Neurotherapeutics. 2025 Mar 20:e00575. doi: 10.1016/j.neurot.2025.e00575. Online ahead of print.

ABSTRACT

Neuroblastoma is an aggressive childhood cancer that arises from the sympathetic nervous system. Despite advances in treatment, high-risk neuroblastoma remains difficult to manage due to its heterogeneous nature and frequent development of drug resistance. Drug repurposing guided by single-cell analysis presents a promising strategy for identifying new therapeutic options. Here, we aim to characterize high-risk neuroblastoma subpopulations and identify effective repurposed drugs for targeted treatment. We performed single-cell transcriptomic analysis of neuroblastoma samples, integrating bulk RNA-seq data deconvolution with clinical outcomes to define distinct malignant cell states. Using a systematic drug repurposing pipeline, we identified and validated potential therapeutic agents targeting specific high-risk neuroblastoma subpopulations. Single-cell analysis revealed 17 transcriptionally distinct neuroblastoma subpopulations. Survival analysis identified a highly aggressive subpopulation characterized by elevated UBE2C/PTTG1 expression and poor patient outcomes, distinct from a less aggressive subpopulation with favorable prognosis. Drug repurposing screening identified Adavosertib as particularly effective against the aggressive subpopulation, validated using SK-N-DZ cells as a representative model. Mechanistically, Adavosertib suppressed cell proliferation through AKT/mTOR pathway disruption, induced G2/M phase cell cycle arrest, and promoted apoptosis. Further analysis revealed UBE2C and PTTG1 as key molecular drivers of drug resistance, where their overexpression enhanced proliferation, Adavosertib resistance, and cell migration. This study establishes a single-cell-based drug repurposing strategy for high-risk neuroblastoma treatment. Our approach successfully identified Adavosertib as a promising repurposed therapeutic agent for targeting specific high-risk neuroblastoma subpopulations, providing a framework for developing more effective personalized treatment strategies.

PMID:40118716 | DOI:10.1016/j.neurot.2025.e00575

Med Mycol. 2025 Mar 21:myaf025. doi: 10.1093/mmy/myaf025. Online ahead of print.

ABSTRACT

Candida albicans is a major human fungal pathogen; however, limited antifungal agents, undesirable drug side effects, and ineffective prevention of drug-resistant strains have become serious problems. Chitosan is a nontoxic, biodegradable, and biocompatible linear polysaccharide made from the deacetylation of chitin. In this study, a ZCF31 putative transcription factor gene was selected from a previous mutant library screen, as zcf31Δ strains exhibited defective cell growth in response to chitosan. Furthermore, chitosan caused notable damage to zcf31Δ cells; however, ZCF31 expression was not significantly changed by chitosan, suggesting that zcf31Δ is sensitive to chitosan could be due to changes in the physical properties of C. albicans. Indeed, zcf31Δ cells displayed significant increases in cell wall thickness. Consistent to the previous study, zcf31Δ strains were resistant to calcofluor white but highly susceptible to SDS. These results implied that chitosan mainly influences membrane function, as zcf31Δ strengthens the stress resistance of the fungal cell wall but lessens cell membrane function. Interestingly, this effect on the cell surface mechanics of the C. albicans zcf31Δ strains was not responsible for the virulence-associated function. RNA-seq analysis further revealed that six mannosyltransferase-related genes were upregulated in zcf31Δ. Although five mannosyltransferase-related mutant strains in the zcf31Δ background partially reduced the cell wall thickness, only zcf31Δ/mnn45Δ showed the recovery of chitosan resistance. Our findings suggest that Zcf31 mediates a delicate and complicated dynamic balance between the cell membrane and cell wall architectures through the mannosyltransferase genes in C. albicans, leading to altered chitosan susceptibility.

PMID:40118513 | DOI:10.1093/mmy/myaf025

Cell Syst. 2025 Mar 17:101239. doi: 10.1016/j.cels.2025.101239. Online ahead of print.

ABSTRACT

A major contributor to poor sensitivity to anti-cancer kinase inhibitor therapy is drug-induced cellular adaptation, whereby remodeling of signaling and gene regulatory networks permits a drug-tolerant phenotype. Here, we resolve the scale and kinetics of critical subcellular events following oncogenic kinase inhibition and preceding cell cycle re-entry, using mass spectrometry-based phosphoproteomics and RNA sequencing (RNA-seq) to monitor the dynamics of thousands of growth- and survival-related signals over the first minutes, hours, and days of oncogenic BRAF inhibition in human melanoma cells. We observed sustained inhibition of the BRAF-ERK axis, gradual downregulation of cell cycle signaling, and three distinct, reversible phase transitions toward quiescence. Statistical inference of kinetically defined regulatory modules revealed a dominant compensatory induction of SRC family kinase (SFK) signaling, promoted in part by excess reactive oxygen species, rendering cells sensitive to co-treatment with an SFK inhibitor in vitro and in vivo, underscoring the translational potential for assessing early drug-induced adaptive signaling. A record of this paper's transparent peer review process is included in the supplemental information.

PMID:40118060 | DOI:10.1016/j.cels.2025.101239

Mol Divers. 2025 Mar 21. doi: 10.1007/s11030-025-11168-9. Online ahead of print.

ABSTRACT

Vitamin D receptor (VDR) agonists play a pivotal role in modulating immune responses and promoting melanocyte survival, making them potential candidates for vitiligo treatment. The VDR gene is integral to mediating the effects of vitamin D in the immune system, and disruptions in its structure due to missense mutations may significantly contribute to the pathogenesis of vitiligo. Missense single-nucleotide polymorphisms (SNPs) can alter the amino acid sequence of the VDR protein, potentially affecting its ligand-binding affinity and downstream signaling. Investigating these missense SNPs provides critical insights into the genetic underpinnings of vitiligo and may help identify biomarkers for early detection and precision-targeted therapies. This study explored the therapeutic potential of vitamin D analogs in vitiligo management, with a particular focus on their binding interactions and molecular efficacy. Using molecular docking and virtual screening, 24 vitamin D analogs were evaluated. Calcipotriol exhibited the highest binding affinity (-11.4 kcal/mol) and unique interactions with key residues in the VDR ligand-binding domain. Additionally, an analysis of structural variations stemming from missense mutations in the VDR gene highlighted potential impacts on receptor-ligand interactions, further emphasizing the importance of genetic factors in treatment response. These findings underscore the potential of calcipotriol to promote melanogenesis and modulate pigmentation in vitiligo. A comparative analysis identified structural variations influencing the efficacy of other analogs, such as calcitriol and tacalcitol. Although the in silico methods provided valuable insights, the study acknowledges the limitations of excluding dynamic cellular environments and emphasizes the need for experimental validation. Overall, this study enhances our understanding of VDR-targeted therapies, and calcipotriol is a promising candidate for further development in the management of vitiligo.

PMID:40117094 | DOI:10.1007/s11030-025-11168-9

Int J Clin Pharm. 2025 Mar 21. doi: 10.1007/s11096-025-01899-y. Online ahead of print.

ABSTRACT

BACKGROUND: Achieving optimal tacrolimus dosing is vital for effectively balancing therapeutic efficacy and safety, as CYP3A5 genetic variants and inter-patient variability emphasize the need for precision strategies.

AIM: This study aimed to optimize tacrolimus dosage prediction for renal transplant recipients by incorporating genetic polymorphisms, specifically profiling CYP3A5 genetic variants, within the DoseOptimal framework to enhance interpretability and accuracy of dosing decisions.

METHOD: The dataset comprised clinical, demographic, and CYP3A5 genetic variants information from 1045 stable tacrolimus-treated patients. The DoseOptimal framework was developed by integrating the strengths of the most effective algorithms from fifteen machine learning models. SHapley Additive exPlanations (SHAP) and decision tree insights were incorporated to enhance the framework's interpretability. The framework's performance was assessed using mean absolute error (MAE) and the coefficient of determination (R2 score). The F-statistic and p value were calculated to validate the framework's statistical significance.

RESULTS: The DoseOptimal framework demonstrated robust performance with an R2 score of 0.884 in the training set and 0.830 in the testing set. The MAE was 0.40 mg/day (95% CI 0.38-0.43) in the training set and 0.41 mg/day (95% CI 0.38-0.45) in the testing set. The framework predicted the ideal tacrolimus dosage in 87.6% (n = 275) of the test cohort, with 3.2% (n = 10) underestimation and 9.2% (n = 29) overestimation. The framework's statistical significance was confirmed with an F-statistic of 266.095 and a p value < 0.001.

CONCLUSION: The framework provides precision medicine-based dosing solutions tailored to individual genetic profiles, minimizing dosing errors and enhancing patient outcomes.

PMID:40117041 | DOI:10.1007/s11096-025-01899-y

Eur J Clin Pharmacol. 2025 Mar 21. doi: 10.1007/s00228-025-03821-x. Online ahead of print.

ABSTRACT

PURPOSE: Direct oral anticoagulants (DOACs) are used to prevent and treat thromboembolic events in adults. We aimed to investigate whether pharmacogenomic variation contributes to the risk of bleeding during DOAC treatment.

METHODS: Cases were recruited from reports of bleeding sent to the Swedish Medical Products Agency (n = 129, 60% men, 93% Swedish, 89% on factor Xa inhibitors) and compared with population controls (n = 4891) and a subset matched for exposure to DOACs (n = 353). We performed a genome-wide association study, with analyses of candidate single nucleotide polymorphisms (SNPs) and candidate gene set analyses.

RESULTS: Forty-four cases had major, 37 minor, and 48 clinically relevant non-major (CRNM) bleeding. When cases were compared with matched controls, BAIAP2L2 rs142001534 was significantly associated with any bleeding and major/CRNM bleeding (P = 4.66 × 10-8 and P = 3.28 × 10-8, respectively). The candidate SNP CYP3A5 rs776746 was significantly associated with major and major/CRNM bleeding (P = 0.00020 and P = 0.00025, respectively), and ABCG2 rs2231142 was nominally associated with any bleeding (P = 0.01499). Rare coding variants in the candidate gene VWF were significantly associated with any bleeding (P = 0.00296).

CONCLUSION: BAIAP2L2, CYP3A5, ABCG2, and VWF may be associated with bleeding in DOAC-treated patients. The risk estimates of the candidate variants in CYP3A5 and ABCG2 were in the same direction as in previous studies. The Von Willebrand Factor gene (VWF) is linked to hereditary bleeding disorders, while there is no previous evidence of bleeding associated with BAIAP2L2.

PMID:40116934 | DOI:10.1007/s00228-025-03821-x

Pharmacogenomics. 2025 Mar 21:1-20. doi: 10.1080/14622416.2025.2481025. Online ahead of print.

ABSTRACT

AIM: To systematically assess clinical studies involving patients undergoing drug therapy, comparing different genotypes to assess the relationship with changes in QT intervals, with no limitations on study design, setting, population, dosing regimens, or duration.

METHODS: This systematic review followed PRISMA guidelines and a pre-registered protocol. Clinical human studies on PGx markers of diQTP were identified, assessed using standardized tools, and categorized by design. Gene associations were classified as pharmacokinetic or pharmacodynamic. Identified genes underwent pathway enrichment analyses. Drugs were classified by third-level Anatomical Therapeutic Chemical (ATC) codes. Descriptive statistics were computed by study category and drug classes.

RESULTS: Of 4,493 reports, 84 studies were included, identifying 213 unique variants across 42 drug classes, of which 10% were replicated. KCNE1-Asp85Asn was the most consistent variant. Most findings (82%) were derived from candidate gene studies, suggesting bias toward known markers. The diQTP-associated genes were mainly linked to "cardiac conduction" and "muscle contraction" pathways (false discovery rate = 4.71 × 10-14). We also found an overlap between diQTP-associated genes and congenital long QT syndrome genes.

CONCLUSION: Key genes, drugs, and pathways were identified, but few consistent PGx markers emerged. Extensive, unbiased studies with diverse populations are crucial to advancing the field.

REGISTRATION: A protocol was pre-registered at PROSPERO under registration number CRD42022296097.

DATA DEPOSITION: Data sets generated by this review are available at figshare: DOI: 10.6084/m9.figshare.27959616.

PMID:40116580 | DOI:10.1080/14622416.2025.2481025

J Med Internet Res. 2025 Mar 21;27:e60148. doi: 10.2196/60148.

ABSTRACT

BACKGROUND: Despite the rapid growth of research in artificial intelligence/machine learning (AI/ML), little is known about how often study results are disclosed years after study completion.

OBJECTIVE: We aimed to estimate the proportion of AI/ML research that reported results through ClinicalTrials.gov or peer-reviewed publications indexed in PubMed or Scopus.

METHODS: Using data from the Clinical Trials Transformation Initiative Aggregate Analysis of ClinicalTrials.gov, we identified studies initiated and completed between January 2010 and December 2023 that contained AI/ML-specific terms in the official title, brief summary, interventions, conditions, detailed descriptions, primary outcomes, or keywords. For 842 completed studies, we searched PubMed and Scopus for publications containing study identifiers and AI/ML-specific terms in relevant fields, such as the title, abstract, and keywords. We calculated disclosure rates within 3 years of study completion and median times to disclosure-from the "primary completion date" to the "results first posted date" on ClinicalTrials.gov or the earliest date of journal publication.

RESULTS: Of 842 completed studies (n=357 interventional; n=485 observational), 5.5% (46/842) disclosed results on ClinicalTrials.gov, 13.9% (117/842) in journal publications, and 17.7% (149/842) through either route within 3 years of completion. Higher disclosure rates were observed for trials: 10.4% (37/357) on ClinicalTrials.gov, 19.3% (69/357) in journal publications, and 26.1% (93/357) through either route. Randomized controlled trials had even higher disclosure rates: 11.3% (23/203) on ClinicalTrials.gov, 24.6% (50/203) in journal publications, and 32% (65/203) through either route. Nevertheless, most study findings (82.3%; 693/842) remained undisclosed 3 years after study completion. Trials using randomization (vs nonrandomized) or masking (vs open label) had higher disclosure rates and shorter times to disclosure. Most trials (85%; 305/357) had sample sizes of ≤1000, yet larger trials (n>1000) had higher publication rates (30.8%; 16/52) than smaller trials (n≤1000) (17.4%; 53/305). Hospitals (12.4%; 42/340), academia (15.1%; 39/259), and industry (13.7%; 20/146) published the most. High-income countries accounted for 82.4% (89/108) of all published studies. Of studies with disclosed results, the median times to report through ClinicalTrials.gov and in journal publications were 505 days (IQR 399-676) and 407 days (IQR 257-674), respectively. Open-label trials were common (60%; 214/357). Single-center designs were prevalent in both trials (83.3%; 290/348) and observational studies (82.3%; 377/458).

CONCLUSIONS: For over 80% of AI/ML studies completed during 2010-2023, study findings remained undisclosed even 3 years after study completion, raising questions about the representativeness of publicly available evidence. While methodological rigor was generally associated with higher publication rates, the predominance of single-center designs and high-income countries may limit the generalizability of the results currently accessible.

PMID:40117574 | DOI:10.2196/60148

Med Sci (Paris). 2025 Mar;41(3):277-280. doi: 10.1051/medsci/2025036. Epub 2025 Mar 21.

ABSTRACT

On October 8, 2024, the Nobel Prize in Physics was awarded to John J. Hopfield, professor at Princeton University, and Geoffrey E. Hinton, professor at the University of Toronto, for their "fundamental discoveries that made possible machine learning through artificial neural networks." According to the Nobel committee, John Hopfield designed an associative memory capable of storing and reconstructing images, while Geoffrey Hinton developed a method enabling tasks such as identifying specific elements within images. This article retraces the career paths of these two researchers and highlights their pioneering contributions.

PMID:40117553 | DOI:10.1051/medsci/2025036

Proc Natl Acad Sci U S A. 2025 Apr;122(13):e2504475122. doi: 10.1073/pnas.2504475122. Epub 2025 Mar 21.

NO ABSTRACT

PMID:40117323 | DOI:10.1073/pnas.2504475122

PLoS One. 2025 Mar 21;20(3):e0320204. doi: 10.1371/journal.pone.0320204. eCollection 2025.

ABSTRACT

Deciphering the complex relationships between cellular morphology and phenotypic manifestations is crucial for understanding cell behavior, particularly in the context of neuropathological states. Despite its importance, the application of advanced image analysis methodologies to central nervous system (CNS) cells, including neuronal and glial cells, has been limited. Furthermore, cutting-edge techniques in the field of cell image analysis, such as deep learning (DL), still face challenges, including the requirement for large amounts of labeled data, difficulty in detecting subtle cellular changes, and the presence of batch effects. Our study addresses these shortcomings in the context of neuroinflammation. Using our in-house data and a DL-based approach, we have effectively analyzed the morphological phenotypes of neuronal and microglial cells, both in pathological conditions and following pharmaceutical interventions. This innovative method enhances our understanding of neuroinflammation and streamlines the process for screening potential therapeutic compounds, bridging a gap in neuropathological research and pharmaceutical development.

PMID:40117300 | DOI:10.1371/journal.pone.0320204

PLoS One. 2025 Mar 21;20(3):e0314111. doi: 10.1371/journal.pone.0314111. eCollection 2025.

ABSTRACT

Glaucoma is the leading cause of irreversible vision impairment, emphasizing the critical need for early detection. Typically, AI-based glaucoma screening relies on fundus imaging. To tackle the resource and time challenges in glaucoma screening with convolutional neural network (CNN), we chose the Data-efficient image Transformers (DeiT), a vision transformer, known for its reduced computational demands, with preprocessing time decreased by a factor of 10. Our approach utilized the meticulously annotated GlauCUTU-DATA dataset, curated by ophthalmologists through consensus, encompassing both unanimous agreement (3/3) and majority agreement (2/3) data. However, DeiT's performance was initially lower than CNN. Therefore, we introduced the "pie method," an augmentation method aligned with the ISNT rule. Along with employing polar transformation to improved cup region visibility and alignment with the vision transformer's input to elevated performance levels. The classification results demonstrated improvements comparable to CNN. Using the 3/3 data, excluding the superior and nasal regions, especially in glaucoma suspects, sensitivity increased by 40.18% from 47.06% to 88.24%. The average area under the curve (AUC) ± standard deviation (SD) for glaucoma, glaucoma suspects, and no glaucoma were 92.63 ± 4.39%, 92.35 ± 4.39%, and 92.32 ± 1.45%, respectively. With the 2/3 data, excluding the superior and temporal regions, sensitivity for diagnosing glaucoma increased by 11.36% from 47.73% to 59.09%. The average AUC ± SD for glaucoma, glaucoma suspects, and no glaucoma were 68.22 ± 4.45%, 68.23 ± 4.39%, and 73.09 ± 3.05%, respectively. For both datasets, the AUC values for glaucoma, glaucoma suspects, and no glaucoma were 84.53%, 84.54%, and 91.05%, respectively, which approach the performance of a CNN model that achieved 84.70%, 84.69%, and 93.19%, respectively. Moreover, the incorporation of attention maps from DeiT facilitated the precise localization of clinically significant areas, such as the disc rim and notching, thereby enhancing the overall effectiveness of glaucoma screening.

PMID:40117284 | DOI:10.1371/journal.pone.0314111

IEEE Trans Neural Syst Rehabil Eng. 2025 Mar 21;PP. doi: 10.1109/TNSRE.2025.3553794. Online ahead of print.

ABSTRACT

Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time monitoring of brain activity, are increasingly being applied in rehabilitation, assistive technology, neurological diseases and behavioral disorders. Functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) are promising techniques for these applications due to their non-invasiveness, portability, low cost, and relatively high spatial resolution. However, real-time processing of fNIRS/DOT data remains a significant challenge as it requires establishing a baseline of the measurement, simultaneously performing real-time motion artifact (MA) correction across all channels, and (in the case of DOT) addressing the time-consuming process of image reconstruction. This study proposes a real-time processing system for fNIRS/DOT that integrates baseline calibration, denoising autoencoder (DAE) based MA correction model with a sliding window strategy, and a pre-calculated inverse Jacobian matrix to streamline the reconstructed 3D brain hemodynamics. The DAE model was trained on an extensive whole-head high-density DOT (HD-DOT) dataset and tested on separate motor imagery dataset augmented with artificial MA. The system demonstrated the capability to simultaneously process approximately 750 channels in real-time. Our results show that the DAE-based MA correction method outperformed traditional MA correction in terms of mean squared error and correlation to the known MA-free data while maintaining low latency, which is critical for effective BCI and NFB applications. The system's high-channel, real-time processing capability provides channel-wise oxygenation information and functional 3D imaging, making it well-suited for fNIRS/DOT applications in BCI and NFB, particularly in movement-intensive scenarios such as motor rehabilitation and assistive technology for mobility support.

PMID:40117159 | DOI:10.1109/TNSRE.2025.3553794

IEEE Trans Pattern Anal Mach Intell. 2025 Mar 21;PP. doi: 10.1109/TPAMI.2025.3553485. Online ahead of print.

ABSTRACT

6D pose estimation of rigid objects is a long-standing and challenging task in computer vision. Recently, the emergence of deep learning reveals the potential of Convolutional Neural Networks (CNNs) to predict reliable 6D poses. Given that direct pose regression networks currently exhibit suboptimal performance, most methods still resort to traditional techniques to varying degrees. For example, top-performing methods often adopt an indirect strategy by first establishing 2D-3D or 3D-3D correspondences followed by applying the RANSAC-based P P or Kabsch algorithms, and further employing ICP for refinement. Despite the performance enhancement, the integration of traditional techniques makes the networks time-consuming and not end-to-end trainable. Orthogonal to them, this paper introduces a fully learning-based object pose estimator. In this work, we first perform an in-depth investigation of both direct and indirect methods and propose a simple yet effective Geometry-guided Direct Regression Network (GDRN) to learn the 6D pose from monocular images in an end-to-end manner. Afterwards, we introduce a geometry-guided pose refinement module, enhancing pose accuracy when extra depth data is available. Guided by the predicted coordinate map, we build an end-to-end differentiable architecture that establishes robust and accurate 3D-3D correspondences between the observed and rendered RGB-D images to refine the pose. Our enhanced pose estimation pipeline GDRNPP (GDRN Plus Plus) conquered the leaderboard of the BOP Challenge for two consecutive years, becoming the first to surpass all prior methods that relied on traditional techniques in both accuracy and speed. The code and models are available at https://github.com/shanice-l/gdrnpp_bop2022.

PMID:40117145 | DOI:10.1109/TPAMI.2025.3553485

IEEE Trans Pattern Anal Mach Intell. 2025 Mar 21;PP. doi: 10.1109/TPAMI.2025.3552309. Online ahead of print.

ABSTRACT

Foundation models are usually pre-trained on large-scale datasets and then adapted to different downstream tasks through tuning. This pre-training and then fine-tuning paradigm has become a standard practice in deep learning. However, the large-scale pre-training datasets, often inaccessible or too expensive to handle, can contain label noise that may adversely affect the generalization of the model and pose unexpected risks. This paper stands out as the first work to comprehensively understand and analyze the nature of noise in pre-training datasets and then effectively mitigate its impacts on downstream tasks. Specifically, through extensive experiments of fully-supervised and image-text contrastive pre-training on synthetic noisy ImageNet-1K, YFCC15M, and CC12M datasets, we demonstrate that, while slight noise in pre-training can benefit in-domain (ID) performance, where the training and testing data share a similar distribution, it always deteriorates out-of-domain (OOD) performance, where training and testing distributions are significantly different. These observations are agnostic to scales of pre-training datasets, pre-training noise types, model architectures, pre-training objectives, downstream tuning methods, and downstream applications. We empirically ascertain that the reason behind this is that the pre-training noise shapes the feature space differently. We then propose a tuning method (NMTune) to affine the feature space to mitigate the malignant effect of noise and improve generalization, which is applicable in both parameter-efficient and black-box tuning manners, considering one may not be able to access or fully fine-tune the pre-trained models. We additionally conduct extensive experiments on popular vision and language models, including APIs, which are supervised and self-supervised pre-trained on realistic noisy data for evaluation. Our analysis and results demonstrate the importance of this novel and fundamental research direction, which we term as Noisy Model Transfer Learning.

PMID:40117144 | DOI:10.1109/TPAMI.2025.3552309

Environ Sci Pollut Res Int. 2025 Mar 21. doi: 10.1007/s11356-025-36265-y. Online ahead of print.

ABSTRACT

Air pollution is a significant global challenge with profound impacts on human health and the environment. Elevated concentrations of various air pollutants contribute to numerous premature deaths each year. In Europe, and particularly in Poland, air quality remains a critical concern due to pollutants such as particulate matter (PM), which pose serious risks to public health and ecological systems. Effective control of PM emissions and accurate forecasting of their concentrations are essential for improving air quality and supporting public health interventions. This paper presents four advanced deep learning-based forecasting methods: extended long short-term memory network (xLSTM), Kolmogorov-Arnold network (KAN), temporal convolutional network (TCN), and variational autoencoder (VAE). Using data from eight cities in Poland, we evaluate our methods' ability to predict particulate matter concentrations through extensive experiments, utilizing statistical hypothesis testing and error metrics such as mean absolute error (MAE) and root mean square error (RMSE). Our findings demonstrate that these methods achieve high prediction accuracy, significantly outperforming several state-of-the-art algorithms. The proposed forecasting framework offers practical applications for policymakers and public health officials by enabling timely interventions to decrease pollution impacts and enhance urban air quality management.

PMID:40117111 | DOI:10.1007/s11356-025-36265-y

Brain Inform. 2025 Mar 21;12(1):8. doi: 10.1186/s40708-025-00252-3.

ABSTRACT

Clinical diagnosis of Alzheimer's disease (AD) is usually made after symptoms such as short-term memory loss are exhibited, which minimizes the intervention and treatment options. The existing screening techniques cannot distinguish between stable MCI (sMCI) cases (i.e., patients who do not convert to AD for at least three years) and progressive MCI (pMCI) cases (i.e., patients who convert to AD in three years or sooner). Delayed diagnosis of AD also disproportionately affects underrepresented and socioeconomically disadvantaged populations. The significant positive impact of an early diagnosis solution for AD across diverse ethno-racial and demographic groups is well-known and recognized. While advancements in high-throughput technologies have enabled the generation of vast amounts of multimodal clinical, and neuroimaging datasets related to AD, most methods utilizing these data sets for diagnostic purposes have not found their way in clinical settings. To better understand the landscape, we surveyed the major preprocessing, data management, traditional machine-learning (ML), and deep learning (DL) techniques used for diagnosing AD using neuroimaging data such as structural magnetic resonance imaging (sMRI), functional magnetic resonance imaging (fMRI), and positron emission tomography (PET). Once we had a good understanding of the methods available, we conducted a study to assess the reproducibility and generalizability of open-source ML models. Our evaluation shows that existing models show reduced generalizability when different cohorts of the data modality are used while controlling other computational factors. The paper concludes with a discussion of major challenges that plague ML models for AD diagnosis and biomarker discovery.

PMID:40117001 | DOI:10.1186/s40708-025-00252-3

Clin Exp Immunol. 2025 Mar 21:uxaf019. doi: 10.1093/cei/uxaf019. Online ahead of print.

ABSTRACT

BACKGROUND: Idiopathic inflammatory myopathy (IIM) is a progressive autoimmune disease characterized by interstitial lung disease (ILD) with limited therapeutics available. Pirfenidone (PFD), a medication utilized for the treatment of idiopathic pulmonary fibrosis, exhibits notable antioxidant, anti-inflammatory and inhibition of collagen synthesis. This study aims to clarify its efficacy and mechanism in treating IIM-ILD.

METHODS: A murine myositis-associated interstitial lung disease (MAILD) model was used to assess the therapeutic effect of PFD. The serum levels of IL-1β, IL-6 and TNF-α were detected by ELISA. PFD was utilized to disrupt neutrophil extracellular traps (NETs) formation in vitro, and its inhibitory effect on NETs was assessed through immunohistochemistry of CitH3 and MPO in the lung tissue and the serum cfDNA level in mice. Immunohistochemical and western blot was utilized to examine alterations in epithelial-mesenchymal transition (EMT) and NLRP3 inflammasome markers.

RESULTS: PFD treatment inhibited pulmonary inflammation and fibrosis in the MAILD model. PFD intervention reduced NETs formation in vitro. PFD treatment significantly reduce NETs infiltration in the lung tissue and the level of cfDNA in the serum of mice. Additionally, PFD down-regulated EMT and NLRP3-related proteins in vivo. PFD treatment also notably reduced serum levels of IL-1β, IL-6 and TNF-α. After NETs stimulation, A549 cells exhibited EMT and activation of NLRP3 inflammasome. PFD attenuated EMT in A549 cells and suppressed the activation of NLRP3 inflammasome.

CONCLUSION: PFD alleviates ILD in a murine MAILD model by inhibiting NETs formation and NLRP3 inflammasome activation, suggesting that PFD might be a potential therapeutic agent for IIM-ILD.

PMID:40117382 | DOI:10.1093/cei/uxaf019

Elife. 2025 Mar 21;14:RP102019. doi: 10.7554/eLife.102019.

ABSTRACT

The mammalian suprachiasmatic nucleus (SCN), situated in the ventral hypothalamus, directs daily cellular and physiological rhythms across the body. The SCN clockwork is a self-sustaining transcriptional-translational feedback loop (TTFL) that in turn coordinates the expression of clock-controlled genes (CCGs) directing circadian programmes of SCN cellular activity. In the mouse, the transcription factor, ZFHX3 (zinc finger homeobox-3), is necessary for the development of the SCN and influences circadian behaviour in the adult. The molecular mechanisms by which ZFHX3 affects the SCN at transcriptomic and genomic levels are, however, poorly defined. Here, we used chromatin immunoprecipitation sequencing to map the genomic localization of ZFHX3-binding sites in SCN chromatin. To test for function, we then conducted comprehensive RNA sequencing at six distinct times-of-day to compare the SCN transcriptional profiles of control and ZFHX3-conditional null mutants. We show that the genome-wide occupancy of ZFHX3 occurs predominantly around gene transcription start sites, co-localizing with known histone modifications, and preferentially partnering with clock transcription factors (CLOCK, BMAL1) to regulate clock gene(s) transcription. Correspondingly, we show that the conditional loss of ZFHX3 in the adult has a dramatic effect on the SCN transcriptome, including changes in the levels of transcripts encoding elements of numerous neuropeptide neurotransmitter systems while attenuating the daily oscillation of the clock TF Bmal1. Furthermore, various TTFL genes and CCGs exhibited altered circadian expression profiles, consistent with an advanced in daily behavioural rhythms under 12 h light-12 h dark conditions. Together, these findings reveal the extensive genome-wide regulation mediated by ZFHX3 in the central clock that orchestrates daily timekeeping in mammals.

PMID:40117332 | DOI:10.7554/eLife.102019

Database (Oxford). 2025 Mar 21;2025:baaf020. doi: 10.1093/database/baaf020.

ABSTRACT

Ontologies are widely used in databases to standardize data, improving data quality, integration, and ease of comparison. Within ontologies tailored to diverse use cases, post-composing user-defined terms reconciles the demands for standardization on the one hand and flexibility on the other. In many instances of Breedbase, a digital ecosystem for plant breeding designed for genomic selection, the goal is to capture phenotypic data using highly curated and rigorous crop ontologies, while adapting to the specific requirements of plant breeders to record data quickly and efficiently. For example, post-composing enables users to tailor ontology terms to suit specific and granular use cases such as repeated measurements on different plant parts and special sample preparation techniques. To achieve this, we have implemented a post-composing tool based on orthogonal ontologies providing users with the ability to introduce additional levels of phenotyping granularity tailored to unique experimental designs. Post-composed terms are designed to be reused by all breeding programs within a Breedbase instance but are not exported to the crop reference ontologies. Breedbase users can post-compose terms across various categories, such as plant anatomy, treatments, temporal events, and breeding cycles, and, as a result, generate highly specific terms for more accurate phenotyping.

PMID:40117331 | DOI:10.1093/database/baaf020