High Diagnostic Potential Research Articles

Targeted thoracic ultrasonography shows high diagnostic potential for interstitial pneumonia in feedyard cattle.

To use a novel approach of targeted thoracic point-of-care ultrasound (TT-POCUS) of the caudodorsal right lung to determine potential associations between TT-POCUS measurements and the presence of interstitial pneumonia (IP) in feedyard cattle. Cross-sectional study evaluated feedyard animals (n = 62; 358.4 ± 10.5 kg; heifer, n = 38; steer, n = 24) using TT-POCUS at time of respiratory disease treatment and microscopic assessment of lesions at subsequent mortality. Evaluations took place from July through December 2023. Pulmonary samples were collected at necropsy for histopathologic assessment, and lesions were used to diagnose IP or non-IP. Statistical models determined potential associations between IP, cattle demographic, and TT-POCUS measured variables. Evaluations of animals enrolled (n = 62) displayed significant associations with IP, including ultrasound lung score (ULS), B-line count, A-line count, and days on feed at treatment. Cattle with a ULS of 5 had the lowest probability of IP (0.12 ± 0.16) compared to cattle with a ULS of 2, 3, or 4 (0.72 ± 0.18, 0.93 ± 0.06, and 0.85 ± 0.08, respectively). Cattle with B-line counts > 5 had a higher probability of IP (0.86 ± 0.11) compared to cattle with 3 to 5 B-lines (0.38 ± 0.14). The probability of IP was higher in cattle with A-line counts < 3 (0.83 ± 0.10) compared to cattle with ≥ 3 A-lines (0.51 ± 0.15). Strategic targeted thoracic ultrasonography has potential a value of timely IP diagnosis in feedyard cattle, thereby offering important insights into therapeutic and management decisions. Targeted thoracic ultrasonography can be utilized as a method for IP diagnosis in feedyard cattle.

SMOC2, OGN, FCN3, and SERPINA3 could be biomarkers for the evaluation of acute decompensated heart failure caused by venous congestion

BackgroundVenous congestion (VC) sets in weeks before visible clinical decompensation, progressively increasing cardiac strain and leading to acute heart failure (HF) decompensation. Currently, the field lacks a universally acknowledged gold standard and early detection methods for VC.MethodsUsing data from the GEO database, we identified VC's impact on HF through key genes using Limma and STRING databases. The potential mechanisms of HF exacerbation were explored via GO and KEGG enrichment analyses. Diagnostic genes for acute decompensated HF were discovered using LASSO, RF, and SVM-REF machine learning algorithms, complemented by single-gene GSEA analysis. A nomogram tool was developed for the diagnostic model's evaluation and application, with validation conducted on external datasets.ResultsOur findings reveal that VC influences 37 genes impacting HF via 8 genes, primarily affecting oxygen transport, binding, and extracellular matrix stability. Four diagnostic genes for HF's pre-decompensation phase were identified: SMOC2, OGN, FCN3, and SERPINA3. These genes showed high diagnostic potential, with AUCs for each gene exceeding 0.9 and a genomic AUC of 0.942.ConclusionsOur study identifies four critical diagnostic genes for HF's pre-decompensated phase using bioinformatics and machine learning, shedding light on the molecular mechanisms through which VC worsens HF. It offers a novel approach for clinical evaluation of acute decompensated HF patient congestion status, presenting fresh insights into its pathogenesis, diagnosis, and treatment.

Urinary exosomal microRNA-200 family: diagnostic biomarkers for Kawasaki disease and their link to inflammatory markers.

The microRNA-200 family plays a key role in inflammatory and vascular processes, making it a relevant target for Kawasaki disease, a vasculitis with coronary complications. This study aimed to evaluate the diagnostic potential of urinary exosomal microRNA-200 family members in Kawasaki disease patients. Urine samples from 15 Kawasaki disease patients and 15 healthy controls underwent total exosome isolation and high-throughput sequencing. Differential expression of microRNA-200 family members was validated using quantitative real-time polymerase chain reaction. Diagnostic potential was assessed via receiver operating characteristic analysis, and correlations with clinical parameters were evaluated using Spearman correlation. High-throughput sequencing identified upregulation of microRNA-429, microRNA-200b-3p/5p, microRNA-141-3p, microRNA-200a-3p/5p, and microRNA-200c-3p in Kawasaki disease patients. We confirmed significant upregulation of microRNA-200a-3p/5p, microRNA-200b-3p/5p, and microRNA-429, with receiver operating characteristic analysis showing high diagnostic potential for these microRNAs (area under the curves of 0.844, 0.791, 0.942, 0.842, and 0.898, respectively) and a combined analysis yielding a perfect area under the curve of 1.000. MicroRNA-141 and microRNA-200c-3p/5p, however, showed no significant diagnostic value. MicroRNA-200a-3p and microRNA-200a-5p were positively correlated with white blood cells, platelet counts, and C-reactive protein, while microRNA-200b-3p and microRNA-429 were positively correlated with white blood cells, platelet counts, erythrocyte sedimentation rate, and C-reactive protein. microRNA-200b-5p showed moderate correlations with platelet counts and erythrocyte sedimentation rate. Urinary exosomal microRNA-200 family members, especially microRNA-200a-3p/5p, microRNA-200b-3p/5p, and microRNA-429, demonstrate strong diagnostic potential for Kawasaki disease, correlating with key inflammatory markers. MicroRNA-141 and microRNA-200c did not demonstrate significant diagnostic utility.

A Prospective Randomized Study of the Potential Biomarker miR-204-5p for the Differential Diagnosis of Gestational Diabetes Mellitus and its Correlation with Pregnancy Outcomes

Background: Gestational diabetes mellitus (GDM) is a common complication of pregnancy that has a certain impact on maternal and infant health. The aim of this study was to investigate the diagnostic value of microRNA-204-5p (miR-204-5p) in GDM by analyzing its differential expression between GDM patients and healthy individuals and to assess its predictive potential for the occurrence of poor maternal and infant outcomes in GDM patients. Methods: GDM patients (107; GDM group) and healthy pregnant women (92; control group) were recruited for this prospective study. GDM patients meet the relevant criteria of the International Association of Diabetes and Pregnancy Study Groups (IADPSG), which are categorized into good and poor outcome groups on the basis of maternal and infant pregnancy outcomes. Serum miR-204-5p expression was quantified via the reverse transcription quantitative polymerase chain reaction (RT-qPCR). The diagnostic potential of miR-204-5p in GDM patients was analyzed by receiver operating characteristic (ROC) curves and further assessed for its ability to predict the occurrence of poor maternal and infant outcomes in GDM patients. The difference in clinical indicators between GDM patients and healthy pregnant women was determined through Student's t-test, and the clinical indicators of GDM patients in the good outcome group and the poor outcome group were evaluated. The potential of miR-204-5p as a risk factor for poor maternal and infant outcomes in GDM patients was evaluated by binary logistic analysis. Results: miR-204-5p expression was decreased in GDM patients compared to healthy pregnant women. The area under the curve (AUC) for distinguishing the healthy people from GDM patients was 0.918, with a sensitivity and specificity of 78.5% and 90.2%. Additionally, miR-204-5p expression in GDM patients in the poor outcome group was lower than that in the good outcome group. The AUC reflecting the potential of miR-204-5p in predicting poor maternal and infant outcomes in GDM patients was 0.855, with a sensitivity of 66.7% and a specificity of 90.5%. miR-204-5p is a risk factor for poor maternal and infant outcomes in GDM patients. Conclusions: Serum miR-204-5p expression is low in GDM and has a high diagnostic potential for predicting the occurrence of GDM and poor maternal and infant pregnancy outcomes. It is expected to be a biomarker for the risk of GDM.

Profiling of the serum MiRNAome in pediatric egyptian patients with wilms tumor.

Wilms tumor (WT) is a pediatric kidney cancer associated with poor outcomes in patients with unfavorable histological features such as anaplasia. Small non-coding RNAs, such as miRNAs, are known to be involved in WT pathogenesis. However, research on the clinical potential of blood-based miRNAs is limited. This study aimed to profile aberrantly expressed miRNAs in WT serum samples, evaluate their potential to differentiate standard-risk patients with favorable histology from those with anaplastic WTs, and assess their clinical value as minimally invasive biomarkers for WT detection. The study used next-generation sequencing (NGS) to analyze miRNA expressions in serum samples from 37 Egyptian children, including 10 healthy individuals, 14 with non-anaplastic WTs (favorable histology FH-WTs), and 13 with anaplastic WTs (unfavorable histology UnFH-WTs). Functional enrichment analysis was conducted to identify critical pathways and biological processes affected by dysregulated miRNAs, and a network was created for the most promising miRNA-target interactions linked to WT. The study identified a distinct miRNA expression signature of 45 miRNAs (3 upregulated and 42 downregulated) in WT serum samples compared to healthy controls, with 29 miRNAs exclusively dysregulated in FH-WTs and 6 miRNAs dysregulated solely in UnFH-WTs. These dysregulated miRNAs displayed significant enrichment in cancer-related pathways, such as PI3K/AKT, FOXO, and MAPK signaling. In relation to WT clinicopathological features, decreased levels of hsa-miR-2355-3p showed a significant positive correlation with clinical stage (r = 0.6597, p = 0.0006) and WT metastasis (r = 0.439, p = 0.021). The ROC curve analysis revealed that multiple dysregulated miRNAs in WT, specifically hsa-miR-7-5p, hsa-miR-146a-5p, hsa-miR-378a-3p, and hsa-miR-483-5p, exhibited high diagnostic potential for WT, with AUC values exceeding 0.86. Among WT histopathology types, the hsa-miR-1180-3p showed a 2.3 log2fold difference in expression between UnFH-WTs and FH-WTs, indicating its potential as a biomarker with 92% sensitivity and 85% specificity for identifying UnFH-WTs. Its target genes were enriched in pathways related to cell division and cell cycle regulation. In conclusion, hsa-miR-1180-3p could be a reliable blood-based biomarker for distinguishing WT histopathological types, and further research is needed to validate its clinical value.

Levels of matrix metalloproteinase-1 (MMP-1), MMP-2, MMP-3, osteopontin, pentraxin-3, and thymic stromal lymphopoietin in crevicular fluid samples from peri-implantitis, periodontitis, and healthy sites.

Periodontitis and peri-implantitis are chronic inflammatory diseases characterized by the destruction of supporting tissues. Despite some similarities, it is essential to understand the differences in how these diseases elicit unique host responses within the oral tissues, including the production of selected matrix metalloproteinases (MMPs) and inflammatory mediators involved in tissue remodelling. The aim of this study was to evaluate the levels of proteolytic enzymes MMP-1, MMP-2, MMP-3, as well as the inflammatory mediators osteopontin (OPN), pentraxin-3 (PTX3), and thymic stromal lymphopoietin (TSLP) in crevicular fluid samples collected from healthy, periodontitis-affected, and peri-implantitis sites. Gingival crevicular fluid (GCF) and peri-implant crevicular fluid (PICF) samples were collected from healthy and diseased teeth and implant sites of 163 patients. The MMP-1, MMP-2, MMP-3, OPN, PTX3, and TSLP levels were determined using commercially available immunoassays. A linear mixed model procedure was adopted for multilevel analyses, using biomarker levels as the outcome variable to compare two types of sites. The diagnostic accuracy of the biomarkers was evaluated by Youden's index to estimate the sensitivity, specificity and the area under curve (AUC). The levels of MMP-1, MMP-2, MMP-3, OPN, and TSLP were higher at sites with periodontitis and peri-implantitis compared to the levels at sites with healthy teeth and healthy implants. No significant differences were observed in the levels of the measured markers between the sites diagnosed with periodontitis and those diagnosed with peri-implantitis. The highest diagnostic potential at implant sites was found for MMP-2 (AUC = 0.74) and TSLP (AUC = 0.72). The highest AUC (0.82) at tooth sites was found for OPN. The findings indicate that the proteolytic enzyme MMP-2 and the cytokine TSLP might be potential biomarkers for both periodontitis and peri-implantitis, whereas the proinflammatory cytokine OPN may serve as a biomarker for periodontitis. Further studies are required to confirm the utility of these biomarkers and explore their potential clinical applications.

Potential diagnostic and prognostic biomarkers of pediatric Burkitt lymphoma identified through miRNA expression profiling.

Pediatric Burkitt lymphoma (pBL) is the most common non-Hodgkin lymphoma in children. These patients require prompt diagnosis and initiation of therapy due to rapid tumor growth. The roles of tumor tissue and circulating microRNAs (miRNAs) in the diagnosis or prognostication have not been fully elucidated in pBLs. Differentially expressed (DE) miRNAs were identified with microRNA sequencing (miRNA-Seq) in tumor tissues and plasma of diagnostic pBLs. The diagnostic potential of total miRNA concentrations and overexpressed miRNAs were evaluated through receiver operating characteristic (ROC) analyses. Log-rank test was employed to evaluate survival differences associated with DE miRNAs. Selected miRNA expressions were cross-validated with quantitative reverse transcription PCR (qRT-PCR). Total circulating cell-free miRNAs were higher in pBL cases compared to controls. Cancer-associated pathways were enriched among miRNAs differentially expressed in pBL tumor tissues. Several upregulated miRNAs in pBL tumors demonstrated high diagnostic potential. Similarly, ROC analysis of overexpressed plasma miRNAs revealed circulating cell-free or exosomal miRNAs that can distinguish pBLs from control cases. Indeed, integrative analysis of overexpressed circulating exosomal miRNAs showed an enhanced diagnostic potential for certain triple combinations. Kaplan-Meier analyses of DE miRNAs in tumor tissues identified miRNAs predicting overall survival. Differentially expressed miRNAs in tumor tissue and plasma of pBL have the potential to improve diagnosis and prognosis. Differentially expressed miRNAs in treatment-naive pediatric Burkitt lymphoma cases have diagnostic or prognostic biomarker potential. This is the first study that applied miRNA-Seq on treatment-naive pediatric Burkitt lymphoma cases for identification of differentially expressed miRNAs both in tumor tissue and plasma samples with diagnostic potential. Through systematic analysis of differentially expressed miRNAs, tumor tissue miRNAs associated with the overall survival of pBLs have been discovered. The clinically significant, differentially expressed miRNAs identified in pediatric Burkitt lymphoma cases can potentially improve the current tissue-based or non-invasive clinical practice in terms of diagnosis or prognostication.

Identification of novel mitophagy-related biomarkers for Kawasaki disease by integrated bioinformatics and machine-learning algorithms.

Kawasaki disease (KD) is a systemic vasculitis primarily affecting the coronary arteries in children. Despite growing attention to its symptoms and pathogenesis, the exact mechanisms of KD remain unclear. Mitophagy plays a critical role in inflammation regulation, however, its significance in KD has only been minimally explored. This study sought to identify crucial mitophagy-related biomarkers and their mechanisms in KD, focusing on their association with immune cells in peripheral blood. This research used four datasets from the Gene Expression Omnibus (GEO) database that were categorized as the merged and validation datasets. Screening for differentially expressed mitophagy-related genes (DE-MRGs) was conducted, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A weighted gene co-expression network analysis (WGCNA) identified the hub module, while machine-learning algorithms [random forest-recursive feature elimination (RF-RFE) and support vector machine-recursive feature elimination (SVM-RFE)] pinpointed the hub genes. Receiver operating characteristic (ROC) curves were generated for these genes. Additionally, the CIBERSORT algorithm was used to assess the infiltration of 22 immune cell types to explore their correlations with hub genes. Interactions between transcription factors (TFs), genes, and Gene-microRNAs (miRNAs) of hub genes were mapped using the NetworkAnalyst platform. The expression difference of the hub genes was validated using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). Initially, 306 DE-MRGs were identified between the KD patients and healthy controls. The enrichment analysis linked these MRGs to autophagy, mitochondrial function, and inflammation. The WGCNA revealed a hub module of 47 KD-associated DE-MRGs. The machine-learning algorithms identified cytoskeleton-associated protein 4 (CKAP4) and serine-arginine protein kinase 1 (SRPK1) as critical hub genes. In the merged dataset, the area under the curve (AUC) values for CKAP4 and SRPK1 were 0.933 [95% confidence interval (CI): 0.901 to 0.964] and 0.936 (95% CI: 0.906 to 0.966), respectively, indicating high diagnostic potential. The validation dataset results corroborated these findings with AUC values of 0.872 (95% CI: 0.741 to 1.000) for CKAP4 and 0.878 (95% CI: 0.750 to 1.000) for SRPK1. The CIBERSORT analysis connected CKAP4 and SRPK1 with specific immune cells, including activated cluster of differentiation 4 (CD4) memory T cells. TFs such as MAZ, SAP30, PHF8, KDM5B, miRNAs like hsa-mir-7-5p play essential roles in regulating these hub genes. The qRT-PCR results confirmed the differential expression of these genes between the KD patients and healthy controls. CKAP4 and SRPK1 emerged as promising diagnostic biomarkers for KD. These genes potentially influence the progression of KD through mitophagy regulation.

Exploring the role of ferroptosis-related genes as biomarkers in acute kidney injury.

Acute kidney injury (AKI) is a severe condition with high morbidity and mortality. Innovative biomarkers and treatments are essential for improving patient outcomes. This study aims to investigate the role of ferroptosis-related genes (FRGs) in AKI for identifying potential biomarkers and therapeutic targets. We analyzed mRNA expression profiles from the Gene Expression Omnibus (GEO: GSE139061) dataset, comparing 36 AKI samples with 9 normal samples. Differentially expressed genes (DEGs) were identified using the R software package limma. Functional enrichment analyses were conducted using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Key biomarkers were validated through area under the curve (AUC) values, and immune cell infiltration was analyzed using CIBERSORT. We identified 78 differentially expressed FRGs, with 27 up-regulated and 51 down-regulated genes. Key signaling pathways included MAPK, ferroptosis, and p53. Five genes-NR4A1, GLRX5, USP35, AEBP2, and MDM4-were identified as potential biomarkers, each demonstrating AUC values greater than 0.800. Specifically, MDM4 showed significant potential by promoting the phosphorylation of p53 at Ser46, enhancing mitochondrial apoptotic activity. Immune analysis revealed a significant elevation of M0 macrophages in AKI samples compared to normal samples (P < 0.01). Our findings highlight the critical role of ferroptosis-related genes in AKI, identifying NR4A1, GLRX5, USP35, AEBP2, and MDM4 as key biomarkers with high diagnostic potential. These results provide novel insights into the molecular mechanisms of AKI.

Enhancing electrochemical detection through machine learning-driven prediction for canine mammary tumor biomarker with green silver nanoparticles

This study developed an innovative biosensor strategy for the sensitive and selective detection of canine mammary tumor biomarkers, cancer antigen 15–3 (CA 15–3) and mucin 1 (MUC-1), integrating green silver nanoparticles (GAgNPs) with machine learning (ML) algorithms to achieve high diagnostic accuracy and potential for noninvasive early detection. The GAgNPs-enhanced electrochemical biosensor demonstrated selective detection of CA 15–3 in serum and MUC-1 in tissue homogenates, with limits of detection (LODs) of 0.07 and 0.11 U mL−1, respectively. The nanoscale dimensions of the GAgNPs endowed them with electrochemically active surface areas, facilitating sensitive biomarker detection. Experimental studies targeted CA 15–3 and MUC-1 biomarkers in clinical samples, and the biosensor exhibited ease of use and good selectivity. Furthermore, ML algorithms were employed to analyze the electrochemical data and predict biomarker concentrations, enhancing the diagnostic accuracy. The Random Forest algorithm achieved 98% accuracy in tumor presence prediction, while an Artificial Neural Network attained 76% accuracy in CA 15–3-based tumor grade classification. The integration of ML techniques with the GAgNPs-based biosensor offers a promising approach for noninvasive, accurate, and early detection of canine mammary tumors, potentially revolutionizing veterinary diagnostics. This multilayered strategy, combining eco-friendly nanomaterials, electrochemical sensing, and ML algorithms, holds significant potential for advancing both biomedical research and clinical practice in the field of canine mammary tumor diagnostics.Graphical

Development and Validation of Diagnostic Models for Transcriptomic Signature Genes for Multiple Tissues in Osteoarthritis.

Progress in research on expression profiles in osteoarthritis (OA) has been limited to individual tissues within the joint, such as the synovium, cartilage, or meniscus. This study aimed to comprehensively analyze the common gene expression characteristics of various structures in OA and construct a diagnostic model. Three datasets were selected: synovium, meniscus, and knee joint cartilage. Modular clustering and differential analysis of genes were used for further functional analyses and the construction of protein networks. Signature genes with the highest diagnostic potential were identified and verified using external gene datasets. The expression of these genes was validated in clinical samples by Real-time (RT)-qPCR and immunohistochemistry (IHC) staining. This study investigated the status of immune cells in OA by examining their infiltration. The merged OA dataset included 438 DEGs clustered into seven modules using WGCNA. The intersection of these DEGs with WGCNA modules identified 190 genes. Using Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest algorithms, nine signature genes were identified (CDADC1, PPFIBP1, ENO2, NOM1, SLC25A14, METTL2A, LINC01089, L3HYPDH, NPHP3), each demonstrating substantial diagnostic potential (areas under the curve from 0.701 to 0.925). Furthermore, dysregulation of various immune cells has also been observed. CDADC1, PPFIBP1, ENO2, NOM1, SLC25A14, METTL2A, LINC01089, L3HYPDH, NPHP3 demonstrated significant diagnostic efficacy in OA and are involved in immune cell infiltration.

Electrogastrography measurement systems and analysis methods used in clinical practice and research: comprehensive review.

https://www.prisma-statement.org/.

A Novel Peptidome Technology for the Diagnosis of Mild Cognitive Impairment and Alzheimer’s Disease by Selected Reaction Monitoring

Background: With the aging of populations worldwide, Alzheimer’s disease (AD) has become a concern due to its high prevalence and the continued lack of established treatments. Early diagnosis is required as a preventive intervention to modify the disease’s progression. In our previous study, we performed peptidomic analysis of serum samples obtained from AD patients and age-matched healthy subjects to seek peptide biomarker candidates for AD by using BLOTCHIP-MS analysis, and identified four peptides as AD biomarker candidates. Objective: The objective was to validate the serum biomarker peptides to distinguish mild cognitive impairment (MCI) and AD in comparison to cognitively healthy controls using a new peptidome technology, the Dementia Risk Test. Methods: We enrolled 195 subjects with normal cognitive function (NC; n = 70), MCI (n = 55), and AD (n = 70), The concentrations of cognitive impairment marker peptides (Fibrinogen α chain (FAC), Fibrinogen β chain (FBC), Plasma protease C1 inhibitor (PPC1I), α2-HS-glycoprotein (AHSG)) were quantified by using a selected reaction monitoring assay based on liquid chromatography-MS/MS. Results: The present study confirmed that three peptides, FAC, FBC, and PPC1I, were significantly upregulated during the onset of AD. This three-peptide set was both highly sensitive in determining AD (sensitivity: 85.7%, specificity: 95.7%, AUC: 0.900) and useful in distinguishing MCI (sensitivity: 61.8%, specificity: 98.6%, AUC: 0.824) from NC. Conclusions: In this validation study, we confirmed the high diagnostic potential of the three peptides identified in our previous study as candidate serum biomarkers for AD. The Dementia Risk Test may be a powerful tool for detecting AD-related pathological changes.

Elevated extracellular particle concentration in plasma predicts in-hospital mortality after severe trauma.

Extracellular particles (EPs), particularly extracellular vesicles, play a crucial role in regulating various pathological mechanisms, including immune dysregulations post-trauma. Their distinctive expression of cell-specific markers and regulatory cargo such as cytokines or micro-ribonucleic acid suggests their potential as early biomarkers for organ-specific damage and for identifying patients at risk for complications and mortality. Given the critical need for reliable and easily assessable makers to identify at-risk patients and guide therapeutic decisions, we evaluated the early diagnostic value of circulating EPs regarding outcomes in severely injured multiple-trauma patients. Plasma samples were collected from 133 severely injured trauma patients (Injury Severity Score (ISS) ≥16) immediately upon arrival at the emergency department (ED). Patients were categorized into survivors and non-survivors. Injury characteristics and outcomes related to sepsis, pneumonia, or early (<1 day after admission) and late mortality were assessed. Circulating EPs, cytokine profiles, and blood counts of platelets and leukocytes were determined. Receiver operating characteristic analyses were conducted. Despite no significant differences in injury pattern or severity, non-survivors exhibited significantly elevated counts of circulating EPs compared to survivors. The optimal cut-off for EPs <200 nm indicating non-survivors was 17380/µl plasma, with a sensitivity of 77% and a specificity of 61% in predicting in-hospital mortality. Later non-survivors received significantly higher numbers of units of packed red blood cells [8.54 ± 5.45 vs. 1.29 ± 0.36 units], had higher serum lactate [38.00 ± 7.51 vs. 26.98 ± 1.58 mg/dL], significantly lower platelet counts [181.30 ± 18.06 vs. 213.60 ± 5.85 *10³/µL] and lower heart rates [74.50 ± 4.93 vs. 90.18 ± 2.06 beats/minute] upon arrival at the ED compared to survivors. Our results demonstrate the high diagnostic potential of elevated concentrations of circulating EPs <200 nm for identifying patients at risk of mortality after severe trauma. This parameter shows comparable sensitivity to established clinical predictors. Early evaluation of EPs concentration could complement assessment markers in guiding early therapeutic decisions.

The value of apparent diffusion coefficient values in predicting Gleason grading of low to intermediate-risk prostate cancer

ObjectivesTo investigate the diagnostic performance of the apparent diffusion coefficient (ADC) for low to intermediate-risk prostate cancer (PCa), as well as its correlation with the prognostic Gleason score (GS).Materials and methodsRetrospective analysis of MRI images and relevant clinical data from patients with prostate disease. The differences in ADC between different GS groups were compared, and the efficacy of ADC in PCa diagnosis were analyzed. Furthermore, the diagnostic performance of the mean ADC (ADCmean) and minimum ADC (ADCmin) values was compared.ResultsThere were 1414 patients with 1631 lesions. In terms of GS, both ADCmin and ADCmean values of the GS 4 + 3 group were significantly lower than those of the GS 3 + 4 group, GS 3 + 3 group, and the benign group, with all differences being statistically significant (p < 0.01). The AUC values for diagnosing PCa based on ADCmin and ADCmean were 0.914 and 0.944, respectively. The corresponding diagnostic thresholds were 0.703 × 10−3 mm2/s for ADCmin and 0.927 × 10−3 mm2/s for ADCmean. The magnitudes of ADCmin and ADCmean values exhibited a negative correlation with GS values (ρ = −0.750, p < 0.001; ρ = −0.752, p < 0.001).ConclusionsADC values demonstrate an inverse relationship with the invasiveness of PCa, indicating that higher invasiveness is associated with lower ADC values. Additionally, ADC values exhibit high diagnostic potential, sensitivity, and specificity for distinguishing between GS 3 + 4 and GS 4 + 3 lesions. Moreover, the diagnostic value of ADCmean is even more significant, highlighting its crucial role in the diagnosis of low to intermediate-risk PCa.Critical relevance statementADC values are a valuable tool for distinguishing different levels of aggressiveness in PCa. They help in the preoperative assessment of the biological characteristics of PCa, allowing clinicians to develop personalized treatment strategies, effectively mitigating the risk of unnecessary interventions.Key PointsThe preoperative GS is crucial for planning the clinical treatment of PCa.The invasiveness of PCa is inversely correlated with ADC values.ADC values play a crucial role in the accurate preoperative evaluation of low to intermediate-risk PCa, thus aiding clinicians in developing tailored treatment plans.Graphical

Non-obese lipedema patients show a distinctly altered quantitative sensory testing profile with high diagnostic potential.

Lipedema is a widespread severe chronic disease affecting mostly women. Characterized by painful bilateral fat accumulation in extremities sparing hands and feet, objective measurement-based diagnosis is currently missing. We tested for characteristic psychometric and/or sensory alterations including pain and for their potential for medical routine diagnosis. Pain psychometry was assessed using the German Pain Questionnaire. Sensory sensitivity toward painful and nonpainful stimuli was characterized in non-obese lipedema patients and matched controls using the validated quantitative sensory testing (QST) protocol of the German Research Network on Neuropathic Pain. Lipedema patients showed no overt psychometric abnormalities. Pain was reported as somatic rather than psychosomatic aversive. All QST measurements were normal, but the z-score of pressure pain thresholds (PPT) was twofold reduced and the z-score of vibration detection thresholds (VDT) was two and a half times increased. Both thresholds were selectively altered at the affected thigh but not the unaffected hand. Receiver operating characteristic analysis of the combination of PPT and VDT of thigh vs hand into a PVTH score (PPT, VDT, thigh, hand-score) shows high sensitivity and specificity, categorizing correctly 95.8% of the participants as lipedema patients or healthy controls. Bayesian inference analysis corroborated the diagnostic potential of such a combined PVTH score. We propose to assess PPT and VDT at the painful thigh and the pain-free hand. Combination in a PVTH score may allow a convenient lipedema diagnosis early during disease development.

A panel based on three-miRNAs as diagnostic biomarker for prostate cancer.

Background: Prostate cancer (PCa) is one of the most prevalent malignancies affecting the male life cycle. The incidence and mortality of prostate cancer are also increasing every year. Detection of MicroRNA expression in serum to diagnose prostate cancer and determine prognosis is a very promising non-invasive modality. Materials and method: A total of 224 study participants were included in our study, including 112 prostate cancer patients and 112 healthy adults. The experiment consisted of three main phases, namely, the screening phase, the testing phase, and the validation phase. The expression levels of serum miRNAs in patients and healthy adults were detected using quantitative reverse transcription-polymerase chain reaction. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to evaluate the diagnostic ability, specificity, and sensitivity of the candidate miRNAs. Result: Eventually, three miRNAs most relevant to prostate cancer diagnosis were selected, namely, miR-106b-5p, miR-129-1-3p and miR-381-3p. We used these three miRNAs to construct a diagnostic panel with very high diagnostic potential for prostate cancer, which had an AUC of 0.912 [95% confidence interval (CI): 0.858 to 0.950; p < 0.001; sensitivity = 91.67%; specificity = 79.76%]. In addition, the three target genes (DTNA, GJB1, and TRPC4) we searched for are also expected to be used for prostate cancer diagnosis and treatment in the future.

Simultaneous detection of breast cancer biomarkers circROBO1 and BRCA1 based on a CRISPR-Cas13a/Cas12a system

Breast cancer is reported to be one of the most lethal cancers in women, and its multi-target detection can help improve the accuracy of diagnosis. In this work, a cluster regularly interspaced short palindromic repeats (CRISPR)-Cas13a/Cas12a-based system was established for the simultaneous fluorescence detection of breast cancer biomarkers circROBO1 and BRCA1. CRISPR-Cas13a and CRISPR-Cas12a were directly activated by their respective targets, resulting in the cleavage of short RNA and DNA reporters, respectively, thus the signals of 6-carboxyfluorescein (FAM) and 6-carboxy-xrhodamine (ROX) were restored. As the fluorescence intensities of FAM and ROX were dependent on the concentrations of circROBO1 and BRCA1, respectively, synchronous fluorescence scanning could achieve one-step detection of circROBO1 and BRCA1 with detection limits of 0.013 pM and 0.26 pM, respectively. The system was highly sensitive and specific, holding high diagnostic potential for the detection of clinical samples. Furthermore, the competing endogenous RNA mechanism between circROBO1 and BRCA1 was also explored, providing a reliable basis for the intrinsic regulatory mechanism of breast cancer.

Diagnostic accuracy of high resolution computed tomography (HRCT) of chest in diagnosing sputum smear positive and sputum smear negative pulmonary tuberculosis (PTB).

Objective: To assess the diagnostic accuracy of high resolution computed tomography (HRCT) of chest in diagnosing sputum smear positive and negative pulmonary tuberculosis (PTB). Study Design: Cross-sectional study. Setting: Tertiary Care Hospital, Kharian Cantt. Period: Oct 2021 to April 2022. Methods: The retrospective reviews of medical records of patients undergone HRCT chest and sputum for AFB smear and culture due to pulmonary tuberculosis (PTB) suspicion were included. The HRCT and AFB findings for sample size of 220 was compared. SPSS was used to stratify outcomes on basis of sputum smear results. Diagnostic accuracy of HRCT chest was calculated by taking AFB as gold standard. The AFB findings and HRCT outcomes were compared through correlation analysis and paired t-test with p value &lt; 0.05 considered as significant. Results: The sensitivity was found to be 86.23% with specificity of 86.48%, positive predictive value of 86.23%, negative predictive value of 86.48% and diagnostic accuracy of 86.36% for HRCT for diagnosing pulmonary tuberculosis (PTB). Conclusion: HRCT chest affirms high diagnostic potential for pulmonary tuberculosis (PTB) and should be used as a routine diagnostic tool for the disease.

Mechanisms of Ferroptosis-Related Genes in Gallbladder Cancer Based on Bioinformatics Analysis.

Gallbladder Cancer (GBC) is a lethal malignancy with limited treatment options and poor prognosis. Recent studies have emphasized the role of ferroptosis, a regulated form of cell death, in various cancers, including GBC. We applied bioinformatics methodologies on four GBC datasets to identify differentially expressed genes (DEGs). An intersection of DEGs from the four datasets with ferroptosis and GBC-associated genes was done to identify key ferroptosis-related genes in GBC. GSVA pathway enrichment analysis and immune cell infiltration assessment were conducted to explore their functional roles and interactions. Seven ferroptosis-related genes, EZH2, MUC1, PVT1, GOT1, CDO1, LIFR, and TFAP2A, were identified to be related to GBC. These genes were associated with vital signaling pathways like the G2/M checkpoint and DNA repair and showed significant correlations with immune cell infiltration in GBC. Receiver Operating Characteristic (ROC) curve analysis revealed their high diagnostic potential, with Area Under the Curve (AUC) values ranging from 0.796 to 0.953. Our findings underscore the pivotal role of ferroptosis in GBC and the potential of ferroptosis-related genes as diagnostic biomarkers. This study lays a foundation for further research into ferroptosis-based therapeutic strategies for GBC.