Relationships of serum VILIP-1, NSE, and ADP levels with postoperative cognitive dysfunction in elderly patients undergoing general anesthesia: a retrospective, observational study.

OBJECTIVE: To investigate the relationships of serum visinin-like protein-1 (VILIP-1), neuron-specific enolase (NSE), and adiponectin (ADP) levels with postoperative cognitive dysfunction (POCD) in elderly patients undergoing general anesthesia and provide a reference for the prevention and treatment of POCD. METHODS: In this retrospective, observational study, 162 elderly patients who underwent general anesthesia were divided into POCD and non-POCD groups according to whether POCD occurred with 24 hours after surgery. Serum VILIP-1, NSE, and ADP levels were measured. RESULTS: Immediately after and 24 hours after surgery, serum VILIP-1 and NSE levels were significantly higher in the POCD group than in the non-POCD group, whereas serum ADP levels were significantly lower in the POCD group. Mini-mental state examination (MMSE) scores significantly differed between the two groups. At 24 hours after surgery, serum VILIP-1 and NSE levels were negatively correlated with MMSE scores in the POCD group, whereas serum ADP levels were positively correlated with MMSE scores in this group. CONCLUSION: Increased serum VILIP-1 and NSE levels and decreased serum ADP levels could be involved in the pathophysiology of POCD in elderly patients after general anesthesia. These serum markers could be used as indicators of POCD in elderly patients undergoing general anesthesia.

A spectral sampling algorithm in dynamic causal modelling for resting-state fMRI.

Resting-state functional magnetic resonance imaging (rs-fMRI) is widely utilized to study the directed influences among neural populations which were called effective connectivity (EC), and the spectral dynamic causal modelling (spDCM) is the state-of-the-art framework to identify them. However, spDCM used variational Laplace to approximate the posterior density by maximizing the free energy, which might underestimate the variability of posterior density and get locked to the local minima. A spectral sampling algorithm (SS-DCM) was proposed to improve the estimation accuracy of the dynamic causal model for rs-fMRI. In SS-DCM, a naïve Bayesian model was constructed in the spectral domain, which described the probabilistic relationship between the sampled parameters and cross spectra of the observed blood oxygen level-dependent signals, and the parameters were sampled using randomly walked Markov Chain Monto Carlo scheme. The root mean square errors of the estimation of EC and hemodynamic parameters of SS-DCM, spDCM and generalized filter scheme were compared in the synthetic data, and SS-DCM was the most accurate and stable. A comparative evaluation using empirical rs-fMRI data was performed to study the EC pattern of the default mode network and compare the accuracy of classification between typically developed subjects and inattentive attention deficit and hyperactivity disorder patients. The results showed high consistency of positivity and negativity of EC between spDCM and SS-DCM, and SS-DCM also provided higher classification accuracy. It is highlighted that SS-DCM improves the accuracy of the estimation of EC and provides accurate information of discrepancies between diseased and healthy subjects using rs-fMRI.

Functional Network Alterations as Markers for Predicting the Treatment Outcome of Cathodal Transcranial Direct Current Stimulation in Focal Epilepsy.

BACKGROUND AND PURPOSE: Transcranial direct current stimulation (tDCS) is an emerging non-invasive neuromodulation technique for focal epilepsy. Because epilepsy is a disease affecting the brain network, our study was aimed to evaluate and predict the treatment outcome of cathodal tDCS (ctDCS) by analyzing the ctDCS-induced functional network alterations. METHODS: Either the active 5-day, -1.0 mA, 20-min ctDCS or sham ctDCS targeting at the most active interictal epileptiform discharge regions was applied to 27 subjects suffering from focal epilepsy. The functional networks before and after ctDCS were compared employing graph theoretical analysis based on the functional magnetic resonance imaging (fMRI) data. A support vector machine (SVM) prediction model was built to predict the treatment outcome of ctDCS using the graph theoretical measures as markers. RESULTS: Our results revealed that the mean clustering coefficient and the global efficiency decreased significantly, as well as the characteristic path length and the mean shortest path length at the stimulation sites in the fMRI functional networks increased significantly after ctDCS only for the patients with response to the active ctDCS (at least 20% reduction rate of seizure frequency). Our prediction model achieved the mean prediction accuracy of 68.3% (mean sensitivity: 70.0%; mean specificity: 67.5%) after the nested cross validation. The mean area under the receiver operating curve was 0.75, which showed good prediction performance. CONCLUSION: The study demonstrated that the response to ctDCS was related to the topological alterations in the functional networks of epilepsy patients detected by fMRI. The graph theoretical measures were promising for clinical prediction of ctDCS treatment outcome.

A multicompartment model for intratumor tissue-specific analysis of DCE-MRI using non-negative matrix factorization.

PURPOSE: A pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data is subject to inaccuracy and instability partly owing to the partial volume effect (PVE). We proposed a new multicompartment model for a tissue-specific pharmacokinetic analysis in DCE-MRI data to solve the PVE problem and to provide better kinetic parameter maps. METHODS: We introduced an independent parameter named fractional volumes of tissue compartments in each DCE-MRI pixel to construct a new linear separable multicompartment model, which simultaneously estimates the pixel-wise time-concentration curves and fractional volumes without the need of the pure-pixel assumption. This simplified convex optimization model was solved using a special type of non-negative matrix factorization (NMF) algorithm called the minimum-volume constraint NMF (MVC-NMF). RESULTS: To test the model, synthetic datasets were established based on the general pharmacokinetic parameters. On well-designed synthetic data, the proposed model reached lower bias and lower root mean square fitting error compared to the state-of-the-art algorithm in different noise levels. In addition, the real dataset from QIN-BREAST-DCE-MRI was analyzed, and we observed an improved pharmacokinetic parameter estimation to distinguish the treatment response to chemotherapy applied to breast cancer. CONCLUSION: Our model improved the accuracy and stability of the tissue-specific estimation of the fractional volumes and kinetic parameters in DCE-MRI data, and improved the robustness to noise, providing more accurate kinetics for more precise prognosis and therapeutic response evaluation using DCE-MRI.

Lycium barbarum polysaccharides attenuates the apoptosis of hippocampal neurons induced by sevoflurane.

Following the application of inhalational anesthetics, including sevoflurane, patients may suffer from neural injury. The present study was conducted to explore the mechanism involved in Lycium barbarum polysaccharides (LBP) treatment of sevoflurane injured hippocampal neurons. Primary hippocampal neurons were isolated from Sprague Dawley embryonic rats. The Cell Counting Kit-8 (CCK-8) assay was used to detect cell viability. Furthermore, flow cytometry (FCM) was used to determine cell proliferation and apoptosis rates. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blot analysis were applied to detect the expression levels of apoptosis-related factors, including activated-Caspase-3, B-cell lymphoma/leukemia-2 (Bcl-2) and Bcl-2 associated X (Bax), phosphorylated extracellular signal-regulated kinase 1/2 (p-ERK1/2) and total ERK1/2. The results showed that LBP promoted cell viability and cell proliferation but inhibited cell apoptosis in neurons injured with 3% sevoflurane, in dose-dependent manners (100, 200 and 400 µg/ml). LBP increased the expression levels of Bcl-2 and p-ERK1/2, and decreased levels of activated-Caspase-3 and Bax in a dose-dependent manner in hippocampal neurons that were injured with sevoflurane. In addition, ERK1/2 inhibitor reversed the above phenomenon in 400 µg/ml LBP and 3% sevoflurane-treated hippocampal neurons. Therefore, the present study indicated that LBP protected hippocampal neurons from sevoflurane injury, including aberrant cell apoptosis, via the ERK1/2 pathway.