TASK DEFINITION: In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.
PROBLEM: Several tiny organisms of various size ranges present in air are called airborne particles or bioaerosol which mainly includes live or dead fungi and bacteria, their secondary metabolites, viruses, pollens, etc. which have been related to health issues of human beings and other life stocks. Bio-terror attacks in 2001 as well as pandemic outbreak of flue due to influenza A H1N1 virus in 2009 have alarmed us about the importance of bioaerosol research. Hence characterization i.e. identification and quantification of different airborne microorganisms in various indoor environments is necessary to identify the associated risks and to establish exposure threshold. Along with the bioaerosol sampling and their analytical techniques, various literatures revealing the concentration levels of bioaerosol have been mentioned in this review thereby contributing to the knowledge of identification and quantification of bioaerosols and their different constituents in various indoor environments (both occupational and non-occupational sections). Apart from recognition of bioaerosol, developments of their control mechanisms also play an important role. Hence several control methods have also been briefly reviewed. However, several individual levels of efforts such as periodic cleaning operations, maintenance activities and proper ventilation system also serve in their best way to improve indoor air quality.

SOLUTION: Review of bioaerosols in indoor environment with special reference to sampling, analysis and control mechanisms

PROBLEM: Background: Adverse events are associated with significant morbidity, mortality and cost in hospitalized patients. Measuring adverse events is necessary for quality improvement, but current detection methods are inaccurate, untimely and expensive. The advent of electronic health records and the development of automated methods for encoding and classifying electronic narrative data, such as natural language processing, offer an opportunity to identify potentially better methods. The objective of this study is to determine the accuracy of using automated methods for detecting three highly prevalent adverse events: a) hospital-acquired pneumonia, b) catheter-associated bloodstream infections, and c) in-hospital falls. Methods/design: This validation study will be conducted at two large Canadian academic health centres: the McGill University Health Centre (MUHC) and The Ottawa Hospital (TOH). The study population consists of all medical, surgical and intensive care unit patients admitted to these centres between 2008 and 2014. An automated detection algorithm will be developed and validated for each of the three adverse events using electronic data extracted from multiple clinical databases. A random sample of MUHC patients will be used to develop the automated detection algorithms (cohort 1, development set). The accuracy of these algorithms will be assessed using chart review as the reference standard. Then, receiver operating characteristic curves will be used to identify optimal cut points for each of the data sources. Multivariate logistic regression and the areas under curve (AUC) will be used to identify the optimal combination of data sources that maximize the accuracy of adverse event detection. The most accurate algorithms will then be validated on a second random sample of MUHC patients (cohort 1, validation set), and accuracy will be measured using chart review as the reference standard. The most accurate algorithms validated at the MUHC will then be applied to TOH data (cohort 2), and their accuracy will be assessed using a reference standard assessment of the medical chart. Discussion: There is a need for more accurate, timely and efficient measures of adverse events in acute care hospitals. This is a critical requirement for evaluating the effectiveness of preventive interventions and for tracking progress in patient safety through time.

SOLUTION: Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol

PROBLEM: was performed with RdRpSeq-Fwd (similar to the first round) and RdRpSeq-Rnest primer by using MyFi ™️ Mix Kit (Bioline, USA) according to the following parameters: 95 C for 1 min, followed by 35 cycles at 95 C for 15 seconds, 56 C for 15 seconds, 72 C for 20 seconds, and 2 min at 72 C for terminal elongation[5]. The resulted amplicons were separated by gel electrophoresis, and the DNA fragments were excised, purified and subjected to sequencing in both directions. The obtained sequences were verified by basic local alignment search tool. As for the amplification of complete N gene region, cDNA was synthesized by using SuperScript III First Strand Synthesis System for RT-PCR (Invitrogen, USA) with NR2 primer (Table 1). Two microliters of the generated cDNA was subjected to one round of PCR cycle by using MyFi ™️ Mix Kit (Bioline, USA) with NF1 and NR2 (Table 1) primers following the parameters: 95 C for 1 min followed by 25 cycles at 95 C for 15 seconds, 52 C for 15 seconds and 72 C for 30 seconds and terminal elongation at 72 C for 5 min. Then, 0.5 L of the resulted PCR amplicon was further subjected to the second round PCR following the abovementioned parameters. The resulted PCR product was separated by gel electrophoresis while the DNA fragment was excised, purified and subjected to sequencing by using NF1, NR1, NF2, NR2 and N350R primers. The obtained sequences were analyzed by using ChromasPro version 1.7.4 (Technelysium Pty Ltd, Australia). Multiple sequence alignment was performed by using Clustal X (version 2.0.11) with 44 human-derived (including the Malaysian case; MyH010414; GenBank accession No: KP769415) and 3 camel-derived MERS-CoV sequences[6]. Maximum likelihood analysis was conducted by using Molecular Evolutionary Genetics Analysis 6.06[7]. The evolutionary history was computed by using Tamura-Nei model with 1 000 replicates of bootstrap analysis. Extracted RNA from combination of throat and nasal swabs from the case was detected positive both in rRT-PCR and conventional RT-PCR for partial RdRp and N gene. The cycle threshold values for upE, ORF1a and ORF1b were 28.19, 29.16 and 33.36, respectively. Amplification of partial RdRp and N gene regions resulted in DNA fragment of ~240 bp and ~310 bp, respectively (data not shown). Initial sequencing result of these fragments showed highly identical nucleotide sequence (99%) to MERS-CoV strain Jeddah_1_2013 (KJ556336.1), further confirming the MERS-CoV infection. JX869059/Human/Jeddah/HCoV EMC/2012 KC776174/Human/Jordan/Jordan N3/2012 Clade A 0.0005 67 63 63 63 66 50 62 Clade B Figure 1. Phylogenetic analysis of N gene region of Malaysia MERS-CoV case together with 3 camel-derived and 43 human-derived MERS-CoV sequences from 2012-2014. Red diamond: Malaysian case; Blank diamond: sequences from the camel-derived MERS-CoV.

SOLUTION:
Nucleocapsid gene analysis from an imported case of Middle East respiratory syndrome coronavirus, Malaysia Asian Pacific Journal of Tropical Disease