Anal Chem. 2025 Mar 4. doi: 10.1021/acs.analchem.4c05138. Online ahead of print.

ABSTRACT

Therapy resistance in breast cancer is increasingly attributed to polyploid giant cancer cells (PGCCs), which arise through whole genome doubling and exhibit heightened resilience to standard treatments. Characterized by enlarged nuclei and increased DNA content, these cells tend to be dormant under therapeutic stress, driving disease relapse. Despite their critical role in resistance, strategies to effectively target PGCCs are limited, largely due to the lack of high-throughput methods for assessing their viability. Traditional assays lack the sensitivity needed to detect PGCC-specific elimination, prompting the development of novel approaches. To address this challenge, we developed a high-throughput single-cell morphological analysis workflow designed to differentiate compounds that selectively inhibit non-PGCCs, PGCCs, or both. Using this method, we screened a library of 2726 FDA Phase 1-approved drugs, identifying promising anti-PGCC candidates, including proteasome inhibitors, FOXM1, CHK, and macrocyclic lactones. Notably, RNA-Seq analysis of cells treated with the macrocyclic lactone Pyronaridine revealed AXL inhibition as a potential strategy for targeting PGCCs. Although our single-cell morphological analysis pipeline is powerful, empirical testing of all existing compounds is impractical and inefficient. To overcome this limitation, we trained a machine learning model to predict anti-PGCC efficacy in silico, integrating chemical fingerprints and compound descriptions from prior publications and databases. The model demonstrated a high correlation with experimental outcomes and predicted efficacious compounds in an expanded library of over 6,000 drugs. Among the top-ranked predictions, we experimentally validated five compounds as potent PGCC inhibitors using cell lines and patient-derived models. These findings underscore the synergistic potential of integrating high-throughput empirical screening with machine learning-based virtual screening to accelerate the discovery of novel therapies, particularly for targeting therapy-resistant PGCCs in breast cancer.

PMID:40040372 | DOI:10.1021/acs.analchem.4c05138

Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10782823.

ABSTRACT

Tuberculosis, caused by Mycobacterium tuberculosis (Mtb), is a highly infectious disease mainly affecting the lungs. Macrophages are key phagocytic immune cells and the preferred hosts for intracellular bacteria growth. Macrophages are also important sites of vitamin D3 synthesis, with vitamin D3 deficiency associated with increased risk of developing active TB. There is great interest in vitamin D3 as adjunctive therapy due to its immunomodulatory and antimicrobial properties, particularly the effect on proinflammatory effectors like bactericidal nitric oxide (NO). NO production requires inducible nitric oxide synthase expression, which is regulated by IFN-γ, a pro-inflammatory cytokine upregulated by IL-12. Vitamin D3 serves an important host protective function to regulate NO production to a level that is sufficient to restrict Mtb growth while avoiding uncontrolled inflammation. While previous in vitro studies have shown that vitamin D3 modulates NO levels and IL-12, in an infection dose-dependent manner, to date, there are no computational models that capture the mechanisms by which vitamin D3 regulates NO production during high and low Mtb infection. Using an integrative systems biology approach, we define key signaling pathways involved in vitamin D3 immunometabolism and determine the impact of vitamin D3 sufficiency/deficiency given infection dosage. Data from multiple computational models and in vitro infection studies are integrated into a mechanistic model, and simulation results compared to in vitro IL-12 and NO concentrations from our in vitro models of infection. Concurrence between our in-silico and in vitro models demonstrates the feasibility of NO modulation in a vitamin D3 and infection level dependent manner.

PMID:40039981 | DOI:10.1109/EMBC53108.2024.10782823

Plant J. 2025 Mar;121(5):e70046. doi: 10.1111/tpj.70046.

ABSTRACT

Spider mites (Tetranychus urticae) are a major threat to economically important crops. Here, we investigated the potential of tetranins, in particular Tet3 and Tet4, as T. urticae protein-type elicitors that stimulate plant defense. Truncated Tet3 and Tet4 proteins showed efficacy in activating the defense gene pathogenesis-related 1 (PR1) and inducing phytohormone production in leaves of Phaseolus vulgaris. In particular, Tet3 caused a drastically higher Ca2+ influx in leaves, but a lower reactive oxygen species (ROS) generation compared to other tetranins, whereas Tet4 caused a low Ca2+ influx and a high ROS generation in the host plants. Such specific and non-specific elicitor activities were examined by knockdown of Tet3 and Tet4 expressions in mites, confirming their respective activities and in particular showing that they function additively or synergistically to induce defense responses. Of great interest is the fact that Tet3 and Tet4 expression levels were higher in mites on their preferred host, P. vulgaris, compared to the levels in mites on the less-preferred host, Cucumis sativus, whereas Tet1 and Tet2 were constitutively expressed regardless of their host. Furthermore, mites that had been hosted on C. sativus induced lower levels of PR1 expression, Ca2+ influx and ROS generation, i.e., Tet3- and Tet4-responsive defense responses, in both P. vulgaris and C. sativus leaves compared to the levels induced by mites that had been hosted on P. vulgaris. Taken together, these findings show that selected tetranins respond to variable host cues that may optimize herbivore fitness by altering the anti-mite response of the host plant.

PMID:40038832 | DOI:10.1111/tpj.70046

Stem Cell Res Ther. 2025 Mar 5;16(1):111. doi: 10.1186/s13287-025-04144-5.

ABSTRACT

BACKGROUND: Rheumatoid arthritis (RA) is a chronic autoimmune disease and the integrity of CX3CR1+ synovial macrophage barrier significantly impacts its progression. However, the mechanisms driving the dynamic changes of this macrophage barrier remain unclear. Traditional drug therapies for RA have substantial limitations. Mesenchymal stem cells (MSCs)-based cell therapy, especially adipose-derived stem cells (ADSCs), hold therapeutic promise. Nevertheless, the underlying therapeutic mechanism of ADSCs, especially their interactions with CX3CR1+ macrophages, require further investigation.

METHODS: To explore the interaction between ADSCs and CX3CR1+ synovial macrophages during barrier reconstruction, underlying the therapeutic mechanism of ADSCs and the mechanisms on the dynamic changes of the macrophage barrier, scRNA-seq analysis was conducted 4 days after ADSCs injection in serum transfer-induced arthritis model mice. The roles of mitochondria transfer and ADSCs transplantation were also explored. Bulk RNA-seq analysis was performed after the co-culture of ADSCs and CX3CR1+ synovial macrophages. To study the in vivo fate of ADSCs, bulk RNA-seq was performed on ADSCs retrieved at 0, 2, 4, and 7 days post-injection.

RESULTS: Intra-articular injection of ADSCs effectively attenuated the pathological progression of mice with serum transfer-induced arthritis. ADSCs gradually adhered to CX3CR1+ macrophages, facilitating the restore of the macrophage barrier, while the absence of this barrier greatly weakened the therapeutic effect of ADSCs. scRNA-seq analysis revealed an Atf3high Ccl3high subset of CX3CR1+ macrophages with impaired oxidative phosphorylation that increased during RA progression. ADSCs-mediated reduction of this subset appeared to be linked to mitochondrial transfer, and transplantation of isolated ADSCs-derived mitochondria also proved effective in treating RA. Both bulk RNA-seq and scRNA-seq analyses revealed multiple interaction mechanisms between ADSCs and CX3CR1+ macrophages, including Cd74/Mif axis and GAS6/MERTK axis, which contribute to barrier restoration and therapeutic effects. Furthermore, bulk RNA-seq analysis showed that ADSCs primarily contribute to tissue repair and immune regulation subsequently.

CONCLUSIONS: Our results suggest that ADSCs ameliorated the energy metabolism signature of CX3CR1+ lining macrophages and may promote barrier restoration through mitochondria transfer. In addition, we elucidated the fate of ADSCs and the therapeutic potential of mitochondria in RA treatment.

PMID:40038808 | DOI:10.1186/s13287-025-04144-5

Front Immunol. 2025 Feb 18;16:1508512. doi: 10.3389/fimmu.2025.1508512. eCollection 2025.

ABSTRACT

OBJECTIVE: To identify predictors of all-grade, grade ≥ 3, and onset time of immune-related adverse events (irAEs) in cancer patients undergoing immune checkpoint inhibitors (ICIs) therapy.

METHODS: This retrospective analysis included cancer patients treated with ICIs at Chongqing Medical University Second Affiliated Hospital from 2018 to 2024. Logistic regression and Cox regression analyses were used to identify predictors of all-grade and grade ≥ 3 irAEs and the time of irAE onset.

RESULTS: Among the 3,795 patients analyzed, 1,101 (29.0%) developed all-grade irAEs, and 175 (4.6%) experienced grade ≥ 3 irAEs. Multivariate logistic regression revealed that female (OR = 1.37, p < 0.001), combination therapy (OR = 1.87, p < 0.001), pre-existing autoimmune diseases (AIDs) (OR = 5.15, p < 0.001), pre-existing cirrhosis (OR = 1.34, p = 0.001), antibiotic use during ICIs treatment (OR = 1.51, p < 0.001), and a higher baseline prognostic nutritional index (PNI) (OR = 1.23, p = 0.01) were significant predictors for the development of all-grade irAEs. The predictors for grade ≥ 3 irAEs included age ≥ 60 (OR = 1.49, p = 0.023) and pre-existing AIDs (OR = 2.09, p = 0.005), For the onset time, predictors included female (HR = 1.26, p = 0.001), combination therapy (HR = 1.80, p < 0.001), pre-existing AIDs (HR = 2.25, p < 0.001), and pre-existing infection (HR = 1.20, p = 0.008).

CONCLUSIONS: Females, combination therapy, pre-existing AIDs and cirrhosis, antibiotics, and a higher baseline PNI are associated with a higher risk of developing all-grade irAEs. Those aged ≥ 60 and with pre-existing AIDs face a higher risk of severe irAEs. Females, undergoing combination therapy, with pre-existing AIDs and infection generally experience a shorter time to irAEs onset. Multicentric prospective studies are warranted to validate these findings.

PMID:40040713 | PMC:PMC11876122 | DOI:10.3389/fimmu.2025.1508512

Are you a signing official or principal investigator planning to be away from the office?  Do not let award management tasks pile up in your absence. Simply use the delegation feature in eRA Commons to allow others to act on your behalf. To accommodate scheduled absences and the need for assistance with award management tasks, delegation of specific duties and privileges are baked into the design of eRA Commons. 

Open the Admin (Account Management) module and click the Delegations menu option to work with delegations. Delegation capabilities vary based on your role and the authority being delegated. Scientific roles, such as a principal investigator (PI) can do direct delegations of their authorities (progress report, Status, Personal Profile, xTrain) to another PI or assistant.  

Signing officials (SOs) and business officials have an additional ability to delegate privileges on behalf of another user. This might be done if a PI is unexpectedly unavailable and someone else needs to work on their progress report.  SOs can also delegate institutional authority, which is the authority to act on behalf of an institution or organization. For instance, usually only SOs can submit reports for an award, but institutional authority granted by an SO to a PI enables the PI to submit these reports.  

Any user can delegate Personal Profile authority, so that others can update their Personal Profile.  

See instructions and a complete list of who can delegate which authorities in the Delegations online help topic. Also learn more about delegations on the Manage Delegation webpage.  

medRxiv [Preprint]. 2025 Feb 18:2025.02.16.25322147. doi: 10.1101/2025.02.16.25322147.

ABSTRACT

Interstitial cystitis/bladder pain syndrome (IC/BPS) is a poorly understood and underdiagnosed syndrome of chronic bladder/pelvic pain with urinary frequency and urgency. Though IC/BPS can be hereditary, little is known of its genetic etiology. Using the eMERGE data, we confirmed known phenotypic associations such as gastroesophageal reflux disease and irritable bowel syndrome and detected new associations, including osteoarthrosis/osteoarthritis and Barrett's esophagus. An exome wide ultra-rare variants analysis in 348 IC/BPS and 11,981 controls extended the previously reported association with ATP2C1 and ATP2A2, implicated in Mendelian desquamating skin disorders, but did not provide evidence for other previously proposed pathogenic pathways such as bladder development, nociception or inflammation. Pathway analysis detected new associations with "anaphase-promoting complex-dependent catabolic process", the "regulation of MAPK cascade" and "integrin binding". These findings suggest perturbations in biological networks for epithelial integrity and cell cycle progression in IC/BPS pathogenesis, and provide a roadmap for its future investigation.

PMID:40034785 | PMC:PMC11875234 | DOI:10.1101/2025.02.16.25322147

BMC Geriatr. 2025 Mar 4;25(1):144. doi: 10.1186/s12877-025-05732-z.

ABSTRACT

BACKGROUND: Older adults are vulnerable to adverse drug events given the pharmacokinetic and pharmacodynamic changes that coming with ageing, as well as they often take multiple medications for their chronic health conditions, especially older co-morbidities. ADEs can cause unnecessary emergency department visits and hospitalization, which contribute to financial burden and decreased quality of life. This study aims to investigate the prevalence of adverse drug events in elderly co-morbid patients in Liaoning province and explore its risk factors, in order to ensure medication safety in elderly patients.

METHODS: This was a cross-sectional study that enrolled elderly patients with co-morbidities, and the data were collected by nurses using a structured interview method for elderly patients with multimorbidity. Risk factors for patient-reported adverse drug events were identified by univariate and logistic regression analyses.

RESULTS: A total of 329 elderly patients were enrolled, among whom 169 were females, with an age ranging from 61 to 90 years. 205 participants (62.3%) had 462 "possible-probable-certain" adverse drug events, and 156 (47.4%) experienced two or more self-reported adverse drug events concurrently. The logistic regression analysis included four variables: female (OR = 2.194, 95% confidence interval 1.281-3.760, P = 0.004), numbers of daily drugs > 12 (OR = 2.257, 95% confidence interval 1.254-4.061, P = 0.007), history of fall within 1 year (OR = 3.106, 95% confidence interval 1.112-8.674, P = 0.031), and medication noncompliance (OR = 3.768, 95% confidence interval 1.535-9.249, P = 0.004).

CONCLUSION: Patient-reported adverse drug events are more prevalent in older co-morbid patients in Liaoning province. Female, numbers of daily drugs, fall history with 1 year and poor medication compliance were significantly and independently associated with adverse drug events. These findings may provide informative interventions for the medication management in elderly patients living with multimorbidity.

PMID:40038590 | DOI:10.1186/s12877-025-05732-z

J Imaging Inform Med. 2025 Mar 4. doi: 10.1007/s10278-025-01458-x. Online ahead of print.

ABSTRACT

BACKGROUND: The increasing rates of lung cancer emphasize the need for early detection through computed tomography (CT) scans, enhanced by deep learning (DL) to improve diagnosis, treatment, and patient survival. This review examines current and prospective applications of 2D- DL networks in lung cancer CT segmentation, summarizing research, highlighting essential concepts and gaps; Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, a systematic search of peer-reviewed studies from 01/2020 to 12/2024 on data-driven population segmentation using structured data was conducted across databases like Google Scholar, PubMed, Science Direct, IEEE (Institute of Electrical and Electronics Engineers) and ACM (Association for Computing Machinery) library. 124 studies met the inclusion criteria and were analyzed.

RESULTS: The LIDC-LIDR dataset was the most frequently used; The finding particularly relies on supervised learning with labeled data. The UNet model and its variants were the most frequently used models in medical image segmentation, achieving Dice Similarity Coefficients (DSC) of up to 0.9999. The reviewed studies primarily exhibit significant gaps in addressing class imbalances (67%), underuse of cross-validation (21%), and poor model stability evaluations (3%). Additionally, 88% failed to address the missing data, and generalizability concerns were only discussed in 34% of cases.

CONCLUSIONS: The review emphasizes the importance of Convolutional Neural Networks, particularly UNet, in lung CT analysis and advocates for a combined 2D/3D modeling approach. It also highlights the need for larger, diverse datasets and the exploration of semi-supervised and unsupervised learning to enhance automated lung cancer diagnosis and early detection.

PMID:40038137 | DOI:10.1007/s10278-025-01458-x

J Imaging Inform Med. 2025 Mar 4. doi: 10.1007/s10278-025-01449-y. Online ahead of print.

ABSTRACT

Accurate segmentation of adrenal glands from CT images is essential for enhancing computer-aided diagnosis and surgical planning. However, the small size, irregular shape, and proximity to surrounding tissues make this task highly challenging. This study introduces a novel pipeline that significantly improves the segmentation of left and right adrenal glands by integrating advanced pre-processing techniques and a robust post-processing framework. Utilising a 2D UNet architecture with various backbones (VGG16, ResNet34, InceptionV3), the pipeline leverages test-time augmentation (TTA) and targeted removal of unconnected regions to enhance accuracy and robustness. Our results demonstrate a substantial improvement, with a 38% increase in the Dice similarity coefficient for the left adrenal gland and an 11% increase for the right adrenal gland on the AMOS dataset, achieved by the InceptionV3 model. Additionally, the pipeline significantly reduces false positives, underscoring its potential for clinical applications and its superiority over existing methods. These advancements make our approach a crucial contribution to the field of medical image segmentation.

PMID:40038136 | DOI:10.1007/s10278-025-01449-y

J Am Med Inform Assoc. 2025 Mar 4:ocaf021. doi: 10.1093/jamia/ocaf021. Online ahead of print.

ABSTRACT

OBJECTIVE: This study aimed to develop a novel multi-stage self-supervised learning model tailored for the accurate classification of optical coherence tomography (OCT) images in ophthalmology reducing reliance on costly labeled datasets while maintaining high diagnostic accuracy.

MATERIALS AND METHODS: A private dataset of 2719 OCT images from 493 patients was employed, along with 3 public datasets comprising 84 484 images from 4686 patients, 3231 images from 45 patients, and 572 images. Extensive internal, external, and clinical validation were performed to assess model performance. Grad-CAM was employed for qualitative analysis to interpret the model's decisions by highlighting relevant areas. Subsampling analyses evaluated the model's robustness with varying labeled data availability.

RESULTS: The proposed model outperformed conventional supervised or self-supervised learning-based models, achieving state-of-the-art results across 3 public datasets. In a clinical validation, the model exhibited up to 17.50% higher accuracy and 17.53% higher macro F-1 score than a supervised learning-based model under limited training data.

DISCUSSION: The model's robustness in OCT image classification underscores the potential of the multi-stage self-supervised learning to address challenges associated with limited labeled data. The availability of source codes and pre-trained models promotes the use of this model in a variety of clinical settings, facilitating broader adoption.

CONCLUSION: This model offers a promising solution for advancing OCT image classification, achieving high accuracy while reducing the cost of extensive expert annotation and potentially streamlining clinical workflows, thereby supporting more efficient patient management.

PMID:40037789 | DOI:10.1093/jamia/ocaf021

Cell Mol Biol Lett. 2025 Mar 5;30(1):27. doi: 10.1186/s11658-025-00710-0.

ABSTRACT

The "War on Cancer" began with the National Cancer Act of 1971 and despite more than 50 years of effort and numerous successes, there still remains much more work to be done. The major challenge remains the complexity and intrinsic polygenicity of neoplastic diseases. Furthermore, the safety of the antitumor therapies still remains a concern given their often off-target effects. Although the amount of money invested in research and development required to introduce a novel FDA-approved drug has continuously increased, the likelihood for a new cancer drug's approval remains limited. One interesting alternative approach, however, is the idea of repurposing of old drugs, which is both faster and less costly than developing new drugs. Repurposed drugs have the potential to address the shortage of new drugs with the added benefit that the safety concerns are already established. That being said, their interactions with other new drugs in combination therapies, however, should be tested. In this review, we discuss the history of repurposed drugs, some successes and failures, as well as the multiple challenges and obstacles that need to be addressed in order to enhance repurposed drugs' potential for new cancer therapies.

PMID:40038587 | DOI:10.1186/s11658-025-00710-0

Nat Commun. 2025 Mar 4;16(1):2159. doi: 10.1038/s41467-025-57476-4.

ABSTRACT

The targeting of cancer stem cells (CSCs) has proven to be an effective approach for limiting tumor progression, thus necessitating the identification of new drugs with anti-CSC activity. Through a high-throughput drug repositioning screen, we identify the antibiotic Nifuroxazide (NIF) as a potent anti-CSC compound. Utilizing a click chemistry strategy, we demonstrate that NIF is a prodrug that is specifically bioactivated in breast CSCs. Mechanistically, NIF-induced CSC death is a result of a synergistic action that combines the generation of DNA interstrand crosslinks with the inhibition of the Fanconi anemia (FA) pathway activity. NIF treatment mimics FA-deficiency through the inhibition of STAT3, which we identify as a non-canonical transcription factor of FA-related genes. NIF induces a chemical HRDness (Homologous Recombination Deficiency) in CSCs that (re)sensitizes breast cancers with innate or acquired resistance to PARP inhibitor (PARPi) in patient-derived xenograft models. Our results suggest that NIF may be useful in combination with PARPi for the treatment of breast tumors, regardless of their HRD status.

PMID:40038300 | DOI:10.1038/s41467-025-57476-4

Nat Commun. 2025 Mar 4;16(1):2152. doi: 10.1038/s41467-025-57361-0.

ABSTRACT

Numerous patients with rheumatoid arthritis (RA) manifest severe syndromes, including elevated synovial fluid volumes (SF) with abundant immune cells, which can be controlled by TNF/JAK inhibitors. Here, we apply single-cell RNA sequencing (scRNA-seq) and subsequent validations in SF from RA patients. These analyses of synovial tissue show reduced density of SF-derived pathogenic cells (e.g., SPP1+ macrophages and CXCL13+CD4+ T cells), altered gene expression (e.g., SPP1 and STAT1), molecular pathway changes (e.g., JAK/STAT), and cell-cell communications in drug-specific manners in samples from patients pre-/post-treated with adalimumab/tofacitinib. Particularly, SPP1+ macrophages exhibit pronounced communication with CXCL13+CD4+ T cells, which are abolished after treatment and correlate with treatment efficacy. These pathogenic cell types alone or in combination can augment inflammation of fibroblast-like synoviocytes in vitro, while conditional Spp1 knocking-out reduces RA-related cytokine expression in collagen-induced arthritis mice models. Our study shows the functional role of SF-derived pathogenic cells in progression and drug-specific treatment outcomes in RA.

PMID:40038288 | DOI:10.1038/s41467-025-57361-0

Mol Pharmacol. 2025 Feb 7;107(3):100021. doi: 10.1016/j.molpha.2025.100021. Online ahead of print.

ABSTRACT

Ritonavir (RTV) is an important drug for anti-human immunodeficiency virus treatment and is mainly metabolized by cytochrome P450 (CYP) 3A4. Clinically, the most common side effect of RTV treatment is hepatoxicity. We previously showed that the long noncoding RNA hepatocyte nuclear factor 4 alpha (HNF4A) antisense 1 (HNF4A-AS1) negatively regulated CYP3A4 expression and participated in RTV-induced hepatotoxicity in vitro, but the mechanism has not been well understood. In this study, similar results were observed in the mouse, where liver-specific knockdown of Hnf4aos (homolog of human HNF4A-AS1) led to increased serum aspartate (∼1.8-fold) and alanine transaminase (∼2.4-fold) levels and enlarged and degenerated hepatocytes 24 hours after RTV administration. Meanwhile, endoplasmic reticulum stress markers GRP78, PDI, and XBP-1 increased about 2.4-fold, 2.1-fold, and 2.7-fold, respectively. The aggravated liver injury correlated with Hnf4aos knockdown, attributable to heightened Cyp3a11 (homolog of human CYP3A4) expression (mRNA and protein levels were 1.8-fold and 2.5-fold, respectively). Importantly, in vitro studies revealed the underlying mechanism that HNF4A-AS1 mediated the interaction between heterogeneous nuclear ribonucleoprotein C and HNF4A, whereas heterogeneous nuclear ribonucleoprotein C promoted HNF4A degradation through the ubiquitination pathway, thereby decreasing CYP3A4 expression and alleviating RTV-induced liver injury. Overall, our findings unveil a novel mechanism by which HNF4A-AS1 regulates CYP3A4 expression to influence RTV-induced liver injury. SIGNIFICANCE STATEMENT: HNF4A-AS1 negatively regulates the expression of CYP3A4, whose overexpression is highly correlated with ritonavir (RTV)-induced liver injury. In this study, the role of Hnf4aos (homolog of human HNF4A-AS1) in RTV-induced hepatotoxicity was confirmed in mice. We found that HNF4A-AS1 and HNRNPC form a complex and facilitate the ubiquitination and degradation of HNF4A protein, thereby decreasing CYP3A4 expression and alleviating RTV hepatotoxicity.

PMID:40037142 | DOI:10.1016/j.molpha.2025.100021

Mol Cell Biochem. 2025 Mar 4. doi: 10.1007/s11010-025-05243-w. Online ahead of print.

ABSTRACT

Emerging evidence indicates that chloride channels (ClCs) significantly affect the pathogenesis of inflammatory bowel disease (IBD) through their regulatory roles in mast cell function and epithelial integrity. IBD, encompassing conditions such as Crohn's disease and ulcerative colitis, involves chronic inflammation of the gastrointestinal tract, where channels influence immune responses, fluid balance, and cellular signalling pathways essential for maintaining mucosal homeostasis. This review examines the specific roles of ClC in mast cells, focussing on the regulation of mast cell activation, degranulation, cytokine release, and immune cell recruitment in inflamed tissues. Key channels, including Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) and ClC-2, are discussed in detail because of their involvement in maintaining intestinal epithelial barrier function, a critical factor disrupted in IBD. For example, CFTR facilitates chloride ion transport across epithelial cells, which is essential for mucosal hydration and maintenance of the intestinal barrier. Reduced CFTR function can compromise this barrier, permitting microbial antigens to penetrate the underlying tissues and triggering excessive immune responses. ClC-2, another chloride channel expressed in mast cells and epithelial cells, supports tight junction integrity, contributes to barrier function, and reduces intestinal permeability. Dysregulation of these channels is linked to altered mast cell activity and excessive release of pro-inflammatory mediators, exacerbating IBD symptoms, such as diarrhoea, abdominal pain, and tissue damage. Here, we review recent pharmacological strategies targeting ClC, including CFTR potentiators and ClC-2 activators, which show the potential to mitigate inflammatory responses. Additionally, experimental approaches for selective modulation of chloride channels in mast cells have been explored. Although targeting ClC offers promising therapeutic avenues, challenges remain in achieving specificity and minimizing side effects. This review highlights the therapeutic potential of Cl channel modulation in mast cells as a novel approach for IBD treatment, aiming to reduce inflammation and restore intestinal homeostasis in affected patients.

PMID:40038149 | DOI:10.1007/s11010-025-05243-w

Eur J Prev Cardiol. 2025 Mar 4:zwaf122. doi: 10.1093/eurjpc/zwaf122. Online ahead of print.

ABSTRACT

AIM: To assess the impact of antimicrobial resistance (AMR) on major adverse cardiovascular event (MACE) risk in patients with bronchiectasis.

METHODS: This retrospective study utilized data from the TriNetX research network, analysing patients with bronchiectasis categorized by the presence or absence of AMR. Primary outcomes included the risk of MACE (myocardial infarction, stroke and systemic thromboembolism, and cardiac arrest) and all-cause death. Cox regression analysis with 1:1 propensity score matching (PSM) was applied to estimate hazard ratios (HR) and 95% confidence intervals (CI) for the primary outcomes. Subgroup analyses were conducted to validate results in clinically relevant subgroups.

RESULTS: Prior to PSM, patients with AMR (n=6,543, 61.0±22.0 years, 55.8% female) were younger, more often male, and presented a higher prevalence of cardiovascular risk factors than those without AMR (n=154,685, 67.3±16.0 years, 59.4% female). After PSM, no significant differences were found between groups. However, AMR patients showed a higher risk of MACE (HR 1.29, 95% CI 1.17-1.41) and all-cause death (HR 1.49, 95% CI 1.38-1.61) compared to non-AMR patients. The MACE risk was notably elevated among AMR patients without prior cardiovascular events (HR 1.56, 95% CI 1.34-1.81). Similar MACE risks were observed in cystic fibrosis (HR 1.24, 95% CI 0.86-1.78) and non-cystic fibrosis subgroups (HR 1.28, 95% CI 1.16-1.41), with consistent findings across different AMR types.

CONCLUSIONS: In patients with bronchiectasis, AMR is associated with an increased risk of MACE and all-cause death, suggesting that controlling AMR spread may confer broader health benefits, particularly in reducing cardiovascular risk.

PMID:40037796 | DOI:10.1093/eurjpc/zwaf122

J Orthop Surg Res. 2025 Mar 4;20(1):236. doi: 10.1186/s13018-025-05620-7.

ABSTRACT

BACKGROUND: Accurate measurement of the spinal alignment parameters is crucial for diagnosing and evaluating adolescent idiopathic scoliosis (AIS). Manual measurement is subjective and time-consuming. The recently developed artificial intelligence models mainly focused on measuring the coronal Cobb angle (CA) and ignored the evaluation of the sagittal plane. We developed a deep-learning model that could automatically measure spinal alignment parameters in biplanar radiographs.

METHODS: In this study, our model adopted ResNet34 as the backbone network, mainly consisting of keypoint detection and CA measurement. A total of 600 biplane radiographs were collected from our hospital and randomly divided into train and test sets in a 3:1 ratio. Two senior spinal surgeons independently manually measured and analyzed spinal alignment and recorded the time taken. The reliabilities of automatic measurement were evaluated by comparing them with the gold standard, using mean absolute difference (MAD), intraclass correlation coefficient (ICC), simple linear regression, and Bland-Altman plots. The diagnosis performance of the model was evaluated through the receiver operating characteristic (ROC) curve and area under the curve (AUC). Severity classification and sagittal abnormalities classification were visualized using a confusion matrix.

RESULTS: Our AI model achieved the MAD of coronal and sagittal angle errors was 2.15° and 2.72°, and ICC was 0.985, 0.927. The simple linear regression showed a strong correction between all parameters and the gold standard (p < 0.001, r2 ≥ 0.686), the Bland-Altman plots showed that the mean difference of the model was within 2° and the automatic measurement time was 9.1 s. Our model demonstrated excellent diagnostic performance, with an accuracy of 97.2%, a sensitivity of 96.8%, a specificity of 97.6%, and an AUC of 0.972 (0.940-1.000).For severity classification, the overall accuracy was 94.5%. All accuracy of sagittal abnormalities classification was greater than 91.8%.

CONCLUSIONS: This deep learning model can accurately and automatically measure spinal alignment parameters with reliable results, significantly reducing diagnostic time, and might provide the potential to assist clinicians.

PMID:40038733 | DOI:10.1186/s13018-025-05620-7

J Transl Med. 2025 Mar 4;23(1):267. doi: 10.1186/s12967-025-06234-7.

ABSTRACT

BACKGROUND: Homologous recombination deficiency (HRD) refers to the dysfunction of homologous recombination repair (HRR) at the cellular level. The assessment of HRD status has the important significance for the formulation of treatment plans, efficacy evaluation, and prognosis prediction of patients with ovarian cancer.

OBJECTIVES: This study aimed to construct a deep learning-based classifier for identifying tumor regions from whole slide images (WSIs) and stratify the HRD status of patients with ovarian cancer (OC).

METHODS: The deep learning models were trained on 205 H&E-stained sections which contained 205 ovarian cancer patients, 64 were found to have HRD status while 141 had homologous recombination proficiency (HRP) status from two institutions Memorial Sloan Kettering Cancer Center (MSKCC) and Zhongda Hospital, Southeast University. The framework includes tumor regions identification by UNet + + and subtypes of ovarian cancer classifier construction. Referring to the EasyEnsemble, we classified the HRP patients into three distributed subsets. These three subsets of HRP patients were combined with the HRD patients to establish three new training groups for subsequent model construction. The three models were integrated into a single model named Ensemble Model.

RESULTS: The UNet + + algorithm segmented tumor regions with 81.8% accuracy, 85.9% recall, 83.8% dice score and 68.3% IoU. The AUC of the Ensemble Model was 0.769 (Precision = 0.800, Recall = 0.727, F1-score = 0.762) in the study. The most discriminative features between HRD and HRP comprised S_mean_dln_obtuse_ratio, S_mean_dln_acute_ratio and mean_Graph_T-S_Betweenness_normed.

CONCLUSIONS: The models we constructed enables accurate discrimination between tumor and non-tumor tissues in ovarian cancer as well as the prediction of HRD status for patients with ovarian cancer.

PMID:40038690 | DOI:10.1186/s12967-025-06234-7

BMC Med Imaging. 2025 Mar 4;25(1):71. doi: 10.1186/s12880-025-01604-5.

ABSTRACT

BACKGROUND: Pulmonary diseases such as COVID-19 and pneumonia, are life-threatening conditions, that require prompt and accurate diagnosis for effective treatment. Chest X-ray (CXR) has become the most common alternative method for detecting pulmonary diseases such as COVID-19, pneumonia, and lung opacity due to their availability, cost-effectiveness, and ability to facilitate comparative analysis. However, the interpretation of CXRs is a challenging task.

METHODS: This study presents an automated deep learning (DL) model that outperforms multiple state-of-the-art methods in diagnosing COVID-19, Lung Opacity, and Viral Pneumonia. Using a dataset of 21,165 CXRs, the proposed framework introduces a seamless combination of the Vision Transformer (ViT) for capturing long-range dependencies, DenseNet201 for powerful feature extraction, and global average pooling (GAP) for retaining critical spatial details. This combination results in a robust classification system, achieving remarkable accuracy.

RESULTS: The proposed methodology delivers outstanding results across all categories: achieving 99.4% accuracy and an F1-score of 98.43% for COVID-19, 96.45% accuracy and an F1-score of 93.64% for Lung Opacity, 99.63% accuracy and an F1-score of 97.05% for Viral Pneumonia, and 95.97% accuracy with an F1-score of 95.87% for Normal subjects.

CONCLUSION: The proposed framework achieves a remarkable overall accuracy of 97.87%, surpassing several state-of-the-art methods with reproducible and objective outcomes. To ensure robustness and minimize variability in train-test splits, our study employs five-fold cross-validation, providing reliable and consistent performance evaluation. For transparency and to facilitate future comparisons, the specific training and testing splits have been made publicly accessible. Furthermore, Grad-CAM-based visualizations are integrated to enhance the interpretability of the model, offering valuable insights into its decision-making process. This innovative framework not only boosts classification accuracy but also sets a new benchmark in CXR-based disease diagnosis.

PMID:40038588 | DOI:10.1186/s12880-025-01604-5