A two-stage Data Envelopment Analysis Approach Incorporating Global Bounded Adjustment Measure to evaluate the efficiency of medical waste recycling systems with undesirable inputs and outputs (preprint)

With the ever-increasing focus on sustainable development, recycling waste and renewable use of waste products has earned immense consideration from academics and policy-makers. The serious pollution, complex types, and strong infectivity of medical waste (MW) have brought serious challenges to management. Although several researchers have addressed the issue of the MW by optimizing MW management networks and systems, there is still a significant gap in systematically evaluating the efficiency of MW recycling systems. Therefore, this paper proposes a two-stage data envelopment analysis (DEA) approach that combines the virtual frontier and the global bounded adjustment measure (BAM-VF-G), considering both undesirable inputs and outputs. In the first stage, the BAM-G model is used to evaluate the efficiency of MW recycling systems, and the BAM-VF-G model is used to further rank super-efficient MW recycling systems. In the second stage, two types of efficiency decomposition models are proposed. The first type of models decomposes unified efficiency into production efficiency (PE) and environment efficiency (EE). Depending upon the systems structure, the second type of models decomposes unified efficiency into the efficiency of the MW collection and transport subsystem (MWCS) and the efficiency of the MW treatment subsystem (MWTS). The novel approach is used to measure the efficiency of the MW recycling systems in China's new first-tier cities (CNFCs), and we find that: (1) Foshan ranks the highest in efficiency, followed by Qingdao and Dongguan, with efficiency values of 0.3593, 0.1765, and 0.1530, respectively. (2) EE has always been lower than PE and is a critical factor influencing the overall efficiency of MW recycling systems in CNFCs. (3) The MWCS lacks resilience, with an efficiency 0.042 lower than that of the MWTC. Following the outbreak of COVID-19, the efficiency of the MWCS has been decreasing year by year, reaching only 0.762 in 2021, which is a decline of 0.111 compared to 2017.

Single cell sequencing reveals cellular landscape alterations in the airway mucosa of patients with pulmonary long COVID (preprint)

To elucidate the important cellular and molecular drivers of pulmonary long COVID, we generated a single-cell transcriptomic map of the airway mucosa using bronchial brushings from patients with long COVID who reported persistent pulmonary symptoms. Adults with and without long COVID were recruited from the general community in greater Vancouver, Canada. The cohort was divided into those with pulmonary long COVID (PLC), which was defined as persons with new or worsening respiratory symptoms following at least one year from their initial acute SARS-CoV-2 infection (N=9); and control subjects defined as SARS-CoV-2 infected persons whose acute respiratory symptoms had fully resolved or individuals who had not experienced acute COVID-19 (N=9). These participants underwent bronchoscopy from which a single cell suspension was created from bronchial brush samples and then sequenced. A total of 56,906 cells were recovered for the downstream analysis, with 34,840 cells belonging to the PLC group. A dimensionality reduction plot shows a unique cluster of neutrophils in the PLC group (p<.05). Ingenuity Pathway Analysis revealed that neutrophil degranulation pathway was enriched across epithelial cells. Differential gene expression analysis between the PLC and control groups demonstrated upregulation of mucin genes in secretory cell clusters. A single-cell transcriptomic landscape of the small airways shows that the PLC airways harbors a dominant neutrophil cluster and an upregulation in the neutrophil-associated activation signature with increased expression of MUC genes in the secretory cells. Together, they suggest that pulmonary symptoms of long COVID may be driven by chronic small airway inflammation.

Characteristics of viral shedding in people infected with SARS-CoV-2 during difference stages

Viral shedding of SARS-CoV-2 is a continuous dynamic process, which can be divided into latent stage, initial stage, peak stage and decreasing stage according to the characteristics of viral shedding. After being infected with SARS-CoV-2, the infected person generally stays in the latent period for 1-3 days, which is characterized by continuous negative nucleic acid test results and no infectiousness, and the risk of infection for close contacts is very low. At the initial stage of viral shedding is characterized by a rapid decline in the Ct value of nucleic acid tests in a short time, and clinical symptoms gradually appear. The infectiousness of the infected person gradually increases during this period, and the risk of infection for close contacts also gradually increases, but it is still in the early stage of infection, the possibility of viral shedding is low, and the risk of infection of secondary close contacts is low. The peak of viral shedding is characterized by low Ct value in nucleic acid test and obvious clinical symptoms;during this period, the infected person is the most infectious, and the risk of infection of the contact is the highest, so the scope of close contacts should be expanded appropriately. The decreasing period is characterized by the gradual increase of Ct value of nucleic acid test and the gradual disappearance of clinical symptoms;during this period, the infectiousness of the infected person gradually decreases to disappear. In an outbreak, an infected person in the decreasing phase is more likely to be an early infected person in the transmission chain. If infected individuals in the decreasing phase are found in an area without a SARS-CoV-2 epidemic, it suggests that the local outbreak epidemic has been spreading for some time and may be larger in scale. According to the characteristics of viral shedding, risk personnel can be determined more scientifically and accurately, so as to minimize the risk and reduce the waste of epidemic prevention resources.

Monitoring and responding to emerging infectious diseases in a university setting: A case study using COVID-19.

Emerging infection diseases (EIDs) are an increasing threat to global public health, especially when the disease is newly emerging. Institutions of higher education (IHEs) are particularly vulnerable to EIDs because student populations frequently share high-density residences and strongly mix with local and distant populations. In fall 2020, IHEs responded to a novel EID, COVID-19. Here, we describe Quinnipiac University's response to SARS-CoV-2 and evaluate its effectiveness through empirical data and model results. Using an agent-based model to approximate disease dynamics in the student body, the University established a policy of dedensification, universal masking, surveillance testing via a targeted sampling design, and app-based symptom monitoring. After an extended period of low incidence, the infection rate grew through October, likely due to growing incidence rates in the surrounding community. A super-spreader event at the end of October caused a spike in cases in November. Student violations of the University's policies contributed to this event, but lax adherence to state health laws in the community may have also contributed. The model results further suggest that the infection rate was sensitive to the rate of imported infections and was disproportionately impacted by non-residential students, a result supported by the observed data. Collectively, this suggests that campus-community interactions play a major role in campus disease dynamics. Further model results suggest that app-based symptom monitoring may have been an important regulator of the University's incidence, likely because it quarantined infectious students without necessitating test results. Targeted sampling had no substantial advantages over simple random sampling when the model incorporated contact tracing and app-based symptom monitoring but reduced the upper boundary on 90% prediction intervals for cumulative infections when either was removed. Thus, targeted sampling designs for surveillance testing may mitigate worst-case outcomes when other interventions are less effective. The results' implications for future EIDs are discussed.

Comparative analysis of epidemiological trends of COVID-19 in Shanghai and New York City

ObjectiveTo assess the effects of countermeasures against coronavirus disease 2019 (COVID-19) in Shanghai from March to May 2022 in comparison with epidemiological trend of COVID-19 in New York City. MethodsDaily confirmed cases, asymptomatic SARS-CoV-2 carriers, and daily deaths were obtained in the Shanghai Municipal Health Commission and the Center for Disease Control and Prevention (CDC) of the United States. Descriptive study was conducted by using these data. ResultsFrom March 1 to May 17, the number of daily asymptomatic SARS-CoV-2 infections in Shanghai was up to 58 times as large as that of daily confirmed cases;however, the number of daily confirmed cases in Shanghai was generally less than that in New York in the same time period. At the peak of the COVID-19 epidemic, the growth of daily attack rate in Shanghai was significantly lower than that in New York (P < 0.05). Moreover, the number of daily death was evidently less than that in New York. In addition, the vaccination rate in the elderly (aged 60 years) in Shanghai was evidently lower than that in New York (aged 65 years). ConclusionThe COVID-19 epidemics in Shanghai from March to May 2022 and in New York after December 2021 were both caused by the Omicron variant. Compared with the Delta variant, the Omicron variant has stronger replication ability and infectivity, resulting in challenges to the containment of the epidemic in metropolis such as Shanghai and New York City. The epidemic in New York City remained crucial due to absence of effective countermeasures, while that in Shanghai has been effectively contained with strict countermeasures. The prevention and control strategies may be adjusted along with the continual evolution of SARS-CoV-2 and increasing trend of imported COVID-19 cases.

Stability of SARS-CoV-2 on inanimate surfaces: A review.

The stability of SARS-CoV-2 for varying periods on a wide range of inanimate surfaces has raised concerns about surface transmission; however, there is still no evidence to confirm this route. In the present review, three variables affecting virus stability, namely temperature, relative humidity (RH), and initial virus titer, were considered from different experimental studies. The stability of SARS-CoV-2 on the surfaces of six different contact materials, namely plastic, metal, glass, protective equipment, paper, and fabric, and the factors affecting half-life period was systematically reviewed. The results showed that the half-life of SARS-CoV-2 on different contact materials was generally 2-10 h, up to 5 d, and as short as 30 min at 22 °C, whereas the half-life of SARS-CoV-2 on non-porous surfaces was generally 5-9 h d, up to 3 d, and as short as 4 min at 22 â„ƒ. The half-life on porous surfaces was generally 1-5 h, up to 2 d, and as short as 13 min at 22 °C. Therefore, the half-life period of SARS-CoV-2 on non-porous surfaces is longer than that on porous surfaces, and thehalf-life of the virus decreases with increasing temperature, whereas RH produces a stable negative inhibitory effect only in a specific humidity range. Various disinfection precautions can be implemented in daily life depending on the stability of SARS-CoV-2 on different surfaces to interrupt virus transmission, prevent COVID-19 infections, and avoid over-disinfection. Owing to the more stringent control of conditions in laboratory studies and the lack of evidence of transmission through surfaces in the real world, it is difficult to provide strong evidence for the efficiency of transmission of the contaminant from the surface to the human body. Therefore, we suggest that future research should focus on exploring the systematic study of the entire transmission process of the virus, which will provide a theoretical basis for optimizing global outbreak prevention and control measures.

Simultaneous detection and phylogenetic analysis of porcine epidemic diarrhea virus and porcine circovirus 4 in Henan province, China.

Porcine circovirus 4 (PCV4) is a recently discovered circovirus that was first reported in 2019 in several pigs in Hunan province of China and has also been identified in pigs infected with porcine epidemic diarrhea virus (PEDV). To further investigate the coinfection and genetic diversity of these two viruses, 65 clinical samples (including feces and intestinal tissues) were collected from diseased piglets on 19 large-scale pig farms in Henan province of China, and a duplex SYBR Green I-based quantitative real-time polymerase chain reaction (qPCR) assay was developed for detecting PEDV and PCV4 simultaneously. The results showed that the limit of detection was 55.2 copies/µL and 44.1 copies/µL for PEDV and PCV4, respectively. The detection rate for PEDV and PCV4 was 40% (26/65) and 38% (25/65), respectively, and the coinfection rate for the two viruses was 34% (22/65). Subsequently, the full-length spike (S) gene of eight PEDV strains and a portion of the genome containing the capsid (Cap) gene of three PCV4 strains were sequenced and analyzed. Phylogenetic analysis showed that all of the PEDV strains from the present study clustered in the G2a subgroup and were closely related to most of the PEDV reference strains from China from 2011 to 2021, but they differed genetically from a vaccine strain (CV777), a Korean strain (virulent DR1), and two Chinese strains (SD-M and LZC). It is noteworthy that two PEDV strains (HEXX-24 and HNXX-24XIA) were identified in one sample, and the HNXX-24XIA strain had a large deletion at amino acids 31-229 of the S protein. Moreover, a recombination event was observed in strain HEXX-24. Phylogenetic analysis based on the amino acid sequence of the PCV4 Cap protein revealed that PCV4 strains were divided into three genotypes: PCV4a1, PCV4a2, and PCV4b. Three strains in the present study belonged to PCV4a1, and they had a high degree of sequence similarity (>98% identity) to other PCV4 reference strains. This study not only provides technical support for field investigation of PEDV and PCV4 coinfection but also provides data for their prevention and control.

N-Butanol Extract of Glycyrrhizae Radix et Rhizoma Inhibits Dengue Virus through Targeting Envelope Protein.

BACKGROUND: At present, about half of the world's population is at risk of being infected with dengue virus (DENV). However, there are no specific drugs to prevent or treat DENV infection. Glycyrrhizae Radix et Rhizome, a well-known traditional Chinese medicine, performs multiple pharmacological activities, including exerting antiviral effects. The aim of this study was to investigate the anti-DENV effects of n-butanol extract from Glycyrrhizae Radix et Rhizome (GRE). METHODS: Compounds analysis of GRE was conducted via ultra-performance liquid chromatography/tandem mass spectrometry (UHPLC-MS/MS). The antiviral activities of GRE were determined by the CCK-8 assay, plaque assay, qRT-PCR, Western blotting, and the immunofluorescence assay. The DENV-infected suckling mice model was constructed to explore the antiviral effects of GRE in vivo. RESULTS: Four components in GRE were analyzed by UHPLC-MS/MS, including glycyrrhizic acid, glycyrrhetnic acid, liquiritigenin, and isoliquiritigenin. GRE inhibited the attachment process of the virus replication cycle and reduced the expression of the E protein in cell models. In the in vivo study, GRE significantly relieved clinical symptoms and prolong survival duration. GRE also significantly decreased viremia, reduced the viral load in multiple organs, and inhibited the release of pro-inflammatory cytokines in DENV-infected suckling mice. CONCLUSIONS: GRE exhibited significant inhibitory activities in the adsorption stage of the DENV-2 replication cycle by targeting the envelope protein. Thus, GRE might be a promising candidate for the treatment of DENV infection.

Different weathering conditions affect the release of microplastics by masks.

A generation of microplastics caused by improper disposal of disposable masks has become a non-negligible environmental concern. In order to investigate the degradation mechanisms of masks and the release of microplastics under different environmental conditions, the masks are placed in 4 common environments. After 30 days of weathering, the total amount and release kinetics of microplastics released from different layers of the mask were studied. The chemical and mechanical properties of the mask were also discussed. The results showed that the mask released 25141±3543 particles/mask into the soil, which is much more than the sea and river water. The release kinetics of microplastics fit the Elovich model better. All samples correspond to the release rate of microplastics from fast to slow. Experiments show that the middle layer of the mask is released more than the other layers, and the amount of release was highest in the soil. And the tensile capacity of the mask is negatively correlated with its ability to release microplastics in the following order, which are soil > seawater > river > air > new masks. In addition, during the weathering process, the C-C/C-H bond of the mask was broken.

Analysis of Compounding and Broadband Extinction Properties of Novel Bioaerosols

Artificially prepared microbial spores have excellent electromagnetic attenuation properties due to their special composition and structure. At present, studies on the optical properties of microbial spores have mainly focused on those with a single band or a single germplasm, which has limitations and cannot reveal the optical properties comprehensively. In this paper, 3 kinds of laboratory-prepared microbial spores were selected for compounding, and the spectral reflectivities of single-germplasm biospores and compound biospores were measured in the wavebands of 0.25–2.4 and 3–15 μm. The complex refractive indices (CRIs) were calculated in combination with the Kramers–Kronig (K-K) algorithm. Relying on the smoke box broadband test system, the transmittance of single-germplasm bioaerosols and compound bioaerosols from the ultraviolet (UV) band to the far-infrared (FIR) band was measured, and the mass extinction coefficients were calculated. The results indicate that the trend of the complex refractive indices of the compound spores is consistent with that of the single-germplasm spores with a larger particle size. For the single-germplasm bioaerosols, the lowest transmittance values were 2.21, 5.70 and 6.27% in the visible (VIS), near-infrared (NIR) and middle-infrared (FIR) bands, and the mass extinction coefficients reached 1.15, 0.87 and 0.84 m2/g, respectively. When AO and BB spores were compounded at 4:1, the extinction performance of the bioaerosols somewhat improved in all wavebands. These results can help to comprehensively analyze the optical properties of bioaerosols and provide ideas for the development of new extinction materials.

Machine Learning-Based Scoring System for Early Prognosis Evaluation of Patients with Coronavirus Disease 2019

Background The global spread of coronavirus disease 2019 (COVID-19) continues to threaten human health security, exerting considerable pressure on healthcare systems worldwide. While prognostic models for COVID-19 hospitalized or intensive care patients are currently available, prognostic models developed for large cohorts of thousands of individuals are still lacking. Methods Between February 4 and April 16, 2020, we enrolled 3,974 patients admitted with COVID-19 disease in the Wuhan Huo-Shen-Shan Hospital and the Maternal and Child Hospital, Hubei Province, China. (1) Screening of key prognostic factors: A univariate Cox regression analysis was performed on 2,649 patients in the training set, and factors affecting prognosis were initially screened. Subsequently, a random survival forest model was established through machine analysis to further screen for factors that are important for prognosis. Finally, multivariate Cox regression analysis was used to determine the synergy among various factors related to prognosis. (2) Establishment of a scoring system: The nomogram algorithm established a COVID-19 patient death risk assessment scoring system for the nine selected key prognostic factors, calculated the C index, drew calibration curves and drew training set patient survival curves. (3) Verification of the scoring system: The scoring system assessed 1,325 patients in the test set, splitting them into high- and low-risk groups, calculated the C-index, and drew calibration and survival curves. Results The cross-sectional study found that age, clinical classification, sex, pulmonary insufficiency, hypoproteinemia, and four other factors (underlying diseases: blood diseases, malignant tumor;complications: digestive tract bleeding, heart dysfunction) have important significance for the prognosis of the enrolled patients with COVID-19. Herein, we report the discovery of the effects of hypoproteinemia and hematological diseases on the prognosis of COVID-19. Meanwhile, the scoring system established here can effectively evaluate objective scores for the early prognoses of patients with COVID-19 and can divide them into high- and low-risk groups (using a scoring threshold of 117.77, a score below which is considered low risk). The efficacy of the system was better than that of clinical classification using the current COVID-19 guidelines (C indexes, 0.95 vs. 0.89). Conclusions Age, clinical typing, sex, pulmonary insufficiency, hypoproteinemia, and four other factors were important for COVID-19 survival. Compared with general statistical methods, this method can quickly and accurately screen out the relevant factors affecting prognosis, provide an order of importance, and establish a scoring system based on the nomogram model, which is of great clinical significance.

Stability of SARS-CoV-2 on Inanimate Surfaces: A Review

The stability of SARS-CoV-2 for varying periods on a wide range of inanimate surfaces has raised concerns about surface transmission;however, there is still no evidence to confirm this route. In the present review, three variables affecting virus stability, namely temperature, relative humidity (RH), and initial virus titer, were considered from different experimental studies. The stability of SARS-CoV-2 on the surfaces of six different contact materials, namely plastic, metal, glass, protective equipment, paper, and fabric, and the factors affecting half-life period was systematically reviewed. The results showed that the half-life of SARS-CoV-2 on different contact materials was generally 2–10 h, up to 5 d, and as short as 30 min at 22°C, whereas the half-life of SARS-CoV-2 on non-porous surfaces was generally 5–9 h d, up to 3 d, and as short as 4 min at 22℃. The half-life on porous surfaces was generally 1–5 h, up to 2 d, and as short as 13 min at 22°C. Therefore, the half-life period of SARS-CoV-2 on non-porous surfaces is longer than that on porous surfaces, and thehalf-life of the virus decreases with increasing temperature, whereas RH produces a stable negative inhibitory effect only in a specific humidity range. Various disinfection precautions can be implemented in daily life depending on the stability of SARS-CoV-2 on different surfaces to interrupt virus transmission, prevent COVID-19 infections, and avoid over-disinfection. Owing to the more stringent control of conditions in laboratory studies and the lack of evidence of transmission through surfaces in the real world, it is difficult to provide strong evidence for the efficiency of transmission of the contaminant from the surface to the human body. Therefore, we suggest that future research should focus on exploring the systematic study of the entire transmission process of the virus, which will provide a theoretical basis for optimizing global outbreak prevention and control measures.

Identifying the determinants of face mask disposal behavior and policy implications: An application of the extended theory of planned behavior.

A study in Nigeria examined the psychological factors affecting face mask disposal behavior (DB) during the COVID-19 pandemic. The Theory of Planned Behavior (TPB) was used, with awareness of consequences and institutional barriers added. 1183 respondents completed an online survey, and structural equation modeling was used to analyze the data. The original TPB model revealed that attitudes, perceived behavioral control, and subjective norms explained 65% of the variance in respondents' behavior. Behavioral intention and perceived behavioral control accounted for 59.3% of the variance in DB. The extended TPB model, which included awareness of consequences and perceived institutional barriers, improved the model's explanatory power by 12.8%. Both TPB models adequately predicted face mask (FM) disposal behavior, with implications for policymakers and waste management authorities to design interventions to promote proper FM disposal behavior.

Separating Daily 1 km PM2.5 Inorganic Chemical Composition in China since 2000 via Deep Learning Integrating Ground, Satellite, and Model Data.

Fine particulate matter (PM2.5) chemical composition has strong and diverse impacts on the planetary environment, climate, and health. These effects are still not well understood due to limited surface observations and uncertainties in chemical model simulations. We developed a four-dimensional spatiotemporal deep forest (4D-STDF) model to estimate daily PM2.5 chemical composition at a spatial resolution of 1 km in China since 2000 by integrating measurements of PM2.5 species from a high-density observation network, satellite PM2.5 retrievals, atmospheric reanalyses, and model simulations. Cross-validation results illustrate the reliability of sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), and chloride (Cl-) estimates, with high coefficients of determination (CV-R2) with ground-based observations of 0.74, 0.75, 0.71, and 0.66, and average root-mean-square errors (RMSE) of 6.0, 6.6, 4.3, and 2.3 µg/m3, respectively. The three components of secondary inorganic aerosols (SIAs) account for 21% (SO42-), 20% (NO3-), and 14% (NH4+) of the total PM2.5 mass in eastern China; we observed significant reductions in the mass of inorganic components by 40-43% between 2013 and 2020, slowing down since 2018. Comparatively, the ratio of SIA to PM2.5 increased by 7% across eastern China except in Beijing and nearby areas, accelerating in recent years. SO42- has been the dominant SIA component in eastern China, although it was surpassed by NO3- in some areas, e.g., Beijing-Tianjin-Hebei region since 2016. SIA, accounting for nearly half (∼46%) of the PM2.5 mass, drove the explosive formation of winter haze episodes in the North China Plain. A sharp decline in SIA concentrations and an increase in SIA-to-PM2.5 ratios during the COVID-19 lockdown were also revealed, reflecting the enhanced atmospheric oxidation capacity and formation of secondary particles.

The whole-of-society approach of mass COVID-19 vaccination in China: a qualitative study.

BACKGROUND: Many countries have an inefficient vaccination system, which hinders global exit from the COVID-19 pandemic. It is vital to summarize COVID-19 vaccination practices in countries with high vaccination coverage and provide implications for other countries. This study aimed to investigate China's COVID-19 vaccination system and to summarize its implementation experience from a health system perspective. METHODS: We conducted key informant interviews in five representative cities of China in late 2021. Guided by the health systems framework proposed by WHO, we developed our interview guidelines which included seven building blocks-leadership and governance, health workforce, vaccination service delivery, vaccination mobilization and communication, financing, access to vaccines, and information systems. Semi-structured interviews and COVID-19 vaccination policy documents were collected and coded using a thematic analysis approach. RESULTS: A total of 61 participants (nine vaccination programme directors of the local Center for Disease Control and Prevention, four government staff and 48 vaccination service workers) were interviewed. We found that China adopted a whole-of-society approach with adequate government engagement and linked health and non-health sectors to promote COVID-19 vaccination. Key measures included the collaboration of multiple systems and departments from a governance perspective, allocating sufficient health workers and resources, large-scale vaccination mobilization and communication, expansion of vaccine financing channels, localized production and digital information systems. With the vaccination system strengthening, the two-doses vaccination coverage reached 89.5% for the total population but relatively lower coverage for older adults as of July 2022. CONCLUSIONS: Our study highlights the importance of a government-led whole-of-society approach to promote mass vaccination. The low vaccination coverage among older adults should be paid the greatest attention to. The experiences and lessons from China may serve as a reference for other countries.

User review analysis of dating apps based on text mining.

With the continuous development of information technology, more and more people have become to use online dating apps, and the trend has been exacerbated by the COVID-19 pandemic in these years. However, there is a phenomenon that most of user reviews of mainstream dating apps are negative. To study this phenomenon, we have used topic model to mine negative reviews of mainstream dating apps, and constructed a two-stage machine learning model using data dimensionality reduction and text classification to classify user reviews of dating apps. The research results show that: firstly, the reasons for the current negative reviews of dating apps are mainly concentrated in the charging mechanism, fake accounts, subscription and advertising push mechanism and matching mechanism in the apps, proposed corresponding improvement suggestions are proposed by us; secondly, using principal component analysis to reduce the dimensionality of the text vector, and then using XGBoost model to learn the low-dimensional data after oversampling, a better classification accuracy of user reviews can be obtained. We hope These findings can help dating apps operators to improve services and achieve sustainable business operations of their apps.

SARS-CoV-2 Infection Causes Hyperglycemia in Cats.

Isolated reports of new-onset diabetes in patients with coronavirus disease 2019 (COVID-19) have led researchers to hypothesize that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects human exocrine and endocrine pancreatic cells ex vivo and in vivo. However, existing research lacks experimental evidence indicating that SARS-CoV-2 can infect pancreatic tissue. Here, we found that cats infected with a high dose of SARS-CoV-2 exhibited hyperglycemia. We also detected SARS-CoV-2 RNA in pancreatic tissues of these cats, and immunohistochemical staining revealed the presence of SARS-CoV-2 nucleocapsid protein (NP) in islet cells. SARS-CoV-2 NP and spike proteins were primarily detected in glucagon-positive cells, and most glucagon-positive cells expressed ACE2. Additionally, immune protection experiments conducted on cats showed that blood glucose levels of immunized cats did not increase postchallenge. Our data indicate cat pancreas as a SARS-CoV-2 target and suggest that the infection of glucagon-positive cells could contribute to the metabolic dysregulation observed in SARS-CoV-2-infected cats.

Peptide valence-induced breaks in plasmonic coupling† † Electronic supplementary information (ESI) available: Materials, synthesis and instrumentations, characterization studies, computational simulations, one-pot assays, table of peptide sequences and molecular weights, table of operation windows of R2 and K2 peptides, structures of ligands and R2 peptide, HPLC and ESI-MS data, CCC, operation windows, LoD, and specificity measurements, initial structures for molecular dynamics simulation, and the colorimetric map of the sensing kit. See DOI: https://doi.org/10.1039/d2sc05837e

Electrostatic interactions are a key driving force that mediates colloidal assembly. The Schulze-Hardy rule states that nanoparticles have a higher tendency to coagulate in the presence of counterions with high charge valence. However, it is unclear how the Schulze–Hardy rule works when the simple electrolytes are replaced with more sophisticated charge carriers. Here, we designed cationic peptides of varying valencies and demonstrate that their charge screening behaviors on anionic gold nanoparticles (AuNPs) follow the six-power relationship in the Schulze–Hardy rule. This finding further inspires a simple yet effective strategy for measuring SARS-CoV-2 main protease (Mpro) via naked eyes. This work provides a unique avenue for fundamental NP disassembly based on the Schulze–Hardy rule and can help design versatile substrates for colorimetric sensing of other proteases. Electrostatic interactions are a key driving force that mediates colloidal assembly.