J Pharmacol Exp Ther. 2025 Feb;392(2):100023. doi: 10.1124/jpet.124.002184. Epub 2024 Nov 22.

ABSTRACT

The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) established S7B and E14 guidelines in 2005 to prevent drug-induced torsade de pointes (TdP), effectively preventing the development of high-risk drugs. However, those guidelines hampered the development of some potentially valuable drug candidates despite not being proven to be proarrhythmic. In response, comprehensive in vitro proarrhythmia assay and exposure-response modeling were proposed in 2013 to reinforce proarrhythmic risk assessment. In 2022, the ICH released E14/S7B questions and answers (stage 1), emphasizing a "double negative" nonclinical scenario for low-risk compounds. For "non-double negative" compounds, new questions and answers are expected to be enacted as stage 2 shortly, in which more detailed recommendations for proarrhythmia models and proarrhythmic surrogate markers will be provided. This review details the onset mechanisms of drug-induced TdP, including IKr inhibition, pharmacokinetic factors, autonomic regulation, and reduced repolarization reserve. It also explores the utility of proarrhythmic surrogate markers (J-Tpeak, Tpeak-Tend, and terminal repolarization period) besides QT interval. Finally, it presents various in silico, in vitro, ex vivo, and in vivo models for proarrhythmic risk prediction, such as comprehensive in vitro proarrhythmia assay in silico model, induced pluripotent stem cell-derived cardiomyocyte sheet, Langendorff-perfused heart preparation, chronic atrioventricular block animals (dogs, monkeys, pigs, and rabbits), acute atrioventricular block rabbits, methoxamine-sensitized rabbits, and genetically engineered rabbits for specific long QT syndromes. Those models along with the surrogate markers can play important roles in quantifying TdP risk of new compounds, impacting late-phase clinical design and regulatory decision-making, and preventing adverse events on postmarketing clinical use. SIGNIFICANCE STATEMENT: Since ICH S7B/E14 guidelines hampered the development of some potentially valuable compounds with unproven proarrhythmic risk, comprehensive in vitro proarrhythmia assay and exposure-response modeling were proposed in 2013 to reinforce proarrhythmic risk assessment of new compounds. In 2022, the ICH released questions and answers (stage 1), emphasizing a "double negative" nonclinical scenario for low-risk compounds, and new questions and answers (stage 2) for "non-double negative" compounds are expected. This review delves into proarrhythmic mechanisms with surrogate markers and explores various models for proarrhythmic risk prediction.

PMID:40023597 | DOI:10.1124/jpet.124.002184

Pest Manag Sci. 2025 Feb 28. doi: 10.1002/ps.8728. Online ahead of print.

ABSTRACT

BACKGROUND: Precision weed mapping in turf according to its susceptibility to selective herbicides allows the smart sprayer to spot-spray the most pertinent herbicides onto the susceptible weeds. The objective of this study was to evaluate the feasibility of implementing herbicide susceptibility-based weed mapping using deep convolutional neural networks (DCNNs) to facilitate targeted and efficient herbicide applications. Additionally, applying path-planning algorithms to weed mapping data to guide the spraying nozzle ensures minimal travel paths for herbicide application.

RESULTS: DenseNet achieved high precision, recall, overall accuracy, and F1 score values for all categories of herbicides and no herbicides, with F1 scores ranging from 0.996 to 0.999 in the validation dataset and from 0.992 to 0.997 in the testing dataset. The average accuracies attained by DenseNet, GoogLeNet and ResNet were 0.9985, 0.9953 and 0.9980, respectively. By considering both accuracy and computational efficiency, the ResNet model was identified as the most effective among the models compared to weed detection. The performance of the Christofides, Greedy and 2-opt algorithms in optimizing path planning for single or dual spraying nozzles was compared and analyzed. The Greedy algorithm proved the most efficient in optimizing the nozzle's trajectory.

CONCLUSION: Implementing herbicide susceptibility-based weed mapping facilitates targeted herbicide application by directing the nozzle to the grid cells containing the weeds susceptible to the herbicides. Moreover, the strategic integration of herbicide susceptibility-based weed mapping with optimized path planning for the spraying mechanism can be adeptly implemented on smart sprayers, which could effectively reduce the herbicide input. © 2025 Society of Chemical Industry.

PMID:40022516 | DOI:10.1002/ps.8728

J Cachexia Sarcopenia Muscle. 2025 Apr;16(2):e13728. doi: 10.1002/jcsm.13728.

ABSTRACT

BACKGROUND: Age-related conditions, such as osteoporosis and sarcopenia, alongside chronic diseases, can result in significant musculoskeletal tissue loss. This impacts individuals' quality of life and increases risk of falls and fractures. Computed tomography (CT) has been widely used for assessing musculoskeletal tissues. Although automatic techniques have been investigated for segmenting tissues in the abdomen and mid-thigh regions, studies in proximal hip remain limited. This study aims to develop a deep learning technique for segmentation and quantification of musculoskeletal tissues in CT scans of proximal hip.

METHODS: We examined 300 participants (men, 73 ± 6 years) from two cohorts of the Osteoporotic Fractures in Men Study (MrOS). We manually segmented cortical bone, trabecular bone, marrow adipose tissue (MAT), haematopoietic bone marrow (HBM), muscle, intermuscular adipose tissue (IMAT) and subcutaneous adipose tissue (SAT) from CT scan images at the proximal hip level. Using these data, we trained a U-Net-like deep learning model for automatic segmentation. The association between model-generated quantitative results and outcome variables such as grip strength, chair sit-to-stand time, walking speed, femoral neck and spine bone mineral density (BMD), and total lean mass was calculated.

RESULTS: An average Dice similarity coefficient (DSC) above 90% was observed across all tissue types in the test dataset. Grip strength showed positive correlations with cortical bone area (coefficient: 0.95, 95% confidence interval: [0.10, 1.80]), muscle area (0.41, [0.19, 0.64]) and average Hounsfield unit for muscle adjusted for height squared (AHU/h2) (1.1, [0.53, 1.67]), while it was negatively correlated with IMAT (-1.45, [-2.21, -0.70]) and SAT (-0.32, [-0.50, -0.13]). Gait speed was directly related to muscle area (0.01, [0.00, 0.02]) and inversely to IMAT (-0.04, [-0.07, -0.01]), while chair sit-to-stand time was associated with muscle area (0.98, [0.98, 0.99]), IMAT area (1.04, [1.01, 1.07]), SAT area (1.01, [1.01, 1.02]) and AHU/h2 for muscle (0.97, [0.95, 0.99]). MAT area showed a potential link to non-trauma fractures post-50 years (1.67, [0.98, 2.83]). Femoral neck BMD was associated with cortical bone (0.09, [0.08, 0.10]), MAT (-0.11, [-0.13, -0.10]), MAT adjusted for total bone marrow area (-0.06, [-0.07, -0.05]) and AHU/h2 for muscle (0.01, [0.00, 0.02]). Total spine BMD showed similar associations and with AHU for muscle (0.02, [0.00, 0.05]). Total lean mass was correlated with cortical bone (517.3, [148.26, 886.34]), trabecular bone (924, [262.55, 1585.45]), muscle (381.71, [291.47, 471.96]), IMAT (-1096.62, [-1410.34, -782.89]), SAT (-413.28, [-480.26, -346.29]), AHU (527.39, [159.12, 895.66]) and AHU/h2 (300.03, [49.23, 550.83]).

CONCLUSION: Our deep learning-based technique offers a fast and accurate method for segmentation and quantification of musculoskeletal tissues in proximal hip, with potential clinical value.

PMID:40022453 | DOI:10.1002/jcsm.13728

Food Res Int. 2025 Feb;203:115905. doi: 10.1016/j.foodres.2025.115905. Epub 2025 Feb 1.

ABSTRACT

Current detection methods for lipid and protein oxidation using hyperspectral imaging (HSI) in conjunction with machine learning (ML) necessitate the involvement of data scientists and domain experts to adjust the model architecture and tune hyperparameters. Additionally, prediction models lack explainability in the predictive outcomes and decision-making process. In this study, ML, automated machine learning (AutoML) and automated deep learning (AutoDL) models were developed for visible near-infrared HSI of mutton samples treated with different freeze-thaw cycles to evaluate the feasibility of building prediction models for lipid and protein oxidation without manual intervention. SHapley Additive exPlanations (SHAP) were utilized to explain the prediction models. The results showed that the AutoDL attained the effective prediction models for lipid oxidation (R2p = 0.9021, RMSEP = 0.0542 mg/kg, RPD = 3.3624) and protein oxidation (R2p = 0.8805, RMSEP = 3.8065 nmol/mg, RPD = 3.0789). AutoML driven stacked ensembles further improved the generalization ability of the models, predicting lipid and protein oxidation with R2p of 0.9237 and 0.9347. The important wavelengths identified through SHAP closely align with the results obtained from spectral analysis, and the analysis also determined the magnitude and direction of the impact of these important wavelengths on the model outputs. Finally, changes in lipid and protein oxidation of mutton in different freeze-thaw cycles were visualized. The research indicated that the combination of HSI, AutoML and SHAP may generate high-quality explainable models without human assistance for monitoring lipid and protein oxidative damage in mutton.

PMID:40022412 | DOI:10.1016/j.foodres.2025.115905

Food Res Int. 2025 Feb;203:115874. doi: 10.1016/j.foodres.2025.115874. Epub 2025 Jan 30.

ABSTRACT

Fixation is a critical step in green tea processing, and the moisture content of the leaves after fixation is a key indicator of the fixation quality. Near-infrared spectroscopy (NIRS)-based moisture detection technology is often applied in the tea processing industry. However, temperature fluctuations during processing can cause changes in the NIRS curves, which in turn affect the accuracy of moisture prediction models based on the spectral data. To address this challenge, NIRS data were collected from samples at various stages of fixation and at different temperatures, and a novel deep learning network (DiSENet) was proposed, which integrates multi-scale feature fusion and attention mechanisms. Using a global modeling approach, the proposed method achieved a coefficient of determination (RP2) of 0.781 for moisture content prediction, with a root mean square error (RMSEP) of 1.720 % and a residual predictive deviation (RPD) of 2.148. On the dataset constructed for this study, DiSENet demonstrated superior predictive accuracy compared to the spectral correction methods of external parameter orthogonalization (EPO) and generalized least squares weighting (GLSW), as well as traditional global modeling methods such as partial least squares regression (PLSR) and support vector regression (SVR). This approach effectively corrects spectral interferences caused by temperature variations, thereby enhancing the accuracy of moisture content prediction. Thus, it offers a reliable solution for real-time, non-destructive moisture detection during tea processing.

PMID:40022390 | DOI:10.1016/j.foodres.2025.115874

Food Res Int. 2025 Feb;203:115810. doi: 10.1016/j.foodres.2025.115810. Epub 2025 Jan 22.

ABSTRACT

The application of high-level data fusion in the detection of agricultural products still presents a significant challenge. In this study, dual-channel feature fusion model (DCFFM) with attention mechanism was proposed to optimize the utilization of both one-dimensional spectral data and two-dimensional image data in the hyperspectral images for achieving high-level data fusion. A comparative analysis of support vector machine (SVM), convolutional neural network (CNN) with DCFFM, demonstrated that DCFFM exhibited superior results, achieving the accuracy, precision, recall, specificity, and F1-score of 95.13 %, 95.49 %, 94.83 %, 98.97 %, 95.12 % in the visible and near-infrared (Vis-NIR), and 94.00 %, 94.43 %, 94.16 %, 98.67 %, 94.27 % in the short-wave infrared (SWIR). This also indicated that Vis-NIR was more suitable for identifying unsound soybeans than SWIR. Furthermore, visualization was employed to demonstrate classification outcomes, thereby illustrating the generalization capacity of DCFFM through model inversion. In summary, this study is to explore a modeling framework that is capable of the comprehensive acquisition of spectra and images in the hyperspectral images, allowing for high-level data fusion, thereby achieving enhanced levels of accuracy.

PMID:40022337 | DOI:10.1016/j.foodres.2025.115810

J Exp Clin Cancer Res. 2025 Feb 28;44(1):77. doi: 10.1186/s13046-025-03308-8.

ABSTRACT

BACKGROUND: Chromosomal instability (CIN) is involved in about 70% of colorectal cancers (CRCs) and is associated with poor prognosis and drug resistance. From a clinical perspective, a better knowledge of these tumour's biology will help to guide therapeutic strategies more effectively.

METHODS: We used high-density chromosomal microarray analysis to evaluate CIN level of patient-derived organoids (PDOs) and their original mCRC tissues. We integrated the RNA-seq and mass spectrometry-based proteomics data from PDOs in a functional interaction network to identify the significantly dysregulated processes in CIN. This was followed by a proteome-wGII Pearson correlation analysis and an in silico validation of main findings using functional genomic databases and patient-tissues datasets to prioritize the high-confidence CIN features.

RESULTS: By applying the weighted Genome Instability Index (wGII) to identify CIN, we classified PDOs and demonstrated a good correlation with tissues. Multi-omics analysis showed that our organoids recapitulated genomic, transcriptomic and proteomic CIN features of independent tissues cohorts. Thanks to proteotranscriptomics, we uncovered significant associations between mitochondrial metabolism and epithelial-mesenchymal transition in CIN CRC PDOs. Correlating PDOs wGII with protein abundance, we identified a subset of proteins significantly correlated with CIN. Co-localisation analysis in PDOs strengthened the putative role of IPO7 and YAP, and, through in silico analysis, we found that some of the targets give significant dependencies in cell lines with CIN compatible status.

CONCLUSIONS: We first demonstrated that PDO models are a faithful reflection of CIN tissues at the genetic and phenotypic level. Our new findings prioritize a subset of genes and molecular processes putatively required to cope with the burden on cellular fitness imposed by CIN and associated with disease aggressiveness.

PMID:40022181 | DOI:10.1186/s13046-025-03308-8

Respir Res. 2025 Feb 28;26(1):77. doi: 10.1186/s12931-025-03140-w.

ABSTRACT

BACKGROUND: Non-cystic fibrosis bronchiectasis is associated with frequent and diverse microbial infections, yet an overall understanding of microbial presence across different disease stages is lacking.

METHODS: A meta-analysis assessed lung microbes in adults with non-CF bronchiectasis, collecting data using both culture-based and sequencing approaches through three international databases and three Chinese databases. Subgroups were categorized by disease stage: the stable group (S), the exacerbation group (E), and unclassified data consolidated into the undetermined group (U). Culture data were analysed in random-effects meta-analyses while sequencing data were processed using QIIME 2.

RESULTS: A total of 98 studies were included with data from 54,384 participants worldwide. Pseudomonas aeruginosa was the most frequently isolated bacterium (S: 26[19-34]%, E: 23[20-25]%, U: 20[16-25]%), while not specified Mycobacterium avium complex exhibited the highest mycobacterial prevalence (S: 3[1-5]%, E: 4[2-5]%, U: 15[3-27]%). Aspergillus spp. (S: 15[-10-39]%, E: 2[1-3]%, U: 10[5-15]%) and Candida spp. (S: not applicable, E: 11[2-20]%, U: 10[-8-27]%) were predominant in fungi culture with variable distributions among groups. Rhinovirus was the most commonly detected virus with varying prevalence across airway sample types rather than disease stages (S-sputum: 18[-16-53]%, S-nasopharyngeal: 4[-1-9]%, E-sputum: 22[16-29]%, E-nasopharyngeal: 6[4-8]%). Sequencing results revealed notable antibiotic persistence of Pseudomonas in 16S, and significant domination of Candida in ITS.

CONCLUSION: Our findings indicate consistent bacterial patterns throughout bronchiectasis stages in both culture and sequencing results. Viruses are extensively detected in stable patients but vary across different airway sample types. Lower bacterial diversity and higher fungal diversity may be associated with exacerbation risks.

PMID:40022075 | DOI:10.1186/s12931-025-03140-w

Mol Med. 2025 Feb 28;31(1):81. doi: 10.1186/s10020-025-01133-5.

ABSTRACT

BACKGROUND AND PURPOSE: Shwachman-Diamond Syndrome (SDS) is an autosomal recessive disease belonging to the inherited bone marrow failure syndromes and characterized by hypocellular bone marrow, exocrine pancreatic insufficiency, and skeletal abnormalities. SDS is associated with increased risk of developing myelodysplastic syndrome (MDS) and/or acute myeloid leukemia (AML). Although SDS is not primarily considered an inflammatory disorder, some of the associated conditions (e.g., neutropenia, pancreatitis and bone marrow dysfunction) may involve inflammation or immune system dysfunctions. We have already demonstrated that signal transducer and activator of transcription (STAT)-3 and mammalian target of rapamycin (mTOR) were hyperactivated and associated with elevated IL-6 levels in SDS leukocytes. In this study, we analyzed the level of phosphoproteins involved in STAT3 and mTOR pathways in SDS lymphoblastoid cells (LCLs) and the secretomic profile of soluble pro-inflammatory mediators in SDS plasma and LCLs in order to investigate the systemic inflammation in these patients and relative pathways.

METHODS: Twenty-six SDS patients and seven healthy donors of comparable age were recruited during the programmed follow-up visits for clinical evaluation at the Verona Cystic Fibrosis Center Human. The obtained samples (plasma and/or LCLs) were analyzed for: phosphoproteins, cytokines, chemokines and growth factors levels by Bio-plex technology; microRNAs profiling by next generation sequencing (NGS) and microRNAs expression validation by Real Time-PCR (RT-PCR) and droplet digital PCR (ddPCR) .

RESULTS: We demonstrated dysregulation of ERK1/2 and AKT phosphoproteins in SDS, as their involvement in the hyperactivation of the STAT3 and mTOR pathways confirmed the interplay of these pathways in SDS pathophysiology. However, both these signaling pathways are strongly influenced by the inflammatory environment. Here, we reported that SDS is characterized by elevated plasma levels of several soluble proinflammatory mediators. In vitro experiments show that these pro-inflammatory genes are closely correlated with STAT3/mTOR pathway activation. In addition, we found that miR-181a-3p is down-regulated in SDS. Since this miRNA acts as a regulator of several pro-inflammatory pathways such as STAT3 and ERK1/2, its down-regulation may be a driver of the constitutive inflammation observed in SDS patients.

CONCLUSIONS: The results obtained in this study shed light on the complex pathogenetic mechanism underlying bone marrow failure and leukemogenesis in SDS, suggesting the need for anti-inflammatory therapies for SDS patients.

PMID:40021961 | DOI:10.1186/s10020-025-01133-5

NPJ Antimicrob Resist. 2025 Feb 28;3(1):17. doi: 10.1038/s44259-025-00088-1.

ABSTRACT

R-pyocins, bacteriocin-like proteins produced by Pseudomonas aeruginosa, present a promising alternative to phage therapy and/or adjunct to currently used antimicrobials in treating bacterial infections due to their targeted specificity, lack of replication, and stability. This review explores the structural, mechanistic, and therapeutic aspects of R-pyocins, including their potential for chronic infection management, and discusses recent advances in delivery methods, paving the way for novel antimicrobial applications in clinical settings.

PMID:40021925 | DOI:10.1038/s44259-025-00088-1

Transplant Proc. 2025 Feb 27:S0041-1345(25)00102-2. doi: 10.1016/j.transproceed.2025.02.020. Online ahead of print.

ABSTRACT

Although active malignancy is a contraindication to lung transplantation, there is increasing uncertainty as to what constitutes "active" malignancy given the rapidly changing therapeutic armamentarium and overall survival of patients with malignancy. Chronic myeloid leukemia (CML) is an example of a previously fatal malignancy that has been transformed into a chronic disease with close-to-normal life expectancy since the advent of tyrosine kinase inhibitor (TKI) therapy. However, it remains relatively unknown if lung transplantation could successfully be performed in patients with CML. We describe the course of a 34-year-old woman with cystic fibrosis and advanced lung disease who was diagnosed with CML while undergoing lung transplant evaluation. She was initiated on imatinib with optimal treatment response; she achieved major molecular response (MMR) and deep molecular response (DMR) at 8 and 10 months of treatment, respectively. She developed progressive respiratory failure and underwent bilateral lung transplantation at close to 3 years after achieving MMR. At 6 years post-transplant, she has excellent graft function and remains in DMR on imatinib. Treated CML in DMR should be regarded as inactive malignancy and should not preclude patients from life-saving transplant consideration. Our case also demonstrates the feasibility of long-term immunosuppression on TKI therapy.

PMID:40021434 | DOI:10.1016/j.transproceed.2025.02.020

Cancer Imaging. 2025 Feb 28;25(1):20. doi: 10.1186/s40644-025-00845-5.

ABSTRACT

OBJECTIVE: Exploring the construction of a fusion model that combines radiomics and deep learning (DL) features is of great significance for the precise preoperative diagnosis of meningioma sinus invasion.

MATERIALS AND METHODS: This study retrospectively collected data from 601 patients with meningioma confirmed by surgical pathology. For each patient, 3948 radiomics features, 12,288 VGG features, 6144 ResNet features, and 3072 DenseNet features were extracted from MRI images. Thus, univariate logistic regression, correlation analysis, and the Boruta algorithm were applied for further feature dimension reduction, selecting radiomics and DL features highly associated with meningioma sinus invasion. Finally, diagnosis models were constructed using the random forest (RF) algorithm. Additionally, the diagnostic performance of different models was evaluated using receiver operating characteristic (ROC) curves, and AUC values of different models were compared using the DeLong test.

RESULTS: Ultimately, 21 features highly associated with meningioma sinus invasion were selected, including 6 radiomics features, 2 VGG features, 7 ResNet features, and 6 DenseNet features. Based on these features, five models were constructed: the radiomics model, VGG model, ResNet model, DenseNet model, and DL-radiomics (DLR) fusion model. This fusion model demonstrated superior diagnostic performance, with AUC values of 0.818, 0.814, and 0.769 in the training set, internal validation set, and independent external validation set, respectively. Furthermore, the results of the DeLong test indicated that there were significant differences between the fusion model and both the radiomics model and the VGG model (p < 0.05).

CONCLUSIONS: The fusion model combining radiomics and DL features exhibits superior diagnostic performance in preoperative diagnosis of meningioma sinus invasion. It is expected to become a powerful tool for clinical surgical plan selection and patient prognosis assessment.

PMID:40022261 | DOI:10.1186/s40644-025-00845-5

Genome Med. 2025 Feb 28;17(1):16. doi: 10.1186/s13073-025-01442-8.

ABSTRACT

Spatial transcriptomics (ST) enables the study of gene expression in spatial context, but many ST technologies face challenges due to limited resolution, leading to cell mixtures at each spot. We present LETSmix to deconvolve cell types by integrating spatial correlations through a tailored LETS filter, which leverages layer annotations, expression similarities, image texture features, and spatial coordinates to refine ST data. Additionally, LETSmix employs a mixup-augmented domain adaptation strategy to address discrepancies between ST and reference single-cell RNA sequencing data. Comprehensive evaluations across diverse ST platforms and tissue types demonstrate its high accuracy in estimating cell-type proportions and spatial patterns, surpassing existing methods (URL: https://github.com/ZhanYangen/LETSmix ).

PMID:40022231 | DOI:10.1186/s13073-025-01442-8

J Neuroeng Rehabil. 2025 Feb 28;22(1):40. doi: 10.1186/s12984-025-01560-9.

ABSTRACT

Accurate detection of gait events in older adults, particularly during perturbed walking, is essential for evaluating balance control and fall risk. Traditional force plate-based methods often face limitations in perturbed walking scenarios due to the difficulty in landing cleanly on the force plates. Subsequently, previous studies have not addressed gait event automatic detection methods for perturbed walking. This study introduces an automated gait event detection method using a bidirectional gated recurrent unit (Bi-GRU) model, leveraging ground reaction force, joint angles, and marker data, for both regular and perturbed walking scenarios from 307 healthy older adults. Our marker-based model achieved over 97% accuracy with a mean error of less than 14 ms in detecting touchdown (TD) and liftoff (LO) events for both walking scenarios. The results highlight the efficacy of kinematic approaches, demonstrating their potential in gait event detection for clinical settings. When integrated with wearable sensors or computer vision techniques, these methods enable real-time, precise monitoring of gait patterns, which is helpful for applying personalized programs for fall prevention. This work takes a significant step forward in automated gait analysis for perturbed walking, offering a reliable method for evaluating gait patterns, balance control, and fall risk in clinical settings.

PMID:40022199 | DOI:10.1186/s12984-025-01560-9

Commun Eng. 2025 Feb 28;4(1):37. doi: 10.1038/s44172-025-00379-5.

ABSTRACT

Computational spectrometers enable low-cost, in-situ, and rapid spectral analysis, with applications in chemistry, biology, and environmental science. Traditional filter-based spectral encoding approaches typically use filter arrays, complicating the manufacturing process and hindering device consistency. Here we propose a computational spectrometer spanning visible to mid-infrared by combining the Single-Spinning Film Encoder (SSFE) with a deep learning-based reconstruction algorithm. Optimization through particle swarm optimization (PSO) allows for low-correlation and high-complexity spectral responses under different polarizations and spinning angles. The spectrometer demonstrates single-peak resolutions of 0.5 nm, 2 nm, 10 nm, and dual-peak resolutions of 3 nm, 6 nm, 20 nm for the visible, near, and mid-infrared wavelength ranges. Experimentally, it shows an average MSE of 1.05 × 10⁻³ for narrowband spectral reconstruction in the visible wavelength range, with average center-wavelength and linewidth errors of 0.61 nm and 0.56 nm. Additionally, it achieves an overall 81.38% precision for the classification of 220 chemical compounds, showcasing its potential for compact, cost-effective spectroscopic solutions.

PMID:40021937 | DOI:10.1038/s44172-025-00379-5

Sci Rep. 2025 Feb 28;15(1):7201. doi: 10.1038/s41598-025-91784-5.

ABSTRACT

The study introduces a new method for predicting software defects based on Residual/Shuffle (RS) Networks and an enhanced version of Fish Migration Optimization (UFMO). The overall contribution is to improve the accuracy, and reduce the manual effort needed. The originality of this work rests in the synergic use of deep learning and metaheuristics to train the software code for extraction of semantic and structural properties. The model is tested on a variety of open-source projects, yielding an average accuracy of 93% and surpassing the performance of the state-of-the-art models. The results indicate an overall increase in the precision (78-98%), recall (71-98%), F-measure (72-96%), and Area Under the Curve (AUC) (78-99%). The proposed model is simple and efficient and proves to be effective in identifying potential defects, consequently decreasing the chance of missing these defects and improving the overall quality of the software as opposed to existing approaches. However, the analysis is limited to open-source projects and warrants further evaluation on proprietary software. The study enables a robust and efficient tool for developers. This approach can revolutionize software development practices in order to use artificial intelligence to solve difficult issues presented in software. The model offers high accuracy to reduce the software development cost, which can improve user satisfaction, and enhance the overall quality of software being developed.

PMID:40021906 | DOI:10.1038/s41598-025-91784-5

Respir Res. 2025 Feb 28;26(1):81. doi: 10.1186/s12931-025-03146-4.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) and progressive pulmonary fibrosis (PPF) are interstitial lung diseases (ILD) that carry a high burden and mortality. IPF/PPF experts and patients call for standardized care, outcome harmonization and holistic management in these complex and devastating diseases, with a focus on person-centeredness. In this cross-sectional international survey study, we aimed to gather information on the person-centred health outcomes European healthcare professionals (HCPs) already use or deem important for use in routine care for IPF/PPF. This work is part of the COCOS-IPF project on developing a Core Outcome Set (COS) for and with patients with IPF/PPF.

METHODS: With the input of IPF/PPF experts, psychologists and patients, we developed an online survey for European multidisciplinary HCPs with IPF/PPF expertise. The survey was programmed in QualtricsXM, piloted and distributed via the networks of the COCOS-IPF consortium. We used content analysis to create an overall list of outcome domains mentioned in the survey, classified these according to the COMET (Core Outcome Measures in Effectiveness Trials) taxonomy and calculated the frequency of all outcomes mentioned.

RESULTS: A total of 149 experts, mainly pulmonologists (n = 120, 81%) working in ILD expert centres, from 31 European countries participated. Of the 40 different outcome domains mentioned, the majority referred to `physiological/clinical` (n = 773, 81%) and `life impact` (n = 138, 14%) outcome domains. Of these, `lung function' (n = 280, 29%), 'exercise capacity' (n = 123, 13%) and `quality of life` (n = 103, 11%) were reported as most frequently used person-centred health outcomes. Survey respondents deemed the same three outcome domains the most important for use in the routine clinical IPF/PPF care, supplemented by chest symptoms. Pulmonologists reported mainly about routine use of `lung function` (n = 252, 26%), while allied HCPs put more focus on outcomes related to physical condition and whole body status.

CONCLUSIONS: HCPs have identified 40 different outcomes domains in a European multidisciplinary survey on person-centred health outcomes in IPF/PPF. Lung function, exercise capacity, quality of life and chest symptoms were rated as the most relevant health outcomes to be assessed routinely in clinical care. These insights can help to support the development of a COS for IPF/PPF clinical care.

PMID:40022111 | DOI:10.1186/s12931-025-03146-4

Respir Res. 2025 Feb 28;26(1):80. doi: 10.1186/s12931-025-03139-3.

ABSTRACT

BACKGROUND: Supplemental oxygen therapy is commonly prescribed in clinical practice for patients with fibrosing interstitial lung disease (ILD) to reduce breathlessness and increase physical capacity. Only a few studies have evaluated the incidence of oxygen therapy use, with evidence lacking in its use among fibrosing ILD subtypes including patients with idiopathic pulmonary fibrosis (IPF) and non-IPF ILD. This study aimed to estimate incidence of oxygen therapy and factors associated with oxygen therapy initiation.

METHODS: This non-interventional study used US administrative claims and electronic health record data from 01 October 2015 to 30 June 2022. Patients aged ≥ 18 years with newly diagnosed fibrosing ILD (≥ 2 fibrosing ILD diagnoses in any position on different dates of service within 365 days) were included; the index date was the first date with ILD diagnosis. Patients were followed until the earlier of health plan disenrollment, death, or end of study period. Oxygen therapy use was evaluated among patients without evidence of oxygen therapy before the index date, stratified by the underlying fibrosing disease (i.e., IPF vs. non-IPF ILD). Factors associated with oxygen therapy use were evaluated using Cox proportional hazards regression.

RESULTS: A total of 114,921 patients (IPF n = 5,555; non-IPF ILD n = 109,366) newly diagnosed with fibrosing ILD were included in the study. The mean (standard deviation) age of patients with ILD was 66.9 (14.2) years, and 47.2% were male. Patients were followed for a mean of 24 months after ILD diagnosis, during which 38% of fibrosing ILD patients initiated oxygen therapy; a higher proportion of patients with IPF initiated oxygen therapy compared to those with non-IPF ILD (68% and 36%, respectively). Factors associated with oxygen therapy initiation included IPF, higher Charlson comorbidity scores, and comorbidities that impair respiratory capacity.

CONCLUSIONS: The study findings demonstrate a substantial proportion of patients with fibrosing ILD initiated oxygen therapy following initial ILD diagnosis, with higher rates of oxygen therapy initiation observed among patients with IPF compared with non-IPF ILD. Respiratory comorbidities were key factors associated with increased initiation of oxygen therapy.

PMID:40022082 | DOI:10.1186/s12931-025-03139-3

Respir Res. 2025 Feb 28;26(1):73. doi: 10.1186/s12931-025-03136-6.

ABSTRACT

BACKGROUND: The prognostic value of patterns and quantitative measures of lung fibrosis on high-resolution computed tomography (HRCT) in patients identified as having progressive pulmonary fibrosis (PPF) has not been established. We investigated whether HRCT patterns and quantitative scores were associated with risk of progression in patients with PPF.

METHODS: Patients enrolled in the ILD-PRO Registry had an interstitial lung disease (ILD) other than idiopathic pulmonary fibrosis, reticular abnormality and traction bronchiectasis, and met criteria for ILD progression. HRCT images taken between 24 months prior to enrollment and 90 days after enrollment were analyzed using a machine learning algorithm to derive quantitative scores. Associations were assessed between HRCT pattern (usual interstitial pneumonia [UIP]-like versus other patterns) and tertiles of quantitative scores and measures of disease severity at enrollment, and between these patterns/tertiles at enrollment and ILD progression (relative decline in forced vital capacity [FVC] % predicted ≥ 10%, lung transplant, or death) over a median follow-up of 17.3 months.

RESULTS: Among 395 patients, 178 (45.1%) had a UIP-like pattern on HRCT. A UIP-like pattern did not associate with worse disease severity at enrollment or an increased risk of ILD progression (HR 1.01 [95% CI: 0.71, 1.44]). The highest quantitative lung fibrosis (QLF) score tertile (≥ 20.5%) was associated with worse disease severity. In unadjusted analyses, patients with QLF scores in the highest tertile had a significantly increased risk of ILD progression versus the middle tertile (HR [95% CI] 1.63 [1.07, 2.49] and a numerically increased risk versus the lowest tertile (HR 1.46 [0.97, 2.18]); however, after adjustment for sex, age, FVC % predicted and oxygen use at enrollment, there were no significant differences. There were no significant associations between tertiles of quantitative ILD score, quantitative ground glass score, or quantitative honeycomb cysts score and risk of ILD progression in unadjusted or adjusted analyses.

CONCLUSIONS: In a real-world cohort of patients with PPF, QLF score associated with subsequent risk of ILD progression, while HRCT pattern did not. The QLF score did not provide additional prognostic information beyond clinical variables.

TRIAL REGISTRATION: ClinicalTrials.gov; No: NCT01915511; registered August 5, 2013; URL: www.

CLINICALTRIALS: gov .

PMID:40022059 | DOI:10.1186/s12931-025-03136-6

BMC Pulm Med. 2025 Feb 28;25(1):98. doi: 10.1186/s12890-025-03555-7.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a chronic, irreversible, and fatal disease characterized by progressive interstitial lung fibrosis. Given its insidious onset and poor outcome, there is an urgent need to elucidate the molecular mechanisms underlying IPF and identify effective therapeutic targets and diagnosis and prognosis biomarkers. Ferroptosis is an iron-dependent form of programmed cell death that occurs as lipid peroxides accumulate. Growing evidence suggests that ferroptosis is important in IPF.

METHODS: Human ferroptosis PCR array was performed on IPF and control lung tissue. The differentially expressed ferroptosis-related genes (DE-FRGs) were identified, underwent functional enrichment analyses, protein-protein interaction network construction, and potential drug target prediction. The DE-FRGs were validated and their value as diagnostic and prognostic blood biomarkers were evaluated using the Gene Expression Omnibus dataset GSE28042.

RESULTS: The array identified 13 DE-FRGs. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that the DE-FRGs were mainly related to iron ion transport, blood microparticles, and oxidoreductase activity, and were involved in porphyrin metabolism, necroptosis, and the p53 signaling pathway in addition to ferroptosis. The 13 DE-FRGs were analyzed using the Drug-Gene Interaction Database to explore novel IPF therapeutic agents, yielding 42 potential drugs. Four DE-FRGs (BBC3, STEAP3, EPRS, SLC39A8) in the peripheral blood of IPF patients from the GSE28042 dataset demonstrated the same expression pattern as that observed in the lung tissue array. The receiver operating characteristic analysis demonstrated that the area under the curve of STEAP3 and EPRS were > 0.75. The survival analysis demonstrated that STEAP3 and EPRS were significantly different between the IPF and control groups.

CONCLUSIONS: The FRG expression profiles in IPF and control lung tissue were characterized. The findings provided valuable ideas to elucidate the role of ferroptosis in IPF and aided the identification of novel IPF therapeutic targets and biomarkers.

PMID:40022042 | DOI:10.1186/s12890-025-03555-7