BACKGROUND/AIMS: After cholecystectomy, patients have reported postcholecystectomic syndromes such as abdominal symptoms, dyspepsia, and diarrhea, which suggest a relationship between cholecystectomic symptoms and diet, although the details of this association remain unclear. The present study investigated the hypothesis that dietary intake of nutrients and foods was significantly associated with postcholecystectomic syndromes. METHODS: Gallstone patients (n = 59) who underwent laparoscopic cholecystectomy were enrolled, and dietary intake and clinical parameters were assessed immediately postcholecystectomy and 3 months later. RESULTS: There were no significant differences in biochemical measurements or characteristics between symptomatic and asymptomatic patients. Immediately postcholecystectomy, there were no significant differences in consumption of nutrients or foods between symptomatic and asymptomatic patients. However, 3 months after cholecystectomy, symptomatic patients consumed more animal protein, cholesterol, and eggs, and fewer vegetables than did asymptomatic patients. Multivariable-adjusted regression analyses also indicated that the risk for symptoms was positively associated with intake of animal protein, cholesterol, and eggs, but negatively associated with intake of vegetables after adjusting for confounders. In addition, symptomatic patients consumed more bread-based breakfast foods, while asymptomatic patients consumed more rice. CONCLUSIONS: Postcholecystectomic syndromes were positively associated with intake of cholesterol, animal protein, and eggs, and negatively associated with intake of vegetables, suggesting that diet was plays a role in postcholecystectomic syndromes.
Animals ; Breakfast ; Cholecystectomy ; Cholecystectomy, Laparoscopic ; Cholesterol ; Diarrhea ; Diet ; Dyspepsia ; Eggs ; Follow-Up Studies ; Gallbladder Diseases* ; Gallbladder* ; Gallstones ; Humans ; Ovum ; Postcholecystectomy Syndrome ; Vegetables

Purpose: Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of sentinel lymph nodes by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is to review a challenge competition (HeLP 2018) to develop automated solutions for the classification of metastases in hematoxylin and eosin–stained frozen tissue sections of SLNs in breast cancer patients. Materials and Methods: A total of 297 digital slides were obtained from frozen SLN sections, which include post–neoadjuvant cases (n = 144, 48.5%) in Asan Medical Center, South Korea. The slides were divided into training, development, and validation sets. All of the imaging datasets have been manually segmented by expert pathologists. A total of 10 participants were allowed to use the Kakao challenge platform for six weeks with two P40 GPUs. The algorithms were assessed in terms of the AUC (area under receiver operating characteristic curve). Results: The top three teams showed 0.986, 0.985, and 0.945 AUCs for the development set and 0.805, 0.776, and 0.765 AUCs for the validation set. Micrometastatic tumors, neoadjuvant systemic therapy, invasive lobular carcinoma, and histologic grade 3 were associated with lower diagnostic accuracy. Conclusion In a challenge competition, accurate deep learning algorithms have been developed, which can be helpful in making frozen diagnosis of intraoperative sentinel lymph node biopsy. Whether this approach has clinical utility will require evaluation in a clinical setting