Development of an artificial intelligence model for predicting implant size in total knee arthroplasty using simple X-ray images.

BACKGROUND: Accurate estimation of implant size before surgery is crucial in preparing for total knee arthroplasty. However, this task is time-consuming and labor-intensive. To alleviate this burden on surgeons, we developed a reliable artificial intelligence (AI) model to predict implant size. METHODS: We enrolled 714 patients with knee osteoarthritis who underwent total knee arthroplasty from March 2010 to February 2014. All surgeries were performed by the same surgeon using implants from the same manufacturer. We collected 1412 knee anteroposterior (AP) and lateral view x-ray images and retrospectively investigated the implant size. We trained the AI model using both AP and lateral images without any clinical or demographic information and performed data augmentation to resolve issues of uneven distribution and insufficient data. Using data augmentation techniques, we generated 500 images for each size of the femur and tibia, which were then used to train the model. Using data augmentation techniques, we generated 500 images for each size of the femur and tibia, which were then used to train the model. We used ResNet-101 and optimized the model with the aim of minimizing the cross-entropy loss function using both the Stochastic Gradient Descent (SGD) and Adam optimizer. RESULTS: The SGD optimizer achieved the best performance in internal validation. The model showed micro F1-score 0.91 for femur and 0.87 for tibia. For predicting within ± one size, micro F1-score was 0.99 for femur and 0.98 for tibia. CONCLUSION: We developed a deep learning model with high predictive power for implant size using only simple x-ray images. This could help surgeons reduce the time and labor required for preoperative preparation in total knee arthroplasty. While similar studies have been conducted, our work is unique in its use of simple x-ray images without any other data, like demographic features, to achieve a model with strong predictive power.

Unique Changes in the Lung Microbiome following the Development of Chronic Lung Allograft Dysfunction.

The importance of lung microbiome changes in developing chronic lung allograft dysfunction (CLAD) after lung transplantation is poorly understood. The lung microbiome-immune interaction may be critical in developing CLAD. In this context, examining alterations in the microbiome and immune cells of the lungs following CLAD, in comparison to the lung condition immediately after transplantation, can offer valuable insights. Four adult patients who underwent lung retransplantation between January 2019 and June 2020 were included in this study. Lung tissues were collected from the same four individuals at two different time points: at the time of the first transplant and at the time of the explantation of CLAD lungs at retransplantation due to CLAD. We analyzed whole-genome sequencing using the Kraken2 algorithm and quantified the cell fractionation from the bulk tissue gene expression profile for each lung tissue. Finally, we compared the differences in lung microbiome and immune cells between the lung tissues of these two time points. The median age of the recipients was 57 years, and most (75%) had undergone lung transplants for idiopathic pulmonary fibrosis. All patients were administered basiliximab for induction therapy and were maintained on three immunosuppressants. The median CLAD-free survival term was 693.5 days, and the median time to redo the lung transplant was 843.5 days. Bacterial diversity was significantly lower in the CLAD lungs than at transplantation. Bacterial diversity tended to decrease according to the severity of the CLAD. Aerococcus, Caldiericum, Croceibacter, Leptolyngbya, and Pulveribacter genera were uniquely identified in CLAD, whereas no taxa were identified in lungs at transplantation. In particular, six taxa, including Croceibacter atlanticus, Caldiserium exile, Dolichospermum compactum, Stappia sp. ES.058, Kinetoplastibacterium sorsogonicusi, and Pulveribacter suum were uniquely detected in CLAD. Among immune cells, CD8+ T cells were significantly increased, while neutrophils were decreased in the CLAD lung. In conclusion, unique changes in lung microbiome and immune cell composition were confirmed in lung tissue after CLAD compared to at transplantation.

Highly Aligned Ternary Nanofiber Matrices Loaded with MXene Expedite Regeneration of Volumetric Muscle Loss.

Current therapeutic approaches for volumetric muscle loss (VML) face challenges due to limited graft availability and insufficient bioactivities. To overcome these limitations, tissue-engineered scaffolds have emerged as a promising alternative. In this study, we developed aligned ternary nanofibrous matrices comprised of poly(lactide-co-ε-caprolactone) integrated with collagen and Ti3C2Tx MXene nanoparticles (NPs) (PCM matrices), and explored their myogenic potential for skeletal muscle tissue regeneration. The PCM matrices demonstrated favorable physicochemical properties, including structural uniformity, alignment, microporosity, and hydrophilicity. In vitro assays revealed that the PCM matrices promoted cellular behaviors and myogenic differentiation of C2C12 myoblasts. Moreover, in vivo experiments demonstrated enhanced muscle remodeling and recovery in mice treated with PCM matrices following VML injury. Mechanistic insights from next-generation sequencing revealed that MXene NPs facilitated protein and ion availability within PCM matrices, leading to elevated intracellular Ca2+ levels in myoblasts through the activation of inducible nitric oxide synthase (iNOS) and serum/glucocorticoid regulated kinase 1 (SGK1), ultimately promoting myogenic differentiation via the mTOR-AKT pathway. Additionally, upregulated iNOS and increased NO- contributed to myoblast proliferation and fiber fusion, thereby facilitating overall myoblast maturation. These findings underscore the potential of MXene NPs loaded within highly aligned matrices as therapeutic agents to promote skeletal muscle tissue recovery.

Association between the skin microbiome and lichen striatus hypopigmentation: Cutibacterium acnes as a potential cause.

BACKGROUND: Lichen striatus (LS) is an acquired skin disorder with a linear pattern along Blaschko's lines. It commonly occurs in childhood, and the lesions spontaneously regress within several months. OBJECTIVES: Although up to 50% of LS cases exhibit hypopigmentation that can persist for several months to years, it is unknown why LS is associated with such a high incidence of hypopigmentation compared to other inflammatory skin diseases. Therefore, this study aimed to analyse the differences in the skin microbiome between LS patients with and without hypopigmentation. METHODS: Differences in skin microbiome were analysed using whole genome sequencing of skin biopsies and subsequent bioinformatics analyses. RESULTS: Some microbes commonly found in hypopigmented skin disorders, including Cutibacterium acnes, were more abundant in patients with LS showing hypopigmentation than in those not showing hypopigmentation. CONCLUSIONS: The skin microbiota may be involved in the development of hypopigmentation in LS and may be considered a treatment target to reduce LS duration and hypopigmentation.

Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy.

PURPOSE: Triple-negative breast cancer (TNBC) is a breast cancer subtype that has poor prognosis and exhibits a unique tumor microenvironment. Analysis of the tumor microbiome has indicated a relationship between the tumor microenvironment and treatment response. Therefore, we attempted to reveal the role of the tumor microbiome in patients with TNBC receiving neoadjuvant chemotherapy. MATERIALS AND METHODS: We collected TNBC patient RNA-sequencing samples from the Gene Expression Omnibus and extracted microbiome count data. Differential and relative abundance were estimated with linear discriminant analysis effect size. We calculated the immune cell fraction with CIBERSORTx and conducted survival analysis using the Cancer Genome Atlas patient data. Correlations between the microbiome and immune cell compositions were analyzed and a prediction model was constructed to estimate drug response. RESULTS: Among the pathological complete response group (pCR), the beta diversity varied considerably; consequently, 20 genera and 24 species were observed to express a significant differential and relative abundance. Pandoraea pulmonicola and Brucella melitensis were found to be important features in determining drug response. In correlation analysis, Geosporobacter ferrireducens, Streptococcus sanguinis, and resting natural killer cells were the most correlated factors in the pCR, whereas Nitrosospira briensis, Plantactinospora sp. BC1, and regulatory T cells were key features in the residual disease group. CONCLUSION: Our study demonstrated that the microbiome analysis of tumor tissue can predict chemotherapy response of patients with TNBC. Further, the immunological tumor microenvironment may be impacted by the tumor microbiome, thereby affecting the corresponding survival and treatment response.

Comparative Analysis of Primers Used for 16S rRNA Gene Sequencing in Oral Microbiome Studies.

Recent advances in genomic technologies have enabled more in-depth study of the oral microbiome. In this study, we compared the amplicons generated by primers targeting different sites of the 16S rRNA gene found in the Human Oral Microbiome Database (HOMD). Six sets of primer targeting V1-V2, V1-V3, V3-V4, V4-V5, V5-V7 and V6-V8 regions of 16S rRNA were tested via in silico simulation. Primers targeting the V1-V2, V3-V4, and V4-V5 regions generated more than 90% of the original input sequences. Primers targeting the V1-V2 and V1-V3 regions exhibited a low number of mismatches and unclassified sequences at the taxonomic level, but there were notable discrepancies at the species level. Phylogenetic tree comparisons showed primers targeting the V1-V2 and V3-V4 regions showed performances similar to primers targeting the whole 16s RNA region in terms of separating total oral microbiomes and periodontopathogens. In an analysis of clinical oral samples, V1-V2 primers showed superior performance for identifying more taxa and had better resolution sensitivity for Streptococcus than V3-V4 primers. In conclusion, primers targeting the V1-V2 region of 16S rRNA showed the best performance for oral microbiome studies. In addition, the study demonstrates the need for careful PCR primer selections.

Proliferation-Related Features of the Human Mesenchymal Stem Cells Derived from Palatine Tonsils, Adipose Tissues, and Bone Marrow.

BACKGROUND: Mesenchymal stem cells (MSCs) are widely used in regenerative medicine and cell-based transplantations. However, an in-depth comparison of the different MSC origins is lacking. This study aimed to compare the expression of adipose-derived (AMSCs), bone marrow-derived (BMSCs), and tonsil-derived (TMSCs) and evaluate whether TMSCs are good alternatives for AMSCs or BMSCs. METHODS: We analyzed the expression levels of 47,000 transcripts in AMSCs (n = 4), BMSCs (n = 4), and TMSCs (n = 4) using GeneChip. Microarray data were analyzed using the LIMMA package to compare the TMSCs, AMSCs, and BMSCs. Hub genes were analyzed using STRING and Cytoscape. To ascertain the functional roles of AURKA and AURKB, small interfering RNA (siRNA) molecules specifically targeting AURKA and AURKB mRNA were synthesized and employed to induce knockdown of AURKA and AURKB in TMSC and AMSC. We analyzed the expression level of OCT4, SOX-2, and NANOG genes in TMSC and AMSCs by cell culture and real-time PCR. RESULTS: We identified commonly increased 256 and decreased 160 genes in TMSCs from the differentially expressed genes (DEGs) between the TMSCs, AMSCs, and BMSCs. In the DEG-based protein-protein interaction and gene set enrichment analysis, hub genes (AURKA, AURKB, CDC20, and BUB1) highly expressed in TMSCs were enriched for development- and progression-related oocyte meiosis, the cell cycle, and ubiquitin-mediated proteolysis. In vitro analysis demonstrated that cells with downregulated expression of AURKA and AURKB exhibited a significant reduction in proliferation compared to control cells. However, silencing of the genes did not affect the differentiation capacity in TMSCs and AMSCs. CONCLUSION: Our study compared MSCs of different origins to better understand the similarities and differences among these cell types.

The oral microbiome of implant-abutment screw holes compared with the peri-implant sulcus and natural supragingival plaque in healthy individuals.

PURPOSE: An implant-supported prosthesis consists of an implant fixture, an abutment, an internal screw that connects the abutment to the implant fixture, and the upper prosthesis. Numerous studies have investigated the microorganisms present on the implant surface, surrounding tissues, and the subgingival microflora associated with peri-implantitis. However, there is limited information regarding the microbiome within the internal screw space. In this study, microbial samples were collected from the supragingival surfaces of natural teeth, the peri-implant sulcus, and the implant-abutment screw hole, in order to characterize the microbiome of the internal screw space in healthy subjects. METHODS: Samples were obtained from the supragingival region of natural teeth, the peri-implant sulcus, and the implant screw hole in 20 healthy subjects. DNA was extracted, and the V3-V4 region of the 16S ribosomal RNA was sequenced for microbiome analysis. Alpha diversity, beta diversity, linear discriminant analysis effect size (LEfSe), and network analysis were employed to compare the characteristics of the microbiomes. RESULTS: We observed significant differences in beta diversity among the samples. Upon analyzing the significant taxa using LEfSe, the microbial composition of the implant-abutment screw hole's microbiome was found to be similar to that of the other sampling sites' microbiomes. Moreover, the microbiome network analysis revealed a unique network complexity in samples obtained from the implant screw hole compared to those from the other sampling sites. CONCLUSIONS: The bacterial composition of the biofilm collected from the implant-abutment screw hole exhibited significant differences compared to the supra-structure of the implant. Therefore, long-term monitoring and management of not only the peri-implant tissue but also the implant screw are necessary.

Exosomal hsa-miR-335-5p and hsa-miR-483-5p are novel biomarkers for rheumatoid arthritis: A development and validation study.

BACKGROUND: Rheumatoid arthritis (RA) is a chronic autoimmune disease that causes cartilage and bone damage. Exosomes are small extracellular vesicles that play a critical role in intercellular communication and various biological processes by serving as vehicles for the transfer of diverse molecules, such as nucleic acids, proteins, and lipids, between cells. The purpose of this study was to develop potential biomarkers for RA in peripheral blood by performing small non-coding RNA (sncRNA) sequencing using circulating exosomes from healthy controls and patients with RA. METHODS: In this study, we investigated extracellular sncRNAs associated with RA in peripheral blood. Using RNA sequencing and differentially expressed sncRNA analysis, we identified a miRNA signature and target genes. Target gene expression was validated via the four GEO datasets. RESULTS: Exosomal RNAs were successfully isolated from the peripheral blood of 13 patients with RA and 10 healthy controls. The hsa-miR-335-5p and hsa-miR-486-5p expression levels were higher in patients with RA than in controls. We identified the SRSF4 gene, which is a common target of hsa-miR-335-5p and hsa-miR-483-5p. As expected, the expression of this gene was found to be decreased in the synovial tissues of patients with RA through external validation. In addition, hsa-miR-335-5p was positively correlated with antiCCP, DAS28ESR, DAS28CRP, and rheumatoid factor. CONCLUSIONS: Our results provide strong evidence that circulating exosomal miRNA (hsa-miR-335-5p and hsa-miR-486-5p) and SRSF4 could be valuable biomarkers for RA.

Single-cell analysis of platelets from patients with periodontitis and diabetes.

Background: Blood platelets are known to play a role in the development of atherosclerotic disease, thrombi, and hemostasis. Investigation of blood platelet transcriptome could provide evidence of disorders that increase vulnerability to cardiovascular disease. However, research on the molecular insights of platelet activation in patients with periodontitis and patients with periodontitis and type 2 diabetes mellitus (DM) is still lacking. Objectives: In this study, we analyzed expression in blood platelets from patients with periodontitis and patients with concurrent periodontitis and DM to examine the transcriptomic profile of platelets induced by periodontitis and the modifying effects of DM. Methods: We obtained the transcriptional profiles of blood platelets from 11 healthy donors, 10 patients with periodontitis, and 6 patients with periodontitis and DM using single-cell RNA sequencing. The biological processes and coexpressed modules of transcriptionally altered genes were further explored. Results: Both the patients with periodontitis and DM and those with periodontitis without DM showed higher levels of platelet activation and coagulation signals than the healthy individuals. Platelets from the patients with periodontitis had higher expression levels of genes for RHO GTPase effectors, whereas platelets from the patients with periodontitis and DM demonstrated higher expression of genes involved in oxidative phosphorylation and cellular responses to stress than those from the controls. However, compared with the patients with only periodontitis, those with periodontitis and DM presented a lower expression level of genes for hemostasis and platelet receptors. Conclusion: These results suggest that periodontitis contributes to establishment of blood coagulation via platelet dysregulation, whereas the comorbidities of patients with periodontitis and DM impair the components of platelets, thus preventing normal functions.

Microbial profiling of peri-implantitis compared to the periodontal microbiota in health and disease using 16S rRNA sequencing.

PURPOSE: The objective of this study was to analyze the microbial profile of individuals with peri-implantitis (PI) compared to those of periodontally healthy (PH) subjects and periodontitis (PT) subjects using Illumina sequencing. METHODS: Buccal, supragingival, and subgingival plaque samples were collected from 109 subjects (PH: 30, PT: 49, and PI: 30). The V3-V4 region of 16S rRNA was sequenced and analyzed to profile the plaque microbiota. RESULTS: Microbial community diversity in the PI group was higher than in the other groups, and the 3 groups showed significantly separated clusters in the buccal samples. The PI group showed different patterns of relative abundance from those in the PH and PT groups depending on the sampling site at both genus and phylum levels. In all samples, some bacterial species presented considerably higher relative abundances in the PI group than in the PH and PT groups, including Anaerotignum lactatifermentans, Bacteroides vulgatus, Faecalibacterium prausnitzii, Olsenella uli, Parasutterella excrementihominis, Prevotella buccae, Pseudoramibacter alactolyticus, Treponema parvum, and Slackia exigua. Network analysis identified that several well-known periodontal pathogens and newly recognized bacteria were closely correlated with each other. CONCLUSIONS: The composition of the microbiota was considerably different in PI subjects compared to PH and PT subjects, and these results could shed light on the mechanisms involved in the development of PI.

Assessing the effect of antihypertensives on plaque microbiota in patients with periodontitis and hypertension using 16S rRNA sequencing: A cross-sectional study.

BACKGROUND: Periodontitis is initiated or accelerated by dysbiosis of oral microorganisms. When hypertension is accompanied in periodontitis patients, changes of oral microbiota occur. Since there are no reports on antihypertensives, we assessed their effect on the oral microbial profiles of patients with periodontitis. METHODS: This study involved 95 participants divided into two groups: those with periodontitis and hypertension (P_HT), and those with periodontitis and taking medications for hypertension (P_mHT). Plaque samples were collected from the buccal, supragingival, and subgingival sites of the oral cavities of these patients. DNA was extracted, and the V3-V4 region of the 16S ribosomal RNA was sequenced and analyzed. RESULTS: The P_HT and P_mHT groups were similar with respect to the alpha- and beta-diversity as well as the dominant phyla and genera, but differed in the relative abundance of bacterial species (85 species). In the P_mHT group, the relative abundance of major periodontal pathogens was greatly increased. In particular, Tannerella forsythia, Treponema denticola, and Fretibacterium fastidiosum increased nearly three times in the linear discriminant analysis score in the supragingival plaque. Also, there was an increase in the relative abundance of Prevotella spp., associated with periodontitis and nitrate reduction, which was also evident in the supragingival plaque. CONCLUSIONS: These findings indicate that antihypertensives induce dysbiotic changes in the oral microbiota of patients with periodontitis, which are associated with increases in the relative abundance of periodontal pathogens. Therefore, more active periodontal treatment and supportive periodontal therapy are required in patients taking antihypertensives.

Differential microbiota network in gingival tissues between periodontitis and periodontitis with diabetes.

Periodontitis and diabetes mellitus (DM) have a bidirectional relationship. Periodontitis is initiated by dysbiosis of oral microorganisms, and in particular, the characteristics of the microorganisms that have penetrated the tissue are directly related to the disease; therefore, we investigated the effect of DM on intragingival microbial profiling of patients with periodontitis. A total of 39 subjects were recruited and divided into three groups in this case control study as follows: healthy (NA, 10), periodontitis only (PD, 18), and periodontitis with DM (PD_DM, 11). Gingival tissue was collected, DNA was extracted, and whole-genome sequencing was performed. PD and PD_DM showed different characteristics from NA in diversity and composition of the microbial community; however, no difference was found between the PD nad PD_DM. PD_DM showed discriminatory characteristics for PD in the network analysis. PD showed a network structure in which six species were connected, including three red complex species, and PD_DM's network was more closely connected and expanded, with six additional species added to the PD network. Although DM did not significantly affect α- and ß-diversity or abundance of phyla and genera of microbiota that invaded the gingival tissue of patients with periodontitis, DM will affect the progression of periodontitis by strengthening the bacterial network in the gingival tissue.

Single-cell RNA sequencing reveals rebalancing of immunological response in patients with periodontitis after non-surgical periodontal therapy.

BACKGROUND: Periodontitis is a major inflammatory disease of the oral mucosa that is not limited to the oral cavity but also has systemic consequences. Although the importance of chronic periodontitis has been emphasized, the systemic immune response induced by periodontitis and its therapeutic effects remain elusive. Here, we report the transcriptomes of peripheral blood mononuclear cells (PBMCs) from patients with periodontitis. METHODS: Using single-cell RNA sequencing, we profiled PBMCs from healthy controls and paired pre- and post-treatment patients with periodontitis. We extracted differentially expressed genes and biological pathways for each cell type and calculated activity scores reflecting cellular characteristics. Intercellular crosstalk was classified into therapy-responsive and -nonresponsive pathways. RESULTS: We analyzed pan-cellular differentially expressed genes caused by periodontitis and found that most cell types showed a significant increase in CRIP1, which was further supported by the increased levels of plasma CRIP1 observed in patients with periodontitis. In addition, activated cell type-specific ligand-receptor interactions, including the BTLA, IFN-γ, and RESISTIN pathways, were prominent in patients with periodontitis. Both the BTLA and IFN-γ pathways returned to similar levels in healthy controls after periodontal therapy, whereas the RESISTIN pathway was still activated even after therapy. CONCLUSION: These data collectively provide insights into the transcriptome changes and molecular interactions that are responsive to periodontal treatment. We identified periodontitis-specific systemic inflammatory indicators and suggest unresolved signals of non-surgical therapy as future therapeutic targets.

Clinical big-data-based design of GLUT2-targeted carbon nanodots for accurate diagnosis of hepatocellular carcinoma.

Despite advances in diagnostic and therapeutic methods, the prognosis of patients with hepatocellular carcinoma (HCC) remains poor due to the delay in diagnosis. Herein, we aimed to discover a highly sensitive and specific biomarker for HCC based on genomic big data analysis and create an HCC-targeted imaging probe using carbon nanodots (CNDs) as contrast agents. In genomic analysis, we selected glucose transporter 2 (GLUT2) as a potential imaging target for HCC. We confirmed the target suitability by immunohisto-chemistry tests of 339 patient samples, where 81.1% of the patients exhibited underexpression of GLUT2, i.e., higher GLUT2 intensity in non-tumor tissues than in tumor tissues. To visualize GLUT2, we conjugated CNDs with glucosamine (GLN) as a targeting ligand to yield glucosamine-labeled CNDs (GLN-CNDs). A series of in vitro and in vivo experiments were conducted on GLUT2-modified HepG2 cells to confirm the specificity of the GLN-CNDs. Since the GLUT2 expression is higher in hepatocytes than in HCC cells, the GLUT2-targeted contrast agent is highly attached to normal cells. However, it is possible to produce images in the same form as the images obtained with a cancer cell-targeted contrast agent by inverting color scaling. Our results indicate that GLUT2 is a promising target for HCC and that GLN-CNDs may potentially be used as targeted imaging probes for diagnosing HCC.

Angiotensin-converting enzyme inhibitors or angiotensin receptor blockers and cancer risk: an updated meta-analysis of observational studies.

Introduction: Debate on the association between the use of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) and the risk of developing cancer has been ongoing for decades. This study aimed to generate reliable results by analysing observational studies published in the decade after our last meta-analysis was conducted. Methods: We searched Embase and Medline databases on 21 January 2021 for cohort and case-control studies. Two researchers independently reviewed the literature and assessed the title and abstract of each publication. The I2 statistic used to evaluate the heterogeneity of the effect measures. Risk of bias was qualitatively assessed using the Newcastle-Ottawa scale. Results and discussion: We included an additional 16 cohort, 6 nested case-control, and 9 conventional case-control studies in the updated analysis. Overall HRs decreased, while overall relative risks increased. Conclusion: Our results show some protective effects through the hazard ratio and some detrimental effects through the relative risk. Large-scale investigations of cohorts followed up for decades are needed to clarify association. Plain Language Summary: Introduction: Two types of drug, angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs), have been linked to the risk of developing cancer. We performed a meta-analysis by aggregating individual studies looking into the cancer risk of ACEIs and ARBs.Methods: We searched for articles on Embase and Medline databases until 21 January, 2021. Two researchers independently reviewed the literature and assessed the title and abstract of each publication.Results: Overall, the hazard ratio showed less than 1, while the relative risks showed higher than 1.Conclusion: Our results show some protective effects through the hazard ratio and some detrimental effects through the relative risk. Evidence supporting the risk of developing cancer is insufficient to prevent prescribing ACEIs or ARBs for patients with high blood pressure.

Microbial and molecular differences according to the location of head and neck cancers.

BACKGROUND: Microbiome has been shown to substantially contribute to some cancers. However, the diagnostic implications of microbiome in head and neck squamous cell carcinoma (HNSCC) remain unknown. METHODS: To identify the molecular difference in the microbiome of oral and non-oral HNSCC, primary data was downloaded from the Kraken-TCGA dataset. The molecular differences in the microbiome of oral and non-oral HNSCC were identified using the linear discriminant analysis effect size method. RESULTS: In the study, the common microbiomes in oral and non-oral cancers were Fusobacterium, Leptotrichia, Selenomonas and Treponema and Clostridium and Pseudoalteromonas, respectively. We found unique microbial signatures that positively correlated with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in oral cancer and positively and negatively correlated KEGG pathways in non-oral cancer. In oral cancer, positively correlated genes were mostly found in prion diseases, Alzheimer disease, Parkinson disease, Salmonella infection, and Pathogenic Escherichia coli infection. In non-oral cancer, positively correlated genes showed Herpes simplex virus 1 infection and Spliceosome and negatively correlated genes showed results from PI3K-Akt signaling pathway, Focal adhesion, Regulation of actin cytoskeleton, ECM-receptor interaction and Dilated cardiomyopathy. CONCLUSIONS: These results could help in understanding the underlying biological mechanisms of the microbiome of oral and non-oral HNSCC. Microbiome-based oncology diagnostic tool warrants further exploration.

DeepCIA: a novel deep-learning model for cancer type identification using class activation map via transcription factor expression.

Deep learning methods are powerful analytical tools for large-scale data analysis. Here, we introduce DeepCIA as a novel diagnostic deep-learning model for cancer type identification using a class activation map via transcription factor expression. Although many deep learning researches attempts have recently been made in relation to cancer diagnosis, there are difficulties in using cancer data due to a large-scale problem. Therefore, From The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) public databases, we selected transcription factor expression profiles of eight cancer types. TCGA included 3496 samples and divided the train and validation sets in an 8:2 ratio. ICGC included 552 samples and was used as a test set for external validation. To compare the performance of 1D-CNN models, we also used SVM and KNN from machine learning. In external validation, 1D-CNN showed a high average accuracy of 98% and was superior to support vector machine (SVM) and k-nearest neighbor (KNN) with a difference in the accuracy of 10-12%. Also, 1D-CNN performed very well in several performance metrics (98.2% Recall, 98.1% Precision, 98.2% F score, 99.8% Specificity, 99.8% AUC, and 99.0% Balanced Accuracy). In each data set evaluation, 1-network, 5-network, and 2-network with high accuracy were selected and visualized through the Class Activation Map. We identified the Cys2Hys2 zinc finger group with the highest distribution across all cancer types. Collectively, DeepCIA can be used as a decision support system for cancer and a classifier for diagnosing unknown primary cancer, while emphasizing its usefulness in cancer diagnosis.

Neuropeptide Y: a potential theranostic biomarker for diabetic peripheral neuropathy in patients with type-2 diabetes.

BACKGROUND: Diabetic peripheral neuropathy (DPN), the most common microvascular complication of type-2 diabetes mellitus (T2DM), results in nontraumatic lower-limb amputations. When DPN is not detected early, disease progression is irreversible. Thus, biomarkers for diagnosing DPN are needed. METHODS: We analyzed three data sets of T2DM DPN: two for mouse models (GSE70852 and GSE34889) and one for a human model (GSE24290). We found common differentially expressed genes (DEGs) in the two mouse data sets and validated them in the human data set. To identify the phenotypic function of the DEGs, we overexpressed them in zebrafish embryos. Clinical information and serum samples of T2DM patients with and without DPN were obtained from the Korea Biobank Network. To assess the plausibility of DEGs as biomarkers of DPN, we performed an enzyme-linked immunosorbent assay. RESULTS: Among the DEGs, only NPY and SLPI were validated in the human data set. As npy is conserved in zebrafish, its mRNA was injected into zebrafish embryos, and it was observed that the branches of the central nervous system became thicker and the number of dendritic branches increased. Baseline characteristics between T2DM patients with and without DPN did not differ, except for the sex ratio. The mean serum NPY level was higher in T2DM patients with DPN than in those without DPN (p = 0.0328), whereas serum SLPI levels did not differ (p = 0.9651). CONCLUSION: In the pathogenesis of DPN, NPY may play a protective role in the peripheral nervous system and may be useful as a biomarker for detecting T2DM DPN.

SOCS3 is Related to Cell Proliferation in Neuronal Tissue: An Integrated Analysis of Bioinformatics and Experiments.

Glioma is the most common primary malignant tumor that occurs in the central nervous system. Gliomas are subdivided according to a combination of microscopic morphological, molecular, and genetic factors. Glioblastoma (GBM) is the most aggressive malignant tumor; however, efficient therapies or specific target molecules for GBM have not been developed. We accessed RNA-seq and clinical data from The Cancer Genome Atlas, the Chinese Glioma Genome Atlas, and the GSE16011 dataset, and identified differentially expressed genes (DEGs) that were common to both GBM and lower-grade glioma (LGG) in three independent cohorts. The biological functions of common DEGs were examined using NetworkAnalyst. To evaluate the prognostic performance of common DEGs, we performed Kaplan-Meier and Cox regression analyses. We investigated the function of SOCS3 in the central nervous system using three GBM cell lines as well as zebrafish embryos. There were 168 upregulated genes and 50 downregulated genes that were commom to both GBM and LGG. Through survival analyses, we found that SOCS3 was the only prognostic gene in all cohorts. Inhibition of SOCS3 using siRNA decreased the proliferation of GBM cell lines. We also found that the zebrafish ortholog, socs3b, was associated with brain development through the regulation of cell proliferation in neuronal tissue. While additional mechanistic studies are necessary, our results suggest that SOCS3 is an important biomarker for glioma and that SOCS3 is related to the proliferation of neuronal tissue.