Wine has been popular with the public for centuries; in the market, there are a variety of wines to choose from. Among all, Bordeaux, France, is considered as the most famous wine region in the world. In this paper, we try to understand Bordeaux wines made in the 21st century through Wineinformatics study. We developed and studied two datasets: the first dataset is all the Bordeaux wine from 2000 to 2016; and the second one is all wines listed in a famous collection of Bordeaux wines, 1855 Bordeaux Wine Official Classification, from 2000 to 2016. A total of 14,349 wine reviews are collected in the first dataset, and 1359 wine reviews in the second dataset. In order to understand the relation between wine quality and characteristics, Naïve Bayes classifier is applied to predict the qualities (90+/89−) of wines. Support Vector Machine (SVM) classifier is also applied as a comparison. In the first dataset, SVM classifier achieves the best accuracy of 86.97%; in the second dataset, Naïve Bayes classifier achieves the best accuracy of 84.62%. Precision, recall, and f-score are also used as our measures to describe the performance of our models. Meaningful features associate with high quality 21 century Bordeaux wines are able to be presented through this research paper.

We aimed to evaluate and compare the growth patterns among pathological types of indeterminate subsolid nodules in patients without a history of cancer as observed on computed tomography (CT). METHODSThis retrospective study included 77 consecutive patients with 80 indeterminate subsolid nodules on unenhanced thin-section CT. Subsolid nodules were classified into 2 growth pattern groups based on volume: growth (n = 35) and non-growth (n = 42). According to the pathological diagnosis, subsolid nodules were further subdivided into 3 groups: adenocarcinoma in situ (growth, n = 8 vs. non-growth, n = 22), minimally invasive adenocarcinoma (n = 14 vs. n = 15), and invasive adenocarcinoma (n = 13 vs. n = 5). Kaplan-Meier and Cox proportional hazards regression analyses were performed to identify the risk factors for subsolid nodules growth. The CT findings of the 35 subsolid nodules in the growth group were compared among the 3 pathological groups. RESULTSIn the growth group, the overall mean volume doubling time and mass doubling time (MDT) were 811.5 days and 616.5 days, respectively. Patient's age (odds ratio = 1.041, P = .045) and CT subtype of non-solid nodule and part-solid nodule (odds ratio = 3.430, P = .002) could predict subsolid nodule growth. The baseline volume, mass, and mean CT value were larger in the invasive adenocarcinoma group than in the adenocarcinoma in situ group (all P < .01). The shortest volume doubling time was observed in the invasive adenocarcinoma group, followed by the minimally invasive adenocarcinoma group and the adenocarcinoma in situ group. A shorter mass doubling time was observed in the minimally invasive adenocarcinoma group than in the adenocarcinoma in situ group (all P < .02). CONCLUSIONAs age increases, the risk of pulmonary subsolid nodule growth increases by 4% each year, and part-solid nodules have a 3 times higher risk of growth compared to non-solid nodules in patients with no history of cancer. Subsolid nodules with more aggressive pathological characteristics grow at a faster rate. I ncidentally detected pulmonary subsolid nodules (SSNs) can commonly present as indeterminate nodules on chest computed tomography (CT) in routine workflows. Pulmonary SSNs can be divided into non-solid nodules (NSNs) and part-solid nodules (PSNs) according to the absence or presence of internal solid components on thin-section CT, respectively. 1,2 Subsolid nodules can be benign or malignant; most benign SSNs can be radiologically diagnosed based on whether the lesion resolves on follow-up CT scans, while a few persistent SSNs must be surgically confirmed as focal organizing pneumonia and non-specific interstitial fibrosis. 3 Pulmonary SSNs that persist after a follow-up period of 3-6 months have a high likelihood of being premalignant or malignant lesions, and many authors consider persistent SSNs to represent early-stage adenocarcinoma or its precursor. [3][4][5] However, it is still important to determine the following: (1) the growth patterns of radiologically malignant SSNs, (2) what g...

Rationale: Pancreatic carcinosarcoma (PCS) is a very rare pancreatic cancer with an extremely poor prognosis. Interestingly, PCS can coexist with other metachronous malignant cancers. Here we report a case of PCS combined with esophageal cancer (EC). Patient concerns: The patient was a 66-year-old man who presented with abdominal pain and progressive nausea. He had undergone esophagectomy for EC 5 years previously. Diagnosis: Both EC and PCS were confirmed via postoperative pathological diagnosis. Interventions: Owing to the patient's previous esophagectomy for EC, pancreaticoduodenectomy for the PCS could not be performed. Instead, he underwent cholecystectomy with bile duct-jejunum Roux-en-Y anastomosis and radioactive seed implantation. Outcomes: The patient is still alive for >1 year. Lessons: To our knowledge, this is the first report of PCS combined with EC and thus of metachronous multiple primary carcinoma. A detailed literature review of the clinical and histologic features of PCS reveals important information about the epidemiology and biology of this rare disease.

Purpose Structural magnetic resonance imaging is widely used to explore brain gray and white matter structure in trigeminal neuralgia (TN) but has yielded conflicting findings. This study investigated the relationship between disease duration as a clinical feature of TN and changes in brain structure. Methods We divided 49 TN patients into three groups (TN1–TN3) based on disease duration (TN1 = 1.1 ± 0.7 (0–2) years, TN2 = 4.8 ± 1.5 (3–7) years, TN3 = 15.1 ± 5.5 (10–30) years). We used voxel-based morphometry (VBM) to compare the gray matter volume (GMV) across groups and between TN patients and 18 matched healthy control subjects. Results The TN1 group showed reduced GMV of pain-related regions in the cerebellum; the TN2 group showed reduced GMV in the thalamus and the motor/sensory cortex; and the TN3 group showed reduced GMV in the emotional and reward circuits compared with healthy controls. Similar brain regions, including bilateral hippocampi, caudate, left insular cortex, and medial superior frontal cortex, were affected in TN2 and TN3 compared with TN1. Conclusion Disease duration can explain differences in structural alterations—especially in pain-related brain regions—in TN. These results highlight the advanced structural neuroimaging method that are valuable tools to assess the trigeminal system in TN and may further our current understanding of TN pathology.