METHODS: 120 primary pterygium participants were selected from patients who visited an ophthalmology clinic. We adopted image analysis software in calculating the size of invading pterygium to the cornea. The marking of the calculated area was done manually, and the total area size was measured in pixel. The computed area is defined as the area from the apex of pterygium to the limbal-corneal border. Then, from the pixel, it was transformed into a percentage (%), which represents the CPTA relative to the entire corneal surface area. Intra- and inter-observer reliability testing were performed by repeating the tracing process twice with a different sequence of images at least one (1) month apart. Intraclass correlation (ICC) and scatter plot were used to describe the reliability of measurement.
RESULTS: The overall mean (N=120) of CPTA was 45.26±13.51% (CI: 42.38-48.36). Reliability for region of interest (ROI) demarcation of CPTA were excellent with intra and inter-agreement of 0.995 (95% CI, 0.994-0.998; P<0.001) and 0.994 (95% CI, 0.992-0.997; P<0.001) respectively. The new method was positively associated with corneal astigmatism (P<0.01). This method was able to predict 37% of the variance in CA compared to 21% using standard method.
CONCLUSIONS: Image analysis method is useful, reliable and practical in the clinical setting to objectively quantify actual pterygium size, shapes and its effects on the anterior corneal curvature.
MATERIALS AND METHODS: Seventy-eight specimens of needle prostate biopsy and its subsequent radical prostatectomy were retrospectively studied. The GSs of the needle biopsy were compared with the corresponding prostatectomy specimens. The percentage of GP4 in GS7 needle biopsy groups was calculated and correlated with the pathological staging.
RESULTS: More than half (60%) of GS 6 needle biopsy cases (PGG 1) were upgraded in the prostatectomy specimen, while the majority (80%) of the GS7 needle biopsy groups (PGG 2 and 3) remain unchanged. Cohen's Kappa shows fair agreement in the Gleason scoring between needle biopsies and prostatectomy specimens, K = 0.324 (95% CI, 6.94 to 7.29), p <0.0005 and in the percentage of GP4 in GS7 needle biopsy groups and their corresponding radical prostatectomy specimens, K = 0.399 (95% CI 34.2 - 49.2), p<0.0005. A significant relationship was seen between the percentage of GP4 in GS7 needle biopsy with the pT and pN stage of its radical prostatectomy (p = 0.008 and p=0.001 respectively).
CONCLUSION: A higher percentage of GP4 in GS7 tumour is associated with worse tumour behaviour, therefore it is crucial for clinicians to realise this in deciding the optimal treatment.
MATERIALS AND METHODS: Following the preferred reporting items for systematic review and meta-analysis (PRISMA) guidelines, online searches of multiple databases were performed to retrieve articles from their inception until December 2017. Inclusion criteria included all English-based original articles of immunohistochemistry (IHC) studies investigating CAIX expression in human RCC tissue. Four articles were finally selected for meta-analysis with a total of 1964 patients. Standard meta-analysis methods were applied to evaluate the role of CAIX in RCC prognosis. The relative risk (RR) and its 95% confidence interval (CI) were recorded for the association between biomarker and prognosis, and data were analysed using MedCalc statistical software.
RESULTS: The meta-analysis showed that high CAIX expression was associated with low tumour stage (RR 0.90%, 95% CI 0.849-0.969, p= 0.004), low tumour grade (RR 0.835%, 95% CI 0.732-0.983, p= 0.028), absence of nodal involvement (RR 0.814%, 95% CI 0.712-0.931, p= 0.003) and better ECOS-PS index (RR 0.888%, 95% CI 0.818-0.969, p= 0.007). The high tissue CAIX expression in RCC is hence an indication of an early malignancy with a potential to predict favourable disease progression and outcome.
CONCLUSION: The measurement of this marker may be beneficial to determine the course of the illness. It is hoped that CAIX can be developed as a specific tissue biomarker for RCC in the near future.
METHODS: A total of 142 patients from the Orthopaedics Oncology Database were included into this retrospective study. Kaplan-Meier curve and multivariate Cox proportional models were used to calculate the overall survival of patients with sarcoma who underwent radical excision surgery.
RESULTS: High preoperative LMR is significantly associated with better overall survival and prognosis in sarcoma patients, whereas high preoperative NLR is significantly associated with shorter overall survival and poorer prognosis. Multivariate analysis shows that LMR and NLR are good predictors for overall survival at 3 and 5 years after surgery, respectively. Patients with high preoperative lymphocytes count are associated with longer overall survival, but this association is not statistically significant. Our findings suggest that preoperative NLR and LMR are good predictive markers for survival of sarcoma patients.
CONCLUSION: LMR and NLR can be used to identify patients at risk for poor clinical outcome, so that a more aggressive course of treatment can be applied to improve outcome. These are cost-effective prognostic tools as they are calculated from routine preoperative peripheral blood counts. In conclusion, preoperative NLR and LMR are good prognostic markers for predicting the clinical outcome of patients with sarcoma.
CASE REPORT: A 69-year-old woman presented with fever and lower limb swelling. She had diabetes mellitus, hypertension, dyslipidaemia and a history of surgical resection of vulvar carcinoma. N. meningitidis was isolated from her blood culture.
DISCUSSION: This report provides additional evidence in support of N. meningitidis as a cause of cellulitis.
METHODS: Primary SFb isolated from knee synovium of OA obese (OA-ob:SFb), OA-pre-obese (OA-Pob:SFb), non-OA arthroscopic (scope:SFb), and non-OA arthroscopic with cartilage damage (scope-CD:SFb) were exposed to OA-conditioned media (OACM), derived from OA obese (OA-ob:CM), OA-pre-obese (OA-Pob:CM), and mechanical stretch at either 0 %, 6 % or 10 % for 24 h. Differences in the mRNA levels of genes involved in extracellular matrix production, inflammation and secretory activity were measured.
RESULTS: Despite the significant BMI differences between the OA-ob and OA-Pob groups, OA-Pob has more patients with underlying dyslipidaemia, and low-grade synovitis with higher levels of secreted proteins, CXCL8, COL4A1, CCL4, SPARC and FGF2 in OA-Pob:CM. All primary SFb exhibited anti-proliferative activity with both OA-CM. Mechanical stretch stimulated lubricin production in scope:SFb, higher TGFβ1 and COL1A1 expressions in scope-CD:SFb. OA-Pob:CM stimulated greater detrimental effects than the OA-ob:CM, with higher pro-inflammatory cytokines, IL1β, IL6, COX2 and proteases such as aggrecanases, ADAMTS4 and ADAMTS5, and lower ECM matrix, COL1A1 expressions in all SFb. OA-ob:SFb were unresponsive but expressed higher pro-inflammatory cytokines under OA-Pob:CM treatment.
CONCLUSION: Both mechanical and inflammatory stressors regulate SFb molecular functions with heterogeneity in responses that are dependent on their pathological tissue of origins. While mechanical stretch promotes a favorable effect with enhanced lubricin production in scope:SFb and TGFβ1 and COL1A1 in scope-CD:SFb, the presence of excessively high OA-associated inflammatory mediators in OA-Pob:CM, predominantly SPARC, CXCL8 and FGF2 drive all SFb regardless of pathology, towards greater pro-inflammatory activities.
METHODS: Overall, 612 patients (306 COVID-19 and 306 non-COVID-19 pneumonia) were recruited. Twenty radiological features were extracted from CT images to evaluate the pattern, location, and distribution of lesions of patients in both groups. All significant CT features were fed in five classifiers namely decision tree, K-nearest neighbor, naïve Bayes, support vector machine, and ensemble to evaluate the best performing CAD system in classifying COVID-19 and non-COVID-19 cases.
RESULTS: Location and distribution pattern of involvement, number of the lesion, ground-glass opacity (GGO) and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features to classify COVID-19 from non-COVID-19 groups. Our proposed CAD system obtained the sensitivity, specificity, and accuracy of 0.965, 93.54%, 90.32%, and 91.94%, respectively, using ensemble (COVIDiag) classifier.
CONCLUSIONS: This study proposed a COVIDiag model obtained promising results using CT radiological routine features. It can be considered an adjunct tool by the radiologists during the current COVID-19 pandemic to make an accurate diagnosis.
KEY POINTS: • Location and distribution of involvement, number of lesions, GGO and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features between COVID-19 from non-COVID-19 groups. • The proposed CAD system, COVIDiag, could diagnose COVID-19 pneumonia cases with an AUC of 0.965 (sensitivity = 93.54%; specificity = 90.32%; and accuracy = 91.94%). • The AUC, sensitivity, specificity, and accuracy obtained by radiologist diagnosis are 0.879, 87.10%, 88.71%, and 87.90%, respectively.