT (0.17 ± 0.03 mSv) scans. ULDCT images were reconstructed with ASiR-V (ULDCT-ASiR-V) and post-processed using DLIR (ULDCT-DLIR). The quality of the three CT images was analyzed. Three radiologists detected and measured pulmonary nodules on all CT images, with LDCT results serving as references. Nodule conspicuity was assessed using a five-point Likert scale, followed by further statistical analyses.

RESULTS: A total of 463 nodules were detected using LDCT. The image noise of ULDCT-DLIR decreased by 60% compared to that of ULDCT-ASiR-V and was lower than that of LDCT (p < 0.001). The subjective image quality scores for ULDCT-DLIR (4.4 [4.1, 4.6]) were also higher than those for ULDCT-ASiR-V (3.6 [3.1, 3.9]) (p < 0.001). The overall nodule detection rates for ULDCT-ASiR-V and ULDCT-DLIR were 82.1% (380/463) and 87.0% (403/463), respectively (p < 0.001). The percentage difference between diameters > 1 mm was 2.9% (ULDCT-ASiR-V vs. LDCT) and 0.5% (ULDCT-DLIR vs. LDCT) (p = 0.009). Scores of nodule imaging sharpness on ULDCT-DLIR (4.0 ± 0.68) were significantly higher than those on ULDCT-ASiR-V (3.2 ± 0.50) (p < 0.001).

CONCLUSION: DLIR-based image domain improves image quality, nodule detection rate, nodule imaging sharpness, and nodule measurement accuracy of ASiR-V on ULDCT.

KEY POINTS: Question Deep learning post-processing is simple and cheap compared with raw data processing, but its performance is not clear on ultralow-dose CT. Findings Deep learning post-processing enhanced image quality and improved the nodule detection rate and accuracy of nodule measurement of ultralow-dose CT. Clinical relevance Deep learning post-processing improves the practicability of ultralow-dose CT and makes it possible for patients with less radiation exposure during lung cancer screening.

PMID:39792163 | DOI:10.1007/s00330-024-11317-y

Eur Radiol. 2025 Jan 10. doi: 10.1007/s00330-024-11308-z. Online ahead of print.

ABSTRACT

OBJECTIVES: The use of deep learning models for quantitative measurements on coronary computed tomography angiography (CCTA) may reduce inter-reader variability and increase efficiency in clinical reporting. This study aimed to investigate the diagnostic performance of a recently updated deep learning model (CorEx-2.0) for quantifying coronary stenosis, compared separately with two expert CCTA readers as references.

METHODS: This single-center retrospective study included 50 patients that underwent CCTA to rule out obstructive coronary artery disease between 2017-2022. Two expert CCTA readers and CorEx-2.0 independently assessed all 150 vessels using Coronary Artery Disease-Reporting and Data System (CAD-RADS). Inter-reader agreement analysis and diagnostic performance of CorEx-2.0, compared with each expert reader as references, were evaluated using percent agreement, Cohen's kappa for the binary CAD-RADS classification (CAD-RADS 0-3 versus 4-5) at patient level, and linearly weighted kappa for the 6-group CAD-RADS classification at vessel level.

RESULTS: Overall, 50 patients and 150 vessels were evaluated. Inter-reader agreement using the binary classification at patient level was 91.8% (45/49) with a Cohen's kappa of 0.80. For the 6-group classification at vessel level, inter-reader agreement was 67.6% (100/148) with a linearly weighted kappa of 0.77. CorEx-2.0 showed 100% sensitivity for detecting CAD-RADS ≥ 4 and kappa values of 0.86 versus both readers using the binary classification at patient level. For the 6-group classification at vessel level, CorEx-2.0 demonstrated weighted kappa values of 0.71 versus reader 1 and 0.73 versus reader 2.

CONCLUSION: CorEx-2.0 identified all patients with severe stenosis (CAD-RADS ≥ 4) compared with expert readers and approached expert reader performance at vessel level (weighted kappa > 0.70).

KEY POINTS: Question Can deep learning models improve objectivity in coronary stenosis grading and reporting as coronary CT angiography (CTA) workloads rise? Findings The deep learning model (CorEx-2.0) identified all patients with severe stenoses when compared with expert readers and approached expert reader performance at vessel level. Clinical relevance CorEx-2.0 is a reliable tool for identifying patients with severe stenoses (≥ 70%), underscoring the potential of using this deep learning model to prioritize coronary CTA reading by flagging patients at risk of severe obstructive coronary artery disease.

PMID:39792162 | DOI:10.1007/s00330-024-11308-z

JAMA Health Forum. 2025 Jan 3;6(1):e244874. doi: 10.1001/jamahealthforum.2024.4874.

ABSTRACT

IMPORTANCE: The prevalence of pharmacies owned by integrated insurers and pharmacy benefit managers (PBMs), or insurer-PBMs, is of growing regulatory concern. However, little is known about the role of these pharmacies in Medicare, in which pharmacy network protections may influence market dynamics.

OBJECTIVE: To evaluate the prevalence of insurer-PBM-owned pharmacies and the extent to which insurer-PBMs steer patients to pharmacies they own in Medicare.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used Medicare Part D claims data on prescription fills for a 20% random sample of US beneficiaries enrolled from January 1 through December 31, 2021. Data were analyzed from March to November 2024.

EXPOSURES: Prescription fills.

MAIN OUTCOMES AND MEASURES: The main outcome was the share of spending filled by insurer-PBM-owned pharmacies overall, by pharmacy type (specialty and nonspecialty), and by drug class. For the top 100 specialty and nonspecialty molecules by claim volume, 2 quantities were identified for 4 major insurer-PBMs (Cigna, CVS, Humana, and UnitedHealth Group): share of the index firm's insurer claims filled by its owned pharmacies and share of other firms' insurer claims filled by the index firm's owned pharmacies. Differences between these quantities were assessed to evaluate the degree to which insurer-PBMs steered patients to their own pharmacies.

RESULTS: Among 10 455 726 patients (54.8% women; mean [SD] age, 71.8 [10.7] years), 34.1% of all pharmacy and 37.1% of specialty pharmacy spending occurred through Cigna, CVS, Humana, or UnitedHealth Group pharmacies. Among specialty molecules, market shares varied by drug class (antivirals: 18.5%; antipsychotics: 29.5%; cancer: 32.5%; disease-modifying antirheumatic drugs: 41.1%; multiple sclerosis: 64.8%; pulmonary arterial hypertension and idiopathic pulmonary fibrosis: 89.7%). Across molecule-firm combinations, a 19.8 (95% CI, 18.0-21.6)-percentage point and 13.9 (95% CI, 13.1-14.7)-percentage point greater share of claims were filled at insurer-PBM-owned pharmacies than would be expected without steering for specialty and nonspecialty categories, respectively.

CONCLUSIONS AND RELEVANCE: This cross-sectional study found that insurer-PBM firms represented an important portion of the Medicare Part D market, especially for certain drug classes, and that insurer-PBM firms steered patients to their own pharmacies, despite certain pharmacy network protections in Medicare. These findings underscore the need to understand the impacts of insurer-PBM and pharmacy integration on medication access and costs for Medicare patients.

PMID:39792403 | DOI:10.1001/jamahealthforum.2024.4874

ACS Nano. 2025 Jan 10. doi: 10.1021/acsnano.4c15130. Online ahead of print.

ABSTRACT

For idiopathic pulmonary fibrosis (IPF), interleukin 11 (IL-11) is a pivotal cytokine that stimulates the transformation of fibroblasts into myofibroblasts, thus accelerating the progression of pulmonary fibrosis. Here, we develop an innovative inhalable small interfering RNA (siRNA) delivery system termed PEI-GBZA, which demonstrates impressive efficiency in loading siIL-11 targeting IL-11 (siIL-11) and substantially suppresses the differentiation of fibroblasts into myofibroblasts and epithelial-mesenchymal transition (EMT), reduces neutrophil and macrophage recruitment, and ultimately relieves the established fibrotic lesions in the IPF model. PEI-GBZA is prepared by modifying low-molecular-weight polyethylenimine (PEI) with 4-guanidinobenzoic acid (GBZA). The resulting PEI-GBZA may effectively encapsulate siIL-11 through a variety of interactions such as hydrophobic, hydrogen bonding, and electrostatic interactions, creating stable carrier/siIL-11 nanoparticles (PEI-GBZA/siIL-11 NPs). Upon inhalation, PEI-GBZA/siIL-11 NPs demonstrate effective retention in fibrotic lesions, leading to a marked mitigation of disease progression in a bleomycin-induced pulmonary fibrosis model. Impressively, this inhalation therapy exhibits negligible systemic toxicity. This work provides a universal and noninvasive RNA therapeutic delivery platform that holds significant promise for respiratory diseases. The potential for clinical application of this platform is substantial, offering a frontier for the treatment of IPF and potentially other pulmonary disorders.

PMID:39791575 | DOI:10.1021/acsnano.4c15130

Cells. 2025 Jan 6;14(1):56. doi: 10.3390/cells14010056.

ABSTRACT

In neurons, the acquisition of a polarized morphology is achieved upon the outgrowth of a single axon from one of several neurites. Small extracellular vesicles (sEVs), such as exosomes, from diverse sources are known to promote neurite outgrowth and thus may have therapeutic potential. However, the effect of fibroblast-derived exosomes on axon elongation in neurons of the central nervous system under growth-permissive conditions remains unclear. Here, we show that fibroblast-derived sEVs promote axon outgrowth and a polarized neuronal morphology in mouse primary embryonic cortical neurons. Mechanistically, we demonstrate that the sEV-induced increase in axon outgrowth requires endogenous Wnts and core PCP components including Prickle, Vangl, Frizzled, and Dishevelled. We demonstrate that sEVs are internalized by neurons, colocalize with Wnt7b, and induce relocalization of Vangl2 to the distal axon during axon outgrowth. In contrast, sEVs derived from neurons or astrocytes do not promote axon outgrowth, while sEVs from activated astrocytes inhibit elongation. Thus, our data reveal that fibroblast-derived sEVs promote axon elongation through the Wnt-PCP pathway in a manner that is dependent on endogenous Wnts.

PMID:39791757 | DOI:10.3390/cells14010056

J Neuropsychiatry Clin Neurosci. 2025 Jan 10:appineuropsych20240072. doi: 10.1176/appi.neuropsych.20240072. Online ahead of print.

ABSTRACT

OBJECTIVE: Catatonia is a neuropsychiatric disorder that is associated with a range of medical and psychiatric illnesses. Although many single-center studies have been conducted, uncertainty over the population-based incidence and prevalence of the disorder remains. This study reports on the incidence and prevalence rates of catatonia extrapolated from two large epidemiologic studies in the United Kingdom and United States.

METHODS: Incidence rates (defined as the number of catatonic episodes per 100,000 person-years) and prevalence rates (defined as the proportion of individuals with catatonia in a given year) were calculated from the two studies.

RESULTS: U.K. data showed an incidence of 4.34 (95% CI=3.98-4.72) catatonic episodes per 100,000 person-years with an average 1-year prevalence of 4.39 (95% CI=4.03-4.77) catatonic episodes per 100,000 persons. U.S. data revealed a 1-year prevalence of 5.15 (95% CI=5.08-5.23) catatonia-related hospitalizations per 100,000 persons.

CONCLUSIONS: Catatonia is a rare disorder, qualifying as an orphan disease under both European Medicines Agency and U.S. Food and Drug Administration criteria. Further research is needed to rigorously define the epidemiology of catatonia in other populations.

PMID:39789943 | DOI:10.1176/appi.neuropsych.20240072

Clin Pharmacol Ther. 2025 Jan 9. doi: 10.1002/cpt.3545. Online ahead of print.

ABSTRACT

Pharmacogenetic testing can prevent severe toxicities from several oncology drug therapies; it also has the potential to improve the outcomes from supportive care drugs. Paired tumor and germline sequencing is increasingly common in oncology practice; these include sequencing of pharmacogenes, but the germline pharmacogenetic variants are rarely included in the clinical reports, despite many being clinically actionable. We established an informatics workflow to evaluate the clinical sequencing results for pharmacogenetic variants. We used the Aldy computational tool, which we have previously shown to determine the variant alleles in 14 pharmacogenes in clinical sequencing data with >99% accuracy, to identify pharmacogenetic variants in the clinical whole exome sequencing from our molecular tumor board. Patients with genetic variants that are clinically actionable for their individual therapy programs, including both treatment and supportive care, are referred to a clinical pharmacogenetics testing laboratory for confirmation. Through an evaluation of our weekly informatics workflow, we determined it took approximately 3.25 hours to complete the analysis of the sequencing data from approximately 20 patients. Using a United States pharmacist's median salary, we estimated the incremental added cost of the process to be only ~$15 per patient. This adds only a minor increase to the patient's cost of testing and has the potential to improve the safety and efficacy of their treatment.

PMID:39789831 | DOI:10.1002/cpt.3545

Hum Gene Ther. 2025 Jan 10. doi: 10.1089/hum.2024.215. Online ahead of print.

ABSTRACT

Cystic fibrosis (CF) is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR). While gene therapy holds promise as a cure, the cell-type-specific heterogeneity of CFTR expression in the lung presents significant challenges. Current CF ferret models closely replicate the human disease phenotype but have limitations in studying functional complementation through cell-type-specific CFTR restoration. To address this, we developed a new transgenic ferret line, CFTRint1-eGFP(lsl), in which a Cre-recombinase (Cre)-excisable enhanced fluorescent protein (eGFP) reporter cassette is knocked in (KI) to intron 1 of the CFTR locus. Breeding this reporter line with CFTRG551D CF ferret resulted in a novel CF model, CFTRint1-eGFP(lsl)/G551D, with disease onset manageable via the administration of CFTR modulator VX770. In this study, we confirmed two key properties of the CFTRint1-eGFP(lsl)/G551D CF ferrets: (1) cell-type-specific expression of the CFTR(N-24)-eGFP fusion protein, driven by the intrinsic CFTR promoter, in polarized epithelial cultures and selected tissues, and (2) functional reversion of the KI allele via Cre-mediated excision of the reporter cassette. This model provides a valuable tool for studying the effects of targeted CFTR reactivation in a cell-type-specific manner, which is crucial for enhancing our understanding of CFTR's roles in modulating airway clearance and innate immunity, and for identifying relevant cellular targets for CF gene therapy.

PMID:39791230 | DOI:10.1089/hum.2024.215

Am J Rhinol Allergy. 2025 Jan 10:19458924241311354. doi: 10.1177/19458924241311354. Online ahead of print.

ABSTRACT

BACKGROUND: Thymic stromal lymphopoietin (TSLP) plays an important role in mediating the type-2-inflammatory response. This study examined how TSLP and interleukin (IL)-4 levels in patients with chronic rhinosinusitis with nasal polyps (CRSwNP) correlated with clinical and postoperative outcomes.

METHODS: Solid-phase sandwich ELISA was used to analyze TSLP and IL-4 levels in mucus (n = 47), plasma (n = 17), polyp (n = 30), inferior (n = 25), and middle (n = 26) turbinate tissue collected during functional endoscopic sinus surgery (FESS) in CRSwNP patients (n = 76) and controls (n = 11). Inclusion criteria includes patients with medical treatment refractory CRSwNP confirmed by endoscopy or maxillofacial CT. Exclusion criteria include history of immunodeficiency, coagulation disorders, fungal sinusitis, or cystic fibrosis. Levels of TSLP and IL-4 were correlated with SNOT-22, UPSIT, and fractional exhaled nitric oxide (FeNO) using MannWhitney U two-tailed test and linear regression with Spearman correlation coefficient test.

RESULTS: TSLP is elevated in the inferior turbinates (effect size = 2.695, p = 0.0007) of CRSwNP patients compared to controls. IL-4 is expressed at elevated levels in the inferior (effect size = 3.092, p < 0.0001) and middle turbinates (effect size = 2.041, p = 0.019) compared to controls. Mucus TSLP (r = 0.4013, p = 0.0153) and IL-4 (r = 0.6138, p < 0.0001) positively correlate with preoperative FeNO levels. Lower TSLP in the inferior (r = -0.5179, p = 0.0231) and middle turbinates (r = -0.5075, p = 0.0224) and lower IL-4 in the inferior turbinates (r = -0.5205, p = 0.0223) correlate with a greater improvement in SNOT-22 post-FESS.

CONCLUSION: TSLP and IL-4 are elevated in patients with CRSwNP and correlated with increased preoperative FeNO levels and decreased sinonasal quality of life benefit after FESS. Expression of TSLP and IL-4 may play a role in guiding postoperative expectations in patients with treatment refractory CRSwNP.

PMID:39791191 | DOI:10.1177/19458924241311354

Med Image Comput Comput Assist Interv. 2024 Oct;15012:465-475. doi: 10.1007/978-3-031-72390-2_44. Epub 2024 Oct 23.

ABSTRACT

Recent advancements in Contrastive Language-Image Pre-training (CLIP) [21] have demonstrated notable success in self-supervised representation learning across various tasks. However, the existing CLIP-like approaches often demand extensive GPU resources and prolonged training times due to the considerable size of the model and dataset, making them poor for medical applications, in which large datasets are not always common. Meanwhile, the language model prompts are mainly manually derived from labels tied to images, potentially overlooking the richness of information within training samples. We introduce a novel language-image Contrastive Learning method with an Efficient large language model and prompt Fine-Tuning (CLEFT) that harnesses the strengths of the extensive pre-trained language and visual models. Furthermore, we present an efficient strategy for learning context-based prompts that mitigates the gap between informative clinical diagnostic data and simple class labels. Our method demonstrates state-of-the-art performance on multiple chest X-ray and mammography datasets compared with various baselines. The proposed parameter efficient framework can reduce the total trainable model size by 39% and reduce the trainable language model to only 4% compared with the current BERT encoder.

PMID:39791126 | PMC:PMC11709740 | DOI:10.1007/978-3-031-72390-2_44

Cureus. 2024 Dec 10;16(12):e75476. doi: 10.7759/cureus.75476. eCollection 2024 Dec.

ABSTRACT

This paper investigates the potential of artificial intelligence (AI) and machine learning (ML) to enhance the differentiation of cystic lesions in the sellar region, such as pituitary adenomas, Rathke cleft cysts (RCCs) and craniopharyngiomas (CP), through the use of advanced neuroimaging techniques, particularly magnetic resonance imaging (MRI). The goal is to explore how AI-driven models, including convolutional neural networks (CNNs), deep learning, and ensemble methods, can overcome the limitations of traditional diagnostic approaches, providing more accurate and early differentiation of these lesions. The review incorporates findings from critical studies, such as using the Open Access Series of Imaging Studies (OASIS) dataset (Kaggle, San Francisco, USA) for MRI-based brain research, highlighting the significance of statistical rigor and automated segmentation in developing reliable AI models. By drawing on these insights and addressing the challenges posed by small, single-institutional datasets, the paper aims to demonstrate how AI applications can improve diagnostic precision, enhance clinical decision-making, and ultimately lead to better patient outcomes in managing sellar region cystic lesions.

PMID:39791061 | PMC:PMC11717160 | DOI:10.7759/cureus.75476

Cardiovasc Diagn Ther. 2024 Dec 31;14(6):1161-1175. doi: 10.21037/cdt-24-409. Epub 2024 Dec 19.

ABSTRACT

BACKGROUND AND OBJECTIVE: Cardiovascular magnetic resonance (CMR) is a routine cross-sectional imaging modality in adults with congenital heart disease. Developing CMR techniques and the knowledge that CMR is well suited to assess long-term complications and to provide prognostic information for single ventricle (SV) patients makes CMR the ideal assessment tool for this patient cohort. Nevertheless, many of the techniques have not yet been incorporated into day-to-day practice. The aim of this review is to provide a comprehensive overview of CMR applications in SV patients together with recent scientific findings.

METHODS: Articles from 2009 to August 2024 retrieved from PubMed on CMR in SV patients were included. Case reports and non-English literature were excluded.

KEY CONTENT AND FINDINGS: CMR is essential for serial follow-up of SV patients and CMR-derived standard markers can improve patient management and prognosis assessment. Advanced CMR techniques likely will enhance our understanding of Fontan hemodynamics and are promising tools for a comprehensive patient evaluation and care.

CONCLUSIONS: There is increasing research that shows the advantages of CMR in Fontan patients. However, further research about the prognostic role of CMR in older Fontan patients and how new methods such as modeling and deep learning pipelines can be clinically implemented is warranted.

PMID:39790200 | PMC:PMC11707479 | DOI:10.21037/cdt-24-409

Neuroradiol J. 2025 Jan 9:19714009251313517. doi: 10.1177/19714009251313517. Online ahead of print.

ABSTRACT

This study evaluates the efficacy of deep learning models in identifying infarct tissue on computed tomography perfusion (CTP) scans from patients with acute ischemic stroke due to large vessel occlusion, specifically addressing the potential influence of varying noise reduction techniques implemented by different vendors. We analyzed CTP scans from 60 patients who underwent mechanical thrombectomy achieving a modified thrombolysis in cerebral infarction (mTICI) score of 2c or 3, ensuring minimal changes in the infarct core between the initial CTP and follow-up MR imaging. Noise reduction techniques, including principal component analysis (PCA), wavelet, non-local means (NLM), and a no denoising approach, were employed to create hemodynamic parameter maps. Infarct regions identified on follow-up diffusion-weighted imaging (DWI) within 48 hours were co-registered with initial CTP scans and refined with ADC maps to serve as ground truth for training a data-augmented U-Net model. The performance of this convolutional neural network (CNN) was assessed using Dice coefficients across different denoising methods and infarct sizes, visualized through box plots for each parameter map. Our findings show no significant differences in model accuracy between PCA and other denoising methods, with minimal variation in Dice scores across techniques. This study confirms that CNNs are adaptable and capable of handling diverse processing schemas, indicating their potential to streamline diagnostic processes and effectively manage CTP input data quality variations.

PMID:39789894 | DOI:10.1177/19714009251313517

Brief Bioinform. 2024 Nov 22;26(1):bbae705. doi: 10.1093/bib/bbae705.

ABSTRACT

Identifying the regulatory effects of noncoding variants presents a significant challenge. Recently, the accumulation of epigenomic profiling data in wheat has provided an opportunity to model the functional impacts of these variants. In this study, we introduce Language of Genome for Wheat (LOGOWheat), a deep learning-based tool designed to predict the regulatory effects of noncoding variants in wheat. LOGOWheat initially employs a self-attention-based, contextualized pretrained language model to acquire bidirectional representations of the unlabeled wheat reference genome. Epigenomic profiling data are also collected and utilized to fine-tune the model, enabling it to discern the regulatory code inherent in genomic sequences. The test results suggest that LOGOWheat is highly effective in predicting multiple chromatin features, achieving an average area under the receiver operating characteristic (AUROC) of 0.8531 and an average area under the precision-recall curve (AUPRC) of 0.7633. Two case studies illustrate and demonstrate the main functions provided by LOGOWheat: assigning scores and prioritizing causal variants within a given variant set and constructing a saturated mutagenesis map in silico to discover high-impact sites or functional motifs in a given sequence. Finally, we propose the concept of extracting potential functional variations from the wheat population by integrating evolutionary conservation information. LOGOWheat is available at http://logowheat.cn/.

PMID:39789857 | DOI:10.1093/bib/bbae705

Plant Commun. 2025 Jan 8:101240. doi: 10.1016/j.xplc.2025.101240. Online ahead of print.

ABSTRACT

In the face of climate change and the growing global population, there is an urgent need to accelerate the development of high-yielding crop varieties. To this end, vast amounts of genotype-to-phenotype data have been collected, and many machine learning (ML) models have been developed to predict phenotype from a given genotype. However, the requirement for high densities of single-nucleotide polymorphisms (SNPs) and the labor-intensive collection of phenotypic data are hampering the use of these models to advance breeding. Furthermore, recently developed genomic selection (GS) models such as deep learning (DL) are complicated and inconvenient for breeders to navigate and optimize within their breeding programs. Here, we present the development of an intelligent breeding platform named AutoGP (http://autogp.hzau.edu.cn), which integrates genotype extraction, phenotypic extraction, and GS models of genotype-to-phenotype within a user-friendly web interface. AutoGP has three main advantages over previously developed platforms: 1) we designed an efficient sequencing chip to identify high-quality, high-confidence SNPs throughout gene regulatory networks; 2) we developed a complete workflow for plant phenotypic extraction (such as plant height and leaf count) from smartphone-captured video; 3) we provided a broad model pool, allowing users to select from five ML models (SVM, XGBoost, GBDT, MLP, and RF) and four commonly used DL models (DeepGS, DLGWAS, DNNGP, and SoyDNGP). For the convenience of breeders, we employ datasets from the maize (Zea mays) CUBIC population as a case study to demonstrate the usefulness of AutoGP. We show that our genotype chips can effectively extract high-quality SNPs associated with the days to tasseling and plant height. The models present reliable predictive accuracy on different populations, which can provide effective guidance for breeders. Overall, AutoGP offers a practical solution to streamline the breeding process, enabling breeders to achieve more efficient and accurate genomic selection.

PMID:39789848 | DOI:10.1016/j.xplc.2025.101240

Expert Rev Proteomics. 2025 Jan 9. doi: 10.1080/14789450.2025.2451715. Online ahead of print.

ABSTRACT

INTRODUCTION: Molecular recognition features (MoRFs) are regions in protein sequences that undergo induced folding upon binding partner molecules. MoRFs are common in nature and can be predicted from sequences based on their distinctive sequence signatures.

AREAS COVERED: We overview twenty years of progress in the sequence-based prediction of MoRFs which resulted in the development of 25 predictors of MoRFs that interact with proteins, peptides and lipids. These methods range from simple discriminant analysis to sophisticated deep transformer networks that use protein language models. They generate relatively accurate predictions as evidenced by the results of a recently published community-driven assessment.

EXPERT OPINION: MoRFs prediction is a mature field of research that is poised to continue at a steady pace in the foreseeable future. We anticipate further expansion of the scope of MoRF predictions to additional partner molecules, such as nucleic acids, and continued use of recent machine learning advances. Other future efforts should concentrate on improving availability of MoRF predictions by releasing, maintaining and popularizing web servers and by depositing MoRF predictions to large databases of protein structure and function predictions. Furthermore, accurate MoRF predictions should be coupled with the equally accurate prediction and modeling of the resulting structures of complexes.

PMID:39789785 | DOI:10.1080/14789450.2025.2451715

Elife. 2025 Jan 10;14:e75393. doi: 10.7554/eLife.75393. Online ahead of print.

ABSTRACT

Proliferating animal cells maintain a stable size distribution over generations despite fluctuations in cell growth and division size. Previously, we showed that cell size control involves both cell size checkpoints, which delay cell cycle progression in small cells, and size-dependent regulation of mass accumulation rates (Ginzberg et al., 2018). While we previously identified the p38 MAPK pathway as a key regulator of the mammalian cell size checkpoint (S. Liu et al., 2018), the mechanism of size-dependent growth rate regulation has remained elusive. Here, we quantified global rates of protein synthesis and degradation in cells of varying sizes, both under unperturbed conditions and in response to perturbations that trigger size-dependent compensatory growth slowdown. We found that protein synthesis rates scale proportionally with cell size across cell cycle stages and experimental conditions. In contrast, oversized cells that undergo compensatory growth slowdown exhibit a superlinear increase in proteasome-mediated protein degradation, with accelerated protein turnover per unit mass, suggesting activation of the proteasomal degradation pathway. Both nascent and long-lived proteins contribute to the elevated protein degradation during compensatory growth slowdown, with long-lived proteins playing a crucial role at the G1/S transition. Notably, large G1/S cells exhibit particularly high efficiency in protein degradation, surpassing that of similarly sized or larger cells in S and G2, coinciding with the timing of the most stringent size control in animal cells. These results collectively suggest that oversized cells reduce their growth efficiency by activating global proteasome-mediated protein degradation to promote cell size homeostasis.

PMID:39791360 | DOI:10.7554/eLife.75393

Plant Direct. 2025 Jan 8;9(1):e70035. doi: 10.1002/pld3.70035. eCollection 2025 Jan.

ABSTRACT

Abscission is a tightly regulated process in which plants shed unnecessary, infected, damaged, or aging organs, as well as ripe fruits, through predetermined abscission zones in response to developmental, hormonal, and environmental signals. Despite its importance, the underlying mechanisms remain incompletely understood. This study highlights the deleterious effects of abscission on chloroplast ultrastructure in the cells of the tomato flower pedicel abscission zone, revealing spatiotemporal differential gene expression and key transcriptional networks involved in chloroplast vesiculation during abscission. Significant changes in chloroplast structure and vesicle formation were observed 8 and 14 h after abscission induction, coinciding with the differential expression of vesiculation-related genes, particularly with upregulation of Senescence-Associated Gene 12 (SAG12) and Chloroplast Vesiculation (CV). This suggests a possible vesicle transport of chloroplast degrading material for recycling by autophagy-independent senescence-associated vacuoles (SAVs) and CV-containing vesicles (CCVs). Ethylene signaling appears to be involved in the regulation of these processes, as treatment with a competitive inhibitor of ethylene action, 1-methylcyclopropene, delayed vesiculation, reduced the expression of SAG12, and increased expression of Curvature Thylakoid 1A (CURT1A). In addition, chloroplast vesiculation during abscission was associated with differential expression of photosynthesis-related genes, particularly those involved in light reactions, underscoring the possible functional impact of the observed structural changes. This work provides new insights into the molecular and ultrastructural mechanisms underlying abscission and offers potential new targets for agricultural or biotechnological applications.

PMID:39790709 | PMC:PMC11710935 | DOI:10.1002/pld3.70035

iScience. 2024 Dec 2;28(1):111510. doi: 10.1016/j.isci.2024.111510. eCollection 2025 Jan 17.

ABSTRACT

Chromothripsis, a hallmark of cancer, is characterized by extensive and localized DNA rearrangements involving one or a few chromosomes. However, its genome-wide frequency and characteristics in urothelial carcinoma (UC) remain largely unknown. Here, by analyzing single-regional and multi-regional whole-genome sequencing (WGS), we present the chromothripsis blueprint in 488 UC patients. Chromothripsis events exhibit significant intertumoral heterogeneity, being detected in 41% of UC patients, with an increase from 30% in non-muscle-invasive disease (Ta/1) to 53% in muscle-invasive disease (T2-4). The presence of chromothripsis correlates with an unstable cancer genome and poor clinical outcomes. Analysis of multi-regional WGS data from 52 patients revealed pronounced intratumoral heterogeneity with chromothripsis events detectable only in specific tumor regions rather than uniformly across all areas. Chromothripsis events evolve under positive selection and contribute to tumor dissemination. This study presents a comprehensive genome-wide chromothripsis landscape in UC, highlighting the significance of chromothripsis in UC development.

PMID:39790556 | PMC:PMC11714673 | DOI:10.1016/j.isci.2024.111510