BACKGROUND AND AIMS: The multitarget stool DNA (mt-sDNA) test is a noninvasive screening tool for colorectal cancer. We aimed to clarify the effects of antiplatelet and anticoagulant medications on the diagnostic performance of this test. METHODS: We retrospectively identified patients undergoing mt-sDNA testing from Mayo Clinic sites across the US during a 5-year period. Participants with positive stool testing results and subsequent high-quality colonoscopy were included. Participants were grouped by medication use: anti-platelets, anticoagulants, both, or none of these medications. The primary outcomes were the effects on positive predictive value (PPV) of the test for identifying advanced adenoma by antithrombotic use. RESULTS: Of the 11,761 persons with a positive mt-sDNA test result, 8926 persons (age range, 45-91 years) underwent colonoscopy at our institution, of which 7750 were deemed high quality. Among these, 2435 patients were diagnosed with advanced adenomas, for a PPV of 31.4% for detecting advanced adenomas with the mt-sDNA test. The PPVs for advanced adenoma were 32.1% in nonantithrombotic users, 29.2% in antiplatelet users, 30.9% in anticoagulant users, and 31.7% in users of both medications. Additionally, among all patients with positive mt-sDNA testing and subsequent followup colonoscopy (n 1/4 8926), colorectal cancer developed in 116patients, for a notable 1.3% risk of cancer after positive test results and colonoscopy. CONCLUSION: In a large retrospective cohort in the US, the PPV of mt-sDNA testing for advanced adenomas was 31.4%. Use of antiplatelet or anticoagulant agents did not affect the PPV for detection of advanced adenomas.

OBJECTIVES:We analyzed annual surgical trends for benign chronic pancreatitis (CP), studying specifically mortality, morbidity, and pancreatic fistula rates. We also aimed to identify predictors of pancreatic fistula formation. MATERIALS AND METHODS:For this analysis, we used data from the American College of Surgeons National Surgical Quality Improvement Program from 2014 to 2021. The study included patients who underwent surgery for benign CP. Data collected included patient demographics, preoperative variables, and postoperative outcomes. Data were analyzed with univariate and multivariate analyses, with significance defined as P  ≤ 0.05. RESULTS:Over the study period, the number of pancreatic surgical procedures increased by 49.3%, although surgery specifically for CP declined by 31.7%. The rate of pancreatic fistula formation decreased 44.9%, and mortality decreased 31.9%. Significant predictors of a pancreatic fistula included no diabetes, preoperative sepsis, soft texture of the pancreatic gland, and greater patient weight. CONCLUSION:Surgery for benign CP decreased substantially despite the established efficacy of surgical intervention for long-term pain management. The concurrent decline in mortality and rates of pancreatic fistula formation suggest advances over the study years in surgical and postoperative care.

A better understanding comes from the AFM surface investigation of irradiated silicon for application as corrosion inhibitors. A Si-surface coated with 2D materials like MoS2, graphene, MXene, MnS, SnxSy family, 2D-transition metal dichalcogenides (2D-TMDs), transition metal chalcogenides (TMCs) and 2D-layered double hydroxides (2D-LDH) nanomaterials can be used for surface protection in modern Industry 4.0 where humidity and corrosive gas pollutants plays a significant role. The role of hydrated molecule complex or ion detection sensors due to their electronic transport phenomenon and high oxygen affinity at the interfaces nearby surface is also a supportive escort. Irradiated surfaces which are coated with complex 2D metal oxide-based nanomaterials show super inhibiting properties. Ar-ion beam induced low energy irradiaton effect on silicon or metal surface holds a firm layer protection of metals underneath and has a longer life compared to general treatments. This review elaborate the interaction of atoms and ripples on Si-substrate for harmful corrosions through AFM micrographs.

Semaglutide, a glucagon-like peptide-1 receptor agonist, used for Type 2 diabetes mellitus and more recently for weight loss, often causes gastrointestinal adverse effects such as delayed gastric emptying and abdominal discomfort. Current literature has not described an associated case of gastric pneumatosis with semaglutide use. We report a 61-year-old man on semaglutide for 9 months with gastric pneumatosis. Symptoms resolved on discontinuation. Clinicians should be vigilant for significant gastrointestinal adverse effects, including pneumatosis with semaglutide use.

Question: A 33-year-old white, non-Hispanic male presented to the hospital with 3 weeks of progressive abdominal pain, rectal bleeding, and constipation. His past medical history was significant for an episode of superior mesenteric vein thrombosis and small-bowel resection due to necrotic bowel 1 year ago due to unclear etiology despite a work-up including JAK2 mutation panel, antineutrophil cytoplasmic antibodies vasculitis panel, protein C, protein S, anti-thrombin 3, activated protein C resistance, lupus anticoagulant profile, along with immunoglobulin G and immunoglobulin M antiphospholipid antibodies and beta 2 glycoprotein antibodies, chronic hepatitis profile, and QuantiFERON, which were nonrevealing.

BACKGROUND AND AIMS:Nearly all routine endoscopy procedures are performed using moderate sedation (MS) or monitored anesthesia care (MAC). In this article, we describe how we improved decision-making and decreased practitioners' cognitive burden for choosing between MAC and MS by using patient data in an automated application within the electronic health record (EHR).METHODS:In our practice, we choose between MS or MAC for routine GI procedures according to written anesthesia-use guidelines and practitioner preferences. To expedite our decision-making for MS versus MAC, we developed an Excel (Microsoft Corp, Redmond, Wash, USA)-based tool from patient demographic characteristics, comorbid conditions, and medication use extracted from the EHR. The data points from Excel were then implemented in the automated application in the EHR to predict the type of sedation for GI procedures.RESULTS:Before use of the new application, nurses spent an average of 4 minutes and gastroenterology practitioners spent 5 minutes reviewing the EHR to determine the appropriate sedation (MS or MAC). After the application was implemented, the use of MS substantially increased. Time spent reviewing the EHR was reduced to 2 minutes. The rate of adverse events for MS (.5%) versus MAC (.6%) was comparable and low overall.CONCLUSIONS:The EHR-based application, which automates and standardizes determination of sedation type, is a highly beneficial tool that eliminates subjectivity in decision-making, thus allowing for appropriate use of MAC. Adverse event rates and sedation failure did not increase with use of the application. With the increased use of MS over MAC, healthcare costs for the more-expensive MAC sedation should also decrease.

Wheat is India’s second-largest staple food crop grown in different agro climatic zones within different crop sequences, as a result of which a diversity of weeds infests to this crop. Diversity of weeds including grassy, broadleaf weeds and sedges are mainly infested to the crop. Phalaris minor (grassy) and Rumex dentatus (broadleaf) are the most dominant weeds in northern region of country. For receiving fast results, use of herbicides is most popular method of weed control and used all over the world. But over reliance on herbicides has created several complications such as environmental quality deterioration, shifting in weed flora and development of herbicide resistant weeds. These problems can be abridged to a great extent by decreasing reliance on chemical method which can be done by integration of various cultural and mechanical methods to the weed management program. Various cultural practices such as nutrient management, seeding rate, spacing, cropping sequence, water management, sowing methods, sowing time and competitive varieties should be followed in such a way that crop plants utilize much more resources in comparison to weed plants. As a mechanical measure, Furrow Irrigated Raised Bed (FIRB) method and stale seed bed technique are recommended for effective weed management. Timely application of herbicides with recommended doses with the help of efficient spray technology improves the efficacy of herbicides. So with the help of Integrated Weed Management (IWM) i.e integration of various cultural, mechanical and chemical methods, we can efficiently managed the diverse weed flora in wheat crop.

Abstract Autoimmune pancreatitis (AIP) is a type of chronic pancreatitis which is likely caused by immune dysregulation. There are two types of AIP. Type 1 AIP is a pancreatic manifestation of immunoglobulin G4 (IgG4)-related disease. It presents with painless obstructive jaundice and diffuse swelling of the pancreas on imaging. It is seen more commonly in elderly males. Approximately two-thirds of patients with type 1 AIP have elevated serum IgG4 levels. It is characterized histologically by dense lymphoplasmacytic infiltration, storiform fibrosis, and obliterated phlebitis. Type 1 AIP responds very well to steroids but has a high risk of relapse and patients frequently need steroids, immunomodulators, or rituximab to maintain remission. Type 2 AIP primarily affects the pancreas. It affects male and female patients equally in the third to fourth decade of life. These patients do not typically have elevated serum IgG4 levels or any other biomarkers, and tend to have more focal or segmental involvement than diffuse involvement of the pancreas. It is characterized histologically by lymphoplasmacytic infiltration and intraluminal and intraepithelial neutrophils in the small and medium ducts. These patients respond to steroids and the likelihood of relapse is low.

PurposeTo develop a two‐stage three‐dimensional (3D) convolutional neural networks (CNNs) for fully automated volumetric segmentation of pancreas on computed tomography (CT) and to further evaluate its performance in the context of intra‐reader and inter‐reader reliability at full dose and reduced radiation dose CTs on a public dataset.MethodsA dataset of 1994 abdomen CT scans (portal venous phase, slice thickness ≤ 3.75‐mm, multiple CT vendors) was curated by two radiologists (R1 and R2) to exclude cases with pancreatic pathology, suboptimal image quality, and image artifacts (n = 77). Remaining 1917 CTs were equally allocated between R1 and R2 for volumetric pancreas segmentation [ground truth (GT)]. This internal dataset was randomly divided into training (n = 1380), validation (n = 248), and test (n = 289) sets for the development of a two‐stage 3D CNN model based on a modified U‐net architecture for automated volumetric pancreas segmentation. Model’s performance for pancreas segmentation and the differences in model‐predicted pancreatic volumes vs GT volumes were compared on the test set. Subsequently, an external dataset from The Cancer Imaging Archive (TCIA) that had CT scans acquired at standard radiation dose and same scans reconstructed at a simulated 25% radiation dose was curated (n = 41). Volumetric pancreas segmentation was done on this TCIA dataset by R1 and R2 independently on the full dose and then at the reduced radiation dose CT images. Intra‐reader and inter‐reader reliability, model’s segmentation performance, and reliability between model‐predicted pancreatic volumes at full vs reduced dose were measured. Finally, model’s performance was tested on the benchmarking National Institute of Health (NIH)‐Pancreas CT (PCT) dataset.ResultsThree‐dimensional CNN had mean (SD) Dice similarity coefficient (DSC): 0.91 (0.03) and average Hausdorff distance of 0.15 (0.09) mm on the test set. Model’s performance was equivalent between males and females (P = 0.08) and across different CT slice thicknesses (P > 0.05) based on noninferiority statistical testing. There was no difference in model‐predicted and GT pancreatic volumes [mean predicted volume 99 cc (31cc); GT volume 101 cc (33 cc), P = 0.33]. Mean pancreatic volume difference was −2.7 cc (percent difference: −2.4% of GT volume) with excellent correlation between model‐predicted and GT volumes [concordance correlation coefficient (CCC)=0.97]. In the external TCIA dataset, the model had higher reliability than R1 and R2 on full vs reduced dose CT scans [model mean (SD) DSC: 0.96 (0.02), CCC = 0.995 vs R1 DSC: 0.83 (0.07), CCC = 0.89, and R2 DSC:0.87 (0.04), CCC = 0.97]. The DSC and volume concordance correlations for R1 vs R2 (inter‐reader reliability) were 0.85 (0.07), CCC = 0.90 at full dose and 0.83 (0.07), CCC = 0.96 at reduced dose datasets. There was good reliability between model and R1 at both full and reduced dose CT [full dose: DSC: 0.81 (0.07), CCC = 0.83 and reduced dose DSC:0.81 (0.08), CCC = 0.87]. Likewise, there was good reliability between model and R2 at both full and reduced dose CT [full dose: DSC: 0.84 (0.05), CCC = 0.89 and reduced dose DSC:0.83(0.06), CCC = 0.89]. There was no difference in model‐predicted and GT pancreatic volume in TCIA dataset (mean predicted volume 96 cc (33); GT pancreatic volume 89 cc (30), p = 0.31). Model had mean (SD) DSC: 0.89 (0.04) (minimum–maximum DSC: 0.79 −0.96) on the NIH‐PCT dataset.ConclusionA 3D CNN developed on the largest dataset of CTs is accurate for fully automated volumetric pancreas segmentation and is generalizable across a wide range of CT slice thicknesses, radiation dose, and patient gender. This 3D CNN offers a scalable tool to leverage biomarkers from pancreas morphometrics and radiomics for pancreatic diseases including for early pancreatic cancer detection.

This protocol for a Cochrane Review is out of date and has been withdrawn in order to adhere to Cochrane policy.

Background Cirrhosis is associated with substantial inpatient morbidity and mortality. This study aimed to determine the trends in 30-day hospital readmission rates among patients with cirrhosis and identify factors associated with these readmissions. Methods We conducted a retrospective analysis of data retrieved from the Nationwide Readmissions Database to determine trends in 30-day readmission for patients discharged with a diagnosis of cirrhosis in 2010 through 2014. Multivariate logistic regression analysis was used to identify predictors of readmission. Results Among 303,346 patients identified from the database, the 30-day readmission rate for patients with a discharge diagnosis of cirrhosis was 31.4% (n=95,298). The trends in the readmission rates remained steady during the study period. On multivariate analysis, female sex, age 45 years or older, esophagogastroduodenoscopy (EGD) during admission, and disposition to a short-term care facility or skilled nursing facility protected against readmissions. In contrast, coverage by Medicaid insurance, admission during a weekend, nonalcoholic cause of cirrhosis, and history of hepatic encephalopathy and ascites were associated with readmission. Conclusions We found an exceptionally high 30-day readmission rate in patients with cirrhosis, although it remained stable during the study period. This study identified some modifiable factors such as disposition to a short-term care facility or skilled nursing facility and patients’ attendance of alcohol rehabilitation facilities that could decrease the likelihood of readmission and could inform local and national healthcare policymakers.

Abstract Purpose: Around 30% of PDAC less than 2-cm tend to go undetected on CT due to their subtle imaging signatures. Automated detection of PDAC using AI represents an opportunity to augment physician expertise and to improve outcomes through early detection of PDAC. Our purpose was to develop a 3D-CNN for fully automated detection of PDAC and to further evaluate the impact of inclusion of pancreas segmentation on the accuracy of this 3D-CNN. Methods: A Medical Imaging Data Readiness Scale (MIDaR) level A dataset (portal venous phase CTs, slice thickness ≤ 3.75 mm) of 466 treatment-naïve biopsy-proven PDAC and 1994 subjects with normal pancreas was created after exclusion of CTs with suboptimal image quality or biliary stents. Volumetric pancreas and tumor segmentations on CTs were done by two radiologists using 3D Slicer. A total of 370 CTs with PDAC and 370 CTs with normal pancreas were randomly selected for separate training and validation sets, and 396 CTs (96 CTs with PDAC and 300 CTs with normal pancreas) were utilized for testing. Two separate 3D-CNNs were trained. A three-stage bounding-box-only model (A): stage 1 was based on a UNET-like architecture and localized the pancreas on CT with a bounding box; stage 2 utilized an Inception ResNet architecture and classified each slice through the pancreas into PDAC vs. normal; and stage 3 utilized the output of stage 2 to generate final classification for a given CT. Conversely, a four-stage pancreas segmentation-based model (B) included stage 1 of model A followed by an additional stage of automated pancreas and tumor segmentation (stage 2), classification of each slice through the pancreas into PDAC vs. normal (stage 3) and, finally, generation of final classification score (stage 4) for a given CT. Area under the receiver operating characteristic curve (AUROC) of the two models were compared on the test set. Results: Mean (SD) PDAC diameter in the test set was 1.1 (0.43) cm. Model A (three-stage bounding-box-only) correctly classified 305 (77%) out of 396 CTs from the test set into PDAC vs. normal. It incorrectly classified 12/96 (12.5%) CTs with PDAC as normal and 79/300 (26%) normal CTs as PDAC. AUROC for model A was 0.85. Model B (four-stage pancreas segmentation-based) correctly classified 351 (88%) out of 396 CTs. It incorrectly classified 13/96 (13.5%) CTs with PDAC as normal and 32/300 (10.7%) normal CTs as PDAC. AUROC for model B was 0.94. AUROC for model B was significantly higher than model A (p<0.005). Conclusion: A 3D-CNN can detect small PDAC with high accuracy using automated localization of pancreas with a bounding box without relying on separate pancreas segmentation. Inclusion of an additional automated pancreas segmentation step reduced false positives with consequent incremental gain in the model’s accuracy. Prospective validation and subsequent integration of such models into clinical workflows has the potential to reduce inadvertent errors in detection of subtle or small PDAC on standard-of-care CT scans. Citation Format: Anurima Patra, Korfiatis Panagiotis, Garima Suman, Ananya Panda, Sushil Kumar Garg, Ajit Goenka. Automated detection of pancreatic ductal adenocarcinoma (PDAC) on CT scans using artificial intelligence (AI): Impact of inclusion of automated pancreas segmentation on the accuracy of 3D-convolutional neural network (CNN) [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-084.