Antibiotics (Basel). 2025 Feb 25;14(3):231. doi: 10.3390/antibiotics14030231.

ABSTRACT

Background/Objectives:Streptococcus pneumoniae (S. pneumoniae) is a major pathogen causing severe infectious diseases, with an escalating issue of antimicrobial resistance that threatens the efficacy of existing antibiotics. Given the challenges in developing traditional antibiotics, drug repurposing strategies offer a novel approach to address the resistance crisis. This study aims to evaluate the antibacterial and anti-biofilm activities of the approved non-antibiotic anticancer drug carmofur against multidrug-resistant S. pneumoniae, and investigate the mechanism of action, and assess therapeutic potential in vivo. Methods/Results: Antimicrobial tests revealed that carmofur exhibited strong antibacterial activity against multidrug-resistant S. pneumoniae strains, with minimum inhibitory concentrations (MICs) ranging from 0.25 to 1 µg/mL. In the biofilm detection experiments, carmofur not only inhibited the formation of biofilms, but also effectively removed biofilms under high concentration conditions. Mechanistic studies showed that carmofur disrupted bacterial membrane permeability and decreased intracellular ATP levels. Molecular docking and dynamics simulation assays indicated that carmofur could stably bind to thymidylate synthase through hydrogen bonding and hydrophobic interactions, thereby exerting antibacterial effects. Meanwhile, carmofur was able to repress the expression of the thyA gene at the mRNA level. In a mouse infection model, the carmofur treatment group showed a reduction of approximately two log levels in bacterial load in lung tissue and blood, a significant decrease in the levels of inflammatory cytokines TNF-α and IL-6, and an improvement in survival rate to 60%. Conclusions: In summary, carmofur demonstrated significant antibacterial and anti-biofilm activities against multidrug-resistant S. pneumoniae and showed good anti-infective effects in vivo, suggesting its potential clinical application as a therapeutic agent against drug-resistant bacteria.

PMID:40149043 | DOI:10.3390/antibiotics14030231

Biol Direct. 2025 Mar 27;20(1):38. doi: 10.1186/s13062-025-00631-0.

ABSTRACT

BACKGROUND: Acute kidney injury (AKI) remains a critical condition with limited therapeutic options, predominantly managed by renal replacement therapy. The challenge of developing targeted treatments persists.

METHODS: We integrated genetic data related to druggable proteins and gene expression with AKI genome-wide association study (GWAS) findings. Based on multi-omics Mendelian randomization (MR), we identified the potential causal influence of 5,883 unique proteins and genes on AKI. We also performed using reverse MR and external cohort-based analysis to verify the robustness of this causal relationship. Expression patterns of these targets were examined using bulk transcriptome and single-cell transcriptome data. In addition, drug repurposing analyses were conducted to explore the potential of existing medications. We also constructed a molecular interaction network to explore the interplay between identified targets and known drugs.

RESULTS: Genetically predicted levels of seven proteins and twelve genes were associated with an increased risk of AKI. Of these, six targets (NCF1, TNFRSF1B, APEH, ACADSB, ADD1, and FAM3B) were prioritized based on robust evidence and validated in independent cohorts. Reverse MR showed a one-way causal relationship of targets. These targets are predominantly expressed in proximal tubular cells, endothelial cells, collecting duct-principal cells, and immune cells within both AKI-affected and normal tissues. Several promising drug repurposing opportunities were identified, such as telmisartan-NCF1, calcitriol-ACADSB, and ethinyl estradiol-ACADSB. The molecular interaction mapping and pathway integration analysis provided further insights, suggesting potential strategies for combinatorial therapies.

CONCLUSIONS: This extensive investigation identified several promising therapeutic targets for AKI and highlighted opportunities for drug repurposing. These findings offer valuable insights that could shape future research and the development of targeted treatments.

PMID:40148878 | DOI:10.1186/s13062-025-00631-0

Sci Rep. 2025 Mar 27;15(1):10606. doi: 10.1038/s41598-025-94283-9.

ABSTRACT

The 2019 pandemic of coronavirus disease (COVID-19) caused by SARS-CoV-2 led to millions of deaths worldwide since its emergence. The viral genomic material can code structural and non-structural proteins including the main protease or 3CLpro, a cysteine protease that cleavages the viral polyprotein generating 11 proteins that participate in viral pre-replication. Thus, 3CLpro is a promising therapeutic target for SARS-CoV-2 inhibition by new drugs or drug repositioning because 3CLpro is dissimilar to human proteases. We conducted in vitro assays demonstrating the modulation activity of ambenonium, a drug already used in Myasthenia gravis that acts by inhibiting the action of acetylcholinesterase, and had its potential inhibitory activity against viral replication pointed out in a previous in silico study. In concentrations of 100 µM, 50 µM, 25 µM, 10 µM, and 1 µM there was no inhibition in the formation of lysis plates, with a slight increase in the genome copy number at the higher concentrations evaluated. However, in the concentrations of 0,1 µM and 0,01 µM, there was a reduction in the number of lysis plates. This behavior suggests that the ambenonium acts as a modulator of viral activity in vitro. To investigate potential conformational changes in the protein between dimeric and monomeric forms in the presence of the compound, a local docking analysis was performed. Results indicated this conformational shift is possible, though further studies are needed to confirm these findings.

PMID:40148508 | DOI:10.1038/s41598-025-94283-9

Sci Rep. 2025 Mar 27;15(1):10563. doi: 10.1038/s41598-024-83178-w.

ABSTRACT

Despite the abundance of large-scale molecular and drug-response data, current research on early-onset Parkinson's disease (EOPD) markers often lacks mechanistic interpretations of drug-gene relationships, limiting our understanding of how drugs exert their therapeutic effects. While existing studies provide valuable EOPD markers, the mechanisms by which targeted drugs act remain poorly understood. We propose DTI-Prox, a novel workflow that identifies potentially overlooked EOPD markers and suggests relevant drug targets. DTI-Prox employs network proximity to measure how closely connected a drug and gene are within a biological network. Additionally, node similarity, which assesses the functional resemblance between network nodes, reveals meaningful drug-gene connections. DTI-Prox identifies 417 novel drug-target pairs and four previously unreported EOPD markers (PTK2B, APOA1, A2M, and BDNF), demonstrating significant pathway enrichment in neurodegenerative processes. Notably, shared pathway analysis shows that prioritized drugs such as Amantadine, Apomorphine, Atropine, Benztropine, Biperiden, Bromocriptine, Cabergoline, Carbidopa, and Citalopram, currently used for other conditions, interact with key EOPD-associated diagnostic markers, suggesting their potential for drug repurposing. The constructed functional network's validity is reinforced by statistically significant drug-target pairs. The findings provide new insights into EOPD drug mechanisms and identify promising therapeutic candidates, potentially leading to more effective, personalized treatment approaches for EOPD patients.

PMID:40148390 | DOI:10.1038/s41598-024-83178-w

BMC Cancer. 2025 Mar 28;25(1):558. doi: 10.1186/s12885-025-13934-2.

ABSTRACT

BACKGROUND: Cancers affecting < 6/100,000/year are classified as rare, but they account for up to 25% of all cancers and are associated with worse 5-year survival than common cancers. Early-phase clinical trials (EPCTs) may represent a viable treatment option for patients with rare cancers as they have evolved significantly with novel designs and the increasing use of precision medicine.

METHODS: A retrospective study of patients with rare cancers referred to a large EPCT team at a UK specialist centre over 5 years (2016-2020) was conducted. Patient demographics, medical and oncological history, genomic variants, EPCT participation, responses and survival outcomes were analysed.

RESULTS: In total, 240 patients with rare cancers were included. The mean age at diagnosis was 51.7 years (range 16-84), 54.2% of the patients were female. The most frequent rare cancers originated from the digestive system (27.1%), female genital tract (20%) and head and neck (H + N) (18.3%). Molecular profiling was offered to 45.5% of the population, median number of gene alterations was 3 per patient (range 1-20) while actionable gene alterations were reported in 60.2% (n = 50) of those with identified gene aberrations. Fifty-one patients participated in EPCTs, with 39.2% achieving SD and 11.8% PR. Median PFS for trial participants was three months (95% CI 1.12 - 4.88) while median OS in the trial patients was 16 months (95% CI 9.10 - 22.90) compared to 7 months for non-trial participants (95% CI 5.50 - 8.51). Finally, poor Royal Marsden Hospital (RMH) prognostic score (2-3) was correlated with worse survival when controlling for age and sex (HR 1.714, 95% CI 1.19 - 2.46, p = 0.004).

CONCLUSIONS: Participation of patients with rare cancers in EPCTs may be associated with a survival benefit and lead to the development of new treatments for these patients. Moreover, expanded use of precision medicine is paramount as it can inform targeted treatment selection in this heterogenous group.

PMID:40148757 | DOI:10.1186/s12885-025-13934-2

Biomedicines. 2025 Feb 21;13(3):553. doi: 10.3390/biomedicines13030553.

ABSTRACT

Background/Objectives: The COVID-19 pandemic resulted in 675 million cases and 6.9 million deaths by 2022. Despite substantial declines in case fatalities following widespread vaccination campaigns, the threat of future coronavirus outbreaks remains a concern. Current treatments for COVID-19 have been repurposed from existing therapies for other infectious and non-infectious diseases. Emerging evidence suggests a role for genetic factors in both susceptibility to SARS-CoV-2 infection and response to treatment. However, comprehensive studies correlating clinical outcomes with genetic variants are lacking. The main aim of our study is the identification of host genetic biomarkers that predict the clinical outcome of COVID-19 pharmacological treatments. Methods: In this study, we present findings from GWAS and candidate gene and pathway enrichment analyses leveraging diverse patient samples from the Spanish Coalition to Unlock Research of Host Genetics on COVID-19 (SCOURGE), representing patients treated with immunomodulators (n = 849), corticoids (n = 2202), and the combined cohort of both treatments (n = 2487) who developed different outcomes. We assessed various phenotypes as indicators of treatment response, including survival at 90 days, admission to the intensive care unit (ICU), radiological affectation, and type of ventilation. Results: We identified significant polymorphisms in 16 genes from the GWAS and candidate gene studies (TLR1, TLR6, TLR10, CYP2C19, ACE2, UGT1A1, IL-1α, ZMAT3, TLR4, MIR924HG, IFNG-AS1, ABCG1, RBFOX1, ABCB11, TLR5, and ANK3) that may modulate the response to corticoid and immunomodulator therapies in COVID-19 patients. Enrichment analyses revealed overrepresentation of genes involved in the innate immune system, drug ADME, viral infection, and the programmed cell death pathways associated with the response phenotypes. Conclusions: Our study provides an initial framework for understanding the genetic determinants of treatment response in COVID-19 patients, offering insights that could inform precision medicine approaches for future epidemics.

PMID:40149530 | DOI:10.3390/biomedicines13030553

Genes (Basel). 2025 Mar 17;16(3):349. doi: 10.3390/genes16030349.

ABSTRACT

In an obesogenic environment, such as the one we have been experiencing in recent decades, epigenetics provides answers to the relationship between hereditary and environmentally acquired patterns that have significantly contributed to the global rise in obesity prevalence. MicroRNA (miRNA) constitutes a diminutive non-coding small RNA molecule, 20 to 24 nucleotides in length, that functions as a regulator of gene regulation at the post-translational level. Circulating miRNAs (c-miRNAs) have been detected in multiple body fluids, including blood, plasma, serum, saliva, milk from breastfeeding mothers, and urine. These molecules hold significant therapeutic value and serve as extracellular biomarkers in metabolic diseases. They aid in the diagnosis and tracking of therapy responses, as well as dietary and physical habit modifications. Researchers have studied c-miRNAs as potential biomarkers for diagnosing and characterizing systemic diseases in people of all ages and backgrounds since then. These conditions encompass dyslipidemia, type 2 diabetes mellitus (T2DM), cardiovascular risk, metabolic syndrome, cardiovascular diseases, and obesity. This review therefore analyzes the usefulness of c-miRNAs as therapeutic markers over the past decades. It also provides an update on c-miRNAs associated with general obesity and overweight, as well as with the most prevalent pathologies in the adult population. It also examines the effect of different nutritional approaches and physical activity regarding the activity of miRNAs in circulation in adults with overweight or general obesity. All of this is done with the aim of evaluating their potential use as biomarkers in various research contexts related to overweight and obesity in adults.

PMID:40149500 | DOI:10.3390/genes16030349

Genes (Basel). 2025 Mar 6;16(3):314. doi: 10.3390/genes16030314.

ABSTRACT

Chronic Obstructive Pulmonary Disease (COPD) manifests as a genetically diverse and intricate lung condition with various subtypes. The development of the disease and response to treatment are influenced by the interplay between genetic and environmental factors. The predominant therapeutic approaches include bronchodilator therapy and corticosteroid treatment. Studies in COPD pharmacogenetics involve genome-wide association (GWA) studies, gene profiling, whole-genome sequencing, and other omics-based investigations. Many of these investigations have focused on the association between genetic variations and the response to β2 agonist treatment. Additionally, several studies have explored the impact of gene variations on the response to inhaled corticosteroid (ICS) treatment, with a specific focus on polymorphisms in the glucocorticoid receptor (GR) signaling pathway. However, a significant challenge lies in the inconclusive or inconsistent results of these pharmacogenetic studies, underscoring the research community's struggle to provide sufficient evidence for the clinical implementation of COPD pharmacogenetics. To address these challenges, further research and larger genome-wide studies are essential. These efforts aim to uncover additional COPD subtypes, identify predictors of treatment response, and discover novel genetic markers for COPD. The integration of genomics, detailed evaluations such as chest CT scans, spirometry tests, and blood analyses, along with DNA collection in clinical research, is critical for translating COPD pharmacogenetics into clinical practice. Furthermore, advancing our understanding of the complex interactions between genetics, phenotypes, and environmental factors will be pivotal for improving individualized prognostic assessments and enhancing treatment outcomes in COPD.

PMID:40149465 | DOI:10.3390/genes16030314

Genes (Basel). 2025 Feb 24;16(3):265. doi: 10.3390/genes16030265.

ABSTRACT

Background/Objectives: The accurate prediction of adverse drug reactions (ADRs) to oncological treatments still poses a clinical challenge. Chemotherapy is usually selected based on clinical trials that do not consider patient variability in ADR risk. Consequently, many patients undergo multiple treatments to find the appropriate medication or dosage, enhancing ADR risks and increasing the chance of discontinuing therapy. We first aimed to develop a pharmacogenetic model for predicting chemotherapy-induced ADRs in cancer patients (the ANTIBLASTIC DRUG MULTIPANEL PLATFORM) and then to assess its feasibility and validate this model in patients with non-small-cell lung cancer (NSCLC) undergoing oncological treatments. Methods: Seventy NSCLC patients of all stages that needed oncological treatment at our facility were enrolled, reflecting the typical population served by our institution, based on geographic and demographic characteristics. Treatments followed existing guidelines, and patients were continuously monitored for adverse reactions. We developed and used a multipanel platform based on 326 SNPs that we identified as strongly associated with response to cancer treatments. Subsequently, a network-based algorithm to link these SNPs to molecular and biological functions, as well as efficacy and adverse reactions to oncological treatments, was used. Results: Data and blood samples were collected from 70 NSCLC patients. A bioinformatic analysis of all identified SNPs highlighted five clusters of patients based on variant aggregations and the associated genes, suggesting potential susceptibility to treatment-related toxicity. We assessed the feasibility of the platform and technically validated it by comparing NSCLC patients undergoing the same course of treatment with or without ADRs against the cluster combination. An odds ratio analysis confirmed the correlation between cluster allocation and increased ADR risk, indicating specific treatment susceptibilities. Conclusions: The ANTIBLASTIC DRUG MULTIPANEL PLATFORM was easily applicable and able to predict ADRs in NSCLC patients undergoing oncological treatments. The application of this novel predictive model could significantly reduce adverse drug reactions and improve the rate of chemotherapy completion, enhancing patient outcomes and quality of life. Its potential for broader prescription management suggests significant treatment improvements in cancer patients.

PMID:40149417 | DOI:10.3390/genes16030265

Cancers (Basel). 2025 Mar 17;17(6):1008. doi: 10.3390/cancers17061008.

ABSTRACT

Cancer, characterized by the uncontrolled proliferation of cells, is one of the leading causes of death globally, with approximately one in five people developing the disease in their lifetime. While many driver genes were identified decades ago, and most cancers can be classified based on morphology and progression, there is still a significant gap in knowledge about genetic aberrations and nuclear DNA damage. The study of two critical groups of genes-tumor suppressors, which inhibit proliferation and promote apoptosis, and oncogenes, which regulate proliferation and survival-can help to understand the genomic causes behind tumorigenesis, leading to more personalized approaches to diagnosis and treatment. Aberration of tumor suppressors, which undergo two-hit and loss-of-function mutations, and oncogenes, activated forms of proto-oncogenes that experience one-hit and gain-of-function mutations, are responsible for the dysregulation of key signaling pathways that regulate cell division, such as p53, Rb, Ras/Raf/ERK/MAPK, PI3K/AKT, and Wnt/β-catenin. Modern breakthroughs in genomics research, like next-generation sequencing, have provided efficient strategies for mapping unique genomic changes that contribute to tumor heterogeneity. Novel therapeutic approaches have enabled personalized medicine, helping address genetic variability in tumor suppressors and oncogenes. This comprehensive review examines the molecular mechanisms behind tumor-suppressor genes and oncogenes, the key signaling pathways they regulate, epigenetic modifications, tumor heterogeneity, and the drug resistance mechanisms that drive carcinogenesis. Moreover, the review explores the clinical application of sequencing techniques, multiomics, diagnostic procedures, pharmacogenomics, and personalized treatment and prevention options, discussing future directions for emerging technologies.

PMID:40149342 | DOI:10.3390/cancers17061008

Cancers (Basel). 2025 Mar 7;17(6):913. doi: 10.3390/cancers17060913.

ABSTRACT

Pharmacogenomics, the study of how genetic variations influence drug response, has become integral to cancer treatment as personalized medicine evolves. This review aims to explore key pharmacogenomic biomarkers relevant to cancer therapy and their clinical implications, providing an updated and comprehensive perspective on how genetic variations impact drug metabolism, efficacy, and toxicity in oncology. Genetic heterogeneity among oncology patients significantly impacts drug efficacy and toxicity, emphasizing the importance of incorporating pharmacogenomic testing into clinical practice. Genes such as CYP2D6, DPYD, UGT1A1, TPMT, EGFR, KRAS, and BRCA1/2 play pivotal roles in influencing the metabolism, efficacy, and adverse effects of various chemotherapeutic agents, targeted therapies, and immunotherapies. For example, CYP2D6 polymorphisms affect tamoxifen metabolism in breast cancer, while DPYD variants can result in severe toxicities in patients receiving fluoropyrimidines. Mutations in EGFR and KRAS have significant implications for the use of targeted therapies in lung and colorectal cancers, respectively. Additionally, BRCA1/2 mutations predict the efficacy of PARP inhibitors in breast and ovarian cancer. Ongoing research in polygenic risk scores, liquid biopsies, gene-drug interaction networks, and immunogenomics promises to further refine pharmacogenomic applications, improving patient outcomes and reducing treatment-related adverse events. This review also discusses the challenges and future directions in pharmacogenomics, including the integration of computational models and CRISPR-based gene editing to better understand gene-drug interactions and resistance mechanisms. The clinical implementation of pharmacogenomics has the potential to optimize cancer treatment by tailoring therapies to an individual's genetic profile, ultimately enhancing therapeutic efficacy and minimizing toxicity.

PMID:40149251 | DOI:10.3390/cancers17060913

Sci Rep. 2025 Mar 27;15(1):10563. doi: 10.1038/s41598-024-83178-w.

ABSTRACT

Despite the abundance of large-scale molecular and drug-response data, current research on early-onset Parkinson's disease (EOPD) markers often lacks mechanistic interpretations of drug-gene relationships, limiting our understanding of how drugs exert their therapeutic effects. While existing studies provide valuable EOPD markers, the mechanisms by which targeted drugs act remain poorly understood. We propose DTI-Prox, a novel workflow that identifies potentially overlooked EOPD markers and suggests relevant drug targets. DTI-Prox employs network proximity to measure how closely connected a drug and gene are within a biological network. Additionally, node similarity, which assesses the functional resemblance between network nodes, reveals meaningful drug-gene connections. DTI-Prox identifies 417 novel drug-target pairs and four previously unreported EOPD markers (PTK2B, APOA1, A2M, and BDNF), demonstrating significant pathway enrichment in neurodegenerative processes. Notably, shared pathway analysis shows that prioritized drugs such as Amantadine, Apomorphine, Atropine, Benztropine, Biperiden, Bromocriptine, Cabergoline, Carbidopa, and Citalopram, currently used for other conditions, interact with key EOPD-associated diagnostic markers, suggesting their potential for drug repurposing. The constructed functional network's validity is reinforced by statistically significant drug-target pairs. The findings provide new insights into EOPD drug mechanisms and identify promising therapeutic candidates, potentially leading to more effective, personalized treatment approaches for EOPD patients.

PMID:40148390 | DOI:10.1038/s41598-024-83178-w

Bioengineering (Basel). 2025 Mar 3;12(3):254. doi: 10.3390/bioengineering12030254.

ABSTRACT

Pancreatitis is a prominent and severe type of inflammatory disorder that has grabbed a lot of scientific and clinical interest to prevent its onset. It should be detected early to avoid the development of serious complications, which occur due to long-term damage to the pancreas. The accurate measurement of biomarkers that are released from the pancreas during inflammation is essential for the detection and early treatment of patients with severe acute and chronic pancreatitis, but this is sub-optimally performed in clinically relevant practices, mainly due to the complexity of the procedure and the cost of the treatment. Clinically available tests for the early detection of pancreatitis are often time-consuming. The early detection of pancreatitis also relates to disorders of the exocrine pancreas, such as cystic fibrosis in the hereditary form and cystic fibrosis-like syndrome in the acquired form of pancreatitis, which are genetic disorders with symptoms that can be correlated with the overexpression of specific markers such as creatinine in biological fluids like urine. In this review, we studied how to develop a minimally invasive system using hydrogel-based biosensors, which are highly absorbent and biocompatible polymers that can respond to specific stimuli such as enzymes, pH, temperature, or the presence of biomarkers. These biosensors are helpful for real-time health monitoring and medical diagnostics since they translate biological reactions into quantifiable data. This paper also sheds light on the possible use of Ayurvedic formulations along with hydrogels as a treatment strategy. These analytical devices can be used to enhance the early detection of severe pancreatitis in real time.

PMID:40150718 | DOI:10.3390/bioengineering12030254

Children (Basel). 2025 Feb 25;12(3):277. doi: 10.3390/children12030277.

ABSTRACT

Background/Objectives: Home spirometry allows people with cystic fibrosis (CF) to monitor their lung function from home. However, there are concerns about its feasibility and validity compared to traditional clinic spirometry. The aim of this study was to evaluate the feasibility and validity of telehealth spirometry for patients with CF living in a regional setting. Methods: This retrospective study included forty-eight people with cystic fibrosis (pwCF) aged 6-33 years. Participants performed home spirometry using a portable flow sensor spirometer over a one-year period, without supervision. Spirometry readings from portable spirometers were compared with the nearest in-clinic spirometry using the intra-correlation coefficient (ICC) and Bland-Altman plots. Data were collected over a period of one year, with regular intervals of measurements. Results: In 427 of the 877 (48.6%) attempted sessions, successful spirometry at home was recorded. Although we showed good reliability between at-home and in-clinic measurements using the Bland-Altman plots and intraclass correlation co-efficient (ICC) (values ranged from 0.76 to 0.88), analysis of the 117 pairs of at-home and in-clinic spirometries showed that mean differences of forced expiratory volume in the 1st sec (FEV1) and forced vital capacity (FVC) obtained at home (both in liter and z-score) had, on average, lower values than the corresponding values at the clinic. Conclusions: Home-based telehealth spirometry is feasible among pwCF and provides advantages, especially for those from remote or secluded areas. However, lower values in FVC and FEV1 obtained through home spirometry should not be used interchangeably with clinic values.

PMID:40150559 | DOI:10.3390/children12030277

Biomedicines. 2025 Feb 22;13(3):558. doi: 10.3390/biomedicines13030558.

ABSTRACT

Background/Objectives: Patients suffering from rare diseases are particularly vulnerable to vitamin D deficiency. The role of vitamin D status in rare disease management remains insufficiently investigated and employed in routine clinical practice. Methods: This review analyses current data on vitamin D status in selected rare diseases of organs involved in vitamin D metabolism: skin (epidermolysis bullosa, morphea), liver (autoimmune hepatitis, primary biliary cholangitis, primary sclerosing cholangitis), kidney (Alport syndrome, Fabry disease), and cystic fibrosis as a model of a systemic rare disease. Additionally, this review critically examines potential drug-vitamin D interactions in the context of rare disease patient polypharmacy. Results: Evidence suggests that vitamin D deficiency is prevalent in rare disease patient populations, often at once exacerbating and being simultaneously exacerbated by the underlying condition. Vitamin D deficiency correlates with worse clinical outcomes and lower quality of life across the examined diseases. Immunoregulatory properties of vitamin D appear relevant for rare diseases with autoimmune components. Conclusions: An urgent need for developing disease-specific clinical practice guidelines, implementing routine vitamin D monitoring in rare disease patient care, and introducing tailored supplementation under the principles of precision medicine is emphasized.

PMID:40149535 | DOI:10.3390/biomedicines13030558

Antibiotics (Basel). 2025 Mar 18;14(3):317. doi: 10.3390/antibiotics14030317.

ABSTRACT

Background:Nocardia species are an emergent pathogen in people with CF (pwCF) or bronchiectasis. Their clinical role and management remain unclear, and their isolation is a challenge. In this paper, we describe four cases of Nocardia detection, in two pwCF and two patients with non-CF bronchiectasis or primary ciliary dyskinesia (PCD). Methods: We conducted a multicenter retrospective study, involving pwCF and non-CF people with bronchiectasis who presented with a Nocardia detection and were followed at three CF Italian centers (Florence, Verona, and Cerignola). Results:Nocardia detection was associated with clinical and radiological respiratory exacerbation and decline in lung function. In one CF patient, Nocardia was not detected in sputum cultures after starting Elexacaftor-Tezacaftor-Ivacaftor therapy. Conclusions: Managing Nocardia detection in patients with underlying lung diseases such as CF, PCD, or bronchiectasis presents significant challenges for clinicians.

PMID:40149127 | DOI:10.3390/antibiotics14030317

Antibiotics (Basel). 2025 Mar 14;14(3):302. doi: 10.3390/antibiotics14030302.

ABSTRACT

Pseudomonas aeruginosa is one of the leading causes of nosocomial respiratory tract infections, significantly affecting morbidity and mortality. It can persist in the lungs of patients with cystic fibrosis (CF) for extended periods because of its adaptive capacity. The main aim of this study was to determine the phenotypic and genotypic resistance to antibiotics of clinical isolates of P. aeruginosa that persist in patients with CF receiving long-term antimicrobial therapy. The study included nine strains of P. aeruginosa isolated from the sputum of patients with CF admitted to the hospital. Susceptibility to antibiotics was determined using the European Committee on Antimicrobial Susceptibility Testing (EUCAST) criteria. Whole-genome sequencing was performed for phylogeny, sequence typing, and to identify antibiotic-resistant genes. The study showed that during long-term persistence in the lungs of patients receiving antibacterial therapy, the restoration of susceptibility to antibiotics occurred in some cases. Multilocus sequence typing and phylogeny revealed six sequence types. Functional annotation identified 72 genes responsible for resistance to antibacterial and chemical substances, with either chromosomal or plasmid localisation.

PMID:40149112 | DOI:10.3390/antibiotics14030302

Technol Cancer Res Treat. 2025 Jan-Dec;24:15330338251327081. doi: 10.1177/15330338251327081. Epub 2025 Mar 28.

ABSTRACT

The aim was to evaluate a deep learning-based auto-segmentation method for liver delineation in Y-90 selective internal radiation therapy (SIRT). A deep learning (DL)-based liver segmentation model using the U-Net3D architecture was built. Auto-segmentation of the liver was tested in CT images of SIRT patients. DL auto-segmented liver contours were evaluated against physician manually-delineated contours. Dice similarity coefficient (DSC) and mean distance to agreement (MDA) were calculated. The DL-model-generated contours were compared with the contours generated using an Atlas-based method. Ratio of volume (RV, the ratio of DL-model auto-segmented liver volume to manually-delineated liver volume), and ratio of activity (RA, the ratio of Y-90 activity calculated using a DL-model auto-segmented liver volume to Y-90 activity calculated using a manually-delineated liver volume), were assessed. Compared with the contours generated with the Atlas method, the contours generated with the DL model had better agreement with the manually-delineated contours, which had larger DSCs (average: 0.94 ± 0.01 vs 0.83 ± 0.10) and smaller MDAs (average: 1.8 ± 0.4 mm vs 7.1 ± 5.1 mm). The average RV and average RA calculated using the DL-model-generated volumes are 0.99 ± 0.03 and 1.00 ± 0.00, respectively. The DL segmentation model was able to identify and segment livers in the CT images and provide reliable results. It outperformed the Atlas method. The model can be applied for SIRT procedures.

PMID:40152005 | DOI:10.1177/15330338251327081

Network. 2025 Mar 28:1-23. doi: 10.1080/0954898X.2025.2476079. Online ahead of print.

ABSTRACT

An accurate classification of brain tumour disease is an important function in diagnosing cancer disease. Several deep learning (DL) methods have been used to identify and categorize the tumour illness. Nevertheless, the better categorized result was not consistently obtained by the traditional DL procedures. Therefore, a superior answer to this problem is offered by the optimized DL approaches. Here, the brain tumour categorization (BTC) is done using the devised Hybrid Fruit Bee Optimization based Deep Convolution Neural Network (HFBO-based DCNN). Here, the noise in the image is removed through pre-processing using a Gaussian filter. Next, the feature extraction process is done using the SegNet and this helps to extract the relevant data from the input image. Then, the feature selection is done with the help of the HFBO algorithm. Additionally, the brain tumour classification is done by the Deep CNN, and the established HFBO algorithm is used to train the weight. The devised model is analysed using the testing accuracy, sensitivity, and specificity and produced the values of 0.926, 0.926, and 0.931, respectively.

PMID:40151966 | DOI:10.1080/0954898X.2025.2476079

CRISPR J. 2025 Mar 27. doi: 10.1089/crispr.2024.0058. Online ahead of print.

ABSTRACT

Design of guide RNA (gRNA) with high efficiency and specificity is vital for successful application of the CRISPR gene editing technology. Although many machine learning (ML) and deep learning (DL)-based tools have been developed to predict gRNA activities, a systematic and unbiased evaluation of their predictive performance is still needed. Here, we provide a brief overview of in silico tools for CRISPR design and assess the CRISPR datasets and statistical metrics used for evaluating model performance. We benchmark seven ML and DL-based CRISPR-Cas9 editing efficiency prediction tools across nine CRISPR datasets covering six cell types and three species. The DL models CRISPRon and DeepHF outperform the other models exhibiting greater accuracy and higher Spearman correlation coefficient across multiple datasets. We compile all CRISPR datasets and in silico prediction tools into a GuideNet resource web portal, aiming to facilitate and streamline the sharing of CRISPR datasets. Furthermore, we summarize features affecting CRISPR gene editing activity, providing important insights into model performance and the further development of more accurate CRISPR prediction models.

PMID:40151952 | DOI:10.1089/crispr.2024.0058