bluuebunny/crossref_metadata_2025 huggingface dataset:
Membrane-bound Heat Shock Protein mHsp70 Is Required for Migration and Invasion of Brain Tumors
Authors: Shevtsov Maxim , Bobkov Danila , Yudintceva Natalia , Likhomanova Ruslana , Kim Alexander , Fedorov Evegeniy , Fedorov Viacheslav , Mikhailova Natalia , Oganesyan Elena , Shabelnikov Sergey , Rozanov Oleg , Garaev Timur , Aksenov Nikolay , Shatrova Alla , Ten Artem , Nechaeva Anastasiya , Goncharova Daria , Ziganshin Rustam , Lukacheva Anastasiya , Sitovskaya Daria , Ulitin Alexey , Pitkin Emil , Samochernykh Konstantin , Shlyakhto Evgeny , Combs Stephanie E.
Published: July 2024

Molecular chaperones, especially 70 kDa heat shock protein, in addition to their intracellular localization in cancer cells, can be exposed on the surface of the plasma membrane. We report that the membrane-associated chaperone mHsp70 of malignant brain tumors is required for high migratory and invasive activity of cancer cells. Live-cell inverted confocal microscopy of tumor samples from adult (n = 23) and pediatric (n = 9) neurooncologic patients showed pronounced protein expression on the membrane, especially in the perifocal zone. Mass spectrometry analysis of lipid rafts isolated from tumor cells confirmed the presence of the protein in the chaperone cluster (including representatives of other families, such as Hsp70, Hsc70, Hsp105, and Hsp90), which in turn, during interactome analysis, was associated with proteins involved in cell migration (e.g., Rac1, RhoC, and myosin-9). The use of small-molecule inhibitors of HSP70 (PES and JG98) led to a substantial decrease in the invasive potential of cells isolated from a tumor sample of patients, which indicates the role of the chaperone in invasion. Moreover, the use of HSP70 inhibitors in animal models of orthotopic brain tumors significantly delayed tumor progression, which was accompanied by an increase in overall survival. Data demonstrate that chaperone inhibitors, particularly JG98, disrupt the function of mHsp70, thereby providing an opportunity to better understand the diverse functions of this protein and offer aid in the development of novel cancer therapies.

Significance:

Membrane-bound mHsp70 is required for brain tumor cell migration and invasion and therefore could be employed as a target for anticancer therapies.
Insights of learning approach towards determination of potentially objectional communication in social networking
Authors: Nanjundappa Praneetha Garagadakuppe , Naganna Kamalakshi
Published: July 2024

Over the last decade, sentiment analysis has evolved significantly towards extracting the contextual knowledge associated with the communication exchanged in social networks. Irrespective of various approaches to natural language processing and constantly evolving machine learning, sentiment analysis has inherent shortcomings, which further act as an obstacle to determining hateful and offensive speech exchanged in social networks. Therefore, this paper offers a compact yet granular insight into the effectiveness of existing sentiment analysis approaches used distinctly for determining hateful and offensive speech with particular emphasis on machine learning-based methodologies. The paper further contributes towards research trend analysis followed by distinct highlights of the research gap. The paper offers a learning outcome that significantly benefits future researchers investigating the same field.
Morphology for hexagonal image processing: a comprehensive simulation analysis
Authors: Cevik Taner , Nematzadeh Sajjad , Rasheed Jawad , Alshammari Abdulaziz
Published: July 2024

Morphological operators for binary and grayscale images are commonly used to eliminate noise, recognize contours or specific structures, and arrange shapes in image processing for physiological modeling and biomechanics applications. Even though morphology has been substantially developed in square-pixelbased-image-processing (SIP), no effort has been made to construct morphological operators in hexagonal-pixel-based-image-processing (HIP) yet. In this paper, we transform basic SIP-domain-morphological operators such as dilation, erosion, closing, and opening into HIP-domain and compare their performance with their SIP counterparts. It is the first time to give the fundamental morphological operators in the HIP domain. The operators developed in this paper initiate the research about morphology in the HIP domain by successfully filling a significant gap by eliminating HIP’s lack of basic operators, thus capable of producing enhanced images for better analysis in anatomical models related to biology and medicine research fields.
The effect of features combination on coloscopy images of cervical cancer using the support vector machine method
Authors: Supriyanti Retno , Aryanto Andreas S. , Akbar Mohammad Irham , Sutrisna Eman , Alqaaf Muhammad
Published: July 2024

Cervical cancer is cancer that grows in cells in the cervix. This cancer generally develops slowly and only shows symptoms when it has entered an advanced stage. Therefore, it is crucial to detect cervical cancer early before serious complications arise. One way to detect cervical cancer early is to use colposcopy, which is to look closely at the condition of the cervix to find changes in cells in the cervix that have the potential to become cancer. However, this method requires the expertise of an obstetrician. This research proposes the use of image processing techniques to create automatic early detection of cervical cancer based on coloscopy images. In this paper, we will discuss image selection using an approach in the form of comparing the weights of feature vectors and then using a data distribution threshold, features that are not too influential can be eliminated. Image classification uses the Support Vector Machine (SVM) method, which makes it possible to distinguish normal images from abnormal images. Classification with feature selection and merging results can improve the consistency of SVM model performance evenly across all four SVM kernels.
Software defined network-based controller system in intelligent transportation system
Authors: Kirthima Arjunan Mari , Krishnaraju Pushpa Sothenahalli
Published: July 2024

Intelligent transportation system (ITS) is meant for redefining the conventional transport by incorporating various analytical features that not only offers safety but also enriches traffic data quality extensively. Review of existing literature shows that there is a significant gap towards utilizing vehicular adhoc network (VANET) for optimal performance in ITS environment. Therefore, this paper contributes towards a simplified and yet intelligent controller system harnessing potential of software defined network (SDN) towards effective directional management of complex transportation system. The novelty of this model is dual-fold. The first novelty is about the usage of locally and globally processed traffic information for undertaking decision towards clearing the waiting vehicles in observation point in specific route segment. The second novelty is associated with relaying of distinct traffic clearance signal to the distinct vehicles unlike any of teh existing transportation management scheme.
Advanced digital competency assessment of vocational teachers': A new approach based on fuzzy-analytical hierarcy process
Authors: Ramadhan Islami Aditya , Abdullah Ade Gafar , Widiaty Isma , Yulia Cica , Lukman Hakim Dadang , Handoko Erfan , Subekti Eri , Rahmawati Sherly
Published: July 2024

Teachers need digital competence to adapt easily to the current digital era. This study tries to discover the perceptions of vocational high school (VHS) teachers in Indonesia related to advanced digital competencies, which include information, communication, content creation, digital security, and problem-solving competencies. The multi-criteria Analytical Hierarchy Process (AHP) problem-solving method is used to rank the priority digital competencies that are most mastered by the respondents, and then their performance is validated by the fuzzy AHP artificial intelligence-based method. A poll was conducted with 392 respondents, with the research instrument adopting the digital competency measurement platform from DigComp. The study's results show that the fuzzy AHP method has proven that the classical AHP method is a very good way to prioritize VHS teachers' digital skills based on several factors. The two methods gave almost identical results in determining the priority order of VHS teacher digital competencies. The survey results reveal that VHS teachers in Indonesia must immediately develop their skills in terms of digital content creation and digital security. Teachers, teacher professional organizations, and decision-makers are expected to use the findings of this study as a reference in implementing VHS teacher digital competency improvement trainings.
A novel approach to wastewater treatment control: A self-organizing fuzzy sliding mode controller
Authors: Kumara Varuna , Ganesan Ezhilarasan
Published: July 2024

The treatment of wastewater plays a crucial role in protecting the environment and ensuring the sustainable use of resources. This research paper presents a new methodology for managing wastewater treatment operations, utilising Self-Organizing Fuzzy Sliding Mode Controller (SOFSMC) to enhance the efficiency of treatment procedures. MATLAB Simulink functions as a simulation tool that facilitates meticulous analysis. SOFSMC presents a control strategy that is both adaptive and robust. This strategy effectively regulates crucial parameters, including dissolved oxygen levels, pH levels, and flow rates. It achieves this within the challenging and complex framework of wastewater treatment, which is characterised by dynamic and nonlinear dynamics. Using a SOFSMC for wastewater treatment control is novel approach. This novel technique creates a self-learning, dynamic system using fuzzy logic (FL) and sliding mode control (SMC). This unique approach can autonomously adapt to wastewater treatment processes' complex and nonlinear dynamics, improving efficiency, resource optimisation, and system dependability. The results emphasise the potential of SOFSMC as a revolutionary approach for wastewater treatment. This approach can improve treatment effectiveness, conserve resources, and protect the environment. The proposed method SOFSMC, exhibits commendable outcomes, with an integrated absolute error of 0.082 mg/L, an integrated square differential error of 0.091 mg/L, and a response time of 1.85 seconds This study offers a substantial advancement in the field of wastewater treatment regulation, highlighting its significance in the context of sustainable water management and environmental conservation.
Fuzzy logic for the management of vaccination during pandemics: A spread-rate-based approach
Authors: Kareem Abdul , Kumara Varuna
Published: July 2024

Pandemics, such as coronavirus disease COVID-19 are known to cause massive damage to the world's economic growth and their impacts are serious and influence across every aspect of social structure. The most inevitable factor in responding to the disaster of pandemics is the right management in terms of allocating a limited vaccine supply. The focus of this research work is to utilize a fuzzy logic inference system in the allocation of vaccine doses to the regional authorities by a central authority. The objective is obtained by designing a system based on fuzzy logic that considers the spread rate as the input to infer the vaccination rate of the local population. This system makes it possible for sufficient doses of vaccines to be allotted to the prioritized regions where the severity of the spread rate is a concern and vaccines are not held up in regions where the severity of the spread rate is lesser. The designed system is verified using MATLAB software, which shows that this method can ensure an effective and efficient allocation of vaccination in the local regions and aid the fight against the disastrous spread of the disease.
Enhancing stroke prediction using the waikato environment for knowledge analysis
Authors: Altayeb Muneera , Arabiat Areen
Published: July 2024

State-of-the-art data mining tools incorporate advanced machine learning (ML) and artificial intelligence (AI) models, and it is widely used in classification, association rules, clustering, prediction, and sequential models. Data mining is important for the process of diagnosing and predicting diseases in the early stages, and this contributes greatly to the development of the health services sector. This study utilized classification to predict the stroke of a sample of the patient dataset that was taken from Kaggle. The classification model was created using the data mining program waikato environment for knowledge analysis (WEKA). This data mining tool helped identify individuals most at risk of stroke based on analysis of features extracted from the patient’s dataset. These features were used in classification processes according to the naive Bayes (NB), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) algorithms. Analysis of the classification results of the previous algorithms showed that the SVM outperformed other algorithms in terms of accuracy (94.4%), sensitivity (100%), and F-measure (97.1%). However, the NB algorithm had the best performance in terms of precision (95.7%).
A design of a brain tumor classifier of magnetic resonance imaging images using ResNet101V2 with hyperparameter tuning
Authors: Maulana Zein Rhendiya , Effendy Nazrul , Basuki Endro , Nopriadi Nopriadi
Published: July 2024

Brain tumors are a disease that is quite dangerous and requires severe treatment. One thing that is quite important is the process of diagnosing the brain tumor. This diagnosis process requires intense attention, and differences in interpretation may arise. Machine learning has been used in several fields, including disease diagnosis. This paper proposes an intelligent diagnostic tool for brain tumors using ResNet101v2. ResNet101V2 is used to classify meningioma, glioma, pituitary, and normal from magnetic resonance imaging (MRI) images. This research includes data collection, data preprocessing, ResNet101v2 design and evaluation. We investigate three models of ResNet101v2 for brain tumor classification. The best model achieves an accuracy of 96.2%.
Review of early and accurate detection of Parkinson’s disease
Authors: Mathew Biju Soly , Al-Khatib Obada , Chuan Lim Hock , Malt Suzanne , Zahid Sheikh Hashir
Published: July 2024

Parkinson’s disease (PD) is a central sensory system-based progressive illness with no cure. The origin of this illness is unknown. According to various research, it has been found that it is caused due to genetics or environmental factors. It is usually found in older people. However, there is no accurate treatment for this disease. So, the patient must be monitored periodically. It usually starts with deterioration in speech performance. The major problem with this disease is that it’s very costly to treat. The paper aims to report details of numerous aspects of detection of PD published in recent years based on the focus and benefits of the study, the methodology being used, accuracy of the system, and future research suggested for the study. A systematic study was done based on a search of the literature. A total of 50 articles were discovered.
Deep retino-network for automatic quantification of diabetic retinopathy
Authors: Amin Jameel Syed , Mohamed Shanavas Abdul Rahim
Published: July 2024

Diabetic retinopathy (DR) is the ocular manifestation of the systemic disease. Since it is the most prevalent cause of blindness in the world, it demands a significant amount of therapeutic attention. As a result, a precise assessment of the DR condition as well as its evolution is very important for treatment. In this work, an automated quantification of diabetic retinopathy state (AQDRS) using fundus images is proposed. The state of DR is classified into 0 (low) to 3 (high) with the help of a deep retino-network (DRN). Before the classification by DRN, an image down-sampling scheme is employed. A DRN consists of convolution layer and max-pooling layers to extract the deep retina features and fully connected layer (FCL) for AQDRS where feed-forward neural network is employed for the classification. The performance of AQDRS by DRN for grading DR is evaluated using methods to evaluate segmentation and indexing techniques in the field of retinal ophthalmology (MESSIDOR) database. Results show that the AQDRS by DRN can able to extract the relevant discriminative information for grading the fundus image. The average accuracy on normal images in MESSIDOR database is 97.9% and it is 95.3% for DR images.
Facemask detection and classification using you only look once version 7
Authors: Al-Rasheedi Gareebah , Ullah Khan Rehan
Published: July 2024

World Health Organization (WHO) suggests that wearing masks and keeping social distancing are the best ways to avoid infection transmission of communicable diseases. Consequently, most governments have forced people to wear masks in public areas to prevent communicable diseases such as COVID-19. Manual monitoring and surveillance are time-consuming and not always possible in crowded areas. Hence, object detection deep learning models can effectively handle these challenges. Therefore, this work aims to investigate the efficiency of different versions of the you only look once version 7 (YOLOv7) model in facemask detection and classification over the privately balanced dataset. The dataset comprises of 1,300 images with four novel classes; including no occlusion, correct mask, incorrect mask, and other use cases. Furthermore, the model’s performance was evaluated based on mean average precision (mAP), recall, precision, and inference time. Finally, a comparative result analysis has been reported to determine the best model for facemask detection and classification. YOLOv7 model versions exhibit widely various performances ranging from 20.7% mAP for YOLOv7-D6 to 95.5% for YOLOv7-tiny. In contrast, the inference time for all YOLOv7 versions covers a narrow range of 3 ms. In conclusion, the YOLOv7-tiny version outperforms other models, achieving a high detection performance and acceptable detection speed.
Intelligent classification and performance prediction of multi-text assessment with recurrent neural networks-long short-term memory
Authors: Paryono Tukino , Sediyono Eko , Hendry Hendry , Huda Baenil , Lia Hananto April , Yuniar Rahman Aviv
Published: July 2024

The assessment document at the time of study program accreditation shows performance achievements that will have an impact on the development of the study program in the future. The description in the assessment document contains unstructured data, making it difficult to identify target indicators. Apart from that, the number of Indonesian-based assessment documents is quite large, and there has been no research on these assessment documents. Therefore, this research aims to classify and predict target indicator categories into 4 categories: deficient, enough, good, and very. Learning testing of the Indonesian language assessment sentence classification model using recurrent neural networks-long short-term memory (RNN-LSTM) using 5 layers and 3 parameters produces performance with an accuracy value of 94.24% and a loss of 10%. In the evaluation with the Adamax optimizer, it had a high level of accuracy, namely 79%, followed by stochastic gradient descent (SGD) of 78%. For the Adam optimizer, Adadelta, and root mean squared propagation (RMSProp) have an accuracy rate of 77%.
Epilepsy detection using wavelet transform, genetic algorithm, and decision tree classifier
Authors: Zougagh Lahcen , Bouyghf Hamid , Nahid Mohammed , Sabiri Issa
Published: July 2024

This work presents a unique detection approach for classifying epilepsy using the CHB_MIT dataset. The suggested system utilizes the discrete wavelet transform (DWT) technique, genetic algorithm (GA), and decision tree (DT). This model consists of three distinct steps. In the first one, we present a feature extraction method that uses a DWT of four levels on electroencephalogram (EEG) and electrocardiogram (ECG) signals. The second step is the process of feature selection, which entails the elimination of irrelevant features in order to produce datasets of superior quality. This is achieved via the use of correlation and GA techniques. The reduction in dimensionality of the dataset serves to decrease the complexity of the training process and effectively addresses the problem of overfitting. The third step utilizes a DT algorithm to make predictions based on the data of epileptic patients. The performance evaluation layer encompasses the implementation of our prediction model on the CHB-MIT dataset. The results achieved from this implementation show that using feature selection techniques and an ECG signal as additional information increases the detection model's performance. The averaging accuracy is 98.3%, the sensitivity is 96%, and the specificity is 99%.
A framework of attribute extraction and dependable aspect term selection from reviews of hospital websites
Authors: Mohammed Basha Nasreen Taj , Gowdra Shivappa Girisha
Published: July 2024

Online reviews found on hospital websites and external platforms constitute user-generated content where patients and their families share their firsthand encounters. As patients increasingly rely on online platforms to share their experiences, understanding the importance of their feedback is paramount for healthcare providers. The novelty of this research lies in the development of advanced frameworks that not only extract relevant information but also offer a more sophisticated and coherent analysis of the multifaceted aspects embedded in patient reviews. Hence, this work involves collecting data from various hospital websites, followed by data pre-processing to ensure accuracy and consistency. Subsequently, two distinct frameworks are proposed. The first framework aims to extract specific attributes (topics) mentioned in reviews, enhancing the granularity of information derived from the collected data. The second framework addresses the efficient extraction of aspect terms from pre-processed data, utilizing a coherence score-based approach called as modified latent dirichlet allocation term frequency-inverse document frequency (M-LDA TF-IDF). The M-LDA TF-IDF has achieved better a coherence score of 0.478 which is much better in comparison with other topic modelling approaches.
A Horner’s polynomial based quadrupedal multi-gaits signal generation controller
Authors: Olivier Akansie Kouame Yann , C. Biradar Rajashekhar , Rajendra Karthik , D. Devanagavi Geetha
Published: July 2024

Animal locomotion is the process through which animals move from one location to another. Self-propelled locomotion is based on the animal performing a series of actions to move towards a predetermined target. All of these motions occur sequentially and repeat themselves during a gait cycle. A gait cycle may be simulated by duplicating each motion in the cycle sequentially. To achieve this goal, a problem known as the gait planning issue was formulated, in which various systems were created to provide suitable signals for the execution of distinct gaits (patterns of steps of an animal at a specified speed). This research approaches the problem using Horner's polynomials for quadruped robots. The approach entails first creating a sequence table for each gait and fit two polynomial equations. In this study, an attempt is made to combine several gaits using Horner's polynomials. An algorithm uses elaborated polynomials to generate the desired gaits signals.
Data-driven farming: implementing internet of things for agricultural efficiency
Authors: Ismail Lafta Mohamed , Dawood Abdullah Wisam
Published: July 2024

Integrating internet of things (IoT) technology into agriculture has become essential to address challenges such as low crop productivity, which is often due to insufficient soil nutrients and suboptimal environmental conditions. This paper discusses the design and implementation of an IoT-based system for agriculture that aims to automate key parameters, facilitate real-time monitoring, and promote sustainable practices. Equipped with a graphical user interface (GUI), the system focuses on improving irrigation, regulating temperatures, and correcting soil nutrient deficiencies to improve crop productivity. Our research includes the use of humidity sensors to monitor soil moisture and temperature sensors. Soil nutrient levels, especially nitrogen, phosphorus, and potassium (NPK), were also assessed. We conducted experiments on three radish varieties using this IoT system and compared the results with traditional farming methods. The germination rate was impressive, reaching 98% within the first four days, while in a traditional greenhouse, it did not exceed 50%. As for plant height and leaf area, the smart greenhouse was better. These results were promising and demonstrated the potential of IoT in enhancing agricultural productivity. These results highlight the significant impact of IoT technology in enhancing agricultural productivity and its potential for broader application in this sector.
Unlocking the Mysteries of Reproduction: Exploring Fecundity and Gonadosomatic Index in the Enigmatic Bornean Pygmy Halfbeak, Dermogenys colletei (Meisner, 2001) (Beloniformes: Zenarchopteridae)
Authors: Latif Kamil ,   Department of Animal Science and Fishery, Faculty of Agricultural and Forestry Sciences, University Putra  Malaysia, Bintulu Campus, Nyabau Road, 97008 Bintulu, Sarawak, Malaysia, Abit Lirong Yu , Al-Asif Abdulla , Pengarah Leonard Umban ,   Curtin Aquaculture Research Laboratories, Faculty of Engineering and Science, Curtin University Malaysia, CDT 250,98009, Miri, Sarawak, Malaysia,   Department of Animal Science and Fishery, Faculty of Agricultural and Forestry Sciences, University Putra  Malaysia, Bintulu Campus, Nyabau Road, 97008 Bintulu, Sarawak, Malaysia,   Department of Animal Science and Fishery, Faculty of Agricultural and Forestry Sciences, University Putra  Malaysia, Bintulu Campus, Nyabau Road, 97008 Bintulu, Sarawak, Malaysia
Published: July 2024

The pygmy halfbeak Dermogenys colletei is known for its viviparous nature, this presents an intriguing case of relatively low fecundity, raising questions about potential compensatory reproductive strategies employed by this species. Our study delves into the examination of fecundity and the Gonadosomatic Index (GSI) in the Pygmy Halfbeak, D. colletei (Meisner, 2001), an intriguing viviparous fish indigenous to Sarawak, Borneo. We hypothesize that the Pygmy halfbeak, D. colletei, may exhibit unique reproductive adaptations to offset its low fecundity, thus enhancing its survival and fitness. To address this, we conducted a comprehensive study utilizing 28 mature female specimens of D. colletei, carefully measuring fecundity and GSI to shed light on the reproductive adaptations of this species. Our findings reveal that D. colletei indeed exhibits low fecundity, with a mean of 16.76 ± 2.01, and a mean GSI of 12.83 ± 1.27, providing crucial insights into the reproductive mechanisms at play in this species. These results underscore the existence of unique reproductive strategies in D. colletei, enabling its adaptation and persistence in Borneo's diverse aquatic ecosystems, and call for further ecological research to elucidate these mechanisms. This study lends to a better understanding of viviparous fish in Borneo and contributes to the broader field of aquatic ecology, enhancing our knowledge of species adaptations to unique ecological challenges.
An Overview of Traditional Medicinal Plants Used in Treating Hepatocellular Carcinoma (HCC) with Emphasis on Mechanisms of Action
Authors: Adekilekun Habeebulahi Ajibola ,   Department of Pharmacology and Toxicology, Kampala International University, P. O. Box 71, Bushenyi,  Western Campus, Ishaka, Uganda
Published: July 2024

Cancer is an incurable fatal disease wreaking havoc on the population of countries either developed or developing. Hepatocellular carcinoma (HCC) is the fifth most frequent type of cancer having a poor prognosis. Globally, HCC management and treatment intervention using medicinal plants from generation to generation is rapidly increasing and remains an area of promising research. This review provided insight into medicinal plants' ethnomedicinal use, photochemistry and mechanisms of action on HCC exhibiting anti-cancer potentials subjected to scientific investigations. Furthermore, clinical, toxicity studies and detailed mechanisms of action would provide an understanding of the development of lead compounds in drug discovery and development for HCC.
Integrating anatomical and functional landmarks for interparticipant alignment of imaging data
Authors: Jeganathan Jayson , Paton Bryan , Koussis Nikitas , Breakspear Michael
Published: July 2024

Aligning brain maps using functional features rather than anatomical landmarks potentially improves individual identifiability and increases power in group neuroimaging studies. However, alignment based purely on functional magnetic resonance imaging (fMRI) risks omitting useful anatomical constraints. An optimized combination of anatomical and functional feature alignment could balance the advantages of each approach. We used 3T fMRI data from 80 Human Connectome Project participants during seven tasks. The effectiveness of functional and anatomical alignment methods was evaluated using interparticipant decoding accuracy. Functional alignment mapped vertices from participants to a template, aligning their fMRI responses to shared responses during movie viewing. The template was derived from the combined fMRI responses of a set of participants. We benchmarked the results against existing functional alignment methods, including the Procrustes method and ridge regression. A common practice in the field is to use the same participants for the alignment cohort and for template generation. We found that this inflates decoding accuracies by mixing anatomical and functional alignment. Based on this, we recommend that a template’s generalizability should be evaluated against held-out participants. Building on these findings, we investigated whether inter-subject alignment could be improved by integrating anatomical and functional information. We studied a modified alignment method where a single parameter interpolates between pure functional alignment and anatomical alignment. Optimizing the parameter with nested cross-validation, we found that integrating anatomical and functional information robustly reduced noise and improved alignment across a variety of alignment methods. Combining anatomical and functional information accounts for individual heterogeneity in functional topographies while incorporating anatomical constraints. The integrated alignment described here improves inter-subject decoding using functional brain maps. These findings also demonstrate that brain anatomy provides a lens into the inherent variability of individual neural landscapes.
Weaning failure due to isolated residual diaphragmatic paralysis after cervical spinal cord ischemia following aortic surgery- a case report
Authors: Overbeek Remco , Behrens Amelie , Zopfs David , Mylonas Spyridon , Dorweiler Bernhard , Dusse Fabian , Böttiger Bernd W. , Stoll Sandra Emily
Published: July 2024

Background

Bilateral diaphragmatic dysfunction can lead to dyspnea and recurrent respiratory failure. In rare cases, it may result from high cervical spinal cord ischemia (SCI) due to anterior spinal artery syndrome (ASAS). We present a case of a patient experiencing persistent isolated diaphragmatic paralysis after SCI at level C3/C4 following thoracic endovascular aortic repair (TEVAR) for Kommerell’s diverticulum. This is, to our knowledge, the first documented instance of a patient fully recovering from tetraplegia due to SCI while still exhibiting ongoing bilateral diaphragmatic paralysis.

Case presentation

The patient, a 67-year-old male, presented to the Vascular Surgery Department for surgical treatment of symptomatic Kommerell’s diverticulum in an aberrant right subclavian artery. After successful surgery in two stages, the patient presented with respiratory insufficiency and flaccid tetraparesis consistent with anterior spinal artery syndrome with maintained sensibility of all extremities. A computerized tomography scan (CT) revealed a high-grade origin stenosis of the left vertebral artery, which was treated by angioplasty and balloon-expandable stenting. Consecutively, the tetraparesis immediately resolved, but weaning remained unsuccessful requiring tracheostomy. Abdominal ultrasound revealed a residual bilateral diaphragmatic paralysis. A repeated magnetic resonance imaging (MRI) 14 days after vertebral artery angioplasty confirmed SCI at level C3/C4. The patient was transferred to a pulmonary clinic with weaning center for further recovery.

Conclusions

This novel case highlights the need to consider diaphragmatic paralysis due to SCI as a cause of respiratory failure in patients following aortic surgery. Diaphragmatic paralysis may remain as an isolated residual in these patients.
A combined endovascular and open repair of innominate artery bifurcation pseudoaneurysm: a case report
Authors: Xu Youyao , Zhu Tao , Chen Guobin , Wu Senyan , Li Xiaoyang
Published: July 2024

Background

Innominate artery aneurysms (IAAs) are rare and may result in rupture, distal arterial embolization, or local compression without timely treatment. Rupture is the most dangerous of these complications. This article reports a case of innominate artery bifurcation pseudoaneurysm.

Case presentation

The patient was a 45-year-old man who was admitted to the emergency department due to chest discomfort. The computed tomographic angiography (CTA) imaging indicated the presence of a 3.6*2.4 cm saccular aneurysm in the bifurcation of the innominate artery, involving both the right proximal subclavian and common carotid arteries. The patient’s vital signs were normal, there was equal blood pressure in the upper arms and no neurological dysfunction was observed. Gadolinium-enhanced magnetic resonance angiography indicated that the circle of Willis was intact. The treatment involved open surgery combined with endovascular therapy. The external carotid artery was first transposed to the right subclavian artery (RSA) and an 8-mm woven Dacron graft was inserted in the middle. The covered stent graft was then placed in the proximal part of the innominate artery to close the entrance of the aneurysm. Lastly, an occluder was implanted at the origin of the RSA. There were no perioperative or postoperative complications. At 1-year follow-up, no aneurysm was observed on CTA and the right vertebral artery was patent.

Conclusions

This study indicated that the combined use of endovascular therapy and open repair surgery is an effective strategy to treat innominate artery bifurcation pseudoaneurysm.
Current situation, strengths and problems in intra- and interprofessional collaboration in German nursing homes – A holistic multiple case study
Authors: Schmüdderich Kathrin , Dörner Jonas , Fahsold Anne , Palm Rebecca , Roes Martina , Holle Bernhard
Published: July 2024

Background

The increasing care complexity of nursing home residents living with dementia requires new care models that strengthen professional collaboration. To contribute to the sustainable implementation of new care models, it is important that they are linked to the care reality. However, little is known about intra- and interprofessional organization and provision of care in German nursing homes. Therefore, the aim of this study was to explore the current care situation, problems and strengths regarding intra- and interprofessional collaboration in the care of residents living with dementia.

Methods

We conducted a holistic multiple case study. The individual care units in which residents living with dementia are cared for were defined as cases. The context was built by the respective nursing homes and their regional affiliation to the federal state of North Rhine-Westphalia. We used qualitative face-to-face interviews, documents and context questionnaires for data collection. The different sources of evidence served to capture complementary perspectives and to validate the findings. First, the collected qualitative data were analyzed using deductive-inductive content analysis. Second, similarities and differences between the cases were identified to elaborate case-specific and cross-case patterns and themes. The reporting followed the EQUATOR reporting guideline for organizational case studies.

Results

We included four care units comprising 21 professionals (nurses, physicians, social worker, physiotherapist, pharmacist) and 14 relatives of residents living with dementia. The analysis revealed four categories to describe current intra- and interprofessional collaboration in all cases: actors and their roles, service delivery, coordination and governance, and communication channel. Moreover, we identified three categories that relate to the strengths and problems of intra- and interprofessional collaboration in all cases: role understanding, teamwork, and communication and exchange. Although we examined similar care units, we found differences in the realization of professional collaboration and resulting problems and strengths that are connected to the organizational contexts and strategies used.

Conclusions

Even though professional collaboration follows given patterns; these patterns do differ context-specifically and are perceived as problematic and fragmentary. Therefore, the identified differences and problems in collaboration need to be addressed in future research to develop and successfully implement tailored innovative care models.
Increased level of TXNIP and nuclear translocation of TXN is associated with end stage renal disease and development of multiplex renal tumours
Authors: Beothe Tamas , Docs Janos , Kovacs Gyula , Peterfi Lehel
Published: July 2024

Background

End-stage and acquired cystic renal disease (ESRD/ACRD) kidneys are characterized by inflammatory remodelling and multiplex renal cell carcinomas (RCC). Eosinophilic vacuolated tumour (EVT) occurs exclusively in ACRD. The aim of this study was to identify the involvement of thioredoxin-interacting protein (TXNIP) and thioredoxin (TXN) in ESRD/ACRD pathology.

Methods

Expression of TXNIP and TXN was examined in histological slides of 6 ESRD and 6 ACRD kidneys, precursor lesions and associated tumours as well as of RCCs from the general population by immunohistochemistry.

Results

Strong TXNIP expression was seen in epithelial cells, myo-fibroblasts and endothelial cells and weak TXN expression in ESRD/ACRD kidneys and tumours. In ACRD specific EVT and its precursors TXN were translocated into nuclei.

Conclusion

The impaired TXNIP/TXN redox homeostasis might be associated with development of multiplex cancer especially of EVT in ESRD/ACRD kidney.
Knowledge, attitude, and perception towards COVID-19 vaccinations among the adults in Rwanda: a cross-sectional study
Authors: Gabriel Abakundana Nsenga Ariston , Wang Xiao-Yang , Jamil Laila , Shimbre Mulugeta Shegaze , Bikorimana Gerard , Zhao Lin , Cao Wu-Chun
Published: July 2024

Background

Multiple vaccinations have received approval for the prevention of the coronavirus illness. Nevertheless, the sluggish vaccination rate is mostly attributed to the general population’s limited understanding and unwillingness to accept the use of vaccinations. Thus, it is important to investigate the Rwandan population’s knowledge, attitudes, and perceptions toward COVID-19 vaccines.

Methods

A cross-sectional survey was conducted among 370 participants from 11th to 17th February 2023. Demographic information was gathered, and knowledge, attitudes, and perceptions of COVID-19 vaccinations were assessed. A binary logistic regression analysis was undertaken to determine the parameters that determine the perception of COVID-19 vaccinations.

Results

This study included 370 participants. Among them, 85% had good knowledge about COVID-19 vaccines, and 84% had a positive attitude towards them. Additionally, the study had a diverse group, with half of the participants being female and nearly half falling between the ages of 30 and 39. Several key findings emerged through logistic regression analysis. Those aged 30–39 had 1.39 times higher odds of positive perception than 18–28 (OR = 1.39, 95% CI = 1.08–3.24). Participants with a university education were twice as likely to have a positive perception compared to those without an education level (OR = 2.43, 95% CI = 1.30–6.20). Additionally, single individuals were three times more likely to have a positive perception than their married counterparts (OR = 3.39, 95% CI = 1.28–9.09). Vaccinated individuals had twice the odds of positive perception than non-vaccinated individuals (OR = 2.89, 95% CI = 1.01–8.89). Those receiving information from government health institutions were three times more likely to have a positive perception than those who received the information from friends (OR = 3.19, 95% CI = 1.02–12.7). Moreover, employed participants were four times more likely to have a positive perception non-employed individuals (OR = 4.21, 95% CI = 1.48–13.6). Besides, gender and COVID-19 diagnosis did not significantly correlate with positive COVID-19 vaccine perception.

Conclusion

The results indicate that the general public in Rwanda has good knowledge, positive attitudes, and a positive perception toward the COVID-19 vaccination, however, some of the participants had some misconceptions towards COVID-19. The findings of this study will be valuable for policymakers and healthcare authorities working to improve vaccination rates.
Tackling social disconnection: an umbrella review of RCT-based interventions targeting social isolation and loneliness
Authors: Hansen Thomas , Nes Ragnhild Bang , Hynek Kamila , Nilsen Thomas Sevenius , Reneflot Anne , Stene-Larsen Kim , Tornes Ragnhild Agathe , Bidonde Julia
Published: July 2024

Background

Social isolation and loneliness are urgent public health concerns associated with negative physical and mental health outcomes. Understanding effective remedies is crucial in addressing these problems. This umbrella review aimed to synthesize and critically appraise scientific evidence on the effectiveness of social isolation and loneliness interventions overall and across subgroups. We focused on systematic reviews (SRs) of randomized controlled trials (RCTs).

Methods

We searched seven databases (June 2022 and updated June 2023) and supplemented the search with grey literature and reference screening to identify SRs published since 2017. Screening, data extraction, and quality assessment using the AMSTAR2 tool were conducted independently by author pairs, with disagreements resolved through discussion.

Results

We included 29 SRs, 16 with meta-analysis and 13 with narrative synthesis. All SRs focused on loneliness, with 12 additionally examining social isolation. Four SRs focused on young people, 11 on all ages, and 14 on older adults. The most frequently examined intervention types were social (social contact, social support), psychological (therapy, psychoeducation, social skills training), and digital (e.g., computer use and online support). Meta-analyses indicated small-to-moderate beneficial effects, while narrative synthesis demonstrated mixed or no effect. Social interventions for social isolation and psychological interventions for loneliness were the most promising. However, caution is warranted due to the effects’ small magnitude, significant heterogeneity, and the variable quality of SRs. Digital and other interventions showed mixed or no effect; however, caution is advised in interpreting these results due to the highly diverse nature of the interventions studied.

Conclusions

This overview of SRs shows small to moderate effectiveness of social interventions in reducing social isolation and psychological ones in tackling loneliness. Further rigorously conducted RCTs and SRs are needed to guide policy decisions regarding the implementation of efficacious and scalable interventions. Evaluation should focus on both preventive structural interventions and tailored mitigating strategies that address specific types and causes of loneliness.
Saturation effects of the relationship between physical exercise and systemic immune inflammation index in the short-sleep population: a cross-sectional study
Authors: You Yanwei , Ablitip Alimjan , Chen Yuquan , Ding Hao , Chen Keshuo , Cui Yicong , Ma Xindong
Published: July 2024

Background

Short sleep can lead to an increase in inflammation and regular exercise has been shown to have a mitigation effect. However, the association between physical exercise (PE) and inflammation in the short sleep population is an unknown and intriguing issue.

Methods

NHANES dataset spanning the years 2007 to 2018 were analyzed. To investigate the relationship mentioned above, we carried out multivariate linear regression models controlling for sociodemographic and lifestyles factors. The systemic immune inflammation index (SII) served as a reflection of inflammatory potential, calculated as the product of platelet count, neutrophil count, and divided by the lymphocyte count. Self-reported questionnaires were used to collect sleep and exercise information.

Results

A total of 14,664 participants were included for final analysis. Across the three models, PE showed significant negative associations with SII as a continuous variable [Crude Model, β (95% CI): -1.261(-1.600, -0.922), p < 0.001; Model 1, β (95% CI): -1.005(-1.344, -0.666), p < 0.001; Model 2, β (95% CI): -0.470(-0.827, -0.112), p = 0.011]. The consistent nature of the findings persisted when investigating physical exercise (PE) as a categorized variable. By two-piecewise linear regression model, we calculated a saturation effect of PE with the inflection point as 2400 MET-minutes/week.

Conclusion

This study suggested that performing no more than 2400 MET-minutes/week of PE was associated with lower SII levels in the short sleep population, while more PE might not bring additional benefits.
Prevalence of trochlear dysplasia in an 1162 retrospective cohort study using CT scans
Authors: Martinique Vella-Baldacchino , Alessandra Cipolla , Sylvain Guy , Sally LiArno , Ahmad Faizan , Jean-Noel Argenson , Matthieu Ollivier
Published: July 2024

Hypothesis/purpose

The prevalence of trochlear dysplasia is common in different populations.

Background

The prevalence of trochlear dysplasia in the general population, categorised by sex, race, age, and body mass index, has been sparse. This study aimed to define the prevalence of trochlear dysplasia based on the latter categories.

Study design

Cohort retrospective study.

Methods

1162 skeletal mature healthy femora were obtained from a CT-scan-based modelling system (SOMA). Thin slice CT scans were acquired exclusively for medical indications such as polytrauma (20%), CT angiography (70%) and other reasons (i.e. Total Joint Replacement) (10%). Trochlear dysplasia was measured using Pfirmann’s method. Patient demographics such as age, race and sex were recorded.

Results

The overall prevalence of trochlear dysplasia is 4.5% and is far more common in Asian female patients compared to Caucasian, African and Middle Eastern knees.

Conclusion

Overall, the prevalence of dysplasia in the general population was determined to be 4.5%, with female patients being more likely to suffer from the condition. Patients of Asian and Caucasian race were more likely to have trochlear dysplasia, while Middle Eastern male patients displayed more dysplastic values than their female counterparts.