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Perfluorobutane ultrasound contrast agent as a new type of contrast agent has a good performance in the diagnosis of hepatocellular carcinoma (HCC). This study aim to evaluate the accuracy and reliability of Perfluorobutane contrast-enhanced ultrasonography (P-CEUS) in the diagnosis of HCC with a systematic review and meta-analysis. Web of Science, EMBASE, Cochrane, Clinical Key, Wan Fang, CBM and CNKI databases were systematically searched and checked for studies using P-CEUS in HCC, from 2007 to 2020. Data necessary to construct 2 × 2 contingency tables were extracted from included studies. The QUADAS tool was utilized to assess the methodologic quality of the studies. Meta-analysis included data pooling, subgroup analyses, meta-regression and investigation of publication bias was comprehensively performed. Nine studies were included in this meta-analysis and the overall diagnostic accuracy in characterization of HCC was as follows: pooled sensitivity, 0.90 (95% confidence interval: 0.82–0.95); pooled specificity, 0.97 (0.93–0.98); pooled positive likelihood ratio, 27.2 (14.1 to − 52.3); and pooled negative likelihood ratio, 0.10 (0.06–0.18). The area under the comprehensive receiving operation characteristic curve was 0.98 (0.97–0.99). The sensitivity and specificity of P-CEUS are more valuable than other imaging techniques (such as computer tomography or magnetic resonance imaging). However, due to the large differences in the data samples collected in this study, statistical heterogeneity results. P-CEUS can significantly improve the diagnostic efficiency of previous contrast-enhanced ultrasound for HCC. PROSPERO registration number: PROSPERO (CRD42020200040).

Obesity is a growing epidemic worldwide, and it is also considered a major environmental factor contributing to the pathogenesis of inflammatory skin diseases, including psoriasis (PSO) and atopic dermatitis (AD). Moreover, obesity worsens the course and impairs the treatment response of these inflammatory skin diseases. Emerging evidence highlights that hypertrophied adipocytes and infiltrated immune cells secrete a variety of molecules, including fatty acids and adipokines, such as leptin, adiponectin, and a panel of cytokines/chemokines that modulate our immune system. In this review, we describe how adipose hypertrophy leads to a chronic low-grade inflammatory state in obesity and how obesity-related inflammatory factors are involved in the pathogenesis of PSO and/or AD. Finally, we discuss the potential role of antimicrobial peptides, mechanical stress and impairment of epidermal barrier function mediated by fast expansion, and dermal fat in modulating skin inflammation. Together, this review summarizes the current literature on how obesity is associated with the pathogenesis of PSO and AD, highlighting the potentially important but overlooked immunomodulatory role of adipose tissue in the skin.

We performed in vitro and in vivo experiments to explore the role of protein kinase C-binding protein 1 (PICK1), an intracellular transporter involved in oxidative stress-related neuronal diseases, in sepsis-related acute kidney injury (AKI). Firstly, PCR, western blotting, and immunohistochemistry were used to observe the expression of PICK1 after lipopolysaccharide- (LPS-) induced AKI. Secondly, by inhibiting PICK1 in vivo and silencing PICK1 in vitro, we further explored the effect of PICK1 on AKI. Finally, the relationship between PICK1 and oxidative stress and the related mechanisms were explored. We found that the expression of PICK1 was increased in LPS-induced AKI models both in vitro and in vivo. PICK1 silencing significantly aggravated LPS-induced apoptosis, accompanied by ROS production in renal tubular epithelial cells. FSC231, a PICK1-specific inhibitor, aggravated LPS-induced kidney injury. Besides, NAC (N-acetylcysteine), a potent ROS scavenger, significantly inhibited the PICK1-silencing-induced apoptosis. In conclusion, PICK1 might protect renal tubular epithelial cells from LPS-induced apoptosis by reducing excessive ROS, making PICK1 a promising preventive target in LPS-induced AKI.

Osteosarcoma is the most common primary malignant bone tumor; its standard treatment includes neoadjuvant chemotherapy combined with surgery. Neoadjuvant chemotherapy has significantly improved the 5-year survival and limb salvage rates in osteosarcoma since the 1870s. The survival rate of patients with limb salvage was not inferior to that of amputees, and therefore, limb salvage has become the main surgical option for patients with osteosarcoma. The 5-year survival rate for osteosarcoma has plateaued. However, new advances in limb salvage therapy in osteosarcoma, including adjuvant chemotherapy, ablation techniques, bone transport techniques, and computer navigation techniques, are now available. This report summarizes the recent advances in limb salvage therapy for osteosarcoma over the past decade.

In the era of big data, digital technologies are increasingly used to solve a range of problems, with the support of the Internet, Cloud computing and Internet of things. Human resource management is a traditional discipline that faces the challenge of ever-increasing diversity and complexity of requirements – something that can be solved by applying new technologies. This paper first introduces the development of big data technology and the importance of human resource management in enterprises and governments. Then, it proposes a novel solution for developing a macro human resource management service platform which not only helps collect data about human resources requirements from both enterprises and local governments but also provides support of data classification, analysis and prediction. This paper describes the solutions mentioned above in detail and discusses their evaluation in the development of a prototype system and a case study. The results show that new technologies have great potential in improving effectiveness and efficiency in traditional management disciplines like human resource management.

The noise source identification is an important issue in noise reduction and condition monitoring(CM) for machines in- site using microphone arrays. In this paper, we propose a new approach to optimize array configuration based on particles swarm optimization algorithm in order to improve noise source identification and condition monitoring performance. Two distinct optimized array configurations are designed under the certain conditions. Furthermore, an acoustic imaging equipment is developed to carry out experiments on transformer substation equipment and wind turbine generator, which demonstrate that the acoustic imaging system allows a high resolution in identifying main noise sources for noise reduction and abnormal noise sources for condition monitoring.

The Infrasonic Monitoring Network (IMN) in China is under construction to detect, identify and located the signals generated by natural disasters. Presently, fifteen sensors are operational and transmitting real time data to Central Data Centre (CDC) in Beijing. Each sensor station is composed of one infrasonic sensor, one humidity and temperature sensor, one digital sampling and transmitting device, which collects and delivers real-time continuous infrasonic, temperature, humidity, GPS raw data to CDC. IMN is designed as a robust sensor network optimized for rapid deployment and provides long-term information monitoring; the system supports data acquisition with GMT time and is remotely configurable. Its high performance has been demonstrated in the initial deployment experience and preliminary analysis of sampling data.