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Abstract This study investigated the prevalence and severity of lower urinary tract symptoms (LUTS) in children with spastic cerebral palsy (SCP) and evaluated the effect of selective dorsal rhizotomy (SDR) in alleviating these symptoms. The study also explored the correlation between postoperative LUTS improvement and intraoperative electrophysiological findings. Prospective data were collected from a consecutive cohort of 247 children with SCP who underwent SDR and were retrospectively analyzed. Pre- and post-operative assessments included muscle tone, motor function, LUTS and intraoperative electrophysiology data were analyzed. Preoperatively, 94 patients (38.1%) had LUTS, and the severity of LUTS negatively correlated with motor function (R=-0.32, P 20µV. SDR effectively reduces spasticity, improves motor function, and alleviates LUTS in most children with SCP. Intraoperative neurophysiology may predict improvements, warranting further research.

Abstract Background Functional role of filum terminale (FT) was not well studied though it contains structure basis for nerve impulse conduction. We aimed to explore the possible functions of the FT from the perspective of triggered electromyography (EMG) during surgery. Methods We retrospectively reviewed intraoperative neurophysiological monitoring data from pediatric patients who underwent intradural surgeries at the lumbar level in Shanghai Children’s. Hospital from January 2018 to March 2023. Altogether 168 cases with complete intraoperative neurophysiological recordings of the FT were selected for further analysis. Triggered EMG recordings of the filum originated from two main types of surgeries: selective dorsal rhizotomy (SDR) and fatty filum transection. Results 96 cases underwent SDR and 72 cases underwent fatty filum transection. Electrical stimulation of the FT with fatty infiltration did not elicit electromyographic activity in the monitored muscles with the maximum stimulus intensity of 4.0 mA, while the average threshold for FT with normal appearance was 0.68 mA, and 89 out of 91 FT could elicit electromyographic responses in monitored channels. The threshold ratio of filum to motor nerve roots at the same surgical segment was significantly higher in patients with fatty filum, and a cut-off point of 21.03 yielded an area under curve of 0.943, with 100% sensitivity and 85.71% specificity. Conclusion Filum with normal appearance can elicit electromyographic activity in the lower limbs/anal sphincter similar to the performance of the cauda equina nerve roots. The threshold of fatty filum is different from that of normal appearing FT. Triggered EMG plays an important role in untethering surgeries.

Abstract Master–slave blockchain is a novel information processing technology that is domain-oriented and uses efficient cryptography principles for trustworthy communication and storage of big data. Existing indexing methods primarily target the creation of a single-structured blockchain, resulting in extensive time and memory requirements. As the scale of domain data continues to grow exponentially, master–slave blockchain systems face increasingly severe challenges with regards to low query efficiency and extended traceback times. To address these issues, this paper propose a multi-level index construction method for the master–slave blockchain (MLI). Firstly, MLI introduces a weight matrix and partitions the entire master–slave blockchain based on the master chain structure, the weight of each partition is assigned. Secondly, for the master blockchain in each partition, a master chain index construction method based on jump consistent hash (JHMI) is proposed, which takes the key value of the nodes and the number of index slots as input and outputs the master chain index. Finally, a bloom filter is introduced to improve the column-based selection function and build a secondary composite index on the subordinate blockchain corresponding to each master block. Experimental results on three constraint conditions and two types of datasets demonstrate that the proposed method reduce the index construction time by an average of 9.28%, improve the query efficiency by 12.07%, and reduce the memory overhead by 24.4%.

Abstract Background Neuroinflammation mediated by microglial pyroptosis is an important pathogenic mechanism of septic encephalopathy (SAE). It has been reported that TRIM45 is associated with tumours and inflammatory diseases. However, the role of TRIM45 in SAE and the relationship between TRIM45 and microglial pyroptosis are unknown. In this study, we found that TRIM45 played an important role in regulating microglial pyroptosis and the molecular mechanism. Methods SAE was induced by intraperitoneal injection of LPS in WT and AAV-shTRIM45 mice. BV2 cells were treated with LPS/ATP in vitro. Cognitive function was assessed by the Morris water maze. Nissl staining was used to evaluate histological and structural lesions. ELISA was used to dectect neuroinflammation. qPCR was used to detect the mRNA levels of inflammatory cytokines, NLRP3, and autophagy genes. Western blotting and immunofluorescence analysis were used to analyse the expression of the proteins. Changes in reactive oxygen species (ROS) in cells were observed by flow cytometry. Changes in mitochondrial membrane potential in BV2 cells were detected by JC-1 staining. Peripheral blood mononuclear cells were extracted from blood by density gradient centrifugation and then used for qPCR, western blotting and flow detection. To further explore the mechanism, we used the overexpression plasmids TRIM45 and Atg5 as well as siRNA-TRIM45 and siRNA-Atg5 to analyse the downstream pathway of NLRP3. The protein and mRNA levels of TRIM45 in peripheral blood mononuclear cells from sepsis patients were examined. Results Knocking down TRIM45 protected against neuronal damage and cognitive impairment in septic mice. TRIM45 knockdown inhibited microglial pyroptosis and the secretion of inflammatory cytokines in vivo and in vitro, which was mediated by NLRP3/Gsdmd-N activation. Overexpression of TRIM45 could activate NLRP3 and downstream proteins. Further examination showed that TRIM45 regulated the activation of NLRP3 by altering Atg5 and regulating autophagic flux. It was also found that overexpression and knockdown of TRIM45 affected the changes in ROS and mitochondrial membrane potential. Thus, knocking down TRIM45 could reduce microglial pyroptosis, the secretion of proinflammatory cytokines, and neuronal damage and improve cognitive function. In addition, the level of TRIM45 protein in septic patients was increased. There was a positive linear correlation between APACHE II score and TRIM45, between SOFA score and TRIM45. Compared to group GCS > 9, level of TRIM45 were increased in group GCS ≤ 8. Conclusion TRIM45 plays a key role in neuroinflammation caused by LPS, and the mechanism may involve TRIM45-mediated exacerbation of microglial pyroptosis via the Atg5/NLRP3 axis. Graphical Abstract

Abstract Selective dorsal rhizotomy (SDR) can reduce the spasticity in patients with spastic cerebral palsy (SCP) and thus improve the motor function in these patients, but different levels of improvement in motor function were observed among patients after SDR. The aim of the present study was to subgroup patients and to predict the possible outcome of SDR based on the pre-operational parameters. A hundred and thirty-five pediatric patients diagnosed with SCP who underwent SDR from January 2015 to January 2021 were retrospectively reviewed. Spasticity of lower limbs, the number of target muscles, motor functions, and other clinical parameters were used as input variables for unsupervised machine learning to cluster all included patients. The postoperative motor function change is used to assess the clinical significance of clustering. After the SDR procedure, the spasticity of muscles in all patients was reduced significantly, and the motor function was promoted significantly at the follow-up duration. All patients were categorized into three subgroups by both hierarchical and K-means clustering methods. The three subgroups showed significantly different clinical characteristics except for the age at surgery, and the post-operational motor function change at the last follow-up in these three clusters was different. Three subgroups clustered by two methods could be identified as “best responders”, “good responders” and “moderate responders” based on the increasement of motor function after SDR. Clustering results achieved by hierarchical and K-means algorithms showed high consistency in subgrouping the whole group of patients. These results indicated that SDR could relieve the spasticity and promote the motor function of patients with SCP. Unsupervised machine learning methods can effectively and accurately cluster patients into different subgroups suffering from SCP based on pre-operative characteristics. Machine learning can be used for the determination of optimal responders for SDR surgery.

In order to foster a modern economic system and facilitate high-quality economic development, it is crucial to establish a conducive business environment. Undoubtedly, the evaluation of the business environment for enterprises constitutes a prominent area of research. Nevertheless, ensuring the authenticity and security of the raw data sources provided by participating enterprises poses a challenge, thereby compromising the accuracy of the evaluation. To tackle this issue, an enterprise composite blockchain construction method for business environment is proposed in this paper, which stores the raw data of enterprises by the means of hybrid on-chain and off-chain. Initially, the enhanced hash function SHA256 is introduced to encrypt the raw data of enterprises. The encrypted data is subsequently stored in an off-chain Level DB database, which is based on non-volatile memory. This approach effectively alleviates the burden on communication and storage. Secondly, a composite storage strategy on-chain is adopted: the key values from the Level DB are stored in the DAG-based Conflux public blockchain, while the enterprise state data is stored in the consortium blockchain, so as to provide trusted evidence of business environment evaluation data. Finally, it is demonstrated through a large number of experimental comparisons that the enterprise composite blockchain construction method proposed in this paper exhibits better read and write performance, lower storage efficiency and storage overhead, and outperforms both the before-improved Level DB database and existing blockchain storage models.

Blockchain-based applications are becoming more and more widespread in business operations. In view of the shortcomings of existing enterprise blockchain evaluation methods, this paper proposes a multi-source heterogeneous blockchain data quality evaluation model for enterprise business activities, so as to achieve efficient evaluation of business activity information consistency, credibility and value. This paper proposes a multi-source heterogeneous blockchain data quality assessment method for enterprise business activities, aiming at the problems that most of the data in enterprise business activities come from different data sources, information representation is inconsistent, information ambiguity between the same block chain is serious, and it is difficult to evaluate the consistency, credibility and value of information. The method firstly proposes an entity information representation method based on the Representation learning for fusing entity category information (CEKGRL) model, which introduces the triad structure of related entities in blockchain, then associates them with enterprise business activity categories, and carries out similarity calculation through contextual information to achieve blockchain information consistency assessment. After that, a trustworthiness characterization method is proposed based on information sources, information comments, and information contents, to obtain the trustworthiness assessment of the business. Finally, based on the information trustworthiness characterization, a value assessment method is introduced to assess the total value of business activity information in the blockchain, and a blockchain quality assessment model is constructed. The experimental results show that the proposed model has great advantages over existing methods in assessing inter-block consistency, intra-block activity information trustworthiness and the value of blockchain.

BACKGROUND: Glycogen synthase kinase-3β (GSK3β), fat mass and obesity-associated protein (FTO), and toll-like receptors 4 (TLR4) take on critical significance in different biological processes, whereas their interactions remain unclear. The objective was the investigation of the interaction effect in cerebral ischemia-reperfusion (I/R) injury. METHODS: The function of the cerebral cortex in the mouse middle cerebral artery occlusion (MCAO) model (each group n = 6) and P12 cells oxygen-glucose deprivation/reoxygenation (OGD/R) model was analyzed using short hairpin GSK3β lentivirus and overexpression of FTO lentivirus (in vitro), TLR4 inhibitor (TAK242), and LiCl to regulate GSK3β, FTO, TLR4 expression, and GSK3β activity, respectively. RESULTS: After GSK3β knockdown in the OGD/R model of PC12 cells, the levels of TLR4 and p-p65 were lower than in the control, and the level of FTO was higher than in the control. Knockdown GSK3β reversed the OGD/R-induced nuclear factor kappa-B transfer to the intranuclear nuclei. As indicated by the result, TLR4 expression was down-regulated by overexpressed FTO, and TLR4 expression was up-regulated notably after inhibition of FTO with the use of R-2HG. After the inhibition of the activity of GSK3β in vivo, the reduction of FTO in mice suffering from MCAO was reversed. CONCLUSIONS: Our research shows that GSK3β/FTO/TLR4 pathway contributes to cerebral I/R injury.

Abstract Time series classification is a basic task in the field of streaming data event analysis and data mining. The existing time series classification methods have the problems of low classification accuracy and low efficiency. To solve these problems, this paper proposes a T-CNN time series classification method based on a Gram matrix. Specifically, we perform wavelet threshold denoising on time series to filter normal curve noise, and propose a lossless transformation method based on the Gram matrix, which converts the time series to the time domain image and retains all the information of events. Then, we propose an improved CNN time series classification method, which introduces the Toeplitz convolution kernel matrix into convolution layer calculation. Finally, we introduce a Triplet network to calculate the similarity between similar events and different classes of events, and optimize the squared loss function of CNN. The proposed T-CNN model can accelerate the convergence rate of gradient descent and improve classification accuracy. Experimental results show that, compared with the existing methods, our T-CNN time series classification method has great advantages in efficiency and accuracy.

ObjectiveThis study aimed to investigate the electro-neurophysiological characteristics of the ventral and dorsal nerves at the L2 segment in a quantitative manner.MethodsMedical records of consecutive patients who underwent single-level approach selective dorsal rhizotomy (SDR) from June 2019 to January 2022 were retrospectively reviewed. Intraoperative electro-neurophysiological data were analyzed.ResultsA total of 74 males and 27 females were included in the current study with a mean age of 6.2 years old. Quadriceps and adductors were two main muscle groups innervated by L2 nerve roots in both ventral and dorsal nerve roots. Dorsal roots have a higher threshold than that of the ventral ones, and muscles that first reached 200 µV innervated by dorsal roots have longer latency and smaller compound muscle action potential (CMAP) than those of the ventral ones. Supervised machine learning can efficiently distinguish ventral/dorsal roots using threshold + latency or threshold + CMAP as predictors.ConclusionElectro-neurophysiological parameters could be used to efficiently differentiate ventral/dorsal fibers during SDR.

Three-dimensional resistivity/chargeability tomography based on distributed data acquisition technology is likely to provide abundant information for mineral exploration. To realize true 3D tomography, establishing transmitter sources with different injection directions and collecting vector signals at receiver points is necessary. We implemented 3D resistivity/ chargeability tomography to search for new ore bodies in the deep and peripheral areas of Huaniushan, China. A distributed data acquisition system was used to form a vector receiver array in the survey area. First, by using the expanding gradient array composed of 11 pairs of transmitter electrodes, we quickly obtained the 3D distributions of the resistivity and chargeability of the whole area. Based on the electrical structure and geological setting, a NE-striking potential area for mineral exploration was determined. Next, a pole–dipole array was employed to depict the locations and shapes of the potential ore bodies in detail. The results showed that the inversion data for the two arrays corresponded well with the known geological setting and that the ore veins controlled by boreholes were located in the low-resistivity and high-chargeability zone. These results provided data for future mineral evaluation. Further research showed that true 3D tomography has obvious advantages over quasi-3D tomography. The expanding gradient array, characterized by a good signal strength and field efficiency, was suitable for the target determination in the early exploration stage. The pole–dipole array with high spatial resolution can be used for detailed investigations. Choosing a reasonable data acquisition scheme is helpful to improve the spatial resolution and economic efficiency.

Abstract Previous studies have demonstrated that cardiomyocyte apoptosis, ferroptosis, and inflammation participate in the progress of sepsis-induced cardiomyopathy (SIC). Although Islet cell autoantigen 69 (ICA69) is an imperative molecule that could regulate inflammation and immune response in numerous illnesses, its function in cardiovascular disease, particularly in SIC, is still elusive. We confirmed that LPS significantly enhanced the expression of ICA69 in wild-type (WT) mice, macrophages, and cardiomyocytes. The knockout of ICA69 in lipopolysaccharide(LPS)-induced mice markedly elevated survival ratio and heart function, while inhibiting cardiac muscle and serum inflammatory cytokines, reactive oxygen (ROS), and ferroptosis biomarkers. Mechanistically, increased expression of ICA69 triggered the production of STING, which further resulted in the production of intracellular lipid peroxidation, eventually triggering ferroptosis and heart injury. Intriguingly, ICA69 deficiency only reversed the ferroptotic marker levels, such as prostaglandin endoperoxide synthase 2 (PTGS2), malonaldehyde (MDA), 4-hydroxynonenal (4HNE), glutathione peroxidase 4 (GPX4), superoxide dismutase (SOD), iron and lipid ROS, but had no effects on the xCT-dependent manner. Additionally, greater ICA69 level was identified in septic patients peripheralblood mononuclear cells (PBMCs) than in normal control groups. Generally, we unveil that ICA69 deficiency can relieve inflammation and ferroptosis in LPS-induced murine hearts and macrophages, making targeting ICA69 in heart a potentially promising treatment method for SIC.

The blockchain runs in a complex topological network which is affected by the principle of consensus, and data storage between nodes needs to maintain global consistency in the entire network, which causes the data storage inefficient. At the same time, the information exchange between large-scale communication node groups leads to the problems of bandwidth expropriation and excessive network load. In response to these problems, this article proposes a second-degree branch structure blockchain expansion model. First, a ternary storage structure is established. Data use the way of fully integrated storage, multi-cell storage, and fully split storage, and data are classified and stored in parallel between the structures. Second, a second-degree branch chain model is constructed. The main chain forks into multiple sub-chains, and a free competition chain structure and a Z-type chain structure are defined; a two-way rotation mechanism is introduced to realize the integration and transition between chain structures. Finally, a set of malicious attacks is simulated to realize the security constraints of the blockchain, to verify the security of the second-degree branch chain model. Experiment shows that the second-degree branch structure expansion model proposed in this article has great advantages in data storage efficiency and network load.

Ferroptosis is a new form of programmed cell death due to iron-dependent excess accumulation of lipid peroxides and differs from other programmed cell deaths in morphological and biochemical characteristics. The process of ferroptosis is precisely regulated by iron metabolism, lipid metabolism, amino acid metabolism, and numerous signaling pathways, and plays a complex role in many pathophysiological processes. Recent studies have found that ferroptosis is closely associated with the development and progression of many lung diseases, including acute lung injury, pulmonary ischemia-reperfusion injury, lung cancer, chronic obstructive pulmonary disease, and pulmonary fibrosis. Here, we present a review of the main regulatory mechanisms of ferroptosis and its research progress in the pathogenesis and treatment of lung diseases, with the aim of providing new ideas for basic and clinical research of lung-related diseases.

A previous study has demonstrated that pretreatment with electroacupuncture (EA) induces rapid tolerance to focal cerebral ischemia. In the present study, we investigated whether adenosine receptor 1 (A1 R) is involved in EA pretreatment-induced cognitive impairment after focal cerebral ischemia in rats. Two hours after EA pretreatment, focal cerebral ischemia was induced by middle cerebral artery occlusion for 120 min in male Sprague-Dawley rats. The neurobehavioral score, cognitive function [as determined by the Morris water maze (MWM) test], neuronal number, and the Bax/Bcl-2 ratio was evaluated at 24 h after reperfusion in the presence or absence of CCPA (a selective A1 receptor agonist), DPCPX (a selective A1 receptor antagonist) into left lateral ventricle, or A1 short interfering RNA into the hippocampus area. The expression of the A1 receptor in the hippocampus was also investigated. The result showed that EA pretreatment upregulated the neuronal expression of the A1 receptor in the rat hippocampus at 90 min. And EA pretreatment reversed cognitive impairment, improved neurological outcome, and inhibited apoptosis at 24 h after reperfusion. Pretreatment with CCPA could imitate the beneficial effects of EA pretreatment. But the EA pretreatment effects were abolished by DPCPX. Furthermore, A1 receptor protein was reduced by A1 short interfering RNA which attenuated EA pretreatment-induced cognitive impairment.

Abstract Background Acupuncture-balanced anesthesia has been found to offer protective benefits. Electrical stimulation at certain acupoints can potentially promote perioperative gastrointestinal function recovery. The purpose of this study is to explore the effects of acupuncture-balanced anesthesia on the postoperative recovery of gastrointestinal function, on anesthesia strategies for abdominal surgery, on postoperative pain treatment, and on any associated complications or alterations in immune function. We further seek to verify the protective effects of transcutaneous electrical acupoint stimulation (TEAS), to explore possible underlying neuroimmune–endocrine mechanisms, and to thereby develop an optimized acupuncture-balanced anesthesia strategy suitable for abdominal surgery. Together, these findings will provide a scientific basis for the clinical utilization of acupuncture-balanced anesthesia in the context of abdominal surgery. Methods/design This study is a multicenter, large-sample, randomized placebo-controlled trial. All subjects will be patients undergoing elective gastric or colorectal surgery. In Part 1, these patients will be stratified according to surgical site (gastric or colorectal), and randomly divided into four groups based on different perioperative interventions: Con group, which will undergo sham TEAS before, during, and after surgery; T1 group, which will receive TEAS during the preoperative and intraoperative periods, and sham TEAS during the postoperative period; T2 group, which will receive TEAS during the preoperative period, sham TEAS during the intraoperative period, and TEAS during the postoperative period; and T3 group, which will receive TEAS before, during, and after operation. Part 2 of this study will focus solely on colorectal surgery patients. All patients will receive TEAS during the preoperative and intraoperative periods, and they will be randomized into four groups according to different postoperative treatments: Con′ group, which will not receive TEAS; T1′ group, which will receive sham TEAS; T2′ group, which will receive 5-Hz TEAS; and T3′ group, which will receive 100-Hz TEAS. Venous blood (5 ml) will be used to measure immunological and inflammatory indexes both at the preoperative stage prior to TEAS and 4–5 days after operation. The primary outcome will be the time to first bowel sounds after surgery. Secondary outcomes will include gastrointestinal functional recovery, analgesic efficacy during the postoperative period, acupuncture-balanced anesthesia efficacy, postoperative nausea and vomiting, and postoperative complications. Discussion This study is designed to investigate the clinical value of TEAS during various perioperative periods in those undergoing abdominal surgery, with the overall goal of evaluating the clinical value and advantages of acupuncture-balanced anesthesia, and of providing new strategies for improving patient prognoses. Trial registration Chinese Clinical Trial Registry, ChiCTR-TRC-14004435. Registered on 26 March 2014.

Introduction The aim of the study was to study the role of the anterior cingulate cortex (ACC)-dorsal midbrain striatum (DMS) in neuropathic pain in mice. Material and methods Optogenetics has been increasingly used in neuroscience research to selectively and precisely control the activity of a defined group of central neurons to determine their roles in behavioral functions in animals. The most important opsins are blue-sensitive ChR2 and yellow-sensitive NpHR. Calcium-calmodulin dependent protein kinase Iiα (CaMKIIα) is mostly expressed in the pyramidal excitatory neurons. Mice were injected with AAV2/9-CamKII-ChR2-mCherry, AAV2/9-CamKII-eNpHR3.0-GFP or AAV2/9-CamKII-mCherry virus in the ACC region, and the optical fiber implantation was performed in the ACC or DMS region. Mice were then followed up for 2 to 8 weeks and behavioral tests were carried out in the presence or absence of the blue/yellow light (473 nm/589 nm). Pain behavioral tests with or without the blue/yellow light at the same time were performed on the third and the seventh day after the chronic constriction injury of sciatic nerve model (CCI) was established. The pain thresholds of left and right hind limbs of mice in all groups were measured. Results No matter whether activating the neurons in ACC or DMS, compared with normal mice in the ChR2-off-right group, and the mCherry-on-right group, the thermal pain threshold and mechanical pain threshold of the normal mice in the ChR2-on-right group were significantly lower. When inhibiting the neurons in the ACC or DMS, on day 3 and day 7 after CCI operation, the thermal pain threshold and mechanical pain threshold of the CCI mice of the NpHR-on-right group were significantly higher compared with the NpHR-off-right and mCherry-on-right groups. Conclusions The anterior cingulate cortex-dorsal midbrain striatum may be involved in the regulation of neuropathic pain in mice.

Background. At present, there is a lack of drug treatment for obese patients, so it is needed to find a drug that is effective and has few side effects to treat obesity. PEX-168 is a novel long-acting glucagon-like peptide-1 receptor agonist for T2DM. It improves blood glucose with fewer side effects. The aim of the present study was to investigate the effect of PEX-168 on blood glucose and body weight of mice with simple obesity. Methods. Thirty healthy and 6-week-old C57BL/6 male mice were randomly divided into a normal control group (NC, n = 6) and obesity model group (n = 24). The obesity model mice were randomly divided into a high-fat diet group (HF) and intervention groups with different doses of PEX-168 (0.03 mg/kg, 0.1 mg/kg, and 0.3 mg/kg). Each group includes 6 mice. Body weight, food intake, and fasting blood glucose (FBG) were evaluated after intraperitoneal injection, and the intervention was performed weekly for 12 weeks. Fasting insulin (FINS) levels were measured at the 12th week. Results. Compared with HF, the food intake of mice in the intervention groups decreased transiently, but there was no difference between different doses (P>0.05). The body weight of mice in the middle and high dose of PEX-168 intervention groups decreased significantly, and the differences were statistically significant (P0.05). At the 10th week, the incidence of transient hypoglycemia was 67% and 50% in the middle- and high-dose groups, respectively. The levels of serum FINS in the intervention groups were significantly lower than those in the HF group, and the differences were statistically significant (P0.05). Conclusions. PEX-168 showed significant improvement in the FBG and FINS levels of simple obese mice. Middle and high doses of PEX-168 could reduce the weight of simple obese mice, but there was a certain risk of hypoglycemia.

Tracheobronchopathia osteochondroplastica (TO) is a rare disease that may cause unexpected difficult intubation. There is no available consensus on the management of difficult intubation that is associated with TO. A 45-year-old woman was scheduled for modified radical mastoidectomy, canaloplasty, and tympanoplasty under general anesthesia. We encountered significant resistance during tracheal intubation, although the laryngeal view was normal with the video laryngoscope. A fiberoptic bronchoscope was then used to facilitate intubation, and we noted that the trachea was obviously narrowed due to cartilaginous ring hypertrophy. The tracheal tube was fully lubricated with tetracaine gel, and smoothly inserted into the trachea. After the operation, bronchoscopy and a computed tomography (CT) scan were performed to confirm the diagnosis of TO. Fiberoptic bronchoscopy-assisted tracheal intubation is safe and effective choice for the patients in whom subglottic intubation is difficult. CT scan and bronchoscopy might be helpful for preoperative airway assessment. Identifying patients with TO is important to avoid unexpected tracheal intubation impediment. Assessment of the subglottic airway should also be taken seriously.

The real-time sensing data in sensor networks are mostly data stream, and due to the inevitable factors such as external natural environment and man-made interference of sensors deployment, the phenomenon of data drift like slowing down or aggravation often appears when the perceived data stream propagates in the time domain, making it impossible to match the real-time data with standardized event templates in real time. Thus, it is difficult to identify the results through the existing data stream monitoring approaches before the ends of disaster events, and the accuracy is extremely low. Therefore, aiming at the deficiencies of the existing real-time identification approaches, this paper proposes a multi-stage real-time identification approach (MRIA) for data stream events with data drift feature based on dynamic time warping. First, the initial identification domain of data stream events is determined, and an anti-aliasing model based on dynamic time warping is constructed for the drift feature of the data stream, realizing the real-time similarity matching between the real-time data and event template. Second, a variable sliding window mechanism is introduced to determine the starting position of events, and an optimized matching approach for the incremental sequence is proposed to reduce the computational cost of re-matching in the process of the matching, and it obtains the identification benchmark of the similarity matching between the real-time data and the event template dynamically by dynamic threshold setting, which can improve the accuracy of matching. On this basis, an event multi-stage real-time identification approach based on identification proportion allocation is proposed, which can obtain the possibilities of events occurrence and the information of final disaster events quickly through the initial real-time identification and the final real-time identification process. The data compression optimization strategy based on the piecewise aggregate approximation approach is proposed to reduce the data scale, which further improves the identification efficiency. Furthermore, it provides an effective way for real-time identification of data stream events. The experimental results show that the approach proposed in this paper has great advantages in the efficiency and accuracy of data stream events identification.

Background: Although N 6 -methyladenosine (m 6 A) plays a very important role in different biological processes, its function in the brain has not been fully explored. Thus, we investigated the roles of the RNA demethylases Alkbh5/Fto in cerebral ischemia-reperfusion injury. Methods: We used a rat model and primary neuronal cell culture to study the role of m 6 A and Alkbh5/Fto in the cerebral cortex ischemic penumbra after cerebral ischemia-reperfusion injury. We used Alkbh5-shRNA and Lv-Fto ( in vitro ) to regulate the expression of Alkbh5/Fto to study their regulation of m 6 A in the cerebral cortex and to study brain function after ischemia-reperfusion injury. Results: We found that RNA m 6 A levels increased consecutive to the increase of Alkbh5 expression in both the cerebral cortex of rats after middle cerebral artery occlusion, and in primary neurons after oxygen deprivation/reoxygenation. In contrast, Fto expression decreased after these perturbations. Our results suggest that knocking down Alkbh5 can aggravate neuronal damage. This is due to the demethylation of Alkbh5 and Fto, which selectively demethylate the Bcl2 transcript, preventing Bcl2 transcript degradation and enhancing Bcl2 protein expression. Conclusion: Collectively, our results demonstrate that the demethylases Alkbh5/Fto co-regulate m 6 A demethylation, which plays a crucial role in cerebral ischemia-reperfusion injury. The results provide novel insights into potential therapeutic mechanisms for stroke.

Ca2+/calmodulin-dependent protein kinase II (CaMKII) is a multifunctional serine/threonine kinase that is ubiquitously distributed in the central and peripheral nervous systems. Moreover, its phosphorylated protein (P-CaMKII) is involved in memory, mood, and pain regulation in the anterior cingulate cortex (ACC). Electroacupuncture (EA) is a traditional Chinese therapeutic technique that can effectively treat chronic inflammatory pain. However, the CaMKII-GluA1 role in EA analgesia in the ACC remains unclear. This study investigated the role of P-CaMKII and P-GluA1 in a mouse model of inflammatory pain induced by complete Freund’s adjuvant (CFA). There were increased P-CaMKII and P-GluA1 levels in the ACC. We found that intracerebroventricular injection of KN93, a CaMKII inhibitor, as well as EA stimulation, attenuated complete Freund’s adjuvant-induced pain behavior. Further, EA increased pCaMKII-PICK1 complex (abbreviated as C-P complex) levels. Our findings demonstrate that EA inhibits inflammatory pain by inhibiting CaMKII-GluA1 phosphorylation. P-CaMKII is involved in EA analgesia as the pCaMKII-PICK1 complex.

With the development of streaming data processing technology, real-time event monitoring and querying has become a hot issue in this field. In this article, an investigation based on coal mine disaster events is carried out, and a new anti-aliasing model for abnormal events is proposed, as well as a multistage identification method. Coal mine micro-seismic signal is of great importance in the investigation of vibration characteristic, attenuation law, and disaster assessment of coal mine disasters. However, as affected by factors like geological structure and energy losses, the micro-seismic signals of the same kind of disasters may produce data drift in the time domain transmission, such as weak or enhanced signals, which affects the accuracy of the identification of abnormal events (“the coal mine disaster events”). The current mine disaster event monitoring method is a lagged identification, which is based on monitoring a series of sensors with a 10-s-long data waveform as the monitoring unit. The identification method proposed in this article first takes advantages of the dynamic time warping algorithm, which is widely applied in the field of audio recognition, to build an anti-aliasing model and identifies whether the perceived data are disaster signal based on the similarity fitting between them and the template waveform of historical disaster data, and second, since the real-time monitoring data are continuous streaming data, it is necessary to identify the start point of the disaster waveform before the identification of the disaster signal. Therefore, this article proposes a strategy based on a variable sliding window to align two waveforms, locating the start point of perceptual disaster wave and template wave by gradually sliding the perceptual window, which can guarantee the accuracy of the matching. Finally, this article proposes a multistage identification mechanism based on the sliding window matching strategy and the characteristics of the waveforms of coal mine disasters, adjusting the early warning level according to the identification extent of the disaster signal, which increases the early warning level gradually with the successful result of the matching of 1/ N size of the template, and the piecewise aggregate approximation method is used to optimize the calculation process. Experimental results show that the method proposed in this article is more accurate and be used in real time.

Personalized recommendation and the processing of real-time data exemplify the processing of massive data which in the field of Internet-of-Things (IoT) received a great extent of attention in recent literature. The incompleteness of massive data in the IoT is widespread. Obtaining personalized information from the incomplete data set is still puzzled by searching efficient and accurate methods at present. Skyline query is a widely used data processing method, especially in the field of multi-objective decision analysis and data visualization. To eliminate the negative effects on massive data processing in IoT, a novel skyline preference query strategy based on massive and the incomplete data set is proposed in this paper. This strategy simply separates and divides massive and incomplete data set into two parts according to dimension importance and executes skyline query, respectively. The strategy mainly resolves the problem of extracting personalized information from massive and incomplete data set and improves the efficiency of skyline query on massive and incomplete data set. First, this paper presents a skyline preference query strategy based on strict clustering and implements it on dimensions that have higher importance. Second, a skyline preference query strategy based on loose clustering is implemented on dimensions that have lower importance. Finally, integrating local skyline query results, this paper calculates global skyline query results by using information entropy theory. The efficiency and effectiveness of Skyline Preference Query (SPQ) algorithm have been evaluated in terms of response time and result set size through the comparative experiments with ISkyline algorithm and sort-based incomplete data skyline algorithm. A large number of simulation results show that the efficiency of SPQ algorithm is higher than that of other common methods.

Sepsis is a systemic inflammatory reaction caused by infection. Multiple organ failure ultimately leads to high morbidity and mortality. Unfortunately, therapies against these responses have been unsuccessful due to the insufficient underlying pathophysiological evidence. Protein interacting with C-kinase 1 (PICK1) has received considerable attention because of its important physiological functions in many tissues. However, its role in sepsis-induced acute lung injury (ALI) is unclear. In this study, we used cecal ligation and puncture (CLP) to establish a septic model and found that decreased microtubule-associated protein-1light chain 3 (LC3)-II/LC3-I in PICK1−/− septic mice was caused by autophagy dysfunction. Consistently, the transmission electron microscopy (TEM) of bone marrow-derived macrophages (BMDMs) from PICK1−/− mice showed the accumulation of autophagosomes as well. However, more serious damage was caused by PICK1 deficiency indicating that the disrupted autophagic flux was harmful to sepsis-induced ALI. We also observed that it was the impaired lysosomal function that mediated autophagic flux blockade, and the autophagy progress was relevant to PI3K-Akt-mTOR pathway. These findings will aid in the potential development of PICK1 with novel evidence of autophagy in sepsis treatment and prevention.

Reperfusion after tourniquet use can induce inflammation and cause remote organ injury. We evaluated the therapeutic effect of transcutaneous electrical acupoint stimulation (TEAS) on inflammatory mediators and lung function in patients receiving lower limb tourniquets. Forty patients undergoing unilateral lower extremity surgery with tourniquet were randomly assigned to two groups: the TEAS group and ischemia-reperfusion (I/R) group. The C-C motif chemokine ligand 2 (CCL2), C-X-C motif chemokine ligand 8 (CXCL8), interleukin-1 (IL-1), interleukin-6 (IL-6), interleukin-10 (IL-10), tumor necrosis factor-α (TNF-α), and arterial blood gas analysis were measured preoperatively and 6 h after tourniquet removal. The levels of CXCL8, IL-1, IL-6, TNF-α, and CCL2 were significantly increased compared to baseline values in both groups, but the increase was significantly smaller in the TEAS group. In the TEAS group, the partial pressure of oxygen and arterial-alveolar oxygen tension ratio were significantly decreased, and the alveolar-arterial oxygen tension difference and respiratory index were significantly increased, compared to those in the I/R group at 6 h after reperfusion. In conclusion, TEAS diminished the upregulation of proinflammatory factors in response to lower limb ischemia-reperfusion and improved pulmonary gas exchange.

As the technology of the Internet of Things (IoT) becomes more widely used in large-scale monitoring networks, this paper proposes an optimized obtaining strategy (OFS) for large-scale sensor monitoring networks. First, because of the large-scale features of sensor node network, this paper proposes a large-scale monitoring network area clustering optimization strategy. Second, based on the characteristics of regular changes in the sensed data in large-scale monitoring networks, this paper proposes a strategy for acquiring sensor data based on an adaptive frequency conversion. The OFS optimization strategy can prolong network lifetime, reduce the transmission bandwidth resources, and reduce average energy consumption of the cluster head and network energy consumption.

The concentration of dissolved oxygen (DO) in the aeration tank(s) of an activated sludge system is one of the most important process control parameters. The DO concentration in the aeration tank(s) is maintained at a desired level by using a Proportional-Integral-Derivative (PID) controller. Since the traditional PID parameter adjustment is not adaptive, the unknown disturbances make it difficult to adjust the DO concentration rapidly and precisely to maintain at a desired level. A Radial Basis Function (RBF) neural network (NN)-based adaptive PID (RBFNNPID) algorithm is proposed and simulated in this paper for better control of DO in an activated sludge process-based wastewater treatment. The powerful learning and adaptive ability of the RBF neural network makes the adaptive adjustment of the PID parameters to be realized. Hence, when the wastewater quality and quantity fluctuate, adjustments to some parameters online can be made by RBFNNPID algorithm to improve the performance of the controller. The RBFNNPID algorithm is based on the gradient descent method. Simulation results comparing the performance of traditional PID and RBFNNPID in maintaining the DO concentration show that the RBFNNPID control algorithm can achieve better control performances. The RBFNNPID control algorithm has good tracking, anti-disturbance and strong robustness performances.

Objective. Our present study tested whether flurbiprofen axetil could reduce perioperative sufentanil consumption and provide postoperative analgesia with decrease in emergency agitation and systemic proinflammatory cytokines release. Methods. Ninety patients undergoing tangential excision surgery were randomly assigned to three groups: (1) preoperative dose of 100 mg flurbiprofen axetil and a postoperative dose of 2 μg/kg sufentanil and 10 mL placebo by patient-controlled analgesia (PCA) pump, (2) preoperative dose of 100 mg flurbiprofen axetil and a postoperative dose of 2 μg/kg sufentanil and 100 mg flurbiprofen axetil by PCA pump, and (3) 10 mL placebo and a postoperative dose of 2 μg/kg sufentanil and 10 mL placebo by PCA pump. Results. Preoperative administration of flurbiprofen axetil decreased postoperative tramadol consumption and the visual analog scale at 4, 6, 12, and 24 h after surgery, which were further decreased by postoperative administration of flurbiprofen axetil. Furthermore, flurbiprofen axetil attenuated emergency agitation score and Ramsay score at 0, 5, and 10 min after extubation and reduced the TNF-α and interleukin- (IL-) 6 levels at 24 and 48 h after the operation. Conclusion. Flurbiprofen axetil enhances analgesic effects of sufentanil and attenuates emergence agitation and systemic proinflammation in patients undergoing tangential excision surgery.

ABSTRACT Neuropathic pain is a chronic pain condition caused by damage or dysfunction of the central or peripheral nervous system. Electroacupuncture (EA) has an antinociceptive effect on neuropathic pain, which is partially due to inhibiting astrocyte activation in the spinal cord. We found that an intrathecal injection of 8-cyclopentyl-1,3-dipropylxanthine (DPCPX), a selective adenosine A1 receptor antagonist, reversed the antinociceptive effects of EA in a chronic constriction injury-induced neuropathic pain model. The expression of GFAP in L4-L6 spinal cord was significantly upgraded, while DPCPX suppressed the effect of the EA-mediating inhibition of astrocyte activation, as well as wiping out the EA-induced suppression of cytokine content (TNF-α). These results indicated that the adenosine A1 receptor is involved in EA actions during neuropathic pain through suppressing astrocyte activation as well as TNF-α upregulation of EA, giving enlightenment to the mechanisms of acupuncture analgesia and development of therapeutic targets for neuropathic pain.