J Orthop Translat. 2025 Mar 15;51:187-197. doi: 10.1016/j.jot.2025.01.007. eCollection 2025 Mar.

ABSTRACT

BACKGROUND: Load-bearing structural degradation is crucial in knee osteoarthritis (KOA) progression, yet limited prediction models use load-bearing tissue radiomics for radiographic (structural) KOA incident.

PURPOSE: We aim to develop and test a Load-Bearing Tissue plus Clinical variable Radiomic Model (LBTC-RM) to predict radiographic KOA incidents.

STUDY DESIGN: Risk prediction study.

METHODS: The 700 knees without radiographic KOA at baseline were included from Osteoarthritis Initiative cohort. We selected 2164 knee MRIs during 4-year follow-up. LBTC-RM, which integrated MRI features of meniscus, femur, tibia, femorotibial cartilage, and clinical variables, was developed in total development cohort (n = 1082, 542 cases vs. 540 controls) using neural network algorithm. Final predictive model was tested in total test cohort (n = 1082, 534 cases vs. 548 controls), which integrated data from five visits: baseline (n = 353, 191 cases vs. 162 controls), 3 years prior KOA (n = 46, 19 cases vs. 27 controls), 2 years prior KOA (n = 143, 77 cases vs. 66 controls), 1 year prior KOA (n = 220, 105 cases vs. 115 controls), and at KOA incident (n = 320, 156 cases vs. 164 controls).

RESULTS: In total test cohort, LBTC-RM predicted KOA incident with AUC (95 % CI) of 0.85 (0.82-0.87); with LBTC-RM aid, performance of resident physicians for KOA prediction were improved, with specificity, sensitivity, and accuracy increasing from 50 %, 60 %, and 55 %-72 %, 73 %, and 72 %, respectively. The LBTC-RM output indicated an increased KOA risk (OR: 20.6, 95 % CI: 13.8-30.6, p < .001). Radiomic scores of load-bearing tissue raised KOA risk (ORs: 1.02-1.9) from 4-year prior KOA whereas 3-dimensional feature score of medial meniscus decreased the OR (0.99) of KOA incident at KOA confirmed. The 2-dimensional feature score of medial meniscus increased the ORs (1.1-1.2) of KOA symptom score from 2-year prior KOA.

CONCLUSIONS: We provided radiomic features of load-bearing tissue to improved KOA risk level assessment and incident prediction. The model has potential clinical applicability in predicting KOA incidents early, enabling physicians to identify high-risk patients before significant radiographic evidence appears. This can facilitate timely interventions and personalized management strategies, improving patient outcomes.

THE TRANSLATIONAL POTENTIAL OF THIS ARTICLE: This study presents a novel approach integrating longitudinal MRI-based radiomics and clinical variables to predict knee osteoarthritis (KOA) incidence using machine learning. By leveraging deep learning for auto-segmentation and machine learning for predictive modeling, this research provides a more interpretable and clinically applicable method for early KOA detection. The introduction of a Radiomics Score System enhances the potential for radiomics as a virtual image-based biopsy tool, facilitating non-invasive, personalized risk assessment for KOA patients. The findings support the translation of advanced imaging and AI-driven predictive models into clinical practice, aiding early diagnosis, personalized treatment planning, and risk stratification for KOA progression. This model has the potential to be integrated into routine musculoskeletal imaging workflows, optimizing early intervention strategies and resource allocation for high-risk populations. Future validation across diverse cohorts will further enhance its clinical utility and generalizability.

PMID:40144553 | PMC:PMC11937290 | DOI:10.1016/j.jot.2025.01.007

Front Physiol. 2025 Mar 12;16:1512835. doi: 10.3389/fphys.2025.1512835. eCollection 2025.

ABSTRACT

BACKGROUND: Pneumonia is considered one of the most important causes of morbidity and mortality in the world. Bacterial and viral pneumonia share many similar clinical features, thus making diagnosis a challenging task. Traditional diagnostic method developments mainly rely on radiological imaging and require a certain degree of consulting clinical experience, which can be inefficient and inconsistent. Deep learning for the classification of pneumonia in multiple modalities, especially integrating multiple data, has not been well explored.

METHODS: The study introduce the PneumoFusion-Net, a deep learning-based multimodal framework that incorporates CT images, clinical text, numerical lab test results, and radiology reports for improved diagnosis. In the experiments, a dataset of 10,095 pneumonia CT images was used-including associated clinical data-most of which was used for training and validation while keeping part of it for validation on a held-out test set. Five-fold cross-validation was considered in order to evaluate this model, calculating different metrics including accuracy and F1-Score.

RESULTS: PneumoFusion-Net, which achieved 98.96% classification accuracy with a 98% F1-score on the held-out test set, is highly effective in distinguishing bacterial from viral types of pneumonia. This has been highly beneficial for diagnosis, reducing misdiagnosis and further improving homogeneity across various data sets from multiple patients.

CONCLUSION: PneumoFusion-Net offers an effective and efficient approach to pneumonia classification by integrating diverse data sources, resulting in high diagnostic accuracy. Its potential for clinical integration could significantly reduce the burden of pneumonia diagnosis by providing radiologists and clinicians with a robust, automated diagnostic tool.

PMID:40144549 | PMC:PMC11937601 | DOI:10.3389/fphys.2025.1512835

Front Physiol. 2025 Mar 12;16:1515881. doi: 10.3389/fphys.2025.1515881. eCollection 2025.

ABSTRACT

Multimodal diagnostic methods for Alzheimer's disease (AD) have demonstrated remarkable performance. However, the inclusion of electroencephalography (EEG) in such multimodal studies has been relatively limited. Moreover, most multimodal studies on AD use convolutional neural networks (CNNs) to extract features from different modalities and perform fusion classification. Regrettably, this approach often lacks collaboration and fails to effectively enhance the representation ability of features. To address this issue and explore the collaborative relationship among multimodal EEG, this paper proposes a multimodal AD diagnosis model based on resting-state EEG and structural magnetic resonance imaging (sMRI). Specifically, this work designs corresponding feature extraction models for EEG and sMRI modalities to enhance the capability of extracting modality-specific features. Additionally, a multimodal joint attention mechanism (MJA) is developed to address the issue of independent modalities. The MJA promotes cooperation and collaboration between the two modalities, thereby enhancing the representation ability of multimodal fusion. Furthermore, a random forest classifier is introduced to enhance the classification ability. The diagnostic accuracy of the proposed model can achieve 94.7%, marking a noteworthy accomplishment. This research stands as the inaugural exploration into the amalgamation of deep learning and EEG multimodality for AD diagnosis. Concurrently, this work strives to bolster the use of EEG in multimodal AD research, thereby positioning itself as a hopeful prospect for future advancements in AD diagnosis.

PMID:40144547 | PMC:PMC11937600 | DOI:10.3389/fphys.2025.1515881

Front Vet Sci. 2025 Mar 12;12:1511522. doi: 10.3389/fvets.2025.1511522. eCollection 2025.

ABSTRACT

Deep learning (DL), a subfield of artificial intelligence (AI), involves the development of algorithms and models that simulate the problem-solving capabilities of the human mind. Sophisticated AI technology has garnered significant attention in recent years in the domain of veterinary medicine. This review provides a comprehensive overview of the research dedicated to leveraging DL for diagnostic purposes within veterinary medicine. Our systematic review approach followed PRISMA guidelines, focusing on the intersection of DL and veterinary medicine, and identified 422 relevant research articles. After exporting titles and abstracts for screening, we narrowed our selection to 39 primary research articles directly applying DL to animal disease detection or management, excluding non-primary research, reviews, and unrelated AI studies. Key findings from the current body of research highlight an increase in the utilisation of DL models across various diagnostic areas from 2013 to 2024, including radiography (33% of the studies), cytology (33%), health record analysis (8%), MRI (8%), environmental data analysis (5%), photo/video imaging (5%), and ultrasound (5%). Over the past decade, radiographic imaging has emerged as most impactful. Various studies have demonstrated notable success in the classification of primary thoracic lesions and cardiac disease from radiographs using DL models compared to specialist veterinarian benchmarks. Moreover, the technology has proven adept at recognising, counting, and classifying cell types in microscope slide images, demonstrating its versatility across different veterinary diagnostic modality. While deep learning shows promise in veterinary diagnostics, several challenges remain. These challenges range from the need for large and diverse datasets, the potential for interpretability issues and the importance of consulting with experts throughout model development to ensure validity. A thorough understanding of these considerations for the design and implementation of DL in veterinary medicine is imperative for driving future research and development efforts in the field. In addition, the potential future impacts of DL on veterinary diagnostics are discussed to explore avenues for further refinement and expansion of DL applications in veterinary medicine, ultimately contributing to increased standards of care and improved health outcomes for animals as this technology continues to evolve.

PMID:40144529 | PMC:PMC11938132 | DOI:10.3389/fvets.2025.1511522

Front Digit Health. 2025 Mar 12;7:1551298. doi: 10.3389/fdgth.2025.1551298. eCollection 2025.

ABSTRACT

COVID-19 remains a significant global public health challenge. While nucleic acid tests, antigen tests, and CT imaging provide high accuracy, they face inefficiencies and limited accessibility, making rapid and convenient testing difficult. Recent studies have explored COVID-19 detection using acoustic health signals, such as cough and breathing sounds. However, most existing approaches focus solely on audio classification, often leading to suboptimal accuracy while neglecting valuable prior information, such as clinical symptoms. To address this limitation, we propose SympCoughNet, a deep learning-based COVID-19 audio classification network that integrates cough sounds with clinical symptom data. Our model employs symptom-encoded channel weighting to enhance feature processing, making it more attentive to symptom information. We also conducted an ablation study to assess the impact of symptom integration by removing the symptom-attention mechanism and instead using symptoms as classification labels within a CNN-based architecture. We trained and evaluated SympCoughNet on the UK COVID-19 Vocal Audio Dataset. Our model demonstrated significant performance improvements over traditional audio-only approaches, achieving 89.30% accuracy, 94.74% AUROC, and 91.62% PR on the test set. The results confirm that incorporating symptom data enhances COVID-19 detection performance. Additionally, we found that incorrect symptom inputs could influence predictions. Our ablation study validated that even when symptoms are treated as classification labels, the network can still effectively leverage cough audio to infer symptom-related information.

PMID:40144457 | PMC:PMC11936986 | DOI:10.3389/fdgth.2025.1551298

Curr Opin Pulm Med. 2025 Mar 27. doi: 10.1097/MCP.0000000000001169. Online ahead of print.

ABSTRACT

PURPOSE OF REVIEW: This review examines the current understanding of telomere biology disorders (TBDs) in advanced lung disease, with particular focus on their implications for lung transplantation outcomes and management.

RECENT FINDINGS: Recent studies have revealed that TBDs are enriched in lung transplant populations, with many idiopathic pulmonary fibrosis transplant recipients having short telomeres and/or carrying variants in telomere-related genes. While survival outcomes remain debated, recipients with short telomeres consistently show increased susceptibility to cytopenias, cytomegalovirus (CMV) infection, and may require modified immunosuppression regimens. New evidence suggests potential protection against acute cellular rejection in some cases, and novel approaches using letermovir for CMV prophylaxis show promise in managing these complex patients.

SUMMARY: Management of lung transplant recipients with TBDs requires careful consideration of multiorgan manifestations and individualized management strategies. A multidisciplinary approach incorporating genetics, haematology, and hepatology expertise is increasingly essential for optimal outcomes in this unique population.

PMID:40145203 | DOI:10.1097/MCP.0000000000001169

JHLT Open. 2023 Oct 20;2:100011. doi: 10.1016/j.jhlto.2023.100011. eCollection 2023 Dec.

ABSTRACT

BACKGROUND: Acute exacerbations of interstitial lung disease (AE-ILD) cause severe respiratory failure, and mortality is high despite treatment. Lung transplantation is an effective therapy for late-stage interstitial lung disease (ILD), but prior studies on post-transplant outcomes for patients trandsplanted in AE-ILD are conflicting.

METHODS: We performed a retrospective evaluation of all first-time lung transplant recipients for ILD performed at our institution between May 1, 2005, and April 1, 2019. Patients were stratified according to a published consensus definition into AE-ILD recipients, other inpatients, or outpatients. One-year survival was compared with a Cox proportional hazards model. Subset analysis was performed on those with idiopathic pulmonary fibrosis (IPF). Patients were also assessed for survival free of long-term chronic lung allograft dysfunction (CLAD).

RESULTS: We identified 717 first-time lung transplant ILD recipients: 41 inpatients in AE-ILD, 31 other inpatients, and 645 outpatients. One-year survival was 93% for AE-ILD recipients, 61% for other inpatient recipients, and 82% for outpatient recipients. Those transplanted in AE-ILD had a lower hazard of death or retransplantation compared to other inpatients (hazard ratio [HR] 0.16, 95% confidence interval [CI] 0.04-0.56) and outpatients (HR 0.29, CI 0.09-1.00). Results were similar among the subset of patients with IPF, but differences were not significant. For those transplanted during AE-ILD, rates of CLAD were not significantly different compared to other inpatients (HR 1.34, CI 0.51-3.54) or to outpatients (HR 1.05, CI 0.52-2.13).

CONCLUSIONS: With careful selection, patients in AE-ILD can be transplanted and have acceptable 1-year outcomes without increased risk of long-term graft dysfunction.

PMID:40144010 | PMC:PMC11935389 | DOI:10.1016/j.jhlto.2023.100011

JHLT Open. 2024 Apr 25;5:100101. doi: 10.1016/j.jhlto.2024.100101. eCollection 2024 Aug.

ABSTRACT

Chimeric antigen receptor T-cell therapy (CAR-T) has been used to treat refractory post-transplant lymphoproliferative disorder (PTLD) in solid organ transplant patients, including heart, kidney, liver, intestine, and pancreas. We report the use of CAR-T for treating refractory PTLD in a 73-year-old female who was 7 years post bilateral lung transplantation for idiopathic pulmonary fibrosis. We discuss the immunosuppression management in this patient, as well as her clinical course and outcome.

PMID:40143897 | PMC:PMC11935479 | DOI:10.1016/j.jhlto.2024.100101

JHLT Open. 2024 Jul 1;5:100122. doi: 10.1016/j.jhlto.2024.100122. eCollection 2024 Aug.

ABSTRACT

BACKGROUND: Predicting post-transplant (PT) survival in lung allocation remains an elusive goal. We analyzed the impact of donor factors on PT survival and how these relationships vary among transplant recipients.

METHODS: We studied primary bilateral lung transplant recipients (n = 7,609) from the US Scientific Registry of Transplant Recipients (19 February 2015-1 February 2020). Main and interaction effects were evaluated and adjusted across candidate age, sex, and diagnosis. Models predicting PT survival were compared to the PT Composite Allocation Score model (PT-CAS): (1) Cox regression donor multivariable model (COX), (2) COX + PT-CAS, (3) random forest model (RF), and (4) RF + PT-CAS. Model discrimination and calibration measures were compared.

RESULTS: Interactions between donor and recipient factors emerged by age: lower survival for donation after circulatory death organs for recipients aged 55 to 69 years, donor smoking for recipients aged 30 to 54 and 70+, Hispanic donor for recipients <30, non-Hispanic Black donor for recipients aged 30+; sex: cytomegalovirus mismatch for males; diagnosis: higher donor recipient weight ratio for diagnosis group C (e.g., cystic fibrosis), donor diabetes for diagnosis group D (e.g., idiopathic pulmonary fibrosis). COX and RF models performed similarly to PT-CAS; however, the combined COX + PT-CAS model had improved discrimination (1-year area under the receiver operator characteristic curve [AUC] PT-CAS 0.609 vs 1-year AUC COX + PT-CAS 0.626) and improved calibration across a broader range of predicted risk.

CONCLUSIONS: The influence of donor factors on recipient PT survival differed by age, sex, and diagnosis. The addition of donor factors to existing models predicting PT survival led to only modest improvement in prediction accuracy. Future efforts may focus on optimizing matching strategies to improve donor utilization.

PMID:40143895 | PMC:PMC11935449 | DOI:10.1016/j.jhlto.2024.100122

Pharmaceuticals (Basel). 2025 Mar 14;18(3):411. doi: 10.3390/ph18030411.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive interstitial lung disease of unknown aetiology. Evidence on the progression of idiopathic pulmonary fibrosis (IPF) following the introduction of antifibrotic therapies still indicates a generally poor prognosis. IPF is associated with both respiratory and non-respiratory comorbidities, which can worsen symptoms and impact overall survival. Background/Objectives: The study aimed to investigate the effect of these comorbidities on the early and permanent discontinuation of pirfenidone or nintedanib in IPF patients. Methods: In this single-centre retrospective study, 101 patients diagnosed with IPF according to ATS/ERS/JRS/ALAT guidelines were treated with AFT. Clinical data were collected at 12 months prior to and up to 24 months following treatment initiation, including age, gender, smoking history, and the presence of respiratory and non-respiratory comorbidities. Results: The data showed that 21 patients (20.8%) discontinued treatment within the first 12 months. Additionally, pre-treatment comorbidities were not statistically correlated with the suspension of antifibrotic treatment. Among the overall cohort, 77 patients (76.2%) had at least one comorbidity and 27 (26.7%) had three or more comorbidities. Notably, 24 (23.8%) had respiratory comorbidities, while 75 (74.3%) had non-respiratory comorbidities. Conclusions: This real-life study emphasises the complexities involved in managing IPF, particularly regarding adherence to treatment when significant comorbidities are present. The evidence suggests that in patients with IPF, pre-treatment respiratory or non-respiratory conditions do not affect AFT discontinuation.

PMID:40143187 | DOI:10.3390/ph18030411

Molecules. 2025 Mar 14;30(6):1311. doi: 10.3390/molecules30061311.

ABSTRACT

Silicosis is an interstitial lung disease (ILD) caused by prolonged inhalation of silica particles. Acute lung injury (ALI) is a critical clinical syndrome involving bilateral lung infiltration and acute hypoxic respiratory failure. However, there is currently no effective treatment for these two diseases. Previous research has established that cyclic adenosine monophosphate (cAMP) is pivotal in the pathogenesis of silicosis and acute lung injury. Phosphodiesterase 4 (PDE4) is a hydrolase enzyme of cAMP, and BI 1015550, as an inhibitor of PDE4B, is expected to be a candidate drug for treating both. BI 1015550 has shown certain anti-inflammatory and anti-fibrotic properties in systemic sclerosis-associated interstitial lung disease (SSc-ILD) and idiopathic pulmonary fibrosis (IPF), but there is a lack of research on silicosis and acute lung injury. In this research, we successfully synthesized BI 1015550 autonomously and demonstrated that it could significantly improve lung fibrosis and inflammation in a silica-induced silicosis mouse model. Furthermore, we found that BI 1015550 could also alleviate lung inflammation in a Lipopolysaccharide (LPS)-induced acute lung injury mouse model. The mechanism of action may involve the regulation of cAMP-related signaling pathways.

PMID:40142089 | DOI:10.3390/molecules30061311

Int J Mol Sci. 2025 Mar 19;26(6):2760. doi: 10.3390/ijms26062760.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive lung disease characterized by the disruption of the alveolar and interstitial architecture due to extracellular matrix deposition. Emerging evidence suggests that genetic susceptibility plays a crucial role in IPF development. This study explores the role of human leukocyte antigen (HLA) alleles and haplotypes in IPF susceptibility and progression within the genetically distinct Sardinian population. Genotypic data were analyzed for associations with disease onset and progression, focusing on allele and haplotype frequencies in patients exhibiting slow (S) or rapid (R) progression. While no significant differences in HLA allele frequencies were observed between IPF patients and controls, the HLA-DRB1*04:05 allele and the extended haplotype (HLA-A*30:02, B*18:01, C*05:01, DQA1*05:01, DQB1*02:01, DRB1*03:01) were associated with a slower disease progression and improved survival (log-rank = 0.032 and 0.01, respectively). At 36 months, carriers of these variants demonstrated significantly better pulmonary function, measured with single-breath carbon monoxide diffusing capacity (DLCO%p) (p = 0.005 and 0.02, respectively). Multivariate analysis confirmed these findings as being independent of confounding factors. These results highlight the impact of HLA alleles and haplotypes on IPF outcomes and underscore the potential of the Sardinian genetic landscape to illuminate immunological mechanisms, paving the way for predictive biomarkers and personalized therapies.

PMID:40141400 | DOI:10.3390/ijms26062760

Int J Mol Sci. 2025 Mar 17;26(6):2705. doi: 10.3390/ijms26062705.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a progressive and incurable chronic interstitial lung disease characterized by excessive fibrosis and impaired lung function. Current treatments, such as pirfenidone and nintedanib, slow disease progression but fail to halt or reverse fibrosis, highlighting the need for novel approaches. Activin A, which belongs to the TGF-β superfamily, is implicated in various fibrosis-related mechanisms, including epithelial-mesenchymal transition (EMT), a process where epithelial cells acquire mesenchymal characteristics, and fibroblast-myofibroblast transformation (FMT), in which fibroblasts differentiate into contractile myofibroblasts. It also promotes inflammatory cytokine release and extracellular matrix buildup. This study aimed to inhibit Activin A activity using synthetic peptides identified through phage display screening. Of the ten peptides isolated, A7, B9, and E10 demonstrated high binding affinity and inhibitory activity. Computational modeling confirmed that these peptides target the receptor-binding domain of Activin A, with peptide E10 exhibiting superior efficacy. Functional assays showed that E10 reduced cell migration, inhibited EMT in A549 cells, and suppressed FMT in fibroblast cultures, even under pro-fibrotic stimulation with TGF-β. These findings underscore the therapeutic potential of targeting Activin A with synthetic peptides, offering a promising avenue for IPF treatment and expanding the arsenal of anti-fibrotic strategies.

PMID:40141346 | DOI:10.3390/ijms26062705

AAPS PharmSciTech. 2025 Mar 26;26(4):94. doi: 10.1208/s12249-025-03086-8.

ABSTRACT

Idiopathic Pulmonary Fibrosis (IPF) is a rare and serious chronic interstitial lung disease that may endanger the lives of patients. The median survival time of patients with idiopathic pulmonary fibrosis is short, and the mortality rate is higher than that of many types of cancer. At present, pirfenidone (PFD) and nintedanib (NDNB) have been approved by FDA for IPF, but they can only delay the process of pulmonary fibrosis and cannot cure the disease. Therefore, it is urgent to develop other drugs with the effect of improving pulmonary fibrosis. Ellagic acid (EA) can inhibit the Wnt-signaling pathway and has an effect in treating pulmonary fibrosis induced by bleomycin (BLM) in mice. However, its solubility is poor, resulting in its low bioavailability and limited therapeutic benefits, so its clinical application has been limited. Herein, based on the characteristics of nano-drug lung delivery system, chitosan (CS) was selected as the carrier, and ellagic acid-loaded chitosan nanoparticles (EA-CS-NPs) were prepared by ionic gelation method. The EE% and DL% of prepared EA-CS-NPs was 73.73 ± 4.52% and 6.23 ± 1.09%, the particle size was 119.6 ± 5.51 nm (PDI = 0.234 ± 0.017), the zeta potential was 29.833 ± 0.503 mV. The morphology of the nanoparticles was observed by TEM microscope, which was round, uniform dispersion, indicating that the preparation process is stable and feasible. The toxicity experiment showed that EA-CS-NPs maintained 80% cell viability, significantly higher than that of the NDNB group, indicating lower toxicity and better inhibitory effects on TGF-β1-stimulated MLg and NIH-3T3 cells. Wound healing assay results showed that the inhibitory effect of EA-CS-NPs on cell migration was more pronounced than that of EA in the same amount of EA-containing drugs. Drug uptake experiments revealed that EA-CS-NPs significantly enhanced drug uptake in MLg and NIH-3T3 cells. In vivo, Cy7-CS-NPs exhibited higher fluorescence intensity in rat lungs compared to Cy7 solution, indicating better lung retention. The in vivo efficacy test showed that compared with the EA group, EA-CS-NPs could better reduce the area of pulmonary fibrosis and collagen deposition, improve lung function, and have a longer retention time in the lung. In summary, our results revealed that EA-CS-NPs may be a good choice for the treatment of pulmonary fibrosis.

PMID:40140157 | DOI:10.1208/s12249-025-03086-8

Front Immunol. 2025 Mar 12;16:1534615. doi: 10.3389/fimmu.2025.1534615. eCollection 2025.

ABSTRACT

Tuberculosis (TB) is one of the leading causes of death due to infectious disease. The sole established vaccine against TB is the Mycobacterium bovis Bacillus Calmette-Guerin (BCG) vaccine. However, owing to the lack of durable immunity with the BCG vaccine and its risk of infection, safer vaccines that can also be used as boosters are needed. Here, we examined whether membrane vesicles (MVs) from BCG (BCG-MVs) isolated from BCG statically cultured in nutrient-restricted Sauton's medium (s-MVs) and from BCG planktonically cultured in nutrient-rich medium commonly used in the laboratory (p-MVs) could be used as novel TB vaccines. MVs are extracellular vesicles produced by various bacteria, including mycobacteria. Differences in the culture conditions affected the morphology, contents, immunostimulatory activity and immunogenicity of BCG-MVs. s-MVs presented greater immunostimulatory activity than p-MVs via the induction of TLR2 signaling. Mouse immunization experiments revealed that s-MVs, but not p-MVs, induced mycobacterial humoral and mucosal immunity, especially when administered in combination with adjuvants. In a BCG challenge experiment using BCG Tokyo type I carrying pMV361-Km, subcutaneous vaccination with s-MVs reduced the bacterial burden in the mouse lung to a level similar to that after intradermal vaccination with live BCG. Furthermore, the administration of s-MVs induced a significant lipopolysaccharide-induced proinflammatory response in macrophages in vitro. These results indicate that BCG-MVs obtained from static culture in Sauton's medium induce not only humoral immunity against mycobacteria but also trained immunity, which can allow the clearance of infectious agents other than mycobacteria. Together, these findings highlight the immunological properties of BCG-MVs and the availability of acellular TB vaccines that confer broad protection against various infectious diseases.

PMID:40145097 | PMC:PMC11937015 | DOI:10.3389/fimmu.2025.1534615

J Allergy Clin Immunol Glob. 2025 Feb 18;4(2):100443. doi: 10.1016/j.jacig.2025.100443. eCollection 2025 May.

ABSTRACT

BACKGROUND: Orchiectomy, which results in hypogonadism, may increase the risk of asthma due to androgen deficiency.

OBJECTIVES: We aimed to investigate the association between orchiectomy and asthma risk.

METHODS: Men aged 18 years or older between 1999 and 2016 were identified from the national real-world database IBM-Explorys. We used multivariable logistic regression adjusted for age and body mass index to determine the risk of asthma among individuals who had and had not undergone orchiectomy. To reproduce our findings, we selected men aged 18 years or older with or without a history of orchiectomy who were enrolled in the globally federated TriNetX database as of May 2024.

RESULTS: In the IBM-Explorys database, the orchiectomy group had a 2-fold increase in the odds of having asthma (adjusted odds ratio = 2.03 [95% CI = 1.91-2.16]; P < .001). Similarly, in the TriNetX database, the risk of asthma was higher in the orchiectomy group than in the nonorchiectomy group (adjusted odds ratio =1.61 [95% CI = 1.42-1.82]; P < .001).

CONCLUSION: Patients who have undergone an orchiectomy are at increased risk of developing asthma. More research is needed to determine the mechanisms underlying the relationship between asthma diagnosis and orchiectomy.

PMID:40144019 | PMC:PMC11938140 | DOI:10.1016/j.jacig.2025.100443

Viruses. 2025 Feb 26;17(3):318. doi: 10.3390/v17030318.

ABSTRACT

The journal Viruses retracts the article "PERK Is Critical for Alphavirus Nonstructural Protein Translation" [...].

PMID:40143380 | DOI:10.3390/v17030318

Viruses. 2025 Feb 28;17(3):355. doi: 10.3390/v17030355.

ABSTRACT

Viruses are ubiquitous across all kingdoms of cellular life, posing a significant threat to human health, and analyzing viral communities is challenging due to their genetic diversity and lack of a single, universally conserved marker gene. To address this challenge, we developed the AliMarko pipeline, a tool designed to streamline virus identification in metagenomic data. Our pipeline uses a dual approach, combining mapping reads with reference genomes and a de novo assembly-based approach involving an HMM-based homology search and phylogenetic analysis, to enable comprehensive detection of viral sequences, including low-coverage and divergent sequences. We applied our pipeline to total RNA sequencing of bat feces and identified a range of viruses, quickly validating viral sequences and assessing their phylogenetic relationships. We hope that the AliMarko pipeline will be a useful resource for the scientific community, facilitating the interpretation of viral communities and advancing our understanding of viral diversity and its impact on human health.

PMID:40143285 | DOI:10.3390/v17030355

Pharmaceutics. 2025 Feb 24;17(3):296. doi: 10.3390/pharmaceutics17030296.

ABSTRACT

Melanoma, a highly aggressive form of skin cancer, poses a major therapeutic challenge due to its metastatic potential, resistance to conventional therapies, and the complexity of the tumor microenvironment (TME). Materials science and nanotechnology advances have led to using nanocarriers such as liposomes, dendrimers, polymeric nanoparticles, and metallic nanoparticles as transformative solutions for precision melanoma therapy. This review summarizes findings from Web of Science, PubMed, EMBASE, Scopus, and Google Scholar and highlights the role of nanotechnology in overcoming melanoma treatment barriers. Nanoparticles facilitate passive and active targeting through mechanisms such as the enhanced permeability and retention (EPR) effect and functionalization with tumor-specific ligands, thereby improving the accuracy of drug delivery and reducing systemic toxicity. Stimuli-responsive systems and multi-stage targeting further improve therapeutic precision and overcome challenges such as poor tumor penetration and drug resistance. Emerging therapeutic platforms combine diagnostic imaging with therapeutic delivery, paving the way for personalized medicine. However, there are still issues with scalability, biocompatibility, and regulatory compliance. This comprehensive review highlights the potential of integrating nanotechnology with advances in genetics and proteomics, scalable, and patient-specific therapies. These interdisciplinary innovations promise to redefine the treatment of melanoma and provide safer, more effective, and more accessible treatments. Continued research is essential to bridge the gap between evidence-based scientific advances and clinical applications.

PMID:40142960 | DOI:10.3390/pharmaceutics17030296

J Clin Med. 2025 Mar 17;14(6):2040. doi: 10.3390/jcm14062040.

ABSTRACT

Oncologists increasingly recognize the microbiome as an important facilitator of health as well as a contributor to disease, including, specifically, cancer. Our knowledge of the etiologies, mechanisms, and modulation of microbiome states that ameliorate or promote cancer continues to evolve. The progressive refinement and adoption of "omic" technologies (genomics, transcriptomics, proteomics, and metabolomics) and utilization of advanced computational methods accelerate this evolution. The academic cancer center network, with its immediate access to extensive, multidisciplinary expertise and scientific resources, has the potential to catalyze microbiome research. Here, we review our current understanding of the role of the gut microbiome in cancer prevention, predisposition, and response to therapy. We underscore the promise of operationalizing the academic cancer center network to uncover the structure and function of the gut microbiome; we highlight the unique microbiome-related expert resources available at the City of Hope of Comprehensive Cancer Center as an example of the potential of team science to achieve novel scientific and clinical discovery.

PMID:40142848 | DOI:10.3390/jcm14062040