A Mission-Oriented Flight Path and Charging Mechanism for Internet of Drones.

In addition to traditional battery exchange services and stationary charging stations, researchers have proposed wireless charging technology, such as decentralized laser charging or drone-to-drone charging in flight, to provide power to drones with insufficient battery electricity. However, the charging methods presented in the literature will inevitably cause drones to wait in line for charging during peak hours and disrupt their scheduled trips when the number of drones grows rapidly in the future. To the best of our knowledge, there have been no integrated solutions for drone flight path and charging planning to alleviate charging congestion, taking into account the different mission characteristics of drones and the charging cost considerations of drone operators. Accordingly, this paper provides adaptive charging options to help drone operators to solve the above-mentioned problems. Drones on ordinary missions can use conventional battery swap services, wired charging stations, or electromagnetic wireless charging stations to recharge their batteries as usual, whereas drones on time-critical missions can choose drone-to-drone wireless charging or decentralized laser charging deployed along the fight paths to charge the batteries of drones in flight. Notably, since fixed-wing drones have larger wing areas to install solar panels, they can also use solar energy to charge during flight if the weather is clear. The simulation results exhibited that the proposed work reduced the power load of the power grid during peak hours, met the charging needs of each individual drone during flight, and cut down the charging costs of drone operators. As a result, an all-win situation for drone operators, drone customers, and power grid operators was achieved.

The impact of COVID-19 on online medical education: a knowledge graph analysis based on co-term analysis.

BACKGROUND: This study aims to identify the characteristics and future directions of online medical education in the context of the novel coronavirus outbreak new through visual analytics using CiteSpace and VOSviewer bibliometric methods. METHOD: From Web of Science, we searched for articles published between 2020 and 2022 using the terms online education, medical education and COVID-19, ended up with 2555 eligible papers, and the articles published between 2010 and 2019 using the terms online education, medical education and COVID-19, and we ended up with 4313 eligible papers. RESULTS: Before the COVID-19 outbreak, Medical students and care were the most frequent keywords and the most cited author was BRENT THOMA with 18 times. The United States is the country with the greatest involvement and research impact in the field of online medical education. The most cited journal is ACAD MED with 1326 citations. After the COVID-19 outbreak, a surge in the number of research results in related fields, and ANXIETY and four secondary keywords were identified. In addition, the concentration of authors of these publications in the USA and China is a strong indication that local epidemics and communication technologies have influenced the development of online medical education research. Regarding the centrality of research institutions, the most influential co-author network is Harvard Medical School in the United States; and regarding the centrality of references, the most representative journal to which it belongs is VACCINE. CONCLUSION: This study found that hey information such as keywords, major institutions and authors, and countries differ in the papers before and after the COVID-19 outbreak. The novel coronavirus outbreak had a significant impact on the online education aspect. For non-medical and medical students, the pandemic has led to home isolation, making it difficult to offer face-to-face classes such as laboratory operations. Students have lost urgency and control over the specifics of face-to-face instruction, which has reduced the quality of teaching. Therefore, we should improve our education model according to the actual situation to ensure the quality of teaching while taking into account the physical and psychological health of students.

Progress and bioapplication of CRISPR-based one-step, quantitative and multiplexed infectious disease diagnostics.

In Vitro Diagnosis (IVD) technology is able to accurately detect pathogens or biomarkers at an initial stage of disease, which works as an important toolbox for disease diagnosis. Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) system, as an emerging IVD method, plays a crucial role in the field of infectious disease detection due to its superior sensitivity and specificity. Recently, an increasing number of scientists have been devoted to improving the performance of CRISPR-based detection and on-site point-of-care testing (POCT) from extraction-free detection, amplification-free, modified Cas/crRNA complexes, quantitative assays, one-pot detection, and multiplexed platform. In this review, we describe the potential roles of these novel approaches and platforms in one-pot methods, quantitative molecular diagnostics as well as multiplexed detection. This review will not only help guide the full use of the CRISPR-Cas tools for quantification, multiplexed detection, POCT and as next-generation diagnostic biosensing platforms but also inspire new ideas, technological advances, and engineering strategies to address real-world challenges like the ongoing COVID-19 pandemic.

Risk of adverse outcomes in inflammatory bowel disease patients infected with SARS-CoV-2: a systematic review and meta-analysis.

BACKGROUND: Between people with and without inflammatory bowel disease (IBD), there was no statistically significant difference in the probability of contracting the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). However, the risk of adverse outcomes in IBD patients after virus infection remains unclear. METHODS: Eligible studies conducted from January 1, 2020 to March 17, 2022 were obtained by searching PubMed, Embase, and Web of Science. Information was collected in tables from the included studies. Random-effects and fixed-effects models were used as measures for the pooled estimates. All data were estimated by R version 4.1.3. RESULTS: Twenty-four studies were included. The risk ratio (RR) of adverse outcomes in COVID-19 patients with IBD increased by 32% (RR 1.32; 95% CI 1.06-1.66) relative to COVID-19 patients without IBD. The RR of mortality was higher in COVID-19 patients with IBD from Europe (RR 1.72; 95% CI 1.11-2.67) than in those that were not from Europe (RR 1.00; 95% CI 0.79-1.26; χ2 = 4.67; P = 0.03). Patients with ulcerative colitis were at higher risk of adverse outcomes after SARS-CoV-2 infection than patients with Crohn's disease patients (RR1.38; 95% CI 1.27-1.50). The IBD drugs treatment was associated with the risk of adverse outcomes, the pooled odds ratio (OR) of mesalazine (1.79; 95% CI 1.59-2.02), immunomodulators (1.30; 95% CI 1.10-1.53), and anti-TNF (0.47; 95% CI 0.41-0.53) were assessed. CONCLUSION: COVID-19 patients with IBD had an increased risk of adverse outcomes than those without IBD, whereas anti-TNF treatment might reduce the risk.

Effect on implant drills and postoperative reactions for pre-extraction interradicular implant bed preparation during the COVID-19 pandemic and beyond.

The aim of the present study was to observe the abrasion of implant drills and postoperative reactions for the preparation of the interradicular immediate implant bed during the COVID-19 pandemic and beyond. Thirty-two implant drills were included in four groups: blank, improved surgery, traditional surgery, and control. In the improved surgery group, a dental handpiece with a surgical bur was used to decoronate the first molar and create a hole in the middle of the retained root complex, followed by the pilot drilling protocol through the hole. The remaining root complex was separated using a surgical bur and then extracted. Subsequently, the implant bed was prepared. Implant drills were used in the traditional surgery group to complete the decoronation, hole creation, and implant-drilling processes. The tooth remained intact until the implant bed was prepared. The surface roughness of the pilot drill was observed and measured. Surgery time, postoperative reactions (swelling, pain, and trismus), and fear of coronavirus disease 2019 scale (FCV-19S) were measured and recorded, respectively. Statistical analysis revealed significant difference with surface roughness among blank group (0.41 ± 0.05 µm), improved surgery group (0.37 ± 0.06 µm), traditional surgery group (0.16 ± 0.06 µm), and control group (0.26 ± 0.04 µm) (P < .001). Significant differences were revealed with surgery time between improved surgery group (5.63 ± 1.77 min) and traditional surgery group (33.63 ± 2.13 min) (P < .001). Swelling, pain, and trismus (improved group: r ≥ 0.864, P ≤ .006; traditional group: r ≥ 0.741, P ≤ .035) were positively correlated with the FCV-19S. This study proved that a new pilot drill could only be used once in traditional surgery but could be used regularly in improved surgery. Improved surgery was more effective, efficient, and economical than the traditional surgery. The higher FCV-19S, the more severe swelling, pain, and trismus.

Highly educated patients have lower dental compliance during the COVID-19 pandemic: an observational study.

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) is rapidly changed medical habits, and dental clinics have been forced to adapt. This study explored the pandemic-induced changes in patient utilization of dental services to assist practitioners in responding efficiently to similar public crises as references in the future. METHODS: We retrospectively analyzed the correlation between patient profiles and dental visits attendance within 2 months before and during the outbreak. RESULTS: A total of 332 patients, 210 women and 122 men (total number of visits: 1068) were enrolled in this study. A significantly lower attendance rate was noted during the COVID-19 period (70.3%) than prior to the pandemic (83.4%). The rate of return visits for patients with a high education level during the COVID-19 period was significantly reduced from 96.5 to 93.1%. In addition, the number of days between two visits significantly increased during the pandemic. CONCLUSIONS: Our results indicate that, during the pandemic period, the attendance rates of return dental appointments decreased, and the rate of missed appointments for patients with a high educational levels was higher than that of patients with a low educational level. CLINICAL RELEVANCE: Preventive management of these patients who are easy to miss dental appointments may enable more effective use of medical resources.

Impact of COVID-19 Pandemic on Trauma CT Imaging.

Purpose: The goal of this study was to understand the impact of COVID-19 pandemic and associated lockdown measures on the volume, rate, and type of trauma presenting to the emergency department (ED) by using trauma-initiated CT studies to capture patient data. Materials and Methods: We performed a retrospective observational study comparing patients undergoing CT scans for trauma during the 1st and 2nd lockdown periods compared to corresponding prepandemic months. During two lockdown periods, public places such as restaurants, libraries, parks, and shops across the province were shut down. Government-led messaging advised that people should stay at home and practice social distancing. The rate of trauma-initiated CT scans and the proportion of different types of traumas were compared between time periods. Results: There was no significant difference in overall trauma-initiated CT scans between the prepandemic and pandemic levels. Motor vehicle collision (MVC) cases decreased from 18.2% to 15.6% during the first lockdown period (p = 0.049) and also reduced from 29.1% to 25.2% during the second lockdown period (p = 0.013). Trauma from falls increased from 19.1% to 27.5% (p = 0.036) during the first lockdown, despite no significant change during the 2nd lockdown. Furthermore, the percentage of stab injuries increased from 25.0% to 38.9% while blunt trauma decreased from 68.5% to 54.3% during two lockdowns (p = 0.015). Conclusion: The total number of trauma-initiated CT scans did not significantly decrease during the lockdown periods. Stabbings and falls increased during lockdown periods while MVCs and blunt trauma decreased.

A Randomized, Single-Blind, Group Sequential, Active-Controlled Study to Evaluate the Clinical Efficacy and Safety of α-Lipoic Acid for Critically Ill Patients With Coronavirus Disease 2019 (COVID-19).

OBJECT: To evaluate the clinical efficacy and safety of α-Lipoic acid (ALA) for critically ill patients with coronavirus disease 2019 (COVID-19). METHODS: A randomized, single-blind, group sequential, active-controlled trial was performed at JinYinTan Hospital, Wuhan, China. Between February 2020 and March 2020, 17 patients with critically ill COVID-19 were enrolled in our study. Eligible patients were randomly assigned in a 1:1 ratio to receive either ALA (1200 mg/d, intravenous infusion) once daily plus standard care or standard care plus equal volume saline infusion (placebo) for 7 days. All patients were monitored within the 7 days therapy and followed up to day 30 after therapy. The primary outcome of this study was the Sequential Organ Failure Estimate (SOFA) score, and the secondary outcome was the all-cause mortality within 30 days. RESULT: Nine patients were randomized to placebo group and 8 patients were randomized to ALA group. SOFA score was similar at baseline, increased from 4.3 to 6.0 in the placebo group and increased from 3.8 to 4.0 in the ALA group (P = 0.36) after 7 days. The 30-day all-cause mortality tended to be lower in the ALA group (3/8, 37.5%) compared to that in the placebo group (7/9, 77.8%, P = 0.09). CONCLUSION: In our study, ALA use is associated with lower SOFA score increase and lower 30-day all-cause mortality as compared with the placebo group. Although the mortality rate was two-folds higher in placebo group than in ALA group, only borderline statistical difference was evidenced due to the limited patient number. Future studies with larger patient cohort are warranted to validate the role of ALA in critically ill patients with COVID-19. CLINICAL TRIAL REGISTRATION: http://www.chictr.org.cn/showproj.aspx?proj=49534.

Aptamer/antibody sandwich method for digital detection of SARS-CoV2 nucleocapsid protein.

Nucleocapsid protein (N protein) is the most abundant protein in SARS-CoV2 and is highly conserved, and there are no homologous proteins in the human body, making it an ideal biomarker for the early diagnosis of SARS-CoV2. However, early detection of clinical specimens for SARS-CoV2 remains a challenge due to false-negative results with viral RNA and host antibodies based testing. In this manuscript, a microfluidic chip with femtoliter-sized wells was fabricated for the sensitive digital detection of N protein. Briefly, ß-galactosidase (ß-Gal)-linked antibody/N protein/aptamer immunocomplexes were formed on magnetic beads (MBs). Afterwards, the MBs and ß-Gal substrate fluorescein-di-ß-d-galactopyranoside (FDG) were injected into the chip together. Each well of the chip would only hold one MB as confined by the diameter of the wells. The MBs in the wells were sealed by fluorocarbon oil, which confines the fluorescent (FL) product generated from the reaction between ß-Gal and FDG in the individual femtoliter-sized well and creates a locally high concentration of the FL product. The FL images of the wells were acquired using a conventional inverted FL microscope. The number of FL wells with MBs (FL wells number) and the number of wells with MBs (MBs wells number) were counted, respectively. The percentage of FL wells was calculated by dividing (FL wells number) by (MBs wells number). The higher the percentage of FL wells, the higher the N protein concentration. The detection limit of this digital method for N protein was 33.28 pg/mL, which was 300 times lower than traditional double-antibody sandwich based enzyme-linked immunosorbent assay (ELISA).

Polymeric and lipid nanoparticles for delivery of self-amplifying RNA vaccines.

Self-amplifying RNA (saRNA) is a next-generation vaccine platform, but like all nucleic acids, requires a delivery vehicle to promote cellular uptake and protect the saRNA from degradation. To date, delivery platforms for saRNA have included lipid nanoparticles (LNP), polyplexes and cationic nanoemulsions; of these LNP are the most clinically advanced with the recent FDA approval of COVID-19 based-modified mRNA vaccines. While the effect of RNA on vaccine immunogenicity is well studied, the role of biomaterials in saRNA vaccine effectiveness is under investigated. Here, we tested saRNA formulated with either pABOL, a bioreducible polymer, or LNP, and characterized the protein expression and vaccine immunogenicity of both platforms. We observed that pABOL-formulated saRNA resulted in a higher magnitude of protein expression, but that the LNP formulations were overall more immunogenic. Furthermore, we observed that both the helper phospholipid and route of administration (intramuscular versus intranasal) of LNP impacted the vaccine immunogenicity of two model antigens (influenza hemagglutinin and SARS-CoV-2 spike protein). We observed that LNP administered intramuscularly, but not pABOL or LNP administered intranasally, resulted in increased acute interleukin-6 expression after vaccination. Overall, these results indicate that delivery systems and routes of administration may fulfill different delivery niches within the field of saRNA genetic medicines.

Deep supervised learning using self-adaptive auxiliary loss for COVID-19 diagnosis from imbalanced CT images.

The outbreak and rapid spread of coronavirus disease 2019 (COVID-19) has had a huge impact on the lives and safety of people around the world. Chest CT is considered an effective tool for the diagnosis and follow-up of COVID-19. For faster examination, automatic COVID-19 diagnostic techniques using deep learning on CT images have received increasing attention. However, the number and category of existing datasets for COVID-19 diagnosis that can be used for training are limited, and the number of initial COVID-19 samples is much smaller than the normal's, which leads to the problem of class imbalance. It makes the classification algorithms difficult to learn the discriminative boundaries since the data of some classes are rich while others are scarce. Therefore, training robust deep neural networks with imbalanced data is a fundamental challenging but important task in the diagnosis of COVID-19. In this paper, we create a challenging clinical dataset (named COVID19-Diag) with category diversity and propose a novel imbalanced data classification method using deep supervised learning with a self-adaptive auxiliary loss (DSN-SAAL) for COVID-19 diagnosis. The loss function considers both the effects of data overlap between CT slices and possible noisy labels in clinical datasets on a multi-scale, deep supervised network framework by integrating the effective number of samples and a weighting regularization item. The learning process jointly and automatically optimizes all parameters over the deep supervised network, making our model generally applicable to a wide range of datasets. Extensive experiments are conducted on COVID19-Diag and three public COVID-19 diagnosis datasets. The results show that our DSN-SAAL outperforms the state-of-the-art methods and is effective for the diagnosis of COVID-19 in varying degrees of data imbalance.

Heterologous vaccination regimens with self-amplifying RNA and adenoviral COVID vaccines induce robust immune responses in mice.

Several vaccines have demonstrated efficacy against SARS-CoV-2 mediated disease, yet there is limited data on the immune response induced by heterologous vaccination regimens using alternate vaccine modalities. Here, we present a detailed description of the immune response, in mice, following vaccination with a self-amplifying RNA (saRNA) vaccine and an adenoviral vectored vaccine (ChAdOx1 nCoV-19/AZD1222) against SARS-CoV-2. We demonstrate that antibody responses are higher in two-dose heterologous vaccination regimens than single-dose regimens. Neutralising titres after heterologous prime-boost were at least comparable or higher than the titres measured after homologous prime boost vaccination with viral vectors. Importantly, the cellular immune response after a heterologous regimen is dominated by cytotoxic T cells and Th1+ CD4 T cells, which is superior to the response induced in homologous vaccination regimens in mice. These results underpin the need for clinical trials to investigate the immunogenicity of heterologous regimens with alternate vaccine technologies.

Innate Inhibiting Proteins Enhance Expression and Immunogenicity of Self-Amplifying RNA.

Self-amplifying RNA (saRNA) is a cutting-edge platform for both nucleic acid vaccines and therapeutics. saRNA is self-adjuvanting, as it activates types I and III interferon (IFN), which enhances the immunogenicity of RNA vaccines but can also lead to inhibition of translation. In this study, we screened a library of saRNA constructs with cis-encoded innate inhibiting proteins (IIPs) and determined the effect on protein expression and immunogenicity. We observed that the PIV-5 V and Middle East respiratory syndrome coronavirus (MERS-CoV) ORF4a proteins enhance protein expression 100- to 500-fold in vitro in IFN-competent HeLa and MRC5 cells. We found that the MERS-CoV ORF4a protein partially abates dose nonlinearity in vivo, and that ruxolitinib, a potent Janus kinase (JAK)/signal transducer and activator of transcription (STAT) inhibitor, but not the IIPs, enhances protein expression of saRNA in vivo. Both the PIV-5 V and MERS-CoV ORF4a proteins were found to enhance the percentage of resident cells in human skin explants expressing saRNA and completely rescued dose nonlinearity of saRNA. Finally, we observed that the MERS-CoV ORF4a increased the rabies virus (RABV)-specific immunoglobulin G (IgG) titer and neutralization half-maximal inhibitory concentration (IC50) by ∼10-fold in rabbits, but not in mice or rats. These experiments provide a proof of concept that IIPs can be directly encoded into saRNA vectors and effectively abate the nonlinear dose dependency and enhance immunogenicity.

Self-amplifying RNA SARS-CoV-2 lipid nanoparticle vaccine candidate induces high neutralizing antibody titers in mice.

The spread of the SARS-CoV-2 into a global pandemic within a few months of onset motivates the development of a rapidly scalable vaccine. Here, we present a self-amplifying RNA encoding the SARS-CoV-2 spike protein encapsulated within a lipid nanoparticle (LNP) as a vaccine. We observe remarkably high and dose-dependent SARS-CoV-2 specific antibody titers in mouse sera, as well as robust neutralization of both a pseudo-virus and wild-type virus. Upon further characterization we find that the neutralization is proportional to the quantity of specific IgG and of higher magnitude than recovered COVID-19 patients. saRNA LNP immunizations induce a Th1-biased response in mice, and there is no antibody-dependent enhancement (ADE) observed. Finally, we observe high cellular responses, as characterized by IFN-γ production, upon re-stimulation with SARS-CoV-2 peptides. These data provide insight into the vaccine design and evaluation of immunogenicity to enable rapid translation to the clinic.

[Logistic regression analysis of death risk factors of patients with severe and critical coronavirus disease 2019 and their predictive value].

OBJECTIVE: To analyze the risk factors of death in patients with severe and critical coronavirus disease 2019 (COVID-19) and their predictive value. METHODS: Using the clinical and epidemiological database of Yangtze River Shipping General Hospital in Wuhan, the clinical and epidemiological data of 105 patients with severe and critical COVID-19 from January to March in 2020 were collected. Multivariate unconditional Logistic regression method was used to analyze the death risk factors of patients during hospitalization. The receiver operating characteristic (ROC) curve was drawn according to the multivariate analysis results to construct a death prediction model; the prediction value of the model was analyzed. RESULTS: The 105 patients with severe and critical COVID-19 were enrolled with 66 males (62.9%) and 39 females (37.1%). The age was (58.2±14.4) years old. Forty-two patients died in hospital and 63 survived. Among the dead patients, 69.0% (29/42) were male, and 78.6% (33/42) were over 60 years old. Compared with survival patients, the non-survival patients were older (years old: 59.2±12.5 vs. 51.2±11.4), and had more comorbidities, including coronary heart disease, hypertension, myocardial damage and thrombocytopenia (coronary heart disease: 33.3% vs. 11.1%, hypertension: 28.6% vs. 9.5%, myocardial damage: 73.8% vs. 11.1%, thrombocytopenia: 61.9% vs. 14.3%), and received more mechanical ventilation (92.9% vs. 44.4%), with significant differences (all P < 0.01). The variables of gender, age, basic diseases, mechanical ventilation and complications were included in the unconditional Logistic regression analysis, which showed that gender [odds ratio (OR) = 2.852, 95% confidence interval (95%CI) was 0.122-66.694], age (OR = 3.257, 95%CI was 0.466-18.584), coronary heart disease (OR = 7.337, 95%CI was 0.227-87.021), hypertension (OR = 5.517, 95%CI was 0.258-65.024) and concurrent myocardial damage (OR = 7.322, 95%CI was 0.278-95.020) and thrombocytopenia (OR = 3.968, 95%CI was 0.325-35.549) were independent risk factors for death in patients with severe and critical COVID-19 during hospitalization. According to the risk factors, the death prediction model was constructed and ROC curve was analyzed, which showed that the area under ROC curve (AUC) of death prediction model for predicting the mortality of patients with severe and critical COVID-19 during hospitalization was 0.804, the sensitivity was 83.8%, and the specificity was 82.3%. CONCLUSIONS: Various risk factors are associated with the death of severe or critical COVID-19 patients, such as gender, age, basic diseases and complications. The death prediction model is constructed by gender, age, basic diseases with coronary heart disease and hypertension, concurrent myocardial damage and thrombocytopenia, which has certain predictive value for the death of patients with severe or critical COVID-19.