Going Full Circle with Organocatalysis and Biocatalysis: The Latent Potential of Cofactor Mimics in Asymmetric Synthesis.

Many enzymes work in tandem with small molecule cofactors, which have inspired organocatalyst designs. Chemical modification of cofactor scaffolds has increased organocatalytic reactivity and reaction scope. This synopsis presents a selection of recent advances in the use of cofactors (native and mimics) in organocatalysis and biocatalysis. We aim to highlight the benefits of combining fundamental knowledge gained in both bio- and organo-catalysis for asymmetric biocatalysis.

Workshop, Assessment, and Validity Evidence for Tools Measuring Performance of Knee and Shoulder Arthrocentesis.

Introduction: Musculoskeletal concerns are common, yet residents at our institution lacked arthrocentesis training. We created a workshop to teach residents knee and shoulder arthrocentesis, developed simulated assessment scenarios (SASs) with tools to measure procedural proficiency, and collected validity evidence. Methods: A multidisciplinary group conducted a modified Delphi to define content for the workshop, SASs, and assessment tools. We defined minimum thresholds for competence in knee and shoulder arthrocentesis using the modified borderline-group method. We implemented the workshop and SASs in 2020 and 2021 and analyzed assessment tool scoring for statistical reliability and validity. Our program evaluation included SAS performance, participants' survey responses, and change in the number of arthrocenteses performed in the internal medicine (IM) resident primary care clinic. Results: Sixty-one residents (53 IM, eight physical medicine and rehabilitation [PM&R]) participated. Fifty-two (85%; 46 IM, six PM&R) completed the evaluation survey. We procured data from 48 knee and 65 shoulder SASs for validity evidence. All arthrocentesis SAS performances met the proficiency standard except one resident's shoulder SAS. Validity evidence revealed strong interrater reliability (α = .82 and .77 for knee and shoulder, respectively) and strong relational validity (p < .001 for both procedures). All participants rated workshop quality and usefulness as good or very good. The number of arthrocenteses performed at our institution's primary care clinic increased. Discussion: We created a workshop to teach residents arthrocentesis and assessment tools with strong validity and reliability evidence. The workshop was well regarded by residents, who applied their arthrocentesis skills during patient care.

Evaluation of thresholding methods for the quantification of [68Ga]Ga-PSMA-11 PET molecular tumor volume and their effect on survival prediction in patients with advanced prostate cancer undergoing [177Lu]Lu-PSMA-617 radioligand therapy.

PURPOSE: The aim of this study was to systematically evaluate the effect of thresholding algorithms used in computer vision for the quantification of prostate-specific membrane antigen positron emission tomography (PET) derived tumor volume (PSMA-TV) in patients with advanced prostate cancer. The results were validated with respect to the prognostication of overall survival in patients with advanced-stage prostate cancer. MATERIALS AND METHODS: A total of 78 patients who underwent [177Lu]Lu-PSMA-617 radionuclide therapy from January 2018 to December 2020 were retrospectively included in this study. [68Ga]Ga-PSMA-11 PET images, acquired prior to radionuclide therapy, were used for the analysis of thresholding algorithms. All PET images were first analyzed semi-automatically using a pre-evaluated, proprietary software solution as the baseline method. Subsequently, five histogram-based thresholding methods and two local adaptive thresholding methods that are well established in computer vision were applied to quantify molecular tumor volume. The resulting whole-body molecular tumor volumes were validated with respect to the prognostication of overall patient survival as well as their statistical correlation to the baseline methods and their performance on standardized phantom scans. RESULTS: The whole-body PSMA-TVs, quantified using different thresholding methods, demonstrate a high positive correlation with the baseline methods. We observed the highest correlation with generalized histogram thresholding (GHT) (Pearson r (r), p value (p): r = 0.977, p < 0.001) and Sauvola thresholding (r = 0.974, p < 0.001) and the lowest correlation with Multiotsu (r = 0.877, p < 0.001) and Yen thresholding methods (r = 0.878, p < 0.001). The median survival time of all patients was 9.87 months (95% CI [9.3 to 10.13]). Stratification by median whole-body PSMA-TV resulted in a median survival time from 11.8 to 13.5 months for the patient group with lower tumor burden and 6.5 to 6.6 months for the patient group with higher tumor burden. The patient group with lower tumor burden had significantly higher probability of survival (p < 0.00625) in eight out of nine thresholding methods (Fig. 2); those methods were SUVmax50 (p = 0.0038), SUV ≥3 (p = 0.0034), Multiotsu (p = 0.0015), Yen (p = 0.0015), Niblack (p = 0.001), Sauvola (p = 0.0001), Otsu (p = 0.0053), and Li thresholding (p = 0.0053). CONCLUSION: Thresholding methods commonly used in computer vision are promising tools for the semiautomatic quantification of whole-body PSMA-TV in [68Ga]Ga-PSMA-11-PET. The proposed algorithm-driven thresholding strategy is less arbitrary and less prone to biases than thresholding with predefined values, potentially improving the application of whole-body PSMA-TV as an imaging biomarker.

Oxadiazolopyridine Derivatives as Efficacious Mitochondrial Uncouplers in the Prevention of Diet-Induced Obesity.

Small-molecule mitochondrial uncouplers are gaining recognition as potential therapeutics for metabolic diseases such as obesity, diabetes, and nonalcoholic steatohepatitis (NASH). Specifically, heterocycles derived from BAM15, a potent and mitochondria-selective uncoupler, have yielded promising preclinical candidates that are efficacious in animal models of obesity and NASH. In this study, we report the structure-activity relationship studies of 6-amino-[1,2,5]oxadiazolo[3,4-b]pyridin-5-ol derivatives. Using oxygen consumption rate as a readout of mitochondrial uncoupling, we established 5-hydroxyoxadiazolopyridines as mild uncouplers. In particular, SHM115, which contains a pentafluoro aniline, had an EC50 value of 17 µM and exhibited 75% oral bioavailability. SHM115 treatment increased the energy expenditure and lowered the body fat mass in two diet-induced obesity mouse models, including an obesity prevention model and an obesity reversal model. Taken together, our findings demonstrate the therapeutic potential of mild mitochondrial uncouplers for the prevention of diet-induced obesity.

Novel measures for the diagnosis of hepatic steatosis using contrast-enhanced computer tomography images.

PURPOSE: Hepatic steatosis is often diagnosed non-invasively. Various measures and accompanying diagnostic thresholds based on contrast-enhanced CT and virtual non-contrast images have been proposed. We compare these established criteria to novel and fully automated measures. METHOD: CT data sets of 197 patients were analyzed. Regions of interest (ROIs) were manually drawn for the liver, spleen, portal vein, and aorta to calculate four established measures of liver-fat. Two novel measures capturing the deviation between the empirical distributions of HU measurements across all voxels within the liver and spleen were calculated. These measures were calculated with both manual ROIs and using fully automated organ segmentations. Agreement between the different measures was evaluated using correlational analysis, as well as their ability to discriminate between fatty and healthy liver. RESULTS: Established and novel measures of fatty liver were at a high level of agreement. Novel methods were statistically indistinguishable from the established ones when taking established diagnostic thresholds or physicians' diagnoses as ground truth and this high performance level persisted for automatically selected ROIs. CONCLUSION: Automatically generated organ segmentations led to comparable results as manual ROIs, suggesting that the implementation of automated methods can prove to be a valuable tool for incidental diagnosis. Differences in the distribution of HU measurements across voxels between liver and spleen can serve as surrogate markers for the liver-fat-content. Novel measures do not exhibit a measurable disadvantage over established methods based on simpler measures such as across-voxel averages in a population with low incidence of fatty liver.

Quantification and reduction of cross-vendor variation in multicenter DWI MR imaging: results of the Cancer Core Europe imaging task force.

OBJECTIVES: In the Cancer Core Europe Consortium (CCE), standardized biomarkers are required for therapy monitoring oncologic multicenter clinical trials. Multiparametric functional MRI and particularly diffusion-weighted MRI offer evident advantages for noninvasive characterization of tumor viability compared to CT and RECIST. A quantification of the inter- and intraindividual variation occurring in this setting using different hardware is missing. In this study, the MRI protocol including DWI was standardized and the residual variability of measurement parameters quantified. METHODS: Phantom and volunteer measurements (single-shot T2w and DW-EPI) were performed at the seven CCE sites using the MR hardware produced by three different vendors. Repeated measurements were performed at the sites and across the sites including a traveling volunteer, comparing qualitative and quantitative ROI-based results including an explorative radiomics analysis. RESULTS: For DWI/ADC phantom measurements using a central post-processing algorithm, the maximum deviation could be decreased to 2%. However, there is no significant difference compared to a decentralized ADC value calculation at the respective MRI devices. In volunteers, the measurement variation in 2 repeated scans did not exceed 11% for ADC and is below 20% for single-shot T2w in systematic liver ROIs. The measurement variation between sites amounted to 20% for ADC and < 25% for single-shot T2w. Explorative radiomics classification experiments yield better results for ADC than for single-shot T2w. CONCLUSION: Harmonization of MR acquisition and post-processing parameters results in acceptable standard deviations for MR/DW imaging. MRI could be the tool in oncologic multicenter trials to overcome the limitations of RECIST-based response evaluation. KEY POINTS: • Harmonizing acquisition parameters and post-processing homogenization, standardized protocols result in acceptable standard deviations for multicenter MR-DWI studies. • Total measurement variation does not to exceed 11% for ADC in repeated measurements in repeated MR acquisitions, and below 20% for an identical volunteer travelling between sites. • Radiomic classification experiments were able to identify stable features allowing for reliable discrimination of different physiological tissue samples, even when using heterogeneous imaging data.

[Potential of radiomics and artificial intelligence in myeloma imaging : Development of automatic, comprehensive, objective skeletal analyses from whole-body imaging data]. / Potenzial von Radiomics und künstlicher Intelligenz in der Myelombildgebung : Entwicklung automatischer, umfassender, objektiver Skelettanalysen aus Ganzkörperbildgebungsdaten.

CLINICAL/METHODICAL ISSUE: Multiple myeloma can affect the complete skeleton, which makes whole-body imaging necessary. With the current assessment of these complex datasets by radiologists, only a small part of the accessible information is assessed and reported. STANDARD RADIOLOGICAL METHODS: Depending on the question and availability, computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) is performed and the results are then visually examined by radiologists. METHODOLOGICAL INNOVATIONS: A combination of automatic skeletal segmentation using artificial intelligence and subsequent radiomics analysis of each individual bone have the potential to provide automatic, comprehensive, and objective skeletal analyses. PERFORMANCE: A few automatic skeletal segmentation algorithms for CT already show promising results. In addition, first studies indicate correlations between radiomics features of bone and bone marrow with established disease markers and therapy response. ACHIEVEMENTS: Artificial intelligence (AI) and radiomics algorithms for automatic skeletal analysis from whole-body imaging are currently in an early phase of development.

Representation of Skin Colors in Images of Patients With Lupus.

OBJECTIVE: Lupus presents earlier and more severely among patients with skin of color (SOC), and this population experiences worse outcomes. Providers rely on medical education materials when developing skills to care for patients, yet these resources historically underrepresent patients with SOC and marginalize vulnerable populations. In this study, we investigated if this publication bias extends to images depicting patients with lupus. METHODS: We reviewed published images of patients with lupus from rheumatology, dermatology, and internal medicine textbooks and medical journals, SOC atlases, online image libraries, UpToDate, and Google Images. We selected materials published from 2014 to 2019 that were available through our university's online medical library. We used the search terms "lupus" and "lupus rash" to identify images. We rated the skin color in each image using the New Immigrant Survey Skin Color Scale and categorized them as light, medium, or dark. We compared the frequencies of published skin tones with chi-square and odds ratio analyses. RESULTS: We assessed the skin tone of 1,417 images. The significant majority (56.4%) of the images represented light skin (χ2  = 490.14, P < 0.001). After SOC atlases, journals were the most inclusive of images depicting dark skin tones. The specialty of dermatology was most inclusive of medium and darker skin tones. CONCLUSION: Published images of lupus underrepresent patients with SOC, which may limit providers' ability to deliver care to the patients who are at greatest risk for complications.

Health care practitioners' confidence assessing lupus-related rashes in patients of color.

Background: Patients with skin of color (P-SOC) are disproportionately burdened by lupus and often have worse disease outcomes than white patients. This is partly because educational materials underrepresent P-SOC, thereby promoting unconscious bias and clinical deficiencies among practitioners.Purpose: We sought to measure providers' confidence in diagnosing the cutaneous manifestations of lupus (i.e., lupus-related rashes) in P-SOC and to assess which factors influenced their confidence.Research Design: We created and distributed a survey that gathered information about participants' personal characteristics, clinical specialty, training, and current practice as well as measuring their confidence assessing lupus-related rashes in various skin tones.Study Sample: Practitioners from the fields of rheumatology, dermatology, and internal medicine in the greater St. Louis area (Missouri, USA) participated in the survey.Analysis: We compared practitioners' mean confidence levels assessing lupus-related rashes in patients with fair skin and P-SOC with a linear mixed effects model and used univariate and multivariate linear regression models to determine if the aforementioned factors correlated with confidence.Results: Participants' mean confidence in diagnosing lupus-related rashes in P-SOC was significantly lower than assessing such findings in patients with fair skin (p = .009). Several factors correlated with confidence level at a univariate level; however, the multivariate model revealed experience as the only factor significantly associated with confidence (p = .001). Conclusions: Providers report significantly less confidence assessing lupus-related rashes in P-SOC than in patients with fair skin. Our analysis demonstrates that experience positively correlates with confidence and suggests that interventions which enhance practitioners' exposure to and experience with these rashes in P-SOC can improve clinical confidence as well as patient outcomes.

Vesseg: An Open-Source Tool for Deep Learning-Based Atherosclerotic Plaque Quantification in Histopathology Images-Brief Report.

Objective: Manual plaque segmentation in microscopy images is a time-consuming process in atherosclerosis research and potentially subject to unacceptable user-to-user variability and observer bias. We address this by releasing Vesseg a tool that includes state-of-the-art deep learning models for atherosclerotic plaque segmentation. Approach and Results: Vesseg is a containerized, extensible, open-source, and user-oriented tool. It includes 2 models, trained and tested on 1089 hematoxylin-eosin stained mouse model atherosclerotic brachiocephalic artery sections. The models were compared to 3 human raters. Vesseg can be accessed at https://vesseg .online or downloaded. The models show mean Soerensen-Dice scores of 0.91+/-0.15 for plaque and 0.97+/-0.08 for lumen pixels. The mean accuracy is 0.98+/-0.05. Vesseg is already in active use, generating time savings of >10 minutes per slide. Conclusions: Vesseg brings state-of-the-art deep learning methods to atherosclerosis research, providing drastic time savings, while allowing for continuous improvement of models and the underlying pipeline.

Discovering Digital Tumor Signatures-Using Latent Code Representations to Manipulate and Classify Liver Lesions.

Modern generative deep learning (DL) architectures allow for unsupervised learning of latent representations that can be exploited in several downstream tasks. Within the field of oncological medical imaging, we term these latent representations "digital tumor signatures" and hypothesize that they can be used, in analogy to radiomics features, to differentiate between lesions and normal liver tissue. Moreover, we conjecture that they can be used for the generation of synthetic data, specifically for the artificial insertion and removal of liver tumor lesions at user-defined spatial locations in CT images. Our approach utilizes an implicit autoencoder, an unsupervised model architecture that combines an autoencoder and two generative adversarial network (GAN)-like components. The model was trained on liver patches from 25 or 57 inhouse abdominal CT scans, depending on the experiment, demonstrating that only minimal data is required for synthetic image generation. The model was evaluated on a publicly available data set of 131 scans. We show that a PCA embedding of the latent representation captures the structure of the data, providing the foundation for the targeted insertion and removal of tumor lesions. To assess the quality of the synthetic images, we conducted two experiments with five radiologists. For experiment 1, only one rater and the ensemble-rater were marginally above the chance level in distinguishing real from synthetic data. For the second experiment, no rater was above the chance level. To illustrate that the "digital signatures" can also be used to differentiate lesion from normal tissue, we employed several machine learning methods. The best performing method, a LinearSVM, obtained 95% (97%) accuracy, 94% (95%) sensitivity, and 97% (99%) specificity, depending on if all data or only normal appearing patches were used for training of the implicit autoencoder. Overall, we demonstrate that the proposed unsupervised learning paradigm can be utilized for the removal and insertion of liver lesions at user defined spatial locations and that the digital signatures can be used to discriminate between lesions and normal liver tissue in abdominal CT scans.

Kinetic and structure-activity studies of the triazolium ion-catalysed benzoin condensation.

Steady-state kinetic and structure-activity studies of a series of six triazolium-ion pre-catalysts 2a-2f were investigated for the benzoin condensation. These data provide quantitative insight into the role of triazolium N-aryl substitution under synthetically relevant catalytic conditions in a polar solvent environment. Kinetic behaviour was significantly different to that previously reported for a related thiazolium-ion pre-catalyst 1, with the observed levelling of initial rate constants to νmax at high aldehyde concentrations for all triazolium catalysts. Values for νmax for 2a-2f increase with electron withdrawing N-aryl substituents, in agreement with reported optimal synthetic outcomes under catalytic conditions, and vary by 75-fold across the series. The levelling of rate constants supports a change in rate-limiting step and evidence supports the assignment of the Breslow-intermediate forming step to the plateau region. Correlation of νmax reaction data yielded a positive Hammett ρ-value (ρ = +1.66) supporting the build up of electron density adjacent to the triazolium N-Ar in the rate-limiting step favoured by electron withdrawing N-aryl substituents. At lower concentrations of aldehyde, both Breslow-intermediate and benzoin formation are partially rate-limiting.

[Artificial intelligence and machine learning in oncologic imaging]. / Künstliche Intelligenz und maschinelles Lernen in der onkologischen Bildgebung.

Machine learning (ML) is entering many areas of society, including medicine. This transformation has the potential to drastically change medicine and medical practice. These aspects become particularly clear when considering the different stages of oncologic patient care and the involved interdisciplinary and intermodality interactions. In recent publications, computers-in collaboration with humans or alone-have been outperforming humans regarding tumor identification, tumor classification, estimating prognoses, and evaluation of treatments. In addition, ML algorithms, e.g., artificial neural networks (ANNs), which constitute the drivers behind many of the latest achievements in ML, can deliver this level of performance in a reproducible, fast, and inexpensive manner. In the future, artificial intelligence applications will become an integral part of the medical profession and offer advantages for oncologic diagnostics and treatment.

6-Amino[1,2,5]oxadiazolo[3,4-b]pyrazin-5-ol Derivatives as Efficacious Mitochondrial Uncouplers in STAM Mouse Model of Nonalcoholic Steatohepatitis.

Small molecule mitochondrial uncouplers have recently garnered great interest for their potential in treating nonalcoholic steatohepatitis (NASH). In this study, we report the structure-activity relationship profiling of a 6-amino[1,2,5]oxadiazolo[3,4-b]pyrazin-5-ol core, which utilizes the hydroxy moiety as the proton transporter across the mitochondrial inner membrane. We demonstrate that a wide array of substituents is tolerated with this novel scaffold that increased cellular metabolic rates in vitro using changes in oxygen consumption rate as a readout. In particular, compound SHS4121705 (12i) displayed an EC50 of 4.3 µM in L6 myoblast cells and excellent oral bioavailability and liver exposure in mice. In the STAM mouse model of NASH, administration of 12i at 25 mg kg-1 day-1 lowered liver triglyceride levels and improved liver markers such as alanine aminotransferase, NAFLD activity score, and fibrosis. Importantly, no changes in body temperature or food intake were observed. As potential treatment of NASH, mitochondrial uncouplers show promise for future development.

Anilinopyrazines as potential mitochondrial uncouplers.

Mitochondrial protonophores transport protons through the mitochondrial inner membrane into the matrix to uncouple nutrient oxidation from ATP production thereby decreasing the proton motive force. Mitochondrial uncouplers have beneficial effects of decrease reactive oxygen species generation and have the potential for treating diseases such as obesity, neurodegenerative diseases, non-alcoholic fatty liver disease (NAFLD), diabetes, and many others. In this study, we report the structure-activity relationship profile of the pyrazine scaffold bearing substituted aniline rings. Our work indicates that a trifluoromethyl group is best at the para position while the trifluoromethoxy group is preferred in the meta position of the aniline rings of 2,3-substituted pyrazines. As proton transport and cycling requires the formation of a negative charge that has to traverse the mitochondrial membrane, a stabilizing internal hydrogen bond is a key feature for efficient mitochondrial uncoupling activity.

[A primer on radiomics]. / Wie funktioniert Radiomics?

CLINICAL ISSUE: The reproducible and exhaustive extraction of information from radiological images is a central task in the practice of radiology. Dynamic developments in the fields of artificial intelligence (AI) and machine learning are introducing new methods for this task. Radiomics is one such method and offers new opportunities and challenges for the future of radiology. METHODOLOGICAL INNOVATIONS: Radiomics describes the quantitative evaluation, interpretation, and clinical assessment of imaging markers in radiological data. Components of a radiomics analysis are data acquisition, data preprocessing, data management, segmentation of regions of interest, computation and selection of imaging markers, as well as the development of a radiomics model used for diagnosis and prognosis. This article explains these components and aims at providing an introduction to the field of radiomics while highlighting existing limitations. MATERIALS AND METHODS: This article is based on a selective literature search with the PubMed search engine. ASSESSMENT: Even though radiomics applications have yet to arrive in routine clinical practice, the quantification of radiological data in terms of radiomics is underway and will increase in the future. This holds the potential for lasting change in the discipline of radiology. Through the successful extraction and interpretation of all the information encoded in radiological images the next step in the direction of a more personalized, future-oriented form of medicine can be taken.

[A primer on machine learning]. / Wie funktioniert maschinelles Lernen?

BACKGROUND: The methods of machine learning and artificial intelligence are slowly but surely being introduced in everyday medical practice. In the future, they will support us in diagnosis and therapy and thus improve treatment for the benefit of the individual patient. It is therefore important to deal with this topic and to develop a basic understanding of it. OBJECTIVES: This article gives an overview of the exciting and dynamic field of machine learning and serves as an introduction to some methods primarily from the realm of supervised learning. In addition to definitions and simple examples, limitations are discussed. CONCLUSIONS: The basic principles behind the methods are simple. Nevertheless, due to their high dimensional nature, the factors influencing the results are often difficult or impossible to understand by humans. In order to build confidence in the new technologies and to guarantee their safe application, we need explainable algorithms and prospective effectiveness studies.