Aim
To evaluate the effect of a ketogenic diet (KD) in women with polycystic ovary syndrome (PCOS) and liver dysfunction who were obese.
Methods
Women with PCOS and liver dysfunction who were obese ...were enrolled in this prospective, open‐label, parallel‐group, controlled pilot trial, and randomly received KD (KD group) or conventional pharmacological treatment (Essentiale plus Yasmin, control group) in a 1:1 ratio for 12 weeks. The primary endpoint was the liver function markers. Secondary endpoints included the menstrual cycle, anthropometric characteristics, body composition, hormonal levels, and metabolic biomarkers.
Results
Of the 20 eligible participants enrolled, 18 participants completed the study. The KD group reported a significant reduction in anthropometric characteristics and body composition from baseline to week 12 (all p < 0.05). In addition, there were significant reductions in menstrual cycle, plasma estradiol, and progesterone levels in two groups (all p < 0.05), but no significant between‐group difference was observed. KD significantly reduced the liver function markers compared with control group (p < 0.05). The signs of fatty liver disappeared in six out of seven fatty liver participants in KD group after 12 weeks of intervention, while only one of 10 fatty liver participants in control group disappeared.
Conclusions
In addition to improving the menstrual cycle, KD had the additional benefits of reducing blood glucose and body weight, improving liver function, and treating fatty liver compared to traditional pharmacological treatment in women with PCOS and liver dysfunction who were obese.
•Impacts of varied carbon sources on denitrification were elucidated.•Metabolic patterns of microbial communities were investigated.•Complex microbial networks were found in the reactors with ...methanol and glycerol.•Intricate network relationship contributed to bioreactor efficiency
The limited information on microbial interactions and metabolic patterns in denitrification systems, especially those fed with different carbon sources, has hindered the establishment of ecological linkages between microscale connections and macroscopic reactor performance. In this work, denitrification performance, metabolic patterns, and ecological structure were investigated in parallel well-controlled bioreactors with four representative carbon sources, i.e., methanol, glycerol, acetate, and glucose. After long-term acclimation, significant differences were observed among the four bioreactors in terms of denitrification rates, organic utilization, and heterotrophic bacterial yields. Different carbon sources induced the succession of denitrifying microbiota toward different ecological structures and exhibited distinct metabolic patterns. Methanol-fed reactors showed distinctive microbial carbon utilization pathways and a more intricate microbial interaction network, leading to significant variations in organic utilization and metabolite production compared to other carbon sources. Three keystone taxa belonging to the Verrucomicrobiota phylum, SJA-15 order and the Kineosphaera genus appeared as network hubs in the methanol, glycerol, and acetate-fed systems, playing essential roles in their ecological functions. Several highly connected species were also identified within the glucose-fed system. The close relationship between microbial metabolites, ecological structures, and system performances suggests that this complex network relationship may greatly contribute to the efficient operation of bioreactors.
Display omitted
4mC is a type of DNA alteration that has the ability to synchronize multiple biological movements, for example, DNA replication, gene expressions, and transcriptional regulations. Accurate prediction ...of 4mC sites can provide exact information to their hereditary functions. The purpose of this study was to establish a robust deep learning model to recognize 4mC sites in Geobacter pickeringii. In the anticipated model, two kinds of feature descriptors, namely, binary and
-mer composition were used to encode the DNA sequences of
. The obtained features from their fusion were optimized by using correlation and gradient-boosting decision tree (GBDT)-based algorithm with incremental feature selection (IFS) method. Then, these optimized features were inserted into 1D convolutional neural network (CNN) to classify 4mC sites from non-4mC sites in
. The performance of the anticipated model on independent data exhibited an accuracy of 0.868, which was 4.2% higher than the existing model.
Cerebral small vessel disease (CSVD) is one of the main causes of vascular dementia in older individuals. Apart from risk containment, efforts to prevent or treat CSVD are ineffective due to the ...unknown pathogenesis of the disease. CSVD, a subtype of stroke, is characterized by recurrent strokes and neurodegeneration. Blood-brain barrier (BBB) impairment, chronic inflammatory responses, and leukocyte infiltration are classical pathological features of CSVD. Understanding how BBB disruption instigates inflammatory and degenerative processes may be informative for CSVD therapy. Antigens derived from the brain are found in the peripheral blood of lacunar stroke patients, and antibodies and sensitized T cells against brain antigens are also detected in patients with leukoaraiosis. These findings suggest that antigen-specific immune responses could occur in CSVD. This review describes the neurovascular unit features of CSVD, the immune responses to specific neuronal and glial processes that may be involved in a distinct mechanism of CSVD, and the current evidence of the association between mechanisms of inflammation and interventions in CSVD. We suggest that autoimmune activity should be assessed in future studies; this knowledge would benefit the development of effective therapeutic interventions in CSVD.
This study examines the impact of customer concentration on corporate charitable donations. Drawing on stakeholder and resource dependency theories, we argue that when firms rely on a small set of ...customers for a significant proportion of sales revenue, they will reduce corporate charitable donations. State ownership and industry competition moderate this relationship. Using data on corporate donations from Chinese listed firms between 2009 and 2019, we find support for our hypotheses. Our study contributes to the literature on customer concentration and corporate philanthropy in emerging economies.
Unraveling the magnetic order in iron chalcogenides and pnictides at atomic scale is pivotal for understanding their unconventional superconducting pairing mechanism, but is experimentally ...challenging. Here, by utilizing spin‐polarized scanning tunneling microscopy, real‐space spin contrasts are successfully resolved to exhibit atomically unidirectional stripes in Fe4Se5 ultrathin films, the plausible closely related compound of bulk FeSe with ordered Fe‐vacancies, which are grown by molecular beam epitaxy. As is substantiated by the first‐principles electronic structure calculations, the spin contrast originates from a pair‐checkerboard antiferromagnetic ground state with in‐plane magnetization, which is modulated by a spin–lattice coupling. These measurements further identify three types of nanoscale antiferromagnetic domains with distinguishable spin contrasts, which are subject to thermal fluctuations into short‐ranged patches at elevated temperatures. This work provides promising opportunities in understanding the emergent magnetic order and the electronic phase diagram for FeSe‐derived superconductors.
Fe4Se5 ultrathin films are experimentally demonstrated to host a pair‐checkerboard antiferromagnetic (AFM) ground state with in‐plane magnetization, evident with magnetic‐field‐dependent spin contrasts in real‐space by spin‐polarized scanning tunneling microscopy. The AFM order is modulated by a spin–lattice coupling and exhibits three types of nanoscale AFM domains, which are subject to thermal fluctuations into short‐ranged patches at elevated temperatures.
Background and Aim
Hepatocellular carcinoma (HCC) remains a serious cause of cancer‐related deaths worldwide. Developing new therapeutic strategies is urgently needed to improve the outcomes of HCC ...patients. Dendritic cell (DC)‐based vaccines and programmed death 1 (PD‐1) immune checkpoint inhibitors have been regarded as potential immunotherapeutics for HCC. However, the therapeutic efficacy of combining these two treatments for HCC remains to be evaluated.
Methods
In this study, DCs were derived from mouse bone marrow and pulsed with mouse HCC cell lysates to generate a DC vaccine. A monoclonal antibody that blocks the interaction of mouse PD‐1 with its ligands was used as a PD‐1 inhibitor. An orthotopic HCC mouse model was established to assess the effect of a DC vaccine in combination with a PD‐1 inhibitor on overall survival and tumor volume.
Results
Compared with the untreated control, single treatment with a DC vaccine or PD‐1 inhibitor prolonged the overall survival and reduced the tumor volume of HCC mice. Further, compared with the single treatment with the DC vaccine or the PD‐1 inhibitor, a combination treatment using both agents elicited a higher cytotoxicity of T cells against HCC cells and resulted in a better overall survival, smaller tumor volume, and greater tumor cell apoptosis in HCC mice.
Conclusions
Our results suggest that a combination treatment with DC vaccine and PD‐1 inhibitor may be a promising therapeutic strategy for HCC.
Accurately predicting the survival rate of breast cancer patients is a major issue for cancer researchers. Machine learning (ML) has attracted much attention with the hope that it could provide ...accurate results, but its modeling methods and prediction performance remain controversial. The aim of this systematic review is to identify and critically appraise current studies regarding the application of ML in predicting the 5-year survival rate of breast cancer.
In accordance with the PRISMA guidelines, two researchers independently searched the PubMed (including MEDLINE), Embase, and Web of Science Core databases from inception to November 30, 2020. The search terms included breast neoplasms, survival, machine learning, and specific algorithm names. The included studies related to the use of ML to build a breast cancer survival prediction model and model performance that can be measured with the value of said verification results. The excluded studies in which the modeling process were not explained clearly and had incomplete information. The extracted information included literature information, database information, data preparation and modeling process information, model construction and performance evaluation information, and candidate predictor information.
Thirty-one studies that met the inclusion criteria were included, most of which were published after 2013. The most frequently used ML methods were decision trees (19 studies, 61.3%), artificial neural networks (18 studies, 58.1%), support vector machines (16 studies, 51.6%), and ensemble learning (10 studies, 32.3%). The median sample size was 37256 (range 200 to 659820) patients, and the median predictor was 16 (range 3 to 625). The accuracy of 29 studies ranged from 0.510 to 0.971. The sensitivity of 25 studies ranged from 0.037 to 1. The specificity of 24 studies ranged from 0.008 to 0.993. The AUC of 20 studies ranged from 0.500 to 0.972. The precision of 6 studies ranged from 0.549 to 1. All of the models were internally validated, and only one was externally validated.
Overall, compared with traditional statistical methods, the performance of ML models does not necessarily show any improvement, and this area of research still faces limitations related to a lack of data preprocessing steps, the excessive differences of sample feature selection, and issues related to validation. Further optimization of the performance of the proposed model is also needed in the future, which requires more standardization and subsequent validation.
Core-shell microspheres with high-entropy alloy (HEA: FeCoNiCrCuAl0.3) as core and metal oxide (Ni–NiO) as shell have been successfully constructed via a two-step hydrothermal method. The chemical ...composition, microstructure, electromagnetic (EM) properties and EM wave absorption properties were characterized in detail. We find that the magnetic loss originated from high-entropy alloy and the dielectric loss caused by Ni–NiO can promote the consumption of EM energy through synergistic effect. The effective absorption bandwidth (fE, RL < −10 dB) of HEA@air@Ni–NiO composites was 4.0 GHz with an ultra-thin matching thickness (d) of 1.3 mm. We believe that this HEA@air@Ni–NiO composite can be a new generation of high-efficiency high-entropy alloy based EM wave absorber because of its strong EM wave absorption capability under an ultra-thin matching thickness. In addition, the new preparation method of core-shell micro-/nanostructure also has important reference value.
Display omitted
•The HEA@air@Ni–NiO composites have been successfully synthesized.•The effective bandwidth is up to 4.0 GHz with an ultra-thin thickness of 1.3 mm.•It promotes the consumption of EM energy through synergistic effect.
PDF
