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  1. Article: Prevalence, Comorbidity and Investigation of Anemia in the Primary Care Office.

    Gandhi, Shivani Jatin / Hagans, Iris / Nathan, Karim / Hunter, Krystal / Roy, Satyajeet

    Journal of clinical medicine research

    2017  Volume 9, Issue 12, Page(s) 970–980

    Abstract: Background: Anemia has a myriad of causes and its prevalence is growing. Anemia is associated with increased all-cause hospitalization and mortality in community-dwelling individuals above age 65 years. Our aim was to determine the prevalence and ... ...

    Abstract Background: Anemia has a myriad of causes and its prevalence is growing. Anemia is associated with increased all-cause hospitalization and mortality in community-dwelling individuals above age 65 years. Our aim was to determine the prevalence and severity of anemia in adult patients in our primary care office and to determine the relationship between anemia and medical comorbidities.
    Methods: Electronic medical records of 499 adult patients in our suburban internal medicine office were reviewed who had had at least one hemoglobin value and did not undergo moderate to high-risk surgery in the preceding 30 days.
    Results: About one-fifth (21.1%) of the patients had anemia. The mean age of patients with anemia was 62.6 years. Among all patients with anemia, 20.3% were males and 79.6% were females. Of these patients, 60.1% had mild anemia (hemoglobin 11 - 12.9 g/dL) and 39.8% had moderate anemia (hemoglobin 8 - 10.9 g/dL). For every year of increase in age, there was 1.8% increased odds of having anemia. African-American race had 5.2 times greater odds of having anemia than the Caucasian race. Hispanic race had 3.2 times greater odds of having anemia compared to the Caucasian race. Patients with anemia had a greater average number of comorbidities compared to patients without anemia (1.74 and 0.96, respectively; P < 0.05). There was a statistically greater percentage of patients with essential hypertension, hypothyroidism, chronic kidney disease, malignancy, rheumatologic disease, congestive heart failure, and coronary artery disease in the anemic population as compared to the non-anemic population. Of the patients, 41% with mild anemia and 62% with moderate anemia underwent additional diagnostic studies. Of the patients, 14.8% had resolution of anemia without therapy in 1 year, 15.7% were on iron replacement therapy, and 6.5% were on cobalamin therapy. No specific etiology of anemia was found in 24% of patients.
    Conclusion: A higher prevalence of anemia was associated with advancing age, African-American and Hispanic ethnicity, and comorbidities, such as essential hypertension, hypothyroidism, chronic kidney disease, malignancy, rheumatologic disease, congestive heart failure, and coronary artery disease. It is important to be aware of the demographic factors and their relationship to anemia in primary care.
    Language English
    Publishing date 2017-11-06
    Publishing country Canada
    Document type Journal Article
    ZDB-ID 2548987-2
    ISSN 1918-3011 ; 1918-3003
    ISSN (online) 1918-3011
    ISSN 1918-3003
    DOI 10.14740/jocmr3221w
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Primary round cell sarcomas of the urinary bladder with EWSR1 rearrangement: a multi-institutional study of thirteen cases with a review of the literature.

    Baisakh, Manas R / Tiwari, Ankit / Gandhi, Jatin S / Naik, Subhasini / Sharma, Shailendra K / Balzer, Bonnie L / Sharma, Shivani / Peddinti, Kamal / Jha, Shilpy / Sahu, Pradeepa K / Pradhan, Dinesh / Geller, Matthew / Amin, Mahul B / Dhillon, Jasreman / Mohanty, Sambit K

    Human pathology

    2020  Volume 104, Page(s) 84–95

    Abstract: Primary Ewing sarcoma (ES) of the urinary bladder is a rare and aggressive small blue round cell malignant neoplasm associated primarily with translocation involving EWSR1 and FLI1 genes located in the 22nd and 11th chromosomes, respectively. To date, 18 ...

    Abstract Primary Ewing sarcoma (ES) of the urinary bladder is a rare and aggressive small blue round cell malignant neoplasm associated primarily with translocation involving EWSR1 and FLI1 genes located in the 22nd and 11th chromosomes, respectively. To date, 18 cases have been published in the literature as single-case reports, based chiefly on CD99 positivity (17 patients). Molecular confirmation by fluorescence in situ hybridization was performed in 9 patients, and FLI1 immunohistochemical (IHC) analysis was not performed in any of these published cases. Herein, we present thirteen patients of more comprehensive primary round cell sarcomas of the urinary bladder with EWSR1 rearrangement. Clinicopathologic parameters including demographics; clinical presentation; histopathologic, IHC, and molecular profiles; and management and follow-up data of 13 patients with primary round cell sarcomas with EWSR1 rearrangement (Ewing family of tumor) of the urinary bladder were analyzed. The studied patients (n = 13) included 6 females and 7 males; their age ranged from 4 years to 81 years (median = 30 years). The most common clinical presentation was hematuria (n = 7), followed by hydronephrosis (n = 2, one with renal failure). The tumor size ranged from 2.9 cm to 15 cm in maximum dimension. Conventional ES architecture and histology was observed in 6 cases, and diverse histology was observed in 7 cases (adamantinomatous pattern [n = 1], alveolar pattern [n = 1], ganglioneuroblastoma-like pattern [n = 2], and small cell carcinoma-like pattern [n = 3]). All the tumors were muscle invasive (invasion into the muscularis propria). IHC analysis showed that all tumors expressed FLI1, CD99, and at least one neuroendocrine marker. Focal cytokeratin staining was positive in 2 patients, and RB1 was retained in all patients. EWSR1 rearrangement was seen in 12 of 12 tumors (in 12 patients) tested. A combined multimodal approach that included surgery with chemotherapy was instituted in all patients. Follow-up was available for 11 patients (ranging from 5 to 24 months). Six patients either died of disease (n = 3) or other causes (n = 3). Five patients were alive with metastases to the liver (n = 1), liver and lung (n = 2), liver and abdominal wall (n = 1), and kidney (n = 1). Based on our experience with the largest series to date and aggregate of the published data, ES/round cell sarcomas with EWSR1 rearrangement occurring in the bladder have bimodal age distribution with poor prognosis despite aggressive therapy. Owing to its rarity and age distribution, the differential diagnosis is wide and requires a systematic approach for ruling out key age-dependent differential diagnoses aided with molecular confirmation.
    MeSH term(s) Adolescent ; Adult ; Aged ; Aged, 80 and over ; Biomarkers, Tumor/genetics ; Child ; Child, Preschool ; Female ; Gene Rearrangement ; Genetic Predisposition to Disease ; Humans ; India ; Male ; Middle Aged ; Phenotype ; RNA-Binding Protein EWS/genetics ; Retrospective Studies ; Sarcoma/genetics ; Sarcoma/mortality ; Sarcoma/secondary ; Sarcoma/therapy ; Urinary Bladder Neoplasms/genetics ; Urinary Bladder Neoplasms/mortality ; Urinary Bladder Neoplasms/pathology ; Urinary Bladder Neoplasms/therapy ; Young Adult
    Chemical Substances Biomarkers, Tumor ; EWSR1 protein, human ; RNA-Binding Protein EWS
    Language English
    Publishing date 2020-08-14
    Publishing country United States
    Document type Journal Article ; Multicenter Study ; Review
    ZDB-ID 207657-3
    ISSN 1532-8392 ; 0046-8177
    ISSN (online) 1532-8392
    ISSN 0046-8177
    DOI 10.1016/j.humpath.2020.08.001
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: A machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys.

    Singh, Prateek / Ujjainiya, Rajat / Prakash, Satyartha / Naushin, Salwa / Sardana, Viren / Bhatheja, Nitin / Singh, Ajay Pratap / Barman, Joydeb / Kumar, Kartik / Gayali, Saurabh / Khan, Raju / Rawat, Birendra Singh / Tallapaka, Karthik Bharadwaj / Anumalla, Mahesh / Lahiri, Amit / Kar, Susanta / Bhosale, Vivek / Srivastava, Mrigank / Mugale, Madhav Nilakanth /
    Pandey, C P / Khan, Shaziya / Katiyar, Shivani / Raj, Desh / Ishteyaque, Sharmeen / Khanka, Sonu / Rani, Ankita / Promila / Sharma, Jyotsna / Seth, Anuradha / Dutta, Mukul / Saurabh, Nishant / Veerapandian, Murugan / Venkatachalam, Ganesh / Bansal, Deepak / Gupta, Dinesh / Halami, Prakash M / Peddha, Muthukumar Serva / Veeranna, Ravindra P / Pal, Anirban / Singh, Ranvijay Kumar / Anandasadagopan, Suresh Kumar / Karuppanan, Parimala / Rahman, Syed Nasar / Selvakumar, Gopika / Venkatesan, Subramanian / Karmakar, Malay Kumar / Sardana, Harish Kumar / Kothari, Anamika / Parihar, Devendra Singh / Thakur, Anupma / Saifi, Anas / Gupta, Naman / Singh, Yogita / Reddu, Ritu / Gautam, Rizul / Mishra, Anuj / Mishra, Avinash / Gogeri, Iranna / Rayasam, Geethavani / Padwad, Yogendra / Patial, Vikram / Hallan, Vipin / Singh, Damanpreet / Tirpude, Narendra / Chakrabarti, Partha / Maity, Sujay Krishna / Ganguly, Dipyaman / Sistla, Ramakrishna / Balthu, Narender Kumar / A, Kiran Kumar / Ranjith, Siva / Kumar, B Vijay / Jamwal, Piyush Singh / Wali, Anshu / Ahmed, Sajad / Chouhan, Rekha / Gandhi, Sumit G / Sharma, Nancy / Rai, Garima / Irshad, Faisal / Jamwal, Vijay Lakshmi / Paddar, Masroor Ahmad / Khan, Sameer Ullah / Malik, Fayaz / Ghosh, Debashish / Thakkar, Ghanshyam / Barik, S K / Tripathi, Prabhanshu / Satija, Yatendra Kumar / Mohanty, Sneha / Khan, Md Tauseef / Subudhi, Umakanta / Sen, Pradip / Kumar, Rashmi / Bhardwaj, Anshu / Gupta, Pawan / Sharma, Deepak / Tuli, Amit / Ray Chaudhuri, Saumya / Krishnamurthi, Srinivasan / Prakash, L / Rao, Ch V / Singh, B N / Chaurasiya, Arvindkumar / Chaurasiyar, Meera / Bhadange, Mayuri / Likhitkar, Bhagyashree / Mohite, Sharada / Patil, Yogita / Kulkarni, Mahesh / Joshi, Rakesh / Pandya, Vaibhav / Mahajan, Sachin / Patil, Amita / Samson, Rachel / Vare, Tejas / Dharne, Mahesh / Giri, Ashok / Paranjape, Shilpa / Sastry, G Narahari / Kalita, Jatin / Phukan, Tridip / Manna, Prasenjit / Romi, Wahengbam / Bharali, Pankaj / Ozah, Dibyajyoti / Sahu, Ravi Kumar / Dutta, Prachurjya / Singh, Moirangthem Goutam / Gogoi, Gayatri / Tapadar, Yasmin Begam / Babu, Elapavalooru Vssk / Sukumaran, Rajeev K / Nair, Aishwarya R / Puthiyamadam, Anoop / Valappil, Prajeesh Kooloth / Pillai Prasannakumari, Adrash Velayudhan / Chodankar, Kalpana / Damare, Samir / Agrawal, Ved Varun / Chaudhary, Kumardeep / Agrawal, Anurag / Sengupta, Shantanu / Dash, Debasis

    Computers in biology and medicine

    2022  Volume 146, Page(s) 105419

    Abstract: Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in ... ...

    Abstract Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in assessing vaccine effectiveness in populations globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines since whole virion vaccines generate antibodies against all the viral proteins. Here, we show how a statistical and machine learning (ML) based approach can be used to discriminate between SARS-CoV-2 infection and immune response to an inactivated whole virion vaccine (BBV152, Covaxin). For this, we assessed serial data on antibodies against Spike and Nucleocapsid antigens, along with age, sex, number of doses taken, and days since last dose, for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, our ensemble ML model classified 724 to be infected. For method validation, we determined the relative ability of a random subset of samples to neutralize Delta versus wild-type strain using a surrogate neutralization assay. We worked on the premise that antibodies generated by a whole virion vaccine would neutralize wild type more efficiently than delta strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, neutralization against Delta strain was more effective, indicating infection. We found 71.8% subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period. Our approach will help in real-world vaccine effectiveness assessments where whole virion vaccines are commonly used.
    MeSH term(s) COVID-19/epidemiology ; COVID-19/prevention & control ; COVID-19 Vaccines/therapeutic use ; Humans ; Machine Learning ; Pandemics ; SARS-CoV-2 ; Vaccines, Inactivated ; Viral Vaccines ; Virion
    Chemical Substances COVID-19 Vaccines ; Vaccines, Inactivated ; Viral Vaccines
    Language English
    Publishing date 2022-04-25
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2022.105419
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: A machine learning-based approach to determine infection status in recipients of BBV152 whole virion inactivated SARS-CoV-2 vaccine for serological surveys

    Singh, Prateek / Ujjainiya, Rajat / Prakash, Satyartha / Naushin, Salwa / Sardana, Viren / Bhatheja, Nitin / Singh, Ajay Pratap / Barman, Joydeb / Kumar, Kartik / Khan, Raju / Tallapaka, Karthik Bharadwaj / Anumalla, Mahesh / Lahiri, Amit / Kar, Susanta / Bhosale, Vivek / Srivastava, Mrigank / Mugale, Madhav Nilakanth / Pandey, C.P / Khan, Shaziya /
    Katiyar, Shivani / Raj, Desh / Ishteyaque, Sharmeen / Khanka, Sonu / Rani, Ankita / Promila / Sharma, Jyotsna / Seth, Anuradha / Dutta, Mukul / Saurabh, Nishant / Veerapandian, Murugan / Venkatachalam, Ganesh / Bansal, Deepak / Gupta, Dinesh / Halami, Prakash M / Peddha, Muthukumar Serva / Sundaram, Gopinath M / Veeranna, Ravindra P / Pal, Anirban / Singh, Ranvijay Kumar / Anandasadagopan, Suresh Kumar / Karuppanan, Parimala / Rahman, Syed Nasar / Selvakumar, Gopika / Venkatesan, Subramanian / Karmakar, MalayKumar / Sardana, Harish Kumar / Kothari, Animika / Parihar, DevendraSingh / Thakur, Anupma / Saifi, Anas / Gupta, Naman / Singh, Yogita / Reddu, Ritu / Gautam, Rizul / Mishra, Anuj / Mishra, Avinash / Gogeri, Iranna / Rayasam, Geethavani / Padwad, Yogendra / Patial, Vikram / Hallan, Vipin / Singh, Damanpreet / Tirpude, Narendra / Chakrabarti, Partha / Maity, Sujay Krishna / Ganguly, Dipyaman / Sistla, Ramakrishna / Balthu, Narender Kumar / A, Kiran Kumar / Ranjith, Siva / Kumar, Vijay B / Jamwal, Piyush Singh / Wali, Anshu / Ahmed, Sajad / Chouhan, Rekha / Gandhi, Sumit G / Sharma, Nancy / Rai, Garima / Irshad, Faisal / Jamwal, Vijay Lakshmi / Paddar, MasroorAhmad / Khan, Sameer Ullah / Malik, Fayaz / Ghosh, Debashish / Thakkar, Ghanshyam / Barik, Saroj K / Tripathi, Prabhanshu / Satija, Yatendra Kumar / Mohanty, Sneha / Khan, Md. Tauseef / Subudhi, Umakanta / Sen, Pradip / Kumar, Rashmi / Bhardwaj, Anshu / Gupta, Pawan / Sharma, Deepak / Tuli, Amit / Chaudhuri, Saumya Ray / Krishnamurthi, Srinivasan / L, Prakash / Rao, Ch V / Singh, B N / Chaurasiya, Arvindkumar / Chaurasiyar, Meera / Bhadange, Mayuri / Likhitkar, Bhagyashree / Mohite, Sharada / Patil, Yogita / Kulkarni, Mahesh / Joshi, Rakesh / Pandya, Vaibhav / Patil, Amita / Samson, Rachel / Vare, Tejas / Dharne, Mahesh / Giri, Ashok / Paranjape, Shilpa / Sastry, G. Narahari / Kalita, Jatin / Phukan, Tridip / Manna, Prasenjit / Romi, Wahengbam / Bharali, Pankaj / Ozah, Dibyajyoti / Sahu, Ravi Kumar / Dutta, Prachurjya / Singh, Moirangthem Goutam / Gogoi, Gayatri / Tapadar, Yasmin Begam / Babu, Elapavalooru VSSK / Sukumaran, Rajeev K / Nair, Aishwarya R / Puthiyamadam, Anoop / Valappil, PrajeeshKooloth / Pillai Prasannakumari, Adrash Velayudhan / Chodankar, Kalpana / Damare, Samir / Agrawal, Ved Varun / Chaudhary, Kumardeep / Agrawal, Anurag / Sengupta, Shantanu / Dash, Debasis

    medRxiv

    Abstract: Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the effectiveness of interventions. Asymptomatic breakthrough infections have been a major ... ...

    Abstract Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the effectiveness of interventions. Asymptomatic breakthrough infections have been a major problem during the ongoing surge of Delta variant globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines used in the higher-income regions. Here, we show for the first time how statistical and machine learning (ML) approaches can discriminate SARS-CoV-2 infection from immune response to an inactivated whole virion vaccine (BBV152, Covaxin, India), thereby permitting real-world vaccine effectiveness assessments from cohort-based serosurveys in Asia and Africa where such vaccines are commonly used. Briefly, we accessed serial data on Anti-S and Anti-NC antibody concentration values, along with age, sex, number of doses, and number of days since the last vaccine dose for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine (SVM) model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, 724 were classified as infected. Since the vaccine contains wild-type virus and the antibodies induced will neutralize wild type much better than Delta variant, we determined the relative ability of a random subset of such samples to neutralize Delta versus wild type strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, Delta variant, was neutralized more effectively than the wild type, which cannot happen without infection. The fraction rose to 71.8% (28 of 39) in subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period.
    Keywords covid19
    Language English
    Publishing date 2021-12-17
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2021.12.16.21267889
    Database COVID19

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