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Background: Despite the benefits of exclusive breastfeeding, the proportion of exclusively breastfed children remains low in rural China. Self-efficacy is one of the most crucial modifiable factors predicting breastfeeding behavior. However, existing instruments in China do not specifically measure self-efficacy for exclusive breastfeeding but rather measure self-efficacy for any breastfeeding. Furthermore, they have been validated only in high-income Chinese settings. We sought to adapt and validate an instrument to measure exclusive breastfeeding self-efficacy within rural Chinese contexts. Methods: We introduced relevant items to Dennis' Breastfeeding Self-Efficacy Scale-Short Form (BSES-SF), which can assess maternal self-efficacy for exclusive breastfeeding. It was then implemented in a multistage random cluster sampling design and cross-sectional survey with home-visit interviews among women 0–6 months postpartum (n = 654) in the rural areas of four counties in Sichuan, China. We performed item-total and adjusted item-total correlations, as well as exploratory factor analysis to remove redundant items and determine the latent factor structure. We further applied confirmatory factor analysis to test the dimensionality of the scale. We then assessed the reliability of the scale and conducted tests of predictive and divergent validity. Known group comparisons were made by primiparous status and breastfeeding support level. We compared the validated Exclusive Breastfeeding Self-Efficacy Scale with the BSES-SF in terms of reliability and validity to explore the added value of scale modification. Results: Our modification of the BSES-SF to target exclusive breastfeeding produced 19 items. This was further reduced to 15 items based on adjusted item-total correlations and exploratory factor analysis, forming the Exclusive Breastfeeding Self-Efficacy Scale. This scale had three dimensions: "Breast milk supply and quality," "Breastfeeding skills," and "Exclusive breastfeeding" subscales. The Exclusive Breastfeeding Self-Efficacy Scale demonstrated strong internal consistency and overall reliability with a Cronbach's alpha coefficient of 0.91. Predictive and divergent validity and known group comparison assessments supported its validity. Robust psychometric evaluations demonstrated enhanced validity and reliability compared to the original BSES-SF. Conclusions: Our Exclusive Breastfeeding Self-Efficacy Scale is valid and reliable for measuring exclusive breastfeeding self-efficacy within rural Chinese contexts and is ready for adaptation and validation for clinical and programmatic use elsewhere, particularly within LMICs. [ABSTRACT FROM AUTHOR]

The dengue virus (DENV) is a mosquito-borne flavivirus transmitted through the infected mosquito, primarily of the Aedes genus. Over recent decades, the significant emergence of DENV infections has posed growing challenges for control efforts. Extensive research indicates that the dengue NS2B–NS3 protease, a non-structural viral protein, is the most promising target in dengue drug research. Therefore, our study synthesized two series of novel allosteric inhibitors of the NS2B–NS3 protease to explore previous allosteric inhibitors of the protease. Meanwhile, enzyme inhibitory activities of all compounds are measured and IC50 values of the optimized compounds are obtained by utilizing a fluorescence-based assay. Notably, compound A24 demonstrates the highest enzyme inhibitory activity with an IC50 value of 16 μM. Further enzyme kinetic experiments confirm that the optimized compound A24 functions as a non-competitive inhibitor of the NS2B–NS3 protease. Through molecular docking studies, compound A24 showed a binding energy of −5.6 kcal mol−1, comparable to the lead compounds, which preliminarily explains its similar inhibitory activity. In addition, compound A24 forms a stable complex with the protein primarily through hydrogen bonds and π–π interaction, with subsequent dynamics simulations confirming the stable binding mode. In conclusion, we designed and synthesized 59 novel scaffold compounds and identified compound A24 with the highest activity through pharmacological experiments and computer-aided drug design, which targets the allosteric sites and contributes significantly to the prevention of the dengue virus. [ABSTRACT FROM AUTHOR]

To address the issue of low yield in the preparation of nanofiber materials using single-needle electrospinning technology, multi-needle electrospinning technology has emerged as a crucial solution for mass production. However, the mutual interference of multiple electric fields between the needles can cause significant randomness in the morphology of the produced nanofibers. To better predict the influence of electric field distribution on nanofiber morphology, simulation analysis of the multi-needle arrangement was conducted using finite element analysis (FEA) software. Nanofiber-coated yarn was produced continuously with the core yarn rotating. The water bath was utilized as the receiver of nanofibers on self-made water bath electrospinning equipment. The electric field distribution and mutual interference under seven different needle arrangements was simulated and analyzed by FEA software ANSYS Maxwell. The results indicated that when the needles were arranged diagonally in a staggered pattern and directly above the core yarn, the simulated electric field distribution was relatively uniform, with less mutual interference. The produced nanofibers exhibited a finer diameter and the diameter distribution was more concentrated. In addition, the nanofiber coating showed higher crystallinity and better mechanical properties. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have achieved breakthrough progress in various fields, largely owing to the support of large-scale datasets with manually annotated labels. However, obtaining such datasets is costly and time-consuming, making high-quality annotation a challenging task. In this work, we propose an improved noisy sample selection method, termed "sample selection framework", based on a mixup loss and recalibration strategy (SMR). This framework enhances the robustness and generalization abilities of models. First, we introduce a robust mixup loss function to pre-train two models with identical structures separately. This approach avoids additional hyperparameter adjustments and reduces the need for prior knowledge of noise types. Additionally, we use a Gaussian Mixture Model (GMM) to divide the entire training set into labeled and unlabeled subsets, followed by robust training using semi-supervised learning (SSL) techniques. Furthermore, we propose a recalibration strategy based on cross-entropy (CE) loss to prevent the models from converging to local optima during the SSL process, thus further improving performance. Ablation experiments on CIFAR-10 with 50% symmetric noise and 40% asymmetric noise demonstrate that the two modules introduced in this paper improve the accuracy of the baseline (i.e., DivideMix) by 1.5% and 0.5%, respectively. Moreover, the experimental results on multiple benchmark datasets demonstrate that our proposed method effectively mitigates the impact of noisy labels and significantly enhances the performance of DNNs on noisy datasets. For instance, on the WebVision dataset, our method improves the top-1 accuracy by 0.7% and 2.4% compared to the baseline method. [ABSTRACT FROM AUTHOR]

Artificial intelligence has shown immense potential in cancer prediction, but existing models cannot estimate prediction uncertainty by themselves. Here, we developed a Bayesian neural network (BNN) model, BNN‐CRC15, for colorectal cancer (CRC) prediction while assessing its reliability. The model was trained on routine laboratory data obtained from 27,911 participants and provided quantified prediction uncertainty, allowing identification of a subset of participants in which the model was confident, mimicking the diagnostic process of human practitioners. Our model exhibited superior performance (area under the curve = 0.918) in the confident participant group, which accounted for 46.4% of the patients, indicating that routine laboratory data alone are sufficient for accurate predictions in this subset. For the non‐confident group, further advanced tests, such as colonoscopy, could be recommended to achieve more accurate predictions. In addition, our model demonstrated superior overall accuracy (0.848) in all patients, outperforming other five traditional algorithms (extreme gradient boosting, support vector machine, logistic regression, random forest, and artificial neural network) and fecal immunochemical test in distinguishing CRC from non‐CRC. These findings suggest that our BNN‐CRC15 model could serve as a valuable tool for improving CRC diagnosis and prevention. [ABSTRACT FROM AUTHOR]

The development of high‐efficient and large‐scale non‐precious electrocatalysts to improve sluggish reaction kinetics plays a key role in enhancing electrocatalytic nitrogen reduction reaction (NRR) for ammonia production under mild condition. Herein, Fe3O4 and Fe supported by porous carbon (denoted as Fe/Fe3O4/PC‐800) composite with a high specific surface area of 1004.1 m2 g−1 was prepared via a simple template method. On one hand, the high surface area of Fe/Fe3O4/PC‐800 provides a large area to enhance N2 adsorption and promote more protons and electrons to accelerate the reaction, thereby greatly improving the dynamics. On the other hand, mesoporous Fe/Fe3O4/PC‐800 provides high electrochemically active surface area for promoting the occurrence of catalytic kinetics. As a result, Fe/Fe3O4/PC‐800 exhibited significantly enhanced NRR performance with an ammonia yield of 31.15 μg h−1 mg−1cat. and faraday efficiency of 22.26 % at −0.1 V vs. reversible hydrogen electrode (RHE). This study is expected to provide a new strategy for the synthesis of catalysts with large specific area and pave the way for the foundational research in NRR. [ABSTRACT FROM AUTHOR]

Early diagnosis of cancer is crucial to improving the long‐term survival rate of patients. However, commonly used tumor markers lack sensitivity and specificity for screening purposes. Herein, 10 diagnostic models for 10 common types of cancer are developed by extreme gradient boosting, incorporating 66 laboratory parameters. The datasets consist of a retrospective cohort of 737 503 training and 184 012 validation cases, and a prospective cohort of 174 894 cases for model testing. The areas under the curve of the 10 diagnostic models range from 0.763 to 0.993. Notably, the different models have varying numbers of identical parameters among the 66 test features. Additionally, SHapley Additive exPlanation analysis reveals that 54 nontumor markers contributed significantly to the models. Cosine similarity analysis and clustering analysis demonstrate that some of the 10 cancers share common pathophysiological characteristics. Feature‐based inference graph models are thus performed and infer relationships between nontumor index parameters and cancers with strong correlations. In conclusion, a machine learning‐based pan‐cancer early warning system has been established in this study, which can guide doctors in selecting more accurate testing indicators and assessing the risk of 10 types of cancer with greater precision. [ABSTRACT FROM AUTHOR]

Background: Lonicera macranthoides Hand.-Mazz. is an important medicinal plant. Xianglei-type (XL) L. macranthoides was formed after many years of cultivation by researchers on the basis of the natural mutant. The corolla of L. macranthoides XL remains unexpanded and its flowering period is nearly three times longer than that of wild-type (WT) plants. However, the molecular mechanism behind this desirable trait remains a mystery. Objective: To understand the floral phenotype differences between L. macranthoides and L. macranthoides XL at the molecular level. Methods: Transcriptome analysis was performed on L. macranthoides XL and WT. One DEG was cloned by RT-PCR amplification and selected for qRT-PCR analysis. Results: Transcriptome analysis showed that there were 5603 differentially expressed genes (DEGs) in XL vs. WT. Enrichment analysis of DEGs showed that pathways related to plant hormone signal transduction were significantly enriched. We identified 23 key genes in ethylene biosynthesis and signal transduction pathways. The most abundant were the ethylene biosynthesis DEGs. In addition, the open reading frames (ORFs) of WT and XL ETR2 were successfully cloned and named LM-ETR2 (GenBank: MW334978) and LM-XL-ETR2 (GenBank: MW334978), respectively. qRT-PCR at different flowering stages suggesting that ETR2 acts in the whole stage of flower development of WT and XL. Conclusions: This study provides new insight into the molecular mechanism that regulates the development of special traits in the flowers of L. macranthoides XL. The plant hormone ethylene plays an important role in flower development and flowering duration prolongation in L. macranthoides. The ethylene synthesis gene could be more responsible for the flower phenotype of XL. The genes identified here can be used for breeding and improvement of other flowering plants after functional verification. [ABSTRACT FROM AUTHOR]

The mellow and thick taste is a unique characteristic of pu−erh ripe tea infusion, and it is closely related to the chemical composition of pu−erh ripe tea, which is less studied. This paper clarifies and compares the chemical composition of pu−erh ripe tea to that of the raw materials of sun−dried green tea, and uses membrane separation technology to separate pu−erh ripe tea into the rejection liquid and the filtration liquid. The results show that microorganisms transformed most physicochemical components, except caffeine, during the pile fermentation. It was found that total tea polyphenols, soluble proteins, total soluble sugars, theabrownin, and galloylated catechins became enriched in the rejection liquid, and the rejection liquid showed a more obvious mellow and thick characteristic. Taste interactions between crude protein, crude polysaccharide, and theabrownin were determined. They illustrated that the mellow and thick taste of pu−erh ripe tea with the addition of theabrownin increased from 4.45 to 5.13. It is of great significance to explore the chemical basis of the mellow and thick taste in pu−erh tea for guiding the pu−erh tea production process and for improving the quality of pu−erh tea. [ABSTRACT FROM AUTHOR]

The electrocatalytic nitrogen reduction reaction (NRR) is known as a promising mean of nitrogen fixation to mitigate the energy crisis and facilitate fertilizer production under mild circumstances. For electrocatalytic reactions, the design of efficient catalysts is conducive to reducing activation energy and accelerating lethargic dynamics. Among them, hollow structural materials possess cavities in their structures, which can slack off the escape rate of N2 and reaction intermediates, prolong the residence time of N2, enrich the reaction intermediates' concentration, and shorten electron transportation path, thereby further enhancing their NRR activity. Here, the basic synthetic strategies of hollow structural materials are introduced first. Then, the recent breakthroughs in hollow structural materials as NRR catalysts are reviewed from the perspective of intrinsic, mesoscopic, and microscopic regulations, aiming to discuss how structures affect and improve the catalytic performance. Finally, the future research directions of hollow structural materials as NRR catalysts are discussed. This review is expected to provide an outlook for optimizing hollow structural NRR catalysts. [ABSTRACT FROM AUTHOR]

Accuracy and efficiency are essential topics in the current biometric feature recognition and security research. This paper proposes a deep neural network using bidirectional feature extraction and transfer learning to improve finger-vein recognition performance. Above all, we make a new finger-vein database with the opposite position information of the original one and adopt transfer learning to make the network suitable for our overall recognition framework. Next, the feature extractor is constructed by adjusting the unidirectional database's parameters, capturing vein features from top to bottom and vice versa. Correspondingly, we concatenate the above two features to form the finger-veins' bidirectional features, which are trained and classified by Support Vector Machines (SVM) to realize recognition. Experiments are conducted on the Malaysian Polytechnic University's published database (FV-USM) and finger veins of Signal and Information Processing Laboratory (FV-SIPL). The accuracy of our proposed algorithm reaches 99.67% and 99.31%, which is significantly higher than the unidirectional recognition under each database. Compared with the algorithms cited in this paper, our proposed model based on bidirectional feature enjoys higher accuracy, faster recognition speed than the state-of-the-art frameworks, and excellent practical value. [ABSTRACT FROM AUTHOR]

Exosomes are small extracellular vesicles (EVs) secreted by almost all cells, which have been recognized as a novel platform for intercellular communication in the central nervous system (CNS). Exosomes are capable of transferring proteins, nucleic acids, lipids, and metabolites between neurons and glial cells, contributing to CNS development and maintenance of homeostasis. Evidence shows that exosomes originating from CNS cells act as suppressors or promoters in the initiation and progression of neurological disorders. Moreover, these exosomes have been shown to transfer molecules associated with diseases through the blood-brain barrier (BBB) and thus can be detected in blood. This unique feature enables exosomes to act as potential diagnostic biomarkers for neurological disorders. In addition, a substantial number of researches have indicated that exosomes derived from mesenchymal stem cells (MSCs) have repair effects on neurological disorders. Herein, we briefly introduce the roles of exosomes under physiological and pathological conditions. In particular, novel roles of exosomes as potential diagnostic biomarkers and therapeutic tools for neurological disorders are highlighted. [ABSTRACT FROM AUTHOR]

Human umbilical cord mesenchymal stem cell-derived exosomes (HucMSC-Ex) are a promising tool for the repair of acute kidney injury (AKI) caused by cisplatin and ischemia/reperfusion. However, the roles of hucMSC-Ex in sepsis-associated AKI repair and its mechanism are largely unknown. Hence, we constructed a sepsis model through cecal ligation and puncture (CLP), testing the benefits of hucMSC-Ex in the sepsis in terms of survival rate, serum renal markers levels, morphological changes and apoptosis. Immunohistochemistry staining and immunofluorescence assay were used to investigate the role of NF-κB activity in the repair of sepsis-associated AKI with hucMSC-Ex. HK-2 cells were transfected with microRNA-146b (miR-146b) mimics and inhibitors, respectively, and the regulatory effect of miR-146b on NF-κB activity was studied. We found that hucMSC-Ex treatment significantly decreased the serum creatinine (Cr) and blood urea nitrogen (BUN) levels, ameliorated the morphological damage and inhibited renal tubular cells apoptosis. More importantly, the survival rate at 72 h was 28% in CLP group and 45% in hucMSC-Ex group, respectively. Treatment with hucMSC-Ex improved survival in mice with sepsis. These effects of hucMSC-Ex were mediated by the inhibition of NF-κB activity and the lessening of pro-inflammatory response. Furthermore, hucMSC-Ex significantly increased miR-146b expression in kidney tissues. Conversely, interleukin (IL)-1 receptor-associated kinase (IRAK1) level, which is the target gene of miR-146b, clearly decreased in hucMSC-Ex group. In brief, this study showed that treatment with hucMSC-Ex decreased IRAK1 expression through the up-regulation of miR-146b level, led to the inhibition of NF-κB activity, and eventually alleviated sepsis-associated AKI and improved survival in mice with sepsis. HucMSC-Ex may be a novel therapeutic agent for the reduction of sepsis-associated AKI. [ABSTRACT FROM AUTHOR]

A number of single-nucleotide polymorphisms within the 3′-UTR of genes have been shown to relate to the occurrence of cancers. In this study, by using polymerase chain reaction-restriction fragment length analysis method, we determined an SNP rs1599795 in the 3′-UTR of CD80 gene in 183 gastric cancer patients and 348 healthy controls. Statistical analysis results showed that SNP rs1599795 genotypes were significantly correlated with the risk of gastric cancer. Compared with the AA homozygotes, the TA heterozygotes were significantly more prevalent in the patients (OR 1.44, 95 % CI 0.98-2.11) with a larger tumor size ( P = 0.001), deeper infiltration ( P = 1.5 × 10), higher possibility of lymph node metastasis ( P = 0.003), and more in the late stage (TNM stage III and IV; P = 0.003); the TT homozygotes had larger tumor size ( P = 0.001) and lower degree of differentiation ( P = 2.2 × 10). Dual-luciferase reporter assays showed that miR-132-3p, miR-212-3p, and miR-361-5p inhibited the expression of CD80 through binding with the CD80 3′-UTR, and this inhibitory role of miR-132-3p, miR-212-3p, and miR-361-5p was impacted by rs1599795. Our findings have shown that the SNP rs1599795 in CD80 3′-UTR, through disrupting the regulatory role of miR-132-3p, miR-212-3p, and miR-361-5p in CD80 expression, contributed to the occurrence of gastric cancer. [ABSTRACT FROM AUTHOR]

Cell wall components such as pectin and hemicelluloses have been proposed to be involved in aluminum resistance mechanisms in plants. However, whether hydroxyproline-rich glycoproteins (HRGPs), one of the most abundant proteins of the cell walls, are involved in Al resistance mechanisms remains elusive. In this study, two rice cultivars Xiushui 03 (Al resistant) and Xiushui 128 (Al sensitive) significantly differing in Al resistance were identified. In the absence of Al, no significant difference was observed in contents of glycoproteins and hydroxyproline in cell wall fractions of these two cultivars. At the early stage of Al toxicity, glycoproteins and hydroxyproline were significantly induced in these two cultivars, but levels of their accumulation in cell walls were much higher in cv. Xiushui 03 than in cv. Xiushui 128. At the late stage of Al toxicity, their accumulation in cell walls dramatically decreased in cv. Xiushui 128 and, however, still kept a high level in cv. Xiushui 03. The finding that Al-induced changes of glycoproteins and hydroxyproline were completely consistent indicates that Al-induced glycoproteins are HRGPs. Further observation utilizing transmission electron microscope showed that HRGPs were greatly accumulated in cell walls leading to thickening of cell walls in cv. Xiushui 03, however, HRGPs and cell walls greatly decreased in cv. Xiushui 128. These data suggest that Al-induced HRGP accumulation in cell walls is involved in alleviating Al toxicity in rice. [ABSTRACT FROM AUTHOR]