Virtual-State Model for Analyzing Electro-Optical Modulation in Ring Resonators.

Ring resonators play a crucial role in optical communication and quantum technology applications. However, these devices lack a simple and intuitive theoretical model to describe their electro-optical modulation. When the resonance frequency is rapidly modulated, the filtering and modulation within a ring resonator become physically intertwined, making it difficult to analyze the complex physical processes involved. We address this by proposing an analytical solution for electro-optic ring modulators based on the concept of a "virtual state." This approach equates a lightwave passing through a dynamic ring modulator to one excited to a virtual state by a cumulative phase and then returning to the real state after exiting the static ring. Our model simplifies the independent analysis of the intertwined physical processes, enhancing its versatility in analyzing various incident signals and modulation formats. Experimental results, including resonant and detuning modulation, align with the numerical simulation of our model. Notably, our findings indicate that the dynamic modulation of the ring resonator under detuning driving approximates phase modulation.

Impacts of mining on vegetation phenology and sensitivity assessment of spectral vegetation indices to mining activities in arid/semi-arid areas.

Measuring the impact of mining activities on vegetation phenology and assessing the sensitivity of vegetation indices (VIs) to it are crucial for understanding land degradation in mining areas and enhancing the carbon sink capacity following the ecological restoration of mines. To this end, we have developed a novel technical framework to quantify the impact of mining activities on vegetation, and applied it to the Bainaimiao copper mining area in Inner Mongolia. Phenological indices are extracted based on the VI time series data of Sentinel-2, and changes in phenological differences in various directions are used to quantify the impact of mining activities on vegetation. Finally, indicators such as mean difference, standard deviation, index value distribution interval, and concentration of index value distribution were selected to assess the sensitivity of the Enhanced Vegetation Index (EVI), Green Chlorophyll Index (GCI), Global Environmental Monitoring Index (GEMI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), Red-Edge Chlorophyll Index (RECI), and Soil-Adjusted Vegetation Index (SAVI) to mining activities. The results of the study show that the impact of mining activities on surrounding vegetation extends to an area three times larger than the actual mining activity area. When compared with the reference and unaffected areas, the affected area experienced a delay of approximately 10 days in seasonal vegetation development. Environmental pollution caused by the tailings pond was identified as the primary factor influencing this delay. Significant variations in the sensitivity of each VI to assess mining activities in arid/semi-arid areas were observed. Notably, GCI, GNDVI and RDVI displayed relatively high sensitivity to discrepancies in the spectral attributes of vegetation within the affected area, while SAVI reflected the overall spectral stability of the vegetation in the affected area. The research findings have the potential to provide valuable technical guidance for holistic environmental management in mining areas and hold great significance in preventing further land degradation and supporting ecological restoration in mining areas.

Published trends and research hotspots of central venous catheter-associated thrombosis from 1973 to 2022: A scientometric analysis.

This study aims to explore the intellectual landscape and research hotspots in the central venous catheter-related thrombosis (CVC-RT) research field. Studies discussing CVC-RT published from 1973 to 2022 in the Web of Science Core Collection database were retrieved on February 24th, 2022. Citespace was used to perform a scientometric analysis to identify the intellectual landscape and research hotspots in the research fields of CVC-RT. A total of 4358 studies were retrieved, with an ascending trend in publication numbers. The United States of America was the most influential country. The Journal of Vascular Access published the most studies, and McMaster University was the most prolific institution. The results showed that the focus population of CVC-RT research has changed from pediatric patients to cancer patients, the management of CVC-RT has become more formal and standardized, and the focused CVC type has shifted to port and peripherally inserted central catheters. In addition, seventeen active burst keywords were detected, such as patient safety, clinical practice guidelines, and postthrombotic syndrome. This study comprehensively reviewed publications related to CVC-RT. The research topics on patient safety, clinical practice guidelines, and postthrombotic syndrome related to CVC-RT may be future hotspots.

Predicting Glaucoma Progression to Surgery with Artificial Intelligence Survival Models.

Purpose: Prior artificial intelligence (AI) models for predicting glaucoma progression have used traditional classifiers that do not consider the longitudinal nature of patients' follow-up. In this study, we developed survival-based AI models for predicting glaucoma patients' progression to surgery, comparing performance of regression-, tree-, and deep learning-based approaches. Design: Retrospective observational study. Subjects: Patients with glaucoma seen at a single academic center from 2008 to 2020 identified from electronic health records (EHRs). Methods: From the EHRs, we identified 361 baseline features, including demographics, eye examinations, diagnoses, and medications. We trained AI survival models to predict patients' progression to glaucoma surgery using the following: (1) a penalized Cox proportional hazards (CPH) model with principal component analysis (PCA); (2) random survival forests (RSFs); (3) gradient-boosting survival (GBS); and (4) a deep learning model (DeepSurv). The concordance index (C-index) and mean cumulative/dynamic area under the curve (mean AUC) were used to evaluate model performance on a held-out test set. Explainability was investigated using Shapley values for feature importance and visualization of model-predicted cumulative hazard curves for patients with different treatment trajectories. Main Outcome Measures: Progression to glaucoma surgery. Results: Of the 4512 patients with glaucoma, 748 underwent glaucoma surgery, with a median follow-up of 1038 days. The DeepSurv model performed best overall (C-index, 0.775; mean AUC, 0.802) among the models studied in this article (CPH with PCA: C-index, 0.745; mean AUC, 0.780; RSF: C-index, 0.766; mean AUC, 0.804; GBS: C-index, 0.764; mean AUC, 0.791). Predicted cumulative hazard curves demonstrate how models could distinguish between patient who underwent early surgery and patients who underwent surgery after > 3000 days of follow-up or no surgery. Conclusions: Artificial intelligence survival models can predict progression to glaucoma surgery using structured data from EHRs. Tree-based and deep learning-based models performed better at predicting glaucoma progression to surgery than the CPH regression model, potentially because of their better suitability for high-dimensional data sets. Future work predicting ophthalmic outcomes should consider using tree-based and deep learning-based survival AI models. Additional research is needed to develop and evaluate more sophisticated deep learning survival models that can incorporate clinical notes or imaging. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

Effectiveness of mindfulness-based stress reduction and mindfulness-based cognitive therapy on depression in poststroke patients-A systematic review and meta-analysis of randomized controlled trials.

OBJECTIVE: To evaluate the effectiveness of mindfulness-based stress reduction (MBSR) and mindfulness-based cognitive therapy (MBCT) on depressive symptoms in people with stroke. METHODS: The PubMed, CINAL, Web of Science, Embase, The Cochrane Library, CNKI, and Wangfang databases were searched for relevant articles from inception to September 1st, 2022. Only randomized controlled trials (RCTs) examining the effect of MBSR and MBCT on depressive symptoms in poststroke patients were included. Data extraction and critical appraisal were conducted independently by two investigators. RESULTS: Seven trials with 502 participants were included. Using standardized mean differences, the meta-analysis showed evidence of a significant effect in depression (SMD = -0.93, 95% CI (-1.34 to -0.53), Z = 4.48, p < 0.001). MBSR and MBCT both affected depressive emotions in poststroke participants with depression (SMD = -1.27, 95% CI (-1.71 to -0.84), p < 0.001) and poststroke participants without clinically defined depression (SMD = -0.46, 95% CI (-0.75 to -0.17), p = 0.002). CONCLUSION: Although populations with stroke seem to potentially improve moods from MBSR/MBCT intervention, the impact on the physiological parameters of the disease has not been determined. Further studies with long-term follow-up and higher qualities are warranted for such interventions to determine the full effectiveness.

Thermo-optic tunable optical filters with GHz-bandwidth and flat-top passband on thin film lithium niobate platform.

Lithium niobate on insulator (LNOI) is a new photonic integrated platform that provides high optical confinement and retains the inherent excellent properties of lithium niobate (LN). Tunable filters are one of the indispensable devices for integrated optics. Here we design and fabricate a thermo-optic (TO) tunable optical filter using two cascaded racetrack microring resonators (MRRs) based on LNOI. The filter shows a narrow and flat top passband with intra band ripple less than 0.3 dB, 3 dB bandwidth of 4.8 GHz and out-of-band rejection of about 35 dB. The insertion loss of the filter is about -14 dB, including grating coupling loss about -6.5 dB and on-chip loss less than -1 dB. The heating power for center wavelength shift of the filter is about 89.4 mW per free spectral range (FSR). Relevant applications of such filters include optical information processing and microwave photonics.

A novel landslide susceptibility optimization framework to assess landslide occurrence probability at the regional scale for environmental management.

Landslide is a hazard that has drastic repercussions on population and the environment worldwide. Landslide susceptibility mapping (LSM) is vital for landslide disaster management and formulating mitigation strategies. In this study, with the support of geographic information system and remote sensing, a new LSM hybrid framework is developed based on random forest (RF) and cusp catastrophe model (CCM). Under the framework, 15 conditioning factors and 2082 historical landslides are selected to test and compare its performance in a landslide-prone area in Liangshan, Southwest China. The results depicted a better performance of the new LSM hybrid framework (RF-CCM) than those of RF or traditional application mode of catastrophe model (Catastrophe fuzzy membership functions, CFMFs) only. The RF-CCM achieved the highest accuracy (0.901), the narrowest confidence interval (0.895-0.907), and the smallest standard error (0.004) among all the models. Notably, RF-CCM successfully decreased the uncertainty of CFMFs in determining the relative importance of conditioning factors, overcame the dependence of the CFMFs on independence among the conditioning factors, and had a higher stability level than RF. Moreover, distance to human engineering activities and slope had the greatest impact on LSM in the modeling process. The study result can provide insights for developing reliable predictive models for other landslide-prone areas with similar geo-environmental conditions.

Leveraging weak supervision to perform named entity recognition in electronic health records progress notes to identify the ophthalmology exam.

OBJECTIVE: To develop deep learning models to recognize ophthalmic examination components from clinical notes in electronic health records (EHR) using a weak supervision approach. METHODS: A corpus of 39,099 ophthalmology notes weakly labeled for 24 examination entities was assembled from the EHR of one academic center. Four pre-trained transformer-based language models (DistilBert, BioBert, BlueBert, and ClinicalBert) were fine-tuned to this named entity recognition task and compared to a baseline regular expression model. Models were evaluated on the weakly labeled test dataset, a human-labeled sample of that set, and a human-labeled independent dataset. RESULTS: On the weakly labeled test set, all transformer-based models had recall > 0.93, with precision varying from 0.815 to 0.843. The baseline model had lower recall (0.769) and precision (0.682). On the human-annotated sample, the baseline model had high recall (0.962, 95 % CI 0.955-0.067) with variable precision across entities (0.081-0.999). Bert models had recall ranging from 0.771 to 0.831, and precision >=0.973. On the independent dataset, precision was 0.926 and recall 0.458 for BlueBert. The baseline model had better recall (0.708, 95 % CI 0.674-0.738) but worse precision (0.399, 95 % CI -0.352-0.451). CONCLUSION: We developed the first deep learning system to recognize eye examination components from clinical notes, leveraging a novel opportunity for weak supervision. Transformer-based models had high precision on human-annotated labels, whereas the baseline model had poor precision but higher recall. This system may be used to improve cohort and feature identification using free-text notes.Our weakly supervised approach may help amass large datasets of domain-specific entities from EHRs in many fields.

High-efficient coupler for thin-film lithium niobate waveguide devices.

Lithium niobate (LN) devices have been widely used in optical communication and nonlinear optics due to its attractive optical properties. The emergence of the thin-film lithium niobate on insulator (LNOI) improves performances of LN-based devices greatly. However, a high-efficient fiber-chip optical coupler is still necessary for the LNOI-based devices for practical applications. In this paper, we demonstrate a highly efficient and polarization-independent edge coupler based on LNOI. The coupler, fabricated by a standard semiconductor process, shows a low fiber-chip coupling loss of 0.54 dB/0.59 dB per facet at 1550 nm for TE/TM light, respectively, when coupled with an ultra-high numerical aperture fiber (UHNAF) of which the mode field diameter is about 3.2 µm. The coupling loss is lower than 1dB/facet for both TE and TM light in the wavelength range of 1527 nm to 1630 nm. A relatively large tolerance for optical misalignment is also proved, due to the coupler's large mode spot size up to 3.2 µm. The coupler shows a promising stability in high optical power and temperature variation.

Understanding the Effects of China's Agro-Environmental Policies on Rural Households' Labor and Land Allocation with a Spatially Explicit Agent-Based Model.

Understanding household labor and land allocation decisions under agro-environmental policies is challenging due to complex human-environment interactions. Here, we develop a spatially explicit agent-based model based on spatial and socioeconomic data to simulate households' land and labor allocation decisions and investigate the impacts of two forest restoration and conservation programs and one agricultural subsidy program in rural China. Simulation outputs reveal that the forest restoration program accelerates labor out-migration and cropland shrink, while the forest conservation program promotes livelihood diversification via increasing non-farm employment. Meanwhile, the agricultural subsidy program keeps labor for cultivation on land parcels with good quality, but appears less effective for preventing marginal croplands from being abandoned. The policy effects on labor allocation substantially differ between rules based on bounded rational and empirical knowledge of defining household decisions, particularly on sending labor out-migrants and engaging in local off-farm jobs. Land use patterns show that the extent to which households pursue economic benefits through shrinking cultivated land is generally greater under bounded rationality than empirical knowledge. Findings demonstrate nonlinear social-ecological impacts of the agro-environmental policies through time, which can deviate from expectations due to complex interplays between households and land. This study also suggests that the spatial agent-based model can represent adaptive decision-making and interactions of human agents and their interactions in dynamic social and physical environments.

Effects of payments for ecosystem services programs in China on rural household labor allocation and land use: Identifying complex pathways.

Payments for Ecosystem Services (PES) is increasingly used in developing countries to secure the sustainable provision of vital ecosystem services. The largest PES programs in the world are embedded in China's new forest policies, which aim to expand forest cover for soil and water conservation and improve livelihoods of rural people. The objective of this study is to identify the complex pathways of impacts of two PES programs - the Conversion of Cropland to Forest Program (CCFP) and the Ecological Welfare Forest Program (EWFP) - on household livelihood decisions, and to quantify the direct and indirect impacts along the identified pathways. We fulfill this objective by developing an integrated conceptual framework and applying a Partial Least Squares-Structural Equation Model (PLS-SEM), based on household survey data from Anhui, China. Labor allocation (for on-farm work, local paid work, local business, and out-migration) and land use decisions (i.e., rent in, maintain, rent out, or abandon cropland) for participating households are key to understand PES program effects on livelihoods. Results show that the PES programs have only small direct effects but significant indirect effects via the mediating factor of capital assets. Moreover, group heterogeneity analysis shows that lower-income households do not benefit any more than the better-off households from the PES, while households with medium wealth increase dependence on agriculture. In addition, household demographics, individual attributes, and geographic settings differ in their impacts on labor allocation and land use decisions. We conclude that CCFP and EWFP programs would be more efficient in conserving the environment while improving the economic welfare of lower-income households if capital assets were taken into account in the design of compensation schemes.

Divergent socioeconomic-ecological outcomes of China's Conversion of Cropland to Forest Program in the subtropical mountainous area and the semi-arid Loess Plateau.

China's Conversion of Cropland to Forest Program (CCFP) is one of the world's largest Payments for Ecosystem Services (PES) programs. Its socioeconomic-ecological effects are of great interest to both scholars and policy-makers. However, little is known about how the socioeconomic-ecological outcomes of CCFP differ across geographic regions. This study integrates household survey data, satellite imagery, and statistical models to examine labor migration and forest dynamics under CCFP. The investigation is carried out at two mountainous sites with distinct biophysical and socioeconomic conditions, one in a subtropical mountainous region (Anhui) and the other in the semi-arid Loess Plateau (Shanxi). We found divergent CCFP outcomes on migration behavior, stimulating both local- and distant-migration in the Anhui site while discouraging distant-migration in the Shanxi site, after controlling for factors at the individual, household, community and regional levels. Forest recovery is positively associated with distant-migration in Anhui but with local-migration in Shanxi. Contextual factors interact with demographic-socioeconomic factors to influence household livelihoods in both areas, leading to various socio-ecological pathways from CCFP participation to enhanced forest sustainability. Regional differences should therefore be taken into account in the design of future large-scale PES programs.

Rural household income distribution and inequality in China: Effects of payments for ecosystem services policies and other factors.

In the late 1990s, China initiated the Conversion of Croplands to Forest Program (CCFP) and the Ecological Welfare Forest Program (EWFP) based on the Payments for Ecosystem Services (PES) principle. Positive socioeconomic outcomes of the programs are essential for the long-term success of eco-environment conservation. However, there is lack of understanding of their longer-term (over 10 years) impacts on rural livelihoods. In this paper, we examine income distribution and inequality of rural households under CCFP and EWFP in rural Anhui, China after 12 years of program implementation. Results show that CCFP-participating households have higher income inequality than non-participants, while the EWFP does not have an significant effect. Local off-farm work and out-migration with remittances are the two principal income sources and both add to inequality. A regression-based decomposition of inequality shows that the CCFP indirectly alters livelihoods by increasing out-migration with remittances, but it also adds to inequality from shifting livelihoods to non-agricultural activities. Meanwhile, EWFP payments positively affect agricultural incomes and contribute 16% to agricultural income inequality. Finally, human capital, natural capital and physical capital all play important roles in generating income and inequality, but the factors affecting inequality from agricultural and non-agricultural activities are different.

Determinants of Out-migration in rural China: effects of payments for Ecosystem Services.

Rural out-migration has been a hallmark of socioeconomic development in China, but rapid economic development including deforestation also resulted in environmental degradation, leading to disastrous floods and droughts. In response, the Chinese government implemented Payments for Ecosystem Services (PES) programs for both environmental conservation and poverty alleviation, notably the Conversion of Cropland to Forest Program (CCFP) and the Ecological Welfare Forest Program (EWFP). In the context of a full model of the determinants of migration incorporating individual, household and community factors, we investigate the manner in which these programs influenced rural out-migration in a mountainous township in Anhui, China. Results show that the CCFP compensation for switching cropland to trees releases farm labor, leading to out-migration. Meanwhile, the EWFP compensation provides poor rural farmers with large areas of forest with sizable cash subsidies that reduces their motivation to migrate. Out-migration was also found to be affected by a number of individual, household and community characteristics.

Silicon nitride O-band (de)multiplexers with low thermal sensitivity.

In this paper, four-channel cascaded Mach-Zehnder interferometer-based wavelength (de)multiplexers in the O-band are demonstrated experimentally by utilizing silicon nitride (SiN) optical waveguides. By reference to the commonly used 100 Gigabit Ethernet standards, two types of (de)multiplexer devices with different channel spacings are designed and fabricated. Both the devices exhibit low insertion loss and flat passbands. The lower thermo-optical coefficient provided by SiN brings benefits of reduction in thermal sensitivity. The fabricated (de)multiplexers show a temperature-dependent wavelength shift of about 18.5 pm/°C, which is reduced by 75% compared to the standard silicon-based devices.

sFRP-4, a potential novel serum marker for chronic hepatitis B-related hepatocellular carcinoma.

BACKGROUND: The current methods used for diagnosing hepatocellular carcinoma (HCC) are unsatisfactory. Here, we assessed the serum levels of secreted frizzled related protein 4 (sFRP-4) for diagnosing HCC in patients infected with chronic hepatitis B (CHB). METHODS: In 272 patients with CHB enrolled, 142 were patients with HCC. Thirty-three healthy subjects were recruited as healthy controls. The CHB patients were assigned to a test group or a validation group based on the time of enrollment. Human antibody arrays were used to screen 15 patients (8 CHB-related HCC patients, 7 CHB patients) for serum markers. Four markers and one candidate marker were assessed in the test group and validation group, respectively. RESULTS: Human antibody assays indicated that the serum levels of sFRP-4 in HCC patients were significantly higher than those in CHB patients (P<0.05). Additionally, serum sFRP-4 levels were significantly higher in the HCC patients than those in the non-HCC patients in both test group (79.7 vs 41.3 ng/mL; P<0.001) and validation group (89.0 vs 39.0 ng/mL; P<0.001). Areas under the Receiver Operating Characteristic curves (AUCs) for alpha-fetoprotein (AFP) and sFRP-4 were similar in both test group and validation group. In the test group, the combination of sFRP-4 (a sensitivity of 94.4%, a specificity of 60.5% at 46.4 ng/mL) and AFP (a sensitivity of 75.0%, a specificity of 87.2% at 11.3 ng/mL) showed better performance for diagnosing HCC (a sensitivity of 79.2% and a specificity of 95.3%). The AUC for combined sFRP-4 and AFP increased to 0.941 (95% CI: 0.908-0.975), and similar results were seen in the validation group. CONCLUSION: sFRP-4 is a candidate serum marker for diagnosing HCC in CHB patients, and the combination of sFRP-4 with AFP may improve the diagnostic accuracy of HCC.

Interaction of TLR-IFN and HLA polymorphisms on susceptibility of chronic HBV infection in Southwest Han Chinese.

BACKGROUND & AIMS: The toll-like receptor-interferon (TLR-IFN) signalling pathway plays a crucial role in HBV infection. Human leucocyte antigen (HLA) polymorphisms are associated with chronic HBV infection by genome wide association study (GWAS). We aimed to explore interaction between TLR-IFN and HLA gene polymorphisms in susceptibility of chronic HBV infection. METHODS: In the Chinese Southwest Han population, 1191 chronic HBV infection patients and 273 HBV clearance were selected. A total of 39 single nucleotide polymorphism loci in 23 genes of the TLR-IFN pathway and four HLA polymorphism loci associated with chronic HBV infection identified by GWAS were selected for genotyping. SNPStats, QVALUE, and multifactor dimensionality reduction were used for statistical analysis. RESULTS: A significant association was seen in several of the TLR-IFN pathway genes, TLR9 rs352140 (OR = 0.70, P = 0.0088), IL1B rs16944 (OR = 0.67, P = 0.016), IL12B rs3212227 (OR = 1.38, P = 0.021), IFNGR1 rs3799488 (OR = 1.48, P = 0.0048), IFNGR2 rs1059293 (OR = 0.27, P = 0.011), MX1 rs467960 (OR = 0.68, P = 0.022), as well as four loci in HLA, rs3077 (OR = 0.55, P < 0.0001), rs2856718 (OR = 0.60, P = 4e-04), rs9277535 (OR = 0.54, P < 0.0001) and rs7453920 (OR = 0.43, P < 0.0001). A synergistic relationship was seen between rs9277535 and rs16944 (0.13%), rs1143623 and rs6613 (0.10%). The combination of rs9277535 in HLA and rs16944 in IL1B was the best model to predict chronic HBV infection (testing accuracy = 0.6040, P = 0.0010, cross-validation consistency = 10/10). CONCLUSIONS: TLR-IFN pathway gene polymorphisms are associated with chronic HBV infection. Interactions with polymorphisms in these genes may be one mechanism by which HLA polymorphisms influence susceptibility to chronic HBV infection, as specific single nucleotide polymorphism combinations are highly predictive of chronic HBV infection.

Expression pattern of serum cytokines in hepatitis B virus infected patients with persistently normal alanine aminotransferase levels.

PURPOSE: About 60-80 % of chronic hepatitis B virus (HBV) carriers are characterized with persistently normal alanine transaminase (ALT). Differences of cytokine expression are associated with the prognosis of HBV infection. We investigated the expression pattern of 30 cytokines associated with anti-HBV immunity in patients with normal ALT. METHODS: Four patient groups (immune tolerance, inactive hepatitis B surface antigen carriers, resolved hepatitis B, and control; 10 subjects per group) were assigned. Thirty cytokines, including IFN-γ, IL-1ß, IL-2, IL-4, IL-6, IL-7, IL-9, IL-10, IL-12p40, IL-12p70, IL-15, IL-17A, IL-17C, IL-21, IL-22, IL-23p19, IL-28A, IL-29, CCL5, CCL16, CCL20, CCL22, CXCL9, CXCL10, CXCL11, TNFRSF8, TNFRSF18, IL-6R, gp130, and TGF-ß1, were measured using a human cytokine antibody array. Signal intensities were obtained by laser scanner. Protein-protein interactions were analyzed by STRING (Search Tool for the Retrieval of Interacting Genes/Proteins). RESULTS: Significant differences of signal intensities were observed for IL-2, IL-4, IL-6, IL-7, IL-9, IL-10, IL-12p40, IL-12p70, IL-15, IL-21, IL-23p19, IL-28A, and IL-29. The lowest intensity was in controls. Among three HBV infection groups, significant differences were observed in IL-2, IL-4, IL-12p70, IL-15, IL-21, IL-23p19, and IL-29. The highest intensity was in the inactive group. All cytokines with significant differences were involved JAK-STAT signaling that up-regulate FOXP3, SOCS3 and MX1. CONCLUSION: Differential expression of cytokines in JAK-STAT signaling is an important factor associated with prognosis of HBV infection. The elevation of γC cytokines, IL-12p70, IL-23p19, and IL-29 may promote spontaneous HBeAg seroconversion and HBV clearance.