Cell Rep Methods. 2025 Jan 5:100938. doi: 10.1016/j.crmeth.2024.100938. Online ahead of print.

ABSTRACT

Single-cell RNA sequencing (scRNA-seq) is invaluable for profiling cellular heterogeneity and transcriptional states, but transcriptomic profiles do not always delineate subsets defined by surface proteins. Cellular indexing of transcriptomes and epitopes (CITE-seq) enables simultaneous profiling of single-cell transcriptomes and surface proteomes; however, accurate cell-type annotation requires a classifier that integrates multimodal data. Here, we describe multimodal classifier hierarchy (MMoCHi), a marker-based approach for accurate cell-type classification across multiple single-cell modalities that does not rely on reference atlases. We benchmark MMoCHi using sorted T lymphocyte subsets and annotate a cross-tissue human immune cell dataset. MMoCHi outperforms leading transcriptome-based classifiers and multimodal unsupervised clustering in its ability to identify immune cell subsets that are not readily resolved and to reveal subset markers. MMoCHi is designed for adaptability and can integrate annotation of cell types and developmental states across diverse lineages, samples, or modalities.

PMID:39814026 | DOI:10.1016/j.crmeth.2024.100938

Microbiol Res. 2025 Jan 9;293:128052. doi: 10.1016/j.micres.2025.128052. Online ahead of print.

ABSTRACT

Pseudomonas aeruginosa is a prominent respiratory pathogen in cystic fibrosis (CF) patients, thriving in the hypoxic airway mucus. Previous studies have established the role of the oxygen-binding hemerythrin, Mhr, in enhancing P. aeruginosa's fitness under microaerobic conditions. However, the specific mechanisms by which Mhr operates remain unclear. This study uniquely identifies Mhr as an effector of the H2-Type VI Secretion System (H2-T6SS) and elucidates its role in the transport and interaction mechanisms that confer a growth advantage under microaerobic conditions. Our findings demonstrate that mhr expression is directly regulated by Anr and Dnr. Western blot analysis confirms that Mhr is secreted extracellularly via the H2-T6SS. The oxygen-binding Mhr re-enters P. aeruginosa through the OprG porin. Then, Mhr interacts with cbb3-type cytochrome c oxidase (cbb3-CcO) subunits CcoP1/CcoP2, significantly impacting intracellular NADH/NAD+ levels. These insights suggest that the T6SS-mediated secretion and transport of Mhr represent a novel mechanism by which P. aeruginosa acquires and delivers oxygen, potentially enhancing microaerobic respiration, energy production, and growth under microaerobic conditions.

PMID:39813750 | DOI:10.1016/j.micres.2025.128052

J Bras Pneumol. 2025 Jan 13;50(6):e20240301. doi: 10.36416/1806-3756/e20240301. eCollection 2025.

ABSTRACT

OBJECTIVE: Surgical resection remains the gold standard treatment for bronchiectasis in patients who present with hemoptysis or suppuration, as well as in those who do not respond to clinical treatment. We sought to investigate the efficacy of sublobar resection (segmentectomy) and compare it with that of lobar resection (lobectomy) in patients with non-cystic fibrosis bronchiectasis.

METHODS: Patients undergoing lobectomy or segmentectomy between 2019 and 2023 were included in the study. We analyzed intraoperative complications and postoperative outcomes, including length of hospital stay, length of ICU stay, and disease recurrence.

RESULTS: There was no significant difference between the lobectomy and segmentectomy groups regarding the occurrence of intraoperative complications such as bleeding > 1000 ml, cardiogenic shock, and ventilatory instability (p > 0.999). However, the frequency of complications was significantly lower in the segmentectomy group than in the lobectomy group (p = 0.016). Hospital stays were longer in the lobectomy group than in the segmentectomy group (16 days vs. 5 days; p = 0.027), as were ICU stays (7 days vs. 1 day; p = 0.006). There was no significant difference between the lobectomy and segmentectomy groups regarding the recurrence rate (p = 0.541).

CONCLUSIONS: Early identification of bronchiectasis patients who are candidates for surgical resection is essential because those who are identified as such early on are candidates for parenchyma-sparing resections, which are similar to lobar resections in terms of disease control and lead to shorter hospital stays and better postoperative outcomes.

PMID:39813502 | DOI:10.36416/1806-3756/e20240301

PLoS One. 2025 Jan 15;20(1):e0317054. doi: 10.1371/journal.pone.0317054. eCollection 2025.

ABSTRACT

The epidemiology of allergic bronchopulmonary aspergillosis (ABPA) in the United States is not well-described. To estimate national ABPA prevalence among patients with asthma or cystic fibrosis, characterize ABPA testing practices, and describe ABPA clinical features, treatment, and 6-month outcomes. We used the 2016-2022 Merative™ MarketScan® Commercial/Medicare and Multi-State Medicaid Databases to identify cohorts of patients with 1) asthma, 2) cystic fibrosis (CF), and 3) ABPA. We calculated ABPA prevalence per 10,000 patients with asthma or CF, assessed diagnostic testing for ABPA among patients with severe asthma, and described features of patients with ABPA using diagnosis and procedure codes. The overall ABPA prevalence among patients with asthma was 2.8/10,000 (Commercial/Medicare) and 1.0/10,000 (Medicaid). ABPA prevalence increased with asthma severity (Commercial/Medicare: mild 1.3, moderate 9.3, severe 70.6, Medicaid: mild 0.3, moderate 2.4, severe 32.4). Among patients with CF, ABPA prevalence was 183.7/10,000 (Commercial/Medicare) and 134.6/10,000 (Medicaid). Among patients with severe asthma, 10.3% (Commercial/Medicare) and 7.4% (Medicaid) received total immunoglobulin E testing, which is recommended for ABPA diagnosis. Among all patients with ABPA (Commercial/Medicare: n = 1,564, Medicaid: n = 410), ABPA treatments included inhaled corticosteroids (>70%), systemic corticosteroids (>62%), and antifungals (>18%). Patients with ABPA and Medicaid were more likely to experience hospitalization (45.1% vs. 22.5% of patients with Commercial/Medicare insurance) and respiratory failure (18.5% vs. 10.9%). This analysis provides initial estimates of national ABPA prevalence. Further studies could identify potential barriers to ABPA testing and investigate potential factors affecting payer-related differences in ABPA burden.

PMID:39813272 | DOI:10.1371/journal.pone.0317054

Proc Natl Acad Sci U S A. 2025 Jan 21;122(3):e2412717122. doi: 10.1073/pnas.2412717122. Epub 2025 Jan 15.

ABSTRACT

Monocytes are critical in controlling tissue infections and inflammation. Monocyte dysfunction contributes to the inflammatory pathogenesis of cystic fibrosis (CF) caused by CF transmembrane conductance regulator (CFTR) mutations, making CF a clinically relevant disease model for studying the contribution of monocytes to inflammation. Although CF monocytes exhibited adhesion defects, the precise mechanism is unclear. Herein, superresolution microscopy showed that an integrin clustering but not an integrin activation defect determines the adhesion defect in CFTR-deficient monocytes, challenging the existing paradigm emphasizing an integrin activation defect in CF patient monocytes. We further found that the clustering defect is accompanied by defects in CORO1A membrane recruitment, actin cortex formation, and CORO1A engagement with integrins. Complementing canonical studies of leukocyte adhesion focusing on integrin activation, we highlight the importance of integrin clustering in cell adhesion and report that integrin clustering and activation are distinctly regulated, warranting further investigation for selective targeting in therapeutic strategy design involving leukocyte-dependent inflammation.

PMID:39813254 | DOI:10.1073/pnas.2412717122

Inflamm Bowel Dis. 2025 Jan 15:izae302. doi: 10.1093/ibd/izae302. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Patients with very early-onset inflammatory bowel disease (VEO-IBD), with an age of onset < 6 years, can present with severe manifestations and may require biologic therapy. Infliximab and adalimumab are approved for induction and maintenance in pediatric IBD patients but are licensed only above the age of 6 years. Effectiveness and safety data on adalimumab in this patient population are lacking. We assessed the therapeutic response to help close this gap.

METHODS: This retrospective study involved 30 sites worldwide. Demographic, clinical, and laboratory data were collected from patients with VEO-IBD who commenced adalimumab therapy before the age of 6 years.

RESULTS: Seventy-eight patients (37 Crohn's disease, 26 ulcerative colitis, and 15 with IBD-unclassified) were included. Median age of IBD onset was 2.6 (1.3-4.1) years, with 30 (38.5%) patients diagnosed at age <2 years. Median age at adalimumab initiation was 4.2 (2.8-5.1) years. Adalimumab was used as second-line biologic therapy in 45 (57.7%) patients after infliximab. The median time to last follow-up was 63 (22-124) weeks. Significant improvement in clinical scores, CRP, fecal calprotectin, and weight Z-score were observed by Week 52. Adalimumab durability rates were 61.9%, 48.1%, and 35.6% after 1, 2, and 3 years, respectively. Drug discontinuation rates were not dependent on IBD type, age, prior anti-TNF exposure, or concomitant immunomodulatory treatment. Four (5.1%) patients developed serious infections, including 1 patient with TTC7A deficiency who died following adenovirus sepsis.

CONCLUSION: Adalimumab therapy is a viable therapeutic option in patients with VEO-IBD with an acceptable safety profile.

PMID:39813158 | DOI:10.1093/ibd/izae302

PLoS One. 2025 Jan 15;20(1):e0316291. doi: 10.1371/journal.pone.0316291. eCollection 2025.

ABSTRACT

Cognitive Radio (CR) technology enables wireless devices to learn about their surrounding spectrum environment through sensing capabilities, thereby facilitating efficient spectrum utilization without interfering with the normal operation of licensed users. This study aims to enhance spectrum sensing in multi-user cooperative cognitive radio systems by leveraging a hybrid model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. A novel multi-user cooperative spectrum sensing model is developed, utilizing CNN's local feature extraction capability and LSTM's advantage in handling sequential data to optimize sensing accuracy and efficiency. Furthermore, a multi-head self-attention mechanism is incorporated to improve information flow, enhancing the model's adaptability and robustness in dynamic and complex environments. Simulation experiments were conducted to quantitatively evaluate the performance of the proposed model. The results demonstrate that the CNN-LSTM model achieves low sensing error rates across various numbers of secondary users (16, 24, 32, 40, 48), with a particularly low sensing error of 9.9658% under the 32-user configuration. Additionally, when comparing the sensing errors of different deep learning models, the proposed model consistently outperformed others, showing a 12% lower sensing error under low-power conditions (100 mW). This study successfully develops a CNN-LSTM-based cooperative spectrum sensing model for multi-user cognitive radio systems, significantly improving sensing accuracy and efficiency. By integrating CNN and LSTM technologies, the model not only enhances sensing performance but also improves the handling of long-term dependencies in time-series data, offering a novel technical approach and theoretical support for cognitive radio research. Moreover, the introduction of the multi-head self-attention mechanism further optimizes the model's adaptability to complex environments, demonstrating significant potential for practical applications.

PMID:39813223 | DOI:10.1371/journal.pone.0316291

PLoS One. 2025 Jan 15;20(1):e0310465. doi: 10.1371/journal.pone.0310465. eCollection 2025.

ABSTRACT

Accurate energy demand forecasting is critical for efficient energy management and planning. Recent advancements in computing power and the availability of large datasets have fueled the development of machine learning models. However, selecting the most appropriate features to enhance prediction accuracy and robustness remains a key challenge. This study proposes an ensemble approach that integrates a genetic algorithm with multiple forecasting models to optimize feature selection. The genetic algorithm identifies the optimal subset of features from a dataset that includes historical energy consumption, weather variables, and temporal characteristics. These selected features are then used to train three base learners: Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU). The predictions from these models are combined using a stacking ensemble technique to generate the final forecast. To enhance model evaluation, we divided the dataset into weekday and weekend subsets, allowing for a more detailed analysis of energy consumption patterns. To ensure the reliability of our findings, we conducted ten simulations and applied the Wilcoxon Signed Rank Test to the results. The proposed model demonstrated exceptional precision, achieving a Root Mean Square Error (RMSE) of 130.6, a Mean Absolute Percentage Error (MAPE) of 0.38%, and a Mean Absolute Error (MAE) of 99.41 for weekday data. The model also maintained high accuracy for weekend predictions, with an RMSE of 137.41, a MAPE of 0.42%, and an MAE of 105.67. This research provides valuable insights for energy analysts and contributes to developing more sophisticated demand forecasting methods.

PMID:39813218 | DOI:10.1371/journal.pone.0310465

PLoS One. 2025 Jan 15;20(1):e0316258. doi: 10.1371/journal.pone.0316258. eCollection 2025.

ABSTRACT

The Covid-19 pandemic has sparked renewed attention to the risks of online misinformation, emphasizing its impact on individuals' quality of life through the spread of health-related myths and misconceptions. In this study, we analyze 6 years (2016-2021) of Italian vaccine debate across diverse social media platforms (Facebook, Instagram, Twitter, YouTube), encompassing all major news sources-both questionable and reliable. We first use the symbolic transfer entropy analysis of news production time-series to dynamically determine which category of sources, questionable or reliable, causally drives the agenda on vaccines. Then, leveraging deep learning models capable to accurately classify vaccine-related content based on the conveyed stance and discussed topic, respectively, we evaluate the focus on various topics by news sources promoting opposing views and compare the resulting user engagement. Our study uncovers misinformation not as a parasite of the news ecosystem that merely opposes the perspectives offered by mainstream media, but as an autonomous force capable of even overwhelming the production of vaccine-related content from the latter. While the pervasiveness of misinformation is evident in the significantly higher engagement of questionable sources compared to reliable ones (up to 11 times higher in median value), our findings underscore the need for consistent and thorough pro-vax coverage to counter this imbalance. This is especially important for sensitive topics, where the risk of misinformation spreading and potentially exacerbating negative attitudes toward vaccines is higher. While reliable sources have successfully promoted vaccine efficacy, reducing anti-vax impact, gaps in pro-vax coverage on vaccine safety led to the highest engagement with anti-vax content.

PMID:39813203 | DOI:10.1371/journal.pone.0316258

PLoS One. 2025 Jan 15;20(1):e0316832. doi: 10.1371/journal.pone.0316832. eCollection 2025.

ABSTRACT

There is growing interest in using deep learning models to automate wildlife detection in aerial imaging surveys to increase efficiency, but human-generated annotations remain necessary for model training. However, even skilled observers may diverge in interpreting aerial imagery of complex environments, which may result in downstream instability of models. In this study, we present a framework for assessing annotation reliability by calculating agreement metrics for individual observers against an aggregated set of annotations generated by clustering multiple observers' observations and selecting the mode classification. We also examined how image attributes like spatial resolution and texture influence observer agreement. To demonstrate the framework, we analyzed expert and volunteer annotations of twelve drone images of migratory waterfowl in New Mexico. Neither group reliably identified duck species: experts showed low agreement (43-56%) for several common species, and volunteers opted out of the task. When simplified into broad morphological categories, there was high agreement for cranes (99% among experts, 95% among volunteers) and ducks (93% among experts, 92% among volunteers), though agreement among volunteers was notably lower for classifying geese (75%) than among experts (94%). The aggregated annotation sets from the two groups were similar: the volunteer count of birds across all images was 91% of the expert count, with no statistically significant difference per image (t = 1.27, df = 338, p = 0.20). Bird locations matched 81% between groups and classifications matched 99.4%. Tiling images to reduce search area and maintaining a constant scale to keep size differences between classes consistent may increase observer agreement. Although our sample was limited, these findings indicate potential taxonomic limitations to aerial wildlife surveys and show that, in aggregate, volunteers can produce data comparable to experts'. This framework may assist other wildlife practitioners in evaluating the reliability of their input data for deep learning models.

PMID:39813190 | DOI:10.1371/journal.pone.0316832

Network. 2025 Jan 15:1-40. doi: 10.1080/0954898X.2024.2449173. Online ahead of print.

ABSTRACT

Skin cancer is one of the most prevalent and harmful forms of cancer, with early detection being crucial for successful treatment outcomes. However, current skin cancer detection methods often suffer from limitations such as reliance on manual inspection by clinicians, inconsistency in diagnostic accuracy, and a lack of personalized recommendations based on patient-specific data. In our work, we presented a Personalized Recommendation System to handle Skin Cancer at an early stage based on Hybrid Model (PRSSCHM). Preprocessing, improved deep joint segmentation, feature extraction, and classification are the major steps to identify the stages of skin cancer. The input image is first preprocessed using the Gaussian filtering method. Improved deep joint segmentation is employed to segment the preprocessed image. A set of features including Median Binary Pattern (MBP), Gray Level Co-occurrence Matrix (GLCM), and Improved Local Direction Texture Pattern (ILDTP) are extracted in the next step. Finally, the hybrid classification includes Improved Bi-directional Long Short-Term Memory (Bi-LSTM) and Deep Belief Network (DBN) used for the classification process, where the training will be carried out by the Integrated Bald Eagle and Average and Subtraction Optimizer (IBEASO) algorithm via optimizing the weights of the models.

PMID:39813094 | DOI:10.1080/0954898X.2024.2449173

PLoS Biol. 2025 Jan 15;23(1):e3002984. doi: 10.1371/journal.pbio.3002984. eCollection 2025 Jan.

ABSTRACT

Noncoding satellite DNA repeats are abundant at the pericentromeric heterochromatin of eukaryotic chromosomes. During interphase, sequence-specific DNA-binding proteins cluster these repeats from multiple chromosomes into nuclear foci known as chromocenters. Despite the pivotal role of chromocenters in cellular processes like genome encapsulation and gene repression, the associated proteins remain incompletely characterized. Here, we use 2 satellite DNA-binding proteins, D1 and Prod, as baits to characterize the chromocenter-associated proteome in Drosophila embryos, ovaries, and testes through quantitative mass spectrometry. We identify D1- and Prod-associated proteins, including known heterochromatin proteins as well as proteins previously unlinked to satellite DNA or chromocenters, thereby laying the foundation for a comprehensive understanding of cellular functions enabled by satellite DNA repeats and their associated proteins. Interestingly, we find that multiple components of the transposon-silencing piRNA pathway are associated with D1 and Prod in embryos. Using genetics, transcriptomics, and small RNA profiling, we show that flies lacking D1 during embryogenesis exhibit transposon expression and gonadal atrophy as adults. We further demonstrate that this gonadal atrophy can be rescued by mutating the checkpoint kinase, Chk2, which mediates germ cell arrest in response to transposon mobilization. Thus, we reveal that a satellite DNA-binding protein functions during embryogenesis to silence transposons, in a manner that is heritable across later stages of development.

PMID:39813297 | DOI:10.1371/journal.pbio.3002984

PLoS One. 2025 Jan 15;20(1):e0316584. doi: 10.1371/journal.pone.0316584. eCollection 2025.

ABSTRACT

BACKGROUND: Rheumatoid arthritis (RA) is a degenerative autoimmune disease, often managed through symptomatic treatment. The co-occurrence of the reported extra-articular comorbidities such as inflammatory bowel disease (IBD), and dementia may complicate the pathology of the disease as well as the treatment strategies. Therefore, in our study, we aim to elucidate the key genes, and regulatory elements implicated in the progression and association of these diseases, thereby highlighting the linked potential therapeutic targets.

METHODOLOGY: Ten microarray datasets each for RA, and IBD, and nine datasets for dementia were obtained from Gene Expression Omnibus. We identified common differentially expressed genes (DEGs) and constructed a gene-gene interaction network. Subsequently, topology analysis for hub gene identification, cluster and functional enrichment, and regulatory network analysis were performed. The hub genes were then validated using independent microarray datasets from Gene Expression Omnibus.

RESULTS: A total of 198 common DEGs were identified from which CD44, FN1, IGF1, COL1A2, and POSTN were identified as the hub genes in our study. These hub genes were mostly enriched in significant processes and pathways like tissue development, collagen binding, cell adhesion, regulation of ERK1/2 cascade, PI3K-AKT signaling, and cell surface receptor signaling. Key transcription factors TWIST2, CEBPA, EP300, HDAC1, HDAC2, NFKB1, RELA, TWIST1, and YY1 along with the miRNA hsa-miR-29 were found to regulate the expression of the hub genes significantly. Among these regulatory molecules, miR-29 emerged as a significant linker molecule, bridging the molecular mechanisms of RA, IBD, and dementia. Validation of our hub genes demonstrated a similar expression trend in the independent datasets used for our study.

CONCLUSION: Our study underscores the significant role of miR-29 in modulating the expression of hub genes and the associated transcription factors, which are crucial in the comorbidity status of RA, dementia, and IBD. This regulatory mechanism highlights miR-29 as a key player in the pathogenesis of these comorbid diseases.

PMID:39813219 | DOI:10.1371/journal.pone.0316584

Anal Chem. 2025 Jan 15. doi: 10.1021/acs.analchem.4c03855. Online ahead of print.

ABSTRACT

The role of peripheral blood platelets as indicators of cancer progression is increasingly recognized, and the significance of abnormal glycosylation in platelet function and related disorders is gaining attention. However, the potential of platelets as a source of protein site-specific glycosylation for cancer diagnosis remains underexplored. In this study, we proposed a general pipeline that integrates quantitative proteomics with site-specific glycoproteomics, allowing for an in-depth investigation of the platelet glycoproteome. With this pipeline, we generated a data set comprising 3,466 proteins with qualitative information, 3,199 proteins with quantitative information, 3,419 site-specific glycans with qualitative information and 3,377 site-specific glycans with quantitative information from peripheral blood platelets of hepatocellular carcinoma (HCC) patients, metastatic liver cancer (mLC) patients, and healthy controls. The integrated analysis revealed significant changes in platelet protein N-glycosylation in liver cancer patients. Further systems biology analysis and lectin pull-down-coupled ELISA assays in independent clinical samples confirmed two N-glycoproteins with specific glycan types, complement C3 (C3) with oligomannose modification and integrin β-3 (ITGB3) with sialylation, as potential biomarkers distinguishing liver cancer patients from healthy individuals, without differentiating between HCC and mLC patient group. These findings highlight the potential of platelet protein glycosylation as biomarkers.

PMID:39813102 | DOI:10.1021/acs.analchem.4c03855

JMIR Res Protoc. 2025 Jan 15;14:e60084. doi: 10.2196/60084.

ABSTRACT

BACKGROUND: Adverse medicine events (AMEs) are unintended effects that occur following administration of medicines. Up to 70% of AMEs are not reported to, and hence remain undetected by, health care professionals and only 6% of AMEs are reported to regulators. Increased reporting by consumers, health care professionals, and pharmaceutical companies to medicine regulatory authorities is needed to increase the safety of medicines.

OBJECTIVE: We describe a project that aims to co-design a digital reporting platform to improve detection and management of AMEs by consumers and health care professionals and improve reporting to regulators.

METHODS: The project will be conducted in 3 phases and uses a co-design methodology that prioritizes equity in designing with stakeholders. Our project is guided by the Consolidated Framework for Implementation Research. In phase 1, we will engage with 3 stakeholder groups-consumers, health care professionals, and regulators-to define digital platform development standards. We will conduct a series of individual interviews, focus group discussions, and co-design workshops with the stakeholder groups. In phase 2, we will work with a software developer and user interaction design experts to prototype, test, and develop the digital reporting platform based on findings from phase 1. In phase 3, we will implement and trial the digital reporting platform in South Australia through general practices and pharmacies. Consumers who have recently started using medicines new to them will be recruited to use the digital reporting platform to report any apparent, suspected, or possible AMEs since starting the new medicine. Process and outcome evaluations will be conducted to assess the implementation process and to determine whether the new platform has increased AME detection and reporting.

RESULTS: This project, initiated in 2023, will run until 2026. Phase 1 will result in persona profiles and user journey maps that define the standards for the user-friendly platform and interactive data visualization tool or dashboard that will be developed and further improved in phase 2. Finally, phase 3 will provide insights of the implemented platform regarding its impact on AME detection, management, and reporting. Findings will be published progressively as we complete the different phases of the project.

CONCLUSIONS: This project adopts a co-design methodology to develop a new digital reporting platform for AME detection and reporting, considering the perspectives and lived experience of stakeholders and addressing their requirements throughout the entire process. The overarching goal of the project is to leverage the potential of both consumers and technology to address the existing challenges of underdetection and underreporting of AMEs to health care professionals and regulators. The project potentially will improve individual patient safety and generate new data for regulatory purposes related to medicine safety and effectiveness.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/60084.

PMID:39813668 | DOI:10.2196/60084

J Chem Inf Model. 2025 Jan 15. doi: 10.1021/acs.jcim.4c02073. Online ahead of print.

ABSTRACT

Accurate prediction of drug-target interactions (DTIs) is pivotal for accelerating the processes of drug discovery and drug repurposing. MVCL-DTI, a novel model leveraging heterogeneous graphs for predicting DTIs, tackles the challenge of synthesizing information from varied biological subnetworks. It integrates neighbor view, meta-path view, and diffusion view to capture semantic features and employs an attention-based contrastive learning approach, along with a multiview attention-weighted fusion module, to effectively integrate and adaptively weight the information from the different views. Tested under various conditions on benchmark data sets, including varying positive-to-negative sample ratios, conducting hard negative sampling experiments, and masking known DTIs with different ratios, as well as redundant DTIs with various similarity metrics, MVCL-DTI exhibits strong robust generalization. The model is then employed to predict novel DTIs, with a particular focus on COVID-19-related drugs, highlighting its practical applicability. Ultimately, through features visualization and computational properties analysis, we've pinpointed critical elements, including Gene Ontology and substituent nodes, along with a proper initialization strategy, underscoring their vital role in DTI prediction tasks.

PMID:39812134 | DOI:10.1021/acs.jcim.4c02073

Platelets. 2025 Dec;36(1):2449344. doi: 10.1080/09537104.2024.2449344. Epub 2025 Jan 15.

ABSTRACT

Platelet-like particles (PLPs), derived from megakaryocytic cell lines MEG-01 and K-562, are widely used as a surrogate to study platelet formation and function. We demonstrate by RNA-Seq that PLPs are transcriptionally distinct from platelets. Expression of key genes in signaling pathways promoting platelet activation/aggregation, such as the PI3K/AKT, protein kinase A, phospholipase C, and α-adrenergic and GP6 receptor pathways, was missing or under-expressed in PLPs. Functionally, PLPs do not aggregate following epinephrine, collagen, or ADP stimulation. While PLPs aggregated in response to thrombin, they did not display enhanced expression of surface markers P-selectin and activated α2bβ3, in contrast to platelets. We have previously demonstrated that platelets physically couple to MDA-PCa-2b and RC77T/E prostate cancer (PCa) cells via specific ligand-receptor interactions, leading to platelet-stimulated cell invasiveness and apoptotic resistance, and reciprocal cell-induced platelet aggregation. In contrast, PLP interactions with PCa cells inhibited both cell invasion and apoptotic resistance while failing to promote PLP aggregation. Moreover, PLPs reduced platelet-PCa cell interactions and antagonized platelet-stimulated oncogenic effects in PCa cells. RNA-Seq analysis identified candidate ligand-transmembrane protein combinations involved in anti-tumorigenic signaling of PLPs to PCa cells. Antibody neutralization of the TIMP3-MMP15 and VEGFB-FGFR1 signaling axes reversed PLP-mediated anti-invasion and apoptotic sensitization, respectively. In summary, PLPs lack many transcriptomic, molecular and functional features of platelets and possess novel anti-tumorigenic properties. These findings indicate that PLPs may have a potential therapeutic role in targeting and disrupting the oncogenic signaling between platelets and cancer cells, offering a new avenue for anti-cancer strategies.

PMID:39812346 | DOI:10.1080/09537104.2024.2449344

Pediatr Pulmonol. 2025 Jan;60(1):e27472. doi: 10.1002/ppul.27472.

ABSTRACT

BACKGROUND: The vast majority of men with CF (mwCF) are infertile. Improvements in assisted reproductive technology (ART) have made it possible for these patients to become biological fathers.

METHODS: Data were examined for all male CF patients attending a large adult CF center over a 23-year period. Azoospermia was confirmed via laboratory analysis of semen and was defined as complete absence of sperm in the ejaculate. Outcome of surgical sperm retrieval (SSR) procedure, post SSR complications, success rate of intracytoplasmic sperm injection (ICSI) and embryo transfer and subsequent live births were evaluated.

RESULTS: Seventy-one mwCF with proven azoospermia were referred to fertility services over the study period. Mean (SD) percentage predicted forced expiratory volume in 1 second (ppFEV1) 67.86 ( ± 25.3), body mass index (BMI) 24.3 ( ± 3.7) kg/m2. Seventy (98.5%) of these men underwent and had successful SSR. 67/71 (94%) couples proceeded to have ICSI post SSR. 11 couples had failed egg fertilisation, implantation, or early miscarriage. 56/67 couples (84%) went on to have live births. To date 10/71 (14%) mwCF referred for fertility treatment have died. Mean ppFEV1 and BMI of mwCF who survived was higher than those who died; FEV1 72.8% ( ± 23.4) versus 38% ( ± 12.0) (p < 0.001), BMI 25.0 ( ± 3.5) kg/m2 versus 20.3 ( ± 2.2) kg/mg2 (p < 0.001).

CONCLUSION: This is the largest study to investigate success rate of fertility treatment in mwCF and demonstrates that fertility treatment is successful in greater than 75% of patients referred. Clinical status and prognosis remain important factors when considering referral to fertility services.

PMID:39812352 | DOI:10.1002/ppul.27472

Pediatr Pulmonol. 2025 Jan;60(1):e27483. doi: 10.1002/ppul.27483.

ABSTRACT

BACKGROUND: Notwithstanding guidance from the European Cystic Fibrosis (CF) Society (ECFS) neonatal screening (NBS) working group, significant variation persists in the evaluation and management of Cystic Fibrosis Screen Positive, Inconclusive Diagnosis (CFSPID) subjects, leaving many aspects of care under debate. This study reports the results of a national survey investigating management and treatment approaches of pre-school CFSPIDs in Italy.

METHODS: In February 2024, a comprehensive questionnaire was distributed to all Italian CF centers. The survey explored various aspects of CFSPID management in the year 2023, including patient visit schedules, sweat tests (ST) timing, screening procedures, therapeutic interventions, and discharge criteria. Data on regional NBS protocols, number of CFSPID cases, and CF:CFSPID ratio were also collected.

RESULTS: By December 31, 2023, CF Italian centers were following 522 CFSPIDs. In 2023, CF NBS identified 85 CF and 68 CFSPID cases, resulting in a CF:CFSPID ratio of 1.25:1. Seven centers diagnosed more CFSPID than CF, with the lowest CF:CFSPID ratio being 0.20:1. A quarter of all centers reported management plans that deviated widely from ECFS guidelines. Respiratory cultures were performed in 16 (69.6%) centers in the absence of symptoms. Nine (38.9%) prescribed antibiotics in any case of positive Pseudomonas aeruginosa cultures, including first detections and asymptomatic subjects. Spirometries were performed by 14/23 centers (60.9%) in procedure-competent children at each visit. Follow up care continued after age 6 for all CFSPIDs in 15 (65.2%) centers regardless of age, genotype or ST results. A diagnosis of CF was established based on repeated pathological STs and/or multiorgan involvement. Children with STs in intermediate range and mono-organ involvement were classified as CFTR-related disorders (CFTR-RD).

CONCLUSIONS: Despite available data on clinical course and recommendations on management of CFSPIDs, different approaches persist in clinical practice. Further efforts should be considered to disseminate and encourage adherence to international guidelines.

PMID:39812351 | DOI:10.1002/ppul.27483

JAMA Cardiol. 2025 Jan 15. doi: 10.1001/jamacardio.2024.4991. Online ahead of print.

ABSTRACT

IMPORTANCE: Lung ultrasound (LUS) aids in the diagnosis of patients with dyspnea, including those with cardiogenic pulmonary edema, but requires technical proficiency for image acquisition. Previous research has demonstrated the effectiveness of artificial intelligence (AI) in guiding novice users to acquire high-quality cardiac ultrasound images, suggesting its potential for broader use in LUS.

OBJECTIVE: To evaluate the ability of AI to guide acquisition of diagnostic-quality LUS images by trained health care professionals (THCPs).

DESIGN, SETTING, AND PARTICIPANTS: In this multicenter diagnostic validation study conducted between July 2023 and December 2023, participants aged 21 years or older with shortness of breath recruited from 4 clinical sites underwent 2 ultrasound examinations: 1 examination by a THCP operator using Lung Guidance AI and the other by a trained LUS expert without AI. The THCPs (including medical assistants, respiratory therapists, and nurses) underwent standardized AI training for LUS acquisition before participation.

INTERVENTIONS: Lung Guidance AI software uses deep learning algorithms guiding LUS image acquisition and B-line annotation. Using an 8-zone LUS protocol, the AI software automatically captures images of diagnostic quality.

MAIN OUTCOMES AND MEASURES: The primary end point was the proportion of THCP-acquired examinations of diagnostic quality according to a panel of 5 masked expert LUS readers, who provided remote review and ground truth validation.

RESULTS: The intention-to-treat analysis included 176 participants (81 female participants [46.0%]; mean [SD] age, 63 [14] years; mean [SD] body mass index, 31 [8]). Overall, 98.3% (95% CI, 95.1%-99.4%) of THCP-acquired studies were of diagnostic quality, with no statistically significant difference in quality compared to LUS expert-acquired studies (difference, 1.7%; 95% CI, -1.6% to 5.0%).

CONCLUSIONS AND RELEVANCE: In this multicenter validation study, THCPs with AI assistance achieved LUS images meeting diagnostic standards compared with LUS experts without AI. This technology could extend access to LUS to underserved areas lacking expert personnel.

TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05992324.

PMID:39813064 | DOI:10.1001/jamacardio.2024.4991