Standard Workflow Research Articles

Shape-matching-based fracture reduction aid concept exemplified on the proximal humerus-a pilot study.

Optimizing fracture reduction quality is key to achieve successful osteosynthesis, especially for epimetaphyseal regions such as the proximal humerus (PH), but can be challenging, partly due to the lack of a clear endpoint. We aimed to develop the prototype for a novel intraoperative C-arm-based aid to facilitate true anatomical reduction of fractures of the PH. The proposed method designates the reduced endpoint position of fragments by superimposing the outer boundary of the premorbid bone shape on intraoperative C-arm images, taking the mirrored intact contralateral PH from the preoperative CT scan as a surrogate. The accuracy of the algorithm was tested on 60 synthetic C-arm images created from the preoperative CT images of 20 complex PH fracture cases (Dataset A) and on 12 real C-arm images of a prefractured human anatomical specimen (Dataset B). The predicted outer boundary shape was compared with the known exact solution by (1) a calculated matching error and (2) two experienced shoulder trauma surgeons. A prediction accuracy of 88% (with 73% 'good') was achieved according to the calculation method and an 87% accuracy (68% 'good') by surgeon assessment in Dataset A. Accuracy was 100% by both assessments for Dataset B. By seamlessly integrating into the standard perioperative workflow and imaging, the intuitive shape-matching-based aid, once developed as a medical device, has the potential to optimize the accuracy of the reduction of PH fractures while reducing the number of X-rays and surgery time. Further studies are required to demonstrate the applicability and efficacy of this method in optimizing fracture reduction quality.

High-throughput Experimentation Enables the Development of a Nickel-catalyzed Cyanation Platform for (Hetero)aryl Halides.

A novel screening platform for the nickel-catalyzed cyanation of (hetero)aryl halides relying on the use of air-stable Ni(COD)DQ at low loading is reported. Through high-throughput experimentation (HTE), various ligand and solvent combinations are systematically explored, allowing the fast identification of suitable conditions. This standardized workflow serves as an excellent starting point for selecting other competent nickel precatalysts and for further optimization of reluctant substrates. The transformation exhibits broad functional group tolerance and can be readily scaled up to gram-scale.

MetaboLabPy—An Open-Source Software Package for Metabolomics NMR Data Processing and Metabolic Tracer Data Analysis

Introduction: NMR spectroscopy is a powerful technique for studying metabolism, either in metabolomics settings or through tracing with stable isotope-enriched metabolic precursors. MetaboLabPy (version 0.9.66) is a free and open-source software package used to process 1D- and 2D-NMR spectra. The software implements a complete workflow for NMR data pre-processing to prepare a series of 1D-NMR spectra for multi-variate statistical data analysis. This includes a choice of algorithms for automated phase correction, segmental alignment, spectral scaling, variance stabilisation, export to various software platforms, and analysis of metabolic tracing data. The software has an integrated help system with tutorials that demonstrate standard workflows and explain the capabilities of MetaboLabPy. Materials and Methods: The software is implemented in Python and uses numerous Python toolboxes, such as numpy, scipy, pandas, etc. The software is implemented in three different packages: metabolabpy, qtmetabolabpy, and metabolabpytools. The metabolabpy package contains classes to handle NMR data and all the numerical routines necessary to process and pre-process 1D NMR data and perform multiplet analysis on 2D-1H, 13C HSQC NMR data. The qtmetabolabpy package contains routines related to the graphical user interface. Results: PySide6 is used to produce a modern and user-friendly graphical user interface. The metabolabpytools package contains routines which are not specific to just handling NMR data, for example, routines to derive isotopomer distributions from the combination of NMR multiplet and GC-MS data. A deep-learning approach for the latter is currently under development. MetaboLabPy is available via the Python Package Index or via GitHub.

Automated Incidental Findings Notification Through the Electronic Health Record Utilizing Dictation Macros.

The objective of this study is to implement an actionable incidental findings (AIFs) communication workflow integrated into the electronic health record (EHR) using dictation macros to improve the quality of radiology reports and facilitate delivery of findings to clinicians. The workflow was implemented across an academic multi-hospital health system and used by over 100 radiologists from 12 divisions. Standardized macros were created for different organ systems including the thyroid, lungs, liver, pancreas, spleen, kidney, female reproductive, and others, designed based on the ACR Novel Quality Measure Set. All macros contained special codes enabling automated notification of clinicians in Epic EHR and unique codes to allow for tracking. When notified, clinicians can fast track ordering of follow-up imaging exams. All alerts were monitored by radiology operations who ensured messages were acknowledged within 73h. From September 2023 to March 2024, 12,919 AIFs alerts were filed for 10,766 patients. Median age was 65years, and 63.6% were female and 36.4% were male. Most alerts were submitted for outpatients (73.5%), and a majority originated from CT exams (57.3%) followed by radiographs (12.2%) and ultrasound (11.5%). Number of submissions per radiologist ranged from 0 to 930 with a median of 62. Median time to alert acknowledgment was 8.1h, and 93.9% were acknowledged within 73h. Follow-up orders were placed for 62.3% of patients. A standardized AIFs communication workflow utilizing dictation macros can help facilitate delivery of findings and follow-up recommendations to clinicians.

Optimizing hip MRI: enhancing image quality and elevating inter-observer consistency using deep learning-powered reconstruction

BackgroundConventional hip joint MRI scans necessitate lengthy scan durations, posing challenges for patient comfort and clinical efficiency. Previously, accelerated imaging techniques were constrained by a trade-off between noise and resolution. Leveraging deep learning-based reconstruction (DLR) holds the potential to mitigate scan time without compromising image quality.MethodsWe enrolled a cohort of sixty patients who underwent DL-MRI, conventional MRI, and No-DL MRI examinations to evaluate image quality. Key metrics considered in the assessment included scan duration, overall image quality, quantitative assessments of Relative Signal-to-Noise Ratio (rSNR), Relative Contrast-to-Noise Ratio (rCNR), and diagnostic efficacy. Two experienced radiologists independently assessed image quality using a 5-point scale (5 indicating the highest quality). To gauge interobserver agreement for the assessed pathologies across image sets, we employed weighted kappa statistics. Additionally, the Wilcoxon signed rank test was employed to compare image quality and quantitative rSNR and rCNR measurements.ResultsScan time was significantly reduced with DL-MRI and represented an approximate 66.5% reduction. DL-MRI consistently exhibited superior image quality in both coronal T2WI and axial T2WI when compared to both conventional MRI (p < 0.01) and No-DL-MRI (p < 0.01). Interobserver agreement was robust, with kappa values exceeding 0.735. For rSNR data, coronal fat-saturated(FS) T2WI and axial FS T2WI in DL-MRI consistently outperformed No-DL-MRI, with statistical significance (p < 0.01) observed in all cases. Similarly, rCNR data revealed significant improvements (p < 0.01) in coronal FS T2WI of DL-MRI when compared to No-DL-MRI. Importantly, our findings indicated that DL-MRI demonstrated diagnostic performance comparable to conventional MRI.ConclusionIntegrating deep learning-based reconstruction methods into standard clinical workflows has the potential to the promise of accelerating image acquisition, enhancing image clarity, and increasing patient throughput, thereby optimizing diagnostic efficiency.Trial registrationRetrospectively registered.

Golden Gate Cloning for the Standardized Assembly of Gene Elements with Modular Cloning in Chlamydomonas.

Modern synthetic biology requires fast and efficient cloning strategies for the assembly of new transcription units or entire pathways. Modular Cloning (MoClo) is a standardized synthetic biology workflow, which has tremendously simplified the assembly of genetic elements for transgene expression. MoClo is based on Golden Gate Assembly and allows to combine genetic elements of a library through a hierarchical syntax-driven pipeline. Here we describe the assembly of a genetic cassette for transgene expression in the single-celled model alga Chlamydomonas reinhardtii.

PTN secreted by cardiac fibroblasts promotes myocardial fibrosis and inflammation of pressure overload-induced hypertrophic cardiomyopathy through the PTN-SDC4 pathway.

Hypertrophic cardiomyopathy (HCM) is characterized by unexplained left ventricular hypertrophy (LVH) with key pathologic processes including myocardial necrosis, fibrosis, inflammation, and hypertrophy, which are involved in heart failure (HF), stroke, and even sudden death. Our aim was to explore the communication network among various cells in the heart of transverse aortic constriction (TAC) surgery induced HCM mice. Single-cell RNA-seq data of GSE137167 was downloaded from the Gene Expression Omnibus (GEO) database. Seurat was used to perform the standard workflow. CellChat was utilized to compute the cell-cell interaction network and analyze the ligand-receptor pairs. Weighted gene co-expression network analysis (WGCNA) was conducted to identify gene co-expression modules. In vitro and in vivo studies were performed to verify bioinformatic analysis findings through real-time quantitative PCR (RT-qPCR), Edu staining, transwell assay, western blot, immunofluorescence assay, CCK-8, hematoxylin and eosin (H&E) staining, and echocardiography based on TAC mouse model. Our results showed that after TAC surgery, the interaction between cardiac fibroblasts and macrophages was very common, and the increasing pleiotrophin (PTN) ligand secreted by cardiac fibroblasts could promote the self-proliferation or invasion for myocardial fibrosis as well as stimulate the inflammatory response of macrophages to contribute TAC surgery induced HCM through acting on Syndecan 4 (SDC4) receptor. Our study demonstrates that PTN derived from cardiac fibroblasts may play potential role in pressure overload-induced HCM through activating the PTN-SDC4 pathway in cardiac fibroblasts and macrophages, which may be a potential therapeutic target for pressure overload-induced HCM patients.

AI-Powered Coding Tools: A Study of Advancements, Challenges, and Future Directions

This review examines the effects of AI-assisted programming in contemporary software development, paying particular attention to tools driven by Large Language Models (LLMs), such as GPT-4o and GitHub Copilot. Starting with data processing and ending with model deployment, the review describes the standard workflow for training machine learning models and how programmers use these models to improve their coding processes. GitHub Copilot, an AI-powered code generator, and GPT-4o, a general-purpose LLM, are compared in terms of accuracy, usability, and efficiency when assisting with programming tasks. The results show that although both tools greatly facilitate coding, they each have particular advantages and disadvantages. GitHub Copilot is excellent at integrating with IDEs, providing contextual code recommendations and streamlining processes. In contrast, GPT-4o shows better accuracy when creating code from scratch, but it does not have Copilot’s seamless IDE integration. The review also identifies some of the current drawbacks of AI-powered coding tools, including the possibility of producing faulty or vulnerable code as a result of training on unreliable datasets and the inability to comprehend context, which can occasionally result in functionally correct but practically incorrect code. In order to filter and fix problematic code before training, the review recommends using advanced algorithms for data pre-processing. It also suggests improving the interpretability of code generated by AI to help developers better comprehend and trust the results.

Setting the foundation for a national collaborative learning health system in acute TBI rehabilitation: CARE4TBI Year 1 experience

AbstractIntroductionA learning health system (LHS) approach is a collaborative model that continuously examines, evaluates, and re‐evaluates data eventually transforming it into knowledge. High quantity of high‐quality data are needed to establish this model. The purpose of this article is to describe the collaborative discovery process used to identify and standardize clinical data documented during daily multidisciplinary inpatient rehabilitation that would then allow access to these data to conduct comparative effectiveness research.MethodsCARE4TBI is a prospective observational research study designed to capture clinical data within the standard inpatient rehabilitation documentation workflow at 15 TBI Model Systems Centers in the US. Three groups of stakeholders guided project development: therapy representative work group (TRWG) consisting of frontline therapists from occupational, physical, speech‐language, and recreational therapies; rehabilitation leader representative group (RLRG); and informatics and information technology team (IIT). Over a 12‐month period, the three work groups and research leadership team identified the therapeutic components captured within daily documentation throughout the duration of inpatient TBI rehabilitation.ResultsData brainstorming among the groups created 98 distinct categories of data with each containing a range of data elements comprising a total of 850 discrete data elements. The free‐form data were sorted into three large categories and through review and discussion, reduced to two categories of prospective data collection—session‐level and therapy activity‐level data. Twelve session data elements were identified, and 54 therapy activities were identified, with each activity containing discrete sub‐categories for activity components, method of delivery, and equipment or supplies. A total of 561 distinct meaningful data elements were identified across the 54 activities.DiscussionThe CARE4TBI data discovery process demonstrated feasibility in identifying and capturing meaningful high quantity and high‐quality treatment data across multiple disciplines and rehabilitation sites, setting the foundation for a LHS coalition for acute traumatic brain injury rehabilitation.

Reproducible protein quantitation of 270 human proteins at increased depth using nanoparticle-based fractionation and multiple reaction monitoring mass spectrometry with stable isotope-labelled internal standards.

Here we show that when using a mix of 274 light synthetic peptide standards (NAT) as surrogates for 270 human plasma proteins, as well as stable isotope-labelled standards (SIS) as normalizers (both from MRM Proteomics Inc.) for targeted quantitative analysis by liquid chromatography multiple reaction monitoring mass spectrometry (LC/MRM-MS), the Seer Proteograph™ platform allowed for the enrichment and absolute quantitation of up to an additional 62 targets (median) compared to two standard proteomic workflows without enrichment, representing an increase of 44%. The nanoparticle-based fractionation workflow resulted in improved reproducibility compared to a traditional proteomic workflow with no fractionation (median 8.3% vs. 13.1% CV). As expected, the protein concentrations in nanoparticle coronas were higher and had more compressed dynamic range in comparison to the concentrations determined either by a 3-hour Trypsin/LysC or overnight tryptic digestion methods. As the nanoparticle-based fractionation technology gains popularity, additional steps such as establishing technique-specific protein reference ranges across plasma samples and comparisons to well-established protein quantitation methods like enzyme-linked immunosorbent assay (ELISA) and LC/MRM-MS may be explored to enable absolute quantification of plasma proteins based on nanoparticle-based fractionation data.

Subsampling Blood Swabs as an Efficient and Good Practice for RapidHIT ID® Analyses

Background/Objectives: Rapid DNA instruments are gaining interest in the forensic community as a means of generating DNA profile information more quickly than standard laboratory workflows, and they have the potential to be carried out in decentralized structures of the main laboratory, such as in French overseas territories, where no laboratory facilities are installed. Although this system has been shown to be effective in analyzing rich traces (such as blood) sampled on a swab, it has the disadvantage of consuming the entire swab for analysis. Since it is impossible to double the sampling from very few blood stains, we designed a subsampling protocol of the main swab for analysis on RapidHIT ID® instruments. Methods: Two volumes of blood (10 µL and 150 µL) from four individuals were applied to a regular Copan flocked swab and then subsampled by three different operators using a Copan mini (subungual) flocked swab to recover a portion of the biological material. Results:The analysis of the mini swabs allows us to obtain a complete genetic profile regardless of the initial volume, with a better genetic profile intensity associated with a higher initial volume deposited on the regular swab. Conclusions: Therefore, this study presents a novel, effective, and adaptable subsampling method that has the potential to significantly improve forensic DNA analysis, particularly in remote or decentralized settings. It offers a flexible solution to current limitations of RapidHIT ID® technology and paves the way for future advancements in genetic forensic science.

A multimodal machine learning model for the stratification of breast cancer risk.

Machine learning models for the diagnosis of breast cancer can facilitate the prediction of cancer risk and subsequent patient management among other clinical tasks. For the models to impact clinical practice, they ought to follow standard workflows, help interpret mammography and ultrasound data, evaluate clinical contextual information, handle incomplete data and be validated in prospective settings. Here we report the development and testing of a multimodal model leveraging mammography and ultrasound modules for the stratification of breast cancer risk based on clinical metadata, mammography and trimodal ultrasound (19,360 images of 5,216 breasts) from 5,025 patients with surgically confirmed pathology across medical centres and scanner manufacturers. Compared with the performance of experienced radiologists, the model performed similarly at classifying tumours as benign or malignant and was superior at pathology-level differential diagnosis. With a prospectively collected dataset of 191 breasts from 187 patients, the overall accuracies of the multimodal model and of preliminary pathologist-level assessments of biopsied breast specimens were similar (90.1% vs 92.7%, respectively). Multimodal models may assist diagnosis in oncology.

Robust self-supervised denoising of voltage imaging data using CellMincer

Voltage imaging is a powerful technique for studying neuronal activity, but its effectiveness is often constrained by low signal-to-noise ratios (SNR). Traditional denoising methods, such as matrix factorization, impose rigid assumptions about noise and signal structures, while existing deep learning approaches fail to fully capture the rapid dynamics and complex dependencies inherent in voltage imaging data. Here, we introduce CellMincer, a novel self-supervised deep learning method specifically developed for denoising voltage imaging datasets. CellMincer operates by masking and predicting sparse pixel sets across short temporal windows and conditions the denoiser on precomputed spatiotemporal auto-correlations to effectively model long-range dependencies without large temporal contexts. We developed and utilized a physics-based simulation framework to generate realistic synthetic datasets, enabling rigorous hyperparameter optimization and ablation studies. This approach highlighted the critical role of conditioning on spatiotemporal auto-correlations, resulting in an additional 3-fold SNR gain. Comprehensive benchmarking on both simulated and real datasets, including those validated with patch-clamp electrophysiology (EP), demonstrates CellMincer’s state-of-the-art performance, with substantial noise reduction across the frequency spectrum, enhanced subthreshold event detection, and high-fidelity recovery of EP signals. CellMincer consistently outperforms existing methods in SNR gain (0.5–2.9 dB) and reduces SNR variability by 17–55%. Incorporating CellMincer into standard workflows significantly improves neuronal segmentation, peak detection, and functional phenotype identification, consistently surpassing current methods in both SNR gain and consistency.

Challenges in clinical translation of cardiac magnetic resonance imaging radiomics in non-ischemic cardiomyopathy: a narrative review.

Radiomics is an emerging technology that facilitates the quantitative analysis of multi-modal cardiac magnetic resonance imaging (MRI). This study aims to introduce a standardized workflow for applying radiomics to non-ischemic cardiomyopathies, enabling clinicians to comprehensively understand and implement this technology in clinical practice. A computerized literature search (up to August 1, 2024) was conducted using PubMed to identify relevant studies on the roles and workflows of radiomics in non-ischemic cardiomyopathy. Expert discussions were also held to ensure the accuracy and relevance of the findings. Only English-language publications were reviewed. The paper details the essential processes of radiomics, including feature extraction, feature engineering, model construction, and data analysis. It emphasizes the role of MRI in assessing cardiac structure and function and demonstrates how MRI-based radiomics can aid in diagnosing and differentiating non-ischemic cardiomyopathies such as hypertrophic cardiomyopathy, dilated cardiomyopathy, and myocarditis. The study also investigates various cardiac MRI sequences to enhance the clinical application of radiomics. The standardized radiomics workflow presented in this study aims to assist clinicians in effectively utilizing MRI-based radiomics for the diagnosis and management of non-ischemic cardiomyopathies, thereby improving clinical decision-making.

Effect of digital cataract workflow on time and resource efficiencies in cataract surgery: time and motion study.

To compare time and resource saving with integration of digital cataract workflow to the existing workflow in high-volume cataract surgery clinics. L V Prasad Eye Institute, Hyderabad, India (site 1), and Narayana Nethralaya, Bengaluru, India (site 2). Prospective, time and motion. The total time to complete each step (preoperative measurements, surgical planning, and surgical procedures) of the cataract workflow, number of data fields entered, and support staff required for both workflows were recorded. All study measurements were determined first for existing electronic medical record (EMR) cataract workflow followed by digital workflow (integrated data management system with data reviewer, surgical planner, and data transfer to operating room) at both sites. A total of 85 (site 1, 44; site 2, 41) cataract workflows were analyzed. The integration of digital workflow into the site's existing EMR workflow reduced the mean time for preoperative measurements by 25.3% ( P = .006), surgical planning by 55.1% ( P = .008), and surgical procedures by 22.6% ( P = .002). The mean ± SD overall time for the surgery was significantly shorter in the digital group (887.3 ± 103.3 vs 1271.3 ± 300.7 seconds; P < .0001). For both sites, the number of data fields recorded and number of support staff needed were significantly lesser for the digital workflow ( P < .0001, for both). Integration of digital workflow significantly reduced the overall cataract surgery time, variability of overall time, number of data fields recorded, and resource utilization. Complete digitalization has important implications for improving the efficiency and standardization of cataract surgery workflow.

Advancing hyperspectral imaging and machine learning tools toward clinical adoption in tissue diagnostics: A comprehensive review

Hyperspectral imaging (HSI) has become an evident transformative apparatus in medical diagnostics. The review aims to appraise the present advancement and challenges in HSI for medical applications. It features a variety of medical applications namely diagnosing diabetic retinopathy, neurodegenerative diseases like Parkinson's and Alzheimer's, which illustrates its effectiveness in early diagnosis, early caries detection in periodontal disease, and dermatology by detecting skin cancer. Regardless of these advances, the challenges exist within every aspect that limits its broader clinical adoption. It has various constraints including difficulties with technology related to the complexity of the HSI system and needing specialist training, which may act as a drawback to its clinical settings. This article pertains to potential challenges expressed in medical applications and probable solutions to overcome these constraints. Successful companies that perform advanced solutions with HSI in terms of medical applications are being emphasized in this study to signal the high level of interest in medical diagnosis for systems to incorporate machine learning ML and artificial intelligence AI to foster precision diagnosis and standardized clinical workflow. This advancement signifies progressive possibilities of HSI in real-time clinical assessments. In conclusion despite HSI has been presented as a significant advanced medical imaging tool, addressing its limitations and probable solutions is for broader clinical adoption.

Flow cytometry FRET reveals post-translational modifications drive Protein Phosphatase-5 conformational changes in mammalian cells.

The serine/threonine Protein Phosphatase-5 (PP5) plays an essential role in regulating hormone and stress-induced signaling networks as well as extrinsic apoptotic pathways in cells. Unlike other Protein Phosphatases, PP5 possesses both regulatory and catalytic domains, and its function is further modulated through post-translational modifications (PTMs). PP5 contains a tetratricopeptide repeat (TPR) domain, which usually inhibits its phosphatase activity by blocking the active site (closed conformation). Certain activators bind to the PP5-TPR domain, alleviating this inhibition and allowing the catalytic domain to adopt an active (open) conformation. While this mechanism has been proposed based on structural and biophysical studies, PP5 conformational changes and activity have yet to be observed in cells. Here, we designed and developed a flow cytometry-based fluorescence resonance energy transfer (FC-FRET) method, enabling real-time observation of PP5 autoinhibition and activation within live mammalian cells. By quantifying FRET efficiency using sensitized emission, we established a standardized and adaptable data acquisition workflow. Our findings revealed that, in a cellular context, PP5 exists in multiple conformational states, none of which alone fully predicts its activity. Additionally, we have demonstrated that PTMs such as phosphorylation and SUMOylation impact PP5 conformational changes, representing a significant advancement in our understanding of its regulatory mechanisms.

Engaging the Space Between Minimal Processing and Inclusive Description to Create More Sustainable and Ethical Workflows

ABSTRACT Archival literature includes two prominent theoretical currents: the implementation of minimal descriptive practices and the need for more inclusive, reparative practices. Although prominent, these threads are not often considered together to explore how minimal processing frameworks may impact efforts for a diverse and representative archival record documenting marginalized communities and experiences. This article discusses these two frameworks, the apparent tension between them, and possible methods for reconciling the two to integrate more inclusive practices directly into standard archival workflows. This article argues that implementing inclusive practices into archival work is necessary to create a more representative historical record. Balancing both perspectives is necessary to create access points discoverable to a broad range of archival researchers. Creating access points for marginalized researchers is part of the core of archivists’ professional responsibilities and cannot be performed as a specialized project or contingent upon specialized funding—it must be embedded directly into all accessioning and processing workflows. By simultaneously considering these two theoretical currents, archivists can incorporate inclusive and reparative practices within an overarching extensible processing workflow.

Learning from wildfires: A scalable framework to evaluate treatment effects on burn severity

AbstractInterruption of frequent burning in dry forests across western North America and the continued impacts of anthropogenic climate change have resulted in increases in fire size and severity compared to historical fire regimes. Recent legislation, funding, and planning have emphasized increased implementation of mechanical thinning and prescribed burning treatments to decrease the risk of undesirable ecological and social outcomes due to fire. As wildfires and treatments continue to interact, managers require consistent approaches to evaluate treatment effectiveness at moderating burn severity. In this study, we present a repeatable, remote sensing–based, analytical framework for conducting fire‐scale assessments of treatment effectiveness that informs local management while also supporting cross‐fire comparisons. We demonstrate this framework on the 2021 Bootleg Fire in Oregon and the 2021 Schneider Springs Fire in Washington. Our framework used (1) machine learning to identify key bioclimatic, topographic, and fire weather drivers of burn severity in each fire, (2) standardized workflows to statistically sample untreated control units, and (3) spatial regression modeling to evaluate the effects of treatment type and time since treatment on burn severity. The application of our framework showed that, in both fires, recent prescribed burning treatments were the most effective at reducing burn severity relative to untreated controls. In contrast, thinning‐only treatments only produced low/moderate‐severity effects under the more moderate fire weather conditions in the Schneider Springs Fire. Our framework offers a robust approach for evaluating treatment effects on burn severity at the scale of individual fires, which can be scaled up to assess treatment effectiveness across multiple fires. As climate change brings increased uncertainty to dry forest ecosystems of western North America, our framework can support more strategic management actions to reduce wildfire risk and foster resilience.

A Cross-Sectional Study of Variant Interpretation and Reporting of NGS Data Using Tertiary Analysis Software: Navify® Mutation Profiler.

Personalized medicine has revolutionized the clinical management of patients with solid tumors. However, the large volumes of molecular data derived from next-generation sequencing (NGS) and the lack of harmonized bioinformatics pipelines drastically impact the clinical management of patients with solid tumors. A possible solution to streamline the molecular interpretation and reporting of NGS data would be to adopt automated data analysis software. In this study, we tested the clinical efficiency of the Navify Mutation Profiler (nMP) software in improving the interpretation of NGS data analysis in diagnostic routine samples from patients with solid tumors. This study included one coordinating institution (Federico II University of Naples) and five other Italian institutions. Variant call format (VCF) files from reference standard samples previously tested by the coordinating institution and from n = 8 diagnostic routine samples (n = 2 from colorectal carcinoma; n = 2 from non-small cell lung cancer; n = 2 from advanced melanoma; and n = 2 from patients with gastrointestinal stromal tumors)and previously analyzed by each participating institution (n = 5) with standardized internal analysis workflows were uploaded onto the Navify® Mutation Profiler (nMP) system (Roche Sequencing Solutions, Pleasanton, CA, USA) for automated analysis and interpretation of DNA and RNA molecular alterations analytical parameters, molecular profiling, and clinical interpretation were carried out by the nMP system and compared with the standard workflow data analyzed by the participating institutions. Overall, all VCF files were successfully submitted and interpreted by the nMP system. A concordance agreement rate of 89.6% was observed between the automated and standard workflow systems. In particular, DNA and RNA molecular profiles obtained with the nMP system matched those obtained with standardized approaches in 44 out of 48 patients (91.7%) and in 11 out of 12 (91.7%) cases, respectively. In addition, the nMP system evidenced wild-type variants in 6 out of 7 (85.7%) cases. The nMP system represents a valid, easily manageable, and clinically useful system to interpret NGS data on diagnostic routine samples from patients with solid tumors.