Daidzein Augments Cholesterol Homeostasis via ApoE to Promote Functional Recovery in Chronic Stroke

Stroke is the world's leading cause of physiological disability, but there are currently no available agents that can be delivered early after stroke to enhance recovery. Daidzein, a soy isoflavone, is a clinically approved agent that has a neuroprotective effect in vitro, and it promotes axon growth in an animal model of optic nerve crush. The current study investigates the efficacy of daidzein on neuroprotection and functional recovery in a clinically relevant mouse model of stroke recovery. In light of the fact that cholesterols are essential lipid substrates in injury-induced synaptic remodeling, we found that daidzein enhanced the cholesterol homeostasis genetic program, including Lxr and downstream transporters, Apoe, Abca1, and Abcg1 genes in vitro. Daidzein also elevated the cholesterol homeostasis genes in the poststroke brain with Apoe, the highest expressing transporter, but did not affect infarct volume or hemispheric swelling. Despite the absence of neuroprotection, daidzein improved motor/gait function in chronic stroke and elevated synaptophysin expression. However, the daidzein-enhanced functional benefits and synaptophysin expression were abolished in Apoe-knock-out mice, suggesting the importance of daidzein-induced ApoE upregulation in fostering stroke recovery. Dissociation between daidzein-induced functional benefits and the absence of neuroprotection further suggest the presence of nonoverlapping mechanisms underlying recovery processes versus acute pathology. With its known safety in humans, early and chronic use of daidzein aimed at augmenting ApoE may serve as a novel, translatable strategy to promote functional recovery in stroke patients without adverse acute effect. SIGNIFICANCE STATEMENT There have been recurring translational failures in treatment strategies for stroke. One underlying issue is the disparity in outcome analysis between animal and clinical studies. The former mainly depends on acute infarct size, whereas long-term functional recovery is an important outcome in patients. In an attempt to identify agents that promote functional recovery, we discovered that an FDA-approved soy isoflavone, daidzein, improved stroke-induced behavioral deficits via enhancing cholesterol homeostasis in chronic stroke, and this occurs without causing adverse effects in the acute phase. With its known safety in humans, the study suggests that the early and chronic use of daidzein serves as a potential strategy to promote functional recovery in stroke patients

Cutoff Values of Surrogate Measures of Insulin Resistance for Metabolic Syndrome in Korean Non-diabetic Adults

We investigated the cutoff values of surrogate of insulin resistance for diagnosing metabolic syndrome in Korean adults. The data from 976 non-diabetic individuals (484 men and 492 women) aged 30-79 yr were analyzed. We determined the odds ratios for the prevalence of metabolic syndrome according to the quartiles of fasting insulin, homeostasis model for insulin resistance (HOMA-IR), and quantitative insulin sensitivity check index (QUICKI) as independent variables, while adjusting for age, sex, and body mass index. The cutoff values of fasting insulin, HOMA-IR, and QUICKI were estimated by the areas under the receiver-operating characteristic (ROC) curves. The cutoff points for defining insulin resistance are a fasting insulin level of 12.94 µU/mL, HOMA-IR=3.04 as the 75th percentile value, and QUICKI=0.32 as the 25th percentile value. Compared with the lowest quartile, the adjusted odds ratios for the prevalence of metabolic syndrome in the highest quartiles of fasting insulin, HOMA-IR, and QUICKI were 1.95 (1.26-3.01), 2.27 (1.45-3.56), and 2.27 (1.45-3.56), respectively. The respective cutoff values for fasting serum insulin, HOMA-IR, and QUICKI by ROC analysis were 10.57 µU/mL (sensitivity 58.5%, specificity 66.8%), 2.34 (sensitivity 62.8%, specificity 65.7%), and 0.33 (sensitivity 61.2%, specificity 66.8%). Fasting insulin, HOMA-IR, and QUICKI can be used as surrogate measures of insulin resistance in Korean non-diabetic adults

Proteogenomic method to identify mutated peptides and immunoglobulin rearrangements using NGS data, and it's application to cancer data

Cancer is driven by the acquisition of somatic DNA lesions. Distinguishing the early driver mutations from subsequent passenger mutations is key to molecular sub- typing of cancers, understanding cancer progression, and the discovery of novel biomarkers. The advances of genomics technologies (whole-genome exome, and transcript sequencing, collectively referred to as NGS(Next Gengeration Sequencing)) have fueled recent studies on somatic mutation discovery. However, the vision is challenged by the complexity, redundancy, and errors in genomic data, and the difficulty of investigating the proteome translated portion of aberrant genes using only genomic approaches. Combination of proteomic and genomic technologies are increasingly being employed. This thesis provides a discussion of applying different strategies relating to large database search, and FDR(False Discovery Rate) based error control, and their implication to cancer proteogenomics. Moreover, it extends and develops the idea of a unified genomic variant database that can be searched by any mass spectrometry sample. Furthermore, we introduce a novel database creation method targeted for immunoglobulin peptide search. Finally, by applying our integrative proteogenomics pipeline, we have identified various types of mutated peptides and immunoglobulin gene rearrangements. Overall statistics and important examples of our proteogenomic discoveries will be shown throughout this stud

Gender Classification Using Sentiment Analysis and Deep Learning in a Health Web Forum

Sentiment analysis is the most common text classification tool that analyzes incoming messages and tells whether the underlying sentiment is positive, negative, or neutral. We can use this technique to understand people by gender, especially people who are suffering from a sensitive disease. People use health-related web forums to easily access health information written by and for non-experts and also to get comfort from people who are in a similar situation. The government operates medical web forums to provide medical information, manage patients’ needs and feelings, and boost information-sharing among patients. If we can classify people’s emotional or information needs by gender, age, or location, it is possible to establish a detailed health policy specialized into patient segments. However, people with sensitive illness such as AIDS tend to hide their information. Especially, in the case of sexually transmitted AIDS, we can detect problems and needs according to gender. In this work, we present a gender detection model using sentiment analysis and machine learning including deep learning. Through the experiment, we found that sentiment features generate low accuracy. However, senti-words give better results with SVM. Overall, traditional machine learning algorithms have a high misclassification rate for the female category. The deep learning algorithm overcomes this drawback with over 90% accuracy

Forecasting Hospital Visits Due to Influenza Based on Emergency Department Visits for Fever: A Feasibility Study on Emergency Department-Based Syndromic Surveillance

This study evaluated the use of chief complaint data from emergency departments (EDs) to detect the increment of influenza cases identified from the nationwide medical service usage and developed a forecast model to predict the number of patients with influenza using the daily number of ED visits due to fever. The National Health Insurance Service (NHIS) and the National Emergency Department Information System (NEDIS) databases from 2015 to 2019 were used. The definition of fever included having an initial body temperature ≥ 38.0 °C at an ED department or having a report of fever as a patient’s chief complaint. The moving average number of visits to the ED due to fever for the previous seven days was used. Patients in the NHIS with the International Classification of Diseases-10 codes of J09, J10, or J11 were classified as influenza cases, with a window duration of 100 days, assuming the claims were from the same season. We developed a forecast model according to an autoregressive integrated moving average (ARIMA) method using the data from 2015 to 2017 and validated it using the data from 2018 to 2019. Of the 29,142,229 ED visits from 2015 to 2019, 39.9% reported either a fever as a chief complaint or a ≥38.0 °C initial body temperature at the ED. ARIMA (1,1,1) (0,0,1)7 was the most appropriate model for predicting ED visits due to fever. The mean absolute percentage error (MAPE) value showed the prediction accuracy of the model. The correlation coefficient between the number of ED visits and the number of patients with influenza in the NHIS up to 14 days before the forecast, with the exceptions of the eighth, ninth, and twelfth days, was higher than 0.70 (p-value = 0.001). ED-based syndromic surveillances of fever were feasible for the early detection of hospital visits due to influenza

Phagocytosis converts infiltrated monocytes to microglia-like phenotype in experimental brain ischemia

Abstract Background Monocyte-derived macrophages (MDMs) and microglia elicit neural inflammation and clear debris for subsequent tissue repair and remodeling. The role of infiltrating MDMs in the injured brain, however, has been controversial due to overlapping antigen expression with microglia. In this study, we define the origin and function of MDMs in cerebral ischemia. Methods Using adoptive transfer of GFP+ splenocytes into adult asplenic mice subjected to transient middle cerebral artery occlusion, we compared the role of CD11b + /CD45+/NK1.1 − /Ly6G − MDMs and microglia in the ischemic brain. The phagocytic activities of MDMs and microglia were measured by the uptake of fluorescent beads both in vivo with mice infused with GFP+ splenocytes and ex vivo with cultures of isolated brain immune cells. Results Stroke induced an infiltration of MDMs [GFP+] into the ipsilateral hemisphere at acute (3 days) and sub-acute phases (7 days) of post-stroke. At 7 days, the infiltrating MDMs contained both CD45High and CD45Low subsets. The CD45High MDMs in the injured hemisphere exhibited a significantly higher proliferation capacity (Ki-67 expression levels) as well as higher expression levels of CD11c when compared to CD45Low MDMs. The CD45High and CD45Low MDM subsets in the injured hemisphere were approximately equal populations, indicating that CD45High MDMs infiltrating the ischemic brain changes their phenotype to CD45Low microglia-like phenotype. Studies with fluorescent beads reveal high levels of MDM phagocytic activity in the post-stroke brain, but this phagocytic activity was exclusive to post-ischemic brain tissue and was not detected in circulating monocytes. By contrast, CD45Low microglia-like cells had low levels of phagocytic activity when compared to CD45High cells. Both in vivo and ex vivo studies also show that the phagocytic activity in CD45High MDMs is associated with an increase in the CD45Low/CD45High ratio, indicating that phagocytosis promotes MDM phenotype conversion. Conclusions This study demonstrates that MDMs are the predominant phagocytes in the post-ischemic brain, with the CD45High subset having the highest phagocytic activity levels. Upon phagocytosis, CD45High MDMs in the post-ischemic brain adopt a CD45Low phenotype that is microglia-like. Together, these studies reveal key roles for MDMs and their phagocytic function in tissue repair and remodeling following cerebral ischemia

Lipid Composition of Latex and Rubber Particles in Hevea brasiliensis and Taraxacum kok-saghyz

Natural rubber is usually synthesized in the rubber particles present in the latex of rubber-producing plants such as the Pará rubber tree (Hevea brasiliensis) and rubber dandelion (Taraxacum kok-saghyz). Since the detailed lipid compositions of fresh latex and rubber particles of the plants are poorly known, the present study reports detailed compound lipid composition, focusing on phospholipids and galactolipids in the latex and rubber particles of the plants. In the fresh latex and rubber particles of both plants, phospholipids were much more dominant (85–99%) compared to galactolipids. Among the nine classes of phospholipids, phosphatidylcholines (PCs) were most abundant, at ~80%, in both plants. Among PCs, PC (36:4) and PC (34:2) were most abundant in the rubber tree and rubber dandelion, respectively. Two classes of galactolipids, monogalactosyl diacylglycerol and digalactosyl diacylglycerol, were detected as 12% and 1%, respectively, of total compound lipids in rubber tree, whereas their percentages in the rubber dandelion were negligible (< 1%). Overall, the compound lipid composition differed only slightly between the fresh latex and the rubber particles of both rubber plants. These results provide fundamental data on the lipid composition of rubber particles in two rubber-producing plants, which can serve as a basis for artificial rubber particle production in the future

The combination of sport and sport-specific diet is associated with characteristics of gut microbiota: an observational study

Background Recently, gut microbiota have been studied extensively for health promotion, disease prevention, disease treatment, and exercise performance. It is recommended that athletes avoid dietary fiber and resistant starch to promote gastric emptying and reduce gastrointestinal distress during exercise, but this diet may reduce microbial diversity and compromise the health of the athlete’s gut microbiota. Objective This study compared fecal microbiota characteristics using high-throughput sequencing among healthy sedentary men (as controls), bodybuilders, and distance runners, as well as the relationships between microbiota characteristics, body composition, and nutritional status. Methods Body composition was measured using DXA, and physical activity level was assessed using IPAQ. Dietary intake was analyzed with the computerized nutritional evaluation program. The DNA of fecal samples was extracted and it was sequenced for the analysis of gut microbial diversity through bioinformatics cloud platform. Results We showed that exercise type was associated with athlete diet patterns (bodybuilders: high protein, high fat, low carbohydrate, and low dietary fiber diet; distance runners: low carbohydrate and low dietary fiber diet). However, athlete type did not differ in regard to gut microbiota alpha and beta diversity. Athlete type was significantly associated with the relative abundance of gut microbiota at the genus and species level: Faecalibacterium, Sutterella, Clostridium, Haemophilus, and Eisenbergiella were the highest (p < 0.05) in bodybuilders, while Bifidobacterium and Parasutterella were the lowest (p < 0.05). At the species level, intestinal beneficial bacteria widely used as probiotics (Bifidobacterium adolescentis group, Bifidobacterium longum group, Lactobacillus sakei group) and those producing short chain fatty acids (Blautia wexlerae, Eubacterium hallii) were the lowest in bodybuilders and the highest in controls. In addition, aerobic or resistance exercise training with an unbalanced intake of macronutrients and low intake of dietary fiber led to similar diversity of gut microbiota. Specifically, daily protein intake was negatively correlated with operation taxonomic unit (r = − 0.53, p < 0.05), ACE (r = − 0.51, p < 0.05), and Shannon index (r = − 0.64, p < 0.01) in distance runners.. Conclusion Results suggest that high-protein diets may have a negative impact on gut microbiota diversity for athletes, while athletes in resistance sports that carry out the high protein low carbohydrates diet demonstrate a decrease in short chain fatty acid-producing commensal bacteria