Dynamic associations between the respiratory tract and gut antibiotic resistome of patients with COVID-19 and its prediction power for disease severity.

The antibiotic resistome is the collection of all antibiotic resistance genes (ARGs) present in an individual. Whether an individual's susceptibility to infection and the eventual severity of coronavirus disease 2019 (COVID-19) is influenced by their respiratory tract antibiotic resistome is unknown. Additionally, whether a relationship exists between the respiratory tract and gut ARGs composition has not been fully explored. We recruited 66 patients with COVID-19 at three disease stages (admission, progression, and recovery) and conducted a metagenome sequencing analysis of 143 sputum and 97 fecal samples obtained from them. Respiratory tract, gut metagenomes, and peripheral blood mononuclear cell (PBMC) transcriptomes are analyzed to compare the gut and respiratory tract ARGs of intensive care unit (ICU) and non-ICU (nICU) patients and determine relationships between ARGs and immune response. Among the respiratory tract ARGs, we found that Aminoglycoside, Multidrug, and Vancomycin are increased in ICU patients compared with nICU patients. In the gut, we found that Multidrug, Vancomycin, and Fosmidomycin were increased in ICU patients. We discovered that the relative abundances of Multidrug were significantly correlated with clinical indices, and there was a significantly positive correlation between ARGs and microbiota in the respiratory tract and gut. We found that immune-related pathways in PBMC were enhanced, and they were correlated with Multidrug, Vancomycin, and Tetracycline ARGs. Based on the ARG types, we built a respiratory tract-gut ARG combined random-forest classifier to distinguish ICU COVID-19 patients from nICU patients with an AUC of 0.969. Cumulatively, our findings provide some of the first insights into the dynamic alterations of respiratory tract and gut antibiotic resistome in the progression of COVID-19 and disease severity. They also provide a better understanding of how this disease affects different cohorts of patients. As such, these findings should contribute to better diagnosis and treatment scenarios.

[Tracking analysis of viral nucleic acid Ct value in patients with re-positive SARS-CoV-2 infection].

OBJECTIVE: To track analysis of viral nucleic acid test results in patients with re-positive SARS-CoV-2 infection, and provide clinical reference for nucleic acid test of re-positive cases. METHODS: A retrospective study was conducted. The multiple nucleic acid results of 96 cases with SARS-CoV-2 infection tested by medical laboratory of Shenzhen Luohu Hospital Group from January to September in 2022 were analyzed. The test dates and cycle threshold (Ct) values of detectable positive virus nucleic acid in the 96 cases were summarized and analyzed. RESULTS: A total of 96 patients with SARS-CoV-2 infection were retested re-sampled for nucleic acid testing at least 12 days after the initial positive screening. Among them, 54 cases (56.25%) had Ct value of < 35 for nucleocapsid protein gene (N) and/or open reading frame 1ab gene (ORF 1ab), 42 cases (43.75%) had Ct value ≥ 35. In the re-sampling of infected patients, N gene titers were 25.08 to 39.98 Ct cycles, and ORF 1ab gene titers were 23.16 to 39.56 Ct cycles. Compared with the positive results of the initial screening, the Ct values of N gene and/or ORF 1ab gene positive were increased in 90 cases (93.75%). Among them, the patients with the longest duration of nucleic acid positive could still be positive for double targets (the Ct value of N gene was 38.60, and the Ct value of ORF 1ab gene was 38.11) at an interval of 178 days after the initial positive screening. CONCLUSIONS: Patients infected with SARS-CoV-2 can be sustained or repeatedly tested positive for nucleic acid for a long period of time, and most of them had Ct values < 35. But whether it is infectious needs to be comprehensively evaluated by combining epidemiology, variant type, samples with the alive virus, and clinical symptoms and signs.

Synthesis and characterization of Au-decorated graphene oxide nanocomposite for magneto-electrochemical detection of SARS-CoV-2 nucleocapsid gene.

Fast and effective diagnosis is the first step in monitoring the current coronavirus 2 (CoV-2) pandemic. Herein, we establish a simple and sensitive electrochemical assay using magnetic nanocomposite and DNA sandwich probes to rapidly quantify the CoV-2 nucleocapsid (N) gene down to the 0.37 fM level. This assay uses a pair of specific DNA probes. The capture probe is covalently conjugated to Au-decorated magnetic reduced graphene oxide (AMrGO) nanocomposite for efficiently capturing target RNA. In contrast, the detection probe is linked to peroxidase for signal amplification. The probes target the COV-2 gene, allowing for specific magnetic separation, enzymatic signal amplification, and subsequent generation of voltammetric current with a total assay time of 45 min. The developed biosensor has high selectivity and can discriminate non-specific gene sequences. Synthetic COV-2 N-gene can be detected efficiently in serum and saliva, while 1-bp mismatch gene yielded a low response. The performance of the genosensor was good in an extensive linear range of 5 aM-50 pM. For synthetic N-gene, we achieved the detection limit of 0.37, 0.33, and 0.19 fM in human saliva, urine, and serum. This simple, selective, and sensitive genosensor could have various genetics-based biosensing and diagnostic applications.

Transmission trends of the global COVID-19 pandemic with combined effects of adaptive behaviours and vaccination.

We developed a mechanism model which allows for simulating the novel coronavirus (COVID-19) transmission dynamics with the combined effects of human adaptive behaviours and vaccination, aiming at predicting the end time of COVID-19 infection in global scale. Based on the surveillance information (reported cases and vaccination data) between 22 January 2020 and 18 July 2022, we validated the model by Markov Chain Monte Carlo (MCMC) fitting method. We found that (1) if without adaptive behaviours, the epidemic could sweep the world in 2022 and 2023, causing 3.098 billion of human infections, which is 5.39 times of current number; (2) 645 million people could be avoided from infection due to vaccination; and (3) in current scenarios of protective behaviours and vaccination, infection cases would increase slowly, levelling off around 2023, and it would end completely in June 2025, causing 1.024 billion infections, with 12.5 million death. Our findings suggest that vaccination and the collective protection behaviour remain the key determinants against the global process of COVID-19 transmission.

At-Home COVID-19 Rapid Antigen Test Down to 0.03 pg mL-1 of Nucleocapsid Protein.

Rapid and ultra-sensitive detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is critical for early screening and management of COVID-19. Currently, the real-time reverse transcription polymerase chain reaction (rRT-PCR) is the primary laboratory method for diagnosing SARS-CoV-2. It is not suitable for at-home COVID-19 diagnostic test due to the long operating time, specific equipment, and professional procedures. Here an all-printed photonic crystal (PC) microarray with portable device for at-home COVID-19 rapid antigen test is reported. The fluorescence-enhanced effect of PC amplifies the fluorescence intensity of the labeled probe, achieving detection of nucleocapsid (N-) protein down to 0.03 pg mL-1 . A portable fluorescence intensity measurement instrument gives the result (negative or positive) by the color of the indicator within 5 s after inserting the reacted PC microarray test card. The N protein in inactivated virus samples (with cycle threshold values of 26.6-40.0) can be detected. The PC microarray provides a general and easy-to-use method for the timely monitoring and eventual control of the global coronavirus pandemic.

Characterization of cross-reactive monoclonal antibodies against SARS-CoV-1 and SARS-CoV-2: Implication for rational design and development of pan-sarbecovirus vaccines and neutralizing antibodies.

Emergence of various circulating SARS-CoV-2 variants of concern (VOCs) promotes the identification of pan-sarbecovirus vaccines and broadly neutralizing antibodies (bNAbs). Here, to characterize monoclonal antibodies cross-reactive against both SARS-CoV-1 and SARS-CoV-2 and to search the criterion for bNAbs against all emerging SARS-CoV-2, we isolated several SARS-CoV-1-cross-reactive monoclonal antibodies (mAbs) from a wildtype SARS-CoV-2 convalescent donor. These antibodies showed broad binding capacity and cross-neutralizing potency against various SARS-CoV-2 VOCs, including B.1.1.7 (Alpha), B.1.351 (Beta), P.1 (Gamma), and B.1.617.2 (Delta), but failed to efficiently neutralize Omicron variant and its sublineages. Structural analysis revealed how Omicron sublineages, but not other VOCs, efficiently evade an antibody family cross-reactive against SARS-CoV-1 through their escape mutations. Further evaluation of a series of SARS-CoV-1/2-cross-reactive bNAbs showed a negative correlation between the neutralizing activities against SARS-CoV-1 and SARS-CoV-2 Omicron variant. Together, these results suggest the necessity of using cross-neutralization against SARS-CoV-1 and SARS-CoV-2 Omicron as criteria for rational design and development of potent pan-sarbecovirus vaccines and bNAbs.

Exploring the influence of COVID-19 on the spread of hand, foot, and mouth disease with an automatic machine learning prediction model.

Hand, foot, and mouth disease (HFMD) is an important public health problem and has received concern worldwide. Moreover, the coronavirus disease 2019 (COVID-19) epidemic also increases the difficulty of understanding and predicting the prevalence of HFMD. The purpose is to prove the usability and applicability of the automatic machine learning (Auto-ML) algorithm in predicting the epidemic trend of HFMD and to explore the influence of COVID-19 on the spread of HFMD. The AutoML algorithm and the autoregressive integrated moving average (ARIMA) model were applied to construct and validate models, based on the monthly incidence numbers of HFMD and meteorological factors from May 2008 to December 2019 in Henan province, China. A total of four models were established, among which the Auto-ML model with meteorological factors had minimum RMSE and MAE in both the model constructing phase and forecasting phase (training set: RMSE = 1424.40 and MAE = 812.55; test set: RMSE = 2107.83, MAE = 1494.41), so this model has the best performance. The optimal model was used to further predict the incidence numbers of HFMD in 2020 and then compared with the reported cases. And, for analysis, 2020 was divided into two periods. The predicted incidence numbers followed the same trend as the reported cases of HFMD before the COVID-19 outbreak; while after the COVID-19 outbreak, the reported cases have been greatly reduced than expected, with an average of only about 103 cases per month, and the incidence peak has also been delayed, which has led to significant changes in the seasonality of HFMD. Overall, the AutoML algorithm is an applicable and ideal method to predict the epidemic trend of the HFMD. Furthermore, it was found that the countermeasures of COVID-19 have a certain influence on suppressing the spread of HFMD during the period of COVID-19. The findings are helpful to health administrative departments.

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.

Internet of Everything and Digital Twin enabled Service Platform for Cold Chain Logistics.

The proliferation of the e-commerce market has posed challenges to staff safety, product quality, and operational efficiency, especially for cold chain logistics (CCL). Recently, the logistics of vaccine supply under the worldwide COVID-19 pandemic rearouses public attention and calls for innovative solutions to tackle the challenges remaining in CCL. Accordingly, this study proposes a cyber-physical platform framework applying the Internet of Everything (IoE) and Digital Twin (DT) technologies to promote information integration and provide smart services for different stakeholders in the CCL. In the platform, reams of data are generated, gathered, and leveraged to interconnect and digitalize physical things, people, and processes in cyberspace, paving the way for digital servitization. Deep learning techniques are used for accident identification and indoor localization based on Bluetooth Low Energy (BLE) to actualize real-time staff safety supervision in the cold warehouse. Both algorithms are designed to take advantage of the IoE infrastructure to achieve online self-adapting in response to surrounding evolutions. Besides, with the help of mobile and desktop applications, paperless operation for shipment, remote temperature and humidity (T&H) monitoring, anomaly detection and warning, and customer interaction are enabled. Thus, information traceability and visibility are highly fortified in this way. Finally, a real-life case study is conducted in a pharmaceutical distribution center to demonstrate the feasibility and practicality of the proposed platform and methods. The dedicated hardware and software are developed and deployed on site. As a result, the effectiveness of staff safety management, operational informatization, product quality assurance, and stakeholder loyalty maintenance shows a noticeable improvement. The insights and lessons harvested in this study may spark new ideas for researchers and inspire practitioners to meet similar needs in the industry.

A data-driven interpretable ensemble framework based on tree models for forecasting the occurrence of COVID-19 in the USA.

This prevalence of coronavirus disease 2019 (COVID-19) has become one of the most serious public health crises. Tree-based machine learning methods, with the advantages of high efficiency, and strong interpretability, have been widely used in predicting diseases. A data-driven interpretable ensemble framework based on tree models was designed to forecast daily new cases of COVID-19 in the USA and to determine the important factors related to COVID-19. Based on a hyperparametric optimization technique, we developed three machine learning algorithms based on decision trees, including random forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), and three linear ensemble models were used to integrate these outcomes for better prediction accuracy. Finally, the SHapley Additive explanation (SHAP) value was used to obtain the feature importance ranking. Our outcomes demonstrated that, among the three basic machine learners, the prediction accuracy was the following in descending order: LightGBM, XGBoost, and RF. The optimized LAD ensemble was the most precise prediction model that reduced the prediction error of the best base learner (LightGBM) by approximately 3.111%, while vaccination, wearing masks, less mobility, and government interventions had positive effects on the control and prevention of COVID-19.

Optimizing variant-specific therapeutic SARS-CoV-2 decoys using deep-learning-guided molecular dynamics simulations.

Treatment of COVID-19 with a soluble version of ACE2 that binds to SARS-CoV-2 virions before they enter host cells is a promising approach, however it needs to be optimized and adapted to emerging viral variants. The computational workflow presented here consists of molecular dynamics simulations for spike RBD-hACE2 binding affinity assessments of multiple spike RBD/hACE2 variants and a novel convolutional neural network architecture working on pairs of voxelized force-fields for efficient search-space reduction. We identified hACE2-Fc K31W and multi-mutation variants as high-affinity candidates, which we validated in vitro with virus neutralization assays. We evaluated binding affinities of these ACE2 variants with the RBDs of Omicron BA.3, Omicron BA.4/BA.5, and Omicron BA.2.75 in silico. In addition, candidates produced in Nicotiana benthamiana, an expression organism for potential large-scale production, showed a 4.6-fold reduction in half-maximal inhibitory concentration (IC50) compared with the same variant produced in CHO cells and an almost six-fold IC50 reduction compared with wild-type hACE2-Fc.

Chinese expert consensus on intestinal microecology and management of digestive tract complications related to tumor treatment (version 2022).

The human gut microbiota represents a complex ecosystem that is composed of bacteria, fungi, viruses, and archaea. It affects many physiological functions including metabolism, inflammation, and the immune response. The gut microbiota also plays a role in preventing infection. Chemotherapy disrupts an organism's microbiome, increasing the risk of microbial invasive infection; therefore, restoring the gut microbiota composition is one potential strategy to reduce this risk. The gut microbiome can develop colonization resistance, in which pathogenic bacteria and other competing microorganisms are destroyed through attacks on bacterial cell walls by bacteriocins, antimicrobial peptides, and other proteins produced by symbiotic bacteria. There is also a direct way. For example, Escherichia coli colonized in the human body competes with pathogenic Escherichia coli 0157 for proline, which shows that symbiotic bacteria compete with pathogens for resources and niches, thus improving the host's ability to resist pathogenic bacteria. Increased attention has been given to the impact of microecological changes in the digestive tract on tumor treatment. After 2019, the global pandemic of novel coronavirus disease 2019 (COVID-19), the development of novel tumor-targeting drugs, immune checkpoint inhibitors, and the increased prevalence of antimicrobial resistance have posed serious challenges and threats to public health. Currently, it is becoming increasingly important to manage the adverse effects and complications after chemotherapy. Gastrointestinal reactions are a common clinical presentation in patients with solid and hematologic tumors after chemotherapy, which increases the treatment risks of patients and affects treatment efficacy and prognosis. Gastrointestinal symptoms after chemotherapy range from nausea, vomiting, and anorexia to severe oral and intestinal mucositis, abdominal pain, diarrhea, and constipation, which are often closely associated with the dose and toxicity of chemotherapeutic drugs. It is particularly important to profile the gastrointestinal microecological flora and monitor the impact of antibiotics in older patients, low immune function, neutropenia, and bone marrow suppression, especially in complex clinical situations involving special pathogenic microbial infections (such as clostridioides difficile, multidrug-resistant Escherichia coli, carbapenem-resistant bacteria, and norovirus).

3- to 24-month Follow-up on COVID-19 with Pulmonary Tuberculosis Survivors after Discharge: Results from a Prospective, Multicenter Study.

Objective: Coronavirus disease 2019 (COVID-19) and tuberculosis (TB) are major public health and social issues worldwide. The long-term follow-up of COVID-19 with pulmonary TB (PTB) survivors after discharge is unclear. This study aimed to comprehensively describe clinical outcomes, including sequela and recurrence at 3, 12, and 24 months after discharge, among COVID-19 with PTB survivors. Methods: From January 22, 2020 to May 6, 2022, with a follow-up by August 26, 2022, a prospective, multicenter follow-up study was conducted on COVID-19 with PTB survivors after discharge in 13 hospitals from four provinces in China. Clinical outcomes, including sequela, recurrence of COVID-19, and PTB survivors, were collected via telephone and face-to-face interviews at 3, 12, and 24 months after discharge. Results: Thirty-two COVID-19 with PTB survivors were included. The median age was 52 (45, 59) years, and 23 (71.9%) were men. Among them, nearly two-thirds (62.5%) of the survivors were moderate, three (9.4%) were severe, and more than half (59.4%) had at least one comorbidity (PTB excluded). The proportion of COVID-19 survivors with at least one sequela symptom decreased from 40.6% at 3 months to 15.8% at 24 months, with anxiety having a higher proportion over a follow-up. Cough and amnesia recovered at the 12-month follow-up, while anxiety, fatigue, and trouble sleeping remained after 24 months. Additionally, one (3.1%) case presented two recurrences of PTB and no re-positive COVID-19 during the follow-up period. Conclusion: The proportion of long symptoms in COVID-19 with PTB survivors decreased over time, while nearly one in six still experience persistent symptoms with a higher proportion of anxiety. The recurrence of PTB and the psychological support of COVID-19 with PTB after discharge require more attention.

Key Drivers of COVID-19 Vaccine Hesitancy: A Perspective of Collectivism.

Vaccination against the COVID-19 pandemic remains a major part of global immunization policy. The aim of this study was to explore young people's willingness to continue to receive vaccination against COVID-19 in a collectivist culture. In this study, an online questionnaire was used to measure willingness to continue vaccination, the tendency towards collectivism, the degree of disease anxiety, vaccine brand loyalty, and perceived infectability in 2022. The results showed that women were more willing to be vaccinated than men (70.1% vs. 29.9%). Young people who were willing to receive continuous vaccination had a relatively higher tendency towards collectivism (p < 0.001), a relatively higher degree of disease anxiety (p < 0.001), and lower vaccine brand loyalty (p = 0.034). The COVID-19 pandemic is still ongoing and, since young people are the most active in group activities, policy-makers should weigh the factors influencing vaccination among the young to create effective policy measures.

Prone positioning improves ventilation-perfusion matching assessed by electrical impedance tomography in patients with ARDS: a prospective physiological study.

BACKGROUND: The physiological effects of prone ventilation in ARDS patients have been discussed for a long time but have not been fully elucidated. Electrical impedance tomography (EIT) has emerged as a tool for bedside monitoring of pulmonary ventilation and perfusion, allowing the opportunity to obtain data. This study aimed to investigate the effect of prone positioning (PP) on ventilation-perfusion matching by contrast-enhanced EIT in patients with ARDS. DESIGN: Monocenter prospective physiologic study. SETTING: University medical ICU. PATIENTS: Ten mechanically ventilated ARDS patients who underwent PP. INTERVENTIONS: We performed EIT evaluation at the initiation of PP, 3 h after PP initiation and the end of PP during the first PP session. MEASUREMENTS AND MAIN RESULTS: The regional distribution of ventilation and perfusion was analyzed based on EIT images and compared to the clinical variables regarding respiratory and hemodynamic status. Prolonged prone ventilation improved oxygenation in the ARDS patients. Based on EIT measurements, the distribution of ventilation was homogenized and dorsal lung ventilation was significantly improved by PP administration, while the effect of PP on lung perfusion was relatively mild, with increased dorsal lung perfusion observed. The ventilation-perfusion matched region was found to increase and correlate with the increased PaO2/FiO2 by PP, which was attributed mainly to reduced shunt in the lung. CONCLUSIONS: Prolonged prone ventilation increased dorsal ventilation and perfusion, which resulted in improved ventilation-perfusion matching and oxygenation. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04725227. Registered on 25 January 2021.

Sleep changes in Taiwanese first graders before and after school closure during the COVID-19 pandemic.

PURPOSE: The objective of this study was (1) to examine sleep changes in first graders before and after school closure and (2) to examine the association between parental work rearrangement and children's sleep change during the coronavirus disease 2019 (COVID-19) pandemic. DESIGN AND METHODS: This was an observational study. The children's sleep habit questionnaire was completed by 103 parents of first-graders before and after school closure. Paired t-test and the general linear model were applied to data analysis. RESULTS: Children delayed their bedtime and rising time, but total sleep duration increased. Moreover, parents who rearranged their work during the pandemic perceived more child parasomnia symptoms (p = .029) and less delayed sleep-wake patterns in their children. PRACTICAL IMPLICATION: Sleep is an indicator that reflects children's behavioral changes in response to the COVID-19 pandemic. As routine changes, parents should be aware of child's parasomnia symptoms. Nursing interventions could aim at promoting sufficient external cues in the daytime during home confinement.

Characterizing Discourse about COVID-19 Vaccines: A Reddit Version of the Pandemic Story

It has been one year since the outbreak of the COVID-19 pandemic. The good news is that vaccines developed by several manufacturers are being actively distributed worldwide. However, as more and more vaccines become available to the public, various concerns related to vaccines become the primary barriers that may hinder the public from getting vaccinated. Considering the complexities of these concerns and their potential hazards, this study is aimed at offering a clear understanding about different population groups' underlying concerns when they talk about COVID-19 vaccines—particularly those active on Reddit. The goal is achieved by applying LDA and LIWC to characterize the pertaining discourse with insights generated through a combination of quantitative and qualitative comparisons. Findings include the following: (1) during the pandemic, the proportion of Reddit comments predominated by conspiracy theories outweighed that of any other topics;(2) each subreddit has its own user bases, so information posted in one subreddit may not reach that from other subreddits;and (3) since users' concerns vary across time and subreddits, communication strategies must be adjusted according to specific needs. The results of this study manifest challenges as well as opportunities in the process of designing effective communication and immunization programs.

Uncovering the Impact of Control Strategies on the Transmission Pattern of SARS-CoV-2 - Ruili City, Yunnan Province, China, February-March 2022.

Introduction: The implementation of public health and social measures (PHSMs) was an effective option for controlling coronavirus disease 2019 (COVID-19). However, evidence is needed to evaluate these PHSMs' effects on the recently emerged variant Omicron. Methods: This study investigated variant Omicron BA.2's outbreak in Ruili City, Yunnan Province, China. The disease transmission dynamics, spatiotemporal interactions, and transmission networks were analyzed to illustrate the effect of PHSM strategies on Omicron spread. Results: A total of 387 cases were related to the outbreak. The time-varying reproduction number was synchronized with PHSM strategies. Spatiotemporal clustering strength presented heterogeneity and hotspots. Restricted strategies suppressed temporal and spatial relative risk compared with routine and upgraded strategies. The transmission network presented a steeper degree distribution and a heavier tail under upgraded strategies. Phase transformation and distinctive transmission patterns were observed from strategy-stratified subnetworks. Conclusions: The tightened response strategy contained reproduction of the virus, suppressed spatiotemporal clustering, and reshaped the networks of COVID-19 Omicron variant transmission. As such, PHSMs against Omicron are likely to benefit future responses as well.

Management of Emerging Health Conditions to Improve Resilience and Mental Health.

Good health is when a person is in a complete, optimal physical, mental, and social condition [...].

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.