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| 1 | C-peptide as a key risk factor for non-alcoholic fatty liver disease in the United States population显示文摘AIM To determine whether fasting C-peptide is an independent predictor for non-alcoholic fatty liver disease(NAFLD) in United States population.METHODS Using the National Health and Nutrition Examination Survey(NHANES) 1988-1994, NAFLD participants aged 20 or greater without any other liver diseases were included in this study. Excessive alcohol intake is defined as > 2 drinks per day for males and > 1 drink per day for females. C-peptide and 27 other factors known to be associated with NAFLD(e.g., age, gender, body mass index, waist circumference, race/ethnicity, liver chemistries, and other diabetes tests) were tested in both univariate and multivariate level using logistic regression with a P-value 0.05.RESULTS Of 18825 participants aged ≥ 20, 3235 participants(n = 3235) met inclusion criteria. There were 23 factors associated with NAFLD by univariate analysis. 9 factors, ranked by the highest change in pseudo R2, were found to be significant predictors of NAFLD in multivariate model: waist circumference, fasting C-peptide, natural log of alanine aminotransferase(ALT), total protein, beingMexican American, natural log of glycated hemoglobin, triglyceride level, being non-Hispanic white, and ferritin level. CONCLUSION Together with waist circumference and ALT, fasting C-peptide is among three most important predictors of NAFLD in United States population in the NHANES data set. Further study is needed to validate the clinical utility of fasting C-peptide in diagnosis or monitoring insulin resistance in NAFLD patients. | Amporn Atsawarungruangkit Jirat Chenbhanich George Dickstein | 2018 | World Journal of Gastroenterology2018，24，32： | 8 |
| 2 | Current understanding of the neuropathophysiology of pain in chronic pancreatitis显示文摘Chronic pancreatitis(CP) is a chronic inflammatory disease of the pancreas. The main symptom of patients with CP is chronic and severe abdominal pain. However, the pathophysiology of pain in CP remains obscure.Traditionally, researchers believed that the pain was caused by anatomical changes in pancreatic structure. However, treatment outcomes based on such beliefs are considered unsatisfactory. The emerging explanations of pain in CP are trending toward neurobiological theories. This article aims to review current evidence regarding the neuropathophysiology of pain in CP and its potential implications for the development of new treatments for pain in CP. | Amporn Atsawarungruangkit Supot Pongprasobchai | 2015 | World Journal of Gastrointestinal Pathophysiology2015，6，4： | 2 |
| 3 | Machine learning models for predicting non-alcoholic fatty liver disease in the general United States population:NHANES database显示文摘BACKGROUND Non-alcoholic fatty liver disease(NAFLD)is the most common chronic liver disease,affecting over 30% of the United States population.Early patient identification using a simple method is highly desirable.AIM To create machine learning models for predicting NAFLD in the general United States population.METHODS Using the NHANES 1988-1994.Thirty NAFLD-related factors were included.The dataset was divided into the training(70%)and testing(30%)datasets.Twentyfour machine learning algorithms were applied to the training dataset.The bestperforming models and another interpretable model(i.e.,coarse trees)were tested using the testing dataset.RESULTS There were 3235 participants(n=3235)that met the inclusion criteria.In the training phase,the ensemble of random undersampling(RUS)boosted trees had the highest F1(0.53).In the testing phase,we compared selective machine learning models and NAFLD indices.Based on F1,the ensemble of RUS boosted trees remained the top performer(accuracy 71.1%and F10.56)followed by the fatty liver index(accuracy 68.8% and F10.52).A simple model(coarse trees)had an accuracy of 74.9% and an F1 of 0.33.CONCLUSION Not every machine learning model is complex.Using a simpler model such as coarse trees,we can create an interpretable model for predicting NAFLD with only two predictors:fasting C-peptide and waist circumference.Although the simpler model does not have the best performance,its simplicity is useful in clinical practice. | Amporn Atsawarungruangkit Passisd Laoveeravat Kittichai Promrat | 2021 | World Journal of Hepatology2021，13，10： | 2 |
| 4 | Bacillus sphaericus Mtxl and Mtx2 toxins co-expressed aegypti larvae显示文摘 | Amporn R Natalia K T Sumarin S | 2009 | Biotechnology Letters2009，31，： | 1 |
| 5 | herbivores in Thailand on Rhodomyrtus tomentosa(myrtaceae),an invasive weed in florida显示文摘 | Amporn Winotai Tony Wright | 2005 | Florida Entomologist2005，88，1： | 1 |
| 6 | Formation of retinyl palmitate-loaded poly(L-lactide) nanoparticles using rapid expansion of su- percritical solutions into liquid solvents (RESOLV)显示文摘 | Amporn S Jumras L | 2009 | J Supercritical Fluids2009，51，2： | 1 |
| 7 | Diversity of type I polyketide synthase genes in the wood-decay fungus Xylaria sp. BCC 1067显示文摘 | Alongkorn Amnuaykanjanasin Juntira Punya Porntip Paungmoung Amporn Rungrod Anuwat Tachaleat Somchai Pongpattanakitshote Supapon Cheevadhanarak Morakot Tanticharoen | 2005 | FEMS Microbiology Letters2005，，1： | 1 |
| 8 | Prevalence and risk factors of steatosis and advanced fibrosis using transient elastography in the United States’ adolescent population显示文摘BACKGROUND Non-alcoholic fatty liver disease(NAFLD)is the leading cause of chronic liver disease in children and adolescents.AIM To determine the prevalence and risk factors of steatosis and advanced fibrosis using transient elastography(TE)in the United States’adolescent population.METHODS Using the National Health and Nutrition Examination Survey 2017-2018,adolescent participants aged 13 to 17 years who underwent TE and controlled attenuation parameter(CAP)were included in this study.Forty-one factors associated with liver steatosis and fibrosis were collected.Univariate and multivariate linear regression analysis were used to identify statistically significant predictors.RESULTS Seven hundred and forty participants met inclusion criteria.Steatosis(S1-S3),based on CAP,and advanced fibrosis(F3-F4),based on TE,were present in 27%and 2.84%of the study population,respectively.Independent predictors of steatosis grade included log of alanine aminotransferase,insulin resistance,waistto-height ratio,and body mass index.Independent predictors of fibrosis grade included steatosis grade,non-Hispanic black race,smoking history,and systolic blood pressure.CONCLUSION This study demonstrated a high prevalence of steatosis in the United States’adolescent population.Almost 3%of United States’adolescents had advanced fibrosis.These findings are concerning because a younger age of onset of NAFLD can lead to an earlier development of severe disease,including steatohepatitis,cirrhosis,and liver decompensation. | Amporn Atsawarungruangkit Yousef Elfanagely Jason Pan Kelsey Anderson James Scharfen Kittichai Promrat | 2021 | World Journal of Hepatology2021，13，7： | 1 |
| 9 | Malignant mesothelioma presenting as colonic tumor显示文摘 | Ifat A. Shah Amporn Somsin Sheila X. Wong Osama S. Gani | 1998 | Human Pathology1998，，： | 1 |
| 10 | Rhoptry neck protein RON2 forms a complex with microneme protein AMA1 in Plasmodium falciparum merozoites显示文摘 | Jun Cao Osamu Kaneko Amporn Thongkukiatkul Mayumi Tachibana Hitoshi Otsuki Qi Gao Takafumi Tsuboi Motomi Torii | 2008 | Parasitology International2008，，1： | 1 |
| 11 | Protective efficacy of hepatitis B vaccine without HBIG in infants of HBeAg-positive carrier mothers in Thailand显示文摘 | SOMSAK L BOONYARAT W AMPORN H | 2002 | Vaccine2002，，20： | 1 |
| 12 | Effect of material properties and processing conditions on RESS of poly(L-lactide)显示文摘 | AMPORN S MARK C | 2007 | J of Supercritical Fluids2007，40，： | 1 |
| 13 | Artificial intelligence for pancreatic cancer detection: Recent development and future direction显示文摘Artificial intelligence(AI)has been increasingly utilized in medical applications,especially in the field of gastroenterology.AI can assist gastroenterologists in imaging-based testing and prediction of clinical diagnosis,for examples,detecting polyps during colonoscopy,identifying small bowel lesions using capsule endoscopy images,and predicting liver diseases based on clinical parameters.With its high mortality rate,pancreatic cancer can highly benefit from AI since the early detection of small lesion is difficult with conventional imaging techniques and current biomarkers.Endoscopic ultrasound(EUS)is a main diagnostic tool with high sensitivity for pancreatic adenocarcinoma and pancreatic cystic lesion.The standard tumor markers have not been effective for diagnosis.There have been recent research studies in AI application in EUS and novel biomarkers to early detect and differentiate malignant pancreatic lesions.The findings are impressive compared to the available traditional methods.Herein,we aim to explore the utility of AI in EUS and novel serum and cyst fluid biomarkers for pancreatic cancer detection. | Passisd Laoveeravat Priya R Abhyankar Aaron R Brenner Moamen M Gabr Fadlallah G Habr Amporn Atsawarungruangkit | 2021 | Artificial Intelligence in Gastroenterology2021，2，2： | 0 |
| 14 | Understanding deep learning in capsule endoscopy: Can artificial intelligence enhance clinical practice?显示文摘Wireless capsule endoscopy(WCE)enables physicians to examine the gastrointestinal tract by transmitting images wirelessly from a disposable capsule to a data recorder.Although WCE is the least invasive endoscopy technique for diagnosing gastrointestinal disorders,interpreting a WCE study requires significant time effort and training.Analysis of images by artificial intelligence,through advances such as machine or deep learning,has been increasingly applied to medical imaging.There has been substantial interest in using deep learning to detect various gastrointestinal disorders based on WCE images.This article discusses basic knowledge of deep learning,applications of deep learning in WCE,and the implementation of deep learning model in a clinical setting.We anticipate continued research investigating the use of deep learning in interpreting WCE studies to generate predictive algorithms and aid in the diagnosis of gastrointestinal disorders. | Amporn Atsawarungruangkit Yousef Elfanagely Akwi W Asombang Abbas Rupawala Harlan G Rich | 2020 | Artificial Intelligence in Gastrointestinal Endoscopy2020，1，2： | 0 |
| 15 | 致力改革的泰国技职教育显示文摘近年来,随着泰国工业和商业的持续发展,社会对劳动力的需求也与日俱增。但职业教育与培训系统培养出来的毕业生数量不足,难以满足劳动力市场的需求。 | Amporn Potsompong | 2007 | 职业技术教育2007，28，27： | 0 |
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