Adjunctive Chinese medicine therapy reduces relapse of psoriasis vulgaris after discontinuation of biologics: a prospective registry-based cohort study.

BACKGROUND: Biologics have revolutionized psoriasis treatment; however, relapse of psoriasis after discontinuation of biologics remains unresolved. OBJECTIVE: To assess the impact of adjunctive Chinese medicine (CM) therapy on relapse of psoriasis vulgaris (PV) after discontinuation of biologics. METHODS: We constructed a prospective cohort study through a psoriasis case registry platform that enrolled patients treated with biologics (in combination with or without CM). The endpoint event was relapse, defined as loss of psoriasis area and severity index (PASI) 75. RESULTS: A total of 391 patients completed the study and were included in the analysis, of whom 169 (43.2%) experienced relapse during follow-up. To minimize the bias, a 1:1 propensity score matching (PSM) was performed, generating matched cohorts of 156 individuals per group. Adjuvant CM therapy significantly associated with reduced incidence of relapse (HR =0.418, 95% CI = 0.289 ∼ 0.604, p < 0.001), and the protective effect of CM in the subgroup analysis was significant. In addition, PASI 90 response and disease duration were associated with relapse (p < 0.05). CONCLUSION: Adjunctive CM therapy is associated with reduced relapse incidence in PV after discontinuation of biologics.

PLAU promotes cell proliferation and migration of head and neck cancer via STAT3 signaling pathway.

It was reported that within the head and neck cancer (HNC) cell line CAL21 the epithelial-mesenchymal transition (EMT) and cell proliferation were promoted by Urokinase-Type Plasminogen Activator (PLAU) proteinase through TNFRSF12A. Additionally, in this paper HNC cell lines refer to Fadu and Tu686. A novel PLAU-STAT3 axis was found to be involved in HNC cell line proliferation and metastasis. PLAU expression in HNC samples was upregulated, besides, the elevated expression of PLAU was linked to the lower overall survival (OS) and disease-free survival (DFS). Ectopic PLAU expression promoted cell proliferation and migration, while PLAU knockdown exhibited opposite results. RNA-seq data identified the JAK-STAT signaling pathway, confirmed by western blotting. A recovery assay using S3I-201, a selective inhibitor of signal transducer and activator of transcription 3 (STAT3), indicated that PLAU promoted HNC cell line progression via STAT3 signaling in vitro. The oncogenic role of PLAU in HNC tumor growth in vivo was confirmed using xenograft models. In summary, we identified the tumorigenic PLAU function in the HNC progress. PLAU may represent a potential prognostic biomarker of HNC and the PLAU-STAT3 pathway might be considered a therapeutic target of HNC.

COVID-19 vaccine hesitancy among the Chinese elderly: A multi-stakeholder qualitative study.

The United Nations reported that the mortality risk of Corona Virus Disease 2019 (COVID-19) is five times higher in the elderly than the global average. Although the COVID-19 vaccine effectively prevents infections and reduce mortality among the elderly, vaccine hesitancy among the Chinese elderly poses a significant threat. This study, utilizing the "Confidence, Convenience and Complacency (3 Cs)" vaccine hesitancy model, aimed to explore factors contributing to vaccine hesitancy among the Chinese elderly and assess national countermeasures and potential improvement approaches. Thirteen elderly with vaccine hesitancy and eleven vaccine-related staff participated in semi-structured interviews. Thematic analysis revealed three key determinants of vaccine hesitancy among the elderly: perceived low threat of COVID-19, lack of confidence in COVID-19 vaccine, and poor accessibility to vaccination. China has implemented strategies, including advocacy through diverse channels, joint multi-sectoral promotion vaccination, and enhancing ongoing vaccination services. Recommendations from the vaccine-related staff emphasize improving vaccine awareness among the elderly, and prioritizing the vaccination environment and process. The study underscores the importance of targeted vaccination promotion programs addressing hesitation reasons to improve vaccination rates. Furthermore, existing countermeasures can serve as a foundation for enhancing vaccination strategies, including improved publicity, administration, and management approaches.

The predictive effect of mid-pregnancy sleep disorders on perinatal depression within women with or without depression in early pregnancy: A prospective cohort study.

BACKGROUND: To investigate the relationship between depression in early pregnancy and sleep quality in mid-pregnancy, and explore whether sleep disorders independently predicts depression across the perinatal period within women with or without depression in early pregnancy. METHODS: Data were collected at 7 time points from 12 weeks of pregnancy to 6 weeks postpartum. Multiple logistic regression and survival analysis were used to explore the relationship between sleep quality in mid-pregnancy and perinatal depression within women with or without depression in early pregnancy. RESULTS: 390 women were included. Women with depression in early pregnancy were more likely to have sleep disorders and perinatal depression. Women with sleep disorders had a higher risk of perinatal depression compared to women without sleep disorders in mid-pregnancy. Stratified analysis based on whether depressed at 12 weeks of pregnancy found that among women without depression, those with sleep disorders in mid-pregnancy were more likely to have subsequent perinatal depression and appeared earlier; whereas, among women with depression, mid-pregnancy sleep disorders was not a predictor of subsequent perinatal depression. LIMITATION: High rates of missed visits may lead to sample bias, with depression and sleep quality being assessed by self-report. CONCLUSIONS: Women with depression in early pregnancy are more likely to have sleep disorders in mid-pregnancy. There is a strong correlation between sleep quality in mid-pregnancy and perinatal depression among women without depression in early pregnancy. Routine screening and intervention for sleep disorders should be a priority in perinatal care to reduce the incidence of perinatal depression.

The bidirectional causal association between psoriasis and psychological illnesses: a 2-sample Mendelian randomization study.

There have been reports of association between psoriasis and psychological illnesses. Nevertheless, it is not easy to draw conclusions with high quality causality from conventional observational studies. The objective of this study was to evaluate the causality of psoriasis and psychological illnesses. All summary-level data from genome-wide association studies for psoriasis and psychological illnesses were collected from Neale Lab and MRC Integrative Epidemiology Unit. Bidirectional Mendelian randomization analysis was performed to determine the random orientation with an independent genetic variation. The main estimated effects are derived using Inverse-Variance Weighted, weighted median, and MR Egger methods. Multiple data sets were validated against each other, and a series of sensitivity studies were conducted to ensure the reliability of the results. Mendelian randomization analysis revealed a causal effect of psoriasis [odds ratio (OR) 1·34, 95% confidence interval (CI) 1·21-1·46, P < 0.001; and OR 1.28, CI 1.17-1.39, P < 0.001] on psychological illnesses, and vice versa (OR 1.23, CI 1.14-1.31, P < 0.001; and OR 1.21, CI 1.11-1.31, P < 0.001). The results were supported by a series of sensitivity analyses. The findings of this Mendelian randomization study support a causal effect between psoriasis and psychological illnesses, and vice versa.

Response of Psoriasis with Systemic Lupus Erythematosus to Secukinumab: A Case Report and Review of the Literature.

Although psoriasis and systemic lupus erythematosus are both immune abnormalities, there are few reports on Ps combined with SLE in China and abroad. We present a case of a 23-year-old female with a 10-year history of psoriasis, who had a relapse with significant joint symptoms and a PASI score of 35.6, along with several positive antibodies, including antinuclear antibodies, elevated CRP and ESR, and was diagnosed with psoriasis and SLE. She has been treated with secukinumab 300 mg subcutaneously, improving her condition. She is currently being followed up. The PubMed, SCOPUS, and ISI Web of Science databases were thoroughly searched for English studies reporting psoriasis with systemic lupus erythematosus and treated using biologics. Three case reports/series were included after the screening. Our case report provides a reference for clinicians using secukinumab to treat psoriasis combined with SLE. However, further research is needed to determine whether it can be widely used or if there are common signaling pathways that can be interfered with the pathogenesis of the diseases.

Mapping the landscape of artificial intelligence in skin cancer research: a bibliometric analysis.

Objective: Artificial intelligence (AI), with its potential to diagnose skin cancer, has the potential to revolutionize future medical and dermatological practices. However, the current knowledge regarding the utilization of AI in skin cancer diagnosis remains somewhat limited, necessitating further research. This study employs visual bibliometric analysis to consolidate and present insights into the evolution and deployment of AI in the context of skin cancer. Through this analysis, we aim to shed light on the research developments, focal areas of interest, and emerging trends within AI and its application to skin cancer diagnosis. Methods: On July 14, 2023, articles and reviews about the application of AI in skin cancer, spanning the years from 1900 to 2023, were selected from the Web of Science Core Collection. Co-authorship, co-citation, and co-occurrence analyses of countries, institutions, authors, references, and keywords within this field were conducted using a combination of tools, including CiteSpace V (version 6.2. R3), VOSviewer (version 1.6.18), SCImago, Microsoft Excel 2019, and R 4.2.3. Results: A total of 512 papers matching the search terms and inclusion/exclusion criteria were published between 1991 and 2023. The United States leads in publications with 149, followed by India with 61. Germany holds eight positions among the top 10 institutions, while the United States has two. The most prevalent journals cited were Cancer, the European Journal of Cancer, and Sensors. The most frequently cited keywords include "skin cancer", "classification", "artificial intelligence", and "deep learning". Conclusions: Research into the application of AI in skin cancer is rapidly expanding, and an increasing number of scholars are dedicating their efforts to this field. With the advancement of AI technology, new opportunities have arisen to enhance the accuracy of skin imaging diagnosis, treatment based on big data, and prognosis prediction. However, at present, the majority of AI research in the field of skin cancer diagnosis is still in the feasibility study stage. It has not yet made significant progress toward practical implementation in clinical settings. To make substantial strides in this field, there is a need to enhance collaboration between countries and institutions. Despite the potential benefits of AI in skin cancer research, numerous challenges remain to be addressed, including developing robust algorithms, resolving data quality issues, and enhancing results interpretability. Consequently, sustained efforts are essential to surmount these obstacles and facilitate the practical application of AI in skin cancer research.

Meta-adaptive-weighting-based bilateral multi-dimensional refined space feature attention network for imbalanced breast cancer histopathological image classification.

Breast cancer histopathological image automatic classification can reduce pathologists workload and provide accurate diagnosis. However, one challenge is that empirical datasets are usually imbalanced, resulting in poorer classification quality compared with conventional methods based on balanced datasets. The recently proposed bilateral branch network (BBN) tackles this problem through considering both representation and classifier learning to improve classification performance. We firstly apply bilateral sampling strategy to imbalanced breast cancer histopathological image classification and propose a meta-adaptive-weighting-based bilateral multi-dimensional refined space feature attention network (MAW-BMRSFAN). The model is composed of BMRSFAN and MAWN. Specifically, the refined space feature attention module (RSFAM) is based on convolutional long short-term memories (ConvLSTMs). It is designed to extract refined spatial features of different dimensions for image classification and is inserted into different layers of classification model. Meanwhile, the MAWN is proposed to model the mapping from a balanced meta-dataset to imbalanced dataset. It finds suitable weighting parameter for BMRSFAN more flexibly through adaptively learning from a small amount of balanced dataset directly. The experiments show that MAW-BMRSFAN performs better than previous methods. The recognition accuracy of MAW-BMRSFAN under four different magnifications still is higher than 80% even when unbalance factor is 16, indicating that MAW-BMRSFAN can make ideal performance under extreme imbalanced conditions.

Improved sparse domain super-resolution reconstruction algorithm based on CMUT.

A novel breast ultrasound tomography system based on a circular array of capacitive micromechanical ultrasound transducers (CMUT) has broad application prospects. However, the images produced by this system are not suitable as input for the training phase of the super-resolution (SR) reconstruction algorithm. To solve the problem, this paper proposes an improved medical image super-resolution (MeSR) method based on the sparse domain. First, we use the simultaneous algebraic reconstruction technique (SART) with high imaging accuracy to reconstruct the image into a training image in a sparse domain model. Secondly, we denoise and enhance the contrast of the SART images to obtain improved detail images before training the dictionary. Then, we use the original detail image as the guide image to further process the improved detail image. Therefore, a high-precision dictionary was obtained during the testing phase and applied to filtered back projection SR reconstruction. We compared the proposed algorithm with previously reported algorithms in the Shepp Logan model and the model based on the CMUT background. The results showed significant improvements in peak signal-to-noise ratio, entropy, and average gradient compared to previously reported algorithms. The experimental results demonstrated that the proposed MeSR method can use noisy reconstructed images as input for the training phase of the SR algorithm and produce excellent visual effects.

Effect and safety of probiotics for treating urticaria: A systematic review and meta-analysis.

BACKGROUND: To assess the effect and safety of probiotics for treating urticaria. METHODS: Randomized controlled trial (RCT) papers on the probiotics treatment published before May 2019 were retrieved from various databases like PubMed, EMbase, MEDLINE (Ovid), SCI-Hub, Springer, ClinicalKey, VIP, and CNKI. The treatment plan that we include are oral administration of single probiotic, multiple probiotics, and the combination of probiotics and antihistamines. Meta-analysis of the data was performed by RevMan 5.3 software. RESULTS: A total of nine RCT papers were included: four papers for oral administration of single probiotic, three papers for oral administration of multiple probiotics, and two papers for oral administration of a probiotic combined with antihistamines. The results of meta-analysis showed that the therapeutic effect of the probiotic group was significantly higher than the control group (placebo or antihistamines) (RR = 1.09, 95% CI: 1.03-1.16, p = 0.006). And compared with the placebo group, the therapeutic effect of single probiotic group was significantly improved (RR = 1.11, 95% CI: 1.01-1.21, p = 0.03). Regarding therapeutic effect, there was no statistically significant difference between the multiple probiotics group and placebo group (RR = 1.00, 95% CI: 0.94 ~ 1.07, p = 0.91); the therapeutic effect of single probiotic combined antihistamine group was significantly higher than the antihistamine group (RR = 1.13, 95% CI: 1.07-1.19, p < 0.0001). Regarding the incidence of adverse reactions, there was no significant difference between the probiotic group and the control group (p = 0.46). CONCLUSION: The treatment plan of oral administration of probiotics has significant therapeutic effects on urticaria, but the therapeutic effects of the administration of multiple probiotics and the safety of probiotic therapy are still not yet obvious. Some large-scale, multi-centered RCT studies are needed in the future for clarification.

NCAPG as a novel prognostic biomarker in numerous cancers: a meta-analysis and bioinformatics analysis.

BACKGROUND: Identification of effective biomarkers for cancer prognosis is a primary research challenge. Recently, several studies have reported the relationship between NCAPG and the occurrence of various tumors. However, none have combined meta-analytical and bioinformatics approaches to systematically assess the role of NCAPG in cancer. METHODS: We searched four databases, namely, PubMed, Web of Science, Embase, and the Cochrane Library, for relevant articles published before April 30, 2022. The overall hazard ratio or odds ratio and 95% confidence intervals were calculated to assess the relationship between NCAPG expression and cancer survival prognosis or clinical characteristics. Furthermore, the aforementioned results were validated using the GEPIA2, Kaplan-Meier plotter, and PrognoScan databases. RESULTS: The meta-analysis included eight studies with 1096 samples. The results showed that upregulation of NCAPG was correlated with poorer overall survival (hazard ratio = 2.90, 95% confidence interval = 2.06-4.10, P < 0.001) in the cancers included in the study. Subgroup analysis showed that in some cancers, upregulation of NCAPG was correlated with age, distant metastasis, lymph node metastasis, TNM stage, relapse, differentiation, clinical stage, and vascular invasion. These results were validated using the GEPIA2, UALCAN, and PrognoScan databases. We also explored the processes of NCAPG methylation and phosphorylation. CONCLUSION: Dysregulated NCAPG expression is associated with the clinical prognostic and pathological features of various cancers. Therefore, NCAPG can serve as a human cancer therapeutic target and a new potential prognostic biomarker.

Knowledge Mapping and Research Hotspots of Comorbidities in Psoriasis: A Bibliometric Analysis from 2004 to 2022.

Background and Objectives: Psoriasis is a chronic inflammatory disease whose impact on health is not only limited to the skin, but is also associated with multiple comorbidities. Early screening for comorbidities along with appropriate treatment plans can provide a positive prognosis for patients. This study aimed to summarize the knowledge structure in the field of psoriasis comorbidities and further explore its research hotspots and trends through bibliometrics. Materials and Methods: A search was conducted in the core collection of the Web of Science for literature on comorbidities of psoriasis from 2004 to 2022. VOSviewer and CiteSpace software were used for collaborative network analysis, co-citation analysis of references, and keyword co-occurrence analysis on these publications. Results: A total of 1803 papers written by 6741 authors from 81 countries was included. The publications have shown a progressive increase since 2004. The United States and Europe were at the forefront of this field. The most prolific institution was the University of California, and the most productive author was A. Armstrong. Research has focused on "psoriatic arthritis", "metabolic syndrome", "cardiovascular disease", "psychosomatic disease", "inflammatory bowel disease", "prevalence", "quality of life", and "risk factor" in the past 18 years. Keywords such as "biologics" and "systemic inflammation", have been widely used recently, suggesting current research hotspots and trends. Conclusions: Over the past 18 years, tremendous progress has been made in research on psoriasis comorbidity. However, collaborations among countries, institutions, and investigators are inadequate, and the study of the mechanisms of interaction between psoriasis and comorbidities and management of comorbidities is insufficient. The treatment of comorbidities with biologic agents, screening of comorbidities, and multidisciplinary co-management are predicted to be the focus of future research.

Effects of Probiotics Supplementation on the Intestinal Metabolites, Muscle Fiber Properties, and Meat Quality of Sunit Lamb.

The development of animal husbandry is closely related to the meat quality of small ruminants. Intestinal metabolites and the muscle fiber types of lambs are important factors that affect their meat quality, but few studies have examined the regulation of the "intestinal muscle axis" by probiotics. In this study, 12 Sunit lambs were divided into a control group (C) and a probiotics group (P). The gene expressions of the myosin heavy chain, metabolic enzyme activity, and short-chain fatty acids in the intestines were analyzed using gas chromatography-mass spectrometry (GC-MS) and quantitative real-time PCR. The results showed that levels of propionic acid and butyric acid in the intestines of group P were significantly higher than in group C (p < 0.05). In addition, probiotics increased the number and area ratio of type I muscle fibers. They also increased the mRNA expression of MyHC IIA and the activity of malate dehydrogenase (MDH) and succinate dehydrogenase (SDH). Propionic acid was negatively correlated with the number ratio of type IIB muscle fibers. Butyric acid was found to be significantly positively correlated with the number ratio of type IIA muscle fibers. Cooking loss, pH24h, and shear force decreased significantly in group P. In conclusion, intestinal metabolites (SCFAs) altered the activity of oxidative-myofibril-metabolizing enzymes and the expression of myosin heavy-chain type IIA, reduced the meat shear values, and improved meat tenderness. This study provides a new basis for improving the production and meat quality of small ruminants.

Dietary linseed supplementation improves meat quality and flavor of sheep by altering muscle fiber characteristics and antioxidant capacity.

Linseed as a common oil crop that has been implicated in regulating the meat quality of animals. The aim of this investigation was to establish the effects of dietary linseed supplementation on carcass traits, meat quality, muscle fiber characteristics, volatile compounds and antioxidant capacity of sheep. Twenty-four Sunit sheep were distributed into 2 groups with different diet: control (CO) fed a standard diet and linseed (LS, 8% of extruded linseed). The results showed that linseed not only increased M. longissimus thoracis (LT) increased the loin-eye area but also decreased pH24h, L* and shear force. Meanwhile, linseed increased mRNA expression of MyHCI and MyHCIIx and decreased in cross-sectional area (CSA) and muscle fiber diameter. In addition, linseed altered the composition of meat volatile flavor compounds, such as Z-10-Pentadecen-1-ol, pentanal, 2-Octenal, (E)-, decanal, butane, and 2-heptanone. Moreover, linseed increased total antioxidative capacity (T-AOC) activity, the mRNA expression of glutathione peroxidase (GSH-Px) and catalase (CAT), decreased malondialdehyde (MDA) content. Overall, the results suggest that linseed is an effective feed additive in improving meat quality and flavor. The underlying mechanisms for its effectiveness may be partly due to a change in muscle fiber characteristics and antioxidant capacity.

Lactobacillus improves meat quality in Sunit sheep by affecting mitochondrial biogenesis through the AMPK pathway.

Sunit sheep are famous for their high meat quality, but the meat quality of them has declined due to the change in feeding methods. Lactobacillus has a variety of probiotic effects and is widely used in animal diets to optimize meat quality. This study aimed to investigate the effect of dietary supplementation with different levels of Lactobacillus on meat quality. A total of 24 3-month-old Sunit sheep with an average body weight of 19.03 ± 3.67 kg were randomly divided into control (C), 1% (L1), 2% (L2), and 3% Lactobacillus groups (L3), with 6 sheep in each group. Myofiber characteristics, meat quality, and metabolic enzyme activity were detected. Moreover, the regulatory mechanism of Lactobacillus on meat quality was explored by using Western blotting and real-time Quantitative polymerase chain reaction (RT-qPCR). The results showed that dietary addition of Lactobacillus decreased LDH activity in the Biceps femoris of Sunit sheep (P < 0.05). Compared to the other groups, the 1% Lactobacillus group showed the conversion of myofibers from the glycolytic to the oxidative type, and the increasing b* values (P < 0.05), decreasing shear force and cooking loss of meat (P < 0.05) and the relative gene and protein expression levels of AMPK, PGC-1α, NRF1, TFAM, and COX IV (P < 0.05) in the Biceps femoris were also increased in the 1% Lactobacillus group. Therefore, the addition of Lactobacillus to the diet of Sunit sheep could regulate the AMPK signaling pathway to promote myofiber type conversion, which improves meat quality. This study provided a theoretical and data basis for improving the meat quality of sheep and supplied a novel way of applying Lactobacillus.

Histopathological image recognition of breast cancer based on three-channel reconstructed color slice feature fusion.

Although computer-aided diagnosis (CAD) have shown excellent performance in Breast cancer (BC) histopathological image, it commonly requires a high-level network, and the recognition efficiency is frequently unsatisfied due to the complex structure of histopathological image. In this study, a ten-layers convolutional neural network (CNN) model called "ColorDeep" is used to extract color features corresponding to the different tissue parts in cell, pure color image slices obtained by a three-channel separation and reconstruction method are used as model input. Two models are tested on the BreaKHis dataset show that images under four magnifications achieved the recognition accuracy of 96.89%-99.67% at the image level, which is better than many state-of-the-art methods. The characteristics contained by the B channel have the largest effect on BC recognition, and compared to other research results, the proposed model improves the recognition speed on a single image by about 0.1s. More importantly, instead of using large histopathological images to input into the model for BC diagnosis, and instead of segmenting the nuclei, only the reconstructed B-channel features containing the nuclei region in the stained BC image need to be input into the model to enable accurate diagnosis of BC.

Breast cancer histopathological images classification based on deep semantic features and gray level co-occurrence matrix.

Breast cancer is regarded as the leading killer of women today. The early diagnosis and treatment of breast cancer is the key to improving the survival rate of patients. A method of breast cancer histopathological images recognition based on deep semantic features and gray level co-occurrence matrix (GLCM) features is proposed in this paper. Taking the pre-trained DenseNet201 as the basic model, part of the convolutional layer features of the last dense block are extracted as the deep semantic features, which are then fused with the three-channel GLCM features, and the support vector machine (SVM) is used for classification. For the BreaKHis dataset, we explore the classification problems of magnification specific binary (MSB) classification and magnification independent binary (MIB) classification, and compared the performance with the seven baseline models of AlexNet, VGG16, ResNet50, GoogLeNet, DenseNet201, SqueezeNet and Inception-ResNet-V2. The experimental results show that the method proposed in this paper performs better than the pre-trained baseline models in MSB and MIB classification problems. The highest image-level recognition accuracy of 40×, 100×, 200×, 400× is 96.75%, 95.21%, 96.57%, and 93.15%, respectively. And the highest patient-level recognition accuracy of the four magnifications is 96.33%, 95.26%, 96.09%, and 92.99%, respectively. The image-level and patient-level recognition accuracy for MIB classification is 95.56% and 95.54%, respectively. In addition, the recognition accuracy of the method in this paper is comparable to some state-of-the-art methods.

Breast cancer histopathological images recognition based on two-stage nuclei segmentation strategy.

Pathological examination is the gold standard for breast cancer diagnosis. The recognition of histopathological images of breast cancer has attracted a lot of attention in the field of medical image processing. In this paper, on the base of the Bioimaging 2015 dataset, a two-stage nuclei segmentation strategy, that is, a method of watershed segmentation based on histopathological images after stain separation, is proposed to make the dataset recognized to be the carcinoma and non-carcinoma recognition. Firstly, stain separation is performed on breast cancer histopathological images. Then the marker-based watershed segmentation method is used for images obtained from stain separation to achieve the nuclei segmentation target. Next, the completed local binary pattern is used to extract texture features from the nuclei regions (images after nuclei segmentation), and color features were extracted by using the color auto-correlation method on the stain-separated images. Finally, the two kinds of features were fused and the support vector machine was used for carcinoma and non-carcinoma recognition. The experimental results show that the two-stage nuclei segmentation strategy proposed in this paper has significant advantages in the recognition of carcinoma and non-carcinoma on breast cancer histopathological images, and the recognition accuracy arrives at 91.67%. The proposed method is also applied to the ICIAR 2018 dataset to realize the automatic recognition of carcinoma and non-carcinoma, and the recognition accuracy arrives at 92.50%.