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Background: Radiofrequency ablation (RFA) has been used to treat various abdominal tumors including pancreatic tumors. Multiple approaches such as laparoscopic, open, and percutaneous have been used for pancreatic tissue ablation. More recently, endoscopic ultrasound (EUS)-guided RFA has emerged as a new technique for pancreatic tissue ablation. The role of EUS-RFA in management of pancreatic lesions is still not well-established. In this study, our aim is to assess efficacy and safety of EUS-RFA for management of pancreatic lesions.Methods: MEDLINE, Scopus, and Cochrane Library databases were searched to identify studies reporting EUS-RFA of pancreatic lesions with outcomes of interest. Studies with <5 patients were excluded. Clinical success was defined as symptom resolution, decrease in tumor size, and/or evidence of necrosis on radiologic imaging. Efficacy was assessed by the pooled clinical response rate whereas safety was assessed by the pooled adverse events rate. Heterogeneity was assessed using I 2 . Pooled estimates and the 95% CI were calculated using random-effect model.Results: Ten studies (5 retrospective and 5 prospective) involving 115 patients with 125 pancreatic lesions were included. 152 EUS-RFA procedures were performed. The lesions comprised of 37.6% non-functional neuroendocrine tumors (NFNETs), 15.4% were insulinomas, 26.5% were pancreatic cystic neoplasms (PCNs), and 19.7% were pancreatic adenocarcinomas. The majority were present in the pancreatic head (40.2%), 38.3% in the body, 11.2% in the tail, and 10.3% in the uncinate process. Pooled overall clinical response rate was 88.9% (95% CI: 82.4-93.7, I 2 =38.1%). Pooled overall adverse events rate was 6.7% (95% CI: 3.4-11.7, I 2 =34.0%). The most common complication was acute pancreatitis (3.3%) followed by pancreatic duct stenosis, peripancreatic fluid collection, and ascites (2.8%) each. Only one case of perforation was reported with pooled rate of (2.1%).Conclusions: This study demonstrates that EUS-RFA is an effective treatment modality for pancreatic lesions, especially functional neuroendocrine tumors such as insulinomas.

Racial and ethnic disparities in cancer care remain a persistent, preventable, and well-documented contributor to poor outcomes among minorities. 1-3 Given increasing proportions of racial/ethnic minorities in the US, these disparities pose an even greater economic and social burden in the future. 1,4 Hence reducing disparities is an ethical and scientific imperative. 2 Lower enrolment rates of racial-ethnic minorities in clinical trials is a well-known factor underlying these disparities. 4-7 Recognizing the racial/ethnic composition of a study population is the first step in understanding barriers to enrolment. However, studies have found infrequent reporting of race/ethnicity in clinical trials, especially for haematological malignancies. 1,5 Herein, we analyse the frequency of race/ethnicity reporting of study populations in oral presentations at an international haematology conference.

the heart into the pulmonary artery, and retrieval of the missing line was considerably more difficult in these cases than in the remainder, where the line was still in the superior vena cava or right atrium. This was because of the additional catheter manipulation required to enter the pulmonary artery and also because it proved difficult to pull the ensnared fragment back through the pulmonary and tricuspid valves. It is therefore important to try to remove a detached intravenous catheter at the earliest opportunity, when it may still lie in a relatively accessible site proximal to the tricuspid valve.In all but one of these cases the lines were inserted strictly in accordance with the manufacturer's instructions; in the one exception a Nutricath catheter was partly severed by a cutaneous stitch. CommentIndwelling catheters inserted into the cephalic, subclavian, or jugular veins are routinely used for the administration of parenteral nutrition, cytotoxic agents, and blood products and measurement of central venous pressure. Although catheter embolisation was first reported as a complication of indwelling lines in 1954,3 it seems to be relatively uncommon: in a study of 355 central venous catheters used for total parenteral nutrition in 200 patients4 the major complications noted were related to catheter sepsis (25 catheters (7%) and 22 patients) and to catheter insertion (14 catheters (4%) and 12 patients); no mention was made of catheter embolisation. One series of 29 cases has been reported,5 however, and a recent review' emphasised the hazards of retained catheter fragments: the overall potential risk of serious complications in that report was 71%.One practical problem encountered during these five procedures was difficulty in visualising the detached catheter fragments at fluoroscopy, which would have been easier had the catheters been more radio-opaqueWe think that the detachment and embolisation of intravenous catheter fragments is occurring more frequently. The use of noninvasive radiological techniques, as described here, should obviate the need for major thoracic surgery in most cases.

Previous research on emotion recognition of Twitter users centered on the use of lexicons and basic classifiers on pack of words models, despite the recent accomplishments of deep learning in many disciplines of natural language processing. The study's main question is if deep learning can help them improve their performance. Because of the scant contextual information that most posts offer, emotion analysis is still difficult. The suggested method can capture more emotion sematic than existing models by projecting emoticons and words into emoticon space, which improves the performance of emotion analysis. In a microblog setting, this aids in the detection of subjectivity, polarity, and emotion. It accomplishes this by utilizing hash tags to create three large emotion-labeled data sets that can be compared to various emotional orders. Then compare the results of a few words and character-based repetitive and convolutional neural networks to the results of a pack of words and latent semantic indexing models. Furthermore, the specifics examine the transferability of the most recent hidden state representations across distinct emotional classes and whether it is possible to construct a unified model for predicting each of them using a common representation. It's been shown that repetitive neural systems, especially character-based ones, outperform pack-of-words and latent semantic indexing models. The semantics of the token must be considered while classifying the tweet emotion. The semantics of the tokens recorded in the hash map may be simply searched. Despite these models' low exchange capacities, the recently presented training heuristic produces a unity model with execution comparable to the three solo models. Keywords: Hashtags, Sentiment Analysis, Facial Recognition, Emotions.