A Study of The Deep Learning-based Monitoring and Efficient Numerical Modeling Methodologies for Crystallization Processes

Driven by the increasing demands of producing consistent and high-quality crystals for high value-added products such as pharmaceutical ingredients, the operation and design of a crystallization process have phased from an empirical trial-and-error approach to the modern frameworks powered by the online process analytical technologies (PATs) and model-based process optimization techniques. The one-dimensional crystal size distribution (CSD) measured by the well-established PATs is inadequate due to the missing particle morphology information. A major contribution of this thesis is to develop an image analysis-based PAT powered by the deep learning image processing techniques, whose accuracy and functionality outperformed the traditional PATs and other image analysis techniques. The PAT was deployed to monitor and study the slurry mixture of glass beads and catalyst particles as well as a taurine-water batch crystallization process. The results confirmed the superb accuracy of two-dimensional size and shape characterization in a challengingly high solids concentration. The classification capability enabled unparalleled functionalities including quantification of agglomeration level and characterization of different polymorphs based on their distinct appearances. A computerized crystallization platform was built with the developed PAT, which could automate the time-consuming experiments for determining the metastable zone width (MSZW) and induction time of a crystallization system. The application of the PAT revealed the potential to simplify and speed up the research and development stage of a crystallization process. The rich two-dimensional crystal size and shape information provided by our PAT enabled more descriptive multi-dimensional modeling for the better prediction of the crystallization process. The novel population array (PA) solver developed in this thesis could solve the multi-dimensional crystallization population balance equation (PBE) more computationally efficient than the existing discretization-based numerical methods without compromising the accuracy. The PA solver could accurately model the complex phenomena including agglomeration, breakage, and size-dependent growth. The efficient computation enables solving the complex multi-dimensional PBE for crystal morphology modeling. The combination of the innovative PAT and modeling technique is a significant contribution to the crystallization field that enables better understanding and more effective control of a crystallization process

A nomogram to predict severe COVID-19 patients with increased pulmonary lesions in early days

ObjectivesThis study aimed to predict severe coronavirus disease 2019 (COVID-19) progression in patients with increased pneumonia lesions in the early days. A simplified nomogram was developed utilizing artificial intelligence (AI)-based quantified computed tomography (CT).MethodsFrom 17 December 2019 to 20 February 2020, a total of 246 patients were confirmed COVID-19 infected in Jingzhou Central Hospital, Hubei Province, China. Of these patients, 93 were mildly ill and had follow-up examinations in 7 days, and 61 of them had enlarged lesions on CT scans. We collected the neutrophil-to-lymphocyte ratio (NLR) and three quantitative CT features from two examinations within 7 days. The three quantitative CT features of pneumonia lesions, including ground-glass opacity volume (GV), semi-consolidation volume (SV), and consolidation volume (CV), were automatically calculated using AI. Additionally, the variation volumes of the lesions were also computed. Finally, a nomogram was developed using a multivariable logistic regression model. To simplify the model, we classified all the lesion volumes based on quartiles and curve fitting results.ResultsAmong the 93 patients, 61 patients showed enlarged lesions on CT within 7 days, of whom 19 (31.1%) developed any severe illness. The multivariable logistic regression model included age, NLR on the second time, an increase in lesion volume, and changes in SV and CV in 7 days. The personalized prediction nomogram demonstrated strong discrimination in the sample, with an area under curve (AUC) and the receiver operating characteristic curve (ROC) of 0.961 and a 95% confidence interval (CI) of 0.917–1.000. Decision curve analysis illustrated that a nomogram based on quantitative AI was clinically useful.ConclusionThe integration of CT quantitative changes, NLR, and age in this model exhibits promising performance in predicting the progression to severe illness in COVID-19 patients with early-stage pneumonia lesions. This comprehensive approach holds the potential to assist clinical decision-making

Microtissues Enhance Smooth Muscle Differentiation and Cell Viability of hADSCs for Three Dimensional Bioprinting

Smooth muscle differentiated human adipose derived stem cells (hADSCs) provide a crucial stem cell source for urinary tissue engineering, but the induction of hADSCs for smooth muscle differentiation still has several issues to overcome, including a relatively long induction time and equipment dependence, which limits access to abundant stem cells within a short period of time for further application. Three-dimensional (3D) bioprinting holds great promise in regenerative medicine due to its controllable construction of a designed 3D structure. When evenly mixed with bioink, stem cells can be spatially distributed within a bioprinted 3D structure, thus avoiding drawbacks such as, stem cell detachment in a conventional cell-scaffold strategy. Notwithstanding the advantages mentioned above, cell viability is often compromised during 3D bioprinting, which is often due to pressure during the bioprinting process. The objective of our study was to improve the efficiency of hADSC smooth muscle differentiation and cell viability of a 3D bioprinted structure. Here, we employed the hanging-drop method to generate hADSC microtissues in a smooth muscle inductive medium containing human transforming growth factor β1 and bioprinted the induced microtissues onto a 3D structure. After 3 days of smooth muscle induction, the expression of α-smooth muscle actin and smoothelin was higher in microtissues than in their counterpart monolayer cultured hADSCs, as confirmed by immunofluorescence and western blotting analysis. The semi-quantitative assay showed that the expression of α-smooth muscle actin (α-SMA) was 0.218 ± 0.077 in MTs and 0.082 ± 0.007 in Controls; smoothelin expression was 0.319 ± 0.02 in MTs and 0.178 ± 0.06 in Controls. Induced MTs maintained their phenotype after the bioprinting process. Live/dead and cell count kit 8 assays showed that cell viability and cell proliferation in the 3D structure printed with microtissues were higher at all time points compared to the conventional single-cell bioprinting strategy (mean cell viability was 88.16 ± 3.98 vs. 61.76 ± 15% for microtissues and single-cells, respectively). These results provide a novel way to enhance the smooth muscle differentiation of hADSCs and a simple method to maintain better cell viability in 3D bioprinting

Nanoarchitectonic Engineering of Thermal-Responsive Magnetic Nanorobot Collectives for Intracranial Aneurysm Therapy

Stent-assisted coiling is a main treatment modality for intracranial aneurysms (IAs) in clinics, but critical challenges remain to be overcome, such as exogenous implant-induced stenosis and reliance on antiplatelet agents. Herein, we report an endovascular approach for IA therapy without stent grafting or microcatheter shaping, enabled by active delivery of thrombin (Th) to target aneurysms using innovative phase-change material (PCM)-coated magnetite-thrombin (Fe3O4-Th@PCM) FTP nanorobots. The nanorobots are controlled by an integrated actuation system of dynamic torque-force hybrid magnetic fields. With robust intravascular navigation guided by real-time ultrasound imaging, nanorobotic collectives can effectively accumulate and retain in model aneurysms constructed in vivo, followed by controlled release of the encapsulated Th for rapid occlusion of the aneurysm upon melting the protective PCM (thermally responsive in a tunable manner) through focused magnetic hyperthermia. Complete and stable aneurysm embolization was confirmed by postoperative examination and 2-week postembolization follow-up using digital subtraction angiography (DSA), contrast-enhanced ultrasound (CEUS) and histological analysis. The safety of the embolization therapy was assessed through biocompatibility evaluation and histopathology assays. Our strategy, seamlessly integrating secure drug packaging, agile magnetic actuation and clinical interventional imaging, avoids possible exogenous implant rejection, circumvents cumbersome microcatheter shaping, and offers a promising option for IA therapy

Intestinal Microbiota-Derived GABA Mediates Interleukin-17 Expression during Enterotoxigenic Escherichia coli Infection

Intestinal microbiota has critical importance in pathogenesis of intestinal infection; however, the role of intestinal microbiota in intestinal immunity during enterotoxigenic Escherichia coli (ETEC) infection is poorly understood. The present study tested the hypothesis that the intestinal microbiota is associated with intestinal interleukin-17 (IL-17) expression in response to ETEC infection. Here, we found ETEC infection induced expression of intestinal IL-17 and dysbiosis of intestinal microbiota, increasing abundance of γ-aminobutyric acid (GABA)-producing Lactococcus lactis subsp. lactis. Antibiotics treatment in mice lowered the expression of intestinal IL-17 during ETEC infection, while GABA or L. lactis subsp. lactis administration restored the expression of intestinal IL-17. L. lactis subsp. lactis administration also promoted expression of intestinal IL-17 in germ-free mice during ETEC infection. GABA enhanced intestinal IL-17 expression in the context of ETEC infection through activating mechanistic target of rapamycin complex 1 (mTORC1)-ribosomal protein S6 kinase 1 (S6K1) signaling. GABA–mTORC1 signaling also affected intestinal IL-17 expression in response to Citrobacter rodentium infection and in drug-induced model of intestinal inflammation. These findings highlight the importance of intestinal GABA signaling in intestinal IL-17 expression during intestinal infection and indicate the potential of intestinal microbiota-GABA signaling in IL-17-associated intestinal diseases

Understanding the intention to donate online in the Chinese context: The influence of norms and trust

Due to the advancement of information and communication technologies, online donations have become unprecedentedly convenient, making money received from individual online donations an important form of revenue for many charitable organizations in China. However, factors contributing to people’s online donation intentions, in turn impacting donating behavior, have been under-examined. The current study aims to understand factors influencing online donation intention in the Chinese cultural context by combining constructs from the extended Theory of Planned Behavior (TPB; including the original TPB constructs and moral norm) and trust-related constructs (i.e., trust in charity organizations and trust in technology). The moderation effect of past donation behavior on the relationship between trust and donation intention was also explored. A total of 721 Chinese participants completed the online survey. SPSS was used to perform hierarchical multiple regressions. The results showed that attitude, perceived behavioral control, moral norm, and subjective norm were all positively related to online donation intention. Moral norm was found to be a stronger predictor than subjective norm, raising the amount of explained variance of the original TPB model. Trust in charity organizations was found to positively predict donation intention while trust in technology was not. The results also revealed that past donation behavior moderated the effect of trust in charity organizations on donation intention. This study not only adds to the body of knowledge on charitable donation in the online context by incorporating two trust-related constructs into the extended TPB model, but also highlights the different roles moral and subjective norms play in predicting people’s prosocial behavior in the context of Chinese culture