In the presence of health threats, precision public health approaches aim to provide targeted, timely, and population-specific interventions. Accurate surveillance methodologies that can estimate ...infectious disease activity ahead of official healthcare-based reports, at relevant spatial resolutions, are important for achieving this goal. Here we introduce a methodological framework which dynamically combines two distinct influenza tracking techniques, using an ensemble machine learning approach, to achieve improved state-level influenza activity estimates in the United States. The two predictive techniques behind the ensemble utilize (1) a self-correcting statistical method combining influenza-related Google search frequencies, information from electronic health records, and historical flu trends within each state, and (2) a network-based approach leveraging spatio-temporal synchronicities observed in historical influenza activity across states. The ensemble considerably outperforms each component method in addition to previously proposed state-specific methods for influenza tracking, with higher correlations and lower prediction errors.
Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the prevalence of COVID-19 across the United States (US). Equipment ...shortages and varying testing capabilities have however hindered the usefulness of the official reported positive COVID-19 case counts. We introduce four complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 in each state in the US as well as Puerto Rico and the District of Columbia, using a combination of excess influenza-like illness reports, COVID-19 test statistics, COVID-19 mortality reports, and a spatially structured epidemic model. Instead of relying on the estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our four approaches emerges the consistent conclusion that on April 4, 2020, the estimated case count was 5 to 50 times higher than the official positive test counts across the different states. Nationally, our estimates of COVID-19 symptomatic cases as of April 4 have a likely range of 2.3 to 4.8 million, with possibly as many as 7.6 million cases, up to 25 times greater than the cumulative confirmed cases of about 311,000. Extending our methods to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 4.9 to 10.1 million, as opposed to 1.5 million positive test counts. The proposed combination of approaches may prove useful in assessing the burden of COVID-19 during resurgences in the US and other countries with comparable surveillance systems.
Given still-high levels of coronavirus disease 2019 (COVID-19) susceptibility and inconsistent transmission-containing strategies, outbreaks have continued to emerge across the United States. Until ...effective vaccines are widely deployed, curbing COVID-19 will require carefully timed nonpharmaceutical interventions (NPIs). A COVID-19 early warning system is vital for this. Here, we evaluate digital data streams as early indicators of state-level COVID-19 activity from 1 March to 30 September 2020. We observe that increases in digital data stream activity anticipate increases in confirmed cases and deaths by 2 to 3 weeks. Confirmed cases and deaths also decrease 2 to 4 weeks after NPI implementation, as measured by anonymized, phone-derived human mobility data. We propose a means of harmonizing these data streams to identify future COVID-19 outbreaks. Our results suggest that combining disparate health and behavioral data may help identify disease activity changes weeks before observation using traditional epidemiological monitoring.
Background and Purpose
Asthma is characterized by airway inflammation, mucus hypersecretion, and airway hyperresponsiveness. The use of nicotinic agents to mimic the cholinergic anti‐inflammatory ...pathway (CAP) controls experimental asthma. Yet, the effects of vagus nerve stimulation (VNS)‐induced CAP on allergic inflammation remain unknown.
Experimental Approach
BALB/c mice were sensitized and challenged with house dust mite (HDM) extract and treated with active VNS (5 Hz, 0.5 ms, 0.05–1 mA). Bronchoalveolar lavage (BAL) fluid was assessed for total and differential cell counts and cytokine levels. Lungs were examined by histopathology and electron microscopy.
Key Results
In the HDM mouse asthma model, VNS at intensities equal to or above 0.1 mA (VNS 0.1) but not sham VNS reduced BAL fluid differential cell counts and alveolar macrophages expressing α7 nicotinic receptors (α7nAChR), goblet cell hyperplasia, and collagen deposition. Besides, VNS 0.1 also abated HDM‐induced elevation of type 2 cytokines IL‐4 and IL‐5 and was found to block the phosphorylation of transcription factor STAT6 and expression level of IRF4 in total lung lysates. Finally, VNS 0.1 abrogated methacholine‐induced hyperresponsiveness in asthma mice. Prior administration of α‐bungarotoxin, a specific inhibitor of α7nAChR, but not propranolol, a specific inhibitor of β2‐adrenoceptors, abolished the therapeutic effects of VNS 0.1.
Conclusion and Implications
Our data revealed the protective effects of VNS on various clinical features in allergic airway inflammation model. VNS, a clinically approved therapy for depression and epilepsy, appears to be a promising new strategy for controlling allergic asthma.
Activation of zymogens within the pancreatic acinar cell is an early feature of acute pancreatitis. Supraphysiological concentrations of cholecystokinin (CCK) cause zymogen activation and ...pancreatitis. The effects of the CCK analog, caerulein, and alcohol on trypsin and chymotrypsin activation in isolated pancreatic acini were examined. Caerulein increased markers of zymogen activation in a time- and concentration-dependent manner. Notably, trypsin activity reached a peak value within 30 min, then diminished with time, whereas chymotrypsin activity increased with time. Ethanol (35 mM) sensitized the acinar cells to the effects of caerulein (10(-10) to 10(-7) M) on zymogen activation but had no effect alone. The effects of ethanol were concentration dependent. Alcohols with a chain length of >or=2 also sensitized the acinar cell to caerulein; the most potent was butanol. Branched alcohols (2-propanol and 2-butanol) were less potent than aliphatic alcohols (1-propanol and 1-butanol). The structure of an alcohol is related to its ability to sensitize acinar cells to the effects of caerulein on zymogen activation.
Chemistry of Methoxonium on (2 × 1)Pt(110) Lu, Chang; Thomas, Fred S; Masel, Richard I
The journal of physical chemistry. B,
09/2001, Letnik:
105, Številka:
36
Journal Article
Recenzirano
In a previous paper, (Chen, N.; Blowers, P.; Masel, R. I. Surf. Sci. 1998, 418, 329−341.) we found that methoxonium ions (CH3OH2)+ can form when methanol and hydrogen coadsorb on Pt(110). In this ...paper, we characterize the chemistry of methoxonium on (2 × 1)Pt(110) using mainly TPD. We observe three reaction pathways: a simple dehydrogenation to CO and hydrogen; an SN1 pathway to form water, methane, and adsorbed CH x groups; and an SN2 pathway yielding dimethyl ether. The SN1 pathway shows a strong secondary isotope effect implying that the transition state is ionic. The SN2 pathway is seen at high hydrogen coverages and shows kinetics typical for an SN2 process. This work demonstrates for the first time that cationic species on surfaces react via the same SN1 and SN2 pathways seen during reactions of cations in solution.
Non-pharmaceutical interventions (NPIs) have been crucial in curbing COVID-19 in the United States (US). Consequently, relaxing NPIs through a phased re-opening of the US amid still-high levels of ...COVID-19 susceptibility could lead to new epidemic waves. This calls for a COVID-19 early warning system. Here we evaluate multiple digital data streams as early warning indicators of increasing or decreasing state-level US COVID-19 activity between January and June 2020. We estimate the timing of sharp changes in each data stream using a simple Bayesian model that calculates in near real-time the probability of exponential growth or decay. Analysis of COVID-19-related activity on social network microblogs, Internet searches, point-of-care medical software, and a metapopulation mechanistic model, as well as fever anomalies captured by smart thermometer networks, shows exponential growth roughly 2-3 weeks prior to comparable growth in confirmed COVID-19 cases and 3-4 weeks prior to comparable growth in COVID-19 deaths across the US over the last 6 months. We further observe exponential decay in confirmed cases and deaths 5-6 weeks after implementation of NPIs, as measured by anonymized and aggregated human mobility data from mobile phones. Finally, we propose a combined indicator for exponential growth in multiple data streams that may aid in developing an early warning system for future COVID-19 outbreaks. These efforts represent an initial exploratory framework, and both continued study of the predictive power of digital indicators as well as further development of the statistical approach are needed.
The Wiskott-Aldrich syndrome (WAS) is an X-linked recessive genetic disease in which the basic molecular defect is unknown. We previously located the WAS gene between two DNA markers, DXS7 (Xp11.3) ...and DXS14 (Xp11), and mapped it to the proximal short arm of the human X chromosome (Kwan et al., 1988, Genomics 3:39-43). In this study, further mapping was performed on 17 WAS families with two additional RFLP markers, TIMP and DXS255. Our data suggest that DXS255 is closer to the WAS locus than any other markers that have been previously described, with a multipoint maximum lod score of Z = 8.59 at 1.2 cM distal to DXS255 and thus further refine the position of the WAS gene on the short arm of the X chromosome. Possible locations for the WAS gene are entirely confined between TIMP (Xp11.3) and DXS255 (Xp11.22). Use of these markers thus represents a major improvement in genetic prediction in WAS families.
PDF
