bluuebunny/crossref_metadata_2025 huggingface dataset:
Immunomagnetic separation for the enrichment of trace enrofloxacin and analysis of degradation byproducts in water
Authors: Yang Ruonan , Zhu Lei , Gao Yuxiao , Li Bingzhi , Fang Longxiang , Meng Shunlong , Song Chao
Published: October 2024

The presence of trace antibiotics in water can lead to the development of drug-resistant bacterial strains, posing risks to ecosystems and human health. Immunomagnetic separation (IMS) using magnetic nanoparticles (MNPs) is an effective technique for targeted enrichment. This study established and optimized a separation system for the immunomagnetic microsphere enrichment of enrofloxacin (ENR) antibiotics, achieving efficient enrichment and isolation of ENR. To address potential elution degradation, an analysis of ENR degradation pathways and toxicity assessment of degradation products was performed. The study manifested the successful conjugation of antibodies to magnetic microspheres, leading to a 97.68% separation efficiency for ENR in water through IMS. Specifically, 1 mg of MNP@Ab could specifically bind to 1.5 ng of ENR at 37 °C for 30 min, and the elution rate exceeded 83%. No degradation products of ENR were detected during the enrichment and isolation procedures. Nevertheless, extending the elution time to 1 h disclosed three major degradation pathways with higher toxicity risks than ENR based on ecological risk assessment. To strictly control the elution temperature and elution time, the increase in temperature and time will heighten the risk of degradation products. This study presents an efficient strategy for water treatment and environmental protection.
Seven Concerns of Innovation Design Process and Analysis of a Smart Bicycle Frame
Authors: Suraj  , Kumar Arun , Nath Tameshwer
Published: October 2024

Though we are improving our living standards with technology, we are
neither bothered about our fitness nor our environment. The article focuses on both,
this research will not only help to keep an individual fit but also help in improving air
pollution. In this article using the concept of seven concerns of the innovation process,
a customized Bicycle frame (Photo Voltaic (PV) frame) is selected after overcoming
the flaws in many designs and final conception is analyzed for maximum deformation
at different loads. In this article, five concerns out of seven are considered to achieve
the final design. Flexible Solar Cell (mentioned in a magazine created by
Massachusetts Institute of Technology, Energy Initiative) on the frame will help to
charge the battery and that battery will operate other devices like Air Purifier, Mobile
Charger, etc. that’s why this bicycle is called smart bicycle by the author. This article
only focuses on innovative design analysis of customized solar frames but not the
working of solar frames or air purifiers. This innovative design is going to change the
concept of cycling in the future.
Emerging Technologies in Fintech: A Case Study
Authors: Patel Keyurkumar , Sunnapu Pujita , Gautam Sunil
Published: October 2024

The Financial Technology (FinTech) industry has been playing a pivotal role
in driving modern day’s economics, social aspects, technology, and many more areas.
FinTech is majorly inspired, motivated, and empowered by Data Science and Artificial
Intelligence Methodologies (DSAIM). With emerging technology, the smart FinTech
industry has revolutionised economic and financial businesses, service industries, and
systems. The global research communities have made significant progress in smart
FinTech for Banking Tech, Trade Tech, InsurTech, Wealth Tech, Pay Tech, Risk Tech,
Cryptocurrencies, Digital Payment Systems, and Blockchain using DSAIM. In this
review paper, we narrow down the overview of smart financial businesses, their
complex challenges, and the entire smart FinTech ecosystem. The DSAIM enables
smart FinTech and poses some research problems among global academic and
researcher communities.
Design and Development of Blockchain Integrated IoT System for Pharmaceutical Applications
Authors: T. Jyothy S. , Sarvagya Mrinal
Published: October 2024

The integration of the Internet of Things (IoT) technology in the
pharmaceutical industry has the potential to bring about significant advancements and
improvements, particularly in areas like supply chain management, drug discovery, and
patient monitoring. By leveraging IoT technology in these ways, the pharmaceutical
industry can enhance efficiency, improve product quality, ensure patient safety, and
mitigate the risks associated with counterfeit drugs in the market. However, it was
essential to address cybersecurity concerns to safeguard sensitive data and maintain the
integrity of the IoT ecosystem in healthcare. The concept of integrating IoT with
blockchain in the pharmaceutical supply chain is indeed a powerful solution to address
the challenges faced by the industry, particularly in supply chain management and the
prevention of counterfeit drugs. Integrating IoT with blockchain technology also results
in many advantages like accurate location tracking, real-time updates and visibility,
counterfeit prevention, timely delivery to customers, environmental parameter control,
expiry date monitoring, pharmaceutical warehouse management, automated decision
making and many more. By combining the strengths of IoT and blockchain, the
pharmaceutical industry can build a robust and secure supply chain ecosystem. This
integrated approach not only enhances transparency and traceability but also addresses
critical issues such as counterfeiting by ensuring the delivery of safe and authentic
pharmaceutical products to consumers. Environmental parameters are also monitored
in a warehouse to avoid spoilage of medicines. The date of expiry of medicines and
related equipment are monitored and categorized into three levels such as discard the
medicines if expired, near to expiry if the expiry date is nearer and store in the
warehouse if the date of expiry is far, and a message is sent accordingly to the
supervisor for necessary action.
Deep Learning Techniques for Predicting Changes in the Ecosystem
Authors: Kruthi  , kumar Anugraha Anil , J. Aromal A. , Ravindra Chaya
Published: October 2024

In the modern age, we depend on technology for our daily needs, from
groceries to booking tickets for rides. The technology supports us by understanding our
requirements. This is done by using Machine Learning. Machine Learning deals with
understanding human behavior and providing suggestions for our requirements. The
changes in the ecosystem affect the living creatures who depend on the ecosystem. One
of the subsets of Machine Learning that play a vital role in saving the lives of living
creatures is Deep Learning. Deep Learning is a representational learning of artificial
neural networks. Deep Learning keeps on improving so that it can imitate human
intelligence more accurately. Artificial Neural Networks is another subset of Machine
Learning and helps in the growth of Deep Learning. There are different classes of
artificial neural networks, two of the important classes are the Convolutional Neural
Network (CNN) and the Recurrent Neural Network (RNN). The patterns in images are
recognized by CNN. So, CNN majorly deals with image recognition and processing.
RNN helps recognize sequential data and uses this pattern of sequential data to predict
the likely scenarios in the ecosystem. The model, which uses the algorithm of RNN and
CNN, should be trained and tested with the data for better efficiency.
A Detailed Analysis of Issues with Solid-State Devices
Authors: Shweta  , Ahmad Hifzan
Published: October 2024

Non-volatile memory technologies, such as NAND flash memory, have
improved storage system performance, reliability, durability, and cost. Due to their
speed and density, solid-state devices (SSDs) that are based on flash memory are being
used as workstations, desktops, and laptops. Despite offering superior performance,
stress resistance, and energy economy as compared to mechanical hard drives, NAND
flash memory has unique features and operating limits and cannot be employed as a
perfect block device. The design of SSDs has developed over time to make use of the
benefits offered by flash memory while, at the same time, hiding their drawbacks. SSD
concurrency techniques make use of the available parallelism of flash memories. This
chapter thoroughly examines SSD subjects, ranging from the physical features of a
flash memory cell to the design of SSDs themselves. The subjects pertaining to the
flash translation layer (FTL) are described within the context of interconnected systemlevel operations. These operations include garbage collection, wear-leveling, address
mapping and bad block management. This chapter also provides a review of the most
current SSD-related studies.
Classification of Deep Learning Techniques for Object Detection
Authors: Raj Purushotham Aras Amruth , Ravindra Manjunath , Ravindra Chaya
Published: October 2024

The object detection framework recognises real-world objects within the
frame of a moving photograph or computer-generated image. The object has a location
to flow to through other objects, such as people or automobiles. Item detection is
widely used in sectors where it is necessary for an organization's security and growth.
The vast range of applications for protest detection include image recovery, security
strategy, reason for inspection, machine framework assessment, and computerised
vehicle structure. In contrast to conventional object localization techniques, machine
learning-based object identification makes use of the machine's greater capacity to
learn and represent knowledge [1]. A difficult problem in the analysis of designs and
computer frameworks is object detection. Later on, the relationship between object
detection, video analysis and image processing was developed. The complicated
structure that is now being constructed includes both fundamental and sophisticated
features, and the evaluation is carried out depending on the classifiers used. A complex
system that can accurately assess and distinguish between numerous aspects is
produced as a result of this combination. Several deep-level characteristics have been
developed as a result of machine learning advancements to address the problems in the
old design [2]. We conducted research on one-stage and two-stage object detectors,
which are further categorised into deep learning methodologies. To enhance object
detection, CNN networks employ these algorithms. An evaluation of the machine
learning method for object detection is presented in this paper [3]. The protest site's
applications have been distilled. The various methods of object localization employ
template-based, region-based, and portion-based methods.
Unlocking Business Insights: Leveraging the Synergy of Business Intelligence and Artifical Intelligence for Effective Data Analytics
Authors: Nayak Manjushree , Pattnaik Ashutosh
Published: October 2024

Nowadays, the market is full of a variety of products for a single item, which
makes it very difficult for customers to choose which product is best for them
according to their price range. At the same time, it also creates a huge problem for
manufacturers and business people to sell their products in this market, with a variety
of customers who have different needs and expectations from the products they
purchase. One of the most important factors in selling a product is its price and
standard, which determine its sales and demand in the market. So, it is most important
to sell the product as per the customer's needs and also at a price so that they can afford
it. As there is a rapid evolution of technology, it is being used by other sectors to make
a profit and also to understand market needs and their performance in the market. So, it
is also very necessary to implement the technology in this sector, as it deals with a huge
number of customers and is very important for the businessperson to make a huge
amount of profit. A business intelligence tool is used, and its purpose is to analyze the
market. It collects complete information about a product in a huge quantity, which
helps it predict future results more accurately. By applying this tool in business, it
predicts the future sale of the product in the market, and if they decide to come up with
a new product in the market, what should be the selling price they need to fix for its
sale, and how can they increase the demand for that product in the market? One of the
biggest advantages of using this tool is that it also predicts the performance of the
manufacturer in the market and the different areas they should focus on to improve
their productivity. This concludes that using business intelligence in business solves
many problems for the manufacturer, starting from analyzing the market to
understanding the needs and expectations of the customers. Not only does it help
businesses, but it also helps customers so that they can purchase the product that meets
their expectations.
Multi-Agent Trading System Using Artificial Intelligence
Authors: Ingle Vaishali
Published: October 2024

Multi-agent systems are concerned with decision-making tasks where
multiple agents act in a shared environment. Agents can observe their environment
(partially or fully), act to impact the environment, and might have different or aligned
goals. Multi-Agent Systems Artificial Intelligence (MAAI) is used for simulating enduser requirements. The models designed are examples of the use of AI in the business
world.
The concept of reinforcement learning can be applied to stock price prediction for a
specific stock, working in an agent-based system to predict higher returns based on the
current environment. The agent's reward will be either profit or loss. A multi-agent
system will use three types of agents: agent 1 (forecasting agent using a basic machine
learning algorithm), agent 2 (judgmental agent; the background algorithms to work on
it are reinforcement learning or fuzzy neural networks), and agent 3 (based on simple
trading rules or neural networks). Alert Agent (AA) guarantees proficient conveying
inside the schema. Signals are one of the alerts. The alert agent sends the foundation
agents (Agent 1, Agent 2, and Agent 3) signals (verdict) delivered by the superior
agent. Depending on these verdicts, the superior policies are understood to be presented
to the users (traders). Depending on the verdict by Superior, investment risk can be
minimized. The multi-agent framework verdict is combined with sentiment collected
from finance news for a particular company. The cognizant behavior of agents in the
stock market is also considered future research work for this framework. AI-based
stock trading systems must be strengthened in the future with the use of various
security measures.
The Role of Artificial Intelligence in Social Welfare: Harnessing AI For Positive Societal Impact
Authors: Rathore Manas
Published: October 2024

This chapter explores the transformative role of Artificial Intelligence (AI)
in promoting social welfare and advancing the well-being of individuals and
communities. AI, with its remarkable capabilities in data analysis, pattern recognition,
and decision-making, has the potential to revolutionize various aspects of social
welfare, ranging from healthcare and education to poverty alleviation and disaster
response. The chapter begins by providing an overview of the concept of social welfare
and its significance in fostering inclusive and equitable societies. It highlights the
challenges faced by traditional approaches in addressing complex social issues and how
AI can act as a catalyst for change. The chapter explores how AI can enhance access to
quality healthcare, especially in underserved regions, and contribute to improved health
outcomes for vulnerable populations. Education is another domain greatly influenced
by AI. The chapter examines the potential of AI in personalized learning, adaptive
tutoring, and intelligent assessment systems. It explores how AI technologies can
bridge educational gaps, promote lifelong learning, and support inclusive education for
students with diverse needs.Furthermore, the chapter discusses the role of AI in poverty alleviation and social
safety net programs. AI-driven analytics and predictive modeling can help identify
vulnerable populations, optimize resource allocation, and design targeted interventions
to reduce poverty and inequality. It emphasizes the importance of ethical
considerations and safeguards to ensure that AI solutions do not perpetuate biases or
deepen existing social divides. In addition, the chapter highlights the significance of AI
in disaster response and humanitarian aid. AI-powered algorithms can analyze vast
amounts of data from various sources, such as social media and satellite imagery, to
facilitate timely and effective disaster management. It explores how AI can enable
rapid needs assessment, aid distribution, and post-disaster recovery efforts, ultimately
saving lives and minimizing human suffering. Finally, the chapter addresses the
challenges and ethical implications associated with the use of AI in social welfare.
Drug Discovery
Authors: Vlasiou Manos C.
Published: October 2024

Drug discovery is a complex process involving target identification, lead
generation, and clinical development. Recent breakthroughs in genomics and AI-driven
approaches have expedited target identification. Rational drug design and advanced
chemistry techniques have improved lead compound optimization—preclinical testing
benefits from organ-on-a-chip systems and 3D cell culture models. Clinical
development is enhanced by personalized medicine and innovative trial designs. Across
all stages, big data, machine learning, and AI play pivotal roles in data analysis and
candidate selection. Collaboration between academia, industry, and regulators fosters a
more efficient drug development ecosystem. These advancements offer promising
prospects for tackling challenging diseases and enhancing global healthcare.
Molecular Docking in Computer-Aided Drug Discovery: A Powerful Tool for Targeted Therapeutics
Authors: Vlasiou Manos C.
Published: October 2024

Computer-aided drug discovery (CADD) has revolutionized the field of
pharmaceutical research by speeding up the identification of potential drug candidates.
Molecular docking, a well-known technique within CADD, plays a crucial role in
predicting and evaluating the binding affinity of small molecules to target proteins.
This essay explores the principles, methodologies, applications, and advancements of
molecular docking in the context of drug discovery. Additionally, it highlights the
impact of molecular docking in accelerating the development of targeted therapeutics.
By shedding light on the remarkable potential of molecular docking, this essay aims to
underscore its significance in the ongoing pursuit of novel drugs and personalized
medicine.
Applications of Machine Learning Practices in Human Healthcare Management Systems
Authors: Satija Ajay , Pahuja Priti , Singh Dipti , Hussain Athar
Published: October 2024

In the modern era, medical institutions offer patients high-quality,
reasonably priced treatment, but they require sophisticated technology. But even with
significant advancements in the computerization and digitalization of medicine,
effective and reliable management solutions are still lacking. Medical operations are
very complex, so high-level management is required. Machine learning techniques
might be very useful in resolving these issues since they are scalable and adaptable to
complex patterns. This study suggests that machine learning could improve human
comprehension and oversight of healthcare operations, leading to more efficient
healthcare delivery. The goal of the current study is to examine how machine learning
methods can be used to detect diseases, various clinical trials, drug development,
robotics-based surgery, organ image processing, and various challenges of machine
learning in the medical industry. Finally, along with challenges, the study concludes
that machine learning practices become essential for healthcare organizations of the
modern era.
Hypertension Detection System Using Machine Learning
Authors: Bhatnagar Amrita , Singh Kamna
Published: October 2024

The medical condition known as hypertension, or high blood pressure, is
characterized by persistently elevated blood pressure against the arterial walls.
Generally speaking, an individual should maintain blood pressure from 120/80 mm Hg.
Whenever blood pressure continuously registers at 130/80 mm Hg or above,
hypertension is frequently diagnosed. The exact origins are unknown, but factors that
accelerate its growth include obesity, high-stress levels, aging, increased sodium
intake, and decreased physical activity. Numerous organs and systems inside the body
can be significantly impacted by hypertension or high blood pressure. It can cause
several major health issues and diseases, including renal disease and stroke if left
unchecked and untreated. When it comes to the identification and treatment of
hypertension, or high blood pressure, machine learning can be an invaluable tool. It can
help medical practitioners with several procedures, such as risk evaluation, early
detection, and individualized care. Decision-support tools that provide treatment
suggestions based on the most recent medical research and patient-specific data are one
way that machine learning can help healthcare providers. This can assist physicians in
making better-informed choices regarding medication and lifestyle modifications.
Patients with hypertension can benefit from individualized therapy regimens designed
with the help of machine learning. A variety of machine learning algorithms are
available for the prediction of hypertension and related risk variables, including
decision trees (DT), Random Forests (RF), gradient boosting machines (GBM),
extreme gradient boosting (XG Boost), logistic regression (LR), and linear discriminant
analysis (LDA). The quality of the available dataset and the suitable technique are
critical to the effectiveness of machine learning in the detection and management of
hypertension.
Challenges and Opportunities for the Healthcare
Authors: Jain Prateek , Behl Rachna , Saxena Abhishek , Bathla Rohit
Published: October 2024

Machine learning technology is a rapidly growing field aiming to create
systems replicating human intelligence. In the healthcare sector, machine learning is
not meant to replace human physicians but to provide better solutions to healthcare
problems. It plays a critical role in the development of automated computational
approaches. It has numerous applications in radiology, computer-aided drug design,
virtual health assistance, clinical decisions, disease outbreaks, healthcare management,
and administration. Security and privacy risks are a significant concern with AIpowered healthcare systems since the healthcare sector has distinct security and privacy
requirements to safeguard patients' medical information. Despite this, using machine
learning in healthcare has many benefits, including faster analysis of large datasets,
improved safety of clinical trials, better insights into predictive screening, higher
accuracy, reduced healthcare costs, and increased efficiency. Although many AI and
machine learning applications have been successfully deployed in medical research and
continue to deliver favorable results, challenges still need to be addressed. In this book
chapter, we delve into the latest challenges and opportunities that the healthcare
industry faces. We explore the changing landscape of healthcare and provide insights
into how technological advancements, regulatory changes, and shifting patient
expectations are shaping the future of healthcare delivery. Whether you're a healthcare
professional, policymaker, or just interested in the industry, this chapter will provide
valuable insights and a fresh perspective on the challenges and opportunities faced by
the healthcare industry today.
Revolutionizing Healthcare: The Power of Machine Learning
Authors: Sharma Monika , Tiwari Dimple , Verma Neeta , Singhal Anjali
Published: October 2024

Machine learning is a challenging platform employed across various
fundamental domains to investigate diverse patterns within extensive datasets.
Gradually, the outcomes of machine learning influence crucial decisions in pertinent
fundamental areas concerning healthcare and biomedicine. Frequent changes in the
domain of technology like deep learning, artificial networks, machine learning, and big
data have been dealt with the best opportunities to give more applications in healthcare.
Efficient healthcare communication is crucial for accurately conveying and
disseminating information to aid and educate patients and the general public. Machine
learning has demonstrated its applicability in healthcare, particularly in facilitating
intricate dialogue management and conversational adaptability. In speedy progress in
the medical environment, some domains like machine learning, deep learning, big data,
and AI-based systems fundamentals are to be managed and held accountable in
healthcare. Machine learning is a subset of Artificial Intelligence that contains some
computer systems which can perform the huge task of developing different
fundamentals on the basis of human needs in healthcare. Machine learning (ML)
technology has had a profound impact on healthcare, offering innovative solutions to
various challenges in the industry. Machine learning algorithms analyze medical
images, clinical data, and genetic information to assist in the early detection and
accurate diagnosis of diseases, such as cancer, diabetes, and cardiovascular conditions.
Machine learning accelerates the drug discovery process by analyzing large datasets to
identify potential drug candidates and predict their efficacy and safety profiles.
Machine learning models predict patient admission rates, optimize resource allocation,
and improve hospital operations, leading to better efficiency and cost-effectiveness.
Nowadays, Machine learning is centered on creating algorithms that can adjust to new
data and uncover patterns. It is a prime exemplar of data mining principles, capable of
inferring correlations and incorporating them into novel algorithms. The objective is to
replicate human learning abilities, leveraging experience to accomplish tasks with
minimal external (human) intervention.
Understanding purchase intentions in crisis: The role of trust and perceived value in Sudan's F-commerce during the COVID-19 pandemic
Authors:   Department of Business Administration, College of Business, University of Bisha, Bisha, Saudi Arabia, Eldin Anwar Yahia Shams
Published: October 2024

This study investigates how purchase intentions on Facebook's commerce platform (F-commerce) are shaped during crises. Focusing on Sudanese customers, the research examines the roles of perceived value, trust, and consumption value in influencing purchase intentions. The study specifically looks at how trust mediates the relationship between perceived value and purchase intentions, as well as how perceived value mediates the relationship between cosmopolitanism value and purchase intentions. The goal is to better understand the mechanisms that enhance purchase intentions in Sudan, a developing country facing crises during and after the COVID-19 pandemic. An analysis of 355 responses reveals that trust mediates some aspects of perceived value's influence on purchase intentions but not others. Notably, the mediation of cosmopolitanism's impact on purchase intentions only occurs when perceived security, safety, and logistics values (affected by fuel shortages) are considered. The findings provide a basis for future research using this mediation model to explore the drivers of e-commerce purchase intentions in similar underdeveloped countries during crises where the use of digital technology for commerce is limited.
Behavioral Nudging With Generative AI for Content Development in SMS Health Care Interventions: Case Study
Authors: Harrison Rachel M , Lapteva Ekaterina , Bibin Anton
Published: October 2024

Background

Brief message interventions have demonstrated immense promise in health care, yet the development of these messages has suffered from a dearth of transparency and a scarcity of publicly accessible data sets. Moreover, the researcher-driven content creation process has raised resource allocation issues, necessitating a more efficient and transparent approach to content development.

Objective

This research sets out to address the challenges of content development for SMS interventions by showcasing the use of generative artificial intelligence (AI) as a tool for content creation, transparently explaining the prompt design and content generation process, and providing the largest publicly available data set of brief messages and source code for future replication of our process.

Methods

Leveraging the pretrained large language model GPT-3.5 (OpenAI), we generate a collection of messages in the context of medication adherence for individuals with type 2 diabetes using evidence-derived behavior change techniques identified in a prior systematic review. We create an attributed prompt designed to adhere to content (readability and tone) and SMS (character count and encoder type) standards while encouraging message variability to reflect differences in behavior change techniques.

Results

We deliver the most extensive repository of brief messages for a singular health care intervention and the first library of messages crafted with generative AI. In total, our method yields a data set comprising 1150 messages, with 89.91% (n=1034) meeting character length requirements and 80.7% (n=928) meeting readability requirements. Furthermore, our analysis reveals that all messages exhibit diversity comparable to an existing publicly available data set created under the same theoretical framework for a similar setting.

Conclusions

This research provides a novel approach to content creation for health care interventions using state-of-the-art generative AI tools. Future research is needed to assess the generated content for ethical, safety, and research standards, as well as to determine whether the intervention is successful in improving the target behaviors.
Auricular Therapy to Control Pain in Women With Breast Cancer: Protocol for Systematic Review and Meta-Analysis
Authors: Ruela Ludmila Oliveira , Moura Caroline de Castro , Shieu Bianca , Cho Yu-Min , Yeh Chao Hsing , Pimentel Franklin Fernandes , Stefanello Juliana
Published: October 2024

Background

The increased incidence of breast cancer implies the appearance of frequent symptoms associated with disease and treatments, such as pain. For the management of this issue, auricular therapy has been used in a complementary manner, especially for its safety and analgesic action.

Objective

This systematic review aims to summarize available evidence on the effects of auricular therapy on pain in women undergoing breast cancer treatment.

Methods

This is a systematic review that includes randomized controlled trials that evaluated the effects of auricular therapy on pain in women with breast cancer, as compared with other interventions (sham or placebo auricular therapy, other nonpharmacological interventions, and routine pain treatments) during the treatment of the disease. Pain, whether induced or not by cancer treatments, is the main outcome to be evaluated. The search for the studies was performed in the following databases: MEDLINE through PubMed, CINAHL, CENTRAL, Embase, Web of Science, Scopus, VHL, TCIM Americas Network, CNKI, and Wanfang Data. The reviewers have independently evaluated the full texts, and in the near future, they will extract the data and assess the risk of bias in the included studies. The certainty of the evidence will be assessed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE), and a meta-analysis will be carried out to evaluate the intervention, considering the homogeneity of the results, using the Cochran Q test and quantified by the Higgins inconsistency index. The guidelines of the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) have been respected in the elaboration of this protocol.

Results

The records screening stage has been completed, and the synthesis and meta-analysis were conducted in February 2024. We hope to have finished the preparation of the paper for publication by September 2024. Review reporting will follow standard guidelines for reporting systematic reviews. The results will be published in peer-reviewed scientific journals.

Conclusions

This review will compile the strength of evidence for the use of auricular therapy in the management of pain in women with breast cancer during the treatment of the disease, identifying gaps in the available evidence as well as assisting health professionals in indicating the intervention for clinical practice.

Trial Registration

PROSPERO CRD42022382433; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=382433

International Registered Report Identifier (IRRID)

DERR1-10.2196/55792
Exploring the Use of a Length AI Algorithm to Estimate Children’s Length from Smartphone Images in a Real-World Setting: Algorithm Development and Usability Study
Authors: Chua Mei Chien , Hadimaja Matthew , Wong Jill , Mukherjee Sankha Subhra , Foussat Agathe , Chan Daniel , Nandal Umesh , Yap Fabian
Published: October 2024

Background

Length measurement in young children younger than 18 months is important for monitoring growth and development. Accurate length measurement requires proper equipment, standardized methods, and trained personnel. In addition, length measurement requires young children’s cooperation, making it particularly challenging during infancy and toddlerhood.

Objective

This study aimed to develop a length artificial intelligence (LAI) algorithm to aid users in determining recumbent length conveniently from smartphone images and explore its performance and suitability for personal and clinical use.

Methods

This proof-of-concept study in healthy children (aged 0-18 months) was performed at KK Women’s and Children’s Hospital, Singapore, from November 2021 to March 2022. Smartphone images were taken by parents and investigators. Standardized length-board measurements were taken by trained investigators. Performance was evaluated by comparing the tool’s image-based length estimations with length-board measurements (bias [mean error, mean difference between measured and predicted length]; absolute error [magnitude of error]). Prediction performance was evaluated on an individual-image basis and participant-averaged basis. User experience was collected through questionnaires.

Results

A total of 215 participants (median age 4.4, IQR 1.9-9.7 months) were included. The tool produced a length prediction for 99.4% (2211/2224) of photos analyzed. The mean absolute error was 2.47 cm for individual image predictions and 1.77 cm for participant-averaged predictions. Investigators and parents reported no difficulties in capturing the required photos for most participants (182/215, 84.7% participants and 144/200, 72% participants, respectively).

Conclusions

The LAI algorithm is an accessible and novel way of estimating children’s length from smartphone images without the need for specialized equipment or trained personnel. The LAI algorithm’s current performance and ease of use suggest its potential for use by parents or caregivers with an accuracy approaching what is typically achieved in general clinics or community health settings. The results show that the algorithm is acceptable for use in a personal setting, serving as a proof of concept for use in clinical settings.

Trial Registration

ClinicalTrials.gov NCT05079776; https://clinicaltrials.gov/ct2/show/NCT05079776
Performance of ChatGPT in Ophthalmic Registration and Clinical Diagnosis: Cross-Sectional Study
Authors: Ming Shuai , Yao Xi , Guo Xiaohong , Guo Qingge , Xie Kunpeng , Chen Dandan , Lei Bo
Published: October 2024

Background

Artificial intelligence (AI) chatbots such as ChatGPT are expected to impact vision health care significantly. Their potential to optimize the consultation process and diagnostic capabilities across range of ophthalmic subspecialties have yet to be fully explored.

Objective

This study aims to investigate the performance of AI chatbots in recommending ophthalmic outpatient registration and diagnosing eye diseases within clinical case profiles.

Methods

This cross-sectional study used clinical cases from Chinese Standardized Resident Training–Ophthalmology (2nd Edition). For each case, 2 profiles were created: patient with history (Hx) and patient with history and examination (Hx+Ex). These profiles served as independent queries for GPT-3.5 and GPT-4.0 (accessed from March 5 to 18, 2024). Similarly, 3 ophthalmic residents were posed the same profiles in a questionnaire format. The accuracy of recommending ophthalmic subspecialty registration was primarily evaluated using Hx profiles. The accuracy of the top-ranked diagnosis and the accuracy of the diagnosis within the top 3 suggestions (do-not-miss diagnosis) were assessed using Hx+Ex profiles. The gold standard for judgment was the published, official diagnosis. Characteristics of incorrect diagnoses by ChatGPT were also analyzed.

Results

A total of 208 clinical profiles from 12 ophthalmic subspecialties were analyzed (104 Hx and 104 Hx+Ex profiles). For Hx profiles, GPT-3.5, GPT-4.0, and residents showed comparable accuracy in registration suggestions (66/104, 63.5%; 81/104, 77.9%; and 72/104, 69.2%, respectively; P=.07), with ocular trauma, retinal diseases, and strabismus and amblyopia achieving the top 3 accuracies. For Hx+Ex profiles, both GPT-4.0 and residents demonstrated higher diagnostic accuracy than GPT-3.5 (62/104, 59.6% and 63/104, 60.6% vs 41/104, 39.4%; P=.003 and P=.001, respectively). Accuracy for do-not-miss diagnoses also improved (79/104, 76% and 68/104, 65.4% vs 51/104, 49%; P<.001 and P=.02, respectively). The highest diagnostic accuracies were observed in glaucoma; lens diseases; and eyelid, lacrimal, and orbital diseases. GPT-4.0 recorded fewer incorrect top-3 diagnoses (25/42, 60% vs 53/63, 84%; P=.005) and more partially correct diagnoses (21/42, 50% vs 7/63 11%; P<.001) than GPT-3.5, while GPT-3.5 had more completely incorrect (27/63, 43% vs 7/42, 17%; P=.005) and less precise diagnoses (22/63, 35% vs 5/42, 12%; P=.009).

Conclusions

GPT-3.5 and GPT-4.0 showed intermediate performance in recommending ophthalmic subspecialties for registration. While GPT-3.5 underperformed, GPT-4.0 approached and numerically surpassed residents in differential diagnosis. AI chatbots show promise in facilitating ophthalmic patient registration. However, their integration into diagnostic decision-making requires more validation.
Exploring the Linkages Among Chronic Illness, Substance Use, and COVID-19 Infection in Adults Aged 50 Years and Older: Retrospective Cross-Sectional Analysis of National Representative Data (Preprint)
Authors: Ruksakulpiwat Suebsarn , Niyomyart Atsadaporn , Riangkam Chontira , Phianhasin Lalipat , Benjasirisan Chitchanok , Adams Jon
Published: October 2024

BACKGROUND

The co-occurrence of chronic illnesses and substance use presents complex challenges for health care systems. Understanding the interplay between these factors, compounded by the context of the COVID-19 pandemic, is essential for effective intervention strategies.

OBJECTIVE

This study aims to investigate the relationships among chronic illness, substance use, and COVID-19 infection in adults aged 50 years and older.

METHODS

Participants were 1196 adults aged 50 years and older. Descriptive statistics were used to describe demographic information. Logistic regressions and multiple regression analyses were used to determine associations between chronic illnesses, substance use, and COVID-19 infection. Mediation analysis was used to determine the effect of chronic illness mediators in the association between COVID-19 concerns and substance use.

RESULTS

The mean age was 68 (SD 10.3) years, with 58.6% (701/1196) being women. Adjusted analysis revealed that age and sex (women) significantly predicted a lower level of substance use (P<.05). However, marital status (separated or widowed) and chronic illness significantly predicted a higher level of substance use (P<.05). Furthermore, having dementia, arthritis, and high cholesterol significantly predicted a higher level of concern about the COVID-19 pandemic (P<.05). Logistic regression analysis indicated that individuals with hypertension (odds ratio [OR] 1.91, 95% CI 1.37-2.66; P<.001), lung disease (OR 2.42, 95% CI 1.23-4.75; P=.01), heart condition (OR 1.99, 95% CI 1.28-3.10; P=.002), stroke (OR 2.35, 95% CI 1.07-5.16; P=.03), and arthritis (OR 1.72, 95% CI 1.25-2.37; P=.001) were more likely to have their work affected by the COVID-19 pandemic. The mediation analysis showed a significant effect of COVID-19 concern on substance use through the mediation of chronic illness, with a 95% CI of –0.02 to –0.01 and an indirect effect of –0.01.

CONCLUSIONS

Our study reveals complex associations among chronic illnesses, substance use, and COVID-19 infection among adults aged 50 years and older. It underscores the impact of demographics and specific chronic conditions on substance use behaviors and COVID-19 concerns. In addition, certain chronic illnesses were linked to heightened vulnerability in employment status during the pandemic. These findings emphasize the need for targeted interventions addressing physical health and substance use in this population during the COVID-19 pandemic.
Design of Terrace-Based Hydroponic System using Hydraulic Ram Pump
Authors: Parami Shemer , Cinense Marvin , Malamug Vitaliana , Somera Carolyn Grace
Published: October 2024

The primary objective of the study is to design a terrace-based hydroponic system incorporating a hydraulic ram pump and to evaluate the effectiveness of the hydraulic ram pump in terms of drive head, flow rate, and delivery head, while also assessing its overall performance, particularly in terms of water loss percentage.  The hydraulic ram pump can deliver a delivery head of 200 cm.  To achieve this 200 cm delivery head, a drive head of 20 cm and a drive pipe length of 60 cm are necessary. The flow rate of the ram pump at the delivery pipe is 1.13 liters per minute.  The water balance equation was employed to quantify water loss, considering parameters such as initial water volume in storage tanks, water collected in excess water collectors, and overall water losses during a system operation.  The study's key findings reveal that during each 15-minute operational cycle, the terrace-based hydroponic system, incorporating a hydraulic ram pump, experienced an average water loss of 4.59 liters, equating to a 2.7% water loss percentage. This 2.7% loss could have a significant impact on sustainability if the system were to be upscaled.
Effectiveness of Citicoline versus Citicoline with Piracetam in Moderate to Severe Acute Ischemic Stroke Patients: A Quasi Experimental Prospective Study
Authors: Murugesan Karthick , Sivadasan Shalini , Saravanan Prarthana , Ramesh Anusha , Muralidharan Madhumidha , Elumalai Senthil Kumar
Published: October 2024

Stroke is a sudden cerebral root neurologic collapse, a major cause for global morbidity and death, necessitates the use of neuroprotectants to prevent further damage to ischemic decline. Citicoline and piracetam are widely used as neuro-protectives, however their benefits in Acute Ischemic Stroke have not yet been established. This prospective, quasi-experimental study compared the effectiveness of citicoline (Group A) and citicoline with piracetam combination (Group B) in moderate-to-severe patients. 75 patients were divided into two groups (25 in Group A and 50 in Group B) and followed for a period of 90 days. Their cognitive and functioning events were examined using National Institute of Health Stroke Scale (NIHSS), modified Rankin Scale (mRS) and Barthel Index (BI). There is no discernible difference (p > 0.05) amongst the two groups by NIHSS whereas, there was a significant difference (p<0.05) in mRS and BI. After 90 days, Group A patients significantly progressed in terms of overall functional and cognitive improvement. The administration of citicoline within the first 24 hours of a stroke helps to prevent further sequelae and minimizes the severity of AIS. This study concludes that citicoline alone responds better than piracetam combination, but its neuroprotective properties have not been proven.
Keywords: Acute Ischemic Stroke. Citicoline. Piracetam. NIHSS. BI. mRS.
Acute toxicity and anti-inflammatory activity of an improved traditional medicine developed in Burkina Faso
Authors: BELEMLILGA Mohamed Bonewendé , TRAORÉ Tata Kadiatou , Boly Gilchrist Abdoul Laurent , BOURGOU Séripouguin Rihanata , TRAORE Ladji , Ouédraogo Salfo , OUEDRAOGO Jean Claude R. P. , ILBOUDO Sylvain , OUEDRAOGO Moussa , KINDA Dieudonné , KINI Félix B. , TRAORE Aristide , OUEDRAOGO Noufou
Published: October 2024

An Improved Traditional Medicine (ITM) in capsule form presented by traditional health practitioners in Burkina Faso as used for hepatoprotective and hepatocurative treatment for viral hepatitis B. The study's main objective was to investigate the anti-inflammatory properties of this ITM, which is used in treating viral hepatitis B in traditional medicine in Burkina Faso.  Phytochemical screening revealed the presence of flavonoids, saponins, tannins, coumarins, alkaloids, sterols and triterpenes. The in vitro anti-inflammatory assay inhibited LOX by 15.69 ± 0.50% at a 100 µg/mL concentration. The LD50 was estimated to be greater than 5000 mg/kg. In vivo activity using the carrageenan anti-oedema test showed good dose-dependent anti-inflammatory activity of ITM at 30, 65 and 130 mg/kg bw. After 3 hours, the extract at various doses reduced edema to over 50% inhibition. After 5 h, oedema was reduced to 85.209% at the 130 mg/kg. However, the reference acetylsalicylic acid (ASA) at 100 mg/kg bw showed a percentage of 85.048% at 5 h. The IC50 obtained after the ABTS, FRAP and LPO antioxidant tests of the phytomedicine were 351.00 µg/mL, 353.20 mM EAA/L and 81.13%. However, the IC50 with the DPPH method was higher than 104 mg/mL. The phytomedicine would have anti-inflammatory and antioxidant properties due to the presence of bioactive phytochemicals on inflammation and free radicals.
Keywords: ITM, Phytochemical, Acute toxicity, Anti-inflammatory, Antioxidants
A Systematic Review of Nano-Encapsulation for Improving the Bioavailability of Dietary Supplements and Nutraceuticals
Authors: Djebbar Badia , Hellali Djaafer Hamza , Merzougui Hanaa
Published: October 2024

Nanoencapsulation is a growing technique in the field of nutraceuticals and dietary supplements, offering several advantages over non-encapsulated forms. This systematic review assesses the impact of nanoencapsulation on key factors such as bioavailability, stability, tolerance, and encapsulation efficiency. The selected studies demonstrate that nanoencapsulation enhances the protection and controlled release of bioactive compounds, leading to improved efficacy and therapeutic potential. Furthermore, it increases the bioavailability of nutrients and active ingredients while maintaining their stability under different conditions. The review also highlights the positive effects on product safety and consumer tolerance, making nanoencapsulation a promising strategy for optimizing the formulation of dietary supplements. These findings suggest that nanoencapsulation could play a critical role in advancing the effectiveness of nutraceutical products.
Keywords: Nanoencapsulation, dietary supplements, nutraceuticals, efficacy, bioavailability, stability