bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving Tumoral Acidic pH-Responsive Drug Efficiency Based on GO@Au Nanocomposite
Authors: Tang Mingqing , Xin Pengliang , Lin Hongjun , Li Huangen 
Published: August 2020

Abstract
Background: to accurately deliver drugs to the lesion site and realize its timed and quantitative release is the focus of tumor therapy. In this paper, a tumor targeted near-infrared light controlled release drug delivery system integrating hyperthermia and chemotherapy was successfully constructed by combining tumor hyperthermia and chemotherapy. Results: by hydrothermal method, Au nanoparticles were firstly loaded onto the GO surface to obtain GO@Au nanocomposites, then PEG was loaded onto the GO@Au by pH sensitive hydrazone bond to obtain GO@Au-PEG, and DOX is finally loaded onto the GO surface via π-π stacking. The target controlled release drug delivery system showed good tumor targeting property, can be the efficient transfer of drug to the tumor cells, and the tumor cells in near-infrared light control of DOX release. In vivo tumor inhibition experiments in mice, the targeted drug delivery system showed obvious tumor inhibition characteristics and had good effects of hyperthermia and chemotherapy. Conclusions: therefore, this tumor targeted near-infrared controlled release drug delivery system has great potential in tumor therapy.
The lncRNA MIR31HG regulates the senescence associated secretory phenotype
Authors: Montes Marta , Lubas Michael , Arendrup Frederic S , Mentz Bettina , Rohatgi Neha , Tumas Sarunas , Harder Lea M , Skanderup Anders J , Andersen Jens S , Lund Anders H 
Published: August 2020

Abstract
Senescent cells secrete cytokines, chemokines and growth factors collectively known as the senescence-associated secretory phenotype (SASP) which can reinforce senescence and activate the immune response. However, it can also negatively impact neighbouring tissues facilitating tumor progression. We have previously shown that in proliferating cells the nuclear long non-coding RNA (lncRNA) MIR31HG inhibits p16/CDKN2A expression through interaction with polycomb repressor complexes (PRC1/2) and that during BRAF-induced senescence MIR31HG is overexpressed and translocates to the cytoplasm. Here, we show that MIR31HG regulates the expression of a subset of SASP components during BRAF-induced senescence. The SASP secreted from senescent cells depleted for MIR31HG fails to induce paracrine invasion without affecting the growth inhibitory effect. Mechanistically, MIR31HG interacts with YBX1 facilitating its phosphorylation at serine 102 (p-YBX1S102) by the kinase RSK. p-YBX1S102 induces IL1A translation which acts as upstream regulator inducing the transcription of the other SASP mRNAs. Our results suggest a dual role for MIR31HG in senescence depending on its cellular localization and points to the lncRNA as a potential therapeutic target in the treatment of senescence-related pathologies.
Tissue dose estimation after extravasation of 177Lu-DOTATATE&nbsp;&nbsp;
Authors: Tylski Perrine , Pina-Jomir Géraldine , Bournaud-Salinas Claire , Jalade Patrice 
Published: August 2020

Abstract
Background. Extravasation of radiopharmaceuticals used for vectorized internal radiotherapy can lead to severe tissue damage (1). Clinical management of these extravasations requires the preliminary estimation of the dose distribution in the extravasation area. Data are scarce regarding the dose estimation in the literature. This work presents a methodology for estimating the dose distribution after an extravasation occurred in September 2017, in the arm of a patient during a 7.4 GBq infusion of Lutathera ® (AAA). Methods. A local quantification procedure initially developed for renal dosimetry was used. A calibration factor was determined and verified by phantom study. Extravasation volume of interest and its variation in time were determined using 4 whole body (WB) planar acquisitions performed at 2h (T2h), 5h (T5h), 20h (T20h) and 26h (T26h) after the beginning of the infusion and three SPECT/CT thoracic acquisitions at T5h, T20h and T26h. For better estimation of initial extravasation volume, 3 volumes were defined on SPECT images using a 3D activity threshold. Cumulated activities and associated absorbed doses (D1, D2, D3) were calculated in the 3 volumes using the MIRD formalism.Results. Volumes estimated using 3D threshold were V1= 1000 mL, V2=400 mL and V3=180 mL. Cumulated activities were evaluated using a mono exponential fit on activities calculated on SPECT images. Estimated local absorbed doses in V1, V2 and V3 were D1 = 2.8 Gy, D2 = 5.4 Gy and D3 = 7.8 Gy. Evolution in time of local activity in the extravasation area was consistent with an effective local period (Teff) of 3 hours.Conclusions. Rapid local doses estimation was permitted thank to knowledge of the calibration factor determined previous to accidental extravasation. Luthatera® lymphatic drainage was quick in the arm (Teff = 3h). Estimated doses were in the lower range of deterministic effects and far under soft tissue necrosis threshold. Thus no surgical rinse was proposed. The patient did not show any clinical consequence of theses extravasation.
Characteristics of registered systematic reviews on traditional Chinese medicine for COVID-19
Authors: Lian Rongna , Gao Ya , Zhang Ruinian , Xie Dairong , Zhang Yi , Zhang Mei , Zhang Junhua , Tian Jinhui 
Published: August 2020

Abstract
Background: Characteristics of registered systematic reviews (SRs) on traditional Chinese medicine (TCM) for coronavirus disease 2019 (COVID-19) remain unclear. This study aimed to analyze research collaboration, interventions, and outcomes in registered SRs of TCM associated with COVID-19.Methods: PROSPERO was searched to obtain SRs of TCM related to COVID-19 up to July 1, 2020. VOSviewer 1.6.14 software was used to generate network maps for countries, institutions, and provinces.Results: A total of 80 SRs were included, which were registered by 81 institutions from 6 countries. China (76, 95.00%) was the country with the highest output. 21 provinces in China contributed to the registration of SRs, Sichuan (25, 30.12%), Beijing (13, 15.66%), and Shaanxi (7, 8.43%) were the top three productive provinces. The top three productive institutions were Chengdu University of traditional Chinese medicine (18, 24.66%), Shaanxi University of traditional Chinese medicine (7, 9.59%), and Beijing University of traditional Chinese medicine (6, 8.22%). Collaborations among countries, provinces, and institutions were sparse. Interventions investigated included traditional Chinese medicine, integrated Chinese and Western medicine, acupuncture, and Taijiquan, but the description was brief, and no specific implementation plan was provided. The most frequently used primary outcome was clinical efficiency (45, 56.25%), and the most frequently used secondary outcome was the adverse event (50, 62.5%). The expression of the outcomes was not standardized.Conclusions: Although there were some collaborations between provinces and institutions, cooperation between countries should be further strengthened. The identified deficiencies in interventions and outcomes should be given more attention by future researchers.
Prediction of fasting plasma glucose and glycated haemoglobin using machine learning based on tongue features
Authors: Li Jun , Huang Jingbin , Tu Liping , Cui Longtao , Cui Ji , Ma Xuxiang , Jiang Tao , Yao Xinghua , Li Jiacai , Shi Yulin , Bi Zijuan , Wang Yu , Fu Hongyuan , Wang Jue , Lin Yenting , Pai ChingHsuan , Guo Xiaojing , Zhou Changle , Hu Xiaojuan , Xu Jiatuo 
Published: August 2020

Abstract
Background

Given tongue features and basic features, this study aimed to develop and assess a non-invasive machine learning model to perform regression prediction on fasting plasma glucose and glycated haemoglobin which will help optimize diabetes risk warning.
Methods

We collected the basic features, tongue features and blood features of the subjects. Using machine learning algorithms to analyze these data, we built models to predict fasting plasma glucose and glycated haemoglobin. Then the performance of the models was evaluated through 5-fold cross-validation results and test set results.
Results

The results of cross validation on the training set showed that given non-invasive input features, the minimum average mean square error of fasting plasma glucose and glycated haemoglobin prediction was 1.227 and 0.438. Our non-invasive fasting plasma glucose prediction model with tongue features and basic features combined achieved a minimum mean square error of 0.601 and a maximum coefficient of determination of 0.606 on the test set. The glycated haemoglobin prediction model product a minimum mean square error of 0.272 and a maximum coefficient of determination of 0.539 on the test set. The Clarke’s Error Grid Analysis showed that the non-invasive blending model had 90.83% of points in zone A and 8.49% of points in zone B on the test set.
Conclusions

We developed an effective non-invasive method for estimating fasting plasma glucose and glycated haemoglobin from tongue features and basic features combined, which may help identify individuals at high risk for diabetes.
Inhibition of Cronobacter Sakazakii Biofilm Formation and Expression of Virulence Factors by Coenzyme Q0
Authors: Guan Ning , Shi Yiqi , Tong Haoyu , Yang Yanpeng , Li Jiahui , Guo Du , Wang Yutang , Xia Xiaodong , Shi Chao 
Published: August 2020

Abstract
Objectives：Here, we investigated the inhibitory effects of coenzyme Q0 (CoQ0) on biofilm formation and the expression of virulence genes by Cronobacter sakazakii. Results：We found that the minimum inhibitory concentration of CoQ0 against C. sakazakii strains ATCC29544 and ATCC29004 was 100 μg/mL, while growth curve assays showed that sub-inhibitory concentrations (SICs) of CoQ0 for both strains were 6.4, 3.2, 1.6 and 0.8 μg/mL. Assays exploring the inhibition of specific biofilm formation showed that SICs of CoQ0 inhibited biofilm formation by C. sakazakii in a dose-dependent manner, which was confirmed by scanning electron microscopy and confocal laser scanning microscopy analyses. CoQ0 inhibited the swimming and swarming motility of C. sakazakii and reduced its ability to adhere to and invade HT-29 cells. In addition, CoQ0 impeded the ability of C. sakazakii to survive and replicate within RAW 264.7 cells. Finally, real time polymerase chain reaction analysis confirmed that nine C. sakazakii genes associated with biofilm formation and virulence were down-regulated in response to CoQ0 treatment. Conclusion：Overall, our findings suggest that CoQ0 is a promising antibiofilm agent and provide new insights for the prevention and control of infections caused by C. sakazakii.
Single-Step Ethanol Production from Raw Cassava Starch Using a Combination of Raw Starch Hydrolysis and Fermentation, Scale-Up from 5-L Laboratory and 200-L Pilot Plant to 3,000-L Industrial Fermenters
Authors: Krajang Morakot , Malairuang Kwanruthai , Sukna Jatuporn , Rattanapradit Krongchan , Chamsart Saethawat 
Published: August 2020

Abstract
Background: A single-step ethanol production is the combination of raw cassava starch hydrolysis and fermentation. For the development of raw starch consolidated bioprocessing (CBP) technologies, this research work was to investigate the optimum conditions and technical procedures for the production of ethanol from raw cassava starch in a single step. This resulted high yields and productivities of all the experiments from the laboratory, the pilot, through the industrial scales. The yields of ethanol concentration are comparable with those in the commercial industries that use molasses and hydrolyzed starch as the raw materials. Results: Before single-step ethanol production, the studies of raw cassava starch hydrolysis by a granular starch hydrolyzing enzyme, StargenTM002, were carefully conducted. It successfully converted 80.19% (w/v) of raw cassava starch to glucose at a concentration of 176.41 g/L with a productivity of 2.45 g/L/h when the raw starch was pretreated at 60 °C for 1 h with 0.10% (v/w dry starch basis) of Distillase ASP before hydrolysis. A single-step ethanol production at 34 °C in a 5-L fermenter showed that S. cerevisiae (Fali, active dry yeast) produced the maximum ethanol concentration, p of 81.86 g/L (10.43% v/v) with a yield coefficient, Y p/s of 0.41 g/g, a productivity or production rate, r p of 1.14 g/L/h with an efficiency, Ef of 71.44%. The scale-up experiments of the single-step ethanol production using this method, from the 5-L fermenter to the 200-L fermenter and further to the 3,000-L industrial fermenter were successfully achieved with essentially good results. The p, Y p/s , r p , and Ef values of the 200-L scale were 80.85 g/L (10.23% v/v), 0.41 g/g, 1.12 g/L/h and 72.47% , respectively ; of the 3,000-L scale were 70.74 g/L (9.01% v/v), 0.34 g/g, 0.98 g/L/h and 59.82% , respectively. Because of using raw starch, the major by-products of all the three scales were very low; glycerol lactic acid and acetic acid, in ranges of 0.94-1.14%, 0.046-0.052%, 0-0.059% (w/v), respectively, where are less than those values in the industries. Conclusions: This single-step ethanol production using a combination of raw cassava starch hydrolysis and fermentation of the three fermentation scales here is practicable and feasible for the scale-up of industrial production of ethanol from raw starch.
Impact of Chimera-less Long Reads on Metagenomics of Human Gut Viromes Treated With Multiple Displacement Amplification
Authors: Kiguchi Yuya , Nishijima Suguru , Kumar Naveen , Hattori Masahira , Suda Wataru 
Published: August 2020

Abstract
Background: The ecological and biological features of the indigenous phage community (virome) in the human gut microbiome are poorly understood, possibly due to many fragmented contigs and fewer complete genomes based on conventional short-read metagenomics. Long-read sequencing technologies have attracted attention as an alternative approach to reconstruct long and accurate contigs from microbial communities. However, the impact of long-read metagenomics on human gut virome analysis has not been well evaluated. Results: Here we present chimera-less PacBio long-read metagenomics of multiple displacement amplification (MDA)-treated human gut virome DNA. The method included the development of a novel bioinformatics tool, SACRA (Split Amplified Chimeric Read Algorithm), which efficiently detects and splits numerous chimeric reads in PacBio reads from the MDA-treated virome samples. SACRA treatment of PacBio reads from five samples markedly reduced the average chimera ratio from 72 to 1.5%, generating chimera-less PacBio reads with an average read-length of 1.8 kb. De novo assembly of the chimera-less long reads generated contigs with an average N50 length of 11.1 kb, whereas those of MiSeq short reads from the same samples were 0.7 kb, dramatically improving contig extension. Alignment of both contig sets generated 378 high-quality merged contigs (MCs) composed of the minimum scaffolds of 434 MiSeq and 637 PacBio contigs, respectively, and also identified numerous MiSeq short fragmented contigs ≤500 bp additionally aligned to MCs, which possibly originated from a small fraction of MiSeq chimeric reads. The alignment also revealed that fragmentations of the scaffolded MiSeq contigs were caused primarily by genomic complexity of the community, including local repeats, hypervariable regions, and highly conserved sequences in and between the phage genomes. We identified 142 complete and near-complete phage genomes including 108 novel genomes, varying from 5 to 185 kb in length, the majority of which were predicted to be Microviridae phages including several variants with homologous but distinct genomes, which were fragmented in MiSeq contigs. Conclusions: Long-read metagenomics coupled with SACRA provides an improved method to reconstruct accurate and extended phage genomes from MDA-treated virome samples of the human gut, and potentially from other environmental virome samples.
Diagnostic Measures for Severe Acute Malnutrition in Indian Infants Under 6 Months of Age: a Secondary Data Analysis
Authors: Chowdhury Ranadip , Nitika Nitika , Choudhary Tarun Shankar , Taneja Sunita , Martines Jose Carlos , Bhandari Nita , Bahl Rajiv 
Published: August 2020

Abstract
Background Weight for length z-score (WLZ) <- 3 is currently used to define severe acute malnutrition (SAM) among infants. However, this approach has important limitations for infants younger than 6 months of age as WLZ cannot be calculated using WHO growth standards if infant length is <45 cm. Moreover, length for age z-score (LAZ) and weight for length z-score (WLZ) are least reliable measures, with high chances of variation, and less chances of detecting undernutrition in under 6 months infants. The objective of the current analysis was to compare WLZ with WAZ and LAZ in a cohort of Indian infants in predicting the deaths between 6 weeks and 6 months of age. Methods The data was from an individually randomized trial conducted in slums of Delhi, India in which infants’ weight and length were measured at 6 weeks of age (at the time of the first immunization visit). Vital status of the infants was documented from 6 weeks to 6 months of age. The sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios, and area under curve (AUC) were calculated for WAZ<-3, WLZ<-3, and LAZ<-3 for deaths between 6 weeks and 6 months of age. Results For deaths occurring between 6 weeks to 6 months of age, the specificity ranged between 85.9-95.9% for all three anthropometric indicators. However, the sensitivity was considerably higher for WAZ; it was 64.6 % for WAZ<-3, 39.1% for LAZ<-3, and 25.0% for WLZ<-3. WAZ <-3 had higher AUC (0.75; 95% CI: 0.68, 0.82) and hence, better discriminated deaths between 6 weeks and 6 months of age than WLZ<-3. The adjusted relative risk (RR 10.6, 95% CI 5.9, 18.9) and the population attributable fraction (PAF 57.9%, 95% CI 38.8, 71.0%) of mortality was highest for WAZ<-3.Conclusions We found WAZ<-3 at 6 weeks of age to be a better predictor of death in the 6 weeks to 6 months of life in comparison to WLZ<-3 and LAZ<-3 and propose that it should be considered to diagnose SAM in this age group.
Non-Operatively Managed Primary Small Bowel Volvulus: A Case Report
Authors: Ewnte Biniam 
Published: August 2020

Abstract
Background: Primary small intestinal volvulus is one of the common causes of intestinal obstruction in various localities of the developing world. Although operative intervention has been the usual mode of treatment; this case report depicts with meticulous follow-up & care, there is a possibility for relief of obstruction with non-operative management.Case presentation: This is a case report of a 20-year-old male patient presented with crampy abdominal pain and frequent bilious vomiting. Plain abdominal film showed multiple distended small bowel loops with air fluid level, consistent with small bowel obstruction. Ruling out other etiologies primary small bowel volvulus was entertained and naso-gastric tube inserted, patient catheterized and kept nil per oral. After 48 hours of admission all symptoms resolved the patient resumed feeding and was discharged home. Conclusions: The reported case shows evidence in which the pa­tient’s primary small bowel volvulus was relieved non-operatively with insertion of naso gastric tube keeping nil per oral.
Text Classification Models for the Automatic Detection of Nonmedical Prescription Medication Use from Social Media
Authors: Al-Garadi Mohammed , Yang Yuan-Chi , Cai Haitao , Ruan Yucheng , O’Connor Karen , Graciela Gonzalez-Hernandez , Perrone Jeanmarie , Sarker Abeed 
Published: August 2020

Abstract
Background
Prescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging—requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter.
Methods
We experimented with state-of-the-art bi-directional transformer-based language models, which utilize tweet-level representations that enable transfer learning (e.g., BERT, RoBERTa, XLNet, AlBERT, and DistilBERT), proposed fusion-based approaches, and compared the developed models with several traditional machine learning, including deep learning, approaches. Using a public dataset, we evaluated the performances of the classifiers on their abilities to classify the non-majority “abuse/misuse” class.
Results
Our proposed fusion-based model performs significantly better than the best traditional model (F1-score [95% CI]: 0.67 [0.64–0.69] vs. 0.45 [0.42–0.48]). We illustrate, via experimentation using differing training set sizes, that the transformer-based models are more stable and require less annotated data compared to the other models. The significant improvements achieved by our best-performing classification model over past approaches makes it suitable for automated continuous monitoring of nonmedical PM use from Twitter.
Conclusions
BERT, BERT-like and fusion-based models not only outperform traditional machine learning and deep learning models, but also show substantial improvements over many years of past research on the topic of prescription medication misuse/abuse classification from social media, which had been shown to be a complex task due to the unique ways in which information about nonmedical use is presented. However, several challenges, such as lack of complete context and the nature of social media language, must be overcome to further improve BERT and BERT-like models despite their advantages over other approaches. These experimental driven challenges are represented as potential future research directions.
Antibiotic-related Gut Dysbiosis Induces Lung Immunodepression and Worsens Lung Infection.
Authors: Dessein Rodrigue , BAUDUIN Marvin , GRANDJEAN Teddy , GUERN Rémi LE , FIGEAC Martin , BEURY Delphine , FAURE Karine , FAVEEUW Christelle , GUERY Benoit , GOSSET Philippe , KIPNIS Eric 
Published: August 2020

Abstract
BackgroundGut dysbiosis due to the adverse effects of antibiotics affects outcomes of lung infection. Previous murine models relied on significant depletion of both gut and lung microbiota, rendering the analysis of immune gut-lung cross-talk difficult.Here we study the effects of antibiotic-induced gut dysbiosis without lung dysbiosis on lung immunity and the consequences on acute P. aeruginosa lung infection.MethodsC57BL6 mice were administered 7 days oral vancomycin-colistin, modulated or not by fecal microbial transplant or exogenous hematopoietic cytokine, Fms-related tyrosine kinase 3 ligand (Flt3-Ligand), followed by intranasal P. aeruginosa strain PAO1. Gut and lung microbiota were studied by next-generation sequencing, and lung infection outcomes were studied at 24h. Effects of vancomycin-colistin on underlying immunity and bone marrow progenitors were studied in uninfected mice by flow cytometry in the lung, spleen, and bone marrow.ResultsVancomycin-colistin administration induces widespread cellular immunosuppression in both lung and spleen, decreases circulating hematopoietic cytokine Flt3-Ligand, and depresses dendritic cell bone marrow progenitors leading to worsening of P. aeruginosa lung infection outcomes (bacterial loads, lung injury and survival). Reversal of these effects by fecal microbial transplant shows that these alterations are related to gut dysbiosis. Recombinant Flt3-Ligand reverses the effects of antibiotics on subsequent lung infection. ConclusionsThese results show that gut dysbiosis strongly impairs monocyte/dendritic progenitors and lung immunity, worsening outcomes of P. aeruginosa lung infection. Treatment with a fecal microbial transplant or immune stimulation by Flt3-Ligand both restore lung cellular responses to and outcomes of P. aeruginosa following antibiotic-induced gut dysbiosis.