Inter-strain cross-fertility tests on cultures from Israel and America in the homothallic fungus, Sordaria fimicola

Inter-strain cross-fertility was studied in relation to geographical origin in a homothallic, self-fertile fungus, by looking for hybrid perithecia in wild-type x ascospore colour mutant crosses. Strains from opposite slopes in \u27Evolution Canyon\u27, Israel, showed no cross-fertility with American or Canadian strains; there was excellent cross-fertility with other strains from the same slope, but an occasional lack of cross-fertility with strains from the other slope

Evolving classification of intensive care patients from event data

Objective: This work aims at predicting the patient discharge outcome on each hospitalization day by introducing a new paradigm—evolving classification of event data streams. Most classification algorithms implicitly assume the values of all predictive features to be available at the time of making the prediction. This assumption does not necessarily hold in the evolving classification setting (such as intensive care patient monitoring), where we may be interested in classifying the monitored entities as early as possible, based on the attributes initially available to the classifier, and then keep refining our classification model at each time step (e.g., on daily basis) with the arrival of additional attributes. / Materials and methods: An oblivious read-once decision-tree algorithm, called information network (IN), is extended to deal with evolving classification. The new algorithm, named incremental information network (IIN), restricts the order of selected features by the temporal order of feature arrival. The IIN algorithm is compared to six other evolving classification approaches on an 8-year dataset of adult patients admitted to two Intensive Care Units (ICUs) in the United Kingdom. / Results: Retrospective study of 3452 episodes of adult patients (≥ 16 years of age) admitted to the ICUs of Guy’s and St. Thomas’ hospitals in London between 2002 and 2009. Random partition (66:34) into a development (training) set n = 2287 and validation set n = 1165. Episode-related time steps: Day 0—time of ICU admission, Day x—end of the x-th day at ICU. The most accurate decision-tree models, based on the area under curve (AUC): Day 0: IN (AUC = 0.652), Day 1: IIN (AUC = 0.660), Day 2: J48 decision-tree algorithm (AUC = 0.678), Days 3–7: regenerative IN (AUC = 0.717–0.772). Logistic regression AUC: 0.582 (Day 0)—0.827 (Day 7). / Conclusions: Our experimental results have not identified a single optimal approach for evolving classification of ICU episodes. On Days 0 and 1, the IIN algorithm has produced the simplest and the most accurate models, which incorporate the temporal order of feature arrival. However, starting with Day 2, regenerative approaches have reached better performance in terms of predictive accuracy

Identification and Distribution of Pathogens in a Major Tertiary Hospital of Indonesia

The nosocomial persistence of multiple drug resistance organisms constitutes a global threat. Healthcare-setting acquired infections are subject to substantial selection pressure and are frequently associated with drug resistance. As part of the microbiological surveillance of the Sanglah tertiary referral hospital in the island province of Bali, the distribution of bacterial pathogen and their relative susceptibilities were recorded over a 30 months period. This is the first such detailed study benchmarking the type and sensitivity of bacterial pathogens in a major tertiary referral hospital within Indonesia and it is hoped that it will lead to similar reports in the near future, while informing local and national antimicrobial stewardship policies

High-throughput pipeline for the de novo viral genome assembly and the identification of minority variants from Next-Generation Sequencing of residual diagnostic samples

Motivation: The underlying genomic variation of a large number of pathogenic viruses can give rise to drug resistant mutations resulting in treatment failure. Next generation sequencing (NGS) enables the identification of viral quasi-species and the quantification of minority variants in clinical samples; therefore, it can be of direct benefit by detecting drug resistant mutations and devising optimal treatment strategies for individual patients. / Results: The ICONIC (InfeCtion respONse through vIrus genomiCs) project has developed an automated, portable and customisable high-throughput computational pipeline to assemble de novo whole viral genomes, either segmented or non-segmented, and quantify minority variants using residual diagnostic samples. The pipeline has been benchmarked on a dedicated High-Performance Computing cluster using paired-end reads from RSV and Influenza clinical samples. The median length of generated genomes was 96% for the RSV dataset and 100% for each Influenza segment. The analysis of each set lasted less than 12 hours; each sample took around 3 hours and required a maximum memory of 10 GB. The pipeline can be easily ported to a dedicated server or cluster through either an installation script or a docker image. As it enables the subtyping of viral samples and the detection of relevant drug resistance mutations within three days of sample collection, our pipeline could operate within existing clinical reporting time frames and potentially be used as a decision support tool towards more effective personalised patient treatments. / Availability: The software and its documentation are available from https://github.com/ICONIC-UCL/pipeline / Contact: t.cassarino{at}ucl.ac.uk, pk5{at}sanger.ac.uk / Supplementary information: Supplementary data are available at Briefings in Bioinformatics online

Laboratory Readiness and Response for SARS-Cov-2 in Indonesia

The laboratory diagnosis of SARS-CoV-2 infection comprises the informational cornerstone in the effort to contain the infections. Therefore, the ability to leverage laboratories' capacity in diagnostic testing and to increase the number of people being tested are critical. This paper reviews the readiness of Indonesian laboratories during the early months of the pandemic. It discusses the success of cross-sectoral collaboration among previously siloed national and sub-national government institutions, international development agencies, and private sector stakeholders. This collaboration managed to scale-up the COVID-19 referral laboratory network from one Ministry of Health NIHRD laboratory in the capital to 685 laboratories across 34 provinces. However, this rapid growth within 12 months since the first Indonesian case was discovered remained insufficient to cater for the constantly surging testing demands within the world's fourth most populous country. Reflecting on how other countries built their current pandemic preparedness from past emergencies, this paper highlights challenges and opportunities in workforce shortage, logistic distribution, and complex administration that need to be addressed

Assessing hepatitis C virus distribution among vulnerable populations in London using whole genome sequencing: results from the TB-REACH study [version 1; peer review: awaiting peer review]

Background: Injecting drugs substantially increases the risk of hepatitis C virus (HCV) infection and is common in vulnerable population groups, such as the homeless and prisoners. Capturing accurate data on relative genotype distribution within these groups is essential to inform strategies to reduce HCV transmission. The aim of this study was to utilise a next-generation whole-genome sequencing method recently validated by Public Health England, in order to produce near complete HCV genomes. / Methods: In total, 98 HCV positive patients were recruited from homeless hostels and drug treatment services through the National Health Services (NHS) Find and Treat (F&T) Service between May 2011 and June 2013 in London, UK. Samples were sequenced by Next-generation sequencing, with 88 complete HCV genomes constructed by a de novo assembly pipeline. They were analysed phylogenetically for an estimate of their genetic distance. / Results: Of the 88 complete HCV genomes, 50/88 (56.8%) were genotype 1; 32/88 (36.4%) genotype 3; 4/88 (4.5%) genotype 2; and 1/88 (1.1%) for genotypes 4 and 6 each. Subtype 1a had the highest number of samples (51.1%), followed by subtype 3a (35.2%), 1b (5.7%), and 2b (3.4%). Samples collected from drug treatment services had the highest number of genotype 1 (69%); genotypes 4 and 6 were only found from samples collected in homeless shelters. Small clusters of highly related genomic sequences were observed both across and within the vulnerable groups sampled. / Conclusions: Subsequent phylogenetic analysis provides a first indication that there are related HCV sequences amongst the three vulnerable population groups, reflecting their overlapping social behaviours. This study is the first presentation of whole genome HCV sequences from such vulnerable groups in London and paves the way for similar research in the future

Knowledge, Attitudes, and Behaviors on Utilizing Mobile Health Technology for TB in Indonesia: A Qualitative Pilot Study

Tuberculosis (TB) infections remain a global health burden with a high incidence rate in South-East Asia, including Indonesia. TB control strategy is founded on early case detection and complete treatment to minimize transmission and prevent the emergence of drug resistance. However, many patients face challenges to comply with daily medication, causing many to adhere inconsistently or stop prematurely. Technological solutions could enhance adherence to treatment and support national screening and follow-up policies. These include telephone video communication, enabling health professionals to watch patients take their medication, address patients' concerns, and provide advice and support. This manuscript describes the outcome of a qualitative pilot study, based on a series of focus group discussions to assess the knowledge, attitudes, and behaviors, on the potential utilization of mobile technology for health purposes with a particular focus on TB treatment follow-up. The findings illustrate that general knowledge of mobile health technologies, of their legal framework of operations, and of their exact potential within the healthcare system is incomplete or poor. The novel findings are as follows: (a) the willingness of participants to learn about these technologies, (b) the open and welcoming attitude toward receiving such information even within frontline community settings, and (c) the willingness to back a government-supported, healthcare-driven set of such initiatives. Potential implementation barriers have also been highlighted. This study is an important first step toward understanding the attitudes and behaviors on utilizing mobile health technology for TB in Indonesia