bluuebunny/crossref_metadata_2025_split huggingface dataset:
A novel lncRNA ROPM-mediated lipid metabolism governs breast cancer stem cell properties
Authors: Liu Shuiqing , Sun Yan , Hou Yixuan , Yang Liping , Wan Xueying , Qin Yilu , Liu Yongcan , Wang Rui , Zhu Pengpeng , Teng Yong , Liu Manran 
Published: October 2021

AbstractBackgroundCancer stem cells (CSCs) are considered as the major cause to tumor initiation, recurrence, metastasis, and drug resistance, driving poor clinical outcomes in patients. Long noncoding RNAs (lncRNAs) have emerged as crucial regulators in cancer development and progression. However, limited lncRNAs involved in CSCs have been reported.MethodsThe novel lncROPM (a regulator of phospholipid metabolism) in breast CSCs (BCSCs) was identified by microarray and validated by qRT-PCR in BCSCs from breast cancer cells and tissues. The clinical significance of lncROPM was evaluated in two breast cancer cohorts and TANRIC database (TCGA-BRCA, RNAseq data). Gain- and loss-of-function assays were performed to examine the role of lncROPM on BCSCs both in vitro and in vivo. The regulatory mechanism of lncROPM was investigated by bioinformatics, RNA FISH, RNA pull-down, luciferase reporter assay, and actinomycin D treatment. PLA2G16-mediated phospholipid metabolism was determined by UHPLC-QTOFMS system. Cells’ chemosensitivity was assessed by CCK8 assay.ResultsLncROPM is highly expressed in BCSCs. The enhanced lncROPM exists in clinic breast tumors and other solid tumors and positively correlates with malignant grade/stage and poor prognosis in breast cancer patients. Gain- and loss-of-function studies show that lncROPM is required for the maintenance of BCSCs properties both in vitro and in vivo. Mechanistically, lncROPM regulates PLA2G16 expression by directly binding to 3'-UTR of PLA2G16 to increase the mRNA stability. The increased PLA2G16 significantly promotes phospholipid metabolism and the production of free fatty acid, especially arachidonic acid in BCSCs, thereby activating PI3K/AKT, Wnt/β-catenin, and Hippo/YAP signaling, thus eventually involving in the maintenance of BCSCs stemness. Importantly, lncROPM and PLA2G16 notably contribute to BCSCs chemo-resistance. Administration of BCSCs using clinic therapeutic drugs such as doxorubicin, cisplatin, or tamoxifen combined with Giripladib (an inhibitor of cytoplasmic phospholipase A2) can efficiently eliminate BCSCs and tumorigenesis.ConclusionsOur study highlights that lncROPM and its target PLA2G16 play crucial roles in sustaining BCSC properties and may serve as a biomarker for BCSCs or other cancer stem cells. Targeting lncROPM-PLA2G16 signaling axis may be a novel therapeutic strategy for patients with breast cancer.
Plasma cell infiltration and treatment effect in breast cancer patients treated with neoadjuvant chemotherapy
Authors: Sakaguchi Asumi , Horimoto Yoshiya , Onagi Hiroko , Ikarashi Daiki , Nakayama Takayuki , Nakatsura Tetsuya , Shimizu Hideo , Kojima Kuniaki , Yao Takashi , Matsumoto Toshiharu , Ogura Kanako , Kitano Shigehisa 
Published: October 2021

Abstract
Background
Tumour-infiltrating lymphocyte (TIL)-high breast tumours have a high rate of pathological complete response (pCR) with neoadjuvant chemotherapy. In our routine pathological diagnoses of biopsy specimens from pCR cases, we have observed a high infiltration of plasma cells (PCs). A positive correlation of PCs with favourable patient outcome has recently been reported, but little is known about how PCs contribute to local tumour immunity.

Methods
We retrospectively examined biopsy specimens from 146 patients with invasive breast cancer who received neoadjuvant chemotherapy. CD138+ PC infiltration was assessed by immunohistochemistry. Multiplexed fluorescent immunohistochemistry (mfIHC) with T and B cell markers was also conducted to elucidate the profile of immune cells.

Results
Greater PC infiltration was observed in the pCR group (p = 0.028) and this trend was confirmed in another patient cohort. With mfIHC, we observed significantly more CD8+, T-bet+CD4+, and CD8+FOXP3+ T cells, total B cells and PCs in pCR cases. Such cases were also characterised by high expression of both PD-1 and PD-L1 on B cells and PCs. In patients with hormone receptor-negative tumours, high PC infiltration was correlated with significantly longer disease-free survival (p = 0.034).

Conclusions
We found that higher PC infiltration in biopsy specimens before neoadjuvant chemotherapy was associated with pCR. With mfIHC, we also revealed that the local cytotoxic immune response was clearly enhanced in pCR cases, as was the infiltration of B cells including PCs. Moreover, higher PC levels were correlated with favourable outcomes in hormone receptor-negative breast cancer patients.
Sustained microglial activation in the area postrema of collagen-induced arthritis mice
Authors: Matsushita Takayuki , Otani Kazuhiro , Oto Yohsuke , Takahashi Yukari , Kurosaka Daitaro , Kato Fusao 
Published: October 2021

AbstractBackgroundCentral nervous system (CNS)-mediated symptoms, such as fatigue, depression, and hyperalgesia, are common complications among patients with rheumatoid arthritis (RA). However, it remains unclear how the peripheral pathology of RA spreads to the brain. Accumulated evidence showing an association between serum cytokine levels and aberrant CNS function suggests that humoral factors participate in this mechanism. In contrast to the well-known early responses of microglia (CNS-resident immune cells) in the area postrema [AP; a brain region lacking a blood–brain barrier (BBB)] to experimental inflammation, microglial alterations in the AP during chronic inflammation like RA remain unclear. Therefore, to determine whether microglia in the AP can react to persistent autoimmune-arthritis conditions, we analyzed these cells in a mouse model of collagen-induced arthritis (CIA).MethodsMicroglial number and morphology were analyzed in the AP of CIA and control mice (administered Freund’s adjuvant or saline). Immunostaining for ionized calcium-binding adaptor molecule-1 was performed at various disease phases: “pre-onset” [post-immunization day (PID) 21], “establishment” (PID 35), and “chronic” (PID 56 and 84). Quantitative analyses of microglial number and morphology were performed, with principal component analysis used to classify microglia. Interleukin-1β (IL-1β) mRNA expression was analyzed by multiple fluorescent in situ hybridization and real-time polymerase chain reaction. Behavioral changes were assessed by sucrose preference test.ResultsMicroglia in the AP significantly increased in density and exhibited changes in morphology during the establishment and chronic phases, but not the pre-onset phase. Non-subjective clustering classification of cell morphology (CIA, 1,256 cells; saline, 852 cells) showed that the proportion of highly activated microglia increased in the CIA group during establishment and chronic phases. Moreover, the density of IL-1β-positive microglia, a hallmark of functional activation, was increased in the AP. Sucrose preferences in CIA mice negatively correlated with IL-1β expression in brain regions containing the AP.ConclusionsOur findings demonstrate that microglia in the AP can sustain their activated state during persistent autoimmune arthritis, which suggests that chronic inflammation, such as RA, may affect microglia in brain regions lacking a BBB and have various neural consequences.
Self-isolation negatively impacts self-management of diabetes during the coronavirus (COVID-19) pandemic
Authors: Pardhan Shahina , Islam Md. Saiful , López-Sánchez Guillermo F. , Upadhyaya Tirthalal , Sapkota Raju P. 
Published: October 2021

Abstract
Background/Aim
People with diabetes are at a greater risk of serious complications from Coronavirus disease (COVID-19). Self-management of diabetes is therefore of paramount importance. The purpose of this study is to compare self-management of diabetes pre-COVID-19 and during the COVID-19 pandemic.

Methods
679 participants with diabetes completed an online structured questionnaire survey. Various exposure variables (demographics, duration, treatment and complications of diabetes, self-isolation, etc.) were analysed to examine associations with the following outcome variables: (i) fluctuation of blood glucose levels, (ii) access to diabetes medicine, (iii) access to healthy diet, (iv) physical activity. Adjusted multiple regression analysis ascertained significant associations for each outcome variable against exposure variables.

Results
Multiple regression analysis showed that self-isolation was significantly associated with greater fluctuation in blood glucose levels (OR = 1.8, 95% CI = 1.2–2.6, p = 0.005), reduced access to diabetes medicine (OR = 1.9, 95% CI = 1.1–3.1, p = 0.02) and reduced access to healthy diet (OR = 3.0, 95% CI = 2.0–4.6, p < 0.001). Fluctuation in blood glucose level was also significantly associated with having at least one complication of diabetes (OR = 2.2, 95% CI = 1.2–3.9, p = 0.008) and reduced access to diabetes medicine was significantly higher in people who were on insulin (OR = 2.1, 95% CI = 1.3–3.3, p = 0.001).

Conclusions
Self-isolation was shown to impact almost all factors that influence self-management of diabetes. A targeted approach to improved access to diabetes medicine, healthy diet for people who needed to self-isolate is vital in order to ensure that they are able to self-manage their diabetes effectively.
The clone devaluation effect: does duplication of local facial features matter?
Authors: Yonemitsu Fumiya , Sasaki Kyoshiro , Gobara Akihiko , Yamada Yuki 
Published: October 2021

Abstract
Objective
The clone devaluation is a phenomenon reported by the latest paper in which eeriness is evoked when people observe individuals with the same face (clone faces) compared to those with different faces. There are two possibilities that explain the clone devaluation effect. One is that the same facial features that clone faces have (duplication of facial features) induce the clone devaluation effect. The other possibility is that duplication of identities between people with clone faces is important for the clone devaluation effect. We thus conducted an experiment to investigate whether the duplication of identities or of facial features induces the clone devaluation effect.

Results
Participants evaluated eeriness of scrambled clone faces and scrambled different faces using the paired comparison method. There was only a slight difference in subjective eeriness between scrambled clone faces and scrambled different faces. Therefore, this study suggests that the duplication of local facial features does not play a key role in inducing the clone devaluation effect.
MS2DeepScore: a novel deep learning similarity measure to compare tandem mass spectra
Authors: Huber Florian , van der Burg Sven , van der Hooft Justin J. J. , Ridder Lars 
Published: October 2021

AbstractMass spectrometry data is one of the key sources of information in many workflows in medicine and across the life sciences. Mass fragmentation spectra are generally considered to be characteristic signatures of the chemical compound they originate from, yet the chemical structure itself usually cannot be easily deduced from the spectrum. Often, spectral similarity measures are used as a proxy for structural similarity but this approach is strongly limited by a generally poor correlation between both metrics. Here, we propose MS2DeepScore: a novel Siamese neural network to predict the structural similarity between two chemical structures solely based on their MS/MS fragmentation spectra. Using a cleaned dataset of > 100,000 mass spectra of about 15,000 unique known compounds, we trained MS2DeepScore to predict structural similarity scores for spectrum pairs with high accuracy. In addition, sampling different model varieties through Monte-Carlo Dropout is used to further improve the predictions and assess the model’s prediction uncertainty. On 3600 spectra of 500 unseen compounds, MS2DeepScore is able to identify highly-reliable structural matches and to predict Tanimoto scores for pairs of molecules based on their fragment spectra with a root mean squared error of about 0.15. Furthermore, the prediction uncertainty estimate can be used to select a subset of predictions with a root mean squared error of about 0.1. Furthermore, we demonstrate that MS2DeepScore outperforms classical spectral similarity measures in retrieving chemically related compound pairs from large mass spectral datasets, thereby illustrating its potential for spectral library matching. Finally, MS2DeepScore can also be used to create chemically meaningful mass spectral embeddings that could be used to cluster large numbers of spectra. Added to the recently introduced unsupervised Spec2Vec metric, we believe that machine learning-supported mass spectral similarity measures have great potential for a range of metabolomics data processing pipelines.
Modeling the time course of ComX: towards molecular process control for Bacillus wild-type cultivations
Authors: Treinen Chantal , Magosch Olivia , Hoffmann Mareen , Klausmann Peter , Würtz Berit , Pfannstiel Jens , Morabbi Heravi Kambiz , Lilge Lars , Hausmann Rudolf , Henkel Marius 
Published: October 2021

AbstractWild-type cultivations are of invaluable relevance for industrial biotechnology when it comes to the agricultural or food sector. Here, genetic engineering is hardly applicable due to legal barriers and consumer’s demand for GMO-free products. An important pillar for wild-type cultivations displays the genus Bacillus. One of the challenges for Bacillus cultivations is the global ComX-dependent quorum sensing system. Here, molecular process control can serve as a tool to optimize the production process without genetic engineering. To realize this approach, quantitative knowledge of the mechanism is essential, which, however, is often available only to a limited extent. The presented work provides a case study based on the production of cyclic lipopeptide surfactin, whose expression is in dependence of ComX, using natural producer B. subtilis DSM 10 T. First, a surfactin reference process with 40 g/L of glucose was performed as batch fermentation in a pilot scale bioreactor system to gain novel insights into kinetic behavior of ComX in relation to surfactin production. Interestingly, the specific surfactin productivity did not increase linearly with ComX activity. The data were then used to derive a mathematic model for the time course of ComX in dependence of existing biomass, biomass growth as well as a putative ComX-specific protease. The newly adapted model was validated and transferred to other batch fermentations, employing 20 and 60 g/L glucose. The applied approach can serve as a model system for molecular process control strategies, which can thus be extended to other quorum sensing dependent wild-type cultivations.
PRCC reduces the sensitivity of cancer cells to DNA damage by inhibiting JNK and ATM/ATR pathways and results in a poor prognosis in hepatocellular carcinoma
Authors: Liu Chunying , Lin Xuejing , Sun Bin , Mao Ziming , Chen Lei , Qian Haihua , Su Changqing 
Published: October 2021

Abstract
Background and aim
The proline rich mitotic checkpoint control factor (PRCC) is involved in the splicing process of pre-mRNA. This study aims to elucidate PRCC molecular function, regulatory mechanism and diagnostic value in hepatocellular carcinoma (HCC).

Methods
The tissue microarray and serum samples from HCC patients were used to investigate the clinical value of PRCC. The biological function and molecular mechanism of PRCC were demonstrated by cell biology, biochemical and animal experiments. The relationship between PRCC and intratumoral heterogeneity (ITH) was analyzed by bioinformatics.

Results
PRCC was highly expressed in HCC tissues and related to the poor prognosis of HCC patients, its contents were elevated in the preoperative sera of HCC patients. PRCC exhibited high application potential as a substitute or adjuvant of alpha-fetoprotein (AFP) for clinical diagnosis of HCC. It had no significant effect on the proliferation of cancer cells, but could inhibit spheroid formation and metastasis of HCC cells in vitro and in vivo. The high ectopic expression of PRCC made cancer cells insensitive to DNA damage, and enhanced the heterogeneity of HCC cells by inhibiting the JNK/ATM/ATR/ATF2 axis. The HCC patients with high PRCC expression had high ITH, which corresponded to a short overall survival in patients.

Conclusions
PRCC has high application potential as a substitute or adjuvant of AFP for clinical diagnosis of HCC. The high ectopic expression of PRCC not only caused HCC cells to resist to cell death induced by DNA damage, but also endowed cancer cells with numerous DNA mutations to become increasingly heterogeneous, finally leading to a poor prognosis in HCC patients. These data suggested PRCC could be a promising therapeutic target in HCC patients.
Noise prediction of chemical industry park based on multi-station Prophet and multivariate LSTM fitting model
Authors: Zeng Qingtian , Liang Yu , Chen Geng , Duan Hua , Li Chunguo 
Published: October 2021

AbstractWith the gradual transformation of chemical industry park to digital and intelligent, various types of environmental data in the park are extremely rich. It has high application value to provide safe production environment by deeply mining environmental data law and providing data support for industrial safety and workers’ health in the park through prediction means. This paper takes the noise data of the chemical industry park as the main research object, and innovatively applies the 3σ principle to the zero-value processing of the noise data, and builds an LSTM model that integrates multivariate information based on the characteristics of the wind direction classification noise data combined with the wind speed and vehicle flow information. The Prophet model integrating multi-site noise information was adopted, and the Multi-PL model was constructed by fitting the above two models to predict the noise. This paper designs and implements a comparative experiment with Kalman filter, BP neural network, Prophet, LSTM, Prophet + LSTM weighted combination prediction model. R2 was used to evaluate the fitting effect of single model in Multi-PL, RMSE and MAE that were used to evaluate the prediction effect of Multi-PL on noise time series. The experimental results show that the RMSE and MAE of the data processed by the 3σ principle are reduced by 32.2% and 23.3% in the multi-station ordered Prophet method, respectively. Compared with the above comparison models, the Multi-PL model prediction method is more stable and accurate. Therefore, the Multi-PL method proposed in this paper can provide a new idea for noise prediction in digital chemical parks.
Protocol for a systematic review and meta-analysis of observational studies examining the impact of COVID-19 safety measures on physical activity patterns in adults
Authors: Schwendinger Fabian , Infanger Denis , Pocecco Elena , Gander Joséphine , Hinrichs Timo , Schmidt-Trucksäss Arno 
Published: October 2021

AbstractBackgroundThe primary objective of this study is twofold: (1) to examine the effect of COVID-19 safety measures, enacted to prevent transmission of SARS-nCOV-2, on total physical activity in the adult general population (≥ 18 years) and (2) to analyze the impact of the factor “severity of safety measures” on potential changes in physical activity. The secondary objective is to investigate the effects of safety measures on the respective PA intensities, i.e., sedentary behavior, light, moderate, and vigorous physical activity.MethodsA systematic literature search will be performed in the following online databases: Medline (on Ovid), Web of Science, Scopus, L.OVE Coronavirus disease by Epistemonikos, and ProQuest Dissertations & Theses A&I. All obtained citations will undergo title and abstract as well as full-text screening by two independent reviewers. Observational studies investigating the effects of safety measures on physical activity patterns in the adult general population will be included. The standardized mean difference in total physical activity per time unit between pre- and during COVID-19 or between normative data and during COVID-19 will be the primary outcome. The standardized mean difference in sedentary time, light, moderate, and vigorous physical activity will be assessed as secondary outcomes. Eligible studies will be divided between the reviewers for data extraction using a pilot-tested data form. Risk of bias assessment will be performed using a standard assessment tool. If suitable, a random-effects meta-analysis and meta-regression with a unit of safety measure severity as the independent variable will be performed.DiscussionThis study will synthesize available data reporting the effect of COVID-19 safety measures on physical activity patterns in adults. Furthermore, we will incorporate a unit for the severity of safety measures for better generalizability of the results. These findings will be of great value for public health policymaking and estimating future health consequences.Systematic review registrationPROSPERO CRD42021231039.
Decision-making in sustainable energy transition in Southeastern Europe: probabilistic network-based model
Authors: Hribar Nena , Šimić Goran , Vukadinović Simonida , Šprajc Polona 
Published: October 2021

Abstract
Background
Sustainable energy transition of a country is complex and long-term process, which requires decision-making in all stages and at all levels, including a large number of different factors, with different causality. The main objective of this paper is the development of a probabilistic model for decision-making in sustainable energy transition in developing countries of SE Europe. The model will be developed according to the specificities of the countries for which it is intended—SE Europe. These are countries where energy transition is slower and more difficult due to many factors: high degree of uncertainty, low transparency, corruption, investment problems, insufficiently reliable data, lower level of economic development, high level of corruption and untrained human resources. All these factors are making decision-making more challenging and demanding.

Methods
Research was done by using content analysis, artificial intelligence methods, software development method and testing. The model was developed by using MSBNx—Microsoft Research’s Bayesian Network Authoring and Evaluation Tool.

Results
Due to the large number of insufficiently clear, but interdependent factors, the model is developed on the principle of probabilistic (Bayesian) networks of factors of interest. The paper presents the first model for supporting decision-making in the field of energy sustainability for the region of Southeastern Europe, which is based on the application of Bayesian Networks.

Conclusion
Testing of the developed model showed certain characteristics, discussed in paper. The application of developed model will make it possible to predict the short-term and long-term consequences that may occur during energy transition by varying these factors. Recommendations are given for further development of the model, based on Bayesian networks.