Our preliminary data on MDCT show that the technique has excellent negative predictive value for vascular invasion and good negative predictive value for overall tumor resectability in patients with pancreatic adenocarcinoma, suggesting an improvement over previous results reported using single-detector CT. The problem of undetected micrometastases to the liver and peritoneum remains.

Electrospray ionization of polyesters composed of isophthalic acid and neopentyl glycol produces carboxylate anions in negative mode and mainly sodium ion adducts in positive mode. A tandem mass spectrometry (MS/MS) study of these ions in a quadrupole ion trap shows that the collisionally activated dissociation pathways of the anions are simpler than those of the corresponding cations. Charge-remote fragmentations predominate in both cases, but the spectra obtained in negative mode are devoid of the complicating cation exchange observed in positive mode. MS/MS of the Na(+) adducts gives rise to a greater number of fragments but not necessarily more structural information. In either positive or negative mode, polyester oligomers with different end groups fragment by similar mechanisms. The observed fragments are consistent with rearrangements initiated by the end groups. Single-stage ESI mass spectra also are more complex in positive mode because of extensive H/Na substitutions; this is also true for matrix-assisted laser desorption ionization (MALDI) mass spectra. Hence, formation and analysis of anions might be the method of choice for determining block length, end group structure and copolymer sequence, provided the polyester contains at least one carboxylic acid end group that is ionizable to anions.

Background Eating disorders affect an increasing number of people. Social networks provide information that can help. Objective We aimed to find machine learning models capable of efficiently categorizing tweets about eating disorders domain. Methods We collected tweets related to eating disorders, for 3 consecutive months. After preprocessing, a subset of 2000 tweets was labeled: (1) messages written by people suffering from eating disorders or not, (2) messages promoting suffering from eating disorders or not, (3) informative messages or not, and (4) scientific or nonscientific messages. Traditional machine learning and deep learning models were used to classify tweets. We evaluated accuracy, F1 score, and computational time for each model. Results A total of 1,058,957 tweets related to eating disorders were collected. were obtained in the 4 categorizations, with The bidirectional encoder representations from transformer–based models had the best score among the machine learning and deep learning techniques applied to the 4 categorization tasks (F1 scores 71.1%-86.4%). Conclusions Bidirectional encoder representations from transformer–based models have better performance, although their computational cost is significantly higher than those of traditional techniques, in classifying eating disorder–related tweets.