Success Rate Research Articles

Investigation of Nosocomial Urinary Tract Infections Post Transplantation, Main Pathogens, and Sensitivity Tests

Background: Regarding end-stage organ disease, transplantation is recommended as the best therapeutic management. After organ transplantation, the incidence of nosocomial urinary tract infections (NUTIs) due to multidrug-resistant Gram-negative bacilli increases. Aim: The study aimed to investigate NUTIs post-transplantation, the main pathogens involved, and sensitivity tests conducted in a tertiary hospital in Isfahan, Iran. Methods: A retrospective survey on patients admitted to a tertiary hospital in Isfahan (Alzahra), Iran, was performed between 27 March, 2017, and 9 February, 2022. The information recorded included the date of infection, date of hospitalization, gender, age, type of pathogens, and resistance or sensitivity to antibiotics. Results: 73 kidney transplant recipients (61% females) with a mean age of 43. 2 ± 15.1 years were included. Within this population involving both genders, the main pathogens involved in NUTIs were as follows: Escherichia coli (30%), Klebsiella pneumonia (19%), Candida albicans and non-albicans (14%), Enterococcus faecalis (12%), Enterobacteriaceae (8%), Pseudomonas aeruginosa (6%), Staphylococcus spp. (6%), Acinetobacter baumannii (4%), and Streptococcus spp. (4%). Antibiotic susceptibility testing showed the most sensitivity of isolates against amikacin (n=29; 66%), meropenem (n= 28; 64%), piperacillin/tazobactam (n=26; 54%), cefepime (n= 25; 40%), ceftazidime (n= 27; 30%), ciprofloxacin (n= 40; 18%), and co-trimoxazole (n= 29; 10%). Conclusion: Escherichia coli, Klebsiella pneumonia, and Candida spp. were the major causes of NUTIs within the studied organ-transplanted recipients. Amikacin, meropenem, and piperacillin/ tazobactam have shown more than 50% sensitivity against isolates. Further evidence-based pharmacotherapy investigations associated with the different spectrum antibiotics and overall antimicrobial success rate is recommended to be advantageous.

Design of A Self-Correctable Docking Mechanism in Disturbed Water Surface Environment

The docking mechanism is critical for unmanned surface vehicle (USV) applications, such as traction, dragging, charging, data transmission, building pontoons, etc. These applications are significant for observation, exploration, data acquisition, patrol, rescuing, etc., in aquatic environments. In this paper, a novel autonomous docking mechanism for USVs is proposed. It is capable of (1) docking the USV subject to disturbance efficiently, (2) latching without power consumption after the docking is completed, and (3) guiding the docking head to three specific angles at the end of the docking process. This paper proved the feasibility and rationality of the docking mechanism through theoretical force analysis. Experiments with three different scenarios are implemented to test the mechanism's performance. The experimental results show that the docking success rate is greater than or equal to 90 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> on the disturbed water surface.

Discourse-Level Communication Success in Aphasia: Unveiling Its Significance through Observer's Ratings.

Audrey Holland (1982) compared test scores to observers' ratings of conversational communicative success in people with aphasia (PWA). This springboarded a body of evidence employing observers to rate discourse. We review the utility of those ratings for assessing PWA's communication success. A traditional literature review identified 16 articles involving naive or trained raters assessing PWAs' communicative success across discourse genres. Another 10 articles reported ratings over time. Collectively, these studies evaluated 349 PWAs. Four studies utilized observers to rate the success of PWA's conversations. Eight studies that reported observers' ratings on other discourse genres found that multimodal communication and facilitative contexts improved success, and ratings of informativeness and comfort related to objective discourse analysis measures. Nine of 10 studies examining treatment effects found that communicative success ratings captured improvements. Observers' ratings provide social validity by reliably assessing the discourse-level communicative success of PWA. Ratings correlated with standardized diagnostic and objective discourse metrics but provided a window into factors that affect communicative success, including the degree to which communication is interactive, multimodal, and contextual. Integrating observers' ratings of discourse success at pretreatment may help identify supports or barriers to successful communication, facilitate individualization of treatments, and offer social validity of change.

Our Single Center Experience in Osteoid Osteoma Patients Treated with CT-Guided Percutaneous Radiofrequency Ablation Treatment and Follow-up.

Introduction Osteoid osteoma (OO) is a painful benign bone tumor. Typically, it causes pain that is most noticeable during the night, which is improved by nonsteroidal anti-inflammatory drugs. In the treatment of symptomatic lesions, open surgery for nidus removal is the gold standard. However, surgical technical difficulties and morbidities vary by location. Percutaneous radiofrequency ablation (RFA) therapy guided by computed tomography (CT) is now a popular treatment option for OO. This study aims to assess our single-center experience with the technique, complications, and procedure effectiveness. Materials and Methods The study included fifteen patients who were treated between 2017 and 2021. A retrospective analysis was carried out on archive images and file records. The lesions' location, nidus width, and affected area (cortical, medullary) were all recorded. The procedure and technical success, as well as postoperative complications and the need for repeat ablation, were all documented. Results A total of 20 patients, 18 men, and 2 women, were included in the study, and 12 of them were pediatric patients. The patients' mean age was 16.9±7.3 years old, and the mean nidus diameter was 7.1±8.7 mm. There were 13 cortical niduses, 2 intramedullary niduses, and 5 corticomedullary niduses. The lesions were in the femur (n=12), tibia (n=6), scapula (n=1), and vertebrae (n=1). Two recurrences (10%) were observed in our patients during the follow-up. Patient with a femoral OO, the pain started again 12 weeks after the procedure and we performed additional RFA. The patient with vertebral OO had fewer symptoms and full recovery was not achieved. Therefore, the vertebral OO was ablated again 4 months later, and clinical success was achieved. One patient had a minor burn at the entry site that went away on its own after a short period of time. Except for the patient who was scheduled for a repeat RFA, no recurrence has been observed so far. The primary and secondary success rates are, respectively, 90% (18/20) and 100% (20/20). Conclusion RFA has a high success rate in treating OO. The procedure failure and recurrence rates are low. There are possibilities for posttreatment pain relief, early discharge, and a quick return to daily life. For inappropriate lesion localization, the RFA process replaces surgical treatment. The procedure-related complication rate is low. On the other hand, the burn during the procedure can be a serious problem.

Electronic Skin as Human–Robot Interface for Human Grasp Recognition in Home-Care Robots: Introducing a Complete Set of Resources

Population aging has highlighted the significance of developing home-care robots to assist human life. However, traditional home-care robots work according to fixed procedures, which lack human–robot interaction to mimic human hand action. Human–robot interfaces provide an opportunity for interactions between robots and users, which play an important role in home-care robots. Traditional human–robot interfaces are based on rigid, cumbersome, and high-priced machines, which prevent their wide application in performing home-care tasks. An e-skin sensor with 11 stretchable resistors evenly distributed on the back of the human hand, which could be applied to monitor human finger motion and control robotic fingers to dexterously grasp diverse objects, is presented in this article. A 3D printer is employed to pattern high-performance wires into structurally soft and stretchable filamentary serpentines. The stretchable resistors are fabricated by a thin sensing film of mixed multiwalled carbon nanotubes (MWCNTs) and silicone elastomer. The e-skin sensor is applied in a hand-in-the-loop system for controlling a robotic hand to help grasp an object synchronously. A robot system is developed to connect the e-skin with the robotic hand to perform grasp actions with a success rate of more than 80%. By analyzing the experimental results of the e-skin sensor and robotic hand grasp, this work obtains insights showing that the skin sensor can help engineers design a home-care assistant plan for patients according to monitoring value or a human–robot interaction mode by robotic fingers mimicking human finger actions.

Fecal Microbiota, Live-jslm for the Prevention of Recurrent Clostridioides difficile Infection : Subgroup Analysis of PUNCH CD2 and PUNCH CD3.

To assess fecal microbiota, live-jslm (REBYOTA, abbreviated as RBL, formerly RBX2660) efficacy and safety in participants grouped by recurrent Clostridioides difficile infection (rCDI) risk factors and treatment-related variables. RBL is the first microbiota-based live biotherapeutic approved by the US Food and Drug Administration for the prevention of rCDI in adults after antibiotic treatment for rCDI. Treatment success rates across subgroups for PUNCH CD3 (NCT03244644) were estimated using a Bayesian hierarchical model, borrowing data from PUNCH CD2 (NCT02299570). Treatment-emergent adverse events were summarized for the double-blind treatment period within 8 weeks. Treatment differences between RBL and placebo at 8 weeks were similar to the total population for most subgroups. Treatment effect sizes were similar between CDI tests, higher for oral vancomycin courses >14 days versus ≤14 days and higher for antibiotic washout periods of 3 days versus ≤2 days. The largest reductions in the rate of rCDI with RBL versus placebo were observed for participants with a 3-day CDI antibiotic washout period and participants with ≥4 previous CDI episodes. Most RBL-treated participants experienced TEAEs that were mild or moderate in severity and related to preexisting conditions. This analysis provides further evidence of RBL efficacy and safety across subgroups, including those at high risk for rCDI.

Overcoming local anesthesia failure during routine dental treatments in children.

Local anesthesia (LA) during routine dental treatment in children fails in 5%-35% of first attempts. No data, however, are available on the success rates of subsequent attempts. To evaluate the effectiveness of primary, secondary, and tertiary LA attempts (P-LA, S-LA, and T-LA, respectively) for anesthetizing molars during routine dental treatments in children. We retrospectively analyzed dental records of all children (2-18 years) who had been administered LA for the treatment of primary or permanent molars by a single paediatric dentist, between 2011 and 2022. All LAs were delivered using a computer-controlled local anesthetic delivery (CCLAD) system. The failure rate of P-LA in 1312 molars was 13% and correlated with age (p < .001), type of tooth (p < .001), type of treatment (p < .001), and treated arch (p < .001). The effectiveness of S-LA for buccal infiltration, intrasulcular, inferior alveolar nerve block, greater palatine nerve block (GPNB), posterior superior alveolar nerve block (PSANB), or a combination of the last two was 50%, 87.2%, 66.7%, 63.6%, 33.3%, and 100%, respectively, and was not significantly associated with age or the type of tooth, treatment, or P-LA. The optimal choice of S-LA for anesthetizing maxillary molars was a combination of PSANB and GPNB, whereas for mandibular molars, it was IS-CCLAD system.

Endovascular therapy in acute ischemic stroke with poor reperfusion is associated with worse outcomes compared with best medical management: a HERMES substudy

BackgroundFunctional outcomes in patients with acute ischemic stroke (AIS) with large vessel occlusion (LVO) undergoing endovascular treatment (EVT) with poor reperfusion were compared with patients with AIS-LVO treated with best...

Subjective Long-Term Outcomes After Vaginal Native Tissue Hysteropexy: Cohort Study.

There are still doubts about long-term satisfaction rates of native tissue uterine preserving surgical techniques for pelvic organ prolapse. The objective of this study was to compare long-term subjective success rates and satisfaction rates between vaginal sacrospinous hysteropexy (SSHP) and vaginal hysterectomy with uterosacral ligament suspension (VH-USLS). This was a retrospective single-center, observational matched cohort study in women receiving either SSHP or VH-USLS between 2004 and 2021. Primary outcome was overall subjective success (combined outcome of absence of bulge nor retreatment, and satisfaction with operation) at least 12 months after surgery. Satisfaction with the operation was defined as a combined Patient Global Impression of Improvement rating ≤ 2 and a patient satisfaction score ≥7. Of 583 patients, 192 patients could be matched (SSHP, 96; VH-USLS, 96), with 55% (SSHP, 60; VH-USLS, 45) participating at the telephone interview. Mean follow-up time was 77 months for VH-USLS, and 36 months for SSHP, respectively. No difference in overall subjective success rates was found between the groups (45% VH-USLS and 51% SSHP; P = 0.54). Overall satisfaction was similar between both groups (70% vs 71%, P = 0.90). Logistic regression found no influence of duration of follow-up and the overall subjective success rate. Both procedures would be recommended to a relative or friend by a large majority of patients (88% vs 85%, P = 0.761). Operative time and hospitalization time were significantly shorter in the SSHP group. No serious complications were reported. Overall success rates did not differ between both procedures after at least 1 year of follow-up with relatively high satisfaction rates. Sacrospinous hysteropexy had shorter operative time and shorter duration of hospitalization.

Value-Based Subgoal Discovery and Path Planning for Reaching Long-Horizon Goals.

Learning to reach long-horizon goals in spatial traversal tasks is a significant challenge for autonomous agents. Recent subgoal graph-based planning methods address this challenge by decomposing a goal into a sequence of shorter-horizon subgoals. These methods, however, use arbitrary heuristics for sampling or discovering subgoals, which may not conform to the cumulative reward distribution. Moreover, they are prone to learning erroneous connections (edges) between subgoals, especially those lying across obstacles. To address these issues, this article proposes a novel subgoal graph-based planning method called learning subgoal graph using value-based subgoal discovery and automatic pruning (LSGVP). The proposed method uses a subgoal discovery heuristic that is based on a cumulative reward (value) measure and yields sparse subgoals, including those lying on the higher cumulative reward paths. Moreover, LSGVP guides the agent to automatically prune the learned subgoal graph to remove the erroneous edges. The combination of these novel features helps the LSGVP agent to achieve higher cumulative positive rewards than other subgoal sampling or discovery heuristics, as well as higher goal-reaching success rates than other state-of-the-art subgoal graph-based planning methods.

Adaptive Constraint Penalty-Based Multiobjective Operation Optimization of an Industrial Dynamic System With Complex Multiconstraint.

Aiming at the operation optimization of the wastewater treatment process (WWTP) with nonstationary time-varying dynamics and complex multiconstraint, this article proposes a novel adaptive constraint penalty decomposed multiobjective evolutionary algorithm with synthetical distance (SD)-based cross-generation crossover. First, the concept of spatial SD is presented to comprehensively evaluate the similarity of individual solutions from two aspects of distance and angle, and the individual information between two adjacent generations is used to enhance the diversity of individuals and accelerate the convergence of the algorithm. Second, aiming at the complex multiconstraint during the operation optimization of WWTP, an adaptive penalty algorithm is further adopted to punish the individual solutions that violate the constraints, so as to improve the handling efficiency and success rate of constraints. Furthermore, in view of the time-varying dynamics of actual WWTP, a recursive bilinear subspace identification method based on sliding window is adopted to establish the optimization models as well as the constraint models with self-learning parameter, which provides accurate model guarantee for high-performance multiobjective operation optimization. Finally, the effectiveness, superiority, and practicability of the proposed method are verified through test function experiments as well as operation optimization control experiments of WWTP.

Adaptive Perturbation for Adversarial Attack.

In recent years, the security of deep learning models achieves more and more attentions with the rapid development of neural networks, which are vulnerable to adversarial examples. Almost all existing gradient-based attack methods use the sign function in the generation to meet the requirement of perturbation budget on L∞ norm. However, we find that the sign function may be improper for generating adversarial examples since it modifies the exact gradient direction. Instead of using the sign function, we propose to directly utilize the exact gradient direction with a scaling factor for generating adversarial perturbations, which improves the attack success rates of adversarial examples even with fewer perturbations. At the same time, we also theoretically prove that this method can achieve better black-box transferability. Moreover, considering that the best scaling factor varies across different images, we propose an adaptive scaling factor generator to seek an appropriate scaling factor for each image, which avoids the computational cost for manually searching the scaling factor. Our method can be integrated with almost all existing gradient-based attack methods to further improve their attack success rates. Extensive experiments on the CIFAR10 and ImageNet datasets show that our method exhibits higher transferability and outperforms the state-of-the-art methods.

Experience of percutaneous closure of small to large patent ductus arteriosus with Nit-Occlud® device in a tertiary referral hospital in Colombia.

The decision to close patent ductus arteriosus should always be individualized and taken together with the child's family once the risks and benefits of both choices have been exposed. This study aims to report the experience and outcomes in patients undergoing endovascular closure of small to medium-size PDA with a Nit-Occlud® device in a tertiary referral hospital in Colombia. Longitudinal descriptive study, which included all patients under 18 years of age who underwent percutaneous ductal closure with Nit-Occlud® device between January 1, 2011, and February 1, 2023. Patients with associated complex congenital heart disease requiring surgical management, pregnant patients, and patients with incomplete data regarding studied variables were excluded from the study. Eighty-seven patients were documented, with a mean age, weight, and height at closure of 51 months, 14 kg, and 95.83 cm, respectively. About 70% of the patients (n = 61) were female, 76% were under 6-years-old and only one patient was over 15. The average size of the ductus at the pulmonary end was 2 mm. Four of the total number of patients did not achieve PDA closure during the procedure. Of the remaining 83, complete immediate closure was achieved in 81 patients. A device exchange for a larger device was required during the same procedure in one of the cases. Two patients presented residual shunt of 0.5 mm during follow-up, and one required a new procedure for device closure 10 months later. Only one device presented repeatedly embolization to the aorta, requiring surgical removal. As a technical difficulty, one device presented repeated passage into the aorta, so it was decided to remove it before releasing it to avoid complications, and given the complex anatomy of the ductus, surgical closure was indicated. Among the complications, one patient presented a hematoma of the subcutaneous tissue in the right thigh, which improved with medical management, and no deaths related to the procedure were registered. Using the Nit-Occlud® device to close small to moderate-sized ductus remains a safe and effective strategy with successful closure rates at 1-year follow-up irrespective of age, weight, height, or whether it involves a small or medium-sized duct. Despite our limitations, results concerning adverse effects are comparable to those observed in multicentric studies conducted in other regions.

Research on Privacy Issues in Smart Metering System: An Improved TCN-Based NILM Attack Method and Practical DRL-Based Rechargeable Battery Assisted Privacy Preserving Method

Smart meters, as a key component of Advanced Metering Infrastructure (AMI), collect fine-grained electricity consumption data for demand response in smart grids. While this data improves the grid’s accuracy, it also poses significant threats to users’ privacy. In this paper, we study the privacy issue in smart metering systems from both attacker and defender perspectives. First, we propose an improved Temporal Convolutional Network (TCN) based Non-Intrusive Load Monitoring (NILM) attack method, which infers electrical appliance usage from public load curves, addressing the gradient vanishing, gradient exploding, and other problems while improving attack accuracy. Second, we develop a rechargeable battery-assisted energy management system to hide load characteristics of electrical appliances by adding physical noise, thus resisting NILM attacks. To address the privacy-cost trade-off optimization problem, we propose a Practical Deep Reinforcement Learning-based Rechargeable Battery assist Privacy Preserving Method (PRoP) that learns optimal battery charging/discharging policies. We design a novel privacy measurement method and constraints to ensure the feasibility of system deployment and prove PRoP’s effectiveness in resisting NILM attacks. Comprehensive evaluations demonstrate that our improved TCN-based NILM method achieves an attack success rate of over 80% on various electrical appliances, improving attack performance (MAE, RMSE) by 20% compared to existing methods while reducing model training time. Moreover, our proposed PRoP achieves a better trade-off between privacy protection and electricity cost than existing battery-assisted methods, reducing costs by 5% and the attack success ratio to 36%, while increasing the MAE and RMAE obtained by NILM by 3 times. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Smart grid, which can support bidirectional information transmission, has a series of advantages, such as high efficiency and high stability. However, it also brings a significant threat to users’ electricity privacy. Although encryption-based privacy protection methods have been deployed on terminal devices of smart grids to prevent privacy leaks, this method can often only defend against intrusive attacks and has little effect on non-intrusive attacks. To this end, this paper studies the privacy issues caused by non-intrusive attacks. Specifically, to better study the protection method, we first investigate the attack mechanism and design an improved TCN-based Non-Intrusive Load Monitoring method. Then, we propose a Practical Reinforcement learning-based rechargeable battery-assisted Privacy preserving method (PRoP) to defend against this attack physically. The most practical contribution of this paper is that, compared with existing battery-assisted privacy protection methods, we do not blindly pursue algorithm performance but fully consider the practical factors of deployment, such as limiting battery capacity and constraining battery charging and discharging behavior. This can guide practitioners to better apply this technology in practice.

FANet: Fast and Accurate Robotic Grasp Detection Based on Keypoints

In practice, the real-time and accuracy of robotic grasp detection are two very important metrics. In the past, researchers had to sacrifice the real-time nature of the detection network in order to obtain higher detection accuracy. How to make the real-time and accuracy of the network co-exist is a problem worth studying. In order to solve this problem, this paper proposes a network, FANet, based on grasp keypoints, which improves the accuracy of grasp detection while ensuring the real-time performance. The key of this paper is how to quickly and accurately detect grasped keypoints. To this end, this paper proposes a local refinement module that optimizes and de-duplicates each feature of the multi-scale feature map, enabling the network to make full use of the multi-scale features. We also propose a global feature refinement module that allows the network to make better use of global features. We also propose a grasp keypoint optimization module that predicts the offset between the actual keypoints and the predicted keypoints, enabling the network to predict the keypoints more accurately. Moreover, we develop two FANets specifically for grasp detection on CPU and GPU, both of which can accomplish real-time grasp detection in real-world scenes. We complete the training and testing of FANet on the Cornell dataset and the Jacquard dataset, achieving SOTA results on the Jacquard dataset. We also test FANet on a dataset of unknown objects, all with good results. Finally, we use the FANet in grasping experiments with an actual Baxter robot and achieve an average grasping success rate of 96%. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Real-time and accuracy are two very important metrics in robotic grasping detection. To achieve high accuracy, more time is often consumed for feature extraction. Similarly, in order to improve the real time performance, we need to reduce the time consumed in the feature extraction process, which may result in a drop in detection accuracy. How to coordinate the relationship between them, so as to have both, is a problem worth investigating. Current methods tend to focus on obtaining higher accuracy and they are willing to spend more time to achieve higher accuracy. But in some practical scenarios, such as on factory assembly lines, objects move fast, and the network needs to be able to detect the grasping position quickly, the real-time performance is more important, which makes some methods difficult to use. In addition, most of the current methods tend to focus on GPU-based robotic grasp detection methods, and in real-world scenarios we may not have such a powerful processing GPU available. In contrast, the CPU is an indispensable unit of the computer that we can use to process images without a high-performance GPU. However, compared to GPUs, the CPUs’ image processing capability is poor, making it difficult to achieve real-time processing. Faced with this situation, the problem of how to achieve real-time and high accuracy in a CPU-only robotic grasp detection network is worth studying, but most of the existing methods ignore this problem. To address these problems, we propose a Fast and Accurate robotic grasp detection Network (FANet), which not only enables the network to combine real-time and accuracy, but also enables real-time detection on CPU or GPU.

Unveiling the Threat: Investigating Distributed and Centralized Backdoor Attacks in Federated Graph Neural Networks

Graph neural networks (GNNs) have gained significant popularity as powerful deep learning methods for processing graph data. However, centralized GNNs face challenges in data-sensitive scenarios due to privacy concerns and regulatory restrictions. Federated learning has emerged as a promising technology that enables collaborative training of a shared global model while preserving privacy. Although federated learning has been applied to train GNNs, no research focuses on the robustness of Federated GNNs against backdoor attacks. This article bridges this research gap by investigating two types of backdoor attacks in Federated GNNs: centralized backdoor attack (CBA) and distributed backdoor attack (DBA). Through extensive experiments, we demonstrate that DBA exhibits a higher success rate than CBA across various scenarios. To further explore the characteristics of these backdoor attacks in Federated GNNs, we evaluate their performance under different scenarios, including varying numbers of clients, trigger sizes, poisoning intensities, and trigger densities. Additionally, we explore the resilience of DBA and CBA against two defense mechanisms. Our findings reveal that both defenses cannot eliminate DBA and CBA without affecting the original task. This highlights the necessity of developing tailored defenses to mitigate the novel threat of backdoor attacks in Federated GNNs.

Development of the Children’s ACuTe UltraSound (CACTUS) point-of-care ultrasound (POCUS)-accredited training in the UK: a descriptive study

Point-of-care ultrasound (POCUS) is an established, evidence-supported tool that can be used in neonatal and paediatric medicine, offering clinicians immediate diagnostic insights, assessment of interventions and improved safety profiles and...

Heatmap creation with YOLO-Deep SORT system customized for in-store customer behavior analysis

Due to the limitations of the hardware system, analysis of retail stores has caused problems such as excessive workload, incomplete analysis, slow analysis speed, difficult data collection, non-real-time data collection, passenger flow statistics, and density analysis. However, heatmaps are a viable solution to these problems and provide adaptable and effective analysis. In this paper, we propose to use the deep sequence tracking algorithm together with the YOLO object recognition algorithm to create heatmap visualizations. We will present key innovations of our customized YOLO-Deep SORT system to solve some fundamental problems in in-store customer behavior analysis. These innovations include our use of footpad targeting to make bounding boxes more precise and less noisy. Finally, we made a comprehensive evaluation and comparison to determine the success rate of our system and found that the success rate was higher than the systems we compared in the literature. The results show that our heatmap visualization enables accurate, timely, and detailed analysis.

Comparative meta-analysis of endovascular strategies for intracranial dissecting aneurysms: Flow diverters versus stents with or without coiling.

Intracranial dissecting aneurysms present a challenging subset linked to considerable morbidity and mortality, necessitating effective therapeutic strategies to prevent complications. Traditional treatments face technical limitations, while emerging endovascular modalities like stent-assisted coiling, multiple stenting, and flow-diverting devices (FDDs) offer promise in reducing periprocedural risks and enhancing patient outcomes. The aim of this study is to compare the safety and efficacy outcomes of endovascular treatment for intracranial dissecting aneurysms using FDDs versus stents (with or without coiling). A systematic review and meta-analysis were conducted following established guidelines. The search included PubMed, Scopus, Web of Science, and Embase databases up to July 30, 2023. Eligible studies reporting outcomes of interest in both FDD and stent-treated groups were included, and the data was extracted and analyzed using STATA software. Six studies were analyzed, involving 131 patients in the FDD group and 199 patients in the stent group. The pooled rates for favorable functional outcomes (86.8% vs. 86%), mortality (3.9% vs. 6%), adequate occlusion (79.7% vs. 86.3%), aneurysm recurrence (1.3% vs. 13.3%), in-stent stenosis/thrombosis (7% vs. 6.9%), ischemic events/infarctions (6.7% vs. 7.8%), retreatment (7% vs. 8.6%), and technical success (100% vs. 98.7%) were comparable in individuals treated with FDDs and stents (p > 0.05 in all cases). Additionally, complete occlusion rates were not significantly different between FDD (62.7%) and stent-treated patients (75.2%) (p = 0.06). However, after excluding one study in a leave-one-out analysis of the random effects meta-analysis, a significant difference in the pooled rates of this outcome was observed between the FDD (59.2%) and stent (75.2%) groups (p = 0.034). FDDs present a promising approach for the treatment of intracranial dissecting aneurysms, yielding outcomes that are roughly comparable to stent-based methods. However, the absence of randomized trials and data limitations highlight the need for further research to enhance treatment strategies.

Evaluation of post-surgical complications between "Rendez-vous" technique vs. standard care in patients with choledocholithiasis

"Rendez-vous" (RV) technique is a mixed-technique which uses both laparoscopic and endoscopic skills; however, the evidence is contradictory regarding the implementation of this technique or the 2-step sequential technique (endoscopic retrograde cholangiopancreatography [ERCP] followed by laparoscopic cholecystectomy [LC]) in the management of cholecysto-choledocholitiasis. To estimate the association between the implementation of RV technique and the presence of post-surgical complications as primary outcome, using as comparator the 2-step sequential technique. An observational, analytical, retrospective study was conducted, using as exposed cohort the medical records from patients with a diagnosis of cholelithiasis, cholecystitis, or mild biliary pancreatitis. The exposed cohort underwent RV technique, while the unexposed cohort were those which underwent two step technique. There was a lower post-surgical complication rate in the RV group (0%) compared with the 10.1% (p = 0.3617) in the control group. Also, RV technique showed a lesser hospitalization time (p = 0.0377) and a lesser post-surgical hospitalization time (p < 0.0001). RV technique is superior when compared with the 2-step sequential technique (ERCP followed by LC), based on a better surgical success rate, a fewer complications rate and less hospitalization time.