bluuebunny/crossref_metadata_2025 huggingface dataset:
Demographics of Cauda Equina Syndrome: A Population-Based Incidence Study
Authors: Woodfield Julie , Lammy Simon , Jamjoom Aimun A.B. , Fadelalla Mohammed A.G. , Copley Phillip C. , Arora Mohit , Glasmacher Stella A. , Abdelsadg Mohamed , Scicluna Gabrielle , Poon Michael T.C. , Pronin Savva , Leung Andraay H.C. , Darwish Stacey , Demetriades Andreas K. , Brown Jennifer , Eames Niall , Statham Patrick F.X. , Hoeritzauer Ingrid ,   UCES Study Collaborators,   British Neurosurgical Trainee Research Collaborative
Published: October 2022

Introduction: Cauda equina syndrome (CES) has significant medical, social, and legal consequences. Understanding the number of people presenting with CES and their demographic features is essential for planning healthcare services to ensure timely and appropriate management. We aimed to establish the incidence of CES in a single country and stratify incidence by age, gender, and socioeconomic status. As no consensus clinical definition of CES exists, we compared incidence using different diagnostic criteria. Methods: All patients presenting with radiological compression of the cauda equina due to degenerative disc disease and clinical CES requiring emergency surgical decompression during a 1-year period were identified at all centres performing emergency spinal surgery across Scotland. Initial patient identification occurred during the emergency hospital admission, and case ascertainment was checked using ICD-10 diagnostic coding. Clinical information was reviewed, and incidence rates for all demographic and clinical groups were calculated. Results: We identified 149 patients with CES in 1 year from a total population of 5.4 million, giving a crude incidence of 2.7 (95% CI: 2.3–3.2) per 100,000 per year. CES occurred more commonly in females and in the 30–49 years age range, with an incidence per year of 7.2 (95% CI: 4.7–10.6) per 100,000 females age 30–39. There was no association between CES and socioeconomic status. CES requiring catheterization had an incidence of 1.1 (95% CI: 0.8–1.5) per 100,000 adults per year. The use of ICD-10 codes alone to identify cases gave much higher incidence rates, but was inaccurate, with 55% (117/211) of patients with a new ICD-10 code for CES found not to have CES on clinical notes review. Conclusion: CES occurred more commonly in females and in those between 30 and 49 years and had no association with socioeconomic status. The incidence of CES in Scotland is at least four times higher than previous European estimates of 0.3–0.6 per 100,000 population per year. Incidence varies with clinical diagnostic criteria. To enable comparison of rates of CES across populations, we recommend using standardized clinical and radiological criteria and standardization for population structure.
Low Baseline CXCL9 Predicts Early Progressive Disease in Unresectable HCC with Atezolizumab Plus Bevacizumab Treatment
Authors: Hosoda Shunichi , Suda Goki , Sho Takuya , Ogawa Koji , Kimura Megumi , Yang Zijian , Yoshida Sonoe , Kubo Akinori , Tokuchi Yoshimasa , Kitagataya Takashi , Maehara Osamu , Ohnishi Shunsuke , Nakamura Akihisa , Yamada Ren , Ohara Masatsugu , Kawagishi Naoki , Natsuizaka Mitsuteru , Nakai Masato , Morikawa Kenichi , Furuya Ken , Baba Masaru , Yamamoto Yoshiya , Suzuki Kazuharu , Izumi Takaaki , Meguro Takashi , Terashita Katsumi , Ito Jun , Miyagishima Takuto , Sakamoto Naoya ,   for the NORTE STUDY Group
Published: October 2022

Introduction: Atezolizumab plus bevacizumab treatment is highly effective in patients with unresectable hepatocellular carcinoma (HCC). However, progressive disease (PD) occurs in approximately 20% of HCC patients treated with atezolizumab plus bevacizumab, resulting in a poor prognosis. Thus, the prediction and early detection of HCC is crucial. Methods: Patients with unresectable HCC treated with atezolizumab plus bevacizumab and had baseline preserved serum (n = 68) were screened and classified according to their PD, 6 weeks after treatment initiation (early PD; n = 13). Of these, 4 patients each with and without early PD were selected for cytokine array and genetic analyses. The identified factors were validated in the validated cohort (n = 60) and evaluated in patients treated with lenvatinib. Results: No significant differences were observed in the genetic alterations in circulating tumor DNA. Cytokine array data revealed that baseline MIG (CXCL9), ENA-78, and RANTES differed substantially between patients with and without early PD. Subsequent analysis in the validation cohort revealed that baseline CXCL9 was significantly lower in patients with early PD than that in patients without early PD, and the best cut-off value of serum CXCL9 to predict early PD was 333 pg/mL (sensitivity: 0.600, specificity: 0.923, AUC = 0.75). In patients with lower serum CXCL9 (<333 pg/mL), 35.3% (12/34) experienced early PD with atezolizumab plus bevacizumab, while progression-free survival (PFS) was significantly shorter relative to that in patients without (median PFS, 126 days vs. 227 days; HR: 2.41, 95% CI: 1.22–4.80, p = 0.0084). While patients with objective response to lenvatinib had significantly lower CXCL9 levels compared with those of patients without. Conclusion: Baseline low serum CXCL9 (<333 pg/mL) levels may predict early PD in patients with unresectable HCC treated with atezolizumab plus bevacizumab.
Application of fuzzy logic technique to track maximum power point in photovoltaic systems
Authors: Rout Kshirod Kumar , Mishra Debani Prasad , Mishra Sivkumar , Teja Rishitha Gangavaram , Salkuti Surender Reddy
Published: October 2022

The use of photovoltaic (PV) systems for generating electric power is increasing in our everyday lives but since the generated voltage and current vary non linearly it has been very difficult to trace the maximum power point (MPP) of the PV systems so to overcome with this problem many power tracking methods were introduced out of which fuzzy logic technique was found to be one of the easy and efficient maximum power point tracking (MPPT) method. In this paper, various MPPT algorithms are observed how they help in improving the efficiency of PV systems by adjusting the duty ratio of the power interface, and also understand why the fuzzy logic control (FLC) technique is preferred over other algorithms. The system was established using MATLAB/Simulink.
New algorithm for localization of iris recognition using deep learning neural networks
Authors: Hussein Ali Ekbal , Abbas Jaber Hanadi , Naji Kadhim Nahida
Published: October 2022

Iris recognition is the most reliable and accurate method for eye identification. A novel strategy for localizing iris printing is proposed in this paper. The median filter and histogram were used for this purpose. To extract iris features from iris photographs, an algebraic method known as semi-discrete matrix decomposition (SDD) is used. For classification, neural network (NN) is used to extract the SDD feature. This study also included the setup of convolution neural network (CNN), a convolution neural network that does not require feature extraction, as well as a comparison of the two types of classifiers is made. Iris images are obtained from the Chinese Academy of Sciences Institute of Automation dataset (CASIA Iris-V1), a common database used for the iris recognition system. The proposed algorithm is straightforward, simple, efficient, and fast. The experimental results showed that the proposed algorithm achieved high classification accuracy of approximately 95.5% and 95% for CNN and NN based on SDD features respectively. The proposed algorithms outperformed literature works and required less time for determining the location of iris region.
Modelling and simulation of optical transmitter for 5G passive optical networks
Authors: Adil Abbas Sura , A. Aldhaibani Jaafar
Published: October 2022

5G is altering the communications future. The 5G literature focuses on wireless and radio technology, but fiber optics play a crucial supporting role in signal transmission to and from the next generation of base stations (BSs) that will serve customers. With the increasing bandwidth requirements of 5G ultra-high-speed applications, passive optical networks (PONs) are the optimal solution. Targeting long-reach 5G PONs, an integrated, high-speed, and cost-effective optical transmitter circuit was constructed and tested. This engine can be combined into a 5G-compliant optical transceiver module. For the purpose of validating the engine performance, a theoretical analysis and an OptiSimTM-based design simulation model are carried out. Bit error rate (BER), phase-shifting, and eye diagram were all taken into account when analyzing the transmitter performance. The article findings validate the optical transmitter design. The design is cost-effective and requires no extra components, such as a filter.
Performance improvement of visible light communication links based on coded unipolar OFDM
Authors: N. Al-Musawi Wasnaa , K. Al-Musawi Hassan
Published: October 2022

Visible light communications are limited by the narrow bandwidth of their sources as the light-emitting diodes, which restricts the high data rates transmission. A potential technique such as multi-carrier modulation is considered promising for achieving a high-speed data rate and enhancing system performance. This paper proposes turbo coding with unipolar orthogonal frequency-division multiplexing (U-OFDM) for indoor environments. The system under U-OFDM considerations can reduce the inter-symbol interference caused by multiple paths and mitigate burst errors by adopting an iterative turbo decoding. The system performance is evaluated regarding the bit error rate (BER) for the range of the signal-to-noise ratio (SNR) under receiver field-of-view (FOV) restriction. The obtained results show that the performance system of the target BER (below the threshold limit of 3.8×10−3) has a power penalty of ~9 dB for modulation order of 64 for quadrature amplitude modulation scheme, FOV of 50°, and incidence angle of half power assigned to 30°. The obtained results also showed a ~10.8 dB improvement in the SNR when comparing the present system with coded-DC-biased optical OFDM under the same considerations.
Boxplot analysis of 4 grade agarwood essential oil for various grades
Authors: Hasnu Al-Hadi Anis Hazirah 'Izzati , Mohd Amidon Aqib Fawwaz , Mohd Huzir Siti Mariatul Hazwa , Ismail Nurlaila , Mohd Yusoff Zakiah , Tajuddin Saiful Nizam , Taib Mohd Nasir
Published: October 2022

Agarwood essential oil is used in most perfumery ingredients, as an incense and in traditional medical preparations. Agarwood essential oil, called "Black Gold," is extremely valued to the global community due to its numerous benefits. As of now, there is still no standard technique of grading different grades of agarwood essential oil. The current grading technique is inefficient since the agarwood essential oil is graded by using human sensory panel. Different people might have different perspective on grading the agarwood essential oil hence, the technique is not practical to adapt it globally. Due to the current technology, numerous intelligent techniques for verifying the grades of agarwood essential oil have been proposed and implemented. The study has conducted a statistical analysis on 4 grade agarwood essential oil using boxplot. Boxplot analysis summarizes the abundances for each chemical compounds from four different grades of agarwood essential oil with a high grade as a reference. This study shows the analysis of boxplot investigated 10-epi-δ-eudesmol, α-agarofuran, β-agarofuran, δ-eudesmol and dihydrocollumellarin as most important chemical compounds in high grade of agarwood essential oil. The chemical compounds that have been identified in high grade of agarwood essential oil can be a reference for future research studies.
Integrated approach of brain segmentation using neuro fuzzy k-means
Authors: Sami Ur Rahman Jawwad , Kumar Selvaperumal Sathish
Published: October 2022

A proposed method using neuro-fuzzy k-means for the segmentation process of brain has been developed successfully, simulated and assessed. The proposed method has been assessed by using clinical brain images of magnetic resonance imaging (MRI) technology, to segment the three main tissues of the brain. The proposed system is able to segment the three important regions of the brain, which are white matter, grey matter and cerebrospinal fluid (CSF) more accurately, as compared to the benchmarked algorithms. Furthermore, the developed method’s misclassification rate (MR) has been significantly minimized by 88%, 27%, 88%; 82%, 71%, 84%; and 82%, 29%, 83%, as compared to k-means, fuzzy logic, and radial basis function (RBF) for white matter, grey matter and CSF, respectively. Also, from the visual interpretation, it is observed that the brain’s edges are well preserved and the tissues are clearly segmented. From these measures, the proposed integrated approach is shown to be accurate in segmenting the MRI brain tissue with reduced misclassified pixels.
Object detection on robosoccer environment using convolution neural network
Authors: Steffi Diana , Mehta Shilpa , Venkatesh Venkatesh
Published: October 2022

Robots with autonomous capabilities depend on vision capabilities to detect and interact with objects and their environment. In the field of robotic research, one of the focus areas is the robosoccer platform that is being used to implement and test new ideas and findings on computer vision and decision making. In this article, an efficient real-time object detection algorithm is employed in a robosoccer simulation environment by deploying a convolution neural network and Kalman filter based tracking algorithms. This study's objective is to classify nao, ball, and the goalpost as well as to validate nao and ball tracking without human intervention from initial frame to last frame. In comparison with the existing methods, the proposed method is robust and fast in identifying three classes namely nao, ball, and goalpost with a speed of 1.67 FPS and a mAP of 95.18%. By implementing this approach, soccer playing robots can make appropriate decisions during game play.
Various object detection algorithms and their comparison
Authors: Mishra Debani Prasad , Rout Kshirod Kumar , Mishra Sivkumar , Salkuti Surender Reddy
Published: October 2022

This paper presents a detailed and comparative analysis of various object detection algorithms. The challenge of object detection is taken care of while studying various algorithms. Throughout the year various methods have been discovered in this field, each having its advantages and drawbacks. This paper aims at providing the systematic study of all the popular algorithms including the conventional ones. Although many methods and techniques come up each year and each of them having superiority other the previous models but at the same time even complexity increases. In this paper, some famous and basic methods of object detection and tracking are discussed. Using these developed techniques good results can be obtained and also the comparison of the efficiency of all the models can be done. Real-time applications and the outcomes are also discussed.
Implementation of ethereum blockchain on transaction recording of white sugar supply chain data
Authors: Ekawati Ratna , Arkeman Yandra , Suprihatin Suprihatin , Candra Sunarti Titi
Published: October 2022

The complex supply chain system for the sugar agroindustry supply chain involves many actors, resulting in the asymmetry of information data. It still leads to a lack of data transparency. In the past, data traceability could not be made efficient at every stage, so the record data transaction was not real-time, less accurate, and inefficient coordination between actors. Blockchain is one of the technologies in the 4.0 era as a distributed ledger technology. It can be transparency, traceability, security, immutability, and decentralization. This study aims to design a white sugar agroindustry system based on blockchain technology using the SDLC waterfall stage public. Ethereum is a proof-of-work convention based on the Ropsten test-net on the Metamask wallet. The sugar supply chain system that has been successfully developed allows consumers to track the purchased sugar products based on the transaction hash code sent by the seller. The data listed is the location of the plantation, the quantity (quintal) and quality of sugarcane (percentage), and the purchase price. A web-based blockchain application could be used as a model by national sugar factories to help them make enough sugar food for themselves.
Correlation based data unification for personality trait prediction
Authors: Divi Radha , Potala Chandra Sekhar
Published: October 2022

The data points created by users with their online behavior are the primary source of data analysis in various research studies. One of such studies that even more contributes directly or indirectly to other application domains is personality trait analysis. There are well-defined models witnessed to be strong enough to characterize the individual personalities. Some prominent models are the Big Five, Interpersonal Circumplex, and DISC. To make it possible the model should always be fed with a huge amount of data. Here comes the limitation is that the user's online behavior is spread across various platforms resulting in various forms of data points. The segregated form of the data limits the model performance, which is the primary focus of the proposed work. Among these, the big five personality traits model has become so familiar because of its simplicity and dominance. The proposed work illustrated the mapping of various data forms namely measures of the source dataset (MBTI) and Interpersonal Circumplex data to Big five data.
Optimizing blockchain technology using a data sharing model
Authors: Nazeeh Israa , Hussain Hadi Teeb , Qahtan Mohammed Zainab , Taha Ahmed Shaymaa , Kanaan Kadhim Qusay
Published: October 2022

The improvement of different data-sharing technology has increasingly permeated many industries as technology continues to improve. As a result, for the value of the data to be realized, data sharing and security are essential. However, a fundamental data sharing mechanism is difficult to check for electronic data usage traces. Furthermore, data providers' unwillingness to provide their data is a challenge. Taking use of the dispersed ledger, smart contract, data trust, and traceability aspects of blockchain technology. This research presents a data-sharing model based on blockchain technology optimizing to overcome the challenges in terms of security and control, of conventional centralized data sharing and management, enabling safe access to the data as a result. Moreover, the research assesses the prototype's usefulness and security. Additionally, this paper suggests a method for using blockchain technology to optimize the efficiency of data sharing. This study showed that data sharing via the blockchain technology paradigm proposed in this work is feasible, secure, controllable, and efficient. This was demonstrated in a novel way employing blockchain technology.
A discrete salp swarm algorithm with weights and Lévy flights: application for Parkinson’s disease detection
Authors: Sureja Nitesh M. , Patel Pratik N. , Patel Hemant , Shingadiya Chetan J.
Published: October 2022

A new hybrid algorithm named discrete salp swarm algorithm that integrates effectiveness of weights, Lévy flights, and an excellent classifier, support vector machine (SVM), has been proposed to predict Parkinson's disease. In the proposed algorithm, salp swarm algorithm (SSA) is used as a feature selection tool, which targets to reduce the noise in features of the speech PD dataset to improve the SVM classifier's prediction accuracy. The efficacy and usefulness of the proposed discrete salp swarm algorithm with Lévy flights have been meticulously assessed against the speech PD dataset in terms of G-mean, accuracy, F-measure, specificity, sensitivity, and precision measures. DWLSSA has achieved values of the measures, 97.76%, 98.75%, 98.77%, 97.37%, 98.15%, and 99.39% respectively. Comparison of DWLSSA with other nature inspired algorithms applied to predict Parkinson’s shows that the proposed DWLSSA performs better. It can be also said that DWLSSA can be an alternative for solving the NP-hard problems.
A deep learning signed medical image based on cryptographic techniques
Authors: H. Elkamchouchi Dalia , D. Algarni Abeer , M. Ghoniem Rania , G. Mohamed Heba
Published: October 2022

Innovative medical multimedia communications technology requirements have enhanced safety principles, allowing significant advancement in security standards. In hospitals and imaging centers, massive amounts of medical images have been created. To successfully access the medical databases and utilize those rich resources in assisting diagnosis and research, image processing enabled communication solutions are necessary. Our article presents a rigorous verified model by employing deep learning to enhance the cryptographic performance of biomedical images using hybrid chaotic Lorentz map diffusion and de-oxyribonucleic acid (DNA) confusion stages. It consists of two encryption/decryption techniques, the initial signal is verified using digital signature and two unique non-consecutive stages of chaotic diffusion with a single DNA scrambling stage in between. The encoded secret bit stream is generated and used to encrypt or decode the original signal in the diffusion manner to disintegrate the redundancy in the plain image statistics, utilizing hybrid chaotic system. Using DNA confusion step to make the relationship between the original signal and the utilized key more ambiguous. These stages make the proposed image cryptosystem more resistant to known/ chosen plaintext assaults. The performance of the suggested technique will be assessed to the most similar techniques reported in the literature for comparative purposes.
Nash equilibrium learning in non-cooperative reputation game in social networks
Authors: Touya Khadija , Baslam Mohamed , El Ayachi Rachid , Jourhmane Mostafa
Published: October 2022

Recently, people became more dependent on online social networks with the increasing use and the rapid development of information technology. Those environments constitute an important area where users interact and create communication ties to maintain their relationships. Furthermore, the time life of these relationships is depending on reputations of the users. Every source (information provider) has a reputation which depends on his frequency of publishing, but also the opinions given by the observers (others users) has an important impact on the determination of this reputation. Since, everyone is trying selfishly to keep a good reputation; a competition is met within these networks. This paper aims to solve this kind of competition through a game theoretic approach; we formulate the said competition as a non-cooperative game, demonstrate the uniqueness of the existent Nash Equilibrium which seems to be the convent solution in this case, then present results clarifying and illustrating the proposed modeling method.
Comparison of ensemble hybrid sampling with bagging and boosting machine learning approach for imbalanced data
Authors: Malek Nur Hanisah Abdul , Yaacob Wan Fairos Wan , Wah Yap Bee , Md Nasir Syerina Azlin , Shaadan Norshahida , Indratno Sapto Wahyu
Published: October 2022

Training an imbalanced dataset can cause classifiers to overfit the majority class and increase the possibility of information loss for the minority class. Moreover, accuracy may not give a clear picture of the classifier’s performance. This paper utilized decision tree (DT), support vector machine (SVM), artificial neural networks (ANN), K-nearest neighbors (KNN) and Naïve Bayes (NB) besides ensemble models like random forest (RF) and gradient boosting (GB), which use bagging and boosting methods, three sampling approaches and seven performance metrics to investigate the effect of class imbalance on water quality data. Based on the results, the best model was gradient boosting without resampling for almost all metrics except balanced accuracy, sensitivity and area under the curve (AUC), followed by random forest model without resampling in term of specificity, precision and AUC. However, in term of balanced accuracy and sensitivity, the highest performance was achieved by random forest with a random under-sampling dataset. Focusing on each performance metric separately, the results showed that for specificity and precision, it is better not to preprocess all the ensemble classifiers. Nevertheless, the results for balanced accuracy and sensitivity showed improvement for both ensemble classifiers when using all the resampled dataset.
Proper insertion of DSTATCOM in distribution networks based on VSM with network reconfiguration
Authors: Fawzi Shafeeq Abdullah , Ibrahim Ali Inaam
Published: October 2022

Voltage stability is necessary to maintain the grid system healthily. The load demand has been steadily increasing that causing high losses and voltage drops, endangering the system's stability. This study proposes the suitable insertion of distribution static compensator (DSTATCOM) based on voltage stability margin (VSM) with optimum network reconfiguration (ONR) to reduce losses and enhance the voltage profile. The findings are acquired utilizing recommended methodologies and test systems such as the IEEE-33 bus and 39 bus of an Iraqi distribution network, which are done using CYME and MATLAB software. The proposed approach may be able to solve the problem by aiding distribution network operators in estimating the size and location of DSTATCOM.
Trajectory optimization using learning from demonstration with meta-heuristic grey wolf algorithm
Authors: Pawlowski Adam , Romaniuk Slawomir , Kulesza Zbigniew , Petrovic Milica
Published: October 2022

Nowadays, most robotic systems perform their tasks in an environment that is generally known. Thus, robot's trajectory can be planned in advance depending on a given task. However, as a part of modern manufacturing systems which are faced with the requirements to produce high product variety, mobile robots should be flexible to adapt to changing and diverse environments and needs. In such scenarios, a modification of the task or a change in the environment, forces the operator to modify robot's trajectory. Such modification is usually expensive and time-consuming, as experienced engineers must be involved to program robot's movements. The current paper presents a solution to this problem by simplifying the process of teaching the robot a new trajectory. The proposed method generates a trajectory based on an initial raw demonstration of its shape. The new trajectory is generated in such a way that the errors between the actual and target end positions and orientations of the robot are minimized. To minimize those errors, the grey wolf optimization (GWO) algorithm is applied. The proposed approach is demonstrated for a two-wheeled mobile robot. Simulation and experimental results confirm high accuracy of generated trajectories.
Unmanned aerial vehicle design for disaster victim search and rescue operation using wireless sensor network localization
Authors: Hutabarat Fajar Sam , Sitanggang Imanuel Maurice Darmawan , Damanik Joy Andrew Immanuel , Knight Guntur Petrus Boy , Sagala Albert
Published: October 2022

<span data-sheets-value="{"1":2,"2":"The search for disaster victims carried out by search-and-rescue (SAR) team mainly uses traditional methods, which are considered to take much time and effort, and pose a high risk for the search team and the victim. Based on this problem, we conducted research to assist disaster victim search. In this research, we designed a system using passive infrared (PIR) sensors to detect and measure the direction angle of the victim. Given the direction angle from different observation points’ perspectives, we determine the victim’s position using the triangulation method. we also designed a quadcopter unmanned aerial vehicle (UAV) to carry this sensor system across the disaster area. For monitoring purposes, a local website was designed to display data generated by the system. Based on test results, the system can determine a victim’s position with a distance difference of less than 5 m. The system can search victim in an empty land ±35 m×15 m wide in 14 minutes 20 seconds. The data monitoring system also displayed the victim’s position, the position of the observation points, and the UAV’s flight path."}" data-sheets-userformat="{"2":13249,"3":{"1":0},"9":0,"10":1,"11":3,"12":0,"15":"Nunito Sans","16":9}">The search for disaster victims carried out by search-and-rescue (SAR) team mainly uses traditional methods, which are considered to take much time and effort, and pose a high risk for the search team and the victim. Based on this problem, we conducted research to assist disaster victim search. In this research, we designed a system using passive infrared (PIR) sensors to detect and measure the direction angle of the victim. Given the direction angle from different observation points’ perspectives, we determine the victim’s position using the triangulation method. we also designed a quadcopter unmanned aerial vehicle (UAV) to carry this sensor system across the disaster area. For monitoring purposes, a local website was designed to display data generated by the system. Based on test results, the system can determine a victim’s position with a distance difference of less than 5 m. The system can search victim in an empty land ±35 m×15 m wide in 14 minutes 20 seconds. The data monitoring system also displayed the victim’s position, the position of the observation points, and the UAV’s flight path.
Single High-Sensitivity Point-of-Care Whole-Blood Cardiac Troponin I Measurement to Rule Out Acute Myocardial Infarction at Low Risk
Authors: Apple Fred S. , Smith Stephen W. , Greenslade Jaimi H. , Sandoval Yader , Parsonage William , Ranasinghe Isuru , Gaikwad Niranjan , Schulz Karen , Stephensen Laura , Schmidt Christian W. , Okeson Brynn , Cullen Louise , Brownlee Emily , Fincher Gavin , Hall Emma , Hancock Rebecca , Gangathimmaiah Vinay , Hamilton-Craig Christian , Hobbins-King Andrew , Keijzers Gerben , Bayat Maryam Khorramshahi , McCormick Ellyse , Mahmoodi Ehsan , Perez Siegfried , Staib Andrew , Zournazi Anna , Than Martin
Published: October 2022

Background:

High-sensitivity cardiac troponin (hs-cTn) laboratory assays are used to rule out myocardial infarction (MI) on presentation, but prolonged result turnaround times can delay patient management. Our primary aim was to identify patients at low risk of index MI using a rapid point-of-care (POC) whole-blood hs-cTnI assay at presentation with potential early patient discharge.

Methods:

Consecutive patients presenting to the emergency department from 2 prospective observational studies with suspected acute coronary syndrome were enrolled. A POC hs-cTnI assay (Atellica VTLi) threshold using whole blood at presentation, which resulted in a negative predictive value of ≥99.5% and sensitivity of >99% for index MI, was derived (SEIGE [Safe Emergency Department Discharge Rate]) and validated with plasma (SAMIE [Suspected Acute Myocardial Infarction in Emergency]). Event adjudications were established with hs-cTnI assay results from routine clinical care. The primary outcome was MI at 30 days.

Results:

A total of 1086 patients (8.1% with MI) were enrolled in a US derivation cohort (SEIGE) and 1486 (5.5% MI) in an Australian validation cohort (SAMIE). A derivation whole-blood POC hs-cTnI concentration of <4 ng/L provided a sensitivity of 98.9% (95% CI, 93.8%–100%) and negative predictive value of 99.5% (95% CI, 97.2%–100%) for ruling out MI. In the validation cohort, the sensitivity was 98.8% (95% CI, 93.3%–100%), and negative predictive value was 99.8% (95% CI, 99.1%–100%); 17.8% and 41.8%, respectively, were defined as low risk for discharge. The 30-day adverse cardiac events were 0.1% (n=1) for SEIGE and 0.8% (n=5) for SAMIE.

Conclusions:

A POC whole-blood hs-cTnI assay permits accessible, rapid, and safe exclusion of MI and may expedite discharge from the emergency department.

Registration:

URL:
https://www.clinicaltrials.gov
; Unique identifier: NCT04772157. URL:
https://www.australianclinicaltrials.gov.au/anzctr_feed/form
; Unique identifier: 12621000053820.
Heterogeneity in the Association Between the Presence of Coronary Artery Calcium and Cardiovascular Events: A Machine-Learning Approach in the MESA Study
Authors: Inoue Kosuke , Seeman Teresa E. , Horwich Tamara , Budoff Matthew J. , Watson Karol E.
Published: October 2022

Background:

Coronary artery calcium (CAC) has been widely recognized as an important predictor of cardiovascular disease (CVD). Given the finite resources, it is important to identify individuals who would receive the most benefit from detecting positive CAC by screening. However, the evidence is limited as to whether the burden of positive CAC on CVD differs by multidimensional individual characteristics. We sought to investigate the heterogeneity in the association between positive CAC and incident CVD.

Methods:

This cohort study included adults from MESA (Multi-Ethnic Study of Atherosclerosis) ages ≥45 years and free of cardiovascular disease. After propensity score matching in a 1:1 ratio, we applied a machine learning causal forest model to (1) evaluate the heterogeneity in the association between positive CAC and incident CVD, and (2) predict the increase in CVD risk at 10-years when CAC>0 (versus CAC=0) at the individual level. We then compared the estimated increase in CVD risk when CAC>0 to the absolute 10-year atherosclerotic CVD (ASCVD) risk calculated by the 2013 American College of Cardiology/American Heart Association pooled cohort equations.

Results:

Across 3328 adults in our propensity score–matched analysis, our causal forest model showed the heterogeneity in the association between CAC>0 and incident CVD. We found a dose–response relationship of the estimated increase in CVD risk when CAC>0 with higher 10-year ASCVD risk. Almost all individuals (2293 of 2428 [94.4%]) with borderline risk of ASCVD or higher showed ≥2.5% increase in CVD risk when CAC>0. Even among 900 adults with low ASCVD risk, 689 (69.2%) showed ≥2.5% increase in CVD risk when CAC>0; these individuals were more likely to be male, Hispanic, and have unfavorable CVD risk factors than others.

Conclusions:

The expected increases in CVD risk when CAC>0 were heterogeneous across individuals. Moreover, nearly 70% of people with low ASCVD risk showed a large increase in CVD risk when CAC>0, highlighting the need for CAC screening among such low-risk individuals. Future studies are needed to assess whether targeting individuals for CAC measurements based on not only the absolute ASCVD risk but also the expected increase in CVD risk when CAC>0 improves cardiovascular outcomes.
Dynamic Change of Cardiovascular Health Metrics and Long‐Term Risk of Sudden Cardiac Death: The ARIC Study
Authors: Zhai Yuan‐Sheng , Bi Wen‐Tao , Li Zhu‐Yu , Qu Li‐ping , Jia Yu‐He , Cheng Yun‐Jiu
Published: October 2022

Background

The change of cardiovascular health (CVH) status has been associated with risk of cardiovascular disease. However, no studies have explored the change patterns of CVH in relation to risk of sudden cardiac death (SCD). We aim to examine the link between baseline CVH and change of CVH over time with the risk of SCD.

Methods and Results

Analyses were conducted in the prospective cohort ARIC (Atherosclerosis Risk in Communities) study, started in 1987 to 1989. ARIC enrolled 15 792 individuals 45 to 64 years of age from 4 US communities (Forsyth County, North Carolina; Jackson, Mississippi; suburbs of Minneapolis, Minnesota; and Washington County, Maryland). Subjects with 0 to 2, 3 to 4, and 5 to 7 ideal metrics of CVH were categorized as having poor, intermediate, or ideal CVH, respectively. Change in CVH over 6 years between 1987 to 1989 and 1993 to 1995 was considered. The primary study outcome was physician adjudicated SCD. The study population consisted of 15 026 subjects, of whom 12 207 had data about CVH change. Over a median follow‐up of 23.0 years, 583 cases of SCD were recorded. There was a strong inverse association between baseline CVH metrics and time varying CVH metrics with risk of SCD. Compared with subjects with consistently poor CVH, risk of SCD was lower in those changed from poor to intermediate/ideal (hazard ratio [HR], 0.67 [95% CI, 0.48–0.94]), intermediate to poor (HR, 0.73 [95% CI, 0.54–0.99]), intermediate to ideal (HR, 0.49 [95% CI, 0.24–0.99]), ideal to poor/intermediate CVH (HR, 0.23 [95% CI, 0.10–0.52]), or those with consistently intermediate (HR, 0.49 [95% CI, 0.36–0.66]) or consistently ideal CVH (HR, 0.31 [95% CI, 0.13–0.76]). Similar results were also observed for non‐SCD.

Conclusions

Compared with consistently poor CVH, other patterns of change in CVH were associated with lower risk of SCD. These findings highlight the importance of promotion of ideal CVH in the primordial prevention of SCD.
Inhaled Carbon Dioxide Improves Neurological Outcomes by Downregulating Hippocampal Autophagy and Apoptosis in an Asphyxia‐Induced Cardiac Arrest and Resuscitation Rat Model
Authors: Wang Chih‐Hung , Huang Chien‐Hua , Tsai Min‐Shan , Wang Chan‐Chi , Chang Wei‐Tien , Liu Shing‐Hwa , Chen Wen‐Jone
Published: October 2022

Background

Protracted cerebral hypoperfusion following cardiac arrest (CA) may cause poor neurological recovery. We hypothesized that inhaled carbon dioxide (CO
2
) could augment cerebral blood flow (CBF) and improve post‐CA neurological outcomes.

Methods and Results

After 6‐minute asphyxia‐induced CA and resuscitation, Wistar rats were randomly allocated to 4 groups (n=25/group) and administered with different inhaled CO
2
concentrations, including control (0% CO
2
), 4% CO
2
, 8% CO
2
, and 12% CO
2
. Invasive monitoring was maintained for 120 minutes, and neurological outcomes were evaluated with neurological function score at 24 hours post‐CA. After the 120‐minute experiment, CBF was 242.3% (median; interquartile range, 221.1%–267.4%) of baseline in the 12% CO
2
group while CBF fell to 45.8% (interquartile range, 41.2%–58.1%) of baseline in the control group (
P
<0.001). CBF increased along with increasing inhaled CO
2
concentrations with significant linear trends (
P
<0.001). At 24 hours post‐CA, compared with the control group (neurological function score, 9 [interquartile range, 8–9]), neurological recovery was significantly better in the 12% CO
2
group (neurological function score, 10 [interquartile range, 9.8–10]) (
P
<0.001) while no survival difference was observed. Brain tissue malondialdehyde (
P
=0.02) and serum neuron‐specific enolase (
P
=0.002) and S100β levels (
P
=0.002) were significantly lower in the 12% CO
2
group. TUNEL (terminal deoxynucleotidyl transferase–mediated biotin–deoxyuridine triphosphate nick‐end labeling)‐positive cell densities in hippocampal CA1 (
P
<0.001) and CA3 (
P
<0.001) regions were also significantly reduced in the 12% CO
2
group. Western blotting showed that beclin‐1 (
P
=0.02), p62 (
P
=0.02), and LAMP2 (lysosome‐associated membrane protein 2) (
P
=0.01) expression levels, and the LC3B‐II:LC3B‐I ratio (
P
=0.02) were significantly lower in the 12% CO
2
group.

Conclusions

Administering inhaled CO
2
augmented post‐CA CBF, mitigated oxidative brain injuries, ameliorated neuronal injury, and downregulated apoptosis and autophagy, thereby improving neurological outcomes.
Telehealth After Stroke Care Pilot Randomized Trial of Home Blood Pressure Telemonitoring in an Underserved Setting
Authors: Naqvi Imama A. , Strobino Kevin , Kuen Cheung Ying , Li Hanlin , Schmitt Kevin , Ferrara Stephen , Tom Sarah E. , Arcia Adriana , Williams Olajide A. , Kronish Ian M. , Elkind Mitchell S.V.
Published: October 2022

Background:

Hypertension is the most important modifiable stroke risk factor, but blood pressure (BP) remains poorly controlled after stroke, especially among Black and Hispanic patients. We tested the feasibility of TASC (Telehealth After Stroke Care), a post-acute stroke care model integrating nurse-supported home BP telemonitoring, tailored infographics, and multidisciplinary team video visits.

Methods:

Acute stroke patients with hypertension were randomized at discharge to usual care or usual care with TASC. Usual care patients received video visits with primary care and stroke. TASC included a tablet and monitor to wirelessly transmit BP data to the electronic health record, with telenursing support, tailored infographics to explain BP readings, and pharmacist visits. Outcomes assessment was blinded. Feasibility outcomes included recruitment, randomization, adherence, and retention. Systolic BP from baseline to 3 months after discharge was evaluated using generalized linear modeling.

Results:

Fifty patients (64±14 years; 36% women‚ 44% Hispanic, 32% Black, 54% ≤high school education, 30% private insurance), and 75% of all eligible were enrolled over 6.3 months. Baseline systolic BP was similar in both (TASC n=25, 140±19 mm Hg; usual care n=25, 142±19 mm Hg). At 3 months, adherence to video visits (91% versus 75%,
P
=0.14) and retention (84% versus 64%,
P
=0.11) were higher with TASC. Home systolic BP declined by 16±19 mm Hg from baseline in TASC and increased by 3±24 mm Hg in usual care (
P
=0.01). Among Black patients, systolic BP control (<130 mm Hg) improved from 40% to 100% with TASC versus 14% to 29%, and among Hispanic patients, from 23% to 62% with TASC, versus 33% to 17% in usual care.

Conclusions:

Enhancing post-acute stroke care with home BP telemonitoring is feasible to improve hypertension in an underserved setting and should be tested in a definitive randomized clinical trial.

Registration:

URL:
https://www.clinicaltrials.gov
; Unique identifier: NCT04640519.
Acute Infarcts on Brain MRI Following Aortic Arch Repair With Circulatory Arrest: Insights From the ACE CardioLink-3 Randomized Trial
Authors: Chen Chih-Hao , Peterson Mark D. , Mazer C. David , Hibino Makoto , Beaudin Andrew E. , Chu Michael W.A. , Dagenais François , Teoh Hwee , Quan Adrian , Dickson Jeffrey , Verma Subodh , Smith Eric E.
Published: October 2022

Background:

to investigate the frequency and distribution of new ischemic brain lesions detected by diffusion-weighted imaging on brain magnetic resonance imaging after aortic arch surgery.

Methods:

This preplanned secondary analysis of the randomized, controlled ACE (Aortic Surgery Cerebral Protection Evaluation) CardioLink-3 trial compared the safety and efficacy of innominate versus axillary artery cannulation during elective proximal aortic arch surgery. Participants underwent pre and postoperative magnetic resonance imaging. New ischemic lesions were defined as lesions visible on postoperative, but not preoperative diffusion weighted imaging.

Results:

Of the 111 trial participants, 102 had complete magnetic resonance imaging data. A total of 391 new ischemic lesions were observed on diffusion-weighted imaging in 71 (70%) patients. The average number of lesions in patients with ischemic lesion were 5.5±4.9 with comparable numbers in the right (2.9±2.0) and left (3.0±2.3) hemispheres (
P
=0.49). Half the new lesions were in the middle cerebral artery territory; 63% of the cohort had ischemic lesions in the anterior circulation, 49% in the posterior circulation, 42% in both, and 20% in watershed areas. A probability mask of all diffusion-weighted imaging lesions revealed that the cerebellum was commonly involved. More severe white matter hyperintensity on preoperative magnetic resonance imaging (odds ratio, 1.80 [95% CI, 1.10–2.95];
P
=0.02) and lower nadir nasopharyngeal temperature during surgery (odds ratio per 1°C decrease, 1.15 [95% CI, 1.00–1.32];
P
=0.05) were associated with the presentation of new ischemic lesion; older age (risk ratio per 1-year increase, 1.02 [95% CI, 1.00–1.04];
P
=0.03) and lower nadir temperature (risk ratio per 1°C decrease, 1.06 [95% CI, 1.00–1.14];
P
=0.06) were associated with greater number of lesions.

Conclusions:

In patients who underwent elective proximal aortic arch surgery, new ischemic brain lesions were common, and predominantly involved the middle cerebral artery territory or cerebellum. Underlying small vessel disease, lower temperature nadir during surgery, and advanced age were risk factors for perioperative ischemic lesions.

Registration:

URL:
https://www.clinicaltrials.gov
; Unique identifier: NCT02554032.
Dangerous Changes: When Military Innovation Harms Combat
Effectiveness
Authors: Kuo Kendrick
Published: October 2022

Prevailing wisdom suggests that innovation dramatically enhances the effectiveness of a state's armed forces. But self-defeating innovation is more likely to occur when a military service's growing security commitments outstrip shrinking resources. This wide commitment-resource gap pressures the service to make desperate gambles on new capabilities to meet overly ambitious goals while cannibalizing traditional capabilities before beliefs about the effectiveness of new ones are justified. Doing so increases the chances that when wartime comes, the service will discover that the new capability cannot alone accomplish assigned missions, and that neglecting traditional capabilities produces vulnerabilities that the enemy can exploit. To probe this argument's causal logic, a case study examines British armor innovation in the interwar period and its impact on the British Army's poor performance in the North African campaign during World War II. The findings suggest that placing big bets on new capabilities comes with significant risks because what is lost in an innovation process may be as important as what is created. The perils of innovation deserve attention, not just its promises.
Changes in Tropical Cyclone–Induced Extreme Hourly Precipitation over China during 1975–2018
Authors: Liu Lu , Yang Baochen , Jiang Xiaoling , Luo Yali , Ren Fuming
Published: October 2022

This study analyzes the spatiotemporal distributions and climate trends of tropical cyclone (TC)-induced extreme hourly precipitation (EXHP) in the warm season (May–September) during 1975–2018 over China and the involved mechanisms. Each TC is classified as a high-, mid-, and low-EXHP TC according to the total amount of EXHP it produces over China during its lifetime. Results show that low-EXHP TCs have a greater contribution to the total TC-induced EXHP over southern and southwestern China, whereas high-EXHP TCs make greater contributions over eastern and northeastern China as they tend to move northwestward after making landfall. It is shown that, although the total frequency of EXHP-producing TCs displays a decreasing trend, the total frequency of TC-induced EXHP over China shows a significant increasing trend, which is largely contributed by the high-EXHP TCs. To explore the possible mechanisms responsible for the different characteristics in EXHP-producing TC groups, we further analyze the large-scale environmental conditions with respect to three groups by composite analysis. The cooperation of large-scale environmental fields between high and low levels provides favorable conditions for the intensification of TCs and the enhancement of the TC-induced precipitation for the high-EXHP group, such as stronger divergence at high levels, weaker vertical wind shear, more sufficient water vapor, and more conducive steering airflow over the southeastern and eastern area of mainland China. The westward and northward movement of western North Pacific subtropical high is conducive to the northward shift of TC tracks, thus contributing to the high frequency of TC-induced EXHP over the eastern area of mainland China.