scholarly journals IgLON 5 Antibody Syndrome: Isolated Case of a Patient With Indolent Disease Progression and Unusual MRI Findings

Cureus ◽  
2021 ◽  
Author(s):  
Sihyeong Park ◽  
Jonathan Doan ◽  
Irfan Sheikh ◽  
Ajaz A Sheikh
Keyword(s):  
Disease Progression ◽  
Mri Findings ◽  
Indolent Disease

scholarly journals Longitudinal MRI findings in patient with SLC25A12 pathogenic variants inform disease progression and classification

2019 ◽  
Vol 179 (11) ◽  
pp. 2284-2291 ◽  
Author(s):  
Brian C. Kavanaugh ◽  
Emily B. Warren ◽  
Ozan Baytas ◽  
Michael Schmidt ◽  
Derek Merck ◽  
...  
Keyword(s):  
Disease Progression ◽  
Mri Findings ◽  
Pathogenic Variants ◽  
Longitudinal Mri

Outcomes of Cryptococcus meningoencephalitis and associated magnetic resonance imaging findings

2020 ◽  
Author(s):  
Woo-Jin Lee ◽  
Young Jin Ryu ◽  
Jangsup Moon ◽  
Soon-Tae Lee ◽  
Keun-Hwa Jung ◽  
...  
Keyword(s):  
Disease Progression ◽  
Brain Mri ◽  
Area Under The Curve ◽  
Mri Findings ◽  
Neurological Outcomes ◽  
Lesion Extension ◽  
Poor Outcomes ◽  
Periventricular Lesion ◽  
Score Range ◽  
Serial Brain

Abstract In Cryptococcus Neoformans meningoencephalitis, brain MRI findings might reflect the phathomechanism of disease progression that is fungal accumulation in the peri-venular space and consequent invasion into the parenchyma. This study analyzed serial brain MRI findings of 76 patients with cryptococcus meningoencephalitis in association with the disease progression and outcomes. MRI parameters included the enlarged periventricular space (ePVS) score (range 0 − 8), periventricular lesion extension, cryptococcoma, and hydrocephalus. Clinical outcomes at 2-week, 10-week, and 6-month were evaluated using modified Rankin scale (mRS) scores. At 6 months, 15 (19.7%) patient died and 34 (44.1%) had poor neurological outcomes (mRS scores > 2). At baseline, an ePVS score of ≥ 5 (Odds-ratio [OR]: 94.173, 95% confidence-interval [95% CI]: 7.507 − 1181.295, P < 0.001), periventricular lesion extension (OR: 51.965, 95% CI: 2.592 − 1041.673, P = 0.010), and presence of encephalitis feature (OR: 44.487, 95% CI: 1.689 − 1172.082, P = 0.023) were associated with 6-month poor outcomes. Presence of two or more risk factors at baseline was highly associated with the 6-month poor outcomes (area under the curve [AUC]: 0.978, P < 0.001) and mortality (AUC: 0.836, P < 0.001). Disease progression was associated with interval development of cryptococcoma and hydrocephalus. In conclusion, brain MRI findings might be useful in predicting poor outcomes and monitoring the disease progression of cryptococcus meningoencephalitis.

Role of Lncrnas in Early Stage Immunoglobulin Heavy Chain Variable Region (IGHV) Unmutated CLL Disease Progression

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 4364-4364 ◽  
Author(s):  
Renee C. Tschumper ◽  
Susan L. Slager ◽  
Tait D. Shanafelt ◽  
Neil E. Kay ◽  
Diane F. Jelinek
Keyword(s):  
Clinical Outcome ◽  
Disease Progression ◽  
Heavy Chain ◽  
Research Funding ◽  
Progressive Disease ◽  
Variable Region ◽  
Differentially Expressed ◽  
Disease Group ◽  
Heavy Chain Variable Region ◽  
Indolent Disease

Abstract BACKGROUND: Chronic lymphocytic leukemia (CLL) remains a heterogeneous disease despite significant advances in disease classification based upon cellular and molecular markers. Rai stage, immunoglobulin heavy chain variable region mutation status (IGHV), cytogenetic abnormalities, beta 2 microglobulin levels, expression of ZAP-70, CD38 and CD49d provide useful guides alone or in prognostic models for progression risk assessment but variability in clinical outcome in early Rai stage (Rai 0/I) unmutated (UM) IGHV patients persists. This variability in clinical outcome suggests that there are yet-to-be defined biologic/genetic factors that contribute to disease heterogeneity. Long non-coding RNAs (lncRNAs) display a range of diverse activities including regulation of gene transcription, post translational regulation and epigenetic regulation and represent an intriguing prospect for influencing CLL disease progression. LncRNAs have been implicated in diagnosis, prognosis and therapy of various cancers however their role in influencing disease progression in early Rai stage UM CLL has not yet been studied. Methods: To investigate a role for lncRNAs in UM Rai 0/I CLL patients we employed the Arraystar Human lncRNA Microarray v4.0. Using blood samples collected within 3 months of diagnosis, two cohorts of UM Rai 0/I patients were compared: one defined as progressive disease with time to first treatment (TTT) ≤ 2 years after diagnosis (n=34) and one as indolent disease with TTT ≥5 years after diagnosis (n=29). Differentially expressed lncRNAs were identified using Agilent GeneSpring GX Software v12.1. To be considered for further analysis, the difference in lncRNA expression had to have a fold change (FC) ≥ 2.0, a t-test p-value ≤ 0.05 and a false discovery rate (FDR) ≤ 0.05. Results: Over 1100 lncRNAs were found to be differentially expressed between the progressive and indolent UM CLL cohorts. Greater than 150 of these lncRNAs have known associated genes and are well annotated and validated. Among the differentially expressed lncRNAs, several are of particular interest and potentially relevant to CLL disease biology. For example, the lncRNA AL833181 was overexpressed in the progressive group. The associated gene for AL833181 is BCL11A and encodes a zinc finger protein that interacts with BCL6 and may itself serve as a proto-oncogene. Translocations involving BCL11A have been shown to identify a very aggressive subset of CLL (Am J Pathology, 2009). Therefore it is possible this lncRNA may be playing a role in deregulated expression of BCL11A. Within the indolent disease group, CTD-2566J3.1 was overexpressed >10-fold as compared to the progressive disease group. Its associated gene RAD51B is essential for DNA repair, suggesting the possibility that CTD-2566J3 may protect CLL cells from further DNA damage and disease progression. Also overexpressed in the indolent disease group were 2 lncRNAs that are associated with genes that encode proteins involved in the ubiquitin-proteasome system. USP2-AS1 lncRNA is associated with the USP2 gene and encodes an ubiquitin-specific protease required for TNF-alpha induced NF-kB signaling and is a specific deubiquitinase for cyclin D1, a known proto-oncogene. Furthermore, USP2-AS1 may also be linked to MYC. RP11-522I20.3 is associated with the QBLN1gene which encodes a protein that mediates the proteasomal degradation of misfolded or accumulated proteins. Thus, the associated lncRNAs may be acting as post-transcriptional gene silencers. Although CLL is not viewed as an "invasive" cancer, there were 5 lncRNAs overexpressed in the indolent group that are associated with Ankyrin genes which play a role in cell motility, activation and proliferation along with the lncRNA RP11-477D19 that is associated with theTIAM2 gene. TIAM2is a Rac guanine nucleotide exchange factor that can promote invasion and motility of cells. The respective lncRNAs may be down-regulating these genes and prohibiting malignant cell expansion. Conclusions: Our study reveals that there are indeed specific lncRNAs that are expressed at different levels in progressive versus indolent early Rai stage UM CLL and have the potential to impact a number of relevant biological processes and pathways. While these data are preliminary, the lncRNA AL833181 and its associated gene BCL11A may prove to be a potential marker and therapeutic target for aggressive disease. Disclosures Shanafelt: Pharmacyclics: Research Funding; Celgene: Research Funding; Cephalon: Research Funding; GlaxoSmithkKine: Research Funding; Genentech: Research Funding; Janssen: Research Funding; Hospira: Research Funding.

scholarly journals Enlarged periventricular space and periventricular lesion extension on baseline brain MRI predicts poor neurological outcomes in cryptococcus meningoencephalitis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Woo-Jin Lee ◽  
Young Jin Ryu ◽  
Jangsup Moon ◽  
Soon-Tae Lee ◽  
Keun-Hwa Jung ◽  
...  
Keyword(s):  
Disease Progression ◽  
Brain Mri ◽  
Area Under The Curve ◽  
Mri Findings ◽  
Neurological Outcomes ◽  
Lesion Extension ◽  
Poor Outcomes ◽  
Periventricular Lesion ◽  
Score Range ◽  
Serial Brain

AbstractIn Cryptococcus neoformans meningoencephalitis, brain MRI findings might reflect the phathomechanism of disease progression that is fungal accumulation in the peri-venular space and consequent invasion into the parenchyma. This study analyzed serial brain MRI findings of 76 patients with cryptococcus meningoencephalitis in association with the disease progression and outcomes. MRI parameters included the enlarged periventricular space (ePVS) score (range 0–8), periventricular lesion extension, cryptococcoma, and hydrocephalus. Clinical outcomes at 2-week, 10-week, and 6-month were evaluated using modified Rankin scale (mRS). At 6 months, 15 (19.7%) patients died and 34 (44.1%) had poor neurological outcomes (mRS scores > 2). At baseline, an ePVS score of ≥ 5 (Odds-ratio [OR]: 94.173, 95% confidence-interval [95%CI]: 7.507–1181.295, P < .001), periventricular lesion extension (OR: 51.965, 95%CI: 2.592–1041.673, P = .010), and presence of encephalitis feature (OR: 44.487, 95%CI: 1.689–1172.082, P = .023) were associated with 6-month poor outcomes. Presence of two or more risk factors among encephalitis feature, ePVS score ≥ 5, and periventricular lesion extension at baseline, was associated with 6-month poor outcomes (area under the curve [AUC]: 0.978, P < .001) and mortality (AUC: 0.836, P < .001). Disease progression was associated with interval development of cryptococcoma and hydrocephalus. Brain MRI findings might be useful in predicting outcomes and monitoring the progression of cryptococcus meningoencephalitis.

Audiovestibular symptoms in patients with multiple sclerosis: A correlation between self-reported symptomatology and MRI findings to monitor disease progression

2020 ◽  
Vol 45 ◽  
pp. 102431 ◽  
Author(s):  
Arianna Di Stadio ◽  
Massimo Ralli ◽  
Marta Altieri ◽  
Antonio Greco ◽  
Daniela Messineo ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Disease Progression ◽  
Mri Findings

scholarly journals Subnetwork-Based Analysis of Chronic Lymphocytic Leukemia Identifies Pathways That Associate with Disease Progression,

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 3564-3564 ◽  
Author(s):  
Han-Yu Chuang ◽  
Laura Z. Rassenti ◽  
Michelle Salcedo ◽  
Kate Licon ◽  
Alexander Kohlmann ◽  
...  
Keyword(s):  
Disease Progression ◽  
Cross Validation ◽  
Risk Groups ◽  
Lymphocytic Leukemia ◽  
Survival Curves ◽  
Survival Analyses ◽  
Expression Levels ◽  
Aggressive Disease ◽  
Indolent Disease ◽  
Fold Cross Validation

Abstract Abstract 3564 The clinical course of patients with chronic lymphocytic leukemia (CLL) is heterogeneous. Whereas some patients develop aggressive disease requiring early treatment, others can have highly indolent disease and not require therapy for many years. Several prognostic factors have been identified that can stratify patients into groups that differ in their relative tendency for disease progression and/or survival. Microarray studies have highlighted differences in mRNA levels found between such CLL subgroups. Here, we pursued a subnetwork-based analysis of gene expression profiles to discriminate between groups of patients with disparate risks for disease progression. The clinical characterization of patients, blood-sample preparation, and microarray processing all follow the unified protocol implemented by the Microarray Innovations in LEukemia (MILE) program, which proposed standards for microarray-based assays in the diagnosis and sub-classification of leukemia. From an initial cohort of 130 patients, we identified 38 prognostic subnetworks that could predict the relative risk for disease progression requiring therapy from the time of sample collection (Fig. 1A). The prognostic power of these subnetworks then was validated on a second cohort of patients in the MILE study and on another set of CLL patients evaluated outside the MILE program (Fig. 1B). The identified subnetworks could assess the risk for requiring therapy at the time of tissue collection more accurately than established markers (Fig. 1C). Statistical analyses of these and the microarray data collected in prior studies revealed the greatest divergence in gene expression was observed using samples collected within 1 year of diagnosis. Thereafter there was increasing congruence in the expression levels of some subnetworks between patients over time. Moreover, the expression levels of such predictive subnetworks could evolve in patients with otherwise indolent disease characteristics to resemble those associated with patients found to have aggressive disease at diagnosis. These analyses suggest that degenerate pathways apparently converge into common pathways that are associated with disease progression. We conclude that, in addition to having predictive power, these identified subnetworks represent an array of pathways associated with disease progression. As such, these results have implications for understanding cancer evolution and for the development of novel treatment strategies for patients with CLL.Figure 1Use of expression levels of genes versus subnetworks to stratify patient samples. (A) Five-fold cross validation on the 130 patients from UCSD. Survival analyses on SC→TX are shown for both the low (dashed lines) and high (solid lines) risk groups predicted by subnetwork signatures (red lines) or by gene signatures (green lines). (B-C) Survival curves on SC→TX for the 17 European patients (B) or for the patient cohort in Friedman et al (2009) (C). The two risk groups are predicted by two sets of markers developed on the UCSD cohort, including the 38 subnetworks (red lines) and the top 230 genes (green lines).Figure 1. Use of expression levels of genes versus subnetworks to stratify patient samples. (A) Five-fold cross validation on the 130 patients from UCSD. Survival analyses on SC→TX are shown for both the low (dashed lines) and high (solid lines) risk groups predicted by subnetwork signatures (red lines) or by gene signatures (green lines). (B-C) Survival curves on SC→TX for the 17 European patients (B) or for the patient cohort in Friedman et al (2009) (C). The two risk groups are predicted by two sets of markers developed on the UCSD cohort, including the 38 subnetworks (red lines) and the top 230 genes (green lines). Disclosures: Foa: Roche: Consultancy, Speakers Bureau.

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