MATERIALS AND METHODS: A total of 53 paired tissue samples from breast cancer patients were frozen-sectioned to characterize the tumour and normal tissues. Only tissues with 80% tumour cells were used in this study. For confirmation, Q-PCR was used to determine the HER-2/neu DNA amplification.
RESULTS: We found 20/53 (37.7%) of the tumour tissues to be positive for HER-2/neu protein overexpression using IHC. Out of these twenty, only 9/53 (17%) cases were in agreement with the Q-PCR results. The concordance rate between IHC and Q-PCR was 79.3%. Approximately 20.7% of positive IHC cases showed no HER-2/neu gene amplification using Q-PCR.
CONCLUSION: In conclusion, IHC can be used as an initial screening method for detection of the HER-2/neu protein overexpression. Techniques such as Q-PCR should be employed to verify the IHC results for uncertain cases as well as determination of HER-2/neu gene amplification.
METHOD: Targeted sequencing of fourteen genes panel was performed to identify the mutations in 29 OI patients with type I, III, IV and V disease. The mutations were determined using Ion Torrent Suite software version 5 and variant annotation was conducted using ANNOVAR. The identified mutations were confirmed using Sanger sequencing and in silico analysis was performed to evaluate the effects of the candidate mutations at protein level.
RESULTS: Majority of patients had mutations in collagen genes, 48% (n = 14) in COL1A1 and 14% (n = 4) in COL1A2. Type I OI was caused by quantitative mutations in COL1A1 whereas most of type III and IV were due to qualitative mutations in both of the collagen genes. Those with quantitative mutations had milder clinical severity compared to qualitative mutations in terms of dentinogenesis imperfecta (DI), bone deformity and the ability to walk with aid. Furthermore, a few patients (28%, n = 8) had mutations in IFITM5, BMP1, P3H1 and SERPINF1.
CONCLUSION: Majority of our OI patients have mutations in collagen genes, similar to other OI populations worldwide. Genotype-phenotype analysis revealed that qualitative mutations had more severe clinical characteristics compared to quantitative mutations. It is crucial to identify the causative mutations and the clinical severity of OI patients may be predicted based on the types of mutations.
METHODS: A case-control study was conducted involving 600 people with type 2 diabetes (300 chronic kidney disease cases, 300 controls) who participated in The Malaysian Cohort project. Retrospective subanalysis was performed on the chronic kidney disease cases to assess chronic kidney disease progression from the recruitment phase. We genotyped 32 single nucleotide polymorphisms using mass spectrometry. The probability of chronic kidney disease and predicted rate of newly detected chronic kidney disease progression were estimated from the significant gene-environment interaction analyses.
RESULTS: Four single nucleotide polymorphisms (eNOS rs2070744, PPARGC1A rs8192678, KCNQ1 rs2237895 and KCNQ1 rs2283228) and five environmental factors (age, sex, smoking, waist circumference and HDL) were significantly associated with chronic kidney disease. Gene-environment interaction analyses revealed significant probabilities of chronic kidney disease for sex (PPARGC1A rs8192678), smoking (eNOS rs2070744, PPARGC1A rs8192678 and KCNQ1 rs2237895), waist circumference (eNOS rs2070744, PPARGC1A rs8192678, KCNQ1 rs2237895 and KCNQ1 rs2283228) and HDL (eNOS rs2070744 and PPARGC1A rs8192678). Subanalysis indicated that the rate of newly detected chronic kidney disease progression was 133 cases per 1000 person-years (95% CI: 115, 153), with a mean follow-up period of 4.78 (SD 0.73) years. There was a significant predicted rate of newly detected chronic kidney disease progression in gene-environment interactions between KCNQ1 rs2283228 and two environmental factors (sex and BMI).
CONCLUSIONS: Our findings suggest that the gene-environment interactions of eNOS rs2070744, PPARGC1A rs8192678, KCNQ1 rs2237895 and KCNQ1 rs2283228 with specific environmental factors could modify the probability for chronic kidney disease.
AIMS: The aim of this study was to analyze the mutations in genes involved in CRC including MLH1, MSH2, KRAS, and APC genes.
METHODS: A total of 76 patients were recruited. We used the polymerase chain reaction-denaturing high-performance liquid chromatography for the detection of mutations in the mismatch repair (MMR) and APC genes and the PCR single-strand conformation polymorphism for screening of the KRAS gene mutations.
RESULTS: We identified 17 types of missense mutations in 38 out of 76 patients in our patients. Nine mutations were identified in the APC gene, five mutations were detected in the KRAS gene, and two mutations were identified in the MSH2 gene. Only one mutation was identified in MLH1. Out of these 17 mutations, eight mutations (47 %) were predicted to be pathogenic. Seven patients were identified with multiple mutations (3: MSH2 and KRAS, 1: KRAS and APC, 1: MLH1 and APC, 2: APC and APC).
CONCLUSIONS: We have established the PCR-DHPLC and PCR-SSCP for screening of mutations in CRC patients. This study has given a snapshot of the spectrum of mutations in the four genes that were analyzed. Mutation screening in patients and their family members will help in the early detection of CRC and hence will reduce mortality due to CRC.
METHODOLOGY: A total of 571 healthcare workers at COVID-19 and non-COVID-19 wards as well as the emergency department and laboratory staff at COVID-19 testing labs were recruited. The presence of novel human coronavirus (SARS-CoV-2) and IgM/IgG antibodies were confirmed in all healthcare workers. The healthcare workers responded to an online Google Forms questionnaire that evaluates demographic information and comorbidities, exposure and adherence to infection prevention and control measures against COVID-19. Descriptive analysis was performed using Statistical Package for the Social Sciences 24.0.
RESULTS: Three healthcare workers (0.5%) tested positive for SARS-CoV-2, while the remaining 568 (99.5%) were negative. All were negative for IgM and IgG antibodies during recruitment (day 1) and follow-up (day 15). More than 90% of the healthcare workers followed infection prevention and control practices recommendations regardless of whether they have been exposed to occupational risk for COVID-19.
CONCLUSIONS: The healthcare workers' high level of adherence to infection prevention practices at this hospital helped reduce and minimize their occupational exposure to COVID-19.
METHODOLOGY: FPG and HbA1c were taken from 40,667 eligible TMC participants that have no previous history of diabetes, aged between 35-70 years and were recruited from 2006 - 2012. Participants were classified as normal, diabetes and pre-diabetes based on the 2006 World Health Organization (WHO) criteria. Statistical analyses were performed using ANOVA and Chi-square test, while Pearson correlation and Cohen's kappa were used to examine the concordance rate between FPG and HbA1c.
RESULTS: The study samples consisted of 16,224 men and 24,443 women. The prevalence of diabetes among the participants was 5.7% and 7.5% according to the FPG and HbA1c level, respectively. Based on FPG, 10.6% of the participants had pre-diabetes but this increased to 14.2% based on HbA1c (r=0.86; P<0.001). HbA1c had a sensitivity of 58.20 (95% CI: 56.43, 59.96) and a specificity of 98.59 (95% CI: 98.46, 98.70).
CONCLUSION: A higher prevalence of pre-diabetes and diabetes was observed when using HbA1c as a diagnosis tool, suggesting that it could possibly be more useful for early detection. However, given that HbA1c may also have lower sensitivity and higher false positive rate, several diagnostic criteria should be used to diagnose diabetes accurately.