Search Results (6,590)

A class of retinal dystrophies known as retinitis pigmentosa (RP) is caused by the loss of photoreceptor cells. RP can be genetically transmitted as an autosomal dominant, autosomal recessive, or X-linked trait. About one-third of genes implicated in retinal degeneration encode for proteins whose functional dysregulation affects the “connecting cilium” in photoreceptors, altering its structure and function. Here we report on a 33-year-old woman who was referred for clinical genetic testing following a previous diagnosis of degenerative retinopathy, which was not informative. She was enrolled in a research program dedicated to undiagnosed retinal disorders, where a whole genome sequencing approach was employed to understand the underlying genetic basis. The genomic analysis documented the occurrence of compound heterozygosity for two functionally relevant missense variants in BAIAP3, which encodes a protein with a well-documented role in SNARE-mediated trafficking and ciliogenesis. Confocal microscopy analysis showed elongated cilia in patient-derived and BAIAP3-depleted fibroblasts compared to control cells. Real-time PCR analyses showed a consistent significant reduction of GLI1 mRNA levels in patient-derived and BAIAP3-depleted cells, both in basal conditions and after treatment with Smoothened agonist, SAG, indicating Sonic hedgehog signaling dysregulation. Collectively, these data suggest that biallelic loss-of-function variants of BAIAP3 may cause photoreceptor degeneration and underlie isolated RP. Full article

With the rapid expansion of multi-platform remote sensing applications, cloud contamination significantly impedes cross-platform data utilization. Current cloud detection methods face critical technical challenges in cross-platform settings, including neglect of atmospheric radiative transfer mechanisms, inadequate multi-scale structural decoupling, high intra-class variability coupled with inter-class similarity, cloud boundary ambiguity, cross-modal feature inconsistency, and noise propagation in pseudo-labels within semi-supervised frameworks. To address these issues, we introduce a Physics-Guided Multi-Representation Network (PGMRN) that adopts a student–teacher architecture and fuses tri-modal representations—Pseudo-NDVI, structural, and textural features—via atmospheric priors and intrinsic image decomposition. Specifically, PGMRN first incorporates an InfoNCE contrastive loss to enhance intra-class compactness and inter-class discrimination while preserving physical consistency; subsequently, a boundary-aware regional adaptive weighted cross-entropy loss integrates PA-CAM confidence with distance transforms to refine edge accuracy; furthermore, an Uncertainty-Aware Quadruple Consistency Propagation (UAQCP) enforces alignment across structural, textural, RGB, and physical modalities; and finally, a dynamic confidence-screening mechanism that couples PA-CAM with information entropy and percentile-based thresholding robustly refines pseudo-labels. Extensive experiments on four benchmark datasets demonstrate that PGMRN achieves state-of-the-art performance, with Mean IoU values of 70.8% on TCDD, 79.0% on HRC_WHU, and 83.8% on SWIMSEG, outperforming existing methods. Full article

The precise mapping of rock joint traces is fundamental to the design and safety assessment of foundations, retaining structures, and underground cavities in building and civil engineering. Existing deep learning approaches either impose prohibitive computational demands for on-site deployment or disrupt the topological continuity of subpixel lineaments that govern rock mass behavior. This study presents BATNet-Lite, a lightweight encoder–decoder architecture optimized for joint segmentation on resource-constrained devices. The encoder introduces a Boundary-Aware Token-Mixing (BATM) block that separates feature maps into patch tokens and directionally pooled stripe tokens, and a bidirectional attention mechanism subsequently transfers global context to local descriptors while refining stripe features, thereby capturing long-range connectivity with negligible overhead. A complementary Multi-Scale Line Enhancement (MLE) module combines depth-wise dilated and deformable convolutions to yield scale-invariant responses to joints of varying apertures. In the decoder, a Skeletal-Contrastive Decoder (SCD) employs dual heads to predict segmentation and skeleton maps simultaneously, while an InfoNCE-based contrastive loss enforces their topological consistency without requiring explicit skeleton labels. Training leverages a composite focal Tversky and edge IoU loss under a curriculum-thinning schedule, improving edge adherence and continuity. Ablation experiments confirm that BATM, MLE, and SCD each contribute substantial gains in boundary accuracy and connectivity preservation. By delivering topology-preserving joint maps with small parameters, BATNet-Lite facilitates rapid geological data acquisition for tunnel face mapping, slope inspection, and subsurface digital twin development, thereby supporting safer and more efficient building and underground engineering practice. Full article

Background/Objectives: While complete loss-of-function (LoF) SPINK1 variants in the simple heterozygous state cause chronic pancreatitis, biallelic complete LoF variants result in a rare pediatric disorder termed severe infantile isolated exocrine pancreatic insufficiency (SIIEPI). To date, only two individuals with a null SPINK1 genotype have been reported—one homozygous for a whole-gene deletion and the other for an Alu insertion in the 3′ untranslated region. Here, we report the genetic basis of a third SIIEPI case, presenting in early infancy with severe exocrine pancreatic insufficiency and diffuse pancreatic lipomatosis. Methods: Targeted next-generation sequencing (NGS) was used to analyze the entire coding region and exon–intron boundaries of the SPINK1 gene. Copy number variant (CNV) analysis was performed with SeqNext, based on normalized amplicon coverage. Results: The proband harbored compound heterozygous complete LoF SPINK1 variants. One was the known NM_001379610.1:c.180_181del (p.(Cys61PhefsTer2)), inherited from the father. The second, initially detected as an exon 2 deletion and confirmed by quantitative fluorescent multiplex PCR (QFM-PCR), was further characterized by long-range PCR as a complex rearrangement comprising a 1185 bp deletion removing exon 2, a 118 bp templated insertion followed by a non-templated nucleotide, and an 8 bp deletion. The mutational signature is consistent with serial replication slippage or template switching involving translesion synthesis. This maternally inherited variant has not been previously reported. Conclusions: This study expands the mutational spectrum of SPINK1-related SIIEPI and suggests that this distinct pediatric disorder may be under recognized in clinical practice. Full article

Background/Objectives: Immune checkpoint inhibitors targeting the PD-1/PD-L1 axis have become a standard in the treatment of all stages of non-small lung cancer. Beyond tumor-intrinsic biomarkers like PD-L1 expression, evidence points to the role of patient-related factors, such as body mass index, sarcopenia, and cachexia. These body composition parameters may reflect metabolic reserve or even immune competence and could help stratify outcomes in patients treated with PD-1 and PD-L1. This systematic review aims to evaluate the impact of body composition—specifically BMI, pretreatment weight loss, sarcopenia, and cachexia—on clinical outcomes such as progression-free and overall survival in NSCLC patients treated with immune checkpoint inhibitors. Methods: A systematic literature search was conducted across multiple databases including PubMed, Google Scholar, and Science Direct. We included full-text original research articles (1 January 2020–1 May 2025) reporting clinical outcomes of NSCLC patients treated with PD-1 or PD-L1 inhibitors, in relation to body composition factors (BMI, pretreatment weight loss, sarcopenia, cachexia). Eligible studies involved adults (>18 years) and included observational cohorts or controlled trials; animal or in vitro studies were excluded. Data extraction and risk of bias assessments were performed independently by two reviewers, with discrepancies being resolved through a third one. Results: From 12,358 records identified, 21 studies met the inclusion criteria. Most were retrospective cohorts assessing the impact of pre-treatment weight loss, cachexia, and sarcopenia on ICI outcomes in NSCLC. These factors consistently predicted poorer survival and response, while BMI alone showed limited prognostic value. Considerable heterogeneity in body composition definitions and outcome reporting was observed. Conclusions: Body composition—particularly weight loss, cachexia, and sarcopenia—significantly impacts survival and response in NSCLC patients treated with ICIs. These factors reflect immune–metabolic dysfunction that may impair treatment efficacy. BMI alone is insufficient; routine assessment of muscle mass and cachexia could improve risk stratification. Full article

Efficient and explainable vehicle damage inspection is essential due to the increasing complexity and volume of vehicular incidents. Traditional manual inspection approaches are not time-effective, prone to human error, and lead to inefficiencies in insurance claims and repair workflows. Existing deep learning methods, such as CNNs, often struggle with generalization, require large annotated datasets, and lack interpretability. This study presents a robust and interpretable deep learning framework for vehicle damage classification, integrating Vision Transformers (ViTs) and ensemble detection strategies. The proposed architecture employs a RetinaNet backbone with a ViT-enhanced detection head, implemented in PyTorch using the Detectron2 object detection technique. It is pretrained on COCO weights and fine-tuned through focal loss and aggressive augmentation techniques to improve generalization under real-world damage variability. The proposed system applies the Weighted Box Fusion (WBF) ensemble strategy to refine detection outputs from multiple models, offering improved spatial precision. To ensure interpretability and transparency, we adopt numerous explainability techniques—Grad-CAM, Grad-CAM++, and SHAP—offering semantic and visual insights into model decisions. A custom vehicle damage dataset with 4500 images has been built, consisting of approximately 60% curated images collected through targeted web scraping and crawling covering various damage types (such as bumper dents, panel scratches, and frontal impacts), along with 40% COCO dataset images to support model generalization. Comparative evaluations show that Hybrid ViT-RetinaNet achieves superior performance with an F1-score of 84.6%, mAP of 87.2%, and 22 FPS inference speed. In an ablation analysis, WBF, augmentation, transfer learning, and focal loss significantly improve performance, with focal loss increasing F1 by 6.3% for underrepresented classes and COCO pretraining boosting mAP by 8.7%. Additional architectural comparisons demonstrate that our full hybrid configuration not only maintains competitive accuracy but also achieves up to 150 FPS, making it well suited for real-time use cases. Robustness tests under challenging conditions, including real-world visual disturbances (smoke, fire, motion blur, varying lighting, and occlusions) and artificial noise (Gaussian; salt-and-pepper), confirm the model’s generalization ability. This work contributes a scalable, explainable, and high-performance solution for real-world vehicle damage diagnostics. Full article

Background: Young-onset colorectal cancer (YO-CRC) has emerged as a distinct clinical entity, often presenting at advanced stages. Despite the increasing incidence, the molecular and clinical underpinnings of YO-CRC remain underexplored. This study aims to characterize the clinical and molecular features of YO-CRC and to evaluate their impact on OS. Methods: We reviewed 110 patients diagnosed with YO-CRC at our institution who underwent next-generation sequencing. Demographic, clinical, and molecular data, including age, gender, race, tumor location, cancer stage, and mutation status (KRAS, NRAS, BRAF, POLE, ERBB-2/HER2, microsatellite status), were collected by reviewing electronic medical records. For OS analysis, we focused on patients diagnosed with de novo stage IV. Cox proportional hazards regression and Kaplan–Meier survival analysis were utilized to assess the association of these factors with OS, with statistical significance determined by a p-value threshold of <0.05. Results: Among 110 patients, n = 44 (40%) presented with local disease (stage 1–3), while n = 66 (60%) presented with de novo metastatic disease at the time of diagnosis. The median age at diagnosis was 44.5 years. The cohort consisted of 64% males and 36% females, with 84% of patients identified as White. Most tumors were left-sided (77%), including the distal colon/sigmoid (44%) and rectum (33%). KRAS and BRAF mutations were present in 36% and 5.5%, respectively. ERBB-2/HER2 amplification and microsatellite instability were observed in 4.5% and 6.4%, respectively. Tumor mutation burden (TMB) was <10 in 57% of patients, with 14% having TMB > 20. CNV analysis revealed that 14% of patients had copy gains, 12% had concurrent gains/losses, and 31% had copy losses. Among 66 patients with de novo metastatic disease, 44% had died by the time of analysis, with a median overall survival (OS) of 43.6 months (95% CI, 28.7—not reached). KRAS mutations were found to be significantly associated with worse survival outcomes. Cox regression analysis reveals the prognostic significance of KRAS status, with a hazard ratio (HR) of 3.52 (95% CI: 1.59–7.76, p = 0.002), indicating a significantly higher risk of death for KRAS-mutant YO-CRC patients. Conclusions: Patients with YO-CRC are more likely to present with de novo metastatic disease and left-sided tumors with distinct molecular characteristics. KRAS mutations are a key prognostic factor in YO-CRC, highlighting the need for therapeutic interventions to improve outcomes in this high-risk group. Full article

The cushion curves of cushioning materials play crucial roles in scientific and reliable cushioning designs and in reducing damage losses for fragile products during distributions. The construction methods of cushion curves of closed-cell foam materials (CFMs) mainly include the Janssen factor, Rusch curve, cushion factor, and energy absorption diagram. The construction principle of these methods is reviewed in detail, and their disadvantages are mainly discussed. According to relevant ASTM and GB/T experimental standards, the peak acceleration–static stress cushion curve is based on dynamic impacts, which are most consistent with the dropping situation of product packages, so this kind of cushion curve is the standard and most widely applied for product cushioning designs. However, when generating the peak acceleration–static stress cushion curves, the experimental work is extremely huge. Three methods, namely the dynamic factor method, dynamic stress–dynamic energy method, and dynamic cushion factor–dynamic energy method, can significantly reduce the experimental workload and simplify constructing cushion curves. The novel dynamic cushion factor–dynamic stress method is proposed to simplify constructing the cushion curves. The practical generation steps of constructing cushion curves based on the four simplified methods are created and presented in detail. Full article

Coal gangue concrete (CGC), a recycled cementitious material derived from industrial solid waste, presents both opportunities and challenges for structural applications due to its heterogeneous composition and variable mechanical behavior. This study develops an ensemble learning framework—incorporating XGBoost, LightGBM, and CatBoost—to predict four key constitutive parameters based on experimental data. The predicted parameters are subsequently incorporated into an ABAQUS finite element model to simulate the compressive–bending response of CGC columns, with simulation results aligning well with experimental observations in terms of failure mode, load development, and deformation characteristics. To enhance model interpretability, a hybrid approach is adopted, combining permutation-based global feature importance analysis with SHAP (SHapley Additive exPlanations)-derived local explanations. This joint framework captures both the overall influence of each feature and its context-dependent effects, revealing a three-stage stiffness evolution pattern—brittle, quasi-ductile, and re-brittle—governed by gangue replacement levels and consistent with micromechanical mechanisms and numerical responses. Coupled feature interactions, such as between gangue content and crush index, are shown to exacerbate stiffness loss through interfacial weakening and pore development. This integrated approach delivers both predictive accuracy and mechanistic transparency, providing a reference for developing physically interpretable, data-driven constitutive models and offering guidance for tailoring CGC toward ductile, energy-absorbing structural materials in seismic and sustainability-focused engineering. Full article

This study proposes an intelligent method for identifying wiring errors in three-phase three-wire electricity meters using a gradient boosting machine (LightGBM) under complex load conditions, including light load and overcompensation. The work addresses a gap where intelligent fault-detection techniques have rarely been applied to three-phase three-wire wiring errors specifically under these conditions, and contributes a mechanism-informed data generation strategy tied to phase-angle behavior that can cause misidentification. Data generation and model training/evaluation were implemented in Python using LightGBM. The experiments demonstrated faster convergence (a 92.4% reduction in loss by the 50th round) and sub-2-s training time for 300 rounds, with >80% overall accuracy and 100% accuracy in specific normal-wiring scenarios relevant to misidentification risk. Feature-importance analysis identified total reactive power as the most informative input (19.8%) and confirmed the consistency between mechanism and model behavior. These results suggest a practical path to automated and accurate wiring-error detection in modern power systems with significant load variability. Full article

Background: A short cervix in the second trimester significantly increases preterm birth risk, yet no reliable first-trimester prediction method exists. Current guidelines lack consensus on which women should undergo transvaginal ultrasound (TVUS) screening for cost-effective prevention. Therefore, it is vital to establish a highly accurate and economical method for use in the early stages of pregnancy to predict short cervix in mid-pregnancy. Methods: A total of 1480 pregnant women with singleton pregnancies and at least one risk factor for spontaneous preterm birth (<37 weeks) were recruited from January 2020 to December 2020 at the Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine. Cervical length was assessed at 20–24 weeks of gestation, with a short cervix defined as <25 mm. Feature selection employed tree models, regularization, and recursive feature elimination (RFE). Seven machine learning models (logistic regression, linear discriminant analysis, k-nearest neighbors, support vector machine, decision tree, random forest, XGBoost) were trained to predict mid-trimester short cervix. The XGBoost model—an ensemble method leveraging sequential decision trees—was analyzed using Shapley Additive Explanation (SHAP) values to assess feature importance, revealing consistent associations between clinical predictors and outcomes that align with known clinical patterns. Results: Among 1480 participants, 376 (25.4%) developed mid-trimester short cervix. The XGBoost-based prediction model demonstrated high predictive performance in the training set (Recall = 0.838, F1 score = 0.848), test set (Recall = 0.850, F1 score = 0.910), and an independent dataset collected in January 2025 (Recall = 0.708, F1 score = 0.791), with SHAP analysis revealing pre-pregnancy BMI as the strongest predictor, followed by second-trimester pregnancy loss history, peripheral blood leukocyte count (WBC), and positive vaginal microbiological culture results (≥10⁵ CFU/mL, measured between 11⁺⁰ and 13⁺⁶ weeks). Conclusions: The XGBoost model accurately predicts mid-trimester short cervix using first-trimester clinical data, providing a 6-week window for targeted interventions before the 20–24-week gestational assessment. This early prediction could help guide timely preventive measures, potentially reducing the risk of spontaneous preterm birth (sPTB). Full article

In the face of increasingly severe cybersecurity threats, incomplete information and environmental dynamics have become central challenges in network attack–defense scenarios. In real-world network environments, defenders often find it difficult to fully perceive attack behaviors and network states, leading to a high degree of uncertainty in the system. Traditional approaches are inadequate in dealing with the diversification of attack strategies and the dynamic evolution of network structures, making it difficult to achieve highly adaptive defense strategies and efficient multi-agent coordination. To address these challenges, this paper proposes a multi-agent network defense approach based on joint game modeling, termed JG-Defense (Joint Game-based Defense), which aims to enhance the efficiency and robustness of defense decision-making in environments characterized by incomplete information. The method integrates Bayesian game theory, graph neural networks, and a proximal policy optimization framework, and it introduces two core mechanisms. First, a Dynamic Communication Graph Neural Network (DCGNN) is used to model the dynamic network structure, improving the perception of topological changes and attack evolution trends. A multi-agent communication mechanism is incorporated within the DCGNN to enable the sharing of local observations and strategy coordination, thereby enhancing global consistency. Second, a joint game loss function is constructed to embed the game equilibrium objective into the reinforcement learning process, optimizing both the rationality and long-term benefit of agent strategies. Experimental results demonstrate that JG-Defense outperforms the Cybermonic model by 15.83% in overall defense performance. Furthermore, under the traditional PPO loss function, the DCGNN model improves defense performance by 11.81% compared to the Cybermonic model. These results verify that the proposed integrated approach achieves superior global strategy coordination in dynamic attack–defense scenarios with incomplete information. Full article

Lotus stems contain cellulose, which can be utilized as a base material for producing green products, specifically degradable plastics. This research investigates the effect of polylactic acid (PLA) blends on cellulose degradable plastics from the lotus stem (Nelumbo nucifera). The mechanical characteristics are as follows: tensile strength of 0.7703–3.3212 MPa, elongation of 0.58–1.16%, Young’s modulus of 78.7894–364.6118 MPa. Compound analysis showed the presence of O-H, C-C, and C=O groups, and the presence of microbial activity in the soil can also lead to the degradation of these groups due to their hydrophilic nature, which allows them to bind water. Thermal analysis within a temperature range of 413.24 °C to 519.80 °C, shows that significant weight loss begins with the formation of crystalline structures. The degradable plastic exhibiting the lowest degree of swelling consists of 1 g of cellulose and 8 g of PLA, resulting in a swelling value of 6.25%. The degradable plastic is anticipated to decompose most rapidly after 52 days, utilizing 2 g of PLA and 7 g of cellulose. This complies with standard requirement, which sets a maximum degradation period of 180 days for polymers. Full article

Background/Objective: At least 25% of colorectal cancer (CRC) patients develop liver metastases (CRLM), and chemotherapeutic regimens based on the fluoropyrimidine (FP) drug 5-fluorouracil (5-FU) provide a survival advantage, but long-term survival is uncommon. The primary molecular target of FP drugs is thymidylate synthase (TS). Methods: A TS/Top1 dual-targeting cytotoxic mechanism for CF10/LV was confirmed by TS ternary complex detection by Western blot and by immunofluorescence detection of Top1 cleavage complexes. CF10/LV activated the ATR/Chk1 pathway consistent with enhanced replication stress and induced apoptosis. In vivo studies showed CF10 and CF10/LV eradicated liver metastasis in a CRLM model without scarring or weight loss, displaying therapeutic advantages relative to legacy FPs. Results: We demonstrated that a nanoscale FP polymer, CF10, displayed greater potency than expected based on FP content in part through more direct conversion to the TS-inhibitory metabolite, FdUMP. In this study, we tested CF10 for potency advantages relative to 5-FU and trifluorothymidine (TFT, the FP component of TAS-102) and confirmed a general potency advantage for CF10 in CRC cell lines in the Broad Institute PRISM screen. We demonstrated that this potency advantage is retained in CRC cells cultured with human-like folate levels and is enhanced by LV co-treatment to a similar extent as that by 5-FU. Our results confirm CF10 development proceeding as a CF10/LV combination. Mechanistically, CF10 cytotoxicity closely correlates with poisons of DNA topoisomerase 1 (Top1) in the PRISM screen relative to 5-FU and TFT. Conclusions: Our pre-clinical data support an early-phase clinical trial for CF10 for treating liver-metastatic CRC. Full article

Adenoma-like adenocarcinoma (ALAC) of the colon is a recently recognized histological subtype of colorectal adenocarcinoma, characterized by a villous architecture, low-grade cytologic atypia, and deceptive bland morphology despite its invasive potential, which can mimic non-invasive adenomas, leading to underdiagnosis in limited biopsy samples. Herein, we report the case of an 81-year-old male presenting with right-upper-quadrant pain that was found to have a hepatic abscess and a 4 cm villous lesion in the ascending colon. Histopathological examination of the right hemicolectomy specimen revealed a villous adenocarcinoma with invasion of the muscularis propria, consistent with adenoma-like adenocarcinoma. Isolated loss of PMS2 indicated a mismatch repair deficiency. However, adjuvant therapy was not indicated. The patient remained recurrence-free for three years, until he died from unrelated causes in the context of progressive frailty and comorbidities, with no evidence of cancer progression. This case highlights the diagnostic challenges posed by ALAC and underscores the importance of recognizing its distinct morphological features. Awareness of this entity is essential to avoid misclassification and ensure adequate treatment, especially given its typically favorable prognosis with low metastatic potential. Full article