A G-triplex and G-quadruplex concatemer-enhanced fluorescence probe coupled with hybridization chain reaction for ultrasensitive aptasensing of ochratoxin A.

Ochratoxin A (OTA), a typical mycotoxin contaminant found in various agricultural products and foods, poses a serious threat to human health. In this study, an aptasensor based on a novel fluorescence probe comprising a G-rich DNA sequence (G43) and thioflavin T (ThT) was designed via hybridization chain reaction (HCR) for the ultrasensitive detection of OTA. G43 is a concatemer of G-quadruplex and G-triplex (a G-quadruplex-like structure with one G-quartet removed), which can drastically enhance the fluorescence intensity of ThT. For this strategy to work, the OTA aptamer is pro-locked in a hairpin structure, denoted "hairpin-locked aptamer" (HL-Apt). OTA binds to HL-Apt, opens the hairpin structure, releases the trigger sequence, and initiates the HCR reaction to form a long DNA duplex and numerous side chains. The side chains combine entirely with the complementary DNA and liberate the pro-locked G43 DNA, dramatically enhancing the intensity of the ThT fluorescence signal. The fluorescence intensity correlates linearly with the OTA concentration between 0.02 and 2.00 ng mL-1, and the method has a detection limit of 0.008 ng mL-1. The developed aptasensor was used to detect OTA in foodstuffs with satisfactory results.

The impact of weather condition and social activity on COVID-19 transmission in the United States.

The coronavirus disease 2019 (COVID-19) has been first reported in December 2019 and rapidly spread worldwide. As other severe acute respiratory syndromes, it is a widely discussed topic whether seasonality affects the COVID-19 infection spreading. This study presents two different approaches to analyse the impact of social activity factors and weather variables on daily COVID-19 cases at county level over the Continental U.S. (CONUS). The first one is a traditional statistical method, i.e., Pearson correlation coefficient, whereas the second one is a machine learning algorithm, i.e., random forest regression model. The Pearson correlation is analysed to roughly test the relationship between COVID-19 cases and the weather variables or the social activity factor (i.e. social distance index). The random forest regression model investigates the feasibility of estimating the number of county-level daily confirmed COVID-19 cases by using different combinations of eight factors (county population, county population density, county social distance index, air temperature, specific humidity, shortwave radiation, precipitation, and wind speed). Results show that the number of daily confirmed COVID-19 cases is weakly correlated with the social distance index, air temperature and specific humidity through the Pearson correlation method. The random forest model shows that the estimation of COVID-19 cases is more accurate with adding weather variables as input data. Specifically, the most important factors for estimating daily COVID-19 cases are the population and population density, followed by the social distance index and the five weather variables, with temperature and specific humidity being more critical than shortwave radiation, wind speed, and precipitation. The validation process shows that the general values of correlation coefficients between the daily COVID-19 cases estimated by the random forest model and the observed ones are around 0.85.

Environment-dependent behavioral traits and experiential factors shape addiction vulnerability.

The transition from controlled drug use to drug addiction depends on an interaction between a vulnerable individual, their environment and a drug. Here we tested the hypothesis that conditions under which individuals live influence behavioral vulnerability traits and experiential factors operating in the drug taking environment to determine the vulnerability to addiction. The role of behavioral vulnerability traits in mediating the influence of housing conditions on the tendency to acquire cocaine self-administration was characterized in 48 rats housed in either an enriched (EE) or a standard (SE) environment. Then, the influence of these housing conditions on the individual vulnerability to develop addiction-like behavior for cocaine or alcohol was measured in 72 EE or SE rats after several months of cocaine self-administration or intermittent alcohol drinking, respectively. The determining role of negative experiential factors in the drug taking context was further investigated in 48 SE rats that acquired alcohol drinking to self-medicate distress in a schedule-induced polydipsia procedure. The environment influenced the acquisition of drug intake through its effect on behavioral markers of resilience to addiction. In contrast, the initiation of drug taking as a coping strategy or in a negative state occasioned by the contrast between enriched housing conditions and a relatively impoverished drug taking setting, facilitated the development of compulsive cocaine and alcohol intake. These data indicate that addiction vulnerability depends on environmentally determined experiential factors, and suggest that initiating drug use through negative reinforcement-based self-medication facilitates the development of addiction in vulnerable individuals. SIGNIFICANCE STATEMENT: The factors that underlie an individual's vulnerability to switch from controlled, recreational drug use to addiction are not well understood. We showed that in individuals housed in enriched conditions, the experience of drugs in the relative social and sensory impoverishment of the drug taking context, and the associated change in behavioral traits of resilience to addiction, exacerbate the vulnerability to develop compulsive drug intake. We further demonstrated that the acquisition of alcohol drinking as a mechanism to cope with distress increases the vulnerability to develop compulsive alcohol intake. Together these results demonstrate that experiential factors in the drug taking context shape the vulnerability to addiction.