The impacts of patient mistreatment on healthcare workers’ role behaviors: a study in Chinese Fangcang shelter hospitals (preprint)

Background: Fangcang shelter hospitals have played an important role in the battle against the COVID-19 epidemic in China. Verbal and physical attacks on patients are prone to occur in such hospitals. This study explored the impacts of patient mistreatment on healthcare workers’ role behaviors (service performance and patient-oriented organizational citizenship behavior). Methods: We examined the influence of patient mistreatment on service performance and patient-oriented organizational citizenship behavior, as well as the mediating effect of emotional exhaustion and the moderating effect of displaced aggression by patients, using hierarchical linear regression and conditional process analysis. Results: Patient mistreatment was positively associated with emotional exhaustion among healthcare workers, while emotional exhaustion was negatively associated with service performance and patient-oriented organizational citizenship behavior. Mediation analysis revealed that emotional exhaustion mediated the association between patient mistreatment and both types of role behaviors. Moderated mediation analysis found that the mediation effect was weaker when the displaced aggression by patients was high. Conclusions: The findings clarified the relationship between patient mistreatment, emotional exhaustion, service performance, and patient-oriented organizational citizenship behavior. Additional assistance should be provided to healthcare workers dealing with patient mistreatment. Displaced aggression by patients attenuates the positive effects of patient mistreatment on the emotional exhaustion of healthcare workers. Our findings reveal the mechanism and boundary conditions of patient mistreatment affecting healthcare workers' service performance and patient-oriented organizational citizenship behavior.

Development and testing of a multi-lingual Natural Language Processing-based deep learning system in 10 languages for COVID-19 pandemic crisis: A multi-center study

Purpose The COVID-19 pandemic has drastically disrupted global healthcare systems. With the higher demand for healthcare and misinformation related to COVID-19, there is a need to explore alternative models to improve communication. Artificial Intelligence (AI) and Natural Language Processing (NLP) have emerged as promising solutions to improve healthcare delivery. Chatbots could fill a pivotal role in the dissemination and easy accessibility of accurate information in a pandemic. In this study, we developed a multi-lingual NLP-based AI chatbot, DR-COVID, which responds accurately to open-ended, COVID-19 related questions. This was used to facilitate pandemic education and healthcare delivery. Methods First, we developed DR-COVID with an ensemble NLP model on the Telegram platform (https://t.me/drcovid_nlp_chatbot). Second, we evaluated various performance metrics. Third, we evaluated multi-lingual text-to-text translation to Chinese, Malay, Tamil, Filipino, Thai, Japanese, French, Spanish, and Portuguese. We utilized 2,728 training questions and 821 test questions in English. Primary outcome measurements were (A) overall and top 3 accuracies;(B) Area Under the Curve (AUC), precision, recall, and F1 score. Overall accuracy referred to a correct response for the top answer, whereas top 3 accuracy referred to an appropriate response for any one answer amongst the top 3 answers. AUC and its relevant matrices were obtained from the Receiver Operation Characteristics (ROC) curve. Secondary outcomes were (A) multi-lingual accuracy;(B) comparison to enterprise-grade chatbot systems. The sharing of training and testing datasets on an open-source platform will also contribute to existing data. Results Our NLP model, utilizing the ensemble architecture, achieved overall and top 3 accuracies of 0.838 [95% confidence interval (CI): 0.826–0.851] and 0.922 [95% CI: 0.913–0.932] respectively. For overall and top 3 results, AUC scores of 0.917 [95% CI: 0.911–0.925] and 0.960 [95% CI: 0.955–0.964] were achieved respectively. We achieved multi-linguicism with nine non-English languages, with Portuguese performing the best overall at 0.900. Lastly, DR-COVID generated answers more accurately and quickly than other chatbots, within 1.12–2.15 s across three devices tested. Conclusion DR-COVID is a clinically effective NLP-based conversational AI chatbot, and a promising solution for healthcare delivery in the pandemic era.

Development and testing of a multi-lingual Natural Language Processing-based deep learning system in 10 languages for COVID-19 pandemic crisis: A multi-center study.

Purpose: The COVID-19 pandemic has drastically disrupted global healthcare systems. With the higher demand for healthcare and misinformation related to COVID-19, there is a need to explore alternative models to improve communication. Artificial Intelligence (AI) and Natural Language Processing (NLP) have emerged as promising solutions to improve healthcare delivery. Chatbots could fill a pivotal role in the dissemination and easy accessibility of accurate information in a pandemic. In this study, we developed a multi-lingual NLP-based AI chatbot, DR-COVID, which responds accurately to open-ended, COVID-19 related questions. This was used to facilitate pandemic education and healthcare delivery. Methods: First, we developed DR-COVID with an ensemble NLP model on the Telegram platform (https://t.me/drcovid_nlp_chatbot). Second, we evaluated various performance metrics. Third, we evaluated multi-lingual text-to-text translation to Chinese, Malay, Tamil, Filipino, Thai, Japanese, French, Spanish, and Portuguese. We utilized 2,728 training questions and 821 test questions in English. Primary outcome measurements were (A) overall and top 3 accuracies; (B) Area Under the Curve (AUC), precision, recall, and F1 score. Overall accuracy referred to a correct response for the top answer, whereas top 3 accuracy referred to an appropriate response for any one answer amongst the top 3 answers. AUC and its relevant matrices were obtained from the Receiver Operation Characteristics (ROC) curve. Secondary outcomes were (A) multi-lingual accuracy; (B) comparison to enterprise-grade chatbot systems. The sharing of training and testing datasets on an open-source platform will also contribute to existing data. Results: Our NLP model, utilizing the ensemble architecture, achieved overall and top 3 accuracies of 0.838 [95% confidence interval (CI): 0.826-0.851] and 0.922 [95% CI: 0.913-0.932] respectively. For overall and top 3 results, AUC scores of 0.917 [95% CI: 0.911-0.925] and 0.960 [95% CI: 0.955-0.964] were achieved respectively. We achieved multi-linguicism with nine non-English languages, with Portuguese performing the best overall at 0.900. Lastly, DR-COVID generated answers more accurately and quickly than other chatbots, within 1.12-2.15 s across three devices tested. Conclusion: DR-COVID is a clinically effective NLP-based conversational AI chatbot, and a promising solution for healthcare delivery in the pandemic era.

Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective.

Background: Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract. Methods: This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning. Results: One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10-12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63-0.83. Conclusion: Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology.

Simultaneous Detection of Eight Antiviral Drugs for Treatment of COVID-19 in Source Water

The continuous outbreak of COVID-19 results in a large number of therapeutic drugs inevitably entering water bodies through different pathways, and there are problems of accurate and rapid detection in the treatment and ecological evaluation of these drugs. Therefore, it is necessary to establish a method for simultaneous detection of multiple antiviral drugs for treatment of COVID-19 in water. A method for the simultaneous detection of 8 antiviral drugs（ribavirin, oseltamivir, nevirapine, lamivudine,abacavir, stavudine, acyclovir and penciclovir） in source water was developed by optimizing the determining parameters of liquid chromatography-tandem mass spectrometry（LC-MS/MS）. The daughter ion response values of 8 antiviral drugs in this method were relatively high, which was beneficial to the detection of target compounds. The correlation coefficient（r） of the standard curve of 8 antiviral drugs was not less than 0.995 0, the method detection limit（MDL） and the lower limit of determination were in the range of 0.004-0.081 μg/L and 0.013-0.267 μg/L, respectively, and the spike recoveries and RSDs of actual water samples were 81.61%-113.03% and 0.84%-8.12%, respectively.

Pharmacological Activity of Quercetin: An Updated Review

Quercetin, a natural flavonoid compound with a widespread occurrence throughout the plant kingdom, exhibits a variety of pharmacological activities. Because of the wide spectrum of health-promoting effects, quercetin has attracted much attention of dietitians and medicinal chemists. An updated review of the literature on quercetin was performed using PubMed, Embase, and Science Direct databases. This article presents an overview of recent developments in pharmacological activities of quercetin including anti-SARS-CoV-2, antioxidant, anticancer, antiaging, antiviral, and anti-inflammatory activities as well as the mechanism of actions involved. The biological activities of quercetin were evaluated both in vitro and in vivo, involving a number of cell lines and animal models, but metabolic mechanisms of quercetin in the human body are not clear. Therefore, further large sample clinical studies are needed to determine the appropriate dosage and form of quercetin for the treatment of the disease.

Clinical and immune response characteristics among vaccinated persons infected with SARS-CoV-2 delta variant: a retrospective study.

OBJECTIVES: This study aimed to observe the clinical and immune response characteristics of vaccinated persons infected with the delta variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Yangzhou, China. METHODS: We extracted the medical data of 129 patients with delta-variant infection who were admitted to Northern Jiangsu People's Hospital (Yangzhou, China) between August and September, 2021. The patients were grouped according to the number of vaccine doses received into an unvaccinated group: a one-dose group and a two-dose group. The vaccine used was SARS-CoV-2-inactivated vaccine developed by Sinovac. We retrospectively analyzed the patients' epidemiological, clinical, laboratory, and imaging data. RESULTS: Almost all patients with delta-variant infection in Yangzhou were elderly, and patients with severe/critical illness were over 70 years of age. The rates of severe/critical illness (P=0.006), fever (P=0.025), and dyspnea (P=0.045) were lower in the two-dose group than in the unvaccinated group. Compared to the unvaccinated group, the two-dose group showed significantly higher lymphocyte counts and significantly lower levels of C-reactive protein (CRP), interleukin-6 (IL-6), and D-dimer during hospitalization and a significantly higher positive rate of immunoglobulin G (IgG) antibodies at admission (all P<0.05). The cumulative probabilities of hospital discharge and negative virus conversion were also higher in the two-dose group than in the unvaccinated group (P<0.05). CONCLUSIONS: Two doses of the SARS-CoV-2-inactivated vaccine were highly effective at limiting symptomatic disease and reducing immune response, while a single dose did not seem to be effective.

Evaluation of burnout among stay-behind healthcare workers during the current Omicron wave of COVID-19 in Taizhou, China.

Background: Since February 2022, a new Omicron wave of COVID-19 emerged in Shanghai, China. Many healthcare workers came to Shanghai from hospitals of other parts of China as aid workers. Hospitals in areas with mild COVID-19 outbreaks will inevitably be understaffed, it is likely to cause job burnout of stay-behind healthcare workers. Stay-behind healthcare workers were those who had not been dispatched to support COVID-19 prevention and control in other regions. This study was designed to evaluate the burnout among stay-behind healthcare workers in the current COVID-19 Omicron wave in Taizhou, China. Methods: A population-based, anonymous, cross-sectional online survey was designed in the Wen-Juan Xing platform. The survey was sent to all stay-behind healthcare workers of the hospital (n = 1739) from April 29 to May 3, 2022. The Maslach Burnout Inventory-General Survey (MBI-GS) was used for the burnout survey. For univariate analysis, the χ2 test and one way ANOVA were used to assess differences in categorical variables and continuous variables, respectively. The effect of independent associated risk factors on each type of burnout was examined using the multinomial logistic regression model. Results: A total of 434 participants completed the survey invitation effectively. A total of 71.2% of stay-behind healthcare workers experienced burnout during COVID-19, including 54.8% experiencing mild to moderate burnout and 16.4% experiencing severe burnout. Night shift, depression, social support, positive coping and number of children appeared to be significantly related to mild to moderate burnout. Night shift, depression, social support, positive coping, number of children, professional title, and anxiety appeared to be significantly related to severe burnout. Conclusion: Job burnout among stay-behind healthcare workers was an important problem during the current Omicron wave of COVID-19. Night shift, depression, social support, positive coping, and number of children were associated with mild to moderate and severe burnout. Anxiety and professional title were associated with severe burnout.

Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective

Background Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract. Methods This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning. Results One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10–12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63–0.83. Conclusion Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology.

Global prediction for monkeypox epidemic (preprint)

The monkeypox epidemic has now spread all over the world and has become an epidemic of widespread concern in the international community. Before the emergence of targeted vaccines and specific drugs, it is necessary to numerically simulate and predict the epidemic. In order to better understand and grasp its transmission situation, and put forward some countermeasures accordingly, we predicted and simulated monkeypox transmission and vaccination scenarios using models developed for COVID-19 predictions. The results suggest the monkeypox epidemic will spread to almost all countries in the world by the end of 2022 based on modified SEIR model prediction. The total number of people infected with monkeypox will reach 100,000. The top five countries will be the United States, Brazil, Germany, France and Britain with more than 28000, 20000, 4000, 4500 and 4000 cases respectively. If 30% of the population is vaccinated, the number of infected people will drop by 35%.

Real-World Evidence for COVID-19 Delta Variant's Effects on the Digestive System and Protection of Inactivated Vaccines from a Medical Center in Yangzhou, China: A Retrospective Observational Study.

Background: Coronavirus disease 2019 (COVID-19) is rapidly disseminated worldwide, and it continues to threaten global public health. Recently, the Delta variant has emerged as the most dreaded variant worldwide. COVID-19 predominantly affects the respiratory tract, and studies have reported the transient effects of COVID-19 on digestive system function. However, the relationship between the severity of the Delta variant and digestive system function remains to be investigated. Additionally, data on the ability of the inactive Chinese vaccines (Sinovac or Sinopharm) to protect against the Delta variant or COVID-19-induced gastrointestinal symptoms in the real world are insufficient. Thus, the present retrospective observational study first attempted to use the total gastrointestinal symptom rating scale scores (GSRS) to quantify the possible changes in digestive system functions following the Delta variant infection in the early stage. In addition, the study discusses the potential of inactivated vaccines in preventing severe or critical symptoms or Delta variant-induced digestive system dysfunction. Methods: To evaluate the difference between mild illness group, moderate illness group, and severe or critical illness group, analysis of variance (ANOVA) was employed to compare the three groups' total gastrointestinal symptom rating scale scores (GSRS). A chi-squared test was used to compare the differences in the ratio of the abnormal biochemical measurements among the three groups first. Then, the percentage of the vaccinated population was compared among the three groups. Additionally, the ratio of the abnormal serum markers between the vaccinated and nonvaccinated cohorts was compared. A P value < 0.05 was considered statistically significant. Results: Significant differences were observed in the abnormal ratio of alanine aminotransferase (ALT), total bilirubin (TBIL), direct bilirubin (DBIL), lactate dehydrogenase (LDH), and Interleukin 6 (IL-6) ratio among the three groups (P < 0.05). Additionally, no significant difference was observed in the abnormal serum markers ratio between day 14 and day 21 after treatment (P > 0.05). A significant difference was observed in the total GSRS scores among the three groups and the ratio of the vaccinated population among the three groups (P < 0.05). A significant difference was observed in the ratio of the abnormal serum ALT and AST levels between the vaccinated and nonvaccinated cohorts (P < 0.05). Conclusions: In summary, serum AST, DBIL, LDH, and IL-6 levels are potential markers for distinguishing severe or critical patients in the early stage of the Delta variant infection. Additionally, changes in the levels of these serum makers are transient, and the levels can return to normal after treatment. Furthermore, severe gastrointestinal discomfort was significantly more prevalent in patients with severe or critical diseases and should thus be considered in patients diagnosed with Delta variant infection. Finally, inactivated vaccines may prevent severe or critical symptoms and Delta variant-induced liver dysfunction. Vaccination programs must be promoted to protect public health.

Metaverse and Virtual Health Care in Ophthalmology: Opportunities and Challenges.

ABSTRACT: The outbreak of the coronavirus disease 2019 has further increased the urgent need for digital transformation within the health care settings, with the use of artificial intelligence/deep learning, internet of things, telecommunication network/virtual platform, and blockchain. The recent advent of metaverse, an interconnected online universe, with the synergistic combination of augmented, virtual, and mixed reality described several years ago, presents a new era of immersive and real-time experiences to enhance human-to-human social interaction and connection. In health care and ophthalmology, the creation of virtual environment with three-dimensional (3D) space and avatar, could be particularly useful in patient-fronting platforms (eg, telemedicine platforms), operational uses (eg, meeting organization), digital education (eg, simulated medical and surgical education), diagnostics, and therapeutics. On the other hand, the implementation and adoption of these emerging virtual health care technologies will require multipronged approaches to ensure interoperability with real-world virtual clinical settings, user-friendliness of the technologies and clinical efficiencies while complying to the clinical, health economics, regulatory, and cybersecurity standards. To serve the urgent need, it is important for the eye community to continue to innovate, invent, adapt, and harness the unique abilities of virtual health care technology to provide better eye care worldwide.

Impact of the COVID-19 pandemic on congenital diaphragmatic hernia patients: a single-center retrospective study.

PURPOSE: To investigate the impact of COVID-19 on the treatment of children with congenital diaphragmatic hernia (CDH). METHODS: We retrospectively collected and compared the data of patients with CDH admitted between January 1, 2020 and December 31, 2021(study group) with the CDH patients admitted before the pandemic between January 1, 2018 and December 31, 2019 (control group). RESULTS: During the pandemic, 41 patients with CDH diagnosed prenatally were transferred to our hospital, and 40 underwent surgical repair. The number of patients treated in our hospital increased by 24.2% compared with the 33 patients before the pandemic. During the pandemic, the overall survival rate, postoperative survival rate and recurrence rate were 85.4%, 87.5% and 7.3%, respectively, and there were no significant differences compared with the control group (75.8%, 83.3% and 9.1%, respectively). The average length of hospital stay in patients admitted during the pandemic was longer than that in the control group (31 days vs. 16 days, P < 0.001), and the incidence of nosocomial infection was higher than that in the control group (19.5% vs. 3%, P = 0.037). CONCLUSIONS: CDH patients confirmed to be SARS-CoV-2 infection-free can receive routine treatment. Our data indicate that the implementation of protective measures during the COVID-19 pandemic, along with appropriate screening and case evaluation, do not have a negative impact on the prognosis of children.

The Prevalence of and Factors Associated With Anxiety and Depression Among Working-Age Adults in Mainland China at the Early Remission Stage of the Coronavirus 2019 Pandemic.

Background: The Coronavirus 2019 (COVID-19) outbreak has led to a considerable proportion of adverse psychological symptoms in different subpopulations. This study aimed to investigate the status of anxiety and depression and their associated factors in the adult, working-age population in Mainland China at the early remission stage of the COVID-19 pandemic. Methods: An online study was conducted among 1,863 participants in 29 provinces in Mainland China from March 23 to 31, 2020. Their mental health was evaluated by the generalized anxiety disorder scale (GAD-7) and the patient health questionnaire (PHQ-9). Descriptive analysis, Chi-square, and multiple logistic regressions were applied. Results: About 44.5% of the participants had anxiety, 49.2% had depression, and 37.9% showed a combination of depression and anxiety. Around 83.7% of the participants claimed that the pandemic had a negative impact on their medical needs, which was the primary predictor of mental health, the degree of impact being positively related to the prevalence of anxiety and depression. More chronic diseases, moderate to bad self-rated health, severe perceived infection risk, and younger age group were the common risk factors for anxiety and depression. Having no children, unemployment, and a college-level educational background were associated with higher anxiety prevalence, whereas unmarried participants were correlated with higher depression prevalence. Conclusion: The working-age population showed a relatively high risk of anxiety and depression in Mainland China at the early remission stage of the pandemic. To improve medical services capacity for routine and delayed medical service needs should be a part of policy-makers' priority agenda during this period of crisis.

Application of artificial intelligence in cataract management: current and future directions.

The rise of artificial intelligence (AI) has brought breakthroughs in many areas of medicine. In ophthalmology, AI has delivered robust results in the screening and detection of diabetic retinopathy, age-related macular degeneration, glaucoma, and retinopathy of prematurity. Cataract management is another field that can benefit from greater AI application. Cataract  is the leading cause of reversible visual impairment with a rising global clinical burden. Improved diagnosis, monitoring, and surgical management are necessary to address this challenge. In addition, patients in large developing countries often suffer from limited access to tertiary care, a problem further exacerbated by the ongoing COVID-19 pandemic. AI on the other hand, can help transform cataract management by improving automation, efficacy and overcoming geographical barriers. First, AI can be applied as a telediagnostic platform to screen and diagnose patients with cataract using slit-lamp and fundus photographs. This utilizes a deep-learning, convolutional neural network (CNN) to detect and classify referable cataracts appropriately. Second, some of the latest intraocular lens formulas have used AI to enhance prediction accuracy, achieving superior postoperative refractive results compared to traditional formulas. Third, AI can be used to augment cataract surgical skill training by identifying different phases of cataract surgery on video and to optimize operating theater workflows by accurately predicting the duration of surgical procedures. Fourth, some AI CNN models are able to effectively predict the progression of posterior capsule opacification and eventual need for YAG laser capsulotomy. These advances in AI could transform cataract management and enable delivery of efficient ophthalmic services. The key challenges include ethical management of data, ensuring data security and privacy, demonstrating clinically acceptable performance, improving the generalizability of AI models across heterogeneous populations, and improving the trust of end-users.

Blockchain applications in health care for COVID-19 and beyond: a systematic review.

The COVID-19 pandemic has had a substantial and global impact on health care, and has greatly accelerated the adoption of digital technology. One of these emerging digital technologies, blockchain, has unique characteristics (eg, immutability, decentralisation, and transparency) that can be useful in multiple domains (eg, management of electronic medical records and access rights, and mobile health). We conducted a systematic review of COVID-19-related and non-COVID-19-related applications of blockchain in health care. We identified relevant reports published in MEDLINE, SpringerLink, Institute of Electrical and Electronics Engineers Xplore, ScienceDirect, arXiv, and Google Scholar up to July 29, 2021. Articles that included both clinical and technical designs, with or without prototype development, were included. A total of 85 375 articles were evaluated, with 415 full length reports (37 related to COVID-19 and 378 not related to COVID-19) eventually included in the final analysis. The main COVID-19-related applications reported were pandemic control and surveillance, immunity or vaccine passport monitoring, and contact tracing. The top three non-COVID-19-related applications were management of electronic medical records, internet of things (eg, remote monitoring or mobile health), and supply chain monitoring. Most reports detailed technical performance of the blockchain prototype platforms (277 [66·7%] of 415), whereas nine (2·2%) studies showed real-world clinical application and adoption. The remaining studies (129 [31·1%] of 415) were themselves of a technical design only. The most common platforms used were Ethereum and Hyperledger. Blockchain technology has numerous potential COVID-19-related and non-COVID-19-related applications in health care. However, much of the current research remains at the technical stage, with few providing actual clinical applications, highlighting the need to translate foundational blockchain technology into clinical use.

The Effect of “Wuhan Closure” on the COVID-19 Pandemic in China

In this study, we analyzed daily records of newly diagnosed cases in Wuhan, Hubei excluding Wuhan (HEW), and China excluding Hubei (CEH) to investigate the impact of the new coronavirus outbreak in Wuhan on cities around it and throughout China. We used multifractal detrended cross-correlation analysis (MF-DXA) method to investigate the correlations between the daily number of patients in Wuhan and HEW as well as in Wuhan and CEH. We concluded that the cross-correlations between the daily number of patients in Wuhan and HEW were higher than those between the daily number of patients in Wuhan and CEH because the multifractal features of Wuhan and HEW are greater than those of Wuhan and CEH. We also found that the “Wuhan closure” conducted on January 23 resulted in a decrease in cross-correlations between Wuhan and CEH.

Untangling the changing impact of non-pharmaceutical interventions and vaccination on European Covid-19 trajectories (preprint)

Non-pharmaceutical interventions (NPIs) and vaccination are two fundamental approaches to mitigate the coronavirus disease 2019 (Covid-19) pandemic. Vaccination strategies are generally less costly and socially/economically disruptive than NPI strategies, such as business closures, social distancing, and face mask mandates, as evidenced by highly vaccinated countries generally rolling back NPIs. However, the respective real-world impact of an NPI strategy versus vaccination strategy, or the combination of both, on mitigating Covid-19 transmission remains uncertain. To address this, we built a Bayesian inference model to explore the changing effectiveness of NPIs and vaccination based on the assembled large-scale dataset, including epidemiological parameters, variants, vaccines, and control variable. Here we show that NPIs were still considerably complementary or even synergistic to vaccination in the effort to curb the Covid-19 infection before reaching herd immunity. We found that (1) the synergistic effect of NPIs and vaccination was 46.9% (reduction in reproduction number) in September 2021, whereas the effects of NPIs and vaccination alone were 20.7% and 28.8%, respectively; (2) effectiveness of NPIs is less sensitive to emerging COVID-19 variants but decreases with vaccination progress, as NPIs may unnecessarily restrict the vaccinated population. The effectiveness of NPIs alone declined approximately 23% since the introduction of vaccination strategies, where the relaxation of NPIs promoted the decline from May 2021. Our results demonstrate that the decision to relax NPIs should consider the real-world vaccination rate of the relevant population, which is determined by the observed vaccine efficacy in relation to extant and emerging variants.

Effects of worldwide interventions and vaccination on COVID-19 between waves and countries (preprint)

Worldwide governments have rapidly deployed non-pharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic, together with the large-scale rollout of vaccines since late 2020. However, the effect of these individual NPI and vaccination measures across space and time has not been sufficiently explored. By the decay ratio in the suppression of COVID-19 infections, we investigated the performance of different NPIs across waves in 133 countries, and their integration with vaccine rollouts in 63 countries as of 25 March 2021. The most effective NPIs were gathering restrictions (contributing 27.83% in the infection rate reductions), facial coverings (16.79%) and school closures (10.08%) in the first wave, and changed to facial coverings (30.04%), gathering restrictions (17.51%) and international travel restrictions (9.22%) in the second wave. The impact of NPIs had obvious spatiotemporal variations across countries by waves before vaccine rollouts, with facial coverings being one of the most effective measures consistently. Vaccinations had gradually contributed to the suppression of COVID-19 transmission, from 0.71% and 0.86% within 15 days and 30 days since Day 12 after vaccination, to 1.23% as of 25 March 2021, while NPIs still dominated the pandemic mitigation. Our findings have important implications for continued tailoring of integrated NPI or NPI-vaccination strategies against future COVID-19 waves or similar infectious diseases.