Improvement of Solder Joint Shear Strength under Formic Acid Atmosphere at A Low Temperature.

With the continuous reduction of chip size, fluxless soldering has brought attention to high-density, three-dimensional packaging. Although fluxless soldering technology with formic acid (FA) atmosphere has been presented, few studies have examined the effect of the Pt catalytic, preheating time, and soldering pad on FA soldering for the Sn-58Bi solder. The results have shown that the Pt catalytic can promote oxidation-reduction and the formation of a large pore in the Sn-58Bi/Cu solder joint, which causes a decrease in shear strength. ENIG (electroless nickel immersion gold) improves soldering strength. The shear strength of Sn-58Bi/ENIG increases under the Pt catalytic FA atmosphere process due to the isolation of the Au layer on ENIG. The Au layer protects metal from corrosion and provides a good contact surface for the Sn-58Bi solder. The shear strength of the Sn-58Bi/ENIG joints under a Pt catalytic atmosphere improved by 44.7% compared to using a Cu pad. These findings reveal the improvement of the shear strength of solder joints bonded at low temperatures under the FA atmosphere.

Exploring chemical markers and identifying phenolic markers using a metabolomics strategy and chemometrics to study the different origins of defatted Coix seed.

Coix seed, a prevalent medicinal and food-homologous plant, is extensively consumed in Asia. It has various pharmacological properties, such as anti-inflammatory and anticancer effects. Coix seed oil, as its main component, is widely produced. However, during the industrial production process of Coix seed oil, substantial byproducts are produced, namely, defatted Coix seeds, which are also worth researching. Currently, it remains unclear whether there will be differences in defatted Coix seeds obtained from different geographical locations, with previous studies reporting that phenolic compounds in defatted Coix seeds have a significant utilization value. In this study, firstly, the TPC and TFC of samples collected in three temperature zones were detected. Subsequently, UPLC-Q-TOF/MS was used to analyze the samples, and a metabolomics data processing strategy and chemometric analysis method were established. We have confirmed the presence of flavonoids and phenolic compounds in 30 batches of Coix seed from different temperature zones in China, and concluded that the overall quality of Coix seed from different batches is relatively stable. With the established strategy, 12 characteristic chemical markers were identified, and 5 valuable phenolic chemical markers were selected for distinguishing the origin of Coix seed and evaluating the quality of defatted Coix seed. Among them, proanthocyanidin A2 has the highest content in defatted Coix seed in subtropical regions, while the content of caffeic acid, naringin, rutin, and chlorogenic acid decreases from north to south. The strategy proposed in this study may provide some basis for the quality control and rational use of defatted Coix seeds.

Multifeature Fusion Classification Method for Adaptive Endoscopic Ultrasonography Tumor Image.

Endoscopic ultrasonography (EUS) has been found to be of great advantage in the diagnosis of digestive tract submucosal tumors. However, EUS-based diagnosis is limited by variability in subjective interpretation on the part of doctors. Tumor classification of ultrasound images with the computer-aided diagnosis system can significantly improve the diagnostic efficiency and accuracy of doctors. In this study, we proposed a multifeature fusion classification method for adaptive EUS tumor images. First, for different ultrasound tumor images, we selected the region of interest based on prior information to facilitate the estimation in the subsequent works. Second, we proposed a method based on image gray histogram feature extraction with principal component analysis dimensionality reduction, which learns the gray distribution of different tumor images effectively. Third, we fused the reduced grayscale features with the improved local binary pattern features and gray-level co-occurrence matrix features, and then used the multiclassification support vector machine. Finally, in the experiment, we selected the 431 ultrasound images of 109 patients in the hospital and compared the experimental effects of different features and different classifiers. The results revealed that the proposed method performed best, with the highest accuracy of 96.18% and an area under the curve of 99%. It is evident that the method proposed in this study can efficiently contribute to the classification of EUS tumor images.

Pharmacological effects and mechanisms of YiYiFuZi powder in chronic heart disease revealed by metabolomics and network pharmacology.

Introduction: YiYiFuZi powder (YYFZ) is a classical formula in Chinese medicine, which is commonly used clinically for the treatment of Chronic Heart Disease (CHD), but it's pharmacological effects and mechanism of action are currently unclear. Methods: An adriamycin-induced CHD model rat was established to evaluate the pharmacological effects of YYFZ on CHD by the results of inflammatory factor level, histopathology and echocardiography. Metabolomic studies were performed on rat plasma using UPLC-Q-TOF/MS to screen biomarkers and enrich metabolic pathways; network pharmacology analysis was also performed to obtain the potential targets and pathways of YYFZ for the treatment of CHD. Results: The results showed that YYFZ significantly reduced the levels of TNF-α and BNP in the serum of rats, alleviated the disorder of cardiomyocyte arrangement and inflammatory cell infiltration, and improved the cardiac function of rats with CHD. The metabolomic analysis identified a total of 19 metabolites, related to amino acid metabolism, fatty acid metabolism, and other metabolic pathways. Network pharmacology showed that YYFZ acts through PI3K/Akt signaling pathway, MAPK signaling pathway and Ras signaling pathway. Discussion: YYFZ treatment of CHD modulates blood metabolic pattern and several protein phosphorylation cascades but importance specific changes for therapeutic effect require further studies.

Radiomics analysis for prediction and classification of submucosal tumors based on gastrointestinal endoscopic ultrasonography.

Objectives: To identify and classify submucosal tumors by building and validating a radiomics model with gastrointestinal endoscopic ultrasonography (EUS) images. Methods: A total of 144 patients diagnosed with submucosal tumors through gastrointestinal EUS were collected between January 2019 and October 2020. There are 1952 radiomic features extracted from each patient's EUS images. The statistical test and the customized least absolute shrinkage and selection operator regression were used for feature selection. Subsequently, an extremely randomized trees algorithm was utilized to construct a robust radiomics classification model specifically tailored for gastrointestinal EUS images. The performance of the model was measured by evaluating the area under the receiver operating characteristic curve. Results: The radiomics model comprised 30 selected features that showed good discrimination performance in the validation cohorts. During validation, the area under the receiver operating characteristic curve was calculated as 0.9203 and the mean value after 10-fold cross-validation was 0.9260, indicating excellent stability and calibration. These results confirm the clinical utility of the model. Conclusions: Utilizing the dataset provided curated from gastrointestinal EUS examinations at our collaborating hospital, we have developed a well-performing radiomics model. It can be used for personalized and non-invasive prediction of the type of submucosal tumors, providing physicians with aid for early treatment and management of tumor progression.