Comprehensive evaluation of Dragon's Blood in combination with borneol in ameliorating ischemic/reperfusion brain injury using RNA sequencing, metabolomics, and 16S rRNA sequencing.

Ischemia/reperfusion (IR) can induce deleterious responses such as apoptosis, inflammation, and oxidative stress; however, there are currently no efficient therapeutics to treat IR brain injury. Dragon's blood (DB) plays a significant role in treating ischemic stroke in China. Borneol (B) is an upper ushering drug that guides drugs to the target organs, including the brain. Therefore, we hypothesized that the combination of DB and B (DB + B) would provide cooperative therapeutic benefits for IR brain injury. To confirm this, we first investigated the protective effect of DB + B in an IR brain injury rat model using the modified neurological severity score (mNSS), infarction size measure, HE staining, and laser speckle contrast imaging. Then, we comprehensively evaluated the mechanism of DB + B in ameliorating IR brain injury based on RNA sequencing, serum untargeted metabolomics, and 16S rRNA sequencing. We have confirmed that DB + B enhanced the efficacy of the ischemic stroke treatment compared to DB or B alone for the first time. Our study provisionally confirms that the mechanism by which DB + B prevents IR brain injury is related to the maintenance of intestinal microecological balance and regulation of metabolic dysfunction, thereby suppressing inflammation and regulating immunity. DB + B may effectively regulate intestinal flora including o_Pseudomonadales, s_Bacteroides_caecimuris, o_unidentified_Bacilli, f-Pseudomonadaceae, and g-Pseudomonas, mainly regulate serum metabolites including improve the protective benefit of IR brain injury lysoPCs and lysoPEs, thus inhibiting TLR4/MyD88/NF-κB and IL-17 signing pathway to reduce inflammatory reactions. hat this mechanism is associated with the maintenance of intestinal flora balance and the regulation of metabolic dysfunction, thereby suppressing inflammation and regulating immunity. This provides scientific support for the clinical translation of DB + B in the prevention and treatment of ischemic stroke and establishes a basis for further investigation of its therapeutic mechanism.

Notoginsenoside R1 alleviates cerebral ischemia/reperfusion injury by inhibiting the TLR4/MyD88/NF-κB signaling pathway through microbiota-gut-brain axis.

BACKGROUND: Ischemic stroke (IS) ranks as the second common cause of death worldwide. However, a narrow thrombolysis timeframe and ischemia-reperfusion (I/R) injury limits patient recovery. Moreover, anticoagulation and antithrombotic drugs do not meet the clinical requirements. Studies have demonstrated close communication between the brain and gut microbiota in IS. Notoginsenoside R1 (NG-R1), a significant component of the total saponins from Panax notoginseng, has been demonstrated to be effective against cerebral I/R injury. Total saponins have been used to treat IS in Chinese pharmacopoeia. Furthermore, previous research has indicated that the absorption of NG-R1 was controlled by gut microbiota. STUDY DESIGN: This study aimed to access the impact of NG-R1 treatment on neuroinflammation and investigate the microbiota-related mechanisms. RESULTS: NG-R1 significantly reduced neuronal death and neuroinflammation in middle cerebral artery occlusion/reperfusion (MCAO/R) models. 16S rRNA sequencing revealed that NG-R1 treatment displayed the reversal of microbiota related with MCAO/R models. Additionally, NG-R1 administration attenuated intestinal inflammation, gut barrier destruction, and systemic inflammation. Furthermore, microbiota transplantation from NG-R1 exhibited a similar effect in the MCAO/R models. CONCLUSION: In summary, NG-R1 treatment resulted in the restoration of the structure of the blood-brain barrier (BBB) and reduction in neuroinflammation via suppressing the stimulation of astrocytes and microglia in the cerebral ischemic area. Mechanistic research demonstrated that NG-R1 treatment suppressed the toll-like receptor 4/myeloid differentiation primary response 88/nuclear factor kappa B (TLR4/MyD88/NF-κB) signaling pathway in both the ischemic brain and colon. NG-R1 treatment enhanced microbiota dysbiosis by inhibiting the TLR4 signaling pathway to protect MCAO/R models. These findings elucidate the mechanisms by which NG-R1 improve stroke outcomes and provide some basis for Panax notoginseng saponins in clinical treatment.

Activation of RhoA/ROCK2 signaling by hypoxia-inducible factor 1α in promoting tumor growth and metastasis in human colon cancer.

Background: Recent evidence strongly suggests the profound role of the tumor microenvironment in cancer development and progression. A hypoxic microenvironment is widely acknowledged to be a typical feature of solid tumors, and altered hypoxia-inducible factor 1α (HIF-1α) expression has been associated with the formation and the progression of many solid tumors; however, the underlying mechanism of this relationship remains obscure. Methods: Clinical colorectal cancer tissue samples were collected to detect the differential expression of HIF-1α, Ras homolog family member A (RhoA), and Rho-associated, coiled-coil containing protein kinase 2 (ROCK2). With hypoxic stress, small interfering RNA (siRNA) targeting HIF-1α, lentivirus transfection of RhoA was used to study the mechanism of HIF-1α and RhoA/ROCK2 signaling pathways in the growth and metastasis of colon cancer. Results: According to Cell Counting Kit 8, wound-healing, and Transwell assays, HIF-1α expression activated the RhoA/ROCK2 pathway within colon cancer cell lines, accelerating their growth and expansion. In cells transfected with LV-RhoA, inactivating the RhoA/ROCK2 pathway with the specific inhibitor Y-27632 decreased tumor growth and metastasis under hypoxic conditions, while activating the RhoA/ROCK2 pathway restored these biological properties. The Western blot results showed that the expression levels of pMYPT1, cyclin D1, and MMP2 in the siRNA + LV-RhoA group were also significantly increased compared with those in the siRNA group. Conclusions: For the first time, this study demonstrated that HIF-1α can promote the growth and metastasis of colon cancer via directly affecting RhoA/ROCK2 signaling and thus represents a novel therapeutic target for colon cancer.

Variation of sugar compounds in Phoebe chekiangensis seeds during natural desiccation.

To investigate the role of sugar metabolism in desiccation-sensitive seeds, we performed a natural desiccation treatment on Phoebe chekiangensis seeds in a room and systematically analyzed the changes in seed germination, sugar compounds, malondialdehyde, and relative electrical conductivity during the seed desiccation. The results revealed that the initial moisture content of P. chekiangensis seed was very high (37.06%) and the seed was sensitive to desiccation, the germination percentage of the seed decreased to 5.33% when the seed was desiccated to 22.04% of moisture content, therefore, the seeds were considered recalcitrant. Based on the logistic model, we know that the moisture content of the seeds is 29.05% when the germination percentage drops to 50% and that it is desirable to keep the seed moisture content above 31.74% during ambient transportation. During seed desiccation, sucrose and trehalose contents exhibited increasing trends, and raffinose also increased during the late stage of desiccation, however, low levels of the non-reducing sugar accumulations may not prevent the loss of seed viability caused by desiccation. Glucose and fructose predominated among sugar compounds, and they showed a slight increase followed by a significant decrease. Their depletion may have contributed to the accumulation of sucrose and raffinose family oligosaccharides. Correlation analysis revealed a significant relationship between the accumulation of sucrose, trehalose, and soluble sugars, and the reduction in seed viability. Sucrose showed a significant negative correlation with glucose and fructose. Trehalose also exhibited the same pattern of correlation. These results provided additional data and theoretical support for understanding the mechanism of sugar metabolism in seed desiccation sensitivity.

Higenamine inhibits acute and chronic inflammatory pain through modulation of TRPV4 channels.

Pain is the cardinal symptom of many debilitating diseases and results in heavy health and economic burdens worldwide. Asarum (Asarum sieboldii Miq.) is a commonly used analgesic in Chinese medicine. However, the analgesic components and mechanisms of asarum in acute and chronic pain mice model remain unknown. In this study, we first generated asarum water extract and confirmed strong analgesic properties in mice in both the acute thermal and mechanical pain models, as well as in the complete Freund's adjuvant (CFA) induced chronic inflammatory pain model. Second, we identified higenamine as a major component of asarum and found that higenamine significantly inhibited thermal and mechanical induced acute pain and CFA induced chronic inflammatory pain. Then, using Trpv4-/- mice, we found that TRPV4 is necessary for CFA induced thermal and mechanical allodynia, and demonstrated that higenamine analgesia in the CFA model is partly through TRPV4 channel inhibition. Finally, we found that GSK1016790A, a TRPV4 agonist, induced calcium response was significantly inhibited by higenamine in both cultured DRG neurons and TRPV4 transfected HEK293 cells. Consistent with calcium imaging results, higenamine pretreatment also dose-dependently inhibited GSK1016790A induced acute pain. Taken together, our behavior and calcium imaging results demonstrate that the asarum component higenamine inhibits acute and chronic inflammatory pain by modulation of TRPV4 channels.

Reconfigurable three multi-mode terahertz metasurface.

We designed a three reconfigurable multi-mode terahertz metasurface based on a concentric elliptical ring structure. The proposed unit cell is a concentric elliptical ring composed of copper, vanadium oxide and photosensitive silicon from the inside ring to the outside ring. The conductivity of photosensitive silicon and vanadium oxide can be adjusted by changing the external operating temperature and pump light intensity. The same unit cell can reconstruct three kinds of states with different properties, and they have completely different transmission characteristics in various terahertz bands. By encoding the arrangement, through changing external stimulus and operating frequencies, the reconfigurable terahertz metasurface can achieve multiple functions including terahertz focusing with adjustable focal length, vortex beam with different topological charge, and near-field imaging with different patterns. It provides what we believe to be a new idea for the field of information security and the design of multifunctional and multifrequency terahertz devices.

An explainable artificial intelligence framework for risk prediction of COPD in smokers.

BACKGROUND: Since the inconspicuous nature of early signs associated with Chronic Obstructive Pulmonary Disease (COPD), individuals often remain unidentified, leading to suboptimal opportunities for timely prevention and treatment. The purpose of this study was to create an explainable artificial intelligence framework combining data preprocessing methods, machine learning methods, and model interpretability methods to identify people at high risk of COPD in the smoking population and to provide a reasonable interpretation of model predictions. METHODS: The data comprised questionnaire information, physical examination data and results of pulmonary function tests before and after bronchodilatation. First, the factorial analysis for mixed data (FAMD), Boruta and NRSBoundary-SMOTE resampling methods were used to solve the missing data, high dimensionality and category imbalance problems. Then, seven classification models (CatBoost, NGBoost, XGBoost, LightGBM, random forest, SVM and logistic regression) were applied to model the risk level, and the best machine learning (ML) model's decisions were explained using the Shapley additive explanations (SHAP) method and partial dependence plot (PDP). RESULTS: In the smoking population, age and 14 other variables were significant factors for predicting COPD. The CatBoost, random forest, and logistic regression models performed reasonably well in unbalanced datasets. CatBoost with NRSBoundary-SMOTE had the best classification performance in balanced datasets when composite indicators (the AUC, F1-score, and G-mean) were used as model comparison criteria. Age, COPD Assessment Test (CAT) score, gross annual income, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), anhelation, respiratory disease, central obesity, use of polluting fuel for household heating, region, use of polluting fuel for household cooking, and wheezing were important factors for predicting COPD in the smoking population. CONCLUSION: This study combined feature screening methods, unbalanced data processing methods, and advanced machine learning methods to enable early identification of COPD risk groups in the smoking population. COPD risk factors in the smoking population were identified using SHAP and PDP, with the goal of providing theoretical support for targeted screening strategies and smoking population self-management strategies.

A hybrid model for tuberculosis forecasting based on empirical mode decomposition in China.

BACKGROUND: Pulmonary Tuberculosis is a major public health problem endangering people's health, a scientifically accurate predictive model is of great practical significance for the prevention and treatment of pulmonary tuberculosis. METHODS: The reported incidence data of pulmonary tuberculosis were from the National Public Health Science Data Center ( https://www.phsciencedata.cn/ ). The ARIMA, LSTM, EMD-SARIMA, EMD-LSTM, EMD-ARMA-LSTM models were established using the reported monthly incidence of tuberculosis reported in China from January 2008 to December 2018. The MSE, MAE, RMSE and MAPE were used to evaluate the performance of the models to determine the best model. RESULTS: Comparing decomposition-based single model with undecomposed single model, it was found that: when predicting the incidence trend in the next year, compared with SARIMA model, the MSE, MAE, RMSE and MAPE of EMD-SARIMA decreased by 39.3%, 19.0%, 22.1% and 19.8%, respectively. The MSE, MAE, RMSE and MAPE of EMD-LSTM were reduced by 40.5%, 12.8%, 22.9% and 12.7%, respectively, compared with the LSTM model; Comparing the decomposition-based hybrid model with the decomposition-based single model, it was found that: when predicting the incidence trend in the next year, compared with EMD-SARIMA model, the MSE, MAE, RMSE and MAPE of EMD-ARMA-LSTM model decreased by 21.7%, 10.6%, 11.5% and 11.2%, respectively. The MSE, MAE, RMSE and MAPE of EMD-ARMA-LSTM were reduced by 16.7%, 9.6%, 8.7% and 12.3%, respectively, compared with EMD-LSTM model. Furthermore, the performance of the model were consistent when predicting the incidence trend in the next 3 months, 6 months and 9 months. CONCLUSION: The prediction performance of the decomposition-based single model is better than that of the undecomposed single model, and the prediction performance of the combined model using the advantages of different models is better than that of the decomposition-based single model, so the EMD-ARMA-LSTM combination model can improve the prediction accuracy better than other models, which can provide a theoretical basis for predicting the epidemic trend of pulmonary tuberculosis and formulating prevention and control policies.

A study on the preparation conditions of lidocaine microemulsion based on multi-objective genetic algorithm.

Background: Topical lidocaine microemulsion preparations with low toxicity, low irritation, strong transdermal capability and convenient administration are urgently needed. Methods: Box-Behnken design was performed for three preparation conditions of 5% lidocaine microemulsions: mass ratio of the mass ratio of surfactant/(oil phase + surfactant) (X1), the mass ratio of olive oil/(α-linolenic acid + linoleic acid) (X2) and the water content W% (X3). Then, five multi-objective genetic algorithms were used to optimize the three evaluation indices to optimize the effects of lidocaine microemulsion preparations. Finally, the ideal optimization scheme was experimentally verified. Results: Non-dominated Sorting Genetic Algorithm-II was used for 30 random searches. Among these, Scheme 2: X1 = 0.75, X2 = 0.35, X3 = 75%, which resulted in Y1 = 0.17 µg/(cm2·s) and Y2 = 0.74 mg/cm2; and the Scheme 19: X1 = 0.68, X2 = 1.42, X3 = 75% which resulted in Y1 = 0.14 µg/(cm2·s) and Y2 = 0.80 mg/cm2, provided the best matches for the objective function requirements. The maximum and average fitness of the method have reached stability after 3 generations of evolution. Experimental verification of the above two schemes showed that there were no statistically significant differences between the measured values of Y1 and Y2 and the predicted values obtained by optimization (p > 0.05) and are close to the target value. Conclusion: Two lidocaine microemulsion preparation protocols were proposed in this study. These preparations resulted in good transdermal performance or long anesthesia duration, respectively.

Diabetes mellitus early warning and factor analysis using ensemble Bayesian networks with SMOTE-ENN and Boruta.

Diabetes mellitus (DM) has become the third chronic non-infectious disease affecting patients after tumor, cardiovascular and cerebrovascular diseases, becoming one of the major public health issues worldwide. Detection of early warning risk factors for DM is key to the prevention of DM, which has been the focus of some previous studies. Therefore, from the perspective of residents' self-management and prevention, this study constructed Bayesian networks (BNs) combining feature screening and multiple resampling techniques for DM monitoring data with a class imbalance in Shanxi Province, China, to detect risk factors in chronic disease monitoring programs and predict the risk of DM. First, univariate analysis and Boruta feature selection algorithm were employed to conduct the preliminary screening of all included risk factors. Then, three resampling techniques, SMOTE, Borderline-SMOTE (BL-SMOTE) and SMOTE-ENN, were adopted to deal with data imbalance. Finally, BNs developed by three algorithms (Tabu, Hill-climbing and MMHC) were constructed using the processed data to find the warning factors that strongly correlate with DM. The results showed that the accuracy of DM classification is significantly improved by the BNs constructed by processed data. In particular, the BNs combined with the SMOTE-ENN resampling improved the most, and the BNs constructed by the Tabu algorithm obtained the best classification performance compared with the hill-climbing and MMHC algorithms. The best-performing joint Boruta-SMOTE-ENN-Tabu model showed that the risk factors of DM included family history, age, central obesity, hyperlipidemia, salt reduction, occupation, heart rate, and BMI.

Three novel dalbergiphenol hybrids from the heartwood of Dalbergia cochinchinensis.

A novel discovery of two hybrid benzodioxepin-dalbergiphenol epimers, named cochindalbergiphenols A-B (1-2), and a benzofuran-dalbergiphenol hybrid, named cochindalbergiphenol C (3), were isolated and identified from the heartwood of Dalbergia cochinchinensis. The structures of all the isolated compounds were identified through NMR and HRESIMS techniques, while the absolute configurations were determined by comparing the experimental and calculated ECD spectra. Compounds 1-3 exhibited potential protective effects against hypoxia/reoxygenation (H/R) induced injury in H9c2 cells.

New insight into gut microbiota and their metabolites in ischemic stroke: A promising therapeutic target.

The gut-brain axis has been shown to play a vital role in the prognosis and recovery of ischemic stroke (IS), which is associated with gut microbiota dysfunction and changes in the gastrointestinal system and epithelial barrier integrity. In turn, gut microbiota and its derived metabolites can influence stroke outcomes. In this review, we first describe the relationship between IS (clinical and experimental IS) and the gut microbiota. Second, we summarize the role and specific mechanisms of microbiota-derived metabolites in IS. Further, we discuss the roles of natural medicines targeting the gut microbiota. Finally, the potential use of the gut microbiota and derived metabolites as a promising therapeutic opportunity for stroke prevention, diagnosis, and treatment is explored.

Study on the prediction effect of a combined model of SARIMA and LSTM based on SSA for influenza in Shanxi Province, China.

BACKGROUND: Influenza is an acute respiratory infectious disease that is highly infectious and seriously damages human health. Reasonable prediction is of great significance to control the epidemic of influenza. METHODS: Our Influenza data were extracted from Shanxi Provincial Center for Disease Control and Prevention. Seasonal-trend decomposition using Loess (STL) was adopted to analyze the season characteristics of the influenza in Shanxi Province, China, from the 1st week in 2010 to the 52nd week in 2019. To handle the insufficient prediction performance of the seasonal autoregressive integrated moving average (SARIMA) model in predicting the nonlinear parts and the poor accuracy of directly predicting the original sequence, this study established the SARIMA model, the combination model of SARIMA and Long-Short Term Memory neural network (SARIMA-LSTM) and the combination model of SARIMA-LSTM based on Singular spectrum analysis (SSA-SARIMA-LSTM) to make predictions and identify the best model. Additionally, the Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to evaluate the performance of the models. RESULTS: The influenza time series in Shanxi Province from the 1st week in 2010 to the 52nd week in 2019 showed a year-by-year decrease with obvious seasonal characteristics. The peak period of the disease mainly concentrated from the end of the year to the beginning of the next year. The best fitting and prediction performance was the SSA-SARIMA-LSTM model. Compared with the SARIMA model, the MSE, MAE and RMSE of the SSA-SARIMA-LSTM model decreased by 38.12, 17.39 and 21.34%, respectively, in fitting performance; the MSE, MAE and RMSE decreased by 42.41, 18.69 and 24.11%, respectively, in prediction performances. Furthermore, compared with the SARIMA-LSTM model, the MSE, MAE and RMSE of the SSA-SARIMA-LSTM model decreased by 28.26, 14.61 and 15.30%, respectively, in fitting performance; the MSE, MAE and RMSE decreased by 36.99, 7.22 and 20.62%, respectively, in prediction performances. CONCLUSIONS: The fitting and prediction performances of the SSA-SARIMA-LSTM model were better than those of the SARIMA and the SARIMA-LSTM models. Generally speaking, we can apply the SSA-SARIMA-LSTM model to the prediction of influenza, and offer a leg-up for public policy.

TAT Nanobody Exerts Antiviral Effect against PRRSV In Vitro by Targeting Viral Nucleocapsid Protein.

Porcine reproductive and respiratory syndrome (PRRS) is caused by the PRRS virus (PRRSV), which has brought huge economic losses to the pork industry worldwide since its first discovery in the late 1980s in North America. To date, there are no effective commercial vaccines or therapeutic drugs available for controlling the spread of PRRSV. Due to their unique advantages of high affinity and high specificity, nanobodies (Nbs) have received increasing attention in the process of disease diagnosis and treatment. Trans-activator transcription (TAT) can serve as a vector to carry specific proteins into cells by passing through cell membranes. In our previous study, a specific Nb against the PRRSV nucleocapsid (N) protein was screened using phage display technology. For this study, we developed a novel recombinant protein constituting a TAT-conjugated Nb, which we call TAT-Nb1. The target cell entry efficiency of TAT-Nb1 and its effect on PRRSV infection and replication were then investigated. Our results indicate that TAT delivered Nb1 into Marc-145 cells and porcine alveolar macrophages (PAMs) in a dose- and time-dependent manner. Furthermore, TAT-Nb1 dose-dependently suppressed PRRSV infection and replication, where this antiviral effect was independent of PRRSV strain. Co-immunoprecipitation results revealed that Nb1 efficiently interacted with the N protein of PRRSV. Taken together, the presented results suggest that TAT-Nb1 can effectively suppress PRRSV replication, and it may be considered as a new anti-PRRSV candidate drug.

Machine learning-enabled risk prediction of chronic obstructive pulmonary disease with unbalanced data.

BACKGROUND AND OBJECTIVE: Since the early symptoms of chronic obstructive pulmonary disease (COPD) are not obvious, patients are not easily identified, causing improper time for prevention and treatment. In present study, machine learning (ML) methods were employed to construct a risk prediction model for COPD to improve its prediction efficiency. METHODS: We collected data from a sample of 5807 cases with a complete COPD diagnosis from the 2019 COPD Surveillance Program in Shanxi Province and extracted 34 potentially relevant variables from the dataset. Firstly, we used feature selection methods (i.e., Generalized elastic net, Lasso and Adaptive lasso) to select ten variables. Afterwards, we employed supervised classifiers for class imbalanced data by combining the cost-sensitive learning and SMOTE resampling methods with the ML methods (Logistic Regression, SVM, Random Forest, XGBoost, LightGBM, NGBoost and Stacking), respectively. Last, we assessed their performance. RESULTS: The cough frequently at age 14 and before and other 9 variables are significant parameters for COPD. The Stacking heterogeneous ensemble model showed relatively good performance in the unbalanced datasets. The Logistic Regression with class weighting enjoyed the best classification performance in the balancing data when these composite indicators (AUC, F1-Score and G-mean) were used as criteria for model comparison. The values of F1-Score and G-mean for the top three ML models were 0.290/0.660 for Logistic Regression with class weighting, 0.288/0.649 for Stacking with synthetic minority oversampling technique (SMOTE), and 0.285/0.648 for LightGBM with SMOTE. CONCLUSIONS: This paper combining feature selection methods, unbalanced data processing methods and machine learning methods with data from disease surveillance questionnaires and physical measurements to identify people at risk of COPD, concluded that machine learning models based on survey questionnaires could provide an automated identification for patients at risk of COPD, and provide a simple and scientific aid for early identification of COPD.

Variation of the ileocolic artery and superior mesenteric artery in a patient with right-sided colon cancer with Lynch syndrome: a case report.

Background: Complete mesangectomy and central vascular detachment are the core elements of laparoscopic right hemicolectomy. Failure to identify vascular variations in patients undergoing laparoscopic right hemicolectomy can result in unwanted bleeding, a prolonged surgical time, transfer to open surgery, and an elevated risk of postoperative complications. In this case report, we describe a new vascular variation that has not yet been reported in the literature. Parallelly vascular variation and the management of vessels in key areas are essential for successful surgery. Case Description: The patient was a 32-year-old female who was referred to the department of gastrointestinal surgery of our hospital due to intermittent abdominal pain accompanied by changes in stool habits for 3 months. She had not experienced other symptoms. Physical examination revealed mild tenderness in the right lower abdomen. Subsequently, she underwent laparoscopic radical right hemicolectomy for ascending colon cancer under general anesthesia in our hospital. Preoperative abdominal contrast-enhanced computed tomography (CT) and intraoperative photos confirmed that there were two ileocolic arteries derived from the superior mesenteric artery (SMA). On the other side, the SMA and superior mesenteric vein (SMV) were found to be accompanied like "X"-shaped variant. The final surgical pathological diagnosis was pT3N1aM0 adenocarcinoma of the ascending colon. Given the patient's family history of colon and uterine cancer combined with the results of immunohistochemical staining and next-generation sequencing, we concluded that she had Lynch syndrome (LS). Conclusions: This report describes the first case of simultaneous variation of the ileocolic artery (ICA) and SMA in a female patient with colon cancer. This type of vascular variation should be fully recognized by surgeons in order to avoid unnecessary intraoperative bleeding.

Epac: A Promising Therapeutic Target for Vascular Diseases: A Review.

Vascular diseases affect the circulatory system and comprise most human diseases. They cause severe symptoms and affect the quality of life of patients. Recently, since their identification, exchange proteins directly activated by cAMP (Epac) have attracted increasing scientific interest, because of their role in cyclic adenosine monophosphate (cAMP) signaling, a well-known signal transduction pathway. The role of Epac in cardiovascular disease and cancer is extensively studied, whereas their role in kidney disease has not been comprehensively explored yet. In this study, we aimed to review recent studies on the regulatory effects of Epac on various vascular diseases, such as cardiovascular disease, cerebrovascular disease, and cancer. Accumulating evidence has shown that both Epac1 and Epac2 play important roles in vascular diseases under both physiological and pathological conditions. Additionally, there has been an increasing focus on Epac pharmacological modulators. Therefore, we speculated that Epac could serve as a novel therapeutic target for the treatment of vascular diseases.

Effects of Periodontal Treatment on Levels of Proinflammatory Cytokines in Patients with Chronic Periodontitis: A Meta-Analysis.

Background: During the progression of chronic periodontitis (CP), changes in the levels of inflammatory factors are detected in serum and gingival sulcus fluid (GCF). The aim of this meta-analysis was to systematically evaluate the effect of periodontal treatment on GCF and serum proinflammatory cytokines (IL-6, TNF-α, and IL-8) in patients with CP. Methods: Literature searches were performed through PubMed, Web of Science, Embase, China National Knowledge Infrastructure (CNKI), and Wanfang Database. Randomized controlled trials comparing cytokine levels in periodontal treatment (experimental group) and control group between 2015 and 2020 were included. Results: There were a total of 13 studies included with 1220 patients. There were 630 cases in the experimental group and 590 cases in the control group. The meta-analysis showed that IL-6 levels in the GCF (SMD = -2.88, 95% CI (-3.68, -2.09), P < 0.001) and serum (SMD = -1.27, 95% CI (-1.72, -0.81), P < 0.001) were significantly lower in the experimental group compared with those before treatment. In addition, IL-8 levels in the GCF (SMD = -2.08, 95% CI (-3.40, -0.76), P < 0.001) and serum (SMD = -1.73, 95% CI (-2.76, -0.70), P < 0.001) were decreased after periodontal treatment, but more than that, a decrease was observed in TNF-α levels of GCF (SMD = -3.98, 95% CI (-5.23, -2.73), P < 0.001) and serum (SMD = -1.80, 95% CI (-3.16, -0.45), P < 0.001) after treatment. Conclusion: After periodontal therapy, the proinflammatory cytokines in the GCF and serum of patients with CP were significantly decreased compared with those before treatment, and the efficacy was remarkable.

K205R specific nanobody-horseradish peroxidase fusions as reagents of competitive ELISA to detect African swine fever virus serum antibodies.

BACKGROUND: African swine fever virus (ASFV) is a highly contagious hemorrhagic disease and often lethal, which has significant economic consequences for the swine industry. Due to lacking of commercial vaccine, the prevention and control of ASF largely depend on early large-scale detection and screening. So far, the commercial ELISA kits have a long operation time and are expensive, making it difficult to achieve large-scale clinical applications. Nanobodies are single-domain antibodies produced by camelid animals, and have unique advantages such as smaller molecular weight, easy genetic engineering modification and low-costing of mass production, thus exhibiting good application prospects. RESULTS: The present study developed a new method for detection of ASFV specific antibodies using nanobody-horseradish peroxidase (Nb-HRP) fusion proteins as probe. By using camel immunization, phage library construction and phage display technology, five nanobodies against K205R protein were screened. Then, Nb-HRP fusion proteins were produced using genetic modification technology. Based on the Nb-HRP fusion protein as specific antibodies against K205R protein, a new type of cELISA was established to detect ASFV antibodies in pig serum. The cut-off value of the cELISA was 34.8%, and its sensitivity, specificity, and reproducibility were good. Furthermore, the developed cELISA exhibited 99.3% agreement rate with the commercial available ELISA kit (kappa value = 0.98). CONCLUSIONS: The developed cELISA method has the advantages of simple operation, rapid and low-costing, and can be used for monitoring of ASFV infection in pigs, thus providing a new method for the prevention and control of ASF.

Diabetic retinopathy: Involved cells, biomarkers, and treatments.

Diabetic retinopathy (DR), a leading cause of vision loss and blindness worldwide, is caused by retinal neurovascular unit dysfunction, and its cellular pathology involves at least nine kinds of retinal cells, including photoreceptors, horizontal and bipolar cells, amacrine cells, retinal ganglion cells, glial cells (Müller cells, astrocytes, and microglia), endothelial cells, pericytes, and retinal pigment epithelial cells. Its mechanism is complicated and involves loss of cells, inflammatory factor production, neovascularization, and BRB impairment. However, the mechanism has not been completely elucidated. Drug treatment for DR has been gradually advancing recently. Research on potential drug targets relies upon clear information on pathogenesis and effective biomarkers. Therefore, we reviewed the recent literature on the cellular pathology and the diagnostic and prognostic biomarkers of DR in terms of blood, protein, and clinical and preclinical drug therapy (including synthesized molecules and natural molecules). This review may provide a theoretical basis for further DR research.