Fast Photochemical Oxidation of Proteins (FPOP) is a promising technique for studying protein structure and dynamics. The quality of insight provided by FPOP depends on the reliability of the determination of the modification site. This study investigates the performance of two search engines, Mascot and PEAKS, for the data processing of FPOP analyses. Comparison of Mascot and PEAKS of the hemoglobin--haptoglobin Bruker timsTOF data set (PXD021621) revealed greater consistency in the Mascot identification of modified peptides, with around 26% of the IDs being mutual for all three replicates, compared to approximately 22% for PEAKS. The intersection between Mascot and PEAKS results revealed a limited number (31%) of shared modified peptides. Principal Component Analysis (PCA) using the peptide-spectrum match (PSM) score, site probability, and peptide intensity was applied to evaluate the results, and the analyses revealed distinct clusters of modified peptides. Mascot showed the ability to assess confident site determination, even with lower PSM scores. However, high PSM scores from PEAKS did not guarantee a reliable determination of the modification site. Fragmentation coverage of the modification position played a crucial role in Mascot assignments, while the AScore localizations from PEAKS often become ambiguous because the software employs MS/MS merging.
Endometrial biopsies are important in the diagnostic workup of women who present with abnormal uterine bleeding or hereditary risk of endometrial cancer. In general, approximately 10% of all endometrial biopsies demonstrate endometrial (pre)malignancy that requires specific treatment. As the diagnostic evaluation of mostly benign cases results in a substantial workload for pathologists, artificial intelligence (AI)-assisted preselection of biopsies could optimize the workflow. This study aimed to assess the feasibility of AI-assisted diagnosis for endometrial biopsies (endometrial Pipelle biopsy computer-aided diagnosis), trained on daily-practice whole-slide images instead of highly selected images. Endometrial biopsies were classified into 6 clinically relevant categories defined as follows: nonrepresentative, normal, nonneoplastic, hyperplasia without atypia, hyperplasia with atypia, and malignant. The agreement among 15 pathologists, within these classifications, was evaluated in 91 endometrial biopsies. Next, an algorithm (trained on a total of 2819 endometrial biopsies) rated the same 91 cases, and we compared its performance using the pathologist's classification as the reference standard. The interrater reliability among pathologists was moderate with a mean Cohen's kappa of 0.51, whereas for a binary classification into benign vs (pre)malignant, the agreement was substantial with a mean Cohen's kappa of 0.66. The AI algorithm performed slightly worse for the 6 categories with a moderate Cohen's kappa of 0.43 but was comparable for the binary classification with a substantial Cohen's kappa of 0.65. AI-assisted diagnosis of endometrial biopsies was demonstrated to be feasible in discriminating between benign and (pre)malignant endometrial tissues, even when trained on unselected cases. Endometrial premalignancies remain challenging for both pathologists and AI algorithms. Future steps to improve reliability of the diagnosis are needed to achieve a more refined AI-assisted diagnostic solution for endometrial biopsies that covers both premalignant and malignant diagnoses.
- MeSH
- biopsie MeSH
- hyperplazie MeSH
- lidé MeSH
- počítače * MeSH
- reprodukovatelnost výsledků MeSH
- studie proveditelnosti MeSH
- umělá inteligence * MeSH
- Check Tag
- lidé MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
BACKGROUND: Electrical stimulation involving temporal interference of two different kHz frequency sinusoidal electric fields (temporal interference (TI)) enables non-invasive deep brain stimulation, by creating an electric field that is amplitude modulated at the slow difference frequency (within the neural range), at the target brain region. OBJECTIVE: Here, we investigate temporal interference neural stimulation using square, rather than sinusoidal, electric fields that create an electric field that is pulse-width, but not amplitude, modulated at the difference frequency (pulse-width modulated temporal interference, (PWM-TI)). METHODS/RESULTS: We show, using ex-vivo single-cell recordings and in-vivo calcium imaging, that PWM-TI effectively stimulates neural activity at the difference frequency at a similar efficiency to traditional TI. We then demonstrate, using computational modelling, that the PWM stimulation waveform induces amplitude-modulated membrane potential depolarization due to the membrane's intrinsic low-pass filtering property. CONCLUSIONS: PWM-TI can effectively drive neural activity at the difference frequency. The PWM-TI mechanism involves converting an envelope amplitude-fixed PWM field to an amplitude-modulated membrane potential via the low-pass filtering of the passive neural membrane. Unveiling the biophysics underpinning the neural response to complex electric fields may facilitate the development of new brain stimulation strategies with improved precision and efficiency.
- MeSH
- elektrická stimulace MeSH
- mozek * MeSH
- počítačová simulace MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
PURPOSE: Ktrans$$ {K}^{\mathrm{trans}} $$ has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for Ktrans$$ {K}^{\mathrm{trans}} $$ quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging-Dynamic Contrast-Enhanced (OSIPI-DCE) challenge was designed to benchmark methods to better help the efforts to standardize Ktrans$$ {K}^{\mathrm{trans}} $$ measurement. METHODS: A framework was created to evaluate Ktrans$$ {K}^{\mathrm{trans}} $$ values produced by DCE-MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for Ktrans$$ {K}^{\mathrm{trans}} $$ quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' Ktrans$$ {K}^{\mathrm{trans}} $$ values, the applied software, and a standard operating procedure. These were evaluated using the proposed OSIPIgold$$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score defined with accuracy, repeatability, and reproducibility components. RESULTS: Across the 10 received submissions, the OSIPIgold$$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score ranged from 28% to 78% with a 59% median. The accuracy, repeatability, and reproducibility scores ranged from 0.54 to 0.92, 0.64 to 0.86, and 0.65 to 1.00, respectively (0-1 = lowest-highest). Manual arterial input function selection markedly affected the reproducibility and showed greater variability in Ktrans$$ {K}^{\mathrm{trans}} $$ analysis than automated methods. Furthermore, provision of a detailed standard operating procedure was critical for higher reproducibility. CONCLUSIONS: This study reports results from the OSIPI-DCE challenge and highlights the high inter-software variability within Ktrans$$ {K}^{\mathrm{trans}} $$ estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real-world clinical setting, many of these tools may perform differently with different benchmarking methodology.
- MeSH
- algoritmy MeSH
- kontrastní látky * MeSH
- lidé MeSH
- magnetická rezonanční tomografie * metody MeSH
- reprodukovatelnost výsledků MeSH
- software MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
PURPOSE OF REVIEW: The aim of the systematic review is to assess AI's capabilities in the genetics of prostate cancer (PCa) and bladder cancer (BCa) to evaluate target groups for such analysis as well as to assess its prospects in daily practice. RECENT FINDINGS: In total, our analysis included 27 articles: 10 articles have reported on PCa and 17 on BCa, respectively. The AI algorithms added clinical value and demonstrated promising results in several fields, including cancer detection, assessment of cancer development risk, risk stratification in terms of survival and relapse, and prediction of response to a specific therapy. Besides clinical applications, genetic analysis aided by the AI shed light on the basic urologic cancer biology. We believe, our results of the AI application to the analysis of PCa, BCa data sets will help to identify new targets for urological cancer therapy. The integration of AI in genomic research for screening and clinical applications will evolve with time to help personalizing chemotherapy, prediction of survival and relapse, aid treatment strategies such as reducing frequency of diagnostic cystoscopies, and clinical decision support, e.g., by predicting immunotherapy response. These factors will ultimately lead to personalized and precision medicine thereby improving patient outcomes.
- MeSH
- biologické markery MeSH
- lidé MeSH
- lokální recidiva nádoru genetika MeSH
- nádory močového měchýře * diagnóza genetika terapie MeSH
- prostata * MeSH
- recidiva MeSH
- umělá inteligence MeSH
- Check Tag
- lidé MeSH
- mužské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
- systematický přehled MeSH
Aim: 2-Thioxothiazolidin-4-one represents a versatile scaffold in drug development. The authors used it to prepare new potent acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibitors that can be utilized, e.g., to treat Alzheimer's disease. Materials & methods: 3-Amino-2-thioxothiazolidin-4-one was modified at the amino group or active methylene, using substituted benzaldehydes. The derivatives were evaluated for inhibition of AChE and BChE (Ellman's method). Results & conclusion: The derivatives were obtained with yields of 52-94%. They showed dual inhibition with IC50 values from 13.15 μM; many compounds were superior to rivastigmine. The structure-activity relationship favors nitrobenzylidene and 3,5-dihalogenosalicylidene scaffolds. AChE was inhibited noncompetitively, whereas BChE was inhibited with a mixed type of inhibition. Molecular docking provided insights into molecular interactions. Each enzyme is inhibited by a different binding mode.
AIMS: The aim of this study was to compare the clinical decision-making for benzodiazepine deprescribing between a healthcare provider (HCP) and an artificial intelligence (AI) chatbot GPT4 (ChatGPT-4). METHODS: We analysed real-world data from a Croatian cohort of community-dwelling benzodiazepine patients (n = 154) within the EuroAgeism H2020 ESR 7 project. HCPs evaluated the data using pre-established deprescribing criteria to assess benzodiazepine discontinuation potential. The research team devised and tested AI prompts to ensure consistency with HCP judgements. An independent researcher employed ChatGPT-4 with predetermined prompts to simulate clinical decisions for each patient case. Data derived from human-HCP and ChatGPT-4 decisions were compared for agreement rates and Cohen's kappa. RESULTS: Both HPC and ChatGPT identified patients for benzodiazepine deprescribing (96.1% and 89.6%, respectively), showing an agreement rate of 95% (κ = .200, P = .012). Agreement on four deprescribing criteria ranged from 74.7% to 91.3% (lack of indication κ = .352, P < .001; prolonged use κ = .088, P = .280; safety concerns κ = .123, P = .006; incorrect dosage κ = .264, P = .001). Important limitations of GPT-4 responses were identified, including 22.1% ambiguous outputs, generic answers and inaccuracies, posing inappropriate decision-making risks. CONCLUSIONS: While AI-HCP agreement is substantial, sole AI reliance poses a risk for unsuitable clinical decision-making. This study's findings reveal both strengths and areas for enhancement of ChatGPT-4 in the deprescribing recommendations within a real-world sample. Our study underscores the need for additional research on chatbot functionality in patient therapy decision-making, further fostering the advancement of AI for optimal performance.
- MeSH
- benzodiazepiny škodlivé účinky MeSH
- depreskripce * MeSH
- klinické rozhodování MeSH
- lidé MeSH
- umělá inteligence * MeSH
- zdravotnický personál MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
The inherent diversity of approaches in proteomics research has led to a wide range of software solutions for data analysis. These software solutions encompass multiple tools, each employing different algorithms for various tasks such as peptide-spectrum matching, protein inference, quantification, statistical analysis, and visualization. To enable an unbiased comparison of commonly used bottom-up label-free proteomics workflows, we introduce WOMBAT-P, a versatile platform designed for automated benchmarking and comparison. WOMBAT-P simplifies the processing of public data by utilizing the sample and data relationship format for proteomics (SDRF-Proteomics) as input. This feature streamlines the analysis of annotated local or public ProteomeXchange data sets, promoting efficient comparisons among diverse outputs. Through an evaluation using experimental ground truth data and a realistic biological data set, we uncover significant disparities and a limited overlap in the quantified proteins. WOMBAT-P not only enables rapid execution and seamless comparison of workflows but also provides valuable insights into the capabilities of different software solutions. These benchmarking metrics are a valuable resource for researchers in selecting the most suitable workflow for their specific data sets. The modular architecture of WOMBAT-P promotes extensibility and customization. The software is available at https://github.com/wombat-p/WOMBAT-Pipelines.
- MeSH
- analýza dat MeSH
- benchmarking * MeSH
- proteiny MeSH
- proteomika * MeSH
- průběh práce MeSH
- software MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
INTRODUCTION: The rapid advancement of artificial intelligence and big data analytics, including descriptive, diagnostic, predictive, and prescriptive analytics, has the potential to revolutionize many areas of medicine, including nephrology and dialysis. Artificial intelligence and big data analytics can be used to analyze large amounts of patient medical records, including laboratory results and imaging studies, to improve the accuracy of diagnosis, enhance early detection, identify patterns and trends, and personalize treatment plans for patients with kidney disease. Additionally, artificial intelligence and big data analytics can be used to identify patients' treatment who are not receiving adequate care, highlighting care inefficiencies in the dialysis provider, optimizing patient outcomes, reducing healthcare costs, and consequently creating values for all the involved stakeholders. OBJECTIVES: We present the results of a comprehensive survey aimed at exploring the attitudes of European physicians from eight countries working within a major hemodialysis network (Fresenius Medical Care NephroCare) toward the application of artificial intelligence in clinical practice. METHODS: An electronic survey on the implementation of artificial intelligence in hemodialysis clinics was distributed to 1,067 physicians. Of the 1,067 individuals invited to participate in the study, 404 (37.9%) professionals agreed to participate in the survey. RESULTS: The survey showed that a substantial proportion of respondents believe that artificial intelligence has the potential to support physicians in reducing medical malpractice or mistakes. CONCLUSION: While artificial intelligence's potential benefits are recognized in reducing medical errors and improving decision-making, concerns about treatment plan consistency, personalization, privacy, and the human aspects of patient care persist. Addressing these concerns will be crucial for successfully integrating artificial intelligence solutions in nephrology practice.
- MeSH
- dialýza ledvin MeSH
- lidé MeSH
- nefrologie * MeSH
- nefrologové MeSH
- průzkumy a dotazníky MeSH
- umělá inteligence * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Sinonasal carcinomas represent a rare and diverse group of tumors, presenting diagnostic complexities due to their varied histological and molecular features. To ensure accurate differentiation among these malignancies, a systematic and stepwise approach is paramount. Even with the morphological similarities between poorly differentiated (non) keratinizing sinonasal squamous cell carcinoma (SNSCC) and DEK::AFF2 SNSCC, the two lesions are distinguishable using the surrogate immunohistochemical marker AFF2 or molecular testing for DEK::AFF2 mutation. We report a rare case of SMARCB1-retained DEK::AFF2 papillary non-keratinizing SNSCC in a 53-year-old female, who presented with a polypoid mass corresponding to the left middle turbinate. Following the surgical resection of the tumor and locoregional lymph nodes, adjuvant radiotherapy was administered to eradicate any residual cancer cells that may have remained after surgery.
- MeSH
- algoritmy MeSH
- buněčná diferenciace MeSH
- jaderné proteiny MeSH
- lidé středního věku MeSH
- lidé MeSH
- lymfatické uzliny MeSH
- nádory vedlejších dutin nosních * diagnóza genetika MeSH
- spinocelulární karcinom * diagnóza genetika MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- ženské pohlaví MeSH
- Publikační typ
- kazuistiky MeSH
