bluuebunny/crossref_metadata_2025 huggingface dataset:
STATISTICAL CONVERGENCE OF MATRIX SEQUENCES
Authors: NURAY YILDIRIM Elif
Published: June 2023

This paper extends the statistical convergence of real or complex numbers to the real square matrices sequences . In this context, we investigate the relation between the statistical convergence, statistical Cauchy condition, strong Ces\`{a}ro summability of matrices sequences. This leads us to an initial analysis of the Tauberian conditions for the statistical convergence of matrix sequences.
Sixteen-Year Longitudinal Evaluation of Blood-Based DNA Methylation Biomarkers for Early Prediction of Alzheimer’s Disease
Authors: Schäfer Hackenhaar Fernanda ,   the Australian Imaging Biomarkers and Lifestyle Study, Josefsson Maria , Nordin Adolfsson Annelie , Landfors Mattias , Kauppi Karolina , Porter Tenielle , Milicic Lidija , Laws Simon M. , Hultdin Magnus , Adolfsson Rolf , Degerman Sofie , Pudas Sara
Published: June 2023

Background: DNA methylation (DNAm), an epigenetic mark reflecting both inherited and environmental influences, has shown promise for Alzheimer’s disease (AD) prediction. Objective: Testing long-term predictive ability (>15 years) of existing DNAm-based epigenetic age acceleration (EAA) measures and identifying novel early blood-based DNAm AD-prediction biomarkers. Methods: EAA measures calculated from Illumina EPIC data from blood were tested with linear mixed-effects models (LMMs) in a longitudinal case-control sample (50 late-onset AD cases; 51 matched controls) with prospective data up to 16 years before clinical onset, and post-onset follow-up. Novel DNAm biomarkers were generated with epigenome-wide LMMs, and Sparse Partial Least Squares Discriminant Analysis applied at pre- (10–16 years), and post-AD-onset time-points. Results: EAA did not differentiate cases from controls during the follow-up time (p > 0.05). Three new DNA biomarkers showed in-sample predictive ability on average 8 years pre-onset, after adjustment for age, sex, and white blood cell proportions (p-values: 0.022-<0.00001). Our longitudinally-derived panel replicated nominally (p = 0.012) in an external cohort (n = 146 cases, 324 controls). However, its effect size and discriminatory accuracy were limited compared to APOE ɛ4-carriership (OR = 1.38 per 1 SD DNAm score increase versus OR = 13.58 for ɛ4-allele carriage; AUCs = 77.2% versus 87.0%). Literature review showed low overlap (n = 4) across 3275 AD-associated CpGs from 8 published studies, and no overlap with our identified CpGs.
Nationwide Database Analysis of Risk Factors Associated with Decreased Activities of Daily Living in Patients with Alzheimer’s Disease
Authors: Akada Keishi , Koyama Noriyuki , Miura Yuji , Takahashi Kentaro , Aoshima Ken
Published: June 2023

Background: Preserving activities of daily living (ADL) is the key issue for Alzheimer’s disease (AD) patients and their caregivers. Objective: To clarify the ADL level of AD patients at diagnosis and the risk factors associated with decreased ADL during long-term care (≤3 years). Methods: Medical records of AD patients in a Japanese health insurance claims database were analyzed retrospectively to determine ADL using the Barthel Index (BI) and identify the risk factors associated with decreased ADL. Results: A total of 16,799 AD patients (mean age at diagnosis: 83.6 years, 61.5% female) were analyzed. Female patients were older (84.6 versus 81.9 years; p < 0.001) and had lower BI (46.8 versus 57.6; p < 0.001) and body mass index (BMI) (21.0 versus 21.7 kg/m2; p < 0.001) than male patients at diagnosis. Disability (BI≤60) increased at age≥80 years and was significantly higher in females. Complete disability was most frequent for bathing and grooming. Risk factors for decreased ADL were determined separately by sex through comparing the ADL-preserved and ADL-decreased groups using propensity score matching by age and BI and multivariable logistic regression analysis. In males, decreased ADL was significantly associated with BMI < 21.5 kg/m2, stroke, and hip fracture, and inversely associated with hyperlipidemia. In females, decreased ADL was significantly associated with BMI < 21.5 kg/m2 and vertebral and hip fractures, and inversely associated with lower back pain. Conclusion: AD patients with low BMI, stroke, and fractures had increased risks of decreased ADL; such patients should be identified early and managed appropriately, including rehabilitation to preserve ADL.
Causal Association Between mTOR-Dependent Protein Levels and Alzheimer’s Disease: A Mendelian Randomization Study
Authors: Cai Hong-Yan , Hou Si-Jia , Wen Rui , Feng Qi-Fan , Xi Yu-Jia , Zhang Sheng-Xiao , Qiao Jun , Wu Mei-Na
Published: June 2023

Background: Most previous studies supported that the mammalian target of rapamycin (mTOR) is over-activated in Alzheimer’s disease (AD) and exacerbates the development of AD. It is unclear whether the causal associations between the mTOR signaling-related protein and the risk for AD exist. Objective: This study aims to investigate the causal effects of the mTOR signaling targets on AD. Methods: We explored whether the risk of AD varied with genetically predicted AKT, RP-S6K, EIF4E-BP, eIF4E, eIF4A, and eIF4G circulating levels using a two-sample Mendelian randomization analysis. The summary data for targets of the mTOR signaling were acquired from published genome-wide association studies for the INTERVAL study. Genetic associations with AD were retrieved from the International Genomics of Alzheimer’s Project. We utilized the inverse variance weighted as the primary approach to calculate the effect estimates. Results: The elevated levels of AKT (OR = 0.910, 95% CI=0.840-0.986, p = 0.02) and RP-S6K (OR = 0.910, 95% CI=0.840-0.986, p = 0.02) may decrease the AD risk. In contrast, the elevated eIF4E levels (OR = 1.805, 95% CI=1.002-1.174, p = 0.045) may genetically increase the AD risk. No statistical significance was identified for levels of EIF4-BP, eIF4A, and eIF4G with AD risk (p > 0.05). Conclusion: There was a causal relationship between the mTOR signaling and the risk for AD. Activating AKT and RP-S6K, or inhibiting eIF4E may be potentially beneficial to the prevention and treatment of AD.
The Efficacy and Safety of Physiotherapy in People with Dementia: A Systematic Review
Authors: Saúde Alexandra , Bouça-Machado Raquel , Leitão Mariana , Benedetti Andrea , Ferreira Joaquim J.
Published: June 2023

Background: Physiotherapy has become increasingly relevant as a new therapeutic intervention for dementia. However, it is unclear which interventions are the most suitable. Objective: This study sought to summarize and critically appraise the evidence on physiotherapy interventions in dementia. Methods: A systematic review conducted using CENTRAL, MEDLINE, and PEDro databases, from their inception to July 2022, identified all experimental studies of dementia that included physiotherapy interventions. Results: Of 194 articles included, the most frequently used interventions were aerobic training (n = 82, 42%), strength training (n = 79, 41%), balance training (n = 48, 25%), and stretching (n = 22, 11%). These were associated with a positive effect on several motor and cognitive outcomes. A total number of 1,119 adverse events were reported. Conclusion: Physiotherapy has several motor and cognitive benefits in dementia. Future research should focus on establishing a physiotherapy prescription protocol for people with mild cognitive impairment and for each stage of dementia.
Looking Behind the Curtain: Patient Stratification According to Genetic or Demographic Factors May Yield Unexpected Results in Studies of Neurodegenerative Diseases
Authors: Oliveira Fabricio Ferreira de
Published: June 2023

Amyloid-PET studies of neurodegenerative diseases may yield inconclusive findings due to lacking stratification according to genetic or demographic variants. APOE ɛ4 alleles are the major variants to increase disease susceptibility and cause earlier onset and more behavioral features in patients with late-onset Alzheimer’s disease, but have no linear effects on cognitive or functional decline; thus, sample stratification according to APOE ɛ4 carrier status may be the best option. Interactions among APOE ɛ4 alleles, sex, and age on amyloid-β deposition may reveal even more innovative findings with sufficiently large samples, suggesting variable genomic effects of cognitive reserve, sex differences, and cerebrovascular risk on neurodegeneration.
The conservation and utilization of museum relics based on internet of things and fuzzy control
Authors: Nan Yifei , Yao Nan , Huang Yukun , Jiao Dandan
Published: June 2023

With the development of Internet of Things and fuzzy control theory, there are new opportunities for the conservation and utilization of museum artifacts. The traditional way of heritage restoration lacks the overall harmony of heritage restoration, which leads to the stagnation of heritage restoration level. At the same time, traditional heritage conservation is also limited to manual management, which seriously hinders the development of the overall level of museums. Artifacts are of great commemorative and research value and are objects that have been handed down to mankind for thousands of years and have been subjected to the natural environment for a long time before being excavated. Heritage conservation requires not only restoring artifacts to the greatest extent possible, but also securing the environment in which they are displayed and stored to ensure that they can be preserved for as long as possible. But the following problems exist in the conservation of cultural relics (1) the lack of strict standards for the environment in which cultural relics are stored. (2) the professional competence of the staff is not strong. (3) The lack of a strict management system (4) heritage conservation technology is backward. To solve these problems our team constructed a fuzzy control method based on fuzzy control theory to assist in the restoration of cultural relics. The model constructed by fuzzy control theory solves this problem. The fuzzy control theory constructs a model for restoration of cultural relics, taking into account the overall harmony of the relics and the accuracy of the points, which is of great significance for the restoration of museum relics. At the same time through the Internet of Things technology to build a library heritage protection system, the main content of the system is (1) the use of Internet of Things technology to build a security protection system for cultural relics (2) the use of Internet of Things technology to detect the storage environment of cultural relics (3) the use of Internet of Things technology to optimize the management process of cultural relics (4) the use of Internet of Things technology to achieve real-time monitoring of the flow of information on cultural relics. And based on this, suggestions are made to (1) improve the museum heritage protection and utilization system (2) improve the infrastructure of museum heritage protection and build a good environment for heritage storage (3) improve the quality of heritage managers. The model constructed in the article has been tested in practice and is feasible, with certain practical and theoretical values.
Current situation and path of foreign minority language protection based on Internet of Things from the perspective of ethnic identity
Authors: Huang Qian
Published: June 2023

Ethnic identity is an important reason for national unity. The formation of ethnic groups is mostly due to external and internal reasons, with a complex mechanism structure. When people’s environment is destroyed, this value identity will be stronger, national cohesion and solidarity will grow sharply, an unprecedented high. Minority languages are characterized by a wide variety, cultural generation, and dynamic evolution. The languages are complex and diverse, and are deeply influenced by their cultural heritage. This has led to the fact that for a long time, minority languages have been learned almost exclusively by people of their own ethnic group. This has led to a steady decline in the number of minority language speakers. However, the birth of the Internet of Things (IoT) has created an opportunity for the development of minority languages. Foreign minority languages have the following status (1) rapid decline (2) many endangered languages (3) low willingness to learn. In order to further investigate the current situation of minority language protection, the team conducted a survey in the minority cluster A. The results of the survey are as follows: most of the villagers are in love with the ethnic language and must inherit and promote the ethnic language, which is the wealth of a nation, but there are also a few residents who think that Mandarin should be used to replace the ethnic language. This reflects that most of the residents love the national language, but their young people as the inherited generation lose their love for the national language and are not willing to inherit and promote it. To solve this problem, an Internet of Things (IoT)-based ethnic language data system has been constructed so that the data system for ethnic minority language protection built through IoT technology can become the last line of defense for protecting ethnic languages, while allowing ethnic languages to be known and understood by more people through IoT technology, fostering the love and reverence of ethnic minority people for ethnic languages, and enhancing other ethnic groups’ sense of identity and support for ethnic languages. We propose to protect minority languages. Ultimately, we propose recommendations for the preservation of minority languages. (1) Increase publicity for minority language preservation. (2) Construct laws and regulations related to minority language protection. (3) Conduct in-depth research on minority language work and build an information base. (4) Promote minority language education.
Seed Amplification Assay as a Diagnostic Tool in Newly-Diagnosed Parkinson’s Disease
Authors: Oftedal Linn , Maple-Grødem Jodi , Tysnes Ole-Bjørn , Alves Guido , Lange Johannes
Published: June 2023

Seed amplification assays (SAA) are the first credible molecular assay for Parkinson’s disease (PD). However, the value of SAA to support the clinicians’ initial diagnosis of PD is not clear. In our study, we analyzed cerebrospinal fluid samples from 121 PD patients recruited through population screening methods and taken within a median delay of 38 days from diagnosis and 51 normal controls without neurodegenerative disease. SAA yielded a sensitivity of 82.6% (95% CI, 74.7% – 88.9%) and specificity of 88.2% (95% CI, 76.1% – 95.6%). These results highlight the potential of SAA to support the initial diagnosis of PD in clinical practice and research.
The Representation of Trust in Artificial Intelligence Healthcare Research
Authors: Kutza Jan-Oliver , Hannemann Niels , Hübner Ursula , Babitsch Birgit
Published: June 2023

Artificial intelligence (AI) tends to emerge as a relevant component of medical care, previously reserved for medical experts. A key factor for the utilization of AI is the user’s trust in the AI itself, respectively the AIt’s decision process, but AI-models are lacking information about this process, the so-called Black Box, potentially affecting usert’s trust in AI. This analysis’ objective is the description of trust-related research regarding AI-models and the relevance of trust in comparison to other AI-related research topics in healthcare. For this purpose, a bibliometric analysis relying on 12985 article abstracts was conducted to derive a co-occurrence network which can be used to show former and current scientific endeavors in the field of healthcare based AI research and to provide insight into underrepresented research fields. Our results indicate that perceptual factors such as “trust” are still underrepresented in the scientific literature compared to other research fields.
Health Data Democratization in Austria: Patients’ Perspective
Authors: Hussein Rada , Stainer-Hochgatterer Andreas , Niebauer Josef , Palfinger Thomas , Kaisler Raphaela
Published: June 2023

Health data democratization requires a transparent, protected, and interoperable data-sharing environment. We conducted a co-creation workshop with patients living with chronic diseases and relevant stakeholders to explore their opinion on health data democratization, ownership, and sharing in Austria. Participants showed their willingness to share their health data for clinical and research purposes; provided that appropriate transparency and data protection measures are provided.
Deep Learning Framework for Categorical Emotional States Assessment Using Electrodermal Activity Signals
Authors: Govarthan Praveen Kumar , Sriram Kumar P  , Ganapathy Nagarajan , Agastinose Ronickom Jac Fredo
Published: June 2023

In this study, we attempted to classify categorical emotional states using Electrodermal Activity (EDA) signals and a configurable Convolutional Neural Network (cCNN). The EDA signals from the publicly available, Continuously Annotated Signals of Emotion dataset were down-sampled and decomposed into phasic components using the cvxEDA algorithm. The phasic component of EDA was subjected to Short-Time Fourier Transform-based time-frequency representation to obtain spectrograms. These spectrograms were input to the proposed cCNN to automatically learn the prominent features and discriminate varied emotions such as amusing, boring, relaxing, and scary. Nested k-Fold cross-validation was used to evaluate the robustness of the model. The results indicated that the proposed pipeline could discriminate the considered emotional states with a high average classification accuracy, recall, specificity, precision, and F-measure scores of 80.20%, 60.41%, 86.8%, 60.05%, and 58.61%, respectively. Thus, the proposed pipeline could be valuable in examining diverse emotional states in normal and clinical conditions.
Automated Emotion Recognition System Using Blood Volume Pulse and XGBoost Learning
Authors: Lebaka Lokesh Naidu , Sriram Kumar P  , Govarthan Praveen Kumar , Rani Priya , Ganapathy Nagarajan , Agastinose Ronickom Jac Fredo
Published: June 2023

In this study, a new method for detecting emotions using Blood Volume Pulse (BVP) signals and machine learning was presented. The BVP of 30 subjects from the publicly available CASE dataset was pre-processed, and 39 features were extracted from various emotional states, such as amusing, boring, relaxing, and scary. The features were categorized into time, frequency, and time-frequency domains and used to build an emotion detection model with XGBoost. The model achieved the highest classification accuracy of 71.88% using the top 10 features. The most significant features of the model were computed from time (5 features), time-frequency (4 features), and frequency (1 feature) domains. The skewness calculated from the time-frequency representation of the BVP was ranked highest and played a crucial role in the classification. Our study suggests the potential of using BVP recorded from wearable devices to detect emotions in healthcare applications.
Diagnostic Classification of ASD Improves with Structural Connectivity of DTI and Logistic Regression
Authors: Ratnaik Ravi , Rakshe Chetan , Kumar Manoj , Agastinose Ronickom Jac Fredo
Published: June 2023

In this study, we examined the structural connectivity (SC) of autism spectrum disorder (ASD) and typical development using the distance correlation and machine learning algorithm. We preprocessed diffusion tensor images using a standard pipeline and parcellated the brain into 48 regions using atlas. We derived diffusion measures in white matter tracts, such as fractional anisotropy, radial diffusivity, axial diffusivity, mean diffusivity, and mode of anisotropy. Additionally, SC is determined by the Euclidean distance between these features. The SC were ranked using XGBoost and significant features were fed as the input to the logistic regression classifier. We obtained an average 10-fold cross-validation classification accuracy of 81% for the top 20 features. The SC computed from the anterior limb of internal capsule L to superior corona radiata R regions significantly contributed to the classification models. Our study shows the potential utility of adopting SC changes as the biomarker for the diagnosis of ASD.
Understanding Health Records in West Slavic Languages: Available Resources, Case Study in Oncology
Authors: Anetta Kristof
Published: June 2023

Currently, there is very little research aimed at developing medical knowledge extraction tools for major West Slavic languages (Czech, Polish, and Slovak). This project lays the groundwork for a general medical knowledge extraction pipeline, introducing the resource vocabularies available for the respective languages (UMLS resources, ICD-10 translations and national drug databases). It demonstrates the utility of this approach on a case study using a large proprietary corpus of Czech oncology records consisting of more than 40 million words written about more than 4,000 patients. After correlating MedDRA terms found in patients’ records with drugs prescribed to them, significant non-obvious associations were found between selected medical conditions being mentioned and the probability of certain drugs being prescribed over the course of the patient’s treatment, in some cases increasing the probability of prescriptions by over 250%. This direction of research, producing large amounts of annotated data, is a prerequisite for training deep learning models and predictive systems.
The Classification Algorithms Applied to the Inpatient Stay for Lower Limb Surgery
Authors: Marino Marta Rosaria , Bottino Vincenzo , Negri Giacomo , Stingone Maria Anna , Russo Mario Alessandro , Triassi Maria
Published: June 2023

The revolutions of recent years in health care have involved several areas ranging from patient treatment to resource management. Therefore, several strategies have been put in place to increase patient value while trying to reduce spending. Several indicators have arisen to evaluate the performance of healthcare processes. The main one is Length of Stay (LOS). In this study, classification algorithms were used to predict the LOS of patients undergoing lower extremity surgery, an increasingly common condition given the progressive aging of the population. The context is the Evangelical Hospital “Betania” in Naples (Italy) in 2019–2020, which augments a multicenter study conducted by the same research team on several hospitals in southern Italy. All selected algorithms show an Accuracy above 90% but among them, the best is Logistic Regression with a value reaching 94%.
Health Digital Twins with Clinical Decision Support
Authors: Grob Moritz , Seisl Philipp , Rappelsberger Andrea , Adlassnig Klaus-Peter
Published: June 2023

This paper studies the feasibility of incorporating clinical decision support (CDS) into health digital twins (HDTs). A HDT is visualized in a web application, health data are stored in a FHIR-based electronic health record, and an Arden-Syntax-based CDS interpretation and alert service is connected. The prototype focuses on interoperability of these components. The study confirms the feasibility of CDS integration into HDTs and provides insight into possibilities for further expansion.
An Ontology Design Pattern for Modeling Experimental Paradigms
Authors: Hilbey Jacques , Aimé Xavier , Charlet Jean
Published: June 2023

We present an ontology design pattern for modeling scientific experiments and examinations conducted in a clinical research study. Integrating heterogeneous data into a common ontological model is a challenge, redoubled if we want them to be explored later. In order to facilitate the development of dedicated ontological modules, this design pattern relies on invariants, is centered on the event of the experiment, and keeps the link to the original data.
Attitudes Toward Plagiarism Among PhD Medical Students in Serbia
Authors: Rajovic Nina , Pavlovic Andrija , Olatunde Daniel , Pavlovic Vedrana , Stanisavljevic Dejana , Milic Natasa
Published: June 2023

In this cross-sectional survey the Attitude Towards Plagiarism (ATP) questionnaire was administered to 100 PhD students to measure their attitudes towards plagiarism. The results showed that the students had low scores for positive attitudes and subjective norms, but moderate scores for negative attitudes towards plagiarism. Additional courses on plagiarism should be introduced in PhD studies to promote responsible research practices in Serbia.
Psychometric Properties of the Serbian Version of the Attitudes Towards Research Questionnaire
Authors: Pavlovic Vedrana , Pavlovic Andrija , Klifopoulos Thomas , Rajovic Nina , Milic Natasa , Stanisavljevic Dejana
Published: June 2023

Participating in research plays an essential role in improving the practice of healthcare. This cross-sectional study enrolled 100PhD students who attended the course “Informatics for researchers” at Medical Faculty University of Belgrade. The total ATR scale demonstrated excellent reliability (α=0.899) with 0.881 for Positive attitudes to 0.695 for Relevance to life. PhD students in Serbia showed high degree of positive attitudes toward research. Faculty could administer ATR scale to determine students’ standpoints towards research, to maximize the impact of the research course and enhance participation in research.
Provision of Decision Support Through Continuous Prediction of Recurring Clinical Actions
Authors: Weisman Raymond Michal , Shahar Yuval
Published: June 2023

We propose a framework for provision of decision support through the continuous prediction of recurring targets, in particular clinical actions, which can potentially occur more than once in the patient’s longitudinal clinical record. We first perform an abstraction of the patient’s raw time-stamped data into intervals. Then, we partition the patient’s timeline into time windows, and perform frequent temporal patterns mining in the features’ window. Finally, we use the discovered patterns as features for a prediction model. We demonstrate the framework on the task of treatment prediction in the Intensive Care Unit, in the domains of Hypoglycemia, Hypokalemia and Hypotension.
Interoperable Universal Resource Identifier for Selective Disclosure of Data
Authors: Latorre Fernando , Hawks Claudia E. , Colmenares Bruno , Verma Deepika , Gil Marisa , Sala Nuria
Published: June 2023

All the information stored in the different information systems is issued in a format that allows the holder (the information owner) to disclose only certain information to a third party, which will act as a requester, receiver and verifier of the information disclosed by the holder. We define the Interoperable Universal Resource Identifier (iURI) as a harmonized method of representing a claim (minimum piece of verifiable information) using disparate encoding systems, agnostic to the original encoding system and data format. Encoding systems are represented in Reverse Domain Name Resolution (Reverse-DNS) format for HL7 FHIR, OpenEHR, and other data formats. The iURI can then be used in JSON Web Token for Selective Disclosure (SD-JWT) and Verifiable Credential (VC), among others. The method enables a person to demonstrate data that already exists in different information systems in disparate data formats, and even an information system, to verify certain claims, in a harmonized way.
Harmonisation of German Health Care Data Using the OMOP Common Data Model – A Practice Report
Authors: Hechtel Nicole , Apfel-Starke Johanna , Köhler Sophia , Fradziak Maikel , Schönfeld Norman , Steinmeyer Jens , Oeltze- Jafra Steffen
Published: June 2023

Data harmonization is an important step in large-scale data analysis and for generating evidence on real world data in healthcare. With the OMOP common data model, a relevant instrument for data harmonization is available that is being promoted by different networks and communities. At the Hannover Medical School (MHH) in Germany, an Enterprise Clinical Research Data Warehouse (ECRDW) is established and harmonization of that data source is the focus of this work. We present MHH’s first implementation of the OMOP common data model on top of the ECRDW data source and demonstrate the challenges concerning the mapping of German healthcare terminologies to a standardized format.
Investigation of Sleep Quality and Mental Health of Greek Physicians During the COVID-19 Pandemic
Authors: Nikolaidou Anna , Frantzidis Christos A. , Haidich Anna-Bettina
Published: June 2023

The global outbreak of COVID-19 has had an impact on physicians, not only as a time of great concern and responsibility, but also as a human performance factor influencing their sleep quality and mental health. However, studies have not yet defined the frequency and the interplay of sleep and mental issues. The purpose of this study was to explore the anxiety and sleep disturbances prevalence in Greek physicians, as well as their relationship with sociodemographic and profession-related traits, aiming to raise awareness for changes in healthcare management and policy making.
Using Redcap to Support the Development of a Learning Healthcare System for Patients with Multiple Sclerosis
Authors: Viguera M. , Martin-Sanchez F. , Marzo M.E.
Published: June 2023

Multiple Sclerosis is a neurodegenerative disease which shows different phenotypes making difficult for clinicians to make short-term decisions related with treatment and prognosis. Diagnosis is usually retrospective. Learning Healthcare Systems (LHS) can support clinical practice as they are devised as constantly improving modules. LHS can identify insights which allow evidence-based clinical decisions and more accurate prognosis. We are developing a LHS with the aim of reducing uncertainty. We are using ReDCAP to collect patients’ data, both from Clinical Reported Outcomes (CRO) and from Patients Reported Outcomes (PRO). Once analyzed, this data will serve as a foundation to our LHS. We conducted bibliographical research to select those CRO and PRO collected in clinical practice or identified as possible risk factors. We designed a data collection and management protocol based on using ReDCAP. We are following a cohort of 300 patients for 18 months. At the moment, we have included 93 patients and received 64 complete responses and 1 partial response. This data will be used to develop a LHS, able to accurate prognosis as well as to automatically include new data and improve its algorithm.
Cervical Intraepithelial Neoplasia Grading from Prepared Digital Histology Images
Authors: Brosnan Bríd , Skarga-Bandurova Inna , Biloborodova Tetiana , Skarha-Bandurov Illia
Published: June 2023

The paper proposes an integrated approach to the automated diagnosis of cervical intraepithelial neoplasia (CIN) in epithelial patches extracted from digital histology images. Experiments were conducted to determine the most suitable deep learning model for the dataset and fuse patch predictions to decide the final CIN grade of the histology samples. Seven candidate CNN architectures were assessed in this study. Three fusion methods were applied to the best CNN classifier. The model ensemble, combined CNN classifier and highest performing fusion method achieved an accuracy of 94.57%. This result shows significant improvement over the state-of-the-art classifiers for cervical cancer histopathology images. It is hoped that this work will contribute towards further research to automate diagnosis of CIN from digital histopathology images.
The Progress of Speech Recognition in Health Care: Surgery as an Example
Authors: Aldosari Bakheet , Babsai Rana , Alanazi Ahmed , Aldosari Hanan , Alanazi Abdullah
Published: June 2023

Artificial Intelligence (AI) is a computer system that simulates intelligent human behavior. The use of AI is rapidly shifting Healthcare. Speech recognition (SR) is a type of AI physicians use to operate Electronic Health records (EHR). This paper aims to demonstrate the technological advancements made thus far concerning speech recognition in health care and explore multiple scholarly studies to generate a wide-ranging and detailed assessment of its current progress. The effectiveness of speech recognition is the heart of this analysis. This review investigates published papers on the progress and effectiveness of speech recognition in Healthcare. Eight research papers exploring the progress and effectiveness of speech recognition in Healthcare were thoroughly reviewed. Articles were identified from Google Scholar, PubMed, and the World Wide Web. The five relevant papers generally discussed the progress and current effectiveness of SR in Healthcare, implementing SR in the EHR, adapting healthcare workers to SR and the problems they face, developing an intelligent healthcare system based on SR and using SR systems in other languages. Conclusion: This report demonstrates the technological improvements realized concerning SR in Healthcare. It proved that SR could be a tremendous help to providers if every medical and health institution continued to progress in using this technology.
Automatic Profiles Collection from Twitter Users with Depressive Symptoms
Authors: Wongkoblap Akkapon
Published: June 2023

Mental illness is a pressing issue that needs urgent attention, as the number of people suffering from mental disorders continues to increase. Diagnosing mental health disorders can be challenging, and gathering information about a patient’s medical history and symptoms is crucial for an accurate diagnosis. Self-disclosure on social media can provide valuable insights into whether users may be suffering from a mental illness. This paper proposes a method for automatically collecting data from social media users who disclosed their depression. The proposed approach yielded a 97% accuracy rate with a majority of 95%.
Arden Syntax on FHIR
Authors: Csarmann Andreas , Zeckl Julia , Haug Peter , Jenders Robert A. , Rappelsberger Andrea , Adlassnig Klaus-Peter
Published: June 2023

Arden Syntax, a medical knowledge representation and processing language for clinical decision support tasks supervised by Health Level Seven International (HL7), was extended with HL7’s Fast Healthcare Interoperability Resources (FHIR) constructs to allow standardized data access. The new version, Arden Syntax version 3.0, was successfully balloted as part of the audited, consensus-based, iterative HL7 standards development process.
Usability Assessment of an Electronic Medical Record-Embedded Clinical Decision Support System for Arterial Blood Gas Interpretation
Authors: Meidani Zahra , Zand Farid , Zare Sahar , Shirdeli Mohammad , Sharifian Roxana , Ouhadian Maryam , Holl Felix
Published: June 2023

Since usability is considered a significant success factor for Clinical decision support systems (CDSSs), this study seeks to assess the usability of an electronic medical records-embedded CDSS for arterial blood gas (ABG) interpretation and ordering. The current study was conducted in the general ICU of a teaching hospital, using the System Usability Scale (SUS) and interviews with all anesthesiology residents and intensive care fellows in two rounds of CDSS usability testing. The feedback from the participants was discussed with the research team across a series of meetings, and the second version of CDSS was designed and tailored to participants’ feedbacks. Subsequently, the CDSS usability score increased from 67.22±4.58 to 80.00±4.84 (P-value<0.001) through participatory, iterative design and the users’ usability testing feedbacks.
Predicting In-Hospital Mortality During the COVID-19 Pandemic in Patients with Heart Failure: A Single-Center Exploratory Study
Authors: D’Amore Antonio , D’Onofrio Gaetano , Fidecicchi Andrea , Triassi Maria , Marino Marta Rosaria
Published: June 2023

The aim of this study was to investigate whether exposure to the pandemic was associated with increased in-hospital mortality for health failure. We collected data from patients hospitalized between 2019 and 2020 and we assessed the likelihood of in-hospital death. Although the positive association of exposure to the COVID period with an increased in-hospital mortality is not statistically significant, this may underscore other factors that may influence mortality. Our study was designed to contribute to a better understanding of the impact of the pandemic on in-hospital mortality and to identify potential areas for intervention in patient care.
Health Informatics Training Programs to Strengthen Health Workforce in Montenegro
Authors: Zoulias Emmanouil , Mantas John , Ognjanovic Ivana , Sendelj Ramo , Lakovic Luka , Kara Peter A. , Bokor Laszlo , Reich Christoph
Published: June 2023

Health informatics plays a crucial role in modern healthcare provision. Training and continuous education are essential to bolster the healthcare workforce on health informatics. In this work, we present the training events within EU-funded DigNest project. The aim of the training events, the subjects offered, and the overall evaluation of the results are described in this paper.
Optical Disc Segmentation from Retinal Fundus Images Using Deep Learning
Authors: Islam Mohammad Tariqul , Ahmed Ferdaus , Househ Mowafa , Alam Tanvir
Published: June 2023

The optical disc in the human retina can reveal important information about a person’s health and well-being. We propose a deep learning-based approach to automatically identify the region in human retinal images that corresponds to the optical disc. We formulated the task as an image segmentation problem that leverages multiple public-domain datasets of human retinal fundus images. Using an attention-based residual U-Net, we showed that the optical disc in a human retina image can be detected with more than 99% pixel-level accuracy and around 95% in Matthew’s Correlation Coefficient. A comparison with variants of UNet with different encoder CNN architectures ascertains the superiority of the proposed approach across multiple metrics.
Predicting Overall Survival in METABRIC Cohort Using Machine Learning
Authors: Banu Afroz , Ahmed Rayyan , Musleh Saleh , Shah Zubair , Househ Mowafa , Alam Tanvir
Published: June 2023

Triple-negative breast cancer (TNBC) is an aggressive form of breast cancer that presents very high relapse and mortality. However, due to differences in the genetic architecture associated with TNBC, patients have different outcomes and respond differently to available treatments. In this study, we predicted the overall survival of TNBC patients in the METABRIC cohort employing supervised machine learning to identify important clinical and genetic features that are associated with better survival. We achieved a slightly higher Concordance index than the state of art and identified biological pathways related to the top genes considered important by our model.
PORNOGRAPHY IN THE PERSPECTIVE OF ISLAMIC CRIMINAL LAW
Authors: Aulia Redhina
Published: June 2023

Social media is currently overgrowing, which certainly impacts people's lifestyles. However, these advances are often exploited, with cyberporn being one example. This essay seeks to evaluate and critique pornography from the point of view of Indonesian Islamic criminal law. Examining the application of criminal sanctions for pornography by Islamic criminal law, as well as the reasons why it is created and prohibited, is the aim of this study. This study examines laws and regulations with a normative legal approach. It is the study of normative juridical law. This research data set comes from legal sources. The research findings suggest several behaviors, weaknesses in judgment, constitutive consequences, additional circumstances, additional requirements for a criminal prosecution, and additional requirements for punishment that fall under the category of pornography crimes of Islamic criminal law. Criminal sanctions in Islamic law allow adultery and ta'zir as punishment for pornography offenders. Criminal acts in pornography include various forms, including sounds, moving images, animations, cartoons, conversations, body movements, and public performances containing sexual exploitation or obscene content that violates the norms of social decency.
Saraiki Language Hybrid Stemmer Using Rule-Based and LSTM-Based Sequence-To-Sequence Model Approach
Authors: Mubasher H. Malik  , Hamid Ghous  , Iqra Ahsan  , Maryem Ismail
Published: July 2023

Converting a word to its original form, is called stemming, which is extremely important in the field of Natural language processing (NLP). It’s an integral part of the linguistic pre-processing of every Natural language processing application. Stemming converts inflectional word forms into their root word. Much work has been done for stemming in different national and regional languages like English, French, Arabic, German, Urdu, and Hindi. Many regional languages still need work to build digital resources using Natural language processing. Saraiki is one of the widely spoken regional languages in Pakistan. Almost eighty million people use this language for communication. There are very limited digital resources using the Saraiki language available to support advancement in Natural language processing technologies. The current research aims to propose a hybrid stemmer to stem Saraiki Work. The hybrid stemmer contains two hundred prefix and postfix rules and Long short-term memory based sequence-to-sequence model for converting Saraiki words into the stem. Firstly, Saraiki text * Corresponding Author: mubasher@isp.edu.pk was pre-processed, and a rule set was implemented. Secondly, the Long short-term memory based sequence-to-sequence model was deployed to stem the Saraiki word correctly. In the last step, The Saraiki Stemmer performance was evaluated by accurately finding stem word accuracy using a rule-set and Long short-term memory sequence to sequence model. After experiments, using the rule set correctly, stem word accuracy was 68.53%, while the Long short-term memory based sequence-to-sequence model produced 93.0% accuracy of correctly stem words. This work contributes significantly to the regional linguistic field by introducing stemmer for the Saraiki language.
Risk factors of COVID-19: immunological aspects
Authors:   Russian Research Institute of Hematology and Transfusiology of the Federal Medical Biological Agency of Russia, St. Petersburg, Russian Federation, Glazanova T.V. , Shilova E.R. ,   Russian Research Institute of Hematology and Transfusiology of the Federal Medical Biological Agency of Russia, St. Petersburg, Russian Federation, Pavlova I.E. ,   Russian Research Institute of Hematology and Transfusiology of the Federal Medical Biological Agency of Russia, St. Petersburg, Russian Federation
Published: June 2023

Recent studies devoted to the patterns of the COVID-19 course in patients of various groups indicate a significant role of the immunity state in the disease development. Risk groups of severe disease course, including senile age, obesity and immunodeficiency conditions, have been identified. Research is underway to identify the genetic predisposition to SARS-CoV-2 infection and determine the poor prognosis in COVID-19. The presented article examines the immunological aspects of pathological conditions associated with an unfavorable prognosis of COVID-19. A complex immune response (including both innate immune mechanisms and an adaptive systemic response) develops in response to SARS-CoV-2 infection. The key mechanism of multisystem organ failure is a hyperimmune reaction with the development of a systemic inflammatory response, the so-called "cytokine storm", and the characteristics of the immune response largely determine the disease severity. In this regard, the immune response characteristics in patients at risk are of particular interest, including studies concerning the immunological aspects of a more severe disease course depending on age, the history of obesity, diabetes mellitus and other pathological conditions, as well as various genetic factors, which is the subject of active study at present. KEYWORDS: immune system, COVID-19, SARS-CoV-2, risk factors, obesity, oncological diseases, genetic predisposition, immune response. FOR CITATION: Glazanova T.V., Shilova E.R., Pavlova I.E. Risk factors of COVID-19: immunological aspects. Russian Medical Inquiry. 2023;7(11):751–758 (in Russ.). DOI: 10.32364/2587-6821-2023-7-11-5.
The Split Singularity Hypothesis
Authors: Bhattacharjee Deep , Roy Sanjeevan Singha , Sadhu Riddhima
Published: June 2023

Based on the hypothesis of the evolving machine intelligence that in essence gives birth to super-intelligent machines, this can be said that as proposed in the previous theories there will be an intelligence evolution hitting a singularity. In this paper, we have hypothesized that machines may split into two categories based on the ‘type’ of intelligence explosion resulting in one ‘type’ hitting hard singularity while one ‘type’ hitting soft singularity for a specific blow-up case called errored singularity marking the endpoint is discussed.
MTHFR and LC, CFS, POTS, MCAS, SIBO, EDS: Methylating the Alphabet
Authors: Chambers Patrick
Published: June 2023

Long Covid (LC), Chronic Fatigue Syndrome (CFS), Postural Orthostatic Tachycardia Syndrome (POTS), Mast Cell Activation Syndrome (MCAS), Small Intestine Bacterial Overgrowth (SIBO), and Ehlers-Danlos Syndrome (EDS) are all loosely connected, some poorly defined, some with overlapping symptoms. The female preponderance, the prominence of fatigue and chronic inflammation, and methylenetetrahydrofolate reductase (MTHFR) abnormalities may connect them all. Indeed differential methylation may lie at the root. Two - EDS and MTHFR - are genetic. But epigenetic factors may ultimately determine their phenotypic expression. Oxidative stress, overloaded mitochondria, an antioxidant and nutrient shortfall, and suboptimal gut microbiome appear to be the primary determinants. A deep dive into the folate and methionine cycles is undertaken in an attempt to connect these syndromes. The active forms of vitamin D and vitamins B2,3,6,9,12 are shown to be biochemically integral to optimal methylation and control of the epigenome. Their status largely determines the symptoms of abnormal MTHFR in all its phenotypes. The wider implications for aging, cancer, cardiovascular disease, neurodegenerative disease, and autoimmune disease are briefly explored.