bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multimorbidity Patterns, Leisure Activities, and Cognitive Function: A Population-Based Longitudinal Study
Authors: Wang Shuojia , Yu Chengyuan , Zhu Jing , Zhao Pengfei , Jin Nana , Cheng Lixin , Kang Lin 
Published: September 2024

Abstract

Background
There is limited knowledge about the effect of leisure activities on cognitive decline related to different multimorbidity patterns. The study aimed to examine the role of leisure activities in the association between multimorbidity patterns and cognitive function.

Methods
We conducted a community-based cohort study based on the 2002–2018 Chinese Longitudinal Health Longevity Survey. Multimorbidity patterns were examined by exploratory factor analysis. Multivariable linear and logistic regression models were used to assess the associations between multimorbidity, leisure activities, and cognitive function.

Results
The study included 14 093 older adults. Those with specific multimorbidity patterns had lower Mini-Mental State Examination (MMSE) scores. Compared with individuals with cardiometabolic and sensory patterns who frequently engaged in activities such as housework, garden work, and watching TV/listening to the radio, those who participated in these activities less regularly had lower MMSE scores. Furthermore, a higher frequency change of participation and a greater variety of leisure activities were associated with better cognitive function.

Conclusions
The older individuals with multimorbidity are associated with lower MMSE scores, while those who participated in more leisure activities had higher cognitive function. Diverse and frequent leisure activities may help delay cognitive decline in Chinese older adults with different multimorbidities.
A novel miniaturized roller pump circuit for simulation of extracorporeal circulation
Authors: Hjärpe Anders K , Jeppsson Anders , Lannemyr Lukas , Pernbro Fredrik , Hesse Camilla , Romlin Birgitta 
Published: September 2024

Abstract

OBJECTIVES
Extracorporeal circulation induces pronounced effects on haemostasis and rheology. To study these, an ex vivo simulation model is an attractive alternative but often requires large amounts of blood. We sought to create a miniaturized roller pump circuit requiring minimal amounts of blood and to test if the circuit could be used to compare coagulation, platelet function and blood rheology between a dextran-based and a crystalloid-based priming solution.

METHODS
A miniaturized roller pump circuit requiring only 27 ml of blood was created. Blood samples from 8 cardiac surgery patients were mixed with either a dextran-based or a crystalloid-based solution and circulated for 60 min. Coagulation was assessed by rotational thromboelastometry, and platelet function by impedance aggregometry and flow cytometry, before and at 5 and 60 min of circulation.

RESULTS
A time-dependent impairment of coagulation was observed in both groups. Maximum clot firmness was lower with dextran-based than with crystalloid-based priming at 5 min (HEPTEM 37 ± 4 vs 43 ± 4 mm, P &lt; 0.001; EXTEM 37 ± 4 vs 43 ± 4 mm, P &lt; 0.001; FIBTEM 3 ± 2 vs 9 ± 2 mm, P &lt; 0.001) and at 60 min (HEPTEM 29 ± 9 vs 38 ± 5 mm, P &lt; 0.001; EXTEM 30 ± 7 vs 39 ± 5 mm, P &lt; 0.001; FIBTEM 3 ± 2 vs 8 ± 3 mm, P = 0.002). The EXTEM clotting time was longer with dextran-based solution at 5 (109 ± 19 vs 63 ± 7 sec, P &lt; 0.001) and at 60 min (176 ± 72 vs 73 ± 7 sec, P = 0.004).

CONCLUSIONS
The novel miniaturized roller pump circuit can be used to mimic extracorporeal circulation for selected research questions. Dextran-based priming caused a significant impairment in haemostasis compared with a standard crystalloid solution.
Advancements in a low global warming potential refrigerants for enhanced thermal management in electric vehicle air conditioning
Authors: Dong Suiju , Huo Ziheng , Liu Yin , Zhai Saina , Ding Chuangchuang , Yang Jingxing , Fu Pengwei , Meng Zhaofeng 
Published: September 2024

Abstract
This article proposes an effective thermal management solution for electric vehicles by incorporating a low global warming potential (GWP) hybrid refrigerant R1234ze(E)/R134a/R32 (with a mass percentage of 85/10/5). By integrating R1234ze(E), the potential of substituting R134a with this mixed refrigerant in electric vehicle air conditioning for thermal analysis was analyzed. The findings are that R1234ze(E) exhibits a COP reduction of 0.51% compared to R134a, while R1234ze(E)/R134a/R32 showcases a coefficient of performance (COP) enhancement of 1.53% over R134a. The compressor exhaust temperatures of R1234ze(E) and R1234ze(E)/R134a/R32 are, respectively, 7.4°C and 5.2°C lower than those of R134a. R1234ze(E) demonstrates a volumetric cooling capacity ~26.3% smaller than that of R134a. However, the volumetric cooling capacity of R1234ze(E)/R134a/R32 aligns closely with R134a, facilitating convenient direct charging. R1234ze(E)/R134a/R32 proves to be an excellent refrigerant for applications in the thermal management of electric vehicles.
Applications of blockchain technologies in artificial electricity market settlement and clearing
Authors: Li Yan , Gong Yuying , Wu Mingbo , Li Yujia , Wang Wenguo 
Published: September 2024

Abstract
Blockchain technology is demonstrating vast potential in the realm of distributed energy transactions. This article employs the Ethereum platform to architect an energy trading settlement framework. By incorporating whitelists, authorization mechanisms, and dedicated energy tokens, transactions are rendered more secure and reliable, ensuring the openness, transparency, traceability, and immutability of transaction information. The proposed transaction settlement mechanism automatically updates users' energy imbalances and levies corresponding charges, thereby ensuring strict adherence to transaction contracts. Furthermore, during the settlement phase, smart contracts are employed to autonomously evaluate the creditworthiness of transaction parties, with results recorded on the blockchain. As the credit levels of the entities across the entire network increase, the block generation time for distributed energy trading blockchains becomes shorter, with the block generation time predominantly influenced by the credit rating of the highest-rated entity in the network. Hence, through smart contract design based on energy trading, blockchain technology is utilized to enhance the transparency and security of transaction settlement and clearing. Future research could focus on improving blockchain scalability, integrating emerging technologies, to advance the effectiveness and adoption of blockchain-based energy trading systems.
Genomic insights into epidemic plasmids carrying blaCTX-M and mcr-1 genes in Escherichia coli from Lebanese broiler production
Authors: Mikhayel Myriam , Praud Karine , Leclercq Sébastien , Karam Sarkis Dolla , Doublet Benoît 
Published: September 2024

Abstract

Background
In a previous nationwide survey in the Lebanese broiler production, multidrug-resistant CTX-M-producing E. coli were found to carry the mobile colistin resistance gene mcr-1.

Objectives
To investigate the mobile genetic supports responsible for the spread of these resistance genes among E. coli in healthy broilers in Lebanon.

Methods
Thirty-three blaCTX-M and mcr-1 positive E. coli of various sequence types from 17 broilers farms were subjected to conjugation assays. Long-read sequencing (Oxford Nanopore Technologies) and hybrid assembly were performed to determine complete plasmid sequences and their phylogenetic diversity.

Results
Twenty-nine conjugative IncFII plasmids harboured the extended-spectrum β-lactamase genes blaCTX-M-3 (n = 25) or blaCTX-M-55 (n = 4). Highly related IncF2:A-:B-/blaCTX-M-3 plasmids differing only through IS-mediated genetic rearrangements in antibiotic resistance gene clusters were found in genetically diverse E. coli strains isolated from distant farms. The mobile colistin resistance genes mcr-1.1 and mcr-1.26 were carried by IncX4 and IncI2 plasmids. Worryingly, in one isolate, the ISEcp1-blaCTX-M-55 transposable unit was found integrated in a mcr-1.26-carrying IncX4 plasmid. Beside expanded cephalosporins and colistin resistances, all E. coli isolates were multidrug-resistant with different additional resistances against aminoglycosides, (fluoro)quinolones, fosfomycin, phenicols, sulphonamides, tetracycline and trimethoprim.

Conclusions
Closely related blaCTX-M-3/55-borne IncF2:A-:B- plasmids harbouring variable MDR regions and mcr-1 carrying IncX4 plasmids are widely disseminated in the E. coli population of healthy broilers in Lebanon. Further surveillance programmes of antimicrobial resistance and interventions to reduce the abusive use of medically important antibiotics are necessary to limit the spread of resistances in food-producing animals in Lebanon.
Empowering diversity: striving for inclusivity by leveraging the American Medical Informatics Association’s “For Your Informatics” Podcast
Authors: Williams Karmen S , Hui Vivian , Ross Mindy , Zamanzadeh Davina J , Nguyen Vickie , Khan Zubin A , Chapman Wendy W , You Kai-Lin , Murcko Anita , Warsame Leyla , Ingram Wendy M , Harman Tiffany , Grando Adela 
Published: September 2024

Abstract

Importance
Starting in 2018, the ‘Women in American Medical Informatics Association (AMIA) Podcast’ was women-focused, in 2021 the podcast was rebranded and relaunched as the “For Your Informatics Podcast” (FYI) to expand the scope of the podcast to include other historically underrepresented groups. That expansion of the scope, together with a rebranding and marketing campaign, led to a larger audience and engagement of the AMIA community.

Objectives
The goals of this case report are to characterize our rebranding and expanding decisions, and to assess how they impacted our listenership and engagement to achieve the Podcast goals of increasing diversity among the Podcast team, guests, audience, and improve audience engagement.

Materials and Methods
This descriptive case study is focused on the FYI Podcast team’s processes to develop a revised mission, vision, and values, increase the diversity of guests, augment listenership through social media, and track the reach through the number of followers, downloads, and impressions.

Results
As of December 2023, 35 FYI Podcast episodes are available with 685 social media followers, over 20 000 downloads, and nearly 145 000 impressions. In addition to introductions to informatics and loyal listeners within AMIA, the FYI Podcast episodes have been used by students as teaching material in a graduate biomedical informatics curriculum, and as introductory material for student clubs and programs.

Discussion
The Podcast relaunching led to 98% of guests from underrepresented groups and growth in listenership by 329% since May 2021.

Conclusion
The FYI Podcast supports AMIA’s diversity mission, and gives voices to underrepresented groups, engages the clinical informatics community in critical conversations on justice, equity, diversity and inclusion, and supports education.
Recreating Fall Risk Appraisal matrix using R to support fall prevention programs
Authors: Suarez Jethro Raphael M , Lafontant Kworweinski , Blount Amber , Park Joon-Hyuk , Thiamwong Ladda 
Published: September 2024

Abstract

Objective
This study aimed to optimize Fall Risk Appraisal (FRA) graphing for use in intervention programs tailored toward reducing the fall risk of older adults by using computing graphic functions in the R language.

Materials and Methods
We utilized RStudio, a free development environment for the R language, as well as the functions within the “ggplot2” and “grid” packages, to develop a code that would recreate the FRA matrix for use in data visualization and analysis, as well as feedback for older adults.

Results
The developed code successfully recreates the FRA matrix in R and allows researchers and clinicians to graph participant data onto the matrix itself.

Discussion
The use of an R code allows for a streamlined approach to manipulating the FRA matrix for use in data visualization and feedback for older adults, which improves upon the traditional paper-pencil method that has been previously used.

Conclusions
The code presented in this study recreates the FRA matrix instrument in the R language and gives researchers the ability to instantaneously add, remove, or change different aspects of the instrument to improve its readability for researchers and older adults.
Machine learning-based delirium prediction in surgical in-patients: a prospective validation study
Authors: Jauk Stefanie , Kramer Diether , Sumerauer Stefan , Veeranki Sai Pavan Kumar , Schrempf Michael , Puchwein Paul 
Published: September 2024

Abstract

Objective
Delirium is a syndrome that leads to severe complications in hospitalized patients, but is considered preventable in many cases. One of the biggest challenges is to identify patients at risk in a hectic clinical routine, as most screening tools cause additional workload. The aim of this study was to validate a machine learning (ML)-based delirium prediction tool on surgical in-patients undergoing a systematic assessment of delirium.

Materials and Methods
738 in-patients of a vascular surgery, a trauma surgery and an orthopedic surgery department were screened for delirium using the DOS scale twice a day over their hospital stay. Concurrently, delirium risk was predicted by the ML algorithm in real-time for all patients at admission and evening of admission. The prediction was performed automatically based on existing EHR data and without any additional documentation needed.

Results
103 patients (14.0%) were screened positive for delirium using the DOS scale. Out of them, 85 (82.5%) were correctly identified by the ML algorithm. Specificity was slightly lower, detecting 463 (72.9%) out of 635 patients without delirium. The AUROC of the algorithm was 0.883 (95% CI, 0.8523-0.9147).

Discussion
In this prospective validation study, the implemented machine-learning algorithm was able to detect patients with delirium in surgical departments with high discriminative performance.

Conclusion
In future, this tool or similar decision support systems may help to replace time-intensive screening tools and enable efficient prevention of delirium.
Ivermectin-Based Products in the Context of Green Pharmaceutical Analysis
Authors: Galvão Natália Sabina dos Santos , Kogawa Ana Carolina 
Published: September 2024

Abstract

Background
Ivermectin (IVE), a broad-spectrum antiparasitic, is used in human and animal health. Analytical methods for evaluating IVE in pharmaceutical products are found in the literature and in official compendiums. However, the vast majority of them do not have an eco-friendly approach.

Objective
The aim of this review is to present an overview of existing analytical methods for evaluating IVE in pharmaceutical matrixes in the context of green analytical chemistry (GAC) and show possibilities for increasing their greenness.

Results
GAC is a current alternative to promote sustainable development in laboratories and chemical-pharmaceutical industries; therefore, through its principles, such as reducing the use of aggressive solvents, it is possible to make processes more ecological. However, the vast majority of analytical methods available in the literature and official compendiums do not present an eco-friendly approach. Seventy percent of the methods are by high performance liquid chromatography (HPLC). Among the various pharmaceutical matrixes, the most evaluated are tablets (37%). Of all the solvents used in HPLC, ultra performance liquid chromatography (UPLC), high performance liquid chromatography coupled to mass spectrometry (HPLC–MS/MS), ultraviolet spectrophotometry (UV) and thin layer chromatography (TLC) methods, the combination of methanol and acetonitrile is the most used, accounting for more than 50% of occurrences.

Conclusion
Analytical methods for evaluating IVE-based products can be leveraged within the scope of GAC, bringing sustainable work opportunities to analytical development laboratories around the world.

Highlights
This review shows an overview of the analytical methods present in the literature and official compendiums to evaluate pharmaceutical IVE matrixes, in the context of GAC.
Unveiling the effects of shrimp hydrolysate as a dietary ingredient in healthy adult Beagle dogs
Authors: Guilherme-Fernandes Joana , Fonseca António J M , Aires Tiago , Lima Sofia A C , Maia Margarida R G , Cabrita Ana R J 
Published: September 2024

Abstract
To be more sustainable, the pet food industry could increase the inclusion of animal byproducts from the human food chain and fish hydrolysates have been reported to benefit dogs’ health. However, there is limited research on the impact of alternative marine hydrolysates in dog food. The current study evaluated the effects of including shrimp hydrolysate as a replacement for wheat gluten (experimental diet) in an extruded complete diet (control diet) on diet palatability, intake, digestibility, fecal characteristics and metabolites, oral volatile sulfur compounds (VSC), and coat quality in dogs. The palatability of diets was assessed in a 2-bowl test, conducted with 12 healthy adult Beagle dogs. No differences were observed in the first approach, first taste, or intake ratio. A randomized block design lasting 12 wk was performed with 12 dogs distributed into 6 blocks, according to sex and body weight; one dog from each block was randomly allocated to each diet. Fecal characteristics and metabolites were measured in weeks 0, 4, 8, and 12, VSC and coat quality in weeks 4, 8, and 12, and apparent total tract digestibility (ATTD) of nutrients and energy in week 12. The inclusion of shrimp hydrolysate did not affect intake, but increased fecal output (dry matter, DM, basis, P &lt; 0.05). Fecal butyrate concentration was lower (P &lt; 0.05) in dogs fed the experimental diet. The inclusion of shrimp hydrolysate did not affect ATTD of nutrients and energy, and VSC. Both diets promoted high coat quality. The experimental diet decreased gloss and general evaluation scores in week 4 (P &lt; 0.05), but improved scale scores in weeks 4 and 12 (P &lt; 0.05). Overall, the findings indicate the potential of including shrimp hydrolysate in diets for dogs, fostering a more sustainable industry.
Practical Knowledge and the Structural Challenge
Authors: Campbell Lucy 
Published: September 2024

Abstract
Elizabeth Anscombe characterised practical knowledge as knowledge ‘in intention’. As Anscombe recognised, accepting this view involves rejecting certain basic orthodox epistemological assumptions. But even once this is done, a challenge remains for a conception of practical knowledge as knowledge ‘in intention’. For while practical knowledge would appear to be a kind of propositional knowledge, intentions would appear to be a kind of non-propositional attitude. I call this the ‘Structural Challenge’ for an intention-based account of practical knowledge. After rejecting two suggested responses – one which views intentions as propositional attitudes; one which views practical knowledge as non-propositional knowledge – I offer my own solution by showing how simply having and carrying out an intention to φ will ordinarily meet a plausible neutral condition on propositional knowledge. Knowing a fact will in general involve being mentally related to it via a successful exercise of relevant concepts. The account I develop turns on viewing a person’s carrying out an intention to φ as their constituting the fact that they are φ-ing, through a practical exercise of their concept of φ-ing. The resulting account sheds light both on the analogies, and on the crucial formal differences, between practical and theoretical knowledge.
Evaluation of machine learning models that predict lncRNA subcellular localization
Authors: Miller Jason R , Yi Weijun , Adjeroh Donald A 
Published: September 2024

Abstract
The lncATLAS database quantifies the relative cytoplasmic versus nuclear abundance of long non-coding RNAs (lncRNAs) observed in 15 human cell lines. The literature describes several machine learning models trained and evaluated on these and similar datasets. These reports showed moderate performance, e.g. 72–74% accuracy, on test subsets of the data withheld from training. In all these reports, the datasets were filtered to include genes with extreme values while excluding genes with values in the middle range and the filters were applied prior to partitioning the data into training and testing subsets. Using several models and lncATLAS data, we show that this ‘middle exclusion’ protocol boosts performance metrics without boosting model performance on unfiltered test data. We show that various models achieve only about 60% accuracy when evaluated on unfiltered lncRNA data. We suggest that the problem of predicting lncRNA subcellular localization from nucleotide sequences is more challenging than currently perceived. We provide a basic model and evaluation procedure as a benchmark for future studies of this problem.
A generalized protein identification method for novel and diverse sequencing technologies
Authors: Bhandari Bikash Kumar , Goldman Nick 
Published: September 2024

Abstract
Protein sequencing is a rapidly evolving field with much progress towards the realization of a new generation of protein sequencers. The early devices, however, may not be able to reliably discriminate all 20 amino acids, resulting in a partial, noisy and possibly error-prone signature of a protein. Rather than achieving de novo sequencing, these devices may aim to identify target proteins by comparing such signatures to databases of known proteins. However, there are no broadly applicable methods for this identification problem. Here, we devise a hidden Markov model method to study the generalized problem of protein identification from noisy signature data. Based on a hypothetical sequencing device that can simulate several novel technologies, we show that on the human protein database (N = 20 181) our method has a good performance under many different operating conditions such as various levels of signal resolvability, different numbers of discriminated amino acids, sequence fragments, and insertion and deletion error rates. Our results demonstrate the possibility of protein identification with high accuracy on many early experimental devices. We anticipate our method to be applicable for a wide range of protein sequencing devices in the future.