MATERIAL AND METHODS: In this retrospective observational study the records of patients attending a tertiary urogynecological unit between January 2012 and December 2014 were analyzed. POP assessment included a standardized interview, clinical examination using Pelvic Organ Prolapse Quantification and four-dimensional translabial ultrasound. Puborectalis muscle trauma was assessed with tomographic ultrasound imaging using two continuous scoring systems and a previously established discrete system. Receiver operating characteristics and adjusted odds ratios were used for comparison of scoring systems in predicting symptoms and signs of POP.

RESULTS: Of 1258 women analyzed, 52.6% complained of prolapse symptoms. On ultrasound imaging, 65.7% of women had sonographically significant POP. Complete avulsion was diagnosed in 25.3% of women, being unilateral in 13.9% and bilateral in 11.4%. A maximum score in the 6-point and the 12-point tomographic ultrasound imaging scale increased the odds for a diagnosis of any significant POP on ultrasound by 4.4 and 4.8 times, respectively, compared with 4.6 times for the discrete diagnosis of bilateral avulsion. For all avulsion scoring systems the relation was strongest for cystocele and uterine prolapse.

OBJECTIVES: The objective of the study is to find a correlation between sonographic measurements of ONSD value with ICP value measured via the gold standard invasive intracranial ICP catheter, and to find the cut-off value of ONSD measurement in predicting raised ICP, along with its sensitivity and specificity value.

METHODS: A prospective observational study was performed using convenience sample of 41 adult neurosurgical patients treated in neurosurgical intensive care unit with invasive intracranial pressure monitoring placed in-situ as part of their clinical care. Portable SonoSite ultrasound machine with 7 MHz linear probe were used to measure optic nerve sheath diameter using the standard technique. Simultaneous ICP readings were obtained directly from the invasive monitoring.

RESULTS: Seventy-five measurements were performed on 41 patients. The non-parametric Spearman correlation test revealed a significant correlation at the 0.01 level between the ICP and ONSD value, with correlation coefficient of 0.820. The receiver operating characteristic curve generated an area under the curve with the value of 0.964, and with standard error of 0.22. From the receiver operating characteristic curve, we found that the ONSD value of 5.205 mm is 95.8% sensitive and 80.4% specific in detecting raised ICP.

AIM: To evaluate the accuracy of MACK-3 for the diagnosis of fibrotic NASH.

METHODOLOGY: Consecutive adult non-alcoholic fatty liver disease (NAFLD) patients who had liver biopsy in a university hospital were included. MACK-3 was calculated using the online calculator using the following variables: fasting glucose, fasting insulin, aspartate aminotransferase (AST) and cytokeratin 18 (CK18). MACK-3 cut-offs ≤0.134 and ≥0.550 were used to predict absence and presence of fibrotic NASH, respectively. Histopathological examination of liver biopsy specimen was reported according to the NASH Clinical Research Network Scoring System.

RESULTS: Data for 196 subjects were analysed. MACK-3 was good for diagnosis of fibrotic NASH (area under receiver-operating characteristics curve [AUROC] 0.80), comparable to the Fibrosis-4 index (FIB4) and the NAFLD fibrosis score (NFS) and superior to the BARD score and CK18. MACK-3 was good for diagnosis of active NASH (AUROC 0.81) and was superior to other blood fibrosis tests. The overall accuracy, percentage of subjects in grey zone, sensitivity, specificity, positive predictive value and negative predictive value of MACK-3 for diagnosis of fibrotic NASH was 79.1%, 46.9%, 100%, 43.8%, 43.1% and 100%, respectively, while for diagnosis of active NASH was 90.0%, 39.3%, 84.2%, 81.4%, 88.9% and 74.5%, respectively.

METHODS: We measured psychophysical contrast thresholds in one eye of 16 control subjects and 19 patients aged 67.8 ± 5.65 and 71.9 ± 7.15, respectively, (mean ± SD). Patients ranged in disease severity from suspects to severe glaucoma. We used the 17-region FDT-perimeter C20-threshold program and a custom 9-region test (R9) with similar visual field coverage. The R9 stimuli scaled their spatial frequencies with eccentricity and were modulated at lower temporal frequencies than C20 and thus did not display a clear spatial frequency-doubling (FD) appearance. Based on the overlapping areas of the stimuli, we transformed the C20 results to 9 measures for direct comparison with R9. We also compared mfVEP-based and psychophysical contrast thresholds in 26 younger (26.6 ± 7.3 y, mean ± SD) and 20 older normal control subjects (66.5 ± 7.3 y) control subjects using the R9 stimuli.

RESULTS: The best intraclass correlations between R9/C20 thresholds were for the central and outer regions: 0.82 ± 0.05 (mean ± SD, p ≤ 0.0001). The areas under receiver operator characteristic plots for C20 and R9 were as high as 0.99 ± 0.012 (mean ± SE). Canonical correlation analysis (CCA) showed significant correlation (r = 0.638, p = 0.029) with 1 dimension of the C20 and R9 data, suggesting that the lower and higher temporal frequency tests probed the same neural mechanism(s). Low signal quality made the contrast-threshold mfVEPs non-viable. The resulting mfVEP thresholds were limited by noise to artificially high contrasts, which unlike the psychophysical versions, were not correlated with age.

OBJECTIVE: Apply machine learning for the prediction and identification of factors associated with short and long-term mortality in Asian STEMI patients and compare with a conventional risk score.

METHODS: The National Cardiovascular Disease Database for Malaysia registry, of a multi-ethnic, heterogeneous Asian population was used for in-hospital (6299 patients), 30-days (3130 patients), and 1-year (2939 patients) model development. 50 variables were considered. Mortality prediction was analysed using feature selection methods with machine learning algorithms and compared to Thrombolysis in Myocardial Infarction (TIMI) score. Invasive management of varying degrees was selected as important variables that improved mortality prediction.

RESULTS: Model performance using a complete and reduced variable produced an area under the receiver operating characteristic curve (AUC) from 0.73 to 0.90. The best machine learning model for in-hospital, 30 days, and 1-year outperformed TIMI risk score (AUC = 0.88, 95% CI: 0.846-0.910; vs AUC = 0.81, 95% CI:0.772-0.845, AUC = 0.90, 95% CI: 0.870-0.935; vs AUC = 0.80, 95% CI: 0.746-0.838, AUC = 0.84, 95% CI: 0.798-0.872; vs AUC = 0.76, 95% CI: 0.715-0.802, p < 0.0001 for all). TIMI score underestimates patients' risk of mortality. 90% of non-survival patients are classified as high risk (>50%) by machine learning algorithm compared to 10-30% non-survival patients by TIMI. Common predictors identified for short- and long-term mortality were age, heart rate, Killip class, fasting blood glucose, prior primary PCI or pharmaco-invasive therapy and diuretics. The final algorithm was converted into an online tool with a database for continuous data archiving for algorithm validation.

METHODS: This study included 159 septic patients admitted to an intensive care unit. Leukocytes count, procalcitonin (PCT), interleukin-6 (IL-6), and paraoxonase (PON) and arylesterase (ARE) activities of PON-1 were assayed from blood obtained on ICU presentation. Logistic regression was used to derive sepsis mortality score (SMS), a prediction equation describing the relationship between biomarkers and 30-day mortality.

RESULTS: The 30-day mortality rate was 28.9%. The SMS was [еlogit(p)/(1+еlogit(p))]×100; logit(p)=0.74+(0.004×PCT)+(0.001×IL-6)-(0.025×ARE)-(0.059×leukocytes count). The SMC had higher area under the receiver operating characteristic curve (95% Cl) than SOFA score [0.814 (0.736-0.892) vs. 0.767 (0.677-0.857)], but is not statistically significant. When the SMS was added to the SOFA score, prediction of 30-day mortality improved compared to SOFA score used alone [0.845 (0.777-0.899), p=0.022].

RESULTS: We developed a fast Bayesian method which uses the sequencing coverage information determined from the concentration of an RNA sample to estimate the posterior distribution of a true gene count. Our method has better or comparable performance compared to NOISeq and GFOLD, according to the results from simulations and experiments with real unreplicated data. We incorporated a previously unused sequencing coverage parameter into a procedure for differential gene expression analysis with RNA-Seq data.

METHODS: Fifty-one adult patients with suspected bacterial sepsis on admission to the Emergency Department (ED) of a teaching hospital were included into the study. All relevant cultures and serology tests were performed. Serum levels for Group II Secretory Phospholipase A2 (sPLA2-IIA) and CD64 were subsequently analyzed.

RESULTS AND DISCUSSION: Sepsis was confirmed in 42 patients from a total of 51 recruited subjects. Twenty-one patients had culture-confirmed bacterial infections. Both biomarkers were shown to be good in distinguishing sepsis from non-sepsis groups. CD64 and sPLA2-IIA also demonstrated a strong correlation with early sepsis diagnosis in adults. The area under the curve (AUC) of both Receiver Operating Characteristic curves showed that sPLA2-IIA was better than CD64 (AUC = 0.93, 95% confidence interval (CI) = 0.83-0.97 and AUC = 0.88, 95% CI = 0.82-0.99, respectively). The optimum cutoff value was 2.13μg/l for sPLA2-IIA (sensitivity = 91%, specificity = 78%) and 45 antigen bound cell (abc) for CD64 (sensitivity = 81%, specificity = 89%). In diagnosing bacterial infections, sPLA2-IIA showed superiority over CD64 (AUC = 0.97, 95% CI = 0.85-0.96, and AUC = 0.95, 95% CI = 0.93-1.00, respectively). The optimum cutoff value for bacterial infection was 5.63μg/l for sPLA2-IIA (sensitivity = 94%, specificity = 94%) and 46abc for CD64 (sensitivity = 94%, specificity = 83%).

METHODS: EGFR GCN was examined by in situ hybridization (ISH) in biopsies from 78 patients with OPMD and 92 patients with early-stage (stages I and II) OSCC. EGFR ISH signals were scored by two pathologists and a category assigned by consensus. The data were correlated with patient demographics and clinical outcomes.

RESULTS: OPMD with abnormal EGFR GCN were more likely to undergo malignant transformation than diploid cases. EGFR genomic gain was detected in a quarter of early-stage OSCC, but did not correlate with clinical outcomes.

CONCLUSION: These data suggest that abnormal EGFR GCN has clinical utility as a biomarker for the detection of OPMD destined to undergo malignant transformation. Prospective studies are required to verify this finding. It remains to be determined if EGFR GCN could be used to select patients for EGFR-targeted therapies.