METHODS: A cross-sectional study was conducted involving 22 cases of glioma diagnosed intraoperatively from January 2013 until August 2019 in Hospital Universiti Sains Malaysia. The selected tissues were processed for cytology smear and frozen section. The remaining tissues were proceeded for paraffin section. The diagnosis was categorized as either low-grade or high-grade glioma based on cellularity, nuclear pleomorphism, mitotic count, microvascular proliferation and necrosis. The sensitivity and specificity of frozen section and cytology smears were determined based on paraffin section being as the gold standard. The accuracy of both techniques was compared using statistical analysis.
RESULTS: The overall sensitivity and specificity of cytology smear were 100% and 76.9%, respectively. Meanwhile, the sensitivity and specificity of frozen section were 100% and 84.6%. There was no significant difference in diagnostic accuracy between cytology smear and frozen section in glioma (p>0.05).
CONCLUSION: Cytology smears provides an alternative method for frozen section due to good cellularity and morphology on smear. Cytology smear is rapid, inexpensive, small amount of tissue requirement and less technical demand. This finding may benefit to the hospital or treatment centres where frozen section facility is unavailable.
METHODS: Baseline characteristics and laboratory results were collected and analyzed. Receiver operating characteristic (ROC) curve analysis was used to joint detection of inflammatory markers for influenza positive children, and the scatter-dot plots were used to compare the differences between severe and non-severe group.
RESULTS: Influenza B positive children had more bronchitis and pneumonia (P
Methods: We enrolled and reviewed 122 biopsy-proven NAFLD patients. Advanced fibrosis was defined as fibrosis stages 3-4. Noninvasive assessments included aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio, AST-to-platelet ratio index (APRI), AST/ALT ratio, diabetes (BARD) score, fibrosis-4 (FIB-4) score, and NAFLD fibrosis score.
Results: FIB-4 score had the highest area under the receiver operating characteristic curve (AUROC) and negative predictive value (NPV) of 0.86 and 94.3%, respectively, for the diagnosis of advanced fibrosis. FIB-4 score
SUBJECTS: Patients who were admitted to the University of Malaya Medical Centre due to cardiac events.
METHODS: Eight different machine learning models were evaluated. The models included 3 different sets of features: full features; significant features from multiple logistic regression; and features selected from recursive feature extraction technique. The performance of the prediction models with each set of features was compared.
RESULTS: The AdaBoost model with the top 20 features obtained the highest performance score of 92.4% (area under the curve; AUC) compared with other prediction models.
CONCLUSION: The findings showed the potential of using machine learning models to predict return to work after cardiac rehabilitation.
METHODS: We retrospectively reviewed two pictures both with white light (WL) and LCI for 54 consecutive neoplastic polyps 2-20 mm in size. All pictures were evaluated by four endoscopists according to a published polyp visibility score from four (excellent visibility) to one (poor visibility). Additionally, we calculated CD value between each polyp and surrounding mucosa in LCI and WL using an original software.
RESULTS: The mean polyp visibility scores of LCI (3.11 ± 1.05) were significantly higher than those of WL (2.50 ± 1.09, P
METHODS: We measured 20 plasma markers i.e. IFN-γ, IL-10, granzyme-B, CX3CL1, IP-10, RANTES, CXCL8, CXCL6, VCAM, ICAM, VEGF, HGF, sCD25, IL-18, LBP, sCD14, sCD163, MIF, MCP-1 and MIP-1β in 141 dengue patients in over 230 specimens and correlate the levels of these plasma markers with the development of dengue without warning signs (DWS-), dengue with warning signs (DWS+) and severe dengue (SD).
RESULTS: Our results show that the elevation of plasma levels of IL-18 at both febrile and defervescence phase was significantly associated with DWS+ and SD; whilst increase of sCD14 and LBP at febrile phase were associated with severity of dengue disease. By using receiver operating characteristic (ROC) analysis, the IL-18, LBP and sCD14 were significantly predicted the development of more severe form of dengue disease (DWS+/SD) (AUC = 0.768, P
METHODS: The study was divided into two phases: (I) Marker discovery by miRNA microarray using paired cancer tissues (n = 30) and blood samples (CRC, n = 42; control, n = 18). (II) Marker validation by stem-loop reverse transcription real time PCR using an independent set of paired cancer tissues (n = 30) and blood samples (CRC, n = 70; control, n = 32). Correlation analysis was determined by Pearson's test. Logistic regression and receiver operating characteristics curve analyses were applied to obtain diagnostic utility of the miRNAs.
RESULTS: Seven miRNAs (miR-150, miR-193a-3p, miR-23a, miR-23b, miR-338-5p, miR-342-3p and miR-483-3p) have been found to be differentially expressed in both tissue and blood samples. Significant positive correlations were observed in the tissue and blood levels of miR-193a-3p, miR-23a and miR-338-5p. Moreover, increased expressions of these miRNAs were detected in the more advanced stages. MiR-193a-3p, miR-23a and miR-338-5p were demonstrated as a classifier for CRC detection, yielding a receiver operating characteristic curve area of 0.887 (80.0% sensitivity, 84.4% specificity and 83.3% accuracy).
CONCLUSION: Dysregulations in circulating blood miRNAs are reflective of those in colorectal tissues. The triple miRNA classifier of miR-193a-3p, miR-23a and miR-338-5p appears to be a potential blood biomarker for early detection of CRC.
METHODOLOGY: We performed a cross-sectional cohort study on healthy subjects and patients with glaucoma. The AngioVue Enhanced Microvascular Imaging System was used to capture the optic nerve head and macula images during one visit. En face segment images of the macular and optic disc were studied in layers. Microvascular density of the optic nerve head and macula were quantified by the number of pixels measured by a novel in-house developed software. Areas under the receiver operating characteristic curves (AUROC) were used to determine the accuracy of differentiating between glaucoma and healthy subjects.
RESULTS: A total of 24 (32 eyes) glaucoma subjects (57.5±9.5-y old) and 29 (58 eyes) age-matched controls (51.17±13.5-y old) were recruited. Optic disc and macula scans were performed showing a greater mean vessel density (VD) in healthy compared with glaucoma subjects. The control group had higher VD than the glaucoma group at the en face segmented layers of the optic disc (optic nerve head: 0.209±0.05 vs. 0.110±0.048, P<0.001; vitreoretinal interface: 0.086±0.045 vs. 0.052±0.034, P=0.001; radial peripapillary capillary: 0.146±0.040 vs. 0.053±0.036, P<0.001; and choroid: 0.228±0.074 vs. 0.165±0.062, P<0.001). Similarly, the VD at the macula was also greater in controls than glaucoma patients (superficial retina capillary plexus: 0.115±0.016 vs. 0.088±0.027, P<0.001; deep retina capillary plexus: 0.233±0.027 vs. 0.136±0.073, P<0.001; outer retinal capillary plexus: 0.190±0.057 vs. 0.136±0.105, P=0.036; and choriocapillaris: 0.225±0.053 vs. 0.153±0.068, P<0.001. The AUROC was highest for optic disc radial peripapillary capillary (0.96), followed by nerve head (0.92) and optic disc choroid (0.76). At the macula, the AUROC was highest for deep retina (0.86), followed by choroid (0.84), superficial retina (0.81), and outer retina (0.72).
CONCLUSIONS: Microvascular density of the optic disc and macula in glaucoma patients was reduced compared with healthy controls. VD of both optic disc and macula had a high diagnostic ability in differentiating healthy and glaucoma eyes.
MATERIALS/METHODS: Multivariable models developed to predict atomised and generalised urinary symptoms, both acute and late, were considered for validation using a dataset representing 754 participants from the TROG 03.04-RADAR trial. Endpoints and features were harmonised to match the predictive models. The overall performance, calibration and discrimination were assessed.
RESULTS: 14 models from four publications were validated. The discrimination of the predictive models in an independent external validation cohort, measured using the area under the receiver operating characteristic (ROC) curve, ranged from 0.473 to 0.695, generally lower than in internal validation. 4 models had ROC >0.6. Shrinkage was required for all predictive models' coefficients ranging from -0.309 (prediction probability was inverse to observed proportion) to 0.823. Predictive models which include baseline symptoms as a feature produced the highest discrimination. Two models produced a predicted probability of 0 and 1 for all patients.
CONCLUSIONS: Predictive models vary in performance and transferability illustrating the need for improvements in model development and reporting. Several models showed reasonable potential but efforts should be increased to improve performance. Baseline symptoms should always be considered as potential features for predictive models.