Borderline resectable pancreatic cancer (BRPC) is frequently associated with positive surgical margins and a poor prognosis because the tumor is in contact with major vessels. This study evaluated the relationship between the margin-negative (R0) resection rate and findings indicating peripancreatic vascular invasion on multidetector computed tomography (MDCT) imaging after neoadjuvant chemoradiotherapy (NACRT) in patients with BRPC.
 
  Twenty-nine BRPC patients who underwent laparotomy after neoadjuvant S-1 with concurrent radiotherapy were studied retrospectively. Peripancreatic major vessel invasion was evaluated based on the length of tumor-vessel contact on MDCT. The R0 resection rates were compared between the progression of vascular invasion (PVI) group and the non-progression of vascular invasion (NVI) group.
 
  There were 3 patients with partial responses (10%), 25 with stable disease (86%), and 1 with progressive disease (3%) according to the RECISTv1.1 criteria. Regarding vascular invasion, 9 patients (31%) were classified as having PVI, and 20 patients (69%) were classified as having NVI. Of the 29 patients, 27 (93%) received an R0 resection, and all the PVI patients received an R0 resection (9/9; R0 resection rate = 100%) while 90% (18/20) of the NVI patients underwent an R0 resection. The exact 95% confidence interval of risk difference between those R0 resection rates was - 10.0% [- 31.7-20.4%].
 
  Patients with BRPC after NACRT achieved high R0 resection rates regardless of the vascular invasion status. BRPC patients can undergo R0 resections unless progressive disease is observed after NACRT.
 
  UMIN-CTR, UMIN000009172 . Registered 23 October 2012.
 UMIN-CTR, UMIN000009172 . Registered 23 October 2012.
  Scores on an outcome measurement instrument depend on the type and settings of the instrument used, how instructions are given to patients, how professionals administer and score the instrument, etc. The impact of all these sources of variation on scores can be assessed in studies on reliability and measurement error, if properly designed and analyzed. The aim of this study was to develop standards to assess the quality of studies on reliability and measurement error of clinician-reported outcome measurement instruments, performance-based outcome measurement instrument, and laboratory values.
 
  We conducted a 3-round Delphi study involving 52 panelists.
 
  Consensus was reached on how a comprehensive research question can be deduced from the design of a reliability study to determine how the results of a study inform us about the quality of the outcome measurement instrument at issue. Consensus was reached on components of outcome measurement instruments, i.e.  https://www.selleckchem.com/products/Temsirolimus.html  the potential sources of variation. Next, we reached consensus on standards on design requirements (n = 5), standards on preferred statistical methods for reliability (n = 3) and measurement error (n = 2), and their ratings on a four-point scale. There was one term for a component and one rating of one standard on which no consensus was reached, and therefore required a decision by the steering committee.
 
  We developed a tool that enables researchers with and without thorough knowledge on measurement properties to assess the quality of a study on reliability and measurement error of outcome measurement instruments.
 We developed a tool that enables researchers with and without thorough knowledge on measurement properties to assess the quality of a study on reliability and measurement error of outcome measurement instruments.
  Pancreatic ductal adenocarcinoma (PDAC) is a grim disease with high mortality rates. Increased macrophage influx in PDAC is a common hallmark and associated with poor prognosis. Macrophages have high cellular plasticity, which can differentiate into both anti- and pro-tumorigenic properties. Here, we investigated how naïve (M0) macrophages differ from other macrophages in their anti-tumorigenic activities.
 
  In vitro BrdU proliferation and Annexin V cell death analyses were performed on PANC-1 and MIA PaCa-2 PDAC cell lines exposed to conditioned medium of different macrophage subsets. Macrophage secreted factors were measured by transcript analysis and ELISA. Therapeutic antibodies were used to functionally establish the impact of the identified cytokine on PDAC proliferation.
 
  Proliferation and cell death assays revealed that only M0 macrophages harbor anti-tumorigenic activities and that M1, M2, and TAMs do not. mRNA analysis and ELISA results suggested TNF-α as a potential candidate to mediate M0 macrod a therapeutic benefit.
  Accurate forecasting model for under-five mortality rate (U5MR) is essential for policy actions and planning. While studies have used traditional time series modeling techniques (e.g., autoregressive integrated moving average (ARIMA) and Holt-Winters smoothing exponential methods), their appropriateness to predict noisy and non-linear data (such as childhood mortality) has been debated. The objective of this study was to model long-term U5MR with group method of data handling (GMDH)-type artificial neural network (ANN), and compare the forecasts with the commonly used conventional statistical methods-ARIMA regression and Holt-Winters exponential smoothing models.
 
  The historical dataset of annual U5MR in Nigeria from 1964 to 2017 was obtained from the official website of World Bank. The optimal models for each forecasting methods were used for forecasting mortality rates to 2030 (ending of Sustainable Development Goal era). The predictive performances of the three methods were evaluated, based on root mean interval - 0.113 to 0.122).
 
  GMDH-type neural network performed better in predicting and forecasting of under-five mortality rates for Nigeria, compared to the ARIMA and Holt-Winters models. Therefore, GMDH-type ANN might be more suitable for data with non-linear or unknown distribution, such as childhood mortality. GMDH-type ANN increases forecasting accuracy of childhood mortalities in order to inform policy actions in Nigeria.
 GMDH-type neural network performed better in predicting and forecasting of under-five mortality rates for Nigeria, compared to the ARIMA and Holt-Winters models. Therefore, GMDH-type ANN might be more suitable for data with non-linear or unknown distribution, such as childhood mortality. GMDH-type ANN increases forecasting accuracy of childhood mortalities in order to inform policy actions in Nigeria.