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BackgroundIn the last few decades, mobile technologies have been widely adopted in the field of health care services to improve the accessibility to and the quality of health services received. Mobile health (mHealth) has emerged as a field of research with increasing attention being paid to it by scientific researchers and a rapid increase in related literature being reported. ObjectiveThe purpose of this study was to analyze the current state of research, including publication outputs, in the field of mHealth to uncover in-depth collaboration characteristics and topic burst of international mHealth research. MethodsThe authors collected literature that has been published in the last 20 years and indexed by Thomson Reuters Web of Science Core Collection (WoSCC). Various statistical techniques and bibliometric measures were employed, including publication growth analysis; journal distribution; and collaboration network analysis at the author, institution, and country collaboration level. The temporal visualization map of burst terms was drawn, and the co-occurrence matrix of these burst terms was analyzed by hierarchical cluster analysis and social network analysis. ResultsA total of 2704 bibliographic records on mHealth were collected. The earliest paper centered on mHealth was published in 1997, with the number of papers rising continuously since then. A total of 21.28% (2318/10,895) of authors publishing mHealth research were first author, whereas only 1.29% (141/10,895) of authors had published one paper. The total degree of author collaboration was 4.42 (11,958/2704) and there are 266 core authors who have collectively published 53.07% (1435/2704) of the total number of publications, which means that the core group of authors has fundamentally been formed based on the Law of Price. The University of Michigan published the highest number of mHealth-related publications, but less collaboration among institutions exits. The United States is the most productive country in the field and plays a leading role in collaborative research on mHealth. There are 5543 different identified keywords in the cleaned records. The temporal bar graph clearly presents overall topic evolutionary process over time. There are 12 important research directions identified, which are in the imbalanced development. Moreover, the density of the network was 0.007, a relatively low level. These 12 topics can be categorized into 4 areas: (1) patient engagement and patient intervention, (2) health monitoring and self-care, (3) mobile device and mobile computing, and (4) security and privacy. ConclusionsThe collaboration of core authors on mHealth research is not tight and stable. Furthermore, collaboration between institutions mainly occurs in the United States, although country collaboration is seen as relatively scarce. The focus of research topics on mHealth is decentralized. Our study might provide a potential guide for future research in mHealth.

To solve the problem of resource shortage of ternary content addressable memory (TCAM) in the data plane of software defined network (SDN), a deep flow table aggregation method was proposed based on content entry trees, and a storage architecture of large-scale SDN flow tables named ADAFT was established. The architecture relaxed the Hamming distance requirement between ag-gregated flow entries, and a content entry tree was constructed to aggregate flow entries with different action sets, for significantly en-hancing the aggregation degree of flow tables. Then a dynamic limitation mechanism was designed for the height of content entry trees based on the awareness of TCAM load ratio, to minimize the lookup overhead of aggregated flow tables. Meanwhile, an adaptive selec-tion strategy of flow entry aggregation was presented in the light of TCAM load ratio, to strike a balance between the aggregation degree and lookup overhead of flow tables. Experimental results indicate that the ADAFT architecture achieves much higher flow table com-pression ratios up to 65.74% than existing methods.

Objective To analyze the risk factors for dismal prognosis in patients with cryptococcal meningitis and construct a prediction scoring system Nomogram model. Methods A total of 100 patients with cryptococcal meningitis who treated with anticryptococcal therapy admitted to Affiliated Hospital of Zunyi Medical University from January 2010 to August 2022 were selected. The patients were divided into favorable prognosis group (n = 19) and dismal prognosis group (n = 81) according to the results of cerebrospinal fluid (CSF) cryptococcal culture during hospitalization and clinical symptoms and signs at the time of discharge. Risk factors were screened by using univariate and multivariate stepwise Logistic regression analyses. A Nomogram model was constructed based on the risk factors, the receiver operating characteristic (ROC) curve and calibration curves of the model were plotted, and Hosmer - Lemeshow goodness-of-fit test was performed. Results The proportion of patients in the dismal prognosis group with Nutritional Risk Screening 2002 (NRS 2002) score (Z = - 3.898, P = 0.000), CSF pressure > 250 mm H2O (χ2 = 9.512，P = 0.002) and duration of antifungal treatment < 14 d (χ2 = 17.847，P = 0.000) on admission were higher than those in the favorable prognosis group, and the blood routine red blood cell count (t = - 2.802, P = 0.006) and lymphocyte count (Z = - 2.878, P = 0.004), plasma albumin (t = - 4.332, P = 0.000), and the proportion of amphotericin B application (χ2 = 4.597, P = 0.032) were lower than those in the favorable prognosis group. Logistic regression analysis showed the admission high NRS 2002 score (OR = 3.258, 95%CI: 1.337-7.940; P = 0.009), CBF pressure > 250 mm H2O (OR = 0.108, 95%CI: 0.018-0.659; P = 0.016), and the duration of antifungal treatment < 14 d (OR = 0.092, 95%CI: 0.011-0.742; P = 0.025) were risk factors for dismal prognosis of cryptococcal meningitis. A Nomogram model was constructed based on the above 3 risk factors, and the area under the ROC curve was 0.927 (95%CI: 0.873-0.980, P = 0.000), which predicted a cut - off value of 53.50 points for dismal prognosis in cryptococcal meningitis; the calibration curve (with good consistency), and the Hosmer-Lemeshow goodness-of-fit test (χ2 = 2.694, P = 0.912) indicated that the model had good discrimination, calibration and stability. Conclusions Patients with cryptococcal meningitis with a high NRS 2002 score, CSF pressure > 250 mm H2O, and antifungal treatment < 14 d had a dismal prognosis, and the Nomogram model constructed accordingly has a high predictive value of dismal prognostic risk.

Papillary thyroid carcinoma (PTC) is the most prevalent malignancy of the thyroid. Fibroblast growth factor receptor 1 (FGFR1) is highly expressed in PTC and works as an oncogenic protein in this disease. In this report, we wanted to uncover a new mechanism that drives overexpression of FGFR1 in PTC. Analysis of FGFR1 expression in clinical specimens and PTC cells revealed that FGFR1 expression was enhanced in PTC. Using siRNA/shRNA silencing experiments, we found that FGFR1 downregulation impeded PTC cell growth, invasion, and migration and promoted apoptosis in vitro, as well as suppressed tumor growth in vivo. Bioinformatic analyses predicted the potential USP7-FGFR1 interplay and the potential binding between YY1 and the FGFR1 promoter. The mechanism study found that USP7 stabilized FGFR1 protein via deubiquitination, and YY1 could promote the transcription of FGFR1. Our rescue experiments showed that FGFR1 re-expression had a counteracting effect on USP7 downregulation-imposed in vitro alterations of cell functions and in vivo suppression of xenograft growth. In conclusion, our study identifies the deubiquitinating enzyme USP7 and the oncogenic transcription factor YY1 as potent inducers of FGFR1 overexpression. Designing inhibitors targeting FGFR1 or its upstream inducers USP7 and YY1 may be foreseen as a promising strategy to control PTC development.

Female adults of the migratory locust, Locusta migratoria manilensis (Meyen), can sense seasonal photoperiod changes, which induces embryonic diapause as a key strategy to overwinter. Serine protease inhibitor genes (SPNs) were thought to play key roles during diapause, while few SPNs were functionally characterized. LmSPN2 was one of those genes differentially expressed between diapause and non-diapause eggs; however, its biological function remained to be explored. So, we conducted RNAi knockdown of LmSPN2, resulting in a significant decrease of the egg diapause rate by 29.7%. Using yeast two-hybrid assays, co-immunoprecipitation, and pull-down methods, we found an interaction between LmSPN2 and LmSPN3, which was proved to be mediated by a glutamate (E331) binding site of LmSPN2. RNAi knockdown of LmSPN3 resulted in a significant increase in diapause rate by 14.6%, indicating an inverse function of LmSPN2 and LmSPN3 on diapause regulation. Double knockdown of two SPN genes resulted in a 26.4% reduction in diapause rate, indicating that LmSPN2 was the dominant regulatory signal. Moreover, we found four Toll pathway genes (easter, spätzle, pelle, and dorsal) upregulated significantly after the knockdown of LmSPN2 while downregulated after the knockdown of LmSPN3. Therefore, we speculate that two SPNs regulate diapause through the Toll pathway. Our results indicated that LmSPN2 positively regulates locust egg entry into diapause, while LmSPN3 is a negative regulator of embryonic commitment to diapause. Their interaction is mediated by the binding site of E331 and influences egg diapause through the Toll pathway. This mechanistic understanding of diapause regulation expands our understanding of insect developmental regulation and provides functional targets for developing locust management strategies.

Rough ice can change the leading edge of airfoil and affect the aerodynamic characteristics. Studying the influence of rough ice caused by supercooled water droplets can provide reference for anti-icing design of aircrafts. A detailed experimental study was conducted to measure the flow field structure of an airfoil model with rough ice in a low-speed wind tunnel by using particle image velocimetry. The parameters include Reynolds number, roughness of rough ice, and angle of attack. The results show that with the increase of Reynolds number, the range and value of spanwise vorticity at the wake of the airfoil with ice increased, while the normalized Reynolds stress decreased slightly. The presence of rough ice reduced the airflow velocity near the airfoil, increased the vorticity of wake, and seriously affected the shear stress distribution. Compared with the clean airfoil, the rough ice caused the air flow to separate earlier and the velocity in the separation bubble fluctuated more violently.