A pan-allelic human SIRPα-blocking antibody, ES004-B5, promotes tumor killing by enhancing macrophage phagocytosis and subsequently inducing an effective T-cell response.

As a major immune cell type in the tumor microenvironment, tumor-associated macrophages secrete suppressive factors that can inhibit antitumor immunity and promote tumor progression. One approach trying to utilize macrophages for immunotherapy has been to block the CD47-SIRPα axis, which mediates inhibitory signaling, to promote phagocytosis of tumor cells. Many CD47-targeted agents, namely, anti-CD47 antibodies and SIRPα fusion proteins, were associated with a diverse spectrum of toxicities that limit their use in clinical settings. Universal expression of CD47 also leads to a severe "antigen sink" effect of CD47-targeted agents. Given that the CD47 receptor, SIRPα, has a more restricted expression profile and may have CD47-independent functions, targeting SIRPα is considered to have distinct advantages in improving clinical efficacy with a better safety profile. We have developed ES004-B5, a potentially best-in-class pan-allelic human SIRPα-blocking antibody using hybridoma technology. ES004-B5 binds to major human SIRPα variants through a unique epitope with high affinity. By blocking CD47-induced inhibitory "don't-eat-me" signaling, ES004-B5 exerts superior antitumor activity in combination with anti-tumor-associated antigen antibodies in vitro and in vivo. Unlike CD47-targeted agents, ES004-B5 exhibits an excellent safety profile in nonhuman primates. ES004-B5 has potential to be an important backbone for SIRPα-based combination therapy and/or bispecific antibodies, which will likely overcome the limitations of CD47-targeted agents encountered in clinical settings.

Deep convolutional neural network model ResNeSt for discrimination of papillary thyroid carcinomas and benign nodules in thyroid nodules diagnosed as atypia of undetermined significance.

Background: A deep convolutional neural network (DCNN) model was employed for the differentiation of thyroid nodules diagnosed as atypia of undetermined significance (AUS) according to the 2023 Bethesda System for Reporting Thyroid Cytopathology (TBSRTC). The aim of this study was to investigate the efficiency of ResNeSt in improving the diagnostic accuracy of fine-needle aspiration (FNA) biopsy. Methods: Fragmented images were used to train and test DCNN models. A training dataset was built from 1,330 samples diagnosed as papillary thyroid carcinoma (PTC) or benign nodules, and a test dataset was built from 173 samples diagnosed as AUS. ResNeSt was trained and tested to provide a differentiation. With regard to AUS samples, the characteristics of the cell nuclei were compared using the Wilcoxon test. Results: The ResNeSt model achieved an accuracy of 92.49% (160/173) on fragmented images and 84.78% (39/46) from a patient wise viewpoint in discrimination of PTC and benign nodules in AUS nodules. The sensitivity and specificity of ResNeSt model were 95.79% and 88.46%. The κ value between ResNeSt and the pathological results was 0.847 (P<0.001). With regard to the cell nuclei of AUS nodules, both area and perimeter of malignant nodules were larger than those of benign ones, which were 2,340.00 (1,769.00, 2,807.00) vs. 1,941.00 (1,567.50, 2,455.75), P<0.001 and 190.46 (167.64, 208.46) vs. 171.71 (154.95, 193.65), P<0.001, respectively. The grayscale (0 for black, 255 for white) of malignant lesions was lower than that of benign ones, which was 37.52 (31.41, 46.67) vs. 45.84 (31.88, 57.36), P <0.001, indicating nuclear staining of malignant lesions were deeper than benign ones. Conclusions: In summary, the DCNN model ResNeSt showed great potential in discriminating thyroid nodules diagnosed as AUS. Among those nodules, malignant nodules showed larger and more deeply stained nuclei than benign nodules.

The value of speckle-tracking stratified strain combined with myocardial work measurement in evaluating left ventricular function in patients with heart failure with preserved ejection fraction.

Background: Heart failure with preserved ejection fraction (HFpEF) is a highly prevalent progressive disease accompanied by poor quality of life, high utilization of medical resources, morbidity, and mortality. However, the role of left ventricular (LV) systolic dysfunction has yet to be well elaborated despite the preservation of the LV ejection fraction. This study aimed to explore the diagnostic value of speckle-tracking stratified strain combined with myocardial work (MW) measurement in evaluating LV systolic dysfunction in patients with HFpEF. Methods: A total of 125 study consecutive individuals, 64 HFpEF patients, and 61 controls were prospectively enrolled in the Fourth Affiliated Hospital of Harbin Medical University. In addition to the conventional echocardiographic parameters, LV stratified strain and MW parameters were statistically compared between the HFpEF and control groups. The global longitudinal strain (GLS) of the subendocardium, myocardium, and subepicardium (GLSendo, GLSmyo, and GLSepi); the transmural gradient (ΔGLS); the global myocardial work index (GWI), global myocardial work efficiency (GWE), global myocardial constructive work (GCW), and the global myocardial wasted work (GWW) were included. Area under the receiver operating characteristic curve analysis was used to evaluate the diagnostic performance of these univariate and multivariable logistic models in detecting impaired LV systolic function in HFpEF. Ten-fold cross-validation was used to evaluate the generalizability of the predictive model. Results: Stratified strains values showed a gradient decline from GLSendo to GLSepi in both control and HFpEF patients. Compared with the control group, HFpEF patients had a significantly reduced GLSepi, GLSmyo, GLSendo, ΔGLS, GWI, GWE, and GCW and a significantly increased GWW (all P<0.001). In the derivation set, the optimal logistic model (combined stratified strain and MW variables) demonstrated the highest performance in predicting LV systolic function impairment in HFpEF patients. The best-performing model with a mean area under the curve (AUC) of 0.966 [95% confidence interval (CI): 0.88 to 1] accessed by 10-fold cross-validation. In the validation set, the AUC of the optimal logistic model was 0.933 (95% CI: 0.85 to 1), the sensitivity was 87%, and the specificity was 93%. Conclusions: Both speck-tracking stratified strain and MW measurement may sensitively detect impairment of LV myocardial function at an early stage for patients with HFpEF. Combining the two techniques may improve the quality of HFpEF diagnosis and may provide a reference value for the early diagnosis of HFpEF in the future.

Therapeutic effect of hydrogen and its mechanisms in kidney disease treatment.

Hydrogen is a simple, colorless, and biologically active small molecule gas that can react with reactive oxygen species. Recent research suggests that hydrogen possesses several biological effects, including antioxidant, anti-inflammatory, and anti-apoptotic effects, while exhibiting an extremely high level of safety. Hydrogen application has shown promise in treating a range of acute and chronic diseases, both benign and malignant. Importantly, an increasing number of clinical studies on hydrogen have demonstrated its efficacy and safety in treating various diseases. This review highlights the beneficial effects of hydrogen in kidney diseases, summarizes potential mechanisms by which hydrogen may act in these diseases, and proposes several promising avenues for future research.

Enhancing the Accuracy of Lymph-Node-Metastasis Prediction in Gynecologic Malignancies Using Multimodal Federated Learning: Integrating CT, MRI, and PET/CT.

Gynecological malignancies, particularly lymph node metastasis, have presented a diagnostic challenge, even with traditional imaging techniques such as CT, MRI, and PET/CT. This study was conceived to explore and, subsequently, to bridge this diagnostic gap through a more holistic and innovative approach. By developing a comprehensive framework that integrates both non-image data and detailed MRI image analyses, this study harnessed the capabilities of a multimodal federated-learning model. Employing a composite neural network within a federated-learning environment, this study adeptly merged diverse data sources to enhance prediction accuracy. This was further complemented by a sophisticated deep convolutional neural network with an enhanced U-NET architecture for meticulous MRI image processing. Traditional imaging yielded sensitivities ranging from 32.63% to 57.69%. In contrast, the federated-learning model, without incorporating image data, achieved an impressive sensitivity of approximately 0.9231, which soared to 0.9412 with the integration of MRI data. Such advancements underscore the significant potential of this approach, suggesting that federated learning, especially when combined with MRI assessment data, can revolutionize lymph-node-metastasis detection in gynecological malignancies. This paves the way for more precise patient care, potentially transforming the current diagnostic paradigm and resulting in improved patient outcomes.

Tamoxifen exerts anti-peritoneal fibrosis effects by inhibiting H19-activated VEGFA transcription.

BACKGROUND: Peritoneal dialysis (PD) remains limited due to dialysis failure caused by peritoneal fibrosis. Tamoxifen (TAM), an inhibitor of estrogen receptor 1 (ESR1), has been reported to treat fibrosis, but the underlying mechanism remains unknown. In this study, we sought to explore whether tamoxifen played an anti-fibrotic role by affecting transcription factor ESR1. METHODS: ESR1 expression was detected in the human peritoneum. Mice were daily intraperitoneally injected with 4.25% glucose PD dialysate containing 40 mM methylglyoxal for 2 weeks to establish PD-induced peritoneal fibrosis. Tamoxifen was administrated by daily gavage, at the dose of 10 mg/kg. Chromatin immunoprecipitation (ChIP) and dual-luciferase reporter assay were performed to validate ESR1 bound H19 promoter. Gain-of-function and loss-of-function experiments were performed to investigate the biological roles of H19 on the mesothelial-mesenchymal transition (MMT) of human peritoneal mesothelial cells (HPMCs). Intraperitoneal injection of nanomaterial-wrapped 2'-O-Me-modified small interfering RNA was applied to suppress H19 in the mouse peritoneum. RNA immunoprecipitation and RNA pull-down assays demonstrated binding between H19 and p300. Exfoliated peritoneal cells were obtained from peritoneal dialysis effluent to analyze the correlations between ESR1 (or H19) and peritoneal solute transfer rate (PSTR). RESULTS: ESR1 was increased significantly in the peritoneum after long-term exposure to PD dialysate. Tamoxifen treatment ameliorated high glucose-induced MMT of HPMCs, improved ultrafiltration rate, and decreased PSTR of mouse peritoneum. Tamoxifen reduced the H19 level by decreasing the ESR1 transcription of H19. Depletion of H19 reversed the pro-fibrotic effect of high glucose while ectopic expression of H19 exacerbated fibrotic pathological changes. Intraperitoneal injection of nanomaterial-wrapped 2'-O-Me-modified siRNAs targeting H19 mitigated PD-related fibrosis in mice. RNA immunoprecipitation (RIP) and RNA pull-down results delineated that H19 activated VEGFA expression by binding p300 to the VEGFA promoter and inducing histone acetylation of the VEGFA promoter. ESR1 and H19 were promising targets to predict peritoneal function. CONCLUSIONS: High glucose-induced MMT of peritoneal mesothelial cells in peritoneal dialysis via activating ESR1. In peritoneal mesothelial cells, ESR1 transcribed the H19 and H19 binds to transcription cofactor p300 to activate the VEGFA. Targeting ESR1/H19/VEGFA pathway provided new hope for patients undergoing peritoneal dialysis.

Radiologists with assistance of deep learning can achieve overall accuracy of benign-malignant differentiation of musculoskeletal tumors comparable with that of pre-surgical biopsies in the literature.

PURPOSE: The purpose of this study was to assess if radiologists assisted by deep learning (DL) algorithms can achieve diagnostic accuracy comparable to that of pre-surgical biopsies in benign-malignant differentiation of musculoskeletal tumors (MST). METHODS: We first conducted a systematic review of literature to get the respective overall diagnostic accuracies of fine-needle aspiration biopsy (FNAB) and core needle biopsy (CNB) in differentiating between benign and malignant MST, by synthesizing data from the articles meeting our inclusion criteria. To compared against the accuracies reported in literature, we then invited 4 radiologists, respectively with 2 (A), 6 (B), 7 (C), and 33 (D) years of experience in interpreting musculoskeletal MRI to perform diagnostic tests on our own dataset (n = 62), with and without assistance of a previously developed DL algorithm. The gold standard for benign-malignant differentiation was histopathologic confirmation or clinical/radiographic follow-up. RESULTS: For FNAB, a meta-analysis containing 4604 samples met the inclusion criteria, with the overall diagnostic accuracy reported to be 0.77. For CNB, an overall accuracy of 0.86 was derived by synthesizing results from 7 original research articles containing a total of 587 samples. On our internal MST dataset, the invited radiologists, respectively, achieved diagnostic accuracies of 0.84 (A), 0.89 (B), 0.87 (C), and 0.90 (D), with the assistance of DL. CONCLUSION: Use of DL algorithms on musculoskeletal dynamic contrast-enhanced MRI improved the benign-malignant differentiation accuracy of radiologists to a level comparable to that of pre-surgical biopsies. The developed DL algorithms have a potential to lower the risk of miss-diagnosing malignancy in radiological practice.

An iron-deficient diet prevents alcohol- or diethylnitrosamine-induced acute hepatotoxicity in mice by inhibiting ferroptosis.

The liver is easily injured by exogenous chemicals through reactive oxygen species (ROS), which lead to ferroptosis, a ROS-dependent programmed cell death characterized by iron accumulation and lipid peroxidation. However, whether iron restriction has a positive role in chemicals-induced liver injuries is unknown. The present study investigated the effects of an iron-deficient diet on liver injuries induced by alcohol or diethylnitrosamine (DEN). Mice were fed an iron-deficient diet for four weeks, then treated with three doses of alcohol (5 g/kg, 24 h interval, gavage) to mimic mild liver injury or five doses of DEN (50 mg/kg, 24 h interval, i. p.) to mimic severe liver failure. The results showed that mice were iron-deficient after four weeks of feeding. Interestingly, as evaluated by H&E staining of liver slices, liver/body weight ratio, serum ALT and AST, iron deficiency significantly alleviated liver injuries triggered by alcohol or DEN. The activities of alcohol dehydrogenase (ADH) and acetaldehyde dehydrogenase (ALDH), and the expression of CYP2E1 were increased by iron deficiency. Mechanistically, iron deficiency prevented the decrease of glutathione peroxidase 4 (GPX4), which eliminated malondialdehyde (MDA) by utilizing glutathione (GSH). In summary, alcohol- or DEN-induced liver injuries were mitigated by the iron-deficient diet by inhibiting ferroptosis, which might be a promising measure for preventing liver injuries induced by alcohol, DEN, or other exogenous chemicals.

Loss of SLC46A1 decreases tumor iron content in hepatocellular carcinoma.

It is interesting that high iron is an independent inducer or cofactor of hepatocellular carcinoma (HCC) while the amount of iron is decreased in the liver tumor tissues. Due to the previous findings that iron deficiency promoted HCC metastasis, it is of significance to identify the underlying mechanism of iron deficiency in HCC. The tumor iron content and expressions of iron-metabolic molecules were observed in the primary liver cancers of rats and mice. The molecules that changed independently of iron were identified by comparing the expression profiles in the human HCC tissues and iron-deprived HCC cells. The downstream effects of these molecules on regulating intracellular iron content were investigated in vitro and further validated in vivo. Both in primary liver cancers of rats and mice, we confirmed the decreased iron content in tumor tissues and the altered expressions of iron-metabolic molecules, including transferrin receptor 1 (TfR1), six-transmembrane epithelial antigen of prostate 3 (STEAP3), divalent metal transporter 1 (DMT1), SLC46A1, ferroportin, hepcidin, and ferritin. Among these, STEAP3, DMT1, and SLC46A1 were altered free of iron deficiency. However, only silence or overexpression of SLC46A1 controlled the intracellular iron content of HCC cells. The interventions of STEAP3 or DMT1 could not change the intracellular iron content. Lentivirus-mediated regain of SLC46A1 expression restored the iron content in orthotopically implanted tumors, with correspondingly changes in the iron-metabolic molecules as iron increasing. Conclusion: Taken together, these results suggest that the loss of SLC46A1 expression leads to iron deficiency in liver tumor tissues, which would be an effective target to manage iron homeostasis in HCC.

Magnesium Hydride Ameliorates Endotoxin-Induced Acute Respiratory Distress Syndrome by Inhibiting Inflammation, Oxidative Stress, and Cell Apoptosis.

Acute respiratory distress syndrome (ARDS) causes uncontrolled pulmonary inflammation, resulting in high morbidity and mortality in severe cases. Given the antioxidative effect of molecular hydrogen, some recent studies suggest the potential use of molecular hydrogen as a biomedicine for the treatment of ARDS. In this study, we aimed to explore the protective effects of magnesium hydride (MgH2) on two types of ARDS models and its underlying mechanism in a lipopolysaccharide (LPS)-induced ARDS model of the A549 cell line. The results showed that LPS successfully induced oxidative stress, inflammatory reaction, apoptosis, and barrier breakdown in alveolar epithelial cells (AEC). MgH2 can exert an anti-inflammatory effect by down-regulating the expressions of inflammatory cytokines (IL-1ß, IL-6, and TNF-α). In addition, MgH2 decreased oxidative stress by eliminating intracellular ROS, inhibited apoptosis by regulating the expressions of cytochrome c, Bax, and Bcl-2, and suppressed barrier breakdown by up-regulating the expression of ZO-1 and occludin. Mechanistically, the expressions of p-AKT, p-mTOR, p-P65, NLRP3, and cleaved-caspase-1 were decreased after MgH2 treatment, indicating that AKT/mTOR and NF-κB/NLRP3/IL-1ß pathways participated in the protective effects of MgH2. Furthermore, the in vivo study also demonstrated that MgH2-treated mice had a better survival rate and weaker pathological damage. All these findings demonstrated that MgH2 could exert an ARDS-protective effect by regulating the AKT/mTOR and NF-κB/NLRP3/IL-1ß pathways to suppress LPS-induced inflammatory reaction, oxidative stress injury, apoptosis, and barrier breakdown, which may provide a potential strategy for the prevention and treatment of ARDS.

Deep Learning Assisted Diagnosis of Musculoskeletal Tumors Based on Contrast-Enhanced Magnetic Resonance Imaging.

BACKGROUND: Misdiagnosis of malignant musculoskeletal tumors may lead to the delay of intervention, resulting in amputation or death. PURPOSE: To improve the diagnostic efficacy of musculoskeletal tumors by developing deep learning (DL) models based on contrast-enhanced magnetic resonance imaging and to quantify the improvement in diagnostic performance obtained by using these models. STUDY TYPE: Retrospective. POPULATION: Three hundreds and four musculoskeletal tumors, including 212 malignant and 92 benign lesions, were randomized into the training (n = 180), validation (n = 62) and testing cohort (n = 62). FIELD STRENGTH/SEQUENCE: A 3 T/T1 -weighted (T1 -w), T2 -weighted (T2 -w), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted (CET1 -w) images. ASSESSMENT: Three DL models based, respectively, on the sagittal, coronal, and axial MR images were constructed to predict the malignancy of tumors. Blinded to the prediction results, a group of specialists made independent initial diagnoses for each patient by reading all image sequences. One month after the initial diagnoses, the same group of doctors made another round of diagnoses knowing the malignancy of each tumor predicted by the three models. The reference standard was the pathological diagnosis of malignancy. STATISTICAL TESTS: Sensitivity, specificity, and accuracy (all with 95% confidential intervals [CI]) corresponding to each diagnostic test were computed. Chi-square tests were used to assess the differences in those parameters with and without DL models. A P value < 0.05 was considered statistically significant. RESULTS: The developed models significantly improved the diagnostic sensitivities of two oncologists by 0.15 (95% CI: 0.06-0.24) and 0.36 (95% CI: 0.24-0.28), one radiologist by 0.12 (95% CI: 0.04-0.20), and three of the four orthopedists, respectively, by 0.12 (95% CI: 0.04-0.20), 0.29 (95% CI: 0.18-0.40), and 0.23 (95% CI: 0.13-0.33), without impairing any of their diagnostic specificities (all P > 0.128). DATA CONCLUSION: The DL models developed can significantly improve the performance of doctors with different training and experience in diagnosing musculoskeletal tumors. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Mild iron overload induces TRIP12-mediated degradation of YY1 to trigger hepatic inflammation.

Increasing populations are found to bear mild hepatic iron overload (HIO) due to unhealthy lifestyles, metabolic diseases, etc., whether this mild but chronic HIO induces hepatic inflammation is unknown. In the present study, mice receiving a 12-months 0.3% dextran-iron diet show mild HIO with no detectable oxidative damages in the liver but have infiltrated macrophages and increased IL-6, TNFα, AST and ALT since 6-months. The HNF4α/miR-122/CCL2 pathway, identified by our previous studies to induce macrophages infiltration, is initiated by chronic mild HIO. After excluding the role of DNA methylation, a modified transcription factor microarray is applied to find that transcription factor YY1 is responsible for HIO-decreased HNF4α expression. Then the E3 ubiquitin ligase TRIP12 is identified by an immunoprecipitation coupled LC-MS/MS and proved to bind and ubiquitinate YY1, leading to its degradation. The overexpression or silence of YY1 in the liver regulates the HNF4α/miR-122/CCL2 pathway. More importantly, YY1 overexpression alleviates chronic mild HIO induced hepatic inflammatory responses. In conclusion, these results elucidate an oxidative-stress-independent, TRIP12/YY1/HNF4α/miR-122/CCL2 pathway of chronic mild HIO inducing hepatic inflammation, implying that effective measures in addition to antioxidants are needed for individuals at the risk of chronic mild HIO.

Lung macrophages are involved in lung injury secondary to repetitive diving.

This study aimed to establish an animal model of decompression-induced lung injury (DILI) secondary to repetitive diving in mice and explore the role of macrophages in DILI and the protective effects of high-concentration hydrogen (HCH) on DILI. Mice were divided into three groups: control group, DILI group, and HCH group. Mice were exposed to hyperbaric air at 600 kPa for 60 min once daily for consecutive 3 d and then experienced decompression. In HCH group, mice were administered with HCH (66.7% hydrogen and 33.3% oxygen) for 60 min after each hyperbaric exposure. Pulmonary function tests were done 6 h after decompression; the blood was harvested for cell counting; the lung tissues were harvested for the detection of inflammatory cytokines, hematoxylin and eosin (HE) staining, and immunohistochemistry; western blotting and polymerase chain reaction (PCR) were done for the detection of markers for M1 and M2 macrophages. Our results showed that bubbles formed after decompression and repeated hyperbaric exposures significantly reduced the total lung volume and functional residual volume. Moreover, repetitive diving dramatically increased proinflammatory factors and increased the markers of both M1 and M2 macrophages. HCH inhalation improved lung function to a certain extent, and significantly reduced the pro-inflammatory factors. These effects were related to the reduction of M1 macrophages as well as the increase in M2 macrophages. This study indicates that repetitive diving damages lung function and activates lung macrophages, resulting in lung inflammation. HCH inhalation after each diving may be a promising strategy for the prevention of DILI.

SLC46A1 contributes to hepatic iron metabolism by importing heme in hepatocytes.

BACKGROUND: Iron is finely regulated due to its vital roles in organisms and the peroxidase reactivity if excess. Solute Carrier Family 46 Member 1 (SLC46A1), also named PCFT or HCP1, is the main importer of heme­iron in the intestine, but has a high abundance in the liver. Since the liver has a central role in iron homeostasis, whether SLC46A1 regulates hepatic iron metabolism is of interest to be identified. METHODS: The recombinant adeno-associated virus vectors were used to hepatic-specifically inhibit SLC46A1 expression to observe its effects on hepatic iron metabolism. Then the abilities of SLC46A1 in importing heme and folate, and consequent alterations of iron content in hepatocytes were determined. Furthermore, effects of iron on SLC46A1 expression were investigated both in vitro and in vivo. RESULTS: The hepatocyte-specific inhibition of SLC46A1 decreases iron content in the liver and increases iron content in serum. Expressions of iron-related molecules, transferrin receptor 1, hepcidin and ferroportin, are correspondingly altered. Interestingly, free heme concentration in serum is increased, indicating a decreased import of heme by the liver. In hepatocytes, SLC46A1 is capable of importing hemin, increasing intracellular iron content. The import of hemin by SLC46A1 is unaffected by its other substrate, folate. Instead, hemin treatment decreases SLC46A1 expression, reducing the import of folate. In addition, SLC46A1 itself shows to be iron-responsive both in vivo and in vitro, making it available for regulating iron metabolism. CONCLUSION: The results elucidate that SLC46A1 regulates iron metabolism in the liver through a folate-independent manner of importing heme. The iron-responsive characters of SLC46A1 give us a new clue to link heme or iron overload with folate deficiency diseases.

Molecular hydrogen regulates PTEN-AKT-mTOR signaling via ROS to alleviate peritoneal dialysis-related peritoneal fibrosis.

As a convenient, effective and economical kidney replacement therapy for end-stage renal disease (ESRD), peritoneal dialysis is available in approximately 11% of ESRD patients worldwide. However, long-term peritoneal dialysis treatment causes peritoneal fibrosis. In recent years, the application potential of molecular hydrogen in the biomedicine has been well recognized. Molecular hydrogen selectively scavenges cytotoxic reactive oxygen species (ROS) and acts as an antioxidant. In this experiment, a high glucose-induced peritoneal fibrosis mouse model was successfully established by intraperitoneal injection of high glucose peritoneal dialysate, and peritoneal fibrosis mice were treated with hydrogen-rich peritoneal dialysate. In addition, in vitro studies of high glucose-induced peritoneal fibrosis were performed using MeT-5A cells. In vitro and in vivo experiments show that molecular hydrogen could inhibit peritoneal fibrosis progress induced by high glucose effectively. Furthermore, it has been found that molecular hydrogen alleviate fibrosis by eliminating intracellular ROS and inhibiting the activation of the PTEN/AKT/mTOR pathway. The present data proposes that molecular hydrogen exerts the capacity of anti-peritoneal fibrosis through the ROS/PTEN/AKT/mTOR pathway. Therefore, molecule hydrogen is a potential, safe, and effective treatment agent, with peritoneal protective property and great clinical significance.

Using deep convolutional neural networks for multi-classification of thyroid tumor by histopathology: a large-scale pilot study.

BACKGROUND: To explore whether deep convolutional neural networks (DCNNs) have the potential to improve diagnostic efficiency and increase the level of interobserver agreement in the classification of thyroid nodules in histopathological slides. METHODS: A total of 11,715 fragmented images from 806 patients' original histological images were divided into a training dataset and a test dataset. Inception-ResNet-v2 and VGG-19 were trained using the training dataset and tested using the test dataset to determine the diagnostic efficiencies of different histologic types of thyroid nodules, including normal tissue, adenoma, nodular goiter, papillary thyroid carcinoma (PTC), follicular thyroid carcinoma (FTC), medullary thyroid carcinoma (MTC) and anaplastic thyroid carcinoma (ATC). Misdiagnoses were further analyzed. RESULTS: The total 11,715 fragmented images were divided into a training dataset and a test dataset for each pathology type at a ratio of 5:1. Using the test set, VGG-19 yielded a better average diagnostic accuracy than did Inception-ResNet-v2 (97.34% vs. 94.42%, respectively). The VGG-19 model applied to 7 pathology types showed a fragmentation accuracy of 88.33% for normal tissue, 98.57% for ATC, 98.89% for FTC, 100% for MTC, 97.77% for PTC, 100% for nodular goiter and 92.44% for adenoma. It achieved excellent diagnostic efficiencies for all the malignant types. Normal tissue and adenoma were the most challenging histological types to classify. CONCLUSIONS: The DCNN models, especially VGG-19, achieved satisfactory accuracies on the task of differentiating thyroid tumors by histopathology. Analysis of the misdiagnosed cases revealed that normal tissue and adenoma were the most challenging histological types for the DCNN to differentiate, while all the malignant classifications achieved excellent diagnostic efficiencies. The results indicate that DCNN models may have potential for facilitating histopathologic thyroid disease diagnosis.

Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study.

Objective: In this study, we exploited a VGG-16 deep convolutional neural network (DCNN) model to differentiate papillary thyroid carcinoma (PTC) from benign thyroid nodules using cytological images. Methods: A pathology-proven dataset was built from 279 cytological images of thyroid nodules. The images were cropped into fragmented images and divided into a training dataset and a test dataset. VGG-16 and Inception-v3 DCNNs were trained and tested to make differential diagnoses. The characteristics of tumor cell nucleus were quantified as contours, perimeter, area and mean of pixel intensity and compared using independent Student's t-tests. Results: In the test group, the accuracy rates of the VGG-16 model and Inception-v3 on fragmented images were 97.66% and 92.75%, respectively, and the accuracy rates of VGG-16 and Inception-v3 in patients were 95% and 87.5%, respectively. The contours, perimeter, area and mean of pixel intensity of PTC in fragmented images were more than the benign nodules, which were 61.01±17.10 vs 47.00±24.08, p=0.000, 134.99±21.42 vs 62.40±29.15, p=0.000, 1770.89±627.22 vs 1157.27±722.23, p=0.013, 165.84±26.33 vs 132.94±28.73, p=0.000), respectively. Conclusion: In summary, after training with a large dataset, the DCNN VGG-16 model showed great potential in facilitating PTC diagnosis from cytological images. The contours, perimeter, area and mean of pixel intensity of PTC in fragmented images were more than the benign nodules.

Protective effects of methane-rich saline on mice with allergic asthma by inhibiting inflammatory response, oxidative stress and apoptosis.

BACKGROUND: Asthma is a common cause of breathing difficulty in children and adults, and is characterized by chronic airway inflammation that is poorly controlled by available treatments. This results in severe disability and applies a huge burden to the public health system. Methane has been demonstrated to function as a therapeutic agent in many diseases. The aim of the present study was to explore the effect of methane-rich saline (MRS) on the pathophysiology of a mouse model of asthma and its underlying mechanism. METHODS: A murine model of ovalbumin (OVA)-induced allergic asthma was applied in this study. Mice were divided into three groups: a control group, an OVA group, and OVA-induced asthmatic mice treated with MRS as the third group. Lung resistance index (RI) and dynamic compliance (Cdyn) were measured to determine airway hyper-responsiveness (AHR). Haematoxylin and eosin (H&E) staining was performed and scored to show histopathological changes. Cell counts of bronchoalveolar lavage fluid (BALF) were recorded. Cytokines interleukin (IL)-4, IL-5, IL-13, tumor necrosis factor α (TNF-α), and C-X-C motif chemokine ligand 15 (CXCL15) from BALF and serum were measured by enzyme-linked immunosorbent assay (ELISA). The oxidative stress indexes, including malondialdehyde (MDA), superoxide dismutase (SOD), catalase (CAT), glutathione (GSH), myeloperoxidase (MPO), and 8-hydroxydeoxyguanosine (8-OHdG), were determined using commercial kits. Apoptosis was evaluated by western blot, quantitative real-time polymerase chain reaction (qRT-PCR), and biochemical examination. RESULTS: MRS administration reversed the OVA-induced AHR, attenuated the pathological inflammatory infiltration, and decreased the cytokines IL-4, IL-5, IL-13, TNF-α, and CXCL15 in serum and BALF. Moreover, following MRS administration, the oxidative stress was alleviated as indicated by decreased MDA, MPO, and 8-OHdG, and elevated SOD and GSH. In addition, MRS exhibited an anti-apoptotic effect in this model, protecting epithelial cells from damage. CONCLUSIONS: Methane improves pulmonary function and decreases infiltrative inflammatory cells in the allergic asthmatic mouse model. This may be associated with its anti-inflammatory, antioxidative, and anti-apoptotic properties.

Deep convolutional neural network Inception-v3 model for differential diagnosing of lymph node in cytological images: a pilot study.

BACKGROUND: In this study, we exploited the Inception-v3 deep convolutional neural network (DCNN) model to differentiate cervical lymphadenopathy using cytological images. METHODS: A dataset of 80 cases was collected through the fine-needle aspiration (FNA) of enlarged cervical lymph nodes, which consisted of 20 cases of reactive lymphoid hyperplasia, 24 cases of non-Hodgkin's lymphoma (NHL), 16 cases of squamous cell carcinoma (SCC), and 20 cases of adenocarcinoma. The images were cropped into fragmented images and divided into a training dataset and a test dataset. Inception-v3 was trained to make differential diagnoses and then tested. The features of misdiagnosed images were further analysed to discover the features that may influence the diagnostic efficiency of such a DCNN. RESULTS: A total of 742 original images were derived from the cases, from which a total of 7,934 fragmented images were cropped. The classification accuracies for the original images of reactive lymphoid hyperplasia, NHL, SCC and adenocarcinoma were 88.46%, 80.77%, 89.29% and 100%, respectively. The total accuracy on the test dataset was 89.62%. Three fragmented images of reactive lymphoid hyperplasia and three fragmented images of SCC were misclassified as NHL. Three fragmented images of NHL were misclassified as reactive lymphoid hyperplasia, one was misclassified as SCC, and one was misclassified as adenocarcinoma. CONCLUSIONS: In summary, after training with a large dataset, the Inception-v3 DCNN model showed great potential in facilitating the diagnosis of cervical lymphadenopathy using cytological images. Analysis of the misdiagnosed cases revealed that NHL was the most challenging cytology type for DCNN to differentiate.