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Acute Intestinal Ischemia in a Patient with COVID-19 Infection
( Balraj Singh ),( Ashesha Mechineni ),( Parminder Kaur ),( Nora Ajdir ),( Michael Maroules ),( Fayez Shamoon ),( Mahesh Bikkina ) 대한소화기학회 2020 대한소화기학회지 Vol.76 No.3
The World Health Organization has declared novel coronavirus disease 2019 (COVID-19) a global public health emergency. Although respiratory symptoms predominate in COVID-19, thrombosis can occur in patients with COVID-19. This paper reports a case of an 82-year-old female with a prior medical history of hypertension, diabetes presenting with fever and cough, and was diagnosed with COVID-19. The patient subsequently developed progressively worsening of abdominal distention, tenderness, and underwent emergent laparotomy. She was found to have a gangrenous colon. This case adds to the limited literature regarding the extrapulmonary complications of COVID-19. (Korean J Gastroenterol 2020;76:164-166)
Achalasia Is Associated With eNOS4a4a, iNOS22GA, and nNOS29TT Genotypes: A Case-control Study
( Rajan Singh ),( Uday C Ghoshal ),( Asha Misra ),( Balraj Mittal ) 대한소화기기능성질환·운동학회 2015 Journal of Neurogastroenterology and Motility (JNM Vol.21 No.3
Background/Aims: Achalasia is known to result from degeneration of inhibitory neurons, which are mostly nitrinergic. Characteristic features of achalasia include incomplete lower esophageal sphincter (LES) relaxation and esophageal aperistalsis. Nitric oxide (NO), produced by NO synthase (NOS), plays an important role in peristalsis and LES relaxation. Therefore, we evaluated genetic polymorphisms of NOS gene isoforms (endothelial NOS [eNOS], inducible NOS [iNOS], and neuronal NOS [nNOS]) in patients with achalasia and healthy subjects (HS). Methods: Consecutive patients with achalasia (diagnosed using esophageal manometry) and HS were genotyped for 27-base pair (bp) eNOS variable number of tandem repeats (VNTR), iNOS22G/A (rs1060826), nNOS C/T (rs2682826) polymorphisms by polymerase chain reaction (PCR) and PCR-restriction fragment length polymorphism (RFLP), respectively. Results: Among 183 patients (118 [64.5%] male, age 39.5 ± 13.0 years) with achalasia and 366 HS (254 [69.4%] male, age 40.8 ± 11.0 years), eNOS4a4a genotype of 27-bp VNTR was more common among achalasia than HS (20 [10.9%] vs 13 [3.6%]; P < 0.001; OR, 3.72; 95% CI, 1.8-7.7). Patients with achalasia had iNOS22GA genotypes more often than HS (95 [51.9%] vs 93 [25.4%]; P < 0.001; OR, 3.0; 95% CI, 2.1-4.4). Frequency of genotypes GA + AA was higher in patients than HS (97 [53%] vs 107 [29.2%]; P < 0.001; OR, 2.7; 95% CI, 1.8-3.9). Also, nNOS29TT variant genotype in rs2682826 was more com - mon among patients compared to HS (14 [7.7%] vs 6 [1.6%]; P < 0.001; OR, 5.91; 95% CI, 2.2-15.8). Conclusions: Achalasia is associated with eNOS4a4a, iNOS22GA, and nNOS29TT genotypes. This may suggest that polymorphisms of eNOS, iNOS, and nNOS genes are risk factors for achalasia. (J Neurogastroenterol Motil 2015;21:380-389)
Comparative Evaluation of Infiltration Models
Alireza Sepah Vand,Parveen Sihag,Balraj Singh,Mehran Zand 대한토목학회 2018 KSCE JOURNAL OF CIVIL ENGINEERING Vol.22 No.10
Infiltration models are very helpful in designing and evaluating surface irrigation systems. The main purpose of this study is to compare infiltration models which are used to evaluate infiltration rates of Davood Rashid, Kelat and Honam in Iran. Field infiltration tests were carried out at sixteen different locations comprising of 155 observations by use of double ring infiltrometer. The potential of four conventional infiltration models (Kostiakov, Modified Kostiakov, Novel and Philip’s models) were evaluated by least–square fitting to observed infiltration data. Three statistical comparison criteria including coefficient of correlation (C.C), coefficient of determination (R2) and root mean square error (RMSE) were used to determine the best performing infiltration models. The novel infiltration model suggests improved performance out of other three models. Further a Multi-linear Regression (MLR) equation has been developed using field infiltration data and compare with Support Vector Machine and Gaussian Process based regression with two kernels (Pearson VII and radial basis) modeling. Results suggest that Pearson VII based SVM works well than other modeling approaches in estimating the infiltration rate of soils. Sensitivity analysis concludes that the parameter, time, plays the most significant role in the estimation of infiltration rate. Comparison of results suggests that there is no significant difference between conventional and soft-computing based infiltration models.