Healthy Control Patients Research Articles

Upregulation of GPR133 expression impaired the phagocytosis of macrophages in recurrent spontaneous miscarriage

ABSTRACT Decidual macrophages are the second-largest immune cell group at the maternal-foetal interface. They participate in apoptotic cell removal, and protect the foetus from microorganisms or pathogens. Dysfunction of decidual macrophages gives rise to pregnancy complications such as preeclampsia and recurrent spontaneous miscarriage (RSM). However, the mechanisms by which decidual macrophages are involved in the occurrence of adverse pregnancy outcomes have not been elucidated. Here we integrated DNA methylation and gene expression data from decidua macrophages to identify potential risk factors related to RSM. GPR133 was significantly hypomethylated and upregulated in decidual macrophages from RSM patients. Further demethylation analysis demonstrated that GPR133 expression in decidual macrophages was significantly increased by 5-Aza-dC treatment. In addition, the influence of GPR133 on the phagocytic ability of macrophages was explored. Phagocytosis was impaired in the decidual macrophages of RSM patients with increased GPR133 expression. Increased GPR133 expression induced by demethylation treatment in the decidual macrophages of healthy control patients led to a significant decrease in phagocytic function. Importantly, knockdown of GPR133 resulted in a significant improvement in the phagocytic function of THP-1 macrophages. In conclusion, the existing studies have shown the influence of GPR133 on the phagocytic function of decidual macrophages and pregnancy outcomes, providing new data and ideas for future research on the role of decidual macrophages in RSM.

Comparison of Val66met Polymorphism of BDNF gene in patients of bipolar disorder and healthy controls.

IntroductionThe study aims to explore the utility of BDNF Val66Met polymorphism as a potential biomarker in Indian bipolar disorder patients and its correlation with clinical characteristics.ObjectivesGenotyping Val66Met in BDNF geneExploring its association with bipolar disorder (BD).Methods150 consenting BD patients and matched controls were recruited using a case-control study design. BD severity was assessed using Young’s mania rating scale and the Clinical Global Impression - Severity (CGI-S) scale. BDNF Val66Met polymorphism was identified through real-time PCR after DNA extraction. Data was tested for normal distribution. Genotype frequencies between two groups were compared and the Hardy-Weinberg equilibrium assumptions were tested using Chi-Square tests. Clinical-genotypic associations were explored using the Kruskal-Wallis test and confirmed using hierarchical regression.ResultsOur sample had more males (60%) than females (40%) with mean age of 35.05 years. Most patients had established bipolar disorder and were severely ill (CGI: 38.75, YMRS). Val66Met SNP genotype frequency differed significantly between cases and controls. Val66Val genotype and Val allele were higher in cases. Results consistent with Hardy-Weinberg equilibrium.Table 1.Genotype frequencies of BDNF (rs6265) in cases and controlsGENOTYPECCCTTTCASES94(62.6%)47(31.3%)9(6%)CONTROL71(47.3%)69(46%)10(6.6%)CHI-SQUARE- 7.431DF(Degree of freedom) - 2p-value- 0.024Table 2.Dominant genotype frequencies in cases and controlsDOMINANT GENOTYPECCCT+TTCASES94(62.6%)56(37.3%)CONTROLS71(47.3%)79(52.6)CHI-SQUARE-7.125DF(Degree of freedom)-1p-value-0.007Table 3.Allelic frequencies of BDNF (rs6265) in BD cases and healthy controlsALLELIC VARIATIONCTCASES235(78.3%)65(21.6%)CONTROLS211(70.3%)89(29.6%)CHI-SQUARE-5.032DF(Degree of freedom)-1p-value- 0.024ConclusionsOur study found that Val66Val genotype and Val allele were higher in cases and could be a potential biomarker for bipolar disorder (BD), which is consistent with previous research conducted on the European population. However, further investigations are required to gain a more comprehensive understanding of its impact on BD, including its association with serum BDNF levels, treatment outcomes, and a more diverse study population.Disclosure of InterestNone Declared

Mitochondrial respiratory capacity in patients with acute episodes of bipolar disorder compared with clinical remission

IntroductionBipolar disorder (BD) is a chronic and recurrent disease characterized by acute mood episodes alternated with periods of euthymia. The available literature postulates that a biphasic dysregulation of mitochondrial bioenergetics might be observed in BD.ObjectivesWe aimed to explore differences in in vivo mitochondrial respiration (1) intra-individually: longitudinally within patients during an acute mood episode of BD and after clinical remission, and (2) inter-individually: between patients with BD on depressive or manic episodes and healthy controls (HC).MethodsPatients admitted to our acute psychiatric ward with a manic episode or bipolar depression were recruited. Different mitochondrial oxygen consumption rates (OCRs) were assessed during the acute episode (T0) and after clinical remission (T1) in one million of peripheral blood mononuclear cells (PBMC): Routine, Leak, ETC and Rox. They were measured as picomoles of oxygen per million cells (pmol O2/million). This experiment was also conducted in HC. High-resolution respirometry was performed at 37°C by polarographic oxygen sensors in a two-chamber Oxygraph-2k system. Manic and depressive symptoms were assessed using standardized psychometric scales. Oxygen consumption capacity was compared (1) intra-individually, during acute episodes and after clinical remission, and (2) inter-individually, during acute manic and depressive episodes, and in HC. Statistical analyses were performed with SPSS, GraphPad and R Statistics.Results20 patients with BD (15 manic, 5 depressed) and 10 HC were included. A significant increase in the maximal oxygen consumption capacity (ETC) was observed in clinical remission (27.4 ± 17.4) compared to the acute episodes (21.1 ± 11.7, p = 0.001), which remained significant after subtracting Rox from the other rates (p = 0.001). At T1, patients admitted with a manic episode tended to show higher mean ETC (31.2 ± 18.7) compared with T0 (24.1 ± 12.0, p = 0.074); the tendency persisted after Rox subtraction (p = 0.076). Patients admitted with a depressive episode also showed higher ETC means in T1 (16.3 ± 3.8) compared to T0 (12.1 ± 3.4), but there were not significant differences (p = 0.231). When HC, manic and depressive patients at T0 were compared between them, significant differences were observed in ETC (H =8.5; p =0.014) and Rox (H =13.8; p = 0.001). After Rox deduction, differences in ETC remained (H =11.7; p = 0.003). Individuals with bipolar depression showed lower ETC rates (12.1 ± 3.4) than those with a manic episode (24.1 ± 12.0; t = -3.5, p = 0.003), which was also found after Rox deduction (p = 0.001).ConclusionsIn both manic and depressive episodes in BD, mitochondrial respiration might be reduced and increase after clinical remission. Further studies with larger samples will allow to confirm these results and also to identify potential mitochondrial state-dependent biomarkers.Disclosure of InterestA. Giménez-Palomo Grant / Research support from: AGP is supported by a Rio Hortega 2021 grant (CM21/00094) from the Spanish Ministry of Health financed by ISCIII and cofinanced by Fondo Social Europeo Plus (FSE+)., M. Guitart-Mampel: None Declared, A. Meseguer: None Declared, M. Valentí: None Declared, L. Bracco: None Declared, H. Andreu: None Declared, E. Vieta: None Declared, G. Garrabou: None Declared, I. Pacchiarotti: None Declared

A Case of Electroencephalography and Machine Learning in Early Diagnosis of Psychotic and Affective Disorders

AbstractElectroencephalography (EEG) serves as a non-invasive, cost-effective, and robust tool, directly measuring in-vivo neuronal mass activity with high temporal resolution. Using state-of-the-art machine learning techniques, EEG recordings have the potential to generate in silico biomarkers for severe mental disorders. In this study, we developed EEG-based classification models for schizophrenia and depression taking into account physiological and pathological aging processes. From a cohort (N=735, 51.6% male) that is acquired in LMU Hospital, Department of Psychiatry and Psychotherapy, comprising healthy control individuals (HC, N=245) and patients with schizophrenia (SCZ, N=250) or major depressive disorder (MDD, N=240), we extracted power spectrum density and connectivity measures based on 60 second resting-state EEG recordings with 19 channels. The support vector machine models were trained to 1) classify patients with SCZ or MDD and HC individuals, and 2) predict age in HC individuals using ten-by-ten repeated nested-cross validation. The age-predicting model was applied to patient groups to calculate EphysAGE (Electrophysiological Age Gap Estimation) by subtracting chronological age from chronological age. The links between diagnosis, medication, and EphysAGE, i.e., accelerated aging, were then further explored with univariate analyses. The EphysAGE Model had an explained variance of 46% (MAE=8.7 years, T=14.31, P1000<0.001). The patients with SCZ had a significantly higher EphysAGE (mean[SD]=0.61[10.32]) than the patients with MDD (mean[SD]=-1.10[10.49], p=0.04). The classification models discriminated SCZ from HC (Balanced Accuracy, BAC=72.7%, p<0.001), MDD from HC (BAC=67.0%, p<0.001), and SCZ from MDD individuals (BAC=63.2%, p<0.001). Higher EphysAGE was associated with an increased likelihood of being misclassified as SCZ in HC and MDD (ρHC=0.23, p<0.001; ρMDD=0.17, p=0.01) based on percentile rank scores from the SCZ Model. Moreover, in the Differential Diagnostic Model, higher EphysAGE is positively correlated with being misclassified as SCZ in patients with MDD (ρMDD=0.14, p=0.03). Machine learning models can extract electrophysiological signatures of MDD and SCZ for potential clinical use. However, the impact of aging processes on diagnostic separability calls for timely application of such models, possibly in early recognition settings.Disclosure of InterestNone Declared

The comparison of hot and cold executive functions in patients with bipolar II disorder, borderline personality disorder, and healthy individuals.

Differential diagnosis of bipolar II disorder (BD-II) and borderline personality disorder (BPD) has always been challenging for clinicians due to symptoms' overlap. This study aimed to compare hot and cold executive functions (EFs) in BD-II patients, as well as BPD and healthy controls (HCs), in order to differentiate these two disorders. In the present study, 30 BD-II and 30 BPD patients undergoing the drug therapy with mood stabilizers, and 30 HC were examined using EFs evaluated tests. The data were then analyzed using ANOVA and Tukey post hoc test. The BD-II Patients performed significantly less in all cold EFs than the HC. Also, BPD patients had meaningfully lesser performance compared to HC in all cold EFs except sustained attention. No significant difference was perceived between the two patient groups in the cold EFs. In BD-II patients, the risky decision-making as a hot EFs' component was not significantly different from HC; nevertheless, its amount was significantly higher in BPD than in the HC and BD-II patients. These findings underline the differences between the two mentioned disorders based on the hot EFs, which may indicate further disorder in the emotional information processing system among the BPD patients.

Prolonged exposure to lung-derived cytokines is associated with activation of microglia in patients with COVID-19.

BACKGROUNDSurvivors of pneumonia, including SARS-CoV-2 pneumonia, are at increased risk for cognitive dysfunction and dementia. In rodent models, cognitive dysfunction following pneumonia has been linked to the systemic release of lung-derived pro-inflammatory cytokines. Microglia are poised to respond to inflammatory signals from the circulation, and their dysfunction has been linked to cognitive impairment in murine models of dementia and in humans.METHODSWe measured levels of 55 cytokines and chemokines in bronchoalveolar lavage fluid and plasma from 341 patients with respiratory failure and 13 healthy controls, including 93 unvaccinated patients with COVID-19 and 203 patients with other causes of pneumonia. We used flow cytometry to sort neuroimmune cells from postmortem brain tissue from 5 patients who died from COVID-19 and 3 patients who died from other causes for single-cell RNA-sequencing.RESULTSMicroglia from patients with COVID-19 exhibited a transcriptomic signature suggestive of their activation by circulating pro-inflammatory cytokines. Peak levels of pro-inflammatory cytokines were similar in patients with pneumonia irrespective of etiology, but cumulative cytokine exposure was higher in patients with COVID-19. Treatment with corticosteroids reduced expression of COVID-19-specific cytokines.CONCLUSIONProlonged lung inflammation results in sustained elevations in circulating cytokines in patients with SARS-CoV-2 pneumonia compared with those with pneumonia secondary to other pathogens. Microglia from patients with COVID-19 exhibit transcriptional responses to inflammatory cytokines. These findings support data from rodent models causally linking systemic inflammation with cognitive dysfunction in pneumonia and support further investigation into the role of microglia in pneumonia-related cognitive dysfunction.FUNDINGSCRIPT U19AI135964, UL1TR001422, P01AG049665, P01HL154998, R01HL149883, R01LM013337, R01HL153122, R01HL147290, R01HL147575, R01HL158139, R01ES034350, R01ES027574, I01CX001777, U01TR003528, R21AG075423, T32AG020506, F31AG071225, T32HL076139.

Influence of analytical procedures on miRNA expression analyses in saliva samples

Aim of this study was to demonstrate the influence of different analytical procedures and techniques on the resulting miRNA expression profile in healthy control subjects and tumor patients using the oral squamous cell carcinoma (OSCC) model and to demonstrate the technical and biological reproducibility. Body fluids such as saliva are suitable for non-invasive miRNA analysis because ubiquitously circulating miRNA can be found in them. It was technically possible to distinguish between healthy and diseased samples based on the miRNA expression profile found. Regardless of the methodology used, good technical reproducibility of the results seems to be achievable. On the other hand, biological reproducibility was inadequate, which is why prompt sampling and sequencing is recommended. The data indicate that malignant lesions can be detected using miRNA signatures extracted from saliva. This could stimulate further research to establish standardized protocols and kits for sample collection, miRNA extraction, sequencing and interpretation of results.

Integrated plasma metabolomic and cytokine analysis reveals a distinct immunometabolic signature in atopic dermatitis.

Disease models for atopic dermatitis (AD) have primarily focused on understanding underlying environmental, immunologic, and genetic etiologies. However, the role of metabolic mechanisms in AD remains understudied. To investigate the circulating blood metabolomic and cytokine profile of AD as compared to healthy control patients. This study collected plasma from 20 atopic dermatitis with moderate-to-severe itch (score of ≥5 on the itch Numeric Rating Scale and IGA score ≥3) and 24 healthy control patients. Mass-spectrometry based metabolite data were compared between AD and healthy controls. Unsupervised and supervised machine learning algorithms and univariate analysis analyzed metabolic concentrations. Metabolite enrichment and pathway analyses were performed on metabolites with significant fold change between AD and healthy control patients. To investigate the correlation between metabolites levels and cytokines, Spearman's rank correlation coefficients were calculated between metabolites and cytokines. Patients were recruited from the Johns Hopkins Itch Center and dermatology outpatient clinics in the Johns Hopkins Outpatient Center. The study included 20 atopic dermatitis patients and 24 healthy control patients. Fold changes of metabolites in AD vs healthy control plasma. In patients with AD, amino acids isoleucine, tyrosine, threonine, tryptophan, valine, methionine, and phenylalanine, the amino acid derivatives creatinine, indole-3-acrylic acid, acetyl-L-carnitine, L-carnitine, 2-hydroxycinnamic acid, N-acetylaspartic acid, and the fatty amide oleamide had greater than 2-fold decrease (all P-values<0.0001) compared to healthy controls. Enriched metabolites were involved in branched-chain amino acid (valine, leucine, and isoleucine) degradation, catecholamine biosynthesis, thyroid hormone synthesis, threonine metabolism, and branched and long-chain fatty acid metabolism. Dysregulated metabolites in AD were positively correlated cytokines TARC and MCP-4 and negatively correlated with IL-1a and CCL20. Our study characterized novel dysregulated circulating plasma metabolites and metabolic pathways that may be involved in the pathogenesis of AD. These metabolic pathways serve as potential future biomarkers and therapeutic targets in the treatment of AD.

Fronto-striato network function is reduced in major depressive disorder.

Major depressive disorder (MDD) is a major cause of poor quality of life and disability and is highly prevalent worldwide. Various pathological mechanisms are implicated in MDD, including the reward system. The human brain is equipped with a reward system that is involved in aspects such as motivation, pleasure, and learning. Several studies including a meta-analysis have been reported on the reward system network and MDD. However, to our knowledge, no studies have examined the relationship between the reward system network of drug-naïve, first-episode MDD patients and the detailed symptoms of MDD or age. The fronto-striato network (FSN) is closely related to the reward system network. The present study primarily aimed to elucidate this point. A total of 89 drug-naïve first-episode MDD patients and 82 healthy controls (HCs) patients were enrolled in the study. The correlation between the FSN and age and the interaction between age and illness in the FSN were investigated in 75 patients in the MDD group and 79 patients in the HC group with available information on the FSN and age. In addition, the association between the FSN and the total scores on the 17-item Hamilton Rating Scale for Depression (HAMD-17) and scores in each symptom item was analyzed in 76 MDD subjects with information on the FSN and HAMD-17. The significance of each result was evaluated according to a p-value of <0.05. Age was inversely correlated with the FSN (p=2.14e-11) in the HC group but not in the MDD group (p=0.79). FSN varied with the presence of MDD and with age, particularly showing an interaction with MDD and age (p=1.04e-08). Specifically, age and the presence or absence of MDD each affected FSN, but the effect of age on FSN changed in the presence of depression. FSN did not correlate with total HAMD-17 scores or scores in each item. The reward system may be dysfunctional in patients with MDD. In addition, the effect could be greater in younger patients. Meanwhile, there is no correlation between the function of the reward system and the severity of MDD or the severity of each symptom. Thus, the reward system network may be an important biological marker of MDD, although careful consideration should be given to age and its association with the severity of the disorder. The reward system function is decreased in MDD patients, and this decrease may be more pronounced in younger patients, although further research is still needed.

Association between elevated serum matrix metalloproteinase-2 and tumor necrosis factor-α, and clinical symptoms in male patients with treatment-resistant and chronic medicated schizophrenia

BackgroundInflammation has an important role in the pathogenesis of schizophrenia. The aim of this study was to investigate the levels of tumor necrosis factor (TNF) and matrix metalloproteinase-2 (MMP-2) in male patients with treatment-resistant schizophrenia (TRS) and chronic medicated schizophrenia (CMS), and the relationship with psychopathology.MethodsThe study enrolled 31 TRS and 49 cm male patients, and 53 healthy controls. Serum MMP-2 and TNF-α levels were measured by the Luminex liquid suspension chip detection method. Positive and Negative Syndrome Scale (PANSS) scores were used to evaluate symptom severity and Repeatable Battery for the Assessment of Neuropsychological Status was used to assess cognitive function.ResultsSerum TNF-α and MMP-2 levels differed significantly between TRS, CMS and healthy control patients (F = 4.289, P = 0.016; F = 4.682, P = 0.011, respectively). Bonferroni correction demonstrated that serum TNF-α levels were significantly elevated in CMS patients (P = 0.022) and MMP-2 levels were significantly higher in TRS patients (P = 0.014) compared to healthy controls. In TRS patients, TNF-α was negatively correlated with age (r=-0.435, P = 0.015) and age of onset (r=-0.409, P = 0.022). In CMS patients, MMP-2 and TNF-α were negatively correlated with PANSS negative and total scores, and TNF-α was negatively correlated with PANSS general psychopathology scores (all P < 0.05). MMP-2 levels were positively correlated with TNF-α levels (P < 0.05), but not with cognitive function (P > 0.05).ConclusionThe results indicate the involvement of inflammation in the etiology of TRS and CMS. Further studies are warranted.

Retinal Nerve Fiber Layer Changes in Patients With Chronic Obstructive Pulmonary Disease Compared to Healthy Controls: A Meta-analysis.

This study investigated changes in retinal nerve fiber layers (RNFL) in patients diagnosed with chronic obstructive pulmonary disease (COPD) compared to healthy control patients, using optical coherence tomography. PubMed, Cochrane Library, and Google Scholar databases were systematically searched for published articles comparing RNFL between patients with COPD and healthy controls. Standardized mean difference (SMD) with 95% confidence interval (CI) was computed to compare continuous variables. Average RNFL thickness was significantly reduced in COPD patients compared to healthy controls (SMD = -0.31, 95% CI = -0.48 to -0.14, P = 0.0004, I2 = 0%). Average RNFL thickness did not differ significantly between patients with mild/moderate COPD and healthy controls (SMD = -0.17, 95% CI = -0.39 to 0.04, P = 0.12, I2 = 2%), while a statistically significant reduction in average RNFL thickness was noticed in patients with severe COPD compared to healthy controls (SMD = -0.72, 95% CI = -1.23 to -0.21, P = 0.006, I2 = 83%). Average RNFL thickness was significantly higher in patients with mild/moderate COPD compared to patients with severe COPD (SMD = 0.69, 95% CI = 0.29 to 1.09, P = 0.0008, I2 = 66%). This meta-analysis showed that RNFL thickness was decreased in patients with COPD compared to healthy controls. Patients diagnosed with severe COPD seem to be more affected and have thinner RNFL. [Ophthalmic Surg Lasers Imaging Retina 2024;55:XX-XX.].

Quantification of Circulating Cell-Free DNA in Idiopathic Parkinson's Disease Patients.

Parkinson's disease (PD) is one of the most common neurodegenerative disorders globally and leads to an excessive loss of dopaminergic neurons in the substantia nigra of the brain. Circulating cell-free DNA (ccf-DNA) are double-stranded DNA fragments of different sizes and origins that are released into the serum and cerebrospinal fluid (CSF) due to cell death (i.e., necrosis and apoptosis) or are actively released by viable cells via exocytosis and NETosis. Using droplet digital polymerase chain reaction (ddPCR), we comprehensively analyzed and distinguished circulating cell-free mitochondrial DNA (ccf mtDNA) and circulating cell-free nuclear DNA (ccfDNA) in the serum and CSF of PD and control patients. The quantitative analysis of serum ccf-DNA in PD patients demonstrated a significant increase in ccf mtDNA and ccfDNA compared to that in healthy control patients and a significantly higher copy of ccf mtDNA when compared to ccfDNA. Next, the serum ccf mtDNA levels significantly increased in male PD patients compared to those in healthy male controls. Furthermore, CSF ccf mtDNA in PD patients increased significantly compared to ccfDNA, and ccf mtDNA decreased in PD patients more than it did in healthy controls. These decreases were not statistically significant but were in agreement with previous data. Interestingly, ccf mtDNA increased in healthy control patients in both serum and CSF as compared to ccfDNA. The small sample size of serum and CSF were the main limitations of this study. To the best of our knowledge, this is the first comprehensive study on serum and CSF of PD patients using ddPCR to indicate the distribution of the copy number of ccf mtDNA as well as ccfDNA. If validated, we suggest that ccf mtDNA has greater potential than ccfDNA to lead the development of novel treatments for PD patients.

Prolonged culturing of colonic epithelial organoids derived from healthy individuals and ulcerative colitis patients results in the decrease of LINE-1 methylation level

Patient-derived human intestinal organoids are becoming an indispensable tool for the research of digestive system in health and disease. However, very little is still known about the long-term culturing effect on global genomic methylation level in colonic epithelial organoids derived from healthy individuals as well as active and quiescent ulcerative colitis (UC) patients. In this study, we aimed to evaluate the epigenetic stability of these organoids by assessing the methylation level of LINE-1 during prolonged culturing. We found that LINE-1 region of both healthy control and UC patient colon tissues as well as corresponding epithelial organoids is highly methylated (exceeding 60%). We also showed that long-term culturing of colonic epithelial organoids generated from stem cells of healthy and diseased (both active and quiescent UC) individuals results in decrease of LINE-1 (up to 8%) methylation level, when compared to tissue of origin and short-term cultures. Moreover, we revealed that LINE-1 methylation level in sub-cultured organoids decreases at different pace depending on the patient diagnosis (healthy control, active or quiescent UC). Therefore, we propose LINE-1 as a potential and convenient biomarker for reliable assessment of global methylation status of patient-derived intestinal epithelial organoids in routine testing of ex vivo cultures.

Dynamics of SARS-CoV-2 immunity after vaccination and breakthrough infection in rituximab-treated rheumatoid arthritis patients: a prospective cohort study.

SARS-CoV-2 vaccination in rheumatoid arthritis (RA) patients treated with B cell-depleting drugs induced limited seroconversion but robust cellular response. We aimed to document specific T and B cell immunity in response to vaccine booster doses and breakthrough infection (BTI). We included 76 RA patients treated with rituximab who received up to four SARS-CoV-2 vaccine doses or three doses plus BTI, in addition to vaccinated healthy donors (HD) and control patients treated with tumor necrosis factor inhibitor (TNFi). We quantified anti-SARS-CoV-2 receptor-binding domain (RBD) Spike IgG, anti-nucleocapsid (NC) IgG, 92 circulating inflammatory proteins, Spike-binding B cells, and Spike-specific T cells along with comprehensive high-dimensional phenotyping and functional assays. The time since the last rituximab infusion, persistent inflammation, and age were associated with the anti-SARS-CoV-2 RBD IgG seroconversion. The vaccine-elicited serological response was accompanied by an incomplete induction of peripheral Spike-specific memory B cells but occurred independently of T cell responses. Vaccine- and BTI-elicited cellular immunity was similar between RA and HD ex vivo in terms of frequency or phenotype of Spike-specific cytotoxic T cells and in vitro in terms of the functionality and differentiation profile of Spike-specific T cells. SARS-CoV-2 vaccination in RA can induce persistent effector T-cell responses that are reactivated by BTI. Paused rituximab medication allowed serological responses after a booster dose (D4), especially in RA with lower inflammation, enabling efficient humoral and cellular immunity after BTI, and contributed overall to the development of potential durable immunity.

Greater accuracy of radiomics compared to deep learning to discriminate normal subjects from patients with dementia: a whole brain 18FDG PET analysis.

18F-FDG brain PET and clinical score were collected in 85 patients with dementia and 125 healthy controls (HC). Patients were assigned to various form of dementia on the basis of clinical evaluation, follow-up and voxels comparison with HC using a two-sample Student's t -test, to determine the regions of brain involved. Radiomic analysis was performed on the whole brain after normalization to an optimized template. After feature selection using the minimum redundancy maximum relevance method and Pearson's correlation coefficients, a Neural Network model was tested to find the accuracy to classify HC and demented patients. Twenty subjects not included in the training were used to test the models. The results were compared with those obtained by conventional CNN model. Four radiomic features were selected. The validation and test accuracies were 100% for both models, but the probability scores were higher with radiomics, in particular for HC group ( P = 0.0004). Radiomic features extracted from standardized PET whole brain images seem to be more accurate than CNN to distinguish patients with and without dementia.

Development of a novel lncRNA-derived immune gene score using machine learning-based ensembles for predicting the survival of HCC

BackgroundLong noncoding RNAs (lncRNAs) are implicated in the tumor immunology of hepatocellular carcinoma (HCC). MethodsHCC mRNA and lncRNA expression profiles were used to extract immune-related genes with the ImmPort database, and immune-related lncRNAs with the ImmLnc algorithm. The MOVICS package was used to cluster immune-related mRNA, immune-related lncRNA, gene mutation and methylation data on HCC from the TCGA. GEO and ICGC datasets were used to validate the model. Data from single-cell sequencing was used to determine the expression of genes from the model in various immune cell types. ResultsWith this model, the area under the curve (AUC) for 1-, 3- and 5-year survival of HCC patients was 0.862, 0.869 and 0.912, respectively. Single-cell sequencing showed EREG was significantly expressed in a variety of immune cell types. Knockdown of the EREG target gene resulted in significant anti-apoptosis, pro-proliferation and pro-migration effects in HepG2 and HUH7 cells. Moreover, serum and liver tissue EREG levels in HCC patients were significantly higher than those of healthy control patients. ConclusionWe built a prognostic model with good accuracy for predicting HCC patient survival. EREG is a potential immunotherapeutic target and a promising prognostic biomarker.

A Composite Blood Biomarker Including AKR1B10 and Cytokeratin 18 for Progressive Types of Nonalcoholic Fatty Liver Disease.

We aimed to evaluate whether composite blood biomarkers including aldo-keto reductase family 1 member B10 (AKR1B10) and cytokeratin 18 (CK-18; a nonalcoholic steatohepatitis [NASH] marker) have clinically applicable performance for the diagnosis of NASH, advanced liver fibrosis, and high-risk NASH (NASH+significant fibrosis). A total of 116 subjects including healthy control subjects and patients with biopsy-proven nonalcoholic fatty liver disease (NAFLD) were analyzed to assess composite blood-based and imaging-based biomarkers either singly or in combination. A composite blood biomarker comprised of AKR1B10, CK-18, aspartate aminotransferase (AST), and alanine aminotransferase (ALT) showed excellent performance for the diagnosis of, NASH, advanced fibrosis, and high-risk NASH, with area under the receiver operating characteristic curve values of 0.934 (95% confidence interval [CI], 0.888 to 0.981), 0.902 (95% CI, 0.832 to 0.971), and 0.918 (95% CI, 0.862 to 0.974), respectively. However, the performance of this blood composite biomarker was inferior to that various magnetic resonance (MR)-based composite biomarkers, such as proton density fat fraction/MR elastography- liver stiffness measurement (MRE-LSM)/ALT/AST for NASH, MRE-LSM+fibrosis-4 index for advanced fibrosis, and the known MR imaging-AST (MAST) score for high-risk NASH. Our blood composite biomarker can be useful to distinguish progressive forms of NAFLD as an initial noninvasive test when MR-based tools are not available.

Mild Cognitive Impairment detection based on EEG and HRV data

Brain volume decrease is usually connected to neurodegeneration and aging. In this environment, an important percentage of elderly persons suffer from mild cognitive impairment (MCI), a kind of dementia that can lead to Alzheimer's disease (AD). Since the symptoms of cognitive impairment are scarcely discernible, developing a safe and effective method for early MCI detection has emerged as an important challenge. According to this regard, numerous cognitive training tests can be targeted to help aging people retain a good quality of life, especially in the case of fragility disorders. A Consortium to Establish a Registry for Alzheimer's Disease (CERAD) task was initially created to detect the early stages of AD. This task specifically targets various tests related to specific cognitive domains. However, it has since developed into a popular diagnostic tool for many kinds of dementia, such as MCI. Several low-cost equipment, such as electroencephalography (EEG) and heart rate variability (HRV), may be useful for predicting MCI. On the other side, various machine learning (ML) models can be employed to extract/analyze relevant features from biomedical and physiological signals, especially in the context of anomaly detection and classification. To this regard, we developed a new method based on ML models to categorize MCI and healthy control (HC) patients during the CERAD task using EEG and HRV multimodal data. Our dataset includes 15 subjects who were randomly assigned to training and testing groups of 7 HC and 8 MCI, respectively. Our raw EEG and HRV data are analyzed to extract time, frequency, and non-linear features. A scaling step is employed to reduce the significant disparity between features. For the classification task, five ML models are evaluated, including support vector machine (SVM), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), and gradient boosting (GB). To enhance accuracy, a hybrid ML model with a voting system is developed, combining the top ML models with the highest accuracy rates. A comparison step is performed between the use of ML and hybrid ML models. The experimental findings demonstrated the efficacy of our proposed technique, which included a hybrid ML model. An average accuracy of 93.86%, a sensitivity of 93.87%, and a specificity of 93.53% are achieved. The obtained results allow one to conclude that the first CERAD test plays a prominent role as a novel biomarker with an ultra-short duration for early MCI identification through the combination of EEG and HRV signals.

Integrative analysis of gene expression profiles of substantia nigra identifies potential diagnosis biomarkers in Parkinson's disease

Parkinson's disease (PD) is a progressive neurodegenerative disease whose etiology is attributed to development of Lewy bodies and degeneration of dopaminergic neurons in the substantia nigra (SN). Currently, there are no definitive diagnostic indicators for PD. In this study, we aimed to identify potential diagnostic biomarkers for PD and analyzed the impact of immune cell infiltrations on disease pathogenesis. The PD expression profile data for human SN tissue, GSE7621, GSE20141, GSE20159, GSE20163 and GSE20164 were downloaded from the Gene Expression Omnibus (GEO) database for use in the training model. After normalization and merging, we identified differentially expressed genes (DEGs) using the Robust rank aggregation (RRA) analysis. Simultaneously, DEGs after batch correction were identified. Gene interactions were determined through venn Diagram analysis. Functional analyses and protein–protein interaction (PPI) networks were used to the identify hub genes, which were visualized through Cytoscape. A Lasso Cox regression model was employed to identify the potential diagnostic genes. The GSE20292 dataset was used for validation. The proportion of infiltrating immune cells in the samples were determined via the CIBERSORT method. Sixty-two DEGs were screened in this study. They were found to be enriched in nerve conduction, dopamine (DA) metabolism, and DA biosynthesis Gene Ontology (GO) terms. The PPI network and Lasso Cox regression analysis revealed seven potential diagnostic genes, namely SLC18A2, TAC1, PCDH8, KIAA0319, PDE6H, AXIN1, and AGTR1, were subsequently validated in peripheral blood samples obtained from healthy control (HC) and PD patients, as well as in the GSE20292 dataset. The results revealed the exceptional sensitivity and specificity of these genes in PD diagnosis and monitoring. Moreover, PD patients exhibited a higher number of plasma cells, compared to HC individuals. The SLC18A2, TAC1, PCDH8, KIAA0319, PDE6H, AXIN1, and AGTR1 are potential diagnostic biomarkers for PD. Our findings also reveal the essential roles of immune cell infiltration in both disease onset and trajectory.

P324 Urine metabolite profile characterizes clinical activity and disease extent in patients with inflammatory bowel disease

Abstract Background Inflammatory Bowel Disease (IBD), which comprises Crohn's Disease (CD) and Ulcerative Colitis (UC), is a disorder in which complex interactions between genetic, environmental, and microbial factors trigger alterations in the immune intestinal response. On the other hand, nutrient catabolism from the diet carried out by the microbiota results in the formation of metabolites that also impact immune responses on the intestinal mucosa. Considering that IBD patients have altered microbiota compared to healthy controls (HC) and that the urinary metabolic profile of an individual is the result of a combination of genetic, dietary, and microbial components, we propose the hypothesis that the urinary metabolic profile distinguishes IBD patients from HC. The aim of the study was to compare metabolomic analysis of urine samples from HC and IBD patients. Methods 27 patients were included [HC (n=10), UC (n=10), and CD (n=7)]. Clinical IBD activity was evaluated with partial Mayo score (UC) and Harvey-Bradshaw index (CD). Metabolites in urine were measured using gas chromatography-mass spectrometry (GC-MS). Metabolomic analysis and machine learning were performed using the MetaboAnalyst software. PLS-DA was used for multivariate analysis, and clustering separation was performed using hierarchical clustering. To identify and interpret patterns of metabolite concentration changes in a biologically meaningful way we perform Over Representation Analysis. Results In total, 62 organic acids were detected. Comparing UC and HC revealed 11 significantly altered metabolites (9 increased and 2 decreased). Main increased metabolites comprised lactic acid (p=0.0015), pyroglutamic acid (p=0.0147), isocitric acid (p=0.0325), and phosphoric acid (p=0.0091). Interestingly, the value of adipic and suberic acids were higher in remission vs. active patients. Furthermore, adipic acid was higher in UC patients with proctitis compared to left-side colitis/extensive colitis. Comparing CD and HC revealed only 2 significantly altered metabolites, glycolic acid (p=0.0214), and lactic acid (p=0.0111). Oxalic acid was more concentrated in CD patient with perianal disease. Finally, propionic and phosphoric acid were able to discriminate UC from CD patients. The most affected metabolic pathways were related to energy metabolism and oxidative stress, including the Warburg effect (p=0.0016), gluconeogenesis (p=0.0106), pyruvate metabolism (p=0.0197) and glutathione metabolism (p=0.0039) (Fig.1). Conclusion We found that the urinary metabolic profile of IBD patients differs from the HC, particularly for UC patients. These metabolites profile could be used to improve diagnosis and monitoring in patients with IBD.