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Abstract Introduction Auron Misheil Therapy was developed based on similarities between carcinogenesis and inflammation. Auron Misheil Therapy is a combination of natural and synthetic compounds, including anti-inflammatory drugs and insulin, expected to exhibit synergistic effects. Case presentation Here, we report the case of a 78-year-old Caucasian male patient who presented with multifocal hepatocellular carcinoma and chronic hepatitis C virus infection. Over a four-year period our patient was treated with radiofrequency ablation and transarterial chemoembolization. After these treatments there was tumor progression, with new hyperperfused lesions without evidence of extrahepatic tumor involvement. Our patient refused sorafenib therapy. Therefore, he received twice daily intramuscular injections of Auron Misheil Therapy on an outpatient basis for two months. Partial remission of the hepatic lesions was observed eight weeks after the start of treatment, and confirmed four weeks later. Unfortunately, at that time our patient refused therapy due to dizziness. During follow-up two target lesions remained stable, but one lesion increased in size. At the latest follow-up, one year later, there was still tumor control. Conclusion While the mechanisms underlying the antitumor effects of Auron Misheil Therapy are not fully understood, stable disease and remissions have been observed in different types of tumors, including hepatocellular carcinoma.

Abstract Background Pancreatic cancer is the fourth most common cause of cancer related death in Western countries. Advantages in surgical techniques, radiation and chemotherapy had almost no impact on the long term survival of affected patients. Therefore, the need for better treatment strategies is urgent. HER2, a receptor tyrosine kinase of the EGFR family, involved in signal transduction pathways leading to cell growth and differentiation is overexpressed in a number of cancers, including breast and pancreatic cancer. While in breast cancer HER2 has already been successfully used as a treatment target, there are only limited data evaluating the effects of inhibiting HER2 tyrosine kinases in patients with pancreatic cancer. Methods Here we report the design of a prospective, non-randomized multi-centered Phase II clinical study evaluating the effects of the Fluoropyrimidine-carbamate Capecitabine (Xeloda ®) and the monoclonal anti-HER2 antibody Trastuzumab (Herceptin®) in patients with non-resectable, HER2 overexpressing pancreatic cancer. Patients eligible for the study will receive Trastuzumab infusions on day 1, 8 and 15 concomitant to the oral intake of Capecitabine from day 1 to day 14 of each three week cylce. Cycles will be repeated until tumor progression. A total of 37 patients will be enrolled with an interim analysis after 23 patients. Discussion Primary end point of the study is to determine the progression free survival after 12 weeks of bimodal treatment with the chemotherapeutic agent Capecitabine and the anti-HER2 antibody Trastuzumab. Secondary end points include patient's survival, toxicity analysis, quality of life, the correlation of HER2 overexpression and clinical response to Trastuzumab treatment and, finally, the correlation of CA19-9 plasma levels and progression free intervals.

Since 2011, there has been much discussion and concern about a "replication crisis" in psychology. An inability to reproduce findings in new samples can undermine even basic tenets of psychology. Much attention has been paid to the following practices, which \textcite {b19} described as "the four horsemen of the reproducibility apocalypse": Publication bias, low statistical power, p-hacking \parencite {sns11} and HARKing \parencite [i.e., hypothesizing after the results are known;][]{k98}. Another practice that has received less attention is overfitting of regression models. \textcite {b04} described overfitting as "capitalizing on the idiosyncratic characteristics of the sample at hand", and argued that it results in findings that "don't really exist in the population and hence will not replicate." The following common data-analytic practices increase the likelihood of model overfitting: Having too few observations (or events) per explanatory variable (OPV/EPV); automated algorithmic selection of variables; univariable pretesting of candidate predictor variables; categorization of quantitative variables; and sequential testing of multiple confounders. We reviewed 170 recent articles from three major psychology journals and found that 96 of them included at least one of the types of regression models \textcite {b04} discussed. We reviewed more fully those 96 articles and found that they reported 286 regression models. Regarding OPV/EPV, Babyak recommended 10 -15 as the minimum number needed to reduce the likelihood of overfitting. When we used the 10 OPV/EPV cut-off, 97 of the 286 models (33.9%) used at least one practice that leads to overfitting; and when we used 15 OPV/EPV as the cut-off, that number rose to 109 models (38.1%). The most frequently occurring practice that yields overfitted models was univariable pretesting of candidate predictor variables: It was found in 61 of the 286 models (21.3%). These findings suggest that overfitting of regression models remains a problem in psychology research, and that we must increase our efforts to educate researchers and students about this important issue.