eConsult Provides a Novel Opportunity to Evaluate Hematuria Referrals for Medicaid Patients in the "Real-World" Community.

Introduction: We utilized an eConsult program to assess the appropriateness and completeness of hematuria evaluation among one of the largest Medicaid networks in California, the Inland Empire Health Plan.

Methods: We retrospectively reviewed all hematuria consults from May 2018 to August 2020. Patient demographic and clinical data were extracted from the electronic health record and dialogues between primary care provider and specialist including laboratory results and imaging.

Analysis of environmental sustainability of e-waste in developing countries - a case study from Pakistan.

The electronic waste generation rate is increasing drastically at a rate of 3 to 5% per year in developing countries. The aim of this study is to analyze the environmental sustainability and economic benefits of such e-waste management in the developing economies like Pakistan. The life cycle assessment (LCA) method has been employed for streamlined impact analysis of the end-of-life processing of e-waste focusing mainly on laptop computers and liquid crystal display (LCD) desktop computers in Pakistan.

ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides.

Although advancing the therapeutic alternatives for treating deadly cancers has gained much attention globally, still the primary methods such as chemotherapy have significant downsides and low specificity. Most recently, Anticancer peptides (ACPs) have emerged as a potential alternative to therapeutic alternatives with much fewer negative side-effects. However, the identification of ACPs through wet-lab experiments is expensive and time-consuming.

ECG-based machine-learning algorithms for heartbeat classification.

Electrocardiogram (ECG) signals represent the electrical activity of the human hearts and consist of several waveforms (P, QRS, and T). The duration and shape of each waveform and the distances between different peaks are used to diagnose heart diseases. In this work, to better analyze ECG signals, a new algorithm that exploits two-event related moving-averages (TERMA) and fractional-Fourier-transform (FrFT) algorithms is proposed.

Accurate prediction of RNA 5-hydroxymethylcytosine modification by utilizing novel position-specific gapped k-mer descriptors.

RNA modification is an essential step towards generation of new RNA structures. Such modification is potentially able to modify RNA function or its stability. Among different modifications, 5-Hydroxymethylcytosine (5hmC) modification of RNA exhibit significant potential for a series of biological processes.

iPromoter-BnCNN: a novel branched CNN-based predictor for identifying and classifying sigma promoters.

Motivation: Promoter is a short region of DNA which is responsible for initiating transcription of specific genes. Development of computational tools for automatic identification of promoters is in high demand. According to the difference of functions, promoters can be of different types.

FRnet-DTI: Deep convolutional neural network for drug-target interaction prediction.

The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI, an auto-encoder based feature manipulation and a convolutional neural network based classifier for drug target interaction prediction. Two convolutional neural networks are proposed: FRnet-Encode and FRnet-Predict.

PyFeat: a Python-based effective feature generation tool for DNA, RNA and protein sequences.

Motivation: Extracting useful feature set which contains significant discriminatory information is a critical step in effectively presenting sequence data to predict structural, functional, interaction and expression of proteins, DNAs and RNAs. Also, being able to filter features with significant information and avoid sparsity in the extracted features require the employment of efficient feature selection techniques. Here we present PyFeat as a practical and easy to use toolkit implemented in Python for extracting various features from proteins, DNAs and RNAs.

CFSBoost: Cumulative feature subspace boosting for drug-target interaction prediction.

Drug target interaction prediction is a very labor-intensive and expensive experimental process which has motivated researchers to focus on in silico prediction to provide information on potential interaction. In recent years, researchers have proposed several computational approaches for predicting new drug target interactions. In this paper, we present CFSBoost, a simple and computationally cheap ensemble boosting classification model for identification and prediction of drug-target interactions using evolutionary and structural features.

iRecSpot-EF: Effective sequence based features for recombination hotspot prediction.

In genetic evolution, meiotic recombination plays an important role. Recombination introduces genetic variations and is a vital source of biodiversity and appears as a driving force in evolutionary development. Local regions of chromosomes where recombination events tend to be concentrated are known as hotspots and regions with relatively low frequencies of recombination are called coldspots.

iDTI-ESBoost: Identification of Drug Target Interaction Using Evolutionary and Structural Features with Boosting.

Prediction of new drug-target interactions is critically important as it can lead the researchers to find new uses for old drugs and to disclose their therapeutic profiles or side effects. However, experimental prediction of drug-target interactions is expensive and time-consuming. As a result, computational methods for predictioning new drug-target interactions have gained a tremendous interest in recent times.

The size and distribution of key populations at greater risk of HIV in Pakistan: implications for resource allocation for scaling up HIV prevention programmes.

Background: With competing interests, limited funding and a socially conservative context, there are many barriers to implementing evidence-informed HIV prevention programmes for sex workers and injection drug users in Pakistan. Meanwhile, the HIV prevalence is increasing among these populations across Pakistan. We sought to propose and describe an approach to resource allocation which would maximise the impact and allocative efficiency of HIV prevention programmes.

Is blood agar an alternative to sabouraud dextrose agar for the isolation of fungi in patients with mycotic keratitis.

To compare the blood agar (BA), sabouraud dextrose agar (SDA) and chocolate agar (CA) for the isolation of fungi in patients with mycotic keratitis. Corneal Scrapings of 229 patients with clinically diagnosed microbial keratitis were inoculated on BA, SDA, CA. The culture media were evaluated for the rate and time taken for the fungal growth.