Comparative analysis of glymphatic system alterations in multiple sclerosis and neuromyelitis optica spectrum disorder using MRI indices from diffusion tensor imaging.

OBJECTIVE: The glymphatic system is a glial-based perivascular network that promotes brain metabolic waste clearance. Glymphatic system dysfunction has been observed in both multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD), indicating the role of neuroinflammation in the glymphatic system. However, little is known about how the two diseases differently affect the human glymphatic system. The present study aims to evaluate the diffusion MRI-based measures of the glymphatic system by contrasting MS and NMOSD. METHODS: This prospective study included 63 patients with NMOSD (n = 21) and MS (n = 42) who underwent DTI. The fractional volume of extracellular-free water (FW) and an index of diffusion tensor imaging (DTI) along the perivascular space (DTI-ALPS) were used as indirect indicators of water diffusivity in the interstitial extracellular and perivenous spaces of white matter, respectively. Age and EDSS scores were adjusted. RESULTS: Using Bayesian hypothesis testing, we show that the present data substantially favor the null model of no differences between MS and NMOSD for the diffusion MRI-based measures of the glymphatic system. The inclusion Bayes factor (BF10) of model-averaged probabilities of the group (MS, NMOSD) was 0.280 for FW and 0.236 for the ALPS index. CONCLUSION: Together, these findings suggest that glymphatic alteration associated with MS and NMOSD might be similar and common as an eventual result, albeit the disease etiologies differ. PRACTITIONER POINTS: Previous literature indicates important glymphatic system alteration in MS and NMOSD. We explore the difference between MS and NMOSD using diffusion MRI-based measures of the glymphatic system. We show support for the null hypothesis of no difference between MS and NMOSD. This suggests that glymphatic alteration associated with MS and NMOSD might be similar and common etiology.

Dynamic Contrast-enhanced MRI Quantification of Altered Vascular Permeability in Autoimmune Encephalitis.

Background Blood-brain barrier (BBB) permeability change is a possible pathologic mechanism of autoimmune encephalitis. Purpose To evaluate the change in BBB permeability in patients with autoimmune encephalitis as compared with healthy controls by using dynamic contrast-enhanced (DCE) MRI and to explore its predictive value for treatment response in patients. Materials and Methods This single-center retrospective study included consecutive patients with probable or possible autoimmune encephalitis and healthy controls who underwent DCE MRI between April 2020 and May 2021. Automatic volumetric segmentation was performed on three-dimensional T1-weighted images, and volume transfer constant (Ktrans) values were calculated at encephalitis-associated brain regions. Ktrans values were compared between the patients and controls, with adjustment for age and sex with use of a nonparametric approach. The Wilcoxon rank sum test was performed to compare Ktrans values of the good (improvement in modified Rankin Scale [mRS] score of at least two points or achievement of an mRS score of ≤2) and poor (improvement in mRS score of less than two points and achievement of an mRS score >2) treatment response groups among the patients. Results Thirty-eight patients with autoimmune encephalitis (median age, 38 years [IQR, 29-59 years]; 20 [53%] female) and 17 controls (median age, 71 years [IQR, 63-77 years]; 12 [71%] female) were included. All brain regions showed higher Ktrans values in patients as compared with controls (P < .001). The median difference in Ktrans between the patients and controls was largest in the right parahippocampal gyrus (25.1 × 10-4 min-1 [95% CI: 17.6, 43.4]). Among patients, the poor treatment response group had higher baseline Ktrans values in both cerebellar cortices (P = .03), the left cerebellar cortex (P = .02), right cerebellar cortex (P = .045), left cerebral cortex (P = .045), and left postcentral gyrus (P = .03) than the good treatment response group. Conclusion DCE MRI demonstrated that BBB permeability was increased in all brain regions in patients with autoimmune encephalitis as compared with controls, and baseline Ktrans values were higher in patients with poor treatment response in the cerebellar cortex, left cerebral cortex, and left postcentral gyrus as compared with the good response group. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Filippi and Rocca in this issue.

New Subependymal Enhancement After Radiation Therapy in High-Grade Glioma: Utilizing Morphological Features and DSC Perfusion MRI in Differentiate Progression and Post-Radiation Changes.

BACKGROUND: The specific patterns of subependymal enhancement (SE) that frequently occur as radiation-induced changes in high-grade gliomas following radiotherapy are often overlooked. Perfusion MRI may offer a diagnostic clue. PURPOSE: To distinguish between radiation-induced SE and progression in adult high-grade diffuse gliomas after standard treatment. STUDY TYPE: Retrospective. POPULATION: Ninety-four consecutive high-grade diffuse glioma patients (mean age, 55 ± 14 years; 54 [57.4%] males) with new SE identified in follow-up MRI after completion of surgery plus chemoradiation: progression (N = 74) vs. regression (N = 20). FIELD STRENGTH/SEQUENCE: 3 T, gradient-echo dynamic susceptibility contrast-enhanced MRI, 3D gradient-echo contrast-enhanced T1-weighted imaging. ASSESSMENT: To differentiate between radiation changes and progression in SE evaluation, multivariable logistic regression was performed using significant variables among SE appearance interval, IDH mutation, morphological features, and rCBV. Cox regression was performed to predict the tumor progression. For the added value of the rCBV, a log-rank test was conducted between the multivariable logistic regression models with and without the rCBV. STATISTICAL TESTS: Logistic regression, Cox regression, receiver operating characteristic analysis, log-rank test. RESULTS: 38.3% (36/94) patients had first specific SE (9.2 ± 9.5 months after surgery), which disappeared in 21.3% (20/94) after 5.8 ± 5.8 months after initial appearance on post-radiation MRI. IDH mutation, elongated, small lesions with lower rCBV tended to regress: IDH mutation, elongation, diameter, and rCBV_p95; odds ratio, 0.32, 1.92, 1.70, and 2.47, respectively. Qualitative evaluation of shape revealed that thin and curvilinear-shaped SE tended to regress, indicating a significant correlation with quantitative shape features (r = 0.31). In Cox regression, rCBV and lesion shape were significant (hazard ratio = 1.09 and 0.54, respectively). For sub-centimeter lesions, the rCBV showed added value in predicting outcomes (area under the curve, 0.873 vs. 0.836; log-rank test). DATA CONCLUSION: Smaller, elongated lesions with lower rCBV and IDH mutation are associated with regression when differentiating radiation changes from progression in high-grade glioma with post-radiotherapy SE. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Diagnostic assessments and treatment results of well-differentiated gastric-type adenocarcinoma of the uterine cervix (Adenoma malignum): A multicenter retrospective analysis of KROG 22-03 study.

OBJECTIVE: Among cervical adenocarcinomas, well-differentiated gastric adenocarcinoma of the uterine cervix (WD-GAS), previously termed adenoma malignum (minimal deviation adenocarcinoma) is not well understood. Because of its rarity and difficulty in diagnosis, there is no standard care for WD-GAS. Thus, we conducted the first multicenter retrospective study on WD-GAS to clarify prognostic factors for long-term survival and recurrence. METHODS: Patients diagnosed with WD-GAS at eight hospitals participated in this multi-center study. Overall survival (OS) and recurrence-free survival (RFS) were calculated with the Kaplan-Meier method. Additionally, OS between the early and advanced FIGO stage groups were compared with the log-rank test. Cox regression analysis was conducted to identify significant factors associated with recurrence-free survival (RFS). RESULTS: A total of 73 patients from eight hospitals in South Korea were included in the analysis. The median follow-up period was 44.8 months, and all patients underwent curative surgical intervention as the primary treatment. Recurrence was observed in 17 patients (23.3%). Ten patients had locoregional recurrence, four patients had distant metastasis, and three patients presented with both locoregional recurrence and distant metastasis. The Cox regression analysis identified several statistically significant factors associated with RFS, including vaginal invasion (VI), parametrial invasion (PMI), resection margin (RM), and nodal and lymphovascular invasion (LVI). When considering these five factors together, patients without any of the factors exhibited recurrence-free survival (RFS) of 97.0% at three years and those with more than one of these factors had a 3-year RFS of 65.4% (P < 0.001). CONCLUSION: WD-GAS showed relatively high locoregional recurrence rate. Positive PMI, VI, RM, nodal involvement, and LVI were associated with a significant increase in recurrence or distant metastasis rates.

Predicting hematoma expansion in acute spontaneous intracerebral hemorrhage: integrating clinical factors with a multitask deep learning model for non-contrast head CT.

PURPOSE: To predict hematoma growth in intracerebral hemorrhage patients by combining clinical findings with non-contrast CT imaging features analyzed through deep learning. METHODS: Three models were developed to predict hematoma expansion (HE) in 572 patients. We utilized multi-task learning for both hematoma segmentation and prediction of expansion: the Image-to-HE model processed hematoma slices, extracting features and computing a normalized DL score for HE prediction. The Clinical-to-HE model utilized multivariate logistic regression on clinical variables. The Integrated-to-HE model combined image-derived and clinical data. Significant clinical variables were selected using forward selection in logistic regression. The two models incorporating clinical variables were statistically validated. RESULTS: For hematoma detection, the diagnostic performance of the developed multi-task model was excellent (AUC, 0.99). For expansion prediction, three models were evaluated for predicting HE. The Image-to-HE model achieved an accuracy of 67.3%, sensitivity of 81.0%, specificity of 64.0%, and an AUC of 0.76. The Clinical-to-HE model registered an accuracy of 74.8%, sensitivity of 81.0%, specificity of 73.3%, and an AUC of 0.81. The Integrated-to-HE model, merging both image and clinical data, excelled with an accuracy of 81.3%, sensitivity of 76.2%, specificity of 82.6%, and an AUC of 0.83. The Integrated-to-HE model, aligning closest to the diagonal line and indicating the highest level of calibration, showcases superior performance in predicting HE outcomes among the three models. CONCLUSION: The integration of clinical findings with non-contrast CT imaging features analyzed through deep learning showed the potential for improving the prediction of HE in acute spontaneous intracerebral hemorrhage patients.

Reliability of brain volume measures of accelerated 3D T1-weighted images with deep learning-based reconstruction.

PURPOSE: The time-intensive nature of acquiring 3D T1-weighted MRI and analyzing brain volumetry limits quantitative evaluation of brain atrophy. We explore the feasibility and reliability of deep learning-based accelerated MRI scans for brain volumetry. METHODS: This retrospective study collected 3D T1-weighted data using 3T from 42 participants for the simulated acceleration dataset and 48 for the validation dataset. The simulated acceleration dataset consists of three sets at different simulated acceleration levels (Simul-Accel) corresponding to level 1 (65% undersampling), 2 (70%), and 3 (75%). These images were then subjected to deep learning-based reconstruction (Simul-Accel-DL). Conventional images (Conv) without acceleration and DL were set as the reference. In the validation dataset, DICOM images were collected from Conv and accelerated scan with DL-based reconstruction (Accel-DL). The image quality of Simul-Accel-DL was evaluated using quantitative error metrics. Volumetric measurements were evaluated using intraclass correlation coefficients (ICCs) and linear regression analysis in both datasets. The volumes were estimated by two software, NeuroQuant and DeepBrain. RESULTS: Simul-Accel-DL across all acceleration levels revealed comparable or better error metrics than Simul-Accel. In the simulated acceleration dataset, ICCs between Conv and Simul-Accel-DL in all ROIs exceeded 0.90 for volumes and 0.77 for normative percentiles at all acceleration levels. In the validation dataset, ICCs for volumes > 0.96, ICCs for normative percentiles > 0.89, and R2 > 0.93 at all ROIs except pallidum demonstrated good agreement in both software. CONCLUSION: DL-based reconstruction achieves clinical feasibility of 3D T1 brain volumetric MRI by up to 75% acceleration relative to full-sampled acquisition.

Predicting mild cognitive impairments from cognitively normal brains using a novel brain age estimation model based on structural magnetic resonance imaging.

Brain age prediction is a practical method used to quantify brain aging and detect neurodegenerative diseases such as Alzheimer's disease (AD). However, very few studies have considered brain age prediction as a biomarker for the conversion of cognitively normal (CN) to mild cognitive impairment (MCI). In this study, we developed a novel brain age prediction model using brain volume and cortical thickness features. We calculated an acceleration of brain age (ABA) derived from the suggested model to estimate different diagnostic groups (CN, MCI, and AD) and to classify CN to MCI and MCI to AD conversion groups. We observed a strong association between ABA and the 3 diagnostic groups. Additionally, the classification models for CN to MCI conversion and MCI to AD conversion exhibited acceptable and robust performances, with area under the curve values of 0.66 and 0.76, respectively. We believe that our proposed model provides a reliable estimate of brain age for elderly individuals and can identify those at risk of progressing from CN to MCI. This model has great potential to reveal a diagnosis associated with a change in cognitive decline.

Heritability of cognitive abilities and regional brain structures in middle-aged to elderly East Asians.

This study examined the single-nucleotide polymorphism heritability and genetic correlations of cognitive abilities and brain structural measures (regional subcortical volume and cortical thickness) in middle-aged and elderly East Asians (Korean) from the Gwangju Alzheimer's and Related Dementias cohort study. Significant heritability was found in memory function, caudate volume, thickness of the entorhinal cortices, pars opercularis, superior frontal gyri, and transverse temporal gyri. There were 3 significant genetic correlations between (i) the caudate volume and the thickness of the entorhinal cortices, (ii) the thickness of the superior frontal gyri and pars opercularis, and (iii) the thickness of the superior frontal and transverse temporal gyri. This is the first study to describe the heritability and genetic correlations of cognitive and neuroanatomical traits in middle-aged to elderly East Asians. Our results support the previous findings showing that genetic factors play a substantial role in the cognitive and neuroanatomical traits in middle to advanced age. Moreover, by demonstrating shared genetic effects on different brain regions, it gives us a genetic insight into understanding cognitive and brain changes with age, such as aging-related cognitive decline, cortical atrophy, and neural compensation.

Association Between QRS Characteristics in Pulseless Electrical Activity and Survival Outcome in Cardiac Arrest Patients: A Systematic Review and Meta-Analysis.

OBJECTIVE: Recent studies have shown inconsistent results regarding the association between QRS characteristics and survival outcomes in patients with cardiac arrest and pulseless electrical activity (PEA) rhythms. This meta-analysis aimed to identify the usefulness of QRS width and frequency as prognostic tools for outcomes in patients with cardiac arrest and PEA rhythm. METHODS: Extensive searches were conducted using Medline, Embase, and the Cochrane Library to find articles published from database inception to 4 June 2023. Studies that assessed the association between the QRS characteristics of cardiac arrest patients with PEA rhythm and survival outcomes were included. The Newcastle-Ottawa Scale was used to assess the methodological quality of the included studies. RESULTS: A total of 9727 patients from seven observational studies were included in this systematic review and meta-analysis. The wide QRS group (QRS ≥ 120 ms) was associated with significantly higher odds of mortality than the narrow QRS group (QRS < 120 ms) (odds ratio (OR) = 1.86, 95% confidence interval (CI) = 1.11-3.11, I2 = 58%). The pooled OR for mortality was significantly higher in patients with a QRS frequency of < 60/min than in those with a QRS frequency of ≥ 60/min (OR = 1.90, 95% CI = 1.19-3.02, I2 = 65%). CONCLUSIONS: Wide QRS width or low QRS frequency is associated with increased odds of mortality in patients with PEA cardiac arrest. These findings may be beneficial to guide the disposition of cardiac arrest patients with PEA during resuscitation.

The 2021 WHO Classification for Gliomas and Implications on Imaging Diagnosis: Part 3-Summary of Imaging Findings on Glioneuronal and Neuronal Tumors.

The fifth edition of the World Health Organization classification of central nervous system tumors published in 2021 reflects the current transitional state between traditional classification system based on histopathology and the state-of-the-art molecular diagnostics. This Part 3 Review focuses on the molecular diagnostics and imaging findings of glioneuronal and neuronal tumors. Histological and molecular features in glioneuronal and neuronal tumors often overlap with pediatric-type diffuse low-grade gliomas and circumscribed astrocytic gliomas (discussed in the Part 2 Review). Due to this overlap, in several tumor types of glioneuronal and neuronal tumors the diagnosis may be inconclusive with histopathology and genetic alterations, and imaging features may be helpful to distinguish difficult cases. Thus, it is crucial for radiologists to understand the underlying molecular diagnostics as well as imaging findings for application on clinical practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning.

BACKGROUND: Accurate and rapid measurement of the MRI volume of meningiomas is essential in clinical practice to determine the growth rate of the tumor. Imperfect automation and disappointing performance for small meningiomas of previous automated volumetric tools limit their use in routine clinical practice. PURPOSE: To develop and validate a computational model for fully automated meningioma segmentation and volume measurement on contrast-enhanced MRI scans using deep learning. STUDY TYPE: Retrospective. POPULATION: A total of 659 intracranial meningioma patients (median age, 59.0 years; interquartile range: 53.0-66.0 years) including 554 women and 105 men. FIELD STRENGTH/SEQUENCE: The 1.0 T, 1.5 T, and 3.0 T; three-dimensional, T1 -weighted gradient-echo imaging with contrast enhancement. ASSESSMENT: The tumors were manually segmented by two neurosurgeons, H.K. and C.-K.P., with 10 and 26 years of clinical experience, respectively, for use as the ground truth. Deep learning models based on U-Net and nnU-Net were trained using 459 subjects and tested for 100 patients from a single institution (internal validation set [IVS]) and 100 patients from other 24 institutions (external validation set [EVS]), respectively. The performance of each model was evaluated with the Sørensen-Dice similarity coefficient (DSC) compared with the ground truth. STATISTICAL TESTS: According to the normality of the data distribution verified by the Shapiro-Wilk test, variables with three or more categories were compared by the Kruskal-Wallis test with Dunn's post hoc analysis. RESULTS: A two-dimensional (2D) nnU-Net showed the highest median DSCs of 0.922 and 0.893 for the IVS and EVS, respectively. The nnU-Nets achieved superior performance in meningioma segmentation than the U-Nets. The DSCs of the 2D nnU-Net for small meningiomas less than 1 cm3 were 0.769 and 0.780 with the IVS and EVS, respectively. DATA CONCLUSION: A fully automated and accurate volumetric measurement tool for meningioma with clinically applicable performance for small meningioma using nnU-Net was developed. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Deep learning-based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI.

OBJECTIVE: To compare the image quality and diagnostic performance between standard turbo spin-echo MRI and accelerated MRI with deep learning (DL)-based image reconstruction for degenerative lumbar spine diseases. MATERIALS AND METHODS: Fifty patients who underwent both the standard and accelerated lumbar MRIs at a 1.5-T scanner for degenerative lumbar spine diseases were prospectively enrolled. DL reconstruction algorithm generated coarse (DL_coarse) and fine (DL_fine) images from the accelerated protocol. Image quality was quantitatively assessed in terms of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) and qualitatively assessed using five-point visual scoring systems. The sensitivity and specificity of four radiologists for the diagnosis of degenerative diseases in both protocols were compared. RESULTS: The accelerated protocol reduced the average MRI acquisition time by 32.3% as compared to the standard protocol. As compared with standard images, DL_coarse and DL_fine showed significantly higher SNRs on T1-weighted images (T1WI; both p < .001) and T2-weighted images (T2WI; p = .002 and p < 0.001), higher CNRs on T1WI (both p < 0.001), and similar CNRs on T2WI (p = .49 and p = .27). The average radiologist assessment of overall image quality for DL_coarse and DL_fine was higher on sagittal T1WI (p = .04 and p < .001) and axial T2WI (p = .006 and p = .01) and similar on sagittal T2WI (p = .90 and p = .91). Both DL_coarse and DL_fine had better image quality of cauda equina and paraspinal muscles on axial T2WI (both p = .04 for cauda equina; p = .008 and p = .002 for paraspinal muscles). Differences in sensitivity and specificity for the detection of central canal stenosis and neural foraminal stenosis between standard and DL-reconstructed images were all statistically nonsignificant (p ≥ 0.05). CONCLUSION: DL-based protocol reduced MRI acquisition time without degrading image quality and diagnostic performance of readers for degenerative lumbar spine diseases. CLINICAL RELEVANCE STATEMENT: The deep learning (DL)-based reconstruction algorithm may be used to further accelerate spine MRI imaging to reduce patient discomfort and increase the cost efficiency of spine MRI imaging. KEY POINTS: • By using deep learning (DL)-based reconstruction algorithm in combination with the accelerated MRI protocol, the average acquisition time was reduced by 32.3% as compared with the standard protocol. • DL-reconstructed images had similar or better quantitative/qualitative overall image quality and similar or better image quality for the delineation of most individual anatomical structures. • The average radiologist's sensitivity and specificity for the detection of major degenerative lumbar spine diseases, including central canal stenosis, neural foraminal stenosis, and disc herniation, on standard and DL-reconstructed images, were similar.

Risk estimation for idiopathic normal-pressure hydrocephalus: development and validation of a brain morphometry-based nomogram.

OBJECTIVES: To develop and validate a nomogram based on MRI features for predicting iNPH. METHODS: Patients aged ≥ 60 years (clinically diagnosed with iNPH, Parkinson's disease, or Alzheimer's disease or healthy controls) who underwent MRI including three-dimensional T1-weighted volumetric MRI were retrospectively identified from two tertiary referral hospitals (one hospital for derivation set and the other for validation set). Clinical and imaging features for iNPH were assessed. Deep learning-based brain segmentation software was used for 3D volumetry. A prediction model was developed using logistic regression and transformed into a nomogram. The performance of the nomogram was assessed with respect to discrimination and calibration abilities. The nomogram was internally and externally validated. RESULTS: A total of 452 patients (mean age ± SD, 73.2 ± 6.5 years; 200 men) were evaluated as the derivation set. One hundred eleven and 341 patients were categorized into the iNPH and non-iNPH groups, respectively. In multivariable analysis, high-convexity tightness (odds ratio [OR], 35.1; 95% CI: 4.5, 275.5), callosal angle < 90° (OR, 12.5; 95% CI: 3.1, 50.0), and normalized lateral ventricle volume (OR, 4.2; 95% CI: 2.7, 6.7) were associated with iNPH. The nomogram combining these three variables showed an area under the curve of 0.995 (95% CI: 0.991, 0.999) in the study sample, 0.994 (95% CI: 0.990, 0.998) in the internal validation sample, and 0.969 (95% CI: 0.940, 0.997) in the external validation sample. CONCLUSION: A brain morphometry-based nomogram including high-convexity tightness, callosal angle < 90°, and normalized lateral ventricle volume can help accurately estimate the probability of iNPH. KEY POINTS: • The nomogram with MRI findings (high-convexity tightness, callosal angle, and normalized lateral ventricle volume) helped in predicting the probability of idiopathic normal-pressure hydrocephalus. • The nomogram may facilitate the prediction of idiopathic normal-pressure hydrocephalus and consequently avoid unnecessary invasive procedures such as the cerebrospinal fluid tap test, drainage test, and cerebrospinal fluid shunt surgery.

Dynamic interfaces for contact-time control of colloidal interactions.

Understanding pairwise interactions between colloidal particles out of equilibrium has a profound impact on dynamical processes such as colloidal self assembly. However, traditional colloidal interactions are effectively quasi-static on colloidal timescales and cannot be modulated out of equilibrium. A mechanism to dynamically tune the interactions during colloidal contacts can provide new avenues for self assembly and material design. In this work, we develop a framework based on polymer-coated colloids and demonstrate that in-plane surface mobility and mechanical relaxation of polymers at colloidal contact interfaces enable an effective, dynamic interaction. Combining analytical theory, simulations, and optical tweezer experiments, we demonstrate precise control of dynamic pair interactions over a range of pico-Newton forces and seconds timescales. Our model helps further the general understanding of out-of-equilibrium colloidal assemblies while providing extensive design freedom via interface modulation and nonequilibrium processing.

Improvement in current drivability and stability in nanoscale vertical channel thin-film transistors via band-gap engineering in In-Ga-Zn-O bilayer channel configuration.

Vertical channel thin film transistors (VTFTs) have been expected to be exploited as one of the promising three-dimensional devices demanding a higher integration density owing to their structural advantages such as small device footprints. However, the VTFTs have suffered from the back-channel effects induced by the pattering process of vertical sidewalls, which critically deteriorate the device reliability. Therefore, to reduce the detrimental back-channel effects has been one of the most urgent issues for enhancing the device performance of VTFTs. Here we show a novel strategy to introduce an In-Ga-Zn-O (IGZO) bilayer channel configuration, which was prepared by atomic-layer deposition (ALD), in terms of structural and electrical passivation against the back-channel effects. Two-dimensional electron gas was effectively employed for improving the operational reliability of the VTFTs by inducing strong confinement of conduction electrons at heterojunction interfaces. The IGZO bilayer channel structure was composed of 3 nm-thick In-rich prompt (In/Ga = 4.1) and 12 nm-thick prime (In/Ga = 0.7) layers. The VTFTs using bilayer IGZO channel showed high on/off ratio (4.8 × 109), low SS value (180 mV dec-1), and high current drivability (13.6µAµm-1). Interestingly, the strategic employment of bilayer channel configurations has secured excellent device operational stability representing the immunity against the bias-dependent hysteretic drain current and the threshold voltage instability of the fabricated VTFTs. Moreover, the threshold voltage shifts of the VTFTs could be suppressed from +5.3 to +2.6 V under a gate bias stress of +3 MV cm-1for 104s at 60 °C, when the single layer channel was replaced with the bilayer channel. As a result, ALD IGZO bilayer configuration could be suggested as a useful strategy to improve the device characteristics and operational reliability of VTFTs.

Genetic characterization of avian polyomaviruses identified from psittacine birds in South Korea.

Budgerigar fledgling disease (BFD) is a contagious disease caused by avian polyomavirus (APV) in psittacine birds and causes high mortality rates. Here, eight APV-positive cases were confirmed from dead parrots or parrot tissue samples by polymerase chain reaction (PCR). Full-length genome sequencing showed high nucleotide identity (98.84-100%) between the APV strains. Phylogenetic analysis revealed that two genogroups were cocirculating in South Korea. The nucleotide sequences of five strains, collected from different parrot species, were identical; however, pathological lesions were observed in only two parrots, both aged 2 months. Pathology included necrotic spots in the liver, subcutaneous haemorrhage, hepatomegaly, ascites, intranuclear inclusion bodies, hepatocyte karyomegaly, hepatic necrosis, and bile duct proliferation. This suggests that the pathogenicity of APV might be host age-dependent regardless of the host species. This study improves our understanding of APV pathogenicity and provides a more detailed genetic characterization of APV strains.RESEARCH HIGHLIGHTS Eight APV strains were identified in South Korea from 2019 to 2021.By phylogenetic analysis, South Korean APV strains were classified into two clades.

Application of metagenomics for diagnosis of broilers displaying neurological symptoms.

BACKGROUND: Thirty-two-day-old broiler chickens at a farm located in northwestern South Korea displayed adverse neurological symptoms including limping, lying down, and head shaking. Approximately 2.1% of chickens died or were culled due to severe symptoms. Five carcasses were submitted to the Avian Disease Division of the Animal and Plant Quarantine Agency (APQA) for disease diagnosis. RESULTS: Broilers displayed severe pericarditis and perihepatitis associated with gross lesions. Broilers also displayed microscopic lesions in the cerebrum and in the granular layer of the cerebellum, which were associated with multifocal perivascular cuffing and purulent necrosis in the cerebrum, and severe meningitis with heterophil and lymphocyte infiltration. Staphylococcus spp. were identified in the liver and heart using bacteriological culture. PCR/RT-PCR assays revealed that broilers were negative for avian Clostridium botulinum, Newcastle disease virus, and avian encephalomyelitis virus. Bacterial and viral metagenomic analysis of brain sample further revealed the presence of Pseudomonas spp. and Marek's disease virus, which are known etiological agents of chicken meningoencephalitis. CONCLUSIONS: This study reports a diagnostic analysis of gross and histopathological lesions from 32-day-old broilers displaying unique neurological symptoms that revealed the presence of the several neurological diseases including meningoencephalitis. The causative agents associated with meningoencephalitis of broilers that had not been identified by routine diagnostic methods could be diagnosed by metagenomics, which proves the usefulness of metagenomics as a diagnostic tool for unknown neurological diseases in broilers.

Comparison of intraocular lens power calculation formulas in patients with a history of acute primary angle-closure attack.

BACKGROUND: To compare the accuracy of nine intraocular lens (IOL) power calculation formulas, including three traditional formulas (SRK/T, Haigis, and Hoffer Q) and six new-generation formulas (Barrett Universal II [BUII], Hill-Radial Basis Function [RBF] 3.0, Kane, Emmetropia verifying optical [EVO], Ladas Super, and Pearl-DGS) in patients who underwent cataract surgery after acute primary angle closure (APAC). METHODS: In this retrospective cross-sectional study, 44 eyes of 44 patients (APAC) and 60 eyes of 60 patients (control) were included. We compared the mean absolute error, median absolute error (MedAE), and prediction error after surgery. Subgroup analyses were performed on whether axial length (AL) or preoperative laser peripheral iridotomy affected the postoperative refractive outcomes. RESULTS: In the APAC group, all formulas showed higher MedAE and more myopic shift than the control group (all P < 0.05). In APAC eyes with AL ≥ 22 mm, there were no differences in MedAEs according to the IOL formulas; however, in APAC eyes with AL < 22 mm, Haigis (0.49 D) showed lower MedAE than SRK/T (0.82 D) (P = 0.036) and Hill-RBF 3.0 (0.54 D) showed lower MedAE than SRK/T (0.82 D), Hoffer Q (0.75 D) or Kane (0.83 D) (P = 0.045, 0.036 and 0.027, respectively). Pearl-DGS (0.63 D) showed lower MedAE than Hoffer Q (0.75 D) and Kane (0.83 D) (P = 0.045 and 0.036, respectively). Haigis and Hill-RBF 3.0 showed the highest percentage (46.7%) of eyes with PE within ± 0.5 D in APAC eyes with AL < 22 mm. Iridectomized eyes did not show superior precision than the non-iridotomized eyes in the APAC group. CONCLUSIONS: Refractive errors in the APAC group were more myopic than those in the control group. Haigis and Hill-RBF 3.0 showed high precision in the eyes with AL < 22 mm in the APAC group.

Impact of extent of resection and postoperative radiotherapy on survival outcomes in intracranial solitary fibrous tumors: a systematic review and meta-analysis.

The knowledge of optimal treatments for patients with intracranial solitary fibrous tumor (SFT) is limited, with inconclusive results from previous studies. In this study, we conducted a meta-analysis of relevant studies to identify the prognostic impact of the extent of resection (EOR) and postoperative radiotherapy (PORT) on survival outcomes of patients with intracranial SFT. We searched the Medline, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) to identify relevant studies published till April 2022. Progression-free survival (PFS) and overall survival (OS) were the outcomes of interest. Differences between two cohorts (gross total resection [GTR] vs. subtotal resection [STR] and PORT vs. surgery only) were estimated by calculating hazard ratios. Twenty-seven studies were selected for the meta-analysis, including data of 1348 patients (GTR, n = 819 vs. STR, n = 381 and PORT, n = 723 vs. surgery only, n = 578). Pooled hazard ratios of PFS (1, 3, 5, and 10 years) and OS (3, 5, and 10 years) revealed that the GTR cohort showed sustained superiority over the STR cohort. In addition, the PORT cohort was superior to the surgery-only cohort with respect to all PFS periods. Although the 10-year OS between the two cohorts was not statistically different, PORT showed significantly better 3- and 5-year OS than surgery only. The study findings suggest that GTR and PORT provide significant benefits for PFS and OS. Aggressive surgical resection of tumors to achieve GTR followed by PORT should be implemented as optimal treatments for all patients with intracranial SFT when feasible.

Predicting vertebral compression fracture prior to spinal SBRT using radiomics from planning CT.

PURPOSE: The purpose of the study was to develop a predictive model for vertebral compression fracture (VCF) prior to spinal stereotactic body radiation therapy (SBRT) using radiomics features extracted from pre-treatment planning CT images. METHODS: A retrospective analysis was conducted on 85 patients (114 spinal lesions) who underwent spinal SBRT. Radiomics features were extracted from pre-treatment planning CT images and used to develop a predictive model using a classification algorithm selected from nine different machine learning algorithms. Four different models were trained, including clinical features only, clinical and radiomics features, radiomics and dosimetric features, and all features. Model performance was evaluated using accuracy, precision, recall, F1-score, and area under the curve (AUC). RESULTS: The model that used all features (radiomics, clinical, and dosimetric) showed the highest performance with an AUC of 0.871. The radiomics and dosimetric features model had the superior performance in terms of accuracy, precision, recall, and F1-score. CONCLUSION: The developed predictive model based on radiomics features extracted from pre-treatment planning CT images can accurately predict the likelihood of VCF prior to spinal SBRT. This model has significant implications for treatment planning and preventive measures for patients undergoing spinal SBRT. Future research can focus on improving model performance by incorporating new data and external validation using independent data sets.