PLoS One. 2025 May 19;20(5):e0317519. doi: 10.1371/journal.pone.0317519. eCollection 2025.

ABSTRACT

This study significantly contributes to the sphere of educational technology by deploying state-of-the-art machine learning and deep learning strategies for meaningful changes in education. The hybrid stacking approach did an excellent implementation using Decision Trees, Random Forest, and XGBoost as base learners with Gradient Boosting as a meta-learner, which managed to record an accuracy of 90%. That indeed puts into great perspective the huge potential it possesses for accuracy measures while predicting in educational setups. The CNN model, which predicted with an accuracy of 89%, showed quite impressive capability in sentiment analysis to acquire further insight into the emotional status of the students. RCNN, Random Forests, and Decision Trees contribute to the possibility of educational data complexity with valuable insight into the complex interrelationships within ML models and educational contexts. The application of the bagging XGBoost algorithm, which attained a high accuracy of 88%, further stamps its utility toward enhancement of academic performance through strong robust techniques of model aggregation. The dataset that was used in this study was sourced from Kaggle, with 1205 entries of 14 attributes concerning adaptability, sentiment, and academic performance; the reliability and richness of the analytical basis are high. The dataset allows rigorous modeling and validation to be done to ensure the findings are considered robust. This study has several implications for education and develops on the key dimensions: teacher effectiveness, educational leadership, and well-being of the students. From the obtained information about student adaptability and sentiment, the developed system helps educators to make modifications in instructional strategy more efficiently for a particular student to enhance effectiveness in teaching. All these aspects could provide critical insights for the educational leadership to devise data-driven strategies that would enhance the overall school-wide academic performance, as well as create a caring learning atmosphere. The integration of sentiment analysis within the structure of education brings an inclusive, responsive attitude toward ensuring students' well-being and, thus, a caring educational environment. The study is closely aligned with sustainable ICT in education objectives and offers a transformative approach to integrating AI-driven insights with practice in this field. By integrating notorious ML and DL methodologies with educational challenges, the research puts the basis for future innovations and technology in this area. Ultimately, it contributes to sustainable improvement in the educational system.

PMID:40388422 | DOI:10.1371/journal.pone.0317519

PLoS One. 2025 May 19;20(5):e0316043. doi: 10.1371/journal.pone.0316043. eCollection 2025.

ABSTRACT

The adoption of deep learning in ECG diagnosis is often hindered by the scarcity of large, well-labeled datasets in real-world scenarios, leading to the use of transfer learning to leverage features learned from larger datasets. Yet the prevailing assumption that transfer learning consistently outperforms training from scratch has never been systematically validated. In this study, we conduct the first extensive empirical study on the effectiveness of transfer learning in multi-label ECG classification, by investigating comparing the fine-tuning performance with that of training from scratch, covering a variety of ECG datasets and deep neural networks. Firstly, We confirm that fine-tuning is the preferable choice for small downstream datasets; however, it does not necessarily improve performance. Secondly, the improvement from fine-tuning declines when the downstream dataset grows. With a sufficiently large dataset, training from scratch can achieve comparable performance, albeit requiring a longer training time to catch up. Thirdly, fine-tuning can accelerate convergence, resulting in faster training process and lower computing cost. Finally, we find that transfer learning exhibits better compatibility with convolutional neural networks than with recurrent neural networks, which are the two most prevalent architectures for time-series ECG applications. Our results underscore the importance of transfer learning in ECG diagnosis, yet depending on the amount of available data, researchers may opt not to use it, considering the non-negligible cost associated with pre-training.

PMID:40388401 | DOI:10.1371/journal.pone.0316043

PLoS One. 2025 May 19;20(5):e0320426. doi: 10.1371/journal.pone.0320426. eCollection 2025.

ABSTRACT

Monitoring the remaining food in patients' trays is a routine activity in healthcare facilities as it provides valuable insights into the patients' dietary intake. However, estimating food leftovers through visual observation is time-consuming and biased. To tackle this issue, we have devised an efficient deep learning-based approach that promises to revolutionize how we estimate food leftovers. Our first step was creating the LeFoodSet dataset, a pioneering large-scale open dataset explicitly designed for estimating food leftovers. This dataset is unique in its ability to estimate leftover rates and types of food. To the best of our knowledge, this is the first comprehensive dataset for this type of analysis. The dataset comprises 524 image pairs representing 34 Indonesian food categories, each with images captured before and after consumption. Our prediction models employed a combined visual feature extraction and late fusion approach utilizing soft parameter sharing. Here, we used multi-task (MT) models that simultaneously predict leftovers and food types in training. In the experiments, we tested the single task (ST) model, the ST Model with Ground Truth (ST-GT), the MT model, and the MT model with Inter-task Connection (MT-IC). Our AI-based models, particularly the MT and MT-IC models, have shown promising results, outperforming human observation in predicting leftover food. These findings show the best with the ResNet101 model, where the Mean Average Error (MAE) of leftover task and food classification accuracy task is 0.0801 and 90.44% in the MT Model and 0.0817 and 92.56% in the MT-IC Model, respectively. It is proved that the proposed solution has a bright future for AI-based approaches in medical and nursing applications.

PMID:40388400 | DOI:10.1371/journal.pone.0320426

N Engl J Med. 2025 May 19. doi: 10.1056/NEJMoa2503643. Online ahead of print.

ABSTRACT

BACKGROUND: Nerandomilast (BI 1015550) is an orally administered preferential inhibitor of phosphodiesterase 4B with antifibrotic and immunomodulatory properties. Nerandomilast has been shown to slow the progression of idiopathic pulmonary fibrosis, but an assessment of its effects in other types of progressive pulmonary fibrosis is needed.

METHODS: In a phase 3, double-blind trial, we randomly assigned patients with progressive pulmonary fibrosis in a 1:1:1 ratio to receive nerandomilast at a dose of 18 mg twice daily, nerandomilast at a dose of 9 mg twice daily, or placebo, with stratification according to background therapy (nintedanib vs. none) and fibrotic pattern on high-resolution computed tomography (usual interstitial pneumonia-like pattern vs. other patterns). The primary end point was the absolute change from baseline in the forced vital capacity (FVC), measured in milliliters, at week 52.

RESULTS: A total of 1176 patients received at least one dose of nerandomilast or placebo, of whom 43.5% were taking background nintedanib therapy at baseline. The adjusted mean change in the FVC at week 52 was -98.6 ml (95% confidence interval [CI], -123.7 to -73.4) in the nerandomilast 18-mg group, -84.6 ml (95% CI, -109.6 to -59.7) in the nerandomilast 9-mg group, and -165.8 ml (95% CI, -190.5 to -141.0) in the placebo group. The adjusted difference between the nerandomilast 18-mg group and the placebo group was 67.2 ml (95% CI, 31.9 to 102.5; P<0.001), and the adjusted difference between the nerandomilast 9-mg group and the placebo group was 81.1 ml (95% CI, 46.0 to 116.3; P<0.001). The most frequent adverse event was diarrhea, reported in 36.6% of the patients in the nerandomilast 18-mg group, 29.5% of those in the nerandomilast 9-mg group, and 24.7% of those in the placebo group. Serious adverse events occurred in similar percentages of patients in the trial groups.

CONCLUSIONS: In patients with progressive pulmonary fibrosis, treatment with nerandomilast led to a smaller decline in the FVC than placebo over a period of 52 weeks. (Funded by Boehringer Ingelheim; FIBRONEER-ILD ClinicalTrials.gov number, NCT05321082.).

PMID:40388329 | DOI:10.1056/NEJMoa2503643

medRxiv [Preprint]. 2025 May 7:2025.05.05.25326966. doi: 10.1101/2025.05.05.25326966.

ABSTRACT

Type 2 diabetes (T2D) is epidemiologically associated with a wide range of non-cardiovascular comorbidities, yet their shared etiology has not been fully elucidated. Leveraging eight non-overlapping mechanistic clusters of T2D genetic profiles, each representing distinct biological pathways, we investigate putative causal links between cluster-stratified T2D genetic predisposition and 21 non-cardiovascular comorbidities. Most of the identified putative causal effects are driven by distinct T2D genetic clusters. For example, the risk-increasing effects of T2D genetic predisposition on cataracts and erectile dysfunction are primarily attributed to obesity and glucose regulation mechanisms, respectively. When surveyed in populations across the globe, we observe opposing effect directions for depression, asthma and chronic obstructive pulmonary disease between populations. We identify a putative causal link between T2D genetic predisposition and osteoarthritis. To underscore the translational potential of our findings, we intersect high-confidence effector genes for osteoarthritis with targets of T2D-approved drugs and identify metformin as a potential candidate for drug repurposing in osteoarthritis.

PMID:40385452 | PMC:PMC12083600 | DOI:10.1101/2025.05.05.25326966

Am J Manag Care. 2025 May;31(5):240-244. doi: 10.37765/ajmc.2025.89673.

ABSTRACT

OBJECTIVES: To quantify the magnitude of altruism value as applied to a hypothetical new treatment for a rare, severe pediatric disease: Duchenne muscular dystrophy (DMD).

STUDY DESIGN: Prospective survey of individuals not planning to have children in the future.

METHODS: A survey was administered to US adults (aged ≥ 21 years) not intending to have a child in the future to elicit willingness to pay (WTP) for government insurance coverage for a new hypothetical DMD treatment that improves mortality and morbidity relative to the current standard of care. A multiple random staircase design was used to identify an indifference point between status quo government insurance coverage and coverage with additional cost in taxes that would cover the treatment if unrelated individuals had a child with DMD. Altruism value was calculated as respondents' mean WTP.

RESULTS: Among 215 respondents, 54.9% (n = 118) were aged 25 to 44 years and 80.0% (n = 172) were women. Mean WTP for insurance coverage of the hypothetical DMD treatment for others was $80.01 (95% CI, $41.64-$118.37) annually, or $6.67 monthly, after adjustment to account for disease probability overestimation. The adjusted altruism value was higher than the ex ante per-person value using traditional cost-effectiveness approaches ($45.30/year). Without adjusting, individuals were willing to pay $799.11 annually ($66.59 monthly).

CONCLUSIONS: Despite no possibility of accruing health benefits directly for themselves or their children, individuals had a high WTP for government insurance coverage of a novel treatment for this rare, severe pediatric disease.

PMID:40387711 | DOI:10.37765/ajmc.2025.89673

Clin Pharmacol Ther. 2025 Jun;117(6):1562-1576. doi: 10.1002/cpt.3675.

ABSTRACT

Over the last three decades, transporters have become increasingly recognized for their important roles in clinical pharmacology. As gatekeepers of drug absorption, disposition and targeting, transporters in the intestine, liver, kidney and blood brain barrier have been the subject of many clinical pharmacology studies. A seminal work published in 2001 was among the first studies to shift the focus of pharmacogenomic research from drug metabolizing enzymes to drug transporters, demonstrating that pharmacogenomic factors in genes in addition to drug metabolizing enzymes, and in particular, in transporter genes, could play an important role in interindividual variation in pharmacokinetics of drugs.

PMID:40388108 | DOI:10.1002/cpt.3675

Minerva Med. 2025 Apr;116(2):89-93. doi: 10.23736/S0026-4806.20.06488-5.

ABSTRACT

BACKGROUND: In acute respiratory distress syndrome (ARDS) obesity is associated with lower mortality but the mechanism(s) have not been elucidated.

METHODS: We aimed at assessing plasma biomarker levels interleukin-8 (IL-8), matrix metalloproteinase-7 (MMP-7), Toll-like receptor 2 (TLR-2), tumor necrosis factor-α (TNF-α) and procalcitonin (PCT) at baseline and 3 days later in 20 consecutive moderate-severe ARDS consecutively admitted to our Center.

RESULTS: Our population includes 20 consecutive mechanically ventilated patients with moderate-to severe ARDS. The incidence of obesity was 40% (8/20). No differences were detectable between obese and normal patients in baseline characteristics. In particular, ICU mortality was comparable between the two subgroups. No differences were detectable between the two subgroups at baseline and after 72 hours in biomarker plasma levels. When examining the behavior of each biomarker, obese patients showed a significant increase in MMP7 and TLR-2 values at 72 hours in respect to baseline, differently from normal patients.

CONCLUSIONS: Our data strongly suggest that obese patients with moderate to severe ARDS have an altered inflammatory response to acute lung injury, since a significant increase in MMP-7 and TLR-2 was detectable at 72 hours only in these patients. Further investigations are needed to confirm our results in larger cohorts.

PMID:40387315 | DOI:10.23736/S0026-4806.20.06488-5

Int J Med Sci. 2025 Apr 28;22(10):2434-2445. doi: 10.7150/ijms.114402. eCollection 2025.

ABSTRACT

Aim and background: Vaccines are a cornerstone of modern medicine, significantly reducing morbidity and mortality worldwide. Their administration in infants requires consideration of physiological maturity. Cytochrome P450 (CYP450) enzymes, crucial for drug metabolism, are underdeveloped at birth and mature over the first two to three years of life. While vaccines are not directly metabolized by CYP450 enzymes, emerging evidence suggests that certain excipients-such as polysorbate 80 and gelatin-could interact with CYP450 pathways, particularly in genetically susceptible infants. This study integrates pharmacogenetics and epidemiology to examine how CYP450 immaturity and variability may influence vaccine excipient metabolism, immune activation, and infant health outcomes. Methods: A systematic review of peer-reviewed literature, pharmacogenetic data, and epidemiological studies was conducted to assess CYP450 enzyme activity in infants, potential metabolic interactions with vaccine excipients, and temporal associations between vaccination and sudden infant death syndrome (SIDS). Gaps in postmortem investigations were also evaluated for their impact to identify metabolic vulnerabilities. Results: CYP450 enzymes exhibit developmental immaturity in infants and genetic polymorphisms-particularly in CYP2D6 and CYP3A5-may affect vaccine excipient clearance. While epidemiological evidence shows temporal clustering of some SIDS cases post-vaccination, causality remains unproven. Inflammation-induced suppression of CYP450 enzymes raise questions about potential metabolic vulnerabilities, which current postmortem protocols often fail to capture. Conclusion: This study highlights the need for further research into the influence of CYP450 variability on vaccine-related outcomes. Incorporating genetic and metabolic profiling into postmortem protocols may improve our understanding of metabolic contributions to SIDS and refine vaccine safety assessments. Clinical significance: Developmental immaturity and genetic variability in CYP450 enzymes may affect vaccine excipient metabolism and interact with immune activation. This interplay could influence metabolic vulnerabilities in infants, particularly with inflammation-induced CYP450 suppression. Genetic and metabolic profiling before vaccination could identify at-risk infants, while postmortem analysis may enhance SIDS understanding and vaccine safety assessments.

PMID:40386062 | PMC:PMC12080585 | DOI:10.7150/ijms.114402

Expert Rev Respir Med. 2025 May 19. doi: 10.1080/17476348.2025.2508313. Online ahead of print.

ABSTRACT

INTRODUCTION: Non-cystic-fibrosis bronchiectasis (NCFB) is an airway disorder with a growing world-wide prevalence that affects predominantly older and female individuals and is associated with high symptom burden and significant healthcare expenditure. Brensocatib is a novel orally bioavailable, selective, and reversible dipeptidyl peptidase 1 (DPP1) inhibitor that leads to a sustained inhibition of neutrophil serine protease activity in both whole blood and sputum.

AREAS COVERED: This drug profile summarizes the role of inflammation in the pathophysiology of bronchiectasis. The mechanism of action of brensocatib in reducing neutrophil-related inflammation is described. We then summarize existing efficacy and safety data from Phase 2 and Phase 3 studies of brensocatib in patients with bronchiectasis, in which the rate of exacerbation was the primary endpoint. Finally, we summarize the current marketplace for brensocatib, including the unmet for effective therapies for bronchiectasis, and the status of other potential treatments undergoing clinical trials.

EXPERT OPINION: Brensocatib is the first-in-class DPP1 inhibitor that shows promise as a treatment for patients with bronchiectasis.

PMID:40387478 | DOI:10.1080/17476348.2025.2508313

JPGN Rep. 2025 Mar 17;6(2):146-152. doi: 10.1002/jpr3.70007. eCollection 2025 May.

ABSTRACT

This is a prospective, multicenter study of a cohort of 224 cystic fibrosis (CF) patients treated with CF transmembrane conductance regulator (CFTR) modulators (CFTRm). Our aim was to prospectively analyze the effect of CFTRm treatment on fat-soluble vitamin serum levels. Demographic and clinical data were recorded, and fat-soluble vitamin levels were analyzed at baseline, and at 6 and 12 months after starting treatment. Two groups were analyzed separately: patients receiving dual therapy lumacaftor/ivacaftor or tezacaftor/ivacaftor (Lum/Tez+Iva), and those on triple therapy with elexacaftor/tezacaftor/ivacaftor (ETI). We found that treatment with ETI produced a significant increase in vitamin D and A levels within the first 6 months, which was maintained at 12 months. However, with dual therapy, we observed an increase only in vitamin A levels within the first 6 months, which was not maintained at 12 months. No differences were found in vitamin E serum levels between the groups.

PMID:40386324 | PMC:PMC12078071 | DOI:10.1002/jpr3.70007

JPGN Rep. 2025 Mar 17;6(2):213. doi: 10.1002/jpr3.70011. eCollection 2025 May.

NO ABSTRACT

PMID:40386322 | PMC:PMC12078063 | DOI:10.1002/jpr3.70011

JCEM Case Rep. 2025 May 15;3(7):luaf104. doi: 10.1210/jcemcr/luaf104. eCollection 2025 Jul.

ABSTRACT

Glucagon-like peptide-1 (GLP-1) agonists are widely used in the management of type 2 diabetes and obesity, with their therapeutic scope expanding to address cardiometabolic and cardiorenal conditions. However, their increasing use has been associated with potential adverse effects, including acute pancreatitis (AP). The exact prevalence of GLP-1 agonist-induced AP remains uncertain and reliable predictors for its onset have yet to be identified. We present 3 cases of class-associated predilection for GLP-1 analog-associated AP in patients with carrier states for hemochromatosis (HC) and cystic fibrosis. Case 1 is a heterozygous carrier for the C282Y HC pathogenic variant. Case 2 is a heterozygous carrier of the Delta F508 deletion of the cystic fibrosis transmembrane regulator (CFTR) gene. Case 3 is compound heterozygous carrier of a single CFTR intron 9 poly T allele pathogenic variant (5T/7T/8T), as well as a single pathogenic variant of the C282Y HC gene. Our observation suggests that carrier states for cystic fibrosis and HC may predispose individuals to GLP-1 agonist-associated AP. Genetic testing for these carrier states should be considered among patients with GLP-1 agonist-associated AP to provide more support and data for this as a potential true risk factor.

PMID:40384889 | PMC:PMC12078934 | DOI:10.1210/jcemcr/luaf104

Quant Imaging Med Surg. 2025 May 1;15(5):4431-4444. doi: 10.21037/qims-24-2205. Epub 2025 Apr 28.

ABSTRACT

BACKGROUND: Pulmonary magnetic resonance imaging (MRI) has the advantage of nonionizing radiation and multiparameter imaging of structure and function, facilitating its clinical use in a variety of pulmonary diseases. This study aimed to identify the research trends and emerging topics in pulmonary MRI by conducting a comprehensive bibliometric analysis of the field over the past decade.

METHODS: A search of the Web of Science Core Collection database was conducted with the words "lung" and "MRI" for literature published from 2014 to 2023. The data were further analyzed with R and CiteSpace software in terms of annual publications and citations, collaborative networks (countries, institutions, and authors), source's local impact, keyword clustering, and burst analysis.

RESULTS: A total of 1,839 publications related to pulmonary MRI have been published over the last decade, with a relatively slow growth trend. The top three journals in terms of total publications and citations were Magnetic Resonance in Medicine, Journal of Magnetic Resonance Imaging, and Radiology. The most productive country was the United States, and the countries with the strongest collaborative links were the United States and the United Kingdom. The most productive institutions and authors were Ruprecht Karls University Heidelberg (articles, n=309) and Wild JM (articles, n=86), respectively. Keyword cluster analysis identified five clusters: "lung cancer", "magnetic resonance imaging", "lung MRI", "cystic fibrosis", and "congenital diaphragmatic hernia". Keyword burst analysis showed that the keywords with the highest burst intensity in the first 5 years and the last 5 years were "mice" and "standardization", respectively.

CONCLUSIONS: Over the past decade, research trends in pulmonary MRI have focused on lung cancer and cystic fibrosis as the dominant clinical diseases. Research has been centered on standardizing pulmonary MRI to promote its clinical application.

PMID:40384665 | PMC:PMC12082580 | DOI:10.21037/qims-24-2205

Bioinformatics. 2025 May 19:btaf165. doi: 10.1093/bioinformatics/btaf165. Online ahead of print.

ABSTRACT

MOTIVATION: Deep learning has deeply influenced protein science, enabling breakthroughs in predicting protein properties, higher-order structures, and molecular interactions.

RESULTS: This paper introduces DeepProtein, a comprehensive and user-friendly deep learning library tailored for protein-related tasks. It enables researchers to seamlessly address protein data with cutting-edge deep learning models. To assess model performance, we establish a benchmark that evaluates different deep learning architectures across multiple protein-related tasks, including protein function prediction, subcellular localization prediction, protein-protein interaction prediction, and protein structure prediction. Furthermore, we introduce DeepProt-T5, a series of fine-tuned Prot-T5-based models that achieve state-of-the-art performance on four benchmark tasks, while demonstrating competitive results on six of others. Comprehensive documentation and tutorials are available which could ensure accessibility and support reproducibility.

AVAILABILITY AND IMPLEMENTATION: Built upon the widely used drug discovery library DeepPurpose, DeepProtein is publicly available at https://github.com/jiaqingxie/DeepProtein.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:40388205 | DOI:10.1093/bioinformatics/btaf165

Interdiscip Cardiovasc Thorac Surg. 2025 May 19:ivaf101. doi: 10.1093/icvts/ivaf101. Online ahead of print.

ABSTRACT

OBJECTIVES: This study aimed to develop an automated method for pulmonary artery and vein segmentation in both left and right lungs from computed tomography (CT) images using artificial intelligence (AI). The segmentations were evaluated using PulmoSR software, which provides 3D visualizations of patient-specific anatomy, potentially enhancing a surgeon's understanding of the lung structure.

METHODS: A dataset of 125 CT scans from lung segmentectomy patients at Erasmus MC was used. Manual annotations for pulmonary arteries and veins were created with 3D Slicer. nnU-Net models were trained for both lungs, assessed using Dice score, sensitivity, and specificity. Intraoperative recordings demonstrated clinical applicability. A paired t-test evaluated statistical significance of the differences between automatic and manual segmentations.

RESULTS: The nnU-Net model, trained at full 3D resolution, achieved a mean Dice score between 0.91 and 0.92. The mean sensitivity and specificity were: left artery: 0.86 and 0.99, right artery: 0.84 and 0.99, left vein: 0.85 and 0.99, right vein: 0.85 and 0.99. The automatic method reduced segmentation time from ∼1.5 hours to under 5 min. Five cases were evaluated to demonstrate how the segmentations support lung segmentectomy procedures. P-values for Dice scores were all below 0.01, indicating statistical significance.

CONCLUSIONS: The nnU-Net models successfully performed automatic segmentation of pulmonary arteries and veins in both lungs. When integrated with visualization tools, these automatic segmentations can enhance preoperative and intraoperative planning by providing detailed 3D views of patients anatomy.

PMID:40388152 | DOI:10.1093/icvts/ivaf101

ACS Nano. 2025 May 19. doi: 10.1021/acsnano.5c04340. Online ahead of print.

ABSTRACT

Conventional point-of-care testing (POCT) has limitations in sensitivity with high risks of missed detection or false positive, which restrains its applications for routine outpatient care analysis and early clinical diagnosis. By merits of the cutting-edge quantum precision metrology, this study devised a mini quantum sensor via magnetofluorescent qubit tagging and tunning on core-shelled fluorescent nanodiamond FND@SiO2. Comprehensive characterizations confirmed the formation of FND biolabels, while spectroscopies secured no degradation in spin-state transition after surface modification. A methodical parametrization was deliberated and decided, accomplishing a wide-field modulation depth ≥15% in ∼ zero field, which laid foundation for supersensitive sensing at single-FND resolution. Using viral nucleocapsid protein as a model marker, an ultralow limit of detection (LOD) was obtained by lock-in analysis, outperforming conventional colorimetry and immunofluorescence by > 2000 fold. Multianalyte and affinity assays were also enabled on this platform. Further by resort to artificial-intelligence (AI) augmentation in the Unet-ConvLSTM-Attention architecture, authentic qubit dots were identified by pixelwise survey through pristine qubit queues. Such processing not just improved pronouncedly the probing precision but also achieved deterministic detections down to a single protein in human saliva with an ultimate LOD as much as 7800-times lower than that of colloidal Au approach, which competed with the RT-qPCR threshold and the certified critical value of SIMOA, the gold standard. Hence, by AI-aided digitization on optic qubits, this REASSURED-compliant contraption may promise a next-generation POCT solution with unparalleled sensitivity, speed, and cost-effectiveness, which in whole confers a conclusive proof of the prowess of the burgeoning quantum metrics in biosensing.

PMID:40388114 | DOI:10.1021/acsnano.5c04340

Afr J Reprod Health. 2025 May 16;29(5s):51-64. doi: 10.29063/ajrh2025/v29i5s.7.

ABSTRACT

This study built a model to forecast the maturity of lungs by blending radiomics and deep learning methods. We examined ultrasound images from 263 pregnancies in the pregnancy stages. Utilizing the GE VOLUSON E8 system we captured images to extract and analyze radiomic features. These features were integrated with clinical data by means of deep learning algorithms such as DenseNet121 to enhance the accuracy of assessing fetal lung maturity. This combined model was validated by receiver operating characteristic (ROC) curve, calibration diagram, as well as decision curve analysis (DCA). We discovered that the accuracy and reliability of the diagnosis indicated that this method significantly improves the level of prediction of fetal lung maturity. This novel non-invasive diagnostic technology highlights the potential advantages of integrating diverse data sources to enhance prenatal care and infant health. The study lays groundwork, for validation and refinement of the model across various healthcare settings.

PMID:40387939 | DOI:10.29063/ajrh2025/v29i5s.7

Langmuir. 2025 May 19. doi: 10.1021/acs.langmuir.5c00769. Online ahead of print.

ABSTRACT

The surface chemistry of nanomaterials, particularly the density of functional groups, governs their behavior in applications such as bioanalysis, bioimaging, and environmental impact studies. Here, we report a precise method to quantify carboxyl groups per nanoparticle by combining anisotropically collapsing agarose gels for nanoparticle immobilization with fluorescence microscopy and acid-base titration. We applied this approach to photon-upconversion nanoparticles (UCNPs) coated with poly(acrylic acid) (PAA) and fluorescence-labeled polystyrene nanoparticles (PNs), which serve as models for bioimaging and environmental pollutants, respectively. UCNPs exhibited 152 ± 14 thousand carboxyl groups per particle (∼11 groups/nm2), while PNs were characterized with 38 ± 3.6 thousand groups (∼1.7 groups/nm2). The limit of detection was 6.4 and 1.9 thousand carboxyl groups per nanoparticle, and the limit of quantification was determined at 21 and 6.2 thousand carboxyl groups per nanoparticle for UCNP-PAAs and PNs, respectively. High intrinsic luminescence enabled direct imaging of UCNPs, while PNs required fluorescence staining with Nile Red to overcome low signal-to-noise ratios. The study also discussed the critical influence of nanoparticle concentration and titration conditions on the assay performance. This method advances the precise characterization of surface chemistry, offering insights into nanoparticle structure that extend beyond the resolution of electron microscopy. Our findings establish a robust platform for investigating the interplay of surface chemistry with nanoparticle function and fate in technological and environmental contexts, with broad applicability across nanomaterials.

PMID:40387864 | DOI:10.1021/acs.langmuir.5c00769

JASA Express Lett. 2025 May 1;5(5):053601. doi: 10.1121/10.0036754.

ABSTRACT

The increasing needs for controlling high noise levels motivate development of automatic sound event detection and classification methods. Little work deals with automatic train pass-by detection despite a high degree of annoyance. To this matter, an innovative approach is proposed in this paper. A generic classifier identifies vehicle noise on the raw audio signal. Then, combined short sound level analysis and mel-spectrogram-based classification refine this outcome to discard anything but train pass-bys. On various long-term signals, a 90% temporal overlap with reference demarcation is observed. This high detection rate allows a proper railway noise contribution estimation in different soundscapes.

PMID:40387613 | DOI:10.1121/10.0036754