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.
METHOD: Relevant studies detecting SMAD4 expression in cancer patients treated with chemo-drugs up till December 2020 were systematically searched in four common scientific databases using selected keywords. The pooled hazard ratio (HR) was the ratio of hazard rate between SMAD4neg population vs SMAD4pos population. The HRs and risk ratios (RRs) with 95% confidence intervals (CIs) were used to explore the association between SMAD4 expression losses with drug resistance in cancers.
RESULT: After an initial screening according to the inclusion and exclusion criteria, eleven studies were included in the meta-analysis. There were a total of 2092 patients from all the included studies in this analysis. Results obtained indicated that loss of SMAD4 expression was significantly correlated with drug resistance with pooled HRs (95% CI) of 1.23 (1.01-1.45), metastasis with pooled RRs (95% CI) of 1.10 (0.97-1.25) and recurrence with pooled RRs (95% CI) of 1.32 (1.06-1.64). In the subgroup analysis, cancer type, drug type, sample size and antibody brand did not affect the significance of association between loss of SMAD4 expression and drug resistance. In addition, there was no evidence of publication bias as suggested by Begg's test.
CONCLUSION: Findings from our meta-analysis demonstrated that loss of SMAD4 expression was correlated with drug resistance, metastasis and recurrence. Therefore, SMAD4 expression could be potentially used as a molecular marker for cancer resistance.
METHODS: Data was collected from 13 Asian countries on patients with CLD, known or newly diagnosed, with confirmed COVID-19.
RESULTS: Altogether, 228 patients [185 CLD without cirrhosis and 43 with cirrhosis] were enrolled, with comorbidities in nearly 80%. Metabolism associated fatty liver disease (113, 61%) and viral etiology (26, 60%) were common. In CLD without cirrhosis, diabetes [57.7% vs 39.7%, OR = 2.1 (1.1-3.7), p = 0.01] and in cirrhotics, obesity, [64.3% vs. 17.2%, OR = 8.1 (1.9-38.8), p = 0.002] predisposed more to liver injury than those without these. Forty three percent of CLD without cirrhosis presented as acute liver injury and 20% cirrhotics presented with either acute-on-chronic liver failure [5 (11.6%)] or acute decompensation [4 (9%)]. Liver related complications increased (p
MATERIALS AND METHODS: We sought to determine the prognostic role of the examined lymph node (LN), negative LN (NLN), and positive LN counts and the LN ratio (LNR), defined as (positive LNs/ENLs), on the survival rate among MBC patients. We performed a large population-based study using the data from the Surveillance, Epidemiology, and End Results program.
RESULTS: Older age, black race, stage IV disease, ≤ 1 NLN, and a > 31.3% LNR were significantly associated with worse survival across all prediction models. Moreover, we demonstrated a decreased risk of mortality in MBC patients across the MBC-specific survival model (hazard ratio, 0.98; 95% confidence interval, 0.96-0.998; P = .03) and 10-year MBC-specific survival model (hazard ratio, 0.98; 95% confidence interval, 0.96-0.999; P = .04).
CONCLUSION: MBC has had an augmented incidence over the years. We found several independent predictors of MBC survival, including age, race, stage, NLNs, and the LNR. We strongly suggest adding the NLN count and/or LNR into the current staging system. Further studies are needed to provide information on the mechanisms underlying the association between the NLN count and MBC survival and the LNR and MBC survival.
METHODS: In this study, plasma miRNA profiles from eight early-stage breast cancer patients and nine age-matched (± 2 years) healthy controls were characterized by miRNA array-based approach, followed by differential gene expression analysis, Independent T-test and construction of Receiver Operating Characteristic (ROC) curve to determine the capability of the assays to discriminate between breast cancer and the healthy control.
RESULTS: Based on the 372-miRNAs microarray profiling, a set of 40 differential miRNAs was extracted regarding to the fold change value at 2 and above. We further sub grouped 40 miRNAs of breast cancer patients that were significantly expressed at 2-fold change and higher. In this set, we discovered that 24 miRNAs were significantly upregulated and 16 miRNAs were significantly downregulated in breast cancer patients, as compared to the miRNA expression of healthy subjects. ROC curve analysis revealed that seven miRNAs (miR-125b-5p, miR-142-3p, miR-145-5p, miR-193a-5p, miR-27b-3p, miR-22-5p and miR-423-5p) had area under curve (AUC) value > 0.7 (AUC p-value < 0.05). Overlapping findings from differential gene expression analysis, ROC analysis, and Independent T-Test resulted in three miRNAs (miR-27b-3p, miR-22-5p, miR-145-5p). Cohen's effect size for these three miRNAs was large with d value are more than 0.95.
CONCLUSION: miR-27b-3p, miR-22-5p, miR-145-5p could be potential biomarkers to distinguish breast cancer patients from healthy controls. A validation study for these three miRNAs in an external set of samples is ongoing.
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MATERIALS AND METHODS: This study was conducted at Chemical Pathology, Department of Pathology and Laboratory Medicine and Department of Medicine, Aga Khan University (AKU), Karachi Pakistan. Electronic medical records of all in-patients including both genders and all age groups with documented COVID-19 from March to August 2020 were reviewed and recorded on a pre-structured performa. The subjects were divided into two categories severe and non-severe COVID-19; and survivors and non-survivors. Between-group differences were tested using the Chi-square and Mann-Whitney's U-test. The receiver operating characteristic curve was plotted for serum PCT with severity and mortality. A binary logistic regression was used to identify variables independently associated with mortality. The data was analysed using SPSS.
RESULTS: 336 patients were reviewed as declared COVID-19 positive during the study duration, and 136 were included in the final analysis including 101 males and 35 females. A statistically significant difference in PCT was found between severe and non-severe COVID-19 (p value=0.01); and survivors and nonsurvivors (p value<0.0001). PCT, older age and increased duration of hospital stay were revealed as variables independently associated with mortality. On ROC analysis, an AUC of 0.76 for mortality prediction was generated for PCT.
CONCLUSION: Baseline serum PCT concentration is a promising predictor of mortality and severity in COVID-19 cases when considered in combination with clinical details and other laboratory tests.
METHODOLOGY: Using a cross-sectional design, invasive breast carcinoma of no special type (NST) and HER2 IHC scores of 2+ and 3+ cases were selected over a 50-month period in Hospital Sultanah Bahiyah (HSB), Alor Setar. IHC staining for HMGCR was performed on paraffin-embedded tissues at the Pathology Laboratory, Hospital Universiti Sains Malaysia (HUSM), Kubang Kerian using the standard staining procedure. The results were correlated with the patient's demographic and clinicopathological data.
RESULTS: A total of 59 cases of HER2 IHC 2+ and 3+ invasive breast carcinoma were identified. The cases were predominant in young Malay women with tumours smaller than 50mm, higher grade and positive for lymphovascular invasion, axillary lymph nodes involvement and ER/PR expressions. HMGCR was positively expressed in HER2 IHC 2+ and 3+ breast cancer cases, which the staining intensities varied from weak, moderate to strong. Majority of the cases were scored 1+ for HMGCR expression. A low-positive HMGCR was more likely to be associated with less favourable outcomes of patients with HER2 IHC 2+ and 3+. However, the associations were statistically not significant.
CONCLUSION: A study in a larger cohort of tumour samples is needed to further validate HMGCR expression as a potential prognostic biomarker for HER2 positive breast cancer. It is also suggested that all the HER2 IHC 2+ and 3+ cases need to be gene amplified using FISH analysis.
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METHODS: A total of 82 archived paraffin embedded HM tissues with subtypes classified based on H&E staining - 39 (47.5%) CHM, 41 (50.0%) PHM and two (2.43%) unclassified molar pregnancy were retrieved. All tissue samples were subjected for p57kip2 IHC analysis and HM subtypes were then reclassified.
RESULTS: A total of 66 cases (80.5%) were re-classified as CHM, 14 cases (17.1%) as PHM and two cases (2.4%) were decidual and cystic tissues. Analysis using p57kip2 immunostaining showed a diagnostic discrepancy of 33.0% from routine H&E staining and helps to improve the characterisation of the HM subtypes specifically at early gestations which have less distinctive morphologies.
CONCLUSIONS: IHC using p57kip2 monoclonal antibody should be considered as a routine ancillary test to H&E in improving the diagnosis of HM subtypes particularly in developing countries with limited resources.
Objective: To determine the additional relationship between factors discovered by searching for sociodemographic and metastasis factors, as well as treatment outcomes, which could help improve the prediction of the survival rate in cancer patients. Material and Methods. A total of 56 patients were recruited from the ambulatory clinic at the Hospital Universiti Sains Malaysia (USM). In this retrospective study, advanced computational statistical modeling techniques were used to evaluate data descriptions of several variables such as treatment, age, and distant metastasis. The R-Studio software and syntax were used to implement and test the hazard ratio. The statistics for each sample were calculated using a combination model that included methods such as bootstrap and multiple linear regression (MLR).
Results: The statistical strategy showed R demonstrates that regression modeling outperforms an R-squared. It demonstrated that when data is partitioned into a training and testing dataset, the hybrid model technique performs better at predicting the outcome. The variable validation was determined using the well-established bootstrap-integrated MLR technique. In this case, three variables are considered: age, treatment, and distant metastases. It is important to note that three things affect the hazard ratio: age (β 1: -0.006423; p < 2e - 16), treatment (β 2: -0.355389; p < 2e - 16), and distant metastasis (β 3: -0.355389; p < 2e - 16). There is a 0.003469102 MSE for the linear model in this scenario.
Conclusion: In this study, a hybrid approach combining bootstrapping and multiple linear regression will be developed and extensively tested. The R syntax for this methodology was designed to ensure that the researcher completely understood the illustration. In this case, a hybrid model demonstrates how this critical conclusion enables us to better understand the utility and relative contribution of the hybrid method to the outcome. The statistical technique used in this study, R, demonstrates that regression modeling outperforms R-squared values of 0.9014 and 0.00882 for the predicted mean squared error, respectively. The conclusion of the study establishes the superiority of the hybrid model technique used in the study.