Associations Between Estimated Pulse Wave Velocity and Five-Year All-Cause Mortality in Patients with Atherosclerotic Cardiovascular Disease with and without Standard Modifiable Risk Factors: Evidence From NHANES 1999-2016.

Aim: The study aimed to analyze the associations between estimated pulse wave velocity (ePWV) and 5-year mortality in atherosclerotic cardiovascular disease (ASCVD) patients with and without standard modifiable risk factors (SMuRFs), which included smoking status, hypertension, diabetes, and hypercholesterolemia. Methods: The present retrospective cohort study utilized data from the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2016. Patients with ASCVD who completed both the questionnaire survey and serum testing were included. Patients were categorized into the ≥1 SMuRF group if they had at least one SMuRF, while those without any SMuRFs were classified into the SMuRF-less group. The ePWV, which was calculated using the age and mean blood pressure, was evenly divided into three categories: low (Q1), medium (Q2), and high (Q3). Multivariable weighted Cox proportional-hazard regression analyses were utilized to explore the risk factors associated with 5-year mortality in patients with and without SMuRFs. And restricted cubic spline curve (RCS) was used to assess their nonlinear correlation. Results: A total of 1901 patients with ASCVD were included in the study. For the patients in ≥1 SMuRF group, the Q3 group included patients who were older, with a higher proportion of males, more comorbidities, and a lower body mass index than the Q1 group (P<0.05). The Cox proportional-hazard regression model results revealed, the Q3 group had a higher risk of 5-year mortality than the Q1 group [hazard ratio (HR) 4.30, 95% confidence interval (CI) (2.66, 6.95), P<0.001]. RCS demonstrated a linear trend between high level of ePWV and decreased risks of mortality. Similar results were observed in the SMuRF-less group [HR 10.62, 95% CI (1.22, 92.06), P=0.032]. Conclusion: A high level of ePWV signified a higher risk of 5-year mortality in ASCVD patients with and without SMuRFs.

Decoupling the mutual promotion of inflammation and oxidative stress mitigates cognitive decline and depression-like behavior in rmTBI mice by promoting myelin renewal and neuronal survival.

BACKGROUND: Repetitive mild traumatic brain injury (rmTBI) can lead to somatic, emotional, and cognitive symptoms that persist for years after the initial injury. Although the ability of various treatments to promote recovery after rmTBI has been explored, the optimal time window for early intervention after rmTBI is unclear. Previous research has shown that hydrogen-rich water (HRW) can diffuse through the blood-brain - barrier, attenuate local oxidative stress, and reduce neuronal apoptosis in patients with severe traumatic brain injury. However, research on the effect of HRW on rmTBI is scarce. AIMS: The objectives of this study were to explore the following changes after rmTBI and HRW treatment: (i) temporal changes in inflammasome activation and oxidative stress-related protein expression through immunoblotting, (ii) temporal changes in neuron/myelin-related metabolite concentrations in vivo through magnetic resonance spectroscopy, (iii) myelin structural changes in late-stage rmTBI via immunofluorescence, and (iv) postinjury anxiety/depression-like behaviors and spatial learning and memory impairment. RESULTS: NLRP-3 expression in the rmTBI group was elevated at 7 and 14 DPI, and inflammasome marker levels returned to normal at 30 DPI. Oxidative stress persisted throughout the first month postinjury. HRW replacement significantly decreased Nrf2 expression in the prefrontal cortex and hippocampal CA2 region at 14 and 30 DPI, respectively. Edema and local gliosis in the hippocampus and restricted diffusion in the thalamus were observed on MR-ADC images. The tCho/tCr ratio in the rmTBI group was elevated, and the tNAA/tCr ratio was decreased at 30 DPI. Compared with the mice in the other groups, the mice in the rmTBI group spent more time exploring the open arms in the elevated plus maze (P < 0.05) and were more active in the maze (longer total distance traveled). In the sucrose preference test, the rmTBI group exhibited anhedonia. In the Morris water maze test, the latency to find the hidden platform in the rmTBI group was longer than that in the sham and HRW groups (P < 0.05). CONCLUSION: Early intervention with HRW can attenuate inflammasome assembly and reduce oxidative stress after rmTBI. These changes may restore local oligodendrocyte function, promote myelin repair, prevent axonal damage and neuronal apoptosis, and alleviate depression-like behavior and cognitive impairment.

Research progress on the association between trimethylamine/trimethylamine-N-oxide and neurological disorders.

Trimethylamine-N-oxide (TMAO) is a common intestinal metabolite. The Choline in the nutrient forms TMA under the action of the gut microbiota, which passes through the liver and eventually forms TMAO. Initial studies of TMAO focused on cardiovascular disease, but as research progressed, TAMO's effects were found to be multisystem and closely related to the development of neurological diseases. Intestinal tract is the organ with the largest concentration of bacteria in human body, and the composition and metabolism of gut microbiota affect human health. As a two-way communication axis connecting the central nervous system and the gastrointestinal tract, the brain-gut axis provides the structural basis for TMAO to play its role. This article will review the correlation between TMA/TMAO and neurological diseases in order to find new directions and new targets for the treatment of neurological diseases.

The effect of green channel for stroke patients on treatment of severe aneurysmal subarachnoid hemorrhage.

PURPOSE: To explore the effect of green channel for stroke patients on the treatment of severe aneurysmal subarachnoid hemorrhage. METHODS: This is a retrospective case-control study. The clinical data of patients with severe aneurysmal subarachnoid hemorrhage admitted to the emergency department of our hospital from January 2015 to June 2022 were retrospectively analyzed. Patients diagnosed with subarachnoid hemorrhage, confirmed intracranial aneurysm by preoperative CT angiography or digital subtraction, graded Hunt-Hess grade III, IV, and V, < 72 h from the onset to the time of consultation received surgical treatment in our hospital were included in this study. Patients with serious underlying diseases, such as heart, liver, kidney diseases, or malignant tumors, traumatic subarachnoid hemorrhage, previous history of cerebral hemorrhage, and incomplete data were excluded. The control group included patients with severe aneurysmal subarachnoid hemorrhage admitted from January 2015 to December 2018 before the establishment of the green channel for stroke patients, and the observation group included patients with severe aneurysmal subarachnoid hemorrhage admitted from January 2019 to June 2022 after the establishment of the green channel. The control group received routine treatment in the emergency department; the observation group received improved treatment of green channel for stroke patients. Gender, age, Hunt-Hess grade on admission, modified Rankin scale (mRS) on admission, aneurysm location, aneurysm size and whether accompanied by intracerebral hemorrhage, the time from onset to emergency department, the time from emergency department to vascular diagnostic examination, the time from onset to surgery, the time from emergency department to surgery, the time from hospital admission to surgery, length of hospital stay, complications, treatment effect were analyzed and compared between the 2 groups. SPSS 23.0 software was utilized to conduct comparisons between the 2 groups. The t-test, Chi-square test, or Mann-Whitney U test was chosen based on the data type. Statistical significance was established when p < 0.05. RESULTS: A total of 71 patients were included in this study, of whom 37 were in the control group and 34 were in the observation group. There were no statistical differences in age, gender, Hunt-Hess grade, mRS scores, aneurysm location, aneurysm size, intracerebral hemorrhage, the time from onset to emergency department, length of hospital stay, complications between the observation group and the control group (all p > 0.05). The time (min) from visit to vascular diagnostic test (60.50 vs. 120.00, p = 0.027), the time (min) from onset to surgery (1792.00 vs. 2868.00, p = 0.023), the time (min) from emergency department to surgery (1568.50 vs. 2778.00, p = 0.016), the time (min) from hospital admission to surgery (1188.50 vs. 2708.00, p = 0.043), all of them were shorter in the observation group than those in the control group. The relative values of admission and 7-day postoperative mRS scores and the relative values of admission and discharge mRS scores ≥ 2 were used as the criteria for determining better efficacy, and the treatment effect was better than that in the control group, and the differences were statistically significant (admission to 7 days postoperative mRS score ≥ 2, 17 (50.0 %) vs. 8 (21.6 %), p = 0.012; admission to discharge mRS score ≥ 2, 19 (55.9 %) vs. 11 (29.7 %), p = 0.026). CONCLUSION: The green channel for stroke patients with severe aneurysmal subarachnoid hemorrhage can effectively shorten the time from arrival at the emergency department to vascular diagnostic examination and the time from the emergency department to surgery, and achieve a better therapeutic effect, which is worth popularizing and applying.

Association between chronic obstructive pulmonary disease and 28-day mortality in patients with sepsis: a retrospective study based on the MIMIC-III database.

BACKGROUND: Sepsis is a common cause of mortality in critically ill patients, and chronic obstructive pulmonary disease (COPD) is one of the most common comorbidities in septic patients. However, the impact of COPD on patients with sepsis remained unclear. Therefore, the purpose of this study aimed to assess the effect of COPD on the prognosis of septic patients based on Medical Information Mart for Intensive Care (MIMIC-III) database. METHODS: In this retrospective study based on the (MIMIC)-III database version 1.4 (v1.4), we collected clinical data and 28-day all-cause mortality from patients with sepsis in intensive care unit (ICU) and these patients met the diagnostic criteria of Sepsis 3 on ICU admission between 2008 and 2012. International Classification of Diseases (ICD-9) (4660, 490, 4910, 4911, 49120, 49121, 4918, 4919, 4920, 4928, 494, 4940, 4941, 496) was used to identified COPD. We applied Kaplan-Meier analysis to compare difference of 28-day all-cause mortality between septic patients with and without COPD. Cox proportional-hazards model was applied to explore the risk factor associated with 28-day all-cause mortality in patients with sepsis. RESULTS: Six thousand two hundred fifty seven patients with sepsis were included in this study, including 955 (15.3%) patients with COPD and 5302 patients without COPD (84.7%). Compared with patients without COPD, patients with COPD were older (median: 73.5 [64.4, 82.0] vs 65.8 [52.9, 79.1], P < 0.001), had higher simplified acute physiology score II (SAPSII) (median: 40.0 [33.0, 49.0] vs 38.0 [29.0,47.0], P < 0.001) and greater proportion of mechanical ventilatory support (MV) (55.0% vs 48.9%, P = 0.001). In our study, septic patients with COPD had higher 28-day all-cause mortality (23.6% vs 16.4%, P < 0.001) than patients without COPD. After adjusting for covariates, the results showed that COPD was an independent risk factor for the 28-day all-cause mortality of patients with sepsis (HR 1.30, 95%CI: 1.12-1.50, P = 0.001). CONCLUSIONS: COPD was an independent risk factor of 28-day all-cause mortality in septic patients. Clinically, septic patients with COPD should be given additional care.

Application of Lineage Tracing in Central Nervous System Development and Regeneration.

The central nervous system (CNS) is a complicated neural network. The origin and evolution of functional neurons and glia cells remain unclear, as do the cellular alterations that occur during the course of cerebral disease rehabilitation. Lineage tracing is a valuable method for tracing specific cells and achieving a better understanding of the CNS. Recently, various technological breakthroughs have been made in lineage tracing, such as the application of various combinations of fluorescent reporters and advances in barcode technology. The development of lineage tracing has given us a deeper understanding of the normal physiology of the CNS, especially the pathological processes. In this review, we summarize these advances of lineage tracing and their applications in CNS. We focus on the use of lineage tracing techniques to elucidate the process CNS development and especially the mechanism of injury repair. Deep understanding of the central nervous system will help us to use existing technologies to diagnose and treat diseases.

Megf10-related engulfment of excitatory postsynapses by astrocytes following severe brain injury.

AIMS: To investigate astrocyte-related phagocytosis of synapses in the ipsilateral hippocampus after traumatic brain injury (TBI). METHODS: We performed controlled cortical impact to simulate TBI in mice. Seven days postinjury, we performed cognitive tests, synapse quantification, and examination of astrocytic phagocytosis in association with Megf10 expression. RESULTS: During the subacute stage post-TBI, we found a reduction in excitatory postsynaptic materials in the ipsilateral hippocampus, which was consistent with poor performance in the cognitive test. The transcriptome data suggested that robust phagocytosis was responsible for this process. Coincidently, we identified phagocytic astrocytes containing secondary lysosomes that were wrapped around the synapses in the ipsilateral hippocampus. Moreover, a significant increase in the co-location of GFAP and PSD-95 in the CA1 region suggested astrocytic engulfment of excitatory postsynaptic proteins. After examining the reported phagocytic pathways, we found that both the transcription level and protein expression of Megf10 were elevated. Co-immunofluorescence of GFAP and Megf10 demonstrated that the expression of Megf10 was spatially upregulated in astrocytes, exclusively in the CA1 region, and was related to the astrocytic engulfment of PSD-95. CONCLUSION: Our study elaborated that the Megf10-related astrocytic engulfment of PSD-95 in the CA1 region of the ipsilateral hippocampus aggravated cognitive dysfunction following severe TBI.

Exhaustive drainage versus fixed-time drainage for chronic subdural hematoma after one-burr hole craniostomy (ECHO): study protocol for a multicenter randomized controlled trial.

BACKGROUND: Chronic subdural hematomas (CSDHs) are one of the most common neurosurgical conditions. The standard surgical technique includes burr-hole craniostomy, followed by intraoperative irrigation and placement of subdural closed-system drainage. The drainage is generally removed after 48 h, which can be described as fixed-time drainage strategy. According to literature, the recurrence rate is 5-33% with this strategy. In our retrospective study, postoperative hematoma volume was found to significantly increase the risk of recurrence. Based on these results, an exhaustive drainage strategy is conducted to minimize postoperative hematoma volume and achieve a low recurrence rate and good outcomes. METHODS: This is a prospective, multicenter, open-label, blinded endpoint randomized controlled trial designed to include 304 participants over the age of 18-90 years presenting with a symptomatic CSDH verified on cranial computed tomography or magnetic resonance imaging. Participants will be randomly allocated to perform exhaustive drainage (treatment group) or fixed-time drainage (control group) after a one-burr hole craniostomy. The primary endpoint will be recurrence indicating a reoperation within 6 months. DISCUSSION: This study will validate the effect and safety of exhaustive drainage after one-burr hole craniostomy in reducing recurrence rates and provide critical information to improve CSDH surgical management. TRIAL REGISTRATION: Clinicaltrials.gov, NCT04573387. Registered on October 5, 2020.

Corrigendum: Construction and validation of a robust prognostic model based on immune features in sepsis.

[This corrects the article DOI: 10.3389/fimmu.2022.994295.].

Evidence-based guideline for the prevention and management of perioperative infection.

BACKGROUND: We have updated the guideline for preventing and managing perioperative infection in China, given the global issues with antimicrobial resistance and the need to optimize antimicrobial usage and improve hospital infection control levels. METHODS: We conducted a comprehensive evaluation of the evidence for prevention and management of perioperative infection, based on the concepts of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. The strength of recommendations was graded and voted using the Delphi method and the nominal group technique. Revisions were made to the guidelines in response to feedback from the experts. RESULTS: There were 17 questions prepared, for which 37 recommendations were made. According to the GRADE system, we evaluated the body of evidence for each clinical question. Based on the meta-analysis results, recommendations were graded using the Delphi method to generate useful information. CONCLUSIONS: This guideline provides evidence to perioperative antimicrobial prophylaxis that increased the rational use of prophylactic antimicrobial use, with substantial improvement in the risk-benefit trade-off.

Effects of Sevoflurane and Propofol on Neurological Recovery of Traumatic Brain Injury Patients in the Early Postoperative Stage: A Retrospective Cohort Study.

Objective To investigate the effects of propofol and sevoflurane on neurological recovery of traumatic brain injury (TBI) patients in the early postoperative stage.Methods We retrospectively analyzed the clinical data of TBI patients who underwent craniotomy or decompressive craniectomy. Generalized additive mixed model (GAMM) was used to analyze effects of propofol and sevoflurane on Glasgow Coma Scale (GCS) on postoperative days 1, 3, and 7. Multivariate regression analysis was used to analyze effects of the two anesthetics on Glasgow Outcome Scale (GOS) at discharge.Results A total of 340 TBI patients were enrolled in this study. There were 110 TBI patients who underwent craniotomy including 75 in the propofol group and 35 in the sevoflurane group, and 134 patients who underwent decompressive craniectomy including 63 in the propofol group and 71 in the sevoflurane group. It showed no significant difference in GCS at admission between the propofol and the sevoflurane groups among craniotomy patients (ß = 0.75, 95%CI: -0.55 to 2.05, P = 0.260). However, elevation in GCS from baseline was 1.73 points (95%CI: -2.81 to -0.66, P = 0.002) less in the sevoflurane group than that in the propofol group on postoperative day 1, 2.03 points (95%CI: -3.14 to -0.91, P < 0.001) less on day 3, and 1.31 points (95%CI: -2.43 to -0.19, P = 0.022) less on day 7. The risk of unfavorable GOS (GOS 1, 2, and 3) at discharge was higher in the sevoflurane group (OR = 4.93, 95%CI: 1.05 to 23.03, P = 0.043). No significant difference was observed among two-group decompressive craniectomy patients in GCS and GOS.Conclusions Compared to propofol, sevoflurane was associated with worse neurological recovery during the hospital stay in TBI patients undergoing craniotomy. This difference was not detected in TBI patients undergoing decompressive craniectomy.

HIF-1α participates in secondary brain injury through regulating neuroinflammation.

A deeper understanding of the underlying biological mechanisms of secondary brain injury induced by traumatic brain injury (TBI) will greatly advance the development of effective treatments for patients with TBI. Hypoxia-inducible factor-1 alpha (HIF-1α) is a central regulator of cellular response to hypoxia. In addition, growing evidence shows that HIF-1α plays the important role in TBI-induced changes in biological processes; however, detailed functional mechanisms are not completely known. The aim of the present work was to further explore HIF-1α-mediated events after TBI. To this end, next-generation sequencing, coupled with cellular and molecular analysis, was adopted to interrogate vulnerable events in a rat controlled cortical impact model of TBI. The results demonstrated that TBI induced accumulation of HIF-1α at the peri-injury site at 24 h post-injury, which was associated with neuronal loss. Moreover, gene set enrichment analysis unveiled that neuroinflammation, especially an innate inflammatory response, was significantly evoked by TBI, which could be attenuated by the inhibition of HIF-1α. Furthermore, the inhibition of HIF-1α could mitigate the activation of microglia and astrocytes. Taken together, all these data implied that HIF-1α might contribute to secondary brain injury through regulating neuroinflammation.

Tandem Mass Tag-based proteomics analysis reveals the vital role of inflammation in traumatic brain injury in a mouse model.

Proteomics is a powerful tool that can be used to elucidate the underlying mechanisms of diseases and identify new biomarkers. Therefore, it may also be helpful for understanding the detailed pathological mechanism of traumatic brain injury (TBI). In this study, we performed Tandem Mass Tag-based quantitative analysis of cortical proteome profiles in a mouse model of TBI. Our results showed that there were 302 differentially expressed proteins in TBI mice compared with normal mice 7 days after injury. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses showed that these differentially expressed proteins were predominantly involved in inflammatory responses, including complement and coagulation cascades, as well as chemokine signaling pathways. Subsequent transcription factor analysis revealed that the inflammation-related transcription factors NF-κB1, RelA, IRF1, STAT1, and Spi1 play pivotal roles in the secondary injury that occurs after TBI, which further corroborates the functional enrichment for inflammatory factors. Our results suggest that inflammation-related proteins and inflammatory responses are promising targets for the treatment of TBI.

A Multimodal Multipath Artificial Intelligence System for Diagnosing Gastric Protruded Lesions on Endoscopy and Endoscopic Ultrasonography Images.

INTRODUCTION: The aim of this study was to develop a novel artificial intelligence (AI) system that can automatically detect and classify protruded gastric lesions and help address the challenges of diagnostic accuracy and inter-reader variability encountered in routine diagnostic workflow. METHODS: We analyzed data from 1,366 participants who underwent gastroscopy at Jiangsu Provincial People's Hospital and Yangzhou First People's Hospital between December 2010 and December 2020. These patients were diagnosed with submucosal tumors (SMTs) including gastric stromal tumors (GISTs), gastric leiomyomas (GILs), and gastric ectopic pancreas (GEP). We trained and validated a multimodal, multipath AI system (MMP-AI) using the data set. We assessed the diagnostic performance of the proposed AI system using the area under the receiver-operating characteristic curve (AUC) and compared its performance with that of endoscopists with more than 5 years of experience in endoscopic diagnosis. RESULTS: In the ternary classification task among subtypes of SMTs using modality images, MMP-AI achieved the highest AUCs of 0.896, 0.890, and 0.999 for classifying GIST, GIL, and GEP, respectively. The performance of the model was verified using both external and internal longitudinal data sets. Compared with endoscopists, MMP-AI achieved higher recognition accuracy for SMTs. DISCUSSION: We developed a system called MMP-AI to identify protruding benign gastric lesions. This system can be used not only for white-light endoscope image recognition but also for endoscopic ultrasonography image analysis.

Neutrophil-derived interleukin-17A participates in neuroinflammation induced by traumatic brain injury.

After brain injury, infiltration and abnormal activation of neutrophils damages brain tissue and worsens inflammation, but the mediators that connect activated neutrophils with neuroinflammation have not yet been fully clarified. To identify regulators of neutrophil-mediated neuroinflammation after traumatic brain injury, a mouse model of traumatic brain injury was established by controlled cortical impact. At 7 days post-injury (sub-acute phase), genome-wide transcriptomic data showed that interleukin 17A-associated signaling pathways were markedly upregulated, suggesting that interleukin 17A may be involved in neuroinflammation. Double immunofluorescence staining showed that interleukin 17A was largely secreted by neutrophils rather than by glial cells and neurons. Furthermore, nuclear factor-kappaB and Stat3, both of which are important effectors in interleukin 17A-mediated proinflammatory responses, were significantly activated. Collectively, our findings suggest that neutrophil-derived interleukin 17A participates in neutrophil-mediated neuroinflammation during the subacute phase of traumatic brain injury. Therefore, interleukin 17A may be a promising therapeutic target for traumatic brain injury.

Construction and validation of a robust prognostic model based on immune features in sepsis.

Purpose: Sepsis, with life-threatening organ failure, is caused by the uncontrolled host response to infection. Immune response plays an important role in the pathophysiology of sepsis. Immune-related genes (IRGs) are promising novel biomarkers that have been used to construct the diagnostic and prognostic model. However, an IRG prognostic model used to predict the 28-day mortality in sepsis was still limited. Therefore, the study aimed to develop a prognostic model based on IRGs to identify patients with high risk and predict the 28-day mortality in sepsis. Then, we further explore the circulating immune cell and immunosuppression state in sepsis. Materials and methods: The differentially expressed genes (DEGs), differentially expressed immune-related genes (DEIRGs), and differentially expressed transcription factors (DETFs) were obtained from the GEO, ImmPort, and Cistrome databases. Then, the TFs-DEIRGs regulatory network and prognostic prediction model were constructed by Cox regression analysis and Pearson correlation analysis. The external datasets also validated the reliability of the prognostic model. Based on the prognostic DEIRGs, we developed a nomogram and conducted an independent prognosis analysis to explore the relationship between DEIRGs in the prognostic model and clinical features in sepsis. Besides, we further evaluate the circulating immune cells state in sepsis. Results: A total of seven datasets were included in our study. Among them, GSE65682 was identified as a discovery cohort. The results of GSEA showed that there is a significant correlation between sepsis and immune response. Then, based on a P value <0.01, 69 prognostic DEIRGs were obtained and the potential molecular mechanisms of DEIRGs were also clarified. According to multivariate Cox regression analysis, 22 DEIRGs were further identified to construct the prognostic model and identify patients with high risk. The Kaplan-Meier survival analysis showed that high-risk groups have higher 28-day mortality than low-risk groups (P=1.105e-13). The AUC value was 0.879 which symbolized that the prognostic model had a better accuracy to predict the 28-day mortality. The external datasets also prove that the prognostic model had an excellent prediction value. Furthermore, the results of correlation analysis showed that patients with Mars1 might have higher risk scores than Mars2-4 (P=0.002). According to the previous study, Mars1 endotype was characterized by immunoparalysis. Thus, the sepsis patients in high-risk groups might exist the immunosuppression. Between the high-risk and low-risk groups, circulating immune cells types were significantly different, and risk score was significantly negatively correlated with naive CD4+ T cells (P=0.019), activated NK cells (P=0.0045), monocytes (P=0.0134), and M1 macrophages (P=0.0002). Conclusions: Our study provides a robust prognostic model based on 22 DEIRGs which can predict 28-day mortality and immunosuppression status in sepsis. The higher risk score was positively associated with 28-day mortality and the development of immunosuppression. IRGs are a promising biomarker that might facilitate personalized treatments for sepsis.

Contralateral synaptic changes following severe unilateral brain injury.

The brain is highly integrated and thus unilateral injury can impact the contralateral hemisphere. However, further research is needed to clarify the changes in the response of the contralateral homotopic area to ipsilateral injury. We hypothesized that severe unilateral brain injury would be accompanied by contralateral synaptic changes that are related to functional recovery. To test this, we divided rats into sham and experimental groups. In the experimental group, we performed right motor cortex resection. These rats were further divided into three subgroups according to post-injury time: 7 days, 14 days, and 30 days post-injury. Rats in each group were evaluated using a beam walking test to quantify the recovery of motor function, and all rats received an injection of adeno-associated virus-containing green fluorescent protein (GFP). Finally, we conducted morphological and histological analyses to identify synaptic changes. Over time, the behavior of the rats that underwent right motor cortex resection recovered. Furthermore, in contrast to the sham group, the experimental groups exhibited an increase in the spine density and expression of synaptic proteins in layer V of the contralateral motor cortex, which was consistent with the GFP-labeled neurons. Moreover, more immature spines were observed 7 days post-injury. Notably, spine morphology matured from 7 to 30 days, and the increase in Synapsin-1 intensity in layer V peaked 14 days after the resection, whereas PSD-95 intensity continued to increase until day 30. Our findings suggested that following motor function recovery from unilateral brain injury, spine morphology and synaptic proteins change dynamically in the contralateral hemisphere.

Transformer-Based Deep-Learning Algorithm for Discriminating Demyelinating Diseases of the Central Nervous System With Neuroimaging.

Background: Differential diagnosis of demyelinating diseases of the central nervous system is a challenging task that is prone to errors and inconsistent reading, requiring expertise and additional examination approaches. Advancements in deep-learning-based image interpretations allow for prompt and automated analyses of conventional magnetic resonance imaging (MRI), which can be utilized in classifying multi-sequence MRI, and thus may help in subsequent treatment referral. Methods: Imaging and clinical data from 290 patients diagnosed with demyelinating diseases from August 2013 to October 2021 were included for analysis, including 67 patients with multiple sclerosis (MS), 162 patients with aquaporin 4 antibody-positive (AQP4+) neuromyelitis optica spectrum disorder (NMOSD), and 61 patients with myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). Considering the heterogeneous nature of lesion size and distribution in demyelinating diseases, multi-modal MRI of brain and/or spinal cord were utilized to build the deep-learning model. This novel transformer-based deep-learning model architecture was designed to be versatile in handling with multiple image sequences (coronal T2-weighted and sagittal T2-fluid attenuation inversion recovery) and scanning locations (brain and spinal cord) for differentiating among MS, NMOSD, and MOGAD. Model performances were evaluated using the area under the receiver operating curve (AUC) and the confusion matrices measurements. The classification accuracy between the fusion model and the neuroradiological raters was also compared. Results: The fusion model that was trained with combined brain and spinal cord MRI achieved an overall improved performance, with the AUC of 0.933 (95%CI: 0.848, 0.991), 0.942 (95%CI: 0.879, 0.987) and 0.803 (95%CI: 0.629, 0.949) for MS, AQP4+ NMOSD, and MOGAD, respectively. This exceeded the performance using the brain or spinal cord MRI alone for the identification of the AQP4+ NMOSD (AUC of 0.940, brain only and 0.689, spinal cord only) and MOGAD (0.782, brain only and 0.714, spinal cord only). In the multi-category classification, the fusion model had an accuracy of 81.4%, which was significantly higher compared to rater 1 (64.4%, p=0.04<0.05) and comparable to rater 2 (74.6%, p=0.388). Conclusion: The proposed novel transformer-based model showed desirable performance in the differentiation of MS, AQP4+ NMOSD, and MOGAD on brain and spinal cord MRI, which is comparable to that of neuroradiologists. Our model is thus applicable for interpretating conventional MRI in the differential diagnosis of demyelinating diseases with overlapping lesions.

Detection and staging of chronic obstructive pulmonary disease using a computed tomography-based weakly supervised deep learning approach.

OBJECTIVES: Chronic obstructive pulmonary disease (COPD) is underdiagnosed globally. The present study aimed to develop weakly supervised deep learning (DL) models that utilize computed tomography (CT) image data for the automated detection and staging of spirometry-defined COPD. METHODS: A large, highly heterogeneous dataset was established, consisting of 1393 participants retrospectively recruited from outpatient, inpatient, and physical examination center settings of four large public hospitals in China. All participants underwent both inspiratory chest CT scans and pulmonary function tests. CT images, spirometry data, demographic information, and clinical information of each participant were collected. An attention-based multi-instance learning (MIL) model for COPD detection was trained using CT scans from 837 participants. External validation of the COPD detection was performed with 620 low-dose CT (LDCT) scans acquired from the National Lung Screening Trial (NLST) cohort. A multi-channel 3D residual network was further developed to categorize GOLD stages among confirmed COPD patients. RESULTS: The attention-based MIL model used for COPD detection achieved an area under the receiver operating characteristic curve (AUC) of 0.934 (95% CI: 0.903, 0.961) on the internal test set and 0.866 (95% CI: 0.805, 0.928) on the LDCT subset acquired from the NLST. The multi-channel 3D residual network was able to correctly grade 76.4% of COPD patients in the test set (423/553) using the GOLD scale. CONCLUSIONS: The proposed chest CT-DL approach can automatically identify spirometry-defined COPD and categorize patients according to the GOLD scale. As such, this approach may be an effective case-finding tool for COPD diagnosis and staging. KEY POINTS: • Chronic obstructive pulmonary disease is underdiagnosed globally, particularly in developing countries. • The proposed chest computed tomography (CT)-based deep learning (DL) approaches could accurately identify spirometry-defined COPD and categorize patients according to the GOLD scale. • The chest CT-DL approach may be an alternative case-finding tool for COPD identification and evaluation.