Search

Background: Chronic kidney disease (CKD) is a significant global health concern often accompanied by cognitive impairment in affected patients. Ludwigia octovalvis extract (LOE) is a traditional medicine widely used for various conditions, including renal disease and its associated complications. However, the pharmacological effects and underlying mechanisms of LOE on CKD remain unclear. Methods: Adult male mice underwent a 4-week oral adenine-induced CKD model, followed by daily administration of either a vehicle, losartan, or LOE for 1 to 2 months. A battery of behavioral tests was conducted to examine CKD-associated cognitive impairments. Additionally, RNA sequencing, Golgi staining, and immunohistochemistry were performed to decipher the underlying mechanisms. Results: Neither losartan nor LOE supplementation could reverse adenine-induced kidney damage. However, during behavioral tests, both losartan and LOE treatments improved memory performance in mice without affecting emotional status and general locomotor activity. Transcriptome profiling and histological analyses revealed altered dendritic arborization and increased spine density of dentate granule cells, along with augmented adult hippocampal neurogenesis, contributing to the improved memory performance of mice treated with LOE. Conclusion: Our study provides comprehensive neurobehavioral and molecular evidence supporting the potential use of LOE for future CKD-associated memory dysfunction treatment.

Abstract Numerous supraglottic airway device (SADs) have been designed for adults; however, their relative efficacy, indicated by parameters such as adequacy of sealing, ease of application, and postinsertion complications, remains unclear. We conducted a systematic review and network meta-analysis to evaluate the efficacy of various SADs. We searched electronic databases for randomized controlled trials comparing at least two types of SADs published before December 2019. The primary outcomes were oropharyngeal leak pressure (OLP), risk of first-attempt insertion failure, and postoperative sore throat rate (POST). We included 108 studies (n = 10,645) comparing 17 types of SAD. The Proseal laryngeal mask airway (LMA), the I-gel supraglottic airway, the Supreme LMA, the Streamlined Liner of the Pharynx Airway, the SoftSeal, the Cobra Perilaryngeal Airway, the Air-Q, the Laryngeal Tube, the Laryngeal Tube Suction II, the Laryngeal Tube Suction Disposable, AuraGain, and Protector had significantly higher OLP (mean difference ranging from 3.98 to 9.18 cmH2O) compared with that of a classic LMA (C-LMA). The Protector exhibited the highest OLP and was ranked first. All SADs had a similar likelihood of first-attempt insertion failure and POST compared with the C-LMA. Our findings indicate that the Protector may be the best SAD because it has the highest OLP. Systematic review registration PROSPERO: CRD42017065273.

According to a recently conducted survey on surgical complication mortality rate, 47% of such cases are due to anesthetics overdose. This indicates that there is an urgent need to moderate the level of anesthesia. Recently deep learning (DL) methods have played a major role in estimating the depth of Anesthesia (DOA) of patients and has played an essential role in control anesthesia overdose. In this paper, Electroencephalography (EEG) signals have been used for the prediction of DOA. EEG signals are very complex signals which may require months of training and advanced signal processing techniques. It is a point of debate whether DL methods are an improvement over the already existing traditional EEG signal processing approaches. One of the DL algorithms is Convolutional neural network (CNN) which is very popular algorithm for object recognition and is widely growing its applications in processing hierarchy in the human visual system. In this paper, various decomposition methods have been used for extracting the features EEG signal. After acquiring the necessary signals values in image format, several CNN models have been deployed for classification of DOA depending upon their Bispectral Index (BIS) and the signal quality index (SQI). The EEG signals were converted into the frequency domain using and Empirical Mode Decomposition (EMD), and Ensemble Empirical Mode Decomposition (EEMD). However, because of the inter mode mixing observed in EMD method; EEMD have been utilized for this study. The developed CNN models were used to predict the DOA based on the EEG spectrum images without the use of handcrafted features which provides intuitive mapping with high efficiency and reliability. The best trained model gives an accuracy of 83.2%. Hence, this provides further scope and research which can be carried out in the domain of visual mapping of DOA using EEG signals and DL methods.

Abstract High risk and geriatric patients are supposed to suffer higher risks of hypotension underwent painless endoscopic procedures. This study evaluated different biomarkers associated with hypotension in off-site patients and aimed to determine the most relevant risk factors in space and monitoring limited environment. The inclusions of this observational cohort study underwent complex endoscopic procedures were sedated with age-adjusted doses of target-controlled infusion of propofol. The following pre-sedative parameters were analysed: time domain, frequency domain, and Deceleration capacity (DC) of heart rate variability, estimated cardiac output data and the index of cardiac contractility from the cardiometer. Patients were divided into hypotension group (blood pressure 35% decrease) and non-hypotension group according to peri-sedative blood pressure, regression analysis is used to examine the association between factors and hypotension. Total data from 178 patients (age range: 33–94 years) were analysed. Age was not significantly different between the hypotension and non-hypotension groups (p = 0.978). Among all the factors, DC was most associated with hypotension (p = 0.05), better than cardiometer, age, and ASA status. In conclusion, DC, which can be interpreted as the indicator of parasympathetic activity and was significantly and negatively correlated with sedation-related hypotension. Pre-sedative measuring DC from routine ECG monitoring is simple and cost-effective and should be added to haemodynamic monitoring in the endoscopic room.

Abstract Microcirculatory dysfunction plays a crucial role in renal ischemia/reperfusion (IR)-induced injury. Dexmedetomidine was reported to ameliorate IR-induced acute kidney injury. This study investigated the effects of dexmedetomidine on renal microcirculation after IR-induced acute kidney injury in rats. In total, 50 rats were randomly allocated to the following five groups (10 in each group): Sham, Control‒IR, Dex (dexmedetomidine) ‒Sham, Dex‒IR, and IR‒Dex group. The microcirculation parameters included total small vessel density, perfused small vessel density (PSVD), proportion of perfused small vessels, microvascular flow index, and tissue oxygen saturation (StO2) were recorded. The repeated measures analysis showed that PSVD on renal surface was higher in the Dex‒IR group than in the Control‒IR group (3.5 mm/mm2, 95% confidence interval [CI] 0.6 to 6.4 mm/mm2, P = 0.01). At 240 min, StO2 on renal surface was lower in the Control‒IR group than in the Sham group (– 7%, 95% CI − 13 to − 1%, P = 0.021), but StO2 did not differ significantly among the Sham, Dex‒IR, and IR‒Dex groups. Our results showed that pretreatment with dexmedetomidine improved renal microcirculation in rats with IR-induced acute kidney injury. However, the adverse effects of low mean arterial pressure and heart rate might offset the protective effect of dexmedetomidine on organ injury.

The term “anesthetic depth” refers to the extent to which a general anesthetic agent sedates the central nervous system with specific strength concentration at which it is delivered. The depth level of anesthesia plays a crucial role in determining surgical complications, and it is imperative to keep the depth levels of anesthesia under control to perform a successful surgery. This study used electroencephalography (EEG) signals to predict the depth levels of anesthesia. Traditional preprocessing methods such as signal decomposition and model building using deep learning were used to classify anesthetic depth levels. This paper proposed a novel approach to classify the anesthesia levels based on the concept of time series feature extraction, by finding out the relation between EEG signals and the bi-spectral Index over a period of time. Time series feature extraction on basis of scalable hypothesis tests were performed to extract features by analyzing the relation between the EEG signals and Bi-Spectral Index, and machine learning models such as support vector classifier, XG boost classifier, gradient boost classifier, decision trees and random forest classifier are used to train the features and predict the depth level of anesthesia. The best-trained model was random forest, which gives an accuracy of 83%. This provides a platform to further research and dig into time series-based feature extraction in this area.

Processed electroencephalogram (EEG) has been considered a useful tool for measuring the depth of anesthesia (DOA). However, because of its inability to detect the activities of the brain stem and spinal cord responsible for most of the vital signs, a new biomarker for measuring the multidimensional activities of the central nervous system under anesthesia is required. Detrended fluctuation analysis (DFA) is a new technique for detecting the scaling properties of nonstationary heart rate (HR) behavior. This study investigated the changes in fractal properties of heart rate variability (HRV), a nonlinear analysis, under intravenous propofol, inhalational desflurane, and spinal anesthesia. We compared the DFA method with traditional spectral analysis to evaluate its potential as an alternative biomarker under different levels of anesthesia. Eighty patients receiving elective procedures were randomly allocated different anesthesia. HRV was measured with spectral analysis and DFA short-term (4–11 beats) scaling exponent (DFAα1). An increase in DFAα1 followed by a decrease at higher concentrations during propofol or desflurane anesthesia is observed. Spinal anesthesia decreased the DFAα1 and low-/high-frequency ratio (LF/HF ratio). DFAα1 of HRV is a sensitive and specific method for distinguishing changes from baseline to anesthesia state. The DFAα1 provides a potential real-time biomarker to measure HRV as one of the multiple dimensions of the DOA.

There are many surgical operations performed daily in operation rooms worldwide. Adequate anesthesia is needed during an operation. Besides hypnosis, adequate analgesia is critical to prevent autonomic reactions. Clinical experience and vital signs are usually used to adjust the dosage of analgesics. Analgesia nociception index (ANI), which ranges from 0 to 100, is derived from heart rate variability (HRV) via electrocardiogram (ECG) signals, for pain evaluation in a non-invasive manner. It represents parasympathetic activity. In this study, we compared the performance of multilayer perceptron (MLP) and long short-term memory (LSTM) algorithms in predicting expert assessment of pain score (EAPS) based on patient′s HRV during surgery. The objective of this study was to analyze how deep learning models differed from the medical doctors′ predictions of EAPS. As the input and output features of the deep learning models, the opposites of ANI and EAPS were used. This study included 80 patients who underwent operations at National Taiwan University Hospital. Using MLP and LSTM, a holdout method was first applied to 60 training patients, 10 validation patients, and 10 testing patients. As compared to the LSTM model, which had a testing mean absolute error (MAE) of 2.633 ± 0.542, the MLP model had a testing MAE of 2.490 ± 0.522, with a more appropriate shape of its prediction curves. The model based on MLP was selected as the best. Using MLP, a seven-fold cross validation method was then applied. The first fold had the lowest testing MAE of 2.460 ± 0.634, while the overall MAE for the seven-fold cross validation method was 2.848 ± 0.308. In conclusion, HRV analysis using MLP algorithm had a good correlation with EAPS; therefore, it can play role as a continuous monitor to predict intraoperative pain levels, to assist physicians in adjusting analgesic agent dosage. Further studies may consider obtaining more input features, such as photoplethysmography (PPG) and other kinds of continuous variable, to improve the prediction performance.

One of the most challenging predictive data analysis efforts is an accurate prediction of depth of anesthesia (DOA) indicators which has attracted growing attention since it provides patients a safe surgical environment in case of secondary damage caused by intraoperative awareness or brain injury. However, many researchers put heavily handcraft feature extraction or carefully tailored feature engineering to each patient to achieve very high sensitivity and low false prediction rate for a particular dataset. This limits the benefit of the proposed approaches if a different dataset is used. Recently, representations learned using the deep convolutional neural network (CNN) for object recognition are becoming a widely used model of the processing hierarchy in the human visual system. The correspondence between models and brain signals that holds the acquired activity at high temporal resolution has been explored less exhaustively. In this paper, deep learning CNN with a range of different architectures is designed for identifying related activities from raw electroencephalography (EEG). Specifically, an improved short-time Fourier transform is used to stand for the time-frequency information after extracting the spectral images of the original EEG as input to CNN. Then CNN models are designed and trained to predict the DOA levels from EEG spectrum without handcrafted features, which presents an intuitive mapping process with high efficiency and reliability. As a result, the best trained CNN model achieved an accuracy of 93.50%, interpreted as CNN's deep learning to approximate the DOA by senior anesthesiologists, which highlights the potential of deep CNN combined with advanced visualization techniques for EEG-based brain mapping.

This study evaluates cardiovascular and cerebral hemodynamics systems by only using non-invasive electrocardiography (ECG) signals. The Massachusetts General Hospital/Marquette Foundation (MGH/MF) and Cerebral Hemodynamic Autoregulatory Information System Database (CHARIS DB) from the PhysioNet database are used for cardiovascular and cerebral hemodynamics, respectively. For cardiovascular hemodynamics, the ECG is used for generating the arterial blood pressure (ABP), central venous pressure (CVP), and pulmonary arterial pressure (PAP). Meanwhile, for cerebral hemodynamics, the ECG is utilized for the intracranial pressure (ICP) generator. A deep convolutional autoencoder system is applied for this study. The cross-validation method with Pearson’s linear correlation (R), root mean squared error (RMSE), and mean absolute error (MAE) are measured for the evaluations. Initially, the ECG is used to generate the cardiovascular waveform. For the ABP system—the systolic blood pressure (SBP) and diastolic blood pressures (DBP)—the R evaluations are 0.894 ± 0.004 and 0.881 ± 0.005, respectively. The MAE evaluations for SBP and DBP are, respectively, 6.645 ± 0.353 mmHg and 3.210 ± 0.104 mmHg. Furthermore, for the PAP system—the systolic and diastolic pressures—the R evaluations are 0.864 ± 0.003 mmHg and 0.817 ± 0.006 mmHg, respectively. The MAE evaluations for systolic and diastolic pressures are, respectively, 3.847 ± 0.136 mmHg and 2.964 ± 0.181 mmHg. Meanwhile, the mean CVP evaluations are 0.916 ± 0.001, 2.220 ± 0.039 mmHg, and 1.329 ± 0.036 mmHg, respectively, for R, RMSE, and MAE. For the mean ICP evaluation in cerebral hemodynamics, the R and MAE evaluations are 0.914 ± 0.003 and 2.404 ± 0.043 mmHg, respectively. This study, as a proof of concept, concludes that the non-invasive cardiovascular and cerebral hemodynamics systems can be potentially investigated by only using the ECG signal.

Dietary restriction (DR; sometimes called calorie restriction) has profound beneficial effects on physiological, psychological, and behavioral outcomes in animals and in humans. We have explored the molecular mechanism of DR-induced memory enhancement and demonstrate that dietary tryptophan-a precursor amino acid for serotonin biosynthesis in the brain-and serotonin receptor 5-hydroxytryptamine receptor 6 (HTR6) are crucial in mediating this process. We show that HTR6 inactivation diminishes DR-induced neurological alterations, including reduced dendritic complexity, increased spine density, and enhanced long-term potentiation (LTP) in hippocampal neurons. Moreover, we find that HTR6-mediated mechanistic target of rapamycin complex 1 (mTORC1) signaling is involved in DR-induced memory improvement. Our results suggest that the HTR6-mediated mTORC1 pathway may function as a nutrient sensor in hippocampal neurons to couple memory performance to dietary intake.

Hypertension affects a huge number of people around the world. It also has a great contribution to cardiovascular- and renal-related diseases. This study investigates the ability of a deep convolutional autoencoder (DCAE) to generate continuous arterial blood pressure (ABP) by only utilizing photoplethysmography (PPG). A total of 18 patients are utilized. LeNet-5- and U-Net-based DCAEs, respectively abbreviated LDCAE and UDCAE, are compared to the MP60 IntelliVue Patient Monitor, as the gold standard. Moreover, in order to investigate the data generalization, the cross-validation (CV) method is conducted. The results show that the UDCAE provides superior results in producing the systolic blood pressure (SBP) estimation. Meanwhile, the LDCAE gives a slightly better result for the diastolic blood pressure (DBP) prediction. Finally, the genetic algorithm-based optimization deep convolutional autoencoder (GDCAE) is further administered to optimize the ensemble of the CV models. The results reveal that the GDCAE is superior to either the LDCAE or UDCAE. In conclusion, this study exhibits that systolic blood pressure (SBP) and diastolic blood pressure (DBP) can also be accurately achieved by only utilizing a single PPG signal.

Estimating the depth of anaesthesia (DoA) in operations has always been a challenging issue due to the underlying complexity of the brain mechanisms. Electroencephalogram (EEG) signals are undoubtedly the most widely used signals for measuring DoA. In this paper, a novel EEG-based index is proposed to evaluate DoA for 24 patients receiving general anaesthesia with different levels of unconsciousness. Sample Entropy (SampEn) algorithm was utilised in order to acquire the chaotic features of the signals. After calculating the SampEn from the EEG signals, Random Forest was utilised for developing learning regression models with Bispectral index (BIS) as the target. Correlation coefficient, mean absolute error, and area under the curve (AUC) were used to verify the perioperative performance of the proposed method. Validation comparisons with typical nonstationary signal analysis methods (i.e., recurrence analysis and permutation entropy) and regression methods (i.e., neural network and support vector machine) were conducted. To further verify the accuracy and validity of the proposed methodology, the data is divided into four unconsciousness-level groups on the basis of BIS levels. Subsequently, analysis of variance (ANOVA) was applied to the corresponding index (i.e., regression output). Results indicate that the correlation coefficient improved to 0.72 ± 0.09 after filtering and to 0.90 ± 0.05 after regression from the initial values of 0.51 ± 0.17. Similarly, the final mean absolute error dramatically declined to 5.22 ± 2.12. In addition, the ultimate AUC increased to 0.98 ± 0.02, and the ANOVA analysis indicates that each of the four groups of different anaesthetic levels demonstrated significant difference from the nearest levels. Furthermore, the Random Forest output was extensively linear in relation to BIS, thus with better DoA prediction accuracy. In conclusion, the proposed method provides a concrete basis for monitoring patients’ anaesthetic level during surgeries.

Depth of anaesthesia (DoA) is an important measure for assessing the degree to which the central nervous system of a patient is depressed by a general anaesthetic agent, depending on the potency and concentration with which anaesthesia is administered during surgery. We can monitor the DoA by observing the patient’s electroencephalography (EEG) signals during the surgical procedure. Typically high frequency EEG signals indicates the patient is conscious, while low frequency signals mean the patient is in a general anaesthetic state. If the anaesthetist is able to observe the instantaneous frequency changes of the patient’s EEG signals during surgery this can help to better regulate and monitor DoA, reducing surgical and post-operative risks. This paper describes an approach towards the development of a 3D real-time visualization application which can show the instantaneous frequency and instantaneous amplitude of EEG simultaneously by using empirical mode decomposition (EMD) and the Hilbert–Huang transform (HHT). HHT uses the EMD method to decompose a signal into so-called intrinsic mode functions (IMFs). The Hilbert spectral analysis method is then used to obtain instantaneous frequency data. The HHT provides a new method of analyzing non-stationary and nonlinear time series data. We investigate this approach by analyzing EEG data collected from patients undergoing surgical procedures. The results show that the EEG differences between three distinct surgical stages computed by using sample entropy (SampEn) are consistent with the expected differences between these stages based on the bispectral index (BIS), which has been shown to be quantifiable measure of the effect of anaesthetics on the central nervous system. Also, the proposed filtering approach is more effective compared to the standard filtering method in filtering out signal noise resulting in more consistent results than those provided by the BIS. The proposed approach is therefore able to distinguish between key operational stages related to DoA, which is consistent with the clinical observations. SampEn can also be viewed as a useful index for evaluating and monitoring the DoA of a patient when used in combination with this approach.

Evaluation of depth of anaesthesia (DoA) is critical in clinical surgery. Indices derived from electroencephalogram (EEG) are currently widely used to quantify DoA. However, there are known to be inaccurate under certain conditions; therefore, experienced anaesthesiologists rely on the monitoring of vital signs such as body temperature, pulse rate, respiration rate, and blood pressure to control the procedure. Because of the lack of an ideal approach for quantifying level of consciousness, studies have been conducted to develop improved methods of measuring DoA. In this study, a short-term index known as the similarity and distribution index (SDI) is proposed. The SDI is generated using heart rate variability (HRV) in the time domain and is based on observations of data distribution differences between two consecutive 32 s HRV data segments. A comparison between SDI results and expert assessments of consciousness level revealed that the SDI has strong correlation with anaesthetic depth. To optimise the effect, artificial neural network (ANN) models were constructed to fit the SDI, and ANN blind cross-validation was conducted to overcome random errors and overfitting problems. An ensemble ANN was then employed and was discovered to provide favourable DoA assessment in comparison with commonly used Bispectral Index. This study demonstrated the effectiveness of this method of DoA assessment, and the results imply that it is feasible and meaningful to use the SDI to measure DoA with the additional use of other measurement methods, if appropriate.

Objectives: Patient-controlled epidural analgesia (PCEA) and continuous epidural infusion (CEI) are popular and effective methods for pain relief during labor; however, there are concerns about increasing rates of cesarean section (C/S) and instrumental delivery. This prospective study investigated the effect of PCEA and CEI with different formulas on labor and the mode of delivery in nulliparous women. Materials and methods: A total of 480 nulliparous women were randomized into four groups, with 120 in each. Group A received a loading dose of 10 mL of 1 mg/mL ropivacaine with 2 μg/mL fentanyl, then an intermittent bolus of 5 mL with a background infusion of 5 mL/hour by PCEA. Group B received the same PCEA formula as Group A with 0.8 mg/mL bupivacaine. Group C received the same formula as Group A by CEI with 1 mg/mL ropivacaine at a rate of 10 mL/hour. Group D received the same formula as Group C with 0.8 mg/mL bupivacaine. The rates of C/S and instrumental delivery and the incidence of side effects were recorded. Results: The rates of C/S were significantly different between Groups A and C, Groups A and D, and Groups B and D. The rates of instrumental delivery for normal spontaneous delivery were significantly different between Groups A and B, A and D, B and C, and C and D. Conclusion: The C/S rate was higher in Groups C and D; however, the instrumental delivery rate was lower in Groups A and C. We conclude that PCEA with 1 mg/mL ropivacaine might provide the greatest benefit for labor analgesia.

Background/purpose: Volatile anesthetics can produce toxic metabolites and lead to postoperative renal or hepatic dysfunction. The aim of this retrospective study is to compare the changes in renal and hepatic function after general anesthesia using two different volatile anesthetics in prolonged surgery for oral malignancy. Materials and methods: Patients anesthetized by desflurane or sevoflurane in prolonged surgery (>10 hours) for oral malignancy during past 18 months were included. Common biomarkers such as blood urea nitrogen, serum creatinine, serum aspartate aminotransferase (AST), alanine aminotransferase (ALT) in postoperative week (POW) 1, 2, and 3 were the primary outcomes. The mean values and the incidence of abnormality of these biochemical markers were also compared. Results: In this study, 21 and 26 patients receiving desflurane and sevoflurane anesthesia, respectively, were included. Blood urea nitrogen and creatinine levels were not significantly different between the two groups. There was a 2.1-fold and a 3-fold increase in AST in POW1 in the desflurane and sevoflurane groups, respectively. The incidence of marked elevation in AST (greater than 100 IU/L) was significantly higher in the sevoflurane group in POW1. There was a 1.9-fold and a 3.2-fold increase in ALT in POW2 in the desflurane and sevoflurane groups, respectively. The incidence of marked elevation in ALT was significantly higher in the sevoflurane group in POW2. Conclusion: Both desflurane and sevoflurane did not cause clinically significant nephrotoxicity but produced a transient deterioration in liver function after prolonged anesthesia for oral cancer surgery. Sevoflurane was associated with a more severe degree of liver damage than desflurane in this study.

EEG (Electroencephalography) signals can express the human awareness activities and consequently it can indicate the depth of anesthesia. On the other hand, Bispectral-index (BIS) is often used as an indicator to assess the depth of anesthesia. This study is aimed at using an advanced signal processing method to analyze EEG signals and compare them with existing BIS indexes from a commercial product (i.e., IntelliVue MP60 BIS module). Multivariate empirical mode decomposition (MEMD) algorithm is utilized to filter the EEG signals. A combination of two MEMD components (IMF2 + IMF3) is used to express the raw EEG. Then, sample entropy algorithm is used to calculate the complexity of the patients’ EEG signal. Furthermore, linear regression and artificial neural network (ANN) methods were used to model the sample entropy using BIS index as the gold standard. ANN can produce better target value than linear regression. The correlation coefficient is 0.790 ± 0.069 and MAE is 8.448 ± 1.887. In conclusion, the area under the receiver operating characteristic (ROC) curve (AUC) of sample entropy value using ANN and MEMD is 0.969 ± 0.028 while the AUC of sample entropy value without filter is 0.733 ± 0.123. It means the MEMD method can filter out noise of the brain waves, so that the sample entropy of EEG can be closely related to the depth of anesthesia. Therefore, the resulting index can be adopted as the reference for the physician, in order to reduce the risk of surgery.

Objective: To investigate the incidence of systemic inflammatory response syndrome (SIRS) on emergency department admission and the prognostic significance of SIRS in patients undergoing emergency surgery. Methods: This is a retrospective study of 889 adults who were admitted as emergency cases and were operated on within 24 hours of admission. Data on patient demography, clinical information including comorbidities, categories of surgery, American Society of Anesthesiologists physical status, SIRS score, postoperative outcomes including duration of mechanical ventilation, intensive care unit (ICU) and hospital lengths of stay, and mortality were collected. Results: SIRS occurred in 43% of the patients and was associated with a significantly worse outcome in terms of duration of ventilator use (10.5 ± 15.4 vs. 3.5 ± 4.4 days, p

Entropy as an estimate of complexity of the electroencephalogram is an effective parameter for monitoring the depth of anesthesia (DOA) during surgery. Multiscale entropy (MSE) is useful to evaluate the complexity of signals over different time scales. However, the limitation of the length of processed signal is a problem due to observing the variation of sample entropy (SE) on different scales. In this study, the adaptive resampling procedure is employed to replace the process of coarse-graining in MSE. According to the analysis of various signals and practical EEG signals, it is feasible to calculate the SE from the adaptive resampled signals, and it has the highly similar results with the original MSE at small scales. The distribution of the MSE of EEG during the whole surgery based on adaptive resampling process is able to show the detailed variation of SE in small scales and complexity of EEG, which could help anesthesiologists evaluate the status of patients.

Objective: Conventional, intermittent, epidural morphine is widely applied as a post-Cesarean delivery analgesia. We compared the analgesic efficacy, motor weakness, and side effects of administering a patient-controlled epidural analgesia (PCEA) of pure ropivacaine versus the intermittent administration of epidural morphine after Cesarean delivery. Materials and Methods: This randomized, double-blind study included 120 full-term parturients who underwent elective Cesarean delivery and received either PCEA with pure ropivacaine or an intermittent bolus epidural of 2 mg/10 mL morphine in normal saline twice per day. The efficacy of pain relief, post-Cesarean side effects, motor blockades, time to first ambulation, and global satisfaction scores were evaluated. Results: Pain scores were recorded at the four evaluation times (2, 12, 24, and 48 hours post-Cesarean delivery), and the time to first ambulation did not statistically differ between the two groups. Patients in the ropivacaine group experienced more motor weakness at 2 and 12 hours, fewer side effects, and higher global satisfaction scores than those in the morphine group (p

Electrocautery procedures generate malodorous smoke. This study quantified five volatile organic compounds detected in the smoke produced during breast surgery, and elucidated the factors that affect their chemical production. Methods: All samplers were assembled in an acrylic chamber with a Tygon tube attached to the tip of a diathermy pencil. The electrocautery smoke was quantified by gas chromatography/mass spectrometry. Results: In all samples, toluene was identified in concentrations of 2.48–5.50 mg/m3. Higher concentrations were observed during modified radical mastectomy procedures. Patients with high body mass index revealed high toluene concentrations. Longer duration of electrocautery tended to produce more toluene. Conclusion: The sampling protocol enabled acquisition of smoke samples near the source without interrupting surgery. The findings suggest that type of surgery, patient body mass index and duration of electrocautery are factors that can alter production of chemicals.

Empirical mode decomposition (EMD) is an adaptive filter bank for processing nonlinear and non-stationary signals, such as electroencephalographic (EEG) signals. EMD works well to decompose a time series into a set of intrinsic mode functions with specific frequency bands. An IMF therefore represents an intrinsic component on its correspondingly intrinsic frequency band. The word of 'intrinsic' means the frequency is totally adaptive to the nature of a signal. In this study, power density and nonlinearity are two critical parameters for characterizing the amplitude and frequency modulations in IMFs. In this study, a nonlinearity level is quantified using degree of waveform distortion (DWD), which represents the characteristic of waveform distortion as an assessment of the intra-wave modulation of an IMF. In the application of anesthesia EEG analysis, the assessments of power density and DWD for a set of IMFs represent dynamic responses in EEG caused by two different anesthesia agents, Ketamine and Alfentanil, on different frequency bands. Ketamine causes the increase of power density and the decrease of nonlinearity on γ-band neuronal oscillation, which cannot be found EEG responses of group B using Alfentanil. Both agents cause an increase of power density and a decrease of nonlinearity on β-band neuronal oscillation accompany with a loss of consciousness. Moreover, anesthesia agents cause the decreases of power density and nonlinearity (i.e. DWD) for the low-frequency IMFs.

Ropivacaine has been shown to induce convulsion following overdose or accidental intravenous injection, but the mechanisms are poorly understood. Using an identifiable central neuron from giant African snail, the authors studied the mechanism of ropivacaine-elicited bursts of potential and explored the possible mechanisms of ropivacaine-induced neurotoxicity. Methods: Ropivacaine action on a central neuron (RP4) of the giant African snail (Achatina fulica Ferussac) was recorded by conventional electrophysiologic technique. Interactions between ropivacaine and prazosin, propranolol, atropine, d-tubocurarine, calcium-free solution, H89, U73122, neomycin, high-magnesium solution, and chelerythrine were also observed. Results: The RP4 neuron showed spontaneous firing of action potentials. Extracellular application of ropivacaine (900 μM) reversibly elicited bursts of potential in the RP4 neuron. The bursts of potential elicited by ropivacaine were not blocked after administration of: (1) prazosin, propranolol, atropine, d-tubocurarine; (2) calcium-free solution; and (3) pretreatment with H89 or chelerythrine. The bursts of potential elicited by ropivacaine were blocked by pretreatment with U73122 (30 μM) or by adding neomycin (3.5 mM) or high-magnesium solution (30 mM). Conclusion: Ropivacaine reversibly elicited bursts of potential in the central snail neuron. The ropivacaine-elicited bursts of potential were associated with phospholipase C activity in the RP4 snail neuron. Our results suggest that ropivacaine-induced neurotoxicity is highly associated with phospholipase C activity and phospholipase C inhibitor may offer a novel therapeutic approach for managing local anesthetic-induced convulsion or other transient neurologic toxicity.

Objective: Placenta accreta is associated with significant maternal morbidity and is the leading indication for peripartum hysterectomy. In our institution, occlusion balloon catheters are commonly placed in bilateral common iliac arteries in order to reduce blood loss and facilitate surgery in patients with this obstetric complication. Few studies, however, have evaluated the effect of different anesthetic methods for cesarean hysterectomy on neonatal outcome. In this study, we compared Apgar scores among neonates born to mothers under general anesthesia with those who received regional anesthesia. Case Reports: A retrospective analysis of 19 women with placenta accreta/percreta who underwent cesarean hysterectomy in our hospital, revealed that the 1-minute Apgar score was 7 after immediate resuscitation in all neonates. There were no significant differences in demographic data, induction-to-delivery period, or Apgar scores between the general and the regional anesthesia groups. Conclusion: We acknowledge that the retrospective nature of this study makes it difficult to conclude whether the different anesthesia management strategies had an impact on Apgar score; however, according to our clinical observation, regional anesthesia may be a better alternative in the induction-to-delivery period, especially for women with accreta/percreta and in situations in which poor neonatal outcome is expected.