Causal inference between pernicious anemia and cancers: a bidirectional two-sample mendelian randomization analysis.

BACKGROUND: Observational study investigated the association between pernicious anemia (PA) and cancers. However, with the exception of gastric cancer, the results are mostly contradictory. The purpose of this study was to investigate the potential causal relationship between PA and cancers through bidirectional two-sample Mendelian randomized (MR) analysis. METHODS: The European sample FinnGen project provided the genetic summary data for PA and 20 site-specific cancers. This bidirectional two-sample MR design mainly used the inverse variance weighting (IVW) method to evaluate the causal relationship between PA and cancer risk. Benjamini-Hochberg correction was performed to reduce the bias caused by multiple tests. RESULTS: Our study shows that there was a causal relationship between PA and gastric cancer, prostate cancer, testicular cancer and malignant melanoma of skin, and there was a reverse causal relationship between prostate cancer or gastric cancer and PA (P < 0.05). After Benjamini-Hochberg correction test, there was still a causal correlation between PA and gastric or prostate cancer (P' < 0.05), while there was only an implied causal association between PA and testicular cancer and malignant melanoma of skin (P'> 0.05). There was still a reverse causal relationship between gastric cancer and PA (P'< 0.05), while prostate cancer shows an implied reverse causal relationship(P'> 0.05). In addition, MR-Egger and MR-PRESSO tests showed no significant horizontal pleiotropy. CONCLUSIONS: PA may be genetically associated with testicular cancer, prostate cancer, gastric cancer, and malignant melanoma of skin.

Analysis of a Length-Structured Density-Dependent Model for Fish.

We present a length-structured matrix model for fish populations in which the probability that a fish grows into the next length class is a decreasing nonlinear function of the total biomass of the population. We present mathematical results classifying the dynamics that this density-dependent model predicts. We illustrate these results with numerical simulations for an invasive white perch population and show how the mathematical results can be used to predict the persistence and/or boundedness of the population as well as an equilibrium structure that is dominated by small fish. We illustrate the results with management recommendations for an invasive white perch population.

Biocontrol Capabilities of Bacillus subtilis E11 against Aspergillus flavus In Vitro and for Dried Red Chili (Capsicum annuum L.).

As a condiment with extensive nutritional value, chili is easy to be contaminated by Aspergillus flavus (A. flavus) during field, transportation, and storage. This study aimed to solve the contamination of dried red chili caused by A. flavus by inhibiting the growth of A. flavus and detoxifying aflatoxin B1 (AFB1). In this study, Bacillus subtilis E11 (B. subtilis) screened from 63 candidate antagonistic bacteria exhibited the strongest antifungal ability, which could not only inhibit 64.27% of A. flavus but could also remove 81.34% of AFB1 at 24 h. Notably, scanning electron microscopy (SEM) showed that B. subtilis E11 cells could resist a higher concentration of AFB1, and the fermentation supernatant of B. subtilis E11 could deform the mycelia of A. flavus. After 10 days of coculture with B. subtilis E11 on dried red chili inoculated with A. flavus, the mycelia of A. flavus were almost completely inhibited, and the yield of AFB1 was significantly reduced. Our study first concentrated on the use of B. subtilis as a biocontrol agent for dried red chili, which could not only enrich the resources of microbial strains for controlling A. flavus but also could provide theoretical guidance to prolong the shelf life of dried red chili.

AT1 Receptors: Their Actions from Hypertension to Cognitive Impairment.

Hypertension is one of the most prevalent cardiovascular disorders worldwide, affecting 1.13 billion people, or 14% of the global population. Hypertension is the single biggest risk factor for cerebrovascular dysfunction. According to the American Heart Association, high blood pressure (BP), especially in middle-aged individuals (~ 40 to 60 years old), is associated with an increased risk of dementia, later in life. Alzheimer's disease and cerebrovascular disease are the two leading causes of dementia, accounting for around 80% of the total cases and usually combining mixed pathologies from both. Little is known regarding how hypertension affects cognitive function, so the impact of its treatment on cognitive impairment has been difficult to assess. The brain renin-angiotensin system (RAS) is essential for BP regulation and overactivity of this system has been established to precede the development and maintenance of hypertension. Angiotensin II (Ang-II), the main peptide within this system, induces vasoconstriction and impairs neuro-vascular coupling by acting on brain Ang-II type 1 receptors (AT1R). In this review, we systemically analyzed the association between RAS and biological mechanisms of cognitive impairment, from the perspective of AT1R located in the central nervous system. Additionally, the possible contribution of brain AT1R to global cognition decline in COVID-19 cases will be discussed as well.

Predictive toxicology: benchmarking molecular descriptors and statistical methods.

The development of drugs depends on finding compounds that have beneficial effects with a minimum of toxic effects. The measurement of toxic effects is typically time-consuming and expensive, so there is a need to be able to predict toxic effects from the compound structure. Predicting toxic effects is expected to be challenging because there are usually multiple toxic mechanisms involved. In this paper, combinations of different chemical descriptors and popular statistical methods were applied to the problem of predictive toxicology. Four data sets were collected and cleaned, and four different sets of chemical descriptors were calculated for the compounds in each of the four data sets. Three statistical methods (recursive partitioning, neural networks, and partial least squares) were used to attempt to link chemical descriptors to the response. Good predictions were achieved in the two smaller data sets; we found for large data sets that the results were less effective, indicating that new chemical descriptors or statistical methods are needed. All of the methods and descriptors worked to a degree, but our work hints that certain descriptors work better with specific statistical methods so there is a need for better understanding and for continued methods development.