bluuebunny/crossref_metadata_2025_split huggingface dataset:
Oxidation characteristic and thermal runaway of isoprene
Authors: Liang Min , Dai Suyi , Cheng Haijun , Yu Chang , Li Weiguang , Lai Fang , Yang Kang , Ma Li , Liu Xiongmin 
Published: September 2023

AbstractIn this study, the oxidation characteristics of isoprene were investigated using a custom-designed mini closed pressure vessel test (MCPVT). The results show that isoprene is unstable and polymerization occurs under a nitrogen atmosphere. Under an oxygen atmosphere, the oxidation process of isoprene was divided into three stages: (1) isoprene reacts with oxygen to produce peroxide; (2) Peroxides produce free radicals through thermal decomposition; (3) Free radicals cause complex oxidation and thermal runaway reactions. The oxidation of isoprene conforms to the second-order reaction kinetics, and the activation energy was 86.88 kJ·mol−1. The thermal decomposition characteristics of the total oxidation product and purified peroxide mixture were determined by differential scanning calorimetry (DSC). The initial exothermic temperatures Ton were 371.17 K and 365.84 K, respectively. And the decomposition heat QDSC were 816.66 J·g−1 and 991.08 J·g−1, respectively. It indicates that high concentration of isoprene peroxide has a high risk of thermal runaway. The results of thermal runaway experiment showed that the temperature and pressure of isoprene oxidation were prone to rise rapidly, which indicates that the oxidation reaction was dangerous. The reaction products of isoprene were analyzed by gas chromatography-mass spectrometry (GC–MS). The main oxidation products were methyl vinyl ketone, methacrolein, 3-methylfuran, etc. The main thermal runaway products were dimethoxymethane, 2,3-pentanedione, naphthalene, etc. Based on the reaction products, the possible reaction pathway of isoprene was proposed.
Effect of C-reactive protein deficiency on insulin resistance reversal in rats with polycystic ovary syndrome through augmented leptin action
Authors: Li Ke , Hu Lingling , Li Xinrun , Yuan Zhibin , He Jia , Liu Dongfang , Yang Gangyi , Yuan Lei 
Published: September 2023

Abstract
Background
C-reactive protein(CRP), is an inflammatory marker that weaken leptin bioavailability and insulin sensitivity to disturb energy and glucose metabolism. Polycystic ovary syndrome (PCOS) exhibit a metabolic component consisting of higher plasma CRP levels, hyperinsulinemic and hyperleptinemia. The ability of leptin to regulation of hepatic glucose production (HGP) in the absence of CRP in PCOS remain unknown.

Methods
Dehydroepiandrosterone (DHEA) was used to induce PCOS in rats. We assessed the effects of CRP gene knockout in PCOS model rats on body weight, energy expenditure glucose metabolism and insulin sensitivity. We conducted experiments involving the administration of leptin to both the peripheral and central systems in PCOS model rats with CRP knockout, and studied the effects on changes in glucose kinetics during hyperinsulinemic-euglycemic clamps.

Results
In female PCOS rats, the lack of CRP resulted in decreased leptin resistance and weight gain, increased energy expenditure, and improved insulin sensitivity. Additionally, the deletion of the CRP gene strengthened the HGP-lowering effects of leptin when administered peripherally or centrally. This effect was accompanied by a decrease in the expression of hepatic gluconeogenic enzymes and an increase in hepatic insulin signaling. Finally, inhibition of glucose production was also enhanced for central leptin administration during lipid infusion in PCOS rats.

Conclusions
Our findings highlight the therapeutic potential of targeting CRP to restore glucose homeostasis and insulin sensitivity for leptin in PCOS.
A systematic review of the measures that have been used to assess surface characteristics in relation to their impact on walking and falling
Authors: Lawson Rebecca , Thomas Nicholas D. A. 
Published: September 2023

AbstractWe investigated whether an impediment to progress in understanding the environmental factors that cause falls may be the difficulty in comparing results across studies because walking surfaces are poorly defined and underspecified. We conducted a systematic review of 384 studies from 370 articles that tested how different surfaces influenced human walking and falling. For each study, we report which categories of surfaces were used (indoor, outdoor, treadmill, virtual reality and qualitative), the nature of each surface (stairs, slopes, slippery, compliant, rough or default) and how information about each surface was measured. We found that minimal information was provided for many surfaces, making it impossible to meaningfully compare results for different types of surfaces across studies. We conclude that most published studies of walking and falling provide insufficient data to describe the surfaces that they used and we provide recommendations about how to improve the reporting of walking surfaces.
Development of machine learning models for the detection of surgical site infections following total hip and knee arthroplasty: a multicenter cohort study
Authors: Wu Guosong , Cheligeer Cheligeer , Southern Danielle A. , Martin Elliot A. , Xu Yuan , Leal Jenine , Ellison Jennifer , Bush Kathryn , Williamson Tyler , Quan Hude , Eastwood Cathy A. 
Published: September 2023

Abstract
Background
Population based surveillance of surgical site infections (SSIs) requires precise case-finding strategies. We sought to develop and validate machine learning models to automate the process of complex (deep incisional/organ space) SSIs case detection.

Methods
This retrospective cohort study included adult patients (age ≥ 18 years) admitted to Calgary, Canada acute care hospitals who underwent primary total elective hip (THA) or knee (TKA) arthroplasty between Jan 1st, 2013 and Aug 31st, 2020. True SSI conditions were judged by the Alberta Health Services Infection Prevention and Control (IPC) program staff. Using the IPC cases as labels, we developed and validated nine XGBoost models to identify deep incisional SSIs, organ space SSIs and complex SSIs using administrative data, electronic medical records (EMR) free text data, and both. The performance of machine learning models was assessed by sensitivity, specificity, positive predictive value, negative predictive value, F1 score, the area under the receiver operating characteristic curve (ROC AUC) and the area under the precision–recall curve (PR AUC). In addition, a bootstrap 95% confidence interval (95% CI) was calculated.

Results
There were 22,059 unique patients with 27,360 hospital admissions resulting in 88,351 days of hospital stay. This included 16,561 (60.5%) TKA and 10,799 (39.5%) THA procedures. There were 235 ascertained SSIs. Of them, 77 (32.8%) were superficial incisional SSIs, 57 (24.3%) were deep incisional SSIs, and 101 (42.9%) were organ space SSIs. The incidence rates were 0.37 for superficial incisional SSIs, 0.21 for deep incisional SSIs, 0.37 for organ space and 0.58 for complex SSIs per 100 surgical procedures, respectively. The optimal XGBoost models using administrative data and text data combined achieved a ROC AUC of 0.906 (95% CI 0.835–0.978), PR AUC of 0.637 (95% CI 0.528–0.746), and F1 score of 0.79 (0.67–0.90).

Conclusions
Our findings suggest machine learning models derived from administrative data and EMR text data achieved high performance and can be used to automate the detection of complex SSIs.
hsa_circ_0003596, as a novel oncogene, regulates the malignant behavior of renal cell carcinoma by modulating glycolysis
Authors: Xie QingZhi , Qin FuQiang , Luo LiHui , Deng ShaoQuan , Zeng Ke , Wu YunChou , Liao DunMing , Luo Lin , Wang KangNing 
Published: September 2023

Abstract
Background
This research was planned to analyze hsa_circ_0003596 (circCOL5A1) and glycolysis-focused mechanisms in renal cell carcinoma (RCC).

Methods
circCOL5A1, miR-370-5p, and PRKCSH levels were determined in RCC tissues and selected cell lines by RT-qPCR and/or Western blot. RCC cells after corresponding transfection were tested by colony formation assay, EdU assay, Transwell assay, and flow cytometry to analyze cell proliferation, invasion, migration, and apoptosis. Meanwhile, glycolysis in cells was evaluated by measuring glucose consumption, lactic acid, and ATP production, as well as immunoblotting for HK2 and PKM2. In addition, circCOL5A1 knockdown was performed in animal experiments to observe tumor growth and glycolysis. Finally, the ceRNA network between circCOL5A1, miR-370-5p, and PRKCSH was studied by luciferase reporter assay and RIP experiment.

Results
circCOL5A1 and PRKCSH were highly expressed and miR-370-5p was poorly expressed in RCC. circCOL5A1 knockdown depressed RCC proliferation, invasion, migration, and glycolysis, and enhanced apoptosis. circCOL5A1 competitively adsorbed miR-370-5p. Artificial upregulation of miR-370-5p saved the pro-tumor effect of circCOL5A1 on RCC cells, as evidenced by suppression of tumor malignancy and glycolysis. miR-370-5p targeted PRKCSH. PRKCSH overexpression contributed to a reversal of the anti-tumor effect of circCOL5A1 silencing. Silencing circCOL5A1 inhibited RCC tumor growth and glycolysis.

Conclusions
circCOL5A1 regulates the malignant behavior of RCC by modulating glycolysis.
Variations of bile bacterial community alongside gallstone disease progression and key taxa involved in poor outcomes after endoscopic surgery
Authors: Cai Xunchao , Peng Yao , Gong Yajie , Huang Xiuting , Liu Lu , Chen Yifan , Du Jingfeng , Dai Zhongming , Qian Yun , Xu Long 
Published: September 2023

AbstractGallstone disease is a prevalent biliary disease worldwide, and bacteria play vital roles in the disease development and progression, as well as the prognosis after endoscopic surgery. However, there have been limited studies to explore the key taxa involved. In this study, bile samples from healthy controls (HCs, liver donors without hepatobiliary disease) and three diseased groups, namely patients with gallbladder stones (GBS), patients with common bile duct stones (CBDS), and patients with stricture in the common bile duct (SCBD), were collected and analyzed. Bacterial community characterization based on 16S rRNA amplicon sequencing showed that bacterial diversities did not change significantly alongside gallstone disease development and progression. The predominant phyla in each group were Proteobacteria, Firmicutes, Bacteroidota, and Fusobacteriota, representing over 80% in abundance of the biliary bacteria community. Specifically, the abundance of Proteobacteria decreased greatly while that of Firmicutes and Bacteroidota increased greatly in the diseased groups when compared to that in HCs. Moreover, linear discriminant analysis identified several genera highly represented in the diseased groups. Among them, Klebsiella, Prevotella, Pseudomonas and Veillonella are persistent in both the HCs group and the diseased groups, indicating an enrichment of local bile bacteria in the diseased bile; while Lachnoanerobaculum, Atopobium, Oribacterium, and Stomatobaculum, those aligned to oral cavity taxa, are persistent in the diseased groups but are transient in the HCs group, and their abundances sequentially increased with the disease development and progression (HCs→GBS→CBDS→SCBD), implying a translocation and colonization of the oral cavity bacteria in the diseased bile. Moreover, co-occurrence network analysis revealed that bacterial infection (e.g., Photobacterium and Plesiomonas) from the intestine was developed during endoscopic surgery with reduced bile bacteria diversity. The results of this study revealed that the bile bacterial community is relatively stable and dominated by a few persistent taxa. Moreover, we hypothesized that translocation and colonization of specific bacteria from the oral cavity happens alongside gallstone disease development and progression, and bacterial infection from the intestinal tract results in poor outcomes after endoscopic surgery.
Translation and psychometric evaluation of the Persian version of “good death inventory- short Form” from the perspective of family-members of cancer patients
Authors: Roshan Hosein Mohammadi , Ebadi Abbas , Karimi Leila , Barasteh Salman 
Published: September 2023

Abstract
Introduction
Achieving good death is among the objectives of palliative care in patients with cancer. There should be an instrument for evaluating the quality of palliative care provided by family members at the end of life. This study was done to assess the psychometric properties of good death inventory- short form according to the perspective of family of patients with cancer.

Method
This methodological study was done in 2022 at two hospitals in Tehran. The translation was done via forward-backward method. Face validity was examined through cognitive interviewing with 10 family members. The content validation, were used by assessment the opinions of 10 palliative care specialists. The construct validity was explored through exploratory factor analysis and examination of convergent validation with care evaluation scale 2.0, as well as inspection of correlation by answering two general questions of satisfaction with treatment and end of life quality of life. The scale’s reliability, internal consistency was calculated using Cronbach’s alpha coefficient and stability via test-retest.

Results
Overall, 204 family members of patients with cancer were included. In the exploratory factor analysis, three factors of peace, hope, and value as well as quality of care were extracted with cumulative variance of 41.8%.A significant and suitable correlation between the total scores of the participants Good death inventory-short form and care evaluation scale2.0 (r = 0.459, P < 0.001) and general satisfaction with end-of-life care (r = 0.423, p < 0.001) as well as the patient’s general quality of life (r = 0.539, p < 0.001). The Cronbach’s alpha coefficient for the questionnaire was found 0.842, and the stability was confirmed with Intra cluster correlation coefficient = 0.851.

Conclusion
the Persian version of good death inventory-short form is a valid and reliable questionnaire which can investigate the factors associated with good death according to patients’ family members’ perspective.
Cerebrovascular disease case identification in inpatient electronic medical record data using natural language processing
Authors: Pan Jie , Zhang Zilong , Peters Steven Ray , Vatanpour Shabnam , Walker Robin L. , Lee Seungwon , Martin Elliot A. , Quan Hude 
Published: September 2023

Abstract
Background
Abstracting cerebrovascular disease (CeVD) from inpatient electronic medical records (EMRs) through natural language processing (NLP) is pivotal for automated disease surveillance and improving patient outcomes. Existing methods rely on coders’ abstraction, which has time delays and under-coding issues. This study sought to develop an NLP-based method to detect CeVD using EMR clinical notes.

Methods
CeVD status was confirmed through a chart review on randomly selected hospitalized patients who were 18 years or older and discharged from 3 hospitals in Calgary, Alberta, Canada, between January 1 and June 30, 2015. These patients’ chart data were linked to administrative discharge abstract database (DAD) and Sunrise™ Clinical Manager (SCM) EMR database records by Personal Health Number (a unique lifetime identifier) and admission date. We trained multiple natural language processing (NLP) predictive models by combining two clinical concept extraction methods and two supervised machine learning (ML) methods: random forest and XGBoost. Using chart review as the reference standard, we compared the model performances with those of the commonly applied International Classification of Diseases (ICD-10-CA) codes, on the metrics of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

Result
Of the study sample (n = 3036), the prevalence of CeVD was 11.8% (n = 360); the median patient age was 63; and females accounted for 50.3% (n = 1528) based on chart data. Among 49 extracted clinical documents from the EMR, four document types were identified as the most influential text sources for identifying CeVD disease (“nursing transfer report,” “discharge summary,” “nursing notes,” and “inpatient consultation.”). The best performing NLP model was XGBoost, combining the Unified Medical Language System concepts extracted by cTAKES (e.g., top-ranked concepts, “Cerebrovascular accident” and “Transient ischemic attack”), and the term frequency-inverse document frequency vectorizer. Compared with ICD codes, the model achieved higher validity overall, such as sensitivity (25.0% vs 70.0%), specificity (99.3% vs 99.1%), PPV (82.6 vs. 87.8%), and NPV (90.8% vs 97.1%).

Conclusion
The NLP algorithm developed in this study performed better than the ICD code algorithm in detecting CeVD. The NLP models could result in an automated EMR tool for identifying CeVD cases and be applied for future studies such as surveillance, and longitudinal studies.
Design of chimeric GLP-1A using oligomeric bile acids to utilize transporter-mediated endocytosis for oral delivery
Authors: Kweon Seho , Lee Jun-Hyuck , Yang Seong-Bin , Park Seong Jin , Subedi Laxman , Shim Jung-Hyun , Cho Seung-Sik , Choi Jeong Uk , Byun Youngro , Park Jooho , Park Jin Woo 
Published: September 2023

Abstract

Background
Despite the effectiveness of glucagon-like peptide-1 agonist (GLP-1A) in the treatment of diabetes, its large molecular weight and high hydrophilicity result in poor cellular permeability, thus limiting its oral bioavailability. To address this, we developed a chimeric GLP-1A that targets transporter-mediated endocytosis to enhance cellular permeability to GLP-1A by utilizing the transporters available in the intestine, particularly the apical sodium-dependent bile acid transporter (ASBT).

Methods

In silico molecular docking and molecular dynamics simulations were used to investigate the binding interactions of
mono
-,
bis
-, and
tetra
-deoxycholic acid (DOCA) (
mono
DOCA,
bis
DOCA, and
tetra
DOCA) with ASBT. After synthesizing the chimeric GLP-1A-conjugated oligomeric DOCAs (
m
D-G1A,
b
D-G1A, and
t
D-G1A) using a maleimide reaction, in vitro cellular permeability and insulinotropic effects were assessed. Furthermore, in vivo oral absorption in rats and hypoglycemic effect on diabetic
db/db
mice model were evaluated.

Results

In silico results showed that
tetra
DOCA had the lowest interaction energy, indicating high binding affinity to ASBT. Insulinotropic effects of GLP-1A-conjugated oligomeric DOCAs were not different from those of GLP-1A-Cys or exenatide. Moreover,
b
D-G1A and
t
D-G1A exhibited improved in vitro Caco-2 cellular permeability and showed higher in vivo bioavailability (7.58% and 8.63%) after oral administration. Regarding hypoglycemic effects on
db/db
mice,
t
D-G1A (50 μg/kg) lowered the glucose level more than
b
D-G1A (50 μg/kg) compared with the control (35.5% vs. 26.4%).

Conclusion
GLP-1A was conjugated with oligomeric DOCAs, and the resulting chimeric compound showed the potential not only for glucagon-like peptide-1 receptor agonist activity but also for oral delivery. These findings suggest that oligomeric DOCAs can be used as effective carriers for oral delivery of GLP-1A, offering a promising solution for enhancing its oral bioavailability and improving diabetes treatment.

Graphical Abstract
Joint aspiration for diagnosis of chronic periprosthetic joint infection: when, how, and what tests?
Authors: Quinlan Nicole Durig , Jennings Jason M. 
Published: September 2023

AbstractDiagnosing chronic periprosthetic joint infection (PJI) requires clinical suspicion in combination with both serological and synovial fluid tests, the results of which are generally applied to validated scoring systems or consensus definitions for PJI. As no single “gold standard” test exists, the diagnosis becomes challenging, especially in the setting of negative cultures or equivocal test results. This review aims to address the workup of chronic PJI and considerations for clinical evaluation to guide treatment. Following aspiration of the joint in question, a multitude of tests has been developed in an attempt to assist with diagnosis, including cell synovial white blood cell count, gram stain, cultures, leukocyte esterase, alpha-defensin, synovial C-reactive protein, multiplex polymerase chain reaction, next-generation sequencing, and interleukins. Each test has advantages and disadvantages and should be used in conjunction with the overall clinical picture to guide further clinical evaluation and treatment in this complex patient population.
Modeling inflation rate factors on present consumption price index in Ethiopia: threshold autoregressive models approach
Authors: Abebe Alebachew , Temesgen Aboma , Kebede Belete 
Published: September 2023

AbstractBackgroundInflation is the industrious and non-stop ascent in the overall prices of any given commodity in an economy. During the global food crisis, Ethiopia experienced an unprecedented increase in inflation ranked the highest in Africa. It is among the most macroeconomic variable described nonlinear behavior.ObjectiveThe main purpose of this study was intended to modeling inflation rate factors on present consumption price index (CPI) in Ethiopia: using the threshold autoregressive (TAR) models.MethodsThe study was utilized the secondary data collected from monthly data of CPI for inflation rate from January 1994 to December 2020 which was obtained from central statistical Agency. The forecast was applied between the nonlinear and linear ARMA models using different techniques. The unit root test of Dickey–Fuller test was made for each variables and applied lag length transformation for the variables that had unit root. A threshold autoregressive models was utilized for data handling technique using least square estimation.ResultsThe results showed that monthly rate of inflation was characterized a non-constant mean and an unstable variance. The outcome of Tsay tests was revealed that non linearity of CPI and SETAR(2,4,4) had the smallest value of AIC under this study. The forecasting performance comparison results were showed that the nonlinear SETAR model outperform the linear ARMA models. Moreover, the out-of-sample forecast indicates that the CPI of inflation has almost a constant trend. The in-sample forecast using the best-fit asymmetric for the SETAR(2,4,4) model the CPI series exhibits an upward trade until 2012; decreases until 2011; slightly increase up to 2018 and then decrease at the end of the study period.ConclusionThe superiority in performance of nonlinear models was attributed to their ability to capture the stochastic nature of the monthly rates as evident in the pattern of the forecast errors. The investigators are recommended that using TAR models policy makers can be able to capture the price volatility persistence and also forecasting can be made.