Growth mechanism prediction for nanoparticles via structure matching polymerization.

Exploring structural and component evolution remains a challenging scientific problem for nanoscience. We propose a novel approach called principle of minimization of structure matching polymerization (SMP) change to rapidly explore the global minimum structure on the potential energy surface (PES). The new method can map low-dimensional stable structures to high-dimensional local minima, and this will make it possible for us to study the growth mechanisms of nanoparticles. Some new lowest-energy structures were found by SMP methods for sulfuric acid (SA)-dimethylamine (DMA) systems relative to previous studies. Additionally, we found that the growth process of boron clusters is mainly that the small-size boron clusters are continuously added to large-size boron clusters by structure matching for Bn (n = 2-36) systems, Bm + Bk → Bn, where m + k = n and 1 ≤ k ≤ 3. The SMP approach can greatly improve the search efficiency of other unbiased global optimization algorithms, such as basin-hopping (BH) and genetic algorithm (GA), with an enhancement of up to 19- and 7-fold relative to traditional BH and GA algorithms for searching the global minima of Bn (n = 14-22) systems. The SMP approach is general and flexible and can be applied to different kinds of problems, such as material structure design, crystal structure prediction, and new drug generation.

New Multicomponent Optimization Scheme for Equiatomic Vanadium-Titanium Nanoparticle Study.

We present a new multicomponent structure prediction method named MRS-Swap searching, which is inspired by space symmetry and swap of different atomic species. For the pure titanium cluster, a new ground-state structure of the Ti20 cluster with higher symmetry relative to a previous study was found by our method. Based on the structural analysis of Tin (n = 2k, k = 2-11), Vn (n = 2k, k = 2-11), and TinVm (n = m = 2-11) systems, we found that the lowest energy structures of these three systems are very similar, which indicates that equiatomic vanadium-titanium-mixed clusters do not change their ground-state structure relative to the same size pure vanadium and titanium cluster. According to the structure-activity relationship, we conclude that the yield strength (σ) of macro vanadium-titanium alloy is between pure titanium and pure vanadium metal, and this can be expressed through σ(Ti) > σ(TiV) > σ(V). The X-ray diffraction results show that the V2Nb, TiVNb, and Ti2Nb alloys also have the same BCC structure, which may be related to their microstructure. Our method and results can be helpful for future multicomponent alloy design.

A polarization image enhancement method for glioma.

Polarization imaging technique (PIT) based on a backward scattering 3 × 3 Mueller matrix polarization imaging experimental setup is able to study the optical information and microstructure of glioma and non-glioblastoma tissues from clinical treatment. However, the image contrast of Mueller Matrix Elements (MME) is far from sufficient to provide supplemental information in the clinic, especially in off-diagonal MME. The aim of this work is to propose an innovative method to improve the contrast and quality of PIT images of glioma and non-glioma tissues. The work first confirms the robustness of the method by evaluating the enhanced images and assessment coefficients on ex vivo unstained glioma and non-glioma sample bulks, then the optimal enhancement results are tested and presented based on the multi-sample tests. This PIT image enhancement method can greatly improve the contrast and detailed texture information of MMEs images, which can provide more useful clinical information, and further be used to identify glioma and residues in the intraoperative environment with PIT.

Ion formation mechanism of cortisone molecules and clusters in charged nanodroplets.

Mass spectrometry measurements coupled with classical molecular dynamics (MD) simulations have been conducted in recent years to understand the final stage of ion formation in electrospray ionization (ESI). Here, to characterize the ion formation mechanism in the recently developed droplet-assisted ionization (DAI) source, MD simulations with various conditions (solute number, temperature, ions, composition) were performed to help explain DAI-based measurements. The specific binding ability of cortisone with preformed ions (ions of sodium, cesium and iodide) in evaporating nanodroplets makes the ion formation process characteristic of both the ion evaporation and charge residue models (IEM and CRM, respectively). Most preformed ions are ejected with dozens of solvent molecules to form gas-phase ions by IEM, while clusters of one or more cortisone molecules with one or more preformed ions remain in the evaporating droplet to form gas-phase ions by CRM. As the ratio of cortisone molecules to preformed ions increases, the number of preformed ions held in the droplet without ejection by the IEM increases. In other words, increasing the molecular solute to preformed ion ratio in the droplet increases the fraction of gas-phase ions formed by CRM relative to IEM. The increase in CRM relative to IEM is accompanied by an increase in the calculated activation energy barrier, which can explain the activation energy measurements by DAI, where droplets without preformed ions exhibit higher activation energies for gas-phase ion formation than droplets containing large numbers of preformed ions.

Experiment-theory hybrid method for studying the formation mechanism of atmospheric new particle formation.

Atmospheric aerosols have a significant influence on climate change through their effect on the cloud lifetime and the radiative balance of the Earth's atmosphere. Despite its importance, the mechanism of aerosol nucleation is still poorly understood. Based on the low-energy structure of cluster molecules, quantum chemical (QC) computations can help us to directly calculate the formation mechanism of atmospheric clusters and formation rates at the molecular level. However, deciphering the formation mechanism of pre-nucleated clusters, especially those close to the critical size (∼1.7 nm), remains extremely challenging because many millions of configuration spaces might need to be explored to find the low-energy structure. We present a new idea that establishes a comprehensive experimental and computational hybrid calculation protocol to integrate experimental data, isomer distributions, hydrogen bond interactions, and interaction sites for exploring the configuration spaces and clarifying the nucleation mechanism of acid-base clusters, whose maximum size can exceed 1.7 nm. This protocol can effectively and accurately explore the configuration space of complex large nucleation clusters on the potential energy surface (PES). The consistency of the cluster concentration and the formation rate between the experiment and the in situ measurement is much better than that of the previous studies and proves its accuracy. In addition, we found that malic acid (MA) can enhance sulfuric acid-dimethylamine (SA-DMA)-based particle formation rates in the atmospheric boundary layer, for example, in Shanghai and Beijing in the summer, with an enhancement of up to 700- and 135-fold, respectively. The enhancement in atmospheric particle formation by MA is critical for new particle formation in the boundary layer with relatively low SA and DMA concentrations and at high temperature. This model greatly improves our understanding of the complex aerosol nucleation mechanism of large-scale multicomponent cluster molecules.

Towards fully ab initio simulation of atmospheric aerosol nucleation.

Atmospheric aerosol nucleation contributes to approximately half of the worldwide cloud condensation nuclei. Despite the importance of climate, detailed nucleation mechanisms are still poorly understood. Understanding aerosol nucleation dynamics is hindered by the nonreactivity of force fields (FFs) and high computational costs due to the rare event nature of aerosol nucleation. Developing reactive FFs for nucleation systems is even more challenging than developing covalently bonded materials because of the wide size range and high dimensional characteristics of noncovalent hydrogen bonding bridging clusters. Here, we propose a general workflow that is also applicable to other systems to train an accurate reactive FF based on a deep neural network (DNN) and further bridge DNN-FF-based molecular dynamics (MD) with a cluster kinetics model based on Poisson distributions of reactive events to overcome the high computational costs of direct MD. We found that previously reported acid-base formation rates tend to be significantly underestimated, especially in polluted environments, emphasizing that acid-base nucleation observed in multiple environments should be revisited.

Differentiation of Human GBM From Non-GBM Brain Tissue With Polarization Imaging Technique.

As for optical techniques, it is difficult for the 5-aminolevulinic (5-ALA) fluorescence guidance technique to completely detect glioma due to residual cells in the blind area and the dead angle of vision under microscopy. The purpose of this research is to characterize different microstructural information and optical properties of formalin-soaked unstained glioblastoma (GBM) and non-GBM tissue with the polarization imaging technique (PIT), and provide a novel method to detect GBM during surgery. In this paper, a 3×3 Mueller matrix polarization experimental system in backscattering mode was built to detect the GBM and non-GBM tissue bulk. The Mueller matrix decomposition and transformation parameters of GBM and non-GBM tissue were calculated and analyzed, and showed that parameters (1- Δ ) and t are good indicators for distinguishing GBM from non-GBM tissues. Furthermore, the central moment coefficients (CMCs) of the frequency distribution histogram (FDH) were also calculated and used to distinguish the cancerous tissues. The results of the experiments confirmed the feasibility of PIT applied in the clinic to detect glioma, laying the foundation for the subsequent non-invasive, non-staining glioma detection.

Effect of the Samples' Surface With Complex Microscopic Geometry on 3 × 3 Mueller Matrix Measurement of Tissue Bulks.

The clinical in vivo tissue bulks' surface is always coarse and shows a complex microscopic geometry which may affect the visual effect of polarization images and calculation of polarization parameters of the sample. To confirm whether this effect would cause identification difficulties and misjudgments on the target recognition when performed the polarization imaging based on 3 × 3 Mueller matrix measurement, cylindrical type and slope type physical models were used to study and analyze the effect of the surface with complex microscopic geometry on the polarization images. Then, clinical tumor bulk samples were used to interact with different sizes of patterns to simulate the different complex microscopic geometry and test the coarse surface effect on polarization images. Meanwhile, assessment parameters were defined to evaluate and confirm the variation between two polarization images quantitatively. The results showed that the polarization imaging of the sample surface with the complex microscopic geometry led to acceptable visual effect and limited quantitative variation on the value of polarization parameters and assessment parameters, and it caused no identification difficulties on target recognition, indicating that it is feasible to apply the polarization imaging based on 3 × 3 Mueller matrix measurement on clinical in vivo tissues with the complex microscopic geometry sample surface.

A high-accuracy machine-learning water model for exploring water nanocluster structures.

Water, the most important molecule on the Earth, possesses many essential and unique physical properties that are far from completely understood, partly due to serious difficulties in identifying the precise microscopic structures of water. Hence, identifying the structures of water nanoclusters is a fundamental and challenging issue for studies on the relationship between the macroscopic physical properties of water and its microscopic structures. For large-scale simulations (at the level of nm and ns) of water nanoclusters, a calculation method with simultaneous accuracy at the level of quantum chemistry and efficiency at the level of an empirical potential method is in great demand. Herein, a machine-learning (ML) water model was utilized to explore the microscopic structural features at different length scales for water nanoclusters with a size up to several nm. The ML water model can be employed to efficiently predict the structures of water nanoclusters with a similar accuracy to that of density functional theory and with substantially lower computational resource demands. To validate the low-lying structure search results with experimental spectral results, an ML water model combined with velocity autocorrelation function analysis was used to simulate the vibrational spectra of water nanoclusters with up to thousands of water molecules. By comparing the simulated and experimentally recorded vibrational spectra, the atomic structures determined by a simulation based on the ML water model are all verified. To demonstrate its ability to represent water's structural evolution at large length and time scales, the ML water model was employed to model the structural evolution during the crystal-liquid transition, and the phase transition temperatures of water clusters with different sizes were precisely predicted. The ML water model provides an efficient theoretical calculation tool for exploring the structures and physical properties of water and their relationships, especially for clusters with relatively large sizes and processes with relatively long durations.

Liver stiffness measurement is a potent predictor of histological fibrosis regression after hepatitis C virus clearance.

BACKGROUND: Most of the studies on fibrosis regression prediction were based on noninvasive fibrosis markers and differ greatly. The 'Beijing fibrosis classification' can use histological results to classify fibrosis into progressive or 'nonprogressive' according to fibrotic septal morphology. We use this standard which served as the gold standard in order to find fibrosis regression predictors. AIM: To study the predictors of fibrosis regression after hepatitis C virus clearance according to histological fibrosis staging by the 'Beijing fibrosis classification'. MATERIALS AND METHODS: This was a prospective cohort study. A total of 68 patients with advanced liver fibrosis or compensated cirrhosis who achieved sustained virological response were enrolled. Patients with the Ishak scores lower than 3 seemed to have fibrosis regression. The others were divided into the fibrosis progressive group and the nonprogressive group according to the 'Beijing fibrosis classification'. Predictors of fibrosis regression were studied by logistic regression using baseline factors and the dynamic change in noninvasive fibrosis factors. RESULTS: Eighteen patients were assigned to the progressive group, and the others were assigned to the nonprogressive group. The baseline liver stiffness measurements (LSMs) of the progressive and nonprogressive groups were 14.35 (11.3, 27.3) kPa and 11.3 (8.3, 14.2) kPa, respectively, P = 0.02. The baseline LSM was the only predictor of fibrosis progression. With a cutoff of 11.85 kPa, the AUC was 0.71 (0.5, 0.9), and the negative predictive value was 0.92. CONCLUSIONS: The baseline LSM was found to be the only predictor of fibrosis regression, 11.85 kPa is a possible 'hepatic fibrosis return point'.

Association of PNPLA3 rs738409 G/C gene polymorphism with nonalcoholic fatty liver disease in children: a meta-analysis.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease worldwide. Current studies have shown that PNPLA3 (Patatin-like phospholipase domain containing 3) rs738409 G/C gene polymorphism is associated with adult nonalcoholic fatty liver disease [1, 2].But there is no consensus on the relationship between PNPLA3 rs738409 G/C gene polymorphism and children NAFLD due to differences in population samples. To this end, a meta-analysis of published research is conducted to comprehensively assess the relationship between PNPLA3 gene polymorphism and NAFLD in children. METHODS: We searched MEDLINE, PubMed, EMBASE, and CENTRAL databases from inception to May 2019. Case-control studies assessing the relationship between PNPLA3 rs738409 G/C gene polymorphism with non-alcoholic fatty liver disease in children were selected according to inclusion and exclusion criteria. Random effects model was used to quantify the association between the PNPLA3 rs738409 G/C gene polymorphism and the susceptibility of children's NAFLD. Fixed effects model was used to quantify the relationship between the PNPLA3 rs738409 G/C gene polymorphism and the severity of NAFLD in children. The Stata 12.0 software was employed for data analysis. RESULTS: A total of nine case-control studies were included in this meta-analysis containing data of 1173 children with NAFLD and 1792 healthy controls. Five studies compared NAFLD children and non-NAFLD healthy populations. Statistical analysis showed that PNPLA3 gene polymorphism was significantly associated with children's NAFLD in the allele contrast, dominant, recessive and over dominant models (G vs C,OR = 3.343, 95% CI = 1.524-7.334; GG + GC vs CC,OR = 3.157, 95% CI = 1.446-6.892;GG vs GC + CC,OR = 5.692, 95% CI = 1.941-16.689; GG + CC vs GC,OR = 2.756, 95% CI = 1.729-4.392). Four case-control studies compared Children with nonalcoholic fatty liver (NAFL) and children with nonalcoholic steatohepatitis (NASH). The results showed that the PNPLA3 gene polymorphism was also significantly associated with the severity of NAFLD in children in recessive gene model (GG vs GC + CC,OR = 14.43, 95% CI = 5.985-34.997); The Egger's test revealed no significant publication bias. CONCLUSIONS: Meta-analysis showed that PNPLA3 gene polymorphism was significantly associated with susceptibility and severity of NAFLD in children.

Ultrasonographic appearance of triple-negative invasive breast carcinoma is associated with novel molecular subtypes based on transcriptomic analysis.

BACKGROUND: Various sonographic features of triple-negative invasive breast carcinomas (TNBC) expected to be associated with the molecular subtypes based on transcriptomic analysis were examined. The effects of clinical, sonographic, pathological, and molecular features on survival outcome was also studied. METHODS: One hundred and fourteen patients with breast cancer with negative expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal receptor 2 (HER2) were included in our retrospective study. Based on the transcriptomic profiles, four stable clusters named immunomodulatory (IM), luminal androgen receptor (LAR), mesenchymal-like (MES), and basal-like and immune-suppressed (BLIS) were identified. Ultrasound (US) images were reviewed by two US physicians according to Breast Imaging Reporting and Data System (BI-RADS). Multivariate Cox regression was used to determine the variables associated with recurrence-free survival (RFS) and overall survival (OS). RESULTS: There were 21 IM, 18 LAR, 36 MES, and 39 BLIS cases. The four molecular subtypes showed significant differences in terms of tumor shape (P=0.008) and posterior acoustic pattern (P=0.028). Compared with the subtypes LAR and MES, the IM and BLIS subtypes had higher probability of presenting benign-like sonographic features, such as regular shape, no angular/spiculated margin, and posterior acoustic enhancement (P<0.05). The independent risk factors for RFS events and death were axillary lymph node metastasis (P<0.05) and BLIS subtype (P<0.05). BLIS subtype showed worse OS than other subtypes (log rank P=0.05). TNBCs with benign sonographic features tended to have less death events (3.3% vs. 15.2%, P=0.088). CONCLUSIONS: Sonographic appearance of TNBCs is associated with transcriptome-based molecular subtypes, and tends to correlate with the survival outcome.

Association of TM6SF2 rs58542926 T/C gene polymorphism with hepatocellular carcinoma: a meta-analysis.

BACKGROUND: Hepatocellular carcinoma (HCC) is the sixth-most common malignancy worldwide. Multiple previous studies have assessed the relationship between TM6SF2 gene polymorphism and the risk of developing HCC, with discrepant conclusions reached. To assess the association of TM6SF2 rs58542926 T/C gene polymorphism with liver cancer, we performed the current meta-analysis. METHODS: This study queried the MEDLINE, PubMed, EMBASE, and CENTRAL databases from inception to April 2019. Case-control studies assessing the relationship between TM6SF2 rs5854292 locus polymorphism and liver cancer were selected according to inclusion and exclusion criteria. The Stata 12.0 software was employed for data analysis. RESULTS: A total of 5 articles, encompassing 6873 patients, met inclusion criteria and were included in the meta-analysis. Statistical analysis showed that the TM6SF2 gene polymorphism was significantly associated with liver cancer in the allele contrast, dominant, recessive and over dominant models (T vs C, OR = 1.621, 95%CI 1.379-1.905; CT + TT vs CC. OR = 1.541, 95%CI 1.351-1.758; TT vs CT + CC, OR = 2.897, 95%CI 1.690-4.966; CC + TT vs TC, OR = 0.693, 95%CI 0.576-0.834). The Egger's test revealed no significant publication bias. CONCLUSION: The present findings suggest a significant association of TM6SF2 gene polymorphism with HCC risk in the entire population studied.

Protein C receptor is a therapeutic stem cell target in a distinct group of breast cancers.

Breast cancer is a heterogeneous disease. In particular, triple-negative breast cancer (TNBC) comprises various molecular subgroups with unclear identities and currently has few targeted treatment options. Our previous study identified protein C receptor (Procr) as a surface marker on mammary stem cells (MaSCs) located in the basal layer of the normal mammary gland. Given the possible connection of TNBC with basal layer stem cells, we conducted comparative analyses of Procr in breast cancers of mouse and human origin. In mouse mammary tumors, we showed that Procr+ cells are enriched for cancer stem cells (CSCs) in Wnt1 basal-like tumors, but not in Brca1 basal-like tumors or PyVT luminal tumors. In human cancers, PROCR was robustly expressed in half of TNBC cases. Experiments with patient-derived xenografts (PDXs) revealed that PROCR marks CSCs in this discrete subgroup (referred to as PROCR+ TNBC). Interfering with the function of PROCR using an inhibitory nanobody reduced the CSC numbers, arrested tumor growth and prevented rapid tumor recurrence. Our data suggest a key role of MaSC in breast tumorigenesis. Moreover, our work indicates that PROCR can be used as a biomarker to stratify TNBC into clinically relevant subgroups and may provide a novel targeted treatment strategy for this clinically important tumor subtype.

A Nomogram Predicting Lymph Node Metastasis in T1 Breast Cancer based on the Surveillance, Epidemiology, and End Results Program.

Background: Patients with early stage breast cancer with lymph nodes metastasis were proven to have more aggressive biologically phenotypes. This study aimed to build a nomogram to predict lymph node metastasis in patients with T1 breast cancer. Methods: We identified female patients with T1 breast cancer diagnosed between 2010 and 2014 in the Surveillance, Epidemiology and End Results database. The patients were randomized into training and validation sets. Univariate and multivariate logistic regressions were carried out to assess the relationships between lymph node metastasis and clinicopathological characteristics. A nomogram was developed and validated by a calibration curve and receptor operating characteristic curve analysis. Result: Age, race, tumour size, tumour primary site, pathological grade, oestrogen receptor (ER) status, progesterone receptor (PR) status and human epidermal growth factor receptor 2 (HER2) status were independent predictive factors of positive lymph node metastasis in T1 breast cancer. Increasing age, tumour size and pathological grade were positively correlated with the risk of lymph node metastasis. We developed a nomogram to predict lymph node metastasis and further validated it in a validation set, with areas under the receiver operating characteristic curves of 0.733 and 0.741 in the training and validation sets, respectively. Conclusions: A better understanding of the clinicopathological characteristics of T1 breast cancer patients might important for assessing their lymph node status. The nomogram developed here, if further validated in other large cohorts, might provide additional information regarding lymph node metastasis. Together with sentinel lymph node biopsy, this nomogram can help comprehensively predict lymph node metastasis.

Enhancement of Atmospheric Nucleation by Highly Oxygenated Organic Molecules: A Density Functional Theory Study.

New particle formation (NPF) by gas-particle conversion is the main source of atmospheric aerosols. Highly oxygenated organic molecules (HOMs) and sulfuric acid (SA) are important NPF participants. 2-Methylglyceric acid (MGA), a kind of HOMs, is a tracer of isoprene-derived secondary organic aerosols. The nucleation mechanisms of MGA with SA were studied using density functional theory and atmospheric cluster dynamics simulation in this study, along with that of MGA with methanesulfonic acid (MSA) as a comparison. Our theoretical works indicate that the (MGA)(SA) and (MGA)(MSA) clusters are the most stable ones in the (MGA) i(SA) j ( i = 1-2, j = 1-2) and (MGA) i(MSA) j ( i = 1-2, j = 1-2) clusters, respectively. Both the formation rates of (MGA)(SA) and (MGA)(MSA) clusters are quite large and could have significant contributions to NPF. The results imply that the homomolecular nucleation of MGA is unlikely to occur in the atmosphere, and MGA and SA can effectively contribute to heteromolecular nucleation mainly in the form of heterodimers. MSA exhibits properties similar to SA in its ability to form clusters with MGA but is slightly weaker than SA.

Genomic and Transcriptomic Landscape of Triple-Negative Breast Cancers: Subtypes and Treatment Strategies.

We comprehensively analyzed clinical, genomic, and transcriptomic data of a cohort of 465 primary triple-negative breast cancer (TNBC). PIK3CA mutations and copy-number gains of chromosome 22q11 were more frequent in our Chinese cohort than in The Cancer Genome Atlas. We classified TNBCs into four transcriptome-based subtypes: (1) luminal androgen receptor (LAR), (2) immunomodulatory, (3) basal-like immune-suppressed, and (4) mesenchymal-like. Putative therapeutic targets or biomarkers were identified among each subtype. Importantly, the LAR subtype showed more ERBB2 somatic mutations, infrequent mutational signature 3 and frequent CDKN2A loss. The comprehensive profile of TNBCs provided here will serve as a reference to further advance the understanding and precision treatment of TNBC.

The endogenous retrovirus-derived long noncoding RNA TROJAN promotes triple-negative breast cancer progression via ZMYND8 degradation.

Human endogenous retroviruses (HERVs) play pivotal roles in the development of breast cancer. However, the detailed mechanisms of noncoding HERVs remain elusive. Here, our genome-wide transcriptome analysis of HERVs revealed that a primate long noncoding RNA, which we dubbed TROJAN, was highly expressed in human triple-negative breast cancer (TNBC). TROJAN promoted TNBC proliferation and invasion and indicated poor patient outcomes. We further confirmed that TROJAN could bind to ZMYND8, a metastasis-repressing factor, and increase its degradation through the ubiquitin-proteasome pathway by repelling ZNF592. TROJAN also epigenetically up-regulated metastasis-related genes in multiple cell lines. Correlations between TROJAN and ZMYND8 were subsequently confirmed in clinical samples. Furthermore, our study verified that antisense oligonucleotide therapy targeting TROJAN substantially suppressed TNBC progression in vivo. In conclusion, the long noncoding RNA TROJAN promotes TNBC progression and serves as a potential therapeutic target.

Protective Effect of Ganshuang Granules () on Liver Cirrhosis by Suppressing Regulatory T Cells in Mouse Model.

OBJECTIVE: To investigate the potential antifibrotic mechanisms of Chinese medicine Ganshuang Granules (, GSG) and to provide clinical therapeutic evidence of its effects. METHODS: A cirrhotic mouse model was established by intraperitoneally injecting a mixture of CCl4 (40%) and oil (60%) at 0.2 mL per 100 g of body weight twice a week for 12 weeks. After 12-week modeling, GSG was intragastric administrated to the mice for 2 weeks, and the mice were divided into low-, medium- and high-dose groups at doses of 1, 2 and 4 g/(kg·day), respectively. Liver morphology changes were observed using Masson's trichrome staining and B-ultrasound. The levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST) and hyaluronic acid (HA) in serum were detected using an automatic biochemistry analyzer. The expressions of desmin, smooth muscle actin (SMA) and Foxp3 in liver were detected by immunoflfluorescence. The regulatory T cell (Treg) frequency was determined through flflow cytometry analysis. Collagen-I, SMA, IL-6, tumor necrosis factor α (TNF-α), interleukin (IL)-1ß and transforming growth factor ß1 (TGF-ß1) expression levels were measured using quantitative polymerase chain reaction (qPCR). RESULTS: Masson's staining result showed fewer pseudolobule structures and fibrous connective tissue in the GSG-treatment groups than in the spontaneous recovery group. Ultrasonography showed that GSG treatment reduced the number of punctate hyperechoic lesions in mice cirrhotic livers. The serum ALT, AST, HA levels were significantly ameliorated by GSG treatment (ALT: F=8.104, P=0.000; AST: F=7.078, P=0.002; and HA: F=7.621, P=0.001). The expression levels of collagen-I and SMA in the cirrhotic livers were also attenuated by GSG treatment (collagen-I: F=3.938, P=0.011; SMA: F=4.115, P=0.009). Tregs, which were elevated in the fibrotic livers, were suppressed by GSG treatment (F=8.268, P=0.001). The expressions of IL-6, TNF-α and IL-1ß increased, and TGF-ß levels decreased in the cirrhotic livers after GSG treatment (IL-6: F=5.457, P=0.004; TNF-α: F=6.023, P=0.002; IL-1ß: F=6.658, P=0.001; and TGF-ß1: F=11.239, P=0.000). CONCLUSIONS: GSG promoted the resolution/regression of cirrhosis and restored liver functions in part by suppressing Treg cell differentiation, which may be mediated by hepatic stellate cells.