Clin Mol Hepatol. 2025 Jun 4. doi: 10.3350/cmh.2025.0292. Online ahead of print.

ABSTRACT

BACKGROUND/AIMS: Hepatitis C virus (HCV) infection remains a global health challenge, leading to chronic liver disease, cirrhosis, and hepatocellular carcinoma (HCC). Despite the high efficacy of direct-acting antiviral (DAA) therapy in achieving sustained virologic response (SVR), concerns persist regarding long-term immune alterations and residual risks, particularly in cirrhotic patients.

METHODS: This study investigates 75 soluble immune mediator (SIM) profiles in 102 chronic HCV patients, stratified by cirrhosis status, at therapy initiation, end of treatment, and long-term follow-up (median 96 weeks). Findings were compared with 51 matched healthy controls and validated in an independent cohort of 47 cirrhotic patients, 17 of whom developed HCC.

RESULTS: We observed significant SIM alterations at baseline, with cirrhotic patients displaying a more profoundly dysregulated inflammatory milieu. Despite an overall decline in inflammatory markers following SVR, persistent alterations were evident, particularly in cirrhotic patients. Notably, those with liver stiffness exceeding 14 kPa exhibited sustained inflammatory dysregulation, correlating with liver elastography values. Key SIM such as IL-6, IL-8, urokinase plasminogen activator (uPA), and hepatocellular growth factor (HGF) remained elevated and were associated with HCC development. Network analysis highlighted their roles in liver fibrosis, regeneration, and carcinogenesis.

CONCLUSIONS: These findings underscore the importance of early antiviral intervention to prevent cirrhosis-related sequelae. Future studies should explore the mechanistic pathways linking chronic inflammation, fibrosis, and oncogenesis to identify predictive biomarkers and novel therapeutic targets. Addressing persistent immune alterations post-HCV clearance may improve long-term outcomes, particularly in patients with advanced liver disease.

PMID:40462644 | DOI:10.3350/cmh.2025.0292

Br J Clin Pharmacol. 2025 Jun 3. doi: 10.1002/bcp.70109. Online ahead of print.

ABSTRACT

Over the past decade there has been considerable and growing enthusiasm about the promise of using genomics to inform healthcare. In particular, using genetic data to inform prescribing practice has emerged as a compelling policy priority for health systems around the world, not least in the NHS. Various initiatives and strategies have been developed to explore the value of pharmacogenomics in the UK National Health Service (NHS) and identify strategies for implementation. The NHS England Network of Excellence for Pharmacogenomics and Medicines Optimisation (PGx-NoE) was launched in 2024 and held two stakeholder meetings over the year in collaboration with the UK Pharmacogenetics and Stratified Medicine Network and the British Pharmacological Society (BPS). This article describes the outputs of those meetings, which are discussed in the context of previously identified challenges and opportunities. Rather than simply identify further barriers or facilitators, outputs are contextualized around tangible recommendations and real-world implementation exercises. These are grouped into three key areas: genetics, data and service. The work of partners across the UK are highlighted, including development of the NHS England Genomic Test Directory, the proof-of-principle informatic patterns demonstrated by the PROGRESS study, and the launch of the Centre for Excellence in Regulatory Science and Innovation (CERSI) in Pharmacogenomics, which will create UK-specific guidance and clarify complex regulatory pathways. Many of the well-defined barriers to the implementation of pharmacogenomics have been addressed in recent years, and this work highlights how the UK has the opportunity to emerge as a global leader in genomics-informed healthcare.

PMID:40460990 | DOI:10.1002/bcp.70109

Virology. 2025 May 22;610:110579. doi: 10.1016/j.virol.2025.110579. Online ahead of print.

ABSTRACT

The global pandemic coronavirus disease 2019 (COVID-19) attributable to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The current study aimed at combination therapies with natural polyphenolic compounds, such as cannabidiol (CBD), green tea polyphenols (Tea-poly), epigallocatechin gallate (EGCG) and theaflavin (TF), to investigate in vitro their inhibitory effects on virus invasion and viral spike (S) protein expression. Among the compounds tested, CBD and Tea-poly exhibited the most significant inhibitory effects on virus entry, comparable to the positive control chloroquine (CQ). EGCG showed the strongest suppression of the expression of the S protein, while CBD remarkably decreased ACE2 expression. CoIP-MS revealed eleven S-protein-interacting proteins that were significantly affected by EGCG. Transcriptome analysis demonstrated similar trends of CBD and EGCG in the modulation of many SARS-CoV-2-associated genes, with CBD showing greater impact on the gene profile than EGCG. GO and KEGG functional enrichment analyses revealed overlapping pathways of EGCG and CBD, including DNA repair, cell-cycle, and ER-, spliceosome- and ribosome-related processes. The combined use of CBD and EGCG can complement each other's advantages in inhibiting the invasion and reinvasion process of the virus at multiple levels, while minimizing the adverse effects of ACE2 expression level changes. Findings in this work offer new information for developing multi-level therapeutic strategies to control SARS-CoV-2 infection and, specifically, provide a novel antiviral agent combination of CBD and EGCG for the control of COVID-19.

PMID:40460494 | DOI:10.1016/j.virol.2025.110579

World J Pediatr. 2025 Jun 3. doi: 10.1007/s12519-025-00916-4. Online ahead of print.

ABSTRACT

BACKGROUND: Pediatric lung transplant (pLTX) is a rare procedure globally; its characteristics and survival outcomes in China remain unknown.

METHODS: This retrospective study analyzed data from pLTX recipients aged ≤ 17 years between January 2019 and December 2023 from the China Lung Transplantation Registry. Pre-, intra-, and post-operative characteristics were described and compared between children aged 2-11 years and 12-17 years and between pLTX conducted in centers with high and low transplant volumes. The Kaplan‒Meier method was used to estimate the postoperative survival rates and 95% confidence intervals (CIs). One-year postoperative survival rates were compared between pediatric and adult lung transplant (LTX) patients via log-rank tests.

RESULTS: Between 2019 and 2023, 63 transplants were performed in 62 pediatric patients, accounting for 1.8% of the total LTX in China. The primary indication for pLTX was bronchiolitis obliterans syndrome (46.0%), followed by cystic fibrosis (12.7%) and idiopathic pulmonary arterial hypertension (11.1%). Infection was the most common complication after pLTX (63.9%), and the incidence of bronchial anastomotic stenosis was slightly higher among recipients aged 2-11 years than among those aged 12-17 years (14.3% vs. 2.9%, P = 0.244). High-volume hospitals had a higher incidence of infections (72.7% vs. 41.2%, P = 0.021) and primary graft failure (20.0% vs. 5.9%, P = 0.260) among pediatric recipients. However, acute rejection was exclusively observed in low-volume hospitals (0.0% vs. 17.6%, P = 0.018). The in-hospital mortality rate was 16.1% (95% CI = 6.7-25.5). The 30-day and one-year survival rates after pLTX were 93.5% (95% CI = 87.6-99.9) and 80.6% (95% CI = 71.4-91.1), respectively, and were significantly higher than those of adult recipients (82.0% and 58.7%, all P < 0.05).

CONCLUSIONS: This research identified the trends, indications, donor and recipient characteristics, and complications of pLTX in China. Despite its small size, pLTX is growing gradually and has favorable outcomes. Future research on the long-term follow-up of pLTX recipients is needed to identify factors associated with the prognosis of pLTX patients.

PMID:40461919 | DOI:10.1007/s12519-025-00916-4

J Cyst Fibros. 2025 Jun 2:S1569-1993(25)01486-9. doi: 10.1016/j.jcf.2025.05.005. Online ahead of print.

ABSTRACT

BACKGROUND: Real-world data demonstrate variability in the response to elexacaftor/tezacaftor/ivacaftor (ETI) treatment among people with CF (pwCF). The aim of this study was to evaluate long-term outcomes in pwCF that had not shown early improvement in the percentage of predicted FEV1 (ppFEV1) following ETI treatment.

METHODS: A single-center prospective study in pwCF who initiated ETI. Patients were categorized as 'early responders' if showing an improvement of at least 10 % in ppFEV1 within three months of treatment or 'non-early responders' if not. Patients with pretreatment ppFEV1 of above 99 % predicted were excluded. Respiratory and non-respiratory outcomes 18 to 24 months after ETI initiation were evaluated.

RESULTS: A total of 52 pwCF (median age 30 (22-34), 22 (42 %) female) were included, of whom 21 (40 %) were 'non-early responders'. In a multivariable analysis, previous CFTR modulator therapy (p = 0.002), higher pretreatment ppFEV1 (p = 0.002), and higher pretreatment BMI (p = 0.018), were negatively associated with early change in ppFEV1. After 18 to 24 months of ETI therapy, ppFEV1 did not significantly improve in the 'non-early responders' group (p = 0.29). However, rates of ppFEV1 decline (p < 0.001), BMI (p < 0.005), number of pulmonary exacerbations (p < 0.02), days of intravenous antibiotic treatment (p < 0.01), and chest CT scores (p < 0.05), all significantly improved in both patient groups.

CONCLUSIONS: This study provides evidence for the long-term clinical benefits of ETI in pwCF lacking an early ppFEV1 response. The data suggest that a lack of early improvement should not deter clinicians from treatment continuation.

PMID:40461393 | DOI:10.1016/j.jcf.2025.05.005

Enferm Infecc Microbiol Clin (Engl Ed). 2025 Jun-Jul;43(6):309-316. doi: 10.1016/j.eimce.2024.08.008.

ABSTRACT

BACKGROUND: We investigate bronchopulmonary colonization patterns in Spanish people with CF (pwCF), gathering clinical, demographic, and microbiological data to supplement nine years of registry information, comparing 2021 findings with a similar multicenter study conducted in 2013.

METHODS: Sixteen CF units from 14 hospitals across Spain participated, each randomly recruiting around 20 patients. Patients provided sputum samples for culture. The clinical, demographical, microbiological, and treatment data from the previous year were recorded.

RESULTS: Overall, 326 patients (48.5% females) were recruited: 185 adult and 141 pediatrics, with a median age [q3-q1] of 30 [38-24] and 12 [16-6] years, respectively. p.Phe508del mutation was present in 30.6% and 46.4% of patients with homozygosis or heterozygosis, respectively. Median FEV1 (%) was significantly lower in adults (62%, range 75-43%) compared with pediatrics (90%, range 104-81%) (p<0.001). Pancreatic insufficiency was observed in 77.3%, carbohydrate metabolism alteration in 27.3%, and CF-related diabetes in 19.6% of patients. Lower prevalence of Staphylococcus aureus and Pseudomonas aeruginosa colonization were noted compared to 2013, along with a significantly lower correlation in lung function among pwCF colonized by P. aeruginosa (p<0.001). Half of pwCF (51%) exhibited a single pathogen in culture, two in 30%, and three or more in 2.4%. Co-colonization of P. aeruginosa and S. aureus (36.1%) was the most prevalent combination. High resistance rates were observed in P. aeruginosa and methicillin resistant S. aureus isolates.

CONCLUSIONS: We provide a valuable and representative current insight into the observed evolution in the clinical, demographic, and microbiological aspects in recent years among pwCF in Spain.

PMID:40461090 | DOI:10.1016/j.eimce.2024.08.008

Enferm Infecc Microbiol Clin (Engl Ed). 2025 Jun-Jul;43(6):307-308. doi: 10.1016/j.eimce.2025.04.004.

NO ABSTRACT

PMID:40461089 | DOI:10.1016/j.eimce.2025.04.004

Commun Med (Lond). 2025 Jun 3;5(1):212. doi: 10.1038/s43856-025-00935-2.

ABSTRACT

BACKGROUND: During radiation therapy, the natural movement of organs can lead to underdosing the cancer and overdosing the healthy tissue, compromising treatment efficacy. Real-time image-guided adaptive radiation therapy can track the tumour and account for the motion. Typically, fiducial markers are implanted as a surrogate for the tumour position due to the low radiographic contrast of soft tissues in kilovoltage (kV) images. A segmentation approach that does not require markers would eliminate the costs, delays, and risks associated with marker implantation.

METHODS: We trained patient-specific conditional Generative Adversarial Networks for prostate segmentation in kV images. The networks were trained using synthetic kV images generated from each patient's own imaging and planning data, which are available prior to the commencement of treatment. We validated the networks on two treatment fractions from 30 patients using multi-centre data from two clinical trials.

RESULTS: Here, we present a large-scale proof-of-principle study of x-ray-based markerless prostate segmentation for globally available cancer therapy systems. Our results demonstrate the feasibility of a deep learning approach using kV images to track prostate motion across the entire treatment arc for 30 patients with prostate cancer. The mean absolute deviation is 1.4 and 1.6 mm in the anterior-posterior/lateral and superior-inferior directions, respectively.

CONCLUSIONS: Markerless segmentation via deep learning may enable real-time image guidance on conventional cancer therapy systems without requiring implanted markers or additional hardware, thereby expanding access to real-time adaptive radiation therapy.

PMID:40461695 | DOI:10.1038/s43856-025-00935-2

Sci Rep. 2025 Jun 3;15(1):19462. doi: 10.1038/s41598-025-04720-y.

ABSTRACT

Recently, with the growth of electronic cars and the advancement of modern vehicles using portable equipment and embedded systems, several in-vehicle networks like the CAN (Controller Area Network) encountered novel risks of security. Because the portal of CAN does not have systems of security, like encryption and authentication in order to contend with cyber-attacks, the necessity for a system of intrusion detection for identifying attacks on the portal of CAN is really essential. In this study, Triple-attention Mechanism (TAN) has been used to recognize different kinds of security intrusions in portals of CAN. The purpose of TAN here is to identify intrusion within 3 steps. Within the initial phase, the major features have been extracted, and TAN functions as a descriptor of feature. Then, the discriminating categorizer classifies the current features. Eventually, with the help of adversarial learning, intrusion has been recognized. The current work utilizes a novel Greylag Goose Optimization algorithm for optimal selection of the network hyperparameters. For checking the effectiveness of the suggested method, an open-source dataset was applied, which recorded the traffic of CAN using a real vehicle throughout injection attacks of message. The results show that this method outperforms certain machine learning algorithms in error rate and false negative for DoS and drive gear and RPM spoofing attack with accuracy of 96.3%, recall of 96.1%, F1-Score of 96.2%, specificity of 97.2%, accuracy of 96.3%, AUC-ROC of 0.97, and MCC of 0.92 for DoS attacks. Therefore, the phase attack is minimized.

PMID:40461686 | DOI:10.1038/s41598-025-04720-y

Sci Rep. 2025 Jun 3;15(1):19495. doi: 10.1038/s41598-025-03932-6.

ABSTRACT

Drug-Target Interaction (DTI) prediction is a vital task in drug discovery, yet it faces significant challenges such as data imbalance and the complexity of biochemical representations. This study makes several contributions to address these issues, introducing a novel hybrid framework that combines advanced machine learning (ML) and deep learning (DL) techniques. The framework leverages comprehensive feature engineering, utilizing MACCS keys to extract structural drug features and amino acid/dipeptide compositions to represent target biomolecular properties. This dual feature extraction method enables a deeper understanding of chemical and biological interactions, enhancing predictive accuracy. To address data imbalance, Generative Adversarial Networks (GANs) are employed to create synthetic data for the minority class, effectively reducing false negatives and improving the sensitivity of the predictive model. The Random Forest Classifier (RFC) is utilized to make precise DTI predictions, optimized for handling high-dimensional data. The proposed framework's scalability and robustness were validated across diverse datasets, including BindingDB-Kd, BindingDB-Ki, and BindingDB-IC50. For the BindingDB-Kd dataset, the GAN+RFC model achieved remarkable performance metrics: accuracy of 97.46%, precision of 97.49%, sensitivity of 97.46%, specificity of 98.82%, F1-score of 97.46%, and ROC-AUC of 99.42%. Similarly, for the BindingDB-Ki dataset, the model attained an accuracy of 91.69%, precision of 91.74%, sensitivity of 91.69%, specificity of 93.40%, F1-score of 91.69%, and ROC-AUC of 97.32%. On the BindingDB-IC50 dataset, the model achieved an accuracy of 95.40%, precision of 95.41%, sensitivity of 95.40%, specificity of 96.42%, F1-score of 95.39%, and ROC-AUC of 98.97%. These results demonstrate the efficacy of the GAN-based approach in capturing complex patterns, significantly improving DTI prediction outcomes. In conclusion, the proposed GAN-based hybrid framework sets a new benchmark in computational drug discovery by addressing critical challenges in DTI prediction. Its robust performance, scalability, and generalizability contribute substantially to therapeutic development and pharmaceutical research.

PMID:40461636 | DOI:10.1038/s41598-025-03932-6

Sci Rep. 2025 Jun 3;15(1):19458. doi: 10.1038/s41598-025-05116-8.

ABSTRACT

Electrical impedance spectroscopy (EIS) is a powerful tool used to investigate the properties of materials and biological tissues. This study presents one of the first applications of EIS for the detection and classification of oral potentially malignant disorders (OPMDs) and oral cancer. We aimed to apply EIS in conjunction with deep learning to assist the clinical diagnosis of OPMD and oral cancer as a non-invasive diagnostic technology. Currently, the diagnosis of OPMD and oral cancer relies on clinical examination and histopathological analysis of invasive scalpel tissue biopsies, which is stressful for patients, time-consuming for clinicians and subject to histopathological interobserver variation in diagnosis, although recent advances in artificial intelligence may circumvent discrepancy. Here we developed a novel deep learning convolutional neural network (CNN)-based method to automatically differentiate normal, OPMD and malignant oral tissues using EIS measurements. EIS readings were initially taken from untreated or glacial acetic acid-treated porcine oral mucosa and analyzed via CNN to determine if this method could discriminate between normal and damaged oral epithelium. CNN models achieved area under the curve (AUC) values of 0.92 ± 0.03, with specificity 0.95 and sensitivity 0.84, showing good discrimination. EIS data from ventral tongue and floor-of-the-mouth were collected from 51 healthy humans and 11 patients with OPMD and oral cancer. When a binary classification (low or high risk of malignancy) was applied, the best CNN model achieved an AUC 0.91 ± 0.1, with accuracy 0.91 ± 0.05, specificity 0.97 and sensitivity 0.74. These results demonstrate the considerable potential of EIS in combination with CNN models as an adjunctive non-invasive diagnostic tool for OPMD and oral cancer.

PMID:40461631 | DOI:10.1038/s41598-025-05116-8

Sci Rep. 2025 Jun 3;15(1):19419. doi: 10.1038/s41598-025-98653-1.

ABSTRACT

EEG is widely applied in emotion recognition, brain disease detection, and other fields due to its high temporal resolution and non-invasiveness. However, artifact removal remains a crucial issue in EEG signal processing. Recently, with the rapid development of deep learning, there has been a significant transformation in the methods of EEG artifact removal. Nonetheless, existing research still exhibits some limitations: (1) insufficient capability to remove unknown artifacts; (2) inability to adapt to tasks where artifact removal needs to be applied to the overall input of multi-channel EEG data. Therefore, this study proposes CLEnet by integrating dual-scale CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory), and incorporating an improved EMA-1D (One-Dimensional Efficient Multi-Scale Attention Mechanism). CLEnet can extract the morphological features and temporal features of EEG, thereby separating EEG from artifacts. We conducted experiments on three datasets, and the results showed that CLEnet performed best. Specifically, in the task of removing artifacts from multi-channel EEG data containing unknown artifacts, CLEnet shows improvements of 2.45% and 2.65% in SNR(signal-to-noise ratio) and CC(average correlation coefficient). Moreover, RRMSEt(relative root mean square error in the temporal domain) and RRMSEf (relative root mean square error in the frequency domain) decrease by 6.94% and 3.30%.

PMID:40461599 | DOI:10.1038/s41598-025-98653-1

Commun Eng. 2025 Jun 3;4(1):100. doi: 10.1038/s44172-025-00431-4.

ABSTRACT

A large number of in-service reinforced concrete structures are now entering the mid-to-late stages of their service life. Efficient detection of damage characteristics and accurate prediction of material performance degradation have become essential for ensuring the safety of these structures. Traditional damage detection methods, which primarily rely on manual inspections and sensor monitoring, are inefficient and lack accuracy. Similarly, performance prediction models for reinforced concrete materials, which are often based on limited experimental data and polynomial fitting, oversimplify the influencing factors. In contrast, partial differential equation models that account for degradation mechanisms are computationally intensive and difficult to solve. Recent advancements in deep learning and machine learning, as part of artificial intelligence, have introduced innovative approaches for both damage detection and material performance prediction in reinforced concrete structures. This paper provides a comprehensive overview of machine learning and deep learning theories and models, and reviews the current research on their application to the durability of reinforced concrete structures, focusing on two main areas: intelligent damage detection and predictive modeling of material durability. Finally, the article discusses future trends and offers insights into the intelligent innovation of concrete structure durability.

PMID:40461590 | DOI:10.1038/s44172-025-00431-4

Sci Rep. 2025 Jun 3;15(1):19425. doi: 10.1038/s41598-025-04552-w.

ABSTRACT

The automatic detection of pain through the analysis of facial expressions is indeed one of the most critical challenges in the healthcare system. One of the significant challenges in automatic pain detection from facial expressions is the variability in how individuals express pain and other emotions through their facial deformations. This research aims to solve this issue by presenting ErAS-Net, an Enhanced Residual Attention-Based Subject-Specific Network that employs various attention mechanisms. Through transfer learning and multiple attention mechanisms, the proposed deep learning model is designed to mimic human perception of facial expressions, thereby enhancing its pain recognition ability and capturing the unique features of each individual's facial expressions based on their specific patterns. The UNBC-McMaster Shoulder Pain dataset is used to demonstrate the effectiveness of the proposed deep learning algorithm, which achieves impressive values of 98.77% accuracy for binary classification and 94.21% for four-level pain intensity classification using tenfold cross-validation. Additionally, the model attained 89.83% accuracy for binary classification with the Leave-One-Subject-Out (LOSO) validation method. To further evaluate generalizability, a cross-dataset experiment was conducted using the BioVid Heat Pain Database, where ErAS-Net achieved 78.14% accuracy for binary pain detection on unseen data without fine-tuning. The fact that this finding supports the attention mechanism and human perception is why the proposed model proves to be a powerful and reliable tool for automatic pain detection.

PMID:40461564 | DOI:10.1038/s41598-025-04552-w

Sci Rep. 2025 Jun 3;15(1):19379. doi: 10.1038/s41598-025-03979-5.

ABSTRACT

Systematic literature review (SLR) is an important tool for Health Economics and Outcomes Research (HEOR) evidence synthesis. SLRs involve the identification and selection of pertinent publications and extraction of relevant data elements from full-text articles, which can be a manually intensive procedure. Previously we developed machine learning models to automatically identify relevant publications based on pre-specified inclusion and exclusion criteria. This study investigates the feasibility of applying Natural Language Processing (NLP) approaches to automatically extract data elements from the relevant scientific literature. First, 239 full-text articles were collected and annotated for 12 important variables including study cohort, lab technique, and disease type, for proper SLR summary of Human papillomavirus (HPV) Prevalence, Pneumococcal Epidemiology, and Pneumococcal Economic Burden. The three resulting annotated corpora are shared publicly at [ https://github.com/Merck/NLP-SLR-corpora ], to provide training data and a benchmark baseline for the NLP community to further research this challenging task. We then compared three classic Named Entity Recognition (NER) algorithms, namely Conditional Random Fields (CRF), Long Short-Term Memory (LSTM), and the Bidirectional Encoder Representations from Transformers (BERT) models, to assess performance on the data element extraction task. The annotation corpora contain 4,498, 579, and 252 annotated entity mentions for HPV Prevalence, Pneumococcal Epidemiology, and Pneumococcal Economic Burden tasks respectively. Deep learning algorithms achieved superior performance in recognizing the targeted SLR data elements, compared to conventional machine learning algorithms. LSTM models have achieved 0.890, 0.646 and 0.615 micro-averaged F1 scores for three tasks respectively. CRF models could not provide comparable performance on most of the elements of interest. Although BERT-based models are known to generally achieve superior performance on many NLP tasks, we did not observe improvement in our three tasks. Deep learning algorithms have achieved superior performance compared with machine learning models on multiple SLR data element extraction tasks. LSTM model, in particular, is more preferable for deployment in supporting HEOR SLR data element extraction, due to its better performance, generalizability, and scalability as it's cost-effective in our SLR benchmark datasets.

PMID:40461545 | DOI:10.1038/s41598-025-03979-5

Sci Rep. 2025 Jun 3;15(1):19407. doi: 10.1038/s41598-025-01791-9.

ABSTRACT

Accurate and timely diagnosis of leukemia, a cancer characterized by an excessive number of abnormal white blood cells (WBCs), is crucial for effective treatment. Manual examination of blood smear images for leukemia diagnosis is often laborious and costly. Computer-aided classification of WBCs has the potential to assist hematologists in improving diagnostic accuracy. However, the subtle visual differences among the five types of immature neutrophils pose a significant challenge, even for experienced professionals. The study proposes a method called densely connected co-occurrence network (DCONN). The method first detects white blood cells in blood smear images using Yolact. Then, the images are pre-processed to minimize the correlation between image channels by transforming RGB color space to LAB color space. Finally, DCONN extracts spatial texture information using a co-occurrence matrix to improve classification accuracy. DCONN achieved 93.46% accuracy in classifying five types of immature neutrophils: myeloblast, promyelocyte, myelocyte, metamyelocyte, and band cells. The results indicate that using a combination of densely connected convolutional layers and a co-occurrence layer improves classification accuracy while using fewer trainable parameters than other deep learning methods such as ResNet and Inception. Additionally, the model is less demanding in terms of training hardware than attentional mechanism-based models that also have local feature representation. DCONN achieves advanced performance based on small-scale models without requiring much training time. The proposed method can be extended to other pathological image analyses in the future.

PMID:40461529 | DOI:10.1038/s41598-025-01791-9

Sci Rep. 2025 Jun 3;15(1):19369. doi: 10.1038/s41598-025-02015-w.

ABSTRACT

Cancer is among the most dangerous diseases contributing to rising global mortality rates. Lung cancer, particularly adenocarcinoma, is one of the deadliest forms and severely impacts human life. Early diagnosis and appropriate treatment significantly increase patient survival rates. Computed Tomography (CT) is a preferred imaging modality for detecting lung cancer, as it offers detailed visualization of tumor structure and growth. With the advancement of deep learning, the automated identification of lung cancer from CT images has become increasingly effective. This study proposes a novel lung cancer detection framework using a Flower Pollination Algorithm (FPA)-based weighted ensemble of three high-performing pretrained Convolutional Neural Networks (CNNs): VGG16, ResNet101V2, and InceptionV3. Unlike traditional ensemble approaches that assign static or equal weights, the FPA adaptively optimizes the contribution of each CNN based on validation performance. This dynamic weighting significantly enhances diagnostic accuracy. The proposed FPA-based ensemble achieved an impressive accuracy of 98.2%, precision of 98.4%, recall of 98.6%, and an F1 score of 0.985 on the test dataset. In comparison, the best individual CNN (VGG16) achieved 94.6% accuracy, highlighting the superiority of the ensemble approach. These results confirm the model's effectiveness in accurate and reliable cancer diagnosis. The proposed study demonstrates the potential of deep learning and neural networks to transform cancer diagnosis, helping early detection and improving treatment outcomes.

PMID:40461493 | DOI:10.1038/s41598-025-02015-w

World J Pediatr. 2025 Jun 3. doi: 10.1007/s12519-025-00916-4. Online ahead of print.

ABSTRACT

BACKGROUND: Pediatric lung transplant (pLTX) is a rare procedure globally; its characteristics and survival outcomes in China remain unknown.

METHODS: This retrospective study analyzed data from pLTX recipients aged ≤ 17 years between January 2019 and December 2023 from the China Lung Transplantation Registry. Pre-, intra-, and post-operative characteristics were described and compared between children aged 2-11 years and 12-17 years and between pLTX conducted in centers with high and low transplant volumes. The Kaplan‒Meier method was used to estimate the postoperative survival rates and 95% confidence intervals (CIs). One-year postoperative survival rates were compared between pediatric and adult lung transplant (LTX) patients via log-rank tests.

RESULTS: Between 2019 and 2023, 63 transplants were performed in 62 pediatric patients, accounting for 1.8% of the total LTX in China. The primary indication for pLTX was bronchiolitis obliterans syndrome (46.0%), followed by cystic fibrosis (12.7%) and idiopathic pulmonary arterial hypertension (11.1%). Infection was the most common complication after pLTX (63.9%), and the incidence of bronchial anastomotic stenosis was slightly higher among recipients aged 2-11 years than among those aged 12-17 years (14.3% vs. 2.9%, P = 0.244). High-volume hospitals had a higher incidence of infections (72.7% vs. 41.2%, P = 0.021) and primary graft failure (20.0% vs. 5.9%, P = 0.260) among pediatric recipients. However, acute rejection was exclusively observed in low-volume hospitals (0.0% vs. 17.6%, P = 0.018). The in-hospital mortality rate was 16.1% (95% CI = 6.7-25.5). The 30-day and one-year survival rates after pLTX were 93.5% (95% CI = 87.6-99.9) and 80.6% (95% CI = 71.4-91.1), respectively, and were significantly higher than those of adult recipients (82.0% and 58.7%, all P < 0.05).

CONCLUSIONS: This research identified the trends, indications, donor and recipient characteristics, and complications of pLTX in China. Despite its small size, pLTX is growing gradually and has favorable outcomes. Future research on the long-term follow-up of pLTX recipients is needed to identify factors associated with the prognosis of pLTX patients.

PMID:40461919 | DOI:10.1007/s12519-025-00916-4

Nat Med. 2025 Jun 3. doi: 10.1038/s41591-025-03743-2. Online ahead of print.

ABSTRACT

Despite substantial progress in artificial intelligence (AI) for generative chemistry, few novel AI-discovered or AI-designed drugs have reached human clinical trials. Here we present the results of the first phase 2a multicenter, double-blind, randomized, placebo-controlled trial testing the safety and efficacy of rentosertib (formerly ISM001-055), a first-in-class AI-generated small-molecule inhibitor of TNIK, a first-in-class target in idiopathic pulmonary fibrosis (IPF) discovered using generative AI. IPF is an age-related progressive lung condition with no current therapies available that reverse the degenerative course of disease. Patients were randomized to 12 weeks of treatment with 30 mg rentosertib once daily (QD, n = 18), 30 mg rentosertib twice daily (BID, n = 18), 60 mg rentosertib QD (n = 18) or placebo (n = 17). The primary endpoint was the percentage of patients who have at least one treatment-emergent adverse event, which was similar across all treatment arms (72.2% in patients receiving 30 mg rentosertib QD (n = 13/18), 83.3% for 30 mg rentosertib BID (n = 15/18), 83.3% for 60 mg rentosertib QD (n = 15/18) and 70.6% for placebo (n = 12/17)). Treatment-related serious adverse event rates were low and comparable across treatment groups, with the most common events leading to treatment discontinuation related to liver toxicity or diarrhea. Secondary endpoints included pharmacokinetic dynamics (Cmax, Ctrough, tmax, AUC0-t/τ/∞ and t1/2), changes in lung function as measured by forced vital capacity, diffusion capacity of the lung for carbon monoxide, forced expiry in 1 s and change in the Leicester Cough Questionnaire score, change in 6-min walk distance and the number and hospitalization duration of acute exacerbations of IPF. We observed increased forced vital capacity at the highest dosage with a mean change of +98.4 ml (95% confidence interval 10.9 to 185.9) for patients in the 60 mg rentosertib QD group, compared with -20.3 ml (95% confidence interval -116.1 to 75.6) for the placebo group. These results suggest that targeting TNIK with rentosertib is safe and well tolerated and warrants further investigation in larger-scale clinical trials of longer duration. ClinicalTrials.gov registration number: NCT05938920 .

PMID:40461817 | DOI:10.1038/s41591-025-03743-2