PERform: assessing model performance with predictivity and explainability readiness formula.

In the rapidly evolving field of artificial intelligence (AI), explainability has been traditionally assessed in a post-modeling process and is often subjective. In contrary, many quantitative metrics have been routinely used to assess a model's performance. We proposed a unified formular named PERForm, by incorporating explainability as a weight into the existing statistical metrics to provide an integrated and quantitative measure of both predictivity and explainability to guide model selection, application, and evaluation. PERForm was designed as a generic formula and can be applied to any data types. We applied PERForm on a range of diverse datasets, including DILIst, Tox21, and three MAQC-II benchmark datasets, using various modeling algorithms to predict a total of 73 distinct endpoints. For example, AdaBoost algorithms exhibited superior performance (PERForm AUC for AdaBoost is 0.129 where Linear regression is 0) in DILIst prediction, where linear regression outperformed other models in the majority of Tox21 endpoints (PERForm AUC for linear regression is 0.301 where AdaBoost is 0.283 in average). This research marks a significant step toward comprehensively evaluating the utility of an AI model to advance transparency and interpretability, where the tradeoff between a model's performance and its interpretability can have profound implications.

Towards accurate indel calling for oncopanel sequencing through an international pipeline competition at precisionFDA.

Accurately calling indels with next-generation sequencing (NGS) data is critical for clinical application. The precisionFDA team collaborated with the U.S. Food and Drug Administration's (FDA's) National Center for Toxicological Research (NCTR) and successfully completed the NCTR Indel Calling from Oncopanel Sequencing Data Challenge, to evaluate the performance of indel calling pipelines. Top performers were selected based on precision, recall, and F1-score. The performance of many other pipelines was close to the top performers, which produced a top cluster of performers. The performance was significantly higher in high confidence regions and coding regions, and significantly lower in low complexity regions. Oncopanel capture and other issues may have occurred that affected the recall rate. Indels with higher variant allele frequency (VAF) may generally be called with higher confidence. Many of the indel calling pipelines had good performance. Some of them performed generally well across all three oncopanels, while others were better for a specific oncopanel. The performance of indel calling can further be improved by restricting the calls within high confidence intervals (HCIs) and coding regions, and by excluding low complexity regions (LCR) regions. Certain VAF cut-offs could be applied according to the applications.

A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document.

Regulatory agencies consistently deal with extensive document reviews, ranging from product submissions to both internal and external communications. Large Language Models (LLMs) like ChatGPT can be invaluable tools for these tasks, however present several challenges, particularly the proprietary information, combining customized function with specific review needs, and transparency and explainability of the model's output. Hence, a localized and customized solution is imperative. To tackle these challenges, we formulated a framework named askFDALabel on FDA drug labeling documents that is a crucial resource in the FDA drug review process. AskFDALabel operates within a secure IT environment and comprises two key modules: a semantic search and a Q&A/text-generation module. The Module S built on word embeddings to enable comprehensive semantic queries within labeling documents. The Module T utilizes a tuned LLM to generate responses based on references from Module S. As the result, our framework enabled small LLMs to perform comparably to ChatGPT with as a computationally inexpensive solution for regulatory application. To conclude, through AskFDALabel, we have showcased a pathway that harnesses LLMs to support agency operations within a secure environment, offering tailored functions for the needs of regulatory research.

Trends in the use of ankle arthrodesis and total ankle replacements in Australia over the past 20 years.

BACKGROUND: End stage ankle osteoarthritis (OA) is debilitating. Surgical management consists of either ankle arthrodesis (AA) or a total ankle replacement (TAR). The purpose of this study is to assess the trends in operative intervention for end stage ankle OA in an Australian population. METHODS: This is a retrospective epidemiological study of 15,046 surgeries. Data were collected from publicly available national registries including the Australian Medicare Database and Australian Orthopaedic Association National Joint Replacement Registrar from 2001 to 2020. RESULTS: There was a significant increase in all ankle surgeries performed across the period of interest. AA remained the more commonly performed procedure throughout the course of the study (11,946 cases, 79.4%) and was never surpassed by TAR (3100, 20.6%). The overall proportions demonstrated no significant changes from 2001 to 2020. CONCLUSION: The incidence of ankle surgeries continues to increase with the ageing and increasingly comorbid population of Australia. Despite demonstrating no significant overall change in the ratio of TAR and AA in our study population and period, there are noticeable trends within the timeframe, with a recent surge favouring TAR in the last 5 years.

2023 White Paper on Recent Issues in Bioanalysis: ISR for ADA Assays, the Rise of dPCR vs qPCR, International Reference Standards for Vaccine Assays, Anti-AAV TAb Post-Dose Assessment, NanoString Validation, ELISpot as Gold Standard (Part 3 - Recommendations on Gene Therapy, Cell Therapy, Vaccines Immunogenicity & Technologies; Biotherapeutics Immunogenicity & Risk Assessment; ADA/NAb Assay/Reporting Harmonization).

The 17th Workshop on Recent Issues in Bioanalysis (17th WRIB) took place in Orlando, FL, USA on June 19-23, 2023. Over 1000 professionals representing pharma/biotech companies, CROs, and multiple regulatory agencies convened to actively discuss the most current topics of interest in bioanalysis. The 17th WRIB included 3 Main Workshops and 7 Specialized Workshops that together spanned 1 week to allow an exhaustive and thorough coverage of all major issues in bioanalysis of biomarkers, immunogenicity, gene therapy, cell therapy and vaccines. Moreover, in-depth workshops on "EU IVDR 2017/746 Implementation and impact for the Global Biomarker Community: How to Comply with these NEW Regulations" and on "US FDA/OSIS Remote Regulatory Assessments (RRAs)" were the special features of the 17th edition. As in previous years, WRIB continued to gather a wide diversity of international, industry opinion leaders and regulatory authority experts working on both small and large molecules as well as gene, cell therapies and vaccines to facilitate sharing and discussions focused on improving quality, increasing regulatory compliance, and achieving scientific excellence on bioanalytical issues. This 2023 White Paper encompasses recommendations emerging from the extensive discussions held during the workshop and is aimed to provide the bioanalytical community with key information and practical solutions on topics and issues addressed, in an effort to enable advances in scientific excellence, improved quality and better regulatory compliance. Due to its length, the 2023 edition of this comprehensive White Paper has been divided into three parts for editorial reasons. This publication (Part 3) covers the recommendations on Gene Therapy, Cell therapy, Vaccines and Biotherapeutics Immunogenicity. Part 1A (Mass Spectrometry Assays and Regulated Bioanalysis/BMV), P1B (Regulatory Inputs) and Part 2 (Biomarkers, IVD/CDx, LBA and Cell-Based Assays) are published in volume 16 of Bioanalysis, issues 8 and 9 (2024), respectively.

Co-surgeon versus Single-surgeon Outcomes in Free Tissue Breast Reconstruction: A Meta-analysis.

BACKGROUND: Autologous breast reconstruction offers superior long-term patient reported outcomes compared with implant-based reconstruction. Universal adoption of free tissue transfer has been hindered by procedural complexity and long operative time with microsurgery. In many specialties, co-surgeon (CS) approaches are reported to decrease operative time while improving surgical outcomes. This systematic review and meta-analysis synthesizes the available literature to evaluate the potential benefit of a CS approach in autologous free tissue breast reconstruction versus single-surgeon (SS). METHODS: A systematic review and meta-analysis was conducted using PubMed, Embase, and MEDLINE from inception to December 2022. Published reports comparing CS to SS approaches in uni- and bilateral autologous breast reconstruction were identified. Primary outcomes included operative time, postoperative outcomes, processes of care, and financial impact. Risk of bias was assessed and outcomes were characterized with effect sizes. RESULTS: Eight retrospective studies reporting on 9,425 patients were included. Compared with SS, CS approach was associated with a significantly shorter operative time (SMD -0.65, 95% confidence interval [CI] -1.01 to -0.29, p < 0.001), with the largest effect size in bilateral reconstructions (standardized mean difference [SMD] -1.02, 95% CI -1.37 to -0.67, p < 0.00001). CS was also associated with a significant decrease in length of hospitalization (SMD -0.39, 95% CI -0.71 to -0.07, p = 0.02). Odds of flap failure or surgical complications including surgical site infection, hematoma, fat necrosis, and reexploration were not significantly different. CONCLUSION: CS free tissue breast reconstruction significantly shortens operative time and length of hospitalization compared with SS approaches without compromising postoperative outcomes. Further research should model processes and financial viability of its adoption in a variety of health care models.

The Use of Hyaluronic Acid in Non-surgical Rhinoplasty: A Systematic Review of Complications, Clinical, and Patient-Reported Outcomes.

INTRODUCTION: In cosmetic practices, non-surgical rhinoplasty using filler injections has become increasingly common. Nevertheless, the outcome and overall complications have not been studied as a systematic review in the literature. This study provides a high-quality systematic review of studies reporting clinical and patient-reported outcomes following non-surgical rhinoplasty with hyaluronic acid (HA) to further guide practitioners. METHODS: This systematic review was conducted in accordance with PRISMA guidelines and was registered in PROSPERO. The search was conducted using MEDLINE, EMBASE, and Cochrane. The literature retrieval was conducted by three independent reviewers, and the remaining articles were screened by two independent reviewers. The quality of included articles was assessed using the MINORS and methodological quality and synthesis of case series and case reports tools. RESULTS: A total of 874 publications were found based on the search criteria. A total of 3928 patients were reviewed for this systematic review from 23 full-text articles. For non-surgical rhinoplasty, Juvéderm ultra was the most commonly used HA filler. The nasal tip was most commonly injected (13 studies), followed by the columella (12 studies). Nasal hump deformities are the most common reason for non-surgical rhinoplasty. All studies showed high patient satisfaction. Among all patients reviewed, eight developed major complications. CONCLUSION: Non-surgical rhinoplasty performed with HA has minimal side effects and a short recovery period. Furthermore, non-surgical rhinoplasty with HA results in high satisfaction. To strengthen the presently available evidence, further well-designed RCTs are needed. LEVEL OF EVIDENCE III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors https://www.springer.com/00266.

Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science.

Artificial intelligence (AI) is increasingly being used in decision making across various industries, including the public health arena. Bias in any decision-making process can significantly skew outcomes, and AI systems have been shown to exhibit biases at times. The potential for AI systems to perpetuate and even amplify biases is a growing concern. Bias, as used in this paper, refers to the tendency toward a particular characteristic or behavior, and thus, a biased AI system is one that shows biased associations entities. In this literature review, we examine the current state of research on AI bias, including its sources, as well as the methods for measuring, benchmarking, and mitigating it. We also examine the biases and methods of mitigation specifically relevant to the healthcare field and offer a perspective on bias measurement and mitigation in regulatory science decision making.

Assessments of tumor mutational burden estimation by targeted panel sequencing: A comprehensive simulation analysis.

Tumor mutational burden (TMB), when at a high level, is an emerging indicative factor of sensitivity to immune checkpoint inhibitors. Previous studies have shown that the more affordable and accurate targeted panels can be used to measure TMB as a substitute for whole exome sequencing (WES). However, additional processes, such as hotspot mutations exclusion and TMB adjustment, are usually required to deal with the effect of the limited panel sizes. A comprehensive investigation of the effective factors is needed for accurate TMB estimation by targeted panels. In this study, we quantitatively evaluated the variances of TMB values calculated by WES and targeted panels using 10,000 simulated targeted panels with panel sizes ranging from 0.2 to 3.1 million bases. With The Cancer Genome Atlas (TCGA) cancer samples and mutation profiles, we fixed regressions on WES-TMBs and panel-TMBs to assess the performance of a given targeted panel. Panel size was found as one of the major effective factors of TMB estimation. Meanwhile, by investigating the well-performing small panels that reported TMB values similar to those of WES, we demonstrated the evidence of the cancer type-specific impacts of genes on TMB estimation and identified high-impact gene sets for different cancer types based on the TCGA data. This study revealed the quantitative correlations between TMB variance and panel size, and the potential impacts of individual genes on TMB estimation. Our results suggested that for cancer patients diagnosed using targeted panels, it would be highly beneficial to have the capability to directly measure TMB from the targeted sequencing data. This would greatly assist in making decisions regarding the use of immunotherapies.

Evaluating the Utility of a Near-Peer Situation-Based Course to Prepare Medical Students for the UK Foundation Programme: A Pilot Study.

Background: Starting the Foundation Programme can be challenging for many medical graduates, as medical school alone may not adequately prepare them for complex tasks like managing comorbidities or emergencies. Growing evidence supports the role of transition interventions to meet this knowledge gap, however data on the utility of situation-based learning are limited. This pilot study aimed to assess the efficacy of a near-peer case-based course in improving knowledge and preparedness for foundation training in recent medical graduates. Methods: Recent Imperial College graduates who attended a "Junior Doctor on Call" course were eligible for inclusion. This transition intervention, designed and delivered by a Foundation Year 1 doctor, covered six patient cases that integrated high-yield clinical concepts and practical tips. An online questionnaire was distributed one week before and after the course to assess perceptions of knowledge, confidence, and preparedness for training. Participants were also invited to attend an online semi-structured after the course. Results: Out of 19 attendees, 17 (89.5% response rate) completed the pre-course questionnaire, 14 (73.7% response rate) completed the post-course questionnaire, and 3 completed the interview. 68.75% (n=11) had not previously attended a preparatory course for foundation training. Results demonstrated that 85.7% of participants felt more knowledgeable than before in the key topics covered. Participants also demonstrated an increase in self-rated confidence in commencing work as a junior doctor following the course, with 92.9% of participants stating that they felt more confident. Conclusion: This study offers support for short-term situation-based courses in enhancing medical students' knowledge and confidence for foundation training. These findings add to the growing evidence-base encouraging implementation of short-term courses in preparing for practice. However, further research on the utility of such transition interventions is critical to inform the development of evidence-based recommendations for recent medical graduates, educators, and programme directors.

Quartet DNA reference materials and datasets for comprehensively evaluating germline variant calling performance.

BACKGROUND: Genomic DNA reference materials are widely recognized as essential for ensuring data quality in omics research. However, relying solely on reference datasets to evaluate the accuracy of variant calling results is incomplete, as they are limited to benchmark regions. Therefore, it is important to develop DNA reference materials that enable the assessment of variant detection performance across the entire genome. RESULTS: We established a DNA reference material suite from four immortalized cell lines derived from a family of parents and monozygotic twins. Comprehensive reference datasets of 4.2 million small variants and 15,000 structural variants were integrated and certified for evaluating the reliability of germline variant calls inside the benchmark regions. Importantly, the genetic built-in-truth of the Quartet family design enables estimation of the precision of variant calls outside the benchmark regions. Using the Quartet reference materials along with study samples, batch effects are objectively monitored and alleviated by training a machine learning model with the Quartet reference datasets to remove potential artifact calls. Moreover, the matched RNA and protein reference materials and datasets from the Quartet project enables cross-omics validation of variant calls from multiomics data. CONCLUSIONS: The Quartet DNA reference materials and reference datasets provide a unique resource for objectively assessing the quality of germline variant calls throughout the whole-genome regions and improving the reliability of large-scale genomic profiling.

Developing a Charlson Comorbidity Index for the American Indian Population Using the Epidemiologic Data from the Strong Heart Study.

Background: The Charlson Comorbidity Index (CCI) is a frequently used mortality predictor based on a scoring system for the number and type of patient comorbidities health researchers have used since the late 1980s. The initial purpose of the CCI was to classify comorbid conditions, which could alter the risk of patient mortality within a one-year time frame. However, the CCI may not accurately reflect risk among American Indians because they are a small proportion of the U.S. population and possibly lack representation in the original patient cohort. A motivating factor in calibrating a CCI for American Indians is that this population, as a whole, experiences a greater burden of comorbidities, including diabetes mellitus, obesity, cancer, cardiovascular disease, and other chronic health conditions, than the rest of the U.S. population. Methods: This study attempted to modify the CCI to be specific to the American Indian population utilizing the data from the still ongoing The Strong Heart Study (SHS) - a multi-center population-based longitudinal study of cardiovascular disease among American Indians.A one-year survival analysis with mortality as the outcome was performed using the SHS morbidity and mortality surveillance data and assessing the impact of comorbidities in terms of hazard ratios with the training cohort. A Kaplan-Meier plot for a subset of the testing cohort was used to compare groups with selected mCCI-AI scores. Results: A total of 3,038 Phase VI participants from the SHS comprised the study population for whom mortality and morbidity surveillance data were available through December 2019. The weights generated by the SHS participants for myocardial infarction, congestive heart failure, and high blood pressure were greater than Charlson's original weights. In addition, the weights for liver illness were equivalent to Charlson's severe form of the disease. Lung cancer had the greatest overall weight derived from a hazard ratio of 8.308. Conclusions: The mCCI-AI was a statistically significant predictor of one-year mortality, classifying patients into different risk strata X2 (8, N = 1,245) = 30.56 (p = .0002). The mCCI-AI exhibited superior performance over the CCI, able to discriminate between participants who died and those who survived 73% of the time.

Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling.

Certified RNA reference materials are indispensable for assessing the reliability of RNA sequencing to detect intrinsically small biological differences in clinical settings, such as molecular subtyping of diseases. As part of the Quartet Project for quality control and data integration of multi-omics profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets between two samples, providing cross-platform and cross-laboratory 'ground truth'. Investigation of the intrinsically subtle biological differences among the Quartet samples enables sensitive assessment of cross-batch integration of transcriptomic measurements at the ratio level. The Quartet RNA reference materials, combined with the ratio-based reference datasets, can serve as unique resources for assessing and improving the quality of transcriptomic data in clinical and biological settings.

Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method.

BACKGROUND: Batch effects are notoriously common technical variations in multiomics data and may result in misleading outcomes if uncorrected or over-corrected. A plethora of batch-effect correction algorithms are proposed to facilitate data integration. However, their respective advantages and limitations are not adequately assessed in terms of omics types, the performance metrics, and the application scenarios. RESULTS: As part of the Quartet Project for quality control and data integration of multiomics profiling, we comprehensively assess the performance of seven batch effect correction algorithms based on different performance metrics of clinical relevance, i.e., the accuracy of identifying differentially expressed features, the robustness of predictive models, and the ability of accurately clustering cross-batch samples into their own donors. The ratio-based method, i.e., by scaling absolute feature values of study samples relative to those of concurrently profiled reference material(s), is found to be much more effective and broadly applicable than others, especially when batch effects are completely confounded with biological factors of study interests. We further provide practical guidelines for implementing the ratio based approach in increasingly large-scale multiomics studies. CONCLUSIONS: Multiomics measurements are prone to batch effects, which can be effectively corrected using ratio-based scaling of the multiomics data. Our study lays the foundation for eliminating batch effects at a ratio scale.

Patient-relevant outcomes following elective, aseptic revision knee arthroplasty: a systematic review.

BACKGROUND: The aim of this systematic review was to summarise the evidence for the clinical effectiveness of revision knee arthroplasty (rKA) compared to non-operative treatment for the management of patients with elective, aseptic causes for a failed knee arthroplasty. METHODS: MEDLINE, Embase, AMED and PsychINFO were searched from inception to 1st December 2020 for studies on patients considering elective, aseptic rKA. Patient-relevant outcomes (PROs) were defined as implant survivorship, joint function, quality of life (QoL), complications and hospital admission impact. RESULTS: No studies compared elective, aseptic rKA to non-operative management. Forty uncontrolled studies reported on PROs following elective, aseptic rKA (434434 rKA). Pooled estimates for implant survivorship were: 95.5% (95% CI 93.2-97.7%) at 1 year [seven studies (5524 rKA)], 90.8% (95% CI 87.6-94.0%) at 5 years [13 studies (5754 rKA)], 87.4% (95% CI 81.7-93.1%) at 10 years [nine studies (2188 rKA)], and 83.2% (95% CI 76.7-89.7%) at 15 years [two studies (452 rKA)]. Twelve studies (2382 rKA) reported joint function and/or QoL: all found large improvements from baseline to follow-up. Mortality rates were low (0.16% to 2% within 1 year) [four studies (353064 rKA)]. Post-operative complications were common (9.1 to 37.2% at 90 days). CONCLUSION: Higher-quality evidence is needed to support patients with decision-making in elective, aseptic rKA. This should include studies comparing operative and non-operative management. Implant survivorship following elective, aseptic rKA was ~ 96% at 1 year, ~ 91% at 5 years and ~ 87% at 10 years. Early complications were common after elective, aseptic rKA and the rates summarised here can be shared with patients during informed consent. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020196922.

2022 White Paper on Recent Issues in Bioanalysis: FDA Draft Guidance on Immunogenicity Information in Prescription Drug Labeling, LNP & Viral Vectors Therapeutics/Vaccines Immunogenicity, Prolongation Effect, ADA Affinity, Risk-based Approaches, NGS, qPCR, ddPCR Assays (Part 3 - Recommendations on Gene Therapy, Cell Therapy, Vaccines Immunogenicity & Technologies; Immunogenicity & Risk Assessment of Biotherapeutics and Novel Modalities; NAb Assays Integrated Approach).

The 2022 16th Workshop on Recent Issues in Bioanalysis (WRIB) took place in Atlanta, GA, USA on September 26-30, 2022. Over 1000 professionals representing pharma/biotech companies, CROs, and multiple regulatory agencies convened to actively discuss the most current topics of interest in bioanalysis. The 16th WRIB included 3 Main Workshops and 7 Specialized Workshops that together spanned 1 week in order to allow exhaustive and thorough coverage of all major issues in bioanalysis, biomarkers, immunogenicity, gene therapy, cell therapy and vaccines. Moreover, in-depth workshops on ICH M10 BMV final guideline (focused on this guideline training, interpretation, adoption and transition); mass spectrometry innovation (focused on novel technologies, novel modalities, and novel challenges); and flow cytometry bioanalysis (rising of the 3rd most common/important technology in bioanalytical labs) were the special features of the 16th edition. As in previous years, WRIB continued to gather a wide diversity of international, industry opinion leaders and regulatory authority experts working on both small and large molecules as well as gene, cell therapies and vaccines to facilitate sharing and discussions focused on improving quality, increasing regulatory compliance, and achieving scientific excellence on bioanalytical issues. This 2022 White Paper encompasses recommendations emerging from the extensive discussions held during the workshop and is aimed to provide the bioanalytical community with key information and practical solutions on topics and issues addressed, in an effort to enable advances in scientific excellence, improved quality and better regulatory compliance. Due to its length, the 2022 edition of this comprehensive White Paper has been divided into three parts for editorial reasons. This publication (Part 3) covers the recommendations on Gene Therapy, Cell therapy, Vaccines and Biotherapeutics Immunogenicity. Part 1 (Mass Spectrometry and ICH M10) and Part 2 (LBA, Biomarkers/CDx and Cytometry) are published in volume 15 of Bioanalysis, issues 16 and 15 (2023), respectively.

A Weakly Supervised Deep Learning Framework for Whole Slide Classification to Facilitate Digital Pathology in Animal Study.

The pathology of animal studies is crucial for toxicity evaluations and regulatory assessments, but the manual examination of slides by pathologists remains time-consuming and requires extensive training. One inherent challenge in this process is the interobserver variability, which can compromise the consistency and accuracy of a study. Artificial intelligence (AI) has demonstrated its ability to automate similar examinations in clinical applications with enhanced efficiency, consistency, and accuracy. However, training AI models typically relies on costly pixel-level annotation of injured regions and is often not available for animal pathology. To address this, we developed the PathologAI system, a "weakly" supervised approach for WSI classification in rat images without explicit lesion annotation at the pixel level. Using rat liver imaging data from the Open TG-GATEs system, PathologAI was applied to predict necrosis of n = 816 WSIs (377 controls). TG-GATEs studied 170 compounds at three dose levels (low, middle, and high) for four time points (3, 7, 14, and 28 days). PathologAI first preprocessed WSIs at the tile level to generate a high-level representation with a Generative Adversarial Network architecture. The prediction of liver necrosis relied on an ensemble model of 5 CNN classifiers trained on 335 WSIs. The ensemble model achieved notable classification accuracy on the holdout test set: 87% among 87 control slides free of findings, 83% among 120 controls with spontaneous necrosis, 67% among 147 treated animals with spontaneous minimal or slight necrosis, and 59% among 127 treated animals with minimal or slight necrosis caused by the treatment. Importantly, PathologAI was able to discriminate WSIs with spontaneous necrosis from those with treatment related necrosis and discriminated mild lesion level findings (slight vs minimal) and between treatment dose levels. PathologAI could provide an inexpensive and rapid screening tool to assist the digital pathology analysis in preclinical applications and general toxicological studies.

Flexor Digitorum Superficialis Excision for Trigger Finger - A Systematic Literature Review.

Background: Division of one or more slips of the flexor digitorum superficialis (FDS) tendon has been posited as an effective surgical modality for advanced or recurrent trigger finger. This may be an effective approach among patients with diabetes or rheumatoid arthritis, or in those with fixed flexion deformities who have poor outcomes from A1 pulley release alone. However, there is limited evidence regarding the effectiveness of this procedure. The role of this study was to systematically review the evidence on functional outcomes and safety of partial or complete FDS resection in the management of trigger finger. Methods: A systematic review was performed according to PRISMA guidelines. PubMed, Cochrane CENTRAL and Ovid Medline databases were electronically queried from their inception until February 2022. English language papers were included if they reported original data on postoperative outcomes and complications following resection of one or more slips of FDS for adult trigger finger. Results: Seven articles were eligible for inclusion, encompassing 420 fingers in 290 patients. All included studies were retrospective. Isolated ulnar slip FDS resection was the most described surgery. Mean postoperative fixed flexion deformity at the proximal interphalangeal joint was 6.0° compared to 31.5° preoperatively, and the proportion of patients with fixed flexion deformity reduced by 58%. Mean postoperative total active motion was 228.7°. Recurrence was seen in 4.7% of digits, and complications occurred in 11.2% of cases. No post-surgical ulnar drift or swan neck deformities were observed. Conclusions: FDS resection for long-standing trigger finger, or in diabetic or rheumatoid populations, is an effective and safe technique with low rates of recurrence. Prospective and comparative studies of this technique would be beneficial. Level of Evidence: Level III (Therapeutic).