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. We calculated the proportions of imaging types and the outcome of the eConsults among patients. χ2 and Fisher's exact tests were used for statistical analysis. RESULTS: A total of 106 hematuria eConsults were submitted. Primary care provider evaluation for risk factors rates were low: 37% gross hematuria, 29% voiding symptoms/dysuria, 49% other urothelial risk factors or benign etiology, and 63% smoking. Only 50% of all referrals were deemed appropriate based on a history of gross hematuria or ≥3 red blood cells/high-power field on urinalysis without evidence of infection or contamination. Thirty-one percent of patients received a renal ultrasound, 2.8% received CT urography, 5.7% received other cross-sectional imaging, and 64% received no imaging. By the conclusion of the eConsult only 54% of patients were referred for a face-to-face visit. CONCLUSIONS: The use of eConsults allows for urological access in the safety-net population and presents a means to assess the urological needs in the community. Our findings suggest eConsults represent an opportunity to reduce the morbidity and mortality associated with hematuria among safety-net patients who are otherwise less likely to receive a proper evaluation.

Analysis of distracted driving crashes in New Jersey using mixed logit model.

INTRODUCTION: Distracted driving is a concern for traffic safety in the 21st century, and can be held responsible for the increasing propensity and severity of traffic crashes. With the advent of mobile technologies, distractions involving the use of cellphones while driving have emerged, and young drivers in particular are getting more and more engaged in these distractions. Texting or receiving phone calls while driving are offenses in most states, and they are punished with fiscal penalties. Awareness campaigns have also been arranged over recent decades across the United States in order to minimize crashes due to distracted driving. The severity of such crashes depends on driver behavior, which can also be affected by various factors like the geometric design of the roadway, lighting and environmental conditions, and temporal variables. METHOD: In this study, we analyzed data on five years (2015-2019) of crashes involving cellphone use in New Jersey using a mixed logit model. As estimated model parameters can vary randomly across roadway segments in this approach, this allowed us to account for unobserved heterogeneities relating to roadway characteristics, environmental factors, and driver behavior. A pseudo-elasticity analysis was further employed to observe the sensitivity of the significant explanatory variables to crash severity. RESULTS: We found that higher speed limits and a larger total number of vehicles involved both increased crash severity, while higher annual average daily traffic (AADT) levels and the presence of an urban road setting reduced it. PRACTICAL APPLICATIONS: These findings will help decision-makers to comprehend what the significant contributing factors associated with crash injury severity due to distracted driving are, and how to implement necessary interventions to reduce this severity.

Enhancing water use efficiency and grain yield of wheat by optimizing irrigation supply in arid and semi-arid regions of Pakistan.

The lack of good irrigation practices and policy reforms in Pakistan triggers major threats to the water and food security of the country. In the future, irrigation will happen under the scarcity of water, as inadequate irrigation water becomes the requirement rather than the exception. The precise application of water with irrigation management is therefore needed. This research evaluated the wheat grain yield and water use efficiency (WUE) under limited irrigation practices in arid and semi-arid regions of Pakistan. DSSAT was used to simulate yield and assess alternative irrigation scheduling based on different levels of irrigation starting from the actual irrigation level up to 65% less irrigation. The findings demonstrated that different levels of irrigation had substantial effects on wheat grain yield and total water consumption. After comparing the different irrigation levels, the high amount of actual irrigation level in semi-arid sites decreased the WUE and wheat grain yield. However, the arid site (Site-1) showed the highest wheat grain yield 2394 kg ha-1 and WUE 5.9 kg-3 on actual irrigation (T1), and with the reduction of water, wheat grain yield decreased continuously. The optimal irrigation level was attained on semi-arid (site-2) with 50% (T11) less water where the wheat grain yield and WUE were 1925 kg ha-1 and 4.47 kg-3 respectively. The best irrigation level was acquired with 40% less water (T9) on semi-arid (site-3), where wheat grain yield and WUE were 1925 kg ha-1 and 4.57 kg-3, respectively. The results demonstrated that reducing the irrigation levels could promote the growth of wheat, resulting in an improved WUE. In crux, significant potential for further improving the efficiency of agricultural water usage in the region relies on effective soil moisture management and efficient use of water.

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. The method of cumulative exergy extraction from the natural environment (CEENE) has also been deployed for the relative assessment of resources' consumption of e-waste recycling versus landfilling scenario. The determined impact scores are 1.79E + 03 kg CO2 eq., 7.19E-07 kg CFC-11 eq., 1.02E + 03 kg 1,4-DCB, 7.13E + 01 kg 1,4-DCB, and 3.41E-03 kg Cu eq. in climate change potential, stratospheric ozone depletion, ecotoxicity potential, human noncarcinogenic potential, and mineral resource depletion impact categories, respectively. The results of CEENE analysis reveal that approximately 80% of the impact on natural resources is reduced by the efficient recycling of e-waste. The comparative assessment of respective scores for current and target material weight recovery (MWR) indicators represented that by increasing the MWR indicator by 33.8% for laptop computers and by 27.2% for LCD computers, the country will achieve an annual economic benefit of US $191.56 million. This is greatly significant for a transitional shift towards e-waste revalorization while realizing the objectives of sustainable resource consumption. Innovative improvement measures ensuring economically feasible, energy-efficient, and environment friendly waste collection, treatment, and recycling practices present an invaluable opportunity for developing countries.

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. Hence, computational methods have emerged as viable alternatives. During the past few years, several computational ACP identification techniques using hand-engineered features have been proposed to solve this problem. In this study, we propose a new multi headed deep convolutional neural network model called ACP-MHCNN, for extracting and combining discriminative features from different information sources in an interactive way. Our model extracts sequence, physicochemical, and evolutionary based features for ACP identification using different numerical peptide representations while restraining parameter overhead. It is evident through rigorous experiments using cross-validation and independent-dataset that ACP-MHCNN outperforms other models for anticancer peptide identification by a substantial margin on our employed benchmarks. ACP-MHCNN outperforms state-of-the-art model by 6.3%, 8.6%, 3.7%, 4.0%, and 0.20 in terms of accuracy, sensitivity, specificity, precision, and MCC respectively. ACP-MHCNN and its relevant codes and datasets are publicly available at: https://github.com/mrzResearchArena/Anticancer-Peptides-CNN . ACP-MHCNN is also publicly available as an online predictor at: https://anticancer.pythonanywhere.com/ .

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. The TERMA algorithm specifies certain areas of interest to locate desired peak, while the FrFT rotates ECG signals in the time-frequency plane to manifest the locations of various peaks. The proposed algorithm's performance outperforms state-of-the-art algorithms. Moreover, to automatically classify heart disease, estimated peaks, durations between different peaks, and other ECG signal features were used to train a machine-learning model. Most of the available studies uses the MIT-BIH database (only 48 patients). However, in this work, the recently reported Shaoxing People's Hospital (SPH) database, which consists of more than 10,000 patients, was used to train the proposed machine-learning model, which is more realistic for classification. The cross-database training and testing with promising results is the uniqueness of our proposed machine-learning model.

Investigation of injury severities in single-vehicle crashes in North Carolina using mixed logit models.

INTRODUCTION: Roadway departure (RwD) crashes, comprising run-off-road (ROR) and cross-median/centerline head-on collisions, are one of the most lethal crash types. According to the FHWA, between 2015 and 2017, an average of 52 percent of motor vehicle traffic fatalities occurred each year due to roadway departure crashes. An avoidance maneuver, inattention or fatigue, or traveling too fast with respect to weather or geometric road conditions are among the most common reasons a driver leaves the travel lane. Roadway and roadside geometric design features such as clear zones play a significant role in whether human error results in a crash. METHOD: In this paper, we used mixed-logit models to investigate the contributing factors on injury severity of single-vehicle ROR crashes. To that end, we obtained five years' (2010-2014) of crash data related to roadway departures (i.e., overturn and fixed-object crashes) from the Federal Highway Administration's Highway Safety Information System Database. RESULTS: The results indicate that factors such as driver conditions (e.g., age), environmental conditions (e.g., weather conditions), roadway geometric design features (e.g., shoulder width), and vehicle conditions significantly contributed to the severity of ROR crashes. CONCLUSIONS: Our results provide valuable information for traffic design and management agencies to improve roadside design policies and implementing appropriately forgiving roadsides for errant vehicles. Practical applications: Our results show that increasing shoulder width and keeping fences at the road can reduce ROR crash severity significantly. Also, increasing road friction by innovative materials and raising awareness campaigns for careful driving at daylight can decrease the ROR crash severity.

COVID-19 infection is related to differences in the use of personal protective equipment by orthopaedic specialist trainees caring for hip fracture patients during the second surge of COVID-19 in the North West of England.

INTRODUCTION: Personal protective equipment (PPE) may protect health-care workers from COVID-19 infection and limit nosocomial spread to vulnerable hip fracture patients. METHODS: We performed a cross-sectional survey amongst orthopaedic trainees to explore PPE practice in 19 hospitals caring for hip fracture patients in the North West of England. RESULTS: During the second wave of the pandemic, 14/19 (74%) hospitals experienced an outbreak of COVID-19 amongst staff or patients on the orthopaedic wards. An FFP3 respirator mask was used by doctors in only 6/19 (32%) hospitals when seeing patients with COVID-19 and a cough and in 5/19 (26%) hospitals when seeing asymptomatic patients with COVID-19. A COVID-19 outbreak was reported in 11/13 (85%) orthopaedic units where staff wore fluid resistant surgical masks compared to 3/6 (50%) units using an FFP3 respirator mask (RR 1.69, 95% CI 0.74-3.89) when caring for symptomatic patients with COVID-19. Similarly, a COVID-19 outbreak was reported in more orthopaedic units caring for asymptomatic patients with COVID-19 where staff wore fluid resistant surgical masks (12/14 (86%)) as compared to an FFP3 respirator mask (2/5 (40%)) (RR 2.14, 95% CI 0.72-6.4). CONCLUSION: Urgent re-evaluation of PPE use is required to reduce nosocomial spread of COVID-19, amongst highly vulnerable patients with hip fracture.

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. Understanding the distribution of 5hmC in RNA is essential to determine its biological functionality. Although conventional sequencing techniques allow broad identification of 5hmC, they are both time-consuming and resource-intensive. In this study, we propose a new computational tool called iRNA5hmC-PS to tackle this problem. To build iRNA5hmC-PS we extract a set of novel sequence-based features called Position-Specific Gapped k-mer (PSG k-mer) to obtain maximum sequential information. Our feature analysis shows that our proposed PSG k-mer features contain vital information for the identification of 5hmC sites. We also use a group-wise feature importance calculation strategy to select a small subset of features containing maximum discriminative information. Our experimental results demonstrate that iRNA5hmC-PS is able to enhance the prediction performance, dramatically. iRNA5hmC-PS achieves 78.3% prediction performance, which is 12.8% better than those reported in the previous studies. iRNA5hmC-PS is publicly available as an online tool at http://103.109.52.8:81/iRNA5hmC-PS. Its benchmark dataset, source codes, and documentation are available at https://github.com/zahid6454/iRNA5hmC-PS.

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. Promoters may have both intra- and interclass variation and similarity in terms of consensus sequences. Accurate classification of various types of sigma promoters still remains a challenge. RESULTS: We present iPromoter-BnCNN for identification and accurate classification of six types of promoters-σ24,σ28,σ32,σ38,σ54,σ70. It is a CNN-based classifier which combines local features related to monomer nucleotide sequence, trimer nucleotide sequence, dimer structural properties and trimer structural properties through the use of parallel branching. We conducted experiments on a benchmark dataset and compared with six state-of-the-art tools to show our supremacy on 5-fold cross-validation. Moreover, we tested our classifier on an independent test dataset. AVAILABILITY AND IMPLEMENTATION: Our proposed tool iPromoter-BnCNN web server is freely available at http://103.109.52.8/iPromoter-BnCNN. The runnable source code can be found https://colab.research.google.com/drive/1yWWh7BXhsm8U4PODgPqlQRy23QGjF2DZ. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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. Here, one model is used for feature manipulation and the other one for classification. Using the first method FRnet-Encode, we generate 4096 features for each of the instances in each of the datasets and use the second method, FRnet-Predict, to identify interaction probability employing those features. We have tested our method on four gold standard datasets extensively used by other researchers. Experimental results shows that our method significantly improves over the state-of-the-art method on three out of four drug-target interaction gold standard datasets on both area under curve for Receiver Operating Characteristic (auROC) and area under Precision Recall curve (auPR) metric. We also introduce twenty new potential drug-target pairs for interaction based on high prediction scores. The source codes and implementation details of our methods are available from https://github.com/farshidrayhanuiu/FRnet-DTI/ and also readily available to use as an web application from http://farshidrayhan.pythonanywhere.com/FRnet-DTI/.

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. To build PyFeat we mainly focused on extracting features that capture information about the interaction of neighboring residues to be able to provide more local information. We then employ AdaBoost technique to select features with maximum discriminatory information. In this way, we can significantly reduce the number of extracted features and enable PyFeat to represent the combination of effective features from large neighboring residues. As a result, PyFeat is able to extract features from 13 different techniques and represent context free combination of effective features. The source code for PyFeat standalone toolkit and employed benchmarks with a comprehensive user manual explaining its system and workflow in a step by step manner are publicly available. RESULTS: https://github.com/mrzResearchArena/PyFeat/blob/master/RESULTS.md. AVAILABILITY AND IMPLEMENTATION: Toolkit, source code and manual to use PyFeat: https://github.com/mrzResearchArena/PyFeat/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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. CFSBoost uses a simple yet novel feature group selection procedure which allows the model to be computationally very cheap while being able to achieve state of the art performance. The ensemble model uses extra tree as weak learners inside a boosting scheme while holding on to the best model per iteration. We tested our method of four benchmark datasets, which are also referred as gold standard datasets. Our method was able to achieve better score in terms of area under receiver operating characteristic (auROC) curve on 2 out of the 4 datasets. It was also able to achieve higher area under precision recall (auPR) curve on 3 out of the 4 datasets. It has been argued by researchers that auPR metric is more suitable than auROC for comparison of performance on imbalanced datasets such our benchmark datasets. Our reported result shows that, despite of its simplicity in design, CFSBoost's performance is very satisfactory comparing to other literatures. We also provide 5 new possible interactions for each dataset based on CFSBoost's prediction score.

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. Predicting hotspots and coldspots can enlighten structure of recombination and genome evolution. In this paper, we proposed a predictor, called iRecSpot-EF to predict recombination hot and cold spots. iRecSpot-EF uses a novel set of features extracted from the genome sequences. We introduce the frequency of (l,k,p)-mers in the sequence as features. Our proposed feature extraction method hinges solely upon the nucleotide sequences, thus being cost-effective and robust. After feature extraction, the most informative features are selected using AdaBoost algorithm. We have selected logistic regression as the classification algorithm. iRecSpot-EF was tested on a standard benchmark dataset using cross-fold validation. It achieved an accuracy of 95.14% and area under Receiver Operating Characteristic curve (auROC) of 0.985. The performance of iRecSpot-EF is significantly better than the state-of-the-art methods. iRecSpot-EF is readily available for use from http://iRecSpot.pythonanywhere.com/server. All relevant codes are available via open repository at: https://github.com/mrzResearchArena/iRecSpot.

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. Here we present iDTI-ESBoost, a prediction model for identification of drug-target interactions using evolutionary and structural features. Our proposed method uses a novel data balancing and boosting technique to predict drug-target interaction. On four benchmark datasets taken from a gold standard data, iDTI-ESBoost outperforms the state-of-the-art methods in terms of area under receiver operating characteristic (auROC) curve. iDTI-ESBoost also outperforms the latest and the best-performing method found in the literature in terms of area under precision recall (auPR) curve. This is significant as auPR curves are argued as suitable metric for comparison for imbalanced datasets similar to the one studied here. Our reported results show the effectiveness of the classifier, balancing methods and the novel features incorporated in iDTI-ESBoost. iDTI-ESBoost is a novel prediction method that has for the first time exploited the structural features along with the evolutionary features to predict drug-protein interactions. We believe the excellent performance of iDTI-ESBoost both in terms of auROC and auPR would motivate the researchers and practitioners to use it to predict drug-target interactions. To facilitate that, iDTI-ESBoost is implemented and made publicly available at: http://farshidrayhan.pythonanywhere.com/iDTI-ESBoost/ .

Assessment of human health risk associated with the presence of pesticides in chicken eggs

The presence of pesticides in the environment is highly toxic to environment and human health. Aim of the study was determination, quantification and assessment of associated health risk due to presence of pesticide residues in chicken eggs using high pressure liquid chromatography. HPLC method was successfully employed and validated. From collected samples pesticides were extracted in presence of petroleum ether and acetonitrile. Bifenthrin and Difenoconazole residues were found in all samples with different concentration exceeding maximum residue limits (MRL) of Codex Alimentarius Commission. However imidacloprid was not detected in any sample. Concentration of bifenthrin in house egg samples ranged from 0.256206 to 4.112387 mg/kg while in poultry farm samples it varied from 1.5862 to 5.80796 mg/kg. Difenoconazole was found in concentration of 0.02835 mg/kg, 1.7668 mg/kg, 3.7205 mg/kg, 21.8937 mg/kg 21.9835 mg/kg, 19.26407 mg/kg in samples collected from houses while and in poultry farm samples its detected concentration was 10.939 mg/kg, 12.3296 mg/kg, 29.3617 mg/kg, 18.6116 mg/kg, 40.0523 mg/kg and 19.2335 mg/kg. Concentrations of both pesticides Bifenthrin and Difenoconazole exceeded the MRLs (0.05 mg/kg). Health risk index surpassed 1 (the cut off value) for Difenoconazole in seven samples while for Bifenthrin values were less than 1, indicating the possibility of potential medium to long term health risk associated with ingestion of contaminated eggs.(AU)

Composite selection signals can localize the trait specific genomic regions in multi-breed populations of cattle and sheep.

BACKGROUND: Discerning the traits evolving under neutral conditions from those traits evolving rapidly because of various selection pressures is a great challenge. We propose a new method, composite selection signals (CSS), which unifies the multiple pieces of selection evidence from the rank distribution of its diverse constituent tests. The extreme CSS scores capture highly differentiated loci and underlying common variants hauling excess haplotype homozygosity in the samples of a target population. RESULTS: The data on high-density genotypes were analyzed for evidence of an association with either polledness or double muscling in various cohorts of cattle and sheep. In cattle, extreme CSS scores were found in the candidate regions on autosome BTA-1 and BTA-2, flanking the POLL locus and MSTN gene, for polledness and double muscling, respectively. In sheep, the regions with extreme scores were localized on autosome OAR-2 harbouring the MSTN gene for double muscling and on OAR-10 harbouring the RXFP2 gene for polledness. In comparison to the constituent tests, there was a partial agreement between the signals at the four candidate loci; however, they consistently identified additional genomic regions harbouring no known genes. Persuasively, our list of all the additional significant CSS regions contains genes that have been successfully implicated to secondary phenotypic diversity among several subpopulations in our data. For example, the method identified a strong selection signature for stature in cattle capturing selective sweeps harbouring UQCC-GDF5 and PLAG1-CHCHD7 gene regions on BTA-13 and BTA-14, respectively. Both gene pairs have been previously associated with height in humans, while PLAG1-CHCHD7 has also been reported for stature in cattle. In the additional analysis, CSS identified significant regions harbouring multiple genes for various traits under selection in European cattle including polledness, adaptation, metabolism, growth rate, stature, immunity, reproduction traits and some other candidate genes for dairy and beef production. CONCLUSIONS: CSS successfully localized the candidate regions in validation datasets as well as identified previously known and novel regions for various traits experiencing selection pressure. Together, the results demonstrate the utility of CSS by its improved power, reduced false positives and high-resolution of selection signals as compared to individual constituent tests.

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. METHODS: Programme performance reports were used to assess current resource allocation. Population size estimates derived from mapping conducted in 2011 among injection drug users and hijra, male and female sex workers and programme costs per person documented from programmes in the province of Sindh and also in India were used to estimate the cost to deliver services to 80% of these key population members across Pakistan. Cities were prioritised according to key population size. RESULTS: To achieve 80% population coverage, HIV prevention programmes should be implemented in 10 major cities across Pakistan for a total annual operating cost of approximately US$3.5 million, which is much less than current annual expenditures. The total cost varies according to the local needs and the purchasing power of the local currency. CONCLUSIONS: By prioritising key populations at greatest risk of HIV in cities with the largest populations and limited resources, may be most effectively harnessed to quell the spread of HIV in Pakistan.

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. Seventy six of 229 patients had fungal keratitis. Fungus grew on BA in 60/76(78.9 %), on SDA in 76/76 (100 %), on CA in 40/76(52.6 %) patients. The fungi which grew on BA (60/76) also grown on SDA at the same time. The colony morphologies of different fungi were better on SDA than BA/CA. Among the different culture media, SDA is essential for the isolation fungi in patients with mycotic keratitis.