Grasp

PhenoScanner consists of a Perl interface (with R command line tool) that connects to a MySQL database. To develop the initial database, we collated 137 genotype–phenotype association datasets, including results for anthropometric traits, blood pressure, lipids, cardiometabolic diseases, renal function measures, glycemic traits, inflammatory diseases, psychiatric diseases and smoking phenotypes (Supplementary Table). We also included the NHGRI-EBI GWAS catalog, NHLBI GRASP (Leslie et al., 2014 (link)) and dbGaP catalogues of associations. To ensure consistent formatting, we aligned alleles to the plus strand, added or updated chromosome positions to build 37 using dbSNP (release 138) (Sherry et al., 2001 (link)) and liftOver (https://genome.ucsc.edu/cgi-bin/hgLiftOver), and updated old rsIDs to dbSNP release 141 (Supplementary Data). Linkage disequilibrium (LD) measures between neighbouring variants in the autosomal chromosomes were calculated using the phased haplotypes from European samples in 1000 Genomes phase 3 (N = 503) (1000 Genomes Project Consortium et al., 2012 (link)). Variants with minor allele frequencies <0.5% were removed along with multiallelic variants and large indels ( $\ge$ 5 bases). For each remaining variant, we calculated $D\prime$ and r² for variants within 500 kb in either direction, and kept LD statistics for pairs of variants with $r^2\ge 0.6$ . LD statistics based on the CEU population from Hapmap 2 release 24 (Frazer et al., 2007 (link)) are also available (Supplementary Data).
The user may enter either one variant into the text box on the website or upload up to 50 variants in a text file. The Perl interface annotates the variant alleles using dbSNP, identifies proxies of the specified variants (if requested) in the database according to a user-specified pairwise r² threshold, and queries the catalogue of genotype–phenotype associations for the specified variants and their proxies. Association results are collated and presented with respect to the same effect and non-effect alleles for each variant. The associations with proxies are aligned according to the effect and non-effect alleles of the corresponding primary variant of interest for added ease of interpretation. The output is a file of associations, which is made available to download. There is also a P value filter option that only retains results with study-specific P values less than the selected threshold.

After logging into Rayyan, users are presented with a dashboard of all their current reviews (Fig. 2). They can either create a new review or work on an existing one. For each review, they upload one or more citation file obtained from searching different databases. Rayyan supports several standard formats, e.g., RefMan RIS and EndNote. At the outset, Rayyan processes the citation file by extracting different metadata, e.g., title, authors, and computing others, e.g., MeSH terms and language of the article, for each article or study in the citation file. These will then populate the facets in the review workbench (Fig. 3) to help explore and filter the studies. MeSH terms are presented as a word cloud allowing users to quickly grasp the main topics presented in the studies. In addition, users can filter studies based on two predefined lists of keywords that will most likely hint to either include or exclude a study. The user can also modify these two lists by removing and adding keywords, thus giving more flexibility in the labeling and selection of studies. Rayyan was seeded with two lists obtained from the EMBASE project to filter RCTs [14 ].
Fig. 2

Rayyan dashboard. The dashboard lists all reviews for this user as well as for each review the progress in terms of decisions made and estimated time spent working on the review for all collaborators

Fig. 3

Rayyan workbench. The workbench shows the different ways users interact with the app

Users can also label their citations and define their individual reasons for exclusion which facilitates the sharing and tracking of these decisions. Citations can be explored through a similarity graph (Fig. 4) in which the citations are represented as nodes in a graph and clustered based on how similar they are (using an edit distance) in terms of title and abstract content as well as common authors. The similarity thresholds can be tuned independently for each attribute, i.e., title, abstract, and authors, as well an overall threshold.
Fig. 4

Similarity graph. Interacting with citations through the similarity graph

In order to achieve the two-fold aim of this study; (1) comparing Bayesian indices and (2) provide visual guides for an intuitive understanding of the numeric values in relation to a known frame of reference (the frequentist p-value), we will start by presenting the relationship between these indices and main sources of variance, such as sample size, noise and null hypothesis (true if absence of effect, false if presence of effect). We will then compare Bayesian indices with the frequentist p-value and its commonly used thresholds (0.05, 0.01, 0.001). Finally, we will show the mutual relationship between three recommended Bayesian candidates. Taken together, these results will help us outline guides to ease the reporting and interpretation of the indices.
In order to provide an intuitive understanding of values, data processing will focus on creating clear visual figures to help the user grasp the patterns and variability that exists when computing the investigated indices. Nevertheless, we decided to also mathematically test our claims in cases where the graphical representation begged for a deeper investigation. Thus, we fitted two regression models to assess the impact of sample size and noise, respectively. For these models (but not for the figures), to ensure that any differences between the indices are not due to differences in their scale or distribution, we converted all indices to the same scale by normalizing the indices between 0 and 1 (note that BFs were transformed to posterior probabilities, assuming uniform prior odds) and reversing the p-values, the MAP-based p-values and the ROPE indices so that a higher value corresponds to stronger “significance.”
The statistical analyses were conducted using R (R Core Team, 2019 ). Computations of Bayesian models were done using the rstanarm package (Goodrich et al., 2019 ), a wrapper for Stan probabilistic language (Carpenter et al., 2017 (link)). We used Markov Chain Monte Carlo sampling (in particular, Hamiltonian Monte Carlo; Gelman et al., 2014 ) with 4 chains of 2000 iterations, half of which used for warm-up. Mildly informative priors (a normal distribution with mean 0 and SD 1) were used for the parameter in all models. The Bayesian indices were calculated using the bayestestR package (Makowski et al., 2019 (link)).

With the idea of an MFH technique limited only to small tumor sizes, a special instrument was needed to deliver the heat in a minimally invasive manner. One potential end use of the LIH is during a laparoscopic procedure, a minimal invasive surgery where the surgeon inserts multiple tube-like instruments (with different functions) through small incisions in the patient’s body [53 –56 (link)]. This instrument was designed with a tube-like structure, considering current laparoscopic instrument designs, and its performance is mainly limited by the magnetic generator parameters and the electrical current flowing through it. The TRIH was designed for prostate cancer malignancies. This instrument could be used to reach the prostate transrectally or to access the prostate by placing it in contact with the perineum. A normal prostate has a volume of approximately 25 cm³. Nevertheless, in cases of benign prostate hyperplasia (BPH) or prostate cancer, the prostate volume can increase to over 30 cm³. This was the rationale behind designing this coil larger than the one in the LIH.
The enclosures for each instrument were designed to have a medical device appearance, while also providing a means to maintain the internal temperature of the coil below 155 °C to avoid damage. Both embodiments were designed using NX software (Siemens, Plano, TX, USA) and were partially constructed by a Zortrax Inkspire 3D printer (Zortrax SA, Olsztyn, Poland) with epoxy-based resin from the same company. The constructs can regulate the coil temperature by circulating water (20 °C–25 °C) throughout the instrument similar as inside MFH coils [57 (link)]. The materials for the LIH design and its measurements were selected considering the dimensions of current laparoscopic instruments. A polycarbonate tubing (⌀_inner = 12.7 mm × ⌀_outer = 15.9 mm × 1.6 mm wall) (Small Parts Inc., Logansport, IN, USA) connected the 3D printed parts (handle and tip). The handle included the water inlet and outlets, as well as a nylon wet-location multi-cord grip (McMaster-Carr, Elmhurst, IL, USA) for the 6 AWG Type 2 Litz wires that connect directly to the circuit. The tip was where the coil was placed and served as a connection point for the Masterflex^® 25 L/S^® inner tubing (Cole-Palmer, Vernon Hills, IL, USA). This ensures that the water flows right into the coil before exiting the instrument. A cap at the tip ensured fast replacement of the coil if a problem was encountered. The overall length of the LIH was approximately 23 cm (see figure 3(a)).
The TRIH design was similar in the parts used, but different in shape. In this case, a tygon flexible tubing (⌀_inner = 25.4 mm × ⌀_outer = 31.8 mm × 3.2 mm wall) (McMaster-Carr, Elmhurst, IL, USA) connected the 3D printed parts (handle and tip). Similar to the LIH, the handle included the water inlet and outlets, as well as the nylon wet-location multi-cord grip for the 6 AWG Type 2 Litz wires that connect directly to the circuit. The tip in this case also enclosed the coil and served as a connection point for the 25 L/S^® inner tubing. This part had a 4-jet nozzle for improved heat removal of this larger coil. This design also had a cap for fast replacement of the coil if a problem was encountered. Because of the 3D printed enclosure, the instrument itself is slightly larger than what we aim to use in the future. However, this does not affect its performance, as the coil has the exact dimensions we designed for. The overall length of the TRIH was approximately 25 cm (see figure 3(b)).

This test was used to evaluate muscle strength of the forelimbs. The mouse was lowered over a grid and permitted to grip the top portion of the grid with its forepaws. The mouse was gently pulled from the grid by its tail. Grip strength was represented as the g-force required for the mouse to release its grip on the grid. This procedure was repeated 3 times and the mean grip strength was calculated for that session.

The inclusion criteria for this meta-analysis were as follows: Study design: randomized controlled trials (RCTs) comparing the effects of quantified versus willful grip exercises in PICC patients; Research population: patients with indwelling PICCs; Interventions: the experimental group underwent quantified grip exercises by designed nursing care plans, the control group underwent grip exercises at their own willingness; Observation indicators: The main indicators are the incidence of venous thrombosis and venous hemodynamics, and the secondary indicators are the infection rate associated to PICC. Venous thrombosis and venous blood flow velocity were examined with Doppler color ultrasound. The maximum venous blood flow velocity refers to the maximum blood flow velocity per second in the blood vessel. The exclusion criteria for this meta-analysis were: Replicated publications; Literature reports for which the full-text could not be obtained or the data could not be extracted.