Oscillatory mechanisms of intrinsic human brain networks.

Non-invasive neuroimaging has revealed specific network-based resting-state dynamics in the human brain, yet the underlying neurophysiological mechanism remains unclear. We employed intracranial electroencephalography to characterize local field potentials within the default mode network (DMN), frontoparietal network (FPN), and salience network (SN) in 42 participants. We identified stronger within-network phase coherence at low frequencies (θ and α band) within the DMN, and at high frequencies (γ band) within the FPN. Hidden Markov modeling indicated that the DMN exhibited preferential low frequency phase coupling. Phase-amplitude coupling (PAC) analysis revealed that the low-frequency phase in the DMN modulated the high-frequency amplitude envelopes of the FPN, suggesting frequency-dependent characterizations of intrinsic brain networks at rest. These findings provide intracranial electrophysiological evidence in support of the network model for intrinsic organization of human brain and shed light on the way brain networks communicate at rest.

Antibacterial mechanism of whey protein isolated-citral nanoparticles and stable synergistic antibacterial eugenol encapsulated Pickering emulsion for grapes preservation.

The purpose of this study was to prepare Pickering emulsion with synergistic antibacterial effect using whey protein isolated-citral (WPI-Cit) nanoparticles with eugenol for grape preservation. In this emulsion, eugenol was encapsulated in oil phase. The particle size, ζ-potential, and antibacterial mechanism of the nanoparticles were characterized. The rheological properties, antibacterial effects and preservation effects of WPI-Cit Pickering emulsion were measured. The results showed that the optimal preparation condition was performed at WPI/Cit mass ratio of 1:1, WPI-Cit nanoparticles were found to damage the cell wall and membrane of bacteria and showed more effective inhibition against S. aureus. Pickering emulsion prepared with WPI-Cit nanoparticles exhibited a better antibacterial effect after eugenol was encapsulated in it, which extended the shelf life of grapes when the Pickering emulsion was applied as a coating. It demonstrated that the Pickering emulsion prepared in this study provides a new way to extend the shelf life.

Identification of biomarkers related to angiogenesis in myocardial ischemia-reperfusion injury and prediction of potential drugs.

Myocardial ischemia-reperfusion injury (MIRI) refers to the secondary damage to myocardial tissue that occurs when blood perfusion is rapidly restored following myocardial ischemia. This process often exacerbates the injury to myocardial fiber structure and function. The activation mechanism of angiogenesis is closely related to MIRI and plays a significant role in the occurrence and progression of ischemic injury. In this study, we utilized sequencing data from the GEO database and employed WGCNA, Mfuzz cluster analysis, and protein interaction network to identify Stat3, Rela, and Ubb as hub genes involved in MIRI-angiogenesis. Additionally, the GO and KEGG analysis of differentially expressed genes highlighted their broad participation in inflammatory responses and associated signaling pathways. Moreover, the analysis of sequencing data and hub genes revealed a notable increase in the infiltration ratio of monocytes and activated mast cells. By establishing key cell ROC curves, using independent datasets, and validating the expression of hub genes, we demonstrated their high diagnostic value. Moreover, by scrutinizing single-cell sequencing data alongside trajectory analysis, it has come to light that Stat3 and Rela exhibit predominant expression within Dendritic cells. In contrast, Ubb demonstrates expression across multiple cell types, with all three genes being expressed at distinct stages of cellular development. Lastly, leveraging the CMap database, we predicted potential small molecule compounds for the identified hub genes and validated their binding activity through molecular docking. Ultimately, our research provides valuable evidence and references for the early diagnosis and treatment of MIRI from the perspective of angiogenesis.

Radiomics in Diagnosis, Grading, and Treatment Response Assessment of Soft Tissue Sarcomas: A Systematic Review and Meta-analysis.

RATIONALE AND OBJECTIVES: To evaluate radiomics in soft tissue sarcomas (STSs) for diagnostic accuracy, grading, and treatment response assessment, with a focus on clinical relevance. METHODS: In this diagnostic accuracy study, radiomics was applied using multiple MRI sequences and AI classifiers, with histopathological diagnosis as the reference standard. Statistical analysis involved meta-analysis, random-effects model, and Deeks' funnel plot asymmetry test. RESULTS: Among 579 unique titles and abstracts, 24 articles were included in the systematic review, with 21 used for meta-analysis. Radiomics demonstrated a pooled sensitivity of 84% (95% CI: 80-87) and specificity of 63% (95% CI: 56-70), AUC of 0.93 for diagnosis, sensitivity of 84% (95% CI: 82-87) and specificity of 73% (95% CI: 68-77), AUC of 0.91 for grading, and sensitivity of 83% (95% CI: 67-94) and specificity of 67% (95% CI: 59-74), AUC of 0.87 for treatment response assessment. CONCLUSION: Radiomics exhibits potential for accurate diagnosis, grading, and treatment response assessment in STSs, emphasizing the need for standardization and prospective trials. CLINICAL RELEVANCE STATEMENT: Radiomics offers precise tools for STS diagnosis, grading, and treatment response assessment, with implications for optimizing patient care and treatment strategies in this complex malignancy.

Advancing musculoskeletal tumor diagnosis: Automated segmentation and predictive classification using deep learning and radiomics.

OBJECTIVES: Musculoskeletal (MSK) tumors, given their high mortality rate and heterogeneity, necessitate precise examination and diagnosis to guide clinical treatment effectively. Magnetic resonance imaging (MRI) is pivotal in detecting MSK tumors, as it offers exceptional image contrast between bone and soft tissue. This study aims to enhance the speed of detection and the diagnostic accuracy of MSK tumors through automated segmentation and grading utilizing MRI. MATERIALS AND METHODS: The research included 170 patients (mean age, 58 years ±12 (standard deviation), 84 men) with MSK lesions, who underwent MRI scans from April 2021 to May 2023. We proposed a deep learning (DL) segmentation model MSAPN based on multi-scale attention and pixel-level reconstruction, and compared it with existing algorithms. Using MSAPN-segmented lesions to extract their radiomic features for the benign and malignant classification of tumors. RESULTS: Compared to the most advanced segmentation algorithms, MSAPN demonstrates better performance. The Dice similarity coefficients (DSC) are 0.871 and 0.815 in the testing set and independent validation set, respectively. The radiomics model for classifying benign and malignant lesions achieves an accuracy of 0.890. Moreover, there is no statistically significant difference between the radiomics model based on manual segmentation and MSAPN segmentation. CONCLUSION: This research contributes to the advancement of MSK tumor diagnosis through automated segmentation and predictive classification. The integration of DL algorithms and radiomics shows promising results, and the visualization analysis of feature maps enhances clinical interpretability.

Neural evidence for attentional capture by salient distractors.

Salient objects often capture our attention, serving as distractors and hindering our current goals. It remains unclear when and how salient distractors interact with our goals, and our knowledge on the neural mechanisms responsible for attentional capture is limited to a few brain regions recorded from non-human primates. Here we conducted a multivariate analysis on human intracranial signals covering most brain regions and successfully dissociated distractor-specific representations from target-arousal signals in the high-frequency (60-100 Hz) activity. We found that salient distractors were processed rapidly around 220 ms, while target-tuning attention was attenuated simultaneously, supporting initial capture by distractors. Notably, neuronal activity specific to the distractor representation was strongest in the superior and middle temporal gyrus, amygdala and anterior cingulate cortex, while there were smaller contributions from the parietal and frontal cortices. These results provide neural evidence for attentional capture by salient distractors engaging a much larger network than previously appreciated.

Single-cell transcriptomics reveals comprehensive microenvironment and highlights the dysfuntional state of NK cells in endometrioid carcinoma.

Endometrioid endometrial cancer (EEC) is one of the most common gynecologic malignancies. The interaction between cancer cells and the cells in the tumor microenvironment (TME) plays a crucial role in determining disease progression and response to treatment. To better understand the diversity in the TME of ECC, we conducted a comprehensive analysis using single-cell RNA sequencing across 21 samples, including 16 ECC and 5 adjacent normal tissues. We primarily focused on tumor-infiltrating natural killer (NK) cells and their cell-cell interactions with other immune cell types. We identified a CD56dim_DNAJB1 NK cells subset, which had low cytotoxic capability and high stress levels, suggesting a dysfunctional state. This subset showed strong interactions with tumor-associated macrophages through several ligand-receptor pairs. Additionally, we observed that tumor-infiltrating LAMP3+ dendritic cells may inhibit CD8+ T cells or attract regulatory T cells to the tumor area. These dendritic cells also had impaired activation effects on NK cells within the TME. Our study provides valuable insights into the role of NK cells in cancer immunity and highlights the potential of targeting specific NK cell subsets for therapeutic purposes.

Differential protein analysis of saline-alkali promoting the oil accumulation in Nitzschia palea.

BACKGROUND: The increasingly severe salinization of the aquatic environment has led to serious damage to the habitats of aquatic organisms. Benthic diatoms are commonly employed as indicator species for assessing water quality and serve as a reflection of the overall health of the aquatic ecosystem. Nitzschia palea is a common diatom found in freshwater, with high oil content, rapid reproductive rate, and it is a commonly dominant species in various rivers. RESULTS: The results showed that after 4 days (d) of saline-alkali stress, the cell density and chlorophyll a content of Nitzschia palea reached their maximum values. Therefore, we selected Nitzschia palea under 4 d stress for Tandem Mass Tag (TMT) quantitative proteomic analysis to explore the molecular adaptation mechanism of freshwater diatoms under saline-alkali stress. Totally, 854 proteins were enriched, of which 439 differentially expressed proteins were identified. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and subcellular fractionation analysis revealed that these proteins were mainly enriched in the photosynthesis pathway, citric acid cycle (TCA cycle), fatty acid synthesis, and glutathione cycle. CONCLUSIONS: This study aims to reveal the physiological, biochemical and proteomic mechanisms of salt and alkali tolerance and molecular adaptation of Nitzschia palea under different saline-alkali concentrations. This study showed that Nitzschia palea is one candidate of the environmental friendly, renewable bioenergy microalgae. Meantime, Nitzschia palea reveals for the proteome of the freshwater and provides the basis, it became a model algal species for freshwater diatoms.

The effect of octenyl succinic anhydride-modified chitosan coating on DHA-loaded nanoemulsions: Physichemical stability and in vitro digestibility.

Octenyl succinic anhydride-modified chitosan (OSA-CS) was synthesized and applied as a coating material to enhance the stability of docosahexaenoic acid (DHA)-loaded nanoemulsion. Due to the presence of the positively charged OSA-CS coating, the nanoemulsion exhibited a high positive zeta potential and two different layers. Compared with natural CS-coated nanoemulsion, OSA-CS-coated nanoemulsion showed improved storage stability (physical and chemical stability) and stability against environmental stresses (ionic strengths, temperatures and pH). Besides, OSA-CS-coated nanoemulsion protected encapsulated DHA from simulated gastric fluid damage better than that of natural CS-coated nanoemulsion, suggesting that OSA-CS-coated nanoemulsion had the potential to deliver more DHA into the small intestine. In conclusion, based on the comparison of two coating materials, natural chitosan and OSA-CS, it was found that the encapsulated nutrient was better protected by the OSA-CS coating. Such a finding will provide insights to broaden the application of modified chitosan in food delivery systems.

DYNC1H1 variants associated with infant-onset epilepsy without neurodevelopmental disorders.

OBJECTIVES: The DYNC1H1 variants are associated with abnormal brain morphology and neuromuscular disorders that are accompanied by epilepsy. This study aimed to explore the relationship between DYNC1H1 variants and epilepsy. MATERIALS AND METHODS: Trios-based whole-exome sequencing was performed on patients with epilepsy. Previously reported epilepsy-related DYNC1H1 variants were systematically reviewed to analyse genotype-phenotype correlation. RESULTS: The DYNC1H1 variants were identified in four unrelated cases of infant-onset epilepsy, including two de novo and two biallelic variants. Two patients harbouring de novo missense variants located in the stem and stalk domains presented with refractory epilepsies, whereas two patients harbouring biallelic variants located in the regions between functional domains had mild epilepsy with infrequent focal seizures and favourable outcomes. One patient presented with pachygyria and neurodevelopmental abnormalities, and the other three patients presented with normal development. These variants have no or low frequencies in the Genome Aggregation Database. All the missense variants were predicted to be damaging using silico tools. Previously reported epilepsy-related variants were monoallelic variants, mainly de novo missense variants, and all the patients presented with severe epileptic phenotypes or developmental delay and malformations of cortical development. Epilepsy-related variants were clustered in the dimerization and stalk domains, and generalized epilepsy-associated variants were distributed in the stem domain. CONCLUSION: This study suggested that DYNC1H1 variants are potentially associated with infant-onset epilepsy without neurodevelopmental disorders, expanding the phenotypic spectrum of DYNC1H1. The genotype-phenotype correlation helps to understand the underlying mechanisms of phenotypic variation.

Identification of epileptic networks with graph convolutional network incorporating oscillatory activities and evoked synaptic responses.

Stereoelectroencephalography (SEEG) offers unique neural data from in-depth brain structures with fine temporal resolutions to better investigate the origin of epileptic brain activities. Although oscillatory patterns from different frequency bands and functional connectivity computed from the SEEG datasets are employed to study the epileptic zones, direct electrical stimulation-evoked electrophysiological recordings of synaptic responses, namely cortical-cortical evoked potentials (CCEPs), from the same SEEG electrodes are not explored for the localization of epileptic zones. Here we proposed a two-stream model with unsupervised learning and graph convolutional network tailored to the SEEG and CCEP datasets in individual patients to perform localization of epileptic zones. We compared our localization results with the clinically marked electrode sites determined for surgical resections. Our model had good classification capability when compared to other state-of-the-art methods. Furthermore, based on our prediction results we performed group-level brain-area mapping analysis for temporal, frontal and parietal epilepsy patients and found that epileptic and non-epileptic brain networks were distinct in patients with different types of focal epilepsy. Our unsupervised data-driven model provides personalized localization analysis for the epileptic zones. The epileptic and non-epileptic brain areas disclosed by the prediction model provide novel insights into the network-level pathological characteristics of epilepsy.

The Correlation of Dynamic Magnetic Resonance Imaging Evaluation With Histological, Biochemical, and Biomechanical Properties in Healing Progress After Achilles Tendon Injury: A Review.

Achilles tendon injury is a common sports injury, and an in-depth understanding of its healing process is essential for improving rehabilitation strategies. As a non-invasive imaging technology with excellent anatomical and functional information extraction abilities, magnetic resonance imaging (MRI) has been widely used in the evaluation and monitoring of Achilles tendon injury. MRI scans at different stages of Achilles tendon healing can provide information about the structure of the Achilles tendon tissue, blood supply, composition, and metabolism. The change pattern on dynamic MRI evaluation is closely related to the specific stage of Achilles tendon healing and tissue characteristics. For example, the signal strength of dynamic enhanced MRI sequences can reflect blood supply to the Achilles tendon, whereas some quantitative MRI techniques can provide information on the recovery of water and collagen contents in the Achilles tendon. This article discusses the pathophysiological changes after Achilles tendon injury and summarizes the clinical and research status of the MRI techniques used for monitoring Achilles tendon healing. The feasibility of various MRI techniques for monitoring Achilles tendon healing and their correlation with histology, biochemistry, and biomechanics are reviewed, along with the challenges, limitations, and potential opportunities for their application. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Multimodal imaging-based diagnostic approach for MRI-negative posterior cortex epilepsy.

Background: Posterior cortex epilepsy (PCE) primarily comprises seizures originating from the occipital, parietal, and/or posterior edge of the temporal lobe. Electroclinical dissociation and subtle imaging representation render the diagnosis of PCE challenging. Improved methods for accurately identifying patients with PCE are necessary. Objectives: To develop a novel voxel-based image postprocessing method for better visual identification of the neuroimaging abnormalities associated with PCE. Design: Multicenter, retrospective study. Methods: Clinical and imaging features of 165 patients with PCE were retrospectively reviewed and collected from five epilepsy centers. A total of 37 patients (32.4% female, 20.2 ± 8.9 years old) with magnetic resonance imaging (MRI)-negative PCE were finally included for analysis. Image postprocessing features were calculated over a neighborhood for each voxel in the multimodality data. The postprocessed maps comprised structural deformation, hyperintense signal, and hypometabolism. Five raters from three different centers were blinded to the clinical diagnosis and determined the neuroimaging abnormalities in the postprocessed maps. Results: The average accuracy of correct identification was 55.7% (range from 43.2 to 62.2%) and correct lateralization was 74.1% (range from 64.9 to 81.1%). The Cronbach's alpha was 0.766 for the correct identification and 0.683 for the correct lateralization with similar results of the interclass correlation coefficient, thus indicating reliable agreement between the raters. Conclusion: The image postprocessing method developed in this study can potentially improve the visual detection of MRI-negative PCE. The technique could lead to an increase in the number of patients with PCE who could benefit from the surgery.

Soy Protein Isolate-Chitosan Nanoparticle-Stabilized Pickering Emulsions: Stability and In Vitro Digestion for DHA.

The purpose of the study was to investigate the stability and oral delivery of DHA-encapsulated Pickering emulsions stabilized by soy protein isolate-chitosan (SPI-CS) nanoparticles (SPI-CS Pickering emulsions) under various conditions and in the simulated gastrointestinal (GIT) model. The stability of DHA was characterized by the retention rate under storage, ionic strength, and thermal conditions. The oral delivery efficiency was characterized by the retention and release rate of DHA in the GIT model and cell viability and uptake in the Caco-2 model. The results showed that the content of DHA was above 90% in various conditions. The retention rate of DHA in Pickering emulsions containing various nanoparticle concentrations (1.5 and 3.5%) decreased to 80%, while passing through the mouth to the stomach, and DHA was released 26% in 1.5% Pickering emulsions, which was faster than that of 3.5% in the small intestine. After digestion, DHA Pickering emulsions proved to be nontoxic and effectively absorbed by cells. These findings helped to develop a novel delivery system for DHA.

EEG-based investigation of effects of mindfulness meditation training on state and trait by deep learning and traditional machine learning.

Introduction: This study examines the state and trait effects of short-term mindfulness-based stress reduction (MBSR) training using convolutional neural networks (CNN) based deep learning methods and traditional machine learning methods, including shallow and deep ConvNets as well as support vector machine (SVM) with features extracted from common spatial pattern (CSP) and filter bank CSP (FBCSP). Methods: We investigated the electroencephalogram (EEG) measurements of 11 novice MBSR practitioners (6 males, 5 females; mean age 35.7 years; 7 Asians and 4 Caucasians) during resting and meditation at early and late training stages. The classifiers are trained and evaluated using inter-subject, mix-subject, intra-subject, and subject-transfer classification strategies, each according to a specific application scenario. Results: For MBSR state effect recognition, trait effect recognition using meditation EEG, and trait effect recognition using resting EEG, from shallow ConvNet classifier we get mix-subject/intra-subject classification accuracies superior to related previous studies for both novice and expert meditators with a variety of meditation types including yoga, Tibetan, and mindfulness, whereas from FBSCP + SVM classifier we get inter-subject classification accuracies of 68.50, 85.00, and 78.96%, respectively. Conclusion: Deep learning is superior for state effect recognition of novice meditators and slightly inferior but still comparable for both state and trait effects recognition of expert meditators when compared to the literatures. This study supports previous findings that short-term meditation training has EEG-recognizable state and trait effects.

Classification of temporal lobe epilepsy based on neuropsychological tests and exploration of its underlying neurobiology.

Objective: To assist improving long-term postoperative seizure-free rate, we aimed to use machine learning algorithms based on neuropsychological data to differentiate temporal lobe epilepsy (TLE) from extratemporal lobe epilepsy (extraTLE), as well as explore the relationship between magnetic resonance imaging (MRI) and neuropsychological tests. Methods: Twenty-three patients with TLE and 23 patients with extraTLE underwent neuropsychological tests and MRI scans before surgery. The least absolute shrinkage and selection operator were firstly employed for feature selection, and a machine learning approach with neuropsychological tests was employed to classify TLE using leave-one-out cross-validation. A generalized linear model was used to analyze the relationship between brain alterations and neuropsychological tests. Results: We found that logistic regression with the selected neuropsychological tests generated classification accuracies of 87.0%, with an area under the receiver operating characteristic curve (AUC) of 0.89. Three neuropsychological tests were acquired as significant neuropsychological signatures for the diagnosis of TLE. We also found that the Right-Left Orientation Test difference was related to the superior temporal and the banks of the superior temporal sulcus (bankssts). The Conditional Association Learning Test (CALT) was associated with the cortical thickness difference in the lateral orbitofrontal area between the two groups, and the Component Verbal Fluency Test was associated with the cortical thickness difference in the lateral occipital cortex between the two groups. Conclusion: These results showed that machine learning-based classification with the selected neuropsychological data can successfully classify TLE with high accuracy compared to previous studies, which could provide kind of warning sign for surgery candidate of TLE patients. In addition, understanding the mechanism of cognitive behavior by neuroimaging information could assist doctors in the presurgical evaluation of TLE.

Bacteriostatic Pickering emulsions stabilized by whey protein isolate-vanillin nanoparticles: Fabrication, characterization and stability in vitro.

The purpose of this work was to prepare Pickering emulsion stabilized by bacteriostatic whey protein isolate-vanillin (WPI-Van) nanoparticles as a carrier for encapsulating vitamin E. The particle size, ζ potential, PDI were used to study the optimal preparation conditions of nanoparticles. The results showed that the optimal preparation condition was achieved at WPI/Van mass ratio of 3:1. FTIR spectra demonstrated the complexation of WPI and Van. SEM image showed spherical and slightly rough surface of nanoparticles. Inhibitory effects of nanoparticles on E. coli and S. aureus were also observed. After storage of 21 days at 4 °C, the retention rate of vitamin E in the emulsions remained 43% higher than that of unencapsulated vitamin E. Moreover, the release rate of vitamin E encapsulated in emulsions in the small intestine was 81%, indicating excellent bioaccessibility. The research can provide a new insight for production and application of antibacterial Pickering emulsions.

Investigating Risk Factors and Magnetic Resonance Imaging (MRI)-Based Grading of Subchondral Incomplete Fracture (SIF) of Medial Femoral Condyle.

BACKGROUND: Subchondral insufficiency fractures (SIF) of the knee joint are prevalent in osteoporosis patients over the age of 55. Early diagnosis of SIF fracture of the medial femoral condyle is crucial for delaying disease progression, early therapy, and potential disease reversal. Magnetic resonance imaging (MRI) is useful in detecting SIF, which is often undetectable on initial radiographs. This study aimed at developing a grading system for subchondral insufficiency fractures (SIF) based on MRI to predict outcomes and evaluate risk factors. METHODS: In this study, MRI was used to examine SIF risk variables in the medial condyle of the femur to help clinicians diagnose, treat, and delay the condition. A total of 386 patients with SIF from 2019 to 2021 were retrospectively analyzed and divided into 106 patients in the disease group and 280 patients in the control group according to whether they had SIF. The lesion site, meniscus, ligament, and other parameters were evaluated and compared. At the same time, a grading system was introduced to stratify and statistically analyze the size of the lesion area, the degree of bone marrow edema (BME), meniscus tears, and other parameters in the patients. RESULTS: Most SIF were low-grade (LG) fractures, and the predictors of LG and high-grade (HG) fractures included heel tear (P =0.031), degree of medial malleolus degeneration (P < 0.001), advanced age (P < 0.001), and lesion size (P < 0.001). The prognostic factors that showed significant differences between the two groups included age (P =0.027), gender (P =0.005), side (P =0.005), medial tibial plateau injury (P < 0.0001), femoral medullary bone marrow edema (P < 0.0001), medial tibial plateau bone marrow edema (P < 0.0001), meniscus body partial injury (P =0.016), heel tear (P =0.001), anterior cruciate ligament injury (P =0.002), and medial collateral ligament injury (P < 0.0001). CONCLUSION: This current study proposed an MRI-based grading system for inferior condylar fractures of the femur, in which HG inferior condylar fractures are associated with severe medial malleolus degeneration, advanced age, lesion size (correlation), and meniscus heel tears.

How to locate the fistula orifice of spinal extradural arachnoid cyst: Surgical experience and clinical outcome.

BACKGROUND: In clinical practice, spinal extradural arachnoid cysts (SEAC) are relatively rare. The key to the treatment of SEAC is recognize and close the dural defects (fistula orifice), but there is currently no convenient method to locate and identify the fistula. We propose a method for predicting the location of lumbar/thoracolumbar SEAC fistula based on surgical experience, subsequently closing the fistula through posterior unilateral interlaminar fenestration. Evaluating its surgical efficacy and investigated its effect on patient prognosis. METHODS: A stepped approach based on clinical experience is proposed. A retrospective analysis was performed on 6 patients diagnosed with thoracolumbar SEAC disease and treated with posterior unilateral interlaminar fenestration through the position by pre-estimated fistula orifice in our hospital's Department of Neurosurgery between January 2017 and January 2022. RESULTS: All patients who received this treatment experienced significantly lower postoperative VAS pain scores and ODI index compared to their corresponding preoperative values (P < 0.01). During the ongoing follow-up after surgery, no unstable vertebral column, adverse effects, or complications were reported. CONCLUSIONS: The use of posterior unilateral interlaminar fenestration for the treatment of large SEAC in the adult lumbar/thoracolumbar segment can reduce spinal cord manipulation and enhance spine stability. The disease can be treated by sealing the fistula orifice through a small fenestra, the position of which is assessed before surgery. This surgical method reduces trauma and improves the prognosis of patients with large SEAC.

OSA-starch stabilized EPA nanoliposomes: Preparation, characterization, stability and digestion in vitro and in vivo.

OSA-starch stabilized EPA nanoliposomes (OSA-EPA-NLs) were prepared by thin film rehydration/dispersion method. The physical properties and morphology of OSA-EPA-NLs were characterized. The best formulated sample was used to measure the storage stability and oxidative properties of EPA under different environmental stresses and to determine release and absorption of OSA-EPA-NLs in vitro and in vivo. The results showed that the encapsulation efficiency of OSA-EPA-NLs was 84.61%. All samples were relatively stable under different environmental stresses, and the release rate of EPA in simulated intestine stage (89.87%) was higher than that in the simulated gastric stage (5.86%). The areas under the EPA concentration-time curve of OSA-EPA-NLs group and EPA-NLs group through in vivo study were 0.42 and 0.32, respectively, which indicated that OSA-starch could improve the stability of EPA nanoliposomes and enhance EPA bioavailability in the form of ethyl esters.