Quantitative detection of high-order Poincaré sphere beams and their polarization evolution.

The high-order Poincaré sphere (PS) introduces a mapping whereby any vector beams with spatially homogeneous ellipticity are represented by a specific point on the surface of the sphere. We propose the quantitative detection of high-order PS beams by introducing three sets of nonuniform polarization bases in the high-order Stokes parameters. Overall polarization detection is realized by directly separating and measuring the respective intensity of different nonuniform polarization bases based on S-plate. The polarization evolution of the PS beams on the high-order PS and between the conventional and the high-order PS are achieved by S-plate. The results provide new insights for the generation, evolution and detection of arbitrary beams on the high-order PS.

Splenic Artery Steal Syndrome in Patients with Liver Cirrhosis: A Retrospective Clinical Study.

BACKGROUND Splenic artery steal syndrome (SASS) can aggravate liver damage in patients with cirrhosis. This study explored whether SASS could be an effective therapeutic target for improving hepatic artery perfusion and liver function in patients with decompensated cirrhosis. MATERIAL AND METHODS Based on inclusion and exclusion criteria, 87 patients with hepatitis B cirrhosis and portal hypertension hypersplenism admitted to our General Surgery Department for splenectomy and pericardial devascularization surgery were selected. A total of 35 cases met the diagnostic criteria of SASS and were assigned to the SASS group; the remaining 52 cases were assigned to the control group. The indicators before, during, and after surgery were compared between the 2 groups. RESULTS There were no significant differences in preoperative and intraoperative indicators between SASS group and control group (P>0.05). The MELD score 7 days after surgery and the hepatic artery diameter and hepatic artery velocity 14 days after surgery in both groups were significantly better than before surgery. The MELD score 7 days after surgery in the SASS group was significantly better than that in the control group, and the hepatic artery diameter and hepatic artery velocity 14 days after surgery in the SASS group were significantly better than those in the control group (P<0.05). CONCLUSIONS Splenectomy and pericardial devascularization surgery was an effective treatment to redirect blood flow to the hepatic artery for cirrhotic patients diagnosed with SASS. The introduction of cirrhotic SASS into clinical practice may benefit more patients with cirrhotic portal hypertension and hypersplenism.

Risk of primary osteoporosis score (RPOPs): an algorithm model for primary osteoporosis risk assessment in grass-roots hospital.

BACKGROUND: This study aimed to develop and validate a lasso regression algorithm model which was established by correlation factors of bone mineral density (BMD) and could be accurately predicted a high-risk population of primary osteoporosis (POP). It provides a rapid, economical and acceptable early screening method for osteoporosis in grass-roots hospitals. METHODS: We collected 120 subjects from primary osteoporosis screening population in Zhejiang Quhua Hospital between May 2021 and November 2021 who were divided into three groups (normal, osteopenia and osteoporosis) according to the BMD T-score. The levels of three micro-RNAs in the plasma of these people were detected and assessed by qRT-PCR. At the same time, the levels of ß-CTX and t-P1NP in serum of the three groups were determined. Based on the cluster random sampling method, 84 subjects (84/120, 70%) were selected as the training set and the rest were the test set. Lasso regression was used to screen characteristic variables and establish an algorithm model to evaluate the population at high risk of POP which was evaluated and tested in an independent test cohort. The feature variable screening process was used 10-fold cross validation to find the optimal lambda. RESULTS: The osteoporosis risk score was established in the training set: Risk of primary osteoporosis score (RPOPs) = -0.1497785 + 2.52Age - 0.19miR21 + 0.35miR182 + 0.17ß-CTx. The sensitivity, precision and accuracy of RPOPs in an independent test cohort were 79.17%, 82.61% and 75%, respectively. The AUC in the test set was 0.80. Some risk factors have a significant impact on the abnormal bone mass of the subjects. These risk factors were female (p = 0.00013), older than 55 (p < 2.2e-16) and BMI < 24 (p = 0.0091) who should pay more attention to their bone health. CONCLUSION: In this study, we successfully constructed and validated an early screening model of osteoporosis that is able to recognize people at high risk for developing osteoporosis and remind them to take preventive measures. But it is necessary to conduct further external and prospective validation research in large sample size for RPOPs prediction models.

Cascaded stimulated Brillouin scattering erbium-doped fiber laser generating orbital angular momentum beams at tunable wavelengths.

We experimentally demonstrate a method to obtain central wavelength tunable orbital angular momentum beams with switchable topological charges (+1 or -1) in a stimulated Brillouin scattering erbium-doped fiber laser. Multiwavelength operation is achieved through cascaded stimulated Brillouin scattering in a single-mode fiber with a length of 6 km initiated by an external Brillouin pump. High-efficiency mode conversion between the fundamental mode and the orbital angular momentum modes is realized through a broadband two-mode long-period fiber grating. High-purity orbital angular momentum beams with up to 10 stable wavelength channels with a tuning range of 35 nm are achieved, which is the highest number of operating wavelengths and tuning range in an all-fiber laser for orbital angular momentum beam emission to the best of our knowledge. Both the operational central wavelength and number of operating wavelengths can be tuned by adjusting the primary pump power and the center wavelength of the tunable bandpass filter in conjunction with changing the Brillouin pump wavelength.

Phylogenomics Reveals an Ancient Hybrid Origin of the Persian Walnut.

Persian walnut (Juglans regia) is cultivated worldwide for its high-quality wood and nuts, but its origin has remained mysterious because in phylogenies it occupies an unresolved position between American black walnuts and Asian butternuts. Equally unclear is the origin of the only American butternut, J. cinerea. We resequenced the whole genome of 80 individuals from 19 of the 22 species of Juglans and assembled the genome of its relatives Pterocarya stenoptera and Platycarya strobilacea. Using phylogenetic-network analysis of single-copy nuclear genes, genome-wide site pattern probabilities, and Approximate Bayesian Computation, we discovered that J. regia (and its landrace J. sigillata) arose as a hybrid between the American and the Asian lineages and that J. cinerea resulted from massive introgression from an immigrating Asian butternut into the genome of an American black walnut. Approximate Bayesian Computation modeling placed the hybrid origin in the late Pliocene, ∼3.45 My, with both parental lineages since having gone extinct in Europe.

A comparison of deltopectoral versus Judet approach for glenoid exposure.

BACKGROUND: Open reduction-internal fixation via an anterior or posterior approach is a widely used method for treating displaced glenoid fractures. This study aimed to identify the exposure range of the glenoid rim by these 2 approaches (deltopectoral and Judet approaches) and provide reference data for the choice of surgical approach. METHODS: Twelve cadaveric shoulders were dissected. Both deltopectoral and Judet approaches were performed on each shoulder to mark the glenoid fracture. In addition, the shoulder was disarticulated to record the exposure range of the glenoid rim. RESULTS: For the deltopectoral approach, the range of the exposed glenoid rim was from 5:50 to 11:30, which accounted for about 47.2% of the clock face. For the Judet approach, the range of the exposed glenoid rim was from 1:30 to 6:20, which accounted for about 40.3% of the clock face. Along the inferior glenoid, there was an area of partial overlap for the 2 approaches. The superior glenoid rim located from 11:30 to 1:30 was considered inaccessible, as it could not be exposed by the 2 approaches. CONCLUSION: Less than 50% of the glenoid rim can be exposed by the deltopectoral or Judet approach. With a single approach, it may be difficult to expose and fix some complex glenoid fractures. The superior part of the glenoid fracture is the non-access area via the deltopectoral or Judet approach.

Metformin mediates induction of miR-708 to inhibit self-renewal and chemoresistance of breast cancer stem cells through targeting CD47.

Breast cancer stem cells (BCSCs) have been considered responsible for cancer progression, recurrence, metastasis and drug resistance. However, the mechanisms by which cells acquire self-renewal and chemoresistance properties are remaining largely unclear. Herein, we evaluated the role of miR-708 and metformin in BCSCs, and found that the expression of miR-708 is significantly down-regulated in BCSCs and tumour tissues, and correlates with chemotherapy response and prognosis. Moreover, miR-708 markedly inhibits sphere formation, CD44+ /CD24- ratio, and tumour initiation and increases chemosensitivity of BCSCs. Mechanistically, miR-708 directly binds to cluster of differentiation 47 (CD47), and regulates tumour-associated macrophage-mediated phagocytosis. On the other hand, CD47 is essential for self-renewal, tumour initiation and chemoresistance of BCSCs, and correlates with the prognosis of breast cancer patients. In addition, the anti-type II diabetes drug metformin are found to be involved in the miR-708/CD47 signalling pathway. Therefore, our study demonstrated that miR-708 plays an important tumour suppressor role in BCSCs self-renewal and chemoresistance, and the miR-708/CD47 regulatory axis may represent a novel therapeutic mechanism of metformin in BCSCs.

A new anatomic locking plate for the treatment of posterolateral tibial plateau fractures.

BACKGROUND: Posterolateral tibial plateau fractures have become more common, and their treatment is of great importance to knee function. Additionally, there is no available literature detailing specialized anatomic locking plate for tibial plateau fractures. Therefore, the aim of the study was to evaluate the safety and clinical efficacy of an innovative anatomic locking plate for treatment of posterolateral tibial plateau fractures. METHODS: Between March 2014 and January 2016, 12 patients with posterolateral tibial plateau fracture underwent surgery with the anatomic locking plate for the posterolateral tibial plateau via the posterolateral approach. Relevant operational data for clinical evaluation were collected. RESULTS: The mean follow-up time was 26 months, and the mean age was 35 years for 12 patients. The mean interval between the time of injury and the surgery was 6.1 days. Radiological fracture union was evident in all patients at 12 weeks. During surgery, the blood loss ranged from 100 to 300 mL, and the duration ranged from 55 to 90 min. The Tegner-Lysholm functional score ranged from 85 to 97 at the final follow-up. Moreover, the final Rasmussen functional score ranged from 25 to 29, and Rasmussen anatomical score ranged from 13 to 18. CONCLUSIONS: The newly designed anatomic locking plate for the posterolateral tibial plateau provided adequate fixation along the posterolateral tibial plateau. It proved to be safe and effective in a small-sample-size population (12 patients) during a 12- to 34-month follow-up.

Single lateral versus medial and lateral plates for treating displaced scapular body fractures: a retrospective comparative study.

BACKGROUND: This study compared the outcomes and complications of single lateral plating vs. dual plating for treating displaced scapular body fractures. METHODS: Open reduction and internal fixation using locking plates was performed in 45 patients with displaced scapular fractures. A single lateral plate fixed in the lateral border was used in 22 patients (group A), and dual plates fixed in both the lateral and medial borders were used in 23 (group B). RESULTS: The average follow-up duration in both groups was 20 months. A remarkable difference was seen between the 2 groups in mean operative time and blood loss, although the Disabilities of the Arm, Shoulder and Hand and Constant Shoulder scores at the final follow-up were similar. The prominence rate of the hardware was 27.3% (6 of 22) in group A and 65.2% (15 of 23) in group B. The plate removal rate was 31.8% (7 of 22) in group A and 78.3% (18 of 23) in group B. CONCLUSIONS: Open reduction using a single plate on the lateral border for treating displaced scapular body fractures can lead to good functional outcomes, shorter operative time, less blood loss, and fewer plate-related complications compared with the dual-plating technique.

[Correction Method of Atmospheric Scattering Effect Based on Three Spectrum Bands].

As a major error of CO2 retrieval, atmospheric scattering effect hampers the application of satellite products. Effect of aerosol and combined effect of aerosol and ground surface are important source of atmospheric scattering, so it needs comprehensive consideration of scattering effect from aerosol and ground surface. Based on the continuum, strong and weak absorption part of three spectrum bands O2-A, CO2 1.6 µm and 2.06 µm, information of aerosol and albedo was analyzed, and improved full physics retrieval method was proposed, which can retrieve aerosol and albedo simultaneously to correct the scattering effect. Simulation study on CO2 error caused by aerosol and ground surface albedo CO2 error by correction method was carried out. CO2 error caused by aerosol optical depth and ground surface albedo can reach up to 8%, and CO2 error caused by different types of aerosol can reach up to 10%, while these two types of error can be controlled within 1% and 2% separately by this correction method, which shows that the method can correct the scattering effect effectively. Through evaluation of the results, the potential of this method for high precision satellite data retrieval is obvious, meanwhile, some problems which need to be noticed in real application were also pointed out.

[Errors Analysis and Correction in Atmospheric Methane Retrieval Based on Greenhouse Gases Observing Satellite Data].

High precision retrieval of atmospheric CH4 is influenced by a variety of factors. The uncertainties of ground properties and atmospheric conditions are important factors, such as surface reflectance, temperature profile, humidity profile and pressure profile. Surface reflectance is affected by many factors so that it is difficult to get the precise value. The uncertainty of surface reflectance will cause large error to retrieval result. The uncertainties of temperature profile, humidity profile and pressure profile are also important sources of retrieval error and they will cause unavoidable systematic error. This error is hard to eliminate only using CH4 band. In this paper, ratio spectrometry method and CO2 band correction method are proposed to reduce the error caused by these factors. Ratio spectrometry method can decrease the effect of surface reflectance in CH4 retrieval by converting absolute radiance spectrometry into ratio spectrometry. CO2 band correction method converts column amounts of CH4 into column averaged mixing ratio by using CO2 1.61 µm band and it can correct the systematic error caused by temperature profile, humidity profile and pressure profile. The combination of these two correction methods will decrease the effect caused by surface reflectance, temperature profile, humidity profile and pressure profile at the same time and reduce the retrieval error. GOSAT data were used to retrieve atmospheric CH4 to test and validate the two correction methods. The results showed that CH4 column averaged mixing ratio retrieved after correction was close to GOSAT Level2 product and the retrieval precision was up to -0.24%. The studies suggest that the error of CH4 retrieval caused by the uncertainties of ground properties and atmospheric conditions can be significantly reduced and the retrieval precision can be highly improved by using ratio spectrometry method and CO2 hand correction method.

LFMNet: a lightweight model for identifying leaf diseases of maize with high similarity.

Maize leaf diseases significantly impact yield and quality. However, recognizing these diseases from images taken in natural environments is challenging due to complex backgrounds and high similarity of disease spots between classes.This study proposes a lightweight multi-level attention fusion network (LFMNet) which can identify maize leaf diseases with high similarity in natural environment. The main components of LFMNet are PMFFM and MAttion blocks, with three key improvements relative to existing essential blocks. First, it improves the adaptability to the change of maize leaf disease scale through the dense connection of partial convolution with different expansion rates and reduces the parameters at the same time. The second improvement is that it replaces a adaptable pooling kernel according to the size of the input feature map on the original PPA, and the convolution layer to reshape to enhance the feature extraction of maize leaves under complex background. The third improvement is that it replaces different pooling kernels to obtain features of different scales based on GMDC and generate feature weighting matrix to enhance important regional features. Experimental results show that the accuracy of the LFMNet model on the test dataset reaches 94.12%, which is better than the existing heavyweight networks, such as ResNet50 and Inception v3, and lightweight networks such as DenseNet 121,MobileNet(V3-large) and ShuffleNet V2. The number of parameters is only 0.88m, which is better than the current mainstream lightweight network. It is also effective to identify the disease types with similar disease spots in leaves.

[Retrieval of atmospheric CO2 from ground-based high resolution FTS spectra].

Atmospheric carbon dioxide is a key greenhouse gas. Global monitoring of carbon dioxide would be helpful to improving the understanding of climate change. Ground-based observations usually have higher resolution and signal-to-noise ratio and more known parameters than satellite-based observations, which make it easier to achieve a better retrieval precision Ground-based retrieval method could be extended to satellite scenarios, and it could also provide validation for satellite data. The main procedure of the retrieval method was established, and this procedure was validated by simulated retrieval. It was proved that the method could get reasonable results under certain system errors. The retrieval of ground-based FTS spectra showed that this method has a potential to obtain atmospheric carbon dioxide column information.

[Study of the effect of surface reflectance on atmospheric CO2 retrieval and ratio spectrometry].

Retrieving atmospheric CO2 concentration from space-based infrared measurements is an ill-posed problem, and the uncertainty of ground properties is an important impacting factor. For the requirement of high precision retrieval of atmospheric CO2, the effects of surface reflectance must be corrected. Thus a surface reflectance correction method is proposed. In this correction method, the selection of non-CO2 absorption (off-line) channel is an important part, so several different types of off-line channels were compared and analyzed. We finally found that averaging all data of multi-channels as off-line channel is the best way, in which the retrieval precision is highest and dispersion is lowest, because the advantage is that averaging many data can reduce random error. The effects of surface reflectance on CO2 retrieval decreased significantly after correction using ratio spectrometry combined with the selected off-line channel, and CO2 retrieval precision improved greatly.

Classification and recognition of milk somatic cell images based on PolyLoss and PCAM-Reset50.

Somatic cell count (SCC) is a fundamental approach for determining the quality of cattle and bovine milk. So far, different classification and recognition methods have been proposed, all with certain limitations. In this study, we introduced a new deep learning tool, i.e., an improved ResNet50 model constructed based on the residual network and fused with the position attention module and channel attention module to extract the feature information more effectively. In this paper, macrophages, lymphocytes, epithelial cells, and neutrophils were assessed. An image dataset for milk somatic cells was constructed by preprocessing to increase the diversity of samples. PolyLoss was selected as the loss function to solve the unbalanced category samples and difficult sample mining. The Adam optimization algorithm was used to update the gradient, while Warm-up was used to warm up the learning rate to alleviate the overfitting caused by small sample data sets and improve the model's generalization ability. The experimental results showed that the classification accuracy, precision rate, recall rate, and comprehensive evaluation index F value of the proposed model reached 97%, 94.5%, 90.75%, and 92.25%, respectively, indicating that the proposed model could effectively classify the milk somatic cell images, showing a better classification performance than five previous models (i.e., ResNet50, ResNet18, ResNet34, AlexNet andMobileNetv2). The accuracies of the ResNet18, ResNet34, ResNet50, AlexNet, MobileNetv2, and the new model were 95%, 93%, 93%, 56%, 37%, and 97%, respectively. In addition, the comprehensive evaluation index F1 showed the best effect, fully verifying the effectiveness of the proposed method in this paper. The proposed method overcame the limitations of image preprocessing and manual feature extraction by traditional machine learning methods and the limitations of manual feature selection, improving the classification accuracy and showing a strong generalization ability.

A radiomics-based model can predict recurrence-free survival of hepatocellular carcinoma after curative ablation.

BACKGROUND: Prediction of early recurrence (ER) of HCC after radical treatment is of great significance for follow-up and subsequent treatment, and there is a lot of unmet needs. Here, our goal is to develop and validate a radiomics nomogram that can predict ER after curative ablation. OBJECTIVE: The aim of this study was to evaluate the efficacy and safety of regorafenib after disease progression with sorafenib in Chinese patients with advanced HCC through this retrospective analysis. METHODS: 149 HCC patients treated between November 2008 and February 2018 were enrolled and randomly divided into training cohort (n = 105) and validation cohort (n = 44). The survival endpoint was recurrence-free survival (RFS). A total of 16908 radiomics features were extracted from the contrast-enhanced MR images of each patient. The minimum redundancy maximum relevance algorithm (mRMR) and random survival forest (RSF) were used for feature selection. Twelve kinds of support vector machine (SVM) models, a Cox regression model (Cox PH), a random survival forest (RSF) model and a gradient boosting model (GBoost) were used to build a radiomics signature. These models were trained after adjusting the model parameters using 5-fold cross-validation. The best models were selected according to the C-index. RESULTS: Using the machine learning (ML) framework, 40 features were identified that demonstrated good prediction of HCC recurrence across all cohorts. The random survival forest (RSF) model showed higher prognostic value, with a C-index of 0.733-0.801 and an integrated Brier score of 0.147-0.165, compared with other SVM models, Cox regression models, etc. (all P < 0.05). Time-dependent receiver operating characteristic (ROC) curve analysis, survival analysis, and decision curve analysis (DCA) were used to verify the performance of the RSF model in predicting tumor recurrence. CONCLUSION: We successfully built a radiomics-based RSF model with integrated radiomics and clinicopathological features that can potentially be used to predict ER after curative ablation in HCC patients.

An Online Mammography Database with Biopsy Confirmed Types.

Breast carcinoma is the second largest cancer in the world among women. Early detection of breast cancer has been shown to increase the survival rate, thereby significantly increasing patients' lifespan. Mammography, a noninvasive imaging tool with low cost, is widely used to diagnose breast disease at an early stage due to its high sensitivity. Although some public mammography datasets are useful, there is still a lack of open access datasets that expand beyond the white population as well as missing biopsy confirmation or with unknown molecular subtypes. To fill this gap, we build a database containing two online breast mammographies. The dataset named by Chinese Mammography Database (CMMD) contains 3712 mammographies involved 1775 patients, which is divided into two branches. The first dataset CMMD1 contains 1026 cases (2214 mammographies) with biopsy confirmed type of benign or malignant tumors. The second dataset CMMD2 includes 1498 mammographies for 749 patients with known molecular subtypes. Our database is constructed to enrich the diversity of mammography data and promote the development of relevant fields.

Recognition of bovine milk somatic cells based on multi-feature extraction and a GBDT-AdaBoost fusion model.

Traditional laboratory microscopy for identifying bovine milk somatic cells is subjective, time-consuming, and labor-intensive. The accuracy of the recognition directly through a single classifier is low. In this paper, a novel algorithm that combined the feature extraction algorithm and fusion classification model was proposed to identify the somatic cells. First, 392 cell images from four types of bovine milk somatic cells dataset were trained and tested. Secondly, filtering and the K-means method were used to preprocess and segment the images. Thirdly, the color, morphological, and texture features of the four types of cells were extracted, totaling 100 features. Finally, the gradient boosting decision tree (GBDT)-AdaBoost fusion model was proposed. For the GBDT classifier, the light gradient boosting machine (LightGBM) was used as the weak classifier. The decision tree (DT) was used as the weak classifier of the AdaBoost classifier. The results showed that the average recognition accuracy of the GBDT-AdaBoost reached 98.0%. At the same time, that of random forest (RF), extremely randomized tree (ET), DT, and LightGBM was 79.9, 71.1, 67.3 and 77.2%, respectively. The recall rate of the GBDT-AdaBoost model was the best performance on all types of cells. The F1-Score of the GBDT-AdaBoost model was also better than the results of any single classifiers. The proposed algorithm can effectively recognize the image of bovine milk somatic cells. Moreover, it may provide a reference for recognizing bovine milk somatic cells with similar shape size characteristics and is difficult to distinguish.

MRI-Based Radiomics Features to Predict Treatment Response to Neoadjuvant Chemotherapy in Locally Advanced Rectal Cancer: A Single Center, Prospective Study.

This is a prospective, single center study aimed to evaluate the predictive power of peritumor and intratumor radiomics features assessed using T2 weight image (T2WI) of baseline magnetic resonance imaging (MRI) in evaluating pathological good response to NAC in patients with LARC (including Tany N+ or T3/4a Nany but not T4b). In total, 137 patients with LARC received NAC between April 2014 and August 2020. All patients were undergoing contrast-enhanced MRI and 129 patients contained small field of view (sFOV) sequence which were performed prior to treatment. The tumor regression grade standard was based on pathological response. The training and validation sets (n=91 vs. n=46) were established by random allocation of the patients. Receiver operating characteristic curve (ROC) analysis was applied to estimate the performance of different models based on clinical characteristics and radiomics features obtained from MRI, including peritumor and intratumor features, in predicting treatment response; these effects were calculated using the area under the curve (AUC). The performance and agreement of the nomogram were estimated using calibration plots. In total, 24 patients (17.52%) achieved a complete or near-complete response. For the individual radiomics model in the validation set, the performance of peritumor radiomics model in predicting treatment response yield an AUC of 0.838, while that of intratumor radiomics model is 0.805, which show no statically significant difference between then(P>0.05). The traditional and selective clinical features model shows a poor predictive ability in treatment response (AUC=0.596 and 0.521) in validation set. The AUC of combined radiomics model was improved compared to that of the individual radiomics models in the validation sets (AUC=0.844). The combined clinic-radiomics model yield the highest AUC (0.871) in the validation set, although it did not improve the performance of the radiomics model for predicting treatment response statically (P>0.05). Good agreement and discrimination were observed in the nomogram predictions. Both peritumor and intratumor radiomics features performed similarly in predicting a good response to NAC in patients with LARC. The clinic-radiomics model showed the best performance in predicting treatment response.

Real-Life Experience of Regorafenib in Patients With Advanced Hepatocellular Carcinoma.

Background: The RESORCE trial reported that regorafenib was effective as the second-line treatment for patients with hepatocellular carcinoma (HCC) after progression on sorafenib. Real-world data are needed to assess clinical outcomes and adverse events in the setting of daily practice. Objective: We aimed to evaluate the efficacy and safety of regorafenib after disease progression with sorafenib in Chinese patients with advanced HCC. Patients and Methods: A total of 41 patients with advanced HCC who did not respond to sorafenib and followed a regorafenib regimen were enrolled in this retrospective study. Overall survival (OS), progression-free survival (PFS), radiological responses, and adverse events (AEs) were evaluated. Survival curves were compared by using the log-rank test and constructed with the Kaplan-Meier method. Results: The median PFS with regorafenib was 6.6 months (range: 5.0-8.2 months), and the median OS with regorafenib was not reached. The 1-year OS rate of regorafenib was 66.4%. The median OS of sequential sorafenib to regorafenib treatment was 35.3 months [95% confidence interval (CI), 24.3-46.3], and the 2-year OS rate of sequential sorafenib to regorafenib treatment was 74.4%. The most common AEs of regorafenib treatment were elevated aspartate aminotransferase [17/41 patients (41.5%)], elevated alanine aminotransferase [16/41 patients (39%)] and hand-foot syndrome [14/41 patients (34.1%)]. Conclusion: Regorafenib appears to be safe and clinically effective in patients with advanced HCC who progressed on first-line sorafenib.