Bioinformatics. 2025 Jan 13:btaf018. doi: 10.1093/bioinformatics/btaf018. Online ahead of print.

ABSTRACT

MOTIVATION: Predicting RNA-binding proteins (RBPs) is central to understanding post-transcriptional regulatory mechanisms. Here, we introduce EnrichRBP, an automated and interpretable computational platform specifically designed for the comprehensive analysis of RBP interactions with RNA.

RESULTS: EnrichRBP is a web service that enables researchers to develop original deep learning and machine learning architectures to explore the complex dynamics of RNA-binding proteins. The platform supports 70 deep learning algorithms, covering feature representation, selection, model training, comparison, optimization, and evaluation, all integrated within an automated pipeline. EnrichRBP is adept at providing comprehensive visualizations, enhancing model interpretability, and facilitating the discovery of functionally significant sequence regions crucial for RBP interactions. In addition, EnrichRBP supports base-level functional annotation tasks, offering explanations and graphical visualizations that confirm the reliability of the predicted RNA binding sites. Leveraging high-performance computing, EnrichRBP provides ultra-fast predictions ranging from seconds to hours, applicable to both pre-trained and custom model scenarios, thus proving its utility in real-world applications. Case studies highlight that EnrichRBP provides robust and interpretable predictions, demonstrating the power of deep learning in the functional analysis of RBP interactions. Finally, EnrichRBP aims to enhance the reproducibility of computational method analyses for RNA-binding protein sequences, as well as reduce the programming and hardware requirements for biologists, thereby offering meaningful functional insights.

AVAILABILITY AND IMPLEMENTATION: EnrichRBP is available at https://airbp.aibio-lab.com/. The source code is available at https://github.com/wangyb97/EnrichRBP, and detailed online documentation can be found at https://enrichrbp.readthedocs.io/en/latest/.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:39804669 | DOI:10.1093/bioinformatics/btaf018

Insights Imaging. 2025 Jan 13;16(1):14. doi: 10.1186/s13244-024-01874-7.

ABSTRACT

OBJECTIVE: To develop an automatic segmentation model to delineate the adnexal masses and construct a machine learning model to differentiate between low malignant risk and intermediate-high malignant risk of adnexal masses based on ovarian-adnexal reporting and data system (O-RADS).

METHODS: A total of 663 ultrasound images of adnexal mass were collected and divided into two sets according to experienced radiologists: a low malignant risk set (n = 446) and an intermediate-high malignant risk set (n = 217). Deep learning segmentation models were trained and selected to automatically segment adnexal masses. Radiomics features were extracted utilizing a feature analysis system in Pyradiomics. Feature selection was conducted using the Spearman correlation analysis, Mann-Whitney U-test, and least absolute shrinkage and selection operator (LASSO) regression. A nomogram integrating radiomic and clinical features using a machine learning model was established and evaluated. The SHapley Additive exPlanations were used for model interpretability and visualization.

RESULTS: The FCN ResNet101 demonstrated the highest segmentation performance for adnexal masses (Dice similarity coefficient: 89.1%). Support vector machine achieved the best AUC (0.961, 95% CI: 0.925-0.996). The nomogram using the LightGBM algorithm reached the best AUC (0.966, 95% CI: 0.927-1.000). The diagnostic performance of the nomogram was comparable to that of experienced radiologists (p > 0.05) and outperformed that of less-experienced radiologists (p < 0.05). The model significantly improved the diagnostic accuracy of less-experienced radiologists.

CONCLUSIONS: The segmentation model serves as a valuable tool for the automated delineation of adnexal lesions. The machine learning model exhibited commendable classification capability and outperformed the diagnostic performance of less-experienced radiologists.

CRITICAL RELEVANCE STATEMENT: The ultrasound radiomics-based machine learning model holds the potential to elevate the professional ability of less-experienced radiologists and can be used to assist in the clinical screening of ovarian cancer.

KEY POINTS: We developed an image segmentation model to automatically delineate adnexal masses. We developed a model to classify adnexal masses based on O-RADS. The machine learning model has achieved commendable classification performance. The machine learning model possesses the capability to enhance the proficiency of less-experienced radiologists. We used SHapley Additive exPlanations to interpret and visualize the model.

PMID:39804536 | DOI:10.1186/s13244-024-01874-7

Int J Cardiovasc Imaging. 2025 Jan 13. doi: 10.1007/s10554-025-03327-8. Online ahead of print.

ABSTRACT

Coronary artery calcification (CAC) is a key marker of coronary artery disease (CAD) but is often underreported in cancer patients undergoing non-gated CT or PET/CT scans. Traditional CAC assessment requires gated CT scans, leading to increased radiation exposure and the need for specialized personnel. This study aims to develop an artificial intelligence (AI) method to automatically detect CAC from non-gated, freely-breathing, low-dose CT images obtained from positron emission tomography/computed tomography scans. A retrospective analysis of 677 PET/CT scans from a medical center was conducted. The dataset was divided into training (88%) and testing (12%) sets. The DLA-3D model was employed for high-resolution representation learning of cardiac CT images. Data preprocessing techniques were applied to normalize and augment the images. Performance was assessed using the area under the curve (AUC), accuracy, sensitivity, specificity and p-values. The AI model achieved an average AUC of 0.85 on the training set and 0.80 on the testing set. The model demonstrated expert-level performance with a specificity of 0.79, a sensitivity of 0.67, and an overall accuracy of 0.73 for the test group. In real-world scenarios, the model yielded a specificity of 0.8, sensitivity of 0.6, and an accuracy of 0.76. Comparison with human experts showed comparable performance. This study developed an AI method utilizing DLA-3D for automated CAC detection in non-gated PET/CT images. Findings indicate reliable CAC detection in routine PET/CT scans, potentially enhancing both cancer diagnosis and cardiovascular risk assessment. The DLA-3D model shows promise in aiding non-specialist physicians and may contribute to improved cardiovascular risk assessment in oncological imaging, encouraging additional CAC interpretation.

PMID:39804436 | DOI:10.1007/s10554-025-03327-8

Clin Oral Investig. 2025 Jan 13;29(1):59. doi: 10.1007/s00784-024-06136-w.

ABSTRACT

OBJECTIVES: To investigate the performance of a deep learning (DL) model for segmenting cone-beam computed tomography (CBCT) scans taken before and after mandibular horizontal guided bone regeneration (GBR) to evaluate hard tissue changes.

MATERIALS AND METHODS: The proposed SegResNet-based DL model was trained on 70 CBCT scans. It was tested on 10 pairs of pre- and post-operative CBCT scans of patients who underwent mandibular horizontal GBR. DL segmentations were compared to semi-automated (SA) segmentations of the same scans. Augmented hard tissue segmentation performance was evaluated by spatially aligning pre- and post-operative CBCT scans and subtracting preoperative segmentations obtained by DL and SA segmentations from the respective postoperative segmentations. The performance of DL compared to SA segmentation was evaluated based on the Dice similarity coefficient (DSC), intersection over the union (IoU), Hausdorff distance (HD95), and volume comparison.

RESULTS: The mean DSC and IoU between DL and SA segmentations were 0.96 ± 0.01 and 0.92 ± 0.02 in both pre- and post-operative CBCT scans. While HD95 values between DL and SA segmentations were 0.62 mm ± 0.16 mm and 0.77 mm ± 0.31 mm for pre- and post-operative CBCTs respectively. The DSC, IoU and HD95 averaged 0.85 ± 0.08; 0.78 ± 0.07 and 0.91 ± 0.92 mm for augmented hard tissue models respectively. Volumes mandible- and augmented hard tissue segmentations did not differ significantly between the DL and SA methods.

CONCLUSIONS: The SegResNet-based DL model accurately segmented CBCT scans acquired before and after mandibular horizontal GBR. However, the training database must be further increased to increase the model's robustness.

CLINICAL RELEVANCE: Automated DL segmentation could aid treatment planning for GBR and subsequent implant placement procedures and in evaluating hard tissue changes.

PMID:39804427 | DOI:10.1007/s00784-024-06136-w

J Gen Virol. 2025 Jan;106(1). doi: 10.1099/jgv.0.002067.

ABSTRACT

The complexity and speed of evolution in viruses with RNA genomes makes predictive identification of variants with epidemic or pandemic potential challenging. In recent years, machine learning has become an increasingly capable technology for addressing this challenge, as advances in methods and computational power have dramatically improved the performance of models and led to their widespread adoption across industries and disciplines. Nascent applications of machine learning technology to virus research have now expanded, providing new tools for handling large-scale datasets and leading to a reshaping of existing workflows for phenotype prediction, phylogenetic analysis, drug discovery and more. This review explores how machine learning has been applied to and has impacted the study of viruses, before addressing the strengths and limitations of its techniques and finally highlighting the next steps that are needed for the technology to reach its full potential in this challenging and ever-relevant research area.

PMID:39804261 | DOI:10.1099/jgv.0.002067

Eur Heart J. 2025 Jan 13:ehae914. doi: 10.1093/eurheartj/ehae914. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Current heart failure (HF) risk stratification strategies require comprehensive clinical evaluation. In this study, artificial intelligence (AI) applied to electrocardiogram (ECG) images was examined as a strategy to predict HF risk.

METHODS: Across multinational cohorts in the Yale New Haven Health System (YNHHS), UK Biobank (UKB), and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), individuals without baseline HF were followed for the first HF hospitalization. An AI-ECG model that defines cross-sectional left ventricular systolic dysfunction from 12-lead ECG images was used, and its association with incident HF was evaluated. Discrimination was assessed using Harrell's C-statistic. Pooled cohort equations to prevent HF (PCP-HF) were used as a comparator.

RESULTS: Among 231 285 YNHHS patients, 4472 had primary HF hospitalizations over 4.5 years (inter-quartile range 2.5-6.6). In UKB and ELSA-Brasil, among 42 141 and 13 454 people, 46 and 31 developed HF over 3.1 (2.1-4.5) and 4.2 (3.7-4.5) years. A positive AI-ECG screen portended a 4- to 24-fold higher risk of new-onset HF [age-, sex-adjusted hazard ratio: YNHHS, 3.88 (95% confidence interval 3.63-4.14); UKB, 12.85 (6.87-24.02); ELSA-Brasil, 23.50 (11.09-49.81)]. The association was consistent after accounting for comorbidities and the competing risk of death. Higher probabilities were associated with progressively higher HF risk. Model discrimination was 0.718 in YNHHS, 0.769 in UKB, and 0.810 in ELSA-Brasil. In YNHHS and ELSA-Brasil, incorporating AI-ECG with PCP-HF yielded a significant improvement in discrimination over PCP-HF alone.

CONCLUSIONS: An AI model applied to a single ECG image defined the risk of future HF, representing a digital biomarker for stratifying HF risk.

PMID:39804243 | DOI:10.1093/eurheartj/ehae914

J Cell Mol Med. 2025 Jan;29(1):e70351. doi: 10.1111/jcmm.70351.

ABSTRACT

Cancer is a complex disease driven by mutations in the genes that play critical roles in cellular processes. The identification of cancer driver genes is crucial for understanding tumorigenesis, developing targeted therapies and identifying rational drug targets. Experimental identification and validation of cancer driver genes are time-consuming and costly. Studies have demonstrated that interactions among genes are associated with similar phenotypes. Therefore, identifying cancer driver genes using molecular network-based approaches is necessary. Molecular network-based random walk-based approaches, which integrate mutation data with protein-protein interaction networks, have been widely employed in predicting cancer driver genes and demonstrated robust predictive potential. However, recent advancements in deep learning, particularly graph-based models, have provided novel opportunities for enhancing the prediction of cancer driver genes. This review aimed to comprehensively explore how machine learning methodologies, particularly network propagation, graph neural networks, autoencoders, graph embeddings, and attention mechanisms, improve the scalability and interpretability of molecular network-based cancer gene prediction.

PMID:39804102 | DOI:10.1111/jcmm.70351

JMIR Med Educ. 2025 Jan 10;11:e62669. doi: 10.2196/62669.

ABSTRACT

BACKGROUND: Artificial intelligence (AI) is set to shape the future of medical practice. The perspective and understanding of medical students are critical for guiding the development of educational curricula and training.

OBJECTIVE: This study aims to assess and compare medical AI-related attitudes among medical students in general medicine and in one of the visually oriented fields (pathology), along with illuminating their anticipated role of AI in the rapidly evolving landscape of AI-enhanced health care.

METHODS: This was a cross-sectional study that used a web-based survey composed of a closed-ended questionnaire. The survey addressed medical students at all educational levels across the 5 public medical schools, along with pathology residents in 4 residency programs in Jordan.

RESULTS: A total of 394 respondents participated (328 medical students and 66 pathology residents). The majority of respondents (272/394, 69%) were already aware of AI and deep learning in medicine, mainly relying on websites for information on AI, while only 14% (56/394) were aware of AI through medical schools. There was a statistically significant difference in awareness among respondents who consider themselves tech experts compared with those who do not (P=.03). More than half of the respondents believed that AI could be used to diagnose diseases automatically (213/394, 54.1% agreement), with medical students agreeing more than pathology residents (P=.04). However, more than one-third expressed fear about recent AI developments (167/394, 42.4% agreed). Two-thirds of respondents disagreed that their medical schools had educated them about AI and its potential use (261/394, 66.2% disagreed), while 46.2% (182/394) expressed interest in learning about AI in medicine. In terms of pathology-specific questions, 75.4% (297/394) agreed that AI could be used to identify pathologies in slide examinations automatically. There was a significant difference between medical students and pathology residents in their agreement (P=.001). Overall, medical students and pathology trainees had similar responses.

CONCLUSIONS: AI education should be introduced into medical school curricula to improve medical students' understanding and attitudes. Students agreed that they need to learn about AI's applications, potential hazards, and legal and ethical implications. This is the first study to analyze medical students' views and awareness of AI in Jordan, as well as the first to include pathology residents' perspectives. The findings are consistent with earlier research internationally. In comparison with prior research, these attitudes are similar in low-income and industrialized countries, highlighting the need for a global strategy to introduce AI instruction to medical students everywhere in this era of rapidly expanding technology.

PMID:39803949 | DOI:10.2196/62669

Cardiovasc Res. 2025 Jan 10:cvae267. doi: 10.1093/cvr/cvae267. Online ahead of print.

ABSTRACT

AIMS: The gastrointestinal (GI) tract is composed of distinct sub-regions, which exhibit segment-specific differences in microbial colonization and (patho)physiological characteristics. Gut microbes can be collectively considered as an active endocrine organ. Microbes produce metabolites, which can be taken up by the host and can actively communicate with the immune cells in the gut lamina propria with consequences for cardiovascular health. Variation in bacterial load and composition along the GI tract may influence the mucosal microenvironment and thus be reflected its interstitial fluid (IF). Characterization of the segment-specific microenvironment is challenging and largely unexplored because of lack of available tools.

METHODS AND RESULTS: Here, we developed methods, namely tissue centrifugation and elution, to collect IF from the mucosa of different intestinal segments. These methods were first validated in rats and mice, and the tissue elution method was subsequently translated for use in humans. These new methods allowed us to quantify microbiota-derived metabolites, mucosa-derived cytokines, and proteins at their site-of-action. Quantification of short-chain fatty acids showed enrichment in the colonic IF. Metabolite and cytokine analyses revealed differential abundances within segments, often significantly increased compared to plasma, and proteomics revealed that proteins annotated to the extracellular phase were site-specifically identifiable in IF. Lipopolysaccharide injections in rats showed significantly higher ileal IL-1β levels in IF compared to the systemic circulation, suggesting the potential of local as well as systemic effect.

CONCLUSION: Collection of IF from defined segments and the direct measurement of mediators at the site-of-action in rodents and humans bypasses the limitations of indirect analysis of faecal samples or serum, providing direct insight into this understudied compartment.

PMID:39804196 | DOI:10.1093/cvr/cvae267

Elife. 2025 Jan 13;13:RP92683. doi: 10.7554/eLife.92683.

ABSTRACT

Many applications in biomedicine and synthetic bioengineering rely on understanding, mapping, predicting, and controlling the complex behavior of chemical and genetic networks. The emerging field of diverse intelligence investigates the problem-solving capacities of unconventional agents. However, few quantitative tools exist for exploring the competencies of non-conventional systems. Here, we view gene regulatory networks (GRNs) as agents navigating a problem space and develop automated tools to map the robust goal states GRNs can reach despite perturbations. Our contributions include: (1) Adapting curiosity-driven exploration algorithms from AI to discover the range of reachable goal states of GRNs, and (2) Proposing empirical tests inspired by behaviorist approaches to assess their navigation competencies. Our data shows that models inferred from biological data can reach a wide spectrum of steady states, exhibiting various competencies in physiological network dynamics without requiring structural changes in network properties or connectivity. We also explore the applicability of these 'behavioral catalogs' for comparing evolved competencies across biological networks, for designing drug interventions in biomedical contexts and synthetic gene networks for bioengineering. These tools and the emphasis on behavior-shaping open new paths for efficiently exploring the complex behavior of biological networks. For the interactive version of this paper, please visit https://developmentalsystems.org/curious-exploration-of-grn-competencies.

PMID:39804159 | DOI:10.7554/eLife.92683

Anal Chem. 2025 Jan 13. doi: 10.1021/acs.analchem.4c04692. Online ahead of print.

ABSTRACT

Simultaneous analysis of multiple phosphorylated metabolites (phosphorylated metabolome) in biological samples is vital to reveal their physiological and pathophysiological functions, which is extremely challenging due to their low abundance in some biological matrices, high hydrophilicity, and poor chromatographic behavior. Here, we developed a new method with ion-pair reversed-phase ultrahigh-performance liquid chromatography and mass spectrometry using BEH C18 columns modified with hybrid surface technology. This method demonstrated good performances for various phosphorylated metabolites, including phosphorylated sugars and amino acids, nucleotides, NAD-based cofactors, and acyl-CoAs in a single run using standard LC systems. Specifically, the method showed good retention (capacity factor > 2) and reproducibility (ΔtR < 0.09 min, n = 6), peak symmetry (tailing factor < 2), and sensitivity (limit-of-detection < 238 fmol-on-column with QTOFMS) for all tested analytes especially for the medium- and/or long-chain acyl-CoAs. The method demonstrated reproducible applicability across numerous biological matrices, including tissue (liver), human biofluids (urine, plasma), cells, and feces, and revealed significant molecular phenotypic differences in phosphorylated metabolite composition.

PMID:39804109 | DOI:10.1021/acs.analchem.4c04692

Arch Dermatol Res. 2025 Jan 13;317(1):231. doi: 10.1007/s00403-024-03763-x.

ABSTRACT

Pityriasis rosea (PR) is an acute exanthematous disease with an uncertain physiopathology, increasingly recognized as potentially drug induced. This study aims to investigate medication triggers associated with PR by analyzing cases reported in the FDA Adverse Event Reporting System (FAERS) database. A retrospective review of 343 PR cases reported in the FAERS database from January 1, 1998, to March 31, 2024, was conducted. Reporting odds ratios (ROR) were calculated to assess associations between PR and specific drug classes, including tumor necrosis factor (TNF) inhibitors and angiotensin-converting enzyme (ACE) inhibitors. Logistic regression analysis evaluated the influence of factors such as sex, age group, and seriousness of outcomes on the occurrence of PR. Females represented 56.3% of cases and the 18-64 age group comprised 55.4% of cases. TNF inhibitors were significantly associated with PR (ROR = 4.1881 [3.1970-5.4865], P < 0.0001), particularly infliximab (ROR = 6.5284 [3.9523-10.7837], P < 0.0001), etanercept (ROR = 3.4921 [2.2873-5.3315], P < 0.0001), and adalimumab (ROR = 3.086 [2.0213-4.7115], P < 0.0001). ACE inhibitors were also associated with PR (ROR = 9.9808 [6.0423-16.4864], P < 0.0001), with higher odds in older patients (OR 14.08 [4.2-47.2], P < 0.0001) and those reporting serious outcomes (OR 9.53 [1.24-72.99], P = 0.03). Based on the FAERS, there has been a consistent rise in PR cases, with TNF inhibitors and ACE inhibitors being associated medication classes tied to PR. Given the limited literature on drug-related triggers and patient demographics, we aimed to highlight the characteristics of PR cases that could enhance awareness and inform better clinical outcomes for affected patients.

PMID:39804489 | DOI:10.1007/s00403-024-03763-x

Egypt J Immunol. 2025 Jan;32(1):27-41.

ABSTRACT

Cryptococcal meningitis is an alarming fungal infection that usually affects the meninges surrounding the brain and spinal cord. The causative organism is Cryptococcus neoformans. Although this infection can occur in normal individuals, it is more often seen in patients with human immunodeficiency virus/acquired immunodeficiency syndrome. Amphotericin B is an antifungal medication often used to treat severe fungal infections. It belongs to the class of polyene antifungal drugs, and it acts by binding to the cell membrane of the fungus. This causes some essential cellular components to leak out and ultimately the fungus dies. However, the administration of Amphotericin B is associated with toxicity. Therefore, lipid formulations are preferred to decrease the toxicity and increase the therapeutic index of the drug. It is widely used since it has a longer tissue half-life, the drug induced toxic effects are lower and it can penetrate the brain tissue efficaciously. This review collects and analyzes several research studies and literature reviews found in the electronic databases. The inclusion criteria prioritize studies focusing on the efficacy and drawbacks of using liposomal Amphotericin B as a treatment for fungal meningitis. In conclusion, liposomal Amphotericin B showed more effective treatment compared to other available antifungal drugs. Patients treated with a single dose of liposomal Amphotericin B coupled with fluconazole and flucytosine exhibited fewer adverse events and the mortality rate was also lower as compared to the control group.

PMID:39803853

Hum Vaccin Immunother. 2025 Dec;21(1):2450878. doi: 10.1080/21645515.2025.2450878. Epub 2025 Jan 13.

ABSTRACT

As infants suffer significant morbidity and mortality due to norovirus-related acute gastroenteritis (AGE), we assessed four formulations of the bivalent virus-like particle (VLP) vaccine candidate (HIL-214) in Panamanian and Colombian infants. 360 infants aged 6 weeks to 5 months were randomly allocated to 8 groups to receive three doses of HIL-214 or two doses of HIL-214 and one dose of placebo (Days 1, 56 and 112), where HIL-214 doses contained 15/15, 15/50, 50/50 or 50/150 μg of GI.1/GII.4c genotype VLPs and 0.5 mg Al(OH)3. Solicited injection-site and systemic adverse events (AE) were collected within 7 days after each dose, unsolicited AEs were collected within 28 days after each, and serious AEs throughout the study. Pan-Ig and histoblood group antigen-blocking antibodies (HBGA) were measured on Days 1, 56, 84, and 140. All formulations were well-tolerated causing mainly mild-to-moderate transient solicited AEs, most frequently local pain and irritability/fussiness, but no vaccine-related serious AEs. Two doses of each formulation induced high titers of high avidity Pan-Ig and also HBGA antibodies; a third dose increased titers against both antigens and the avidity of Pan-Ig antibodies against GII.4c but not against GI.1. Two and three doses of HIL-214 were well-tolerated and induced potent immune responses at 4-6 months of age supporting further clinical assessment to protect against norovirus-related AGE.

PMID:39803784 | DOI:10.1080/21645515.2025.2450878

Discov Oncol. 2025 Jan 13;16(1):39. doi: 10.1007/s12672-024-01616-7.

ABSTRACT

The occurrence and progression of breast cancer (BCa) are complex processes involving multiple factors and multiple steps. The tumor microenvironment (TME) plays an important role in this process, but the functions of immune components and stromal components in the TME require further elucidation. In this study, we obtained the RNA-seq data of 1086 patients from The Cancer Genome Atlas (TCGA) database. We calculated the proportions of tumor-infiltrating immune cells (TICs) and immune and stromal components using the CIBERSORT and ESTIMATE methods, and we screened differentially expressed genes (DEGs). Univariate Cox regression analysis of overall survival was performed on the DEGs, and a protein-protein interaction network of their protein products was generated. Finally, the hub gene CD5 was obtained. High CD5 expression was found to be associated with longer survival than low expression. Gene set enrichment analysis showed that DEGs upregulated in the high-CD5 expression group were mainly enriched in tumor- and immune-related pathways, while those upregulated in the low-expression group were enriched in protein export and lipid synthesis. TIC analysis showed that CD5 expression was positively correlated with the infiltration of CD8+ T cells, activated memory CD4+ T cells, gamma delta T cells, and M1 macrophages and negatively correlated with the infiltration of M2 macrophages. CD5 can increase anticancer immune cell infiltration and reduce M2 macrophage infiltration. These results suggest that CD5 is likely a potential prognostic biomarker and therapeutic target, providing novel insights into the treatment and prognostic assessment of BCa.

PMID:39804513 | DOI:10.1007/s12672-024-01616-7

Endocr Connect. 2025 Jan 1:EC-24-0617. doi: 10.1530/EC-24-0617. Online ahead of print.

ABSTRACT

BACKGROUND: Arterial hypertension and left ventricular hypertrophy and remodeling are independent cardiovascular risk factors in patients with Cushing's syndrome. Changes in the renin-angiotensin system and in the mineralocorticoid axis activity could be involved as potential mechanisms in their pathogenesis, in addition to cortisol excess.

METHODS: In this ancillary study of our previous study prospectively investigating patients with ACTH-dependent Cushing's syndrome by cardiac magnetic resonance imaging (NCT02202902), 11 patients without any interfering medication were cross-sectionally compared to 20 control subjects matched for age, sex and body mass index. Angiotensin metabolites and adrenal steroids were measured by liquid chromatography tandem mass spectrometry and their relation to blood pressure and cardiac structure was evaluated.

RESULTS: Concentrations of angiotensin I and angiotensin II were comparable, but the angiotensin-converting enzyme activity was significantly lower (2.19 (1.67;3.08) vs 4.07 (3.1;5.6); p<0.001) in patients compared to controls. Aldosterone concentrations were significantly lower (6.9 (6.9;124.1) vs 239.9 (181.4;321.9) pmol/l; p<0.001) in the group of patients, but adrenal aldosterone precursor metabolites were comparable between patients and controls. Inverse correlations were observed for 24h urinary free cortisol and aldosterone with the ratio of left ventricular mass to end-diastolic volume (r=0.470, p=0.012 and r= -0.367, p=0.046, respectively).

CONCLUSIONS: We describe a disease specific profile of angiotensin metabolites in patients with ACTH dependent Cushing's syndrome. Low levels of aldosterone in the presence of unchanged precursor metabolites indicate a direct inhibitory action of cortisol excess on the aldosterone synthase. Further, glucocorticoid excess per se drives cardiac muscle remodeling.

PMID:39804209 | DOI:10.1530/EC-24-0617

RSC Adv. 2025 Jan 9;15(2):748-753. doi: 10.1039/d4ra07449a. eCollection 2025 Jan 9.

ABSTRACT

Due to their considerable chemical diversity, metal compounds are attracting increasing and renewed attention from the scientific and medical communities as potential antimicrobial agents to combat the growing problem of antibiotic resistance. The development of metal compounds as antimicrobial agents typically follows classical drug discovery procedures and suffers from the same problems; indeed, these procedures can be very expensive and time-consuming, and carry an intrinsically high risk of failure. Here, we show how some established drug discovery approaches can be conveniently and successfully applied to antimicrobial metal compounds to provide some shortcuts for faster clinical translation of new treatments. Specifically, we refer to (i) drug repurposing, (ii) drug combination and (iii) drug targeting by bioconjugation; some relevant examples will be illustrated.

PMID:39802470 | PMC:PMC11712697 | DOI:10.1039/d4ra07449a

Mol Ther Oncol. 2024 Nov 23;32(4):200911. doi: 10.1016/j.omton.2024.200911. eCollection 2024 Dec 19.

ABSTRACT

Drug repurposing has potential to improve outcomes for high-grade serous ovarian cancer (HGSOC). Repurposing drugs with PARP family binding activity may produce cytotoxic effects through the multiple mechanisms of PARP including DNA repair, cell-cycle regulation, and apoptosis. The aim of this study was to determine existing drugs that have PARP family binding activity and can be repurposed for treatment of HGSOC. In silico ligand-based virtual screening (BLAZE) was used to identify drugs with potential PARP-binding activity. The list was refined by dosing, known cytotoxicity, lipophilicity, teratogenicity, and side effects. The highest ranked drug, efavirenz, progressed to in vitro testing. Molecularly characterized HGSOC cell lines, 3D hydrogel-encapsulated models, and patient-derived organoid models were used to determine the IC50 for efavirenz, cell death, apoptosis, PARP1 enzyme expression, and activity in intact cancer cells following efavirenz treatment. The IC50 for efavirenz was 26.43-45.85 μM for cells in two dimensions; 27.81 μM-54.98 μM in three dimensions, and 14.52 μM-42.27 μM in HGSOC patient-derived organoids. Efavirenz decreased cell viability via inhibition of PARP; increased CHK2 and phosphor-RB; increased cell-cycle arrest via decreased CDK2; increased γH2AX, DNA damage, and apoptosis. The results of this study suggest that efavirenz may be a viable treatment for HGSOC.

PMID:39802157 | PMC:PMC11719850 | DOI:10.1016/j.omton.2024.200911

Fundam Clin Pharmacol. 2025 Feb;39(1):e13052. doi: 10.1111/fcp.13052.

ABSTRACT

Drug repurposing of well-established drugs to be targeted against lung cancer has been a promising strategy. Bosentan is an endothelin 1 (ET-1) blocker widely used in pulmonary hypertension. The current experiment intends to inspect the anticancer and antiangiogenic mechanism of bosentan targeting epidermal growth factor receptor (EGFR) /extra-cellular Signal Regulated Kinase (ERK) /c-Jun/vascular endothelial growth factor (VEGF) carcinogenic pathway. BALB/c mice were randomized into four groups, the first received the vehicle, the second received 100 mg/kg oral bosentan alone, the third has non-small cell lung cancer (NSCLC) induced by two doses of 1.5 g/kg urethane i.p. and finally the fourth has NSCLC received bosentan. To determine the anti-proliferative impact of bosentan, cytokeratin 19 fragments (CYFRA 21-1) level was assessed, and Ki-67 positive cells were counted by immunohistochemical (IHC). Molecular expression of EGFR via IHC, relative expression of p-ERK1/2 and p-c-Jun via western blotting and caspase 3, Bcl-2 Associated X-protein (BAX)/B-cell lymphoma 2 (Bcl-2) ratio and VEGF via ELISA were quantified. Bosentan showed pronounced improvement in lung index and histopathological examinations. Bosentan exerted a noticeable arrest of lung cancer growth indicated by the attenuation of CYFRA 21-1 and Ki-67 positive cell counts besides the boost of BAX/Bcl-2 ratio and caspase 3. Bosentan induced a remarkable decline of EGFR, T-ERK1/2/p-ERK1/2, T-c-Jun/p-c-Jun, and VEGF. Bosentan induced cytotoxic and anti-angiogenic impact through regulation of EGFR/ERK/c-Jun/VEGF axis suggesting its potential therapeutic impact against lung cancer.

PMID:39801131 | DOI:10.1111/fcp.13052

Turk J Pharm Sci. 2025 Jan 10;21(6):566-576. doi: 10.4274/tjps.galenos.2024.49768.

ABSTRACT

INTRODUCTION: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), from the family Coronaviridae, is the seventh known coronavirus to infect humans and cause acute respiratory syndrome. Although vaccination efforts have been conducted against this virus, which emerged in Wuhan, China, in December 2019 and has spread rapidly around the world, the lack of an Food and Drug Administration-approved antiviral agent has made drug repurposing an important approach for emergency response during the COVID-19 pandemic. The aim of this study was to investigate the potential of H1-antihistamines as antiviral agents against SARS-CoV-2 RNA-dependent RNA polymerase enzyme.

MATERIALS AND METHODS: Using molecular docking techniques, we explored the interactions between H1-antihistamines and RNA-dependent RNA polymerase (RdRp), a key enzyme involved in viral replication. The three-dimensional structure of 37 H1-antihistamine molecules was drawn and their energies were minimized using Spartan 0.4. Subsequently, we conducted a docking study with Autodock Vina to assess the binding affinity of these molecules to the target site. The docking scores and conformations were then visualized using Discovery Studio.

RESULTS: The results examined showed that the docking scores of the H1-antihistamines were between 5.0 and 8.3 kcal/mol. These findings suggested that among all the analyzed drugs, bilastine, fexofenadine, montelukast, zafirlukast, mizolastine, and rupatadine might bind with the best binding energy (< -7.0 kcal/mol) and inhibit RdRp, potentially halting the replication of the virus.

CONCLUSION: This study highlights the potential of H1-antihistamines in combating COVID-19 and underscores the value of computational approaches in rapid drug discovery and repurposing efforts. Finally, experimental studies are required to measure the potency of H1-antihistamines before their clinical use against COVID-19 as RdRp inhibitors.

PMID:39801109 | DOI:10.4274/tjps.galenos.2024.49768