LAYN Serves as a Prognostic Biomarker and Downregulates Tumor-Infiltrating CD8+ T Cell Function in Hepatocellular Carcinoma.

Background: Layilin (LAYN) represents a valuable prognostic biomarker across various tumor types, while also serving as an innovative indicator of dysfunctional or exhausted CD8+ T cells and exhibiting correlation with immune context. However, the immune function and prognostic significance of LAYN in hepatocellular carcinoma (HCC) remain unexplored. Therefore, our objective is to investigate the role of LAYN in CD8+ T cell exhaustion, clinical prognosis, and the tumor microenvironment within HCC. Methods: TIMER or GEPIA databases were used to analyze LAYN expression level and its correlation with immune infiltration in HCC. Bioinformatics analysis was conducted on TCGA and scRNA-seq cohorts. The evaluation of LAYN expression level in fresh specimens was performed through IF, IHC, and ELISA assays. Flow cytometry and mRNA-seq were employed to investigate co-expressed genes of LAYN, the LAYN+CD8+ T cell exhaustion signature and immune function. Cell proliferation ability and killing activity were assessed using CCK8 and CFSE/PI. Results: The expression level of LAYN in HCC tumors was significantly higher compared to peri-tumors. Patients with high levels of LAYN exhibited poorer OS. GO or KEGG analysis confirmed that LAYN was involved in immune response and was positively associated with CD8+ T cell immune infiltration levels. Furthermore, LAYN negatively regulated the immune function of CD8+ T cells, leading to dysfunctional phenotypes characterized by elevated levels of CD39, TIM3 and reduced levels of perforin, TNF-α, Ki-67. CFSE/PI assays demonstrated that LAYN+CD8+ T cells displayed decreased cytotoxic activity. Additionally, there was a positive correlation between LAYN and CD146 levels, which are involved in adhesion and localization processes of CD8+ T cells. Interestingly, blocking LAYN partially restored the exhaustion properties of CD8+ T cells. Conclusion: LAYN exhibits a strong correlation with immune infiltration in the TME and represents a novel biomarker for predicting clinical prognosis in HCC. Moreover, targeting LAYN may hold promise as an effective strategy for HCC immunotherapy.

Tumor-associated macrophage-induced circMRCKα encodes a peptide to promote glycolysis and progression in hepatocellular carcinoma.

The tumor-associated macrophages (TAMs) play multifaceted roles in the progression of hepatocellular carcinoma (HCC). However, the involvement of circular RNAs in the interplay between TAMs and HCC remains unclear. Based on Transwell co-culturing and circular RNA sequencing, this study revealed that TAMs enhanced tumor glycolysis and progression by upregulating circMRCKα in HCC cells. Patients with HCC who exhibited elevated circMRCKα levels presented significantly reduced overall survival and greater cumulative recurrence. Notably, we identified a novel functional peptide of 227 amino acids named circMRCKα-227aa, encoded by circMRCKα. Mechanistically, circMRCKα-227aa bound to USP22 and enhanced its protein level to obstruct HIF-1α degradation via the ubiquitin-proteasome pathway, thereby augmenting HCC glycolysis and progression. In clinical HCC samples, a positive correlation was observed between the expression of circMRCKα and the number of infiltrating CD68+ TAMs and expression of USP22. Furthermore, circMRCKα emerged as an independent prognostic risk factor both individually and in conjunction with CD68+ TAMs and USP22. This study illustrated that circMRCKα-227aa, a novel TAM-induced peptide, promotes tumor glycolysis and progression via USP22 binding and HIF-1α upregulation, suggesting that circMRCKα and TAMs could be combined as therapeutic targets in HCC.

Multielement Flame-Retardant System Constructed with Metal POSS-Organic Frameworks for Epoxy Resin.

The direct coordination between polyhedral oligomeric silsesquioxane (POSS) and Co forms an assembly of nanoparticles with low specific surface area and leads to a poor dispersion state in the epoxy resin matrix, resulting in unsatisfactory flame-retardant efficiency. Metal-organic frameworks (MOFs), for instance, ZIF-67, provide not only the cobalt element but also the porous framework that endows the nanocomposite of MOFs and POSS with high specific surface area and abundant Co sites in the silica skeleton. Herein, ZIF-67 is hybridized with octacarboxyl POSS, resulting in the removal of the alkaline ligand to form novel metal POSS-organic frameworks (MPOFs). The size differences for organic groups and silica nanocages of POSS vs. micropores of ZIF-67 gave rise to a reverse click reaction, reforming octavinyl POSS isolated on the outer surface of the Co complex, which could be further modified by a phosphorous flame retardant using an addition reaction. The obtained MPOFs-P with 2 wt % loading in epoxy resin could improve the limiting oxygen index value of the composites to 27.0% and pass the V-0 rating in the UL-94 test. Meanwhile, the peaks of the heat release rate and especially the total smoke production were reduced by 46.6 and 25.2%, respectively. The robust char layer reduces the emission of toxic gas CO by 39.8%. The above epoxy product with promising flame retardancy also improved mechanical properties, thanks to the filler with a unique nanostructure. The ingenious work offers enlightenment for the hybridization method of MOFs and POSS to fabricate a multielement flame-retardant system for epoxy resin with high efficacy.

Identification of the Exercise and Time Effects on Human Skeletal Muscle through Bioinformatics Methods.

Background: The human body has more than 600 kinds of skeletal muscles, which accounts for about 40% of the whole weight. Most skeletal muscles can make bones move, and their strength and endurance directly affect their performance during exercise. Methods: To determine the effects of exercise and time on human skeletal muscle, we downloaded the microarray expression profile of GSE1832 and analyzed it to select differentially expressed genes (DEGs). Then, a protein-protein interaction (PPI) network was established, and the hub genes were identified. Afterwards, DEGs were applied to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Finally, with the help of Gene Set Enrichment Analysis (GSEA), the gene sets in the 7 samples were enriched in the KEGG pathway. Results: Through a series of bioinformatics analyses, we obtained a total of 271 DEGs. After that, four hub genes were determined through the PPI network, namely, EP300, STAT1, CDKN1A, and RAC2. In addition, we got that these DEGs were enriched in GO, such as regulation of cell population proliferation, cellular water homeostasis, and so on, and in KEGG, namely, hepatitis B, Epstein-Barr virus infection, small cell lung cancer, pathways in cancer, and others. Finally, the gene set in the samples obtained by GSEA was enriched in the cell cycle, chemokine signaling pathway, DNA replication, cytokine receptor interaction, ECM receptor interaction, and focal adhesion in KEGG. Conclusion: The findings obtained in this study will provide new clues for elucidating the mechanism of exercise and time on human skeletal muscles.

Bioinformatics Analysis of Exercise-Related Biomarkers in Diabetes.

Background: Exercise is a regular behavioral activity that not only helps to lose weight but also reduces the risk of cardiovascular and cerebrovascular diseases. Diabetes is a common disease that plagues human health. It is shown that regular exercise can improve the insulin sensitivity of diabetic patients and have an important function in adjuvant therapy. Methods: We downloaded the GSE101931 dataset from the Gene Expression Omnibus (GEO) database, 10 samples were obtained from the GSE101931 dataset, including 5 before exercise and 5 postexercise samples, and GEO2R was used to screen the differentially expressed genes (DEGs) exhibited by a heat map. Then, the enrichment analysis of DEGs in Gene Ontology (GO) function was analyzed by Metascape, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of DEGs was also analyzed by gene set enrichment analysis (GSEA). Next, the protein-protein interaction (PPI) network maps were drawn, and the hub genes were identified through Metascape. Finally, the expressions of the hub genes in the dataset were analyzed. Results: Totally, 116 upregulated DEGs and 1017 downregulated DEGs were identified from these data. These DEGs were mainly enriched in the platelet-derived growth factor receptor signaling pathway and mRNA processing. Then, the GSEA analysis showed that 6 KEGG pathways were associated with postexercise prediabetic samples, namely, ABC transporters, focal adhesion, MAPK signaling pathway, prion diseases, melanogenesis, and gap junction. Afterward, three hub genes (HSPA8, STIP1, and HSPH1) were highly expressed after exercise through the box plot analysis. Conclusion: A myriad of research results confirms that there is a certain connection between exercise and diabetes, which provides a favorable basis for emerging exercise into the treatment of diabetic patients.

Cancelable HD-SEMG Biometric Identification via Deep Feature Learning.

Conventional biometric modalities, such as the face, fingerprint, and iris, are vulnerable against imitation and circumvention. Accordingly, secure biometric modalities with cancelable properties are needed for personal identification, especially in smart healthcare applications. Here we developed a person identification model using high-density surface electromyography (HD-sEMG) as biometric traits. In this model, the HD-sEMG biometric templates are cancelable and could be customized by the users through finger isometric contractions. A deep feature learning approach, implemented by convolutional neural networks (CNNs) is used to capture user-specific patterns from HD-sEMG signals and make identification decisions. This model has been validated on twenty-two subjects, with training and testing data acquired from two different days. The rank-1 identification accuracy and equal error rate for 44 identities (22 subjects × 2 accounts) can reach 87.23% and 4.66%, respectively. The cross-day identification accuracy of the proposed model is higher than the results of previous methods reported in the literature. The usability and efficiency of the proposed model are also investigated, indicating its potentials for practical applications.

Changes in synchronization of the motor unit in muscle fatigue condition during the dynamic and isometric contraction in the Biceps Brachii muscle.

The fatigue-induced neuromuscular mechanism remains to be fully elucidated. So far, the macroscopic mechanism using global surface electromyogram (sEMG) has been widely investigated. However, the microscopic mechanism using high-level neural information based on motor unit (MU) spike train from the spinal cord lacks attention, especially for the conditions under dynamic contraction task. The synchronization of the MU spike train is generally assumed to be an excellent indicator to represent the activities of spinal nerves. Accordingly, this study employed synchronization of MU spike train decomposed from high-density sEMG (HD-sEMG) to investigate the fatigue condition in muscular contractions within the Biceps Brachii muscle under both isometric and dynamic contraction tasks, giving a complete picture of the microscopic fatigue mechanism. We compared the synchronization of MU in Delta (1-4 Hz), alpha (8-12 Hz), Beta (15-30 Hz), and Gamma (30-60 Hz) frequency bands during the fatigue condition induced by different contractions. Our results showed that MU synchronization increased significantly (p<0.05) in all frequency bands across the two contraction tasks. The results indicate that the microscopic fatigue mechanism of Biceps Brachii muscle does not vary due to different contraction tasks.

Open Access Dataset, Toolbox and Benchmark Processing Results of High-Density Surface Electromyogram Recordings.

We provide an open access dataset of High densitY Surface Electromyogram (HD-sEMG) Recordings (named "Hyser"), a toolbox for neural interface research, and benchmark results for pattern recognition and EMG-force applications. Data from 20 subjects were acquired twice per subject on different days following the same experimental paradigm. We acquired 256-channel HD-sEMG from forearm muscles during dexterous finger manipulations. This Hyser dataset contains five sub-datasets as: (1) pattern recognition (PR) dataset acquired during 34 commonly used hand gestures, (2) maximal voluntary muscle contraction (MVC) dataset while subjects contracted each individual finger, (3) one-degree of freedom (DoF) dataset acquired during force-varying contraction of each individual finger, (4) N-DoF dataset acquired during prescribed contractions of combinations of multiple fingers, and (5) random task dataset acquired during random contraction of combinations of fingers without any prescribed force trajectory. Dataset 1 can be used for gesture recognition studies. Datasets 2-5 also recorded individual finger forces, thus can be used for studies on proportional control of neuroprostheses. Our toolbox can be used to: (1) analyze each of the five datasets using standard benchmark methods and (2) decompose HD-sEMG signals into motor unit action potentials via independent component analysis. We expect our dataset, toolbox and benchmark analyses can provide a unique platform to promote a wide range of neural interface research and collaboration among neural rehabilitation engineers.

Quantifying Spatial Activation Patterns of Motor Units in Finger Extensor Muscles.

The ability to expertly control different fingers contributes to hand dexterity during object manipulation in daily life activities. The macroscopic spatial patterns of muscle activations during finger movements using global surface electromyography (sEMG) have been widely researched. However, the spatial activation patterns of microscopic motor units (MUs) under different finger movements have not been well investigated. The present work aims to quantify MU spatial activation patterns during movement of distinct fingers (index, middle, ring and little finger). Specifically, we focused on extensor muscles during extension contractions. Motor unit action potentials (MUAPs) during movement of each finger were obtained through decomposition of high-density sEMG (HD-sEMG). First, we quantified the spatial activation patterns of MUs for each finger based on 2-dimension (2-D) root-mean-square (RMS) maps of MUAP grids after spike-triggered averaging. We found that these activation patterns under different finger movements are distinct along the distal-proximal direction, but with partial overlap. Second, to further evaluate MU separability, we classified the spatial activation pattern of each individual MU under distinct finger movement and associated each MU with its corresponding finger with Regularized Uncorrelated Multilinear Discriminant Analysis (RUMLDA). A high accuracy of MU-finger classification tested on 12 subjects with a mean of 88.98% was achieved. The quantification of MU spatial activation patterns could be beneficial to studies of neural mechanisms of the hand. To the best of our knowledge, this is the first work which manages to quantify MU behaviors under different finger movements.

Generalized Finger Motion Classification Model Based on Motor Unit Voting.

Surface electromyogram-based finger motion classification has shown its potential for prosthetic control. However, most current finger motion classification models are subject-specific, requiring calibration when applied to new subjects. Generalized subject-nonspecific models are essential for real-world applications. In this study, the authors developed a subject-nonspecific model based on motor unit (MU) voting. A high-density surface electromyogram was first decomposed into individual MUs. The features extracted from each MU were then fed into a random forest classifier to obtain the finger label (primary prediction). The final prediction was selected by voting for all primary predictions provided by the decomposed MUs. Experiments conducted on 14 subjects demonstrated that our method significantly outperformed traditional methods in the context of subject-nonspecific finger motion classification models.

Fabrication of carbon nanotube-polylactic acid composite electrode by melt compounding for capillary electrophoretic determination of tectoridin and irigenin in Belamcandae Rhizoma.

A sensitive detection electrode based on the composite of carbon nanotubes (CNTs) and polylactic acid (PLA) was fabricated for measuring the bioactive constituents in Belamcandae Rhizoma by capillary electrophoresis (CE). The composite was facilely fabricated by packing a blend of CNTs and melted PLA into a fused silica capillary under heat. The prepared CNT-PLA composite was characterized by scanning electron microscopy, Raman spectroscopy, Fourier transform infrared spectroscopy and cyclic voltammetry. The results indicated that PLA bound electrically conductive CNTs to form a well-dispersed composite network for electrochemical sensing. The electrode was employed to the off-line detection of tectoridin and irigenin in Belamcandae Rhizoma to demonstrate its performance in capillary electrophoresis. At a separation voltage of 12 kV, the two isoflavones were well separated and detected within 8 min in a 40-cm fused silica capillary in a borate buffer at pH 9.8. The detection limits for tectoridin and irigenin were measured to be 0.24 and 0.21 µM, respectively.

Reduction of urinary uric acid excretion in patients with proteinuria.

Serum uric acid (UA) concentration is positively associated with proteinuria. However, the relationship between proteinuria and urinary metabolites of purine metabolism remains unknown. This study developed a hydrophilic interaction chromatography (HILIC)-based HPLC method with ultraviolet detection (UV) to quantify creatinine (Cr), UA, xanthine, and hypoxanthine in human urine simultaneously. The urinary concentrations of UA and Cr obtained by our method are consistent with those measured by an autoanalyzer. The HPLC-HILIC-UV method was validated as selective and robust with simple sample preparation for measuring UA, xanthine, hypoxanthine and Cr, which is suitable for large clinical studies. The UA/Cr ratios in random urine samples were 5.5 times lower in proteinuria patients (0.077±0.008) than in healthy individuals (0.424±0.037). Moreover, the UA/hypoxanthine ratio in proteinuria patients was approximately 10 times lower than that in healthy individuals. Our findings revealed a reduced urinary UA excretion, which is one of the factors leading to increased serum UA in proteinuria patients.

Alteration of gut bacteria and metabolomes after glucaro-1,4-lactone treatment contributes to the prevention of hypercholesterolemia.

D-Glucaro-1,4-lactone (1,4-GL) has been shown to have a hypocholesterolemic effect in rats and human subjects. However, little information is known concerning the alteration of metabolome associated with the effect. Here, we show that 1,4-GL delays the development of hypercholesterolemia with the coadministration of a high-fat, high-cholesterol diet (HFHC) in rats. Metabonomic results based on proton nuclear magnetic resonance indicate that urinary trimethylamine N-oxide, trimethylamine, lactate, acetate, formate, and creatinine are significantly altered after 1,4-GL and HFHC treatments. Colonic flora test results reveal that the quantity of Bifidobacterium and Lactobacillus in the intestines respectively increase by about 1.7- and 4.2-fold in rats treated with 1,4-GL compared with those in the control group. Rats that were coadministered with HFHC and 1,4-GL exhibit normal levels of lactate and acetate in serum and display urinary excretions of lactate and acetate that are 2 to 3 times higher compared with those treated with HFHC alone. The results imply that the increased probiotic quantities and urinary excretion of breakdown products of fat/cholesterol after 1,4-GL treatment contribute to the prevention of hypercholesterolemia. Our study offers insights into the model of action for 1,4-GL in preventing hypercholesterolemia.