PLoS One. 2025 Apr 21;20(4):e0319760. doi: 10.1371/journal.pone.0319760. eCollection 2025.

ABSTRACT

Accurate assessment of training status in team sports is crucial for optimising performance and reducing injury risk. This pilot study investigates the feasibility of using machine learning (ML) models to estimate oxygen uptake (VO2) with wearable sensors during team sports activities. Six healthy male team sports athletes participated in the study. Data were collected using inertial measurement units (IMU), heart rate monitors, and breathing rate sensors during incremental fitness tests. The performance of different ML models, including multiple linear regression (MLR), XGBoost, and deep learning models (LSTM, CNN, MLP), was compared using raw and engineered features from IMU data. Results indicate that while LSTM models with raw IMU data provided the most accurate predictions (RMSE: 4.976, MAE: 3.698 [Formula: see text]), MLR models remained competitive, especially with engineered features. Multi-sensor configurations, particularly those including sensors on the torso and limbs, enhanced prediction accuracy. The findings demonstrate the potential of ML models to monitor VO2 noninvasively in real-time, offering valuable insights into the internal physiological demand during team sports activities.

PMID:40258017 | DOI:10.1371/journal.pone.0319760

IEEE Trans Neural Syst Rehabil Eng. 2025 Apr 21;PP. doi: 10.1109/TNSRE.2025.3562922. Online ahead of print.

ABSTRACT

Brain-computer interfaces (BCI) acquire electroencephalogram (EEG) signals to effectively address postoperative motor dysfunction in stroke patients by discerning their motor intentions during significant movements. Traditionally, noninvasive BCIs have been constrained by limitations in their usage environments; whereas, invasive BCIs damage neural permanently. Therefore, we proposed a novel interventional BCI, in which electrodes are implanted along the veins into the brain to acquire intracerebral EEG signals without an open craniotomy. We collect EEG signals from the primary motor cortex in the superior sagittal sinus of sheep during three different significant movements: laying down; standing; and walking. The first three month data are used to train the neural network, and The fourth month of data were used to validate. The deep learning model achieved an 86% accuracy rate in classifying motion states in validation. Furthermore, the results of the power spectral density (PSD) show that the signal power in the main frequency band did not decrease over a period of five months, which demonstrates that the interventional BCI has the ability to effectively capture EEG signals over long periods of time.

PMID:40257874 | DOI:10.1109/TNSRE.2025.3562922

Proc Natl Acad Sci U S A. 2025 Apr 29;122(17):e2414342122. doi: 10.1073/pnas.2414342122. Epub 2025 Apr 21.

ABSTRACT

Genome modification is essential for studying and engineering bacteria, yet making efficient modifications to most species remains challenging. Bacteriophage-encoded single-stranded DNA-annealing proteins (SSAPs) can facilitate efficient genome editing by homologous recombination, but their typically narrow host range limits broad application. Here, we demonstrate that a single library of 227 SSAPs enables efficient genome-editing across six diverse bacteria from three divergent classes: Actinomycetia (Mycobacterium smegmatis and Corynebacterium glutamicum), Alphaproteobacteria (Agrobacterium tumefaciens and Caulobacter crescentus), and Bacilli (Lactococcus lactis and Staphylococcus aureus). Surprisingly, the most effective SSAPs frequently originated from phyla distinct from their bacterial hosts, challenging the assumption that phylogenetic relatedness is necessary for recombination efficiency, and supporting the value of a large unbiased library. Across these hosts, the identified SSAPs enable genome modifications requiring efficient homologous recombination, demonstrated through three examples. First, we use SSAPs with Cas9 in C. crescentus to introduce single amino acid mutations with >70% efficiency. Second, we adapt SSAPs for dsDNA editing in C. glutamicum and S. aureus, enabling one-step gene knockouts using PCR products. Finally, we apply SSAPs for multiplexed editing in S. aureus to precisely map the interaction between a conserved protein and a small-molecule inhibitor. Overall, this library-based SSAP screen expands engineering capabilities across diverse, previously recalcitrant microbes, enabling efficient genetic manipulation for both fundamental research and biotechnological applications.

PMID:40258142 | DOI:10.1073/pnas.2414342122

PLoS Comput Biol. 2025 Apr 21;21(4):e1012995. doi: 10.1371/journal.pcbi.1012995. Online ahead of print.

ABSTRACT

Over the past decade, pathogen genome sequencing has become well established as a powerful approach to study infectious disease epidemiology. In particular, when multiple genomes are available from several geographical locations, comparing them is informative about the relative size of the local pathogen populations as well as past migration rates and events between locations. The structured coalescent model has a long history of being used as the underlying process for such phylogeographic analysis. However, the computational cost of using this model does not scale well to the large number of genomes frequently analysed in pathogen genomic epidemiology studies. Several approximations of the structured coalescent model have been proposed, but their effects are difficult to predict. Here we show how the exact structured coalescent model can be used to analyse a precomputed dated phylogeny, in order to perform Bayesian inference on the past migration history, the effective population sizes in each location, and the directed migration rates from any location to another. We describe an efficient reversible jump Markov Chain Monte Carlo scheme which is implemented in a new R package StructCoalescent. We use simulations to demonstrate the scalability and correctness of our method and to compare it with existing software. We also applied our new method to several state-of-the-art datasets on the population structure of real pathogens to showcase the relevance of our method to current data scales and research questions.

PMID:40258093 | DOI:10.1371/journal.pcbi.1012995

PLoS Biol. 2025 Apr 21;23(4):e3003139. doi: 10.1371/journal.pbio.3003139. Online ahead of print.

ABSTRACT

During plant fertilization, excess male gametes compete for a limited number of female gametes. The dormant male gametophyte, encapsulated in the pollen grain, consists of two sperm cells enclosed in a vegetative cell. After reaching the stigma of a compatible flower, quick and efficient germination of the vegetative cell to a tip-growing pollen tube is crucial to ensure fertilization success. Rho of Plants (ROP) signaling and their activating ROP Guanine Nucleotide Exchange Factors (ROPGEFs) are essential for initiating polar growth processes in multiple cell types. However, which ROPGEFs activate pollen germination is unknown. We investigated the role of ROPGEFs in initiating pollen germination and the required cell polarity establishment. Of the five pollen-expressed ROPGEFs, we found that GEF8, GEF9, and GEF12 are required for pollen germination and male fertilization success, as gef8;gef9;gef12 triple mutants showed almost complete loss of pollen germination in vitro and had a reduced allele transmission rate. Live-cell imaging and spatiotemporal analysis of subcellular protein distribution showed that GEF8, GEF9, and GEF11, but not GEF12, displayed transient polar protein accumulations at the future site of pollen germination minutes before pollen germination, demonstrating specific roles for GEF8 and GEF9 during the initiation of pollen germination. Furthermore, this novel GEF accumulation appears in a biphasic temporal manner and can shift its location laterally. We showed that the C-terminal domain of GEF8 and GEF9 confers their protein accumulation and demonstrated that GEFs locally activate ROPs and alter Ca2+ levels, which is required for pollen tube germination. We demonstrated that not all GEFs act redundantly during pollen germination, and we described for the first time a polar domain with spatiotemporal flexibility, which is crucial for the de novo establishment of a polar growth domain within a cell and, thus, for pollen function and fertilization success.

PMID:40258071 | DOI:10.1371/journal.pbio.3003139

PLoS Comput Biol. 2025 Apr 21;21(4):e1012999. doi: 10.1371/journal.pcbi.1012999. Online ahead of print.

ABSTRACT

Cancer involves dynamic changes caused by (epi)genetic alterations such as mutations or abnormal DNA methylation patterns which occur in cancer driver genes. These driver genes are divided into oncogenes and tumor suppressors depending on their function and mechanism of action. Discovering driver genes in different cancer (sub)types is important not only for increasing current understanding of carcinogenesis but also from prognostic and therapeutic perspectives. We have previously developed a framework called Moonlight which uses a systems biology multi-omics approach for prediction of driver genes. Here, we present an important development in Moonlight2 by incorporating a DNA methylation layer which provides epigenetic evidence for deregulated expression profiles of driver genes. To this end, we present a novel functionality called Gene Methylation Analysis (GMA) which investigates abnormal DNA methylation patterns to predict driver genes. This is achieved by integrating the tool EpiMix which is designed to detect such aberrant DNA methylation patterns in a cohort of patients and further couples these patterns with gene expression changes. To showcase GMA, we applied it to three cancer (sub)types (basal-like breast cancer, lung adenocarcinoma, and thyroid carcinoma) where we discovered 33, 190, and 263 epigenetically driven genes, respectively. A subset of these driver genes had prognostic effects with expression levels significantly affecting survival of the patients. Moreover, a subset of the driver genes demonstrated therapeutic potential as drug targets. This study provides a framework for exploring the driving forces behind cancer and provides novel insights into the landscape of three cancer sub(types) by integrating gene expression and methylation data.

PMID:40258059 | DOI:10.1371/journal.pcbi.1012999

Int J Palliat Nurs. 2025 Apr 2;31(4):181-188. doi: 10.12968/ijpn.2023.0039.

ABSTRACT

BACKGROUND: Low-prevalence diseases (LPDs), previously referred to as orphan diseases or rare diseases, entail a substantial potential for mortality and impose a remarkable burden of symptoms for patients. The process of diagnosing these diseases is often lengthy, and viable treatment options for such conditions are scarce, or in some cases, non-existent.

METHODS: A narrative review was carried out following the Scale for the Assessment of Narrative Review Articles (SANRA) methodology to establish the role of palliative care in the treatment and follow-up of patients with LPDs. A search was carried out by a multidisciplinary team in EMBASE, PUBMED, Web of Science, CINHAL and OVID. Peer-reviewed articles reporting on the role of palliative care in the multidisciplinary treatment of LPDs were included.

RESULTS: The review identified significant areas where palliative care specialists play a crucial role in caring for LPDs. These areas include addressing complex physical and emotional symptoms, assisting patients in adjusting their expectations through genetic counselling, facilitating decision-making across short, medium and long-term perspectives based on disease prognosis, and offering support with care transitions, advanced planning and the grieving process for families.

CONCLUSION: Patients with LPDs and their caregivers experience complex care needs that should be assessed by a palliative care specialist and supported by a multidisciplinary medical group.

PMID:40257763 | DOI:10.12968/ijpn.2023.0039

Mol Cell Pediatr. 2025 Apr 21;12(1):5. doi: 10.1186/s40348-025-00190-4.

ABSTRACT

BACKGROUND: Intercellular communication is a critical process that ensures cooperation between distinct cell types and maintains homeostasis. In the past decades, extracellular vesicles (EVs) have been recognized as key components in cell-to-cell communication. These EVs carry multiple factors such as active enzymes, metabolites, nucleic acids and surface molecules that can alter the behavior of recipient cells. Thus, the role of EVs in exacerbating disease pathology by transporting inflammatory mediators, and other molecular signals that contribute to chronic inflammation and immune dysregulation in various diseases including cystic fibrosis (CF) is well documented.

MAIN BODY: CF is a genetic disorder characterized by chronic inflammation and persistent infections, primarily affecting the respiratory system. This review explores the multifaceted roles of EVs in CF lung disease, focusing on their biogenesis, cargo, and contributions to disease progression. It is well known that CF results from mutations in the CFTR (cystic fibrosis transmembrane conductance regulator) gene, leading to defective ion transport, thick mucus secretion, and a propensity for bacterial infections. However, it has been observed that EVs derived from CF patients carry altered molecular cargo, including proteins, lipids, RNA, and DNA, which can exacerbate these conditions by promoting inflammation, and modulating immune responses. Beyond their pathogenic roles, EVs also hold significant therapeutic potential. Their natural ability to transfer bioactive molecules positions them as promising vectors for delivering therapeutic agents, such as gene therapy constructs and anti-inflammatory compounds. Accordingly, a study has shown that these EVs can act as a carrier molecule for transport of functional CFTR mRNA, helping to restore proper chloride ion channel function by correcting defective CFTR proteins in affected cells.

CONCLUSION: This review aims to summarize the role of EVs and their molecular cargo in pathogenesis of CF lung disease via modulation of intracellular signaling leading to persistent inflammation and increased disease severity. We also explored the mechanisms of EV biogenesis, cargo selection, and their effects on recipient cells which may provide novel insights into CF pathogenesis and open new avenues for EV-based therapies aimed at improving disease management.

PMID:40257719 | DOI:10.1186/s40348-025-00190-4

J Asthma. 2025 Apr 21:1-14. doi: 10.1080/02770903.2025.2494222. Online ahead of print.

ABSTRACT

BACKGROUND: Asthma is a common condition among women of childbearing age, requiring careful management, particularly during pregnancy. Despite existing guidelines, significant gaps remain in asthma management during pregnancy, notably for women with moderate-to-severe asthma.

AIM: This study aimed to explore the awareness, limitations, and challenges of asthma management during childbearing age and pregnancy from both asthmatic women (AW) and physician perspectives in Italy. Additionally, it sought to identify unmet needs and collect real-life experiences from Italian centers specialized in severe asthma care.

METHODS: An anonymous online survey was disseminated through scientific networks and patient associations. Separate questionnaires were developed for doctors and AW by a task force of specialists.

RESULTS: 76 doctors and 54 AW completed the survey, with 70% of AW reporting moderate-to-severe asthma. While most physicians had experience managing asthma in pregnancy, 40% lacked systematic collaboration with gynecologists recognizing the need for integrated care. Despite guidelines supporting asthma medication continuity, 60% of doctors reported discontinuing treatments due to perceived risks. However, surveyed AW generally expressed greater confidence in medication safety. Physicians and AW highlighted the lack of pre-pregnancy counseling, with 55% of AW reporting they had never discussed pregnancy plans when starting asthma treatment. Both groups emphasized the need for improved interdisciplinary collaboration and structured asthma care pathways during pregnancy.

CONCLUSIONS: This study reveals significant gaps in asthma management for women of childbearing age and during pregnancy, especially those with moderate-to-severe asthma. Improving outcomes requires better education for patients and healthcare providers, along with a structured multidisciplinary network.

PMID:40257168 | DOI:10.1080/02770903.2025.2494222

Environ Sci Pollut Res Int. 2025 Apr 21. doi: 10.1007/s11356-025-36405-4. Online ahead of print.

ABSTRACT

Accurate and timely access to the spatial distribution of crops is crucial for sustainable agricultural development and food security. However, extracting multi-crop areas based on high-resolution time-series data and deep learning still faces challenges. Therefore, this study aims to provide an effective model for multi-crop classification using high-resolution remote sensing time-series data. We designed two deep learning models based on convolutional neural network-long short-term memory (CNN-LSTM) and bidirectional long short-term memory (Bi-LSTM). The monthly synthetic time series of the normalized difference vegetation index (NDVI) from Sentinel-2 data will be used as input features to extract the multi-crop planting area in Shandong province's northwestern, southwestern, and eastern regions. The results showed that deep learning models achieved higher accuracy compared to the random forest (RF) and extreme gradient boosting (XGBoost) models, with CNN-LSTM achieving the highest overall accuracy of 96.48%. At the county level, the coefficients of determination (R2) for the CNN-LSTM model were 0.91 for wheat, 0.88 for maize, and 0.73 for spring cotton. This study demonstrates that the CNN-LSTM model combined with monthly synthetic time-series NDVI provides a feasible approach for accurately mapping high-resolution multi-crop planting areas and also contributes significantly to decision support and resource management in agricultural production.

PMID:40257731 | DOI:10.1007/s11356-025-36405-4

Med Biol Eng Comput. 2025 Apr 21. doi: 10.1007/s11517-025-03361-7. Online ahead of print.

ABSTRACT

Nonalcoholic steatohepatitis (NASH) is a contributing factor to liver cancer, with ultrasound B-mode imaging as the first-line diagnostic tool. This study applied deep learning to ultrasound B-scan images for NASH detection and introduced an ultrasound-specific data augmentation (USDA) technique with a dual-branch global-local feature fusion architecture (DG-LFFA) to improve model performance and adaptability across imaging conditions. A total of 137 participants were included. Ultrasound images underwent data augmentation (rotation and USDA) for training and testing convolutional neural networks-AlexNet, Inception V3, VGG16, VGG19, ResNet50, and DenseNet201. Gradient-weighted class activation mapping (Grad-CAM) analyzed model attention patterns, guiding the selection of the optimal backbone for DG-LFFA implementation. The models achieved testing accuracies of 0.81-0.83 with rotation-based data augmentation. Grad-CAM analysis showed that ResNet50 and DenseNet201 exhibited stronger liver attention. When USDA simulated datasets from different imaging conditions, DG-LFFA (based on ResNet50 and DenseNet201) improved accuracy (0.79 to 0.84 and 0.78 to 0.83), recall (0.72 to 0.81 and 0.70 to 0.78), and F1 score (0.80 to 0.84 for both models). In conclusion, deep architectures (ResNet50 and DenseNet201) enable focused analysis of liver regions for NASH detection. Under USDA-simulated imaging variations, the proposed DG-LFFA framework further improves diagnostic performance.

PMID:40257712 | DOI:10.1007/s11517-025-03361-7

Int J Comput Assist Radiol Surg. 2025 Apr 21. doi: 10.1007/s11548-025-03372-7. Online ahead of print.

ABSTRACT

PURPOSE: Laparoscopic cholecystectomy (LC) operative difficulty (LCOD) is highly variable and influences outcomes. Despite extensive LC studies in surgical workflow analysis, limited efforts explore LCOD using intraoperative video data. Early recognition of LCOD could allow prompt review by expert surgeons, enhance operating room (OR) planning, and improve surgical outcomes.

METHODS: We propose the clinical task of early LCOD assessment using limited video observations. We design SurgPrOD, a deep learning model to assess LCOD by analyzing features from global and local temporal resolutions (snapshots) of the observed LC video. Also, we propose a novel snapshot-centric attention (SCA) module, acting across snapshots, to enhance LCOD prediction. We introduce the CholeScore dataset, featuring video-level LCOD labels to validate our method.

RESULTS: We evaluate SurgPrOD on 3 LCOD assessment scales in the CholeScore dataset. On our new metric assessing early and stable correct predictions, SurgPrOD surpasses baselines by at least 0.22 points. SurgPrOD improves over baselines by at least 9 and 5 percentage points in F1 score and top1-accuracy, respectively, demonstrating its effectiveness in correct predictions.

CONCLUSION: We propose a new task for early LCOD assessment and a novel model, SurgPrOD, analyzing surgical video from global and local perspectives. Our results on the CholeScore dataset establish a new benchmark to study LCOD using intraoperative video data.

PMID:40257703 | DOI:10.1007/s11548-025-03372-7

Med Phys. 2025 Apr 21. doi: 10.1002/mp.17844. Online ahead of print.

ABSTRACT

BACKGROUND: AI modeling CDM can improve the quality and efficiency of clinical practice or provide secondary opinion consultations for patients with limited medical resources to address healthcare disparities.

PURPOSE: In this study, we developed an interpretable AI model to select radiotherapy treatment options, that is, whole-brain radiation therapy (WBRT) versus stereotactic radiosurgery (SRS), for patients with brain metastases.

MATERIALS/METHODS: A total of 232 patients with brain metastases treated by radiation therapy from 2018 to 2023 were obtained. CT/MR images with contoured target lesions and organs-at-risk (OARs) as well as non-image-based clinical parameters were extracted and digitized as inputs to the model. These parameters included (1) tumor size, shape, location, and proximity of lesions to OARs; (2) age; (3) the number of brain metastases; (4) Eastern Cooperative Oncology Group (ECOG) performance status; (5) presence of neurologic symptoms; (6) if surgery was performed (either pre/post-op RT); (7) newly diagnosed cancer with brain metastases (de-novo) versus re-treatment (either local or distant in the brain); (8) primary cancer histology; (9) presence of extracranial metastases; (10) extent of extracranial disease (progression vs. stable); and (11) receipt of systemic therapy. One vanilla and two interpretable 3D convolutional neural networks (CNN) models were developed. The vanilla one-path model (VM-1) uses only images as input, while the two interpretable models use both images and clinical parameters as inputs with two (IM-2) and 11 (IM-11) independent paths, respectively. This novel design allowed the model to calculate a class activation score for each input to interpret its relative weighting and importance in decision-making. The actual radiotherapy treatment (WBRT or SRS) used for the patients was used as ground truth for model training. The model performance was assessed by Stratified-10-fold cross-validation, with each fold consisting of selected 184 training, 24 validation, and 24 testing subjects.

RESULT: A total of 232 brain metastases patients treated by WBRT or SRS were evaluated, including 80 WBRT and 152 SRS patients. Based on the images alone, the VM-1 model prescribed correctly for 143 (94%) SRS and 67 (84%) WBRT cases. Based on both images and clinical parameters, the IM-2 model prescribed correctly for 149 (98%) SRS and 74 (93%) WBRT cases. IM-11 provided the most interpretability with a relative weighting for each input as follows: CT image (59.5%), ECOG performance status (7.5%), re-treatment (5%), extracranial metastases (1.5%), number of brain metastases (9.5%), neurologic symptoms (3%), pre/post-surgery (2%), primary cancer histology (2%), age (1%), progressive extracranial disease (6%), and receipt of systemic therapy (4.5%), reflecting the importance of all these inputs in clinical decision-making.

CONCLUSION: Interpretable CNN models were successfully developed to use CT/MR images and non-image-based clinical parameters to predict the treatment selection between WBRT and SRS for brain metastases patients. The interpretability makes the model more transparent, carrying profound importance for the prospective integration of these models into routine clinical practice, particularly for informing real-time clinical decision-making.

PMID:40257121 | DOI:10.1002/mp.17844

Med Phys. 2025 Apr 21. doi: 10.1002/mp.17838. Online ahead of print.

ABSTRACT

BACKGROUND: The extension of onboard cone-beam CT (CBCT) imaging for real-time treatment planning is constrained by limitations in image quality. Synthetic CT (sCT) generation using deep learning provides a potential solution to these limitations.

PURPOSE: This study was dedicated to creating a model capable of rapidly generating sCT images from CBCT scans, specifically for the entire spine. This work aims to be a step towards a CT simulation-free workflow by using onboard imaging for real-time palliative radiotherapy treatments for patients with spinal metastases.

METHODS: Using CBCT and planning fan-beam CT images from 220 patients, we developed and validated a two-stage sCT generation model. The initial stage used a conditional generative adversarial network (GAN) to minimize streaking artifacts in CBCT images, using 7400 images for training and 1000 for validation. The second stage used a cycle-consistent GAN to produce sCT images, training on 14,700 images and validating on 500 images. The quality of the sCT images was evaluated quantitatively using a distinct dataset from 33 patients who received same-day palliative radiotherapy for spinal metastases.

RESULTS: Our two-stage model generated high-quality sCT images from CBCT scans across the entire spine, significantly improving HU accuracy and dosimetric agreement with planning CT images. Mean Absolute Error was reduced from 225 ± $\,\pm\,$ 62 HU in CBCT to 86 ± $\,\pm\,$ 24 HU in sCT images, and Mean Error was improved from 178 ± $\,\pm\,$ 91 HU to -8 ± $\,\pm\,$ 20 HU. Dosimetric comparison for a subset of 20 patients indicated that the mean dose discrepancy for sCT-based calculations was lower than CBCT-based calculations by 4.5%, with the gamma (2 mm/2%) pass rate increasing by 34% on average.

CONCLUSIONS: This study demonstrates how a two-stage network facilitates CBCT-based sCT generation across the entire spine without prior CT knowledge, improving HU accuracy and potentially enabling highly-conformal palliative treatment planning for spinal metastases in real time.

PMID:40257079 | DOI:10.1002/mp.17838

J Int Med Res. 2025 Apr;53(4):3000605251330578. doi: 10.1177/03000605251330578. Epub 2025 Apr 21.

ABSTRACT

ObjectiveCompared with anatomical magnetic resonance imaging modalities, metabolite images from magnetic resonance spectroscopic imaging often suffer from low quality and detail due to their larger voxel sizes. Conventional interpolation techniques aim to enhance these low-resolution images; however, they frequently struggle with issues such as edge preservation, blurring, and input quality limitations. This study explores an artificial intelligence-driven approach to improve the quality of synthetically generated metabolite maps.MethodsUsing an open-access database of 450 participants, we trained and tested a model on 350 participants, evaluating its performance against spline and nearest-neighbor interpolation methods. Metrics such as structural similarity index, peak signal-to-noise ratio, and learned perceptual image patch similarity were used for comparison.ResultsOur model not only increased spatial resolution but also preserved critical image details, outperforming traditional interpolation methods in both image fidelity and edge preservation.ConclusionsThis artificial intelligence-powered super-resolution technique could substantially enhance magnetic resonance spectroscopic imaging quality, aiding in more accurate neurological assessments.

PMID:40257058 | DOI:10.1177/03000605251330578

Thyroid. 2025 Apr 21. doi: 10.1089/thy.2024.0584. Online ahead of print.

NO ABSTRACT

PMID:40256961 | DOI:10.1089/thy.2024.0584

Small Methods. 2025 Apr 21:e2500236. doi: 10.1002/smtd.202500236. Online ahead of print.

ABSTRACT

Natural Killer (NK) cells, as key effector cells of the innate immune system, display high heterogeneity in their ability to kill target cells. The underlying mechanisms remain poorly understood. Here, a droplet-based microfluidic platform is presented to identify and select NK cells with serial killing ability. To this end, primary human NK cells are encapsulated with several target cells using an efficient negative pressure-based droplet generator. Capitalizing on the large number of possible killing events due to quantization into droplets, a convolutional neural network analysis pipeline is developed to quantify the cytotoxicity and abundance of serial killing events with high accuracy. To physically select NK cells based on their serial killing ability, MultiCell-Sort - an advanced real-time image-based droplet sorting module - is presented. While conventional single-cell sorters mostly evaluate intrinsically-encoded properties, such as protein expression levels, MultiCell-Sort can select living NK cells based on complex functional phenotypes emerging from multiple cell-cell interactions within the droplet. This novel microfluidic phenotyping platform hereby allows the potential integration of complementary techniques to provide an understanding of regulators and markers underlying the heterogeneous nature of NK cell functional phenotypes.

PMID:40256918 | DOI:10.1002/smtd.202500236

Am J Physiol Lung Cell Mol Physiol. 2025 Apr 21. doi: 10.1152/ajplung.00039.2025. Online ahead of print.

ABSTRACT

Type 2 inflammation and epithelial-to-mesenchymal transitions (EMTs) play critical roles in airway repair after damage from allergens or parasites. The matricellular protein periostin (POSTN) has increased expression in inflammatory conditions and has been implicated in fibrosis and EMT, suggesting a role in airway repair. This study investigates the role of periostin in airway epithelial and lung fibroblast wound repair using an in vitro wound model. Our results demonstrate that the type 2 cytokine IL-13 induces periostin secretion from primary human airway epithelial basal cells. Periostin knockdown in human airway epithelial cells (HAEs) and human lung fibroblasts (HLFs) impairs wound closure, indicating that periostin is required for airway repair. In a coculture model of HAE and HLFs, fibroblast-secreted POSTN is required for airway epithelial wound repair, suggesting that periostin is involved in paracrine signaling between the two cell types. These findings highlight periostin's critical function in epithelial and fibroblast-mediated wound repair, suggesting its potential as a therapeutic target for diseases characterized by aberrant wound healing and fibrosis, such as asthma and idiopathic pulmonary fibrosis.

PMID:40257107 | DOI:10.1152/ajplung.00039.2025

Sports Med. 2025 Apr 21. doi: 10.1007/s40279-025-02217-2. Online ahead of print.

ABSTRACT

BACKGROUND: In response to exercise, protein kinases and signaling networks are engaged to blunt homeostatic threats generated by acute contraction-induced increases in skeletal muscle energy and oxygen demand, as well as serving roles in the adaptive response to chronic exercise training to blunt future disruptions to homeostasis. High-intensity interval training (HIIT) is a time-efficient exercise modality that induces superior or similar health-promoting skeletal muscle and whole-body adaptations compared with prolonged, moderate-intensity continuous training (MICT). However, the skeletal muscle signaling pathways underlying HIIT's exercise intensity-specific adaptive responses are unknown.

OBJECTIVE: We mapped human muscle kinases, substrates, and signaling pathways activated/deactivated by an acute bout of HIIT versus work-matched MICT.

METHODS: In a randomized crossover trial design (Australian New Zealand Clinical Trials Registry number ACTRN12619000819123; prospectively registered 6 June 2019), ten healthy male participants (age 25.4 ± 3.2 years; BMI 23.5 ± 1.6 kg/m2; V ˙ O 2 max 37.9 ± 5.2 ml/kg/min, mean values ± SD) completed a single bout of HIIT and MICT cycling separated by ≥ 10 days and matched for total work (67.9 ± 10.2 kJ) and duration (10 min). Mass spectrometry-based phosphoproteomic analysis of muscle biopsy samples collected before, during (5 min), and immediately following (10 min) each exercise bout, to map acute temporal signaling responses to HIIT and MICT, identified and quantified 14,931 total phosphopeptides, corresponding to 8509 phosphorylation sites.

RESULTS: Bioinformatic analyses uncovered exercise intensity-specific signaling networks, including > 1000 differentially phosphorylated sites (± 1.5-fold change; adjusted P < 0.05; ≥ 3 participants) after 5 min and 10 min HIIT and/or MICT relative to rest. After 5 and 10 min, 92 and 348 sites were differentially phosphorylated by HIIT, respectively, versus MICT. Plasma lactate concentrations throughout HIIT were higher than MICT (P < 0.05), and correlation analyses identified > 3000 phosphosites significantly correlated with lactate (q < 0.05) including top functional phosphosites underlying metabolic regulation.

CONCLUSIONS: Collectively, this first global map of the work-matched HIIT versus MICT signaling networks has revealed rapid exercise intensity-specific regulation of kinases, substrates, and pathways in human skeletal muscle that may contribute to HIIT's skeletal muscle adaptations and health-promoting effects. Preprint: The preprint version of this work is available on medRxiv, https://doi.org/10:1101/2024.07.11.24310302 .

PMID:40257739 | DOI:10.1007/s40279-025-02217-2

Eur J Nucl Med Mol Imaging. 2025 Apr 21. doi: 10.1007/s00259-025-07287-y. Online ahead of print.

ABSTRACT

AIM: To assess the ability of [18F]F-fluciclovine-PET/CT to stage muscle invasive bladder cancer (MIBC) before radical cystectomy.

METHODS: This single-site prospective pilot study enrolled patients with MIBC and T2-T4, N0 disease on CT/MRI slated to undergo radical cystectomy (RC). Dynamic and static [18F]F-fluciclovine-PET/CT images were acquired. Clinical readers assessed for confirmation of the primary bladder lesion on imaging and the presence of pelvic nodal metastases. Findings were compared to pathology at RC. Kinetic parameters from dynamic PET/CT were compared across bladder lesions of different clinical stages.

RESULTS: The study enrolled sixteen patients (median age: 73 years, range: 57-88 years, 11 males, 5 females), twelve receiving neoadjuvant chemotherapy before RC. There was high specificity amongst all three readers for detecting lymph node metastases (overall specificity: 0.91, 95%CI: 0.81-1.00) with good overall agreement rate with pathology (0.67, 95%CI: 0.44-0.83). The overall PPV for all readers for identifying node-positive disease was 0.4 (95%CI: 0-1.00), and the overall sensitivity was 0.13 (95%CI: 0-0.44). The overall PPV for detecting the primary tumor was 0.69 (95%CI: 0.47-0.88), and the sensitivity was 0.89 (95%CI: 0.78-1.00), with NPV and specificity being 0.70 (95%CI: 0.33, 1.00) and 0.39 (95%CI: 0.33, 0.50), respectively. Compartmental analysis of the primary bladder tumor revealed that k1 and vb parameters significantly differentiated between low (pT0-pT1) and high (pT2-pT4) risk disease (p < 0.05). Immunohistochemical assessment showed no significant correlation of tumor [18F]F-fluciclovine uptake nor kinetic parameter with amino acid transporter expression.

CONCLUSIONS: [18F]F-fluciclovine demonstrates good specificity and agreement rate for MIBC staging, with sensitivity like CT/MRI. Kinetic parameters such as k1 was able to delineate higher-stage ( ≥ = pT2) primary lesions. Heterogeneous amino acid transporter expression can be seen across lesions. Further studies are warranted to understand [18F]F-fluciclovine PET/CT use in the context of other imaging modalities in this disease.

CLINICAL TRIAL REGISTRATION: NCT04018053 Registered 2/26/2020.

PMID:40257614 | DOI:10.1007/s00259-025-07287-y