SIAL: A simple image analysis library for wet-lab scientists.
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ABSTRACT:
SUBMITTER: Tyrpak DR
PROVIDER: S-EPMC8373214 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Publications
SIAL: A simple image analysis library for wet-lab scientists.
Tyrpak David R DR Li Yaocun Y Lei Siqi S MacKay J Andrew JA
Journal of open source software 20201214 56
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Project description:BackgroundTissue handling is one of the pivotal parts of surgical procedures. We aimed to elucidate the characteristics of experts' left-hand during laparoscopic tissue dissection.MethodsParticipants performed tissue dissection around the porcine aorta. The grasping force/point of the grasping forceps were measured using custom-made sensor forceps, and the forceps location was also recorded by motion capture system (Mocap). According to the global operative assessment of laparoscopic skills (GOALS), two experts scored the recorded movies, and based on the mean scores, participants were divided into three groups: novice (<10), intermediate (10≤ to <20), and expert (≤20). Force-based metrics were compared among the three groups using the Kruskal-Wallis test. Principal component analysis (PCA) using significant metrics was also performed.ResultsA total of 42 trainings were successfully recorded. The statistical test revealed that novices frequently regrasped a tissue (median total number of grasps, novices: 268.0 times, intermediates: 89.5, experts: 52.0, p < 0.0001), the traction angle became stable against the aorta (median weighted standard deviation of traction angle, novices: 30.74°, intermediates: 26.80, experts: 23.75, p = 0.0285), and the grasping point moved away from the aorta according to skill competency [median percentage of grasping force applied in close zone (0 to 2.0 cm from aorta), novices: 34.96 %, intermediates: 21.61 %, experts: 10.91 %, p = 0.0032]. PCA showed that the efficiency-related (total number of grasps) and effective tissue traction-related (weighted average grasping position in Y-axis and distribution of grasping area) metrics mainly contributed to the skill difference (proportion of variance of first principal component: 60.83 %).ConclusionThe present results revealed experts' left-hand characteristics, including correct tissue grasping, sufficient tissue traction from the aorta, and stable traction angle. Our next challenge is the provision of immediate and visual feedback onsite after the present wet-lab training, and shortening the learning curve of trainees.
| S-EPMC11639329 | biostudies-literature
Project description:Most glioblastoma studies incorporate the layer of tumor molecular subtype based on the four-subtype classification system proposed in 2010. Nevertheless, there is no universally recognized and convenient tool for glioblastoma molecular subtyping, and each study applies a different set of markers and/or approaches that cause inconsistencies in data comparability and reproducibility between studies. Thus, this study aimed to create an applicable user-friendly tool for glioblastoma classification, with high accuracy, while using a significantly smaller number of variables. The study incorporated a TCGA microarray, sequencing datasets, and an independent cohort of 56 glioblastomas (LUHS cohort). The models were constructed by applying the Agilent G4502 dataset, and they were tested using the Affymetrix HG-U133a and Illumina Hiseq cohorts, as well as the LUHS cases. Two classification models were constructed by applying a logistic regression classification algorithm, based on the mRNA levels of twenty selected genes. The classifiers were translated to a RT-qPCR assay and validated in an independent cohort of 56 glioblastomas. The classification accuracy of the 20-gene and 5-gene classifiers varied between 90.7-91% and 85.9-87.7%, respectively. With this work, we propose a cost-efficient three-class (classical, mesenchymal, and proneural) tool for glioblastoma molecular classification based on the mRNA analysis of only 5-20 genes, and we provide the basic information for classification performance starting from the wet-lab stage. We hope that the proposed classification tool will enable data comparability between different research groups.
| S-EPMC9784712 | biostudies-literature
Project description:How to use bioinformatics methods to quickly and accurately locate the effective targets of traditional Chinese medicine monomer (TCM) is still an urgent problem needing to be solved. Here, we used high-throughput sequencing to identify the genes that were up-regulated after cells were treated with TCM monomers and used bioinformatics methods to analyze which transcription factors activated these genes. Then, the binding proteins of these transcription factors were analyzed and cross-analyzed with the docking proteins predicted by small molecule reverse docking software to quickly and accurately determine the monomer's targets. Followeding this method, we predicted that the TCM monomer Daphnoretin (DT) directly binds to JAK2 with a binding energy of -5.43 kcal/mol, and activates the JAK2/STAT3 signaling transduction pathway. Subsequent Western blotting and in vitro binding and kinase experiments further validated our bioinformatics predictions. Our method provides a new approach for quickly and accurately locating the effective targets of TCM monomers, and we also have discovered for the first time that TCM monomer DT is an agonist of JAK2.
| S-EPMC9504604 | biostudies-literature
Project description:COMPARE Food Ring Trial - wet lab
| PRJEB27238 | ENA
Project description:BackgroundOur aim was to build a skill assessment system, providing objective feedback to trainees based on the motion metrics of laparoscopic surgical instruments.MethodsParticipants performed tissue dissection around the aorta (tissue dissection task) and renal parenchymal closure (parenchymal-suturing task), using swine organs in a box trainer under a motion capture (Mocap) system. Two experts assessed the recorded movies, according to the formula of global operative assessment of laparoscopic skills (GOALS: score range, 5-25), and the mean scores were utilized as objective variables in the regression analyses. The correlations between mean GOALS scores and Mocap metrics were evaluated, and potential Mocap metrics with a Spearman's rank correlation coefficient value exceeding 0.4 were selected for each GOALS item estimation. Four regression algorithms, support vector regression (SVR), principal component analysis (PCA)-SVR, ridge regression, and partial least squares regression, were utilized for automatic GOALS estimation. Model validation was conducted by nested and repeated k-fold cross validation, and the mean absolute error (MAE) was calculated to evaluate the accuracy of each regression model.ResultsForty-five urologic, 9 gastroenterological, and 3 gynecologic surgeons, 4 junior residents, and 9 medical students participated in the training. In both tasks, a positive correlation was observed between the speed-related parameters (e.g., velocity, velocity range, acceleration, jerk) and mean GOALS scores, with a negative correlation between the efficiency-related parameters (e.g., task time, path length, number of opening/closing operations) and mean GOALS scores. Among the 4 algorithms, SVR showed the highest accuracy in the tissue dissection task ([Formula: see text]), and PCA-SVR in the parenchymal-suturing task ([Formula: see text]), based on 100 iterations of the validation process of automatic GOALS estimation.ConclusionWe developed a machine learning-based GOALS scoring system in wet lab training, with an error of approximately 1-2 points for the total score, and motion metrics that were explainable to trainees. Our future challenges are the further improvement of onsite GOALS feedback, exploring the educational benefit of our model and building an efficient training program.
| S-EPMC9399206 | biostudies-literature
Project description:While in the last decade there has been significant technical infrastructure development to support standards-based image exchange through organizations like Integrating the Healthcare Enterprise, Carequality, DICOM, and HL7 FHIR, the human operationalization of such infrastructure using centralized, intuitive, standards-based applications remains the cornerstone of effective and reliable electronic image exchange. Image libraries managing the highly transactional and often uncertain inflows and outflows of images have a unique perspective on the challenges of image exchange. This manuscript will summarize frequent collaboration and communication, release of information, staffing, technology, information localization, and analytics difficulties for image exchange from the perspective of the image library staff managing the transactions.
| S-EPMC9485382 | biostudies-literature
Project description:Here we describe a new, non-human, ex-vivo model (goat eye model) for training surgeons in DMEK surgeons. In a wet lab setting, goat eyes were used to obtain a pseudo-DMEK graft of 8 mm from the goat lens capsule that was injected into another goat eye with the same maneuvers described for human DMEK. The DMEK pseudo-graft can be easily prepared, stained, loaded, injected, and unfolded into the goat eye model reproducing the similar maneuvers used for DMEK in a human eye, except for the descemetorhexis, which cannot be performed. The pseudo-DMEK graft behaves similar to human DMEK graft and useful for surgeons to experience and understand steps of DMEK early in learning curve. The concept of a non-human ex-vivo eye model is simple and reproducible and obviates the need for human tissue and the issues of poor visibility in stored corneal tissue.
| S-EPMC10391481 | biostudies-literature
Project description:BackgroundDespite the large volume of genome sequencing data produced by next-generation sequencing technologies and the highly sophisticated software dedicated to handling these types of data, gaps are commonly found in draft genome assemblies. The existence of gaps compromises our ability to take full advantage of the genome data. This study aims to identify a practical approach for biologists to complete their own genome assemblies using commonly available tools and resources.ResultsA pipeline was developed to assemble complete genomes primarily from the next generation sequencing (NGS) data. The input of the pipeline is paired-end Illumina sequence reads, and the output is a high quality complete genome sequence. The pipeline alternates the employment of computational and biological methods in seven steps. It combines the strengths of de novo assembly, reference-based assembly, customized programming, public databases utilization, and wet lab experimentation. The application of the pipeline is demonstrated by the completion of a bacterial genome, Thermotoga sp. strain RQ7, a hydrogen-producing strain.ConclusionsThe developed pipeline provides an example of effective integration of computational and biological principles. It highlights the complementary roles that in silico and wet lab methodologies play in bioinformatical studies. The constituting principles and methods are applicable to similar studies on both prokaryotic and eukaryotic genomes.
| S-EPMC4331810 | biostudies-literature
Project description:IntroductionWe undertook a systematic review of the use of wet lab (animal and cadaveric) simulation models in urological training, with an aim to establishing a level of evidence (LoE) for studies and level of recommendation (LoR) for models, as well as evaluating types of validation.MethodsMedline, EMBASE, and Cochrane databases were searched for English-language studies using search terms including a combination of "surgery," "surgical training," and "medical education." These results were combined with "wet lab," "animal model," "cadaveric," and "in-vivo." Studies were then assigned a LoE and LoR if appropriate as per the education-modified Oxford Centre for Evidence-Based Medicine classification.ResultsA total of 43 articles met the inclusion criteria. There was a mean of 23.1 (±19.2) participants per study with a median of 20. Overall, the studies were largely of low quality, with 90.7% of studies being lower than LoE 2a (n=26 for LoE 2b and n=13 for LoE 3). The majority (72.1%, n=31) of studies were in animal models and 27.9% (n=12) were in cadaveric models.ConclusionsSimulation in urological education is becoming more prevalent in the literature, however, there is a focus on animal rather than cadaveric simulation, possibly due to cost and ethical considerations. Studies are also predominately of a low LoE; higher LoEs, especially randomized controlled studies, are needed.
| S-EPMC7673832 | biostudies-literature
Project description:Humanized monoclonal antibodies (mAbs) are among the most promising modern therapeutics, but defined engineering strategies are still not available. Antibody humanization often leads to a loss of affinity, as it is the case for our model antibody Ab2/3H6 (PDB entry 3BQU). Identifying appropriate back-to-mouse mutations is needed to restore binding affinity, but highly challenging. In order to get more insight, we have applied molecular dynamics simulations and correlated them to antibody binding and expression in wet lab experiments. In this study, we discuss six mAb variants and investigate a tyrosine conglomeration, an isopolar substitution and the improvement of antibody binding towards wildtype affinity. In the 3D structure of the mouse wildtype, residue R94h is surrounded by three tyrosines which form a so-called 'tyrosine cage'. We demonstrate that the tyrosine cage has a supporting function for the CDRh3 loop conformation. The isopolar substitution is not able to mimic the function appropriately. Finally, we show that additional light chain mutations can restore binding to wildtype-comparable level, and also improve the expression of the mAb significantly. We conclude that the variable light chain of Ab2/3H6 is of underestimated importance for the interaction with its antigen mAb 2F5.
| S-EPMC6277173 | biostudies-literature