bluuebunny/crossref_metadata_2025_split huggingface dataset:
A mixed methods investigation of end-of-life surrogate decisions among older adults
Authors: Batteux Eleonore , Ferguson Eamonn , Tunney Richard J. 
Published: April 2020

AbstractBackgroundA large number of end-of-life decisions are made by a next-of-kin for a patient who has lost their decision-making capacity. This has given rise to investigations into how surrogates make these decisions. The experimental perspective has focused on examining how the decisions we make for others differ from our own, whereas the qualitative perspective has explored surrogate insights into making these decisions.MethodsWe conducted a mixed methods study to bring these two perspectives together. This is crucial to comparing decision outcomes to the decision process. We asked older adult partners to make end-of-life decisions for each other. They then took part in a semi-structured interview about their decision process. Transcripts were analysed using thematic analysis.Results24 participants took part in the study. Surrogates were more likely to take a life-saving treatment at the risk of a diminished quality of life for their partner than for themselves. This was consistent with their transcripts which showed that they wanted to give their partner a better chance of living. Although there was evidence of surrogate inaccuracy in the decision task, participants overwhelmingly reported their intention to make a decision which aligns with the substituted judgment standard. However, uncertainty about their wishes pushed them to consider other factors.ConclusionsTaking a mixed methods approach allowed us to make novel comparisons between decision outcome and process. We found that the intentions of surrogates broadly align with the expectations of the substituted judgment standard and that previous discussions with their partner helps them to make a decision.
A novel protein encoded by circFNDC3B inhibits tumor progression and EMT through regulating Snail in colon cancer
Authors: Pan Zihao , Cai Jianye , Lin Jiatong , Zhou Huinian , Peng Jingwen , Liang Jinliang , Xia Long , Yin Qi , Zou Baojia , Zheng Jun , Qiao Liang , Zhang Lei 
Published: April 2020

Abstract
Background
Colon cancer (CC) is a common malignant cancer. Recently, circFNDC3B was found to exert biological function in multiple cancers. However, it was unclear whether the potential protein encoded by circFNDC3B is involved in carcinogenesis of CC.

Methods
We used Sanger sequence and RNase R digestion assay to confirm the existence of circFNDC3B, and quantitative real-time PCR was used to evaluate the circRNA’s expression. Then fluorescence in situ hybridization (FISH) was performed to study location of circFNDC3B. The identification of protein encoded by circFNDC3B was performed using LC-MS/MS. The function of circFNDC3B-218aa on proliferation, invasion and migration were assessed by CCK8 assays, colony formation assays, transwell assays, wound-healing assays and animal experiments. RNA-sequencing and western blot were used to identify the gene regulated by circFNDC3B-218aa. Finally, glucose metabolism-related assays were performed to further investigate function of circFNDC3B-218aa.

Results
CircFNDC3B was localized mostly in the cytoplasm, and was decreased in CC cell lines and tissues. The patients with low circFNDC3B expression had a shorter OS (P = 0.0014) than patients with high expression. Moreover, circFNDC3B inhibited the proliferation, invasion and migration of CC cells. Next, we identified that circFNDC3B could encode a novel protein circFNDC3B-218aa. Furthermore, circFNDC3B-218aa, not circFNDC3B, inhibited the proliferation, invasion and migration of CC. Additionally, the in vivo experiments implied that up-regulated circFNDC3B-218aa exhibited an inhibitory effect on CC progression. By RNA-sequencing, western blot and glucose metabolism-related assays, we found that circFNDC3B-218aa inhibited the expression of Snail, and subsequently promoted the tumor-suppressive effect of FBP1 in CC.

Conclusions
The novel circFNDC3B-218aa may serve as a tumor suppressive factor and potential biomarker which may supply the potential therapeutic target for CC.
Comparison and development of machine learning tools for the prediction of chronic obstructive pulmonary disease in the Chinese population
Authors: Ma Xia , Wu Yanping , Zhang Ling , Yuan Weilan , Yan Li , Fan Sha , Lian Yunzhi , Zhu Xia , Gao Junhui , Zhao Jiangman , Zhang Ping , Tang Hui , Jia Weihua 
Published: April 2020

AbstractBackgroundChronic obstructive pulmonary disease (COPD) is a major public health problem and cause of mortality worldwide. However, COPD in the early stage is usually not recognized and diagnosed. It is necessary to establish a risk model to predict COPD development.MethodsA total of 441 COPD patients and 192 control subjects were recruited, and 101 single-nucleotide polymorphisms (SNPs) were determined using the MassArray assay. With 5 clinical features as well as SNPs, 6 predictive models were established and evaluated in the training set and test set by the confusion matrix AU-ROC, AU-PRC, sensitivity (recall), specificity, accuracy, F1 score, MCC, PPV (precision) and NPV. The selected features were ranked.ResultsNine SNPs were significantly associated with COPD. Among them, 6 SNPs (rs1007052, OR = 1.671,P = 0.010; rs2910164, OR = 1.416,P < 0.037; rs473892, OR = 1.473,P < 0.044; rs161976, OR = 1.594,P < 0.044; rs159497, OR = 1.445,P < 0.045; and rs9296092, OR = 1.832,P < 0.045) were risk factors for COPD, while 3 SNPs (rs8192288, OR = 0.593,P < 0.015; rs20541, OR = 0.669,P < 0.018; and rs12922394, OR = 0.651,P < 0.022) were protective factors for COPD development. In the training set, KNN, LR, SVM, DT and XGboost obtained AU-ROC values above 0.82 and AU-PRC values above 0.92. Among these models, XGboost obtained the highest AU-ROC (0.94), AU-PRC (0.97), accuracy (0.91), precision (0.95), F1 score (0.94), MCC (0.77) and specificity (0.85), while MLP obtained the highest sensitivity (recall) (0.99) and NPV (0.87). In the validation set, KNN, LR and XGboost obtained AU-ROC and AU-PRC values above 0.80 and 0.85, respectively. KNN had the highest precision (0.82), both KNN and LR obtained the same highest accuracy (0.81), and KNN and LR had the same highest F1 score (0.86). Both DT and MLP obtained sensitivity (recall) and NPV values above 0.94 and 0.84, respectively. In the feature importance analyses, we identified that AQCI, age, and BMI had the greatest impact on the predictive abilities of the models, while SNPs, sex and smoking were less important.ConclusionsThe KNN, LR and XGboost models showed excellent overall predictive power, and the use of machine learning tools combining both clinical and SNP features was suitable for predicting the risk of COPD development.
Microglial physiological properties and interactions with synapses are altered at presymptomatic stages in a mouse model of Huntington’s disease pathology
Authors: Savage Julie C. , St-Pierre Marie-Kim , Carrier Micaël , El Hajj Hassan , Novak Sammy Weiser , Sanchez Maria Gabriela , Cicchetti Francesca , Tremblay Marie-Ève 
Published: April 2020

Abstract
Background
Huntington’s disease (HD) is a dominantly inherited neurodegenerative disorder that affects cognitive and motor abilities by primarily targeting the striatum and cerebral cortex. HD is caused by a mutation elongating the CAG repeats within the Huntingtin gene, resulting in HTT protein misfolding. Although the genetic cause of HD has been established, the specific susceptibility of neurons within various brain structures has remained elusive. Microglia, which are the brain’s resident macrophages, have emerged as important players in neurodegeneration. Nevertheless, few studies have examined their implication in HD.

Methods
To provide novel insights, we investigated the maturation and dysfunction of striatal microglia using the R6/2 mouse model of HD. This transgenic model, which presents with 120+/-5 CAG repeats, displays progressive motor deficits beginning at 6 weeks of age, with full incapacitation by 13 weeks. We studied microglial morphology, phagocytic capacity, and synaptic contacts in the striatum of R6/2 versus wild-type (WT) littermates at 3, 10, and 13 weeks of age, using a combination of light and transmission electron microscopy. We also reconstructed dendrites and determined synaptic density within the striatum of R6/2 and WT littermates, at nanoscale resolution using focused ion beam scanning electron microscopy.

Results
At 3 weeks of age, prior to any known motor deficits, microglia in R6/2 animals displayed a more mature morphological phenotype than WT animals. Microglia from R6/2 mice across all ages also demonstrated increased phagocytosis, as revealed by light microscopy and transmission electron microscopy. Furthermore, microglial processes from 10-week-old R6/2 mice made fewer contacts with synaptic structures than microglial processes in 3-week-old R6/2 mice and age-matched WT littermates. Synaptic density was not affected by genotype at 3 weeks of age but increased with maturation in WT mice. The location of synapses was lastly modified in R6/2 mice compared with WT controls, from targeting dendritic spines to dendritic trunks at both 3 and 10 weeks of age.

Conclusions
These findings suggest that microglia may play an intimate role in synaptic alteration and loss during HD pathogenesis.
Machine learning and clinical epigenetics: a review of challenges for diagnosis and classification
Authors: Rauschert S. , Raubenheimer K. , Melton P. E. , Huang R. C. 
Published: April 2020

Abstract
Background
Machine learning is a sub-field of artificial intelligence, which utilises large data sets to make predictions for future events. Although most algorithms used in machine learning were developed as far back as the 1950s, the advent of big data in combination with dramatically increased computing power has spurred renewed interest in this technology over the last two decades.

Main body
Within the medical field, machine learning is promising in the development of assistive clinical tools for detection of e.g. cancers and prediction of disease. Recent advances in deep learning technologies, a sub-discipline of machine learning that requires less user input but more data and processing power, has provided even greater promise in assisting physicians to achieve accurate diagnoses.
Within the fields of genetics and its sub-field epigenetics, both prime examples of complex data, machine learning methods are on the rise, as the field of personalised medicine is aiming for treatment of the individual based on their genetic and epigenetic profiles.

Conclusion
We now have an ever-growing number of reported epigenetic alterations in disease, and this offers a chance to increase sensitivity and specificity of future diagnostics and therapies. Currently, there are limited studies using machine learning applied to epigenetics. They pertain to a wide variety of disease states and have used mostly supervised machine learning methods.
Innovation in the public sector in a small open economy-initial investigation of patent activity at the Czech universities
Authors: Maresova Petra , Soukal Ivan , Stemberkova Ruzena , Kuca Kamil 
Published: April 2020

AbstractFor a successful economy, the interaction between research and its application in practice plays a key role. The aim of this paper is to analyze the institutional factors of patent activity at universities in the Czech Republic. The following five factors were selected for detailed investigation: budget from government, number of employees: professors, associate professor, and assistant professor, number of researchers and other employees, number of students (PhD, master, and bachelor), technology transfer workers. The patent activity analysis used methods of retrospective data analysis in the field of R&D activities, unstructured interviews, and statistical data processing. Following from the basic characteristics of the collected data, patent activity is related to the nature of universities, where technical universities are in leading positions in the monitored period of industrial-legal protection. However, the importance of encouraging innovation activity across all disciplines is crucial. The result of monitoring the dependency shows that a relationship of efficiencies and patent output is influenced by the size of a university. Secondly, there can be observed that technology transfer efficiency contributes more to the patent output that research staff efficiency.
Current understanding of metal ions in the pathogenesis of Alzheimer’s disease
Authors: Wang Lu , Yin Ya-Ling , Liu Xin-Zi , Shen Peng , Zheng Yan-Ge , Lan Xin-Rui , Lu Cheng-Biao , Wang Jian-Zhi 
Published: April 2020

AbstractBackgroundThe homeostasis of metal ions, such as iron, copper, zinc and calcium, in the brain is crucial for maintaining normal physiological functions. Studies have shown that imbalance of these metal ions in the brain is closely related to the onset and progression of Alzheimer’s disease (AD), the most common neurodegenerative disorder in the elderly.Main bodyErroneous deposition/distribution of the metal ions in different brain regions induces oxidative stress. The metal ions imbalance and oxidative stress together or independently promote amyloid-β (Aβ) overproduction by activating β- or γ-secretases and inhibiting α-secretase, it also causes tau hyperphosphorylation by activating protein kinases, such as glycogen synthase kinase-3β (GSK-3β), cyclin-dependent protein kinase-5 (CDK5), mitogen-activated protein kinases (MAPKs), etc., and inhibiting protein phosphatase 2A (PP2A). The metal ions imbalances can also directly or indirectly disrupt organelles, causing endoplasmic reticulum (ER) stress; mitochondrial and autophagic dysfunctions, which can cause or aggravate Aβ and tau aggregation/accumulation, and impair synaptic functions. Even worse, the metal ions imbalance-induced alterations can reversely exacerbate metal ions misdistribution and deposition. The vicious cycles between metal ions imbalances and Aβ/tau abnormalities will eventually lead to a chronic neurodegeneration and cognitive deficits, such as seen in AD patients.ConclusionThe metal ions imbalance induces Aβ and tau pathologies by directly or indirectly affecting multiple cellular/subcellular pathways, and the disrupted homeostasis can reversely aggravate the abnormalities of metal ions transportation/deposition. Therefore, adjusting metal balance by supplementing or chelating the metal ions may be potential in ameliorating AD pathologies, which provides new research directions for AD treatment.
Inference-based accuracy of metagenome prediction tools varies across sample types and functional categories
Authors: Sun Shan , Jones Roshonda B. , Fodor Anthony A. 
Published: April 2020

Abstract
Background
Despite recent decreases in the cost of sequencing, shotgun metagenome sequencing remains more expensive compared with 16S rRNA amplicon sequencing. Methods have been developed to predict the functional profiles of microbial communities based on their taxonomic composition. In this study, we evaluated the performance of three commonly used metagenome prediction tools (PICRUSt, PICRUSt2, and Tax4Fun) by comparing the significance of the differential abundance of predicted functional gene profiles to those from shotgun metagenome sequencing across different environments.

Results
We selected 7 datasets of human, non-human animal, and environmental (soil) samples that have publicly available 16S rRNA and shotgun metagenome sequences. As we would expect based on previous literature, strong Spearman correlations were observed between predicted gene compositions and gene relative abundance measured with shotgun metagenome sequencing. However, these strong correlations were preserved even when the abundance of genes were permuted across samples. This suggests that simple correlation coefficient is a highly unreliable measure for the performance of metagenome prediction tools. As an alternative, we compared the performance of genes predicted with PICRUSt, PICRUSt2, and Tax4Fun to sequenced metagenome genes in inference models associated with metadata within each dataset. With this approach, we found reasonable performance for human datasets, with the metagenome prediction tools performing better for inference on genes related to “housekeeping” functions. However, their performance degraded sharply outside of human datasets when used for inference.

Conclusion
We conclude that the utility of PICRUSt, PICRUSt2, and Tax4Fun for inference with the default database is likely limited outside of human samples and that development of tools for gene prediction specific to different non-human and environmental samples is warranted.
PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers
Authors: Martí-Bonmatí Luis , Alberich-Bayarri Ángel , Ladenstein Ruth , Blanquer Ignacio , Segrelles J. Damian , Cerdá-Alberich Leonor , Gkontra Polyxeni , Hero Barbara , García-Aznar J. M. , Keim Daniel , Jentner Wolfgang , Seymour Karine , Jiménez-Pastor Ana , González-Valverde Ismael , Martínez de las Heras Blanca , Essiaf Samira , Walker Dawn , Rochette Michel , Bubak Marian , Mestres Jordi , Viceconti Marco , Martí-Besa Gracia , Cañete Adela , Richmond Paul , Wertheim Kenneth Y. , Gubala Tomasz , Kasztelnik Marek , Meizner Jan , Nowakowski Piotr , Gilpérez Salvador , Suárez Amelia , Aznar Mario , Restante Giuliana , Neri Emanuele 
Published: April 2020

AbstractPRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in children. It is a 4-year European Commission-financed project that has 16 European partners in the consortium, including the European Society for Paediatric Oncology, two imaging biobanks, and three prominent European paediatric oncology units. The project is constructed as an observational in silico study involving high-quality anonymised datasets (imaging, clinical, molecular, and genetics) for the training and validation of machine learning and multiscale algorithms. The open cloud-based platform will offer precise clinical assistance for phenotyping (diagnosis), treatment allocation (prediction), and patient endpoints (prognosis), based on the use of imaging biomarkers, tumour growth simulation, advanced visualisation of confidence scores, and machine-learning approaches. The decision support prototype will be constructed and validated on two paediatric cancers: neuroblastoma and diffuse intrinsic pontine glioma. External validation will be performed on data recruited from independent collaborative centres. Final results will be available for the scientific community at the end of the project, and ready for translation to other malignant solid tumours.
Decrease of 14–3-3 proteins by glutamate exposure in the cerebral cortex of newborn rats
Authors: Kang Ju-Bin , Lee Seung-Yun , Park Dong-Ju , Koh Phil-Ok 
Published: April 2020

AbstractGlutamate is a representative excitatory neurotransmitter. However, excessive glutamate exposure causes neuronal cell damage by generating neuronal excitotoxicity. Excitotoxicity in neonates caused by glutamate treatment induces neurological deficits in adults. The 14–3-3 family proteins are conserved proteins that are expressed ubiquitously in a variety of tissues. These proteins contribute to cellular processes, including signal transduction, protein synthesis, and cell cycle control. We proposed that glutamate induces neuronal cell damage by regulating 14–3-3 protein expression in newborn animals. In this study, we investigated the histopathological changes and 14–3-3 proteins expressions as a result of glutamate exposure in the neonatal cerebral cortex. Rat pups at post-natal day 7 were intraperitoneally administrated with vehicle or glutamate (10 mg/kg). Animals were sacrificed 4 h after treatment, and brain tissues were fixed for histological study. Cerebral cortices were isolated and frozen for proteomic study. We observed serious histopathological damages including shrunken dendrites and atypical neurons in glutamate-treated cerebral cortices. In addition, we identified that 14–3-3 family proteins decreased in glutamate-exposed cerebral cortices using a proteomic approach. Moreover, Western blot analysis provided results that glutamate treatment in neonates decreased 14–3-3 family proteins expressions, including the β/α, ζ/δ, γ, ε, τ, and η isoforms. 14–3-3 proteins are involved in signal transduction, metabolism, and anti-apoptotic functions. Thus, our findings suggest that glutamate induces neonatal neuronal cell damage by modulating 14–3-3 protein expression.
Current Perspectives on Therapy for Chronic Lymphocytic Leukemia
Authors: Awan Farrukh T. , Al-Sawaf Othman , Fischer Kirsten , Woyach Jennifer A. 
Published: April 2020

Therapy for chronic lymphocytic leukemia has improved dramatically over the past decade with the introduction of new targeted therapies and a paradigm shift toward targeted therapies for the majority of patients. Better understanding of prognostic factors has helped tailor therapy for individual patients, and work continues to identify optimal therapy for each patient. When therapy is required, most patients will be treated with targeted therapies, either the Bruton tyrosine kinase (BTK) inhibitors ibrutinib or acalabrutinib or the BCL-2 inhibitor venetoclax in combination with obinutuzumab. Without head-to-head comparisons showing differential efficacy among these options, considerations regarding safety, patient preference, and ability to sequence therapy currently influence treatment decisions. Also, clinical trials investigating combinations of these therapies have the potential to further change the standard of care. In this review, we cover the currently available options for the frontline treatment of chronic lymphocytic leukemia (CLL) and discuss safety considerations and toxicity management with each agent as well as novel combination strategies currently under investigation.
Approach to Palliative Care Consultation for Patients With Malignant Bowel Obstruction in Gynecologic Oncology: A Qualitative Analysis of Physician Perspectives
Authors: Hoppenot Claire , Hlubocky Fay J. , Chor Julie , Yamada S. Diane , Lee Nita K. 
Published: April 2020

PURPOSE: Malignant bowel obstruction (MBO) from gynecologic cancer is associated with increased symptoms and short survival. A gynecologic oncologist’s approach to palliative care consultation in the setting of MBO has not been well studied—it could be an opportune time for collaboration with palliative care. MATERIALS AND METHODS: This qualitative analysis of interviews with gynecologic oncologists focuses on their perspectives on palliative care consultation at the time of MBO. Interviews were analyzed using a framework analysis, and key themes and quotations were extracted. RESULTS: We interviewed 15 gynecologic oncologists from 8 institutions in Chicago. They described a variety of expectations from palliative care consultation. Most frequently, they consulted palliative care for specific questions but managed the remainder of the care. Most participants frequently consulted palliative care, but they also worried about fragmentation of care, the timing of when to introduce a new team during MBO, and the selection of appropriate patients for a limited resource. Many participants preferred earlier palliative care consultation, and many described an emotional toll of caring for patients with MBO. Palliative care consultation was most readily discussed for nonsurgical patients. CONCLUSION: Participants’ expectations of palliative care consultations during MBO varied and were not always met. We recommend strengthening communication and protocols for palliative care involvement that meet the needs of specific patient populations and physician teams for surgical and nonsurgical patients. More research is needed to better understand how to integrate palliative care into oncologic and surgical care with gynecologic oncologists.