bluuebunny/crossref_metadata_2025_split huggingface dataset:
Synthesis of greenish-yellow fluorescent copper nanocluster for the selective and sensitive detection of fipronil pesticide in vegetables and grain samples
Authors: Ghinaiya Nirav Vajubhai , Mehta Vaibhavkumar N . , Jha Sanjay , Park Tae Jung , Kailasa Suresh Kumar 
Published: September 2023

Abstract
A new synthetic route for Avaram senna (A. Senna) and polyvinylpyrrolidone (PVP) based high-luminescent greenish-yellow fluorescent copper nanocluster (PVP@A. Senna-Cu NCs) is introduced in this work. A. Senna plant extract mainly contains phytochemicals are glycoside, sugar, saponins, phenols, and terpenoids that show good pharmacological activities such as anti-inflammatory, antioxidant, and antidiabetic. PVP is a stable and biocompatible polymer that is used as a stabilizing agent for the synthesis of PVP@A. Senna-Cu NCs. The size, surface functionality, and element composition of the fabricated Cu NCs was confirmed by various analytical techniques. The resulting copper nanoclusters (Cu NCs) exhibit significant selectivity towards fipronil. These PVP@A. Senna-Cu NCs show selective detection of fipronil pesticides through the aggregation-caused quenching mechanism. As synthesized sensor shows linear concentration ranging from 3 to 30 µM with a detection limit at a nanomolar scale (65.19 nM). More importantly, PVP@A. Senna-Cu NCs successfully apply for the determination of fipronil in vegetables and grain samples.
Interleukin-36 is overexpressed in human sepsis and IL-36 receptor deletion aggravates lung injury and mortality through epithelial cells and fibroblasts in experimental murine sepsis
Authors: Wang Huachen , Wang Meixiang , Chen Junlan , Hou Hongda , Chen Bing , Tang Hua 
Published: September 2023

Abstract
Background: Sepsis is defined as a life-threatening syndrome caused by an unbalanced host response to infection. The role of interleukin (IL)-36 cytokines binding to the IL-36 receptor (IL-36R) in host response during sepsis remains unknown.
Methods: Serum IL-36 level was measured in 47 septic patients sampled on the day of intensive care unit (ICU) and emergency department admission, 21 non-septic ICU patient controls, and 21 healthy volunteers. In addition, the effects of IL-36R deletion on host inflammatory response in cecal ligation and puncture (CLP)-induced polymicrobial sepsis was determined.
Results: On the day of ICU and emergency department admission, the patients with sepsis showed a significant increase in serum IL-36 levels compared with ICU patient controls and healthy volunteers, and the serum IL-36 levels were related to the severity of sepsis. Non-survivors of septic patients displayed significantly lower serum IL-36 levels compared with survivors. A high serum IL-36 level in ICU and emergency department admission was associated with 28-day mortality, and IL-36 was found to be an independent predictor of 28-day mortality in septic patients by logistic regression analysis. Furthermore, IL-36R deletion increased lethality in CLP-induced polymicrobial sepsis. Septic mice with IL-36R deletion had higher bacterial load and demonstrated more severe multiple organ injury (including lung, liver, and kidney) as indicated by clinical chemistry and histopathology. Mechanistically, IL-36R ligands released upon lung damage activated IL-36R+lung fibroblasts thereby inducing expression of the antimicrobial protein lipocalin 2 (LCN2) . Moreover, they induced the apoptosis of lung epithelial cells (IECs).
Conclusions: Septic patients had elevated serum IL-36 levels, which may correlate with disease severity and mortality. In experimental sepsis, we demonstrated a previously unrecognized role of IL-36R deletion in increasing lethality.
Examining the Reliability of Brain Age Algorithms Under Varying Degrees of Subject Motion
Authors: Hanson Jamie , Adkins Dorthea , Zhou Peiran 
Published: September 2023

Abstract
Brain age, defined as the predicted age of an individual’s brain based on neuroimaging data, shows promise as a biomarker for healthy aging and age-related neurodegenerative conditions. However, noise and motion artifacts during MRI scanning may introduce systematic bias into brain age estimates. This study leveraged a novel dataset with repeated structural MRI scans from participants during no motion, low motion, and high motion conditions. This allowed us to evaluate the impact of motion artifacts for brain age derived from 5 commonly used algorithms. Intraclass correlation coefficients, Bland-Altman analyses, and linear mixed-effect models were used to assess reliability. Results demonstrated variable resilience to motion artifacts depending on the algorithm utilized. The DeepBrainNet and pyment algorithms showed the greatest invariance to motion conditions, with high intraclass correlations and minimal mean differences on Bland-Altman plots between motion and no motion scans. In contrast, the brainageR algorithm was most affected by motion, with lower intraclass correlations and a high degree of bias. Findings elucidate the critical need for careful benchmarking of brain age algorithms on datasets with controlled motion artifacts in order to rigorously assess suitability for clinical deployment. Moreover, targeted efforts to improve model robustness to image quality and motion are warranted to strengthen the validity of brain age as a predictive biomarker. Overall, this study highlights open questions regarding the sensitivity of different brain age algorithms to noise and movement and motivates future optimization to derive biologically-meaningful brain aging metrics.
Building a Maternal and Child Cohort Amidst Lebanon’s Socioeconomic Collapse: Preliminary Results and Navigating Research Challenges
Authors: Kaddoura Rima , Elbejjani Martine , Tamim Hani , Mahfoud Ziyad R , Salameh Pascale , Mirza Fadi , Charafeddine Lama 
Published: September 2023

Abstract
In recent years, research has shown how connected systems influence health. Lebanon's financial predicament and political unrest have created a multifaceted crisis landscape with shortage in essential services. Social determinants like socioeconomic status and social support along with maternal mental health have been linked to preterm birth. Preterm infants are particularly disadvantaged, needing comprehensive, multidisciplinary support for optimal development. This study delves into exploring the impact of the social environment on preterm infants' developmental outcomes amidst Lebanon’s complex crises. Exploring the links between determinants of health and preterm outcomes gains urgency in this context. Lebanon's healthcare and economic deterioration over the past years is expected to amplify these risks, emphasizing the need to understand their impacting on both birth outcomes and infants’ cognitive development.
We aimed to establish a prospective cohort study to evaluate the impact of the social environment on birth and developmental outcomes. This multicenter prospective cohort faced challenges that can be categorized into recruitment, follow-up, and in data collection challenges. Emigration of Lebanese citizens affected recruitment, reducing the pool of eligible participants. Birth rates decreased due to the socioeconomic and political crises. Follow-up assessments were hindered by rising healthcare costs and obstacles with transportation.
Whilst the study is still ongoing, preliminary results highlight the importance of the social environment and contextual conditions. The WHO Quality of Life Questionnaire scores of the preterm infants’ mothers were significantly lower than those of term infants across physical, psychological, and social domains. Stress levels were noted to be high across most of the mothers. Economic challenges decreased the purchasing power and limited income measurement in the cohort.
Findings from this study aim to provide critical data on the crises’ effect on social determinants, child developmental outcomes, and their relationship. Results can have important implications for informed resource allocation and timely interventions.
Nurturing Multiculturalism in Iranian EFL Teacher Education: An In-Depth Scrutiny of Experts’ and Teachers’ Perceptions
Authors: Salimi Esmaeel Ali , Rad Mostafa Rahimi 
Published: September 2023

Abstract

Given that Iran is the host of a variety of cultures characterized with diverse languages, viewpoints, and customs, the empty place of multiculturalism in its ELT system is deeply felt. The present study sought to tackle multiculturalism in EFL teacher education in the context of Iran by exploring Iranian EFL experts’ and teachers’ perceptions. The participants of this study consisted of the two groups. The first group included 15 male and female multicultural education experts. The second group of the participants included of 60 (30 males and 30 females) Iranian EFL teachers. To collect the required data, a demographic information scale, two semi-structured interviews and an audio-reflective journal were used. Data analysis was done via qualitative thematic analysis by MAXQDA. As revealed by the results, several themes were extracted as the components of the multicultural EFL teacher education, as uncovered by the experts’ views some of which include Eliminating Race-Related Inequalities, Eliminating Gender-Related Inequalities, and Eliminating Social Class-Related Inequalities. Moreover, the Necessary Communicative Skills, Positive Thinking, Difference Acceptance, and Multicultural Content, Various Evaluation Methods were found as some of the extracted EFL teachers’ perceptions of multicultural EFL teacher education. The findings have some implications for different groups of stakeholders including EFL teachers, teacher educators and teacher education material developers who can take useful insights from the findings in teaching EFL, educating student-teachers and developing future curricula, respectively.
The real-time detection method for coal gangue based on YOLOv8s-GSC
Authors: Chen Kaiyun , Du Bo , Wang Yanwei , Wang Guoxin , He Junxi 
Published: September 2023

Abstract
To address the issues of complex algorithm models, poor accuracy, and low real-time performance in the coal industry's coal gangue sorting, a lightweight real-time detection method called YOLOv8s-GSC is proposed based on the characteristics of coal gangue. This method incorporates the ghost module into the YOLOv8s backbone network to reduce the network's parameter count. Additionally, a slim-neck model is used for feature fusion, and a coordinate attention module is added to the backbone network to enhance the network's feature representation capability. The experimental results show: 1) The average precision of the YOLOv8s-GSC model is 91.2%, which is a 0.6% improvement over the YOLOv8s model. The parameters and floating-point computation are reduced by 36.0% and 41.6%, respectively. 2) Compared to other models such as FasterRCNN-resnet50, SSD-VGG16, YOLOv5s, YOLOv7, and YOLOv8s-Mobilenetv3, the average precision is improved to varying degrees. 3) The YOLOv8s-GSC model achieves a detection speed of 115FPS, meeting the real-time requirements for coal gangue detection. In conclusion, the proposed YOLOv8s-GSC model provides a lightweight, real-time, and efficient detection method for coal gangue separation in the coal industry, demonstrating high practical value.
Understanding the impact of covariates on the classification of implementation units for soil-transmitted helminths control: A case study from Kenya
Authors: Puranik Amitha , Diggle Peter J. , Odiere Maurice R. , Gass Katherine , Kepha Stella , Okoyo Collins , Mwandawiro Charles , Wakesho Florence , Omondi Wycliff , Sultani Hadley Matendechero , Giorgi Emanuele 
Published: September 2023

Abstract
Background
Soil-transmitted helminthiasis (STH) is a parasitic infection that predominantly affects impoverished regions. Model-based geostatistics (MBG) has been established as a set of modern statistical methods that enable mapping of disease risk in a geographical area of interest. We investigate how the use of remotely sensed covariates can help to improve the predictive inferences on STH prevalence using MBG methods. In particular, we focus on how the covariates impact on the classification of areas into distinct class of STH prevalence.
Methods
This study used secondary data obtained from a sample of 1551 schools in Kenya, gathered through a combination of longitudinal and cross-sectional surveys. We compare the performance of two geostatistical models: one that does not make use of any spatially referenced covariate; and a second model that uses remotely sensed covariates to assist STH prevalence prediction. We also carry out a simulation study in which we compare the performance of the two models in the classifications of areal units with varying sample sizes and prevalence levels.
Results
The model with covariates generated lower levels of uncertainty and was able to classify 88 more districts into prevalence classes than the model without covariates, which instead left those as “unclassified”. The simulation study showed that the mode with covariates also yielded a higher proportion of at least 40% for all sub-counties.
Conclusion
Covariates can substantially reduce the uncertainty of the predictive inference generated from geostatistical models. Using covariates can thus contribute to the design of more effective STH control strategies by reducing sample sizes without compromising the predictive performance of geostatical models.
Unraveling the intercellular communication disruption and key pathways in Alzheimer's disease: An integrative study of single-nucleus transcriptomes and genetic association
Authors: Liu Andi , Fernandes Brisa S , Citu Citu , Zhao Zhongming 
Published: September 2023

Abstract

Background Recently, single-nucleus RNA-seq (snRNA-seq) analyses have revealed important cellular and functional features of Alzheimer's disease (AD), a prevalent neurodegenerative disease. However, our knowledge regarding intercellular communication mediated by dysregulated ligand-receptor (LR) interactions remains very limited in AD brains. Methods We systematically assessed the intercellular communication networks by using a discovery snRNA-seq dataset comprising 69,499 nuclei from 48 human postmortem prefrontal cortex (PFC) samples. We replicated the findings using an independent snRNA-seq dataset of 56,440 nuclei from 18 PFC samples. By integrating genetic signals from AD genome-wide association studies (GWAS) summary statistics and whole genome sequencing (WGS) data, we prioritized AD-associated Gene Ontology (GO) terms containing dysregulated LR interactions. We further explored drug repurposing for the prioritized LR pairs using the Therapeutic Targets Database. Results We identified 316 dysregulated LR interactions across six major cell types in AD PFC, of which 210 pairs were replicated. Among the replicated LR signals, we found globally downregulated communications in astrocytes-to-neurons signaling axis, characterized, for instance, by the downregulation of APOE-related and Calmodulin (CALM)-related LR interactions and their potential regulatory connections to target genes. Pathway analyses revealed 60 GO terms significantly linked to AD, highlighting Biological Processes such as ‘amyloid precursor protein processing’ and ‘ion transmembrane transport’, among others. We prioritized several drug repurposing candidates, such as cromoglicate, targeting the identified dysregulated LR pairs. Conclusions Our integrative analysis identified key dysregulated LR interactions in a cell type-specific manner and the associated GO terms in AD, offering novel insights into potential therapeutic targets involved in disrupted cell-cell communication in AD.
A review of using duckweed (Lemnaceae) in fish feeds
Authors: Minich Jeremiah J , Michael Todd P 
Published: September 2023

Abstract

One of the primary sustainability challenges in aquaculture is replacing fish meal with plant based ingredients in aquafeeds. Plants are not optimal however due to low protein content and antinutritional factors which can cause gut dysbiosis. Duckweed (Lemnaceae) is a family of aquatic plants with high protein content and has been used successfully for various types of animal feeds. In this systematic review and meta analysis of 58 papers, we summarize the extent by which duckweed has been used in fish production including the species of fish tested, the grow out stage of fish, and method of application. Duckweed studies spanned a total of 18 species of fish (16 freshwater and 2 marine) that collectively are valued at 263 billion USD annually, and comprise 28% of total aquaculture production by mass. The average experiment length was 72 days (S.D. 42) at the fingerling life stage. Duckweed was fed to fish through live grazing, dried, and pelleted forms with 20% inclusion as the most common formulation. The Lemna spp.were the most commonly used for feeds, dominated by L. minor, L. gibba, and unknown Lemna species. Spirodela polyrhiza was the second most common. Duckweed inclusion levels between 15-30% was associated with positive outcomes on fish growth and feed conversion ratio without any negative impact on survival rates. Most duckweed species, especially from Wollfiella have not been tested as a fish feed, but should be explored whereas most studies focused on freshwater fishes rather than marine.
Reclaiming Narratives of Empowerment around Black Maternal Health: A Strengths-Based, Community-Informed Focus Group Study
Authors: Mollard Elizabeth K , Elya Anabelle , Riordan Elizabeth , Salahshurian Erin , Moore Tiffany , Maloney Shannon , Balas Michele C. , Shah Neel , Owens Deirdre Cooper 
Published: September 2023

Abstract

Background:
Research on Black maternal populations often focus on deficits that can reinforce biases against Black individuals and communities. To counteract this, the research landscape must shift towards a strengths-based approach, focusing on the protective assets of Black individuals and communities. To inform the design of a future clinical trial focused on reducing Black maternal health disparities, this study engaged the local Black community using a strengths-based approach to explore the assets of Black maternal populations.
Methods:
Guided by the Theory of Maternal Adaptive Capacity, we conducted three purposive focus group sessions with Black adult community members. The focus groups were semi-structured to cover specific topics, including (1) strengths of the local community, (2) strengths specific to pregnant community members, (3) how the strengths of the community members can support pregnant individuals, and (4) how the strengths of pregnant community members can facilitate a healthy pregnancy. Focus group interviews were analyzed by using thematic content analysis.
Results:
Three focus group sessions were conducted with sixteen female individuals identifying as Black or African American. Central themes include (1) The power of pregnancy and motherhood in Black women; (2) Challenging negative perceptions and media representation of Black mothers; (3) Recognizing history and reclaiming cultural traditions surrounding birth; and (4) Community as the foundation to Black motherhood.
Conclusion:
Black community members identified powerful themes on Black maternal health through a strengths-based lens. These focus groups fostered important relationships with the Black community, elucidated possible solutions to improving Black women’s health and wellness, and offered direction on our research design and intervention.
Methods for the Clinical Validation of Digital Endpoints: Protocol for a Scoping Review Abstract
Authors: Rego Sílvia , Henriques Ana Rita , Serra Sofia Silvério , Costa Teresa , Rodrigues Ana Maria , Nunes Francisco 
Published: September 2023

Background
Clinical trials often use digital technologies to collect data continuously outside the clinic and use the derived digital endpoints as trial endpoints. Digital endpoints are also being developed to support diagnosis, monitoring, or therapeutic interventions in clinical care. However, clinical validation stands as a significant challenge, as there are no specific guidelines orienting the validation of digital endpoints.

Objective
This paper presents the protocol for a scoping review that aims to map the existing methods for the clinical validation of digital endpoints.

Methods
The scoping review will comprise searches from the electronic literature databases MEDLINE (PubMed), Scopus (including conference proceedings), Embase, IEEE (Institute of Electrical and Electronics Engineers) Xplore, ACM (Association for Computing Machinery) Digital Library, CENTRAL (Cochrane Central Register of Controlled Trials), Web of Science Core Collection (including conference proceedings), and Joanna Briggs Institute Database of Systematic Reviews and Implementation Reports. We will also include various sources of gray literature with search terms related to digital endpoints. The methodology will adhere to the Joanna Briggs Institute Scoping Review and the Guidance for Conducting Systematic Scoping Reviews.

Results
A search for reviews on the existing evidence related to this topic was conducted and has shown that no such review was previously undertaken. This review will provide a systematic assessment of the literature on methods for the clinical validation of digital endpoints and highlight any potential need for harmonization or reporting of methods. The results will include the methods for the clinical validation of digital endpoints according to device, digital endpoint, and clinical application goal of digital endpoints. The study started in January 2023 and is expected to end by December 2023, with results to be published in a peer-reviewed journal.

Conclusions
A scoping review of methodologies that validate digital endpoints is necessary. This review will be unique in its breadth since it will comprise digital endpoints collected from several devices and not focus on a specific disease area. The results of our work should help guide researchers in choosing validation methods, identify potential gaps in the literature, or inform the development of novel methods to optimize the clinical validation of digital endpoints. Resolving these gaps is the key to presenting evidence in a consistent way to regulators and other parties and obtaining regulatory acceptance of digital endpoints for patient benefit.

International Registered Report Identifier (IRRID)
PRR1-10.2196/47119
MATERNAL OBESITY AS A PREDICTIVE MARKER FOR ADVERSE PREGNANCY OUTCOME: A CASE–CONTROL STUDY
Authors: JYOTI SAINI  , SAWAI SINGH LORA  , MONICA CHETANI GUPTA  
Published: September 2023

Objective: Obesity in pregnancy is an important risk factor for adverse maternal and neonatal outcomes. In the past, various studies have compared adverse pregnancy-related outcomes between obese and normal body mass index (BMI) mothers. In the present study, we aimed to examine the impact of obesity class on maternal and perinatal outcomes. The aim of the study was to evaluate the association between maternal adiposity and pregnancy outcomes.
Methods: A prospective and cross-sectional study conducted at Zanana Hospital SMS Medical College Jaipur on 68 antenatal obese/overweight woman (BMI>25 kg/m2) and 68 control antenatal woman (BMI<25 kg/m2) with singleton pregnancies.
Results: The incidence of gestational diabetes was 8.82% and 2.94%, respectively, in obese and control group. The incidence of pre-eclampsia was 14.70% and 5.88% in obese and control group. The incidence of gestational hypertension was 8.82% and 5.88% in obese and control group. The cesarean delivery rates were higher in obese group (55.88%) than control group (32.35%). About 20.58% of babies born to obese women and 8.82% of babies born to control were admitted in NICU (p<0.05). The incidence of pre-term delivery, fetal abnormality, and macrosomia was higher in obese group as compared to control group.
Conclusions: Obesity increases the risk of many adverse maternal and neonatal outcomes. Therefore, appropriate measures must be taken to reduce obesity incidence in women of reproductive age to circumvent the adverse maternal and neonatal outcomes associated with obesity.