A high security coding and anti-counterfeiting method based on the nonlinear magnetization response of superparamagnetic nanomaterials.

Traditional coding methods based on graphics and digital or magnetic labels have gradually decreased their anti-counterfeiting because of market popularity. This paper presents a new magnetic anti-counterfeiting coding method. This method uses a high-performance coding material, which, along with small changes to the material itself and the particle size of the superparamagnetic nanomaterials, results in a large difference in the nonlinear magnetization response. This method, which adopts 12-site coding and establishes a screening model by measuring the voltage amplitude of 12-site variables, can code different kinds of products, establishing long-term stable coding and decoding means. Through the anti-counterfeiting experiment of wine, the experiment results show that the authenticity of the coded products can be verified using the self-developed magnetic encoding and decoding system. The new coding technology can verify the anti-counterfeiting of 9000 products, with a single detection accuracy of 97% and a detection time of less than one minute. Moreover, this coding method completely depends on the production batch of the superparamagnetic nanomaterials, which is difficult to imitate, and it provides a new coding anti-counterfeiting technology for related industries with a wide range of potential applications.

Study on the correlation and difference of qualitative information among three types of UPLC-HRMS and potential generalization in metabolites annotation.

The variation of qualitative information among different types of mainstream hyphenated instruments of ultra-performance liquid chromatography coupled to high-resolution mass spectrometry (UPLC-HRMS) makes data sharing and standardization, and further comparison of results consistency in metabolite annotation not easy to attain. In this work, a quantitative study of correlation and difference was first achieved to systematically investigate the variation of retention time (tR), precursor ion (MS1), and product fragment ions (MS2) generated by three typical UPLC-HRMS instruments commonly used in metabolomics area. In terms of the findings of systematic and correlated variation of tR, MS1, and MS2 between different instruments, a computational strategy for integrated metabolite annotation was proposed to reduce the influence of differential ions, which made full use of the characteristic (common) and non-common fragments for scoring assessment. The regular variations of MS2 among three instruments under four collision energy voltages of high, medium, low, and hybrid levels were respectively inspected with three technical replicates at each level. These discoveries could improve general metabolite annotation with a known database and similarity comparison. It should provide the potential for metabolite annotation to generalize qualitative information obtained under different experimental conditions or using instruments from various manufacturers, which is still a big headache in untargeted metabolomics. The mixture of standard compounds and serum samples with the addition of standards were applied to demonstrate the principle and performance of the proposed method. The results showed that it could be an optional strategy for general use in HRMS-based metabolomics to offset the difference in metabolite annotation. It has some potential in untargeted metabolomics.

Application of improved machine learning in large-scale investigation of plastic waste distribution in tourism Intensive artificial coastlines.

Oceans are ultimately a sink of plastic waste. Complex artificial coastlines pose remarkable challenges for coastal plastic waste monitoring. With the development of machine learning methods, high detection accuracy can be achieved; however, many false positives have been noted in various network models used for plastic waste investigation. In this study, extensive surveys of artificial coastlines were conducted using drones along the Dongjiang Port artificial coastline in the Binhai District, Tianjin, China. The deep learning model YOLOv8 was enhanced by integrating the InceptionNeXt and LSK modules into the network to improve its detection accuracy for plastic waste and reduce instances of tourists being misidentified as plastic. In total, 553 high-resolution coastline images with 3488 items of detected plastic waste were compared using the original and improved YOLOv8 models. The improved YOLOv8s-IL model achieved a detection rate of 64.9%, a notable increase of 11.5% compared with that of the original model. The number of false positives in the improved YOLOv8s-IL model was reduced to 32.3%, the multi-class F-score reached 76.5%, and the average detection time per image was only 2.7 s. The findings of this study provide technical support for future large-scale monitoring of plastic waste on artificial coastlines.

Identification of male sterility-related genes in Saccharum officinarum and Saccharum spontaneum.

KEY MESSAGE: Candidate male sterility genes were identified in sugarcane, which interacts with kinase-related proteins, transcription factors, and plant hormone signaling pathways to regulate stamen and anther development. Saccharum officinarum is a cultivated sugarcane species that its predominant feature is high sucrose content in stems. Flowering is necessary for breeding new cultivars but will terminate plant growth and reduce sugar yield. The wild sugarcane species Saccharum spontaneum has robust and viable pollen, whereas most S. officinarum accessions are male sterile, which is a desirable trait of a maternal parent in sugarcane breeding. To study male sterility and related regulatory pathways in sugarcane, we carried out RNAseq using flowers in different developmental stages between male-sterile S. officinarum accession 'LA Purple' and fertile S. spontaneum accession 'SES208'. Gene expression profiles were used to detect how genes are differentially expressed between male sterile and fertile flowers and to identify candidate genes for male sterility. Weighted gene correlation networks analysis (WGCNA) was conducted to investigate the regulatory networks. Transcriptomic analyses showed that 988 genes and 2888 alleles were differentially expressed in S. officinarum compared to S. spontaneum. Ten differentially expressed genes and thirty alleles were identified as candidate genes and alleles for male sterility in sugarcane. The gene Sspon.03G0007630 and two alleles of the gene Sspon.08G0002270, Sspon.08G0002270-2B and Sspon.08G0014700-1A, were involved in the early stamen or carpel development stages, while the remaining genes were classified into the post-meiosis stage. Gibberellin, auxin, and jasmonic acid signaling pathways are involved in the stamen development in sugarcane. The results expanded our knowledge of male sterility-related genes in sugarcane and generated genomic resources to facilitate the selection of ideal maternal parents to improve breeding efficiency.

The Basic Units of Working Memory Manipulation Are Boolean Maps, Not Objects.

Determining the manipulation unit of working memory is one of the fundamental questions in understanding how working memory functions. The prevalent object-based theory in cognitive research predicts that memory manipulation is performed on the level of objects. Here we show instead that the basic units of working memory manipulation are Boolean maps, a data structure describing what can be perceived in an instant. We developed four new manipulation tasks (with data from 80 adults) and showed that manipulation times only increased when the number of Boolean maps manipulated increased. Increasing the number of orientations manipulated did not induce longer manipulation times, consistent with a key prediction of the Boolean map theory. Our results show that Boolean maps are the manipulation unit of working memory.

LncRNA MALAT1-miR-339-5p-NIS axis is involved in the increased level of thyroid stimulating hormone (TSH) induced by combined exposure of high iodine and hyperlipidemia.

Hypothyroidism and subclinical hypothyroidism were both characterized by elevated levels of thyroid stimulating hormone (TSH). Previous studies had found that high iodine or hyperlipidemia alone was associated with increased TSH level. However, their combined effects on TSH have not been elucidated. In this study, combination of high iodine and hyperlipidemia was established through the combined exposure of high-water iodine and high fat diet in Wistar rats. The results showed that combined exposure of high iodine and high fat can induce higher TSH level. The mRNA and protein levels of sodium iodide transporters (NIS) and type 1 deiodinase (D1) in thyroid tissues, which were crucial genes in the synthesis of thyroid hormones, decreased remarkably in combined exposure group. Mechanistically, down-regulated long non-coding RNA (lncRNA) metastasis associated in lung denocarcinoma transcript 1 (MALAT1) may regulate the expression of NIS by increasing miR-339-5p, and regulating D1 by increasing miR-224-5p. Then, the above findings were explored in subjects exposed to high water iodine and hyperlipidemia. The results indicated that in population combined with high iodine and hyperlipidemia, TSH level increased to higher level and lncRNA MALAT1-miR-339-5p-NIS axis was obviously activated. Collectively, this study found that combined exposure of high iodine and hyperlipidemia induced a higher level of TSH, and lncRNA MALAT1-miR-339-5p-NIS axis may play important role.

Comparison of Prompt Engineering and Fine-Tuning Strategies in Large Language Models in the Classification of Clinical Notes.

The emerging large language models (LLMs) are actively evaluated in various fields including healthcare. Most studies have focused on established benchmarks and standard parameters; however, the variation and impact of prompt engineering and fine-tuning strategies have not been fully explored. This study benchmarks GPT-3.5 Turbo, GPT-4, and Llama-7B against BERT models and medical fellows' annotations in identifying patients with metastatic cancer from discharge summaries. Results revealed that clear, concise prompts incorporating reasoning steps significantly enhanced performance. GPT-4 exhibited superior performance among all models. Notably, one-shot learning and fine-tuning provided no incremental benefit. The model's accuracy sustained even when keywords for metastatic cancer were removed or when half of the input tokens were randomly discarded. These findings underscore GPT-4's potential to substitute specialized models, such as PubMedBERT, through strategic prompt engineering, and suggest opportunities to improve open-source models, which are better suited to use in clinical settings.

Fruit Quality Analysis and Flavor Comprehensive Evaluation of Cherry Tomatoes of Different Colors.

Cherry tomatoes are popular vegetables worldwide owing to their variety of colors and nutrients. However, an integrated evaluation of color and flavor has rarely been reported. This study examined the differences among red, brown, yellow, and green cherry tomatoes grown in the Jiuquan area. A comprehensive analysis of the flavor quality of these tomatoes, including sensory evaluation, electronic nose analysis, nutritional and flavor quality measurements, targeted metabolomics, and chemometrics, was conducted. Red tomatoes had the highest lycopene content, and green tomatoes had the highest soluble protein and vitamin C content. In cherry tomatoes, K is the most abundant macro element and Fe and Zn are the most abundant trace elements. Brown cherry tomatoes had significantly higher K, P, Mg, Cu and Fe contents than other colored tomatoes, and red tomatoes had significantly higher Zn content than other cherry tomatoes (218.8-724.3%). Yellow cherry tomatoes had the highest soluble sugar content, followed by red, brown and green tomatoes. A total of 20 amino acids of tomatoes were simultaneously determined by LC-MS. Yellow cherry tomatoes have the highest content of essential amino acids, aromatic amino acids and sweetness amino acids. Red tomatoes have the highest levels of non-essential and sourness amino acid contents. An analysis of 30 flavor indicators revealed that yellow tomatoes had the best flavor, followed by red, brown, and green tomatoes. Our work lays the foundation for future research on color and flavor formation in cherry tomatoes.

Suppress Austenite Grain Coarsening by Nb Alloying in High-Temperature-Pseudo-Carburized Bearing Steel.

The effect of Nb alloying on the suppression of austenite grain coarsening behavior during pseudo-carburizing is investigated in high-temperature-carburized SAE4320 bearing steel. To explore the role of the Nb element in the pseudo-carburizing process, the morphology, composition, size, and distribution of NbC precipitates were analyzed. The results show that the fine austenite grain observed in Nb micro-alloyed steel is caused by the pinning effect of NbC precipitates, which hinders the coarsening of austenite grains and changes the growth dynamics of austenite grains. After the SAE4320 carburized bearing steel with the addition of 0.45 wt.% Nb element is kept at 1150 °C for 4 h, the PAG size is still below 20 µm, which indicates the Nb element has obvious advantages in limiting PAG growth at high temperatures and shows great potential for the development of high-temperature carburized bearing steel.

CYTOR-NFAT1 feedback loop regulates epithelial-mesenchymal transition of retinal pigment epithelial cells.

Epithelial mesenchymal transition (EMT) occurring in retinal pigment epithelial cells (RPE) is a crucial mechanism that contributes to the development of age-related macular degeneration (AMD), a pivotal factor leading to permanent vision impairment. Long non-coding RNAs (lncRNAs) have emerged as critical regulators orchestrating EMT in RPE cells. In this study, we explored the function of the lncRNA CYTOR (cytoskeleton regulator RNA) in EMT of RPE cells and its underlying mechanisms. Through weighted correlation network analysis, we identified CYTOR as an EMT-related lncRNA associated with AMD. Experimental validation revealed that CYTOR orchestrates TGF-ß1-induced EMT, as well as proliferation and migration of ARPE-19 cells. Further investigation demonstrated the involvement of CYTOR in regulating the WNT5A/NFAT1 pathway and NFAT1 intranuclear translocation in the ARPE-19 cell EMT model. Mechanistically, CHIP, EMSA and dual luciferase reporter assays confirmed NFAT1's direct binding to CYTOR's promoter, promoting transcription. Reciprocally, CYTOR overexpression promoted NFAT1 expression, while NFAT1 overexpression increased CYTOR transcription. These findings highlight a mutual promotion between CYTOR and NFAT1, forming a positive feedback loop that triggers the EMT phenotype in ARPE-19 cells. These discoveries provide valuable insights into the molecular mechanisms of EMT and its association with AMD, offering potential avenues for targeted therapies in EMT-related conditions, including AMD.

Magnetic leakage behavior assessment in pipeline stress-concentrated areas using improved force-magnetic coupling model.

Magnetic flux leakage (MFL) technology is remarkable for its capability to detect pipeline geometric deformation and general corrosion defects. However, it cannot characterize the MFL behavior in stress-concentrated areas, thereby greatly challenging the subsequent pipeline maintenance. This study suggests that the MFL characteristics of pipeline in stress-concentrated areas are caused by the combined effect of the face magnetic charge on the deformed end-face and the body magnetic charge of the dislocation stack. In addition, an improved force-magnetic coupling model of the pipeline in stress-concentrated areas is established based on the magnetic dipole model and Jiles-Atherton (J-A) theory. In the verification experiment, the Q235 steel plate is magnetized along the extension direction (axis of the pipeline) through the solenoid coil to obtain the distribution law of the MFL signal in the stress-concentrated area under different excitation intensities. The results show that with the increase in excitation intensity, the deformation of the MFL field signal caused by the end-face of the stress-concentrated area gradually increases to a stable state. Moreover, the internal stress of the MFL field signal generated by the pipe dislocation rapidly increases to a peak value and then decays exponentially to a certain base value. The overall change trend is in good agreement with the calculation results of the established force-magnetic coupling model. Meanwhile, the differentiation research between deformation and internal stress MFL field signals under different magnetic field intensities can provide a reliable theoretical basis for the subsequent accurate identification and quantification of pipeline stress-concentrated areas.

Optimal channel dynamic selection for Constructing lightweight Data EEG-based emotion recognition.

At present, most methods to improve the accuracy of emotion recognition based on electroencephalogram (EEG) are achieved by means of increasing the number of channels and feature types. This is to use the big data to train the classification model but it also increases the code complexity and consumes a large amount of computer time. We propose a method of Ant Colony Optimization with Convolutional Neural Networks and Long Short-Term Memory (ACO-CNN-LSTM) which can attain the dynamic optimal channels for lightweight data. First, transform the time-domain EEG signal to the frequency domain by Fast Fourier Transform (FFT), and the Differential Entropy (DE) of the three frequency bands (α, ß and γ) are extracted as the feature data; Then, based on the DE feature dataset, ACO is employed to plan the path where the electrodes are located in the brain map. The classification accuracy of CNN-LSTM is used as the objective function for path determination, and the electrodes on the optimal path are used as the optimal channels; Next, the initial learning rate and batchsize parameters are exactly matched the data characteristics, which can obtain the best initial learning rate and batchsize; Finally, the SJTU Emotion EEG Dataset (SEED) dataset is used for emotion recognition based on the ACO-CNN-LSTM. From the experimental results, it can be seen that: the average accuracy of three-classification (positive, neutral, negative) can achieve 96.59 %, which is based on the lightweight data by means of ACO-CNN-LSTM proposed in the paper. Meanwhile, the computer time consumed is reduced. The computational efficiency is increased by 15.85 % compared with the traditional CNN-LSTM method. The accuracy can achieve more than 90 % when the data volume is reduced to 50 %. In summary, the proposed method of ACO-CNN-LSTM in the paper can get higher efficiency and accuracy.

Numerical simulation of the influence of nasal cycle on nasal airflow.

To study the characteristics of nasal airflow in the presence of nasal cycle by computational fluid dynamics. CT scan data of a healthy Chinese individual was used to construct a three-dimensional model of the nasal cavity to be used as simulation domain. A sinusoidal airflow velocity is set at the nasal cavity entrance to reproduce the breathing pattern of a healthy human. There was a significant difference in the cross-sectional area between the two sides of the nasal cavity. Particularly, the decongested side is characterized by a larger cross-section area, and consequently, by a larger volume with respect to the congested side. The airflow velocity, pressure, and nasal resistance were higher on the congested narrow side. The temperature regulation ability on the congested narrow side was stronger than that on the decongested wider side. During the nasal cycle, there are differences in the nasal cavity function between the congested and decongested sides. Therefore, when evaluating the impact of various factors on nasal cavity function, the nasal cycle should be considered.

A study on the application of radiomics based on cardiac MR non-enhanced cine sequence in the early diagnosis of hypertensive heart disease.

BACKGROUND: The prevalence of hypertensive heart disease (HHD) is high and there is currently no easy way to detect early HHD. Explore the application of radiomics using cardiac magnetic resonance (CMR) non-enhanced cine sequences in diagnosing HHD and latent cardiac changes caused by hypertension. METHODS: 132 patients who underwent CMR scanning were divided into groups: HHD (42), hypertension with normal cardiac structure and function (HWN) group (46), and normal control (NOR) group (44). Myocardial regions of the end-diastolic (ED) and end-systolic (ES) phases of the CMR short-axis cine sequence images were segmented into regions of interest (ROI). Three feature subsets (ED, ES, and ED combined with ES) were established after radiomic least absolute shrinkage and selection operator feature selection. Nine radiomic models were built using random forest (RF), support vector machine (SVM), and naive Bayes. Model performance was analyzed using receiver operating characteristic curves, and metrics like accuracy, area under the curve (AUC), precision, recall, and specificity. RESULTS: The feature subsets included first-order, shape, and texture features. SVM of ED combined with ES achieved the highest accuracy (0.833), with a macro-average AUC of 0.941. AUCs for HHD, HWN, and NOR identification were 0.967, 0.876, and 0.963, respectively. Precisions were 0.972, 0.740, and 0.826; recalls were 0.833, 0.804, and 0.863, respectively; and specificities were 0.989, 0.863, and 0.909, respectively. CONCLUSIONS: Radiomics technology using CMR non-enhanced cine sequences can detect early cardiac changes due to hypertension. It holds promise for future use in screening for latent cardiac damage in early HHD.

The impacts of natural product miltirone and the CYP2D6 pharmacogenetic phenotype on fluoxetine metabolism.

Introduction: To study the effects of drug-induced CYP2D6 activity inhibition and genetic polymorphisms on fluoxetine metabolism, rat liver microsomes (RLMs) and SD rats were used to investigate the potential drug‒drug interactions (DDIs), and CYP2D6 http://muchong.com/t-10728934-1 recombinant baculosomes were prepared and subjected to catalytic reactivity studies. Methods and Results: All analytes were detected by ultraperformance liquid chromatography-tandem mass spectrometry (UPLC‒MS/MS). After screening for 27 targeted natural products, miltirone was identified as having obvious inhibitory effect on fluoxetine metabolism in RLMs. In vivo, the concentration of fluoxetine in rat blood increased markedly after miltirone administration. The molecular docking results showed that miltirone bound more strongly to CYP2D6 than fluoxetine, and PHE120 may be the key residue leading to the inhibition of CYP2D6-mediated fluoxetine N-demethylation by miltirone. In terms of the genetic polymorphism of CYP2D6 on fluoxetine metabolism, the intrinsic clearance values of most variants were significantly altered. Among these variants, CYP2D6*92 and CYP2D6*96/Q424X were found to be catalytically inactive for fluoxetine metabolism, five variants (CYP2D6*89/L142S, *97/F457L, *R497, *V342M and *R344Q) exhibited markedly increased clearance values (>125.07%) and seven variants (CYP2D6*2, *10, *87/A5V, *93/T249P, *E215K, *R25Q and *R440C) exhibited significantly decreased clearance values (from 6.62% to 66.79%) compared to those of the wild-type. Conclusion: Our results suggest that more attention should be given to subjects in the clinic who take fluoxetine and also carry one of these infrequent CYP2D6 alleles or are coadministered drugs containing miltirone.

Textured Perovskite/Silicon Tandem Solar Cells Achieving Over 30% Efficiency Promoted by 4-Fluorobenzylamine Hydroiodide.

Monolithic textured perovskite/silicon tandem solar cells (TSCs) are expected to achieve maximum light capture at the lowest cost, potentially exhibiting the best power conversion efficiency. However, it is challenging to fabricate high-quality perovskite films and preferred crystal orientation on commercially textured silicon substrates with micrometer-size pyramids. Here, we introduced a bulky organic molecule (4-fluorobenzylamine hydroiodide (F-PMAI)) as a perovskite additive. It is found that F-PMAI can retard the crystallization process of perovskite film through hydrogen bond interaction between F- and FA+ and reduce (111) facet surface energy due to enhanced adsorption energy of F-PMAI on the (111) facet. Besides, the bulky molecular is extruded to the bottom and top of perovskite film after crystal growth, which can passivate interface defects through strong interaction between F-PMA+ and undercoordinated Pb2+/I-. As a result, the additive facilitates the formation of large perovskite grains and (111) preferred orientation with a reduced trap-state density, thereby promoting charge carrier transportation, and enhancing device performance and stability. The perovskite/silicon TSCs achieved a champion efficiency of 30.05% based on a silicon thin film tunneling junction. In addition, the devices exhibit excellent long-term thermal and light stability without encapsulation. This work provides an effective strategy for achieving efficient and stable TSCs.

MFPINC: prediction of plant ncRNAs based on multi-source feature fusion.

Non-coding RNAs (ncRNAs) are recognized as pivotal players in the regulation of essential physiological processes such as nutrient homeostasis, development, and stress responses in plants. Common methods for predicting ncRNAs are susceptible to significant effects of experimental conditions and computational methods, resulting in the need for significant investment of time and resources. Therefore, we constructed an ncRNA predictor(MFPINC), to predict potential ncRNA in plants which is based on the PINC tool proposed by our previous studies. Specifically, sequence features were carefully refined using variance thresholding and F-test methods, while deep features were extracted and feature fusion were performed by applying the GRU model. The comprehensive evaluation of multiple standard datasets shows that MFPINC not only achieves more comprehensive and accurate identification of gene sequences, but also significantly improves the expressive and generalization performance of the model, and MFPINC significantly outperforms the existing competing methods in ncRNA identification. In addition, it is worth mentioning that our tool can also be found on Github ( https://github.com/Zhenj-Nie/MFPINC ) the data and source code can also be downloaded for free.

Effect of acupuncture combined with Ningshen mixture on climacteric insomnia: A randomized controlled trial.

BACKGROUND: In recent years, the incidence of menopause insomnia has gradually increased, seriously affecting women's physical and mental health. METHODS: Total 82 climacteric insomnia patients received from January 2021 to January 2023 were divided into 2 groups at random. In control group, 41 cases received conventional Western medicine, and in study group, 41 cases received acupuncture combined with Ningshen mixture. Clinical effectiveness of both groups was compared, neurotransmitter levels, TCM syndrome integral and Pittsburgh Sleep Quality Index (PSQI) were assessed in both groups. Meanwhile, the recurrence rate and safety were evaluated in 2 groups. RESULTS: The curative effect in study group was better than that in control group (P < .05). After treatment, the expressions of 5-hydroxytryptamine and ß-endorphin (ß-EP) in study group were higher than control group (P < .05); TCM syndrome scores and PSQI scores in study group were lower than control group (P < .05). The total recurrence rate in study group was obviously lower than control group at 3 months after treatment (P < .05). There were no serious adverse reactions in both group, and no distinct difference between 2 groups was found (P > .05). CONCLUSION: Acupuncture united with Ningshen mixture has a significant therapeutic effect and high safety in climacteric insomnia patients. It can effectively improve the neurotransmitter levels, clinical symptoms and sleep quality, and reduce the recurrence rate of climacteric insomnia patients, which has high clinical application value and is worthy of clinical promotion.

NFKBIZ regulates NFκB signaling pathway to mediate tumorigenesis and metastasis of hepatocellular carcinoma by direct interaction with TRIM16.

Hepatocellular carcinoma (HCC) is a malignant tumor with high incidence and mortality rates. NFKBIZ, a member of the nuclear factor kappa B inhibitory family, is closely related to tumor progression. However, the precise role of NFKBIZ in HCC remains unclear. To explore this, we conducted a series of experiments from clinic to cells. Western blot and qPCR revealed a significant downregulation of NFKBIZ in human HCC tissues. Clinical character analysis showed that the patients with lower NFKBIZ expression had poorer prognosis and higher clinical stage. By using CCK-8, wound healing, transwell invasion and migration assay, we discovered that NFKBIZ expression was reversely associated with the proliferation, invasion, and migration ability of HCC cells in vitro. Additionally, the results obtained from xenograft assay and lung metastasis models showed that NFKBIZ overexpression inhibited the growth and metastasis of HCC cells in vivo. Western blot and immunofluorescence assay further revealed that NFKBIZ mediated HCC cell growth and migration by regulating NFκB signaling transduction. Finally, flow cytometry, protein degradation assay and Co-immunoprecipitation indicated that TRIM16 can enhance NFKBIZ ubiquitination by direct interactions at its K48 site, which may thereby alleviate HCC cell apoptosis to induce the insensitivity to sorafenib. In conclusion, our study demonstrated that NFKBIZ regulated HCC tumorigenesis and metastasis by mediating NFκB signal transduction and TRIM16/NFKBIZ/NFκB axis may be the underlying mechanism of sorafenib insensitivity in HCC.

UV-aged polystyrene nanoplastics aggravate intestinal barrier damage by overproduction of ROS.

UV irradiation significantly alters nanoplastics (NPs) physicochemical properties, thus affecting their biological toxicity. This study is the first to assess the influence of virgin and UV-aged polystyrene NPs (v-PS NPs, a-PS NPs) on the intestinal barrier of ICR mice. We found that a-PS NPs can cause more severe intestinal barrier damage compared with v-PS NPs. The reason may be attributed to that a-PS NPs produced more ROS in intestinal tissue. Moreover, the strong oxidizing property of hydroxyl radicals (·OH) generated from the a-PS NPs can damage cell membranes through lipid peroxidation, thereby leading to a low clearance rate of ·OH due to the impaired intestinal tissue function, in turn, causing more ROS to accumulate and inducing severe oxidative damage. This research underscores the crucial role of ·OH in mediating oxidative damage from UV-aged nanoparticles, emphasizing the need to consider environmental factors in assessing NPs toxicity.