As a common consensus that the appearances of different persons with the same age diverge widely, we have an opinion that the estimated result of a facial image should be a dynamic range or discrete candidate ages, not a specific one or classified into predefined age groups. Therefore, this paper presents a new method to estimate a set of possible ages of a facial image for large image database with a novel measurement. Firstly by transferring the shape and appearance features of a face into a set of Landmark-Terms, then the famous technology TFIDF in information retrieval and text mining fields is introduced to build a weight matrix of these Landmark-Terms for all age groups, and then some possible ages of a facial image are estimated by this matrix. Secondly, a new clustering method is also used to find the density peaks for each age group by processing the LBP features, then according to the distances of the facial image to the peaks, we obtain another possible estimated ages. Thirdly, we find the first density peak among the two sets of possible ages mentioned above, then choose those ages whose distances to the peak age are short enough in the two set as final estimated ages. Finally, a novel measurement is proposed to evaluate the performance for methods that provide more than one possible estimated ages. The experiments show that our method is promising, the best MAE and CS are close to the best performance of state-ofthe-art, and the best BPMAE and NBPMAE also indicate the top possible ages could cover the neighborhood of the the ground-truth age with small errors, in other words, it narrows the age scope effectively.

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Immune checkpoint inhibitors, widely used in the treatment of malignancies, can improve the prognosis of patients, while it also can induce various immune-related adverse events, and type 1 diabetes induced by anti-programmed cell death protein-1 is a rare but severe complication. Here we reported a case of type 1 diabetes induced by anti-PD-1 which was to treat intrahepatic cholangiocarcinoma. The case was a 61-year-old female who developed diabetes and ketoacidosis symptoms at the 16th week after anti-PD-1 therapy. Her blood glucose was 30.32 mmol/L, HBA1c was 8.10%, and C-peptide was <0.10 ng/ml. The patient was diagnosed as fulminant type 1 diabetes mellitus complicated with ketoacidosis induced by anti-PD-1, and was treated with massive fluid rehydration, intravenous infusion of insulin and correction of acid-base electrolyte disorder. Hepatectomy was performed after stabilization, and the patient was treated with long-term insulin. Through the case report and literature review, this study aims to improve oncologists' understanding of anti-PD-1 induced type 1 diabetes, so as to make early diagnosis and treatment of the complications and ensure medical safety.