Marker For Diagnosis Research Articles

Exploring biomarkers for diagnosing and predicting organ dysfunction in patients with perioperative sepsis: a preliminary investigation

ObjectiveEarly diagnosis and prediction of organ dysfunction are critical for intervening and improving the outcomes of septic patients. The study aimed to find novel diagnostic and predictive biomarkers of organ dysfunction for perioperative septic patients.MethodThis is a prospective, controlled, preliminary, and single-center study of emergency surgery patients. Mass spectrometry, Gene Ontology (GO) functional analysis, and the protein-protein interaction (PPI) network were performed to identify the differentially expressed proteins (DEPs) from sepsis patients, which were selected for further verification via enzyme-linked immunosorbent assay (ELISA). Logistic regression analysis was used to estimate the relative correlation of selected serum protein levels and clinical outcomes of septic patients. Calibration curves were plotted to assess the calibration of the models.ResultsFive randomized serum samples per group were analyzed via mass spectrometry, and 146 DEPs were identified. GO functional analysis and the PPI network were performed to evaluate the molecular mechanisms of the DEPs. Six DEPs were selected for further verification via ELISA. Cathepsin B (CatB), vascular cell adhesion protein 1 (VCAM-1), neutrophil gelatinase-associated lipocalin (NGAL), protein S100-A9, prosaposin, and thrombospondin-1 levels were significantly increased in the patients with sepsis compared with those of the controls (p < 0.001). Logistic regression analysis showed that CatB, S100-A9, VCAM-1, prosaposin, and NGAL could be used for preoperative diagnosis and postoperative prediction of organ dysfunction. CatB and S100-A9 were possible predictive factors for preoperative diagnosis of renal failure in septic patients. Internal validation was assessed using the bootstrapping validation. The preoperative diagnosis of renal failure model displayed good discrimination with a C-index of 0.898 (95% confidence interval 0.843–0.954) and good calibration.ConclusionSerum CatB, S100-A9, VCAM-1, prosaposin, and NGAL may be novel markers for preoperative diagnosis and postoperative prediction of organ dysfunction. Specifically, S100-A9 and CatB were indicators of preoperative renal dysfunction in septic patients. Combining these two biomarkers may improve the accuracy of predicting preoperative septic renal dysfunction.Trial registrationThe study was registered at the Chinese Clinical Trials Registry (ChiCTR2200060418) on June 1, 2022.

Investigating the molecular mechanisms underlying the co-occurrence of Parkinson’s disease and inflammatory bowel disease through the integration of multiple datasets

Parkinson’s disease (PD) and inflammatory bowel disease (IBD) are chronic diseases affecting the central nervous system and gastrointestinal tract, respectively. Recent research suggests a bidirectional relationship between neurodegeneration in PD and intestinal inflammation in IBD. PD patients may experience gastrointestinal dysfunction over a decade before motor symptom onset, and IBD may increase the risk of developing PD. Despite the “gut-brain axis” concept, the underlying pathophysiological mechanisms of this potential association remain unclear. This study aimed to investigate the biological mechanisms of differentially expressed genes in PD and IBD using bioinformatics tools, providing novel insights into the co-diagnosis and treatment of these diseases. We constructed a gene marker for disease diagnosis and identified five important genes (BTK, NCF2, CRH, FCGR3A and SERPINA3). Through nomogram and decision tree analyses, we found that both the IBD and PD required only the expression levels of BTK and NCF2 for accurate discrimination. Additionally, small molecule drugs RO-90-7501 and MST-312 may be useful for the treatment of both IBD and PD. These findings offer new perspectives on the co-diagnosis and treatment of PD and IBD, and suggest that targeting BTK may be a promising therapeutic strategy for both diseases.

A novel dual-recognition fluorescent biosensor for sialyl-Lewisx sensitive detection.

Sialyl-Lewisx (SLex) is a tetrasugar, which plays an important role in initial inflammation and cancer cell metastasis, and can be used as a marker for cancer diagnosis and prognosis or a therapeutic target. Detecting SLex from complex biological media remains a significant challenge. Herein, a single-stranded DNA aptamer of SLex was screened based on the double-stranded DNA library-modified magnetic bead (MB)-SELEX technology. After 14 rounds of screening, 12,639 sequences were obtained and divided into nine families. Three representative sequences were selected based on the number of sequence repeats and Gibbs binding free energy, and the aptamer SLex-Apt2 with 80 nt length (Kd = 23.01nM) had the best affinity and relatively high specificity for targeting SLex. Then, a novel dual-recognition fluorescent biosensor for SLex-sensitive detection based on aptamer SLex-Apt2 bio-dots and 3-aminobenzoboric acid-modified MB was developed. This method can detect SLex as low as 32 μM and has a good linear response in the range 100 μM to 2 mM. It has the advantages of low preparation cost, good targeting, and avoiding the occurrence of false-positive and false-negative detection results, which makes the biosensor more valuable in biological detection and clinical diagnosis.

Urinary Extracellular Vesicles as a Readily Available Biomarker Source: A Simplified Stratification Method.

Urine, a common source of biological markers in biomedical research and clinical diagnosis, has recently generated a new wave of interest. It has recently become a focus of study due to the presence of its content of extracellular vesicles (EVs). These uEVs have been found to reflect physiological and pathological conditions in kidney, urothelial, and prostate tissue and can illustrate further molecular processes, leading to a rapid expansion of research in this field In this work, we present the advantages of an immunoaffinity-based method for uEVs' isolation with respect to the gold standard purification approach performed by differential ultracentrifugation [in terms of purity and antigen presence. The immunoaffinity method was made feasible by combining specific antibodies with a functionalized polymethacrylate polymer. Flow cytometry indicated a significant fluorescence shift, validating the presence of the markers (CD9, CD63, CD81) and confirming the effectiveness of the isolation method. Microscopy evaluations have shown that the morphology of the vesicles remained intact and corresponded to the expected shapes and dimensions of uEVs. The described protocol is inexpensive, fast, easy to process, has good reproducibility, and can be applied to further biological samples.

The D-HCM score, a new diagnostic tool for distinguishing hypertrophic cardiomyopathy from hypertensive cardiopathy.

The diagnosis of hypertrophic cardiomyopathy (HCM) with moderate hypertrophy is challenging. Hypertensive heart disease (HHD) is the most common differential diagnosis that mimics the LVH of HCM. The aim of this study was to compare the QRS duration in HCM and HHD to create a novel diagnostic tool to identify primary HCM. We conducted an international retrospective multicentre study enrolling patients with true HCM and HHD. A total of 547 individuals with HCM and 139 with HHD were included. The median QRS duration was significantly shorter in HCM than in HHD (88ms [80-94] vs. 98ms [88-108]; P<0.01). Multivariable logistic regression identified for the novel diagnostic HCM (D-HCM) score: absence of antihypertensive drugs (+2); family history of unexplained sudden death (+2); QRS duration [<95ms]=+1; maximum wall thickness (mm) [≥17]=+1. A cumulative QRS-HCM score ≥2 supports the diagnostic certainty of true HCM with a sensitivity of 79%, specificity of 99%, negative predictive value (NPV) of 55%, and positive predictive value (PPV) of 99%. The QRS duration in patient with HCM is significantly shorter compared with patients with HHD-related LVH. QRS duration can be used as a diagnosis marker to distinguish between HCM and HHD. The D-HCM score is a novel, simple, and accurate diagnosis tool for HCM patients with mild to moderate phenotypes.

In Pursuit of Novel Markers: Unraveling the Potential of miR-106, CEA and CA 19-9 in Gastric Adenocarcinoma Diagnosis and Staging.

Gastric cancer stands as the fourth leading cause of cancer-related deaths globally, primarily comprising adenocarcinomas, categorized by anatomic location and histologic type. Often diagnosed at advanced stages, gastric cancer prognosis remains poor. To address the critical need for accurate tumoral markers for gastric cancer diagnosis, we conducted a study to assess classical markers like CEA and CA-19-9 alongside the novel marker miR-106. Our investigation revealed distinct dynamics of these markers compared to non-cancerous groups, although no disparities were observed across different disease stages. Univariable and multivariable logistic regression analyses demonstrated that elevated levels of miR-106, CEA and CA 19-9 were predictive of a positive histopathological exam, with the respective odds ratios of 12.032 (95% CI: 1.948-74.305), 30 (95% CI: 3.141-286.576), and 55.866 (95% CI: 4.512-691.687). Subsequently, we utilized predicted probabilities from regression models to construct receiver operating characteristic (ROC) curves, identifying CA 19-9 as the optimal predictor for gastric adenocarcinoma diagnosis when considering age and gender, with an area under the curve (AUC) of 0.936 (p < 0.001). Hence, classical markers exhibit superior performance compared to the novel marker miR-106 in predicting gastric adenocarcinoma.

Functional analysis of a panel of molecular markers for diagnosis of systemic lupus erythematosus in rats.

Systemic lupus erythematosus (SLE) is a diverse autoimmune disease that arises from a combination of complex genetic factors and environmental influences. While circRNAs and miRNAs have recently been identified as promising biomarkers for disease diagnosis, their specific expression patterns, and clinical implications in SLE are not yet fully understood. The aim of the present study was to determine the role of a panel of noncoding-RNAs specifically circRNAs (circ-TubD1, circ-CDC27, and circ-Med14), along with miRNA (rno-miR-146a-5p) and mRNA (TRAF6), as novel minimally invasive diagnostic biomarkers for experimentally induced SLE. Additionally, the study involved an insilico bioinformatics analysis to explore potential pathways involved in the pathogenesis of SLE, aiming to enhance our understanding of the disease, enable early diagnosis, and facilitate improved treatment strategies. SLE was induced in rats using single IP injection of incomplete Freund's adjuvant (IFA). The Induction was confirmed by assessing the ANA and anti-ds DNA levels using ELSA technique. qPCR analysis was conducted to assess the expression of selected RNAs in sera collected from a group of 10 rats with induced SLE and a control group of 10 rats. In addition, bioinformatics and functional analysis were used to construct a circRNA-miRNA-mRNA network and to determine the potential function of these differentially expressed circRNAs. SLE rats demonstrated significantly higher expression levels of circ-CDC27, circ-Med14, and rno-miR-146a-5p as well as TRAF6, with lower expression level of circ-TubD1 in sera of SLE rats relative to controls. ROC curve analysis indicated that all the selected non-coding RNAs could serve as potential early diagnostic markers for SLE. In addition, the expression level of circ-TubD1 was negatively correlated with rno-miR-146a-5p, however, rno-miR-146a-5p was positively correlated with TRAF6. Bioinformatic analysis revealed the incorporation of the circRNAs targeted genes in various immune system and neurodegeneration pathways. Therefore, circRNAs; circ-TubD1, circ-CDC27, and circ-Med14, in addition to the miRNA (rno-miR-146a-5p) and mRNA (TRAF6) may be involved in the development of SLE and may have promising roles for future diagnosis and targeted therapy.

Fetuin-A: a relevant novel serum biomarker for non-invasive diagnosis of metabolic dysfunction-associated steatotic liver disease (MASLD): a retrospective case-control study

ObjectivesTo determine how fetuin-A contributes to diagnosing and assessing MASLD severity.MethodsFifty MASLD patients and fifty healthy control participants were involved in this retrospective case-control research. Abdominal ultrasonography, fibroscan with controlled attenuated parameter scan (CAP scan), laboratory investigation (including fetuin-A assessment), clinical examination, and history-taking were performed on every case.ResultsFetuin-A level was considerably higher in the Cases group (1154.85 ± 629.89) than in the Control group (505.29 ± 150.4) (p < 0.001). Fetuin-A had significant validity in the prediction of MASLD at a cut-off > 702.5 with 82% sensitivity, 90% specificity, and 86% overall accuracy.ConclusionOne possible marker for MASLD diagnosis could be fetuin-A. Furthermore, a substantial association between such marker and the severity of the disease as it revealed a significant correlation with ultrasound grading and fibroscan with controlled attenuated parameters.Trial registration1- Pan African Clinical Trial Registry.Unique Identifying number/registration ID: PACTR202309644280965.URL: https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=26860.Registration Approval date: 21/09/2023.2- ClinicalTrials.gov.Unique Identifying number /registration ID: NCT06097039.URL: https://clinicaltrials.gov/study/NCT06097039?cond=NCT06097039&rank=1.Registration Approval date: 25/10/2023.

A transformer-based weakly supervised computational pathology method for clinical-grade diagnosis and molecular marker discovery of gliomas

A transformer-based weakly supervised computational pathology method for clinical-grade diagnosis and molecular marker discovery of gliomas

Long noncoding RNA KCNMA1-AS2 regulates the function of colorectal cancer cells and sponges miR-1227-5p

BackgroundMany long noncoding RNAs (lncRNAs) with altered expression significantly influence colorectal cancer (CRC) progression and behavior. The functions of many lncRNAs in CRC are not clear yet. This study aimed to discover novel lncRNA entities and comprehensively examine and validate their roles and underlying molecular mechanisms in CRC.MethodsTissue samples, both tumourous and non-tumourous, from three CRC patients were submitted for sequencing. Following expression validation in samples from ten patients and four CRC cell lines. The lncRNA KCNMA1-AS2 was synthesized by In-vitro transcription RNA synthesis and the lncRNA was directly transfected into CRC cell lines to overexpress. Functional assays including MTT proliferation assay, Annexin-V/propidium iodide apoptosis assay, wound healing migration assay and cell cycle assays were performed to evaluate the effect of overexpression of KCNMA1-AS2. Furthermore, the binding of KCNMA1-AS2 to miR-1227-5p was confirmed using dual luciferase reporter assays and qPCR analyses. Subsequent bioinformatics analyses identified 58 potential downstream targets of miR-1227-5p across three databases.ResultsIn this study, we identified the lncRNA KCNMA1-AS2, the expression of which was down-regulated consistently in cancer tissues and CRC cell lines compared to non-cancerous tissues. The overexpression of lncRNA KCNMA1-AS2 led to significant reduction in CRC cell proliferation and migration, increase in cell apoptosis, and more cells arrested in S phase. Additionally, the interaction between KCNMA1-AS2 and miR-1227-5p was confirmed through dual luciferase reporter assay and qPCR analysis. It is also putatively predicted that MTHFR and ST8SIA2 may be linked to CRC based on bioinformatics analyses.ConclusionsLncRNA KCNMA1-AS2 exhibited distinct gene expression patterns in both CRC tissue and cell lines, impacting various cellular functions while also acting as a sponge for miR-1227-5p.The findings spotlight lncRNA KCNMA1-AS2 as a potential marker for diagnosis and treatment of CRC.

A Novel Fluorescent Probe for Nitroreductase Detection and Imaging of cancer Cells under Hypoxia Conditions.

Nitroreductase (NTR) is to be pivotal in the biodegradation of nitroaromatics. NTR is produced in tumor tissues under hypoxic conditions, which is one of the markers for early tumor diagnosis. In this study, a novel probe FD-NTR was developed for NTR detection. Probe FD-NTR can exhibit a specific reaction with NTR in the presence of NADH. The probe displayed satisfactory selectivity and sensitivity towards NTR with a calculated detection limit of 12 ng/mL. Under the conditions of low cytotoxic hypoxia, the FD-NTR probe has shown successful application in imaging both MCF-7 cells and tumor tissues, which indicated that the FD-NTR probe holds promising application prospects for detecting NTR in tumors.

γδ+ T-Cells Is a Useful Biomarker for the Differential Diagnosis between Celiac Disease and Non-Celiac Gluten Sensitivity in Patients under Gluten Free Diet.

The differential diagnosis between patients with celiac disease (CD) and non-celiac gluten sensitivity (NCGS) is difficult when a gluten-free diet (GFD) has been initiated before the diagnostic work-up. Isolated increases in TCRγδ+ and celiac lymphogram (increased TCRγδ+ plus decreased CD3-) may enable differential diagnosis in this challenging clinical setting. This study evaluated: (1) the accuracy of %TCRγδ+ and celiac lymphogram for diagnosing CD before and after GFD and for differentiation with NCGS; (2) TCRγδ+ kinetics at baseline and after starting GFD in both CD and NCGS. The inclusion criteria were patients with CD (n = 104), NCGS (n = 37), and healthy volunteers (n = 18). An intestinal biopsy for intraepithelial lymphogram by flow cytometry was performed at baseline and after GFD. The optimal cutoff for CD diagnostic accuracy was established by maximizing the Youden index and via logistic regression. %TCRγδ+ showed better diagnostic accuracy than celiac lymphogram for identifying CD before and after GFD initiation. With a cutoff > 13.31, the accuracy for diagnosing CD in patients under GFD was 0.88 [0.80-0.93], whereas the accuracy for diagnosing NCGS (%TCRγδ+ ≤ 13.31) was 0.84 [0.76-0.89]. The percentage of TCRγδ+ cells showed differential kinetics between CD (baseline 22.7% [IQR, 16.4-33.6] vs. after GFD 26.4% [IQR, 17.8-36.8]; p = 0.026) and NCGS (baseline 9.4% [IQR, 4.1-14.6] vs. after GFD 6.4% [IQR, 3.2-11]; p = 0.022). TCRγδ+ T cell assessment accurately diagnoses CD before and after a GFD. Increased TCRγδ+ was maintained in the long term after GFD in CD but not in NCGS. Altogether, this suggests the potential usefulness of this marker for the differential diagnosis of these two entities in patients on a GFD.

Unraveling shared molecular signatures and potential therapeutic targets linking psoriasis and acute myocardial infarction

Psoriasis, a chronic inflammatory skin disorder, is associated with comorbidities such as acute myocardial infarction (AMI). However, the molecular mechanisms connecting these conditions are unclear. In this study, we conducted bioinformatics analyses using gene expression datasets to identify differentially expressed genes and hub genes associated with both psoriasis and AMI. Our findings emphasize the involvement of immune-related pathways in the pathogenesis of both conditions. Furthermore, we investigated the expression levels of hub genes in AMI patients and myocardial infarction (MI) mice. ELISA measurements revealed significantly higher levels of CXCL8, IL1B, S100A9, and S100A12 in the serum of AMI patients compared to normal individuals. Immunohistochemical staining of heart tissue from MI mice showed a progressive increase in the expression of CXCL8 and IL-1B as MI advanced, while S100A9 exhibited high expression at day 3 post-MI. mRNA expression analysis validated these findings. Additionally, we explored the skin lesions of psoriasis patients and found significantly higher expression of CXCL8, IL-1B, S100A9, and S100A12 in the affected skin areas compared to unaffected regions. These results highlight the consistent upregulation of hub genes in both AMI and psoriasis patients, as well as in myocardial infarction mice, underscoring their potential as reliable markers for disease diagnosis. Moreover, molecular docking simulations revealed potential interactions between simvastatin and key target proteins, suggesting a potential therapeutic avenue. Overall, our study uncovers shared molecular signatures and potential therapeutic targets, providing a foundation for future investigations targeting common pathways in psoriasis and AMI.

Scaling-law mechanical marker for liver fibrosis diagnosis and drug screening through machine learning.

Studies of cell and tissue mechanics have shown that significant changes in cell and tissue mechanics during lesions and cancers are observed, which provides new mechanical markers for disease diagnosis based on machine learning. However, due to the lack of effective mechanic markers, only elastic modulus and iconographic features are currently used as markers, which greatly limits the application of cell and tissue mechanics in disease diagnosis. Here, we develop a liver pathological state classifier through a support vector machine method, based on high dimensional viscoelastic mechanical data. Accurate diagnosis and grading of hepatic fibrosis facilitates early detection and treatment and may provide an assessment tool for drug development. To this end, we used the viscoelastic parameters obtained from the analysis of creep responses of liver tissues by a self-similar hierarchical model and built a liver state classifier based on machine learning. Using this classifier, we implemented a fast classification of healthy, diseased, and mesenchymal stem cells (MSCs)-treated fibrotic live tissues, and our results showed that the classification accuracy of healthy and diseased livers can reach 0.99, and the classification accuracy of the three liver tissues mixed also reached 0.82. Finally, we provide screening methods for markers in the context of massive data as well as high-dimensional viscoelastic variables based on feature ablation for drug development and accurate grading of liver fibrosis. We propose a novel classifier that uses the dynamical mechanical variables as input markers, which can identify healthy, diseased, and post-treatment liver tissues.

Practical issues related to immunocytochemistry on cytological smears: Tips and recommendations.

Immunocytochemistry (ICC) is essential for enhancing diagnostic accuracy and identifying markers for diagnosis, prognosis and targeted therapies. While cell blocks (CBs) are preferred for standardization and optimized staining, cytological smears are an alternative when CBs are unavailable. However, the literature on ICC protocols for smears is sparse. This review addresses preparation, fixation and protocols for nuclear and cytoplasmic antibodies on smears, drawing from our laboratory's experience. We reviewed procedures for ICC on cytological smears using existing literature and practical insights from our laboratory. Commercially available antibodies were found to be reliable for ICC on smears if specimens are properly prepared and fixed. Protocols developed in our laboratory maintained antigenicity and provided clear staining results. Although ICC on CBs is the gold standard for standardization, cytological smears are a viable alternative when CBs are unavailable. Success in ICC on smears depends on proper preparation and fixation. This review offers practical protocols and insights to help laboratories optimize ICC on cytological smears. Further research and standardization are necessary to enhance reproducibility and reliability of ICC on smears. The practical information provided is based on personal experience in our laboratory.

Biomarkers of Early Diagnosis of Parkinson’s Disease

The World Health Organization (WHO) has stated that neurodegenerative diseases will be the biggest health problem in the future. Among neurodegenerative diseases, Parkinson’s disease is the second most common after Alzheimer’s disease. Parkinson’s disease (PD) is the most common pathology of a person with movement disorders. Among neurological diseases, it has a high social significance due to the negative effect on the quality of life (early loss of the ability to work, progressive disorders of motor and cognitive skills). With increasing life expectancy, the prevalence of Parkinson’s disease increases. Biological markers of early diagnosis of Parkinson’s disease are being actively studied. The review presents the most clinically significant biological markers for the preclinical diagnosis of Parkinson’s disease. Biomarkers of cerebrospinal fluid, serum biomarkers and biomarkers of other biological fluids in Parkinson's disease are discussed.

Structured neurological soft signs examination reveals motor coordination deficits in adults diagnosed with high-functioning autism

Neurological soft signs (NSS), discrete deficits in motor coordination and sensory integration, have shown promise as markers in autism diagnosis. While motor impairments, partly associated with core behavioral features, are frequently found in children with autism, there is limited evidence in adults. In this study, NSS were assessed in adults undergoing initial diagnosis of high-functioning autism (HFA), a subgroup difficult to diagnose due to social adaptation and psychiatric comorbidity. Adults with HFA (n = 34) and 1:1 sex-, age-, and intelligence-matched neurotypical controls were administered a structured NSS examination including motor, sensory, and visuospatial tasks. We showed that adults with HFA have significantly increased motor coordination deficits compared with controls. Using hierarchical cluster analysis within the HFA group, we also identified a subgroup that was particularly highly affected by NSS. This subgroup differed from the less affected by intelligence level, but not severity of autism behavioral features nor global psychological distress. It remains questionable whether motor impairment represents a genuinely autistic trait or is more a consequence of factors such as intelligence. Nevertheless, we conclude that examining NSS in terms of motor coordination may help diagnose adults with HFA and identify HFA individuals who might benefit from motor skills interventions.

Integrated Transcriptomic Landscape and Deep Learning Based Survival Prediction in Uterine Sarcomas.

The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the USs. Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), 3 adenosarcomas, 2 carcinosarcomas, and 1 uterine tumor resembling an ovarian sex-cord tumor (UTROSCT). ESS (including high-grade ESS and low-grade ESS) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A - PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uDEGs were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named MMN-MIL showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Decreased free D-aspartate levels in the blood serum of patients with schizophrenia.

Schizophrenia (SCZ) and autism spectrum disorder (ASD) are neurodevelopmental diseases characterized by different psychopathological manifestations and divergent clinical trajectories. Various alterations at glutamatergic synapses have been reported in both disorders, including abnormal NMDA and metabotropic receptor signaling. We conducted a bicentric study to assess the blood serum levels of NMDA receptors-related glutamatergic amino acids and their precursors, including L-glutamate, L-glutamine, D-aspartate, L-aspartate, L-asparagine, D-serine, L-serine and glycine, in ASD, SCZ patients and their respective control subjects. Specifically, the SCZ patients were subdivided into treatment-resistant and non-treatment-resistant SCZ patients, based on their responsivity to conventional antipsychotics. D-serine and D-aspartate serum reductions were found in SCZ patients compared to controls. Conversely, no significant differences between cases and controls were found in amino acid concentrations in the two ASD cohorts analyzed. This result further encourages future research to evaluate the predictive role of selected D-amino acids as peripheral markers for SCZ pathophysiology and diagnosis.

Multiplexed detection platform implemented with magnetic encoding and deep learning-based decoding for quantitative analysis of exosomes from cancers

Although a number of biosensing technologies have been reported for the detection of cancer-derived exosomes used as early diagnosis markers for cancers, it is necessary to identify them with various biomarkers to distinguish the stages and types of cancers owing to the extreme heterogeneity of cancer. Here, we developed a new multiplexed assay platform for the detection of exosomes using magnetic encoded microparticles (MEMPs), which can recognize multiple proteins expressed on exosomes, and a deep learning-based decoding algorithm. This platform, in which the accuracy of the decoding algorithm was evaluated to be 93 %, was applied to detect exosomes from four types of cancer cell lines and plasma from patients with cancer using three cancer biomarkers: PD-L1 (Programmed Death-Ligand 1), EpCAM (Epithelial cell adhesion molecule), and EGFR (Epidermal growth factor receptor). The limit of detections (LODs) of this platform when applied to the detection of exosomes from MDA-MB-231 cell line were calculated as 4.03 × 106 mL−1 for PD-L1, 1.00 ×107 mL−1 for EpCAM, and 7.17×106 mL−1 for EGFR, respectively. In a clinical study, four types of samples from patients with cancer (n = 92) showed higher signals than those of healthy controls (n = 18). Based on these results, we confirmed that this platform can distinguish patients with cancer from healthy individuals.