Shared Manifold Regularized Joint Feature Selection for Joint Classification and Regression in Alzheimer's Disease Diagnosis.

In Alzheimer's disease (AD) diagnosis, joint feature selection for predicting disease labels (classification) and estimating cognitive scores (regression) with neuroimaging data has received increasing attention. In this paper, we propose a model named Shared Manifold regularized Joint Feature Selection (SMJFS) that performs classification and regression in a unified framework for AD diagnosis. For classification, unlike the existing works that build least squares regression models which are insufficient in the ability of extracting discriminative information for classification, we design an objective function that integrates linear discriminant analysis and subspace sparsity regularization for acquiring an informative feature subset. Furthermore, the local data relationships are learned according to the samples' transformed distances to exploit the local data structure adaptively. For regression, in contrast to previous works that overlook the correlations among cognitive scores, we learn a latent score space to capture the correlations and employ the latent space to design a regression model with l2,1 -norm regularization, facilitating the feature selection in regression task. Moreover, the missing cognitive scores can be recovered in the latent space for increasing the number of available training samples. Meanwhile, to capture the correlations between the two tasks and describe the local relationships between samples, we construct an adaptive shared graph to guide the subspace learning in classification and the latent cognitive score learning in regression simultaneously. An efficient iterative optimization algorithm is proposed to solve the optimization problem. Extensive experiments on three datasets validate the discriminability of the features selected by SMJFS.

Arterial Stiffness and Obesity as Predictors of Diabetes: Longitudinal Cohort Study.

BACKGROUND: Previous studies have confirmed the separate effect of arterial stiffness and obesity on type 2 diabetes; however, the joint effect of arterial stiffness and obesity on diabetes onset remains unclear. OBJECTIVE: This study aimed to propose the concept of arterial stiffness obesity phenotype and explore the risk stratification capacity for diabetes. METHODS: This longitudinal cohort study used baseline data of 12,298 participants from Beijing Xiaotangshan Examination Center between 2008 and 2013 and then annually followed them until incident diabetes or 2019. BMI (waist circumference) and brachial-ankle pulse wave velocity were measured to define arterial stiffness abdominal obesity phenotype. The Cox proportional hazard model was used to estimate the hazard ratio (HR) and 95% CI. RESULTS: Of the 12,298 participants, the mean baseline age was 51.2 (SD 13.6) years, and 8448 (68.7%) were male. After a median follow-up of 5.0 (IQR 2.0-8.0) years, 1240 (10.1%) participants developed diabetes. Compared with the ideal vascular function and nonobese group, the highest risk of diabetes was observed in the elevated arterial stiffness and obese group (HR 1.94, 95% CI 1.60-2.35). Those with exclusive arterial stiffness or obesity exhibited a similar risk of diabetes, and the adjusted HRs were 1.63 (95% CI 1.37-1.94) and 1.64 (95% CI 1.32-2.04), respectively. Consistent results were observed in multiple sensitivity analyses, among subgroups of age and fasting glucose level, and alternatively using arterial stiffness abdominal obesity phenotype. CONCLUSIONS: This study proposed the concept of arterial stiffness abdominal obesity phenotype, which could improve the risk stratification and management of diabetes. The clinical significance of arterial stiffness abdominal obesity phenotype needs further validation for other cardiometabolic disorders.

MTMG: A multi-task model with multi-granularity information for drug-drug interaction extraction.

Drug-drug interactions (DDIs) extraction includes identifying drug entities and interactions between drug pairs from the biomedical corpus. The discovery of potential DDIs aids in our understanding of the mechanisms underlying adverse reactions or combination therapy to improve patient safety. The manual extraction of DDIs is very time-consuming and expensive; therefore, computer-aided extraction of DDIs is vital. Many neural network-based methods have been proposed and achieved good efficiency in the extraction of DDIs over the years. However, most studies improved the performance of DDIs extraction with various external drug features while directly using golden drug entities, leading to error propagation and low universality in practical application. In this paper, we propose a new multi-task framework called MTMG, which changes DDIs extraction from a sentence-level classification task to a sequence labeling task named Drug-Specified Token Classification (DSTC). The proposed approach, MTMG, jointly trains DSTC with drug named entity recognition (DNER) and two sentence-level auxiliary tasks we designed. We aim to improve the performance of the entire DDIs extraction pipeline by better using the correlation between entities and relationships and, to the extent possible, using the information of varying granularity implied in the dataset. Experimental results show that MTMG can both improve the accuracy of DNER and DDIs extraction and outperforms state-of-the-art technique.

Predicting in-hospital outcomes of patients with acute kidney injury.

Acute kidney injury (AKI) is prevalent and a leading cause of in-hospital death worldwide. Early prediction of AKI-related clinical events and timely intervention for high-risk patients could improve outcomes. We develop a deep learning model based on a nationwide multicenter cooperative network across China that includes 7,084,339 hospitalized patients, to dynamically predict the risk of in-hospital death (primary outcome) and dialysis (secondary outcome) for patients who developed AKI during hospitalization. A total of 137,084 eligible patients with AKI constitute the analysis set. In the derivation cohort, the area under the receiver operator curve (AUROC) for 24-h, 48-h, 72-h, and 7-day death are 95·05%, 94·23%, 93·53%, and 93·09%, respectively. For dialysis outcome, the AUROC of each time span are 88·32%, 83·31%, 83·20%, and 77·99%, respectively. The predictive performance is consistent in both internal and external validation cohorts. The model can predict important outcomes of patients with AKI, which could be helpful for the early management of AKI.

Comparison of the effects of AgNPs on the morphological and mechanical characteristics of cancerous cells.

Currently, silver nanoparticles (AgNPs) are the most produced nanoparticles in global market and have been widely utilized in the biomedical field. Here, we investigated the morphological and mechanical effects of AgNPs on cancerous cells of A549 cells and SMMC-7721 cells with atomic force microscope (AFM). The influence of AgNPs on the morphological properties and mechanical properties of cancerous cells were characterized utilizing the force-volume (FV) mode and force spectroscopy (FS) mode of AFM measurement. We mainly focus on the comparison of the effects of AgNPs on the two types of cancerous cells based on the fitting results of calculating the Young's moduli utilizing the Sneddon model. The results showed that the morphology changed little, but the mechanical properties of height, roughness, adhesion force and Young's moduli of two cancerous cells varied significantly with the stimulation of different concentrations of AgNPs. This research has provided insights into the classification and characterization of the effects of the various concentrations of AgNPs on the cancerous cells in vitro by utilizing AFM methodologies for disease therapy.

A prognostic signature based on cuprotosis-related long non-coding RNAs predicts the prognosis and sensitivity to chemotherapy in patients with colorectal cancer.

Cuprotosis, a newly proposed mechanism of cell death, can trigger acute oxidative stress that leads to cell death by mediating protein lipidation in the tricarboxylic acid cycle. However, cuprotosis-related long non-coding RNAs (CRLNCs) and their relationship with prognosis and the immunological landscape of colorectal cancer (CRC) are unclear. We have developed a lncRNA signature to predict survival time, immune infiltration, and sensitivity to chemotherapy. CRLNCs were screened using the Cor function of the R software and the differentially expressed lncRNAs were collected with the limma package. Differentially expressed long non-coding RNAs (lncRNAs) associated with prognosis were selected using univariate regression analysis. A prognostic signature was developed using the least absolute shrinkage and selection operator (LASSO) and multivariate regression analysis. Patients with CRC were divided into two groups based on the risk score. The low-risk group had a more favorable prognosis, higher expression of immune checkpoints, and a higher level of immune cell infiltration compared with the high-risk group. Furthermore, there was a close association between the risk score and the clinical stage, tumor mutational burden, cancer stem cell index, and microsatellite instability. We also assessed chemotherapy response in the two risk groups. Our study analyzed the role of CRLNCs in CRC and provided novel targets and strategies for CRC chemotherapy and immunotherapy.

Water-in-salt electrolytes achieve high energy densities at an ultralow-temperature for aqueous symmetrical supercapacitors.

LiTFSI/H2O water-in-salt electrolytes with different concentrations show high energy densities for capacitive charge storage at sub-zero-temperatures (e.g. 32.23, 38.35 and 35 W h kg-1 at 0, -10 and -20 °C, which are 1.44, 1.71 and 1.56 times that of normal temperature).

The effects of measurement parameters on the cancerous cell nucleus characterisation by atomic force microscopy in vitro.

Cancer is now responsible for the major leading cause of death worldwide. It is noteworthy that lung cancer has been recognised as the highest incidence (11.6%) and mortality (18.4%) for combined sexes among a variety of cancer diseases. Therefore, it is of great value to investigate the mechanical properties of lung cancerous cells for early diagnosis. This paper focus on the influence of measurement parameters on the measured central Young's moduli of single live A549 cell in vitro based on the force spectroscopy mode of atomic force microscopy (AFM). The effects of the measurement parameters on the measured central Young's moduli were analysed by fitting the force-depth curves utilising the Sneddon model. The results revealed that the Young's moduli of A549 cells increased with the larger indentation force, higher indentation speed, less retraction time, deeper Z length and lower purity percentage of serum. The Young's moduli of cells increased first and then decreased with the increasing dwell time. Hence, this research may have potential significance to provide reference for the standardised detection of a single cancerous cell in vitro using AFM methodologies.

Bipolar clavicle fractures treatment using medial and lateral double plates: A case report.

RATIONALE: Clavicle fractures are common, accounting for 2.6 to 4% of all fractures, which typically result from direct injuries, including direct force on the shoulder after falling. However, bipolar clavicle fractures are rare, accounting for only 2.8% of all clavicle fractures, and their injury mechanism is speculated to evolve from two independent and continuous forces affecting the clavicle. Due to its low incidence, there is great controversy regarding the treatment of this fracture, as there is no relevant treatment standard or guideline to date. PATIENT CONCERNS: In this case report, we describe a rare case of bipolar clavicle fracture in a 76-year-old man with multiple systemic fracture complications due to a traffic injury. He presented with limited shoulder function and movement upon arrival in the emergency room. DIAGNOSIS: Bipolar clavicle fracture in the right shoulder (Robinson type 1B2, Robinson type 3B2). INTERVENTIONS: We performed trans-sternoclavicular locking plate and lateral clavicular hook plate treatments and instructed patients to perform reasonable postoperative functional exercises. OUTCOMES: Three months postoperatively, the pain was almost completely relieved with a DASH score of 40.0. Furthermore, radiographic examination of the clavicle showed satisfactory fracture healing. The patient had no further demands for shoulder function and no irritative symptoms of internal fixation and refused to undergo a second operation. The patient had a satisfactory prognosis after the treatment. LESSONS: The treatment of bipolar clavicle fractures remains controversial. This study provides evidence of a feasible method to treat bipolar clavicle fractures: trans-sternoclavicular locking plate and lateral clavicular hook plate treatment.

Multi-Attribute Discriminative Representation Learning for Prediction of Adverse Drug-Drug Interaction.

Adverse drug-drug interaction (ADDI) is a significant life-threatening issue, posing a leading cause of hospitalizations and deaths in healthcare systems. This paper proposes a unified Multi-Attribute Discriminative Representation Learning (MADRL) model for ADDI prediction. Unlike the existing works that equally treat features of each attribute without discrimination and do not consider the underlying relationship among drugs, we first develop a regularized optimization problem based on CUR matrix decomposition for joint representative drug and discriminative feature selection such that the selected drugs and features can well approximate the original feature spaces and the critical factors discriminative to ADDIs can be properly explored. Different from the existing models that ignore the consistent and unique properties among attributes, a Generative Adversarial Network (GAN) framework is then designed to capture the inter-attribute shared and intra-attribute specific representations of adverse drug pairs for exploiting their consensus and complementary information in ADDI prediction. Meanwhile, MADRL is compatible with any kind of attributes and capable of exploring their respective effects on ADDI prediction. An iterative algorithm based on the alternating direction method of multipliers is developed for optimization. Experiments on publicly available dataset demonstrate the effectiveness of MADRL when compared with eleven baselines and its six variants.

Upper-Limb Motion Recognition Based on Hybrid Feature Selection: Algorithm Development and Validation.

BACKGROUND: For rehabilitation training systems, it is essential to automatically record and recognize exercises, especially when more than one type of exercise is performed without a predefined sequence. Most motion recognition methods are based on feature engineering and machine learning algorithms. Time-domain and frequency-domain features are extracted from original time series data collected by sensor nodes. For high-dimensional data, feature selection plays an important role in improving the performance of motion recognition. Existing feature selection methods can be categorized into filter and wrapper methods. Wrapper methods usually achieve better performance than filter methods; however, in most cases, they are computationally intensive, and the feature subset obtained is usually optimized only for the specific learning algorithm. OBJECTIVE: This study aimed to provide a feature selection method for motion recognition of upper-limb exercises and improve the recognition performance. METHODS: Motion data from 5 types of upper-limb exercises performed by 21 participants were collected by a customized inertial measurement unit (IMU) node. A total of 60 time-domain and frequency-domain features were extracted from the original sensor data. A hybrid feature selection method by combining filter and wrapper methods (FESCOM) was proposed to eliminate irrelevant features for motion recognition of upper-limb exercises. In the filter stage, candidate features were first selected from the original feature set according to the significance for motion recognition. In the wrapper stage, k-nearest neighbors (kNN), Naïve Bayes (NB), and random forest (RF) were evaluated as the wrapping components to further refine the features from the candidate feature set. The performance of the proposed FESCOM method was verified using experiments on motion recognition of upper-limb exercises and compared with the traditional wrapper method. RESULTS: Using kNN, NB, and RF as the wrapping components, the classification error rates of the proposed FESCOM method were 1.7%, 8.9%, and 7.4%, respectively, and the feature selection time in each iteration was 13 seconds, 71 seconds, and 541 seconds, respectively. CONCLUSIONS: The experimental results demonstrated that, in the case of 5 motion types performed by 21 healthy participants, the proposed FESCOM method using kNN and NB as the wrapping components achieved better recognition performance than the traditional wrapper method. The FESCOM method dramatically reduces the search time in the feature selection process. The results also demonstrated that the optimal number of features depends on the classifier. This approach serves to improve feature selection and classification algorithm selection for upper-limb motion recognition based on wearable sensor data, which can be extended to motion recognition of more motion types and participants.

A Core Drug Discovery Framework from Large-Scale Literature for Cold Pathogenic Disease Treatment in Traditional Chinese Medicine.

Cold pathogenic disease is a widespread disease in traditional Chinese medicine, which includes influenza and respiratory infection associated with high incidence and mortality. Discovering effective core drugs in Chinese medicine prescriptions for treating the disease and reducing patients' symptoms has attracted great interest. In this paper, we explore the core drugs for curing various syndromes of cold pathogenic disease from large-scale literature. We propose a core drug discovery framework incorporating word embedding and community detection algorithms, which contains three parts: disease corpus construction, drug network generation, and core drug discovery. First, disease corpus is established by collecting and preprocessing large-scale literature about the Chinese medicine treatment of cold pathogenic disease from China National Knowledge Infrastructure. Second, we adopt the Chinese word embedding model SSP2VEC for mining the drug implication implied in the literature; then, a drug network is established by the semantic similarity among drugs. Third, the community detection method COPRA based on label propagation is adopted to reveal drug communities and identify core drugs in the drug network. We compute the community size, closeness centrality, and degree distributions of the drug network to analyse the patterns of core drugs. We acquire 4681 literature from China national knowledge infrastructure. Twelve significant drug communities are discovered, in which the top-10 drugs in every drug community are recognized as core drugs with high accuracy, and four classical prescriptions for treating different syndromes of cold pathogenic disease are discovered. The proposed framework can identify effective core drugs for curing cold pathogenic disease, and the research can help doctors to verify the compatibility laws of Chinese medicine prescriptions.

Effect of trypsin concentration on living SMCC-7721 cells studied by atomic force microscopy.

Trypsin is playing an important role in the processes of cancer proliferation, invasion and metastasis which require the precise information of morphology and mechanical properties on the nano-scale for the related research. In this work, living human hepatoma (SMCC-7721) cells were treated with different concentrations of trypsin solution. The morphology and mechanical properties of the cells were measured via atomic force microscope (AFM). Statistical analyses of measurement data indicated that with the increase of trypsin concentration, the average cell height and the surface roughness were both increased, but the cell viability, the cell surface adhesion and the elasticity modulus were decreased significantly. The force required to puncture the cells was also gradually reduced. It indicates that trypsin not only hydrolyses the proteins between the cell and the substrate but also the membrane proteins. The results offer valuable clues for the cancerous process study, pathological analysis and trypsin inhibitor drug development. And this work provides an effective way for overcoming the cell membrane in drug injection for cell-targeted therapy.

Investigation of the mechanical effects of targeted drugs on cancerous cells based on atomic force microscopy.

Cancer is currently drawing more and more attention as the leading factor in death worldwide. However, little research has been directed towards investigating the micro/nanoscale mechanical properties of cancer cells treated by targeted drugs to evaluate the model systems of targeted drugs using atomic force microscopy (AFM) nano-indentation, especially in light of the multiple drugs targeting various cancerous cells. This paper aims to compare the mechanical effects of sorafenib tosylate and osimertinib mesylate on hepatoma carcinoma cells and lung cancerous cells using atomic force microscopy from the perspective of a model system based on nano-indentation at the micro/nanoscale, which has rarely been investigated. The Sneddon model is applied to fit the force-distance curves, and the mechanical properties, i.e., Young's moduli, can then be calculated. For the SMMC-7721 cells, osimertinib mesylate is a more effective inhibitor than sorafenib tosylate. For the A549 cells, osimertinib mesylate and sorafenib tosylate both have an obvious inhibitory effect. The experimental results may make possible contributions to the diagnosis and treatment of early-stage cancers.

Attribute Supervised Probabilistic Dependent Matrix Tri-Factorization Model for the Prediction of Adverse Drug-Drug Interaction.

Adverse drug-drug interaction (ADDI) becomes a significant threat to public health. Despite the detection of ADDIs is experimentally implemented in the early development phase of drug design, many potential ADDIs are still clinically explored by accidents, leading to a large number of morbidity and mortality. Several computational models are designed for ADDI prediction. However, they take no consideration of drug dependency, although many drugs usually produce synergistic effects and own highly mutual dependency in treatments, which contains underlying information about ADDIs and benefits ADDI prediction. In this paper, we design a dependent network to model the drug dependency and propose an attribute supervised learning model Probabilistic Dependent Matrix Tri-Factorization (PDMTF) for ADDI prediction. In particular, PDMTF incorporates two drug attributes, molecular structure and side effect, and their correlation to model the adverse interactions among drugs. The dependent network is represented by a dependent matrix, which is first formulated by the row precision matrix of the predicted attribute matrices and then regularized by the molecular structure similarities among drugs. Meanwhile, an efficient alternating algorithm is designed for solving the optimization problem of PDMTF. Experiments demonstrate the superior performance of the proposed model when compared with eight baselines and its two variants.

The effects of platelet-rich plasma injection in knee and hip osteoarthritis: a meta-analysis of randomized controlled trials.

OBJECTIVE: We conducted this updated meta-analysis to evaluate the effects of PRP in patients with knee or hip OA. METHOD: PubMed, Embase, and Web of Science were searched to identify randomized controlled trials (RCTs) that compared the efficacy of PRP with other intra-articular injections. The outcomes of interest included Western Ontario and McMaster (WOMAC), Knee Injury and Osteoarthritis Outcome Score (KOOS), Visual Analog Scale (VAS), Harris Hip Score (HHS), and International Knee Documentation Committee (IKDC). RESULTS: Twenty-four RCTs with 21 at knee OA and three at hip OA were included in this meta-analysis. The PRP injections significantly improved the WOMAC score, VAS score, IKDC score, and HHS score as compared with comparators. The WOMAC pain, stiffness, and physical function scores were also significantly better in the PRP group than in the control group. Most of the evaluated parameters that favored PRP were observed in knee OA but not in hip OA, at short-term (at 1, 2, 3, 6, 12 months) but not long-term follow-up (at 18 months), in RCTs with low risk of bias. CONCLUSIONS: Intra-articular PRP injection provided better effects than other injections for OA patients, especially in knee OA patients, in terms of pain reduction and function improvement at short-term follow-up. Key Points • This updated meta-analysis, based on great sample size and high-quality studies, evaluates the effects of PRP in patients with knee or hip OA. • Intra-articular PRP injection provided better effects than other injections for OA patients. • Most of the evaluated parameters that favored PRP were observed in knee OA at short term (at 1, 2, 3, 6, 12 months).

The effects of tacrolimus plus phototherapy in the treatment of vitiligo: a meta-analysis.

The objective of this meta-analysis was performed to compare the effects of tacrolimus plus phototherapy in the treatment of patients with vitiligo. Relevant studies were identified by searching PubMed, Embase, and Web of Science databases. The main outcomes of interest included excellent response (≥ 75% repigmentation), good response (50-75% repigmentation), moderate response (25%-50% repigmentation), and poor response (< 25% repigmentation). Risk ratio (RR) with 95% confidence intervals (95% CIs) was used to calculate the data. Eleven studies were included in this study. Compared with phototherapy alone, combination treatment of tacrolimus and phototherapy significantly improved excellent response rate (RR = 1.40, 95% CI 1.16, 1.69; P < 0.001) and reduced the poor response rate (RR = 0.37, 95% CI 0.22, 0.61; P = 0.001). However, the good response rate (RR = 1.00, 95% CI 0.59, 1.69, P = 1.000) and moderate response rate (RR = 0.91, 95% CI 0.60, 1.38; P = 0.653) were not significantly different between the two treatments. Subgroup analysis suggested that combination treatment had a higher excellent response rate than phototherapy alone for lesions located in the face and proximal limbs. Both NB-UVB and EL, when added to tacrolimus, resulted in a significantly higher excellent response rate than they were used alone. Meta-regression analysis showed that age was a predictive factor that influenced the effect of combination treatment on an excellent response, in which children had a high excellent response to the treatment. Other demographic and clinical variables, including gender, disease duration, family history, and type of vitiligo, did not have any impact on the treatment effect. Combination treatment with tacrolimus and phototherapy was more effective than phototherapy monotherapy for patients with vitiligo, especially in the lesions located in the face and proximal limbs. More large-scale, well-performed trials are needed to verify our findings.

A Semantic Analysis and Community Detection-Based Artificial Intelligence Model for Core Herb Discovery from the Literature: Taking Chronic Glomerulonephritis Treatment as a Case Study.

The Traditional Chinese Medicine (TCM) formula is the main treatment method of TCM. A formula often contains multiple herbs where core herbs play a critical therapeutic effect for treating diseases. It is of great significance to find out the core herbs in formulae for providing evidences and references for the clinical application of Chinese herbs and formulae. In this paper, we propose a core herb discovery model CHDSC based on semantic analysis and community detection to discover the core herbs for treating a certain disease from large-scale literature, which includes three stages: corpus construction, herb network establishment, and core herb discovery. In CHDSC, two artificial intelligence modules are used, where the Chinese word embedding algorithm ESSP2VEC is designed to analyse the semantics of herbs in Chinese literature based on the stroke, structure, and pinyin features of Chinese characters, and the label propagation-based algorithm LILPA is adopted to detect herb communities and core herbs in the herbal semantic network constructed from large-scale literature. To validate the proposed model, we choose chronic glomerulonephritis (CGN) as an example, search 1126 articles about how to treat CGN in TCM from the China National Knowledge Infrastructure (CNKI), and apply CHDSC to analyse the collected literature. Experimental results reveal that CHDSC discovers three major herb communities and eighteen core herbs for treating different CGN syndromes with high accuracy. The community size, degree, and closeness centrality distributions of the herb network are analysed to mine the laws of core herbs. As a result, we can observe that core herbs mainly exist in the communities with more than 25 herbs. The degree and closeness centrality of core herb nodes concentrate on the range of [15, 40] and [0.25, 0.45], respectively. Thus, semantic analysis and community detection are helpful for mining effective core herbs for treating a certain disease from large-scale literature.

The anti-fibrotic effect of human fetal skin-derived stem cell secretome on the liver fibrosis.

BACKGROUND: Liver fibrosis resulting from chronic liver injury is one of the major causes of mortality worldwide. Stem cell-secreted secretome has been evaluated for overcoming the limitations of cell-based therapy in hepatic disease, while maintaining its advantages. METHODS: In this study, we investigated the effect of human fetal skin-derived stem cell (hFSSC) secretome in the treatment of liver fibrosis. To determine the therapeutic potential of the hFSSC secretome in liver fibrosis, we established the CCl4-induced rat liver fibrosis model and administered hFSSC secretome in vivo. Moreover, we investigated the anti-fibrotic mechanism of hFSSC secretome in hepatic stellate cells (HSCs). RESULTS: Our results showed that hFSSC secretome effectively reduced collagen content in liver, improved the liver function and promoted liver regeneration. Interestingly, we also found that hFSSC secretome reduced liver fibrosis through suppressing the epithelial-mesenchymal transition (EMT) process. In addition, we found that hFSSC secretome inhibited the TGF-ß1, Smad2, Smad3, and Collagen I expression, however, increased the Smad7 expression. CONCLUSIONS: In conclusions, our results suggest that hFSSC secretome treatment could reduce CCl4-induced liver fibrosis via regulating the TGF-ß/Smad signal pathway.

Anti-degranulation response of herbal formula in RBL-2H3 cells.

Allergic diseases not only bring serious economic burden to the patients, but also consume a lot of substantial resources of social medical systems. Thus, the prevention and treatment of allergic diseases are imperative. In this study, the anti-degranulation activity of herbal formula was evaluated using the rat basophil leukemia cells (RBL-2H3) as in vitro model. The morphological and biophysical properties of RBL-2H3 cells before and after treatment with herbal formula were also determined. Notably, the herbal formula exhibits clearly inhibited degranulation by RBL-2H3 cells in a concentration-dependent manner without cytotoxic effect. Therefore, this herbal formula can be used as an alternative and promising therapeutic agent to ameliorate allergic diseases.