Tumor-Targeted Delivery of IL-2 by Fusing with a pH Low Insertion Peptide for Antitumor Immunotherapy.

As a pleiotropic cytokine, interleukin-2 (IL-2) can effectively regulate lymphocyte proliferation, survival, and active antitumor immune responses in tumor microenvironments. Although the ability of IL-2 to boost immune responses was reported in cancer patients, its short circulating half-life and high toxicity hinder its broad and continual clinical application. Herein, we developed a novel tumor target agent by fusing pH low insertion peptides (pHLIP) with IL-2, forming the fusion protein pHLIP-IL2. Based on the low pH insertion property of pHLIP, the pHLIP-IL2 fusion protein could be selectively delivered to the acidic tumor microenvironments and then promote the proliferation of killer immune cells to elicit tumor regression. We found that pHLIP-IL2 fusion proteins can be significantly enriched in tumor tissues and can effectively reduce tumor size in diverse tumor models, including breast cancer and melanoma, without apparent adverse effects. These data suggest that the pHLIP-IL2 fusion protein may be a promising solution for the continual and extensive application of IL-2, and pHLIP-IL2 is a potential and valuable therapeutic drug for cancer patients with antitumor immunotherapy.

Construction and effect evaluation of prediction model for red blood cell transfusion requirement in cesarean section based on artificial intelligence.

OBJECTIVES: This study intends to build an artificial intelligence model for obstetric cesarean section surgery to evaluate the intraoperative blood transfusion volume before operation, and compare the model prediction results with the actual results to evaluate the accuracy of the artificial intelligence prediction model for intraoperative red blood cell transfusion in obstetrics. The advantages and disadvantages of intraoperative blood demand and identification of high-risk groups for blood transfusion provide data support and improvement suggestions for the realization of accurate blood management of obstetric cesarean section patients during the perioperative period. METHODS: Using a machine learning algorithm, an intraoperative blood transfusion prediction model was trained. The differences between the predicted results and the actual results were compared by means of blood transfusion or not, blood transfusion volume, and blood transfusion volume targeting postoperative hemoglobin (Hb). RESULTS: Area under curve of the model is 0.89. The accuracy of the model for blood transfusion was 96.85%. The statistical standard for the accuracy of the model blood transfusion volume is the calculation of 1U absolute error, the accuracy rate is 86.56%, and the accuracy rate of the blood transfusion population is 45.00%. In the simulation prediction results, 93.67% of the predicted and actual cases in no blood transfusion surgery; 63.45% of the same predicted blood transfusion in blood transfusion surgery, and only 20.00% of the blood transfusion volume is the same. CONCLUSIONS: In conclusion, this study used machine learning algorithm to process, analyze and predict the results of a large sample of cesarean section clinical data, and found that the important predictors of blood transfusion during cesarean section included preoperative RBC, surgical method, the site of surgery, coagulation-related indicators, and other factors. At the same time, it was found that the overall accuracy of the AI model was higher than actual blood using. Although the prediction of blood transfusion volume was not well matched with the actual blood using, the model provided a perspective of preoperative identification of high blood transfusion risks. The results can provide good auxiliary decision support for preoperative evaluation of obstetric cesarean section, and then promote the realization of accurate perioperative blood management for obstetric cesarean section patients.

Retrospective study of the impact of diabetes on the severity and prognosis of COVID­19.

Patients with diabetes coexisting with viral infection tend to have poor outcomes, but the association between diabetes and coronavirus disease 2019 (COVID-19) prognosis is controversial at present. The present study reviewed and analyzed the data of 1,892 patients with COVID-19 admitted to Shaanxi Provincial People's Hospital (Xi'an, China). Demographic, clinical, laboratory and treatment data as well as clinical outcomes were extracted from the electronic medical records and compared between patients with and without diabetes. Multivariate logistic regression analysis was used to determine the risk factors affecting the prognosis of COVID-19. Compared with patients without diabetes, the levels of glucose, C-reactive protein, procalcitonin, creatinine, total bilirubin and plasma D-dimer were significantly increased in patients with diabetes, while the levels of lymphocytes and albumin were significantly decreased (P<0.05). Multivariate logistic regression analysis revealed that platelet count, albumin, total bilirubin and lymphocytes were significantly correlated with the severity of COVID-19. Diabetes mellitus was an independent prognostic factor that affected the mortality outcome of patients with COVID-19. Additionally, an age of ≥80 years, male sex, cerebral infarction complications and a critical diagnosis of COVID-19 at admission were risk factors for critical illness during hospitalization. The results of the present study suggest that diabetes may be a risk factor for the rapid progression and poor prognosis of COVID-19. Therefore, further attention should be paid to individuals with diabetes in order to prevent rapid deterioration.

OTX1 regulates tumorigenesis and metastasis in glioma.

The most prevalent kind of primary brain tumors, gliomas, have a dismal prognosis. Recent advances in the tumor-promoting ability of OTX1 have drawn increasing attention. The overexpression of OTX1 has been reported to be associated with tumor-promoting effects in several malignancies, but its expression in gliomas is unknown. The oncogene OTX1 is increased in gliomas and is linked to a poor prognosis, as we show here. The degree of OTX1 positive expression is doubtlessly concomitant with the grade of glioma. We observed that OTX1 was up-regulated in gliomas, influenced the epithelial-mesenchymal transition (EMT), encouraged glioma cell growth and proliferation, and was linked to a poor clinical outcome for patients. At present, the prognosis of glioma is still not optimistic, and further research is needed to find a new target for treatment. According to our research, OTX1 is anticipated to emerge as a novel biological target for determining glioma prognosis and treatment.

The role of preoperative systemic immune-inflammation index in predicting the prognosis of patients with digestive tract cancers: A meta-analysis.

BACKGROUND: Systemic immune-inflammation index (SII) is correlated with prognosis in several types of cancers. In this study, we conducted a meta-analysis to systematically evaluate the prognostic significance of SII in digestive tract cancers. METHODS: PubMed, Web of Science, Embase and Cochrane databases were searched for articles reporting an association between preoperative SII and the survival of patients with digestive tract cancers. The baseline information was extracted, with overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), and cancer-specific survival (CSS) as outcome indicators. The hazard ratio (HR) was used to calculate the relationship between SII and patient survival. RESULTS: On the basis of specified inclusion and exclusion criteria, a total of 17 eligible studies covering 8384 patients were included in this meta-analysis. The meta-analysis showed that high preoperative SII was associated with low OS (HR = 1.86, 95% confidence interval [CI] = 1.57-2.21) and DFS (HR = 2.03, 95% CI = 1.63-2.52) in patients with digestive tract cancers. Subgroup analysis indicated that higher SII was consistently associated with lower OS regardless of geographic region, sample size, disease type, or SII thresholds. CONCLUSIONS: Systemic immune-inflammation index can act as a reliable prognostic indicator in patients with digestive tract cancers.

[Effect of fluoride ion on corrosion of two dental titanium alloys].

OBJECTIVE: To investigate the effect of fluoride concentration on the corrosion behavior of Ti-12Zr alloy and Ti-6Al-4V alloy in a simulated oral environment. METHODS: Each specimen was put into the acidic artificial saliva (pH = 4.0) with different NaF concentration (0%, 0.05%, 0.1%, 0.2%) and electrochemical experiment was carried out. Then the surface microstructure and compositions of specimen were analyzed by scanning electron microscope (SEM) after immersed in the test solution with constant temperature. RESULTS: The corrosion potential (E(corr)) and polarization resistance (R(p)) values of titanium alloy decreased with the increasing of NaF concentration, while the corrosion current density (I(corr)) values increased with the increasing of NaF concentration in acidic artificial saliva. The E(corr), R(p) and I(corr) values of titanium alloy changed significantly when the NaF concentration was over 0.1% (P < 0.05). The R, between Ti-12Zr alloy and Ti-6Al-4V alloy had a statistically significant difference, and the R(p) of Ti-12Zr alloy was higher than that of Ti-6Al-4V alloy when the NaF concentration was 0.2% (P < 0.05). CONCLUSION: The fluoride ions have a negative influence on the corrosion resistance of Ti-12Zr alloy and Ti-6Al-4V alloy, especially in the acidic artificial saliva which contained over 0.1% NaF.