Adverse Pregnancy Outcomes Following Exposure to Biologics in Women With Crohn's Disease: A Systematic Review and Meta-Analysis

Crohn's disease is a chronic disease, which commonly affects women during their reproductive years. Poorly treated Crohn's disease is associated with adverse pregnancy outcomes. Biologics, a group of therapeutic drugs targeting inflammatory mediators including anti-TNF, anti-integrins and anti-interleukins, are increasingly used in pregnant women with Crohn's disease, exposing both the women and their fetuses to treatment-related complications. At present, it is unclear which biologics are more superior. This study performed a systematic review and meta-analysis to assess the risk of adverse pregnancy outcomes in women with Crohn's disease after exposure to biologics. Bibliographic databases were searched from inception to May 2021. The outcomes of interest were preterm delivery, low birth weight, spontaneous abortion, and congenital abnormalities. A total of 11 studies comprised of 1,875 pregnancies among women with Crohn's disease were included. Of these, 1,162 received biologics and 713 received non-biologic therapy. During the remission phase of the disease, the use of biological therapy increased the risk of adverse pregnancy outcomes, of which anti-integrins were associated with a higher incidence of adverse pregnancy outcomes than anti-TNF and anti-interleukins.Systematic Review Registration:http://www.crd.york.ac.uk/PROSPERO, identifier: CRD42020191275

Safety Assessment of Channel Seepage by Using Monitoring Data and Detection Information

Seepage analysis has always been the focus of channel safety and stability research. Establishing a diagnosis method based on osmotic pressure monitoring data and combining the detection information to achieve osmotic safety is also an effective way to ensure the safety and stability of osmotic engineering. In this paper, a high-fill channel section of a water diversion project is taken as an example, and the study of osmotic safety is carried out by analyzing the engineering characteristics of linear engineering. High-fill channel sections were selected to study the temporal and spatial characteristics of various monitoring data reflecting the osmotic behavior of linear engineering; that is, these data reflect the time-varying regularity characteristics of the osmotic pressure value and the changing regularity of environmental variables. A single-point multifactor model of the monitoring data was established by establishing an evaluation index system, combining the monitoring index value method and the cloud model theory method according to the distribution law of the measured data and considering the uncertainty of the osmotic pressure data. Additionally, this model was integrated with the set pair analysis method to determine the monitoring data evaluation level; channel detection data information was collected, the abnormal detection of detection information was realized, and the monitoring data results were used to verify the detection results. In this way, an adaptive evaluation method reflecting the working behavior of high-filled channel sections is established, and a diagnostic technology for the safe operation of high-filled channel sections of linear engineering is proposed. The application results show that this method is suitable for engineering an osmotic safety assessment

Forest Conversion Changes Soil Particulate Organic Carbon and Mineral-Associated Organic Carbon via Plant Inputs and Microbial Processes

Primary forest conversion greatly influences soil organic carbon (SOC) sequestration. However, our understanding of how primary forest conversion affects SOC fractions and chemical component evenness remains limited. We examined how primary forest conversion (from primary mixed broadleaved Korean pine forest to secondary broadleaved forest and coniferous plantation) affects free particulate OC (POC), aggregate-occluded POC, mineral-associated OC (MAOC), and their chemical component evenness via plant inputs (e.g., litter and fine roots) and microbial properties (e.g., microbial biomass and residue C) in Northeast China. Primary forest conversion led to a large increase in litter and fine root quality (lower C/N ratio), SOC, and MBC of secondary forests and a reduction in litter and fine root quantity and quality, SOC, MBC, and microbial residue C of plantations, which drove changes in POC and MAOC. As a result, after conversion to secondary forests, free POC decreased by 20.3% and aggregate-occluded POC increased by 57.2%. After conversion to plantations, free POC increased by 49.1%, while aggregate-occluded POC and MAOC decreased by 42.4% and 9.0%, respectively. Free POC was negatively correlated with fine root biomass. Aggregate-occluded POC and MAOC were positively correlated with litter and fine root quality, MBC, and microbial residue C. Meanwhile, forest conversion decreased the evenness of free and aggregate-occluded POC chemical components in secondary forests, with O-alky C being higher and aromatic C being lower, while MAOC was not affected by forest conversion. The evenness of free and aggregate-occluded POC chemical components was associated with litter and fine root quality, and that of MAOC was associated with MBC and microbial residue C. High-quality plant inputs benefit OC sequestration in soil aggregates and MAOM through microbial assimilation and residue accumulation after primary forest conversion. Future forest management should consider tree species with high-quality input as a possible compensation for climate change by sequestering more OC in soil aggregates

Development and Validation of a Predictive Model Based on LASSO Regression: Predicting the Risk of Early Recurrence of Atrial Fibrillation after Radiofrequency Catheter Ablation

Background: Although recurrence rates after radiofrequency catheter ablation (RFCA) in patients with atrial fibrillation (AF) remain high, there are a limited number of novel, high-quality mathematical predictive models that can be used to assess early recurrence after RFCA in patients with AF. Purpose: To identify the preoperative serum biomarkers and clinical characteristics associated with post-RFCA early recurrence of AF and develop a novel risk model based on least absolute shrinkage and selection operator (LASSO) regression to select important variables for predicting the risk of early recurrence of AF after RFCA. Methods: This study collected a dataset of 136 atrial fibrillation patients who underwent RFCA for the first time at Peking University Shenzhen Hospital from May 2016 to July 2022. The dataset included clinical characteristics, laboratory results, medication treatments, and other relevant parameters. LASSO regression was performed on 100 cycles of data. Variables present in at least one of the 100 cycles were selected to determine factors associated with the early recurrence of AF. Then, multivariable logistic regression analysis was applied to build a prediction model introducing the predictors selected from the LASSO regression analysis. A nomogram model for early post-RFCA recurrence in AF patients was developed based on visual analysis of the selected variables. Internal validation was conducted using the bootstrap method with 100 resamples. The model’s discriminatory ability was determined by calculating the area under the curve (AUC), and calibration analysis and decision curve analysis (DCA) were performed on the model. Results: In a 3-month follow-up of AF patients (n = 136) who underwent RFCA, there were 47 recurrences of and 89 non-recurrences of AF after RFCA. P, PLR, RDW, LDL, and CRI-II were associated with early recurrence of AF after RFCA in patients with AF (p < 0.05). We developed a predictive model using LASSO regression, incorporating four robust factors (PLR, RDW, LDL, CRI-II). The AUC of this prediction model was 0.7248 (95% CI 0.6342–0.8155), and the AUC of the internal validation using the bootstrap method was 0.8403 (95% CI 0.7684–0.9122). The model demonstrated a strong predictive capability, along with favorable calibration and clinical applicability. The Hosmer–Lemeshow test indicated that there was good consistency between the predicted and observed values. Additionally, DCA highlighted the model’s advantages in terms of its clinical application. Conclusions: We have developed and validated a risk prediction model for the early recurrence of AF after RFCA, demonstrating strong clinical applicability and diagnostic performance. This model plays a crucial role in guiding physicians in preoperative assessment and clinical decision-making. This novel approach also provides physicians with personalized management recommendations

Prediction of Seepage Pressure Based on Memory Cells and Significance Analysis of Influencing Factors

Seepage analysis is always a concern in dam safety and stability research. The prediction and analysis of seepage pressure monitoring data is an effective way to ensure the safety and stability of dam seepage. With the timeliness of a change in a monitoring value and lag due to external influences, a RS-LSTM model written in Python is developed in this paper which combines rough set theory (RS) and the long- and short-term memory network model (LSTM). The model proposed calculates the prediction score of the seepage pressure of a dam experiencing multiple effects by preordering factor importance values to eliminate the interference of redundant factors. A case study shows that the water level, rainfall, temperature, and duration are all factors that affect the seepage pressure, and their importance values decrease successively. Thus, the seepage pressure of a dam can be predicted with a determination coefficient R2 of 0.96. Compared with the recurrent neural network (RNN) model and BP neural network model, the training time of the RS-LSTM model proposed is 6.37 s, and the operation efficiency is 41% and 59% higher than that of the RNN and BP models, respectively. The mean relative error is also 3.00%, which is 50% lower than that of the RNN model and 31% lower than that of the BP model. Based on these results, this model has the advantages of fast computation speed and high accuracy in prediction

New Insights into Xanthophylls and Lipidomic Profile Changes Induced by Glucose Supplementation in the Marine Diatom Nitzschia laevis

Nitzschia laevis is a candidate microorganism for bioactive compounds (fucoxanthin and eicosapentaenoic acid (EPA)) production. In this study, the impacts of glucose-induced trophic transition on biomass, photosynthesis, pigments, and lipid profiles were examined. The specific growth rate was increased under glucose addition, achieved at 0.47 day&minus;1 (0.26 &plusmn; 0.01 day&minus;1 for the group without glucose in medium). However, the photosynthetic parameters and pigments including chlorophylls, fucoxanthin, and diatoxanthin were reduced. The net yield of EPA doubled under glucose addition, reaching 20.36 &plusmn; 1.22 mg/L in 4 days. In addition, the alteration in detailed lipid molecular species was demonstrated with a focus on EPA-enriched lipids. The effects of 2-deoxyglucose (2DG) indicated that glucose phosphorylation was involved in glucose-induced regulation. These findings provide novel data for guiding the application of this diatom strain in the functional food industries

Effect of Heat Stress on Egg Production, Steroid Hormone Synthesis, and Related Gene Expression in Chicken Preovulatory Follicular Granulosa Cells

This study was conducted to elucidate the molecular mechanisms underlying heat stress (HS)-induced abnormal egg-laying in laying hens. Hy-Line brown laying hens were exposed to HS at 32 &deg;C or maintained at 22 &deg;C (control) for 14 days. In addition, granulosa cells (GCs) from preovulatory follicles were subjected to normal (37 &deg;C) or high (41 &deg;C or 43 &deg;C) temperatures in vitro. Proliferation, apoptosis, and steroidogenesis were investigated, and the expression of estrogen and progesterone synthesis-related genes was detected. The results confirmed that laying hens reared under HS had impaired laying performance. HS inhibited proliferation, increased apoptosis, and altered the GC ultrastructure. HS also elevated progesterone secretion by increasing the expression of steroidogenic acute regulatory protein (StAR), cytochrome P450 family 11 subfamily A member 1 (CYP11A1), and 3b-hydroxysteroid dehydrogenase (3&beta;-HSD). In addition, HS inhibited estrogen synthesis in GCs by decreasing the expression of the follicle-stimulating hormone receptor (FSHR) and cytochrome P450 family 19 subfamily A member 1 (CYP19A1). The upregulation of heat shock 70 kDa protein (HSP70) under HS was also observed. Collectively, laying hens exposed to high temperatures experienced damage to follicular GCs and steroidogenesis dysfunction, which reduced their laying performance. This study provides a molecular mechanism for the abnormal laying performance of hens subjected to HS

Advances in preparation and bioactivity of phosvitin phosphopeptides

Phosvitin and its phosphopeptides contain a large amount of phosphorylated amino groups and exhibit a series of unique bioactivities including metal-binding, antioxidant, bacteriostatic and biomineralization activities due to strong interactions between phosphate groups and metal ions. In this article, the structures and preparation of phosvitin phosphopeptides, the interactions of phosphopeptides with metal ions, and the biological activities associated with their metal-binding capacity, as well as future potential application prospects are discussed.This article is published as Liu, Wei, Mengdie Zhao, Songming Li, Dong Uk Ahn, Ning Chen, and Xi Huang. "Advances in preparation and bioactivity of phosvitin phosphopeptides." Journal of Future Foods 2, no. 3 (2022): 213-222. doi:10.1016/j.jfutfo.2022.06.003. Posted with permission. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

GAME: Generative-Based Adaptive Model Extraction Attack

The outstanding performance of deep learning has prompted the rise of Machine Learning as a Service (MLaaS), which significantly reduces the difficulty for users to train and deploy models. For privacy and security considerations, most models in the MLaaS scenario only provide users with black-box access. However, previous works have shown that this defense mechanism still faces potential threats, such as model extraction attacks, which aim at stealing the function or parameters of a black-box victim model. To further study the vulnerability of publicly deployed models, we propose a novel model extraction attack named Generative-Based Adaptive Model Extraction (GAME), which augments query data adaptively in a sample limited scenario using auxiliary classifier GANs (AC-GAN). Compared with the previous work, our attack has the following advantages: adaptive data generation without original datasets, high fidelity, high accuracy, and high stability under different data distributions. According to extensive experiments, we observe that: (1) GAME poses a threat to victim models despite the model architectures and the training sets; (2) synthetic samples closed to decision boundary without deviating from the center of the target distribution can accelerate the extraction process; (3) compared to state-of-the-art work, GAME improves relative accuracy by 12% at much lower data and query costs without the reliance on domain relevance of proxy datasets