bluuebunny/crossref_metadata_2025 huggingface dataset:
Model Prediction Using Artificial Neural Network (ANN) to Strengthen Diagnostic Analysis of Diabetes Melitus
Authors: Kurniawan Deddy , Wulansari Tina Tri , Febrianti Niken Ayu Dwi
Published: January 2025

The incidence of Diabetes Mellitus (DM) is one of the urgent and increasing health issues every year. Hence, this condition requires high urgency to be handled. The research aimed to develop a prediction model for DM that could be used in general for the purpose of diagnostic analysis of DM cases against suspected individuals. The dataset was sourced from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), which had closely related parameters in diagnostic analysis without favoring certain groups. The targeted contribution was the result of a new prediction model that was specifically tested on the dataset using the Artificial Neural Network (ANN) algorithm. This model was developed through a baseline model that was tested and improved in performance through hyperparameter cross-validation therapy and L1 regularization. The formation of the model architecture through experiments to adjust the conditions of hidden layers and neurons in several configurations results in a model architecture with 8 input parameters. It contains 3 hidden layers with a total of 14, 20, and 26 neurons, with the ReLU activation function on each hidden layer and the Sigmoid activation function on the output part. The second test is carried out on a hyperparameter configuration. It produces maximum performance with a k-fold value of 10 and L1 regularization of 0.0001. The model performance results obtain an accuracy value of 0.947, precision of 0.895, recall of 0.914, and model loss of 0.215
A Novel Machine Learning for Ethanol and Methanol Classification with Capacitive Soil Moisture (CSM) Sensors
Authors: Sari Devina Intan , Trihandaru Suryasatriya , Parhusip Hanna Arini
Published: January 2025

Although Gas Chromatography (GC) is highly accurate, it is costly, highlighting the need for a more affordable method for alcohol detection. Ethanol and methanol have different evaporation rates and dielectric constants, suggesting the potential for classification as an alternative initial step to GC based on differences in dielectric due to evaporation using Capacitive Soil Moisture (CSM) sensors, although it has not been previously attempted. The research aimed to present a novel machine learning for ethanol and methanol classification with CSM sensors. The method involved placing evaporated samples on CSM plates and measuring the change in evaporative dielectric properties over time. The data were then processed using Python, preprocessing data, splitting data, and training various classifiers with key differentiators based on standard deviation, mean, difference, and cumulative summary. Then, model accuracy was evaluated. The research results show that the approach can distinguish between pure ethanol and methanol based on the dielectric differences in each substance's evaporation rate using machine learning training methods with classifiers such as Random Forest, Extra Trees, Gaussian Naive Bayes, AdaBoost, and Logistic Regression with seven folds in cross-validation, L2 regularization, and Newton-Cholesky solver, with accuracies of 96.67%, 96.67%, 96.67%, 93.33%, and 93.33%, respectively. Although the research is limited to the classification of two types of alcohol, the novel approach can classify methanol and ethanol, leading to a potential initial step in determining alcohol content in the future. It can be an alternative to GC with a simpler and more affordable setup using CSM sensors.
Robotics and AI in museums – the future of the present
Authors: de Mello Janaina Cardoso , Secci Gyamarco Pereira Nascimento , Ribeiro Pedro Henrique
Published: January 2025

The contemporary concerns regarding the provision of Basic Education in the Humanities, capable of preparing students for cognitive exercise as critical citizens and professionals who meet the demands of the 21st century, underpin the inquiry that guided this project: “How can the Teaching of History and Cultural Heritage in Basic Education operate significantly in the face of new demands in the relationship between human societies and digital technologies?” It is understood that enabling technologies 4.0 also encompass Artificial Intelligence (AI) and Robotics (used in industry, but also in education), and their incorporation into teaching and learning for new generations is important. The BNCC (2017) emphasizes the importance of using material objects to assist teachers and students in questioning the meaning of things in the world, stimulating the production of historical knowledge in the school environment. Understanding this practice as an action in which teachers and students can play the role of agents in the teaching and learning process, both assuming a “historical attitude” towards the proposed contents. Therefore, the aim was to present the results of the research and technological developments carried out during the “SayHist – AI and Robotics in Museums of Sergipe” project, between 2022 and 2023, aiming to create a mini robot connected to an application capable of presenting exhibitions from two museums in Sergipe (Museum of Sergipe History – MUHSE) through audio and video, with speakers and a micro-projector.
Prefácio
Authors: Machado Regina
Published: January 2025

Neste trabalho, fazendo uso de ferramentas analíticas dedicadas à canção e ao canto popular, Ricardo examina as vozes de intérpretes de uma geração atual em diálogo com vozes icônicas e amplamente reconhecidas pelo grande público. Essa dimensão analítica exposta revigora nossa escuta e, ao mesmo
tempo, chama a atenção para elementos mais sutis nem sempre percebidos em uma escuta menos atenta. Cabe destacar que os mistérios e encantamentos produzidos por uma voz nunca são completamente revelados por processos de análise. Como já mencionava a etnomusicóloga Elizabeth Travassos, “a voz é um objeto fugidio, de difícil apreensão”. Ao mesmo tempo que isso torna a atividade do analista instigante, ela garante ao apaixonado a permanência de camadas do mistério que faz com que vozes nos capturem emocionalmente sem que haja uma explicação total para esse acontecimento
Corrigendum to: Neuropeptide Network of Polycystic Ovary Syndrome – A Review
Authors: Jayamurali Dheepthi , Ravishankar Nivetha , Manoharan Nivedita , Parasuraman Rajeshwari , Jayashankar Sri Kameshwaran , Narayanan Govindarajulu Sathya
Published: January 2025

An error was found in the affiliations of the author in the article titled ‘Neuropeptide Network of Polycystic Ovary Syndrome –
A Review’’, published in Protein and Peptide Letters, 2024, 31(9), 667-680 [1].

Details of the error and a correction are provided here.

Original:
*Address correspondence Department of Physiology, Dr. ALM PG Institute of Basic Medical Sciences, University of Madras,
Taramani, Chennai 600 113, India; E-mail: drgsathyanarayanan@gmail.com

Corrected:
*Address correspondence to this author at the Department of Physiology, Dr. ALM PG Institute of Basic Medical Sciences,
University of Madras, Taramani, Chennai 600 113, India; E-mail: drgsathyanarayanan@gmail.com

We regret the error and apologize to readers.

The original article can be found online at: http://dx.doi.org/10.2174/0109298665309949240822105900 PMID: 39313871
Blockchain-Powered IoT Innovations in Healthcare
Authors: J. Mangaiyarkkarasi  , J. Shanthalakshmi Revathy  , Shashi Kant Gupta  , Shilpa Mehta
Published: January 2025

The convergence of blockchain technology and the Internet of Things (IoT)
has become a transformative catalyst in healthcare. “Blockchain-Powered IoT
Innovations in Healthcare”, a chapter in this book, explores the dynamic interplay
between these technologies and their far-reaching impact on healthcare systems. This
chapter commences with an introduction to blockchain and IoT, emphasizing their
relevance within the healthcare domain. It underscores blockchain's pivotal role in
fortifying patient data security, addressing privacy concerns, and rectifying
vulnerabilities in healthcare data management and highlights the diverse applications of
IoT devices in healthcare, including wearables, remote patient monitoring, and smart
hospital equipment. It also addresses how blockchain facilitates secure cross-platform
data sharing while preserving data integrity and confidentiality. The implementation of
smart contracts in healthcare is explored, showcasing their influence on patient
empowerment, data transparency, and the management of clinical trials. The chapter
also illustrates how blockchain technology enhances supply chain management and
ensures drug traceability, countering the proliferation of counterfeit medications. A
comprehensive discussion on patient empowerment and data control reveals how
blockchain is revolutionizing patient engagement and informed decision-making. The
chapter analyzes the intricate landscape of legal and regulatory aspects, offering
guidance on compliance and potential legal implications of blockchain adoption in
healthcare. In conclusion, the chapter addresses the challenges, future prospects, and
emerging trends in the field of healthcare. It serves as an essential resource for
healthcare professionals, researchers, policymakers, and technology enthusiasts
navigating the intersection of blockchain and IoT in the ever-evolving realm of
healthcare.
Stride-Based Threat Modeling for Blockchain Based Healthcare Supply Chain Management System
Authors: S.V. Harish  , K. Chandrasekaran  , Rathnamma  , Usha Divakarla  , Venkata Ramana
Published: January 2025

The increasing use of blockchain technology in supply chain management
has made it imperative to understand the possible security risks associated with its
implementation. This research aims to identify important security issues related to
supply chain management's use of blockchain technology by doing a thorough analysis
of the body of existing literature and looking at actual cases of blockchain
deployments. These dangers include the possibility of data privacy breaches, smart
contract weaknesses, and 51% attack vulnerability. The report also offers suggestions
for reducing these risks, including using multi-factor authentication, regularly carrying
out security audits, and enforcing strict access rules. The study's conclusions broaden
our knowledge of the security risks associated with blockchain-based supply chain
management (BC-SCM) and offer useful guidance to companies thinking about
implementing this technology.
Efforts to enhance dental health behavior and status of stunted toddlers through the utilization of the 'Towards Healthy Teeth Card' in integrated health posts across Taebenu Subdistrict, Kupang Regency
Authors: Wali Agusthinus , Nubatonis Melkisedek O. , Salikun Salikun , Siregar Irma Hadia Yuliana
Published: January 2025

Dental and oral diseases, such as caries, pose risks to toddlers. The Indonesian government's Ministry of Health Regulation No. 89 of 2015 emphasizes community empowerment in dental health by involving local health cadres. The 'Towards Healthy Teeth Card' facilitates early detection and promotes community independence in dental health. Experimental studies on its effectiveness are needed, with parental involvement being vital to support toddler dental health. This study aims to determine the effect of the 'Towards Healthy Teeth Card' on improving stunted toddlers' dental health behavior and status. The research involved developing materials and concepts for the 'Towards Healthy Teeth Card' for toddlers and employed a pre-experimental one-group pretest-posttest design at Integrated Health Posts across Taebenu Subdistrict, Kupang Regency. The respondents were mothers and stunted toddlers, with total sampling used as the sampling method. The study assessed changes in behavior and dental health status using the def-t score. The Wilcoxon Signed Ranks Test analysis showed a significant effect of the 'Towards Healthy Teeth Card' on maternal behavior in maintaining dental health and the dental caries status of stunted toddlers, with a p-value of 0.000 (< 0.05).
Pengaruh Pijat Endorfin terhadap Tingkat Kecemasan Inpartu Kala I Fase Laten di Puskesmas Gesang Kabupaten Lumajang
Authors:   Program Studi Sarjana Kebidanan, Fakultas Ilmu Kesehatan, Institut Teknologi Sains dan Kesehatan RS dr Soepraoen, Malang, Mualimah Sa'iratul , Retnaningsih Reny ,   Program Studi Sarjana Kebidanan, Fakultas Ilmu Kesehatan, Institut Teknologi Sains dan Kesehatan RS dr Soepraoen, Malang, Purwati Anik ,   Program Studi Sarjana Kebidanan, Fakultas Ilmu Kesehatan, Institut Teknologi Sains dan Kesehatan RS dr Soepraoen, Malang
Published: January 2025

Endorphin massage is known to help reduce anxiety in laboring mothers by stimulating the body’s natural pain-relief mechanisms. This study aims to assess the effectiveness of endorphin massage in reducing anxiety levels in mothers during the latent phase of labor (stage 1). The research focuses on determining the difference in anxiety levels before and after receiving the massage. This study employed a pre-experimental design with a one-group pretest-posttest approach. The sample consisted of 30 mothers in the latent phase of labor at Puskesmas Gesang, selected through purposive sampling. Anxiety levels were measured using a validated anxiety scale before and after the 20-minute endorphin massage. The message targeted specific areas known to stimulate the release of endorphins, such as the shoulders and lower back. The results showed a significant reduction in anxiety levels following the endorphin massage intervention. The average anxiety score before the intervention was 7.5, which decreased to 4.0 after the massage (p < 0.05). This indicates that endorphin massage has a positive effect on reducing anxiety in mothers during the latent phase of labor. Endorphin massage is an effective method to alleviate anxiety in laboring mothers during the latent phase. The intervention provides a non-invasive, simple, low-cost option that can be easily implemented in primary healthcare settings. Endorphin massage should be incorporated into routine maternity care, especially during the early stages of labor, to promote maternal relaxation and potentially improve labor progression and outcomes.
Development of Personas and Journey Maps for Artificial Intelligence Agents Supporting the Use of Health Big Data: Human-Centered Design Approach
Authors: Lee Yoon Heui , Choi Hanna , Lee Soo-Kyoung
Published: January 2025

Background

The rapid proliferation of artificial intelligence (AI) requires new approaches for human-AI interfaces that are different from classic human-computer interfaces. In developing a system that is conducive to the analysis and use of health big data (HBD), reflecting the empirical characteristics of users who have performed HBD analysis is the most crucial aspect to consider. Recently, human-centered design methodology, a field of user-centered design, has been expanded and is used not only to develop types of products but also technologies and services.

Objective

This study was conducted to integrate and analyze users’ experiences along the HBD analysis journey using the human-centered design methodology and reflect them in the development of AI agents that support future HBD analysis. This research aims to help accelerate the development of novel human-AI interfaces for AI agents that support the analysis and use of HBD, which will be urgently needed in the near future.

Methods

Using human-centered design methodology, we collected data through shadowing and in-depth interviews with 16 people with experience in analyzing and using HBD. We identified users’ empirical characteristics, emotions, pain points, and needs related to HBD analysis and use and created personas and journey maps.

Results

The general characteristics of participants (n=16) were as follows: the majority were in their 40s (n=6, 38%) and held a PhD degree (n=10, 63%). Professors (n=7, 44%) and health care personnel (n=10, 63%) represented the largest professional groups. Participants’ experiences with big data analysis varied, with 25% (n=4) being beginners and 38% (n=6) having extensive experience. Common analysis methods included statistical analysis (n=7, 44%) and data mining (n=6, 38%). Qualitative findings from shadowing and in-depth interviews revealed key challenges: lack of knowledge on using analytical solutions, crisis management difficulties during errors, and inadequate understanding of health care data and clinical decision-making, especially among non–health care professionals. Three types of personas and journey maps—health care professionals as big data analysis beginners, health care professionals who have experience in big data analytics, and non–health care professionals who are experts in big data analytics—were derived. They showed a need for personalized platforms tailored to the user level, appropriate direction through a navigation function, a crisis management support system, communication and sharing among users, and expert linkage service.

Conclusions

The knowledge obtained from this study can be leveraged in designing an AI agent to support future HBD analysis and use. This is expected to further increase the usability of HBD by helping users perform effective use of HBD more easily.
Exploring Older Adults’ Needs for a Healthy Life and eHealth: Qualitative Interview Study (Preprint)
Authors: Valkonen Paula , Kujala Sari , Savolainen Kaisa , Helminen Riina-Riitta
Published: January 2025

BACKGROUND

Aging brings physical and life changes that could benefit from eHealth services. eHealth holistically combines technology, tasks, individuals, and contexts, and all these intertwined elements should be considered in eHealth development. As users’ needs change with life situations, including aging and retirement, it is important to identify these needs at different life stages to develop eHealth services for well-being and active, healthy lives.

OBJECTIVE

This study aimed to (1) understand older adults’ everyday lives in terms of well-being and health, (2) investigate older adults’ needs for eHealth services, and (3) create design recommendations based on the findings.

METHODS

A total of 20 older adults from 2 age groups (55 to 74 years: n=12, 60%; >75 years: n=8, 40%) participated in this qualitative interview study. The data were collected remotely using a cultural probes package that included diary-based tasks, sentence completion tasks, and 4 background questionnaires; we also performed remote, semistructured interviews. The data were gathered between the fall of 2020 and the spring of 2021 in Finland as a part of the Toward a Socially Inclusive Digital Society: Transforming Service Culture (DigiIN) project (2019 to 2025).

RESULTS

In the daily lives of older adults, home-based activities, such as exercising (72/622, 11.6% of mentions), sleeping (51/622, 8.2% of mentions), and dining and cooking (96/622, 15.4% of mentions), promoted well-being and health. When discussing their needs for eHealth services, participants highlighted a preference for a chat function. However, they frequently mentioned barriers and concerns such as the lack of human contact, inefficiency, and difficulties using eHealth systems. Older adults value flexibility; testing possibilities (eg, trial versions); support for digital services; and relevant, empathetically offered content with eHealth services on short-term and long-term bases in their changing life situations.

CONCLUSIONS

Many older adults value healthy routines and time spent at home. The diversity of older adults’ needs should be considered by making it possible for them to manage their health safely and flexibly on different devices and channels. eHealth services should adapt to older adults’ life changes through motivation, personalized content, and appropriate functions. Importantly, older adults should still have the option to not use eHealth services.
Real-world effectiveness of glucose-guided eating using the Data-Driven Fasting app: An observational study (Preprint)
Authors: Jospe Michelle R , Kendall Martin , Schembre Susan M , Roy Melyssa
Published: January 2025

BACKGROUND

The Data-Driven Fasting (DDF) app implements glucose-guided eating (GGE), an innovative dietary intervention that encourages individuals to eat when their glucose level falls below a personalised threshold to improve metabolic health. Clinical trials using GGE, facilitated by paper logging of glucose and hunger symptoms, have shown promising results. However, the real-world effectiveness and adherence to GGE supported by a mobile app remain unexplored.

OBJECTIVE

To describe DDF users’ demographics, app engagement, adherence to preprandial glucose monitoring, and the resulting impact on weight and glucose levels.

METHODS

Data were collected over three years from users of the Data-Driven Fasting (DDF) app. The analysis covered the first 30 days of app use and included users with at least two days of preprandial glucose entries. App engagement and changes in body weight and fasting glucose levels by weight and diabetes status were examined.

RESULTS

6197 people used the DDF app for at least two days. Participants used the app for a median of 19 days (25th, 75th percentiles: 9, 28 days), with a median of 7 weight entries (25th, 75th percentiles: 3, 13 entries) and 52 glucose entries (25th, 75th percentiles: 25, 82 entries). Last observation carried forward analysis revealed a weight loss of 0.7 kg (95% CI -0.8, -0.6) in the normal weight category, 1.0 kg (95% CI -1.1, -0.9) in the overweight category, and 1.2 kg (95% CI -1.3, -1.1) in the obese category. Fasting glucose levels increased by 0.11 mmol/L (95% CI 0.09, 0.12) in the normal range, decreased by 0.14 mmol/L (95% CI -0.16, -0.12) in the prediabetes range, and decreased by 0.50 mmol/L (95% CI -0.58, -0.42) in the diabetes range.

CONCLUSIONS

The implementation of GGE through the DDF app in a real-world setting led to consistent weight loss across all weight categories and significant improvements in fasting glucose levels for users with prediabetes and diabetes. This study underscores the potential of the GGE to facilitate improved metabolic health.

CLINICALTRIAL
Development of Personas and Journey Maps for Artificial Intelligence Agents Supporting the Use of Health Big Data: Human-Centered Design Approach (Preprint)
Authors: Lee Yoon Heui , Choi Hanna , Lee Soo-Kyoung
Published: January 2025

BACKGROUND

The rapid proliferation of artificial intelligence (AI) requires new approaches for human-AI interfaces that are different from classic human-computer interfaces. In developing a system that is conducive to the analysis and use of health big data (HBD), reflecting the empirical characteristics of users who have performed HBD analysis is the most crucial aspect to consider. Recently, human-centered design methodology, a field of user-centered design, has been expanded and is used not only to develop types of products but also technologies and services.

OBJECTIVE

This study was conducted to integrate and analyze users’ experiences along the HBD analysis journey using the human-centered design methodology and reflect them in the development of AI agents that support future HBD analysis. This research aims to help accelerate the development of novel human-AI interfaces for AI agents that support the analysis and use of HBD, which will be urgently needed in the near future.

METHODS

Using human-centered design methodology, we collected data through shadowing and in-depth interviews with 16 people with experience in analyzing and using HBD. We identified users’ empirical characteristics, emotions, pain points, and needs related to HBD analysis and use and created personas and journey maps.

RESULTS

The general characteristics of participants (n=16) were as follows: the majority were in their 40s (n=6, 38%) and held a PhD degree (n=10, 63%). Professors (n=7, 44%) and health care personnel (n=10, 63%) represented the largest professional groups. Participants’ experiences with big data analysis varied, with 25% (n=4) being beginners and 38% (n=6) having extensive experience. Common analysis methods included statistical analysis (n=7, 44%) and data mining (n=6, 38%). Qualitative findings from shadowing and in-depth interviews revealed key challenges: lack of knowledge on using analytical solutions, crisis management difficulties during errors, and inadequate understanding of health care data and clinical decision-making, especially among non–health care professionals. Three types of personas and journey maps—health care professionals as big data analysis beginners, health care professionals who have experience in big data analytics, and non–health care professionals who are experts in big data analytics—were derived. They showed a need for personalized platforms tailored to the user level, appropriate direction through a navigation function, a crisis management support system, communication and sharing among users, and expert linkage service.

CONCLUSIONS

The knowledge obtained from this study can be leveraged in designing an AI agent to support future HBD analysis and use. This is expected to further increase the usability of HBD by helping users perform effective use of HBD more easily.
Emerging Role of Digital Resources in Caregiver Approaches to Common Challenges in Dementia Care: Mixed Methods Survey (Preprint)
Authors: Scott Julia A , Cepukenas Emma , Himes McKenzie , Anderson Kennedy , Grewal Kiren , Mai An , Yuter Sheila , Simone Patricia
Published: January 2025

BACKGROUND

As the number of individuals diagnosed with dementia continues to grow, collective caregiving capacity is simultaneously declining. As a result, caregivers often face significant stress and burnout, which negatively impacts both their well-being and the quality of care they are able to provide. To address these challenges, various forms of technology have been developed to support caregiving responsibilities for people with dementia. Barriers to widespread adoption include the cost of technology, lack of caregiver training, limited awareness of available solutions, privacy and ethical concerns, and resistance to change from both caregivers and care recipients. By identifying these key insights, this study seeks to inform the development of more accessible, user-friendly, and effective technological solutions that can better support caregivers in their vital roles.

OBJECTIVE

This study aimed to assess the way dementia caregivers solve common challenges, focusing on technology adoption and barriers to its use.

METHODS

Online surveys were distributed on the topics of technology usage and caregiving experience which were distributed to the communities of informal and formal dementia caregivers. Surveys quantified categories of technology use and caregiver needs. The survey also gathered open responses for joys, struggles, and technology use. Follow up interviews with caregivers discussed how specific challenges were met. The responses were thematically coded to characterize findings by the type--Struggles, Social Supports, Technology, and Joy.

RESULTS

Respondents (N=69) identified access to resources as the most important unmet need for caregivers (53%). The survey found that medication and routine tracking applications and mobility assistive devices were the most commonly used forms of technology. The dominant need expressed by caregivers was for better access to resources, with improved access to healthcare services, and financial support. Interviewees reported barriers to accessing resources, including high costs and difficulties in selecting appropriate technology resources. Additionally, centralizing resources and simplifying the process of locating support was recommended as a solution.

CONCLUSIONS

The study showed that caregivers felt inadequately supported and did not frequently use technology due to barriers of access and usability of products. Strategies in product design and user communication that engage dementia care partners would likely accelerate adoption.
Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments (Preprint)
Authors: Xia Zongqi , Chikersal Prerna , Venkatesh Shruthi , Walker Elizabeth , Dey Anind , Goel Mayank
Published: January 2025

UNSTRUCTURED

We present machine learning (ML) approaches that enable longitudinal monitoring of patient-reported symptoms for people with multiple sclerosis (pwMS) by harnessing passively collected data from sensors in smartphones and fitness trackers. For each patient, we divided collected data into discrete periods. From each period, we extract patient-level behavioral features from the current period (action features) and the previous period (context features). Next, we apply Support Vector Machine with Radial Bias Function Kernel and AdaBoost to predict the presence of depressive symptoms (every 2-weeks) and high global MS symptom burden, severe fatigue, and poor sleep quality (every 4-weeks). We collected ~12,500 days of passive sensor and behavioral health data from 104 pwMS participants who completed 12-weeks and a subset of 44 pwMS who completed 24-weeks of data collection. Among the best-performing models with the least sensor data requirement, ML algorithm predicts depressive symptoms with an accuracy of 80.6% (35.5% improvement over baseline; F1-score: 0.76), high global MS symptom burden with an accuracy of 77.3% (51.3% improvement over baseline; F1-score: 0.77), severe fatigue with an accuracy of 73.8% (45.0% improvement over baseline; F1-score: 0.74), and poor sleep quality with an accuracy of 72.0% (28.1% improvement over baseline; F1-score: 0.70). Sensor data were largely sufficient for predicting symptom severity, while the prediction of depressive symptoms benefited from minimal active patient input (i.e., response to two brief questions on the day before prediction). Our digital phenotyping approach using passive sensors on smartphones and fitness trackers may help patients with real-world, continuous, self-monitoring of common symptoms in their own environment and assist clinicians with better triage of patient needs for timely interventions in MS.
Dying in Darkness: Deviations from Data-Sharing Ethics in the US Public Health System and the Data Genocide of Indian and Alaska Native Communities (Preprint)
Authors: Schmit Cason D. , O’Connell Meghan Curry , Shewbrooks Sarah , Abourezk Charles , Cochlin Fallon J. , Doerr Megan , Kum Hye-Chung
Published: January 2025

UNSTRUCTURED

Tribal governments and Tribal Epidemiology Centers face persistent challenges in obtaining the public health data that is essential to their legal and ethical duties to promote health in American Indian and Alaska Native (AI/AN) communities. We assess the ethical implications of current impediments to data sharing between federal, state, and Tribal public health partners. Public health ethics obligates public health data sharing and opposes data collection without dissemination to affected communities. Privacy practices often obstruct data access, with de-identification and data suppression disproportionately affecting AI/AN populations, exacerbating health disparities. The 2020-2024 syphilis outbreak illustrates how restricted data access impedes effective public health responses. These practices represent a source of structuralized violence throughout the US public health system that contributes to the data genocide of AI/AN populations. Good governance, through transparent data practices and establishing a social license, is essential to ethically balance collective well-being with individual privacy in public health.
Waiting Time for Prescription Services on Social-Health-Insurance Patients at X Kendal Hospitals: Why Takes Too Long?
Authors: Astuti Eka Kristia Ayu , Sriatmi Ayun , Agushybana Farid
Published: January 2025

Background: Excessive waiting time for prescription services at the pharmacy can negatively impact a hospital's reputation. Exceeding the minimum service standard for prescription service waiting time may indicate bottlenecks in the process, leading to inefficient service delivery.Objectives: This research aimed to determine the average waiting time for the prescription services for social health insurance patients and identify external and internal factors that prolong the prescription process.Methods: The research employed a mixed-method approach using an explanatory sequential design. Quantitative data was collected by measuring prescription waiting times from the submission of the prescription sheet by the patient to dispensing medicine to the patients. Qualitative data was gathered through in-depth interviews.Results: The results of the analysis of 100 prescriptions (38 compound and 62 non-compound) showed that the average waiting time for compound prescriptions was 50.42 minutes and 41.03 minutes for non-compound prescriptions. The longest stage in compound and non-compound prescription processes was prescription review, averaging 19 minutes from all outpatient clinic prescriptions sampels. Results of in-depth interviews revealed that incomplete prescription information by doctors, the total number of medicine items, distraction from patients, inadequate staff skills, and a shortage of prescription pharmacists contributed to delays in prescription services.Conclusion: Duration of prescription services for social health insurance that exceeded the standard time was observed in non-compound prescriptions, with bottlenecks occurring during the prescription review. Therefore, pharmacy management should evaluate and improve prescription services to enhance the efficiency of service time.
Cost-Effectiveness Analysis of ERACS Compared to Non-ERACS at Hospital
Authors: Basleman Balqis Hisyam Saleh , Susiyadi Susiyadi , Setiawan Didik
Published: January 2025

Background: Cesarean section has a 5 to 7 times greater risk of maternal and child mortality than prevaginal delivery. Several other complications, namely reduced mobility, prolonged pain in the surgical wound, and longer hospital stays affect the patient's quality of life. The ERACS protocol, which is a multidisciplinary approach, is a good strategy to reduce the negative effects of cesarean section.Objectives: To find out whether the ERACS protocol is more cost-effective than the non-ERACS protocol from a patient’s perspective.Methods: Researchers took data on costs and quality of life (using EQ-5D-5L) prospectively at private hospitals in Wonosobo which then calculated the value of the ICER. The cost components measured include direct medical and non-medical costs as well as indirect costs. The effectiveness of the measures in this study was reported in terms of reducing the LOS to 1 day, which represents a faster recovery and reduced costs. In this study, there were 2 sample groups, namely the ERACS protocol group with 24 respondents and the non-ERACS protocol group with 75 respondents.Results: Based on the research, the effectiveness of the protocol in the form of improving the quality of life of patients was seen from the utility value and higher costs in the ERACS group compared to the non-ERCAS group (utility 0.771 vs 0.715; cost IDR.16,127,183 ± 5,023,356 vs IDR. 10,459,562 ± 3,826 .424) and obtained an ICER of 94,311,767 which means that the patient needs to add IDR.94,311,767 to improve the quality of life of post-cesarean section patients, which value is higher than Indonesia's 2022 GDP.Conclusion: ERACS measures are more cost-effective when compared to non-ERACS measures because the ICER value is below three times Indonesia's 2022 GDP according to the perspective of patients at private hospitals in Wonosobo.
Marketing Strategy of “AL” Traditional Medicine Products: Case Study in “SP” Company in Yogyakarta
Authors: Putri Felicita Eka , Ardiningtyas Bondan , Endarti Dwi
Published: January 2025

Background: Exclusive breastfeeding is an effort to avoid cases of stunting in children. However, the breastmilk produced by the mother is not always enough to meet the needs of the baby. PT. SP is one of the companies that manufacture “AL” as a traditional herbal medication      (a combination of katuk and moringa leaves) that can boost milk production. However, the sales of this product are not optimal yet, meaning competent marketing strategies are needed. Objective: This study aimed to identify and analyze the internal and external factors of PT. SP to gain an understanding of the company's position so that the best marketing strategy can be determined.Methods: This study was carried out using an analytical descriptive method involving key informants including the supervisor of production, Quality Control, Quality Assurance, and the marketing division. Data analysis utilized the Internal Factor Evaluation (IFE), External Factor Evaluation (EFE) Matrix, and Internal-External (IE) Matrix.Results: Based on the analysis PT. SP had significant internal factors for capturing opportunities and facing threats, as shown in cell IV.                      It indicated that PT. SP is a growing and developing company. The most suitable marketing strategy needed is an intensive strategy, such as market penetration, market expansion, and product development.        An integration strategy is carried out, which is forward integration.Conclusion: The company was positioned in cell IV in the IE matrix, namely growing and developing. It is suggested that the strategies that can be used to improve marketing are intensive and integration strategies.
Cost-effectiveness Study in Type 2 Diabetes Mellitus in Asia: A Review
Authors: Wiracana I Made , Priyatni Nunung , Virginia Dita Maria
Published: January 2025

Background: Type 2 Diabetes Mellitus (DM) requires intensive treatment to prevent progression and complications. One of the intensive treatments is insulin therapy. The increase in insulin price and variation in insulin therapy results in differences in treatment costs. In Asia, commonly used types of insulin therapy are NPH insulin and glargine insulin. It is urgent to define the most cost-effective insulin therapy among type 2 DM patients because of the economic burden.Objectives: This study aims to assess the cost-effectiveness of these insulin types. We did a narrative review using literature discussing the cost-effectiveness of insulin for type 2 DM patients.Methods: This study employs a narrative review approach using the PRISMA-P 2015 structured approach to examine studies. This article's methodological quality was evaluated using the Drummond checklist. The terms "cost-effectiveness analysis," "diabetes mellitus type 2," and "insulin" were used in the literature search for this study.Results: We obtained five pieces of literature fulfilling inclusion and exclusion criteria. The results indicated that the age range of type 2 DM patients in this study is 57-62 years, with a majority being women. The most frequently occurring complication is cardiovascular complications. NPH insulin and glargine insulin were the most extensively studied insulins in the literature review.Conclusion: Based on the cost-effectiveness analysis, glargine insulin is more cost-effective than NPH insulin in Asia due to the rare occurrence of hypoglycemia which is a common side effect as a treatment outcomes.
Ursolic Acid and Polydatin in Melinjo Seeds Inhibit AKT1 and GAPDH Protein and HTB-179 Cells Migration
Authors: Febriansah Rifki , Julita Irna
Published: January 2025

The incidence of lung cancer in Indonesia by 2020 has reached 34.783 cases. Melinjo contains ursolic acid and polydatin, which can suppress cell proliferation and induce cell apoptosis, respectively. This study aims to determine the Melinjo seed ethyl acetate fraction (MSEAF) ability to inhibit lung cancer proliferation and migration towards HTB-179 cells using in vitro and in silico methods. Melinjo seed powder was macerated using 70% ethanol and fractionated with ethyl acetate. The fraction obtained was then analyzed using HPLC to detect the active compounds. The compounds obtained were further analyzed using bioinformatics to determine the target proteins. The docking method was performed between ursolic acid and polydatin compounds with each target protein to determine the binding affinity. The in vitro test was done using the MTT cytotoxicity assay and scratch wound healing assay methods. The results showed that MSEAF contains ursolic acid and polydatin with retention times of 12,475 minutes and 16,564 minutes, respectively. Ursolic acid protein targets were TP53 and AKT1 with docking scores of -6,3 kcal/mol and -7,4 kcal/mol, while polydatin target proteins were GAPDH and VEGFA with docking scores of -8,8 kcal/mol and -5,5 kcal/mol. The results of the MTT assay showed an IC50 value of 35,539 g/mL, and MSEAF inhibited the migration of HTB-179 cells by slowing the migration rate. This study suggested that the MSEAF contained ursolic acid and polydatin, which exhibited the ability to prevent the growth and migration of HTB-179 lung cancer, supported by the prediction of their ability to bind to TP53 and AKT1 proteins.
The Effect of Cocamide Dea on the Characteristics of Ginger and Celery Extract Shampoo Preparations
Authors: Anastasia Desy Siska , Septiani Rahmi , Isnindar Isnindar
Published: January 2025

Ginger and celery extracts are recognized anti-dandruff agents. Managing dandruff can be accomplished by creating a cleanser formulation. Shampoo contains an essential cleansing ingredient known as surfactant. SLS enables good foams, but at >10% concentration, it irritates skin. Adding a nonionic surfactant like cocamide DEA reduces irritation. This study examined how cocamide DEA concentrations affected the physical qualities and hedonic evaluation of a shampoo containing ginger and celery extracts. The shampoo was produced in three formulas with differing cocamide DEA concentrations: F1 (6%), F2 (8%), and F3 (10%). The formulation is evaluated using organoleptic, homogeneity, viscosity, pH, bulk density, foam height, foam stability, cycling, hedonic, and irritant studies. The result of this study revealed that shampoo had a brownish-orange color, a ginger-mint scent, and different looks. All three formulations were homogeneous, did not separate during cycling, and had significant viscosity variances, pH, bulk density, foam height, and foam stability. The formula with an 8% cocamide DEA concentration was the most popular and did not produce irritation. In conclusion, varying cocamide DEA concentrations affect the physical properties and preferences of panelists in a shampoo formulation that includes ginger and celery extracts.
Formulation and Antioxidant Activity of Gotu Kola Jelly Candy with Plant-based Polymers as a Gelling Agent
Authors: Devi Dwitya Devi Nurlistyo , Darsih Cici , Yuniarti Nunung , Ardiningtyas Bondan , Laksitorini Marlyn Dian
Published: January 2025

Centella asiatica or gotu kola has a long history as a brain supplement. Gotu kola supplements are sold as liquid and dried extract which is less attractive for a younger generation. Jelly candy is an alternative dosage form with better acceptability across ages. However, the use of animal-derived polymers such as pork gelatine in the candy restricts those who practice vegetarian and halal lifestyles from consuming the products. This study aims to explore plant-based polymers glucomannan and kappa-carrageenan as gelling agents in the preparation of gotu kola jelly candy. Preparation of the jelly candy formula was designed based on Simplex Lattice Design. Evaluation of physical characteristics of jelly candy includes organoleptic, weight uniformity, moisture content, pH, and elasticity. The antioxidant activity of gotu kola before and after the manufacturing process was evaluated. The results showed that a combination of kappa-carrageenan 1.33% and glucomannan 0.67% is the optimum formula. Adding more proportion of kappa-carrageenan reduced jelly elasticity and moisture content. While adding glucomannan improved its elasticity responses but increased moisture content. Evaluation of the antioxidant activity of gotu kola in jelly candy suggested that gotu kola experienced a significant reduction in antioxidant activity following the production process. The IC50 of the crude extract initially was129.23 ppm while post jelly candy manufacturing, the IC50 increased to 197.49 ppm. This study suggested that improvement in extraction and production processes is necessary to maintain gotu kola antioxidant activity.
Mauli Banana Stem Gel: A Potential Material for Dentin Remineralization Analyzed Using Fourier Transform Infrared Spectroscopy
Authors: Carabelly Amy Nindia , Rahman Yajma Kamiila , Puspitasati Dewi , Erlita Isyana , Wydiamala Erida
Published: January 2025

Dental caries requires remineralizing agents to restore the dentin. Mauli banana stem gel is predicted to be an alternative material for mineral deposition during the remineralization process. Fourier transform infrared spectroscopy (FTIR) can analyze substances that indicate dentin remineralization. This study aimed to conduct an FTIR analysis of Mauli banana stem gel as a potential substitute material for dentin remineralization. The study utilized dentin slices obtained from the first premolars of the maxilla, which were demineralized using EDTA. Subsequently, the dentin slices were treated with CPP-ACP, 50% MBSG, 62,5% MBSG, and artificial saliva. A test tube containing 15 ml of artificial saliva was placed inside the smeared material, which was applied twice a day. It was left for three minutes and then rinsed with deionized water before being incubated at 37 °C. Basting was carried out for 21 days, and FTIR observations were performed. The major absorbance peaks detected were O-H and N-H stretch; C-H stretch; SCN stretch; Amide I; CO32-(v3) and collagen; Amide II; CH2 scissoring; C-H deformation; C-H stretching; Amide III; and PO43-(V3). MBSG demonstrated superior retention of dentin collagen compared to the control group, but it could not maintain the mineral content level on day 21. MBSG demonstrated a decrease in crystallinity due to a rise in carbonate content in the hydroxyapatite lattice. The study's findings indicate that MBSG remains unsuitable for dentin remineralization. Additional additives are necessary to enhance the levels of collagen and minerals in remineralized dentin.
Antioxidant and Anti-Breast Cancer from Uncaria gambir Roxb Leaves: In Silico & In Vitro Study
Authors: Iskandar Dodi , Rollando Rollando , Susana Susana , Amperawati Suharyani , Indrastuti Y Erning , Afthoni Muhammad Hilmi
Published: January 2025

Uncaria gambir Roxb (URG) is one of the plants from West Kalimantan predicted to contain antioxidant and anti-breast cancer. This study aims to test the antioxidant and anti-breast cancer potential. UGR leaves were extracted by infusion method using water for 15 minutes with 4 repetitions at a temperature 70oC. A thick extract of 96.2351 grams (29.979%) was obtained from a sample weight of 321 grams of dried UGR leaves. In vitro antioxidant assay of the extract was investigated using 2,2-diphenylpicrylhydrazyl (DPPH) with positive control using quercetin and ascorbic acid, and the Ferric Reducing Antioxidant Power (FRAP) method. In silico screening showed that the biological agents in UGR had the potential as TP53 expression enhancer, antioxidant, anticarcinogenic, chemopreventive, and free radical scavenger. The antioxidant bioassay results showed IC50 values of 81.21 μg/mL, 73.39 μg/mL, and 9.17 μg/mL in DPPH for extract samples with positive control quercetin, sample extracts with positive control Vitamin C and Vitamin C with positive control quercetin, respectively. Meanwhile, the antioxidant activity of extract samples with the FRAP method showed value of 66,05 μg/mL. Anticancer bioassay result showed that UGR leaves extract with water solvent had the stron potentgial to inhibit 4T1 cells with IC50  87.72 μg/mL.