Int J Stem Cells. 2025 Apr 7. doi: 10.15283/ijsc25011. Online ahead of print.

ABSTRACT

The etiology of chronic and lethal interstitial lung disease, termed idiopathic pulmonary fibrosis (IPF), remains unidentified. IPF induces pathological lung scarring that results in rigidity and impairs gas exchange, eventually resulting in premature mortality. Recent findings indicate that deubiquitinating enzymes play a key role in stabilizing fibrotic proteins and contribute to pulmonary fibrosis. The ubiquitin-specific protease 11 (USP11) promotes pro-fibrotic proteins, and its expression elevated in tissue samples from patients with IPF. Thus, this study aimed to examine the effects of loss of function of USP11 gene on the progression of pulmonary fibrosis by utilizing 3D cell culture alveolar organoids (AOs) that replicate the structure and functions of the proximal and distal airways and alveoli. Here, we applied the CRISPR/Cas9 system to knock out the USP11 gene in human induced pluripotent stem cells (hiPSCs) and then differentiated these hiPSCs into AOs. Loss of USP11 gene resulted in abnormalities in type 2 alveolar epithelial cells in the hiPSC-USP11KO-AOs. Moreover, knock out of the USP11 mitigates pulmonary fibrosis caused by TGF-β in hiPSC-USP11KO-AOs by reducing collagen formation and fibrotic markers, suggesting it has the therapeutic potential to treat IPF patients.

PMID:40189830 | DOI:10.15283/ijsc25011

Oncol Res. 2025 Mar 19;33(4):821-836. doi: 10.32604/or.2024.053772. eCollection 2025.

ABSTRACT

Immunotherapies have demonstrated notable clinical benefits in the treatment of cervical cancer (CC). However, the development of therapeutic resistance and diverse adverse effects in immunotherapy stem from complex interactions among biological processes and factors within the tumor immune microenvironment (TIME). Advanced omic technologies offer novel insights into a more expansive and thorough layer of the TIME. Furthermore, integrating multidimensional omics within the frameworks of systems biology and computational methodologies facilitates the generation of interpretable data outputs to characterize the clinical and biological trajectories of tumor behavior. In this review, we present advanced omics technologies that utilize various clinical samples to address scientific inquiries related to immunotherapies for CC, highlighting their utility in identifying metastasis dissemination, recurrence risk, and therapeutic resistance in patients treated with immunotherapeutic approaches. This review elaborates on the strategy for integrating multi-omics data through artificial intelligence algorithms. Additionally, an analysis of the obstacles encountered in the multi-omics analysis process and potential avenues for future research in this domain are presented.

PMID:40191729 | PMC:PMC11964870 | DOI:10.32604/or.2024.053772

Front Genet. 2025 Mar 21;16:1543056. doi: 10.3389/fgene.2025.1543056. eCollection 2025.

ABSTRACT

INTRODUCTION: Rapid advances in personalized medicine and direct-to-consumer genomic applications could increase the risk that physicians will apply genomic results inappropriately. To address a persistent lack of understanding of genomics, we implemented a pathology-supported genetic testing (PSGT) approach, guided by insights from a clinician needs assessment conducted in 2010.

METHODS: Findings from the previous clinician survey were used to develop a new patient screening tool that integrates non-communicable disease (NCD) and post-COVID-19 care pathways. In parallel to the application of this solution for stratification of patients in different treatment groups, an updated version of the original survey questionnaire was used to reassess the knowledge and willingness of healthcare professionals to apply PSGT.

RESULTS: Thirty-six respondents completed the revised needs assessment survey in October 2022, while attending a genomics session at the Annual General Practitioner Congress, Stellenbosch University, South Africa. Nearly 89% of the respondents reported having insufficient knowledge to offer genetic testing; 80% were supportive of using PSGT to differentiate inherited from lifestyle- or therapy-associated NCDs and 83.3% supported integrating wellness screening with genetic testing to identify high-risk individuals.

DISCUSSION: It appears that while clinicians are interested in learning about genomics, they continue to report significant knowledge deficits in this area, highlighting the need for targeted clinician training and tools like multidisciplinary NCD-COVID pathway analysis to improve clinical decision-making. The co-development of a genomic counseling report for ongoing studies, guided the selection of Long COVID patients for whole-genome sequencing across the illness and wellness domains.

PMID:40191609 | PMC:PMC11970434 | DOI:10.3389/fgene.2025.1543056

NAR Genom Bioinform. 2025 Apr 4;7(2):lqaf031. doi: 10.1093/nargab/lqaf031. eCollection 2025 Jun.

ABSTRACT

Defective interfering particles (DIPs) are viral deletion mutants that hamper virus replication and are, thus, potent novel antiviral agents. To evaluate possible antiviral treatments, we first need to get a deeper understanding of DIP characteristics. Thus, we performed a meta-analysis of 20 already published sequencing datasets of influenza A and B viruses (IAV and IBV) from in vivo and in vitro experiments. We analyzed each dataset for characteristics, such as deletion-containing viral genome (DelVG) length distributions, direct repeats, and nucleotide enrichment at the deletion site. Our analysis suggests differences in the length of the 3'- and 5'-end retained in IAV and IBV viral sequences upon deletion. Moreover, in vitro DelVGs tend to be shorter than those in vivo, which is a novel finding with potential implications for future DIP treatment design. Additionally, our analysis demonstrates the presence of DelVGs with longer than expected sequences, possibly related to an alternative mechanism of DelVG formation. Finally, a joint ranking of DelVGs originating from 7 A/Puerto Rico/8/1934 datasets revealed 11 highly abundant, yet unnoticed, candidates. Together, our study highlights the importance of meta-analyses to uncover yet unknown DelVG characteristics and to pre-select candidates for antiviral treatment design.

PMID:40191586 | PMC:PMC11970370 | DOI:10.1093/nargab/lqaf031

Front Psychol. 2025 Mar 21;16:1587001. doi: 10.3389/fpsyg.2025.1587001. eCollection 2025.

ABSTRACT

[This corrects the article DOI: 10.3389/fpsyg.2025.1548905.].

PMID:40191577 | PMC:PMC11970553 | DOI:10.3389/fpsyg.2025.1587001

Front Psychol. 2025 Feb 19;16:1548905. doi: 10.3389/fpsyg.2025.1548905. eCollection 2025.

ABSTRACT

INTRODUCTION: The present study aimed to evaluate the impact of time-out on power-play outcomes both in elite senior and youth matches and in relation to final (margin of victory, MoV) and current (margin of advantage, MoA) match scores (i.e., winning in unbalanced games, MW; winning-draw-losing in close games, W-D-L; losing in unbalanced games, ML).

MATERIALS AND METHODS: A total of 97 (seniors, n = 50; youth, n = 47) European Championship matches were analyzed, comparing power-plays preceded or not by a time-out in relation to the following offensive indicators: goal, exclusion, penalty, and no-goal.

RESULTS: The results reported that both senior and youth levels have been characterized by better power-play outcomes without time-out (higher goals scored: senior, p ≤ 0.01, youth, p ≤ 0.001; and lower "no goal" events: p ≤ 0.01, youth, p ≤ 0.01). Similar trends were observed with respect to the MoV. Specifically, in senior close games, there were both significantly higher goals scored (p ≤ 0.05) and fewer 'no goal' events (p ≤ 0.05), and these patterns were also evident among youth losing teams in unbalanced games. Differently, for MoA, both higher goals scored (p ≤ 0.01) and lower "no goal" events (p ≤ 0.01) emerged for senior losing teams in unbalanced games and youth close games (higher goals scored, p ≤ 0.01; and lower "no goal" events, p ≤ 0.05).

DISCUSSION: Therefore, the present study demonstrated that time-out tends to limit the success of the following power-play action and that MoV and MoA approaches do not overlap. As a consequence, coaches could benefit from these findings by being more aware of the actual time-out consequences on the following power-play as well as their defensive potentialities when the opponents call time-out.

PMID:40191572 | PMC:PMC11970554 | DOI:10.3389/fpsyg.2025.1548905

Res Pharm Sci. 2025 Feb 20;20(1):121-141. doi: 10.4103/RPS.RPS_128_23. eCollection 2025 Feb.

ABSTRACT

BACKGROUND AND PURPOSE: Colorectal cancer (CRC) holds the position of being the third most prevalent cancer and the second primary cause of cancer-related fatalities on a global scale. Approximately 65% of CRC patients survive for 5 years following diagnosis. Metastasis and recurrence frequently occur in half of CRC patients diagnosed at the late stage. This study used bioinformatics analysis to identify key signaling pathways, hub genes, transcription factors, and protein kinases involved in transforming primary CRC with liver metastasis potential. Prognostic markers in CRC were also identified.

EXPERIMENTAL APPROACH: The GSE81582 dataset was re-analyzed to identify differentially expressed genes (DEGs) in early CRC compared to non-tumoral tissues. A protein interaction network (PIN) was constructed, revealing significant modules and hub genes. Prognostic markers, transcription factors, and protein kinases were determined. Boxplot and gene set enrichment analyses were performed.

FINDINGS/RESULTS: This study identified 1113 DEGs in primary CRC compared to healthy controls. PIN analysis revealed 75 hub genes and 8 significant clusters associated with early CRC. The down-regulation of SUCLG2 and KPNA2 correlated with poor prognosis. SIN3A and CDK6 played crucial roles in early CRC transformation, affecting rRNA processing pathways.

CONCLUSION AND IMPLICATIONS: This study demonstrated several pathways, biological processes, and genes mediating the malignant transformation of healthy colorectal tissues to primary CRC and may help the prognosis and treatment of patients with early CRC.

PMID:40190820 | PMC:PMC11972027 | DOI:10.4103/RPS.RPS_128_23

J Sci Med Sport. 2025 Mar 14:S1440-2440(25)00067-2. doi: 10.1016/j.jsams.2025.03.004. Online ahead of print.

ABSTRACT

OBJECTIVES: To analyze the effects of different PA intensities during active learning on cognitive performance and physical fitness in preschool children.

DESIGN: Cluster randomized controlled trial.

METHODS: Four classrooms (n = 99 children aged 3-6 years) were randomly allocated to two intervention groups that performed either light PA (LPA, n = 26) or moderate-to-vigorous PA (MVPA, n = 25) during foreign language (English) lessons, or to a control group (n = 48) that maintained their usual sedentary lessons. The intervention consisted of two 45-min lessons per week and was performed over a 10-week period. Children's PA levels and intensity during sessions were assessed through accelerometry. Primary outcomes included the retention of foreign language vocabulary (free- and cued-recall tests), cognitive performance (BENCI battery), and physical fitness (PREFIT battery).

RESULTS: Both LPA and particularly MVPA groups resulted in greater total PA levels and intensity compared with the control group (p < 0.001) and provided significantly larger benefits in the free-recall test and verbal memory (all p < 0.05 compared to the control group). Additionally, MVPA group provided larger benefits in the free- and cued-recall tests, speed agility and cardiorespiratory fitness (all p < 0.05 compared to LPA).

CONCLUSIONS: Physically active learning appears as an effective strategy for enhancing foreign language vocabulary, cognitive performance, and physical fitness in preschool children. Increasing PA intensity seems to maximize these benefits.

PMID:40189956 | DOI:10.1016/j.jsams.2025.03.004

J Chem Theory Comput. 2025 Apr 6. doi: 10.1021/acs.jctc.5c00121. Online ahead of print.

ABSTRACT

We have developed and tested an absolute free energy perturbation (FEP) protocol, which combines all-atom molecular dynamics, replica exchange with solute tempering (REST) enhanced sampling, and a spherical harmonic restraint applied to a ligand. Our objective was to compute the binding free energy together with the underlying binding mechanism for a ligand, which binds diffusively to a protein. Such ligands represent nearly impossible targets for traditional FEP simulations. To test our FEP/REST protocol, we selected a conserved motif peptide KKPK termed minNLS from the nuclear localization signal sequence of the Venezuelan equine encephalitis virus capsid protein. This peptide fragment binds diffusively to importin-α transport protein without forming well-defined poses. Our FEP/REST simulations with a spherical restraint provided a converged estimate of minNLS binding free energy. We found that minNLS binds with moderate affinity to importin-α utilizing an unusual, purely entropic mechanism in which binding free energy is determined by favorable entropic gain. For this cationic minNLS peptide, a favorable binding entropic gain is primarily associated with the release of water from the solvation shells of charged amino acids. We demonstrated that FEP/REST simulations sample the KKPK bound ensemble well, allowing us to characterize the distribution of bound structures, binding interactions, and locations on the importin-α surface. Analysis of experimental studies offered support to our rationale behind the KKPK entropic binding mechanism.

PMID:40189800 | DOI:10.1021/acs.jctc.5c00121

Front Pharmacol. 2025 Mar 21;16:1549526. doi: 10.3389/fphar.2025.1549526. eCollection 2025.

ABSTRACT

Drug-induced liver injury (DILI) results from the liver toxicity caused by drugs or their metabolites. Gallic acid (GA) is a naturally occurring secondary metabolite found in many fruits, plants, and nuts. Recently, GA has drawn increasing attention due to its potent pharmacological properties, particularly its anti-inflammatory and antioxidant capabilities. To the best of our knowledge, this is the first review to focus on the pharmacological properties of GA and related molecular activation mechanisms regarding protection against hepatotoxicity. We also provide a thorough explanation of the physicochemical properties, fruit sources, toxicity, and pharmacokinetics of GA after reviewing a substantial number of studies. Pharmacokinetic studies have shown that GA is quickly absorbed and eliminated when taken orally, which restricts its use in development. However, the bioavailability of GA can be increased by optimizing its structure or changing its form of administration. Notably, according to toxicology studies conducted on a range of animals and clinical trials, GA rarely exhibits toxicity or side effects. The antioxidation mechanisms mainly involved Nrf2, while anti-inflammatory mechanisms involved MAPKs and NF-κB signaling pathways. Owing to its marked pharmacological properties, GA is a prospective candidate for the management of diverse xenobiotic-induced hepatotoxicity. We also discuss the applications of cutting-edge technologies (nano-delivery systems, network pharmacology, and liver organoids) in DILI. In addition to guiding future research and development of GA as a medicine, this study offers a theoretical foundation for its clinical application.

PMID:40191418 | PMC:PMC11968354 | DOI:10.3389/fphar.2025.1549526

Curr Med Res Opin. 2025 Apr 7:1-17. doi: 10.1080/03007995.2025.2470755. Online ahead of print.

ABSTRACT

Some current prostate cancer (PCa) treatment regimens are known to have adverse effects on bone, for example androgen deprivation therapy (ADT), and on cardiovascular health, for example ADT and antiandrogen therapy. Strengthened recommendations for the practical assessment and management of bone and cardiovascular health in men with PCa are needed. This review aims to provide practical guidance for healthcare providers along the continuum of patient care on the management of bone and cardiovascular health in men with PCa undergoing ADT and antiandrogen therapy based on real-world evidence. Evidence was identified by searching PubMed for publications that reported the effects of PCa treatment on bone or cardiovascular health in a real-world setting and were published between January 2017 and August 2023. Review articles were excluded. The evidence identified indicates that ADT decreases bone mineral density (BMD) and increases the risk of osteoporosis and fractures. Bone-protecting agents (BPAs) are effective at improving bone health in patients undergoing ADT and antiandrogen therapy at all stages of the PCa pathway. Despite this, the use and timing of initiation of BPAs are variable. Furthermore, real-world studies have confirmed an association between ADT and cardiovascular risk. As survival outcomes improve, maintenance of bone and cardiovascular health is increasingly important in men with PCa. Risk is a continuous variable that must be assessed throughout the continuum of PCa treatment. Therefore, all men starting ADT should be assessed for bone and cardiovascular risk. Lifestyle adjustments, dietary supplementation and pharmacological intervention may be advised.

PMID:40190143 | DOI:10.1080/03007995.2025.2470755

Sci Rep. 2025 Apr 6;15(1):11776. doi: 10.1038/s41598-025-90533-y.

ABSTRACT

Accurate segmentation of biomedical time-series, such as intracardiac electrograms, is vital for understanding physiological states and supporting clinical interventions. Traditional rule-based and feature engineering approaches often struggle with complex clinical patterns and noise. Recent deep learning advancements offer solutions, showing various benefits and drawbacks in segmentation tasks. This study evaluates five segmentation algorithms, from traditional rule-based methods to advanced deep learning models, using a unique clinical dataset of intracardiac signals from 100 patients. We compared a rule-based method, a support vector machine (SVM), fully convolutional semantic neural network (UNet), region proposal network (Faster R-CNN), and recurrent neural network for electrocardiographic signals (DENS-ECG). Notably, Faster R-CNN has never been applied to 1D signals segmentation before. Each model underwent Bayesian optimization to minimize hyperparameter bias. Results indicated that deep learning models outperformed traditional methods, with UNet achieving the highest segmentation score of 88.9 % (root mean square errors for onset and offset of 8.43 ms and 7.49 ms), closely followed by DENS-ECG at 87.8 %. Faster R-CNN and SVM showed moderate performance, while the rule-based method had the lowest accuracy (77.7 %). UNet and DENS-ECG excelled in capturing detailed features and handling noise, highlighting their potential for clinical application. Despite greater computational demands, their superior performance and diagnostic potential support further exploration in biomedical time-series analysis.

PMID:40189617 | DOI:10.1038/s41598-025-90533-y

J Microbiol Methods. 2025 Apr 4:107125. doi: 10.1016/j.mimet.2025.107125. Online ahead of print.

ABSTRACT

Conventional clinical microbiological techniques are enhanced by the introduction of artificial intelligence (AI). Comprehensive data processing and analysis enabled the development of curated datasets that has been effectively used in training different AI algorithms. Recently, a number of machine learning (ML) and deep learning (DL) algorithms are developed and evaluated using diverse microbiological datasets. These datasets included spectral analysis (Raman and MALDI-TOF spectroscopy), microscopic images (Gram and acid fast stains), and genomic and protein sequences (whole genome sequencing (WGS) and protein data banks (PDBs)). The primary objective of these algorithms is to minimize the time, effort, and expenses linked to conventional analytical methods. Furthermore, AI algorithms are incorporated with quantitative structure-activity relationship (QSAR) models to predict novel antimicrobial agents that address the continuing surge of antimicrobial resistance. During the COVID-19 pandemic, AI algorithms played a crucial role in vaccine developments and the discovery of new antiviral agents, and introduced potential drug candidates via drug repurposing. However, despite their significant benefits, the implementation of AI encounters various challenges, including ethical considerations, the potential for bias, and errors related to data training. This review seeks to provide an overview of the most recent applications of artificial intelligence in clinical microbiology, with the intention of educating a wider audience of clinical practitioners regarding the current uses of machine learning algorithms and encouraging their implementation. Furthermore, it will discuss the challenges related to the incorporation of AI into clinical microbiology laboratories and examine future opportunities for AI within the realm of infectious disease epidemiology.

PMID:40188989 | DOI:10.1016/j.mimet.2025.107125

Med Image Anal. 2025 Apr 5;102:103567. doi: 10.1016/j.media.2025.103567. Online ahead of print.

ABSTRACT

Artifacts in histology images pose challenges for accurate diagnosis with deep learning models, often leading to misinterpretations. Existing artifact restoration methods primarily rely on Generative Adversarial Networks (GANs), which approach the problem as image-to-image translation. However, those approaches are prone to mode collapse and can unexpectedly alter morphological features or staining styles. To address the issue, we propose ArtiDiffuser, a counterfactual diffusion model tailored to restore only artifact-distorted regions while preserving the integrity of the rest of the image. Additionally, we show an innovative perspective by addressing the misdiagnosis stemming from artifacts via artifact synthesis as data augmentation, and thereby leverage ArtiDiffuser to unify the artifact synthesis and the restoration capabilities. This synergy significantly surpasses the performance of conventional methods which separately handle artifact restoration or synthesis. We propose a Swin-Transformer denoising network backbone to capture both local and global attention, further enhanced with a class-guided Mixture of Experts (MoE) to process features related to specific artifact categories. Moreover, it utilizes adaptable class-specific tokens for enhanced feature discrimination and a mask-weighted loss function to specifically target and correct artifact-affected regions, thus addressing issues of data imbalance. In downstream applications, ArtiDiffuser employs a consistency regularization strategy that assures the model's predictive accuracy is maintained across original and artifact-augmented images. We also contribute the first comprehensive histology dataset, comprising 723 annotated patches across various artifact categories, to facilitate further research. Evaluations on four distinct datasets for both restoration and synthesis demonstrate ArtiDiffuser's effectiveness compared to GAN-based approaches, used for either pre-processing or augmentation. The code is available at https://github.com/wagnchogn/ArtiDiffuser.

PMID:40188685 | DOI:10.1016/j.media.2025.103567

Environ Int. 2025 Mar 30;198:109427. doi: 10.1016/j.envint.2025.109427. Online ahead of print.

ABSTRACT

Wastewater-based epidemiology (WBE) represents a powerful technique for quantifying the attenuation characteristics and consumption of pharmaceuticals. In addition to WBE, no further methods have been developed to assess the wastewater stability related to antidepressants (ADs). In this study, the biodegradability, solubility, and adsorption or partition of 66 ADs were objectively scored according to the relevant guidelines of the Organisation for Economic Cooperation and Development. An assessment framework and the MSSL-RealFormer classification model of ADs wastewater stability were constructed based on physicochemical properties to predict the ADs wastewater stability and the quantitative structure-activity relationship. The constructed MSSL-RealFormer classification model exhibited a markedly higher prediction accuracy than traditional methods. Furthermore, 15 high-stable ADs in wastewater with low biodegradability, high solubility, and low adsorption or partition were identified. SHapley Additive exPlanation method demonstrated that group hydrophobicity, electrostatic and van der Waals forces exerted a significant influence on the ADs wastewater stability. And molecular stability was found to be significantly correlated with the ADs wastewater stability. A combination of density functional theory and MSSL-RealFormer classification model was employed to identify 17 high-stable transformation products of nine medium- and low-stable ADs in wastewater. The Ecological Structure Activity Relationships model demonstrated that bupropion, tapentadol and chlorpheniramine exhibited significant acute toxicity to the aquatic food chain. In this study, a novel deep learning model was constructed to rapidly screen the correlation between the ADs wastewater stability and their molecular structures. It is anticipated to prove a favorable tool for optimizing the wastewater stability screening of pharmaceuticals.

PMID:40188602 | DOI:10.1016/j.envint.2025.109427

Comput Methods Programs Biomed. 2025 Mar 27;265:108728. doi: 10.1016/j.cmpb.2025.108728. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: In the medical field of digital pathology, many tasks rely on visual assessments of tissue patterns or cells, presenting an opportunity to apply computer vision methods. However, acquiring a substantial number of annotations for developing deep learning algorithms remains a bottleneck. The annotation process is inherently biased due to various constraints, including labor shortages, high costs, time inefficiencies, and a strongly imbalanced distribution of labels. This study explores available solutions for reducing the costs of annotation bootstrapping in the challenging task of follicular lymphoma diagnosis.

METHODS: We compare three distinct approaches to annotation bootstrapping: extensive manual annotations, active learning, and weak supervision. We propose a hybrid architecture for centroblast and centrocyte detection from whole slide images, based on a custom cell encoder and contextual encoding derived from foundation models for digital pathology. We collected a dataset of 41 whole slide images scanned with a 20x objective lens and resolution 0.24μm/pixel, from which 12,704 cell annotations were gathered.

RESULTS: Applying our proposed active learning workflow led to an almost twofold increase in the number of samples within the minority class. The best bootstrapping method improved the overall performance of the detection algorithm by 18 percentage points, yielding a macro-averaged F1-score, precision, and recall of 63%.

CONCLUSIONS: The results of this study may find applications in other digital pathology problems, particularly for tasks involving a lack of homogeneous cell clusters within whole slide images.

PMID:40188578 | DOI:10.1016/j.cmpb.2025.108728

BMC Med. 2025 Apr 7;23(1):200. doi: 10.1186/s12916-025-04022-0.

ABSTRACT

BACKGROUND: Influenza A virus (IAV) is a major cause of seasonal and global pandemics, posing serious health risks. Repositioning approved drugs offers an efficient antiviral strategy, particularly as calcium (Ca2⁺) is crucial for IAV infection, making Ca2⁺ channel blockers (CCBs) promising candidates for antiviral agents.

METHODS: The in vitro antiviral activity of cilnidipine was evaluated using MTT assays, qRT-PCR, plaque assays, and western blotting. Mechanistic studies involved time-of-addition, viral internalization, pseudovirus neutralization, and HA (hemagglutinin) syncytium assays. For in vivo analysis, BALB/c mice were intranasally infected to evaluate the effects of cilnidipine on viral titer, lung index, pulmonary inflammatory mediators, and survival rate.

RESULTS: In vitro, cilnidipine exhibits antiviral activity against IAV during the early stages of infection. It disrupts clathrin- and caveolin-mediated endocytosis to inhibit the internalization of IAV and interacts with the viral HA2 subunit to impede virus membrane fusion. Additionally, cilnidipine suppresses the PI3K-AKT and p38 MAPK pathways activated by IAV infections. In vivo, cilnidipine reduces virus titers and lung index, ameliorates lung pathology, and inhibits pulmonary inflammatory mediator expression, improving survival rates.

CONCLUSIONS: These findings highlight the promising anti-IAV properties of cilnidipine both in vitro and in vivo, suggesting its potential as a clinical agent for emergencies against influenza outbreaks.

PMID:40189517 | DOI:10.1186/s12916-025-04022-0

J Microbiol Methods. 2025 Apr 4:107125. doi: 10.1016/j.mimet.2025.107125. Online ahead of print.

ABSTRACT

Conventional clinical microbiological techniques are enhanced by the introduction of artificial intelligence (AI). Comprehensive data processing and analysis enabled the development of curated datasets that has been effectively used in training different AI algorithms. Recently, a number of machine learning (ML) and deep learning (DL) algorithms are developed and evaluated using diverse microbiological datasets. These datasets included spectral analysis (Raman and MALDI-TOF spectroscopy), microscopic images (Gram and acid fast stains), and genomic and protein sequences (whole genome sequencing (WGS) and protein data banks (PDBs)). The primary objective of these algorithms is to minimize the time, effort, and expenses linked to conventional analytical methods. Furthermore, AI algorithms are incorporated with quantitative structure-activity relationship (QSAR) models to predict novel antimicrobial agents that address the continuing surge of antimicrobial resistance. During the COVID-19 pandemic, AI algorithms played a crucial role in vaccine developments and the discovery of new antiviral agents, and introduced potential drug candidates via drug repurposing. However, despite their significant benefits, the implementation of AI encounters various challenges, including ethical considerations, the potential for bias, and errors related to data training. This review seeks to provide an overview of the most recent applications of artificial intelligence in clinical microbiology, with the intention of educating a wider audience of clinical practitioners regarding the current uses of machine learning algorithms and encouraging their implementation. Furthermore, it will discuss the challenges related to the incorporation of AI into clinical microbiology laboratories and examine future opportunities for AI within the realm of infectious disease epidemiology.

PMID:40188989 | DOI:10.1016/j.mimet.2025.107125

Respir Med. 2025 Apr 4:108076. doi: 10.1016/j.rmed.2025.108076. Online ahead of print.

ABSTRACT

The European Cystic Fibrosis Society-Clinical Trials Network (ECFS-CTN) herein proposes guidance for the use of chest CT-scans for the regular monitoring of lung disease in CF. Statements were completed in a 3-step process: the questions were identified via an anonymous online survey, followed by a comprehensive literature search, and a final Delphi process. The guidance recommends the use of ultra-low dose CT scans (effective radiation dose, 0.08 mSv; equivalent to 2 to 4 chest X-rays), tracking of patients' cumulative radiation and effective communication strategies using "de-medicalised" information for shared decision making. Chest CT scans (with lung volume monitoring) are not recommended systematically in both children and adults. Ultimate responsibility for justifying a chest CT scan lies with the individual professionals directly involved, the final decision being influenced by indications, costs, expertise, available material, resources and/or the patient's values, as well as possible impact on treatment modalities.

PMID:40189162 | DOI:10.1016/j.rmed.2025.108076

Respir Med. 2025 Apr 4:108087. doi: 10.1016/j.rmed.2025.108087. Online ahead of print.

ABSTRACT

BACKGROUND: Diagnostic tests for cystic fibrosis (CF) are not readily available in resource-limited settings, which leads to delay in diagnosis and treatment of children with CF.

METHODS: In a multicentric prospective study, children with recurrent/ persistent pneumonia, failure to thrive, or steatorrhea with suspicion of CF were enrolled. Sweat chloride concentration was measured to confirm the diagnosis of CF. Diagnostic accuracy of various clinical features, ancillary laboratory investigations (serum electrolytes, blood gas, stool fat globules), and aquagenic wrinkling for CF diagnosis was estimated. CF clinical diagnostic score (CF-CDS) was developed by combining significant parameters in stepwise logistic regression.

RESULTS: Of 860 children enrolled, 313 (36.7%) were diagnosed with CF. History of a sibling with CF, clubbing, hyponatremia, metabolic alkalosis, stool fat globule positivity, sputum Pseudomonas isolation, and aquagenic wrinkling within 3 minutes were found to be independently associated with a diagnosis of CF. CF-CDS score developed by combining these parameters demonstrated excellent diagnostic accuracy for diagnosis of CF [AUROC of 0.923 (95%CI: 0.899, 0.946)]. At a cut-off of ≥ 2.5, CF-CDS had sensitivity and specificity of 87.64% and 81.02%, respectively.

CONCLUSION: CF-CDS has excellent diagnostic accuracy for diagnosis of CF in children and can be used to decide on starting treatment of CF pending confirmatory tests when confirmatory tests are not readily available.

PMID:40189161 | DOI:10.1016/j.rmed.2025.108087