Previous studies have suggested that Taiji practice may improve immune function. This study was intended to examine whether 5 months of moderate Taiji and Qigong (TQ) practice could improve the immune response to influenza vaccine in older adults. Fifty older adults (mean age 77.2 ± 1.3 years) participated in this study (TQ N = 27; wait-list control [CON] N = 23). Baseline pre-vaccine blood samples were collected. All subjects then received the 2003–2004 influenza vaccine during the first week of the intervention. Post-vaccine blood samples were collected 3, 6 and 20 weeks post-intervention for analysis of anti-influenza hemagglutination inhibition (HI) titers. We found a significant (p < 0.05) increase in the magnitude and duration of the antibody response to influenza vaccine in TQ participants when compared to CON. The vaccination resulted in a 173, 130, and 109% increase in HI titer at 3, 6, and 20 weeks post-vaccine, respectively, in the TQ group compared to 58, 54, and 10% in CON. There was a significant between group difference at 3 and 20 weeks post-vaccine and at 20 weeks the TQ group had significantly higher titers compared to the pre-vaccine time point, whereas the CON group did not. A higher percentage of TQ subjects also responded to the influenza A strains with a protective (> 40HI) antibody response (37% TQ vs. 20% CON for the H1N1 strain and 56% TQ vs. 45% CON for the H3N2 strain), but the differences between groups were not statistically significant. Traditional TQ practice improves the antibody response to influenza vaccine in older adults, but further study is needed to determine whether the enhanced response is sufficient to provide definitive protection from influenza infection.
The aim of this research is to evaluate the clinical evidence of an herbal medicine (HM) treatment on influenza and describe the potential benefits and adverse events by reviewing all relevant randomized controlled trials. All papers published from 2010 to 2019 in all languages in six databases were searched, including all randomized controlled trials on adults and children, testing herbal medicine for treatment of influenza, alone or in combination with conventional antiviral therapy. The main outcome parameters of interest were total effective rate, time to resolution of fever, adverse events, complications, and duration of viral shedding. 25 trials of 3044 patients were included. Herbal medicine compared to placebo significantly reduced time to fever resolution by 4.96h (mean difference, −4.96; 95% CI, −7.11 to −2.80; P<0.00001), herbal medicine compared to oseltamivir showed no significant difference (mean difference, −1.82; 95% CI, −6.08 to 2.44; P=0.40), and herbal medicine plus oseltamivir combined treatment significantly reduced duration of fever by 7.84h compared to a single treatment with oseltamivir (mean difference, −7.84; 95% CI, −12.51 to −3.17; p=0.001). Herbal medicine compared to placebo showed a significantly better total effective rate (risk ratio, 1.90; 95% CI, 1.18 to 3.07; P=0.008), herbal medicine compared to oseltamivir indicated significantly better effective rate (risk ratio, 1.15; 95% CI, 1.03 to 1.29; P=0.01), and combined treatment showed a significantly better total effective rate compared to a single treatment with oseltamivir (risk ratio, 1.20; 95% CI, 1.06 to 1.36; P=0.004). Regarding safety, no serious adverse events were reported in HM treatment. HM presented fewer adverse events compared to oseltamivir, but the difference was not significant (risk difference, −0.04; 95% CI, −0.09 to 0.00; P=0.08), and the combined treatment did not increase adverse events compared to oseltamivir (risk difference, −0.02; 95% CI, −0.06 to 0.02; P=0.30). Research findings show that herbal medicine treatments have beneficial therapeutic effects on influenza and could decrease duration of fever and improve total effective rate. In addition, herbal medicine plus oseltamivir combined therapy could increase the therapeutic effect compared to a single treatment with oseltamivir.
Influenza is a sudden and serious viral breathing and lung-related infectious disease that causes significant deadliness and death worldwide. Now, the international treatment is oseltamivir. Chinese patent medicine (CPM) as a kind of different therapy is used in the treatment of influenza in China. The aim of this study was to interpret the clinical efficacy and safety of CPM combined with oseltamivir in the treatment of adult influenza by reviewing all relevant randomized controlled trials, and to provide new ideas and methods for the treatment of influenza. PubMed, Embase, Cochrane Library, SinoMed, CNKI, and Wanfang Database were searched from the date of beginning until 1 June 2021, for the references on treatment of influenza with CPM. According to standard information extraction tables, two people worked to find and aggregate information independently. Review Manager 5.2 was used to study data carefully and evaluate risk of bias. A total of nine trials of 906 patients were included. Based on the meta-analysis, compared to oseltamivir, CPM combined with oseltamivir had better effect in the time of defervescence [MD = –17.68, 95% CI (–25.93, –9.44), P < 0.0001], the time of symptom improvement [MD = –22.28, 95% CI (–26.77, –17.80), P < 0.00001], and the time of hospitalization [MD = –2.04, 95% CI (–3.45, –0.63), P = 0.005]. Related to safety [RR = 0.69, 95% CI (0.38, 1.23), P = 0.21], the experimental group had fewer adverse reactions than the control group, but there is no statistical significance. The findings show that CPM combined with oseltamivir in adult influenza has a better efficacy in shortening the time of defervescence and symptom improvement, reducing the time of hospitalization. However, publication bias is inevitable due to the low methodological quality check of the clinical research about diagnostic criteria, definition of adult influenza, and small number of articles, and further large sample sizes and multi-center clinical trials are needed to give better proof for its efficacy and safety.
We present an optimal control model for influenza vaccination strategies in an open population. The model is based on an extended Kermack–McKendrick model with the vaccination rate being a measurable function. The objective of this optimal control model is to describe the vaccination strategies so that the total cost arising from vaccination and infections is minimized. We show that the optimal control is a non-singular bang-bang control which has a finite number of switchings. A scheme for the solution of the optimal control problem is formulated using the shooting method. We also carry out numerical simulations to illustrate the general results and to examine the effects of parameters on the optimal vaccination strategy. The simulations show that the ratio of the per capita treatment cost and per capita vaccination cost has a significant effect on the optimal strategy, while the vaccination rate of the newly recruited class turns out to have less effect.
Flu is an acute respiratory infection caused by the influenza virus. The outbreak usually occurs every year in temperate region during the fall and winter seasons, but it is present year-round in tropics. Perceived risk of infection, affordability and lack of awareness among the population results in a low level of vaccination coverage. To control disease transmission and promote vaccination, public health officials use media coverage to spread awareness on vaccine safety, vaccine coverage, disease prevalence in the population through public health websites, advertisements, and other social media web pages. Media coverage acts as an incentive as it helps to decrease overall transmission potential and also at the same time increases the vaccination coverage in the population. Since the public health department has a limited budget, it needs to make optimum allocation of its effort to reduce the total cost of infection. Our paper investigates the effect of media coverage using SIR model of disease transmission. We look at three possible functional relationships — linear, exponential, and hyperbolic — the way media coverage may affect the disease transmission and vaccination rate. We derive necessary conditions of optimal solution using Optimal Control Theory and Pontryagin Maximum Principle (PMP) to minimize the total cost for infection. Analysis of our paper demonstrates that the cost of optimal management is four times less than the cost of constant control effort, and putting more effort into reducing transmission is optimal rather than an effort to increase vaccination at the beginning of the outbreak. Analysis of the role of media coverage under three different scenarios may help in formulating policies for public health programs in mitigating the influenza outbreak.
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Avian influenza viruses from migratory birds have managed to cross host species barriers and infected various hosts like human and swine. Epidemics and pandemics might occur when influenza viruses are adapted to humans, causing deaths and enormous economic loss. Receptor-binding specificity of the virus is one of the key factors for the transmission of influenza viruses across species. The determination of host tropism and understanding of molecular properties would help identify the mechanism why zoonotic influenza viruses can cross species barrier and infect humans. In this study, we have constructed computational models for host tropism prediction on human-adapted subtypes of influenza HA proteins using random forest. The feature vectors of the prediction models were generated based on seven physicochemical properties of amino acids from influenza sequences of three major hosts. Feature aggregation and associative rules were further applied to select top 20 features and extract host-associated physicochemical signatures on the combined model of nonspecific subtypes. The prediction model achieved high performance (Accuracy=0.948, Precision=0.954, MCC=0.922). Support and confidence rates were calculated for the host class-association rules. The results indicated that secondary structure and normalized Van der Waals volume were identified as more important physicochemical signatures in determining the host tropism.
Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics due to rapid viral evolution. Vaccines are used to prevent influenza infections but the composition of the influenza vaccines have to be updated regularly to ensure its efficacy. Computational tools and analyses have become increasingly important in guiding the process of vaccine selection. By constructing time-series training samples with splittings and embeddings, we develop a computational method for predicting suitable strains as the recommendation of the influenza vaccines using recurrent neural networks (RNNs). The Encoder-decoder architecture of RNN model enables us to perform sequence-to-sequence prediction. We employ this model to predict the prevalent sequence of the H3N2 viruses sampled from 2006 to 2017. The identity between our predicted sequence and recommended vaccines is greater than 98% and the Pepitope<0.2 indicates their antigenic similarity. The multi-step vaccine prediction further demonstrates the robustness of our method which achieves comparable results in contrast to single step prediction. The results show significant matches of the recommended vaccine strains to the circulating strains. We believe it would facilitate the process of vaccine selection and surveillance of seasonal influenza epidemics.
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