ObjectiveThe most prevalent mRNA modification, N6-methyladenosine (m⁶A) plays an important role in various RNA metabolism, including gene expression and translation. By recruiting different “reader” proteins and their cofactors, m⁶A modification can affect messenger RNA (mRNA) degradation, splicing, nuclear export and translation. However, the selective mechanism by which m⁶A sites regulate mRNA translation through m⁶A reader YTHDF1 binding remains poorly understood, due to a lack of computational methods for identifying context-specific m⁶A sites that regulate translation. To address this, we developed a novel computational framework named m⁶ATEpre, the first tool designed to predict cell-specific m⁶A sites that regulate translation efficiency. Methodsm⁶ATEpre integrates multi-omics data, introduces a novel feature representation strategy for m⁶A site sequences, and employs an autoencoder to effectively capture embedded feature representations. Specifically, m⁶ATEpre first integrated MeRIP-seq data and PAR-CLIP data through overlapping m⁶A sites with YTHDF1 binding sites and identified YTHDF1-mediated m⁶A sites. Then, m⁶ATEpre detected the translation gene by analyzing the Ribo-seq data under YTHDF1 knockdown vs control condition. Genes whose translation is mediated by YTHDF1 in an m⁶A-dependent manner were identified by a significant decrease in translation efficiency upon YTHDF1 knockdown. Next, we proposed a binary vector indicating the presence or absence of YTHDF1 binding motifs to characterize each m⁶A site sequence. This represents a novel feature representation strategy for m⁶A sites. m⁶ATEpre utilized the autoencoder to extract the potentially important feature representations and constructed a multilayer perceptron neural networks model to predict potential m⁶A sites that regulating translation efficiency. ResultsA comprehensive evaluation of m⁶ATEpre was conducted through a series of experiments. We compared its performance against that of a similar prediction task model, as well as other classifiers. The results indicate that m⁶ATEpre achieved the best prediction performance. In addition, we analyzed different feature representation strategies and performed ablation experiments to validate the rationality of the model design. The results demonstrate that our proposed feature representation strategy has a greater advantage in improving prediction performance. In the HeLa cell line, bioinformatic analysis of the metagene distribution and sequence minimum free energy of m⁶A sites regulating translation efficiency (m⁶A-reg-TE sites) revealed their specific properties in translation regulation. Functional enrichment analysis indicated that m⁶A-reg-TE genes are associated with specific biological processes and KEGG pathways. By integrating the binding sites of YTHDF1 co-factors with m⁶A-reg-TE sites, we revealed that YTHDF1-mediated and m⁶A-dependent translation efficiency regulation requires the cooperation of multiple translation-regulatory RNA-binding proteins among its co-factors in the HeLa cell line. Furthermore, we extended our predictions to the dataset of the HEK293T cell line. Similarly, bioinformatic analysis of the metagene distribution and functional enrichment revealed the cell-specific characteristic of these predicted m⁶A-reg-TE sites in HEK293T cells. Likewise, integrated analysis of multiple YTHDF1 co-factors and m⁶A-reg-TE sites predicted in the HEK293T cell line reveals their m⁶A-dependent cooperation in regulating translation efficiency. Conclusionm⁶ATEpre is a timely tool that will advance our understanding of the mechanisms of m⁶A regulation in translation efficiency. The source code and datasets used in this work can be downloaded from https://www.scidb.cn/s/bAZZFr.

In March 2024, three male and two female specimens of the genus Lispe were intercepted on cargo ships inbound for Wan Zai Port under Gongbei Customs. Based on morphological characteristics and molecular analysis, the specimens were identified as Lispe pumila. In this study, we describe the morphological features, diagnostic characteristics, and geographical distribution of L. pumila, with the aim of providing a reference for the identification of this species when intercepted at ports.

Background: High-mobility group box protein 1 (HMGB1) is a chromatin-binding protein involved in arthritis, ischemia, sepsis, atherosclerosis, neurodegenerative disorders, meningitis, and cancer. HMGB1 exhibits dual roles in cancer, acting as either a tumor suppressor or oncoprotein depending on context. Objective: This research aimed to elucidate HMGB1’s functional significance in cutaneous squamous cell carcinoma (cSCC). Methods: We overexpressed HMGB1 in cSCC cell lines using recombinant adenovirus and examined its effects on cell proliferation, colony formation, and cell migration. Results: Immunohistochemical analysis revealed elevated HMGB1 expression levels in cSCC tissue relative to normal epidermis. To assess the influence of HMGB1, we employed recombinant adenoviruses expressing HMGB1 to transduce SCC cell lines (SCC12 and SCC13). Enhanced HMGB1 expression significantly promoted cellular proliferation and colony formation capacity.Notably, HMGB1 overexpression elevated the levels of proliferation regulators, including P63, SOX2, CDK4 and CDK6. Furthermore, HMGB1 overexpression substantially enhanced tumor invasiveness, accompanied by upregulation of epithelial-mesenchymal transition (EMT) biomarkers. Mechanistically, overexpression of HMGB1 enhanced transforming growth factor-β signaling by increasing phosphorylation of SMAD2/3, the key mediators of EMT. Conclusion These data imply that HMGB1 acts as a tumor-promoting factor in cSCC.

Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

Objective This study aims to investigate the effects of varying durations of light exposure on body weight and learning and memory abilities of pubertal NIH mice. Methods Forty pubertal NIH mice, evenly split by gender and with similar initial weights, were subjected to a 12 h light-dark cycle for one week. They were then randomly assigned to groups with daily light exposure durations of 0, 6, 12, 18, and 24 hours, with 8 mice in each group. The experimental period lasted for 7 weeks, with the first 5 weeks as the feeding phase under different light exposure conditions, and the last 2 weeks as the behavioral testing phase. Their body weight was monitored, and learning and memory abilities were assessed using the T-maze, object location test, and eight-arm maze tests. Results During the light exposure period, there were no significant differences in body weight among groups (P>0.05). However, the weight gain of mice in the 24 h group was significantly higher than that of the 0 h group and the 6 h group during the second and third weeks of light exposure (P<0.05). After five weeks of light exposure, in the T-maze test, the latency time of the 0 h light exposure group was significantly longer than that of the 12 h group (P<0.01), and the latency time of the 24 h light exposure group was significantly longer than that of the 12 h group (P<0.05). In the object location test, the mice in 12 h group exhibited a higher discrimination index and spent more time observing the new location compared to the other groups, with significant differences in comparison to the 18 h group (P<0.01) and the 24 h group (P<0.05). In the eight-arm maze test, the time to find food, the reference memory error rate, and the working memory error rate in the 12 h group were all lower than those in the 0 h group, with significant differences (P<0.05). Moreover, the working memory error rate in the 24 h group was higher than that in the 12 h group, with significant differences (P<0.05). Conclusion Continuous 24 h light exposure affects body weight gain, while light exposure durations exceeding 18 h or below 6 h per day weaken the learning and memory abilities of NIH mice.

AIM To study the chemical constituents from ethyl acetate fraction of Balanophora harlandii Hook.f.and their tyrosinase inhibitory activity.METHODS Separation and purification were performed using silica gel,MCI,ODS,Sephadex LH-20 and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The monophenolase inhibitory activity was determined by the tyrosinase-catalyzed oxidation of L-tyrosine.RESULTS Twenty-four compounds were isolated and identified as sesamin(1),methyl caffeate(2),quercetin(3),5,7-dihydroxychromanone(4),methyl 3,4-dihydroxybenzoate(5),esculetin(6),kaempferol(7),naringenin(8),pyrogallic acid(9),pinosylvin(10),methyl propionate(11),caffeic acid(12),saccharinol(13),ferulic acid(14),trans-p-hydroxycinnamic acid(15),cinnamic acid(16),vanillic acid(17),vanillin(18),4-hydroxyacetophenone(19),4-hydroxybenzaldehyde(20),apigenin(21),(-)-isolariciresinol(22),(-)-secoisolariciresinol(23)and meso-2,3-di(3′,4′-methylenedioxybenzyl)butane-1,4-diol(24).The IC50 values of compounds 3,5,7,8,19,and 20 ranged from(0.246 5±0.028 3)to(1.278 2±0.021 3)mmol/L.CONCLUSION Compounds 1-9、11、15、17-21、24 are isolated from this plant for the first time,and 1,6,9,17-19,24 are first isolated from genus Balanophora.Compounds 3、5、7、8、19 and 20 have tyrosinase inhibitory activity.

Dental caries is the most common oral disease in children,not only impairing chewing,speech,and other functions while also impacting maxillofacial development and overall health.Recent researches have established the"ecological plaque theory",which posits that an imbalance in the oral plaque microecology leads to the demineralization of teeth hard tissues,as the widely ac-cepted etiology of dental caries.However,the significant variability in the composition and function of children's plaque microorgan-isms,influenced by primary tooth eruption and replacement,changes in diet,and increase in social interactions,makes traditional culture methods inadequate for studying this complex micro-ecosystem.Advances in multi-omics technologies have recently created new opportunities for studiing complex microecology.High-throughput sequencing technologies,such as 16S rRNA gene sequencing and metagenomics,allow for the exploration of microbial diversity and the analysis of genomic functions and their associations with dental caries.Mass spectrometry-based technologies,including metabolomics and proteomics,might elucidate the molecular mecha-nisms of dental caries by analyzing differential metabolites and pathways.This review focuses on the application of multi-omics tech-nologies to explore the relationships between the structure,function,and metabolic state of oral microbial communities and pediatric dental caries.It further discusses the future of oral microorganisms as diagnosis and therapeutic targets for caries,and is expected to provide novel strategies for the prevention and treatment of pediatric dental caries.

Aim To explore the molecular mechanism of Tongluo Tangtai(TLTT)prescription in the preven-tion and treatment of diabetic peripheral neuropathy(DPN)based on network pharmacology and in vitro experimental verification.Methods The chemical composition information of Tongluo Tangtai was searched by TCMSP database and literature search,and the target of chemical composition was collected by PubChem and SwissTargetPrediction database.DPN targets were collected through GeneCards database.GO function and KEGG pathway enrichment of common target proteins were analyzed using DAVID database.The correlation network diagram was constructed using Cytoscape software,and the main active components and target genes were screened for molecular docking study.The in vitro model of DPN was constructed in Schwann cells induced by high glucose.The effect of TLTT on the survival rate of RSC96 cells was detected by CCK-8 method,and the gene expression of Wnt/β-catenin pathway related target molecules in RSC96 cells was detected by Real-time PCR.The expression levels of Wnt/β-catenin pathway-related proteins in RSC96 cells were detected by Western blot.Results The main active components such as stigmasterol,β-si-tosterol and quercetin were screened,which mainly ac-ted on EGFR,AKT1,MAPK3 and Wnt,PI3K-Akt and MAPK signaling pathways.The molecular docking re-sults showed that stigmasterol,β-sitosterol,quercetin and other components could dock well with EGFR,AKT1,MAPK3 and other targets.The results of cell experiment showed that 10％TLTT drug-containing ser-um could promote the viability of high-glucose Schwann cells,up-regulate the expression of β-catenin protein,and down-regulate the expression of GSK-3β and Wif-1 protein.Conclusions TLTT has the characteristics of multi-target-multi-pathway in the treatment of DPN.The possible mechanism is that TLTT activates the Wnt/β-catenin signaling pathway,improves the inhibi-tory effect of high glucose on the proliferation of Schwann cells,promotes the proliferation of Schwann cells,and thus improves the status of DPN.

Aim To explore the molecular mechanism of Tongluo Tangtai(TLTT)prescription in the preven-tion and treatment of diabetic peripheral neuropathy(DPN)based on network pharmacology and in vitro experimental verification.Methods The chemical composition information of Tongluo Tangtai was searched by TCMSP database and literature search,and the target of chemical composition was collected by PubChem and SwissTargetPrediction database.DPN targets were collected through GeneCards database.GO function and KEGG pathway enrichment of common target proteins were analyzed using DAVID database.The correlation network diagram was constructed using Cytoscape software,and the main active components and target genes were screened for molecular docking study.The in vitro model of DPN was constructed in Schwann cells induced by high glucose.The effect of TLTT on the survival rate of RSC96 cells was detected by CCK-8 method,and the gene expression of Wnt/β-catenin pathway related target molecules in RSC96 cells was detected by Real-time PCR.The expression levels of Wnt/β-catenin pathway-related proteins in RSC96 cells were detected by Western blot.Results The main active components such as stigmasterol,β-si-tosterol and quercetin were screened,which mainly ac-ted on EGFR,AKT1,MAPK3 and Wnt,PI3K-Akt and MAPK signaling pathways.The molecular docking re-sults showed that stigmasterol,β-sitosterol,quercetin and other components could dock well with EGFR,AKT1,MAPK3 and other targets.The results of cell experiment showed that 10％TLTT drug-containing ser-um could promote the viability of high-glucose Schwann cells,up-regulate the expression of β-catenin protein,and down-regulate the expression of GSK-3β and Wif-1 protein.Conclusions TLTT has the characteristics of multi-target-multi-pathway in the treatment of DPN.The possible mechanism is that TLTT activates the Wnt/β-catenin signaling pathway,improves the inhibi-tory effect of high glucose on the proliferation of Schwann cells,promotes the proliferation of Schwann cells,and thus improves the status of DPN.

Objective:To establish a pure water direct dilution-inductively coupled plasma mass spectrometry (ICP-MS) detection method for rapid determination of urinary iodine.Methods:Pure water was used to directly dilute the urine samples. The washing solution was 5.0 g/L ascorbic acid, the internal standard solution was 5.0 g/L ascorbic acid and 100 μg/L 128Te, the standard solution was prepared with the solution of lyophilized urine iodine biological component analysis reference material. The method was evaluated in terms of linear range, detection limit, quantification limit, precision and method comparision experiment. Results:The linear correlation coefficient of the standard curve for iodine concentration range from 0 to 50.0 μg/L was 0.999 7, with a detection limit of 0.2 μg/L and a quantification limit of 0.6 μg/L. The spiked recovery rates of low, medium, and high concentration iodine standard solutions added to actual urine samples were 100.8%, 99.1% and 99.7%, respectively, with relative standard deviations of 0.8%, 1.3% and 1.6%, respectively. There was no statistically significant difference ( t = - 0.14, P = 0.890) between the results of measuring actual urine and assessment urine using this method and "Determination of Iodine in Urine-Part 2: Inductively Coupled Plasma Mass Spectrometry (WS/T 107.2-2016)". Conclusions:We have successfully established a pure water direct dilution-ICP-MS method for determining urinary iodine. This method provides accurate and highly sensitive results, making it suitable for sudden public health emergencies and large-scale clinical measurement of urinary iodine.