Signalling pathway crosstalk stimulated by L-proline drives mouse embryonic stem cells to primitive-ectoderm-like cells.

The amino acid L-proline exhibits growth factor-like properties during development - from improving blastocyst development to driving neurogenesis in vitro. Addition of 400 µM L-proline to self-renewal medium drives naïve mouse embryonic stem cells (ESCs) to early primitive ectoderm-like (EPL) cells - a transcriptionally distinct primed or partially primed pluripotent state. EPL cells retain expression of pluripotency genes, upregulate primitive ectoderm markers, undergo a morphological change and have increased cell number. These changes are facilitated by a complex signalling network hinging on the Mapk, Fgfr, Pi3k and mTor pathways. Here, we use a factorial experimental design coupled with statistical modelling to understand which signalling pathways are involved in the transition between ESCs and EPL cells, and how they underpin changes in morphology, cell number, apoptosis, proliferation and gene expression. This approach reveals pathways which work antagonistically or synergistically. Most properties were affected by more than one inhibitor, and each inhibitor blocked specific aspects of the naïve-to-primed transition. These mechanisms underpin progression of stem cells across the in vitro pluripotency continuum and serve as a model for pre-, peri- and post-implantation embryogenesis.

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation.

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

Exploring the Performance of Linear and Nonlinear Models of Time-of-Flight Secondary Ion Mass Spectrometry Spectra.

Multivariate statistical tools and machine learning (ML) techniques can deconvolute hyperspectral data and control the disparity between the number of samples and features in materials science. Nevertheless, the importance of generating sufficient high-quality sample replicates in training data cannot be overlooked, as it fundamentally affects the performance of ML models. Here, we present a quantitative analysis of time-of-flight secondary ion mass spectrometry (ToF-SIMS) spectra of a simple microarray system of two food dyes using partial least-squares (PLS, linear) and random forest (RF, nonlinear) algorithms. This microarray was generated by a high-throughput sample preparation and analysis workflow for fast and efficient acquisition of quality and reproducible spectra via ToF-SIMS. We drew insights from the bias-variance trade-off, investigated the performances of PLS and RF regression models as a function of training data size, and inferred the amount of data needed to construct accurate and reliable regression models. In addition, we found that the spectral concatenation of positive and negative ToF-SIMS spectra improved the model performances. This study provides an empirical basis for future design of high-throughput microarrays and multicomponent systems, for the purpose of analysis with ToF-SIMS and ML.

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery.

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro/photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro/photocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized.

Turbulence explains the accelerations of an eagle in natural flight.

Turbulent winds and gusts fluctuate on a wide range of timescales from milliseconds to minutes and longer, a range that overlaps the timescales of avian flight behavior, yet the importance of turbulence to avian behavior is unclear. By combining wind speed data with the measured accelerations of a golden eagle (Aquila chrysaetos) flying in the wild, we find evidence in favor of a linear relationship between the eagle's accelerations and atmospheric turbulence for timescales between about 1/2 and 10 s. These timescales are comparable to those of typical eagle behaviors, corresponding to between about 1 and 25 wingbeats, and to those of turbulent gusts both larger than the eagle's wingspan and smaller than large-scale atmospheric phenomena such as convection cells. The eagle's accelerations exhibit power spectra and intermittent activity characteristic of turbulence and increase in proportion to the turbulence intensity. Intermittency results in accelerations that are occasionally several times stronger than gravity, which the eagle works against to stay aloft. These imprints of turbulence on the bird's movements need to be further explored to understand the energetics of birds and other volant life-forms, to improve our own methods of flying through ceaselessly turbulent environments, and to engage airborne wildlife as distributed probes of the changing conditions in the atmosphere.

Multi-Dimensional Machine Learning Analysis of Polyaniline Films Using Stitched Hyperspectral ToF-SIMS Data.

The self-organizing map with relational perspective mapping (SOM-RPM) is an unsupervised machine learning method that can be used to visualize and interpret high-dimensional hyperspectral data. We have previously used SOM-RPM for the analysis of time-of-flight secondary ion mass spectrometry (ToF-SIMS) hyperspectral images and three-dimensional (3D) depth profiles. This provides insightful visualization of features and trends of 3D depth profile data, using a slice-by-slice view, which can be useful for highlighting structural flaws including molecular characteristics and transport of contaminants to a buried interface and characterization of spectra. Here, we apply SOM-RPM to stitched ToF-SIMS data sets, whereby the stitched data are used to train the same model to provide a direct comparison in both 2D and 3D. We conduct an analysis of spin-coated polyaniline (PANI) films on indium tin oxide-coated glass slides that were subjected to heat treatment under atmospheric conditions to model PANI as a conformal aerospace industry coating. Replicates were shown to be precisely equivalent, both spatially and by composition, indicating a clear threshold for annealing of the film. Quantitative assessment was performed on the chemical breakdown trends accompanying annealing based on peak ratios, while spectral analysis alone shows only very subtle differences which are difficult to evaluate quantitatively. The SOM-RPM method considers data sets in their totality and highlights subtle differences between samples often simply differences in peak intensity ratios.

Staging of colorectal cancer using lipid biomarkers and machine learning.

INTRODUCTION: Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Alteration in lipid metabolism and chemokine expression are considered hallmark characteristics of malignant progression and metastasis of CRC. Validated diagnostic and prognostic biomarkers are urgently needed to define molecular heterogeneous CRC clinical stages and subtypes, as liver dominant metastasis has poor survival outcomes. OBJECTIVES: The aim of this study was to integrate lipid changes, concentrations of chemokines, such as platelet factor 4 and interleukin 8, and gene marker status measured in plasma samples, with clinical features from patients at different CRC stages or who had progressed to stage-IV colorectal liver metastasis (CLM). METHODS: High-resolution liquid chromatography-mass spectrometry (HR-LC-MS) was used to determine the levels of candidate lipid biomarkers in each CRC patient's preoperative plasma samples and combined with chemokine, gene and clinical data. Machine learning models were then trained using known clinical outcomes to select biomarker combinations that best classify CRC stage and group. RESULTS: Bayesian neural net and multilinear regression-machine learning identified candidate biomarkers that classify CRC (stages I-III), CLM patients and control subjects (cancer-free or patients with polyps/diverticulitis), showing that integrating specific lipid signatures and chemokines (platelet factor-4 and interluken-8; IL-8) can improve prognostic accuracy. Gene marker status could contribute to disease prediction, but requires ubiquitous testing in clinical cohorts. CONCLUSION: Our findings demonstrate that correlating multiple disease related features with lipid changes could improve CRC prognosis. The identified signatures could be used as reference biomarkers to predict CRC prognosis and classify stages, and monitor therapeutic intervention.

Birds advancing lay dates with warming springs face greater risk of chick mortality.

In response to a warming planet with earlier springs, migratory animals are adjusting the timing of essential life stages. Although these adjustments may be essential for keeping pace with resource phenology, they may prove insufficient, as evidenced by population declines in many species. However, even when species can match the tempo of climate change, other consequences may emerge when exposed to novel conditions earlier in the year. Here, using three long-term datasets on bird reproduction, daily insect availability, and weather, we investigated the complex mechanisms affecting reproductive success in an aerial insectivore, the tree swallow (Tachycineta bicolor). By examining breeding records over nearly half a century, we discovered that tree swallows have continuously advanced their egg laying by ∼3 d per decade. However, earlier-hatching offspring are now exposed to inclement weather events twice as often as they were in the 1970s. Our long-term daily insect biomass dataset shows no long-term trends over 25 y but precipitous drops in flying insect numbers on days with low ambient temperatures. Insect availability has a considerable impact on chick survival: Even a single inclement weather event can reduce offspring survival by >50%. Our results highlight the multifaceted threats that climate change poses on migrating species. The decoupling between cold snap occurrence and generally warming spring temperatures can affect reproductive success and threaten long-term persistence of populations. Understanding the exact mechanisms that endanger aerial insectivores is especially timely because this guild is experiencing the steepest and most widespread declines across North America and Europe.

A bright future for engineering piezoelectric 2D crystals.

The piezoelectric effect, mechanical-to-electrical and electrical-to-mechanical energy conversion, is highly beneficial for functional and responsive electronic devices. To fully exploit this property, miniaturization of piezoelectric materials is the subject of intense research. Indeed, select atomically thin 2D materials strongly exhibit the piezoelectric effect. The family of 2D crystals consists of over 7000 chemically distinct members that can be further manipulated in terms of strain, functionalization, elemental substitution (i.e. Janus 2D crystals), and defect engineering to induce a piezoelectric response. Additionally, most 2D crystals can stack with other similar or dissimilar 2D crystals to form a much greater number of complex 2D heterostructures whose properties are quite different to those of the individual constituents. The unprecedented flexibility in tailoring 2D crystal properties, coupled with their minimal thickness, make these emerging highly attractive for advanced piezoelectric applications that include pressure sensing, piezocatalysis, piezotronics, and energy harvesting. This review summarizes literature on piezoelectricity, particularly out-of-plane piezoelectricity, in the vast family of 2D materials as well as their heterostructures. It also describes methods to induce, enhance, and control the piezoelectric properties. The volume of data and role of machine learning in predicting piezoelectricity is discussed in detail, and a prospective outlook on the 2D piezoelectric field is provided.

Identifying SARS-CoV-2 Drugs Binding to the Spike Fatty Acid Binding Pocket Using In Silico Docking and Molecular Dynamics.

Drugs against novel targets are needed to treat COVID-19 patients, especially as SARS-CoV-2 is capable of rapid mutation. Structure-based de novo drug design and repurposing of drugs and natural products is a rational approach to discovering potentially effective therapies. These in silico simulations can quickly identify existing drugs with known safety profiles that can be repurposed for COVID-19 treatment. Here, we employ the newly identified spike protein free fatty acid binding pocket structure to identify repurposing candidates as potential SARS-CoV-2 therapies. Using a validated docking and molecular dynamics protocol effective at identifying repurposing candidates inhibiting other SARS-CoV-2 molecular targets, this study provides novel insights into the SARS-CoV-2 spike protein and its potential regulation by endogenous hormones and drugs. Some of the predicted repurposing candidates have already been demonstrated experimentally to inhibit SARS-CoV-2 activity, but most of the candidate drugs have yet to be tested for activity against the virus. We also elucidated a rationale for the effects of steroid and sex hormones and some vitamins on SARS-CoV-2 infection and COVID-19 recovery.

Rational Atom Substitution to Obtain Efficient, Lead-Free Photocatalytic Perovskites Assisted by Machine Learning and DFT Calculations.

Inorganic lead-free halide perovskites, devoid of toxic or rare elements, have garnered considerable attention as photocatalysts for pollution control, CO2 reduction and hydrogen production. In the extensive perovskite design space, factors like substitution or doping level profoundly impact their performance. To address this complexity, a synergistic combination of machine learning models and theoretical calculations were used to efficiently screen substitution elements that enhanced the photoactivity of substituted Cs2 AgBiBr6 perovskites. Machine learning models determined the importance of d10 orbitals, highlighting how substituent electron configuration affects electronic structure of Cs2 AgBiBr6 . Conspicuously, d10 -configured Zn2+ boosted the photoactivity of Cs2 AgBiBr6 . Experimental verification validated these model results, revealing a 13-fold increase in photocatalytic toluene conversion compared to the unsubstituted counterpart. This enhancement resulted from the small charge carrier effective mass, as well as the creation of shallow trap states, shifting the conduction band minimum, introducing electron-deficient Br, and altering the distance between the B-site cations d band centre and the halide anions p band centre, a parameter tuneable through d10 configuration substituents. This study exemplifies the application of computational modelling in photocatalyst design and elucidating structure-property relationships. It underscores the potential of synergistic integration of calculations, modelling, and experimental analysis across various applications.

Two-Dimensional and Three-Dimensional Time-of-Flight Secondary Ion Mass Spectrometry Image Feature Extraction Using a Spatially Aware Convolutional Autoencoder.

Feature extraction algorithms are an important class of unsupervised methods used to reduce data dimensionality. They have been applied extensively for time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging─commonly, matrix factorization (MF) techniques such as principal component analysis have been used. A limitation of MF is the assumption of linearity, which is generally not accurate for ToF-SIMS data. Recently, nonlinear autoencoders have been shown to outperform MF techniques for ToF-SIMS image feature extraction. However, another limitation of most feature extraction methods (including autoencoders) that is particularly important for hyperspectral data is that they do not consider spatial information. To address this limitation, we describe the application of the convolutional autoencoder (CNNAE) to hyperspectral ToF-SIMS imaging data. The CNNAE is an artificial neural network developed specifically for hyperspectral data that uses convolutional layers for image encoding, thereby explicitly incorporating pixel neighborhood information. We compared the performance of the CNNAE with other common feature extraction algorithms for two biological ToF-SIMS imaging data sets. We investigated the extracted features and used the dimensionality-reduced data to train additional ML algorithms. By converting two-dimensional convolutional layers to three-dimensional (3D), we also showed how the CNNAE can be extended to 3D ToF-SIMS images. In general, the CNNAE produced features with significantly higher contrast and autocorrelation than other techniques. Furthermore, histologically recognizable features in the data were more accurately represented. The extension of the CNNAE to 3D data also provided an important proof of principle for the analysis of more complex 3D data sets.

Severe oligomeric tau toxicity can be reversed without long-term sequelae.

Tau is a microtubule stabilizing protein that forms abnormal aggregates in many neurodegenerative disorders, including Alzheimer's disease. We have previously shown that co-expression of fragmented and full-length tau in P301SxTAU62on tau transgenic mice results in the formation of oligomeric tau species and causes severe paralysis. This paralysis is fully reversible once expression of the tau fragment is halted, even though P301S tau expression is maintained. Whereas various strategies to target tau aggregation have been developed, little is known about the long-term consequences of reverted tau toxicity. Therefore, we studied the long-term motor fitness of recovered, formerly paralysed P301SxTAU62on-off mice. To assess the seeding competence of oligomeric toxic tau species, we also inoculated ALZ17 mice with brainstem homogenates from paralysed P301SxTAU62on mice. Counter-intuitively, after recovery from paralysis due to oligomeric tau species expression, ageing P301SxTAU62on-off mice did not develop more motor impairment or tau pathology when compared to heterozygous P301S tau transgenic littermates. Thus, toxic tau species causing extensive neuronal dysfunction can be cleared without inducing seeding effects. Moreover, these toxic tau species also lack long-term tau seeding effects upon intrahippocampal inoculation into ALZ17 mice. In conclusion, tau species can be neurotoxic in the absence of seeding-competent tau aggregates, and mice can clear these tau forms permanently without tau seeding or spreading effects. These observations suggest that early targeting of non-fibrillar tau species may represent a therapeutically effective intervention in tauopathies. On the other hand, the absent seeding competence of early toxic tau species also warrants caution when using seeding-based tests for preclinical tauopathy diagnostics.

Overcoming resistance to checkpoint blockade therapy by targeting PI3Kγ in myeloid cells.

Recent clinical trials using immunotherapy have demonstrated its potential to control cancer by disinhibiting the immune system. Immune checkpoint blocking (ICB) antibodies against cytotoxic-T-lymphocyte-associated protein 4 or programmed cell death protein 1/programmed death-ligand 1 have displayed durable clinical responses in various cancers. Although these new immunotherapies have had a notable effect on cancer treatment, multiple mechanisms of immune resistance exist in tumours. Among the key mechanisms, myeloid cells have a major role in limiting effective tumour immunity. Growing evidence suggests that high infiltration of immune-suppressive myeloid cells correlates with poor prognosis and ICB resistance. These observations suggest a need for a precision medicine approach in which the design of the immunotherapeutic combination is modified on the basis of the tumour immune landscape to overcome such resistance mechanisms. Here we employ a pre-clinical mouse model system and show that resistance to ICB is directly mediated by the suppressive activity of infiltrating myeloid cells in various tumours. Furthermore, selective pharmacologic targeting of the gamma isoform of phosphoinositide 3-kinase (PI3Kγ), highly expressed in myeloid cells, restores sensitivity to ICB. We demonstrate that targeting PI3Kγ with a selective inhibitor, currently being evaluated in a phase 1 clinical trial (NCT02637531), can reshape the tumour immune microenvironment and promote cytotoxic-T-cell-mediated tumour regression without targeting cancer cells directly. Our results introduce opportunities for new combination strategies using a selective small molecule PI3Kγ inhibitor, such as IPI-549, to overcome resistance to ICB in patients with high levels of suppressive myeloid cell infiltration in tumours.

PI3Kγ is a molecular switch that controls immune suppression.

Macrophages play critical, but opposite, roles in acute and chronic inflammation and cancer. In response to pathogens or injury, inflammatory macrophages express cytokines that stimulate cytotoxic T cells, whereas macrophages in neoplastic and parasitic diseases express anti-inflammatory cytokines that induce immune suppression and may promote resistance to T cell checkpoint inhibitors. Here we show that macrophage PI 3-kinase γ controls a critical switch between immune stimulation and suppression during inflammation and cancer. PI3Kγ signalling through Akt and mTor inhibits NFκB activation while stimulating C/EBPß activation, thereby inducing a transcriptional program that promotes immune suppression during inflammation and tumour growth. By contrast, selective inactivation of macrophage PI3Kγ stimulates and prolongs NFκB activation and inhibits C/EBPß activation, thus promoting an immunostimulatory transcriptional program that restores CD8+ T cell activation and cytotoxicity. PI3Kγ synergizes with checkpoint inhibitor therapy to promote tumour regression and increased survival in mouse models of cancer. In addition, PI3Kγ-directed, anti-inflammatory gene expression can predict survival probability in cancer patients. Our work thus demonstrates that therapeutic targeting of intracellular signalling pathways that regulate the switch between macrophage polarization states can control immune suppression in cancer and other disorders.

Safety of Brodalumab in Plaque Psoriasis: Integrated Pooled Data from Five Clinical Trials.

Brodalumab is approved for treatment of moderate-to-severe plaque psoriasis. Here, we assess the safety profile of brodalumab using pooled safety data from 5 phase II/III trials of brodalumab 140 mg or 210 mg. In total, 4,464 patients received brodalumab, representing 8,891.6 patient-years of exposure. During the placebo-controlled 12-week induction period, rates of serious adverse events per 100 patient-years were 10.8 and 9.6 (brodalumab 140 mg and 210 mg, respectively) vs 4.3 and 6.5 (ustekinumab and placebo, respectively); infections were the most frequent serious adverse event. Rates of serious adverse events during the comparator-controlled 52-week period were 14.4, 10.2 and 8.3 per 100 patient-years for brodalumab 210 mg, brodalumab 140 mg, and ustekinumab, respectively. Brodalumab was not associated with increased risks of malignancy, major adverse cardiac events, suicidal ideation and behaviour, or fatal events. Overall, brodalumab demonstrated an acceptable safety profile in short- and long-term treatment.

Missing the blind spot in a HeartMate 3 LVAD outflow graft obstruction caused by fungal infection.

A patient was admitted in cardiogenic shock and a constant decrease of pump flow requiring combined inotropic support. To evaluate the cause, echocardiography and a ramp test were performed. The results suggested a LVAD related problem - particularly a suspected outflow graft obstruction. Wether CT scan nor angiography confirmed the assumption. However, a post-mortem LVAD examination revealed an outflow obstruction caused by a fungal thrombus formation invisible for standard imaging procedures.

A critical overview of computational approaches employed for COVID-19 drug discovery.

COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19.

Trans-seeding of Alzheimer-related tau protein by a yeast prion.

Abnormal tau protein aggregates constitute a hallmark of Alzheimer's disease. The mechanisms underlying the initiation of tau aggregation in sporadic neurodegeneration remain unclear. Here we investigate whether a non-human prion can seed tau aggregation. Due to their structural similarity with tau aggregates, we chose Sup35NM yeast prion domain fibrils for explorative tau seedings. Upon in vitro incubation with tau monomers, Sup35NM fibrils promoted the formation of morphologically distinct tau fibril strains. In vivo, intrahippocampal inoculation of Sup35NM fibrils accentuated tau pathology in P301S tau transgenic mice. Thus, our results provide first in vivo evidence for heterotypic cross-species seeding of a neurodegenerative human prion-like protein by a yeast prion. This opens up the conceptual perspective that non-mammalian prions present in the human microbiome could be involved in the initiation of protein misfolding in neurodegenerative disorders, a mechanism for which we propose the term "trans-seeding."

Potential COVID-19 Therapies from Computational Repurposing of Drugs and Natural Products against the SARS-CoV-2 Helicase.

Repurposing of existing drugs is a rapid way to find potential new treatments for SARS-CoV-2. Here, we applied a virtual screening approach using Autodock Vina and molecular dynamic simulation in tandem to screen and calculate binding energies of repurposed drugs against the SARS-CoV-2 helicase protein (non-structural protein nsp13). Amongst the top hits from our study were antivirals, antihistamines, and antipsychotics, plus a range of other drugs. Approximately 30% of our top 87 hits had published evidence indicating in vivo or in vitro SARS-CoV-2 activity. Top hits not previously reported to have SARS-CoV-2 activity included the antiviral agents, cabotegravir and RSV-604; the NK1 antagonist, aprepitant; the trypanocidal drug, aminoquinuride; the analgesic, antrafenine; the anticancer intercalator, epirubicin; the antihistamine, fexofenadine; and the anticoagulant, dicoumarol. These hits from our in silico SARS-CoV-2 helicase screen warrant further testing as potential COVID-19 treatments.