Three-dimensional laser combined with C-arm computed tomography-assisted puncture of intracerebral hemorrhage

BackgroundIntracerebral hemorrhage (ICH) is the deadliest subtype of stroke, with a 30-day case fatality rate of approximately 40%. Timely and accurate treatment is essential to facilitate recovery. The introduction of stereotactic instruments and navigation systems has greatly improved the accuracy of surgical treatment. In this study, we explored the application and effects of a three-dimensional (3D) laser combined with C-arm computed tomography (CT) on ICH puncture.Materials and methodsAccording to the principle of randomness, 118 patients with ICH were divided into control and experimental groups. The control group was treated with CT-guided puncture, and the experimental group was treated with 3D laser combined with C-arm CT puncture. The hematoma clearance rates at 3, 5, and 7 days after surgery and the prognosis at 1, 3, and 6 months after surgery were compared between the two groups.ResultsThe hematoma clearance rates of the group using 3D laser combined with C-arm CT at 3, 5, and 7 days after surgery were significantly higher than those of the control group, and the difference was statistically significant (p < 0.05). One month postoperatively, the daily living ability (ADL) grading and recovery of the patients in the test group was significantly better than those of the control group (p < 0.05), but there was no statistically significant difference in ADL 3 and 6 months after surgery (p > 0.05).Conclusion3D laser combined with C-arm CT puncture has the advantages of real-time guidance, accurate positioning, and simple operation. It is an effective minimally invasive surgical method that is easy to master

Cognitive impairment in diffuse axonal injury patients with favorable outcome

Background and purposeTraumatic brain injury (TBI), especially the severe TBI are often followed by persistent cognitive sequalae, including decision-making difficulties, reduced neural processing speed and memory deficits. Diffuse axonal injury (DAI) is classified as one of the severe types of TBI. Part of DAI patients are marginalized from social life due to cognitive impairment, even if they are rated as favorable outcome. The purpose of this study was to elucidate the specific type and severity of cognitive impairment in DAI patients with favorable outcome.MethodsThe neurocognition of 46 DAI patients with favorable outcome was evaluated by the Chinese version of the Montreal Cognitive Assessment Basic (MoCA-BC), and the differences in the domains of cognitive impairment caused by different grades of DAI were analyzed after data conversion of scores of nine cognitive domains of MoCA-BC by Pearson correlation analysis.ResultsAmong the 46 DAI patients with favorable outcome, eight had normal cognitive function (MoCA-BC ≥ 26), and 38 had cognitive impairment (MoCA-BC < 26). The MoCA-BC scores were positively correlated with pupillary light reflex (r = 0.361, p = 0.014), admission Glasgow Coma Scale (GCS) (r = 0.402, p = 0.006), and years of education (r = 0.581, p < 0.001). Return of consciousness (r = −0.753, p < 0.001), Marshall CT (r = −0.328, p = 0.026), age (r = −0.654, p < 0.001), and DAI grade (r = −0.403, p = 0.006) were found to be negatively correlated with the MoCA-BC scores. In patients with DAI grade 1, the actually deducted scores (Ads) of memory (r = 0.838, p < 0.001), abstraction (r = 0.843, p < 0.001), and calculation (r = 0.782, p < 0.001) were most related to the Ads of MoCA-BC. The Ads of nine cognitive domains and MoCA-BC were all proved to be correlated, among patients with DAI grade 2. However, In the DAI grade 3 patients, the highest correlation with the Ads of MoCA-BC were the Ads of memory (r = 0.904, p < 0.001), calculation (r = 0.799, p = 0.006), orientation (r = 0.801, p = 0.005), and executive function (r = 0.869, p = 0.001).ConclusionDAI patients with favorable outcome may still be plagued by cognitive impairment, and different grades of DAI cause different domains of cognitive impairment

Evaluating the efficiency of a nomogram based on the data of neurosurgical intensive care unit patients to predict pulmonary infection of multidrug-resistant Acinetobacter baumannii

BackgroundPulmonary infection caused by multidrug-resistant Acinetobacter baumannii (MDR-AB) is a common and serious complication after brain injury. There are no definitive methods for its prediction and it is usually accompanied by a poor prognosis. This study aimed to construct and evaluate a nomogram based on patient data from the neurosurgical intensive care unit (NSICU) to predict the probability of MDR-AB pulmonary infection.MethodsIn this study, we retrospectively collected patient clinical profiles, early laboratory test results, and doctors’ prescriptions (66 variables). Univariate and backward stepwise regression analyses were used to screen the variables to identify predictors, and a nomogram was built in the primary cohort based on the results of a logistic regression model. Discriminatory validity, calibration validity, and clinical utility were evaluated using validation cohort 1 based on receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). For external validation based on predictors, we prospectively collected information from patients as validation cohort 2.ResultsAmong 2115 patients admitted to the NSICU between December 1, 2019, and December 31, 2021, 217 were eligible for the study, including 102 patients with MDR-AB infections (102 cases) and 115 patients with other bacterial infections (115 cases). We randomly categorized the patients into the primary cohort (70%, N=152) and validation cohort 1 (30%, N=65). Validation cohort 2 consisted of 24 patients admitted to the NSICU between January 1, 2022, and March 31, 2022, whose clinical information was prospectively collected according to predictors. The nomogram, consisting of only six predictors (age, NSICU stay, Glasgow Coma Scale, meropenem, neutrophil to lymphocyte ratio, platelet to lymphocyte ratio), had significantly high sensitivity and specificity (primary cohort AUC=0.913, validation cohort 1 AUC=0.830, validation cohort 2 AUC=0.889) for early identification of infection and had great calibration (validation cohort 1,2 P=0.3801, 0.6274). DCA confirmed that the nomogram is clinically useful.ConclusionOur nomogram could help clinicians make early predictions regarding the onset of pulmonary infection caused by MDR-AB and implement targeted interventions

On asymptotic likelihood inference, removing p-value singularities

grantor: University of TorontoRecent likelihood asymptotics initiated by Barndorff-Nielsen (1986) and Lugannani & Rice (1980) has developed two combining formulas, which are often called third order formulas, for computing 'p'-values and confidence coefficients with high accuracy. Fraser & Reid (1995) and many others have extended these results to very general contexts and extensively explored their applications to various statistical problems. These two formulas, however, have certain singularities near the maximum likelihood value. In this thesis we develop a theory for removing the singularities in a general statistical context using the tangent exponential model developed by Cakmak ' et al' (1998) and Abebe 'et al' (1995). In doing so, the asymptotic expansions of the signed likelihood ratio statistic and the standardized measure of departure are first obtained in terms of the standardized third and fourth derivatives of the log density and are then used to form a bridge for the 'p'-value functions in the neighborhood of the maximum likelihood value. The concept of a nonnormality measure is also developed and its implications to the singularity problems are discussed. In addition, its expressions for two types of tangent exponential model are related. We have also developed several alternative combining formulas for obtaining highly accurate 'p'-value and confidence interval. Unlike the Lugannani & Rice formula, these formulas are generally continuous at the extremes. Numerical studies are used to determine and compare the accuracy and reliability of the different combining formulas. These studies show that these new formulas outperform the existing ones in many cases.Ph.D

Anti-cyclooxygenase, anti-glycation, and anti-skin aging effect of Dendrobium officinale flowers’ aqueous extract and its phytochemical validation in aging

IntroductionDendrobium officinale Kimura et Migo (D. officinale) , widely called as “life-saving immortal grass” by Chinese folk, is a scarce and endangered species. The edible stems of D. officinale have been extensively studied for active chemical components and various bioactivities. However, few studies have reported the well-being beneficial effects of D. officinale flowers (DOF). Therefore, the present study aimed to investigate the in vitro biological potency of its aqueous extract and screen its active components.MethodsAntioxidant tests, including 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), the ferric reducing ability of plasma (FRAP), and intracellular reactive oxygen species (ROS) level analyses in primary human epidermal keratinocytes, anti-cyclooxygenase2 (COX-2) assay, anti-glycation assay (both fluorescent AGEs formation in a BSA fructose/glucose system and glycation cell assay), and anti-aging assay (quantification of collagen types I and III, and SA-β-gal staining assay) were conducted to determine the potential biological effects of DOF extracts and its major compounds. Ultra-performance liquid chromatography-electrospray ionisation-quadrupole-time-of-flight-mass spectrometry (UPLC-ESI-QTOF-MS/MS) was performed to investigate the composition of DOF extracts. Online antioxidant post-column bioassay tests were applied to rapidly screen major antioxidants in DOF extracts.Results and discussionThe aqueous extract of D. officinale flowers was found to have potential antioxidant capacity, anti-cyclooxygenase2 (COX-2) effect, anti-glycation potency, and anti-aging effects. A total of 34 compounds were identified using UPLC-ESI-QTOF-MS/MS. Online ABTS radical analysis demonstrated that 1-O-caffeoyl-β-D-glucoside, vicenin-2, luteolin-6-C-β-D-xyloside-8-C-β--D-glucoside, quercetin-3-O-sophoroside, rutin, isoquercitrin, and quercetin 3-O-(6″-O-malonyl)-β-D-glucoside are the major potential antioxidants. In addition, all selected 16 compounds exerted significant ABTS radical scavenging ability and effective AGE suppressive activities. However, only certain compounds, such as rutin and isoquercitrin, displayed selective and significant antioxidant abilities, as shown by DPPH and FRAP, as well as potent COX-2 inhibitory capacity, whereas the remaining compounds displayed relatively weak or no effects. This indicates that specific components contributed to different functionalities. Our findings justified that DOF and its active compound targeted related enzymes and highlighted their potential application in anti-aging

On Parameter Estimation for Semi-linear Errors-in-Variables Models

AbstractThis paper studies a semi-linear errors-in-variables model of the formYi=x′iβ+g(Ti)+ei,Xi=xi+ui(1⩽i⩽n). The estimators of parametersβ,σ2and of the smooth functiongare derived by using the nearest neighbor-generalized least square method. Under some weak conditions, it is shown that the estimators of unknown vectorβand the unknown parameterσ2are strongly consistent and asymptotically normal. The estimator ofgalso achieves an optimal rate of convergence

On Parameter Estimation for Semi-linear Errors-in-Variables Models

This paper studies a semi-linear errors-in-variables model of the formYi=x'i[beta]+g(Ti)+ei,Xi=xi+ui(1[less-than-or-equals, slant]i[less-than-or-equals, slant]n). The estimators of parameters[beta],[sigma]2and of the smooth functiongare derived by using the nearest neighbor-generalized least square method. Under some weak conditions, it is shown that the estimators of unknown vector[beta]and the unknown parameter[sigma]2are strongly consistent and asymptotically normal. The estimator ofgalso achieves an optimal rate of convergence.semi-linear errors-in-variables model, asymptotic normality, rate of convergence

miR-539 mediates osteoblast mineralization by regulating Distal-less genes 2 in MC3T3-E1 cell line

microRNAs (miRNAs) play an important role in osteoblast differentiation. However, the mechanisms of miRNAs regulating osteoblast mineralization still needs to be further cleared. Distal-less genes 2 (Dlx2) plays an important role in osteoblast differentiation. We have found that miR-539 was significantly downregulated and Dlx2 was found to be inversely correlated with miR-539 in MC3T3-E1 cell line during osteoblast mineralization. The overexpression of miR-539 significantly decreased the expression level of Dlx2 and suppressed the osteogenic marker gene expression level, alkaline phosphatase activity and matrix mineralization. Our study showed that miR-539 was a negative regulator in osteoblast mineralization and that the targeting of Dlx2 gene partly contributes to this inhibitory effect exerted by miR-539

Research on Fault Feature Extraction Method of Rolling Bearing Based on SSA–VMD–MCKD

In response to the problem that nonlinear and non-stationary rolling bearing fault signals are easily disturbed by noise, which leads to the difficulty of fault feature extraction, to take full advantage of the superiority of variational mode decomposition (VMD) in noise reduction, and of maximum correlation kurtosis deconvolution (MCKD) in highlighting continuous pulses masked by noise, a method based on sparrow search algorithm (SSA), VMD, and MCKD is proposed, namely, SSA–VM–MCKD, for rolling bearing faint fault extraction. To improve the feature extraction effect, the method uses the inverse of the peak factor squared of the envelope spectrum as the fitness function, and the parameters to be determined in both algorithms are searched adaptively by SSA. Firstly, the parameter-optimized VMD is used to decompose the fault signal to obtain the intrinsic mode function (IMF) components, from which the optimal mode component is selected, and then the optimal component signal is deconvoluted by the parameter-optimized MCKD to enhance the periodic fault pulses in the optimal component signal, and finally extracts the rolling bearing fault characteristic frequency by envelope demodulation. Experiments on simulated signals and measured data show that the method can adaptively determine the parameters in VMD and MCKD, enhance the fault impact components in the signals, and effectively extract the fault characteristic frequencies of rolling bearings, with a success rate up to 100%, providing a new idea for rolling bearing fault feature extraction

Research on Fault Feature Extraction Method of Rolling Bearing Based on SSA–VMD–MCKD

In response to the problem that nonlinear and non-stationary rolling bearing fault signals are easily disturbed by noise, which leads to the difficulty of fault feature extraction, to take full advantage of the superiority of variational mode decomposition (VMD) in noise reduction, and of maximum correlation kurtosis deconvolution (MCKD) in highlighting continuous pulses masked by noise, a method based on sparrow search algorithm (SSA), VMD, and MCKD is proposed, namely, SSA–VM–MCKD, for rolling bearing faint fault extraction. To improve the feature extraction effect, the method uses the inverse of the peak factor squared of the envelope spectrum as the fitness function, and the parameters to be determined in both algorithms are searched adaptively by SSA. Firstly, the parameter-optimized VMD is used to decompose the fault signal to obtain the intrinsic mode function (IMF) components, from which the optimal mode component is selected, and then the optimal component signal is deconvoluted by the parameter-optimized MCKD to enhance the periodic fault pulses in the optimal component signal, and finally extracts the rolling bearing fault characteristic frequency by envelope demodulation. Experiments on simulated signals and measured data show that the method can adaptively determine the parameters in VMD and MCKD, enhance the fault impact components in the signals, and effectively extract the fault characteristic frequencies of rolling bearings, with a success rate up to 100%, providing a new idea for rolling bearing fault feature extraction