Brief Bioinform. 2024 Nov 22;26(1):bbaf039. doi: 10.1093/bib/bbaf039.

ABSTRACT

Pathway analysis plays a critical role in bioinformatics, enabling researchers to identify biological pathways associated with various conditions by analyzing gene expression data. However, the rise of large, multi-center datasets has highlighted limitations in traditional methods like Over-Representation Analysis (ORA) and Functional Class Scoring (FCS), which struggle with low signal-to-noise ratios (SNR) and large sample sizes. To tackle these challenges, we use a deep learning-based classification method, Gene PointNet, and a novel $P$-value computation approach leveraging the confusion matrix to address pathway analysis tasks. We validated our method effectiveness through a comparative study using a simulated dataset and RNA-Seq data from The Cancer Genome Atlas breast cancer dataset. Our method was benchmarked against traditional techniques (ORA, FCS), shallow machine learning models (logistic regression, support vector machine), and deep learning approaches (DeepHisCom, PASNet). The results demonstrate that GPNet outperforms these methods in low-SNR, large-sample datasets, where it remains robust and reliable, significantly reducing both Type I error and improving power. This makes our method well suited for pathway analysis in large, multi-center studies. The code can be found at https://github.com/haolu123/GPNet_pathway">https://github.com/haolu123/GPNet_pathway.

PMID:39879387 | DOI:10.1093/bib/bbaf039

PLoS One. 2025 Jan 29;20(1):e0314288. doi: 10.1371/journal.pone.0314288. eCollection 2025.

ABSTRACT

BACKGROUND: Bipolar Disorder (BD) is a complex disease. It is heterogeneous, both at the phenotypic and genetic level, although the extent and impact of this heterogeneity is not fully understood. One way to assess this heterogeneity is to look for patterns in the subphenotype data. Because of the variability in how phenotypic data was collected by the various BD studies over the years, homogenizing this subphenotypic data is a challenging task, and so is replication. An alternative methodology, taken here, is to set aside the intricacies of subphenotype and allow the genetic data itself to determine which subjects define a homogeneous genetic subgroup (termed 'bicluster' below).

RESULTS: In this paper, we leverage recent advances in heterogeneity analysis to look for genetically-driven subgroups (i.e., biclusters) within the broad phenotype of Bipolar Disorder. We first apply this covariate-corrected biclustering algorithm to a cohort of 2524 BD cases and 4106 controls from the Bipolar Disease Research Network (BDRN) within the Psychiatric Genomics Consortium (PGC). We find evidence of genetic heterogeneity delineating a statistically significant bicluster comprising a subset of BD cases which exhibits a disease-specific pattern of differential-expression across a subset of SNPs. This disease-specific genetic pattern (i.e., 'genetic subgroup') replicates across the remaining data-sets collected by the PGC containing 5781/8289, 3581/7591, and 6825/9752 cases/controls, respectively. This genetic subgroup (discovered without using any BD subtype information) was more prevalent in Bipolar type-I than in Bipolar type-II.

CONCLUSIONS: Our methodology has successfully identified a replicable homogeneous genetic subgroup of bipolar disorder. This subgroup may represent a collection of correlated genetic risk-factors for BDI. By investigating the subgroup's bicluster-informed polygenic-risk-scoring (PRS), we find that the disease-specific pattern highlighted by the bicluster can be leveraged to eliminate noise from our GWAS analyses and improve risk prediction. This improvement is particularly notable when using only a relatively small subset of the available SNPs, implying improved SNP replication. Though our primary focus is only the analysis of disease-related signal, we also identify replicable control-related heterogeneity.

PMID:39879180 | DOI:10.1371/journal.pone.0314288

Rheumatology (Oxford). 2025 Jan 29:keaf028. doi: 10.1093/rheumatology/keaf028. Online ahead of print.

ABSTRACT

OBJECTIVES: COVID-19 and systemic sclerosis (SSc) share multiple similarities in their clinical manifestations, alterations in immune response, and therapeutic options. These resemblances have also been identified in other immune-mediated inflammatory diseases where a common genetic component has been found. Thus, we decided to evaluate for the first time this shared genetic architecture with SSc.

METHODS: For this study, we retrieved genomic data from two European-ancestry cohorts: 2,597 856 individuals from The COVID-19 Host Genetics Initiative consortium, and 26 679 individuals from the largest genomic scan in SSc. We performed a cross-trait meta-analyses including >9.3 million SNPs. Finally, we conducted functional annotation to prioritize potential causal genes and performed drug repurposing analysis.

RESULTS: Our results revealed a total of 19 non-HLA pleiotropic loci, including 2 novel associations for both conditions (BMP1 and PPARG), and 12 emerging as new shared loci. Functional annotation of these regions underscored their potential regulatory role and identified potential causal genes, many of which are implicated in fibrotic and inflammatory pathways. Remarkably, we observed an antagonistic pleiotropy model of the IFN signalling between COVID-19 and SSc, including the well-known TYK2 P1104A missense variant, showing a protective effect for SSc while being a risk factor for COVID-19, along with two additional novel pleiotropic associations (IRF8 and SENP7). Finally, our findings provide new therapeutic options that could potentially benefit both conditions.

CONCLUSION: Our study confirms the genetic resemblance between susceptibility to and severity of COVID-19 and SSc, revealing a novel common genetic contribution affecting fibrotic and immune pathways.

PMID:39878951 | DOI:10.1093/rheumatology/keaf028

J Med Microbiol. 2025 Jan;74(1). doi: 10.1099/jmm.0.001943.

ABSTRACT

Introduction. Increased virulence and drug resistance in species of Candida resulted in reduced disease control and further demand the development of potent antifungal drugs.Hypothesis. The repurposing of non-antifungal drugs and combination therapy has become an attractive alternative to counter the emerging drug resistance and toxicity of existing antifungal drugs against Candida albicans and non-albicans species.Aim. This study aimed to accelerate antifungal drug development process by drug repurposing approach.Methodology. In this study, the antifungal effects of the antiviral drug, 2-adamantylamine hydrochloride (2-AM), were explored against C. albicans and C. parapsilosis. Broth microdilution measured in vitro efficacy of 2-AM, whereas reactive oxygen species (ROS) accumulation and ergosterol quantification, cell cycle and phosphatidylserine externalization studies were detailed to investigate the antifungal mode of 2-AM action.Results. Results showed that 2-AM had fungicidal action against both the strains where, 2-AM further inhibited morphogenic transitions as well. Antibiofilm action of 2-AM on C. albicans was evidenced on urinary catheters. G2/M phase arrest and apoptosis indicated ROS induced antifungal effect of 2-AM on both strains.Conclusions. Results of in vitro studies offers insight into the antifungal activity of 2-AM and may serve as an effective antifungal repurposed candidate against C. albicans and C. parapsilosis.

PMID:39878161 | DOI:10.1099/jmm.0.001943

Mol Biol Cell. 2025 Jan 29:mbcE24110512. doi: 10.1091/mbc.E24-11-0512. Online ahead of print.

ABSTRACT

Rare inherited diseases caused by mutations in the copper transporters SLC31A1 (CTR1) or ATP7A induce copper deficiency in the brain, causing seizures and neurodegeneration in infancy through poorly understood mechanisms. Here, we used multiple model systems to characterize the molecular mechanisms by which neuronal cells respond to copper deficiency. Targeted deletion of CTR1 in neuroblastoma cells produced copper deficiency that produced a metabolic shift favoring glycolysis over oxidative phosphorylation. Proteomic and transcriptomic analysis of CTR1 KO cells revealed simultaneous upregulation of mTORC1 and S6K signaling and reduced PERK signaling. Patterns of gene and protein expression and pharmacogenomics show increased activation of the mTORC1-S6K pathway as a pro-survival mechanism, ultimately resulting in increased protein synthesis. Spatial transcriptomic profiling of Atp7aflx/Y:: Vil1Cre/+ mice identified upregulated protein synthesis machinery and mTORC1-S6K pathway genes in copper-deficient Purkinje neurons in the cerebellum. Genetic epistasis experiments in Drosophila demonstrated that copper deficiency dendritic phenotypes in class IV neurons are improved or rescued by increased S6k expression or 4E-BP1 (Thor) RNAi, while epidermis phenotypes are exacerbated by Akt, S6k, or raptor RNAi. Overall, we demonstrate that increased mTORC1-S6K pathway activation and protein synthesis is an adaptive mechanism by which neuronal cells respond to copper deficiency.

PMID:39878654 | DOI:10.1091/mbc.E24-11-0512

Eur J Nucl Med Mol Imaging. 2025 Jan 29. doi: 10.1007/s00259-025-07091-8. Online ahead of print.

ABSTRACT

PURPOSE: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.

METHODS: We trained a generative model on 99mTc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis. A blinded reader study was performed to assess the clinical validity and quality of the generated data. We investigated the added value of the generated data by augmenting an independent small single-center dataset with synthetic data and by training a deep learning model to detect abnormal uptake in a downstream classification task. We tested this model on 7,472 scans from 6,448 patients across four external sites in a cross-tracer and cross-scanner setting and associated the resulting model predictions with clinical outcomes.

RESULTS: The clinical value and high quality of the synthetic imaging data were confirmed by four readers, who were unable to distinguish synthetic scans from real scans (average accuracy: 0.48% [95% CI 0.46-0.51]), disagreeing in 239 (60%) of 400 cases (Fleiss' kappa: 0.18). Adding synthetic data to the training set improved model performance by a mean (± SD) of 33(± 10)% AUC (p < 0.0001) for detecting abnormal uptake indicative of bone metastases and by 5(± 4)% AUC (p < 0.0001) for detecting uptake indicative of cardiac amyloidosis across both internal and external testing cohorts, compared to models without synthetic training data. Patients with predicted abnormal uptake had adverse clinical outcomes (log-rank: p < 0.0001).

CONCLUSIONS: Generative AI enables the targeted generation of bone scintigraphy images representing different clinical conditions. Our findings point to the potential of synthetic data to overcome challenges in data sharing and in developing reliable and prognostic deep learning models in data-limited environments.

PMID:39878897 | DOI:10.1007/s00259-025-07091-8

Br J Radiol. 2025 Jan 29:tqaf018. doi: 10.1093/bjr/tqaf018. Online ahead of print.

ABSTRACT

In a rapidly evolving healthcare environment, artificial intelligence (AI) is transforming diagnostic techniques and personalised medicine. This is also seen in osseous biopsies. AI applications in radiomics, histopathology, predictive modelling, biopsy navigation, and interdisciplinary communication are reshaping how bone biopsies are conducted and interpreted. We provide a brief review of AI in image- guided biopsy of bone tumours (primary and secondary) and specimen handling, in the era of personalised medicine. This paper explores AI's role in enhancing diagnostic accuracy, improving safety in biopsies, and enabling more precise targeting in bone lesion biopsies, ultimately contributing to better patient outcomes in personalised medicine. We dive into various AI technologies applied to osseous biopsies, such as traditional machine learning, deep learning, radiomics, simulation and generative models. We explore their roles in tumour board meetings, communication between clinicians, radiologists, and pathologists. Additionally, we inspect ethical considerations associated with the integration of AI in bone biopsy procedures, technical limitations, and we delve into health equity, generalisability, deployment issues, and reimbursement challenges in AI-powered healthcare. Finally, we explore potential future developments and offer a list of open-source AI tools and algorithms relevant to bone biopsies, which we include to encourage further discussion and research.

PMID:39878877 | DOI:10.1093/bjr/tqaf018

Clin Oral Investig. 2025 Jan 29;29(2):97. doi: 10.1007/s00784-025-06183-x.

ABSTRACT

OBJECTIVES: To evaluate recent advances in the automatic multimodal registration of cone-beam computed tomography (CBCT) and intraoral scans (IOS) and their clinical significance in dentistry.

METHODS: A comprehensive literature search was conducted in October 2024 across the PubMed, Web of Science, and IEEE Xplore databases, including studies that were published in the past decade. The inclusion criteria were as follows: English-language studies, randomized and nonrandomized controlled trials, cohort studies, case-control studies, cross-sectional studies, and retrospective studies.

RESULTS: Of the 493 articles identified, 22 met the inclusion criteria. Among these, 14 studies used geometry-based methods, 7 used artificial intelligence (AI) techniques, and 1 compared the accuracy of both approaches. Geometry-based methods primarily utilize two-stage coarse-to-fine registration algorithms, which require relatively fewer computational resources. In contrast, AI methods leverage advanced deep learning models, achieving significant improvements in automation and robustness.

CONCLUSIONS: Recent advances in CBCT and IOS registration technologies have considerably increased the efficiency and accuracy of 3D dental modelling, and these technologies show promise for application in orthodontics, implantology, and oral surgery. Geometry-based algorithms deliver reliable performance with low computational demand, whereas AI-driven approaches demonstrate significant potential for achieving fully automated and highly accurate registration. Future research should focus on challenges such as unstable registration landmarks or limited dataset diversity, to ensure their stability in complex clinical scenarios.

PMID:39878846 | DOI:10.1007/s00784-025-06183-x

Bioresour Bioprocess. 2025 Jan 29;12(1):7. doi: 10.1186/s40643-024-00835-8.

ABSTRACT

Feruloyl esterases (FEs, EC 3.1.1.73) play a crucial role in biological synthesis and metabolism. However, the identification of versatile FEs, capable of catalyzing a wide range of substrates, remains a challenge. In this study, we obtained 2085 FE sequences from the BRENDA database and initiated with an enzyme similarity network analysis, revealing three main clusters (1-3). Notably, both cluster 1 and cluster 3 included the characterized FEs, which exhibited significant differences in sequence length. Subsequent phylogenetic analysis of these clusters unveiled a correlation between phylogenetic classification and substrate promiscuity, and enzymes with broad substrate scope tended to locate within specific branches of the phylogenetic tree. Further, molecular dynamics simulations and dynamical cross-correlation matrix analysis were employed to explore structural dynamics differences between promiscuous and substrate-specific FEs. Finally, to expand the repertoire of versatile FEs, we employed deep learning models to predict potentially promiscuous enzymes and identified 38 and 75 potential versatile FEs from cluster 1 and cluster 3 with a probability score exceeding 90%. Our findings underscore the utility of integrating phylogenetic and structural features with deep learning approaches for mining versatile FEs, shedding light on unexplored enzymatic diversity and expanding the repertoire of biocatalysts for synthetic applications.

PMID:39878830 | DOI:10.1186/s40643-024-00835-8

Physiol Rep. 2025 Feb;13(3):e70187. doi: 10.14814/phy2.70187.

ABSTRACT

The maintenance of an appropriate ratio of body fat to muscle mass is essential for the preservation of health and performance, as excessive body fat is associated with an increased risk of various diseases. Accurate body composition assessment requires precise segmentation of structures. In this study we developed a novel automatic machine learning approach for volumetric segmentation and quantitative assessment of MRI volumes and investigated the efficacy of using a machine learning algorithm to assess muscle, subcutaneous adipose tissue (SAT), and bone volume of the thigh before and after a strength training. Eighteen healthy, young, female volunteers were randomly allocated to two groups: intervention group (IG) and control group (CG). The IG group followed an 8-week strength endurance training plan that was conducted two times per week. Before and after the training, the subjects of both groups underwent MRI scanning. The evaluation of the image data was performed by a machine learning system which is based on a 3D U-Net-based Convolutional Neural Network. The volumes of muscle, bone, and SAT were each examined using a 2 (GROUP [IG vs. CG]) × 2 (TIME [pre-intervention vs. post-intervention]) analysis of variance (ANOVA) with repeated measures for the factor TIME. The results of the ANOVA demonstrate significant TIME × GROUP interaction effects for the muscle volume (F1,16 = 12.80, p = 0.003, ηP 2 = 0.44) with an increase of 2.93% in the IG group and no change in the CG (-0.62%, p = 0.893). There were no significant changes in bone or SAT volume between the groups. This study supports the use of artificial intelligence systems to analyze MRI images as a reliable tool for monitoring training responses on body composition.

PMID:39878619 | DOI:10.14814/phy2.70187

Med Phys. 2025 Jan 29. doi: 10.1002/mp.17642. Online ahead of print.

ABSTRACT

BACKGROUND: Computed tomography angiography (CTA) is used to screen for coronary artery calcification. As the coronary artery has complicated structure and tiny lumen, manual screening is a time-consuming task. Recently, many deep learning methods have been proposed for the segmentation (SEG) of coronary artery and calcification, however, they often neglect leveraging related anatomical prior knowledge, resulting in low accuracy and instability.

PURPOSE: This study aims to build a deep learning based SEG framework, which leverages anatomical prior knowledge of coronary artery and calcification, to improve the SEG accuracy. Moreover, based on the SEG results, this study also try to reveal the predictive ability of the volume ratio of coronary artery and calcification for rotational atherectomy (RA).

METHODS: We present a new SEG framework, which is composed of four modules: the variational autoencoder based centerline extraction (CE) module, the self-attention (SA) module, the logic operation (LO) module, and the SEG module. Specifically, the CE module is used to crop a series of 3D CTA patches along the coronary artery, from which the continuous property of vessels can be utilized by the SA module to produce vessel-related features. According to the spatial relations between coronary artery lumen and calcification regions, the LO module with logic union and intersection is designed to refine the vessel-related features into lumen- and calcification-related features, based on which SEG results can be produced by the SEG module.

RESULTS: Experimental results demonstrate that our framework outperforms the state-of-the-art methods on CTA image dataset of 72 patients with statistical significance. Ablation experiments confirm that the proposed modules have positive impacts to the SEG results. Moreover, based on the volume ratio of segmented coronary artery and calcification, the prediction accuracy of RA is 0.75.

CONCLUSIONS: Integrating anatomical prior knowledge of coronary artery and calcification into the deep learning based SEG framework can effectively enhance the performance. Moreover, the volume ratio of segmented coronary artery and calcification is a good predictive factor for RA.

PMID:39878608 | DOI:10.1002/mp.17642

Med Phys. 2025 Jan 29. doi: 10.1002/mp.17635. Online ahead of print.

ABSTRACT

BACKGROUND: Stereotactic radiosurgery (SRS) is widely used for managing brain metastases (BMs), but an adverse effect, radionecrosis, complicates post-SRS management. Differentiating radionecrosis from tumor recurrence non-invasively remains a major clinical challenge, as conventional imaging techniques often necessitate surgical biopsy for accurate diagnosis. Machine learning and deep learning models have shown potential in distinguishing radionecrosis from tumor recurrence. However, their clinical adoption is hindered by a lack of explainability, limiting understanding and trust in their diagnostic decisions.

PURPOSE: To utilize a novel neural ordinary differential equation (NODE) model for discerning BM post-SRS radionecrosis from recurrence. This approach integrates image-deep features, genomic biomarkers, and non-image clinical parameters within a synthesized latent feature space. The trajectory of each data sample towards the diagnosis decision can be visualized within this feature space, offering a new angle on radiogenomic data analysis foundational for AI explainability.

METHODS: By hypothesizing that deep feature extraction can be modeled as a spatiotemporally continuous process, we designed a novel model based on heavy ball NODE (HBNODE) in which deep feature extraction was governed by a second-order ODE. This approach enabled tracking of deep neural network (DNN) behavior by solving the HBNODE and observing the stepwise derivative evolution. Consequently, the trajectory of each sample within the Image-Genomic-Clinical (I-G-C) space became traceable. A decision-making field (F) was reconstructed within the feature space, with its gradient vectors directing the data samples' trajectories and intensities showing the potential. The evolution of F reflected the cumulative feature contributions at intermediate states to the final diagnosis, enabling quantitative and dynamic comparisons of the relative contribution of each feature category over time. A velocity curve was designed to determine key intermediate states (locoregional ∇F = 0) that are most predictive. Subsequently, a non-parametric model aggregated the optimal solutions from these key states to predict outcomes. Our dataset included 90 BMs from 62 NSCLC patients, and 3-month post-SRS T1+c MR image features, seven NSCLC genomic features, and seven clinical features were analyzed. An 8:2 train/test assignment was employed, and five independent models were trained to ensure robustness. Performance was benchmarked in sensitivity, specificity, accuracy, and ROCAUC, and results were compared against (1) a DNN using only image-based features, and (2) a combined "I+G+C" features without the HBNODE model.

RESULTS: The temporal evolution of gradient vectors and potential fields in F suggested that clinical features contribute the most during the initial stages of the HBNODE implementation, followed by imagery features taking dominance in the latter ones, while genomic features contribute the least throughout the process. The HBNODE model successfully identified and assembled key intermediate states, exhibiting competitive performance with an ROCAUC of 0.88 ± 0.04, sensitivity of 0.79 ± 0.02, specificity of 0.86 ± 0.01, and accuracy of 0.84 ± 0.01, where the uncertainties represent standard deviations. For comparison, the image-only DNN model achieved an ROCAUC of 0.71 ± 0.05 and sensitivity of 0.66 ± 0.32 (p = 0.086), while the "I+G+C" model without HBNODE reported an ROCAUC of 0.81 ± 0.02 and sensitivity of 0.58 ± 0.11 (p = 0.091).

CONCLUSION: The HBNODE model effectively identifies BM radionecrosis from recurrence, enhancing explainability within XAI frameworks. Its performance encourages further exploration in clinical settings and suggests potential applicability across various XAI domains.

PMID:39878595 | DOI:10.1002/mp.17635

J Chem Inf Model. 2025 Jan 29. doi: 10.1021/acs.jcim.4c01338. Online ahead of print.

ABSTRACT

Cell-penetrating peptides (CPPs) are short peptides capable of penetrating cell membranes, making them valuable for drug delivery and intracellular targeting. Accurate prediction of CPPs can streamline experimental validation in the lab. This study aims to assess pretrained protein language models (pLMs) for their effectiveness in representing CPPs and develop a reliable model for CPP classification. We evaluated peptide embeddings generated from BEPLER, CPCProt, SeqVec, various ESM variants (ESM, ESM-2 with expanded feature set, ESM-1b, and ESM-1v), ProtT5-XL UniRef50, ProtT5-XL BFD, and ProtBERT. We developed pLM4CCPs, a novel deep learning architecture using convolutional neural networks (CNNs) as the classifier for binary classification of CPPs. pLM4CCPs demonstrated superior performance over existing state-of-the-art CPP prediction models, achieving improvements in accuracy (ACC) by 4.9-5.5%, Matthews correlation coefficient (MCC) by 9.3-10.2%, and sensitivity (Sn) by 14.1-19.6%. Among all the tested models, ESM-1280 and ProtT5-XL BFD demonstrated the highest overall performance on the kelm data set. ESM-1280 achieved an ACC of 0.896, an MCC of 0.796, a Sn of 0.844, and a specificity (Sp) of 0.978. ProtT5-XL BFD exhibited superior performance with an ACC of 0.901, an MCC of 0.802, an Sn of 0.885, and an Sp of 0.917. pLM4CCPs combine predictions from multiple models to provide a consensus on whether a given peptide sequence is classified as a CPP or non-CPP. This approach will enhance prediction reliability by leveraging the strengths of each individual model. A user-friendly web server for bioactivity predictions, along with data sets, is available at https://ry2acnp6ep.us-east-1.awsapprunner.com. The source code and protocol for adapting pLM4CPPs can be accessed on GitHub at https://github.com/drkumarnandan/pLM4CPPs. This platform aims to advance CPP prediction and peptide functionality modeling, aiding researchers in exploring peptide functionality effectively.

PMID:39878455 | DOI:10.1021/acs.jcim.4c01338

Int J Gynecol Cancer. 2025 Jan;35(1):100017. doi: 10.1016/j.ijgc.2024.100017. Epub 2024 Dec 17.

ABSTRACT

OBJECTIVE: Evaluation of prognostic factors is crucial in patients with endometrial cancer for optimal treatment planning and prognosis assessment. This study proposes a deep learning pipeline for tumor and uterus segmentation from magnetic resonance imaging (MRI) images to predict deep myometrial invasion and cervical stroma invasion and thus assist clinicians in pre-operative workups.

METHODS: Two experts consensually reviewed the MRIs and assessed myometrial invasion and cervical stromal invasion as per the International Federation of Gynecology and Obstetrics staging classification, to compare the diagnostic performance of the model with the radiologic consensus. The deep learning method was trained using sagittal T2-weighted images from 142 patients and tested with a 3-fold stratified test with 36 patients in each fold. Our solution is based on a segmentation module, which employed a 2-stage pipeline for efficient uterus in the whole MRI volume and then tumor segmentation in the uterus predicted region of interest.

RESULTS: A total of 178 patients were included. For deep myometrial invasion prediction, the model achieved an average balanced test accuracy over 3-folds of 0.702, while experts reached an average accuracy of 0.769. For cervical stroma invasion prediction, our model demonstrated an average balanced accuracy of 0.721 on the 3-fold test set, while experts achieved an average balanced accuracy of 0.859. Additionally, the accuracy rates for uterus and tumor segmentation, measured by the Dice score, were 0.847 and 0.579 respectively.

CONCLUSION: Despite the current challenges posed by variations in data, class imbalance, and the presence of artifacts, our fully automatic approach holds great promise in supporting in pre-operative staging. Moreover, it demonstrated a robust capability to segment key regions of interest, specifically the uterus and tumors, highlighting the positive impact our solution can bring to health care imaging.

PMID:39878275 | DOI:10.1016/j.ijgc.2024.100017

ACS Electrochem. 2024 Oct 3;1(1):52-62. doi: 10.1021/acselectrochem.4c00014. eCollection 2025 Jan 2.

ABSTRACT

In electrochemical analysis, mechanism assignment is fundamental to understanding the chemistry of a system. The detection and classification of electrochemical mechanisms in cyclic voltammetry set the foundation for subsequent quantitative evaluation and practical application, but are often based on relatively subjective visual analyses. Deep-learning (DL) techniques provide an alternative, automated means that can support experimentalists in mechanism assignment. Herein, we present a custom DL architecture dubbed as EchemNet, capable of assigning both voltage windows and mechanism classes to electrochemical events within cyclic voltammograms of multiple redox events. The developed technique detects over 96% of all electrochemical events in simulated test data and shows a classification accuracy of up to 97.2% on redox events with 8 known mechanisms. This newly developed DL model, the first of its kind, proves the feasibility of redox-event detection and electrochemical mechanism classification with minimal a priori knowledge. The DL model will augment human researchers' productivity and constitute a critical component in a general-purpose autonomous electrochemistry laboratory.

PMID:39878149 | PMC:PMC11728721 | DOI:10.1021/acselectrochem.4c00014

Respir Care. 2023 Dec 28;69(1):114-127. doi: 10.4187/respcare.11298.

ABSTRACT

BACKGROUND: People with interstitial lung disease (ILD) want to actively manage their condition; however, the effects of self-management interventions (SMIs) in this population have not been synthesized. This review summarizes the effects of SMIs on health-related quality of life (HRQOL), functional status, psychological and social factors, symptoms, exacerbations, health care utilization, and survival in people with ILD.

METHODS: The protocol of this systematic review was registered (PROSPERO ID: CRD42022329199). Six digital databases were searched in May 2022 with monthly updates until August 2023. Studies implementing SMIs in people with any type of ILD were included. Risk of bias (RoB) and quality of evidence were assessed with the Cochrane tool for RoB assessment and the grading of recommendations, assessment, development, and evaluations. Meta-analysis was used to summarize the results.

RESULTS: Four studies that examined 217 participants (81% male, 71 y old, 91% idiopathic pulmonary fibrosis) were included. SMIs were highly heterogeneous. Meta-analysis showed no difference in HRQOL (standardized mean difference: 0.08 [95% CI -0.21 to 0.37], P .58) nor in the secondary outcomes. No evidence for the effects of SMIs on functional capacity, exacerbations, and survival was found. The quality of evidence ranged from low to very low.

CONCLUSIONS: There is low to very low-quality evidence that SMIs have no effect in people with ILD. However, this conclusion is limited by high methodological heterogeneity. A consensus definition of SMIs is needed to implement more comparable interventions and strengthen results.

PMID:39878768 | DOI:10.4187/respcare.11298

Biochem Cell Biol. 2025 Jan 1;103:1-7. doi: 10.1139/bcb-2024-0221.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a progressive and irreversible lung disease with high mortality and limited treatment options. While single-dose bleomycin-induced models are commonly used to investigate the pathogenesis of IPF, they fail to adequately replicate the complex pathological features in human patients, thereby hindering comprehensive investigations. Previous studies utilizing repetitive bleomycin injections have demonstrated a closer resemblance to human IPF pathology; however, the time- and resource-intensive nature of this approach presents significant drawbacks. Here, we propose a novel methodology involving twice-repeated oropharyngeal administration of bleomycin in mice, which closely mirrors the pathological manifestations observed in IPF patients. This model exhibited the honeycomb-like cyst formation, fibroblastic foci, bronchiolization of alveolar epithelium, emergence of metaplastic alveolar KRT5+ basal cells, and sustainability of these fibrotic phenotypes, thereby providing a robust model for IPF. Our findings establish a more efficient and translatable preclinical platform for investigating IPF pathogenesis and exploring potential therapeutic strategies.

PMID:39878201 | DOI:10.1139/bcb-2024-0221

J Biol Rhythms. 2025 Jan 29:7487304241310923. doi: 10.1177/07487304241310923. Online ahead of print.

ABSTRACT

The nature of biological research is changing, driven by the emergence of big data, and new computational models to parse out the information therein. Traditional methods remain the core of biological research but are increasingly either augmented or sometimes replaced by emerging data science tools. This presents a profound opportunity for those circadian researchers interested in incorporating big data and related analyses into their plans. Here, we discuss the emergence of novel sources of big data that could be used to gain real-world insights into circadian biology. We further discuss technical considerations for the biologist interested in including data science approaches in their research. We conversely discuss the biological considerations for data scientists so that they can more easily identify the nuggets of biological rhythms insight that might too easily be lost through application of standard data science approaches done without an appreciation of the way biological rhythms shape the variance of complex data objects. Our hope is that this review will make bridging disciplines in both directions (biology to computational and vice versa) easier. There has never been such rapid growth of cheap, accessible, real-world research opportunities in biology as now; collaborations between biological experts and skilled data scientists have the potential to mine out new insights with transformative impact.

PMID:39878301 | DOI:10.1177/07487304241310923

Comput Struct Biotechnol J. 2024 Dec 25;27:307-320. doi: 10.1016/j.csbj.2024.12.020. eCollection 2025.

ABSTRACT

According to global cancer statistics for the year 2022, based on updated estimates from the International Agency for Research on Cancer, there were approximately 20 million new cases of cancer in 2022 alongside 9.7 million related deaths. Lung, breast, colorectal, gastric, and liver cancers are the most common types of cancer. Despite advancements in anticancer drugs and optimised chemotherapy regimens that have improved cure rates for malignant tumours, the presence of tumour heterogeneity has resulted in substantial variations among patients in terms of disease progression, clinical response, sensitivity to therapy, and prognosis, posing significant challenges in attaining optimal therapeutic outcomes for each patient. Here, we collected five single-cell transcriptome datasets from patients with lung, breast, colorectal, gastric, and liver cancers and constructed multiple cancer blueprints of tumour cell heterogeneity. By integrating multiple bioinformatics analyses, we explored the biological differences underlying tumour cell heterogeneity at the single-cell level and identified tumour cell subcluster-specific biomarkers and potential therapeutic drugs for each subcluster. Interestingly, although tumour cell subpopulations exhibit dramatic differences within the same cancer type and between different cancers at both the genomic and transcriptomic levels, some demonstrate similar oncogenic pathway activities and phenotypes. Tumour cell subpopulations from the five cancers listed above were classified into three major groups corresponding to different treatment strategies. The findings of this study not only focus on the differences but also on the similarities among tumour cell subpopulations across different cancers, providing new insights for individualised therapy.

PMID:39877290 | PMC:PMC11773088 | DOI:10.1016/j.csbj.2024.12.020

Front Oncol. 2025 Jan 14;14:1514120. doi: 10.3389/fonc.2024.1514120. eCollection 2024.

ABSTRACT

Drug repositioning, the practice of identifying novel applications for existing drugs beyond their originally intended medical indications, stands as a transformative strategy revolutionizing pharmaceutical productivity. In contrast to conventional drug development approaches, this innovative method has proven to be exceptionally effective. This is particularly relevant for cancer therapy, where the demand for groundbreaking treatments continues to grow. This review focuses on drug repositioning for ovarian cancer treatment, showcasing a comprehensive exploration grounded in thorough in vitro experiments across diverse cancer cell lines, which are validated through preclinical in vivo models. These insights not only shed light on the efficacy of these drugs but also expand in potential synergies with other pharmaceutical agents, favoring the development of cost-effective treatments for cancer patients.

PMID:39876896 | PMC:PMC11772297 | DOI:10.3389/fonc.2024.1514120