bluuebunny/crossref_metadata_2025_split huggingface dataset:
Chitosan Nanomedicine in Cancer Therapy: Targeted Delivery and Cellular Uptake
Authors: Mushtaq Asim , Li Li , A. Anitha , Grøndahl Lisbeth 
Published: March 2021

AbstractNanomedicine has gained much attention for the management and treatment of cancers due to the distinctive physicochemical properties of the drug‐loaded particles. Chitosan's cationic nature is attractive for the development of such particles for drug delivery, transfection, and controlled release. The particle properties can be improved by modification of the polymer or the particle themselves. The physicochemical properties of chitosan particles are analyzed in 126 recent studies, which allows to highlight their impact on passive and active targeted drug delivery, cellular uptake, and tumor growth inhibition (TGI). From 2012 to 2019, out of 40 in vivo studies, only 4 studies are found reporting a reduction in tumor size by using chitosan particles while all other studies reported tumor growth inhibition relative to controls. A total of 23 studies are analyzed for cellular uptake including 12 studies reporting cellular uptake mechanisms. Understanding and exploiting the processes involved in targeted delivery, endocytosis, and exocytosis by controlling the physicochemical properties of chitosan particles are important for the development of safe and efficient nanomedicine. It is concluded based on the recent literature available on chitosan particles that combination therapies can play a pivotal role in transformation of chitosan nanomedicine from bench to bedside.
Hygroresponsive Torsional Yarns and Actuators Based on Cascade Amplification of the Deformation
Authors: Chen Hui , Ye Sunjie , Tao Qianyi , Chen Zhuang , Tu Yingfeng , Yang Xiaoming 
Published: March 2021

AbstractFiber‐based hygroresponsive torsional actuators provide desirable merits, such as light weight and shapeability, for developing smart systems to harvest energy from moisture which is a ubiquitous natural resource. A key challenge in this development is to realize moisture‐triggered actuation combining large actuation and rapid responses. Here, a multiscale design strategy is explored to create high‐performance hygroresponsive torsional actuators consisting of chitosan and multiwalled carbon nanotubes (MWCNTs). The superior actuation performance arises from the synergism of contributing factors at different scales, including 1) MWNCTs accelerate the water transport in primary twisted fibers (PTFs), fostering the rotation of PTFs upon moisture stimuli; 2) in situ‐formed hierarchically‐assembled twists realize cascade amplification of moisture‐triggered actuation. Specifically, PTFs are self‐twisted to generate secondary helical yarns, that are subsequently over‐twisted to yield tertiary coiled yarn. The resultant yarn actuator can reach a maximum rotation speed of 11 400 rpm in 5 s, output gravitational potential energy of 2.4 J kg−1 and gravitational potential power of 0.053 W kg−1 during contraction. This work represents the first design of fiber‐based actuators by virtue of moisture‐triggered in situ formation of yarns. The established principles of multiscale design will enable high‐performance fiber‐based hygroresponsive actuators toward advanced intelligent textile and soft robotics.
Interfacial Reaction Induced Disruption and Dissolution of Dynamic Polymer Networks
Authors: Zhao Bin , Yuan Qingqing , Yang Hongkun , Russell Thomas P. , Wang Dong 
Published: March 2021

AbstractThe reaction of amine‐terminated polystyrene (PS‐NH2) with an epoxy‐based dynamic polymer networks (DPNs) above the topology freezing transition temperature of the DPN, results in the disruption of the network by the formation of graft copolymers at the interface between the linear homopolymer and the network. The rate of the disruption decreases with annealing time and is strongly dependent on the molecular weight of the PS‐NH2, with the lower molecular weight PS‐NH2 reacting much more rapidly than the higher molecular weight PS‐NH2. A higher catalyst concentration in the DPN also promotes the interfacial reaction, indicating a reaction‐rate‐controlled process.
Alleviating Growth and Recruitment Overfishing through Simple Management Changes: Insights from an Overexploited Long-Lived Fish
Authors: Ben-Hasan Abdulrahman , Walters Carl , Hordyk Adrian , Christensen Villy , Al-Husaini Mohsen 
Published: March 2021

Abstract
Growth and recruitment overfishing can co-occur when a fishery is subjecting small and immature fish in conjunction with adult fish to excessive exploitation rates such that it reduces the spawning biomass to the point where recruitment is significantly impaired. Such conditions are generally evident in open-access fisheries and are especially detrimental to long-lived species as they reach maturity at older ages. Here, we investigate the conditions of a long-lived lutjanid, Malabar Blood Snapper Lutjanus malabaricus, in Kuwait waters, for which catches declined by about 95% between 1995 and 2009 with negligible recovery afterward, yet exploitation rates are likely high and remain hardly regulated. Using an age-structured model and length and age distributions for over 47,000 Malabar Blood Snapper, we (1) underscore the impacts of recruitment and growth overfishing on fish biomass and catch and (2) demonstrate the efficacy of improving the size limit policy to address both issues. The proportion of small fish (length-classes &lt;50 cm; age-classes = 1–4 years) in the catch rose from 40–50% in 1981 to over 70% between 1992 and 1998, indicating growth overfishing. Due to the selection of immature fish at high exploitation rates, the age-structured model showed that recruitment dropped virtually linearly with decreasing biomass by the mid-1990s, implying recruitment overfishing. Future scenarios show that by increasing the current mean age at vulnerability (1 year or 34 cm) to the age at first maturity (5 years or 61 cm), biomass and catch would increase by at least 300% and 130%, respectively, relative to status quo. Biomass would rebuild to higher levels if exploitation rates are regulated at sustainable levels. Our study highlights the importance of simple management changes in alleviating both types of overfishing, particularly when open-access conditions cannot be rapidly remedied due to weak management institutions.
Effect of Learning Dataset for Identification of Active Molecules: A Case Study of Integrin αIIbβ3 Inhibitors
Authors: Kawai Kentaro , Tomonou Mami , Machida Yume , Karuo Yukiko , Tarui Atsushi , Sato Kazuyuki , Ikeda Yoshiki , Kinashi Tatsuo , Omote Masaaki 
Published: March 2021

AbstractEfficient in silico approaches are needed to identify strong integrin αIIbβ3 inhibitors through a small number of measurements. To address the challenge, we investigated the effect of learning dataset on the classification performance of machine learning models focusing on weak and inactive compounds. The structure and activity information of the compounds were obtained from ChEMBL, and pCHEMBL values were used to classify them as active, inactive, or weak. Datasets with various imbalance levels from active:inactive=1 : 1 to 1 : 1000 were used for the machine learning. The prediction scores of the weak samples were found to lie between the predictive values of active and inactive compounds. In addition, another dataset that consists of 149 actives and 6.9 million inactives was screened; the results indicated that the number of positive predictions decreased for models trained with a higher number of inactives. Although there is a trade‐off between false positives and false negatives, for determination of compounds with strong activity using a reduced number of measurements, it is better to use a large number of inactives for learning and identifying compounds that score higher than the weak samples.
Interactive contouring through contextual deep learning
Authors: Trimpl Michael J. , Boukerroui Djamal , Stride Eleanor P. J. , Vallis Katherine A. , Gooding Mark J. 
Published: March 2021

PurposeTo investigate a deep learning approach that enables three‐dimensional (3D) segmentation of an arbitrary structure of interest given a user provided two‐dimensional (2D) contour for context. Such an approach could decrease delineation times and improve contouring consistency, particularly for anatomical structures for which no automatic segmentation tools exist.MethodsA series of deep learning segmentation models using a Recurrent Residual U‐Net with attention gates was trained with a successively expanding training set. Contextual information was provided to the models, using a previously contoured slice as an input, in addition to the slice to be contoured. In total, 6 models were developed, and 19 different anatomical structures were used for training and testing. Each of the models was evaluated for all 19 structures, even if they were excluded from the training set, in order to assess the model's ability to segment unseen structures of interest. Each model's performance was evaluated using the Dice similarity coefficient (DSC), Hausdorff distance, and relative added path length (APL).ResultsThe segmentation performance for seen and unseen structures improved when the training set was expanded by addition of structures previously excluded from the training set. A model trained exclusively on heart structures achieved a DSC of 0.33, HD of 44 mm, and relative APL of 0.85 when segmenting the spleen, whereas a model trained on a diverse set of structures, but still excluding the spleen, achieved a DSC of 0.80, HD of 13 mm, and relative APL of 0.35. Iterative prediction performed better compared to direct prediction when considering unseen structures.ConclusionsTraining a contextual deep learning model on a diverse set of structures increases the segmentation performance for the structures in the training set, but importantly enables the model to generalize and make predictions even for unseen structures that were not represented in the training set. This shows that user‐provided context can be incorporated into deep learning contouring to facilitate semi‐automatic segmentation of CT images for any given structure. Such an approach can enable faster de‐novo contouring in clinical practice.
PART 2: Mouse models for multiple sclerosis research
Authors: Ramasamy Ramalakshmi , Smith Phillip P. 
Published: March 2021

AbstractLower urinary tract symptoms and dysfunction (LUTS/LUTD) contribute to loss of quality of life, morbidity, and need for medical intervention in most patients with multiple sclerosis (MS). Although MS is an inflammatory neurodegenerative disease, clinical manifestations including continence control disorders have traditionally been attributed to the loss of neural signaling due to neurodegeneration. Clinical approaches to MS‐LUTS/LUTD have focused on addressing symptoms in the context of urodynamic dysfunctions as pathophysiologic understandings are incomplete. The mouse model provides a useful research platform for discovery of more detailed molecular, cellular, and tissue‐level knowledge of the disease and its clinical manifestations. The aim of this two‐part review is to provide a state‐of‐the‐art update on the use of the mouse model for MS research, with a focus on lower urinary tract symptoms. Part I presents a summary of current understanding of MS pathophysiology, the impact on lower urinary tract symptoms, and briefly introduces the types of mouse models available to study MS. Part II presents the common animal models that are currently available to study MS, their mechanism, relevance to MS‐LUTS/LUTD and their urinary pathophysiology, advantages and disadvantages.
Longer is better than shorter: The added value of the seven‐day pad test in the post‐radical prostatectomy urinary incontinence
Authors: Martínez‐Cuenca Esther , Sánchez Jose Vicente , Bonillo Miguel Ángel , Morán Eduardo , Broseta Enric , Arlandis Salvador 
Published: March 2021

AbstractAimsTo investigate the feasibility and reliability of the seven‐day pad test (7DPT), the correlation between 7DPT and 24‐hour pad test (24HPT), and International Consultation on Incontinence Questionnaire‐Short Form (ICIQ‐SF) questionnaire, and the different categorization by the severity of each pad test. Pad weighting tests have been recommended in the evaluation of post‐radical prostatectomy urinary incontinence severity, being considered the 24HPT gold standard. Some authors consider that the longer the testing, the better assessment. We propose a self‐filled pad weight, the 7DPT.MethodsA prospective study of incontinent male patients after radical prostatectomy. We carried out the study in two phases. All patients underwent urodynamic study. The first phase evaluated the feasibility and reliability of 7DPT. The second evaluated the correlation between 7DPT, 24HPT, and ICIQ‐SF questionnaire in a larger sample.ResultsFirst phase: 32 patients were recruited. Test‐retest reliability was excellent, with good agreement between mean 7DPT and 24HPT. The number of pads and mean 7DPT showed a good correlation. Second phase: 72 patients were screened, 51 (71%) met inclusion criteria. Mean 7DPT and 24HPT showed a strong association. Mean 7DPT and 24HPT correlated moderately with ICIQ‐SF score. 7DPT and 24HPT agreement with ICIQ‐SF groups was slight. As 7DPT captures better days with maximum urinary leakage, up to 12 patients would have been misclassified according to 24HPT (number need to treat for 7DPT was seven).Conclusions7DPT is a feasible and reliable tool to evaluate post‐radical prostatectomy urinary incontinence, with a strong correlation with the 24HPT and moderate with the ICIQ‐SF.
Improved breakdown strengths and energy storage properties of polyimide composites: The effect of internal interfaces of C/SiO2 hybrid nanoparticles
Authors: Yin Peng , Shi Zhicheng , Sun Liang , Xie Peitao , Dastan Davoud , Sun Kai , Fan Runhua 
Published: March 2021

AbstractPolymer nanocomposites with high energy‐storage capability have been widely used in electronic devices. To achieve further enhanced energy density, developing nanofillers with tailored compositions and nanostructures is demonstrated to be an effective strategy. Especially, hybrid nanoparticles (NPs) with abundant internal interfaces are acknowledged as a promising candidate. Here, a class of carbon/silica hybrid nanoparticles (C/SiO2 h‐NPs) with disorderly mixed nanostructures are synthesized and employed to prepare C/SiO2 h‐NPs/polyimide nanocomposites. To reveal the influence of internal interfaces on dielectric properties, a series of comparative composites co‐filled with C and SiO2 NPs are also prepared. The breakdown strength of the C/SiO2 h‐NPs/polyimide composites can reach 303.96 kV/mm, which is over 200% that of their counterparts. It is believed that the abundant heterogeneous interfaces inside the C/SiO2 h‐NPs can effectively impede the development of conductive paths, thereby obviously enhancing breakdown strengths. Furthermore, the C/SiO2 h‐NPs/polyimide composite with 5 wt% C/SiO2 h‐NPs demonstrates a high discharged energy density of ~0.612 J/cm3 at 150 kV/mm, which is about 2.29 times higher than pure polyimide (PI) (~0.267 @ 150 kV/mm), along with a high discharge energy efficiency of 85.9%. This work offers a practicable strategy for the design of functional NPs toward high‐performance dielectric composites.
Hope, bias and survival expectations of advanced cancer patients: A cross‐sectional study
Authors: Finkelstein Eric A. , Baid Drishti , Cheung Yin Bun , Schweitzer Maurice E. , Malhotra Chetna , Volpp Kevin , Kanesvaran Ravindran , Lee Lai Heng , Dent Rebecca Alexandra , Ng Chau Hsien Matthew , Bin Harunal Rashid Mohamad Farid , Somasundaram Nagavali 
Published: March 2021

AbstractObjectiveMany patients with advanced illness are unrealistically optimistic about their prognosis. We test for the presence of several cognitive biases, including optimism bias, illusion of superiority, self‐deception, misattribution, and optimistic update bias, that could explain unrealistically optimistic prognostic beliefs among advanced cancer patients and quantifies the extent to which hope exacerbates these biases.MethodsA cross‐sectional survey was administered to 200 advanced cancer patients with physician‐estimated prognoses of one year or less. Hope was measured using the Herth Hope Index (HHI). Hypotheses were tested using linear and logistic regressions and a structural‐equation model.ResultsResults are consistent with the presence of optimism bias, illusion of superiority, self‐deception, and misattribution. All of these biases are amplified by higher levels of hope. Each 1‐point higher HHI is associated with a 6% (OR: 1.06; 95% CI: 1.01–1.11) greater odds of believing their illness is curable, a 0.33‐year (95% CI: 0.17–0.49) longer expected survival, a 6% (OR: 1.06; 95% CI: 1.02–1.11) higher probability of believing that survival outcomes are better than the average patient, a 5% higher odds of believing primary intent of treatment is curative (OR: 1.05; 95% CI: 1.00–1.10), and a 12% (OR: 1.12; 95% CI: 1.05–1.17) higher odds of believing they are well‐informed. Mediation analyses revealed that hope significantly mediates the effect of mental‐well‐being and loneliness on expected survival.ConclusionsResults suggest advanced cancer patients succumb to several cognitive biases which are exacerbated by greater levels of hope. As a result, they are susceptible to possible over‐treatment and regret.
Logistic retainment interval dose exploration design for Phase I clinical trials of cytotoxic agents
Authors: Murray Thomas A. 
Published: March 2021

AbstractPhase I studies of a cytotoxic agent often aim to identify the dose that provides an investigator specified target dose‐limiting toxicity (DLT) probability. In practice, an initial cohort receives a dose with a putative low DLT probability, and subsequent dosing follows by consecutively deciding whether to retain the current dose, escalate to the adjacent higher dose, or de‐escalate to the adjacent lower dose. This article proposes a Phase I design derived using a Bayesian decision‐theoretic approach to this sequential decision‐making process. The design consecutively chooses the action that minimizes posterior expected loss where the loss reflects the distance on the log‐odds scale between the target and the DLT probability of the dose that would be given to the next cohort under the corresponding action. A logistic model is assumed for the log odds of a DLT at the current dose with a weakly informative t‐distribution prior centered at the target. The key design parameters are the pre‐specified odds ratios for the DLT probabilities at the adjacent higher and lower doses. Dosing rules may be pre‐tabulated, as these only depend on the outcomes at the current dose, which greatly facilitates implementation. The recommended default version of the proposed design improves dose selection relative to many established designs across a variety of scenarios.
Tip of the iceberg: Measuring racial discrimination in studies of health
Authors: Cuevas Adolfo G. , Boen Courtney 
Published: March 2021

AbstractThere is compelling evidence that racial discrimination is a risk factor for illness and disease. But what are health scientists measuring—and what do they think they are measuring—when they include measures of racial discrimination in health research? We synthesize theoretical conceptualizations of racial discrimination in health research and critically assess whether and how these concepts correspond (or not) to widely used measures of racial discrimination. In doing so, we show that while researchers often use terms such as ‘self‐reported discrimination', ‘perceptions of discrimination', and ‘exposure to discrimination' interchangeably, these concepts are indeed unique, with each holding a distinct epistemological position and theoretical and methodological capacity to uncover the impact of racial discrimination on health and health disparities. Importantly, we argue that commonly used measures of self‐reported or perceived racial discrimination are just the ‘tip of the iceberg' in terms of revealing the ways in which discrimination shapes health inequities. Scientists and practitioners must be cognizant of and intentional in their measurement choices and language, as the framing of these processes will inform policy and intervention efforts aimed at eliminating discrimination.
Photosynthetic Microorganisms‐Based Biophotothermal Therapy with Enhanced Immune Response
Authors: Wang Haoran , Guo Yunfei , Gan Shaoju , Liu Honghui , Chen Qian , Yuan Ahu , Hu Yiqiao , Wu Jinhui 
Published: March 2021

AbstractThe production of oxygen by photosynthetic microorganisms (PSMs) has recently attracted interest concerning the in vivo treatment of multiple diseases for their photosynthetic oxygen production in vivo, since PSMs have good biological safety. Here, the first evidence that PSMs can be used as a photothermal source to perform biophotothermal therapy (bio‐PTT) is provided. In vitro and in vivo experiments proved that PSMs can generate heat for the direct elimination of tumors and release a series of pathogen‐associated molecular patterns and adjuvants for immune stimulation under light irradiation. Bio‐PTT enabled a local tumor inhibition rate exceeding 90% and an abscopal tumor inhibition rate exceeding 75%. This strategy also produced a stronger antitumor immune memory effect to prevent tumor recurrence. The bio‐PTT strategy provides a novel direction for photothermal therapy as it simultaneously produces local and abscopal antitumor effects.
Analysis and availability of lignocellulosic wastes: Assessments for Indonesia and Peru
Authors: Sari Reka M. , Torres Fernando G. , Troncoso Omar P. , De‐la‐Torre Gabriel E. , Gea Saharman 
Published: March 2021

AbstractDeveloping countries located in the tropics have favorable climates to develop modern agriculture and agriculture‐based industry. Peru and Indonesia are examples of two developing countries located in the tropics with economies based on the export of agricultural products. The generation of agricultural and forest residues has continuously increased in these countries. In this paper, we analyze the availability of agricultural and forest products in Peru and Indonesia in order to calculate the number of residues that could potentially be used for different applications such as energy generation and composite materials production. A single index taking into account the technical, economic, and environmental performance of these residues‐based products was utilized to compare them. The results showed that, in addition to the generation of energy, the production of plastic bio‐based composites and activated carbon are viable alternatives for the use of these residues.
Multilingual automation of transcript preprocessing in Alzheimer's disease detection
Authors: Abiven Frédéric , Ratté Sylvie 
Published: March 2021

AbstractIntroductionAnalyzing linguistic functions can improve early detection of Alzheimer's disease (AD). To date, no studies have focused on creating a universal pipeline for clinical transcript preprocessing.MethodsThis article presents a simple and efficient method for processing linguistic and phonetic data, sequencing subproblems of cleaning, normalization, and measure extraction tasks. Because some of these tasks are language‐ and context‐ dependent, they were designed to be easily configurable, thus increasing their scalability when dealing with new corpora.ResultsResults show improved performances over previous studies in this time‐consuming preprocessing task. Moreover, our findings showed that some discursive markers extracted from transcripts revealed a significant correlation (>0.5) with cognitive impairment severity.DiscussionThis article contributes to the literature on AD by presenting an efficient pipeline that allows speeding up the transcripts preprocessing task. We further invite other researchers to contribute to this work to help improve the quality of this pipeline (https://github.com/LiNCS-lab/usAge).