Development of a fit-for-purpose multi-marker panel for early diagnosis of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) suffers from lack of an effective diagnostic method, which hampers improvement in patient survival. Carbohydrate antigen 19-9 (CA19-9) is the only FDA-approved blood biomarker for PDAC, yet its clinical utility is limited due to suboptimal performance. Liquid chromatography-mass spectrometry (LC-MS) has emerged as a burgeoning technology in clinical proteomics for discovery, verification, and validation of novel biomarkers. A plethora of protein biomarker candidates for PDAC have been identified using LC-MS, yet few has successfully transitioned into clinical practice. This translational standstill is owed partly to insufficient considerations of practical needs and perspectives of clinical implementation during biomarker development pipelines, such as demonstrating analytical robustness of proposed biomarkers which is critical for transitioning from research-grade to clinical-grade assays. Moreover, throughput and cost-effectiveness of proposed assays ought to be considered concomitantly from early phases of the biomarker pipelines for enhancing widespread adoption in clinical settings. Here, we developed a fit-for-purpose multi-marker panel for PDAC diagnosis by consolidating analytically robust biomarkers as well as employing a relatively simple LC-MS protocol. In discovery phase, we comprehensively surveyed putative PDAC biomarkers from both in-house data and prior studies. In verification phase, we developed a multiple-reaction monitoring (MRM)-MS-based proteomic assay using surrogate peptides that passed stringent analytical validation tests. We adopted a high-throughput protocol including short gradient (<10 min) and simple sample preparation (no depletion or enrichment steps). Additionally, we developed our assay using serum samples, which are usually the preferred biospecimen in clinical settings. We developed predictive models based on our final panel of 12 protein biomarkers combined with CA19-9, which showed improved diagnostic performance compared to using CA19-9 alone in discriminating PDAC from non-PDAC controls including healthy individuals and patients with benign pancreatic diseases. A large-scale clinical validation is underway to demonstrate the clinical validity of our novel panel.

Diagnostic value of a deep learning-based hyoid bone tracking model for aspiration in patients with post-stroke dysphagia.

Objective: Hyoid bone movement is potentially related to aspiration risk in post-stroke dysphagia (PSD) patients but is difficult to assess quantitatively. This study aimed to measure the distance of hyoid bone movement more efficiently and accurately using a deep learning model and determine the clinical usefulness of the model in PSD patients. Methods: This study included 85 patients with PSD within 6 months from onset. Patients were grouped into an aspiration group (n = 35) and a non-aspiration group (n = 50) according to the results of a videofluoroscopic swallowing study. Hyoid bone movement was tracked using a deep learning model constructed with the BiFPN-U-Net(T) architecture. The maximum distance of hyoid bone movement was measured horizontally (H max), vertically (V max), and diagonally (D max). Results: Compared with the non-aspiration group, the aspiration group showed significant decreases in hyoid bone movement in all directions. The area under the curve of V max was highest at 0.715 with a sensitivity of 0.680 and specificity of 0.743. The V max cutoff value for predicting aspiration risk was 1.61 cm. The success of oral feeding at the time of discharge was significantly more frequent when hyoid movement was equal to or larger than the cutoff value although no significant relationship was found between hyoid movement and other clinical characteristics. Conclusion: Hyoid bone movement of PSD patients can be measured quantitatively and efficiently using a deep learning model. Deep learning model-based analysis of hyoid bone movement seems to be useful for predicting aspiration risk and the possibility of resuming oral feeding.

Electromagnetic Interference Shielding Properties of Highly Flexible Poly(styrene-co-butyl acrylate)/PEDOT:PSS Films Fabricated by Latex Technology.

As the use of stretchable electronic devices increases, the importance of flexible electromagnetic interference (EMI) shielding films is emerging. In this study, a highly flexible shielding film was fabricated using poly(styrene-co-butyl acrylate) (p(St-co-BA)) latex as a matrix and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) as a conductive filler, and then the mechanical properties and EMI shielding performance of the film were examined. Styrene and butyl acrylate were copolymerized to lower the high glass transition temperature and increase the ductility of brittle polystyrene. The latex blending technique was used to produce a shielding film in which the aqueous filler dispersion was uniformly dispersed in the emulsion polymerized resin. To determine the phase change in the copolymer matrix with temperature, the storage modulus was measured, and a time-temperature superposition master curve was constructed. The drying temperature of water-based copolymer resin suitable for film fabrication was set based on this curve. The glass transition temperature and flexibility of the blends were determined by evaluating the thermomechanical analysis and tensile tests. The EMI shielding effectiveness (SE) of the films was analyzed at frequencies from 50 MHz to 1.5 GHz, covering the VHF and UHF ranges. As the filler content increased, the SE of the blend film increased, but the elongation increased until a certain content and then decreased. The optimal content of PEDOT:PSS that satisfied both the ductility and shielding performance of the film was found to be 10 wt%. In this case, the elongation at break reached 300%, and the SE of a 1.6 mm thick film was about 35 dB. The film developed in this study can be used as an EMI shielding material that requires high flexibility.

Characterizing the Microparticles Deposition Structure and its Photonic Nature in Surfactant-Laden Evaporating Colloidal Sessile Droplets.

This work presents a simple method to create photonic microstructures via the natural evaporation of surfactant-laden colloidal sessile droplets on a flat substrate. In the absence of dissolved surfactant, the evaporating colloidal droplet forms a well-known coffee ring deposition. In contrast, the presence of surfactant leads to the formation of multiple ring structures due to the repetitive pinning-depinning behavior of the droplet contact line (CL). It is found that the multiring structure shows vibrant iridescent structural colors while the coffee ring lacks a photonic nature. This difference in the structural color for the presence and absence of the surfactant is found to be dependent on the arrangement of the particles in the deposition structure. The particle arrangement in the multirings is monolayered and well-ordered. The ordering of the particles is strongly influenced by the particle dynamics, contact angle (CA), and CL dynamics of the evaporating colloidal solution droplet. Furthermore, the iridescent nature of the multiring deposition is demonstrated and explained. The dependence of the multiring deposition structure on the concentration of the dissolved surfactant and the suspended particles is also studied. The findings demonstrate that an intermediate surfactant concentration is desirable for the formation of a multiring structure. Further, the pinning-depinning CL dynamics that causes the formation of the multiring deposition structure is discussed. Finally, we demonstrate the applicability of the approach to smaller droplet volumes.

Integrated Machine Learning Approach for the Early Prediction of Pressure Ulcers in Spinal Cord Injury Patients.

(1) Background: Pressure ulcers (PUs) substantially impact the quality of life of spinal cord injury (SCI) patients and require prompt intervention. This study used machine learning (ML) techniques to develop advanced predictive models for the occurrence of PUs in patients with SCI. (2) Methods: By analyzing the medical records of 539 patients with SCI, we observed a 35% incidence of PUs during hospitalization. Our analysis included 139 variables, including baseline characteristics, neurological status (International Standards for Neurological Classification of Spinal Cord Injury [ISNCSCI]), functional ability (Korean version of the Modified Barthel Index [K-MBI] and Functional Independence Measure [FIM]), and laboratory data. We used a variety of ML methods-a graph neural network (GNN), a deep neural network (DNN), a linear support vector machine (SVM_linear), a support vector machine with radial basis function kernel (SVM_RBF), K-nearest neighbors (KNN), a random forest (RF), and logistic regression (LR)-focusing on an integrative analysis of laboratory, neurological, and functional data. (3) Results: The SVM_linear algorithm using these composite data showed superior predictive ability (area under the receiver operating characteristic curve (AUC) = 0.904, accuracy = 0.944), as demonstrated by a 5-fold cross-validation. The critical discriminators of PU development were identified based on limb functional status and laboratory markers of inflammation. External validation highlighted the challenges of model generalization and provided a direction for future research. (4) Conclusions: Our study highlights the importance of a comprehensive, multidimensional data approach for the effective prediction of PUs in patients with SCI, especially in the acute and subacute phases. The proposed ML models show potential for the early detection and prevention of PUs, thus contributing substantially to improving patient care in clinical settings.

Correction to "Electrical and Thermal Properties of Surface-Modified Copper Nanowire/Polystyrene Nanocomposites through Latex Blending".

[This corrects the article DOI: 10.1021/acsomega.3c06775.].

Acoustofluidic separation of prolate and spherical micro-objects.

Most microfluidic separation techniques rely largely on object size as a separation marker. The ability to separate micro-objects based on their shape is crucial in various biomedical and chemical assays. Here, we develop an on-demand, label-free acoustofluidic method to separate prolate ellipsoids from spherical microparticles based on traveling surface acoustic wave-induced acoustic radiation force and torque. The freely rotating non-spherical micro-objects were aligned under the progressive acoustic field by the counterrotating radiation torque, and the major axis of the prolate ellipsoids was parallel to the progressive wave propagation. The specific alignment of the ellipsoidal particles resulted in a reduction in the cross-sectional area perpendicular to the wave propagation. As a consequence, the acoustic backscattering decreased, resulting in a decreased magnitude of the radiation force. Through the variation in radiation force, which depended on the micro-object morphology enabled the acoustofluidic shape-based separation. We conducted numerical simulations for the wave scattering of spherical and prolate objects to elucidate the working mechanism underlying the proposed method. A series of experiments with polystyrene microspheres, prolate ellipsoids, and peanut-shaped microparticles were performed for validation. Through quantitative analysis of the separation efficiency, we confirmed the high purity and high recovery rate of the proposed acoustofluidic shape-based separation of micro-objects. As a bioparticle, we utilize Thalassiosira eccentrica to perform shape-based separation, as the species has a variety of potential applications in drug delivery, biosensing, nanofabrication, bioencapsulation and immunoisolation.

Immobilized Candida antarctica lipase B as an sn-1,3 regiospecific biocatalyst for the interesterification of triacylglycerols with fatty acid ethyl esters.

Candida antarctica lipase B (CALB) is regarded as non-regiospecific. This study aimed to investigate the regiospecificity of CALB in the solvent-free interesterification of high-oleic sunflower oil with stearic acid ethyl ester for 1,3-distearoyl-2-oleoylglycerol (SOS)-rich fat preparation using a packed bed reactor. The content ratio of 1,2-distearoyl-3-oleoylglycerol (SSO) to SOS (denoted by SSO/SOS content) obtained using Lipozyme 435 (a commercially immobilized CALB; 0-4.1%), at residence times (1-32 min) was similar to that obtained using Lipozyme RM IM (0-3.0%), but lower than that obtained using Lipozyme TL IM (6.0-39.4%). When immobilized on Lewatit VP OC 1600, Lipozyme CALB had an SSO/SOS content of 0-10.4%, which was greater than that of Palatase 20,000 L (0-1.1%) but was lower than that of Lipozyme TL 100 L (8.8-97.7%). Our findings suggest that immobilized CALB shows distinct sn-1,3 regiospecificity in the interesterification of triacylglycerol with fatty acid ethyl esters.

Electrical and Thermal Properties of Surface-Modified Copper Nanowire/Polystyrene Nanocomposites through Latex Blending.

The incorporation of conductive nanofillers into an insulating polymer matrix commonly leads to nanocomposites with good electrical, thermal, and mechanical properties. In this study, copper nanowires (CuNWs) and polystyrene (PS) microspheres were synthesized along with the fabrication of CuNW/PS polymer nanocomposites. The electrical, thermal, mechanical, rheological, and morphological properties of the CuNW/PS nanocomposites were examined. The CuNWs were homogeneously dispersed in the PS matrix through latex blending. For the CuNW/PS nanocomposites, the storage modulus was higher than the loss modulus at all frequencies, indicating their elastic-dominant behavior. The electrical and thermal conductivities of the nanocomposites increased with an increasing CuNW content. Using a mixed dispersion of two monodisperse PS particles of 500 nm and 5 µm in diameter resulted in the highest electrical conductivity (ca. 10° S/m for 30 wt % nanofillers) among the nanocomposites. In addition, the introduction of silica- and polydopamine-coated CuNWs as nanofillers imparted insulation properties to the nanocomposites, with electrical conductivities to 10-10-10-8 S/m. When using 500 nm PS particles, the thermal conductivity of the surface-modified CuNW/PS nanocomposite at 30 wt % of CuNW was enhanced to 0.22 W/m·K compared to 0.17 W/m·K for its unmodified counterpart. We have achieved multiple innovative approaches, including the use of mixed particle sizes, surface modification of CuNW, and the exploration of elastic-dominant behavior. This enhanced thermal conductivity, coupled with the attainment of insulation properties, presents a distinct advantage for thermal interface material (TIM) applications.

Light-Scattering Analysis of Drying Behavior in Suspension Droplets with Silica and Polystyrene Particles and a Hydrosoluble Polymer.

The coffee-ring structure, which is the final drying pattern of a sessile suspension droplet, is a key factor in controlling the uniformity of the particulate deposits in various coatings. Two light-scattering methods, diffusing wave spectroscopy (DWS) and multispeckle DWS (MSDWS), were used to quantitatively distinguish temporal changes in particle mobility in evaporating suspension droplets containing micrometer-sized silica and polystyrene (PS) particles. The characteristic particle mobility was measured in terms of the mean square displacement in the early stage of drying, and the local particle dynamics around the edge and center regimes of the droplets during drying were analyzed using MSDWS. Hydroxyethyl cellulose (HEC), a hydrosoluble polymer, was added to the silica and PS suspensions to further investigate its role in suppressing or enhancing coffee-ring patterns based on particle-polymer interactions. Consequently, dried microstructures can be directly correlated with real-time drying dynamics, as well as the interactions between solutes by comprehensive light-scattering methods.

Clinical Practice Guideline for Stroke Rehabilitation in Korea-Part 1: Rehabilitation for Motor Function (2022).

This clinical practice guideline (CPG) is the fourth edition of the Korean guideline for stroke rehabilitation, which was last updated in 2016. The development approach has been changed from a consensus-based approach to an evidence-based approach using the Grading of Recommendations Assessment Development and Evaluation (GRADE) method. This change ensures that the guidelines are based on the latest and strongest evidence available. The aim is to provide the most accurate and effective guidance to stroke rehabilitation teams, and to improve the outcomes for stroke patients in Korea. Fifty-five specialists in stroke rehabilitation and one CPG development methodology expert participated in this development. The scope of the previous clinical guidelines was very extensive, making it difficult to revise at once. Therefore, it was decided that the scope of this revised CPG would be limited to Part 1: Rehabilitation for Motor Function. The key questions were selected by considering the preferences of the target population and referring to foreign guidelines for stroke rehabilitation, and the recommendations were completed through systematic literature review and the GRADE method. The draft recommendations, which were agreed upon through an official consensus process, were refined after evaluation by a public hearing and external expert evaluation.

Retardation of Capillary Force between Janus Particles at the Oil-Water Interface.

Interactions among colloidal particles govern the hierarchical microstructure and its physical properties. Here, optical laser tweezers and Monte Carlo simulations are used to evaluate the effects of azimuthal rotation of Janus particles at the oil-water interface on interparticle interactions. We find that the capillary-induced attractive force between two Janus particles at the interface can be relaxed by azimuthal rotation around the critical separation region, at which the capillary force is ∼0.053 pN. Force relaxation leads to a decrease in capillary force around the critical separation region, resulting in a slight increase in the scaling exponent, compared to the theoretical prediction.

Quantitative Analysis in Cervical Spinal Cord Injury Patients Using Diffusion Tensor Imaging and Tractography.

OBJECTIVE: To investigate the clinical usefulness of diffusion tensor imaging (DTI) and tractography in the prediction of outcomes after traumatic cervical spinal cord injury (SCI) and to assess whether the predictability is different between DTI and tractography administered before and after surgery. METHODS: Sixty-one subjects with traumatic cervical SCI were randomly assigned to preop or postop groups and received DTI accordingly. Among the patients who had DTI before surgery, we assigned 10 patients who had received repeated DTI examinations at 8 weeks after injury to the follow-up group. Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values were obtained from DTI, and imaginary fiber and crossing fiber numbers were calculated from the tractography. Neurological status and functional status were assessed at 4 and 8 weeks after SCI. RESULTS: The neurologic and functional statuses of both groups improved after 4 weeks. Out of the initial 61 patients who were enrolled in the study, the failure rate of DTI image analysis was significantly higher in the postop group (n=17, 41.5%) than in the preop group (n=6, 20%). The FA values and fiber numbers in the preop group tended to be higher than those in the postop group, whereas ADC values were lower in the preop group. When comparing the tractographic findings in the follow-up group, imaginary fiber numbers at the C6 and C7 levels and crossing fiber numbers from the C3 to C6 levels were significantly decreased after surgery. Several DTI and tractographic parameters (especially the ADC value at the C4 level and imaginary fiber numbers at the C6 level) showed significant correlations with neurologic and functional statuses in both the preop and postop groups. These findings were most prominent when DTI and physical examination were simultaneously performed. CONCLUSION: Preoperative DTI and tractography demonstrated better FA and ADC values with lower interpretation failure rates than those obtained after surgery, whereas postoperative data significantly reflected the patient's clinical state at the time of evaluation. Therefore, DTI and tractography could be useful in predicting clinical outcomes after traumatic cervical SCI and should be interpreted separately before and after spine surgery.

Prediction of Poststroke Depression Based on the Outcomes of Machine Learning Algorithms.

Poststroke depression (PSD) is a major psychiatric disorder that develops after stroke; however, whether PSD treatment improves cognitive and functional impairments is not clearly understood. We reviewed data from 31 subjects with PSD and 34 age-matched controls without PSD; all subjects underwent neurological, cognitive, and functional assessments, including the National Institutes of Health Stroke Scale (NIHSS), the Korean version of the Mini-Mental Status Examination (K-MMSE), computerized neurocognitive test (CNT), the Korean version of the Modified Barthel Index (K-MBI), and functional independence measure (FIM) at admission to the rehabilitation unit in the subacute stage following stroke and 4 weeks after initial assessments. Machine learning methods, such as support vector machine, k-nearest neighbors, random forest, voting ensemble models, and statistical analysis using logistic regression were performed. PSD was successfully predicted using a support vector machine with a radial basis function kernel function (area under curve (AUC) = 0.711, accuracy = 0.700). PSD prognoses could be predicted using a support vector machine linear algorithm (AUC = 0.830, accuracy = 0.771). The statistical method did not have a better AUC than that of machine learning algorithms. We concluded that the occurrence and prognosis of PSD in stroke patients can be predicted effectively based on patients' cognitive and functional statuses using machine learning algorithms.

Electrically Conductive Silicone-Based Nanocomposites Incorporated with Carbon Nanotubes and Silver Nanowires for Stretchable Electrodes.

Stretchable electrode materials have attracted great attention as next-generation electronic materials because of their ability to maintain intrinsic properties with rare damage when undergoing repetitive deformations, such as folding, twisting, and stretching. In this study, an electrically conductive PDMS nanocomposite was manufactured by combining the hybrid nanofillers of carbon nanotubes (CNTs) and silver nanowires (AgNWs). The amphiphilic isopropyl alcohol molecules temporarily adhered simultaneously to the hydrophobic CNT and hydrophilic AgNW surfaces, thereby improving the dispersity. As the CNT/AgNW ratio (wt %/wt %) decreased under the constant nanofiller content, the tensile modulus decreased and the elongation at break increased owing to the poor interaction between the AgNWs and matrix. The shear storage moduli of all nanocomposites were higher than the loss moduli, indicating the elastic behavior with a cross-linked network. The electrical conductivities of the nanocomposite containing the hybrid nanofillers were superior to those of the nanocomposite containing either CNT or AgNW at the same filler content (4 wt %). The hybrid nanofillers were rearranged and deformed by 5000 cyclic strain tests, relaxing the PDMS matrix chain and weakening the interfacial bonding. However, the elastic behavior was maintained. The dynamic electrical conductivities gradually increased under the cyclic strain tests due to the rearrangement and tunneling effect of the nanofillers. The highest dynamic electrical conductivity (10 S/m) was obtained for the nanocomposite consisting of 2 wt % of CNTs and 2 wt % of AgNWs.

Reliability of Machine and Human Examiners for Detection of Laryngeal Penetration or Aspiration in Videofluoroscopic Swallowing Studies.

Computer-assisted analysis is expected to improve the reliability of videofluoroscopic swallowing studies (VFSSs), but its usefulness is limited. Previously, we proposed a deep learning model that can detect laryngeal penetration or aspiration fully automatically in VFSS video images, but the evidence for its reliability was insufficient. This study aims to compare the intra- and inter-rater reliability of the computer model and human raters. The test dataset consisted of 173 video files from which the existence of laryngeal penetration or aspiration was judged by the computer and three physicians in two sessions separated by a one-month interval. Intra- and inter-rater reliability were calculated using Cohen's kappa coefficient, the positive reliability ratio (PRR) and the negative reliability ratio (NRR). Intrarater reliability was almost perfect for the computer and two experienced physicians. Interrater reliability was moderate to substantial between the model and each human rater and between the human raters. The average PRR and NRR between the model and the human raters were similar to those between the human raters. The results demonstrate that the deep learning model can detect laryngeal penetration or aspiration from VFSS video as reliably as human examiners.

Hyoid Bone Tracking in a Videofluoroscopic Swallowing Study Using a Deep-Learning-Based Segmentation Network.

Kinematic analysis of the hyoid bone in a videofluorosopic swallowing study (VFSS) is important for assessing dysphagia. However, calibrating the hyoid bone movement is time-consuming, and its reliability shows wide variation. Computer-assisted analysis has been studied to improve the efficiency and accuracy of hyoid bone identification and tracking, but its performance is limited. In this study, we aimed to design a robust network that can track hyoid bone movement automatically without human intervention. Using 69,389 frames from 197 VFSS files as the data set, a deep learning model for detection and trajectory prediction was constructed and trained by the BiFPN-U-Net(T) network. The present model showed improved performance when compared with the previous models: an area under the curve (AUC) of 0.998 for pixelwise accuracy, an accuracy of object detection of 99.5%, and a Dice similarity of 90.9%. The bounding box detection performance for the hyoid bone and reference objects was superior to that of other models, with a mean average precision of 95.9%. The estimation of the distance of hyoid bone movement also showed higher accuracy. The deep learning model proposed in this study could be used to detect and track the hyoid bone more efficiently and accurately in VFSS analysis.

Identification of Nucleolin as a Novel AEG-1-Interacting Protein in Breast Cancer via Interactome Profiling.

Breast cancer is one of the most common malignant diseases worldwide. Astrocyte elevated gene-1 (AEG-1) is upregulated in breast cancer and regulates breast cancer cell proliferation and invasion. However, the molecular mechanisms by which AEG-1 promotes breast cancer have yet to be fully elucidated. In order to delineate the function of AEG-1 in breast cancer development, we mapped the AEG-1 interactome via affinity purification followed by LC-MS/MS. We identified nucleolin (NCL) as a novel AEG-1 interacting protein, and co-immunoprecipitation experiments validated the interaction between AEG-1 and NCL in breast cancer cells. The silencing of NCL markedly reduced not only migration/invasion, but also the proliferation induced by the ectopic expression of AEG-1. Further, we found that the ectopic expression of AEG-1 induced the tyrosine phosphorylation of c-Met, and NCL knockdown markedly reduced this AEG-1 mediated phosphorylation. Taken together, our report identifies NCL as a novel mediator of the oncogenic function of AEG-1, and suggests that c-Met could be associated with the oncogenic function of the AEG-1-NCL complex in the context of breast cancer.

Preparation of Low-Diacylglycerol Cocoa Butter Equivalents by Hexane Fractionation of Palm Stearin and Shea Butter.

Herein, we prepared 1,3-dipalmitoyl-2-oleoyl glycerol (POP)-rich fats with reduced levels of diacylglycerols (DAGs), adversely affecting the tempering of chocolate, via two-step hexane fractionation of palm stearin. DAG content in the as-prepared fats was lower than that in POP-rich fats obtained by previously reported conventional two-step acetone fractionation. Cocoa butter equivalents (CBEs) were fabricated by blending the as-prepared fats with 1,3-distearoyl-2-oleoyl glycerol (SOS)-rich fats obtained by hexane fractionation of degummed shea butter. POP-rich fats achieved under the best conditions for the fractionation of palm stearin had a significantly lower DAG content (1.6 w/w%) than that in the counterpart (4.6 w/w%) prepared by the previously reported method. The CBEs fabricated by blending the POP- and SOS-rich fats in a weight ratio of 40:60 contained 63.7 w/w% total symmetric monounsaturated triacylglycerols, including 22.0 w/w% POP, 8.6 w/w% palmitoyl-2-oleoyl-3-stearoyl-rac-glycerol, 33.1 w/w% SOS, and 1.3 w/w% DAGs, which was not substantially different from the DAG content in cocoa butter (1.1 w/w%). Based on the solid-fat content results, it was concluded that, when these CBEs were used for chocolate manufacture, they blended with cocoa butter at levels up to 40 w/w%, without distinctively altering the hardness and melting behavior of cocoa butter.

Latex-Based Polystyrene Nanocomposites with Non-Covalently Modified Carbon Nanotubes.

We prepared electrically conductive polystyrene (PS) nanocomposites by incorporating non-covalently surface-modified carbon nanotubes (CNTs) with hydrophilic polymers such as polydopamine (PDA) and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS). Further, ethylene glycol (EG) was introduced as a second dopant to improve the electrical properties of the nanocomposites prepared with PEDOT:PSS-wrapped CNTs. All conductive PS nanocomposites were prepared through latex-based process, and the morphology and properties of the nanocomposites were investigated. The electrical properties of the nanocomposites with PEDOT:PSS-wrapped CNTs were better than those of the nanocomposites with PDA-coated CNTs owing to the conducting nature of PEDOT:PSS, although the dispersions of both types of modified CNTs in the PS matrix were excellent, as evidenced by morphology and rheology. In the case of PEDOT:PSS modification, the electrical properties of the nanocomposites with EG-doped PEDOT:PSS-wrapped CNTs were superior to those of the nanocomposites without EG treatment.