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34. Prevalence of HIV in Patients with Malignancy and of Malignancy in HIV Patients in a Tertiary Care Center from North India

Author

Sinha, Sanjeev, Agarwal, Ashish, Gupta, Kartik, Mandal, Dibyakanti, Jain, Mitul, Detels, Roger, Nandy, Karabi, DeVos, Michelle A, Sharma, SK, Manoharan, N, Julka, PK, Rath, GK, Ambinder, Richard F, and Mitsuyasu, Ronald T

Subjects
Biomedical and Clinical Sciences, Health Services and Systems, Health Sciences, Clinical Sciences, Oncology and Carcinogenesis, Infectious Diseases, HIV/AIDS, Sexually Transmitted Infections, Prevention, Cancer, Hematology, Rare Diseases, 4.4 Population screening, 2.1 Biological and endogenous factors, Infection, Good Health and Well Being, Adult, Aged, Cross-Sectional Studies, Female, HIV Infections, Humans, India, Male, Middle Aged, Neoplasms, Prevalence, Risk Factors, Tertiary Care Centers, AIDS-defining cancer, cancer, HIV, invasive cervical cancer, non-Hodgkin's lymphoma, ART, non-Hodgkin's lymphoma., Medical and Health Sciences, Virology, Biomedical and clinical sciences, Health sciences
Abstract

Background and objectivesPeople living with HIV/AIDS are at an increased risk of developing cancer. The goals of this study were to obtain data on the prevalence of HIV in the cancer population and vice versa at a major tertiary cancer and HIV center in North India.MethodsThis cross-sectional study was conducted over a 3-year period from July 2013 to June 2016, wherein successive HIV positive patients from an anti-retroviral therapy (ART) center were screened for malignancy. Simultaneously, successive cancer patients at the cancer center were screened for HIV. Baseline demographic details, risk factors, and laboratory investigations were obtained for all the patients.ResultsAmong the 999 HIV-positive patients at the ART center, the prevalence of malignancy was 2% (n=20; 95% confidence interval (CI) 1.13, 2.87). Among the 998 patients with a malignancy, the prevalence of HIV infection was 0.9% (n=9; 95% CI 0.31, 1.49). Weight loss, loss of appetite, and fever were the most common symptoms in patients with HIV and cancer. Among 29 patients with HIV and cancer, AIDS-defining cancer was found in 19 patients; non-Hodgkin's lymphoma was the most common malignancy reported (n=13).Interpretation and conclusionThere is a low prevalence of HIV in cancer patients as well as a low prevalence of cancer in HIV patients. AIDS-defining cancers remain much more common than non-AIDS-defining cancers. With the increased coverage of ART, it is expected that non-AIDSdefining cancers will increase, as is evident from data from more developed countries.

Published
2018

36. Contrast-Induced Encephalopathy Following Transcatheter Aortic Valve Replacement: A Case Series

Author

Vinayak, M, Ahmed, A, Koshy, AN, Bronster, DJ, Dangas, GD, Tang, GHL, Kini, AS, Sharma, SK, Khera, S, Vinayak, M, Ahmed, A, Koshy, AN, Bronster, DJ, Dangas, GD, Tang, GHL, Kini, AS, Sharma, SK, and Khera, S

Abstract

Contrast-induced encephalopathy (CIE) is an idiopathic reaction following iodine-contrast dye administration in patients undergoing angiographic procedures. While it has been well-documented following coronary and carotid interventions, literature on CIE following transcatheter aortic valve replacement is limited. We report the multidisciplinary management of 3 patients with CIE following transcatheter aortic valve replacement.

Published
2024

37. Optimised deep k-nearest neighbour's based diabetic retinopathy diagnosis(ODeep-NN) using retinal images

Author

Hans, R, Sharma, SK, Aickelin, U, Hans, R, Sharma, SK, and Aickelin, U

Abstract

Diabetes mellitus has been regarded as one of the prime health issues in present days, which can often lead to diabetic retinopathy, a complication of the disease that affects the eyes, causing loss of vision. For precisely detecting the condition's existence, clinicians are required to recognise the presence of lesions in colour fundus images, making it an arduous and time-consuming task. To deal with this problem, a lot of work has been undertaken to develop deep learning-based computer-aided diagnosis systems that assist clinicians in making accurate diagnoses of the diseases in medical images. Contrariwise, the basic operations involved in deep learning models lead to the extraction of a bulky set of features, further taking a long period of training to predict the existence of the disease. For effective execution of these models, feature selection becomes an important task that aids in selecting the most appropriate features, with an aim to increase the classification accuracy. This research presents an optimised deep k-nearest neighbours'-based pipeline model in a bid to amalgamate the feature extraction capability of deep learning models with nature-inspired metaheuristic algorithms, further using k-nearest neighbour algorithm for classification. The proposed model attains an accuracy of 97.67 and 98.05% on two different datasets considered, outperforming Resnet50 and AlexNet deep learning models. Additionally, the experimental results also portray an analysis of five different nature-inspired metaheuristic algorithms, considered for feature selection on the basis of various evaluation parameters.

Published
2024

41. Enhanced Cytotoxic Efficacy of Ocimum basilicum Leaf Extract-Mediated TiO2 Nanocrystals.

Author

Kapoor, Anu, Kumar, Naveen, Sharma, J. K., Sharma, Vandana, Sharma, SK, Pundir, Prem, Anand, Gagan, Kandwal, Abhishek, and Kumar, Suresh

Subjects
NANOCRYSTALS, PARTICLE size distribution, BASIL, BAND gaps, SYMMETRY groups, TITANIUM oxides
Abstract

Breast cancer has become a global health burden, and therefore requires necessitating action against the development of affordable, target-oriented, and safe chemotherapeutic agents to mitigate its prevalence worldwide. In the present report, titanium oxide (TiO2) nanocrystals (NCs) were synthesized using the green route utilizing Ocimum basilicum leaf extract (OBLE). X-ray diffraction (XRD) studies revealed that TiO2 NCs exhibit tetragonal crystal configuration with anatase-type symmetry with space group. The phenolic groups (–OH) present in OBLE were detected as the bend appeared at 3420 cm− 1 in the infrared spectrum. The characteristic hump positioned at 331 nm in the UV–visible diffuse reflectance spectrum (UV–DRS) of biosynthesized TiO2 NCs confirmed the formation of nanoparticles and the optical band gap of TiO2 NCs was found to be 3.41 eV as evaluated from Tauc's plot. The surface morphology of TiO2 NCs showed minute agglomeration with an average particle size of 11.8 ± 0.66 nm as obtained from statistical analysis of the particle size distribution. The cytotoxic activity of TiO2 NCs was investigated against MDA–MB 231 cancer cells and dose-dependent cell inhibition was observed with a low IC50 value of 13.35 µg/mL after 48 h of incubation time. The present study underscores the remarkable cytotoxicity demonstrated by TiO2 NCs against breast cancer cell lines. [ABSTRACT FROM AUTHOR]

Published
2024
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43. Germline BRCA1 and BRCA2 deleterious mutations and variants of unknown clinical significance associated with breast/ovarian cancer: a report from North India

Author

Mehta A, Vasudevan S, Sharma SK, Kumar D, Panigrahi M, Suryavanshi M, and Gupta G

Subjects
Genetic screening, high-throughput sequencing, Multiplex Ligation-Dependent Probe Amplification assay, novel mutations, recurrent mutations, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Anurag Mehta,1 Smreti Vasudevan,2 Sanjeev Kumar Sharma,1 Dushyant Kumar,1 Manoj Panigrahi,1 Moushumi Suryavanshi,1 Garima Gupta2 1Department of Laboratory and Transfusion Services, Rajiv Gandhi Cancer Institute and Research Centre, Rohini, Delhi 110085, India; 2Department of Research, Rajiv Gandhi Cancer Institute and Research Centre, Rohini, Delhi 110085, India Background: The spectrum of BRCA mutations that predispose to development of breast/ovarian cancer in Indian population remains unexplored. We report incidence and various types of pathogenic, likely pathogenic and variants of unknown significance (VUS) mutations in BRCA1 and BRCA2 genes observed at a tertiary cancer center in North India.Materials and methods: A total of 206 unrelated breast and/or ovarian cancer patients, who met the National Comprehensive Cancer Network (NCCN) guidelines for genetic testing, were screened for germline BRCA1/BRCA 2 mutations on high-throughput sequencing platform; large genomic rearrangements were assessed by multiple ligation probe assay. Mutations were mined in mutational databases, PubMed, and discerned into classes. Furthermore, the clinicopathological correlation of BRCA mutation status with prognostic markers in breast cancer and tumor histology in ovarian cancer was performed.Results: In total, 45/206 and 17/206 cases showed positivity for BRCA1 and BRCA2 mutations, respectively, whereas 1/206 was positive for a mutation in both the genes. Altogether, 33 distinct BRCA1 mutations were observed, among which 27 were deleterious (12 frameshifts, 8 nonsense, 1 missense, 3 splice-site variants, 2 big deletions and 1 large duplication) and 6 were VUS. Five novel BRCA1 mutations (c.541G>T, c.1681delT, c.2295delG, c.4915C>T and exon 23 deletion) were identified. Seven mutations (c.2214_2215insT, c.2295delG, c.3607C>T, c.4158_4162delCTCTC, c.4571C>A, splicesite_3 (C>T) and exon 21–23 duplication) occurred more than once, whereas 16 distinct BRCA2 mutations were noted – 9 were lethal (6 frameshifts, 2 nonsense and 1 big deletion) and 7 VUS. One unique pathogenic BRCA2 mutation (c.932_933insT) was recognized. Two mutations (c.9976A>T and c.10089A>G) recurred twice. No significant difference in hormone receptor status was observed among BRCA1 carriers, BRCA2 carriers and noncarriers.Conclusion: We have documented various pathogenic and VUS mutations in BRCA1 and BRCA2 genes observed in the cohort. Six novel mutations were identified. The knowledge shared would assist genetic testing in enabling more focused site-specific screening for mutations in biological relatives. Keywords: genetic screening, high-throughput sequencing, multiplex ligation-dependent probe amplification assay, novel mutations, recurrent mutations

Published
2018

46. Length of stay and 30 day mortality in patients suffering an uncomplicated ST-elevation myocardial infarction treated with primary percutaneous coronary intervention in New York State

Author

Shah ND, Yost GW, Hannan EL, Zhong Y, Venditti FJ, Holmes DR, Stamato N, Sharma SK, Jacobs AK, Iii SBK, and Berger PB

Subjects
General Medicine
Abstract

We studied the duration of hospitalization and clinical outcome of patients with an uncomplicated STEMI (ST-elevation myocardial infarction) treated with primary percutaneous coronary intervention (PCI) in New York State between 2007 and 2009. Among the 4,939 such patients, 1,818 were discharged after 2 nights, and 1,922 were discharged after 3 nights. There were no deaths on the day after discharge among patients discharged after 2 nights. There was also no significant difference in 30-day mortality or readmission between patients discharged after 2 and 3 nights. In conclusion, 24-hour mortality was very low in patients with an uncomplicated STEMI discharged after 2 nights in the hospital, based on physician judgment. Thirty-day mortality is also low and similar to that of patients discharged after 3 nights in the hospital.

Published
2023
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49. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants

Author

Zhou, B, Lu, Y, Hajifathalian, K, Bentham, J, Di Cesare, M, Danaei, G, Bixby, H, Cowan, MJ, Ali, MK, Taddei, C, Lo, WC, Reis-Santos, B, Stevens, GA, Riley, LM, Miranda, JJ, Bjerregaard, P, Rivera, JA, Fouad, HM, Ma, G, Mbanya, JC, McGarvey, ST, Mohan, V, Onat, A, Pilav, A, Ramachandran, A, Romdhane, HB, Paciorek, CJ, Bennett, JE, Ezzati, M, Abdeen, ZA, Abdul Kadir, K, Abu-Rmeileh, NM, Acosta-Cazares, B, Adams, R, Aekplakorn, W, Aguilar-Salinas, CA, Agyemang, C, Ahmadvand, A, Al-Othman, AR, Alkerwi, A, Amouyel, P, Amuzu, A, Andersen, LB, Anderssen, SA, Anjana, RM, Aounallah-Skhiri, H, Aris, T, Arlappa, N, Arveiler, D, Assah, FK, Avdicová, M, Azizi, F, Balakrishna, N, Bandosz, P, Barbagallo, CM, Barceló, A, Batieha, AM, Baur, LA, Benet, M, Bernabe-Ortiz, A, Bharadwaj, S, Bhargava, SK, Bi, Y, Bjertness, E, Bjertness, MB, Björkelund, C, Blokstra, A, Bo, S, Boehm, BO, Boissonnet, CP, Bovet, P, Brajkovich, I, Breckenkamp, J, Brenner, H, Brewster, LM, Brian, GR, Bruno, G, Bugge, A, Cabrera de León, A, Can, G, Cândido, AP, Capuano, V, Carlsson, AC, Carvalho, MJ, Casanueva, FF, Casas, JP, Caserta, CA, Castetbon, K, Chamukuttan, S, Chaturvedi, N, Chen, CJ, Chen, F, Chen, S, Cheng, CY, Chetrit, A, Chiou, ST, Cho, Y, Chudek, J, Cifkova, R, Claessens, F, Concin, H, Cooper, C, Cooper, R, Costanzo, S, Cottel, D, Cowell, C, Crujeiras, AB, D'Arrigo, G, Dallongeville, J, Dankner, R, Dauchet, L, de Gaetano, G, De Henauw, S, Deepa, M, Dehghan, A, Deschamps, V, Dhana, K, Di Castelnuovo, AF, Djalalinia, S, Doua, K, Drygas, W, Du, Y, Dzerve, V, Egbagbe, EE, Eggertsen, R, El Ati, J, Elosua, R, Erasmus, RT, Erem, C, Ergor, G, Eriksen, L, Escobedo-de la Peña, J, Fall, CH, Farzadfar, F, Felix-Redondo, FJ, Ferguson, TS, Fernández-Bergés, D, Ferrari, M, Ferreccio, C, Feskens, EJ, Finn, JD, Föger, B, Foo, LH, Forslund, AS, Francis, DK, Franco Mdo, C, Franco, OH, Frontera, G, Furusawa, T, Gaciong, Z, Garnett, SP, Gaspoz, JM, Gasull, M, Gates, L, Geleijnse, JM, Ghasemian, A, Ghimire, A, Giampaoli, S, Gianfagna, F, Giovannelli, J, Giwercman, A, Gross, MG, González Rivas, JP, Gorbea, MB, Gottrand, F, Grafnetter, D, Grodzicki, T, Grøntved, A, Gruden, G, Gu, D, Guan, OP, Guerrero, R, Guessous, I, Guimaraes, AL, Gutierrez, L, Hambleton, IR, Hardy, R, Hari Kumar, R, Hata, J, He, J, Heidemann, C, Herrala, S, Hihtaniemi, IT, Ho, SY, Ho, SC, Hofman, A, Hormiga, CM, Horta, BL, Houti, L, Howitt, C, Htay, TT, Htet, AS, Htike, MM, Hu, Y, Hussieni, AS, Huybrechts, I, Hwalla, N, Iacoviello, L, Iannone, AG, Ibrahim, MM, Ikeda, N, Ikram, MA, Irazola, VE, Islam, M, Iwasaki, M, Jacobs, JM, Jafar, T, Jamil, KM, Jasienska, G, Jiang, CQ, Jonas, JB, Joshi, P, Kafatos, A, Kalter-Leibovici, O, Kasaeian, A, Katz, J, Kaur, P, Kavousi, M, Keinänen-Kiukaanniemi, S, Kelishadi, R, Kengne, AP, Kersting, M, Khader, YS, Khalili, D, Khang, YH, Kiechl, S, Kim, J, Kolsteren, P, Korrovits, P, Kratzer, W, Kromhout, D, Kujala, UM, Kula, K, Kyobutungi, C, Laatikainen, T, Lachat, C, Laid, Y, Lam, TH, Landrove, O, Lanska, V, Lappas, G, Laxmaiah, A, Leclercq, C, Lee, J, Lehtimäki, T, Lekhraj, R, León-Muñoz, LM, Li, Y, Lim, WY, Lima-Costa, MF, Lin, HH, Lin, X, Lissner, L, Lorbeer, R, Lozano, JE, Luksiene, D, Lundqvist, A, Lytsy, P, Machado-Coelho, GL, Machi, S, Maggi, S, Magliano, DJ, Makdisse, M, Mallikharjuna Rao, K, Manios, Y, Manzato, E, Margozzini, P, Marques-Vidal, P, Martorell, R, Masoodi, SR, Mathiesen, EB, Matsha, TE, McFarlane, SR, McLachlan, S, McNulty, BA, Mediene-Benchekor, S, Meirhaeghe, A, Menezes, AM, Merat, S, Meshram, II, Mi, J, Miquel, JF, Mohamed, MK, Mohammad, K, Mohammadifard, N, Mohd Yusoff, MF, Møller, NC, Molnár, D, Mondo, CK, Morejon, A, Moreno, LA, Morgan, K, Moschonis, G, Mossakowska, M, Mostafa, A, Mota, J, Motta, J, Mu, TT, Muiesan, ML, Müller-Nurasyid, M, Mursu, J, Nagel, G, Námešná, J, Nang, EE, NangThetia, VB, Navarrete-Muñoz, EM, Ndiaye, NC, Nenko, I, Nervi, F, Nguyen, ND, Nguyen, QN, Nieto-Martínez, RE, Ning, G, Ninomiya, T, Noale, M, Noto, D, Nsour, MA, Ochoa-Avilés, AM, Oh, K, Ordunez, P, Osmond, C, Otero, JA, Owusu-Dabo, E, Pahomova, E, Palmieri, L, Panda-Jonas, S, Panza, F, Parsaeian, M, Peixoto, SV, Pelletier, C, Peltonen, M, Peters, A, Peykari, N, Pham, ST, Pitakaka, F, Piwonska, A, Piwonski, J, Plans-Rubió, P, Porta, M, Portegies, ML, Poustchi, H, Pradeepa, R, Price, JF, Punab, M, Qasrawi, RF, Qorbani, M, Radisauskas, R, Rahman, M, Raitakari, O, Rao, SR, Ramke, J, Ramos, R, Rampal, S, Rathmann, W, Redon, J, Reganit, PF, Rigo, F, Robinson, SM, Robitaille, C, Rodríguez-Artalejo, F, Rodriguez-Perez Mdel, C, Rodríguez-Villamizar, LA, Rojas-Martinez, R, Ronkainen, K, Rosengren, A, Rubinstein, A, Rui, O, Ruiz-Betancourt, BS, Russo Horimoto, RV, Rutkowski, M, Sabanayagam, C, Sachdev, HS, Saidi, O, Sakarya, S, Salanave, B, Salonen, JT, Salvetti, M, Sánchez-Abanto, J, Santos, D, dos Santos, RN, Santos, R, Saramies, JL, Sardinha, LB, Sarrafzadegan, N, Saum, KU, Scazufca, M, Schargrodsky, H, Scheidt-Nave, C, Sein, AA, Sharma, SK, Shaw, JE, Shibuya, K, Shin, Y, Shiri, R, Siantar, R, Sibai, AM, Simon, M, Simons, J, Simons, LA, Sjostrom, M, Slowikowska-Hilczer, J, Slusarczyk, P, Smeeth, L, Snijder, MB, So, HK, Sobngwi, E, Söderberg, S, Solfrizzi, V, Sonestedt, E, Soumare, A, Staessen, JA, Stathopoulou, MG, Steene-Johannessen, J, Stehle, P, Stein, AD, Stessman, J, Stöckl, D, Stokwiszewski, J, Stronks, K, Strufaldi, MW, Sun, CA, Sundström, J, Sung, YT, Suriyawongpaisal, P, Sy, RG, Tai, ES, Tamosiunas, A, Tang, L, Tarawneh, M, Tarqui-Mamani, CB, Taylor, A, Theobald, H, Thijs, L, Thuesen, BH, Tolonen, HK, Tolstrup, JS, Topbas, M, Torrent, M, Traissac, P, Trinh, OT, Tulloch-Reid, MK, Tuomainen, TP, Turley, ML, Tzourio, C, Ueda, P, Ukoli, FA, Ulmer, H, Uusitalo, HM, Valdivia, G, Valvi, D, van Rossem, L, van Valkengoed, IG, Vanderschueren, D, Vanuzzo, D, Vega, T, Velasquez-Melendez, G, Veronesi, G, Verschuren, WM, Verstraeten, R, Viet, L, Vioque, J, Virtanen, JK, Visvikis-Siest, S, Viswanathan, B, Vollenweider, P, Voutilainen, S, Vrijheid, M, Wade, AN, Wagner, A, Walton, J, Wan Mohamud, WN, Wang, F, Wang, MD, Wang, Q, Wang, YX, Wannamethee, SG, Weerasekera, D, Whincup, PH, Widhalm, K, Wiecek, A, Wijga, AH, Wilks, RJ, Willeit, J, Wilsgaard, T, Wojtyniak, B, Wong, TY, Woo, J, Woodward, M, Wu, FC, Wu, SL, Xu, H, Yan, W, Yang, X, Ye, X, Yoshihara, A, Younger-Coleman, NO, Zambon, S, Zargar, AH, Zdrojewski, T, Zhao, W, Zheng, Y, and Zuñiga Cisneros, J

Published
2016
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50. Characterizing Topographic Influences of Bushfire Severity Using Machine Learning Models: A Case Study in a Hilly Terrain of Victoria, Australia

Author

Sharma, SK, Aryal, J, Shao, Q, Rajabifard, A, Sharma, SK, Aryal, J, Shao, Q, and Rajabifard, A

Abstract

Topography plays a significant role in determining bushfire severity over a hilly landscape. However, complex interrelationships between topographic variables and bushfire severity are difficult to quantify using traditional statistical methods. More recently, different machine learning (ML) models are becoming popular in characterizing complex relationships between different environmental variables. Yet, few studies have specifically evaluated the suitability of ML models in predictive bushfire severity analysis. Hence, the aim of this research is twofold. First, to determine suitable ML models by assessing their performances in bushfire severity predictions using remote sensing data analytics, and second to identify and investigate topographic variables influencing bushfire severity. The results showed that random forest (RF) and gradient boosting (GB) models had their distinct advantages in predictive modeling of bushfire severity. RF model showed higher precision (86% to 100%) than GB (59% to 72%) while predicting low, moderate, and high severity classes. Whereas GB model demonstrated better recall, i.e., completeness of positive predictions (56% to 75%) than RF (49% to 61%) for those classes. Closer investigations on topographic characteristics showed a varying relationship of severity patterns across different morphological landform classes. Landforms having lower slope curvatures or with unchanging slopes were more prone to severe burning than those landforms with higher slope curvatures. Our results provide insights into how topography influences potential bushfire severity risks and recommends purpose-specific choice of ML models.

Published
2023

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