Giant Cell Tumor of the Mandible

A 53-year-old woman presented with left mandibular area pain, trismus, and facial numbness that had persisted for 4 years. Physical examination revealed a 3×5 cm, hard, non-tender, and round mass on the left mandibular area. Computed tomography and magnetic resonance imaging revealed an expansile tumor involving the left mandibular ramus and temporomandibular joint area with bone destruction, extending to the base of middle cranial fossa and left zygomatic bone. The mass at the segment of left mandible and zygomatic bone, and base of middle cranial fossa was removed. Pathological examination of the mass revealed a giant cell tumor. The defect was reconstructed with iliac bone for the mandible and temporal bone and fascia for the cranial bone and dura. The case is described along with a review of the literature

Biofilter aquaponic system for nutrients removal from fresh market wastewater

Aquaponics is a significant wastewater treatment system which refers to the combination of conventional aquaculture (raising aquatic organism) with hydroponics (cultivating plants in water) in a symbiotic environment. This system has a high ability in removing nutrients compared to conventional methods because it is a natural and environmentally friendly system (aquaponics). The current chapter aimed to review the possible application of aquaponics system to treat fresh market wastewater with the intention to highlight the mechanism of phytoremediation occurs in aquaponic system. The literature revealed that aquaponic system was able to remove nutrients in terms of nitrogen and phosphorus

Association between hair mineral and age, BMI and nutrient intakes among Korean female adults

This study was performed to investigate the association between hair mineral levels and nutrient intakes, age, and BMI in female adults who visited a woman's clinic located in Seoul. Dietary intakes were assessed by food frequency questionnaire and mineral levels were measured in collected hairs, and the relationship between these was examined. The average daily nutrient intakes of subjects were compared to those of the KDRIs, and the energy intake status was fair. The average intake of calcium in women of 50 years and over was 91.35% of KDRIs and the potassium intake was greatly below the recommended levels in all age groups. In the average hair mineral contents in subjects, calcium and copper exceeded far more than the reference range while selenium was very low with 85.19% of subjects being lower than the reference value. In addition, the concentrations of sodium, potassium, iron, and manganese in the hair were below the reference ranges in over 15% of subjects. The concentrations of sodium, chromium, sulfur, and cadmium in the hair showed positive correlations (P < 0.05) with age, but the hair zinc level showed a negative correlation (P < 0.05) with age. The concentrations of sodium, potassium, chromium, and cadmium in the hair showed positive correlations (P < 0.05) with BMI. Some mineral levels in subjects of this study showed significant correlations with nutrient intakes, but it seems that the hair mineral content is not directly influenced by each mineral intake. As described above, some hair mineral levels in female adults deviated from the normal range, and it is considered that nutritional intervention to control the imbalance of mineral nutrition is required. Also, as some correlations were shown between hair mineral levels and age, BMI, and nutrient intakes, the possibility of utilizing hair mineral analysis for specific purposes in the future is suggested

Analysis of cardiac signals using spatial filling index and time-frequency domain

BACKGROUND: Analysis of heart rate variation (HRV) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system (ANS). HRV analysis is based on the concept that fast fluctuations may specifically reflect changes of sympathetic and vagal activity. It shows that the structure generating the signal is not simply linear, but also involves nonlinear contributions. These signals are essentially non-stationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random in the time scale. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming. METHODS: This paper presents the spatial filling index and time-frequency analysis of heart rate variability signal for disease identification. Renyi's entropy is evaluated for the signal in the Wigner-Ville and Continuous Wavelet Transformation (CWT) domain. RESULTS: This Renyi's entropy gives lower 'p' value for scalogram than Wigner-Ville distribution and also, the contours of scalogram visually show the features of the diseases. And in the time-frequency analysis, the Renyi's entropy gives better result for scalogram than the Wigner-Ville distribution. CONCLUSION: Spatial filling index and Renyi's entropy has distinct regions for various diseases with an accuracy of more than 95%

Ocular Application of the Kinin B1 Receptor Antagonist LF22-0542 Inhibits Retinal Inflammation and Oxidative Stress in Streptozotocin-Diabetic Rats

Purpose: Kinin B1 receptor (B1R) is upregulated in retina of Streptozotocin (STZ)-diabetic rats and contributes to vasodilation of retinal microvessels and breakdown of the blood-retinal barrier. Systemic treatment with B 1R antagonists reversed the increased retinal plasma extravasation in STZ rats. The present study aims at determining whether ocular application of a water soluble B1R antagonist could reverse diabetes-induced retinal inflammation and oxidative stress. Methods: Wistar rats were made diabetic with STZ (65 mg/kg, i.p.) and 7 days later, they received one eye drop application of LF22-0542 (1 % in saline) twice a day for a 7 day-period. The impact was determined on retinal vascular permeability (Evans blue exudation), leukostasis (leukocyte infiltration using Fluorescein-isothiocyanate (FITC)-coupled Concanavalin A lectin), retinal mRNA levels (by qRT-PCR) of inflammatory (B1R, iNOS, COX-2, ICAM-1, VEGF-A, VEGF receptor type 2, IL-1b and HIF-1a) and anti-inflammatory (B2R, eNOS) markers and retinal level of superoxide anion (dihydroethidium staining). Results: Retinal plasma extravasation, leukostasis and mRNA levels of B 1R, iNOS, COX-2, VEGF receptor type 2, IL-1b and HIF-1a were significantly increased in diabetic retinae compared to control rats. All these abnormalities were reversed to control values in diabetic rats treated with LF22-0542. B1R antagonist also significantly inhibited the increased production of superoxide anion in diabetic retinae. Conclusion: B1R displays a pathological role in the early stage of diabetes by increasing oxidative stress and proinflammator

Reversal of type 2 diabetes: normalisation of beta cell function in association with decreased pancreas and liver triacylglycerol

The presence of Fc-receptor-blocking factors in the sera of normal and insulin-dependent diabetic pregnant women was investigated by means of an antibody-dependent cell-mediated cytotoxicity assay. Sera from normal pregnant women induced a significant depression of antibody dependent cell-mediated cytotoxicity when compared with sera from normal and diabetic non-pregnant women (p less than 0.0001; p less than 0.002, respectively). The effect of sera from diabetic pregnant women, however, was not different from that observed with sera from normal and diabetic non-pregnant women. Thus, we confirm the presence of Fc-receptor-blocking factors in the sera of normal pregnant women. The higher cytotoxicity levels measured in the presence of sera from pregnant women with insulin-dependent diabetes suggests that the titres of such factors are reduced in this conditio

Unplanned readmission rates, length of hospital stay, mortality, and medical costs of ten common medical conditions: a retrospective analysis of Hong Kong hospital data

<p>Abstract</p> <p>Background</p> <p>Studies on readmissions attributed to particular medical conditions, especially heart failure, have generally not addressed the factors associated with readmissions and the implications for health outcomes and costs. This study aimed to investigate the factors associated with 30-day unplanned readmission for 10 common conditions and to determine the cost implications.</p> <p>Methods</p> <p>This population-based retrospective cohort study included patients admitted to all public hospitals in Hong Kong in 2007. The sample consisted of 337,694 hospitalizations in Internal Medicine. The disease-specific risk-adjusted odd ratio (OR), length of stay (LOS), mortality and attributable medical costs for the year were examined for unplanned readmissions for 10 medical conditions, namely malignant neoplasms, heart diseases, cerebrovascular diseases, pneumonia, injury and poisoning, nephritis and nephrosis, diabetes mellitus, chronic liver disease and cirrhosis, septicaemia, and aortic aneurysm.</p> <p>Results</p> <p>The overall unplanned readmission rate was 16.7%. Chronic liver disease and cirrhosis had the highest OR (1.62, 95% confidence interval (CI) 1.39-1.87). Patients with cerebrovascular disease had the longest LOS, with mean acute and rehabilitation stays of 6.9 and 3.0 days, respectively. Malignant neoplasms had the highest mortality rate (30.8%) followed by aortic aneurysm and pneumonia. The attributed medical cost of readmission was highest for heart disease (US$3 199 418, 95$2 579 443-803 393).</p> <p>Conclusions</p> <p>Our findings showed variations in readmission rates and mortality for different medical conditions which may suggest differences in the quality of care provided for various medical conditions. In-hospital care, comprehensive discharge planning, and post-discharge community support for patients need to be reviewed to improve the quality of care and patient health outcomes.</p

Candidate gene prioritization by network analysis of differential expression using machine learning approaches

<p>Abstract</p> <p>Background</p> <p>Discovering novel disease genes is still challenging for diseases for which no prior knowledge - such as known disease genes or disease-related pathways - is available. Performing genetic studies frequently results in large lists of candidate genes of which only few can be followed up for further investigation. We have recently developed a computational method for constitutional genetic disorders that identifies the most promising candidate genes by replacing prior knowledge by experimental data of differential gene expression between affected and healthy individuals.</p> <p>To improve the performance of our prioritization strategy, we have extended our previous work by applying different machine learning approaches that identify promising candidate genes by determining whether a gene is surrounded by highly differentially expressed genes in a functional association or protein-protein interaction network.</p> <p>Results</p> <p>We have proposed three strategies scoring disease candidate genes relying on network-based machine learning approaches, such as kernel ridge regression, heat kernel, and Arnoldi kernel approximation. For comparison purposes, a local measure based on the expression of the direct neighbors is also computed. We have benchmarked these strategies on 40 publicly available knockout experiments in mice, and performance was assessed against results obtained using a standard procedure in genetics that ranks candidate genes based solely on their differential expression levels (<it>Simple Expression Ranking</it>). Our results showed that our four strategies could outperform this standard procedure and that the best results were obtained using the <it>Heat Kernel Diffusion Ranking </it>leading to an average ranking position of 8 out of 100 genes, an AUC value of 92.3% and an error reduction of 52.8% relative to the standard procedure approach which ranked the knockout gene on average at position 17 with an AUC value of 83.7%.</p> <p>Conclusion</p> <p>In this study we could identify promising candidate genes using network based machine learning approaches even if no knowledge is available about the disease or phenotype.</p

The Induction of MicroRNA Targeting IRS-1 Is Involved in the Development of Insulin Resistance under Conditions of Mitochondrial Dysfunction in Hepatocytes

BACKGROUND: Mitochondrial dysfunction induces insulin resistance in myocytes via a reduction of insulin receptor substrate-1 (IRS-1) expression. However, the effect of mitochondrial dysfunction on insulin sensitivity is not understood well in hepatocytes. Although research has implicated the translational repression of target genes by endogenous non-coding microRNAs (miRNA) in the pathogenesis of various diseases, the identity and role of the miRNAs that are involved in the development of insulin resistance also remain largely unknown. METHODOLOGY: To determine whether mitochondrial dysfunction induced by genetic or metabolic inhibition causes insulin resistance in hepatocytes, we analyzed the expression and insulin-stimulated phosphorylation of insulin signaling intermediates in SK-Hep1 hepatocytes. We used qRT-PCR to measure cellular levels of selected miRNAs that are thought to target IRS-1 3' untranslated regions (3'UTR). Using overexpression of miR-126, we determined whether IRS-1-targeting miRNA causes insulin resistance in hepatocytes. PRINCIPAL FINDINGS: Mitochondrial dysfunction resulting from genetic (mitochondrial DNA depletion) or metabolic inhibition (Rotenone or Antimycin A) induced insulin resistance in hepatocytes via a reduction in the expression of IRS-1 protein. In addition, we observed a significant up-regulation of several miRNAs presumed to target IRS-1 3'UTR in hepatocytes with mitochondrial dysfunction. Using reporter gene assay we confirmed that miR-126 directly targeted to IRS-1 3'UTR. Furthermore, the overexpression of miR-126 in hepatocytes caused a substantial reduction in IRS-1 protein expression, and a consequent impairment in insulin signaling. CONCLUSIONS/SIGNIFICANCE: We demonstrated that miR-126 was actively involved in the development of insulin resistance induced by mitochondrial dysfunction. These data provide novel insights into the molecular basis of insulin resistance, and implicate miRNA in the development of metabolic disease