Abdom Radiol (NY). 2025 Apr 28. doi: 10.1007/s00261-025-04951-7. Online ahead of print.

ABSTRACT

Accurate, reproducible body composition analysis from abdominal computed tomography (CT) images is critical for both clinical research and patient care. We present a fully automated, artificial intelligence (AI)-based pipeline that streamlines the entire process-from data normalization and anatomical landmarking to automated tissue segmentation and quantitative biomarker extraction. Our methodology ensures standardized inputs and robust segmentation models to compute volumetric, density, and cross-sectional area metrics for a range of organs and tissues. Additionally, we capture selected DICOM header fields to enable downstream analysis of scan parameters and facilitate correction for acquisition-related variability. By emphasizing portability and compatibility across different scanner types, image protocols, and computational environments, we ensure broad applicability of our framework. This toolkit is the basis for the Opportunistic Screening Consortium in Abdominal Radiology (OSCAR) and has been shown to be robust and versatile, critical for large multi-center studies.

PMID:40293521 | DOI:10.1007/s00261-025-04951-7

Abdom Radiol (NY). 2025 Apr 28. doi: 10.1007/s00261-025-04963-3. Online ahead of print.

ABSTRACT

Renal cell carcinoma (RCC) as a significant health concern, with incidence rates rising annually due to increased use of cross-sectional imaging, leading to a higher detection of incidental renal lesions. Differentiation between benign and malignant renal lesions is essential for effective treatment planning and prognosis. Renal tumors present numerous histological subtypes with different prognoses, making precise subtype differentiation crucial. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), shows promise in radiological analysis, providing advanced tools for renal lesion detection, segmentation, and classification to improve diagnosis and personalize treatment. Recent advancements in AI have demonstrated effectiveness in identifying renal lesions and predicting surveillance outcomes, yet limitations remain, including data variability, interpretability, and publication bias. In this review we explored the current role of AI in assessing kidney lesions, highlighting its potential in preoperative diagnosis and addressing existing challenges for clinical implementation.

PMID:40293518 | DOI:10.1007/s00261-025-04963-3

Int Orthop. 2025 Apr 28. doi: 10.1007/s00264-025-06531-2. Online ahead of print.

ABSTRACT

AIM: We aimed to compare the diagnostic performance of physicians in the detection of arthroscopically confirmed meniscus and anterior cruciate ligament (ACL) tears on knee magnetic resonance imaging (MRI), with and without assistance from a deep learning (DL) model.

METHODS: We obtained preoperative MR images from 88 knees of patients who underwent arthroscopic meniscal repair, with or without ACL reconstruction. Ninety-eight MR images of knees without signs of meniscus or ACL tears were obtained from a publicly available database after matching on age and ACL status (normal or torn), resulting in a global dataset of 186 MRI examinations. The Keros® (Incepto, Paris) DL algorithm, previously trained for the detection and characterization of meniscus and ACL tears, was used for MRI assessment. Magnetic resonance images were individually, and blindly annotated by three physicians and the DL algorithm. After three weeks, the three human raters repeated image assessment with model assistance, performed in a different order.

RESULTS: The Keros® algorithm achieved an area under the curve (AUC) of 0.96 (95% CI 0.93, 0.99), 0.91 (95% CI 0.85, 0.96), and 0.99 (95% CI 0.98, 0.997) in the detection of medial meniscus, lateral meniscus and ACL tears, respectively. With model assistance, physicians achieved higher sensitivity (91% vs. 83%, p = 0.04) and similar specificity (91% vs. 87%, p = 0.09) in the detection of medial meniscus tears. Regarding lateral meniscus tears, sensitivity and specificity were similar with/without model assistance. Regarding ACL tears, physicians achieved higher specificity when assisted by the algorithm (70% vs. 51%, p = 0.01) but similar sensitivity with/without model assistance (93% vs. 96%, p = 0.13).

CONCLUSIONS: The current model consistently helped physicians in the detection of medial meniscus and ACL tears, notably when they were combined.

LEVEL OF EVIDENCE: Diagnostic study, Level III.

PMID:40293511 | DOI:10.1007/s00264-025-06531-2

Sleep. 2025 Apr 28:zsaf113. doi: 10.1093/sleep/zsaf113. Online ahead of print.

ABSTRACT

Wearable devices with sleep tracking functionalities can prompt behavioral changes to promote sleep, but proactively preventing poor sleep when it is likely to occur remains a challenge due to a lack of prediction models that can forecast sleep parameters prior to sleep onset. We developed models that forecast low sleep efficiency 4 and 8 hours prior to sleep onset using gradient boosting (CatBoost) and deep learning (Convolutional Neural Network Long Short-Term Memory, CNN-LSTM) algorithms trained exclusively on accelerometer data from 80,811 adults in the UK Biobank. Associations of various sleep and activity parameters with sleep efficiency were further examined. During repeated cross-validation, both CatBoost and CNN-LSTM exhibited excellent predictive performance (median AUCs > 0.90, median AUPRCs > 0.79). U-shaped relationships were observed between total activity within 4 and 8 hours of sleep onset and low sleep efficiency. Functional data analyses revealed higher activity 6 to 8 hours prior to sleep onset had negligible associations with sleep efficiency. Higher activity 4 to 6 hours prior had moderate beneficial associations, while higher activity within 4 hours had detrimental associations. Additional analyses showed that increased variability in sleep duration, efficiency, onset timing, and offset timing over the preceding four days was associated with lower sleep efficiency. Our study represents a first step towards wearable-based machine learning systems that proactively prevent poor sleep by demonstrating that sleep efficiency can be accurately forecasted prior to bedtime and by identifying pre-bed activity targets for subsequent intervention.

PMID:40293116 | DOI:10.1093/sleep/zsaf113

Sensors (Basel). 2025 Mar 19;25(6):1924. doi: 10.3390/s25061924.

ABSTRACT

With the development of UAV technology, the composition of UAVs has become increasingly complex, interconnected, and tightly coupled. Fault features are characterized by weakness, nonlinearity, coupling, and uncertainty. A promising approach is the use of deep learning methods, which can effectively extract useful diagnostic information from weak, coupled, nonlinear data from inputs with background noise. However, due to the diversity of flight environments and missions, the distribution of the obtained sample data varies. The types of fault data and corresponding labels under different conditions are unknown, and it is time-consuming and expensive to label sample data. These challenges reduce the performance of traditional deep learning models in anomaly detection. To overcome these challenges, a novel weighted-transfer domain-adaptation network (WTDAN) method is introduced to realize the online anomaly detection and fault diagnosis of UAV electromagnetic-sensitive flight data. The method is based on unsupervised transfer learning, which can transfer the knowledge learnt from existing datasets to solve problems in the target domain. The method contains three novel multiscale modules: a feature extractor, used to extract multidimensional features from the input; a domain discriminator, used to improve the imbalance of the data distribution between the source domain and the target domain; and a label classifier, used to classify data categories for the target domain. Multilayer domain adaptation is used to reduce the distance between the source domain datasets and the target domain datasets distributions. The WTDAN assigns different weights to the source domain samples in order to weight the different contributions of source samples to solve the problem during the training process. The dataset adopts not only open datasets from the website but also test datasets from experiments to evaluate the transferability of the proposed WTDAN model. The experimental results show that, under the condition of fewer anomalous target data samples, the proposed method had a classification accuracy of up to 90%, which is higher than that of the other compared methods, and performed with superior transferability on the cross-domain datasets. The capability of fault diagnosis can provide a novel method for online anomaly detection and the prognostics and health management (PHM) of UAVs, which, in turn, would improve the reliability, repairability, and safety of UAV systems.

PMID:40293102 | DOI:10.3390/s25061924

Sensors (Basel). 2025 Mar 20;25(6):1942. doi: 10.3390/s25061942.

ABSTRACT

Fringe projection profilometry (FPP) is a widely employed technique owing to its rapid speed and high accuracy. However, when FPP is utilized to measure shiny surfaces, the fringes tend to be saturated or too dark, which significantly compromises the accuracy of the 3D measurement. To overcome this challenge, this paper proposes an efficient method for the 3D measurement of shiny surfaces based on FPP. Firstly, polarizers are employed to alleviate fringe saturation by leveraging the polarization property of specular reflection. Although polarizers reduce fringe intensity, a deep learning method is utilized to enhance the quality of fringes, especially in low-contrast regions, thereby improving measurement accuracy. Furthermore, to accelerate measurement efficiency, a dual-frequency complementary decoding method is introduced, requiring only two auxiliary fringes for accurate fringe order determination, thereby achieving high-efficiency and high-dynamic-range 3D measurement. The effectiveness and feasibility of the proposed method are validated through a series of experimental results.

PMID:40293081 | DOI:10.3390/s25061942

Sensors (Basel). 2025 Mar 20;25(6):1926. doi: 10.3390/s25061926.

ABSTRACT

This paper presents a new methodology for localizing impact events on plate-like structures using a proposed two-dimensional convolutional neural network (CNN) and received impact signals. A network of four piezoelectric wafer active sensors (PWAS) was installed on the tested plate to acquire impact signals. These signals consisted of reflection waves that provided valuable information about impact events. In this methodology, each of the received signals was divided into several equal segments. Then, a wavelet transform (WT)-based time-frequency analysis was used for processing each segment signal. The generated WT diagrams of these segments' signals were cropped and resized using MATLAB code to be used as input image datasets to train, validate, and test the proposed CNN model. Two scenarios were adopted from PAWS transducers. First, two sensors were positioned in two corners of the plate, while, in the second scenario, four sensors were used to monitor and collect the signals. Eight datasets were collected and reshaped from these two scenarios. These datasets presented the signals of two, three, four, and five impacts. The model's performance was evaluated using four metrics: confusion matrix, accuracy, precision, and F1 score. The proposed model demonstrated exceptional performance by accurately localizing all of the impact points of the first scenario and 99% of the second scenario. The main limitation of the proposed model is how to differentiate the data samples that have similar features. From our point of view, the similarity challenge arose from two factors: the segmentation interval and the impact distance. First, applying the segmenting procedure to the PWAS signals led to an increase in the number of data samples. The procedure segmented each PWAS signal to 30 samples with equal intervals, regardless of the features of the signal. Segmenting and transforming different PWAS signals into image-based data points led to data samples that had similar features. Second, some of the impacts had a close distance to the PWAS sensors, which resulted in similar segmented signals. Therefore, the second scenario was more challenging for the proposed model.

PMID:40293079 | DOI:10.3390/s25061926

Sensors (Basel). 2025 Mar 19;25(6):1902. doi: 10.3390/s25061902.

ABSTRACT

Advanced communication systems, particularly in the context of autonomous driving and integrated sensing and communication (ISAC), require high precision and refresh rates for environmental perception, alongside reliable data transmission. This paper presents a novel approach to enhance the ISAC performance in existing 4G and 5G systems by utilizing a two-dimensional offset in the Delay-Doppler (DD) domain, effectively leveraging the sounding reference signal (SRS) resources. This method aims to improve spectrum efficiency and sensing accuracy in vehicular networks. However, a key challenge arises from interference between multiple users after the wireless propagation of signals. To address this, we propose a deep learning-based interference mitigation solution using an UNet architecture, which operates on the Range-Doppler maps. The UNet model, with its encoder-decoder structure, efficiently filters out unwanted signals, therefore enhancing the system performance. Simulation results show that the proposed method significantly improves the accuracy of environmental sensing and resource utilization while mitigating interference, even in dense network scenarios. Our findings suggest that this DD-domain-based approach offers a promising solution to optimizing ISAC capabilities in current and future communication systems.

PMID:40293069 | DOI:10.3390/s25061902

Sensors (Basel). 2025 Mar 19;25(6):1909. doi: 10.3390/s25061909.

ABSTRACT

Echocardiography is a widely used cardiac imaging modality in clinical practice. Physicians utilize echocardiography images to measure left ventricular volumes at end-diastole (ED) and end-systole (ES) frames, which are pivotal for calculating the ejection fraction and thus quantitatively assessing cardiac function. However, most existing approaches focus on features from ES frames and ED frames, neglecting the inter-frame correlations in unlabeled frames. Our model is based on an encoder-decoder architecture and consists of two modules: the Temporal Feature Fusion Module (TFFA) and the Vision Retentive Network (Vision RetNet) encoder. The TFFA leverages self-attention to learn inter-frame correlations across multiple consecutive frames and aggregates the features of the temporal-channel dimension through channel aggregation to highlight ambiguity regions. The Vision RetNet encoder introduces explicit spatial priors by constructing a spatial decay matrix using the Manhattan distance. We conducted experiments on the EchoNet-Dynamic dataset and the CAMUS dataset, where our proposed model demonstrates competitive performance. The experimental results indicate that spatial prior information and inter-frame correlations in echocardiography images can enhance the accuracy of semantic segmentation, and inter-frame correlations become even more effective when spatial priors are provided.

PMID:40293054 | DOI:10.3390/s25061909

Sensors (Basel). 2025 Mar 19;25(6):1916. doi: 10.3390/s25061916.

ABSTRACT

BACKGROUND: IgA nephropathy (IgAN) is a leading cause of renal failure, characterized by significant clinical and pathological heterogeneity. Accurate subtype classification remains challenging due to overlapping clinical manifestations and the multidimensional nature of data. Traditional methods often fail to fully capture IgAN's complexity, limiting their clinical applicability. This study introduces MAL-Net, a deep learning framework for multi-label classification of IgAN subtypes, leveraging multidimensional clinical data and incorporating sensor-based inputs such as laboratory indices and symptom tracking.

METHODS: MAL-Net integrates Long Short-Term Memory (LSTM) networks with Multi-Head Attention (MHA) mechanisms to effectively capture sequential and contextual dependencies in clinical data. A memory network module extracts features from clinical sensors and records, while the MHA module emphasizes critical features and mitigates class imbalance. The model was trained and validated on clinical data from 500 IgAN patients, incorporating demographic, laboratory, and symptomatic variables. Performance was evaluated against six baseline models, including traditional machine learning and deep learning approaches.

RESULTS: MAL-Net outperformed all baseline models, achieving 91% accuracy and an AUC of 0.97. The integration of MHA significantly enhanced classification performance, particularly for underrepresented subtypes. The F1-score for the Ni-du subtype improved by 0.8, demonstrating the model's ability to address class imbalance and improve precision.

CONCLUSIONS: MAL-Net provides a robust solution for multi-label IgAN subtype classification, tackling challenges such as data heterogeneity, class imbalance, and feature interdependencies. By integrating clinical sensor data, MAL-Net enhances IgAN subtype prediction, supporting early diagnosis, personalized treatment, and improved prognosis evaluation.

PMID:40293045 | DOI:10.3390/s25061916

JAMA. 2025 Apr 28. doi: 10.1001/jama.2025.2680. Online ahead of print.

ABSTRACT

IMPORTANCE: Non-cystic fibrosis (CF) bronchiectasis is a chronic lung condition caused by permanent bronchial dilatation and inflammation and is characterized by daily cough, sputum, and recurrent exacerbations. Approximately 500 000 people in the US have non-CF bronchiectasis.

OBSERVATIONS: Non-CF bronchiectasis may be associated with prior pneumonia, infection with nontuberculous mycobacteria or tuberculosis, genetic conditions (eg, α1-antitrypsin deficiency, primary ciliary dyskinesia), autoimmune diseases (eg, rheumatoid arthritis, inflammatory bowel disease), allergic bronchopulmonary aspergillosis, and immunodeficiency syndromes (eg, common variable immunodeficiency). Up to 38% of cases are idiopathic. According to US data, conditions associated with non-CF bronchiectasis include gastroesophageal reflux disease (47%), asthma (29%), and chronic obstructive pulmonary disease (20%). The prevalence of non-CF bronchiectasis increases substantially with age (7 per 100 000 in individuals 18-34 years vs 812 per 100 000 in those ≥75 years) and is more common in women than men (180 vs 95 per 100 000). Diagnosis is confirmed with noncontrast chest computed tomography showing dilated airways and often airway thickening and mucus plugging. Initial diagnostic evaluation involves blood testing (complete blood cell count with differential); immunoglobulin quantification testing (IgG, IgA, IgE, and IgM); sputum cultures for bacteria, mycobacteria, and fungi; and prebronchodilator and postbronchodilator spirometry. Treatment includes airway clearance techniques; nebulization of saline to loosen tenacious secretions; and regular exercise, participation in pulmonary rehabilitation, or both. Inhaled bronchodilators (β-agonists and antimuscarinic agents) and inhaled corticosteroids are indicated for patients with bronchiectasis who have asthma or chronic obstructive pulmonary disease. Exacerbations of bronchiectasis, which typically present with increased cough and sputum and worsened fatigue, are associated with progressive decline in lung function and decreased quality of life. Exacerbations should be treated with oral or intravenous antibiotics. Individuals with 3 or more exacerbations of bronchiectasis annually may benefit from long-term inhaled antibiotics (eg, colistin, gentamicin) or daily oral macrolides (eg, azithromycin). Lung transplant may be considered for patients with severely impaired pulmonary function, frequent exacerbations, or both. Among patients with non-CF bronchiectasis, mortality is higher for those with frequent and severe exacerbations, infection with Pseudomonas aeruginosa, and comorbidities, such as chronic obstructive pulmonary disease.

CONCLUSIONS AND RELEVANCE: Non-CF bronchiectasis is a chronic lung condition that typically causes chronic cough and daily sputum production. Exacerbations are associated with progressive decline in lung function and decreased quality of life. Management involves treatment of conditions associated with bronchiectasis, airway clearance techniques, oral or intravenous antibiotics for acute exacerbations, and consideration of long-term inhaled antibiotics or oral macrolides for patients with 3 or more exacerbations annually.

PMID:40293759 | DOI:10.1001/jama.2025.2680

J Inflamm Res. 2025 Apr 23;18:5499-5517. doi: 10.2147/JIR.S505229. eCollection 2025.

ABSTRACT

AIM: To identify the molecular signature of differentially expressed genes (DEGs) associated with PANoptosis in idiopathic pulmonary fibrosis (IPF) and to interpret their immune landscape and cellular distribution characteristics.

METHODS AND RESULTS: We acquired two IPF datasets from the Gene Expression Omnibus (GEO) database to identify PANoptosis-related DGEs (PAN-DEGs), initially identifying thirty PAN-DEGs. Utilizing machine learning algorithms, we established a five-gene PANoptosis-related signature comprising IGF1, GPX3, GADD45β, SMAD7, and TIMP3, each demonstrating robust diagnostic performance. The expression of these hub genes was subsequently validated using a third GEO dataset and a bleomycin-induced pulmonary fibrosis model. Immune infiltration analysis revealed a close association of these genes with various immune cells, and single-cell RNA sequencing indicated significant expression changes in diverse pulmonary cell types, particularly endothelial cells and fibroblasts.

CONCLUSION: We identified and validated a PANoptosis-related gene signature in IPF, providing insights into their immune infiltration and potential cellular distribution. Further research is necessary to elucidate the biological functions and mechanisms of these genes in the pathogenesis of IPF.

PMID:40291456 | PMC:PMC12034268 | DOI:10.2147/JIR.S505229

Toxicol Res. 2024 Dec 28;41(3):235-244. doi: 10.1007/s43188-024-00269-6. eCollection 2025 May.

ABSTRACT

Protein kinase B (PKB/AKT) is a very important member of the protein kinase family, playing significant roles in various crucial processes including insulin-signaling, cell survival, growth, and metabolism. The carboxyl-terminal modulator protein 1 (CTMP1) inhibits PKB, primarily by attenuating its phosphorylation. Idiopathic pulmonary fibrosis (IPF) is an irreversible, chronic, progressive pulmonary disorder; the clinical treatment options are limited. Of the various experimental models, bleomycin-induced lung fibrosis is the most extensively studied. It closely resembles human lung fibrosis. We explored the impact of CTMP1 on bleomycin-induced fibrosis. In vitro experiments involved knockdown of CTMP1 in A549 cells (human alveolar epithelial cells), followed by bleomycin treatment. In vivo, lung fibrosis was induced in mice with ablated CTMP1 via intratracheal bleomycin administration at 2 mg/kg. CTMP1 deletion reduced pulmonary fibrosis and the epithelial-to-mesenchymal transition by inhibiting PKB phosphorylation. These findings suggest that CTMP1 plays a pivotal role in the regulation of lung fibrosis, offering new insights into potential therapeutic approaches for IPF patients.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s43188-024-00269-6.

PMID:40291111 | PMC:PMC12021751 | DOI:10.1007/s43188-024-00269-6

Front Med (Lausanne). 2025 Apr 11;12:1526530. doi: 10.3389/fmed.2025.1526530. eCollection 2025.

ABSTRACT

BACKGROUND: For patients with interstitial lung diseases (ILDs) other than idiopathic pulmonary fibrosis (IPF) with a progressive pulmonary fibrosis (PPF) phenotype, current knowledge of patient experience and symptom burden is limited. This study aimed to describe the patient journey for patients with PPF and IPF in a real-world setting in Japan.

METHODS: Data were analyzed from the Adelphi Real World PPF-ILD Disease Specific Programme™, a cross-sectional survey with elements of retrospective data collection of pulmonologists and rheumatologists in Japan from April to October 2022. Participants provided data for up to 12 consecutive patients with physician-confirmed ILD with a progressive phenotype. Analyses were descriptive, except Kappa (κ) statistic was used to measure the alignment between physician- and patient-reported symptom burden in the 4 weeks prior to survey date (poor agreement: κ =<0.00; slight 0.00-0.20; fair 0.21-0.40; moderate 0.41-0.60; substantial 0.61-0.80; almost perfect 1.00).

RESULTS: A total of 63 physicians (43 pulmonologists and 20 rheumatologists) provided data on 382 patients (312 with PPF and 70 with IPF). These patients were also asked to complete a voluntary survey on their experience and symptoms. Mean time from first symptom to consultation was 14.1 months for IPF, 8.0 months for non-connective tissue disease (CTD)-associated ILDs, and 10.7 months for CTD-ILDs. Mean times from consultation to diagnosis were 7.1, 4.8, and 3.6 months, respectively. Perception of symptoms differed between physicians and patients with alignment ranging from poor (dysphagia, κ = -0.0296, p = 0.6217) to substantial (weight loss, κ = 0.6174, p = 0.001). Health-related quality of life (HRQoL) was consistently impaired in patients overall, but too few patients completed HRQoL instruments to compare IPF with other forms of ILD.

CONCLUSIONS: This real-world study expands our understanding of the patient journey for patients with PPF and IPF in Japan. Greater communication between patients and physicians is needed to shorten diagnostic delays and target treatment strategies to improve patient experience and overall outcomes.

PMID:40291022 | PMC:PMC12023007 | DOI:10.3389/fmed.2025.1526530

J Med Internet Res. 2025 Apr 28;27:e66231. doi: 10.2196/66231.

ABSTRACT

BACKGROUND: A substantially lower proportion of female individuals participate in sufficient daily activity compared to male individuals despite the known health benefits of exercise. Investment in female sports and exercise medicine research may help close this gap; however, female individuals are underrepresented in this research. Hesitancy to include female participants is partly due to assumptions that biological rhythms driven by menstrual cycles and occurring on the timescale of approximately 28 days increase intraindividual biological variability and weaken statistical power. An analysis of continuous skin temperature data measured using a commercial wearable device found that temperature cycles indicative of menstrual cycles did not substantially increase variability in female individuals' skin temperature. In this study, we explore physical activity (PA) data as a variable more related to behavior, whereas temperature is more reflective of physiological changes.

OBJECTIVE: We aimed to determine whether intraindividual variability of PA is affected by biological sex, and if so, whether having menstrual cycles (as indicated by temperature rhythms) contributes to increased female intraindividual PA variability. We then sought to compare the effect of sex and menstrual cycles on PA variability to the effect of PA rhythms on the timescales of days and weeks and to the effect of nonrhythmic temporal structure in PA on the timescale of decades of life (age).

METHODS: We used minute-level metabolic equivalent of task data collected using a wearable device across a 206-day study period for each of 596 individuals as an index of PA to assess the magnitudes of variability in PA accounted for by biological sex and temporal structure on different timescales. Intraindividual variability in PA was represented by the consecutive disparity index.

RESULTS: Female individuals (regardless of whether they had menstrual cycles) demonstrated lower intraindividual variability in PA than male individuals (Kruskal-Wallis H=29.51; P<.001). Furthermore, individuals with menstrual cycles did not have greater intraindividual variability than those without menstrual cycles (Kruskal-Wallis H=0.54; P=.46). PA rhythms differed at the weekly timescale: individuals with increased or decreased PA on weekends had larger intraindividual variability (Kruskal-Wallis H=10.13; P=.001). In addition, intraindividual variability differed by decade of life, with older age groups tending to have less variability in PA (Kruskal-Wallis H=40.55; P<.001; Bonferroni-corrected significance threshold for 15 comparisons: P=.003). A generalized additive model predicting the consecutive disparity index of 24-hour metabolic equivalent of task sums (intraindividual variability of PA) showed that sex, age, and weekly rhythm accounted for only 11% of the population variability in intraindividual PA variability.

CONCLUSIONS: The exclusion of people from PA research based on their biological sex, age, the presence of menstrual cycles, or the presence of weekly rhythms in PA is not supported by our analysis.

PMID:40293784 | DOI:10.2196/66231

Synth Syst Biotechnol. 2025 Apr 8;10(3):827-834. doi: 10.1016/j.synbio.2025.03.011. eCollection 2025 Sep.

ABSTRACT

Synthetic RNA-based switches provide distinctive merits in modulating gene expression. Simple and flexible RNA-based switches are crucial for advancing the field of gene regulation, paving the way for innovative tools that can sense and manipulate cellular processes. In this research, we have developed programmable ribozymes that are capable of suppressing gene expression in response to specific, endogenously expressed trigger RNAs. We engineer ribozymes by introducing upstream antisense sequences (anti-ribozymes) to inhibit the self-cleaving activity of the hammerhead ribozyme and open the expression of the target gene. The trigger RNA is designed to recognize and bind to complementary sequences within the anti-ribozymes, thereby inhibiting their ability to direct protein synthesis. The anti-ribozyme performance is optimized by regulating the essential sequence modules that play a crucial role in determining the specificity and efficiency of the anti-ribozyme's interaction with its trigger RNA. By applying this switch mechanism to various ribozyme designs, we have shown that it is possible to achieve control over gene expression across a wide range of trigger RNAs. By exploiting these programmable anti-ribozymes, we aim to create a powerful tool for controlling gene expression in mammalian cells, which could have important implications for basic research, disease diagnosis, and therapeutic interventions.

PMID:40291978 | PMC:PMC12033390 | DOI:10.1016/j.synbio.2025.03.011

F1000Res. 2024 Sep 5;13:792. doi: 10.12688/f1000research.153244.2. eCollection 2024.

ABSTRACT

Sphingosine 1-phosphate receptor 1 (S1PR1) is a G-coupled protein receptor that induces crucial biological processes when bound by sphingosine 1-phosphate. Here, we have characterized nine S1PR1 commercial antibodies for western blot, immunoprecipitation, and immunofluorescence using a standardized experimental protocol based on comparing read-outs in knockout cell lines and isogenic parental controls. These studies are part of a larger, collaborative initiative seeking to address antibody reproducibility issues by characterizing commercially available antibodies for human proteins and publishing the results openly as a resource for the scientific community. While use of antibodies and protocols vary between laboratories, we encourage readers to use this report as a guide to select the most appropriate antibodies for their specific needs.

PMID:40291769 | PMC:PMC12022542 | DOI:10.12688/f1000research.153244.2

ACS Omega. 2025 Apr 8;10(15):15204-15218. doi: 10.1021/acsomega.4c10939. eCollection 2025 Apr 22.

ABSTRACT

Selective serotonin reuptake inhibitors (SSRIs) are known to have anticancer activity against different types of cancer. In this study, an integrative informatics approach was applied to identify compound and genetic perturbations that produce similar effects to SSRIs to formulate systems biology hypotheses and identify biological pathways involved in the putative anticancer effects of SSRIs in prostate cancer. An 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay assessed the antiproliferative effects of SSRIs and drug combinations. Cell death mechanisms were studied using annexin V-FITC/PI staining, and the cell cycle analysis was carried out by counterstaining with propidium iodide. Relative gene expression was assessed using a real-time polymerase chain reaction (PCR). Computational results hypothesized that SSRIs could potentially exert anticancer effects in prostate cancer cell lines by modulating apoptotic and tumorigenesis pathways and significantly inhibiting the growth of prostate cancer cells in a time and concentration-dependent manner. The combination of SSRIs with cisplatin, 5-fluorouracil, and raloxifene resulted in either synergistic or additive effects. SSRIs resulted in a significant increase in the early and late apoptotic activity in PC3 cells. Dapoxetine, paroxetine, and sertraline resulted in cell cycle arrest at the G0/G1 phase. Treatment with either dapoxetine or paroxetine decreases the expression of Bcl-2, CASP8, DR5, and VEGF. At the same time, sertraline decreases the expression of Bcl-2 and VEGF and increases the expression of CASP8 and DR5. Results revealed that SSRIs can potentially act as antiproliferative agents against prostate cancer cells, and their activity is mediated through different signaling pathways.

PMID:40290959 | PMC:PMC12019733 | DOI:10.1021/acsomega.4c10939

JHEP Rep. 2025 Jan 30;7(5):101340. doi: 10.1016/j.jhepr.2025.101340. eCollection 2025 May.

ABSTRACT

BACKGROUND & AIMS: Actionable candidates of hepatocarcinogenesis remain elusive, and tools for early detection are suboptimal. Our aim was to demonstrate that serum metabolome profiles reflect the initiation of hepatocellular carcinoma (HCC) and enable the identification of biomarkers for early HCC detection and actionable candidates for chemoprevention.

METHODS: This global cohort study included 654 patients and 801 biospecimens. Following serum metabolome profiling across the spectrum of hepatocarcinogenesis, we conducted a phase II biomarker case-control study for early HCC detection. Findings were independently validated through in silico analysis, mRNA sequencing, and proteome profiling of primary HCC and non-tumoral tissue, and in vitro experiments.

RESULTS: Aspartic acid, glutamic acid, taurine, and hypoxanthine were differentially abundant in the serum across chronic liver disease, cirrhosis, initial HCC, and progressed HCC, independent of sex, age, and etiology. In a phase II biomarker case-control study, a blood-based metabolite signature yielded an AUC of 94% to discriminate between patients with early-stage HCC and controls with cirrhosis, including independent validation. Unsupervised biclustering (MoSBi), lipid network analysis (LINEX2), and pathway enrichment analysis confirmed alterations in amino acid-, lipid-, and nucleotide-related pathways. In tumor tissue, these pathways were significantly deregulated regarding gene and protein expression in two independent datasets, including actionable targets RRM2, GMPS, BCAT1, PYCR2, and NEU1. In vitro knockdown confirmed a functional role in proliferation and migration, as exemplified for PYCR2.

CONCLUSIONS: These findings demonstrate that serum metabolome profiling indicates deregulated metabolites and pathways during hepatocarcinogenesis. Our liquid biopsy approach accurately detects early-stage HCC outperforming currently recommended surveillance tools and facilitates identification of actionable candidates for chemoprevention.

IMPACT AND IMPLICATIONS: Deregulated cellular metabolism is a hallmark of cancer. In smaller studies, circulating metabolite profiles have been associated with HCC, although mainly in the context of fatty liver disease. Translation strategies for primary prevention or early detection are lacking. In this global study, we present an unsupervised landscape of the altered serum metabolome profile during hepatocarcinogenesis, independent of age, sex, and etiology. We provide a blood-based metabolite signature that accurately identifies early-stage HCC in a phase II biomarker study including independent validation. Further RRM2, GMPS, BCAT1, PYCR2, and NEU1 are identified in tumor tissue as actionable candidates for prevention. Our data provide the rationale for clinical trials testing liquid biopsy metabolome-based signatures for early HCC detection and the development of chemoprevention strategies.

PMID:40290517 | PMC:PMC12023797 | DOI:10.1016/j.jhepr.2025.101340

N Engl J Med. 2025 Apr 28. doi: 10.1056/NEJMoa2503704. Online ahead of print.

ABSTRACT

BACKGROUND: Innovative oral targeted therapies are warranted for patients with human epidermal growth factor receptor 2 (HER2)-mutant non-small-cell lung cancer (NSCLC). Zongertinib is an oral, irreversible, HER2-selective tyrosine kinase inhibitor that has been shown to have efficacy in persons with advanced or metastatic solid tumors with HER2 alterations in a phase 1 study.

METHODS: We evaluated zongertinib in a multicohort, phase 1a-1b trial involving patients with advanced or metastatic HER2-mutant NSCLC. Here we report the primary analysis of zongertinib in previously treated patients: those with tumors harboring a mutation in the tyrosine kinase domain (cohort 1), those with tumors harboring a mutation in the tyrosine kinase domain previously treated with a HER2-directed antibody-drug conjugate (cohort 5), and those with tumors harboring a non-tyrosine kinase domain mutation (cohort 3). In cohort 1, patients were initially randomly assigned to receive zongertinib at a dose of 120 mg or 240 mg once daily. Patients in cohorts 5 and 3 initially received 240 mg daily. After an interim analysis of data from cohort 1, subsequently recruited patients across all cohorts received zongertinib at a dose of 120 mg. The primary end point was an objective response assessed by blinded independent central review (cohorts 1 and 5) or by investigator review (cohort 3). Secondary end points included the duration of response and progression-free survival.

RESULTS: In cohort 1, a total of 75 patients received zongertinib at a dose of 120 mg. At the data cutoff (November 29, 2024), 71% of these patients (95% confidence interval [CI], 60 to 80; P<0.001 against a ≤30% benchmark) had a confirmed objective response; the median duration of response was 14.1 months (95% CI, 6.9 to not evaluable), and the median progression-free survival was 12.4 months (95% CI, 8.2 to not evaluable). Grade 3 or higher drug-related adverse events occurred in 13 patients (17%). In cohort 5 (31 patients), 48% of the patients (95% CI, 32 to 65) had a confirmed objective response. Grade 3 or higher drug-related adverse events occurred in 1 patient (3%). In cohort 3 (20 patients), 30% of the patients (95% CI, 15 to 52) had a confirmed objective response. Grade 3 or higher drug-related adverse events occurred in 5 patients (25%). Across all three cohorts, no cases of drug-related interstitial lung disease occurred.

CONCLUSIONS: Zongertinib showed clinical benefit with mainly low-grade adverse events in patients with previously treated HER2-mutant NSCLC. (Funded by Boehringer Ingelheim; Beamion LUNG-1 ClinicalTrials.gov number, NCT04886804.).

PMID:40293180 | DOI:10.1056/NEJMoa2503704