Microglia lactylation in relation to central nervous system diseases.

The development of neurodegenerative diseases is closely related to the disruption of central nervous system homeostasis. Microglia, as innate immune cells, play important roles in the maintenance of central nervous system homeostasis, injury response, and neurodegenerative diseases. Lactate has been considered a metabolic waste product, but recent studies are revealing ever more of the physiological functions of lactate. Lactylation is an important pathway in lactate function and is involved in glycolysis-related functions, macrophage polarization, neuromodulation, and angiogenesis and has also been implicated in the development of various diseases. This review provides an overview of the lactate metabolic and homeostatic regulatory processes involved in microglia lactylation, histone versus non-histone lactylation, and therapeutic approaches targeting lactate. Finally, we summarize the current research on microglia lactylation in central nervous system diseases. A deeper understanding of the metabolic regulatory mechanisms of microglia lactylation will provide more options for the treatment of central nervous system diseases.

Pharmacological targeting cGAS/STING/NF-κB axis by tryptanthrin induces microglia polarization toward M2 phenotype and promotes functional recovery in a mouse model of spinal cord injury.

ABSTRACT: The M1/M2 phenotypic shift of microglia after spinal cord injury plays an important role in the regulation of neuroinflammation during the secondary injury phase of spinal cord injury. Regulation of shifting microglia polarization from M1 (neurotoxic and proinflammatory type) to M2 (neuroprotective and anti-inflammatory type) after spinal cord injury appears to be crucial. Tryptanthrin possesses an anti-inflammatory biological function. However, its roles and the underlying molecular mechanisms in spinal cord injury remain unknown. In this study, we found that tryptanthrin inhibited microglia-derived inflammation by promoting polarization to the M2 phenotype in vitro. Tryptanthrin promoted M2 polarization through inactivating the cGAS/STING/NF-κB pathway. Additionally, we found that targeting the cGAS/STING/NF-κB pathway with tryptanthrin shifted microglia from the M1 to M2 phenotype after spinal cord injury, inhibited neuronal loss, and promoted tissue repair and functional recovery in a mouse model of spinal cord injury. Finally, using a conditional co-culture system, we found that microglia treated with tryptanthrin suppressed endoplasmic reticulum stress-related neuronal apoptosis. Taken together, these results suggest that by targeting the cGAS/STING/NF-κB axis, tryptanthrin attenuates microglia-derived neuroinflammation and promotes functional recovery after spinal cord injury through shifting microglia polarization to the M2 phenotype.

Adaptive Memory Broad Learning System for Unsupervised Time Series Anomaly Detection.

Time series anomaly detection is the process of identifying anomalies within time series data. The primary challenge of this task lies in the necessity for the model to comprehend the characteristics of time-independent and abnormal data patterns. In this study, a novel algorithm called adaptive memory broad learning system (AdaMemBLS) is proposed for time series anomaly detection. This algorithm leverages the rapid inference capabilities of the broad learning algorithm and the memory bank's capacity to differentiate between normal and abnormal data. Furthermore, an incremental algorithm based on multiple data augmentation techniques is introduced and applied to multiple ensemble learners, thereby enhancing the model's effectiveness in learning the characteristics of time series data. To bolster the model's anomaly detection capabilities, a more diverse ensemble approach and a discriminative anomaly score are recommended. Extensive experiments conducted on various real-world datasets demonstrate that the proposed method exhibits superior inference speed and more accurate anomaly detection compared to the existing competitors. A detailed experimental investigation is presented to elucidate the effectiveness of the proposed method and the underlying reasons for its efficacy.

Directional thermal emission and display using pixelated non-imaging micro-optics.

Thermal radiation is intrinsically broadband, incoherent and non-directional. The ability to beam thermal energy preferentially in one direction is not only of fundamental importance, but it will enable high radiative efficiency critical for many thermal sensing, imaging, and energy devices. Over the years, different photonic materials and structures have been designed utilizing resonant and propagating modes to generate directional thermal emission. However, such thermal emission is narrowband and polarised, leading to limited thermal transfer efficiency. Here we experimentally demonstrate ultrabroadband polarisation-independent directional control of thermal radiation with a pixelated directional micro-emitter. Our compact pixelated directional micro-emitter facilitates tunable angular control of thermal radiation through non-imaging optical principles, producing a large emissivity contrast at different view angles. Using this platform, we further create a pixelated infrared display, where information is only observable at certain directions. Our pixelated non-imaging micro-optics approach can enable efficient radiative cooling, infrared spectroscopy, thermophotovoltaics, and thermal camouflaging.

Optimizing prediction accuracy for early recurrent lumbar disc herniation with a directional mutation-guided SVM model.

Percutaneous endoscopic lumbar discectomy (PELD) is one of the main means of minimally invasive spinal surgery, and is an effective means of treating lumbar disc herniation, but its early recurrence is still difficult to predict. With the development of machine learning technology, the auxiliary model based on the prediction of early recurrent lumbar disc herniation (rLDH) and the identification of causative risk factors have become urgent problems in current research. However, the screening ability of current models for key factors affecting the prediction of rLDH, as well as their predictive ability, needs to be improved. Therefore, this paper presents a classification model that utilizes wrapper feature selection, developed through the integration of an enhanced bat algorithm (BDGBA) and support vector machine (SVM). Among them, BDGBA increases the population diversity and improves the population quality by introducing directional mutation strategy and guidance-based strategy, which in turn allows the model to secure better subsets of features. Furthermore, SVM serves as the classifier for the wrapper feature selection method, with its classification prediction results acting as a fitness function for the feature subset. In the proposed prediction method, BDGBA is used as an optimizer for feature subset filtering and as an objective function for feature subset evaluation based on the classification results of the support vector machine, which improves the interpretability and prediction accuracy of the model. In order to verify the performance of the proposed method, this paper proves the performance of the model through global optimization experiments and prediction experiments on real data sets. The accuracy of the proposed rLDH prediction model is 93.49% and sensitivity is 88.33%. The experimental results show that Level of herniated disk, Modic change, Disk height, Disk length, and Disk width are the key factors for predicting rLDH, and the proposed method is an effective auxiliary diagnosis method.

Columbianadin suppresses glioblastoma progression by inhibiting the PI3K-Akt signaling pathway.

Glioblastoma (GBM) is the most common malignant glioma among brain tumors with low survival rate and high recurrence rate. Columbianadin (CBN) has pharmacological properties such as anti-inflammatory, analgesic, thrombogenesis-inhibiting and anti-tumor effects. However, it remains unknown that the effect of CBN on GBM cells and its underlying molecular mechanisms. In the present study, we found that CBN inhibited the growth and proliferation of GBM cells in a dose-dependent manner. Subsequently, we found that CBN arrested the cell cycle in G0/G1 phase and induced the apoptosis of GBM cells. In addition, CBN also inhibited the migration and invasion of GBM cells. Mechanistically, we chose network pharmacology approach by screening intersecting genes through targets of CBN in anti-GBM, performing PPI network construction followed by GO analysis and KEGG analysis to screen potential candidate signaling pathway, and found that phosphatidylinositol 3-kinase/Protein Kinase-B (PI3K/Akt) signaling pathway was a potential target signaling pathway of CBN in anti-GBM. As expected, CBN treatment indeed inhibited the PI3K/Akt signaling pathway in GBM cells. Furthermore, YS-49, an agonist of PI3K/Akt signaling, partially restored the anti-GBM effect of CBN. Finally, we found that CBN inhibited GBM growth in an orthotopic mouse model of GBM through inhibiting PI3K/Akt signaling pathway. Together, these results suggest that CBN has an anti-GBM effect by suppressing PI3K/Akt signaling pathway, and is a promising drug for treating GBM effectively.

Tenacissoside H repressed the progression of glioblastoma by inhibiting the PI3K/Akt/mTOR signaling pathway.

Glioblastoma (GBM) is one of the most common intracranial primary malignancies with the highest mortality rate, and there is a lack of effective treatments. In this study, we examined the anti-GBM activity of Tenacissoside H (TH), an active component isolated from the traditional Chinese medicine Marsdenia tenacissima (Roxb.) Wight & Arn (MT), and investigated the potential mechanism. Firstly, we found that TH decreased the viability of GBM cells by inducing cell cycle arrest and apoptosis, and inhibited the migration of GBM cells. Furthermore, combined with the Gene Expression Omnibus database (GEO) and network pharmacology as well as molecular docking, TH was shown to inhibit GBM progression by directly regulating the PI3K/Akt/mTOR pathway, which was further validated in vitro. In addition, the selective PI3K agonist 740 y-p partially restored the inhibitory effects of TH on GBM cells. Finally, TH inhibited GBM progression in an orthotopic transplantation model by inactivating the PI3K/Akt/mTOR pathway in vivo. Conclusively, our results suggest that TH represses GBM progression by inhibiting the PI3K/Akt/mTOR signaling pathway in vitro and in vivo, and provides new insight for the treatment of GBM patients.

Tryptanthrin promotes pressure ulcers healing in mice by inhibiting macrophage-mediated inflammation via cGAS/STING pathways.

BACKGROUND: Pressure ulcers (PUs) is ischemic necrosis caused by long-term local tissue pressure, directly affecting postoperative functional recovery. There is evidence that inflammation has an adverse impact on the development of PUs and contributes to unfavorable outcomes, suggesting that blocking the inflammatory response may be a promising therapeutic strategy for PUs. Tryptanthrin (Tryp), a natural product isolated from indigenous plants, has an anti-inflammatory biological function. However, the efficacy of Tryp in PUs remains unclear. METHODS: Efficacy of Tryp suppressed inflammation was assessed using magnets-induced PUs model in mice. Hematoxylin-Eosin staining, masson staining and immunohistochemistry were used to evaluate the histologic changes after the formation of PUs. The expression of inflammatory cytokines was detected by qRT-PCR. And we detected the expression of protein by Western blotting. RESULTS: Tryp could promote wound healing, such as epidermal thickening, revascularization, and nerve regeneration. Then the treatment of Tryp was able to promote fibroblast migration and collagen deposition. Moreover, Tryp attenuated inflammation through inducing macrophage polarization to M2 phenotype by suppressing the activation of cGAS-STING pathway. CONCLUSION: Tryp could reduce the release of inflammatory cytokines, and induce RAW 264.7 polarization to M2 phenotype by targeting cGAS/STING/TBK1 pathways. In summary, Tryp may be a novel medicine for the treatment of PUs in the future.

Assessing Risk of Health Outcomes From Brain Activity in Sleep: A Retrospective Cohort Study.

Background and Objectives: Patterns of electrical activity in the brain (EEG) during sleep are sensitive to various health conditions even at subclinical stages. The objective of this study was to estimate sleep EEG-predicted incidence of future neurologic, cardiovascular, psychiatric, and mortality outcomes. Methods: This is a retrospective cohort study with 2 data sets. The Massachusetts General Hospital (MGH) sleep data set is a clinic-based cohort, used for model development. The Sleep Heart Health Study (SHHS) is a community-based cohort, used as the external validation cohort. Exposure is good, average, or poor sleep defined by quartiles of sleep EEG-predicted risk. The outcomes include ischemic stroke, intracranial hemorrhage, mild cognitive impairment, dementia, atrial fibrillation, myocardial infarction, type 2 diabetes, hypertension, bipolar disorder, depression, and mortality. Diagnoses were based on diagnosis codes, brain imaging reports, medications, cognitive scores, and hospital records. We used the Cox survival model with death as the competing risk. Results: There were 8673 participants from MGH and 5650 from SHHS. For all outcomes, the model-predicted 10-year risk was within the 95% confidence interval of the ground truth, indicating good prediction performance. When comparing participants with poor, average, and good sleep, except for atrial fibrillation, all other 10-year risk ratios were significant. The model-predicted 10-year risk ratio closely matched the observed event rate in the external validation cohort. Discussion: The incidence of health outcomes can be predicted by brain activity during sleep. The findings strengthen the concept of sleep as an accessible biological window into unfavorable brain and general health outcomes.

Insights into the Phase Purity and Storage Mechanism of Nonstoichiometric Na3.4 Fe2.4 (PO4 )1.4 P2 O7 Cathode for High-Mass-Loading and High-Power-Density Sodium-Ion Batteries.

Mixed-anion-group Fe-based phosphate materials, such as Na4 Fe3 (PO4 )2 P2 O7 , have emerged as promising cathode materials for sodium-ion batteries (SIBs). However, the synthesis of pure-phase material has remained a challenge, and the phase evolution during sodium (de)intercalation is debating as well. Herein, a solid-solution strategy is proposed to partition Na4 Fe3 (PO4 )2 P2 O7 into 2NaFePO4 ⋅ Na2 FeP2 O7 from the angle of molecular composition. Via regulating the starting ratio of NaFePO4 and Na2 FeP2 O7 during the synthesis process, the nonstoichiometric pure-phase material could be successfully synthesized within a narrow NaFePO4 content between 1.6 and 1.2. Furthermore, the proposed synthesis strategy demonstrates strong applicability that helps to address the impurity issue of Na4 Co3 (PO4 )2 P2 O7 and nonstoichiometric Na3.4 Co2.4 (PO4 )1.4 P2 O7 are evidenced to be the pure phase. The model Na3.4 Fe2.4 (PO4 )1.4 P2 O7 cathode (the content of NaFePO4 equals 1.4) demonstrates exceptional sodium storage performances, including ultrahigh rate capability under 100 C and ultralong cycle life over 14000 cycles. Furthermore, combined measurements of ex situ nuclear magnetic resonance, in situ synchrotron radiation diffraction and X-ray absorption spectroscopy clearly reveal a two-phase transition during Na+ extraction/insertion, which provides a new insight into the ionic storage process for such kind of mixed-anion-group Fe-based phosphate materials and pave the way for the development of high-power sodium-ion batteries.

Comparative study of two models predicting the risk of deep vein thrombosis progression in spinal trauma patients after operation.

OBJECTIVE: Patients with preoperative deep vein thrombosis (DVT) exhibit a notable incidence of postoperative deep vein thrombosis progression (DVTp), which bears a potential for silent, severe consequences. Consequently, the development of a predictive model for the risk of postoperative DVTp among spinal trauma patients is important. METHODS: Data of 161 spinal traumatic patients with preoperative DVT, who underwent spine surgery after admission, were collected from our hospital between January 2016 and December 2022. The least absolute shrinkage and selection operator (LASSO) combined with multivariable logistic regression analysis was applied to select variables for the development of the predictive logistic regression models. One logistic regression model was formulated simply with the Caprini risk score (Model A), while the other model incorporated not only the previously screened variables but also the age variable (Model B). The model's capability was evaluated using sensitivity, specificity, positive predictive value, negative predictive value, accuracy, F1 score, and receiver operating characteristic (ROC) curve. Nomograms simplified and visually presented Model B for the clinicians and patients to understand the predictive model. The decision curve was used to analyze the clinical value of Model B. RESULTS: A total of 161 DVT patients were enrolled in this study. Postoperative DVTp occurred in 48 spinal trauma patients, accounting for 29.81% of the total patient enrolled. Model A inadequately predicted postoperative DVTp in spinal trauma patients, with ROC AUC values of 0.595 for the training dataset and 0.593 for the test dataset. Through the application of LASSO regression and multivariable logistic regression, a screening process was conducted for seven risk factors: D-dimer, blood platelet, hyperlipidemia, blood group, preoperative anticoagulant, spinal cord injury, lower extremity varicosities. Model B demonstrated superior and consistent predictive performance, with ROC AUC values of 0.809 for the training dataset and 0.773 for the test dataset. According to the calibration curves and decision curve analysis, Model B could accurately predict the probability of postoperative DVTp after spine surgery. The nomograms enhanced the interpretability of Model B in charts and graphs. CONCLUSIONS: In summary, we established a logistic regression model for the accurate predicting of postoperative deep vein thrombosis progression in spinal trauma patients, utilizing D-dimer, blood platelet, hyperlipidemia, blood group, preoperative anticoagulant, spinal cord injury, lower extremity varicosities, and age as predictive factors. The proposed model outperformed a logistic regression model based simply on CRS. The proposed model has the potential to aid frontline clinicians and patients in identifying and intervening in postoperative DVTp among traumatic patients undergoing spinal surgery.

Dual targeting agent Thiotert inhibits the progression of glioblastoma by inducing ER stress-dependent autophagy.

Glioblastoma (GBM) is the most aggressive and lethal type of tumor in the central nervous system, characterized by a high incidence and poor prognosis. Thiotert, as a novel dual targeting agent, has potential inhibitory effects on various tumors. Here, we found that Thiotert effectively inhibited the proliferation of GBM cells by inducing G2/M cell cycle arrest and suppressed the migratory ability in vitro. Furthermore, Thiotert disrupted the thioredoxin (Trx) system while causing cellular DNA damage, which in turn caused endoplasmic reticulum (ER) stress-dependent autophagy. Knockdown of ER stress-related protein ATF4 in U251 cells inhibited ER stress-dependent autophagy caused by Thiotert to some extent. Orthotopic transplantation experiments further showed that Thiotert had the same anti-GBM activity and mechanism as in vitro. Conclusively, these results suggest that Thiotert induces ER stress-dependent autophagy in GBM cells by disrupting redox homeostasis and causing DNA damage, which provides new insight for the treatment of GBM.

Comprehensive analysis of different types of ginsenosides in the different parts of American ginseng by targeted and nontargeted MS/MS scanning.

To comprehensively study the ginsenosides distribution in the various tissues of American ginseng, the qualitative and quantitative-targeted and nontargeted mass spectroscopic methods were established using the high-performance liquid chromatography coupled with Qtrap triple quadrupole mass spectrometry (HPLC-QtrapQQQ-MS). The total ginsenosides of the root, stem, and leaf of American ginseng were determined by a colorimetric method, and the contents showed the order from high to low root, stem, and leaf. Eighty-two kinds of ginsenosides were detected in the different parts of American ginseng by enhanced mass scan-information-dependent data acquisition (IDA)-enhanced product ion (EPI) scan mode, including 69 from the root, 62 from the stem, and 48 from the leaf. An HPLC-multiple reaction monitoring (MRM) method was established, and 28 representative ginsenosides were further quantified in the three parts. Nearly all ginsenosides had the highest contents in the root and the lowest content in the leaf. Three types of ginsenosides (protopanaxadiol [PPD]-, protopanaxatiol [PPT]-, and oleanolic acid [OA]-types) were analyzed by precursor ion-IDA-EPI and MRM-IDA-EPI scan modes. Root had the most abundant ginsenosides in PPD- and PPT-type ginsenosides. Meanwhile, the OA-type ginsenosides are significantly enriched in the stem and leaf of American ginseng. The results provided a supplement to the quality assessment of American ginseng. PRACTICAL APPLICATION: The distribution profile of ginsenosides in the parts of American ginseng is different. Except for the root, the stem, and leaf of American ginseng have the most abundant ginsenosides in oleanolic acid type. The results reported herein can help the manufacturers choose appropriate materials to extract the ginsenosides.

Automated detection of immune effector cell-associated neurotoxicity syndrome via quantitative EEG.

OBJECTIVE: To develop an automated, physiologic metric of immune effector cell-associated neurotoxicity syndrome among patients undergoing chimeric antigen receptor-T cell therapy. METHODS: We conducted a retrospective observational cohort study from 2016 to 2020 at two tertiary care centers among patients receiving chimeric antigen receptor-T cell therapy with a CD19 or B-cell maturation antigen ligand. We determined the daily neurotoxicity grade for each patient during EEG monitoring via chart review and extracted clinical variables and outcomes from the electronic health records. Using quantitative EEG features, we developed a machine learning model to detect the presence and severity of neurotoxicity, known as the EEG immune effector cell-associated neurotoxicity syndrome score. RESULTS: The EEG immune effector cell-associated neurotoxicity syndrome score significantly correlated with the grade of neurotoxicity with a median Spearman's R2 of 0.69 (95% CI of 0.59-0.77). The mean area under receiving operator curve was greater than 0.85 for each binary discrimination level. The score also showed significant correlations with maximum ferritin (R2 0.24, p = 0.008), minimum platelets (R2 -0.29, p = 0.001), and dexamethasone usage (R2 0.42, p < 0.0001). The score significantly correlated with duration of neurotoxicity (R2 0.31, p < 0.0001). INTERPRETATION: The EEG immune effector cell-associated neurotoxicity syndrome score possesses high criterion, construct, and predictive validity, which substantiates its use as a physiologic method to detect the presence and severity of neurotoxicity among patients undergoing chimeric antigen receptor T-cell therapy.

Inhibition of TANK-binding kinase1 attenuates the astrocyte-mediated neuroinflammatory response through YAP signaling after spinal cord injury.

AIMS: TANK-binding kinase 1 (TBK1) is involved in regulating the pathological process of a variety of inflammatory diseases in the central nervous system. However, its role and underlying molecular mechanisms in spinal cord injury (SCI) remain largely unknown. METHODS: We employed the TBK1 inhibitor amlexanox (ALX) to address this question. An in vivo clip-compressive SCI model and in vitro lipopolysaccharide (LPS)-induced astrocyte inflammation model were established to examine the effects of TBK1 inhibition on the expression of proinflammatory cytokines. RESULTS: In this study, we found that TBK1 and TBK1-medicated innate immune pathways, such as TBK1/IRF3 and noncanonical NF-κB signaling, were activated in astrocytes and neurons after SCI. Furthermore, inhibition of TBK1 by ALX alleviated neuroinflammation response, reduced the loss of motor neurons, and improved the functional recovery after SCI. Mechanistically, inhibition of TBK1 activity promoted the activation of the noncanonical NF-κB signaling pathway and inhibited p-IRF3 activity in LPS-induced astrocytes, and the TBK1 activity was required for astrocytic activation through yes-associated protein (YAP) signaling after SCI and in LPS-induced astrocytes inflammation model. CONCLUSION: TBK1-medicated innate immune pathway in astrocytes through YAP signaling plays an important role in the pathogenesis of SCI and inhibition of TBK1 may be a potential therapeutic drug for SCI.

Development of Expert-Level Classification of Seizures and Rhythmic and Periodic Patterns During EEG Interpretation.

BACKGROUND AND OBJECTIVES: Seizures (SZs) and other SZ-like patterns of brain activity can harm the brain and contribute to in-hospital death, particularly when prolonged. However, experts qualified to interpret EEG data are scarce. Prior attempts to automate this task have been limited by small or inadequately labeled samples and have not convincingly demonstrated generalizable expert-level performance. There exists a critical unmet need for an automated method to classify SZs and other SZ-like events with expert-level reliability. This study was conducted to develop and validate a computer algorithm that matches the reliability and accuracy of experts in identifying SZs and SZ-like events, known as "ictal-interictal-injury continuum" (IIIC) patterns on EEG, including SZs, lateralized and generalized periodic discharges (LPD, GPD), and lateralized and generalized rhythmic delta activity (LRDA, GRDA), and in differentiating these patterns from non-IIIC patterns. METHODS: We used 6,095 scalp EEGs from 2,711 patients with and without IIIC events to train a deep neural network, SPaRCNet, to perform IIIC event classification. Independent training and test data sets were generated from 50,697 EEG segments, independently annotated by 20 fellowship-trained neurophysiologists. We assessed whether SPaRCNet performs at or above the sensitivity, specificity, precision, and calibration of fellowship-trained neurophysiologists for identifying IIIC events. Statistical performance was assessed by the calibration index and by the percentage of experts whose operating points were below the model's receiver operating characteristic curves (ROCs) and precision recall curves (PRCs) for the 6 pattern classes. RESULTS: SPaRCNet matches or exceeds most experts in classifying IIIC events based on both calibration and discrimination metrics. For SZ, LPD, GPD, LRDA, GRDA, and "other" classes, SPaRCNet exceeds the following percentages of 20 experts-ROC: 45%, 20%, 50%, 75%, 55%, and 40%; PRC: 50%, 35%, 50%, 90%, 70%, and 45%; and calibration: 95%, 100%, 95%, 100%, 100%, and 80%, respectively. DISCUSSION: SPaRCNet is the first algorithm to match expert performance in detecting SZs and other SZ-like events in a representative sample of EEGs. With further development, SPaRCNet may thus be a valuable tool for an expedited review of EEGs. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that among patients with epilepsy or critical illness undergoing EEG monitoring, SPaRCNet can differentiate (IIIC) patterns from non-IIIC events and expert neurophysiologists.

Development of a Dynamic Nomogram for Predicting the Probability of Satisfactory Recovery after 6 Months for Cervical Traumatic Spinal Cord Injury.

OBJECTIVE: Cervical traumatic spinal cord injury (CTSCI) is a seriously disabling disease that severely affects the physical and mental health of patients and imposes a huge economic burden on patients and their families. Accurate identification of the prognosis of CTSCI patients helps clinicians to design individualized treatment plans for patients. For this purpose, a dynamic nomogram was developed to predict the recovery of CTSCI patients after 6 months. METHODS: We retrospectively included 475 patients with CTSCI in our institution between March 2013 and January 2022. The outcome variable of the current study was a satisfactory recovery of patients with CTSCI at 6 months. Univariate analyses and univariate logistic regression analyses were used to assess the factors affecting the prognosis of patients with CTSCI. Subsequently, variables (P < 0.05) were included in the multivariate logistic regression analysis to evaluate these factors further. Eventually, a nomogram model was constructed according to these independent risk factors. The concordance index (C-index) and the calibration curve were utilized to assess the model's predictive ability. The discriminating capacity of the prediction model was measured by the receiver operating characteristic (ROC) area under the curve (AUC). One hundred nine patients were randomly selected from 475 patients to serve as the center's internal validation test cohort. RESULTS: The multivariate logistic regression model further screened out six independent factors that impact the recovery of patients with CTSCI. Including admission to the American Spinal Injury Association Impairment Scale (AIS) grade, the length of high signal in the spinal cord, maximum spinal cord compression (MSCC), spinal segment fractured, admission time, and hormonal therapy within 8 h after injury. A nomogram prediction model was developed based on the six independent factors above. In the training cohort, the AUC of the nomogram that included these predictors was 0.879, while in the test cohort, it was 0.824. The nomogram C-index incorporating these predictors was 0.872 in the training cohort and 0.813 in the test cohort, while the calibration curves for both cohorts also indicated good consistency. Furthermore, this nomogram was converted into a Web-based calculator, which provided individual probabilities of recovery to be generated for individuals with CTSCI after 6 months and displayed in a graphical format. CONCLUSION: The nomogram, including ASIA grade, the length of high signal in the spinal cord, MSCC, spinal segment fractured, admission time, and hormonal therapy within 8 h after injury, is a promising model to predict the probability of content recovery in patients with CTSCI. This nomogram assists clinicians in stratifying patients with CTSCI, enhancing evidence-based decision-making, and individualizing the most appropriate treatment.

Interrater Reliability of Expert Electroencephalographers Identifying Seizures and Rhythmic and Periodic Patterns in EEGs.

BACKGROUND AND OBJECTIVES: The validity of brain monitoring using electroencephalography (EEG), particularly to guide care in patients with acute or critical illness, requires that experts can reliably identify seizures and other potentially harmful rhythmic and periodic brain activity, collectively referred to as "ictal-interictal-injury continuum" (IIIC). Previous interrater reliability (IRR) studies are limited by small samples and selection bias. This study was conducted to assess the reliability of experts in identifying IIIC. METHODS: This prospective analysis included 30 experts with subspecialty clinical neurophysiology training from 18 institutions. Experts independently scored varying numbers of ten-second EEG segments as "seizure (SZ)," "lateralized periodic discharges (LPDs)," "generalized periodic discharges (GPDs)," "lateralized rhythmic delta activity (LRDA)," "generalized rhythmic delta activity (GRDA)," or "other." EEGs were performed for clinical indications at Massachusetts General Hospital between 2006 and 2020. Primary outcome measures were pairwise IRR (average percent agreement [PA] between pairs of experts) and majority IRR (average PA with group consensus) for each class and beyond chance agreement (κ). Secondary outcomes were calibration of expert scoring to group consensus, and latent trait analysis to investigate contributions of bias and noise to scoring variability. RESULTS: Among 2,711 EEGs, 49% were from women, and the median (IQR) age was 55 (41) years. In total, experts scored 50,697 EEG segments; the median [range] number scored by each expert was 6,287.5 [1,002, 45,267]. Overall pairwise IRR was moderate (PA 52%, κ 42%), and majority IRR was substantial (PA 65%, κ 61%). Noise-bias analysis demonstrated that a single underlying receiver operating curve can account for most variation in experts' false-positive vs true-positive characteristics (median [range] of variance explained ([Formula: see text]): 95 [93, 98]%) and for most variation in experts' precision vs sensitivity characteristics ([Formula: see text]: 75 [59, 89]%). Thus, variation between experts is mostly attributable not to differences in expertise but rather to variation in decision thresholds. DISCUSSION: Our results provide precise estimates of expert reliability from a large and diverse sample and a parsimonious theory to explain the origin of disagreements between experts. The results also establish a standard for how well an automated IIIC classifier must perform to match experts. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that an independent expert review reliably identifies ictal-interictal injury continuum patterns on EEG compared with expert consensus.

EEG-based grading of immune effector cell-associated neurotoxicity syndrome.

CAR-T cell therapy is an effective cancer therapy for multiple refractory/relapsed hematologic malignancies but is associated with substantial toxicity, including Immune Effector Cell Associated Neurotoxicity Syndrome (ICANS). Improved detection and assessment of ICANS could improve management and allow greater utilization of CAR-T cell therapy, however, an objective, specific biomarker has not been identified. We hypothesized that the severity of ICANS can be quantified based on patterns of abnormal brain activity seen in electroencephalography (EEG) signals. We conducted a retrospective observational study of 120 CAR-T cell therapy patients who had received EEG monitoring. We determined a daily ICANS grade for each patient through chart review. We used visually assessed EEG features and machine learning techniques to develop the Visual EEG-Immune Effector Cell Associated Neurotoxicity Syndrome (VE-ICANS) score and assessed the association between VE-ICANS and ICANS. We also used it to determine the significance and relative importance of the EEG features. We developed the Visual EEG-ICANS (VE-ICANS) grading scale, a grading scale with a physiological basis that has a strong correlation to ICANS severity (R = 0.58 [0.47-0.66]) and excellent discrimination measured via area under the receiver operator curve (AUC = 0.91 for ICANS ≥ 2). This scale shows promise as a biomarker for ICANS which could help to improve clinical care through greater accuracy in assessing ICANS severity.

High-altitude exposure decreases bone mineral density and its relationship with gut microbiota: Results from the China multi-ethnic cohort (CMEC) study.

BACKGROUND: Geographic altitude is a potent environmental factor for human microbiota and bone mineral density. However, little evidence exists in population-based studies with altitude diversity ranges across more than 3000 m. This study assessed the associations between a wide range of altitudes and bone mineral density, as well as the potential mediating role of microbiota in this relationship. METHODS: A total of 99,556 participants from the China Multi-Ethnic Cohort (CMEC) study were enrolled. The altitude of each participant was extracted from global Shuttle Radar Topography Mission (SRTM) 4 data. Bone mineral density was measured by calcaneus quantitative ultrasound index (QUI). Stool samples were collected for 16S rRNA gene sequencing (n = 1384). The metabolites of gut microbiota, seven kinds of short-chain fatty acids (SCFAs), were detected by gas chromatography-mass spectrometry (GC-MS, n = 128). After screening, 73,974 participants were selected for the "altitude-QUI" analysis and they were placed into the low-altitude (LA) and high-altitude (HA) groups. Additionally, a subgroup (n = 1384) was further selected for the "altitude-microbiota-QUI" analysis. Multivariate linear regression models and mediation analyses were conducted among participants. RESULTS: A significant negative association between high-altitude and QUI was obtained (mean difference = -0.373 standard deviation [SD], 95% confidence interval [CI]: -0.389, -0.358, n = 73,974). The same negative association was also observed in the population with microbiota data (mean difference = -0.185 SD, 95%CI: -0.360, -0.010, n = 1384), and a significant mediating effect of Catenibacteriumon on the association between altitude and QUI (proportion mediated = 25.2%, P = 0.038) was also noticed. Additionally, the acetic acid, butyric acid, and total amount of seven SCFAs of the low-altitude group were significantly higher than that of the high-altitude group (P < 0.05). CONCLUSION: High-altitude exposure may decrease bone mineral density in adults, thus increasing the risk of osteoporosis. The modulation of gut microbiota may be a potential strategy for alleviating the decrease of bone mineral density.