Study of Relationships between Ceiling Smoke Leakage Rate and Evacuation Time in the Ward

With reference to the requirements of CNS 15038 and testing principles, this study proposes a set of equipment for measuring the leakage volume of ceilings and provides detailed assembly specifications for future users. In this study, a total of 405 tests were conducted as part of a set of experiments for measuring the leakage volume of ceilings, using various ceiling materials, ceiling sizes, and construction methods, in conjunction with the principles of fluid mechanics, to propose a method for evaluating the leakage volume of ceilings of various sizes and materials. Two cases—bottom-up airflow and top-down airflow—were considered. According to our research findings, in the case of bottom-up airflow, the pressure difference, panel weight, and panel size were correlated with the leakage volume; the more significant the pressure difference, the larger the leakage volume; the heavier the panel weight, the more minor the leakage volume; and the larger the panel size, the more significant the leakage volume. On the other hand, in the case of top-down airflow, different leakage volumes were observed for different ceiling materials, even if the ceiling size was identical. On the other hand, when the ceiling material was the same, and the ceiling size was different, there was not a positive relationship between the leakage volume and a larger panel size; instead, the leakage volume observed for the largest panel was the smallest. Finally, in this study we propose a volumetric leakage assessment table for assessing a ceiling as a whole, which can be utilized by engineers in the future to calculate the smoke leakage value and to estimate the smoke fall time for ward escape designs.

Panel Informativity Optimizer (PIO): an R package to improve cancer NGS panel informativity

Mutation detection by next generation sequencing (NGS) is routinely used for cancer diagnosis. Selecting an optimal set of genes for a given cancer is not trivial as it has to optimize informativity (i.e. the number of patients with at least one mutation in the panel), while minimizing panel length in order to reduce sequencing costs and increase sensitivity. We propose herein Panel Informativity Optimizer (PIO), an open-source software developed as an R package with a user-friendly graphical interface to help optimize cancer NGS panel informativity. Using patient-level mutational data from either private datasets or preloaded dataset of 91 independent cohort from 31 different cancer type, PIO selects an optimal set of genomic intervals to maximize informativity and panel size in a given cancer type. Different options are offered such as the definition of genomic intervals at the gene or exon level, and the use of optimization strategy at the patient or patient per kilobase level. PIO can also propose an optimal set of genomic intervals to increase informativity of custom panels. A panel tester function is also available for panel benchmarking. Using public databases, as well as data from real-life settings, we demonstrate that PIO allows panel size reduction of up to 1000kb, and accurately predicts the performance of custom or commercial panels. PIO is available online at https://vincentalcazer.shinyapps.io/Panel_informativity_optimizer/ or can be set on a locale machine from https://github.com/VincentAlcazer/PIO.

Exploring Factors that May Influence Ontario Nurse Practitioners’ Patient Panel Size in Primary Healthcare Settings

Limited knowledge exists about the factors that may influence nurse practitioner (NP) patient panel size. Patient panel size refers to the number of patients for whom a NP is their usual care provider. Increased knowledge of these factors may improve patient care, NP practice, and primary health care (PHC) workforce planning. Two hundred and eighty-three NPs working in Ontario PHC were surveyed to explore patient, NP, and organizational factors that may influence NP patient panel size. Three factors were associated with NP panel size. Higher percentages of certain health conditions and/or longer appointment time for multi-morbid and palliative care were associated with smaller NP patient panel size. NPs who worked more hours per week had larger patient panels. Also, the PHC practice model was related to NP patient panel size, which was largest in NP-led clinics. Decision makers can use these findings to support optimization of NP patient panel size.

Exploring Factors that May Influence Ontario Nurse Practitioners’ Patient Panel Size in Primary Healthcare Settings

Limited knowledge exists about the factors that may influence nurse practitioner (NP) patient panel size. Patient panel size refers to the number of patients for whom a NP is their usual care provider. Increased knowledge of these factors may improve patient care, NP practice, and primary health care (PHC) workforce planning. Two hundred and eighty-three NPs working in Ontario PHC were surveyed to explore patient, NP, and organizational factors that may influence NP patient panel size. Three factors were associated with NP panel size. Higher percentages of certain health conditions and/or longer appointment time for multi-morbid and palliative care were associated with smaller NP patient panel size. NPs who worked more hours per week had larger patient panels. Also, the PHC practice model was related to NP patient panel size, which was largest in NP-led clinics. Decision makers can use these findings to support optimization of NP patient panel size.

MSIFinder: A python package for detecting MSI status using random forest classifier.

2601 Background: Microsatellite instability (MSI) is a common genomic alteration in several tumors, such as colorectal cancer, endometrial carcinoma, and stomach, which is characterized as microsatellite instability-high (MSI-H) and microsatellite stable (MSS) based on a high degree of polymorphism in microsatellite lengths. MSI is a predictive biomarker for immunotherapy efficacy in advanced/metastatic solid tumors, especially in colorectal cancer (CRC) patients. Several computational approaches based on target panel sequencing data have been used to detect MSI; However, they are considerably affected by the sequencing depth and panel size. Methods: We developed MSIFinder, a python package for automatic MSI classification, using random forest classifier (RFC)-based genome sequencing, which is a machine learning technology. We included 19 MSI-H and 25 MSS samples as training sets. First, RFC model were built by 54 feature markers from the training sets. Second. The software was validated the classifier using a test set comprising 21 MSI-H and 379 MSS samples. Results: With this test set, MSIFinder achieved a sensitivity (recall) of 0.997, a specificity of 1, an accuracy of 0.998, a positive predictive value (PPV) of 0.954, an F1 score of 0.977, and an area under curve (AUC) of 0.999. We discovered that MSIFinder is less affected by low sequencing depth and can achieve a concordance of 0.993, while exhibiting a sequencing depth of 100×. Furthermore, we realized that MSIFinder is less affected by the panel size and can achieve a concordance of 0.99 when the panel size is 0.5 m (million base). Conclusions: These results indicated that MSIFinder is a robust MSI classification tool and not affected by the panel size and sequencing depth. Furthermore, MSIFinder can provide reliable MSI detection for scientific and clinical purposes.[Table: see text]

MSIFinder: a python package for detecting MSI status using random forest classifier

Background Microsatellite instability (MSI) is a common genomic alteration in colorectal cancer, endometrial carcinoma, and other solid tumors. MSI is characterized by a high degree of polymorphism in microsatellite lengths owing to the deficiency in the mismatch repair system. Based on the degree, MSI can be classified as microsatellite instability-high (MSI-H) and microsatellite stable (MSS). MSI is a predictive biomarker for immunotherapy efficacy in advanced/metastatic solid tumors, especially in colorectal cancer patients. Several computational approaches based on target panel sequencing data have been used to detect MSI; however, they are considerably affected by the sequencing depth and panel size. Results We developed MSIFinder, a python package for automatic MSI classification, using random forest classifier (RFC)-based genome sequencing, which is a machine learning technology. We included 19 MSI-H and 25 MSS samples as training sets. First, we selected 54 feature markers from the training sets, built an RFC model, and validated the classifier using a test set comprising 21 MSI-H and 379 MSS samples. With this test set, MSIFinder achieved a sensitivity (recall) of 1.0, a specificity of 0.997, an accuracy of 0.998, a positive predictive value of 0.954, an F1 score of 0.977, and an area under the curve of 0.999. To further verify the robustness and effectiveness of the model, we used a prospective cohort consisting of 18 MSI-H samples and 122 MSS samples. MSIFinder achieved a sensitivity (recall) of 1.0 and a specificity of 1.0. We discovered that MSIFinder is less affected by a low sequencing depth and can achieve a concordance of 0.993 while exhibiting a sequencing depth of 100×. Furthermore, we realized that MSIFinder is less affected by the panel size and can achieve a concordance of 0.99 when the panel size is 0.5 M (million bases). Conclusion These results indicate that MSIFinder is a robust and effective MSI classification tool that can provide reliable MSI detection for scientific and clinical purposes.

Revisiting the Delphi Technique in Orthodontics: A Systematic Review

Introduction: The Delphi technique is an iterative, multi-stage process that consists of questioning a panel of experts through a structured group communication process to reach a consensus on specific issues. The study is a systematic review of the available literature in orthodontics which has utilised the Delphi technique to seek consensus on a range of issues. Aim: To identify and summarise the studies which have utilised the Delphi technique as a method for gathering consensus in the speciality field of orthodontics. The study evaluated the various characteristics of the Delphi technique. Materials and Methods: This systematic review followed the methodology of a preset article inclusion and exclusion criteria using an electronic database search using the keywords consensus, Delphi, Delphi technique, Delphi studies, expert opinion was conducted in March 2021. A range of electronic databases comprising PubMed, Excerpta Medica database (EMBASE), Google Scholar, Web of Science and Scopus were searched dated from (January 1990 to March 2021) to identify the studies which involved the use of Delphi in orthodontics. Following this, two authors reviewed and scored each of the studies before finalising a list of five studies to be included in this review. Results: The searches revealed a total of 187 studies out of which only five studies met the inclusion criteria and were included in this study. Each of the five studies was assessed by two independent assessors. The five studies involved an expert panel from multiple health professionals involving orthodontic specialists, maxillofacial surgeons, nurses, speech therapists, and nutritionists. Five domains (theme, panel constitution, panel size, number of iterations and the level at which consensus reached) were identified and assessed in each of the five studies. Conclusion: The study has identified and reviewed the Delphi technique and its usage in orthodontics but has also provided a sound description and elaboration of the various components and characteristics of the Delphi technique in addition to providing some correlations between expert panel size and the number of iterations.