Programmed death-ligand 1 expression and tumor-infiltrating lymphocytes in non-small cell lung cancer: association with clinicopathologic parameters

Gaurav Garg,Kuruswamy Thurai Prasad,Navneet Singh,Parul Gupta,Valliappan Muthu,Ashim Das,Amanjit Bal 대한병리학회 2021 Journal of Pathology and Translational Medicine Vol.55 No.6

Background: Data on the prevalence of programmed death-ligand 1 (PD-L1) expression and tumor-infiltrating lymphocytes (TILs) in non–small cell lung cancer (NSCLC) and their clinical significance in Indian patients are limited. Methods: Newly diagnosed NSCLC cases (adenocarcinoma or squamous cell carcinoma [SqCC] histology) were included in the present study. The TILs were evaluated based on morphology on hematoxylin and eosin–stained slides. PD-L1 expression in tumors was assessed using immunohistochemistry with rabbit monoclonal antibody (SP263) on the Ventana automated immunostainer. Tumors with PD-L1 expression > 50% on tumor cells were considered PD-L1–positive. Tumors in which TILs occupy > 25% of stroma were considered to have high TILs. The association of PD-L1 expression and TILs with various clinical parameters including overall survival (OS) was investigated. Results: The present study included 128 cases of NSCLC (67 adenocarcinoma, 61 SqCC). PD-L1 positivity was observed in 17.2% of the patients with NSCLC. Baseline characteristics of PD-L1–positive subjects were similar to PD-L1–negative subjects except for a higher prevalence of liver metastasis (18.2% vs. 2.8%; p = .018) and a higher probability of diagnosis from extrapulmonary biopsies. High TILs were observed in 26.6% of the subjects. However, PD-L1 expression and high TIL did not affect OS. Conclusions: PD-L1 positivity and high TILs were observed in 20% and 25% of the patients with NSCLC, respectively, however, neither were predictors of survival in SqCC.

Using multi-source data and decision tree classification in mapping vegetation diversity

Gaurav Shukla,Rahul Dev Garg,Pradeep Kumar,Hari Shanker Srivastava,Pradeep Kumar Garg 대한공간정보학회 2018 Spatial Information Research Vol.26 No.5

This study acknowledges the problem of land cover demarcation in diverse vegetation condition. The Normalized Difference Vegetation Index is used for the preparation of base map. Further identification of mix and incorrect classes was done using ground truth. Radar data in combination with optical indices are used. In different NDVI classes, rRV with additional criteria on Normalized Difference Water Index successfully demarcated waterlogged area, polarization ratio rRV/rRH and backscattering coefficient rRH are found suitable to separate bare land from dry grass land, sparse and dense scrub could be separated by - (rRV ? rRH)/2 and NDVI is efficient to identify dense vegetation. The study area is taken as Keoladeo National Park in Bharatpur, India. Statistical similarity between ground truth and classified class has been assessed using Jaccard coefficient (JC), Jaccard distance (JD), Dice coefficient (DC) and F-score. High similarity values of JC, JD, DC and F-score are achieved for all land cover types except bare land. Although, dry grassland showed low value of F-score; the reason could be low precision of class. The overall accuracy (87.17%), producer’s accuracy (86.39%), user’s accuracy (85.81%) and Kappa Coefficient (0.84) are also utilized to analyze performance of classifier.

Controlling Home Appliances in IOT Environment

Gourav Garg,Devang Goyal,Himanshu Aggarwal,Kriti Baindail,Gaurav Verma 보안공학연구지원센터 2016 International Journal of Smart Home Vol.10 No.8

With increasing technology and decreasing amount of time in everyone's lives, the luxury of home automation is being deeply valued. This paper will provide an efficient solution for a reliable, economical and user friendly home automation system. In this system we will be using Raspberry pi to connect to the home network through which we can access the home appliances from across the globe. PHP Script has been used for creation of web portal through which the user can provide the inputs for controlling the appliances. Python program has been used in Raspberry pi which will turn devices on and off. Connection between PHP and Python has been maintained using My SQL Database.

Are there any predictive risk factors for failure of ureteric stent in patients with obstructive urolithiasis with sepsis?

Siddharth Pandey,Deepanshu Sharma,Satyanarayan Sankhwar,Manmeet Singh,Gaurav Garg,Ajay Aggarwal,Ashish Sharma,Samarth Agarwal 대한비뇨의학회 2018 Investigative and Clinical Urology Vol.59 No.6

Purpose: To compare patients with sepsis due to obstructive urolithiasis (Sep-OU) and underwent drainage by percutaneous nephrostomy (PCN) or a double-J (DJ)-ureteral stent and to identify predictive risk factors of DJ stent failure in these patients. Materials and Methods: We reviewed our records from January 2013 to July 2018 and identified 286 adult patients with Sep-OU out of which 36 had bilateral involvement, thus total 322 renal units were studied. Urologic residents in training carried out both ureteral stenting and PCN tube placement. Demographic data and stone characteristics were recorded along with Charlson comorbidity index. For predicting risk factors of DJ stent failure, those variables that had a p-value <0.1 in univariate analysis were combined in a multinomial regression analysis model. Results: The patients with PCN placement were significantly older than those with DJ stent placement (p=0.001) and also had significant number of units with multiple calculi (p=0.018). PCN was also placed more frequently in those patients with a upper ureteric calculi (p<0.05). On multinomial regression analysis multiple calculi (p=0.014; odds ratio [OR], 4.878; 95% confidence interval [CI], 1.377–17.276) and larger calculi size (p=0.040; OR, 0.974; 95% CI, 0.950–0.999) were the significant predictors of DJ stent failure. Conclusions: In patients with sepsis from obstructive urolithiasis due to larger and multiple calculi a PCN placement might be better suited although this data requires further prospective randomized studies to be extrapolated.

Unplanned 30-day readmission rates in patients undergoing endo-urological surgeries for upper urinary tract calculi

Manoj Kumar,Siddharth Pandey,Ajay Aggarwal,Deepanshu Sharma,Gaurav Garg,Samarth Agarwal,Ashish Sharma,Satyanarayan Sankhwar 대한비뇨의학회 2018 Investigative and Clinical Urology Vol.59 No.5

Purpose: To see the 30-day unplanned readmission rates in patients underdoing endo-urological surgeries for upper urinary tract calculi we conducted this retrospective study at King George's Medical University, Lucknow, India. Unplanned readmissions not only add to healthcare costs but also are bothersome for the patients. There are many studies on 30-day unplanned readmissions in general surgical patients. Although similar studies have been done in certain urological procedures, no study has reported readmission rates or its risk factors in patients undergoing surgeries for upper urinary tract calculi. Materials and Methods: We retrospectively reviewed our prospectively maintained database from 1st January 2009 to 31st December 2017, for the patients who underwent endo-urological procedures for upper urinary tract calculi and identified the patients who were re-admitted within 30 days of discharge. Results: Out of the total 3,209 patients undergoing endo-urological procedures for upper urinary tract calculi 56 were re-admitted. The readmission rate was 1.74% over the study period. The most common etiology for readmission was sepsis followed by hematuria. The significant risk factors for readmission in bivariate analysis included male gender, age >65 years, current smoking, chronic obstructive pulmonary disease, diabetes mellitus, bleeding disorder, prior cardiac disease, and American Society of Anesthesiologists (ASA) class ≥3. In multivariate risk adjusted logistic regression analysis ASA class ≥3 was the only independent risk factor for readmission. Conclusions: The readmission rates in endo-urological procedures for urolithiasis are less compared to other procedures. ASA class ≥3 is the most important independent predictor of unplanned 30-day readmissions.