TLR5-Mediated Reactivation of Quiescent Ranavirus FV3 in Xenopus Peritoneal Macrophages.

Ranaviruses such as frog virus 3 (FV3) are large double-stranded DNA (dsDNA) viruses causing emerging infectious diseases leading to extensive morbidity and mortality of amphibians and other ectothermic vertebrates worldwide. Among the hosts of FV3, some are highly susceptible, whereas others are resistant and asymptomatic carriers that can take part in disseminating the infectious virus. To date, the mechanisms involved in the processes of FV3 viral persistence associated with subclinical infection transitioning to lethal outbreaks remain unknown. Investigation in Xenopus laevis has revealed that in asymptomatic FV3 carrier animals, inflammation induced by heat-killed (HK) Escherichia coli stimulation can provoke the relapse of active infection. Since Toll-like receptors (TLRs) are critical for recognizing microbial molecular patterns, we investigated their possible involvement in inflammation-induced FV3 reactivation. Among the 10 different TLRs screened for changes in expression levels following FV3 infection and HK E. coli stimulation, only TLR5 and TLR22, both of which recognize bacterial products, showed differential expression, and only the TLR5 ligand flagellin was able to induce FV3 reactivation similarly to HK E. coli Furthermore, only the TLR5 ligand flagellin induced FV3 reactivation in peritoneal macrophages both in vitro and in vivo These data indicate that the TLR5 signaling pathway can trigger FV3 reactivation and suggest a role of secondary bacterial infections or microbiome alterations (stress or pollution) in initiating sudden deadly disease outbreaks in amphibian populations with detectable persistent asymptomatic ranavirus.IMPORTANCE This study in the amphibian Xenopus laevis provides new evidence of the critical role of macrophages in the persistence of ranaviruses in a quiescent state as well as in the reactivation of these pathogens into a virulent infection. Among the multiple microbial sensors expressed by macrophages, our data underscore the preponderant involvement of TLR5 stimulation in triggering the reactivation of quiescent FV3 in resident peritoneal macrophages, unveiling a mechanistic connection between the reactivation of persisting ranavirus infection and bacterial coinfection. This suggests a role for secondary bacterial infections or microbiome alterations (stress or pollution) in initiating sudden deadly disease outbreaks in amphibian populations with detectable persistent asymptomatic ranavirus.

Critical Role of an MHC Class I-Like/Innate-Like T Cell Immune Surveillance System in Host Defense against Ranavirus (Frog Virus 3) Infection.

Besides the central role of classical Major Histocompatibility Complex (MHC) class Ia-restricted conventional Cluster of Differentiation 8 (CD8) T cells in antiviral host immune response, the amphibian Xenopuslaevis critically rely on MHC class I-like (mhc1b10.1.L or XNC10)-restricted innate-like (i)T cells (iVα6 T cells) to control infection by the ranavirus Frog virus 3 (FV3). To complement and extend our previous reverse genetic studies showing that iVα6 T cells are required for tadpole survival, as well as for timely and effective adult viral clearance, we examined the conditions and kinetics of iVα6 T cell response against FV3. Using a FV3 knock-out (KO) growth-defective mutant, we found that upregulation of the XNC10 restricting class I-like gene and the rapid recruitment of iVα6 T cells depend on detectable viral replication and productive FV3 infection. In addition, by in vivo depletion with XNC10 tetramers, we demonstrated the direct antiviral effector function of iVα6 T cells. Notably, the transitory iV6 T cell defect delayed innate interferon and cytokine gene response, resulting in long-lasting negative inability to control FV3 infection. These findings suggest that in Xenopus and likely other amphibians, an immune surveillance system based on the early activation of iT cells by non-polymorphic MHC class-I like molecules is important for efficient antiviral immune response.

Review of Machine Learning Algorithms for Diagnosing Mental Illness.

OBJECTIVE: Enhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice. METHODS: Researches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized. RESULTS: Based on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics. CONCLUSION: Researchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.

Hemoglobin assay in anemic patients with a photothermal spectral-domain optical coherence reflectometric sensor.

BACKGROUND: We recently introduced an optical sensor, termed photothermal spectral-domain optical coherence reflectometer (PT SD-OCR), capable of direct measurement of hemoglobin concentration ([Hb]) without chemicals and pre-processing of blood. PT SD-OCR measures [Hb] by quantifying refractive index changes of blood samples due to photothermal effect under the irradiation of 532-nm light. We evaluated diagnostic accuracy and precision of PT SD-OCR in anemic patients. METHODS: We used PT SD-OCR to measure [Hb] in 50 anemic patients, and examined its accuracy against a hematology analyzer (ADVIA 2120i, Siemens AG). Its precision was assessed by examining the SD and CV based on 20 repeated measurements on 3 blood samples. Its performance was also compared with a hemoglobinometer (HemoCue 201+, HemoCue). RESULTS: The PT SD-OCR demonstrated good correspondence with the hematology analyzer with a Pearson's correlation coefficient of 0.992 and a bias of -0.055 g/dl. Standard deviation and CV were measured to be <0.25 g/dl and <2.05%. CONCLUSIONS: Simple and chemical-free operation of PT SD-OCR enabled rapid and accurate measurement of [Hb] in anemic patients. The sensor is expected to facilitate clinical procedures related to blood-related disorders in patient care.

Photothermal spectral-domain optical coherence reflectometry for direct measurement of hemoglobin concentration of erythrocytes.

A novel optical detection method for hemoglobin concentration is described. The hemoglobin molecules consisting mainly of iron generate heat upon their absorption of light energy at 532 nm, which subsequently changes the refractive index of the blood. We exploit this photothermal effect to determine the hemoglobin concentration of erythrocytes without any preprocessing of blood. Highly sensitive measurement of refractive index alteration of blood samples is enabled by a spectral-domain low coherence reflectometric sensor with subnanometer-level optical path-length sensitivity. The performance and validity of the sensor are presented by comparing the measured results against the reference data acquired from an automatic hematology analyzer.