Effect of temperature and humidity on mechanical properties and constitutive modeling of pressure-sensitive adhesives.

Pressure-sensitive adhesives (PSAs) are crucial for the structural and functional integrity of flexible displays. Investigating the intricate mechanical properties of PSAs can help enhance product quality and performance. This study conducts systematic mechanical tests, including uniaxial tensile, compression, planar shear, and stress relaxation, on PSAs at temperatures ranging from - 25 to 85 â„ƒ and relative humidity levels from 0 to 90%. Our findings reveal that the Anssari-Benam model accurately describes the hyperelastic behavior of PSA materials under large deformation, outperforming the Ogden model by requiring fewer parameters and better preserving convexity. Moreover the results show that temperature markedly affects PSA properties, particularly near the glass transition temperature (Tg), with lower temperatures leading to decreased elasticity and higher temperatures aiding in stress relaxation. Similarly, humidity impacts PSA behavior, increasing elasticity and decreasing stiffness, especially noticeable in stress relaxation tests. These findings highlight the substantial influence of environmental conditions on the material properties of PSAs and underscore the necessity of understanding both hyperelastic and viscoelastic responses for their application in flexible technologies. This research provides critical insights for the optimal utilization of PSAs in the rapidly evolving field of flexible electronics, including OLED displays.

Label-Free Differentiation of Cancer and Non-Cancer Cells Based on Machine-Learning-Algorithm-Assisted Fast Raman Imaging.

This paper proposes a rapid, label-free, and non-invasive approach for identifying murine cancer cells (B16F10 melanoma cancer cells) from non-cancer cells (C2C12 muscle cells) using machine-learning-assisted Raman spectroscopic imaging. Through quick Raman spectroscopic imaging, a hyperspectral data processing approach based on machine learning methods proved capable of presenting the cell structure and distinguishing cancer cells from non-cancer muscle cells without compromising full-spectrum information. This study discovered that biomolecular information-nucleic acids, proteins, and lipids-from cells could be retrieved efficiently from low-quality hyperspectral Raman datasets and then employed for cell line differentiation.

Clinical Study on Long-Term Sinus Reversion Rate and Left Atrial Function Recovery of Mitral Valve Disease with Atrial Fibrillation under Modified Surgical Radiofrequency Ablation.

We aimed to study the long-term sinus reversion rate and recovery of left atrial function after modified surgical radiofrequency ablation for permanent atrial fibrillation caused by mitral valve disease. From March 2014 to May 2020, 35 patients who underwent modified surgical radiofrequency ablation during cardiac valve surgery in our hospital were selected as the study group, and 25 normal individuals without cardiac structural changes were selected as the control group. The time of modified surgical radiofrequency ablation and long-term sinus reversion rate were measured, and left atrial anteroposterior, superoinferior, left and right diameters, left atrial ejection fraction, left atrial filling index, and left atrial ejection force were measured before and 6 months after surgery. The mean ablation time was 23.2 min, and the long-term sinus reversion rate was 80.0%. The left atrium diameter decreased and the left atrium ejection fraction increased after the operation (P < 0.05). The left atrium filling index and ejection force were significantly increased in 28 patients with sinus reversion (P < 0.05). The decrease in left atrial diameter and the increase in left atrial ejection fraction were correlated with sinus conversion after surgery (P < 0.05). The modified operation is simple, the curative effect is definite, and the sinus reversion rate is high, which is beneficial to the restoration of left atrial structure, ejection function, and hemodynamic function.

Predicting Breast Cancer in Breast Imaging Reporting and Data System (BI-RADS) Ultrasound Category 4 or 5 Lesions: A Nomogram Combining Radiomics and BI-RADS.

Radiomics reflects the texture and morphological features of tumours by quantitatively analysing the grey values of medical images. We aim to develop a nomogram incorporating radiomics and the Breast Imaging Reporting and Data System (BI-RADS) for predicting breast cancer in BI-RADS ultrasound (US) category 4 or 5 lesions. From January 2017 to August 2018, a total of 315 pathologically proven breast lesions were included. Patients from the study population were divided into a training group (n = 211) and a validation group (n = 104) according to a cut-off date of March 1st, 2018. Each lesion was assigned a category (4A, 4B, 4C or 5) according to the second edition of the American College of Radiology (ACR) BI-RADS US. A radiomics score was generated from the US image. A nomogram was developed based on the results of multivariate regression analysis from the training group. Discrimination, calibration and clinical usefulness of the nomogram for predicting breast cancer were assessed in the validation group. The radiomics score included 9 selected radiomics features. The radiomics score and BI-RADS category were independently associated with breast malignancy. The nomogram incorporating the radiomics score and BI-RADS category showed better discrimination (area under the receiver operating characteristic curve [AUC]: 0.928; 95% confidence interval [CI]: 0.876, 0.980) between malignant and benign lesions than either the radiomics score (P = 0.029) or BI-RADS category (P = 0.011). The nomogram demonstrated good calibration and clinical usefulness. In conclusion, the nomogram combining the radiomics score and BI-RADS category is potentially useful for predicting breast malignancy in BI-RADS US category 4 or 5 lesions.

[Explore new clinical application of Huanglian and corresponding compound prescriptions from their traditional use].

Huanglian is a commonly used Chinese medicinal herb in the ancient and the present. It has a history of 2000 years in clinical application, having the efficacy of "Clear away heat and remove dampness, purge the sthenic fire and eliminate toxic materials", therefore can be used for various diseases or syndromes in types of dampness-heat and fire-toxin by internal or external use. Compound prescriptions mainly based on Huanglian or prescribed by Huanglian, such as Puji Xiaodu Yin, Huanglian Jiedu Tang, Zhusha Anshen Wan, Qingying Tang, Angong Niuhuang Wan, Niuhuang Qingxin Wan, Jiaotai Wan, Huanglian Ejiao Tang, Zuojin Wan, Danggui Longhui Wan, Huanglian Yanggan Wan, Wu Xiexin Tang, Lianpu Yin, Gegen Huangqin Huanglian Tang, Baitouweng Tang, Xianglian Wan etc. All of these are well-known formulas for clearing away toxin of heat-fire of heart and liver, as well as dampness-heat of stomach and intestines. Nowadays, Huanglian is generally considered as a kind of antibiotic and antivirus herb and is widely used for many infective diseases. In fact, it is also used to cure cardiovascular and cerebrovascular diseases, diabetes and cancer based on pharmacological studies. Having been using Huanglian in treating the above diseases and having conducted clinical and experimental research on cancer and liver diseases, the author observed that Huanglian and its compound prescriptions have obvious effects on liver diseases such as acute or chronic hepatitis, liver fibrosis, liver cirrhosis and liver cancer due to types of dampness-heat and fire-toxin. Part of the effects has been proved by experimental research and it is worth carrying out more research in this area for development and clinical application.