Meta-analysis of the adoption of omalizumab in the treatment of pediatric allergic diseases.

Introduction: Allergic diseases are common chronic conditions in children, omalizumab has a wide range of adoptions in various diseases. A meta-analysis was implemented to demonstrate the efficacy of omalizumab in the therapy of pediatric allergic diseases. Materials and methods: English databases were searched. The search terms included "Omalizumab", "Children", "Allergic asthma", and "Atopic dermatitis". The literature was screened regarding inclusion and exclusion criteria, and data were extracted and analyzed using RevMan5.3. Results: a total of six suitable studies, comprising 2761 patients, were selected for inclusion. The meta-analysis results implied that at 24 weeks, OR for worsening of symptoms in children was 0.10 (95 % confidence interval [CI] 0.03-0.41), Z = 3.24, P = 0.001 (P < 0.05); at 52 weeks, OR was 0.27 (95 % CI 0.09-0.83), Z = 2.28, P = 0.02 (P < 0.05); and during treatment, OR for adverse events in children was 0.87 (95 % CI 0.60-1.29), Z = 0.68, P = 0.49 (P > 0.05). Conclusion: the study comprised six investigations that examined the effectiveness of omalizumab in treating pediatric allergic diseases. The findings demonstrated that, in comparison to standard treatment, omalizumab can greatly alleviate allergy-related clinical symptoms in children, slow down disease progression, and has a higher safety profile with fewer adverse reactions. These results have practical implications and highlight the potential value of omalizumab in pediatric allergy treatment.

Effectiveness of a nurse-led online educational programme based on basic insulin therapy in patients with diabetes mellitus: A quasi-experimental trial.

AIMS AND OBJECTIVES: To evaluate the effectiveness of a nurse-led online educational programme based on patients with diabetes mellitus treated with initial basal insulin therapy. BACKGROUND: Patients with type 2 diabetes mellitus (T2DM) need to be treated with insulin to control hyperglycaemia and reduce the risk of diabetic complications when oral hypoglycaemic drugs are not effective or contraindicated. Current practices emphasise the leading role of nurses in patients treated with initial basal insulin therapy after discharge. The introduction of nurse-led online education within this area is a relatively new programme. DESIGN: This study was a quasi-experimental, nonequivalent, two-group, comparison group design. METHODS: The study selected 800 patients with T2DM hospitalised in the Department of Endocrinology at a Chinese hospital from July 2018 to June 2020 who were initially treated with insulin. According to the time sequence, 400 patients from July 2018 to June 2019 were divided into the control group and 400 patients from July 2019 to June 2020 into the intervention group. The control group received routine health education and doctor-led follow-up based on routine health education. The intervention group received systematic health education and online insulin injection activities led by nurses. The effects were evaluated after 3 and 6 months of intervention. The TREND checklist was followed to ensure rigour in the study. RESULTS: In total, 339 patients were enrolled in the intervention group and 333 patients within the control group. According to the analysis, 3 months after the intervention, the compliance rate of fasting blood glucose (FBG) (rate difference: 0.078, 95% CI: 0.006-1.150, p < .05) and HbA1c (%) (rate difference: 0.070, 95% CI: 0.001-0.137, p < .05) between the intervention and control groups were statistically significant; 6 months after the intervention, the compliance rate of FBG (rate difference: 0.077, 95% CI: 0.007-0.14, p < .05) and HbA1c (%) (rate difference: 0.106, 95% CI: 0.324-0.180, p < .01) between the intervention and the control groups were statistically significant. The total score of the 'My Opinion on Insulin' scale in the intervention group was (80.18 ± 6.68), and in the control group was (71.15 ± 8.17), there was a significant difference in the scale between the two groups (mean difference: 9.03, 95% CI: 7.900-10.160, p < .01). Through a multivariable regression model, in order to correct the important baseline characteristics, the daily insulin dosage, and the total score of the 'My Opinion on Insulin' scale after 6 months of intervention were independent risk factors for the two intervention methods in diabetic patients treated with initial basal insulin therapy (p < .05). CONCLUSIONS: A nurse-led online programme was feasible and effective for patients with diabetes mellitus treated with initial basal insulin therapy. This programme could effectively be used to reduce the patient's daily insulin dose, as well as improve the patients' compliance using insulin therapy. RELEVANCE TO CLINICAL PRACTICE: Nurse-led online education has a role in implementing a safe, standardised, and sustained approach to patients with diabetes mellitus treated with initial basal insulin therapy during follow-up after discharge.

Transcriptome Characterization and Identification of Molecular Markers (SNP, SSR, and Indels) in the Medicinal Plant Sarcandra glabra spp.

Sarcandra glabra has significant metabolically active bioingredients of pharmaceutical importance. The deficiency of molecular markers for S. glabra is a hindrance in molecular breeding for genetic improvement. In this study, 57.756 million pair-end reads were generated by transcriptome sequencing in S. glabra (Thunb.) Nakai and its subspecies S. glabra ssp. brachystachys. A total of 141,954 unigenes with 646.63 bp average length were assembled. A total of 25,620 simple sequence repeats, 726,476 single nucleotide polymorphisms, and 42,939 insertions and deletions were identified, and the associated unigenes and differentially expressed genes were characterized. This work enhanced the molecular marker resources and will facilitate molecular breeding and gene mining in S. glabra spp.

Learning Macromanagement in Starcraft by Deep Reinforcement Learning.

StarCraft is a real-time strategy game that provides a complex environment for AI research. Macromanagement, i.e., selecting appropriate units to build depending on the current state, is one of the most important problems in this game. To reduce the requirements for expert knowledge and enhance the coordination of the systematic bot, we select reinforcement learning (RL) to tackle the problem of macromanagement. We propose a novel deep RL method, Mean Asynchronous Advantage Actor-Critic (MA3C), which computes the approximate expected policy gradient instead of the gradient of sampled action to reduce the variance of the gradient, and encode the history queue with recurrent neural network to tackle the problem of imperfect information. The experimental results show that MA3C achieves a very high rate of winning, approximately 90%, against the weaker opponents and it improves the win rate about 30% against the stronger opponents. We also propose a novel method to visualize and interpret the policy learned by MA3C. Combined with the visualized results and the snapshots of games, we find that the learned macromanagement not only adapts to the game rules and the policy of the opponent bot, but also cooperates well with the other modules of MA3C-Bot.

Hinokiflavone induces apoptosis and inhibits migration of breast cancer cells via EMT signalling pathway.

Hinokiflavone is a natural product, isolated from Selaginella P. Beauv, Juniperus phoenicea and Rhus succedanea. Even though hinokiflavone was reported to possess cytotoxicity to many cancer cells, and has potential in cancer treatment, the anti-proliferation and anti-metastasis efficacy of hinokiflavone on human breast cancer cells has not a further research. In this study, we investigated the anti-cancer activity of hinokiflavone in human breast cancer cells in vitro and in vivo. Hinokiflavone exhibited a time- and dose-dependent manner apoptosis induction by upregulating expression of Bax and downregulating Bcl-2 in breast cancer cells. Furthermore, hinokiflavone significantly inhibited the migration and invasion of breast cancer cells by impairing the process of epithelial-to-mesenchymal transition. In addition, the tumour growth was distinctly inhibited by treatment of hinokiflavone in a xenograft tumour mouse model of MDA-MB-231 cells. Immunohistochemical analysis of tumour sections showed that MMP-2+ cells and Ki-67+ cells were remarkably decreased in tumour tissues of mice after treatment of hinokiflavone, indicating that hinokiflavone inhibits not only proliferation but also metastasis of breast cancer cells. Our study suggested that hinokiflavone can be a potential drug to breast cancer. SIGNIFICANCE OF THE STUDY: Hinokiflavone significantly inhibited proliferation and induced apoptosis in breast cancer cells. In addition, hinokiflavone remarkably inhibited migration and invasion of breast cancer cells via EMT signalling pathway. It is worth noting that hinokiflavone possesses anti-tumour effect in tumour mouse xenograft model of breast cancer. Overall, our results indicated that hinokiflavone may be a potential anticancer drug for breast cancer treatment.

Integration of Global and Local Metrics for Domain Adaptation Learning Via Dimensionality Reduction.

Domain adaptation learning (DAL) investigates how to perform a task across different domains. In this paper, we present a kernelized local-global approach to solve domain adaptation problems. The basic idea of the proposed method is to consider the global and local information regarding the domains (e.g., maximum mean discrepancy and intraclass distance) and to convert the domain adaptation problem into a bi-object optimization problem via the kernel method. A solution for the optimization problem will help us identify a latent space in which the distributions of the different domains will be close to each other in the global sense, and the local properties of the labeled source samples will be preserved. Therefore, classic classification algorithms can be used to recognize unlabeled target domain data, which has a significant difference on the source samples. Based on the analysis, we validate the proposed algorithm using four different sources of data: synthetic, textual, object, and facial image. The experimental results indicate that the proposed method provides a reasonable means to improve DAL algorithms.