Stump appendicitis is inflammation of remnant appendix tissue due to incomplete removal of the appendix. Due to appendectomy history, stump appendicitis diagnosis is usually delay and that can cause increase morbidity. Medical records of patients who had surgery for acute appendicitis at a single center from 2008 to 2017 were retrospectively reviewed. During the evaluation of medical records, patients that had a previous operation for acute appendicitis or had “stump appendicitis” as an exploratory finding in operation notes were included. Appendectomy was performed in 3130 patients (2630 open surgeries and 380 laparoscopic surgeries). Stump appendicitis was diagnosed in five patients (0.15%). The appendectomies had been performed 4, 5, 7, 7, and 11 years previously. Mean time taken for surgery was 36 h after symptoms began. Open surgery was performed in three patients, laparoscopic procedures in others. Awareness of stump appendicitis before radiological examinations may facilitate accurate diagnosis and decrease the duration of the decision-making process, leading to decreased morbidity.

Indroduction: Machine learning is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. Aim: Our aim is to predict acute appendicitis, which is the most common indication for emergency surgery, using machine learning algorithms with an easy and inexpensive method. Materials and methods: Patients who were treated surgically with a prediagnosis of acute appendicitis in a single center between 2011 and 2021 were analyzed. Patients with right lower quadrant pain were selected. A total of 189 positive and 156 negative appendectomies were found. Gender and hemogram were used as features. Machine learning algorithms and data analysis were made in Python (3.7) programming language. Results: Negative appendectomies were found in 62% (n = 97) of the women and in 38% (n = 59) of the men. Positive appendectomies were present in 38% (n = 72) of the women and 62% (n = 117) of the men. The accuracy in the test data was 82.7% in logistic regression, 68.9% in support vector machines, 78.1% in k-nearest neighbors, and 83.9% in neural networks. The accuracy in the voting classifier created with logistic regression, k-nearest neighbor, support vector machines, and artificial neural networks was 86.2%. In the voting classifier, the sensitivity was 83.7% and the specificity was 88.6%. Conclusions: The results of our study show that machine learning is an effective method for diagnosing acute appendicitis. This study presents a practical, easy, fast, and inexpensive method to predict the diagnosis of acute appendicitis.

Poly(ADP-ribose) polymerase (PARP) inhibitors (PARPi) have attracted significant attention in triple negative breast cancer (TNBC) treatment. However, the acquired or de novo PARP inhibitor resistance limits treatment success. Ataxia telangiectasia and Rad3-related (ATR) regulates genome integrity, and thus, the aberrant activation of ATR could play a significant role in the pathogenesis of TNBC and be associated with PARPi resistance in especially homologous recombinant deficiency tumors. We aimed to assess the efficacy of ATR and PARP inhibitors combination in TNBC cells and the reversal of PARPi resistance in resistant cells. HCC1937 and HCC1937-R Talazoparib (TAL) resistant cells were treated with Elimusertib (ELI) alone as ATR inhibitor (ATRi) and ELI and TAL combination. Then, the cytotoxic, apoptotic and ATR based DNA damage response (DDR) were evaluated by WST-1, Annexin V, AO/PI, cell cycle and western blot analysis. Our results showed that the ELI and TAL combination could overcome TAL resistance by downregulating cell cycle checkpoint proteins and ATR-based DDR pathways through synergistic effects (ZIP score > 10). The overexpression of ATR and associated cell cycle proteins could play a role in PARPi resistance. However, this combination did not exert synergism in TNBC cells despite a higher apoptotic rate and increased DNA damage compared with the drug alone. Therefore, the dual targeting of ATR and PARP is a promising modality to reverse PARPi resistance with the downregulation of ATR-Chk1 based DNA damage response. However, further preclinical and clinical investigations should be required to elucidate the underlying molecular mechanisms behind ATRi and PARPi interactions in TNBC cells.

Breast cancer is one of the rarest types of cancer in men. Its incidence increases with age, as in women. Neurofibromatosis type 1 is an autosomal dominant genetic disease that predisposes to many cancer types. Neurofibromatosis and breast cancer have been more frequently distinguished and better studied in women. Characteristically, estrogen, progesterone receptors, negative, and overexpression of human epidermal growth factor receptor 2 (HER2) are detected in patients with female neurofibromatosis breast cancers. In cases reported so far, estrogen and progesterone receptors have been evaluated as positive and HER2 negative in male patients. With the high possibility of breast cancer development in female patients with neurofibromatosis at an earlier age, prompt and careful evaluation is required in terms of both examination and radiological imaging. Unfortunately, there are no thorough recommendations for breast cancer follow-up in male patients with neurofibromatosis. Here, we present a breast cancer, which is an uncommon type of cancer in male neurofibromatosis.