Syntaxin6 contributes to hepatocellular carcinoma tumorigenesis via enhancing STAT3 phosphorylation.

BACKGROUND: Syntaxin6 (STX6) is a SNARE (Soluble N-ethylmaleimide-sensitive factor attachment protein receptors) protein complex located in the trans-Golgi network and endosomes, which is closely associated with a variety of intracellular membrane transport events. STX6 has been shown to be overexpressed in a variety of human malignant tumors such as esophageal, colorectal, and renal cell carcinomas, and participates in tumorigenesis and development. METHODS: Based on clinical public database and clinical liver samples analysis, the expression of STX6 in hepatocellular carcinoma (HCC) tissues was investigated. The effects of STX6 on proliferation, migration and invasion of HCC cell in vitro and in vivo were evaluated through gain- and loss-of-function studies. We further performed RNA-seq analysis and protein interactome analysis, to further decifer the detailed mechanisms of STX6 in the regulation of the JAK-STAT pathway in HCC. RESULTS: STX6 expression was upregulated in HCC tissues and its expression was highly correlated with the high histological grade of the tumor. STX6 promoted HCC cell proliferation, migration and invasion both in vitro and in vivo. Mechanistically, STX6 mediated tumor progression depending on promoting the activation of JAK-STAT signaling pathway. Receptor for activated protein kinase C (RACK1) as an essential adaptor protein mediating STX6 regulation of JAK-STAT pathway. Specifically, STX6 interacted with RACK1 and then recruited signal transducer and activator of transcription 3 (STAT3) to form a protein-binding complex and activates STAT3 transcriptional activity. CONCLUSIONS: This study provided a novel concept that STX6 exerted oncogenic effects by activating the STAT3 signaling pathway, and STX6 might be a promising therapeutic target for HCC.

UPLC-Q-TOF-MS/MS combined with machine learning methods for screening quality indicators of Hypericum perforatum L.

Hypericum perforatum L. (HPL), also known as St. John's wort, is one of the extensively researched domestically and internationally as a medicinal plant. In this study, non-targeted metabolomics combined with machine learning methods were used to identify reasonable quality indicators for the holistic quality control of HPL. First, the high-resolution MS data from different samples of HPL were collected, and visualized the chemical compounds through the MS molecular network. A total of 122 compounds were identified. Then, the orthogonal partial least squares-discriminant analysis (OPLS-DA) model was established for comparing the differences in metabolite expression between flower, leaf, and branches. A total of 46 differential metabolites were screened out. Subsequently, analyzing the pharmacological activities of these differential metabolites based on protein-protein interaction (PPI) network. A total of 25 compounds associated with 473 gene targets were retrieved. Among them, 13 highly active compounds were selected as potential quality markers, and five compounds were ultimately selected as quality control markers for HPL. Finally, three different classifiers (support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN)) were used to validate whether the selected quality control markers are qualified. When the feature count is set to 122 and 46, the RF model demonstrates optimal performance. As the number of variables decreases, the performance of the RF model degrades. The KNN model and the SVM model also exhibit a decrease in performance but still manage to satisfy the intended requirements. The strategy can be applied to the quality control of HPL and can provide a reference for the quality control of other herbal medicines.

The polysaccharides from seeds of Glycyrrhiza uralensis ameliorate metabolic disorders and restructure gut microbiota in type 2 diabetic mice.

T2D and its complications are significant threats to human health and are among the most concerning metabolic diseases worldwide. Previous studies have revealed that Glycyrrhiza uralensis polysaccharide extract (GUP) exhibits remarkable antioxidant capabilities and inhibits alpha-glucosidase activity. However, whether GUP improves glycemic control in T2D is unknown. This study aims to investigate the effects of GUP on glucose and lipid metabolism as well as the intestinal microbiota in HFD/STZ-induced T2D. The results demonstrated that GUP could significantly ameliorate hyperglycemia, insulin resistance, oxidative stress, and reduce liver lipid levels in T2D mice. Furthermore, it also enhanced the integrity of the intestinal barrier in T2D mice by reducing the levels of pro-inflammatory cytokines and serum LPS levels. Interestingly, GUP treatment significantly lowered serum creatinine and urea nitrogen levels, mitigating renal function deterioration and interstitial fibrosis. Additionally, GUP intervention increased the α diversity of gut microbiota, promoting beneficial species like Akkermansia, Lactobacillus, Romboutsia and Faecalibaculum, while decreasing harmful ones such as Bacteroides, Escherichia-Shigella, and Clostridium sensu stricto 1 in T2D mice. Overall, this study highlights the potential of GUP in alleviating complications and enhancing intestinal health in T2D mice, providing valuable insights into dietary strategies for diabetes control and overall health improvement.

Succinylated starch emulsified Eugenol and Carvacrol nanoemulsions improved digestive stability, bio-accessibility and Salmonella typhimurium inhibition.

The ultrasonically processed Eugenol (EU) and Carvacrol (CAR) nanoemulsions (NE) were successfully optimized via response surface methodology (RSM) to achieve broad spectrum antimicrobial efficacy. These NE were prepared using 2 % (w/w) purity gum ultra (i.e., succinylated starch), 10 % (v/v) oil phase, 80 % (800 W) sonication power, and 10 min of processing time as determined via RSM. The second order Polynomial method was suitable to RSM with a co-efficient of determination >0.90 and a narrow polydispersity index (PDI) ranging 0.12-0.19. NE had small droplet sizes (135.5-160 nm) and low volatility at high temperatures. The EU & CAR entrapment and heat stability (300 °C) confirmed by Fourier transform infrared spectroscopy (FTIR) and thermogravimetric analysis (TGA). Further, the volatility of EU & CAR NE was 18.18 ± 0.13 % and 12.29 ± 0.11 % respectively, being lower than that of bulk/unencapsulated EU & CAR (i.e., 23.48 ± 0.38 % and 19.11 ± 0.08 %) after 2 h at 90 °C. Interestingly, both EU & CAR NE showed sustained release behaviour till 48 h. Their digest could inhibit Salmonella typhimurium (S. typhimurium) via membrane disruption and access to cellular machinery as evident from SEM images. Furthermore, in-vivo bio-accessibility of EU & CAR in mice serum was up to 80 %. These cost-effective and short-processed EU/CAR NE have the potential as green preservatives for food industry.

Size-Related Pathway Flux Analysis of Ultrasmall Iron Oxide Nanoparticles in Macrophage Cell RAW264.7 for Safety Evaluation.

The endocytosis, intracellular transport, and exocytosis of different-sized nanoparticles were reported to greatly affect their efficacy and biosafety. The quantitation of endocytosis and exocytosis as well as subcellular distribution of nanoparticles might be an effective approach based on transport pathway flux analysis. Thus, the key parameters that could present the effects of three different-sized ultrasmall iron oxide nanoparticles (USIONPs) were systematically investigated in RAW264.7 cells. The endocytosis and exocytosis of USIONPs were related to their sizes; 15.4 nm of S2 could be quickly and more internalized and excreted in comparison to S1 (7.8 nm) and S3 (30.7 nm). In RAW264.7 cells, USIONPs were observed in endosomes, lysosomes, the Golgi apparatus, and autophagosomes via a transmission electron microscope. Based on flux analysis of intracellular transport pathways of USIONPs, it was found that 43% of S1, 40% of S2, and 44% of S3 were individually transported extracellularly through the Golgi apparatus-involved middle-fast pathway, while 24% of S1, 23% of S2, and 26% of S3 were transported through the fast recycling endosomal pathway, and the residues were transported through the slower speed lysosomal pathway. USIONPs might be transported via size-related endocytosis and exocytosis pathways. The pathway flux could be calculated on the basis of disturbance analysis of special transporters as well as their coding genes. Because there were rate differences among these transport pathways, this pathway flux could anticipate the intracellular remaining time and distribution of different-sized nanoparticles, the function exertion, and side effects of nanomaterials. The size of the nanomaterials could be optimized for improving functions and safety.

Holistic quality evaluation method of Epimedii Folium based on NIR spectroscopy and chemometrics.

INTRODUCTION: There are some problems in the quality control of Epimedii Folium (leaves of Epimedium brevicornum Maxim.), such as the mixed use of Epimedii Folium from different harvesting periods and regions, incomplete quality evaluation, and time-consuming analysis methods. OBJECTIVE: Near-infrared (NIR) spectroscopy was conducted to establish a rapid overall quality evaluation method for Epimedii Folium. MATERIALS AND METHODS: Quantitative models of the total solid, moisture, total flavonoid, and flavonol glycoside (Epimedin A, Epimedin B, Epimedin C, Icariin) contents of Epimedii Folium were established by partial least squares regression (PLSR). The root mean square error (RMSE) and correlation coefficient (R) were used to evaluate the performance of models. The qualitative models of Epimedii Folium from different geographic origins and harvest periods were established based on K-nearest neighbor (KNN), back-propagation neural network (BPNN), and random forest (RF). Accuracy and Kappa values were used to evaluate the performance of models. A new multivariable signal conversion strategy was proposed, which combines NIR spectroscopy with the PLSR model to predict the absorbance values of retention time points in the high-performance liquid chromatography (HPLC) fingerprint to obtain the predicted HPLC fingerprint. The Pearson correlation coefficient and cosine coefficient were used to evaluate the similarity between real and predicted HPLC fingerprints. RESULTS: Qualitative models, quantitative models, and the similarity between real and predicted HPLC fingerprints are satisfactory. CONCLUSION: The method serves as a fast and green analytical quality evaluation method of Epimedii Folium and can replace traditional methods to achieve the overall quality evaluation of Epimedii Folium.

Genome-wide identification of STATs and analysis of their role in sex determination in Pacific oysters (Crassostrea gigas).

STAT (signal transducer and activator of the transcription) proteins, are a group of highly conserved transcription factors and fundamental components of the JAK-STAT signaling pathway. They play crucial roles in a variety of biological processes, such as immunity, proliferation, differentiation, and growth. However, little information is known regarding their role in gonad development and sex determination in mollusks. In this study, we identified 3 STAT genes in Pacific Oyster Crassostrea gigas. Phylogenetic analysis showed that STATs from mollusks were highly conserved, and most of them had four identical motif regions, except for the STAT1 and STAT3 predicted sequences from Crassostrea hongkongensis. Tissue expression analysis indicated CgSTAT1 had a high expression level in most tissues, while CgSTAT3 had a low expression level in most tissues. Expression analysis of early developmental stages showed CgSTAT1 had a higher expression level from egg to D shaped larva and a lower expression level in subsequent stages. In contrast CgSTAT1, CgSTAT2 had a reverse expression pattern. Expression analysis of different developmental stages of diploid gonads indicated that CgSTAT1 had a higher expression level at the S1 and S3 stages relative to the S2 stage in females, while in males the S3 stage had a higher expression than than the S2 stage. The expression level of CgSTAT1 between diploids and triploids in females differed significantly, but there were no significant differences in males. Expression of CgSTAT2 differed significantly between diploid and triploid males. These data suggest an important role for STATs in sex differentiation in diploid and triploid oysters. Our study is the first to explore the role of STATs in sex differentiation and gonadal development in oysters, and will help us better understand the molecular mechanisms of sex differentiation in shellfish.

Phage cocktail alleviated type 2 diabetes by reshaping gut microbiota and decreasing proinflammatory cytokines.

Type 2 diabetes (T2D), a global health concern, is closely associated with the gut microbiota. Restoration of a balanced microbiota and intestinal homeostasis benefit therapy of T2D. Some special phages may selectively alter the gut microbiota without causing dysbiosis, such as MS2 and P22. However, scarcely systematic analysis of cascading effects triggered by MS2 and P22 phages on the microbiota, as well as interactions between specific gut bacteria and systemic metabolism, seriously inhibit the development of positive interventions of phages. Based on multi-omic analysis, we analyzed the intrinsic correlations among specific microbiota, their bioactive metabolites, and key indicators of T2D. We found that gavage of the MS2-P22 phage cocktail could significantly alter the gut microbiome to attenuate dysbiosis of diabetic C57BL/6 mice caused by high-fat diets (HFDs) and streptozotocin (STZ), by affecting microbial compositions as well as their metabolic pathways and metabolites, especially increasing amounts of short-chain fatty acid-producing (SCFA-producing) bacteria (e.g., Blautia and Romboutsia) and short-chain fatty acids (SCFAs). Correspondingly, a noteworthy reduction in the number of several opportunistic pathogens occurred, e.g., Candidatus Saccharimonas, Aerococcus, Oscillibacter, Desulfovibrio, and Clostridium sensu stricto 1. Synchronously, the levels of proinflammatory cytokines and lipopolysaccharide (LPS) were reduced to recover gut barrier function in T2D mice. These findings might benefit the development of a new dietary intervention for T2D based on phage cocktails. KEY POINTS: • Intestinal barrier integrity of T2D mice is improved by a phage cocktail • Negative relationship between Muribaculaceae and Corynebacterium reshaped gut microbiota • Acetate, propionate, and butyrate decreased the level of proinflammatory factors.

Positive interactions among Corynebacterium glutamicum and keystone bacteria producing SCFAs benefited T2D mice to rebuild gut eubiosis.

Accumulating evidences strongly support the correlations between the compositions of gut microbiome and therapeutic effects on Type 2 diabetes (T2D). Notably, gut microbes such as Akkermansia muciniphila are found able to regulate microecological balance and alleviate dysmetabolism of mice bearing T2D. In order to search out similarly functional bacteria, bacteriophage MS2 with a good specificity to bacteria carrying fertility (F) factor were used to treat T2D mice. Based on multi-omics analysis of microbiome and global metabolism of mice, we observed that gavage of bacteriophage MS2 and metformin led to a significant increase in the abundance of Corynebacterium glutamicum and A. muciniphila, respectively. Consequently, the gut microbiota were remodeled, leading to variations in metabolites and a substantial increase in short-chain fatty acids (SCFAs). In which, the amount of acetate, propionate, and butyrate presented negative correlations to that of proinflammatory cytokines, which was beneficial to repairing the intestinal barriers and improving their functions. Moreover, main short fatty acid (SCFA) producers exhibited positive interactions, further facilitating the restoration of gut eubiosis. These findings revealed that C. glutamicum and its metabolites may be potential dietary supplements for the treatment of T2D. Moreover, our research contributes to a novel understanding of the underlying mechanism by which functional foods exert their anti-diabetic effects.

DNA binding peptide CF-14 enhances bactericidal efficacy of eugenol/carvacrol nanoparticles to Escherichia coli.

To evaluate the bactericidal action of antimicrobial peptide CF-14, Eugenol (EU) and carvacrol (CAR) nanoparticles (NPs) less than 200 nm were surface-modified with CF14, gaining approximately 200 nm of EU-CF and CAR-CF NPs with swollen morphology. EU-CF and CAR-CF NPs were bactericidal to E. coli at dosage of 0.09% and 0.07% (v/v), respectively; while they were just bacteriostatic to Staphylococcus aureus at 0.10% and 0.08% (v/v). Spectral variations in bacterial carbohydrates (1185-900 cm-1), lipids (3000-2800 cm-1) and DNA (1500-1185 cm-1) were obvious as evident from Fourier transform infrared spectroscopy (FTIR). A higher percentage of membrane damaged (non-revivable) E. coli than S. aureus was found, which indicated electrostatic interactions between Gram-negative E. coli with cationic CF conjugated NPs leading to DNA disintegration. Interestingly, EU-CF and CAR-CF NPs inhibited E. coli growth in orange juice without impacting flavour compounds.

Evaluation of safety, effectiveness and treatment patterns of sodium zirconium cyclosilicate in management of hyperkalaemia in China: a real-world study protocol.

INTRODUCTION: Hyperkalaemia (HK) is a potentially life-threatening electrolyte imbalance associated with several adverse clinical outcomes. The efficacy and negative effects of currently existing treatment options have made HK management questionable. Sodium zirconium cyclosilicate (SZC), a novel highly selective potassium binder, is approved for the treatment of HK. The present study will be aimed to assess the safety, effectiveness and treatment patterns of SZC in Chinese patients with HK in a real-world clinical setting as it is required by China's drug review and approval process. METHODS AND ANALYSIS: This is a multicentre, prospective cohort study which plans to enrol 1000 patients taking SZC or willing to take SZC from approximately 40 sites in China. Patients ≥18 years of age at the time of signing the written informed consent and with documented serum potassium levels ≥5.0 mmol/L within 1 year before study enrolment day will be included. Eligible patients will receive SZC treatment and will be followed up for 6 months from enrolment day. The primary objective will be to evaluate the safety of SZC for the management of HK in Chinese patients in terms of adverse events (AEs), serious AEs as well as discontinuation of SZC. The secondary objectives will include understanding the SZC dosage information in terms of its effectiveness and treatment patterns under real-world clinical practice and assessing effectiveness of SZC during the observational period. ETHICS AND DISSEMINATION: This study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Dalian Medical University (approval number: YJ-JG-YW-2020). All the participating sites have received the ethics approval. Results will be disseminated through national and international presentations and peer-reviewed publications. TRIAL REGISTRATION NUMBER: NCT05271266.

Effects of dialysis modality on mortality in patients with end-stage renal disease: A cohort study.

METHODS: Using a retrospective 15-year cohort, stratified by age, this study aimed to analyze the effect of dialysis modality on mortality of ESRD patients in a city of China. Study data were from the medical insurance information system of Kunshan, Jiangsu Province of China, and 1484 patients with ESRD, enrolled from 1 January 2005 to 31 December 2019 were included in this study. The primary outcome event was all-cause mortality, which was calculated in months. Dialysis modalities included hemodialysis (HD) and peritoneal dialysis (PD). Survival analysis and competing-risk regression model were performed in this study. RESULTS: HD costs significantly higher medical expense than the PD treatment regimen. The mean survival time was 121.28 (SE = 3.020) months for HD patients, while that was 94.68 (SE = 3.534) months for the PD. Ten-year survival rates of the young, middle-aged, and elderly were 0.82, 0.56, and 0.26, respectively. For the young (SHR = 0.869, 95% CI: 0.525-1.436) and middle-aged (SHR = 0.715, 95% CI: 0.484-1.057) ESRD patients, different dialysis modalities exhibited no statistical significance on the survival, but for the elderly, HD had a lower risk of mortality than PD (SHR = 0.747, 95% CI: 0.581-0.961). CONCLUSION: Survival of the young and middle-aged ESRD patients was superior to that of the elderly. Considering both survival time and direct medical costs, we recommend that PD could be a better choice for young and middle-aged ESRD patients, while HD may be suitable for older patients.

Economic evaluation of three dialysis methods in patients with end-stage renal disease in China.

OBJECTIVES: End-stage renal disease (ESRD) may result in different degrees of physical and psychological pain. Automated peritoneal dialysis (APD), continuous ambulatory peritoneal dialysis (CAPD), and hemodialysis (HD) as the main treatment methods lead to a heavy burden on social economic and family financial. However, there are few studies on the economic evaluation of the three dialysis methods in China. METHODS: Cost-effectiveness analyses were performed using Markov models based on longitudinal data for 15 years of different modalities in Kunshan City, China. Direct cost derived from medical insurance information system, and indirect cost referred to as loss of productivity. Sensitivity analyses were conducted to study uncertainty. RESULTS: The per capita total cost of CAPD was 664,027.00 yuan, the per capita utility is 5.9105. The per capita total cost of APD was 858,800.65 yuan, the per capita utility is 6.4548. The per capita total cost of HD was 1,281,213.64 yuan, the per capita utility is 6.1356. When CAPD was compared with HD, Incremental Cost-Effectiveness Ratio (ICER) was 1,323,389.53 yuan per QALY, compared with APD, ICER was 357,848.13 yuan per QALY. ICER value suggests that APD was cost-effective compared with CAPD and HD at a willingness-to-pay threshold of 538,200 yuan. CONCLUSION: Our research showed that APD is the most appropriate and HD is the worst in terms of cost-effectiveness. However, in fact, HD accounts for a high proportion in China, so some relevant policy suggestions need to be implemented to change the current situation.

Non-invasive prediction of overall survival time for glioblastoma multiforme patients based on multimodal MRI radiomics.

Glioblastoma multiforme (GBM) is the most common and deadly primary malignant brain tumor. As GBM tumor is aggressive and shows high biological heterogeneity, the overall survival (OS) time is extremely low even with the most aggressive treatment. If the OS time can be predicted before surgery, developing personalized treatment plans for GBM patients will be beneficial. Magnetic resonance imaging (MRI) is a commonly used diagnostic tool for brain tumors with high-resolution and sound imaging effects. However, in clinical practice, doctors mainly rely on manually segmenting the tumor regions in MRI and predicting the OS time of GBM patients, which is time-consuming, subjective and repetitive, limiting the effectiveness of clinical diagnosis and treatment. Therefore, it is crucial to segment the brain tumor regions in MRI, and an accurate pre-operative prediction of OS time for personalized treatment is highly desired. In this study, we present a multimodal MRI radiomics-based automatic framework for non-invasive prediction of the OS time for GBM patients. A modified 3D-UNet model is built to segment tumor subregions in MRI of GBM patients; then, the radiomic features in the tumor subregions are extracted and combined with the clinical features input into the Support Vector Regression (SVR) model to predict the OS time. In the experiments, the BraTS2020, BraTS2019 and BraTS2018 datasets are used to evaluate our framework. Our model achieves competitive OS time prediction accuracy compared to most typical approaches.

A continuous learning approach to brain tumor segmentation: integrating multi-scale spatial distillation and pseudo-labeling strategies.

Introduction: This study presents a novel continuous learning framework tailored for brain tumour segmentation, addressing a critical step in both diagnosis and treatment planning. This framework addresses common challenges in brain tumour segmentation, such as computational complexity, limited generalisability, and the extensive need for manual annotation. Methods: Our approach uniquely combines multi-scale spatial distillation with pseudo-labelling strategies, exploiting the coordinated capabilities of the ResNet18 and DeepLabV3+ network architectures. This integration enhances feature extraction and efficiently manages model size, promoting accurate and fast segmentation. To mitigate the problem of catastrophic forgetting during model training, our methodology incorporates a multi-scale spatial distillation scheme. This scheme is essential for maintaining model diversity and preserving knowledge from previous training phases. In addition, a confidence-based pseudo-labelling technique is employed, allowing the model to self-improve based on its predictions and ensuring a balanced treatment of data categories. Results: The effectiveness of our framework has been evaluated on three publicly available datasets (BraTS2019, BraTS2020, BraTS2021) and one proprietary dataset (BraTS_FAHZU) using performance metrics such as Dice coefficient, sensitivity, specificity and Hausdorff95 distance. The results consistently show competitive performance against other state-of-the-art segmentation techniques, demonstrating improved accuracy and efficiency. Discussion: This advance has significant implications for the field of medical image segmentation. Our code is freely available at https://github.com/smallboy-code/A-brain-tumor-segmentation-frameworkusing-continual-learning.

The controversy of klotho as a potential biomarker in chronic kidney disease.

Klotho is an identified longevity gene with beneficial pleiotropic effects on the kidney. Evidence shows that a decline in serum Klotho level occurs in early chronic kidney disease (CKD) and continues as CKD progresses. Klotho deficiency is associated with poor clinical outcomes and CKD mineral bone disorders (CKD-MBD). Klotho has been postulated as a candidate biomarker in the evaluation of CKD. However, the evidence for the clinical significance of the relationship between Klotho and kidney function, CKD stage, adverse kidney and/or non-kidney outcomes, and CKD-MBD remains inconsistent and in some areas, contradictory. Therefore, there is uncertainty as to whether Klotho is a potential biomarker in CKD; a general consensus regarding the clinical significance of Klotho in CKD has not been reached, and there is limited evidence synthesis in this area. To address this, we have systematically assessed the areas of controversy, focusing on the inconsistencies in the evidence base. We used a PICOM strategy to search for relevant studies and the Newcastle-Ottawa Scale scoring to evaluate included publications. We reviewed the inconsistent clinical findings based on the relationship of Klotho with CKD stage, kidney and/or non-kidney adverse outcomes, and CKD-MBD in human studies. Subsequently, we assessed the underlying sources of the controversies and highlighted future directions to resolve these inconsistencies and clarify whether Klotho has a role as a biomarker in clinical practice in CKD.

3D FRN-ResNet: An Automated Major Depressive Disorder Structural Magnetic Resonance Imaging Data Identification Framework.

Major Depressive Disorder (MDD) is the most prevalent psychiatric disorder, seriously affecting people's quality of life. Manually identifying MDD from structural magnetic resonance imaging (sMRI) images is laborious and time-consuming due to the lack of clear physiological indicators. With the development of deep learning, many automated identification methods have been developed, but most of them stay in 2D images, resulting in poor performance. In addition, the heterogeneity of MDD also results in slightly different changes reflected in patients' brain imaging, which constitutes a barrier to the study of MDD identification based on brain sMRI images. We propose an automated MDD identification framework in sMRI data (3D FRN-ResNet) to comprehensively address these challenges, which uses 3D-ResNet to extract features and reconstruct them based on feature maps. Notably, the 3D FRN-ResNet fully exploits the interlayer structure information in 3D sMRI data and preserves most of the spatial details as well as the location information when converting the extracted features into vectors. Furthermore, our model solves the feature map reconstruction problem in closed form to produce a straightforward and efficient classifier and dramatically improves model performance. We evaluate our framework on a private brain sMRI dataset of MDD patients. Experimental results show that the proposed model exhibits promising performance and outperforms the typical other methods, achieving the accuracy, recall, precision, and F1 values of 0.86776, 0.84237, 0.85333, and 0.84781, respectively.

Prevalence and Clinical Characteristics of Calciphylaxis in Chinese Hemodialysis Patients.

Background: Calciphylaxis is a grievous life-threatening vascular disease that commonly affects dialysis population. This is the first epidemiological survey of calciphylaxis initiated in China. Methods: In the cross-sectional survey, a stratified sampling method was used to select 24 dialysis centers in Jiangsu Province. The participants were all adult patients in each center who had been on hemodialysis for more than 6 months. Calciphylaxis patients were uniformly diagnosed based on characteristic skin lesions and histopathological features. Results: A total of 3,867 hemodialysis patients (average age of 55.33 ± 13.89 years; 61.81% of males) were included. Forty eight cases were diagnosed with calciphylaxis, and prevalence was 1.24%. Among calciphylaxis patients, 33 cases were male, and the average age and median dialysis duration were 53.85 ± 15.17 years and 84.00 (48.00, 138.75) months, respectively. Skin biopsy was performed in 70.83% of calciphylaxis patients, and positive rate was 64.71%. Meanwhile, the positive rate of bone scintigraphy in the diagnosis of calciphylaxis was 62.5%. The prevalence of hyperparathyroidism in case group was as high as 72.92% with longer duration, and 42.86% had undergone parathyroidectomy. Multivariate analysis indicated that increased BMI, prolonged dialysis duration, warfarin therapy, hyperparathyroidism, diabetes, tumors, low serum albumin and high serum alkaline phosphatase levels were high-risk factors for calciphylaxis. Conclusions: The prevalence of calciphylaxis in Chinese hemodialysis patients was 1.24% according to regional epidemiological survey, but its actual prevalence would be presumably far beyond present data. It's urgent to improve clinical understanding of calciphylaxis, and multifaceted diagnostic methods should be applied for early screening.

scSE-NL V-Net: A Brain Tumor Automatic Segmentation Method Based on Spatial and Channel "Squeeze-and-Excitation" Network With Non-local Block.

Intracranial tumors are commonly known as brain tumors, which can be life-threatening in severe cases. Magnetic resonance imaging (MRI) is widely used in diagnosing brain tumors because of its harmless to the human body and high image resolution. Due to the heterogeneity of brain tumor height, MRI imaging is exceptionally irregular. How to accurately and quickly segment brain tumor MRI images is still one of the hottest topics in the medical image analysis community. However, according to the brain tumor segmentation algorithms, we could find now, most segmentation algorithms still stay in two-dimensional (2D) image segmentation, which could not obtain the spatial dependence between features effectively. In this study, we propose a brain tumor automatic segmentation method called scSE-NL V-Net. We try to use three-dimensional (3D) data as the model input and process the data by 3D convolution to get some relevance between dimensions. Meanwhile, we adopt non-local block as the self-attention block, which can reduce inherent image noise interference and make up for the lack of spatial dependence due to convolution. To improve the accuracy of convolutional neural network (CNN) image recognition, we add the "Spatial and Channel Squeeze-and-Excitation" Network (scSE-Net) to V-Net. The dataset used in this paper is from the brain tumor segmentation challenge 2020 database. In the test of the official BraTS2020 verification set, the Dice similarity coefficient is 0.65, 0.82, and 0.76 for the enhanced tumor (ET), whole tumor (WT), and tumor core (TC), respectively. Thereby, our model can make an auxiliary effect on the diagnosis of brain tumors established.

Combined Channel Attention and Spatial Attention Module Network for Chinese Herbal Slices Automated Recognition.

Chinese Herbal Slices (CHS) are critical components of Traditional Chinese Medicine (TCM); the accurate recognition of CHS is crucial for applying to medicine, production, and education. However, existing methods to recognize the CHS are mainly performed by experienced professionals, which may not meet vast CHS market demand due to time-consuming and the limited number of professionals. Although some automated CHS recognition approaches have been proposed, the performance still needs further improvement because they are primarily based on the traditional machine learning with hand-crafted features, resulting in relatively low accuracy. Additionally, few CHS datasets are available for research aimed at practical application. To comprehensively address these problems, we propose a combined channel attention and spatial attention module network (CCSM-Net) for efficiently recognizing CHS with 2-D images. The CCSM-Net integrates channel and spatial attentions, focusing on the most important information as well as the position of the information of CHS image. Especially, pairs of max-pooling and average pooling operations are used in the CA and SA module to aggregate the channel information of the feature map. Then, a dataset of 14,196 images with 182 categories of commonly used CHS is constructed. We evaluated our framework on the constructed dataset. Experimental results show that the proposed CCSM-Net indicates promising performance and outperforms other typical deep learning algorithms, achieving a recognition rate of 99.27%, a precision of 99.33%, a recall of 99.27%, and an F1-score of 99.26% with different numbers of CHS categories.