Customizable Tool Research Articles

The Roles of Micro- and Nanoscale Materials in Cell-Engineering Systems.

Customizable manufacturing of ex vivo cell engineering is driven by the need for innovations in the biomedical field and holds substantial potential for addressing current therapeutic challenges; but it is still only in its infancy. Micro- and nanoscale-engineered materials are increasingly used to control core cell-level functions in cellular engineering. By reprogramming or redirecting targeted cells for extremely precise functions, these advanced materials offer new possibilities. This influences the modularity of cell reprogramming and reengineering, making these materials part of versatile and emerging technologies. Here, the roles of micro- and nanoscale materials in cell engineering are highlighted, demonstrating how they can be adaptively controlled to regulate cellular reprogramming and core cell-level functions, including differentiation, proliferation, adhesion, user-defined gene expression, and epigenetic changes. The current reprogramming routes used to achieve pluripotency from somatic cells and the significant potential of induced pluripotent stem cell technology for translational biomedical research are covered. Recent advances in nonviral intracellular delivery modalities for cell reprogramming and their constraints are evaluated. This paper focuses on emerging physical and combinatorial approaches of intracellular delivery for cell engineering, revealing the capabilities and limitations of these routes. It is showcased how these programmable materials are continually being explored as customizable tools for inducing biophysical stimulation. Harnessing the power of micro- and nanoscale-engineered materials will be a step change in the design of cell engineering, producing a suite of powerful tools for addressing potential future challenges in therapeutic cell engineering.

Automating COVID-19 epidemiological situation reports based on multiple data sources, the Netherlands, 2020 to 2023

BackgroundDuring the COVID-19 pandemic, the National Institute for Public Health and the Environment in the Netherlands developed a pipeline of scripts to automate and streamline the production of epidemiological situation reports (epi‑sitrep). The pipeline was developed for the Automation of Data Import, Summarization, and Communication (hereafter called the A-DISC pipeline). ObjectiveThis paper describes the A-DISC pipeline and provides a customizable scripts template that may be useful for other countries wanting to automate their infectious disease surveillance processes. MethodsThe A-DISC pipeline was developed using the open-source statistical software R. It is organized in four modules: Prepare, Process data, Produce report, and Communicate. The Prepare scripts set the working environment (e.g., load packages). The (data-specific) Process data scripts import, validate, verify, transform, save, analyze, and summarize data as tables and figures and store these data summaries. The Produce report scripts gather summaries from multiple data sources and integrate them into a RMarkdown document – the epi‑sitrep. The Communicate scripts send e-mails to stakeholders with the epi‑sitrep. ResultsAs of March 2023, up to ten data sources were automatically summarized into tables and figures by A-DISC. These data summaries were featured in routine extensive COVID-19 epi‑sitreps, shared as open data, plotted on RIVM's website, sent to stakeholders and submitted to European Centre for Disease Prevention and Control via the European Surveillance System -TESSy [38]. DiscussionIn the face of an unprecedented high number of cases being reported during the COVID-19 pandemic, the A-DISC pipeline was essential to produce frequent and comprehensive epi‑sitreps. A-DISC's modular and intuitive structure allowed for the integration of data sources of varying complexities, encouraged collaboration among people with various R-scripting capabilities, and improved data lineage. The A-DISC pipeline remains under active development and is currently being used in modified form for the automatization and professionalization of various other disease surveillance processes at the RIVM, with high acceptance from the participant epidemiologists. ConclusionThe A-DISC pipeline is an open-source, robust, and customizable tool for automating epi‑sitreps based on multiple data sources.

TÍCH HỢP WEB SKETCHPAD VÀO KHÓA HỌC TRỰC TUYẾN TRONG DẠY HỌC CHUYÊN ĐỀ PHÉP QUAY

Web Sketchpad is a new technology based on The Geometer’s Sketchpad software, featuring a highly simplified user interface and customizable tools, allowing learners to focus on mathematical exploration experiences to construct their own knowledge. This study integrated two dynamic mathematical representations designed on Web Sketchpad into an online course to support students in exploring the topic of Rotations in the plane. The results show that Web Sketchpad on the Moodle platform supports students well in conducting mathematical explorations related to rotations. It can serve as a cognitive tool and support different stages of mathematical problem-solving. The students in the experiment demonstrated problem-solving and creative competency, independently constructing new mathematical objects, searching for and discovering mathematical relationships. Additionally, they had the opportunity to practice self-directed learning and self-study through heuristic exploratory activities with dynamic mathematical representations. Keywords: Web Sketchpad; rotation; online-learning; moodle platform

Selecting appropriate excipients for paediatric dosage form − Paediatric excipients risk assessment (PERA) framework – Part 1

Excipients are often the major component of the formulation that critically affect the dosage form, manufacturing process, product performance, stability and safety. They exert different roles and functions in a dosage form. Selecting excipients with appropriate safety and tolerability is a major hurdle in paediatric formulation development. The suitability of a particular excipient will be dependent on the context of its use with regard to the paediatric age range, acute versus chronic use, and clinical risk–benefit of the disease, active and excipient. Scientists are encouraged to apply the principle of risk–benefit to assess the suitability of excipients to the specific paediatric population. Indicative list of parameters that should be taken into consideration and hierarchy of information sources when assessing the excipients risks is provided by regulatory agencies. However, the approach to be taken and details of how the risk evaluation should be undertaken are lacking. There is a need for a systematic approach to selection of excipients and assessment of the risk of excipient exposure. The Paediatric Excipients Risk Assessment (PERA) framework developed and proposed in this paper provides a structured, systematic decision-making framework via customizable tools and processes that can help to improve the transparency and communications on the selection and justification of use of excipients in a paediatric formulation.

GenoTools: An Open-Source Python Package for Efficient Genotype Data Quality Control and Analysis.

GenoTools, a Python package, streamlines population genetics research by integrating ancestry estimation, quality control (QC), and genome-wide association studies (GWAS) capabilities into efficient pipelines. By tracking samples, variants, and quality-specific measures throughout fully customizable pipelines, users can easily manage genetics data for large and small studies. GenoTools' "Ancestry" module renders highly accurate predictions, allowing for high-quality ancestry-specific studies, and enables custom ancestry model training and serialization, specified to the user's genotyping or sequencing platform. As the genotype processing engine that powers several large initiatives, including the NIH's Center for Alzheimer's and Related Dementias (CARD) and the Global Parkinson's Genetics Program (GP2). GenoTools was used to process and analyze the UK Biobank and major Alzheimer's Disease (AD) and Parkinson's Disease (PD) datasets with over 400,000 genotypes from arrays and 5000 sequences and has led to novel discoveries in diverse populations. It has provided replicable ancestry predictions, implemented rigorous QC, and conducted genetic ancestry-specific GWAS to identify systematic errors or biases through a single command. GenoTools is a customizable tool that enables users to efficiently analyze and scale genotype data with reproducible and scalable ancestry, QC, and GWAS pipelines.

Manzaneda's Tool: Adaptation of the Piezotome for Rib Remodeling Surgery without Incisions.

For rib remodeling surgery without incisions (RibXcar), a piezotome is used, which is a piezoelectric device that allows for selective and delicate manipulation of the bone, thanks to microvibrations that facilitate precise and smooth cuts. The piezotome, in its original form, requires the adaptation of the tip shape to perform costal surgery; therefore, Manzaneda's tool was created, which is composed of three parts: first, modification of the shape of the tip of the piezotome; second, the experimentation with materials for coating the tip of the piezotome; and third, the sterilization of the piece. We found that the material that best adheres and protects the adapted tip of the piezotome is the Rust-Oleum High Heat. After applying the tip coating, the piece was sterilized, and finally, Manzaneda's tool was obtained, which is specifically for use in RibXcar rib remodeling surgery. Manzaneda's tool is a safe and customizable tool for RibXcar surgery.

Understanding the financial aspects of digital pathology: A dynamic customizable return on investment calculator for informed decision-making

BackgroundThe adoption of digital pathology has transformed the field of pathology, however, the economic impact and cost analysis of implementing digital pathology solutions remain a critical consideration for institutions to justify. Digital pathology implementation requires a thorough evaluation of associated costs and should identify and optimize resource allocation to facilitate informed decision-making. A dynamic cost calculator to estimate the financial implications of deploying digital pathology systems was needed to estimate the financial effects on transitioning to a digital workflow. MethodsA systematic approach was used to comprehensively assess the various components involved in implementing and maintaining a digital pathology system. This consisted of: (1) identification of key cost categories associated with digital pathology implementation; (2) data collection and analysis of cost estimation; (3) cost categorization and quantification of direct and indirect costs associated with different use cases, allowing customization of each factor based on specific intended uses and market rates, industry standards, and regional variations; (4) opportunities for savings realized by digitization of glass slides and (5) integration of the cost calculator into a unified framework for a holistic view of the financial implications associated with digital pathology implementation. The online tool enables the user to test various scenarios specific to their institution and provides adjustable parameters to assure organization specific relatability. ResultsThe Digital Pathology Association has developed a web-based calculator as a companion tool to provide an exhaustive list of the necessary concepts needed when assessing the financial implications of transitioning to a digital pathology system. The dynamic return on investment (ROI) calculator successfully integrated relevant cost and cost-saving components associated with digital pathology implementation and maintenance. Considerations include factors such as digital pathology infrastructure, clinical operations, staffing, hardware and software, information technology, archive and retrieval, medical–legal, and potential reimbursements. The ROI calculator developed for digital pathology workflows offers a comprehensive, customizable tool for institutions to assess their anticipated upfront and ongoing annual costs as they start or expand their digital pathology journey. It also offers cost-savings analysis based on specific user case volume, institutional geographic considerations, and actual costs. In addition, the calculator also serves as a tool to estimate number of required whole slide scanners, scanner throughput, and data storage (TB). This tool is intended to estimate the potential costs and cost savings resulting from the transition to digital pathology for business plan justifications and return on investment calculations. ConclusionsThe digital pathology online cost calculator provides a comprehensive and reliable means of estimating the financial implications associated with implementing and maintaining a digital pathology system. By considering various cost factors and allowing customization based on institution-specific variables, the calculator empowers pathology laboratories, healthcare institutions, and administrators to make informed decisions and optimize resource allocation when adopting or expanding digital pathology technologies. The ROI calculator will enable healthcare institutions to assess the financial feasibility and potential return on investment on adopting digital pathology, facilitating informed decision-making and resource allocation.

RNA3DB: A structurally-dissimilar dataset split for training and benchmarking deep learning models for RNA structure prediction

With advances in protein structure prediction thanks to deep learning models like AlphaFold, RNA structure prediction has recently received increased attention from deep learning researchers. RNAs introduce substantial challenges due to the sparser availability and lower structural diversity of the experimentally resolved RNA structures in comparison to protein structures. These challenges are often poorly addressed by the existing literature, many of which report inflated performance due to using training and testing sets with significant structural overlap. Further, the most recent Critical Assessment of Structure Prediction (CASP15) has shown that deep learning models for RNA structure are currently outperformed by traditional methods.In this paper we present RNA3DB, a dataset of structured RNAs, derived from the Protein Data Bank (PDB), that is designed for training and benchmarking deep learning models. The RNA3DB method arranges the RNA 3D chains into distinct groups (Components) that are non-redundant both with regard to sequence as well as structure, providing a robust way of dividing training, validation, and testing sets. Any split of these structurally-dissimilar Components are guaranteed to produce test and validations sets that are distinct by sequence and structure from those in the training set. We provide the RNA3DB dataset, a particular train/test split of the RNA3DB Components (in an approximate 70/30 ratio) that will be updated periodically. We also provide the RNA3DB methodology along with the source-code, with the goal of creating a reproducible and customizable tool for producing structurally-dissimilar dataset splits for structural RNAs.

Designing for caregiving networks: a case study of primary caregivers of children with medical complexity.

The study aimed to characterize the experiences of primary caregivers of children with medical complexity (CMC) in engaging with other members of the child's caregiving network, thereby informing the design of health information technology (IT) for the caregiving network. Caregiving networks include friends, family, community members, and other trusted individuals who provide resources, information, health, or childcare. We performed a secondary analysis of two qualitative studies. Primary studies conducted semi-structured interviews (n = 50) with family caregivers of CMC. Interviews were held in the Midwest (n = 30) and the mid-Atlantic region (n = 20). Interviews were transcribed verbatim for thematic analysis. Emergent themes were mapped to implications for the design of future health IT. Thematic analysis identified 8 themes characterizing a wide range of primary caregivers' experiences in constructing, managing, and ensuring high-quality care delivery across the caregiving network. Findings evidence a critical need to create flexible and customizable tools designed to support hiring/training processes, coordinating daily care across the caregiving network, communicating changing needs and care updates across the caregiving network, and creating contingency plans for instances where caregivers are unavailable to provide care to the CMC. Informaticists should additionally design accessible platforms that allow primary caregivers to connect with and learn from other caregivers while minimizing exposure to sensitive or emotional content as indicated by the user. This article contributes to the design of health IT for CMC caregiving networks by uncovering previously underrecognized needs and experiences of CMC primary caregivers and drawing direct connections to design implications.

SCDetect: A SeisComP Module for Real-Time Waveform Cross-Correlation-Based Earthquake Detection

Abstract Enhanced earthquake catalogs based on waveform cross correlation (template matching) have become routine when studying regional or sequence specific seismicity. Currently, there is no existing open-source cross-correlation software that is designed to be fully integrated in real-time operations of seismic networks. To fill this gap, we introduce SCDetect, a software that implements real-time earthquake detection based on waveform cross correlation in the time domain. SCDetect with the extension module scdetect-cc is an open-source SeisComP package written in C++. scdetect-cc can be used to process both archived waveform data (playback mode), and real-time data. In the real-time application, waveforms are fetched through one of the SeisComP RecordStream interfaces, and its output (picks, origin times, amplitudes, and magnitudes) are sent to the SeisComP messaging system. The new origins are associated either with existing events detected by other pick-based SeisComP modules, or create new events. Optionally, the hypocenter location can be refined by downstream application of existing SeisComP modules. scdetect-cc offers two magnitude estimation methods that are based on the amplitudes of the template earthquakes and the new detections. In the real-time application, scdetect-cc can be scaled to handle thousands of templates without overloading the application or becoming too latent, unable to keep up with the data flow. In the playback mode, we applied scdetect-cc to three recent earthquake sequences occurring in Switzerland and surrounding regions between 2019 and 2020. Two scenarios are tested to simulate its performance in real time. The first scenario cross correlates the signals of the nearest station, whereas the second requires four stations. In both cases, we successfully detected most of the cataloged events and added hundreds of new detections. Overall, scdetect-cc is a computationally efficient and highly customizable tool to detect earthquakes for regional networks that implement the SeisComP system for earthquake monitoring.

LUSTR: a new customizable tool for calling genome-wide germline and somatic short tandem repeat variants

BackgroundShort tandem repeats (STRs) are widely distributed across the human genome and are associated with numerous neurological disorders. However, the extent that STRs contribute to disease is likely under-estimated because of the challenges calling these variants in short read next generation sequencing data. Several computational tools have been developed for STR variant calling, but none fully address all of the complexities associated with this variant class.ResultsHere we introduce LUSTR which is designed to address some of the challenges associated with STR variant calling by enabling more flexibility in defining STR loci, allowing for customizable modules to tailor analyses, and expanding the capability to call somatic and multiallelic STR variants. LUSTR is a user-friendly and easily customizable tool for targeted or unbiased genome-wide STR variant screening that can use either predefined or novel genome builds. Using both simulated and real data sets, we demonstrated that LUSTR accurately infers germline and somatic STR expansions in individuals with and without diseases.ConclusionsLUSTR offers a powerful and user-friendly approach that allows for the identification of STR variants and can facilitate more comprehensive studies evaluating the role of pathogenic STR variants across human diseases.

Mobile health in the aging surgical patient

Mobile health includes the use of mobile devices, patient monitoring devices, and digital assistants to improve the delivery of healthcare. Aging surgical patients (ie, 65 years and older) represent a unique patient population that demands increased resources to prepare for surgery and optimize recovery. Mobile health has the potential to improve surgical patient outcomes by increasing the accessibility of personalized care and reducing costs. However, there are some challenges to consider when using mobile health in older surgical patients, such as technological literacy, visual and hearing impairment, and cognitive changes before or after anesthesia. Despite the rapid uptake of mobile health in medical specialties, its application in the surgical field is gradual. The complexity of aging surgical patients requires surgical care teams, surgical leaders, and healthcare policymakers to consider unique solutions, such as mobile health, to address this growing population’s needs before and after surgery. This article will discuss the potential benefits and challenges of mobile health among aging surgical patients, as well as opportunities to support these patients and families with customizable tools to meet their preferences and needs.

PyComp: A Versatile Tool for Efficient Data Extraction, Conversion, and Management in High-throughput Virtual Drug Screening.

Virtual screening (VS) is essential for analyzing potential drug candidates in drug discovery. Often, this involves the conversion of large volumes of compound data into specific formats suitable for computational analysis. Managing and processing this wealth of information, especially when dealing with vast numbers of compounds in various forms, such as names, identifiers, or SMILES strings, can present significant logistical and technical challenges. To streamline this process, we developed PyComp, a software tool using Python's PyQt5 library, and compiled it into an executable with Pyinstaller. PyComp provides a systematic way for users to retrieve and convert a list of compound names, IDs (even in a range), or SMILES strings into the desired 3D format. PyComp greatly enhances the efficiency of data extraction, conversion, and storage processes involved in VS. It searches for similar compounds coupled with its ability to handle misidentified compounds and offers users an easy-to-use, customizable tool for managing largescale compound data. By streamlining these operations, PyComp allows researchers to save significant time and effort, thus accelerating the pace of drug discovery research. PyComp effectively addresses some of the most pressing challenges in highthroughput VS: efficient management and conversion of large volumes of compound data. As a user-friendly, customizable software tool, PyComp is pivotal in improving the efficiency and success of large-scale drug screening efforts, paving the way for faster discovery of potential therapeutic compounds.

CuticleTrace: A toolkit for capturing cell outlines from leaf cuticle with implications for paleoecology and paleoclimatology.

Leaf epidermal cell morphology is closely tied to the evolutionary history of plants and their growth environments and is therefore of interest to many plant biologists. However, cell measurement can be time consuming and restrictive with current methods. CuticleTrace is a suite of Fiji and R-based functions that streamlines and automates the segmentation and measurement of epidermal pavement cells across a wide range of cell morphologies and image qualities. We evaluated CuticleTrace-generated measurements against those from alternate automated methods and expert and undergraduate hand tracings across a taxonomically diverse 50-image data set of variable image qualities. We observed ~93% statistical agreement between CuticleTrace and expert hand-traced measurements, outperforming alternate methods. CuticleTrace is a broadly applicable, modular, and customizable tool that integrates data visualization and cell shape measurement with image segmentation, lowering the barrier to high-throughput studies of epidermal morphology by vastly decreasing the labor investment required to generate high-quality cell shape data sets.

An integrated methodology using geospatial data and remote sensing techniques for sustainability indicators integration: Sustainable development assessment in the Suez Canal Zone in Egypt

This research paper proposes a new framework for evaluating the impact of national projects on land sustainability by utilizing remote sensing techniques and geospatial data to assess various sustainability indicators. The proposed framework integrates different indicators to evaluate the three dimensions of sustainability, providing decision-makers with a flexible and customizable tool for promoting sustainable and equitable development. The study addresses the lack of a suitable framework for applying this evaluation to different study regions at different scales and highlights the inefficiency of relying on outdated census data. The proposed framework was applied to the Suez Canal Zone in Egypt, a vital and ecologically important area. we produced the necessary data for the evaluation process using different techniques after ensuring their accuracy for producing such important data. The framework can be easily modified and customized to fit the needs of study areas at different scales. The results obtained reveal that the study area has a high potential for sustainable development, with over 65% of it classified as "High or Very High Sustainability level". The study also evaluated the population equity distribution through the study area and found that approximately 73.9% of the population is living in areas categorized as "high sustainability level".

Open-source controller for dynamic cardiovascular models

Cardiovascular pressure sensors require dedicated, reliable, and customisable performance testing equipment. Devices available on the market, such as pulsatile pumps and pulse multipliers, offer limited adaptability to the needs of pressure sensor testing or are highly complex tools designed for other purposes. Therefore, there is a strong need to provide an adaptable and versatile device for characterisation during prototype development, prior to animal model testing. Early development requires detailed characterisation of a sensor performance in a realistic environmental scenario. To address this need, we adapted an off-the-shelf pressure chamber with a custom Arduino-based controller to achieve a rapid change in pressure that simulates the pulsatile profile of human blood pressure. The system is a highly customisable tool, and we have experimentally shown that it works successfully in a wide range of pressures from 30 mmHg to 400 mmHg with a resolution of 2 mmHg. By adjusting the chamber volume using a water balloon, we achieved a cycle rate of up to 120 beats per minute. The device can be operated directly from the Arduino IDE or with a customised graphical user interface developed by our research group. The proposed system is intended to assist other researchers in the development of industrial and biomedical pressure sensors.

Closing the patient-provider gap along the surgical journey one click at a time: results of a phase I pilot trial of a patient navigation tool

BackgroundPatients diagnosed with complex hepato-pancreaticobiliary (HPB) conditions experience a challenging journey through the healthcare system. Patient navigation is commonly offered to patients and their caregivers throughout this process. Here, we report on the development and phase I testing of a prototype web-based dual (patient and provider) navigation application ‘PatientNav.’MethodsEvidence-based recommendations were determined through a needs assessment. The PatientNav app was designed to be a highly customizable tool based on the needs of the patients, the care team, and the characteristics of the institution. Our phase I pilot trial targeted adult patients who reported the capacity to use a mobile app or desktop website presenting to our HPB transplant clinic at MedStar Georgetown University Hospital/Lombardi Comprehensive Cancer Center over two months. Usability, functionality, and reliability testing were conducted by applying multiple strategies, including biometric data analysis, task completion, questionnaires, and interviews.ResultsOut of 22 patients, 18 (81.8%) completed the in-app survey whose responses were included in the analysis. The usability rate of PatientNav among patient app users was 95.4%. Among patient app users who completed the in-app survey questionnaire, 66.6% reported that the content in the PatientNav app was relevant. In terms of the app's reliability, none of the patient app users reported technical issues with accessing PatientNav throughout the study period. To further elucidate the characteristics of individuals who exhibited high usage of the PatientNav assigned task functions, we divided our cohort based on the median number of tasks used (N = 75 total tasks). The univariable comparison showed that high function users were older, with a median (IQR) age of 61.5 (57.8, 71.3) compared to 52.2 (34.5, 65.7) years among low function users. No differences were observed based on gender, racial distribution, living condition, or occupation. However, 41.7% of low function users had Medicare/Medicaid insurance, whereas all high function users had private or combined insurance.ConclusionOur phase I pilot study showed that PatientNav is a feasible, usable, and functional technological tool that enables patients with complex HPB diagnoses and their care team to interface in real-time using patient-reported outcome measures (PROMs). PatientNav is a reliable tool that can be used by clinical and support staff to help navigate patients through surgery and aftercare and by patients to assist in navigation and self-management.Trial registrationThe trial was registered at ClinicalTrials.gov; Registration number: NCT04892927; date of registration: 5/19/2021.

Employees’ reskilling and upskilling for industry 5.0: Selecting the best professional development programmes

The current research aims at developing a multi-criteria decision-making methodology, based on analytic network process (ANP), for selecting the appropriate professional development programmes for enhancing employees' reskilling and upskilling, in the Industry 5.0 context. Thus, a four-stage methodology, which combines the qualitative approach of the Delphi study with the quantitative approach of the multi-criteria decision-making technique of ANP, is proposed and tested in the three largest companies from the Romanian manufacture of coke and refined petroleum products industry. The results show that teamwork, online communities of practice and enterprise social networks are among the best options. Furthermore, the most influential factors are employees' psychological profile (namely, interpersonal trust and self-efficacy) and human capital (especially, inclusiveness and job satisfaction). These findings have both theoretical and practical implications. At the theoretical level, they provide a nexus between human resource management and operations management by showing how a methodology based on ANP can be applied for solving a human resource issue. At the practical level, they help managers to decide which professional development programmes to implement for employees' reskilling and upskilling by providing them an easily customizable tool that takes into account employees' characteristics and company's human and structural capital.

Budget Impact Tool for Implementing Contingency Management for Co-occurring Alcohol Use Disorders and Serious Mental Illness.

Contingency management (CM) is a behavioral intervention in which tangible incentives are provided to patients when they achieve a desired behavior (e.g., reducing or abstaining from alcohol use). The authors sought to describe the resource requirements and associated costs of various CM versions (usual, high magnitude, and shaping) tailored to a high-risk population with co-occurring serious mental illness and severe alcohol use disorder. A microcosting analysis was conducted to identify the resource requirements of the different CM versions. This approach included semistructured interviews with site investigators, who also staffed the intervention. The resource costing method-multiplying the number of units of each resource utilized by its respective unit cost-was used to value the resources from a provider's perspective. All cost estimates were calculated in 2021 U.S. dollars. The cost of setting up a CM program was $6,038 per site. Assuming full capacity and 56% of urine samples meeting the requirement for receipt of the CM incentive, the average cost of 16 weeks of usual and shaping CM treatments was $1,119-$1,136 and of high-magnitude CM was $1,848-$1,865 per participant. A customizable tool was created to estimate the costs associated with various levels of treatment success and CM design features. After the trial, the tool will be updated and used to finalize per-participant cost for incorporation into a comprehensive economic evaluation. This costing tool will help a growing number of treatment providers who are interested in implementing CM with budgeting for and sustaining CM in their practices.

A comparison of neuroelectrophysiology databases

As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration. This paper compares four freely available intracranial neuroelectrophysiology data repositories: Data Archive for the BRAIN Initiative (DABI), Distributed Archives for Neurophysiology Data Integration (DANDI), OpenNeuro, and Brain-CODE. The aim of this review is to describe archives that provide researchers with tools to store, share, and reanalyze both human and non-human neurophysiology data based on criteria that are of interest to the neuroscientific community. The Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB) are utilized by these archives to make data more accessible to researchers by implementing a common standard. As the necessity for integrating large-scale analysis into data repository platforms continues to grow within the neuroscientific community, this article will highlight the various analytical and customizable tools developed within the chosen archives that may advance the field of neuroinformatics.