In the last decade, we have gained significant understanding of the mechanism by which vesicular stomatitis virus (VSV) specifically kills cancer cells. Dysregulation of translation and defective innate immunity are both thought to contribute to VSV oncolysis. Safety and efficacy are important objectives to consider in evaluating VSV as a therapy for malignant disease. Ongoing efforts may enable VSV virotherapy to be considered in the near future to treat drug-resistant ovarian cancer when other options have been exhausted. In this article, we review the development of VSV as a potential therapeutic approach for recurrent or drug-resistant ovarian cancer.
Animals ; Antineoplastic Agents ; pharmacology ; Apoptosis ; Cell Proliferation ; Drug Resistance, Neoplasm ; Female ; Humans ; Neoplasm Recurrence, Local ; Oncolytic Virotherapy ; methods ; Ovarian Neoplasms ; pathology ; therapy ; virology ; Vesicular stomatitis Indiana virus ; physiology ; Virus Replication

The infusion and persistence in a transplant recipient of donor-derived bone marrow cells (DBMC) of multi-lineage can lead to a state of permanent chimerism. In solid vascular organ transplantation, the donor bone marrow lineage cells can even be derived from the transplant organ, and these cells can be detected in very small numbers in the recipient. This has been called microchimerism. Much controversy has developed with respect to the function of chimeric cells in organ transplantation. One idea is that the occurrence of these donor cells found in microchimerism in the recipient are coincidental and have no long-term beneficial effect on engraftment. A second and opposing view, is that these donor cells have immunoregulatory function that affect both the acute and chronic phases of the recipient anti-donor responses. It follows that detecting quantitative changes in chimerism might serve as an indication of the donor-specific alloimmune or regulatory response that could occur in concert with or independent of other adaptive immune responses. The latter, including autoimmune native disease, need to be controlled in the transplant organ. The safety and immune tolerance potential of DBMC infusion with deceased and living donor renal transplants was evaluated in a non-randomized trial at this center and compared with non-infused controls given identical immunosuppression. Overall DBMC infusions were well tolerated by the recipients. There were no complications from the infusion (s), no episodes of graft-vs-host disease (GVHD) and no increase infections or other complications. In the deceased DBMC- kidney trial, actuarial graft survival at 5 years was superior especially when graft survival was censored for recipient death. Acute rejections were significant reduced in patients given two DBMC infusions, and chronic rejection was dramatically reduced in all DBMC treated patients. The most interesting finding was that the degree of microchimerism slowly increased over the years the DBMC group that had exhibited no rejection episodes. In the DBMC-living related trial, the incidence of acute rejection did not differ between groups. However, DBMC chimerism in recipient iliac crest marrow had increased more rapidly than might be predicted from results previously seen in the cadaver group, despite four times fewer DBMC infused, with the generation of T- regulartory cells in-vitro assays.
*Bone Marrow Transplantation ; Humans ; *Kidney Transplantation ; *Living Donors ; *Tissue Donors ; *Transplantation Chimera ; *Transplantation Tolerance

Background: This study aimed to develop a diabetic kidney disease (DKD) prediction model using long short term memory (LSTM) neural network and evaluate its performance using accuracy, precision, recall, and area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Methods: The study identified DKD risk factors through literature review and physician focus group, and collected 7 years of data from 6,040 type 2 diabetes mellitus patients based on the risk factors. Pytorch was used to build the LSTM neural network, with 70% of the data used for training and the other 30% for testing. Three models were established to examine the impact of glycosylated hemoglobin (HbA1c), systolic blood pressure (SBP), and pulse pressure (PP) variabilities on the model’s performance. Results: The developed model achieved an accuracy of 83% and an AUC of 0.83. When the risk factor of HbA1c variability, SBP variability, or PP variability was removed one by one, the accuracy of each model was significantly lower than that of the optimal model, with an accuracy of 78% (P<0.001), 79% (P<0.001), and 81% (P<0.001), respectively. The AUC of ROC was also significantly lower for each model, with values of 0.72 (P<0.001), 0.75 (P<0.001), and 0.77 (P<0.05). Conclusion The developed DKD risk predictive model using LSTM neural networks demonstrated high accuracy and AUC value. When HbA1c, SBP, and PP variabilities were added to the model as featured characteristics, the model’s performance was greatly improved.

Background: This study aimed to develop a diabetic kidney disease (DKD) prediction model using long short term memory (LSTM) neural network and evaluate its performance using accuracy, precision, recall, and area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Methods: The study identified DKD risk factors through literature review and physician focus group, and collected 7 years of data from 6,040 type 2 diabetes mellitus patients based on the risk factors. Pytorch was used to build the LSTM neural network, with 70% of the data used for training and the other 30% for testing. Three models were established to examine the impact of glycosylated hemoglobin (HbA1c), systolic blood pressure (SBP), and pulse pressure (PP) variabilities on the model’s performance. Results: The developed model achieved an accuracy of 83% and an AUC of 0.83. When the risk factor of HbA1c variability, SBP variability, or PP variability was removed one by one, the accuracy of each model was significantly lower than that of the optimal model, with an accuracy of 78% (P<0.001), 79% (P<0.001), and 81% (P<0.001), respectively. The AUC of ROC was also significantly lower for each model, with values of 0.72 (P<0.001), 0.75 (P<0.001), and 0.77 (P<0.05). Conclusion The developed DKD risk predictive model using LSTM neural networks demonstrated high accuracy and AUC value. When HbA1c, SBP, and PP variabilities were added to the model as featured characteristics, the model’s performance was greatly improved.

Background: This study aimed to develop a diabetic kidney disease (DKD) prediction model using long short term memory (LSTM) neural network and evaluate its performance using accuracy, precision, recall, and area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Methods: The study identified DKD risk factors through literature review and physician focus group, and collected 7 years of data from 6,040 type 2 diabetes mellitus patients based on the risk factors. Pytorch was used to build the LSTM neural network, with 70% of the data used for training and the other 30% for testing. Three models were established to examine the impact of glycosylated hemoglobin (HbA1c), systolic blood pressure (SBP), and pulse pressure (PP) variabilities on the model’s performance. Results: The developed model achieved an accuracy of 83% and an AUC of 0.83. When the risk factor of HbA1c variability, SBP variability, or PP variability was removed one by one, the accuracy of each model was significantly lower than that of the optimal model, with an accuracy of 78% (P<0.001), 79% (P<0.001), and 81% (P<0.001), respectively. The AUC of ROC was also significantly lower for each model, with values of 0.72 (P<0.001), 0.75 (P<0.001), and 0.77 (P<0.05). Conclusion The developed DKD risk predictive model using LSTM neural networks demonstrated high accuracy and AUC value. When HbA1c, SBP, and PP variabilities were added to the model as featured characteristics, the model’s performance was greatly improved.

BACKGROUND: We aimed to compare the diagnostic accuracy of the Alere Triage Cardio3 Tropinin I (TnI) assay (Alere, Inc., USA) and the PathFast cTnI-II (Mitsubishi Chemical Medience Corporation, Japan) against the central laboratory assay Singulex Erenna TnI assay (Singulex, USA). METHODS: Using the Markers in the Diagnosis of Acute Coronary Syndromes (MIDAS) study population, we evaluated the ability of three different assays to identify patients with acute myocardial infarction (AMI). The MIDAS dataset, described elsewhere, is a prospective multicenter dataset of emergency department (ED) patients with suspected acute coronary syndrome (ACS) and a planned objective myocardial perfusion evaluation. Myocardial infarction (MI) was diagnosed by central adjudication. RESULTS: The C-statistic with 95% confidence intervals (CI) for diagnosing MI by using a common population (n=241) was 0.95 (0.91-0.99), 0.95 (0.91-0.99), and 0.93 (0.89-0.97) for the Triage, Singulex, and PathFast assays, respectively. Of samples with detectable troponin, the absolute values had high Pearson (R(P)) and Spearman (R(S)) correlations and were R(P)=0.94 and R(S)=0.94 for Triage vs Singulex, R(P)=0.93 and R(S)=0.85 for Triage vs PathFast, and R(P)=0.89 and R(S)=0.73 for PathFast vs Singulex. CONCLUSIONS: In a single comparative population of ED patients with suspected ACS, the Triage Cardio3 TnI, PathFast, and Singulex TnI assays provided similar diagnostic performance for MI.
Acute Coronary Syndrome/*diagnosis ; Biomarkers/analysis ; Emergency Service, Hospital ; Humans ; Laboratories/standards ; Myocardial Infarction/diagnosis ; *Point-of-Care Systems ; Prospective Studies ; Reagent Kits, Diagnostic ; Sensitivity and Specificity ; Troponin I/*analysis