ABSTRACT
The recent spread of coronavirus (COVID-19) has meant that online conference presentations are becoming more and more frequent at national and international level. We believe that these online presentations will remain an option even after the pandemic has subsided. One of the challenges of online conference presentations is that it is difficult to convey nonverbal information such as gestures and the facial expressions of the presenter. In this paper, we propose the “Stage-like Presentation Method”, which involves projecting the whole body, and investigates how the presence or absence of nonverbal information from the presenter affects the audience. A comparison of the proposed method with two other presentation methods confirmed that the audience considered it the most effective. The method was used by seven people in actual conference presentations, and it was found that the audience’s impressions changed according to the details of the setting. This research confirmed that the Stage-like Presentation Method left the audience at online conferences with a good impression of presentations. It also suggests that audiences find visual nonverbal information useful. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The present study aimed to identify useful biomarkers to predict deterioration in patients with coronavirus disease 2019 (COVID‑19). A total of 201 COVID‑19 patients were classified according to their disease severity into non‑severe (n=125) and severe (n=76) groups, and the behavior of laboratory biomarkers was examined according to the prognosis. Neutrophil count, aspartate aminotransferase (AST), alanine aminotransferase, lactate dehydrogenase (LDH), C‑reactive protein (CRP), sialylated carbohydrate antigen KL‑6 (KL‑6), procalcitonin (PCT), presepsin (PSP) and D‑dimer levels were significantly higher, and lymphocyte count and platelet count were significantly lower in the non‑severe group compared with the severe group. In the non‑severe group, ROC analysis demonstrated that only four biomarkers, CRP, PSP, AST and LDH were useful for differentiating the prognosis between improvement and deterioration subgroups. No strong correlation was revealed for any of the markers. Multivariate analysis identified CRP as a significant prognostic factor in non‑severe cases (odds ratio, 41.45;95% confidence interval, 4.91‑349.24;P<0.001). However, there were no blood biomarkers that could predict the outcome of patients in the severe group. Overall, several blood markers changed significantly according to disease severity in the course of COVID‑19 infection. Among them, CRP, PSP, LDH and AST were the most reliable markers for predicting the patient's prognosis in non‑severe COVID‑19 cases.
ABSTRACT
Automatic and long-term monitoring of respiratory is in great demand for lung diseases. It gets required greater in these years due to COVID-19 pandemic to reduce medical staff fatigue for checking patient conditions frequently for long time. Kobayashi et al., in our team, developed a device measuring respiratory condition by quantizing the displacement between the 6th and 8th ribs. We introduce long short-term memory (LSTM) neural network to classify patient respiratory signals into the two states of normal and low-functional respirations. The signals were checked by a medical doctor manually for classified into the two states. In the process, they were transformed to frequency-domain spectra with complex-valued wavelet transform, and then quantized the respiratory wavelet spectra due to the large number of spectra patterns. After that, the LSTM learned and classified the processed respiratory signals. The experimental results showed the feasibility to detect the two states. © 2021, Japan Soc. of Med. Electronics and Biol. Engineering. All rights reserved.
ABSTRACT
A 12-year-old indoor cat showed severe respiratory symptoms such as sneezing, nasal discharge and cough. On Day 5 after disease onset, an oral swab was collected and a real-time RT-PCR test was performed to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), indicating that the sample from the cat was positive for SARS-CoV-2 genes. Since the symptoms worsened on Day 8, a general physical examination, blood tests, chest x-rays and treatment were carried out and oral, nasal and rectal swabs were collected. Mild bronchitis and increased serum amyloid A (SAA) were observed, but it did not lead to pneumonia. In addition, whole-genome analysis revealed that it was the delta variant of SARS-CoV-2. Then the cat recovered, and a significant increase of virus-neutralizing antibody titer was observed in the convalescent serum. In conclusion, this is the first report on a cat with respiratory symptoms caused by SARS-CoV-2 infection in Japan.
ABSTRACT
The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe from the beginning of 2020 and people worldwide have been receiving news about the same from government offices, press conferences and various other media outlets. The COVID-19 Information Watcher Project started in 2020 to collect and organize reliable information sources worldwide. However, it is difficult to automatically identify reliable information sources in foreign countries for several reasons. First, what kind of information sources are reliable heavily depend on each county situation. In some countries people trust their government's official information but in other countries they do not. Secondly, such reliable information sources often provide information in their local languages. Reliable information sources are not necessarily top-ranked by search engines. Crowdsourcing is a promising way to deal with such a case. However, crowd-sourcing platforms do not cover crowds in all countries. In this study, we report some results of our attempt to collect local information regarding COVID-19 from several countries through multi-hop crowdsourcing, in which we allow crowd workers on a crowdsourcing platform to use other platforms in other countries. We show two case studies, Russia and Afghanistan. Our results show that the multi-hop crowdsourcing is a promising way to collect COVID-19 information from different countries. © 2021 IEEE.