Controlled attenuation parameter for the detection of steatosis severity in chronic liver disease: a meta-analysis of diagnostic accuracy.

BACKGROUND AND AIM: Controlled attenuation parameter (CAP) is a novel ultrasound-based elastography method for detection of steatosis severity. This meta-analysis aimed to assess the performance of CAP. METHODS: PubMed, the Cochrane Library, and the Web of Knowledge were searched to find studies, published in English, relating to accuracy evaluations of CAP for detecting stage 1 (S1), stage 2 (S2), or stage 3 (S3) hepatic steatosis which was diagnosed by liver biopsy. Sensitivities, specificities, and hierarchical summary receiver operating characteristic (HSROC) curves were used to examine CAP performance. The clinical utility of CAP was also evaluated. RESULTS: Nine studies, with 11 cohorts were analyzed. The summary sensitivities and specificities values were 0.78 (95% confidence interval [CI], 0.69-0.84) and 0.79 (95% CI, 0.68-0.86) for ≥ S1, 0.85 (95% CI, 0.74-0.92) and 0.79 (95% CI, 0.71-0.85) for ≥ S2, and 0.83 (95% CI, 0.76-0.89) and 0.79 (95% CI, 0.68-0.87) for ≥ S3. The HSROCs were 0.85 (95% CI, 0.81-88) for ≥ S1, 0.88 (95% CI, 0.85-0.91) for ≥ S2, and 0.87 (95% CI, 0.84-0.90) for ≥ S3. Following a "positive" measurement (over the threshold value) for ≥ S1, ≥ S2, and ≥ S3, the corresponding post-test probabilities for the presence of steatosis (pretest probability was 50%) were 78%, 80% and 80%, respectively; if the values were below these thresholds ("negative" results), the post-test probabilities were 22%, 16%, and 17%, respectively. CONCLUSIONS: CAP has good sensitivity and specificity for detecting hepatic steatosis; however, based on a meta-analysis, CAP was limited in their accuracy of steatosis, which precluded widespread use in clinical practice.

Bidirectional flow in the vertebral artery is not always indicative of the subclavian steal phenomenon.

OBJECTIVE: To evaluate the causes of bidirectional flow in the vertebral artery detected by Doppler sonography and its differential diagnosis. METHODS: Twenty-nine patients with bidirectional flow in the vertebral artery were retrospectively studied. The vertebral artery parameters, including peak antegrade velocity (PAV), peak reversed velocity (PRV), maximum peak velocity (MPV), peak systolic velocity, resistive index (RI), and diameter, were measured. The MPV was defined as the MPV of bidirectional flow regardless of the velocity of antegrade or retrograde flow. To better predict the cause of bidirectional flow, receiver operating characteristic curves were constructed for these parameters, and the best cutoff values were obtained. The cause of bidirectional flow was determined by angiography. RESULTS: The causes of bidirectional flow were classified as the subclavian steal phenomenon (n = 21) and factors unrelated to the steal phenomenon (n = 8, including a hypoplastic vertebral artery [n = 4] and proximal vertebral artery stenosis and occlusion [n = 4]). Significant differences were observed between the steal phenomenon and non-steal phenomenon groups (P< .05) for MPV, PRV, PAV, target vertebral artery diameter, and contralateral RI. To determine the cause of bidirectional flow, areas under the receiver operating characteristic curves for the different parameters were obtained: 0.929 for MPV, 0.881 for PRV, 0.824 for PAV, 0.753 for target vertebral artery diameter, and 0.845 for contralateral RI. The cutoff value for MPV was 26.1 cm/s, and the accuracy was 93% (27 of 29). CONCLUSIONS: Bidirectional flow in the vertebral artery is not always indicative of the subclavian steal phenomenon. Measurement of hemodynamic parameters in the vertebral artery, such as MPV, can facilitate determination of the cause of bidirectional flow.

Case report: A double pathogenic mutation in a patient with late-onset MELAS/PEO overlap syndrome.

Mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes (MELAS) and progressive external ophthalmoplegia (PEO) are established phenotypes of mitochondrial disorders. They are maternally-inherited, multisystem disorder that is characterized by variable clinical, biochemical, and imaging features. We described the clinical and genetic features of a Chinese patient with late-onset MELAS/PEO overlap syndrome, which has rarely been reported. The patient was a 48-year-old woman who presented with recurrent ischemic strokes associated with characteristic brain imaging and bilateral ptosis. We assessed her clinical characteristics and performed mutation analyses. The main manifestations of the patient were stroke-like episodes and seizures. A laboratory examination revealed an increased level of plasma lactic acid and a brain MRI showed multiple lesions in the cortex. A muscle biopsy demonstrated ragged red fibers. Genetic analysis from a muscle sample identified two mutations: TL1 m.3243A>G and POLG c.3560C>T, with mutation loads of 83 and 43%, respectively. This suggested that mitochondrial disorders are associated with various clinical presentations and an overlap between the syndromes and whole exome sequencing is important, as patients may carry multiple mutations.

Classification of 5-HT(1A) receptor agonists and antagonists using GA-SVM method.

AIM: To construct a reliable computational model for the classification of agonists and antagonists of 5-HT(1A) receptor. METHODS: Support vector machine (SVM), a well-known machine learning method, was employed to build a prediction model, and genetic algorithm (GA) was used to select the most relevant descriptors and to optimize two important parameters, C and r of the SVM model. The overall dataset used in this study comprised 284 ligands of the 5-HT(1A) receptor with diverse structures reported in the literatures. RESULTS: A SVM model was successfully developed that could be used to predict the probability of a ligand being an agonist or antagonist of the 5-HT(1A) receptor. The predictive accuracy for training and test sets was 0.942 and 0.865, respectively. For compounds with probability estimate higher than 0.7, the predictive accuracy of the model for training and test sets was 0.954 and 0.927, respectively. To further validate our model, the receiver operating characteristic (ROC) curve was plotted, and the Area-Under-the-ROC- Curve (AUC) value was calculated to be 0.883 for training set and 0.906 for test set. CONCLUSION: A reliable SVM model was successfully developed that could effectively distinguish agonists and antagonists among the ligands of the 5-HT(1A) receptor. To our knowledge, this is the first effort for the classification of 5-HT(1A) receptor agonists and antagonists based on a diverse dataset. This method may be used to classify the ligands of other members of the GPCR family.