Gen Physiol Biophys. 2025 Jan;44(1):1-11. doi: 10.4149/gpb_2024038.

ABSTRACT

Pulmonary alveolar proteinosis (PAP) is a rare disease characterised by excessive accumulation of surfactant components in alveolar macrophages, alveoli, and peripheral airways. The accumulation of surfactant is associated with only a minimal inflammatory response but can lead to the development of pulmonary fibrosis. Three clinical forms of PAP are distinguished - primary, secondary and congenital. In recent years, significant findings have helped to clarify the ethiology and pathogenesis of the disease. Apart from impaired surfactant protein function, a key role in the development of PAP is played by signal pathway of granulocyte and macrophage colonies stimulating growth factor (GM-CSF) which is necessary for the functioning of alveolar macrophages and for surfactant homeostasis. Surfactant is partially degraded by alveolar macrophages that are stimulated by GM-CSF. The role of GM-CSF has been shown especially in primary PAP, which is currently considered an autoimmune disease involving the development of GM-CSF neutralising autoantibodies. Clinically, the disease may be silent or manifest with dyspnoeic symptoms triggered by exertion and cough. However, there is a 10 to 15% rate of patients who develop respiratory failure. Total pulmonary lavage is regarded as the standard method of treatment. In addition, recombinant human GM-CSF has been studied as a prospective therapy for the treatment of PAP.

PMID:39815895 | DOI:10.4149/gpb_2024038

J Clin Endocrinol Metab. 2025 Jan 16:dgaf025. doi: 10.1210/clinem/dgaf025. Online ahead of print.

ABSTRACT

CONTEXT: The response to treatment with vitamin D varies between patients.

OBJECTIVE: To identify genetic variants associated with the biochemical response to vitamin D3 supplementation.

DESIGN: Randomized placebo-controlled trial conducted between 2017 and 2019.

SETTING: The trial was nested in an ongoing community-based cohort study, the Multi-Ethnic Study of Atherosclerosis (MESA).

INTERVENTION: 2,000 International Units of vitamin D3 or placebo daily for 16 weeks.

PARTICIPANTS: The analytic sample included 427 participants assigned to vitamin D3 (mean age 73 y, 54% females) and was 36% White, 33% Black, 18% Hispanic, and 14% Chinese.

MAIN OUTCOME MEASURES: The biochemical response to vitamin D3 included changes in serum concentrations of 1,25-dihydroxyvitamin D3 [1,25(OH)2D3], parathyroid hormone (PTH), and 25-hydroxyvitamin D3 [25(OH)D3].

RESULTS: In genome-wide analyses, SNPs in 8 regions of the genome had significant association (p < 5E-08) with one of the traits (2 with change in 1,25(OH)2D3, 1 with change in PTH, and 5 with change in 25(OH)D3). rs16867276 within an intergenic region on 2q31 was associated with change in serum 1,25(OH)2D3 (+8.37 pg/mL difference per effect allele; p = 4.93E-08) and was the only locus that achieved genome-wide significance in transethnic meta-analysis. rs114044709 adjacent to FAM20A, which encodes a protein required for biomineralization, was associated with change in PTH among Black participants (+20.32 pg/mL difference per effect allele; p = 1.34E-08). In candidate analyses, SNPs within SULT2A1 and CYP24A1 had significant association (p < 0.05 ÷ 36 = 0.0014) with the changes in 1,25(OH)2D3 and PTH, respectively.

CONCLUSIONS: Our results reveal potential new pathways of vitamin D regulation that require replication in other vitamin D trials.

PMID:39815761 | DOI:10.1210/clinem/dgaf025

Mol Genet Metab Rep. 2024 Dec 26;42:101182. doi: 10.1016/j.ymgmr.2024.101182. eCollection 2025 Mar.

ABSTRACT

BACKGROUND: Immediately after birth, adaptation to the extrauterine environment includes an upregulation of fatty acid catabolism. Cystic fibrosis and untreated hypothyroidism exert a life-long impact on fatty acid metabolism, but their influence during this transitional period is unknown. Children and adults with cystic fibrosis exhibit unbalanced fatty acid composition, most prominently a relative deficit of linoleic acid. Lipid catabolism is downregulated in hypothyroidism.

METHODS: We analyzed acylcarnitine data in newborn screening blood spot samples from infants with cystic fibrosis, with congenital hypothyroidism, or without congenital disorders. Eight long-chain acylcarnitine species were quantified. Of primary interest was the relative composition of linoleoylcarnitine (C18:2), the acylcarnitine of linoleic acid. Mixed effects modeling was used to determine the impact of disease status on acylcarnitine levels, accounting for possible covariates including birth weight, gestational age, sex and race.

RESULTS: Total long-chain acylcarnitine levels were diminished in newborns with cystic fibrosis and with congenital hypothyroidism. Contrary to expectations, C18:2 composition was elevated in newborns with cystic fibrosis and with congenital hypothyroidism, as compared to those without congenital disorders. Furthermore, higher thyroid-stimulating hormone levels, indicative of more severe hypothyroidism, predicted higher C18:2 composition.

CONCLUSIONS: Decreased total long-chain acylcarnitine concentrations in newborns with cystic fibrosis and congenital hypothyroidism suggest diminished beta-oxidation. However, the unexpected relative increase in C18:2 indicates selective preservation of linoleic acid beta-oxidation in both conditions. This is especially surprising in cystic fibrosis where linoleic acid levels become diminished and suggests that linoleic acid beta-oxidation contributes to the deficiency of linoleic acid in cystic fibrosis.

PMID:39816991 | PMC:PMC11732690 | DOI:10.1016/j.ymgmr.2024.101182

Mycoses. 2025 Jan;68(1):e70024. doi: 10.1111/myc.70024.

ABSTRACT

BACKGROUND: The airways of patients with cystic fibrosis (pwCF) harbour complex fungal and bacterial microbiota involved in pulmonary exacerbations (PEx) and requiring antimicrobial treatment. Descriptive studies analysing bacterial and fungal microbiota concomitantly are scarce, especially using both culture and high-throughput-sequencing (HTS).

OBJECTIVES: We analysed bacterial-fungal microbiota and inter-kingdom correlations in two French CF centres according to clinical parameters and antimicrobial choices.

METHODS: Forty-eight pwCF with PEx from Creteil (n = 24) and Lille (n = 24) CF centres were included over 2 years. Sputa were collected for culture and targeted-HTS (ITS2 and V3-V4 targets). Sequencing and culture data, along with clinical, radiological and treatment data, were analysed. Two-level stratified analysis was performed to study potential confounding factors (age, CF mutation, FEV1 and antibiotics) on the centre factor. Inter-kingdom correlations were analysed.

RESULTS: Significant differences in the bacterial microbiota profile were found between centres (p-value = 0.03). For mycobiota, the taxonomic distribution and diversity were comparable. HTS provided concordant but more detailed information than culture and increased detection of main CF fungi (> 25% more positive samples for Aspergillus or Scedosporium). FEV1 and systemic antibiotic before PEx influenced bacterial microbiota, but no clinical association was found with the mycobiota. No inter-kingdom correlation between Pseudomonas and fungi was found.

CONCLUSIONS: Describing concomitant bacterial and fungal communities of pwCF at the beginning of PEx using culture and HTS shows greater diversity in HTS and better detection in case of low microbial load. Interesting inter-kingdom correlations were observed, requiring further research on larger cohorts to understand the potential microbial interactions.

PMID:39816006 | DOI:10.1111/myc.70024

Ann Transl Med. 2024 Dec 24;12(6):116. doi: 10.21037/atm-24-171. Epub 2024 Dec 18.

ABSTRACT

BACKGROUND AND OBJECTIVE: Patients with thoracic aortic aneurysm and dissection (TAAD) are often asymptomatic but present acutely with life threatening complications that necessitate emergency intervention. Aortic diameter measurement using computed tomography (CT) is considered the gold standard for diagnosis, surgical planning, and monitoring. However, manual measurement can create challenges in clinical workflows due to its time-consuming, labour-intensive nature and susceptibility to human error. With advancements in artificial intelligence (AI), several models have emerged in recent years for automated aortic diameter measurement. This article aims to review the performance and clinical relevance of these models in relation to clinical workflows.

METHODS: We performed literature searches in PubMed, Scopus, and Web of Science to identify relevant studies published between 2014 and 2024, with the focus on AI and deep learning aortic diameter measurements in screening and diagnosis of TAAD.

KEY CONTENT AND FINDINGS: Twenty-four studies were retrieved in the past ten years, highlighting a significant knowledge gap in the field of translational medicine. The discussion included an overview of AI-powered models for aortic diameter measurement, as well as current clinical guidelines and workflows.

CONCLUSIONS: This article provides a thorough overview of AI and deep learning models designed for automatic aortic diameter measurement in the screening and diagnosis of thoracic aortic aneurysms (TAAs). We emphasize not only the performance of these technologies but also their clinical significance in enabling timely interventions for high-risk patients. Looking ahead, we envision a future where AI and deep learning-powered automatic aortic diameter measurement models will streamline TAAD clinical management.

PMID:39817238 | PMC:PMC11729799 | DOI:10.21037/atm-24-171

Adv Appl Bioinform Chem. 2025 Jan 10;17:159-178. doi: 10.2147/AABC.S488604. eCollection 2024.

ABSTRACT

PURPOSE: The incidence of cancer, which is a serious public health concern, is increasing. A predictive analysis driven by machine learning was integrated with haematology parameters to create a method for the simultaneous diagnosis of several malignancies at different stages.

PATIENTS AND METHODS: We analysed a newly collected dataset from various hospitals in Jordan comprising 19,537 laboratory reports (6,280 cancer and 13,257 noncancer cases). To clean and obtain the data ready for modelling, preprocessing steps such as feature standardization and missing value removal were used. Several cutting-edge classifiers were employed for the prediction analysis. In addition, we experimented with the dataset's missing values using the histogram gradient boosting (HGB) model.

RESULTS: The feature ranking method demonstrated the ability to distinguish cancer patients from healthy individuals based on hematological features such as WBCs, red blood cell (RBC) counts, and platelet (PLT) counts, in addition to age and creatinine level. The random forest (RF) classifier, followed by linear discriminant analysis (LDA) and support vector machine (SVM), achieved the highest prediction accuracy (ranging from 0.69 to 0.72 depending on the scenario and method investigated), reliably distinguishing between malignant and benign conditions. The HGB model showed improved performance on the dataset.

CONCLUSION: After investigating a number of machine learning methods, an efficient screening platform for non-invasive cancer detection is provided by the integration of haematological indicators with proper analytical data. Exploring deep learning methods in the future work, could provide insights into more complex patterns within the dataset, potentially improving the accuracy and robustness of the predictions.

PMID:39817190 | PMC:PMC11734259 | DOI:10.2147/AABC.S488604

Healthc Technol Lett. 2025 Jan 15;12(1):e12120. doi: 10.1049/htl2.12120. eCollection 2025 Jan-Dec.

ABSTRACT

Over the past decade, there has been a global increase in the incidence of skin cancers. Skin cancer has serious consequences if left untreated, potentially leading to more advanced cancer stages. In recent years, deep learning based convolutional neural network have emerged as powerful tools for skin cancer detection. Generally, deep learning approaches are computationally expensive and require large storage space. Therefore, deploying such a large complex model on resource-constrained devices is challenging. An ultra-light and accurate deep learning model is highly desirable for better inference time and memory in low-power-consuming devices. Knowledge distillation is an approach for transferring knowledge from a large network to a small network. This small network is easily compatible with resource-constrained embedded devices while maintaining accuracy. The main aim of this study is to develop a deep learning-based lightweight network based on knowledge distillation that identifies the presence of skin cancer. Here, different training strategies are implemented for the modified benchmark (Phase 1) and custom-made model (Phase 2) and demonstrated various distillation configurations on two datasets: HAM10000 and ISIC2019. In Phase 1, the student model using knowledge distillation achieved accuracies ranging from 88.69% to 93.24% for HAM10000 and from 82.14% to 84.13% on ISIC2019. In Phase 2, the accuracies ranged from 88.63% to 88.89% on HAM10000 and from 81.39% to 83.42% on ISIC2019. These results highlight the effectiveness of knowledge distillation in improving the classification performance across diverse datasets and enabling the student model to approach the performance of the teacher model. In addition, the distilled student model can be easily deployed on resource-constrained devices for automated skin cancer detection due to its lower computational complexity.

PMID:39816700 | PMC:PMC11733311 | DOI:10.1049/htl2.12120

Heliyon. 2024 Nov 19;11(1):e40148. doi: 10.1016/j.heliyon.2024.e40148. eCollection 2025 Jan 15.

ABSTRACT

Deformable image registration is a cornerstone of many medical image analysis applications, particularly in the context of fetal brain magnetic resonance imaging (MRI), where precise registration is essential for studying the rapidly evolving fetal brain during pregnancy and potentially identifying neurodevelopmental abnormalities. While deep learning has become the leading approach for medical image registration, traditional convolutional neural networks (CNNs) often fall short in capturing fine image details due to their bias toward low spatial frequencies. To address this challenge, we introduce a deep learning registration framework comprising multiple cascaded convolutional networks. These networks predict a series of incremental deformation fields that transform the moving image at various spatial frequency levels, ensuring accurate alignment with the fixed image. This multi-resolution approach allows for a more accurate and detailed registration process, capturing both coarse and fine image structures. Our method outperforms existing state-of-the-art techniques, including other multi-resolution strategies, by a substantial margin. Furthermore, we integrate our registration method into a multi-atlas segmentation pipeline and showcase its competitive performance compared to nnU-Net, achieved using only a small subset of annotated images as atlases. This approach is particularly valuable in the context of fetal brain MRI, where annotated datasets are limited. Our pipeline for registration and multi-atlas segmentation is publicly available at https://github.com/ValBcn/CasReg.

PMID:39816514 | PMC:PMC11732682 | DOI:10.1016/j.heliyon.2024.e40148

Heliyon. 2024 Jul 10;10(14):e34245. doi: 10.1016/j.heliyon.2024.e34245. eCollection 2024 Jul 30.

ABSTRACT

Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive neuroimaging technique widely utilized in the research of Autism Spectrum Disorder (ASD), providing preliminary insights into the potential biological mechanisms underlying ASD. Deep learning techniques have demonstrated significant potential in the analysis of rs-fMRI. However, accurately distinguishing between healthy control group and ASD has been a longstanding challenge. In this regard, this work proposes a model featuring a dual-path cross-attention framework for spatial and temporal patterns, named STDCformer, aiming to enhance the accuracy of ASD identification. STDCformer can preserve both temporal-specific patterns and spatial-specific patterns while explicitly interacting spatiotemporal information in depth. The embedding layer of the STDCformer embeds temporal and spatial patterns in dual paths. For the temporal path, we introduce a perturbation positional encoding to improve the issue of signal misalignment caused by individual differences. For the spatial path, we propose a correlation metric based on Gramian angular field similarity to establish a more specific whole-brain functional network. Subsequently, we interleave the query and key vectors of dual paths to interact spatial and temporal information. We further propose integrating the dual-path attention into a tensor that retains spatiotemporal dimensions and utilizing 2D convolution for feed-forward processing. Our attention layer allows the model to represent spatiotemporal correlations of signals at multiple scales to alleviate issues of information distortion and loss. Our STDCformer demonstrates competitive results compared to state-of-the-art methods on the ABIDE dataset. Additionally, we conducted interpretative analyses of the model to preliminarily discuss the potential physiological mechanisms of ASD. This work once again demonstrates the potential of deep learning technology in identifying ASD and developing neuroimaging biomarkers for ASD.

PMID:39816341 | PMC:PMC11734066 | DOI:10.1016/j.heliyon.2024.e34245

Cogn Neurodyn. 2025 Dec;19(1):22. doi: 10.1007/s11571-024-10202-0. Epub 2025 Jan 13.

ABSTRACT

Autism spectrum disorder (ASD) is one of the complicated neurodevelopmental disorders that impacts the daily functioning and social interactions of individuals. It includes diverse symptoms and severity levels, making it challenging to diagnose and treat efficiently. Various deep learning (DL) based methods have been developed for diagnosing ASD, which rely heavily on behavioral assessment. However, existing techniques have suffered from poor diagnostic outcomes, higher computational complexity, and overfitting issues. To address these challenges, this research work introduces an innovative framework called 3T Dilated Inception Network (3T-DINet) for effective ASD diagnosis using resting-state functional Magnetic Resonance Imaging (rs-fMRI) images. The proposed 3T-DINet technique designs a 3T dilated inception module that incorporates dilated convolutions along with the inception module, allowing it to extract multi-scale features from brain connectivity patterns. The 3T dilated inception module uses three distinct dilation rates (low, medium, and high) in parallel to determine local, mid-level, and global features from the brain. In addition, the proposed approach implements Residual networks (ResNet) to avoid the vanishing gradient problem and enhance the feature extraction ability. The model is further optimized using a Crossover-based Black Widow Optimization (CBWO) algorithm that fine-tunes the hyperparameters thereby enhancing the overall performance of the model. Further, the performance of the 3T-DINet model is evaluated using the five ASD datasets with distinct evaluation parameters. The proposed 3T-DINet technique achieved superior diagnosis results compared to recent previous works. From this simulation validation, it's clear that the 3T-DINet provides an excellent contribution to early ASD diagnosis and enhances patient treatment outcomes.

PMID:39816217 | PMC:PMC11729590 | DOI:10.1007/s11571-024-10202-0

Biomed Opt Express. 2024 Dec 11;16(1):126-141. doi: 10.1364/BOE.541570. eCollection 2025 Jan 1.

ABSTRACT

Gastric cancer is a leading cause of cancer-related deaths globally. As mortality rates continue to rise, predicting cancer survival using multimodal data-including histopathological images, genomic data, and clinical information-has become increasingly crucial. However, extracting effective predictive features from this complex data has posed challenges for survival analysis due to the high dimensionality and heterogeneity of histopathology images and genomic data. Furthermore, existing methods often lack sufficient interaction between intra- and inter-modal features, significantly impacting model performance. To address these challenges, we developed a deep learning-based multimodal feature fusion model, MultiDeepsurv, designed to predict the survival of gastric cancer patients by integrating histopathological images, clinical data, and gene expression data. Our approach includes a two-branch hybrid network, GLFUnet, which leverages the attention mechanism for enhanced pathology image representation learning. Additionally, we employ a graph convolutional neural network (GCN) to extract features from gene expression data and clinical information. To capture the correlations between different modalities, we utilize the SFusion fusion strategy that employs a self-attention mechanism to learn potential correlations across modalities. Finally, these deeply processed features are fed into Cox regression models for an end-to-end survival analysis. Comprehensive experiments and analyses conducted on a gastric cancer cohort from The Cancer Genome Atlas (TCGA) demonstrate that our proposed MultiDeepsurv model outperforms other methods in terms of prognostic accuracy, with a C-index of 0.806 and an AUC of 0.842.

PMID:39816158 | PMC:PMC11729289 | DOI:10.1364/BOE.541570

Biomed Opt Express. 2024 Dec 23;16(1):303-319. doi: 10.1364/BOE.547176. eCollection 2025 Jan 1.

ABSTRACT

Lung cancer with heterogeneity has a high mortality rate due to its late-stage detection and chemotherapy resistance. Liquid biopsy that discriminates tumor-related biomarkers in body fluids has emerged as an attractive technique for early-stage and accurate diagnosis. Exosomes, carrying membrane and cytosolic information from original tumor cells, impart themselves endogeneity and heterogeneity, which offer extensive and unique advantages in the field of liquid biopsy for cancer differential diagnosis. Herein, we demonstrate a Gramian angular summation field and MobileNet V2 (GASF-MobileNet)-assisted surface-enhanced Raman spectroscopy (SERS) technique for analyzing exosomes, aimed at precise diagnosis of lung cancer. Specifically, a composite substrate was synthesized for SERS detection of exosomes based on Ti3C2Tx Mxene and the array of gold-silver core-shell nanocubes (MGS), that combines sensitivity and signal stability. The employment of MXene facilitates the non-selective capture and enrichment of exosomes. To overcome the issue of potentially overlooking spatial features in spectral data analysis, 1-D spectra were first transformed into 2-D images through GASF. By using transformed images as the input data, a deep learning model based on the MobileNet V2 framework extracted spectral features from higher dimensions, which identified different non-small cell lung cancer (NSCLC) cell lines with an overall accuracy of 95.23%. Moreover, the area under the curve (AUC) for each category exceeded 0.95, demonstrating the great potential of integrating label-free SERS with deep learning for precise lung cancer differential diagnosis. This approach allows routine cancer management, and meanwhile, its non-specific analysis of SERS signatures is anticipated to be expanded to other cancers.

PMID:39816152 | PMC:PMC11729284 | DOI:10.1364/BOE.547176

Biomed Opt Express. 2024 Dec 3;16(1):28-41. doi: 10.1364/BOE.539721. eCollection 2025 Jan 1.

ABSTRACT

The study aimed to identify differences in the biochemical composition of corneal stroma lenses across varying degrees of myopia using Raman spectrum characteristics. Corneal stroma lens samples from 38 patients who underwent small incision lens extraction (SMILE) surgery, were categorized into low (n = 9, spherical power ≧ -3.00D), moderate (n = 23, spherical power < -3.00D and > -6.00D), and high myopia (n = 6, spherical power ≦-6.00D) groups. A custom-built microscopic confocal Raman system (MCRS) was used to collect Raman spectra, which were processed by smoothing, denoising, and baseline calibrating to refine raw data. Independent sample t-tests were used to analyze spectral feature peaks among sample types. Significant differences (P < 0.001) were found in multiple Raman spectral characteristic peaks (854 cm-1, 937 cm-1, 1002 cm-1, 1243 cm-1, 1448 cm-1, and 2940 cm-1) between low and high myopia samples, particularly at 2940 cm-1. Differences were also found between low and moderate, and moderate and high myopia samples, although fewer than between low and high myopia samples. The three-classification model, particularly with PLS-KNN training, exhibited superior discriminative performance with accuracy rates of 95%. Similarly, the two-classification model for low and high myopia achieved high accuracy with PLS-KNN (94.4%) compared to PCA-KNN (93.3%). PLS dimensionality reduction slightly outperformed PCA, enhancing classification accuracy. In addition, in both reduction methods, the KNN algorithm demonstrated the highest accuracy and performance. The optimal PLS-KNN classification model showed AUC values of 0.99, 0.98, and 1.00 for ROC curves corresponding to low, moderate, and high myopia, respectively. Classification accuracy rates were 89.7% and 96.9%, and 100% for low and high myopia, respectively. For the two-classification model, accuracy reached 94.4% with an AUC of 0.98, indicating strong performance in distinguishing between high and low myopic corneal stroma. We found significant biochemical differences such as collagen, lipids, and nucleic acids in corneal stroma lenses across varying degrees of myopia, suggesting that Raman spectroscopy holds substantial potential in elucidating the pathogenesis of myopia.

PMID:39816144 | PMC:PMC11729285 | DOI:10.1364/BOE.539721

Narra J. 2024 Dec;4(3):e1512. doi: 10.52225/narra.v4i3.1512. Epub 2024 Dec 19.

ABSTRACT

Psoriasis is a chronic skin condition with challenges in the accurate assessment of its severity due to subtle differences between severity levels. The aim of this study was to evaluate deep learning models for automated classification of psoriasis severity. A dataset containing 1,546 clinical images was subjected to pre-processing techniques, including cropping and applying noise reduction through median filtering. The dataset was categorized into four severity classes: none, mild, moderate, and severe, based on the Psoriasis Area and Severity Index (PASI). It was split into 1,082 images for training (70%) and 463 images for validation and testing (30%). Five modified deep convolutional neural networks (DCNN) were evaluated, including ResNet50, VGGNet19, MobileNetV3, MnasNet, and EfficientNetB0. The data were validated based on accuracy, precision, sensitivity, specificity, and F1-score, which were weighted to reflect class representation; Pairwise McNemar's test, Cochran's Q test, Cohen's Kappa, and Post-hoc test were performed on the model performance, where overall accuracy and balanced accuracy were determined. Findings revealed that among the five deep learning models, ResNet50 emerged as the optimum model with an accuracy of 92.50% (95%CI: 91.2-93.8%). The precision, sensitivity, specificity, and F1-score of this model were found to be 93.10%, 92.50%, 97.37%, and 92.68%, respectively. In conclusion, ResNet50 has the potential to provide consistent and objective assessments of psoriasis severity, which could aid dermatologists in timely diagnoses and treatment planning. Further clinical validation and model refinement remain required.

PMID:39816098 | PMC:PMC11731931 | DOI:10.52225/narra.v4i3.1512

ACS Pharmacol Transl Sci. 2024 Dec 28;8(1):256-269. doi: 10.1021/acsptsci.4c00671. eCollection 2025 Jan 10.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a debilitating, incurable, and life-threatening disease that lacks effective therapy. The overexpression of phosphodiesterase 10A (PDE10A) plays a vital role in pulmonary fibrosis (PF). However, the impact of selective PDE10A inhibitors on the tumor growth factor-β (TGF-β)/small mother against decapentaplegic (Smad) signaling pathway remains unclear. Herein, we have exploited a novel carbonyl sulfide (COS)/hydrogen sulfide (H2S)-donor hybrid PDE10A inhibitor called COS-2080 with a well-defined mechanism of H2S-releasing action. It exhibited highly potent inhibitory activity against PDE10A and excellent PDE subfamily selectivity. Moreover, COS-2080 demonstrated significant antifibrotic effects by inhibiting cell proliferation and mitigating fibroblast-to-myofibroblast transition (FMT). A dry powder inhalation formulation called COS-2080-DPI has been developed using the ultrasonic spray freeze drying (USFD) technique, demonstrating significant antifibrotic efficacy in mice with bleomycin-induced PF at a dosage approximately 600 times lower than pirfenidone. This remarkable antifibrotic efficacy of COS-2080 on TGF-β1-induced FMT could be primarily attributed to its inhibition of the Smad2/Smad3 phosphorylation. Moreover, COS-2080 effectively attenuated fibrosis in MRC-5 cells by activating the cAMP/protein kinase A (PKA)/CREB pathway and potentially increasing levels of p53 protein. Our findings suggest that effective inhibition of PDE10A potentially confers a protective effect on FMT in PF by impeding TGF-β signaling and activating the cAMP/PKA/CREB/p53 axis.

PMID:39816787 | PMC:PMC11729434 | DOI:10.1021/acsptsci.4c00671

Natl Sci Rev. 2024 Nov 26;12(1):nwae425. doi: 10.1093/nsr/nwae425. eCollection 2025 Jan.

ABSTRACT

Defining metabolic health is critical for the earlier reversing of metabolic dysfunction and disease, and fasting-based diagnosis may not adequately assess an individual's metabolic adaptivity under stress. We constructed a novel Health State Map (HSM) comprising a Health Phenotype Score (HPS) with fasting features alone and a Homeostatic Resilience Score (HRS) with five time-point features only (t = 30, 60, 90, 180, 240 min) following a standardized mixed macronutrient tolerance test (MMTT). Among 111 Chinese adults, when the same set of fasting and post-MMTT data as for the HSM was used, the mixed-score was highly correlated with the HPS. The HRS was significantly associated with metabolic syndrome prevalence, independently of the HPS (OR [95% CI]: 0.41 [0.18, 0.92]) and the mixed-score (0.34 [0.15, 0.69]). Moreover, the HRS could discriminate metabolic characteristics unseparated by the HPS and the mixed-score. Participants with higher HRSs had better metabolic traits than those with lower HRSs. Large interpersonal variations were also evidenced by evaluating postprandial homeostatic resiliencies for glucose, lipids and amino acids when participants had similar overall HRSs. Additionally, the HRS was positively associated with physical activity level and specific gut microbiome structure. Collectively, our HSM model might offer a novel approach to precisely define an individual's metabolic health and nutritional capacity.

PMID:39816947 | PMC:PMC11734281 | DOI:10.1093/nsr/nwae425

R Soc Open Sci. 2025 Jan 15;12(1):241252. doi: 10.1098/rsos.241252. eCollection 2025 Jan.

ABSTRACT

Adaptive divergence and increased genetic differentiation among populations can lead to reproductive isolation. In Lake Constance, Germany, a population of invasive three-spined stickleback (Gasterosteus aculeatus) is currently diverging into littoral and pelagic ecotypes, which both nest in the littoral zone. We hypothesized that assortative mating behaviour contributes to reproductive isolation between these ecotypes and performed a behavioural experiment in which females could choose between two nest-guarding males. Behaviour was recorded, and data on traits relevant to mate choice were collected. Both females of the same and different ecotypes were courted with equal vigour. However, there was a significant interaction effect of male and female ecotypes on the level of aggression in females. Littoral females were more aggressive towards pelagic males, and pelagic females were more aggressive towards littoral males. This indicates rejection of males of different ecotypes in spite of the fact that littoral males were larger, more intensely red-coloured and more aggressive than the pelagic males-all mating traits female sticklebacks generally select for. This study documents the emergence of behavioural barriers during early divergence in an invasive and rapidly diversifying stickleback population and discusses their putative role in facilitating reproductive isolation and adaptive radiation within this species.

PMID:39816745 | PMC:PMC11732402 | DOI:10.1098/rsos.241252

Front Antibiot. 2022 Sep 16;1:957942. doi: 10.3389/frabi.2022.957942. eCollection 2022.

ABSTRACT

We report here the identification of four gene functions of principal importance for the tolerance of meropenem stress in Escherichia coli: cell division, cell envelope synthesis and maintenance, ATP metabolism, and transcription regulation. The primary mechanism of β-lactam antibiotics such as meropenem is inhibition of penicillin binding proteins, thus interfering with peptidoglycan crosslinking, weakening the cell envelope, and promoting cell lysis. However, recent systems biology approaches have revealed numerous downstream effects that are triggered by cell envelope damage and involve diverse cell processes. Subpopulations of persister cells can also arise, which can survive elevated concentrations of meropenem despite the absence of a specific resistance factor. We used Transposon-Directed Insertion Sequencing with inducible gene expression to simultaneously assay the effects of upregulation, downregulation, and disruption of every gene in a model E. coli strain on survival of exposure to four concentrations of meropenem. Automated Gene Functional Classification and manual categorization highlighted the importance at all meropenem concentrations of genes involved in peptidoglycan remodeling during cell division, suggesting that cell division is the primary function affected by meropenem. Genes involved in cell envelope synthesis and maintenance, ATP metabolism, and transcriptional regulation were generally important at higher meropenem concentrations, suggesting that these three functions are therefore secondary or downstream targets. Our analysis revealed the importance of multiple two-component signal transduction mechanisms, suggesting an as-yet unexplored coordinated transcriptional response to meropenem stress. The inclusion of an inducible, transposon-encoded promoter allowed sensitive detection of genes involved in proton transport, ATP production and tRNA synthesis, for which modulation of expression affects survival in the presence of meropenem: a finding that would not be possible with other technologies. We were also able to suggest new targets for future antibiotic development or for synergistic effects between gene or protein inhibitors and existing antibiotics. Overall, in a single massively parallel assay we were able to recapitulate many of the findings from decades of research into β-lactam antibiotics, add to the list of genes known to be important for meropenem tolerance, and categorize the four principal gene functions involved.

PMID:39816415 | PMC:PMC11731830 | DOI:10.3389/frabi.2022.957942

Brain Commun. 2025 Jan 6;7(1):fcae478. doi: 10.1093/braincomms/fcae478. eCollection 2025.

ABSTRACT

Parkinson's disease is primarily marked by mitochondrial dysfunction and metabolic abnormalities. We recently reported that the combined metabolic activators improved the immunohistochemical parameters and behavioural functions in Parkinson's disease and Alzheimer's disease animal models and the cognitive functions in Alzheimer's disease patients. These metabolic activators serve as the precursors of nicotinamide adenine dinucleotide and glutathione, and they can be used to activate mitochondrial metabolism and eventually treat mitochondrial dysfunction. Here, we designed a randomized, double-blinded, placebo-controlled phase II study in Parkinson's disease patients with 84 days combined metabolic activator administration. A single dose of combined metabolic activator contains L-serine (12.35 g), N-acetyl-L-cysteine (2.55 g), nicotinamide riboside (1 g) and L-carnitine tartrate (3.73 g). Patients were administered either one dose of combined metabolic activator or a placebo daily for the initial 28 days, followed by twice-daily dosing for the next 56 days. The main goal of the study was to evaluate the clinical impact on motor functions using the Unified Parkinson's Disease Rating Scale and to determine the safety and tolerability of combined metabolic activator. A secondary objective was to assess cognitive functions utilizing the Montreal Cognitive Assessment and to analyse brain activity through functional MRI. We also performed comprehensive plasma metabolomics and proteomics analysis for detailed characterization of Parkinson's disease patients who participated in the study. Although no improvement in motor functions was observed, cognitive function was shown to be significantly improved (P < 0.0000) in Parkinson's disease patients treated with the combined metabolic activator group over 84 days, whereas no such improvement was noted in the placebo group (P > 0.05). Moreover, a significant reduction (P = 0.001) in Montreal Cognitive Assessment scores was observed in the combined metabolic activator group, with no decline (P > 0.05) in the placebo group among severe Parkinson's disease patients with lower baseline Montreal Cognitive Assessment scores. We showed that improvement in cognition was associated with critical brain network alterations based on functional MRI analysis, especially relevant to areas with cognitive functions in the brain. Finally, through a comprehensive multi-omics analysis, we elucidated the molecular mechanisms underlying cognitive improvements observed in Parkinson's disease patients. Our results show that combined metabolic activator administration leads to enhanced cognitive function and improved metabolic health in Parkinson's disease patients as recently shown in Alzheimer's disease patients. The trial was registered in ClinicalTrials.gov NCT04044131 (17 July 2019, https://clinicaltrials.gov/ct2/show/NCT04044131).

PMID:39816194 | PMC:PMC11733689 | DOI:10.1093/braincomms/fcae478

Immunometabolism (Cobham). 2025 Jan 10;7(1):e00053. doi: 10.1097/IN9.0000000000000053. eCollection 2025 Jan.

ABSTRACT

The nucleotide-binding domain, leucine-rich repeat, and pyrin domain containing-protein 3 (NLRP3) inflammasome is a multiprotein complex that plays a critical role in the innate immune response to both infections and sterile stressors. Dysregulated NLRP3 activation has been implicated in a variety of autoimmune and inflammatory diseases, including cryopyrin-associated periodic fever syndromes, diabetes, atherosclerosis, Alzheimer's disease, inflammatory bowel disease, and cancer. Consequently, fine-tuning NLRP3 activity holds significant therapeutic potential. Studies have implicated several organelles, including mitochondria, lysosomes, the endoplasmic reticulum (ER), the Golgi apparatus, endosomes, and the centrosome, in NLRP3 localization and inflammasome assembly. However, reports of conflict and many factors regulating interactions between NLRP3 and subcellular organelles remain unknown. This review synthesizes the current understanding of NLRP3 spatiotemporal dynamics, focusing on recent literature that elucidates the roles of subcellular localization and organelle stress in NLRP3 signaling and its crosstalk with other innate immune pathways converging at these organelles.

PMID:39816134 | PMC:PMC11731036 | DOI:10.1097/IN9.0000000000000053