Oral administration of geranylgeranylacetone plus local somatothermal stimulation: A simple, effective, safe and operable preconditioning combination for conferring tolerance against ischemia-reperfusion injury in rat livers

AIM： To explore a simple, effective, safe and operable pretreatment for conferring tolerance against ischemia- reperfusion （I-R） injury in rat livers. METHODS： Forty-five rats were divided into five groups （each group n = 9）. Group C： control group; group G： geranylgeranylacetone （GGA） was administered without heat stress; group S： local heat stress alone; group WG： GGA plus whole-body heat stress; group SG： GGA administration plus local heat stress. After completion of the I-R procedure, the ischemic-reperfused liver lobes in five groups were resected and tested for heat shock protein （HSP70） by RT-PCR, Western blotting analysis and immunohistochemical staining. The blood samples were collected for ALT and AST measurement at the end of occlusion of blood supply, 30 min after reperfusion, 24, 48, 72 h after surgery from the inferior vena cava. Survival was monitored for 1 wk. RESULTS： The production of HSP70 after I-R injury increased, the liver enzyme levels after reperfusion decreased rapidly, and the survival rates increased in groups C-SG. CONCLUSION： The combination of GGA plus local somatothermal stimulation is a simple, effective, safe and operable pretreatment to induce HSP70 in patients with liver tumor and cirrhosis before hepatectomy and in donors before harvesting graft for liver transplantation.2005 The WJG Press and Elsevier Inc. All rights reserved.

Intraperitoneal perfusion of cytokine-induced killer cells with local hyperthermia for advanced hepatocellular carcinoma

AIM:To study the effect and tolerance of intraperitoneal perfusion of cytokine-induced killer(CIK) cells in combination with local radio frequency(RF) hyperthermia in patients with advanced primary hepatocellular carcinoma(HCC).METHODS:Patients with advanced primary HCC were included in this study.CIK cells were perfused intraperitoneal twice a week,using 3.2 × 10 9 to 3.6 × 10 9 cells each session.Local RF hyperthermia was performed 2 h after intraperitoneal perfusion.Following an interval of one month,the next course of treatment was administered.Patients received treatment until disease progression.Tumor size,immune indices(CD3 +,CD4 +,CD3 + CD8 +,CD3 + CD56 +),alpha-fetoprotein(AFP) level,abdominal circumference and adverse events were recorded.Time to progression and overall survival(OS) were calculated.RESULTS:From June 2010 to July 2011,31 patients diagnosed with advanced primary HCC received intraperitoneal perfusion of CIK cells in combination with local RF hyperthermia in our study.Patients received an average of 4.2 ± 0.6 treatment courses(range,1-8 courses).Patients were followed up for 8.3 ± 0.7 mo(range,2-12 mo).Following combination treatment,CD4 +,CD3 + CD8 + and CD3 + CD56 + cells increased from 35.78% ± 3.51%,24.61% ± 4.19% and 5.94% ± 0.87% to 45.83% ± 2.48%(P = 0.016),39.67% ± 3.38%(P = 0.008) and 10.72% ± 0.67%(P = 0.001),respectively.AFP decreased from 167.67 ± 22.44 to 99.89 ± 22.05 ng/mL(P = 0.001) and abdominal circumference decreased from 97.50 ± 3.45 cm to 87.17 ± 4.40 cm(P = 0.002).The disease control rate was 67.7%.The most common adverse events were low fever and slight abdominal erubescence,which resolved without treatment.The median time to progression was 6.1 mo.The 3-,6-and 9-mo and 1-year survival rates were 93.5%,77.4%,41.9% and 17.4%,respectively.The median OS was 8.5 mo.CONCLUSION:Intraperitoneal perfusion of CIK cells in combination with local RF hyperthermia is safe,can efficiently improve immunological status,and may prolong survival in HCC patients.

INFLUENCE OF LOCAL INFILITRATION ANESTHESIA OF SCALP-POINT ON ACUPUNCTURE INDUCED CHANGES OF CEREBRAL BLOOD PERFUSION DETECTED BY SPECT

Objective: To observe the influence of local infiltration anesthesia at the scalp point on acupuncture stimulation induced changes of cerebral blood perfusion in the brain. Methods: Experiments were conducted in 10 healthy volunteer subjects (8 males and 2 females) who were ordered to take a lying position on a specific bed with their heads keeping in a fixed position. Scalp point used was Motor Area (MS 6). The first syringe needle (gauge 5) was inserted into the scalp from the upper 1/5 of MS 6 and the second syringe needle inserted into the scalp from the middle 2/5 of MS 6 and advanced downward, with the two needles connected to a HAN’s Therapeutic Apparatus. Images of the brain were then taken before and after electroacupuncture (EA) stimulation, and after local injection of 1% lidocaine [mixed with epinephrine (1∶200,000, 3 mL)] plus EA by using Siemens ECAM/ICON Single Photo Emission Computed Tomography (SPECT). Intravenous injection of Ethyl cysteinate dimmer 555 MBq was performed before displaying cerebral images. Data of blood functional changing rat (BFCR%) were analyzed quantitatively using a mathematic model. Results: Before EA stimulation, the blood perfusion and function of cerebral cortex, thalamus, basal ganglion and cerebellum on both sides of the brain were basically symmetry. Following EA of MS 6, BFCR% of the contralateral thalamus, parietal lobe and the frontal lobe increased significantly. Following local infiltration anesthesia, BFCR% of the contralatral thalamus declined markedly (P<0.05). The results of quantitative analysis were in agreement with those of visual observation. Conclusion: Local infiltration anesthesia of the scalp point can significantly weaken or block EA stimulation induced changes of BFCR%.

Comparative pilot study on the effects of pulsating and static cupping on non-specific neck pain and local skin blood perfusion

Objective:To compare the effects of pulsating and static cupping on non-specific neck pain and local skin microcirculation blood perfusion,which is a pilot study.Methods:Seventy participants with non-specific neck pain were randomized to the following groups:low-frequency pulsating cupping(LF,n=20);high-frequency pulsating cupping(HF,n=20);static cupping(SC,n=20),or waiting list(WL,n=10).The LF,HF,and SC received a bilateral 10-minute cupping treatment at Jianzhongshu(SI 15).Outcomes were pain intensity(visual analog scale,VAS),functional status(Neck Disability Index,NDI),and skin blood perfusion at the SI 15,Dazhui(GV 14),and Shenzhu(GV 12)acupoint areas,measured using Laser Speckle Contrast Analysis technology.Results:Both LF and HF groups showed a significant reduction in VAS scores compared with the SC group(9.00,95%Cl 1.05-16.95,P=.027;8.75,95%CI 0.80-16.70,P=.031).There was no significant difference in VAS scores between the LF and HF groups(P>.05)and between NDI scores measured 3 days after intervention among the four groups(P>.05).In the SI 15 area,blood perfusion in the three treatment groups was higher than that in WL group(P<.01),and the perfusion unit(PU)of the HF pulsating group at 5 minutes after intervention was significantly higher compared with the SC group(P<.05).In the GV 14 area,blood perfusion in the two pulsating cupping groups was higher compared with the WL and SC groups after cupping(P<.05).In the GV 12 area,the PU of the LF group was higher compared with the other three groups only at the time of cup removal(P<.05).Conclusion:This study showed that pulsating cupping may have more favorable analgesic effects on non-specific neck pain compared with static cupping,which may be related to its better effect on improving the local skin blood perfusion.

Routine utilization of machine perfusion in liver transplantation:Ready for prime time?

The last decade has been notable for increasing high-quality research and dramatic improvement in outcomes with dynamic liver preservation.Robust evidence from numerous randomized controlled trials has been pooled by meta-analyses,providing the highest available evidence on the protective effect of machine perfusion(MP)over static cold storage in liver transplantation(LT).Based on a protective effect with less complications and improved graft survival,the field has seen a paradigm shift in organ preservation.This editorial focuses on the role of MP in LT and how it could become the new“gold standard”.Strong collaborative efforts are needed to explore its effects on long-term outcomes.

Effect of systemic intravenous chemotherapy combined with regional arterial perfusion chemoembolization on the malignancy of locally advanced gastric cancer

Objective:To study the effect of systemic intravenous chemotherapy combined with regional arterial perfusion chemoembolization on the malignancy of locally advanced gastric cancer. Methods:A total of 90 patients with primary gastric cancer who received treatment in Tianyou Hospital Affiliated to Wuhan University of Science & Technology between January 2014 and May 2016 were collected and divided into control group and observation group according to the random number table method, 45 cases in each group. The control group of patients received routine systemic intravenous chemotherapy + surgical treatment, and the observation group of patients received systemic intravenous chemotherapy combined with local arterial perfusion chemoembolization + surgical treatment. Levels of tumor markers and angiogenesis factors in serum as well as the expression of oncogenes and tumor suppressor genes in gastric cancer tissue were compared between the two groups of patients before and after chemotherapy.Results:Before chemotherapy, the levels of tumor markers and angiogenesis factors in serum as well as the expression of oncogenes and tumor suppressor genes in gastric cancer tissue were not significantly different between the two groups of patients;after chemotherapy, serum CEA, CA724, CA242, AFP, VEGF, Ang-2, COX2 and PD-ECGF levels of observation group were lower than those of control group, andiASPP, p130Cas, ERBB2 and C-myc mRNA expression in gastric cancer tissue were lower than those of control group while GKN1, p16, PTEN, TSPYL5 and merlin mRNA expression in gastric cancer tissue were higher than those of control group.Conclusions: Preoperative systemic intravenous chemotherapy combined with regional arterial perfusion chemoembolization can effectively reduce the malignancy of locally advanced gastric cancer and provide favorable conditions for the operation.

Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

Survey of Indoor Localization Based on Deep Learning

This study comprehensively examines the current state of deep learning (DL) usage in indoor positioning.It emphasizes the significance and efficiency of convolutional neural networks (CNNs) and recurrent neuralnetworks (RNNs). Unlike prior studies focused on single sensor modalities like Wi-Fi or Bluetooth, this researchexplores the integration of multiple sensor modalities (e.g.,Wi-Fi, Bluetooth, Ultra-Wideband, ZigBee) to expandindoor localization methods, particularly in obstructed environments. It addresses the challenge of precise objectlocalization, introducing a novel hybrid DL approach using received signal information (RSI), Received SignalStrength (RSS), and Channel State Information (CSI) data to enhance accuracy and stability. Moreover, thestudy introduces a device-free indoor localization algorithm, offering a significant advancement with potentialobject or individual tracking applications. It recognizes the increasing importance of indoor positioning forlocation-based services. It anticipates future developments while acknowledging challenges such as multipathinterference, noise, data standardization, and scarcity of labeled data. This research contributes significantly toindoor localization technology, offering adaptability, device independence, and multifaceted DL-based solutionsfor real-world challenges and future advancements. Thus, the proposed work addresses challenges in objectlocalization precision and introduces a novel hybrid deep learning approach, contributing to advancing locationcentricservices.While deep learning-based indoor localization techniques have improved accuracy, challenges likedata noise, standardization, and availability of training data persist. However, ongoing developments are expectedto enhance indoor positioning systems to meet real-world demands.

Improved PSO-Extreme Learning Machine Algorithm for Indoor Localization

Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related features.The basic assumption of fingerprinting localization is that the received signal strength indication(RSSI)distance is accord with the location distance.Therefore,how to efficiently match the current RSSI of the user with the RSSI in the fingerprint database is the key to achieve high-accuracy localization.In this paper,a particle swarm optimization-extreme learning machine(PSO-ELM)algorithm is proposed on the basis of the original fingerprinting localization.Firstly,we collect the RSSI of the experimental area to construct the fingerprint database,and the ELM algorithm is applied to the online stages to determine the corresponding relation between the location of the terminal and the RSSI it receives.Secondly,PSO algorithm is used to improve the bias and weight of ELM neural network,and the global optimal results are obtained.Finally,extensive simulation results are presented.It is shown that the proposed algorithm can effectively reduce mean error of localization and improve positioning accuracy when compared with K-Nearest Neighbor(KNN),Kmeans and Back-propagation(BP)algorithms.

Deep brain implantable microelectrode arrays for detection and functional localization of the subthalamic nucleus in rats with Parkinson’s disease

The subthalamic nucleus(STN)is considered the best target for deep brain stimulation treatments of Parkinson’s disease(PD).It is difficult to localize the STN due to its small size and deep location.Multichannel microelectrode arrays(MEAs)can rapidly and precisely locate the STN,which is important for precise stimulation.In this paper,16-channel MEAs modified with multiwalled carbon nanotube/poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate)(MWCNT/PEDOT:PSS)nanocomposites were designed and fabricated,and the accurate and rapid identification of the STN in PD rats was performed using detection sites distributed at different brain depths.These results showed that nuclei in 6-hydroxydopamine hydrobromide(6-OHDA)-lesioned brains discharged more intensely than those in unlesioned brains.In addition,the MEA simultaneously acquired neural signals from both the STN and the upper or lower boundary nuclei of the STN.Moreover,higher values of spike firing rate,spike amplitude,local field potential(LFP)power,and beta oscillations were detected in the STN of the 6-OHDA-lesioned brain,and may therefore be biomarkers of STN localization.Compared with the STNs of unlesioned brains,the power spectral density of spikes and LFPs synchronously decreased in the delta band and increased in the beta band of 6-OHDA-lesioned brains.This may be a cause of sleep and motor disorders associated with PD.Overall,this work describes a new cellular-level localization and detection method and provides a tool for future studies of deep brain nuclei.

Hemoperfusion and continuous veno-venous hemodiafiltration for eliminating chlorfenapyr in poisoning patients

Chlorfenapyr is a liposoluble insecticide belonging to the pyrrole family.Chlorfenapyr is activated when the N-ethoxymethyl side chain breaks,forming a toxic metabolite,which uncouples oxidative phosphorylation in the mitochondria,inhibits the production of adenosine triphosphate (ATP),and leads to the death of cells and targe organisms.[1] Symptoms of chlorfenapyr poisoning in patients are mild and atypical in the early stage,especially in patients receiving low dose exposure;however,such cases are rare and may be ignored by physicians,often leading to delayed treatment.[2,3].

Assessment of earthquake location uncertainties for the design of local seismic networks

The ability to estimate earthquake source locations,along with the appraisal of relevant uncertainties,is paramount in monitoring both natural and human-induced micro-seismicity.For this purpose,a monitoring network must be designed to minimize the location errors introduced by geometrically unbalanced networks.In this study,we first review different sources of errors relevant to the localization of seismic events,how they propagate through localization algorithms,and their impact on outcomes.We then propose a quantitative method,based on a Monte Carlo approach,to estimate the uncertainty in earthquake locations that is suited to the design,optimization,and assessment of the performance of a local seismic monitoring network.To illustrate the performance of the proposed approach,we analyzed the distribution of the localization uncertainties and their related dispersion for a highly dense grid of theoretical hypocenters in both the horizontal and vertical directions using an actual monitoring network layout.The results expand,quantitatively,the qualitative indications derived from purely geometrical parameters(azimuthal gap(AG))and classical detectability maps.The proposed method enables the systematic design,optimization,and evaluation of local seismic monitoring networks,enhancing monitoring accuracy in areas proximal to hydrocarbon production,geothermal fields,underground natural gas storage,and other subsurface activities.This approach aids in the accurate estimation of earthquake source locations and their associated uncertainties,which are crucial for assessing and mitigating seismic risks,thereby enabling the implementation of proactive measures to minimize potential hazards.From an operational perspective,reliably estimating location accuracy is crucial for evaluating the position of seismogenic sources and assessing possible links between well activities and the onset of seismicity.

New strategy for distal limb perfusion in veno-arterial extracorporeal membrane oxygenation treatment: application of continuous renal replacement therapy tubing

Extracorporeal membrane oxygenation(ECMO)is a technology that can temporarily take over the functions of the heart and lungs.Acute kidney injury is a common issue in patients receiving ECMO treatment,with reported incidence rates ranging from 70%to 85%.[1]To maintain the balance of fluids and electrolytes in patients,continuous renal replacement therapy(CRRT)is frequently employed.

SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach:Multistory Round Building Scenario over LoRa Network

In situations when the precise position of a machine is unknown,localization becomes crucial.This research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based technique.In order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based algorithm.Received signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout building.The noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI measurement.This study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference point.The findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.

Early peripheral perfusion index predicts 28-day outcome in patients with septic shock

BACKGROUND:To investigate the prognostic value of the peripheral perfusion index(PPI)in patients with septic shock.METHODS:This prospective cohort study,conducted at the emergency intensive care unit of Peking University People's Hospital,recruited 200 patients with septic shock between January 2023 and August 2023.These patients were divided into survival(n=84)and death(n=116)groups based on 28-day outcomes.Clinical evaluations included laboratory tests and clinical scores,with lactate and PPI values assessed upon admission to the emergency room and at 6 h and 12 h after admission.Risk factors associated with mortality were analyzed using univariate and multivariate Cox regression analyses.Receiver operator characteristic(ROC)curve was used to assess predictive performance.Mortality rates were compared,and Kaplan-Meier survival plots were created.RESULTS:Compared to the survival group,patients in the death group were older and had more severe liver damage and coagulation dysfunction,necessitating higher norepinephrine doses and increased fl uid replacement.Higher lactate levels and lower PPI levels at 0 h,6 h,and 12 h were observed in the death group.Multivariate Cox regression identifi ed prolonged prothrombin time(PT),decreased 6-h PPI and 12-h PPI as independent risk factors for death.The area under the curves for 6-h PPI and 12-h PPI were 0.802(95%CI 0.742-0.863,P<0.001)and 0.945(95%CI 0.915-0.974,P<0.001),respectively,which were superior to Glasgow Coma Scale(GCS),Sequential Organ Failure Assessment(SOFA)scores(0.864 and 0.928).Cumulative mortality in the low PPI groups at 6 h and 12 h was signifi cantly higher than in the high PPI groups(6-h PPI:77.52%vs.22.54%;12-h PPI:92.04%vs.13.79%,P<0.001).CONCLUSION:PPI may have value in predicting 28-day mortality in patients with septic shock.

Dynamical localization in a non-Hermitian Floquet synthetic system

We investigate the non-Hermitian effects on quantum diffusion in a kicked rotor model where the complex kicking potential is quasi-periodically modulated in the time domain.The synthetic space with arbitrary dimension can be created by incorporating incommensurate frequencies in the quasi-periodical modulation.In the Hermitian case,strong kicking induces the chaotic diffusion in the four-dimension momentum space characterized by linear growth of mean energy.We find that the quantum coherence in deep non-Hermitian regime can effectively suppress the chaotic diffusion and hence result in the emergence of dynamical localization.Moreover,the extent of dynamical localization is dramatically enhanced by increasing the non-Hermitian parameter.Interestingly,the quasi-energies become complex when the non-Hermitian parameter exceeds a certain threshold value.The quantum state will finally evolve to a quasi-eigenstate for which the imaginary part of its quasi-energy is large most.The exponential localization length decreases with the increase of the non-Hermitian parameter,unveiling the underlying mechanism of the enhancement of the dynamical localization by nonHermiticity.

Scenario Modeling-Aided AP Placement Optimization Method for Indoor Localization and Network Access

Owing to the ubiquity of wireless networks and the popularity of WiFi infrastructures,received signal strength(RSS)-based indoor localization systems have received much attention.The placement of access points(APs)significantly influences localization accuracy and network access.However,the indoor scenario and network access are not fully considered in previous AP placement optimization methods.This study proposes a practical scenario modelingaided AP placement optimization method for improving localization accuracy and network access.In order to reduce the gap between simulation-based and field measurement-based AP placement optimization methods,we introduce an indoor scenario modeling and Gaussian process-based RSS prediction method.After that,the localization and network access metrics are implemented in the multiple objective particle swarm optimization(MOPSO)solution,Pareto front criterion and virtual repulsion force are applied to determine the optimal AP placement.Finally,field experiments demonstrate the effectiveness of the proposed indoor scenario modeling method and RSS prediction model.A thorough comparison confirms the localization and network access improvement attributed to the proposed anchor placement method.

A UWB/IMU-Assisted Fingerprinting Localization Framework with Low Human Efforts

With the rapid development of smart phone,the location-based services(LBS)have received great attention in the past decades.Owing to the widespread use of WiFi and Bluetooth devices,Received Signal Strength Indication(RSSI)fingerprintbased localization method has obtained much development in both academia and industries.In this work,we introduce an efficient way to reduce the labor-intensive site survey process,which uses an UWB/IMU-assisted fingerprint construction(UAFC)and localization framework based on the principle of Automatic radio map generation scheme(ARMGS)is proposed to replace the traditional manual measurement.To be specific,UWB devices are employed to estimate the coordinates when the collector is moved in a reference point(RP).An anchor self-localization method is investigated to further reduce manual measurement work in a wide and complex environment,which is also a grueling,time-consuming process that is lead to artificial errors.Moreover,the measurements of IMU are incorporated into the UWB localization algorithm and improve the label accuracy in fingerprint.In addition,the weighted k-nearest neighbor(WKNN)algorithm is applied to online localization phase.Finally,filed experiments are carried out and the results confirm the effectiveness of the proposed approach.

Source localization in signed networks with effective distance

While progress has been made in information source localization,it has overlooked the prevalent friend and adversarial relationships in social networks.This paper addresses this gap by focusing on source localization in signed network models.Leveraging the topological characteristics of signed networks and transforming the propagation probability into effective distance,we propose an optimization method for observer selection.Additionally,by using the reverse propagation algorithm we present a method for information source localization in signed networks.Extensive experimental results demonstrate that a higher proportion of positive edges within signed networks contributes to more favorable source localization,and the higher the ratio of propagation rates between positive and negative edges,the more accurate the source localization becomes.Interestingly,this aligns with our observation that,in reality,the number of friends tends to be greater than the number of adversaries,and the likelihood of information propagation among friends is often higher than among adversaries.In addition,the source located at the periphery of the network is not easy to identify.Furthermore,our proposed observer selection method based on effective distance achieves higher operational efficiency and exhibits higher accuracy in information source localization,compared with three strategies for observer selection based on the classical full-order neighbor coverage.

Two-person device-free localization system based on ZigBee and transformer

Most studies on device-free localization currently focus on single-person scenarios.This paper proposes a novel method for device-free localization that utilizes ZigBee received signal strength indication(RSSI)and a Transformer network structure.The method aims to address the limited research and low accuracy of two-person device-free localization.This paper first describes the construction of the sensor network used for collecting ZigBee RSSI.It then examines the format and features of ZigBee data packages.The algorithm design of this paper is then introduced.The box plot method is used to identify abnormal data points,and a neural network is used to establish the mapping model between ZigBee RSSI matrix and localization coordinates.This neural network includes a Transformer encoder layer as the encoder and a fully connected network as the decoder.The proposed method's classification accuracy was experimentally tested in an online test stage,resulting in an accuracy rate of 98.79%.In conclusion,the proposed two-person localization system is novel and has demonstrated high accuracy.