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BackgroundPrevious studies have suggested that gut microbiota (GM) may be involved in the pathogenesis of epilepsy through the microbiota-gut-brain axis (MGBA). However, the causal relationship between GM and different epilepsy subtypes and whether circulating inflammatory proteins act as mediators to participate in epileptogenesis through the MGBA remain unclear. Therefore, it is necessary to identify specific GM associated with epilepsy and its subtypes and explore their underlying inflammatory mechanisms for risk prediction, personalized treatment, and prognostic monitoring of epilepsy.MethodsWe hypothesized the existence of a pathway GM-inflammatory proteins-epilepsy. We found genetic variants strongly associated with GM, circulating inflammatory proteins, epilepsy and its subtypes, including generalized and partial seizures, from large-scale genome-wide association studies (GWAS) summary data and used Multivariate Mendelian Randomization to explore the causal relationship between the three and whether circulating inflammatory proteins play a mediating role in the pathway from GM to epilepsy, with inverse variance weighted (IVW) method as the primary statistical method, supplemented by four methods: MR-Egger, weighted median estimator (WME), Weighted mode and Simple mode.Results16 positive and three negative causal associations were found between the genetic liability of GM and epilepsy and its subtypes. There were nine positive and nine negative causal associations between inflammatory proteins and epilepsy and its subtypes. Furthermore, we found that C-X-C motif chemokine 11 (CXCL11) levels mediated the causal association between Genus Family XIII AD3011 group and epilepsy.ConclusionOur study highlights the possible causal role of specific GM and specific inflammatory proteins in the development of epilepsy and suggests that circulating inflammatory proteins may mediate epileptogenesis through the MGBA.

In order to study the influence law of gas desorption accumulation and emission characteristics under hydraulic fracturing, this experiment uses coal-rock adsorption–desorption test equipment to carry out isothermal desorption tests of water-bearing coal under various stress paths. The experimental object is anthracite from Four Seasons Chun coal mine in Guizhou Province. In this experiment, the influence law of the desorption emission characteristics of coal under different stresses is analyzed. Research shows that the stress directly affects the gas desorption of coal and plays a decisive role in the gas desorption and emission characteristics of water-bearing coal in the stress-affected zone. Under equivalent gas adsorption of water-bearing coal, the total accumulated gas desorption displayed by coal increases with the increase in stress under certain conditions and the increase rate slows down with the time; coal samples differing in moisture content are subjected to various stress paths, leading to the difference in the total gas desorption. The total accumulated gas desorption displayed by coal with higher moisture content is generally smaller than that with lower moisture content. Through field observation, a zone with high accumulated gas desorption is formed in the stress-affected zone beyond the radius of effective fracture influence, generating an imbalance of gas desorption and emission. The study results are of theoretical and practical engineering significance for the prevention and control of stress-induced disasters and gas disasters in deep coal seams.

To investigate the influence of hydraulic permeability enhancement on the gas desorption and accumulation characteristics of water-bearing coal bodies and deeply implement hydraulic fracturing measures to prevent and control gas disasters in coal seams, isothermal adsorption/desorption tests, low-pressure CO2 adsorption tests, and X-ray diffraction (XRD) tests were performed on anthracite from Anbao Coal Mine in Guizhou and bituminous coal from Huainan Pan’er Coal Mine. The study results showed that the gas desorption by kinds of water-bearing coal samples (anthracite) and bituminous coal (PE) was evidently promoted by stress. After each stress path, differences were observed between coal samples in gas desorption, which, however, presented similar variation trends. The gas desorption of the AB coal samples was greater than that of PE coal samples, and the increment of the limiting gas desorption by the AB coal samples was greater than that by the PE coal samples. The specific surface area of the AB coal samples was larger than that of the PE coal samples, and they both contained hydrophilic minerals and moisture. It was found through field observation that after hydraulic permeability enhancement acted upon water-bearing coal, the desorbed gas passively migrated under the stress action. When the coal body was free from the hydraulic stress action, gas started flowing back. The study results reveal the influence of hydraulic permeability enhancement on the accumulated gas desorption characteristics of water-bearing coal, and will be of theoretical and practical engineering significance for the prevention and control of gas disasters in coal seams using hydraulic measures.

Abstract Background Ischemic stroke (IS) is a principal contributor to long-term disability in adults. A new cell death mediated by iron is ferroptosis, characterized by lethal aggregation of lipid peroxidation. However, a paucity of ferroptosis-related biomarkers early identify IS until now. This study investigated potential ferroptosis-related gene pair biomarkers in IS and explored their roles in immune infiltration. Results In total, we identified 6 differentially expressed ferroptosis-related genes (DEFRGs) in the metadata cohort. Of these genes, 4 DEFRGs were incorporated into the competitive endogenous RNA (ceRNA) network, including 78 lncRNA-miRNA and 16 miRNA-mRNA interactions. Based on relative expression values of DEFRGs, we constructed gene pairs. An integrated scheme consisting of machine learning algorithms, ceRNA network, and gene pair was proposed to screen the key DEFRG biomarkers. The receiver operating characteristic (ROC) curve witnessed that the diagnostic performance of DEFRG pair CDKN1A/JUN was superior to that of single gene. Moreover, the CIBERSORT algorithm exhibited immune infiltration landscapes: plasma cells, resting NK cells, and resting mast cells infiltrated less in IS samples than controls. Spearman correlation analysis confirmed a significant correlation between plasma cells and CDKN1A/JUN (CDKN1A: r = − 0.503, P

To study the fatigue failure of an intermittent jointed rock mass under repeated stress waves, numerical models of jointed rock masses with different joint angles were created using Autodyne software, and crack propagation behavior was simulated using the Drucker–Prager strength model and cumulative damage failure criteria. In this numerical simulation, the influence of stress wave amplitude and the mode of disturbance on fatigue failure of the rock mass were analyzed. The simulation results showed a significant difference between the failure process of jointed rock masses subjected to repeated stress waves and those exposed to a single stress wave, including crack initiation locations, propagation paths, and rock mass failure patterns. With increasing angles of inclination, the fatigue life of the rock mass first decreased and then increased under repeated stress waves. As the joint inclination angle, β, increased from 20° to 50°, it had a significant influence on the fatigue life of the rock mass, which decreased rapidly with increases in β. The variation in the disturbance form (the change in amplitude of the stress waves from small to large, or from large to small) did not affect the final macro failure pattern of the rock mass; but the extent of damage to the rock mass was affected.

Abstract Background Screening carotid B-mode ultrasonography is a frequently used method to detect subjects with carotid atherosclerosis (CAS). Due to the asymptomatic progression of most CAS patients, early identification is challenging for clinicians, and it may trigger ischemic stroke. Recently, machine learning has shown a strong ability to classify data and a potential for prediction in the medical field. The combined use of machine learning and the electronic health records of patients could provide clinicians with a more convenient and precise method to identify asymptomatic CAS. Methods Retrospective cohort study using routine clinical data of medical check-up subjects from April 19, 2010 to November 15, 2019. Six machine learning models (logistic regression [LR], random forest [RF], decision tree [DT], eXtreme Gradient Boosting [XGB], Gaussian Naïve Bayes [GNB], and K-Nearest Neighbour [KNN]) were used to predict asymptomatic CAS and compared their predictability in terms of the area under the receiver operating characteristic curve (AUCROC), accuracy (ACC), and F1 score (F1). Results Of the 18,441 subjects, 6553 were diagnosed with asymptomatic CAS. Compared to DT (AUCROC 0.628, ACC 65.4%, and F1 52.5%), the other five models improved prediction: KNN + 7.6% (0.704, 68.8%, and 50.9%, respectively), GNB + 12.5% (0.753, 67.0%, and 46.8%, respectively), XGB + 16.0% (0.788, 73.4%, and 55.7%, respectively), RF + 16.6% (0.794, 74.5%, and 56.8%, respectively) and LR + 18.1% (0.809, 74.7%, and 59.9%, respectively). The highest achieving model, LR predicted 1045/1966 cases (sensitivity 53.2%) and 3088/3566 non-cases (specificity 86.6%). A tenfold cross-validation scheme further verified the predictive ability of the LR. Conclusions Among machine learning models, LR showed optimal performance in predicting asymptomatic CAS. Our findings set the stage for an early automatic alarming system, allowing a more precise allocation of CAS prevention measures to individuals probably to benefit most.

The effect of gas drainage is often affected by water after hydraulic infiltration enhancement measures are adopted for soft and weak permeability coal. In order to understand the permeability characteristics of coal in the state of gas and water, a three-phase pipe domain model is established. Using the improved triaxial seepage test platform, three groups of cyclic seepage tests under different pressure and water injection conditions are carried out on three groups of raw coal samples under the conditions of constant temperature, fixed axial pressure, and confining pressure. The test results show that (1) after the test of three groups of circulating coal samples, the moisture content of each group of coal samples is different. The change trend of permeability of the first cycle natural coal sample and the second cycle pressureless water injection coal sample is similar to that of gas pressure, which decreases slowly with the increase of gas pressure and the permeability of the third cycle pressure water injection coal sample increases with the increase of gas pressure, which is different from the first two cycles. (2) In the ultrasonic wave velocity test of pressure water injection, the change trend of longitudinal wave velocity of coal sample is opposite to that of gas pressure, that is, the longitudinal wave velocity decreases with the increase of gas pressure. When the gas pressure is 0.9 MPa to 1.0 MPa, the reduction of longitudinal wave velocity is more significant. (3) During the test, when the gas pressure of the coal sample is less than 1.11 MPa, the gas sensitivity coefficient of the third cycle pressurized water injection coal sample is lower than that of the first two cycles. When the gas pressure increases to 1.11 MPa, there is an inflection point; that is, the gas sensitivity coefficient of the third cycle pressurized water injection coal sample is higher than that of the first two cycles.

Objective. To investigate the expression status of Girdin in glioma and the relationship between Girdin expression and the biological behavior of glioma. Materials and methods. The expression status of Girdin in glioma from 560 cases was evaluated by RT-PCR, Western Blot and immunohistochemistry. The relationship between Girdin expression and clinic-pathological parameters as well as prognosis was also studied. Results. The expression of Girdin in high grade glioma was significantly higher than low grade glioma. After universal analysis, the expression of Girdin protein is closely related to KPS score, extent of resection, Ki67 and WHO grade, but it was not related to sex and age. Finally, extent of resection, Ki67 and WHO grade were indentified to be related to the Girdin protein expression in logistic regression. Interestingly, we found that the expression of Girdin is significantly related to the distant metastasis of glioma. After COX regression analysis, KPS score, Extent of resection, Ki67, WHO grade as well as Girdin were observed to be independent prognostic factors. Conclusions. Girdin is differential expressed in the glioma patients and closely related to the biological behavior of Glioma. Finally, Girdin was found to be a strong predictor for the post-operative prognosis.

In order to study the influence law of gas desorption accumulation and emission characteristics under hydraulic fracturing, this experiment uses coal-rock adsorption–desorption test equipment to carry out isothermal desorption tests of water-bearing coal under various stress paths. The experimental object is anthracite from Four Seasons Chun coal mine in Guizhou Province. In this experiment, the influence law of the desorption emission characteristics of coal under different stresses is analyzed. Research shows that the stress directly affects the gas desorption of coal and plays a decisive role in the gas desorption and emission characteristics of water-bearing coal in the stress-affected zone. Under equivalent gas adsorption of water-bearing coal, the total accumulated gas desorption displayed by coal increases with the increase in stress under certain conditions and the increase rate slows down with the time; coal samples differing in moisture content are subjected to various stress paths, leading to the difference in the total gas desorption. The total accumulated gas desorption displayed by coal with higher moisture content is generally smaller than that with lower moisture content. Through field observation, a zone with high accumulated gas desorption is formed in the stress-affected zone beyond the radius of effective fracture influence, generating an imbalance of gas desorption and emission. The study results are of theoretical and practical engineering significance for the prevention and control of stress-induced disasters and gas disasters in deep coal seams. [ABSTRACT FROM AUTHOR]

To investigate the influence of hydraulic permeability enhancement on the gas desorption and accumulation characteristics of water-bearing coal bodies and deeply implement hydraulic fracturing measures to prevent and control gas disasters in coal seams, isothermal adsorption/desorption tests, low-pressure CO2 adsorption tests, and X-ray diffraction (XRD) tests were performed on anthracite from Anbao Coal Mine in Guizhou and bituminous coal from Huainan Pan'er Coal Mine. The study results showed that the gas desorption by kinds of water-bearing coal samples (anthracite) and bituminous coal (PE) was evidently promoted by stress. After each stress path, differences were observed between coal samples in gas desorption, which, however, presented similar variation trends. The gas desorption of the AB coal samples was greater than that of PE coal samples, and the increment of the limiting gas desorption by the AB coal samples was greater than that by the PE coal samples. The specific surface area of the AB coal samples was larger than that of the PE coal samples, and they both contained hydrophilic minerals and moisture. It was found through field observation that after hydraulic permeability enhancement acted upon water-bearing coal, the desorbed gas passively migrated under the stress action. When the coal body was free from the hydraulic stress action, gas started flowing back. The study results reveal the influence of hydraulic permeability enhancement on the accumulated gas desorption characteristics of water-bearing coal, and will be of theoretical and practical engineering significance for the prevention and control of gas disasters in coal seams using hydraulic measures. [ABSTRACT FROM AUTHOR]

Background: Ischemic stroke (IS) is a principal contributor to long-term disability in adults. A new cell death mediated by iron is ferroptosis, characterized by lethal aggregation of lipid peroxidation. However, a paucity of ferroptosis-related biomarkers early identify IS until now. This study investigated potential ferroptosis-related gene pair biomarkers in IS and explored their roles in immune infiltration. Results: In total, we identified 6 differentially expressed ferroptosis-related genes (DEFRGs) in the metadata cohort. Of these genes, 4 DEFRGs were incorporated into the competitive endogenous RNA (ceRNA) network, including 78 lncRNA-miRNA and 16 miRNA-mRNA interactions. Based on relative expression values of DEFRGs, we constructed gene pairs. An integrated scheme consisting of machine learning algorithms, ceRNA network, and gene pair was proposed to screen the key DEFRG biomarkers. The receiver operating characteristic (ROC) curve witnessed that the diagnostic performance of DEFRG pair CDKN1A/JUN was superior to that of single gene. Moreover, the CIBERSORT algorithm exhibited immune infiltration landscapes: plasma cells, resting NK cells, and resting mast cells infiltrated less in IS samples than controls. Spearman correlation analysis confirmed a significant correlation between plasma cells and CDKN1A/JUN (CDKN1A: r = − 0.503, P < 0.001, JUN: r = − 0.330, P = 0.025). Conclusions: Our findings suggested that CDKN1A/JUN could be a robust and promising gene-pair diagnostic biomarker for IS, regulating ferroptosis during IS progression via C9orf106/C9orf139-miR-22-3p-CDKN1A and GAS5-miR-139-5p/miR-429-JUN axes. Meanwhile, plasma cells might exert a vital interplay in IS immune microenvironment, providing an innovative insight for IS therapeutic target. [ABSTRACT FROM AUTHOR]

The effect of gas drainage is often affected by water after hydraulic infiltration enhancement measures are adopted for soft and weak permeability coal. In order to understand the permeability characteristics of coal in the state of gas and water, a three-phase pipe domain model is established. Using the improved triaxial seepage test platform, three groups of cyclic seepage tests under different pressure and water injection conditions are carried out on three groups of raw coal samples under the conditions of constant temperature, fixed axial pressure, and confining pressure. The test results show that (1) after the test of three groups of circulating coal samples, the moisture content of each group of coal samples is different. The change trend of permeability of the first cycle natural coal sample and the second cycle pressureless water injection coal sample is similar to that of gas pressure, which decreases slowly with the increase of gas pressure and the permeability of the third cycle pressure water injection coal sample increases with the increase of gas pressure, which is different from the first two cycles. (2) In the ultrasonic wave velocity test of pressure water injection, the change trend of longitudinal wave velocity of coal sample is opposite to that of gas pressure, that is, the longitudinal wave velocity decreases with the increase of gas pressure. When the gas pressure is 0.9 MPa to 1.0 MPa, the reduction of longitudinal wave velocity is more significant. (3) During the test, when the gas pressure of the coal sample is less than 1.11 MPa, the gas sensitivity coefficient of the third cycle pressurized water injection coal sample is lower than that of the first two cycles. When the gas pressure increases to 1.11 MPa, there is an inflection point; that is, the gas sensitivity coefficient of the third cycle pressurized water injection coal sample is higher than that of the first two cycles. [ABSTRACT FROM AUTHOR]

The article presents a case study of a 50-year-old man with left-sided pulsating headache and left shoulder pain. Topics include subsequent development of left-sided neck pain, dysphonia, dysphagia, choking on water, and numbness of the tongue; and Doppler angiography of the cervical arteries showing intimal surface of the bilateral cervical arteries being rough with plaque formation.

Heterozygous missense mutations in TUBB3 have been implicated in various neurological disorders encompassing either isolated congenital fibrosis of the extraocular muscles type 3 (CFEOM3) or complex cortical dysplasia with other brain malformations 1 (CDCBM1). The description of seizures in patients with TUBB3 mutations is rare. Here, we reported a patient who had febrile seizures before and focal seizure this time, which was diagnosed as epilepsy in combination with an abnormal EEG. MRI showed hypoplastic corpus callosum. Mutation analysis showed a novel de novo heterozygous variant of the TUBB3 gene (NM_006086), c.763G > A (p.V255I). The patient had global developmental delay, photophobia and elliptic pupils, but lacking extraocular muscle involvement and malformations of cortical development, which might be a less severe phenotype of TUBB3 mutations. This is the first report of elliptic pupils in a patient with TUBB3 mutations and expands the spectrum of TUBB3 phenotypes. It indicates that the phenotypic range of TUBB3 mutations might exist on more of a continuum than as a discrete entity, with severity ranging from mild to severe. Further studies are needed to elucidate the complete spectrum of TUBB3-related phenotypes. [ABSTRACT FROM AUTHOR]

Background: Screening carotid B-mode ultrasonography is a frequently used method to detect subjects with carotid atherosclerosis (CAS). Due to the asymptomatic progression of most CAS patients, early identification is challenging for clinicians, and it may trigger ischemic stroke. Recently, machine learning has shown a strong ability to classify data and a potential for prediction in the medical field. The combined use of machine learning and the electronic health records of patients could provide clinicians with a more convenient and precise method to identify asymptomatic CAS.Methods: Retrospective cohort study using routine clinical data of medical check-up subjects from April 19, 2010 to November 15, 2019. Six machine learning models (logistic regression [LR], random forest [RF], decision tree [DT], eXtreme Gradient Boosting [XGB], Gaussian Naïve Bayes [GNB], and K-Nearest Neighbour [KNN]) were used to predict asymptomatic CAS and compared their predictability in terms of the area under the receiver operating characteristic curve (AUCROC), accuracy (ACC), and F1 score (F1).Results: Of the 18,441 subjects, 6553 were diagnosed with asymptomatic CAS. Compared to DT (AUCROC 0.628, ACC 65.4%, and F1 52.5%), the other five models improved prediction: KNN + 7.6% (0.704, 68.8%, and 50.9%, respectively), GNB + 12.5% (0.753, 67.0%, and 46.8%, respectively), XGB + 16.0% (0.788, 73.4%, and 55.7%, respectively), RF + 16.6% (0.794, 74.5%, and 56.8%, respectively) and LR + 18.1% (0.809, 74.7%, and 59.9%, respectively). The highest achieving model, LR predicted 1045/1966 cases (sensitivity 53.2%) and 3088/3566 non-cases (specificity 86.6%). A tenfold cross-validation scheme further verified the predictive ability of the LR.Conclusions: Among machine learning models, LR showed optimal performance in predicting asymptomatic CAS. Our findings set the stage for an early automatic alarming system, allowing a more precise allocation of CAS prevention measures to individuals probably to benefit most. [ABSTRACT FROM AUTHOR]