WiFi mobility models for COVID-19 enable less burdensome and more localized interventions for university campuses

Infectious diseases, like COVID-19, pose serious challenges to university campuses, which typically adopt closure as a non-pharmaceutical intervention to control spread and ensure a gradual return to normalcy. Intervention policies, such as remote instruction (RI) where large classes are offered online, reduce potential contact but also have broad side-effects on campus by hampering the local economy, students learning outcomes, and community wellbeing. In this paper, we demonstrate that university policymakers can mitigate these tradeoffs by leveraging anonymized data from their WiFi infrastructure to learn community mobility --- a methodology we refer to as WiFi mobility models (WO_SCPLOWIC_SCPLOWMO_SCPLOWOBC_SCPLOW). This approach enables policymakers to explore more granular policies like localized closures (LC). WO_SCPLOWIC_SCPLOWMO_SCPLOWOBC_SCPLOW can construct contact networks that capture behavior in various spaces, highlighting new potential transmission pathways and temporal variation in contact behavior. Additionally, WO_SCPLOWIC_SCPLOWMO_SCPLOWOBC_SCPLOW enables us to design LC policies that close super-spreader locations on campus. By simulating disease spread with contact networks from WO_SCPLOWIC_SCPLOWMO_SCPLOWOBC_SCPLOW, we find that LC maintains the same reduction in cumulative infections as RI while showing greater reduction in peak infections and internal transmission. Moreover, LC reduces campus burden by closing fewer locations, forcing fewer students into completely online schedules, and requiring no additional isolation. WO_SCPLOWIC_SCPLOWMO_SCPLOWOBC_SCPLOW can empower universities to conceive and assess a variety of closure policies to prevent future outbreaks.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naive baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance StatementThis paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting

How do we forecast an emerging pandemic in real time in a purely data-driven manner? How to leverage rich heterogeneous data based on various signals such as mobility, testing, and/or disease exposure for forecasting? How to handle noisy data and generate uncertainties in the forecast? In this paper, we present DO_SCPLOWEEPC_SCPLOWCO_SCPLOWOVIDC_SCPLOW, an operational deep learning frame-work designed for real-time COVID-19 forecasting. DO_SCPLOWEEPC_SCPLOW-CO_SCPLOWOVIDC_SCPLOW works well with sparse data and can handle noisy heterogeneous data signals by propagating the uncertainty from the data in a principled manner resulting in meaningful uncertainties in the forecast. The deployed framework also consists of modules for both real-time and retrospective exploratory analysis to enable interpretation of the forecasts. Results from real-time predictions (featured on the CDC website and FiveThirtyEight.com) since April 2020 indicates that our approach is competitive among the methods in the COVID-19 Forecast Hub, especially for short-term predictions.

COVID-19 diagnosis and study of serum SARS-CoV-2 specific IgA, IgM and IgG by a quantitative and sensitive immunoassay

BackgroundThe pandemic of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is causing great loss. Detecting viral RNAs is standard approach for SARS-CoV-2 diagnosis with variable success. Currently, studies describing the serological diagnostic methods are emerging, while most of them just involve the detection of SARS-CoV-2-specific IgM and IgG by ELISA or "flow immunoassay" with limited accuracy. MethodsDiagnostic approach depends on chemiluminescence immunoanalysis (CLIA) for detecting IgA, IgM and IgG specific to SARS-CoV-2 nucleocapsid protein (NP) and receptor-binding domain (RBD) was developed. The approach was tested with 216 sera from 87 COVID-19 patients and 483 sera from SARS-CoV-2 negative individuals. The diagnostic accuracy was evaluated by receiver operating characteristic (ROC) analysis. Concentration kinetics of RBD-specific serum antibodies were characterized. The relationship of serum RBD-specific antibodies and disease severity was analyzed. ResultsThe diagnostic accuracy based on RBD outperformed those based on NP. Adding IgA to a conventional serological test containing IgM and IgG improves sensitivity of SARS-CoV-2 diagnosis at early stage. CLIA for detecting RBD-specific IgA, IgM and IgG showed diagnostic sensitivities of 98.6%, 96.8% and 96.8%, and specificities of 98.1%, 92.3% and 99.8%, respectively. Median concentration of IgA and IgM peaked during 16-20 days after illness onset at 8.84 g/mL and 7.25 g/mL, respectively, while IgG peaked during 21-25 days after illness onset at 16.47 g/mL. Furthermore, the serum IgA level positively correlates with COVID-19 severity. ConclusionCLIA for detecting SARS-CoV-2 RBD-specific IgA, IgM and IgG in blood provides additional values for diagnosing and monitoring of COVID-19. SummaryChemiluminescence immunoanalysis of SARS-CoV-2 RBD-specific serum IgA as well as IgM and IgG improves accuracy of COVID-19 diagnosis. Concentration kinetics of serum RBD-specific IgA, IgM and IgG are revealed. Serum IgA levels positively correlate with COVID-19 severity.

Clinical and Laboratory Profiles of 75 Hospitalized Patients with Novel Coronavirus Disease 2019 in Hefei, China

The outbreak of the novel coronavirus disease 2019 (COVID-19) infection began in December 2019 in Wuhan, and rapidly spread to many provinces in China. The number of cases has increased markedly in Anhui, but information on the clinical characteristics of patients is limited. We reported 75 patients with COVID-19 in the First Affiliated Hospital of USTC from Jan 21 to Feb 16, 2020, Hefei, Anhui Province, China. COVID-19 infection was confirmed by real-time RT-PCR of respiratory nasopharyngeal swab samples. Epidemiological, clinical and laboratory data were collected and analyzed. Of the 75 patients with COVID-19, 61 (81.33%) had a direct or indirect exposure history to Wuhan. Common symptoms at onset included fever (66 [88.0%] of 75 patients) and dry cough (62 [82.67%]). Of the patients without fever, cough could be the only or primary symptom. The most prominent laboratory abnormalities were lymphopenia, decreased percentage of lymphocytes (LYM%), decreased CD4+ and CD8+ T cell counts, elevated C-reactive protein (CRP) and lactate dehydrogenase (LDH). Patients with elevated interleukin 6 (IL-6) showed significant decreases in the LYM%, CD4+ and CD8+ T cell counts. Besides, the percentage of neutrophils, CRP, LDH and Procalcitonin levels increased significantly. We concluded that COVID-19 could cause different degrees of hematological abnormalities and damage of internal organs. Hematological profiles including LYM, LDH, CRP and IL-6 could be indicators of diseases severity and evaluation of treatment effectiveness. Antiviral treatment requires a comprehensive and supportive approach. Further targeted therapy should be determined based on individual clinical manifestations and laboratory indicators.

Study on the design and clinical application of Preventing Jaundice and Antibacterial Biological Medical Gel in newborn / 中国实用护理杂志

Objective:To evaluate the effects of Preventing Jaundice and Antibacterial Biological Medical Gel in prevention of hyperbilirubinemia and umbilical infection in newborn.Methods:A total of 600 healthy neonates in a tertiary hospital were selected. Participants were randomly divided into the control group ( n=300) and the observation group ( n=300). The control group was given routine nursing guidance while the observation group was treated with Preventing Jaundice and Antibacterial Biological Medical Gel. The differences in the number of times of the fetus feces in 3 days after birth, the first fetal feces, yellow discharge time of the fetus feces, the incidence of hyperbilirubinemia, the incidence of neonatal phototherapy and the incidence of umbilical infection between the two groups were compared. Results:The number of times of the fetus feces in 3 days after birth and the first fetal feces and yellow discharge time of the fetus feces of the observation group were (8.12±1.36) times, (7.39±3.71) hours, (26.05±3.98) hours, respectively. The control group were (5.31±1.02) times, (13.04±5.26) hours, (28.65±3.54) hours, respectively. The difference between the two groups was significant ( Z value was -6.133, -6.483, t value was -19.011, P<0.05). The incidence of hyperbilirubinemia, being in neonatal intensive care unit, the incidence of blue light irradiation and the incidence of umbilical infection of the observation group was 0.67%(2/300), 0, 1.00%(3/300) and 0, respectively. The control group was 3.33%(10/300), 2.00%(6/300), 5.00%(15/300) and 3.33%(10/300), respectively. the difference between the two groups was significant ( χ2 value was 4.209-8.247, P<0.01). Conclusions:Preventing Jaundice and Antibacterial Biological Medical Gel could help control the incidence of hyperbilirubinemia and reduce the umbilical infection. It is worth clinical spreading.

Epidemiological characteristics of bacillary dysentery from 2009 to 2016 and its incidence prediction model based on meteorological factors

BACKGROUND@#This study aimed to analyse the epidemiological characteristics of bacillary dysentery (BD) caused by Shigella in Chongqing, China, and to establish incidence prediction models based on the correlation between meteorological factors and BD, thus providing a scientific basis for the prevention and control of BD.@*METHODS@#In this study, descriptive methods were employed to investigate the epidemiological distribution of BD. The Boruta algorithm was used to estimate the correlation between meteorological factors and BD incidence. The genetic algorithm (GA) combined with support vector regression (SVR) was used to establish the prediction models for BD incidence.@*RESULTS@#In total, 68,855 cases of BD were included. The incidence declined from 36.312/100,000 to 23.613/100,000, with an obvious seasonal peak from May to October. Males were more predisposed to the infection than females (the ratio was 1.118:1). Children < 5 years old comprised the highest incidence (295.892/100,000) among all age categories, and pre-education children comprised the highest proportion (34,658 cases, 50.335%) among all occupational categories. Eight important meteorological factors, including the highest temperature, average temperature, average air pressure, precipitation and sunshine, were correlated with the monthly incidence of BD. The obtained mean absolute percent error (MAPE), mean squared error (MSE) and squared correlation coefficient (R) of GA_SVR_MONTH values were 0.087, 0.101 and 0.922, respectively.@*CONCLUSION@#From 2009 to 2016, BD incidence in Chongqing was still high, especially in the main urban areas and among the male and pre-education children populations. Eight meteorological factors, including temperature, air pressure, precipitation and sunshine, were the most important correlative feature sets of BD incidence. Moreover, BD incidence prediction models based on meteorological factors had better prediction accuracies. The findings in this study could provide a panorama of BD in Chongqing and offer a useful approach for predicting the incidence of infectious disease. Furthermore, this information could be used to improve current interventions and public health planning.

HPLC fingerprint for Xinjiaxiangruyin standard decoction / 国际药学研究杂志

Objective To establish HPLC fingerprint of Xinjiaxiangruyin standard decoction soasto provide an analytical method for the quality control of Xinjiaxiangruyin granules. Methods HPLC was conducted on a phenonmenex Luna C18 column us?ing acetonitrile-0.1%H3PO4 in gradient elution modes as mobile phase,where detection wavelength was 280 nm and detection time was 130 min. With chlorogenic acid as reference peak,the Edition 2012 ofchromatographic fingerprint similarity evaluation systemsoftware was used to establish the standard decoction reference fingerprint,which was then compared with the fingerprint of 5 batchs of the granules for similarity evaluation. Results The similarity of the fingerprints between the 5 batches of granules and the standard decoction was higher than 0.90. Conclusion The method is simple,stable and reproducible,which could be used for the quality control of the granules as an adjuvant method.

Ultrasound molecular imaging of endothelial vascular endothelial growth factor recptor-2 expression during hind-limb ischemia-mediated angiogensis / 中华超声影像学杂志

Objective To explore the feasibility of evaluation of mice hind limb ischemia-mediated angiogenesis with ultrasound molecular imaging using molecular probes targeted to angiogenesis endothelial marker VEGFR-2.Methods A mice model of unilateral hind-limb ischemia was induced by femoral artery excision in 12 experimental mice.Ultrasound molecular imaging of the ischemia and contralateral non-ischemia hind-limbs was performed in all mice on day 7 after surgery at 8 minutes after intravenous injection of either VEGFR-2 targeting microbubbles or isotype control microbubbles in random with 30 min interval,and the video intensity (VI) was measured.Following ultrasound imaging,the hind-limb was harvested for immunohistochemical analysis.Results As expected,VI in the ischemia hind-limb was significantly higher (P ＜0.05) for MBvEGFR-2 [(25.6 ± 4.3)U] as compared with MBIso[(6.7 ± 1.6)U].However,the ultrasound signal in the non-ischemia hind-limb was low for both MBvEGFR-2 [4.4 ± 1.5)U] and MBIso [(4.6 ± 1.6)U].A marked endothelial VEGFR-2 expression in ischemia hind-limb was confirmed by immunohistochemistry.Conclusions Ultrasound molecular imaging using molecular probes targeted to angiogenesis endothelial VEGFR-2 can effectively evaluate ischemia-mediated angiogenesis.

MicroRNA-548 down-regulates host antiviral response via direct targeting of IFN-λ1

Interferon (IFN)-mediated pathways are a crucial part of the cellular response against viral infection. Type III IFNs, which include IFN-λ1, 2 and 3, mediate antiviral responses similar to Type I IFNs via a distinct receptor complex. IFN-λ1 is more effective than the other two members. Transcription of IFN-λ1 requires activation of IRF3/7 and nuclear factor-kappa B (NF-κB), similar to the transcriptional mechanism of Type I IFNs. Using reporter assays, we discovered that viral infection induced both IFN-λ1 promoter activity and that of the 3'-untranslated region (UTR), indicating that IFN-λ1 expression is also regulated at the post-transcriptional level. After analysis with microRNA (miRNA) prediction programs and 3'UTR targeting site assays, the miRNA-548 family, including miR-548b-5p, miR-548c-5p, miR-548i, miR-548j, and miR-548n, was identified to target the 3'UTR of IFN-λ1. Further study demonstrated that miRNA-548 mimics down-regulated the expression of IFN-λ1. In contrast, their inhibitors, the complementary RNAs, enhanced the expression of IFN-λ1 and IFN-stimulated genes. Furthermore, miRNA-548 mimics promoted infection by enterovirus-71 (EV71) and vesicular stomatitis virus (VSV), whereas their inhibitors significantly suppressed the replication of EV71 and VSV. Endogenous miRNA-548 levels were suppressed during viral infection. In conclusion, our results suggest that miRNA-548 regulates host antiviral response via direct targeting of IFN-λ1, which may offer a potential candidate for antiviral therapy.

Preparation and evaluation of nanometer-scale bubbles with surfaces of N-palmitoyl chitosan / 中华超声影像学杂志

Objective To develop nanometer-scale bubbles with surfaces of N-palmitoyl chitosan(PLCS) as ultrasound contrast agent and evaluate its characteristics and acoustic effects in vivo. Methods The PLCS nanobubbles were prepared using a cutting technique at differential high-frequency of shear speed. Both optical and transmission electron micrography were performed to determine the nanobubble size and morphology. Concentration, size-distribution and zeta potential of the PLCS nanobubbles were measured by cell counting chamber, Malvern lazer particle analyzer and zeta-sizer at 1-day, 45-day and 90-day. The acoustic effects of the PLCS nanobubbles on myocardium and renal tissue in 6 normal rats were observed using bolus infusion of the nanobubbles intravenously. The maximum video intensity(VI) was measured.Results The PLCS nanobubbles with nice round-shape and uniform site-distribution were demonstrated.The mean diameter,concentration and zeta potential of the PLCS nanobubbles were (617 ± 12) nm, (7.2 ±0.6) × 109/ml and (52.9 ± 1.3)mV at the 1-day,and all of parameters did not change significantly in 45-day and 90-day ( P ＞ 0. 05). A significant contrast-enhancement was noted on myocardium and renal tissue during infusion of the nanobubbles. VI on both tissues was (15.6 ± 1.1)GU and (27.3 ± 2.5)GU,respectively. The visual contrast-enhancement last up to (10 ± 2)min. Conclusions The PLCS nanometerscale bubbles have excellent physical-features and contrast-enhanced ultrasound effects in vivo. It may develop as a novel contrast ultrasound agent which could cross endothelial cell membrances.

Visually assessment of matrigel angiogenesis with ultrasound molecular imaging using microbubbles targeted to endothelial αv-integrins / 中华超声影像学杂志

Objective To explore the feasibility of visually assessment of angiogenesis in a murine model of subcutaneous matrigel plugs with ultrasound molecular imaging(UMI) using microbubbles(MB)targeted to endothelial αv-integrins. Methods Matrigel angiogenesis was created by subcutaneous implantation of FGF-2 enriched matrigel in 10 mice. On day 10, UMI of the matrigel was performed in all mice at 6 minutes after intravenous injection of either αv-integrin targeting microbubbles(MBα) or isotype control microbubbles(MBc) in random with 30 min interval,and the video intensity(Ⅵ) was measured. To further test the specificity of the signal coming from MBα,antibody against αv-integrin was injected 10 min before microbubbles injection. Following UMI,all matrigels were harvested for histological analysis. Results As expected,VI of the matrigel was significantly higher ( P ＜0.05) for MBα (20. 5 ± 3.3)U as compared with MBc (4. 8 ± 1.5)U. After blocking with antibody against αv-integrin,a great decrease was observed in the MBα group [VI (4.6 ± 1.2) U, P ＜0.05] while no significant difference was noted for MBc [VI (4. 9 ±1.5)U, P ＞ 0.05 ]. Neovessels within matrigel was positive for αv-integrin. Conclusions UMI with microbubbles targeted to αv-integrins can be effective and specific in evaluating the angiogenesis in a murine model of subcutaneous matrigel plugs.