Adv Sci (Weinh). 2025 Jan 30:e2413571. doi: 10.1002/advs.202413571. Online ahead of print.

ABSTRACT

DNA methylation plays a critical role in gene regulation, affecting cellular differentiation and disease progression, particularly in non-coding regions. However, predicting the epigenetic consequences of non-coding mutations at single-cell resolution remains a challenge. Existing tools have limited prediction capacity and struggle to capture dynamic, cell-type-specific regulatory changes that are crucial for understanding disease mechanisms. Here, Methven, a deep learning framework designed is presented to predict the effects of non-coding mutations on DNA methylation at single-cell resolution. Methven integrates DNA sequence with single-cell ATAC-seq data and models SNP-CpG interactions over 100 kbp genomic distances. By using a divide-and-conquer approach, Methven accurately predicts both short- and long-range regulatory interactions and leverages the pre-trained DNA language model for enhanced precision in classification and regression tasks. Methven outperforms existing methods and demonstrates robust generalizability to monocyte datasets. Importantly, it identifies CpG sites associated with rheumatoid arthritis, revealing key pathways involved in immune regulation and disease progression. Methven's ability to detect progressive epigenetic changes provides crucial insights into gene regulation in complex diseases. These findings demonstrate Methven's potential as a powerful tool for basic research and clinical applications, advancing this understanding of non-coding mutations and their role in disease, while offering new opportunities for personalized medicine.

PMID:39888214 | DOI:10.1002/advs.202413571

Oral Dis. 2025 Jan 30. doi: 10.1111/odi.15269. Online ahead of print.

ABSTRACT

OBJECTIVES: Utilizing a deep learning approach is an emerging trend to improve the efficiency of periodontitis diagnosis and classification. This study aimed to use an object detection model to automatically annotate the anatomic structure and subsequently classify the stages of radiographic bone loss (RBL).

MATERIALS AND METHODS: In all, 558 panoramic radiographs were cropped to 7359 pieces of individual teeth. The detection performance of the model was assessed using mean average precision (mAP), root mean squared error (RMSE). The classification performance was evaluated using accuracy, precision, recall, and F1 score. Additionally, receiver operating characteristic (ROC) curves and confusion matrices were presented, and the area under the ROC curve (AUC) was calculated.

RESULTS: The mAP was 0.88 when the difference between the ground truth and prediction was 10 pixels, and 0.99 when the difference was 25 pixels. For all images, the mean RMSE was 7.30 pixels. Overall, the accuracy, precision, recall, F1 score, and micro-average AUC of the prediction were 0.72, 0.76, 0.64, 0.68, and 0.79, respectively.

CONCLUSIONS: The current model is reliable in assisting with the detection and staging of radiographic bone levels.

PMID:39888112 | DOI:10.1111/odi.15269

Mol Inform. 2025 Feb;44(2):e202400039. doi: 10.1002/minf.202400039.

ABSTRACT

Currently, numerous metrics allow chemists and computational chemists to refine and filter libraries of virtual molecules in order to prioritize their synthesis. Some of the most commonly used metrics and models are QSAR models, docking scores, diverse druggability metrics, and synthetic feasibility scores to name only a few. To our knowledge, among the known metrics, a function which estimates the price of a novel virtual molecule and which takes into account the availability and price of starting materials has not been considered before in literature. Being able to make such a prediction could improve and accelerate the decision-making process related to the cost-of-goods. Taking advantage of recent advances in the field of Computer Aided Synthetic Planning (CASP), we decided to investigate if the predicted retrosynthetic pathways of a given molecule and the prices of its associated starting materials could be good features to predict the price of that compound. In this work, we present a deep learning model, RetroPriceNet, that predicts the price of molecules using their predicted synthetic pathways. On a holdout test set, the model achieves better performance than the state-of-the-art model. The developed approach takes into account the synthetic feasibility of molecules and the availability and prices of the starting materials.

PMID:39887833 | DOI:10.1002/minf.202400039

J Magn Reson Imaging. 2025 Jan 29. doi: 10.1002/jmri.29682. Online ahead of print.

ABSTRACT

BACKGROUND: Conventional quantitative MRI (qMRI) scan is time-consuming and highly sensitive to movements, posing great challenges for quantitative images of individuals with involuntary movements, such as Huntington's disease (HD).

PURPOSE: To evaluate the potential of our developed ultra-fast qMRI technique, multiple overlapping-echo detachment (MOLED), in overcoming involuntary head motion and its capacity to quantitatively assess tissue changes in HD.

STUDY TYPE: Prospective.

PHANTOM/SUBJECTS: A phantom comprising 13 tubes of MnCl2 at varying concentrations, 5 healthy volunteers (male/female: 1/4), 22 HD patients (male/female: 14/8) and 27 healthy controls (male/female: 15/12).

FIELD STRENGTH/SEQUENCE: 3.0 T. MOLED-T2 sequence, MOLED-T2* sequence, T2-weighted spin-echo sequence, T1-weighted gradient echo sequence, and T2-dark-fluid sequence.

ASSESSMENT: T1-weighted images were reconstructed into high-resolution images, followed by segmentation to delineate regions of interest (ROIs). Subsequently, the MOLED T2 and T2* maps were aligned with the high-resolution images, and the ROIs were transformed into the MOLED image space using the transformation matrix and warp field. Finally, T2 and T2* values were extracted from the MOLED relaxation maps.

STATISTICAL TESTS: Bland-Altman analysis, independent t test, Mann-Whitney U test, Pearson correlation analysis, and Spearman correlation analysis, P < 0.05 was considered statistically significant.

RESULTS: MOLED-T2 and MOLED-T2* sequences demonstrated good accuracy (Meandiff = - 0.20%, SDdiff = 1.05%, and Meandiff = -1.73%, SDdiff = 10.98%, respectively), and good repeatability (average intraclass correlation coefficient: 0.856 and 0.853, respectively). More important, MOLED T2 and T2* maps remained artifact-free across all HD patients, even in the presence of apparent head motions. Moreover, there were significant differences in T2 and T2* values across multiple ROIs between HD and controls.

DATA CONCLUSION: The ultra-fast scanning capabilities of MOLED effectively mitigate the impact of head movements, offering a robust solution for quantitative imaging in HD. Moreover, T2 and T2* values derived from MOLED provide powerful capabilities for quantifying tissue changes.

PLAIN LANGUAGE SUMMARY: Quantitative MRI scan is time-consuming and sensitive to movements. Consequently, obtaining quantitative images is challenging for patients with involuntary movements, such as those with Huntington's Disease (HD). In response, a newly developed MOLED technique has been introduced, promising to resist motion through ultra-fast scan. This technique has demonstrated excellent accuracy and reproducibility and importantly all HD patient's MOLED maps remained artifacts-free. Additionally, there were significant differences in T2 and T2∗ values across ROIs between HD and controls. The robust resistance of MOLED to motion makes it particularly suitable for quantitative assessments in patients prone to involuntary movements.

LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

PMID:39887812 | DOI:10.1002/jmri.29682

Med Phys. 2025 Jan 30. doi: 10.1002/mp.17651. Online ahead of print.

ABSTRACT

BACKGROUND: Deep learning image reconstruction (DLIR) algorithms allow strong noise reduction while preserving noise texture, which may potentially improve hypervascular focal liver lesions.

PURPOSE: To assess the impact of DLIR on image quality (IQ) and detectability of simulated hypervascular hepatocellular carcinoma (HCC) in fast kV-switching dual-energy CT (DECT).

METHODS: An anthropomorphic phantom of a standard patient morphology (body mass index of 23 kg m-2) with customized liver, including mimickers of hypervascular lesions in both late arterial phase (AP) and portal venous phase (PVP) enhancement, was scanned on a DECT. Virtual monoenergetic images were reconstructed from raw data at four energy levels (40/50/60/70 keV) using filtered back-projection (FBP), adaptive statistical iterative reconstruction-V 50% and 100% (ASIRV-50 and ASIRV-100), DLIR low (DLIR-L), medium (DLIR-M), and high (DLIR-H). The contrast between the lesion and the liver parenchyma, the noise magnitude, the average and peak frequencies (favg and fpeak) of the noise power spectrum (NPS) reflecting noise texture, and the task-based measure of the modulation transfer function (MTFtask) were measured to evaluate spatial resolution. A detectability index (d') was computed to model the detection of hypervascular lesions in both AP and PVP. Metrics were compared between reconstructions and between energy levels using a Friedman test with follow-up post-hoc multiple comparison.

RESULTS: Lesion-to-liver contrast significantly increased with decreasing energy level in both AP and PVP (p ≤ 0.042) but was not affected by reconstruction algorithm (p ≥ 0.57). Overall, noise magnitude increased with decreasing energy levels and was the lowest with ASIRV-100 at all energy levels in both AP and PVP (p ≤ 0.01) and significantly lower with DLIR-M and DLIR-H reconstructions compared to ASIRV-50 and DLIR-L (p < 0.001). For all reconstructions, noise texture within the liver tended to get smoother with decreasing energy; favg significantly shifted towards lower frequencies from 70 to 40 keV (p ≤ 0.01). Noise texture was the smoothest with ASIRV-100 (p < 0.001) while DLIR-L had the noise texture closer to the one of FBP. The spatial resolution was not significantly affected by the energy level, but it was degraded when increasing the level of ASIRV and DLIR. For all reconstructions, the detectability indices increased with decreasing energy levels and peaked at 40 and 50 keV in AP and PVP, respectively. In both AP and PVP, the highest d' values were observed with ASIRV-100 and DLIR-H, whatever the energy level studied (p ≤ 0.01) without statistical significance between those two reconstructions.

CONCLUSIONS: Compared to the routinely used level of iterative reconstruction, DLIR reduces noise without consequential noise texture modification, and may improve the detectability of hypervascular liver lesions while enabling the use of lower energy virtual monoenergetic images. The optimal energy level and DLIR level may depend on the lesion enhancement.

PMID:39887750 | DOI:10.1002/mp.17651

Clin Transl Med. 2025 Feb;15(2):e70217. doi: 10.1002/ctm2.70217.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a fibrotic disease driven by both environmental and genetic factors. Epigenetics refers to changes in gene expression or cellular phenotype that do not involve alterations to DNA sequence. KMT2A is a member of the SET family which catalyses H3K4 methylation.

RESULTS: Through microarray and single-cell sequencing data, we discovered KMT2A-positive fibroblasts were increased in IPF lung tissues. KMT2A level was increased in IPF and bleomycin-induced pulmonary fibrosis mice lung tissues collected in our centre. Mice with AAV6-induced KMT2A knockdown in fibroblast showed attenuated pulmonary fibrosis after bleomycin treatment. Bioinformation also revealed that transcription factor PU.1 was a target of KMT2A. We demonstrated that PU.1 levels were increased in IPF tissues, bleomycin-induced mice lung tissues and primary fibrotic fibroblasts. KMT2A knockdown decreases PU.1 expression in vitro while KMT2A overexpression induces PU.1 activation. PU.1 fibroblast-specific knockout mice showed attenuated lung fibrosis induced by bleomycin. Furthermore, we demonstrated KMT2A up-regulated PU.1 in fibroblasts by catalysing H3K4me3 at the promoter of the PU.1 gene. The KMT2A transcription complex inhibitor mm102 treatment attenuated bleomycin-induced pulmonary fibrosis.

CONCLUSION: The current study indicated that histone modification participates in the pathogenesis of IPF and KMT2A may have the potential to be a therapeutic target of IPF treatment.

KEY POINTS: KMT2A plays a role in pulmonary fibrogenesis. KMT2A regulates PU.1 transcription in fibroblasts through H3K4me3 at promoter. KMT2A inhibitor attenuates pulmonary fibrosis in mice.

PMID:39888275 | DOI:10.1002/ctm2.70217

ChemMedChem. 2025 Jan 30:e202401001. doi: 10.1002/cmdc.202401001. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a progressive and chronic interstitial lung disease characterized by irreversible loss of lung function and a poor prognosis. Type I collagen, a major component of the extracellular matrix, plays a central role in the pathogenesis of fibrosis and is considered a key molecular target for therapeutic intervention. While current anti-fibrotic therapies demonstrate limited efficacy in slowing disease progression, their clinical impact remains suboptimal due to poor pharmacokinetic properties and non-curative therapy. Moreover, the development of effective anti-fibrotic agents targeting collagen synthesis is hindered by the absence of robust, cost-effective, high-throughput drug screening platforms. In this study, we established a novel screening system designed to identify small molecules that inhibit the expression of the COL1A2 gene, which encodes type I collagen. Utilizing this system, we screened a library of natural and synthetic compounds developed at Nagasaki University and identified lamellarin D as a potent inhibitor of COL1A2 expression and subsequent type I collagen production. These findings suggest that lamellarin D, through its unique molecular mechanism, may serve as the foundation for the development of a new class of IPF treatments aimed at targeting the underlying fibrotic processes.

PMID:39887929 | DOI:10.1002/cmdc.202401001

Proteomics. 2025 Jan 31:e202400109. doi: 10.1002/pmic.202400109. Online ahead of print.

NO ABSTRACT

PMID:39888210 | DOI:10.1002/pmic.202400109

New Phytol. 2025 Jan 31. doi: 10.1111/nph.20398. Online ahead of print.

ABSTRACT

HIGH EXPRESSION OF OSMOTICALLY RESPONSIVE GENE 15 (HOS15) acts as a substrate receptor of E3 ligase complex, which plays a negative role in drought stress tolerance. However, whether and how HOS15 participates in controlling important transcriptional regulators remains largely unknown. Here, we report that HOS15 physically interacts with and tightly regulates DROUGHT-INDUCED LIKE 19 (DIL9) protein stability. Moreover, application of exogenous abscisic acid (ABA) stabilizes the interaction between DIL9 and HOS15, leading to ABA-induced proteasomal degradation of DIL9 by HOS15. Genetic analysis revealed that DIL9 functions downstream to HOS15 and that the drought tolerance of hos15-2 plants was impaired in dil9/hos15 double mutants. Notably, DIL9 is directly associated with the promoter regions of ABF transcription factors and facilitates their expression, which is pivotal in enhancing ABA-dependent drought tolerance. Collectively, these findings demonstrate that HOS15 consistently degrades DIL9 under normal condition, while stress (drought/ABA) promotes the DIL9 activity for binding to the promoter regions of ABFs and positively regulates their expression in response to dehydration.

PMID:39888052 | DOI:10.1111/nph.20398

J Fish Dis. 2025 Jan 31:e14093. doi: 10.1111/jfd.14093. Online ahead of print.

ABSTRACT

White tail disease in Macrobrachium rosenbergii is caused by M. rosenbergii nodavirus (MrNV) infection, resulting in up to 100% mortality in larvae and post-larvae stages, severely impacting aquaculture production. Existing genome-based detection methods for MrNV are costly and time-consuming, highlighting the need for rapid and cost-effective diagnostic tests. This study evaluated the effects of truncating selected aptamer on its binding affinity to the MrNV capsid protein. The previously isolated and identified aptamer through magnetic-capture SELEX and Next Generation Sequencing demonstrated high binding affinity to the MrNV capsid protein. Truncation at the primer overhang was found to improve binding affinity, reducing the dissociation constant from 347 nM to 30.1 nM. The calculated limit of detection for the truncated aptamer decreased from 5.64 nM to 1.7 nM, while the limit of quantification decreased from 17.1 nM to 5.16 nM. These reductions indicate that the truncated aptamer has higher sensitivity compared to the full-length aptamer. In tests with MrNV-infected M. rosenbergii samples, both the enzyme-linked aptamer assay and the gold nanoparticle aptasensor assay showed consistent results when 0.5 μg of total protein lysate was used. This indicates that the prawn protein concentration interferes with the detection of the viral protein. These findings suggest the potential application of the truncated aptamer as a sensor in the development of a practical aptamer-based diagnostic kit. For instance, an aptamer-based lateral flow assay test kit could provide a user-friendly, cost-effective solution that eliminates the need for sophisticated instrumentation for diagnosis or data interpretation, making it ideal for detecting MrNV infection in M. rosenbergii aquaculture.

PMID:39887434 | DOI:10.1111/jfd.14093

Expert Opin Drug Saf. 2025 Jan 31. doi: 10.1080/14740338.2024.2446414. Online ahead of print.

ABSTRACT

OBJECTIVES: Medroxyprogesterone acetate (MPA), a steroid progesterone, is widely used to treat endometriosis, menstrual disorders, and uterine bleeding in clinical practice. However, the safety profile of MPA requires comprehensive evaluation.

METHODS: This study performed a retrospective analysis using real-world data extracted from the US Food and Drug Administration Adverse Event Reporting System (FAERS) database. Case reports from 2003 to 2023 were analyzed using methods like reporting advantage ratio (ROR), proportional report ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and empirical Bayes geometric mean (EBGM).

RESULTS: In the case reports spanning from 2003 to 2023, showed 26,437 adverse events (AEs) related to MPA, mostly in females (25,639). Disproportionality analysis identified 116 ADRs across 19 system organ class (SOC) levels, including expected AEs like 'female breast cancer'(n = 8717) and 'ovarian cancer' (n = 459). Unexpected AEs, such as 'acquired diaphragmatic eventration'(n = 3), were also noted.

CONCLUSION: Our study identifies potential new and unexpected ADR signals linked to MPA, which align with clinical observations. Additional research is necessary to confirm these associations and address previously unrecognized safety concerns. This research provides a novel and distinctive approach to exploring drug-related AEs.

PMID:39888632 | DOI:10.1080/14740338.2024.2446414

Int J Clin Pharm. 2025 Jan 31. doi: 10.1007/s11096-025-01867-6. Online ahead of print.

ABSTRACT

BACKGROUND: Drug-induced hypofibrinogenemia has received increasing scrutiny; however, the specific drugs involved remain poorly characterized. Hypofibrinogenemia can have significant clinical implications, including increased bleeding risks.

AIM: This study aimed to utilize the FDA Adverse Event Reporting System (FAERS) to identify and analyze drugs frequently implicated in drug-induced hypofibrinogenemia.

METHOD: A disproportionality analysis was conducted using FAERS data from January 2004 to March 2024. Various statistical tools were used, including the Reporting Odds Ratio (ROR), Proportional Reporting Ratio, Medicines and Healthcare Products Regulatory Agency metrics, and Bayesian confidence propagation neural network.

RESULTS: The analysis included 17,627,340 cases involving 52,373,206 adverse events, with 1,661 cases identified as hypofibrinogenemia. The top five drugs associated with hypofibrinogenemia by case number were methotrexate (124 cases), tigecycline (119 cases), tocilizumab (100 cases), pegaspargase (83 cases), and alteplase (57 cases). The drugs ranked by signal strength based on ROR included eravacycline (ROR 2173.84, 95% CI 1208.80-3909.30), tigecycline (ROR 747.34, 95% CI 619.03-902.24), crotalidae polyvalent immune Fab (ROR 407.67, 95% CI 291.07-570.99), pegaspargase (ROR 216.06, 95% CI 173.15-269.61), and asparaginase (ROR 184.93, 95% CI 132.18-258.72).

CONCLUSION: This analysis of FAERS data identified 52 drugs associated with hypofibrinogenemia, most (88.5%) of which do not mention this risk in their prescribing information. These findings demonstrate the need for the monitoring of blood fibrinogen and may serve as a reference for the explore of the characteristics and underlying mechanism of drug-induced hypofibrinogenemia in the real world.

PMID:39888490 | DOI:10.1007/s11096-025-01867-6

J Int AIDS Soc. 2025 Feb;28(2):e26414. doi: 10.1002/jia2.26414.

ABSTRACT

INTRODUCTION: Elvitegravir/cobicistat/emtricitabine/tenofovir alafenamide (E/C/F/TAF) was efficacious and well tolerated in children/adolescents with HIV (aged ≥6 years, weighing ≥25 kg) in a Phase 2/3 study. Here, we report data from children aged ≥2 years and weighing ≥14-<25 kg.

METHODS: This is an analysis of data from the youngest cohort in an open-label, multicentre, multi-cohort, single-group, international study of children/adolescents with HIV. Participants in this cohort were children aged ≥2 years, weighing ≥14-<25 kg at screening and able to swallow tablets, on stable antiretroviral therapy with virologic suppression (HIV-1 RNA <50 copies/ml for ≥6 consecutive months) and a CD4 count ≥400 cells/µl. Eligible participants received low-dose E/C/F/TAF (90/90/120/6 mg) once daily through Week 48. The study included pharmacokinetic evaluation of the low-dose E/C/F/TAF tablet at Week 2. Safety, efficacy, palatability and acceptability were also evaluated.

RESULTS: Between 16 January and 25 November 2019, 27 participants were enrolled with a median (quartile [Q]1, Q3) age of 6 (4, 8) years, body weight of 19.3 (17.0, 20.5) kg, CD4 count of 1061 (895, 1315) cells/µl and CD4 cell percentage of 37.4 (30.6, 40.3). Most (92.6%) participants acquired HIV through vertical transmission. On 6 October 2020 (data-cut), median (Q1, Q3) exposure to E/C/F/TAF was 48.3 (48.0, 60.1) weeks. Pharmacokinetic parameters were within the safe and efficacious range of previous data in adult and paediatric populations. Drug-related treatment-emergent adverse events occurred in 4/27 (15%) participants. There were no Grade 3/4 adverse events, or adverse events leading to E/C/F/TAF discontinuation. One participant experienced a serious treatment-emergent adverse event (Grade 2 pneumonia not considered E/C/F/TAF related). Virologic suppression (US FDA Snapshot algorithm) was maintained by 26/27 (96%) participants at Weeks 24 and 48. At Week 48, most children reported positive palatability (84.6%) and acceptability (96.2%).

CONCLUSIONS: These data support the use of single-tablet E/C/F/TAF (90/90/120/6 mg) regimen for the treatment of HIV in children aged ≥2 years and weighing ≥14-<25 kg.

CLINICAL TRIAL NUMBER: NCT01854775.

PMID:39888251 | DOI:10.1002/jia2.26414

Front Cell Infect Microbiol. 2025 Jan 16;14:1496176. doi: 10.3389/fcimb.2024.1496176. eCollection 2024.

ABSTRACT

BACKGROUND: Drug repurposing has become a widely adopted strategy to minimise research time, costs, and associated risks. Combinations of protease inhibitors such as lopinavir and darunavir with ritonavir have been repurposed as treatments for COVID-19. Although lopinavir-ritonavir (LPV/r) and darunavir-ritonavir (DRV/r) have shown in vitro efficacy against COVID-19, the results in human studies have been inconsistent. Therefore, our objective was to compare the efficacy of LPV/r and DRV/r in COVID-19 patients admitted to a tertiary centre in Romania.

RESEARCH DESIGN AND METHODS: A clinical dataset from 417 hospitalised patients was analysed. Patients were assigned to the LPV/r, DRV/r, or control (standard-of-care) group based on clinical decisions made by the attending infectious disease specialists, aligned with national treatment protocols. Kaplan-Meier and Cox proportional hazards regression analyses were conducted to compare in-hospital mortality and to identify factors associated with clinical improvement or fatal outcomes.

RESULTS: By day 10, more patients showed improvement with LPV/r and DRV/r (p=0.03 and 0.01, respectively), but only LPV/r was associated with improved survival compared to the control group (p=0.05). Factors associated with mortality included male gender (HR: 3.63, p=0.02), diabetes (HR: 2.49, p=0.03), oxygen saturation below 90% at admission (HR: 5.23, p<0.01), high blood glucose levels (HR: 3.68, p=0.01), age (HR: 1.04, p=0.02), and more than 25% lesion extension on chest CT scan (HR: 2.28, p=0.03).

CONCLUSIONS: LPV/r, but not DRV/r, showed a survival benefit in patients hospitalised with COVID-19, but these findings deserve further investigation in a randomised clinical trial.

PMID:39885967 | PMC:PMC11779713 | DOI:10.3389/fcimb.2024.1496176

J Law Med Ethics. 2024;52(4):940-949. doi: 10.1017/jme.2024.171. Epub 2025 Jan 31.

ABSTRACT

To help academic and non-profit investigators interested in drug repurposing navigate regulatory approval processes, we compared pathways for repurposed drugs to obtain approval at EMA, UK MHRA, and the US FDA. Though we found no pathways specifically for repurposed drugs, pathways to market are available in all repurposing scenarios.

PMID:39885757 | DOI:10.1017/jme.2024.171

Expert Rev Clin Immunol. 2025 Jan 31. doi: 10.1080/1744666X.2025.2461584. Online ahead of print.

ABSTRACT

INTRODUCTION: Inflammatory bowel diseases (IBDs), comprised of ulcerative colitis (UC) and Crohn's disease (CD), are chronic inflammatory diseases of the gastrointestinal tract. Clinicians and patients must vigilantly manage these complex diseases over the course of the patient's lifetime to mitigate risks of the disease, surgical complications, progression to neoplasia, and complications from medical or surgical therapies. Over the past several decades, the armamentarium of IBD therapeutics has expanded; now with biologics and advanced small molecules complementing conventional drugs such as aminosalicylates, corticosteroids and thiopurines. Significant attention has been paid to the potential of precision medicine to assist clinicians in tailoring therapeutics based on patients' genetic signatures to maximize therapeutic benefit while minimizing adverse effects.

AREAS COVERED: In this paper, we review the published literature on genetic polymorphisms relevant to each class of IBD therapeutics.

EXPERT OPINION: Finally, we envision a paradigm shift in IBD research toward an omics-based network analysis approach. Through global collaboration, organization and goal setting, we predict the next decade of IBD research will revolutionize existing disease frameworks by developing precise molecular diagnoses, validated biomarkers, predictive models and novel molecularly targeted therapeutics.

PMID:39885730 | DOI:10.1080/1744666X.2025.2461584

Front Plant Sci. 2025 Jan 16;15:1442686. doi: 10.3389/fpls.2024.1442686. eCollection 2024.

ABSTRACT

The stomatal phenotype is a crucial microscopic characteristic of the leaf surface, and modulating the stomata of maize leaves can enhance photosynthetic carbon assimilation and water use efficiency, thereby playing a vital role in maize yield formation. The evolving imaging and image processing technologies offer effective tools for precise analysis of stomatal phenotypes. This study employed Jingnongke 728 and its parental inbred to capture stomatal images from various leaf positions and abaxial surfaces during key reproductive stages using rapid scanning electron microscopy. We uesd a target detection and image segmentation approach based on YOLOv5s and Unet to efficiently obtain 11 phenotypic traits encompassing stomatal count, shape, and distribution. Manual validation revealed high detection accuracies for stomatal density, width, and length, with R2 values of 0.92, 0.97, and 0.95, respectively. Phenotypic analyses indicated a significant positive correlation between stomatal density and the percentage of guard cells and pore area (r=0.36), and a negative correlation with stomatal area and subsidiary cell area (r=-0.34 and -0.46). Additionally, stomatal traits exhibited notable variations with reproductive stages and leaf layers. Specifically, at the monocot scale, stomatal density increased from 74.35 to 87.19 Counts/mm2 from lower to upper leaf layers. Concurrently, the stomatal shape shifted from sub-circular (stomatal roundness = 0.64) to narrow and elongated (stomatal roundness = 0.63). Throughout the growth cycle, stomatal density remained stable during vegetative growth, decreased during reproductive growth with smaller size and narrower shape, and continued to decline while increasing in size and tending towards a rounded shape during senescence. Remarkably, hybrid 728 differed notably from its parents in stomatal phenotype, particularly during senescence. Moreover, the stomatal density of the hybrids showed negative super parental heterosis (heterosis rate = -0.09), whereas stomatal dimensions exhibited positive super parental heterosis, generally resembling the parent MC01. This investigation unveils the dynamic variations in maize stomatal phenotypes, bolstering genetic analyses and targeted improvements in maize, and presenting a novel technological instrument for plant phenotype studies.

PMID:39886688 | PMC:PMC11779725 | DOI:10.3389/fpls.2024.1442686

Front Plant Sci. 2025 Jan 16;15:1457360. doi: 10.3389/fpls.2024.1457360. eCollection 2024.

ABSTRACT

With the rapid advancement of plant phenotyping research, understanding plant genetic information and growth trends has become crucial. Measuring seedling length is a key criterion for assessing seed viability, but traditional ruler-based methods are time-consuming and labor-intensive. To address these limitations, we propose an efficient deep learning approach to enhance plant seedling phenotyping analysis. We improved the DeepLabv3+ model, naming it DFMA, and introduced a novel ASPP structure, PSPA-ASPP. On our self-constructed rice seedling dataset, the model achieved a mean Intersection over Union (mIoU) of 81.72%. On publicly available datasets, including Arabidopsis thaliana, Brachypodium distachyon, and Sinapis alba, detection scores reached 87.69%, 91.07%, and 66.44%, respectively, outperforming existing models. The model generates detailed segmentation masks, capturing structures such as the embryonic shoot, axis, and root, while a seedling length measurement algorithm provides precise parameters for component development. This approach offers a comprehensive, automated solution, improving phenotyping analysis efficiency and addressing the challenges of traditional methods.

PMID:39886686 | PMC:PMC11779734 | DOI:10.3389/fpls.2024.1457360

Front Plant Sci. 2025 Jan 16;15:1458978. doi: 10.3389/fpls.2024.1458978. eCollection 2024.

ABSTRACT

Hyperspectral images are rich in spectral and spatial information, providing a detailed and comprehensive description of objects, which makes hyperspectral image analysis technology essential in intelligent agriculture. With various corn seed varieties exhibiting significant internal structural differences, accurate classification is crucial for planting, monitoring, and consumption. However, due to the large volume and complex features of hyperspectral corn image data, existing methods often fall short in feature extraction and utilization, leading to low classification accuracy. To address these issues, this paper proposes a spectral-spatial attention transformer network (SSATNet) for hyperspectral corn image classification. Specifically, SSATNet utilizes 3D and 2D convolutions to effectively extract local spatial, spectral, and textural features from the data while incorporating spectral and spatial morphological structures to understand the internal structure of the data better. Additionally, a transformer encoder with cross-attention extracts and refines feature information from a global perspective. Finally, a classifier generates the prediction results. Compared to existing state-of-the-art classification methods, our model performs better on the hyperspectral corn image dataset, demonstrating its effectiveness.

PMID:39886680 | PMC:PMC11781253 | DOI:10.3389/fpls.2024.1458978

Proc IEEE Int Symp Biomed Imaging. 2024 May;2024. doi: 10.1109/ISBI56570.2024.10635138. Epub 2024 Aug 22.

ABSTRACT

Physics-driven deep learning (PD-DL) methods have gained popularity for improved reconstruction of fast MRI scans. Though supervised learning has been used in early works, there has been a recent interest in unsupervised learning methods for training PD-DL. In this work, we take inspiration from statistical image processing and compressed sensing (CS), and propose a novel convex loss function as an alternative learning strategy. Our loss function evaluates the compressibility of the output image while ensuring data fidelity to assess the quality of reconstruction in versatile settings, including supervised, unsupervised, and zero-shot scenarios. In particular, we leverage the reweighted l 1 norm that has been shown to approximate the l 0 norm for quality evaluation. Results show that the PD-DL networks trained with the proposed loss formulation outperform conventional methods, while maintaining similar quality to PD-DL models trained using existing supervised and unsupervised techniques.

PMID:39886655 | PMC:PMC11779509 | DOI:10.1109/ISBI56570.2024.10635138