Myricetin, a natural inhibitor of CD147, increases sensitivity of cisplatin in ovarian cancer.

BACKGROUND: Ovarian cancer (OC) is the most lethal gynecological tumor, but it currently lacks effective therapeutic targets. CD147, which is overexpressed in OC, plays a crucial role in promoting malignant progression and is associated with poor prognosis in patients. Therefore, CD147 has been identified as a potential therapeutic target. However, there is a limited amount of research on the development of CD147 inhibitors. METHODS: Surface plasmon resonance (SPR) assay and virtual molecular docking analysis were performed to identify potential natural compounds targeting CD147. The anti­tumor effects of myricetin were evaluated using various assays, including CCK8, Alkaline comet, immunofluorescence and xenograft mouse models. The underlying mechanism was investigated through western blot analysis and lentivirus short hairpin RNA (LV-shRNA) transfection. RESULTS: Myricetin, a flavonoid commonly found in plants, was discovered to be a potent inhibitor of CD147. Our findings demonstrated that myricetin exhibited a strong affinity for CD147 and down-regulated the protein level of CD147 by facilitating its proteasome-dependent degradation. Additionally, we observed synergistic antitumor effects of myricetin and cisplatin both in vivo and in vitro. Mechanistically, myricetin suppressed the expression of FOXM1 and its downstream DNA damage response (DDR) genes E×O1and BRIP1, thereby enhancing the DDR induced by cisplatin. CONCLUSION: Our data demonstrate that myricetin, a natural inhibitor of CD147, may have clinical utility in the treatment of OC due to its ability to increase genomic toxicity when combined with cisplatin.

Relationship between multi-slice computed tomography features and pathological risk stratification assessment in gastric gastrointestinal stromal tumors.

BACKGROUND: Computed tomography (CT) imaging features are associated with risk stratification of gastric gastrointestinal stromal tumors (GISTs). AIM: To determine the multi-slice CT imaging features for predicting risk stratification in patients with primary gastric GISTs. METHODS: The clinicopathological and CT imaging data for 147 patients with histologically confirmed primary gastric GISTs were retrospectively analyzed. All patients had received dynamic contrast-enhanced CT (CECT) followed by surgical resection. According to the modified National Institutes of Health criteria, 147 lesions were classified into the low malignant potential group (very low and low risk; 101 lesions) and high malignant potential group (medium and high-risk; 46 lesions). The association between malignant potential and CT characteristic features (including tumor location, size, growth pattern, contour, ulceration, cystic degeneration or necrosis, calcification within the tumor, lymphadenopathy, enhancement patterns, unenhanced CT and CECT attenuation value, and enhancement degree) was analyzed using univariate analysis. Multivariate logistic regression analysis was performed to identify significant predictors of high malignant potential. The receiver operating curve (ROC) was used to evaluate the predictive value of tumor size and the multinomial logistic regression model for risk classification. RESULTS: There were 46 patients with high malignant potential and 101 with low-malignant potential gastric GISTs. Univariate analysis showed no significant differences in age, gender, tumor location, calcification, unenhanced CT and CECT attenuation values, and enhancement degree between the two groups (P > 0.05). However, a significant difference was observed in tumor size (3.14 ± 0.94 vs 6.63 ± 3.26 cm, P < 0.001) between the low-grade and high-grade groups. The univariate analysis further revealed that CT imaging features, including tumor contours, lesion growth patterns, ulceration, cystic degeneration or necrosis, lymphadenopathy, and contrast enhancement patterns, were associated with risk stratification (P < 0.05). According to binary logistic regression analysis, tumor size [P < 0.001; odds ratio (OR) = 26.448; 95% confidence interval (CI): 4.854-144.099)], contours (P = 0.028; OR = 7.750; 95%CI: 1.253-47.955), and mixed growth pattern (P = 0.046; OR = 4.740; 95%CI: 1.029-21.828) were independent predictors for risk stratification of gastric GISTs. ROC curve analysis for the multinomial logistic regression model and tumor size to differentiate high-malignant potential from low-malignant potential GISTs achieved a maximum area under the curve of 0.919 (95%CI: 0.863-0.975) and 0.940 (95%CI: 0.893-0.986), respectively. The tumor size cutoff value between the low and high malignant potential groups was 4.05 cm, and the sensitivity and specificity were 93.5% and 84.2%, respectively. CONCLUSION: CT features, including tumor size, growth patterns, and lesion contours, were predictors of malignant potential for primary gastric GISTs.

Study on contaminant distribution in a mobile BSL-4 laboratory based on multi-region directional airflow.

The outbreak of COVID-19 has caused increasing public attention to laboratory-acquired infections (LAIs), especially for a mobile Bio-Safety Level 4 Lab (BSL-4) with high potential of exposure. In this paper, the distribution and removal mechanism of bioaerosols in the biosafety laboratory were studied. A simulation model of airflow distribution in the opening and closing state of air-tight door was established and verified. The results showed that the airflow entrainment velocity during the opening of the door was approximately 0.12 m/s. It increased the probability of vortex generation in the laboratory. The deposition rate of particles was doubled when the air-tight door opening is compared with air-tight door closing. Besides, nearly 80% of the particles deposited on the surface of the wall and ceiling, increasing the possibility of LAIs. The findings of this paper could provide new scientific methods for high-level biosafety laboratories to avoid cross-infection. Moreover, future work regarding air-tight door rotation speed regulation and control should be emphasized.

Leveraging machine learning techniques for predicting pancreatic neuroendocrine tumor grades using biochemical and tumor markers.

BACKGROUND: The incidence of pancreatic neuroendocrine tumors (PNETs) is now increasing rapidly. The tumor grade of PNETs significantly affects the treatment strategy and prognosis. However, there is still no effective way to non-invasively classify PNET grades. Machine learning (ML) algorithms have shown potential in improving the prediction accuracy using comprehensive data. AIM: To provide a ML approach to predict PNET tumor grade using clinical data. METHODS: The clinical data of histologically confirmed PNET cases between 2012 and 2018 were collected. A method of minimum P for the Chi-square test was used to divide the continuous variables into binary variables. The continuous variables were transformed into binary variables according to the cutoff value, while the P value was minimum. Four classical supervised ML models, including logistic regression, support vector machine (SVM), linear discriminant analysis (LDA) and multi-layer perceptron (MLP) were trained by clinical data, and the models were labeled with the pathological tumor grade of each PNET patient. The performance of each model, including the weight of the different parameters, were evaluated. RESULTS: In total, 91 PNET cases were included in this study, in which 32 were G1, 48 were G2 and 11 were G3. The results showed that there were significant differences among the clinical parameters of patients with different grades. Patients with higher grades tended to have higher values of total bilirubin, alpha fetoprotein, carcinoembryonic antigen, carbohydrate antigen 19-9 and carbohydrate antigen 72-4. Among the models we used, LDA performed best in predicting the PNET tumor grade. Meanwhile, MLP had the highest recall rate for G3 cases. All of the models stabilized when the sample size was over 70 percent of the total, except for SVM. Different parameters varied in affecting the outcomes of the models. Overall, alanine transaminase, total bilirubin, carcinoembryonic antigen, carbohydrate antigen 19-9 and carbohydrate antigen 72-4 affected the outcome greater than other parameters. CONCLUSION: ML could be a simple and effective method in non-invasively predicting PNET grades by using the routine data obtained from the results of biochemical and tumor markers.

RBPJ polymorphisms associated with cerebral infarction diseases in Chinese Han population: A Clinical Trial/Experimental Study (CONSORT Compliant).

TRIAL DESIGN: Cerebral small vessel diseases (CSVDs) are a group of brain pathological processes involving cerebral small arteries, brain venules, and capillaries. The recombination signal-binding protein Jκ (RBPJ) is implicated in the pathogenesis of these diseases but its actual roles need confirmation. The aim of this work was to evaluate variations in RBPJ gene for their possible associations with the disease. METHODS: The RBPJ gene was sequenced for 400 patients with cerebral infarction disease and 600 normal controls. The statistical analyses and Hardy-Weinberg equilibrium tests of the patients and control populations were conducted using the SPSS software (version 19.0) and Plink (version 1.9), Haploview software, and online software SNPSpD. RESULTS: We characterized variants rs2871198, rs1397731, rs3822223, rs2077777, rs2270226, and rs2788861 within or near the RBPJ gene. The genetic heterozygosity of rs2871198, rs1397731, rs3822223, rs2077777, and rs2270226 was very high. Statistical analysis showed that the variants rs2270226 and rs2077777 in the gene were associated with the risk of cerebral infarction diseases in the Chinese Han population. CONCLUSIONS: rs2270226 and rs2077777 in the RBPJ gene were associated with the risk of cerebral infarction diseases in the Chinese Han population.

Combined effects of age and polymorphisms in Notch3 in the pathogenesis of cerebral infarction disease.

Cerebral infarction disease is a severe hypoxic ischemic tissue necrosis in the brain, often leading to long-term functional disability and residual impairments. The Notch signaling pathway plays key roles in proliferation and survival of the stem/progenitor cells of the central and peripheral nervous systems. Notch3 is an important member of the pathway, but the relationships between the genetic abnormalities and cerebral infarction disease still remain unclear. The aim of this work was to evaluate variations in Notch3 gene for their possible associations with the cerebral infarction disease. We sequenced the Notch3 gene for 260 patients with cerebral infarction disease, 300 normal controls with old ages and 300 normal controls with younger ages, and identified the variations. The statistical analyses were conducted using Chi-Square Tests as implemented in SPSS (version 19.0). The Hardy-Weinberg equilibrium test of the population was carried out using the online software OEGE. Six variations, including rs1044116, rs1044009, rs1044006, rs10408676, rs1043996 and rs16980398 within or near the Notch3 gene, were found. The genetic heterozygosity of rs1044116, rs1044009, rs1044006, and rs1043996 was very high, whereas that of rs10408676 and rs16980398 was very low. Statistical analyses showed that rs1044009 and rs1044006 were associated with the risk of cerebral infarction disease in the Chinese Han agedness population. The SNPs rs1044009 and rs1044006 in the Notch3 gene were associated with the risk of cerebral infarction diseases in the Chinese Han agedness population.

Identification of two novel critical mutations in PCNT gene resulting in microcephalic osteodysplastic primordial dwarfism type II associated with multiple intracranial aneurysms.

Microcephalic osteodysplastic primordial dwarfism type II (MOPD II) is a highly detrimental human autosomal inherited recessive disorder. The hallmark characteristics of this disease are intrauterine and postnatal growth restrictions, with some patients also having cerebrovascular problems such as cerebral aneurysms. The genomic basis behind most clinical features of MOPD II remains largely unclear. The aim of this work was to identify the genetic defects in a Chinese family with MOPD II associated with multiple intracranial aneurysms. The patient had typical MOPD II syndrome, with subarachnoid hemorrhage and multiple intracranial aneurysms. We identified three novel mutations in the PCNT gene, including one single base alteration (9842A>C in exon 45) and two deletions (Del-C in exon 30 and Del-16 in exon 41). The deletions were co-segregated with the affected individual in the family and were not present in the control population. Computer modeling demonstrated that the deletions may cause drastic changes on the secondary and tertiary structures, affecting the hydrophilicity and hydrophobicity of the mutant proteins. In conclusion, we identified two novel mutations in the PCNT gene associated with MOPD II and intracranial aneurysms, and the mutations were expected to alter the stability and functioning of the protein by computer modeling.

Identification of NS1 domains of avian H5N1 influenza virus which influence the interaction with the NOLC1 protein.

Non-structural protein 1 (NS1) is an important virulence factor encoded by influenza A virus. NS1 can interact with a variety of host cell proteins to interfere with the host innate immune response and to promote effective viral replication. Our previous work has shown that only the effector domain of NS1 (amino acid residues 74-230/237) is sufficient to interact with nucleolar and coiled-body phosphoprotein 1 (NOLC1). To investigate the exact region of NS1 that interacts with NOLC1, we used only the effector domain of NS1 and constructed various mutants having different deletions, and then tested their ability to interact with NOLC1 via pull-down assay. Only the mutant containing amino acid residues 104-200 showed positive interaction with NOLC1. To further determine the key amino acids of the NS1 effector domain which are crucial for interaction with NOLC1, several mutants containing a single amino acid substitution were made and their interaction with NOLC1 was tested. Only the mutant D120A or R195A showed reduced binding with NOLC1, suggesting that D120 and R195 were crucial to the binding of NS1 to NOLC1. This study lays the foundation for further research aiming at furthering our understanding of the interaction between NS1 and host cells.

Knockdown of MDR1 increases the sensitivity to adriamycin in drug resistant gastric cancer cells.

Gastric cancer is one of the most frequently occurring malignancies in the world. Development of multiple drug resistance (MDR) to chemotherapy is known as the major cause of treatment failure for gastric cancer. Multiple drug resistance 1/P-glycoprotein (MDR1/p-gp) contributes to drug resistance via ATP-dependent drug efflux pumps and is overexpressed in many solid tumors including gastric cancer. To investigate the role of MDR1 knockdown on drug resistance reversal, we knocked down MDR1 expression using shRNA in drug resistant gastric cancer cells and examined the consequences with regard to adriamycin (ADR) accumulation and drug- sensitivity. Two shRNAs efficiently inhibited mRNA and protein expression of MDR1 in SGC7901-MDR1 cells. MDR1 knockdown obviously decreased the ADR accumulation in cells and increased the sensitivity to ADR treatment. Together, our results revealed a crucial role of MDR1 in drug resistance and confirmed that MDR1 knockdown could reverse this phenotype in gastric cancer cells.