Molecular Epidemiology of Multi-Drug Resistant Acinetobacter baumannii Isolated in Shandong, China

Acinetobacter baumannii is an emerging nosocomial pathogen prevalent in hospitals worldwide. In order to understand the molecular epidemiology of multi-drug resistant (MDR) A. baumannii, we investigated the genotypes of A. baumannii isolated from ten hospitals in Shandong, China, from August 2013 to December 2013, by pulsed field gel electrophoresis (PFGE) and multilocus sequence typing (MLST). Antimicrobial resistance genes were analyzed by PCR and DNA sequencing. By PFGE analysis, we discovered 11 PFGE types in these ten hospitals. By MLST, we assigned these isolates to 12 sequence types (STs), 10 of which belong to the cloning complex CC92, including the prevalent ST369, ST208, ST195, and ST368. Two new STs, namely ST794 and ST809, were detected only in one hospital. All isolates of the MDR A. baumannii were resistant to carbapenem, except 2 isolates, which did not express the blaOXA-23 carbapenemase gene, indicating blaOXA-23 is the major player for carbapenem resistance. We also discovered armA is likely to be responsible for amikacin resistance, and may play a role in gentamicin and tobramycin resistance. aac(3)-I is another gene responsible for gentamicin and tobramycin resistance. In summary, we discovered that the majority of the isolates in Shandong, China, were the STs belonging to the CC92. Besides, two new STs were detected in one hospital. These new STs should be further investigated for prevention of outbreaks caused by A. baumannii

Caloric restriction augments radiation efficacy in breast cancer.

Dietary modification such as caloric restriction (CR) has been shown to decrease tumor initiation and progression. We sought to determine if nutrient restriction could be used as a novel therapeutic intervention to enhance cytotoxic therapies such as radiation (IR) and alter the molecular profile of triple-negative breast cancer (TNBC), which displays a poor prognosis. In two murine models of TNBC, significant tumor regression is noted with IR or diet modification, and a greater regression is observed combining diet modification with IR. Two methods of diet modification were compared, and it was found that a daily 30% reduction in total calories provided more significant tumor regression than alternate day feeding. At the molecular level, tumors treated with CR and IR showed less proliferation and more apoptosis. cDNA array analysis demonstrated the IGF-1R pathway plays a key role in achieving this physiologic response, and multiple members of the IGF-1R pathway including IGF-1R, IRS, PIK3ca and mTOR were found to be downregulated. The innovative use of CR as a novel therapeutic option has the potential to change the biology of tumors and enhance the opportunity for clinical benefit in the treatment of patients with TNBC

Energy-related CO₂ emission accounts and datasets for 40 emerging economies in 2010-2019

Since 2000, CO2 emissions from emerging economies have outstripped those of developed economies. To limit global warming to under 1.5gg C by 2100, over 100 emerging economies have proposed net-zero carbon targets. Yet the supportive data are lacking-no inventory of CO2 emission outlines detailed sources by sector or distribution at the subnational level for these economies. Here, we redress the balance by establishing a dataset for an energy-related CO2 emission inventory that covers 47 sectors and eight energy types in 40 emerging economies (10.5281/zenodo.7309360, Cui et al., 2021). Their emissions, growing rapidly by 3.0g%gyr-1, reached 7.5gGt in 2019 and were sourced primarily in coal and oil (34.6g% and 28.1g%, respectively) and consumed by the power and transportation sectors. Meanwhile, among African countries in this group, biomass combustion was responsible for 34.7g%-96.2g% of emissions. Our dataset fills a data gap by providing a detailed, robust emission accounting baseline for emerging economies-an advance that will support emission reduction policymaking at global, national, and subnational levels.</p

Characteristic Quality Evaluation of Nang from the Yili Region in Xinjiang Based on Principal Component and Cluster Analysis

This study determined 13 quality indexes (weight, transverse diameter, height, moisture content, water activity, L*, a*, b*, hardness, cohesion, elasticity, adhesiveness, and chewability) in 55 different Nang samples from the Yili region in Xinjiang to examine their quality characteristics. The main indexes were selected for the Nang quality evaluation via principal component analysis, cluster analysis, and sensory quality assessment. The results showed an association between the 13 quality indexes of the 55 Nang samples. The height was highly significantly correlated with moisture content, water activity, elasticity, and chewiness (P<0.01), while the L* value and adhesiveness were substantially related (P<0.05). Three principal components were extracted via principal component analysis, with a cumulative variance contribution rate of 76.856%. Cluster analysis showed that six quality indexes, namely elasticity, chewability, adhesiveness, hardness, L* values, and b* values, were crucial for evaluating Nang quality. Then, the 55 Nang samples were classified into three categories: Large Nang, thick Nang, and snack Nang. The sensory quality analysis revealed significant differences between the quality of the three Nang categories (P<0.05). The color, flavor, and hardness scores of the large Nang were the highest at 14.85, 13.52, and 14.37, respectively, with a golden color and rich flavor. The elasticity and chewability scores of the thick Nang were the highest at 14.82 and 14.54, respectively, while the crisp score of the snack Nang was the highest at 14.47. These results revealed the key indexes for assessing the quality characteristics of three types of Nang from the Yili region in Xinjiang, providing a scientific classification method for constructing a Xinjiang Nang quality evaluation system

High-order brain network feature extraction and classification method of first-episode schizophrenia: an EEG study

IntroductionA multimodal persistent topological feature extraction and classification method is proposed to enhance the recognition accuracy of first-episode schizophrenia patients. This approach addresses the limitations of traditional higher-order brain network analyses that rely on single persistent features (e.g., persistent images).MethodsThe study utilized resting-state EEG data from 198 subjects recruited at Huilongguan Hospital in Beijing, comprising 102 males and 96 females, with a mean age of 30 years and mean education of 14 years. Persistent topological features were extracted using adaptive thresholding during persistent homology (PH) filtrations. The distribution of these features was visualized through heatmaps and persistence entropies, while the generation process was elucidated using Betti curves and persistence landscapes.ResultsThe classification performance of the multimodal persistent topological features was assessed using various machine learning classifiers. The classifier yielding the highest performance was selected for comparison with traditional brain network features derived from graph theory and single persistent topological features. The results revealed significant topological changes in first-episode schizophrenia patients throughout the persistent homology filtering compared to healthy subjects. The univariate feature selection algorithm achieved a classification accuracy of 94.6% with a combination of attributes meeting the criterion of AC ≥ 0.6.DiscussionThe proposed method demonstrates clinical significance for the early identification and diagnosis of first-episode schizophrenia patients, offering a new research perspective for constructing higher-order functional connectivity networks and extracting topological structure features

Neutrophil-lymphocyte and platelet-lymphocyte ratios as systemic inflammatory biomarkers for atopic dermatitis in US adults: a cross-sectional NHANES study revealing subgroup heterogeneity

BackgroundNeutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) are systemic inflammation markers, but their association with adult atopic dermatitis (AD) remains underexplored.MethodsThis cross-sectional study analyzed 2001–2006 NHANES data from 10,890 US adults. AD was defined by self-reported physician diagnosis. Cutoffs for NLR (1. 81×109/L) and PLR (136. 13×109/L) were determined via ROC analysis. Multivariable models adjusted for sociodemographic and clinical covariates.ResultsElevated NLR (≥1. 81×109/L) and PLR (≥136. 13×109/L) were independently associated with higher AD prevalence after full adjustment (NLR: OR=1. 23, 95%CI:1. 08–1. 40; PLR: OR=1. 24, 95%CI:1. 10–1. 41). Subgroup analyses revealed stronger associations in males, normal-BMI individuals, and asthmatics (PLR: OR=1. 84), but inverse correlations in nonsmokers (NLR: OR=0. 33; PLR: OR=0. 34). Significant interactions occurred with BMI and asthma (PLR-interaction P=0. 0077).ConclusionNLR and PLR are accessible systemic inflammatory biomarkers for AD, with subgroup heterogeneity suggesting roles for lymphocyte depletion (skin homing), neutrophilic (Th17), and platelet-mediated (Th2) inflammation pathways

DIFFERENCES IN THE PHYSICAL AND MECHANICAL PROPERTIES OF DIFFERENT VARIETIES OF LOTUS ROOT

ABSTRACT The traditional way of harvesting lotus root consumes a lot of labor and has low harvesting efficiency. Therefore, it is necessary to explore the automated harvesting device of lotus root. And the physical and mechanical properties of lotus root are crucial for the design of harvesting device. In this study, the mechanical properties of "Baoying Beauty Red" and "E-Lian No.7" lotus roots in different positions were tested by compression, bending and tensile methods. The results showed that the mechanical properties of "Baoying Beauty Red" were better than those of "E-Lian No.7", which were not easy to be damaged during harvesting, and the minimum compressive strength of "Baoying Beauty Red" was 4.04 MPa, the minimum bending force of lotus root breakage was 97 N, and the minimum tensile force was 182.73 N

Research Progress on the Efficacy and Mechanism of Collagen Peptides in Delaying Skin Aging

With the emergence of social aging and the increasing emphasis on skin health, the demand in the market for active ingredients to prevent and delay skin aging is increasing. Collagen peptides have various physiological activities and great potential in delaying skin aging. In this paper, the source, preparation, structure and biological activities of collagen peptides are introduced, and the mechanisms of action of collagen peptides in delaying skin aging are summarized. Moreover, the progress made in the past decade in clinic research on oral collagen peptides in alleviating skin aging is reviewed. At present, collagen peptides have been widely applied in various fields such as foods, oral beauty drinks, health products and cosmetics. However, there are still some problems and challenges in terms of optimal intake, evaluation methods, identification and exploration of functional peptides, and interaction with other components. With the progress of production technology and the continuous deepening of research, collagen peptides with anti-skin aging activity have enormous potential and broad application prospects in food nutrition, healthy skincare, clinical therapy and medical beauty

Advance in Application of Deep Learning in Food Quality and Safety Detection

With the improvement of people's living standards, consumers' demand for food quality and safety is growing. Traditional methods for detecting food quality and safety can no longer meet the demand for efficient, accurate and reliable detection. Therefore, it becomes imperative to seek a more efficient and convenient detection method. On this basis, the rapid development of deep neural network-based machine learning technology, i.e., deep learning, has brought new opportunities for food quality and safety detection. This paper focuses on the application progress of deep learning in food quality and safety detection. It introduces the principles of traditional machine learning and deep learning, and elaborates on the applications of deep learning in food origin tracing and food quality, including the detection of food defects, freshness, adulteration, and pathogens. Furthermore, it looks forward to the development trends of deep learning in the field of food quality and safety detection, aiming to provide theoretical references and research ideas for this field