The impact of dynamic adaptive teaching model on surgical education.

Objective To investigate the impact of dynamic adaptive teaching model on surgical education. Methods Due to the COVID-19 pandemic in 2020, we adopted dynamic adaptive teaching model in the Department of Breast Surgery, Peking Union Medical College Hospital, which divided the whole curriculum into several individual modules and recombined different modules to accommodate to student's levels and schedules. Meanwhile, adaptive strategy also increased the proportion of online teaching and fully utilized electronic medical resources. The present study included quantitative teaching score (QTS) recorded from January 2020 to June 2020, and used the corresponding data from 2019 as control. The main endpoint was to explore the impact of dynamic adaptive teaching model on overall QTS and its interaction effect with trainer's experience and student category. Results Totally, 20 trainers and 181 trainees were enrolled in the present study. With implementation of dynamic adaptive strategy, the overall QTS decreased dramatically (1.76+/-0.84 vs. 4.91+/-1.15, t=4.85, P=0.005). The impact was consistent irrespective of trainers' experience (high experience trainers: 0.85+/-0.40 vs. 2.12+/-0.44, t=4.98, P=0.004;medium experience trainers: 0.85+/-0.29 vs. 2.06+/-0.53, t=4.51, P=0.006;and low experience trainers: 0.10+/-0.16 vs. 0.44+/-0.22, t=2.62, P=0.047). For resident (including graduate) and undergraduate student teaching, both QTS was lower with dynamic strategy (residents: 0.18+/-0.34 vs. 0.97+/-0.14, t=4.35, P=0.007;undergraduate students 1.57+/-0.55 vs. 3.77+/-1.24, t=3.62, P=0.015), but dynamic strategy was effective for post-doc student subgroup and reached comparable QTS as traditional model (0.00+/-0.00 vs. 0.17+/-0.41, t=1.00, P=0.363). Conclusions Dynamic adaptive teaching strategy could be a useful alternative to traditional teaching model for post-doc students. It could be a novel effective solution for saving teaching resources and providing individualized surgical teaching modality.Copyright © 2023, Peking Union Medical College Hospital. All rights reserved.

Data analysis and prediction on world happiness score 2020

Happiness is vague and multifaceted, with a plethora of philosophers who have sought to define and understand how it works through the ages. This is the go-to phrase for many people when encouraging someone to live their best life within their means. This article uses the machine learning approach to explain and predict happiness scores. The contribution of this work has three parts: data understanding and cleaning, data analysis and visualization, and modeling and prediction. The following five prediction models are used: linear regression, random forest regressor, decision tree, Bayesian linear model, and Lasso Lars. GDP per capita, freedom to make life choices, and Life expectancy are significant determinants of happiness scores to predict future scores. This is not conclusive as unforeseen occurrences like pandemic, natural disasters, and economic meltdowns happen, even to the most stable countries. Thus, these scores change. Family is also important as we see a reasonable correlation between Social status and Happiness score, so, hold your loved ones dear. © 2023 Author(s).

FedISM: Enhancing Data Imbalance via Shared Model in Federated Learning

Considering the sensitivity of data in medical scenarios, federated learning (FL) is suitable for applications that require data privacy. Medical personnel can use the FL framework for machine learning to assist in analyzing large-scale data that are protected within the institution. However, not all clients have the same distribution of datasets, so data imbalance problems occur among clients. The main challenge is to overcome the performance degradation caused by low accuracy and the inability to converge the model. This paper proposes a FedISM method to enhance performance in the case of Non-Independent Identically Distribution (Non-IID). FedISM exploits a shared model trained on a candidate dataset before performing FL among clients. The Candidate Selection Mechanism (CSM) was proposed to effectively select the most suitable candidate among clients for training the shared model. Based on the proposed approaches, FedISM not only trains the shared model without sharing any raw data, but it also provides an optimal solution through the selection of the best shared model. To evaluate performance, the proposed FedISM was applied to classify coronavirus disease (COVID), pneumonia, normal, and viral pneumonia in the experiments. The Dirichlet process was also used to simulate a variety of imbalanced data distributions. Experimental results show that FedISM improves accuracy by up to 25% when privacy concerns regarding patient data are rising among medical institutions.

Intelligent medicine and beyond

With the developments of medical artificial intelligence (AI), meta-data analysis, intelligence-aided drug design and discovery, surgical robots and image-navigated precision treatments, intelligent medicine (IM) as a new era evolved from ancient medicine and biomedical medicine, has become an emerging topic and important criteria for clinical applications. It is fully characterized by fundamental research-driven, new-generation technique-directed as well as state-of-the-art paradigms for advanced disease diagnosis and therapy leading to an even broader future of modern medicine. As a fundamental subject and also a practice-oriented field, intelligent medicine is highly trans-disciplinary and cross-developed, which has emerged the knowledge of modern medicine, basic sciences and engineering. Basically, intelligent medicine has three domains of intelligent biomaterials, intelligent devices and intelligent techniques. Intelligent biomaterials derive from traditional biomedical materials, and currently are endowed with multiple functionalities for medical uses. For example, micro-/nanorobots, smart responsive biomaterials and digital drugs are representative intelligent biomaterials which have been already commercialized and applied to clinical uses. Intelligent devices, such as surgical robots, rehabilitation robots and medical powered exoskeleton, are an important majority in the family of intelligent medicine. Intelligent biomaterials and intelligent devices are more and more closely integrated with each other especially on the occasions of intelligence acquisition, remote transmission, AI-aided analysis and management. In comparison, intelligent techniques are internalized in the former two domains and are playing a critical role in the development of intelligent medicine. Representative intelligent techniques of telemedicine, image-navigated surgery, virtual/augmented reality and AI-assisted image analysis for early-stage disease assessments have been employed in nowadays clinical operations which to a large extent relieved medical labors. In the past decades, China has been in the leading groups compared to international colleagues in the arena of intelligent medicine, and a series of eminent research has been clinically translated for practical uses in China. For instance, the first 5G-aided remote surgery has been realized in Fujian Province in January 2019, which for the first time validated their applicability for human uses. The surgical robots have found China as the most vigorous market, and more than 10 famous Chinese companies are developing versatile surgical robots for both Chinese people and people all over the world. China also applied AI techniques to new drug developments especially in early 2020 when COVID-19 epidemic roared, and several active molecules and drug motifs have been discovered for early-stage COVID-19 screening and treatments. Based on the significance of intelligent medicine and its rapid developments in both basic research and industrials, this review summarized the comprehensive viewpoints of the Y6 Xiangshan Science Conferences titled with Fundamental Principles and Key Technologies of Intelligent Medicine, and gave an in-depth discussion on main perspectives of future developments of the integration of biomaterial and devices, the integration of bioinformatics and medical hardware, and the synergy of biotechnology and intelligence information. It is expected that this featuring article will further promote intelligent medicine to an even broader community not only for scientists but also for industrials, and in the long run embrace a perspective future for its blooming and rich contributions in China in the coming 5 years.

An International Student, a Researcher, or a Work-Ready Graduate? Exploring the Self-formation of International Students in Coursework Master's Programmes

Providing research pathways for coursework master's programme (CMP) students is a feature in the Australian higher education system. While a burgeoning number of international students in Australian CMPs participate in research units, it is constraining to rigidly categorise international students as belonging to either research or coursework streams. Acknowledging that, this chapter explores the detailed experiences of international students who have recently completed the research pathway in their CMP. Combining the concepts of self-formation and positioning theory, it investigates international students' interactions with social actors, negotiations with the self and external rules, such as social codes and educational structures. Through collaborative autoethnography, this chapter identifies four stages of self-formation, namely pre-positioning, positioning, performing, and transformation, and highlights the influences of COVID-19 on these four stages. This chapter suggests that collective efforts on an institutional level are required to improve the wellbeing of international students in four main areas, namely developing agency, resolving career anxiety, addressing financial difficulties, and handling mental issues. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.

Consumer decisions toward halal purchase before and during COVID-19 pandemic: a grey relational analysis approach

Purpose: The main objective of this research is to investigate the factors that influence consumer purchase decisions for halal products before and during the COVID-19 pandemic, based on the Engel-Kollat-Blackwell (EKB) theory. Design/methodology/approach: The research was conducted as a survey. The influencing factors were determined based on the grey relational analysis (GRA) approach. Findings: The findings indicate before the COVID-19 pandemic, consumers mainly purchased halal products based on four key factors: purchasing experience, certification label, Internet searches and past consumption experience. However, during the pandemic, the ranking and factors have changed to six indicators, which are past consumption experience, purchasing experience, certification labels, standardized specifications, Internet searches and halal certification labels. Research limitations/implications: The study was limited by the sample size and geographical area. Nevertheless, the findings could be further explored by expanding related theories toward understand human decisions based on spiritual beliefs. Practical implications: The findings of this study have important implications for research, practice and society. Understanding the factors influencing halal purchase decisions before and during the pandemic can help businesses, policymakers and halal certification bodies to better cater to consumers' needs and preferences and ensure the continued growth and development of the halal industry. Originality/value: This study evaluates halal purchasing decisions between periods of certainty and uncertainty by using the GRA. Changes in halal consumption and purchase decisions in response to COVID-19 pandemic have become an emerging topic of discovery. The study addresses the gap in the literature regarding changes in consumer decision pattern. © 2023, Emerald Publishing Limited.

Prophylactic Corticosteroid Use with Axicabtagene Ciloleucel (Axi-Cel) in Patients (Pts) with Relapsed/ Refractory Large B-Cell Lymphoma (R/R LBCL): 2-Year Follow-up of Zuma-1 Cohort 6

Background: The ZUMA-1 safety management Cohort 6 (N=40), which evaluated whether prophylactic corticosteroids and earlier corticosteroids and/or tocilizumab could improve safety outcomes, demonstrated an improved safety profile (no Grade >=3 cytokine release syndrome [CRS];15% Grade >=3 neurologic events [NEs]) vs pivotal Cohorts 1+2, without compromising response rate or durability (95% ORR, 80% CR rate, and 53% ongoing response rate with >=1 y of follow-up;Oluwole, et al. ASH 2021. 2832). Here, 2-y updated outcomes are reported. Method(s): Eligible pts with R/R LBCL underwent leukapheresis (followed by optional bridging therapy) and conditioning chemotherapy, then a single axi-cel infusion. Pts received corticosteroid prophylaxis (once-daily oral dexamethasone 10 mg on Days 0 [before axi-cel], 1, and 2) and earlier corticosteroids and/or tocilizumab for CRS and NE management vs Cohorts 1+2 (Oluwole, et al. Br J Haematol. 2021). The primary endpoints were incidence and severity of CRS and NEs. Secondary endpoints included ORR (investigator-assessed), duration of response (DOR), progression-free survival (PFS), overall survival (OS), and chimeric antigen receptor (CAR) T-cell levels in blood. Result(s): As of December 16, 2021, the median follow-up time for the 40 treated pts was 26.9 mo. Since the 1-y analysis, no new CRS events were reported (no pts had Grade >=3 CRS to date). The incidence of Grade >=3 NEs increased from 15% to 18%between the 1-y and 2-y analyses. Two new NEs occurred in 2 pts: 1 pt had Grade 2 dementia (onset on Day 685 and ongoing at time of data cutoff;not related to axi-cel) and 1 had Grade 5 axi-cel-related leukoencephalopathy. Since the 1-y analysis, 6 new infections were reported (Grades 1, 2, and 5 COVID-19 [n=1 each], Grade 3 Pneumocystis jirovecii pneumonia [n=1], Grade 3 unknown infectious episode with inflammatory syndrome [n=1], and Grade 2 herpes zoster [n=1]). In total, 8 deaths occurred since the 1-y analysis (progressive disease [n=5], leukoencephalopathy [n=1], and COVID-19 [n=2]). The ORR was 95% (80% CR), which was unchanged from the 1-y analysis. Median DOR and PFS were since reached (25.9 mo [95% CI, 7.8-not estimable] and 26.8 mo [95% CI, 8.7-not estimable], respectively). Median OS was still not reached. Kaplan- Meier estimates of the 2-y DOR, PFS, and OS rates were 53%, 53%, and 62%, respectively. Of 18 pts (45%) in ongoing response at data cutoff, all achieved CR as the best response. By Month 24, 14/20 pts with evaluable samples (70%) had detectable CAR T cells (vs 23/36 pts [64%] in Cohorts 1+2). Conclusion(s): With 2 y of follow-up, the ZUMA-1 Cohort 6 toxicity management strategy continued to demonstrate an improved long-term safety profile of axi-cel in pts with R/R LBCL. Further, responses remained high, durable, and similar to those observed in Cohorts 1+2 (Locke, et al. Lancet Oncol. 2019).Copyright © 2023 American Society for Transplantation and Cellular Therapy

Intelligent medicine and beyond

With the developments of medical artificial intelligence (AI), meta-data analysis, intelligence-aided drug design and discovery, surgical robots and image-navigated precision treatments, intelligent medicine (IM) as a new era evolved from ancient medicine and biomedical medicine, has become an emerging topic and important criteria for clinical applications. It is fully characterized by fundamental research-driven, new-generation technique-directed as well as state-of-the-art paradigms for advanced disease diagnosis and therapy leading to an even broader future of modern medicine. As a fundamental subject and also a practice-oriented field, intelligent medicine is highly trans-disciplinary and cross-developed, which has emerged the knowledge of modern medicine, basic sciences and engineering. Basically, intelligent medicine has three domains of intelligent biomaterials, intelligent devices and intelligent techniques. Intelligent biomaterials derive from traditional biomedical materials, and currently are endowed with multiple functionalities for medical uses. For example, micro-/nanorobots, smart responsive biomaterials and digital drugs are representative intelligent biomaterials which have been already commercialized and applied to clinical uses. Intelligent devices, such as surgical robots, rehabilitation robots and medical powered exoskeleton, are an important majority in the family of intelligent medicine. Intelligent biomaterials and intelligent devices are more and more closely integrated with each other especially on the occasions of intelligence acquisition, remote transmission, AI-aided analysis and management. In comparison, intelligent techniques are internalized in the former two domains and are playing a critical role in the development of intelligent medicine. Representative intelligent techniques of telemedicine, image-navigated surgery, virtual/augmented reality and AI-assisted image analysis for early-stage disease assessments have been employed in nowadays clinical operations which to a large extent relieved medical labors. In the past decades, China has been in the leading groups compared to international colleagues in the arena of intelligent medicine, and a series of eminent research has been clinically translated for practical uses in China. For instance, the first 5G-aided remote surgery has been realized in Fujian Province in January 2019, which for the first time validated their applicability for human uses. The surgical robots have found China as the most vigorous market, and more than 10 famous Chinese companies are developing versatile surgical robots for both Chinese people and people all over the world. China also applied AI techniques to new drug developments especially in early 2020 when COVID-19 epidemic roared, and several active molecules and drug motifs have been discovered for early-stage COVID-19 screening and treatments. Based on the significance of intelligent medicine and its rapid developments in both basic research and industrials, this review summarized the comprehensive viewpoints of the Y6 Xiangshan Science Conferences titled with Fundamental Principles and Key Technologies of Intelligent Medicine, and gave an in-depth discussion on main perspectives of future developments of the integration of biomaterial and devices, the integration of bioinformatics and medical hardware, and the synergy of biotechnology and intelligence information. It is expected that this featuring article will further promote intelligent medicine to an even broader community not only for scientists but also for industrials, and in the long run embrace a perspective future for its blooming and rich contributions in China in the coming 5 years. © 2023 Chinese Academy of Sciences. All rights reserved.

COVID-19 Mortality by Demographics in a Brooklyn Hospital

Previous publications have shown worse COVID-19 outcomes in African American and LatinX patients. We are sharing the experience of a 750-bed tertiary safety net hospital in Brooklyn, NY. Copyright © Wolters Kluwer Health, Inc. All rights reserved.

Prognosis Score for Overall Survival with CAR-T Therapy in Aggressive Lymphoma

Background: Despite the transformative potential of chimeric antigen receptor T (CAR-T) therapy, more tools to assist with identifying patients with increased likelihood of benefitting from this therapy will be helpful, particularly given the logistical complexity and socio-economic demands for CAR-T relative to other therapies. Health care resource restriction during the COVID-19 pandemic highlights the need for these tools. We present a simple survival score that uses 3 readily available clinical labs: platelet (plt), absolute lymphocyte count (ALC), and Lactate dehydrogenase (LDH), to predict the risk of dying within 6 months of CAR-T therapy in patients with aggressive lymphoma. Method(s): We conducted a retrospective chart review of patients with aggressive non-Hodgkin lymphoma (NHL) who received FDA-approved CAR-T between Jan 2018 to Jan 2022 at Mayo Clinic Rochester.(Table Presented)Results: Among a total of 110 pts who received CAR-T, 27 (25%) pts died within the first 6 months post CAR-T infusion (OS <= 6 months). Disease progression was the main cause of death (18/25, 72%), followed by infection (4/25, 16%), CAR-T related (HLH/MAS, 2/25, 8%), second primary malignancy (1/25, 4%) and unknown (2/25, 8%).Baseline demographics were comparable between the OS>6months and <=6months groups (Table 1). Patients' ECOG, Karnofsky performance status and 11 labs at the time of evaluation for CAR-T therapy (initial eligibility assessment, prior to leukapheresis) were compared between those who died from any cause within 6 months of CAR-T infusion and those who did not. Hemoglobin, plt, ALC, absolute monocyte count, CRP, ferritin, and LDH were selected as clinically and/or statistically significant variables for multivariate testing. Multivariate regression with boot-strap testing identified plt, ALC, and LDH as the most predictive variables with 80.9+/-11.7% accuracy for predicting death within 6 months of CAR-T infusion. Patients were scored 0-3 using these 3 labs, with 1 point assigned for plt <= 100 X109/L, ALC <= 0.4 X109/L, or LDH > 222 U/L (upper limit of normal). OS by this survival score is shown in Figure 1.(Figure Presented)Discussion: Due to the curative potential of CAR-T, patients with broader characteristics than those treated on registration studies have been treated in standard of care practice. While an estimated 5%-10% risk of CAR-T associated deaths in the first 3 months is seen across all patients in clinical trials, predictors for early death after CAR-T in real-world patient populations can provide additional context for pts and providers when selecting treatment. This survival score is important proof of concept that a simple model using readily accessible clinical labs at the time of CAR-T evaluation could provide additional context to help with additional clinical decision-making. Multicenter prospective studies will help define and validate the definitive survival scoring system.Copyright © 2023 American Society for Transplantation and Cellular Therapy

Co-Evolutionary Location-Routing Model of Medical Isolation Areas for Major Public Health Emergencies Considering the Uncertainty of Future Traffic Environment

With the continuous development of the COVID-19 pandemic, the selection of locations for medical isolation areas has not always been optimal for the timely transportation of infected people, or those suspected of being infected. This has resulted in failure to control the rate of spread of infection cases in time. To address this problem, this paper proposes a co-evolutionary location-routing optimization (CELRO) model of medical isolation areas for use in major public health emergencies to develop a rapid location-routing scheme for epidemic isolation, including the selection of locations of medical isolation facilities per area and the optimal route per vehicle to each infected person. Specifically, this paper solves the following two sub-problems: (i) calculate the shortest transportation times and corresponding routes from any medical isolation area to any person infected or suspected of being infected, and (ii) calculate the location scheme for distribution of isolation areas. Different from previous studies, the vehicle operating characteristics and the interference of uncertainty of the traffic environment are considered in the proposed model. To find an appropriate scheme for location of medical isolation areas with the shortest travel times, a co-evolutionary clustering algorithm (CECA), which is a combination of some separated evolutionary programming operations, is proposed to solve the model. Various network sizes and uncertainty combinations are used to design some comparative tests, which aim to verify the effectiveness of the proposed model. In the experiment section, CELRO reduced travel time by at least 14% compared with other methods. This finding can provide an effective theoretical basis for optimizing the spatial layout of medical isolation areas or the location planning of new medical facilities. © National Academy of Sciences.

ENLIGHTENMENT OF INTERNATIONAL CARBON EMISSION TRADING PRACTICE TO CHINA

In the era of "Post-epidemic" and "Dual-carbon targets", the focus of research on China's carbon trading market has changed from basic framework design to problem solving and development paths in the process of practice. Foreign carbon trading markets have developed for many years, and have experienced the financial crisis and the impact of the coronavirus epidemic. By analyzing the important problems and countermeasures encountered in the process of carbon trading market by representative organizations such as EU, USA, New Zealand, Korea and Japan, the valuable experience and reference significance of foreign carbon trading practice were summarized. At the same time, comparing the similarities and differences between Chinese and foreign carbon trading national conditions, and taking into account the current development of China's carbon trading market, this paper put forward some carbon trading strategies with Chinese characteristics and absorbing foreign advanced experience, such as choosing appropriate emission caps, balancing regulation, formulating price stabilization mechanism, and leaving interfaces for international cooperation. © 2023 Research Institute of Petroleum Processing, SINOPEC. All rights reserved.

Resilient Practices of Small Businesses to Survive from COVID-19 Pandemic: Perspectives from S-DL and Social Capital

The devastating outbreaks of COVID-19 pandemic has negatively impacted social and economic sustainability of the world, particularly in routine services that require physical interactions, such as dining services. With the retrospective analysis via case study, we identified three cases in dining service from USA, Indonesia, Taiwan, respectively, and investigated their service systems with the Service-Dominant Logic to understand the interactions among actors and how they integrated resources to cope with the pandemic. We identified their resilient practices heavily relied on various types of social capital to quickly respond to demand shifts, reconnect value networks, and leverage ICTs for marketing and sales. These resilient practices could be used for guiding small and medium enterprises to cope with devastating unexpected crises, taking COVID-19 as an example. More cases collected and analyzed from corresponding regions in the follow-up study could further conclude a more general causal relationship in resilience toward the theory for resilience. © 2023 IEEE Computer Society. All rights reserved.

An Integrated Neutrosophic SNA-MABAC Approach for Blockchain Applicability Evaluation in Sustainable Supply Chains

The outbreak of the novel coronavirus pneumonia and the turbulent international situation in recent years have seriously disrupted the normal operation of the entire supply chain (SC). As an emerging technology, blockchain is characterized by decentralization, reliability, transparency and traceability, which can be effectively applied to solve social, environmental and economic concerns and achieve sustainability of supply chain. However, whether blockchain is suitable for every function of a sustainable supply chain (SSC), or what function is best suited for the application of a set of blockchain criteria, can be viewed as a multi-criteria group decision-making (MCGDM) problem. This paper presents a combined MCGDM technique utilizing the social network analysis (SNA) and Multi-Attributive Border Approximation Area Comparison (MABAC), for selecting an appropriate function of SSCs to implement blockchain technology with Neutrosophic information. The framework gives quantitative consideration to the weight of relevant blockchain criteria and decision makers under high uncertainty. This study can also facilitate the effective allocation of resources and enhance the competitiveness of SSCs in the coordinated planning of various blockchain deployments. © 2022 IEEE.

Clinical characteristics of patients with diabetes mellitus complicated with COVID-19.

Objective: To study the clinical characteristics of diabetes mellitus with Coronavirus disease 2019 (COVID-19) and explore the possible mechanism of diabetes predisposition. Method(s): A single center, retrospective and observational study was used to collect 48 inpatients diagnosed with COVID-19 who were admitted to the first ward of the third department of infection, Raytheon hospital, Wuhan from February 23, 2020 to March 30, 2020. Demographic data, symptoms, laboratory tests, comorbidities, treatments and clinical outcomes have been collected. The patients were divided into non-diabetic group and diabetic group according to the combination of diabetes. The clinical data and laboratory test results of the two groups were observed, and the t test, non-parametric test and Chi square test were used for comparison. Result(s): All the 5 patients with COVID-19 diabetes mellitus had fever and respiratory symptoms, chest CT was consistent with typical COVID-19 imaging features, and novel coronavirus nucleic acid test results were positive. There were no statistically significant differences in age, gender composition, co-existing diseases, clinical symptoms, clinical typing, disease course and treatment plan between the diabetic group and the non-diabetic group (P>0.05).There was a statistically significant difference in fasting blood glucose between the non-diabetic group and the diabetic group (P<0.05).The difference of fasting blood glucose at discharge from the diabetes group compared with that at admission was also statistically significant (P<0.05).There was no statistically significant difference between the two groups in other laboratory examination indexes (P>0.05). Conclusion(s): COVID-19 patients with diabetes are mainly manifested by fever and respiratory symptoms.Chest CT shows typical COVID-19 imaging features.Copyright © 2020 by the Chinese Medical Association.

Getting the Most Out of the Evidence for Transfusion Medicine and Lessons from the COVID-19 Pandemic

Evidence-based medicine (EBM) has been described by Sackett et al. as ‘the integration of best research evidence with clinical expertise and patient values'. This chapter discusses core elements of EBM with particular reference to clinical research in transfusion medicine, and provides a practical approach to searching for evidence and critical appraisal, with some considerations of different study designs. It also includes a review of how the evidence base for transfusion medicine was collated in response to the COVID-19 pandemic. One important component of EBM is the critical appraisal of the evidence generated from a study. One important aspect of clinical trial appraisal concerns the understanding of chance variation and sample-size calculation. Appraising the evidence base for transfusion medicine is one part of improving practice;another is the effective dissemination of the evidence to clinicians. © 2022 John Wiley and Sons Ltd.

Analysis of adverse reactions of lopinavir/ritonavir in the treatment of coronavirus disease 2019.

Objective: To explore the occurrence of adverse reactions of lopinavir/ritonavir (LPV/r) in the treatment of coronavirus disease 2019 (COVID-19). Method(s): The medical records of patients with COVID-19 who received LPV/r treatment in the Fourth People's Hospital of Nanning from January 24th to February 6th, 2020 were collected and the occurrence of adverse events during the treatment was retrospectively analyzed. According to the 5 principles of adverse drug reaction correlation evaluation proposed in the Handbook of Adverse Drug Reaction Reporting and Monitoring in China, adverse events that were certainly related, probably related, and possibly related to LPV/r were defined as LPV/r-related adverse reactions. The incidence of adverse reactions was calculated and the main clinical manifestations and severity of adverse reactions [grade 1 (mild), grade 2 (moderate), grade 3 (severe), grade 4 (life-threatening), and grade 5 (death);grade 3-5 was defined as severe adverse reaction] were analyzed. Result(s): A total of 28 patients were enrolled in the analysis, including 13 males and 15 females, aged from 18 to 70 years with an average age of 44 years. The courses of treatment with LPV/r of patients ranged from 2 to 12 days, with a median course of 6 days. Of the 28 patients, 18 developed LPV/r related adverse reactions, with an incidence of 64.3%. The LPV/r-related adverse reactions in 18 patients included gastrointestinal reactions in 14 patients (grade 1 in 13 patients and grade 2 in 1 patient), bradycardia in 2 patients (grade 2 in both patients), and acute hemolysis in 1 patient (grade 3), and liver injury in 1 patient (grade 3), and no grade 4 or 5 adverse reactions occurred. The incidence of severe adverse reactions was 7.1%. Thirteen patients with grade 1 adverse reactions did not affect the treatment, and the symptoms were relieved after 2-7 days of continuous medication. LPV/r was discontinued in 5 patients with grade 2 or 3 adverse reactions, 4 of whom received symptomatic treatment, and the symptoms disappeared 2-10 days later. Conclusion(s): The incidence of adverse reactions in COVID-19 patients treated with LPV/r in our hospital was 64.3%. LPV/r mainly leads to mild gastrointestinal reactions and can also lead to bradycardia, acute hemolysis, and liver injury. Blood routine, liver function, and electrocardiogram need to be monitored during the treatment.Copyright © 2020 by the Chinese Medical Association.

Jing Si Herbal Drink as a prospective adjunctive therapy for COVID-19 treatment: Molecular evidence and mechanisms

Background: SARS-CoV-2 has led to a sharp increase in the number of hospitalizations and deaths from pneumonia and multiorgan disease worldwide;therefore, SARS-CoV-2 has become a global health problem. Supportive therapies remain the mainstay treatments against COVID-19, such as oxygen inhalation, antiviral drugs, and antibiotics. Traditional Chinese medicine (TCM) has been shown clinically to relieve the symptoms of COVID-19 infection, and TCMs can affect the pathogenesis of SARS-CoV-2 infection in vitro. Jing Si Herbal Drink (JSHD), an eight herb formula jointly developed by Tzu Chi University and Tzu Chi Hospital, has shown potential as an adjuvant treatment for COVID-19 infection. A randomized controlled trial (RCT) of JSHD as an adjuvant treatment in patients with COVID-19 infection is underway Objectives: This article aims to explore the efficacy of the herbs in JSHD against COVID-19 infection from a mechanistic standpoint and provide a reference for the rational utilization of JSHD in the treatment of COVID-19. Method(s): We compiled evidence of the herbs in JSHD to treat COVID-19 in vivo and in vitro. Result(s): We described the efficacy and mechanism of action of the active ingredients in JSHD to treat COVID-19 based on experimental evidence. JSHD includes 5 antiviral herbs, 7 antioxidant herbs, and 7 anti-inflammatory herbs. In addition, 2 herbs inhibit the overactive immune system, 1 herb reduces cell apoptosis, and 1 herb possesses antithrombotic ability. Conclusion(s): Although experimental data have confirmed that the ingredients in JSHD are effective against COVID-19, more rigorously designed studies are required to confirm the efficacy and safety of JSHD as a COVID-19 treatment.Copyright © 2021

A bibliometric analysis of artificial intelligence chatbots in educational contexts

Purpose: The application of artificial intelligence chatbots is an emerging trend in educational technology studies for its multi-faceted advantages. However, the existing studies rarely take a perspective of educational technology application to evaluate the application of chatbots to educational contexts. This study aims to bridge the research gap by taking an educational perspective to review the existing literature on artificial intelligence chatbots. Design/methodology/approach: This study combines bibliometric analysis and citation network analysis: a bibliometric analysis through visualization of keyword, authors, organizations and countries and a citation network analysis based on literature clustering. Findings: Educational applications of chatbots are still rising in post-COVID-19 learning environments. Popular research issues on this topic include technological advancements, students' perception of chatbots and effectiveness of chatbots in different educational contexts. Originating from similar technological and theoretical foundations, chatbots are primarily applied to language education, educational services (such as information counseling and automated grading), health-care education and medical training. Diversifying application contexts demonstrate specific purposes for using chatbots in education but are confronted with some common challenges. Multi-faceted factors can influence the effectiveness and acceptance of chatbots in education. This study provides an extended framework to facilitate extending artificial intelligence chatbot applications in education. Research limitations/implications: The authors have to acknowledge that this study is subjected to some limitations. First, the literature search was based on the core collection on Web of Science, which did not include some existing studies. Second, this bibliometric analysis only included studies published in English. Third, due to the limitation in technological expertise, the authors could not comprehensively interpret the implications of some studies reporting technological advancements. However, this study intended to establish its research significance by summarizing and evaluating the effectiveness of artificial intelligence chatbots from an educational perspective. Originality/value: This study identifies the publication trends of artificial intelligence chatbots in educational contexts. It bridges the research gap caused by previous neglection of treating educational contexts as an interconnected whole which can demonstrate its characteristics. It identifies the major application contexts of artificial intelligence chatbots in education and encouraged further extending of applications. It also proposes an extended framework to consider that covers three critical components of technological integration in education when future researchers and instructors apply artificial intelligence chatbots to new educational contexts. © 2023, Emerald Publishing Limited.