instruction:
In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.
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Acute cough is a major symptom of viral respiratory tract infection and causes excessive morbidity in human populations across the world. A wide variety of viruses play a role in the development of cough after acute infection and all of these manifest a similar clinical picture across different age groups. Despite the large disease burden surprisingly little is known about the mechanism of acute cough following viral infection. Both in vitro and in vivo experiments show that increased production of neuropeptides and leukotrienes mediate cough after viral infection, along with altered expression of neural receptors. Increased airway mucus production is also likely to play a significant role. This work is reviewed in this article. Following the recent development of a mouse model for rhinovirus infection and the establishment of experimental models of rhinovirus challenge in human subjects with both asthma and COPD the field is expanding to translate in vitro research into clinical studies and hopefully eventually into clinical practice. Developing a clearer understanding of the mechanisms underlying virus induced cough may lead to more specific and effective therapies.
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Cough and viruses in airways disease: Mechanisms


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Purpose: COVID-19 has become global threaten. CT acts as an important method of 60 diagnosis. However, human-based interpretation of CT imaging is time consuming. 61 More than that, substantial inter-observer-variation cannot be ignored. We aim at 62 developing a diagnostic tool for artificial intelligence (AI)-based classification of CT 63 images for recognizing COVID-19 and other common infectious diseases of the lung. 64 Experimental Design: In this study, images were retrospectively collected and 65 prospectively analyzed using machine learning. CT scan images of the lung that show 66 or do not show COVID-19 were used to train and validate a classification framework 67 based on convolutional neural network. Five conditions including COVID-19 68 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, pulmonary 69 tuberculosis, and normal lung were evaluated. Training and validation set of images 70 were collected from Wuhan Jin Yin-Tan Hospital whereas test set of images were 71 collected from Zhongshan Hospital Xiamen University and the fifth Hospital of 72 Wuhan. 73 Results: Accuracy, sensitivity, and specificity of the AI framework were reported. For 74 test dataset, accuracies for recognizing normal lung, COVID-19 pneumonia, 75 non-COVID-19 viral pneumonia, bacterial pneumonia, and pulmonary tuberculosis 76 were 99.4%, 98.8%, 98.5%, 98.3%, and 98.6%, respectively. For the test dataset, 77 accuracy, sensitivity, specificity, PPV, and NPV of recognizing COVID-19 were 78 98.8%, 98.2%, 98.9%, 94.5%, and 99.7%, respectively. 79 All rights reserved. No reuse allowed without permission. the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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Deep Learning-Based Recognizing COVID-19 Deep Learning-Based Recognizing COVID-19 and other Common Infectious Diseases of the Lung by Chest CT 2 Scan Images


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A prospective cohort study was undertaken to determine calf-level factors that affected calf health status between birth and 6 months of age. A convenience sample of approximately 3300 female Holstein calves born in 1991 on two large Florida dairy farms was used for the study. Data collected on each calf at birth included farm of origin, weight, height at the pelvis, birth date, and serum total protein (a measure of colostral immunoglobulin absorption). Birth season was dichotomized into summer and winter using meteorological data collected by University of Florida Agricultural Research Stations. Health data including date of initial treatment and number of treatments were collected for the diseases diarrhea, omphalitis, septicemia and pneumonia. All calves were followed for 6 months. Cumulative incidences of mortality and occurrence of diarrhea, omphalitis, septicemia and pneumonia were 0.12, 0.35, 0.11, 0.24 and 0.21, respectively. Serum total protein (TP) was a significant risk factor for mortality. The association of TP and mortality was quadratic and showed a dramatic decrease in mortality as TP increased from 4.0 to 5.0 g/dl, a small improvement from 5.0 to 6.0 g/d1 and virtually no improvement in mortality rates as TP increased over 6.0 g/dl. The hazard mortality ratio was constant from birth to six months, indicating that the increased risk of mortality associated with low levels of TP was evident through six months of age. No interactions between TP, farm, season, or birth weight were found in these analyses. Serum total protein concentration was a significant risk factor for the 32 G. A. Donovan et al. /Preventive Veterinary Medicine 34 (1998) 31-46 occurrences, age of onset and severity of septicemia and pneumonia. The association between TP and septicemia was linear and an interaction with birth season was found. The association between TP and pneumonia was quadratic, and in contrast to the TP-and-septicemia relationship, the morbidity hazard ratio for pneumonia was not constant over the time measured; that is, colostral immunity protected the calf from developing pneumonia early in life, but this effect disappeared as the calf got older. Total protein was not a significant risk factor for diarrhea or omphalitis. 0 1998 Elsevier Science B.V.
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PREVENTIVE Associations between passive immunity and morbidity and mortality in dairy heifers in Florida, USA