Enhancing Earth data analysis in 5G satellite networks: A novel lightweight approach integrating improved deep learning.

Efficiently handling huge data amounts and enabling processing-intensive applications to run in faraway areas simultaneously is the ultimate objective of 5G networks. Currently, in order to distribute computing tasks, ongoing studies are exploring the incorporation of fog-cloud servers onto satellites, presenting a promising solution to enhance connectivity in remote areas. Nevertheless, analyzing the copious amounts of data produced by scattered sensors remains a challenging endeavor. The conventional strategy of transmitting this data to a central server for analysis can be costly. In contrast to centralized learning methods, distributed machine learning (ML) provides an alternative approach, albeit with notable drawbacks. This paper addresses the comparative learning expenses of centralized and distributed learning systems to tackle these challenges directly. It proposes the creation of an integrated system that harmoniously merges cloud servers with satellite network structures, leveraging the strengths of each system. This integration could represent a major breakthrough in satellite-based networking technology by streamlining data processing from remote nodes and cutting down on expenses. The core of this approach lies in the adaptive tailoring of learning techniques for individual entities based on their specific contextual nuances. The experimental findings underscore the prowess of the innovative lightweight strategy, LMAED2L (Enhanced Deep Learning for Earth Data Analysis), across a spectrum of machine learning assignments, showcasing remarkable and consistent performance under diverse operational conditions. Through a strategic fusion of centralized and distributed learning frameworks, the LMAED2L method emerges as a dynamic and effective remedy for the intricate data analysis challenges encountered within satellite networks interfaced with cloud servers. The empirical findings reveal a significant performance boost of our novel approach over traditional methods, with an average increase in reward (4.1 %), task completion rate (3.9 %), and delivered packets (3.4 %). This report suggests that these advancements will catalyze the integration of cutting-edge machine learning algorithms within future networks, elevating responsiveness, efficiency, and resource utilization to new heights.

Selective Elucidation of Living Microbial Communities in Fermented Grains of Chinese Baijiu: Development of a Technique Integrating Propidium Monoazide Probe Pretreatment and Amplicon Sequencing.

The fermentation process of Chinese Baijiu's fermented grains involves the intricate succession and metabolism of microbial communities, collectively shaping the Baijiu's quality. Understanding the composition and succession of these living microbial communities within fermented grains is crucial for comprehending fermentation and flavor formation mechanisms. However, conducting high-throughput analysis of living microbial communities within the complex microbial system of fermented grains poses significant challenges. Thus, this study addressed this challenge by devising a high-throughput analysis framework using light-flavor Baijiu as a model. This framework combined propidium monoazide (PMA) pretreatment technology with amplicon sequencing techniques. Optimal PMA treatment parameters, including a concentration of 50 µM and incubation in darkness for 5 min followed by an exposure incubation period of 5 min, were identified. Utilizing this protocol, viable microorganism biomass ranging from 8.71 × 106 to 1.47 × 108 copies/µL was successfully detected in fermented grain samples. Subsequent amplicon sequencing analysis revealed distinct microbial community structures between untreated and PMA-treated groups, with notable differences in relative abundance compositions, particularly in dominant species such as Lactobacillus, Bacillus, Pediococcus, Saccharomycopsis, Issatchenkia and Pichia, as identified by LEfSe analysis. The results of this study confirmed the efficacy of PMA-amplicon sequencing technology for analyzing living microbial communities in fermented grains and furnished a methodological framework for investigating living microbial communities in diverse traditional fermented foods. This technical framework holds considerable significance for advancing our understanding of the fermentation mechanisms intrinsic to traditional fermented foods.

An advanced optic-fiber differential sensing system enhanced by molecularly imprinted polymer for specific sodium benzoate detection.

A molecularly imprinted polymer (MIP) based microfiber differential demodulation sensing system for sodium benzoate (SB) concentration detection is proposed. The specific binding of MIP on the surface of microfibers with SB can lead to changes in local refractive index (RI). RI change induces a drift in the interference wavelength, which can be monitored by the power difference between two fiber Bragg gratings (FBGs). The sensing system can detect SB in the concentration range of 0.1-50 µg/ml, and interference wavelength and FBG power difference sensitivities are 0.55 nm/(µg/ml) and 2.64 dB/(µg/ml) in the low concentration range of 0.1-1 µg/ml, respectively, with a limit of detection (LOD) of 0.1 µg/ml. This microfiber differential demodulation sensing system is not only simple to fabricate, but also simplifies the demodulation equipment to reduce the cost, which providing a simple, reliable and low-cost technique for the quantitative detection of SB concentration in beverages and flavoured foods.

Development and validation of a nomogram for predicting specific mortality risk: A study of competing risk model based on real endometrial cancer patients.

OBJECTIVE: This study aimed to construct a competing risk prediction model for predicting specific mortality risks in endometrial cancer patients from the SEER database based on their demographic characteristics and tumor information. METHODS: We collected relevant clinical data on patients with histologically confirmed endometrial cancer in the SEER database between 2010 and 2015. Univariate and multivariate competing risk models were used to analyze the risk factors for endometrial cancer-specific death, and a predictive nomogram was constructed. C-index and receiver operating characteristic curve (ROC) at different time points were used to verify the accuracy of the constructed nomogram. RESULTS: There were 26 109 eligible endometrial cancer patients in the training cohort and 11 189 in the validation cohort. Univariate and multivariate analyses revealed that Age, Marriage, Grade, Behav, FIGO, Size, Surgery, SurgOth, Radiation, ParaAortic_Nodes, Peritonea, N positive, DX_liver, and DX_lung were independent prognostic factors for specific mortality in endometrial cancer patients. Based on these factors, a nomogram was constructed. Internal validation showed that the nomogram had a good discriminative ability (C-index = 0.883 [95% confidence interval [CI]: 0.881-0.884]), and the 1-, 3-, and 5-year AUC values were 0.901, 0.886 and 0.874, respectively. External validation indicated similar results (C-index = 0.883 [95%CI: 0.882-0.883]), and the 1-, 3-, and 5- AUC values were 0.908, 0.885 and 0.870, respectively. CONCLUSION: We constructed a competing risk model to predict the specific mortality risk among endometrial cancer patients. This model has favorable accuracy and reliability and can provide a reference for the development and update of endometrial cancer prognostic risk assessment tools.

Engineering of molecularly imprinted cavity within 3D covalent organic frameworks: An innovation for enhanced extraction and removal of microcystins.

The scarcity of selective adsorbents for efficient extraction and removal of microcystins (MCs) from complex samples greatly limits the precise detection and effective control of MCs. Three-dimensional covalent organic frameworks (3D COFs), characterized by their large specific surface areas and highly ordered rigid structure, are promising candidates, but suffer from lack of specific recognition. Herein, we design to engineer molecularly imprinted cavities within 3D COFs via molecularly imprinted technology, creating a novel adsorbent with exceptional selectivity, kinetics and capacity for the efficient extraction and removal of MCs. As proof-of-concept, a new CC bond-containing 3D COF, designated JNU-7, is designed and prepared for copolymerization with methacrylic acid, the pseudo template L-arginine and ethylene dimethacrylate to yield the JNU-7 based molecularly imprinted polymer (JNU-7-MIP). The JNU-7-MIP exhibits a great adsorption capacity (156 mg g-1) for L-arginine. Subsequently, the JNU-7-MIP based solid-phase extraction coupled with high performance liquid chromatography-mass spectrometry achieves low detection limit of 0.008 ng mL-1, wide linear range of 0.025-100 ng mL-1, high enrichment factor of 186, rapid extraction of 10 min, and good recoveries of 92.4%-106.5% for MC-LR. Moreover, the JNU-7-MIP can rapidly remove the MC-LR from 1 mg L-1 to levels (0.26-0.35 µg L-1) lower than the WHO recommended limit for drinking water (1 µg L-1). This work reveals the considerable potential of 3D COF based MIPs as promising adsorbents for the extraction and removal of contaminants in complex real samples.

Determination of insecticide residues in beverages based on MIL-100(Fe) dispersive solid-phase microextraction in combination with dispersive liquid-liquid microextraction followed by HPLC-MS/MS.

A novel dispersive solid-phase microextraction method based on a metal-organic framework (MIL-100(Fe)) combined with a dispersive liquid-liquid microextraction technique was proposed for the extraction and enrichment of four insecticides in beverages. The qualitative and quantitative analysis of these insecticides was conducted using HPLC-MS/MS. To optimize the extraction process, several parameters were investigated, and the main variables were optimized using CCD-based RSM. The developed method displayed a wide linear range of 1.000-1000 ng/L and R2 values >0.993 for all four calibration curves. The method demonstrated high sensitivity, with LODs and LOQs of 0.3-0.6 ng/L and 0.8-1.0 ng/L, respectively. In addition, the greenness of the proposed method was assessed using the Complex GAPI tool, and the results showed that the proposed method exhibits benefits, such as minimal usage of organic solvents and negligible matrix influence, making it a suitable method for the detection of insecticide residues in beverages.

Separation and Detection of Catechins and Epicatechins in Shanxi Aged Vinegar Using Solid-Phase Extraction and Hydrophobic Deep Eutectic Solvents Combined with HPLC.

This research presents a new, eco-friendly, and swift method combining solid-phase extraction and hydrophobic deep eutectic solvents (DES) with high-performance liquid chromatography (SPE-DES-HPLC) for extracting and quantifying catechin and epicatechin in Shanxi aged vinegar (SAV). The parameters, such as the elution solvent type, the XAD-2 macroporous resin dosage, the DES ratio, the DES volume, the adsorption time, and the desorption time, were optimized via a one-way experiment. A central composite design using the Box-Behnken methodology was employed to investigate the effects of various factors, including 17 experimental runs and the construction of three-dimensional response surface plots to identify the optimal conditions. The results show that the optimal conditions were an HDES (tetraethylammonium chloride and octanoic acid) ratio of 1:3, an XAD-2 macroporous resin dosage of 188 mg, and an adsorption time of 11 min. Under these optimal conditions, the coefficients of determination of the method were greater than or equal to 0.9917, the precision was less than 5%, and the recoveries ranged from 98.8% to 118.8%. The environmentally friendly nature of the analytical process and sample preparation was assessed via the Analytical Eco-Scale and AGREE, demonstrating that this method is a practical and eco-friendly alternative to conventional determination techniques. In summary, this innovative approach offers a solid foundation for the assessment of flavanol compounds present in SAV samples.

Local prediction-learning in high-dimensional spaces enables neural networks to plan.

Planning and problem solving are cornerstones of higher brain function. But we do not know how the brain does that. We show that learning of a suitable cognitive map of the problem space suffices. Furthermore, this can be reduced to learning to predict the next observation through local synaptic plasticity. Importantly, the resulting cognitive map encodes relations between actions and observations, and its emergent high-dimensional geometry provides a sense of direction for reaching distant goals. This quasi-Euclidean sense of direction provides a simple heuristic for online planning that works almost as well as the best offline planning algorithms from AI. If the problem space is a physical space, this method automatically extracts structural regularities from the sequence of observations that it receives so that it can generalize to unseen parts. This speeds up learning of navigation in 2D mazes and the locomotion with complex actuator systems, such as legged bodies. The cognitive map learner that we propose does not require a teacher, similar to self-attention networks (Transformers). But in contrast to Transformers, it does not require backpropagation of errors or very large datasets for learning. Hence it provides a blue-print for future energy-efficient neuromorphic hardware that acquires advanced cognitive capabilities through autonomous on-chip learning.

An age classification model based on DNA methylation biomarkers of aging in human peripheral blood using random forest and artificial neural network.

Recent epigenetic studies have revealed a strong association between DNA methylation and aging and lifespan, which changes (increases or decreases) with age. Based on these, the construction of age prediction models associated with DNA methylation levels can be used to infer biological ages closer to the functional state of the organism. We downloaded methylation data from the Gene Expression Omnibus (GEO) public database for normal peripheral blood samples from people of different ages. We grouped the samples according to age (18-35 years and >50 years), screened the methylation sites that differed between the two groups, identified 44 differentially methylated sites, and subsequently obtained 11 age-related characteristic methylation sites using the random forest method. Then, we constructed an age classification model with these 11 characteristic methylation sites using an artificial neural network and evaluated its efficacy. The age classification model was constructed by an artificial neural network and its efficacy was evaluated. The model predicted an area under the curve (AUC) of 0.97 in the validation set and accurately distinguished between those aged 18-35 and >50 years. Furthermore, the levels of these 11 characteristic methylation sites also differed significantly between the two sets of samples in the validation set, including six newly identified age-related methylation sites (P<0.001). Finally, we constructed a multifactor regulatory network based on the corresponding genes of age-related methylation sites to reveal the transcriptional and post-transcriptional regulation patterns. As a result of the increasing problem of aging, the age classification model we constructed allows us to accurately distinguish different age groups at the molecular level, which will be more predictive than chronological age for assessing individual aging and future health status.

Self-powered molecularly imprinted photoelectrochemical sensor based on Ppy/QD/HOF heterojunction for the detection of bisphenol A.

As an emerging porous material, hydrogen-bonded organic framework materials (HOFs) still pose application challenges. In this work, the designed type "I + II" heterojunction extracted hot electrons from HOFs using quantum dots (QDs) and polypyrrole (Ppy), improving the stability and photoelectrochemical performance of materials. In addition to serving as a potential well, electropolymerized Ppy was used as a recognition element for bisphenol A (BPA), and a novel self-powered molecularly imprinted photoelectrochemical (MIP-PEC) sensor was designed. The sensing platform showed a linear relationship from 1 × 10-10 to 1 × 10-7 mol∙L-1 and from 1 × 10-7 to 1 mol∙L-1 with an acceptable detection limit of 4.2 × 10-11 mol∙L-1. This is the first application of HOFs in constructing MIP-PEC sensors and a new attempt to improve the stability of HOFs for the application of porous crystal materials in the sensing field.

An all-fiber system biosensor for trace ß-lactam antibiotics detection enhanced by functionalized microfiber and fiber bragg grating.

An all-fiber-optic system for rapid detection of antibiotic concentration, based on an optical enzyme biosensor with microfiber interferometer (MFI) and fiber gratings (FBGs) power variation, is proposed and experimentally validated. During the experiment, ß-lactamase(ß-LS) is fixed on the polyaniline (PANI)-coated optical fiber by cross-linking through glutaraldehyde (GA) covalent bonding. ß-LS can hydrolyze ß-lactam antibiotics to generate acidic by-products that transform polyaniline from the form of the emerald base to emerald salt, which results in the surface refractive index (RI) variation of MFI, to convert MFI wavelength and FBGs power macroscopic change for feedbackingly detecting the concentration of ß-lactam antibiotics. The detection of amoxicillin (AMX) in deionized water at concentrations in the range of 0.01-100 nM resulted in a wavelength change sensitivity of 0.6 nm/nM, and FBGs power difference change sensitivity of 1.3 dB/nM, with a detection limit LOD = 0.04 nM in real food and urine samples. The sensing system by the same calibration technique can detect antibiotic concentrations in different substances (tap water, milk and artificial urine). This developed all-fiber-optic system can be used as a rapid solution for the measurement of ß-lactam antibiotic residues in food and the environment.

Stimulating creativity in the classroom: examining the impact of sense of place on students' creativity and the mediating effect of classmate relationships.

BACKGROUND: Although previous studies have found a close relationship between sense of place and creativity, few studies have been conducted considering the micro-environment of the classroom. The mediating role of classmate relationships in the association between students' sense of place and creativity remains unclear. METHODS: This study explores classmate relationships as a mediating factor in the relationship between sense of place and creativity. Therefore, we considered a sample of 1555 Chinese high-school students and used a paper-based questionnaire survey. Data analysis was performed using SPSS 24.0, PROCESS 3.2 plugin, and AMOS. RESULTS: Sense of place in the micro-environment of the classroom has a significant positive predictive effect on creativity. Sense of place also has a significant positive predictive effect on peer relationships. The mediation analysis reveals that peer relationships play a mediating role in the relationship between the sense of place and creativity. CONCLUSIONS: This study revealed the associations between sense of place, classmate relationships, and creativity. Creativity is better expressed in students with a strong sense of place in the classroom. Moreover, a student's sense of place can enhance their creativity by influencing their peer relationships. These findings enrich the research in educational psychology within the classroom, providing new insights for fostering creativity.

A novel approach to forecast dust concentration in open pit mines by integrating meteorological parameters and production intensity.

Mine dust pollution poses a hindrance to achieving green and climate-smart mining. This paper uses weather forecast data and mine production intensity data as model inputs to develop a novel model for forecasting daily dust concentration values in open pit mines by employing and integrating multiple machine learning techniques. The results show that the forecast model exhibits high accuracy, with a Pearson correlation coefficient exceeding 0.87. The PM2.5 forecast model performs best, followed by the total suspended particle and PM10 models. The inclusion of production intensity significantly enhances model performance. Total column water vapor exerts the most significant impact on the model's predictive performance, while the impacts of rock production and coal production are also notable. The proposed daily forecast model leverages production intensity data to predict future dust concentrations accurately. This tool offers valuable insights for optimizing mine design parameters, enabling informed decisions based on real-time forecasts. It effectively prevents severe pollution in the mining area while maximizing the use of natural meteorological conditions for effective dust removal and diffusion.

The Separation, Purification, Structure Identification, and Antioxidant Activity of Elaeagnus umbellata Polysaccharides.

In order to investigate the antioxidant activity of Elaeagnus umbellata polysaccharides, the physicochemical characteristics of purified Elaeagnus umbellata polysaccharides (EUP, consisting of two fractions, EUP1 and EUP2) were investigated using UV spectrophotometry, high-performance liquid chromatography (HPLC), high-performance gel permeation chromatography (HPGPC), and Fourier transform infrared spectroscopy (FT-IR). This revealed that EUP1 and EUP2 were acidic polysaccharides with an average molecular weight (MW) of 63 and 38 kDa, respectively. EUP1 mainly consisted of L-rhamnose and D-galactose in a molar ratio of 2.05:1, and EUP2 consisted of D-mannose, L-rhamnose, D-galactose, and D-arabinose in a molar ratio of 2.06:1:2.78:1. Furthermore, EUP exhibited considerable antioxidant potential for scavenging hydroxyl, superoxide anion, DPPH, and ABTS radicals. Therefore, EUP can be developed as a potential antioxidant for the functional food or pharmaceutical field.

Correlation Analysis of Microbial Community Changes and Physicochemical Characteristics in Aged Vinegar Brewing.

This study aimed to explore key physicochemical characteristics and evolutionary patterns of microbial community structure during the fermentation of aged vinegar. The correlation between microorganisms and physicochemical characteristics during fermentation was examined. The results revealed significant differences in genera at different stages of fermentation. The dominant bacteria in R1 were Bacillus, Lactobacillus, Aspergillus, and Issatchenkia. During the R2 fermentation stage, Lactobacillus, Acetobacter, and Saccharomyces exhibited an upward trend and finally became the dominant bacteria. Aspergillus was the main bacterial genus at the end of overall fermentation. The correlation analysis showed that the bacterial genera significantly positively and negatively correlated with reducing sugars and amino acid nitrogen were the same in Cuqu. Similarly, the bacterial genera significantly positively and negatively correlated with pH and saccharification power were the same. pH, reducing sugar, and saccharification ability were mainly positively correlated with bacterial genera during fermentation. Further, studies found that the overall correlation between fungal communities and physicochemical characteristics was weaker than the correlation with bacteria during fermentation.

Determination of Flavonoid Compounds in Shanxi Aged Vinegars Based on Hydrophobic Deep Eutectic Solvent VALLME-HPLC Method: Assessment of the Environmental Impact of the Developed Method.

This research presents a novel, eco-friendly, vortex-assisted liquid-liquid microextraction (VALLME) approach, integrating hydrophobic deep eutectic solvents (DESs) with HPLC for the identification and quantification of nine specific flavonoids in Shanxi aged vinegar (SAV). The parameters of DES-VALLME, including the ratio of trioctylmethylammonium chloride to 1,4-butanediol (1:6), DES volume (150 µL), vortex duration (5 min), the concentration of NaCl (0.40 g), and centrifugation time (10 min), were optimized to achieve the maximum extraction efficiency of target substances. Under these optimal conditions, quantitative analyses performed via HPLC demonstrated a broad linear range of 0.20-50.00 µg/mL and correlation coefficients (r2) greater than 0.9944 for all nine calibration curves. The limits of detection (LOD) and limits of quantitation (LOQ) were 0.09-0.18 µg/mL and 0.30-0.60 µg/mL, respectively, ensuring high sensitivity. The relative standard deviations for intra-day and inter-day variability were within the acceptable range, 2.34-3.77% and 3.04-4.96%, respectively, demonstrating the method's reliability. The recovery rates ranged from 85.97% to 108.11%, underscoring the method's precision. This technique exhibited a significant enrichment effect (enrichment factor: 43 to 296) on SAV flavonoids. Notably, the eco-friendliness of this procedure was evaluated using the Analytical Eco-Scale, Green Analytical Procedure Index, and Analytical Greenness Metric. The results suggested that this technique is a viable green alternative to traditional flavonoid determination methods in SAV. In summary, this novel method provides a theoretical basis for assessing flavonoid content in SAV samples and tracing SAV products. This contribution has significant implications for enhancing analytical techniques in food chemistry and environmental science and the sustainable development of the food industry.

A novel technique of penetrating keratoplasty to prevent intraocular contents extrusion for infectious keratitis.

PURPOSE: To evaluate the safety and the effectiveness of our novel penetrating keratoplasty for infectious keratitis. METHODS: Retrospective, noncomparative, interventional case series of patients with infectious keratitis who received the novel penetrating keratoplasty technique were analyzed. A prepared plastic sheet was located between the diseased cornea and iris-lens diaphragm. After the diseased lesions were removed, the graft was positioned on the plastic sheet and sutured to the recipient bed. The plastic sheet was pulled out from the anterior chamber before the all interrupted sutures were placed. The intra- and post-operative complications, the outcome of the graft and the number of corneal endothelial cells were analyzed. RESULTS: A total of 82 eyes of 82 patients was included. The mean follow-up period was 29 ± 16 months (range from 13 to 45 months). No intraocular content extrusion, simultaneous cataract extraction and suprachoroidal hemorrhage occurred. Direct contact between the infectious cornea and the graft was successfully avoided. Greater than expected endothelial cell reduction or complications were not found. CONCLUSIONS: This modified technique effectively prevents the extrusion of intraocular contents while avoiding the direct contact with donor endothelium during the procedure. The occurrence rate of complications such as endothelial cell loss is not higher than the conventional methods.

Semaphorin3A Exacerbates Cardiac Microvascular Rarefaction in Pressure Overload-Induced Heart Disease.

Microvascular endothelial cells (MiVECs) impair angiogenic potential, leading to microvascular rarefaction, which is a characteristic feature of chronic pressure overload-induced cardiac dysfunction. Semaphorin3A (Sema3A) is a secreted protein upregulated in MiVECs following angiotensin II (Ang II) activation and pressure overload stimuli. However, its role and mechanism in microvascular rarefaction remain elusive. The function and mechanism of action of Sema3A in pressure overload-induced microvascular rarefaction, is explored, through an Ang II-induced animal model of pressure overload. RNA sequencing, immunoblotting analysis, enzyme-linked immunosorbent assay, quantitative reverse transcription polymerase chain reaction (qRT-PCR), and immunofluorescence staining results indicate that Sema3A is predominantly expressed and significantly upregulated in MiVECs under pressure overload. Immunoelectron microscopy and nano-flow cytometry analyses indicate small extracellular vesicles (sEVs), with surface-attached Sema3A, to be a novel tool for efficient release and delivery of Sema3A from the MiVECs to extracellular microenvironment. To investigate pressure overload-mediated cardiac microvascular rarefaction and cardiac fibrosis in vivo, endothelial-specific Sema3A knockdown mice are established. Mechanistically, serum response factor (transcription factor) promotes the production of Sema3A; Sema3A-positive sEVs compete with vascular endothelial growth factor A to bind to neuropilin-1. Therefore, MiVECs lose their ability to respond to angiogenesis. In conclusion, Sema3A is a key pathogenic mediator that impairs the angiogenic potential of MiVECs, which leads to cardiac microvascular rarefaction in pressure overload-induced heart disease.

A universal design of turn-on fluorescent aptasensor based on luminescent MOFs: Application for the detection of bisphenol A in water, milk and chicken samples.

A universal design of turn-on fluorescent aptasensor based on aptamer functionalized gold nanoparticles (AuNPs) and luminescent metal-organic frameworks (LMOFs) complex (AuNPs-Apt/NH2-MIL-125(Ti)) was realized for bisphenol A (BPA) detection. LMOF NH2-MIL-125(Ti) was prepared using facial hydrothermal method. BPA aptamer functionalized AuNPs were prepared and adsorbed on the surface of NH2-MIL-125(Ti) to obtain platform of the fluorescent aptasensor. The fabrication process, sensing performance and applicability of the proposed aptasensor were characterized and investigated carefully. Linear detection range of the constructed aptasensor was from 1 × 10-9 mol L-1 to 1 × 10-4 mol L-1 with good selectivity, repeatability, stability and reproducibility under optimal experimental conditions. Meanwhile, the fluorescent aptasensor was successfully utilized for BPA detection in real samples with the recoveries of 95.80%-103.12%. The proposed aptasensor based on AuNPs-Apt/NH2-MIL-125(Ti) holds significant potential for BPA detection in environmental and food samples, promoting the construction and application of LMOFs-based aptasensor.

Dysregulated expression of microRNA involved in resistance to osimertinib in EGFR mutant non-small cell lung cancer cells.

Background: An increasing amount of evidence has confirmed that the altered expression of microRNAs (miRNAs) is critical to the mechanism underlying primary and even acquired resistance to tyrosine kinase inhibitors (TKIs). However, studies on the linkage between the altered miRNAs expression and osimertinib resistance are few, and the effect of miRNAs in this context is still unclear. In the light of this, we hypothesized that the differential expression of multiple miRNAs is the driver in the osimertinib resistance process. Thus, the aim of our study was to find differentially expressed miRNAs in non-small cell lung cancer cells resistant to osimertinib. Methods: An AZD9291(Osimertinib)-resistant cell line model was constructed, and the differential miRNAs between epidermal growth factor receptor (EGFR)-sensitive cell lines A549 and H1975 and the corresponding drug-resistant cell lines were identified via biosynthesis analysis. Results: In the A549 osimertinib-resistant cell line, 93 miRNAs were upregulated and 94 miRNAs were downregulated. In the H1975 osimertinib-resistant cell line, 124 miRNAs were upregulated and 53 miRNAs were downregulated. Finally, 7 significantly different miRNAs were screened using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Conclusions: This study on the mechanism of target therapy in lung cancer systematically and comprehensively examined the miRNAs involved in osimertinib resistance. It was found that miR-708-5p, miR-708-3p, miR-10395-3p, miR-7704 miR-34a-5p, miR-19b-1-5p, and miR-219a-5p may play key roles in osimertinib resistance.