Association between hydroxyethyl starch 130/0.4 administration during noncardiac surgery and postoperative acute kidney injury: A propensity score-matched analysis of a large cohort in China.

STUDY OBJECTIVE: The use of hydroxyethyl starch 130/0.4 has been linked to renal injury in critically ill patients, but its impact on surgical patients remains uncertain. DESIGN: A retrospective cohort study. SETTING: This study was conducted at one tertiary care hospital in China. PATIENTS: We evaluated the records of 51,926 Chinese adults who underwent noncardiac surgery from 2013 to 2022. Patients given a combination of hydroxyethyl starch 130/0.4 and crystalloids were propensity-matched at a 1: 1 ratio of baseline characteristics to patients given only crystalloids (11,725 pairs). INTERVENTIONS: Eligible patients were divided into those given a combination of hydroxyethyl starch 130/0.4 and crystalloid during surgery and a reference crystalloid group consisting of patients who were not given any colloid. MEASUREMENTS: The primary outcome was the incidence of acute kidney injury. Secondarily, acute kidney injury stage, need for renal replacement therapy, intensive care unit transfer rate, and duration of postoperative hospitalization were considered. MAIN RESULTS: After matching, hydroxyethyl starch use [8.5 (IQR: 7.5-10.0) mL/kg] did not increase the incidence of acute kidney injury compared with that in the crystalloid group [2.0 vs. 2.2%, OR: 0.90 (0.74-1.08), P = 0.25]. Nor did hydroxyethyl starch use worsen acute kidney injury stage [OR 0.90 (0.75-1.08), P = 0.26]. No significant differences between the fluid groups were observed in renal replacement therapy [OR 0.60 (0.41-0.90), P = 0.02)] or intensive care unit transfers [OR 1.02 (0.95-1.09), P = 0.53] after Bonferroni correction. Even in a subset of patients at high risk of renal injury, hydroxyethyl starch use was not associated with worse outcomes. CONCLUSIONS: Hydroxyethyl starch 130/0.4 use was not significantly associated with a greater incidence of postoperative acute kidney injury compared to receiving crystalloid solutions only.

Fast multi-view clustering via correntropy-based orthogonal concept factorization.

Owing to its ability to handle negative data and promising clustering performance, concept factorization (CF), an improved version of non-negative matrix factorization, has been incorporated into multi-view clustering recently. Nevertheless, existing CF-based multi-view clustering methods still have the following issues: (1) they directly conduct factorization in the original data space, which means its efficiency is sensitive to the feature dimension; (2) they ignore the high degree of factorization freedom of standard CF, which may lead to non-uniqueness factorization thereby causing reduced effectiveness; (3) traditional robust norms they used are unable to handle complex noises, significantly challenging their robustness. To address these issues, we establish a fast multi-view clustering via correntropy-based orthogonal concept factorization (FMVCCF). Specifically, FMVCCF executes factorization on a learned consensus anchor graph rather than directly decomposing the original data, lessening the dimensionality sensitivity. Then, a lightweight graph regularization term is incorporated to refine the factorization process with a low computational burden. Moreover, an improved multi-view correntropy-based orthogonal CF model is developed, which can enhance the effectiveness and robustness under the orthogonal constraint and correntropy criterion, respectively. Extensive experiments demonstrate that FMVCCF can achieve promising effectiveness and robustness on various real-world datasets with high efficiency.

Fast Multiview Anchor-Graph Clustering.

Due to its high computational complexity, graph-based methods have limited applicability in large-scale multiview clustering tasks. To address this issue, many accelerated algorithms, especially anchor graph-based methods and indicator learning-based methods, have been developed and made a great success. Nevertheless, since the restrictions of the optimization strategy, these accelerated methods still need to approximate the discrete graph-cutting problem to a continuous spectral embedding problem and utilize different discretization strategies to obtain discrete sample categories. To avoid the loss of effectiveness and efficiency caused by the approximation and discretization, we establish a discrete fast multiview anchor graph clustering (FMAGC) model that first constructs an anchor graph of each view and then generates a discrete cluster indicator matrix by solving the discrete multiview graph-cutting problem directly. Since the gradient descent-based method makes it hard to solve this discrete model, we propose a fast coordinate descent-based optimization strategy with linear complexity to solve it without approximating it as a continuous one. Extensive experiments on widely used normal and large-scale multiview datasets show that FMAGC can improve clustering effectiveness and efficiency compared to other state-of-the-art baselines.

Intranasal dexmedetomidine improves postoperative sleep quality in older patients with chronic insomnia: a randomized double-blind controlled trial.

Objective: This study was determined to investigate the impact of intranasal dexmedetomidine (DEX) on postoperative sleep quality in older patients (age over 65) with chronic insomnia during their hospitalization after surgery. Design: A randomized double-blind controlled trial was conducted to compare the effects of intranasal dexmedetomidine spray with a placebo group. Setting and Participants: The study was carried out at Xiangya Hospital, Central South University. 110 participants with chronic insomnia were analyzed. Methods: This trial enrolled older patients who underwent total hip/knee arthroplasty and randomized them to receive intranasal dexmedetomidine (2.0 µg/kg) or saline daily at around 9 p.m. after surgery until discharge. The primary outcomes were subjective sleep quality assessed with the Leeds Sleep Evaluation Questionnaire (LSEQ). The secondary outcomes included the objective sleep quality measured with the Acti-graph, the Pittsburgh Sleep Quality Index (PSQI), the Insomnia Severity Index (ISI). The other outcomes included the incidence of delirium, levels of inflammatory factors, visual analog scale (VAS) pain scores, postoperative opioid consumption, and treatment-related adverse events. Results: 174 patients were screened for eligibility, and 110 were recruited and analyzed. The DEX group had significantly higher scores on both the LSEQ-Getting to sleep and LSEQ-Quality of Sleep at each time point compared to the placebo (p < 0.0001), The least squares (LS) mean difference in LSEQ-GTS score at T0 between placebo group and DEX group was 2 (95% CI, -1-6), p = 0.4071 and at T5 was -14 (95% CI, -17 to -10), p < 0.0001; The LS mean difference in the LSEQ-QOS score at T0 was -1 (95% CI, -4 to 1), p = 0.4821 and at T5 was -16 (95% CI, -21 to -10), p < 0.0001. The DEX group exhibited significant improvement in Total Sleep Time (TST), Sleep Onset Latency (SOL), and Sleep Efficiency (SE), at each time point after treatment compared to the placebo group (p < 0.0001). The PSQI and ISI scores in the DEX group were reduced after treatment (p < 0.001). No significant adverse events were reported with the use of dexmedetomidine. Conclusion and Implications: This study demonstrates that intranasal administration of dexmedetomidine improves postoperative sleep quality in older patients with chronic insomnia who undergo surgery, without increasing the incidence of adverse effects. Clinical Trial Registration: http://www.chictr.org.cn/, identifier ChiCTR2200057133.

Emergence delirium and postoperative delirium associated with high plasma NfL and GFAP: an observational study.

Background: Neuroinflammation and neuronal injury have been reported to be associated with the development of postoperative delirium in both preclinical and clinical settings. This study aimed to investigate the potential correlation between biomarkers of neurofilament light chain and glial fibrillary acidic protein and emergence and postoperative delirium in elderly patients undergoing surgery. Methods: Patients who developed emergence delirium (n = 30) and postoperative delirium (n = 32), along with their matched controls, were enrolled after obtaining ethics approval and written informed consent. Delirium was assessed using the Confusion Assessment Method for the Intensive Care Unit or Confusion Assessment Method scale, and blood samples were collected before and after surgery for plasma neurofilament light chain and glial fibrillary acidic protein measurements using a single-molecule array. Results: The study found that in patients with emergence delirium, the increase in plasma neurofilament light chain protein levels during surgery was significantly higher than in non-delirium patients (P = 0.002). Additionally, in patients with postoperative delirium, both the increase in plasma neurofilament light chain protein levels (P < 0.001) and the increase in plasma glial fibrillary acidic protein levels during surgery (P = 0.008) were significantly higher than in non-delirium patients. Multivariate logistic regression analysis showed that the increase in plasma neurofilament light chain protein was associated with emergence delirium (adjusted OR = 1.872, P = 0.005), and the increase in plasma glial fibrillary acidic protein was associated with postoperative delirium (adjusted OR = 1.419, P = 0.016). Moreover, the American Society of Anesthesiologists Physical Status Classification and surgical duration were also found to be associated with delirium in elderly patients. Conclusion: Our findings suggest that emergence delirium is linked to elevated levels of neurofilament light chain, a biomarker of axonal injury, during surgery. Furthermore, in addition to axonal injury, postoperative delirium was also associated with an increase in glial fibrillary acidic protein, a marker of astrocyte activation.

Sepsis-Induced Acute Lung Injury Is Alleviated by Small Molecules from Dietary Plants via Pyroptosis Modulation.

Sepsis-induced acute respiratory distress syndrome (ARDS) has high morbidity and mortality, and it has three major pathogeneses, namely alveolar-capillary barrier destruction, elevated gut permeability, and reduced neutrophil extracellular traps (NETS), all of which are pyroptosis-involved. Due to limitations of current agents like adverse reaction superposition, inevitable drug resistance, and relatively heavier financial burden, naturally extracted small-molecule compounds have a broad market even though chemically modified drugs have straightforward efficacy. Despite increased understanding of the molecular biology and mechanism underlying sepsis-induced ARDS, there are no specific reviews concerning how small molecules from dietary plants alleviate sepsis-induced acute lung injury (ALI) via regulating pyroptotic cell death. Herein, we traced and reviewed the molecular underpinnings of sepsis-induced ALI with a focus on small-molecule compounds from dietary plants, the top three categories of which are respectively flavonoids and flavone, terpenoids, and polyphenol and phenolic acids, and how they rescued septic ALI by restraining pyroptosis.

Regioselective C-H chalcogenylation and halogenation of arenes and alkenes under metal-free conditions.

The reactions of direct Csp2-H chalcogenylation and halogenation of N-arylpyrrolidone under the action of PIFA without a directing group and under metal-free conditions were reported in this paper. Diphenyl selenide/sulfur and selenium phenyl halides were used as reaction reagents to obtain chalcogenylated and halogenated N-arylpyrrolidone products, respectively. The mechanistic studies indicated that a radical pathway was likely involved in these reactions. Preliminary antitumor tests showed that these compounds have moderate to potent activities against human acute leukemia cells K562 in vitro, which may be used as lead compounds for subsequent research.

Family history of cancer is associated with poorer prognosis in oral squamous cell carcinoma.

OBJECTIVE: The purpose of this study was to investigate the prognostic value of the family history of cancer (FHC) in predicting survival and clinicopathological features in oral squamous cell carcinoma (OSCC) patients. MATERIALS AND METHODS: This single-institution study utilized data from 610 patients undergoing surgery from 2014 to 2020 that was prospectively collected and cataloged for research purposes. All patients underwent standard surgery with/without radiotherapy or chemoradiotherapy. We statistically evaluated whether FHC was associated with changes in disease-free survival (DFS) and disease-specific survival (DSS). RESULTS: Among 610 patients, 141 (23.1%) reported a family history of cancer. The distribution of clinicopathological characteristics was balanced between FHC-positive and FHC-negative OSCC patients. FHC-positive patients had decreased DFS (p = 0.005) and DSS (p = 0.018) compared to FHC-negative patients. CONCLUSIONS: FHC-positive OSCC patients have a poorer prognosis. FHC positivity is an independent predictor of negative outcomes based on DFS and DSS. FHC should be a consideration in screening, evaluating, counseling, and treating OSCC patients.

A novel silent speech recognition approach based on parallel inception convolutional neural network and Mel frequency spectral coefficient.

Silent speech recognition breaks the limitations of automatic speech recognition when acoustic signals cannot be produced or captured clearly, but still has a long way to go before being ready for any real-life applications. To address this issue, we propose a novel silent speech recognition framework based on surface electromyography (sEMG) signals. In our approach, a new deep learning architecture Parallel Inception Convolutional Neural Network (PICNN) is proposed and implemented in our silent speech recognition system, with six inception modules processing six channels of sEMG data, separately and simultaneously. Meanwhile, Mel Frequency Spectral Coefficients (MFSCs) are employed to extract speech-related sEMG features for the first time. We further design and generate a 100-class dataset containing daily life assistance demands for the elderly and disabled individuals. The experimental results obtained from 28 subjects confirm that our silent speech recognition method outperforms state-of-the-art machine learning algorithms and deep learning architectures, achieving the best recognition accuracy of 90.76%. With sEMG data collected from four new subjects, efficient steps of subject-based transfer learning are conducted to further improve the cross-subject recognition ability of the proposed model. Promising results prove that our sEMG-based silent speech recognition system could have high recognition accuracy and steady performance in practical applications.

Novel benzodiazepine remimazolam tosylate delays neurodegeneration of aged mice via decreasing tau phosphorylation.

Benzodiazepines like midazolam were generally considered one of the possible causes affecting postoperative cognitive recovery. As a new kind of rapid-effect benzodiazepine, remimazolam tosylate was widely used in clinical anesthesia for its pharmacological advantage, but few studies reported its effects on cognitive function in the elderly. Here, we aimed to research the effects of remimazolam tosylate on cognitive function in aged mice and its underlying biological mechanisms. We measured the memory function of aged mice immediately and one month after intraperitoneal injection of remimazolam tosylate compared to the saline control. The brain metabolism level was detected by Positron Emission Tomography/Computed tomography (PET/CT). Compared with the control, we observed a decrease in memory ability， as well as an increase in tau phosphorylation level and a decrease in phosphatase level in the short term; however, one month later, contrary to the previous results, we observed better memory and brain metabolism and lower tau phosphorylation levels in the experimental group compared to the control. Therefore, we concluded that remimazolam tosylate did not cause long-term damage to the cognitive function of aged mice and even delayed the decline of memory function in the aging process to some extent.

Parallel-Inception CNN Approach for Facial sEMG based Silent Speech Recognition.

With the purpose of providing an external human-machine interaction platform for the elderly in need, a novel facial surface electromyography based silent speech recognition system was developed. In this study, we propose a deep learning architecture named Parallel-Inception Convolutional Neural Network (PICNN), and employ up-to-date feature extraction method log Mel frequency spectral coefficients (MFSC). To better meet the requirements of our target users, a 100-class dataset containing daily life-related demands was designed and generated for the comparative experiments. According to experimental results, the highest recognition accuracy of 88.44% was achieved by proposed recognition framework based on MFSC and PICNN, exceeding the performance of state-of-the-art deep learning algorithms such as CNN, VGGNet and Inception CNN (3.22%, 4.09% and 1.19%, respectively). These findings suggest that the newly developed silent speech approach holds promise to provide a more reliable communication channel, and the application scenery of speech recognition technology has been expanded at the same time.

Temporal Combination Pattern Optimization Based on Feature Selection Method for Motor Imagery BCIs.

Common spatial pattern (CSP) method is widely used for spatial filtering and brain pattern extraction from electroencephalogram (EEG) signals in motor imagery (MI)-based brain-computer interfaces (BCIs). The participant-specific time window relative to the visual cue has a significant impact on the effectiveness of the CSP. However, the time window is usually selected experientially or manually. To solve this problem, we propose a novel feature selection approach for MI-based BCIs. Specifically, multiple time segments were obtained by decomposing each EEG sample of the MI task. Furthermore, the features were extracted by CSP from each time segment and were combined to form a new feature vector. Finally, the optimal temporal combination patterns for the new feature vector were selected based on four feature selection algorithms, i.e., mutual information, least absolute shrinkage and selection operator, principal component analysis and stepwise linear discriminant analysis (denoted as MUIN, LASSO, PCA, and SWLDA, respectively), and the classification algorithm was employed to evaluate the average classification accuracy. With three BCI competition datasets, the results of the four proposed algorithms were compared with traditional CSP algorithm in classification accuracy. Experimental results show that compared with traditional algorithm, the proposed methods significantly improve performance. Specifically, the LASSO achieved the highest accuracy (88.58%) among the proposed methods. Importantly, the average classification accuracies using the proposed approaches significantly improved 10.14% (MUIN), 11.40% (LASSO), 6.08% (PCA), and 10.25% (SWLDA) compared to that using CSP. These results indicate that the proposed approach is expected to be practical in MI-based BCIs.