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er to confirm prognostic features and the choice of surgical volume.

Our results showed that more radical surgical treatment of metastases to ovaries has no increase of survival among patients. However, it should be noted that this may be affected by different stage of primary disease. Thus, larger and more standardized studies need to be done in order to confirm prognostic features and the choice of surgical volume.

The rate of caesarean delivery between 22 and 28 weeks of gestation (weeks) has increased for several years. The aim of the study was to describe subsequent pregnancies in women with a history of caesarean delivery between 22 and 28 weeks.

We performed a retrospective, observational, bicentric cohort study in tertiary care maternity units. We included women who had a caesarean delivery between 22 and 28 weeks from December 1, 2014 to December 31, 2017. We then retrospectively collected data on subsequent pregnancies of these patients up to March 2020. We described the subsequent pregnancy rate and the outcomes of these pregnancies.

Among the 186 women who had a caesarean between 22 and 28 weeks, data from 103 of them could be collected, including 47 (45.6%) women who had 64 new pregnancies. Of the 47 first pregnancies after the preterm cesarean, 19 (40.4%) were completed at≥37 weeks. The mode of delivery was a cesarean in 23 cases (79.3%). A trial of labor after cesarean was only considered in 7 cases (24.1%), and 6 women (20.7%) gave birth vaginally.

If pregnancy is desired after a caesarean between 22 and 28 weeks, the pregnancy rate is high without recurrence of prematurity in the majority of cases. Cesarean delivery is the most common mode of delivery. In case of trial of labor after cesarean, the success rate is reasonable.

If pregnancy is desired after a caesarean between 22 and 28 weeks, the pregnancy rate is high without recurrence of prematurity in the majority of cases. Cesarean delivery is the most common mode of delivery. In case of trial of labor after cesarean, the success rate is reasonable.Various stressors including temperature, environmental chemicals, and toxins can have profound impacts on immunity to pathogens. Increased eutrophication near rivers and lakes coupled with climate change are predicted to lead to increased algal blooms. Currently, the effects of cyanobacterial toxins on disease resistance in mammals is a largely unexplored area of research. Recent studies have suggested that freshwater cyanotoxins can elicit immunomodulation through interaction with specific components of innate immunity, thus potentially altering disease susceptibility parameters for fish, wildlife, and human health owing to the conserved nature of the vertebrate immune system. In this study, we investigated the effects of three microcystin congeners (LR, LA, and RR), nodularin-R, and cylindrospermopsin for their ability to directly interact with nine different human Toll-like receptors (TLRs)-key pathogen recognition receptors for innate immunity. Toxin concentrations were verified by LC/MS/MS prior to use. Using an established HEK293-hTLR NF-κB reporter assay, we concluded that none of the tested toxins (29-90 nM final concentration) directly interacted with human TLRs in either an agonistic or antagonistic manner. These results suggest that earlier reports of cyanotoxin-induced NF-κB responses likely occur through different surface receptors to mediate inflammation.

The purpose of this study was to develop a quantitative structure-activity relationship (QSAR) model for the prediction of blood brain barrier (BBB) permeability by using artificial neural networks (ANN) in combination with molecular structure and property descriptors.

Using a database composed of 300 compounds, 52 structure descriptors obtained based on the universal quasichemical functional group activity coefficients (UNIFAC) group contribution method and the selected 8 molecular property descriptors were used as the network inputs, whereas logBB values of compounds constituted its output.

The correlation coefficient R of the constructed prediction model, the relative error (RE) and the root mean square error (RMSE) was 0.956, 0.857, and 0.171, respectively. These indicators reflected the feasibility, robustness and accuracy of the prediction model. Compared with the previously published results, a significant improvement in the predictions of the proposed ANN model was observed.

ANN model based on the group contribution method could achieve a satisfactory performance for logBB prediction.

ANN model based on the group contribution method could achieve a satisfactory performance for logBB prediction.

Auditory brainstem responses (ABRs) offer a unique opportunity to assess the neural integrity of the peripheral auditory nervous system in individuals presenting with listening difficulties. ABRs are typically recorded and analyzed by an audiologist who manually measures the timing and quality of the waveforms. The interpretation of ABRs requires considerable experience and training, and inappropriate interpretation can lead to incorrect judgments about the integrity of the system. Machine learning (ML) techniques may be a suitable approach to automate ABR interpretation and reduce human error.

The main objective of this paper was to identify a suitable ML technique to automate the analysis of ABR responses recorded as a part of the electrophysiological testing in the Auditory Processing Disorder clinical test battery.

ABR responses recorded during routine clinical assessment from 136 children being evaluated for auditory processing difficulties were analyzed using several common ML algorithms Support Vl be translated into an evaluation tool that can be used by audiologists in the clinic. Furthermore, this work may aid future researchers in exploring ML paradigms to improve clinical test batteries used by audiologists in achieving accurate diagnoses.

The findings of the present study demonstrate that it is possible to develop accurate ML models to automate the process of analyzing ABR waveforms recorded at suprathreshold levels. There is currently no ML-based application to screen children with listening difficulties. Therefore, it is expected that this work will be translated into an evaluation tool that can be used by audiologists in the clinic. Furthermore, this work may aid future researchers in exploring ML paradigms to improve clinical test batteries used by audiologists in achieving accurate diagnoses.

The hybrid artificial pancreas regulates glucose levels in people with type 1 diabetes. It delivers (i) insulin boluses at meal times based on the meals' carbohydrate content and the carbohydrate ratios (CRs) and (ii) insulin basal, between meals and at night, continuously modulated around individual-specific programmed basal rate. Immunology antagonist The CRs and programmed basal rate significantly vary between individuals and within the same individual with type 1 diabetes, and using suboptimal values in the hybrid artificial pancreas may degrade glucose control. We propose a reinforcement learning algorithm to adaptively optimize CRs and programmed basal rate to improve the performance of the hybrid artificial pancreas.

The proposed reinforcement learning algorithm was designed using the Q-learning approach. The algorithm learns the optimal actions (CRs and programmed basal rate) by applying them to the individual's state (previous day's glucose levels and insulin delivery) based on an exploration and exploitation trade-ofat the proposed algorithm has the potential to improve glucose control in people with type 1 diabetes using the hybrid artificial pancreas. The proposed algorithm is a key in making the hybrid artificial pancreas adaptive for the long-term real life outpatient studies.The pregnane X receptor (PXR) is one of the major transcription factors that regulate the expression of different drug-metabolizing enzymes and transporters. Adenosine-to-inosine RNA editing, the most frequent nucleotide conversion on RNA, which is catalyzed by adenosine deaminase acting on RNA (ADAR) enzymes, may modulate gene expression and function. Here, we investigated the potential regulation of human PXR expression by adenosine-to-inosine RNA editing. Knockdown of ADAR1 increased PXR mRNA level, and the knockdown of ADAR1 or ADAR2 significantly increased PXR protein level in HepaRG cells. In HepG2 cells, the knockdown of ADAR1 or ADAR2 significantly increased PXR mRNA and protein levels. The increase in the PXR protein by ADAR1 knockdown resulted in increased cytochrome P450 3A4 (CYP3A4) transactivity and CYP3A4 and UDP-glucuronosyltransferase 1A1 (UGT1A1) expression. A reporter assay revealed that the 3'-untranslated region (UTR) of PXR mRNA, especially from +3371 to +3440, is responsible for the ADAR-mediated post-transcriptional control of PXR expression, despite the lack of RNA edited sites in this region. Collectively, we found that PXR is negatively regulated by ADAR1 via an indirect mechanism, which facilitates the degradation of PXR mRNA. We could demonstrate that ADAR1 can cause interindividual variability in hepatic drug metabolism potencies.Alkali-mediated disintegration is efficient to improve the anaerobic digestion of waste activated sludge (WAS). In the present study, the role and potential of refinery spent caustic (RSC), an alkaline hazardous waste, in enhancing the disintegration of refinery waste activated sludge (RWAS) was investigated. The high alkalinity and free ammonia of RSC destroyed the microbial cell wall and promoted the release of intracellular substances. The contents of N-acetylglucosamine and proteins in the disintegrated liquid greatly increased to 0.41 mg/L and 1147 mg/L, respectively, relative to no disintegration (0.04 mg/L and 3.3 mg/L). The methane production (66.1 mL/g-VS) from RWAS anaerobic digestion increased by 226% compared to without disintegration (20.3 mL/g-VS). This study provides a newly developed "wastes-treat-wastes" management approach of refinery wastewater using combined treatment processes for RWAS and RSC using a cost-efficient and environmentally friendly disintegration of RWAS.Atmospheric Polycyclic Aromatic Hydrocarbons (PAHs) emissions cause non-negligible damage to human health and well-being. Effective regional cooperation is urgently required to mitigate PAHs emissions to maintain satisfactory air quality. This study quantified and tracked China's PAHs emissions flows embodied in interprovincial trade. A production-based emissions inventory of 16 U.S. EPA priority PAHs based on commercial energy consumption in China in 2012 was compiled using the emissions factor approach. Then, a multiregional input-output model was constructed to reveal consumption-based emissions and to track the PAHs emissions embodied in the trade of 27 major sectors across 30 regions in China. Key structural paths were also identified using structural path analysis (SPA). In 2012, the total industrial energy-derived PAHs emissions were estimated to be 47.7 tons of BaP-toxic equivalents (8032.7 tons of mass). Shandong, Hebei, and Hubei accounted for more than 24.0% of the production-side PAHs emissions in the whole country. Approximately 30.8% of China's PAHs emissions were embodied in goods consumed outside of the province in which they were produced. PAHs flow tended to start in the western regions and ended in the eastern regions along the coast. The results of the SPA showed that critical paths, such as from the Metallurgy sector to the Construction sector, embodied a large amount of emissions and had the potential to affect the performance of the entire system. By paying attention to the consumption-based accounting as well as the production-based accounting of emissions and by focusing on vital transfer paths, policymakers can devise effective and targeted environmental protection and sustainable development policies in China.