Bioinformatics. 2025 Apr 2:btaf149. doi: 10.1093/bioinformatics/btaf149. Online ahead of print.

ABSTRACT

SUMMARY: In single-cell transcriptomics, inconsistent cell type annotations due to varied naming conventions and hierarchical granularity impede data integration, machine learning applications, and meaningful evaluations. To address this challenge, we developed the unified Hierarchical Annotation Framework (uHAF), which includes organ-specific hierarchical cell type trees (uHAF-T) and a mapping tool (uHAF-Agent) based on large language models. uHAF-T provides standardized hierarchical references for 38 organs, allowing for consistent label unification and analysis at different levels of granularity. uHAF-Agent leverages GPT-4 to accurately map diverse and informal cell type labels onto uHAF-T nodes, streamlining the harmonization process. By simplifying label unification, uHAF enhances data integration, supports machine learning applications, and enables biologically meaningful evaluations of annotation methods. Our framework serves as an essential resource for standardizing cell type annotations and fostering collaborative refinement in the single-cell research community.

AVAILABILITY AND IMPLEMENTATION: uHAF is publicly available at: https://uhaf.unifiedcellatlas.org and https://github.com/SuperBianC/uhaf.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:40172934 | DOI:10.1093/bioinformatics/btaf149

Mycorrhiza. 2025 Apr 2;35(2):26. doi: 10.1007/s00572-025-01188-6.

ABSTRACT

Like other plant-microbe symbioses, the establishment of orchid mycorrhiza (ORM) is likely to require specific communication and metabolic adjustments between the two partners. However, while modulation of plant and fungal metabolism has been investigated in fully established mycorrhizal tissues, the molecular changes occurring during the pre-symbiotic stages of the interaction remain largely unexplored in ORM. In this study, we investigated the pre-symbiotic responses of the ORM fungus Tulasnella sp. SV6 to plantlets of the orchid host Serapias vomeracea in a dual in vitro cultivation system. The fungal mycelium was harvested prior to physical contact with the orchid roots and the fungal transcriptome and metabolome were analyzed using RNA-seq and untargeted metabolomics approaches. The results revealed distinct transcriptomic and metabolomic remodelling of the ORM fungus in the presence of orchid plantlets, as compared to the free-living condition. The ORM fungus responds to the presence of the host plant with a significant up-regulation of genes associated with protein synthesis, amino acid and lipid biosynthesis, indicating increased metabolic activity. Metabolomic analysis supported the RNA-seq data, showing increased levels of amino acids and phospholipids, suggesting a remodelling of cell structure and signalling during the pre-symbiotic interaction. In addition, we identified an increase of transcripts of a small secreted protein that may play a role in early symbiotic signalling. Taken together, our results suggest that Tulasnella sp. SV6 may perceive information from orchid roots, leading to a readjustment of its transcriptomic and metabolomic profiles.

PMID:40172721 | DOI:10.1007/s00572-025-01188-6

Philos Trans A Math Phys Eng Sci. 2025 Apr 2;383(2293):20240412. doi: 10.1098/rsta.2024.0412. Epub 2025 Apr 2.

ABSTRACT

During infectious disease outbreaks, the time-dependent reproduction number ([Formula: see text]) can be estimated to monitor pathogen transmission. In previous work, we developed a simulation-based method for estimating [Formula: see text] from temporally aggregated disease incidence data (e.g. weekly case reports). While that approach is straightforward to use, it assumes implicitly that all cases are reported and the computation can be slow when applied to large datasets. In this article, we extend our previous approach and develop a computationally efficient simulation-based method for estimating [Formula: see text] in real-time accounting for both temporal aggregation of incidence data and under-reporting (with a fixed reporting probability per case). Using simulated data, we show that failing to consider stochastic under-reporting can lead to inappropriately precise estimates, including scenarios in which the true [Formula: see text] value lies outside inferred credible intervals more often than expected. We then apply our approach to data from the 2018 to 2020 Ebola outbreak in the Democratic Republic of the Congo (DRC), again exploring the effects of case under-reporting. Finally, we show how our method can be extended to account for temporal variations in reporting. Given information about the level of case reporting, our framework can be used to estimate [Formula: see text] during future outbreaks with under-reported and temporally aggregated case data.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.

PMID:40172553 | DOI:10.1098/rsta.2024.0412

Infect Immun. 2025 Apr 2:e0033324. doi: 10.1128/iai.00333-24. Online ahead of print.

ABSTRACT

Babesia microti is the primary cause of human babesiosis in North America. Despite the emergence of the disease in recent years, the pathogenesis and immune response to B. microti infection remain poorly understood. Studies in laboratory mice have shown a critical role for macrophages in the elimination of parasites and infected red blood cells (iRBCs). Importantly, the underlying mechanisms that activate macrophages are still unknown. Recent evidence identified the release of extracellular vesicles (EVs) from Babesia iRBCs. EVs are spherical particles released from cell membranes under natural or pathological conditions that have been suggested to play roles in host-pathogen interactions among diseases caused by protozoan parasites. The present study examined whether EVs released from cultured Babesia iRBCs could activate macrophages and alter cytokine secretion. An analysis of vesicle size in EV fractions from Babesia iRBCs showed diverse populations in the <100 nm size range compared to EVs from uninfected RBCs. In co-culture experiments, EVs released by B. microti iRBCs appeared to be associated with macrophage membranes and cytoplasm, indicating uptake of these vesicles in vitro. Interestingly, the incubation of macrophages with EVs isolated from Babesia iRBC culture supernatants resulted in the activation of NF-κB and modulation of pro-inflammatory cytokines. These results support a role for Babesia-derived EVs in macrophage activation and provide new insights into the mechanisms involved in the induction of the innate immune response during babesiosis.

PMID:40172538 | DOI:10.1128/iai.00333-24

Adv Sci (Weinh). 2025 Apr 2:e2412775. doi: 10.1002/advs.202412775. Online ahead of print.

ABSTRACT

Chronic noncommunicable diseases (NCDS) are often characterized by gradual onset and slow progression, but the difficulty in early prediction remains a substantial health challenge worldwide. This study aims to explore the interconnectedness of disease occurrence through multi-omics studies and validate it in large-scale electronic health records. In response, the research examined multi-omics data from 160 sub-healthy individuals at high altitude and then a deep learning model called Omicsformer is developed for detailed analysis and classification of routine blood samples. Omicsformer adeptly identified potential risks for nine diseases including cancer, cardiovascular conditions, and psychiatric conditions. Analysis of risk trajectories from 20 years of large clinical patients confirmed the validity of the group in preclinical risk assessment, revealing trends in increased disease risk at the time of onset. Additionally, a straightforward NCDs risk prediction system is developed, utilizing basic blood test results. This work highlights the role of multiomics analysis in the prediction of chronic disease risk, and the development and validation of predictive models based on blood routine results can help advance personalized medicine and reduce the cost of disease screening in the community.

PMID:40171841 | DOI:10.1002/advs.202412775

Ir J Med Sci. 2025 Apr 1. doi: 10.1007/s11845-025-03948-x. Online ahead of print.

ABSTRACT

BACKGROUND: This study aims to conduct a systematic review and meta-analysis of the currently present literature analyzing the effectiveness and safety profile of prusogliptin, a novel dipeptidyl peptidase-IV (DPP-4) inhibitor, as compared to placebo in type 2 diabetes mellitus (T2DM) patients.

METHODS: This systemic review and meta-analysis complied with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search strategy based on various MeSH terms was run on: PubMed/Medline, SCOPUS, and Cochrane Central, which were then systematically searched from inception till March 2024 to select all relevant Randomized Control Trials (RCT).

RESULTS: The analysis of the findings from three RCTs with 957 patients revealed that prusogliptin reduced Hemoglobin A1c (HbA1c)% levels in T2DM patients significantly [Mean Difference (MD): -0.62, 95% Confidence Interval (CI): -0.74 to -0.50, I2 = 0%, p < 0.001] and led to more patients with a HbA1c% ≤ 7% [Odds Ratio (OR): 2.65, 95%CI: 1.94 to 3.61, I2 = 0%, p < 0.00001]. However, prusogliptin led to a non-significant increase in weight when compared with placebo (MD: 0.22, 95% CI: -0.50 to 0.93, I2 = 60%, p = 0.551). The safety profile of prusogliptin revealed a non-significant decrease in treatment-emergent adverse events (OR: 0.90, 95% CI: 0.59 to 1.38, I2 = 43%, p = 0.64) and a non-significant increase in treatment-emergent serious adverse events (OR: 1.02, 95% CI: 0.43 to 2.44, I2 = 0%, p = 0.96) and drug-related adverse events (OR: 1.07, 95%CI: 0.68 to 1.69, I2 = 0%, p = 0.76).

CONCLUSION: Prusogliptin has a favorable efficacy in attaining glycemic control in patients with T2DM. However, its safety profile yields uncertain outcomes. More literature is required for a definitive result.

PMID:40172782 | DOI:10.1007/s11845-025-03948-x

Front Microbiol. 2025 Mar 18;16:1534659. doi: 10.3389/fmicb.2025.1534659. eCollection 2025.

ABSTRACT

INTRODUCTION: Streptococcus pneumoniae is a Gram-positive bacterium responsible for severe infections such as meningitis and pneumonia. The increasing prevalence of antibiotic resistance necessitates the identification of new therapeutic targets. This study aimed to discover potential drug targets against S. pneumoniae using an in silico subtractive genomics approach.

METHODS: The S. pneumoniae genome was compared to the human genome to identify non-homologous sequences using CD-HIT and BLASTp. Essential genes were identified using the Database of Essential Genes (DEG), with consideration for human gut microflora. Protein-protein interaction analyses were conducted to identify key hub genes, and gene ontology (GO) studies were performed to explore associated pathways. Due to the lack of crystal structure data, a potential target was modeled in silico and subjected to structure-based virtual screening.

RESULTS: Approximately 2,000 of the 2,027 proteins from the S. pneumoniae genome were identified as non-homologous to humans. The DEG identified 48 essential genes, which was reduced to 21 after considering human gut microflora. Key hub genes included gpi, fba, rpoD, and trpS, associated with 20 pathways. Virtual screening of 2,509 FDA-approved compounds identified Bromfenac as a leading candidate, exhibiting a binding energy of -26.335 ± 29.105 kJ/mol.

DISCUSSION: Bromfenac, particularly when conjugated with AuAgCu2O nanoparticles, has demonstrated antibacterial and anti-inflammatory properties against Staphylococcus aureus. This suggests that Bromfenac could be repurposed as a potential therapeutic agent against S. pneumoniae, pending further experimental validation. The approach highlights the potential for drug repurposing by targeting proteins essential in pathogens but absent in the host.

PMID:40170924 | PMC:PMC11958985 | DOI:10.3389/fmicb.2025.1534659

Clin Transl Med. 2025 Apr;15(4):e70290. doi: 10.1002/ctm2.70290.

ABSTRACT

Single-cell omics has emerged as a powerful tool for elucidating cellular heterogeneity in health and disease. Parallel advances in artificial intelligence (AI), particularly in pattern recognition, feature extraction and predictive modelling, now offer unprecedented opportunities to translate these insights into clinical applications. Here, we propose single-cell -omics-based Disease Predictor through AI (scDisPreAI), a unified framework that leverages AI to integrate single-cell -omics data, enabling robust disease and disease-stage prediction, alongside biomarker discovery. The foundation of scDisPreAI lies in assembling a large, standardised database spanning diverse diseases and multiple disease stages. Rigorous data preprocessing, including normalisation and batch effect correction, ensures that biological rather than technical variation drives downstream models. Machine learning pipelines or deep learning architectures can then be trained in a multi-task fashion, classifying both disease identity and disease stage. Crucially, interpretability techniques such as SHapley Additive exPlanations (SHAP) values or attention weights pinpoint the genes most influential for these predictions, highlighting biomarkers that may be shared across diseases or disease stages. By consolidating predictive modelling with interpretable biomarker identification, scDisPreAI may be deployed as a clinical decision assistant, flagging potential therapeutic targets for drug repurposing and guiding tailored treatments. In this editorial, we propose the technical and methodological roadmap for scDisPreAI and emphasises future directions, including the incorporation of multi-omics, standardised protocols and prospective clinical validation, to fully harness the transformative potential of single-cell AI in precision medicine.

PMID:40170267 | DOI:10.1002/ctm2.70290

Ann Hum Genet. 2025 Apr 2:e12596. doi: 10.1111/ahg.12596. Online ahead of print.

ABSTRACT

Pharmacological responses can vary significantly among patients from different ethnogeographic backgrounds. This variability can, at least in part, be attributed to population-specific genetic patterns in genes involved in drug absorption, distribution, metabolism, and excretion, as well as in genes associated with drug-induced toxicity. Identification of such ethnogeographic variability is thus crucial for the optimization of precise population-specific drug treatments. In this review, we summarize the current knowledge about the clinically actionable pharmacogenetic diversity of genes involved in drug metabolism (CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A5, DPYD, TPMT, NUDT15, UGT1A1, and NAT2), drug-induced hypersensitivity reactions (HLA-A and HLA-B), and drug-induced acute hemolytic anemia (G6PD). We highlight risk populations with distinct allele frequencies and discuss implications for the customization of treatment. Subsequently, we discuss key challenges and opportunities in population pharmacogenomics, including the importance of considering distinct allele frequency patterns in indigenous or founder populations, interpreting pharmacogenomic response in admixed populations, addressing the investigation bias of the pharmacogenomic literature, and difficulties in including rare and population-specific variants into drug response predictions. The information provided here underscores the critical role of population pharmacogenomics in refining pharmacological treatment strategies and aspires to provide further guidance to maximize the benefits of precision medicine across populations.

PMID:40171627 | DOI:10.1111/ahg.12596

Front Pharmacol. 2025 Mar 18;16:1577547. doi: 10.3389/fphar.2025.1577547. eCollection 2025.

NO ABSTRACT

PMID:40170722 | PMC:PMC11959060 | DOI:10.3389/fphar.2025.1577547

Ann Lab Med. 2025 Apr 2. doi: 10.3343/alm.2024.0652. Online ahead of print.

ABSTRACT

Pharmacogenomics is a rapidly evolving field with a strong foundation in basic science dating back to 1960. Pharmacogenomic findings have been translated into clinical care through collaborative efforts of clinical practitioners, pharmacists, clinical laboratories, and research groups. The methods used have transitioned from targeted genotyping of relatively few variants in individual genes to multiplexed multi-gene panels, and sequencing-based methods are likely on the horizon; however, no system exists for classifying and reporting rare variants identified via sequencing-based approaches. Laboratory testing in pharmacogenomics is complex for several genes, including cytochrome P450 2D6 (CYP2D6), HLA-A, and HLA-B, owing to a high degree of polymorphisms, homology with other genes, and copy-number variation. These loci require specialized methods and familiarity with each gene, which may persist during the transition to next-generation sequencing. Increasing implementation across laboratories and clinical facilities has required cooperative efforts to develop standard testing targets, nomenclature, and reporting practices and guidelines for applying the results clinically. Beyond standardization, harmonization between pharmacogenomics and the broader field of genomic medicine may be essential for facilitating further adoption and realizing the full potential of personalized medicine. In this review, we describe the evolution of clinical laboratory testing for pharmacogenomics, including standardization efforts and the anticipated transition from targeted genotyping to sequencing-based pharmacogenomics. We speculate on potential upcoming developments, including pharmacoepigenetics, improved understanding of the impact of non-coding variants, use of large-scale functional genomics to characterize rare variants, and a renewed interest in polygenic risk or combinatorial approaches, which will drive the progression of the field.

PMID:40170583 | DOI:10.3343/alm.2024.0652

Front Med (Lausanne). 2025 Mar 18;12:1527843. doi: 10.3389/fmed.2025.1527843. eCollection 2025.

ABSTRACT

BACKGROUND: Established morpho-functional chest magnetic resonance imaging (MRI) detects abnormalities in lung morphology and perfusion in people with cystic fibrosis (pwCF) using a dedicated scoring system. Functional assessment is performed using contrast-enhanced (CE) perfusion MRI. Novel matrix pencil decomposition MRI (MP-MRI) is a contrast agent-free alternative, but further validation of this technique is needed.

OBJECTIVES: The aim of this study was to evaluate the applicability of the validated morpho-functional chest MRI score for CE perfusion and MP perfusion MRI in a multireader approach.

METHODS: Twenty-seven pwCF (mean age 20.8 years, range 8.4-45.7 years) underwent morpho-functional MRI including CE perfusion and MP perfusion MRI in the same examination. Nine blinded chest radiologists of different experience levels assessed lung perfusion and applied the validated chest MRI score to CE- and MP-MRI. Inter-reader agreement of perfusion scores in CE- and MP-MRI were compared with each other and with the MRI morphology score. Differences according to the readers' experience were also analyzed.

RESULTS: The CE perfusion scores were overall lower than the MP perfusion scores (6.2 ± 3.3 vs. 6.9 ± 2.0; p < 0.05) with a strong correlation between both perfusion scores (r = 0.74; p < 0.01). The intraclass correlation coefficient (ICC) as measure for inter-reader agreement was good and significant for both perfusion scores, but higher for the CE perfusion score (0.75, p < 0.001) than for MP perfusion scores (0.61, p < 0.001). The Bland-Altman analysis revealed a difference in CE and MP perfusion scores with more extreme values in CE perfusion scores compared to MP perfusion scores (r = 0.62, p < 0.001). The morphology score showed a moderate to good correlation with the CE perfusion score (r = 0.73, p < 0.01) and the MP perfusion score (r = 0.55, p < 0.01). We did not find a difference in scoring according to the radiological experience level.

CONCLUSION: The established chest MRI score can be applied both to validated CE and novel MP perfusion MRI with a good interreader reliability. The remaining difference between CE and MP-MRI scores may be explained by a lack of routine in visual analysis of MP-MRI and may favor an automated analysis for use of MP-MRI as a noninvasive outcome measure.

PMID:40171501 | PMC:PMC11958188 | DOI:10.3389/fmed.2025.1527843

NEJM Catal Innov Care Deliv. 2025 Feb;6(2). doi: 10.1056/CAT.24.0095. Epub 2025 Jan 15.

ABSTRACT

Cystic fibrosis (CF) affects more than 160,000 individuals globally and has seen improved survival rates due to multidisciplinary care models and pharmacotherapy innovations. However, the associated costs remain substantial, prompting the authors to study and evaluate the expense of CF ambulatory care to understand how care structure influences costs. People with CF (PwCF) at large pediatric CF centers in both the United States and Ireland were recruited for parallel observational, prospective studies. Based upon the process of care, the lead clinicians at both sites identified and agreed on three strata of patients (0-11 months, 1-5 years, and 6-17 years of age). Process maps were developed for each of the age cohorts at each site, and the costs of ambulatory care - with emphasis on routine CF clinic visits - were measured utilizing time-driven activity-based costing (TDABC). A dollar-per-minute capacity cost rate (CCR) was calculated for all resources used in the care cycle. The total direct cost was obtained by multiplying the CCR for each resource by the time the resource was used during the patient's care cycle. The cost was summed across all resource types to obtain the cost over the entire care cycle for each site. Service operations were benchmarked to one site and variance analysis was performed. In total, 58 PwCF were included in the analysis (49 in the United States and 9 in Ireland); 4 were 0-11 months, 17 were 1-5 years, and 37 were 6-17 years of age. Physicians (United States) and respiratory consultants (Ireland) had the highest CCRs. Physicians and registered dietitians spent the most time with patients in the United States, compared with the clinical nurse specialists and dietitians in Ireland. The total variance in cost for clinical visits was largest in the 6- to 17-year-old group (28% variance, with 100% in the United States vs. 128% in Ireland). In the 6- to 17-year-old group, the largest drivers in total variance were quantity variance (variance in duration of time spent with patients), which was 108% greater in Ireland); the skill mix variance (variance in clinician type performing service for a given time), which was 49% greater in the United States; and the rate variance (variance in compensation levels across sites), which was 31% greater in the United States. The authors' use of TDABC to characterize the cost of multidisciplinary care during ambulatory clinic visits for PwCF, in combination with variance analysis (the quantitative investigation of the difference between actual and expected costs), provides new and innovative ways to compare costs across similar health care service delivery sites, providing insights into the distinctive features of each. A granular understanding of cost and comparison of resource utilization between centers provides valuable, organizationally relevant insights.

PMID:40171477 | PMC:PMC11960789 | DOI:10.1056/CAT.24.0095

Front Pediatr. 2025 Mar 18;13:1564156. doi: 10.3389/fped.2025.1564156. eCollection 2025.

ABSTRACT

INTRODUCTION: Primary Ciliary Dyskinesia (PCD) is a rare genetic disorder affecting motile cilia, leading to impaired mucociliary clearance and increased susceptibility to respiratory infections. These infections contribute to long-term complications such as bronchiectasis and lung function decline.

OBJECTIVES: This review explores both the acute and long-term impact of respiratory infections in children with PCD, while highlighting the multiple contributors to infection susceptibility. The review also evaluates emerging personalized approaches such as gene and mRNA therapy that hold promise for restoring ciliary function and reducing the burden of acute infections in pediatric PCD.

KEY FINDINGS AND CONCLUSIONS: Acute respiratory infections have a significant impact on morbidity in pediatric PCD, driving progressive airway remodeling. While current treatment strategies focus on managing infections directly, emerging therapies targeting inflammation and genetic causes hold promise for reducing infection burden and improving long-term outcomes. Future advances in personalized medicine could further enhance therapeutic approaches in this population.

PMID:40171169 | PMC:PMC11958984 | DOI:10.3389/fped.2025.1564156

Front Cell Infect Microbiol. 2025 Mar 18;15:1445660. doi: 10.3389/fcimb.2025.1445660. eCollection 2025.

ABSTRACT

Mycobacterium abscessus is a nontuberculous mycobacterium emerging as a significant pathogen in individuals with chronic lung diseases, including cystic fibrosis and chronic obstructive pulmonary disease. Current therapeutics have poor efficacy. Strategies of bacterial control based on host defenses are appealing; however, antimycobacterial immunity remains poorly understood and is further complicated by the appearance of smooth and rough morphotypes, which elicit distinct host responses. We investigated the role of serum components in neutrophil-mediated clearance of M. abscessus morphotypes. M. abscessus opsonization with complement enhanced bacterial killing compared to complement-deficient opsonization. Killing of rough isolates was less reliant on complement. Complement C3 and mannose-binding lectin 2 (MBL2) were deposited on M. abscessus morphotypes in distinct patterns, with a greater association of MBL2 on rough M. abscessus. Killing was dependent on C3; however, depletion and competition experiments indicate that canonical complement activation pathways are not involved. Complement-mediated killing relied on natural IgG and IgM for smooth morphotypes and on IgG for rough morphotypes. Both morphotypes were recognized by complement receptor 3 in a carbohydrate- and calcium-dependent manner. These findings indicate a role for noncanonical C3 activation pathways for M. abscessus clearance by neutrophils and link smooth-to-rough adaptation to complement activation.

PMID:40171164 | PMC:PMC11959001 | DOI:10.3389/fcimb.2025.1445660

Pediatr Pulmonol. 2025 Apr;60(4):e71078. doi: 10.1002/ppul.71078.

ABSTRACT

BACKGROUND: As the life expectancy of people with cystic fibrosis (PwCF) increases, understanding long-term complications, including CF-related bone disease (CFBD), is crucial.

OBJECTIVE: This study aimed to longitudinally characterize CFBD and to compare the bone status of pancreatic sufficient (PS) and pancreatic insufficient (PI) PwCF.

METHODS: This longitudinal analysis included PwCF older than 8 years of age who had at least one dual-energy X-ray absorptiometry test between 2008 and 2021. Data were collected on serum parameters of bone metabolism, nutritional history, habitual activity, and fractures in addition to other demographic and clinical characteristics.

RESULTS: The study included 80 PwCF: 32 (40%) were PS and 48 (60%) PI. Normal dual-energy X-ray absorptiometry results were found in 42 (53%) patients: 16 (50%) in the PS group and 26 (54%) in the PI group (p = 0.72). Three (9%) of the PS group and seven (15%) of the PI group had at least one Z-score below -2 (p = 0.49). The longitudinal bone density decline over a mean of 4.8 years was similar in the two groups. In a logistic regression analysis, pancreatic insufficiency was not found to be a risk factor for CFBD. Female sex was the only significant risk factor for a pathological Z-score.

CONCLUSIONS: The prevalence and severity of CFBD were not found to correlate with pancreatic sufficiency. The similar prevalence of CFBD between patients with PS and PI suggests that screening, and eventually treatment, should be offered to all PwCF, irrespective of pancreatic status.

PMID:40170622 | DOI:10.1002/ppul.71078

Br J Clin Pharmacol. 2025 Apr 2. doi: 10.1002/bcp.70027. Online ahead of print.

ABSTRACT

AIM: Dipeptidyl peptidase-1 (DPP-1) inhibitors have been studied for the treatment of neutrophil-mediated inflammatory diseases including bronchiectasis, bronchial asthma and cystic fibrosis. This study evaluated the pharmacokinetics, pharmacodynamics, safety and tolerability of DPP-1 inhibitor HSK31858 in healthy Chinese volunteers.

METHODS: Volunteers in Part A randomly received single doses of HSK31858 (15, 40, 60 and 80 mg) or placebo in fasted states. The 40-mg cohort also received HSK31858 40 mg or placebo in fed states. In Part B, volunteers randomly received HSK31858 10, 20 and 40 mg or placebo once daily for 28 days in fasted states. The primary endpoints were safety and tolerability of HSK31858.

RESULTS: Among 38 volunteers in Part A and 36 in Part B, HSK31858 was well tolerated; no deaths, serious adverse events, or discontinuations due to adverse events occurred. The median Tmax was 0.75 to 1.0 h and the mean terminal t1/2 was 16.5 to 21.0 h in the fasted state with single doses of HSK31858. Both Cmax and AUC0-t exhibited a dose-dependent rise. Food had no effect on AUC. Multiple doses of HSK31858 demonstrated a similar pharmacokinetics profile, with about 2-fold accumulation in AUC. HSK31858 dose-dependently inhibited neutrophil count-normalized neutrophil elastase (NEnorm) activity. The maximal percentage decrease in NEnorm activity relative to baseline during 28 days of HSK31858 treatments was 13.6% and 76.4% with HSK31858 10 and 40 mg once-daily, respectively.

CONCLUSION: HSK31858 was safe and well tolerated. The pharmacokinetics and pharmacodynamics profile of HSK31858 supports further clinical development for the treatment of neutrophil-mediated inflammatory diseases.

TRIAL REGISTRATION: NCT05663593.

PMID:40170587 | DOI:10.1002/bcp.70027

Ther Adv Respir Dis. 2025 Jan-Dec;19:17534666251329792. doi: 10.1177/17534666251329792. Epub 2025 Apr 1.

NO ABSTRACT

PMID:40170358 | DOI:10.1177/17534666251329792

Front Plant Sci. 2025 Mar 18;16:1498913. doi: 10.3389/fpls.2025.1498913. eCollection 2025.

ABSTRACT

The decline of insect biomass, including pollinators, represents a significant ecological challenge, impacting both biodiversity and ecosystems. Effective monitoring of pollinator habitats, especially floral resources, is essential for addressing this issue. This study connects drone and deep learning technologies to their practical application in ecological research. It focuses on simplifying the application of these technologies. Updating an object detection toolbox to TensorFlow (TF) 2 enhanced performance and ensured compatibility with newer software packages, facilitating access to multiple object recognition models - Faster Region-based Convolutional Neural Network (Faster R-CNN), Single-Shot-Detector (SSD), and EfficientDet. The three object detection models were tested on two datasets of UAV images of flower-rich grasslands, to evaluate their application potential in practice. A practical guide for biologists to apply flower recognition to Unmanned Aerial Vehicle (UAV) imagery is also provided. The results showed that Faster RCNN had the best overall performance with a precision of 89.9% and a recall of 89%, followed by EfficientDet, which excelled in recall but at a lower precision. Notably, EfficientDet demonstrated the lowest model complexity, making it a suitable choice for applications requiring a balance between efficiency and detection performance. Challenges remain, such as detecting flowers in dense vegetation and accounting for environmental variability.

PMID:40171479 | PMC:PMC11959073 | DOI:10.3389/fpls.2025.1498913

Cureus. 2025 Mar 1;17(3):e79871. doi: 10.7759/cureus.79871. eCollection 2025 Mar.

ABSTRACT

Artificial intelligence (AI) has rapidly progressed in the last decade and will inevitably become incorporated into trauma and surgical systems. In such settings, surgeons often need to make high-stakes, time-sensitive, and complex decisions with limited or uncertain information. AI has great potential to augment the pre-operative, intra-operative, and post-operative phases of trauma care. Despite the expeditious advancement of AI, many surgeons lack a foundational understanding of AI terminology, its processes, and potential applications in clinical practice. This narrative review aims to educate general surgeons about the basics of AI, highlight its applications in thoraco-abdominal trauma, and discuss the implications of incorporating its use into the Australian health care system. This review found that studies of AI in trauma care have predominantly focused on machine learning and deep learning applied to diagnostics, risk prediction, and decision-making. Other subfields of AI include natural language processing and computer vision. While AI tools have many potential applications in trauma care, current clinical use is limited. Future prospective, locally validated research is required prior to incorporating AI into clinical practice.

PMID:40171361 | PMC:PMC11958818 | DOI:10.7759/cureus.79871