Severe Acute Pharyngitis Caused by Group C Streptococcus

INTRODUCTION: Adult group C beta-hemolytic streptococcal pharyngitis has a prevalence of approximately 5%. It can present with a broad spectrum of severity. CASE REPORT: We report a 30-year-old woman who presented with severe Group C streptococcal pharyngitis. DISCUSSION: She presented with a 9-day history of progressive symptoms, including fever, sore throat, neck swelling, and recent onset of hoarseness. In the 9 days before the emergency room (ER) presentation, the patient had visited the ER twice complaining of a sore throat. At both visits, the physicians performed rapid antigen strep testing. Each time her test was negative and the physicians recommended symptomatic therapy. Her symptoms continued to worsen leading to her repeat presentation. At this time she had severe pharyngitis with markedly enlarged tonsils. Neck CT excluded peritonsillar abscess. Rapid strep testing was again negative, but her throat culture grew group C beta-hemolytic streptococcus. CONCLUSION: This presentation illustrates the importance of a systematic approach to evaluating patients with negative rapid strep tests and worsening pharyngitis

Investigating centering, scan length, and arm position impact on radiation dose across 4 countries from 4 continents during pandemic: mitigating key radioprotection issues

Purpose: Optimization of CT scan practices can help achieve and maintain optimal radiation protection. The aim was to assess centering, scan length, and positioning of patients undergoing chest CT for suspected or known COVID-19 pneumonia and to investigate their effect on associated radiation doses. Methods: With respective approvals from institutional review boards, we compiled CT imaging and radiation dose data from four hospitals belonging to four countries (Brazil, Iran, Italy, and USA) on 400 adult patients who underwent chest CT for suspected or known COVID-19 pneumonia between April 2020 and August 2020. We recorded patient demographics and volume CT dose index (CTDIvol) and dose length product (DLP). From thin-section CT images of each patient, we estimated the scan length and recorded the first and last vertebral bodies at the scan start and end locations. Patient mis-centering and arm position were recorded. Data were analyzed with analysis of variance (ANOVA). Results: The extent and frequency of patient mis-centering did not differ across the four CT facilities (>0.09). The frequency of patients scanned with arms by their side (11–40% relative to those with arms up) had greater mis-centering and higher CTDIvol and DLP at 2/4 facilities (p = 0.027–0.05). Despite lack of variations in effective diameters (p = 0.14), there were significantly variations in scan lengths, CTDIvol and DLP across the four facilities (p < 0.001). Conclusions: Mis-centering, over-scanning, and arms by the side are frequent issues with use of chest CT in COVID-19 pneumonia and are associated with higher radiation doses

Salinity management alternatives for the Rechna Doab, Punjab, Pakistan. Volume 4 - Field data collection and processing

Irrigation management / Soil salinity / Agricultural development / Water quality / Data processing / Groundwater / Crop production / Intensive cropping / Models / Farm surveys / Pakistan / Punjab / Rechna Doab

Integrative analysis for COVID-19 patient outcome prediction

While image analysis of chest computed tomography (CT) for COVID-19 diagnosis has been intensively studied, little work has been performed for image-based patient outcome prediction. Management of high-risk patients with early intervention is a key to lower the fatality rate of COVID-19 pneumonia, as a majority of patients recover naturally. Therefore, an accurate prediction of disease progression with baseline imaging at the time of the initial presentation can help in patient management. In lieu of only size and volume information of pulmonary abnormalities and features through deep learning based image segmentation, here we combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit (ICU) admission. To our knowledge, this is the first study that uses holistic information of a patient including both imaging and non-imaging data for outcome prediction. The proposed methods were thoroughly evaluated on datasets separately collected from three hospitals, one in the United States, one in Iran, and another in Italy, with a total 295 patients with reverse transcription polymerase chain reaction (RT-PCR) assay positive COVID-19 pneumonia. Our experimental results demonstrate that adding non-imaging features can significantly improve the performance of prediction to achieve AUC up to 0.884 and sensitivity as high as 96.1, which can be valuable to provide clinical decision support in managing COVID-19 patients. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia. The source code of our work is available at https://github.com/DIAL-RPI/COVID19-ICUPrediction. © 2020 Elsevier B.V

Natural Variation in Decision-Making Behavior in Drosophila melanogaster

There has been considerable recent interest in using Drosophila melanogaster to investigate the molecular basis of decision-making behavior. Deciding where to place eggs is likely one of the most important decisions for a female fly, as eggs are vulnerable and larvae have limited motility. Here, we show that many natural genotypes of D. melanogaster prefer to lay eggs near nutritious substrate, rather than in nutritious substrate. These preferences are highly polymorphic in both degree and direction, with considerable heritability (0.488) and evolvability

Direct targets of the transcription factors ABA-Insensitive(ABI)4 and ABI5 reveal synergistic action by ABI4 and several bZIP ABA response factors

The plant hormone abscisic acid (ABA) is a key regulator of seed development. In addition to promoting seed maturation, ABA inhibits seed germination and seedling growth. Many components involved in ABA response have been identified, including the transcription factors ABA insensitive (ABI)4 and ABI5. The genes encoding these factors are expressed predominantly in developing and mature seeds, and are positive regulators of ABA mediated inhibition of seed germination and growth. The direct effects of ABI4 and ABI5 in ABA response remain largely undefined. To address this question, plants over-expressing ABI4 or ABI5 were used to allow identification of direct transcriptional targets. Ectopically expressed ABI4 and ABI5 conferred ABA-dependent induction of slightly over 100 genes in 11 day old plants. In addition to effector genes involved in seed maturation and reserve storage, several signaling proteins and transcription factors were identified as targets of ABI4 and/or ABI5. Although only 12% of the ABA- and ABI-dependent transcriptional targets were induced by both ABI factors in 11 day old plants, 40% of those normally expressed in seeds had reduced transcript levels in both abi4 and abi5 mutants. Surprisingly, many of the ABI4 transcriptional targets do not contain the previously characterized ABI4 binding motifs, the CE1 or S box, in their promoters, but some of these interact with ABI4 in electrophoretic mobility shift assays, suggesting that sequence recognition by ABI4 may be more flexible than known canonical sequences. Yeast one-hybrid assays demonstrated synergistic action of ABI4 with ABI5 or related bZIP factors in regulating these promoters, and mutant analyses showed that ABI4 and these bZIPs share some functions in plants

How corporate social responsibility contributes to strengthening brand loyalty, hotel positioning and intention to revisit?

This study aims to investigate if the visitors’ perception of corporate social responsibility influences both hotel brand positioning and intention to revisit. Furthermore, it examines the indirect impact of corporate social responsibility on hotel brand positioning and intention to revisit through other major factors (identification, satisfaction, and loyalty). In total, 348 valid questionnaires were collected from customers reserved a hotel room in the UK within the last six months at the time of this investigation. Structural equation modeling was conducted to advance insight into the various influences and relationships. The results showed that there is a significant direct relationship between CSR with hotel brand positioning and indirect relationship between CSR and intention to revisit through identification, loyalty. However, surprisingly there are no relationships between CSR with satisfaction and satisfaction with loyalty. This study contributes to the existing literature on CSR in hotel management by investigating the impact of the customers’ perception of a hotel’s CSR on both hotel brand positioning and customers’ intention to revisit. Moreover, this study also contributes to hotel management literature by investigating the indirect impact of identification, satisfaction, and loyalty on the relationship between CSR with hotel brand positioning and intention to revisit

Investigating centering, scan length, and arm position impact on radiation dose across 4 countries from 4 continents during pandemic: Mitigating key radioprotection issues

Purpose: Optimization of CT scan practices can help achieve and maintain optimal radiation protection. The aim was to assess centering, scan length, and positioning of patients undergoing chest CT for suspected or known COVID-19 pneumonia and to investigate their effect on associated radiation doses. Methods: With respective approvals from institutional review boards, we compiled CT imaging and radiation dose data from four hospitals belonging to four countries (Brazil, Iran, Italy, and USA) on 400 adult patients who underwent chest CT for suspected or known COVID-19 pneumonia between April 2020 and August 2020. We recorded patient demographics and volume CT dose index (CTDIvol) and dose length product (DLP). From thin-section CT images of each patient, we estimated the scan length and recorded the first and last vertebral bodies at the scan start and end locations. Patient mis-centering and arm position were recorded. Data were analyzed with analysis of variance (ANOVA). Results: The extent and frequency of patient mis-centering did not differ across the four CT facilities (>0.09). The frequency of patients scanned with arms by their side (11�40 relative to those with arms up) had greater mis-centering and higher CTDIvol and DLP at 2/4 facilities (p = 0.027�0.05). Despite lack of variations in effective diameters (p = 0.14), there were significantly variations in scan lengths, CTDIvol and DLP across the four facilities (p < 0.001). Conclusions: Mis-centering, over-scanning, and arms by the side are frequent issues with use of chest CT in COVID-19 pneumonia and are associated with higher radiation doses. © 202

CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase-polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method; however, its accuracy in detection is only ~70�75. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80�98, but similar accuracy of 70. To enhance the accuracy of CT imaging detection, we developed an open-source framework, CovidCTNet, composed of a set of deep learning algorithms that accurately differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 95 compared to radiologists (70). CovidCTNet is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. To facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and model parameter details as open-source. Open-source sharing of CovidCTNet enables developers to rapidly improve and optimize services while preserving user privacy and data ownership. © 2021, The Author(s)