ABSTRACT
Since 2020, the precedence of COVID-19 and its variants has made a significant impact on the global fashion industry and instigated a fundamental change in consumer purchasing behaviour. A series of lockdowns, travel restrictions, social distancing has forced consumers to rely on and adapt to online shopping methods, causing major branding retail companies to innovate new online based consumer interaction systems. Moreover, work from home has significantly cut down sales in formal wear, while outing restrictions and social distancing has further cut down demand for fast and luxury fashion. Consumer needs and preferences in turn have reoriented towards home comfort and athleisure wear, as well as essential wearables. However, the current online shopping platforms do not provide a way for consumers to specify their own needs and preferences which leads to dissatisfaction for consumers, uncertainty in consumer purchases resulting in high inventory risk for branding retailers, as well as suppliers. This paper introduces how consumer's needs and preferences can be linked with product performance on e-shopping platforms with the novel consumer interactive system "Fashion Big Data (FBD) API plugins". It will describe how the FBD API plugins can enable consumers to set their own needs and preferences;compare consumers' needs and preferences with certified product performance;and provide respective smart personalized product recommendations. The paper will demonstrate real business case examples with hand feel, skin feel and thermal wear comfort FBD API plugins from the EU Horizon 2020, Fashion Big Data Model (FBD B_MODEL) project. This will be followed by FBD B_MODEL business case partner and consumer feedback. © Textile Bioengineering and Informatics Symposium Proceedings 2022 - 15th Textile Bioengineering and Informatics Symposium, TBIS 2022.
ABSTRACT
The COVID-19 pandemic has catalysed the change in fashion consumer demand. With more apparel consumers turning to online shopping, brand owners, retailers, and suppliers in the textile industry supply chain are facing new challenges in transitioning from the traditional offline business model to the new online business model. This paper first discusses the behavioural changes of fashion consumers, the supply chain participants, and the policymakers, and identifies the pain points of the industry. To address these pain points, the FBD digital fashion business model was then proposed, and the C2B2B ICT platform was developed to enable fashion brandings, retailers, and manufacturers to adopt the new business model. The architecture of the digital platform is explained. Finally, the paper introduces how the retailer, garment manufacturer, fabric manufacturer, yarn manufacturer and fibre manufacturer can build up their digital business models by using the FBD C2B2B platform with real case studies. © Textile Bioengineering and Informatics Symposium Proceedings 2022 - 15th Textile Bioengineering and Informatics Symposium, TBIS 2022.
ABSTRACT
For rapid discovery of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro) inhibitors from a natural product library, a novel colorimetric screening assay was developed. According to the colorimetric principle, the synthetic peptide TSAVLQ-para-nitroanilide (pNA) was used as the Mpro hydrolysis substrate. Subsequently, the working concentration of pNA substrate, Mpro working concentration, hydrolysis time and DMSO tolerance were optimized for the development of a simple and robust colorimetric screening assay. Through these systematic optimizations, we selected 0.4 mumol.L-1 Mpro and 100 mumol.L-1 pNA substrate as the optimal working concentrations in this colorimetric screening assay, and a high Z' factor of 0.9 was achieved. Using this screening assay, natural product ginkgolic acid C13: 0 (GA13: 0) was identified as a novel competitive Mpro inhibitor in vitro. Taken together, we have successfully developed a simple and optimized colorimetric screening assay, which will be vital for the discovery of novel SARS-CoV-2 Mpro inhibitors. Copyright © 2022, Chinese Pharmaceutical Association. All rights reserved.
ABSTRACT
Objective: To explore a modified CT scoring system, its feasibility for disease severity evaluation and its predictive value in coronavirus disease 2019 (COVID-19) patients. Methods: This study was a multi-center retrospective cohort study. Patients confirmed with COVID-19 were recruited in three medical centers located in Beijing, Wuhan and Nanchang from January 27, 2020 to March 8, 2020. Demographics, clinical data, and CT images were collected. CT were analyzed by two emergency physicians of more than ten years' work experience independently through a modified scoring system. Final score was determined by average score from the two reviewers if consensus was not reached. The lung was divided into 6 zones (upper, middle, and lower on both sides) by the level of trachea carina and the level of lower pulmonary veins. The target lesion types included ground-glass opacity (GGO), consolidation, overall lung involvement, and crazy-paving pattern. Bronchiectasis, cavity, pleural effusion, etc., were not included in CT reading and analysis because of low incidence. The reviewers evaluated the extent of the targeted patterns (GGO, consolidation) and overall affected lung parenchyma for each zone, using Likert scale, ranging from 0-4 (0=absent; 1=1%-25%; 2=26%-50%; 3=51%-75%; 4=76%-100%). Thus, GGO score, consolidation score, and overall lung involvement score were sum of 6 zones ranging from 0-24. For crazy-paving pattern, it was only coded as absent or present (0 or 1) for each zone and therefore ranging from 0-6. Results: A total of 197 patients from 3 medical centers and 522 CT scans entered final analysis. The median age of the patients was 64 years, and 54.8% were male. There were 76(38.8%) patients had hypertension and 30(15.3%) patients had diabetes mellitus. There were 75 of the patients classified as moderate cases, as well as 95 severe cases and 27 critical cases. As initial symptom, dry cough occurred in 170 patients, 134 patients had fever, and 125 patients had dyspnea. Reparatory rate, oxygen saturation, lymphocyte count and CURB 65 score on admission day varied among patients with different disease severity scale. There were 50 of the patients suffered from deterioration during hospital stay. The median time consumed for each CT by clinicians was 86.5 seconds. Cronbach's alpha for GGO, consolidation, crazy-paving pattern, and overall lung involvement between two clinicians were 0.809, 0.712, 0.678, and 0.906, respectively, showing good or excellent inter-rater correlation. There were 193 (98.0%) patients had GGO, 147 (74.6%) had consolidation, and 126(64.0%) had crazy-paving pattern throughout clinical course. Bilateral lung involvement was observed in 183(92.9%) patients. Median time of interval for CT scan in our study was 7 days so that the whole clinical course was divided into stages by week for further analysis. From the second week on, the CT scores of various types of lesions in severe or critically patients were higher than those of moderate cases. After the fifth week, the course of disease entered the recovery period. The CT score of the upper lung zones was lower than that of other zones in moderate and severe cases. Similar distribution was not observed in critical patients. For moderate cases, the ground glass opacity score at the second week had predictive value for the escalation of the severity classification during hospitalization. The area under the receiver operating characteristic curve was 0.849, the best cut-off value was 5 points, with sensitivity of 84.2% and specificity of 75.0%. Conclusions: It is feasible for clinicians to use the modified semi-quantitative CT scoring system to evaluate patients with COVID-19. Severe/critical patients had higher scores for ground glass opacity, consolidation, crazy-paving pattern, and overall lung involvement than moderate cases. The ground glass opacity score in the second week had an optimal predictive value for escalation of disease severity during hospitalization in moderate patients on admission. The frequency of CT scan should be reduced after entering the recovery stage.