bluuebunny/crossref_metadata_2025 huggingface dataset:
A comparative study of the PRF mediated cellular reaction of tumour cells
Authors:   , Parkhoo Kamelia
Published: November 2024

The use of platelet rich fibrin (PRF) has been proven as beneficial in the context of improved wound healing, pain management and tissue engineering applications. To the best of our knowledge, this study is the first that evaluated the effect of PRF on osteoblastic and fibroblastic tumour cell lines. Within our findings, we could determine decreased tumour cell proliferation of MG63 and HT1080 when treated directly or indirectly with PRF as compared to cell cultures without PRF. Low-RCF PRF generally yielded more distinct results than high-RCF PRF within both cell types. This observation aligns with previous studies that have found higher concentrations of growth factors, leukocytes and platelets in PRF with reduced RCF. However, the cellular reaction regarding released growth factors and cytokines of MG63 treated with PRF differed partly compared to that of HT1080 cultivated with PRF. Similar observations could be made in regard to altered regulation of gene-expression between MG63 and HT1080 treated with PRF. Nevertheless, generally tumour-suppressive gene expression was up-regulated and anti-apoptotic gene expression was down-regulated in both cell types treated with high- and especially low-RCF PRF. As angiogenesis is crucial for tumour growth and spread, a limitation on this process with PRF could contribute to tumour suppression. The impact of PRF regarding angiogenesis was evaluated in co-culture models of tumour cells with HDMEC in order to imitate the conditions in organisms. Even though, there were great differences regarding angiogenic activity between both tumour cell types in co-culture, (low-RCF) PRF led to inhibition of angiogenic–like processes in both. The effect of released growth factors, cytokines, altered gene expression and angiogenic potential is dependent on co-factors, the environment and the cell type as MG63 seems to be more sensitive towards (low-RCF) PRF treatment than HT1080 treated with PRF. Overall, PRF treatment led to distinct cellular reactions in both tumour cell lines, ultimately decreasing tumour growth and proliferation.
Assessing the Protective Effects of Benoxacor, Fenclorim, Melatonin, and 2,4,6-Trichlorophenoxyacetic Acid Against Herbicide Injury in Tomato
Authors: de Oliveira Tabata Raissa , Correa Tavares Antonio Augusto , Broderick Shaun , Bararpour Taghi , Tseng Te Ming
Published: November 2024

Safeners protect crops by enhancing their ability to metabolize various compounds, including herbicides. They increase a crop’s tolerance to herbicide damage, activating herbicide-metabolizing proteins, and aiding in their detoxification. This study aimed to investigate the chemical effects of safeners in tomato cultivation and focus on injury reduction and tissue protection. The experiment followed a randomized factorial design (5 × 4) with four replications repeated twice. We evaluated the effects of herbicides dicamba, 2,4-D, metribuzin, and sulfentrazone (diluted to 1% of the recommended field rate) and safeners benoxacor, fenclorim, melatonin, 2,4,6-T, and an untreated control. Safeners were applied to the seeds before sowing, and herbicides were applied as a foliar spray 25 days after sowing. Visual injury was evaluated 7, 14, and 21 days after application (DAA). Biomass measurements were taken 21 DAA. Results showed that preconditioning tomato seeds with 2,4,6-T, melatonin, and fenclorim 7 DAA significantly decreased injury by 25, 25, and 23%, respectively. Moreover, applying melatonin, benoxacor, and 2,4,6-T 21 DAA led to significantly greater dry biomass, which increased by 1.5, 1.42, and 1.44 times, respectively, compared with the control. This research provides valuable insights into the chemical effects of benoxacor, fenclorim, 2,4,6-T, and melatonin safeners in tomato cultivation. The findings demonstrate the potential for preconditioning tomato plants with 2,4,6-T, melatonin, and fenclorim to reduce herbicide injury. Additionally, melatonin, benoxacor, and 2,4,6-T stimulated growth, increasing tomato dry biomass. Understanding plant defense mechanisms and the protective effects of safeners against herbicide damage contributes to developing effective weed management strategies.
Diabetes-Related Hospital Mortality in Rural America: A Significant Cause for Concern
Authors: Ferdinand Alva O. , Akinlotan Marvellous , Callaghan Timothy H. , Towne Jr Samuel D. , Bolin Jane
Published: November 2024

It has been reported that diabetes prevalence is higher in rural areas than in urban areas. Other studies have shown that rural persons with diabetes have higher morbidity from diabetes-related complications than urban persons with diabetes. This study used data from the Healthcare Cost and Utilization Project Nationwide Inpatient Sample (2009-2014) to examine hospital-based diabetes-related mortality and whether there were urban-rural differences across census regions. On average 2.63% of all diabetes-related urban hospital admissions resulted in death, while 2.73% of all diabetes-related rural hospital admissions resulted in death (p<0.001). Mortality rates were highest within rural areas of the South and Midwest (21.0 and 15.1 deaths per 100,000 population, respectively) compared to other areas in both regions. The noncore, micropolitan, and small metropolitan areas of the South had the highest average diabetes-related hospital mortality rates (21.0, 20.3, and 14.0 deaths per 100,000 population, respectively) compared to corresponding areas in the Northeast, Midwest, and West regions. Results suggest that substantial differences exist between rural and urban diabetes-related hospital mortality. Furthermore, the burden is especially pronounced for rural residents in the South and Midwest census regions.
Screening of Natural Compounds as Inhibitor of Mpro SARS-CoV-2 Protein; A
Molecular Dynamics Approach
Authors: Javaid Anum , Bibi Nousheen , Mahmood Malik Siddique , Batool Hina , Batool Sana , Hamid Arslan , Saleem Mahjabeen , Ashraf Naeem Mahmood , Afsar Tayyaba , Almajwal Ali , Razak Suhail
Published: November 2024

Background:

New strains of SARS-CoV-2 are continually emerging worldwide. Recently, WHO
warned of a severe new wave in Europe. Current vaccines cannot fully prevent reinfection in vaccinated individuals.

Aim:

Given this issue, recent research focuses on new antiviral candidates with high efficacy and minimal
side effects.

Objectives:

Screen natural compounds as inhibitors of Mpro SARS-CoV-2 protein using molecular dynamics.

Methods:

In this study, we have screened the potential of plant-based natural anti-viral compounds. A library
of the 579 compounds was generated using currently available literature and online databases. All these compounds
were screened based on their binding affinities as predicted by molecular docking analysis and compounds
having binding affinity values ≤ -10 Kcal/mol were considered for analysis. Furthermore, from physicochemical
assessment, drug-likeness initially nine compounds were identified as the antiviral targets for the
selected viral proteins. After ADMET analysis and simulations, the compound 9064 with the lowest RMSD,
Coul-SR interaction energy (-71.53 kJ/mol), and LJ-SR energy (-95.32 kJ/mol) was selected as the most stable
drug candidate against COVID-19 main protease Mpro.

Results:

The ΔG value, calculated using MMGBSA also revealed strong binding of the compound with Mpro.
The selected antiviral compound 9064 is an antioxidant flavonoid (Catechin or Cianidanol), which was previously
known to have significant immunomodulatory, anti-inflammatory, and antioxidant properties.

Conclusion:

Considering the limitations of currently available vaccines, our study may provide new insight
into potential drugs that may prevent SARS-CoV-2 infection in humans.
Novel Therapies for Nonalcoholic Steatohepatitis (NASH) and Cardiovascular Risk Reduction
Authors: Biswas Tarun , Lehker Angelica , Mukherjee Debabrata
Published: November 2024

Nonalcoholic steatohepatitis (NASH) is a type of nonalcoholic fatty liver disease
(NAFLD) characterized by hepatocyte injury and inflammation, in addition to only the presence
of steatosis NAFLD. We review the existing data on available novel therapies for NASH and
NAFLD and also discuss several therapies in development. We assessed therapies for NASH by
searching the databases of PubMed, EMBASE, and Web of Science (SCI) from their inception
dates until September 15, 2024. Search terms used were: nonalcoholic fatty liver disease, nonalcoholic
steatohepatitis, liver inflammation and hepatocyte injury.Until very recently, therapeutic
lifestyle change was the primary modality of treatment for NASH, including modification of diet
and physical activity. The FDA recently approved resmetirom using its expedited approval mechanism
for NASH. There are also several pharmacotherapies in development for NASH which aim
at weight loss, insulin sensitization and improvement in lipid levels, although some drugs may
have multiple effects which are discussed. The availability of resmetirom offers patients with
NASH an effective adjunctive therapy in addition to lifestyle changes. Several other novel therapies
are also currently being tested and will add to our therapeutic armamentarium.
Applications of Neural Network in Physics: Cosmology and Molecular Dynamics
Authors: Mohapatra Vivekanand , Agrawal Dhruv , Avishek Shubhamshree
Published: November 2024

Understanding the underlying physics of a physical system at both the
cosmological and molecular scales has been a focus of attention for decades. Modeling
the system using ordinary and partial differential equations along with the Markov
Chain Monte Carlo technique are the conventional methods being used. These methods
have been proven to reconcile accurate results, however, they fail miserably when the
physics is not completely known, which leads to the presence of a large number of free
parameters in the model describing the system. Recently, conventional methods have
been aided by the use of machine learning techniques to solve real-world problems,
which include the use of artificial neural networks such as convolutional neural
networks, generative adversarial networks, and random forests. The ability of these
techniques to understand the complexity of a physical system and predict new physics
solely from data has given a new edge to conventional methods. Their prevalent
applications lie in parameter prediction, where available data is used to train a neural
network model, and then physical quantities are predicted using the trained model.
Classification is another fundamental aspect of machine learning that involves
predicting the specific family or category to which the provided data pertains. These
techniques find an essential place in physics, providing important insights into complex
systems.
AI Tools for Teaching-Learning Chemistry
Authors: Raza Saman , Satya  , Khan Tahmeena , Singh Manisha
Published: November 2024

Artificial Intelligence (AI) is quickly becoming ubiquitous, with applications
in all spheres of life. The education sector is also not untouched, in fact students are
now relying on AI tools for studying, doing homework, making assignments and
reports, and preparing for exams. Teachers are also using AI tools to enhance
classwork and assessments. The use of AI in chemistry education is rapidly growing
and many AI tools are proving to be quite useful in this regard. However, chemistry
being a vast subject with lots of concepts, laws, formulae, reactions, and applications,
requires deep understanding and comprehension, which is a challenge for these tools as
they are not always accurate and consistent in providing answers. The present chapter
gives a brief account of the uses of AI in chemistry, with teaching-learning chemistry,
in particular. It explores the advantages and disadvantages of using AI in chemistry
education and how AI can be incorporated in classrooms.
Transformation in the World of Commerce and Economics through AI
Authors: Tandon Umang , Tandon Apoorva , Mehrotra Tarang
Published: November 2024

This chapter constitutes a comprehensive exploration of the far-reaching
impact of Artificial Intelligence (AI) on the domains of commerce and economics. It
conducts a thorough analysis, meticulously concentrating on the intricate aspects of
analytics and decision-making. Within this dynamic landscape, the research not only
elucidates the transformative role of Artificial Intelligence in predictive and descriptive
analytics but also delves into its nuanced contributions to tracking market trends, risk
mitigation, and the enhancement of operational decision-making processes. Examining
the economic implications of Artificial Intelligence, the chapter goes beyond a cursory
overview, providing a detailed understanding of documented instances where Artificial
Intelligence has led to heightened productivity and subtle yet impactful shifts in labour
dynamics. By grounding its analysis in real-world examples, the study aims to uncover
the intricacies of how Artificial Intelligence is actively reshaping traditional economic
paradigms, potentially ushering in a redefined fabric of commerce. Recognizing the
pivotal role of Artificial Intelligence in driving efficiency and innovation, the research
places a heightened emphasis on addressing inherent biases embedded within AI
models. It advocates not only for the identification of these biases but also proposes
meticulous rectification processes to ensure the fostering of fairness and equity in AIdriven decision-making. The study acknowledges ethical dimensions associated with
the integration of Artificial Intelligence, highlighting the imperative for responsible
Artificial Intelligence development and deployment, especially in economic settings.
Furthermore, the paper identifies the essence of the AI-driven era in commerce and
economics, foreseeing a landscape where Artificial Intelligence becomes indispensable
for informed decision-making. It not only outlines the current landscape but also
proposes future lines of inquiry to guide ongoing research. This paper, therefore, stands
as a substantive and forward-thinking resource for scholars, practitioners, and
policymakers alike, seeking to navigate and understand the evolving intersection of
Artificial Intelligence, commerce, and economics.
Revolutionizing Learning Landscapes: Unleashing the Potential of AI in the Realm of Academic Research
Authors: Zahra Waseem , Rautela Gunjan
Published: November 2024

Artificial Intelligence (AI) has become a game-changer in education,
transforming how we learn, and improving the way we conduct research. By using
smart algorithms, machine learning, and data analysis, AI is applied in diverse areas,
such as intelligent tutoring systems, personalized learning platforms, and automated
grading systems. This article explores why it is crucial to use Artificial Intelligence
(AI) in academic research nowadays. With research questions getting more complex
and lots of data to handle, traditional methods struggle to keep up. AI comes in as a
game-changer, helping researchers by doing routine tasks, sorting through big datasets,
and finding important patterns. In academic research, AI has proven invaluable. It helps
researchers explore complex topics, gain insights, and streamline the overall research
process. With its ability to analyze large sets of data quickly, recognize patterns, and
offer insights, AI speeds up research significantly. The point is that AI is becoming a
must-have tool for universities and researchers. We need it to make our research better,
spark new ideas, and keep up with the ever-evolving world of academic exploration.
This research article illustrates the development of AI in research and the comparison
between AI data analysis and conventional data analysis. This article tries to explore
various AI tools and techniques used in academic research, discusses the diverse
applications of AI in academic research, outlines the benefits, addresses challenges,
and considers ethical considerations in using AI for academic research.
Identifying Safeguards Disabled by Epstein-Barr Virus Infections in Genomes From Patients With Breast Cancer: Chromosomal Bioinformatics Analysis
Authors: Friedenson Bernard
Published: November 2024

Background

The causes of breast cancer are poorly understood. A potential risk factor is Epstein-Barr virus (EBV), a lifelong infection nearly everyone acquires. EBV-transformed human mammary cells accelerate breast cancer when transplanted into immunosuppressed mice, but the virus can disappear as malignant cells reproduce. If this model applies to human breast cancers, then they should have genome damage characteristic of EBV infection.

Objective

This study tests the hypothesis that EBV infection predisposes one to breast cancer by causing permanent genome damage that compromises cancer safeguards.

Methods

Publicly available genome data from approximately 2100 breast cancers and 25 ovarian cancers were compared to cancers with proven associations to EBV, including 70 nasopharyngeal cancers, 90 Burkitt lymphomas, 88 diffuse large B-cell lymphomas, and 34 gastric cancers. Calculation algorithms to make these comparisons were developed.

Results

Chromosome breakpoints in breast and ovarian cancer clustered around breakpoints in EBV-associated cancers. Breakpoint distributions in breast and EBV-associated cancers on some chromosomes were not confidently distinguished (P>.05), but differed from controls unrelated to EBV infection. Viral breakpoint clusters occurred in high-risk, sporadic, and other breast cancer subgroups. Breakpoint clusters disrupted gene functions essential for cancer protection, which remain compromised even if EBV infection disappears. As CRISPR (clustered regularly interspaced short palindromic repeats)–like reminders of past infection during evolution, EBV genome fragments were found regularly interspaced between Piwi-interacting RNA (piRNA) genes on chromosome 6. Both breast and EBV-associated cancers had inactivated genes that guard piRNA defenses and the major histocompatibility complex (MHC) locus. Breast and EBV-associated cancer breakpoints and other variations converged around the highly polymorphic MHC. Not everyone develops cancer because MHC differences produce differing responses to EBV infection. Chromosome shattering and mutation hot spots in breast cancers preferentially occurred at incorporated viral sequences. On chromosome 17, breast cancer breakpoints that clustered around those in EBV-mediated cancers were linked to estrogen effects. Other breast cancer breaks affected sites where EBV inhibits JAK-STAT and SWI-SNF signaling pathways. A characteristic EBV-cancer gene deletion that shifts metabolism to favor tumors was also found in breast cancers. These changes push breast cancer into metastasis and then favor survival of metastatic cells.

Conclusions

EBV infection predisposes one to breast cancer and metastasis, even if the virus disappears. Identifying this pathogenic viral damage may improve screening, treatment, and prevention. Immunizing children against EBV may protect against breast, ovarian, other cancers, and potentially even chronic unexplained diseases.
The Significance of a Cerebrovascular Accident Outcome Prediction Model for Patients, Family Members, and Health Care Professionals: Qualitative Evaluation Study
Authors: Allaart Corinne G , van Houwelingen Sanne , Hilkens Pieter HE , van Halteren Aart , Biesma Douwe H , Dijksman Lea , van der Nat Paul B
Published: November 2024

Background

Patients with cerebrovascular accident (CVA) should be involved in setting their rehabilitation goals. A personalized prediction of CVA outcomes would allow care professionals to better inform patients and informal caregivers. Several accurate prediction models have been created, but acceptance and proper implementation of the models are prerequisites for model adoption.

Objective

This study aimed to assess the added value of a prediction model for long-term outcomes of rehabilitation after CVA and evaluate how it can best be displayed, implemented, and integrated into the care process.

Methods

We designed a mock-up version, including visualizations, based on our recently developed prediction model. We conducted focus groups with CVA patients and informal caregivers, and separate focus groups with health care professionals (HCPs). Their opinions on the current information management and the model were analyzed using a thematic analysis approach. Lastly, a Measurement Instrument for Determinants of Innovations (MIDI) questionnaire was used to collect insights into the prediction model and visualizations with HCPs.

Results

The analysis of 6 focus groups, with 9 patients, 4 informal caregivers, and 8 HCPs, resulted in 10 themes in 3 categories: evaluation of the current care process (information absorption, expectations of rehabilitation, prediction of outcomes, and decision aid), content of the prediction model (reliability, relevance, and influence on the care process), and accessibility of the model (ease of understanding, model type preference, and moment of use). We extracted recommendations for the prediction model and visualizations. The results of the questionnaire survey (9 responses, 56% response rate) underscored the themes of the focus groups.

Conclusions

There is a need for the use of a prediction model to assess CVA outcomes, as indicated by the general approval of participants in both the focus groups and the questionnaire survey. We recommend that the prediction model be geared toward HCPs, as they can provide the context necessary for patients and informal caregivers. Good reliability and relevance of the prediction model will be essential for its wide adoption.
Effectiveness of Frequent Point-of-Care Molecular COVID-19 Surveillance in a Rural Workplace: Nonrandomized Controlled Clinical Trial Among Miners
Authors: Sood Akshay , “Cotton” Jarrell William , Shore Xin W , Sosa Nestor , Parada Alisha , Edwardson Nicholas , Yingling Alexandra V , Amirkabirian Teah , Cheng Qiuying , Hurwitz Ivy , Cook Linda S , Leng Shuguang , Myers Orrin B , Perkins Douglas J
Published: November 2024

Background

Numerous studies have assessed the risk of SARS-CoV-2 exposure and infection among health care workers during the pandemic. However, far fewer studies have investigated the impact of SARS-CoV-2 on essential workers in other sectors. Moreover, guidance for maintaining a safely operating workplace in sectors outside of health care remains limited. Workplace surveillance has been recommended by the Centers for Disease Control and Prevention, but few studies have examined the feasibility or effectiveness of this approach.

Objective

The objective of this study was to investigate the feasibility and effectiveness of using frequent point-of-care molecular workplace surveillance as an intervention strategy to prevent the spread of SARS-CoV-2 at essential rural workplaces (mining sites) where physical distancing, remote work, and flexible schedules are not possible.

Methods

In this nonrandomized controlled clinical trial conducted from February 2021, to March 2022, 169 miners in New Mexico (intervention cohort) and 61 miners in Wyoming (control cohort) were enrolled. Investigators performed point-of-care rapid antigen testing on midnasal swabs (NSs) self-collected by intervention miners. Our first outcome was the intervention acceptance rate in the intervention cohort. Our second outcome was the rate of cumulative postbaseline seropositivity to SARS-CoV-2 nucleocapsid protein, which was analyzed in the intervention cohort and compared to the control cohort between baseline and 12 months. The diagnostic accuracy of detecting SARS-CoV-2 using rapid antigen testing on NSs was compared to laboratory-based reverse transcriptase polymerase chain reaction (RT-PCR) on nasopharyngeal swabs (NPSs) in a subset of 68 samples.

Results

Our intervention had a mean acceptance rate of 96.4% (11,413/11,842). The intervention miners exhibited a lower cumulative postbaseline incident seropositivity at 12 months compared to control miners (14/97, 14% vs 17/45, 38%; P=.002). Analysis of SARS-CoV-2 antigen detection in self-administered NSs revealed 100% sensitivity and specificity compared to laboratory-based RT-PCR testing on NPSs.

Conclusions

Our findings establish frequent point-of-care molecular workplace COVID-19 surveillance as a feasible option for keeping essential rural workplaces open and preventing SARS-CoV-2 spread. These findings extend beyond this study, providing valuable insights for designing interventions to maintain employees’ safety at other essential workplaces during an infectious disease outbreak.
The Challenges and Lessons Learned Building a New UK Infrastructure for Finding and Accessing Population-Wide COVID-19 Data for Research and Public Health Analysis: The CO-CONNECT Project (Preprint)
Authors: Jefferson Emily , Milligan Gordon , Johnston Jenny , Mumtaz Shahzad , Cole Christian , Best Joseph , Giles Thomas Charles , Cox Samuel , Masood Erum , Horban Scott , Urwin Esmond , Beggs Jillian , Chuter Antony , Reilly Gerry , Morris Andrew , Seymour David , Hopkins Susan , Sheikh Aziz , Quinlan Philip
Published: November 2024

UNSTRUCTURED

The COVID-19-Curated and Open Analysis and Research Platform (CO-CONNECT) project worked with 22 organizations across the United Kingdom to build a federated platform, enabling researchers to instantaneously and dynamically query federated datasets to find relevant data for their study. Finding relevant data takes time and effort, reducing the efficiency of research. Although data controllers could understand the value of such a system, there were significant challenges and delays in setting up the platform in response to COVID-19. This paper aims to present the challenges and lessons learned from the CO-CONNECT project to support other similar initiatives in the future. The project encountered many challenges, including the impacts of lockdowns on collaboration, understanding the new architecture, competing demands on people’s time during a pandemic, data governance approvals, different levels of technical capabilities, data transformation to a common data model, access to granular-level laboratory data, and how to engage public and patient representatives meaningfully on a highly technical project. To overcome these challenges, we developed a range of methods to support data partners such as explainer videos; regular, short, “touch base” videoconference calls; drop-in workshops; live demos; and a standardized technical onboarding documentation pack. A 4-stage data governance process emerged. The patient and public representatives were fully integrated team members. Persistence, patience, and understanding were key. We make 8 recommendations to change the landscape for future similar initiatives. The new architecture and processes developed are being built upon for non–COVID-19–related data, providing an infrastructural legacy.
Effectiveness of Digital Health Interventions in Promoting Physical Activity Among College Students: Systematic Review and Meta-Analysis (Preprint)
Authors: Bi Siyuan , Yuan Junfeng , Wang Yanling , Zhang Wenxin , Zhang Luqin , Zhang Yongjuan , Zhu Rui , Luo Lin
Published: November 2024

BACKGROUND

Recent studies offer conflicting conclusions about the effectiveness of digital health interventions in changing physical activity behaviors. In addition, research focusing on digital health interventions for college students remains relatively scarce.

OBJECTIVE

This study aims to examine the impact of digital health interventions on physical activity behaviors among college students, using objective measures as outcome indicators.

METHODS

In accordance with the 2020 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a comprehensive literature search was conducted across several databases, including MEDLINE (PubMed), Web of Science, Cochrane Library, and EBSCO (CINAHL Plus with full text), to identify relevant intervention studies published up to June 6, 2023. The inclusion criteria specified studies that examined the quantitative relationships between digital health interventions and physical activity among adults aged 18 years to 29 years, focusing on light physical activity (LPA), moderate to vigorous physical activity (MVPA), sedentary time (ST), or steps. Non–randomized controlled trials were excluded. The quality of the studies was assessed using the Cochrane Risk of Bias tool. Results were synthesized both narratively and quantitatively, where applicable. When sufficient homogeneity was found among studies, a random-effects model was used for meta-analysis to account for variability.

RESULTS

In total, 8 studies, encompassing 569 participants, were included in the analysis. The primary outcomes measured were LPA, MVPA, ST, and steps. Among these studies, 3 reported on LPA, 5 on MVPA, 5 on ST, and 3 on steps. The meta-analysis revealed a significant increase in steps for the intervention group compared with the control group (standardized mean difference [SMD] 0.64, 95% CI 0.37-0.92; P<.001). However, no significant differences were observed between the intervention and control groups regarding LPA (SMD –0.08, 95% CI –0.32 to 0.16; P=.51), MVPA (SMD 0.02, 95% CI –0.19 to 0.22; P=.88), and ST (SMD 0.03, 95% CI –0.18 to 0.24; P=.78).

CONCLUSIONS

Digital health interventions are effective in increasing steps among college students; however, their effects on LPA, MVPA, and sedentary behavior are limited.

CLINICALTRIAL

PROSPERO CRD42024533180; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=533180
Community Versus Facility-Based Services to Improve the Screening of Active Hepatitis C Virus Infection in Cambodia: The ANRS 12384 CAM-C Cluster Randomized Controlled Trial—Protocol for a Mixed Methods Study (Preprint)
Authors: Mosnier Emilie , Ségéral Olivier , Neth Sansothy , Sagaon-Teyssier Luis , Khuon Dyna , Phoeung Chan Leakhena , Mam Sovatha , Chhay Chhingsrean , Heang Kimeang , Duclos-Vallée Jean Charles , Saphonn Vonthanak
Published: November 2024

BACKGROUND

In Cambodia, hepatitis C constitutes a significant public health challenge, particularly among older adults (>45 years) for whom prevalence is estimated to be 5%. To facilitate the elimination of hepatitis C among the general population, enhancing access to screening and treatment is imperative. In this regard, the evaluation of community-based screening programs emerges as a crucial step toward improving health care accessibility.

OBJECTIVE

This study aims to assess the comparative efficacy of a community-based versus a facility-based approach in enhancing the uptake of hepatitis C antibody testing among the general population older than 40 years of age in Cambodia.

METHODS

The CAM-C (Community Versus Facility-Based Services to Improve the Screening of Active Hepatitis C Virus Infection in Cambodia) study uses a cluster-randomized controlled trial design across two Cambodian provinces to compare community-based and facility-based hepatitis testing interventions. Sampling involves a multistage cluster approach, targeting individuals older than 40 years of age due to their higher prevalence and risk of chronic hepatitis complications. This study incorporates a qualitative analysis of acceptability and a cost-effectiveness comparison. Interventions include facility-based testing with subsequent referral and community-based testing with direct in-home assessments. Follow-up for positive cases involves comprehensive management and potential direct-acting antiviral treatment. This study aims to identify a significant increase in testing uptake, requiring the screening of 6000 individuals older than 40 years of age, facilitated by a structured sampling and intervention approach to minimize contamination risks.

RESULTS

The final protocol including the quantitative, qualitative, and cost-effectiveness part of the study was registered and was approved in 2019 by the National Ethical Cambodian for Health Research. Inclusions were completed by mid-2024, with analyses starting in May 2024.

CONCLUSIONS

Using a mixed methods approach that combines a robust methodology (cluster-randomized controlled trial) with a cost-effectiveness analysis and qualitative research, such a study should provide invaluable information to guide the Ministry of Health in its hepatitis C virus screening strategy and move toward elimination.

CLINICALTRIAL

ClinicalTrials.gov NCT03992313; https://clinicaltrials.gov/study/NCT03992313

INTERNATIONAL REGISTERED REPORT

DERR1-10.2196/63376
Improving Robustness and Clinical Applicability of Automatic Respiratory Sound Classification Using Deep Learning-Based Audio Enhancement: Algorithm Development and Validation Study (Preprint)
Authors: Tzeng Jing-Tong , Li Jeng-Lin , Chen Huan-Yu , Huang Chu-Hsiang , Chen Chi-Hsin , Fan Cheng-Yi , Huang Edward Pei-Chuan , Lee Chi-Chun
Published: November 2024

BACKGROUND

eep learning techniques have shown promising results in the automatic classification of respiratory sounds. However, accurately distinguishing these sounds in real-world noisy conditions poses challenges for clinical deployment. Additionally, predicting signals with only background noise could undermine user trust in the system.

OBJECTIVE

This paper aims to investigate the feasibility and effectiveness of incorporating a deep learning-based audio enhancement preprocessing step into automatic respiratory sound classification systems to improve robustness and clinical applicability.

METHODS

Multiple experiments were conducted using different audio enhancement model structures and classification models. The classification performance was compared to the baseline method of noise injection data augmentation. Experiments were performed on two datasets: the ICBHI respiratory sound dataset, which includes 5.5 hours of recordings, and the Formosa Archive of Breath Sounds (FABS) dataset, comprising 14.6 hours of recordings. Additionally, a physician validation study was conducted by 7 senior physicians to assess the clinical utility of the system.

RESULTS

The integration of the audio enhancement pipeline resulted in a 21.88% increase in the ICBHI classification score on the ICBHI dataset and a 4.10% improvement on the FABS dataset in multi-class noisy scenarios. Quantitative analysis from the physician validation study revealed improvements in efficiency, diagnostic confidence, and trust during model-assisted diagnosis, with workflows integrating enhanced audio leading to an 11.61% increase in diagnostic sensitivity and facilitating high-confidence diagnoses.

CONCLUSIONS

Incorporating an audio enhancement algorithm significantly enhances the robustness and clinical utility of automatic respiratory sound classification systems, improving performance in noisy environments and fostering greater trust among medical professionals.

CLINICALTRIAL

n/a
Predicting Postoperative Delirium in Older Patients: a multicenter retrospective cohort study (Preprint)
Authors: Wu Shun-Chin Jim , Sharma Nitin , Bauch Anne , Yang Hao-Chun , Hect Jasmine L. , Thomas Christine , Wagner Sören , Förstner Bernd R. , von Arnim Christine A.F. , Kaufmann Tobias , Eschweiler Gerhard W. , Wolfers Thomas ,   PAWEL Study Group
Published: November 2024

BACKGROUND

Elective surgeries for older adults are increasing. Machine learning could enhance risk assessment, influencing surgical planning and postoperative care. Preoperative cognitive assessment may facilitate early detection and management of postoperative delirium (POD).

OBJECTIVE

This study aims to assess machine learning models' predictive ability for POD, focusing on adding neuropsychological assessments before surgery.

METHODS

This retrospective cohort study analyzed data from the multicenter PAWEL and PAWEL-R studies, encompassing older patients (≥70 years) undergoing elective surgeries from July 2017 to April 2019. A total of 1624 patients were included, with POD diagnosis made before discharge. Data included demographics, clinical, surgical, and neuropsychological features collected pre- and perioperatively. Machine learning model performance was evaluated using the area under the receiver operating characteristic curve (AUC), with permutation testing for significance and SHapley Additive exPlanations to identify effective neuropsychological assessments.

RESULTS

In this cohort of 1624 patients, 52.3% (N=850) were male, with a mean (s.d.) age of 77.9 (4.9) years. Predicting POD before surgery achieved an AUC of 0.786. Incorporating all pre- and perioperative features into the model yielded a slightly higher AUC of 0.806, with no statistically significant difference observed (P= .193). Notably, cognitive factors alone were not strong predictors (AUC=0.611). However, specific tests within neuropsychological assessments, such as the Montreal Cognitive Assessment memory subdomain and Trail Making Test Part B, were found to be crucial for prediction.

CONCLUSIONS

Preoperative risk prediction for POD can increase risk awareness in presurgical assessment and improve postoperative management in older patients with a high risk for delirium.

CLINICALTRIAL

The study was registered with the German Clinical Trials Register under the identifiers DRKS12797 and DRKS13311.

INTERNATIONAL REGISTERED REPORT

RR2-2019 Jan 21;20(1):71. doi: 10.1186/s13063-018-3148-8
Systematic Review: Effect of extended reality simulation intervention on midwifery students’ anxiety (Preprint)
Authors: Pérez de los Cobos Cintas Clara , Vuillerme Nicolas , Thomann Guillaume , Di Marco Lionel
Published: November 2024

BACKGROUND

Midwifery students often experience anxiety due to several factors such as the clinical experiences faced. Simulation-based learning in nursing and midwifery studies using extended reality (XR) tools offers the opportunity to better manage educational processes while reducing this anxiety.

OBJECTIVE

This study aims to evaluate the current understanding of how the use of XR gesture-simulation-based tools allows a better understanding of the anxiety levels of midwives and nurses in educational settings.

METHODS

We conducted a systematic review. A scientific literature search using PubMed, IEEE, Scopus, and Web of Science, up to March 2024, was performed to identify studies that reported the effectiveness of these technologies for gesture simulation in education and training on nursing and midwifery student anxiety.

RESULTS

Seven articles, involving a total of 428 participants, were included in this review. The findings indicate that XR can effectively reduce anxiety in midwifery and nursing education.

CONCLUSIONS

However, the limited number of studies highlights a research gap in the field, particularly in the area of mixed reality, which warrants further exploration.
Virtual streamer responsiveness and consumer purchase intention in live streaming commerce: Social presence as a mediator
Authors: Liu Xiaoli , Zhang Lei
Published: November 2024

This study investigated how virtual streamer responsiveness affects consumers' purchase intention through social presence in the context of live streaming commerce. We recruited 432 consumers who had watched virtual streamers in the context of live streaming commerce, and asked them
to complete a survey assessing responsiveness, social presence, and purchase intention. Our findings demonstrated that virtual streamer responsiveness had a positive impact on consumers' purchase intention in live streaming commerce, and that this relationship was mediated by social presence.
We discuss the theoretical and practical implications of these results.
On proving the robustness of algorithms for early fault-tolerant quantum computers
Authors: Kshirsagar Rutuja , Katabarwa Amara , Johnson Peter D.
Published: November 2024

The hope of the quantum computing field is that quantum architectures are able to scale up and realize fault-tolerant quantum computing. Due to engineering challenges, such ''cheap'' error correction may be decades away. In the meantime, we anticipate an era of ''costly'' error correction, or early fault-tolerant quantum computing. Costly error correction might warrant settling for error-prone quantum computations. This motivates the development of quantum algorithms which are robust to some degree of error as well as methods to analyze their performance in the presence of error. Several such algorithms have recently been developed; what is missing is a methodology to analyze their robustness. To this end, we introduce a randomized algorithm for the task of phase estimation and give an analysis of its performance under two simple noise models. In both cases the analysis leads to a noise threshold, below which arbitrarily high accuracy can be achieved by increasing the number of samples used in the algorithm. As an application of this general analysis, we compute the maximum ratio of the largest circuit depth and the dephasing scale such that performance guarantees hold. We calculate that the randomized algorithm can succeed with arbitrarily high probability as long as the required circuit depth is less than 0.916 times the dephasing scale.
LIGHTLIKE HYPERSURFACES OF AN INDEFINITE (α, β)-TYPE ALMOST CONTACT METRIC STATISTICAL MANIFOLD WITH AN (l, m)-TYPE CONNECTION
Authors: Bhatti Shagun , Kaur Jasleen
Published: November 2024

This paper aims to present the theory of hypersurfaces for a novel class of manifolds called an indefinite (α, β)-type almost contact metric statistical manifold or trans-Sasakian statistical manifold with an (l, m)-type connection. The study delves into the analysis of screen semi-invariant lightlike hypersurfaces within this framework. Specific conditions on the recurrent and Lie recurrent structure tensor field have been established. Additionally, illustrative examples are provided to enhance the comprehension of the introduced concept.
Clash of sciences: from Aristotle to the scientific revolution
Authors: Pires Guilherme Nunes
Published: November 2024

This book is an introductory guide to understanding a centuries-old and complex phenomenon: the transition from pre-modern science to the establishment of scientific thought in the early stages of modernity. This process, as we know, is neither linear nor peaceful. It is, above all, a turbulent phenomenon, marked by intense debates and clashes. All of this revolves around a question humanity has pondered since its beginnings: how can we coherently explain the reality around us? To answer this question, modern thinkers had to direct their efforts against Aristotle. This was because the Greek philosopher offered the most comprehensive and coherent explanation of the world, one that remained dominant for over eighteen centuries. The process of refuting Aristotle’s thought took centuries to materialize, beginning with the fundamental challenges posed by Islamic thinkers during the so-called Islamic Golden Age, between the 8th and 13th centuries. Islamic thinkers played a crucial role in enabling European Renaissance thinkers to begin the definitive challenge to the Aristotelian worldview, paving the way for the modern Scientific Revolution. It was within this context of everyone against Aristotle, combined with the fundamental elements of capitalism in its mercantile stage, that the Scientific Revolution flourished, laying the foundational pillars of modern science in its classical period (classical science).
REWARD SYSTEM FOR ACHIEVING MARKETING TARGETS ACCORDING TO ISLAMIC ECONOMIC LAW
Authors: Aksar Muhammad Azrial , Muhammad Iqbal
Published: November 2024

The Federal International Finance (Hence called PT.FIF) branch Banda Aceh city is a financing institution that facilitates the people to financing of their needs. This company collaborates with reseller shops and distributors, allowing customers to buy goods using an installment system. Partner stores act as facilitators between PT FIF and customers, helping to market financing products. This research examines the rewards or commissions given by PT FIF to partner stores based on their marketing performance, with a focus on whether they are in line with the ji'alah agreement (marketing achievement commission) from the Islamic Economic Law perspective. Using a normative-sociological approach and descriptive-analytical methods, this research aims to analyze the concept and practice of the ji'alah agreement in sharia financing marketing partnerships. The research results show that the practice of giving commissions by PT FIF to partner shops is generally in line with the principle of the ji'alah agreement. However, several aspects were found that needed adjustment to be more in line with sharia provisions, such as clarity in determining the commission amount and target achievement period. Research also reveals that partner shops' understanding of the ji'alah concept is still limited, so further education is needed to increase awareness of the sharia aspects of this partnership.
Selection of pulverized coal fuel components for blast furnaces based on the study of physico-chemical processes during their heating
Authors: Amdur A.M. , Fedorov S.A.
Published: November 2024

There is a global tendency to use technologies to save energy costs per unit of production in blast furnace production. This is due to the use of pulverized coal fuel (PCF) instead of part of the coke. Selecting the components of the PCF charge from a wide range of carbon-containing materials to expand the raw material base and minimize costs is an important task. One of these materials is peat. The aim of this work is to develop a methodology for the selection of pulverized coal fuel components based on a comprehensive study using peat milling as an example. Industrial experiments were carried out in an operating metallurgical plant. The particle size of PCF is no more than 70-100 ?m. Thermal destruction of peat in an inert atmosphere is responsible for 59% of the mass loss. The maximum destruction rate is 2.69%/min compared to 2.0%/min for long?burning coal. The release of volatiles starts at 425?C for lean coal, 300?C for brown coal and 220?C for peat. The destruction of the coal also leads to an increase in the specific surface area. It increases from 2.04 to 161.3 m2/g for brown coal. Combustion accounts for about 27% of the total loss of peat mass; this value is twice as high for longburning coal. The total thermal effect for coal ranges from 15510 to 17751 kJ/kg, for peat from 12816 to 14148 kJ/kg. The pulverized coal fuel produced during the pilot phase was blown into an industrial blast furnace. The equipment of the PCF feed path, the poppet mill, worked stably without changing the operating parameters. There were no deviations in the operation of the blast furnace. The introduction of peat reduced the consumption of lean coal. The consumption of PCF and natural gas has practically not changed.