Patterns, Risk Factors, and Outcomes of Recurrence After Hepatectomy for Hepatocellular Carcinoma with and without Microvascular Invasion.

Purpose: The patterns and risk factors of postsurgical recurrence of patient with hepatocellular carcinoma (HCC) with microvascular invasion (MVI) are not clarified. This study aimed to decipher and compare the postoperative recurrent patterns and the risk factors contributing to recurrence between MVI positive (MVI(+)) and MVI negative (MVI(-)) HCC after hepatectomy. Patients and methods: Patients with HCC who underwent hepatectomy in three Chinese academic hospitals between January 1, 2009, and December 31, 2018, were enrolled. Recurrent patterns included early (≤2 years) or late (>2 years) recurrence, recurrent sites and number, and risk factors of recurrence were compared between the MVI(+)and MVI(-) groups by propensity score-matching (PSM). Results: Of 1756 patients included, 581 (33.1%) were MVI(+), and 875 (49.8%) patients developed early recurrence. Compared with the MVI(-) group, the MVI(+) group had a higher 2-year recurrence rate in the PSM cohort (hazard ratio [HR], 1.82; 95% confidence interval [CI], 1.59-2.10; P < 0.001), and more patients with multiple tumor recurrence. Patients with early recurrence in the MVI(+) group had a worse overall survival (OS) than those in the MVI(-) group (HR, 1.24; 95% CI, 1.02-1.50; P = 0.034). Resection margin (RM) ≤1.0 cm is a surgical predictor of early recurrence for the MVI(+) group (HR, 0.68; 95% CI, 0.54-0.87; P = 0.002), but not for the MVI(-) group. Conclusion: Compared to MVI(-) HCC, MVI(+) HCC tends to be early, multiple recurrence and lung and lymph node metastasis after resection. RM ≤1.0 cm is a surgical risk factor of early recurrence for patient with MVI.

Intranasal PAMAM-G3 scavenges cell-free DNA attenuating the allergic airway inflammation.

Allergic airway inflammation (AAI), including allergic rhinitis (AR) and allergic asthma, is driven by epithelial barrier dysfunction and type 2 inflammation. However, the underlying mechanism remains uncertain and available treatments are constrained. Consequently, we aim to explore the role of cell-free DNA (cfDNA) in AAI and assess the potential alleviating effects of cationic polymers (CPs) through cfDNA elimination. Levels of cfDNA were evaluated in AR patients, allergen-stimulated human bronchial epithelium (BEAS-2B cells) and primary human nasal epithelium from both AR and healthy control (HC), and AAI murine model. Polyamidoamine dendrimers-generation 3 (PAMAM-G3), a classic type of cationic polymers, were applied to investigate whether the clearance of cfDNA could ameliorate airway epithelial dysfunction and inhibit AAI. The levels of cfDNA in the plasma and nasal secretion from AR were higher than those from HC (P < 0.05). Additionally, cfDNA levels in the exhaled breath condensate (EBC) were positively correlated with Interleukin (IL)-5 levels in EBC (R = 0.4191, P = 0.0001). Plasma cfDNA levels negatively correlated with the duration of allergen immunotherapy treatment (R = -0.4297, P = 0.006). Allergen stimulated cfDNA secretion in vitro (P < 0.001) and in vivo (P < 0.0001), which could be effectively scavenged with PAMAM-G3. The application of PAMAM-G3 inhibited epithelial barrier dysfunction in vitro and attenuated the development of AAI in vivo. This study elucidates that cfDNA, a promising biomarker for monitoring disease severity, aggravates AAI and the application of intranasal PAMAM-G3 could potentially be a novel therapeutic intervention for AAI. Allergen stimulates the secretion of cell-free DNA (cfDNA) in both human and mouse airway. Intranasal polyamidoamine dendrimers-generation 3 (PAMAM-G3) scavenges cfDNA and alleviates allergic airway inflammation.

Small extracellular vesicles derived from human mesenchymal stem cells prevent Th17-dominant neutrophilic airway inflammation via immunoregulation on Th17 cells.

Type 17 helper T cells (Th17)-dominant neutrophilic airway inflammation is critical in the pathogenesis of steroid-resistant airway inflammation such as severe asthma. Small extracellular vesicles (sEV) derived from human mesenchymal stem cells (MSCs) display extensive therapeutic effects and advantages in many diseases. However, the role of MSC-sEV in Th17-dominant neutrophilic airway inflammation and the related mechanisms are still poorly studied. Here we found that MSC-sEV significantly alleviated the infiltration of inflammatory cells in peribronchial interstitial tissues and reduced levels of inflammatory cells, especially neutrophils, in bronchoalveolar lavage fluids (BALF) of mice with neutrophilic airway inflammation. Consistently, MSC-sEV significantly decreased levels of IL-17A in BALF and Th17 in lung tissues. Furthermore, we found that labelled MSC-sEV were taken up by human CD4+ T cells most obviously at 12 h after incubation, and distributed mostly in mouse lungs. More importantly, potential signaling pathways involved in the MSC-sEV mediated inhibition of Th17 polarization were found using RNA sequencing. Using Western blot, JAK2-STAT3 pathway was identified as an important role in the inhibition of Th17 polarization by MSC-sEV. We found that proteins in MSC-sEV were mostly involved in the therapeutic effects of MSC-sEV. In total, our study suggested that MSC-sEV could be a potential therapeutic strategy for the treatment of neutrophilic airway inflammation.

Generating lymphoma ultrasound image description with transformer model.

Lymphoma, the most prevalent hematologic tumor originating from the lymphatic hematopoietic system, can be accurately diagnosed using high-resolution ultrasound. Microscopic ultrasound performance enables clinicians to identify suspected tumors and subsequently obtain a definitive pathological diagnosis through puncture biopsy. However, the complex and diverse ultrasonographic manifestations of lymphoma pose challenges for accurate characterization by sonographers. To address these issues, this study proposes a Transformer-based model for generating descriptive ultrasound images of lymphoma, aiming to provide auxiliary guidance for ultrasound doctors during screening procedures. Specifically, deep stable learning is integrated into the model to eliminate feature dependencies by training sample weights. Additionally, a memory module is incorporated into the model decoder to enhance semantic information modeling in descriptions and utilize learned semantic tree branch structures for more detailed image depiction. Experimental results on an ultrasonic diagnosis dataset from Shanghai Ruijin Hospital demonstrate that our proposed model outperforms relevant methods in terms of prediction performance.

Optimized Design of Lithium Niobate Tuning Forks for the Measurement of Fluid Characteristic Parameters.

The unique double-cantilever beam structure and vibration mode of the tuning fork enable the measuring of fluid density and viscosity synchronously in a decoupling manner. Therefore, it is widely employed in oil and gas development and in petrochemical, food, textile, and other industries. In this paper, quality factors are used to characterize the energy losses of lithium niobate tuning forks when vibrating in a fluid, and the influence parameters, such as length, width, and thickness of the tuning fork arm, etc., of different quality factors are examined with a focus on the viscous quality factor of the fluid. The optimized design of lithium niobate tuning fork dimensions is carried out on this premise, and the analytical solution of the optimal dimension of the lithium niobate tuning fork in the air is obtained. Secondly, the optimal dimension of the lithium niobate tuning fork in fluids is given out by finite element simulation, and the sensitivity of the optimized fork to the viscosity of fluids is investigated. The results show that the optimized tuning fork has a higher quality factor, and thus has a larger parameter measurement range as well as being more sensitive to the change in the fluid density and viscosity. Therefore, the results are of great significance for guiding the preparation and practical application of lithium niobate tuning forks.

Multi-bubble scattering acoustic fields in viscoelastic tissues under dual-frequency ultrasound.

Bubbles are widely used in the medical field due to their strong acoustic scattering properties, and the interaction between bubbles affects the scattering acoustic field caused by the bubble cluster. In this study, the dynamic equations of bubbles oscillating in viscoelastic tissues are solved numerically. The effect of bubble interaction on the scattered acoustic pressure under dual-frequency ultrasound is analyzed. In addition, the frequency spectra of the scattered acoustic waves due to the bubbles with and without considering the interaction are compared. The results show that the suppression or enlargement of the scattered sound pressure caused by the interaction between bubbles is related to the bubble radius and the incident frequency. Moreover, when the incident frequency is equal to the resonant frequency of the bubble with equilibrium radius R0, the effect of resonant bubbles is stronger than that of non-resonant bubbles. Meanwhile, for the multi-bubble system with a small bubble number density, the total response of the bubble cluster can be approximated as an algebraic sum of the dynamical behavior of individual bubbles.

Type 17 mucosal-associated invariant T cells contribute to neutrophilic inflammation in patients with nasal polyps.

BACKGROUND: Immune regulation in chronic rhinosinusitis with nasal polyps (CRSwNP) with a neutrophilic endotype remains unclear. Mucosal-associated invariant T (MAIT) cells are tissue-resident innate T lymphocytes that respond quickly to pathogens and promote chronic mucosal inflammation. OBJECTIVE: We aimed to investigate the roles of MAIT cells in neutrophilic CRSwNP. METHODS: Nasal tissues were obtained from 113 patients with CRSwNP and 29 control subjects. Peripheral and tissue MAIT cells and their subsets were analyzed by flow cytometry. Polyp-derived MAIT cells were analyzed by RNA sequencing to study their effects on neutrophils. RESULTS: Endotypes of CRSwNP were classified as paucigranulocytic (n = 21), eosinophilic (n = 29), neutrophilic (n = 39), and mixed granulocytic (n = 24). Frequencies of MAIT cells were significantly higher in neutrophilic (3.62%) and mixed granulocytic (3.60%) polyps than in control mucosa (1.78%). MAIT cell percentages positively correlated with local neutrophil counts. MAIT cells were more enriched in tissues than in matched PBMCs. The frequencies of MAIT1 subset or IFN-γ+ MAIT cells were comparable among control tissues and CRSwNP subtypes. The proportions of MAIT17 subset or IL-17A+ MAIT cells were significantly increased in neutrophilic or mixed granulocytic polyps compared with controls. RNA sequencing revealed type 17 and pro-neutrophil profiles in neutrophilic polyp-derived MAIT cells. In patients with neutrophilic CRSwNP, the proportions of MAIT and MAIT17 cells were positively correlated with local proinflammatory cytokines and symptom severity. In vitro experiments demonstrated that neutrophilic polyp-derived MAIT cells promoted neutrophil migration, survival, and activation. CONCLUSIONS: MAIT cells from neutrophilic CRSwNP demonstrate type 17 functional properties and promote neutrophil infiltration in nasal mucosa.

Dendritic cells mediated by small extracellular vesicles derived from MSCs attenuated the ILC2 activity via PGE2 in patients with allergic rhinitis.

BACKGROUND: Mesenchymal stromal cells-derived small extracellular vesicles (MSC-sEVs) have recently attracted considerable attention because of their therapeutic potential in various immune diseases. We previously reported that MSC-sEVs could exert immunomodulatory roles in allergic airway inflammation by regulating group 2 innate lymphoid cell (ILC2) and dendritic cell (DC) functions. Therefore, this study aimed to investigate the indirect effects of MSC-sEVs on ILC2s from patients with allergic rhinitis (AR) via DCs. METHODS: Here, we isolated sEVs from induced pluripotent stem cells-MSCs using anion-exchange chromatography and mature DCs (mDCs) were treated with MSC-sEVs. sEV-mDCs were co-cultured with peripheral blood mononuclear cells from patients with AR or purified ILC2s. The levels of IL-13 and GATA3 in ILC2s were examined by flow cytometry. Bulk RNA sequence for mDCs and sEV-mDCs was employed to further probe the potential mechanisms, which were then validated in the co-culture systems. RESULTS: sEV-mDCs showed impaired capacity in priming the levels of IL-13 and GATA3 in ILC2s when compared with mDCs. Furthermore, there was higher PGE2 and IL-10 production from sEV-mDCs, and the blockade of them especially the former one reversed the inhibitory effects of sEV-mDCs. CONCLUSIONS: We demonstrated that MSC-sEVs were able to dampen the activating effects of mDCs on ILC2s in patients with AR. Mechanismly, the PGE2-EP2/4 axis played an essential role in the immunomodulatory effects of sEV-mDCs on ILC2s. Herein, we provided new insights into the mechanism underlying the therapeutic effects of MSC-sEVs in allergic airway inflammation.

Sensitive dynamics of brain cognitive networks and its resource constraints.

It is well known that brain functions are closely related to the synchronization of brain networks, but the underlying mechanisms are still not completely understood. To study this problem, we here focus on the synchronization of cognitive networks, in contrast to that of a global brain network, as individual brain functions are in fact performed by different cognitive networks but not the global network. In detail, we consider four different levels of brain networks and two approaches, i.e., either with or without resource constraints. For the case of without resource constraints, we find that global brain networks have fundamentally different behaviors from that of the cognitive networks; i.e., the former has a continuous synchronization transition, while the latter shows a novel transition of oscillatory synchronization. This feature of oscillation comes from the sparse links among the communities of cognitive networks, resulting in coupling sensitive dynamics of brain cognitive networks. While for the case of resource constraints, we find that at the global level, the synchronization transition becomes explosive, in contrast to the continuous synchronization for the case of without resource constraints. At the level of cognitive networks, the transition also becomes explosive and the coupling sensitivity is significantly reduced, thus guaranteeing the robustness and fast switch of brain functions. Moreover, a brief theoretical analysis is provided.

Effect of Low Temperature on Insecticidal Protein Contents of Cotton (Gossypium herbaceum L.) in the Boll Shell and Its Physiological Mechanism.

Low temperature is the main factor for global natural disasters affecting the growth and distribution of plants, and cotton may be affected by low temperature and cold damage at all growth stages. In addition, the insecticidal resistance of cultivars has been reported to perform poorly or unstably due to adverse environments. The present study aimed to investigate the impact of low temperature on the levels of insecticidal protein in Bacillus thuringiensis (Bt) transgenic cotton plants during the peak boll stage. To achieve this, two Bt cotton cultivars, Sikang1 (SK1) and Sikang3 (SK3), were subjected to different temperature regimes and durations. The findings of the study demonstrated that the expression of insecticidal protein in the boll shell of Bt transgenic cotton plants was significantly inhibited under low-temperature stress. Specifically, in 2020, compared to the CK (27 °C), the insecticidal protein content in the boll shell of SK3 decreased by 28.19% after a 48 h of a 16 °C temperature. These results suggest that low-temperature stress can negatively impact the expression of insecticidal protein in Bt transgenic cotton, highlighting the need for appropriate measures to minimize its adverse effects on cotton production. In addition, the threshold temperature that leads to a significant decrease in the content of insecticidal proteins symbolizes an upward trend as the duration of stress prolongs. Decreased Bt protein content at low temperatures is associated with changes in the N metabolism. The present study revealed a significant positive correlation between the levels of glutamate oxaloacetate transaminase (GOT) and glutamate pyruvate transaminase (GPT) activities, as well as in the soluble protein levels in the boll shell and the content of the Bt protein. On the other hand, a significant negative correlation was observed between the levels of free amino acids, peptidase, and protease activities, as well as of Bt protein content. These findings suggest that, in Bt cotton production, it is crucial to remain vigilant of prolonged low-temperature disasters, which last for over 12 h and drop below 17-20 °C during the peak boll stage. Such conditions may reduce insecticidal resistance, leading to substantial economic losses.

A method for extracting tumor events from clinical CT examination reports.

Accurate and efficient extraction of key information related to diseases from medical examination reports, such as X-ray and ultrasound images, CT scans, and others, is crucial for accurate diagnosis and treatment. These reports provide a detailed record of a patient's health condition and are an important part of the clinical examination process. By organizing this information in a structured way, doctors can more easily review and analyze the data, leading to better patient care. In this paper, we introduce a new technique for extracting useful information from unstructured clinical text examination reports, which we refer to as a medical event extraction (EE) task. Our approach is based on Machine Reading Comprehension (MRC) and involves two sub-tasks: Question Answerability Judgment (QAJ) and Span Selection (SS). We use BERT to build a question answerability discriminator (Judger) that determines whether a reading comprehension question can be answered or not, thereby avoiding the extraction of arguments from unanswerable questions. The SS sub-task first obtains the encoding of each word in the medical text from the final layer of BERT's Transformer, then utilizes the attention mechanism to identify important information related to the answer from these word encodings. This information is then input into a bidirectional LSTM (BiLSTM) module to obtain a global representation of the text, which is used, along with the softmax function, to predict the span of the answer (i.e., the start and end positions of the answer in the text report). We use interpretable methods to calculate the Jensen-Shannon Divergence (JSD) score between various layers of the network and confirm that our model has strong word representation capabilities, enabling it to effectively extract contextual information from medical reports. Our experiments demonstrate that our method outperforms existing medical event extraction methods, achieving state-of-the-art results with a notable F1 score.

Apparent Diffusion Coefficient in the Differentiation of Common Pediatric Brain Tumors in the Posterior Fossa: Different Region-of-Interest Selection Methods for Time Efficiency, Measurement Reproducibility, and Diagnostic Utility.

OBJECTIVES: This study aimed to explore the diagnostic ability of apparent diffusion coefficient (ADC) values obtained from different region of interest (ROI) measurements in tumor parenchyma for differentiating posterior fossa tumors (PFTs) and the correlations between ADC values and Ki-67. METHODS: Seventy-three pediatric patients with PFTs who underwent conventional diffusion-weighted imaging were recruited in this study. Five different ROIs were manually drawn by 2 radiologists (ROI-polygon, ROI-3 sections, ROI-3-5 ovals, ROI-more ovals, and ROI-whole). The interreader/intrareader repeatability, time required, diagnostic ability, and Ki-67 correlation analysis of the ADC values based on these ROI strategies were calculated. RESULTS: Both interreader and intrareader reliabilities were excellent for ADC values among the different ROI strategies (intraclass correlation coefficient, 0.899-0.992). There were statistically significant differences in time consumption among the 5 ROI selection methods ( P < 0.001). The time required for the ROI-3-5 ovals was the shortest (32.23 ± 5.14 seconds), whereas the time required for the ROI-whole was the longest (204.52 ± 92.34 seconds). The diagnostic efficiency of the ADC values showed no significant differences among the different ROI measurements ( P > 0.05). The ADC value was negatively correlated with Ki-67 ( r = -0.745 to -0.798, all P < 0.0001). CONCLUSIONS: The ROI-3-5 ovals method has the best interobserver repeatability, the shortest amount of time spent, and the best diagnostic ability. Thus, it is considered an effective measurement to produce ADC values in the evaluation of pediatric PFTs.

Phase frustration induced remote synchronization.

Remote synchronization (RS) may take an important role in brain functioning and its study has attracted much attention in recent years. So far, most studies of RS are focused on the Stuart-Landau oscillators with mean-field coupling. However, realistic cases may have more complicated couplings and behaviors, such as the brain networks. To make the study of RS a substantial progress toward realistic situations, we here present a model of RS with phase frustration and show that RS can be induced for those systems where no RS exists when there is no phase frustration. By numerical simulations on both the Stuart-Landau and Kuramoto oscillators, we find that the optimal range of RS depends on the match of phase frustrations between the hub and leaf nodes and a fixed relationship of this match is figured out. While for the non-optimal range of RS, we find that RS exists only in a linear band between the phase frustrations of the hub and leaf nodes. A brief theoretical analysis is provided to explain these results.

Chinese Guideline on Allergen Immunotherapy for Allergic Rhinitis: The 2022 Update.

In the last few decades, there has been a progressive increase in the prevalence of allergic rhinitis (AR) in China, where it now affects approximately 250 million people. AR prevention and treatment include allergen avoidance, pharmacotherapy, allergen immunotherapy (AIT), and patient education, among which AIT is the only curative intervention. AIT targets the disease etiology and may potentially modify the immune system as well as induce allergen-specific immune tolerance in patients with AR. In 2017, a team of experts from the Chinese Society of Allergy (CSA) and the Chinese Allergic Rhinitis Collaborative Research Group (C2AR2G) produced the first English version of Chinese AIT guidelines for AR. Since then, there has been considerable progress in basic research of and clinical practice for AIT, especially regarding the role of follicular regulatory T (TFR) cells in the pathogenesis of AR and the use of allergen-specific immunoglobulin E (sIgE) in nasal secretions for the diagnosis of AR. Additionally, potential biomarkers, including TFR cells, sIgG4, and sIgE, have been used to monitor the incidence and progression of AR. Moreover, there has been a novel understanding of AIT during the coronavirus disease 2019 pandemic. Hence, there was an urgent need to update the AIT guideline for AR by a team of experts from CSA and C2AR2G. This document aims to serve as professional reference material on AIT for AR treatment in China, thus improving the development of AIT across the world.

Estimation of Mycophenolic Acid Exposure in Chinese Renal Transplant Patients by a Joint Deep Learning Model.

BACKGROUND: To predict mycophenolic acid (MPA) exposure in renal transplant recipients using a deep learning model based on a convolutional neural network with bilateral long short-term memory and attention methods. METHODS: A total of 172 Chinese renal transplant patients were enrolled in this study. The patients were divided into a training group (n = 138, Ruijin Hospital) and a validation group (n = 34, Zhongshan Hospital). Fourteen days after renal transplantation, rich blood samples were collected 0-12 hours after MPA administration. The plasma concentration of total MPA was measured using an enzyme-multiplied immunoassay technique. A limited sampling strategy based on a convolutional neural network-long short-term memory with attention (CALS) model for the prediction of the area under the concentration curve (AUC) of MPA was established. The established model was verified using the data from the validation group. The model performance was compared with that obtained from multiple linear regression (MLR) and maximum a posteriori (MAP) methods. RESULTS: The MPA AUC 0-12 of the training and validation groups was 54.28 ± 18.42 and 41.25 ± 14.53 µg·ml -1 ·h, respectively. MPA plasma concentration after 2 (C 2 ), 6 (C 6 ), and 8 (C 8 ) hours of administration was the most significant factor for MPA AUC 0-12 . The predictive performance of AUC 0-12 estimated using the CALS model of the validation group was better than the MLR and MAP methods in previous studies (r 2 = 0.71, mean prediction error = 4.79, and mean absolute prediction error = 14.60). CONCLUSIONS: The CALS model established in this study was reliable for predicting MPA AUC 0-12 in Chinese renal transplant patients administered mycophenolate mofetil and enteric-coated mycophenolic acid sodium and may have good generalization ability for application in other data sets.

Integrated CNN and Federated Learning for COVID-19 Detection on Chest X-Ray Images.

Currently, Coronavirus Disease 2019 (COVID-19) is still endangering world health and safety and deep learning (DL) is expected to be the most powerful method for efficient detection of COVID-19. However, patients' privacy concerns prohibit data sharing between medical institutions, leading to unexpected performance of deep neural network (DNN) models. Fortunately, federated learning (FL), as a novel paradigm, allows participating clients to collaboratively train models without exposing source data outside original location. Nevertheless, the current FL-based COVID-19 detection methods prefer optimizing secondary objectives including delay, energy consumption and privacy, while few works focus on improving the model accuracy and stability. In this paper, we propose a federated learning framework with dynamic focus for COVID-19 detection on CXR images, named FedFocus. Specifically, to improve the training efficiency and accuracy, the training loss of each model is taken as the basis for parameter aggregation weights. As training layer deepens, a constantly updated dynamic factor is designed to stabilize the aggregation process. In addition, to highly restore the real dataset, the training sets in our experiments are divided based on the population and the infection of three real cities. Extensive experiments conducted on the real-world CXR images dataset demonstrate that FedFocus outperforms the baselines in model training efficiency, accuracy and stability.

Deep Personal Multitask Prediction of Diabetes Complication with Attentive Interactions Predicting Diabetes Complications by Multitask-Learning.

Objective: Diabetic complications have brought a tremendous burden for diabetic patients, but the problem of predicting diabetic complications is still unresolved. Our aim is to explore the relationship between hemoglobin A1C (HbA1c), insulin (INS), and glucose (GLU) and diabetic complications in combination with individual factors and to effectively predict multiple complications of diabetes. Methods: This was a real-world study. Data were collected from 40,913 participants with an average age of 48 years from the Department of Endocrinology of Ruijin Hospital in Shanghai. We proposed deep personal multitask prediction of diabetes complication with attentive interactions (DPMP-DC) to predict the five complication models of diabetes, including diabetic retinopathy, diabetic nephropathy, diabetic peripheral neuropathy, diabetic foot disease, and diabetic cardiovascular disease. Results: Our model has an accuracy rate of 88.01% for diabetic retinopathy, 89.58% for diabetic nephropathy, 85.77% for diabetic neuropathy, 80.56% for diabetic foot disease, and 82.48% for diabetic cardiovascular disease. The multitasking accuracy of multiple complications is 84.67%, and the missed diagnosis rate is 9.07%. Conclusion: We put forward the method of interactive integration with individual factors of patients for the first time in diabetic complications, which reflect the differences between individuals. Our multitask model using the hard sharing mechanism provides better prediction than prior single prediction models.

Mesenchymal stromal cells-derived small extracellular vesicles modulate DC function to suppress Th2 responses via IL-10 in patients with allergic rhinitis.

Mesenchymal stromal cells (MSCs) are well known for their immunoregulatory roles on allergic inflammation particularly by acting on T cells, B cells, and dendritic cells (DCs). MSC-derived small extracellular vesicles (MSC-sEV) are increasingly considered as one of the main factors for the effects of MSCs on immune responses. However, the effects of MSC-sEV on DCs in allergic diseases remain unclear. MSC-sEV were prepared from the induced pluripotent stem cells (iPSC)-MSCs by anion-exchange chromatography, and were characterized with the size, morphology, and specific markers. Human monocyte-derived DCs were generated and cultured in the presence of MSC-sEV to differentiate the so-called sEV-immature DCs (sEV-iDCs) and sEV-mature DCs (sEV-mDCs), respectively. The phenotypes and the phagocytic ability of sEV-iDCs were analyzed by flow cytometry. sEV-mDCs were co-cultured with isolated CD4+ T cells or peripheral blood mononuclear cells (PBMCs) from patients with allergic rhinitis. The levels of Th1 and Th2 cytokines produced by T cells were examined by ELISA and intracellular flow staining. And the following mechanisms were further investigated. We demonstrated that MSC-sEV inhibited the differentiation of human monocytes to iDCs with downregulation of the expression of CD40, CD80, CD86, and HLA-DR, but had no effects on mDCs with these markers. However, MSC-sEV treatment enhanced the phagocytic ability of mDCs. More importantly, using anti-IL-10 monoclonal antibody or IL-10Rα blocking antibody, we identified that sEV-mDCs suppressed the Th2 immune response by reducing the production of IL-4, IL-9, and IL-13 via IL-10. Furthermore, sEV-mDCs increased the level of Treg cells. Our study identified that mDCs treated with MSC-sEV inhibited the Th2 responses, providing novel evidence of the potential cell-free therapy acting on DCs in allergic airway diseases.

Prediction of coronary heart disease based on combined reinforcement multitask progressive time-series networks.

Coronary heart disease is the first killer of human health. At present, the most widely used approach of coronary heart disease diagnosis is coronary angiography, a surgery that could potentially cause some physical damage to the patients, together with some complications and adverse reactions. Furthermore, coronary angiography is expensive thus cannot be widely used in under development country. On the other hand, the heart color Doppler echocardiography report, blood biochemical indicators and personal information, such as gender, age and diabetes, can reflect the degree of heart damage in patients to some extent. This paper proposes a combined reinforcement multitask progressive time-series networks (CRMPTN) model to predict the grade of coronary heart disease through heart color Doppler echocardiography report, blood biochemical indicators and ten basic body information items about the patients. In this model, the first step is to perform deep reinforcement learning (DRL) pre-training through asynchronous advantage actor-critic (A3C). Training data is adopted to optimize the recurrent neural network (RNN) that parameterizes the stochastic policy. In the second step, soft parameter sharing module, hard parameter sharing module and progressive time-series networks are used to predict the status of coronary heart disease. The experimental results show that after DRL pre-training, the multiple tasks in the model interact with each other and learn together to achieve satisfactory results and outperform other state-of-the-art methods.