Biol Reprod. 2025 Jan 20:ioaf009. doi: 10.1093/biolre/ioaf009. Online ahead of print.

ABSTRACT

Economic losses in cattle farms are frequently associated with failed pregnancies. Some studies found that the transcriptomic profiles of blood and endometrial tissues in cattle with varying pregnancy outcomes display discrepancies even before artificial insemination (AI) or embryo transfer (ET). In the study, 330 samples from seven distinct sources and two tissue types were integrated and divided into two groups based on the ability to establish and maintain pregnancy after AI or ET: P (pregnant) and NP (nonpregnant). By analyzing gene co-variation and employing machine learning algorithms, the objective was to identify genes that could predict pregnancy outcomes in cattle. Initially, within each tissue type, the top 100 differentially co-expressed genes (DCEG) were identified based on the analysis of changes in correlation coefficients and network topological structure. Subsequently, these genes were used in models trained by seven different machine learning algorithms. Overall, models trained on DCEGs exhibited superior predictive accuracy compared to those trained on an equivalent number of differential expression genes (DEGs). Among them, the deep learning models based on differential co-expression genes in blood and endometrial tissue achieved prediction accuracies of 91.7% and 82.6%, respectively. Finally, the importance of DCEGs was ranked using SHapley Additive exPlanations (SHAP) and enrichment analysis, identifying key signaling pathways that influence pregnancy. In summary, this study identified a set of genes potentially affecting pregnancy by analyzing the overall co-variation of gene connections between multiple sources. These key genes facilitated the development of interpretable machine learning models that accurately predict pregnancy outcomes in cattle.

PMID:39832283 | DOI:10.1093/biolre/ioaf009

Dentomaxillofac Radiol. 2025 Jan 20:twaf006. doi: 10.1093/dmfr/twaf006. Online ahead of print.

ABSTRACT

OBJECTIVES: This study aimed to develop an automated method for generating clearer, well-aligned panoramic views by creating an optimized three-dimensional (3D) reconstruction zone centered on the teeth. The approach focused on achieving high contrast and clarity in key dental features, including tooth roots, morphology, and periapical lesions, by applying a 3D U-Net deep learning model to generate an arch surface and align the panoramic view.

METHODS: This retrospective study analyzed anonymized cone-beam CT (CBCT) scans from 312 patients (mean age 40 years; range 10-78; 41.3% male, 58.7% female). A 3D U-Net deep learning model segmented the jaw and dentition, facilitating panoramic view generation. During preprocessing, CBCT scans were binarized, and a cylindrical reconstruction method aligned the arch along a straight coordinate system, reducing data size for efficient processing. The 3D U-Net segmented the jaw and dentition in two steps, after which the panoramic view was reconstructed using 3D spline curves fitted to the arch, defining the optimal 3D reconstruction zone. This ensured the panoramic view captured essential anatomical details with high contrast and clarity. To evaluate performance, we compared contrast between tooth roots and alveolar bone and assessed intersection over union (IoU) values for tooth shapes and periapical lesions (#42, #44, #46) relative to the conventional method, demonstrating enhanced clarity and improved visualization of critical dental structures.

RESULTS: The proposed method outperformed the conventional approach, showing significant improvements in the contrast between tooth roots and alveolar bone, particularly for tooth #42. It also demonstrated higher IoU values in tooth morphology comparisons, indicating superior shape alignment. Additionally, when evaluating periapical lesions, our method achieved higher performance with thinner layers, resulting in several statistically significant outcomes. Specifically, average pixel values within lesions were higher for certain layer thicknesses, demonstrating enhanced visibility of lesion boundaries and better visualization.

CONCLUSIONS: The fully automated AI-based panoramic view generation method successfully created a 3D reconstruction zone centered on the teeth, enabling consistent observation of dental and surrounding tissue structures with high contrast across reconstruction widths. By accurately segmenting the dental arch and defining the optimal reconstruction zone, this method shows significant advantages in detecting pathological changes, potentially reducing clinician fatigue during interpretation while enhancing clinical decision-making accuracy. Future research will focus on further developing and testing this approach to ensure robust performance across diverse patient cases with varied dental and maxillofacial structures, thereby increasing the model's utility in clinical settings.

ADVANCES IN KNOWLEDGE: This study introduces a novel method for achieving clearer, well-aligned panoramic views focused on the dentition, providing significant improvements over conventional methods.

PMID:39832267 | DOI:10.1093/dmfr/twaf006

J Food Sci. 2025 Jan;90(1):e70003. doi: 10.1111/1750-3841.70003.

ABSTRACT

Bruises can affect the appearance and nutritional value of apples and cause economic losses. Therefore, the accurate detection of bruise levels and bruise time of apples is crucial. In this paper, we proposed a method that combines a self-designed multispectral imaging system with deep learning to accurately detect the level and time of bruising on apples. To enhance the accuracy of extracting bruised regions with subtle features and irregular edges, an improved DeepLabV3+ was proposed. More specifically, depthwise separable convolution and efficient channel attention were employed, and the loss function was replaced with a focal loss. With these improvements, DeepLabV3+ achieved the maximum intersection over union of 95.5% and 91.0% for segmenting bruises on two types of apples in the test set, as well as maximum F1-score of 97.5% and 95.2%. In addition, the spectral data of the bruised regions were extracted. After spectral preprocessing, EfficientNetV2, DenseNet121, and ShuffleNetV2 were utilized to identify the bruise levels and times and DenseNet121 exhibited the best performance. To improve the identification accuracy, an improved DenseNet121 was proposed. The learning rate was adjusted using the cosine annealing algorithm, and squeeze-and-excitation attention mechanism and the Gaussian error linear unit activation function were utilized. Test set results demonstrated that the accuracies of the bruising levels were 99.5% and 99.1%, and those of the bruise time were 99.0% and 99.3%, respectively. This provides a new method for detecting bruise levels and bruised time on apples.

PMID:39832229 | DOI:10.1111/1750-3841.70003

Network. 2025 Jan 20:1-21. doi: 10.1080/0954898X.2025.2451388. Online ahead of print.

ABSTRACT

The image retrieval is the process of retrieving the relevant images to the query image with minimal searching time in internet. The problem of the conventional Content-Based Image Retrieval (CBIR) system is that they produce retrieval results for either colour images or grey scale images alone. Moreover, the CBIR system is more complex which consumes more time period for producing the significant retrieval results. These problems are overcome through the proposed methodologies stated in this work. In this paper, the General Image (GI) and Medical Image (MI) are retrieved using deep learning architecture. The proposed system is designed with feature computation module, Retrieval Convolutional Neural Network (RETCNN) module, and Distance computation algorithm. The distance computation algorithm is used to compute the distances between the query image and the images in the datasets and produces the retrieval results. The average precision and recall for the proposed RETCNN-based CBIRS is 98.98% and 99.15% respectively for GI category, and the average precision and recall for the proposed RETCNN-based CBIRS are 99.04% and 98.89% respectively for MI category. The significance of these experimental results is used to produce the higher image retrieval rate of the proposed system.

PMID:39832139 | DOI:10.1080/0954898X.2025.2451388

Oncologist. 2025 Jan 17;30(1):oyae341. doi: 10.1093/oncolo/oyae341.

ABSTRACT

BACKGROUND: Peritoneal metastasis (PM) after the rupture of hepatocellular carcinoma (HCC) is a critical issue that negatively affects patient prognosis. Machine learning models have shown great potential in predicting clinical outcomes; however, the optimal model for this specific problem remains unclear.

METHODS: Clinical data were collected and analyzed from 522 patients with ruptured HCC who underwent surgery at 7 different medical centers. Patients were assigned to the training, validation, and test groups in a random manner, with a distribution ratio of 7:1.5:1.5. Overall, 78 (14.9%) patients experienced postoperative PM. Five different types of models, including logistic regression, support vector machines, classification trees, random forests, and deep learning (DL) models, were trained using these data and evaluated based on their receiver operating characteristic curve and area under the curve (AUC) values and F1 scores.

RESULTS: The DL models achieved the highest AUC values (10-fold training cohort: 0.943, validation set: 0.928, and test set: 0.892) and F1 scores (10-fold training set: 0.917, validation cohort: 0.908, and test set:0.899) The results of the analysis indicate that tumor size, timing of hepatectomy, alpha-fetoprotein levels, and microvascular invasion are the most important predictive factors closely associated with the incidence of postoperative PM.

CONCLUSION: The DL model outperformed all other machine learning models in predicting postoperative PM after the rupture of HCC based on clinical data. This model provides valuable information for clinicians to formulate individualized treatment plans that can improve patient outcomes.

PMID:39832130 | DOI:10.1093/oncolo/oyae341

EJNMMI Phys. 2025 Jan 20;12(1):3. doi: 10.1186/s40658-024-00713-4.

ABSTRACT

BACKGROUND: The topic of the effect of the patient table on attenuation in myocardial perfusion imaging (MPI) SPECT is gaining new relevance due to deep learning methods. Existing studies on this effect are old, rare and only consider phantom measurements, not patient studies. This study investigates the effect of the patient table on attenuation based on the difference between reconstructions of phantom scans and polar maps of patient studies.

METHODS: Jaszczak phantom scans are acquired according to quality control and MPI procedures. An algorithm is developed to automatically remove the patient table from the CT for attenuation correction. The scans are then reconstructed with attenuation correction either with or without the patient table in the CT. The reconstructions are compared qualitatively and on the basis of their percentage difference. In addition, a small retrospective cohort of 15 patients is examined by comparing the resulting polar maps. Polar maps are compared qualitatively and based on the segment perfusion scores.

RESULTS: The phantom reconstructions look qualitatively similar in both the quality control and MPI procedures. The percentage difference is highest in the lower part of the phantom, but it always remains below 17.5%. Polar maps from patient studies also look qualitatively similar. Furthermore, the segment scores are not significantly different (p=0.83).

CONCLUSIONS: The effect of the patient table on attenuation in MPI SPECT is negligible.

PMID:39832088 | DOI:10.1186/s40658-024-00713-4

Int J Comput Assist Radiol Surg. 2025 Jan 20. doi: 10.1007/s11548-025-03323-2. Online ahead of print.

ABSTRACT

PURPOSE: Pulmonary perfusion imaging is a key lung health indicator with clinical utility as a diagnostic and treatment planning tool. However, current nuclear medicine modalities face challenges like low spatial resolution and long acquisition times which limit clinical utility to non-emergency settings and often placing extra financial burden on the patient. This study introduces a novel deep learning approach to predict perfusion imaging from non-contrast inhale and exhale computed tomography scans (IE-CT).

METHODS: We developed a U-Net Transformer architecture modified for Siamese IE-CT inputs, integrating insights from physical models and utilizing a self-supervised learning strategy tailored for lung function prediction. We aggregated 523 IE-CT images from nine different 4DCT imaging datasets for self-supervised training, aiming to learn a low-dimensional IE-CT feature space by reconstructing image volumes from random data augmentations. Supervised training for perfusion prediction used this feature space and transfer learning on a cohort of 44 patients who had both IE-CT and single-photon emission CT (SPECT/CT) perfusion scans.

RESULTS: Testing with random bootstrapping, we estimated the mean and standard deviation of the spatial Spearman correlation between our predictions and the ground truth (SPECT perfusion) to be 0.742 ± 0.037, with a mean median correlation of 0.792 ± 0.036. These results represent a new state-of-the-art accuracy for predicting perfusion imaging from non-contrast CT.

CONCLUSION: Our approach combines low-dimensional feature representations of both inhale and exhale images into a deep learning model, aligning with previous physical modeling methods for characterizing perfusion from IE-CT. This likely contributes to the high spatial correlation with ground truth. With further development, our method could provide faster and more accurate lung function imaging, potentially expanding its clinical applications beyond what is currently possible with nuclear medicine.

PMID:39832070 | DOI:10.1007/s11548-025-03323-2

Radiol Med. 2025 Jan 20. doi: 10.1007/s11547-025-01949-5. Online ahead of print.

ABSTRACT

BACKGROUND: Accurate differentiation between benign and malignant pancreatic lesions is critical for effective patient management. This study aimed to develop and validate a novel deep learning network using baseline computed tomography (CT) images to predict the classification of pancreatic lesions.

METHODS: This retrospective study included 864 patients (422 men, 442 women) with confirmed histopathological results across three medical centers, forming a training cohort, internal testing cohort, and external validation cohort. A novel hybrid model, Multi-Scale Large Kernel Attention with Mobile Vision Transformer (MVIT-MLKA), was developed, integrating CNN and Transformer architectures to classify pancreatic lesions. The model's performance was compared with traditional machine learning methods and advanced deep learning models. We also evaluated the diagnostic accuracy of radiologists with and without the assistance of the optimal model. Model performance was assessed through discrimination, calibration, and clinical applicability.

RESULTS: The MVIT-MLKA model demonstrated superior performance in classifying pancreatic lesions, achieving an AUC of 0.974 (95% CI 0.967-0.980) in the training set, 0.935 (95% CI 0.915-0.954) in the internal testing set, and 0.924 (95% CI 0.902-0.945) in the external validation set, outperforming traditional models and other deep learning models (P < 0.05). Radiologists aided by the MVIT-MLKA model showed significant improvements in diagnostic accuracy and sensitivity compared to those without model assistance (P < 0.05). Grad-CAM visualization enhanced model interpretability by effectively highlighting key lesion areas.

CONCLUSION: The MVIT-MLKA model efficiently differentiates between benign and malignant pancreatic lesions, surpassing traditional methods and significantly improving radiologists' diagnostic performance. The integration of this advanced deep learning model into clinical practice has the potential to reduce diagnostic errors and optimize treatment strategies.

PMID:39832039 | DOI:10.1007/s11547-025-01949-5

Brief Bioinform. 2024 Nov 22;26(1):bbaf009. doi: 10.1093/bib/bbaf009.

ABSTRACT

Single-cell high-throughput chromosome conformation capture (Hi-C) technology enables capturing chromosomal spatial structure information at the cellular level. However, to effectively investigate changes in chromosomal structure across different cell types, there is a requisite for methods that can identify cell types utilizing single-cell Hi-C data. Current frameworks for cell type prediction based on single-cell Hi-C data are limited, often struggling with features interpretability and biological significance, and lacking convincing and robust classification performance validation. In this study, we propose four new feature sets based on the contact matrix with clear interpretability and biological significance. Furthermore, we develop a novel deep learning framework named scHiClassifier based on multi-head self-attention encoder, 1D convolution and feature fusion, which integrates information from these four feature sets to predict cell types accurately. Through comprehensive comparison experiments with benchmark frameworks on six datasets, we demonstrate the superior classification performance and the universality of the scHiClassifier framework. We further assess the robustness of scHiClassifier through data perturbation experiments and data dropout experiments. Moreover, we demonstrate that using all feature sets in the scHiClassifier framework yields optimal performance, supported by comparisons of different feature set combinations. The effectiveness and the superiority of the multiple feature set extraction are proven by comparison with four unsupervised dimensionality reduction methods. Additionally, we analyze the importance of different feature sets and chromosomes using the "SHapley Additive exPlanations" method. Furthermore, the accuracy and reliability of the scHiClassifier framework in cell classification for single-cell Hi-C data are supported through enrichment analysis. The source code of scHiClassifier is freely available at https://github.com/HaoWuLab-Bioinformatics/scHiClassifier.

PMID:39831891 | DOI:10.1093/bib/bbaf009

J Thorac Dis. 2024 Dec 31;16(12):8379-8388. doi: 10.21037/jtd-24-1165. Epub 2024 Dec 11.

ABSTRACT

BACKGROUND: The benefits of pulmonary rehabilitation (PR) for patients with idiopathic pulmonary fibrosis (IPF) have been limited to improving dyspnea, exercise capacity, and quality of life (QoL). This study aimed to assess the current status of PR and its effect on prognosis.

METHODS: The Nationwide Korean Health Insurance Review and Assessment Service (HIRA) database was used in this study. Annual PR implementation rate since 2016 following its coverage in the health insurance was analyzed. IPF cases were defined using the International Classification of Diseases 10th Revision (ICD-10) codes and rare intractable diseases (RID) codes. Risk of acute exacerbation (AE) and mortality of IPF patients with or without PR were analyzed.

RESULTS: Of the 4,228 patients with IPF, only 205 (4.85%) received PR. Patients in the PR group were more frequently treated with pirfenidone and systemic steroids than non-PR group. In patients treated with steroids, mortality risk increased regardless of PR application, with hazard ratio (HR) of 1.63 [95% confidence interval (CI): 1.26-2.10, P<0.001] in the PR group and 1.38 (95% CI: 1.21-1.57, P<0.001) in the non-PR group, compared to those not treated with steroids. Additionally, PR did not significant affect mortality risk in patients not receiving steroids (HR, 1.49, 95% CI: 0.87-2.54, P=0.15). Similar patterns were seen for the risk of AE.

CONCLUSIONS: PR was applied in only a minority of patients with IPF. It did not succeed in reducing the risk of AE or mortality. A prospective study targeting early-stage patients is needed to evaluate the impact of PR considering the progressive nature of IPF disease itself.

PMID:39831231 | PMC:PMC11740027 | DOI:10.21037/jtd-24-1165

J Thorac Dis. 2024 Dec 31;16(12):8528-8537. doi: 10.21037/jtd-24-1356. Epub 2024 Dec 28.

ABSTRACT

BACKGROUND: Patients with idiopathic pulmonary fibrosis (IPF) are at risk of lung cancer development. Antifibrotic therapy could slow disease progression of IPF, but there is limited data on its effectiveness on lung cancer. Here, we aimed to investigate lung cancer incidence and the risk of mortality of patients with IPF receiving antifibrotic therapy.

METHODS: Data from the Korean National Health Insurance service database between October 2015 and September 2021 were used. The incidence of lung cancer and all-cause mortality in the IPF cohort was analyzed depending on pirfenidone treatment. Those who were diagnosed with lung cancer prior to IPF diagnosis were excluded.

RESULTS: Among the 5,038 patients with IPF who were eligible for the study, pirfenidone was administered to 880 patients. Median follow-up duration was 4,872.8 and 23,612.1 person-years in the groups receiving and not receiving pirfenidone, respectively. The incidence of lung cancer was significantly higher in the pirfenidone group compared to non-users [2.44 vs. 1.56 per 100 person-years; risk ratio 1.56; 95% confidence interval (CI), 1.27-1.92]. However, the risk of mortality did not differ significantly between patients receiving pirfenidone and those who did not. Further analysis was conducted to assess lung cancer development and pirfenidone therapy. Among patients with lung cancer, those treated with pirfenidone demonstrated significantly improved survival compared to those not receiving pirfenidone therapy (log-rank test, P<0.001). Pirfenidone therapy was associated with a protective effect on mortality in IPF patients with lung cancer [hazard ratio, 0.61; 95% CI, 0.43-0.85].

CONCLUSIONS: Antifibrotic therapy was associated with improved survival in patients with IPF who develop lung cancer, even though the incidence of lung cancer was higher in those receiving antifibrotic treatment compared to those do not.

PMID:39831219 | PMC:PMC11740043 | DOI:10.21037/jtd-24-1356

J Thorac Dis. 2024 Dec 31;16(12):8338-8349. doi: 10.21037/jtd-23-1908. Epub 2024 Dec 20.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) has high mortality and poor prognosis, which brings enormous burdens to families and society. We conducted this meta-analysis to analyze and summarize the risk factors associated with mortality in IPF, hoping to provide reference for clinical prevention and treatment of IPF.

METHODS: We conducted a comprehensive search of PubMed, Cochrane Library, Embase, and Web of Science from inception to August 10, 2023, to include cohort studies on mortality in patients with IPF. Two researchers independently screened the studies and extracted data. The Newcastle-Ottawa Scale (NOS) was used to assess the methodological quality of studies. Hazard ratios (HRs) and 95% confidence intervals (CIs) were reported to identify risk factors for mortality in IPF. In addition, we also carried out sensitivity analysis, Begg's and Egger's tests to evaluate the heterogeneity and publication bias.

RESULTS: Eighteen studies comprising 8,408 patients were included. The meta-analysis suggested that age (HR =1.03; 95% CI: 1.01, 1.04; P<0.001), forced vital capacity (FVC) (HR =0.97; 95% CI: 0.96, 0.99; P=0.005), FVC to predicted value ratio (FVC% pred) (HR =0.98; 95% CI: 0.97, 0.99; P<0.001), diffusing capacity of the lungs for carbon monoxide to predicted value ratio (DLCO% pred) (HR =0.98; 95% CI: 0.97, 0.99; P<0.001), gender-age-physiology (GAP) index (HR =1.70; 95% CI: 1.20, 2.40; P=0.003), and lung cancer (HR =2.75, 95% CI: 1.23, 6.15; P=0.01) were mortality-related risk factors in patients with IPF. Whereas, gender, smoking, body mass index (BMI), diffusing capacity of the lungs for carbon monoxide (DLCO), C-reactive protein (CRP), 6-minute walking distance (6MWD), pulmonary hypertension, gastroesophageal reflux, and cardiovascular disease were not statistically associated with death.

CONCLUSIONS: Age, FVC, FVC% pred, DLCO% pred, GAP index, and lung cancer have been identified as potential risk factors for mortality in patients with IPF. Due to the limited number and quality of included studies, the conclusions need to be verified by further studies.

PMID:39831203 | PMC:PMC11740034 | DOI:10.21037/jtd-23-1908

NPJ Biosens. 2025;2(1):2. doi: 10.1038/s44328-024-00018-7. Epub 2025 Jan 16.

ABSTRACT

The reactivation of heterotrimeric protein phosphatase 2A (PP2A) through small molecule activators is of interest to therapeutic intervention due to its dysregulation, which is linked to chronic conditions. This study focuses on the PP2A scaffold subunit PR65 and a small molecule activator, ATUX-8385, designed to bind directly to this subunit. Using a label-free single-molecule approach with nanoaperture optical tweezers (NOT), we quantify its binding, obtaining a dissociation constant of 13.6 ± 2.5 μM, consistent with ensemble fluorescence anisotropy results but challenging to achieve with other methods due to low affinity. Single-molecule NOT measurements reveal that binding increases optical scattering, indicating PR65 elongation. This interpretation is supported by all-atom molecular dynamics simulations showing PR65 adopts more extended conformations upon binding. This work highlights NOT's utility in quantifying binding kinetics and structural impact, offering insights valuable for drug discovery.

PMID:39830999 | PMC:PMC11738983 | DOI:10.1038/s44328-024-00018-7

Iran J Pharm Res. 2024 Sep 15;23(1):e149463. doi: 10.5812/ijpr-149463. eCollection 2024 Jan-Dec.

ABSTRACT

BACKGROUND: Dry eye disease (DED) is a multifactorial condition characterized by ocular surface inflammation, tear film instability, and corneal epithelial damage. Current treatments often provide temporary relief without addressing the underlying inflammatory mechanisms.

OBJECTIVES: This study examined the therapeutic potential of crocin and nobiletin, two naturally derived compounds with well-known antioxidant and anti-inflammatory properties, in a mouse model of DED induced by lacrimal gland excision (LGE).

METHODS: Thirty female Balb/c mice were divided into five groups (n = 6 each): Control (sham surgery), untreated DED, nobiletin-treated DED (32.75 µM), crocin-treated DED (34 µM), and 1% betamethasone-treated DED. Treatments were administered three times daily for 28 days. Ocular tissues were evaluated using Hematoxylin and Eosin (H&E) staining and fluorescein staining. Conjunctival inflammatory cytokines, including interleukin-6 (IL-6), interleukin-1 beta (IL-1β), and tumor necrosis factor-alpha (TNF-α), were measured by enzyme-linked immunosorbent assay (ELISA).

RESULTS: Histological analysis showed that the crocin and nobiletin treatment groups exhibited reduced epithelial disruption, keratinization, and inflammatory cell infiltration compared to the untreated DED group. The ELISA assay revealed that both compounds efficiently inhibited the production of the pro-inflammatory cytokines IL-6, TNF-α, and IL-1β, which are key mediators of DED pathogenesis. Fluorescein staining further confirmed the protective impact of crocin and nobiletin on corneal epithelial integrity. Moreover, the anti-inflammatory and epithelial-preserving effects of these compounds were comparable to those of the corticosteroid betamethasone.

CONCLUSIONS: Overall, these findings suggest that crocin and nobiletin have therapeutic potential for DED management by modulating inflammatory responses and enhancing ocular surface healing. These naturally derived compounds offer promising avenues for the development of safer and more effective treatments for this challenging condition. However, further investigations, including clinical trials, are essential to elucidate the underlying mechanisms of action and optimize therapeutic approaches.

PMID:39830666 | PMC:PMC11742122 | DOI:10.5812/ijpr-149463

Natl Sci Rev. 2024 Dec 9;12(2):nwae441. doi: 10.1093/nsr/nwae441. eCollection 2025 Feb.

ABSTRACT

The enormous computational requirements and unsustainable resource consumption associated with massive parameters of large language models and large vision models have given rise to challenging issues. Here, we propose an interpretable 'small model' framework characterized by only a single core-neuron, i.e. the one-core-neuron system (OCNS), to significantly reduce the number of parameters while maintaining performance comparable to the existing 'large models' in time-series forecasting. With multiple delay feedback designed in this single neuron, our OCNS is able to convert one input feature vector/state into one-dimensional time-series/sequence, which is theoretically ensured to fully represent the states of the observed dynamical system. Leveraging the spatiotemporal information transformation, the OCNS shows excellent and robust performance in forecasting tasks, in particular for short-term high-dimensional systems. The results collectively demonstrate that the proposed OCNS with a single core neuron offers insights into constructing deep learning frameworks with a small model, presenting substantial potential as a new way for achieving efficient deep learning.

PMID:39830389 | PMC:PMC11737406 | DOI:10.1093/nsr/nwae441

ISME Commun. 2024 Dec 18;5(1):ycae164. doi: 10.1093/ismeco/ycae164. eCollection 2025 Jan.

ABSTRACT

Harmful Algal Blooms (HABs) of the toxigenic dinoflagellate Karenia brevis (KB) are pivotal in structuring the ecosystem of the Gulf of Mexico (GoM), decimating coastal ecology, local economies, and human health. Bacterial communities associated with toxigenic phytoplankton species play an important role in influencing toxin production in the laboratory, supplying essential factors to phytoplankton and even killing blooming species. However, our knowledge of the prevalence of these mechanisms during HAB events is limited, especially for KB blooms. Here, we introduced native microbial communities from the GoM, collected during two phases of a Karenia bloom, into KB laboratory cultures. Using bacterial isolation, physiological experiments, and shotgun metagenomic sequencing, we identified both putative enhancers and mitigators of KB blooms. Metagenome-assembled genomes from the Roseobacter clade showed strong correlations with KB populations during HABs, akin to symbionts. A bacterial isolate from this group of metagenome-assembled genomes, Mameliella alba, alleviated vitamin limitations of KB by providing it with vitamins B1, B7 and B12. Conversely, bacterial isolates belonging to Bacteroidetes and Gammaproteobacteria, Croceibacter atlanticus, and Pseudoalteromonas spongiae, respectively, exhibited strong algicidal properties against KB. We identified a serine protease homolog in P. spongiae that putatively drives the algicidal activity in this isolate. While the algicidal mechanism in C. atlanticus is unknown, we demonstrated the efficiency of C. atlanticus to mitigate KB growth in blooms from the GoM. Our results highlight the importance of specific bacteria in influencing the dynamics of HABs and suggest strategies for future HAB management.

PMID:39830096 | PMC:PMC11740886 | DOI:10.1093/ismeco/ycae164

ACS Omega. 2024 Dec 20;10(1):1419-1428. doi: 10.1021/acsomega.4c09036. eCollection 2025 Jan 14.

ABSTRACT

The human gut microbiota (HGM) is a complex ecosystem subtly dependent on the interplay between hundreds of bacterial species and numerous metabolites. Dietary phenols, whether ingested (e.g., plant-derived guaiacol, mequinol, or resveratrol) or products of bacterial fermentation (e.g., p-cresol), have been attributed with influencing bacterial growth and host health. They are cleared by phase II metabolism, one form utilizing β-d-glucuronidation, but encounter bacterially derived glucuronidases capable of hydrolyzing them to release their phenolic and glucuronic acid moieties with potential effects on host cells or the surrounding bacterial population. Tools to enable the detailed study of their activity are currently lacking. Syntheses of β-d-glucuronides from methyl 1,2,3,4 tetra-acetyl β-d-glucopyranosyluronate by direct glycosylation with 2-, 3-, or 4-methoxy- and 4-fluorophenol acceptors employing trimethylsilyl triflate catalysis are reported. Yields (methoxy series) were modest. An improved route from methyl 1,2,3,4-tetra-acetyl β-d-glucopyranosyluronate via selective anomeric deprotection (N-methyl piperazine) and conversion to an α-trichloroacetimidate glycosyl donor was employed. Coupling with 2- and 3-methoxyphenol acceptors and deprotection provided 2- and 3-methoxyphenyl β-d-glucuronides in 2-fold improved overall yield. These naturally occurring methoxyphenyl glucuronides augment available model substrates of dietary glucuronides, which include 3- and 4'-linked resveratrol. The use of model glucuronides as substrates was illustrated in studies of β-d-glucuronidase activity employing cell lysates of 9 species of HGM (Bacteroidetes), revealing distinct outcomes. Contrasting effects on bacterial growth were also observed between the free phenolic components, their respective glucuronides, and glucuronic acid. The glucuronide of 4-fluorophenol provided sensitive and background-free detection of β-glucuronidase activity using 19F NMR.

PMID:39829562 | PMC:PMC11740244 | DOI:10.1021/acsomega.4c09036

Psychopharmacology (Berl). 2025 Jan 20. doi: 10.1007/s00213-025-06743-9. Online ahead of print.

ABSTRACT

RATIONALE AND OBJECTIVES: In vivo receptor interactions vary as a function of behavioral endpoint, with key differences between reflexive and non-reflexive measures that assess the motivational aspects of pain and pain relief. There have been no assessments of D1 dopamine agonist / mu opioid receptor (MOR) agonist interactions in non-reflexive behavioral measures of pain. We examined the hypothesis that D1/MOR mixtures show enhanced effectiveness in blocking pain depressed behaviors while showing decreased side effects such as sedation and drug reward.

METHODS: SKF82958 and methadone were used as selective/high efficacy D1 and mu agonists, respectively. An FR10 operant schedule was utilized in the presence and absence of a lactic acid inflammatory pain-like manipulation, to measure antinociceptive and operant-rate-suppressing effects, respectively. Rewarding properties of the drug combinations were determined using a conditioned place preference procedure.

RESULTS: Methadone alone, but not SKF82958 alone, produced dose-dependent restoration of pain-depressed responding. Both SKF82958 and methadone produced dose-dependent response rate suppression. Three fixed proportion mixtures, based on the relative potencies of the drugs in the rate suppression assay, produced dose-dependent antinociception and sedation. Isobolographic analysis indicated that the 0.17:1 mixture produced supra-additive antinociception and additive sedation. The 0.055:1 mixture produced additive antinociception with sub-additive sedation, and the 0.018:1 mixture had the highest therapeutic index (TI) relative to other mixtures and drugs alone. The antinociceptive doses and component doses for the 0.018:1 mixture did not produce conditioned place preference.

CONCLUSIONS: These results suggest that D1-selective dopamine agonists may have utility as candidate opioid-sparing analgesics.

PMID:39832015 | DOI:10.1007/s00213-025-06743-9

Pharmacotherapy. 2025 Jan 20. doi: 10.1002/phar.4648. Online ahead of print.

ABSTRACT

INTRODUCTION: Heart failure (HF) affects more than 6 million adults in the United States, contributing to substantial morbidity, mortality, and health care costs. Despite advances in medical care, many medications can exacerbate HF, yet their prevalence of use remains unknown. This study examined the national use of prescription medications that could exacerbate HF in adults with self-reported HF.

METHODS: We analyzed data from US adults with self-reported HF in the National Health and Nutrition Examination Survey (NHANES) from 2011 to March 2020. Medications known to exacerbate HF, identified from HF guidelines, were documented through pill bottle reviews. Weighted estimates were used to calculate prevalence overall and by sex, race and ethnicity, and level of evidence for avoidance. Multivariable logistic regression models calculated adjusted odds ratios (aORs) and 95% confidence intervals (95% CIs) for the use of these high-risk medications by sex and race and ethnicity.

RESULTS: A total of 687 participants, representing 5.2 million U.S. adults with HF after applying sampling weights, were included (mean age, 66.1 [95% CI 64.9, 67.4] years; 50.4% female [95% CI 45.9%, 55.0%]). Overall, 14.5% (95% CI 10.4%, 19.5%; n = 92) of adults with HF were prescribed at least one medication known to exacerbate HF, with the most common being diltiazem, meloxicam, and ibuprofen. Use of these medications was not significantly different by sex nor by race and ethnicity. Of these medications, 21.7% (95% CI 10.7%, 38.8%) had level A evidence warning against use, and 78.3% (95% CI 61.2%, 89.3%) had B level evidence.

CONCLUSION: Over one-seventh of U.S. adults with HF were likely to have been prescribed medications that could exacerbate the condition, underscoring the need to optimize care. Reducing high-risk medication use may mitigate HF exacerbations and improve outcomes in this vulnerable population.

PMID:39831652 | DOI:10.1002/phar.4648

Br J Hosp Med (Lond). 2024 Dec 30;85(12):1-12. doi: 10.12968/hmed.2024.0380. Epub 2024 Jul 16.

ABSTRACT

Aims/Background Tinnitus is a very common condition, and is a side effect of many medications. The panorama of drug-induced tinnitus has widened in recent decades, and post-marketing data are needed to gain a better insight into adverse drug reactions related to tinnitus. However, there are currently few studies on drug-induced tinnitus. We aimed to explore the details of real-world drug-related tinnitus. Methods We collected data on adverse drug reactions related to tinnitus from the Food and Drug Administration Adverse Event Reporting System (FAERS) database for the fourth quarter of 2012 to the fourth quarter of 2023. The top 25 tinnitus-associated drugs and indications were analyzed, and reporting odds ratios (RORs) were used to assess the association between drugs and adverse events (AEs). Results A total of 29,460 patients were enrolled in our study, with a greater proportion of women (59.1%) than men (31.7%). Among all tinnitus-related drugs, duloxetine (n = 1510, ROR [95% confidence interval (CI)] = 11.99 [11.38-12.63]), ciprofloxacin (n = 938, ROR [95% CI] = 9.96 [9.33-10.63]), and adalimumab (n = 759, ROR [95% CI] = 0.68 [0.64-0.73]) displayed the strongest associations. Among all tinnitus-related indications, depression (n = 1172), rheumatoid arthritis (n = 947), and multiple sclerosis (n = 914) were the most relevant indications. Vertigo (n = 2443, ROR [95% CI] = 7.51 [7.21-7.82]), deafness (n = 1740, ROR [95% CI] = 13.50 [12.86-14.18]), and hypoacusis (n = 1550, ROR [95% CI] = 6.11 [5.81-6.43]) were the most common concomitant ototoxic AEs in patients reporting tinnitus. Conclusion Our study mined and analyzed the AEs signals of drug-induced tinnitus and provided a reference for the safe clinical application of the drugs.

PMID:39831503 | DOI:10.12968/hmed.2024.0380