Advancements in the understanding and management of histiocytic neoplasms.

Histiocytic neoplasms are rare diseases involving macrophages, dendritic cells, and monocytes. They include Langerhans cell histiocytosis (LCH), Erdheim-Chester disease (ECD), Rosai-Dorfman disease (RDD), juvenile xanthogranuloma (JXG), and histiocytic sarcoma. Histiocytic neoplasms are characterized by varied clinical courses and prognoses, necessitating a nuanced understanding of their classification, epidemiology, and clinical manifestations. Genetic studies have revealed somatic mutations, predominantly in the MAPK pathway, suggesting a clonal neoplastic nature. This review covers the current understanding of histiocytic neoplasms, molecular pathophysiology, with a particular focus on mutations in genes such as BRAF, MAP2K1, and the PI3K-AKT signaling pathways, and evolving treatment strategies, especially focusing on LCH, ECD, RDD, and JXG. The treatment landscape has evolved with advancements in targeted therapies. BRAF inhibitors, such as vemurafenib and dabrafenib, have shown efficacy, especially in high-risk LCH cases; however, challenges remain, including relapse post-treatment discontinuation, and adverse effects. MEK inhibitors have also demonstrated effectiveness, and cobimetinib has recently been approved for use in adults. Further research is required to determine the optimal treatment duration and strategies for managing therapy interruptions. Advancements in molecular genetics and targeted therapies have revolutionized the management of histiocytic neoplasms. However, ongoing research is crucial for optimizing patient outcomes.

Tunable translation-level CRISPR interference by dCas13 and engineered gRNA in bacteria.

Although CRISPR-dCas13, the RNA-guided RNA-binding protein, was recently exploited as a translation-level gene expression modulator, it has still been difficult to precisely control the level due to the lack of detailed characterization. Here, we develop a synthetic tunable translation-level CRISPR interference (Tl-CRISPRi) system based on the engineered guide RNAs that enable precise and predictable down-regulation of mRNA translation. First, we optimize the Tl-CRISPRi system for specific and multiplexed repression of genes at the translation level. We also show that the Tl-CRISPRi system is more suitable for independently regulating each gene in a polycistronic operon than the transcription-level CRISPRi (Tx-CRISPRi) system. We further engineer the handle structure of guide RNA for tunable and predictable repression of various genes in Escherichia coli and Vibrio natriegens. This tunable Tl-CRISPRi system is applied to increase the production of 3-hydroxypropionic acid (3-HP) by 14.2-fold via redirecting the metabolic flux, indicating the usefulness of this system for the flux optimization in the microbial cell factories based on the RNA-targeting machinery.

Myricetin ameliorates the severity of pancreatitis in mice by regulating cathepsin B activity and inflammatory cytokine production.

Cathepsin B (CTSB) and inflammatory cytokines are critical in initiating and developing pancreatitis. Calcineurin, a central calcium (Ca2+)-responsive signaling molecule, mediates acinar cell death and inflammatory responses leading to pancreatitis. However, the detailed mechanisms for regulating CTSB activity and inflammatory cytokine production are unknown. Myricetin (MC) exhibits various biological activities, including anti-inflammatory effects. Here, we aimed to investigate MC effects on pancreatitis and the underlying mechanisms. Prophylactic and therapeutic MC treatment ameliorated the severity of cerulein-, L-arginine-, and PDL-induced acute pancreatitis (AP). The inhibition of CTSB activity by MC was mediated via decreased calcineurin activity and macrophage infiltration, not neutrophils infiltration, into the pancreas. Additionally, calcineurin activity inhibition by MC prevented the phosphorylation of Ca2+/CaM-dependent protein kinase kinase 2 (CaMKK2) during AP, resulting in the inhibition of CaMKIV phosphorylation and adenosine monophosphate-activated protein kinase (AMPK) dephosphorylation. Furthermore, MC reduced nuclear factor-κB activation by modulating the calcineurin-CaMKIV-IKKα/ß-Iκ-Bα and calcineurin-AMPK-sirtuin1 axes, resulting in reduced production of tumor necrosis factor-α, interleukin (IL)-1ß, and IL-6. Our results showed that MC alleviated AP severity by inhibiting acinar cell death and inflammatory responses, suggesting that MC as a calcineurin and CaMKK2 signaling modulator may be a potential treatment for AP.

Multilayer network analysis in patients with juvenile myoclonic epilepsy.

INTRODUCTION: We conducted a multilayer network analysis in patients with juvenile myoclonic epilepsy (JME) and healthy controls, to investigate the gray matter layer using a morphometric similarity network and analyze the white matter layer using structural connectivity. METHODS: We enrolled 42 patients with newly diagnosed JME and 53 healthy controls. Brain magnetic resonance imaging (MRI) using a three-tesla MRI scanner, including T1-weighted imaging and diffusion tensor imaging (DTI) were performed. We created a gray matter layer matrix with a morphometric similarity network using T1-weighted imaging, and a white matter layer matrix with structural connectivity using the DTI. Subsequently, we performed a multilayer network analysis by applying graph theory. RESULTS: There were significant differences in network at the global level in the multilayer network analysis between the groups. The average multiplex participation of patients with JME was lower than that of healthy controls (0.858 vs. 0.878, p = 0.007). In addition, several regions showed significant differences in multiplex participation at the nodal level in the multilayer network analysis. Multiplex participation in the right entorhinal cortex was lower, whereas multiplex participation in the right supramarginal gyrus was higher at the nodal level in the multilayer network analysis of patients with JME compared to healthy controls. CONCLUSION: We demonstrated differences in network at the global and nodal levels in the multilayer network analysis between patients with JME and healthy controls. These features may be associated with the pathophysiology of JME and could help us understand the complex brain network in patients with JME.

StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records.

Determining acute ischemic stroke (AIS) etiology is fundamental to secondary stroke prevention efforts but can be diagnostically challenging. We trained and validated an automated classification tool, StrokeClassifier, using electronic health record (EHR) text from 2039 non-cryptogenic AIS patients at 2 academic hospitals to predict the 4-level outcome of stroke etiology adjudicated by agreement of at least 2 board-certified vascular neurologists' review of the EHR. StrokeClassifier is an ensemble consensus meta-model of 9 machine learning classifiers applied to features extracted from discharge summary texts by natural language processing. StrokeClassifier was externally validated in 406 discharge summaries from the MIMIC-III dataset reviewed by a vascular neurologist to ascertain stroke etiology. Compared with vascular neurologists' diagnoses, StrokeClassifier achieved the mean cross-validated accuracy of 0.74 and weighted F1 of 0.74 for multi-class classification. In MIMIC-III, its accuracy and weighted F1 were 0.70 and 0.71, respectively. In binary classification, the two metrics ranged from 0.77 to 0.96. The top 5 features contributing to stroke etiology prediction were atrial fibrillation, age, middle cerebral artery occlusion, internal carotid artery occlusion, and frontal stroke location. We designed a certainty heuristic to grade the confidence of StrokeClassifier's diagnosis as non-cryptogenic by the degree of consensus among the 9 classifiers and applied it to 788 cryptogenic patients, reducing cryptogenic diagnoses from 25.2% to 7.2%. StrokeClassifier is a validated artificial intelligence tool that rivals the performance of vascular neurologists in classifying ischemic stroke etiology. With further training, StrokeClassifier may have downstream applications including its use as a clinical decision support system.

Structural connectivity as a predictive factor for perampanel response in patients with epilepsy.

OBJECTIVES: This study aimed to identify clinical characteristics that could predict the response to perampanel (PER) and determine whether structural connectivity is a predictive factor. METHODS: We enrolled patients with epilepsy who received PER and were followed-up for a minimum of 12 months. Good PER responders, who were seizure-free or presented with more than 50 % seizure reduction, were classified separately from poor PER responders who had seizure reduction of less than 50 % or non-responders. A graph theoretical analysis was conducted based on diffusion tensor imaging to calculate network measures of structural connectivity among patients with epilepsy. RESULTS: 106 patients with epilepsy were enrolled, including 26 good PER responders and 80 poor PER responders. Good PER responders used fewer anti-seizure medications before PER administration compared to those by poor PER responders (3 vs. 4; p = 0.042). Early PER treatment was more common in good PER responders than poor PER responders (46.2 vs. 21.3 %, p = 0.014). Regarding cortical structural connectivity, the global efficiency was higher and characteristic path length was lower in good PER responders than in poor PER responders (0.647 vs. 0.635, p = 0.006; 1.726 vs. 1,759, p = 0.008, respectively). For subcortical structural connectivity, the mean clustering coefficient and small-worldness index were higher in good PER responders than in poor PER responders (0.821 vs. 0.791, p = 0.009; 0.597 vs. 0.560, p = 0.009, respectively). CONCLUSION: This study demonstrated that early PER administration can predict a good PER response in patients with epilepsy, and structural connectivity could potentially offer clinical utility in predicting PER response.

Temporal Muscle Thickness and Sarcopenia Components in Healthy Adults, Validated through Allgeun Diagnostic Tool.

Sarcopenia, characterized by progressive muscle loss and functional decline, poses significant risks, including falls, impaired daily activities, and increased mortality. We developed Allgeun, a novel device that measures handgrip strength, muscle mass, and physical performance. This study aimed to investigate whether temporal muscle thickness (TMT) could be used as a sarcopenia marker and to evaluate the usability of Allgeun. This prospective study enrolled 28 participants without medical or neurological disorders. They underwent three-dimensional T1-weighted imaging using a 3 Tesla magnetic resonance imaging scanner. TMT was measured based on T1-weighted images by a board-certified neuroradiologist. Allgeun was used to measure the following three key components of sarcopenia: muscle strength (handgrip strength), muscle mass (calf and thigh circumference), and physical performance (five times the chair stand test). Correlation analysis was conducted between TMT and the results of the handgrip strength, calf and thigh circumferences, and chair stand tests. There were moderate positive correlations between TMT and calf circumference (r = 0.413, p = 0.029), thigh circumference (r = 0.486, p = 0.008), and handgrip strength (r = 0.444, p = 0.018). However, no significant correlation was observed between TMT and physical performance (r = -0.000, p = 0.998). Our findings underscore TMT's potential as an indicator of sarcopenia, particularly regarding muscle mass and strength. Additionally, we demonstrated that the new device, Allgeun, is useful for screening and diagnosing the severity of sarcopenia.

Correlation of diffusion tensor tractography with obstructive sleep apnea severity.

INTRODUCTION: Using correlation tractography, this study aimed to find statistically significant correlations between white matter (WM) tracts in participants with obstructive sleep apnea (OSA) and OSA severity. We hypothesized that changes in certain WM tracts could be related to OSA severity. METHODS: We enrolled 40 participants with OSA who underwent diffusion tensor imaging (DTI) using a 3.0 Tesla MRI scanner. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and quantitative anisotropy (QA)-values were used in the connectometry analysis. The apnea-hypopnea index (AHI) is a representative measure of the severity of OSA. Diffusion MRI connectometry that was used to derive correlational tractography revealed changes in the values of FA, MD, AD, RD, and QA when correlated with the AHI. A false-discovery rate threshold of 0.05 was used to select tracts to conduct multiple corrections. RESULTS: Connectometry analysis revealed that the AHI in participants with OSA was negatively correlated with FA values in WM tracts that included the cingulum, corpus callosum, cerebellum, inferior longitudinal fasciculus, fornices, thalamic radiations, inferior fronto-occipital fasciculus, superior and posterior corticostriatal tracts, medial lemnisci, and arcuate fasciculus. However, there were no statistically significant results in the WM tracts, in which FA values were positively correlated with the AHI. In addition, connectometry analysis did not reveal statistically significant results in WM tracts, in which MD, AD, RD, and QA values were positively or negatively correlated with the AHI. CONCLUSION: Several WM tract changes were correlated with OSA severity. However, WM changes in OSA likely involve tissue edema and not neuronal changes, such as axonal loss. Connectometry analyses are valuable tools for detecting WM changes in sleep disorders.

Alteration of multilayer network perspective on gray and white matter connectivity in obstructive sleep apnea.

PURPOSE: The objective of this research was to examine changes in the neural networks of both gray and white matter in individuals with obstructive sleep apnea (OSA) in comparison to those without the condition, employing a comprehensive multilayer network analysis. METHODS: Patients meeting the criteria for OSA were recruited through polysomnography, while a control group of healthy individuals matched for age and sex was also assembled. Utilizing T1-weighted imaging, a morphometric similarity network was crafted to represent gray matter, while diffusion tensor imaging provided structural connectivity for constructing a white matter network. A multilayer network analysis was then performed, employing graph theory methodologies. RESULTS: We included 40 individuals diagnosed with OSA and 40 healthy participants in our study. Analysis revealed significant differences in various global network metrics between the two groups. Specifically, patients with OSA exhibited higher average degree overlap and average multilayer clustering coefficient (28.081 vs. 23.407, p < 0.001; 0.459 vs. 0.412, p = 0.004), but lower multilayer modularity (0.150 vs. 0.175, p = 0.001) compared to healthy controls. However, no significant differences were observed in average multiplex participation, average overlapping strength, or average weighted multiplex participation between the patients with OSA and healthy controls. Moreover, several brain regions displayed notable differences in degree overlap at the nodal level between patients with OSA and healthy controls. CONCLUSION: Remarkable alterations in the multilayer network, indicating shifts in both gray and white matter, were detected in patients with OSA in contrast to their healthy counterparts. Further examination at the nodal level unveiled notable changes in regions associated with cognition, underscoring the effectiveness of multilayer network analysis in exploring interactions across brain layers.

The Factors Influencing Chronic Kidney Disease Incidence: Database from the Korean National Health Insurance Sharing Service (NHISS).

Background: The global prevalence of chronic kidney disease (CKD) is increasing, with diabetes accounting for the highest proportion. We analyzed the influence of clinical factors on the incidence of CKD according to the renal function, primary focusing on patients with diabetes. Methods: We used the Sample Cohorts Database provided by the National Health Insurance Sharing Service (NHISS) in Korea. Participants aged ≥ 40 years who underwent a health checkup in 2009 were categorized into six groups based on their eGFR values (<60 mL/min, 60-89 mL/min, ≥90 mL/min) and the presence of diabetes. And all patients with CKD at 2009 screening were excluded. The participants were tracked from 2010 to 31 December 2019. The CKD incidence rate according to the eGFR values and the effect of the accompanying factors on CKD incidence were confirmed. Results: 148,089 people without CKD were analyzed. The CKD incidence rate was highest in those with eGFR < 60 mL/min with diabetes and lowest in those with eGFR ≥ 90 mL/min without diabetes. The CKD incidence rates were similar between the eGFR < 60 mL/min group without diabetes and the eGFR 60-89 mL/min group with diabetes. Compared to under 44 years of age, the hazard ratio of CKD incidence was 8 times higher in over 75 years of age. Men had a 1.7-fold higher risk of developing CKD than women. Current smoker, hypertension, dyslipidemia, myocardial infarction history, and atrial fibrillation and flutter increased the risk of CKD incidence. Age, diabetes, and baseline eGFR are important factors in the occurrence of CKD. As age increases, the risk of developing CKD in men increases compared to women. Conclusions: These results will be helpful in predicting risk groups for CKD and establishing strategies to lowering CKD incidence.

Amoebicidal effect of chlorine dioxide gas against pathogenic Naegleria fowleri and Acanthamoeba polyphaga.

The pathogenic free-living amoebae, Naegleria fowleri and Acanthamoeba polyphaga, are found in freshwater, soil, and unchlorinated or minimally chlorinated swimming pools. N. fowleri and A. polyphaga are becoming problematic as water leisure activities and drinking water are sources of infection. Chlorine dioxide (ClO2) gas is a potent disinfectant that is relatively harmless to humans at the concentration used for disinfection. In this study, we examined the amoebicidal effects of ClO2 gas on N. fowleri and A. polyphaga. These amoebae were exposed to ClO2 gas from a ready-to-use product (0.36 ppmv/h) for 12, 24, 36, and 48 h. Microscopic examination showed that the viability of N. fowleri and A. polyphaga was effectively inhibited by treatment with ClO2 gas in a time-dependent manner. The growth of N. fowleri and A. polyphaga exposed to ClO2 gas for 36 h was completely inhibited. In both cases, the mRNA levels of their respective actin genes were significantly reduced following treatment with ClO2 gas. ClO2 gas has an amoebicidal effect on N. fowleri and A. polyphaga. Therefore, ClO2 gas has been proposed as an effective agent for the prevention and control of pathogenic free-living amoeba contamination.

Thin-slice elbow MRI with deep learning reconstruction: Superior diagnostic performance of elbow ligament pathologies.

PURPOSE: With the slice thickness routinely used in elbow MRI, small or subtle lesions may be overlooked or misinterpreted as insignificant. To compare 1 mm slice thickness MRI (1 mm MRI) with deep learning reconstruction (DLR) to 3 mm slice thickness MRI (3 mm MRI) without/with DLR, and 1 mm MRI without DLR regarding image quality and diagnostic performance for elbow tendons and ligaments. METHODS: This retrospective study included 53 patients between February 2021 and January 2022, who underwent 3 T elbow MRI, including T2-weighted fat-saturated coronal 3 mm and 1 mm MRI without/with DLR. Two radiologists independently assessed four MRI scans for image quality and artefacts, and identified the pathologies of the five elbow tendons and ligaments. In 19 patients underwent elbow surgery after elbow MRI, diagnostic performance was evaluated using surgical records as a reference standard. RESULTS: For both readers, 3 mm MRI with DLR had significant higher image quality scores than 3 mm MRI without DLR and 1 mm MRI with DLR (all P < 0.01). For common extensor tendon and elbow ligament pathologies, 1 mm MRI with DLR showed the highest number of pathologies for both readers. The 1 mm MRI with DLR had the highest kappa values for all tendons and ligaments. For reader 1, 1 mm MRI with DLR showed superior diagnostic performance than 3 mm MRI without/with DLR. For reader 2, 1 mm MRI with DLR showed the highest diagnostic performance; however, there was no significant difference. CONCLUSIONS: One mm MRI with DLR showed the highest diagnostic performance for evaluating elbow tendon and ligament pathologies, with similar subjective image qualities and artefacts.

Sarcopenia and anti-seizure medication response in juvenile myoclonic epilepsy.

INTRODUCTION: This study aimed to investigate the presence of sarcopenia in patients with juvenile myoclonic epilepsy (JME) and the association between sarcopenia and response to anti-seizure medication (ASM) in patients with JME. METHODS: We enrolled 42 patients with JME and 42 healthy controls who underwent brain magnetic resonance imaging with three-dimensional T1-weighted imaging. We measured the temporal muscle thickness (TMT), a radiographic marker for sarcopenia, using T1-weighted imaging. We compared the TMT between patients with JME and healthy controls and analyzed it according to the ASM response in patients with JME. We also performed a receiver operating characteristic (ROC) curve analysis to evaluate how well the TMT differentiated the groups. RESULTS: The TMT in patients with JME did not differ from that in healthy controls (9.630 vs. 9.956 mm, p = .306); however, ASM poor responders had a lower TMT than ASM good responders (9.109 vs. 10.104 mm, p = .023). ROC curve analysis revealed that the TMT exhibited a poor performance in differentiating patients with JME from healthy controls, with an area under the ROC curve of .570 (p = .270), but good performance in differentiating between ASM good and poor responders, with an area under the ROC curve of .700 (p = .015). CONCLUSION: The TMT did not differ between patients with JME and healthy controls; however, it was reduced in ASM poor responders compared to ASM good responders, suggesting a link between ASM response and sarcopenia in patients with JME. TMT can be used to investigate sarcopenia in various neurological disorders.

Brain connectivity in status epilepticus as a predictor of outcome: A diffusion tensor imaging study.

BACKGROUND AND PURPOSE: We aimed to explore structural connectivity in status epilepticus. METHODS: We enrolled participants who underwent diffusion tensor imaging. We applied graph theory to investigate structural connectivity. We compared the structural connectivity measures between patients and healthy controls and between patients with poor (modified Rankin Scale [mRS] >3) and good (mRS ≤3) admission outcomes. RESULTS: We enrolled 28 patients and 31 healthy controls (age 65.5 vs.62.0 years, p = .438). Of these patients, 16 and 12 showed poor and good admission outcome (age 65.5 vs.62.0 years, p = .438). The assortative coefficient (-0.113 vs. -0.121, p = .021), mean clustering coefficient (0.007 vs.0.006, p = .009), global efficiency (0.023 vs.0.020, p = .009), transitivity (0.007 vs.0.006, p = .009), and small-worldness index (0.006 vs.0.005, p = .021) were higher in patients with status epilepticus than in healthy controls. The assortative coefficient (-0.108 vs. -0.119, p = .042), mean clustering coefficient (0.007 vs.0.006, p = .042), and transitivity (0.008 vs.0.007, p = .042) were higher in patients with poor admission outcome than in those with good admission outcome. MRS score was positively correlated with structural connectivity measures, including the assortative coefficient (r = 0.615, p = .003), mean clustering coefficient (r = 0.544, p = .005), global efficiency (r = 0.515, p = .007), transitivity (r = 0.547, p = .007), and small-worldness index (r = 0.435, p = .024). CONCLUSION: We revealed alterations in structural connectivity, showing increased integration and segregation in status epilepticus, which might be related with neuronal synchronization. This effect was more pronounced in patients with a poor admission outcome, potentially reshaping our understanding for comprehension of status epilepticus mechanisms and the development of more targeted treatments.

Local control and toxicity outcomes following consolidative radiation therapy in patients with high-risk neuroblastoma: a 20-year experience at a single center.

BACKGROUND: Intensive multimodal treatment can improve survival in patients with high-risk neuroblastoma, and consolidative radiation therapy has contributed to local control. We examined the clinical outcomes of patients who underwent consolidative radiation therapy at our institution. METHODS: We retrospectively reviewed the records of patients with high-risk neuroblastoma who underwent consolidative radiation therapy from March 2001 to March 2021 at Asan Medical Center. Patients underwent multimodal treatment including high-dose chemotherapy, surgery, stem cell transplantation, and maintenance therapy. Radiation (median, 21.0 Gy; range, 14-36) was administered to the primary site and surrounding lymph nodes. RESULTS: This study included 37 patients, and the median age at diagnosis was 2.8 years (range, 1.3-10.0). Four patients exhibited local failure, and 5-year free-from locoregional failure rate was 88.7%, with a median followup period of 5.7 years. The 5-year disease-free survival (DFS) and overall survival (OS) rates were 59.1% and 83.6%, respectively. Univariate analysis revealed that patients with neuron-specific enolase levels > 100 ng/mL had significantly worse DFS and OS (P = 0.036, 0.048), and patients with no residual disease before radiation therapy showed superior OS (P = 0.029). Furthermore, patients with 11q deletion or 17q gain exhibited poor DFS and OS, respectively (P = 0.021, 0.011). Six patients experienced grade 1 acute toxicity. Late toxicity was confirmed in children with long-term survival, predominantly hypothyroidism and hypogonadism, typically < grade 3, possibly attributed to combination treatment. Four patients experienced late toxicity ≥ grade 3 with chronic kidney disease, growth hormone abnormality, ileus, premature epiphyseal closure, and secondary tumor, and recovered by hospitalization or surgical treatment. CONCLUSIONS: In patients with high-risk neuroblastoma, consolidative radiotherapy to the primary tumor site resulted in excellent local control and a tolerable safety profile.

Alterations in the multilayer network in patients with rapid eye movement sleep behaviour disorder.

This study aimed to reveal the pathophysiology of isolated rapid eye movement sleep behaviour disorder (RBD) in patients using multilayer network analysis. Participants eligible for isolated RBD were included and verified via polysomnography. Both iRBD patients and healthy controls underwent brain MRI, including T1-weighted imaging and diffusion tensor imaging. Grey matter matrix was derived from T1-weighted images using a morphometric similarity network. White matter matrix was formed from diffusion tensor imaging-based structural connectivity. Multilayer network analysis of grey and white matter was performed using graph theory. We studied 29 isolated RBD patients and 30 healthy controls. Patients exhibited a higher average overlap degree (27.921 vs. 23.734, p = 0.002) and average multilayer clustering coefficient (0.474 vs. 0.413, p = 0.002) compared with controls. Additionally, several regions showed significant differences in the degree of overlap and multilayer clustering coefficient between patients with isolated RBD and healthy controls at the nodal level. The degree of overlap in the left medial orbitofrontal, left posterior cingulate, and right paracentral nodes and the multilayer clustering coefficients in the left lateral occipital, left rostral middle frontal, right fusiform, right inferior posterior parietal, and right parahippocampal nodes were higher in patients with isolated RBD than in healthy controls. We found alterations in the multilayer network at the global and nodal levels in patients with isolated RBD, and these changes may be associated with the pathophysiology of isolated RBD. Multilayer network analysis can be used widely to explore the mechanisms underlying various neurological disorders.

Identification of amnestic mild cognitive impairment by structural and functional MRI using a machine-learning approach.

OBJECTIVE: The importance of early treatment for mild cognitive impairment (MCI) has been extensively shown. However, classifying patients presenting with memory complaints in clinical practice as having MCI vs normal results is difficult. Herein, we assessed the feasibility of applying a machine learning approach based on structural volumes and functional connectomic profiles to classify the cognitive levels of cognitively unimpaired (CU) and amnestic MCI (aMCI) groups. We further applied the same method to distinguish aMCI patients with a single memory impairment from those with multiple memory impairments. METHODS: Fifty patients with aMCI were enrolled and classified as having either verbal or visual-aMCI (verbal or visual memory impairment), or both aMCI (verbal and visual memory impairments) based on memory test results. In addition, 26 CU patients were enrolled in the control group. All patients underwent structural T1-weighted magnetic resonance imaging (MRI) and resting-state functional MRI. We obtained structural volumes and functional connectomic profiles from structural and functional MRI, respectively, using graph theory. A support vector machine (SVM) algorithm was employed, and k-fold cross-validation was performed to discriminate between groups. RESULTS: The SVM classifier based on structural volumes revealed an accuracy of 88.9% at classifying the cognitive levels of patients with CU and aMCI. However, when the structural volumes and functional connectomic profiles were combined, the accuracy increased to 92.9%. In the classification of verbal or visual-aMCI (n = 22) versus both aMCI (n = 28), the SVM classifier based on structural volumes revealed a low accuracy of 36.7%. However, when the structural volumes and functional connectomic profiles were combined, the accuracy increased to 53.1%. CONCLUSION: Structural volumes and functional connectomic profiles obtained using a machine learning approach can be used to classify cognitive levels to distinguish between aMCI and CU patients. In addition, combining the functional connectomic profiles with structural volumes results in a better classification performance than the use of structural volumes alone for identifying both "aMCI versus CU" and "verbal- or visual-aMCI versus both aMCI" patients.

Harmonic field extension for QSM with reduced spatial coverage using physics-informed generative adversarial network.

Quantitative susceptibility mapping (QSM) is frequently employed in investigating brain iron related to brain development and diseases within deep gray matter (DGM). Nonetheless, the acquisition of whole-brain QSM data is time-intensive. An alternative approach, focusing the QSM specifically on areas of interest such as the DGM by reducing the field-of-view (FOV), can significantly decrease scan times. However, severe susceptibility value underestimations have been reported during QSM reconstruction with a limited FOV, largely attributable to artifacts from incorrect background field removal in the boundary region. This presents a considerable barrier to the clinical use of QSM with small spatial coverages using conventional methods alone. To mitigate the propagation of these errors, we proposed a harmonic field extension method based on a physics-informed generative adversarial network. Both quantitative and qualitative results demonstrate that our method outperforms conventional methods and delivers results comparable to those obtained with full FOV. Furthermore, we demonstrate the versatility of our method by applying it to data acquired prospectively with limited FOV and to data from patients with Parkinson's disease. The method has shown significant improvements in local field results, with QSM outcomes. In a clear illustration of its feasibility and effectiveness in real clinical environments, our proposed method addresses the prevalent issue of susceptibility underestimation in QSM with small spatial coverage.

Impact of deep learning-based reconstruction and anti-peristaltic agent on the image quality and diagnostic performance of magnetic resonance enterography comparing single breath-hold single-shot fast spin echo with and without anti-peristaltic agent.

Background: While anti-peristaltic agents are beneficial for high quality magnetic resonance enterography (MRE), their use is constrained by potential side effects and increased examination complexity. We explored the potential of deep learning-based reconstruction (DLR) to compensate for the absence of anti-peristaltic agent, improve image quality and reduce artifact. This study aimed to evaluate the need for an anti-peristaltic agent in single breath-hold single-shot fast spin-echo (SSFSE) MRE and compare the image quality and artifacts between conventional reconstruction (CR) and DLR. Methods: We included 45 patients who underwent MRE for Crohn's disease between October 2021 and September 2022. Coronal SSFSE images without fat saturation were acquired before and after anti-peristaltic agent administration. Four sets of data were generated: SSFSE CR with and without an anti-peristaltic agent (CR-A and CR-NA, respectively) and SSFSE DLR with and without an anti-peristaltic agent (DLR-A and DLR-NA, respectively). Two radiologists independently reviewed the images for overall quality and artifacts, and compared the three images with DLR-A. The degree of distension and inflammatory parameters were scored on a 5-point scale in the jejunum and ileum, respectively. Signal-to-noise ratio (SNR) levels were calculated in superior mesenteric artery (SMA) and iliac bifurcation level. Results: In terms of overall quality, DLR-NA demonstrated no significant difference compared to DLR-A, whereas CR-NA and CR-A demonstrated significant differences (P<0.05, both readers). Regarding overall artifacts, reader 1 rated DLR-A slightly better than DLR-NA in four cases and rated them as identical in 41 cases (P=0.046), whereas reader 2 demonstrated no difference. Bowel distension was significantly different in the jejunum (Reader 1: P=0.046; Reader 2: P=0.008) but not in the ileum. Agreements between the images (Reader 1: ĸ=0.73-1.00; Reader 2: ĸ=1.00) and readers (ĸ=0.66 for all comparisons) on inflammation were considered good to excellent. The sensitivity, specificity, and accuracy in diagnosing inflammation in the terminal ileum were the same among DLR-NA, DLR-A, CR-NA and CR-A (94.42%, 81.83%, and 89.69 %; and 83.33%, 90.91%, and 86.21% for Readers 1 and 2, respectively). In both SMA and iliac bifurcation levels, SNR of DLR images exhibited no significant differences. CR images showed significantly lower SNR compared with DLR images (P<0.001). Conclusions: SSFSE without anti-peristaltic agents demonstrated nearly equivalent quality to that with anti-peristaltic agents. Omitting anti-peristaltic agents before SSFSE and adding DLR could improve the scanning outcomes and reduce time.

Sarcopenia in patients with isolated rapid eye movement sleep behavior disorder.

OBJECTIVES: Evaluating of sarcopenia is important for promoting healthy aging, preventing functional decline, reducing the risk of falls and fractures, and improving overall quality of life. This study aimed to investigate sarcopenia in patients with isolated rapid eye movement sleep behavior disorder (RBD) using temporal muscle thickness (TMT) measurement. METHODS: This investigation was retrospectively conducted at a single tertiary hospital. We recruited patients diagnosed with isolated RBD confirmed by polysomnography and clinical history and healthy participants as controls. Patients with isolated RBD and healthy controls underwent brain MRI scans, including three-dimensional T1-weighted imaging. We measured TMT, a radiographic marker of sarcopenia, based on the T1-weighted imaging. We compared the TMT between the groups and performed receiver operating characteristic (ROC) curve analysis to evaluate how well the TMT differentiated patients with isolated RBD from healthy controls. We also conducted a correlation analysis between the TMT and clinical factors. RESULTS: Our study included 28 patients with isolated RBD and 30 healthy controls. There was a significant difference in the TMT of both groups. The TMT was reduced in patients with isolated RBD than in healthy controls (11.843 vs. 10.420 mm, p = 0.002). In the ROC curve analysis, the TMT exhibited good performance in differentiating patients with isolated RBD from healthy controls, with an area under the curve of 0.708. Furthermore, age was negatively correlated with TMT in patients with isolated RBD (r = -0.453, p = 0.015). CONCLUSION: We demonstrate that TMT is reduced in patients with isolated RBD compared with healthy controls, confirming sarcopenia in patients with isolated RBD. The result suggests an association between neurodegeneration and sarcopenia. TMT can be used to evaluate sarcopenia in sleep disorders.