scholarly journals Detection of a Locally-Acquired Zika Virus Outbreak in Hidalgo County, Texas through Increased Antenatal Testing in a High-Risk Area

2020 ◽  
Vol 5 (3) ◽  
pp. 128 ◽  
Author(s):  
Steven Hinojosa ◽  
Alexander Alquiza ◽  
Clarissa Guerrero ◽  
Diana Vanegas ◽  
Niko Tapangan ◽  
...  

Hidalgo County (HC), located along the Texas–Mexico border, was listed as a high-risk county for Zika virus (ZIKV) in 2017 by the Texas Department of State Health Services, based on its historical presence of Dengue. Due to its subtropical climate, active binational travel, and population of low socioeconomic status, Hidalgo County focused on disease detection activities for the prevention of further transmission. Therefore, Hidalgo County Health and Human Services enacted public health surveillance, reviewed laboratory results, and conducted epidemiological investigations from 2016 to 2018. In 2017, Hidalgo County experienced a locally-acquired outbreak of Zika virus disease, resulting in the highest local mosquito-borne acquisition case count for the year within the United States. This resulted in Hidalgo County reviewing epidemiological data for disease detection and risk areas. With the data review, key outcomes of testing were identified. This included the importance of both RT-PCR and IgM-ELISA/PRNT testing methods. In addition, increased antenatal testing and surveillance also recognized the need of improved disease identification and testing among the general population, especially during localized outbreaks.

2016 ◽  
Vol 95 (1) ◽  
pp. 212-215 ◽  
Author(s):  
Morgan J. Hennessey ◽  
Marc Fischer ◽  
Amanda J. Panella ◽  
Robert S. Lanciotti ◽  
J. Erin Staples ◽  
...  

Plant Disease ◽  
2007 ◽  
Vol 91 (4) ◽  
pp. 466-466 ◽  
Author(s):  
T. Isakeit ◽  
A. M. Idris ◽  
G. Sunter ◽  
M. C. Black ◽  
J. K. Brown

Tomato yellow leaf curl virus (TYLCV), a monopartite virus in the genus Begomovirus (family, Geminiviridae) from the Middle East, is one of the most damaging whitefly-transmitted viruses of tomato (Lycopersicon esculentum) worldwide. TYLCV was first identified in the United States in 1997 in Florida (4), and most recently, in the Pacific Coast states of Mexico where fresh market tomatoes are grown for the U.S. market (1). During September 2006, tomatoes grown from transplants in Waller County, TX exhibited shortened internodes, stunting and puckering of leaflets, green vein banding, and diffuse chlorosis. The disease incidence in two fields (4 ha total) was 95% and yield was substantially reduced. Many of the transplants were symptomatic at planting. The transplants originated from two facilities in Hidalgo County, TX. Both facilities had experienced heavy infestations of the whitefly, Bemisia tabaci (Genn.), during transplant production. At the same time, transplants produced in Uvalde and Bexar counties, TX, where whitefly infestations were also prevalent, had similar virus symptoms. Total DNA was extracted from the leaves of symptomatic tomato plants from 10 samples from these four counties and amplified by PCR (2). DNA samples from Waller, Hidalgo, and Uvalde counties were cloned, and a partial fragment of the viral coat protein gene (core Cp) was sequenced. BLAST analysis of the core Cp sequences of each sample confirmed the presence of TYLCV. No other begomovirus was detected, and all attempts to amplify a bipartite begomovirus by PCR using degenerate DNA-B specific primers (3) were unsuccessful. The full-length TYLCV DNA was amplified from three samples using the rolling circle amplification method as described (1), cloned, and the sequences were determined. The three sequences shared 99.6 to 100% nt identity and so only one sequence was deposited in the NCBI GenBank database (Accession No. EF110890) (1). Analysis of the complete genome nucleotide sequence corroborated TYLCV identity predicted by core Cp analysis that was 98.1% identical with TYLCV from Egypt (GenBank Accession No. AY594174) and Spain (GenBank Accession No. AJ489258), 97.6% with TYLCV from Mexico (GenBank Accession No. DQ631892), and 96.5% with TYLCV-Is (GenBank Accession No. X15656). Additionally, a Southern blot with TYLCV as the probe detected replicating (double-stranded) TYLCV DNA in all samples consisting of three plants from Uvalde County and 21 plants from Bexar County. To our knowledge, this is the first report of TYLCV in Texas that occurred in two transplant production areas approximately 400 km apart. Transplants produced in Uvalde and Bexar counties were planted there, while Hidalgo County transplants were shipped outside of the usual range of the whitefly. Hidalgo County has a subtropical climate, which can allow overwintering of TYLCV and the whitefly vector, allowing the establishment and spread of this virus in the future. References: (1) J. K. Brown and A. M. Idris. Plant Dis. 90:1360, 2006. (2) J. K. Brown et al. Arch. Virol. 146:1581, 2001. (3) A. M. Idris and J. K. Brown. Phytopathology 88:648, 1998. (4) J. E. Polston et al. Plant Dis. 83:984, 1999.


2020 ◽  
Vol 40 (8) ◽  
pp. 1808-1817 ◽  
Author(s):  
J. Aaron Barnes ◽  
Mark A. Eid ◽  
Mark A. Creager ◽  
Philip P. Goodney

Peripheral artery disease (PAD) stems from atherosclerosis of lower extremity arteries with resultant arterial narrowing or occlusion. The most severe form of PAD is termed chronic limb-threatening ischemia and carries a significant risk of limb loss and cardiovascular mortality. Diabetes mellitus is known to increase the incidence of PAD, accelerate disease progression, and increase disease severity. Patients with concomitant diabetes mellitus and PAD are at high risk for major complications, such as amputation. Despite a decrease in the overall number of amputations performed annually in the United States, amputation rates among those with both diabetes mellitus and PAD have remained stable or even increased in high-risk subgroups. Within this cohort, there is significant regional, racial/ethnic, and socioeconomic variation in amputation risk. Specifically, residents of rural areas, African-American and Native American patients, and those of low socioeconomic status carry the highest risk of amputation. The burden of amputation is severe, with 5-year mortality rates exceeding those of many malignancies. Furthermore, caring for patients with PAD and diabetes mellitus imposes a significant cost to the healthcare system—estimated to range from $84 billion to $380 billion annually. Efforts to improve the quality of care for those with PAD and diabetes mellitus must focus on the subgroups at high risk for amputation and the disparities they face in the receipt of both preventive and interventional cardiovascular care. Better understanding of these social, economic, and structural barriers will prove to be crucial for cardiovascular physicians striving to better care for patients facing this challenging combination of chronic diseases.


2016 ◽  
Vol 65 (12) ◽  
Author(s):  
Naomi K. Tepper ◽  
Howard I. Goldberg ◽  
Manuel I. Vargas Bernal ◽  
Brenda Rivera ◽  
Meghan T. Frey ◽  
...  

2020 ◽  
Author(s):  
Carson Lam ◽  
Jacob Calvert ◽  
Gina Barnes ◽  
Emily Pellegrini ◽  
Anna Lynn-Palevsky ◽  
...  

BACKGROUND In the wake of COVID-19, the United States has developed a three stage plan to outline the parameters to determine when states may reopen businesses and ease travel restrictions. The guidelines also identify subpopulations of Americans that should continue to stay at home due to being at high risk for severe disease should they contract COVID-19. These guidelines were based on population level demographics, rather than individual-level risk factors. As such, they may misidentify individuals at high risk for severe illness and who should therefore not return to work until vaccination or widespread serological testing is available. OBJECTIVE This study evaluated a machine learning algorithm for the prediction of serious illness due to COVID-19 using inpatient data collected from electronic health records. METHODS The algorithm was trained to identify patients for whom a diagnosis of COVID-19 was likely to result in hospitalization, and compared against four U.S policy-based criteria: age over 65, having a serious underlying health condition, age over 65 or having a serious underlying health condition, and age over 65 and having a serious underlying health condition. RESULTS This algorithm identified 80% of patients at risk for hospitalization due to COVID-19, versus at most 62% that are identified by government guidelines. The algorithm also achieved a high specificity of 95%, outperforming government guidelines. CONCLUSIONS This algorithm may help to enable a broad reopening of the American economy while ensuring that patients at high risk for serious disease remain home until vaccination and testing become available.


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