Mol Divers. 2025 Apr 25. doi: 10.1007/s11030-025-11197-4. Online ahead of print.

ABSTRACT

Predicting molecular properties with high accuracy is essential across scientific fields, from drug discovery and biotechnology to materials science and environmental research. In biomedical sciences, accurate molecular property prediction is crucial for elucidating disease mechanisms, identifying potential drug candidates, and optimising various processes. However, existing approaches, often based on low-dimensional representations, fail to capture the intricate spatial and structural complexities of molecular data. This study introduces a novel hybrid deep learning model, the Graph-Aware AURA-LSTM (Attentive Unified Representation Architecture-Long Short-Term Memory), designed to determine molecular properties with unprecedented accuracy using advanced graphical representations. AURA-LSTM combines multiple Graph Neural Network (GNN) architectures, specifically Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and Graph Isomorphism Networks (GINs), in a parallel structure to comprehensively capture the multidimensional structural features of molecules. Within this architecture, GCNs incorporate local structural relationships, GATs apply attention mechanisms to highlight critical structural elements, and GINs capture intricate molecular details through isomorphic distinction, resulting in a richly detailed feature matrix. The feature layer then processes this BiLSTM matrix, which evaluates temporal relationships to enhance molecular feature classification. Evaluated on eight benchmark datasets, AURA-LSTM demonstrated superior performance, consistently achieving over 90% accuracy and outperforming state-of-the-art methods. These results position AURA-LSTM as a robust tool for molecular feature classification, uniquely capable of integrating temporally aware insights from distinct GNN architectures.

PMID:40279083 | DOI:10.1007/s11030-025-11197-4

Discov Oncol. 2025 Apr 25;16(1):614. doi: 10.1007/s12672-025-02346-0.

ABSTRACT

OBJECTIVE: Oral cancer is an important health challenge worldwide and accurate survival time prediction of this disease can guide treatment decisions. This study aims to propose a deep learning-based model, DeepOmicsSurv, to predict survival in oral cancer patients using clinical and multi-omics data.

METHODS: DeepOmicsSurv builds on the DeepSurv model, incorporating multi-head attention convolutional layers, dropout, pooling, and batch normalization to boost its strength and precision. Various dimensionality reduction techniques, including Principal Component Analysis (PCA), Kernel PCA, Non-Negative Matrix Factorization (NMF), Singular Value Decomposition (SVD), Partial Least Squares (PLS), Multidimensional Scaling (MDS), and Autoencoders, were employed to manage the high-dimensional omics data. The model's performance was evaluated against DeepSurv, DeepHit, Cox Proportional Hazards (CoxPH), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). Additionally, SHapley Additive Explanations (SHAP) was used to analyze the impact of clinical features on survival predictions.

RESULTS: DeepOmicsSurv achieved a C-index of 0.966, MSE of 0.0138, RMSE of 0.1174, MAE of 0.0795, and MedAE of 0.0515, outperforming other deep learning models. Among various dimensionality reduction techniques, autoencoder performed the best with DeepOmicsSurv. SHAP analysis showed that Age, AJCC N Stage, alcohol history and patient smoking history are prevalent clinical features for survival time.

CONCLUSION: In conclusion, DeepOmicsSurv has the potential to predict survival time in oral cancer patients. This model achieved high accuracy with various data types including Clinical, DNAmethylation + clinical, mRNA + clinical, Copy number alteration + clinical, or multi-omics data. Additionally, SHAP analysis reveals clinical factors that influence survival time.

PMID:40278990 | DOI:10.1007/s12672-025-02346-0

Tomography. 2025 Apr 17;11(4):48. doi: 10.3390/tomography11040048.

ABSTRACT

BACKGROUND: Deep learning (DL) reconstruction techniques have shown promise in reducing MRI acquisition times while maintaining image quality. However, the impact of different acceleration factors on diagnostic accuracy in shoulder MRI remains unexplored in clinical practice.

PURPOSE: The purpose of this study was to evaluate the diagnostic accuracy of 2-fold and 4-fold DL-accelerated shoulder MRI protocols compared to standard protocols in clinical practice.

MATERIALS AND METHODS: In this prospective single-center study, 88 consecutive patients (49 males, 39 females; mean age, 51 years) underwent shoulder MRI examinations using standard, 2-fold (DL2), and 4-fold (DL4) accelerated protocols between June 2023 and January 2024. Four independent radiologists (experience range: 4-25 years) evaluated the presence of bone marrow edema (BME), rotator cuff tears, and labral lesions. The sensitivity, specificity, and interobserver agreement were calculated. Diagnostic confidence was assessed using a 4-point scale. The impact of reader experience was analyzed by stratifying the radiologists into ≤10 and >10 years of experience.

RESULTS: Both accelerated protocols demonstrated high diagnostic accuracy. For BME detection, DL2 and DL4 achieved 100% sensitivity and specificity. In rotator cuff evaluation, DL2 showed a sensitivity of 98-100% and specificity of 99-100%, while DL4 maintained a sensitivity of 95-98% and specificity of 99-100%. Labral tear detection showed perfect sensitivity (100%) with DL2 and slightly lower sensitivity (89-100%) with DL4. Interobserver agreement was excellent across the protocols (Kendall's W = 0.92-0.98). Reader experience did not significantly impact diagnostic performance. The area under the ROC curve was 0.94 for DL2 and 0.90 for DL4 (p = 0.32).

CLINICAL IMPLICATIONS: The implementation of DL-accelerated protocols, particularly DL2, could improve workflow efficiency by reducing acquisition times by 50% while maintaining diagnostic reliability. This could increase patient throughput and accessibility to MRI examinations without compromising diagnostic quality.

CONCLUSIONS: DL-accelerated shoulder MRI protocols demonstrate high diagnostic accuracy, with DL2 showing performance nearly identical to that of the standard protocol. While DL4 maintains acceptable diagnostic accuracy, it shows a slight sensitivity reduction for subtle pathologies, particularly among less experienced readers. The DL2 protocol represents an optimal balance between acquisition time reduction and diagnostic confidence.

PMID:40278715 | DOI:10.3390/tomography11040048

Tomography. 2025 Apr 1;11(4):41. doi: 10.3390/tomography11040041.

ABSTRACT

INTRODUCTION: An efficient pipeline for rosette trajectory magnetic resonance imaging reconstruction is proposed, combining the inverse Fourier transform with a vision transformer (ViT) network enhanced with a convolutional layer. This method addresses the challenges of reconstructing high-quality images from non-Cartesian data by leveraging the ViT's ability to handle complex spatial dependencies without extensive preprocessing.

MATERIALS AND METHODS: The inverse fast Fourier transform provides a robust initial approximation, which is refined by the ViT network to produce high-fidelity images.

RESULTS AND DISCUSSION: This approach outperforms established deep learning techniques for normalized root mean squared error, peak signal-to-noise ratio, and entropy-based image quality scores; offers better runtime performance; and remains competitive with respect to other metrics.

PMID:40278708 | DOI:10.3390/tomography11040041

Toxins (Basel). 2025 Apr 1;17(4):171. doi: 10.3390/toxins17040171.

ABSTRACT

The aaccurate prediction of enzymes with environment detoxification functions is crucial, not only to achieve a better understanding of bioremediation strategies, but also to alleviate environmental pollution. In the present study, a novel machine learning model was introduced which classifies enzymes by their toxin degradation ability. In this model, two different sets of data were used which include enzymes that can catalyze the toxin degradation as a positive dataset and non-toxin-degrading enzymes as a negative dataset. Further, a comparison of multiple classifiers was performed to find the best model and a Random Forest (RF) classifier was selected due to its strong performance. To enhance the accuracy, we combined RF with a Deep Neural Network (DNN), forming an ensemble model which effectively integrated both techniques. This combination achieved 95% precision, surpassing individual models. Our ensemble model not only ensures high prediction accuracy but also reliably differentiates toxin-degrading enzymes from non-degrading ones. This study highlights the power of combining classical machine learning with deep learning to advance prediction. Our model represents a significant step in enzyme classification and serves as a valuable resource for environmental biotechnology, food nutrition, and health applications.

PMID:40278669 | DOI:10.3390/toxins17040171

Toxins (Basel). 2025 Mar 22;17(4):156. doi: 10.3390/toxins17040156.

ABSTRACT

Aflatoxin B1, a toxic carcinogen frequently contaminating almonds, nuts, and food products, poses significant health risks. Therefore, a rapid and non-destructive detection method is crucial to detect aflatoxin B1-contaminated almonds to ensure food safety. This study introduces a novel deep learning approach utilizing 3D Inception-ResNet architecture with fine-tuning to classify aflatoxin B1-contaminated almonds using hyperspectral images. The proposed model achieved higher classification accuracy than traditional methods, such as support vector machine (SVM), random forest (RF), quadratic discriminant analysis (QDA), and decision tree (DT), for classifying aflatoxin B1 contaminated almonds. A feature selection algorithm was employed to enhance processing efficiency and reduce spectral dimensionality while maintaining high classification accuracy. Experimental results demonstrate that the proposed 3D Inception-ResNet (Lightweight) model achieves superior classification performance with a 90.81% validation accuracy, an F1-score of 0.899, and an area under the curve value of 0.964, outperforming traditional machine learning approaches. The Lightweight 3D Inception-ResNet model, with 381 layers, offers a computationally efficient alternative suitable for real-time industrial applications. These research findings highlight the potential of hyperspectral imaging combined with deep learning for aflatoxin B1 detection in almonds with higher accuracy. This approach supports the development of real-time automated screening systems for food safety, reducing contamination-related risks in almonds.

PMID:40278655 | DOI:10.3390/toxins17040156

Toxics. 2025 Apr 13;13(4):305. doi: 10.3390/toxics13040305.

ABSTRACT

Bacillus subtilis exhibits a great affinity to soluble U(VI) through non-reducing biomineralization. The pH value, temperature, initial uranium concentration, bacterial concentration, and adsorption time are recognized as the five environmental sensitive factors that can regulate the degree of non-reductive biomineralization. Most of the current studies have focused on the regulatory mechanisms of these factors on uranium non-reductive mineralization. However, there are still few reports on the importance of these factors in influencing non-reductive mineralization, as well as on how to regulate these factors to increase the efficiency of non-reductive mineralization and enhance the enrichment of Bacillus subtilis on uranium. In this work, a deep learning neural network model was constructed to effectively predict the effects of changes in these five environmental sensitivity factors on the non-reducing mineralization of Bacillus subtilis to uranium. Accuracy (99.6%) and R2 (up to 0.89) confirm a high degree of agreement between the predicted output and the observed values. Sensitivity analysis shows that in this model, pH value is the most important influencing factor. However, under different pH values, temperature, initial uranium concentration, adsorption time, and bacterial concentration have different effects. When the pH value is lower than 6, the most important factor is temperature, and once the pH value is greater than 6, the initial concentration is the most important factor. The results are expected to provide a theoretical basis for regulating the enrichment degree of U(VI) by Bacillus subtilis, achieving the maximum long-term stable fixation of U(VI), and understanding the environmental chemical behavior of uranium under different conditions.

PMID:40278621 | DOI:10.3390/toxics13040305

Toxics. 2025 Mar 28;13(4):254. doi: 10.3390/toxics13040254.

ABSTRACT

Surface air pollution affects ecosystems and people's health. However, traditional models have low prediction accuracy. Therefore, a hybrid model for accurately predicting daily surface PM2.5 concentrations was integrated with wavelet (W), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and bidirectional gated recurrent unit (BiGRU). The data for meteorological factors and air pollutants in Guangzhou City from 2014 to 2020 were utilized as inputs to the models. The W-CNN-BiGRU-BiLSTM hybrid model demonstrated strong performance during the predicting phase, achieving an R (correlation coefficient) of 0.9952, a root mean square error (RMSE) of 1.4935 μg/m3, a mean absolute error (MAE) of 1.2091 μg/m3, and a mean absolute percentage error (MAPE) of 7.3782%. Correspondingly, the accurate prediction of surface PM2.5 concentrations is beneficial for air pollution control and urban planning.

PMID:40278570 | DOI:10.3390/toxics13040254

J Intell. 2025 Apr 7;13(4):45. doi: 10.3390/jintelligence13040045.

ABSTRACT

This study proposes a multimodal deep learning model for automated scoring of image-based divergent thinking tests, integrating visual and semantic features to improve assessment objectivity and efficiency. Utilizing 708 Chinese high school students' responses from validated tests, we developed a system combining pretrained ResNet50 (image features) and GloVe (text embeddings), fused through a fully connected neural network with MSE loss and Adam optimization. The training set (603 images, triple-rated consensus scores) showed strong alignment with human scores (Pearson r = 0.810). Validation on 100 images demonstrated generalization capacity (r = 0.561), while participant-level analysis achieved 0.602 correlation with total human scores. Results indicate multimodal integration effectively captures divergent thinking dimensions, enabling simultaneous evaluation of novelty, fluency, and flexibility. This approach reduces manual scoring subjectivity, streamlines assessment processes, and maintains cost-effectiveness while preserving psychometric rigor. The findings advance automated cognitive evaluation methodologies by demonstrating the complementary value of visual-textual feature fusion in creativity assessment.

PMID:40278054 | DOI:10.3390/jintelligence13040045

J Imaging. 2025 Apr 21;11(4):125. doi: 10.3390/jimaging11040125.

ABSTRACT

Computer vision aims to enable machines to understand the visual world. Computer vision encompasses numerous tasks, namely action recognition, object detection and image classification. Much research has been focused on solving these tasks, but one that remains relatively uncharted is light enhancement (LE). Low-light enhancement (LLE) is crucial as computer vision tasks fail in the absence of sufficient lighting, having to rely on the addition of peripherals such as sensors. This review paper will shed light on this (focusing on video enhancement) subfield of computer vision, along with the other forementioned computer vision tasks. The review analyzes both traditional and deep learning-based enhancers and provides a comparative analysis on recent models in the field. The review also analyzes how popular computer vision tasks are improved and made more robust when coupled with light enhancement algorithms. Results show that deep learners outperform traditional enhancers, with supervised learners obtaining the best results followed by zero-shot learners, while computer vision tasks are improved with light enhancement coupling. The review concludes by highlighting major findings such as that although supervised learners obtain the best results, due to a lack of real-world data and robustness to new data, a shift to zero-shot learners is required.

PMID:40278041 | DOI:10.3390/jimaging11040125

Cell Biol Toxicol. 2025 Apr 25;41(1):73. doi: 10.1007/s10565-025-10031-9.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF), characterized by fibroblast activation and collagen deposition, is a progressive lung disease that lacks effective interventions. Ubiquitin-specific peptidase 10 (USP10) acts as a multifunctional player in inflammatory response and progression of cancers, the effect on pulmonary fibrosis is unknown. Here, we demonstrated downregulated expression of USP10 in fibrotic lung tissues of IPF patients. In the current study, lung tissues were collected at the end of weeks 1, 2, or 3 post bleomycin (BLM)-intratracheal delivery. Consistently, USP10 expression levels were reduced after BLM challenge in a time-dependent manner. Mice treated with lentivirus overexpressing USP10 exhibited mitigative lung injury and reduced collagen deposition. USP10 overexpression enhanced autophagy in BLM-treated mouse lungs. Interestingly, the protective effect of USP10 was attenuated as the pulmonary autophagy flux was blocked by autophagy inhibitor 3-methyladenine (3-MA). Primary human and mouse lung fibroblasts were treated with pro-fibrotic TGF-β1 to verify the role of USP10 in vitro. Mechanically, the deubiquitinating enzyme USP10 interacted with Sirtuin 6 (Sirt6) and inhibited its degradation. Furthermore, USP10 overexpression inhibited the activation of Sirt6-mediated AKT/mTOR pathway in both lung tissues and fibroblasts. Our findings suggest that USP10 might attenuate pulmonary fibrosis through the promotion of Sirt6/AKT/mTOR-mediated autophagy. These data prioritize USP10 as a therapeutic target for treating IPF.

PMID:40278953 | DOI:10.1007/s10565-025-10031-9

Infect Dis Rep. 2025 Apr 3;17(2):28. doi: 10.3390/idr17020028.

ABSTRACT

BACKGROUND: Interstitial lung diseases (ILDs) are a heterogeneous group of conditions that can cause fibrosis of the lung interstitium, resulting in respiratory failure and death. Patients with an ILD, particularly idiopathic pulmonary fibrosis (IPF) or connective tissue disease-associated ILDs (CTD-ILDs), are prone to develop chronic pulmonary infections such as tuberculosis (TB) and non-tuberculous mycobacterial pulmonary disease (NTM-PD).

METHODS: This case series examines the management of three ILD patients with a usual interstitial pneumonia (UIP) pattern and concomitant NTM-PD or TB at National Institute for Infectious Diseases "Lazzaro Spallanzani" in Rome, Italy, over three years (2019-2022).

RESULTS AND CONCLUSIONS: Multi-disciplinary discussion (MDD) was crucial to define the therapeutic approach due to the increased risk of side effects and drug interactions. Our work underscored how a comprehensive diagnostic evaluation, enriched by MDD, is useful for optimizing the management and reducing drug-related adverse effects and interactions in ILD patients with cavitary lesions.

PMID:40277955 | DOI:10.3390/idr17020028

Adv Respir Med. 2025 Mar 28;93(2):6. doi: 10.3390/arm93020006.

ABSTRACT

Background: Acute exacerbation (AE) of idiopathic pulmonary fibrosis (IPF) is one of the most challenging events in the disease course due to the high mortality despite treatment. The role of corticosteroid treatment in AE-IPF has never been defined, even though it is used in current clinical practice. We performed a meta-analysis to determine the effects of steroid treatment on the acute exacerbation outcomes in idiopathic pulmonary fibrosis (IPF). Objectives: To evaluate the impact of steroids on mortality in patients affected by an acute exacerbation of IPF. Methods: This meta-analysis was performed in accordance with the PRISMA statement. A systemic literature search was conducted through Google Scholar, Scopus, WoS, PubMed, and JSTOR. Manuscripts from January 2014 to September 2024 were included in the analysis. Articles were included on whether participants had an acute exacerbation of IPF. Regarding the intervention performed, we evaluated the studies in which patients underwent treatment with corticosteroids. As outcomes, studies were included if they analyzed the overall mortality. Results: A total of 2156 records were initially identified. Nineteen studies (3277 patients) were ultimately included in the final analysis, comparing 1552 patients who received steroids to 1725 patients without steroids. Steroid treatment poses a higher risk, as suggested by the summary measures (RR of 1.78; CI 1.29-2.76, p = 0.00001). Conclusions: This meta-analysis investigated the debated role of corticosteroid treatment during acute exacerbation of idiopathic pulmonary fibrosis. Overall, steroid therapy is associated with increased risk. Clinicians should carefully weigh the risks and benefits of corticosteroid therapy in acute exacerbation of IPF.

PMID:40277510 | DOI:10.3390/arm93020006

J Agric Food Chem. 2025 Apr 25. doi: 10.1021/acs.jafc.5c02528. Online ahead of print.

ABSTRACT

Nutmeg (Myristica fragrans) has been traditionally valued for its culinary and medicinal properties. In our ongoing efforts to discover pharmacologically active compounds from this spice, five new furan acids (2-6, jusahos B-F), one new neolignan (7, jusaho G), and six known compounds (1 and 8-12) were isolated from its nutmegs. The chemical structures of the compounds were elucidated using NMR spectroscopy and HRESIMS. Among them, compound 3 (jusaho C) demonstrated promising antineuroinflammatory and neuroprotective effects in BV2 and HT22 cells by modulating the MAPK/NF-κB signaling pathway, which was explored through network pharmacology, molecular docking, and experimental verification. Compound 3 also showed the improvement of locomotor activity in Caenorhabditis elegans model infected with Pseudomonas aeruginosa. These findings expand the phytochemical profile of M. fragrans, where only one furan acid was previously reported, and highlight nutmeg-derived compounds, particularly jusaho C, as potential functional food ingredients or nutraceuticals for managing neuroinflammatory conditions.

PMID:40278862 | DOI:10.1021/acs.jafc.5c02528

Metabolites. 2025 Apr 15;15(4):274. doi: 10.3390/metabo15040274.

ABSTRACT

BACKGROUND: The presence and concentration of lipids in serum of dairy cows have significant implications for both animal health and productivity and are potential biomarkers for several common diseases. However, information on serum lipid composition is rather fragmented, and lipid remodelling during the transition period is only partially understood.

METHODS: Using a combination of reversed-phase liquid chromatography-mass spectrometry (RP-LC-MS), hydrophilic interaction-mass spectrometry (HILIC-MS), and lipid annotation software, we performed a comprehensive identification and quantification of serum of dairy cows in pasture-based Holstein-Friesian cows. The lipid remodelling induced by negative energy balance was investigated by comparing the levels of all identified lipids between the fresh lactation (5-14 days in milk, DIM) and full lactation (65-80 DIM) stages.

RESULTS: We identified 535 lipid molecular species belonging to 19 classes. The most abundant lipid class was cholesteryl ester (CE), followed by phosphatidylcholine (PC), sphingomyelin (SM), and free fatty acid (FFA), whereas the least abundant lipids included phosphatidylserine (PS), phosphatidic acid (PA), phosphatidylglycerol (PG), acylcarnitine (AcylCar), ceramide (Cer), glucosylceramide (GluCer), and lactosylceramide (LacCer).

CONCLUSIONS: A remarkable increase in most lipids and a dramatic decrease in FFAs, AcylCar, and DHA-containing species were observed at the full lactation compared to fresh lactation stage. Several serum lipid biomarkers for detecting negative energy balance in cows were also identified.

PMID:40278403 | DOI:10.3390/metabo15040274

Metabolites. 2025 Apr 9;15(4):255. doi: 10.3390/metabo15040255.

ABSTRACT

BACKGROUND/OBJECTIVES: Drug development for complex diseases such as NAFLD is often lengthy and expensive. Drug repurposing, the process of finding new therapeutic uses for existing drugs, presents a promising alternative to traditional approaches. This study aims to identify potential repurposed drugs for NAFLD by leveraging disease-disease relationships and drug-target data from the BioSNAP database.

METHODS: A bipartite network was constructed between drugs and their target genes, followed by the application of the BiClusO bi-clustering algorithm to identify high-density clusters. Clusters with significant associations with NAFLD risk genes were considered to predict potential drug candidates. Another set of candidates was determined based on disease similarity.

RESULTS: A novel ranking methodology was developed to evaluate and prioritize these candidates, supported by a comprehensive literature review of their effectiveness in NAFLD treatment.

CONCLUSIONS: This research demonstrates the potential of drug repurposing to accelerate the development of therapies for NAFLD, offering valuable insights into novel treatment strategies for complex diseases.

PMID:40278384 | DOI:10.3390/metabo15040255

Elife. 2025 Apr 25;12:RP88794. doi: 10.7554/eLife.88794.

ABSTRACT

Plasmid construction is central to life science research, and sequence verification is arguably its costliest step. Long-read sequencing has emerged as a competitor to Sanger sequencing, with the principal benefit that whole plasmids can be sequenced in a single run. Nevertheless, the current cost of nanopore sequencing is still prohibitive for routine sequencing during plasmid construction. We develop a computational approach termed Simple Algorithm for Very Efficient Multiplexing of Oxford Nanopore Experiments for You (SAVEMONEY) that guides researchers to mix multiple plasmids and subsequently computationally de-mixes the resultant sequences. SAVEMONEY defines optimal mixtures in a pre-survey step, and following sequencing, executes a post-analysis workflow involving sequence classification, alignment, and consensus determination. By using Bayesian analysis with prior probability of expected plasmid construction error rate, high-confidence sequences can be obtained for each plasmid in the mixture. Plasmids differing by as little as two bases can be mixed as a single sample for nanopore sequencing, and routine multiplexing of even six plasmids per 180 reads can still maintain high accuracy of consensus sequencing. SAVEMONEY should further democratize whole-plasmid sequencing by nanopore and related technologies, driving down the effective cost of whole-plasmid sequencing to lower than that of a single Sanger sequencing run.

PMID:40277466 | DOI:10.7554/eLife.88794

Anal Chem. 2025 Apr 25. doi: 10.1021/acs.analchem.5c01648. Online ahead of print.

ABSTRACT

As a visualization technology for the in situ characterization of surface material molecules, mass spectrometry imaging (MSI) analysis is being increasingly used in various fields, especially in the biomedical field. However, the preparation of biological tissue section samples for MSI analysis remains time-consuming and labor-intensive, and sample loss or damage occurs frequently. The inability to stably and consecutively obtain suitable section samples and perform concise and efficient imaging analysis limits the analysis throughput. Herein, a preparation method is proposed. It enables consecutive sectioning, batch preservation, and dry processing through the use of ordinary adhesive tape, enhancing the adhesion between section and tape and rapid freeze-drying. Furthermore, based on the air flow assisted desorption electrospray ionization (AFADESI) MSI system, a vacuum adsorption platform is introduced, which simplifies the process of MSI analysis. Moreover, compared with general tape-based MSI methods, the signal intensity of 73%-85% of the annotated ions is improved for positive ion mode. The signal-to-noise (S/N) ratios of characteristic ions in the corresponding regions in the images of the tissue section samples increase by an average of more than two times, and a clearer organ outline can be seen in the images. By integrating the sample preparation method with the adsorption platform, high-throughput imaging of serial whole-body or scattered organ tissue sections can be conducted more easily and concise and efficient MSI analysis can be performed, which will provide a new strategy to meet rapidly growing MSI research demands.

PMID:40277201 | DOI:10.1021/acs.analchem.5c01648

Infect Dis Rep. 2025 Apr 3;17(2):28. doi: 10.3390/idr17020028.

ABSTRACT

BACKGROUND: Interstitial lung diseases (ILDs) are a heterogeneous group of conditions that can cause fibrosis of the lung interstitium, resulting in respiratory failure and death. Patients with an ILD, particularly idiopathic pulmonary fibrosis (IPF) or connective tissue disease-associated ILDs (CTD-ILDs), are prone to develop chronic pulmonary infections such as tuberculosis (TB) and non-tuberculous mycobacterial pulmonary disease (NTM-PD).

METHODS: This case series examines the management of three ILD patients with a usual interstitial pneumonia (UIP) pattern and concomitant NTM-PD or TB at National Institute for Infectious Diseases "Lazzaro Spallanzani" in Rome, Italy, over three years (2019-2022).

RESULTS AND CONCLUSIONS: Multi-disciplinary discussion (MDD) was crucial to define the therapeutic approach due to the increased risk of side effects and drug interactions. Our work underscored how a comprehensive diagnostic evaluation, enriched by MDD, is useful for optimizing the management and reducing drug-related adverse effects and interactions in ILD patients with cavitary lesions.

PMID:40277955 | DOI:10.3390/idr17020028

J Eval Clin Pract. 2025 Apr;31(3):e70110. doi: 10.1111/jep.70110.

ABSTRACT

RATIONALE: Patient safety has become a major concern in healthcare today as 5%-10% of patients experience serious adverse events (SAE) during their hospital stay. The causal assessment of SAE is the responsibility of healthcare professionals (HCP), who use their judgment or a standardize tool. Whether those two methods are replicable to provide similar results remains unclear.

OBJECTIVE: Our aim was to evaluate if causality assessment performed by HCP is replicable when systematically assessed with the Naranjo tool and to validate its performance in Canadian clinical context.

METHODS: We performed pilot retrospective cohort study which included patients with SAE admitted to a Quebec hospital in 2021. Twelve SAE were randomly selected, and two reviewers independently assessed their causality using Naranjo tool. Inter-rater reliability among two reviewers and between HCP was evaluated. Along with criterion validity, sensitivity and specificity were calculated for validation study.

RESULTS: Weighted kappa was 0.92 (good inter-rater reliability) where kappa was 0.84 (good agreement between reviewers). No causality assessment by HCP was documented leading to impossibility in computing replicability. The Naranjo tool showed positive monotonic correlation with expert opinion resulting in rs = 0.208 (p < 0.001). Classification of Naranjo scores to binary variables resulted in sensitivity of 1.00 and specificity of 0.31.

CONCLUSION: Our study suggested that Naranjo tool is reliable and valid to be used in a clinical setting and was able to classify all drug products involved in the occurrence of SAE. Larger scale studies need to be conducted in real-time clinical settings to investigate its performance and utility.

PMID:40275458 | DOI:10.1111/jep.70110