common risk factors
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2022 ◽  
Vol 19 (1) ◽  
pp. 97-100
Author(s):  
Piush Kanodia ◽  
Arun Kumar Verma ◽  
Sumit Adhikari

Introduction: Small for gestational age (SGA) refers to birth weight of neonates less than 10th percentile for gestational age or 2nd standard deviation below the population norms on the growth charts. Aims: To identify common risk factors and common morbidities for small for gestational age babies. Methods: This is a cross sectional descriptive study and it has been conducted at Department of pediatrics, Nepalgunj Medical college which is a tertiary level teaching hospital located in western part of Nepal. All term small for gestational age neonates born during study period from January 2020 to December 2020 were included. Detailed baseline demographic and clinical profile has been collected and recorded in the predesigned Proforma. Results: The most common risk factors associated with small for gestational age babies in our study were maternal hypertension (14.6%) , maternal GDM(9.6%), Urinary Tract Infection (UTI) in 1st or 2nd trimester of pregnancy, maternal anemia, smoking, alcohol consumption, hypothyroidism and congenital heart disease. The most common short term complications associated with Small for gestational age babies were hypoglycemia and Meconium aspiration syndrome. Conclusion: The most common risk factors associated with Small for gestational age  babies in our study were maternal hypertension, maternal Gestational diabetes Mellitus (GDM), Urinary Tract Infection  in 1st or 2nd trimester of pregnancy, maternal anemia, smoking, alcohol consumption, hypothyroidism and congenital heart disease. The most common short term complications associated with Small for gestational age  babies were hypoglycemia and Meconium aspiration syndrome (MAS).


Author(s):  
Jun Wei Ng ◽  
Eric Tzyy Jiann Chong ◽  
Ping-Chin Lee

Abstract: Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and recently has become a serious global pandemic. Age, gender, and comorbidities are known to be common risk factors for severe COVID-19 but are not enough to fully explain the magnitude of their effect on the risk of severity of the disease. Single nucleotide polymorphisms (SNPs) in several genes have been reported as a genetic factor contributing to COVID-19 severity. This comprehensive review focuses on the association between SNPs in four important genes and COVID-19 severity in a global aspect. We discuss a total of 39 SNPs in this review: five SNPs in the ABO gene, nine SNPs in the angiotensin-converting enzyme 2 (ACE2) gene, 19 SNPs in the transmembrane protease serine 2 (TMPRSS2) gene, and six SNPs in the toll-like receptor 7 (TLR7) gene. These SNPs data could assist in monitoring an individual's risk of severe COVID-19 disease, and therefore personalized management and pharmaceutical treatment could be planned in COVID-19 patients.


Author(s):  
Ahmad Shaker Abdalrada ◽  
Jemal Abawajy ◽  
Tahsien Al-Quraishi ◽  
Sheikh Mohammed Shariful Islam

Abstract Background Diabetic mellitus (DM) and cardiovascular diseases (CVD) cause significant healthcare burden globally and often co-exists. Current approaches often fail to identify many people with co-occurrence of DM and CVD, leading to delay in healthcare seeking, increased complications and morbidity. In this paper, we aimed to develop and evaluate a two-stage machine learning (ML) model to predict the co-occurrence of DM and CVD. Methods We used the diabetes complications screening research initiative (DiScRi) dataset containing >200 variables from >2000 participants. In the first stage, we used two ML models (logistic regression and Evimp functions) implemented in multivariate adaptive regression splines model to infer the significant common risk factors for DM and CVD and applied the correlation matrix to reduce redundancy. In the second stage, we used classification and regression algorithm to develop our model. We evaluated the prediction models using prediction accuracy, sensitivity and specificity as performance metrics. Results Common risk factors for DM and CVD co-occurrence was family history of the diseases, gender, deep breathing heart rate change, lying to standing blood pressure change, HbA1c, HDL and TC\HDL ratio. The predictive model showed that the participants with HbA1c >6.45 and TC\HDL ratio > 5.5 were at risk of developing both diseases (97.9% probability). In contrast, participants with HbA1c >6.45 and TC\HDL ratio ≤ 5.5 were more likely to have only DM (84.5% probability) and those with HbA1c ≤5.45 and HDL >1.45 were likely to be healthy (82.4%. probability). Further, participants with HbA1c ≤5.45 and HDL <1.45 were at risk of only CVD (100% probability). The predictive accuracy of the ML model to detect co-occurrence of DM and CVD is 94.09%, sensitivity 93.5%, and specificity 95.8%. Conclusions Our ML model can significantly predict with high accuracy the co-occurrence of DM and CVD in people attending a screening program. This might help in early detection of patients with DM and CVD who could benefit from preventive treatment and reduce future healthcare burden.


2022 ◽  
Author(s):  
Srikanth Umakanthan ◽  
Saudah Ghany ◽  
La Donna Gay ◽  
Tia Gilkes ◽  
Jamila Freeman ◽  
...  

Abstract Background: Cervical cancer, the fourth most frequent cancer in women, is associated with the human papilloma virus (HPV). This study focuses on identifying any risk factors and clinical findings for abnormal cervical cytology and histopathology in relation to the Trinidad and Tobago population. Some risk factors include early age of first coitus, high number of sexual partners, high parity, smoking, and use of certain medications such as oral contraception. This study is aimed to identify the significance of Papanicolaou (pap) smears and to identify the common risk factors that contribute to the development of premalignant and malignant cervical lesions. Method: A three-year retrospective, descriptive study of cervical cancer was conducted at the Eric Williams Medical Sciences Complex to assess the risk factors and clinical findings using cervical cytology and histopathology data of patients with premalignant lesions. The subject population included 215 female patients aged 18 years and older who had the following documented abnormal cervical cytology: (ASCUS), ASC-H, LSIL, HSIL, Atypical Glandular cells, HPV, Adenocarcinoma, Invasive Squamous Cell Carcinoma. Histopathology records were analysed for thirty-three of these patients. Patients’ information were recorded on data collection sheets adapted from the North Central Regional Health Authority’s cytology laboratory standardised reporting format request form (Appendix I). Results and findings: The data were analysed via Statistical Package for Social Sciences (SPSS) software edition 23 using frequency tables and descriptive analysis. The sample mean age of the population was 36.7 years, first age of coitus was 18.1 years, number of sexual partners was 3.8 and number of live births was 2. LSIL was the most popular abnormal finding, 32.6 %, followed by HSIL, 28.8% and ASCUS, 27.4%. Most histopathological reports resulted in CIN I and II.Conclusion: The major risk factors observed for cytology abnormalities and premalignant lesions were early age of coitus, high number of sexual partners, and no use of contraception.Patients mostly presented as asymptomatic despite obtaining abnormal cytology results. Hence, regular pap smear screening should continue to be highly encouraged.


2022 ◽  
Vol 12 (1) ◽  
pp. 29
Author(s):  
Chiara Villa ◽  
Eleonora Rivellini ◽  
Marialuisa Lavitrano ◽  
Romina Combi

The current coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus (SARS-CoV)-2, is affecting every aspect of global society, including public healthcare systems, medical care access, and the economy. Although the respiratory tract is primarily affected by SARS-CoV-2, emerging evidence suggests that the virus may also reach the central nervous system (CNS), leading to several neurological issues. In particular, people with a diagnosis of Alzheimer’s disease (AD) are a vulnerable group at high risk of contracting COVID-19, and develop more severe forms and worse outcomes, including death. Therefore, understanding shared links between COVID-19 and AD could aid the development of therapeutic strategies against both. Herein, we reviewed common risk factors and potential pathogenetic mechanisms that might contribute to the acceleration of neurodegenerative processes in AD patients infected by SARS-CoV-2.


2022 ◽  
pp. 296-319
Author(s):  
Lisa Ogilvie ◽  
Julie Prescott ◽  
Terry Hanley ◽  
Jerome Carson

Chatbots are programmed conversational agents that emulate communication systematically using natural language processing. They can be programmed to assume a range of roles where regular human interaction occurs. Within mental health services, they are not as well represented as in other areas of healthcare, with research suggesting that uptake has been hindered by concerns over the accuracy of the information they provide, undeveloped technology, lack of adherence to an ethical framework, and the unconvincing portrayal of human authenticity. Technological improvements have addressed some of these concerns, and as the resultant solution choice increases, the potential for chatbots within mental health is receiving greater attention. In this chapter, two novel uses for chatbots are showcased. Foxbot, a recovery friend, accessible at the point of need to help mitigate some of the common risk factors to sustaining addiction recovery; and ERIC, a counselling client who allows trainee counsellors to practise their counselling skills without having to enlist an actual client.


Diseases ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 93
Author(s):  
Tamara Alhobayb ◽  
Rahul Peravali ◽  
Motaz Ashkar

Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease with poor prognosis, leading to significant cancer-related mortality and an overall five-year survival rate of about nine percent. Acute and chronic pancreatitis have been associated with PDAC through common risk factors based on multiple epidemiological studies. Acute pancreatitis (AP) might be one of the earliest manifestations of PDAC, but evolving chronic pancreatitis (CP) following recurrent bouts of AP has been proposed as a risk factor for cancer development in the setting of persistent inflammation and ongoing exposure to carcinogens. This review aims to highlight the evidence supporting the relationship between acute and chronic pancreatitis with PDAC.


2021 ◽  
Vol 7 (12) ◽  
pp. 1092
Author(s):  
Ana Goyeneche-García ◽  
Juan Rodríguez-Oyuela ◽  
Guillermo Sánchez ◽  
Carolina Firacative

Invasive aspergillosis (IA) is a severe mycosis caused by Aspergillus species. The infection mainly affects immunocompromised patients with a significant clinical burden. This study aimed to determine the clinical and epidemiological characteristics of patients diagnosed with IA in a fourth level hospital in Colombia, as these data are scarce in the country. A retrospective, observational study, from a single center was conducted with 34 male and 32 female patients, between 1 month- and 90-year-old, diagnosed with proven (18.2%), probable (74.2%) and possible (7.6%) IA, during a 21-year period. The most frequent underlying conditions for IA were chemotherapy (39.4%) and corticosteroid use (34.8%). The lung was the most common affected organ (92.4%). Computed tomography (CT) imaging findings were mainly nodules (57.6%) and consolidation (31.8%). A low positive correlation was found between serum galactomannan and hospitalization length. Aspergillus fumigatus prevailed (73.3%) in sputum and bronchoalveolar lavage cultures. Most patients were hospitalized in general wards (63.6%) and treated with voriconazole (80.3%). Mortality rate was 15.2%. Common risk factors for IA were identified in the Colombian cohort, including medications and underlying diseases. However, their frequency differs from other countries, reinforcing the idea that local surveillance is essential and at-risk patients should be carefully monitored.


Author(s):  
Tayse Caroline Cunha de Medeiros ◽  
Alessandra Areas e Souza ◽  
Rodolfo Coelho Prates ◽  
Iain Chapple ◽  
João Paulo Steffens

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