Antioxidants (Basel). 2025 Mar 27;14(4):396. doi: 10.3390/antiox14040396.

ABSTRACT

BACKGROUND: All the processes leading to neurodegeneration cannot be addressed with just one medication. Combinations of drugs affecting various disease mechanisms concurrently could demonstrate improved effect in slowing the course of Parkinson's disease (PD).

OBJECTIVE: This was a drug-repurposing experiment designed to assess several combinations of nine drugs for possible added or synergistic efficacy using in vitro models of PD.

METHODS: We evaluated 44 combinations of the nine medications (sodium phenylbutyrate, terazosin, exenatide, ambroxol, deferiprone, coenzyme-Q10, creatine, dasatinib and tauroursodeoxycholic acid) selected for their previously demonstrated evidence of their impact on different targets, showing neuroprotective properties in preclinical models of PD. We utilized wild-type induced pluripotent stem-cell-derived human dopaminergic neurons treated with 1-methyl-4-phenylpyridinium for initial screening. We retested some combinations using an idiopathic PD patient-derived induced pluripotent stem cell line and alpha-synuclein triplication line. We assessed anti-neuroinflammatory effects using human microglia cells. As metrics, we evaluated neurite length, number of branch points per mm2, the number of live neurons, neurofilament heavy chain and pro-inflammatory cytokines.

RESULTS: We have identified four combinations of two to three drugs that showed an additive protective effect in some endpoints. Only the combination of sodium phenylbutyrate, exenatide and tauroursodeoxycholic acid showed improvement in four endpoints studied.

CONCLUSIONS: We demonstrated that some of the medications, used in combination, can exert an additive neuroprotective effect in preclinical models of PD that is superior to that of each of the compounds individually. This project can lead to the development of the first treatment for PD that can slow or prevent its progression.

PMID:40298667 | DOI:10.3390/antiox14040396

Antibiotics (Basel). 2025 Apr 14;14(4):402. doi: 10.3390/antibiotics14040402.

ABSTRACT

Background/Objectives: Antibiotic therapy faces challenges from rising acquired and biofilm-related antibiotic resistance rates. High resistance levels to commonly used antibiotics have been observed in methicillin-resistant Staphylococcus aureus (MRSA) strains among cystic fibrosis (CF) patients, indicating an urgent need for new antibacterial agents. This study aimed to identify potential novel therapeutics with antibacterial and antibiofilm activities against an MRSA CF strain by screening, for the first time, the Drug Repurposing Compound Library (MedChem Express). Methods/Results: Among the 3386 compounds, a high-throughput screening-based spectrophotometric approach identified 2439 (72%), 654 (19.3%), and 426 (12.6%) drugs active against planktonic cells, biofilm formation, and preformed biofilm, respectively, although to different extents. The most active hits were 193 (5.7%), against planktonic cells, causing a 100% growth inhibition; 5 (0.14%), with excellent activity against biofilm formation (i.e., reduction ≥ 90%); and 4, showing high activity (i.e., 60% ≤ biofilm reduction < 90%) against preformed biofilms. The potential hits belonged to several primary research areas, with "cancer" being the most prevalent. After performing a literature review to identify other, already published biological properties that could be relevant to the CF lung environment (i.e., activity against other CF pathogens, and anti-inflammatory and anti-virulence potential), the most interesting hits were the following: 5-(N,N-Hexamethylene)-amiloride (diuretic), Toremifene (anticancer), Zafirlukast (antiasthmatic), Fenretide (anticancer), and Montelukast (antiasthmatic) against planktonic S. aureus cells; Hemin against biofilm formation; and Heparin, Clemastine (antihistaminic), and Bromfenac (nonsteroidal anti-inflammatory) against established biofilms. Conclusions: These findings warrant further in vitro and in vivo studies to confirm the potential of repurposing these compounds for managing lung infections caused by S. aureus in CF patients.

PMID:40298549 | DOI:10.3390/antibiotics14040402

Hum Mol Genet. 2025 Apr 29:ddaf061. doi: 10.1093/hmg/ddaf061. Online ahead of print.

ABSTRACT

The copper ionophore disulfiram (DSF) is commonly used to treat chronic alcoholism and has potential anti-cancer activity. Using a yeast-based screening assay of FDA-approved compounds, DSF was herein identified for its ability to improve oxidative phosphorylation-dependent growth of various yeast models of mitochondrial diseases caused by a wide range of defects in ATP synthase, complexes III and IV, cardiolipin remodeling, maintenance and translation of the mitochondrial genome. This compound also showed beneficial effects in cells derived from patients suffering from Barth or MELAS syndromes, two mitochondrial diseases associated respectively with a lack in cardiolipin remodeling and protein synthesis inside the organelle. We provide evidence that the rescuing activity of DSF results from its ability to transport copper ions across biological membranes. Indeed, other copper ionophores (pyrithione and elesclomol) and supplementation of the growth media with copper ions had also beneficial effects in yeast and human cells with dysfunctional mitochondria. Our data suggest that the copper-dependent rescuing activity in these cells results from a better capacity to assemble cytochrome c oxidase. Altogether, our findings hold promise for the development of new therapeutic strategies for mitochondrial disorders.

PMID:40298238 | DOI:10.1093/hmg/ddaf061

J Manag Care Spec Pharm. 2025 May;31(5):491-498. doi: 10.18553/jmcp.2025.31.5.491.

ABSTRACT

BACKGROUND: More than 10,000 rare diseases affect more than 30 million Americans, nearly 70% of which manifest in childhood. The drug development pipeline boasts hundreds of candidates for pediatric-onset rare disease, but little is known about the impact of potential approvals.

OBJECTIVE: To quantify US projected product approvals, patients treated, and product revenues for pediatric-onset rare disease treatments through 2033.

METHODS: Four-stage model consisting of a Markov Chain Monte Carlo simulation of US Food and Drug Administration approvals, calculation of eligible patients per clinical trial criteria, and projection of adoption and list price revenues, all using publicly available data.

RESULTS: By 2033 the pipeline will yield approximately 45 new product approvals, a 14% growth in annual treated patients, and an incremental $10.7B in list price drug revenues ($28.2B: 2023; $38.9B: 2033) prior to any health care cost offsets, caregiving impacts, long-term social benefits, or other benefits from treating the additional patients.

CONCLUSIONS: The projected approvals over the next decade will undoubtedly be transformational for the patient communities impacted, many of whom have no currently approved treatments. However, the number of newly identified rare diseases is likely to outpace the rate of new therapies to treat them. Resources are needed to accelerate progress as 95% of pediatric-onset rare diseases are projected to still have no approved treatments in the next decade, and even for the 5% that have some options, more is needed.

PMID:40298308 | DOI:10.18553/jmcp.2025.31.5.491

Nucleic Acids Res. 2025 Apr 29:gkaf337. doi: 10.1093/nar/gkaf337. Online ahead of print.

ABSTRACT

Biomedical ontologies, such as Gene Ontology (GO), Disease Ontology (DO), and the Human Phenotype Ontology (HPO), have been extensively applied to characterize molecular roles and their semantic relationships in biomedical research and clinical practice. Although numerous algorithms have been developed to quantify relationships between ontology terms or to explore molecular functions, the absence of a comprehensive tool to integrate these algorithms has limited effective ontology applications. To address this, we developed OntoTiger, a platform of Ontology-based application Tools for InteGrativE biomedical exploRation. OntoTiger combines >20 classic algorithms, supporting six prevalent molecular types as well as five widespread biomedical ontologies. The platform comprises four modules: (i) Annotation module, which qualifies the relationships between ontology terms and molecules; (ii) Similarity module, quantifying functional similarity between/across pairwise ontology terms or between molecules; (iii) Prediction module, characterizing the molecular roles from an ontological perspective; and (iv) Enrichment module, elucidating the potential biological significance of a particular list of molecules. OntoTiger provides a freely accessible, user-friendly web server dedicated to enabling one-stop ontology-based applications and is freely available at https://bio-computing.hrbmu.edu.cn/OntoTiger.

PMID:40297993 | DOI:10.1093/nar/gkaf337

Biomedicines. 2025 Mar 28;13(4):814. doi: 10.3390/biomedicines13040814.

ABSTRACT

Background/Objectives: Activating epidermal growth factor receptor (EGFR) variants is the most common targetable alteration in non-small cell lung cancer (NSCLC). Clinical decision-making requires fast and reliable detection of EGFR variants in early and advanced NSCLC, but limited available tissue necessitates tissue-sparing approaches and optimized sample management. The objective of this study was to assess the performance of the commercial EasyPGX® ready EGFR assay using real-world clinical NSCLC samples. Methods: A consecutive cohort of 804 non-squamous NSCLC samples was prospectively analyzed with the real-time quantitative polymerase chain reaction (RT-qPCR)-based EasyPGX® ready EGFR assay (Diatech Pharmacogenetics, Jesi, Ancona, Italy) and compared to next-generation sequencing (NGS) assays. Results: NGS revealed conclusive results in 99.7% samples, of which 11.1% had at least one EGFR variant. The most common variants were exon 19 deletions and p.L858R. The RT-qPCR-based assay identified EGFR variants with high accuracy (overall concordance rate 94.3%) over a broad range of clinical sample types, variant allele frequencies, tumor cell contents and deoxyribonucleic acid (DNA) input amounts. Conclusions: This study demonstrates that the EasyPGX® ready EGFR assay is a valid approach for the rapid detection of common EGFR variants in real-world clinical NSCLC samples with DNA inputs as low as 5 ng (less than the 15 ng recommended by the manufacturer), improving sample management in small specimens with limited quantity of nucleic acids.

PMID:40299437 | DOI:10.3390/biomedicines13040814

Transl Stroke Res. 2025 Apr 29. doi: 10.1007/s12975-025-01350-4. Online ahead of print.

ABSTRACT

To investigate the association between rs402007 polymorphism in the ADAMTS-1 gene and carotid atherosclerotic plaque vulnerability, as well as the lipid-lowering efficacy of atorvastatin in cerebral infarction patients. Clinical data from 684 cerebral infarction patients admitted to The First Hospital of Hebei Medical University (2016-2019) were analyzed. Patients were stratified into stable plaque (n = 338) and vulnerable plaque (n = 346) groups based on carotid ultrasound. General information, biochemical markers, rs402007 (G/C) genotypes (dominant model), and allele frequencies were compared. Polymorphism genotyping was performed using TaqMan SNP assays (Applied Biosystems) on an ABI 7500 Fast Real-Time PCR system. Logistic regression evaluated plaque vulnerability risk factors and gene-risk factor interactions. Atorvastatin's lipid-lowering efficacy was compared across genotypes. Diabetes prevalence, LDL-C, TC, HCY, and FIB levels differed significantly between groups (P < 0.05). Genotypic distribution analysis revealed a higher frequency of the GG genotype in the stable plaque group (29.59% vs. 21.68%, χ2 = 5.618, P = 0.018). Diabetes, LDL-C, HCY, and FIB were independent risk factors for plaque vulnerability (P < 0.05). A significant interaction between rs402007 polymorphism and LDL-C was observed (P < 0.05). Atorvastatin efficacy rates were 82.29% (GG), 84.27% (GC), and 89.27% (CC), with significant post-treatment lipid improvements in all genotypes (P < 0.05). The CC genotype exhibited superior efficacy compared to GG (P < 0.05). The rs402007 polymorphism influences carotid plaque vulnerability and modulates atorvastatin efficacy, underscoring its potential role in genotype-guided therapeutic strategies.

PMID:40299202 | DOI:10.1007/s12975-025-01350-4

Biomedicines. 2025 Apr 4;13(4):879. doi: 10.3390/biomedicines13040879.

ABSTRACT

Background: Organoids are a valuable model for studying hereditary diseases such as cystic fibrosis (CF). Recombinant adenoviral (rAdV) and adeno-associated viral (rAAV) vectors are promising tools for CF gene therapy and genome editing. Objective: This study aims to determine the most efficient viral vector (rAdV5, rAAV serotypes 5, 6 and 9) and transduction protocol for delivering transgenes to lung organoids (LOs), providing a foundation for future CF gene therapy development. Methods: Three transduction protocols were used taking into account the specificities of LOs' cultivation in specific matrices, both with and without organoid extraction from the matrix. This work was carried out on organoids from a healthy donor (LOs-WT) and on a patient with cystic fibrosis (LOs-CF). Results: High transduction efficiency was observed with rAdV5 (30% cells), rAAV6 (>80% cells), and rAAV9 (>40% cells). rAdV5 and rAAV9 transduced basal and secretory cells with >90% efficiency. For rAAV9, Protocol 1 (without extraction of organoids from the matrix) showed lower transduction efficiency (33% for LOs-WT, 9% for LOs-CF), significantly lower than that of Protocols 2 (60% for LOs-WT, 59% for LOs-CF) and 3 (46% for LOs-WT, 35% for LOs-CF) with organoid extraction from the matrix (p < 0.005). Conclusions: rAdV5 and rAAV9 are the most promising vectors for the delivery of transgenes to basal and secretory cells in a lung organoid model, providing a solid foundation for CF gene therapy development.

PMID:40299508 | DOI:10.3390/biomedicines13040879

Antibiotics (Basel). 2025 Apr 14;14(4):402. doi: 10.3390/antibiotics14040402.

ABSTRACT

Background/Objectives: Antibiotic therapy faces challenges from rising acquired and biofilm-related antibiotic resistance rates. High resistance levels to commonly used antibiotics have been observed in methicillin-resistant Staphylococcus aureus (MRSA) strains among cystic fibrosis (CF) patients, indicating an urgent need for new antibacterial agents. This study aimed to identify potential novel therapeutics with antibacterial and antibiofilm activities against an MRSA CF strain by screening, for the first time, the Drug Repurposing Compound Library (MedChem Express). Methods/Results: Among the 3386 compounds, a high-throughput screening-based spectrophotometric approach identified 2439 (72%), 654 (19.3%), and 426 (12.6%) drugs active against planktonic cells, biofilm formation, and preformed biofilm, respectively, although to different extents. The most active hits were 193 (5.7%), against planktonic cells, causing a 100% growth inhibition; 5 (0.14%), with excellent activity against biofilm formation (i.e., reduction ≥ 90%); and 4, showing high activity (i.e., 60% ≤ biofilm reduction < 90%) against preformed biofilms. The potential hits belonged to several primary research areas, with "cancer" being the most prevalent. After performing a literature review to identify other, already published biological properties that could be relevant to the CF lung environment (i.e., activity against other CF pathogens, and anti-inflammatory and anti-virulence potential), the most interesting hits were the following: 5-(N,N-Hexamethylene)-amiloride (diuretic), Toremifene (anticancer), Zafirlukast (antiasthmatic), Fenretide (anticancer), and Montelukast (antiasthmatic) against planktonic S. aureus cells; Hemin against biofilm formation; and Heparin, Clemastine (antihistaminic), and Bromfenac (nonsteroidal anti-inflammatory) against established biofilms. Conclusions: These findings warrant further in vitro and in vivo studies to confirm the potential of repurposing these compounds for managing lung infections caused by S. aureus in CF patients.

PMID:40298549 | DOI:10.3390/antibiotics14040402

Microbiol Resour Announc. 2025 Apr 29:e0027525. doi: 10.1128/mra.00275-25. Online ahead of print.

ABSTRACT

We describe the genomes of two Pseudomonas aeruginosa phages of the genus Bruynoghevirus, WRAIR_EPa83 and WRAIR_EPa87. They consist of 45,622 and 45,077 bp, with 52.52% and 52.11% guanine-cytosine content, contain 81 and 80 coding sequences, two and three tRNA genes, and direct terminal repeats of 183 and 184 bp, respectively.

PMID:40298417 | DOI:10.1128/mra.00275-25

mSphere. 2025 Apr 29:e0023325. doi: 10.1128/msphere.00233-25. Online ahead of print.

ABSTRACT

Persistent bacterial airway infection is a hallmark feature of cystic fibrosis (CF). Achromobacter spp. are gram-negative rods that can cause persistent airway infection in people with CF (pwCF), but the knowledge of host immune responses to these bacteria is limited. The aim of this study was to investigate if patients develop antibodies against Achromobacter xylosoxidans, the most common Achromobacter species, and to identify the bacterial antigens that induce specific IgG responses. Seven serum samples from pwCF with Achromobacter infection were screened for antibodies against bacteria in an ELISA coated with A. xylosoxidans, A. insuavis, or Pseudomonas aeruginosa. Sera from pwCF with or without P. aeruginosa infection (n = 22 and 20, respectively) and healthy donors (n = 4) were included for comparison. Serum with high titers to A. xylosoxidans was selected for affinity purification of bacterial antigens using serum IgGs bound to protein G beads. The resulting IgG-antigen complexes were then analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Selected antigens of interest were produced in recombinant form and used in an ELISA to confirm the results. Four of the seven patients with Achromobacter infection had serum antibodies against Achromobacter. Using patient serum-IgG for affinity purification of A. xylosoxidans proteins, we identified eight antigens. Three of these, which were not targeted by anti-P. aeruginosa antibodies, were expressed recombinantly for further validation: dihydrolipoyl dehydrogenase (DLD), type I secretion C-terminal target domain-containing protein, and domain of uncharacterized function 336 (DUF336). While specific IgG against all three recombinant antigens was confirmed in the patient serum with high titers against Achromobacter, DLD and DUF336 showed the least binding to serum IgG from pwCF without Achromobacter spp. infection. Using serum IgG affinity purification in combination with LC-MS/MS and confirming the results using ELISA against recombinant proteins, we have identified bacterial antigens from A. xylosoxidans.IMPORTANCEAchromobacter species are opportunistic pathogens that can cause airway infections in people with cystic fibrosis. In this patient population, persistent Achromobacter infection is associated with low lung function, but the knowledge about bacterial interactions with the host is currently limited. In this study, we identify protein antigens that induce specific antibody responses in the host. The identified antigens may potentially be useful in serological assays, serving as a complement to culturing methods for the diagnosis and surveillance of Achromobacter infection.

PMID:40298413 | DOI:10.1128/msphere.00233-25

JMIR AI. 2025 Apr 29;4:e76150. doi: 10.2196/76150.

ABSTRACT

[This corrects the article DOI: 10.2196/67239.].

PMID:40299541 | DOI:10.2196/76150

Biomedicines. 2025 Mar 31;13(4):836. doi: 10.3390/biomedicines13040836.

ABSTRACT

Pancreatic cancer is one of the most lethal neoplasms. Despite considerable research conducted in recent decades, not much has been achieved to improve its survival rate. That may stem from the lack of effective screening strategies in increased pancreatic cancer risk groups. One population that may be appropriate for screening is new-onset diabetes (NOD) patients. Such a conclusion stems from the fact that pancreatic cancer can cause diabetes several months before diagnosis. The most widely used screening tool for this population, the ENDPAC (Enriching New-Onset Diabetes for Pancreatic Cancer) model, has not achieved satisfactory results in validation trials. This provoked the first attempts at using artificial intelligence (AI) to create larger, multi-parameter models that could better identify the at-risk population, which would be suitable for screening. The results shown by the authors of these trials seem promising. Nonetheless, the number of publications is limited, and the downfalls of using AI are not well highlighted. This narrative review presents a summary of previous publications, recent advancements and feasible solutions for effective screening of patients with NOD for pancreatic cancer.

PMID:40299428 | DOI:10.3390/biomedicines13040836

Biomedicines. 2025 Apr 1;13(4):846. doi: 10.3390/biomedicines13040846.

ABSTRACT

Improved image quality, better scanners, innovative software technologies, enhanced computational power, superior network connectivity, and the ease of virtual image reproduction and distribution are driving the potential use of digital pathology for diagnosis and education. Although relatively common in clinical oncology, its application in liver pathology is under development. Digital pathology and improving subjective histologic scoring systems could be essential in managing obesity-associated steatotic liver disease. The increasing use of digital pathology in analyzing liver specimens is particularly intriguing as it may offer a more detailed view of liver biology and eliminate the incomplete measurement of treatment responses in clinical trials. The objective and automated quantification of histological results may help establish standardized diagnosis, treatment, and assessment protocols, providing a foundation for personalized patient care. Our experience with artificial intelligence (AI)-based software enhances reproducibility and accuracy, enabling continuous scoring and detecting subtle changes that indicate disease progression or regression. Ongoing validation highlights the need for collaboration between pathologists and AI developers. Concurrently, automated image analysis can address issues related to the historical failure of clinical trials stemming from challenges in histologic assessment. We discuss how these novel tools can be incorporated into liver research and complement post-diagnosis scenarios where quantification is necessary, thus clarifying the evolving role of digital pathology in the field.

PMID:40299404 | DOI:10.3390/biomedicines13040846

Int J Comput Assist Radiol Surg. 2025 Apr 29. doi: 10.1007/s11548-025-03369-2. Online ahead of print.

ABSTRACT

PURPOSE: Automated localization of critical anatomical structures in endoscopic pituitary surgery is crucial for enhancing patient safety and surgical outcomes. While deep learning models have shown promise in this task, their predictions often suffer from frame-to-frame inconsistency. This study addresses this issue by proposing ConsisTNet, a novel spatio-temporal model designed to improve prediction stability.

METHODS: ConsisTNet leverages spatio-temporal features extracted from consecutive frames to provide both temporally and spatially consistent predictions, addressing the limitations of single-frame approaches. We employ a semi-supervised strategy, utilizing ground-truth label tracking for pseudo-label generation through label propagation. Consistency is assessed by comparing predictions across consecutive frames using predicted label tracking. The model is optimized and accelerated using TensorRT for real-time intraoperative guidance.

RESULTS: Compared to previous state-of-the-art models, ConsisTNet significantly improves prediction consistency across video frames while maintaining high accuracy in segmentation and landmark detection. Specifically, segmentation consistency is improved by 4.56 and 9.45% in IoU for the two segmentation regions, and landmark detection consistency is enhanced with a 43.86% reduction in mean distance error. The accelerated model achieves an inference speed of 202 frames per second (FPS) with 16-bit floating point (FP16) precision, enabling real-time intraoperative guidance.

CONCLUSION: ConsisTNet demonstrates significant improvements in spatio-temporal consistency of anatomical localization during endoscopic pituitary surgery, providing more stable and reliable real-time surgical assistance.

PMID:40299263 | DOI:10.1007/s11548-025-03369-2

Nanomicro Lett. 2025 Apr 29;17(1):241. doi: 10.1007/s40820-025-01757-6.

ABSTRACT

Bimodal pressure sensors capable of simultaneously detecting static and dynamic forces are essential to medical detection and bio-robotics. However, conventional pressure sensors typically integrate multiple operating mechanisms to achieve bimodal detection, leading to complex device architectures and challenges in signal decoupling. In this work, we address these limitations by leveraging the unique piezotronic effect of Y-ion-doped ZnO to develop a bimodal piezotronic sensor (BPS) with a simplified structure and enhanced sensitivity. Through a combination of finite element simulations and experimental validation, we demonstrate that the BPS can effectively monitor both dynamic and static forces, achieving an on/off ratio of 1029, a gauge factor of 23,439 and a static force response duration of up to 600 s, significantly outperforming the performance of conventional piezoelectric sensors. As a proof-of-concept, the BPS demonstrates the continuous monitoring of Achilles tendon behavior under mixed dynamic and static loading conditions. Aided by deep learning algorithms, the system achieves 96% accuracy in identifying Achilles tendon movement patterns, thus enabling warnings for dangerous movements. This work provides a viable strategy for bimodal force monitoring, highlighting its potential in wearable electronics.

PMID:40299192 | DOI:10.1007/s40820-025-01757-6

Eur Radiol Exp. 2025 Apr 29;9(1):44. doi: 10.1186/s41747-025-00584-z.

ABSTRACT

BACKGROUND: T2-weighted images are a critical component of prostate magnetic resonance imaging (MRI), and it would be useful to automatically assess image quality (IQ) on a patient-specific basis without radiologist oversight.

METHODS: This retrospective study comprised 1,412 axial T2-weighted prostate scans. Four experienced uroradiologists graded IQ using a 0-to-3 scale (0 = uninterpretable; 1 = marginally interpretable; 2 = adequately diagnostic; 3 = more than adequately diagnostic), binarized into nondiagnostic (IQ0 or IQ1), requiring rescanning, and diagnostic (IQ2 or IQ3), not requiring rescanning. The deep learning (DL) model was trained on 1,006 scans; 203 other scans were used for validation of multiple convolutional neural networks; the remaining 203 exams were used as a test set. 3D-DenseNet_169 was chosen among 11 models based on multiple evaluation criteria. The rescan predictions were compared to the number of rescans performed on a subset of 174 exams.

RESULTS: The model accurately predicts radiologist IQ scores (Cohen κ = 0.658), similar to the human inter-rater reliability (κ = 0.688-0.791). The model also predicts rescanning necessity similarly to radiologists: model κ = 0.537; reviewer κ = 0.577-0.703. The rescan model prediction area under the curve was 0.867.

CONCLUSION: The DL model showed a strong ability to differentiate diagnostic from nondiagnostic axial T2-weighted prostate images, accurately mimicking expert radiologists' IQ scores. Using the model, the clinical unnecessary rescan rate could be reduced from over 50% to less than 30%.

RELEVANCE STATEMENT: DL assessment of T2-weighted prostate MRI scans can accurately assess IQ, determining the need to repeat inadequate scans as well as avoiding repeat scans of those with adequate diagnostic quality, resulting in reduced unnecessary rescanning.

KEY POINTS: Artificial intelligence assessment of prostate MRI T2-weighted image quality can improve exam time management. The model showed over 75% accuracy in assessing prostate MRI T2-weighted image quality. Expert radiologists have a substantial agreement in evaluating prostate MRI T2-weighted image quality.

PMID:40299162 | DOI:10.1186/s41747-025-00584-z

Anal Chem. 2025 Apr 29. doi: 10.1021/acs.analchem.5c00306. Online ahead of print.

ABSTRACT

Cell-cell interactions are crucial for understanding various physiological and pathological processes, yet conventional population-level methods fail to disclose the heterogeneity at a single-cell resolution. Single-cell coculture systems that isolate and cultivate single-cell pairs can help reveal heterogeneous interactions between different types of individual cells. However, precise and high-throughput pairing of individual cells for long-term coculture remains challenging. Meanwhile, tools for analyzing single-cell data sets have lagged due to the increased data throughput. Herein, we report a deep learning-assisted high-throughput addressable single-cell coculture system (DL-HASCCS), enabling fast pairing of individual heterogeneous cells and quantitative analysis of single-cell interactions in a high-throughput manner by integrating high-throughput single-cell cocultivation and automated data processing. By analyzing the interaction between single breast cancer cells and single endothelial cells under normal and chemotherapy conditions, the effect of cell-cell interactions on cell proliferation and migration is revealed at the single-cell level, providing valuable insights into cellular heterogeneity.

PMID:40298933 | DOI:10.1021/acs.analchem.5c00306

Adv Sci (Weinh). 2025 Apr 29:e2501750. doi: 10.1002/advs.202501750. Online ahead of print.

ABSTRACT

Despite being a serious health condition that significantly increases cardiovascular and metabolic disease risks, sleep apnea syndrome (SAS) remains largely underdiagnosed. While polysomnography (PSG) remains the gold standard for diagnosis, its clinical application is limited by high costs, complex setup requirements, and sleep quality interference. Although wearable devices using photoplethysmography (PPG) have shown promise in SAS detection, their continuous operation demands substantial power consumption, hindering long-term monitoring capabilities. Here, a dual-modal wearable system is presented integrating a piezoelectric nanogenerator (PENG) and PPG sensor with a biomimetic fingertip structure for SAS detection. A two-stage detection strategy is adopted where the self-powered PENG performs continuous preliminary screening, activating the PPG sensor only when suspicious events are detected. Combined with a Vision Transformer-based deep learning model, the high-accuracy configuration achieves 99.59% accuracy, while the low-power two-stage approach maintained 94.95% accuracy. This dual-modal wearable pulse detection system provides a practical solution for long-term SAS monitoring, overcoming the limitations of traditional PSG while maintaining high detection accuracy. The system's versatility in both home and clinical settings offers the potential for improving early detection rates and treatment outcomes for SAS patients.

PMID:40298874 | DOI:10.1002/advs.202501750

Radiology. 2025 Apr;315(1):e242570. doi: 10.1148/radiol.242570.

ABSTRACT

Background Precise assessment of myocardial ischemia burden and cardiovascular risk stratification based on dynamic CT myocardial perfusion imaging (MPI) is lacking. Purpose To develop and validate a deep learning (DL) model for automated quantification of myocardial blood flow (MBF) and ischemic myocardial volume (IMV) percentage and to explore the prognostic value for major adverse cardiovascular events (MACE). Materials and Methods This multicenter study comprised three cohorts of patients with clinically indicated CT MPI and coronary CT angiography (CCTA). Cohorts 1 and 2 were retrospective cohorts (May 2021 to June 2023 and January 2018 to December 2022, respectively). Cohort 3 was prospectively included (November 2016 to December 2021). The DL model was developed in cohort 1 (training set: 211 patients, validation set: 57 patients, test set: 90 patients). The diagnostic performance of MBF derived from the DL model (MBFDL) for myocardial ischemia was evaluated in cohort 2 based on the area under the receiver operating characteristic curve (AUC). The prognostic value of the DL model-derived IMV percentage was assessed in cohort 3 using multivariable Cox regression analyses. Results Across three cohorts, 1108 patients (mean age: 61 years ± 12 [SD]; 667 men) were included. MBFDL showed excellent agreement with manual measurements in the test set (segment-level intraclass correlation coefficient = 0.928; 95% CI: 0.921, 0.935). MBFDL showed higher diagnostic performance (vessel-based AUC: 0.97) over CT-derived fractional flow reserve (FFR) (vessel-based AUC: 0.87; P = .006) and CCTA-derived diameter stenosis (vessel-based AUC: 0.79; P < .001) for hemodynamically significant lesions, compared with invasive FFR. Over a mean follow-up of 39 months, MACE occurred in 94 (14.2%) of 660 patients. IMV percentage was an independent predictor of MACE (hazard ratio = 1.12, P = .003), with incremental prognostic value (C index: 0.86; 95% CI: 0.84, 0.88) over conventional risk factors and CCTA parameters (C index: 0.84; 95% CI: 0.82, 0.86; P = .02). Conclusion A DL model enabled automated CT MBF quantification and accurate diagnosis of myocardial ischemia. DL model-derived IMV percentage was an independent predictor of MACE and mildly improved cardiovascular risk stratification. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Zhu and Xu in this issue.

PMID:40298595 | DOI:10.1148/radiol.242570