J Proteomics. 2025 Apr 15:105442. doi: 10.1016/j.jprot.2025.105442. Online ahead of print.

ABSTRACT

This white paper presents a comprehensive biobanking framework developed at the European Cancer Moonshot Lund Center that merges rigorous sample handling, advanced automation, and multi-omic analyses to accelerate precision oncology. Tumor and blood-based workflows, supported by automated fractionation systems and standardized protocols, ensure the collection of high-quality biospecimens suitable for proteomic, genomic, and metabolic studies. A robust informatics infrastructure, integrating LIMS, barcoding, and REDCap, supports end-to-end traceability and realtime data synchronization, thereby enriching each sample with critical clinical metadata. Proteogenomic integration lies at the core of this initiative, uncovering tumor- and blood-based molecular profiles that inform cancer heterogeneity, metastasis, and therapeutic resistance. Machine learning and AI-driven models further enhance these datasets by stratifying patient populations, predicting therapeutic responses, and expediting the discovery of actionable targets and companion biomarkers. This synergy between technology, automation, and high-dimensional data analytics enables individualized treatment strategies in melanoma, lung, and other cancer types. Aligned with international programs such as the Cancer Moonshot and the ICPC, the Lund Center's approach fosters open collaboration and data sharing on a global scale. This scalable, patient-centric biobanking paradigm provides an adaptable model for institutions aiming to unify clinical, molecular, and computational resources for transformative cancer research.

PMID:40246065 | DOI:10.1016/j.jprot.2025.105442

Cell Stem Cell. 2025 Apr 11:S1934-5909(25)00102-X. doi: 10.1016/j.stem.2025.03.012. Online ahead of print.

ABSTRACT

Considerable phenotypic variation under identical culture conditions limits the potential of stem-cell-based embryo models (SEMs) in basic and applied research. The biological processes causing this seemingly stochastic variation remain unclear. Here, we investigated the roots of phenotypic variation by parallel recording of transcriptomic states and morphological history in individual structures modeling embryonic trunk formation. Machine learning and integration of time-resolved single-cell RNA sequencing with imaging-based phenotypic profiling identified early features predictive of phenotypic end states. Leveraging this predictive power revealed that early imbalance of oxidative phosphorylation and glycolysis results in aberrant morphology and a neural lineage bias, which we confirmed by metabolic measurements. Accordingly, metabolic interventions improved phenotypic end states. Collectively, our work establishes divergent metabolic states as drivers of phenotypic variation and offers a broadly applicable framework to chart and predict phenotypic variation in organoids and SEMs. The strategy can be used to identify and control underlying biological processes, ultimately increasing reproducibility.

PMID:40245869 | DOI:10.1016/j.stem.2025.03.012

Cell. 2025 Apr 14:S0092-8674(25)00352-6. doi: 10.1016/j.cell.2025.03.034. Online ahead of print.

ABSTRACT

Regulatory DNA provides a platform for transcription factor binding to encode cell-type-specific patterns of gene expression. However, the effects and programmability of regulatory DNA sequences remain difficult to map or predict. Here, we develop variant effects from flow-sorting experiments with CRISPR targeting screens (Variant-EFFECTS) to introduce hundreds of designed edits to endogenous regulatory DNA and quantify their effects on gene expression. We systematically dissect and reprogram 3 regulatory elements for 2 genes in 2 cell types. These data reveal endogenous binding sites with effects specific to genomic context, transcription factor motifs with cell-type-specific activities, and limitations of computational models for predicting the effect sizes of variants. We identify small edits that can tune gene expression over a large dynamic range, suggesting new possibilities for prime-editing-based therapeutics targeting regulatory DNA. Variant-EFFECTS provides a generalizable tool to dissect regulatory DNA and to identify genome editing reagents that tune gene expression in an endogenous context.

PMID:40245860 | DOI:10.1016/j.cell.2025.03.034

Plant Physiol Biochem. 2025 Apr 1;224:109868. doi: 10.1016/j.plaphy.2025.109868. Online ahead of print.

ABSTRACT

Stress-associated proteins (SAPs), characterized by zinc finger domains, play a crucial role in regulating plant responses to various stresses. These proteins modulate stress-related gene expression and are integral to enhancing plant immunity, development, cell proliferation, and hormone regulation. In this study, we conducted a genome-wide analysis of the SAP gene family in Suaeda glauca (S. glauca), identifying 15 SAP genes encoding A20/AN1 zinc finger proteins. Functional analyses of three candidate genes under salinity stress were performed, examining phenotypic and physiological responses to better understand their role in stress tolerance. Sequence alignment, conserved domain analysis, and gene structure analysis revealed high conservation among S. glauca SAPs. Phylogenetic analysis identified two major groups within the gene family, providing insights into their evolutionary relationships. Transcription profiling analysis demonstrated significant expression of most SAP genes in response to salt stress, with qPCR validation confirming the upregulation of specific genes. Notably, transgenic Arabidopsis lines heterologously overexpressing the candidate genes SgSAP4, SgSAP5, and SgSAP7 demonstrated enhanced tolerance to salinity stress. This was evident from improved seed germination, root elongation, and reduced levels of stress markers, including malondialdehyde and free proline, compared to wild-type plants. These findings highlight the potential of these SAP genes in breeding programs aimed at improving salinity tolerance in crops.

PMID:40245556 | DOI:10.1016/j.plaphy.2025.109868

Lancet Child Adolesc Health. 2025 May;9(5):325-336. doi: 10.1016/S2352-4642(25)00067-7.

ABSTRACT

BACKGROUND: Respiratory syncytial virus (RSV) is a particularly dangerous infection in some populations, including very young infants. This study examined the efficacy and safety of ziresovir in hospitalised infants aged 6 months or younger with RSV infection.

METHODS: In this double-blind, randomised, placebo-controlled trial conducted across 28 hospitals in China, patients aged 1-24 months hospitalised for virologically confirmed RSV infection were randomly allocated (2:1) to receive ziresovir (10-40 mg by weight twice daily) or placebo orally for 5 days, with 2 years of follow-up. Patients were included if they had a Wang bronchiolitis clinical score (WBCS) of at least 5 at first dosing and were administered their first dose of study drug within 7 days of the onset of symptoms of RSV infection. In this prespecified subanalysis of patients aged 6 months and younger at randomisation, we analysed the primary endpoint (change from baseline in WBCS on day 3 [48 h post-baseline]) in the intention-to-treat infected (ITT-i) population (comprising patients who received at least one dose of study drug and who had PCR-confirmed RSV infection). Safety endpoints were assessed in all patients who received at least one dose. This study is registered with ClinicalTrials.gov (NCT04231968) and is completed.

FINDINGS: Participants were recruited from Sept 22, 2020, to Jan 19, 2022, and followed up to Feb 4, 2024. Among patients aged 6 months or younger, 188 participants (125 in the ziresovir group and 63 in the placebo group) received at least one dose of study drug and were included in the safety analysis, while the ITT-i population included 150 patients (100 in the ziresovir group and 50 in the placebo group). In the ziresovir group, 33 (26%) of 125 patients were female, 92 (74%) were male, mean age was 3·4 months (SD 1·4), and mean baseline WBCS was 6·8 (SD 1·7). In the placebo group, 15 (24%) of 63 patients were female, 48 (76%) were male, mean age was 3·3 months (1·5), and mean baseline WBCS was 6·9 (1·8). The least-squares mean change in WBCS from baseline to day 3 was -3·5 points (95% CI -3·9 to -3·1) with ziresovir versus -2·2 points (-2·8 to -1·7) with placebo (difference -1·2 [95% CI -1·9 to -0·6], p=0·0004). Drug-related treatment-emergent adverse events occurred in 22 (18%) of 125 patients who received ziresovir and seven (11%) of 63 patients who received placebo. No drug-related serious adverse events were observed and no deaths occurred.

INTERPRETATION: Ziresovir had a favourable safety profile and was associated with a significant clinical benefit during the treatment period compared with placebo in patients aged 6 months or younger.

FUNDING: Shanghai Ark Biopharmaceutical, National Clinical Research Center for Respiratory Diseases, and National Major Science and Technology Projects of China.

PMID:40246359 | DOI:10.1016/S2352-4642(25)00067-7

Endocr Pract. 2025 Apr 15:S1530-891X(25)00118-1. doi: 10.1016/j.eprac.2025.04.004. Online ahead of print.

ABSTRACT

OBJECTIVES: Research has increasingly explored the benefits of thiazolidinediones (TZDs) beyond diabetes management, particularly in reducing stroke and dementia risks. However, concerns about cardiovascular adverse events, especially heart failure, necessitate a re-evaluation of TZD-associated cardiovascular risks using real-world data.

METHODS: This study re-evaluates the cardiovascular risks of TZDs and their efficacy in stroke prevention. We conducted a real-world pharmacovigilance study using the FDA Adverse Event Reporting System (FAERS) database (January 2004 to December 2023) to assess cardiovascular risks associated with TZDs, including myocardial infarction, heart failure, and stroke. Multivariable logistic regression adjusted the Reporting Odds Ratio from the disproportional analysis. Additionally, a network meta-analysis of clinical studies (January 2000 to March 2024) examined the efficacy of TZDs in stroke prevention.

RESULTS: Our analysis of the FAERS database revealed significantly higher cardiovascular risks associated with TZDs. However, clear differences exist in cardiovascular risks between pioglitazone and rosiglitazone. Rosiglitazone was linked to a markedly increased incidence of myocardial infarction, heart failure, and stroke. In contrast, we didn't observe strong cardiovascular risks associated with pioglitazone. Instead, pioglitazone was shown to slightly heighten the risk of heart failure. Further, the network meta-analysis, based on SUCRA rankings and ranking probabilities also disclosed similar findings: when compared to placebo, rosiglitazone increased stroke risk, while pioglitazone reduced stroke incidence in individuals with diabetes and pre-diabetes.

CONCLUSIONS: Our analysis shows that pioglitazone has potential therapeutic effects on stroke prevention and fewer cardiovascular adverse events compared to rosiglitazone, underscoring the importance of re-assessing TZD safety for optimal patient outcomes.

PMID:40246234 | DOI:10.1016/j.eprac.2025.04.004

Lancet. 2025 Apr 11:S0140-6736(25)00628-2. doi: 10.1016/S0140-6736(25)00628-2. Online ahead of print.

ABSTRACT

BACKGROUND: Gepotidacin, a first-in-class, bactericidal, triazaacenaphthylene antibacterial that inhibits bacterial DNA replication, was shown to be efficacious and well tolerated in the treatment of uncomplicated urinary tract infections. We evaluated the efficacy and safety of gepotidacin for the treatment of uncomplicated urogenital gonorrhoea.

METHODS: EAGLE-1 (NCT04010539) was a phase 3, open-label, sponsor-blinded, multicentre, non-inferiority study evaluating oral gepotidacin (two 3000 mg doses administered 10-12 h apart) compared with 500 mg intramuscular ceftriaxone plus 1 g oral azithromycin for the treatment of gonorrhoea. Eligible participants were aged 12 years and older, had a bodyweight over 45 kg, and had suspected uncomplicated urogenital gonorrhoea (including mucopurulent discharge), a positive laboratory test for Neisseria gonorrhoeae, or both. Participants were randomly allocated in a 1:1 ratio to each treatment group, stratified by sex (original urogenital anatomy at birth) and sexual orientation (men who have sex with men [MSM], men who have sex with women [MSW], and female) in combination, and age group (age <18 years, ≥18 to 65 years, or >65 years). The primary efficacy endpoint was microbiological success, defined as culture-confirmed bacterial eradication of N gonorrhoeae from the urogenital body site at test-of-cure (days 4-8). The non-inferiority margin was prespecified at -10%. The primary outcome was assessed in the microbiological intention-to-treat (micro-ITT) population, all participants randomly allocated to a study treatment who received at least one dose of their study treatment and had confirmed ceftriaxone-susceptible N gonorrhoeae isolated from the baseline culture of their urogenital specimen. The safety population comprised all participants who received one or more doses of any study treatment.

FINDINGS: Between Oct 21, 2019, and Oct 10, 2023, 628 participants were randomly allocated (314 allocated to each treatment group). Overall, 39 (6%) of 628 participants discontinued the study prematurely (20 in the gepotidacin group and 19 in the ceftriaxone plus azithromycin group), with the primary reason being lost to follow-up. The micro-ITT population included 406 participants (202 in the gepotidacin group and 204 in the ceftriaxone plus azithromycin group). Most participants in the micro-ITT population were male (372 [92%] vs 34 [8%] female), and there was a higher percentage of participants who were MSM (290 [71%]) compared with participants who were MSW (82 [20%]). Participants were predominantly White (299 [74%]) or Black or African American (61 [15%]), with 70 (17%) identifying as Hispanic or Latino. Results of the primary analysis of microbiological response at test-of-cure demonstrated microbiological success rates of 92·6% (187 of 202 [95% CI 88·0 to 95·8]) in the gepotidacin group and 91·2% (186 of 204 [86·4 to 94·7]) in the ceftriaxone plus azithromycin group (adjusted treatment difference -0·1% [95% CI -5·6 to 5·5]). Gepotidacin was non-inferior to ceftriaxone plus azithromycin. No bacterial persistence of urogenital N gonorrhoeae was observed at test-of-cure for either group. The gepotidacin group had higher rates of adverse events and drug-related adverse events, mainly due to gastrointestinal adverse events, and almost all were mild or moderate. No treatment-related severe or serious adverse events occurred in either group.

INTERPRETATION: Gepotidacin demonstrated non-inferiority to ceftriaxone plus azithromycin for urogenital N gonorrhoeae, with no new safety concerns, offering a novel oral treatment option for uncomplicated urogenital gonorrhoea.

FUNDING: GSK and federal funds from the Office of the Assistant Secretary for Preparedness and Response, Biomedical Advanced Research and Development Authority.

PMID:40245902 | DOI:10.1016/S0140-6736(25)00628-2

Ann Clin Transl Neurol. 2025 Apr 17. doi: 10.1002/acn3.70053. Online ahead of print.

ABSTRACT

This study assessed whether continued treatment with anti-CGRP monoclonal antibodies (mAbs) is driven more by reductions in monthly migraine days (MMDs) or patients' global impression of change (PGIC), a patient-reported outcome. Among 169 patients treated with anti-CGRP mAbs, 21.3% discontinued due to ineffectiveness. PGIC responders (≥ 5) at month 12 were 69.8%, whereas MMD responders (≥ 50% reduction) were 59.2%. Both were significantly associated with discontinuation (PGIC: χ2 = 33.474, φ = 0.445; MMD: χ2 = 29.884, φ = 0.421; p < 0.0001). PGIC showed a stronger correlation with discontinuation (rpb = 0.541) than MMD reduction (rpb = 0.470). These findings highlight PGIC as strongly associated with treatment response, supporting the need for PROMs in evaluating migraine treatment effectiveness.

PMID:40244898 | DOI:10.1002/acn3.70053

J Med Internet Res. 2025 Apr 17;27:e63130. doi: 10.2196/63130.

ABSTRACT

BACKGROUND: Artificial patient technology could transform health care by accelerating diagnosis, treatment, and mapping clinical pathways. Deep learning methods for generating artificial data in health care include data augmentation by variational autoencoders (VAE) technology.

OBJECTIVE: We aimed to test the feasibility of generating artificial patients with reliable clinical characteristics by using a geometry-based VAE applied, for the first time, on high-dimension, low-sample-size tabular data.

METHODS: Clinical tabular data were extracted from 521 real patients of the "MAX" digital conversational agent (BOTdesign) created for preparing patients for anesthesia. A 3-stage methodological approach was implemented to generate up to 10,000 artificial patients: training the model and generating artificial data, assessing the consistency and confidentiality of artificial data, and validating the plausibility of the newly created artificial patients.

RESULTS: We demonstrated the feasibility of applying the VAE technique to tabular data to generate large artificial patient cohorts with high consistency (fidelity scores>94%). Moreover, artificial patients could not be matched with real patients (filter similarity scores>99%, κ coefficients of agreement<0.2), thus guaranteeing the essential ethical concern of confidentiality.

CONCLUSIONS: This proof-of-concept study has demonstrated our ability to augment real tabular data to generate artificial patients. These promising results make it possible to envisage in silico trials carried out on large cohorts of artificial patients, thereby overcoming the pitfalls usually encountered in in vivo trials. Further studies integrating longitudinal dynamics are needed to map patient trajectories.

PMID:40245392 | DOI:10.2196/63130

PLoS One. 2025 Apr 17;20(4):e0314447. doi: 10.1371/journal.pone.0314447. eCollection 2025.

ABSTRACT

The functional near-infrared spectroscopy-based brain-computer interface (fNIRS-BCI) systems recognize patterns in brain signals and generate control commands, thereby enabling individuals with motor disabilities to regain autonomy. In this study hand gripping data is acquired using fNIRS neuroimaging system, preprocessing is performed using nirsLAB and features extraction is performed using deep learning (DL) Algorithms. For feature extraction and classification stack and fft methods are proposed. Convolutional neural networks (CNN), long short-term memory (LSTM), and bidirectional long-short-term memory (Bi-LSTM) are employed to extract features. The stack method classifies these features using a stack model and the fft method enhances features by applying fast Fourier transformation which is followed by classification using a stack model. The proposed methods are applied to fNIRS signals from twenty participants engaged in a two-class hand-gripping motor activity. The classification performance of the proposed methods is compared with conventional CNN, LSTM, and Bi-LSTM algorithms and one another. The proposed fft and stack methods yield 90.11% and 87.00% classification accuracies respectively, which are significantly higher than those achieved by CNN (85.16%), LSTM (79.46%), and Bi-LSTM (81.88%) conventional algorithms. The results show that the proposed stack and fft methods can be effectively used for the classification of the two and three-class problems in fNIRS-BCI applications.

PMID:40245060 | DOI:10.1371/journal.pone.0314447

IEEE J Biomed Health Inform. 2025 Apr 17;PP. doi: 10.1109/JBHI.2025.3562072. Online ahead of print.

ABSTRACT

Quantitatively deciphering the relationship between muscle activation and thickness deformation is essential for diagnosing muscle-related diseases and monitoring muscle health (e.g., Facioscapulohumeral Dystrophy). Despite the potential of ultrasound (US) imaging and sensing to measure changes in muscle thickness during movements, it remains challenging to make a fully portable device, considering the wiring and data collection. On the other hand, surface electromyography (sEMG) can record muscle bioelectrical signals and measure muscle activations, offering a unique perspective that correlates with underlying changes in muscle thickness. This paper introduces a deep-learning-based approach that used sEMG signals to infer muscle deformation. Using a hierarchical combination of self-attention and cross-attention mechanisms, this method predicted muscle deformation directly from sEMG data, eliminating the dependency on applying ultrasound imaging techniques. The experimental results on six healthy subjects indicated that our approach could accurately predict muscle excursion with an average precision of 0.923$\pm$0.900mm, showing benefits in measuring muscle deformation only with a sEMG device. This technique facilitates real-time portable muscle health monitoring by sEMG to provide bioelectrical signals and biomechanical information. It indicates the great potential of using this technique in clinical diagnostics, sports science, and rehabilitation.

PMID:40244835 | DOI:10.1109/JBHI.2025.3562072

Phys Chem Chem Phys. 2025 Apr 17. doi: 10.1039/d5cp00024f. Online ahead of print.

ABSTRACT

Fentanyl is a synthetic opioid with higher potency compared to morphine and heroin, making it an essential drug for pain management and also an abused drug. Beyond fentanyl, derivatives, such as o-fluoro fentanyl and furanyl fentanyl, also possess similar potency and present a significant risk of misuse, but without medical utility. A major challenge for law enforcement is detecting fentanyl and its analogues in their degraded forms. While the degradation fragments of fentanyl are well-known, those of its analogues are not as well studied. Here, we investigated the thermal degradation pathways of fentanyl analogues using extensive ab initio molecular dynamics simulations combined with enhanced sampling techniques, including multiple walker metadynamics and umbrella sampling. We calculated the free energy profiles for each bond previously identified as a potential degradation site to map out the thermodynamic driving forces. Additionally, we estimated the forward attempt rate of each bond degradation reaction to gain insights into the kinetics of those degradation processes. Our results show that, despite high similarity in structure, the bond breaking pathways differ for the analogues compared with fentanyl. We also observed that traditional force fields with fixed charges are insufficient for studies of fentanyl and its analogues due to polarizability of the electronic structure. Distribution Statement A. Approved for Public Release. Distribution Unlimited.

PMID:40245086 | DOI:10.1039/d5cp00024f

Cell Rep. 2025 Apr 16;44(5):115591. doi: 10.1016/j.celrep.2025.115591. Online ahead of print.

ABSTRACT

We present an isotope-based metabolic flux analysis (MFA) approach to simultaneously quantify metabolic fluxes in the liver, heart, and skeletal muscle of individual mice. The platform was scaled to examine metabolic flux adaptations in age-matched cohorts of mice exhibiting varying levels of chronic obesity. We found that severe obesity increases hepatic gluconeogenesis and citric acid cycle flux, accompanied by elevated glucose oxidation in the heart that compensates for impaired fatty acid oxidation. In contrast, skeletal muscle fluxes exhibit an overall reduction in substrate oxidation. These findings demonstrate the dichotomy in fuel utilization between cardiac and skeletal muscle during worsening metabolic disease and demonstrate the divergent effects of obesity on metabolic fluxes in different organs. This multi-tissue MFA technology can be extended to address important questions about in vivo regulation of metabolism and its dysregulation in disease, which cannot be fully answered through studies of single organs or isolated cells/tissues.

PMID:40244853 | DOI:10.1016/j.celrep.2025.115591

J Toxicol Environ Health A. 2025 Apr 17:1-12. doi: 10.1080/15287394.2025.2491556. Online ahead of print.

ABSTRACT

Myricetin (ME) is a major constituent of various foods and beverages consumed by humans, including vegetables, teas and fruits, and is primarily recognized for its iron-chelating, antioxidant, anti-inflammatory and anti-cancer properties. This study evaluated the cytostatic, genotoxic, and chemopreventive effects of ME in CHO-K1 cells using the Cytokinesis-Block Micronucleus (CBMN) assay and explored molecular interactions through in silico systems biology analysis. CHO-K1 cells were exposed to ME (2.5-40 µM). Cytostasis was assessed by the Cytokinesis-Block Proliferation Index (CBPI), and chromosomal instability was measured by the frequency of micronuclei (MNi), nucleoplasmic bridges (NPBs), and nuclear buds (NBUDs). ME at 40 µM significantly reduced CBPI, while concentrations of 20 and 40 µM increased chromosomal instability (p < 0.05). For chemoprevention, ME (2.5-10 µM) was administered in pre-, co-, and post-treatment with bleomycin (BLM). ME significantly reduced BLM-induced MNi and NPBs in all protocols (p < 0.05). In silico analysis revealed strong interactions between ME and key proteins related to DNA damage response, apoptosis, and bleomycin detoxification. Notably, the in silico analysis revealed a strong association between ME and bleomycin hydrolase (BLMH) and the interaction of ME with proteins related to DNA damage response and apoptosis regulation. Overall, ME exhibited genotoxicity at high concentrations but demonstrated a significant chemopreventive effect at lower, nontoxic doses. These findings provide insights into the dual biological activity of ME and support its potential use as a protective agent against genotoxic damage.

PMID:40244708 | DOI:10.1080/15287394.2025.2491556

Mol Divers. 2025 Apr 17. doi: 10.1007/s11030-025-11187-6. Online ahead of print.

ABSTRACT

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the leading cause of chronic liver disease globally. A low-carbohydrate diet (LCD) offers benefits to MASLD patients, albeit its exact mechanism is not fully understood. Using public liver transcriptome data from MASLD patients before/after LCD intervention, we applied differential expression analysis and machine learning to identify key genes. We initially identified 162 differentially expressed genes in the GSE107650 dataset. Secondly, employing two machine learning algorithms, we found that PRAMENP, LEAP2, LOC105379013, and argininosuccinate synthetase 1 (ASS1) are potential hub genes. Additionally, protein-protein interaction and single-cell RNA location analyses suggested that ASS1 was the most crucial hub gene. Then, L1000CDS2 analysis of the gene-expression signatures was employed for drug repurposing studies. CGP71683, an appetite suppressant, was predicted to improve MASLD and may mimic the ASS1 expression pattern induced by an LCD. Molecular dynamics confirmed spontaneous, stable CGP71683-ASS1 complex formation. Overall, this work based on analysis of machine learning algorithms, essential gene identification, and drug repurposing studies suggested that ASS1 is an essential gene in MASLD and CGP71683 is a potential drug candidate for treating MASLD by targeting ASS1 and mimicking the beneficial effects of an LCD. However, due to the inherent limitations of a purely computational approach, further experimental investigation is necessary to validate the anticipated results.

PMID:40244373 | DOI:10.1007/s11030-025-11187-6

Int J Mol Sci. 2025 Mar 24;26(7):2931. doi: 10.3390/ijms26072931.

ABSTRACT

Cardiovascular disease remains the leading global cause of mortality, largely driven by atherosclerosis, a chronic inflammatory condition characterized by lipid accumulation and immune-cell infiltration in arterial walls. Macrophages play a central role by forming foam cells and secreting pro-atherogenic cytokines, such as TNF-α, IFN-γ, and IL-1β, which destabilize atherosclerotic plaques, expanding the lipid core and increasing the risk of thrombosis and ischemia. Despite the significant health burden of subclinical atherosclerosis, few targeted therapies exist. Current treatments, including monoclonal antibodies, are limited by high costs and immunosuppressive side effects, underscoring the urgent need for alternative therapeutic strategies. In this study, we employed in silico drug repositioning to identify multitarget inhibitors against TNF-α, IFN-γ, and IL-1β, leveraging a virtual screening of 2750 FDA-approved drugs followed by molecular dynamics simulations to assess the stability of selected cytokine-ligand complexes. This computational approach provides structural insights into potential inhibitors. Additionally, we highlight nutraceutical options, such as fatty acids (oleic, linoleic and eicosapentaenoic acid), which exhibited strong and stable interactions with key cytokine targets. Our study suggests that these bioactive compounds could serve as effective new therapeutic approaches for atherosclerosis.

PMID:40243563 | DOI:10.3390/ijms26072931

Int J Mol Sci. 2025 Mar 25;26(7):2955. doi: 10.3390/ijms26072955.

ABSTRACT

The development of novel therapeutics in neuro-oncology faces significant challenges, often marked by high costs and low success rates. Despite advances in molecular biology and genomics, targeted therapies have had limited impact on improving patient outcomes in brain tumors, particularly gliomas, due to the complex, multigenic nature of these malignancies. While significant efforts have been made to design drugs that target specific signaling pathways and genetic mutations, the clinical success of these rational approaches remains sparse. This review critically examines the landscape of neuro-oncology drug discovery, highlighting instances where serendipity has led to significant breakthroughs, such as the unexpected efficacy of repurposed drugs and off-target effects that proved beneficial. By exploring historical and contemporary cases, we underscore the role of chance in the discovery of impactful therapies, arguing that embracing serendipity alongside rational drug design may enhance future success in neuro-oncology drug development.

PMID:40243541 | DOI:10.3390/ijms26072955

Pediatr Pulmonol. 2025 Apr;60(4):e71096. doi: 10.1002/ppul.71096.

ABSTRACT

The field of pediatric rare and diffuse lung diseases continues to advance, with ongoing research deepening our understanding of the diagnosis and treatment of conditions such as children's interstitial and diffuse lung disease (chILD), non-cystic fibrosis (CF) bronchiectasis, and pulmonary complications of childhood cancer. Recent publications in Pediatric Pulmonology and other journals in 2024 have highlighted new insights into the pathophysiology, disease progression, and emerging diagnostic tools for these rare lung conditions, as well as innovative therapeutic approaches. This review features these important advancements within the context of current diagnostic practices and clinical care for pediatric patients with rare and diffuse lung diseases.

PMID:40243387 | DOI:10.1002/ppul.71096

Arterioscler Thromb Vasc Biol. 2025 Apr 17. doi: 10.1161/ATVBAHA.125.319221. Online ahead of print.

ABSTRACT

Advances in genomic technologies have significantly enhanced our understanding of both monogenic and polygenic etiologies of cardiovascular disease. In this review, we explore how the utilization of genomic information is bringing personalized medicine approaches to the forefront of cardiovascular disease management. We discuss how genomic data can resolve diagnostic uncertainty, support cascade screening, and inform treatment strategies. The role that genome-wide association studies have had in identifying thousands of risk variants for polygenic cardiovascular diseases, and how these insights, harnessed through the development of polygenic risk scores, could advance personalized risk prediction beyond traditional clinical algorithms. We detail how pharmacogenomics approaches leverage genotype information to guide drug selection and mitigate adverse events. Finally, we present the paradigm-shifting approach of gene therapy, which holds the promise of being a curative intervention for cardiovascular conditions.

PMID:40244646 | DOI:10.1161/ATVBAHA.125.319221

J Community Genet. 2025 Apr 17. doi: 10.1007/s12687-025-00794-3. Online ahead of print.

NO ABSTRACT

PMID:40244505 | DOI:10.1007/s12687-025-00794-3