Polypyrrole microcontainers with morphology like bowls, cups, and bottles have been electrochemically generated by direct oxidation of pyrrole in the aqueous solution of β-naphthalenesulfonic acid (β-NSA). The well-ordered microcontainers stand upright on the working electrode surface in a density of 2000−8000 units cm-2. Their morphological features can be simply controlled by electrochemical polymerization condition, and the results have a good reproducibility. The growth process of microcontainers was studied by scanning electron microscopy. A “soup bubble” growth mechanism was postulated and confirmed by electrolysis of water using a polypyrrole film-coated electrode. The walls of the microcontainers were made of polypyrrole in the oxidized (conductive) state according to the results of Raman and infrared spectroscopies. Electrochemical studies demonstrated that the PPy film with microcontainers had a large surface area which resulted in high film/electrolyte double-layer capacitive charges.

Selective laser sintering (SLS) is a three-dimensional printing (3DP) technology employed to manufacture plastic, metallic or ceramic objects. The aim of this study was to demonstrate the feasibility of using SLS to fabricate novel solid dosage forms with accelerated drug release properties, and with a view to create orally disintegrating formulations. Two polymers (hydroxypropyl methylcellulose (HPMC E5) and vinylpyrrolidone-vinyl acetate copolymer (Kollidon VA 64)) were separately mixed with 5% paracetamol (used as a model drug) and 3% Candurin Gold Sheen colorant; the powder mixes were subjected to SLS printing, resulting in the manufacture of printlets (3DP tablets). Modulating the SLS printing parameters altered the release characteristics of the printlets, with faster laser scanning speeds accelerating drug release from the HPMC formulations. The same trend was observed for the Kollidon based printlets. At a laser scanning speed of 300 mm/s, the Kollidon printlets exhibited orally disintegrating characteristics by completely dispersing in <4 s in a small volume of water. X-ray micro-CT analysis of these printlets indicated a reduction in their density and an increase in open porosity, therefore, confirming the unique disintegration behaviour of these formulations. The work reported here is the first to demonstrate the feasibility of SLS 3DP to fabricate printlets with accelerated drug release and orally disintegrating properties. This investigation has confirmed that SLS is amenable to the pharmaceutical research of modern medicine manufacture.

Objective Hepatocellular carcinoma (HCC) has become a pressing health problem facing the world today due to its high morbidity, high mortality, and late discovery. As a diagnostic criteria of HCC, the exact threshold of Alpha-fetoprotein (AFP) is controversial. Therefore, this study was aimed to systematically estimate the performance of AFP in diagnosing HCC and to clarify its optimal threshold. Methods Medline and Embase databases were searched for articles indexed up to November 2019. English language studies were included if both the sensitivity and specificity of AFP in the diagnosis of HCC were provided. The basic information and accuracy data included in the studies were extracted. Combined estimates for sensitivity and specificity were statistically analyzed by random-effects model using MetaDisc 1.4 and Stata 15.0 software at the prespecified threshold of 400 ng/mL, 200 ng/mL, and the range of 20-100 ng/mL. The optimal threshold was evaluated by the area under curve (AUC) of the summary receiver operating characteristic (SROC). Results We retrieved 29,828 articles and included 59 studies and 1 review with a total of 11,731 HCC cases confirmed by histomorphology and 21,972 control cases without HCC. The included studies showed an overall judgment of at risk of bias. Four studies with AFP threshold of 400 ng/mL showed the summary sensitivity and specificity of 0.32 (95%CI 0.31-0.34) and 0.99 (95%CI 0.98-0.99), respectively. Four studies with AFP threshold of 200 ng/mL showed the summary sensitivity and specificity of 0.49 (95%CI 0.47-0.50) and 0.98 (95%CI 0.97-0.99), respectively. Forty-six studies with AFP threshold of 20-100 ng/mL showed the

Graph neural networks (GNNs) have received intense interest as a rapidly expanding class of machine learning models remarkably well-suited for materials applications. To date, a number of successful GNNs have been proposed and demonstrated for systems ranging from crystal stability to electronic property prediction and to surface chemistry and heterogeneous catalysis. However, a consistent benchmark of these models remains lacking, hindering the development and consistent evaluation of new models in the materials field. Here, we present a workflow and testing platform, MatDeepLearn, for quickly and reproducibly assessing and comparing GNNs and other machine learning models. We use this platform to optimize and evaluate a selection of top performing GNNs on several representative datasets in computational materials chemistry. From our investigations we note the importance of hyperparameter selection and find roughly similar performances for the top models once optimized. We identify several strengths in GNNs over conventional models in cases with compositionally diverse datasets and in its overall flexibility with respect to inputs, due to learned rather than defined representations. Meanwhile several weaknesses of GNNs are also observed including high data requirements, and suggestions for further improvement for applications in materials chemistry are discussed.