Effect of oxidized starch on the storage stability of frozen raw noodles: Water distribution, protein structure, and quality attributes.

Freezing is a popular method of food preservation with multiple advantages. However, it may change the internal composition and quality of food. This study aimed to investigate the effect of modified starch on the storage stability of frozen raw noodles (FRNs) under refrigerated storage conditions. Oxidized starch (OS), a modified starch, is widely used in the food industry. In the present study, texture and cooking loss rate analyses showed that the hardness and chewiness of FRNs with added OS increased and the cooking loss rate decreased during the frozen storage process. Low-field nuclear magnetic resonance characterization confirmed that the water-holding capacity of FRNs with OS was enhanced. When 6% OS was added, the maximum freezable water content of FRNs was lower than the minimum freezable water content (51%) of FRNs without OS during freezing. Fourier-transform infrared spectroscopy showed that after the addition of OS, the secondary structures beneficial for structural maintenance were increased, forming a denser protein network and improving the microstructure of FRNs. In summary, the water state, protein structure, and quality characteristics of FRNs were improved by the addition of OS within an appropriate range.

Autophagy-related CMTM6 promotes glioblastoma progression by activating Wnt/ß-catenin pathway and acts as an onco-immunological biomarker.

BACKGROUND: Glioblastoma multiforme (GBM) is identified as one of the most prevalent and malignant brain tumors, characterized by poor treatment outcomes and a limited prognosis. CMTM6, a membrane protein, has been found to upregulate the expression of programmed cell death 1 ligand 1 protein (PD-L1) and acts as an immune checkpoint inhibitor by inhibiting the programmed death 1 protein/PD-L1 signaling pathway. Recent research has demonstrated a high expression of CMTM6 in GBM, suggesting its potential role in influencing the pathogenesis and progression of GBM, as well as its association with immune cell infiltration in the tumor microenvironment. However, the underlying mechanism of CMTM6 in GBM requires further investigation. METHODS: Data from cancer patients in The Cancer Genome Atlas, Gene Expression Omnibus and Chinese Glioma Genome Atlas cohorts were consolidated for the current study. Through multi-omics analysis, the study systematically examined the expression profile of CMTM6, epigenetic modifications, prognostic significance, biological functions, potential mechanisms of action and alterations in the immune microenvironment. Additionally, the study investigated CMTM6 expression in GBM cell lines and normal cells using reverse transcription PCR and western blot analysis. The impact of CMTM6 on GBM cell proliferation, migration and invasion was evaluated using a combination of cell counting kit-8 assay, clone formation assay, 5-ethynyl-2'-deoxyuridine incorporation assay, wound healing assay and Transwell assay. In order to explore the mechanism of CMTM6, the Wnt/ß-catenin signaling pathway and autophagy-related genes were further verified through western blot analysis. RESULTS: CMTM6 is highly expressed in multiple tumors, particularly GBM. CMTM6 has been shown to be a valuable diagnostic and prognostic biomarker by various bioinformatics approaches. Additionally, CMTM6 plays a pivotal role in the pathogenesis of cancer, specifically GBM, by modulating various biological processes such as DNA methyltransferase expression, RNA modification, copy number variation, genomic heterogeneity, tumor stemness and DNA methylation. The findings of the experiment indicate a significant correlation between elevated CMTM6 expression and the proliferation, invasion, migration and autophagy of GBM cells, with potential key roles mediated through the Wnt/ß-catenin signaling pathway. Furthermore, CMTM6 is implicated in modulating tumor immune cell infiltration and is closely linked to the expression of various immune checkpoint inhibitors and immune modulators, particularly within the context of GBM. High levels of CMTM6 expression also enhance the responsiveness of GBM patients to radiotherapy and chemotherapy, thereby offering valuable insights for guiding treatment strategies for GBM. CONCLUSIONS: Autophagy-related CMTM6 is highly expressed in various types of cancer, especially GBM, and it can regulate GBM progression through the Wnt/ß-catenin signaling pathway and is capable of being used as an underlying target for the diagnosis, treatment selection and prognosis of patients with GBM.

Study on physicochemical properties and anti-aging mechanism of wheat starch by anionic polysaccharides.

The anti-aging effects of two anionic polysaccharides AG (sodium alginate)/SSPS (soluble soybean polysaccharide) and WS (wheat starch) were evaluated, and their different mechanisms were explored. The rheological properties, gelatinization properties and aging properties were characterized. The addition of AG and SSPS changed the gelatinization parameters of WS, decreased the peak viscosity, breakdown viscosity and setback viscosity, and enhanced the fluidity of the gel system. Additionally, the starch molecular orderliness experiment showed that the relative crystallinity of starch gels decreased with the increase in AG and SSPS concentrations, indicating that the rearrangement of amylopectin was disturbed, which inhibited the cross-linking of starch molecules. The water state analysis showed that the hydrophilicity of AG and SSPS and their interactions with starch molecules influenced the relaxation behavior of water protons in the gel system in a concentration-dependent manner. In conclusion, the addition of AG and SSPS could significantly inhibit the aging of WS gels, probably due to the competition effect of AG and SSPS on water and the interaction with starch molecules. The present study results would provide new theoretical insights into WS-based food research.

Interpretation of the effects of hydroxypropyl starch and hydroxypropyl distarch phosphate on frozen raw noodles quality during frozen storage: Studies on water state and starch-gluten network properties.

The impacts of both structural variations induced by low temperature and physiochemical changes induced by modified starch on the qualities of frozen raw noodles (FRNs) were investigated during long-term freezing storage. The addition of modified starch was a potentially effective method to delay the loss of FRNs qualities during storage. In this study, hydroxypropyl starch (HPS) and hydroxypropyl distarch phosphate (HPDSP) were added to improve the cooking and textural characteristics of FRNs. The cooking loss rate of FRNs with the addition of 12%HPS was consistent with that of the control (4.39 % and 4.37 %, respectively), while after 8 weeks of storage showed the significant decrease effect (5.01 % and 5.78 %, respectively). In addition, adding HPS or HPDSP could change the colour and lustre of FRNs to that preferred by consumers. When 6 % HPS or HPDSP were added, the FRNs showed the lowest of freezable water content during storage. The test results of FTIR showed the secondary structure of FRNs was maintained with the introduction of HPS or HPDSP during refrigeration, and the microstructure was improved during the frozen storage period. Consequently, the results provided a theoretical basis and new insight for the production and transportation of FRNs.

An Efficient, Multiplexed Strategy for Instant Detection of Bacterial Biomarker by a Lanthanide-Organic Material.

Water contamination is a highly critical issue owing to its strong relationship to human health. In addition to chemical pollutants, microorganisms such as multiresistant pathogenic bacteria have received significant attention from the World Health Organization. The main problem associated with monitoring pathogenic bacteria in water is the interference from concomitant species and their low concentrations. To address this problem, we synthesized a bilanthanide-organic material as an efficient luminescence sensor for the detection of Pseudomonas aeruginosa, a representative bacterium, via its two unique biomarkers: 1-hydroxyphenazine (1-HX) and 2-aminoacetophenone (2-AA). This multiplexed sensing approach overcomes a common issue encountered by single-marker luminescence sensors that may report false positives due to coexisting species in the complex environment. High sensitivities and low limits of detection for 1-HX and 2-AA were obtained with very fast response time. The key structural factors governing the high-performance sensing function were revealed. This work provides an alternative route for the effortless and instant detection of bacterial biomarkers in water.

Effect of ß-cyclodextrins on the physical properties and anti-staling mechanisms of corn starch gels during storage.

The aim of this study was to investigate the effects of ß-cyclodextrin (ß-CD) and hydroxypropyl-ß-cyclodextrin (HP-ß-CD) on the physical and anti-staling properties of corn starch (CS). It was found that the addition of ß-CDs significantly decreased the storage and loss modulus of CS gel. Moreover, it reduced CS gel setback viscosity and hardness, in which HP-ß-CD primarily exhibited a more pronounced effect. The long and short-range ordered structure showed that the relative crystallinity of CS gel was delayed with the ß-CDs incorporation. Meanwhile, the ß-CDs promoted the inhibition of water migration. In addition, the microstructure observation showed that ß-CDs can better maintain the stability of CS gel structure during storage. Consequently, the addition of ß-CDs could delay the retrogradation of CS gel, and the introduction of hydroxypropyl groups was more effective, which provided a theoretical basis and new insights for the production of starch-based food industrial products.

Machine-Aided Self-diagnostic Prediction Models for Polycystic Ovary Syndrome: Observational Study.

BACKGROUND: Artificial intelligence and digital health care have substantially advanced to improve and enhance medical diagnosis and treatment during the prolonged period of the COVID-19 global pandemic. In this study, we discuss the development of prediction models for the self-diagnosis of polycystic ovary syndrome (PCOS) using machine learning techniques. OBJECTIVE: We aim to develop self-diagnostic prediction models for PCOS in potential patients and clinical providers. For potential patients, the prediction is based only on noninvasive measures such as anthropomorphic measures, symptoms, age, and other lifestyle factors so that the proposed prediction tool can be conveniently used without any laboratory or ultrasound test results. For clinical providers who can access patients' medical test results, prediction models using all predictor variables can be adopted to help health providers diagnose patients with PCOS. We compare both prediction models using various error metrics. We call the former model the patient model and the latter, the provider model throughout this paper. METHODS: In this retrospective study, a publicly available data set of 541 women's health information collected from 10 different hospitals in Kerala, India, including PCOS status, was acquired and used for analysis. We adopted the CatBoost method for classification, K-fold cross-validation for estimating the performance of models, and SHAP (Shapley Additive Explanations) values to explain the importance of each variable. In our subgroup study, we used k-means clustering and Principal Component Analysis to split the data set into 2 distinct BMI subgroups and compared the prediction results as well as the feature importance between the 2 subgroups. RESULTS: We achieved 81% to 82.5% prediction accuracy of PCOS status without any invasive measures in the patient models and achieved 87.5% to 90.1% prediction accuracy using both noninvasive and invasive predictor variables in the provider models. Among noninvasive measures, variables including acanthosis nigricans, acne, hirsutism, irregular menstrual cycle, length of menstrual cycle, weight gain, fast food consumption, and age were more important in the models. In medical test results, the numbers of follicles in the right and left ovaries and anti-Müllerian hormone were ranked highly in feature importance. We also reported more detailed results in a subgroup study. CONCLUSIONS: The proposed prediction models are ultimately expected to serve as a convenient digital platform with which users can acquire pre- or self-diagnosis and counsel for the risk of PCOS, with or without obtaining medical test results. It will enable women to conveniently access the platform at home without delay before they seek further medical care. Clinical providers can also use the proposed prediction tool to help diagnose PCOS in women.

Fluorinated Graphite (FG)-Modified Li-S Batteries with Superhigh Primary Specific Capacity and Improved Cycle Stability.

Lithium-sulfur (Li-S) batteries have received extensive attention because of their high theoretical energy density and low cost. However, the low sulfur utilization and the shuttle effect of polysulfide cause low initial capacity and serious capacity decay. Herein, fluorinated graphite (FG) is introduced to the cathode to alleviate these issues. The results indicated that the FG could provide additional capacity during the first discharge process and increase the porosity and polarity of the cathode via in situ formation of lithium fluoride (LiF) nanocrystals, which can enhance the infiltration of electrolyte and polysulfide adsorption. As a result, the as-prepared cathode containing FG shows a high initial specific capacity of 1602 mA h g-1 and the reversible specific capacity is 650 mA h g-1 at 0.5C after 300 cycles. Moreover, its specific capacity remains at 860 mA h g-1 at 5C, which is 367% higher than that of the sample without FG. This paper provides a new strategy to improve the energy density and the cycle stability of Li-S batteries.

Conversion-type NiCoMn triple perovskite fluorides for advanced aqueous supercapacitors, batteries and supercapatteries.

Conversion-type NiCoMn triple perovskite fluorides (KNCMF-622) have been explored for advanced aqueous supercapacitors (ASCs), batteries (ABs) and supercapatteries (i.e., ASC/Bs). The ASC/Bs outperform the ASCs and ABs, owing to the synergistic effect of capacitive, pseudocapacitive and faradaic characteristics, showing great significance in developing energy storage devices.

Pseudocapacitive trimetallic NiCoMn-111 perovskite fluorides for advanced Li-ion supercabatteries.

Exploring advanced electrochemical energy storage systems and clarifying their charge storage mechanisms are key scientific frontiers presenting a great challenge. Herein, we demonstrate a novel concept of Li-ion supercabatteries (i.e., Li-ion capacitors/batteries, LICBs), which were realized using a novel trimetallic Ni-Co-Mn perovskite fluoride (K0.97Ni0.31Co0.34Mn0.35F2.98, denoted as KNCMF-111 (8#)) anode and a high-performance activated carbon/LiFePO4 (AC/LFP) cathode, which makes the boundary between LICs and LIBs less distinctive. Thanks to the pseudocapacitive conversion mechanism of the KNCMF-111 (8#) anode with superior kinetics and the enhanced capacity of the capacitor/battery hybrid AC/LFP cathode, the designed KNCMF-111 (8#)//AC/LFP LICBs, integrating the synergistic superiority of pseudocapacitive, capacitive and faradaic characteristics, exhibit remarkable energy/power densities and a long cycle life, indicating a high-efficiency energy storage application. Overall, this work provides new insights into exploring advanced Li-ion supercabatteries and clarifying their charge storage mechanisms based on trimetallic Ni-Co-Mn perovskite fluoride electrode materials, which sheds light on the development of advanced electrochemical energy storage systems and in-depth understanding of their charge storage mechanisms.

Trimetallic NiCoMo/graphene multifunctional electrocatalysts with moderate structural/electronic effects for highly efficient alkaline urea oxidation reaction.

Trimetallic NiCoMo/graphene (NCM/G 811) multifunctional electrocatalysts demonstrate remarkable catalytic activity, fast kinetics, a low onset potential and high stability towards alkaline urea oxidation reaction (UOR). Moderate structural/electronic effects among Ni, Co and Mo species are responsible for the outstanding catalytic behavior.

A new method for quantitative detection of Lactobacillus casei based on casx gene and its application.

BACKGROUND: The traditional method of bacterial identification based on 16S rRNA is a widely used and very effective detection method, but this method still has some deficiencies, especially in the identification of closely related strains. A high homology with little differences is mostly observed in the 16S sequence of closely related bacteria, which results in difficulty to distinguish them by 16S rRNA-based detection method. In order to develop a rapid and accurate method of bacterial identification, we studied the possibility of identifying bacteria with other characteristic fragments without the use of 16S rRNA as detection targets. RESULTS: We analyzed the potential of using cas (CRISPR-associated proteins) gene as a target for bacteria detection. We found that certain fragment located in the casx gene was species-specific and could be used as a specific target gene. Based on these fragments, we established a TaqMan MGB Real-time PCR method for detecting bacteria. We found that the method used in this study had the advantages of high sensitivity and good specificity. CONCLUSIONS: The casx gene-based method of bacterial identification could be used as a supplement to the conventional 16 s rRNA-based detection method. This method has an advantage over the 16 s rRNA-based detection method in distinguishing the genetic relationship between closely-related bacteria, such as subgroup bacteria, and can be used as a supplement to the 16 s rRNA-based detection method.