bluuebunny/crossref_metadata_2025 huggingface dataset:
Development and Application of a Self-Attention Residual Network Model in Diagnosing Abnormal Structural Health Monitoring Data
Authors: Cao Yanmei , Zhang Yuxian , Chen Jialiang , Li Zhaoyang , Pang Yuanen , Xia Chaoyi
Published: August 2024

In the structural health monitoring system, a large amount of testing data from sensors has been used to detect the health status of a structure. However, due to factors such as sensor failure and electromagnetic interference, the data collected by the structural health monitoring system may exhibit various abnormal states, which will affect the accuracy of structural damage identification and state assessment. How to process large amounts of data with high accuracy and recognize those abnormal data with high efficiency poses significant challenges. This paper proposes an innovative deep-learning method for data abnormality diagnosis by designing and establishing a self-attention residual network (SARN) model. In the method, all the monitoring data, including the abnormal data, are first converted into a time–frequency diagram by continuous wavelet transform, which are then used as the input of the SARN model for training. Then the convolutional layer, batch normalization, max pooling layer, the set self-attention residual building unit-1, and unit-2 are established successively to process the data, and finally the abnormal data can be identified by deep-learning technique. The proposed method broadens the receptive field of convolutional neural networks limited by the size of convolution kernels, allowing for the extraction of richer global features of data, thereby enabling high-precision automatic diagnosis and classification of a large amount of abnormal monitoring data. As an application case, a long-span cable-stayed bridge being monitored is introduced, and its acceleration monitoring data during one month are used as a dataset, and the developed SARN model is used for data anomaly diagnosis. The test accuracy of the model is found to be 97.80%, indicating its efficiency and accuracy in identifying abnormal data in the structural health monitoring dataset.
Web-Based Maintenance Prediction of Machine Conditions and Failure Modes Using Machine Learning
Authors: Al-Refaie Abbas , Al-Atrash Majd , Melhem Abdullah , Lepkova Natalija
Published: August 2024

Machine failure may result in unplanned downtime, production losses, safety risks, and increased costs. Hence, predicting machine failures is a significant challenge faced by many industries. Predictive Maintenance (PdM) techniques can help mitigate these risks by predicting machine failures before they occur. This research, therefore, develops two supervised Machine Learning (ML) algorithms to predict failure conditions and the associated failure modes for a milling machine. Six machine features were investigated. The ML models were then developed using a four-stage process including data pre-processing, training, evaluation, and deployment. Several ML algorithms were applied then the results were evaluated using accuracy, recall, precision, and [Formula: see text]1-score. The results showed that the Random Forest algorithm was the most effective in predicting machine conditions and failure modes of the milling machine. A web application for PdM was finally developed and tested for the PdM of the milling machine. In conclusion, the developed web application including ML algorithms can support the effective PdM for the machine, which leads to enhancing its availability and improving productivity. Future research considers adopting ML algorithms for predicting machine conditions and failure types of other machines.
An Intelligent Framework of Equipment Fault Diagnosis Based on Knowledge Graph
Authors: Zhai Shichen , Lu Xiaoping , Wang Chao , Liu Haiying , Ma Zongmin
Published: August 2024

Equipment fault information typically exhibits the characteristics of fragmentation and diversified structure. The existing fault diagnosis methods are incapable of fully exploiting the prior knowledge and expert knowledge within the field, and the diagnosis results are overly one-sided. Given that it is challenging to obtain effective fault diagnostic knowledge for complex equipment and complex faults, this paper proposes the application of the domain knowledge graph (KG) for fault diagnosis. Different from the existing fault diagnosis methods involving the KG, our framework consists of two major parts. The first part is to construct an equipment fault KG from semi-structured and unstructured text. We further enrich the graph knowledge through knowledge completion, which can furnish high-quality knowledge sources for downstream applications. The second part is to employ the built fault KG either online or offline for fault diagnosis. We offer two approaches: the deep learning plus KG approach; the question-answering approach. The former not only guarantees the diagnostic accuracy but also provides more comprehensive diagnostic information. This constitutes the online utilization of the KG. The latter realizes the offline use of the KG, providing users with a natural and user-friendly manner to retrieve fault diagnosis information. We demonstrate and verify our proposed framework in the context of the bearing fault diagnosis.
The U.S. Factor in China–Iran Relations: A Historical Perspective on Beijing’s Dual Policy
Authors: Abdollahpour Behzad , Falarti Maziar Mozaffari
Published: August 2024

The China–U.S.–Iran relationship offers an intriguing strategic triangle in which the United States has always loomed large and disproportionately influenced the relationship between the other two actors. Although Tehran and Beijing had a relatively friendly relationship that endured the volatile Cold War era, neither treated the other as a strategic priority in their foreign relations. The Obama administration’s diplomatic overture and the subsequent multi-nation nuclear deal helped relieve persistent restrictions on Tehran and Beijing and created a relatively permissive environment for both to foster more resilient economic, political, and strategic ties. The U.S. factor has long compelled China to adopt a dual policy toward Iran — restricted cooperation and limited distancing. But more recently, in an evolving global geopolitical and geoeconomic landscape, Beijing has begun to tilt more frequently toward Tehran, telegraphing its growing frustration with Washington’s unilateralism and protectionism on the one hand, and a greater willingness to raise its profile in the Middle East on the other.
Machine Learning-Based Scrap Steel Price Forecasting for the Northeast Chinese Market
Authors: Jin Bingzi , Xu Xiaojie
Published: August 2024

Throughout history, governments and investors have relied on predictions of prices for a broad spectrum of commodities. Using time-series data covering 08/23/2013–04/15/2021, this study investigates the challenging problem of predicting scrap steel prices, which are issued daily for the northeast China market. Previous research has not sufficiently taken into account estimates for this significant commodity price measurement. In this instance, Gaussian process regression methods are created using Bayesian optimisation approaches and cross-validation processes, and the resulting price forecasts are constructed. This empirical prediction methodology provides reasonably accurate price estimates for the out-of-sample period from 09/17/2019 to 04/15/2021, with a root mean square error of 9.6951, mean absolute error of 5.4218, and correlation coefficient of 99.9122%. Governments and investors can arrive at informed decisions regarding regional scrap steel markets by using pricing research models.
Socio-Cognitive Framework for Personal Informatics: A Preliminary Framework for Socially-Enabled Health Technologies
Authors: Saksono Herman , Parker Andrea G.
Published: August 2024

Personal health informatics systems have been centered around individual efforts, overlooking the role of social factors in health. Over seven years of research (
n

\(=\)

153), we examined how socially-enabled personal informatics systems can support physical activity—a behavior critical in promoting physical and mental health. We prioritized exploring this topic with families in low-socioeconomic status (SES) neighborhoods because they face increased barriers to being active due to inequities. Through our systems development, qualitative studies, and theoretical foundation, we developed the Socio-Cognitive Framework for Personal Health Informatics systems that shows how five socio-cognitive concepts (aspirations, data exposure, stories, belongingness, and impediments) influence self-efficacy and outcome expectations that are linked to health behavior. We then provide recommendations on how to design and evaluate such systems. We further argue that socially-enabled health informatics tools can support marginalized communities in reducing health disparities through the collective efforts of families, neighbors, and peers.
RFL-LSU: A Robust Federated Learning Approach with Localized Stepwise Updates
Authors: Fan Shuming , Shi Hongjian , Wang Chenpei , Ma Ruhui , Wang Xiaoming
Published: August 2024

Distributed intelligence enables the widespread deployment of AI technology, greatly promoting the development of AI. Federated learning is a widely used distributed intelligence technology that allows iterative optimization of global model while protecting user data privacy. Currently, federated learning faces some security threats, as its open architecture provides attackers with opportunities to disrupt the learning process by submitting malicious updates or inserting backdoors. In this article, we propose a robust federated learning method to defend against potential malicious attacks. Specifically, we enhance the algorithm’s performance and stability by implementing localized stepwise updates on the client side and element-wise anomaly detection on the server side. We conducted experiments in a more realistic non-i.i.d. scenario and compared the results with other typical federated learning methods. Our results demonstrate that our approach exhibits strong robustness under non-i.i.d. data distributions, outperforming other methods in terms of test accuracy and resilience to attacks.
Design and Performance Analysis of a Pneumatic Garlic Peeling Machine
Authors: Unal H. Guran
Published: August 2024

Garlic production and consumption are rapidly increasing due to its recognized health benefits. Peeling garlic before consumption is one of the most important basic operations. The garlic cloves must have the thin, inedible layer of membranous skin peeled off. The peeled garlic cloves are also used to make value‐added and long shelf life products such as garlic sauce, garlic pickle, dehydrated garlic (cips‐granul‐powder), garlic extract, garlic tablet, and garlic oil. For this purpose, a pneumatic garlic peeling machine, which has two peeling units, has been developed and tested. Trials were carried out with 5, 7, and 9 bar peeling air pressure; 10, 15, and 20 s peeling time; and 0.2, 0.4, 0.6, 0.8, and 1.0 s loading time. The peeled garlic percentage was between 49.2% and 99.8%, and the machine efficiency was between 32.8 and 100.6 kg/h. Since the amount of unpeeled garlic coming out of the machine will significantly increase the labor required for separation, 96.4 kg/h peeled and 1.4 kg/h unpeeled garlic amounts with the parameters 9 bar peeling air pressure, 0.6 s loading time, and 10 s peeling time were found to be the most successful result. If both units are operated together, the total machine efficiency will be about 200 kg/h. Compared to manual peeling, about 99.6% labor saving is achieved. Compared to other mechanic and pneumatic garlic peelers in the literature, it has been determined that they can be used easily because the percentage of peeled garlic and machine efficiency is relatively high.
Factors Correlated to the Renoprotective Effect of Sitagliptin in Patients with Type 2 Diabetes Mellitus: Retrospective Observational Study
Authors: Khunkit Pirawan , Wattana Konkanok
Published: August 2024

Background. Sitagliptin functions similarly to GLP‐1RAs by incretin and insulin secretion and has a renoprotective effect. Diabetic kidney disease (DKD) is a kidney complication that increases the mortality rate in type 2 diabetes mellitus (T2DM) patients. The important parameters that predict appropriate sitagliptin treatment are known as factors. This study aimed to assess factors that correlated with the renoprotective effect of sitagliptin in patients with T2DM. Methods. This retrospective study collected data from a tertiary hospital in Thailand. All T2DM patients who were treated with sitagliptin and had complete data were recruited to analyze the outcome. The primary outcome was a correlation between demographics, laboratory data, and kidney outcome. The secondary outcome was the different laboratory results between pre‐ and posttreatment of patients treated with sitagliptin. Results. The number of patients who were treated for T2DM with sitagliptin was 191. Only 102 patients had complete laboratory parameters. Results showed a positive correlation between baseline FBS, HbA1c, and Scr change (p value = 0.042 and 0.005) at 6 months and baseline age, TG, and Scr change (p value = 0.010 and 0.022) at 18 months; while a negative correlation was observed between baseline FBS, HbA1c, and eGFR change (p value = 0.017 and 0.007) at 6 months and baseline age and eGFR change (p value = 0.010) and between HDL‐cholesterol and Scr change at 18 months (p value = 0.044). The eGFR stage 1 subgroup showed a positive correlation between baseline HbA1c and Scr change (p value <0.001) and baseline DM duration and eGFR change (p value = 0.004). Moreover, sitagliptin showed statistically significant FBS, HbA1c, LDL‐cholesterol, and TC reduction. Furthermore, HDL‐cholesterol showed statistically significant elevation. Conclusion. FBS, HbA1c, and age were factors that correlated with the renoprotective effect of sitagliptin. The eGFR ≥90.00 ml/min/1.73 m2 patients group showed a duration of DM in which factors correlated with renoprotective effect. Moreover, sitagliptin also can improve glucose levels and lipid profile.
The Value of Human Epididymal Protein 4, Carcinoembryonic Antigen and Alpha-Fetoprotein in the Early Diagnosis of Cervical Cancer
Authors: Ye Xiaoyan , Xie Shanyan
Published: August 2024

Objectives: This research aimed to unveil the value of human epididymal protein 4 (HE4), carcinoembryonic antigen (CEA) and alpha-fetoprotein (AFP) in the early diagnosis of cervical cancer. Design: This was a clinical study. Participants: Sixty patients with cervical cancer stage IA-IIA (early stage cervical cancer group), 60 patients with cervical intraepithelial neoplasia (CIN) (disease control group), and 60 healthy women who had passed the physical examination (healthy control group) were selected. Setting: The review was conducted in a Jiaxing First Hospital. Methods: Sixty patients with cervical cancer stage IA-IIA (early stage cervical cancer group), 60 patients with CIN (disease control group), and 60 healthy women who had passed the physical examination (healthy control group) were selected. The expression levels of serum HE4, CEA, and AFP in the three groups were detected, and the correlation between the levels of serum HE4, CEA, and AFP and the clinicopathological characteristics of patients with early stage cervical cancer were analyzed, and the receiver operating characteristic (ROC) curves were plotted to identify the value of the single and triple tests of serum HE4, CEA, and AFP for the early stage diagnosis of cervical cancer. Results: The levels of serum HE4, CEA, and AFP in the early stage cervical cancer group were higher than those in the disease control and the healthy control groups (p < 0.05). The levels of serum HE4, CEA, and AFP were related to the FIGO stage as well as the histological grading of patients with early stage cervical cancer (p < 0.05). The results of the ROC curves revealed that the AUC areas of HE4, CEA, and AFP for single as well as triple diagnosis of patients with early stage cervical cancer were 0.725, 0.679, 0.663, and 0.811, respectively, and the AUC of the three combined tests was markedly higher than that of HE4, CEA, AFP single test (p < 0.05). Limitations: There is a lack of larger sample sizes to test whether the combined HE4, CEA, and AFP detection has sufficient validity at the individual level and there are not enough serum samples in this study to perform circulating HPV-DNA detection and compare it with the levels of serum markers. Conclusion: The combination of HE4, CEA, and AFP has good clinical reference value analysis in the auxiliary diagnosis of early stage cervical cancer, and it is worthy of further validation and popularization.
CaCl2-Citrate Regional Anticoagulation with Continuous Veno-Venous Haemodialysis Leads to Unwanted Chloride Loading Compared to Continuous Veno-Venous Hemofiltration with Systemic Anticoagulation
Authors: Chivot Matthieu , Baldwin Ian , Deniel Guillaume , David Guillaume , Eastwood Glenn M. , Richard Jean-Christophe , Bellomo Rinaldo , Bitker Laurent
Published: August 2024

Introduction: Chloride transfers during continuous renal replacement therapy (CRRT) have not been adequately described and may differ based on CRRT technique. We aimed to measure chloride mass transfer (JS,Cl) during CRRT and identify associated determinants. Methods: We performed a two-centre, prospective, observational study in France and Australia in ICU patients with CRRT initiated for <24 h. Patients received continuous veno-venous hemofiltration (CVVH) or continuous veno-venous haemodialysis (CVVHD, with citrate-CaCl2 regional anticoagulation). Over a 24 h period, plasma and effluent chloride concentrations were measured every 4 h to compute chloride mass transfer (JS,Cl, in mmol.min-1) using a modality-specific model, with negative value indicating chloride transfer towards the patient. Secondary outcomes were the identification of CRRT settings associated with JS,Cl (using multivariate mixed effects regression). Results are presented with median (interquartile range). Results: Between February 2021 and August 2022, we enrolled 37 patients (64 [56–71] years, 67% male), for a total of 20 CVVHD and 20 CVVH sessions. Over 24 h, plasma chloride concentrations were significantly higher, and JS,Cl significantly lower during CVVHD, compared to CVVH (−0.10 [−0.33 to 0.15] vs. 0.01 [−0.10 to 0.13] mmol.min-1, p < 0.05). With both modalities, net ultrafiltration (QUFNET) and plasma chloride concentrations were the principal determinants of JS,Cl, with higher QUFNET being associated with an increase in JS,Cl during CVVHD. Also, CVVHD sessions demonstrated a concentration gradient between the plasma and the effluent chamber of −6 [−9 to −4] mmol.L-1. Finally, CaCl2 reinjection during CVVHD accounted for 35% [32–60%] of total JS,Cl in sessions with a negative JS,Cl. Conclusion: Compared to CVVH, CVVHD with regional citrate anticoagulation was associated with greater chloride mass transfer to the patient and higher plasma chloride concentrations. This was due to high dialysate chloride concentrations and CaCl2 reinjection. This effect could only be controlled by high net ultrafiltration flow rates.
Towards automated classification of cognitive states: Riemannian geometry and spectral embedding in EEG data
Authors: Siddappa Manjunatha , Ravikumar Kempahanumaiah M. , Madegowda Nagendra Kumar
Published: August 2024

Our research explores the application of Riemannian geometry and spectral embedding in the context of electroencephalogram (EEG) signal analysis for cognitive state classification. Leveraging the PyRiemann library and the AlphaWaves dataset, our study employs covariance estimation and the minimum distance to mean (MDM) classifier within a machine learning pipeline. The classification accuracy is assessed through stratified k-fold cross-validation. Furthermore, we introduce a novel visualization approach by calculating the spectral embedding of covariance matrices, providing insights into the underlying structure of the EEG epochs. Our findings showcase the potential of Riemannian geometry and spectral embedding as powerful tools in the domain of EEG-based cognitive state classification, contributing to the broader field of brain signal analysis and paving the way for automated and advanced neurocognitive studies.
Detection of colorization based image forgeries using convolutional autoencoder method
Authors: Panchal Soumyashree Muralidhar , Hanumanthaiah Asha Kethaganahalli , Doddasiddavanahalli Bindushree Channabasavaraju , Eshwar Rao Manju More , Jayaramu Ambika Belekere
Published: August 2024

Recently, it has become difficult to recognize and easier to misuse digital images due to the large number of editing tools available. Detecting forgeries in images is crucial for security and forensic purposes. Therefore, this research implements a deep learning (DL) method of convolutional autoencoder (CAE) which improves colorization-based image forgery detection by leveraging spatial and color information, increasing the detection accuracy. At first, the pre-processed input forgery images are used with the wiener filtering-contrast restricted improved histogram equalization (WE-CLAHE) technique. Hybrid dual-tree complex wavelet trigonometric transform (H‑DTCWT) and VGG-16 are used to extract effective features from the clustered data. Improved horse herd optimization (IHH) is employed to reduce the dimensionality of a feature. At last, the CAE model is implemented to significantly recognize the image forgery. The accuracy of CASIA V1 and GRIP datasets of 99.95% and 99.97%, respectively is achieved. Hence, this implemented method obtains a high forgery detection performance than the existing methods.
Experimental of information gain and AdaBoost feature for machine learning classifier in media social data
Authors: Jasmir Jasmir , Abidin Dodo Zaenal , Fachruddin Fachruddin , Riyadi Willy
Published: August 2024

In this research, we use several machine learning methods and feature selection to process social media data, namely restaurant reviews.
 The selection feature used is a combination of information gain (IG) and adaptive boosting (AdaBoost) which is used to see its effect on the classification performance evaluation value of machine learning methods such as Naïve Bayes (NB), K-nearest neighbor (KNN), and random forest (RF) which is the aim of this research. NB is very simple and efficient and very sensitive to feature selection. Meanwhile, KNN is known for its weaknesses such as biased k values, overly complex computation, memory limitations, and ignoring irrelevant attributes. Then RF has weaknesses, including that the evaluation value can change significantly with only small data changes. In text classification, feature selection can improve the scalability, efficiency and accuracy of text classification. Based on tests that have been carried out on several machine learning methods and a combination of the two selection features, it was found that the best classifier is the RF algorithm. RF produces a significant increase in value after using the IG and AdaBoost features. Increased accuracy by 10%, precision by 12.43%, recall by 8.14% and F1-score by 10.37%. RF also produces even accuracy, precision, recall, and F1-score values after using IG and AdaBoost with an accuracy value of 84.5%; precision of 85.58%; recall was 86.36%; and F1-score was 85.97%.
Optimizing hyperspectral classification: spectral similarity-based band selection with chord k-means
Authors: Chander Goud Origanti Subhash , Sarma Thogarachetti Hitendra , Bindu Chigarapalle Shobha
Published: August 2024

Band selection is crucial for achieving high classification accuracy in hyperspectral image (HSI) analysis, especially when ground truth data are limited. While unsupervised algorithms are preferred in such scenarios, the effectiveness of k-means clustering depends heavily on the choice of similarity measure. This article presents a novel two-level clustering approach for band selection. In the first level, bands are clustered using k-means with various similarity measures such as Euclidean distance, spectral angle mapper (SAM), and spectral information divergence (SID). Subsequently, the second level leverages the chord metric k-means clustering to form clusters of HSI scenes upon optimal band clusters from the first level. This initial band selection reduces dimensionality and guides subsequent k-means clustering. The proposed chord-based clustering method, utilizing the chord metric, outperforms standard k-means variants, demonstrating significant improvements in accuracy. Experimental results on publicly available hyperspectral datasets confirm the effectiveness of the proposed approach as an alternative to traditional k-means algorithms, showcasing significant improvements in accuracy.
Neurons to heartbeats: spiking neural networks for electrocardiogram pattern recognition
Authors: Mohamad Noor Nor Amalia Dayana Binti , Chiew Wong Yan , Noh Zarina Mohd , Sarban Singh Ranjit Singh
Published: August 2024

The electrocardiogram (ECG) is one of the most significant methods of diagnostics for determining heart rhythm disorders. For this study, raw ECG signals from the Physio Bank database are subjected to an important preprocessing step that uses empirical mode decomposition (EMD) on signal denoising and distortion elimination. Establishing functioning spiking neural networks (SNN) involves figuring out the neuron’s state through its activity level, challenging due to its resemblance to the human brain’s data processing, yet appealing due to factors like improved unsupervised learning methods, with ten parameters chosen for the learning algorithm of SNN. A comprehensive set of 15 different time-domain features and 10 Cepstral domain features is precisely extracted to train the SNN classifier. An extensive study is conducted to analyse the learning parameters that affect SNN performance, significantly influencing result accuracy. Through a two-classification process, the differentiation between normal and abnormal ECG patterns can be achieved in this study. A maximum testing accuracy of 91.6667% and a maximum training accuracy of 99.1667% have been attained by the process. These results demonstrate the competency of the system in distinguishing between distinct ECG classes, particularly in identifying normal and abnormal cardiac rhythms through ECG classification.
Electroencephalography biometric authentication using eye blink artifacts
Authors: Madile Thamang Teddy , Hlomani Hlomani B. , Zlotnikova Irina
Published: August 2024

This study presents a novel approach to electroencephalography (EEG) biometric authentication using eye blink artifacts. Unlike traditional methods that rely on imagination and mental tasks, which are susceptible to emotional and physical variations, this approach leverages the consistent effects of eye blinks on brainwaves for authentication. Brainwaves were recorded using the NeuroSky Mindwave Mobile 2 headset, and eye blinks were extracted via NeuroSky’s blink detection algorithm. An authentication algorithm was developed based on blink strength, time, and frequency. The proposed method demonstrated high performance with an accuracy (ACC) of 97%, a false acceptance rate (FAR) of 5%, and a false rejection rate (FRR) of 1%. This study also explored the impact of emotions and physical exercise on the authentication process, confirming the method's robustness under varying conditions. These findings suggest that eye blink artifacts offer a reliable and practical biometric trait for EEG-based authentication systems, providing a secure alternative to traditional biometric methods. The substantial contribution of this research lies in demonstrating the superior stability and usability of eye blink-based EEG authentication under diverse conditions, compared to existing EEG authentication methods that often require mental tasks or multi-channel recordings.
The Enterprise Value and The Financial Leverage of Manufacturing Industries in the Municipality of Bulan Sorsogon
Authors: Bongalonta Michael B. , Bongalonta Michelle M.
Published: August 2024

This research was conducted to determine and assess the financial leverage and the enterprise value of manufacturing industries in the Municipality of Bulan Sorsogon, Philippines and to establish the relationship of these two variables. This also determines the factors that influence the financial leverage decisions of the business owners. This study used mixed design.  A total of 92 registered manufacturing business owners operating in the said Municipality were the primary respondents of the study. Vertical analyses and linear regression analyses were utilized.
This study concludes that the manufacturing business enterprises in the Municipality of Bulan Sorsogon have significant amount of debt relative to its equity as manifested by its financial leverage. The respondents were likewise found to have a low enterprise value, with exception to company’s book value, which suggests a lower market perception of the company's worth, potentially reflecting weaker financial performance or growth prospects. Lack of personal capital, financial risk tolerance and cost of debt, cash flow stability, growth opportunities and regulatory and legal considerations were the identified factors that influence the financial leverage of the manufacturing business owners. From the results, the researcher highly recommends that manufacturing business owners may consider optimizing their capital structure to ensure an appropriate balance between debt and equity.
FABP4 Enhances Lipidic and Fibrotic Cardiac Structural and Ca
2+
Dynamic Changes
Authors: Couselo-Seijas Marinela , Vázquez-Abuín Xocas , Gómez-Lázaro María , Pereira Laetitia , Gómez Ana M. , Caballero Ricardo , Delpón Eva , Bravo Susana , González-Juanatey José Ramón , Eiras Sonia
Published: August 2024

BACKGROUND:

Adipocyte FABP4 (fatty acid-binding protein 4) is augmented in the epicardial stroma of patients with long-standing persistent atrial fibrillation. Because this molecule is released mainly by adipocytes, our objective was to study its role in atrial cardiomyopathy, focusing our attention on fibrosis, metabolism, and electrophysiological changes. These results might clarify the role of adiposity as a mediator of atrial cardiomyopathy.

METHODS:

We used several preclinical cellular models, epicardial and subcutaneous stroma primary cell cultures from patients undergoing open heart surgery, human atrial fibroblasts, atrial cardiomyocytes derived from human induced pluripotent stem cells and isolated from adult mice, and Nav1.5 transfected Chinese hamster ovary cells. Fibrosis, glucose, mitochondrial and adipogenesis activity, gene expression, and proteomics were determined by wound healing, enzymatic, colorimetric, fluorescence assays, real-time quantitative polymerase chain reaction, and TripleTOF proteomics. Molecular changes were analyzed by Raman confocal microspectroscopy, calcium dynamics by confocal microscopy, and ion currents by patch clamp. Epicardial, subcutaneous, and atrial fibroblasts and cardiomyocytes were incubated with FABP4 at 100 ng/mL.

RESULTS:

Our results showed that FABP4 induced fibrosis, glucose metabolism, and lipid accumulation on epicardial and subcutaneous stroma cells and atrial fibroblasts. Besides, it modified lipid content and calcium dynamics in atrial cardiomyocytes without effects on I
Na
.

CONCLUSIONS:

FABP4 exerts fibrotic and metabolic changes on epicardial stroma and modifies lipid content and calcium dynamic on atrial cardiomyocytes. These results suggest its possible role as an atrial cardiomyopathy mediator.
c-Src Is Responsible for Mitochondria-Mediated Arrhythmic Risk in Ischemic Cardiomyopathy
Authors: Xie An , Kang Gyeoung-Jin , Kim Eun Ji , Liu Hong , Feng Feng , Dudley Samuel C.
Published: August 2024

BACKGROUND:

Increased mitochondrial Ca
2+
uptake has been implicated in the QT prolongation and lethal arrhythmias associated with nonischemic cardiomyopathy. We attempted to define the role of mitochondria in ischemic arrhythmic risk and to identify upstream regulators.

METHODS:

Myocardial infarction (MI) was induced in wild-type FVB/NJ mice by ligation of the left anterior descending coronary artery. Western blot, immunoprecipitation, ECG telemetry, and patch-clamp techniques were used.

RESULTS:

After MI, c-Src (proto-oncogene tyrosine-protein kinase Src) and its active form (phosphorylated Src, p-Src) were increased. The activation of c-Src was associated with increased diastolic Ca
2+
sparks, action potential duration prolongation, and arrhythmia in MI mice. c-Src upregulation and arrhythmia could be reversed by treatment of mice with the Src inhibitor PP1 but not with the inactive analogue PP3. Tyrosine phosphorylated mitochondrial Ca
2+
uniporter (MCU) was upregulated in the heart tissues of MI mice and patients with ischemic cardiomyopathy. In a heterologous expression system, c-Src could bind MCU and phosphorylate MCU tyrosines. Overexpression of wild-type c-Src significantly increased the mitochondrial Ca
2+
transient while overexpression of dominant-negative c-Src significantly decreased the mitochondrial Ca
2+
transient. c-Src inhibition by PP1, MCU inhibition by Ru360, or MCU knockdown could reduce the action potential duration, Ca
2+
sparks, and arrhythmia after MI. The human heart tissue showed that patients with ischemic cardiomyopathy had significantly increased c-Src active form associated with increased MCU tyrosine phosphorylation and ventricular arrhythmia.

CONCLUSIONS:

MI leads to increased c-Src active form that results in MCU tyrosine phosphorylation, increased mitochondrial Ca
2+
uptake, QT prolongation, and arrhythmia, suggesting c-Src or MCU may represent novel antiarrhythmic targets.
Cardiovascular, Kidney, and Safety Outcomes With GLP-1 Receptor Agonists Alone and in Combination With SGLT2 Inhibitors in Type 2 Diabetes: A Systematic Review and Meta-Analysis
Authors: Neuen Brendon L. , Fletcher Robert A. , Heath Lauren , Perkovic Adam , Vaduganathan Muthiah , Badve Sunil V. , Tuttle Katherine R. , Pratley Richard , Gerstein Hertzel C. , Perkovic Vlado , Heerspink Hiddo J.L.
Published: August 2024

BACKGROUND:

GLP-1 (glucagon-like peptide-1) receptor agonists and SGLT2 (sodium-glucose cotransporter 2) inhibitors both improve cardiovascular and kidney outcomes in people with type 2 diabetes. We conducted a systematic review and meta-analysis to assess the effects of GLP-1 receptor agonists on clinical outcomes with and without SGLT2 inhibitors.

METHODS:

We searched MEDLINE and Embase databases from inception until July 12, 2024, for randomized, double-blind, placebo-controlled outcome trials of GLP-1 receptor agonists in type 2 diabetes that reported treatment effects by baseline use of SGLT2 inhibitors, with findings supplemented by unpublished data. We estimated treatment effects by baseline SGLT2 inhibitor use using inverse variance-weighted meta-analysis. The main cardiovascular outcomes were major adverse cardiovascular events (nonfatal myocardial infarction, stroke, or cardiovascular death) and hospitalization for heart failure. Kidney outcomes included a composite of ≥50% reduction in estimated glomerular filtration rate, kidney failure or death caused by kidney failure, and annualized rate of decline in estimated glomerular filtration rate (estimated glomerular filtration rate slope). Serious adverse events and severe hypoglycemia were also evaluated. This meta-analysis was registered on the International Prospective Register of Systematic Reviews (PROSPERO; CRD42024565765).

RESULTS:

We identified 3 trials with 1743 of 17 072 (10.2%) participants with type 2 diabetes receiving an SGLT2 inhibitor at baseline. GLP-1 receptor agonists reduced the risk of major adverse cardiovascular events by 21% (hazard ratio [HR], 0.79 [95% CI, 0.71–0.87]), with consistent effects in those receiving and not receiving SGLT2 inhibitors at baseline (HR, 0.77 [95% CI, 0.54–1.09] and HR, 0.79 [95% CI, 0.71–0.87], respectively;
P
-heterogeneity=0.78). The effect on hospitalization for heart failure was similarly consistent regardless of SGLT2 inhibitor use (HR, 0.58 [95% CI, 0.36–0.93] and HR, 0.73 [95% CI, 0.63–0.85];
P
-heterogeneity=0.26). Effects on the composite kidney outcome (risk ratio, 0.79 [95% CI, 0.66–0.95]) and estimated glomerular filtration rate slope (0.78 mL/min/1.73 m
2
/y [95% CI, 0.57–0.98]) also did not vary according to SGLT2 inhibitor use (
P
-heterogeneity=0.53 and 0.94, respectively). Serious adverse effects and severe hypoglycemia were also similar regardless of SGLT2 inhibitor use (
P
-heterogeneity=0.29 and 0.50, respectively).

CONCLUSIONS:

In people with type 2 diabetes, the cardiovascular and kidney benefits of GLP-1 receptor agonists are consistent regardless of SGLT2 inhibitor use.
Regional deep atrophy: Using temporal information to automatically identify regions associated with Alzheimer’s disease progression from longitudinal MRI
Authors: Dong Mengjin , Xie Long , Das Sandhitsu R. , Wang Jiancong , Wisse Laura E.M. , deFlores Robin , Wolk David A. , Yushkevich Paul A.
Published: August 2024

Longitudinal assessment of brain atrophy, particularly in the hippocampus, is a well-studied biomarker for neurodegenerative diseases, such as Alzheimer’s disease (AD). Estimating brain progression patterns can be applied to understanding the therapeutic effects of amyloid-clearing drugs in research and detecting the earliest sign of accelerated atrophy in clinical settings. However, most state-of-the-art measurements calculate changes directly by segmentation and/or deformable registration of MRI images, and may misreport head motion or MRI artifacts as neurodegeneration, impacting their accuracy. In our previous study, we developed a deep learning method DeepAtrophy that uses a convolutional neural network to quantify differences between longitudinal MRI scan pairs that are associated with time. DeepAtrophy has high accuracy in inferring temporal information from longitudinal MRI scans, such as temporal order or relative interscan interval. DeepAtrophy also provides an overall atrophy score that was shown to perform well as a potential biomarker of disease progression and treatment efficacy. However, DeepAtrophy is not interpretable, and it is unclear what changes in the MRI contribute to progression measurements. In this paper, we propose Regional Deep Atrophy (RDA), which combines the temporal inference approach from DeepAtrophy with a deformable registration neural network and attention mechanism that highlights regions in the MRI image where longitudinal changes are contributing to temporal inference. RDA has similar prediction accuracy as DeepAtrophy, but its additional interpretability makes it more acceptable for use in clinical settings, and may lead to more sensitive biomarkers for disease monitoring and progression understanding in preclinical AD.
Individual Differences in Accent Imitation
Authors: Myers Emily B. , Olson Hannah E. , Scapetis-Tycer Jennifer
Published: August 2024

All talkers show some flexibility in their speech, and the ability to imitate an unfamiliar accent is a skill that shows vast individual differences. Yet the source of these individual differences, in particular whether they originate from perceptual, motor, or social/personality factors, is not yet clear. In the current study, we ask how individual differences in these factors predict individual differences in deliberate accent imitation. Participants imitated three accents, and attempts were rated for accuracy. A set of measures tracking individual differences in perceptual, motor, cognitive, personality, and demographic factors were also acquired. Imitation ability was related to differences in musical perception, vocal articulation, and the personality characteristic of “openness to experience,” and was affected by attitudes towards the imitated talkers. Taken together, results suggest that deliberate accent imitation skill is modulated not only by core perceptual and motor skills, but also by personality and affinity to the talker, suggesting that some aspects of deliberate imitation are a function of domain-general constraints on perceptual-motor systems, while others may be modulated by social context.
Trade Gap and Public Debt Sustainability in Nigeria
Authors: Kamo Kpeyol , Andohol Jerome , Anjande Gbatsoron , Ijirshar Victor Ushahemba
Published: August 2024

This study assessed the effect of the trade gap on public debt sustainability and the transmission from the trade gap to public debt sustainability through the revenue channel using annual data for Nigeria from 1970 to 2022. The novelty of this study lies in its investigation of factors affecting trade gap shocks and its departure from conventional debt sustainability metrics, offering a deeper understanding of the connection between trade gap and public debt sustainability. The study has been anchored on the theory of trade misinvoicing. The researchers applied the structural vector autoregressive (SVAR) method, which has been estimated using the impulse response function tool. The findings established a negative effect of the trade gap on public debt sustainability. The results further show that the revenue variable is, on average, related to a larger (negative) effect of the trade gap. However, the effect of the trade gap on debt sustainability is shown to be communicated through budget deficits and not the revenue channel. The study recommends that policymakers should prioritise measures to counteract the negative impact of the trade gap on the economy. This may include boosting exports, decreasing import reliance and enhancing competitiveness in global markets.

JEL Codes: F14, H62, H63
Less gives less, more gives more: Socialisation and religiosity in the Lisbon Metropolitan Area
Authors: COUTINHO José P. , TEIXEIRA Alfredo , FRANCA Margarida
Published: August 2024

This article explores the theme of religious socialisation in an environment marked by modernity. It has three goals: first, to analyse the changes in religious socialisation across age groups; second, to analyse the impact of religious socialisation on religiosity; and third, to analyse the impact of religiosity on religious socialisation. Based on quantitative data about the Lisbon Metropolitan Area, results show the following: The level of religious socialisation increases with age, but young respondents have been significantly less socialised than older age groups; the level of religious education provided by respondents to their children increases with age; socialisation and religiosity are biunivocal, mutually reinforcing. In short, this article reinforces the theory of cohort replacement, which states that religiosity decreases in younger groups due to the decrease in religious socialisation over time.
Radiographic assessment of incidental bone lesions of the proximal humerus: a prevalence study
Authors: Kara Mustafa , Bayram Serkan , Kocazeybek Emre , Demirtaş Gökhan
Published: August 2024

Background

An increased prevalence of benign lesions has been associated with the increased use of radiological tools in orthopedic practice.

Purpose

To investigate the inter-observer reliability of the radiographic detection of lesions on the proximal humerus between different observers.

Material and Methods

X-ray radiological examinations of 17,243 patients were performed by an orthopedic surgeon to assess the presence of proximal humeral bone lesions. After this initial screening, 201 patients with lesions were identified and these 201 scans were assessed by three different physicians: an orthopedic oncologist, an orthopedic surgeon, and a musculoskeletal radiologist. Of the 201 patients with lesions, 80 had magnetic resonance imaging in addition to X-rays and were assessed. The diagnosis of the lesions was determined by each participant according to their radiological appearance to assess inter-observer reliability.

Results

The prevalence of proximal humeral lesions was determined by the orthopedic surgeon to be 1.165% (201 of 17,243 patients). In 201 patients with a lesion, the mean age was 48 ± 17 years (range = 18–91 years). According to the assessments of the orthopedic oncologist, orthopedic surgeon, and musculoskeletal radiologist, the most common bone lesion identified was enostosis, with prevalence percentages of 53.7%, 59.7%, and 64.7%, respectively. A noteworthy consensus exists in the collective diagnosis of bone lesions among the three investigators, demonstrated by a substantial agreement with Fleiss’ κ at 0.74 ( P < 0.001).

Conclusion

There was remarkable agreement between the three investigators on the collective diagnosis of bone lesions. Enostosis was the most common bone lesion at the proximal humerus in our series.
Routine cognitive screening for older people undergoing major elective surgery: Benefits, risks and costs
Authors: Chan Luke KM , Chuan Alwin , Berney Christophe R , Chan Daniel KY
Published: August 2024

Cognitive impairment and older age are major risk factors for postoperative delirium. Professional societies have advocated preoperative screening to identify at-risk individuals for implementation of interventions, which have moderate effectiveness in preventing delirium. However, it remains unclear from the guidelines whether screening should be completed routinely for all older individuals or targeted, and also which specific screening tool is preferred. In addition, the responsibility for screening remains undesignated in the guidelines provided. We reviewed the benefits and risks of routine screening. We also performed a cost-benefit analysis of routine screening (versus no screening). Furthermore, we summarised the sensitivities and specificities of commonly used screening tools and reviewed evolving screening tools that may have an increasing role in future practice. We concluded that routine screening is useful and appears to be cost-effective for reducing postoperative delirium, with a cost-benefit ratio of 2.89 (adjusted ratio of 2.34), and has additional advantages over other approaches such as targeted screening or routine intervention.
A single-centre retrospective study of the utility of routine chest X-ray post intrathoracic drain removal in cardiothoracic surgical patients
Authors: Rowbottom Reece D , Doshi Hemang P , Bowen David
Published: August 2024

Routine chest X-ray (CXR) post intrathoracic drain removal in cardiac surgical patients is common practice to identify the presence of a pneumothorax following drain removal. Such pneumothoraces occur infrequently and rarely require intervention. We investigated the utility of routine CXR post drain removal and hypothesised that the practice is unnecessary and a possible area for significant cost saving. We conducted a single-centre, retrospective study of 390 patients who underwent cardiac surgery over a one-year period. Routine CXR post drain removal was reviewed for the presence of a pneumothorax. Rates of intervention post routine CXR were analysed to assess for clinical benefit obtained from this practice. Potential cost savings were calculated by the cost of a mobile CXR and by considering the radiographer’s time. There were 15 pneumothoraces detected on routine CXR post drain removal. All pneumothoraces detected on routine post drain removal CXR were defined as small. No patients had a clinically significant pneumothorax requiring re-insertion of a chest drain. The potential cost saved by omitting routine CXR post drain removal was estimated to be approximately A$7750 per year. This study did not detect any clinically significant pneumothoraces requiring intervention. It also suggests that routine CXR post drain removal does not provide any clinical benefit and indicates that current practice should be reviewed.