Activation of mGluR5 Attenuates Microglial Activation and Neuronal Apoptosis in Early Brain Injury After Experimental Subarachnoid Hemorrhage in Rats.

Activation of metabotropic glutamate receptor 5 (mGluR5) provided neuroprotection in multiple central nervous system injury, but the roles of mGluR5 in subarachnoid hemorrhage (SAH) remain unclear. In present study, we aimed to evaluate whether activation of mGluR5 attenuates early brain injury (EBI) after experimental SAH in rats. We found that selective mGluR5 orthosteric agonist CHPG or positive allosteric modulator VU0360172 administration significantly improves neurological function and attenuates brain edema at 24 h after SAH. Furthermore, mGluR5 obviously expresses in activated microglia (ED-1 positive) after SAH. CHPG or VU0360172 administration significantly reduces the numbers of activated microglia and the protein and mRNA levels of pro-inflammatory cytokines IL-1ß, IL-6 and TNF-α at 24 h after SAH. Moreover, CHPG or VU0360172 administration obviously reduces the number of TUNEL-positive cells and active caspase-3/NeuN-positive neurons in cortex at 24 h after SAH. CHPG or VU0360172 administration significantly up-regulates the expression of Bcl-2, and down-regulates the expression of Bax and active caspase-3, which in turn increases the ratio of Bcl-2/Bax. Our results indicate that activation of mGluR5 attenuates microglial activation and neuronal apoptosis, and improves neurological function in EBI after SAH.

Disentangled representation for sequential treatment effect estimation.

BACKGROUND AND OBJECTIVE: Treatment effect estimation, as a fundamental problem in causal inference, focuses on estimating the outcome difference between different treatments. However, in clinical observational data, some patient covariates (such as gender, age) not only affect the outcomes but also affect the treatment assignment. Such covariates, named as confounders, produce distribution discrepancies between different treatment groups, thereby introducing the selection bias for the estimation of treatment effects. The situation is even more complicated in longitudinal data, because the confounders are time-varying that are subject to patient history and meanwhile affect the future outcomes and treatment assignments. Existing methods mainly work on cross-sectional data obtained at a specific time point, but cannot process the time-varying confounders hidden in the longitudinal data. METHODS: In this study, we address this problem for the first time by disentangled representation learning, which considers the observational data as consisting of three components, including outcome-specific factors, treatment-specific factors, and time-varying confounders. Based on this, the proposed approach adopts a recurrent neural network-based framework to process sequential information and learn the disentangled representations of the components from longitudinal observational sequences, captures the posterior distributions of latent factors by multi-task learning strategy. Moreover, mutual information-based regularization is adopted to eliminate the time-varying confounders. In this way, the association between patient history and treatment assignment is removed and the estimation can be effectively conducted. RESULTS: We evaluate our model in a realistic set-up using a model of tumor growth. The proposed model achieves the best performance over benchmark models for both one-step ahead prediction (0.70% vs 0.74% for the-state-of-the-art model, when γ = 3. Measured by normalized root mean square error, the lower the better) and five-step ahead prediction (1.47% vs 1.83%) in most cases. By increasing the effect of confounders, our proposed model always shows superiority against the state-of-the-art model. In addition, we adopted T-SNE to visualize the disentangled representations and present the effectiveness of disentanglement explicitly and intuitively. CONCLUSIONS: The experimental results indicate the powerful capacity of our model in learning disentangled representations from longitudinal observational data and dealing with the time-varying confounders, and demonstrate the surpassing performance achieved by our proposed model on dynamic treatment effect estimation.

Leveraging online shopping behaviors as a proxy for personal lifestyle choices: New insights into chronic disease prevention literacy.

Objective: Ubiquitous internet access is reshaping the way we live, but it is accompanied by unprecedented challenges in preventing chronic diseases that are usually planted by long exposure to unhealthy lifestyles. This paper proposes leveraging online shopping behaviors as a proxy for personal lifestyle choices to improve chronic disease prevention literacy, targeted for times when e-commerce user experience has been assimilated into most people's everyday lives. Methods: Longitudinal query logs and purchase records from 15 million online shoppers were accessed, constructing a broad spectrum of lifestyle features covering various product categories and buyer personas. Using the lifestyle-related information preceding online shoppers' first purchases of specific prescription drugs, we could determine associations between their past lifestyle choices and whether they suffered from a particular chronic disease. Results: Novel lifestyle risk factors were discovered in two exemplars-depression and type 2 diabetes, most of which showed reasonable consistency with existing healthcare knowledge. Further, such empirical findings could be adopted to locate online shoppers at higher risk of these chronic diseases with decent accuracy [i.e. (area under the receiver operating characteristic curve) AUC=0.68 for depression and AUC=0.70 for type 2 diabetes], closely matching the performance of screening surveys benchmarked against medical diagnosis. Conclusions: Mining online shopping behaviors can point medical experts to a series of lifestyle issues associated with chronic diseases that are less explored to date. Hopefully, unobtrusive chronic disease surveillance via e-commerce sites can grant consenting individuals a privilege to be connected more readily with the medical profession and sophistication.

Greater Curvature Plication with Duodenal-Jejunal Bypass: a Novel Metabolic Surgery for Type 2 Diabetes Mellitus.

OBJECTIVE: The study investigated the use of great curvature plication with duodenal-jejunal bypass (GCP-DJB) in a type 2 diabetic with obesity rat model. METHODS: Twenty-two Sprague-Dawley rats were given a high fat and sugar diet with subsequent intraperitoneal injection of a small dosage of streptozotocin (30 mg/kg) and randomly assigned to either GCP-DJB (n = 12) or Sham surgery (n = 10). Body weight, peripheral blood glucose, and fasting serum insulin were assayed, and insulin resistance index (IRI) was calculated, before and at 1, 2, 4, and 8 weeks after surgery. RESULTS: No differences were found in the preoperative characteristics of the two groups (P > 0.05). At week 1, the body weights decreased significantly, but there was no significant difference between the two groups (P > 0.05).The fasting blood glucose was significantly lower in the GCP-DJB than in the Sham group (P < 0.05), serum insulin levels were higher (P < 0.05), and IRI began to decline (P < 0.05). From 2 to 8 weeks, the body weight of Sham group gradually recovered and continued to rise, while the GCP-DJB group remained at a relatively lower state. Compared to the Sham group, the body weight, fasting blood glucose as well as IRI of GCP-DJB rats had significantly decreased (P < 0.05). But, the fasting insulin concentrations had significantly increased (P < 0.05). CONCLUSION: This novel GCP-DJB procedure established a stable animal model for the study of metabolic surgery to treat type 2 diabetes mellitus (T2DM).

Effects of Diet on Bile Acid Metabolism and Insulin Resistance in Type 2 Diabetic Rats after Roux-en-Y Gastric Bypass.

BACKGROUND: Roux-en-Y gastric bypass (RYGB) is effective for the treatment of type 2 diabetes mellitus; however, the mechanism remains unclear. METHODS: The effects of RYGB on postprandial responses to three different diets (low carbohydrate (CH)-rich diet, high CH-rich diet, and fat-rich diet) of different nutritional composition in a Goto-Kakizaki (GK) diabetic rat model were assessed by measuring glucose tolerance, insulin resistance, incretin responses, and bile acid (BA) metabolism. RESULTS: GK-RYGB group rats lost weight and preferred low CH-rich diet, but there were no significant differences in BW among the different diets. Glucose tolerance and insulin resistance were improved in rats who underwent RYGB, together with higher levels of circulating BAs, plasma GLP-1, and PYY levels. GK-RYGB rats fed high CH-rich or fat-rich diet showed increased glucose level and insulin resistance, together with high plasma BA, GIP, and PYY levels compared to those fed a low CH-rich diet. CONCLUSION: RYGB improves glucose tolerance and insulin resistance which may be related to BA metabolism and hormone levels, and the nutrient composition of the diet affects the treatment effect of RYGB on T2DM.

A novel surgical strategy for secondary hyperparathyroidism: Purge parathyroidectomy.

OBJECTIVE: This study was intended to demonstrate the feasibility and efficacy of purge parathyroidectomy (PPTX) for patients with secondary hyperparathyroidism (SHPT). METHODS: The "seed, environment, and soil" medical hypothesis was first raised, following review of the literatures, to demonstrate the possible causes of persistence or recurrence of SHPT after parathyroidectomy. Subsequently, the novel surgical strategy of PPTX was proposed, which involves comprehensive resection of the fibro-fatty tissues, including visible or invisible parathyroid, within the region surrounded by the thyroid cartilage, bilateral carotid artery sheath, and the brachiocephalic artery. The perioperative information and clinical outcomes of patients who underwent PPTX from June 2016 to December 2016 were analyzed. RESULTS: In total, PPTX was performed safely in nine patients with SHPT from June 2016 to December 2016. The operative time for PPTX ranged from 95 to 135 min, and blood loss ranged from 20 to 40 mL. No patients with perioperative death, bleeding, convulsions, or recurrent laryngeal nerve injury were reported. The preoperative concentration of PTH ranged from 1062 to 2879 pg/mL, and from 12.35 to 72.69 pg/mL on the first day after surgery. In total, 37 parathyroid glands were resected. The postoperative pathologic examination showed that supernumerary or ectopic parathyroid tissues were found within the "non-parathyroid" tissues in three patients. No cases encountered persistence or recurrence of SHPT, or severe hypocalcemia during the follow-up period. CONCLUSION: PPTX involves comprehensive resection of supernumerary and ectopic parathyroid tissues, which may provide a more permanent means of reducing PTH levels.

To assess the effects of parathyroidectomy (TPTX versus TPTX+AT) for Secondary Hyperparathyroidism in chronic renal failure: A Systematic Review and Meta-Analysis.

BACKGROUND: Secondary Hyperparathyroidism (SHPT) requiring parathyroidectomy (PTX) occurs more commonly in patients with progressive chronic kidney disease and in those on long-term lithium therapy. Successful PTX often results in a dramatic drop of parathyroid hormone level, relieves the patient from clinical symptoms, and reduces mortality. However, there is an ongoing debate on the optimal surgical treatment of SHPT. Currently, no clinical guidelines or trials have definitely answered the question of whether Total Parathyroidectomy (TPTX) is superior or equal to Total Parathyroidectomy with Autotransplantation (TPTX + AT). OBJECTIVE: The aims of the study were to compare the efficacy of two different surgical procedures and to develop evidence-based practice guidelines for the treatment of SHPT. METHODS: Citations were identified in the Medline, Cochrane, EMBASE, and Chinese Biomedical Literature databases through November 2016. The Newcastle-Ottawa Scale (NOS) score was used to assess the methodological quality of the studies included. All data were analyzed using Review Manager 5.3. RESULTS: A total of nine cohort studies and one Randomized Controlled Trials (RCT), comprising 1283 patients, were identified. The NOS score of all the studies included was 5 or above. Compared with TPTX + AT, patients in the TPTX group had lower rates of "recurrence" (OR = 0.20; 95%CI, 0.11-0.38; P < 0.01), "recurrence or persistence" (OR = 0.18; 95%CI, 0.10-0.33; P < 0.01), "reoperation due to recurrence or persistence" (OR = 0.17; 95%CI, 0.06-0.54; P = 0.002), and shorter "operative time" (WMD = -17.30; 95%CI, -30.53 to -4.06; P < 0.05), except for a higher risk of "hypoparathyroidism" (OR = 2.97; 95%CI, 1.09-8.08; P = 0.01). However, none of the patients had developed permanent hypocalcemia or adynamic bone disease. No significant difference was found for "symptomatic improvement", "complications", "drug requirements", and "hospital stay" (P > 0.05). CONCLUSION: The findings indicate that TPTX is superior to TPTX + AT, while referring to the rate of recurrent SHPT. However, this conclusion needs to be tested in large-scale confirmatory trials. TPTX seems to be a feasible alternative therapeutic option for the surgical treatment of refractory SHPT.

General, Nested, and Constrained Wiberg Minimization.

Wiberg matrix factorization breaks a matrix Y into low-rank factors U and V by solving for V in closed form given U, linearizing V(U) about U, and iteratively minimizing ||Y - UV(U)||2 with respect to U only. This approach factors the matrix while effectively removing V from the minimization. Recently Eriksson and van den Hengel extended this approach to L1 , minimizing ||Y - UV(U)||1 . We generalize their approach beyond factorization to minimize ||Y - f(U, V)||1 for more general functions f(U, V) that are nonlinear in each of two sets of variables. We demonstrate the idea with a practical Wiberg algorithm for L1 bundle adjustment. One Wiberg minimization can be nested inside another, effectively removing two of three sets of variables from a minimization. We demonstrate this idea with a nested Wiberg algorithm for L1 projective bundle adjustment, solving for camera matrices, points, and projective depths. Wiberg minimization also generalizes to handle nonlinear constraints, and we demonstrate this idea with Constrained Wiberg Minimization for Multiple Instance Learning (CWM-MIL), which removes one set of variables from the constrained optimization. Our experiments emphasize isolating the effect of Wiberg by comparing against the algorithm it modifies, successive linear programming.

Maximum margin multiple instance clustering with applications to image and text clustering.

In multiple instance learning problems, patterns are often given as bags and each bag consists of some instances. Most of existing research in the area focuses on multiple instance classification and multiple instance regression, while very limited work has been conducted for multiple instance clustering (MIC). This paper formulates a novel framework, maximum margin multiple instance clustering (M(3)IC), for MIC. However, it is impractical to directly solve the optimization problem of M(3)IC. Therefore, M(3)IC is relaxed in this paper to enable an efficient optimization solution with a combination of the constrained concave-convex procedure and the cutting plane method. Furthermore, this paper presents some important properties of the proposed method and discusses the relationship between the proposed method and some other related ones. An extensive set of empirical results are shown to demonstrate the advantages of the proposed method against existing research for both effectiveness and efficiency.

A Bayesian framework for knowledge driven regression model in micro-array data analysis.

This paper addresses the sparse data problem in the linear regression model, namely the number of variables is significantly larger than the number of the data points for regression. We assume that in addition to the measured data points, the prior knowledge about the input variables may be provided in the form of pair wise similarity. We presented a full Bayesian framework to effectively exploit the similarity information of the input variables for linear regression. Empirical studies with gene expression data show that the regression errors can be reduced significantly by incorporating the similarity information derived from gene ontology.

A knowledge driven regression model for gene expression and microarray analysis.

The linear regression model has been widely used in the analysis of gene expression and microarray data to identify a subset of genes that are important to a given metabolic function. One of the key challenges in applying the linear regression model to gene expression data analysis arises from the sparse data problem, in which the number of genes is significantly larger than the number of conditions. To resolve this problem, we present a knowledge driven regression model that incorporates the knowledge of genes from the Gene Ontology (GO) database into the linear regression model. It is based on the assumption that two genes are likely to be assigned similar weights when they share similar sets of GO codes. Empirical studies show that the proposed knowledge driven regression model is effective in reducing the regression errors, and furthermore effective in identifying genes that are relevant to a given metabolite.