Comput Methods Programs Biomed. 2025 Apr 23;267:108801. doi: 10.1016/j.cmpb.2025.108801. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Exploring the dependencies between multimodal brain networks and integrating node features to enhance brain disease diagnosis remains a significant challenge. Some work has examined only brain connectivity changes in patients, ignoring important information about radiomics features such as shape and texture of individual brain regions in structural images. To this end, this study proposed a novel deep learning approach to integrate multimodal brain connectome information and regional radiomics features for brain disease diagnosis.

METHODS: A dual-branch autoencoder (ConnectomeAE) based on multimodal brain connectomes was proposed for brain disease diagnosis. Specifically, a matrix of radiomics feature extracted from structural magnetic resonance image (MRI) was used as Rad_AE branch inputs for learning important brain region features. Functional brain network built from functional MRI image was used as inputs to Cycle_AE for capturing brain disease-related connections. By separately learning node features and connection features from multimodal brain networks, the method demonstrates strong adaptability in diagnosing different brain diseases.

RESULTS: ConnectomeAE was validated on two publicly available datasets. The experimental results show that ConnectomeAE achieved excellent diagnostic performance with an accuracy of 70.7 % for autism spectrum disorder and 90.5 % for Alzheimer's disease. A comparison of training time with other methods indicated that ConnectomeAE exhibits simplicity and efficiency suitable for clinical applications. Furthermore, the interpretability analysis of the model aligned with previous studies, further supporting the biological basis of ConnectomeAE.

CONCLUSIONS: ConnectomeAE could effectively leverage the complementary information between multimodal brain connectomes for brain disease diagnosis. By separately learning radiomic node features and connectivity features, ConnectomeAE demonstrated good adaptability to different brain disease classification tasks.

PMID:40294455 | DOI:10.1016/j.cmpb.2025.108801

J Med Internet Res. 2025 Apr 28;27:e58723. doi: 10.2196/58723.

ABSTRACT

In this viewpoint, we explore the use of big data analytics and artificial intelligence (AI) and discuss important challenges to their ethical, effective, and equitable use within opioid use disorder (OUD) treatment settings. Applying our collective experiences as OUD policy and treatment experts, we discuss 8 key challenges that OUD treatment services must contend with to make the most of these rapidly evolving technologies: data and algorithmic transparency, clinical validation, new practitioner-technology interfaces, capturing data relevant to improving patient care, understanding and responding to algorithmic outputs, obtaining informed patient consent, navigating mistrust, and addressing digital exclusion and bias. Through this paper, we hope to critically engage clinicians and policy makers on important ethical considerations, clinical implications, and implementation challenges involved in big data analytics and AI deployment in OUD treatment settings.

PMID:40294410 | DOI:10.2196/58723

PLoS One. 2025 Apr 28;20(4):e0321529. doi: 10.1371/journal.pone.0321529. eCollection 2025.

ABSTRACT

Accurate power load forecasting helps reduce energy waste and improve grid stability. This paper proposes a hybrid forecasting model, BiStacking+TCN-GRU, which leverages both ensemble learning and deep learning techniques. The model first applies the Pearson correlation coefficient (PCC) to select features highly correlated with the power load. Then, BiStacking is used for preliminary predictions, followed by a temporal convolutional network (TCN) enhanced by a gated recurrent unit (GRU) to produce the final predictions. The experimental validation based on Panama's 2020 electricity load data demonstrated the effectiveness of the model, with the model achieving an RMSE of 29.1213 and an MAE of 22.5206, respectively, with an R² of 0.9719. These results highlight the model's superior performance in short-term load forecasting, demonstrating its strong practical applicability and theoretical contributions.

PMID:40294011 | DOI:10.1371/journal.pone.0321529

IEEE Trans Med Imaging. 2025 Apr 28;PP. doi: 10.1109/TMI.2025.3565000. Online ahead of print.

ABSTRACT

Due to the difficulty of collecting multi-label annotations for retinal diseases, fundus images are usually annotated with only one label, while they actually have multiple labels. Given that deep learning requires accurate training data, incomplete disease information may lead to unsatisfactory classifiers and even misdiagnosis. To cope with these challenges, we propose a co-pseudo labeling and active selection method for Fundus Single-Positive multi-label learning, named FSP. FSP trains two networks simultaneously to generate pseudo labels through curriculum co-pseudo labeling and active sample selection. The curriculum co-pseudo labeling adjusts the thresholds according to the model's learning status of each class. Then, the active sample selection maintains confident positive predictions with more precise pseudo labels based on loss modeling. A detailed experimental evaluation is conducted on seven retinal datasets. Comparison experiments show the effectiveness of FSP and its superiority over previous methods. Downstream experiments are also presented to validate the proposed method.

PMID:40293917 | DOI:10.1109/TMI.2025.3565000

IEEE Trans Neural Netw Learn Syst. 2025 Apr 28;PP. doi: 10.1109/TNNLS.2025.3558596. Online ahead of print.

ABSTRACT

Accurate prediction of pathological subtypes on radiological images is one of the most important deep learning (DL) tasks for the appropriate selection of clinical treatment. It is challenging for conventional DL models to obtain sufficient pathological labels for training because of the heavy workload, invasive surgery, and knowledge requirements in pathological analysis. However, existing methods based on limited annotations, such as active learning (AL) and semi-supervised learning (SSL), have difficulty in capturing lesion's effective features because of the complicated semantic information of radiologic images. In this article, we introduce an efficient domain knowledge-guided semantic prediction framework that integrates domain knowledge-guided AL and SSL methods. This framework can effectively predict pathological subtypes on the basis of radiologic images with limited pathological annotations via three key modules: 1) the discriminative spatial-semantic feature extraction module captures the spatial-semantic features of lesions as semantic information that can better reflect the semantic relationship and effectively mitigate overfitting risk; 2) the explicit sign-guided anchor attention module measures the multimodal semantic distribution of samples under the guidance of clinical domain knowledge, thus selecting the most representative AL samples for pathological labeling; and 3) the implicit radiomics-guided dual-task entanglement module exploits the inherent constraint relationships between implicit radiomics features (IRFs) and pathological subtypes, facilitating the aggregation of unlabeled data. Experiments have been extensively conducted to evaluate our method in two clinical tasks: the pathological grading prediction in pancreatic neuroendocrine neoplasms (pNENs) and muscular invasiveness prediction in bladder cancer (BCa). The experimental results on both tasks demonstrate that the proposed method consistently outperforms the state-of-the-art approaches by a large margin.

PMID:40293902 | DOI:10.1109/TNNLS.2025.3558596

IEEE Trans Biomed Eng. 2025 Apr 28;PP. doi: 10.1109/TBME.2025.3561338. Online ahead of print.

ABSTRACT

Coronary CT angiography (CCTA) is an essential technique for clinical coronary assessment. However, the risks associated with ionizing radiation cannot be ignored, especially its stochastic effects, which increase the risk of cancer. Although it can effectively alleviate radiation problems, low-dose CCTA can reduce imaging quality and interfere with the diagnosis of the radiologist. Existing deep learning methods based on image restoration suffer from subtle structure degradation after noise suppression, leading to unclear coronary boundaries. Furthermore, in the absence of prior guidance on coronary location, subtle coronary branches will be lost after aggressive noise suppression and are difficult to restore successfully. To address the above issues, this paper proposes a novel Guided Refinement Network (GRN) model based on joint learning for restoring high-quality images from low-dose CCTA. GRN integrates coronary segmentation, which provides coronary location, into the denoising, and the two leverage mutual guidance for effective interaction and collaborative optimization. On the one hand, denoising provides images with lower noise levels for segmentation to assist in generating coronary masks. Furthermore, segmentation provides a prior coronary location for denoising, aiming to preserve and restore subtle coronary branches. GRN achieves noise suppression and subtle structure enhancement for low-dose CCTA imaging through joint denoising and segmentation, while also generating segmentation results with reference value. Quantitative and qualitative results show that GRN outperforms existing methods in noise suppression, subtle structure restoration, and visual perception improvement, and generates coronary masks that can serve as a reference for radiologists to assist in diagnosing coronary disease.

PMID:40293900 | DOI:10.1109/TBME.2025.3561338

IEEE J Biomed Health Inform. 2025 Apr 28;PP. doi: 10.1109/JBHI.2025.3561146. Online ahead of print.

ABSTRACT

Cerebrovascular segmentation is essential for diagnosing and treating cerebrovascular diseases. However, accurately segmenting cerebral vessels in TOF-MRA remains challenging due to significant interindividual variations in cerebrovascular morphology, low image con-trast, and class imbalance. The present study proposes an advanced deep learning model called PPA Net, consisting of VesselMRA Net and VesselConvLSTM components. Firstly, VesselMRA Net utilizes rectangular convolutional blocks to fuse multi-scale features, enhancing feature extraction per-formance. VesselMRA Net employs the attention mechanism to boost certain valuable semantic weighting, addressing segmentation challenges arising from class imbalance and low contrast. Secondly, VesselConvLSTM, a pixel-level prediction model, employs a gating mechanism to learn cerebral vessel morphology across individuals. It reduces individual differences in segmentation and restores inter-voxel correlations disrupted by data slicing, aiding VesselMRA Net in accurately segmenting cerebrovascular pixels. Lastly, integrating VesselMRA Net and VesselConv-LSTM results in a modular cerebral vessel segmentation framework, PPA Net, facilitating separate optimization of the backbone network and predicted model components. The performance of this model has been extensively validated through experimental evaluations on three publicly available datasets, obtaining significant competitiveness when compared to the state-of-the-art of the current cerebral vessel segmentation models.

PMID:40293899 | DOI:10.1109/JBHI.2025.3561146

IEEE J Biomed Health Inform. 2025 Apr 28;PP. doi: 10.1109/JBHI.2025.3564981. Online ahead of print.

ABSTRACT

Knee osteoarthritis (KOA) as a prevalent chronic disease, detrimentally impacts the quality of life among affected individuals. The knee adduction moment (KAM) during the stance phase has been identified as a potential biomechanical measure for assessing the severity of KOA. Traditional KAM assessment relies on expensive equipment, which limits its popularization. In contrast, current KAM estimation methods based on wearables and deep-learning technology offer the advantage of lower costs. However, it still suffers challenges in achieving accurate estimation. To address this challenge, a novel deep-learning framework is proposed in this work, which estimates the KAM from Inertial Measurement Units (IMU) and Electromyography (EMG) data by a well-designed self-aware fusion model. Walking data from 18 effective subjects were recorded with 4 IMUs and 6 EMGs. Results show that the model significantly improves KAM estimation accuracy. The relative root-mean-square error of the proposed model is 9.15% BW BH lower than counterpart estimation methods.

PMID:40293894 | DOI:10.1109/JBHI.2025.3564981

Drug Dev Res. 2025 May;86(3):e70094. doi: 10.1002/ddr.70094.

ABSTRACT

Idiopathic pulmonary fibrosis is a progressive, irreversible lung disease of unknown cause, characterized by gradual thickening and scarring of lung tissue, impairing oxygen transfer into the bloodstream. As a result, symptoms such as shortness of breath, fatigue, and a persistent dry cough occur. Currently, the FDA-approved antifibrotic agents Pirfenidone and Nintedanib can slow the progression of the disease. However, these treatments cannot completely stop the loss of lung function and do not provide a significant improvement in the quality of life of patients. As fibrosis progresses, lung capacity decreases, shortness of breath increases, and general health deteriorates significantly. Therefore, new more effective, and targeted therapies that can halt the progression of IPF are urgently needed. This review addresses novel strategies to slow or halt the disease-related loss of lung function by targeting key mechanisms involved in the pathogenesis of IPF. The molecular structure-activity relationships (SARs) of synthesized compounds targeting JAK/STAT, TGF-β/Smad, Wnt/β-catenin, PI3K, JNK1, and other critical signaling pathways were examined. These targeted approaches have great potential for the development of more potent and selective therapeutic agents for the treatment of IPF. The insights provided in this review may contribute to the future development of more efficient and selective antifibrotic drugs.

PMID:40293838 | DOI:10.1002/ddr.70094

Proc Natl Acad Sci U S A. 2025 May 6;122(18):e2410092122. doi: 10.1073/pnas.2410092122. Epub 2025 Apr 28.

ABSTRACT

Plasmacytoid Dendritic cells (pDCs) are the most potent producers of interferons, which are critical antiviral cytokines. pDC development is, however, compromised following a viral infection, and this phenomenon, as well as its relationship to conventional (c)DC development is still incompletely understood. By using lymphocytic choriomeningitis virus (LCMV) infection in mice as a model system, we observed that DC progenitors skewed away from pDC and toward cDC development during in vivo viral infection. Subsequent characterization of the transcriptional and epigenetic landscape of fms-like tyrosine kinase 3+ (Flt3+) DC progenitors and follow-up studies revealed increased apoptosis and reduced proliferation in different individual DC-progenitors as well as a profound type I interferon (IFN-I)-dependent ablation of pre-pDCs, but not pre-DC precursors, after both acute and chronic LCMV infections. In addition, integrated genomic analysis identified altered activity of 34 transcription factors in Flt3+ DC progenitors from infected mice, including two regulators of Glucocorticoid (GC) responses. Subsequent studies demonstrated that addition of GCs to DC progenitors led to downregulated pDC-primed-genes while upregulating cDC-primed-genes, and that endogenous GCs selectively decreased pDC, but not cDC, numbers upon in vivo LCMV infection. These findings demonstrate a significant ablation of pre-pDCs in infected mice and identify GCs as suppressors of pDC generation from early progenitors. This provides a potential explanation for the impaired pDC development following viral infection and links pDC numbers to the hypothalamic-pituitary-adrenal axis.

PMID:40294270 | DOI:10.1073/pnas.2410092122

Proc Natl Acad Sci U S A. 2025 May 6;122(18):e2411446122. doi: 10.1073/pnas.2411446122. Epub 2025 Apr 28.

ABSTRACT

Transposable elements (TEs) make up the bulk of eukaryotic genomes and examples abound of TE-derived sequences repurposed for organismal function. The process by which TEs become coopted remains obscure because most cases involve ancient, transpositionally inactive elements. Reports of active TEs serving beneficial functions are scarce and often contentious due to difficulties in manipulating repetitive sequences. Here, we show that recently active TEs in zebrafish encode products critical for embryonic development. Knockdown and rescue experiments demonstrate that the endogenous retrovirus family BHIKHARI-1 (Bik-1) encodes a Gag protein essential for mesoderm development. Mechanistically, Bik-1 Gag associates with the cell membrane, and its ectopic expression in chicken embryos alters cell migration. Similarly, depletion of BHIKHARI-2 Gag, a relative of Bik-1, causes defects in neural crest development in zebrafish. We propose an "addiction" model to explain how active TEs can be integrated into conserved developmental processes.

PMID:40294259 | DOI:10.1073/pnas.2411446122

Cancer Res Commun. 2025 Apr 1;5(4):706-718. doi: 10.1158/2767-9764.CRC-25-0030.

ABSTRACT

This study presents a novel approach that integrates LLMs with real-time biomedical literature to uncover drug-gene relationships, transforming how cancer researchers identify therapeutic targets, repurpose drugs, and interpret complex molecular interactions. GeneRxGPT, our user-friendly tool, enables researchers to leverage this approach without requiring computational expertise.

PMID:40293950 | DOI:10.1158/2767-9764.CRC-25-0030

Mol Neurobiol. 2025 Apr 28. doi: 10.1007/s12035-025-04957-8. Online ahead of print.

ABSTRACT

L-3,4-Dihydroxyphenylalanine (L-DOPA) is effective at alleviating motor impairments in Parkinson's disease (PD) patients but has mixed effects on nonmotor symptoms and causes adverse effects after prolonged treatment. Here, we analyzed the spatial profile of L-DOPA-induced gene expression in the forebrain of mice with an inducible progressive loss of dopaminergic neurons (the TIF-IADATCreERT2 strain), with a focus on the similarities and differences in areas relevant to different PD symptoms. The animals received a 14-day L-DOPA treatment, and 1 h after the final drug injection, a spatial transcriptome analysis was performed on coronal forebrain sections. A total of 121 genes were identified as being regulated by L-DOPA. We found that the treatment had widespread effects extending beyond the primary areas involved in dopamine-dependent movement control. An unsupervised clustering analysis of the transcripts recapitulated the forebrain anatomy and indicated both ubiquitous and region-specific effects on transcription. The changes were most pronounced in layers 2/3 and 5 of the dorsal cortex and the dorsal striatum, where a robust increase in the abundance of activity-regulated transcripts, including Fos, Egr1, and Junb, was observed. Conversely, transcripts with a decreased abundance, e.g., Plekhm2 or Pgs1, were identified primarily in the piriform cortex, the adjacent endopiriform nucleus, and the claustrum. Taken together, our spatial analysis of L-DOPA-induced alterations in gene expression reveals the anatomical complexity of treatment effects, identifying novel genes affected by the drug, as well as molecular activation in brain areas relevant to the nonmotor symptoms of PD.

PMID:40293707 | DOI:10.1007/s12035-025-04957-8

Front Pharmacol. 2025 Apr 1;16:1519383. doi: 10.3389/fphar.2025.1519383. eCollection 2025.

ABSTRACT

BACKGROUND: Aspirin is a cornerstone medication for acute ischemic stroke (AIS), but its efficacy varies significantly among individuals. This study aimed to develop a pharmacogenetic polygenic response score (PgxRS) to predict the incidence of adverse outcomes in aspirin-treated AIS patients.

METHODS: We conducted a retrospective study involving 828 AIS patients who received aspirin therapy. Fifteen candidate single nucleotide variants (SNPs) in genes related to aspirin's mechanism of action, transport, metabolism, and platelet function were genotyped. The association between SNPs and the risk of unfavorable prognosis (defined as modified Rankin Scale score >1 at 90 days) was assessed using logistic regression analysis. Multivariable models incorporating SNPs and clinical factors were developed to predict adverse outcomes.

RESULTS: The rs1045642GG genotype in the ABCB1 gene was significantly associated with a lower risk of unfavorable prognosis, while the rs1371097T allele in the P2Y1 gene was linked to a higher risk. A prediction model incorporating these two SNPs along with clinical variables demonstrated moderate diagnostic accuracy for predicting unfavorable prognosis (AUC = 0.78, 95% CI: 0.74-0.81).

CONCLUSION: Our findings suggest that rs1045642 and rs1371097 genotypes contribute to variability in aspirin response among AIS patients. The developed PgxRS, incorporating these SNPs and clinical factors, can potentially aid in risk stratification and guide personalized antiplatelet therapy decisions. However, further validation in larger, diverse cohorts is warranted.

PMID:40290439 | PMC:PMC12023276 | DOI:10.3389/fphar.2025.1519383

Front Pharmacol. 2025 Apr 11;16:1548991. doi: 10.3389/fphar.2025.1548991. eCollection 2025.

ABSTRACT

The sequencing of the human genome in 2003 marked a transformative shift from a one-size-fits-all approach to personalized medicine, emphasizing patient-specific molecular and physiological characteristics. Advances in sequencing technologies, from Sanger methods to Next-Generation Sequencing (NGS), have generated vast genomic datasets, enabling the development of tailored therapeutic strategies. Pharmacogenomics (PGx) has played a pivotal role in elucidating how the genetic make-up influences inter-individual variability in drug efficacy and toxicity discovering predictive and prognostic biomarkers. However, challenges persist in interpreting polymorphic variants and translating findings into clinical practice. Multi-omics data integration and bioinformatics tools are essential for addressing these complexities, uncovering novel molecular insights, and advancing precision medicine. In this review, starting from our experience in PGx studies performed by DMET microarray platform, we propose a guideline combining machine learning, statistical, and network-based approaches to simplify and better understand complex DMET PGx data analysis which can be adapted for broader PGx applications, fostering accessibility to high-performance bioinformatics, also for non-specialists. Moreover, we describe an example of how bioinformatic tools can be used for a comprehensive integrative analysis which could allow the translation of genetic insights into personalized therapeutic strategies.

PMID:40290426 | PMC:PMC12022492 | DOI:10.3389/fphar.2025.1548991

JAMA. 2025 Apr 28. doi: 10.1001/jama.2025.2680. Online ahead of print.

ABSTRACT

IMPORTANCE: Non-cystic fibrosis (CF) bronchiectasis is a chronic lung condition caused by permanent bronchial dilatation and inflammation and is characterized by daily cough, sputum, and recurrent exacerbations. Approximately 500 000 people in the US have non-CF bronchiectasis.

OBSERVATIONS: Non-CF bronchiectasis may be associated with prior pneumonia, infection with nontuberculous mycobacteria or tuberculosis, genetic conditions (eg, α1-antitrypsin deficiency, primary ciliary dyskinesia), autoimmune diseases (eg, rheumatoid arthritis, inflammatory bowel disease), allergic bronchopulmonary aspergillosis, and immunodeficiency syndromes (eg, common variable immunodeficiency). Up to 38% of cases are idiopathic. According to US data, conditions associated with non-CF bronchiectasis include gastroesophageal reflux disease (47%), asthma (29%), and chronic obstructive pulmonary disease (20%). The prevalence of non-CF bronchiectasis increases substantially with age (7 per 100 000 in individuals 18-34 years vs 812 per 100 000 in those ≥75 years) and is more common in women than men (180 vs 95 per 100 000). Diagnosis is confirmed with noncontrast chest computed tomography showing dilated airways and often airway thickening and mucus plugging. Initial diagnostic evaluation involves blood testing (complete blood cell count with differential); immunoglobulin quantification testing (IgG, IgA, IgE, and IgM); sputum cultures for bacteria, mycobacteria, and fungi; and prebronchodilator and postbronchodilator spirometry. Treatment includes airway clearance techniques; nebulization of saline to loosen tenacious secretions; and regular exercise, participation in pulmonary rehabilitation, or both. Inhaled bronchodilators (β-agonists and antimuscarinic agents) and inhaled corticosteroids are indicated for patients with bronchiectasis who have asthma or chronic obstructive pulmonary disease. Exacerbations of bronchiectasis, which typically present with increased cough and sputum and worsened fatigue, are associated with progressive decline in lung function and decreased quality of life. Exacerbations should be treated with oral or intravenous antibiotics. Individuals with 3 or more exacerbations of bronchiectasis annually may benefit from long-term inhaled antibiotics (eg, colistin, gentamicin) or daily oral macrolides (eg, azithromycin). Lung transplant may be considered for patients with severely impaired pulmonary function, frequent exacerbations, or both. Among patients with non-CF bronchiectasis, mortality is higher for those with frequent and severe exacerbations, infection with Pseudomonas aeruginosa, and comorbidities, such as chronic obstructive pulmonary disease.

CONCLUSIONS AND RELEVANCE: Non-CF bronchiectasis is a chronic lung condition that typically causes chronic cough and daily sputum production. Exacerbations are associated with progressive decline in lung function and decreased quality of life. Management involves treatment of conditions associated with bronchiectasis, airway clearance techniques, oral or intravenous antibiotics for acute exacerbations, and consideration of long-term inhaled antibiotics or oral macrolides for patients with 3 or more exacerbations annually.

PMID:40293759 | DOI:10.1001/jama.2025.2680

Lancet Reg Health Eur. 2025 Apr 19;53:101303. doi: 10.1016/j.lanepe.2025.101303. eCollection 2025 Jun.

ABSTRACT

BACKGROUND: According to estimates, globally more than 200,000 pregnant women develop tuberculosis (TB) annually. Despite this, data on perinatal TB remain scarce. This study aimed to describe perinatal TB, comprising congenital (cTB) and postnatal (pTB) TB, in a European setting.

METHODS: Retrospective cohort study via the Paediatric Tuberculosis Network European Trials Group (ptbnet) capturing and comparing cases of cTB and pTB diagnosed at 104 participating European healthcare institutions between 1995 and 2019.

FINDINGS: Forty-six cases reported by 20 centres were included in the final analysis (cTB, n = 27; pTB, n = 19). Median age at symptom onset was one week in cTB (IQR: 0-1 weeks), and 12 weeks in pTB patients (IQR: 5-18 weeks). Prematurity was more common in cTB than pTB patients [57.9% (11/19); 95% CI: 36.3-76.9% vs. 21.1% (4/19); 95% CI: 8.5-43.3%; p = 0.049], and the average birth weight was significantly lower [1680 g; IQR: 932-2805 g vs. 2890 g; IQR: 2461-3400 g; p = 0.0043]. Microbiological confirmation was achieved in most patients [85.2% (23/27); 95% CI: 67.5-94.1% vs. 78.9% (15/19); 95% CI: 56.7-91.5%; p = 0.70]. The sensitivity of interferon-gamma release assays was poor in both groups [25.0% (3/12) 95% CI: 8.9-53.2% vs. 35.7% (5/14) 95% CI: 16.3-61.2%; p = 0.68]; in contrast, the sensitivity of the tuberculin skin tests (at 5 mm cut-off) was significantly higher in pTB patients [16.7% (2/12) 95% CI: 4.7-44.8% vs. 66.7% (10/15); 95% CI: 41.7-84.8%; p = 0.0185]. Approximately half of the patients required intensive care support [51.9% (14/27) 95% CI: 34.0-69.3% vs. 47.4% (9/19); 95% CI: 27.3-68.3%; p > 0.99]. Four (4/46; 8.7%) patients died, and four (4/46; 8.7%) had severe long-term sequelae.

INTERPRETATION: There was substantial mortality and morbidity in this patient cohort, despite the high-resource setting. cTB was associated with premature birth and low birth weight. In contrast to microbiological tests, immunological tests perform poorly in perinatal TB, and should therefore not be used as rule-out tests.

FUNDING: No study-specific funding.

PMID:40291400 | PMC:PMC12032942 | DOI:10.1016/j.lanepe.2025.101303

APL Bioeng. 2025 Apr 25;9(2):026110. doi: 10.1063/5.0220367. eCollection 2025 Jun.

ABSTRACT

Dysregulated neutrophil recruitment drives many pulmonary diseases, but most preclinical screening methods are unsuited to evaluate pulmonary neutrophilia, limiting progress toward therapeutics. Namely, high-throughput therapeutic assays typically exclude critical neutrophilic pathophysiology, including blood-to-lung recruitment, dysfunctional activation, and resulting impacts on the air-blood barrier. To meet the conflicting demands of physiological complexity and high throughput, we developed an assay of 96-well leukocyte recruitment in an air-blood barrier array (L-ABBA-96) that enables in vivo-like neutrophil recruitment compatible with downstream phenotyping by automated flow cytometry. We modeled acute respiratory distress syndrome (ARDS) with neutrophil recruitment to 20 ng/mL epithelial-side interleukin 8 and found a dose-dependent reduction in recruitment with physiologic doses of baricitinib, a JAK1/2 inhibitor recently Food and Drug Administration-approved for severe Coronavirus Disease 2019 ARDS. Additionally, neutrophil recruitment to patient-derived cystic fibrosis sputum supernatant induced disease-mimetic recruitment and activation of healthy donor neutrophils and upregulated endothelial e-selectin. Compared to 24-well assays, the L-ABBA-96 reduces required patient sample volumes by 25 times per well and quadruples throughput per plate. Compared to microfluidic assays, the L-ABBA-96 recruits two orders of magnitude more neutrophils per well, enabling downstream flow cytometry and other standard biochemical assays. This novel pairing of high-throughput in vitro modeling of organ-level lung function with parallel high-throughput leukocyte phenotyping substantially advances opportunities for pathophysiological studies, personalized medicine, and drug testing applications.

PMID:40290728 | PMC:PMC12033047 | DOI:10.1063/5.0220367

Biofilm. 2025 Apr 11;9:100279. doi: 10.1016/j.bioflm.2025.100279. eCollection 2025 Jun.

ABSTRACT

Co-infections by Staphylococcus aureus and Pseudomonas aeruginosa are frequent in the airways of patients with cystic fibrosis. These co-infections show higher antibiotic tolerance in vitro compared to mono-infections. In vitro models have been developed to study the interspecies interactions between P. aeruginosa and S. aureus. However, these model systems fail to incorporate clinical isolates with diverse phenotypes, do not reflect the nutritional environment of the CF airway mucus, and/or do not model the biofilm mode of growth observed in the CF airways. Here, we established a dual-species biofilm model grown in artificial sputum medium, where S. aureus was inoculated before P. aeruginosa to facilitate the maintenance of both species over time. It was successfully applied to ten pairs of clinical isolates exhibiting different phenotypes. Co-isolates from individual patients led to robust, stable co-cultures, supporting the theory of cross-adaptation in vivo. Investigation into the cross-adaptation of the VBB496 co-isolate pair revealed that both the P. aeruginosa and S. aureus isolates had reduced antagonism, in part due to reduced production of P. aeruginosa secondary metabolites as well as higher tolerance to those metabolites by S. aureus. Together, these results indicate that the two-species biofilm model system provides a useful tool for exploring interspecies interactions of P. aeruginosa and S. aureus in the context of CF airway infections.

PMID:40290724 | PMC:PMC12033965 | DOI:10.1016/j.bioflm.2025.100279

Pak J Med Sci. 2025 Apr;41(4):1052-1057. doi: 10.12669/pjms.41.4.10877.

ABSTRACT

BACKGROUND & OBJECTIVES: Bronchiectasis is the permanent enlargement of the bronchi following damage to the respiratory tract (bronchi) in the lungs. Bronchiectasis not associated with cystic fibrosis is gaining an increasing place among chronic respiratory diseases worldwide. The purpose of this study was to identify the levels of MMP-9, known to cause bronchial damage in chronic pulmonary illness, and SIRT-1, an anti-aging and anti-infective regulatory protein, in patients with bronchiectasis and to evaluate the importance of these biomarkers in diagnosis.

METHODS: This cross-sectional study was conducted in the Chest Diseases Clinic of Sivas Cumhuriyet University Medical Faculty Hospital between November 2020 and September 2022. We recruited 30 patients with bronchiectasis whose diagnosis was verified by high-resolution chest CT scan and 30 healthy controls. SIRT-1 and MMP-9 levels in the serum of the study group were determined by the ELISA method.

RESULTS: SIRT-1 and MMP-9 concentrations were found to be statistically significant. In comparison to the control group, it was observed that the bronchiectasis group had a lower serum SIRT-1 levels (p<0.001). The bronchiectasis group had higher serum MMP-9 values than the control group (p<0.001). Age-related differences in SIRT-1 and MMP-9 concentrations were not observed. No correlation was found between MMP-9 and SIRT-1. The results of Receiver Operating Characteristic (ROC) analysis indicated that MMP-9 has relatively high sensitivities.

CONCLUSIONS: We concluded that, higher inflammation elevates MMP-9 levels while decreasing SIRT-1 levels. MMP-9 and SIRT-1 may be potential biomarkers in the diagnosis of bronchiectasis.

PMID:40290232 | PMC:PMC12022576 | DOI:10.12669/pjms.41.4.10877