The improvement of pain symptoms in patients with burning mouth syndrome through combined laser and medication therapy.

BACKGROUND: The etiology of Burning Mouth Syndrome (BMS) remains unclear. OBJECTIVE: To explore the differences in the therapeutic efficacy of pain improvement between medication therapy and laser therapy in patients with BMS. METHODS: 45 BMS patients were randomly divided into three groups: The Combination therapy group (Group A, n= 15), The Medication therapy group (Group B, n= 15), and the Laser therapy group (Group C, n= 15). The pain condition of the patients was evaluated using the Numeric Rating Scale (NRS), and the improvement in pain before and after treatment was compared among the three groups. RESULTS: All three groups (A, B, and C) showed a significant reduction in NRS scores after treatment, with statistically significant differences observed among the different groups. Group A exhibited the most significant improvement, with a statistically significant difference before and after treatment. CONCLUSION: Laser and medication therapy are effective methods for reducing oral burning pain * symptoms, and their combined use yields more significant therapeutic effects.

The improvement of pain symptoms in patients with burning mouth syndrome through combined laser and medication therapy.

BACKGROUND: The etiology of Burning Mouth Syndrome (BMS) remains unclear. OBJECTIVE: To explore the differences in the therapeutic efficacy of pain improvement between medication therapy and laser therapy in patients with BMS. METHODS: 45 BMS patients were randomly divided into three groups: The Combination therapy group (Group A, n= 15), The Medication therapy group (Group B, n= 15), and the Laser therapy group (Group C, n= 15). The pain condition of the patients was evaluated using the Numeric Rating Scale (NRS), and the improvement in pain before and after treatment was compared among the three groups. RESULTS: All three groups (A, B, and C) showed a significant reduction in NRS scores after treatment, with statistically significant differences observed among the different groups. Group A exhibited the most significant improvement, with a statistically significant difference before and after treatment. CONCLUSION: Laser and medication therapy are effective methods for reducing oral burning pain * symptoms, and their combined use yields more significant therapeutic effects.

Single-sEV profiling identifies the TACSTD2 + sEV subpopulation as a factor of tumor susceptibility in the elderly.

BACKGROUND: Aging is a very complex physiological phenomenon, and sEVs are involved in the regulation of this mechanism. Serum samples from healthy individuals under 30 and over 60 years of age were collected to analyze differences in sEVs proteomics. RESULTS: Based on PBA analysis, we found that sEVs from the serum of elderly individuals highly express TACSTD2 and identified a subpopulation marked by TACSTD2. Using ELISA, we verified the upregulation of TACSTD2 in serum from elderly human and aged mouse. In addition, we discovered that TACSTD2 was significantly increased in samples from tumor patients and had better diagnostic value than CEA. Specifically, 9 of the 13 tumor groups exhibited elevated TACSTD2, particularly for cervical cancer, colon cancer, esophageal carcinoma, liver cancer and thyroid carcinoma. Moreover, we found that serum sEVs from the elderly (especially those with high TACSTD2 levels) promoted tumor cell (SW480, HuCCT1 and HeLa) proliferation and migration. CONCLUSION: TACSTD2 was upregulated in the serum of elderly individuals and patients with tumors, and could serve as a dual biomarker for aging and tumors.

ScLSTM: single-cell type detection by siamese recurrent network and hierarchical clustering.

MOTIVATION: Categorizing cells into distinct types can shed light on biological tissue functions and interactions, and uncover specific mechanisms under pathological conditions. Since gene expression throughout a population of cells is averaged out by conventional sequencing techniques, it is challenging to distinguish between different cell types. The accumulation of single-cell RNA sequencing (scRNA-seq) data provides the foundation for a more precise classification of cell types. It is crucial building a high-accuracy clustering approach to categorize cell types since the imbalance of cell types and differences in the distribution of scRNA-seq data affect single-cell clustering and visualization outcomes. RESULT: To achieve single-cell type detection, we propose a meta-learning-based single-cell clustering model called ScLSTM. Specifically, ScLSTM transforms the single-cell type detection problem into a hierarchical classification problem based on feature extraction by the siamese long-short term memory (LSTM) network. The similarity matrix derived from the improved sigmoid kernel is mapped to the siamese LSTM feature space to analyze the differences between cells. ScLSTM demonstrated superior classification performance on 8 scRNA-seq data sets of different platforms, species, and tissues. Further quantitative analysis and visualization of the human breast cancer data set validated the superiority and capability of ScLSTM in recognizing cell types.

PTPRC Overexpression Predicts Poor Prognosis and Correlates with Immune Cell Infiltration in Pediatric Acute Myeloid Leukemia.

BACKGROUND: Acute myeloid leukemia (AML) is a molecularly heterogeneous disease that accounts for approximately 25% of childhood leukemia cases. In this study, we aimed to identify survival-associated genes in pediatric AML patients and investigate potential immunotherapy targets. METHODS: After retrieving and processing the data from Gene Expression Omnibus (GEO) web resource, we determined hub genes in AML. Bioinformatics technology was applied to identify key genes and perform functional analysis. Finally, we investigated the correlation between the key gene and the infiltration levels of tumor-infiltrating immune cells. RESULTS: High protein tyrosine phosphatase receptor-type C (PTPRC) expression was associated with worse overall survival rate (p < 0.001) in 287 pediatric AML patients. The results of risk subgroup analyses were similar in the high-risk and low-risk groups (p = 0.007; p = 0.013). Meanwhile, high expression of PTPRC was an independent adverse prognostic factor for overall survival (p = 0.04). Moreover, the results of immune infiltration assessment demonstrated that the expression level of PTPRC was significantly correlated with the infiltration level of activated dendritic cells (p < 0.001). CONCLUSIONS: Overexpression of PTPRC indicates poor prognosis, and its expression level is correlated with the infiltration level of activated dendritic cells. PTPRC could be a promising immunotherapy target for pediatric AML.

Spectral clustering of single cells using Siamese nerual network combined with improved affinity matrix.

Limitations of bulk sequencing techniques on cell heterogeneity and diversity analysis have been pushed with the development of single-cell RNA-sequencing (scRNA-seq). To detect clusters of cells is a key step in the analysis of scRNA-seq. However, the high-dimensionality of scRNA-seq data and the imbalances in the number of different subcellular types are ubiquitous in real scRNA-seq data sets, which poses a huge challenge to the single-cell-type detection.We propose a meta-learning-based model, SiaClust, which is the combination of Siamese Convolutional Neural Network (CNN) and improved spectral clustering, to achieve scRNA-seq cell type detection. To be specific, with the help of the constrained Sigmoid kernel, the raw high-dimensionality data is mapped to a low-dimensional space, and the Siamese CNN learns the differences between the cell types in the low-dimensional feature space. The similarity matrix learned by Siamese CNN is used in combination with improved spectral clustering and t-distribution Stochastic Neighbor Embedding (t-SNE) for visualization. SiaClust highlights the differences between cell types by comparing the similarity of the samples, whereas blurring the differences within the cell types is better in processing high-dimensional and imbalanced data. SiaClust significantly improves clustering accuracy by using data generated by nine different species and tissues through different scNA-seq protocols for extensive evaluation, as well as analogies to state-of-the-art single-cell clustering models. More importantly, SiaClust accurately locates the exact site of dropout gene, and is more flexible with data size and cell type.

LncRNAWiki 2.0: a knowledgebase of human long non-coding RNAs with enhanced curation model and database system.

LncRNAWiki, a knowledgebase of human long non-coding RNAs (lncRNAs), has been rapidly expanded by incorporating more experimentally validated lncRNAs. Since it was built based on MediaWiki as its database system, it fails to manage data in a structured way and is ineffective to support systematic exploration of lncRNAs. Here we present LncRNAWiki 2.0 (https://ngdc.cncb.ac.cn/lncrnawiki), which is significantly improved with enhanced database system and curation model. In LncRNAWiki 2.0, all contents are organized in a structured manner powered by MySQL/Java and curators are able to submit/edit annotations based on the curation model that includes a wider range of annotation items. Moreover, it is equipped with popular online tools to help users identify lncRNAs with potentially important functions, and provides more user-friendly web interfaces to facilitate data curation, retrieval and visualization. Consequently, LncRNAWiki 2.0 incorporates a total of 2512 lncRNAs and 106 242 associations for disease, function, drug, interacting partner, molecular signature, experimental sample, CRISPR design, etc., thus providing a comprehensive and up-to-date resource of functionally annotated lncRNAs in human.

Comparison of lorlatinib, alectinib and brigatinib in ALK inhibitor-naive/untreated ALK-positive advanced non-small-cell lung cancer: a systematic review and network meta-analysis.

Because of lacking of head-to-head comparison among lorlatinib, alectinib and brigatinib for patients with ALK inhibitor-naive or untreated (ALK inhibitor-naive and chemotherapy-naive) ALK-positive advanced non-small-cell lung cancer (NSCLC), the optimal option for these patients still remains undefined. We searched published reports that described the activity and safety of those novel ALK inhibitors (lorlatinib, alectinib and brigatinib) for ALK inhibitor-naive or untreated (ALK inhibitor-naive and chemotherapy-naive) ALK-positive advanced NSCLC. Five randomized controlled trials were identified, covering 1111 subjects. In the network meta-analysis, lorlatinib seemed to prolong progression free survival than brigatinib (Hazard Ratio: 0.57, P = 0.03) and alectinib (Hazard ratio: 0.65, P = 0.05) for previously untreated patients with ALK-positive advanced NSCLC as assessed by the independent review committee. Meanwhile, lorlatinib significantly improved significant progression free survival than brigatinib (Hazard ratio: 0.57, P = 0.03) and alectinib (Hazard ratio: 0.59, P = 0.03) for ALK inhibitor-naive patients. Among lorlatinib, alectinib, brigatinib, and crizotinib, lorlatinib had the highest probability to reach the best overall confirmed response rates (probability of 48%) and intracranial confirmed response rates (probability of 44%). No significant difference was found among them in overall survival and adverse events analysis. In terms of progression free survival, our results indicated that lorlatinib was the best treatment choice for patients with ALK inhibitor-naive or untreated (ALK inhibitor-naive and chemotherapy-naive) ALK-positive advanced NSCLC. The future head-to-head trials assessing the relative efficacy of lorlatinib, alectinib and brigatinib were warranted.

BrainBase: a curated knowledgebase for brain diseases.

Brain is the central organ of the nervous system and any brain disease can seriously affect human health. Here we present BrainBase (https://ngdc.cncb.ac.cn/brainbase), a curated knowledgebase for brain diseases that aims to provide a whole picture of brain diseases and associated genes. Specifically, based on manual curation of 2768 published articles along with information retrieval from several public databases, BrainBase features comprehensive collection of 7175 disease-gene associations spanning a total of 123 brain diseases and linking with 5662 genes, 16 591 drug-target interactions covering 2118 drugs/chemicals and 623 genes, and five types of specific genes in light of expression specificity in brain tissue/regions/cerebrospinal fluid/cells. In addition, considering the severity of glioma among brain tumors, the current version of BrainBase incorporates 21 multi-omics datasets, presents molecular profiles across various samples/conditions and identifies four groups of glioma featured genes with potential clinical significance. Collectively, BrainBase integrates not only valuable curated disease-gene associations and drug-target interactions but also molecular profiles through multi-omics data analysis, accordingly bearing great promise to serve as a valuable knowledgebase for brain diseases.

LncExpDB: an expression database of human long non-coding RNAs.

Expression profiles of long non-coding RNAs (lncRNAs) across diverse biological conditions provide significant insights into their biological functions, interacting targets as well as transcriptional reliability. However, there lacks a comprehensive resource that systematically characterizes the expression landscape of human lncRNAs by integrating their expression profiles across a wide range of biological conditions. Here, we present LncExpDB (https://bigd.big.ac.cn/lncexpdb), an expression database of human lncRNAs that is devoted to providing comprehensive expression profiles of lncRNA genes, exploring their expression features and capacities, identifying featured genes with potentially important functions, and building interactions with protein-coding genes across various biological contexts/conditions. Based on comprehensive integration and stringent curation, LncExpDB currently houses expression profiles of 101 293 high-quality human lncRNA genes derived from 1977 samples of 337 biological conditions across nine biological contexts. Consequently, LncExpDB estimates lncRNA genes' expression reliability and capacities, identifies 25 191 featured genes, and further obtains 28 443 865 lncRNA-mRNA interactions. Moreover, user-friendly web interfaces enable interactive visualization of expression profiles across various conditions and easy exploration of featured lncRNAs and their interacting partners in specific contexts. Collectively, LncExpDB features comprehensive integration and curation of lncRNA expression profiles and thus will serve as a fundamental resource for functional studies on human lncRNAs.

Multi-omics annotation of human long non-coding RNAs.

LncRNAs (long non-coding RNAs) are pervasively transcribed in the human genome and also extensively involved in a variety of essential biological processes and human diseases. The comprehensive annotation of human lncRNAs is of great significance in navigating the functional landscape of the human genome and deepening the understanding of the multi-featured RNA world. However, the unique characteristics of lncRNAs as well as their enormous quantity have complicated and challenged the annotation of lncRNAs. Advances in high-throughput sequencing technologies give rise to a large volume of omics data that are generated at an unprecedented rate and scale, providing possibilities in the identification, characterization and functional annotation of lncRNAs. Here, we review the recent important discoveries of human lncRNAs through analysis of various omics data and summarize specialized lncRNA database resources. Moreover, we highlight the multi-omics integrative analysis as a powerful strategy to efficiently discover and characterize the functional lncRNAs and elucidate their potential molecular mechanisms.

Genome­wide DNA methylation regulation analysis of long non­coding RNAs in glioblastoma.

Glioblastoma (GBM) is a malignant brain tumor associated with high mortality. Long non­coding RNAs (lncRNAs) are increasingly being recognized as its modulators. However, it remains mostly unexplored how lncRNAs are mediated by DNA methylation in GBM. The present study integrated multi­omics data to analyze the epigenetic dysregulation of lncRNAs in GBM. Widely aberrant methylation in the lncRNA promoters was observed, and the lncRNA promoters exhibited a more hypomethylated pattern in GBM. By combining transcriptional datasets, it was possible identify the lncRNAs whose transcriptional changes might be associated with the aberrant promoter methylation. Then, a methylation­mediated lncRNA regulatory network and functional enrichment analysis of aberrantly methylated lncRNAs showed that lncRNAs with different methylation patterns were involved in diverse GBM progression­related biological functions and pathways. Specifically, four lncRNAs whose increased expression may be regulated by the corresponding promoter hypomethylation were evaluated to have an excellent diagnostic effect and clinical prognostic value. Finally, through the construction of drug­target association networks, the present study identified potential therapeutic targets and small­molecule drugs for GBM treatment. The present study provides novel insights for understanding the regulation of lncRNAs by DNA methylation and developing cancer biomarkers in GBM.

Compositional Variability and Mutation Spectra of Monophyletic SARS-CoV-2 Clades.

COVID-19 and its causative pathogen SARS-CoV-2 have rushed the world into a staggering pandemic in a few months, and a global fight against both has been intensifying. Here, we describe an analysis procedure where genome composition and its variables are related, through the genetic code to molecular mechanisms, based on understanding of RNA replication and its feedback loop from mutation to viral proteome sequence fraternity including effective sites on the replicase-transcriptase complex. Our analysis starts with primary sequence information, identity-based phylogeny based on 22,051 SARS-CoV-2 sequences, and evaluation of sequence variation patterns as mutation spectra and its 12 permutations among organized clades. All are tailored to two key mechanisms: strand-biased and function-associated mutations. Our findings are listed as follows: 1) The most dominant mutation is C-to-U permutation, whose abundant second-codon-position counts alter amino acid composition toward higher molecular weight and lower hydrophobicity, albeit assumed most slightly deleterious. 2) The second abundance group includes three negative-strand mutations (U-to-C, A-to-G, and G-to-A) and a positive-strand mutation (G-to-U) due to DNA repair mechanisms after cellular abasic events. 3) A clade-associated biased mutation trend is found attributable to elevated level of negative-sense strand synthesis. 4) Within-clade permutation variation is very informative for associating non-synonymous mutations and viral proteome changes. These findings demand a platform where emerging mutations are mapped onto mostly subtle but fast-adjusting viral proteomes and transcriptomes, to provide biological and clinical information after logical convergence for effective pharmaceutical and diagnostic applications. Such actions are in desperate need, especially in the middle of the War against COVID-19.

MicroRNA-378-3p/5p suppresses the migration and invasiveness of oral squamous carcinoma cells by inhibiting KLK4 expression.

Distant metastasis frequently occurs in oral squamous cell carcinoma (OSCC) and contributes to the adverse prognosis for patients with OSCC. However, the potential mechanisms behind the metastasis have not yet been clarified. This study investigated the role of miR-378 in the migration and invasiveness of OSCC in vitro and in vivo. According to our results, the migration and invasiveness of OSCC cells were increased in cells overexpressing miR-378, and reduced in cells where miR-378-3p/5p was silenced. In addition, overexpression of miR-378 suppressed the expressions and activities of matrix metalloproteinase 9 (MMP-9) and MMP-2. Epithelial-mesenchymal transition (EMT) was restrained by overexpression of miR-378, as evidenced by an increase in E-cadherin expression and decrease in N-cadherin and uPA expression. However, knockdown of miR-378-3p/5p produced the opposite results. Moreover, kallikrein-related peptidase 4 (KLK4) was confirmed to be a target gene of miR-378. Overexpression of KLK4 reversed the induced decrease in migration and invasiveness of cells overexpressing miR-378 by upregulating the levels of MMP-9, MMP-2, and N-cadherin, and downregulating the level of E-cadhrin. Finally, the number of metastasis nodules in the lung tissues of nude mice was reduced by overexpression of miR-378, whereas the number of metastases increased with knockdown of miR-378. Taken together, our results suggest that the miR-378-KLK4 axis is involved in the mechanisms behind the migration and invasiveness of OSCC cells. Targeting the miR-378-KLK4 axis may be an effective measure for treating OSCC.

MicroRNA-378-3p/5p represses proliferation and induces apoptosis of oral squamous carcinoma cells via targeting KLK4.

Oral squamous cell carcinoma (OSCC) is one of the most common types of head and neck neoplasm. Down-regulation of hsa-microRNA-378 (miR-378) has been proved in OSCC tissues, suggesting that miR-378 might play crucial roles in the progression of OSCC. The present study aimed to evaluate the effect of miR-378-3p/5p on the proliferation and apoptosis of OSCC in vitro and in vivo. According to the results, lentivirus-mediated overexpression of miR-378 lowered the colony formation efficiency, blocked cell cycle progression, and decreased the percentage of Ki-67 positive cells, whereas knockdown of miR-378-3p/5p led to the opposite results. Furthermore, the apoptosis of OSCC cells was induced by the overexpression of miR-378 as evidenced by decreasing Bcl-2/Bax ratio, increasing cleaved caspase-9, cleaved caspase-3, and cleaved PARP levels, and promoting the release of cytochrome c into the cytoplasm. However, the above results were reversed by miR-378-3p/5p silencing. In addition, the overexpression of miR-378 inhibited the activation of PI3K/AKT signalling pathway. Conversely, miR-378-3p/5p knockdown resulted in the inactivation of PI3K/AKT signalling pathway. Mechanically, we validated that miR-378-3p/5p could target kallikrein-related peptidase 4 (KLK4), and enforced overexpression of KLK4 counteracted miR-378 overexpression-induced apoptosis. Finally, tumourigenesis in nude mice was suppressed by the overexpression of miR-378, which was promoted by miR-378-3p/5p silencing. Taken together, these results suggest that miR-378 may be a potential target in the diagnoses and treatment of OSCC.

Comparison and analysis of lncRNA-mediated ceRNA regulation in different molecular subtypes of glioblastoma.

Glioblastoma multiforme (GBM) is the most malignant brain tumor with a poor prognosis. A molecular level classification of GBM can provide insight into accurate patient-specific treatment. Competitive endogenous RNAs (ceRNAs), such as long non-coding RNAs (lncRNAs), play an essential role in the development of tumors and are associated with survival. However, the pattern of lncRNA-mediated ceRNA (LMce) crosstalk in different GBM subtypes is still unclear. In this study, we present a computational cascade to construct LMce networks of different GBM subtypes and investigate the lncRNA-mRNA regulations among them. Our results showed that although most lncRNAs and mRNAs in the different GBM subtype networks were the same, the regulation relationships of these RNAs were different among subtypes. 42.5%, 50.9%, 43.5% and 65.0% lncRNA-mRNA regulatory pairs were classic (CL)-, mesenchymal (MES)-, proneural (PN)- and neural (NE)-specific. In addition, our study identified 61, 132, 24 and 16 modules in which lncRNAs and mRNAs synergically competed with each other for miRNAs as CL-, MES-, PN- and NE-specific. CL- and MES-specific modules were mainly involved in biological functions such as cell proliferation, apoptosis and migration, while PN- and NE-specific modules were mainly related to DNA damage and cell cycle dysregulation. Survival analysis demonstrated that some modules could be potential prognostic markers of patients of CL and MES subtypes. This study uncovered the LMce interaction patterns in different GBM subtypes, identified subtype-specific modules with distinct biological functions, and revealed the potential prognostic markers of patients of different GBM subtypes. These results might contribute to the discovery of the GBM prognostic biomarkers and development of a more accurate therapeutic process.

Brain-derived neurotrophic factor exerts antinociceptive effects by reducing excitability of colon-projecting dorsal root ganglion neurons in the colorectal distention-evoked visceral pain model.

The mechanism underlying visceral pain is still largely unclear. Recently, much attention has focused on a potential modulatory role of brain-derived neurotrophic factor (BDNF) in visceral pain. In the present study, we investigated the expression of BDNF in dorsal root ganglia (DRG) primary sensory neurons and its role in a colorectal distention (CRD)-induced model of visceral pain. Results obtained from enzyme-linked immunosorbant assay (ELISA) revealed that BDNF protein was upregulated after CRD. An abdominal withdrawal reflex (AWR) assay confirmed that BDNF played an antinociceptive role in this model. Application of BDNF directly to DRG neurons decreased their hypersensitivity when evoked by CRD. Pretreatment with k252a partially blocked the effect of BDNF. These findings suggest that BDNF might be a novel analgesic agent for the treatment of visceral pain.