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In recent years, facial emotion recognition (FER) has been a popular topic in affective computing. However, FER still faces many challenges in automatic recognition for several reasons, including quality control of sample data, extraction of effective features, creation of models, and multi-feature fusion, which have not been thoroughly researched and therefore are still hot topics in computer visualization. In view of the mature development of deep learning, deep learning methods are increasingly being used in FER. However, because deep learning requires a large amount of data to achieve effective training, many studies have employed transfer learning to compensate for this drawback. Nevertheless, there has been no universal approach for transfer learning in FER. Accordingly, this study used the five classic models in FER (i.e., ResNet-50, Xception, EfficientNet-B0, Inception, and DenseNet121) to conduct a series of experiments: data preprocessing, training type, and the applicability of multi-stage pretraining. According to the results, class wight was the optimal technique for data balance. In addition, the freeze + fine-tuning training type can produce higher accuracy, regardless of the size of the dataset. Multi-stage training was also effective. Compared with the model accuracy in previous studies, the accuracy achieved in this study using the proposed transfer learning method was superior for both large and small datasets. Specifically, on AffectNet, the accuracy for the ResNet-50, Xception, EfficientNet-B0, Inception, and DenseNet-121 models increased by 8.37%, 10.45%, 10.45%, 8.55%, and 5.47%, respectively. On FER2013, the accuracy for these models increased by 5.72%, 2%, 10.45%, 5%, and 9%, respectively. These results proved the validity and advantages of the experiments in this study.

Abstract Background Osteoarthritis (OA) is the most prevalent chronic joint disease in China. The aim of this study was to examine the association between metabolic syndrome (MetS) and knee OA in a population-based Chinese study. Methods Data included in this analysis is from a cross-sectional study, i.e., the Xiangya Hospital Health Management Center Study. MetS was diagnosed according to the criteria defined by the Chinese Diabetes Society. Radiographic knee OA was defined as changes equivalent to Kellgren-Lawrence (K-L) grade 2 or above at least one side. Associations between MetS and its components with OA were evaluated by conducting multivariable adjusted logistic regression. Results A total of 5764 participants were included in the present study. The unadjusted OR (1.27, 95%CI: 1.10–1.47, P = 0.001), age-sex adjusted OR (1.17, 95%CI: 1.01–1.36, P = 0.041) and multivariable adjusted OR (1.17, 95%CI: 1.01–1.36, P = 0.043) all suggested a positive association between MetS and knee OA. Besides, its components (e.g., overweight, hypertension and dyslipidemia) were also associated with the prevalence of radiographic knee OA respectively, after adjusting for some confounding factors. In addition, with the accumulation of MetS components, the prevalence of knee OA increased. Furthermore, MetS as a whole was associated with the prevalence of knee osteophyte (OSP) (OR = 1.72, 95%CI: 1.42–2.09, P

Acyclovir (ACV) is an effective and selective antiviral drug, and the study of its toxicology and the use of appropriate detection techniques to control its toxicity at safe levels are extremely important for medicine efforts and human health. This review discusses the mechanism driving ACV’s ability to inhibit viral coding, starting from its development and pharmacology. A comprehensive summary of the existing preparation methods and synthetic materials, such as 5-aminoimidazole-4-carboxamide, guanine and its derivatives, and other purine derivatives, is presented to elucidate the preparation of ACV in detail. In addition, it presents valuable analytical procedures for the toxicological studies of ACV, which are essential for human use and dosing. Analytical methods, including spectrophotometry, high performance liquid chromatography (HPLC), liquid chromatography/tandem mass spectrometry (LC-MS/MS), electrochemical sensors, molecularly imprinted polymers (MIPs), and flow injection–chemiluminescence (FI-CL) are also highlighted. A brief description of the characteristics of each of these methods is also presented. Finally, insight is provided for the development of ACV to drive further innovation of ACV in pharmaceutical applications. This review provides a comprehensive summary of the past life and future challenges of ACV.

Objective(s):miR-125b has been identified as a tumor suppressor in many tumors, but its role in giant cell tumor (GCT) of bone remains poorly understood. The current study aimed to investigate the potential role and mechanism of miR-125b in GCT. Materials and Methods:Expression levels of miR-125b in GCT tissues were determined using RT-PCR. The cell proliferation was surveyed by direct cell counting, MTS and CCK-8, and the apoptotic cells were evaluated by Annexin V-FITC and propidium iodine staining assay. The target gene expression was determined using RT-PCR and western blot. Parathyroid hormone 1 receptor (PTH1R) 3’-UTR was cloned into luciferase reporter plasmid to confirm direct targeting. Results: We found that miR-125b was significantly down-regulated in GCT tissues. Using both gain- and loss-of-function analyses, we further revealed that miR-125b suppressed GCT stromal cell proliferation and induced cell apoptosis. Furthermore, we revealed that PTH/PTHrP type 1 receptor is a direct and functional target of miR-125b. Conclusion:Our results suggest that miR-125b acts as a tumor suppressor through suppression of the PTH1R/RANKL signaling pathway. These findings contribute to our understanding of the functions of miR-125b in GCT.