Global temporal trends and projections of acute hepatitis E incidence among women of childbearing age: Age-period-cohort analysis 2021.

BACKGROUND & AIMS: Acute hepatitis E (AHE) poses a significant threat to global public health, particularly among women of childbearing age (WCBA), who are at heightened risk for severe pregnancy-related complications. This study aimed to delineate the temporal trends and project future incidence of AHE in WCBA, providing insights crucial for targeted prevention and control strategies. METHODS: Data on AHE incidence from the Global Health data 2021. The age-period-cohort (APC) model was applied to analyze trends across different age groups, periods, and birth cohorts, and the Bayesian APC model was utilized for forecasting future epidemiological trajectories. RESULTS: Globally, AHE incidence numbers among WCBA rose from 2,831,075 in 1992 to 3,420,786 in 2021, while the age-standardized incidence rate (ASIR) declined from 194.66 to 179.54 per 100,000 with a global net drift of -0.28%. However, high SDI regions showed a contrasting trend with a positive net drift of 0.02%. The age effect was consistent across SDI regions and globally, showing a decrease with advancing age, while unfavorable period and cohort effects were exhibited in high-SDI region. At the national level, locations exhibited varying trends of change. The BAPC model predicted a total of 3,759,384 AHE global cases in WCBA by 2030, with an expected mild increase in the ASIR. The outlook for the management and containment of AHE is grim in certain countries, including India. CONCLUSIONS: The study revealed a complex epidemiological landscape of AHE in WCBA, with increasing global incidence numbers juxtaposed against a declining ASIR. The AHE burden by 2030 remain severe among WCBA. Young WCBA and high SDI region merit particular attention. The findings underscore the need for region-specific strategies to curb the projected rise in AHE incidence and align with the 2030 WHO goals.

Liver histological changes in untreated chronic hepatitis B patients in indeterminate phase.

BACKGROUND: Studies with large size samples on the liver histological changes of indeterminate phase chronic hepatitis B (CHB) patients were not previously conducted. AIM: To assess the liver histological changes in the indeterminate phase CHB patients using liver biopsy. METHODS: The clinical and laboratory data of 1532 untreated CHB patients were collected, and all patients had least once liver biopsy from January 2015 to December 2021. The significant differences among different phases of CHB infection were compared with t-test, and the risk factors of significant liver histological changes were analyzed by the multivariate logistic regression analysis. RESULTS: Among 1532 untreated CHB patients, 814 (53.13%) patients were in the indeterminate phase. Significant liver histological changes (defined as biopsy score ≥ G2 and/or ≥ S2) were found in 488/814 (59.95%) CHB patients in the indeterminate phase. Significant liver histological changes were significant differences among different age, platelets (PLTs), and alanine aminotransferase (ALT) subgroup in indeterminate patient. Multivariate logistic regression analysis indicated that age ≥ 40 years old [adjust odd risk (aOR), 1.44; 95% confidence interval (CI): 1.06-1.97; P = 0.02], PLTs ≤ 150 × 109/L (aOR, 2.99; 95%CI: 1.85-4.83; P < 0.0001), and ALT ≥ upper limits of normal (aOR, 1.48; 95%CI: 1.08, 2.05, P = 0.0163) were independent risk factors for significant liver histological changes in CHB patients in the indeterminate phase. CONCLUSION: Our results suggested that significant liver histological changes were not rare among the untreated CHB patients in indeterminate phase, and additional strategies are urgently required for the management of these patients.

A transformer-guided cross-modality adaptive feature fusion framework for esophageal gross tumor volume segmentation.

BACKGROUND AND OBJECTIVE: Accurate segmentation of esophageal gross tumor volume (GTV) indirectly enhances the efficacy of radiotherapy for patients with esophagus cancer. In this domain, learning-based methods have been employed to fuse cross-modality positron emission tomography (PET) and computed tomography (CT) images, aiming to improve segmentation accuracy. This fusion is essential as it combines functional metabolic information from PET with anatomical information from CT, providing complementary information. While the existing three-dimensional (3D) segmentation method has achieved state-of-the-art (SOTA) performance, it typically relies on pure-convolution architectures, limiting its ability to capture long-range spatial dependencies due to convolution's confinement to a local receptive field. To address this limitation and further enhance esophageal GTV segmentation performance, this work proposes a transformer-guided cross-modality adaptive feature fusion network, referred to as TransAttPSNN, which is based on cross-modality PET/CT scans. METHODS: Specifically, we establish an attention progressive semantically-nested network (AttPSNN) by incorporating the convolutional attention mechanism into the progressive semantically-nested network (PSNN). Subsequently, we devise a plug-and-play transformer-guided cross-modality adaptive feature fusion model, which is inserted between the multi-scale feature counterparts of a two-stream AttPSNN backbone (one for the PET modality flow and another for the CT modality flow), resulting in the proposed TransAttPSNN architecture. RESULTS: Through extensive four-fold cross-validation experiments on the clinical PET/CT cohort. The proposed approach acquires a Dice similarity coefficient (DSC) of 0.76 ± 0.13, a Hausdorff distance (HD) of 9.38 ± 8.76 mm, and a Mean surface distance (MSD) of 1.13 ± 0.94 mm, outperforming the SOTA competing methods. The qualitative results show a satisfying consistency with the lesion areas. CONCLUSIONS: The devised transformer-guided cross-modality adaptive feature fusion module integrates the strengths of PET and CT, effectively enhancing the segmentation performance of esophageal GTV. The proposed TransAttPSNN has further advanced the research of esophageal GTV segmentation.

Inverse relationship between HBV DNA levels and liver histopathological changes in immune-tolerant CHB patients.

Limited data exist regarding the association between hepatitis B virus (HBV) DNA levels and liver histopathological changes in patients with chronic hepatitis B (CHB) during the immune tolerant (IT) phase. In this study, we retrospectively analysed liver biopsy results from 150 adult IT-CHB patients. The liver tissue necroinflammation and fibrosis were evaluated by the Scheuer scoring system. Multivariate logistic regression, smooth curve fitting, and segmented regression models were used to examine the association between HBV DNA levels and liver histopathological changes. A total of 26%, 30.67% and 42% of IT patients had significant necroinflammation (≥G2), significant fibrosis (≥S2) and significant histopathological changes (≥G2 and/or ≥S2), respectively. HBV DNA levels were independently and non-linear inversely associated with significant necroinflammation and histopathological changes in IT-CHB patients. Patients with HBV DNA levels <107 IU/mL had a higher risk of significant histopathological changes compared to those with levels >107 IU/mL. The findings were further confirmed by smooth curve fitting analyses, subgroup and sensitivity analyses. In segmented regression model analyses, the optimal DNA value for the lowest odds ratio of significant histopathological changes was 7.26 log10 IU/mL. A non-linear inverse association between HBV DNA levels and significant histopathological changes in IT-CHB patients. DNA 7.26 log10 IU/mL may serve as a potential cut-off point to define a 'true immune tolerant phase' with minimal liver histopathological changes.

Combining quantitative and qualitative analysis for scoring pleural line in lung ultrasound.

Objective.Accurate assessment of pleural line is crucial for the application of lung ultrasound (LUS) in monitoring lung diseases, thereby aim of this study is to develop a quantitative and qualitative analysis method for pleural line.Approach.The novel cascaded deep learning model based on convolution and multilayer perceptron was proposed to locate and segment the pleural line in LUS images, whose results were applied for quantitative analysis of textural and morphological features, respectively. By using gray-level co-occurrence matrix and self-designed statistical methods, eight textural and three morphological features were generated to characterize the pleural lines. Furthermore, the machine learning-based classifiers were employed to qualitatively evaluate the lesion degree of pleural line in LUS images.Main results.We prospectively evaluated 3770 LUS images acquired from 31 pneumonia patients. Experimental results demonstrated that the proposed pleural line extraction and evaluation methods all have good performance, with dice and accuracy of 0.87 and 94.47%, respectively, and the comparison with previous methods found statistical significance (P< 0.001 for all). Meanwhile, the generalization verification proved the feasibility of the proposed method in multiple data scenarios.Significance.The proposed method has great application potential for assessment of pleural line in LUS images and aiding lung disease diagnosis and treatment.

Secoemestrin C Ameliorates Psoriasis-like Skin Inflammation in Mice by Suppressing the TNF-α/NF-κB Signaling Pathway.

OBJECTIVE: Secoemestrin C (SC), an epitetrathiodioxopiperazine isolated from Aspergillus nidulans, has been previously reported to have immunomodulatory and hepatoprotective effects against acute autoimmune hepatitis. However, the effect of SC on regulating the inflammation and its underlying mechanisms in the pathogenesis of psoriasis remain unclear. This study aimed to evaluate the effects of SC on inflammatory dermatosis both in vitro and in vivo. METHODS: In vitro, HaCaT cells were induced with tumor necrosis factor-alpha (TNF-α, 10 ng/mL) to establish an inflammatory injury model, and the expression of nuclear transcription factor-κB (NF-κB) pathway components was measured using qRT-PCR and Western blotting. An in vivo mouse model of imiquimod (IMQ)-induced psoriasis-like skin inflammation was used to evaluate the effectiveness of SC in alleviating psoriasis. RESULTS: SC significantly blocked the activation of NF-κB signaling in TNF-α-stimulated HaCaT cells. In addition, systemic and local administration of SC improved psoriatic dermatitis in the IMQ-induced mouse model. SC reduced skin scale and significantly inhibited the secretion of inflammatory factors in skin lesions. CONCLUSION: The protective effect of SC against psoriatic-associated inflammation reveals its potential therapeutic value for treating psoriasis.

Condition control training-based ConVMLP-ResU-Net for semantic segmentation of esophageal cancer in 18F-FDG PET/CT images.

The precise delineation of esophageal gross tumor volume (GTV) on medical images can promote the radiotherapy effect of esophagus cancer. This work is intended to explore effective learning-based methods to tackle the challenging auto-segmentation problem of esophageal GTV. By employing the progressive hierarchical reasoning mechanism (PHRM), we devised a simple yet effective two-stage deep framework, ConVMLP-ResU-Net. Thereinto, the front-end ConVMLP integrates convolution (ConV) and multi-layer perceptrons (MLP) to capture localized and long-range spatial information, thus making ConVMLP excel in the location and coarse shape prediction of esophageal GTV. According to the PHRM, the front-end ConVMLP should have a strong generalization ability to ensure that the back-end ResU-Net has correct and valid reasoning. Therefore, a condition control training algorithm was proposed to control the training process of ConVMLP for a robust front end. Afterward, the back-end ResU-Net benefits from the yielded mask by ConVMLP to conduct a finer expansive segmentation to output the final result. Extensive experiments were carried out on a clinical cohort, which included 1138 pairs of 18F-FDG positron emission tomography/computed tomography (PET/CT) images. We report the Dice similarity coefficient, Hausdorff distance, and Mean surface distance as 0.82 ± 0.13, 4.31 ± 7.91 mm, and 1.42 ± 3.69 mm, respectively. The predicted contours visually have good agreements with the ground truths. The devised ConVMLP is apt at locating the esophageal GTV with correct initial shape prediction and hence facilitates the finer segmentation of the back-end ResU-Net. Both the qualitative and quantitative results validate the effectiveness of the proposed method.

A new difference of anisotropic and isotropic total variation regularization method for image restoration.

Total variation (TV) regularizer has diffusely emerged in image processing. In this paper, we propose a new nonconvex total variation regularization method based on the generalized Fischer-Burmeister function for image restoration. Since our model is nonconvex and nonsmooth, the specific difference of convex algorithms (DCA) are presented, in which the subproblem can be minimized by the alternating direction method of multipliers (ADMM). The algorithms have a low computational complexity in each iteration. Experiment results including image denoising and magnetic resonance imaging demonstrate that the proposed models produce more preferable results compared with state-of-the-art methods.

[Design and implementation of a modular pulse wave preprocessing and analysis system based on a new detection algorithm].

As one of the standard electrophysiological signals in the human body, the photoplethysmography contains detailed information about the blood microcirculation and has been commonly used in various medical scenarios, where the accurate detection of the pulse waveform and quantification of its morphological characteristics are essential steps. In this paper, a modular pulse wave preprocessing and analysis system is developed based on the principles of design patterns. The system designs each part of the preprocessing and analysis process as independent functional modules to be compatible and reusable. In addition, the detection process of the pulse waveform is improved, and a new waveform detection algorithm composed of screening-checking-deciding is proposed. It is verified that the algorithm has a practical design for each module, high accuracy of waveform recognition and high anti-interference capability. The modular pulse wave preprocessing and analysis software system developed in this paper can meet the individual preprocessing requirements for various pulse wave application studies under different platforms. The proposed novel algorithm with high accuracy also provides a new idea for the pulse wave analysis process.

Safety, tolerance, and pharmacokinetics of salvianolic acid B in healthy Chinese volunteers: A randomized, double-blind, placebo-controlled phase 1 clinical trial.

Background: Salvianolic acid B (Sal B) is one of the main active ingredients of Salvia miltiorrhiza Bunge. In China, many traditional Chinese medicines have been modified into injections for higher bioavailability and better efficacy. Salvianolic acid injection has been widely used in the clinic. Objective: This phase 1, randomized, double-blind, placebo-controlled, single-center study aimed to evaluate the safety, tolerance, and pharmacokinetics of Sal B injection in healthy Chinese volunteers. Methods: For the single-ascending-dose study, forty-seven healthy volunteers were randomly divided into 25, 75, 150, 200, 250, and 300 mg groups. For the multiple-ascending-dose study, sixteen healthy volunteers were randomly divided into 150 and 300 mg groups. In each group, volunteers were treated with Sal B or placebo randomly. Their safety was evaluated by a skin test, physical examination, vital sign, laboratory examination, 12-lead electrocardiogram, Holter, and clinical symptoms and signs. Blood samples were collected in 75, 150, and 300 mg single-ascending-dose study groups and 150 mg multiple-ascending-dose study groups to determine the concentration of salvianolic acid B. Results: In single-ascending-dose study groups, there were 41 adverse events in 24 cases (51.1%, 24/47). In multiple-ascending-dose study groups, there were 13 adverse events in eight cases (50.0%, 8/16). Sixty-six volunteers received the skin test, and three of them were excluded because of the positive result. Adverse events related to the treatment included increased alanine aminotransferase (4.0%), increased bilirubin (2.0%), increased creatinine kinase-MB (2.0%), increased brain natriuretic peptide (8.0%), increased urine N-acetyl-ß-D-glucosidase (4.0%), dizziness (2.0%), and chest discomfort (2.0%). No serious adverse events occurred. No volunteers withdrew from the trial. Peak plasma concentration and the area under the plasma concentration-time curve of salvianolic acid B progressively increased in a dose-dependent manner in 75, 150, and 300 mg single-ascending-dose study groups. There was no accumulation after 5 consecutive days of administration of 150 mg salvianolic acid B. Conclusion: Salvianolic acid B injections administered up to 300 mg in a single dose and 250 mg for 5 consecutive days showed excellent safety and tolerability in healthy Chinese volunteers. Clinical Trial Registration: www.chinadrugtrials.org.cn, identifier CTR20192236.

Automatic segmentation of esophageal gross tumor volume in 18F-FDG PET/CT images via GloD-LoATUNet.

BACKGROUND AND OBJECTIVE: For esophageal squamous cell carcinoma, radiotherapy is one of the primary treatments. During the planning before radiotherapy, the intractable task is to precisely delineate the esophageal gross tumor volume (GTV) on medical images. In current clinical practice, the manual delineation suffers from high intra- and inter-rater variability, while also exhausting the oncologists on a treadmill. There is an urgent demand for effective computer-aided automatic segmentation methods. To this end, we designed a novel deep network, dubbed as GloD-LoATUNet. METHODS: GloD-LoATUNet follows the effective U-shape structure. On the contractile path, the global deformable dense attention transformer (GloDAT), local attention transformer (LoAT), and convolution blocks are integrated to model long-range dependencies and localized information. On the center bridge and the expanding path, convolution blocks are adopted to upsample the extracted representations for pixel-wise semantic prediction. Between the peer-to-peer counterparts, enhanced skip connections are built to compensate for the lost spatial information and dependencies. By exploiting complementary strengths of the GloDAT, LoAT, and convolution, GloD-LoATUNet has remarkable representation learning capabilities, performing well in the prediction of the small and variable esophageal GTV. RESULTS: The proposed approach was validated in the clinical positron emission tomography/computed tomography (PET/CT) cohort. For 4 different data partitions, we report the Dice similarity coefficient (DSC), Hausdorff distance (HD), and Mean surface distance (MSD) as: 0.83±0.13, 4.88±9.16 mm, and 1.40±4.11 mm; 0.84±0.12, 6.89±12.04 mm, and 1.18±3.02 mm; 0.84±0.13, 3.89±7.64 mm, and 1.28±3.68 mm; 0.86±0.09, 3.71±4.79 mm, and 0.90±0.37 mm; respectively. The predicted contours present a desirable consistency with the ground truth. CONCLUSIONS: The inspiring results confirm the accuracy and generalizability of the proposed model, demonstrating the potential for automatic segmentation of esophageal GTV in clinical practice.

MELD score < 18 rule out 28-day ACLF development among inpatients with hepatitis B-related previous compensated liver disease.

The acute-on-chronic liver failure (ACLF) development is highly dynamic. Currently, no satisfactory algorithm identifies patients with HBV at risk of this complication. The aim of the study was to characterize ACLF development in hospitalized HBV-related patients without previous decompensation and to test the performance of traditional prognostic models in ruling out ACLF development within 28 days on admission we conducted a cohort study. Two multi-center cohorts with hospitalized HBV-related previous compensated patients were analyzed. Performances of MELD, MELD-Na, CLIF-C AD, and CLIF-C ACLF-D in ruling out ACLF development within 28 days were compared and further validated by ROC analyses. In the derivation cohort (n = 892), there were 102 patients developed ACLF within 28 days, with profound systemic inflammatory levels and higher 28-day mortality rate (31.4% vs. 1.0%) than those without ACLF development. The MELD score (cut-off = 18) achieved acceptable missing rate (missed/total ACLF development) at 2.9%. In the validation cohort (n = 1656), the MELD score (<18) was able to rule out ACLF development within 28 days with missing rate at 3.0%. ACLF development within 28 days were both lower than 1% (0.6%, derivation cohort; 0.5%, validation cohort) in patients with MELD < 18. While in patients with MELD ≥ 18, 26.6% (99/372, derivation cohort) and 17.8% (130/732, validation cohort) developed into ACLF within 28 days, respectively. While MELD-Na score cut-off at 20 and CLIF-AD score cut-off at 42 did not have consistent performance in our two cohorts. MELD < 18 was able to safely rule out patients with ACLF development within 28 days in HBV-related patients without previous decompensation, which had a high 28-day mortality.

Emotion Recognition Based on Energy-related Features of Peripheral Physiological Signals.

The interest in development of methods and tools for recognizing human emotions has increased continuously. Using physiological information, especially the peripheral physiological signals, to identify emotions is an important direction for this area. This paper proposes an approach for emotion recognition based on energy-related features extracted from peripheral physiological signals. Three emotions: calm, happiness and fear, were elicited in 54 volunteers using video clips while three peripheral physiological signals were recorded: Electrocardiography (ECG), Photoplethysmography (PPG) and Respiration. Given that energy-related features of physiological signals are closely related to autonomic nervous systems activities, nine energy-related features were extracted from the recorded physiological signals. To find the optimal feature subset to represent the target emotions, the correlation between features and emotion state, as well as the discrimination ability of feature for emotion recognition were both analyzed. Four optimal features were then selected for further classification. Moreover, models based on Decision Tree (DT) were built to evaluate the performance of these features for purpose of recognition of emotion states of calm, happiness, and fear. The results show that the DT models based on these four optimal features could distinguish fear from calm (AUC=0.879, Accuracy=87.8%), happiness from calm (AUC=0.915, Accuracy=91.8%), and fear from happiness (AUC=0.822, Accuracy=81.8%), with a global recognition accuracy of 70.8%. These results indicate that energy-related features of peripheral physiological signals can reliably identify emotions, especially intense emotions.

CM-SegNet: A deep learning-based automatic segmentation approach for medical images by combining convolution and multilayer perceptron.

Accurate segmentation of lesions in medical images is of great significance for clinical diagnosis and evaluation. The low contrast between lesions and surrounding tissues increases the difficulty of automatic segmentation, while the efficiency of manual segmentation is low. In order to increase the generalization performance of segmentation model, we proposed a deep learning-based automatic segmentation model called CM-SegNet for segmenting medical images of different modalities. It was designed according to the multiscale input and encoding-decoding thoughts, and composed of multilayer perceptron and convolution modules. This model achieved communication of different channels and different spatial locations of each patch, and considers the edge related feature information between adjacent patches. Thus, it could fully extract global and local image information for the segmentation task. Meanwhile, this model met the effective segmentation of different structural lesion regions in different slices of three-dimensional medical images. In this experiment, the proposed CM-SegNet was trained, validated, and tested using six medical image datasets of different modalities and 5-fold cross validation method. The results showed that the CM-SegNet model had better segmentation performance and shorter training time for different medical images than the previous methods, suggesting it is faster and more accurate in automatic segmentation and has great potential application in clinic.

Long non-coding RNA Linc01133 promotes osteogenic differentiation of human periodontal ligament stem cells via microRNA-30c / bone gamma-carboxyglutamate protein axis.

Periodontitis is a chronic inflammation caused by the deposition of dental plaque on the tooth surface. Human periodontal ligament stem cells (hPDLSCs) have the potential of osteogenic differentiation. Long non-coding RNAs (lncRNAs) are collectively involved in periodontitis. This study was designed to explore the roles of Linc01133 in osteogenic differentiation of hPDLSCs. hPDLSCs obtained from the periodontal ligament (PDL) of patients with periodontitis were used to collect Linc01133, microRNA-30c (miR-30c), and bone gamma-carboxyglutamate protein (BGLAP) expression data, and their expression changes were traced during osteogenic differentiation of hPDLSCs. Quantitative reverse-transcription polymerase chain reaction as well as western blotting were used to analyze the levels of RNAs and proteins. Dual-luciferase reporter and RNA pull-down assays demonstrated the relationship between Linc01133, miR-30c, and BGLAP. Furthermore, alkaline phosphatase (ALP) staining and alizarin red staining were applied to evaluate the degree of osteogenic differentiation. Linc01133 was downregulated in the PDL of patients with periodontitis. Upregulated Linc01133 promoted osteogenic differentiation of hPDLSCs. Linc01133 could inhibit miR-30c expression by sponging miR-30c. miR-30c suppressed osteogenic differentiation. Additionally, miR-30c targeted BGLAP. Knockdown of BGLAP abrogated the effects of decreased miR-30c on osteogenic differentiation of hPDLSCs. Linc01133 acted as a ceRNA to regulate osteogenic differentiation of hPDLSCs via the miR-30c/BGLAP axis. Therefore, Linc01133 may participate in the progress of periodontitis.

Global Quantification of Glutathione S-Transferases in Human Serum Using LC-MS/MS Coupled with Affinity Enrichment.

The members of the glutathione S-transferase (GST) superfamily often exhibit functional overlap and can compensate for each other. Their concentrations in serum are considered as disease biomarkers. A global and quantitative evaluation of serum GSTs is therefore urgent, but there is a lack of efficient approaches due to technological limitations. GSH magnetic beads were examined for their affinity to enrich GSTs in serum, and the enriched GSTs were quantitatively targeted using a Q Exactive HF-X mass spectrometer in parallel reaction monitoring (PRM) mode. To optimize the quantification of GST peptides, sample types, trypsin digestion, and serum loading were carefully assessed; a biosynthetic method was employed to generate isotope-labeled GST peptides, and instrumental parameters were systematically optimized. A total of 134 clinical sera were collected for GST quantification from healthy donors and patients with four liver diseases. Using the new approach, GSTs in healthy sera were profiled: 14 GST peptides were quantified, and the abundance of five GST families was ranked GSTM > GSTP > GSTA > MGST1 > GSTT1, ranging from 0.1 to 4 pmol/L. Furthermore, combining the abundance of multiple GST peptides could effectively distinguish different types of liver diseases. Quantification of serum GSTs through targeted proteomics, therefore, has apparent clinical potential for disease diagnosis.

Impulse Noise Image Restoration Using Nonconvex Variational Model and Difference of Convex Functions Algorithm.

In this article, the problem of impulse noise image restoration is investigated. A typical way to eliminate impulse noise is to use an L1 norm data fitting term and a total variation (TV) regularization. However, a convex optimization method designed in this way always yields staircase artifacts. In addition, the L1 norm fitting term tends to penalize corrupted and noise-free data equally, and is not robust to impulse noise. In order to seek a solution of high recovery quality, we propose a new variational model that integrates the nonconvex data fitting term and the nonconvex TV regularization. The usage of the nonconvex TV regularizer helps to eliminate the staircase artifacts. Moreover, the nonconvex fidelity term can detect impulse noise effectively in the way that it is enforced when the observed data is slightly corrupted, while is less enforced for the severely corrupted pixels. A novel difference of convex functions algorithm is also developed to solve the variational model. Using the variational method, we prove that the sequence generated by the proposed algorithm converges to a stationary point of the nonconvex objective function. Experimental results show that our proposed algorithm is efficient and compares favorably with state-of-the-art methods.

Tri-typing of hepatitis B-related acute-on-chronic liver failure defined by the World Gastroenterology Organization.

BACKGROUND AND AIM: Tri-typing of acute-on-chronic liver failure (ACLF), as proposed by the World Gastroenterology Organization (WGO), has not been validated in patients infected with hepatitis B virus (HBV). We aim to compare the three types of ACLF patients in clinic characteristics. METHODS: Hospitalized ACLF patients with chronic hepatitis B from five hepatology centers were retrospectively selected and grouped according to the WGO classification. For each group, we investigated laboratory tests, precipitating events, organ failure, and clinical outcome. RESULTS: Compared with type-B (n = 262, compensated cirrhosis) and type-C (n = 129, decompensated cirrhosis) ACLF, type-A patients (n = 195, non-cirrhosis) were associated with a younger age, the highest platelet counts, the highest aminotransferase levels, and the most active HBV replications. HBV reactivation were more predominant in type-A, while bacterial infections in type-B and type-C ACLF cases. Liver failure (97.4%) and coagulation failure (86.7%) were most common in type-A compared with type-B or type-C ACLF patients. Kidney failure was predominantly identified in type-C subjects (41.9%) and was highest (23/38, 60.5%) in grade 1 ACLF patients. Furthermore, type-C ACLF showed the highest 28-day (65.2%) and 90-day (75.3%) mortalities, compared with type-A (48.7% and 54.4%, respectively) and type-B (48.4% and 62.8%, respectively) ACLF cases. Compared with type-A (11.7%) ACLF patients, the increased mortality from 28 to 90 days was higher in type-B (31.6%) and type-C (37.5%). CONCLUSION: Tri-typing of HBV-related ACLF in accordance with the WGO definition was able to distinguish clinical characteristics, including precipitating events, organ failure, and short-term prognosis in ACLF patients.

Correction to: LncRNA NEAT1 mediates progression of oral squamous cell carcinoma via VEGF-A and Notch signaling pathway.

An amendment to this paper has been published and can be accessed via the original article.

LncRNA NEAT1 mediates progression of oral squamous cell carcinoma via VEGF-A and Notch signaling pathway.

BACKGROUND: lncRNAs and VEGF have been shown to have close connections with oral squamous cell carcinoma (OSCC). We explored the interaction between lncRNA NEAT1 and VEGF-A in OSCC. METHODS: RT-qPCR was implemented to measure levels of lncRNA NEAT1 and VEGF-A in OSCC cell lines and normal cell lines. Cell functions then were checked after regulating the expressions of lncRNA NEAT1 and VEGF-A separately. Cell viabilities were examined with CCK-8 and apoptosis rate was checked with flow cytometry. Meanwhile, EMT-related genes E-cadherin, N-cadherin, Vimentin, and Snail and Notch signaling genes Notch1, Notch2, and Jagged were evaluated by RT-qPCR. IMR-1 was applied for impeding Notch signaling pathway. Later, cell viabilities, apoptosis, and EMT were assessed. RESULTS: Expressions of lncRNA NEAT1 and VEGF-A were both increased significantly in OSCC cell lines especially in TSCC1 cell line. Suppression of lncNRA NEAT1 was associated with lower cell viabilities and EMT and higher apoptosis rate in the TSCC1 cell line. Meanwhile, knockdown of VEGF-A significantly repressed cell viabilities and EMT in the TSCC1 cell line. Magnifying functions of inhibited lncRNA NEAT1 Notch signaling pathway was obviously activated with overexpressions of lncRNA NEAT1 and VEGF-A. Adding IMR-1 significantly downregulated cell viabilities and EMT and sharply increased apoptosis in the context of lncRNA NEAT1 and VEGF-A overexpression. CONCLUSION: LncRNA NEAT1 may upregulate proliferation and EMT and repress apoptosis through activating VEGF-A and Notch signaling pathway in vitro, suggesting an underlying regulatory factor in OSCC. Nevertheless, further research is necessary to gain a greater understanding of lncRNA NEAT1 and connections with VEGF-A in vivo and in clinical study.