Aligning Synthetic Medical Images with Clinical Knowledge using Human Feedback

Generative models capable of capturing nuanced clinical features in medical images hold great promise for facilitating clinical data sharing, enhancing rare disease datasets, and efficiently synthesizing annotated medical images at scale. Despite their potential, assessing the quality of synthetic medical images remains a challenge. While modern generative models can synthesize visually-realistic medical images, the clinical validity of these images may be called into question. Domain-agnostic scores, such as FID score, precision, and recall, cannot incorporate clinical knowledge and are, therefore, not suitable for assessing clinical sensibility. Additionally, there are numerous unpredictable ways in which generative models may fail to synthesize clinically plausible images, making it challenging to anticipate potential failures and manually design scores for their detection. To address these challenges, this paper introduces a pathologist-in-the-loop framework for generating clinically-plausible synthetic medical images. Starting with a diffusion model pretrained using real images, our framework comprises three steps: (1) evaluating the generated images by expert pathologists to assess whether they satisfy clinical desiderata, (2) training a reward model that predicts the pathologist feedback on new samples, and (3) incorporating expert knowledge into the diffusion model by using the reward model to inform a finetuning objective. We show that human feedback significantly improves the quality of synthetic images in terms of fidelity, diversity, utility in downstream applications, and plausibility as evaluated by experts

Pan-active imidazolopiperazine antimalarials target the Plasmodium falciparum intracellular secretory pathway.

A promising new compound class for treating human malaria is the imidazolopiperazines (IZP) class. IZP compounds KAF156 (Ganaplacide) and GNF179 are effective against Plasmodium symptomatic asexual blood-stage infections, and are able to prevent transmission and block infection in animal models. But despite the identification of resistance mechanisms in P. falciparum, the mode of action of IZPs remains unknown. To investigate, we here combine in vitro evolution and genome analysis in Saccharomyces cerevisiae with molecular, metabolomic, and chemogenomic methods in P. falciparum. Our findings reveal that IZP-resistant S. cerevisiae clones carry mutations in genes involved in Endoplasmic Reticulum (ER)-based lipid homeostasis and autophagy. In Plasmodium, IZPs inhibit protein trafficking, block the establishment of new permeation pathways, and cause ER expansion. Our data highlight a mechanism for blocking parasite development that is distinct from those of standard compounds used to treat malaria, and demonstrate the potential of IZPs for studying ER-dependent protein processing

Two inhibitors of yeast plasma membrane ATPase 1 (ScPma1p): toward the development of novel antifungal therapies

Given that many antifungal medications are susceptible to evolved resistance, there is a need for novel drugs with unique mechanisms of action. Inhibiting the essential proton pump Pma1p, a P-type ATPase, is a potentially effective therapeutic approach that is orthogonal to existing treatments. We identify NSC11668 and hitachimycin as structurally distinct antifungals that inhibit yeast ScPma1p. These compounds provide new opportunities for drug discovery aimed at this important target

Drug Target Discovery Using Designer Drug Sensitive Yeast

Determining the protein target(s) and mechanism(s) of drug candidates found in phenotypic screens is critical to subsequent structure-activity-based development and optimization, but existing methods for target identification are limited. Here we present a method that applies directed evolution to a genetically engineered, drug sensitive Saccharomyces cerevisiae strain. Whole genome sequencing of yeast clones that have evolved drug resistance, in concert with in vitro cell free assays and computer modeling, can be a useful tool for target identification and binding site characterization.To demonstrate the ease and utility of this method, we applied it to the identification of the molecular target and binding site of a range of cytotoxic molecular compounds with activity against eukaryotic pathogens and human cancers. These studies include known drug target combinations, as well as application to experimental compounds with unknown drug targets. As proof of concept, the method correctly identified the precise binding pocket of the protein synthesis inhibitor, cycloheximide, as the ribosomal protein Rpl28. We also correctly identified topoisomerase II inhibitor as the target of the human cancer chemotherapeutic, etoposide.We next used the method to identify novel drug target combinations, which were then validated using a combination of genetic, biochemical, structural and chemical structure activity relationships (SAR)-based assays. We identified a p-type ATPase, ScPma1, as the target of the spiroindolone antimalarials, of which KAE609 is currently in stage 2b clinical trials. We determined that the pre- clinical phenyl-amino-methyl-quinolinols (PAMQ) antimalarials inhibit the cyclic AMP signaling pathway, a mechanism of action that is different from existing commercial antimicrobials. We also demonstrated that MMV001239, a compound with antitrypanosomal activity, targets ScErg11, the yeast homolog of the T, cruzi Cyp51p, and a protein crucial for ergosterol biosynthesis. Taken together, our approach expands on the number of tools available for analyzing compound-target interactions and can be applied to studies of other eukaryotic antimicrobials and chemotherapeutics

Drug Target Discovery Using Designer Drug Sensitive Yeast

Determining the protein target(s) and mechanism(s) of drug candidates found in phenotypic screens is critical to subsequent structure-activity-based development and optimization, but existing methods for target identification are limited. Here we present a method that applies directed evolution to a genetically engineered, drug sensitive Saccharomyces cerevisiae strain. Whole genome sequencing of yeast clones that have evolved drug resistance, in concert with in vitro cell free assays and computer modeling, can be a useful tool for target identification and binding site characterization.To demonstrate the ease and utility of this method, we applied it to the identification of the molecular target and binding site of a range of cytotoxic molecular compounds with activity against eukaryotic pathogens and human cancers. These studies include known drug target combinations, as well as application to experimental compounds with unknown drug targets. As proof of concept, the method correctly identified the precise binding pocket of the protein synthesis inhibitor, cycloheximide, as the ribosomal protein Rpl28. We also correctly identified topoisomerase II inhibitor as the target of the human cancer chemotherapeutic, etoposide.We next used the method to identify novel drug target combinations, which were then validated using a combination of genetic, biochemical, structural and chemical structure activity relationships (SAR)-based assays. We identified a p-type ATPase, ScPma1, as the target of the spiroindolone antimalarials, of which KAE609 is currently in stage 2b clinical trials. We determined that the pre- clinical phenyl-amino-methyl-quinolinols (PAMQ) antimalarials inhibit the cyclic AMP signaling pathway, a mechanism of action that is different from existing commercial antimicrobials. We also demonstrated that MMV001239, a compound with antitrypanosomal activity, targets ScErg11, the yeast homolog of the T, cruzi Cyp51p, and a protein crucial for ergosterol biosynthesis. Taken together, our approach expands on the number of tools available for analyzing compound-target interactions and can be applied to studies of other eukaryotic antimicrobials and chemotherapeutics

Discovery of a Generalization Gap of Convolutional Neural Networks on COVID-19 X-Rays Classification

A number of recent papers have shown experimental evidence that suggests it is possible to build highly accurate deep neural network models to detect COVID-19 from chest X-ray images. In this paper, we show that good generalization to unseen sources has not been achieved. Experiments with richer data sets than have previously been used show models have high accuracy on seen sources, but poor accuracy on unseen sources. The reason for the disparity is that the convolutional neural network model, which learns features, can focus on differences in X-ray machines or in positioning within the machines, for example. Any feature that a person would clearly rule out is called a confounding feature. Some of the models were trained on COVID-19 image data taken from publications, which may be different than raw images. Some data sets were of pediatric cases with pneumonia where COVID-19 chest X-rays are almost exclusively from adults, so lung size becomes a spurious feature that can be exploited. In this work, we have eliminated many confounding features by working with as close to raw data as possible. Still, deep learned models may leverage source specific confounders to differentiate COVID-19 from pneumonia preventing generalizing to new data sources (i.e. external sites). Our models have achieved an AUC of 1.00 on seen data sources but in the worst case only scored an AUC of 0.38 on unseen ones. This indicates that such models need further assessment/development before they can be broadly clinically deployed. An example of fine-tuning to improve performance at a new site is given

Kalkipyrone B, a marine cyanobacterial γ-pyrone possessing cytotoxic and anti-fungal activities

Bioassay-guided fractionation of two marine cyanobacterial extracts using the H-460 human lung cancer cell line and the OVC-5 human ovarian cancer cell line led to the isolation of three related α-methoxy-β, β\u27-dimethyl-γ-pyrones each containing a modified alkyl chain, one of which was identified as the previously reported kalkipyrone and designated kalkipyrone A. The second compound was an analog designated kalkipyrone B. The third was identified as the recently reported yoshinone A, also isolated from a marine cyanobacterium. Kalkipyrone A and B were obtained from a field-collection of the cyanobacterium Leptolyngbya sp. from Fagasa Bay, American Samoa, while yoshinone A was isolated from a field-collection of cyanobacteria (cf. Schizothrix sp.) from Panama. One-dimensional and two-dimensional NMR experiments were used to determine the overall structures and relative configurations of the kalkipyrones, and the absolute configuration of kalkipyrone B was determined by (1)H NMR analysis of diastereomeric Mosher\u27s esters. Kalkipyrone A showed good cytotoxicity to H-460 human lung cancer cells (EC50=0.9μM), while kalkipyrone B and yoshinone A were less active (EC50=9.0μM and \u3e10μM, respectively). Both kalkipyrone A and B showed moderate toxicity to Saccharomyces cerevisiae ABC16-Monster strain (IC50=14.6 and 13.4μM, respectively), whereas yoshinone A was of low toxicity to this yeast strain (IC50=63.8μM)

Magnitude and Kinetics of Anti-Severe Acute Respiratory Syndrome Coronavirus 2 Antibody Responses and Their Relationship to Disease Severity.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can be detected indirectly by measuring the host immune response. For some viruses, antibody concentrations correlate with host protection and viral neutralization, but in rare cases, antiviral antibodies can promote disease progression. Elucidation of the kinetics and magnitude of the SARS-CoV-2 antibody response is essential to understand the pathogenesis of coronavirus disease 2019 (COVID-19) and identify potential therapeutic targets. METHODS: Sera (n = 533) from patients with real-time polymerase chain reaction-confirmed COVID-19 (n = 94 with acute infections and n = 59 convalescent patients) were tested using a high-throughput quantitative immunoglobulin M (IgM) and immunoglobulin G (IgG) assay that detects antibodies to the spike protein receptor binding domain and nucleocapsid protein. Individual and serial samples covered the time of initial diagnosis, during the disease course, and following recovery. We evaluated antibody kinetics and correlation between magnitude of the response and disease severity. RESULTS: Patterns of SARS-CoV-2 antibody production varied considerably. Among 52 patients with 3 or more serial specimens, 44 (84.6%) and 42 (80.8%) had observed IgM and IgG seroconversion at a median of 8 and 10 days, respectively. Compared to those with milder disease, peak measurements were significantly higher for patients admitted to the intensive care unit for all time intervals between 6 and 20 days for IgM, and all intervals after 5 days for IgG. CONCLUSIONS: High-sensitivity assays with a robust dynamic range provide a comprehensive picture of host antibody response to SARS-CoV-2. IgM and IgG responses were significantly higher in patients with severe than mild disease. These differences may affect strategies for seroprevalence studies, therapeutics, and vaccine development