The Role of Polymerase Chain Reaction (PCR) in Diagnosis of Spine Tuberculosis after Pre-operative Anti-tuberculosis Treatment

Objective: The aim of this study was to evaluate the role of polymerase chain reaction (PCR) in the diagnosis of spinal tuberculosis after 2 weeks of preoperative anti-tuberculosis treatment and to compare PCR to the Löwenstein - Jensen Culture (LJC) and histopathological examination (HPE) methods. Methods: Twenty-five patients were included in this study. Sixteen patients were diagnosed and treated for spinal tuberculosis based on clinical and radiological evidence. Nine patients were controls. The LJC method and HPE of the specimen were performed according to hospital protocol. PCR was performed using primer encoding insertion of sequences IS6110 for mycobacterium tuberculosis complex. Clinical findings and radiological features were the gold standard for comparison. Results: PCR results were 15 positive and one negative. The sensitivity and specificity of PCR was 94% and 100% respectively (with 95% confidence interval [CI] 67% to 99% and 63% to 100%, respectively). HPE results showed 13 were positive and 3 negative in the spinal tuberculosis group; for the control group, all were negative. Sensitivity and specificity value of HPE was 82 % and 100% respectively (with 95% confidence interval [CI] 54% to 95% and 63% to 100%, respectively). Use of LJC showed only one was positive and 15 were negative in the spinal tuberculosis group whole all nine in the control group were negative. Sensitivity and specificity value of LJC was 6% and 100% respectively (with 95% confidence interval [CI] 0.3% to 32% and 63% to 100%, respectively). Conclusion: Our findings showed that the PCR for Mycobacterium tuberculosis is reliable as a method for diagnosis of spinal tuberculosis, even after of 2 weeks of anti-TB treatment, with an overall sensitivity of 94% and specificity of 100%

The influence of perfusion solution on renal graft viability assessment

BACKGROUND: Kidneys from donors after cardiac or circulatory death are exposed to extended periods of both warm ischemia and intra-arterial cooling before organ recovery. Marshall’s hypertonic citrate (HOC) and Bretschneider’s histidine-tryptophan-ketoglutarate (HTK) preservation solutions are cheap, low viscosity preservation solutions used clinically for organ flushing. The aim of the present study was to evaluate the effects of these two solutions both on parameters used in clinical practice to assess organ viability prior to transplantation and histological evidence of ischemic injury after reperfusion. METHODS: Rodent kidneys were exposed to post-mortem warm ischemia, extended intra-arterial cooling (IAC) (up to 2 h) with preservation solution and reperfusion with either Krebs-Hensleit or whole blood in a transplant model. Control kidneys were either reperfused directly after retrieval or stored in 0.9% saline. Biochemical, immunological and histological parameters were assessed using glutathione-S-transferase (GST) enzymatic assays, polymerase chain reaction and mitochondrial electron microscopy respectively. Vascular function was assessed by supplementing the Krebs-Hensleit perfusion solution with phenylephrine to stimulate smooth muscle contraction followed by acetylcholine to trigger endothelial dependent relaxation. RESULTS: When compared with kidneys reperfused directly post mortem, 2 h of IAC significantly reduced smooth muscle contractile function, endothelial function and upregulated vascular cellular adhesion molecule type 1 (VCAM-1) independent of the preservation solution. However, GST release, vascular resistance, weight gain and histological mitochondrial injury were dependent on the preservation solution used. CONCLUSIONS: We conclude that initial machine perfusion viability tests, including ischemic vascular resistance and GST, are dependent on the perfusion solution used during in situ cooling. HTK-perfused kidneys will be heavier, have higher GST readings and yet reduced mitochondrial ischemic injury when compared with HOC-perfused kidneys. Clinicians should be aware of this when deciding which kidneys to transplant or discard

A Robust Deep Model for Classification of Peptic Ulcer and Other Digestive Tract Disorders Using Endoscopic Images

Accurate patient disease classification and detection through deep-learning (DL) models are increasingly contributing to the area of biomedical imaging. The most frequent gastrointestinal (GI) tract ailments are peptic ulcers and stomach cancer. Conventional endoscopy is a painful and hectic procedure for the patient while Wireless Capsule Endoscopy (WCE) is a useful technology for diagnosing GI problems and doing painless gut imaging. However, there is still a challenge to investigate thousands of images captured during the WCE procedure accurately and efficiently because existing deep models are not scored with significant accuracy on WCE image analysis. So, to prevent emergency conditions among patients, we need an efficient and accurate DL model for real-time analysis. In this study, we propose a reliable and efficient approach for classifying GI tract abnormalities using WCE images by applying a deep Convolutional Neural Network (CNN). For this purpose, we propose a custom CNN architecture named GI Disease-Detection Network (GIDD-Net) that is designed from scratch with relatively few parameters to detect GI tract disorders more accurately and efficiently at a low computational cost. Moreover, our model successfully distinguishes GI disorders by visualizing class activation patterns in the stomach bowls as a heat map. The Kvasir-Capsule image dataset has a significant class imbalance problem, we exploited a synthetic oversampling technique BORDERLINE SMOTE (BL-SMOTE) to evenly distribute the image among the classes to prevent the problem of class imbalance. The proposed model is evaluated against various metrics and achieved the following values for evaluation metrics: 98.9%, 99.8%, 98.9%, 98.9%, 98.8%, and 0.0474 for accuracy, AUC, F1-score, precision, recall, and loss, respectively. From the simulation results, it is noted that the proposed model outperforms other state-of-the-art models in all the evaluation metrics

Enhancing river health monitoring: Developing a reliable predictive model and mitigation plan

The escalating environmental harm inflicted upon rivers is an unavoidable outcome resulting from climatefluctuations and anthropogenic activities, leading to a catastrophic impact on water quality and thousands ofindividuals succumb to waterborne diseases. Consequently, the water quality monitoring stations have beenestablished worldwide. Regrettably, the real-time evaluation of Water Quality Index (WQI) is hindered by theintricate nature of off-site water quality parameters. Thus, there is a pressing need to create a precise and robustwater quality prediction model. The dynamic and non-linear characteristics of water quality parameters posesignificant challenges for conventional machine learning algorithms like multi-linear regression, as they struggleto capture these complexities. In this particular investigation, machine learning model called FeedforwardArtificial Neural Networks (FANNs) was employed to develop WQI prediction model of Batu Pahat River,Malaysia exclusively utilizing on-site parameters. The proposed method involves a consideration of whether toinclude or exclude parameters such as BOD and COD, which are not measured in real time and can be costly tomonitor as model inputs. Validation accuracy values of 99.53%, 97.99%, and 91.03% were achieved in threedifferent scenarios: the first scenario utilized the full input, the second scenario excluded BOD, and the thirdscenario excluded both BOD and COD. It was suggested that the model has better predictive power between inputvariables and output variables. Factor contributed to river pollution has been identified and mitigation plan forBatu Pahat river pollution has been proposed. This could provide an effective alternative to compute thepollution, better manage water resources and mitigate negative impacts of climate change of river ecosystems

The effect of transurethral resection of the prostate on erectile and ejaculatory functions in patients with benign prostatic hyperplasia

Introduction: The aim of this study was to investigate the effect of TURP on erectile function (EF) and ejaculatory function (EJF). Methods: A total of 91 patients who underwent TURP were retrospectively assessed. Patients were divided into two groups based on International Index of Erectile Function (IIEF-5): group A included 41 patients with normal EF, and group B included 50 patients with erectile dysfunction (ED). All patients were evaluated for EF and EJF at baseline, 1, 3, and 6 months after TURP by using IIEF-5, Ejaculatory Domain-Male Sexual-Health Inventory (Ej-MSHQ). Results: In group A, there were no significant statistical differences in mean IIEF-5 at baseline and after TURP 22.88 ± 0.81 versus 22.63 ± 2.63 (p = 0.065). However, in group B, there was significant improvement in IIEF-5 after TURP all over the follow-up time points in comparison to the baseline (p = <0.001). The loss of EJF was significant among patients in group A. There was significant improvement of IPSS and Qmax in group A after surgery compared to group B. Conclusion: The results confirmed that TURP has no significant negative influence on EF, and patients with preexisting ED were improved after TURP. On the contrary, the loss of EJF was significant

BindN+ for accurate prediction of DNA and RNA-binding residues from protein sequence features

Abstract Background Understanding how biomolecules interact is a major task of systems biology. To model protein-nucleic acid interactions, it is important to identify the DNA or RNA-binding residues in proteins. Protein sequence features, including the biochemical property of amino acids and evolutionary information in terms of position-specific scoring matrix (PSSM), have been used for DNA or RNA-binding site prediction. However, PSSM is rather designed for PSI-BLAST searches, and it may not contain all the evolutionary information for modelling DNA or RNA-binding sites in protein sequences. Results In the present study, several new descriptors of evolutionary information have been developed and evaluated for sequence-based prediction of DNA and RNA-binding residues using support vector machines (SVMs). The new descriptors were shown to improve classifier performance. Interestingly, the best classifiers were obtained by combining the new descriptors and PSSM, suggesting that they captured different aspects of evolutionary information for DNA and RNA-binding site prediction. The SVM classifiers achieved 77.3% sensitivity and 79.3% specificity for prediction of DNA-binding residues, and 71.6% sensitivity and 78.7% specificity for RNA-binding site prediction. Conclusions Predictions at this level of accuracy may provide useful information for modelling protein-nucleic acid interactions in systems biology studies. We have thus developed a web-based tool called BindN+ (http://bioinfo.ggc.org/bindn+/) to make the SVM classifiers accessible to the research community

Magnetic resonance imaging (MRI) brain findings in severe pre–eclampsia/eclampsia

MRI is mainly used in obstetrics in the evaluation of maternal bony pelvis, the cervix for cervical incompetence, localisation of placental site, diagnosis of gestational trophoblastic disease and its severity, and in diagnosing fetal malformations such as renal agenesis. This study aimed to correlate the MRI brain findings in patients with symptomatic and asymptomatic severe pre – eclampsia / eclampsia, and to determine the value of MRI as a predictive diagnostic tool in the management of such cases. This is a prospective descriptive study of 30 pregnant mothers with clinical signs and symptoms of pre-eclampsia / severe pre-eclampsia / eclampsia admitted to the pre-eclampsia room (High Dependency Unit) of the labour room of the Hospital Tengku Ampuan Afzan Kuantan Pahang, Malaysia from 1st January 2004 to 30th June 2004. General findings indicate that there were no conclusive results with regard to the correlation between the MRI brain changes seen in both groups of patients

Insight into potential mechanisms of hypobaric hypoxia–induced learning and memory deficit – Lessons from rat studies

Impairment of memory is one of the most frequently reported symptoms during sudden hypoxia exposure in human. Cortical atrophy has been linked to the impaired memory function and is suggested to occur with chronic high-altitude exposure. However, the precise molecular mechanism(s) of hypoxia-induced memory impairment remains an enigma. In this work, we review hypoxia-induced learning and memory deficit in human and rat studies. Based on data from rat studies using different protocols of continuous hypoxia, we try to elicit potential mechanisms of hypobaric hypoxia–induced memory deficit

Hypoxia induces dilated cardiomyopathy in the chick embryo: mechanism, intervention, and long-term consequences

Background: Intrauterine growth restriction is associated with an increased future risk for developing cardiovascular diseases. Hypoxia in utero is a common clinical cause of fetal growth restriction. We have previously shown that chronic hypoxia alters cardiovascular development in chick embryos. The aim of this study was to further characterize cardiac disease in hypoxic chick embryos. Methods: Chick embryos were exposed to hypoxia and cardiac structure was examined by histological methods one day prior to hatching (E20) and at adulthood. Cardiac function was assessed in vivo by echocardiography and ex vivo by contractility measurements in isolated heart muscle bundles and isolated cardiomyocytes. Chick embryos were exposed to vascular endothelial growth factor (VEGF) and its scavenger soluble VEGF receptor-1 (sFlt-1) to investigate the potential role of this hypoxia-regulated cytokine. Principal Findings: Growth restricted hypoxic chick embryos showed cardiomyopathy as evidenced by left ventricular (LV) dilatation, reduced ventricular wall mass and increased apoptosis. Hypoxic hearts displayed pump dysfunction with decreased LV ejection fractions, accompanied by signs of diastolic dysfunction. Cardiomyopathy caused by hypoxia persisted into adulthood. Hypoxic embryonic hearts showed increases in VEGF expression. Systemic administration of rhVEGF165 to normoxic chick embryos resulted in LV dilatation and a dose-dependent loss of LV wall mass. Lowering VEGF levels in hypoxic embryonic chick hearts by systemic administration of sFlt-1 yielded an almost complete normalization of the phenotype. Conclusions/Significance: Our data show that hypoxia causes a decreased cardiac performance and cardiomyopathy in chick embryos, involving a significant VEGF-mediated component. This cardiomyopathy persists into adulthood

Clinical phenogroups are more effective than left ventricular ejection fraction categories in stratifying heart failure outcomes

Aims Heart failure (HF) guidelines place patients into 3 discrete groups according to left ventricular ejection fraction (LVEF): reduced (<40%), mid-range (40–49%), and preserved LVEF (≥50%). We assessed whether clinical phenogroups offer better prognostication than LVEF. Methods and results This was a sub-study of the Patient-Centered Care Transitions in HF trial. We analysed baseline characteristics of hospitalized patients in whom LVEF was recorded. We used unsupervised machine learning to identify clinical phenogroups and, thereafter, determined associations between phenogroups and outcomes. Primary outcome was the composite of all-cause death or rehospitalization at 6 and 12 months. Secondary outcome was the composite cardiovascular death or HF rehospitalization at 6 and 12 months. Cluster analysis of 1693 patients revealed six discrete phenogroups, each characterized by a predominant comorbidity: coronary heart disease, valvular heart disease, atrial fibrillation (AF), sleep apnoea, chronic obstructive pulmonary disease (COPD), or few comorbidities. Phenogroups were LVEF independent, with each phenogroup encompassing a wide range of LVEFs. For the primary composite outcome at 6 months, the hazard ratios (HRs) for phenogroups ranged from 1.25 [95% confidence interval (CI) 1.00–1.58 for AF] to 2.04 (95% CI 1.62–2.57 for COPD) (log-rank P < 0.001); and at 12 months, the HRs for phenogroups ranged from 1.15 (95% CI 0.94–1.41 for AF) to 1.87 (95% 1.52–3.20 for COPD) (P < 0.002). LVEF-based classifications did not separate patients into different risk categories for the primary outcomes at 6 months (P = 0.69) and 12 months (P = 0.30). Phenogroups also stratified risk of the secondary composite outcome at 6 and 12 months more effectively than LVEF. Conclusion Among patients hospitalized for HF, clinical phenotypes generated by unsupervised machine learning provided greater prognostic information for a composite of clinical endpoints at 6 and 12 months compared with LVEF-based categories. Trial Registration: ClinicalTrials.gov Identifier: NCT0211222