Diagnostic Panel Research Articles

A systematic review and standardized clinical validity assessment of genes involved in female reproductive failure.

Genetic testing is becoming increasingly required at almost every stage of failed female reproduction/infertility. Nonetheless, clinical evidence for the majority of identified gene–disease relationships is ill-defined, thus leading to difficult gene variant interpretation and poor translation of existing knowledge into clinics. We aimed to identify the genes that have ever been implicated in monogenic female reproductive failure in humans and to classify the identified gene–disease relationship pairs using a standardized clinical validity assessment. A PubMed search following PRISMA guidelines was conducted on 20 September 2021 aiming to identify studies pertaining to genetic causes of phenotypes of female reproductive failure. The clinical validity of identified gene–disease pairs was assessed using standardized criteria, counting whether sufficient genetic and experimental evidence has been accumulated to consider a single gene ‘characterized’ for a single Mendelian disease. In total, 1256 articles were selected for the data extraction; 183 unique gene–disease pairs were classified spanning the following phenotypes: hypogonadotropic hypogonadism, ovarian dysgenesis, premature ovarian failure/insufficiency, ovarian hyperstimulation syndrome, empty follicle syndrome, oocyte maturation defect, fertilization failure, early embryonic arrest, recurrent hydatidiform mole, adrenal disfunction and Mullerian aplasia. Twenty-four gene–disease pairs showed definitive evidence, 36 – strong, 19 – moderate, 81 – limited and 23 – showed no evidence. Here, we provide comprehensive, systematic and timely information on the genetic causes of female infertility. Our classification of genetic causes of female reproductive failure will facilitate the composition of up-to-date guidelines on genetic testing in female reproduction, the development of diagnostic gene panels and the advancement of reproductive decision-making.

Gene panel diagnostics reveals new pathogenic variants in pulmonary arterial hypertension

BackgroundA genetic predisposition can lead to the rare disease pulmonary arterial hypertension (PAH). Most mutations have been identified in the gene BMPR2 in heritable PAH. However, as of today 15 further PAH genes have been described. The exact prevalence across these genes particularly in other PAH forms remains uncertain. We present the distribution of mutations across PAH genes identified at the largest German referral centre for genetic diagnostics in PAH over a course of > 3 years.MethodsOur PAH-specific gene diagnostics panel was used to sequence 325 consecutive PAH patients from March 2017 to October 2020. For the first year the panel contained thirteen PAH genes: ACVRL1, BMPR1B, BMPR2, CAV1, EIF2AK4, ENG, GDF2, KCNA5, KCNK3, KLF2, SMAD4, SMAD9 and TBX4.These were extended by the three genes ATP13A3, AQP1 and SOX17 from March 2018 onwards following the genes’ discovery.ResultsA total of 79 mutations were identified in 74 patients (23%). Of the variants 51 (65%) were located in the gene BMPR2 while the other 28 variants were found in ten further PAH genes. We identified disease-causing variants in the genes AQP1, KCNK3 and SOX17 in families with at least two PAH patients. Mutations were not only detected in patients with heritable and idiopathic but also with associated PAH.ConclusionsGenetic defects were identified in 23% of the patients in a total of 11 PAH genes. This illustrates the benefit of the specific gene panel containing all known PAH genes.

Plasma α-synuclein and phosphorylated tau 181 as a diagnostic biomarker panel for de novo Parkinson's disease.

The use of a diagnostic panel comprising multiple biomarkers has the potential to accurately diagnose Parkinson’s disease (PD). However, a panel consisting solely of plasma biomarkers to diagnose PD is not available. This study aimed to examine the diagnostic ability of plasma biomarker panels for de novo PD using novel digital ultrasensitive immunoassay technology. We recruited 45 patients with de novo PD and 20 healthy controls (HCs). The concentrations of plasma α‐synuclein (α‐syn), amyloid β‐42 (Aβ42), Aβ40, phosphorylated tau 181 (p‐tau181), neurofilament light (NFL), and glial fibrillary acidic protein (GFAP) were quantified using the ultrasensitive single molecule array (Simoa) platform. Patients with de novo PD had higher plasma levels of α‐syn and p‐tau181 than HCs, adjusting for age and sex. Plasma levels of α‐syn and p‐tau181 were positively correlated in de novo PD patients. Higher plasma α‐syn levels were significantly associated with worse Unified Parkinson’s Disease Rating Scale (UPDRS) Part III motor scores, modified Hoehn and Yahr (H‐Y) stages, and increased risk of PD with mild cognitive impairment (PD‐MCI). Higher plasma p‐tau181 concentrations were linked to worse H‐Y stages. The diagnostic panel using plasma α‐syn and p‐tau181, combined with age and sex, showed good performance in discriminating de novo PD patients from HCs (area under the curve = 0.806). These findings suggest that plasma α‐syn and p‐tau181 together may be a promising diagnostic biomarker panel for de novo PD patients.

Proteomic profiling identifies novel diagnostic biomarkers and molecular subtypes for mucinous tubular and spindle cell carcinoma of the kidney.

Mucinous tubular and spindle cell carcinoma (MTSCC) is a relatively rare renal epithelial neoplasm resembling type 1 papillary renal cell carcinoma (PRCC) morphologically and immunohistochemically. The accurate diagnosis of MTSCC remains a challenge. Here, by using proteomic profiling, we characterized MTSCC and PRCC to identify diagnostic biomarkers. We found that the MTSCC tumor proteome was significantly enriched in B‐cell‐mediated immunity when compared with the proteome of adjacent normal tissues of MTSCC or tumors of PRCC. Importantly, we identified MZB1, VCAN, and SOSTDC1 as diagnostic biomarkers to distinguish MTSCC from the solid variant of type 1 PRCC, with an AUC of 0.985 when combined. MZB1 was inversely correlated with tumor clinical stage and may play an anti‐tumor role by activating the complement system. Finally, unsupervised clustering revealed two molecular subtypes of MTSCC, displaying different morphology, expression signatures of oxidative phosphorylation, and aggravation. In summary, our analyses identified a three‐protein diagnostic panel and molecular subtypes for MTSCC. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

The use of alkaline phosphatase and runx2 to distinguish osteosarcoma from other common malignant primary bone tumors in dogs.

In dogs, primary bone tumors can be difficult to distinguish with histopathology. Of those tumors, osteosarcoma (OSA) is the most common and aggressive. In this study, 4 immunohistochemistry markers-alkaline phosphatase (ALP), osteonectin (ON), osteopontin (OP), and runx2-were evaluated for their ability to distinguish OSA from other primary bone tumors. The 42 formalin-fixed, paraffin-embedded, primary canine bone tumors included 15 OSAs, 8 chondrosarcomas, 11 fibrosarcomas, and 8 histiocytic sarcomas. All 4 antibodies were highly sensitive for detection of osteosarcoma. ALP was the most sensitive at 100% and runx2 the most specific at 78%. Running ALP and runx2 in series resulted in a sensitivity of 87% and a specificity of 85%. This combination of immunomarkers resulted in a diagnostic panel for distinguishing osteosarcoma from other primary bone tumors.

A Panel of Three Serum MicroRNAs as a Potential Diagnostic Biomarker for Urothelial Carcinoma

Objective: Urothelial carcinoma (UCa) is one of the most common malignancies of the genitourinary system, and its early diagnosis is vital to improve the survival of UCa patients. Therefore, novel noninvasive markers are urgently needed to improve the diagnosis of UCa. The present study aims to identify microRNAs (miRNAs) relevant for the diagnosis of UCa. Materials and Methods: We enrolled a total of 152 UCa patients and 135 healthy controls at a single institution, between 2012 and 2020. The expression levels of candidate miRNAs were calculated from serum samples based on quantitative reverse transcription-polymerase chain reaction. miRNAs with a good diagnostic value were selected and confirmed step by step in a four-phase test. The area under the curve (AUC) of each miRNA was obtained by analyzing the receiver operating characteristic (ROC) curve, which was used to evaluate the sensitivity, specificity, and corresponding cutoff values of miRNAs. Backward stepwise logistic regression was used to identify a panel of miRNAs that could distinguish UCa from healthy controls. Results: Four miRNAs were relevant for diagnosis: miR17-5p (AUC = 0.786, p < 0.001), miR-125a-5p (AUC = 0.681, p < 0.01), miR145-5p (AUC = 0.737, p < 0.001), and miR-224-5p (AUC = 0.872, p < 0.001). These miRNAs were used to construct a diagnostic panel. The final optimal combination to diagnose UCa included three miRNAs, namely, miR17-5p, miR145-5p, and miR-224-5p. The ROC curve of the panel was constructed, and its AUC was 0.961 (95% CI: 0.931–0.991; sensitivity = 93.8%, and specificity = 87.5%). Conclusion: In this study, we discovered four miRNAs (miR17-5p, miR-125a-5p, miR145-5p, and miR-224-5p) that were relevant for UCa diagnosis, and successfully developed a panel using three of these miRNAs. This panel may serve as a new biomarker tool with high sensitivity and specificity to diagnose UCa in patients.

Liver proliferating cell nuclear antigen, BAX/Bcl-2 ratio, collagen, and polysaccharide accumulation as diagnostic tools in experimental hepatocellular carcinoma

Objectives: This experimental study was conducted to look for a sensitive diagnostic panel for early detection of HCC.&#x0D; Methods: Combination of diethyl nitrosamine (DENA, 200 mg/kg, IP, once), two weeks later, CCl4(3ml/kg/week), subcutaneously, for 6 weeks) induced HCC in rats. Sixteen male Wistar rats were divided into 2 groups, control, and HCC.&#x0D; Results: DENA plus CCl4 elevated serum alpha-fetoprotein, liver enzyme activity and depressed superoxide dismutase, reduced glutathione (GSH), elevated malondialdehyde. Also, depressed caspase-3, elevated collagen, polysaccharide accumulation, proliferating cell nuclear antigen in liver tissues, and depressed BAX / Bcl-2 ratio. &#x0D; Conclusion: Decreased BAX/Bcl-2 ratio, elevated collagen deposition, polysaccharide accumulation, nuclear proliferation, and tissue oxidative stress help in the early diagnosis of liver cancer.

Multi-omic characterization of genome-wide abnormal DNA methylation reveals diagnostic and prognostic markers for esophageal squamous-cell carcinoma

This study investigates aberrant DNA methylations as potential diagnosis and prognosis markers for esophageal squamous-cell carcinoma (ESCC), which if diagnosed at advanced stages has <30% five-year survival rate. Comparing genome-wide methylation sites of 91 ESCC and matched adjacent normal tissues, we identified 35,577 differentially methylated CpG sites (DMCs) and characterized their distribution patterns. Integrating whole-genome DNA and RNA-sequencing data of the same samples, we found multiple dysregulated transcription factors and ESCC-specific genomic correlates of identified DMCs. Using featured DMCs, we developed a 12-marker diagnostic panel with high accuracy in our dataset and the TCGA ESCC dataset, and a 4-marker prognostic panel distinguishing high-risk patients. In-vitro experiments validated the functions of 4 marker host genes. Together these results provide additional evidence for the important roles of aberrant DNA methylations in ESCC development and progression. Our DMC-based diagnostic and prognostic panels have potential values for clinical care of ESCC, laying foundations for developing targeted methylation assays for future non-invasive cancer detection methods.

Cathepsin F and Fibulin-1 as novel diagnostic biomarkers for brain metastasis of non-small cell lung cancer

BackgroundThe lack of non-invasive methods for detection of early micro-metastasis is a major cause of the poor prognosis of non-small cell lung cancer (NSCLC) brain metastasis (BM) patients. Herein, we aimed to identify circulating biomarkers based on proteomics for the early diagnosis and monitoring of patients with NSCLC BM.MethodsUpregulated proteins were detected by secretory proteomics in the animal-derived high brain metastatic lung cancer cell line. A well-designed study composed of three independent cohorts was then performed to verify these blood-based protein biomarkers: the serum discovery and verification cohorts (n = 80; n = 459), and the tissue verification cohort (n = 76). Logistic regression was used to develop a diagnostic biomarker panel. Model validation cohort (n = 160) was used to verify the stability of the constructed predictive model. Changes in serum Cathepsin F (CTSF) levels of patients were tracked to monitor the treatment response. Progression-free survival (PFS) and overall survival (OS) were analysed to assess their prognostic relevance.ResultsCTSF and Fibulin-1 (FBLN1) levels were specifically upregulated in sera and tissues of patients with NSCLC BM compared with NSCLC without BM and primary brain tumour. The combined diagnostic performance of CTSF and FBLN1 was superior to their individual ones. CTSF serum changes were found to reflect the therapeutic response of patients with NSCLC BM and the trends of progression were detected earlier than the magnetic resonance imaging changes. Elevated expression of CTSF in NSCLC BM tissues was associated with poor PFS, and was found to be an independent prognostic factor.ConclusionsWe report a novel blood-based biomarker panel for early diagnosis, monitoring of therapeutic response, and prognostic evaluation of patients with NSCLC BM.

Machine Learning-Based Identification of Colon Cancer Candidate Diagnostics Genes.

Simple SummaryWe developed a predictive approach using different machine learning methods to identify a number of genes that can potentially serve as novel diagnostic colon cancer biomarkers.Background: Colorectal cancer (CRC) is the third leading cause of cancer-related death and the fourth most commonly diagnosed cancer worldwide. Due to a lack of diagnostic biomarkers and understanding of the underlying molecular mechanisms, CRC’s mortality rate continues to grow. CRC occurrence and progression are dynamic processes. The expression levels of specific molecules vary at various stages of CRC, rendering its early detection and diagnosis challenging and the need for identifying accurate and meaningful CRC biomarkers more pressing. The advances in high-throughput sequencing technologies have been used to explore novel gene expression, targeted treatments, and colon cancer pathogenesis. Such approaches are routinely being applied and result in large datasets whose analysis is increasingly becoming dependent on machine learning (ML) algorithms that have been demonstrated to be computationally efficient platforms for the identification of variables across such high-dimensional datasets. Methods: We developed a novel ML-based experimental design to study CRC gene associations. Six different machine learning methods were employed as classifiers to identify genes that can be used as diagnostics for CRC using gene expression and clinical datasets. The accuracy, sensitivity, specificity, F1 score, and area under receiver operating characteristic (AUROC) curve were derived to explore the differentially expressed genes (DEGs) for CRC diagnosis. Gene ontology enrichment analyses of these DEGs were performed and predicted gene signatures were linked with miRNAs. Results: We evaluated six machine learning classification methods (Adaboost, ExtraTrees, logistic regression, naïve Bayes classifier, random forest, and XGBoost) across different combinations of training and test datasets over GEO datasets. The accuracy and the AUROC of each combination of training and test data with different algorithms were used as comparison metrics. Random forest (RF) models consistently performed better than other models. In total, 34 genes were identified and used for pathway and gene set enrichment analysis. Further mapping of the 34 genes with miRNA identified interesting miRNA hubs genes. Conclusions: We identified 34 genes with high accuracy that can be used as a diagnostics panel for CRC.

Does pleural fluid cell block is an alternative to Thoracoscopy guided diagnostic techniques in malignant pleural effusion? Study of 500 cases in tertiary care setting in India

Background: Malignant pleural effusion missed routinely because of less diagnostic yield of conventional fluid cytology like fluid cytology. Fluid cell block is underutilsed techniques routinely and less utilized in diagnostic panel due to lack of expertise in filed of cytopathology. Materials and methods: Prospective multicentric study conducted during Jan 2014 to June 2020 in Venkatesh chest hospital, and Pulmonary Medicine, MIMSR medical college Latur, to find out diagnostic yield of conventional pleural fluid cytology&amp; pleural fluid ‘cell block’ in malignant pleural effusion and compare yield of pleural fluid cell block with conventional cytology technique. The study included 500 cases of unexplained, exudative pleural effusion with ADA&lt;30/IU/liter and pleural fluid cytology is either positive for malignant cell with or without cell type differentiation, or cytology suspicious for malignant cell. All cases were subjected to cell block preparation. Statistical analysis was done by using chi-test. Observation and analysis: In study of 500 cases, mean age of group was 68±9.5 years and adenocarcinoma were predominant malignancy in 79% cases, mesothelioma in 6% cases, squamous cell carcinoma in 7% cases &amp; 8% cases were having primary tumor outside the thoracic cavity. In study cases pleural fluid cytology was positive in 42% cases (210/500), and pleural fluid cell block was positive in 96% cases (480/200) in detecting malignant pleural effusion (p&lt;0.0001), Remaining six and two cases were diagnosed by using image guided and thoracoscopy guided pleural biopsies respectively. IHC was done in all pleural fluid cell block preparation for calretinin, cytokeratin and EGFR. Conclusion: Pleural fluid cell block is sensitive, superior, cost effective and specific diagnostic method over conventional pleural fluid cytology. 'Cell block' specimens are enough for primary diagnosis and IHC analysis necessary for cell typing. It will decrease need for more invasive and costlier diagnostic methods like thoracoscopy and image guided pleural biopsies. We recommend cell block for every exudative pleural fluid sample with ADA&lt;30 IU/liter.

Analysis of Gene Expression Omnibus high-throughput sequencing data for the determination of microribonucleic acids in the blood plasma of patients with glioblastomas

Purpose of the study. This work is devoted to the study of blood plasma miRNA patterns in blood plasma using high-throughput sequencing of the Omnibus Gene Expression base and the search for candidate miRNA molecules for the development of a minimally invasive diagnostic panel.Materials and methods. Basing on the open dataset of Omnibus Expression of the NCBI GSE150956 Gene, groups of samples with glioblastoma and conventionally healthy donors were formed. For each sample, information on the levels of miRNA expression was extracted. Determination of significant miRNAs using machine learning algorithms of the R 4.0.4 project. For significant miRNAs, target genes have been performed, an analysis of the improvement of functional characteristics and interactome analysis of target genes of miRNA were performed.Results. The study analyzed the data of 131 samples, where 35 samples with glioblastoma and 96 samples of the conditionally healthy group. Differential expression data were obtained for 945 miRNA. Two panels were obtained using machine learning methods, common miRNA – hsa-miR 3180, hsa-miR 3180-3p, hsa-miR 6782-5p, hsa-miR 182-5p, hsa-miR 133b and hsa-miR 670-3p. For significant miRNAs, information was obtained on experimentally confirmed target genes, a gene ontology demonstrating their participation in enzyme binding, participation in the regulation of primary cellular metabolic processes, and the development of glioblastomas and cancer in general.Conclusion. As a result of layer-by-layer filtering and application of machine learning algorithms, significant miRNAs were identified that are candidates for a diagnostic panel of a minimally invasive method of high-grade glial tumors.

Cancer MiRNA biomarker classification based on Improved Generative Adversarial Network optimized with Mayfly Optimization Algorithm

Nowadays, cancer diagnosis becomes a paradigm shift by incorporating molecular biomarkers as part of a routine diagnostic panel. Ranges of molecular changes include DNA, RNA, micro RNA (miRNAs) and proteins. In recent years, deep learning based methods have been more inspired to health researcher’s regarding the performance of cancer diagnosis. The application of deep learning-based approach gradually becomes clearer in classification accuracy for a problem that separates data related to cancer survival. In this manuscript, an Improved Generative Adversarial Network optimized with Mayfly Optimization Algorithm is proposed to overcome the super class issues. Improved Generative Adversarial Network is the combination of deep convolutional generative adversarial network (DCG) and modified convolutional neural network (MCNN); hence it is called DCG-MCNN. Initially, the DCG is used to balance the dataset by creating more samples in the training dataset. Based on the training dataset, cancer miRNA biomarker classification is improved with the help of modified CNN diagnosis model. The proposed method is activated in python, moreover, its efficiency is analyzed with Cancer Genome Atlas dataset. Here, performance metrics, viz accuracy, sensitivity, specificity, precision, F-measure balanced error rate are calculated. The experimental results of the proposed method shows higher accuracy 99.26%, higher sensitivity 95.23%, higher specificity 92.56% compared with the existing methods, like Validation of miRNAs as breast cancer biomarkers with a machine learning approach (CMiRNA-BC-RF-SVM), Cancer miRNA biomarkers classification using a new representation algorithm and evolutionary deep learning (CMiRNA-BC-CNN) and multi-omics data using graph convolutional networks allowing patient classification and biomarker identification (CMiRNA-BC-GCNN).

Abstract P2-01-16: Targeted sequencing of plasma-derived vs. urinary cfDNA from patients with triple negative breast cancer

Abstract Background: In breast cancer, genetic profiling of circulating cell-free DNA (cfDNA) from blood plasma was shown to have good potential for clinical use and might help to find individual treatment options and to monitor metastatic relapse as well as intratumoral heterogeneity. Due to limited therapy options and high risk of acquired treatment resistance based on genetic alterations of the tumor, particularly, women diagnosed with the aggressive triple negative breast cancer (TNBC) subtype might benefit from serial mutation profiling during disease courses. Here, cfDNA derived from patients’ urine might offer promising opportunities for non-invasive monitoring at frequent intervals. In this pilot study, we investigated plasma-derived and matching urinary cfDNA samples obtained from 15 presurgical TNBC patients using a targeted sequencing approach to identify common breast cancer-related genetic alterations in both bodyfluids. Methods: Blood plasma and urine samples were processed within 4 hrs after collection. We successfully adapted the manufacturer’s instructions to yield increased concentrations of extracted plasma-derived and urinary cfDNA (QIAamp MinElute ccfDNA Kit, QIAGEN). After quality control of cfDNA fragments, matched samples were applied to library preparation and target enrichment with a commercially available diagnostic breast cancer specific 93-gene panel service (QIAGEN). Libraries were analyzed by paired-end sequencing on a NextSeq550 (Illumina Inc.) instrument followed by bioinformatical analysis with QIAGEN Biomedical Genomics Workbench and subsequent interpretation of variants using QCI Interpret Translational Software (QIAGEN). Results: In the cohort, 3339 somatic breast-cancer related genetic alterations (VAF ≥ 3%) were detected, whereof 1222 vs. 2117 variants were found in plasma-derived vs. urinary cfDNA, respectively. Further, 431 variants were found in cfDNA derived from both body fluids. Per sample, we found a median number of 66 variants in plasma vs. 110 variants in urinary cfDNA. Interestingly, a median number of 31 shared variants was found per matched plasma-derived and urinary cfDNA sample resulting in a 46% recovery rate of plasma-derived mutations in urinary cfDNA (and vice versa only a 16% recovery rate of urinary cfDNA variants in plasma) throughout the cohort. Interestingly, QCI analysis revealed that 48 of the mutations found in both body fluids were pathogenic, resulting in a median number of 4 pathogenic variants per sample. The recovery rate of pathogenic plasma-derived variants in urinary cfDNA was 63% and vice versa, 66% of pathogenic urinary variants could be detected in the matched plasma samples. Further, 33 of the shared mutations were likely pathogenic. Among the patients, the most frequently occurring pathogenic and likely pathogenic mutated genes were NF1, CHEK2, KMT2C and PTEN in both body fluids. Noteworthy, a pathogenic CHEK2 (T519M) variant was found in all 30 samples and two pathogenic NF1 variants (T467I + c.2325+3A&amp;gt;G) as well as a pathogenic PTEN variant (C136Y) were also very frequent in the cohort. Conclusion: Taken together, our results indicated that both plasma-derived and urinary cfDNA from TNBC patients could be analyzed in a sufficient manner using a targeted sequencing approach. Both body fluids appear to be valuable sources bearing complementary information regarding the genetic tumor profile, which might be relevant for disease monitoring and individual treatment decisions. Further analyses of a larger patient cohort using adequate bioinformatics algorithms are required to find associations between somatic variants in both cfDNA sources. Key words: cfDNA, breast cancer, targeted sequencing, somatic variants, mutation, urine, plasma Citation Format: Henrike Herzog, Bahriye Aktas, Ivonne Nel. Targeted sequencing of plasma-derived vs. urinary cfDNA from patients with triple negative breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-01-16.

Characterization of the Metabolites and Construction of a Novel Diagnostic Panel in Calcium Oxalate Urolithiasis by Electrospray Ionization – Mass Spectrometry (ESI-MS) Metabolomics

The metabolic differences between patients with calcium oxalate (CaOx) urolithiasis and the healthy population remain unclear. Currently there is no diagnostic panel for CaOx urolithiasis. This study aimed to identify the full-scale metabolic changes in the urine of CaOx urolithiasis patients and to establish a novel diagnostic panel as a noninvasive early prevention method. 240 urine samples were collected from 137 patients diagnosed with CaOx urolithiasis and 103 healthy controls. Electrospray ionization – mass spectrometry (ESI-MS) metabolomics and univariate, multivariate, and bioinformatic analyses were combined to identify the differential metabolites and related metabolic pathways between the groups. Integrated machine learning methods were adopted to discover potential biomarkers and establish a diagnostic panel. 79 differential metabolites were identified in the urine of patients compared with healthy controls. Pathways closely associated with these abnormal metabolites included metabolic pathways, amino acid biosynthesis, microbial metabolism in diverse environments, and protein digestion and absorption. Five potential metabolites, n-acetylputrescine, n-alpha-acetyl-L-arginine, ethyl 3-hydroxybutyrate, s-adenosylmethionine, and l-pyroglutamic acid, were selected as biomarkers by machine learning models, which included logistic regression (LR), random forest (RF), and support vector machine (SVM) models. The panel showed an excellent classification to differentiate patients from healthy controls with areas under the curve (AUC) of 0.998 in the training cohort and 0.993 in the validation cohort. The results revealed differential metabolites between the two groups. Furthermore, a panel of potential biomarkers was established for the first time and showed promising diagnostic value.

Comprehensive analysis of peripheral blood non-coding RNAs identifies a diagnostic panel for fungal infection after transplantation

The occurrence of fungal infection seriously affects the survival and life quality of transplanted patients. The accurate diagnosis is of particular importance in the early stage of infection. To develop a novel diagnostic method for this kind of patient, we established a post-transplant immunosuppressed mice model with fungus inoculation and collected their peripheral blood at specific time points after infection. After screening by microarray, differentially expressed miRNAs and lncRNAs were selected and homologously analyzed with those of human beings from the gene database. These miRNAs and lncRNAs candidates were validated by qRT-PCR in peripheral blood samples from transplanted patients. We found that, compared with normal transplanted patients, the levels of miR-215 and miR-let-7 c were up-regulated in the plasma of patients with fungal infection (P < 0.01), while levels of miR-154, miR-193a, NR_027669.1, and NR_036506.1 were down-regulated in their peripheral blood mononuclear cells (P < 0.01). Principal component analysis shows that the expression pattern of the above RNAs was different between the two groups. A 6-noncoding-RNA detection panel was established by the support vector machine analysis, whose area under the ROC curve was 0.927. The accuracy, precision, sensitivity, and specificity of this model were 0.928, 0.919, 0.944, and 0.910, respectively. Though our detection panel has excellent diagnostic efficacy, its clinical application value still needs to be further confirmed by multi-center prospective clinical trials.

Comprehensive Laboratory Diagnostic Workup for Patients with Suspected Intraocular Lymphoma including Flow Cytometry, Molecular Genetics and Cytopathology.

Background: Intraocular lymphoma (IOL) presents a real challenge in daily diagnostics. Cyto- and/or histopathology of vitreous body represent the diagnostic cornerstones. Yet, false negative results remain common. Therefore, we analyzed the diagnostic significance of flow cytometry (FC) within the workup algorithm of IOL and compared its sensitivity with the results obtained from routine cytopathology and molecular genetics; Methods: Seven patients undergoing vitrectomy due to suspected IOL were investigated by FC and parallel cytopathology and, if available, digital droplet PCR (ddPCR) for MYD88 L265P; Results: Four out of seven patients were finally diagnosed with IOL. Among the IOL patients, cytopathology confirmed the presence of lymphoma cells in only two cases. In contrast, FC was positive for IOL in all four cases, and FC additionally confirmed the lack of IOL in the remaining patients. In IOL patients diagnosed by FC and with available ddPCR, the diagnosis of IOL was confirmed by the presence of the MYD88 L265P mutation in all three patients; Conclusions: The combination with FC was superior to cytopathology alone in the diagnostic work-up of IOL, and it showed an excellent correlation with ddPCR results. A comprehensive diagnostic panel consisting of cytopathology, FC and molecular genetics should be considered for the work-up of suspected IOL.

Evaluation of the Potential Impact of a Multiplex Rapid Diagnostic Panel in Critically Ill Patients With Hospital-Acquired Pneumonia.

BackgroundRapid diagnostic tools have emerged as valuable assets assisting clinicians in decision-making regarding patient management in the hospital setting. Our study sought to identify the potential impact of the BioFire® FilmArray® Pneumonia Panel (FP Panel) (BioFire Diagnostics, Salt Lake City, UT, USA) in patients with hospital-acquired pneumonia (HAP).MethodsRespiratory samples obtained by bronchoalveolar lavage (BAL) or tracheal aspiration (TA) from ICU patients with a diagnosis of HAP were tested by the FP panel in addition to routine bacterial cultures. In addition, the electronic health records of these patients were reviewed to determine what potential changes in antimicrobial therapy could have been implemented if the panel results were known to the treatment team in real-time. A cost analysis was also performed incorporating the cost of the pneumonia panel and the savings associated with the potential decrease of antibiotic use and avoidance of the rapid viral diagnostic panel. ResultsFifty-six patients met the study criteria. The FP panel results could have prompted a change in therapy in 36 (64.3%) patients, with an anticipated mean reduction in time to optimized therapy of approximately 51 hours. In addition, the panel identified three cases where antimicrobials should have been altered because patients were not receiving empiric therapy with activity against the causative pathogen and 34 opportunities for antibiotic de-escalation. The cost analysis calculated an additional cost of $10 per patient associated with using the FP panel. ConclusionsThe FP panel could have prompted a change in therapy in about two-thirds of patients studied. Its potential benefits include a more rapid time to optimized therapy, reduced exposure to and cost of broad-spectrum antimicrobials, and reduced cost of other rapid diagnostic tests.

Genomic Hatchery Introgression in Brown Trout (Salmo trutta L.): Development of a Diagnostic SNP Panel for Monitoring the Impacted Mediterranean Rivers

Brown trout (Salmo trutta L.) populations have been restocked during recent decades to satisfy angling demand and counterbalance the decline of wild populations. Millions of fertile brown trout individuals were released into Mediterranean and Atlantic rivers from hatcheries with homogeneous central European stocks. Consequently, many native gene pools have become endangered by introgressive hybridization with those hatchery stocks. Different genetic tools have been used to identify and evaluate the degree of introgression starting from pure native and restocking reference populations (e.g., LDH-C* locus, microsatellites). However, due to the high genetic structuring of brown trout, the definition of the "native pool" is hard to achieve. Additionally, although the LDH-C* locus is useful for determining the introgression degree at the population level, its consistency at individual level is far from being accurate, especially after several generations were since releases. Accordingly, the development of a more powerful and cost-effective tool is essential for an appropriate monitoring to recover brown-trout-native gene pools. Here, we used the 2b restriction site-associated DNA sequencing (2b-RADseq) and Stacks 2 with a reference genome to identify single-nucleotide polymorphisms (SNPs) diagnostic for hatchery-native fish discrimination in the Atlantic and Mediterranean drainages of the Iberian Peninsula. A final set of 20 SNPs was validated in a MassARRAY® System genotyping by contrasting data with the whole SNP dataset using samples with different degree of introgression from those previously recorded. Heterogeneous introgression impact was confirmed among and within river basins, and was the highest in the Mediterranean Slope. The SNP tool reported here should be assessed in a broader sample scenario in Southern Europe considering its potential for monitoring recovery plans.

Metabolic (Dysfunction)-Associated Fatty Liver Disease in Chinese Patients with Type 2 Diabetes from a Subcenter of the National Metabolic Management Center.

Background Few studies have investigated the epidemiological metabolic (dysfunction) associated with fatty liver disease (MAFLD) in China, especially among those with type 2 diabetes. Methods We recruited 3553 patients aged 18-75 years with type 2 diabetes who underwent abdominal ultrasound and serum biochemical analyses. Patient information including demographic and anthropometric parameters was also collected. Results Overall, 63.2% of type 2 diabetic patients had MAFLD. Among the MAFLD patients, the proportions of lean, nonobese, and obese MAFLD were 23.1%, 75.7%, and 24.3%, respectively, and the percentage of previously undiagnosed MAFLD was 42.2%. MAFLD patients were younger, had shorter diabetic duration, and had greater BMI, aspartate aminotransferase (AST), alanine aminotransferase (ALT), fasting insulin, postprandial insulin, total cholesterol, and insulin resistance levels (HOMA-IR and TyG index). Liver fibrosis diagnostic panels revealed that the proportions of elevated AST (≥40 U/L) and ALT (≥40 U/L) were 7.3% and 18.5%, respectively. The distributions of AST-to-platelet ratio index (APRI), fibrosis-4 (FIB-4) index, and nonalcoholic fatty liver disease fibrosis score (NFS) per stage were as follows: APRI—low 55.1%, indeterminate 35.3%, and high 9.5%; FIB-4—low 48.2%, indeterminate 45.3%, and high 6.5%; and NFS—low 15.0%, indeterminate 70.0%, and high 13.0%. Conclusions MAFLD is a very common condition and generally had greater frequency of metabolic characteristics among type 2 diabetics in China. Many MAFLD patients were in the “indeterminate” or “high” stage when APRI, FIB-4, and NFS were assessed. Assessment of MAFLD should be included in the management of type 2 diabetes.