Hepatitis E virus infection during pregnancy: Advances in animal models.

Hepatitis E virus (HEV) is one of the major pathogens causing acute viral hepatitis worldwide, which usually causes acute self-limited diseases in general individuals. However, it can lead to high mortality and adverse pregnancy outcomes in pregnant women. Due to the lack of effective and stable cell culture models for HEV, the establishment of suitable animal models for HEV infection during pregnancy is necessary. An electronic search of the relevant database was conducted to identify eligible articles. Main animal models for the study of HEV infection during pregnancy include rabbits, swine, nonhuman primates and Mongolian gerbils. These animal models have been used to study the prevention, treatment and possible mechanisms of HEV infection during pregnancy. Studies using these animal models have investigated the potential pathogenesis of HEV infection during pregnancy. It has been found that immune mechanism (changes in the CD4/CD8 ratio and cytokines), hormonal changes (increase in pregnancy-related hormones) and viral factors (different genotypes and genome structures) can lead to HEV-related adverse pregnancy outcomes in animal models. In this review, we aimed to comprehensively present the characteristics of different animal models and the pathogenesis of HEV-related adverse pregnancy outcomes.

Transgenic poplar (Populus davidiana×P. bolleana Loucne) expressing dsRNA of insect chitinase gene: lines identification and resistance assay.

Poplar is a valuable tree species that is distributed all over the world. However, many insect pests infest poplar trees and have caused significant damage. To control poplar pests, we transformed a poplar species, Populus davidiana × P. bolleana Loucne, with the dsRNA of the chitinase gene of a poplar defoliator, Clostera anastomosis (Linnaeus) (Lepidoptera: Notodontidae), employing an Agrobaterium-mediated approach. The transgenic plant has been identified by cloning the T-DNA flanking sequences using TAIL-PCR and quantifying the expression of the dsRNA using qPCR. The toxicity assay of the transgenic poplar lines was carried out by feeding the target insect species (C. anastomosis). The results showed that, in C. anastomosis, the activity of chitinase was significantly decreased, consistent with the expression on mRNA levels, and the larval mortality was significantly increased. These results suggested that the transgenic poplar of dsRNA could be used for pest control.

Identification and verification of four candidate biomarkers for early diagnosis of osteoarthritis by machine learning.

Background: Osteoarthritis (OA) is a common chronic joint disease. This study aimed to investigate possible OA diagnostic biomarkers and to verify their significance in clinical samples. Methods: We exploited three datasets from the Gene Expression Omnibus (GEO) database, serving as the training set. We first determined differentially expressed genes and screened candidate diagnostic biomarkers by applying three machine learning algorithms (Random Forest, Least Absolute Shrinkage and Selection Operator logistic regression, Support Vector Machine-Recursive Feature Elimination). Another GEO dataset was used as the validation set. The test set consisted of RNA-sequenced peripheral blood samples collected from patients and healthy donors. Blood samples and chondrocytes were collected for quantitative real-time PCR to confirm expression levels. Receiver operating characteristic curves were generated for individual and combined biomarkers. Results: In total, 251 DEGs were screened, where B3GALNT1, SCRG1 and ZNF423 were screened by all three algorithms. The area under the curve (AUC) of various biomarkers in our test set did not reach as high as that in public datasets. GRB10 exhibited highest AUC of 0.947 in the training set but 0.691 in our test set, while the favorable combined model comprising B3GALNT1, GRB10, KLF9 and SCRG1 demonstrated an AUC of 0.986 in the training set, 1.000 in the validation set and 0.836 in our test set. Conclusion: We identified a combined model for early diagnosis of OA that includes B3GALNT1, GRB10, KLF9 and SCRG1. This finding offers new avenues for further exploration of mechanisms underlying OA.

Analysis of the efficacy of hyperbaric oxygen therapy for disorders of consciousness: A retrospective cohort study.

OBJECTIVE: To analyze the efficacy and associated factors affecting the prognosis in patients with disturbance of consciousness after hyperbaric oxygen (HBO) treatment. METHODS: A retrospective study was carried out on patients with disorders of consciousness (DOC) receiving HBO treatment from January to January 2022 in the Second Department of Rehabilitation Medicine of the Second Hospital of Hebei Medical University, China. RESULTS: HBO therapy improved the Glasgow Coma Scale (GCS) and Chinese Nanjing Persistent Vegetative State Scale (CNPVSS), as well as the clinical efficacy in patients with DOC. The comparison of GCS and CNPVSS scores in patients with DOC before and after HBO treatment was all statistically significant, with 325 patients (67.1%) showing effective results and 159 patients (32.9%) having unchanged outcomes. Univariate analysis indicated that there were statistically significant differences in age, HBO intervention time, HBO treatment times, pre-treatment GCS score, and etiology and underlying diseases between the good and poor prognoses groups. Multivariate regression analysis showed that HBO intervention time ≤7 days, HBO treatment ＞ times, high GCS score before HBO treatment, and brain trauma were independent influencing factors in achieving a good prognosis for patients with DOC. Low pre-treatment GCS scores were an independent risk factor for a poor prognosis in patients with brain trauma while being male, late HBO intervention time, fewer HBO treatment times, and low pre-treatment GCS scores were independent risk factors for a poor prognosis in patients with DOC after a stroke. Being ≥50 years of age, late HBO intervention time, and low pre-treatment GCS scores were independent risk factors for a poor prognosis in patients with DOC after hypoxic-ischaemic encephalopathy. CONCLUSION: HBO therapy can improve the GCS, CNPVSS scores and clinical efficacy in patients with DOC, and the timing of HBO intervention ≤7 days, times of HBO treatment, high pre-treatment GCS score, and brain trauma were the independent influencing factors of good prognosis in patients with DOC.

Delayed postoperative spontaneous spinal epidural hematoma: Case based review.

INTRODUCTION AND IMPORTANCE: Postoperative spontaneous spinal epidural hematoma (SSEDH) is a rare complication in clinical practice. Despite its rarity, SSEDH is a critical emergency situation associated with neurological deficits, and improper or delayed management may lead to severe consequences. Therefore, surgical operators should familiarize themselves with SSEDH and give it more attention. CASE PRESENTATION: This study describes the case of an elderly woman diagnosed with a left unilateral femoral neck fracture, severe osteoporosis, and multi-segmental vertebral compression fracture. Following artificial femoral head replacement surgery, the patient developed postoperative SSEDH. Subsequently, the patient underwent surgical removal of the posterior epidural hematoma and spinal cord decompression. The postoperative recovery was favorable, with normal muscle strength and tension in both lower limbs. A 4-year follow-up showed no complications. CLINICAL DISCUSSION: The occurrence of SSEDH during the perioperative period of non-spinal surgeries is relatively uncommon. However, SSEDH is a neurosurgical emergency associated with neurological deficits, and prompt surgical intervention is crucial for successful treatment. CONCLUSION: Clinicians should enhance their knowledge of SSEDH and remain vigilant towards this condition. Literature review highlights the significance of factors such as aging in the development of SSEDH following non-spinal surgeries in the perioperative period.

A statistical symptomatic evaluation on SAPHO syndrome from 56 cases of confirmed diagnosis and 352 cases of non-SAPHO involvement.

OBJECTIVE: To report a statistical evaluation of symptomatology based on 56 cases of SAPHO syndrome and 352 non-SAPHO involvement cases, to propose a symptomatic scoring system in consideration of early warning for SAPHO syndrome. METHODS: A cohort comprising 56 subjects diagnosed with SAPHO syndrome was reported, as well as 352 non-SAPHO involvement cases, including their chief complaints, skin manifestations, radiological findings, and laboratory tests. We systematically reviewed previous published five representative huge cohorts from different countries to conclude several specific features of SAPHO by comparing with our case series. The score of each specific index is based on respective incidence and comparison of two cohorts was performed. RESULT: In terms of complaint rates, all subjects of two cohorts suffered from osseous pain, which appeared in the anterior chest wall, spine, and limb which were calculated. In respect to dermatological lesions, SAPHO patients suffered from severe acne, and other patients (82.14%) accompanied with palmoplantar pustulosis. Having received radiological examinations, most SAPHO subjects rather than non-SAPHO involvement cases showed abnormal osteoarticular lesions under CT scanning and more detailed information under whole-body bone scintigraphy. Differences also emerged in elevation of inflammation values and rheumatic markers like HLA-B27. Based on our cases and huge cohorts documented, the early warning standard is set to be 5 scores. CONCLUSIONS: SAPHO syndrome case series with 56 subjects were reported and an accumulative scoring system for the early reminder on SAPHO syndrome was proposed. The threshold of this system is set to be 5 points. Key Points • Fifty-six patients diagnosed by SAPHO syndrome with detailed symptoms and radiological findings were reported. • Comparison was made between the 56 SAPHO patients and 352 non-SAPHO involvement cases. • An accumulative scoring system for the early reminder on SAPHO syndrome was proposed and the threshold of this system is set to be five points.

CellT-Net: A Composite Transformer Method for 2-D Cell Instance Segmentation.

Cell instance segmentation (CIS) via light microscopy and artificial intelligence (AI) is essential to cell and gene therapy-based health care management, which offers the hope of revolutionary health care. An effective CIS method can help clinicians to diagnose neurological disorders and quantify how well these deadly disorders respond to treatment. To address the CIS task challenged by dataset characteristics such as irregular morphology, variation in sizes, cell adhesion, and obscure contours, we propose a novel deep learning model named CellT-Net to actualize effective cell instance segmentation. In particular, the Swin transformer (Swin-T) is used as the basic model to construct the CellT-Net backbone, as the self-attention mechanism can adaptively focus on useful image regions while suppressing irrelevant background information. Moreover, CellT-Net incorporating Swin-T constructs a hierarchical representation and generates multi-scale feature maps that are suitable for detecting and segmenting cells at different scales. A novel composite style named cross-level composition (CLC) is proposed to build composite connections between identical Swin-T models in the CellT-Net backbone and generate more representational features. The earth mover's distance (EMD) loss and binary cross entropy loss are used to train CellT-Net and actualize the precise segmentation of overlapped cells. The LiveCELL and Sartorius datasets are utilized to validate the model effectiveness, and the results demonstrate that CellT-Net can achieve better model performance for dealing with the challenges arising from the characteristics of cell datasets than state-of-the-art models.

Construction of a hepatocellular carcinoma high-risk population rating scale and independent predictors' assessment.

BACKGROUND: With increasing mortality and incidence, hepatocellular carcinoma (HCC) has become a major public health problem. The early diagnosis of HCC can improve its prognosis. The aim of this study was to identify potential risk factors related to HCC development and to establish a high-risk population rating scale. METHODS: A total of 853 patients with chronic hepatitis B (CHB) were enrolled in this study, including 403 patients with HCC as the case group and others as the control group. Their demographic and clinical characteristics were compared and the independent risk factors for HCC were assessed. Then, the optimal cutoff levels of these factors were analyzed by the receiver operating characteristic (ROC) method. A high-risk population rating scale was constructed based on the factors and then evaluated in the modeling population. RESULTS: The factors that presented statistically significant differences between the two groups included age, smoking, alcohol abuse, body mass index, triglyceride, high‒density lipoprotein cholesterol, aspartate transaminase, alanine transaminase, fasting plasma glucose, creatinine and uric acid. The ROC curve showed that the cutoff score for the HCC high risk population was 5 (AUC=0.74, P<0.001) and the Hosmer‒Lemeshow analysis showed that the fitting effect of this rating scale was good (P = 0.294). CONCLUSIONS: The integration of these factors can contribute to a prognostic score for the risk of HCC development, which offered certain clinical practicability.

Development and evaluation of a CRISPR-Cas13a system-based diagnostic for hepatitis E virus.

OBJECTIVES: Hepatitis E virus (HEV) is the leading cause of acute viral hepatitis worldwide. HEV RNA detection is the gold standard for HEV infection diagnosis and PCR methods are commonly used but are usually time-consuming and expensive, resulting in low detection efficiency and coverage, especially in low-income areas. Here, we developed a simpler and more accessible HEV RNA detection method based on CRISPR-Cas13a system. METHODS: A total of 265 samples of different types and sources, including 89 positive samples and 176 negative samples, were enrolled for evaluations. The sensitivity and specificity of the Cas13a-crRNA detection system were evaluated. The World Health Organization reference panel for HEV genotypes was used to evaluate the capability for detecting different HEV genotypes. The validity of the assay was compared with RT-qPCR. RESULTS: The 95 % limits of detection (LOD) of Cas13a-crRNA-based fluorescence assay and strip assay were 12.5 and 200 IU/mL, respectively. They did not show cross-reactivity with samples positive for hepatitis A virus, hepatitis B virus, hepatitis C virus, coxsackievirus A16, rotavirus, enterovirus 71, norovirus or enteropathic Escherichia coli. Different HEV genotypes (HEV1-4) can be detected by the assay. Compared to RT-qPCR, the positive predictive agreements of Cas13a-crRNA-based fluorescence and strip assay were 98.9 % (95 % CI: 93.9-99.8 %) and 91.0 % (95 % CI: 83.3-95.4 %), respectively. The negative predictive agreements were both 100 % (95 % CI: 97.8-100 %). CONCLUSIONS: In conclusion, we established a rapid and convenient HEV RNA detection method with good sensitivity and specificity based on CRISPR-Cas13a system, providing a new option for HEV infection diagnosis.

Establishment and Characterization of the First Chinese National Standard for Nucleic Acid Amplification Technology Assays for Hepatitis E Virus Nucleic Acid Detection.

The detection of hepatitis E virus (HEV) RNA is the gold standard for HEV infection diagnosis. In order to address the quality control requirements for HEV RNA detection kits within China, we aimed to establish the first Chinese national standard for HEV RNA detection through a collaborative study. The candidate standard was quantified using digital PCR (dPCR). A total of five laboratories were invited to determine the estimated mean value of this national standard relative to the World Health Organization International Standard (WHO IS). Additionally, four commercial kits were used to assess the applicability of the candidate standard. The stability was determined by freeze-thaw cycles and storage at 37 °C, 25 °C and 4 °C. The estimated mean value of this national standard relative to the WHO IS was 5.67 log10 IU/mL. Two out of the four commercial kits can detect as low as the estimated limit of detection (LOD). The degradation rates of samples in the stability study ranged from 4% to 19%. In conclusion, we have established the first Chinese national standard for HEV nucleic acid detection against WHO IS, which can be employed to evaluate the quality of HEV RNA detection kits.

C2BNet: A Deep Learning Architecture with Coupled Composite Backbone for Parasitic EGG Detection in Microscopic Images.

Internet of Medical Things (IoMT) enabled by artificial intelligence (AI) technologies can facilitate automatic diagnosis and management of chronic diseases (e.g., intestinal parasitic infection) based on two-dimensional (2D) microscopic images. To improve the model performance of object detection challenged by microscopic image characteristics (e.g., focus failure, motion blur, and whether zoomed or not), we propose Coupled Composite Backbone Network (C2BNet) to execute the parasitic egg detection task using 2D microscopic images. In particular, the C2BNet backbone adopts a two-path structure-based backbone and leverages model heterogeneity to learn object features from different perspectives. A novel feature composition style is proposed to flow the feature within the coupled composite backbone, and ensure mutual enhancement of feature representation ability among the different paths of the backbone. To further improve the accuracy of the detection results, we propose Multiscale Weighted Box Fusion (WBF) to fuse the location and confidence scores of all bounding boxes predicted from the multiscale feature maps, and iteratively refine the box coordinates to form the final prediction. Experimental results on Chula-ParasiteEgg-11 dataset demonstrate that the C2BNet not only performs satisfactorily compared with state-of-the-art methods, but also can focus more on learning detailed morphology features and abundant semantic features, resulting in more precise detection for parasitic eggs located in the 2D microscopic image.

Pentraxin 3 deficiency ameliorates the severity of osteoarthritis and alleviates inflammation.

BACKGROUND: Osteoarthritis is one of the most common degenerative joint disorders, characterized by articular cartilage breakdown, synovitis, osteophytes generation and subchondral bone sclerosis. Pentraxin 3 (PTX3) is a long pentraxin protein, secreted by immune cells, and PTX3 is identified to play a critical role in inflammation and macrophage polarization. However, the underlying mechanism of PTX3 in osteoarthritis under the circumstance of Ptx3-knockout (KO) mice model is still unknown. METHODS: Murine destabilization of the medial meniscus (DMM) OA model was created in Ptx3-knockout (KO) and wildtype mice, respectively. The degenerative status of cartilage was detected by Safranin O, H&E staining, immunohistochemistry (IHC) and micro-CT. OARSI scoring was employed to assess the proteoglycan of cartilage. Serum inflammatory cytokines were examined by ELISA and systematic macrophage polarization in spleen was analyzed by flow cytometry. RESULTS: Safranin O and H&E staining confirmed that the joint cartilage was mostly with reduced degeneration in both the senior KO mice and the DMM model generated from the KO mice, compared to the WT group. This is also supported by micro-CT examination and OARSI scoring. Immunohistochemistry illustrated an up-regulation of Aggrecan and Collagen 2 and down-regulation of ADAMTS-5 and MMP13 in KO mice in comparison with the WT mice. ELISA indicated a dramatical decrease in the serum levels of TNF-α and IL-6 in KO mice. Polarization of M2-like macrophages was observed in the KO group. CONCLUSION: Pentraxin 3 deficiency significantly ameliorated the severity of osteoarthritis by preventing cartilage degeneration and alleviated systematic inflammation by inducing M2 polarization.

ARA-net: an attention-aware retinal atrophy segmentation network coping with fundus images.

Background: Accurately detecting and segmenting areas of retinal atrophy are paramount for early medical intervention in pathological myopia (PM). However, segmenting retinal atrophic areas based on a two-dimensional (2D) fundus image poses several challenges, such as blurred boundaries, irregular shapes, and size variation. To overcome these challenges, we have proposed an attention-aware retinal atrophy segmentation network (ARA-Net) to segment retinal atrophy areas from the 2D fundus image. Methods: In particular, the ARA-Net adopts a similar strategy as UNet to perform the area segmentation. Skip self-attention connection (SSA) block, comprising a shortcut and a parallel polarized self-attention (PPSA) block, has been proposed to deal with the challenges of blurred boundaries and irregular shapes of the retinal atrophic region. Further, we have proposed a multi-scale feature flow (MSFF) to challenge the size variation. We have added the flow between the SSA connection blocks, allowing for capturing considerable semantic information to detect retinal atrophy in various area sizes. Results: The proposed method has been validated on the Pathological Myopia (PALM) dataset. Experimental results demonstrate that our method yields a high dice coefficient (DICE) of 84.26%, Jaccard index (JAC) of 72.80%, and F1-score of 84.57%, which outperforms other methods significantly. Conclusion: Our results have demonstrated that ARA-Net is an effective and efficient approach for retinal atrophic area segmentation in PM.

GDNet-EEG: An attention-aware deep neural network based on group depth-wise convolution for SSVEP stimulation frequency recognition.

Background: Steady state visually evoked potentials (SSVEPs) based early glaucoma diagnosis requires effective data processing (e.g., deep learning) to provide accurate stimulation frequency recognition. Thus, we propose a group depth-wise convolutional neural network (GDNet-EEG), a novel electroencephalography (EEG)-oriented deep learning model tailored to learn regional characteristics and network characteristics of EEG-based brain activity to perform SSVEPs-based stimulation frequency recognition. Method: Group depth-wise convolution is proposed to extract temporal and spectral features from the EEG signal of each brain region and represent regional characteristics as diverse as possible. Furthermore, EEG attention consisting of EEG channel-wise attention and specialized network-wise attention is designed to identify essential brain regions and form significant feature maps as specialized brain functional networks. Two publicly SSVEPs datasets (large-scale benchmark and BETA dataset) and their combined dataset are utilized to validate the classification performance of our model. Results: Based on the input sample with a signal length of 1 s, the GDNet-EEG model achieves the average classification accuracies of 84.11, 85.93, and 93.35% on the benchmark, BETA, and combination datasets, respectively. Compared with the average classification accuracies achieved by comparison baselines, the average classification accuracies of the GDNet-EEG trained on a combination dataset increased from 1.96 to 18.2%. Conclusion: Our approach can be potentially suitable for providing accurate SSVEP stimulation frequency recognition and being used in early glaucoma diagnosis.

Using landscape genomics to assess local adaptation and genomic vulnerability of a perennial herb Tetrastigma hemsleyanum (Vitaceae) in subtropical China.

Understanding adaptive genetic variation of plant populations and their vulnerabilities to climate change are critical to preserve biodiversity and subsequent management interventions. To this end, landscape genomics may represent a cost-efficient approach for investigating molecular signatures underlying local adaptation. Tetrastigma hemsleyanum is, in its native habitat, a widespread perennial herb of warm-temperate evergreen forest in subtropical China. Its ecological and medicinal values constitute a significant revenue for local human populations and ecosystem. Using 30,252 single nucleotide polymorphisms (SNPs) derived from reduced-representation genome sequencing in 156 samples from 24 sites, we conducted a landscape genomics study of the T. hemsleyanum to elucidate its genomic variation across multiple climate gradients and genomic vulnerability to future climate change. Multivariate methods identified that climatic variation explained more genomic variation than that of geographical distance, which implied that local adaptation to heterogeneous environment might represent an important source of genomic variation. Among these climate variables, winter precipitation was the strongest predictor of the contemporary genetic structure. F ST outlier tests and environment association analysis totally identified 275 candidate adaptive SNPs along the genetic and environmental gradients. SNP annotations of these putatively adaptive loci uncovered gene functions associated with modulating flowering time and regulating plant response to abiotic stresses, which have implications for breeding and other special agricultural aims on the basis of these selection signatures. Critically, modelling revealed that the high genomic vulnerability of our focal species via a mismatch between current and future genotype-environment relationships located in central-northern region of the T. hemsleyanum's range, where populations require proactive management efforts such as assistant adaptation to cope with ongoing climate change. Taken together, our results provide robust evidence of local climate adaption for T. hemsleyanum and further deepen our understanding of adaptation basis of herbs in subtropical China.

EEGformer: A transformer-based brain activity classification method using EEG signal.

Background: The effective analysis methods for steady-state visual evoked potential (SSVEP) signals are critical in supporting an early diagnosis of glaucoma. Most efforts focused on adopting existing techniques to the SSVEPs-based brain-computer interface (BCI) task rather than proposing new ones specifically suited to the domain. Method: Given that electroencephalogram (EEG) signals possess temporal, regional, and synchronous characteristics of brain activity, we proposed a transformer-based EEG analysis model known as EEGformer to capture the EEG characteristics in a unified manner. We adopted a one-dimensional convolution neural network (1DCNN) to automatically extract EEG-channel-wise features. The output was fed into the EEGformer, which is sequentially constructed using three components: regional, synchronous, and temporal transformers. In addition to using a large benchmark database (BETA) toward SSVEP-BCI application to validate model performance, we compared the EEGformer to current state-of-the-art deep learning models using two EEG datasets, which are obtained from our previous study: SJTU emotion EEG dataset (SEED) and a depressive EEG database (DepEEG). Results: The experimental results show that the EEGformer achieves the best classification performance across the three EEG datasets, indicating that the rationality of our model architecture and learning EEG characteristics in a unified manner can improve model classification performance. Conclusion: EEGformer generalizes well to different EEG datasets, demonstrating our approach can be potentially suitable for providing accurate brain activity classification and being used in different application scenarios, such as SSVEP-based early glaucoma diagnosis, emotion recognition and depression discrimination.

Preliminary investigation of hepatitis E virus detection by a recombinase polymerase amplification assay combined with a lateral flow strip.

Hepatitis E virus (HEV) is a zoonotic pathogen that is a significant public health problem. Detecting HEV relies mainly on conventional PCR, which is time-consuming and requires sophisticated instruments and trained staff. We aimed to establish a reverse-transcription (RT)-recombinase polymerase amplification (RPA) assay (RT-RPA) combined with a lateral flow strip (LFS; RT-RPA-LFS) to rapidly detect HEV RNA in human and rabbit samples. With the optimal reaction conditions (37°C for 30 min), our assay detected as few as 1.0 × 102 copies/mL of HEV and showed no cross-reactivity with other hepatitis viruses. We tested 28 human samples (4 fecal and 24 serum samples) and 360 rabbit samples (180 fecal and 180 serum samples) with our RT-RPA-LFS assay and compared our assay to an RT-qPCR method. There was no significant difference (p > 0.05) in the test results between the 2 assays. Our RT-RPA-LFS assay detected both HEV3 and HEV4 genotypes. Our rapid, sensitive, and specific RT-RPA-LFS assay for the detection of HEV may provide a useful detection tool for limited-resource areas.

Type 2 diabetes mellitus characteristics affect hepatocellular carcinoma development in chronic hepatitis B patients with cirrhosis.

BACKGROUND: Type 2 diabetes mellitus (T2DM) has been shown to be correlated with hepatocellular carcinoma (HCC) development. However, further investigation is needed to understand how T2DM characteristics affect the prognosis of chronic hepatitis B (CHB) patients. AIM: To assess the effect of T2DM on CHB patients with cirrhosis and to determine the risk factors for HCC development. METHODS: Among the 412 CHB patients with cirrhosis enrolled in this study, there were 196 with T2DM. The patients in the T2DM group were compared to the remaining 216 patients without T2DM (non-T2DM group). Clinical characteristics and outcomes of the two groups were reviewed and compared. RESULTS: T2DM was significantly related to hepatocarcinogenesis in this study (P = 0.002). The presence of T2DM, being male, alcohol abuse status, alpha-fetoprotein > 20 ng/mL, and hepatitis B surface antigen > 2.0 log IU/mL were identified to be risk factors for HCC development in the multivariate analysis. T2DM duration of more than 5 years and treatment with diet control or insulin ± sulfonylurea significantly increased the risk of hepatocarcinogenesis. CONCLUSION: T2DM and its characteristics increase the risk of HCC in CHB patients with cirrhosis. The importance of diabetic control should be emphasized for these patients.

MyopiaDETR: End-to-end pathological myopia detection based on transformer using 2D fundus images.

Background: Automated diagnosis of various retinal diseases based on fundus images can serve as an important clinical decision aid for curing vision loss. However, developing such an automated diagnostic solution is challenged by the characteristics of lesion area in 2D fundus images, such as morphology irregularity, imaging angle, and insufficient data. Methods: To overcome those challenges, we propose a novel deep learning model named MyopiaDETR to detect the lesion area of normal myopia (NM), high myopia (HM) and pathological myopia (PM) using 2D fundus images provided by the iChallenge-PM dataset. To solve the challenge of morphology irregularity, we present a novel attentional FPN architecture and generate multi-scale feature maps to a traditional Detection Transformer (DETR) for detecting irregular lesion more accurate. Then, we choose the DETR structure to view the lesion from the perspective of set prediction and capture better global information. Several data augmentation methods are used on the iChallenge-PM dataset to solve the challenge of insufficient data. Results: The experimental results demonstrate that our model achieves excellent localization and classification performance on the iChallenge-PM dataset, reaching AP50 of 86.32%. Conclusion: Our model is effective to detect lesion areas in 2D fundus images. The model not only achieves a significant improvement in capturing small objects, but also a significant improvement in convergence speed during training.

Whole Genome Resequencing Identifies Single-Nucleotide Polymorphism Markers of Growth and Reproduction Traits in Zhedong and Zi Crossbred Geese.

The broodiness traits of domestic geese are a bottleneck that prevents the rapid development of the goose industry. To reduce the broodiness of the Zhedong goose and thus improve it, this study hybridized it with the Zi goose, which has almost no broody behavior. Genome resequencing was performed for the purebred Zhedong goose, as well as the F2 and F3 hybrids. The results showed that the F1 hybrids displayed significant heterosis in growth traits, and their body weight was significantly greater than those of the other groups. The F2 hybrids showed significant heterosis in egg-laying traits, and the number of eggs laid was significantly greater than those of the other groups. A total of 7,979,421 single-nucleotide polymorphisms (SNPs) were obtained, and three SNPs were screened. Molecular docking results showed that SNP11 located in the gene NUDT9 altered the structure and affinity of the binding pocket. The results suggested that SNP11 is an SNP related to goose broodiness. In the future, we will use the cage breeding method to sample the same half-sib families to accurately identify SNP markers of growth and reproductive traits.