Am J Respir Crit Care Med. 2025 Mar 4. doi: 10.1164/rccm.202409-1698OC. Online ahead of print.

ABSTRACT

OBJECTIVES: To evaluate the effect of genetic variant status on sotatercept efficacy and effect of sotatercept treatment on biomarkers in pulmonary arterial hypertension Methods: PULSAR (NCT03496207) was a phase 2, randomized, controlled study of sotatercept vs placebo added to background therapy for pulmonary arterial hypertension. Participants had DNA sequencing done at baseline to detect genetic variants in disease-associated genes (ACVRL1, BMPR2, CAV1, EIF2AK4, ENG, KCNA3, KCNK3, and SMAD9). Safety (adverse events) and efficacy (pulmonary vascular resistance, 6-minute walk distance) were assessed by variant status and treatment at 24 weeks. Serum levels of BMPR2 mRNA and N-terminal pro-hormone B-type natriuretic peptide were assessed at baseline and 24 weeks by treatment and variant status. Analysis of covariance was used to compare the change from baseline by treatment and variant status.

RESULTS: Of 76 participants included, 25 had pathogenic variants detected (23 BMPR2; 2 other) and 51 had no variants or variants of uncertain significance. BMPR2 mutation carriers were younger and more frequently on triple therapy but had less severe clinical characteristics at baseline. Changes at 24 weeks in pulmonary vascular resistance and 6-minute walk distance did not differ by variant status. BMPR2 gene expression varied less than twofold from baseline over time, irrespective of treatment or variant status. The adverse events profile was generally consistent with that seen in the parent PULSAR study.

CONCLUSIONS: These results suggest consistent safety and clinical efficacy of sotatercept for treatment of pulmonary arterial hypertension, irrespective of BMPR2 variant status. Clinical trial registration available at www.

CLINICALTRIALS: gov, ID: NCT03496207.

PMID:40035659 | DOI:10.1164/rccm.202409-1698OC

Int Rev Psychiatry. 2025 Feb;37(1):52-58. doi: 10.1080/09540261.2024.2432369. Epub 2024 Dec 2.

ABSTRACT

Substance Use Disorders (SUD) lead to a collection of health challenges such as overdoses and clinical diseases. Populations that are vulnerable and lack straightforward treatment access are vulnerable to significant economic and social effects linked to SUD. The ongoing advances in technology, especially Artificial Intelligence (AI), promise new ways to reduce the effects of SUD, refine treatment standards, and minimize the risk of relapse through tailored treatment plans. Recent innovations in functional neuroimaging techniques, such as fMRI, have led to the ability to detect brain patterns associated with drug use, and biomarkers in blood testing provide crucial diagnostic support. In addition, digital platforms applied for behavioral assessment supported by AI and natural language processing improve the early recognition of substance consumption trends, allowing for targeted interventions reliant on real-time data. Using pharmacogenetics and resources like mobile apps and wearable devices makes the development of care programs that continuously track substance use, mental health, and physical changes possible. At the core of ethical issues related to the application of AI for SUD are the rights of patients to have their privacy protected to ensure that all people justly have access to these technologies. The advancement of AI models provides significant possibilities to support clinical judgment and enhance patient outcomes.

PMID:40035372 | DOI:10.1080/09540261.2024.2432369

Appl Environ Microbiol. 2025 Mar 4:e0240224. doi: 10.1128/aem.02402-24. Online ahead of print.

ABSTRACT

Quorum sensing (QS) plays a crucial role in regulating key traits, including the upregulation of phage receptors, which leads to heightened phage susceptibility in Pseudomonas aeruginosa. As a result, higher cell densities typically increase the risk of phage invasions. This has led to speculation that bacteria may have evolved strategies to counterbalance this increased susceptibility. Additionally, non-synonymous mutations in LasR, the master regulator of QS, are common among cystic fibrosis patients, but the impact of these mutations on phage interactions remains poorly understood. Here, we systematically investigated the role of QS in shaping these interactions using bacterial strains with functional or altered QS systems. In the QS-functional strain ZS-PA-35, disruption of the Las system reduces cell susceptibility to the type IV pili-dependent phage phipa2, delaying bacterial lysis during the early logarithmic growth phase. At high cell densities, Las-induced dormancy further inhibits phage proliferation despite enhanced phage adsorption. Notably, nutrient supplementation fully restores phage proliferation in the strains with a functional Las system. In contrast, the QS-deficient strain ZS-PA-05, carrying a LasR mutation, fails to regulate phage-host interactions via QS. Moreover, our findings reveal that within mixed microbial populations, cells benefit from the presence of closely related kin, which collectively reduce prey density and limit phage-host interaction frequencies under nutrient-rich conditions. These results underscore the flexibility of QS-regulated defense strategies, highlighting their critical role in optimizing bacterial resilience against phage predation, particularly in heterogeneous communities most vulnerable to phages.IMPORTANCEBacteria have developed various strategies to combat phage infection, posing challenges to phage therapy. In this study, we demonstrate that Pseudomonas aeruginosa strains with functional or altered quorum sensing (QS) systems may adapt different survival tactics for prolonged coexistence with phages, contingent upon bacterial population dynamics. The dynamics of phage infection highlight the influence of intrinsic heterogeneity mediated by QS, which leads to the emergence of different phage-host outcomes. These variants may arise as a result of coevolutionary processes or coexistence mechanisms of mutational and non-mutational defense strategies. These insights enhance our comprehension of how bacteria shield themselves against phage attacks and further underscore the complexity of such approaches for successful therapeutic interventions.

PMID:40035599 | DOI:10.1128/aem.02402-24

Radiol Phys Technol. 2025 Mar 4. doi: 10.1007/s12194-025-00892-4. Online ahead of print.

ABSTRACT

Cone-beam computed tomography (CBCT) is widely used in radiotherapy to image patient configuration before treatment but its image quality is lower than planning CT due to scattering, motion, and reconstruction methods. This reduces the accuracy of Hounsfield units (HU) and limits its use in adaptive radiation therapy (ART). However, synthetic CT (sCT) generation using deep learning methods for CBCT intensity correction faces challenges due to deformation. To address these issues, we propose enhancing CBCT quality using a conditional denoising diffusion probability model (CDDPM), which is trained on pseudo-CBCT created by adding pseudo-scatter to planning CT. The CDDPM transforms CBCT into high-quality sCT, improving HU accuracy while preserving anatomical configuration. The performance evaluation of the proposed sCT showed a reduction in mean absolute error (MAE) from 81.19 HU for CBCT to 24.89 HU for the sCT. Peak signal-to-noise ratio (PSNR) improved from 31.20 dB for CBCT to 33.81 dB for the sCT. The Dice and Jaccard coefficients between CBCT and sCT for the colon, prostate, and bladder ranged from 0.69 to 0.91. When compared to other deep learning models, the proposed sCT outperformed them in terms of accuracy and anatomical preservation. The dosimetry analysis for prostate cancer revealed a dose error of over 10% with CBCT but nearly 0% with the sCT. Gamma pass rates for the proposed sCT exceeded 90% for all dose criteria, indicating high agreement with CT-based dose distributions. These results show that the proposed sCT improves image quality, dosimetry accuracy, and treatment planning, advancing ART for pelvic cancer.

PMID:40035984 | DOI:10.1007/s12194-025-00892-4

J Imaging Inform Med. 2025 Mar 4. doi: 10.1007/s10278-025-01464-z. Online ahead of print.

ABSTRACT

Effective segmentation of cervical cancer tissue from magnetic resonance (MR) images is crucial for automatic detection, staging, and treatment planning of cervical cancer. This study develops an innovative deep learning model to enhance the automatic segmentation of cervical cancer lesions. We obtained 4063 T2WI small-field sagittal, coronal, and oblique axial images from 222 patients with pathologically confirmed cervical cancer. Using this dataset, we employed a convolutional neural network (CNN) along with TransUnet models for segmentation training and evaluation of cervical cancer tissues. In this approach, CNNs are leveraged to extract local information from MR images, whereas Transformers capture long-range dependencies related to shape and structural information, which are critical for precise segmentation. Furthermore, we developed three distinct segmentation models based on coronal, axial, and sagittal T2WI within a small field of view using multidirectional MRI techniques. The dice similarity coefficient (DSC) and mean Hausdorff distance (AHD) were used to assess the performance of the models in terms of segmentation accuracy. The average DSC and AHD values obtained using the TransUnet model were 0.7628 and 0.8687, respectively, surpassing those obtained using the U-Net model by margins of 0.0033 and 0.3479, respectively. The proposed TransUnet segmentation model significantly enhances the accuracy of cervical cancer tissue delineation compared to alternative models, demonstrating superior performance in overall segmentation efficacy. This methodology can improve clinical diagnostic efficiency as an automated image analysis tool tailored for cervical cancer diagnosis.

PMID:40035972 | DOI:10.1007/s10278-025-01464-z

Brain Topogr. 2025 Mar 4;38(3):35. doi: 10.1007/s10548-025-01111-4.

ABSTRACT

EEG is the most powerful tool for epilepsy discharge detection in brain. Visual evaluation is hard in long term monitoring EEG data as huge amount of data needs to be inspected. Considering the fast and efficient results from deep learning networks especially convolutional networks, and its capability for detection of complex epileptic wave forms, inspired us to evaluate YOLO network for spike detection solution.The most used versions of YOLO (V3, V4 and V7) were evaluated for various epileptic signals. The epileptic discharge wave-forms were first labeled to 9 different signal types, but classified to four group combinations based on their features. EEG data from 20 patients were used under guidance of expert epileptologist. The YOLO networks were all trained for four various class-grouping strategies. The most suitable network to recommend was found to be YOLO-V4, for all four classifying methods giving average sensitivity, specificity, and accuracy of 96.7, 94.3, and 92.8, respectively. YOLO networks have shown promising results in detection of epileptic signals, which by adding some extra measurements this can become a great assistant tool for epileptologists. In addition, besides YOLO's High speed and accuracy in detection of epileptic signals in EEG, it can classify these signals to different morphologies.

PMID:40035961 | DOI:10.1007/s10548-025-01111-4

Geroscience. 2025 Mar 4. doi: 10.1007/s11357-025-01581-1. Online ahead of print.

ABSTRACT

The aim of this study is to investigate the association between retinal age gap and multimorbidity. Retinal age gap was calculated based on a previously developed deep learning model for 45,436 participants. The number of age-related conditions reported at baseline was summed and categorized as zero, one, or at least two conditions at baseline (multimorbidity). Incident multimorbidity was defined as having two or more age-related diseases onset during the follow-up period. Linear regressions were fit to examine the associations of disease numbers at baseline with retinal age gaps. Cox proportional hazard regression models were used to examine associations of retinal age gaps with the incidence of multimorbidity. In the fully adjusted model, those with multimorbidity and one disease both showed significant increases in retinal age gaps at baseline compared to participants with zero disease number (β = 0.254, 95% CI 0.154, 0.354; P < 0.001; β = 0.203, 95% CI 0.116, 0.291; P < 0.001; respectively). After a median follow-up period of 11.38 (IQR, 11.26-11.53; range, 0.02-11.81) years, a total of 3607 (17.29%) participants had incident multimorbidity. Each 5-year increase in retinal age gap at baseline was independently associated with an 8% increase in the risk of multimorbidity (HR = 1.08, 95% CI 1.02, 1.14, P = 0.008). Our study demonstrated that an increase of retinal age gap was independently associated with a greater risk of incident multimorbidity. By recognizing deviations from normal aging, we can identify individuals at higher risk of developing multimorbidity. This early identification facilitates patients' self-management and personalized interventions before disease onset.

PMID:40035945 | DOI:10.1007/s11357-025-01581-1

Med Biol Eng Comput. 2025 Mar 4. doi: 10.1007/s11517-025-03338-6. Online ahead of print.

ABSTRACT

Diabetes is a metabolic condition that can lead to chronic illness and organ failure if it remains untreated. Accurate detection is essential to reduce these risks at an early stage. Recent advancements in predictive models show promising results. However, these models exhibit inadequate accuracy, struggle with class imbalance, and lack interpretability of the decision-making process. To overcome these issues, we propose two novel deep models for early and accurate diabetes prediction: LeDNet (inspired by LeNet and the Dual Attention Network) and HiDenNet (influenced by the highway network and DenseNet). The models are trained using the Diabetes Health Indicators dataset, which has an inherent class imbalance problem and results in biased predictions. This imbalance is mitigated by employing the majority-weighted minority over-sampling technique. Experimental findings demonstrate that LeDNet achieves an F1-score of 85%, recall of 84%, accuracy of 85%, and precision of 86%. Similarly, HiDenNet achieves accuracy, F1-score, recall, and precision of 85%, 86%, 86%, and 86%, respectively. Both proposed models outperform the state-of-the-art deep learning (DL) models. K-fold cross-validation is applied to ensure models' stability at different data splits. Local interpretable model-agnostic explanations and Shapley additive explanations techniques are utilized to enhance interpretability and overcome the traditional black-box nature of DL models. By providing both local and global insights into feature contributions, these explainable artificial intelligence techniques provide transparency to LeDNet and HiDenNet in diabetes prediction. LeDNet and HiDenNet not only improve decision-making transparency but also enhance diabetes prediction accuracy, making them reliable tools for clinical decision-making and early diagnosis.

PMID:40035798 | DOI:10.1007/s11517-025-03338-6

Biomol Biomed. 2025 Mar 3. doi: 10.17305/bb.2025.12056. Online ahead of print.

ABSTRACT

A critical predictive marker for anti-PD-1/PD-L1 therapy is programmed death-ligand 1 (PD-L1) expression, assessed by immunohistochemistry (IHC). This paper explores a novel automated framework using deep learning to accurately evaluate PD-L1 expression from whole slide images (WSIs) of non-small cell lung cancer (NSCLC), aiming to improve the precision and consistency of Tumor Proportion Score (TPS) evaluation, which is essential for determining patient eligibility for immunotherapy. Automating TPS evaluation can enhance accuracy and consistency while reducing pathologists' workload. The proposed automated framework encompasses three stages: identifying tumor patches, segmenting tumor areas, and detecting cell nuclei within these areas, followed by estimating the TPS based on the ratio of positively stained to total viable tumor cells. This study utilized a Reference Medicine (Phoenix, Arizona) dataset containing 66 NSCLC tissue samples, adopting a hybrid human-machine approach for annotating extensive WSIs. Patches of size 1000x1000 pixels were generated to train classification models such as EfficientNet, Inception, and Vision Transformer models. Additionally, segmentation performance was evaluated across various UNet and DeepLabV3 architectures, and the pre-trained StarDist model was employed for nuclei detection, replacing traditional watershed techniques. PD-L1 expression was categorized into three levels based on TPS: negative expression (TPS < 1%), low expression (TPS 1-49%), and high expression (TPS ≥ 50%). The Vision Transformer-based model excelled in classification, achieving an F1-score of 97.54%, while the modified DeepLabV3+ model led in segmentation, attaining a Dice Similarity Coefficient of 83.47%. The TPS predicted by the framework closely correlated with the pathologist's TPS at 0.9635, and the framework's three-level classification F1-score was 93.89%. The proposed deep learning framework for automatically evaluating the TPS of PD-L1 expression in NSCLC demonstrated promising performance. This framework presents a potential tool that could produce clinically significant results more efficiently and cost-effectively.

PMID:40035693 | DOI:10.17305/bb.2025.12056

Radiology. 2025 Mar;314(3):e231650. doi: 10.1148/radiol.231650.

ABSTRACT

Background The detection and classification of adrenal nodules are crucial for their management. Purpose To develop and test a deep learning model to automatically depict adrenal nodules on abdominal CT images and to simulate triaging performance in combination with human interpretation. Materials and Methods This retrospective study (January 2000-December 2020) used an internal dataset enriched with adrenal nodules for model training and testing and an external dataset reflecting real-world practice for further simulated testing in combination with human interpretation. The deep learning model had a two-stage architecture, a sequential detection and segmentation model, trained separately for the right and left adrenal glands. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) for nodule detection and intersection over union for nodule segmentation. Results Of a total of 995 patients in the internal dataset, the AUCs for detecting right and left adrenal nodules in internal test set 1 (n = 153) were 0.98 (95% CI: 0.96, 1.00; P < .001) and 0.93 (95% CI: 0.87, 0.98; P < .001), respectively. These values were 0.98 (95% CI: 0.97, 0.99; P < .001) and 0.97 (95% CI: 0.96, 0.97; P < .001) in the external test set (n = 12 080) and 0.90 (95% CI: 0.84, 0.95; P < .001) and 0.89 (95% CI: 0.85, 0.94; P < .001) in internal test set 2 (n = 1214). The median intersection over union was 0.64 (IQR, 0.43-0.71) and 0.53 (IQR, 0.40-0.64) for right and left adrenal nodules, respectively. Combining the model with human interpretation achieved high sensitivity (up to 100%) and specificity (up to 99%), with triaging performance from 0.77 to 0.98. Conclusion The deep learning model demonstrated high performance and has the potential to improve detection of incidental adrenal nodules. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Malayeri and Turkbey in this issue.

PMID:40035671 | DOI:10.1148/radiol.231650

Radiology. 2025 Mar;314(3):e250387. doi: 10.1148/radiol.250387.

NO ABSTRACT

PMID:40035670 | DOI:10.1148/radiol.250387

Network. 2025 Mar 4:1-43. doi: 10.1080/0954898X.2025.2457955. Online ahead of print.

ABSTRACT

PD is a progressive neurodegenerative disorder that leads to gradual motor impairments. Early detection is critical for slowing the disease's progression and providing patients access to timely therapies. However, accurately detecting PD in its early stages remains challenging. This study aims to develop an optimized deep learning model for PD classification using voice signals and hand-drawn spiral images, leveraging a ZFNet-LHO-DRN. The proposed model first preprocesses the input voice signal using a Gaussian filter to remove noise. Features are then extracted from the preprocessed signal and passed to ZFNet to generate output-1. For the hand-drawn spiral image, preprocessing is performed with a bilateral filter, followed by image augmentation. Here also, the features are extracted and forwarded to DRN to form output-2. Both classifiers are trained using the LHO algorithm. Finally, from the output-1 and output-2, the best one is selected based on the majority voting. The ZFNet-LHO-DRN model demonstrated excellent performance by achieving a premium accuracy of 89.8%, a NPV of 89.7%, a PPV of 89.7%, a TNR of 89.3%, and a TPR of 90.1%. The model's high accuracy and performance indicate its potential as a valuable tool for assisting in the early diagnosis of PD.

PMID:40035544 | DOI:10.1080/0954898X.2025.2457955

Int Rev Psychiatry. 2025 Feb;37(1):39-51. doi: 10.1080/09540261.2024.2384727. Epub 2024 Jul 30.

ABSTRACT

Modern psychiatry aims to adopt precision models and promote personalized treatment within mental health care. However, the complexity of factors underpinning mental disorders and the variety of expressions of clinical conditions make this task arduous for clinicians. Globally, major depression is a common mental disorder and encompasses a constellation of clinical manifestations and a variety of etiological factors. In this context, the use of Artificial Intelligence might help clinicians in the screening and diagnosis of depression on a wider scale and could also facilitate their task in predicting disease outcomes by considering complex interactions between prodromal and clinical symptoms, neuroimaging data, genetics, or biomarkers. In this narrative review, we report on the most significant evidence from current international literature regarding the use of Artificial Intelligence in the diagnosis and treatment of major depression, specifically focusing on the use of Natural Language Processing, Chatbots, Machine Learning, and Deep Learning.

PMID:40035375 | DOI:10.1080/09540261.2024.2384727

Proc Natl Acad Sci U S A. 2025 Mar 11;122(10):e2420499122. doi: 10.1073/pnas.2420499122. Epub 2025 Mar 4.

ABSTRACT

Cardiomyocyte hypertrophy is a key clinical predictor of heart failure. High-throughput and AI-driven screens have the potential to identify drugs and downstream pathways that modulate cardiomyocyte hypertrophy. Here, we developed LogiRx, a logic-based mechanistic machine learning method that predicts drug-induced pathways. We applied LogiRx to discover how drugs discovered in a previous compound screen attenuate cardiomyocyte hypertrophy. We experimentally validated LogiRx predictions in neonatal cardiomyocytes, adult mice, and two patient databases. Using LogiRx, we predicted antihypertrophic pathways for seven drugs currently used to treat noncardiac disease. We experimentally validated that escitalopram (Lexapro) and mifepristone inhibit hypertrophy of cultured cardiomyocytes in two contexts. The LogiRx model predicted that escitalopram prevents hypertrophy through an "off-target" serotonin receptor/PI3Kγ pathway, mechanistically validated using additional investigational drugs. Further, escitalopram reduced cardiomyocyte hypertrophy in a mouse model of hypertrophy and fibrosis. Finally, mining of both FDA and University of Virginia databases showed that patients with depression on escitalopram have a lower incidence of cardiac hypertrophy than those prescribed other serotonin reuptake inhibitors that do not target the serotonin receptor. Mechanistic machine learning by LogiRx discovers drug pathways that perturb cell states, which may enable repurposing of escitalopram and other drugs to limit cardiac remodeling through off-target pathways.

PMID:40035765 | DOI:10.1073/pnas.2420499122

FASEB J. 2025 Mar 15;39(5):e70443. doi: 10.1096/fj.202403156R.

ABSTRACT

AGGF1 encodes an angiogenic factor that causes vascular disease Klippel-Trenaunay syndrome when mutated. AGGF1 also acts at the top of the genetic regulatory hierarchy for mesodermal differentiation of hemangioblasts, multipotent stem cells for differentiation of blood cells and vascular cells. Alternative splicing (AS) is a post-transcriptional process that generates multiple mature mRNAs from a single primary transcript (pre-mRNA), producing protein diversity. Deregulation of AS leads to many human diseases. The physiological role and mechanism of AGGF1 in AS are not clear. Full-length transcriptome sequencing of human pulmonary artery endothelial cells (HPAECs) with AGGF1 silencing revealed 63 121 genes, including 1144 new unannotated genes, and showed that AGGF1 is a general splicing factor regulating AS of 436 genes, including SRSF6 regulating AS of many target genes. AGGF1 promoted the skipping of exon 3 that produces the full-length SRSF6 protein, an evolutionarily conserved AS event. Overexpression of full-length SRSF6 reversed the reduced cell proliferation, migration, and capillary tube formation of HPAECs with AGGF1 silencing. Knockdown of SRSF6 and overexpression of the shorter, alternatively spliced isoform of SRSF6 both inhibited HPAEC proliferation, migration, and capillary tube formation, whereas opposite results were obtained for overexpression of full-length SRSF6. Knockdown of srsf6 impaired development of ISVs in zebrafish, whereas overexpression of srsf6 enhanced vascular development and partially rescued impaired ISV development in zebrafish embryos with aggf1 knockdown. Overall, our findings reveal that AGGF1 is a general splicing factor, and that AGGF1-mediated exon 3 skipping of SRSF6 pre-mRNA is important for endothelial cell functions, angiogenesis, and vascular development.

PMID:40035560 | DOI:10.1096/fj.202403156R

Expert Opin Drug Saf. 2024 Dec 30:1-5. doi: 10.1080/14740338.2024.2448830. Online ahead of print.

ABSTRACT

BACKGROUND: Antineoplastic drug-associated adverse cardiovascular events (ACEs) are a concern; however, information on new antineoplastic drugs is limited. In this situation, signal detection and hypothesis building by analyzing the pharmacovigilance database are useful. However, increased numbers of reports on COVID-19 vaccine-related ACEs in the pharmacovigilance database have affected the results of the disproportionality analysis. Therefore, examining the effect of increased reports on COVID-19 vaccine-related ACEs on detecting anticancer drug-related ACEs is critical.

RESEARCH DESIGN AND METHODS: Disproportionality analysis was performed using the Japanese Adverse Drug Event Report (JADER) database. Reporting odds ratio and information components were used as indicators to detect potential associations between drugs and adverse events. The analysis was performed in two situations: using all data in the JADER database and excluding cases with the COVID-19 vaccine.

RESULTS: Various antineoplastic drugs were associated with diverse ACEs. Additionally, safety signals for ACEs of some antineoplastic drugs were masked by reports on COVID-19 vaccine-related ACEs.

CONCLUSIONS: The rapid increased reports on COVID-19 vaccine-related ACEs in the JADER database had an impact on signal detection activities for antineoplastic drug-associated ACEs. Therefore, the impact of reporting COVID-19 vaccine-related ACEs on current signal detection activities should be evaluated over time.

PMID:40035831 | DOI:10.1080/14740338.2024.2448830

Ceska Slov Farm. 2025;73(2):93-102. doi: 10.36290/csf.2024.015.

ABSTRACT

Drug-related hospitalizations - insights from the Czech Republic Background and objective: Drug-related hospitalizations represent a significant burden on healthcare. The aim of the study was to determine the prevalence of drug-related hospitalizations and identify medications and clinical manifestations associated with drug-related hospitalizations in patients admitted to hospital through the emergency department.

METHODS: This cross-sectional study examined unplanned hospitalizations at the University Hospital Hradec Kralove through the Department of Emergency Medicine between August and November 2018. Data were obtained from electronic health records. The methodology for identifying drug-related hospitalizations was based on the guideline of the European project OPERAM. This article focuses on a subgroup of drug-related problems related to the medication safety.

RESULTS: Of the total 1252 hospitalizations analyzed, 145 cases were identified as drug-related. The prevalence of drug-related hospitalizations was 12% (95% confidence interval 10-13). In 62% of cases, medications only contributed to the cause of hospitalization. Antithrombotics, cytostatics, diuretics, and systemic corticosteroids were the most common medication classes leading to drug-related hospitalizations. Gastrointestinal bleeding was the most common cause of drug-related hospitalizations. The potential preventability of drug-related hospitalizations was 34%.

CONCLUSION: Drug-related hospitalizations remain relatively common, while some of them could be potentially prevented. Pharmacists can contribute to enhancing patient safety by detecting drug-related problems and proposing measures to minimize risks.

PMID:40035300 | DOI:10.36290/csf.2024.015

iScience. 2025 Jan 31;28(3):111931. doi: 10.1016/j.isci.2025.111931. eCollection 2025 Mar 21.

ABSTRACT

FoxP3+ regulatory T cells (Tregs) are responsible for immune homeostasis by suppressing excessive anti-self-immunity. Tregs facilitate tumor growth by inhibiting anti-tumor immunity. Here, we explored the targeting of FoxP3 as a basis for new immunotherapies. In a high-throughput phenotypic screening of a drug repurposing library using human primary T cells, we identified quinacrine as a FoxP3 downregulator. In silico searches based on the structure of quinacrine, testing of sub-libraries of analogs in vitro, and validation identified a subset of 9-amino-acridines that selectively abrogated Treg suppressive functions. Mechanistically, these acridines interfered with the DNA-binding activity of FoxP3 and inhibited FoxP3-regulated downstream gene regulation. Release from Treg suppression by 9-amino-acridines increased anti-tumor immune responses both in cancer patient samples and in mice in a syngeneic tumor model. Our study highlights the feasibility of screening for small molecular inhibitors of FoxP3 as an approach to pursuing Treg-based immunotherapy.

PMID:40034859 | PMC:PMC11872463 | DOI:10.1016/j.isci.2025.111931

Front Pharmacol. 2025 Feb 17;16:1504618. doi: 10.3389/fphar.2025.1504618. eCollection 2025.

ABSTRACT

Achyranthes japonica (Miq.) Nakai (AJN) and Melandrium firmum (Siebold and Zucc.) Rohrb. (MFR) are medicinal plants recognized for their bioactive phytochemicals, including ecdysteroids, anthraquinones, and flavonoids. This study investigates the anticancer properties of key constituents of these plants, focusing on the BK002 formulation, a novel combination of AJN and MFR. Specifically, the research employs advanced molecular docking and in silico analyses to assess the interactions of bioactive compounds ecdysterone, inokosterone, and 20-hydroxyecdysone (20-HE) with key prostate cancer-related network proteins, including 5α-reductase, CYP17, DNMT1, Dicer, PD-1, and PD-L1. Molecular docking techniques were applied to evaluate the binding affinities contributions of the bioactive compounds in BK002 against prostate cancer-hub network targets. The primary focus was on enzymes like 5α-reductase and CYP17, which are central to androgen biosynthesis, as well as on cancer-related proteins such as DNA methyltransferase 1 (DNMT1), Dicer, programmed death-1 (PD-1), and programmed death ligand-1 (PD-L1). Based on data from prostate cancer patients, key target networks were identified, followed by in silico analysis of the primary bioactive components of BK002.In silico assessments were conducted to evaluate the safety profiles of these compounds, providing insights into their therapeutic potential. The docking studies revealed that ecdysterone, inokosterone, and 20-hydroxyecdysonec demonstrated strong binding affinities to the critical prostate cancer-related enzymes 5α-reductase and CYP17, contributing to a potential reduction in androgenic activity. These compounds also exhibited significant inhibitory interactions with DNMT1, Dicer, PD-1, and PD-L1, suggesting a capacity to interfere with key oncogenic and immune evasion pathways. Ecdysterone, inokosterone, and 20-hydroxyecdysone have demonstrated the ability to target key oncogenic pathways, and their favorable binding affinity profiles further underscore their potential as novel therapeutic agents for prostate cancer. These findings provide a strong rationale for further preclinical and clinical investigations, supporting the integration of BK002 into therapeutic regimens aimed at modulating tumor progression and immune responses.

PMID:40034825 | PMC:PMC11872924 | DOI:10.3389/fphar.2025.1504618

medRxiv [Preprint]. 2025 Feb 21:2025.02.18.25321035. doi: 10.1101/2025.02.18.25321035.

ABSTRACT

Identification of drug-repurposing targets with genetic and biological support is an economically and temporally efficient strategy for improving treatment of diseases. We employed a cross-disciplinary approach to identify potential treatments for metabolic dysfunction associated steatotic liver disease (MASLD) using humans as a model organism. We identified 212 putative causal genes associated with MASLD using data from a large multi-ancestry genetic association study, of which 158 (74.5%) are novel. From this set we identified 57 genes that encode for druggable protein targets, and where the effects of increasing genetically predicted gene expression on MASLD risk align with the function of that drug on the protein target. These potential targets were then evaluated for evidence of efficacy using Mendelian randomization, pathway analysis, and protein structural modeling. Using these approaches, we present compelling evidence to suggest activation of FADS1 by icosopent ethyl as well as S1PR2 by fingolimod could be promising therapeutic strategies for MASLD.

PMID:40034783 | PMC:PMC11875238 | DOI:10.1101/2025.02.18.25321035