Triple-negative breast cancers (TNBCs) are defined as lack of expression of estrogen, progesterone, and Her2neu receptors by immunohistochemistry. 1 This subgroup has an aggressive clinical behavior. 2 JAK2 (V617F) exon 14 is a most probable mutation in occurring in pseudo kinase domain with loss of intrinsic auto inhibitory activity and can result in malignant transformation and uncontrolled proliferation leading to TNBC. It is because of this reason for particularly choosing this mutation for study. 3 Recent studies suggest that an amplification of JAK2 gene on somatic chromosome 9p24.1 region in patients with TNBC. 4 Ruxolitinib is an orally available JAK2 inhibitor that can be useful in this subgroup of patients. 5 A total of two hundred and fifty consecutively selected premenopausal, pretreatment female patients at CMH Lahore between 25 and 60 years diagnosed during past 2 years (Jan 2017Jan 2019) as triple-negative breast cancer (estrogen, progesterone, and Her 2 neu negative) in stage M1 on immuno histochemistry examination of breast tissue were included in the study. The presence of myeloproliferative neoplasms (MPN) was ruled out in all patients. Informed consent was obtained from all patients involved in the study. All the blood samples were taken before the start of treatment in TNBC cases. A cohort of age-matched consecutively selected female volunteers (n = 100) was included as unpaid healthy controls after written informed consent. Permission for the study was taken from hospital ethical committee. Real-time polymerase chain reaction was done on all the samples. Real-time polymerase chain reaction was used for amplification of DNA and was carried out in thermal cycler Rotor-Gene-Q (Corbett Research) having quality of rapidly heating and cooling of samples. The results of RT-PCR were recorded as cycle threshold (C t ), and C t < 20 was taken as positive and rest was taken as negative. Positive and negative control DNA samples for JAK2 V617F mutations were included in all batches. Positive samples were further tested for additional mutations that affect signaling pathway notably JAK2 exon12, STAT, KRAS, NRAS, CBL1, PTPN11, RAF, MAP, MYC, and PI3K mutations by Sanger sequencing. JAK2 exon 14-positive DNA of an age-matched MPN negative TNBC patient was taken as positive control while exon 14-negative DNA of an age-matched MPN negative TNBC patient was taken as negative control for PCR. To check the robustness of JAK2 V617F exon 14 mutation assays, quantitative results performed were found to be reliable across all mutation loads with moderate variability at low allele burden (0.1 and 1%; CV = 0.45 and 0.76, respectively). The percentage of samples correctly identified as positive and negative for the JAK2 V617F exon 14 mutation in both groups was calculated. The kappa value (k) was used to determine the level of agreement between each alternative assay and the PCR sequencing. All healthy controls (n = 100) were negative (DNA) for JAK2 V617F exon 14 mutation by real-time PCR. A total of twenty-five (n = ...
Background: Colorectal cancer is a varied illness with an expected heritability of 25-30%. Many CRC disorders occur because of solid family history, a high penetrance of the infection, and developing various tumors at an early age. A few novel genes are recognized that shows associations with CRC, such as AURKA, BCAS1, GNAS and MLH1. Material and methods:In this research work FDA rejected Alpidem and Propoxyphene were selected. Drugs were changed on the basis of side effects; modified drugs were docked with AURKA, BCAS1, GNAS and MLH1 proteins and QSAR analysis was performed.Results: Docked and QSAR results demonstrated the better interaction of both modified Alpidem and Propoxyphene along with proteins of CRC causing genes. The toxicity value and side-effects of modified Alpidem and modified Propoxyphene are less than original drugs. Conclusion:The fewer side effects and docking results of both modified Alpidem and Propoxyphene suggest that both the drugs can be used to cure mutations of genes in colorectal cancer as both modified drugs have fewer side-effects and toxicity, as compared to original drugs, both drugs have demonstrated greater interactions with the amino acid residues lying in the pockets of mutated proteins that demonstrates their stability and soundness.
Objective: To analyze internal quality control of blood components, including red cell concentrates, fresh frozen plasma, cryoprecipitate and random donor platelets, to measure our blood bank performance. Study Design: Cross-sectional study. Place and Duration of Study: Armed Forces Institute of Transfusion (AFIT), Rawalpindi Pakistan from Jul to Dec 2021. Methodology: Whole blood units were separated into red cell concentrates, fresh frozen plasma and random platelets by the platelet-rich plasma method. Cryoprecipitates were prepared from FFPs. Quality control was done on representative components. For red cell concentrates, hematocrit was measured. For platelet concentrates, pH and platelet yield were measured. For fresh frozen plasma, Factor-VIII assay and for cryoprecipitate, Factor VIII and fibrinogen assays were done. The blood components were also tested for bacterial cultures. Results: A total of 1130 units were analyzed for quality control, including 360 red cell concentrates, fresh frozen plasma, random donor platelets each and 50 cryoprecipitates. Red cell concentrates had a mean hematocrit of 68.5±3.7%. Random donor platelets had a yield of 10.1±1.4× 1010 / unit and a mean pH of 6.7±0.2. Fresh frozen plasma had a Factor VIII level of 2.2±0.98 IU/ml. Cryoprecipitate had a mean fibrinogen level of 202.8±27.8 mg/unit and Factor VIII132.5±54.1IU/unit. All blood components met internal quality control standards. Blood cultures were negative in 99.2% of random donor platelets tested. Conclusion: The internal quality control of blood products was in concordance with the national and international standards for quality control in blood banks.
Background: Quantitative cryptococcal cultures provide a measure of disease severity in cryptococcal meningitis, and the rate of fungal clearance by quantitative culture has become a widely accepted surrogate outcome measure in phase II clinical trials. Various quantitative methodologies have been used to quantify CSF fungal burden; however, the reproducibility of between techniques is unknown.Methods & Materials: 213 CSF samples were prospectively collected from 70 individuals with cryptococcal meningitis at Mulago Hospital in Kampala, Uganda during Sept-Nov 2013. Each sample was simultaneously cultured by three different quantitative culturing techniques: 1) "standard" 100mcL input volume of CSF with an additional five 1:10 serial dilutions; 2) a AIDS Clinical Trials Group (ACTG) method using various input volumes (1000,100,10mcL) and two 1:100 dilutions with (100,10 mcL input volume) per dilution; 3) 10mcL calibrated plastic loop of undiluted and 1:100 diluted CSF. Colony forming units (CFU)/mL were quantified on the tenth day of culture. In addition, CSF at time of diagnosis was analyzed by automated cell counter and cryptococcal antigen (CRAG) lateral flow assay (LFA) titers.Results: Mean log10 transformed CFU counts suggested no significant differences between the standard method and either of the two alternative methods by paired t-test (difference=+0.036 ACTG,p=.690;-0.053 log10CFU/mL loop, p=.671), although the ACTG and loop methods differed significantly (difference=+0.55,p=.001). Correlation between tests was high at r=0.82, 0.85, and 0.83 for the standard-ACTG, standard-loop, and ACTG-loop methods, respectively. A weighted kappa statistic allowing for 1 log10 difference between methods showed moderate agreement between all tests, with k=0.50, 0.57, and 0.45 for the standard-ACTG, standard-loop, and ACTG-loop methods, respectively. No significant relationships were identified between culture methods and automated cell counts (diff=-1.13,p<0.001 vs. standard, R2=0.49). Regression analysis showed a significant association between being in the highest tertile of LFA titers and higher CFUs by the standard method (p=.042) Conclusion: Overall, the three methods of quantitative culture produced highly comparable but not identical results. There were significant differences between ACTG and loop techniques. The choice of quantitative method should be made based on lab-specific capacity and reproducibility of each technique.
Objective: To assess the correlation between biofilm formation and azole antifungal susceptibility against plank tonic and sessile clinical isolates of C.albicans. Study Design: Prospective observational study. Place and Duration of Study: Combined Military Hospital Peshawar, from Jun 2016 to Sep 2017. Methodology: All standard microbiological procedures were carried out according to latest Clinical & laboratory standard institute (CLSI) guidelines. After gram staining and presumptive identification on CHRO Magar Candida, the isolates were biochemically identified by API AUX Candida as C.albicans. Planktonic antifungal susceptibility was carried out by Kirby Bauer disk diffusion method on 300 C.albicans isolates. Broth microdilution method was used to determine Minimum inhibitory concentration (MICs) of plank tonic cells and micro titer assay was used for assessment of biofilm formation by C.albicans. Results: In planktonic antifungal susceptibility, fluconazole was susceptible against 195 (65%) and voriconazole against 241 (80%) C. albicans isolates. C. albicans was found susceptible dose dependent (SDD) to fluconazole in 28 (9%) and to voriconazole in 21 (7%) isolates. Seventy-seven (26%) and 38 (13%) C.albicans isolates were found fluconazole and voriconazole resistant, respectively. Sessile antifungal susceptibility was carried out through broth micro dilution method in which 160 (53%) were susceptible, 42 (14%) were susceptible dose dependent SDD and 98 (33%) were resistance to voriconazole, and 161 (54%) were susceptible, 36 (12%) were SDD and 103 (34%) were found resistant to fluconazole. Biofilm forming isolates of C.albicans were observed to be 285 (95%).The p-value is highly significance i.e. <0.01 between......
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