Normal Samples Research Articles

A ferroptosis-related signature predicts the clinical diagnosis and prognosis, and associates with the immune microenvironment of lung cancer

Targeting ferroptosis-related pathway is a potential strategy for treatment of lung cancer (LC). Consequently, exploration of ferroptosis-related markers is important for treating LC. We collected LC clinical data and mRNA expression profiles from TCGA and GEO database. Ferroptosis-related genes (FRGs) were obtained through FerrDB database. Expression analysis was performed to obtain differentially expressed FRGs. Diagnostic and prognostic models were constructed based on FRGs by LASSO regression, univariate, and multivariate Cox regression analysis, respectively. External verification cohorts GSE72094 and GSE157011 were used for validation. The interrelationship between prognostic risk scores based on FRGs and the tumor immune microenvironment was analyzed. Immunocytochemistry, Western blotting, and RT-qPCR detected the FRGs level. Eighteen FRGs were used for diagnostic models, 8 FRGs were used for prognostic models. The diagnostic model distinguished well between LC and normal samples in training and validation cohorts of TCGA. The prognostic models for TCGA, GSE72094, and GSE157011 cohorts significantly confirmed lower overall survival (OS) in high-risk group, which demonstrated excellent predictive properties of the survival model. Multivariate Cox regression analysis further confirmed risk score was an independent risk factor related with OS. Immunoassays revealed that in high-risk group, a significantly higher proportion of Macrophages_M0, Neutrophils, resting Natural killer cells and activated Mast cells and the level of B7H3, CD112, CD155, B7H5, and ICOSL were increased. In conclusion, diagnostic and prognostic models provided superior diagnostic and predictive power for LC and revealed a potential link between ferroptosis and TIME.

Early Identification of Rotten Potatoes Using an Electronic Nose Based on Feature Discretization and Ensemble Convolutional Neural Network.

The early identification of rotten potatoes is one of the most important challenges in a storage facility because of the inconspicuous symptoms of rot, the high density of storage, and environmental factors (such as temperature, humidity, and ambient gases). An electronic nose system based on an ensemble convolutional neural network (ECNN, a powerful feature extraction method) was developed to detect potatoes with different degrees of rot. Three types of potatoes were detected: normal samples, slightly rotten samples, and totally rotten samples. A feature discretization method was proposed to optimize the impact of ambient gases on electronic nose signals by eliminating redundant information from the features. The ECNN based on original features presented good results for the prediction of rotten potatoes in both laboratory and storage environments, and the accuracy of the prediction results was 94.70% and 90.76%, respectively. Moreover, the application of the feature discretization method significantly improved the prediction results, and the accuracy of prediction results improved by 1.59% and 3.73%, respectively. Above all, the electronic nose system performed well in the identification of three types of potatoes by using the ECNN, and the proposed feature discretization method was helpful in reducing the interference of ambient gases.

Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types

The complexity of the tumor microenvironment poses significant challenges in cancer therapy. Here, to comprehensively investigate the tumor-normal ecosystems, we perform an integrative analysis of 4.9 million single-cell transcriptomes from 1070 tumor and 493 normal samples in combination with pan-cancer 137 spatial transcriptomics, 8887 TCGA, and 1261 checkpoint inhibitor-treated bulk tumors. We define a myriad of cell states constituting the tumor-normal ecosystems and also identify hallmark gene signatures across different cell types and organs. Our atlas characterizes distinctions between inflammatory fibroblasts marked by AKR1C1 or WNT5A in terms of cellular interactions and spatial co-localization patterns. Co-occurrence analysis reveals interferon-enriched community states including tertiary lymphoid structure (TLS) components, which exhibit differential rewiring between tumor, adjacent normal, and healthy normal tissues. The favorable response of interferon-enriched community states to immunotherapy is validated using immunotherapy-treated cancers (n = 1261) including our lung cancer cohort (n = 497). Deconvolution of spatial transcriptomes discriminates TLS-enriched from non-enriched cell types among immunotherapy-favorable components. Our systematic dissection of tumor-normal ecosystems provides a deeper understanding of inter- and intra-tumoral heterogeneity.

Exploring the mystery of colon cancer from the perspective of molecular subtypes and treatment

The molecular categorization of colon cancer patients remains elusive. Gene set enrichment analysis (GSEA), which investigates the dysregulated genes among tumor and normal samples, has revealed the pivotal role of epithelial-to-mesenchymal transition (EMT) in colon cancer pathogenesis. In this study, we employed multi-clustering method for grouping data, resulting in the identification of two clusters characterized by varying prognostic outcomes. These two subgroups not only displayed disparities in overall survival (OS) but also manifested variations in clinical variables, genetic mutation, and gene expression profiles. Using the nearest template prediction (NTP) method, we were able to replicate the molecular classification effectively within the original dataset and validated it across multiple independent datasets, underscoring its robust repeatability. Furthermore, we constructed two prognostic signatures tailored to each of these subgroups. Our molecular classification, centered on EMT, hold promise in offering fresh insights into the therapy strategies and prognosis assessment for colon cancer.

Identification of potential biomarkers of leprosy: A study based on GEO datasets.

Leprosy has a high rate of cripplehood and lacks available early effective diagnosis methods for prevention and treatment, thus novel effective molecule markers are urgently required. In this study, we conducted bioinformatics analysis with leprosy and normal samples acquired from the GEO database(GSE84893, GSE74481, GSE17763, GSE16844 and GSE443). Through WGCNA analysis, 85 hub genes were screened(GS > 0.7 and MM > 0.8). Through DEG analysis, 82 up-regulated and 3 down-regulated genes were screened(|Log2FC| > 3 and FDR < 0.05). Then 49 intersection genes were considered as crucial and subjected to GO annotation, KEGG pathway and PPI analysis to determine the biological significance in the pathogenesis of leprosy. Finally, we identified a gene-pathway network, suggesting ITK, CD48, IL2RG, CCR5, FGR, JAK3, STAT1, LCK, PTPRC, CXCR4 can be used as biomarkers and these genes are active in 6 immune system pathways, including Chemokine signaling pathway, Th1 and Th2 cell differentiation, Th17 cell differentiation, T cell receptor signaling pathway, Natural killer cell mediated cytotoxicity and Leukocyte transendothelial migration. We identified 10 crucial gene markers and related important pathways that acted as essential components in the etiology of leprosy. Our study provides potential targets for diagnostic biomarkers and therapy of leprosy.

ZNF692 promotes the migration and response to immunotherapy of clear cell renal cell carcinoma cells by targeting metabolic pathway

Clear cell renal cell carcinoma (ccRCC), with high mortality and poor prognosis, is the most common type of renal malignancy. It is necessary to identify new biomarkers that can serve as indicators for the detection of ccRCC at its early stages. In this study, we analyzed the role of classical zinc finger protein 692 (ZNF692) in ccRCC using datasets from The Cancer Genome Atlas (TCGA) and Single Cell Portal and immunohistochemical (IHC) staining of a tissue-microarray, and analyzed the function of ZNF692 in ccRCC cells. The analyses indicated that ZNF692 was upregulated in ccRCC samples compared with normal or paracancerous control samples (P < 0.001) and that the expression of this gene was linked to poor overall survival (HR = 2.1, P < 0.0001). The knockdown of ZNF692 inhibited the proliferation and migration of ccRCC cells by target GTPase-activating protein (SH3 domain)-binding protein 2 (G3BP2), and transmembrane 9 superfamily member 2 (TM9SF2)). T, B, proximal, and collecting tubule cells are the dominant cell types in normal kidney tissue where ZNF692 is expressed. In addition, immune checkpoint blockade (ICB) therapy dramatically changed the expression patterns of ZNF692. Collectively, these data indicate that ZNF692 may serve as prognosis, and as a potential indicator of the response to ICB therapy, a possibility needs to be verified by a case‒control study.

A novel disulfide death-related genes prognostic signature identifies the role of IPO4 in glioma progression

Background“Disulfide death,” a form of cellular demise, is triggered by the abnormal accumulation of intracellular disulfides under conditions of glucose deprivation. However, its role in the prognosis of glioma remains undetermined. Therefore, the main objective of this study is to establish prognostic signature based on disulfide death-related genes (DDRGs) and to provide new solutions in choosing the effective treatment of glioma.MethodsThe RNA transcriptome, clinical information, and mutation data of glioma samples were sourced from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), while normal samples were obtained from the Genotype-Tissue Expression (GTEx). DDRGs were compiled from previous studies and selected through differential analysis and univariate Cox regression analysis. The molecular subtypes were determined through consensus clustering analysis. Further, LASSO analysis was employed to select characteristic genes, and subsequently, a risk model comprising seven DDRGs was constructed based on multivariable Cox analysis. Kaplan-Meier survival curves were employed to assess survival differences between high and low-risk groups. Additionally, functional analyses (GO, KEGG, GSEA) were conducted to explore the potential biological functions and signaling pathways of genes associated with the model. The study also explored immune checkpoint (ICP) genes, immune cell infiltration levels, and immune stromal scores. Finally, the effect of Importin-4(IPO4) on glioma has been further confirmed through RT-qPCR, Western blot, and cell functional experiments.Results7 genes associated with disulfide death were obtained and two subgroups of patients with different prognosis and clinical characteristics were identified. Risk signature was subsequently developed and proved to serve as an prognostic predictor. Notably, the high-risk group exhibited an immunosuppressive microenvironment characterized by a high concentration of M2 macrophages and regulatory T cells (Tregs). In contrast, the low-risk group showed lower half-maximal inhibitory concentration (IC50) values. Therefore, patients in the high-risk group may benefit more from immunotherapy, while patients in the low-risk group may benefit more from chemotherapy. In addition, in vitro experiments have shown that inhibition of the expression of IPO4 leads to a significant reduction in the proliferation, migration, and invasion of glioma cells.ConclusionThis study identified two glioma subtypes and constructed a prognostic signature based on DDRGs. The signature has the potential to optimize the selection of patients for immune- and chemotherapy and provided a potential therapeutic target for glioma.

Characterization of alternative splicing events and prognostic signatures in gastric cancer

BackgroundAccumulating evidences indicate that the specific alternative splicing (AS) events are linked to the occurrence and prognosis of gastric cancer (GC). Nevertheless, the impact of AS is still unclear and needed to further elucidation.MethodsThe expression profile of GC and normal samples were downloaded from TCGA. AS events were achieved from SpliceSeq database. Cox regression together with LASSO analysis were employed to identify survival-associated AS events (SASEs) and calculate risk scores. PPI and pathway enrichment analysis were implemented to determine the function and pathways of these genes. Kaplan-Meier (K-M) analysis and Receiver Operating Characteristic Curves were used to evaluate the clinical significance of genes of SASEs. Q-PCR were applied to validate the hub genes on the survival prognosis in 47 GC samples. Drug sensitivity and immune cell infiltration analysis were conducted.ResultsIn total, 48 140 AS events in 10 610 genes from 361 GC and 31 normal samples were analyzed. Through univariate Cox regression, 855 SASEs in 763 genes were screened out. Further, these SASEs were analyzed by PPI and 17 hub genes were identified. Meanwhile, using Lasso and multivariate Cox regression analysis, 135 SASEs in 132 genes related to 7 AS forms were further screened and a GC prognostic model was constructed. K-M curves indicates that high-risk group has poorer prognosis. And the nomogram analysis on the basis of the multivariate Cox analysis was disclosed the interrelationships between 7 AS forms and clinical parameters in the model. Five key genes were then screened out by PPI analysis and Differential Expression Gene analysis based on TCGA and Combined-dataset, namely STAT3, RAD51B, SOCS2, POLE2 and TSR1. The expression levels of AS in STAT3, RAD51B, SOCS2, POLE2 and TSR1 were all significantly correlated with survival by qPCR verification. Nineteen drugs were sensitized to high-risk patients and eight immune cells showed significantly different infiltration between the STAD and normal groups.ConclusionsIn this research, the prognostic model constructed by SASEs can be applied to predict the prognosis of GC patients and the selected key genes are expected to become new biomarkers and therapeutical targets for GC treatment.

Identification of Molecular Subtypes Associated with PCD in Esophageal Squamous Cell Carcinoma and Analysis of Immune Microenvironment.

Esophageal squamous cell carcinoma (ESCC) is a highly fatal malignancy with increasing incidence, and programmed cell death (PCD) plays an important role in homeostasis. This study aimed to explore the ESCC of heterogeneity based on the PCD signatures for the diagnosis and treatment of patients. The clinical information and RNA-seq data of patients with ESCC and the PCD-related genes set were used to identify PCD signatures.The "limma" package was used to identify the differentially expressed genes (DEGs). "Clusterprofiler" package was used for function enrichment analysis, and the "ConsensusClusterPlus" package was performed for consensus clustering. Finally, the "GSVA" package and the Cibersort algorithm were used for the immune infiltration analysis. We performed differential expression analysis between ESCC and normal samples and identified 1659 DEGs, of which 124 DEGs were PCD genes. Then, the patients were divided into cluster1 and cluster2 based on the expression of 124 PCD genes. There was a significant difference in immune infiltration between the two clusters. The patients in cluster 1 had a higher immune score and more CD56dim natural killer cells, monocytes, activated CD4 T cells, eosinophil, and activated B cells infiltration, while cluster2 had a higher stromal score, more immune regulation, and immune checkpoint genes expression. We identified two clusters based on PCD gene expression and characterized their tumor microenvironment and immune checkpoint difference. Our findings may provide some new insight into the treatment of ESCC.

Determination of lignin composition in compression wood-like reaction wood of angiosperm Gardenia jasminoides by pyrolysis–gas chromatography–mass spectrometry

Abstract Using pyrolysis–gas chromatography–mass spectrometry, the lignin composition was analysed in normal and reaction wood samples grown at three stem inclination angles in Gardenia jasminoides, which forms compression wood-like reaction wood. Lignin content among the samples was not significantly different. However, the reaction wood samples with larger stem inclination angles showed a lower syringyl/guaiacyl ratio. In conclusion, the degree of inclination affected the lignin composition in G. jasminoides reaction wood, and qualitative changes in lignin might be necessary to mechanically support the stems in this species.

Comprehensive molecular analyses of cuproptosis-related genes with regard to prognosis, immune landscape, and response to immune checkpoint blockers in lung adenocarcinoma

BackgroundRecent studies have emphasized the importance of the biological processes of different forms of cell death in tumor heterogeneity and anti-tumor immunity. Nonetheless, the relationship between cuproptosis and lung adenocarcinoma (LUAD) remains largely unexplored.MethodsData for 793 LUAD samples and 59 normal lung tissues obtained from TCGA-LUAD cohort GEO datasets were used in this study. A total of 165 LUAD tissue samples and paired normal lung tissue samples obtained from our hospital were used to verify the prognostic value of dihydrolipoamide S-acetyltransferase (DLAT) and dihydrolipoamide branched chain transacylase E2 (DBT) for LUAD. The cuproptosis-related molecular patterns of LUAD were identified using consensus molecular clustering. Recursive feature elimination with random forest and a tenfold cross-validation method was applied to construct the cuproptosis score (CPS) for LUAD.ResultsBioinformatic and immunohistochemistry (IHC) analyses revealed that 13 core genes of cuproptosis were all significantly elevated in LUAD tissues, among which DBT and DLAT were associated with poor prognosis (DLAT, HR = 6.103; DBT, HR = 4.985). Based on the expression pattern of the 13 genes, two distinct cuproptosis-related patterns have been observed in LUAD: cluster 2 which has a relatively higher level of cuproptosis was characterized by immunological ignorance; conversely, cluster 1 which has a relatively lower level of cuproptosis is characterized by TILs infiltration and anti-tumor response. Finally, a scoring scheme termed the CPS was established to quantify the cuproptosis-related pattern and predict the prognosis and the response to immune checkpoint blockers of each individual patient with LUAD.ConclusionCuproptosis was found to influence tumor microenvironment (TME) characteristics and heterogeneity in LUAD. Patients with a lower CPS had a relatively better prognosis, more abundant immune infiltration in the TME, and an enhanced response to immune checkpoint inhibitors.

Vesicle-associated membrane protein 8 knockdown exerts anti-proliferative, pro-apoptotic, anti-autophagic, and pro-ferroptotic effects on colorectal cancer cells by inhibition of the JAK/STAT3 pathway.

Vesicle-associated membrane protein 8 (VAMP8), a soluble n-ethylmaleimide-sensitive factor receptor protein, acts as an oncogenic gene in the progression of several malignancies. Nevertheless, the roles and mechanisms of VAMP8 in colorectal cancer (CRC) progression remain unknown. The expression and prognostic significance of VAMP8 in CRC samples were analyzed through bioinformatics analyses. Cell proliferation was detected using CCK-8 and EdU incorporation assays and apoptosis was evaluated via flow cytometry. Western blot analysis was conducted to examine the protein expression. Ferroptosis was evaluated by measurement of iron metabolism, lipid peroxidation, and glutathione (GSH) content. VAMP8 was increased in CRC samples relative to normal samples on the basis of GEPIA and HPA databases. CRC patients with high level of VAMP8 had a worse overall survival. VAMP8 depletion led to a suppression of proliferation and promotion of apoptosis in CRC cells. Additionally, VAMP8 knockdown suppressed beclin1 expression and LC3-II/LC3-I ratio, elevated p62 expression, increased Fe2+, labile iron pool, lipid reactive oxygen species, and malondialdehyde levels, and repressed GSH content and glutathione peroxidase activity. Moreover, VAMP8 knockdown inhibited the activation of janus kinase (JAK)/signal transducer and activator of transcription 3 (STAT3) pathway in CRC cells. Mechanistically, activation of the JAK/STAT3 pathway by JAK1 or JAK2 overexpression attenuated VAMP8 silencing-mediated anti-proliferative, pro-apoptotic, anti-autophagic, and pro-ferroptotic effects on CRC cells. In conclusion, VAMP8 knockdown affects the proliferation, apoptosis, autophagy, and ferroptosis by the JAK/STAT3 pathway in CRC cells.

Molecular alterations in claudin 18 suppressed and non-suppressed gastric adenocarcinomas to guide targeted therapies

ABSTRACT Background Gastric adenocarcinoma represents an aggressive type of cancer and an important cause of cancer mortality. Progress in gastric cancer therapeutics has resulted from a better understanding of the molecular pathogenesis of the disease and introduction of targeted therapies, but most gastric cancer patients still rely on non-targeted chemotherapy as the mainstay of treatment for advanced disease. Methods An analysis of publicly available series from The Cancer Genome Atlas (TCGA) gastric cancer cohort was undertaken to delineate the clinical and genomic landscape of gastric cancers with suppressed expression of claudin 18 compared with cancers with non-suppressed claudin 18. Claudin 18 suppressed cancers were defined as having an mRNA expression z-score relative to normal samples (log RNA Seq V2) of less than −1. Claudin 18 non-suppressed cancers were defined as having an mRNA expression z-score relative to normal samples (log RNA Seq V2) above 0.5. Results Gastric cancers with claudin 18 mRNA suppression represented 7.7% of the gastric adenocarcinomas of TCGA cohort, while non-suppressed cancers represented 46.6% of the cases. The two groups did not differ in clinical and genomic characteristics, such as mean age, histology, grade, and stage. The mutation landscape of claudin 18 suppressed cases included high mutation rates of TP53, of genes of the WNT/β-catenin pathway and of ubiquitin ligase FBXW7. Moreover, a subset of both claudin 18 suppressed and non-suppressed cancers displayed mutations in Mismatch Repair (MMR) associated genes or a high tumor mutation burden (TMB). At the mRNA expression level, claudin 18 suppressed gastric cancers showed up-regulation of EMT core transcription factor Snail 2 and down-regulation of genes of HLA cluster. The survival of gastric cancer patients with claudin 18 mRNA suppression was not significantly different compared with patients with non-suppressed claudin 18. Conclusion Sub-sets of gastric cancers with claudin 18 mRNA suppression displayed characteristics of potential therapeutic interest, such as mutations in WNT and PI3K pathways and MMR defects. These may guide the development of alternative targeted therapies, in this sub-set of gastric cancers which are not candidates for claudin 18 targeting therapies.

Less-supervised learning with knowledge distillation for sperm morphology analysis

ABSTRACT Sperm Morphology Analysis (SMA) is pivotal in diagnosing male infertility. However, manual analysis is subjective and time-intensive. Artificial intelligence presents automated alternatives, but hurdles like limited data and image quality constraints hinder its efficacy. These challenges impede Deep Learning (DL) models from grasping crucial sperm features. A solution enabling DL models to learn sample nuances, even with limited data, would be invaluable. This study proposes a Knowledge Distillation (KD) method to distinguish normal from abnormal sperm cells, leveraging the Modified Human Sperm Morphology Analysis dataset. Despite low-resolution, blurry images, our method yields relevant results. We exclusively utilize normal samples to train the model for anomaly detection, crucial in scenarios lacking abnormal data – a common issue in medical tasks. Our aim is to train an Anomaly Detection model using a dataset comprising unclear images and limited samples, without direct exposure to abnormal data. Our method achieves Receiver ROC/AUC scores of 70.4%, 87.6%, and 71.1% for head, vacuole, and acrosome, respectively, our method matches traditional DL model performance with less than 70% of the data. This less-supervised approach shows promise in advancing SMA despite data scarcity. Furthermore, KD enables model adaptability to edge devices in fertility clinics, requiring less processing power.

An association study of m6A methylation with major depressive disorder

ObjectiveTo find the relationship between N6-methyladenosine (m6A) genes and Major Depressive Disorder (MDD).MethodsDifferential expression of m6A associated genes between normal and MDD samples was initially identified. Subsequent analysis was conducted on the functions of these genes and the pathways they may affect. A diagnostic model was constructed using the expression matrix of these differential genes, and visualized using a nomogram. Simultaneously, an unsupervised classification method was employed to classify all patients based on the expression of these m6A associated genes. Following this, common differential genes among different clusters were computed. By analyzing the functions of the common differential expressed genes among clusters, the role of m6A-related genes in the pathogenesis of MDD patients was elucidated.ResultsDifferential expression was observed in ELAVL1 and YTHDC2 between the MDD group and the control group. ELAVL1 was associated with comorbid anxiety in MDD patients. A linear regression model based on these two genes could accurately predict whether patients in the GSE98793 dataset had MDD and could provide a net benefit for clinical decision-making. Based on the expression matrix of ELAVL1 and YTHDC2, MDD patients were classified into three clusters. Among these clusters, there were 937 common differential genes. Enrichment analysis was also performed on these genes. The ssGSEA method was applied to predict the content of 23 immune cells in the GSE98793 dataset samples. The relationship between these immune cells and ELAVL1, YTHDC2, and different clusters was analyzed.ConclusionAmong all the m6A genes, ELAVL1 and YTHDC2 are closely associated with MDD, ELAVL1 is related to comorbid anxiety in MDD. ELAVL1 and YTHDC2 have opposite associations with immune cells in MDD.

LncRNA-NEAT1 blocks the Wnt/β-catenin signaling pathway by targeting miR-217 to inhibit trophoblast cell migration and invasion.

This study aimed to study the correlation between preeclampsia (PE) and lncRNA nuclear paraspeckle assembly transcript 1 (NEAT1), and to examine the molecular mechanisms behind the development of PE. 30 PE and 30 normal pregnant women placental samples were assessed the levels of NEAT1 and miR-217 by quantitative real-time PCR (qRT-PCR). The trophoblast cell line HTR8/SVneo was used for silencing NEAT1 or miR-217 inhibitor in the absence or presence of an inhibitor and H2O2. Cell counting Kit 8 (CCK-8), flow cytometry, and Transwell were used to detect cell proliferation, apoptosis, migration, and invasion. Luciferase reporter gene assay was utilized to verify the binding between miR-217 and Wnt family member 3 (Wnt3), and between the miR-217 and NEAT1. Proteins related to the Wnt/β-catenin signaling pathway were detected using western blotting. The PE group exhibited a significantly downregulated expression of miR-217 and a significantly upregulated expression of NEAT1. NEAT1 targeted miR-217, and Wnt is a miR-217 target gene. siRNA-NEAT1 inhibited the apoptosis of trophoblast cells, but promoted their invasion, migration, and proliferation. MiR-217 inhibitor could partially reverse the effects of siRNA-NEAT1. The expression of the Wnt/β-catenin signaling pathway-related proteins, WNT signaling pathway inhibitor 1 (DKK1), cyclin-D1 and β-catenin, was significantly increased after siRNA-NEAT1. NEAT1 could reduce trophoblast cell invasion and migration by suppressing miR-217/Wnt signaling pathway, leading to PE.

Specific discrimination of pathogenic bacteria causing septic arthritis using Raman spectroscopy: In-vitro study

In this study we performed preliminary experiments using Raman spectroscopy as an evolving technology in biofluid and microbial characterization, to explore its potential for rapid diagnosis of pathogenic bacteria in an in-vitro synovial fluid infection model. Normal human synovial fluids samples were collected from patients undergoing knee surgery and the three most common pathogenic bacteria introduced in-vitro into the samples. The bacterial growth was systematically monitored using a Raman spectroscopy. Multivariate regression analysis of acquired spectra showed bacterial characteristic Raman bands related to bacterial cell membranes and DNA structures to increase continuously as the incubation period was increased. Spectra signature recorded from cultured synovial fluid samples showed a significant loss in synovial quality and protein morphology over time compared to control samples. In this study, Raman spectroscopy shows promise for rapid pathogenic bacteria identification in synovial fluid. Marker peaks distinguished inoculated bacteria, while chemical changes reveal infection dynamics.

Evaluation of the Expression Level of Some Coding and Non-coding Genes in Breast Cancer Samples Based on Bioinformatics and Laboratory Studies

Background: Breast cancer (BC) is the leading cause of cancer-associated mortality in women worldwide. However, the molecular mechanism underlying the process is still unclear. In this regard, bioinformatics studies play a decisive role in facilitating the path of biological investigations and can ultimately lead to the identification of better molecular candidates for further study. Objectives: Due to the abnormal expression of many coding and non-coding genes in all types of cancers and their relationship with various mechanisms of carcinogenesis, this study aimed at evaluating the expression levels of certain coding and non-coding genes involved in BC based on bioinformatics findings and laboratory investigations. Methods: Gene expression dataset, module extraction, functional enrichment analysis, protein-protein interaction network construction, and RT-qPCR were performed based on bioinformatics methods and laboratory investigations. Additionally, the promoter region mutations of these genes were investigated, using sequencing of extracted DNAs from formalin-fixed paraffin-embedded (FFPE) tumor tissues. Results: A module was selected as a candidate for further investigation. Estrogen receptor 1 (ESR1) and forkhead box A1 (FOXA1) showed the highest degrees in the PPI network with 9 and 7 links, respectively. Furthermore, the expression levels of the FOXA1 gene, RNA component of mitochondrial RNA processing endoribonuclease (RMRP), and nuclear enriched abundant transcript 1 (NEAT1) were significantly upregulated in the tumor group compared to the control group (in order, P = 0.044, P = 0.014, and P = 0.0004). The tumors of patients with positive metastasis displayed significantly higher levels of NEAT1 and RMRP expression compared to those of negative metastasis samples (P &lt; 0.05). Moreover, the expression level of RMRP dramatically decreased in HER2-positive patients compared to negative samples (P = 0.011). Finally, no mutations were observed in the promoter sequencing of positive metastasis samples compared to normal samples. Conclusions: The upregulation levels of all three examined genes may correlate with BC progression. Therefore, they could potentially be used as biomarkers for detecting BC development.

Autoencoder-Based Unsupervised Surface Defect Detection Using Two-Stage Training.

Accurately detecting defects while reconstructing a high-quality normal background in surface defect detection using unsupervised methods remains a significant challenge. This study proposes an unsupervised method that effectively addresses this challenge by achieving both accurate defect detection and a high-quality normal background reconstruction without noise. We propose an adaptive weighted structural similarity (AW-SSIM) loss for focused feature learning. AW-SSIM improves structural similarity (SSIM) loss by assigning different weights to its sub-functions of luminance, contrast, and structure based on their relative importance for a specific training sample. Moreover, it dynamically adjusts the Gaussian window's standard deviation (σ) during loss calculation to balance noise reduction and detail preservation. An artificial defect generation algorithm (ADGA) is proposed to generate an artificial defect closely resembling real ones. We use a two-stage training strategy. In the first stage, the model trains only on normal samples using AW-SSIM loss, allowing it to learn robust representations of normal features. In the second stage of training, the weights obtained from the first stage are used to train the model on both normal and artificially defective training samples. Additionally, the second stage employs a combined learned Perceptual Image Patch Similarity (LPIPS) and AW-SSIM loss. The combined loss helps the model in achieving high-quality normal background reconstruction while maintaining accurate defect detection. Extensive experimental results demonstrate that our proposed method achieves a state-of-the-art defect detection accuracy. The proposed method achieved an average area under the receiver operating characteristic curve (AuROC) of 97.69% on six samples from the MVTec anomaly detection dataset.

Identification of TP53 germline variants in pediatric patients undergoing tumor testing: strategy and prevalence.

TP53 alterations are common in certain pediatric cancers, making identification of putative germline variants through tumor genomic profiling crucial for patient management. We analyzed TP53 alterations in 3123 tumors from 2788 pediatric patients sequenced using tumor-only or tumor-normal paired panels. Germline confirmatory testing was performed when indicated. Somatic and germline variants were classified following published guidelines. In 248 tumors from 222 patients, 284 Tier 1/2 TP53 sequence and small copy number variants were detected. Following germline classification, 73.9% of 142 unique variants were pathogenic/likely pathogenic (P/LP). Confirmatory testing on 118 patients revealed germline TP53 variants in 28 patients (23 P/LP and 5 uncertain significance), suggesting a minimum Li-Fraumeni syndrome (LFS) incidence of 0.8% (23/2788) in this cohort, 10.4% (23/222) in patients with TP53 variant-carrying tumors, and 19.5% (23/118) with available normal samples. About 25% (7/28) of patients with germline TP53 variants did not meet LFS diagnostic/testing criteria while 20.9% (28/134) with confirmed or inferred somatic origins did. TP53 biallelic inactivation occurred in 75% of germline carrier tumors and was also prevalent in other groups, causing an elevated tumor-observed variant allelic fraction (VAF). However, somatic evidence including low VAF correctly identified only 27.8% (25/90) of patients with confirmed somatic TP53 variants. The high incidence and variable phenotype of LFS in this cohort highlights the importance of assessing germline status of TP53 variants identified in all pediatric tumors. Without clear somatic evidence, distinguishing somatic from germline origins is challenging. Classifying germline and somatic variants should follow appropriate guidelines.