Therap Adv Gastroenterol. 2025 May 8;18:17562848251337515. doi: 10.1177/17562848251337515. eCollection 2025.

ABSTRACT

BACKGROUND: Eosinophilic esophagitis (EoE) is a chronic, progressive type 2 inflammatory disorder of the esophagus, characterized by abnormal eosinophil accumulation in esophageal epithelium. Undiagnosed or undertreated EoE leads to increased risk of fibrostenosis, strictures, and food impaction due to persistent inflammation, deeply impacting patients' health-related quality of life (HRQoL).

OBJECTIVES: To gather insights on comprehensive assessment of EoE, comprising clinical, endoscopic, histological outcomes, adaptive behaviors and HRQoL; to define proper evaluation of disease control and impact of continuous versus noncontinuous treatment to reach full disease control. Finally, to validate an algorithm for disease control, switching criteria, and follow-up.

DESIGN: Literature review, survey, and panel expert opinion building by a multidisciplinary Italian EoExpert Panel (EoExpert) of nine specialists from various Italian institutions.

METHODS: Non-systematic literature review, followed by a survey including 21 questions on the different topics. Results were then discussed and validated by EoExpert.

RESULTS: The current diagnostic pathway often does not allow early detection of EoE patients, especially in the presence of adaptive behaviors and unawareness of EoE best practices. In addition, there is a lack of a shared "control" definition. EoExpert reviewed, shared, and recommended two novel management tools for EoE, represented by I.M.P.A.C.T. Questionnaire to uncover adaptive behaviors and S.C.O.P.E. (Symptoms Control, Observation, Pathological Evaluation) scheme for comprehensive treatment efficacy evaluation. EoExpert's recommendations were gathered and turned into a therapeutic management algorithm for the definition of disease control and switching criteria.

CONCLUSION: This document provides a standardized approach to EoE management in pediatric and adult settings, highlighting the importance of timely diagnosis in a multidisciplinary setting, of using unified criteria for assessment of disease control through the adoption of a comprehensive approach and of following up patients. These recommendations highlight the critical role of increased awareness and standardized care in EoE clinical setting for lifelong management.

PMID:40351381 | PMC:PMC12062651 | DOI:10.1177/17562848251337515

Digit Health. 2025 May 8;11:20552076251341163. doi: 10.1177/20552076251341163. eCollection 2025 Jan-Dec.

ABSTRACT

OBJECTIVE: Telemedicine platforms played a crucial role during the COVID-19 pandemic, alleviating issues related to the shortage and unequal distribution of healthcare resources. The purpose of this study is to identify key factors affecting the service quality of telemedicine platforms in China, with the dual objectives of advancing patient wellbeing and informing evidence-based service innovations for industry stakeholders.

METHODS: To quantitatively assess the impact of these key factors on health and wellbeing from the perspective of healthcare consumers, a total of 25,499 valid online reviews were collected from telemedicine platforms. To establish a service quality evaluation framework, this study proposes a novel approach that combines the Servqual quality assessment model with a CNN-BiLSTM deep learning model enhanced by an attention mechanism.

RESULTS: Analysis of the full sample shows that healthcare consumers are most concerned about the quality of services provided by telemedicine platforms, with the most important being the professional competence of doctors, a critical factor for promoting consumer health and wellbeing. The proposed hybrid deep learning approach demonstrates superior performance in sentiment classification accuracy, outperforming conventional methods by 11.11 percentage points. This methodological innovation enables more precise identification of consumer sentiment patterns across service dimensions.

CONCLUSION: The novel quality assessment framework introduced here provides actionable insights for advancing telemedicine platforms, driving progress toward precision healthcare and consumer-centric wellbeing. Furthermore, it enables healthcare consumers to select telemedicine services aligned with their personalized needs.

PMID:40351848 | PMC:PMC12065986 | DOI:10.1177/20552076251341163

J Med Signals Sens. 2025 Apr 19;15:10. doi: 10.4103/jmss.jmss_37_24. eCollection 2025.

ABSTRACT

BACKGROUND: Clinical decisions for stroke treatments, such as thrombolytic drugs for ischemic strokes or anticoagulants for hemorrhagic strokes, rely on accurate diagnosis and severity assessment. Our study uses diffusion-weighted magnetic resonance imaging and Convolutional Neural Networks (CNNs) to differentiate healthy and stroke samples, classify stroke types, and predict severity, aiding in decision-making for stroke management.

METHODS: We evaluated 143 patients: 85 with ischemic stroke and 58 with hemorrhagic stroke. For stroke diagnosis, we compared multimodal (apparent diffusion coefficient and diffusion-weighted imaging [DWI]) and single-modal (using separate images) preprocessing techniques. Our study introduced two models, Added CNN Layer-ResNet-50 (ACL-ResNet-50) and Added CNN Layer-MobileNetV1 (ACL-MobileNetV1), based on transfer learning (MobileNetV1 and ResNet-50), enhancing performance through reinforced layers. We compared our proposed models with a scenario in which only the final layer was replaced in ResNet-50 and MobileNetV1. Furthermore, we predicted National Institutes of Health Stroke Scale (NIHSS) scores in three ranges based on DWI images to gauge stroke severity. Evaluation criteria for the models included accuracy, sensitivity, specificity, and area under the curve (AUC).

RESULTS: In stroke classification (normal, ischemic, and hemorrhagic), ACL-MobileNetV1 outperformed other models, achieving 98% accuracy, 99% sensitivity, 98% specificity, and 99% AUC. For assessing ischemic stroke severity using NIHSS ranges, ACL-ResNet-50 showed the optimal performance with an accuracy of 0.92, sensitivity of 0.84, specificity of 0.92, and AUC of 0.95.

CONCLUSION: Our study's proposed method effectively classified stroke type and severity based on multimodal MR images, potentially as a practical decision support tool for stroke treatments.

PMID:40351777 | PMC:PMC12063969 | DOI:10.4103/jmss.jmss_37_24

JAMIA Open. 2025 May 9;8(3):ooaf033. doi: 10.1093/jamiaopen/ooaf033. eCollection 2025 Jun.

ABSTRACT

OBJECTIVES: This work evaluated algorithmic bias in biomarkers classification using electronic pathology reports from female breast cancer cases. Bias was assessed across 5 subgroups: cancer registry, race, Hispanic ethnicity, age at diagnosis, and socioeconomic status.

MATERIALS AND METHODS: We utilized 594 875 electronic pathology reports from 178 121 tumors diagnosed in Kentucky, Louisiana, New Jersey, New Mexico, Seattle, and Utah to train 2 deep-learning algorithms to classify breast cancer patients using their biomarkers test results. We used balanced error rate (BER), demographic parity (DP), equalized odds (EOD), and equal opportunity (EOP) to assess bias.

RESULTS: We found differences in predictive accuracy between registries, with the highest accuracy in the registry that contributed the most data (Seattle Registry, BER ratios for all registries >1.25). BER showed no significant algorithmic bias in extracting biomarkers (estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2) for race, Hispanic ethnicity, age at diagnosis, or socioeconomic subgroups (BER ratio <1.25). DP, EOD, and EOP all showed insignificant results.

DISCUSSION: We observed significant differences in BER by registry, but no significant bias using the DP, EOD, and EOP metrics for socio-demographic or racial categories. This highlights the importance of employing a diverse set of metrics for a comprehensive evaluation of model fairness.

CONCLUSION: A thorough evaluation of algorithmic biases that may affect equality in clinical care is a critical step before deploying algorithms in the real world. We found little evidence of algorithmic bias in our biomarker classification tool. Artificial intelligence tools to expedite information extraction from clinical records could accelerate clinical trial matching and improve care.

PMID:40351508 | PMC:PMC12063583 | DOI:10.1093/jamiaopen/ooaf033

Front Med (Lausanne). 2025 Apr 25;12:1475362. doi: 10.3389/fmed.2025.1475362. eCollection 2025.

ABSTRACT

Decalcification is crucial in enhancing the diagnostic accuracy and interpretability of cardiac CT images, particularly in cardiovascular imaging. Calcification in the coronary arteries and cardiac structures can significantly impact the quality of the images and hinder precise diagnostics. This study introduces a novel approach, Hybrid Models for Decalcify Cardiac CT (HMDC), aimed at enhancing the clarity of cardiac CT images through effective decalcification. Decalcification is critical in medical imaging, especially in cardiac CT scans, where calcification can hinder accurate diagnostics. The proposed HMDC leverages advanced deep-learning techniques and traditional image-processing methods for efficient and robust decalcification. The experimental results demonstrate the superior performance of HMDC, achieving an outstanding accuracy of 97.22%, surpassing existing decalcification methods. The hybrid nature of the model harnesses the strengths of both deep learning and traditional approaches, leading to more transparent and more diagnostically valuable cardiac CT images. The study underscores the potential impact of HMDC in improving the precision and reliability of cardiac CT diagnostics, contributing to advancements in cardiovascular healthcare. This research introduces a cutting-edge solution for decalcifying cardiac CT images and sets the stage for further exploration and refinement of hybrid models in medical imaging applications. The implications of HMDC extend beyond decalcification, opening avenues for innovation and improvement in cardiac imaging modalities, ultimately benefiting patient care and diagnostic accuracy.

PMID:40351458 | PMC:PMC12062122 | DOI:10.3389/fmed.2025.1475362

AI (Basel). 2024 Dec;5(4):2725-2738. doi: 10.3390/ai5040131. Epub 2024 Dec 3.

ABSTRACT

Substance use disorders affect 17.3% of Americans. Digital health solutions that use machine learning to detect substance use from wearable biosignal data can eventually pave the way for real-time digital interventions. However, difficulties in addressing severe between-subject data heterogeneity have hampered the adaptation of machine learning approaches for substance use detection, necessitating more robust technological solutions. We tested the utility of personalized machine learning using participant-specific convolutional neural networks (CNNs) enhanced with self-supervised learning (SSL) to detect drug use. In a pilot feasibility study, we collected data from 9 participants using Fitbit Charge 5 devices, supplemented by ecological momentary assessments to collect real-time labels of substance use. We implemented a baseline 1D-CNN model with traditional supervised learning and an experimental SSL-enhanced model to improve individualized feature extraction under limited label conditions. Results: Among the 9 participants, we achieved an average area under the receiver operating characteristic curve score across participants of 0.695 for the supervised CNNs and 0.729 for the SSL models. Strategic selection of an optimal threshold enabled us to optimize either sensitivity or specificity while maintaining reasonable performance for the other metric. Conclusion: These findings suggest that Fitbit data have the potential to enhance substance use monitoring systems. However, the small sample size in this study limits its generalizability to diverse populations, so we call for future research that explores SSL-powered personalization at a larger scale.

PMID:40351335 | PMC:PMC12065672 | DOI:10.3390/ai5040131

Small. 2025 May 12:e2503130. doi: 10.1002/smll.202503130. Online ahead of print.

ABSTRACT

In recent years, smart textile sensors have gained exponential growth in various sectors such as wearable technology and healthcare. However, addressing the demand for wearable textiles that offer both exceptional functionality (e.g., air-permeability, flexibility) and comfort remains a significant challenge. In this context, a rotary jet-spun textile piezoelectret is demonstrated, which is not reported so far. The piezoelectric output of the all-organic textile sensor is improved by 150% in voltage and 200% for current upon electrical poling. The finite element method revealed that the enhanced piezo-potential is attributed to the trapped polarized charges within the piezoelectret matrix. It exhibited outstanding piezoelectric properties with sensitivity of 400 mV kPa-1 (pressure range, 0.6-7 kPa), waterproofness (water contact angle ≈134°) and high breathability (10 kg m-2 per day), ensuring wearer comfort. Apart from monitoring different physiological signals such as pulse and respiratory rate, it also acted as a sensor array that displays the deep learning-aided pressure mapping with the accuracy of 98%. In addition, this textile accelerated faster proliferation and migration of L929 cell due to its piezoelectricity induced electrical stimulation, suggesting its potential application in wound dressings. Thus, this approach has huge potential to offer a scalable and versatile solution for biomedical technology.

PMID:40351106 | DOI:10.1002/smll.202503130

J Periodontal Implant Sci. 2025 Apr 2. doi: 10.5051/jpis.2500280014. Online ahead of print.

ABSTRACT

PURPOSE: The aim of this study was to evaluate the diagnostic and classification performance of a deep learning (DL) model for peri-implantitis-related bone defects using panoramic radiographs, focusing on defect morphology and severity.

METHODS: A dataset comprising 1,075 panoramic radiographs from 426 patients with peri-implantitis was analyzed. A total of 2,250 implant sites were annotated and categorized based on defect morphology (intraosseous [class I], supracrestal/horizontal [class II], or combined [class III]) and severity (slight, moderate, or severe). The ensemble-based YOLOv8 DL model was trained on 80% of the dataset, with the remaining 20% reserved for testing. Performance was assessed using classification metrics, including accuracy, precision, recall, and F1 score. The diagnostic accuracy of the DL model was also compared with that of 2 board-certified periodontal surgeons.

RESULTS: The DL model achieved an overall accuracy of 85.33%, significantly outperforming the periodontal surgeons, who exhibited a mean accuracy of 75.6%. The DL model performed especially well for slight class II defects, with precision and recall values of 100% and 98%, respectively. In contrast, the periodontal surgeons demonstrated higher accuracy in severe cases, particularly for class II defects.

CONCLUSIONS: DL enables reliable and accurate detection of peri-implantitis bone defects. It outperformed periodontal surgeons in overall accuracy, demonstrating its potential as a valuable second-opinion tool to support clinical decision-making. Future research should focus on expanding datasets and incorporating multimodal imaging.

PMID:40350773 | DOI:10.5051/jpis.2500280014

J Xray Sci Technol. 2025 May 11:8953996251335115. doi: 10.1177/08953996251335115. Online ahead of print.

ABSTRACT

In recent years Covid-19 impact is causing unprecedented difficulties worldwide, affecting lifestyle choices. The post-pandemic era has made this even more critical.COVID-19 triggers widespread inflammation throughout the body, potentially causing damage to the heart and other vital organs. Mortality data from COVID-19 clearly show that the highest death rates occur in individuals with chronic conditions, such as diabetes, pneumonia, cardiovascular disease (CVD), and acute renal failure.CVD is a particular concern in the medical field. The early detection of CVD remains a significant challenge, as early identification can prompt lifestyle changes and ensure appropriate medical interventions when needed. Individuals with CVD are at an increased risk for heart attack and other serious complications. There is a limited amount of data available to study the effects of COVID-19 on CVD in COVID-19 patients. However, it is essential to monitor these patients to ensure full recovery without complications. The proposed system is specifically designed for individuals experiencing prolonged symptoms following a COVID-19 infection, commonly referred to as long COVID patients. This research introduces a novel Decision-Making System for CVD Prediction, utilizing an improved dual-attention residual bi-directional gated recurrent neural network unit (DA-ResBiGRU) algorithm with AI-Biruni Earth Radius Optimization (ABER). The proposed system employs state-of-the-art predictive algorithms and real-time monitoring to assess individual patient risk profiles accurately. This research addresses the critical need for personalized risk assessment in patients with long-term COVID, aiming to assist healthcare providers in timely and targeted interventions. By analyzing intricate patterns in patient data, the decision-making system enhances the precision of CVD prediction. Additionally, the system's adaptive nature allows it to continuously learn from new patient data, ensuring that its predictions remain up-to-date and reflective of the evolving understanding of long COVID-related cardiovascular risks. The simulation findings of this research highlight the potential of the proposed algorithm to be integrated into clinical decision-making, helping healthcare professionals identify high-risk patients more effectively. The proposed method outperformed existing algorithms, such as Deep Neural Network (DNN), Long short-term memory (LSTM), Inception-v3, Xception, and MobileNetV2, achieving the highest accuracy (97.88%), sensitivity (95.50%), specificity (94.29%), precision (96.68%), and F-measure (95.85%).

PMID:40350710 | DOI:10.1177/08953996251335115

J Xray Sci Technol. 2025 May 11:8953996251331790. doi: 10.1177/08953996251331790. Online ahead of print.

ABSTRACT

ObjectiveThe purpose of this study is to perform multiple (≥3) material decomposition with deep learning method for spectral cone-beam CT (CBCT) imaging based on ultra-slow kV switching.ApproachIn this work, a novel deep neural network called SkV-Net is developed to reconstruct multiple material density images from the ultra-sparse spectral CBCT projections acquired using the ultra-slow kV switching technique. In particular, the SkV-Net has a backbone structure of U-Net, and a multi-head axial attention module is adopted to enlarge the perceptual field. It takes the CT images reconstructed from each kV as input, and output the basis material images automatically based on their energy-dependent attenuation characteristics. Numerical simulations and experimental studies are carried out to evaluate the performance of this new approach.Main ResultsIt is demonstrated that the SkV-Net is able to generate four different material density images, i.e., fat, muscle, bone and iodine, from five spans of kV switched spectral projections. Physical experiments show that the decomposition errors of iodine and CaCl2 are less than 6%, indicating high precision of this novel approach in distinguishing materials.SignificanceSkV-Net provides a promising multi-material decomposition approach for spectral CBCT imaging systems implemented with the ultra-slow kV switching scheme.

PMID:40350700 | DOI:10.1177/08953996251331790

Brief Bioinform. 2025 May 1;26(3):bbaf202. doi: 10.1093/bib/bbaf202.

ABSTRACT

Cyclic peptides containing unnatural amino acids possess many excellent properties and have become promising candidates in drug discovery. Therefore, accurately predicting the 3D structures of cyclic peptides containing unnatural residues will significantly advance the development of cyclic peptide-based therapeutics. Although deep learning-based structural prediction models have made tremendous progress, these models still cannot predict the structures of cyclic peptides containing unnatural amino acids. To address this gap, we introduce a novel model, HighFold2, built upon the AlphaFold-Multimer framework. HighFold2 first extends the pre-defined rigid groups and their initial atomic coordinates from natural amino acids to unnatural amino acids, thus enabling structural prediction for these residues. Then, it incorporates an additional neural network to characterize the atom-level features of peptides, allowing for multi-scale modeling of peptide molecules while enabling the distinction between various unnatural amino acids. Besides, HighFold2 constructs a relative position encoding matrix for cyclic peptides based on different cyclization constraints. Except for training using spatial structures with unnatural amino acids, HighFold2 also parameterizes the unnatural amino acids to relax the predicted structure by energy minimization for clash elimination. Extensive empirical experiments demonstrate that HighFold2 can accurately predict the 3D structures of cyclic peptide monomers containing unnatural amino acids and their complexes with proteins, with the median RMSD for Cα reaching 1.891 Å. All these results indicate the effectiveness of HighFold2, representing a significant advancement in cyclic peptide-based drug discovery.

PMID:40350698 | DOI:10.1093/bib/bbaf202

Front Microbiol. 2025 Apr 25;16:1583746. doi: 10.3389/fmicb.2025.1583746. eCollection 2025.

ABSTRACT

Lignocellulose biomass is one of the most abundant resources for sustainable biofuels. However, scaling up the biomass-to-biofuels conversion process for widespread usage is still pending. One of the main bottlenecks is the high cost of enzymes used in key process of biomass degradation. Current research efforts are therefore targeted at creative solutions to improve the feasibility of lignocellulosic-degrading enzymes. One way is to engineer multi-enzyme complexes that mimic the bacterial cellulosomal system, known to increase degradation efficiency up to 50-fold when compared to freely-secreted enzymes. However, these designer cellulosomes are instable and less efficient than wild type cellulosomes. In this review, we aim to extensively analyze the current knowledge on the lignocellulosic-degrading enzymes through three aspects. We start by reviewing and comparing sets of enzymes in bacterial and fungal lignocellulose degradation. Next, we focus on the characteristics of cellulosomes in both systems and their feasibility to be engineered. Finally, we highlight three key strategies to enhance enzymatic lignocellulose degradation efficiency: discovering novel lignocellulolytic species and enzymes, bioengineering enzymes for improved thermostability, and structurally optimizing designer cellulosomes. We anticipate these insights to act as resources for the biomass community looking to elevate the usage of lignocellulose as biofuel.

PMID:40351319 | PMC:PMC12063362 | DOI:10.3389/fmicb.2025.1583746

ChemistryOpen. 2025 May 12:e2500004. doi: 10.1002/open.202500004. Online ahead of print.

ABSTRACT

Phlomis species (family Lamiaceae) are highly valued as food and herbal medicine. The present study is designed to investigate the chemical composition and antioxidant and enzyme inhibitory activities of extracts from P. fruticosa, P. herba-venti, and P. kurdica aerial parts. Different classes of metabolites, including phenolic acids, phenylethanoids, flavonoids, iridoids, organic acids, terpenes, and fatty acids, are identified in the three species, with methanol as the best solvent to recover bioactive compounds from the three species in addition to ethyl acetate for P. kurdica. Around 70% methanol extract of P. herba-venti exerts the best radical scavenging and ions-reducing properties, while its methanol extract exhibits the highest acetylcholinesterase inhibitory activity. The ethyl acetate extract of P. fruticosa displays the best chelating power, and its other polar extracts have the highest total antioxidant activity. Furthermore, molecular docking and molecular dynamics simulations have underscored the therapeutic potential of bioactive compounds, including isoverbascoside, samioside, forsythoside B, and hattushoside. In conclusion, the study indicates that these three Phlomis species are a rich source of bioactive molecules with possible therapeutic applications, and the selection of appropriate extraction solvents is crucial for the targeted biological activity.

PMID:40351016 | DOI:10.1002/open.202500004

Zhongguo Zhong Yao Za Zhi. 2025 Feb;50(3):812-816. doi: 10.19540/j.cnki.cjcmm.20241108.501.

ABSTRACT

Because of the unclear active substances, metabolic pathways, and targets of new drugs of traditional Chinese medicine(TCM), non-clinical safety evaluation often fails to accurately locate the target organs and tissue exposed to medicinal toxicity. The human use experience(HUE) contains important safety information of TCM, while the clinical safety data in the past HUE are few and have not been effectively applied. Standardized prospective HUE studies should be carried out to collect the clinical safety data, in which appropriate physical and chemical indicators(including blood, urine, and stool routine), liver biochemical indicators, kidney biochemical indicators, and cardiovascular biochemical indicators should be selected for safety evaluation, and the detection time point and sample size should be rationally designed. Importance should be attached to the observation of symptoms and signs of adverse events/reactions in patients as well as the safety information of special groups such as the elderly, children, and pregnant women. The adverse events of TCM should be observed, judged, and treated according to the theory and the diagnosis and treatment mode of TCM. The clinical safety information about the HUE should be comprehensively collected for new drugs of TCM to make up for the lack of extrapolation of toxicological test results to humans. The unique advantages of clinical origin of new drugs of TCM should be given full play for cross-reference of the results of toxicological research and the conclusions of HUE safety evaluation. In addition, benefit-risk assessment should be conducted based on HUE, and a panoramic safety evaluation system characterized by macro and micro combination and in line with the characteristics of TCM should be established to improve the success rate in the research and development of new drugs of TCM.

PMID:40350857 | DOI:10.19540/j.cnki.cjcmm.20241108.501

Adv Med Sci. 2025 May 9:S1896-1126(25)00025-2. doi: 10.1016/j.advms.2025.05.001. Online ahead of print.

ABSTRACT

PURPOSE: Ferritin amounts that accumulate in the meconium may provide new postnatal insights into intrauterine iron homeostasis and neonatal preparedness for the postnatal period. The most dynamic increases in fetal iron stores and fetal growth occur during the third trimester.

MATERIALS AND METHODS: This study involved 122 neonates born between 36 and 41 weeks of gestation, with birth weights from 2,650 g to 4,960 g and birth lengths ranging from 50 cm to 60 cm. Ferritin amounts per gram of meconium were determined via ELISA in the first meconium passed after birth.

RESULTS: A significant week-by-week increase in the birth weight and length (p<0.05) was accompanied by decreasing meconium ferritin amounts (p=0.021) across the gestational age range of 36-41 weeks. There were negative correlations (p<0.05) between the systematic decrease in meconium ferritin amounts and the gestational age across the same range (r = -0.18) and between ferritin amounts and the birth weight and length of newborns (r= -0.20 and r= -0.31). Neonates born at 36-37 weeks of gestation had lower birth weight and length, while their meconium ferritin amounts were nearly twice as high as in neonates born at 38-39 weeks or 40-41 weeks (p<0.05).

CONCLUSIONS: Systematic decreases in meconium ferritin amounts from 36 to 41 weeks of gestation may suggest a gradual and gestational age-appropriate maturation of the mechanisms responsible for adaptation of the fetus to postnatal life. Determining a cut-off value for meconium ferritin amounts could aid in optimal management of newborns after birth.

PMID:40349924 | DOI:10.1016/j.advms.2025.05.001

Chest. 2025 May 9:S0012-3692(25)00574-4. doi: 10.1016/j.chest.2025.05.005. Online ahead of print.

ABSTRACT

BACKGROUND: Limited data exists to inform and appropriately counsel female lung transplant (LuT) recipients regarding post-transplant pregnancy.

QUESTION: What are the modifiable factors that impact pregnancy outcomes in female LuT recipients?

STUDY DESIGN AND METHODS: Retrospective observational analysis was performed on female LuT recipients who reported post-transplant pregnancies to the Transplant Pregnancy Registry International (TPRI).

RESULTS: Fifty-three recipients transplanted from 1991-2021 reported 72 pregnancies to TPRI. Predominant indications for transplant were cystic fibrosis (60%) and pulmonary hypertension (19%). Contraceptive use post-transplant was 36%. The majority of recipients (54%) had unplanned pregnancies. The livebirth rate was 62% resulting in 46 livebirths. Approximately 60% were premature (<37 weeks gestational age, GA) and low birth weight (LBW, <2500 grams). Birth defects were seen in 7 (16%) children; none with mycophenolic acid (MPA) embryopathy. Three neonatal deaths resulted from extreme prematurity; 43 remaining children are healthy. Twenty recipients (38%) have died a median of 23.6 years post-LuT. Recipients with transplant-to-conception interval ≤2 years had no difference in mortality compared to >2 years (HR 1.26 95% CI 0.50-3.12, p=0.625). Recipients whose first pregnancy post-transplant was unplanned had lower survival (HR 7.02, 95% CI 1.35-36.45, p=0.020). Newborns of LuT recipients with planned compared to unplanned pregnancies have higher median GA (36.9 versus 34 weeks, p=0.025) and birthweight (2639 versus 2155 grams, p=0.047), and significantly lower risk of LBW for singletons (OR 0.26, 95% CI 0.07-0.94, p=0.036).

INTERPRETATION: Successful pregnancy after lung transplantation is achievable, however not without risks for mother and offspring. Planned pregnancies resulted in higher GA and birth weight liveborn and had lower post-pregnancy mortality. TPRI data show 54% of recipients reported unplanned pregnancies, an obvious area for improvement. Planning pregnancy is the most modifiable factor for mitigating risks.

PMID:40350146 | DOI:10.1016/j.chest.2025.05.005

Sci Rep. 2025 May 11;15(1):16383. doi: 10.1038/s41598-025-99561-0.

ABSTRACT

Safety risks management is a critical part during the subway construction. However, conventional methods for risk identification heavily rely on experience from experts and fail to effectively identify the relationship between risk factors and events embedded in accident texts, which fail to provide substantial guidance for subway safety risks management. With a dataset comprising 562 occurrences of subway construction accidents, this study devised a domain-specific entity recognition model for identifying safety hazards during the subway construction. The model was constructed by a Bidirectional Long Short-Term Memory Network with Conditional Random Fields (BiLSTM-CRF). Additionally, a domain-specific entity causal relation extraction model employing Convolutional Neural Networks (CNN) was also developed in thsi model. The constructed models automatically extract safety risk factors, safety events, and their causal relationships from the texts about subway accidents. The precision, recall, and F1 scores of Metro Construction Safety Risk Named Entity Recognition Model (MCSR-NER-Model) all exceeded 77%. Its performance in the specialized domain named entity recognition (NER) with a limited volume of textual data is satisfactory. The Metro Construction Safety Risk Domain Entity Causal Relationship Extraction Model (MCSR-CE-Model) achieved an impressive accuracy, recall, and F1 score of 98.96%, exhibiting excellent performance. Moreover, the extracted entities were normalized and domain dictionary was developed. Based on the processed entities and relationships processed by the domain dictionary, 533 domain entity causal relation triplets were obtained, facilitating the establishment of the directed and unweighted complex network and case database about the risks of subway construction. This research successfully converted accident texts into a causal chain structure of "safety risk factors to risk events," providing detailed categorization of safety risks and events. Concurrently, it revealed the interrelationships and historical statistical patterns among various safety risk factors and categories of risk events through the complex safety risks network. The construction of the database facilitated project managers in conducting management decisions about safety risks.

PMID:40350479 | DOI:10.1038/s41598-025-99561-0

BMC Biol. 2025 May 12;23(1):126. doi: 10.1186/s12915-025-02226-7.

ABSTRACT

BACKGROUND: Inosine monophosphate dehydrogenase 2 (IMPDH2) is an enzyme that catalyses the rate-limiting step of guanine nucleotides. In mouse embryonic stem cells (ESCs), IMPDH2 forms large multi-protein complexes known as rod-ring (RR) structures that dissociate when ESCs differentiate. Manual analysis of RR structures from confocal microscopy images, although possible, is not feasible on a large scale due to the quantity of RR structures present in each field of view. To address this analysis bottleneck, we have created a fully automatic RR image classification pipeline to segment, characterise and measure feature distributions of these structures in ESCs.

RESULTS: We find that this model can automatically segment images with a Dice score of over 80% for both rods and rings for in-domain images compared to expert annotation, with a slight drop to 70% for datasets out of domain. Important feature measurements derived from these segmentations show high agreement with the measurements derived from expert annotation, achieving an R2 score of over 90% for counting the number of RRs over the dataset.

CONCLUSIONS: We have established for the first time a quantitative baseline for RR distribution in pluripotent ESCs and have made a pipeline available for training to be applied to other models in which RR remain an open topic of study.

PMID:40350411 | DOI:10.1186/s12915-025-02226-7

J Cardiovasc Magn Reson. 2025 May 9:101909. doi: 10.1016/j.jocmr.2025.101909. Online ahead of print.

ABSTRACT

BACKGROUND: We propose an approach to adapt a segmentation foundation model, segment-anything-model (SAM), for cine Cardiac Magnetic Resonance (CMR) segmentation and evaluate its generalization performance on unseen datasets.

METHODS: We present our model, cineCMR-SAM, which introduces a temporal-spatial attention mechanism to produce segmentation across one cardiac cycle. We freeze the pre-trained SAM's weights to leverage SAM's generalizability while fine-tuning the rest of the model on two public cine CMR datasets. Our model also enables text prompts to specify the view type (short-axis or long-axis) of the input slices and box prompts to guide the segmentation region. We evaluated our model's generalization performance on three external testing datasets including a public multi-center, multi-vendor testing dataset of 136 cases and two retrospectively collected in-house datasets from two different centers with specific pathologies: aortic stenosis (40 cases) and heart failure with preserved ejection fraction (HFpEF) (53 cases).

RESULTS: Our approach achieved superior generalization in both the public testing dataset (Dice for LV = 0.94 and for myocardium = 0.86) and two in-house datasets (Dice ≥ 0.90 for LV and ≥ 0.82 for myocardium) compared to existing CMR deep learning segmentation methods. Clinical parameters derived from automatic and manual segmentations showed a strong correlation (r ≥ 0.90). The use of both text prompts and box prompts enhanced the segmentation accuracy.

CONCLUSION: cineCMR-SAM effectively adapts SAM for cine CMR segmentation, achieving high generalizability and superior accuracy on unseen datasets.

PMID:40350082 | DOI:10.1016/j.jocmr.2025.101909

J Invest Dermatol. 2025 May 9:S0022-202X(25)00469-5. doi: 10.1016/j.jid.2025.04.021. Online ahead of print.

NO ABSTRACT

PMID:40350056 | DOI:10.1016/j.jid.2025.04.021