bluuebunny/crossref_metadata_2025 huggingface dataset:
Two Decades Rituximab Therapy in Anti-Neutrophil Cytoplasmic Antibody Associated Vasculitis
Authors: Habibi Mohammad Amin , Ahmadpour Sajjad , Tafaroji Javad , Eazi Seyed Mohammad , Minaee Poriya , Mohammadpour Yousef , Tavakolpour Soheil
Published: September 2024

Remission failure and relapse numerate as one of the main problems in patients with
antineutrophil cytoplasmic antibody-associated vasculitis (AAVs). The need for new agents that
provide effective and safe induction accompanied by sustained remission seems to be urgent in
clinical care. The efficacy and safety of rituximab (RTX) for AAVs therapy has been reported in
various studies. RTX therapy offers several advantages to treating AAVs patients compared to
other therapeutic approaches including reduction of Glucocorticoids (GCs) and conventional Immunosuppressive
therapy (IST) usage during both the induction of remission and maintenance
phases. This reduction can lead to a lower rate of serious complications making RTX therapy a
safer option. It seems that RTX may provide improved clinical outcomes in these patients mediated
via B-lymphocyte depletion, Proteinase 3-antineutrophilic cytoplasmic antibody (PR3-ANCA),
and myeloperoxidase-antineutrophilic cytoplasmic antibody (MPO-ANCA) titers reduction. In
this regard, some uncertainties have been reported to validate the association between such depletion
and clinical improvement, as suggested by other sources of autoreactive B cells that did not
target with RTX. Due to the prolonged B cell depletion, fixed intervals and adjusted dosage of
RTX may be required in patients with AAVs. In this narrative review, we aimed to insight better
understand regarding the efficacy of RTX for effective induction and sustained remission in patients
with AAVs. It seems that discovering new biomarkers predicting relapse in AAVs patients
can lead to future targeted therapy.
Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures
Authors: Abdelkader Gelany Aly , Kim Jeong-Dong
Published: September 2024

Background:

Drug discovery is a complex and expensive procedure involving several
timely and costly phases through which new potential pharmaceutical compounds must pass to get
approved. One of these critical steps is the identification and optimization of lead compounds,
which has been made more accessible by the introduction of computational methods, including
deep learning (DL) techniques. Diverse DL model architectures have been put forward to learn the
vast landscape of interaction between proteins and ligands and predict their affinity, helping in the
identification of lead compounds.

Objective:

This survey fills a gap in previous research by comprehensively analyzing the most
commonly used datasets and discussing their quality and limitations. It also offers a comprehensive
classification of the most recent DL methods in the context of protein-ligand binding affinity
prediction, providing a fresh perspective on this evolving field.

Methods:

We thoroughly examine commonly used datasets for BAP and their inherent characteristics.
Our exploration extends to various preprocessing steps and DL techniques, including graph
neural networks, convolutional neural networks, and transformers, which are found in the literature.
We conducted extensive literature research to ensure that the most recent deep learning approaches
for BAP were included by the time of writing this manuscript.

Results:

The systematic approach used for the present study highlighted inherent challenges to
BAP via DL, such as data quality, model interpretability, and explainability, and proposed considerations
for future research directions. We present valuable insights to accelerate the development
of more effective and reliable DL models for BAP within the research community.

Conclusion:

The present study can considerably enhance future research on predicting affinity between
protein and ligand molecules, hence further improving the overall drug development process.
Post-Hoc Assessment of Cognitive Efficacy in Alzheimer’s Disease Using a Latent Growth Mixture Model in AMBAR, a Phase 2B Randomized Controlled Trial
Authors: Ayasse Nicolai D. , Stewart Walter F. , Lipton Richard B. , Gomez-Ulloa David , Runken M. Chris
Published: September 2024

Background:

Disease progression in Alzheimer’s Dementia (AD) is typically characterized
by accelerated cognitive and functional decline, where heterogeneous trajectories can impact
the observed treatment response.

Methods:

We hypothesized that unobserved heterogeneity could obscure treatment benefits in
AD. The effect of unobserved heterogeneity was empirically quantified within the Alzheimer’s
Management By Albumin Replacement (AMBAR) phase 2b trial data. The ADAS-Cog 12 cognition
endpoint was reanalyzed in a 2-class latent growth mixture model initially fit to the treatment
arm. The model with the best fit was then applied across both treatment arms to a larger (n=1000)
simulated dataset that was representative of AMBAR trial cognitive data.

Results:

Two classes of patients were observed: a stable cognitive trajectory class and a highly
variable class. Removal of the latter (n=48, 22%) from the analysis and refitting efficacy models
comparing the stable class to full placebo yielded significant treatment efficacy on cognition
(p=0.007, Cohen’s D=-0.4). Comparison of the stable class of each arm within the simulated dataset
revealed a significant difference in treatment efficacy favoring the simulated stable treatment
arm.

Conclusion:

This post hoc exploratory analysis suggests that prespecified strategies for addressing
unobserved heterogeneity may yield improved effect detection in AD trials. The generalizability
of the analytic strategy is limited by latent stratification in only the treatment arm, a requirement
given the small placebo arm in AMBAR. This limitation was partially addressed by the simulation
modeling.

Clinical Trial Registration Number::

NCT01561053
Current Strategies for Atrial Fibrillation Prevention and Management: Taming the Commonest Cardiac Arrhythmia
Authors: Manolis Antonis A. , Manolis Theodora A. , Manolis Antonis S.
Published: September 2024

Atrial fibrillation (AF) is the commonest cardiac arrhythmia, constituting a major cause
of morbidity and mortality, with an age-dependent incidence and prevalence ranging from 1-2%
in the general population to ~10% in persons aged >60 years. The global prevalence of AF is
rapidly increasing, mostly due to the aging population. If not properly and timely managed, this arrhythmia
adversely affects left ventricular function, increases the risk of stroke five-fold, impairs
quality of life, and shortens longevity. There is a genetic, hence non-modifiable, predisposition to
the arrhythmia, while several life-style and cardiometabolic inciting factors, such as hypertension,
heart failure, coronary disease, metabolic syndrome, alcohol use, and thyroid disorders, can be addressed,
attesting to the importance of a holistic approach to its management. Thromboembolism
is a serious consequence of AF, which could lead to a disabling stroke or have a lethal outcome.
The risk of a thromboembolic complication can be estimated as based on a scoring system that
takes into consideration the patient’s age, previous thromboembolic events, and clinical comorbidities.
In addition, rapid AF could affect cardiac performance, leading to an elusive type of arrhythmia-
induced cardiomyopathy and heart failure with grave consequences if undetected and untreated.
Furthermore, AF may cause silent brain infarcts and/or its hemodynamic perturbations can account
for a type of dementia that needs to be taken into account, emphasizing the need for AF
screening and prevention strategies. All these issues are herein detailed, the causes of the arrhythmia
are tabulated, and an algorithm illustrates our current approach to its management.
Iodine Promoted Desulfurization of Isothiocyanate in Water: Synthesis of 5-Amino-1-Aryl/Alkyl Tetrazoles, Aryl/Alkyl Guanidines and their Conversion
Authors: K Hema Kumar Venkata , Sannapaneni Janardan , Tamminana Ramana
Published: September 2024

A general and efficient multi-component reaction has been established for the construction
of 5-amino tetrazoles and guanidines under mild reaction conditions. All the substrates are readily
carried out under optimized reaction conditions to provide their respective target products in
good to excellent yields. The regioselectivity of the reaction is also explained in this manuscript.
Further, this method is free, uses water as a green solvent, and is practical (at room temperature and
under an open atmosphere). In addition, we could not identify any other products during the reaction
procedure. Phenyltetrazoleamines are readily converted into their C-N cross-coupled products in
good to high yield under mild reaction conditions.
Exploring the Mechanism of Action of Lycium barbarum in Treating Allergic Rhinitis Using Network Pharmacology and Molecular Docking
Authors: Shao Hui , Wang Ning , Kang Jing , Zheng Kaizhi , Ma Zheng , Yang Jing , Yang Xiaoling , Hou Li
Published: September 2024

Objective:

Approximately 10%-40% of the global population is affected by allergic
rhinitis, and Lycium barbarum has a long history of dual use as a herbal medicine and also as a
food. This study utilized network pharmacology and molecular docking to explore the mechanism
of action of Lycium barbarum in treating allergic rhinitis.

Methods:

We screened active ingredients and potential action targets of Lycium barbarum using
the TCMSP database. Relevant targets for allergic rhinitis diseases were accessed through the
GeneCards and OMIM databases. GO function enrichment and KEGG pathway enrichment
analyses of key targets were conducted using the DAVID database. Molecular docking validation
was performed using Autodock Vina software to confirm the affinity between the core protein
targets and the key active ingredients.

Results:

Network pharmacological analysis identified 35 major active ingredients of Lycium
barbarum, 87 drug targets, and 2200 disease targets. GO functional analysis and KEGG pathway
enrichment analysis revealed that the mechanism of action of Lycium barbarum in treating
allergic rhinitis involves multiple pathways, including the HIF-1 signaling pathway, IL-17 signaling
pathway, and TNF signaling pathway. Molecular docking results demonstrated good binding
activity between the main active ingredients and the core protein target.

Conclusion:

Lycium barbarum exhibits multi-component, multi-target, and multi-pathway actions
in treating allergic rhinitis by regulating relevant pathways and targets.
Acyl Urea Compounds Therapeutics and its Inhibition for Cancers in Women: A Review
Authors: Kumari Preeti , Mishra Rakhi , Mazumder Rupa , Mazumder Avijit
Published: September 2024

Acyl urea compounds have garnered significant attention in cancer therapeutics, particularly for their
potential effectiveness against cancers that predominantly affect women, such as breast and ovarian cancers. The
paper presents a report on the investigation of acyl urea compounds that are reported to involve a multi-faceted
approach, including synthetic chemistry, biological assays, and computational modeling. A wealth of information
on acyl urea and its purported effects on cancer affecting women has been gathered from different sources and
condensed to provide readers with a broad understanding of the role of acyl urea in combating cancer. Acylureas
demonstrate promising results by selectively inhibiting key molecular targets associated with cancer progressions,
such as EGFR, ALK, HER2, and the Wnt/β-catenin signaling pathway. Specifically, targeting acyl ureas impedes
tumor proliferation and metastasis while minimizing harm to healthy tissues, offering a targeted therapeutic approach
with reduced side effects compared to conventional chemotherapy. Continued research and clinical trials
are imperative to optimize the efficacy and safety profiles of acylurea-based therapies and broaden their applicability
across various cancer types. Acyl urea compounds represent a promising class of therapeutics for the treatment
of cancers in women, particularly due to their ability to selectively inhibit key molecular targets involved in
tumor growth and progression. The combination of synthetic optimization, biological evaluation, and computational
modeling has facilitated the identification of several lead compounds with significant anticancer potential.
This abstract explores the therapeutic mechanisms and targeted pathways of acyl ureas in combating these malignancies,
which will be useful for future studies.
Formulation and Comparison of Herbal Gels Containing Essential Oils and their Antimicrobial Evaluation
Authors: Agrawal Karan , Vibha  , Murti Yogesh , Agrawal Migul , Kulshreshtha Mayank
Published: September 2024

Objective:

The current study aimed to formulate and evaluate herbal gels con-taining essential oils for the treatment of infection caused by microbial species, i.e., S. au-reus, E. coli, P. aeruginosa, and B. subtilis. These species are also responsible for acne directly and indirectly.

Material and Methods:

The gels were prepared using a gelling agent with 1% Carbopol-940, and they were further evaluated for physical appearance, drug content, in-vitro drug release, viscosity, extrudability, pH, and spreadability. Further, anti-microbial screening was done with various microbial species.

Results:

Studies revealed that the gel was highly effective against the selected species ex-cept for the fungal strain. ZOI was observed ranging from 3.1 ± 0.01 mm to 13.4 ± 0.14 mm. The maximum ZOI was observed at 13.4 ± 0.14 mm against S. aureus. The physical properties of the gel satisfied the standard parameters.

Conclusion:

The prepared herbal gel was found to have highly promising activity against bacterial species associated with bacterial infection but in a dose-dependent manner. How-ever, more research is required.
Identification of miR-20a as a Diagnostic and Prognostic Biomarker in Colorectal Cancer: MicroRNA Sequencing and Machine Learning Analysis
Authors: Jamialahmadi Hamid , Asadnia Alireza , Khalili-Tanha Ghazaleh , Mohit Reza , Azari Hanieh , Khazaei Majid , Maftooh Mina , Nassiri Mohammadreza , Hassanian Seyed Mahdi , Ghayour-Mobarhan Majid , Ferns Gordon A , Nazari Elham , Avan Amir
Published: September 2024

Introduction:

The differential expression of miRNAs, a key regulator in many cell
signaling pathways, has been studied in various malignancies and may have an important role in
cancer progression, including colorectal cancer (CRC).

Methods:

The present study used machine learning and gene interaction study tools to explore
the prognostic and diagnostic value of miRNAs in CRC. Integrative analysis of 353 CRC samples
and normal tissue data was obtained from the TCGA database and further analyzed by R packages
to define the deferentially expressed miRNAs (DEMs). Furthermore, machine learning and
Kaplan Meier survival analysis helped better specify the significant prognostic value of miRNAs.
A combination of online databases was then used to evaluate the interactions between target
genes, their molecular pathways, and the correlation between the DEMs.

Results:

The results indicated that miR-19b and miR-20a have a significant prognostic role and
are associated with CRC progression. The ROC curve analysis discovered that miR-20a alone
and combined with other miRNAs, including hsa-mir-21 and hsa-mir-542, are diagnostic biomarkers
in CRC. In addition, 12 genes, including NTRK2, CDC42, EGFR, AGO2, PRKCA,
HSP90AA1, TLR4, IGF1, ESR1, SMAD2, SMAD4, and NEDD4L, were found to be the highest
score targets for these miRNAs. Pathway analysis identified the two correlated tyrosine kinase
and MAPK signaling pathways with the key interaction genes, i.e., EGFR, CDC42, and
HSP90AA1.

Conclusion:

To better define the role of these miRNAs, the ceRNA network, including lncRNAs,
was also prepared. In conclusion, the combination of R data analysis and machine learning provides
a robust approach to resolving complicated interactions between miRNAs and their targets.
Evaluating the Effect of a Probiotic on Prevention of Diarrhea, Stress Response, and Social Interactions in a Colony of Macaca fascicularis
Authors: McGrew Keely , Kosten Therese A.
Published: September 2024

Background:

Social housing changes are associated with diarrhea in macaques, presumably
due to stress.

Objective:

Since probiotics are utilized in humans for diarrhea, we tested the effectiveness of a species-
specific probiotic, SDPro™ (composition: ~2 billion CFU each of live Lactobacillus reuteri
and Saccharomyces boulardii), to prevent relocation-associated diarrhea. Assessments of the gutmicrobiome
and diarrhea severity were made. Behavioral observations and cortisol levels were examined
after relocation to test the effects of the probiotic treatment.

Methods:

The probiotic was administered to 60 juvenile macaques of both sexes (Probiotic +) and
outcomes were compared to 60 control (Probiotic -) macaques. The effects on gut microbiome composition
were analyzed via 16s microbiome analysis in half the animals from both groups. Social
behaviors were monitored twice-weekly in the morning and afternoon for five weeks following 10-
days of SDPro™ administration in 56 subjects and hair cortisol was assayed.

Results:

Probiotic administration altered beta, but not alpha, diversity, and caused changes in taxa
abundance at the phylum, genus, and species levels. The Probiotic + group was enriched in Firmicutes,
Clostridium, and Lactobacillus and diminished in Bacteriodies (p’s < 0.05) compared to controls.
Although SDPro™ did not alter diarrhea incidence following relocation, it reduced diarrhea
severity in males only. Males also exhibited higher cortisol levels than females but there was no
probiotic effect. Probiotic treatment had minor behavioral effects; the typical reduction in locomotion
seen in the afternoon was eliminated.

Conclusion:

The results suggest that SDPro™ may be a viable tool to prevent relocation-induced
diarrhea in juvenile male macaques.
Potential Role of Indian Spices in the Management of Diabetic Complication: A Pre-Clinical and Clinical Review
Authors: Mohapatra Swarnalata , Ranjit Santosh , Pattnaik Gurudutta , Parida Patitapabana , Dutta Sonali , Ghosh Goutam , Rath Goutam , Kar Biswakanth
Published: September 2024

Diabetic is a metabolic disorder that is concerning for people worldwide, caused by a
lack of insulin or ineffective production of insulin in the pancreas. Diabetic retinopathy, nephropathy,
and neuropathy are significant microvascular complications of diabetes mellitus, contributing
to substantial morbidity and mortality worldwide. Several synthetic medications have been developed.
However, none of the compounds provides complete recovery. Long-term use of some synthetic
medications might have serious negative effects, thus, there is a need for safe, affordable,
and effective medications. Throughout human history, traditional ailments have been much respected
as a source of treatment. Their widespread usage across the globe suggests that herbs/
spices are becoming an increasingly important component of cutting-edge, contemporary medications.
Therefore, the objective of this review is mainly based on the beneficial effect of Indian
spices in managing diabetes. We review the current primary and clinical evidence about the potential
of Indian spices, including curcumin, ginger, coriander, cumin seed, garlic, clove, cinnamon,
curry leaves, and fenugreek seed with mainly their hypoglycemic and antioxidant properties, for
treating diabetes mellitus, also managing diabetic-associated complications, such as neuropathy,
retinopathy, and nephropathy. Here, we present the pre-clinical and clinical studies demonstrating
how these spices can improve glucose metabolism, enhance insulin secretion, and mitigate oxidative
stress, potentially alleviating diabetic complications.
The Accuracy and Capability of Artificial Intelligence Solutions in Health Care Examinations and Certificates: Systematic Review and Meta-Analysis
Authors: Waldock William J , Zhang Joe , Guni Ahmad , Nabeel Ahmad , Darzi Ara , Ashrafian Hutan
Published: September 2024

Background

Large language models (LLMs) have dominated public interest due to their apparent capability to accurately replicate learned knowledge in narrative text. However, there is a lack of clarity about the accuracy and capability standards of LLMs in health care examinations.

Objective

We conducted a systematic review of LLM accuracy, as tested under health care examination conditions, as compared to known human performance standards.

Methods

We quantified the accuracy of LLMs in responding to health care examination questions and evaluated the consistency and quality of study reporting. The search included all papers up until September 10, 2023, with all LLMs published in English journals that report clear LLM accuracy standards. The exclusion criteria were as follows: the assessment was not a health care exam, there was no LLM, there was no evaluation of comparable success accuracy, and the literature was not original research.The literature search included the following Medical Subject Headings (MeSH) terms used in all possible combinations: “artificial intelligence,” “ChatGPT,” “GPT,” “LLM,” “large language model,” “machine learning,” “neural network,” “Generative Pre-trained Transformer,” “Generative Transformer,” “Generative Language Model,” “Generative Model,” “medical exam,” “healthcare exam,” and “clinical exam.” Sensitivity, accuracy, and precision data were extracted, including relevant CIs.

Results

The search identified 1673 relevant citations. After removing duplicate results, 1268 (75.8%) papers were screened for titles and abstracts, and 32 (2.5%) studies were included for full-text review. Our meta-analysis suggested that LLMs are able to perform with an overall medical examination accuracy of 0.61 (CI 0.58-0.64) and a United States Medical Licensing Examination (USMLE) accuracy of 0.51 (CI 0.46-0.56), while Chat Generative Pretrained Transformer (ChatGPT) can perform with an overall medical examination accuracy of 0.64 (CI 0.6-0.67).

Conclusions

LLMs offer promise to remediate health care demand and staffing challenges by providing accurate and efficient context-specific information to critical decision makers. For policy and deployment decisions about LLMs to advance health care, we proposed a new framework called RUBRICC (Regulatory, Usability, Bias, Reliability [Evidence and Safety], Interoperability, Cost, and Codesign–Patient and Public Involvement and Engagement [PPIE]). This presents a valuable opportunity to direct the clinical commissioning of new LLM capabilities into health services, while respecting patient safety considerations.

Trial Registration

OSF Registries osf.io/xqzkw; https://osf.io/xqzkw
Implementation of a Social Media Strategy for Public Health Promotion in Black, American Indian or Alaska Native, and Hispanic or Latino Communities During the COVID-19 Pandemic: Cross-Sectional Study
Authors: Mora Pinzon Maria , Hills Ornella , Levy George , James Taryn T , Benitez Ashley , Lawrence Sacheen , Ellis Tiffany , Washington Venus , Solorzano Lizbeth , Tellez-Giron Patricia , Cano Ospina Fernando , Metoxen Melissa F , Gleason Carey E
Published: September 2024

Background

Individuals identifying as Black, American Indian or Alaska Native, or Hispanic or Latino lack access to culturally appropriate accurate information and are the target of disinformation campaigns, which create doubt in science and health care providers and might play a role in sustaining health disparities related to the COVID-19 pandemic.

Objective

This study aims to create and disseminate culturally and medically appropriate social media messages for Black, Latino, and American Indian or Alaska Native communities in Wisconsin and evaluate their reach and effectiveness in addressing the information needs of these communities.

Methods

Our team identified relevant COVID-19 topics based on feedback from their respective community, developed lay format materials, and translated materials into culturally appropriate social media messages that community advocates delivered across their respective communities. Social media metrics (reach, engagement, and impressions) were collected using Sprout Social and Facebook Analytics. We hosted 9 focus groups with community members to learn about their social media use. These data were analyzed using an inductive approach, using NVivo software (release 1.7) to code content.

Results

Between August 2021 and January 2023, we created 980 unique social media posts that reached 88,790 individuals and gathered >6700 engagements. Average reach per post was similar across the 3 communities, despite differences in the number of posts and followers on each page: 119.46 (Latino individuals), 111.74 (Black individuals), and 113.11 (Oneida Nation members). The type of posts that had higher engagement rate per reached person (ERR) varied across communities and platforms, with the highest being live videos for the Latino community on Facebook (ERR 9.4%), videos for the Black community on Facebook (ERR 19.53%), and social media messages for the Oneida Nation community (ERR 59.01%).

Conclusions

Our project presents a unique and effective model for health messages and highlights the need for tailoring social media messages and approaches for minoritized audiences (eg, age, gender, race, and ethnicity). Further research studies are needed to explore how specific types of information affect the dissemination of information and the implications for health communications.
Machine Learning Driven by Magnetic Resonance Imaging for the Classification of Alzheimer Disease Progression: Systematic Review and Meta-Analysis (Preprint)
Authors: Battineni Gopi , Chintalapudi Nalini , Amenta Francesco
Published: September 2024

BACKGROUND

To diagnose Alzheimer disease (AD), individuals are classified according to the severity of their cognitive impairment. There are currently no specific causes or conditions for this disease.

OBJECTIVE

The purpose of this systematic review and meta-analysis was to assess AD prevalence across different stages using machine learning (ML) approaches comprehensively.

METHODS

The selection of papers was conducted in 3 phases, as per PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020 guidelines: identification, screening, and final inclusion. The final analysis included 24 papers that met the criteria. The selection of ML approaches for AD diagnosis was rigorously based on their relevance to the investigation. The prevalence of patients with AD at 2, 3, 4, and 6 stages was illustrated through the use of forest plots.

RESULTS

The prevalence rate for both cognitively normal (CN) and AD across 6 studies was 49.28% (95% CI 46.12%-52.45%; P=.32). The prevalence estimate for the 3 stages of cognitive impairment (CN, mild cognitive impairment, and AD) is 29.75% (95% CI 25.11%-34.84%, P<.001). Among 5 studies with 14,839 participants, the analysis of 4 stages (nondemented, moderately demented, mildly demented, and AD) found an overall prevalence of 13.13% (95% CI 3.75%-36.66%; P<.001). In addition, 4 studies involving 3819 participants estimated the prevalence of 6 stages (CN, significant memory concern, early mild cognitive impairment, mild cognitive impairment, late mild cognitive impairment, and AD), yielding a prevalence of 23.75% (95% CI 12.22%-41.12%; P<.001).

CONCLUSIONS

The significant heterogeneity observed across studies reveals that demographic and setting characteristics are responsible for the impact on AD prevalence estimates. This study shows how ML approaches can be used to describe AD prevalence across different stages, which provides valuable insights for future research.
Effect of Addition of Metal Oxide Nanoparticles on the Strength of Heat-Cured Denture Base Resins: Protocol for Systematic Review and Meta-Analysis of In Vitro Studies (Preprint)
Authors: Kaurani Pragati , Hindocha Amit , Porwal Amit , Tambe Abhijit , Price Carrie , Goel Vidhani , Krasner Henry , Khubchandani Jagdish , Batra Kavita
Published: September 2024

BACKGROUND

Metal oxide nanoparticle–reinforced polymethyl methacrylate (PMMA) has been shown to improve mechanical properties, such as strength. Different types of metal oxide nanoparticles have been used previously, but the comparative effect on the strength of heat-cured denture base resins remains unclear.

OBJECTIVE

This is a protocol for a systematic review and meta-analysis that will aim to pool evidence to compare and analyze the effects of the addition of different metal oxide nanoparticles, with varied sizes and concentrations, on the strength (flexural, impact, transverse, compressive tensile strength, and fracture toughness) of heat-cured PMMA. In addition, this review aims to analyze methodological factors, such as adherence to testing and sample-making guidelines, and the effects of surface treatments of the nanoparticles on the strength of heat-cured denture base resins.

METHODS

The protocol has been registered in the Open Science Framework. Search strategies to identify studies on the effect of metal oxide nanoparticles on the strength of heat-cured PMMA were developed by the subject matter expert in library science. Following this, a systematic search of 5 electronic databases (PubMed [NCBI], Scopus [Elsevier], Cochrane Library [Wiley], CINAHL Plus with Full Text [EBSCO], and Dimensions Free Web App) was conducted to retrieve in vitro studies published in English from January 2012 to October 2023. Along with this citation chasing, other online sources and gray literature were also searched. Furthermore, papers will be screened, and appropriate data elements will be extracted in a standardized manner. A risk-of-bias assessment will be performed using a modified Cochrane Risk of Bias Tool. A meta-analysis will be performed using a random-effects model.

RESULTS

Search in databases resulted in 1837 papers, of which 1752 were duplicates, leaving 85 records that were screened for titles and abstracts based on the eligibility criteria. A similar search run on other online sources identified 129 papers that will be further analyzed for inclusion. The study was initiated in November 2023 and research questions and search strategies were formulated. The proposed study is expected to be completed by December 2024.

CONCLUSIONS

This systematic review will comprehensively analyze the effects of the incorporation of metal oxide nanoparticles in heat-cured denture base resins on the strength of the material. We anticipate gaining a deeper understanding of the effects and method of use of metal oxide nanoparticles to improve the strength of PMMA denture base resins.

INTERNATIONAL REGISTERED REPORT

PRR1-10.2196/59999
Human-centred design (HCD) and digital transformation of mental health services: A narrative review and personal view from the United Kingdom (Preprint)
Authors: Fleming William , Coutts Adam , Pochard Diane , Trivedi Daksha , Sanderson Kristy
Published: September 2024

UNSTRUCTURED

Mental health services face a multitude of challenges, such as increasing demand, underfunding and limited workforce capacity. The accelerated digital transformation of public services is positioned by government, private sector and some academic researchers as the solution. Alongside, human-centred design (HCD) has emerged as a guiding paradigm for this transformation to ensure user needs are met. We define what digital transformation and HCD are, how they are implemented in the UK policy context, and their role within the evolving delivery of mental health services. Our co-author’s involvement in the design and delivery of these policies over the past five years provides unique insights into the decision-making process and policy story. We review the promises, pitfalls and ongoing challenges identified across a multi-disciplinary literature. Finally, we propose future research questions and policy options to ensure that services are designed and delivered to meet the mental health needs of the population.
Electronic Clinical Decision Support System to Screen for Stroke Risk among Patients with Atrial Fibrillation: Perspectives of Clinicians in a Mental Healthcare Setting (Preprint)
Authors: Farran Dina , Cheang Hou Wang , Onwumere Juliana , Ashworth Mark , Gaughran Fiona
Published: September 2024

BACKGROUND

Electronic Clinical Decision Support Systems (eCDSSs) have been an integral component of the digital transformation in healthcare.

OBJECTIVE

This study aims to explore mental health clinician experience in screening for stroke risk among patients with atrial fibrillation and their perception of the potential impact of an eCDSS in improving care quality.

METHODS

A mixed method study, employing a questionnaire and individual semi-structured interviews, was conducted in a large London national health service trust. Interviews were analysed using an inductive thematic approach.

RESULTS

The sample comprised 10 clinicians. Two overarching themes related to prevention of AF-related stroke were identified from interviews: challenges faced on wards and strategies needed to improve practice. Challenges included difficulty identifying relevant medical histories of patients, clinician perceived lack of expertise, fragmented medical care, and mental health symptoms Suggestions to improve clinical practice included clinicians receiving alerts containing the latest guidelines and policies on stroke management, and bespoke training sessions designed to advance the knowledge, competencies and confidence of clinicians.
As for the potential impact of an eCDSS in improving quality of care, two themes emerged: perceived benefits and perceived risks. Potential benefits included enhanced clinical effectiveness, saving time and improved outcomes. Reported perceived risks were rigidity in decision making, annoyance, and increased workload.

CONCLUSIONS

A positive attitude towards an eCDSS to screen for stroke risk was identified although some concerns were noted. Understanding clinician perceptions of how an eCDSS may enhance health care and outcomes could serve as a basis for creating impactful digital health tools.
More Than Just a Vision: Confronting the Practical Barriers in Fostering Sustainable Government-Academic Data Infrastructures (Preprint)
Authors: Lee Jian-Sin , Tyler Allison R. B. , Veinot Tiffany Christine , Yakel Elizabeth
Published: September 2024

UNSTRUCTURED

N/A
Evaluating the Performance of Large Language Models (ChatGPT 4, Gemini 1.5 Pro, Command R+, and Llama 3 70B) on the Turkish Medical Specialty Training Entrance Exam (Preprint)
Authors: Koçak Murat , Oğuz Ali Kemal , Akçalı Zafer
Published: September 2024

UNSTRUCTURED

This study aims to evaluate the performance of advanced large language models (LLMs) - OpenAI-ChatGPT 4, Google AI-Gemini 1.5 Pro, Cohere-Command R+ and Meta AI-Llama 3 70B on questions within the scope of Basic Medical Sciences and Clinical Medical Sciences exams of the Turkish Medical Specialty Training Entrance Exam (2021, 1st semester) and to analyze the answers for user interpretability in languages other than English.
This study used ChatGPT 4, Gemini 1.5 Pro, Command R+, and Llama 3 70B to test artificial intelligence models' medical knowledge and ethical evaluation capabilities. The research was conducted on the questions of Turkish Medical Specialty Training Entrance Exam held on March 21, 2021. Turkish Medical Specialty Training Entrance Exam is organized by the Student Selection and Placement Centre with 240 questions. These questions are divided into two main categories: questions that test the basic medical knowledge and ethics required of a doctor and clinical scenarios covering various diseases.
Regarding Model Comparison, ChatGPT 4 was the best-performing model in most domains, with an overall accuracy of 88.75%. Its strengths lie in its ability to accurately answer basic and clinical medical science questions. Llama 3 70B followed closely behind with an overall accuracy of 79.17%. Although it could not quite match the performance of ChatGPT 4, it still showed strong knowledge consistency across various fields. Gemini 1.5 Pro and Command R+ lagged with overall accuracy rates of 78.13% and 50%, respectively. Furthermore, GPT-4 and Llama 3 70B achieved satisfactory results on the Turkish Medical Specialty Training Entrance Exam, demonstrating that it is a safe source for basic medical sciences and clinical medical sciences in languages other than English.
ChatGPT 4 is currently the most suitable model for answering medical science questions in various domains. Gemini 1.5 Pro and Command R+ show potential but need significant improvement to compete with the best-performing models. LLMS could be a helpful resource for medical education and clinical support in areas where English is not the primary language, like Turkey.
Endothelial dysfunction in patients with different obesity phenotypes
Authors: Sekret T.V. , Vlasenko M.V.
Published: September 2024

Background. Obesity is a progressive problem in society. The degree of cardiovascular risk in obesity depends on the distribution of adipose tissue, as well as on the endothelial function, since it is endothelial dysfunction that is one of the first links in triggering the development of cardiovascular diseases. The purpose of the study was to determine the functional state of the endothelium in patients with different obesity phenotypes. Materials and methods. During 2019–2021, 88 people were exa­mined: 25 men, 63 women. Their average age was 37.42 ± 11.77 years. A history was collected from all patients in order to determine risk factors for cardiovascular pathology. The blood levels of adipocytokines (leptin and adiponectin) and proinflammatory cytokines (interleukin (ІL) 6, ІL-8, ІL-17) were evaluated using the AU480 biochemical analyzer (Beckman Coulter, Inc., USA). Statistical processing of the results was carried out with the help of the Statistica 7 package (StatSoft, USA). Results. The study had demonstrated that leptin levels in all obesity phenotypes were higher than the reference values of the reagent sets. Consequently, adiponectin level with the progression of visceral obesity was as close as possible to the lower indicators of reference norms. It was found that ІL-17 is an interleukin that triggered an inflammatory reaction in all examined patients. ІL-17 stimulated the activity of pro-inflammatory cytokines ІL-8, ІL-6. The highest cytokine activity was recorded in the presence of visceral obesity without metabolic disorders (phenotype III): ІL-6 was 3.28 ± 1.52 pg/ml, ІL-8 was 96.55 ± 20.30 pg/ml. Due to the activation of the inflammatory reaction, NO synthase is secreted in a state of functional tension, therefore, its level is higher in phenotypes III and IV. Over time, its activity is expected to drop sharply, which will provoke endothelial remodeling. Conclusions. Patients with different phenotypes have a predisposition to endothelial dysfunction. Visceral obesity is the main predictor of the further development of insulin resistance, cardiometabolic disorders, as evidenced by an increase in the level of NO synthase, ІL-17, ІL-8, leptin.
Источники одиночных фотонов. Обзор. Часть 1
Authors: Криштоп В.Г.
Published: September 2024

В обзоре рассмотрены различные способы создания однофотонных источников (ИОФ).
Задача генерации одиночных фотонов может решаться разными способами, и на текущий момент среди них нет такого, который был бы существенно предпочтителен. Обширный список литературы дает возможность провести анализ перспектив развития
источников одиночных фотонов.
Tight and Efficient Gradient Bounds for Parameterized Quantum Circuits
Authors: Letcher Alistair , Woerner Stefan , Zoufal Christa
Published: September 2024

The training of a parameterized model largely depends on the landscape of the underlying loss function. In particular, vanishing gradients are a central bottleneck in the scalability of variational quantum algorithms (VQAs), and are known to arise in various ways. However, a caveat of most existing gradient bound results is the requirement of t-design circuit assumptions that are typically not satisfied in practice. In this work, we loosen these assumptions altogether and derive tight upper and lower bounds on loss and gradient concentration for a large class of parameterized quantum circuits and arbitrary observables, which are significantly stronger than prior work. Moreover, we show that these bounds, as well as the variance of the loss itself, can be estimated efficiently and classically-providing practical tools to study the loss landscapes of VQA models, including verifying whether or not a circuit/observable induces barren plateaus. In particular, our results can readily be leveraged to rule out barren plateaus for a realistic class of ansätze and mixed observables, namely, observables containing a non-vanishing local term. This insight has direct implications for hybrid Quantum Generative Adversarial Networks (qGANs). We prove that designing the discriminator appropriately leads to 1-local weights that stay constant in the number of qubits, regardless of discriminator depth. This implies that qGANs with appropriately chosen generators do not suffer from barren plateaus even at scale-making them a promising candidate for applications in generative quantum machine learning. We demonstrate this result by training a qGAN to learn a 2D mixture of Gaussian distributions with up to 16 qubits, and provide numerical evidence that global contributions to the gradient, while initially exponentially small, may kick in substantially over the course of training.
Agradabilidade percebida e design emocional: Um estudo de caso com home offices
Authors: Raposo Felipe , Costa Ramon , Rodrigues Lohuaine , Landim Paula Da Cruz , Demaison André
Published: September 2024

A aceitação de um produto por um público depende de diversos fatores, dentre eles, as funções práticas e estéticas, aspectos cuja porta de entrada se dá pela visão. Dessa maneira, ao projetar um produto, é de extrema importância pensar em quais sistemas, variáveis e forças serão percebidas, de forma que a informação visual vá ao encontro das preferências e que atenda às necessidades da maioria dos usuários. Para compreender melhor a relação entre a percepção visual e a noção de preferência e agradabilidade, este estudo analisa, por meio de uma Sentença Estruturadora, instrumento básico da Teoria das Facetas, 9 ambientes de home office. Observa-se uma grande demanda de pesquisas com este objeto de estudo, ocasionada pela ascensão do trabalho remoto, então objetiva-se aqui entender se as variáveis contraste e complexidade podem influenciar na decisão de uso do home office. A partir dos dados obtidos pelas respostas dos participantes, foi possível elaborar uma discussão sobre a influência das variáveis estéticas na agradabilidade percebida.
Local mass transfer and flow visualization around a cylinder in a liquid-solid fluidized bed
Authors: Boskovic-Vragolovic Nevenka , Brzic Danica , Sucurovic Katarina , Pjanovic Rada , Jacimovski Darko , Garic-Grulovic Radmila
Published: September 2024

Adsorption method has been used for flow visualisation and determination of the local and average mass transfer coefficients around a horizontal cylinder in a liquid-solid fluidized bed. The obtained concentration fields on the adsorbent (silica-foils) represent a clear qualitative flow pattern around the cylinder. By comparison of the concentration fields around the cylinder in the single-phase flow and in the fluidized bed, a significant disturbance of the boundary layer by fluidized particles can be observed. Local mass transfer coefficients were shown to be dependent on the angular position around the cylinder with the maximum determined at the angle 140? (measured from the stagnant point 0?). Selected correlations were applied to predict the average mass transfer coefficients and the two best have shown deviations from the experimental data up to 5 %.
La Maison Des Comptables: A Tax Research, Data Management, and Data Visualization Case
Authors: Adams Mollie T. , Inger Kerry K. , Long James H. , Loraas Tina M.
Published: September 2024

This teaching case integrates the tax and accounting information systems curricula to help students apply research-based outcomes to critical business processes. The setting involves sales and excise tax compliance for out-of-state alcohol shipments, a complex context given the variation between state laws and nuances in the application of general tax rules created by the 21st amendment. The various case components require students to research state tax law, employ data management techniques to apply those laws to a large dataset, and create data visualizations to aid managers with business decision-making. Components of the case can be implemented concurrently or sequentially across multiple courses in the accounting curriculum or as a stand-alone case in a single course. Requiring students to identify and apply tax law across multiple courses helps students see through individual course silos and understand how various accounting disciplines are interconnected.

JEL Classifications: M49; M41.
Harmonizing soft tissue subnasale and chin position in a forehead-based framework: interracial commonalities and differences between Asian and Caucasian females
Authors: Chen Tian , Yang Xianrui , Xue Chaoran , Bai Ding , Xu Hui
Published: September 2024

Objectives

To establish a reference system for assessing the anteroposterior (A-P) position of the subnasal and lower-facial soft tissues for whole facial harmony.

Materials and Methods

Forty Asian and 40 Caucasian females with attractive profiles were selected as the “attractive” samples, with “ordinary” samples for comparison. Each profile was analyzed, and comparisons were made to reveal the interracial commonalities and differences. Esthetically essential parameters were established. An averaged attractive profile for each race was created by digital morphing and then modified into 30 variations based on combined variations of the esthetically essential parameters. Assessments were performed to investigate the esthetic ranges.

Results

A-P position of the subnasal and lower-facial landmarks harmonized with the forehead for female profile esthetics. In addition to balanced soft tissue subnasale (sSn)- and soft titssue pogonion (Pos)-to-forehead A-P relations, harmonizing lower-facial soft tissues to sSn was indispensable for profile attractiveness. sSn-to-glabella, Pos-to-glabella, and Pos-to-sSn A-P relations were esthetically essential. Perceived by orthodontists, the attractive Asian female profiles had sSn-to-glabella A-P relations ranging from 0.5 mm to 4.5 mm, Pos-to-sSn from −9.0 mm to −5.5 mm, and Pos-to-glabella from −8.5 mm to −1.0 mm. Compared with Asians, the attractive Caucasian female profiles had more anteriorly and widely distributed sSn relative to the forehead, wider ranges of Pos-to-sSn A-P relations, and more prominent chins.

Conclusions

A reference system comprising sSn-glabella, Pos-sSn, and Pos-glabella horizontal distances was constructed for facial profile analysis. This system could aid treatment planning for surgical or orthopedic repositioning of the maxilla and chin.
The Sugar-Coated Truth: The Quality of Diabetes Health Information on TikTok
Authors: Vasistha Spruhaa , Kanchibhatla Anuhya , Blanchette Julia E. , Rieke Jorden , Hughes Allyson S.
Published: September 2024

This study aimed to assess diabetes health information found on TikTok and quantify misinformation on TikTok. The authors assessed 171 videos through two health literacy tools, DISCERN and the Patient Education Materials Assessment Tool for Audiovisual Materials, to rate the understandability and actionability of online medical content. The findings from this study encourage health care professionals to use social media platforms to provide factual information about diabetes and advise online health care consumers to use reputable sources such as trusted diabetes organizations’ social media accounts, which tend to validate content with clinicians.