Background and Aims: The aim of this study was to analyse the landscape of publications on rectal cancer (RC) over the past 25 years by machine learning and semantic analysis. Methods: Publications indexed in PubMed under the Medical Subject Headings (MeSH) term ‘Rectal Neoplasms’ from 1994 to 2018 were downloaded in September 2019. R and Python were used to extract publication date, MeSH terms and abstract from the metadata of each publication for bibliometric assessment. Latent Dirichlet allocation was applied to analyse the text from the articles’ abstracts to identify more specific research topics. Louvain algorithm was used to establish a topic network resulting in identifying the relationship between the topics. Results: A total of 23,492 papers published were identified and analysed in this study. The changes of research focus were analysed by the changing of MeSH terms. Studied contents extracted from the publications were divided into five areas, including surgical intervention, radiotherapy and chemotherapy intervention, clinical case management, epidemiology and cancer risk as well as prognosis studies. Conclusions: The number of publications indexed on RC has expanded rapidly over the past 25 years. Studies on RC have mainly focused on five areas. However, studies on basic research, postoperative quality of life and cost-effective research were relatively lacking. It is predicted that basic research, inflammation and some other research fields might become the potential hotspots in the future.

Machine learning and semantic analysis are computer-based methods to evaluate complex relationships and predict future perspectives. We used these technologies to define recent, current and future topics in pancreatic cancer research. Publications indexed under the Medical Subject Headings (MeSH) term ‘Pancreatic Neoplasms’ from January 1996 to October 2021 were downloaded from PubMed. Using the statistical computing language R and the interpreted, high-level, general-purpose programming language Python, we extracted publication dates, geographic information, and abstracts from each publication’s metadata for bibliometric analyses. The generative statistical algorithm “latent Dirichlet allocation” (LDA) was applied to identify specific research topics and trends. The unsupervised “Louvain algorithm” was used to establish a network to identify relationships between single topics. A total of 60,296 publications were identified and analyzed. The publications were derived from 133 countries, mostly from the Northern Hemisphere. For the term “pancreatic cancer research”, 12,058 MeSH terms appeared 1,395,060 times. Among them, we identified the four main topics “Clinical Manifestation and Diagnosis”, “Review and Management”, “Treatment Studies”, and “Basic Research”. The number of publications has increased rapidly during the past 25 years. Based on the number of publications, the algorithm predicted that “Immunotherapy”, Prognostic research”, “Protein expression”, “Case reports”, “Gemcitabine and mechanism”, “Clinical study of gemcitabine”, “Operation and postoperation”, “Chemotherapy and resection”, and “Review and management” as current research topics. To our knowledge, this is the first study on this subject of pancreatic cancer research, which has become possible due to the improvement of algorithms and hardware.

Background and AimSlingshot 1 protein (SSH1) plays a critical role in cytoskeleton dynamic regulation. Increasing evidence suggest that SSH1 expression is upregulated in several cancers and relates to tumor progression and drug resistance. Here, we evaluated the role of SSH1 in colorectal cancer (CRC) development and its prognostic value in patients with CRC.MethodsSSH1 expression was examined by quantitative real‐time polymerase chain reaction, western blot analysis, or immunohistochemistry. The association between SSH1 expression and clinical characteristics and prognosis was evaluated. Stable SSH1 knockdown cells were used for in vitro assays and xenograft models. Correlation between SSH1 expression and epithelial–mesenchymal transition (EMT) was analyzed by western blot and online data analysis.ResultsSSH1 expression was upregulated in cancer tissue compared with paired non‐cancerous tissue in patients with CRC. SSH1 expression level in CRC tissue was associated with tumor stage, lymph node metastasis, and correlated with poor prognosis as indicated by univariate and multivariate analyses. In vitro, loss of SSH1 impaired colony formation, migration, and invasion of CRC cells. In vivo data suggest that SSH1 could promote the progression and metastasis of CRC. Interestingly, E‐cadherin, ZEB1, and Snail, which are markers of EMT, had a significant expression correlation with SSH1.ConclusionsSSH1 expression is associated with CRC progression and predicts poor prognosis. SSH1 may promote CRC tumor progression by regulating EMT.

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With the development of information technology, indoor positioning technology has been rapidly evolving. Due to the advantages of high positioning accuracy, low cost, and wide coverage simultaneously, received signal strength- (RSS-) based WLAN indoor positioning technology has become one of the mainstream technologies. A radio map is the basis for the realization of the WLAN positioning system. However, by reasons of the huge workload of RSS collection, the instability of wireless signal strength, and the disappearance of signals caused by the occlusion of people and objects, the construction of a radio map is time-consuming and inefficient. In order to rapidly deploy the WLAN indoor positioning system, an improved low-rank matrix completion method is proposed to construct the radio map. Firstly, we evenly arrange a small number of reference points (RP) in the positioning area and collect RSS data on the RP to construct the radio map. Then, the low-rank matrix completion method is used to fill a small amount of data in the radio map into a complete database. The Frobenius parameter (F-parameter) is introduced into the traditional low-rank matrix completion model to control the instability of the model solution when filling the data. To solve the noise problem caused by environment and equipment, a low-rank matrix recovery algorithm is used to eliminate noise. The experimental results show that the improved algorithm achieves the expected goal.