bluuebunny/crossref_metadata_2025 huggingface dataset:
APPLICATION OF OWASP ZAP FRAMEWORK FOR SECURITY ANALYSIS OF LMS USING PENTEST METHOD
Authors: Umar Rusydi , Riadi Imam , Wicaksono Sonny Abriantoro
Published: November 2024

Learning Management System (LMS) is an application currently popular for online learning. The presence of LMS offers better prospects for the world of education, where its highly efficient use allows learning anywhere and anytime through the internet or other computer media. This study focuses on analyzing the security of the Learning Management System (LMS) on the domain e-learning.ibm.ac.id using the Pentest method with the Owasp Zap Framework. Security is a crucial step that needs to be considered by IBM Bekasi in protecting data and information from hacker threats. In this study, the method used is Pentest. Pentest is a series of methods used to test the security of a system by conducting literature studies, searching for data information, and domain information, followed by testing using Owasp Zap to find security-related vulnerabilities. The results of the testing using the Pentest method involve several stages of testing and scanning. The first step is checking domain information using Whois Lookup tools and then scanning using ZenMap on e-learning.ibm.ac.id. In this domain information search, the domain status serverTransferProhibited and clientTransferProhibited was found. The next stage is Vulnerability Analysis, where scanning is performed on the domain e-learning.ibm.ac.id using Owasp Zap tools. Based on the results from Owasp Zap scan, 16 vulnerabilities were found, with the breakdown being 2 high risk, 3 medium risk, 6 low risk, and 5 informational. In the exploitation stage using SQLMap, errors were found in the tested parameters, preventing injection.
A COMPARATIVE EVALUATING NUMERICAL MEASURE VARIATIONS IN K-MEDOIDS CLUSTERING FOR EFFECTIVE DATA GROUPING
Authors: Buaton Relita , Solikhun Solikhun
Published: November 2024

The K-Medoids Clustering algorithm is a frequently employed technique among researchers for data categorization. The primary difficulty addressed in this investigation pertains to the extent of optimality achieved when varying distance computation methodologies are applied within the framework of K-Medoids Clustering. This study is primarily concerned with the application of K-Medoids Clustering, employing a multitude of distance calculation methods, specifically those involving numerical metrics. The aim is to undertake a comparative analysis of Davies-Bouldin Index (DBI) values in order to ascertain the most productive distance calculation technique. In this research, the distance calculation methodologies include Manhattan Distance, Jaccard Similarity, Dynamic Time Warping Distance, Cosine Similarity, Chebyshev Distance, Canberra Distance and Euclidean Distance. The dataset consists of sales data from Devi Cosmetics, covering the period between January and April 2022 and comprising 56 distinct sales items. The research provides an exhaustive evaluation of numerical metrics concerning the K-Medoids Clustering algorithm. The findings indicate that the optimal clustering is achieved using the Chebyshev distance, resulting in 9 clusters with a DBI value of 166.632. The study's contribution is that it can improve more optimal data grouping to help make decisions correctly.
ENHANCING UNDERWATER IMAGE QUALITY: EVALUATING COMBINATIVE APPROACHES FOR EFFECTIVE IN SEAGRASS BED ECOSYSTEM
Authors: Asri Sri Dianing , Jaya Indra , Buono Agus , Wijaya Sony Hartono
Published: November 2024

The Complex underwater characteristics, challenges for image processing tasks. These images often have poor visibility due to low contrast, light scattering and various types of interference. There is a lack of exploration into the effectiveness of existing underwater image enhancement methods, particularly in the context of seagrass ecosystems, allows for further investigation. This study aims to explore and evaluate the effectiveness of various methods in underwater image enhancement, including Colour Balanced, CLAHE, and Unsharp Masking and their combinations, starting with converting video data from UTS devices into two-dimensional images. Furthermore, the quality of images taken from underwater cameras placed in a complex and wild seagrass meadow environment was improved using the proposed method, and the quality was evaluated by the SSIM value. The results show that the CLAHE method has the highest average SSIM value of 0.898. Meanwhile, the combined Color Balanced-CLAHE method achieved an SSIM value of 0.683 in a separate evaluation. This combination is an innovative approach to address complex underwater image quality problems, providing a more specific and adaptive solution. Overall, the proposed method is able to improve the visual quality of images on aspects such as clarity, color, and visibility of objects in the image
EXPLORING AGILE EFFORT ESTIMATION ISSUES: A SYSTEMATIC LITERATURE REVIEW
Authors: Sinaga Tetti , Raharjo Teguh , Trisnawaty Ni Wayan
Published: November 2024

Effort estimation is crucial in software development, especially in Agile projects. The 2020 Standish Group survey found that only 31% of software projects success. The success of a software development project depends on the accuracy of effort estimation. This research aims to analyze studies related to effort estimation methods in Agile software development to identify related issues. A systematic literature review by Kitchenham was conducted across Emerald, Science Direct, Scopus, SpringerLink, and IEEE databases and identified 239 relevant studies from 2018 and 2023, ultimately focusing on 40 studies about effort estimation challenges in Agile software development. The research revealed 59 issues related to various estimation methods. The main challenge in effort estimation for Agile software development is team experience and limited knowledge about the domain, which results in inaccurate estimation result. Requirements’ details, tasks complexity, and lack of data will complicate problem-solving and the prediction of the duration of completion. Reliance on expert judgment will increase the risk of bias and inaccuracy in estimates. These challenges increase the likelihood of project failure due to a mismatch between initial planning and reality as development progresses.
COMPARISON OF PROFILE MATCHING AND MOORA METHODS IN DETERMINING LOAN ELIGIBILITY
Authors: Ariawan Wayan Eka , Indrawan Gede , Gunadi I Gede Aris
Published: November 2024

The objective of this research is to analyze the comparison between the profileimatching method and MOORA in supporting decision-making for loan approvals at the Widya Dharma Student Cooperative (KOPMA). The criteria used in this research include basic salary, length of service, loan duration, membership status, loan amount, and number of dependents. These two methods are compared based on their accuracy levels. The accuracy levels are obtained through testing with the Mean Average Precision (MAP) technique, which measures the accuracy in ranking. The testing is conducted by comparing the ranking results from the method calculations with the rankings from the KOPMA chairman. The analysis results show that the Profile Matching method has a higher accuracy rate, which is 67.83%, compared to the MOORA method, which has an accuracy rate of 45.46%. Besides method testing, system testing was also conducted using the User Acceptance Test (UAT) technique. The UAT results indicate that the developed system aligns with the business processes in determining loan eligibility, the menu layout and contents within the system are well-organized, the system features function properly and are easy to understand, and the system meets expectations.
MULTICLASS CLASSIFICATION FOR STUNTING PREDICTION USING DEEP NEURAL NETWORKS
Authors: Lestari Wulan Sri , Saragih Yuni Marlina , Caroline Caroline
Published: November 2024

Stunting is a chronic nutritional issue that hinders child growth and leads to serious long-term health and developmental impacts, particularly in developing countries. Therefore, early and accurate prediction of stunting is crucial for implementing effective interventions. This research aims to develop a multiclass classification model based on Deep Neural Networks (DNNs) to predict stunting status. The model is trained using a comprehensive dataset that encompasses various health variables related to stunting. The research process includes data collection, data preprocessing, dataset splitting, and training and evaluation of the DNNs model. The model can classify stunting status into four categories: stunted, severely stunted, normal, and tall. Further analysis is conducted to evaluate the influence of various parameters on the model's performance, including dataset splitting ratios (80:20 and 70:30) and learning rates (0.001, 0.0001, and 0.00001). The results show that a learning rate of 0.0001 yields the highest prediction accuracy, at 93.64% and 93.83% for the two data-splitting schemes. This indicates that this learning rate has achieved an optimal balance between convergence speed and the model's generalization capability. Additionally, the developed DNNs model can identify complex patterns hidden within the data without being affected by noise. These findings confirm that appropriate parameter selection, particularly the dataset splitting ratio and learning rate, can significantly enhance the DNNs model's ability to identify complex data patterns.
CONTENT-BASED FILTERING CULINARY RECOMMENDATION SYSTEM USING DEEP CONVOLUTIONAL NEURAL NETWORK ON TWITTER (X)
Authors: Artika Zahwa Dewi , Setiawan Erwin Budi
Published: November 2024

Along with the development of technology, social media has become integral to everyday life, especially for sharing content like culinary reviews. Social media platform X (formerly Twitter) is often used for sharing culinary recommendations, but the abundance of information makes it difficult for users to find relevant suggestions. In order to improve rating prediction performance, this study suggests a recommendation system model that is more thoroughly created utilizing Content-Based Filtering (CBF) combined with Deep Convolutional Neural Network (CNN) and optimised with Particle Swarm Optimization (PSO). Data was collected from PergiKuliner and Twitter, totaling 2644 reviews and 200 cuisines. The preprocessing involved text processing, translation, and polarity assessment. Post-labeling, 7438 data were labeled with 0 and 1562 with 1. Label 0 means not recommended while label 1 means recommended. The imbalance is handled by applying the SMOTE method after observing that the fraction of data labeled 0 and 1 is 65.2%. CBF employed TF-IDF feature extraction and FastText word embedding, while Deep CNN handled classification. PSO optimisation was applied to enhance the accuracy of culinary rating predictions. The results showed an initial accuracy of 76.32% with the baseline Deep CNN model, which increased to 86.06% after Nadam optimisation with the best learning rate, and further reached 86.18% after PSO optimisation on dense units. The 9.86% accuracy improvement from the baseline model demonstrates the effectiveness of the combined methods.
The HYBRID CONTENT-BASED FILTERING AND CLASSIFICATION RNN WITH PARTICLE SWARM OPTIMIZATION FOR TOURISM RECOMMENDATION SYSTEM
Authors: Ihsan Syahdan Naufal Nur , Setiawan Erwin Budi
Published: November 2024

Economic recovery in the tourism sector after the COVID-19 pandemic is one of the main focuses of the Indonesian government at the moment, especially in Bandung City. This research aims to develop a personalized tourist spot recommendation system, by addressing the gaps in the existing literature through the integration of Content-Based Filtering (CBF) and Simple Recurrent Neural Network (RNN) methods that aim to improve recommendation accuracy. This study uses a hybrid approach that combines Term Frequency - Inverse Document Frequency (TF-IDF) and word embedding with the Robustly Optimized BERT (RoBERTa) model to identify similarities between tourist destinations based on their content characteristics. Simple RNN is used to analyze user preference patterns over time, which is then further optimized using Particle Swarm Optimization (PSO). As a result, the Simple RNN model that has been optimized with PSO shows an increased accuracy of up to 94.37%, outperforming other optimizations such as Adam and SGD. This research makes a novel contribution by applying advanced machine learning techniques to improve personalization in travel recommendation systems.
UNLEASHING THE POWER OF SVM AND KNN: ENHANCED EARLY DETECTION OF HEART DISEASE
Authors: Pangaribuan Jefri Junifer , Maulana Ade , Romindo Romindo
Published: November 2024

Heart disease is a fatal illness responsible for approximately 36% of deaths in 2020. Therefore, it is important to pay attention to and better anticipate the risk of heart disease. One technological contribution that can be made is through information related to the risk of heart disease. Classification techniques in data mining can be used to diagnose and identify the risk of heart disease earlier by processing medical data and making predictions. This study compares the effectiveness of two classification algorithms, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), in predicting the risk of heart disease using a Kaggle dataset consisting of 303 records with 14 attribute columns. The data is divided into 70% for training and 30% for testing. The software used in this study is Orange Data Mining to build the SVM and KNN models. The results show that the SVM accuracy is 85.6%, while KNN achieves 81.1%. Based on the confusion matrix, the SVM algorithm has a lower error rate compared to KNN. In conclusion, the SVM algorithm is superior to KNN in predicting the risk of heart disease. These findings indicate that SVM has a better potential in identifying individuals at high risk of experiencing a heart attack. This research can contribute to the development of a more accurate medical decision support system for early detection of heart disease.
IMPLEMENTATION OF MULTIPLE LINEAR REGRESSION ALGORITHM IN PREDICTING RED CHILI PRICES IN GARUT REGENCY
Authors: Agustin Yoga Handoko , Nuraeni Fitri , Lestari Rika
Published: November 2024

Vegetables, including red chili peppers, play an important role in food and economic balance. Significant price fluctuations and inflation are often problems for farmers and traders. Garut Regency, as the center of red chili production in West Java, faces similar challenges. This research aims to implement a Multiple Linear Regression algorithm to predict the price of red chili peppers in the Garut Regency, highlighting the novelty of using a combination of One Hot Encoding, Feature Engineering, Standard Scaler, and Hyperparameter Tuning techniques. The method used is CRISP-DM with 6 stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The data used is the price and production of red chili peppers per week in 2018-2023, with a total of 702 records. This research involved 8 trials with data transformation and normalization scenarios. The model evaluation used MSE, RMSE, MAPE, R-squared, and statistical hypothesis testing metrics. Results showed 5 significantly influential attributes: year, month, production, net harvested area, and productivity. The best model yielded MSE 202,134,650, RMSE 14,217, MAPE 29.16%, and R-squared 0.320. This approach is simpler yet effective and is able to provide fairly accurate predictions. This research is expected to contribute to providing predictive models that help farmers and traders anticipate price fluctuations, as well as provide insights for policymakers in price management.
UTILIZING RETRIEVAL-AUGMENTED GENERATION IN LARGE LANGUAGE MODELS TO ENHANCE INDONESIAN LANGUAGE NLP
Authors: Tohir Herdian , Merlina Nita , Haris Muhammad
Published: November 2024

The improvement of Large Language Models (LLM) such as ChatGPT through Retrieval-Augmented Generation (RAG) techniques has urgency in the development of natural language translation technology and dialogue systems. LLMs often experience obstacles in addressing special requests that require information outside the training data. This study aims to discuss the use of Retrieval-Augmented Generation (RAG) on large-scale language models to improve the performance of Natural Language Processing (NLP) in Indonesian, which has so far been poorly supported by high-quality data and to overcome the limitations of traditional language models in understanding the context of Indonesian better. The method used is a combination of retrieval capabilities (external information search) with generation (text generation), where the model utilizes broader and more structured basic data through the retrieval process to produce more accurate and relevant text. The data used includes the Indonesian corpus of the 30 Juz Quran translation into Indonesian. The results of the trial show that the RAG approach significantly improves the performance of the model in various NLP tasks, including token usage optimization, text classification, and context understanding, by increasing the accuracy and relevance of the results
ANALYSIS STUDENT EMOTIONS AND MENTAL HEALTH ON CUMULATIVE GPA USING MACHINE LEARNING AND SMOTE
Authors: Basysyar Fadhil Muhammad , Dwilestari Gifthera , Purnamasari Ade Irma
Published: November 2024

This research investigates the impact of emotions and mental health on students' cumulative grade point average (CGPA) using machine learning classification algorithms while addressing data imbalances with the Synthetic Minority Oversampling Technique (SMOTE). Emotional well-being and mental health are acknowledged as vital determinants of academic achievement. Data imbalance, particularly in mental health metrics such as anxiety and depression, frequently compromises forecast accuracy. This study improves the accuracy of CGPA prediction based on emotional and mental health factors by utilizing SMOTE in machine learning models such as logistic regression and random forest. A dataset including 226 university students, including academic records and self-reported mental health evaluations, was evaluated. The random forest model attained an accuracy of 87.63%, exceeding the logistic regression model's accuracy of 86.56%. These findings emphasize the significant role of emotions and mental health in academic outcomes and validate SMOTE’s efficacy in addressing class imbalance. This work offers a fresh technique in educational data mining by revealing the possibility for improved academic achievement forecasts based on psychological characteristics, helping to the development of targeted therapies for students experiencing emotional issues. Implications for educational policy emphasize the significance of mental health support systems in promoting academic achievement. Subsequent research should investigate supplementary psychological variables and comprehensible models to improve predictive accuracy and facilitate evidence-based policymaking.
The Notion of WorldManuscript pages from ‘La notion de monde’
Authors: Derrida Jacques
Published: November 2024

This text is the translation of the first of two sessions of a lecture course given by Derrida at the Sorbonne in 1961–62. It was clearly intended as an introduction or survey of the concept or notion of ‘world’ from the Greek kosmos and the Latin mundus to the Christian sense of a fallen world, the Kantian ‘idea’ of world, and the Heideggerian rethinking of the very question of world in ‘On the Essence of Ground’.
Tout le monde. Descartes’ World
Authors: Olesen Toft Kristian
Published: November 2024

René Descartes plays a small but pivotal role in Jacques Derrida’s 1961–62 course, ‘The Notion of World’. In contrast, one finds the world everywhere in Descartes’ own texts, from the Discourse on Method to The World. A more extensive discussion of Descartes’ world is found in Derrida’s 1981/82 seminar, Language and the Discourse on Method, which takes up Descartes’ travels throughout the world, a spacing before Cartesian extension, and the tension between universal reason, shared by the whole world, and the necessity of writing philosophy in one language rather than others.
Predictability shapes meaning resolution for lexicosemantic ambiguities
Authors: Poulton Victoria , Davis Matt , MacGregor Lucy
Published: November 2024

Spoken sentence comprehension requires listeners to integrate information across levels of representation. The predictability of linguistics features, including words and phonemes/forms, has been proposed to facilitate this process. In our MEG/EEG study (N=31), participants heard sentences like “While sailing down the river, she noticed the trees along the bank/shore/cashpoint”. On average, ambiguous words (bank) were most predictable (i.e., highest cloze probability); followed by synonyms (shore); while alternative meaning words (cashpoint) were unpredictable. We wanted to test whether predictability would alter the processing of ambiguous words in context. Research into lexicosemantic ambiguities has shown increased amplitude BOLD responses in fMRI (e.g., Rodd et al. 2005), and increased amplitude ERP/ERF (e.g., MacGregor et al. 2020) for ambiguous words over single-meaning words. Thus, neural responses may be impacted by lexicosemantic uncertainty. We hypothesized an effect of Semantic Congruency – where neural response amplitude increases for anomalous words over non-anomalous words (ambiguities+synonyms) – and Lexicosemantic (Un)Certainty – where amplitude differs between ambiguities and synonyms. Using spatiotemporal cluster-based permutation tests, we observed a significant Semantic Congruency effect: ERP/ERF amplitude was greater for anomalous words, consistent with a canonical N400. One cluster was observed in the Lexicosemantic (Un)Certainty comparison: amplitudes diverged between ambiguities and synonyms. However, this approach cannot separate ambiguity and predictability. Follow-up analyses will use mixed-effects modelling to predict ERP/ERF amplitude in cluster-defined time windows. Overall, the results demonstrate the top-down influence of context and lexical knowledge on word meaning resolution, highlighting the flexible nature of linguistic representations and adaptability of the language system.
Device for automated aseptic sampling: Automated sampling solution for future cell and gene manufacturing
Authors: Dan Liu , Ying Ying Wu , Prabhu Akshaya V. , bin Abdul Rahim Ahmad Amirul , Jia Sheng Zach Lee
Published: November 2024

Cell sampling is a key step performed regularly throughout the cell manufacturing process to gather cell samples for cell growth, progress, and characteristics analysis. While the current method of sampling by pipetting in a biosafety cabinet is commonly used, it is labour-intensive and susceptible to contamination risks. We have developed Device for Automated Aseptic Sampling (DAAS), to enable automated, small volume (0.02–1.00 mL) aseptic sampling with minimal dead volume primarily for cell and gene therapy manufacturing. The aim of DAAS is to enable an accurate and consistent sampling process, with minimal contamination risks and interruption to the cells in culture. DAAS can potentially interface with other automated solutions to enable automated and streamlined cell manufacturing workflow and reduce overall manufacturing costs. DAAS has been verified as an aseptic sampling solution via repeated microbial ingression tests. It has also been tested for achieving comparable cell density and viability compared to manual pipetting, with negligible cross-sample carryover when used to sample Jurkat cells of different cell concentrations. The application of using DAAS to sample cell periodically and monitor cell growth and viability continuously for prolonged cell culture was successfully demonstrated with Jurkat cell culture in a static culture flask and donor T cell culture in an automated bioreactor system over a culture duration of 10 days in a Biosafety Level-2 laboratory. Overall, DAAS presents great potential as an automated and aseptic sampling solution, offering cell and gene therapy manufacturers easier and more frequent access to cell samples with minimal interruptions to the cell culture. This enables close monitoring of cell culture and a more automated, connected and cost-effect cell and gene therapy manufacturing process.
Biological and structural properties of curcumin-loaded graphene oxide incorporated collagen as composite scaffold for bone regeneration
Authors: Xie Qi , Wang Tianqi , He Lina , Liang Hongbo , Sun Jingxuan , Huang Xiaoxiao , Xie Weili , Niu Yumei
Published: November 2024

Introduction

To address the challenges related to bone defects, including osteoinductivity deficiency and post-implantation infection risk, this study developed the collagen composite scaffolds (CUR-GO-COL) with multifunctionality by integrating the curcumin-loaded graphene oxide with collagen through a freeze-drying-cross-linking process.

Methods

The morphological and structural characteristics of the composite scaffolds were analyzed, along with their physicochemical properties, including water absorption capacity, water retention rate, porosity, in vitro degradation, and curcumin release. To evaluate the biocompatibility, cell viability, proliferation, and adhesion capabilities of the composite scaffolds, as well as their osteogenic and antimicrobial properties, in vitro cell and bacterial assays were conducted. These assays were designed to assess the impact of the composite scaffolds on cell behavior and bacterial growth, thereby providing insights into their potential for promoting osteogenesis and inhibiting infection.

Results

The CUR-GO-COL composite scaffold with a CUR-GO concentration of 0.05% (w/v) exhibits optimal biological compatibility and stable and slow curcumin release rate. Furthermore, in vitro cell and bacterial tests demonstrated that the prepared CUR-GO-COL composite scaffolds enhance cell viability, proliferation and adhesion, and offer superior osteogenic and antimicrobial properties compared with the CUR-GO composite scaffold, confirming the osteogenesis promotion and antimicrobial effects.

Discussion

The introduction of CUR-GO into collagen scaffold creates a bone-friendly microenvironment, and offers a theoretical foundation for the design, investigation and utilization of multifunctional bone tissue biomaterials.
Embryonic piRNAs target horizontally transferred vertebrate transposons in assassin bugs
Authors: Brito Tarcísio Fontenele de , Arruda Cardoso Maira , Atinbayeva Nazerke , Alexandre de Abreu Brito Ingrid , Amaro da Costa Lucas , Iovino Nicola , Pane Attilio
Published: November 2024

Introduction

Piwi proteins and the associated Piwi-interacting RNAs (piRNAs) coordinate a surveillance system that protects the animal genome from DNA damage induced by transposable element (TE) mobilization. While the pathway has been described in detail in the fruit fly Drosophila melanogaster, much less is known in more basal insects. Rhodnius prolixus is an hemipteran insect and one of the major vectors of Chagas disease. Rhodnius acquired specific classes of horizontally transferred transposons (HTTs) by feeding on bats, opossums and squirrel monkeys, thus providing the opportunity to investigate the piRNA-base response against HTTs in this species.

Methods

SmallRNA-Seq reads mapping to HTTs and resident transposable elements were quantified and checked for piRNA features like 1U a 10A biases, ping-pong and phasing signatures. Uniquely mapped piRNAs were used to identify piRNA clusters in Rhodnius’ genome. RNA-Seq data was used to quantify transposon and Rp-PIWI genes expression levels and were validated by qRT-PCR.

Results

By analyzing the temporal dynamics of piRNA cluster expression and piRNA production during critical stages of Rhodnius development, we show that peak levels of ∼28 nt long piRNAs correlate with reduced HTT and resident TE expression primarily during embryogenesis. Strikingly, while resident TEs piRNAs seem to engage in a typical ping-pong amplification mechanism, sense and antisense HTT piRNAs instead overlap by ∼20 nt or do not display ping-pong signatures.

Discussion

Our data shed light on the biogenesis and functions of the piRNAs in Rhodnius prolixus and reveal that piRNAs, but not the siRNA pathway, responded to HTTs that were recently transferred from vertebrate tetrapods to a hematophagous insect of medical relevance.
Effect of plant fibers on the physical properties of slurry-processed reconstituted tobacco
Authors: Liu Tong , Wang Yixuan , Wang Chunping , Zhang Qi , Wang Le , Li Yuhan , Xu Linyang , Jin Xinyan , Yin Xianzhong , Zhang Zhan , Luo Chong , Fu Lili , Wen Yangbing , Li Bin
Published: November 2024

The primary function of plant fibers in reconstituted tobacco is to enhance the physical strength, and it can quite modify their physical properties. This study demonstrated the effect of various plant fibers and their beating degrees on the physical properties of reconstituted tobacco. Tensile index, burst index, uniformity, tensile stiffness orientation, and thermal conductivity coefficient were examined. The result revealed that the mechanical properties of reconstituted tobacco varied according to the type and beating degree of the fibers. The mechanical properties of softwood, cotton, and bast fibers showed an initial increase followed by a decrease with increasing beating degree, while bamboo fiber showed a continuous improvement in mechanical properties proportional to the beating degree. Conversely, hardwood fiber displayed an inverse relationship with its beating degree. Under identical beating conditions, reconstituted tobacco containing softwood fibers showed the greatest improvement in tensile properties, achieving the highest tensile strength, thermal conductivity, and specific heat capacity. In particular, when softwood fibers were beaten to 50 °SR, the physical properties of the reconstituted tobacco peaked, with longitudinal and transverse tensile indices improving by 42.48% and 12.11%, respectively. Additionally, the bursting resistance index increased by 61.93%, and the thermal conductivity coefficient increased by 5.94%.
Quantitative and qualitative assessment of a pituitary neuroendocrine tumor’s T2-signal intensity in acromegaly – a call for unification
Authors: Godlewska-Nowak Magdalena , Grochowska Anna , Zieliński Grzegorz , Bogusławska Anna , Adamek Dariusz , Maksymowicz Maria , Hubalewska-Dydejczyk Alicja , Gilis-Januszewska Aleksandra
Published: November 2024

Introduction

The T2-signal intensity (SI) of somatotroph pituitary neuroendocrine tumors (sPitNET) is associated with treatment response and granulation pattern. Our aim was to evaluate SI assessment methods and their clinical implications, including responsiveness to preoperative first-generation somatostatin analogs (SSA).

Methods

This single-center, observational study included unselected, consecutive patients with newly diagnosed acromegaly. Out of 109 treatment-naïve patients, 69 were eligible. The qualitative Visual Method involved a visual comparison of the sPitNET with the temporal gray matter. The Three Tissue Method compared the quantified SI of the sPitNET, temporal white matter, and gray matter. The signal intensity ratio of the sPitNET vs. gray matter (GM-SIR) was calculated. Tumors were divided into three groups: hyperintense (HYPER), isointense (ISO), and hypointense (HYPO) according to the Visual Method, Three Tissue Method, and GM-SIR. These groups were compared in terms of demographic, radiological, and biochemical features. The SI assessment methods were investigated for their ability to predict preoperative SSA responsiveness.

Results

SI assessment methods classified SI type correspondingly in 58-75.4% of cases. ISO constituted 39-49% of the analyzed sPitNETs. All methods identified significant differences in tumor volume between the SI groups, with HYPO being more biochemically active per tumor volume unit. According to the Three Tissue Method, patients with ISO had the youngest age at diagnosis and onset. According to the Visual Method, ISO had a lower chance of achieving insulin-like growth factor 1 (IGF1) normalization compared to HYPO (odds ratio (OR) 0.089, confidence interval (CI) 0.015-0.538, p= 0.008)), with no differences between HYPER and HYPO. Only the Visual Method predicted the IGF1 normalization after SSA. HYPER and ISO sPitNETs were classified in electron microscopy as both densely and sparsely granulated. Bihormonal tumors presented only as HYPO and ISO. According to the Three Tissue Method, no HYPO was diagnosed with sparse granulation.

Discussion

We demonstrated discrepancies between the SI assessment methods. The Visual Method predicted the outcome of preoperative treatment with SSA. Clinically, ISO behaved similarly to HYPER. Further studies are needed to unify SI assessment and improve its clinical applicability in acromegaly.
Application of shear wave elastography in the management of thyroid nodules in children and adolescents: our experience and a review of the literature
Authors: Borysewicz-Sańczyk Hanna , Bossowski Filip , Anikiej Katarzyna , Sawicka Beata , Michalak Justyna , Dzięcioł Janusz , Bossowski Artur
Published: November 2024

Introduction

Shear wave elastography (SWE) is an ultrasound diagnostic method used to measure tissue stiffness. Since the mechanical properties of tissue involved in the pathological process change, SWE might indicate regions of the examined tissue covered by the disease. It is well documented that SWE helps to differentiate benign and malignant nodules in thyroid glands in adults, however, there are few studies on the application of SWE in thyroid diagnosis in children. The purpose of the study was to assess the application of SWE based on Young’s modulus expressed in kPa in the management of thyroid nodules in children and adolescents.

Methods

In total, 116 pediatric patients (81 girls and 35 boys) with 168 thyroid nodules were enrolled in the study and qualified for SWE followed by fine needle aspiration biopsy.

Results

According to the result of the cytological examination presented in the Bethesda System, nodules were classified as benign (147 nodules classified as category II according to the Bethesda System) or indeterminate or suspicious (21 nodules classified as categories III, IV, and V according to the Bethesda System). Benign cytological diagnoses were nodular goiter, parenchymal goiter, nodular colloid goiter, or lymphocytic inflammation. Among the indeterminate or suspicious nodules, 15 were diagnosed as category III according to the Bethesda System (atypia of undetermined significance (AUS) or follicular lesion of undetermined significance (FLUS) in cytology), 1 nodule was diagnosed as category IV according to the Bethesda System (suspicious for follicular neoplasm – oxyphilic cell tumor), and 5 as category V according to the Bethesda System (suspicious for malignancy). There were no significant differences in thyrotropin (TSH) and free thyroxine (fT4) concentrations between the benign and suspicious groups. Patients with benign and indeterminate or suspicious thyroid nodules were of comparable age. Mean SWE in benign nodules was statistically significantly lower than in nodules with indeterminate or suspicious cytology (42.22 ± 16.69 vs. 57.4 ± 24.0 kPa, p=0.0004). Six patients from the indeterminate or suspicious group were revealed to be malignant in the final histopathological examination.

Conclusion

Our results suggest that SWE is a viable diagnostic method, however, it still seems to need some adjustment for pediatric patients.
Long term effects of aromatase inhibitor treatment in patients with aromatase excess syndrome
Authors: Giannopoulou Eleni Z. , Brandt Stephanie , Zorn Stefanie , Denzer Christian , von Schnurbein Julia , Fukami Maki , Kaiser Alexander , Schmidt Martin , Wabitsch Martin
Published: November 2024

Introduction

Aromatase excess syndrome (AEXS) is a rare, autosomal dominant disorder, characterized by enhanced aromatization of androgens and estrogen excess. In males it is characterized by pre-/peripubertal gynecomastia, hypogonadotropic hypogonadism, advanced bone age and short adult height. Only a few female patients have been described so far.

Methods

We report on a family with four members with AEXS and present the long-term effects of aromatase inhibitor use in three of them. Genetic analysis showed a monoallelic 0.3-Mb deletion in 15q21, involving parts of CYP19A1, GLDN and DMXL2 in all four patients with AEXS.

Results

The index patient (male, 8 years old) presented with gynecomastia and accelerated growth and bone age. With start of puberty, estradiol levels increased, while testosterone levels remained low. Gynecomastia progressed and a mastectomy was performed twice. Presuming AEXS, a therapy with letrozole was initiated at the age of 19 years. Low-dose letrozole treatment was associated with an increase in testicular volume, increase in virilization and improvement in physical strength and libido. His brother (age 3 years) presented with accelerated growth and bone age. Treatment with letrozole, which was started at the age of 7 years, resulted in achieving an adult height of 179 cm and prevented the appearance of gynecomastia. His sister (age 6 years), who presented with premature thelarche and accelerated growth and bone age, was treated with an estrogen receptor modulator and a GnRH analog followed by letrozole treatment. Menarche occurred at age 13.5 years and adult height was 158 cm. Their father had an early, accelerated growth with an adult height of 171 cm, a delayed puberty and no gynecomastia. In vitro studies provided evidence for involvement of aromatase induction in atypical cells and an increased range of potential mechanisms regulating aromatase activity due to the presence of the mutated allele.

Discussion

In conclusion, we observed a phenotypic variability within family members with AEXS carrying the same CYP19A1 microdeletion. When started early, treatment with letrozole was found to prevent the development of gynecomastia and increase adult height in one patient. In adult life, low-dose letrozole treatment resulted in improved physical strength and libido in the index patient.
Research on fault localization of distribution transformers based on frequency response analysis and support vector machine (SVM)
Authors: Liu Xingting , Wang Haiqi , Gao Zhuo , Han Xutao
Published: November 2024

Distribution transformers are vital to the stability of the power system, but long-term operation and environmental factors can cause distribution transformers to discharge, posing a potential threat. The impulse voltage withstand test rigorously evaluates the insulation of the transformer windings, to prevent accidents caused by insulation breakdown. However, due to the small size of distribution transformers, impulse voltage withstand test breakdown usually cause less damage, and it is challenging to locate the point of failure using traditional methods such as spatial positioning or disassembly. Therefore, the study of the discharge location method based on the characteristic value of the frequency response waveform is of great significance to find and deal with potential faults. This paper discusses the waveform characteristics of different discharge signals transmitted from different positions of the winding to the end. By analyzing the frequency response of different breakdown current signals measured at the end of the transformer winding, support vector machine is used to identify the breakdown position of the distribution transformer. The approximate location of the fault was identified with more than 80% accuracy. The feasibility and effectiveness of this localization method are verified by experiments.
Hepatic and pulmonary macrophage activity in a mucosal challenge model of Ebola virus disease
Authors: Wanninger Timothy G. , Saldarriaga Omar A. , Arroyave Esteban , Millian Daniel E. , Comer Jason E. , Paessler Slobodan , Stevenson Heather L.
Published: November 2024

Background

The inflammatory macrophage response contributes to severe Ebola virus disease, with liver and lung injury in humans.

Objective

We sought to further define the activation status of hepatic and pulmonary macrophage populations in Ebola virus disease.

Methods

We compared liver and lung tissue from terminal Ebola virus (EBOV)-infected and uninfected control cynomolgus macaques challenged via the conjunctival route. Gene and protein expression was quantified using the nCounter and GeoMx Digital Spatial Profiling platforms. Macrophage phenotypes were further quantified by digital pathology analysis.

Results

Hepatic macrophages in the EBOV-infected group demonstrated a mixed inflammatory/non-inflammatory profile, with upregulation of CD163 protein expression, associated with macrophage activation syndrome. Hepatic macrophages also showed differential expression of gene sets related to monocyte/macrophage differentiation, antigen presentation, and T cell activation, which were associated with decreased MHC-II allele expression. Moreover, hepatic macrophages had enriched expression of genes and proteins targetable with known immunomodulatory therapeutics, including S100A9, IDO1, and CTLA-4. No statistically significant differences in M1/M2 gene expression were observed in hepatic macrophages compared to controls. The significant changes that occurred in both the liver and lung were more pronounced in the liver.

Conclusion

These data demonstrate that hepatic macrophages in terminal conjunctivally challenged cynomolgus macaques may express a unique inflammatory profile compared to other macaque models and that macrophage-related pharmacologically druggable targets are expressed in both the liver and the lung in Ebola virus disease.
Rhabdomyolysis secondary to COVID-19 infection and vaccination: a review of literature
Authors: Karimi Mehdi , Faal Hamedanchi Neda , Ansari Kazem , Nahavandi Reza , Mazdak Mahsa , Javaherchian Fateme , Koochaki Pooneh , Asadi Anar Mahsa , Shirforoush Sattari Mahsa , Mohamaditabar Mona
Published: November 2024

Rhabdomyolysis (RML), characterized by the breakdown of skeletal muscle fibers and the release of muscle contents into the bloodstream, has emerged as a notable complication associated with Coronavirus disease 2019 (COVID-19) infection and vaccination. Studies have reported an increased incidence of RML in individuals with severe COVID-19 infection. However, the exact mechanisms remain unclear and are believed to involve the host’s immune response to the virus. Furthermore, RML has been documented as a rare adverse event following COVID-19 vaccination, particularly with mRNA vaccines. Proposed mechanisms include immune responses triggered by the vaccine and T-cell activation against viral spike proteins. This study aims to review the current literature on the incidence, pathophysiology, clinical presentation, and outcomes of RML secondary to COVID-19 infection and vaccination. We identify common risk factors and mechanisms underlying this condition by analyzing case reports, clinical studies, and pharmacovigilance data. Our findings suggest that while RML is a relatively rare adverse event, it warrants attention due to its potential severity and the widespread prevalence of COVID-19 and its vaccines. This review underscores the need for heightened clinical awareness and further research to optimize management strategies and improve patient outcomes in this context.
Comparison of volumetric brain analysis in subjects with rheumatoid arthritis and ulcerative colitis
Authors: Cox Jennifer G. , de Groot Marius , Kempton Matthew J. , Williams Steven C. R. , Cole James H.
Published: November 2024

Background

Rheumatoid arthritis (RA) and ulcerative colitis (UC) are two autoimmune diseases where patients report high levels of fatigue, pain, and depression. The effect of systemic inflammation from these diseases is likely affecting the brain, however, it is unknown whether there are measurable neuroanatomical changes and whether these are a contributing factor to these central symptoms.

Methods

We included 258 RA patients with 774 age and sex matched controls and 249 UC patients with 747 age and sex matched controls in a case control study utilizing the UK Biobank dataset. We used imaging derived phenotypes (IDPs) to determine whether there were differences in (1) hippocampal volume and (2) additional subcortical brain volumes between patients compared to controls and if there were common regions affected between these two diseases.

Results

Patients with UC had moderately smaller hippocampi compared to age and sex matched controls (difference: 134.15 mm3, SD ± 64.76, p = 0.035). This result was not seen in RA patients. RA patients had a significantly smaller amygdala volume than age and sex matched controls (difference: 91.27 mm3, SD ± 30.85, p = 0.0021, adjusted p = 0.012). This result was not seen in UC patients. All other subcortical structures analyzed were comparable between the patients and control groups.

Conclusion

These results indicate there are subcortical brain differences between UC, RA and controls but different regions of the limbic system are preferentially affected by UC and RA. This study may provide evidence for different neurodegenerative mechanisms in distinct autoimmune diseases.
Case report: Robotically-treated spontaneous interstitial pregnancy on tubal stump
Authors: Ascione Mario , Della Corte Luigi , D’Angelo Giuseppe , Palumbo Mario , Watrowski Rafał , Di Spiezio Sardo Attilio , Bifulco Giuseppe
Published: November 2024

To report a rare case of a right interstitial pregnancy spontaneously occurring in a patient who had previously undergone homolateral salpingo-oophorectomy, and to propose possible explanations for the mechanisms involved in the genesis of this rare scenario. A 32-year-old G3P1 female presented to our emergency room with symptoms related to a suspected ectopic interstitial pregnancy managed in another hospital using a conservative pharmacological approach. After discussing the risks, firstly she underwent a transvaginal ultrasound examination, then a diagnostic hysteroscopy to clarify the unclear ultrasound finding, followed by a successful robot-assisted laparoscopic cornual resection. Hysteroscopy demonstrated an empty uterine cavity, confirming the suspect of pregnancy localization into the interstitial portion of the tubal stump. Through the robot-assisted laparoscopic approach, all the trophoblastic tissue was removed without causing significant damage to the surrounding myometrium and preserving the patient’s fertility. No post-operative complications were recorded. The robotic approach successfully allowed the cornual resection, with minimal blood loss and optimal suturing of the uterine defect. Although our knowledge is still limited, it is possible that the pregnancy nested in the tubal residue after being properly fertilized into the intact tube. However, it cannot be ruled out that there have been remodeling phenomena of the tubal residue so that it has acquired the ability to intercept the oocyte.