Pharmacovariome scanning using whole pharmacogene resequencing coupled with deep computational analysis and machine learning for clinical pharmacogenomics.

BACKGROUND: This pilot study aims to identify and functionally assess pharmacovariants in whole exome sequencing data. While detection of known variants has benefited from pharmacogenomic-dedicated bioinformatics tools before, in this paper we have tested novel deep computational analysis in addition to artificial intelligence as possible approaches for functional analysis of unknown markers within less studied drug-related genes. METHODS: Pharmacovariants from 1800 drug-related genes from 100 WES data files underwent (a) deep computational analysis by eight bioinformatic algorithms (overall containing 23 tools) and (b) random forest (RF) classifier as the machine learning (ML) approach separately. ML model efficiency was calculated by internal and external cross-validation during recursive feature elimination. Protein modelling was also performed for predicted highly damaging variants with lower frequencies. Genotype-phenotype correlations were implemented for top selected variants in terms of highest possibility of being damaging. RESULTS: Five deleterious pharmacovariants in the RYR1, POLG, ANXA11, CCNH, and CDH23 genes identified in step (a) and subsequent analysis displayed high impact on drug-related phenotypes. Also, the utilization of recursive feature elimination achieved a subset of 175 malfunction pharmacovariants in 135 drug-related genes that were used by the RF model with fivefold internal cross-validation, resulting in an area under the curve of 0.9736842 with an average accuracy of 0.9818 (95% CI: 0.89, 0.99) on predicting whether a carrying individuals will develop adverse drug reactions or not. However, the external cross-validation of the same model indicated a possible false positive result when dealing with a low number of observations, as only 60 important variants in 49 genes were displayed, giving an AUC of 0.5384848 with an average accuracy of 0.9512 (95% CI: 0.83, 0.99). CONCLUSION: While there are some technologies for functionally assess not-interpreted pharmacovariants, there is still an essential need for the development of tools, methods, and algorithms which are able to provide a functional prediction for every single pharmacovariant in both large-scale datasets and small cohorts. Our approaches may bring new insights for choosing the right computational assessment algorithms out of high throughput DNA sequencing data from small cohorts to be used for personalized drug therapy implementation.

The effect of the combined use of complex decongestive therapy with electrotherapy modalities for the treatment of breast cancer-related lymphedema: a randomized clinical trial.

BACKGROUND: We evaluated the effects of combined complex decongestive therapy (CDT) with electrotherapy modalities (ultrasound and faradic currents) in patients with breast cancer-related lymphedema (BCRL), investigating upper extremity circumference, volume, pain, and functional disability. METHODS: Thirty-nine patients with unilateral BCRL were randomly allocated to three groups (n = 13) as the following: The control group received CDT, the ultrasound group received CDT and therapeutic ultrasound, and the faradic group received CDT and faradic current. All the participants underwent treatment for 10 sessions. The outcomes including volume, circumference (measured at five points), pain intensity, and functional disability of the affected upper extremity were evaluated at baseline and after the treatment. RESULTS: Following the treatment, an improvement was noted in lymphedema volume, pain, and functional disability in all the three groups and there was a significant difference between the groups (P < 0.05). However, changes in limb circumference at the end of the treatment were not significantly different among the three groups in any sites (P > 0.05). CONCLUSION: The combination of electrotherapy modalities, faradic current or ultrasound, with CDT can result in a greater reduction in lymphedema volume, pain, and functional disability in patients with BCRL. TRIAL REGISTRATION: IRCT, IRCT201310292391N14, registered 03/01/2016.

Comparison of serum progesterone in non-viable pregnancy and ectopic pregnancy.

Ectopic pregnancy (EP) and miscarriage are important differential diagnosis of first trimester vaginal bleeding. In first trimester, serum progesterone value can be 5-20 ng/ml in EP and miscarriage. Since delay in diagnosis of EP could have high mortality and morbidity, the aim of this study was comparison of serum progesterone value in EP and miscarriage in order to differentiate these conditions. A total of 60 patients (30 EP and 30 miscarriages) with gestational age of 5-7 weeks by LMP were enrolled in this study. The titer of progesterone and ßhCG of serum were measured. The mean of progesterone and ßhCG titers were compared in two groups by Student's t-test. The mean progesterone titer for all patients was 6.36±5.62 ng/ml, with a minimum value of 0.44 and maximum value of 21.50 ng/ml. Serum progesterone level in 33 patients was lower than 5 ng/ml and in 27 patients was between 5-25 ng/ml. Mean serum progesterone for miscarriage was 6.803±5.72 and for EP was 5.915±5.45 ng/ml. Difference between two values was not significant statistically (P=0.067). Mean ßhCG for miscarriage was 1313.04 IU/l and for EP 1805.56 IU/l. Mean patients age were 27.2 totally that for miscarriage was 25.8 and for EP 28.5 years. This study indicated, the mean value of progesterone could not differentiate EP from miscarriage.