bluuebunny/crossref_metadata_2025_split huggingface dataset:
GMMID: genetically modified mice information database
Authors: Xu Menglin , Fang Minghui , Chen Qiyang , Xiao Wenjun , Xu Zhixuan , Cai Bao , Zhao Zhenyang , Wang Tao , Zhu Zhu , Chen Yingshan , Zhu Yue , Dai Mingzhou , Jiang Tiancheng , Li Xinyi , Chun Siuwing , Zhou Runhua , Li Yafei , Gou Yueyue , He Jingjing , Luo Lin , You Linlin , Jiang Xuan 
Published: August 2024

Abstract
Genetically engineered mouse models (GEMMs) are vital for elucidating gene function and disease mechanisms. An overwhelming number of GEMM lines have been generated, but endeavors to collect and organize the information of these GEMMs are seriously lagging behind. Only a few databases are developed for the information of current GEMMs, and these databases lack biological descriptions of allele compositions, which poses a challenge for nonexperts in mouse genetics to interpret the genetic information of these mice. Moreover, these databases usually do not provide information on human diseases related to the GEMM, which hinders the dissemination of the insights the GEMM provides as a human disease model. To address these issues, we developed an algorithm to annotate all the allele compositions that have been reported with Python programming and have developed the genetically modified mice information database (GMMID; http://www.gmmid.cn), a user-friendly database that integrates information on GEMMs and related diseases from various databases, including National Center for Biotechnology Information, Mouse Genome Informatics, Online Mendelian Inheritance in Man, International Mouse Phenotyping Consortium, and Jax lab. GMMID provides comprehensive genetic information on &gt;70 055 alleles, 65 520 allele compositions, and ∼4000 diseases, along with biologically meaningful descriptions of alleles and allele combinations. Furthermore, it provides spatiotemporal visualization of anatomical tissues mentioned in these descriptions, shown alongside the allele compositions. Compared to existing mouse databases, GMMID considers the needs of researchers across different disciplines and presents obscure genetic information in an intuitive and easy-to-understand format. It facilitates users in obtaining complete genetic information more efficiently, making it an essential resource for cross-disciplinary researchers.
Database URL: http://www.gmmid.cn
Role of 3D transoesophageal echocardiography in the study of infective endocarditis. Demonstration in a case collection
Authors: Baltodano-Arellano Roberto , Huaman-Carrasco Daniel , Cupe-Chacalcaje Kelly , Cachicatari-Beltran Angela , Benites-Yshpilco Lindsay , Urdanivia-Ruiz Dante , Rafael-Horna Eliana , Falcón-Quispe Luis , Demarini-Orellana Alessio , Velarde-Acosta Kevin , Ortiz-Leon Xochitl , Levano-Pachas Gerald 
Published: August 2024

Abstract
Infective endocarditis (IE) is a condition that predominantly affects native or prosthetic heart valves, which is currently on the rise due to the increase in invasive cardiology procedures, such as the utilization of cardiac implantable electronic devices and transcatheter interventions. The recommended imaging tests for diagnosis are 2D transthoracic echocardiography (2D TTE) and 2D transoesophageal echocardiography (2D TOE). However, these modalities present limitations in detecting vegetations and estimating their dimensions. These disadvantages can be overcome by 3D transoesophageal echocardiography (3D TOE), particularly with the multiplanar reconstruction tool, which allows for the visualization of infinite valve planes, thus optimizing the detection of lesions and precise measurements. Furthermore, the volume rendering provides insight into the anatomical relationships between lesions, which is particularly useful for surgical planning. In this review, we aim to discuss the role of this recent imaging modality in the diagnosis, prognosis, and therapeutic of IE. Finally, we present a collection of images that illustrate the use of 3D TOE tools.
Tissue versus mechanical mitral valve replacement in patients aged 50–70: a propensity-matched analysis
Authors: Fialka Nicholas M , Watkins Abeline R , Alam Abrar , EL-Andari Ryaan , Kang Jimmy J H , Hong Yongzhe , Bozso Sabin J , Moon Michael C , Nagendran Jeevan 
Published: August 2024

Abstract

OBJECTIVES
There remains debate over the optimal mitral valve replacement (MVR) option for patients aged 50–70 years. The objective of this study was to retrospectively compare the long-term outcomes of mechanical and bioprosthetic MVR in this patient population.

METHODS
Data from patients undergoing MVR between 2004 and 2018 were retrospectively reviewed. The primary outcome was all-cause mortality. Secondary outcomes included perioperative and late morbidity.

RESULTS
Two hundred and eight-six propensity-matched patients (n = 143 mechanical; n = 143 bioprosthetic) aged 50–70 years were included in the final analysis. Maximum follow-up was 15.8 years. There was no significant difference in all-cause mortality between the groups at 30 days, 1 year, 5 years, 10 years, and at the longest follow-up. Patients who underwent mechanical MVR experienced significantly lower rates of postoperative atrial fibrillation (P = 0.001). There were no significant differences in rates of sepsis, acute kidney injury, superficial and deep sternal wound infection, mediastinal bleeding, and permanent pacemaker implantation. At the longest follow-up, there were no differences in myocardial infarction, stroke, heart failure or overall rehospitalization. At the same time point, there was an increased rate of MVR in patients receiving a bioprosthetic valve (P = 0.015).

CONCLUSIONS
Survival following mechanical and bioprosthetic MVR in patients 50–70 years of age is similar to up to 15 years of follow-up. Bioprosthetic MVR is associated with an increased risk of repeat MVR. Mechanical MVR is not associated with an increased risk of stroke. Valve selection in this patient population requires diligent consideration of structural valve deterioration and subsequent reoperation risk as well as bleeding and thromboembolic risk.
The emerging H3K9me3 chromatin landscape during zebrafish embryogenesis
Authors: Duval Katherine L , Artis Ashley R , Goll Mary G 
Published: August 2024

Abstract
The structural organization of eukaryotic genomes is contingent upon the fractionation of DNA into transcriptionally permissive euchromatin and repressive heterochromatin. However, we have a limited understanding of how these distinct states are first established during animal embryogenesis. Histone 3 lysine 9 trimethylation (H3K9me3) is critical to heterochromatin formation, and bulk establishment of this mark is thought to help drive large-scale remodeling of an initially naive chromatin state during animal embryogenesis. However, a detailed understanding of this process is lacking. Here, we leverage CUT&RUN to define the emerging H3K9me3 landscape of the zebrafish embryo with high sensitivity and temporal resolution. Despite the prevalence of DNA transposons in the zebrafish genome, we found that LTR transposons are preferentially targeted for embryonic H3K9me3 deposition, with different families exhibiting distinct establishment timelines. High signal-to-noise ratios afforded by CUT&RUN revealed new, emerging sites of low-amplitude H3K9me3 that initiated before the major wave of zygotic genome activation (ZGA). Early sites of establishment predominated at specific subsets of transposons and were particularly enriched for transposon sequences with maternal piRNAs and pericentromeric localization. Notably, the number of H3K9me3 enriched sites increased linearly across blastula development, while quantitative comparison revealed a &gt;10-fold genome-wide increase in H3K9me3 signal at established sites over just 30 min at the onset of major ZGA. Continued maturation of the H3K9me3 landscape was observed beyond the initial wave of bulk establishment.
Patterns of Family Conflict and Accusations of Abuse in Dementia Family Caregivers: A Latent Class Analysis
Authors: Browning Wesley R , Yildiz Mustafa , Maxwell Christopher D , Sullivan Tami P , Yefimova Maria , Hernandez Chilatra Jessica A , Pickering Carolyn E Z 
Published: August 2024

Abstract

Background and Objectives
Family conflict is a stressor for dementia family caregivers, yet its impact may differ based on the relationship between caregivers and their recipients. This study’s objectives were to categorize caregivers into groups based on family conflict, examine whether the relationship to the recipient influences group membership, and determine whether these groups are associated with engaging in abusive and neglectful behaviors.

Research Design and Methods
This national, cross-sectional study of 453 dementia family caregivers used latent class analysis to generate groups based on family conflict and abuse accusations. A multinomial logistic regression determined if relationship type (i.e., being a spouse, child, or grandchild to the care recipient or having a nontraditional relationship) predicted group membership. Groups were examined as predictors of abusive and neglectful behaviors using analysis of variance.

Results
A 4-class solution emerged as the best fit: 3 groups with varying probabilities of family conflict and 1 group with elevated probabilities of abuse and neglect accusations. Relationship typed predicted membership in these classes. Group membership predicted abusive and neglectful behaviors.

Discussion and Implications
Adult children were more likely to experience high amounts of family conflict, whereas nontraditional caregivers were less likely to experience abuse and neglect accusations. Membership in the accusations group was a unique risk factor for abusive and neglectful behaviors. These findings support the need for continued investigation of family conflict in dementia caregivers. They also call to examine how family relationship types, including nontraditional family structures, influence caregiving outcomes.
Performance Under Fire: Older Adult Cognitive Risks and Protections Under Heat Strain
Authors: Shin Su Hyun , Walker Susan Lee , Ji Hyunjung , Lee Hee Yun 
Published: August 2024

Abstract

Background and Objectives
Although extreme heat events disproportionately affect older adults and the importance of cognition is known, research examining older adult cognition under heat stress is limited. This study examines the relationship between risk/protective factors and heat strain on older adult cognition, employing a social-ecological model.

Research Design and Methods
Retrieved from the 1996–2016 waves of the Health and Retirement Study, our study used older adults aged 50 and older and their spouses residing in the United States. Individual-fixed effects models estimated changes in cognition as measured by fluid and crystallized intelligence scores in response to extreme heat days. This study further estimated interactions of extreme heat with protective/risk factors for cognition (i.e., education, physical activity, social engagement, and genetic risk for Alzheimer’s disease).

Results
Our results demonstrated that extreme heat days were associated with fluid but not crystallized intelligence scores. Educational attainment, mild physical activity, and social contacts with children moderated this relationship. Furthermore, Alzheimer’s disease polygenic scores moderated the correlation between extreme heat days and crystallized intelligence scores.

Discussion and Implications
An increasing frequency of extreme heat events and an aging population globally highlight the need for policies and interventions building resiliency in older adults. Actions promoting the protective modifiable behaviors to older adult cognition identified by our study can lead to healthier individuals and communities.
The impact of using an IoT-based automatic solar panel cleaner on building energy performance
Authors: Anber Maha , Aboshosha Bassam W 
Published: August 2024

Abstract
Solar energy is a great provider of renewable environmentally friendly energy. Thanks to solar technologies, it converts sunlight into electrical energy using multiple devices such as photovoltaic cells. The generated electrical energy can be used for multiple purposes such as commercial, industrial, and domestic uses. Generating electrical energy without releasing toxic gases is considered one of the biggest advantages of using solar energy. While solar panels are a reliable source of clean electrical energy, their power output can be decreased due to the lack of maintenance and especially cleaning. The efficiency of solar panels decreases by more than 40% due to the lack of cleaning. That is why routine cleaning is crucial to ensure the optimum performance of them. Providing an automatic solar panel cleaning system will ensure that routine maintenance is done and the electricity output is optimum. Integrating the use of photovoltaic cells in buildings helps improve the energy performance and energy efficiency of buildings. This research paper aims to determine the impact of integrating an internet of things (IoT)-based automatic solar panel cleaner and measure the energy efficiency of using it. The IoT-based automatic solar panel cleaner contributed to raising the solar panel power wattage by 89.33% in a hot desert climatic region.
Charging prediction for new energy electric vehicles in the context of vehicle to grid using a hybrid ROCNN-BILSTM model
Authors: Yang Ye , Wang Wen , Qin Jian , Wang Mingcai , Xia Yansong , Li Yanan , Jia Qi 
Published: August 2024

Abstract
Vehicle to grid refers to the interaction between electric vehicles and the power grid through charging stations. It aims to guide owners of new energy vehicles to charge in an orderly and staggered manner, and even enabling power supply back to the grid. In the context of vehicle to grid, the charging behavior of new energy vehicles becomes different from the past due to uncertainties introduced by user plug-in/plug-out actions and weather conditions, which may disrupt owners’ future scheduling plans. In this article, we propose a charging prediction study based on the Reordering Convolutional Neural Network-Bidirectional Long Short-Term Memory (ROCNN-BILSTM) hybrid model specifically designed for the vehicle to grid context. The proposed model employs wavelet threshold denoising as a data preprocessing operation to remove unnecessary noise factors that could affect predictions. Subsequently, the 2-Dimensional Convolutional Neural Network (2D-CNN) component retains temporal features while extracting spatial features. Notably, the features are rearranged, combining highly correlated ones, to facilitate the extraction of high-level, abstract spatial features by the 2D-CNN. Finally, the Bidirectional Long Short-Term Memory (BILSTM) component utilizes a bidirectional structure to capture comprehensive dynamic information and assist in achieving the final charging prediction. Our proposed ROCNN-BILSTM eliminates uncertainty in the data, allowing deep learning models to better focus on important features. Additionally, our model emphasizes high-level spatiotemporal feature extraction, which helps achieve high-performance charging prediction. In the context of vehicle to grid, a real-world dataset of new energy vehicle charging data was used for multi-step prediction, different starting point predictions, and comparison with advanced models. The experimental results show that the proposed model outperforms CNN-LSTM and 2D-CNN models by up to 50.1% and 57.1% in terms of mean absolute error (MAE), and 45.8% and 51.5% in terms of mean squared error (MSE). The results validate the strong predictive performance of the hybrid model and provide robust support for the demands of the vehicle to grid market and new energy vehicle charging prediction technology. In future work, we will place greater emphasis on designing high-performance and interpretable models to explore the fundamental reasons behind different charging trends in new energy vehicles.
Evaluation of a Benzodiazepine Immunoassay for Urine Drug Testing in Clinical Specimens
Authors: Ge Mengyuan , Alabi Adekunle , Kelner Michael J , Fitzgerald Robert L , Suhandynata Raymond T 
Published: August 2024

Abstract

Background
Benzodiazepines are commonly prescribed medications frequently linked to instances of abuse and overdose. Historically, FDA-cleared benzodiazepine urine immunoassays cross-react poorly with glucuronidated benzodiazepine metabolites, leading to false negatives. Clinical laboratories have addressed this deficiency by creating laboratory-developed tests (LDTs) that incorporate a beta-glucuronidase hydrolysis step to increase the clinical sensitivity of these assays.

Methods
Performance characteristics of 2 FDA-cleared benzodiazepine urine immunoassays (Benzodiazepines Plus, no glucuronidase and Benzodiazepines II, with glucuronidase; Roche Diagnostics) and a previously described benzodiazepine immunoassay LDT (with glucuronidase) were evaluated using 258 clinical urine specimens. The positive immunoassay cutoff was set at 200 ng/mL of nordiazepam and results were compared to an LC-MS/MS benzodiazepine LDT. Clinical sensitivity, specificity, precision, and immunoassay cross-reactivity were determined for all 3 immunoassays.

Results
The Benzodiazepines II and LDT immunoassays exhibited greater clinical sensitivity (100% and 95.2%) compared to the Benzodiazepines Plus assay (66.7%). Clinical specificity of 100% was observed for all 3 assays. Immunoassay response of the Benzodiazepines II assay was greater across the range of concentrations tested (100–1000 ng/mL) relative to the other immunoassays and was the most sensitive immunoassay for the detection of lorazepam glucuronide.

Conclusions
The Benzodiazepines II immunoassay demonstrated the greatest clinical and analytical sensitivity compared to the Benzodiazepines Plus and LDT immunoassays. The incorporation of beta-glucuronidase was crucial, as the Benzodiazepines II and LDT immunoassays demonstrated superior clinical sensitivity when compared to the Benzodiazepines Plus immunoassay that does not incorporate a beta-glucuronidase hydrolysis step.
Validation of the Microlab Salmonella for Detection of Salmonella O Group A-E in Selected Foods: AOAC Performance Tested MethodSM 052401
Authors: Alonso Raúl , Razquin Pedro , Carrascón Vanesa , Mata Luis 
Published: August 2024

Abstract

Background
Microlab Salmonella is an all-in-one solution for detection of Salmonella O Groups A-E that integrates, in a single disposable device, all elements to perform the analysis: a ready-to-use enrichment broth, a lateral flow immunochromatography test, and a chemical agent for bacterial inactivation. Microlab Salmonella can be easily used on-site and does not require specific laboratory equipment or technical skills. The device is sealed during the analysis, avoiding risks of contamination, and is therefore safe to use in food production environments.

Objective
This report details the method validation study for raw ground beef, raw turkey (thermal processed, marinated), fresh cheese, deli ham, and pasteurized liquid egg.

Methods
Matrix studies and inclusivity/exclusivity, product consistency/stability, and robustness testing were conducted to assess the method’s performance.

Results
Inclusivity/exclusivity testing showed that the Microlab Salmonella method was able to detect Salmonella O Group A-E isolates while excluding the non-Salmonella strains. In the matrix studies, the differences between the candidate and reference methods were not statistically significant. Minor variations in test parameters (enrichment time, time to read results, and enrichment temperature) did not affect the performance of the Microlab assay. There were no statistically significant differences between recently manufactured lots and those that were halfway through their expiration period or between those that were close to expiring.

Conclusions
The reported data confirm that Microlab Salmonella is reliable for detecting Salmonella in raw ground beef, raw turkey (thermal processed, marinated), fresh cheese, pasteurized liquid egg, and deli ham.

Highlights
Microlab Salmonella is validated to detect Salmonella in select food matrixes. Microlab Salmonella integrates the steps of enrichment, detection, and inactivation in a single device.
Development and reporting of artificial intelligence in osteoporosis management
Authors: Gatineau Guillaume , Shevroja Enisa , Vendrami Colin , Gonzalez-Rodriguez Elena , Leslie William D , Lamy Olivier , Hans Didier 
Published: August 2024

Abstract
An abundance of medical data and enhanced computational power have led to a surge in artificial intelligence (AI) applications. Published studies involving AI in bone and osteoporosis research have increased exponentially, raising the need for transparent model development and reporting strategies. This review offers a comprehensive overview and systematic quality assessment of AI articles in osteoporosis while highlighting recent advancements. A systematic search in the PubMed database, from December 17, 2020 to February 1, 2023 was conducted to identify AI articles that relate to osteoporosis. The quality assessment of the studies relied on the systematic evaluation of 12 quality items derived from the minimum information about clinical artificial intelligence modeling checklist. The systematic search yielded 97 articles that fell into 5 areas; bone properties assessment (11 articles), osteoporosis classification (26 articles), fracture detection/classification (25 articles), risk prediction (24 articles), and bone segmentation (11 articles). The average quality score for each study area was 8.9 (range: 7–11) for bone properties assessment, 7.8 (range: 5–11) for osteoporosis classification, 8.4 (range: 7–11) for fracture detection, 7.6 (range: 4–11) for risk prediction, and 9.0 (range: 6–11) for bone segmentation. A sixth area, AI-driven clinical decision support, identified the studies from the 5 preceding areas that aimed to improve clinician efficiency, diagnostic accuracy, and patient outcomes through AI-driven models and opportunistic screening by automating or assisting with specific clinical tasks in complex scenarios. The current work highlights disparities in study quality and a lack of standardized reporting practices. Despite these limitations, a wide range of models and examination strategies have shown promising outcomes to aid in the earlier diagnosis and improve clinical decision-making. Through careful consideration of sources of bias in model performance assessment, the field can build confidence in AI-based approaches, ultimately leading to improved clinical workflows and patient outcomes.