Utilizing the open-source programming language Python to create interactive Quality Assurance dashboards for diagnostic and screening performance in Cytology

INTRODUCTION

Effective feedback on cytology performance relies on navigating complex laboratory information system data, which is prone to errors and lacks flexibility. As a comprehensive solution, we used the Python programming language to create a dashboard application for screening and diagnostic quality metrics.

MATERIAL AND METHODS

Data from the 5-year period (2018-2022) were accessed. Versatile open-source Python libraries (user developed program code packages) were used from the first step of LIS data cleaning through the creation of the application. To evaluate performance, we selected 3 gynecologic metrics: the ASC/LSIL ratio, the ASC-US/ASC-H ratio, and the proportion of cytologic abnormalities in comparison to the total number of cases (abnormal rate). We also evaluated the referral rate of cytologists/cytotechnologists (CTs) and the ratio of thyroid AUS interpretations by cytopathologists (CPs). These were formed into colored graphs that showcase individual results in established, color-coded laboratory "goal," "borderline," and "attention" zones based on published reference benchmarks. A representation of the results distribution for the entire laboratory was also developed.

RESULTS

We successfully created a web-based test application that presents interactive dashboards with different interfaces for the CT, CP, and laboratory management (https://drkvcsstvn-dashboards.hf.space/app). The user can choose to view the desired quality metric, year, and the anonymized CT or CP, with an additional automatically generated written report of results.

CONCLUSIONS

Python programming proved to be an effective toolkit to ensure high-level data processing in a modular and reproducible way to create a personalized, laboratory specific cytology dashboard.

Modeling assessment of groundwater vulnerability to contamination risk in a typical basement terrain using TOPSIS-entropy developed vulnerability data mining technique

This study involved a comparative analysis in the groundwater vulnerability domain, which is a crucial component of groundwater management decision support systems (DSS). This was achieved by creating models that covered the range of algorithms from the subjective to the data-driven. The study was conducted in a basement complex area. Databases of climatic, remote sensing, and geophysical datasets were created using varieties of data acquisition techniques. The datasets included in this assessment were: rainfall (R), land use (LU), bedrock topography (BT), recharge rate (Re), and slope (S). The slope and rainfall were determined to have the highest (0.78) and lowest (0.01) weighted factors, respectively, using the entropy method. For the development of the TOPSIS-Entropy model algorithm, the weights results were combined with the TOPSIS outranking method. To generate the Groundwater Vulnerability Model map of the study area, the hybrid model was applied to griddled raster layers of the factors. Also, the TOPSIS and Entropy-WLA model algorithms were also explored and used to generate groundwater vulnerability maps. The TOPSIS-Entropy algorithms produced an accuracy of 70%, while TOPSIS and Entropy-WLA produced accuracy of 50 and 47%, respectively. The resulting model maps were validated by using correlation technique on the produced map and the longitudinal conductance map of the study area. The TOPSIS-Entropy, which followed an object-oriented model pattern, demonstrates greater accuracy and has the potential to provide appropriate insights and alternatives to decision-making in the field of groundwater hydrology in the study area and other regions of the world with comparable geology.

The Use of Bi-Potentiostat as a Simple and Accurate Electrochemical Approach for the Determination of Orthophosphate in Seawater

Autonomous on-site monitoring of orthophosphate (PO₄^3-), an important nutrient for primary production in natural waters, is urgently needed. Here, we report on the development and validation of an on-site autonomous electrochemical analyzer for PO₄^3- in seawater. The approach is based on the use of flow injection analysis in conjunction with a dual electrochemical cell (i.e., a bi-potentiostat detector (FIA-DECD) that uses two working electrodes sharing the same reference and counter electrode. The two working electrodes are used (molybdate/carbon paste electrode (CPE) and CPE) to correct for matrix effects. Optimization of squarewave voltammetry parameters (including step potential, amplitude, and frequency) was undertaken to enhance analytical sensitivity. Possible interferences from non-ionic surfactants and humic acid were investigated. The limit of quantification in artificial seawater (30 g/L NaCl, pH 0.8) was 0.014 µM for a linear concentration range of 0.02-3 µM. The system used a Python script for operation and data processing. The analyzer was tested for ship-board PO₄^3- determination during a four-day research cruise in the North Sea. The analyzer successfully measured 34 samples and achieved a good correlation (Pearson' R = 0.91) with discretely collected water samples analyzed using a laboratory-based colorimetric reference analyzer.

A fuzzy cognitive map approach to predict the hazardous effects of malathion to environment (air, water and soil)

Malathion is an organophosphorus insecticide and pesticide commonly used in crops and residential applications. The negative effects of Malathion on human health and ecosystems are of great concern. In this work, a mathematical model pivot on Fuzzy Cognitive Map (FCM) is used to analyse the causes and hazardous effects of Malathion to the environmental components (air, water and soil). Based on expert's opinion the possible factors that cause damage to health and ecosystems due to Malathion is identified, which serve as the input to the FCM. The FCM mathematically establishes the causal relation between these factors. The mathematical simulation is done by Python Programming. This approach can be used to study the interdependencies between the adverse effects of any pesticide in human health and environment due to prolonged exposure.

Development of image analysis using Python: Relationship between matrix ratio of composite resin and curing temperature

The aim of this study was to establish a measurement method for filler and matrix in cured resin composite (RC) using Python programming and to investigate the correlation between matrix ratio and curing temperature rise. Eight kinds of RCs were used. Backscattered electron images were taken for each cured specimen. Matrix and filler contents were calculated using Python programming with the K-means or area segmentation method. Volume measurement methods were assessed for comparison. Heat released during the polymerization reaction was measured. The matrix ratio was calculated without human intervention. Three specimens contained only inorganic filler, and other specimens contained multiple types of fillers. Almost the same values of the matrix ratio were obtained by programming and the volume measurement methods for specimens containing a single type of inorganic filler. Moreover, a strong correlation was found between the matrix ratio obtained by the programming method and curing temperature rise (R=0.9826).

Determination of an inflection point for a dosimetric analysis of unflattened beam using the first principle of derivatives by python code programming

Background

Practice of Unflattened or Flattening filter free (FFF) beam has become the high dose standard in radiotherapy (RT), such as stereotactic radio-surgery (SRS) and stereotactic radiotherapy (SRT). The removal of a flattening filter (FF) from the path of a photon beam alters the characteristics of FFF beam. Since the conventional route for dosimetric analysis of FF beam cannot be applied to FFF beam, the procedure of analyzing beam characteristics for FFF beam based on inflection points (IPs) is used. IP is a point where the concavity change observed corresponds to its change in sign (±) of the second derivative.

Aim

The objective of the study is to determine IPs for dosimetric analysis of the FFF beam profile.

Methods and materials

In this study, IPs are determined through the python code programming based on the mathematical first principle of the derivative. They are compared with IPs estimated by the conventional graphical manual method using Microsoft Excel (MS). IPs and their dependent dosimetric parameters determined by both mathematical and graphical manual methods are compared.

Result

Percentage differences between the IPs determined by both methods, for 6MVFFF inline and crossline beam profile are found to be 2.7% and 0.8% respectively. Similarly, the average penumbra differences for 6MVFFF inline and crossline beam profile are found to be 0.15 mm and 0.9 mm, respectively. However, differences in the field width between both methods are found insignificant.

Conclusion

Graphical manual method is very time-consuming, tedious and user dependent. However, the mathematical method through python code programming is more precise, faster and independent of individual users.

xINTERPDF: a graphical user interface for analyzing intermolecular pair distribution functions of organic compounds from X-ray total scattering data

A new software program, xINTERPDF, that analyzes the intermolecular correlations in organic compounds via measured X-ray total scattering data is described.

A new software program, xINTERPDF, that analyzes the intermolecular correlations in organic compounds via measured X-ray total scattering data is described.

SamuROI, a Python-Based Software Tool for Visualization and Analysis of Dynamic Time Series Imaging at Multiple Spatial Scales

The measurement of activity in vivo and in vitro has shifted from electrical to optical methods. While the indicators for imaging activity have improved significantly over the last decade, tools for analysing optical data have not kept pace. Most available analysis tools are limited in their flexibility and applicability to datasets obtained at different spatial scales. Here, we present SamuROI (Structured analysis of multiple user-defined ROIs), an open source Python-based analysis environment for imaging data. SamuROI simplifies exploratory analysis and visualization of image series of fluorescence changes in complex structures over time and is readily applicable at different spatial scales. In this paper, we show the utility of SamuROI in Ca²⁺-imaging based applications at three spatial scales: the micro-scale (i.e., sub-cellular compartments including cell bodies, dendrites and spines); the meso-scale, (i.e., whole cell and population imaging with single-cell resolution); and the macro-scale (i.e., imaging of changes in bulk fluorescence in large brain areas, without cellular resolution). The software described here provides a graphical user interface for intuitive data exploration and region of interest (ROI) management that can be used interactively within Jupyter Notebook: a publicly available interactive Python platform that allows simple integration of our software with existing tools for automated ROI generation and post-processing, as well as custom analysis pipelines. SamuROI software, source code and installation instructions are publicly available on GitHub and documentation is available online. SamuROI reduces the energy barrier for manual exploration and semi-automated analysis of spatially complex Ca²⁺ imaging datasets, particularly when these have been acquired at different spatial scales.