bluuebunny/crossref_metadata_2025_split huggingface dataset:
Internet of Healthcare Things Based Detection of EEG Epileptic Seizures:
A Smart System
Authors: Thakur Tanishk , Rana Naresh , Jain Shruti 
Published: July 2024

Background:
A seizure is a sudden and uncontrolled electrical activity in the brain that
can cause a variety of symptoms, depending on the location and severity of the abnormal activity. It
can be a symptom of an underlying neurological disorder or can occur without an apparent cause.
Epilepsy is one of the most common causes of seizures. Overactive electrical discharges disrupt normal brain electrical activity and interfere with nerve cell communication.

Aim and Objective:
A comprehensive analysis of the literature revealed that several CAD system
designs have shown to be useful to radiologists in routine medical practice as second-opinion aids for
epileptic seizure detection in circumstances where a clear differentiation cannot be formed subjectively.
CAD systems are made to help radiologists by automating the examination of medical data and images, improving the efficiency and accuracy of diagnosis. These systems examine patterns in medical
imaging using machine learning approaches, which can be quite helpful in spotting small abnormalities that the human eye can miss. Moreover, the objective of this study was to design a smart
healthcare system using a combination of DWT, Hjorth, and statistical parameters for seizure detection.

Method:
In this research article, the authors proposed the framework of the Internet of Healthcare
Things (IoHT) for performing seizure detection. The authors used different pre-processing techniques
and extracted different features like Hjorth, wavelets, and statistics, which were classified using different machine-learning techniques. This novel methodology combines a number of technologies and
techniques to improve seizure detection's precision and dependability.

Results:
DWT + Hjorth + Statistical parameters with bior 1.5 as the pre-processing technique yielding
the best outcomes. 86% accuracy was obtained with kNN for k = 5, 93% accuracy was obtained with
a linear kernel for an SVM classifier, and 95.5% accuracy was obtained using a decision tree and
logistic regression. The authors also considered another dataset for validation and received 96.83%
accuracy with decision tree and logistic regression classifiers considering the bior1.5 wavelet filter
as a preprocessing technique.

Conclusion:
The IoHT framework offers a multi-modal, adaptive method of seizure detection that
enables the dynamic modification of detection parameters and the incorporation of extra sensor signals. This improves seizure detection's precision and dependability, which has important implications
for patient care and monitoring. This work shows how IoHT and machine learning can be combined
to build a reliable, real-time seizure detection system. These developments, which make it possible
for prompt interventions and individualized treatment plans, can significantly improve the quality of
care for individuals with epilepsy.
TNF-alpha Inhibitors Induced Eosinophilia: A Case Report of an Undervalued Side Effect of Such Biologicals
Authors: Diomeda Federico , Calogiuri Gianfranco , Congedo Maurizio , Filoni Angela , Sordi Emiliano , Fiore Massimo , Pietroleonardo Lucia , Piscitelli Prisco , Civino Adele 
Published: July 2024

Introduction:
We present an unusual case of a 28-year-old rheumatologic male patient
who developed eosinophilia while he was on etanercept therapy previously and then on
golimumab.

Case Representation:
Although eosinophilia is rarely reported in the literature as a side-effect
of various Tumor Necrosis Factor-alpha (TNF-alpha) antagonists, it represents a riddle about
the future treatments of these patients since the persistence of therapy might lead to the onset of
dermatologic or visceral eosinophilic complications in such patients.

Conclusion:
Furthermore, the pathogenesis of eosinophilia is still unknown, and all the proposed
hypotheses do not explain the eosinophilic proliferation in certain subjects.
The Role of Local Angiotensin II/Angiotensin Type 1 Receptor in Endometriosis: A Potential Target for New Treatment Approaches
Authors: Moazen Shirin , Arjmand Mohammad-Hasan 
Published: July 2024

:
Endometriosis is a chronic inflammatory disorder described by the presence of functional endometrial-like tissues at extra-uterine locations that are
related to chronic pelvic pain and infertility. Multiple molecular mechanisms, including inflammation, reactive oxygen species (ROS) generation,
fibrotic reactions, and angiogenesis, are involved in the pathogenesis of endometriosis; however, the exact cause of this disorder still remains a
matter of discussion. Recently, it has been shown that the local renin-angiotensin system (RAS) has been expressed in different tissues, like the
gynecological tract, and alterations in its expression are associated with multiple pathological conditions like endometriosis. Angiotensin II (Ang
II), as a main peptide of the RAS through angiotensin type 1 receptor (AT1R), upregulates signal transduction pathways such as nuclear factor
kappa B (NF-κB), mitogen activation protein kinase (MAPK), and transforming growth factor beta (TGF-β) to promote inflammation, oxidative
stress, and fibrogenesis. Angiotensin receptor blockers (ARBs) control high blood pressure, which is increased by excessive AT1R activity.
Recently, it has been recognized that ARBs have tissue protective effects because of their anti-inflammatory and antifibrotic effects. In this review,
we focused on the role of local Ang II/AT1R axis activity in endometriosis pathogenesis and justified the use of ARB agents as a potential
therapeutic strategy to improve endometriosis.
An Energy-Balance Clustering Routing Protocol for Intra-Body Wireless Nanosensor Networks
Authors: S Santhosh , B Vamshi Krishna , Mudarakola Lakshmi Prasad , Mukherjee Saptarshi , Yesubabu Mannava , Sharma Vikas 
Published: July 2024

Aims and Background:
Numerous sensor nodes spread out across the surveillance region
form the Wireless-Sensor Network (WSN), a smart, self-organizing network. Since the lumps
can typically only be motorized by batteries, creating a WSN while maintaining an optimal energy
balance and extending the network's lifetime is the biggest issue.

Methods:
A novel network architecture that integrates nanotechnology with sensor networks is
known as a Wireless-NanoSensor-Network(WNSN). A new area of focus in research is intra-bodyiWNSNs,
which are WNSNs with promising potential applications in biomedicine, damage detection,
and intra-body health monitoring. We suggest an energy-balance-clustering-routing protocol
(EBCR) for iSN nodes that have limited energy storage, short communication range, and low computation
and processing capabilities. The protocol uses a novel hierarchical clustering approach to
lessen the communication burden on nano-nodes.

Results:
Cluster nano-nodes can use one-hop routing to send data directly to the Cluster-Head(CH)
nodes, and the CH-nodes can utilize multi-hop routing to send data to the nano control node. In addition,
selecting the next hop node to minimize energy usage while guaranteeing successful data
packet delivery involves balancing distance and channel capacity. The protocol's strengths in energy
efficiency, network-lifetime extension, and data-packet transmission success rate were highlighted
by the simulation results.

Conclusion:
It is clear that the EBCR protocol is a viable option for iWNSNs' routing system.
Accelerating Evidence Synthesis in Observational Studies: Development of a Living Natural Language Processing–Assisted Intelligent Systematic Literature Review System
Authors: Manion Frank J , Du Jingcheng , Wang Dong , He Long , Lin Bin , Wang Jingqi , Wang Siwei , Eckels David , Cervenka Jan , Fiduccia Peter C , Cossrow Nicole , Yao Lixia 
Published: July 2024

Abstract

Background
Systematic literature review (SLR), a robust method to identify and summarize evidence from published sources, is considered to be a complex, time-consuming, labor-intensive, and expensive task.

Objective
This study aimed to present a solution based on natural language processing (NLP) that accelerates and streamlines the SLR process for observational studies using real-world data.

Methods
We followed an agile software development and iterative software engineering methodology to build a customized intelligent end-to-end living NLP-assisted solution for observational SLR tasks. Multiple machine learning–based NLP algorithms were adopted to automate article screening and data element extraction processes. The NLP prediction results can be further reviewed and verified by domain experts, following the human-in-the-loop design. The system integrates explainable articificial intelligence to provide evidence for NLP algorithms and add transparency to extracted literature data elements. The system was developed based on 3 existing SLR projects of observational studies, including the epidemiology studies of human papillomavirus–associated diseases, the disease burden of pneumococcal diseases, and cost-effectiveness studies on pneumococcal vaccines.

Results
Our Intelligent SLR Platform covers major SLR steps, including study protocol setting, literature retrieval, abstract screening, full-text screening, data element extraction from full-text articles, results summary, and data visualization. The NLP algorithms achieved accuracy scores of 0.86-0.90 on article screening tasks (framed as text classification tasks) and macroaverage F1 scores of 0.57-0.89 on data element extraction tasks (framed as named entity recognition tasks).

Conclusions
Cutting-edge NLP algorithms expedite SLR for observational studies, thus allowing scientists to have more time to focus on the quality of data and the synthesis of evidence in observational studies. Aligning the living SLR concept, the system has the potential to update literature data and enable scientists to easily stay current with the literature related to observational studies prospectively and continuously.
Diagnostic Accuracy of a Mobile AI-Based Symptom Checker and a Web-Based Self-Referral Tool in Rheumatology: Multicenter Randomized Controlled Trial
Authors: Knitza Johannes , Tascilar Koray , Fuchs Franziska , Mohn Jacob , Kuhn Sebastian , Bohr Daniela , Muehlensiepen Felix , Bergmann Christina , Labinsky Hannah , Morf Harriet , Araujo Elizabeth , Englbrecht Matthias , Vorbrüggen Wolfgang , von der Decken Cay-Benedict , Kleinert Stefan , Ramming Andreas , Distler Jörg H W , Bartz-Bazzanella Peter , Vuillerme Nicolas , Schett Georg , Welcker Martin , Hueber Axel 
Published: July 2024

Background
The diagnosis of inflammatory rheumatic diseases (IRDs) is often delayed due to unspecific symptoms and a shortage of rheumatologists. Digital diagnostic decision support systems (DDSSs) have the potential to expedite diagnosis and help patients navigate the health care system more efficiently.

Objective
The aim of this study was to assess the diagnostic accuracy of a mobile artificial intelligence (AI)–based symptom checker (Ada) and a web-based self-referral tool (Rheport) regarding IRDs.

Methods
A prospective, multicenter, open-label, crossover randomized controlled trial was conducted with patients newly presenting to 3 rheumatology centers. Participants were randomly assigned to complete a symptom assessment using either Ada or Rheport. The primary outcome was the correct identification of IRDs by the DDSSs, defined as the presence of any IRD in the list of suggested diagnoses by Ada or achieving a prespecified threshold score with Rheport. The gold standard was the diagnosis made by rheumatologists.

Results
A total of 600 patients were included, among whom 214 (35.7%) were diagnosed with an IRD. Most frequent IRD was rheumatoid arthritis with 69 (11.5%) patients. Rheport’s disease suggestion and Ada’s top 1 (D1) and top 5 (D5) disease suggestions demonstrated overall diagnostic accuracies of 52%, 63%, and 58%, respectively, for IRDs. Rheport showed a sensitivity of 62% and a specificity of 47% for IRDs. Ada’s D1 and D5 disease suggestions showed a sensitivity of 52% and 66%, respectively, and a specificity of 68% and 54%, respectively, concerning IRDs. Ada’s diagnostic accuracy regarding individual diagnoses was heterogenous, and Ada performed considerably better in identifying rheumatoid arthritis in comparison to other diagnoses (D1: 42%; D5: 64%). The Cohen κ statistic of Rheport for agreement on any rheumatic disease diagnosis with Ada D1 was 0.15 (95% CI 0.08-0.18) and with Ada D5 was 0.08 (95% CI 0.00-0.16), indicating poor agreement for the presence of any rheumatic disease between the 2 DDSSs.

Conclusions
To our knowledge, this is the largest comparative DDSS trial with actual use of DDSSs by patients. The diagnostic accuracies of both DDSSs for IRDs were not promising in this high-prevalence patient population. DDSSs may lead to a misuse of scarce health care resources. Our results underscore the need for stringent regulation and drastic improvements to ensure the safety and efficacy of DDSSs.

Trial Registration
German Register of Clinical Trials DRKS00017642; https://drks.de/search/en/trial/DRKS00017642
Exploring the Efficacy of Large Language Models in Summarizing Mental Health Counseling Sessions: Benchmark Study
Authors: Adhikary Prottay Kumar , Srivastava Aseem , Kumar Shivani , Singh Salam Michael , Manuja Puneet , Gopinath Jini K , Krishnan Vijay , Gupta Swati Kedia , Deb Koushik Sinha , Chakraborty Tanmoy 
Published: July 2024

Background
Comprehensive session summaries enable effective continuity in mental health counseling, facilitating informed therapy planning. However, manual summarization presents a significant challenge, diverting experts’ attention from the core counseling process. Leveraging advances in automatic summarization to streamline the summarization process addresses this issue because this enables mental health professionals to access concise summaries of lengthy therapy sessions, thereby increasing their efficiency. However, existing approaches often overlook the nuanced intricacies inherent in counseling interactions.

Objective
This study evaluates the effectiveness of state-of-the-art large language models (LLMs) in selectively summarizing various components of therapy sessions through aspect-based summarization, aiming to benchmark their performance.

Methods
We first created Mental Health Counseling-Component–Guided Dialogue Summaries, a benchmarking data set that consists of 191 counseling sessions with summaries focused on 3 distinct counseling components (also known as counseling aspects). Next, we assessed the capabilities of 11 state-of-the-art LLMs in addressing the task of counseling-component–guided summarization. The generated summaries were evaluated quantitatively using standard summarization metrics and verified qualitatively by mental health professionals.

Results
Our findings demonstrated the superior performance of task-specific LLMs such as MentalLlama, Mistral, and MentalBART evaluated using standard quantitative metrics such as Recall-Oriented Understudy for Gisting Evaluation (ROUGE)-1, ROUGE-2, ROUGE-L, and Bidirectional Encoder Representations from Transformers Score across all aspects of the counseling components. Furthermore, expert evaluation revealed that Mistral superseded both MentalLlama and MentalBART across 6 parameters: affective attitude, burden, ethicality, coherence, opportunity costs, and perceived effectiveness. However, these models exhibit a common weakness in terms of room for improvement in the opportunity costs and perceived effectiveness metrics.

Conclusions
While LLMs fine-tuned specifically on mental health domain data display better performance based on automatic evaluation scores, expert assessments indicate that these models are not yet reliable for clinical application. Further refinement and validation are necessary before their implementation in practice.
El modo subjuntivo en la enseñanza del español a hablantes de persa
Authors: Razavi Rozhin , Helan Nafise Amiri 
Published: July 2024

La enseñanza de una lengua abarca numerosos aspectos, entre los que se puede destacar la gramática. En estas notas de investigación se analiza el modo subjuntivo con la intención de hacer una comparación entre su estructura y uso en español y persa. Los puntos analizados comprenden sus diversos usos y las similitudes existentes entre ambas lenguas. El aspecto que se analiza es la presencia de más puntos de convergencia que de divergencia, así como el papel de la técnica de traducción en clase de ELE para posibilitar una mejor comprensión de este modo verbal por parte de estudiantes iraníes.
Con el presente trabajo se pretende mostrar que la enseñanza del modo subjuntivo puede resultar más sencilla si el estudiantado (en el caso que nos compete, hablante de persa) puede realizar comparaciones, buscar equivalencias o traducir desde su lengua materna hasta comprobar que el resultado obtenido en la L2 es muy parecido al de la L1.
Para entender a los mexicanos, conversemos sobre su historia
Authors: Vivaldo Juan Pablo 
Published: July 2024

El objetivo de este reporte de experiencia docente es mostrar la práctica didáctica que ha representado la puesta en marcha del curso sabatino “Para entender a los mexicanos, conversemos sobre su historia”. La materia se imparte desde la perspectiva de la historia social, sobre todo a partir de una arista urbana, aunque también toma en cuenta la vertiente rural. Se muestra la manera en que hombres y mujeres de todas las edades formaron parte de los procesos históricos que constituyen la memoria colectiva mexicana. En el desarrollo de este curso el grupo de estudiantes debe realizar una serie de lecturas para que cada sesión sea más provechosa, lo cual contribuye a que mejore sus habilidades lectoras y discursivas. La importancia de la propuesta consiste en que, al tratarse de una aproximación a la historia de México que relaciona cinco temas centrales —educación, género, cultura popular, asuntos públicos y relaciones sociales—, el alumnado del extranjero se acerca a una comprensión integral de la sociedad mexicana.
Las sesiones se complementan con material audiovisual (videos musicales, fragmentos de películas), fotografías y documentos que se encuentran disponibles en internet. Eso ha contribuido a que quienes asisten se involucren en la asignatura y conozcan mejor la historia y la cultura mexicana.
Large-Scale Proteomics Improve Prediction of Chronic Kidney Disease in People With Diabetes
Authors: Ye Ziliang , Zhang Yuanyuan , Zhang Yanjun , Yang Sisi , He Panpan , Liu Mengyi , Zhou Chun , Gan Xiaoqin , Huang Yu , Xiang Hao , Hou Fan Fan , Qin Xianhui 
Published: July 2024

OBJECTIVE
To develop and validate a protein risk score for predicting chronic kidney disease (CKD) in patients with diabetes and compare its predictive performance with a validated clinical risk model (CKD Prediction Consortium [CKD-PC]) and CKD polygenic risk score.

RESEARCH DESIGN AND METHODS
This cohort study included 2,094 patients with diabetes who had proteomics and genetic information and no history of CKD at baseline from the UK Biobank Pharma Proteomics Project. Based on nearly 3,000 plasma proteins, a CKD protein risk score including 11 proteins was constructed in the training set (including 1,047 participants; 117 CKD events).

RESULTS
The median follow-up duration was 12.1 years. In the test set (including 1,047 participants; 112 CKD events), the CKD protein risk score was positively associated with incident CKD (per SD increment; hazard ratio 1.78; 95% CI 1.44, 2.20). Compared with the basic model (age + sex + race, C-index, 0.627; 95% CI 0.578, 0.675), the CKD protein risk score (C-index increase 0.122; 95% CI 0.071, 0.177), and the CKD-PC risk factors (C-index increase 0.175; 95% CI 0.126, 0.217) significantly improved the prediction performance of incident CKD, but the CKD polygenic risk score (C-index increase 0.007; 95% CI −0.016, 0.025) had no significant improvement. Adding the CKD protein risk score into the CKD-PC risk factors had the largest C-index of 0.825 (C-index from 0.802 to 0.825; difference 0.023; 95% CI 0.006, 0.044), and significantly improved the continuous 10-year net reclassification (0.199; 95% CI 0.059, 0.299) and 10-year integrated discrimination index (0.041; 95% CI 0.007, 0.083).

CONCLUSIONS
Adding the CKD protein risk score to a validated clinical risk model significantly improved the discrimination and reclassification of CKD risk in patients with diabetes.
Kontruksi konsep go green dalam perspektif Hadis (Studi analisis ma‘ani al-Ḥadῑs terhadap hadis-hadis ekologi pada ṣaḥīḥ al-bukhārī kitab al-hartṡ wa al-muzāra‘ah)
Authors: Ma'rifatulloh Khoirunnisa , Daib Insan Labib Muhammad Alfreda , Muchlis Ahmad 
Published: July 2024

AbstractThis study analyzes the development of the concept of going green from the perspective of Hadith. The book Ṣaḥīḥ al-Bukhārῑ kitab al-Ḥarṡ wa al-Muzāraʻah has various hadiths on environmental protection (go green). This study employs a qualitative method based on library research, books, journals, and scholarly works. The study is based on the Prophet's Hadith, confirmed by Yusuf al-Qaradawi's hadith matan. It involves compiling relevant hadiths, understanding sabāb al-wurūd, determining the meaning of vocabulary in the hadith text, and concluding the concept of go green. This study aims to investigate how the concept of go green, as expressed in the Prophet's Hadith through Bukhari's narration, can be understood using Yusuf al-Qaradhawi's theory of understanding. The study's findings include utilizing vacant land by planting trees to prevent it from becoming neglected. Second, become accustomed to planting trees because it is a noble activity that merits charity. Third, do not cut down trees illegally, as this can harm the environment and humanity.Keywords: Concept; Go green; Maʻānῑ al-Ḥadῑṡ; Qardhawi. AbstrakTulisan ini membahas tentang kontruksi konsep go green dalam perspektif Hadis. Dalam hadis Nabi Muhammad saw yang tercantum dalam kitab Ṣaḥīḥ al-Bukhārῑ kitab al-Ḥarṡ wa al-Muzāraʻah tercantum beberapa hadis yang terkait dengan konsep pelestarian lingkungan (go green). Penelitian ini menggunakan metode kualitatif yang didasarkan pada library research, buku, jurnal maupun kitab para ulama’. Penelitian ini juga berlandaskan pada hadis Nabi dan diteguhkan dengan pemikiran matan hadis menurut Yusuf al-Qaradawi, yakni dengan menghimpun hadis-hadis yang relevan, memahami sabāb al-wurūd, memastikan makna kosakata dalam teks hadis kemudian menyimpulkan konsep go green. Penelitian ini bertujuan untuk mengetahui bagaimana konsep go green berdasarkan hadis Nabi melalui riwayat Bukhari jika ditelaah menggunakan teori pemahaman Yusuf al-Qaradhawi. Hasil penelitian diantaranya, pertama, memanfaatkan lahan kosong dengan menanami pohon sehingga lahan tersebut tidak terbengkalai. Kedua, membiasakan diri untuk mulai menanam pohon karena hal tersebut merupakan perilaku mulia dan bernilai sedekah. Ketiga, tidak menebang pohon secara liar karena hal tersebut dapat menimbulkan dampak negatif baik bagi bumi maupun bagi manusia. Kata Kunci: Go green; Konsep; Maʻānῑ al-Ḥadῑṡ; Qardhawi.