PLoS One. 2025 Jan 27;20(1):e0317450. doi: 10.1371/journal.pone.0317450. eCollection 2025.

ABSTRACT

In 2019, the novel coronavirus swept the world, exposing the monitoring and early warning problems of the medical system. Computer-aided diagnosis models based on deep learning have good universality and can well alleviate these problems. However, traditional image processing methods may lead to high false positive rates, which is unacceptable in disease monitoring and early warning. This paper proposes a low false positive rate disease detection method based on COVID-19 lung images and establishes a two-stage optimization model. In the first stage, the model is trained using classical gradient descent, and relevant features are extracted; in the second stage, an objective function that minimizes the false positive rate is constructed to obtain a network model with high accuracy and low false positive rate. Therefore, the proposed method has the potential to effectively classify medical images. The proposed model was verified using a public COVID-19 radiology dataset and a public COVID-19 lung CT scan dataset. The results show that the model has made significant progress, with the false positive rate reduced to 11.3% and 7.5%, and the area under the ROC curve increased to 92.8% and 97.01%.

PMID:39869555 | DOI:10.1371/journal.pone.0317450

PLoS One. 2025 Jan 27;20(1):e0317662. doi: 10.1371/journal.pone.0317662. eCollection 2025.

ABSTRACT

Patients with type 1 diabetes and their physicians have long desired a fully closed-loop artificial pancreas (AP) system that can alleviate the burden of blood glucose regulation. Although deep reinforcement learning (DRL) methods theoretically enable adaptive insulin dosing control, they face numerous challenges, including safety and training efficiency, which have hindered their clinical application. This paper proposes a safe and efficient adaptive insulin delivery controller based on DRL. It employed ten tricks to enhance the proximal policy optimization (PPO) algorithm, improving training efficiency. Additionally, a dual safety mechanism of 'proactive guidance + reactive correction' was introduced to reduce the risks of hyperglycemia and hypoglycemia and to prevent emergencies. Performance evaluations in the Simglucose simulator demonstrate that the proposed controller achieved an 87.45% time in range (TIR) median, superior to baseline methods, with a lower incidence of hypoglycemia, notably eliminating severe hypoglycemia and treatment failures. These encouraging results indicate that the DRL-based fully closed-loop AP controller has taken an essential step toward clinical implementation.

PMID:39869550 | DOI:10.1371/journal.pone.0317662

Acta Bioeng Biomech. 2025 Jan 27;26(3):123-134. doi: 10.37190/abb-02474-2024-02. Print 2024 Sep 1.

ABSTRACT

Purpose: Monitoring and assessing the level of lower limb motor skills using the Biodex System plays an important role in the training of football players and in post-traumatic rehabilitation. The aim of this study was to build and test an artificial intelligence-based model to assess the peak torque of the lower limb extensors and flexors. The model was based on real-world results in three groups: hearing (n = 19) and deaf football players (n = 28) and non-training deaf pupils (n = 46). Methods: The research used a 4-layer forward CNN neural network with two hidden layers with typical normalization for small data sets and Multilayer Perceptron (MLP) based on MatlabR2023a software with Neural Networks and Deep Learning toolkits and semiautomated learning algorithm selection using ML.NET. Results: The 70-90% accuracy shown in the article is sufficient here. AI provides a highly accurate, objective and efficient means of assessing neuromuscular performance, which can improve injury prevention and rehabilitation strategies. Conclusions: The high accuracy shows that AI-based models can help with this, but their wider practical implementation requires further cross-disciplinary research. AI, and in particular MLP and CNN can support both training methods and various gaming aspects. The contribution of the research is to use an innovative approach to derive computational rules/guidelines from an explicitly given dataset and then identify the relevant physiological torque of the lower limb extensors and flexors in the knee joint. The model complements existing methodologies for describing physiology of peak torque of lower limbs with using fuzzy logic, with a so-called dynamic norm built into the model.

PMID:39869478 | DOI:10.37190/abb-02474-2024-02

PLoS Genet. 2025 Jan 27;21(1):e1011577. doi: 10.1371/journal.pgen.1011577. Online ahead of print.

ABSTRACT

Some animals can regenerate large missing regions of their nervous system, requiring mechanisms to restore the pattern, numbers, and wiring of diverse neuron classes. Because injuries are unpredictable, regeneration must be accomplished from an unlimited number of starting points. Coordinated regeneration of neuron-glia architecture is thus a major challenge and remains poorly understood. In planarians, neurons and glia are regenerated from distinct progenitors. We found that planarians first regenerate neurons expressing a Delta-encoding gene, delta-2, at key positions in the central and peripheral nervous systems. Planarian glia are specified later from dispersed Notch-1-expressing mesoderm-like phagocytic progenitors. Inhibition of delta-2 or notch-1 severely reduced glia in planarians, but did not affect the specification of other phagocytic cell types. Loss of several delta-2-expressing neuron classes prevented differentiation of the glia associated with them, whereas transplantation of delta-2-expressing photoreceptor neurons was sufficient for glia formation at an ectopic location. Our results suggest a model in which patterned delta-2-expressing neurons instruct phagocytic progenitors to locally differentiate into glia, presenting a mechanism for coordinated regeneration of numbers and pattern of cell types.

PMID:39869602 | DOI:10.1371/journal.pgen.1011577

Int J Syst Evol Microbiol. 2025 Jan;75(1). doi: 10.1099/ijsem.0.006648.

ABSTRACT

An obligately anaerobic, spore-forming sulphate-reducing bacterium, strain SB140T, was isolated from a long-term continuous enrichment culture that was inoculated with peat soil from an acidic fen. Cells were immotile, slightly curved rods that stained Gram-negative. The optimum temperature for growth was 28 °C. Strain SB140T grew at pH 4.0-7.5 with an optimum pH of 6.0-7.0 using various electron donors and electron acceptors. Yeast extract, sugars, alcohols and organic acids were used as electron donors for sulphate reduction. SB140T additionally used elemental sulphur and nitrate as electron acceptors but not sulphite, thiosulphate or iron(III) provided as ferrihydrite and fumarate. The 16S rRNA gene sequence placed strain SB140T in the genus Desulfosporosinus of the phylum Bacillota. The predominant cellular fatty acids were iso-C15 : 0 (52.6%) and 5,7 C15 : 2 (19.9%). The draft genome of SB140T (5.42 Mbp in size) shared 77.4% average nucleotide identity with the closest cultured relatives Desulfosporosinus acididurans M1T and Desulfosporosinus acidiphilus SJ4T. On the basis of phenotypic, phylogenetic and genomic characteristics, SB140T was identified as a novel species within the genus Desulfosporosinus, for which we propose the name Desulfosporosinus paludis sp. nov. The type strain is SB140T (=DSM 117342T=JCM 39521T).

PMID:39869511 | DOI:10.1099/ijsem.0.006648

Clin Pharmacol Ther. 2025 Jan 27. doi: 10.1002/cpt.3552. Online ahead of print.

ABSTRACT

Clopidogrel, an anti-platelet drug, is used to prevent thrombosis after percutaneous coronary intervention. Clopidogrel resistance results in recurring ischemic events, with African Americans (AA) suffering disproportionately. The aim of this study was to discover novel biomarkers of clopidogrel resistance in African Americans using genome and transcriptome data. We conducted a genome-wide association study (GWAS), including local ancestry adjustment, in 141 AA on clopidogrel to identify genetic associations with high on-treatment platelet reactivity (HTPR), with validation of genome-wide significant and suggestive loci in an independent cohort of AA clopidogrel patients (N = 823) from the Million Veteran's Program (MVP) along with in vitro functional analysis. We performed differential gene expression (DGE) analysis in whole blood to identify transcriptomic predictors of response, followed by functional validation in MEG-01 cells. GWAS identified one signal on Chromosome 7 as significantly associated with increasing risk of HTPR. The lead single-nucleotide polymorphism (SNP), rs7807369, within thrombospondin 7A (THSD7A) was associated with an increased risk of HTPR (odds ratio (OR) = 4.02, P = 4.56 × 10-9). Higher THSD7A gene expression was associated with HTPR in an independent cohort of clopidogrel-treated patients (P = 0.004) and carrying a risk allele showed increased gene expression in primary human endothelial cells. Notably, the CYP2C19*2 variants showed no association with clopidogrel response in the discovery or MVP cohorts. DGE analysis identified an association with decreased LAIR1 and AP3B2 expression to HTPR. LAIR1 knockdown in MEG-01 cells resulted in increased expression of SYK and AKT1, suggesting an inhibitory role of LAIR1 in the Glycoprotein VI pathway. In summary, these findings suggest that other variants and genes outside of CYP2C19 star alleles play an important role in clopidogrel response in AA.

PMID:39868839 | DOI:10.1002/cpt.3552

Am J Med Genet B Neuropsychiatr Genet. 2025 Jan 27:e33024. doi: 10.1002/ajmg.b.33024. Online ahead of print.

ABSTRACT

Pharmacogenetic studies involving Carboxylesterase 1 (CES1), Latrophilin-3 (LPHN3), and Catechol-O-methyltransferase (COMT) revealed individual differences regarding therapeutic response in children with attention deficit hyperactivity disorder (ADHD) under methylphenidate (MPH) treatment. This study aimed to evaluate MPH's association with the adverse effect status in children and its relationship with CES1, LPHN3, and COMT in the Turkish population. The study included 102 children and adolescents with ADHD, who were categorized as responders, or the adverse effect group based on their treatment response. The Naranjo Adverse Drug Reaction Probability Scale evaluated the presence and severity of adverse effects. Saliva sample was taken from the patients and genotype distribution of CES1 rs3815583, CES1 rs2307227, LPHN3 rs6551665, LPHN3 rs1947274, LPHN3 rs6858066, LPHN3 rs2345039, and COMT rs4680 were examined. In the adverse effect group, instances of carrying the GG genotype in CES1 rs2307227, having G vs. T genotype and GG vs. GT were significantly higher. In LPHN3 rs2345039, carrying the C genotype vs. G was associated with a serious adverse effect. In COMT rs4680, individuals with the AA or GG genotype were significantly higher in the adverse effect group. Our study suggests a relationship between genetic polymorphisms and the side effect status in children receiving MPH.

PMID:39868802 | DOI:10.1002/ajmg.b.33024

bioRxiv [Preprint]. 2025 Jan 13:2025.01.10.632500. doi: 10.1101/2025.01.10.632500.

ABSTRACT

The Arabian Peninsula is considered the initial site of historic human migration out of Africa. The modern-day indigenous Arabians are believed to be the descendants who remained from the ancient split of the migrants into Eurasia. Here, we investigated how the population history and cultural practices such as endogamy have shaped the genetic variation of the Saudi Arabians. We genotyped 3,352 individuals and identified twelve genetic sub-clusters that corresponded to the geographical distribution of different tribal regions, differentiated by distinct components of ancestry based on comparisons to modern and ancient DNA references. These sub-clusters also showed variation across ranges of the genome covered in runs of homozygosity, as well as differences in population size changes over time. Using 25,488,981 variants found in whole genome sequencing data (WGS) from 302 individuals, we found that the Saudi tend to show proportionally more deleterious alleles than neutral alleles when compared to Africans/African Americans from gnomAD (e.g. a 13% increase of deleterious alleles annotated by AlphaMissense between 0.5 -5% frequency in Saudi, compared to 7% decrease of the benign alleles; P < 0.001). Saudi sub-clusters with greater inbreeding and lower effective population sizes showed greater enrichment of deleterious alleles as well. Additionally, we found that approximately 10% of the variants discovered in our WGS data are not observed in gnomAD; these variants are also enriched with deleterious annotations. To accelerate studying the population-enriched deleterious alleles and their health consequences in this population, we made available the allele frequency estimates of 25,488,981 variants discovered in our samples. Taken together, our results suggest that Saudi's population history impacts its pattern of genetic variation with potential consequences to the population health. It further highlights the need to sequence diverse and unique populations so to provide a foundation on which to interpret medical-and pharmaco-genomic findings from these populations.

PMID:39868174 | PMC:PMC11761371 | DOI:10.1101/2025.01.10.632500

Pediatr Pulmonol. 2025 Jan;60(1):e27493. doi: 10.1002/ppul.27493.

ABSTRACT

BACKGROUND: New CFTR Modulator triple therapy Elexacaftor-Ivacaftor-Tezacaftor (ETI) prove efficacy in pulmonary outcomes. However, its impact on nasal sinus symptoms in children has not been specifically studied. The aim of this study is to evaluate the impact of this therapy on nasal sinus symptomatology in children aged 6-12 years.

METHODS: A prospective, single-center cohort study was conducted over a 12-month follow-up period in children aged 6-12 years at the initiation of ETI therapy. The primary outcome was evolution of the SN-5 score, a validated pediatric questionnaire measuring quality of life related to nasal sinus symptoms. A decrease of 0.5 points is considered clinically significant. Secondary outcomes included changes in clinical examination findings (obstructive turbinate hypertrophy, polyps, presence of pus in the middle meatus, and externalized mucocele), quality of life measured by the Visual Analog Scale (VAS), and number of antibiotic courses during the study period.

RESULTS: Twenty-six patients were included between March and September 2023, with no lost to follow-up. The initial mean SN-5 score was 2.88 (95% CI {1.91; 3.85}). After 1 year, the mean SN-5 score was significantly lower (1.41, 95% CI {1.00; 1.88}, Delta = 1.47, p < 0.001). The VAS related to symptoms also improved (Delta = 1.7, p < 0.001), and the number of antibiotic courses decreased (25 vs. 69, p < 0.001). A trend toward improvement in clinical examination parameters was observed.

CONCLUSION: ETI therapy appears to significantly improve nasal sinus symptoms in children aged 6-12 years, as evidenced by improved quality-of-life scales and reduced antibiotic use.

PMID:39868969 | DOI:10.1002/ppul.27493

Pediatr Pulmonol. 2025 Jan;60(1):e27497. doi: 10.1002/ppul.27497.

ABSTRACT

BACKGROUND: Cystic Fibrosis Foundation guidelines recommend human milk (HM) as the ideal source of nutrition for children with CF (cwCF). Despite known pulmonary and nutritional benefits, fewer cwCF ever receive HM compared to the general population. Early nutrition choices are preference-sensitive, yet little is known about the factors that impede or sustain HM feeding among parents of cwCF.

OBJECTIVES: Explore perceptions and experiences of mothers of cwCF who initiated HM feeding.

METHODS: Mothers of cwCF aged ≤ 10 years completed audio-taped, semi-structured interviews describing their experiences with HM feeding. Interviews were transcribed and two researchers independently coded the transcripts and conducted content and thematic analysis using an inductive approach.

RESULTS: Participants included 28 mothers who initiated HM feeding. Major themes included: (1) the impact of a CF diagnosis on HM feeding plans; (2) CF-specific challenges to HM feeding; (3) mixed perceptions of the CF care team's support for HM feeding and of the role of formula in CF nutritional care; and (4) the benefit of lactation consultants as part of the CF care team.

CONCLUSION: Many parents prioritize HM for their cwCF given the well-established health benefits. However, CF-specific barriers to HM feeding are common and nutritional challenges necessitating fortification add additional barriers to sustained HM feeding efforts. While HM may improve long-term pulmonary outcomes, our findings demonstrate the need for personalized support for mothers desiring to HM feed to facilitate shared decision-making around options to optimize early nutritional status among cwCF.

PMID:39868923 | DOI:10.1002/ppul.27497

bioRxiv [Preprint]. 2025 Jan 14:2025.01.14.632786. doi: 10.1101/2025.01.14.632786.

ABSTRACT

Pf bacteriophages, lysogenic viruses that infect Pseudomonas aeruginosa (Pa), are implicated in the pathogenesis of chronic Pa infections; phage-infected (Pf+) strains are known to predominate in people with cystic fibrosis (pwCF) who are older and have more severe disease. However, the transmission patterns of Pf underlying the progressive dominance of Pf+ strains are unclear. In particular, it is unknown whether phage transmission commonly occurs horizontally between bacteria within the airway via viral particles or if Pf+ bacteria are mostly acquired via new Pseudomonas infections. Here, we have studied Pa genomic sequences from 3 patient cohorts totaling 663 clinical isolates from 105 pwCF. We identify Pf+ isolates and analyze transmission patterns of Pf within patients between genetically similar groups of bacteria called "clone types". We find that Pf is predominantly passed down vertically within Pa lineages and rarely via horizontal transfer between clone types within the airway. Conversely, we find extensive evidence of Pa superinfection by a new, genetically distinct Pa that is Pf+. Finally, we find that clinical isolates show reduced activity of the type IV pilus and reduced susceptibility to Pf in vitro. These results cast new light on the transmission of virulence-associated phages in the clinical setting.

PMID:39868244 | PMC:PMC11761399 | DOI:10.1101/2025.01.14.632786

Int J Surg. 2025 Jan 24. doi: 10.1097/JS9.0000000000002232. Online ahead of print.

ABSTRACT

BACKGROUND: The impact of aortic arch (AA) morphology on the management of the procedural details and the clinical outcomes of the transfemoral artery (TF)-transcatheter aortic valve replacement (TAVR) has not been evaluated. The goal of this study was to evaluate the AA morphology of patients who had TF-TAVR using an artificial intelligence algorithm and then to evaluate its predictive value for clinical outcomes.

MATERIALS AND METHODS: A total of 1480 consecutive patients undergoing TF-TAVR using a new-generation transcatheter heart valve at 12 institutes were included in this retrospective study. The AA measurements were evaluated by deep learning, and then the approach index (IA) was determined. The machine learning algorithm was used to construct the predictive model and was validated externally.

RESULTS: The area under the curve of the IA model using random forest and logistic regression was 0.675 [95% confidence interval (CI): 0.586-0.764] and 0.757 (95% CI: 0.665-0.849), respectively. The IA model was validated externally, and consistent distinctions were obtained. After we used a generalized propensity score matching method for continuous exposure, the IA was the strongest correlation factor for major procedural events (odds ratio: 3.87; 95% CI: 2.13-7.59, P < 0.001). When leaflet morphology or transcatheter heart valve type was an interactive item with IA, neither of them was statistically significant in terms of clinical outcomes.

CONCLUSION: IA may be used to identify the impact of AA morphology on procedural and clinical outcomes in patients having TF-TAVR and to help to predict the procedural complications.

PMID:39869394 | DOI:10.1097/JS9.0000000000002232

Int J Surg. 2025 Jan 24. doi: 10.1097/JS9.0000000000002221. Online ahead of print.

ABSTRACT

BACKGROUND: Detecting kidney trauma on CT scans can be challenging and is sometimes overlooked. While deep learning (DL) has shown promise in medical imaging, its application to kidney injuries remains underexplored. This study aims to develop and validate a DL algorithm for detecting kidney trauma, using institutional trauma data and the Radiological Society of North America (RSNA) dataset for external validation.

METHODS: We developed RenoTrNet, a DL model trained on institutional data. We evaluated the model's performance through external validation on randomly selected cases from the RSNA dataset. Performance metrics included the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Heatmap visualizations were used to aid interpretability.

RESULTS: In the internal testing dataset, the model achieved an accuracy of 0.88 (95% CI: 0.82-0.92), with a sensitivity of 0.75 (95% CI: 0.62-0.85) and a specificity of 0.95 (95% CI: 0.89-0.98). PPV and NPV were 0.89 (95% CI: 0.76-0.95) and 0.88 (95% CI: 0.81-0.93), respectively. In external RSNA validation, the algorithm c demonstrated robust performance with an accuracy of 0.93 (0.91-0.95), a sensitivity of 0.73 (0.60-0.83), a specificity of 0.94 (0.93-0.96), a PPV of 0.45 (0.35-0.56), and an NPV of 0.98 (0.97-0.99).

CONCLUSION: The RenoTrNet DL algorithm demonstrated high accuracy in detecting kidney trauma on CT scans, both in internal and external validation. By optimizing image segmentation and computational efficiency, this model has potential for clinical deployment, potentially aiding in trauma diagnosis in real-world clinical scenarios.

PMID:39869390 | DOI:10.1097/JS9.0000000000002221

J Chem Inf Model. 2025 Jan 27. doi: 10.1021/acs.jcim.4c01735. Online ahead of print.

ABSTRACT

Drug-drug interaction can lead to diminished therapeutic effects or increased toxicity, posing significant risks, especially in polypharmacy, and cytochrome P450 plays an indispensable role in this interaction. Cytochrome P450, responsible for the metabolism and detoxification of most drugs, metabolizes about 90% of Food and Drug Administration-approved drugs, making early detection of potential drug-drug interactions. Over the years, in-silico modeling has become a valuable tool for predicting drug-drug interactions. Still, conventional molecular descriptors focusing on the structural properties of drugs often overlook complex electronic interactions critical for accurate predictions. To address this, we implemented the Manifold Embedding of Molecular Surface (MEMS) approach, which retains the quantum mechanical characteristics of molecules. MEMS-generated electronic attributes were embedded and featurized for deep learning using the DeepSets architecture, where our models achieved high accuracy, particularly for cytochrome P450 enzyme 1A2 (CYP1A2), with F1 scores reaching up to 0.866. This study highlights the potential of integrating detailed electronic properties with deep learning to improve predictive models for drug-drug interactions, addressing the limitations of traditional molecular descriptors and machine-learning techniques.

PMID:39869197 | DOI:10.1021/acs.jcim.4c01735

J Mater Chem B. 2025 Jan 27. doi: 10.1039/d4tb02494j. Online ahead of print.

ABSTRACT

Tissue regeneration after a wound occurs through three main overlapping and interrelated stages namely inflammatory, proliferative, and remodelling phases, respectively. The inflammatory phase is key for successful tissue reconstruction and triggers the proliferative phase. The macrophages in the non-healing wounds remain in the inflammatory loop, but their phenotypes can be changed via interactions with nanofibre-based scaffolds mimicking the organisation of the native structural support of healthy tissues. However, the organisation of extracellular matrix (ECM) is highly complex, combining order and disorder, which makes it difficult to replicate. The possibility of predicting the desirable biomimetic geometry and chemistry of these nanofibre scaffolds would streamline the scaffold design process. Fifteen families of nanofibre scaffolds, electrospun from combinations of polyesters (polylactide, polyhydroxybutyrate), polysaccharides (polysucrose, carrageenan, cellulose), and polyester ether (polydioxanone) were investigated and analysed using machine learning (ML). The Random Forest model had the best performance (92.8%) in predicting inflammatory responses of macrophages on the nanoscaffolds using tumour necrosis factor-alpha as the output. CellProfiler proved to be an effective tool to process scanning electron microscopy (SEM) images of the macrophages on the scaffolds, successfully extracting various features and measurements related to cell phenotypes M0, M1, and M2. Deep learning modelling indicated that convolutional neural network models have the potential to be applied to SEM images to classify macrophage cells according to their phenotypes. The complex organisation of the nanofibre scaffolds can be analysed using graph theory (GT), revealing the underlying connectivity patterns of the nanofibres. Analysis of GT descriptors showed that the electrospun membranes closely mimic the connectivity patterns of the ECM. We conclude that ML-facilitated, GT-quantified engineering of cellular scaffolds has the potential to predict cell interactions, streamlining the pipeline for tissue engineering.

PMID:39869000 | DOI:10.1039/d4tb02494j

Anal Chem. 2025 Jan 27. doi: 10.1021/acs.analchem.4c04531. Online ahead of print.

ABSTRACT

In metabolomic analysis based on liquid chromatography coupled with mass spectrometry, detecting and quantifying intricate objects is a massive job. Current peak picking methods still cause high rates of incorrectly picked peaks to influence the reliability and reproducibility of results. To address these challenges, we developed QuanFormer, a deep learning method based on object detection designed to accurately quantify peak signals. Our algorithm combines the feature extraction capabilities of convolutional neural networks (CNNs) with the global computation capability of Transformer architecture. Data training in QuanFormer by using nearly 20,000 annotated regions-of-interest (ROIs) ensures unique prediction via bipartite matching, achieving 96.5% of the average precision value on the test set. Even without retraining, QuanFormer achieves over 90% accuracy in distinguishing true from false peaks. Performance was further analyzed using visualization techniques applied to the encoder and decoder layers. We also demonstrated that QuanFormer could correct retention time shifts for peak alignment and generally surpass the existing methods, including MZmine 3 and PeakDetective, to obtain a larger number of picked peaks and higher accurate quantification. Finally, we also carried out metabolomic analysis in a clinical cohort of breast cancer patients and utilized QuanFormer to detect and quantify the potential biomarkers. QuanFormer is open-source and available at https://github.com/LinShuhaiLAB/QuanFormer.

PMID:39868899 | DOI:10.1021/acs.analchem.4c04531

J Intern Med. 2025 Jan 27. doi: 10.1111/joim.20053. Online ahead of print.

ABSTRACT

BACKGROUND: Steatotic liver disease (SLD) is a potentially reversible condition but often goes unnoticed with the risk for end-stage liver disease.

PURPOSE: To opportunistically estimate SLD on lung screening chest computed tomography (CT) and investigate its prognostic value in heavy smokers participating in the National Lung Screening Trial (NLST).

MATERIAL AND METHODS: We used a deep learning model to segment the liver on non-contrast-enhanced chest CT scans of 19,774 NLST participants (age 61.4 ± 5.0 years; 41.2% female) at baseline and on the 1-year follow-up scan if no cancer was detected. SLD was defined as hepatic fat fraction (HFF) ≥5% derived from Hounsfield unit measures of the segmented liver. Participants with SLD were categorized as lean (body mass index [BMI] < 25 kg/m2) and overweight (BMI ≥ 25 kg/m2). The primary outcome was all-cause mortality. Cox proportional hazard regression assessed the association between (1) SLD and mortality at baseline and (2) the association between a change in HFF and mortality within 1 year.

RESULTS: There were 5.1% (1000/19,760) all-cause deaths over a median follow-up of 6 (range, 0.8-6) years. At baseline, SLD was associated with increased mortality in lean but not in overweight/obese participants as compared to participants without SLD (hazard ratio [HR] adjusted for risk factors: 1.93 [95% confidence interval 1.52-2.45]; p = 0.001). Individuals with an increase in HFF within 1 year had a significantly worse outcome than participants with stable HFF (HR adjusted for risk factors: 1.29 [1.01-1.65]; p = 0.04).

CONCLUSION: SLD is an independent predictor for long-term mortality in heavy smokers beyond known clinical risk factors.

PMID:39868889 | DOI:10.1111/joim.20053

J Magn Reson Imaging. 2025 Jan 27. doi: 10.1002/jmri.29720. Online ahead of print.

ABSTRACT

BACKGROUND: The spinal column is a frequent site for metastases, affecting over 30% of solid tumor patients. Identifying the primary tumor is essential for guiding clinical decisions but often requires resource-intensive diagnostics.

PURPOSE: To develop and validate artificial intelligence (AI) models using noncontrast MRI to identify primary sites of spinal metastases, aiming to enhance diagnostic efficiency.

STUDY TYPE: Retrospective.

POPULATION: A total of 514 patients with pathologically confirmed spinal metastases (mean age, 59.3 ± 11.2 years; 294 males) were included, split into a development set (360) and a test set (154).

FIELD STRENGTH/SEQUENCE: Noncontrast sagittal MRI sequences (T1-weighted, T2-weighted, and fat-suppressed T2) were acquired using 1.5 T and 3 T scanners.

ASSESSMENT: Two models were evaluated for identifying primary sites of spinal metastases: the expert-derived features (EDF) model using radiologist-identified imaging features and a ResNet50-based deep learning (DL) model trained on noncontrast MRI. Performance was assessed using accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve (ROC-AUC) for top-1, top-2, and top-3 indicators.

STATISTICAL TESTS: Statistical analyses included Shapiro-Wilk, t tests, Mann-Whitney U test, and chi-squared tests. ROC-AUCs were compared via DeLong tests, with 95% confidence intervals from 1000 bootstrap replications and significance at P < 0.05.

RESULTS: The EDF model outperformed the DL model in top-3 accuracy (0.88 vs. 0.69) and AUC (0.80 vs. 0.71). Subgroup analysis showed superior EDF performance for common sites like lung and kidney (e.g., kidney F1: 0.94 vs. 0.76), while the DL model had higher recall for rare sites like thyroid (0.80 vs. 0.20). SHapley Additive exPlanations (SHAP) analysis identified sex (SHAP: -0.57 to 0.68), age (-0.48 to 0.98), T1WI signal intensity (-0.29 to 0.72), and pathological fractures (-0.76 to 0.25) as key features.

DATA CONCLUSION: AI techniques using noncontrast MRI improve diagnostic efficiency for spinal metastases. The EDF model outperformed the DL model, showing greater clinical potential.

PLAIN LANGUAGE SUMMARY: Spinal metastases, or cancer spreading to the spine, are common in patients with advanced cancer, often requiring extensive tests to determine the original tumor site. Our study explored whether artificial intelligence could make this process faster and more accurate using noncontrast MRI scans. We tested two methods: one based on radiologists' expertise in identifying imaging features and another using a deep learning model trained to analyze MRI images. The expert-based method was more reliable, correctly identifying the tumor site in 88% of cases when considering the top three likely diagnoses. This approach may help doctors reduce diagnostic time and improve patient care.

LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

PMID:39868626 | DOI:10.1002/jmri.29720

Acta Crystallogr D Struct Biol. 2025 Feb 1. doi: 10.1107/S2059798325000592. Online ahead of print.

ABSTRACT

Predicting the 3D structure of RNA is a significant challenge despite ongoing advancements in the field. Although AlphaFold has successfully addressed this problem for proteins, RNA structure prediction raises difficulties due to the fundamental differences between proteins and RNA, which hinder its direct adaptation. The latest release of AlphaFold, AlphaFold3, has broadened its scope to include multiple different molecules such as DNA, ligands and RNA. While the AlphaFold3 article discussed the results for the last CASP-RNA data set, the scope of its performance and the limitations for RNA are unclear. In this article, we provide a comprehensive analysis of the performance of AlphaFold3 in the prediction of 3D structures of RNA. Through an extensive benchmark over five different test sets, we discuss the performance and limitations of AlphaFold3. We also compare its performance with ten existing state-of-the-art ab initio, template-based and deep-learning approaches. Our results are freely available on the EvryRNA platform at https://evryrna.ibisc.univ-evry.fr/evryrna/alphafold3/.

PMID:39868559 | DOI:10.1107/S2059798325000592

Anal Chem. 2025 Jan 27. doi: 10.1021/acs.analchem.4c05913. Online ahead of print.

ABSTRACT

Timely and accurate detection of trace mycotoxins in agricultural products and food is significant for ensuring food safety and public health. Herein, a deep learning-assisted and entropy-driven catalysis (EDC)-Argonaute powered fluorescence single-particle aptasensing platform was developed for ultrasensitive detection of fumonisin B1 (FB1) using single-stranded DNA modified with biotin and red fluorescence-encoded microspheres as a signal probe and streptavidin-conjugated magnetic beads as separation carriers. The binding of aptamer with FB1 releases the trigger sequence to mediate EDC cycle to produce numerous 5'-phosphorylated output sequences, which can be used as the guide DNA to activate downstream Thermus thermophilus Argonaute (TtAgo) for cleaving the signal probe, resulting in increased number of fluorescence microspheres remaining in the final reaction supernatant after magnetic separation. Subsequently, through fast and accurate counting of red bright particles in the captured confocal fluorescence images from the supernatant via a YOLOv9 deep learning model, the sensitive and specific detection of FB1 could be realized. This approach has a limit of detection (LOD) of 0.89 pg/mL with a linear range from 1 pg/mL to 100 ng/mL, and satisfactory recovery (87.2-113.5%) in real food samples indicates its practicality. The integration of the aptamer and EDC with TtAgo broadens the target range of Argonaute and enhances sensitivity. Furthermore, incorporating deep learning significantly improves the analytical efficiency of single-particle detection. This work provides a promising analytical strategy in biosensing and promotes the application of fluorescence single-particle detection in food safety monitoring.

PMID:39868471 | DOI:10.1021/acs.analchem.4c05913