EBSRMF: Ensemble Based Similarity-Regularized Matrix Factorization to Predict Anticancer Drug Responses

Drug sensitivity prediction to a panel of cancer cell lines using computational approaches has been a challenge for two decades. With the emergence of high-throughput screening technologies, thousands of compounds and cancer cell lines panels with drug sensitivity data are publicly available at various pharmacogenomics databases. Analyzing these data is crucial to improve cancer treatment and develop new anticancer drugs. In this work, we propose EBSRMF: Ensemble Based Similarity-Regularized Matrix Factorization, which is a bagging based framework to improve the drug sensitivity prediction on the Cancer Cell Line Encyclopedia (CCLE) data. Based on the fact that similar drugs and cell lines exhibit similar drug response, we have investigated cell line and drug similarity matrices based on gene expression profiles and chemical structure respectively. The drug sensitivity value is used as outcome values which are the half maximal inhibitory concentrations (IC50). In order to improve the generalization ability of the proposed model, a homogeneous ensemble based bagging learning approach is also investigated where multiple SRMF models are used to train N subsets of the input data. The outcome of each training algorithm is aggregated using the averaging method to predict the outcome. Experiments are conducted on two benchmark datasets: CCLE and GDSC. The proposed model is compared with state-of-the-art models using multiple evaluation metrics including Root Means Square Error (RMSE) and Pearson Correlation Coefficient (PCC). The proposed model is quite promising and achieves better performance on CCLE dataset when compared with the existing approaches

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Deep Ensembling for Perceptual Image Quality Assessment

Blind image quality assessment is a challenging task particularly due to the unavailability of reference information. Training a deep neural network requires a large amount of training data which is not readily available for image quality. Transfer learning is usually opted to overcome this limitation and different deep architectures are used for this purpose as they learn features differently. After extensive experiments, we have designed a deep architecture containing two CNN architectures as its sub-units. Moreover, a self-collected image database BIQ2021 is proposed with 12,000 images having natural distortions. The self-collected database is subjectively scored and is used for model training and validation. It is demonstrated that synthetic distortion databases cannot provide generalization beyond the distortion types used in the database and they are not ideal candidates for general-purpose image quality assessment. Moreover, a large-scale database of 18.75 million images with synthetic distortions is used to pretrain the model and then retrain it on benchmark databases for evaluation. Experiments are conducted on six benchmark databases three of which are synthetic distortion databases (LIVE, CSIQ and TID2013) and three are natural distortion databases (LIVE Challenge Database, CID2013 and KonIQ-10 k). The proposed approach has provided a Pearson correlation coefficient of 0.8992, 0.8472 and 0.9452 subsequently and Spearman correlation coefficient of 0.8863, 0.8408 and 0.9421. Moreover, the performance is demonstrated using perceptually weighted rank correlation to indicate the perceptual superiority of the proposed approach. Multiple experiments are conducted to validate the generalization performance of the proposed model by training on different subsets of the databases and validating on the test subset of BIQ2021 database

Mixed convective thermally radiative micro nanofluid flow in a stretchable channel with porous medium and magnetic field

A numerical study is carried out for two dimensional steady incompressible mixed convective flow of electrically conductive micro nanofluid in a stretchable channel. The flow is generated due to the stretching walls of the channel immersed in a porous medium. The magnetic field is applied perpendicular to the walls. The impact of radiation, viscous dissipation, thermophoretic and Brownian motion of nanoparticles appear in the energy equation. A numerical technique based on Runge-Kutta-Fehlberg fourth-fifth order (RFK45) method is used to express the solutions of velocity, microrotation, temperature and concentration fields. The dimensionless physical parameters are discussed both in tabular and graphical forms. The results are also found in a good agreement with previously published literature work

Sperm Abnormality Detection Using Sequential Deep Neural Network

Sperm morphological analysis (SMA) is an essential step in diagnosing male infertility. Using images of human sperm cells, this research proposes a unique sequential deep-learning method to detect abnormalities in semen samples. The proposed technique identifies and examines several components of human sperm. In order to conduct this study, we used the online Modified Human Sperm Morphology Analysis (MHSMA) dataset containing 1540 sperm images collected from 235 infertile individuals. For research purposes, this dataset is freely available online. To identify morphological abnormalities in different parts of human sperm, such as the head, vacuole, and acrosome, we proposed sequential deep neural network (SDNN) architecture. This technique is also particularly effective with low-resolution, unstained images. Sequential deep neural networks (SDNNs) demonstrate high accuracy in diagnosing morphological abnormalities based on the given dataset in our tests on the benchmark. Our proposed algorithm successfully detected abnormalities in the acrosome, head, and vacuole with an accuracy of 89%, 90%, and 92%, respectively. It is noteworthy that our system detects abnormalities of the acrosome and head with greater accuracy than current state-of-the-art approaches on the suggested benchmark. On a low-specification computer/laptop, our algorithm also requires less execution time. Additionally, it can classify photos in real time. Based on the results of our study, an embryologist can quickly decide whether to use the given sperm

Triethyl orthoformate mediated a novel crosslinking method for the preparation of hydrogels for tissue engineering applications:Characterization and in vitro cytocompatibility analysis

This paper describes the development of a new crosslinking method for the synthesis of novel hydrogel films from chitosan and PVA for potential use in various biomedical applications. These hydrogel membranes were synthesized by blending different ratios of chitosan (CS) and poly(vinyl alcohol) (PVA) solutions and were crosslinked with 2.5% (w/v) triethyl orthoformate (TEOF) in the presence of 17% (w/v) sulfuric acid. The physical/chemical interactions and the presence of specific functional groups in the synthesized materials were evaluated by Fourier transform infrared (FT-IR) spectroscopy. The morphology, structure and pore size of the materials were investigated by scanning electron microscopy (SEM). Thermal gravimetric analysis (TGA) proved that these crosslinked hydrogel films have good thermal stability which was decreased as the CS ratio was increased. Differential scanning calorimetry (DSC) exhibited that CS and PVA were present in the amorphous form. The solution absorption properties were performed in phosphate buffer saline (PBS) solution of pH 7.4. The 20% PVA-80% CS crosslinked hydrogel films showed a greater degree of solution absorption (183%) as compared to other compositions. The hydrogels with greater CS concentration (60% and 80%) demonstrated relatively more porous structure, better cell viability and proliferation and also revealed good blood clotting ability even after crosslinking. Based on the observed facts these hydrogels can be tailored for their potential utilization in wound healing and skin tissue engineering applications. © 2015 Elsevier B.V. All rights reserved

Translation and validation of the Functional Assessment of Cancer Therapy-Bone Marrow Transplant (FACT-BMT) version 4 quality of life instrument into Arabic language

Abstract Background Functional Assessment of Cancer Therapy-Bone Marrow Transplant (FACT-BMT) has been translated from English into several languages. Currently, there is no validated translation of FACT-BMT in Arabic. Here, we are reporting the first Arabic translation and validation of the FACT-BMT. Methods The study was approved by the Institutional Research Advisory Council. The Arabic translation followed the standard Functional Assessment of Chronic Illness Therapy (FACIT.org) translation methodology (with permission). Arabic FACT-BMT (50- items) was statistically validated. Cronbach’s alpha for internal consistency, Spearman’s rank correlation coefficients method for Inter-scale correlations and Principal Component Analysis for factorial construct validity was used. Results One hundred and eight consecutive relapsed /refractory lymphoma patients who underwent high dose chemotherapy and autologous stem cell transplant were enrolled. There were 68 males (63%) and 40 females (37%) with a median age of 29 years (range 14–62). After Arabic questionnaire pre-testing (Cronbach’s alpha 0.744), the study included 108 patients. Cronbach’s alpha for the entire FACT-BMT indicated an excellent internal consistency (0.90); range (0.67 to 0.91). Cronbach’s alpha for sub-groups of social (0.78), emotional (0.67) and functional wellbeing was (0.88). Cronbach’s alpha for bone marrow transplant (0.81), FACT-General (0.89), and FACT- Trial Outcome Index (TOI); (0.91) also revealed excellent internal consistency. Patients had high scores in all domains of quality of life, indicating that most patients were leading a normal life. This translation of FACT-BMT in Arabic was reviewed and approved for submission by the FACIT.org. Conclusions Our data reports the first translated, validated and approved Arabic version of FACT-BMT. This will help large numbers of Arabic speaking patients undergoing stem cell/bone marrow transplantation, across the globe

A new synthetic methodology for the preparation of biocompatible and organo-soluble barbituric- and thiobarbituric acid based chitosan derivatives for biomedical applications

Chitosan's poor solubility especially in organic solvents limits its use with other organo-soluble polymers; however such combinations are highly required to tailor their properties for specific biomedical applications. This paper describes the development of a new synthetic methodology for the synthesis of organo-soluble chitosan derivatives. These derivatives were synthesized from chitosan (CS), triethyl orthoformate and barbituric or thiobarbituric acid in the presence of 2-butannol. The chemical interactions and new functional motifs in the synthesized CS derivatives were evaluated by FTIR, DSC/TGA, UV/VIS, XRD and 1 H NMR spectroscopy. A cytotoxicity investigation for these materials was performed by cell culture method using VERO cell line and all the synthesized derivatives were found to be non-toxic. The solubility analysis showed that these derivatives were readily soluble in organic solvents including DMSO and DMF. Their potential to use with organo-soluble commercially available polymers was exploited by electrospinning; the synthesized derivatives in combination with polycaprolactone delivered nanofibrous membranes