bluuebunny/crossref_metadata_2025_split huggingface dataset:
Adaptation of the brainwriting premortem technique to inform the co-creation of COVID-19 testing strategies in underserved communities in South San Diego
Authors: Rabin Borsika A. , Cain Kelli L. , Ayers Lawrence O. , Lomeli Angel , Escoto Arleth , Burola Maria Linda , Aguilar Melanie , Calvillo Stephenie Tinoco , Reyes Breanna , Salgin Linda , Tukey Robert , Laurent Louise C. , Stadnick Nicole A. 
Published: January 2024

Abstract
Introduction
Meaningful engagement of partners in co-creating and refining health-related programs can increase the initial uptake, sustained implementation, broad reach, and effectiveness of these programs. This is especially important for underserved communities where resources are limited and need to be prioritized. Brainwriting premortem is a novel qualitative approach to partner engagement that combines the strengths of individual idea generation with the concept of premortem exercise that addresses failure points prior to the implementation of new programs.

Methods
An adapted form of brainwriting premortem was used to inform iterative refinements to a COVID-19 testing program at a Federally Qualified Health Center (FQHC) in San Diego. Patients and providers from the FQHC participated in interviews at two time points (early- and mid-implementation of the program). Interview data were transcribed, translated, and analyzed using a rapid qualitative approach. Key themes and sub-themes were identified and used to inform refinements to the program.

Results
A total of 11 patients (7 Spanish- and 4 English-speaking) and 8 providers participated in the brainwriting premortem interviews. Key themes related to possible reasons for COVID-19 testing program failure: advertising/sharing information; access to testing; handling of test results; staff and patient safety; patient beliefs and views regarding the SARS-CoV-2 virus; and COVID-19 testing options offered. Proposed solutions were offered for the key failures except for patient beliefs and views regarding the SARS-CoV-2 virus. Additional solutions offered were related to education, physical operations, and recruitment strategies. Real-time changes to the program flow and components were made in response to 7 suggestions from patients and 11 from providers. Changes related to the process of returning results were the most common, and included sending results via email with distinct workflows based on the test result.

Conclusion
The implementation of the adapted brainwriting premortem technique allowed us to incorporate the perspective of key partners in the delivery and iterative refinement of the COVID-19 testing program. This was an effective tool in the context of an FQHC and can be a promising and approach to incorporate iterative input from patients and providers to ensure successful program implementation. Future studies, particularly those requiring rapid response to public health emergencies, should consider the use of this technique.
Morphology-based deep learning enables accurate detection of senescence in mesenchymal stem cell cultures
Authors: He Liangge , Li Mingzhu , Wang Xinglie , Wu Xiaoyan , Yue Guanghui , Wang Tianfu , Zhou Yan , Lei Baiying , Zhou Guangqian 
Published: January 2024

Abstract
Background
Cell senescence is a sign of aging and plays a significant role in the pathogenesis of age-related disorders. For cell therapy, senescence may compromise the quality and efficacy of cells, posing potential safety risks. Mesenchymal stem cells (MSCs) are currently undergoing extensive research for cell therapy, thus necessitating the development of effective methods to evaluate senescence. Senescent MSCs exhibit distinctive morphology that can be used for detection. However, morphological assessment during MSC production is often subjective and uncertain. New tools are required for the reliable evaluation of senescent single cells on a large scale in live imaging of MSCs.

Results
We have developed a successful morphology-based Cascade region-based convolution neural network (Cascade R-CNN) system for detecting senescent MSCs, which can automatically locate single cells of different sizes and shapes in multicellular images and assess their senescence state. Additionally, we tested the applicability of the Cascade R-CNN system for MSC senescence and examined the correlation between morphological changes with other senescence indicators.

Conclusions
This deep learning has been applied for the first time to detect senescent MSCs, showing promising performance in both chronic and acute MSC senescence. The system can be a labor-saving and cost-effective option for screening MSC culture conditions and anti-aging drugs, as well as providing a powerful tool for non-invasive and real-time morphological image analysis integrated into cell production.
DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model
Authors: Pang Yihe , Liu Bin 
Published: January 2024

AbstractIntrinsically disordered proteins and regions (IDPs/IDRs) are functionally important proteins and regions that exist as highly dynamic conformations under natural physiological conditions. IDPs/IDRs exhibit a broad range of molecular functions, and their functions involve binding interactions with partners and remaining native structural flexibility. The rapid increase in the number of proteins in sequence databases and the diversity of disordered functions challenge existing computational methods for predicting protein intrinsic disorder and disordered functions. A disordered region interacts with different partners to perform multiple functions, and these disordered functions exhibit different dependencies and correlations. In this study, we introduce DisoFLAG, a computational method that leverages a graph-based interaction protein language model (GiPLM) for jointly predicting disorder and its multiple potential functions. GiPLM integrates protein semantic information based on pre-trained protein language models into graph-based interaction units to enhance the correlation of the semantic representation of multiple disordered functions. The DisoFLAG predictor takes amino acid sequences as the only inputs and provides predictions of intrinsic disorder and six disordered functions for proteins, including protein-binding, DNA-binding, RNA-binding, ion-binding, lipid-binding, and flexible linker. We evaluated the predictive performance of DisoFLAG following the Critical Assessment of protein Intrinsic Disorder (CAID) experiments, and the results demonstrated that DisoFLAG offers accurate and comprehensive predictions of disordered functions, extending the current coverage of computationally predicted disordered function categories. The standalone package and web server of DisoFLAG have been established to provide accurate prediction tools for intrinsic disorders and their associated functions.
Development of prescribing indicators related to opioid-related harm in patients with chronic pain in primary care—a modified e-Delphi study
Authors: Bansal Neetu , Campbell Stephen M. , Lin Chiu-Yi , Ashcroft Darren M. , Chen Li-Chia 
Published: January 2024

Abstract
Background
Long-term opioid use is associated with dependency, addiction, and serious adverse events. Although a framework to reduce inappropriate opioid prescribing exists, there is no consensus on prescribing indicators for preventable opioid-related problems in patients with chronic pain in primary care in the UK. This study aimed to identify opioid prescription scenarios for developing indicators for prescribing opioids to patients with chronic pain in primary care.

Methods
Scenarios of opioid prescribing indicators were identified from a literature review, guidelines, and government reports. Twenty-one indicators were identified and presented in various opioid scenarios concerning opioid-related harm and adverse effects, drug-drug interactions, and drug-disease interactions in certain disease conditions. After receiving ethics approval, two rounds of electronic Delphi panel technique surveys were conducted with 24 expert panellists from the UK (clinicians, pharmacists, and independent prescribers) from August 2020 to February 2021. Each indicator was rated on a 1–9 scale from inappropriate to appropriate. The score’s median, 30th and 70th percentiles, and disagreement index were calculated.

Results
The panel unanimously agreed that 15 out of the 21 opioid prescribing scenarios were inappropriate, primarily due to their potential for causing harm to patients. This consensus was reflected in the low appropriateness scores (median ranging from 1 to 3). There were no scenarios with a high consensus that prescribing was appropriate. The indicators were considered inappropriate due to drug-disease interactions (n = 8), drug-drug interactions (n = 2), adverse effects (n = 3), and prescribed dose and duration (n = 2). Examples included prescribing opioids during pregnancy, concurrently with benzodiazepines, long-term without a laxative prescription and prescribing > 120-mg morphine milligram equivalent per day or long-term duration over 3 months after surgery.

Conclusions
The high agreement on opioid prescribing indicators indicates that these potentially hazardous consequences are relevant and concerning to healthcare practitioners. Future research is needed to evaluate the feasibility and implementation of these indicators within primary care settings. This research will provide valuable insights and evidence to support opioid prescribing and deprescribing strategies. Moreover, the findings will be crucial in informing primary care practitioners and shaping quality outcome frameworks and other initiatives to enhance the safety and quality of care in primary care settings.
Anti-cancer effects of DHP107 on canine mammary gland cancer examined through in-vitro and in-vivo mouse xenograft models
Authors: Chae Hyung-Kyu , Oh Ye-In , Lim Ga-Hyun , Jung Yun-Chan , Park Seol-Hee , An Ju-Hyun , Park Su-Min , Seo Kyoung-Won , Chu Sung-Nam , Li Qiang , Youn Hwa-Young 
Published: January 2024

Abstract
Background
Canine mammary gland cancer (CMGC) is a common neoplasm in intact bitches. However, the benefit of adjuvant chemotherapy is unclear. The aim of this study was to investigate the anti-proliferative effects of paclitaxel on CMGC in in-vitro and in-vivo settings.

Results
Paclitaxel dose-dependently inhibited viability and induced G2/M phase cell cycle arrest and apoptosis in both primary and metastatic CMGC cell lines (CIPp and CIPm). In animal experiments, the average tumour volume decreased significantly in proportion to the administered oral paclitaxel dose. By examining tumour tissue using a TUNEL assay and immunohistochemical staining with anti-CD31 as a marker of endothelial differentiation, respectively, it was confirmed that oral paclitaxel induced apoptosis and exerted an anti-angiogenetic effect in tumour tissues. Further, downregulation of cyclin D1 in tumour tissues suggested that oral paclitaxel induced cell cycle arrest in tumour tissues in-vivo.

Conclusions
Our results suggest that paclitaxel may have anti-cancer effects on CMGC through cell cycle arrest, induction of apoptosis, and anti-angiogenesis. This study could provide a novel approach to treat CMGC.
Characterization and optimization of 5´ untranslated region containing poly-adenine tracts in Kluyveromyces marxianus using machine-learning model
Authors: Zeng Junyuan , Song Kunfeng , Wang Jingqi , Wen Haimei , Zhou Jungang , Ni Ting , Lu Hong , Yu Yao 
Published: January 2024

Abstract
Background
The 5´ untranslated region (5´ UTR) plays a key role in regulating translation efficiency and mRNA stability, making it a favored target in genetic engineering and synthetic biology. A common feature found in the 5´ UTR is the poly-adenine (poly(A)) tract. However, the effect of 5´ UTR poly(A) on protein production remains controversial. Machine-learning models are powerful tools for explaining the complex contributions of features, but models incorporating features of 5´ UTR poly(A) are currently lacking. Thus, our goal is to construct such a model, using natural 5´ UTRs from Kluyveromyces marxianus, a promising cell factory for producing heterologous proteins.

Results
We constructed a mini-library consisting of 207 5´ UTRs harboring poly(A) and 34 5´ UTRs without poly(A) from K. marxianus. The effects of each 5´ UTR on the production of a GFP reporter were evaluated individually in vivo, and the resulting protein abundance spanned an approximately 450-fold range throughout. The data were used to train a multi-layer perceptron neural network (MLP-NN) model that incorporated the length and position of poly(A) as features. The model exhibited good performance in predicting protein abundance (average R2 = 0.7290). The model suggests that the length of poly(A) is negatively correlated with protein production, whereas poly(A) located between 10 and 30 nt upstream of the start codon (AUG) exhibits a weak positive effect on protein abundance. Using the model as guidance, the deletion or reduction of poly(A) upstream of 30 nt preceding AUG tended to improve the production of GFP and a feruloyl esterase. Deletions of poly(A) showed inconsistent effects on mRNA levels, suggesting that poly(A) represses protein production either with or without reducing mRNA levels.

Conclusion
The effects of poly(A) on protein production depend on its length and position. Integrating poly(A) features into machine-learning models improves simulation accuracy. Deleting or reducing poly(A) upstream of 30 nt preceding AUG tends to enhance protein production. This optimization strategy can be applied to enhance the yield of K. marxianus and other microbial cell factories.
Heterologous production of 3-hydroxypropionic acid in Methylorubrum extorquens by introducing the mcr gene via a multi-round chromosomal integration system based on cre-lox71/lox66 and transposon
Authors: Zhu Liping , Song Yazhen , Ma Shunan , Yang Song 
Published: January 2024

Abstract
Background and aim
Reprogramming microorganisms to enhance the production of metabolites is a part of contemporary synthetic biology, which relies on the availability of genetic tools to successfully manipulate the bacteria. Methylorubrum extorquens AM1 is a platform microorganism used to convert C1 compounds into various value-added products. However, the repertoire of available plasmids to conveniently and quickly fine-tune the expression of multiple genes in this strain is extremely limited compared with other model microorganisms such as Escherichia coli. Thus, this study aimed to integrate existing technologies, such as transposon-mediated chromosomal integration and cre-lox-mediated recombination, to achieve the diversified expression of target genes through multiple chromosomal insertions in M. extorquens AM1.

Results
A single plasmid toolkit, pSL-TP-cre-km, containing a miniHimar1 transposon and an inducible cre-lox71/lox66 system, was constructed and characterized for its multiple chromosomal integration capacity. A co-transcribed mcr-egfp cassette [for the production of 3-hydroxypropionic acid (3-HP) and a reporting green fluorescent protein] was added to construct pTP-cre-mcr-egfp for evaluating its utility in mediating the expression of heterologous genes, resulting in the production of 3-HP with a titer of 34.7–55.2 mg/L by two chromosomal integration copies. Furthermore, in association with the expression of plasmid-based mcr, 3-HP production increased to 65.5–92.4 mg/L.

Conclusions
This study used a multi-round chromosomal integration system based on cre-lox71/lox66 and a transposon to construct a single constructed vector. A heterologous mcr gene was introduced through this vector, and high expression of 3-hydroxypropionic acid was achieved in M. extorquens. This study provided an efficient genetic tool for manipulating M. extorquens, which not only help increase the expression of heterologous genes in M. extorquens but also provide a reference for strains lacking genetic manipulation vectors.
KCNA1 promotes the growth and invasion of glioblastoma cells through ferroptosis inhibition via upregulating SLC7A11
Authors: Wang Weichao , Zhang Yang , Li Xuetao , E Qinzi , Jiang Zuoyu , Shi Qikun , Huang Yu , Wang Jian , Huang Yulun 
Published: January 2024

Abstract
Background
The high invasiveness and infiltrative nature of Glioblastoma (GBM) pose significant challenges for surgical removal. This study aimed to investigate the role of KCNA1 in GBM progression.

Methods
CCK8, colony formation assay, scratch assay, transwell assay, and 3D tumor spheroid invasion assays were to determine how KCNA1 affects the growth and invasion of GBM cells. Subsequently, to confirm the impact of KCNA1 in ferroptosis, western blot, transmission electron microscopy and flow cytometry were conducted. To ascertain the impact of KCNA1 in vivo, patient-derived orthotopic xenograft models were established.

Results
In functional assays, KCNA1 promotes the growth and invasion of GBM cells. Besides, KCNA1 can increase the expression of SLC7A11 and protect cells from ferroptosis. The vivo experiments demonstrated that knocking down KCNA1 inhibited the growth and infiltration of primary tumors in mice and extended survival time.

Conclusion
Therefore, our research suggests that KCNA1 may promote tumor growth and invasion by upregulating the expression of SLC7A11 and inhibiting ferroptosis, making it a promising therapeutic target for GBM.
Signal-to-noise of linear and volume measures of left ventricular and left atrial size
Authors: Duan Yunbo , Amir Nezar , Armstrong Guy P. 
Published: January 2024

Abstract
Background
Serial echocardiographic assessments are common in clinical cardiology, e.g., for timing of intervention in mitral and aortic regurgitation. When following patients with serial echocardiograms, each new measurement is a combination of true change and confounding noise.
The current investigation compares linear chamber dimensions with volume estimates of chamber size. The aim is to assess which measure is best for serial echocardiograms, when the ideal parameter will be sensitive to change in chamber size and have minimal spurious variation (noise). We present a method that disentangles true change from noise. Linear regression of chamber size against elapsed time gives a slope, being the ability of the method to detect change. Noise is the scatter of individual points away from the trendline, measured as the standard error of the slope. The higher the signal-to-noise ratio (SNR), the more reliably a parameter will distinguish true change from noise.

Methods
LV and LA parasternal dimensions and apical biplane volumes were obtained from serial clinical echocardiogram reports. Change over time was assessed as the slope of the linear regression line, and noise was assessed as the standard error of the regression slope. Signal-to-noise ratio is the slope divided by its standard error.

Results
The median number of LV studies was 5 (4–11) for LV over a mean duration of 5.9 ± 3.0 years in 561 patients (diastole) and 386 (systole). The median number of LA studies was 5 (4–11) over a mean duration of 5.3 ± 2.0 years in 137 patients.
Linear estimates of LV size had better signal-to-noise than volume estimates (p < 0.001 for diastolic and p = 0.035 for systolic). For the left atrium, the difference was not significant (p = 0.214). This may be due to sample size; the effect size was similar to that for LV systolic size. All three parameters had a numerical value of signal-to-noise that favoured linear dimensions over volumes.

Conclusion
Linear measures of LV size have better signal-to-noise than volume measures. There was no difference in signal-to-noise between linear and volume measures of LA size, although this may be a Type II error. The use of regression lines may be better than relying on single measurements. Linear dimensions may clarify whether changes in volumes are real or spurious.

Graphical Abstract
NET1 is a critical regulator of spindle assembly and actin dynamics in mouse oocytes
Authors: Wang Shiwei , Wu Xuan , Zhang Mengmeng , Chang Siyu , Guo Yajun , Song Shuang , Dai Shizhen , Wu Keliang , Zeng Shenming 
Published: January 2024

Abstract
Background
Neuroepithelial transforming gene 1 (NET1) is a RhoA subfamily guanine nucleotide exchange factor that governs a wide array of biological processes. However, its roles in meiotic oocyte remain unclear. We herein demonstrated that the NET1-HACE1-RAC1 pathway mediates meiotic defects in the progression of oocyte maturation.

Methods
NET1 was reduced using a specific small interfering RNA in mouse oocytes. Spindle assembly, chromosomal alignment, the actin cap, and chromosomal spreads were visualized by immunostaining and analyzed under confocal microscopy. We also applied mass spectroscopy, and western blot analysis for this investigation.

Results
Our results revealed that NET1 was localized to the nucleus at the GV stage, and that after GVBD, NET1 was localized to the cytoplasm and predominantly distributed around the chromosomes, commensurate with meiotic progression. NET1 resided in the cytoplasm and significantly accumulated on the spindle at the MI and MII stages. Mouse oocytes depleted of Net1 exhibited aberrant first polar body extrusion and asymmetric division defects. We also determined that Net1 depletion resulted in reduced RAC1 protein expression in mouse oocytes, and that NET1 protected RAC1 from degradation by HACE1, and it was essential for actin dynamics and meiotic spindle formation. Importantly, exogenous RAC1 expression in Net1-depleted oocytes significantly rescued these defects.

Conclusions
Our results suggest that NET1 exhibits multiple roles in spindle stability and actin dynamics during mouse oocyte meiosis.
Diagnostic and prognostic potential of long non-coding RNA NORAD in patients with acute deep vein thrombosis and its role in endothelial cell function
Authors: Zhou Kun , Li Na , Qi Jia , Tu Pingping , Yang Yan , Duan Hui 
Published: January 2024

Abstract
Background
Deep venous thrombosis (DVT) is the common clinical cardiovascular disease, and easily develops into post-thrombotic syndrome (PTS). The study aimed to examine the clinical value of long non-coding RNA NORAD gene in the development of DVT and PTS. In vitro, the underlying mechanism was explored.

Methods
Serum levels of lncRNA NORAD gene in 85 DVT cases and 85 healthy individuals were tested. The role of lncRNA NORAD gene in human umbilical vein endothelial cells (HUVECs) proliferation, migration and inflammation was examined. The candidate downstream target gene was predicted via bioinformatic analysis. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were done for the function annotation and pathway enrichment.

Results
LncRNA NORAD gene was at high expression in the serum of DVT patients, it can distinguish DVT patients from healthy controls with the area under the curve of 0.919. Elevated expression of lncRNA NORAD gene in PTS patients was detected, DVT cases with high expression of lncRNA NORAD gene were more susceptible to PTS. LncRNA NORAD gene knockdown promoted HUVECs’ proliferation, migration while suppressing cell apoptosis and inflammation. MiR-93-5p served as a target of lncRNA NORAD gene, and its overexpression reversed the role of lncRNA NORAD gene in the biological function of HUVECs. The target genes of miR-93-5p were enriched in HIF-1 signaling, TGF-beta signaling and PI3K-Akt signaling, protein-protein interaction (PPI) network indicated STAT3, MAPK1 to be the key targets.

Conclusions
Upregulation of expression of lncRNA NORAD gene was a potential diagnostic biomarker for DVT and related to the development of PTS. LncRNA NORAD/miR-93-5p axis was involved in the progress of DVT through regulating endothelial cell function.