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Objective. Infectious diseases usually spread rapidly. This study aims to develop a model that can provide fine-grained early warnings of infectious diseases using real hospital data combined with disease transmission characteristics, weather, and other multi-source data. Methods. Based on daily data reported for infectious diseases collected from several large general hospitals in China between 2012 and 2020, seven common infectious diseases in medical institutions were screened and a multi self-regression deep (MSRD) neural network was constructed. Using a recurrent neural network as the basic structure, the model can effectively model the epidemiological trend of infectious diseases by considering the current influencing conditions while taking into account the historical development characteristics in time-series data. The fitting and prediction accuracy of the model were evaluated using mean absolute error (MAE) and root mean squared error. Results. The proposed approach is significantly better than the existing infectious disease dynamics model, susceptible-exposed-infected-removed (SEIR), as it addresses the concerns of difficult-to-obtain quantitative data such as latent population, overfitting of long time series, and considering only a single series of the number of sick people without considering the epidemiological characteristics of infectious diseases. We also compare certain machine learning methods in this study. Experimental results demonstrate that the proposed approach achieves an MAE of 0.6928 and 1.3782 for hand, foot, and mouth disease and influenza, respectively. Conclusion. The MRSD-based infectious disease prediction model proposed in this paper can provide daily and instantaneous updates and accurate predictions for epidemic trends. [ABSTRACT FROM AUTHOR]

The hand foot, and mouth disease (HFMI)) epidemic has become a serious public health problem worldwide with a high economic and health burden. In addition, the prevalence of (HFMI) varies greatly between cities. Therefore, this paper aims to reveal high-risk cities for HFMD and associated meteorological and air pollution factors in Guangdong Province during 2014-2018. Data on the incidence of HFMD mid meteorological and air pollution. factors were obtained from the Guangdong Provincial Center for Disease Control and Prevention and Guangdong Meteorological Service. Three different grey incidence analysis (GIA) were used, namely Deng's grey incidence analysis, absolute degree of GIA, and second synthetic degree of GIA. Additionally, GMC (1,1) model was used to predict the trend of HFMD in 2019-2028. According to the second synthetic degree of GIA, the top three cities most affected by HFMD are Zhuhai, Guangzhou, and Foshan. However, the prediction model .found that the incidence of HFMD in Guangdong Province will generally decline in the next 10 years, indicating that the prevention and control measures are still relatively in place. Deng's grey incidence analysis found that CO, %O2, PM2.5, PM10), and wind scale are closely related to the incidence of HFMD. It is recommended to closely monitor weather change and urban air quality and take protective measures against HFMD in advance. The results of this study hare substantial implications for the control of HFMD. [ABSTRACT FROM AUTHOR]

The prediction model is a major component within public health cyber‐physical systems, which supports decisions on prevention and control of diseases. Hand, foot, and mouth disease (HFMD) is one of the most common global infectious diseases with the highest incidence rate. Previous HFMD prediction models are mainly based on the time series that counted in equal‐grained time intervals. However, there are details in the time series counted in fine‐grained time intervals. To benefit from both equal‐grained and fine‐grained data, we proposed a dual‐grained representation (DGR) model. The DGR first represents inputted data to temporal patterns. Then, the represented patterns are consolidated to generate predictions. Experimental comparisons of the short‐term prediction performance are figured out by using real outpatient collections in Xiamen, China. [ABSTRACT FROM AUTHOR]

Hand, Foot, and Mouth Disease (HFMD) is an outbreak infectious disease that can easily spread among children under the age of five. The most common causative agents of HFMD are enterovirus 71 (EV71) and coxsackievirus A16 (CVA16), but infection caused by EV71 is more associated with fatalities due to severe neurological disorders. The present diagnosis methods rely on physical examinations by the doctors and further confirmation by laboratories detection methods such as viral culture and polymerase chain reaction. Clinical signs of HFMD infection and other childhood diseases such as chicken pox, and allergies are similar, yet the genetics and pathogenicity of the viruses are substantially different. Thus, there is an urgent need for an early screening of HFMD using an inexpensive and user-friendly device that can directly detect the causative agents of the disease. This paper reviews current HFMD diagnostic methods based on various target types, such as nucleic acid, protein, and whole virus. This was followed by a thorough discussion on the emerging sensing technologies for HFMD detection, including surface plasmon resonance, electrochemical sensor, and surface enhanced Raman spectroscopy. Lastly, optical absorption spectroscopic method was critically discussed and proposed as a promising technology for HFMD screening and detection. [Display omitted] • The HFMD diagnostic methods measure various target types including nucleic acid, protein, and whole virus. • The current lab-based HFMD diagnostic methods have limitations of relatively high cost, lack of simplicity, and time-consuming. • Present emerging biosensor technologies for HFMD detection offer direct measurements, user-friendly, lab-free and tag-free. • Optical spectroscopy is proposed due to its simplicity, without requiring ligand development and ligand immobilization. [ABSTRACT FROM AUTHOR]

As the proportion of non-enterovirus 71 and non-coxsackievirus A16 which proportion of composition in the hand, foot, and mouth pathogenic spectrum gradually increases worldwide, the attention paid to other enteroviruses has increased. As a member of the species enterovirus A, coxsackievirus A14 (CVA14) has been epidemic around the world until now since it has been isolated. However, studies on CVA14 are poor and the effective population size, evolutionary dynamics, and recombination patterns of CVA14 are not well understood. In this study, 15 CVA14 strains were isolated from HFMD patients in mainland China from 2009 to 2019, and the complete sequences of CVA14 in GenBank as research objects were analyzed. CVA14 was divided into seven genotypes A-G based on an average nucleotide difference of the full-length VP1 coding region of more than 15%. Compared with the CVA14 prototype strain, the 15 CVA14 strains showed 84.0–84.7% nucleotide identity in the complete genome and 96.9–97.6% amino acid identity in the encoding region. Phylodynamic analysis based on 15 CVA14 strains and 22 full-length VP1 sequences in GenBank showed a mean substitution rate of 5.35 × 10−3 substitutions/site/year (95% HPD: 4.03–6.89 × 10−3) and the most recent common ancestor (tMRCA) of CVA14 dates back to 1942 (95% HPD: 1930–1950). The Bayesian skyline showed that the effective population size had experienced a decrease–increase–decrease fluctuation since 2004. The phylogeographic analysis indicated two and three possible migration paths in the world and mainland China, respectively. Four recombination patterns with others of species enterovirus A were observed in 15 CVA14 strains, among which coxsackievirus A2 (CVA2), coxsackievirus A4 (CVA4), coxsackievirus A6 (CVA6), coxsackievirus A8 (CVA8), and coxsackievirus A12 (CVA12) may act as recombinant donors in multiple regions. This study has filled the gap in the molecular epidemiological characteristics of CVA14, enriched the global CVA14 sequence database, and laid the epidemiological foundation for the future study of CVA14 worldwide. [ABSTRACT FROM AUTHOR]

Although most patients with acute viral myocarditis recover spontaneously, some patients progress to heart failure. Perturbations in innate immunity may partially explain the heterogeneity of clinical outcomes. As the most abundant immune cells in the heart, cardiac macrophages have heterogeneous origins, including embryonic‐derived resident macrophages (ResMϕs) and monocyte‐derived macrophages (MoMFs). However, the time course change and role of cardiac macrophage subsets has not been fully explored. In the present study, we found that BALB/c mice had prolonged MoMF accumulation and low proportions of ResMϕs that could not be restored to normal levels. MoMFs of BALB/c mice generally exhibit an M1‐dominant functional phenotype. Moreover, the preferential depletion of MoMF by a C‐C chemokine receptor type 2 (CCR2) inhibitor resulted in improved acute myocarditis and chronic fibrosis, as well as the recovery of ResMϕs number and reduced CD4+ T cell expansion. Hence, immunomodulatory therapy that targets the balance among cardiac macrophages and modulates their function is expected to prevent the progression of cardiac injury to overt heart failure and improve adverse outcomes. [ABSTRACT FROM AUTHOR]

Type 1 diabetes (T1D) is a common multifactorial autoimmune disease resulting from the destruction of insulin-producing -cells in the pancreas as a consequence of dysregulated immune responses.[1] In the past few years, there has been a flurry of discoveries and advancements in our understanding of the immunopathogenic mechanisms at the basis of T1D development and progression. Herein, conventional dendritic cells display a pivotal role in inducing T1D upon pathogen infections as these cells can capture and present -cell-derived proteins and/or viral antigens, initiating the diabetogenic T cell-specific immune response[7] (Figure 1). Anti-viral innate immunity: Is it where type 1 diabetes really begins?. [Extracted from the article]

Hand, foot and mouth disease (HFMD) is a susceptible viral infectious disease to infants and children, which led to millions of cases and hundreds of deaths annually in China. Existing predictive methods commonly learn the development patterns from historical observations. However, almost all these methods are neglect the immediate impact of exogenous factors on HFMD transmission. To solve the limitation, we consider the approximately unidirectional influences from temperature to confirmed cases and then propose a transfer one-step-ahead learning (Tr-OSH) method to establish their association. The Tr-OSH method first extract the unidirectional representation from temperature observations, and subsequently transfer the obtained representation for HFMD cases prediction. Moreover, we notice the independent correlation of each time step and period, and generate the independent representation by the relevance to upcoming values. Intensive experiments on real-world HFMD datasets demonstrate that our Tr-OSH method much efficaciously improves prediction accuracy. [ABSTRACT FROM AUTHOR]

Aim: To study the associations between baseline blood glucose levels (BGL), glycemic variability and clinical outcomes in patients with severe acute kidney injury (AKI) receiving continuous renal replacement therapy (CRRT). Methods: We performed a secondary analysis of the Randomized Evaluation of Normal versus Augmented Level of RRT (RENAL) study. A multivariate Cox regression model was used to assess the association between baseline BGL, glycemic variability and clinical outcomes. The primary outcome was all-cause mortality, and secondary outcomes were duration of hospital and intensive care unit (ICU) stay. Results: Baseline BGL data were available in 1404 out of 1508 patients from the RENAL study. Among them, 627 patients died within 90 days of randomization. Compared to patients in the second quartile (BGL 5.8–7.2 mmol/L), patients in the first quartile (BGL < 5.8 mmol/L) had increased mortality rate (90-day HR 1.48; p = 0.001; 28-day HR 1.47; p = 0.042). However, there were no significant differences in ICU and hospital length of stay (LOS) (p = 0.82 and p = 0.33, respectively). Glycemic variability data were from 1345 out of 1404 patients who had data for BG values within 28 days. Higher coefficient of variation (CV) (HR 1.02; P trend = 0.002) and standard deviation value (SD) (HR 1.29; P trend = 0.027) were associated with higher risk of death at day 90. Conclusions: We identified a low BGL within the normal physiological range at baseline and greater CV and SD values as significant modifiable risk factors for mortality in severe AKI patients in ICU, which may be a target for intervention. [ABSTRACT FROM AUTHOR]

Coxsackievirus B2 (CVB2) is an enterovirus B (EV‐B) species and can cause aseptic meningitis, myocarditis and hand, foot, and mouth disease (HFMD). We characterized a novel CVB2 (YN31V3) associated with HFMD in Yunnan, Southwest China, in 2019. Although YN31V3 and other Mainland China epidemic strains mainly belonged to genotype C, YN31V3 formed an independent branch. The genome sequence of the strain YN31V3 from this study showed a 12.91% nucleotide difference to its closest strain RW41‐2/YN/CHN/2012. Recombination analyses showed that the newly isolated YN31V3 was probably a recombinant, which was closely related to CVB2 strains in the genomic P1 region and other EV‐B strains in the P2 and P3 regions, respectively. YN31V3 strain had a temperature‐sensitive phenotype. The challenge of suckling BALB/c mice with YN31V3 could cause symptoms of disease and severe pathological lesions. Highlights: Coxsackievirus B2 strain YN31V3 associated with hand, foot, and mouth disease was isolated and analyzed in Southwest China.The isolate YN31V3 belonged to a novel clade.The sequence diversity of coxsackievirus B2 affects its clinical manifestations and tissue tropism.It is essential to persistent surveillance of enterovirus. [ABSTRACT FROM AUTHOR]

Background: Measles caused by measles virus (MeV) is a highly contagious viral disease which has also been associated with complications including pneumonia, myocarditis, encephalitis, and subacute sclerosing panencephalitis. The current study isolated 33 strains belonging to 2 groups, outbreak and sporadic strains, in 13 cities of Shandong province, China from 2013 to 2019. Comparison of genetic characterization among 15 outbreak strains and 18 sporadic strains was performed by analyzing nucleotide sequences of the C-terminal region of N protein gene (N-450). Results: All 33 stains belonged to genotype H1. The outbreak strains and sporadic strains distributed crossly in phylogenetic tree. Sequences alignment revealed some interesting G to A transversion which changed the amino acids on genomic sites 1317, 1422, and 1543. The nucleotide and amino acid similarities among outbreak isolates were 98–100% (0–10 nucleotide variations) and 97.7–100%, respectively; They were 97.3–100% and 96.6–100%, respectively for sporadic isolates. Evolutionary genetics analysis revealed that the mean evolution rates of outbreak and sporadic isolates were 1.26 N 10− 3 and 1.48 N 10− 3 substitutions per site per year separately, which were similar with corresponding data before 2012. Local transmission analysis suggested that there were three transmission chains in this study, two of them originated from Japan. Outbreak cases and sporadic cases emerged alternatively and were reciprocal causation on the transmission chains. Conclusions: Our study investigated the phylogeny and evolutional genetics of MeV during a 7-year surveillance, and compared epidemic and genetic characteristics of outbreak strains and sporadic strains. These results underscore the importance of evolutionary study alongside with sporadic cases in discovering and tracing possible outbreaks, especially in the stage of measles elimination. [ABSTRACT FROM AUTHOR]

Hand, foot and mouth disease (HFMD) is a virulent disease most commonly found in East and Southeast Asia. Symptoms include ulcers or sores, inside or around the mouth. In this research, we formulate the dynamic model of HFMD by using the SEIQR model. We separated the infection episodes where there is a higher outbreak and a lower outbreak of the disease associated with regional residency, with the higher level of outbreak occurring in the urban region, and a lower outbreak level occurring in the rural region. We developed two different optimal control programs for the types of outbreaks. Optimal Control Policy 1 (OPC1) is limited to the use of treatment only, whereas Optimal Control Policy 2 (OPC2) includes vaccination along with the treatment. The Pontryagin's maximum principle is used to establish the necessary and optimal conditions for the two policies. Numerical solutions are presented along with numerical sensitivity analyses of the required control efforts needed as the control parameters are changed. Results show that the time t m a x required for the optimal control effort to stay at the maximum amount u m a x exhibits an intrinsic logarithmic relationship with respect to the control parameters. [ABSTRACT FROM AUTHOR]