iScience. 2025 Jan 24;28(3):111883. doi: 10.1016/j.isci.2025.111883. eCollection 2025 Mar 21.

ABSTRACT

Early stages of metastasis depend on the collective behavior of cancer cells and their interaction with the extracellular matrix (ECM). Cancer cell clusters are known to exhibit higher metastatic potential than single cells. To explore clustering dynamics, we developed a calibrated computational model describing how motile cancer cells biochemically and biomechanically interact with the ECM during the initial invasion phase, including ECM degradation and mechanical remodeling. The model reveals that cluster formation time, size, and shape are influenced by ECM degradation rates and cellular compliance to external stresses (durotaxis). The results align with experimental observations, demonstrating distinct cell trajectories and cluster morphologies shaped by biomechanical parameters. The simulations provide valuable insights into cancer invasion dynamics and may suggest potential therapeutic strategies targeting early-stage invasive cells.

PMID:40104056 | PMC:PMC11914804 | DOI:10.1016/j.isci.2025.111883

Prostate. 2025 Mar 18. doi: 10.1002/pros.24880. Online ahead of print.

ABSTRACT

BACKGROUND: 177Lu-vipivotide tetraxetan (177Lu-PSMA-617, LuPSMA) improves overall survival in patients with metastatic castration-resistant prostate cancer (mCRPC) after at least one taxane chemotherapy and androgen receptor pathway inhibitor. There are limited data on the clinical course and outcomes of patients with mCRPC after receipt of LuPSMA.

METHODS: We queried an IRB-approved prospectively maintained registry of all patients with mCRPC who received standard-of-care LuPSMA at our institution between June 2022 and January 2024. Clinical data about LuPSMA and subsequent therapies were extracted from the electronic medical record, including the type and number of subsequent systemic therapies, reason for treatment cessation, hematologic toxicity and supportive treatment, and PSA50 response to subsequent therapy (defined as a ≥ 50% decrease in PSA).

RESULTS: A total of 146 patients were evaluated; mean age 72 (range 52-87), observed median follow-up 5.9 months (range 0.51-18.7). Forty-four received systemic treatment after LuPSMA. The most common subsequent treatment after LuPSMA was chemotherapy (n = 27), primarily cabazitaxel ± carboplatin/cisplatin (n = 23), and the median number of cycles received was 4 (range 1-7). In 35/44 men with available hematologic toxicity data, 13 developed grade ≥ 3 anemia, 7 had ≥ grade 3 thrombocytopenia, and 16 received hematologic support. PSA50 to post-LuPSMA treatment occurred in 10/36 (28%) evaluable patients. Median overall survival from subsequent systemic therapy was 7.6 months (95% CI 5.81-NR).

CONCLUSIONS: 30% of patients receiving standard-of-care LuPSMA received subsequent therapy, mostly cabazitaxel-containing regimens. Post-LuPSMA treatment appeared tolerable and was associated with a PSA50 response rate of 28%. These outcomes may be biased by limited standard-of-care life-prolonging treatment options at the time of LuPSMA FDA approval, but it also highlights the continued need to develop novel therapeutic strategies for mCRPC post-LuPSMA.

PMID:40103237 | DOI:10.1002/pros.24880

Nat Commun. 2025 Mar 18;16(1):2643. doi: 10.1038/s41467-025-57929-w.

NO ABSTRACT

PMID:40102433 | DOI:10.1038/s41467-025-57929-w

Respir Med Case Rep. 2025 Feb 26;54:102183. doi: 10.1016/j.rmcr.2025.102183. eCollection 2025.

ABSTRACT

Lung cancer often causes bone metastasis, and denosumab is administered to bone metastases to prevent bone-related adverse events. One of the important side effects of denosumab is hypocalcemia, but this is generally not a problem, as it is used with calcium supplementation. A 48-year-old non-smoker male was diagnosed with lung adenocarcinoma with EGFR L858R mutation with diffuse bone metastases. Three days after receiving denosumab, the patient developed weakness and numbness in his limbs and was diagnosed with drug-induced hypocalcemia due to denosumab. It takes more than 4 months for treating the hypocalcemia in this case with continuous intravenous infusion of calcium gluconate with oral calcium supplementation for 2 months of hospitalization and subsequent 2 months of outpatient treatment with intermittent intravenous infusion of calcium gluconate three times a week along with oral supplementation. Tartrate-resistant acid phosphatase-5b (TRACP-5b), a marker of bone resorption, was a biomarker for the required amount of calcium in this case. Patients with lung cancer with diffuse osteoblastic bone metastases could develop severe hypocalcemia and require long-term calcium supplementation.

PMID:40104434 | PMC:PMC11915153 | DOI:10.1016/j.rmcr.2025.102183

Br J Sports Med. 2025 Mar 18:bjsports-2024-109184. doi: 10.1136/bjsports-2024-109184. Online ahead of print.

ABSTRACT

OBJECTIVE: To synthesise data on the associations between cardiorespiratory fitness (CRF) and health in children and adolescents, evaluate the certainty of evidence and identify knowledge gaps.

DESIGN: An overview of systematic reviews with meta-analyses. Results were pooled using forest plots and certainty of evidence evaluated with GRADE.

DATA SOURCES: Medline, Embase, Scopus, CINAHL and SPORTDiscus were searched from January 2002 to March 2024.

ELIGIBILITY CRITERIA FOR SELECTED STUDIES: Systematic reviews with meta-analyses exploring CRF and health in children and adolescents aged <18 years.

RESULTS: From the 9062 records identified, 14 reviews were included. Meta-analysed data from 125 164 observations covering 33 health outcomes were compiled, showing favourable (n=26) or null (n=7) associations with CRF. Among general populations, the associations were weak-to-moderate, with favourable links between CRF and indicators of anthropometry and adiposity, cardiometabolic and vascular health, and mental health and well-being. Among clinical populations, CRF was lower in participants with a condition compared with healthy controls, with the largest difference for newly diagnosed cancer (mean difference=-19.6 mL/kg/min; 95%CI: -21.4,-17.8). Patients with cystic fibrosis had a greater risk for all-cause mortality when comparing low CRF vs. high (relative risk=4.9; 95%CI: 1.1, 22.1). The certainty of evidence ranged from very low to moderate.

CONCLUSION: CRF shows promising links to numerous health outcomes in paediatric populations, though the low certainty of evidence calls for further research. High-quality longitudinal evidence is warranted to confirm the findings and investigate a predictive role of childhood CRF for future health.

PMID:40101938 | DOI:10.1136/bjsports-2024-109184

Med Phys. 2025 Mar 18. doi: 10.1002/mp.17757. Online ahead of print.

ABSTRACT

BACKGROUND: Unsupervised domain adaptation (UDA) seeks to mitigate the performance degradation of deep neural networks when applied to new, unlabeled domains by leveraging knowledge from source domains. In medical image segmentation, prevailing UDA techniques often utilize adversarial learning to address domain shifts for cross-modality adaptation. Current research on adversarial learning tends to adopt increasingly complex models and loss functions, making the training process highly intricate and less stable/robust. Furthermore, most methods primarily focused on segmentation accuracy while neglecting the associated confidence levels and uncertainties.

PURPOSE: To develop a simple yet effective UDA method based on histogram matching-enhanced adversarial learning (HMeAL-UDA), and provide comprehensive uncertainty estimations of the model predictions.

METHODS: Aiming to bridge the domain gap while reducing the model complexity, we developed a novel adversarial learning approach to align multi-modality features. The method, termed HMeAL-UDA, integrates a plug-and-play histogram matching strategy to mitigate domain-specific image style biases across modalities. We employed adversarial learning to constrain the model in the prediction space, enabling it to focus on domain-invariant features during segmentation. Moreover, we quantified the model's prediction confidence using Monte Carlo (MC) dropouts to assess two voxel-level uncertainty estimates of the segmentation results, which were subsequently aggregated into a volume-level uncertainty score, providing an overall measure of the model's reliability. The proposed method was evaluated on three public datasets (Combined Healthy Abdominal Organ Segmentation [CHAOS], Beyond the Cranial Vault [BTCV], and Abdominal Multi-Organ Segmentation Challenge [AMOS]) and one in-house clinical dataset (UTSW). We used 30 MRI scans (20 from the CHAOS dataset and 10 from the in-house dataset) and 30 CT scans from the BTCV dataset for UDA-based, cross-modality liver segmentation. Additionally, 240 CT scans and 60 MRI scans from the AMOS dataset were utilized for cross-modality multi-organ segmentation. The training and testing sets for each modality were split with ratios of approximately 4:1-3:1.

RESULTS: Extensive experiments on cross-modality medical image segmentation demonstrated the superiority of HMeAL-UDA over two state-of-the-art approaches. HMeAL-UDA achieved a mean (± s.d.) Dice similarity coefficient (DSC) of 91.34% ± 1.23% and an HD95 of 6.18 ± 2.93 mm for cross-modality (from CT to MRI) adaptation of abdominal multi-organ segmentation, and a DSC of 87.13% ± 3.67% with an HD95 of 2.48 ± 1.56 mm for segmentation adaptation in the opposite direction (MRI to CT). The results are approaching or even outperforming those of supervised methods trained with "ground-truth" labels in the target domain. In addition, we provide a comprehensive assessment of the model's uncertainty, which can help with the understanding of segmentation reliability to guide clinical decisions.

CONCLUSION: HMeAL-UDA provides a powerful segmentation tool to address cross-modality domain shifts, with the potential to generalize to other deep learning applications in medical imaging.

PMID:40102198 | DOI:10.1002/mp.17757

Med Phys. 2025 Mar 18. doi: 10.1002/mp.17764. Online ahead of print.

ABSTRACT

BACKGROUND: To minimize radiation exposure while obtaining high-quality Positron Emission Tomography (PET) images, various methods have been developed to derive standard-count PET (SPET) images from low-count PET (LPET) images. Although deep learning methods have enhanced LPET images, they rarely utilize the rich complementary information from MR images. Even when MR images are used, these methods typically employ early, intermediate, or late fusion strategies to merge features from different CNN streams, failing to fully exploit the complementary properties of multimodal fusion.

PURPOSE: In this study, we introduce a novel multimodal feature-guided diffusion model, termed MFG-Diff, designed for the denoising of LPET images with the full utilization of MRI.

METHODS: MFG-Diff replaces random Gaussian noise with LPET images and introduces a novel degradation operator to simulate the physical degradation processes of PET imaging. Besides, it uses a novel cross-modal guided restoration network to fully exploit the modality-specific features provided by the LPET and MR images and utilizes a multimodal feature fusion module employing cross-attention mechanisms and positional encoding at multiple feature levels for better feature fusion.

RESULTS: Under four counts (2.5%, 5.0%, 10%, and 25%), the images generated by our proposed network showed superior performance compared to those produced by other networks in both qualitative and quantitative evaluations, as well as in statistical analysis. In particular, the peak-signal-to-noise ratio of the generated PET images improved by more than 20% under a 2.5% count, the structural similarity index improved by more than 16%, and the root mean square error reduced by nearly 50%. On the other hand, our generated PET images had significant correlation (Pearson correlation coefficient, 0.9924), consistency, and excellent quantitative evaluation results with the SPET images.

CONCLUSIONS: The proposed method outperformed existing state-of-the-art LPET denoising models and can be used to generate highly correlated and consistent SPET images obtained from LPET images.

PMID:40102174 | DOI:10.1002/mp.17764

ISA Trans. 2025 Mar 11:S0019-0578(25)00145-4. doi: 10.1016/j.isatra.2025.03.004. Online ahead of print.

ABSTRACT

Deep learning has significantly advanced bearing fault diagnosis. Traditional models rely on the assumption of independent and identically distributed, which is frequently violated due to variations in rotational speeds and loads during bearing fault diagnosis. The fault diagnosis of the bearing based on representation learning lacks the consideration of spectrum knowledge and representation diversity under multiple working conditions. Therefore, this study presents a domain-invariant representation learning strategy (DIRLs) for diagnosing bearing faults across differing working conditions. DIRLs, by leveraging envelope spectrum knowledge distillation, captures the Fourier characteristics as domain-invariant features and secures robust health state representations by aligning high-order statistics of the samples under different working conditions. Moreover, an innovative loss function, which maximizes the two-paradigm metric of the health state representation, is designed to enrich representation diversity. Experimental results demonstrate an average AUC improvement of 28.6 % on the Paderborn-bearing dataset and an overall diagnostic accuracy of 88.7 % on a private bearing dataset, validating the effectiveness of the proposed method.

PMID:40102111 | DOI:10.1016/j.isatra.2025.03.004

Z Med Phys. 2025 Mar 17:S0939-3889(25)00034-0. doi: 10.1016/j.zemedi.2025.02.004. Online ahead of print.

ABSTRACT

This work demonstrated the development of a fast, deep-learning framework (DeepSMCP) to mitigate noise in Monte Carlo dose distributions (MC-DDs) of photon treatment plans with high statistical uncertainty (SU) and its integration into the Swiss Monte Carlo Plan (SMCP). To this end, a two-channel input (MC-DD and computed tomography (CT) scan) 3D U-net was trained, validated and tested (80%/10%/10%) on high/low-SU MC-DD-pairs of 106 clinically-motivated VMAT arcs for 29 available CTs, augmented to 3074 pairs. The model was integrated into SMCP to enable a "one-click" workflow of calculating and denoising MC-DDs of high SU to obtain MC-DDs of low SU. The model accuracy was evaluated on the test set using Gamma passing rate (2% global, 2 mm, 10% threshold) comparing denoised and low-SU MC-DD. Calculation time for the whole workflow was recorded. Denoised MC-DDs match low-SU MC-DDs with average (standard deviation) Gamma passing rate of 82.9% (4.7%). Additional application of DeepSMCP to 12 unseen clinically-motivated cases of different treatment sites, including treatment sites not present during training, resulted in an average Gamma passing rate of 91.0%. Denoised DDs were obtained on average in 35.1 s, a 340-fold efficiency gain compared to low-SU MC-DD calculation. DeepSMCP presented a first seamlessly integrated promising denoising framework for MC-DDs.

PMID:40102103 | DOI:10.1016/j.zemedi.2025.02.004

J Chromatogr A. 2025 Mar 13;1748:465874. doi: 10.1016/j.chroma.2025.465874. Online ahead of print.

ABSTRACT

INTRODUCTION: High-Performance Liquid Chromatography (HPLC) is widely used for its high sensitivity, stability, and accuracy. Nonetheless, it often involves lengthy analysis times and considerable solvent consumption, especially when dealing with complex systems and quality control, posing challenges to green and eco-friendly analytical practices.

OBJECTIVE: This study proposes a compressed fingerprint chromatogram analysis technique that combines a one-dimensional convolutional neural network (1D-CNN) with HPLC, aiming to improve the analytical efficiency of various compounds in complex systems while reducing the use of organic solvents.

MATERIALS AND METHODS: The natural product Artemisiae argyi Folium (AAF) was selected as the experimental subject. Firstly, HPLC fingerprints of AAF were developed based on conventional programs. Next, a compressed fingerprint was obtained without losing compound information. Finally, a 1D-CNN deep learning model was used to analyze and identify the compressed chromatograms, enabling quantitative analysis of 10 compounds in complex systems.

RESULTS: The results indicate that the 1D-CNN model can effectively extract features from complex data, reducing the analysis time for each sample by about 40 min. In addition, the consumption of mobile phase has significantly decreased by 78 % compared to before. Among the ten compounds to be analyzed, nine of them achieved good results, with the highest correlation coefficient reaching above 0.95, indicating that the model has strong explanatory power.

CONCLUSION: The proposed compressed fingerprint chromatograms recognition technique enhances the environmental sustainability and efficiency of traditional HPLC methods, offering valuable insights for future advancements in analytical methodologies and equipment development.

PMID:40101658 | DOI:10.1016/j.chroma.2025.465874

Comput Biol Med. 2025 Mar 17;189:109970. doi: 10.1016/j.compbiomed.2025.109970. Online ahead of print.

ABSTRACT

Digital twins (DTs) are advancing biotechnology by providing digital models for drug discovery, digital health applications, and biological assets, including microorganisms. However, the hypothesis posits that implementing micro- and nanoscale DTs, especially for biological entities like bacteria, presents substantial challenges. These challenges stem from the complexities of data extraction, transmission, and computation, along with the necessity for a specialized Internet of Things (IoT) infrastructure. To address these challenges, this article proposes a novel framework that leverages bio-network technologies, including the Internet of Bio-Nano Things (IoBNT), and decentralized deep learning algorithms such as federated learning (FL) and convolutional neural networks (CNN). The methodology involves using CNNs for robust pattern recognition and FL to reduce bandwidth consumption while enhancing security. IoBNT devices are utilized for precise microscopic data acquisition and transmission, which ensures minimal error rates. The results demonstrate a multi-class classification accuracy of 98.7% across 33 bacteria categories, achieving over 99% bandwidth savings. Additionally, IoBNT integration reduces biological data transfer errors by up to 98%, even under worst-case conditions. This framework is further supported by an adaptable, user-friendly dashboard, expanding its applicability across pharmaceutical and biotechnology industries.

PMID:40101583 | DOI:10.1016/j.compbiomed.2025.109970

Ultrasonics. 2025 Mar 9;151:107631. doi: 10.1016/j.ultras.2025.107631. Online ahead of print.

ABSTRACT

Pipe wall loss assessment is crucial in oil and gas transportation. Ultrasonic guided wave is an effective technology to detect pipe defects. However, accurately inverting weak-feature defects under limited view conditions remains challenging due to constraints in transducer arrangements and inconsistent signal characteristics. This paper proposes a stepwise inversion method based on feature compensation and network reconstruction through deep learning, combined with high-order helical guided waves to expand the imaging view and achieve high-resolution imaging of pipe defects. A forward model was established using the finite difference method, with the two-dimensional Pearson correlation coefficient and maximum wall loss estimation accuracy defined as imaging metrics to evaluate and compare the method. Among 50 randomly selected defect samples in the test set, the inversion model achieved a correlation coefficient of 0.9669 and a maximum wall loss estimation accuracy of 96.65 %. Additionally, Gaussian noise was introduced to assess imaging robustness under pure signal, 5 dB, and 3 dB conditions. Laboratory experiments validated the practical feasibility of the proposed method. This approach is generalizable and holds significant potential for nondestructive testing in cylindrical waveguide structures represented by pipes.

PMID:40101471 | DOI:10.1016/j.ultras.2025.107631

Food Chem. 2025 Mar 12;479:143850. doi: 10.1016/j.foodchem.2025.143850. Online ahead of print.

ABSTRACT

The quality of chili peppers is closely related to their variety and geographical origin. The market often substitutes high-quality chili peppers with inferior ones, and cross-contamination occurs during processing. The existing methods cannot quickly and conveniently distinguish between different chili varieties or origins, which require expensive experimental equipment and professional skills. Techniques such as energy-dispersive X-ray fluorescence and inductively coupled plasma spectroscopy have been used for chili pepper classification and origin tracing, but these methods are either costly or destructive. To address the challenges of accurately identifying chili pepper varieties and origin tracing of chili peppers, this paper presents a sensor-aware convolutional network (SACNet) integrated with an electronic nose (e-nose) for accurate variety classification and origin traceability of chili peppers. The e-nose system collects gas samples from various chili peppers. We introduce a sensor attention module that adaptively focuses on the importance of each sensor in gathering gas information. Additionally, we introduce a local sensing and wide-area sensing structure to specifically capture gas information features, enabling high-precision identification of chili pepper gases. In comparative experiments with other networks, SACNet demonstrated excellent performance in both variety classification and origin traceability, and it showed significant advantages in terms of parameter quantity. Specifically, SACNet achieved 98.56 % accuracy in variety classification with Dataset A, 97.43 % accuracy in origin traceability with Dataset B, and 99.31 % accuracy with Dataset C. In summary, the combination of SACNet and an e-nose provides an effective strategy for identifying the varieties and origins of chili peppers.

PMID:40101378 | DOI:10.1016/j.foodchem.2025.143850

Med Image Anal. 2025 Mar 12;102:103542. doi: 10.1016/j.media.2025.103542. Online ahead of print.

ABSTRACT

Histopathological image classification using deep learning is crucial for accurate and efficient cancer diagnosis. However, annotating a large amount of histopathological images for training is costly and time-consuming, leading to a scarcity of available labeled data for training deep neural networks. To reduce human efforts and improve efficiency for annotation, we propose a Unified Semi-supervised Active Learning framework (UniSAL) that effectively selects informative and representative samples for annotation. First, unlike most existing active learning methods that only train from labeled samples in each round, dual-view high-confidence pseudo training is proposed to utilize both labeled and unlabeled images to train a model for selecting query samples, where two networks operating on different augmented versions of an input image provide diverse pseudo labels for each other, and pseudo label-guided class-wise contrastive learning is introduced to obtain better feature representations for effective sample selection. Second, based on the trained model at each round, we design novel uncertain and representative sample selection strategy. It contains a Disagreement-aware Uncertainty Selector (DUS) to select informative uncertain samples with inconsistent predictions between the two networks, and a Compact Selector (CS) to remove redundancy of selected samples. We extensively evaluate our method on three public pathological image classification datasets, i.e., CRC5000, Chaoyang and CRC100K datasets, and the results demonstrate that our UniSAL significantly surpasses several state-of-the-art active learning methods, and reduces the annotation cost to around 10% to achieve a performance comparable to full annotation. Code is available at https://github.com/HiLab-git/UniSAL.

PMID:40101375 | DOI:10.1016/j.media.2025.103542

Phys Med Biol. 2025 Mar 18. doi: 10.1088/1361-6560/adc236. Online ahead of print.

ABSTRACT

&#xD;To evaluate the impact of beam mask implementation and data aggregation on artificial intelligence-based dose prediction accuracy in proton therapy, with a focus on scenarios involving limited or highly heterogeneous datasets.&#xD;Approach:&#xD;In this study, 541 prostate and 632 head and neck (H&N) proton therapy plans were used to train and evaluate convolutional neural networks designed for the task of dose prediction. Datasets were grouped by anatomical site and beam configuration to assess the impact of beam masks-graphical depictions of radiation paths-as a model input. We also evaluated the effect of combining datasets. Model performance was measured using dose-volume histograms (DVH) scores, mean absolute error, mean absolute percent error, Dice similarity coefficients (DSC), and gamma passing rates.&#xD;Main results:&#xD;DSC analysis revealed that the inclusion of beam masks improved dose prediction accuracy, particularly in low-dose regions and for datasets with diverse beam configurations. Data aggregation alone produced mixed results, with improvements in high-dose regions but potential degradation in low-dose areas. Notably, combining beam masks and data aggregation yielded the best overall performance, effectively leveraging the strengths of both strategies. Additionally, the magnitude of the improvements was larger for datasets with greater heterogeneity, with the combined approach increasing the DSC score by as much as 0.2 for a subgroup of H&N cases characterized by small size and heterogeneity in beam arrangement. DVH scores reflected these benefits, showing statistically significant improvements (p < 0.05) for the more heterogeneous H&N datasets.&#xD;Significance:&#xD;Artificial intelligence-based dose prediction models incorporating beam masks and data aggregation significantly improve accuracy in proton therapy planning, especially for complex cases. This technique could accelerate the planning process, enabling more efficient and effective cancer treatment strategies.&#xD.

PMID:40101365 | DOI:10.1088/1361-6560/adc236

Respir Investig. 2025 Mar 17;63(3):365-372. doi: 10.1016/j.resinv.2025.02.009. Online ahead of print.

ABSTRACT

BACKGROUND: Prognostic factors in patients with newly diagnosed idiopathic interstitial pneumonia (IIP) have rarely been analyzed using prospective data. This study investigated prognostic factors in patients with IIP.

METHODS: Central interstitial lung disease (ILD) experts established the diagnoses for fibrotic ILD. Prognostic factors using baseline data, including the pathological confidence level of usual interstitial pneumonia (UIP) assessed on a 0%-100% linear analog scale by high-resolution CT (HRCT), pulmonary function tests, and patient-reported outcomes were investigated.

RESULTS: Overall, 866 eligible patients were registered. Patients with unclassifiable idiopathic interstitial pneumonia (n = 272) survived longer than those with idiopathic pulmonary fibrosis (IPF) (n = 469) (hazard ratio [HR] = 0.67; [95% confidence interval [CI]: 0.47-0.95]; P = 0.022); however, IPF as IIPs classification was not a significant prognostic factor at diagnosis (P = 0.577). UIP pattern on HRCT, age, body mass index, forced vital capacity, diffusing capacity of the lungs for carbon monoxide, and St. George's Respiratory Questionnaire were risk factors for survival (P < 0.05). Patients with proposed progressive pulmonary fibrosis (PPF) had poorer prognoses than those without proposed PPF (HR = 5.63; [95% CI: 3.17-10.00]; P < 0.001). Patients with progressive fibrosing ILD (PF-ILD) had poorer prognoses than those without PF-ILD (HR = 7.85; [95% CI: 3.38-18.3]; P < 0.001).

CONCLUSIONS: A prospective registry of patients with newly diagnosed IIP provided evidence that the UIP pattern on HRCT by analog scale was a prognostic predictor. Proposed PPF and PF-ILD were valuable for discriminating prognosis. (JIPS Registry, ClinTrials.gov, NCT03041623).

PMID:40101437 | DOI:10.1016/j.resinv.2025.02.009

Open Biol. 2025 Mar;15(3):240302. doi: 10.1098/rsob.240302. Epub 2025 Mar 19.

ABSTRACT

The molecular clock rests upon the assumption that the observed changes among sequences capture the differentiation of lineages, or kinship, as dissimilarity increases with time. Although it has been questioned over the years, this paradigmatic principle continues to underlie the idea that the polymorphic space of a gene is so vast that it is unattainable in evolutionary time. Thus, the molecular clock has been used to obtain taxonomic annotations, proving to be very effective at delivering testable results. In this article, however, we ask how often this assumption leads to inaccuracies when inferring the lineage of prokaryotic genes. Thus, we open an interesting discussion by simulating, in realistic scenarios, the critical times in which specific 5S rRNA sequences of two distant lineages are exhausting the polymorphic space. We contend that certain genes in one lineage will become increasingly similar to those in another over time, as the space for new variants is finite, mimicking phylogenetic features by convergence or by chance, without implying true kinship.

PMID:40101780 | DOI:10.1098/rsob.240302

Cell Rep Methods. 2025 Mar 13:101005. doi: 10.1016/j.crmeth.2025.101005. Online ahead of print.

ABSTRACT

Generating metagenome-assembled genomes from DNA shotgun sequencing datasets can demand considerable computational resources. Here, we describe a sequential co-assembly method that reduces the assembly of duplicate reads through successive application of single-node computing tools for read assembly and mapping. Using a simulated mouse microbiome DNA shotgun sequencing dataset, we demonstrated that this approach shortens assembly time, uses less memory than traditional co-assembly, and produces significantly fewer assembly errors. Applying sequential co-assembly to shotgun sequencing reads from (1) a longitudinal study of gut microbiomes from undernourished Bangladeshi children and (2) a 2.3-terabyte dataset generated from gnotobiotic mice colonized with pooled microbiomes from these children that was too large to be handled by a traditional co-assembly approach also demonstrated significant reductions in assembly time and memory requirements. These results suggest that this approach should be useful in resource-constrained settings, including in low- and middle-income countries.

PMID:40101714 | DOI:10.1016/j.crmeth.2025.101005

Vaccine. 2025 Mar 17;54:126860. doi: 10.1016/j.vaccine.2025.126860. Online ahead of print.

ABSTRACT

Rift Valley fever (RVF) is a zoonotic viral disease that causes epidemics and epizootics among humans and livestock, resulting in substantial health and socioeconomic consequences. Currently, there are no RVF vaccines licensed for humans, but several candidates show promise in early-stage development. Existing gaps in RVF epidemiological data and challenges associated with predicting RVF outbreak risk complicate the planning of efficacy studies, making the pathway to licensure for promising candidates unclear. In June 2024, the Coalition for Epidemic Preparedness Innovations (CEPI) convened a two-day workshop in Nairobi, Kenya, to discuss RVF epidemiology, modeling priorities, and specific gaps relevant to human RVF vaccine development. The workshop included representatives from multiple RVF-endemic countries, key global collaborators, and international health organizations. Workshop participants identified five key priorities: (1) Looking beyond outbreaks: There is a need to better characterize the complex One Health epidemiology of RVF and understand interepidemic persistence of the virus; (2) Better data for better models: Epidemiological modeling is crucial for research, prediction, and planning, but it requires accurate and representative data; (3) New, improved and accessible diagnostics and serological assays: These are needed to inform epidemiology and case definitions, without which RVF research will continue to suffer due to paucity of data and challenges in determining infection and exposure; (4) Defining use cases, regulatory pathways, and implementation strategies for human vaccines: Clarity on these topics will facilitate licensure and effective use of RVF vaccines; and (5) People-centered approaches: Community engagement and involvement of social and behavioral scientists are key to the success of human vaccine research and development and implementation, particularly as the virus impacts livestock and livelihoods. Workshop participants welcomed a renewed focus for RVF epidemiology and modeling, and expressed enthusiasm for continued multidisciplinary collaborations to support enabling sciences for human RVF vaccine research and development.

PMID:40101455 | DOI:10.1016/j.vaccine.2025.126860

IEEE J Biomed Health Inform. 2025 Mar 18;PP. doi: 10.1109/JBHI.2025.3552536. Online ahead of print.

ABSTRACT

Central nervous system (CNS) diseases such as glioblastoma (GBM), multiple sclerosis (MS), and Alzheimer's disease (AD) remain challenging due to their complexity and limited treatments. Conventional drug repurposing strategies often rely on bulk RNA sequencing data, which can overlook cellular heterogeneity and mask rare but critical cell populations. Here, we introduce scDrugLink, a computational method that integrates single-cell transcriptomic data with drug targets and perturbation signatures to improve repurposing. For each cell type, scDrugLink constructs a Drug2Cell matrix based on drug targets to estimate promotion/inhibition scores and derives sensitivity/resistance scores by reverse matching signatures and disease-associated genes. These scores are then "linked," yielding robust therapeutic rankings. In our study, we present a systematic evaluation of single-cell drug repurposing methods for CNS diseases. Applied to atlas data for GBM, MS, and AD, scDrugLink surpassed three state-of-the-art methods (ASGARD, DrugReSC, and scDrugPrio), achieving area under the receiver operating characteristic curve (AUC) ranges of 0.6286-0.7242 and area under the precision-recall curve (AUPRC) ranges of 0.3412-0.5484. It also ranked top when comparing AUC and AUPRC at the level of individual cell types. Moreover, applying the "linking" principle to baseline methods boosted their performance, on average improving AUC and AUPRC by 0.0160 and 0.0244, respectively. Despite the advancements, the complexity and heterogeneity of CNS diseases, along with incomplete drug data, indicate that further improvement is necessary. We discuss these challenges and suggest directions for enhancing single-cell drug repurposing in the future.

PMID:40100675 | DOI:10.1109/JBHI.2025.3552536