Left bundle branch pacing set to outshine biventricular pacing for cardiac resynchronization therapy?

The deleterious effects of long-term right ventricular pacing necessitated the search for alternative pacing sites which could prevent or alleviate pacinginduced cardiomyopathy.Until recently,biventricular pacing(BiVP)was the only modality which could mitigate or prevent pacing induced dysfunction.Further,BiVP could resynchronize the baseline electromechanical dssynchrony in heart failure and improve outcomes.However,the high non-response rate of around 20%-30%remains a major limitation.This non-response has been largely attributable to the direct non-physiological stimulation of the left ventricular myocardium bypassing the conduction system.To overcome this limitation,the concept of conduction system pacing(CSP)came up.Despite initial success of the first CSP via His bundle pacing(HBP),certain drawbacks including lead instability and dislodgements,steep learning curve and rapid battery depletion on many occasions prevented its widespread use for cardiac resynchronization therapy(CRT).Subsequently,CSP via left bundle branch-area pacing(LBBP)was developed in 2018,which over the last few years has shown efficacy comparable to BiVP-CRT in small observational studies.Further,its safety has also been well established and is largely free of the pitfalls of the HBP-CRT.In the recent metanalysis by Yasmin et al,comprising of 6 studies with 389 participants,LBBPCRT was superior to BiVP-CRT in terms of QRS duration,left ventricular ejection fraction,cardiac chamber dimensions,lead thresholds,and functional status amongst heart failure patients with left bundle branch block.However,there are important limitations of the study including the small overall numbers,inclusion of only a single small randomized controlled trial(RCT)and a small follow-up duration.Further,the entire study population analyzed was from China which makes generalizability a concern.Despite the concerns,the meta-analysis adds to the growing body of evidence demonstrating the efficacy of LBBP-CRT.At this stage,one must acknowledge that the fact that still our opinions on this technique are largely based on observational data and there is a dire need for larger RCTs to ascertain the position of LBBPCRT in management of heart failure patients with left bundle branch block.

Left bundle branch pacing vs biventricular pacing in heart failure patients with left bundle branch block:A systematic review and meta-analysis

BACKGROUND Left bundle branch pacing(LBBP)is a novel pacing modality of cardiac resynchronization therapy(CRT)that achieves more physiologic native ventricular activation than biventricular pacing(BiVP).AIM To explore the validity of electromechanical resynchronization,clinical and echocardiographic response of LBBP-CRT.METHODS Systematic review and Meta-analysis were conducted in accordance with the standard guidelines as mentioned in detail in the methodology section.RESULTS In our analysis,the success rate of LBBP-CRT was determined to be 91.1%.LBBP CRT significantly shortened QRS duration,with significant improvement in echocardiographic parameters,including left ventricular ejection fraction,left ventricular end-diastolic diameter and left ventricular end-systolic diameter in comparison with BiVP-CRT.CONCLUSION A significant reduction in New York Heart Association class and B-type natriuretic peptide levels was also observed in the LBBP-CRT group vs BiVP-CRT group.Lastly,the LBBP-CRT cohort had a reduced pacing threshold at follow-up as compared to BiVP-CRT.

COMPLETE KAHLER METRICS WITH POSITIVE HOLOMORPHIC SECTIONAL CURVATURES ON CERTAIN LINE BUNDLES(RELATED TO A COHOMOGENEITY ONE POINT OF VIEW ON A YAU CONJECTURE)

In this article,we study Kahler metrics on a certain line bundle over some compact Kahler manifolds to find complete Kahler metrics with positive holomorphic sectional(or bisectional)curvatures.Thus,we apply a strategy to a famous Yau conjecture with a co-homogeneity one geometry.

Optimized CUDA Implementation to Improve the Performance of Bundle Adjustment Algorithm on GPUs

The 3D reconstruction pipeline uses the Bundle Adjustment algorithm to refine the camera and point parameters. The Bundle Adjustment algorithm is a compute-intensive algorithm, and many researchers have improved its performance by implementing the algorithm on GPUs. In the previous research work, “Improving Accuracy and Computational Burden of Bundle Adjustment Algorithm using GPUs,” the authors demonstrated first the Bundle Adjustment algorithmic performance improvement by reducing the mean square error using an additional radial distorting parameter and explicitly computed analytical derivatives and reducing the computational burden of the Bundle Adjustment algorithm using GPUs. The naïve implementation of the CUDA code, a speedup of 10× for the largest dataset of 13,678 cameras, 4,455,747 points, and 28,975,571 projections was achieved. In this paper, we present the optimization of the Bundle Adjustment algorithm CUDA code on GPUs to achieve higher speedup. We propose a new data memory layout for the parameters in the Bundle Adjustment algorithm, resulting in contiguous memory access. We demonstrate that it improves the memory throughput on the GPUs, thereby improving the overall performance. We also demonstrate an increase in the computational throughput of the algorithm by optimizing the CUDA kernels to utilize the GPU resources effectively. A comparative performance study of explicitly computing an algorithm parameter versus using the Jacobians instead is presented. In the previous work, the Bundle Adjustment algorithm failed to converge for certain datasets due to several block matrices of the cameras in the augmented normal equation, resulting in rank-deficient matrices. In this work, we identify the cameras that cause rank-deficient matrices and preprocess the datasets to ensure the convergence of the BA algorithm. Our optimized CUDA implementation achieves convergence of the Bundle Adjustment algorithm in around 22 seconds for the largest dataset compared to 654 seconds for the sequential implementation, resulting in a speedup of 30×. Our optimized CUDA implementation presented in this paper has achieved a 3× speedup for the largest dataset compared to the previous naïve CUDA implementation.

Preventing surgical site infection using operating room bundle of care in patients undergoing elective exploratory laparotomy cholecystectomy surgery

Objective:Surgical procedures manifest immense risks to patients.One of the adverse events that healthcare professionals see as a threat to the patient’s health is the development of complications known as surgical site infection(SSI).Although several effor ts are being under taken to determine the proper means to reduce such complications,there is still a high incidence of SSI worldwide.Surgery requires knowledge in infection control and high precision in maintaining a clean surgical site.This study tested the effectiveness of an operating room(OR)bundle of care in preventing SSI in patients undergoing elective exploratory laparotomy cholecystectomy surgery.Methods:A quasi-experimental pretest and posttest design was utilized to determine its effectiveness.The study was composed of 60 par ticipants divided into two groups:30 subjects were selected to receive the OR bundle of care,while the other 30 subjects received the usual care.The groupings were determined through a systematic random sampling technique.The OR bundle of care had three interventions,namely:(1)maintaining perioperative normothermia,(2)no pre-operative surgical site hair removal,and(3)changing gloves before abdominal wall closure.These patients were evaluated using the standard instrument,Bates–Jensen Wound Assessment Tool(BWAT)in the post-intervention phases of the wound healing process,which are as follows:hemostasis,inflammatory,and proliferative phases.To describe the difference in the patients’wound status after implementation of the OR bundle of care in each post-intervention phase,Friedman’s test was used.To describe the difference in the patients’wound status in both groups after implementation of the OR bundle of care,the Mann–Whitney U test was used.Results:The patient’s wound status was lower,indicating a more healing process.Differences between the wound status of the control and the experimental group were observed on the third postoperative day.This indicates that the experimental group’s wound status healed much faster and more effectively than the control group based on the BWAT scoring severity scoring.A significant difference in the patient’s wound status from the hemostasis phase compared with the proliferative phase was observed.Conclusions:The OR bundle of care has been shown to be effective in preventing SSI in patients who had undergone exploratory laparotomy cholecystectomy surgery in the selected hospital,if there is uniform and consistent implementation of the said intervention.

MAXWELL-EINSTEIN METRICS ON COMPLETIONS OF CERTAIN C* BUNDLES

In this paper,we prove that for some completions of certain fiber bundles there is a Maxwell-Einstein metric conformally related to any given Kahler class.

His bundle pacing versus left bundle branch pacing on ventricular function in atrial fibrillation patients referred for pacing:a prospective crossover comparison

BACKGROUND His bundle pacing(HBP)and left bundle branch pacing(LBBP)both provide physiologic pacing which maintain left ventricular synchrony.They both improve heart failure(HF)symptoms in atrial fibrillation(AF)patients.We aimed to assess the intra-patient comparison of ventricular function and remodeling as well as leads parameters corresponding to two pacing modalities in AF patients referred for pacing in intermediate term.METHODS Uncontrolled tachycardia AF patients with both leads implantation successfully were randomized to either modality.Echocardiographic measurements,New York Heart Association(NYHA)classification,quality-of-life assessments and leads parameters were obtained at baseline and at each 6-month follow up.Left ventricular function including the left ventricular endosystolic volume(LVESV),left ventricular ejection fraction(LVEF)and right ventricular(RV)function quantified by tricuspid annular plane systolic excursion(TAPSE)were all assessed.RESULTS Consecutively twenty-eight patients implanted with both HBP and LBBP leads successfully were enrolled(69.1±8.1 years,53.6% male,LVEF 59.2%±13.7%).The LVESV was improved by both pacing modalities in all patients(n=23)and the LVEF was improved in patients with baseline LVEF at less than 50%(n=6).The TAPSE was improved by HBP but not LBBP(n=23).CONCLUSION In this crossover comparison between HBP and LBBP,LBBP was found to have an equivalent effect on LV function and remodeling but better and more stable parameters in AF patients with uncontrolled ventricular rates referred for atrioventricular node(AVN)ablation.HBP could be preferred in patients with reduced TAPSE at baseline rather than LBBP.

Bundles策略在预防全麻患者术中低体温中的应用

目的:探讨Bundles策略在预防全麻患者术中低体温中的应用效果。方法:将2021年7月1日~10月31日拟行全麻腹部手术的300例成年患者随机分为G1常规保温组和G2 Bundles保温组各150例,G1常规保温组术中实施常规保温方式,G2 Bundles保温组实施基于循证干预措施和可能引发热量损失途径制订预防术中低温的Bundles干预策略。比较两组不同时间的体温、血氧饱和度(SpO_(2))、心率、血压、呼吸,统计两组寒战、低体温、躁动、术中出血、异常心律事件发生率,记录气管导管拔管时间、PACU停留时间。结果:G2 Bundles保温组术中及术后体温、SpO_(2)、心率、呼吸等均高于G1常规保温组,而同期血压低于G1常规保温组(P<0.05);G2 Bundles保温组寒战、低体温、躁动、术中出血事件、异常心律事件发生率均低于G1常规保温组(P<0.05);G2 Bundles保温组气管导管拔管时间、PACU停留时间均短于G1常规保温组(P<0.01)。结论:将Bundles策略应用于全麻患者术中,有助于预防低体温、调节患者心电指标,减少不良事件。

A Self-calibration Bundle Adjustment Algorithm Based on Block Matrix Cholesky Decomposition Technology

In this study,the problem of bundle adjustment was revisited,and a novel algorithm based on block matrix Cholesky decomposition was proposed to solve the thorny problem of self-calibration bundle adjustment.The innovation points are reflected in the following aspects:①The proposed algorithm is not dependent on the Schur complement,and the calculation process is simple and clear;②The complexities of time and space tend to O(n)in the context of world point number is far greater than that of images and cameras,so the calculation magnitude and memory consumption can be reduced significantly;③The proposed algorithm can carry out self-calibration bundle adjustment in single-camera,multi-camera,and variable-camera modes;④Some measures are employed to improve the optimization effects.Experimental tests showed that the proposed algorithm has the ability to achieve state-of-the-art performance in accuracy and robustness,and it has a strong adaptability as well,because the optimized results are accurate and robust even if the initial values have large deviations from the truth.This study could provide theoretical guidance and technical support for the image-based positioning and 3D reconstruction in the fields of photogrammetry,computer vision and robotics.

Enhancing the awakening to family engagement bundle with music therapy

Survivors of prolonged intensive care unit(ICU)admissions may present undesirable long-term outcomes.In particular,physical impairment and cognitive dysfunction have both been described in patients surviving episodes requiring mechanical ventilation and sedation.One of the strategies to prevent the aforementioned outcomes involves the implementation of a bundle composed by:(1)Spontaneous awakening trial;(2)Spontaneous breathing trial;(3)Choosing proper sedation strategies;(4)Delirium detection and management;(5)Early ICU mobility;and(6)Family engagement(ABCDEF bundle).The components of this bundle contribute in shortening length of stay on mechanical ventilation and reducing incidence of delirium.Since the first description of the ABCDEF bundle,other relevant therapeutic factors have been proposed,such as introducing music therapy.This mini-review describes the current evidence supporting the use of the ABCDEF bundle,as well as current knowledge on the implementation of music therapy.

Model-based deep learning for fiber bundle infrared image restoration

As the representative of flexibility in optical imaging media,in recent years,fiber bundles have emerged as a promising architecture in the development of compact visual systems.Dedicated to tackling the problems of universal honeycomb artifacts and low signal-to-noise ratio(SNR)imaging in fiber bundles,the iterative super-resolution reconstruction network based on a physical model is proposed.Under the constraint of solving the two subproblems of data fidelity and prior regularization term alternately,the network can efficiently“regenerate”the lost spatial resolution with deep learning.By building and calibrating a dual-path imaging system,the real-world dataset where paired low-resolution(LR)-high-resolution(HR)images on the same scene can be generated simultaneously.Numerical results on both the United States Air Force(USAF)resolution target and complex target objects demonstrate that the algorithm can restore high-contrast images without pixilated noise.On the basis of super-resolution reconstruction,compound eye image composition based on fiber bundle is also embedded in this paper for the actual imaging requirements.The proposed work is the first to apply a physical model-based deep learning network to fiber bundle imaging in the infrared band,effectively promoting the engineering application of thermal radiation detection.

Deep learning framework for multi‐round service bundle recommendation in iterative mashup development

Recent years have witnessed the rapid development of service‐oriented computing technologies.The boom of Web services increases software developers'selection burden in developing new service‐based systems such as mashups.Timely recommending appropriate component services for developers to build new mashups has become a fundamental problem in service‐oriented software engineering.Existing service recom-mendation approaches are mainly designed for mashup development in the single‐round scenario.It is hard for them to effectively update recommendation results according to developers'requirements and behaviours(e.g.instant service selection).To address this issue,the authors propose a service bundle recommendation framework based on deep learning,DLISR,which aims to capture the interactions among the target mashup to build,selected(component)services,and the following service to recommend.Moreover,an attention mechanism is employed in DLISR to weigh selected services when rec-ommending a candidate service.The authors also design two separate models for learning interactions from the perspectives of content and invocation history,respectively,and a hybrid model called HISR.Experiments on a real‐world dataset indicate that HISR can outperform several state‐of‐the‐art service recommendation methods to develop new mashups iteratively.

Characterization of retinal ganglion cell damage at single axon bundle level in mice by visible-light optical coherence tomography fibergraphy

Retinal ganglion cells(RGCs)receive synaptic inputs through their dendritic trees in the inner plexiform layer(IPL)and convey the visual information via their axons which form the optic nerve to the brain(Sanes and Masland,2015).In glaucoma,RGCs and their axons degenerate and die,leading to irreversible vision loss and eventually blindness if left untreated(Quigley,2016).

Effects of Hydrothermal Environment on the Deformation of the Thin Bamboo Bundle Veneer Laminated Composites

To overcome warping in thin bamboo bundle veneer laminated composites(TBLC),their hydrothermal deformation characteristics were systematically investigated in this study.It was found that TBLCs accelerated the release of internal stress in the thickness direction in a hydrothermal environment,which increased their warpage.TBLCs showed increased warpage in the width and diagonal directions upon increasing the temperature.The warpage of Type E increased by 155.88%and 66.67%in the width and diagonal directions,respectively,when the temperature increased from 25
C to 100
C.The symmetrical TBLC with cross-lay-up and odd layers displayed better hydrothermal stability.We revealed that the deformation of the TBLCs could be regulated under the synergistic effect of water and temperature.These results provide a scientific basis for improving the uniformity of bamboo bundle composite materials and for developing thin bamboo bundle fiber composite materials with designable structures and controllable performance.

Acute hospital-community hospital care bundle for elderly orthopedic surgery patients:A propensity score-matched economic analysis

BACKGROUND While Singapore attains good health outcomes,Singapore’s healthcare system is confronted with bed shortages and prolonged stays for elderly people recovering from surgery in acute hospitals.An Acute Hospital-Community Hospital(AHCH)care bundle has been developed to assist patients in postoperative rehabilitation.The core concept is to transfer patients out of AHs when clinically recommended and into CHs,where they can receive more beneficial dedicated care to aid in their recovery,while freeing up bed capacities in AHs.AIM To analyze the AH length of stay(LOS),costs,and savings associated with the AH-CH care bundle intervention initiated and implemented in elderly patients aged 75 years and above undergoing elective orthopedic surgery.METHODS A total of 8621:1 propensity score-matched patients aged 75 years and above who underwent elective orthopedic surgery in Singapore General Hospital(SGH)before(2017-2018)and after(2019-2021)the care bundle intervention period was analyzed.Outcome measures were AH LOS,CH LOS,hospitalization metrics,postoperative 30-d mortality,and modified Barthel Index(MBI)scores.The costs of AH inpatient hospital stay in the matched cohorts were compared using cost data in Singapore dollars.RESULTS Of the 862 matched elderly patients undergoing elective orthopedic surgery before and after the care bundle intervention,the age distribution,sex,American Society of Anesthesiologists classification,Charlson Comorbidity Index,and surgical approach were comparable between both groups.Patients transferred to CHs after the surgery had a shorter median AH LOS(7 d vs 9 d,P<0.001).The mean total AH inpatient cost per patient was 14.9%less for the elderly group transferred to CHs(S$24497.3 vs S$28772.8,P<0.001).The overall AH U-turn rates for elderly patients within the care bundle were low,with a 0%mortality rate following orthopedic surgery.When elderly patients were discharged from CHs,their MBI scores increased significantly(50.9 vs 71.9,P<0.001).CONCLUSION The AH-CH care bundle initiated and implemented in the Department of Orthopedic Surgery appears to be effective and cost-saving for SGH.Our results indicate that transitioning care between acute and community hospitals using this care bundle effectively reduces AH LOS in elderly patients receiving orthopedic surgery.Collaboration between acute and community care providers can assist in closing the care delivery gap and enhancing service quality.

Masquerading bundle branch block with intermittent right ventricular pacing in a patient with acute coronary syndrome

The relatively uncommon masquerading bundle branch block(MBBB),a concept initially introduced in 1958 by UNGER,et al.[1]and colleagues,refers to a conduction abnormality that consists of the right bundle branch block(RBBB)pattern in the precordial leads,whereas in the limb leads it resembles a left bundle branch block(LBBB).

Improving Accuracy and Computational Burden of Bundle Adjustment Algorithm Using GPUs

Bundle adjustment is a camera and point refinement technique in a 3D scene reconstruction pipeline. The camera parameters and the 3D points are refined by minimizing the difference between computed projection and observed projection of the image points formulated as a non-linear least-square problem. Levenberg-Marquardt method is used to solve the non-linear least-square problem. Solving the non-linear least-square problem is computationally expensive, proportional to the number of cameras, points, and projections. In this paper, we implement the Bundle Adjustment (BA) algorithm and analyze techniques to improve algorithmic performance by reducing the mean square error. We investigate using an additional radial distortion camera parameter in the BA algorithm and demonstrate better convergence of the mean square error. We also demonstrate the use of explicitly computed analytical derivatives. In addition, we implement the BA algorithm on GPUs using the CUDA parallel programming model to reduce the computational time burden of the BA algorithm. CUDA Streams, atomic operations, and cuBLAS library in the CUDA programming model are proposed, implemented, and demonstrated to improve the performance of the BA algorithm. Our implementation has demonstrated better convergence of the BA algorithm and achieved a speedup of up to 16× on the use of the BA algorithm on various datasets.

Multi-View Hybrid Contrastive Learning for Bundle Recommendation

Bundle recommendation aims to provide users with convenient one-stop solutions by recommending bundles of related items that cater to their diverse needs. However, previous research has neglected the interaction between bundle and item views and relied on simplistic methods for predicting user-bundle relationships. To address this limitation, we propose Hybrid Contrastive Learning for Bundle Recommendation (HCLBR). Our approach integrates unsupervised and supervised contrastive learning to enrich user and bundle representations, promoting diversity. By leveraging interconnected views of user-item and user-bundle nodes, HCLBR enhances representation learning for robust recommendations. Evaluation on four public datasets demonstrates the superior performance of HCLBR over state-of-the-art baselines. Our findings highlight the significance of leveraging contrastive learning and interconnected views in bundle recommendation, providing valuable insights for marketing strategies and recommendation system design.

DTN网络中的基于Bundle协议的数据包发送算法

DTN传输协议引入束和分片的概念,将数据分块传输以适应经常发生链接中断的链路。研究了DTN传输层的Bundle协议和LTP(Licklider transmission protocol)协议,针对网络环境可能在短时间内剧烈变化的问题,研究了数据块的大小对传输效率的影响,提出了DTN网络中基于Bundle协议的数据包发送算法,动态选择最适合当前网络状况的发送策略以获得最佳的系统性能和传输效率。仿真结果表明:适当选取发送数据块的大小可以大幅度提高传输效率。

基于Douglas-Peucker和Quick Bundles算法的水上交通模式识别

针对船舶航迹数据量大、数据冗余、航迹特征不明显等问题,提出一种融合道格拉斯-普克(Douglas-Peucker,DP)压缩算法和基于距离的快速捆绑包(Quick Bundles,QB)聚类算法的水上交通模式识别方法。该方法根据航迹数据特征、压缩率和压缩误差等指标选择合理的压缩阈值实现大规模船舶自动识别系统(automatic identification system,AIS)数据的压缩。在此基础上,提出一种基于最小直接翻转距离的聚类指标,利用QB算法实现船舶航迹的有效聚类。实验结果表明,提出的方法既可以简化航迹聚类过程,也可准确高效地实现航迹聚类,为水上交通精细化管理与决策提供数据支撑。