Identification and validation of oxidative stress-related diagnostic markers for recurrent pregnancy loss: insights from machine learning and molecular analysis.

It has been recognized that oxidative stress (OS) is implicated in the etiology of recurrent pregnancy loss (RPL), yet the biomarkers reflecting oxidative stress in association with RPL remain scarce. The dataset GSE165004 was retrieved from the Gene Expression Omnibus (GEO) database. From the GeneCards database, a compendium of 789 genes related to oxidative stress-related genes (OSRGs) was compiled. By intersecting differentially expressed genes (DEGs) in normal and RPL samples with OSRGs, differentially expressed OSRGs (DE-OSRGs) were identified. In addition, four machine learning algorithms were employed for the selection of diagnostic markers for RPL. The Receiver Operating Characteristic (ROC) curves for these genes were generated and a predictive nomogram for the diagnostic markers was established. The functions and pathways associated with the diagnostic markers were elucidated, and the correlations between immune cells and diagnostic markers were examined. Potential therapeutics targeting the diagnostic markers were proposed based on data from the Comparative Toxicogenomics Database and ClinicalTrials.gov. The candidate biomarker genes from the four models were further validated in RPL tissue samples using RT-PCR and immunohistochemistry. A set of 20 DE-OSRGs was identified, with 4 genes (KRAS, C2orf69, CYP17A1, and UCP3) being recognized by machine learning algorithms as diagnostic markers exhibiting robust diagnostic capabilities. The nomogram constructed demonstrated favorable predictive accuracy. Pathways including ribosome, peroxisome, Parkinson's disease, oxidative phosphorylation, Huntington's disease, and Alzheimer's disease were co-enriched by KRAS, C2orf69, and CYP17A1. Cell chemotaxis terms were commonly enriched by all four diagnostic markers. Significant differences in the abundance of five cell types, namely eosinophils, monocytes, natural killer cells, regulatory T cells, and T follicular helper cells, were observed between normal and RPL samples. A total of 180 drugs were predicted to target the diagnostic markers, including C544151, D014635, and CYP17A1. In the validation cohort of RPL patients, the LASSO model demonstrated superiority over other models. The expression levels of KRAS, C2orf69, and CYP17A1 were significantly reduced in RPL, while UCP3 levels were elevated, indicating their suitability as molecular markers for RPL. Four oxidative stress-related diagnostic markers (KRAS, C2orf69, CYP17A1, and UCP3) have been proposed to diagnose and potentially treat RPL.

Single-cell Transcriptomics, Web-based Systems Pharmacology and Integrated Transcriptomics Network Analysis Identified Diagnostic Targets and Drug Candidates for Type 2 Diabetes.

AIM: To discover new therapeutic targets for Type 2 diabetes (T2D) and develop a new diagnostic model. BACKGROUND: T2D is a chronic disease that can be controlled by oral hypoglycemic drugs, however, it cannot be fully cured. The continued increase in the prevalence of T2D and the limitations of existing treatments urgently call for the development of new drugs to be able to effectively control the progression of the disease. OBJECTIVE: We aimed to discover new therapeutic targets for T2D and to develop a new diagnostic model. METHOD: Single-cell transcriptome, web-based systematic pharmacology, and transcriptology were applied to identify T2D diagnostic targets and drug candidates and to analyze the underlying molecular mechanisms. RESULTS: By single-cell clustering analysis, we identified seven subsets between the normal islet ß-cell samples and T2D islet ß-cell samples. A total of 27 key genes in the intersection of insulin- related genes and diabetes-related genes were selected by protein-protein interaction (PPI) analysis and MolecularComplexDetection (MCODE) analysis. Notably, ESR1, MME, and CCR5 had the area under curves (AUC) values as high as 67.95%, 66.67%, and 66.03% for the diagnosis of T2D, respectively. Since the expression of MME in T2D samples was significantly higher than in normal samples, we screened 155 drug candidates against MME in T2D. Finally, the molecular docking revealed a strong binding strength between MME and DB05490, which was one of the most effective candidate drugs for treating T2D. CONCLUSION: Our study screens for diagnostic signatures and potential therapeutic agents for T2D, which provides valuable insights into the development of T2D biomarkers and their drug discovery.

Current understanding of infection of the ventricles and its complications.

INTRODUCTION: Ventriculitis, characterized by inflammation of the ventricles in the brain, frequently occurs as a complication of neurosurgical interventions such as the insertion of cerebrospinal fluid (CSF) shunts or external ventricular drains. It can also present as a community-acquired pathology, broadening its clinical significance and complicating diagnosis and treatment. This condition presents significant challenges, primarily due to its association with various medical devices and the predisposing conditions of patients which enhance infection risks. AREAS COVERED: The review comprehensively explores the etiology, risk factors, diagnostic methodologies, and treatment options for ventriculitis. A thorough literature search was conducted, focusing on recent studies, meta-analyses, and clinical reports that discuss the incidence rates, the effectiveness of different management strategies, and the impact of device-related and community-acquired infections. Particular attention is given to the role of CSF drains and shunts, biofilms, and the prophylactic measures employed in clinical settings to mitigate infection risks. EXPERT OPINION: Despite advances in medical technology and infection control protocols, ventriculitis remains a severe complication in both neurosurgical and community settings. The review highlights the need for continued research into innovative diagnostic tools and more effective infection control strategies.

Lipid metabolites abnormally expressed in pelvic fluid as potential biomarkers for ovarian cancer: A case-control study.

BACKGROUND: Ovarian cancer is insidious and usually detected in advanced stages of the disease. As the ovaries are pelvic organs, changes in their pelvic fluid metabolites may be associated with ovarian cancer. METHODS: Metabolomic changes in the pelvic fluid were detected using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in patients with ovarian cancer, ovarian cysts and uterine fibroids. Area under the curve (AUC) analysis was used to assess the diagnostic performance of lipid metabolites and blood tumor indices. The Pearson correlation algorithm was used to analyze the correlation between clinical characteristics and lipid metabolites in ovarian cancer patients. RESULTS: There were 24 lipid metabolites significantly changed in the pelvic fluid of ovarian cancer patients (p < 0.05). Palmitoylcarnitine, lipoamide, lipid metabolites, and blood tumor indices (CA15-3 and CA125) showed AUC > 0.8, with palmitoylcarnitine reaching a high of 0.942. In addition, we found that some lipid metabolites were significantly associated with the clinical stage, abdominal water volume, lymphatic metastasis, and recurrence (p < 0.05, r > 0.5). CONCLUSION: Levels of specific lipid metabolites are potential biomarkers of ovarian cancer and may play a key role in the early diagnosis and prognostic assessment of ovarian cancer. SIGNIFICANCE: Our results showed that pelvic metabolites, especially some lipid metabolites, play an important role in the diagnosis of ovarian cancer. Meanwhile, partial lipid metabolites were closely associated with the clinical presentation and prognosis of patients with ovarian cancer. We believe that our study makes a significant contribution to the literature because it provides a potential approach that is more effective for ovarian cancer detection.

Acute Appendicitis Incidence, Diagnostic Markers, and Sequelae; Dallah Hospital-Based Study in Riyadh, Saudi Arabia.

Background: Acute appendicitis is currently considered one of the most common acute surgical diseases of the abdomen and requires emergency surgery. It is also one of the most common abdominal emergencies in both developed and developing countries. The current study is aimed at assessing the specificity and sensitivity of Ultrasound and Computed Tomography in the diagnosis of appendicitis as well as the prevalence of its complications. The association of appendicitis with several laboratory findings will also be evaluated. Methodology: A retrospective study was conducted at Dallah-Namar Hospital, Riyadh City, Saudi Arabia. Results: 720 patients were differentially diagnosed with appendicitis in the study area. Of these, 618 patients had US abdomen/pelvis, with 15 (2.4%) showing positive findings. Amongst the 203 patients noted to have undergone abdominal CT,8 (4.0%) was found to have positive findings. Conclusion: The sensitivity of the US was determined to be relatively low. Also, serum creatinine was found to be elevated among all patients diagnosed with appendicitis. Further research with a larger sample size is needed to be conducted to further support the current findings.

Identification and Validation of Diagnostic Model Based on Angiogenesis- and Epithelial Mesenchymal Transition-Related Genes in Myocardial Infarction.

Background: Myocardial infarction (MI) is a chronic cardiovascular disease. This study aims to discern potentially angiogenesis- and epithelial mesenchymal transition (EMT)-related genes as biomarkers for MI diagnosis through bioinformatics. Methods: All datasets and angiogenesis- and EMT-related genes were collected from the public database. The differentially expressed genes (DEGs) of MI and MI-related genes were acquired. DEGs, MI-related genes, and angiogenesis- and EMT-related genes were intersected to obtain hub genes. Functional enrichment, immune microenvironment, and transcription factors (TFs)-hub genes regulatory network analysis were performed. The diagnostic markers and models were developed and validated. Drug prediction and molecular docking were performed. Finally, diagnostic markers expressions were validated using RT-qPCR. Results: A total of 224 angiogenesis- and EMT-related genes, 2,897 DEGs, 1,217 MI-related genes, and 9 hub genes were acquired. The immune infiltration levels of plasma cells, T cells CD4 memory activated, monocytes, macrophages M0, mast cells resting, and neutrophils were higher in patients with MI. LRPAP1, COLGALT1, QSOX1, THBD, VCAN, PLOD1, and PLAUR as the diagnostic markers were identified and used to construct diagnostic models, which can distinguish MI from controls well. Then, 9 drugs were screened, and the binding energies ranged from -7.08 to -5.21 kcal/mol. RT-qPCR results showed that the expression of LRPAP1, PLAUR, and PLOD1 was significantly increased in the MI group. Conclusion: The 7 diagnostic markers may play potential roles in MI and could contribute to improved future diagnostics.

Human disease-related long noncoding RNAs: Impact of ginsenosides.

Ginsenosides in ginseng are known for their potential health benefits, including antioxidant properties and their potential to exhibit anticancer effects. Besides a various range of coding genes, ginsenosides impose their efficacy by targeting noncoding RNAs. Long noncoding RNA ( lncRNA) has gained significant attention from both basic and clinical oncology fields due to its involvement in various cancer cell activities such as proliferation, apoptosis, metastasis, and autophagy. These events can be achieved either by lncRNA alone or in association with microRNAs or proteins. This review aims to summarize the diverse activities of lncRNAs that are regulated by ginsenosides, focusing on their role in regulating target genes through signaling pathways in human diseases. We highlight the results of studies on the expression profiles of lncRNAs induced by ginsenosides in efforts to inhibit cancer cell proliferation. Finally, we discuss the potential and challenges of utilizing lncRNAs as diagnostic markers for disease treatment.

Exploring liquid-liquid phase separation-related diagnostic biomarkers in osteoarthritis based on machine learning algorithms and experiment.

BACKGROUND: Osteoarthritis (OA) is a prevalent joint disorder characterized by cartilage degeneration and joint inflammation. Liquid-liquid phase separation (LLPS), a biophysical process involved in cellular organization, has recently gained attention in OA research. However, the relationship between LLPS and OA remains poorly understood. METHODS: We analyzed gene expression data from the GSE48556 dataset to identify LLPS-related genes associated with OA. Differential expression analysis, enrichment analyses, and machine learning algorithms were employed to explore the functional significance of LLPS-related genes in OA and then construct a diagnostic model for OA. In addition, IL-1ß as a pro-inflammatory factor to establish an in vitro OA model, and the protein expression levels of OA biomarkers were detected by western blot. RESULTS: A total of 145 LLPS-related genes were screened in OA samples. Enrichment analyses revealed these genes were mainly enriched in mRNA metabolic processes, cytoplasmic granules, and insulin resistance. Four characteristic genes for OA were selected by using machine learning algorithms, including ADRB2, H3F3B, GNL3L, and PELO. These genes showed satisfactory diagnostic values. Furthermore, there were association between these biomarkers and immune cells, including T cells CD8 and monocytes. In vitro experiments showed that IL-1ß stimulation significantly inhibited the cell viability of chondrocytes and enhanced the levels of pro-inflammatory factors, that could mimic the inflammatory state of OA. The expression levels of GNL3L and H3F3B proteins in IL-1ß group were obviously lower than those in control group, while levels of ADRB2 and PELO were higher, which was consistent with the results of bioinformatics analysis. CONCLUSION: Our study identifies LLPS-related genes as potential diagnostic biomarkers for OA. These findings provide insights into the molecular mechanisms underlying OA pathogenesis and offer opportunities for the development of novel therapeutic strategies.

Potential diagnostic, prognostic, and predictive biomarkers of gastric cancer.

Background: Gastric cancer (GC), a malignant epithelial tumor, is the fourth leading cause of cancer-related death worldwide. Therapeutic strategies for GC, despite the biggest challenges, can significantly improve survival rates through early detection and effective screening methods. Aim: To provide brief information on the necessity of multiple specific diagnostic, prognostic, and predictive markers for GC. Methods: This review was conducted using a variety of search engines, including PubMed Central, Scopus, Web of Science, Google Scholar, and others. Results: Some potential biomarkers that provide essential information include circulating tumor cells (CTCs), DNA methylation, claudin 18.2, fibroblast growth factor receptor 2 (FGFR2), long noncoding RNAs (lncRNAs), cell-free DNA (cfDNA), microRNAs, and serum pepsinogens. Conclusion: Multiple tumor markers are essential for screening, tumor identification, staging, prognostic assessment, and monitoring recurrence after therapy due to the absence of a single tumor indicator for diagnosing, prognosticating, and predicting GC.

The shared mechanism and potential diagnostic markers for premature ovarian failure and dry eye disease.

Premature ovarian failure (POF), which is often comorbid with dry eye disease (DED) is a key issue affecting female health. Here, we explored the mechanism underlying comorbid POF and DED to further elucidate disease mechanisms and improve treatment. Datasets related to POF (GSE39501) and DED (GSE44101) were identified from the Gene Expression Omnibus (GEO) database and subjected to weighted gene coexpression network (WGCNA) and differentially expressed genes (DEGs) analyses, respectively, with the intersection used to obtain 158 genes comorbid in POF and DED. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses of comorbid genes revealed that identified genes were primarily related to DNA replication and Cell cycle, respectively. Protein-Protein interaction (PPI) network analysis of comorbid genes obtained the 15 hub genes: CDC20, BIRC5, PLK1, TOP2A, MCM5, MCM6, MCM7, MCM2, CENPA, FOXM1, GINS1, TIPIN, MAD2L1, and CDCA3. To validate the analysis results, additional POF- and DED-related datasets (GSE48873 and GSE171043, respectively) were selected. miRNAs-lncRNAs-genes network and machine learning methods were used to further analysis comorbid genes. The DGIdb database identified valdecoxib, amorfrutin A, and kaempferitrin as potential drugs. Herein, the comorbid genes of POF and DED were identified from a bioinformatics perspective, providing a new strategy to explore the comorbidity mechanism, opening up a new direction for the diagnosis and treatment of comorbid POF and DED.

Dysregulation of exosomal miRNAs and their related genes in head and neck cancer patients.

Aim: The present study aimed to figure out the potential role of exosomal microRNAs, and their targeted genes in HNC detection/diagnosis. Methods: In the present study, exosomes were extracted from the serum samples of 400 HNC patients and 400 healthy controls. Exosomes were characterized using TEM, NTA, TEM-immunogold labeling and ELISA. Quantitative PCR was used to measure the expression level of exosomal miRNA-19a, miRNA-19b and targeted genes SMAD2 and SMAD4 in HNC patients and controls. Results: The deregulation of miR-19a (p < 0.01), miR-19b (p < 0.03), SMAD2 (p < 0.04) and SMAD4 (p < 0.04) was observed in HNC patients vs controls. Conclusion: ROC curve and Kaplan-Meier analysis showed the good diagnostic/prognostic value of selected exosomal microRNAs and related genes in HNC patients.

[Box: see text].

Differential expression and clinical significance of long non-coding RNAs in the development and progression of lung adenocarcinoma.

With the development of gene testing technology, we have found many different genes, and lncRNA is one of them. LncRNAs refer to a non-protein coding RNA molecule with a length of more than 200bp, which is one of the focuses of research on human malignant diseases such as LUAD. LncRNAs act as an oncogene or inhibitor to regulate the occurrence and progression of tumors. The differential expression of LncRNAs promotes or inhibits the progression of lung adenocarcinoma by affecting cell proliferation, metastasis, invasion, and apoptosis, thus affecting the prognosis and survival rate of patients. Therefore, LncRNAs can be used as a potential target for diagnosis and treatment of cancer. The early diagnosis of the disease was made through the detection of tumor markers. Because lung adenocarcinoma is not easy to diagnose in the early stage and tumor markers are easy to ignore, LncRNAs play an important role in the diagnosis and treatment of lung adenocarcinoma. The main purpose of this article is to summarize the known effects of LncRNAs on lung adenocarcinoma, the effect of differential expression of LncRNAs on the progression of lung adenocarcinoma, and related signal transduction pathways. And to provide a new idea for the future research of lung adenocarcinoma-related LncRNAs.

Identification of subclusters and prognostic genes based on GLS-associated molecular signature in ulcerative colitis.

Ulcerative colitis (UC) is a chronic and recurrent inflammatory disease that affects the colon and rectum. The response to treatment varies among individuals with UC. Therefore, the aim of this study was to identify and explore potential biomarkers for different subtypes of UC and examine their association with immune cell infiltration. We obtained UC RNA sequencing data from the GEO database, which included the training set GSE92415 and the validation set GSE87473 and GSE72514. UC patients were classified based on GLS and its associated genes using consensus clustering analysis. We identified differentially expressed genes (DEGs) in different UC subtypes through a differential expression analysis of the training cohort. Machine learning algorithms, including Weighted Gene Co-Expression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine Recursive Feature Elimination (SVM-RFE), were utilized to identify marker genes for UC. The CIBERSORT algorithm was used to determine the abundance of various immune cells in UC and their correlation with UC signature genes. Finally, we validated the expression of GLS through in vivo and ex vivo experiments. The expression of GLS was found to be elevated in patients with UC compared to normal patients. GLS and its related genes were able to classify UC patients into two subtypes, C1 and C2. The C1 subtype, as compared to the C2 subtype, showed a higher Mayo score and poorer treatment response. A total of 18 DEGs were identified in both subtypes, including 7 up-regulated and 11 down-regulated genes. Four UC signature genes (CWH43, HEPACAM2, IL24, and PCK1) were identified and their diagnostic value was validated in a separate cohort (AUC > 0.85). Furthermore, we found that UC signature biomarkers were linked to the immune cell infiltration. CWH43, HEPACAM2, IL24, and PCK1 may serve as potential biomarkers for diagnosing different subtypes of UC, which could contribute to the development of targeted molecular therapy and immunotherapy for UC.

Diagnostic, predictive and prognostic molecular biomarkers in clear cell renal cell carcinoma: A retrospective study.

Clear cell renal cell carcinoma (ccRCC) is a common and aggressive subtype of kidney cancer. Many patients are diagnosed at advanced stages, making early detection crucial. Unfortunately, there are currently no noninvasive tests for ccRCC, emphasizing the need for new biomarkers. Additionally, ccRCC often develops resistance to treatments like radiotherapy and chemotherapy. Identifying biomarkers that predict treatment outcomes is vital for personalized care. The integration of artificial intelligence (AI), multi-omics analysis, and computational biology holds promise in bolstering detection precision and resilience, opening avenues for future investigations. The amalgamation of radiogenomics and biomaterial-basedimmunomodulation signifies a revolutionary breakthrough in diagnostic medicine. This review summarizes existing literature and highlights emerging biomarkers that enhance diagnostic, predictive, and prognostic capabilities for ccRCC, setting the stage for future clinical research.

Unveiling immune tolerance pathways in preeclampsia placenta: implications for molecular targets and discovery of potential biomarkers.

During pregnancy, there is a link between disruption of maternal immune tolerance and preeclampsia, but the molecular mechanisms that regulate maternal and fetal immune tolerance remain unclear. This study employs bioinformatics to identify new markers related to placental immune tolerance and explore their potential role in predicting preeclampsia. Analyzing preeclampsia-related gene expression profiles in the Gene Expression Omnibus (GEO) dataset reveals 211 differentially expressed genes (DEGs) in the placenta, mainly influencing immune cell differentiation and response pathways. Employing weighted gene co-expression network analysis (WGCNA) and lasso regression, four potential target genes (ANKRD37, CRH, LEP, SIGLEC6) are identified for potential prediction of preeclampsia. Validation using the GSE4707 dataset confirmed the diagnostic and predictive potential of these candidate genes. RT-qPCR verified up-regulation in the placenta, while ELISA showed their correlation with immune tolerance factors associated with placental immune tolerance. As a result of this study, identifies potential biomarkers associated with placental immunity and contributes to understanding the molecular mechanism of preeclampsia.

Identification of ion channel-related genes as diagnostic markers and potential therapeutic targets for osteoarthritis through bioinformatics and machine learning-based approaches.

BACKGROUND: Osteoarthritis (OA) is a debilitating joint disorder characterized by the progressive degeneration of articular cartilage. Although the role of ion channels in OA pathogenesis is increasingly recognized, diagnostic markers and targeted therapies remain limited. METHODS: In this study, we analyzed the GSE48556 dataset to identify differentially expressed ion channel-related genes (DEGs) in OA and normal controls. We employed machine learning algorithms, least absolute shrinkage and selection operator(LASSO), and support vector machine recursive feature elimination(SVM-RFE) to select potential diagnostic markers. Then the gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed to explore the potential diagnostic markers' involvement in biological pathways. Finally, weighted gene co-expression network analysis (WGCNA) was used to identify key genes associated with OA. RESULTS: We identified a total of 47 DEGs, with the majority involved in transient receptor potential (TRP) pathways. Seven genes (CHRNA4, GABRE, HTR3B, KCNG2, KCNJ2, LRRC8C, and TRPM5) were identified as the best characteristic genes for distinguishing OA from healthy samples. We performed clustering analysis and identified two distinct subtypes of OA, C1, and C2, with differential gene expression and immune cell infiltration profiles. Then we identified three key genes (PPP1R3D, ZNF101, and LOC651309) associated with OA. We constructed a prediction model using these genes and validated it using the GSE46750 dataset, demonstrating reasonable accuracy and specificity. CONCLUSIONS: Our findings provide novel insights into the role of ion channel-related genes in OA pathogenesis and offer potential diagnostic markers and therapeutic targets for the treatment of OA.

As society ages, the incidence of knee osteoarthritis continues to rise, bringing with it a series of social impacts and medical pressure. Despite the increasing recognition of the role of ion channels in the pathogenesis of OA, diagnostic markers and targeted therapies remain limited.This study investigated the role of TRP as possible diagnostic tools for OA.Seven TRP-related genes were identified as the best traits to distinguish OA from healthy samples, and then we constructed and validated risk scores for three key genes (PPP1R3D, ZNF101, and LOC651309) relevant to OA ion channel gene modules.Our findings provide novel insights into the role of ion channel-related genes in OA pathogenesis and offer a reference for further clinical diagnosis.

Comprehensive lipidomic analysis of the genus Cutibacterium.

Cutibacterium are part of the human skin microbiota and are opportunistic microorganisms that become pathogenic in immunodeficient states. These lipophilic bacteria willingly inhabit areas of the skin where sebaceous glands are abundant; hence, there is a need to thoroughly understand their metabolism. Lipids are no longer considered only structural elements but also serve as signaling molecules and may have antigenic properties. Lipidomics remains a major research challenge, mainly due to the diverse physicochemical properties of lipids. Therefore, this study aimed to perform a large comparative lipidomic analysis of eight representatives of the Cutibacterium genus, including four phylotypes of C. acnes and two strains of C. granulosum, C. avidum, and C. namnetense. Lipidomic analysis was performed by liquid chromatography‒mass spectrometry (LC-MS) in both positive and negative ion modes, allowing the detection of the widest range of metabolites. Fatty acid analysis by gas chromatography‒mass spectrometry (GC-MS) corroborated the lipidomic data. As a result, 128 lipids were identified, among which it was possible to select marker compounds, some of which were characteristic even of individual C. acnes phylotypes. These include phosphatidylcholine PC 30:0, sphingomyelins (SM 33:1, SM 35:1), and phosphatidylglycerol with an alkyl ether substituent PG O-32:0. Moreover, cardiolipins and fatty acid amides were identified in Cutibacterium spp. for the first time. This comparative characterization of the cutibacterial lipidome with the search for specific molecular markers reveals its diagnostic potential for clinical microbiology. IMPORTANCE: Cutibacterium (previously Propionibacterium) represents an important part of the human skin microbiota, and its role in clinical microbiology is growing due to opportunistic infections. Lipidomics, apart from protein profiling, has the potential to prove to be a useful tool for defining the cellular fingerprint, allowing for precise differentiation of microorganisms. In this work, we presented a comparative analysis of lipids found in eight strains of the genus Cutibacterium, including a few C. acnes phylotypes. Our results are one of the first large-scale comprehensive studies regarding the bacterial lipidome, which also enabled the selection of C. acnes phylotype-specific lipid markers. The increased role of lipids not only as structural components but also as diagnostic markers or potential antigens has led to new lipid markers that can be used as diagnostic tools for clinical microbiology. We believe that the findings in our paper will appeal to a wide range of researchers.

Identification of diagnostic markers related to inflammatory response and cellular senescence in endometriosis using machine learning and in vitro experiment.

OBJECTIVE: To understand the association between chronic inflammation, cellular senescence, and immunological infiltration in endometriosis. METHODS: Datasets from GEO comprising 108 endometriosis and 97 healthy human samples and the human endometrial stromal cell. Differentially expressed genes were identified using Limma and WGCNA. Inflammatory response-related subtypes were constructed using consensus clustering analysis. The CIBERSORT algorithm and correlation analyses assessed immune cell infiltration. LASSO, SVM-RFE, and RF identified diagnostic genes. Functional enrichment analysis and multifactor regulatory networks established functional effects. Nomograms, internal and external validations, and in vitro experiments validated the diagnostic genes. RESULTS: Inflammatory response subtypes were highly correlated with the immune activities of B and NK cells. Sixteen genes were associated with inflammatory response and cellular senescence and six diagnostic genes (NLK, RAD51, TIMELESS, TBX3, MET, and BTG3) were identified. The six diagnostic gene models had an area under the curve of 0.828 and their expression was significantly downregulated in endometriosis samples. Low expression of NLK and BTG3 promoted the proliferation, migration, and invasion of endometriotic cells. CONCLUSIONS: Inflammatory response subtypes were successfully constructed for endometriosis. Six diagnostic genes related to inflammatory response and cellular senescence were identified and validated. Our study provides novel insights for inflammatory response in endometriosis and markers for endometriosis diagnosis and treatment.

Diagnostic Biomarkers in Renal Cell Tumors According to the Latest WHO Classification: A Focus on Selected New Entities.

The fifth edition of the World Health Organization (WHO) classification for urogenital tumors, released in 2022, introduces some novelties in the chapter on renal epithelial tumors compared to the previous 2016 classification. Significant changes include the recognition of new disease entities and adjustments in the nomenclature for certain pathologies. Notably, each tumor entity now includes minimum essential and desirable criteria for reliable diagnosis. This classification highlights the importance of biological and molecular characterization alongside traditional cytological and architectural features. In this view, immunophenotyping through immunohistochemistry (IHC) plays a crucial role in bridging morphology and genetics. This article aims to present and discuss the role of key immunohistochemical markers that support the diagnosis of new entities recognized in the WHO classification, focusing on critical topics associated with single markers, in the context of specific tumors, such as the clear cell capillary renal cell tumor (CCPRCT), eosinophilic solid and cystic renal cell carcinoma (ESC-RCC), and so-called "other oncocytic tumors", namely the eosinophilic vacuolated tumor (EVT) and low-grade oncocytic tumor (LOT). Their distinctive characteristics and immunophenotypic profiles, along with insights regarding diagnostic challenges and the differential diagnosis of these tumors, are provided. This state-of-the-art review offers valuable insights in biomarkers associated with novel renal tumors, as well as a tool to implement diagnostic strategies in routine practice.

Nuclear Magnetic Resonance (NMR) Metabolomics: Current Applications in Equine Health Assessment.

Metabolomics can allow for the comprehensive identification of metabolites within biological systems, at given time points, in physiological and pathological conditions. In the last few years, metabolomic analysis has gained popularity both in human and in veterinary medicine, showing great potential for novel applications in clinical activity. The aim of applying metabolomics in clinical practice is understanding the mechanisms underlying pathological conditions and the influence of certain stimuli (i.e., drugs, nutrition, exercise) on body systems, in the attempt of identifying biomarkers that can help in the diagnosis of diseases. Proton Nuclear Magnetic Resonance spectroscopy (1H-NMR) is well tailored to be used as an analytical platform for metabolites' detection at the base of metabolomics studies, due to minimal sample preparation and high reproducibility. In this mini-review article, the scientific production of NMR metabolomic applications to equine medicine is examined. The research works are very different in methodology and difficult to compare. Studies are mainly focused on exercise, reproduction, and nutrition, other than respiratory and musculoskeletal diseases. The available information on this topic is still scant, but a greater collection of data could allow researchers to define new reliable markers to be used in clinical practice for diagnostic and therapeutical purposes.