Anal Chem. 2025 Apr 29. doi: 10.1021/acs.analchem.4c06014. Online ahead of print.

ABSTRACT

The efficiency and resolution of dispersive spectrometers play crucial roles in optical spectroscopy. Achieving optimal analytical performance in optical spectroscopy requires striking a delicate balance between employing a narrow spectrometer input slit to enhance spectral resolution while sacrificing throughput or utilizing a wider slit to increase throughput at the expense of resolution. Here, we introduce a spectrometer slit empowered by a deep learning model SlitNET. We trained a neural network to reconstruct synthetic Raman spectra with enhanced resolution from low-resolution inputs. Subsequently, we performed transfer learning from synthetic data to experimental Raman data of materials. By fine-tuning the model with experimental data, we recovered high-resolution Raman spectra. This enhancement enabled us to distinguish between materials that were previously indistinguishable when using a wide slit. SlitNET achieved a resolution enhancement equivalent to employing a 10 μm slit size but with a physical input slit of 100 μm. This, in turn, enables us to simultaneously achieve high throughput and resolution, thereby enhancing the analytic sensitivity and specificity in optical spectroscopy. The incorporation of deep learning into spectrometers highlights the convergence of photonic instrumentation and artificial intelligence, offering improved measurement accuracy across various optical spectroscopy applications.

PMID:40298458 | DOI:10.1021/acs.analchem.4c06014

Biomedicines. 2025 Mar 27;13(4):808. doi: 10.3390/biomedicines13040808.

ABSTRACT

Great progress has been made in the treatment of pulmonary arterial hypertension (WHO group 1 PAH) over the past two decades, which has significantly improved the morbidity and mortality in this patient population. Likewise, the more recent availability of antifibrotic medications for interstitial lung disease (ILD) have also been effective in slowing down the progression of disease. There is no known cure for either of these disease states. When this combination coexists, treatment can be challenging. Interstitial lung disease is a heterogenous group of chronic inflammatory and/or fibrotic parenchymal lung disorders. A subset of patients with ILD, not related to connective tissue disease, can initially present with inflammatory-predominant disease which progresses to irreversible fibrosis. This population of patients is also at risk for developing pulmonary hypertension (PH) or World Health Organization (WHO) group 3 PH. This coexistence of ILD and PH is associated with early morbidity and mortality. The early identification, diagnosis, and treatment of this combination of ILD and PH is vital. Medications available for both ILD and PH require an individualized approach with the intention of slowing down disease progression. Referral to expert centers for clinical trials and transplant evaluation is recommended. The combination of PH-ILD can be challenging to diagnose and treat effectively. Patients require a thorough clinical evaluation to enable the most accurate diagnosis. A vital part of that evaluation is the early recognition of PH. Medications can help improve disease progression along with clinical trials that will further improve our gaps in knowledge.

PMID:40299367 | DOI:10.3390/biomedicines13040808

Biomedicines. 2025 Mar 26;13(4):796. doi: 10.3390/biomedicines13040796.

ABSTRACT

Rationale: The role of the innate immune system in idiopathic pulmonary fibrosis (IPF) remains poorly understood. However, a functional myeloid compartment is required to remove dying cells and cellular debris, as well as to mediate innate immune responses against pathogens. Aberrant macrophage activity has been described in patients with post-acute sequelae of COVID fibrosis (PASC-F), and caveolin scaffolding domain (CSD) peptides have been found to attenuate inflammation and fibrosis in mouse lung injury models. Therefore, we examined, for the first time, the effects of CSD peptide LTI-2355 on the functional and synthetic properties of human myeloid cells isolated from lung explant tissue of donor lungs as well as IPF and PASC-F lung explant tissue. Methods and Results: CD45+ myeloid cells isolated from lung explant tissue from IPF and PASC-F patients exhibited an impaired capacity to clear autologous dead cells and cellular debris. The uptake of pathogen-coated bioparticles was impaired in myeloid cells from both fibrotic patient groups independent of the type of pathogen, highlighting an intrinsic functional cell impairment. LTI-2355 improved the phagocytic activity of both IPF and PASC-F myeloid cells, and this improvement was paired with decreased proinflammatory and pro-fibrotic synthetic activity. LTI-2355 was also shown to primarily target CD206-expressing IPF and PASC-F myeloid cells. Conclusions: Primary myeloid cells from IPF and PASC-F patients exhibit dysfunctional phagocytic and synthetic properties that are modulated by LTI-2355. LTI-2355 treatment of IPF myeloid cells resulted in significantly reduced sCD163, IFN-α2, IFN-γ, IL-2, IL-10, IL-12p40, and MMP-1 in the cell supernatant. This study highlights an additional mechanism of action of the CSD peptide in the treatment of IPF and progressive fibrotic lung disease.

PMID:40299362 | DOI:10.3390/biomedicines13040796

Discov Oncol. 2025 Apr 29;16(1):632. doi: 10.1007/s12672-025-02395-5.

ABSTRACT

OBJECTIVE: Invasive lung cancer staging poses significant challenges, often requiring painful and costly biopsy procedures. This study aims to identify non-invasive biomarkers for detecting bronchogenic carcinoma and its various stages by analyzing gene expression data using bioinformatics and machine learning techniques. By leveraging these advanced computational methods, we seek to eliminate the need for surgical intervention in the diagnostic process.

METHODS: We utilized the TCGA-LUAD dataset, including gene expression data from healthy and cancerous samples. To identify robust biomarkers, we applied eight metaheuristic algorithms for feature selection, combined with four classification methods and two data fusion techniques to optimize performance.

RESULTS: Our approach achieved 100% accuracy in distinguishing healthy samples from cancerous ones, outperforming existing methods that reported 97% accuracy. Notably, while prior methods have struggled to separate bronchogenic carcinoma stages effectively, our research achieved an approximate accuracy of 77% in stage classification. Furthermore, using gene enrichment methods, we identified 5, 7, and 16 diagnostic biomarker candidates for stages I, II, III, and IV, respectively.

CONCLUSION: This study demonstrates that integrating bioinformatics, gene set enrichment, and biological pathway analysis can enable non-invasive diagnostics for bronchogenic carcinoma stages. These findings hold promise for developing alternatives to traditional, invasive staging systems, potentially improving patient outcomes and reducing healthcare costs.

PMID:40299256 | DOI:10.1007/s12672-025-02395-5

Antioxidants (Basel). 2025 Apr 18;14(4):486. doi: 10.3390/antiox14040486.

ABSTRACT

Transforming Growth Factor-Beta 1 (TGF-β1) plays a pivotal role in the differentiation of fibroblasts into myofibroblasts, which is a critical process in tissue repair, fibrosis, and wound healing. Upon exposure to TGF-β1, fibroblasts acquire a contractile phenotype and secrete collagen and extracellular matrix components. Numerous studies have identified hydrogen peroxide (H2O2) as a key downstream effector of TGF-β1 in this pathway. H2O2 functions as a signalling molecule, regulating various cellular processes mostly through post-translational redox modifications of cysteine thiol groups of specific proteins. In this study, we used primary human skin fibroblast cultures to investigate the oxidative mechanisms triggered by TGF-β1. We analyzed the expression of redox-related genes, evaluated the effects of the genetic and pharmacological inhibition of H2O2-producing enzymes, and employed an unbiased redox proteomics approach (OxICAT) to identify proteins undergoing reversible cysteine oxidation. Our findings revealed that TGF-β1 treatment upregulated the expression of oxidant-generating genes while downregulating antioxidant genes. Low concentrations of diphenyleneiodonium mitigated myofibroblast differentiation and mitochondrial oxygen consumption, suggesting the involvement of a flavoenzyme in this process. Furthermore, we identified the increased oxidation of highly conserved cysteine residues in key proteins such as the epidermal growth factor receptor, filamin A, fibulin-2, and endosialin during the differentiation process. Collectively, this study provides insights into the sources of H2O2 in fibroblasts and highlights the novel redox mechanisms underpinning fibroblast-to-myofibroblast differentiation.

PMID:40298862 | DOI:10.3390/antiox14040486

J Acquir Immune Defic Syndr. 2025 Apr 29. doi: 10.1097/QAI.0000000000003689. Online ahead of print.

ABSTRACT

BACKGROUND: Pulmonary complications in people living with HIV (PWH) have shifted away from infectious disease and towards chronic disease. HIV is an independent risk factor for chronic obstructive pulmonary disease (COPD), with PWH developing COPD younger and declining faster in pulmonary function. As an accelerated decline is associated with greater mortality, there is a need to identify individuals at high risk of longitudinal decline.

SETTING: 59 adults with HIV enrolled from the Pittsburgh Lung HIV study cohort.

METHODS: Targeted metabolite profiling was performed on baseline bronchoalveolar lavage fluid (BALF, n=35) and serum samples (n=54) using liquid chromatography-high resolution mass spectrometry. Longitudinal pulmonary function tests (median 3 measurements over 2.95 years with a follow-up interval of 1.34 years) were used to determine rates of decline. Predictive modeling and feature selection algorithms identified baseline clinical and metabolomic factors associated with longitudinal decline across forced expiratory volume, forced vital capacity, and diffusing capacity of the lung.

RESULTS: Predictive models found the BALF metabolome to successfully predict outcomes more consistently than serum. Key BALF metabolites such as elevated carnitine and reduced pyruvate predicted greater risk of longitudinal decline. Low serum citrate levels were a robust predictor of decline across multiple tests. Probabilistic graphical models supported direct relationships between these metabolites and lung function decline.

CONCLUSION: Baseline metabolomic profiling, especially using BALF, can help identify PWH at risk for accelerated lung function decline. Key metabolic pathways related to glucose oxidation, fatty acid metabolism, and amino acid metabolism underlie observed lung function changes.

PMID:40298290 | DOI:10.1097/QAI.0000000000003689

Protein Sci. 2025 May;34(5):e70146. doi: 10.1002/pro.70146.

ABSTRACT

Canonical serine protease inhibitor proteins occupy the substrate-binding groove of their target enzyme via a surface loop. Unlike true substrates, inhibitors are cleaved by the target protease extremely slowly. Here, we applied an unbiased directed evolution approach to investigate which loop residues hamper proteolytic cleavage while maintaining high-affinity binding. As a protease inhibitor model system, we used human chymotrypsin C (CTRC) and Schistocerca gregaria protease inhibitor 2 (SGPI-2). We created an SGPI-2 library displayed on M13 phage by randomizing the binding loop amino acid positions, with the exception of the structurally indispensable Cys residues. We selected binding phage clones against active CTRC and the inactive mutant Ser195Ala. All CTRC-selected binders inhibited CTRC activity and also bound to the inactive Ser195Ala mutant, but the Ser195Ala-selected clones proved to be either inhibitors or substrates of active CTRC. Substrate-like behavior of SGPI-2 variants was associated with the absence of the P2 Thr, the residue next to the specificity determinant P1 amino acid. The selected SGPI-2 variants containing a P2 Thr bound strongly to CTRC even if the other loop residues deviated from the optimal inhibitory consensus sequence. In the absence of a P2 Thr, however, SGPI-2 variants became substrates unless all other loop residues were optimal for binding. Structural modeling confirmed that P2 Thr is important for organizing a stabilizing H-bond network. The observations indicate that binding loops of canonical serine protease inhibitors evolved amino acids not only to support tight binding to the target enzyme but also to inhibit proteolytic cleavage.

PMID:40298105 | DOI:10.1002/pro.70146

New Phytol. 2025 Apr 29. doi: 10.1111/nph.70159. Online ahead of print.

ABSTRACT

Tomato fruit size is a crucial trait in domestication, determined by cell division and cell expansion. Despite the identification of several quantitative trait loci associated with fruit size in tomatoes, the underlying molecular mechanisms that govern cell division and expansion to control fruit size remain unclear. CRISPR/Cas9 gene editing was used to generate single and double loss-of-function mutants of the tomato STERILE APETALA1 (c) and SlSAP2. The results demonstrate that the two SlSAP genes function redundantly in regulating leaf and fruit size by positively regulating cell proliferation and expansion, with SlSAP1 having the predominant effect. Consistently, overexpression of either SlSAP1 or SlSAP2 leads to enlarged fruits due to an increase in both cell layers and cell size in the pericarp. Biochemical evidence suggests that both SlSAP1 and SlSAP2 can form an SCF complex and physically interact with SlKIX8 and SlKIX9, which are crucial negative regulators of fruit size. Further results reveal that SlSAP1 and SlSAP2 target them for degradation. This study uncovers that the ubiquitination pathway plays an important role in the determination of tomato fruit size, and offers new genetic loci for improving fruit yield and biomass by manipulating pericarp thickness.

PMID:40298065 | DOI:10.1111/nph.70159

Biomedicines. 2025 Mar 29;13(4):822. doi: 10.3390/biomedicines13040822.

ABSTRACT

Background: Optimizing sedative techniques for drug-induced sleep endoscopy (DISE) enhances accuracy and reproducibility in tailoring treatment for obstructive sleep apnea (OSA). The Schnider and Eleveld pharmacokinetic-pharmacodynamic (PK-PD) models, which predict propofol concentration in effect-site compartment based on patient-specific parameters, were utilized to guide intravenous sedation in this study. We compared the effectiveness of propofol sedation guided by the novel general-purpose Eleveld model versus the Schnider model using target-controlled infusion (TCI) systems. Methods: We investigated twenty-five adult OSA patients, randomized into two groups: the Schnider model group (n = 12) and the Eleveld model group (n = 13). DISE was conducted following standardized protocols, targeting effect-site concentration TCI mode. Data concerning sedation levels, effect-site concentration of propofol, procedural timing, propofol dosages, respiratory and cardiovascular parameters, and any procedural incidents were collected. Results: DISE was performed successfully in all enrolled patients from both groups. A significant difference was observed in the effect-site concentration of propofol (CeP) at the moment of endoscopy between the Eleveld and Schnider groups (2.1 ± 0.4 µg/mL vs. 3.3 ± 0.7 µg/mL, respectively; p < 0.001). The E group also demonstrated a shorter time to attain the optimal sedation plane compared to the S group (6.1 ± 1.7 vs. 9.8 ± 2.2 min, respectively; p < 0.001) and a reduced total procedural time (11.2 ± 1.4 vs. 15.0 ± 2.1 min, respectively; p < 0.001). The incidence of adverse events was comparable between groups. Conclusions: The Eleveld model demonstrated a shorter time to achieve the optimal sedation plane, a shorter total procedural time, and a significant difference in effect-site concentration at the time of endoscopy compared to the Schnider model. The incidence of adverse events was comparable between the two groups, suggesting that the Eleveld model may offer improved efficiency without compromising safety during DISE.

PMID:40299425 | DOI:10.3390/biomedicines13040822

medRxiv [Preprint]. 2025 Apr 8:2025.04.07.25325038. doi: 10.1101/2025.04.07.25325038.

ABSTRACT

The urgent need for safe and effective therapies for Alzheimer's disease (AD) has spurred a growing interest in repurposing existing drugs to treat or prevent AD. In this study, we combined multi-omics and clinical data to investigate possible repurposing opportunities for AD. We performed transcriptome-wide association studies (TWAS) to construct gene expression signatures of AD from publicly available GWAS summary statistics, using both transcriptome prediction models for 49 tissues from the Genotype-Tissue Expression (GTEx) project and microglia-specific models trained on eQTL data from the Microglia Genomic Atlas (MiGA). We then identified compounds capable of reversing the AD-associated changes in gene expression observed in these signatures by querying the Connectivity Map (CMap) drug perturbation database. Out of >2,000 small-molecule compounds in CMap, aspirin emerged as the most promising AD repurposing candidate. To investigate the longitudinal effects of aspirin use on AD, we collected drug exposure and AD coded diagnoses from three independent sources of real-world data: electronic health records (EHRs) from Vanderbilt University Medical Center (VUMC) and the National Institutes of Health All of Us Research Program, along with national healthcare claims from the MarketScan Research Databases. In meta-analysis of EHR data from VUMC and All of Us , we found that aspirin use before age 65 was associated with decreased risk of incident AD (hazard ratio=0.76, 95% confidence interval [CI]: 0.64-0.89, P =0.001). Consistent with the findings utilizing EHR data, analysis of claims data from MarketScan revealed significantly lower odds of aspirin exposure among AD cases compared to matched controls (odds ratio=0.32, 95% CI: 0.28-0.38, P <0.001). Our results demonstrate the value of integrating genetic and clinical data for drug repurposing studies and highlight aspirin as a promising repurposing candidate for AD, warranting further investigation in clinical trials.

PMID:40297415 | PMC:PMC12036415 | DOI:10.1101/2025.04.07.25325038

Clin Transl Sci. 2025 May;18(5):e70225. doi: 10.1111/cts.70225.

ABSTRACT

Simvastatin, an HMG-CoA reductase inhibitor, is widely used for hypercholesterolemia but may cause myotoxicity linked to its plasma concentration. Pharmacokinetic gene polymorphisms influence inter-individual variability in simvastatin exposure. This study investigated the effects of pharmacokinetic gene polymorphisms on steady-state simvastatin plasma levels in Thai patients. Eighty-nine Thai patients with dyslipidemia or coronary artery disease on simvastatin treatment for at least 2 weeks without dose adjustment were recruited from King Chulalongkorn Memorial Hospital. Simvastatin lactone and acid concentrations were measured 12 h post-dose using UHPLC-MS/MS. Pharmacokinetic gene polymorphisms, including ABCB1, ABCC2, ABCG2, SLCO1B1, SLCO1B3, CYP3A4, and CYP3A5, were genotyped by MassARRAY System. The results showed that patients with the SLCO1B1 c.521TC+CC genotype had significantly higher simvastatin acid levels than those with c.521TT (0.53 vs. 0.19 ng/mL, p = 0.03). Similarly, the SLCO1B1*1b/*15 genotype was associated with higher simvastatin acid levels than SLCO1B1*1a/*1a (0.58 vs. 0.16 ng/mL, p < 0.001). These findings suggest that SLCO1B1 c.521T>C, alone or with c.388A>G (SLCO1B1*1b/*15), reduces OATP1B1 function, leading to elevated simvastatin acid levels and increased myotoxicity risk. This study confirms the association of SLCO1B1 rs4149056 (c.521T>C) with higher simvastatin plasma levels in Thai patients. The study highlights the potential role of SLCO1B1 genotyping, particularly rs4149056 (c.521T>C) and rs2306283 (c.388A>G), in guiding statin therapy for Thai patients, which could help optimize treatment and reduce adverse effects such as statin-induced myotoxicity.

PMID:40297930 | DOI:10.1111/cts.70225

Diabetes Metab Syndr Obes. 2025 Apr 23;18:1255-1262. doi: 10.2147/DMSO.S500336. eCollection 2025.

ABSTRACT

BACKGROUND/OBJECTIVES: This subgroup analysis of a randomised, open-label, two-period crossover trial in Aotearoa New Zealand (February 2019 to March 2020) assessed whether the glucose-lowering effects of vildagliptin, vs pioglitazone varied by the CREBRF (p.Arg457Gln) rs373863828 genotype.

METHODS: Adults with type 2 diabetes and HbA1c > 58 mmol/mol (>7.5%) received either pioglitazone (30 mg) or vildagliptin (50 mg) for 16 weeks, then switched medications for another 16 weeks. Differences in HbA1c between treatments (pioglitazone vs vildagliptin) were tested for an interaction with CREBRF rs373863828 A-allele carrier status and controlling for baseline HbA1c using linear mixed models. Secondary endpoints included weight, systolic blood pressure, and diabetes treatment satisfaction.

RESULTS: Participants with the AA/AG genotype had a higher baseline weight than those with the GG genotype (121.4 kg vs 106.6 kg, respectively; p<0.01). No significant difference in achieved HbA1c was found based on A-allele carrier status (0.43 mmol/mol; 95% CI -4.83, 5.69; p=0.87). Among Māori and Pacific participants with the A-allele, a smaller weight difference was observed after pioglitazone vs vildagliptin compared to those with the GG genotype (interaction effect -1.66 kg; 95% CI -3.27, -0.05; p=0.04).

CONCLUSION: CREBRF rs373863828 A-allele carriers show a similar HbA1c-lowering response to pioglitazone vs vildagliptin compared to non-carriers but exhibit less weight gain with pioglitazone, despite having significantly higher baseline weights.

PMID:40297769 | PMC:PMC12035406 | DOI:10.2147/DMSO.S500336

Front Pharmacol. 2025 Apr 14;16:1524623. doi: 10.3389/fphar.2025.1524623. eCollection 2025.

ABSTRACT

INTRODUCTION: Thyroid cancer, a prevalent endocrine malignancy, has an age-standardized incidence rate of 9.1 per 100,000 people and a mortality rate of 0.44 per 100,000 as of 2024. Despite significant advances in precision oncology driven by large-scale international consortia, gaps persist in understanding the genomic landscape of thyroid cancer and its impact on therapeutic efficacy across diverse populations.

METHODS: To address this gap, we performed comprehensive data mining and in silico analyses to identify pathogenic variants in thyroid cancer driver genes, calculate allele frequencies, and assess deleteriousness scores across global populations, including African, Amish, Ashkenazi Jewish, East and South Asian, Finnish and non-Finnish European, Latino, and Middle Eastern groups. Additionally, pharmacogenomic profiling, in silico drug prescription, and clinical trial data were analyzed to prioritize targeted therapeutic strategies.

RESULTS: Our analysis examined 56,622 variants in 40 thyroid cancer-driver genes across 76,156 human genomes, identifying 5,001 known and predicted oncogenic variants. Enrichment analysis revealed critical pathways such as MAPK, PI3K-AKT-mTOR, and p53 signaling, underscoring their roles in thyroid cancer pathogenesis. High-throughput validation strategies confirmed actionable genomic alterations in RET, BRAF, NRAS, KRAS, and EPHA7. Ligandability assessments identified these proteins as promising therapeutic targets. Furthermore, our findings highlight the clinical potential of targeted drug inhibitors, including vandetanib, dabrafenib, and selumetinib, for improving treatment outcomes.

DISCUSSION: This study underscores the significance of integrating genomic insights with pharmacogenomic strategies to address disparities in thyroid cancer treatment. The identification of population-specific oncogenic variants and actionable therapeutic targets provides a foundation for advancing precision oncology. Future efforts should focus on including underrepresented populations, developing population-specific prevention strategies, and fostering global collaboration to ensure equitable access to pharmacogenomic testing and innovative therapies. These initiatives have the potential to transform thyroid cancer care and align with the broader goals of personalized medicine.

PMID:40297138 | PMC:PMC12034932 | DOI:10.3389/fphar.2025.1524623

Acta Biochim Biophys Sin (Shanghai). 2025 Apr 28. doi: 10.3724/abbs.2025013. Online ahead of print.

ABSTRACT

Platinum-based chemotherapy remains the mainstay for non-small cell lung cancer (NSCLC), but it frequently causes dose-limiting myelosuppression, with significant individual variability in susceptibility. However, the genetic basis of myelosuppression side effects remains elusive, greatly hindering personalized therapeutic approaches. In this study, we perform a comprehensive genome-wide association analysis on 491 NSCLC patients receiving platinum-based chemotherapy, examining 4,690,998 single-nucleotide polymorphisms (SNPs) to identify relevant genetic variants. LDBlockShow, FUMA, and MAGMA are utilized to explore linkage disequilibrium, expression quantitative trait loci (eQTLs), chromatin interaction, and conduct gene-based and gene set-based analysis of candidate SNPs. The GWAS results reveal that rs6856089 and its linked SNPs are significantly associated with platinum-based chemotherapy-induced myelosuppression. Specifically, patients with the A allele of rs6856089 have a significantly lower risk of myelosuppression (odds ratio (OR) = 0.1300, P = 7.59 × 10 -8). Furthermore, gene-based analysis reveals that EMCN ( P = 2.47 × 10 -5), which encodes endomucin, a marker for hematopoietic stem cells, might mediate myelosuppression. This study provides a scientific basis for the individual differences in platinum-based chemotherapy-induced myelosuppression.

PMID:40296719 | DOI:10.3724/abbs.2025013

Vavilovskii Zhurnal Genet Selektsii. 2025 Apr;29(2):279-289. doi: 10.18699/vjgb-25-31.

ABSTRACT

Cystic fibrosis (CF) is a disease with a broad clinical and genetic spectrum of manifestations, significantly impacting the quality and duration of life of patients. At present, a diagnosis of CF enables the disease to be identified at the earliest stages of its development. The accelerated advancement of scientific knowledge and contemporary research techniques has transformed the methodology employed in the treatment of CF, encompassing a spectrum of approaches from symptomatic management to pathogenetic therapies. Pathogenetic therapy represents an approach to treatment that aims to identify methods of restoring the function of the CFTR gene. The objective of this review was to analyse and summarize the available scientific data on the pathogenetic therapy of CF. This paper considers various approaches to the pathogenetic therapy of CF that are based on the use of targeted drugs known as CFTR modulators. The article presents studies employing gene therapy techniques for CF, which are based on the targeted delivery of a normal copy of the CFTR gene cDNA to the respiratory tract via viral or non-viral vectors. Some studies have demonstrated the efficacy of RNA therapeutic interventions in restoring splicing, promoting the production of mature RNA, and increasing the functional expression of the CFTR protein. The review also analyzes literature data that consider methods of etiotropic therapy for CF, which consists of targeted correction of the CFTR gene using artificial restriction enzymes, the CRISPR/Cas9 system and a complex of peptide-nucleic acids. In a prospective plan, the use of cell therapy methods in the treatment of lung damage in CF is considered.

PMID:40297296 | PMC:PMC12036567 | DOI:10.18699/vjgb-25-31

J Pathol Inform. 2025 Mar 27;17:100438. doi: 10.1016/j.jpi.2025.100438. eCollection 2025 Apr.

ABSTRACT

Immune cell differentials are most commonly performed manually or with the use of automated cell sorting devices. However, manual review by research personnel can be both subjective and time consuming, and cell sorting approaches consume samples and demand additional reagents to perform the differential. We have created an artificial intelligence (AI) image processing pipeline using the Biodock.ai platform to classify immune cell types from Giemsa-stained cytospins of mouse bronchoalveolar lavage fluid. Through multiple rounds of training and refinement, we have created a tool that is as accurate as manual review of slide images while removing the subjectivity and making the process mostly hands off, saving researcher time for other tasks and improving core turnaround for experiments. This AI-based image processing is directly compatible with current workflows utilizing stained slides, in contrast to a change to a flow cytometry-based approach, which requires both specialized equipment, reagents, and expertise.

PMID:40297061 | PMC:PMC12036075 | DOI:10.1016/j.jpi.2025.100438

Res Sq [Preprint]. 2025 Apr 7:rs.3.rs-6149503. doi: 10.21203/rs.3.rs-6149503/v1.

ABSTRACT

Recently, topological deep learning (TDL), which integrates algebraic topology with deep neural networks, has achieved tremendous success in processing point-cloud data, emerging as a promising paradigm in data science. However, TDL has not been developed for data on differentiable manifolds, including images, due to the challenges posed by differential topology. We address this challenge by introducing manifold topological deep learning (MTDL) for the first time. To highlight the power of Hodge theory rooted in differential topology, we consider a simple convolutional neural network (CNN) in MTDL. In this novel framework, original images are represented as smooth manifolds with vector fields that are decomposed into three orthogonal components based on Hodge theory. These components are then concatenated to form an input image for the CNN architecture. The performance of MTDL is evaluated using the MedMNIST v2 benchmark database, which comprises 717,287 biomedical images from eleven 2D and six 3D datasets. MTDL significantly outperforms other competing methods, extending TDL to a wide range of data on smooth manifolds.

PMID:40297704 | PMC:PMC12036455 | DOI:10.21203/rs.3.rs-6149503/v1

Head Neck Tumor Segm MR Guid Appl (2024). 2025;15273:99-111. doi: 10.1007/978-3-031-83274-1_7. Epub 2025 Mar 3.

ABSTRACT

Accurate segmentation of gross tumor volume (GTV) is essential for effective MRI-guided adaptive radiotherapy (MRgART) in head and neck cancer. However, manual segmentation of the GTV over the course of therapy is time-consuming and prone to interobserver variability. Deep learning (DL) has the potential to overcome these challenges by automatically delineating GTVs. In this study, our team, UW LAIR, tackled the challenges of both pre-radiotherapy (pre-RT) (Task 1) and mid-radiotherapy (mid-RT) (Task 2) tumor volume segmentation. To this end, we developed a series of DL models for longitudinal GTV segmentation. The backbone of our models for both tasks was SegResNet with deep supervision. For Task 1, we trained the model using a combined dataset of pre-RT and mid-RT MRI data, which resulted in the improved aggregated Dice similarity coefficient (DSCagg) on a hold-out internal testing set compared to models trained solely on pre-RT MRI data. In Task 2, we introduced mask-aware attention modules, enabling pre-RT GTV masks to influence intermediate features learned from mid-RT data. This attention-based approach yielded slight improvements over the baseline method, which concatenated mid-RT MRI with pre-RT GTV masks as input. In the final testing phase, the ensemble of 10 pre-RT segmentation models achieved an average DSCagg of 0.794, with 0.745 for primary GTV (GTVp) and 0.844 for metastatic lymph nodes (GTVn) in Task 1. For Task 2, the ensemble of 10 mid-RT segmentation models attained an average DSCagg of 0.733, with 0.607 for GTVp and 0.859 for GTVn, leading us to achieve 1st place. In summary, we presented a collection of DL models that could facilitate GTV segmentation in MRgART, offering the potential to streamline radiation oncology workflows.

PMID:40297614 | PMC:PMC12036643 | DOI:10.1007/978-3-031-83274-1_7

medRxiv [Preprint]. 2025 Apr 11:2025.04.08.25325262. doi: 10.1101/2025.04.08.25325262.

ABSTRACT

Late-onset Tay-Sachs (LOTS) disease and late-onset Sandhoff disease (LOSD) have long been considered indistinguishable due to similar clinical presentations and shared biochemical deficits. However, recent magnetic resonance imaging (MRI) studies have shown distinct cerebellar atrophy associated with LOTS. In this study, we furthered this investigation to determine if the cerebellar atrophy is globally uniform or preferentially targets certain cerebellar regions. We utilized DeepCERES , a deep learning cerebellar specific segmentation and cortical thickness pipeline to analyze differences between LOTS (n=20), LOSD (n=5), and neurotypical controls (n=1038). LOTS had smaller volumes of the whole cerebellum as well as cerebellar lobules IV, V, VI, VIIB, VIIIA, VIIIB, IX, and both Crus I and II compared to both LOSD and neurotypical controls. LOTS patients also had smaller cortical thickness of cerebellar lobules V, VI, VIIB, VIIIA, VIIIB, and both Crus I and II compared to both LOSD and neurotypical controls. Cerebellar functional and lesion localization studies have implicated lobules V and VI in speech articulation and execution while lobules VI, Crus I, VIIA, among others, have been implicated in a variety of behaviors and neuropsychiatric symptoms. Our observations provide a possible anatomical substrate to the higher prevalence of dysarthria and psychosis in our LOTS but not LOSD patients. Future studies are needed for direct comparisons considering phenotypic aspects such as age of symptom onset, presence and severity of dysarthria and ataxia, full characterization of neuropsychiatric profiles, molecular pathology and biochemical differences to fully understand the dichotomy observed in these two diseases.

PMID:40297453 | PMC:PMC12036421 | DOI:10.1101/2025.04.08.25325262

medRxiv [Preprint]. 2025 Apr 7:2025.04.06.25325337. doi: 10.1101/2025.04.06.25325337.

ABSTRACT

Alzheimer's disease (AD) and AD-related dementias (ADRD) exhibit heterogeneous progression rates, with rapid progression (RP) posing significant challenges for timely intervention and treatment. The increasingly available patient-centered electronic health records (EHRs) have made it possible to develop advanced machine learning models for risk prediction of disease progression by leveraging comprehensive clinical, demographic, and laboratory data. In this study, we propose AutoRADP, an interpretable autoencoder-based framework that predicts rapid AD/ADRD progression using both structured and unstructured EHR data from UFHealth. AutoRADP incorporates a rule-based natural language processing method to extract critical cognitive assessments from clinical notes, combined with feature selection techniques to identify essential structured EHR features. To address the data imbalance issue, we implement a hybrid sampling strategy that combines similarity-based and clustering-based upsampling. Additionally, by utilizing SHapley Additive exPlanations (SHAP) values, we provide interpretable predictions, shedding light on the key factors driving the rapid progression of AD/ADRD. We demonstrate that AutoRADP outperforms existing methods, highlighting the potential of our framework to advance precision medicine by enabling accurate and interpretable predictions of rapid AD/ADRD progression, and thereby supporting improved clinical decision-making and personalized interventions.

PMID:40297450 | PMC:PMC12036374 | DOI:10.1101/2025.04.06.25325337