MiR-146a reduces fibrosis after glaucoma filtration surgery in rats.

PURPOSE: To explore the impact of microRNA 146a (miR-146a) and the underlying mechanisms in profibrotic changes following glaucoma filtering surgery (GFS) in rats and stimulation by transforming growth factor (TGF)-ß1 in rat Tenon's capsule fibroblasts. METHODS: Cultured rat Tenon's capsule fibroblasts were treated with TGF-ß1 and analyzed with microarrays for mRNA profiling to validate miR-146a as the target. The Tenon's capsule fibroblasts were then respectively treated with lentivirus-mediated transfection of miR-146a mimic or inhibitor following TGF-ß1 stimulation in vitro, while GFS was performed in rat eyes with respective intraoperative administration of miR-146a, mitomycin C (MMC), or 5-fluorouracil (5-FU) in vivo. Profibrotic genes expression levels (fibronectin, collagen Iα, NF-KB, IL-1ß, TNF-α, SMAD4, and α-smooth muscle actin) were determined through qPCR, Western blotting, immunofluorescence staining and/or histochemical analysis in vitro and in vivo. SMAD4 targeting siRNA was further used to treat the fibroblasts in combination with miR-146a intervention to confirm its role in underlying mechanisms. RESULTS: Upregulation of miR-146a reduced the proliferation rate and profibrotic changes of rat Tenon's capsule fibroblasts induced by TGF-ß1 in vitro, and mitigated subconjunctival fibrosis to extend filtering blebs survival after GFS in vivo, where miR-146a decreased expression levels of NF-KB-SMAD4-related genes, such as fibronectin, collagen Iα, NF-KB, IL-1ß, TNF-α, SMAD4, and α-smooth muscle actin(α-SMA). Additionally, SMAD4 is a key target gene in the process of miR-146a inhibiting fibrosis. CONCLUSIONS: MiR-146a effectively reduced TGF-ß1-induced fibrosis in rat Tenon's capsule fibroblasts in vitro and in vivo, potentially through the NF-KB-SMAD4 signaling pathway. MiR-146a shows promise as a novel therapeutic target for preventing fibrosis and improving the success rate of GFS.

Static and Temporal Dynamic in Functional Connectivity of Large-scale Brain Networks During Acute Stress Regulate Stress Resilience Differently: The Promotion Role of Trait Resilience.

Stress resilience has been largely regarded as a process in which individuals actively cope with and recover from stress. Over the past decade, the emergence of large-scale brain networks has provided a new perspective for the study of the neural mechanisms of stress. However, the role of inter-network functional-connectivity (FC) and its temporal fluctuations in stress resilience is still unclear. To bridge this knowledge gap, seventy-seven participants (age, 17-22 years, 37 women) were recruited for a ScanSTRESS brain imaging study. A static perspective was initially adopted, using changes in FC that obtained from stress vs. control condition during the entire stress induction phase as a static indicator. Further, changes in FC between different stress runs were analyzed as an index of temporal dynamics. Stress resilience was gauged using salivary cortisol levels, while trait resilience was measured via behavioral-activation-system (BAS) sensitivity. Results found that, for the static index, enhanced FC between the salience-network (SN), default-mode-network (DMN) and limbic-network (LBN) during acute stress could negatively signal stress resilience. For the temporal dynamics index, FC among the dorsal-attention-network (DAN), central-executive-network (CEN) and visual-network (VN) decreased significantly during repeated stress induction. Moreover, the decline of FC positively signaled stress resilience, and this relationship only exist in people with high BAS. The current research elucidates the intricate neural underpinnings of stress resilience, offering insights into the adaptive mechanisms underlying effective stress responses.

Lysine lactylation in the regulation of tumor biology.

Lysine lactylation (Kla), a newly discovered post-translational modification (PTM) of lysine residues, is progressively revealing its crucial role in tumor biology. A growing body of evidence supports its capacity of transcriptional regulation through histone modification and modulation of non-histone protein function. It intricately participates in a myriad of events in the tumor microenvironment (TME) by orchestrating the transitions of immune states and augmenting tumor malignancy. Its preferential modification of metabolic proteins underscores its specific regulatory influence on metabolism. This review focuses on the effect and the probable mechanisms of Kla-mediated regulation of tumor metabolism, the upstream factors that determine Kla intensity, and its potential implications for the clinical diagnosis and treatment of tumors.

Histopathology images-based deep learning prediction of prognosis and therapeutic response in small cell lung cancer.

Small cell lung cancer (SCLC) is a highly aggressive subtype of lung cancer characterized by rapid tumor growth and early metastasis. Accurate prediction of prognosis and therapeutic response is crucial for optimizing treatment strategies and improving patient outcomes. In this study, we conducted a deep-learning analysis of Hematoxylin and Eosin (H&E) stained histopathological images using contrastive clustering and identified 50 intricate histomorphological phenotype clusters (HPCs) as pathomic features. We identified two of 50 HPCs with significant prognostic value and then integrated them into a pathomics signature (PathoSig) using the Cox regression model. PathoSig showed significant risk stratification for overall survival and disease-free survival and successfully identified patients who may benefit from postoperative or preoperative chemoradiotherapy. The predictive power of PathoSig was validated in independent multicenter cohorts. Furthermore, PathoSig can provide comprehensive prognostic information beyond the current TNM staging system and molecular subtyping. Overall, our study highlights the significant potential of utilizing histopathology images-based deep learning in improving prognostic predictions and evaluating therapeutic response in SCLC. PathoSig represents an effective tool that aids clinicians in making informed decisions and selecting personalized treatment strategies for SCLC patients.

Prediction of prognosis and treatment response in ovarian cancer patients from histopathology images using graph deep learning: a multicenter retrospective study.

BACKGROUND: Ovarian cancer (OV) is a prevalent and deadly disease with high mortality rates. The development of accurate prognostic tools and personalized therapeutic strategies is crucial for improving patient outcomes. METHODS: A graph-based deep learning model, the Ovarian Cancer Digital Pathology Index (OCDPI), was introduced to predict prognosis and response to adjuvant therapy using hematoxylin and eosin (H&E)-stained whole-slide images (WSIs). The OCDPI was developed using formalin-fixed, paraffin-embedded (FFPE) WSIs from the TCGA-OV cohort, and was externally validated in two independent cohorts from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) and Harbin Medical University Cancer Hospital (HMUCH). RESULTS: The OCDPI showed prognostic ability for overall survival prediction in the PLCO (HR, 1.916; 95% CI, 1.380-2.660; log-rank test, P < 0.001) and HMUCH (HR, 2.796; 95% CI, 1.404-5.568; log-rank test, P = 0.0022) cohorts. Patients with low OCDPI experienced better survival benefits and lower recurrence rates following adjuvant therapy compared to those with high OCDPI. Multivariable analyses, adjusting for clinicopathological factors, consistently identified OCDPI as an independent prognostic factor across all cohorts (all P < 0.05). Furthermore, OCDPI performed well in patients with low-grade tumors or fresh-frozen slides, and could differentiate between HRD-deficient or HRD-intact patients with and without sensitivity to adjuvant therapy. CONCLUSION: The results from this multicenter cohort study indicate that the OCDPI may serve as a valuable and labor-saving tool to improve prognostic and predictive clinical decision-making in patients with OV.

Inhibition of Ferroptosis Ameliorates Photoreceptor Degeneration in Experimental Diabetic Mice.

Diabetic retinopathy (DR) is a leading cause of vision impairment in the working-age population worldwide. Various modes of photoreceptor cell death contribute to the development of DR, including apoptosis and autophagy. However, whether ferroptosis is involved in the pathogenesis of photoreceptor degeneration in DR is still unclear. High-glucose (HG)-stimulated 661W cells and diabetic mice models were used for in vitro and in vivo experiments, respectively. The levels of intracellular iron, glutathione (GSH), reactive oxygen species (ROS), lipid peroxidation (MDA), and ferroptosis-related proteins (GPX4, SLC7A11, ACSL4, FTH1, and NCOA4) were quantified to indicate ferroptosis. The effect of ferroptosis inhibition was also assessed. Our data showed the levels of iron, ROS, and MDA were enhanced and GSH concentration was reduced in HG-induced 661W cells and diabetic retinas. The expression of GPX4 and SLC7A11 was downregulated, while the expression of ACSL4, FTH1, and NCOA4 was upregulated in the 661W cells cultured under HG conditions and in the photoreceptor cells in diabetic mice. Furthermore, the administration of the ferroptosis inhibitor ferrostatin-1 (Fer-1) obviously alleviated ferroptosis-related changes in HG-cultured 661W cells and in retinal photoreceptor cells in diabetic mice. Taken together, our findings suggest that ferroptosis is involved in photoreceptor degeneration in the development of the early stages of DR.

Macular changes following cataract surgery in eyes with early diabetic retinopathy: an OCT and OCT angiography study.

Background: To evaluate changes in macular status and choroidal thickness (CT) following phacoemulsification in patients with mild to moderate nonproliferative diabetic retinopathy (NPDR) using optical coherence tomography. Methods: In this prospective study, all of the patients underwent uncomplicated phacoemulsification. Retinal superficial capillary plexus vascular density (SCP-VD), macular thickness (MT), and CT were measured pre- and postoperatively. Results: Twenty-two eyes of 22 cataract patients with mild to moderate NPDR without diabetic macular edema (DME) and 22 controls were enrolled. BCVA increased in two groups at 3 months postoperatively. At 1 and 3 months postoperatively, SCP-VD in the diabetic retinopathy (DR) group significantly increased; changes in SCP-VD in parafovea were significantly greater in the DR group than in the control group. MT and CT in the DR group significantly increased at all visits postoperatively in the fovea and perifovea. Changes in parafoveal MT were significantly greater in the DR group than in the control group at all visits postoperatively. Changes in CT and MT in the fovea were significantly greater in patients with DR than in the controls 1 and 3 months postoperatively. Conclusion: Uncomplicated phacoemulsification resulted in greater increases in SCP-VD, MT and CT in patients with early DR without preoperative DME than in controls.

Perceived stress mediates the association between perceived control and emotional distress: The moderating role of psychological resources and sex differences.

Studies have confirmed that perceived control is strongly negatively correlated with emotional distress. However, few studies have explored whether perceived stress plays a potential mediating role in this relationship and whether the association between perceived stress and emotional distress is moderated by psychological resources, such as self-esteem and social support. Furthermore, it is unclear whether there are sex differences in the moderating effects of psychological resources on emotional distress. A total of 951 healthy adults (51.84% females) from different regions of mainland China participated in the study and completed questionnaires in early December 2022, when prevention and control policies concerning COVID-19 in China underwent rapid change. Perceived control negatively correlated with emotional distress, and perceived stress mediated the association between perceived control and emotional distress. In addition, both internal (i.e., self-esteem) and external psychological resources (i.e., social support) moderated the association between perceived stress and emotional distress, and the positive correlation between perceived stress and emotional distress was higher in individuals with low social support (and self-esteem) than in those with high social support (and self-esteem). We found sex differences in the moderating roles of psychological resources. Specifically, self-esteem had a moderating effect on both men and women, whereas social support had a moderating effect only on women. These findings improve understanding of the relationship between perceived control and emotional distress and suggest that intervention programs should be designed to target men and women differently.

Automatic detection and differential diagnosis of age-related macular degeneration from color fundus photographs using deep learning with hierarchical vision transformer.

Age-related macular degeneration (AMD) is a leading cause of vision loss in the elderly, highlighting the need for early and accurate detection. In this study, we proposed DeepDrAMD, a hierarchical vision transformer-based deep learning model that integrates data augmentation techniques and SwinTransformer, to detect AMD and distinguish between different subtypes using color fundus photographs (CFPs). The DeepDrAMD was trained on the in-house WMUEH training set and achieved high performance in AMD detection with an AUC of 98.76% in the WMUEH testing set and 96.47% in the independent external Ichallenge-AMD cohort. Furthermore, the DeepDrAMD effectively classified dryAMD and wetAMD, achieving AUCs of 93.46% and 91.55%, respectively, in the WMUEH cohort and another independent external ODIR cohort. Notably, DeepDrAMD excelled at distinguishing between wetAMD subtypes, achieving an AUC of 99.36% in the WMUEH cohort. Comparative analysis revealed that the DeepDrAMD outperformed conventional deep-learning models and expert-level diagnosis. The cost-benefit analysis demonstrated that the DeepDrAMD offers substantial cost savings and efficiency improvements compared to manual reading approaches. Overall, the DeepDrAMD represents a significant advancement in AMD detection and differential diagnosis using CFPs, and has the potential to assist healthcare professionals in informed decision-making, early intervention, and treatment optimization.

Coexistence of muscle atrophy and high subcutaneous adipose tissue radiodensity predicts poor prognosis in hepatocellular carcinoma.

Introduction: We aimed to assess the prognostic implications of muscle atrophy and high subcutaneous adipose tissue (SAT) radiodensity in patients with hepatocellular carcinoma (HCC). Methods: In this retrospective study, muscle atrophy was assessed using the psoas muscle index (PMI) obtained from computed tomography. SAT radiodensity was evaluated based on radiodensity measurements. Survival and multivariate analyses were performed to identify factors associated with prognosis. The impact of muscle atrophy and high SAT radiodensity on prognosis was determined through survival analysis. Results: A total of 201 patients (median age: 71 years; 76.6% male) with HCC were included. Liver cirrhosis was observed in 72.6% of patients, and the predominant Child-Pugh grade was A (77.1%). A total of 33.3% of patients exhibited muscle atrophy based on PMI values, whereas 12.9% had high SAT radiodensity. Kaplan-Meier survival analysis demonstrated that patients with muscle atrophy had significantly poorer prognosis than those without muscle atrophy. Patients with high SAT radiodensity had a significantly worse prognosis than those without it. Muscle atrophy, high SAT radiodensity, the Barcelona Clinic Liver Cancer class B, C, or D, and Child-Pugh score ≥ 6 were significantly associated with overall survival. Further classification of patients into four groups based on the presence or absence of muscle atrophy and high SAT radiodensity revealed that patients with both muscle atrophy and high SAT radiodensity had the poorest prognosis. Conclusion: Muscle atrophy and high SAT radiodensity are significantly associated with poor prognosis in patients with HCC. Identifying this high-risk subgroup may facilitate the implementation of targeted interventions, including nutritional therapy and exercise, to potentially improve clinical outcomes.

Classification of attention deficit/hyperactivity disorder based on EEG signals using a EEG-Transformer model∗.

Objective. Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in adolescents that can seriously impair a person's attention function, cognitive processes, and learning ability. Currently, clinicians primarily diagnose patients based on the subjective assessments of the Diagnostic and Statistical Manual of Mental Disorders-5, which can lead to delayed diagnosis of ADHD and even misdiagnosis due to low diagnostic efficiency and lack of well-trained diagnostic experts. Deep learning of electroencephalogram (EEG) signals recorded from ADHD patients could provide an objective and accurate method to assist physicians in clinical diagnosis.Approach. This paper proposes the EEG-Transformer deep learning model, which is based on the attention mechanism in the traditional Transformer model, and can perform feature extraction and signal classification processing for the characteristics of EEG signals. A comprehensive comparison was made between the proposed transformer model and three existing convolutional neural network models.Main results. The results showed that the proposed EEG-Transformer model achieved an average accuracy of 95.85% and an average AUC value of 0.9926 with the fastest convergence speed, outperforming the other three models. The function and relationship of each module of the model are studied by ablation experiments. The model with optimal performance was identified by the optimization experiment.Significance. The EEG-Transformer model proposed in this paper can be used as an auxiliary tool for clinical diagnosis of ADHD, and at the same time provides a basic model for transferable learning in the field of EEG signal classification.

Brain-derived extracellular vesicles as serologic markers of brain injury following cardiac arrest: A pilot feasibility study.

AIM: Assessment of neurologic injury within the immediate hours following out-of-hospital cardiac arrest (OHCA) resuscitation remains a major clinical challenge. Extracellular vesicles (EVs), small bodies derived from cytosolic contents during injury, may provide the opportunity for "liquid biopsy" within hours following resuscitation, as they contain proteins and RNA linked to cell type of origin. We evaluated whether micro-RNA (miRNA) from serologic EVs were associated with post-arrest neurologic outcome. METHODS: We obtained serial blood samples in an OHCA cohort. Using novel microfluidic techniques to isolate EVs based on EV surface marker GluR2 (present on excitatory neuronal dendrites enriched in hippocampal tissue), we employed reverse transcription quantitative polymerase chain reaction (RT-qPCR) methods to measure a panel of miRNAs and tested association with dichotomized modified Rankin Score (mRS) at discharge. RESULTS: EVs were assessed in 27 post-arrest patients between 7/3/2019 and 7/21/2022; 9 patients experienced good outcomes. Several miRNA species including miR-124 were statistically associated with mRS at discharge when measured within 6 hours of resuscitation (AUC = 0.84 for miR-124, p < 0.05). In a Kendall ranked correlation analysis, miRNA associations with outcome were not strongly correlated with standard serologic marker measurements, or amongst themselves, suggesting that miRNA provide distinct information from common protein biomarkers. CONCLUSIONS: This study explores the associations between miRNAs from neuron-derived EVs (NDEs) and circulating protein biomarkers within 6 hours with neurologic outcome, suggesting a panel of very early biomarker may be useful during clinical care. Future work will be required to test larger cohorts with a broader panel of miRNA species.

Multi-omics Deep-learning Prediction of Homologous Recombination Deficiency-like Phenotype Improved Risk Stratification and Guided Therapeutic Decisions in Gynecological Cancers.

Homologous recombination deficiency (HRD) is a well-recognized important biomarker in determining the clinical benefits of platinum-based chemotherapy and PARP inhibitor therapy for patients diagnosed with gynecologic cancers. Accurate prediction of HRD phenotype remains challenging. Here, we proposed a novel Multi-Omics integrative Deep-learning framework named MODeepHRD for detecting HRD-positive phenotype. MODeepHRD utilizes a convolutional attention autoencoder that effectively leverages omics-specific and cross-omics complementary knowledge learning. We trained MODeepHRD on 351 ovarian cancer (OV) patients using transcriptomic, DNA methylation and mutation data, and validated it in 2133 OV samples of 22 datasets. The predicted HRD-positive tumors were significantly associated with improved survival (HR = 0.68; 95% CI, 0.60-0.77; log-rank p < 0.001 for meta-cohort; HR = 0.5; 95% CI, 0.29-0.86; log-rank p = 0.01 for ICGC-OV cohort) and higher response to platinum-based chemotherapy compared to predicted HRD-negative tumors. The translational potential of MODeepHRDs was further validated in multicenter breast and endometrial cancer cohorts. Furthermore, MODeepHRD outperforms conventional machine-learning methods and other similar task approaches. In conclusion, our study demonstrates the promising value of deep learning as a solution for HRD testing in the clinical setting. MODeepHRD holds potential clinical applicability in guiding patient risk stratification and therapeutic decisions, providing valuable insights for precision oncology and personalized treatment strategies.

Multi-dimensional characterization of immunological profiles in small cell lung cancer uncovers clinically relevant immune subtypes with distinct prognoses and therapeutic vulnerabilities.

Small-cell lung cancer (SCLC) is generally considered a 'homogenous' disease, with little documented inter-tumor heterogeneity in treatment guidance or prognosis evaluation. The precise identification of clinically relevant molecular subtypes remains incomplete and their translation into clinical practice is limited. In this retrospective cohort study, we comprehensively characterized the immune microenvironment in SCLC by integrating transcriptional and protein profiling of formalin-fixation-and-paraffin-embedded (FFPE) samples from 29 patients. We identified two distinct disease subtypes: immune-enriched (IE-subtype) and immune-deprived (ID-subtype), displaying heterogeneity in immunological, biological, and clinical features. The IE-subtype was characterized by abundant immune infiltrate and elevated levels of interferon-alpha/gamma (IFNα/IFNγ) and inflammatory response, while the ID-subtype featured a complete lack of immune infiltration and a more proliferative phenotype. These two immune subtypes are associated with clinical benefits in SCLC patients treated with adjuvant therapy, with the IE-subtype exhibiting a more favorable response leading to improved survival and reduced disease recurrence risk. Additionally, we identified and validated a personalized prognosticator of immunophenotyping, the CCL5/CXCL9 chemokine index (CCI), using machine learning. The CCI demonstrated superior predictive abilities for prognosis and clinical benefits in SCLC patients, validated in our institute immunohistochemistry cohort and multicenter bulk transcriptomic data cohorts. In conclusion, our study provides a comprehensive and multi-dimensional characterization of the immune architecture of SCLC using clinical FFPE samples and proposes a new immune subtyping conceptual framework enabling risk stratification and the appropriate selection of individualized therapy.

Pharmaco-proteogenomic characterization of liver cancer organoids for precision oncology.

Organoid models have the potential to recapitulate the biological and pharmacotypic features of parental tumors. Nevertheless, integrative pharmaco-proteogenomics analysis for drug response features and biomarker investigation for precision therapy of patients with liver cancer are still lacking. We established a patient-derived liver cancer organoid biobank (LICOB) that comprehensively represents the histological and molecular characteristics of various liver cancer types as determined by multiomics profiling, including genomic, epigenomic, transcriptomic, and proteomic analysis. Proteogenomic profiling of LICOB identified proliferative and metabolic organoid subtypes linked to patient prognosis. High-throughput drug screening revealed distinct response patterns of each subtype that were associated with specific multiomics signatures. Through integrative analyses of LICOB pharmaco-proteogenomics data, we identified the molecular features associated with drug responses and predicted potential drug combinations for personalized patient treatment. The synergistic inhibition effect of mTOR inhibitor temsirolimus and the multitargeted tyrosine kinase inhibitor lenvatinib was validated in organoids and patient-derived xenografts models. We also provide a user-friendly web portal to help serve the biomedical research community. Our study is a rich resource for investigation of liver cancer biology and pharmacological dependencies and may help enable functional precision medicine.

Novel endoscopic management of gastroenterological anastomosis leakage by injecting gel-forming solutions: an experimental animal study.

BACKGROUND: Anastomotic leakage (AL) after gastrointestinal surgery remains a challenging complication that requires surgical or non-surgical treatment. Although various therapeutic endoscopic techniques are available, no definitive interventions exist. We developed a therapeutic endoscopic submucosal injection method using novel gel-forming mixed solutions to close AL and evaluated the elasticity of the developed hydrogel. The safety and efficacy of the injection method were explored in porcine AL models. METHODS: We developed a novel gel-forming solution, and the formed gel lasted approximately one week within the gastrointestinal wall. An indentation test evaluated the elasticity of the novel hydrogel. After the confirmation of AL on porcine anterior gastric walls, sodium alginate was endoscopically injected into the submucosal layer around the leakage site circularly, followed by a calcium lactate/chitosan-based solution. After that, the outcomes data were collected, and histopathological effectiveness was evaluated. RESULTS: The increased sodium alginate elasticity with the addition of calcium lactate/chitosan-based solution facilitated long-lasting gel formation. Four pigs with AL underwent this intervention consecutively. Each endoscopic injection was completed in less than 5 min. No significant complications were observed for 3 weeks after the intervention. All AL sites were macroscopically healed. Histopathologic findings at 3 weeks showed that the wall defect was filled with collagen fibers that had grown around the site of the muscle layer tear. No tissue necrosis was observed. CONCLUSION: This preclinical study demonstrated that the therapeutic injection method for gastroenterological AL using gel-forming solutions could be an alternative endoscopic treatment, especially in patients with severe conditions or comorbidities. The optimal target of this treatment is small size and early AL without poor blood flow or intense hypertrophic scar lesions.

Recent prevalence and characteristics of patients with hepatitis delta virus in Hokkaido, Japan.

AIM: Although hepatitis delta virus (HDV) coinfection with hepatitis B virus (HBV) is a global health concern, the global prevalence of HDV infections remains unknown due to insufficient data in many countries. In Japan, HDV prevalence has not been updated for over 20 years. We aimed to investigate the recent prevalence of HDV infections in Japan. METHODS: We screened 1264 consecutive patients with HBV infection at Hokkaido University Hospital between 2006 and 2022. Patients' serums were preserved and subsequently tested for HDV antibody (immunoglobulin-G). Available clinical information was collected and analyzed. We compared the changes in liver fibrosis using the Fibrosis-4 (FIB-4) index between propensity-matched patients with and without the evidence of anti-HDV antibodies and corrected for baseline FIB-4 index, nucleoside/nucleotide analog treatment, alcohol intake, sex, HIV coinfection, liver cirrhosis, and age. RESULTS: After excluding patients without properly stored serums and those lacking appropriate clinical information, 601 patients with HBV were included. Of these, 1.7% of patients had detectable anti-HDV antibodies. Patients with anti-HDV antibody serum positivity had a significantly higher prevalence of liver cirrhosis, significantly lower prothrombin time, and a higher prevalence of HIV coinfection than those who demonstrated serum anti-HDV antibody negativity. A propensity-matched longitudinal analysis revealed that liver fibrosis (FIB-4 index) progressed more rapidly in patients with positive results for anti-HDV antibody tests. CONCLUSIONS: The recent prevalence of HDV infections in Japanese patients with HBV was 1.7% (10/601). These patients experienced rapid liver fibrosis progression, highlighting the importance of routine HDV testing.

DeepDrRVO: A GAN-auxiliary two-step masked transformer framework benefits early recognition and differential diagnosis of retinal vascular occlusion from color fundus photographs.

Retinal vascular occlusion (RVO) are common causes of visual impairment. Accurate recognition and differential diagnosis of RVO are unmet medical needs for determining appropriate treatments and health care to properly manage the ocular condition and minimize the damaging effects. To leverage deep learning as a potential solution to detect RVO reliably, we developed a deep learning model on color fundus photographs (CFPs) using a two-step masked SwinTransformer with a Few-Sample Generator (FSG)-auxiliary training framework (called DeepDrRVO) for early and differential RVO diagnosis. The DeepDrRVO was trained on the training set from the in-house cohort and achieved consistently high performance in early recognition and differential diagnosis of RVO in the validation set from the in-house cohort with an accuracy of 86.3%, and other three independent multi-center cohorts with the accuracy of 92.6%, 90.8%, and 100%. Further comparative analysis showed that the proposed DeepDrRVO outperforms conventional state-of-the-art classification models, such as ResNet18, ResNet50d, MobileNetv3, and EfficientNetb1. These results highlight the potential benefits of the deep learning model in automatic early RVO detection and differential diagnosis for improving clinical outcomes and providing insights into diagnosing other ocular diseases with a few-shot learning challenge. The DeepDrRVO is publicly available on https://github.com/ZhouSunLab-Workshops/DeepDrRVO.

Fast-Convergence Reinforcement Learning for Routing in LEO Satellite Networks.

Fast convergence routing is a critical issue for Low Earth Orbit (LEO) constellation networks because these networks have dynamic topology changes, and transmission requirements can vary over time. However, most of the previous research has focused on the Open Shortest Path First (OSPF) routing algorithm, which is not well-suited to handle the frequent changes in the link state of the LEO satellite network. In this regard, we propose a Fast-Convergence Reinforcement Learning Satellite Routing Algorithm (FRL-SR) for LEO satellite networks, where the satellite can quickly obtain the network link status and adjust its routing strategy accordingly. In FRL-SR, each satellite node is considered an agent, and the agent selects the appropriate port for packet forwarding based on its routing policy. Whenever the satellite network state changes, the agent sends "hello" packets to the neighboring nodes to update their routing policy. Compared to traditional reinforcement learning algorithms, FRL-SR can perceive network information faster and converge faster. Additionally, FRL-SR can mask the dynamics of the satellite network topology and adaptively adjust the forwarding strategy based on the link state. The experimental results demonstrate that the proposed FRL-SR algorithm outperforms the Dijkstra algorithm in the performance of average delay, packet arriving ratio, and network load balance.

Potential Correlation between Changes in Serum FGF21 Levels and Lenvatinib-Induced Appetite Loss in Patients with Unresectable Hepatocellular Carcinoma.

Lenvatinib, used for unresectable hepatocellular carcinoma (HCC), causes appetite loss, but the underlying mechanisms, clinical impact, and predictive factors have been unclear. The endocrine factor FGF21 modulates appetite and is involved in cachexia. We evaluated the association between FGF21 level changes during lenvatinib treatment for unresectable HCC and appetite loss. Sixty-three eligible unresectable HCC patients who started lenvatinib treatment between 2018 and 2021 were included. We analyzed FGF21 levels at baseline; 1, 2, and 4 weeks after lenvatinib initiation, and before the onset of appetite loss. Grade ≥ 2 lenvatinib-induced appetite loss led to liver functional reserve deterioration at disease progression and a poor prognosis. Baseline characteristics and serum FGF21 levels were similar between patients with and without appetite loss. However, the serum FGF21 change rate increased significantly at 4 weeks post-lenvatinib initiation in patients with grade ≥ 2 appetite loss, as compared to those without appetite loss. Similar significant increases in the serum FGF21 level change rate were observed prior to grade ≥ 2 appetite loss onset. This suggests that changes in FGF21 levels can be used to predict patients with a greater risk of marked appetite loss and provides insights into the mechanisms underlying lenvatinib-induced appetite loss in patients with HCC.