单增李斯特菌胎盘感染的体内外模型研究进展

单核细胞增生李斯特氏菌（Listeria monocytogenes）是重要的食源性致病菌,能引发人类的李斯特菌病,是全球公共卫生问题之一。该菌易感染孕妇,引起胎儿和新生儿的侵袭性李斯特菌病,严重威胁母婴健康。因此,建立有效的单增李斯特菌感染胎盘体内外模型,解析和探究单增李斯特菌经胎盘感染机制,是预防和控制单增李斯特菌感染母婴的关键所在。本文综述了可用于研究单增李斯特菌母婴感染的体内外胎盘模型,总结和讨论了各类模型的优势和局限性;并着重分析了体外三维胎盘屏障模型在单增李斯特菌感染方面的研究进展和未来研究方向。以期为深入解析该菌经胎盘感染的途径、发病机制提供支持,并为预防和控制母婴李斯特菌病提供科学参考。     

Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models

ObjectivesWhen the prognosis of COVID-19 disease can be detected early, the intense-pressure and loss of workforce in health-services can be partially reduced. The primary-purpose of this article is to determine the feature-dataset consisting of the routine-blood-values (RBV) and demographic-data that affect the prognosis of COVID-19. Second, by applying the feature-dataset to the supervised machine-learning (ML) models, it is to identify severely and mildly infected COVID-19 patients at the time of admission.Material and methodsThe sample of this study consists of severely (n = 192) and mildly (n = 4010) infected-patients hospitalized with the diagnosis of COVID-19 between March-September, 2021. The RBV-data measured at the time of admission and age-gender characteristics of these patients were analyzed retrospectively. For the selection of the features, the minimum-redundancy-maximum-relevance (MRMR) method, principal-components-analysis and forward-multiple-logistics-regression analyzes were used. The features set were statistically compared between mild and severe infected-patients. Then, the performances of various supervised-ML-models were compared in identifying severely and mildly infected-patients using the feature set.ResultsIn this study, 28 RBV-parameters and age-variable were found as the feature-dataset. The effect of features on the prognosis of the disease has been clinically proven. The ML-models with the highest overall-accuracy in identifying patient-groups were found respectively, as follows: local-weighted-learning (LWL)-97.86%, K-star (K*)-96.31%, Naive-Bayes (NB)-95.36% and k-nearest-neighbor (KNN)-94.05%. Also, the most successful models with the highest area-under-the-receiver-operating-characteristic-curve (AUC) values in identifying patient groups were found respectively, as follows: LWL-0.95%, K*-0.91%, NB-0.85% and KNN-0.75%.ConclusionThe findings in this article have significant a motivation for the healthcare professionals to detect at admission severely and mildly infected COVID-19 patients.     

Propolis Enhances 5-Fluorouracil Mediated Antitumor Efficacy and Reduces Side Effects in Colorectal Cancer: An in Vitro and in Vivo Study

In this study, we investigated the combined treatment of 5-fluorouracil (5-FU) and Anatolian propolis extract (PE) on colorectal cancer (CRC)using in vitro and in vivo studies. We exposed luciferase-transfected (Lovo-Luc CRC) cells and healthy colon cells (CCD-18Co) to varying concentrations of 5-FU and PE to assess their genotoxic, apoptotic, and cytotoxic effects, as well as their intracellular reactive oxygen species (iROS) levels. We also developed a xenograft model in nude mice and evaluated the anti-tumor effects of PE and 5-FU using various methods. Our findings showed that the combination of PE and 5-FU had selectivity against cancer cells, particularly at higher doses, and enhanced the anti-tumor effectiveness of 5-FU against colon CRC. The results suggest that PE can reduce side effects and increase the effectiveness of 5-FU through iROS generation in a dose-dependent manner.     

Inferring landscape resistance to gene flow when genetic drift is spatially heterogeneous

In connectivity models, land cover types are assigned cost values characterizing their resistance to species movements. Landscape genetic methods infer these values from the relationship between genetic differentiation and cost distances. The spatial heterogeneity of population sizes, and consequently genetic drift, is rarely included in this inference although it influences genetic differentiation. Similarly, migration rates and population spatial distributions potentially influence this inference. Here, we assessed the reliability of cost value inference under several migration rates, population spatial patterns and degrees of population size heterogeneity. Additionally, we assessed whether considering intra-population variables, here using gravity models, improved the inference when drift is spatially heterogeneous. We simulated several gene flow intensities between populations with varying local sizes and spatial distributions. We then fit gravity models of genetic distances as a function of (i) the ‘true’ cost distances driving simulations or alternative cost distances, and (ii) intra-population variables (population sizes, patch areas). We determined the conditions making the identification of the ‘true’ costs possible and assessed the contribution of intra-population variables to this objective. Overall, the inference ranked cost scenarios reliably in terms of similarity with the ‘true’ scenario (cost distance Mantel correlations), but this ‘true’ scenario rarely provided the best model goodness of fit. Ranking inaccuracies and failures to identify the ‘true’ scenario were more pronounced when migration was very restricted (<4 dispersal events/generation), population sizes were most heterogeneous and some populations were spatially aggregated. In these situations, considering intra-population variables helps identify cost scenarios reliably, thereby improving cost value inference from genetic data.     

Hepatic Gluconeogenesis and the Activity of PDH in Individual Tissues of GTG-Obese Mice Following Adrenalectomy

Adrenalectomy (ADX) lowers circulating glucose levels in animal models of non-insulin dependent diabetes (NIDDM) and obesity. To investigate the role of hepatic glucose production (HGP) and tissue glucose oxidation in the improvement in glucose tolerance, hepatocyte gluconeogenesis and the activity of pyruvate dehydrogenase (PDH) were examined in different tissues of gold thioglucose (GTG) obese mice 2 weeks after ADX or sham ADX. GTG-obese mice which had undergone ADX weighed significantly less than their adrenal intact counterparts (GTG ADX: 37.5 ± 0.7g; GTG: 44.1 ± 0.4g; p<0.05), and demonstrated lower serum glucose (GTG ADX: 22.5 ± 1.6 mmol/L; GTG: 29.4 ± 1.9 mmol/L; p<0.05) and serum insulin levels (GTG ADX: 76 ± 10μ.U/mL; GTG: 470 ± 63μU/mL; p<0.05). Lactate conversion to glucose by hepatocytes isolated from ADX GTG mice was significantly reduced compared with that of hepatocytes from GTG mice (GTG ADX: 125 ± 10 nmol glucose/10⁶ cells; GTG: 403 ± 65 nmol glucose/10⁶ cells; p<0.05). ADX also significantly reduced both the glycogen (GTG ADX: 165 ± 27 μmol/liver; GTG: 614 ± 60 pmol/Iiver; p<0.05) and fatty acid content (GTG ADX: 101 ± 9 mg fatty acid/g liver; GTG: 404 ± 40 mg fatty acid/g liver; p<0.05) of the liver of GTG-obese mice. ADX of GTG-obese mice reduced PDH activity by varying degrees in all tissues, except quadriceps muscle. These observations are consistent with an ADX induced decrease in hepatic lipid stores removing fatty acid-induced increases in gluconeogenesis and increased peripheral availability of fatty acids inhibiting PDH activity via the glucose/fatty acid cycle. It is also evident that the improvement in glucose tolerance which accompanies ADX of GTG-obese mice is not due to increased PDH activity resulting in enhanced peripheral glucose oxidation. Instead, it is more likely that reduced blood glucose levels after ADX of GTG-obese mice are the result of decreased gluconeogenesis in the liver.     

Some population consequences of variation in preference among individual predators

A number of foraging studies have demonstrated that populations of predators rarely consist of individuals with identical preferences for particular types of prey. Variation among predators can lead to frequency-dependent changes in population preference, because those predators mat prefer the rarer type of prey generally have the greatest influence on population preference. In this study we develop a series of theoretical models which demonstrate how anti-apostatic selection (i.e. selection against the rare form) can arise out of (a) bimodal and (b) normal variation in preference among individuals of the same species. We show that population level anti-apostatic selection can occur even when individual predators show pro-apostatic selection (i.e. selection against the common form). Furthermore, patterns of population prey selection that arise out of variation in preference can potentially be pro-apostatic over one range of relative densities and anti-apostatic over another range of relative densities. Finally, we examine a case study involving predation by female waterboatmen Notonecta glauca and show that the variation in preference in this species is large enough to generate higher anti-apostatic selection than would be expected from the diet selected by the average individual.     

Chironomid community structure deduced from larvae and pupal exuviae of a chalk stream

Species abundances of Chironomidae (Insecta: Diptera) have often been excluded from studies of benthic river communities because of difficulties associated with sampling and identifying larvae. Chironomid pupal exuviae are easier to collect and identify and could be used to determine community structure if shown to be representative of local larval assemblages. Larvae were sampled along a 20 m chain secured over mid-channel gravels, upstream of two collection points for pupal exuviae. Proportional taxa abundances of pupal exuviae and larvae sampled from 130 m of stream were directly compared by a ² test of independence and also separately fitted to four models of species abundance distribution. Observed proportions of taxa were not independent of the life stage sampled. The greatest discrepancies occurred with species of pupal exuviae that were absent as larvae from the gravel. The log series model provided the best fit with both pupal and larval data. Collections of pupal exuviae had greater species richness and evenness than samples of larvae. This was considered to be a consequence of sampling larvae from the gravel habitat alone.     

Evaluation of several methods for estimating phylogenetic trees when substitution rates differ over nucleotide sites

Several maximum likelihood and distance matrix methods for estimating phylogenetic trees from homologous DNA sequences were compared when substitution rates at sites were assumed to follow a gamma distribution. Computer simulations were performed to estimate the probabilities that various tree estimation methods recover the true tree topology. The case of four species was considered, and a few combinations of parameters were examined. Attention was applied to discriminating among different sources of error in tree reconstruction, i.e., the inconsistency of the tree estimation method, the sampling error in the estimated tree due to limited sequence length, and the sampling error in the estimated probability due to the number of simulations being limited. Compared to the least squares method based on pairwise distance estimates, the joint likelihood analysis is found to be more robust when rate variation over sites is present but ignored and an assumption is thus violated. With limited data, the likelihood method has a much higher probability of recovering the true tree and is therefore more efficient than the least squares method. The concept of statistical consistency of a tree estimation method and its implications were explored, and it is suggested that, while the efficiency (or sampling error) of a tree estimation method is a very important property, statistical consistency of the method over a wide range of, if not all, parameter values is prerequisite.     

On-line state recognition in a yeast fed-batch culture using error vectors

The physiological states with respect to cell growth and ethanol production in a yeast fed-batch culture expressed in linguistic form could be recognized on-line by fuzzy inferencing based on error vectors. The error vector was newly defined here in a macroscopic elemental balance equation. The physiological states for cell growth and ethanol production were characterized by error vectors using many experimental data from fed-batch cultures. Fuzzy membership functions were constructed from the frequency distributions of the error vectors and state recognition was performed by fuzzy inferencing. In particular, an unusual physiological state for a yeast cultivation, in which aerobic ethanol production was accompanied by very low cell growth, could be recognized accurately. According to the results of the state recognition, an energy parameter, the P/O ratio in the metabolic reaction model was adaptively estimated, and the cell growth was successfully evaluated with the estimated P/O. (c) 1995 John Wiley & Sons, Inc.