bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of Perinatal HIV Exposure and Infection on Salivary Properties Among Nigerian Children
Authors: Onyia Nonso Emmanuel , Osagie Esosa , Akhigbe Paul , Idemudia Nosakhare L , Obuekwe Ozoemene , Omoigberale Augustine , Richards Vincent , Coker Modupe 
Published: February 2024

Abstract
Background: There is growing evidence that perinatal HIV infection and exposure affect salivary pH and flow rate in children in most parts of the world, but not against the background of caries and the African demographic. This study aimed to evaluate the impact of HIV infection as well as exposure on salivary properties and their influence upon the dental caries experience among school-aged children in Nigeria.
Method: This cross-sectional study assessed the salivary flow rates and salivary pH of HIV infected and exposed school-aged (4-11) children receiving care at a Nigerian tertiary hospital. A total of 266 consenting participants which comprised of three groups as follows: 1) HIV Infected (HI) (n = 87), 2) HIV Exposed and Uninfected (HEU) (n=82) and 3) HIV Unexposed and Uninfected (HUU) (n=97) were recruited for the study. Questionnaires completed by parents/guardians were used for data collection. Three calibrated dentists performed oral examinations for dental caries. International Caries Detection and Assessment Scores (ICDAS) was used and presented as dmft/DMFT. Salivary pH was measured using MColourpHastTM pH indicator strips, while salivary flow rate was determined by collecting unstimulated whole saliva using the suction method. Data analysis relied on comparative statistics to determine the correlation between HIV exposure and infection on salivary pH and flow rates.
Result: Across the groups, (HI, HEU, and HUU) mean pH of the HI was significantly less than that of HEU and HUU. Similarly, there was a statistically significant difference in the SFR across the three groups (p = 0.004). Other variables such as gender, age and oral hygiene status expressed by the gingival inflammatory scores had no significant influence on the pH and SFR of study participants. There was a rather unexpected positive correlation of DMFT of HI and HEU groups with increasing salivary flow rate; though, the relationship was weak and not significant.
Conclusion: Perinatal HIV exposure and infection significantly impact salivary pH and flow rate among school-aged children in Nigeria. The findings of this study imply that HIV infection influenced the salivary pH, while HIV maternal exposure (without infection) impacted salivary flow rates when compared to the controls.
METTL14 contributes to the progression of nasopharyngeal carcinoma through regulating the stability of AOC1 mRNA
Authors: Xue Zhen , Hu Changan , Song Shengguan , Zhao Shanglong 
Published: February 2024

Abstract
Background: Nasopharyngeal carcinoma (NPC) is a malignant epithelial tumor of the nasopharyngeal mucosa with a high incidence rate all over the world. Methyltransferase-like 14 (METTL14) is a major RNA N6-adenosine methyltransferase implicated in tumor progression by regulating RNA function. This study is designed to explore the biological function and mechanism of METTL14 in NPC.
Methods: METTL14 and Amine oxidase copper containing 1 (AOC1)expression were detected by real-time quantitative polymerase chain reaction (RT-qPCR). The protein levels of METTL14, AOC1, Cyclin D1, B-cell lymphoma-2 (Bcl-2), and N-cadherin were measured using western blot. Cell proliferation, cycle progression, apoptosis, migration, and invasion were assessed using 5-ethynyl-2’-deoxyuridine (EdU), Colony formation, flow cytometry, wound scratch, and transwell assays. The interaction between METTL14 and AOC1 was verified using RNA immunoprecipitation (RIP), methylated RNA immunoprecipitation (MeRIP), and dual-luciferase reporter assays. The biological role of METTL14 on NPC tumor growth was examined by the xenograft tumor model in vivo.
Results: METTL14 and AOC1were highly expressed in NPC tissues and cells. Moreover, METTL14 knockdown might block NPC cell proliferation, migration, invasion, and induce cell apoptosis in vitro. In mechanism, METTL14 might enhance the stability of AOC1 mRNA via m6A methylation. METTL14 silencing might repress NPC tumor growth in vivo.
Conclusion: METTL14 might boosted the development of NPC cells partly by regulating the stability of AOC1 mRNA, which provided a promising therapeutic target for NPC treatment.
Medical cannabis use in Australia seven years after legalisation: findings from the online Cannabis as Medicine Survey 2022 (CAMS-22)
Authors: MILLS LLEWELLYN , ARNOLD JONATHON C. , SURAEV ANASTASIA , ABELEV SARAH V. , ZHOU CILLA , ARKELL THOMAS R. , McGREGOR IAIN S. , LINTZERIS NICHOLAS 
Published: February 2024

Abstract

Background Cannabis was legalised for medical purposes in 2016. Uptake was initially slow, but since 2019 there has been a large increase in the number of Australians who have been prescribed cannabis for medical reasons. Yet a significant number of consumers continue to treat their medical conditions via illicitly-sourced cannabis. Little is known about how these two groups of medical cannabis consumers differ. Methods The anonymous Cannabis-As-Medicine Survey 2022 (CAMS-22) was available for completion online from December 2022 to April 2023 to adult Australians who had used cannabis to treat a medical condition in the previous year. Recruitment occurred through social media, consumer forums, and medical practices. Questions included demographic characteristics, patterns of cannabis use, conditions treated, and self-rated efficacy. Results Of the 3323 respondents included in these analyses, 2352 {73%) mainly used prescribed medical cannabis, 871 (27%) mainly used illicit. Prescribed users were significantly more likely than illicit users to have had their health condition diagnosed (OR = 1.7, 95%CI: 1.3, 2.2) and to consume their cannabis via oral (OR = 1.9; CI: 1.5, 2.4) or vaporised (OR = 5.2; CI: 4.0, 6.8) routes, and were significantly less likely to have used cannabis non-medically before medical use (OR = 0.6, CI: 0.5, 0.7) and consume cannabis via smoked routes (OR = 0.2, CI: 0.1, 0.2). The most common conditions among both prescribed and illicit users were pain (37%), mental health (36%), and sleep (15%) conditions. Prescribed users were significantly more likely to use cannabis to mainly treat a pain (OR = 1.3; CI: 1.1, 1.5) or sleep condition (OR = 1.4; CI: 1.1, 1.7) and less likely to treat a mental health condition (OR = 0.8; CI: 0.7, 0.9). There were no between-group differences in efficacy with over 96% saying medical cannabis had improved their symptoms. Conclusions From a harm-reduction perspective there is much to recommend prescribed medical cannabis; it tends to be less harmful and has fewer side-effects than illicit and does not risk consumers being exposed to the criminal justice system. Of concern however is the increased willingness of prescribers to prescribe for indications for which there is no evidence of efficacy, such as mental health and sleep conditions.
Comprehensive wheat straw processing with deep eutectic solvent to deliver reducing sugar and lignin streams
Authors: Piedade Patrícia , Venkat Veshal , Al-Shwafy Khaled , Aregawi Mearg , Dudek Gabriela , Zygadło Mateusz , Lukasik Rafal 
Published: February 2024

Abstract

Deep eutectic solvents are potential candidates for being used in chemistry and chemical engineering as novel solvents. In this work, the deep eutectic solvent composed of choline chloride and acetic acid was studied for its use in wheat straw fractionation, as a greener and low-cost substitute for conventional solvents. The pretreated biomass was assessed concerning the lignin and glucan content. The processed wheat straw, under optimized time and temperature conditions, of 3 h 47 min and 139.6°C, contained as much as 42.5 ± 0.42 wt.% and 38.59 ± 1.26 wt.% of glucan and lignin contents, respectively. The need for biomass washing after the pretreatment with deep eutectic solvents and before the enzymatic hydrolysis step was also evaluated. The obtained enzymatic hydrolysis results, i.e. glucan to glucose yield of 27.13 ± 0.25 vs. 25.73 ± 0.08 for washed or unwashed biomass correspondingly, are equally good substrates. Hence, biomass washing proved to be unnecessary.
The Chilean exposome-based system for ecosystems project: Protocol for the development of an informatics platform for national data integration
Authors: Matus Patricia , Sepúlveda-Peñaloza Alejandro , Page Keneth , Rodriguez Claudia , Cárcamo Marcela , Bustamante Francisco , Garrido Marcela , Urquidi Cinthya 
Published: February 2024

Abstract
The double burden of diseases and scarce resources in developing countries highlight the need to change conceptualization of health problems and development of translational research. Contrary to the traditional paradigm focused on genetics, the exposome approach proposed in 2005 that complements the genome is an innovative theory. It involves a holistic approach to understanding the complexity of the interactions between the human being’s environment throughout their life and health. Herein, we have describe the conceptual model and technological system development of the Chilean exposome-based system for ecosystems (CHiESS). It is an intelligent and dynamic system for human exposome research, which leverages available administrative data routinely collected by national agencies, in clinical records, and by biobanks. Based on the ecological theory and one-health ecosystem approach, CHiESS considers a multilevel exposure for exposome operationalization, including the ecosystem, community, population, and individual levels. CHiESS will include four consecutive stages for development into an informatic platform: 1) environmental data integration and harmonization system, 2) clinical and omics data integration, 3) advanced analytical algorithm development, and 4) visualization interface development and targeted population-based cohort recruitment. The ChiESS platform aims to integrate and harmonize available secondary administrative data and provide a complete geospatial mapping of the external exposome. Additionally, it aims to analyze complex interactions between environmental stressors of the ecosystem and molecular processes of the human being and their effect on human health. Moreover, by identifying exosome-based hotspots, CHiESS allows the targeted and efficient recruitment of population-based cohorts for translational research and impact evaluation.
Assessment of the impact of the Kenya Government fertilizer subsidy on the performance of domestic private sector fertilizer trade
Authors: Njagi Timothy , Riungu Claris , Opiyo Joseph , Mwadime Robert KN , Aloo Seth Yake 
Published: February 2024

Abstract
Introduction
Fertilizer subsidy was reinstated by the Kenyan government in 2022 to cushion farmers against increasing fertilizer prices on the domestic market caused by shocks to the global fertilizer supply chain. This study assessed the efficiency and effectiveness of the distribution process; the impact of the fertilizer subsidy on the domestic private sector trade; and proposed optional delivery mechanisms for better outcomes to actors in fertilizer supply chain.
Methodology
The study used mixed method approaches; secondary data, qualitative survey of key stakeholders and quantitative survey of beneficiaries.
Results
Findings show that the private sector and the government incurred about the same cost of delivering the fertilizer to last-mile agro-dealer. The absorption of the subsidised fertilizer increased over time especially when the fertiliser price reduced to Ksh 2,500 per 50kg bag. However, farmers faced challenges in accessing the fertiliser such as long distances and increased transport costs to the nearest NCPB depots, late delivery of subsidised fertilizer; mismatch between fertilizer types demanded by farmers. Distribution of the subsidised fertilizer was skewed towards the high potential maize growing region of the country. Certain farmers received subsidized fertilizer from both the NFSP and the county, while other farmers received did not receive since the NFSP and the county farmer database were not combined resulting to uneven distribution of fertiliser.
Conclusions
While all stakeholders viewed the decision to subsidise fertilizer favourably, the model used was suboptimal. The discounted fertiliser is not easily accessible because to high transportation costs, long queues at collection depots, and a delayed process during fertiliser pickup. To enhance access, the government should incorporate the private sector in distributing subsidised fertilizer. The NCPB storage facilities should be centrally positioned to facilitate farmers' quick access to subsidised fertiliser. Timely fertilizer delivery, proper fertilizer distribution, and subsidy predictability are essential for achieving the goal of increased productivity.
A comparative study of zero-shot inference with large language models and supervised modeling in breast cancer pathology classification
Authors: Sushil Madhumita , Zack Travis , Mandair Divneet , Zheng Zhiwei , Wali Ahmed , Yu Yan-Ning , Quan Yuwei , Butte Atul 
Published: February 2024

Abstract
Although supervised machine learning is popular for information extraction from clinical notes, creating large, annotated datasets requires extensive domain expertise and is time-consuming. Meanwhile, large language models (LLMs) have demonstrated promising transfer learning capability. In this study, we explored whether recent LLMs can reduce the need for large-scale data annotations. We curated a manually labeled dataset of 769 breast cancer pathology reports, labeled with 13 categories, to compare zero-shot classification capability of the GPT-4 model and the GPT-3.5 model with supervised classification performance of three model architectures: random forests classifier, long short-term memory networks with attention (LSTM-Att), and the UCSF-BERT model. Across all 13 tasks, the GPT-4 model performed either significantly better than or as well as the best supervised model, the LSTM-Att model (average macro F1 score of 0.83 vs. 0.75). On tasks with a high imbalance between labels, the differences were more prominent. Frequent sources of GPT-4 errors included inferences from multiple samples and complex task design. On complex tasks where large annotated datasets cannot be easily collected, LLMs can reduce the burden of large-scale data labeling. However, if the use of LLMs is prohibitive, the use of simpler supervised models with large annotated datasets can provide comparable results. LLMs demonstrated the potential to speed up the execution of clinical NLP studies by reducing the need for curating large annotated datasets. This may increase the utilization of NLP-based variables and outcomes in observational clinical studies.
Smartphone Use in the Management of Neurological Emergencies: A Simulation-based Study
Authors: Pergakis Melissa B. , Ali Afrah A. , Chang WanTsu Wendy , Neustein Benjamin , Albin Casey , Aysenne Aimee , Tisherman Samuel A. , Morris Nicholas A. 
Published: February 2024

Abstract

Background and Objectives
Smartphone use in medicine is nearly universal despite a dearth of research assessing utility in clinical performance. We sought to identify and define smartphone use during simulated neuro-emergencies.
Methods
In this retrospective review of a prospective, observational, single-center simulation-based study, participants, ranging from sub-interns to attending physicians and stratified by training level (novice, intermediate, and advanced) managed a variety of neurological emergencies. The primary outcome was frequency and purpose of smartphone use. Secondary outcomes included success rate of smartphone use and performance (measured by completion of critical tasks) of participants who used smartphones vs. those that did not. In subgroup analyses we compared outcomes across participants by level of training using t-tests and Chi-square statistics.
Results
One hundred and three participants completed 245 simulation scenarios. Smartphones were used in 109 (45%) simulations. Of participants using smartphones, 102 participants looked up medication doses, 52 participants looked up management guidelines, 11 participants looked up hospital protocols, and 13 participants used smartphones for assistance with an exam scale. Participants found the correct answer 73% of the time using smartphones. There was an association between participant level and smartphone use with intermediate participants being more likely to use their smartphones than novice or advanced participants, 53% vs. 29% and 26%, respectively (p < .05). Of the intermediate participants, those who used smartphones did not perform better during the simulation scenario than participants who did not use smartphones (smartphone users’ mean score [standard deviation (SD)] = 12.3 (2.9) vs. non-smartphone users’ mean score (SD) = 12.9 (2.7), p = .85).
Discussion
Participants commonly used smartphones in simulated neuro-emergencies but use didn’t confer improved clinical performance. Less experienced participants were the most likely to use smartphones, were less likely to arrive at correct conclusions, and thus are the most likely to benefit from an evidence-based smartphone application for neuro-emergencies.
Exploring Microbial Signatures in Individuals with Prolactin-Secreting Pituitary Adenoma: Unraveling Microbiome Variations and Underlying Markers
Authors: Jiang Haixiao , Yang Fang , Zhang Xiaoli , Fang Huie , Qiu Tao , Peng Aijun 
Published: February 2024

Abstract

Background
Alterations in the intestinal flora composition can influence host inflammation, metabolism, and immune response. While extensive research has explored gut dysbiosis in various pituitary adenomas (PA), the changes in gut microbiota composition and their correlations with clinical parameters in prolactin-secreting pituitary adenoma (PPA) patients remain unknown. This study investigates these alterations and associations and explores microbial markers for PPA diagnosis.
Methods
A total of 101 participants were enrolled, comprising 72 PA patients (31 with prolactin-secreting adenomas and 41 with non-functioning adenomas, i.e., PPA and NFPA groups) and 29 age and sex-matched healthy controls (HC). Utilizing 16S rRNA gene amplicon sequencing, we examined the gut microbiota community in the PPA group and investigated its associations with clinical characteristics.
Results
Our results revealed significantly reduced microbial ecosystem richness and evenness in PPA patients compared to healthy controls. The PA group, especially PPA, exhibited substantial alterations in gut microbiota structure, including increased abundance of gram-negative pathogenic bacteria such as Desulfovibrio and Enterobacter, and decreased levels of probiotic bacteria like Bifidobacterium. We also identified significant positive correlations between PPA-enriched bacteria and serum lipid levels. A biomarker panel (including Bifidobacterium, Dorea, Blautia, Morganella, Desulfovibrio, and Enterobacter) demonstrated good performance in differentiating between PA patients and healthy controls (AUC: 0.959). Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis unveiled dysregulations in fundamental physiological pathways, particularly lipid metabolism, within the PPA group.
Conclusions
Our findings suggest that PA patients, particularly those with PPA, exhibit distinct host-microbe interactions compared to healthy controls. Notably, the intestinal flora, particularly in the PPA microenvironment, may play a role in contributing to tumor development by impacting fundamental metabolism, especially lipid metabolism. Our comprehensive findings, including the development of a biomarker panel, suggest the potential of intestinal flora as a diagnostic and predictive tool, emphasizing its significance as a preventive target for PPA.
Intraspecific variations in activities of four classes of fine root phosphatases in Quercus serrata, a dominant deciduous oak, occurring across a wide soil phosphorus gradient in Japan
Authors: Mizukami Chika , Mukai Mana , Wagai Rota , Kitayama Kanehiro 
Published: February 2024

Abstract

Background and aims
Phosphatase secretion by fine roots is an important phosphorus (P) acquisition strategy for plants growing under soil P deficiency. Four different classes of phosphatases degrade specific fractions of soil organic P respectively (phosphomonoesterase, PME degrading labile monoester P; pyrophosphatase, PyP for pyrophosphate; phytase, PhT for phytate; and phosphodiesterase, PDE for diester P). We investigated how plants depend on these phosphatases and how their activities are regulated.
Methods
Activities of four classes of phosphatases were measured in fine roots of Quercus serrata, which is a dominant deciduous oak in Japan. Root samples were collected from 14 sites that formed a wide soil P gradient. Activities of four classes of phosphatases were analyzed as releasing rate of product by colorimetric methods under a common laboratory condition.
Results
Activities of the three phosphatases that produced phosphate as their end product (i.e. PME, PyP and PhT) showed significant inter-site variations, while PDE activity was invariant. PME activity was higher by one to two-order(s) of magnitude than the others in all sites. In structural equation modeling, PME activity was negatively related to the concentration of labile inorganic P in soils, implying that PME was regulated by its product but not by substrate.
Conclusion
Quercus serrata appears to be mainly dependent on PME and the other phosphatases are complementary to PME. The variations patterns of activities among sites probably reflect cost-benefit balance of acquiring phosphate in Q. serrata. This regulatory mechanism may allow Q. serrata to grow across a wide soil P gradient.
Multi-Modal Emotion Recognition Using Tensor Decomposition Fusion and Self-Supervised Multi-Tasking
Authors: Zhu Jiawei , Zhu Xianxun , Wang Shoujin , Wang Tao , Huang Jingze , Wang Rui 
Published: February 2024

Abstract

With technological advancements, we can now capture rich dialogue content, tones, textual information, and visual data through tools like microphones, the internet, and cameras. However, relying solely on a single modality for emotion analysis often fails to accurately reflect the true emotional state, as this approach overlooks the dynamic correlations between different modalities. To address this, our study introduces a multimodal emotion recognition method that combines tensor decomposition fusion and self-supervised multi-task learning. This method first employs Tucker decomposition techniques to effectively reduce the model’s parameter count, lowering the risk of overfitting. Subsequently, by building a learning mechanism for both multimodal and unimodal tasks and incorporating the concept of label generation, it more accurately captures the emotional differences between modalities. We conducted extensive experiments and analyses on public datasets like CMU-MOSI and CMU-MOSEI, and the results show that our method significantly outperforms existing methods in terms of performance. The related code is open-sourced at https://github.com/ZhuJw31/MMER-TD.