Direct Analysis of Pharmaceutical Drugs in Biofluids using Miniature Mass Spectrometry System

Miniature mass spectrometry (MS) system is key for establishing MS as a point-of-care chemical and biological analysis within clinical settings. In order to provide point-of-care diagnostics, ionization methods for direct analysis of biofluids are required. We have previously introduced paper spray and developed the cartridges for direct MS ionization and sampling. Our goal of this research is to identify a viable way to improve the spray efficacy of paper spray for its coupling with miniature MS. As a result, paper capillary spray (PCS) was developed. PCS utilizes ET31 paper with a fused-silica capillary fixated to the tip. The design was optimized through investigations on capillary length, blood deposition and paper coating. Further experiments were done to compare the efficiency of paper capillary spray and paper spray utilizing Q-Trap 4000 and Mini 12. These methods were tested through direct analysis of different pharmaceutical drugs such as amitriptyline, imatinib, verapamil, and methamphetamines. The resulting spectra showed good signal to noise ratios and peak intensities for spray with Grade 1 paper or capillary with ET31 Paper. ET31 paper spray provided a lower abundance of target ions as well as decrease signal to noise ratios. It was concluded that larger droplets generated by the ET31 paper resulted in a less efficient desolvation with the atmospheric pressure interface. By applying the fused silica tubing to the tip of the ET31 paper as a spray emitter, the desolvation was greatly improved. In the future this study can contribute to develop commercial products for point-of-care MS analysis

Retraction: Novel two-stage surgical treatment for Cantrell syndrome complicated by severe pulmonary hypertension: a case report

INTRODUCTION: Cantrell syndrome is a rare syndrome of congenital defects, which can be complicated by severe pulmonary hypertension and left ventricular diverticulum; it has proved difficult to treat in clinical practice. CASE PRESENTATION: A 6-month-old Han Chinese baby girl weighing 3.5kg was diagnosed, using ultrasonography and radiography, as having Cantrell syndrome complicated by severe pulmonary hypertension. For safety, we divided management into two stages. For the first stage, we dealt with the left ventricular diverticulum and pulmonary hypertension. Three months later, we performed diorthosis for an intracardiac malformation. CONCLUSIONS: Cantrell syndrome with pulmonary hypertension may respond well to this novel two-stage operation, which needs more verification via clinical practice

Validity of self-reported weight, height and resultant body mass index in Chinese adolescents and factors associated with errors in self-reports

<p>Abstract</p> <p>Background</p> <p>Validity of self-reported height and weight has not been adequately evaluated in diverse adolescent populations. In fact there are no reported validity studies conducted in Asian children and adolescents. This study aims to examine the accuracy of self-reported weight, height, and resultant BMI values in Chinese adolescents, and of the adolescents' subsequent classification into overweight categories.</p> <p>Methods</p> <p>Weight and height were self-reported and measured in 1761 adolescents aged 12-16 years in a cross-sectional survey in Xi'an city, China. BMI was calculated from both reported values and measured values. Bland-Altman plots with 95% limits of agreement, Pearson's correlation and Kappa statistics were calculated to assess the agreement.</p> <p>Results</p> <p>The 95% limits of agreement were -11.16 and 6.46 kg for weight, -4.73 and 7.45 cm for height, and -4.93 and 2.47 kg/m²for BMI. Pearson correlation between measured and self-reported values was 0.912 for weight, 0.935 for height and 0.809 for BMI. Weighted Kappa was 0.859 for weight, 0.906 for height and 0.754 for BMI. Sensitivity for detecting overweight (includes obese) in adolescents was 56.1%, and specificity was 98.6%. Subjects' area of residence, age and BMI were significant factors associated with the errors in self-reporting weight, height and relative BMI.</p> <p>Conclusions</p> <p>Reported weight and height does not have an acceptable agreement with measured data. Therefore, we do not recommend the application of self-reported weight and height to screen for overweight adolescents in China. Alternatively, self-reported data could be considered for use, with caution, in surveillance systems and epidemiology studies.</p

EGTSyn: Edge-based Graph Transformer for Anti-Cancer Drug Combination Synergy Prediction

Combination therapy with multiple drugs is a potent therapy strategy for complex diseases such as cancer, due to its therapeutic efficacy and potential for reducing side effects. However, the extensive search space of drug combinations makes it challenging to screen all combinations experimentally. To address this issue, computational methods have been developed to identify prioritized drug combinations. Recently, Convolutional Neural Networks based deep learning methods have shown great potential in this community. Although the significant progress has been achieved by existing computational models, they have overlooked the important high-level semantic information and significant chemical bond features of drugs. It is worth noting that such information is rich and it can be represented by the edges of graphs in drug combination predictions. In this work, we propose a novel Edge-based Graph Transformer, named EGTSyn, for effective anti-cancer drug combination synergy prediction. In EGTSyn, a special Edge-based Graph Neural Network (EGNN) is designed to capture the global structural information of chemicals and the important information of chemical bonds, which have been neglected by most previous studies. Furthermore, we design a Graph Transformer for drugs (GTD) that combines the EGNN module with a Transformer-architecture encoder to extract high-level semantic information of drugs.Comment: 15 pages,4 figures,6 table

Meta-Transformer: A Unified Framework for Multimodal Learning

Multimodal learning aims to build models that can process and relate information from multiple modalities. Despite years of development in this field, it still remains challenging to design a unified network for processing various modalities ($\textit{e.g.}$ natural language, 2D images, 3D point clouds, audio, video, time series, tabular data) due to the inherent gaps among them. In this work, we propose a framework, named Meta-Transformer, that leverages a $\textbf{frozen}$ encoder to perform multimodal perception without any paired multimodal training data. In Meta-Transformer, the raw input data from various modalities are mapped into a shared token space, allowing a subsequent encoder with frozen parameters to extract high-level semantic features of the input data. Composed of three main components: a unified data tokenizer, a modality-shared encoder, and task-specific heads for downstream tasks, Meta-Transformer is the first framework to perform unified learning across 12 modalities with unpaired data. Experiments on different benchmarks reveal that Meta-Transformer can handle a wide range of tasks including fundamental perception (text, image, point cloud, audio, video), practical application (X-Ray, infrared, hyperspectral, and IMU), and data mining (graph, tabular, and time-series). Meta-Transformer indicates a promising future for developing unified multimodal intelligence with transformers. Code will be available at https://github.com/invictus717/MetaTransformerComment: Project website: https://kxgong.github.io/meta_transformer