Optical Analysis of Nasal Endoscopic Images From a Patient With Severe Acute Respiratory Syndrome Coronavirus 2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide; a simple diagnostic test is becoming increasingly important. Polymerase chain reaction tests of nasal swab samples are expensive and time-consuming. Detailed observation of the nasal cavity is required. No nasal endoscopic study regarding patients with SARS-CoV-2 has been published. Here, we compared endoscopic images of normal subjects and patients with allergic rhinitis (AR) to images from a patient with confirmed SARS-CoV-2 infection. Nasal cavity endoscopic images (of the inferior turbinate) were compared using the hue, saturation, and luminance (HSL) color system of Matrox Design Assistant, version 5.0. The multiple allergen simultaneous test was used to distinguish normal subjects from patients with AR. HSL patterns were compared among images. We enrolled 14 normal subjects and patients with AR and 1 patient with confirmed SARS-CoV-2 infection. The inferior turbinates of normal subjects and patients with AR were smooth and uniform, while those of the patient with SARS-CoV-2 were scratched and stretched. Although additional data are needed, optical analysis of nasal endoscopy images may be useful to screen for SARS-CoV-2 infection. This imaging method is rapid, non-invasive, and inexpensive

Applications of pre-open sets

[EN] Using the concept of pre-open set, we introduce and study topological properties of pre-limit points, pre-derived sets, preinterior and pre-closure of a set, pre-interior points, pre-border, prefrontier and pre-exterior. The relations between pre-derived set (resp. pre-limit point, pre-interior (point), pre-border, pre-frontier, and preexterior) and α-derived set (resp. α-limit point, α-interior (point), α-border, α-frontier, and α-exterior) are investigatedJun, YB.; Jeong, SW.; Lee, HJ.; Lee, JW. (2008). Applications of pre-open sets. Applied General Topology. 9(2):213-228. https://doi.org/10.4995/agt.2008.18022132289

Size distributions of atmospheric particulate matter and associated trace metals in the multi-industrial city of Ulsan, Korea

Particulate matter (PM) was collected using micro-orifice uniform deposit impactors from a residential (RES) site and an industrial (IND) site in Ulsan, South Korea, in September-October 2014. The PM samples were measured based on their size distributions (11 stages), ranging from 0.06 ??m to over 18.0 ??m. Nine trace metals (As, Se, Cr, V, Cd, Pb, Ba, Sb, and Zn) associated with PM were analyzed. The PM samples exhibited weak bimodal distributions irrespective of sampling sites and events, and the mean concentrations of total PM (TPM) measured at the IND site (56.7 ??g/m3) was higher than that measured at the RES site (38.2 ??g/m3). The IND site also showed higher levels of nine trace metals, reflecting the influence of industrial activities and traffic emissions. At both sites, four trace metals (Ba, Zn, V, and Cr) contributed to over 80% of the total concentrations in TPM. The modality of individual trace metals was not strong except for Zn; however, the nine trace metals in PM2.5 and PM10 accounted for approximately 50% and 90% of the total concentrations in TPM, respectively. This result indicates that the size distributions of PM and trace metals are important to understand how respirable PM affects public health

InMD-X: Large Language Models for Internal Medicine Doctors

In this paper, we introduce InMD-X, a collection of multiple large language models specifically designed to cater to the unique characteristics and demands of Internal Medicine Doctors (IMD). InMD-X represents a groundbreaking development in natural language processing, offering a suite of language models fine-tuned for various aspects of the internal medicine field. These models encompass a wide range of medical sub-specialties, enabling IMDs to perform more efficient and accurate research, diagnosis, and documentation. InMD-X's versatility and adaptability make it a valuable tool for improving the healthcare industry, enhancing communication between healthcare professionals, and advancing medical research. Each model within InMD-X is meticulously tailored to address specific challenges faced by IMDs, ensuring the highest level of precision and comprehensiveness in clinical text analysis and decision support. This paper provides an overview of the design, development, and evaluation of InMD-X, showcasing its potential to revolutionize the way internal medicine practitioners interact with medical data and information. We present results from extensive testing, demonstrating the effectiveness and practical utility of InMD-X in real-world medical scenarios