Informational support, risk perception, anti-pandemic motivation and behavior: a longitudinal study in China.

Mobilizing the public to take anti-pandemic behavior (APB) by strengthening informational support has been recognized as an effective strategy to combat the COVID-19 pandemic. However, it remains unclear how health-related informational support from different channels affects individual factors and, thus, the adoption of different types of APB as the pandemic situation changes. To resolve this issue, we build a multiple mediation model to investigate the associations among informational support from three different channels, two individual internal factors, and two kinds of APB. A three-stage longitudinal study administered to Chinese citizens from February to October 2020 revealed that informational support from media played the most critical role in facilitating individuals' adoption of compliance APB, while informational support from family was the most significant predictor of the adoption of participation APB. Meanwhile, these effects were mediated by risk perception and anti-pandemic motivation, and weakened to varying degrees as the pandemic situation eased. It is recommended that authorities adjust the focus of publicity strategies in light of the changing situation, and make efforts to heighten the public's risk perception and anti-pandemic motivation. This study contributes to deepening the understanding of the dynamic efficacy of informational support from different channels on individuals' adoption of two heterogeneous APBs, and thus to the formulation of more scientific, and situation-based publicity strategies during a public health crisis.

Immune-related adverse events induced by programmed death protein-1 inhibitors from the perspective of lymphoma immunotherapy.

Lymphoma, which is highly malignant, stems from lymph nodes and lymphoid tissue. Lymphoma cells express programmed death-ligand 1/2 (PD-L1/PD-L2), which binds with programmed cell death 1 protein (PD-1) to establish inhibitory signaling that impedes the normal function of T cells and allows tumor cells to escape immune system surveillance. Recently, immune checkpoint inhibitor immunotherapies such as PD-1 inhibitors (nivolumab and pembrolizumab) have been introduced into the lymphoma treatment algorithm and have shown remarkable clinical efficacy and greatly improve prognosis in lymphoma patients. Accordingly, the number of lymphoma patients who are seeking treatment with PD-1 inhibitors is growing annually, which results in an increasing number of patients developing immune-related adverse events (irAEs). The occurrence of irAEs inevitably affects the benefits provided by immunotherapy, particularly when PD-1 inhibitors are applied. However, the mechanisms and characteristics of irAEs induced by PD-1 inhibitors in lymphoma need further investigation. This review article summarizes the latest research advances in irAEs during treatment of lymphoma with PD-1 inhibitors. A comprehensive understanding of irAEs incurred in immunotherapy can help to achieve better efficacy with PD-1 inhibitors in lymphoma.

Applications and prospects of cryo-EM in drug discovery.

Drug discovery is a crucial part of human healthcare and has dramatically benefited human lifespan and life quality in recent centuries, however, it is usually time- and effort-consuming. Structural biology has been demonstrated as a powerful tool to accelerate drug development. Among different techniques, cryo-electron microscopy (cryo-EM) is emerging as the mainstream of structure determination of biomacromolecules in the past decade and has received increasing attention from the pharmaceutical industry. Although cryo-EM still has limitations in resolution, speed and throughput, a growing number of innovative drugs are being developed with the help of cryo-EM. Here, we aim to provide an overview of how cryo-EM techniques are applied to facilitate drug discovery. The development and typical workflow of cryo-EM technique will be briefly introduced, followed by its specific applications in structure-based drug design, fragment-based drug discovery, proteolysis targeting chimeras, antibody drug development and drug repurposing. Besides cryo-EM, drug discovery innovation usually involves other state-of-the-art techniques such as artificial intelligence (AI), which is increasingly active in diverse areas. The combination of cryo-EM and AI provides an opportunity to minimize limitations of cryo-EM such as automation, throughput and interpretation of medium-resolution maps, and tends to be the new direction of future development of cryo-EM. The rapid development of cryo-EM will make it as an indispensable part of modern drug discovery.

PSAC-PDB: Analysis and classification of protein structures.

This paper presents a novel framework, called PSAC-PDB, for analyzing and classifying protein structures from the Protein Data Bank (PDB). PSAC-PDB first finds, analyze and identifies protein structures in PDB that are similar to a protein structure of interest using a protein structure comparison tool. Second, the amino acids (AA) sequences of identified protein structures (obtained from PDB), their aligned amino acids (AAA) and aligned secondary structure elements (ASSE) (obtained by structural alignment), and frequent AA (FAA) patterns (discovered by sequential pattern mining), are used for the reliable detection/classification of protein structures. Eleven classifiers are used and their performance is compared using six evaluation metrics. Results show that three classifiers perform well on overall, and that FAA patterns can be used to efficiently classify protein structures in place of providing the whole AA sequences, AAA or ASSE. Furthermore, better classification results are obtained using AAA of protein structures rather than AA sequences. PSAC-PDB also performed better than state-of-the-art approaches for SARS-CoV-2 genome sequences classification.

Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms

The ability to estimate current GDP growth before official data are released, known as "nowcasting”, is crucial for the Chinese government to effectively implement economic policy and manage economic uncertainties;however, there is limited research on nowcasting China's GDP in a data-rich environment. We evaluate the performance of various machine learning algorithms, dynamic factor models, static factor models, and MIDAS regressions in nowcasting the Chinese annualised real GDP growth rate in pseudo out-of-sample exercise, using 89 macroeconomic variables from years 1995 to 2020. We find that some machine learning methods outperform the benchmark dynamic factor model. The machine learning method that deserves more attention is ridge regression, which dominates all other models not only in terms of nowcast error but also in effective recognition of the impacts of the Global Financial Crisis and Covid-19 shocks. Policy-wise, our study guides practitioners in selecting appropriate nowcasting models for China's macroeconomy.

VV116 versus Nirmatrelvir-Ritonavir for Oral Treatment of Covid-19.

BACKGROUND: Nirmatrelvir-ritonavir has been authorized for emergency use by many countries for the treatment of coronavirus disease 2019 (Covid-19). However, the supply falls short of the global demand, which creates a need for more options. VV116 is an oral antiviral agent with potent activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: We conducted a phase 3, noninferiority, observer-blinded, randomized trial during the outbreak caused by the B.1.1.529 (omicron) variant of SARS-CoV-2. Symptomatic adults with mild-to-moderate Covid-19 with a high risk of progression were assigned to receive a 5-day course of either VV116 or nirmatrelvir-ritonavir. The primary end point was the time to sustained clinical recovery through day 28. Sustained clinical recovery was defined as the alleviation of all Covid-19-related target symptoms to a total score of 0 or 1 for the sum of each symptom (on a scale from 0 to 3, with higher scores indicating greater severity; total scores on the 11-item scale range from 0 to 33) for 2 consecutive days. A lower boundary of the two-sided 95% confidence interval for the hazard ratio of more than 0.8 was considered to indicate noninferiority (with a hazard ratio of >1 indicating a shorter time to sustained clinical recovery with VV116 than with nirmatrelvir-ritonavir). RESULTS: A total of 822 participants underwent randomization, and 771 received VV116 (384 participants) or nirmatrelvir-ritonavir (387 participants). The noninferiority of VV116 to nirmatrelvir-ritonavir with respect to the time to sustained clinical recovery was established in the primary analysis (hazard ratio, 1.17; 95% confidence interval [CI], 1.01 to 1.35) and was maintained in the final analysis (median, 4 days with VV116 and 5 days with nirmatrelvir-ritonavir; hazard ratio, 1.17; 95% CI, 1.02 to 1.36). In the final analysis, the time to sustained symptom resolution (score of 0 for each of the 11 Covid-19-related target symptoms for 2 consecutive days) and to a first negative SARS-CoV-2 test did not differ substantially between the two groups. No participants in either group had died or had had progression to severe Covid-19 by day 28. The incidence of adverse events was lower in the VV116 group than in the nirmatrelvir-ritonavir group (67.4% vs. 77.3%). CONCLUSIONS: Among adults with mild-to-moderate Covid-19 who were at risk for progression, VV116 was noninferior to nirmatrelvir-ritonavir with respect to the time to sustained clinical recovery, with fewer safety concerns. (Funded by Vigonvita Life Sciences and others; ClinicalTrials.gov number, NCT05341609; Chinese Clinical Trial Registry number, ChiCTR2200057856.).

Surveillance of virus in ward environment of patients with SARS-CoV-2 omicron infection in hotel-style Fangcang shelter hospital after discharge

OBJECTIVE: To monitor the virus residues in the ward environment of the patients infected with the new coronavirus Omicron BA.2 after discharge from the hotel-renovated Fangcang shelter hospital, and to provide basis and guidance for the clinical prevention and control and disinfection work. METHODS: Thirty Omicron BA.2-infected patients admitted to the Dapengshan hotel Fangcang shelter hospital in Cixi city of Ningbo from Apr. 5 to 27, 2022 were selected as the research subjects. The general features of 30 patients with Omicron variant infection on admission were collected, and the samples of the ward environment such as door handle, bedside table, pillow, wooden floor, toilet, wall, and power switch were taken after discharge, and nucleic acid detection and analysis were conducted. RESULTS: The median age of the 30 Omicron BA.2-infected patients was 36.00 years, there were 40%(12/30) cases having fever, the average hospitalization time was(13.33+or-2.10) days, and there were 93.33%(28/30) cases receiving two and three doses of vaccination. The mean value of the cycle threshold of nucleic acid detection of the N gene was 23.71, and the average Ct of ORF1 ab gene was 24.82. From 1 d before discharge to 6 d after discharge, the nucleic acid positive detection rate of the bedside table in the ward was 80.00%-21.44%, and the positive detected rate of the wooden floor was 83.33%-42.86%, and the positive detection rate of the door handle was 15.03%-12.50%, and the positive detection rate of the pillow was 46.70%-14.33%.and the positive detection rate of the toilet is 26.76%-14.33%, and the positive detection rate of the power switch is 27.56%-14.33%, whereas the positive detection rate of the wall is 0. CONCLUSION: The positive detection rate of Omicron BA.2 in the hotel Fangcang hospital ward was the highest with wooden floor and bedside table, followed by pillow, power switch, toilet, door handle and wall, which had high application value and reference significance for the prevention and control of nosocomial infection and environmental disinfection in the hotel Fangcang shelter hospital.

Resilience of supply-chain systems under perturbations: A network approach.

Supply-chain systems (SCSs) are an indispensable part of our daily infrastructures. Note that a small perturbation in a SCS can be amplified, eliciting cascading failures. It is of significant value to ensure a high resilience of SCSs. However, due to the complexity of SCSs, it is quite challenging to study their resilience under conditions of perturbations. In view of this, this paper presents a complex network perspective toward the resilience of SCSs. To achieve this goal, a complex SCS is modeled as a multilayer supply-chain network (SCN) with physical organizations being modeled as nodes and interactions among them as edges. A modeled SCN contains three types of nodes, i.e., suppliers, manufacturers, and retailers. An algorithm is proposed to construct a multilayer SCN. For each layer of a multilayer SCN, two kinds of networks, i.e., networks with Poisson degree distributions and networks with power-law degree distributions, are considered. For a given multilayer SCN, a ripple-effect network model is proposed to analyze its resilience under perturbations. Regarding the perturbations, two scenarios, i.e., random node failures and target node failures, are adopted in this research. In order to validate the effectiveness of the proposed network perspective, simulations on computer-generated SCNs are carried out. Interestingly, it is found that the resilience of SCNs under both random and target perturbations presents a discontinuous phase-change phenomenon, which indicates that SCNs are quite fragile under perturbations. It is further noticed that SCNs with power-law degree distributions are relatively more robust than SCNs with Poisson degree distributions. Although SCNs are found to be fragile, it has been discovered that denser interactions between different system organizations can enhance the network's resilience.

Microbiological characteristics of different tongue coatings in adults.

BACKGROUND: Tongue coating is an important health indicator in traditional Chinese medicine (TCM). The tongue coating microbiome can distinguish disease patients from healthy controls. To study the relationship between different types of tongue coatings and health, we analyzed the species composition of different types of tongue coatings and the co-occurrence relationships between microorganisms in Chinese adults. From June 2019 to October 2020, 158 adults from Hangzhou and Shaoxing City, Zhejiang Province, were enrolled. We classified the TCM tongue coatings into four different types: thin white tongue fur (TWF), thin yellow tongue fur (TYF), white greasy tongue fur (WGF), and yellow greasy tongue fur (YGF). Tongue coating specimens were collected and used for 16S rRNA gene sequencing using the Illumina MiSeq system. Wilcoxon rank-sum and permutational multivariate analysis of variance tests were used to analyze the data. The microbial networks in the four types of tongue coatings were inferred independently using sparse inverse covariance estimation for ecological association inference. RESULTS: The microbial composition was similar among the different tongue coatings; however, the abundance of microorganisms differed. TWF had a higher abundance of Fusobacterium periodonticum and Neisseria mucosa, the highest α-diversity, and a highly connected community (average degree = 3.59, average closeness centrality = 0.33). TYF had the lowest α-diversity, but the most species in the co-occurrence network diagram (number of nodes = 88). The platelet-to-lymphocyte ratio (PLR) was associated with tongue coating (P = 0.035), and the YGF and TYF groups had higher PLR values. In the co-occurrence network, Aggregatibacter segnis was the "driver species" of the TWF and TYF groups and correlated with C-reactive protein (P < 0.05). Streptococcus anginosus was the "driver species" in the YGF and TWF groups and was positively correlated with body mass index and weight (P < 0.05). CONCLUSION: Different tongue coatings have similar microbial compositions but different abundances of certain bacteria. The co-occurrence of microorganisms in the different tongue coatings also varies. The significance of different tongue coatings in TCM theory is consistent with the characteristics and roles of the corresponding tongue-coating microbes. This further supports considering tongue coating as a risk factor for disease.

Bibliometrics and visual analysis of international food safety research

Since the 1990s, food safety has caused widespread concern from all walks of life. According to the theory of bibliometrics and scientific knowledge map and knowledge mining method, this paper quantitatively analyzed and visualized 3024 food safety papers and 31032 references in the core collection of Web of Science (WoS) to explore their research status, topic evolution and development trend. The number of academic papers in the field of international food safety increased by 11 times during the past 31 years;the knowledge structure of this field consists of foodborne pathogenic bacteria, food safety cognition, public health and food safety management;its research topics showed an evolution from consumer behavior and food-borne diseases to food safety risk management, food safety governance, food traceability system, food testing, to food global value chain, heavy metal excess, pesticide residues, food safety culture, to blockchain technology, Corona virus disease 2019 (COVID-19) epidemic, food fraud;food safety knowledge and willingness to pay premium, food contamination, aquatic seafood safety, vegetable safety, blockchain, COVID-19 represent the frontier trends, so as to provide references for academic research and government supervision in this field.

Applications of Markov Decision Process Model and Deep Learning in Quantitative Portfolio Management during the COVID-19 Pandemic

Whether for institutional investors or individual investors, there is an urgent need to explore autonomous models that can adapt to the non-stationary, low-signal-to-noise markets. This research aims to explore the two unique challenges in quantitative portfolio management: (1) the difficulty of representation and (2) the complexity of environments. In this research, we suggest a Markov decision process model-based deep reinforcement learning model including deep learning methods to perform strategy optimization, called SwanTrader. To achieve better decisions of the portfolio-management process from two different perspectives, i.e., the temporal patterns analysis and robustness information capture based on market observations, we suggest an optimal deep learning network in our model that incorporates a stacked sparse denoising autoencoder (SSDAE) and a long–short-term-memory-based autoencoder (LSTM-AE). The findings in times of COVID-19 show that the suggested model using two deep learning models gives better results with an alluring performance profile in comparison with four standard machine learning models and two state-of-the-art reinforcement learning models in terms of Sharpe ratio, Calmar ratio, and beta and alpha values. Furthermore, we analyzed which deep learning models and reward functions were most effective in optimizing the agent's management decisions. The results of our suggested model for investors can assist in reducing the risk of investment loss as well as help them to make sound decisions.

Association between sleep disturbance and mental health of healthcare workers: A systematic review and meta-analysis.

Objectives: Sleep disturbance and mental health are challenges for healthcare workers (HCWs). Especially during the COVID-19 pandemic, they experienced more severe sleep and mental health problems. However, the association between sleep disturbance and the mental health of HCWs is still controversial. This study aimed to systematically review the relationship by conducting a systematic review and meta-analysis. Method: Two researchers retrieved the literature from Web of Science, PubMed, EMBASE, CINAHL, Psyclnfo, and Cochrane Library from the establishment of the databases until November 20, 2021. We used the New Castle-Ottawa Scale (NOS) and Agency for Healthcare Research and Quality (AHRQ) to evaluate the risk of bias in prospective research and cross-sectional research, respectively. The major exposure was HCWs' sleep disturbance, and the major outcome was mental health. The correlation coefficients (r), regression coefficients (ß) and odds ratios (OR) of the included studies were integrated. Result: Fifty-nine studies were included for qualitative analysis, of which 30 studies could be combined and entered into quantitative analysis. There were 23 studies during the COVID-19 pandemic among the 59 included studies. The results of the meta-analysis showed that the correlation coefficient between sleep disturbance and mental health was 0.43 (95% CI: 0.39-0.47). HCWs with sleep disturbance had a 3.74 (95% CI: 2.76-5.07) times higher risk of mental health problems than those without sleep disturbance. The correlation coefficient during the COVID-19 epidemic was 0.45 (95% CI: 0.37-0.53), while it was 0.40 (95% CI: 0.36-0.44) during the non-epidemic period. Subgroup analysis compared the OR results in epidemic and non-epidemic periods of COVID-19, which were 4.48 (95% CI: 2.75-5.07) and 3.74 (95% CI: 2.74-7.32), respectively. Conclusion: Sleep disturbance and mental health problems were positively correlated among HCWs. Particularly in the COVID-19 pandemic, more attention should be given to this issue.

An AIEgen/graphene oxide nanocomposite (AIEgen@GO)‐based two‐stage “turn‐on” nucleic acid biosensor for rapid detection of SARS‐CoV‐2 viral sequence

The ongoing outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV‐2) pandemic has posed significant challenges in early viral diagnosis. Hence, it is urgently desirable to develop a rapid, inexpensive, and sensitive method to aid point‐of‐care SARS‐CoV‐2 detection. In this work, we report a highly sequence‐specific biosensor based on nanocomposites with aggregation‐induced emission luminogens (AIEgen)‐labeled oligonucleotide probes on graphene oxide nanosheets (AIEgen@GO) for one step‐detection of SARS‐CoV‐2‐specific nucleic acid sequences (Orf1ab or N genes). A dual “turn‐on” mechanism based on AIEgen@GO was established for viral nucleic acids detection. Here, the first‐stage fluorescence recovery was due to dissociation of the AIEgen from GO surface in the presence of target viral nucleic acid, and the second‐stage enhancement of AIE‐based fluorescent signal was due to the formation of a nucleic acid duplex to restrict the intramolecular rotation of the AIEgen. Furthermore, the feasibility of our platform for diagnostic application was demonstrated by detecting SARS‐CoV‐2 virus plasmids containing both Orf1ab and N genes with rapid detection around 1 h and good sensitivity at pM level without amplification. Our platform shows great promise in assisting the initial rapid detection of the SARS‐CoV‐2 nucleic acid sequence before utilizing quantitative reverse transcription‐polymerase chain reaction for second confirmation. An AIEgen‐graphene oxide (GO) nanocomposite‐based assay is designed for rapid detection of SARS‐CoV‐2 nucleic acids. The sensing mechanism is based on two‐stage fluorescence signal recovery due to fluorescence resonance energy transfer (FRET) effect by detaching AIEgen from GO surface and restricted intramolecular rotation (RIR) effect by formation of nucleic acid duplexes.

Distribution and Drug Resistance of Bacterial Pathogens Associated with Lower Respiratory Tract Infection in Children and the Effect of COVID-19 on the Distribution of Pathogens.

By studying the distribution and drug resistance of bacterial pathogens associated with lower respiratory tract infection (LRTI) in children in Chengdu and the effect of the COVID-19 on the distribution of pathogens and by analyzing the epidemic trend and drug resistance changes of the main pathogens of LRTI, this research is supposed to provide a useful basis for the prevention of LRTI in children and the rational use of drugs in clinical practice. Hospitalized children clinically diagnosed with LRTI in Chengdu Women and Children's Central Hospital from 2011 to 2020 were selected as the study subjects. The pathogens of LRTI in children and the distribution of pathogens in different ages, genders, seasons, years, and departments and before and after the pandemic situation of COVID-19 were counted. The drug resistance distribution of the top six pathogens with the highest infection rate in the past three years and the trend of drug resistance in the past decade were analyzed. A total of 26,469 pathogens were isolated. Among them, 6240 strains (23.6%) were Gram-positive bacteria, 20152 strains (76.1%) were Gram-negative bacteria, and 73 strains (0.3%) were fungi. Klebsiella pneumoniae, Escherichia coli, Enterobacter cloacae, and Staphylococcus aureus were highly isolated in the group of infants aged 0-1 (P < 0.01), Moraxella catarrhalis and Streptococcus pneumoniae were highly isolated in children aged 1-6 (P < 0.01), and Haemophilus influenzae was highly isolated in children over 1 (P < 0.01). The isolation rates of Enterobacteriaceae, Acinetobacter baumannii, Pseudomonas aeruginosa, Stenotrophomonas maltophilia, Staphylococcus aureus, and Candida albicans in the lower respiratory tract of 0-1 year-old male infants were higher than those of female infants (p < 0.05). Haemophilus influenzae was highly isolated in spring and summer, and Moraxella catarrhalis was highly isolated in autumn and winter, while the infection of Streptococcus pneumoniae was mainly concentrated in winter. This difference was statistically significant (P < 0.01). Affected by the COVID-19 pandemic, the isolation rates of Haemophilus influenzae and Streptococcus pneumoniae were significantly lower than those before the pandemic, and the isolation rate of Moraxella catarrhalis was significantly higher. The difference was statistically significant (P < 0.01). The proportion of isolated negative bacteria in NICU and PICU was higher than that in positive bacteria, and the infection rates of Klebsiella pneumoniae, Escherichia coli, Enterobacter cloacae, and Acinetobacter baumannii were higher than those in other departments. The differences were statistically significant (P < 0.01). The results of drug sensitivity test showed that the drug resistance of Haemophilus influenzae and Moraxella catarrhalis was mainly concentrated in Ampicillin, First- and Second-generation cephalosporins, and Cotrimoxazole, with stable sensitivity to Third-generation cephalosporins, while the drug resistance of Streptococcus pneumoniae was concentrated in Macrolides, Sulfonamides, and Tetracyclines, with stable sensitivity to Penicillin. Staphylococcus aureus is highly resistant to penicillins and macrolides and susceptible to vancomycin. Enterobacteriaceae resistance is concentrated in cephalosporins, with a low rate of carbapenem resistance. From 2018 to 2020, 1557 strains of Staphylococcus aureus were isolated, of which 416 strains were MRSA, accounting for 27% of the isolates; 1064 strains of Escherichia coli were isolated, of which 423 strains were ESBL and 23 strains were CRE, accounting for 40% and 2% of the isolates, respectively; and 1400 strains of Klebsiella pneumoniae were isolated, of which 385 strains were ESBL and 402 strains were CRE, accounting for 28% and 29% of the isolates, respectively. Since 2011, the resistance of Escherichia coli and Klebsiella pneumoniae to Third-generation cephalosporins has increased, peaking in 2017, and has decreased after 2018, years after which carbapenem resistance has increased significantly, corresponding to an increase in the detection rate of Carbapenem-resistant Enterobacteriaceae CRE. Findings from this study revealed that there are significant differences in community-associated infectious pathogens before and after the COVID-19 pandemic, and there are significant age differences, seasonal epidemic trends, and high departmental correlation of pathogens related to lower respiratory tract disease infection in children. There was a significant gender difference in the isolation rate of pathogens associated with LRTI in infants under one year. Vaccination, implementation of isolation measures and social distance, strengthening of personal protective measures, aseptic operation of invasive medical treatment, hand hygiene, and environmental disinfection are beneficial to reducing community-associated pathogen infection, opportunistic pathogen infection, and an increase in resistant bacteria. The strengthening of bacterial culture of lower respiratory tract samples by pediatricians is conducive to the diagnosis of respiratory tract infections caused by different pathogens, contributing to the selection of effective drugs for treatment according to drug susceptibility results, which is important for the rational use of antibiotics and curbing bacterial resistance.

Supermeres are functional extracellular nanoparticles replete with disease biomarkers and therapeutic targets.

Extracellular vesicles and exomere nanoparticles are under intense investigation as sources of clinically relevant cargo. Here we report the discovery of a distinct extracellular nanoparticle, termed supermere. Supermeres are morphologically distinct from exomeres and display a markedly greater uptake in vivo compared with small extracellular vesicles and exomeres. The protein and RNA composition of supermeres differs from small extracellular vesicles and exomeres. Supermeres are highly enriched with cargo involved in multiple cancers (glycolytic enzymes, TGFBI, miR-1246, MET, GPC1 and AGO2), Alzheimer's disease (APP) and cardiovascular disease (ACE2, ACE and PCSK9). The majority of extracellular RNA is associated with supermeres rather than small extracellular vesicles and exomeres. Cancer-derived supermeres increase lactate secretion, transfer cetuximab resistance and decrease hepatic lipids and glycogen in vivo. This study identifies a distinct functional nanoparticle replete with potential circulating biomarkers and therapeutic targets for a host of human diseases.

SARS-CoV-2 detection using quantum dot fluorescence immunochromatography combined with isothermal amplification and CRISPR/Cas13a.

The development of reliable, sensitive, and fast devices for the diagnosis of COVID-19 is of great importance in the pandemic of the new coronavirus. Here, we proposed a new principle of analysis based on a combination of reverse transcription and isothermal amplification of a fragment of the gene encoding the S protein of the SARS-CoV-2 and the CRISPR/Cas13a reaction for cleavage of the specific probe. As a result, the destroyed probe cannot be detected on an immunochromatographic strip using quantum fluorescent dots. Besides, the results can be obtained by an available and inexpensive portable device. By detecting SARS-CoV-2 negative (n = 25) and positive (n = 62) clinical samples including throat swabs, sputum and anal swabs, the assay showed good sensitivity and specificity of the method and could be completed within 1 h without complicated operation and expensive equipment. These superiorities showed its potential for fast point-of-care screening of SARS-CoV-2 during the outbreak, especially in remote and underdeveloped areas with limited equipment and resources.

Magnetic-Responsive Surface-Enhanced Raman Scattering Platform with Tunable Hot Spot for Ultrasensitive Virus Nucleic Acid Detection.

Surface-enhanced Raman scattering (SERS)-based biosensors are promising tools for virus nucleic acid detection. However, it remains challenging for SERS-based biosensors using a sandwiching strategy to detect long-chain nucleic acids such as nucleocapsid (N) gene of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) because the extension of the coupling distance (CD) between the two tethered metallic nanostructures weakens electric field and SERS signals. Herein, we report a magnetic-responsive substrate consisting of heteoronanostructures that controls the CD for ultrasensitive and highly selective detection of the N gene of SARS-CoV-2. Significantly, our findings show that this platform reversibly shortens the CD and enhances SERS signals with a 10-fold increase in the detection limit from 1 fM to 100 aM, compared to those without magnetic modulation. The optical simulation that emulates the CD shortening process confirms the CD-dependent electric field strength and further supports the experimental results. Our study provides new insights into designing a stimuli-responsive SERS-based platform with tunable hot spots for long-chain nucleic acid detection.

Differentiation between acute macular neuroretinopathy and paracentral acute middle maculopathy in elderly persons: two case reports.

BACKGROUND: We report one case of rare acute macular neuroretinopathy (AMN) in an elderly patient with hypertension and one case of common paracentral acute middle maculopathy (PAMM) in a patient with diabetes mellitus to illustrate the difference between the two diseases. CASE PRESENTATION: This report describes two cases, one involving AMN and the other PAMM. The first patient was a 70-year-old man complaining of blurred vision for 3 days. He was examined with fundus photography, optical coherence tomography angiography (OCTA) and optical coherence tomography (OCT); a diagnosis of AMN was established. The second patient was a 50-year-old woman who complained of decreased vision during the past month. She had had diabetes mellitus for 6 years. From the ophthalmic imaging examination, the patient was diagnosed with PAMM and non-proliferative diabetic retinopathy (NPDR). Both patients were treated with drugs for improving microcirculation and neurotrophic drugs; however, there was no significant improvement in visual acuity. CONCLUSIONS: AMN is more common in young patients and is rarely observed in elderly patients with systemic diseases. The OCTA examination has an auxiliary diagnostic value for deep retinal capillary network ischaemia. Meanwhile, OCT examination has important imaging value in differentiating AMN from PAMM and can help avoid missed diagnoses.

Segmentation of infected region in CT images of COVID-19 patients based on QC-HC U-net.

Since the outbreak of COVID-19 in 2019, the rapid spread of the epidemic has brought huge challenges to medical institutions. If the pathological region in the COVID-19 CT image can be automatically segmented, it will help doctors quickly determine the patient's infection, thereby speeding up the diagnosis process. To be able to automatically segment the infected area, we proposed a new network structure and named QC-HC U-Net. First, we combine residual connection and dense connection to form a new connection method and apply it to the encoder and the decoder. Second, we choose to add Hypercolumns in the decoder section. Compared with the benchmark 3D U-Net, the improved network can effectively avoid vanishing gradient while extracting more features. To improve the situation of insufficient data, resampling and data enhancement methods are selected in this paper to expand the datasets. We used 63 cases of MSD lung tumor data for training and testing, continuously verified to ensure the training effect of this model, and then selected 20 cases of public COVID-19 data for training and testing. Experimental results showed that in the segmentation of COVID-19, the specificity and sensitivity were 85.3% and 83.6%, respectively, and in the segmentation of MSD lung tumors, the specificity and sensitivity were 81.45% and 80.93%, respectively, without any fitting.

The Experience of the Fight Against COVID-19 in Clinical Laboratory Departments from Chengdu, China.

OBJECTIVE: In order to fight against coronavirus disease 2019 (COVID-19) better and to share our experience as a reference for clinical laboratory departments. METHODS: This was a retrospective study conducted in the clinical laboratory department of Chengdu Women's and Children's Central Hospital in Chengdu, China, from April 2020 to January 2021. The number of nucleic acid and antibody testing specimens of suspected COVID-19 cases was analyzed. The key points of suspected-case sample processing and detection in the clinical laboratory department were summarized. The laboratory was directly involved in the sample processing and testing of suspected cases, the release of reports, and the transfer of specimens to the fever clinic. RESULTS: The number of COVID-19 nucleic acid test specimens in our laboratory ranged from 102 to 2170 per day, and the number of antibody test specimens ranged from 24 to 391 per day. There were four main considerations in the treatment and detection of suspected-case specimens in the clinical laboratory: biosafety management in clinical laboratory departments, measures to ensure the health of the staff, the eight time points for processing suspected-case samples (turn-around time), and key points for the detection of suspected case specimens. CONCLUSION: The laboratory developed a protective process for COVID-19 antibody and nucleic acid detection during the pandemic. At present, the detection of COVID-19 antibodies and nucleic acids in the clinical laboratory department is orderly, and there have been no cases of laboratory infection.