Search Results (5)

The 2019 coronavirus disease (COVID-19) pandemic is affecting millions of people worldwide. Chest high-resolution computed tomography (HRCT) is commonly used as a diagnostic test for suspected COVID-19; however, despite numerous attempts, there is no single scoring system that is widely accepted and used in clinical practice to estimate the probability of SARS-CoV-2 pneumonia. The aim of this single-center retrospective study is to develop a radiological score to predict the probability of COVID-19 with HRCT. Patients admitted to the emergency department with symptoms suggestive of COVID-19 who underwent both HRCT and RT-PCR on nasopharyngeal swab to detect SARS-CoV-2 infection between 1 March and 30 April 2020 were included. A multivariable regression analysis was conducted to identify all HRCT signs independently associated with a positive RT-PCR assay for SARS-CoV-2 and build the HRCT score. A total of 1153 patients were enrolled in this study. The number of segments with ground glass opacities (OR 1.18, 95% CI 1.11–1.26), number of segments with linear opacities (OR 1.21, 95% CI 1.05–1.42), crazy paving patterns (OR 6, 95% CI 3.79–9.76), and vascular ectasia in each segment (OR 2.46, 95% CI 1.1.5–5.8) were included in the score. The HRCT score showed high discriminatory power (area under the ROC curve of 0.8267 [95% CI 0.8–0.85]) with 72.2% sensitivity, 86.6% specificity, 78% PPV, and 83% NPV for its best cut-off. In summary, the HRCT score has good diagnostic and discriminatory accuracy for COVID-19 and is easy and quick to perform. Full article

Truthteller liar puzzles are popular in science and also in recreational mathematics. In this paper, we compare five different types of puzzles. In each of our puzzles, the persons of the puzzle may state some statements about their types. In strong truthteller–strong liar puzzles (SS puzzles), each statement of a truthteller must be true and each statement of a liar must be false, and there is no third type of person in these puzzles. It is known that there is no good SS puzzle, where a puzzle is good if it has exactly one solution. In fact, because of symmetry, by flipping the type of person in a solution, another (dual) solution is obtained. Therefore, to break this symmetry, there are various ways to introduce a third type of person, e.g., Mutes and crazies. In SSS puzzles, crazy people may appear, each of whom can tell only a self-contradicting statement. In SSW puzzles, a crazy person may say some additional statements apart from his or her self-contradicting statement. In SSM puzzles, that we investigate here, there can also be some Mute people (as the third type together with truthtellers and liars). We differentiate two types of SSM puzzles. In SSM_W puzzles a mute person may be a Mute (type), but he or she could also be either a truthteller or a liar (type). In SSM_S puzzles, each person who did not say any statement must be a Mute in the solution. Various examples are presented and it is also highlighted how a puzzle changes from unsolvable to solvable or to a good puzzle when the interpretation, the type of the puzzle changes, i.e., shifted from one to other, and symmetry breaks. Among other data, the number of solvable and good puzzles are counted and compared for the five types when few people appear. Full article

This study’s aim was to investigate CT (computed tomography) pattern dynamics differences within surviving and deceased adult patients with COVID-19, revealing new prognostic factors and reproducing already known data with our patients’ cohort: 635 hospitalized patients (55.3% of them were men, 44.7%—women), of which 87.3% had a positive result of RT-PCR (reverse transcription-polymerase chain reaction) at admission. The number of deaths was 53 people (69.8% of them were men and 30.2% were women). In total, more than 1500 CT examinations were performed on patients, using a GE Optima CT 660 computed tomography (General Electric Healthcare, Chicago, IL, USA). The study was performed at hospital admission, the frequency of repetitive scans further varied based on clinical need. The interpretation of the imaging data was carried out by 11 radiologists with filling in individual registration cards that take into account the scale of the lesion, the location, contours, and shape of the foci, the dominating types of changes, as well as the presence of additional findings and the dynamics of the process—a total of 45 parameters. Statistical analysis was performed using the software packages SPSS Statistics version 23.0 (IBM, Armonk, NY, USA) and R software version 3.3.2. For comparisons in pattern dynamics across hospitalization we used repeated measures general linear model with outcome and disease phase as factors. The crazy paving pattern, which is more common and has a greater contribution to the overall CT picture in different phases of the disease in deceased patients, has isolated prognostic significance and is probably a reflection of faster dynamics of the process with a long phase of progression of pulmonary parenchyma damage with an identical trend of changes in the scale of the lesion (as recovered) in this group of patients. Already known data on typical pulmonological CT manifestations of infection, frequency of occurrence, and the prognostic significance of the scale of the lesion were reproduced, new differences in the dynamics of the process between recovered and deceased adult patients were also found that may have prognostic significance and can be reflected in clinical practice. Full article

Accompanied by the rapid economic and social development, there is a phenomenon of the crazy spread of many infectious diseases. It has brought the rapid growth of the number of people infected with hand-foot-and-mouth disease (HFMD), and children, especially infants and young children’s health is at great risk. So it is very important to predict the number of HFMD infections and realize the regional early-warning of HFMD based on big data. However, in the current field of infectious diseases, the research on the prevalence of HFMD mainly predicts the number of future cases based on the number of historical cases in various places, and the influence of many related factors that affect the prevalence of HFMD is ignored. The current early-warning research of HFMD mainly uses direct case report, which uses statistical methods in time and space to have early-warnings of outbreaks separately. It leads to a high error rate and low confidence in the early-warning results. This paper uses machine learning methods to establish a HFMD epidemic prediction model and explore constructing a variety of early-warning models. By comparison of experimental results, we finally verify that the HFMD prediction algorithm proposed in this paper has higher accuracy. At the same time, the early-warning algorithm based on the comparison of threshold has good results. Full article

In this paper, we use commonsense reasoning and graph representation to study logical puzzles with three types of people. Strong Truth-Tellers say only true atomic statements, Strong Liars say only false atomic statements, and Strong Crazy people say only self-contradicting statements. Self-contradicting statements are connected to the Liar paradox, i.e., no Truth-Teller or a Liar could say “I am a Liar”. A puzzle is clear if it only contains its given statements to solve it, and a puzzle is good if it has exactly one solution. It is known that there is no clear and good Strong Truth-Teller–Strong Liar (also called SS) puzzle. However, as we prove here, there are good and clear Strong Truth-Teller, Strong Liar and Strong Crazy puzzles (SSS-puzzles). The newly investigated type ‘Crazy’ drastically changes the scenario. Some properties of the new types of puzzles are analyzed, and some statistics are also given. Full article