Global dynamics of two-strain epidemic model with single-strain vaccination in complex networks.

Contagious pathogens, such as influenza and COVID-19, are known to be represented by multiple genetic strains. Different genetic strains may have different characteristics, such as spreading more easily, causing more severe diseases, or even evading the immune response of the host. These facts complicate our ability to combat these diseases. There are many ways to prevent the spread of infectious diseases, and vaccination is the most effective. Thus, studying the impact of vaccines on the dynamics of a multi-strain model is crucial. Moreover, the notion of complex networks is commonly used to describe the social contacts that should be of particular concern in epidemic dynamics. In this paper, we investigate a two-strain epidemic model using a single-strain vaccine in complex networks. We first derive two threshold quantities, R 1 and R 2 , for each strain. Then, by using the basic tools for stability analysis in dynamical systems (i.e., Lyapunov function method and LaSalle's invariance principle), we prove that if R 1 < 1 and R 2 < 1 , then the disease-free equilibrium is globally asymptotically stable in the two-strain model. This means that the disease will die out. Furthermore, the global stability of each strain dominance equilibrium is established by introducing further critical values. Under these stability conditions, we can determine which strain will survive. Particularly, we find that the two strains can coexist under certain condition; thus, a coexistence equilibrium exists. Moreover, as long as the equilibrium exists, it is globally stable. Numerical simulations are conducted to validate the theoretical results.

Discovery of a Metabolic Signature Predisposing High Risk Patients with Mild Cognitive Impairment to Converting to Alzheimer's Disease.

Assessing dementia conversion in patients with mild cognitive impairment (MCI) remains challenging owing to pathological heterogeneity. While many MCI patients ultimately proceed to Alzheimer's disease (AD), a subset of patients remain stable for various times. Our aim was to characterize the plasma metabolites of nineteen MCI patients proceeding to AD (P-MCI) and twenty-nine stable MCI (S-MCI) patients by untargeted metabolomics profiling. Alterations in the plasma metabolites between the P-MCI and S-MCI groups, as well as between the P-MCI and AD groups, were compared over the observation period. With the help of machine learning-based stratification, a 20-metabolite signature panel was identified that was associated with the presence and progression of AD. Furthermore, when the metabolic signature panel was used for classification of the three patient groups, this gave an accuracy of 73.5% using the panel. Moreover, when specifically classifying the P-MCI and S-MCI subjects, a fivefold cross-validation accuracy of 80.3% was obtained using the random forest model. Importantly, indole-3-propionic acid, a bacteria-generated metabolite from tryptophan, was identified as a predictor of AD progression, suggesting a role for gut microbiota in AD pathophysiology. Our study establishes a metabolite panel to assist in the stratification of MCI patients and to predict conversion to AD.

Bifurcation analysis of a network-based SIR epidemic model with saturated treatment function.

In this paper, we present a study on a network-based susceptible-infected-recovered (SIR) epidemic model with a saturated treatment function. It is well known that treatment can have a specific effect on the spread of epidemics, and due to the limited resources of treatment, the number of patients during severe disease outbreaks who need to be treated may exceed the treatment capacity. Consequently, the number of patients who receive treatment will reach a saturation level. Thus, we incorporated a saturated treatment function into the model to characterize such a phenomenon. The dynamics of the present model is discussed in this paper. We first obtained a threshold value R0, which determines the stability of a disease-free equilibrium. Furthermore, we investigated the bifurcation behavior at R0=1. More specifically, we derived a condition that determines the direction of bifurcation at R0=1. If the direction is backward, then a stable disease-free equilibrium concurrently exists with a stable endemic equilibrium even though R0<1. Therefore, in this case, R0<1 is not sufficient to eradicate the disease from the population. However, if the direction is forward, we find that for a range of parameters, multiple equilibria could exist to the left and right of R0=1. In this case, the initial infectious invasion must be controlled to a lower level so that the disease dies out or approaches a lower endemic steady state.

Predicting neuropsychiatric symptoms of persons with dementia in a day care center using a facial expression recognition system.

BACKGROUND: Behavioral and psychological symptoms of dementia (BPSD) affect 90% of persons with dementia (PwD), resulting in various adverse outcomes and aggravating care burdens among their caretakers. This study aimed to explore the potential of artificial intelligence-based facial expression recognition systems (FERS) in predicting BPSDs among PwD. METHODS: A hybrid of human labeling and a preconstructed deep learning model was used to differentiate basic facial expressions of individuals to predict the results of Neuropsychiatric Inventory (NPI) assessments by stepwise linear regression (LR), random forest (RF) with importance ranking, and ensemble method (EM) of equal importance, while the accuracy was determined by mean absolute error (MAE) and root-mean-square error (RMSE) methods. RESULTS: Twenty-three PwD from an adult day care center were enrolled with ≥ 11,500 FERS data series and 38 comparative NPI scores. The overall accuracy was 86% on facial expression recognition. Negative facial expressions and variance in emotional switches were important features of BPSDs. A strong positive correlation was identified in each model (EM: r = 0.834, LR: r = 0.821, RF: r = 0.798 by the patientwise method; EM: r = 0.891, LR: r = 0.870, RF: r = 0.886 by the MinimPy method), and EM exhibited the lowest MAE and RMSE. CONCLUSIONS: FERS successfully predicted the BPSD of PwD by negative emotions and the variance in emotional switches. This finding enables early detection and management of BPSDs, thus improving the quality of dementia care.

Artificial-Intelligence-Assisted Discovery of Genetic Factors for Precision Medicine of Antiplatelet Therapy in Diabetic Peripheral Artery Disease.

An increased risk of cardiovascular events was identified in patients with peripheral artery disease (PAD). Clopidogrel is one of the most widely used antiplatelet medications. However, there are heterogeneous outcomes when clopidogrel is used to prevent cardiovascular events in PAD patients. Here, we use an artificial intelligence (AI)-assisted methodology to identify genetic factors potentially involved in the clopidogrel-resistant mechanism, which is currently unclear. Several discoveries can be pinpointed. Firstly, a high proportion (>50%) of clopidogrel resistance was found among diabetic PAD patients in Taiwan. Interestingly, our result suggests that platelet function test-guided antiplatelet therapy appears to reduce the post-interventional occurrence of major adverse cerebrovascular and cardiac events in diabetic PAD patients. Secondly, AI-assisted genome-wide association study of a single-nucleotide polymorphism (SNP) database identified a SNP signature composed of 20 SNPs, which are mapped into 9 protein-coding genes (SLC37A2, IQSEC1, WASHC3, PSD3, BTBD7, GLIS3, PRDM11, LRBA1, and CNR1). Finally, analysis of the protein connectivity map revealed that LRBA, GLIS3, BTBD7, IQSEC1, and PSD3 appear to form a protein interaction network. Intriguingly, the genetic factors seem to pinpoint a pathway related to endocytosis and recycling of P2Y12 receptor, which is the drug target of clopidogrel. Our findings reveal that a combination of AI-assisted discovery of SNP signatures and clinical parameters has the potential to develop an ethnic-specific precision medicine for antiplatelet therapy in diabetic PAD patients.

Artificial Intelligence-Assisted Identification of Genetic Factors Predisposing High-Risk Individuals to Asymptomatic Heart Failure.

Heart failure (HF) is a global pandemic public health burden affecting one in five of the general population in their lifetime. For high-risk individuals, early detection and prediction of HF progression reduces hospitalizations, reduces mortality, improves the individual's quality of life, and reduces associated medical costs. In using an artificial intelligence (AI)-assisted genome-wide association study of a single nucleotide polymorphism (SNP) database from 117 asymptomatic high-risk individuals, we identified a SNP signature composed of 13 SNPs. These were annotated and mapped into six protein-coding genes (GAD2, APP, RASGEF1C, MACROD2, DMD, and DOCK1), a pseudogene (PGAM1P5), and various non-coding RNA genes (LINC01968, LINC00687, LOC105372209, LOC101928047, LOC105372208, and LOC105371356). The SNP signature was found to have a good performance when predicting HF progression, namely with an accuracy rate of 0.857 and an area under the curve of 0.912. Intriguingly, analysis of the protein connectivity map revealed that DMD, RASGEF1C, MACROD2, DOCK1, and PGAM1P5 appear to form a protein interaction network in the heart. This suggests that, together, they may contribute to the pathogenesis of HF. Our findings demonstrate that a combination of AI-assisted identifications of SNP signatures and clinical parameters are able to effectively identify asymptomatic high-risk subjects that are predisposed to HF.

Inhibition of the lethality of Shiga-like toxin-1 by functional gold nanoparticles.

Escherichia coli O157:H7 is a pathogen, which can generate Shiga-like toxins (SLTs) and cause hemolytic-uremic syndrome. Foodborne illness outbreaks caused by E. coli O157:H7 have become a global issue. Since SLTs are quite toxic, effective medicines that can reduce the damage caused by SLTs should be explored. SLTs consist of a single A and five B subunits, which can inhibit ribosome activity for protein synthesis and bind with the cell membrane of host cells, respectively. Pigeon ovalbumin (POA), i.e. a glycoprotein, is abundant in pigeon egg white (PEW) proteins. The structure of POA contains Gal-α(1→4)-Gal-ß(1→4)-GlcNAc ligands, which have binding affinity toward the B subunit in SLT type-1 (SLT-1B). POA immobilized gold nanoparticles (POA-Au NPs) can be generated by reacting PEW proteins with aqueous tetrachloroauric acid in one-pot. The generated POA-Au NPs have been demonstrated to have selective trapping-capacity toward SLT-1B previously. Herein, we explore that POA-Au NPs can be used as protective agents to neutralize the toxicity of SLT-1 in SLT-1-infected model cells. The results show that the cells can be completely rescued when a sufficient amount of POA-Au NPs is used to treat the SLT-1-infected cells within 1 h.

Selective Detection of Shiga-like Toxin 1 from Complex Samples Using Pigeon Ovalbumin Functionalized Gold Nanoparticles as Affinity Probes.

Escherichia coli O157:H7 is a foodborne pathogen. This bacterial strain can generate Shiga-like toxins (SLTs), which can cause serious sickness and even death. Thus, it is important to develop effective and sensitive methods that can be used to rapidly identify the presence of SLTs from complex samples. Pigeon egg white (PEW) contains abundant glycoproteins, including pigeon ovalbumin (POA) (∼60%). POA possesses Gal-α(1→4)-Gal-ß(1→4)-GlcNAc termini, which can recognize the B subunits in SLT type 1 (SLT-1B). Thus, POA is a suitable probe for trapping SLT-1B. In this work, we used PEW proteins as starting materials to react with aqueous tetrachloroauric acid for generation of PEW-protein-immobilized gold nanoparticles (AuNPs@PEW) via one-pot reactions. We demonstrated that the generated AuNPs@PEW were mainly dominated by POA-immobilized Au NPs. The as-prepared AuNPs@PEW were used as affinity probes to selectively probe SLT-1B from complex cell lysates derived from E. coli O157:H7. The selective trapping step can be completed within ∼90 s under microwave heating (power = 450 W) to enrich sufficient SLT-1B for matrix-assisted laser desorption/ionization (MALDI) mass spectrometric analysis. Furthermore, this approach can be used to detect SLT-1B at a concentration as low as ∼40 pM. The feasibility of using the proposed method to selectively detect SLT-1B from ham contaminated by E. coli O157:H7 was also demonstrated.

Capillary hydrodynamic chromatography reveals temporal profiles of cell aggregates.

Microbial cells are known to form aggregates. Such aggregates can be found in various matrices; for example, functional drinks. Capillary hydrodynamic chromatography (HDC) enables separation of particles by size using nanoliter-scale volumes of samples. Here we propose an approach based on HDC for characterisation of real samples containing aggregated and non-aggregated bacterial and fungal cells. Separation of cells and cell aggregates in HDC arises from the parabolic flow profile under laminar flow conditions. In the presented protocol, hydrodynamic separation is coupled with different on-line and off-line detectors (light absorption/scattering and microscopy). The method has successfully been applied in the monitoring of dynamic changes in the microbiome of probiotic drinks. Chromatographic profiles of yogurt and kefir samples obtained at different times during fermentation are in a good agreement with microscopic images. Moreover, thanks to the implementation of an area imaging detector, capillary HDC could be multiplexed and used to profile spatial gradients in cell suspensions, which arise in the course of sedimentation of cells and cell aggregates. This result shows compatibility of sedimentation analysis and capillary HDC. We believe that the approach may find applications in the profiling of functional foods and other matrices containing aggregated bioparticles.