Posterior Vaginoplasty With Perineoplasty: A Canadian Experience With Vaginal Tightening Surgery

Following vaginal trauma, most commonly vaginal delivery, women may experience vaginal laxity as a result of local tissue stretching and separation of the pelvic floor musculature. In addition to this generalized sensation of laxity, women may complain of decreased sexual satisfaction, gaping of the perineum, and excessive vaginal secretions. Since 2014, the authors have used a posterior vaginoplasty with perineoplasty technique for the surgical management of vaginal laxity. To date, the authors have performed surgical vaginal tightening in 30 consecutive patients and found that the posterior vaginoplasty with perineoplasty technique has allowed us to achieve reproducible outcomes with no postoperative complications. This article will review the authors’ approach to patients presenting for surgical vaginal tightening and the authors’ experience to date, including our preoperative screening, perioperative management, and detailed steps of the procedure.

Periareolar Augmentation-Mastopexy

The authors describe their surgical technique for single-stage periareolar mastopexy with subglandular breast augmentation. They have performed this procedure in 85 patients since 2009 and found that this operative technique has allowed them to achieve reproducible outcomes in a single-stage procedure. Periareolar mastopexy with subglandular breast augmentation is an excellent procedure for patients who desire a larger breast size and who present with mild to moderate nipple ptosis with a paucity of excess skin in the lower pole of the breast. This article will review the perioperative management and detailed steps of the procedure and outline its indications for utilization and some of the common complications the authors have encountered.

Moringa oleifera and glycemic control: A review of current evidence and possible mechanisms

Maintaining glycemic control in diabetes and prediabetes is necessary to prevent many health complications and mortality. Although different hypoglycemic drugs are used for this purpose, there is still a growing interest in the use of medicinal plants due to their low price, easy availability, and fewer or no side effects. Moringa (Moringa oleifera Lam.) is a medicinal plant that has been traditionally used in the management of diabetes. This review aims to present the existing literature published until February 2019 on the role of moringa leaves in glycemia and their physiological mechanisms. In the conducted studies, moringa leaves have shown to reduce glycemia, without causing any adverse effects. The proposed mechanisms for reducing glycemia include inhibition of α-amylase and α-glucosidase activities, increased glucose uptake in the muscles and liver, inhibition of glucose uptake from the intestine, decreased gluconeogenesis in the liver, and increased insulin secretion and sensitivity. However, these studies are limited in numbers and mostly conducted in animals, in vitro and in vivo. Therefore, long-term human studies are required to determine the hypoglycemic effect of moringa leaves, their physiological mechanisms, active ingredients, and safety. Overall, this review provides evidence that moringa leaves have the possibility to be used as a glycemic control agent in diabetes and prediabetes.

Formal model of the interplay between TGFβ1 and MMP-9 and their dynamics in hepatocellular carcinoma

Transforming growth factor beta1 (TGFβ1) and matrix metalloproteinase-9 (MMP-9) have been associated with migration and invasion in hepatocellular carcinoma (HCC). Recent studies have suggested a positive feedback loop between TGFβ1 and MMP-9 mediated by the PI3K signaling pathway that confers acquired invasion and metastasis in HCC via induction of the epithelial-mesenchymal transition (EMT), which grows into invasive carcinoma. But the potential molecular mechanism of this loop on HCC has not been clarified yet. Therefore, this study is designed to explore the association between the two entities and their key determinants such as NFκB, TIMP-1, and hepatic stellate cells (HSCs). Hence, a qualitative modeling framework is implemented that predict the role of biological regulatory network (BRN) during recovery and HCC metastasis. Qualitative modeling predicts discrete trajectories, stable states, and cycles that highlight the paths leading to disease recovery and homeostasis, respectively. The deadlock stable state (1, 1, 1, 1, 1) predicts high expression of all the entities in the BRN, resulting in the progression of HCC. The qualitative model predicts 30 cycles representing significant paths leading to recovery and homeostasis and amongst these the most significant discrete cycle was selected based on the highest betweenness centralities of the discrete states. We further verified our model with network modeling and simulation analysis based on petri net modeling approach. The BRN dynamics were analyzed over time. The results implied that over the course of disease condition or homeostasis, the biological entities are activated in a variable manner. Taken together, our findings suggest that the TGFβ1 and the MMP-9 feedback loop is critical in tumor progression, as it may aid in the development of treatment strategies that are designed to target both TGFβ and MMP-9.

Variants in KIAA0825 underlie autosomal recessive postaxial polydactyly

Postaxial polydactyly (PAP) is a common limb malformation that often leads to cosmetic and functional complications. Molecular evaluation of polydactyly can serve as a tool to elucidate genetic and signaling pathways that regulate limb development, specifically, the anterior-posterior specification of the limb. To date, only five genes have been identified for nonsyndromic PAP: FAM92A, GLI1, GLI3, IQCE and ZNF141. In this study, two Pakistani multiplex consanguineous families with autosomal recessive nonsyndromic PAP were clinically and molecularly evaluated. From both pedigrees, a DNA sample from an affected member underwent exome sequencing. In each family, we identified a segregating frameshift (c.591dupA [p.(Q198Tfs*21)]) and nonsense variant (c.2173A > T [p.(K725*)]) in KIAA0825 (also known as C5orf36). Although KIAA0825 encodes a protein of unknown function, it has been demonstrated that its murine ortholog is expressed during limb development. Our data contribute to the establishment of a catalog of genes important in limb patterning, which can aid in diagnosis and obtaining a better understanding of the biology of polydactyly.

Identification of self-regulatory network motifs in reverse engineering gene regulatory networks using microarray gene expression data

Gene Regulatory Networks (GRNs) are reconstructed from the microarray gene expression data through diversified computational approaches. This process ensues in symmetric and diagonal interaction of gene pairs that cannot be modelled as direct activation, inhibition, and self-regulatory interactions. The values of gene co-expressions could help in identifying co-regulations among them. The proposed approach aims at computing the differences in variances of co-expressed genes rather than computing differences in values of mean expressions across experimental conditions. It adopts multivariate co-variances using principal component analysis (PCA) to predict an asymmetric and non-diagonal gene interaction matrix, to select only those gene pair interactions that exhibit the maximum variances in gene regulatory expressions. The asymmetric gene regulatory interactions help in identifying the controlling regulatory agents, thus lowering the false positive rate by minimizing the connections between previously unlinked network components. The experimental results on real as well as in silico datasets including time-series RTX therapy, Arabidopsis thaliana, DREAM-3, and DREAM-8 datasets, in comparison with existing state-of-the-art approaches demonstrated the enhanced performance of the proposed approach for predicting positive and negative feedback loops and self-regulatory interactions. The generated GRNs hold the potential in determining the real nature of gene pair regulatory interactions.

MicroRNAs and their target mRNAs as potential biomarkers among smokers and non-smokers with lung adenocarcinoma

Lung adenocarcinoma is one of the major causes of mortality. Current methods of diagnosis can be improved through identification of disease specific biomarkers. MicroRNAs are small non-coding regulators of gene expression, which can be potential biomarkers in various diseases. Thus, the main objective of this study was to gain mechanistic insights into genetic abnormalities occurring in lung adenocarcinoma by implementing an integrative analysis of miRNAs and mRNAs expression profiles in the case of both smokers and non-smokers. Differential expression was analysed by comparing publicly available lung adenocarcinoma samples with controls. Furthermore, weighted gene co-expression network analysis is performed which revealed mRNAs and miRNAs significantly correlated with lung adenocarcinoma. Moreover, an integrative analysis resulted in identification of several miRNA-mRNA pairs which were significantly dysregulated in non-smokers with lung adenocarcinoma. Also two pairs (miR-133b/Protein Kinase C Zeta (PRKCZ) and miR-557/STEAP3) were found specifically dysregulated in smokers. Pathway analysis further revealed their role in important signalling pathways including cell cycle. This analysis has not only increased the authors' understanding about lung adenocarcinoma but also proposed potential biomarkers. However, further wet laboratory studies are required for the validation of these potential biomarkers which can be used to diagnose lung adenocarcinoma.

PanRV: Pangenome-reverse vaccinology approach for identifications of potential vaccine candidates in microbial pangenome

Background

A revolutionary diversion from classical vaccinology to reverse vaccinology approach has been observed in the last decade. The ever-increasing genomic and proteomic data has greatly facilitated the vaccine designing and development process. Reverse vaccinology is considered as a cost-effective and proficient approach to screen the entire pathogen genome. To look for broad-spectrum immunogenic targets and analysis of closely-related bacterial species, the assimilation of pangenome concept into reverse vaccinology approach is essential. The categories of species pangenome such as core, accessory, and unique genes sets can be analyzed for the identification of vaccine candidates through reverse vaccinology.

Results

We have designed an integrative computational pipeline term as “PanRV” that employs both the pangenome and reverse vaccinology approaches. PanRV comprises of four functional modules including i) Pangenome Estimation Module (PGM) ii) Reverse Vaccinology Module (RVM) iii) Functional Annotation Module (FAM) and iv) Antibiotic Resistance Association Module (ARM). The pipeline is tested by using genomic data from 301 genomes of Staphylococcus aureus and the results are verified by experimentally known antigenic data.

Conclusion

The proposed pipeline has proved to be the first comprehensive automated pipeline that can precisely identify putative vaccine candidates exploiting the microbial pangenome. PanRV is a Linux based package developed in JAVA language. An executable installer is provided for ease of installation along with a user manual at https://sourceforge.net/projects/panrv2/.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2713-9) contains supplementary material, which is available to authorized users.

Incorporating Time Delays in Process Hitting Framework for Dynamical Modeling of Large Biological Regulatory Networks

Modeling and simulation of molecular systems helps in understanding the behavioral mechanism of biological regulation. Time delays in production and degradation of expressions are important parameters in biological regulation. Constraints on time delays provide insight into the dynamical behavior of a Biological Regulatory Network (BRN). A recently introduced Process Hitting (PH) Framework has been found efficient in static analysis of large BRNs, however, it lacks the inference of time delays and thus determination of their constraints associated with the evolution of the expression levels of biological entities of BRN is not possible. In this paper we propose a Hybrid Process Hitting scheme for introducing time delays in Process Hitting Framework for dynamical modeling and analysis of Large Biological Regulatory Networks. It provides valuable insights into the time delays corresponding to the changes in the expression levels of biological entities thus possibly helping in identification of therapeutic targets. The proposed framework is applied to a well-known BRNs of Bacteriophage λ and ERBB Receptor-regulated G1/S transition involved in the breast cancer to demonstrate the viability of our approach. Using the proposed approach, we are able to perform goal-oriented reduction of the BRN and also determine the constraints on time delays characterizing the evolution (dynamics) of the reduced BRN.

Computational modeling and analysis of the impacts of sleep deprivation on glucose stimulated insulin secretion

Circadian clock is an exquisite internal biological clock functioning in all living organisms. Lifestyle changes such as shift work or frequent travelling might result in malfunctioning of the central and consequently the peripheral clocks leading to different metabolic disorders. Disruptions in β cell clock have been found to be a potential reason behind β cell failure that makes a person prone towards developing type 2 diabetes (T2DM). In this study, a Petri net model for β cell circadian clock has been developed, followed by analysis of the negative impacts of sleep deprivation conditions on the process of glucose stimulated insulin secretion (GSIS) through misalignment of circadian clock. The analysis of structural properties of the Petri net model reveals robustness of the circadian system. The simulation results predict that sleep loss negatively affects the expression of circadian genes which eventually leads to impaired GSIS and β cell failure. These results suggest that sleep/wake cycle is a vital contributor for the entrainment of the circadian clock and normal functioning of β cell.

Biological Pathways Leading From ANGPTL8 to Diabetes Mellitus-A Co-expression Network Based Analysis

Angiopoietin like protein 8 (ANGPTL8) is a newly identified hormone with unique nature due to its ability to regulate both glucose and lipid metabolic pathways. It is characterized as an important molecular player of insulin induced nutrient storage and utilization pathway during fasting to re-feeding metabolic transition. Several studies have contributed to increase our knowledge regarding its function and mechanism of action. Moreover, its altered expression levels have been observed in Insulin Resistance, Diabetes Mellitus (Types I & II) and Non Alcohlic Fatty Liver Disease emphasizing its assessment as a drug target. However, there is still a great deal of information that remains to be investigated including its associated biological processes, partner proteins in these processes, its regulators and its association with metabolic pathogenesis. In the current study, the analysis of a transcriptomic data set was performed for functional assessment of ANGPTL8 in liver. Weighted Gene Co-expression Network Analysis coupled with pathway analysis tools was performed to identify genes that are significantly co-expressed with ANGPTL8 in liver and investigate their presence in biological pathways. Gene ontology term enrichment analysis was performed to select the gene ontology classes that over-represent the hepatic ANGPTL8-co-expressed genes. Moreover, the presence of diabetes linked SNPs within the genes set co-expressed with ANGPTL8 was investigated. The co-expressed genes of ANGPTL8 identified in this study (n = 460) provides narrowed down list of molecular targets which are either co-regulated with it and/or might be regulation partners at different levels of interaction. These results are coherent with previously demonstrated roles and regulators of ANGPTL8. Specifically, thirteen co-expressed genes (MAPK8, CYP3A4, PIK3R2, PIK3R4,PRKAB2, G6PC, MAP3K11, FLOT1, PIK3C2G, SHC1, SLC16A2, and RAPGEF1) are also present in the literature curated pathway of ANGPTL8 (WP3915¹). Moreover, the gene-SNP analysis of highly associated biological processes with ANGPTL8 revealed significant genetic signals associated to Diabetes Mellitus and similar phenotypic traits. It provides meaningful insights on the influencing genes involved and co-expressed in these pathways. Findings of this study have implications in functional characterization of ANGPTL8 with emphasis on the identified genes and pathways and their possible involvement in the pathogenesis of Diabetes Mellitus and Insulin Resistance.

Unknown mutations and genotype/phenotype correlations of autosomal recessive congenital ichthyosis in patients from Saudi Arabia and Pakistan

BACKGROUND

Autosomal recessive congenital ichthyosis (ARCI) is a genetically and phenotypically heterogeneous skin disease, associated with defects in the skin permeability barrier. Several but not all genes with underlying mutations have been identified, but a clear correlation between genetic causes and clinical picture has not been described to date.

METHODS

Our study included 19 families from Saudi Arabia, Yemen, and Pakistan. All patients were born to consanguineous parents and diagnosed with ARCI. Mutations were analyzed by homozygosity mapping and direct sequencing.

RESULTS

We have detected mutations in all families in five different genes: TGM1, ABCA12, CYP4F22, NIPAL4, and ALOXE3. Five likely pathogenic variants were unknown so far, a splice site and a missense variant in TGM1, a splice site variant in NIPAL4, and missense variants in ABCA12 and CYP4F22. We attributed TGM1 and ABCA12 mutations to the most severe forms of lamellar and erythematous ichthyoses, respectively, regardless of treatment. Other mutations highlighted the presence of a phenotypic spectrum in ARCI.

CONCLUSION

Our results contribute to expanding the mutational spectrum of ARCI and revealed new insights into genotype/phenotype correlations. The findings are instrumental for a faster and more precise diagnosis, a better understanding of the pathophysiology, and the definition of targets for more specific therapies for ARCI.

Two-dimensional transition metal carbide (Ti3C2Tx) as an efficient adsorbent to remove cesium (Cs+)

Industrial utilization of nuclear resources greatly depends on the effective treatment of nuclear waste.

XPC gene mutations in families with xeroderma pigmentosum from Pakistan; prevalent founder effect

Xeroderma pigmentosum (XP) is a rare autosomal recessive skin disorder characterized by hyperpigmentation, premature skin aging, ocular and cutaneous photosensitivity, and increased risk of skin carcinoma. We investigated seven consanguineous XP families with nine patients from Pakistan. All the Patients exhibited typical clinical symptoms of XP since first year of life. Whole genome SNP genotyping identified a 14 Mb autozygous region segregating with the disease phenotype on chromosome 3p25.1. DNA sequencing of XPC gene revealed a founder homozygous splice site mutation (c.2251-1G>C) in patients from six families (A-F) and a homozygous nonsense mutation (c.1399C>T; p.Gln467*) in patients of family G. This is the first report of XPC mutations, underlying XP phenotype, in Pakistani population.

Parameter estimation of qualitative biological regulatory networks on high performance computing hardware

BACKGROUND

Biological Regulatory Networks (BRNs) are responsible for developmental and maintenance related functions in organisms. These functions are implemented by the dynamics of BRNs and are sensitive to regulations enforced by specific activators and inhibitors. The logical modeling formalism by René Thomas incorporates this sensitivity with a set of logical parameters modulated by available regulators, varying with time. With the increase in complexity of BRNs in terms of number of entities and their interactions, the task of parameters estimation becomes computationally expensive with existing sequential SMBioNET tool. We extend the existing sequential implementation of SMBioNET by using a data decomposition approach using a Java messaging library called MPJ Express. The approach divides the parameters space into different regions and each region is then explored in parallel on High Performance Computing (HPC) hardware.

RESULTS

The performance of the parallel approach is evaluated on BRNs of different sizes, and experimental results on multicore and cluster computers showed almost linear speed-up. This parallel code can be executed on a wide range of concurrent hardware including laptops equipped with multicore processors, and specialized distributed memory computer systems. To demonstrate the application of parallel implementation, we selected a case study of Hexosamine Biosynthetic Pathway (HBP) in cancer progression to identify potential therapeutic targets against cancer. A set of logical parameters were computed for HBP model that directs the biological system to a state of recovery. Furthermore, the parameters also suggest a potential therapeutic intervention that restores homeostasis. Additionally, the performance of parallel application was also evaluated on a network (comprising of 23 entities) of Fibroblast Growth Factor Signalling in Drosophila melanogaster.

CONCLUSIONS

Qualitative modeling framework is widely used for investigating dynamics of biological regulatory networks. However, computation of model parameters in qualitative modeling is computationally intensive. In this work, we presented results of our Java based parallel implementation that provides almost linear speed-up on both multicore and cluster platforms. The parallel implementation is available at https://psmbionet.github.io .

Model-based in silico analysis of the PI3K/Akt pathway: the elucidation of cross-talk between diabetes and breast cancer

BACKGROUND

A positive association between diabetes and breast cancer has been identified by various epidemiological and clinical studies. However, the possible molecular interactions between the two heterogeneous diseases have not been fully determined yet. There are several underlying mechanisms which may increase the risk of breast cancer in diabetic patients.

INTRODUCTION

In this study, we focused on the role of O-GlcNAc transferase (OGT) enzyme in the regulation of phosphatidylinositol-3 kinase (PI3K) pathway through activation/deactivation of Akt protein. The efficiency of insulin signaling in adipocytes is reduced as a result of OGT overexpression which further attenuates Akt signaling; as a result, the efficiency of insulin signaling is reduced by downregulation of insulin-responsive genes. On the other hand, increased expression of OGT results in Akt activation in breast cancer cells, leading to enhanced cell proliferation and inhibition of the apoptosis. However, the interplay amongst these signaling pathways is still under investigation.

METHODS

In this study, we used Petri nets (PNs) to model and investigate the role of PI3K and OGT pathways, acting as key players in crosstalk between diabetes and breast cancer, resulting in progression of these chronic diseases. Moreover, in silico perturbation experiments were applied on the model to analyze the effects of anti-cancer agents (shRNA and BZX) and anti-diabetic drug (Metformin) on the system.

RESULTS

Our PN model reflects the alterations in protein expression and behavior and the correlation between breast cancer and diabetes. The analysis proposed two combination therapies to combat breast cancer progression in diabetic patients including combination of OGTmRNA silencing and OGT inhibitor (BZX) as first combination and BZX and Metformin as the second.

CONCLUSION

The PN model verified that alterations in O-GlcNAc signaling affect both insulin resistance and breast cancer. Moreover, the combination therapy for breast cancer patients consisting of anti-diabetic drugs such as Metformin along with OGT inhibitors, for example BZX, can produce better treatment regimens.

The Canadian Plastic Surgery Workforce Analysis: Forecasting Future Need

BACKGROUND

Projecting the demand for plastic surgeons has become increasingly important in a climate of scarce public resource within a single payer health-care system. The goal of this study is to provide a comprehensive workforce update and describe the perceptions of the workforce among Canadian Plastic Surgery residents and surgeons.

METHODS

Two questionnaires were developed by a national task force under the Canadian Plastic Surgery Research Collaborative. The surveys were distributed to residents and practicing surgeons, respectively.

RESULTS

Two-hundred fifteen (49%) surgeons responded, with a mean age of 51.4 years (standard deviation [SD] = 11.5); 78% were male. Thirty-three percent had been in practice for 25 years or longer. More than half of respondents were practicing in a large urban center. Fifty-nine percent believed their group was going to hire in the next 2 to 3 years; however, only 36% believed their health authority/provincial government had the necessary resources. The mean desired age of retirement was 67 years (SD = 6.4). We predict the surgeons-to-population ratio to be 1.55:100 000 and the graduate-to-retiree ratio to be 2.16:1 within the next 5 to 10 years. Seventy-seven (49%) residents responded. Most were "very satisfied" with their training (61%) and operative experience (90%). Eighty-nine percent of respondents planned to pursue addqitional training after residency, with 70% stating that the current job market was contributing to their decision. Most residents responded that they were concerned with the current job market.

CONCLUSIONS

The results of this study predict an adequate number of plastic surgeons will be trained within the next 10 years to suit the population's requirements; however, there is concern that newly trained surgeons will not have access to the necessary resources to meet growing demands. Furthermore, there is an evident shortage of those practicing in rural areas. Many trainees worry about the availability of jobs, despite evidence of active recruitment. The workforce may benefit from structured career mentorship in residency and improved transparency in hiring practices, particularly to attract young surgeons to smaller communities. It may also benefit from a coordinated national approach to recruitment and succession planning.

Modeling and analysis of the impacts of jet lag on circadian rhythm and its role in tumor growth

Circadian rhythms maintain a 24 h oscillation pattern in metabolic, physiological and behavioral processes in all living organisms. Circadian rhythms are organized as biochemical networks located in hypothalamus and peripheral tissues. Rhythmicity in the expression of circadian clock genes plays a vital role in regulating the process of cell division and DNA damage control. The oncogenic protein, MYC and the tumor suppressor, p53 are directly influenced by the circadian clock. Jet lag and altered sleep/wake schedules prominently affect the expression of molecular clock genes. This study is focused on developing a Petri net model to analyze the impacts of long term jet lag on the circadian clock and its probable role in tumor progression. The results depict that jet lag disrupts the normal rhythmic behavior and expression of the circadian clock proteins. This disruption leads to persistent expression of MYC and suppressed expression of p53. Thus, it is inferred that jet lag altered circadian clock negatively affects the expressions of cell cycle regulatory genes and contribute in uncontrolled proliferation of tumor cells.