Multimodal magnetic resonance longitudinal study on the deep gray matter in multiple sclerosis patients with teriflunomide.

Disease-modifying therapies (DMTs) are used in an increasing number of patients with multiple sclerosis (MS). However, whether DMTs have intrinsic effects on deep gray matter (DGM) microstructure and atrophy is still poorly understood. In this study, we described the quantitative susceptibility values (QSV) and diffusion kurtosis imaging (DKI) metrics of DGM in relapsing-remitting MS (RRMS) patients and their association with cognitive deficits. We recruited 62 patients with RRMS receiving DMTs and 30 patients with RRMS not receiving DMTs underwent MRI on a 3T scanner. Fractional anisotropy (FA), kurtosis fractional anisotropy (KFA), mean diffusivity (MD), mean kurtosis (MK), QSV and volumes of bilateral caudate nucleus (CAU), amygdala (AMYG), putamen (PUT), hippocampus (Hipp), globus pallidus (GP) and thalamus (THA) were measured. Correlation analysis was performed between those image indexes with longitudinal significant changes and clinical neurological scores, including Expanded Disability Status Scale (EDSS), Digit Span Testand (DST), Symbol Digit Modalities Test (SDMT), Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Significant longitudinal increases in FA, KFA and MK values were found in both groups in bilateral CAU, AMYG, PUT, Hipp, GP and THA (all p < 0.005). MD values of the right of CAU in the two groups were significant longitudinal increase (p = 0.009, p = 0.047); MD values of the right of GP (p = 0.042), the left of THA (p = 0.003), the right of THA (p = 0.001) in treated MS were significant longitudinal decrease; There were no significant longitudinal changes between treated and untreated groups in normalized deep gray matter volume. For QSV, longitudinal increase in the right of PUT (p = 0.022) in the treated MS group and in the left of Hipp (p = 0.045) in the untreated MS group. The QSV and DKI measures were highly correlated with cognitive and disability tests. The treated RRMS patients showed different longitudinal changes of MD value and QSV with untreated in several DGM regions, and these differences were correlated with cognitive and microstructural integrity.

A Simple Score (MOG-AR) to Identify Individuals at High Risk of Relapse After MOGAD Attack.

BACKGROUND AND OBJECTIVES: To identify predictors for relapse in patients with myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) and to develop and validate a simple risk score for predicting relapse. METHODS: In China National Registry of Neuro-Inflammatory Diseases (CNRID), we identified patients with MOGAD from March 2023 and followed up prospectively to September 2023. The primary endpoint was MOGAD relapse, confirmed by an independent panel. Patients were randomly divided into model development (75%) and internal validation (25%) cohorts. Prediction models were constructed and internally validated using Andersen-Gill models. Nomogram and relapse risk score were generated based on the final prediction models. RESULTS: A total of 188 patients (comprising 612 treatment episodes) were included in cohorts. Female (HR: 0.687, 95% CI 0.524-0.899, p = 0.006), onset age 45 years or older (HR: 1.621, 95% CI 1.242-2.116, p < 0.001), immunosuppressive therapy (HR: 0.338, 95% CI 0.239-0.479, p < 0.001), oral corticosteroids >3 months (HR 0.449, 95% CI 0.326-0.620, p < 0.001), and onset phenotype (p < 0.001) were identified as factors associated with MOGAD relapse. A predictive score, termed MOG-AR (Immunosuppressive therapy, oral Corticosteroids, Onset Age, Sex, Attack phenotype), derived in prediction model, demonstrated strong predictive ability for MOGAD relapse. MOG-AR score of 13-16 indicates a higher risk of relapse (HR: 3.285, 95% CI 1.473-7.327, p = 0.004). DISCUSSION: The risk of MOGAD relapse seems to be predictable. Further validation of MOG-AR score developed from this cohort to determine appropriate treatment and monitoring frequency is warranted. TRIAL REGISTRATION INFORMATION: CNRID, NCT05154370, registered December 13, 2021, first enrolled December 15, 2021.

Radiomics Nomograms Based on Multi-sequence MRI for Identifying Cognitive Impairment and Predicting Cognitive Progression in Relapsing-Remitting Multiple Sclerosis.

RATIONALE AND OBJECTIVES: To build radiomics nomograms based on multi-sequence MRI to facilitate the identification of cognitive impairment (CI) and prediction of cognitive progression (CP) in patients with relapsing-remitting multiple sclerosis (RRMS). MATERIALS AND METHODS: We retrospectively included two RRMS cohorts with multi-sequence MRI and Symbol Digit Modalities Test (SDMT) data: dataset1 (n = 149, for training and validation) and dataset2 (n = 29, for external validation). 80 patients of dataset1 had a 2-year follow-up SDMT. CI and CP were evaluated using SDMT scores at baseline and follow-up. The included DIR sequence aided in identifying cortical lesions. Lesion radiomics and structural features were extracted and selected from multi-sequence MRI, followed by the computation of radiomics and structural scores. The nomogram was developed through multivariate logistic regression, integrating clinical data, radiomics, and structural scores to identify CI in patients. Moreover, a similar method was employed to further construct a nomogram predicting CP in patients. RESULTS: The nomogram demonstrated superior performance in identifying patients with CI, with area under the curve (AUC) values of 0.937 (95% Conf. Interval: 0.898-0.975) and 0.876 (0.810-0.943) in internal and external validation sets, compared to models solely based on clinical data, lesion radiomics, and structural features. Furthermore, another nomogram constructed in predicting CP also exhibited outstanding performance, with an AUC value of 0.969 (0.875-1.000) in the validation set. CONCLUSION: These nomograms, integrating clinical data, multi-sequence lesions radiomics, and structural features, enable more effective identification of CI and early prediction of CP in RRMS patients, providing important support for clinical decision-making.

Efgartigimod as rescue treatment in acute phase of neuromyelitis optica spectrum disorder: A Case Report.

Introduction: Neuromyelitis optica spectrum disorder (NMOSD) is a central nervous system demyelinating disease. Current therapy methods, however, have limited effect on acute attacks except for intravenous methylprednisolone (IVMP). Efgartigimod is a first-in-class novel human immunoglobulin G1 (IgG1) Fc fragment approved for the treatment of generalized myasthenia gravis. Its capacity to rapidly decrease serum IgG levels, including pathogenic autoantibodies, positions it as a potentially effective option for managing the acute phase of NMOSD. Case presentation: We report the case of a 59-year-old female patient with acute NMOSD, presenting with vision loss and numbness in all four limbs. Despite an initial inadequate response to intravenous methylprednisolone (IVMP), the addition of Efgartigimod to her treatment regimen led to rapid improvement, notably including a significant reduction in serum aquaporin-4 antibody titers, total IgG levels, and inflammation cytokine levels. Furthermore, no adverse events were reported during a four-month follow-up period. Conclusion: As an adjunct to glucocorticoid therapy, Efgartigimod has proven effective and safe for this patient. However, to ascertain its potential as a novel therapeutic option for acute NMOSD, larger-scale prospective clinical trials are required.

Cross-sectional and longitudinal evaluation of white matter microstructure damage and cognitive correlations by automated fibre quantification in relapsing-remitting multiple sclerosis patients.

The purpose of this study was to characterize whole-brain white matter (WM) fibre tracts by automated fibre quantification (AFQ), capture subtle changes cross-sectionally and longitudinally in relapsing-remitting multiple sclerosis (RRMS) patients and explore correlations between these changes and cognitive performance A total of 114 RRMS patients and 71 healthy controls (HCs) were enrolled and follow-up investigations were conducted on 46 RRMS patients. Fractional anisotropy (FA), mean diffusion (MD), axial diffusivity (AD), and radial diffusivity (RD) at each node along the 20 WM fibre tracts identified by AFQ were investigated cross-sectionally and longitudinally in entire and pointwise manners. Partial correlation analyses were performed between the abnormal metrics and cognitive performance. At baseline, compared with HCs, patients with RRMS showed a widespread decrease in FA and increases in MD, AD, and RD among tracts. In the pointwise comparisons, more detailed abnormalities were localized to specific positions. At follow-up, although there was no significant difference in the entire WM fibre tract, there was a reduction in FA in the posterior portion of the right superior longitudinal fasciculus (R_SLF) and elevations in MD and AD in the anterior and posterior portions of the right arcuate fasciculus (R_AF) in the pointwise analysis. Furthermore, the altered metrics were widely correlated with cognitive performance in RRMS patients. RRMS patients exhibited widespread WM microstructure alterations at baseline and alterations in certain regions at follow-up, and the altered metrics were widely correlated with cognitive performance in RRMS patients, which will enhance our understanding of WM microstructure damage and its cognitive correlation in RRMS patients.

Elevated SCN11A concentrations associated with lower serum lipid levels in patients with major depressive disorder.

The pathogenesis of major depressive disorder (MDD) involves lipid metabolism. Our earlier research also revealed that MDD patients had much lower total cholesterol (TC) concentrations than healthy controls (HCs). However, it is still unclear why TC decreased in MDD. Here, based on the Ingenuity Knowledge Base's ingenuity pathway analysis, we found that sodium voltage-gated channel alpha subunit 11A (SCN11A) might serve as a link between low lipid levels and MDD. We analyzed the TC levels and used ELISA kits to measure the levels of SCN11A in the serum from 139 MDD patients, and 65 HCs to confirm this theory and explore the potential involvement of SCN11A in MDD. The findings revealed that TC levels were considerably lower and SCN11A levels were remarkably increased in MDD patients than those in HCs, while they were significantly reversed in drug-treatment MDD patients than in drug-naïve MDD patients. There was no significant difference in SCN11A levels among MDD patients who used single or multiple antidepressants, and selective serotonin reuptake inhibitors or other antidepressants. Pearson correlation analysis showed that the levels of TC and SCN11A were linked with the Hamilton Depression Rating Scales score. A substantial association was also found between TC and SCN11A. Moreover, a discriminative model made up of SCN11A was discovered, which produced an area under a curve of 0.9571 in the training set and 0.9357 in the testing set. Taken together, our findings indicated that SCN11A may serve as a link between low lipid levels and MDD, and showed promise as a candidate biomarker for MDD.

Interpretable and Intuitive Machine Learning Approaches for Predicting Disability Progression in Relapsing-Remitting Multiple Sclerosis Based on Clinical and Gray Matter Atrophy Indicators.

RATIONALE AND OBJECTIVES: To investigate whether clinical and gray matter (GM) atrophy indicators can predict disability in relapsing-remitting multiple sclerosis (RRMS) and to enhance the interpretability and intuitiveness of a predictive machine learning model. MATERIALS AND METHODS: 145 and 50 RRMS patients with structural MRI and at least 1-year follow-up Expanded Disability Status Scale (EDSS) results were retrospectively enrolled and placed in the discovery and external test cohorts, respectively. Six clinical and radiomics feature-based machine learning classifiers were trained and tested to predict disability progression in the discovery cohort and validated in the external test set. Partial dependence plot (PDP) analysis and a Shiny web application were conducted to enhance the interpretability and intuitiveness. RESULTS: In the discovery cohort, 98 patients had disability stability, and 47 patients were classified as having disability progression. In the external test set, 35 patients were disability stable, and 15 patients had disability progression. Models trained with both clinical and radiomics features (area under the curve (AUC), 0.725-0.950) outperformed those trained with clinical (AUC, 0.600-0.740) or radiomics features only (AUC, 0.615-0.945). Among clinical+ radiomics feature models, the logistic regression (LR) classifier-based model performed best, with an AUC of 0.950. Only the radiomics feature-only models were applied in the external test set due to the data collection problem and showed fair performance, with AUCs ranging from 0.617 to 0.753. PDP analysis showed that female patients and those with lower volume, surface area, and symbol digit modalities test (SDMT) scores; greater mean curvature and age; and no disease modifying therapy (DMT) had increased probabilities of disease progression. Finally, a Shiny web application (https://lauralin1104.shinyapps.io/LRshiny/) was developed to calculate the risk of disability progression. CONCLUSION: Interpretable and intuitive machine learning approaches based on clinical and GM atrophy indicators can help physicians predict disability progression in RRMS patients for clinical decision-making and patient management.

Identification of Y‒linked biomarkers and exploration of immune infiltration of normal-appearing gray matter in multiple sclerosis by bioinformatic analysis.

Background: The knowledge of normal‒appearing cortical gray matter (NAGM) in multiple sclerosis (MS) remains unclear. In this study, we aimed to identify diagnostic biomarkers and explore the immune infiltration characteristics of NAGM in MS through bioinformatic analysis and validation in vivo. Methods: Differentially expressed genes (DEGs) were analyzed. Subsequently, the functional pathways of the DEGs were determined. After screening the overlapping DEGs of MS with two machine learning methods, the biomarkers' efficacy and the expression levels of overlapping DEGs were calculated. Quantitative reverse transcription polymerase chain reaction (qRT‒PCR) identified the robust diagnostic biomarkers. Additionally, infiltrating immune cell populations were estimated and correlated with the biomarkers. Finally, the characteristics of immune infiltration of NAGM from MS were evaluated. Results: A total of 98 DEGs were identified. They participated in sensory transduction of the olfactory system, synaptic signaling, and immune responses. Nine overlapping genes were screened by machine learning methods. After verified by ROC curve, four genes, namely HLA‒DRB1, RPS4Y1, EIF1AY and USP9Y, were screened as candidate biomarkers. The mRNA expression of RPS4Y1 and USP9Y was significantly lower in MS patients than that in the controls. They were selected as the robust diagnostic biomarkers for male MS patients. RPS4Y1 and USP9Y were both positively correlated with memory B cells. Moreover, naive CD4+ T cells and monocytes were increased in the NAGM of MS patients compared with those in controls. Conclusions: Low expressed Y‒linked genes, RPS4Y1 and USP9Y, were identified as diagnostic biomarkers for MS in male patients. The inhomogeneity of immune cells in NAGM might exacerbate intricate interplay between the CNS and the immune system in the MS.

Oral D-ribose causes depressive-like behavior by altering glycerophospholipid metabolism via the gut-brain axis.

Our previous work has shown that D-ribose (RIB)-induced depressive-like behaviors in mice. However, the relationship between variations in RIB levels and depression as well as potential RIB participation in depressive disorder is yet unknown. Here, a reanalysis of metabonomics data from depressed patients and depression model rats is performed to clarify whether the increased RIB level is positively correlated with the severity of depression. Moreover, we characterize intestinal epithelial barrier damage, gut microbial composition and function, and microbiota-gut-brain metabolic signatures in RIB-fed mice using colonic histomorphology, 16 S rRNA gene sequencing, and untargeted metabolomics analysis. The results show that RIB caused intestinal epithelial barrier impairment and microbiota-gut-brain axis dysbiosis. These microbial and metabolic modules are consistently enriched in peripheral (fecal, colon wall, and serum) and central (hippocampus) glycerophospholipid metabolism. In addition, three differential genera (Lachnospiraceae_UCG-006, Turicibacter, and Akkermansia) and two types of glycerophospholipids (phosphatidylcholine and phosphatidylethanolamine) have greater contributions to the overall correlations between differential genera and glycerophospholipids. These findings suggest that the disturbances of gut microbiota by RIB may contribute to the onset of depressive-like behaviors via regulating glycerophospholipid metabolism, and providing new insight for understanding the function of microbiota-gut-brain axis in depression.

Comparison of diffusion tensor imaging (DTI) tissue characterization parameters in white matter tracts of patients with multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD).

OBJECTIVE: To investigate the microstructural properties of T2 lesion and normal-appearing white matter (NAWM) in 20 white matter tracts between multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) and correlations between the tissue damage and clinical variables. METHODS: The white matter (WM) compartment of the brain was segmented for 56 healthy controls (HC), 48 patients with MS, and 38 patients with NMOSD, and for the patients further subdivided into T2 lesion and NAWM. Subsequently, the diffusion tensor imaging (DTI) tissue characterization parameters of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were compared for 20 principal white matter tracts. The correlation between tissue damage and clinical variables was also investigated. RESULTS: The higher T2 lesion volumes of 14 fibers were shown in MS compared to NMOSD. MS showed more microstructure damage in 13 fibers of T2 lesion, but similar microstructure in seven fibers compared to NMOSD. MS and NMOSD had microstructure damage of NAWM in 20 fibers compared to WM in HC, with more damage in 20 fibers in MS compared to NMOSD. MS patients showed higher correlation between the microstructure of T2 lesion areas and NAWM. The T2 lesion microstructure damage was correlated with duration and impaired cognition in MS. CONCLUSIONS: Patients with MS and NMOSD show different patterns of microstructural damage in T2 lesion and NAWM areas. The prolonged disease course of MS may aggravate the microstructural damage, and the degree of microstructural damage is further related to cognitive impairment. CLINICAL RELEVANCE STATEMENT: Microstructure differences between T2 lesion areas and normal-appearing white matter help distinguish multiple sclerosis and neuromyelitis optica spectrum disorder. In multiple sclerosis, lesions rather than normal-appearing white matter should be a concern, because the degree of lesion severity correlated both with normal-appearing white matter damage and cognitive impairment. KEY POINTS: • Multiple sclerosis and neuromyelitis optica spectrum disorder have different damage patterns in T2 lesion and normal-appearing white matter areas. • The microstructure damage of normal-appearing white matter is correlated with the microstructure of T2 lesion in multiple sclerosis and neuromyelitis optica spectrum disorder. • The microstructure damage of T2 lesion in multiple sclerosis is correlated with duration and cognitive impairment.

Radiomics models based on cortical damages for identification of multiple sclerosis with cognitive impairment.

BACKGROUND: Cognitive impairment (CI) is a common symptom in multiple sclerosis (MS) patients. Cortical damages can be closely associated with cognitive network dysfunction and clinically significant CI in MS. So, in this study, We aimed to develop a radiomics model to efficiently identify the MS patients with CI based on clinical data and cortical damages. METHODS: One hundred and eighteen patients with MS were divided into CI and normal cognitive (NC) cohorts (62/56) as defined by the Montreal Cognitive Assessment (MoCA). All participants were randomly divided into train and test sets with a ratio of 7:3. The radiomic features were selected by using the least absolute shrinkage and selection operator (LASSO) method. The discrimination models were built with the support vector machines (SVM) by the clinical data, radiomic features, and merge data, respectively. And the patients were further divided according to each cognitive domain including memory, visuospatial, language, attention and executive, and each domain model was applied by the most suitable classifier. RESULTS: A total of 2298 features were extracted, of which 36 were finally selected. The merge model showed the greatest performance with the area under the curve (AUC) of 0.86 (95 % confidence interval: 0.81-0.91), accuracy (ACC) of 0.78, sensitivity of 0.79 and specificity of 0.77 in test cohort. However, although the visuospatial domain model showed the highest AUC of 0.71 (95 % confidence interval: 0.61-0.81) among five domain models, other domain models did not meet satisfactory results with a relatively low AUC, ACC, sensitivity and specificity. CONCLUSIONS: The radiomics model based on clinical data and cortical damages had a great potential to identify the MS patients with CI for clinical cognitive assessment.

TCF4 and RBFOX1 as peripheral biomarkers for the differential diagnosis and treatment of major depressive disorder.

BACKGROUND: Recent genome-wide association studies on major depressive disorder (MDD) have indicated the involvement of LRFN5 and OLFM4; however, the expression levels and roles of these molecules in MDD remain unclear. The present study aimed to determine the serum levels of TCF4 and RBFOX1 in patients with MDD and to investigate whether these molecules could be used as biomarkers for MDD diagnosis. METHODS: The study included 99 drug-naïve MDD patients, 90 drug-treated MDD patients, and 81 healthy controls (HCs). Serum TCF4 and RBFOX1 levels were measured by ELISA. Pearson's correlation analysis was conducted to determine the association between TCF4/RBFOX1 and clinical variables. Linear support vector machine classifier was used to evaluate the diagnostic capabilities of TCF4 and RBFOX1. RESULTS: Serum TCF4 and RBFOX1 levels were substantially higher in MDD patients than in HCs and significantly lower in drug-treated MDD patients than in drug-naïve MDD patients. Moreover, serum TCF4 and RBFOX1 levels were associated with the Hamilton Depression Scale score, duration of illness, serum lipids levels, and hepatic function. Thus, both these molecules showed potential as biomarkers for MDD. TCF4 and RBFOX1 combination exhibited a higher diagnostic performance, with the mean area under the curve values of 0.9861 and 0.9936 in the training and testing sets, respectively. LIMITATIONS: Small sample size and investigation of only the peripheral nervous system. CONCLUSIONS: TCF4 and RBFOX1 may be involved in the pathogenesis of MDD, and their combination may serve as a diagnostic biomarker panel for MDD.

Multimodal Investigation of Deep Gray Matter Nucleus in Patients with Multiple Sclerosis and Their Clinical Correlations: A Multivariate Pattern Analysis Study.

Deep gray matter (DGM) nucleus are involved in patients with multiple sclerosis (MS) and are strongly associated with clinical symptoms. We used machine learning approach to further explore microstructural alterations in DGM of MS patients. One hundred and fifteen MS patients and seventy-one healthy controls (HC) underwent brain MRI. The fractional anisotropy (FA), mean diffusivity (MD), quantitative susceptibility value (QSV) and volumes of the caudate nucleus (CN), putamen (PT), globus pallidus (GP), and thalamus (TH) were measured. Multivariate pattern analysis, based on a machine-learning algorithm, was applied to investigate the most damaged regions. Partial correlation analysis was used to investigate the correlation between MRI quantitative metrics and clinical neurological scores. The area under the curve of FA-based classification model was 0.83, while they were 0.93 for MD and 0.81 for QSV. The Montreal cognitive assessment scores were correlated with the volume of the DGM and the expanded disability status scale scores were correlated with the MD of the GP and PT. The study results indicated that MS patients had involvement of DGM with the CN being the most affected. The atrophy of DGM in MS patients mainly affected cognitive function and the microstructural damage of DGM was mainly correlated with clinical disability.

A multicentre, prospective, double-blind study comparing the accuracy of autoantibody diagnostic assays in myasthenia gravis: the SCREAM study.

Background: Laboratory determination of autoantibodies against acetylcholine receptor (AChR), muscle-specific kinase (MuSK) and other autoantigens have been integrated into the diagnosis of myasthenia gravis (MG). However, evidence supporting the selection of methodologies is lacking. Methods: In this prospective, multicentre cohort study, we recruited patients with suspected MG to evaluate the diagnostic accuracy of cell-based assay (CBA), radioimmunoprecipitation assay (RIPA) and enzyme-linked immunosorbent assay (ELISA) in detecting AChR and MuSK autoantibodies. This study is registered with www.clinicaltrials.gov, number NCT05219097. Findings: 2272 eligible participants were recruited, including 2043 MG, 229 non-MG subjects. AChR antibodies were detected in 1478, 1310, and 1280 out of a total of 2043 MG patients by CBA, RIPA, and ELISA, respectively; sensitivity, 72.3% (95% CI, 70.3-74.3), 64.1% (95% CI, 62.0-66.2), 62.7% (95% CI, 60.5-64.8); specificity, 97.8% (95% CI, 95.0-99.3), 97.8% (95% CI, 95.0-99.3), 94.8% (95% CI, 91.9-97.7). MuSK antibodies were found in 59, 50, and 54 from 2043 MG patients by CBA, RIPA and ELISA, respectively; sensitivity, 2.9% (95% CI, 2.2-3.7), 2.4% (95% CI, 1.8-3.2), 2.6% (95% CI, 2.0-3.4); specificity, 100% (95% CI, 98.4-100), 100% (95% CI, 98.4-100), and 99.1% (95% CI, 96.9-99.9). The area under the curve of AChR antibodies tested by CBA was 0.858, and there were statistical differences with RIPA (0.843; p = 0.03) and ELISA (0.809; p < 0.0001). Interpretation: CBA has a higher diagnostic accuracy compared to RIPA or ELISA in detecting AChR and MuSK autoantibodies for MG diagnosis. Funding: New Terrain Biotechnology, Inc., Tianjin, China.

LRFN5 and OLFM4 as novel potential biomarkers for major depressive disorder: a pilot study.

Evidences have shown that both LRFN5 and OLFM4 can regulate neural development and synaptic function. Recent genome-wide association studies on major depressive disorder (MDD) have implicated LRFN5 and OLFM4, but their expressions and roles in MDD are still completely unclear. Here, we examined serum concentrations of LRFN5 and OLFM4 in 99 drug-naive MDD patients, 90 drug-treatment MDD patients, and 81 healthy controls (HCs) using ELISA methods. The results showed that both LRFN5 and OLFM4 levels were considerably higher in MDD patients compared to HCs, and were significantly lower in drug-treatment MDD patients than in drug-naive MDD patients. However, there were no significant differences between MDD patients who received a single antidepressant and a combination of antidepressants. Pearson correlation analysis showed that they were associated with the clinical data, including Hamilton Depression Scale score, age, duration of illness, fasting blood glucose, serum lipids, and hepatic, renal, or thyroid function. Moreover, these two molecules both yielded fairly excellent diagnostic performance in diagnosing MDD. In addition, a combination of LRFN5 and OLFM4 demonstrated a better diagnostic effectiveness, with an area under curve of 0.974 in the training set and 0.975 in the testing set. Taken together, our data suggest that LRFN5 and OLFM4 may be implicated in the pathophysiology of MDD and the combination of LRFN5 and OLFM4 may offer a diagnostic biomarker panel for MDD.

Altered MANF and RYR2 concentrations associated with hypolipidemia in the serum of patients with schizophrenia.

Schizophrenia (SCZ) is associated with abnormal serum lipid profiles, but their relationship is poorly understood. Mesencephalic astrocyte-derived neurotrophic factor (MANF) is an important regulator of lipid metabolism. Previous studies have shown its involvement in the pathogenesis of numerous neuropsychiatric disorders, while its role in SCZ is still unknown. Therefore, this study was conducted to examine serum MANF levels in patients with SCZ, and to investigate the potential relationship between MANF, serum lipid levels and SCZ. The results showed that total cholesterol (TC) levels were significantly lower in 225 patients with SCZ than in 233 healthy controls (HCs). According to Ingenuity Pathway Analysis, hypolipidemia is associated with SCZ via MANF/ryanodine receptor 2 (RYR2) pathway. This theory was supported by another sample set, which showed significantly lower MANF levels and higher RYR2 levels in the serum of 170 SCZ patients compared to 80 HCs. Moreover, MANF and RYR2 levels both were significantly correlated with the severity of psychotic symptoms and TC levels. In addition, a model consisting of MANF and RYR2 was found to be effective in distinguishing SCZ patients from HCs. These findings suggested that the MANF/RYR2 pathway might serve as a bridge between hypolipidemia and SCZ, and MANF and RYR2 held promise as biomarkers for SCZ.

Subacute cutaneous lupus erythematosus as a rare complication of disease-modifying therapy administration in multiple sclerosis: case report.

BACKGROUND: Teriflunomide, the active metabolite of leflunomide, is a disease-modifying therapy drug used for the treatment of multiple sclerosis (MS), yet the complications associated with this drug remain not fully understood. Here we present the rare case of a 28-year-old female MS patient who developed subacute cutaneous lupus erythematosus (SCLE) following teriflunomide treatment. Though SCLE has been reported to be associated with leflunomide, the current report represents the first documented evidence demonstrating SCLE as a potential teriflunomide treatment-related complication. Additionally, a literature review on the leflunomide-induced SCLE was conducted to emphasize the association of SCLE with teriflunomide, specifically amongst the female demographic with a preexisting autoimmune diathesis. CASE PRESENTATION: A 28-year-old female first presented with MS symptoms in the left upper limb along with blurred vision in the left eye. Medical and family histories were unremarkable. The patient exhibited positive serum biomarkers including ANA, Ro/SSA, La/SSB, and Ro-52 antibodies. Relapsing-remitting MS was diagnosed according to the 2017 McDonald's diagnostic criteria, and remission was achieved upon intravenous administration of methylprednisolone followed by teriflunomide sequential therapy. Three months post-teriflunomide treatment, the patient developed multiple facial cutaneous lesions. SCLE was subsequently diagnosed and was attributed to treatment-related complication. Interventions include oral administration of hydroxychloroquine and tofacitinib citrate effectively resolved cutaneous lesions. Discontinuation of hydroxychloroquine and tofacitinib citrate treatment led to recurring SCLE symptoms under continuous teriflunomide treatment. Full remission of facial annular plaques was achieved after re-treatment with hydroxychloroquine and tofacitinib citrate. The patient's clinical condition remained stable in long-term outpatient follow-ups. CONCLUSIONS: As teriflunomide has become a standard disease-modifying therapy for MS, the current case report highlights the importance of monitoring treatment-related complications, specifically in relation to SCLE symptoms.

The case report of AQP4 and MOG IgG double positive NMOSD treated with subcutaneous Ofatumumab.

Neuromyelitis optica spectrum disorders (NMOSD) is an autoimmune demyelinating disease with IgG against aquaporin 4 (AQP4) in more than two thirds of patients. Anti-myelin-oligodendrocyte glycoprotein (MOG) antibody is found in some AQP4-negative NMOSD patients and MOG antibody-associated disease (MOGAD) is thought to be distinct from NMOSD. Due to the high disabling nature of NMOSD, treatment strategy on first attack is crucial for good prognosis. Rituximab (RTX), an anti-CD20 monoclonal antibody (mAb), is the first-line treatment for NMOSD. However, RTX can be limited by the relatively high rate of systemic allergic reaction. Herein, we reported a rare case of AQP4 and MOG-IgG double positive NMOSD patient effectively and safely treated with ofatumumab (OFA), a novel fully humanized anti-CD20 mAb.