Novel plant oil-based thermosets and polymer composites

Because of the continuously increasing price of petroleum resources and an increase in environmental awareness, researchers are actively trying to produce polymers based on biorenewable resources to replace the traditional petroleum-based plastics. This presentation will discuss: (1) a novel thermoset based on acrylated epoxidized soybean oil (AESO) and methacrylated eugenol (ME); (2) a biocomposites from tall oil-based polyamide (PA) with lignin-cellulose fiber (LCF) as fillers. Project 1: A biorenewable thermoset was prepared by copolymerizing AESO/ME via free radical polymerization. Both of the starting materials, soybean oil and eugenol, are biorenewable. The thermal, mechanical, and rheological properties of this thermoset system were studied as a function of composition. After extensive material characterization, it is concluded that this high biorenewable content resin system possesses good mechanical properties, high thermal stability, and fast curing speed, making it a suitable matrix resin for the pultrusion process and other composite manufacturing processes. Project 2: Tall oil-based PA was blended with LCF to produced biocomposites. SEM shows that a good filler distribution and a good interfacial adhesion between the fiber and the matrix were achieved. This study demonstrates that the lignin-cellulose fiber can be blended with tall oil-based polyamides via melt processing to produce biorenewable composites with lower cost, higher mechanical properties, and higher biorenewable content when compared to the neat PA polymer

Biodegradation behavior of bacterial-based polyhydroxyalkanoate (PHA) and DDGS composites

The extensive use of plastics in agriculture has increased the need for development and implementation of polymer materials that can degrade in soils under natural conditions. The biodegradation behavior in soil of polyhydroxyalkanoate (PHA) composites with 10 wt% distiller\u27s dried grains with solubles (DDGS) was characterized and compared to pure PHA over 24 weeks. Injection-molded samples were measured for degradation weight loss every 4 weeks, and the effects of degradation times on morphological, thermomechanical, and viscoelastic properties were evaluated by scanning electron microscopy (SEM), dynamic mechanical analysis (DMA), and small-amplitude oscillatory shear flow experiments. Incorporation of DDGS had a strong effect on biodegradation rate, mechanical properties, and production cost. Material weight loss increased linearly with increasing biodegradation time for both neat PHA and the PHA/DDGS 90/10 composites. Weight loss after 24 weeks was approximately six times greater for the PHA/DDGS 90/10 composites than for unaltered PHA under identical conditions. Rough surface morphology was observed in early biodegradation stages (≥8 weeks). With increasing biodegradation time, the composite surface eroded and was covered with well-defined pits that were evenly distributed, giving an areolate structure. Zero shear viscosity, Tg, gelation temperature, and cold crystallization temperature of the composites decreased linearly with increasing biodegradation time. Addition of DDGS to PHA establishes mechanical and biodegradation properties that can be utilized in sustainable plastics designed to end their lifecycle as organic matter in soil. Our results provide information that will guide development of PHA composites that fulfill application requirements then degrade harmlessly in soil

Fine-Grained Assessment of COVID-19 Severity Based on Clinico-Radiological Data Using Machine Learning

Background: The severe and critical cases of COVID-19 had high mortality rates. Clinical features, laboratory data, and radiological features provided important references for the assessment of COVID-19 severity. The machine learning analysis of clinico-radiological features, especially the quantitative computed tomography (CT) image analysis results, may achieve early, accurate, and fine-grained assessment of COVID-19 severity, which is an urgent clinical need. Objective: To evaluate if machine learning algorithms using CT-based clinico-radiological features could achieve the accurate fine-grained assessment of COVID-19 severity. Methods: The clinico-radiological features were collected from 78 COVID-19 patients with different severities. A neural network was developed to automatically measure the lesion volume from CT images. The severity was clinically diagnosed using two-type (severe and non-severe) and fine-grained four-type (mild, regular, severe, critical) classifications, respectively. To investigate the key features of COVID-19 severity, statistical analyses were performed between patients’ clinico-radiological features and severity. Four machine learning algorithms (decision tree, random forest, SVM, and XGBoost) were trained and applied in the assessment of COVID-19 severity using clinico-radiological features. Results: The CT imaging features (CTscore and lesion volume) were significantly related with COVID-19 severity (p < 0.05 in statistical analysis for both in two-type and fine-grained four-type classifications). The CT imaging features significantly improved the accuracy of machine learning algorithms in assessing COVID-19 severity in the fine-grained four-type classification. With CT analysis results added, the four-type classification achieved comparable performance to the two-type one. Conclusions: CT-based clinico-radiological features can provide an important reference for the accurate fine-grained assessment of illness severity using machine learning to achieve the early triage of COVID-19 patients

Interpretable Machine Learning for COVID-19:An Empirical Study on Severity Prediction Task

The black-box nature of machine learning models hinders the deployment of some high-accuracy models in medical diagnosis. It is risky to put one's life in the hands of models that medical researchers do not fully understand. However, through model interpretation, black-box models can promptly reveal significant biomarkers that medical practitioners may have overlooked due to the surge of infected patients in the COVID-19 pandemic. This research leverages a database of 92 patients with confirmed SARS-CoV-2 laboratory tests between 18th Jan. 2020 and 5th Mar. 2020, in Zhuhai, China, to identify biomarkers indicative of severity prediction. Through the interpretation of four machine learning models, decision tree, random forests, gradient boosted trees, and neural networks using permutation feature importance, Partial Dependence Plot (PDP), Individual Conditional Expectation (ICE), Accumulated Local Effects (ALE), Local Interpretable Model-agnostic Explanations (LIME), and Shapley Additive Explanation (SHAP), we identify an increase in N-Terminal pro-Brain Natriuretic Peptide (NTproBNP), C-Reaction Protein (CRP), and lactic dehydrogenase (LDH), a decrease in lymphocyte (LYM) is associated with severe infection and an increased risk of death, which is consistent with recent medical research on COVID-19 and other research using dedicated models. We further validate our methods on a large open dataset with 5644 confirmed patients from the Hospital Israelita Albert Einstein, at S\~ao Paulo, Brazil from Kaggle, and unveil leukocytes, eosinophils, and platelets as three indicative biomarkers for COVID-19.Comment: 14 pages, 10 figure

Retrospective analysis of 119 Chinese noninflammatory locally advanced breast cancer cases treated with intravenous combination of vinorelbine and epirubicin as a neoadjuvant chemotherapy: a median follow-up of 63.4 months

<p>Abstract</p> <p>Background</p> <p>This study is a retrospective evaluation of the efficacy of neoadjuvant chemotherapy (NC) with a vinorelbine (V) and epirubicin (E) intravenous combination regimen and is aimed at identification of predictive markers for the long-term outcome in noninflammatory locally advanced breast cancer (NLABC).</p> <p>Methods</p> <p>One-hundred-and-nineteen patients with NLABC were identified from September 2001 to May 2006. Analysis was performed in March 2008, with a median follow-up of 63.4 months (range, 9-76 months). All patients were diagnosed with invasive breast cancer using 14 G core needle biopsy and treated with three cycles of VE before surgery. Local-regional radiotherapy was offered to all patients after the completion of chemotherapy followed by hormonal therapy according to hormone receptor status. Tissue sections cut from formalin-fixed paraffin-embedded blocks from biopsy specimens and postoperative tumor tissues were stained for the presence of estrogen receptor (ER), progesterone receptor (PgR), HER-2 (human epidermal growth factor receptor-2), and MIB-1(Ki-67).</p> <p>Results</p> <p>Patients characteristics were median age 52 years (range: 25-70 years); clinical TNM stage, stage IIB (n = 32), stage IIIA (n = 56), stage IIIB (n = 22) and stage IIIC (n = 9). All patients were evaluable for response: clinically complete response was documented in 27 patients (22.7%); 78 (65.6%) obtained partial response; stable disease was observed in 13 (10.9%); 1 patient (0.8%) had progressive disease. Pathological complete response was found in 22 cases (18.5%). Seventy-five patients were alive with no recurrence after a median follow-up of 63.4 months, the 5-year rates for disease-free survival and overall survival were 58.7% and 71.3%, respectively, after the start of NC. On multivariate analysis, the independent variables associated with increased risk of relapse and death were high pre-Ki-67(p = 0.012, p = 0.017, respectively), high post-Ki-67 expression (p = 0.045, p = 0.001, respectively), and non-pCR (p = 0.034, p = 0.027, respectively). A significantly increased risk of death was associated with lack of pre-ER expression (p = 0.002). Among patients with non-pCR, those with a pathological response at the tumor site with special involvement (i.e. skin, vessel and more than one quadrant) were at a higher risk of disease relapse and death (p < 0.001, p = 0.001, respectively).</p> <p>Conclusion</p> <p>This study suggests the promising use of a VE regimen as NC for Chinese NLABC after a median follow-up of 63.4 months. Pathological response in the tumor site, pre-Ki-67 and post-Ki-67 expression, and pre-ER expression were the important variables that predicted long-term outcome. Patients with pathological special involvement at the primary site after NC had the lowest survival rates.</p

Combination of everolimus with trastuzumab plus paclitaxel as first-line treatment for patients with HER2-positive advanced breast cancer (BOLERO-1) : a phase 3, randomised, double-blind, multicentre trial

BACKGROUND : mTOR inhibition has been shown to reverse trastuzumab resistance from hyperactivated the PIK/AKT/mTOR pathway due to PTEN loss, by sensitizing PTEN-deficient tumors towards trastuzumab. The BOLERO-1 study evaluated the efficacy and safety of adding everolimus to trastuzumab and paclitaxel as first-line therapy for HER2+ advanced breast cancer (ABC). METHODS : In this phase III, randomized, double-blind trial, patients were enrolled across 141 sites in 28 countries. Eligible patients were ≥18 years of age, with locally assessed HER2+ advanced breast cancer (ABC), with Eastern Cooperative Oncology Group performance status of 0-1, who had not received prior trastuzumab or chemotherapy for ABC, had measurable disease as per Response Evaluation Criteria in Solid Tumors or bone lesions in the absence of measurable disease, without prior systemic therapy for advanced disease except endocrine therapy. The patients were randomized 2:1 (with an interactive voice and web response system) to receive either daily everolimus (10 mg/day) orally or placebo plus weekly trastuzumab intravenously at 4 mg/kg loading dose on Day-1 with subsequent weekly doses of 2 mg/kg of each 4-week cycle plus paclitaxel intravenously at a dose of 80 mg/m2 on days 1, 8, and 15 of each 4- week cycle. Randomization was stratified according to prior use of trastuzumab and visceral metastasis. Patients and investigators were blinded to the assigned treatments. Identity of experimental treatments was concealed by use of everolimus and placebo that were identical in packaging, labelling, appearance, and administration schedule. The two primary objectives were investigator-assessed progression-free survival (PFS) in the full study population and in the subset of patients with hormone receptor-negative (HR) breast cancer at baseline; the latter was added during the course of the study, prior to unblinding based on new clinical and biological findings from other studies. All efficacy analyses were based on the intention-to-treat population. Enrolment for this trial is closed and results of the final PFS analyses are presented here. Clinicaltrials.gov identifier: NCT00876395. FINDINGS : Between 10-Sep-2009 and 16-Dec-2012, 719 patients were randomized to receive everolimus (n=480) or placebo (n=239). Median follow-up was 41.3 months (IQR: 35.4 – 46.6 months). INTERPRETATION : The primary objective in the full population was not met; median PFS was 15.0 months with everolimus vs 14.5 months with placebo (hazard ratio, 0.89; 95% CI, 0.73-1.08; p=0.1166). In the HR subpopulation (n=311), median PFS with everolimus was 20.3 months vs 13.1 months with placebo (hazard ratio, 0.66; 95% CI, 0.48-0.91; p=0.0049), however, the protocol-specified statistical significance threshold (p=0.0044) was not crossed. The most common adverse events (AEs) with everolimus vs placebo were stomatitis (314 [66.5%] vs 77 [32.4%] patients), diarrhea (267 [56.6%] vs 111 [46.6%] patients), and alopecia (221 [46.8%] vs 125 [52.5%]). The most frequently reported grade 3/4 AEs in the EVE arm vs PBO arm were neutropenia (117 [24.8%] of 472 patients vs 35 [14.7%] of 238 patients), stomatitis (59 [12.5%] of 472 patients vs 3 [1.3%] of 238 patients), anemia (46 [9.7%] of 472 patients vs 6 [2.5%] of 238 patients) and diarrhea (43 [9.1%] of 472 patients vs 10 [4.2%] of 238 patients) On-treatment AE-related deaths were reported in 17 [3.6%] vs 0% of patients respectively.Interpretation: The primary objective of PFS was not met. However, consistent with the preliminary observations from BOLERO-3, everolimus prolonged median PFS by 7.2 months in patients with HR, HER2+ ABC, which warrants further investigation. The safety profile was generally consistent with what was previously reported in BOLERO-3. Proactive monitoring and early management of AEs in patients treated with everolimus and chemotherapy is critical..Novartis Pharmaceuticals Corporation.http://www.journals.elsevier.com/the-lancet-oncology2016-07-31hb201