Calibration-free reaction yield quantification by HPLC with a machine-learning model of extinction coefficients.

Reaction optimization and characterization depend on reliable measures of reaction yield, often measured by high-performance liquid chromatography (HPLC). Peak areas in HPLC chromatograms are correlated to analyte concentrations by way of calibration standards, typically pure samples of known concentration. Preparing the pure material required for calibration runs can be tedious for low-yielding reactions and technically challenging at small reaction scales. Herein, we present a method to quantify the yield of reactions by HPLC without needing to isolate the product(s) by combining a machine learning model for molar extinction coefficient estimation, and both UV-vis absorption and mass spectra. We demonstrate the method for a variety of reactions important in medicinal and process chemistry, including amide couplings, palladium catalyzed cross-couplings, nucleophilic aromatic substitutions, aminations, and heterocycle syntheses. The reactions were all performed using an automated synthesis and isolation platform. Calibration-free methods such as the presented approach are necessary for such automated platforms to be able to discover, characterize, and optimize reactions automatically.

Open-Source Chromatographic Data Analysis for Reaction Optimization and Screening.

Automation and digitalization solutions in the field of small molecule synthesis face new challenges for chemical reaction analysis, especially in the field of high-performance liquid chromatography (HPLC). Chromatographic data remains locked in vendors' hardware and software components, limiting their potential in automated workflows and data science applications. In this work, we present an open-source Python project called MOCCA for the analysis of HPLC-DAD (photodiode array detector) raw data. MOCCA provides a comprehensive set of data analysis features, including an automated peak deconvolution routine of known signals, even if overlapped with signals of unexpected impurities or side products. We highlight the broad applicability of MOCCA in four studies: (i) a simulation study to validate MOCCA's data analysis features; (ii) a reaction kinetics study on a Knoevenagel condensation reaction demonstrating MOCCA's peak deconvolution feature; (iii) a closed-loop optimization study for the alkylation of 2-pyridone without human control during data analysis; (iv) a well plate screening of categorical reaction parameters for a novel palladium-catalyzed cyanation of aryl halides employing O-protected cyanohydrins. By publishing MOCCA as a Python package with this work, we envision an open-source community project for chromatographic data analysis with the potential of further advancing its scope and capabilities.

Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back.

A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties. We demonstrated two case studies on dye-like molecules, targeting absorption wavelength, lipophilicity, and photooxidative stability. In the first study, the platform experimentally realized 294 unreported molecules across three automatic iterations of molecular design-make-test-analyze cycles while exploring the structure-function space of four rarely reported scaffolds. In each iteration, the property prediction models that guided exploration learned the structure-property space of diverse scaffold derivatives, which were realized with multistep syntheses and a variety of reactions. The second study exploited property models trained on the explored chemical space and previously reported molecules to discover nine top-performing molecules within a lightly explored structure-property space.

Imaging atherosclerosis with hybrid [18F]fluorodeoxyglucose positron emission tomography/computed tomography imaging: what Leonardo da Vinci could not see.

Prodigious efforts and landmark discoveries have led toward significant advances in our understanding of atherosclerosis. Despite significant efforts, atherosclerosis continues globally to be a leading cause of mortality and reduced quality of life. With surges in the prevalence of obesity and diabetes, atherosclerosis is expected to have an even more pronounced impact upon the global burden of disease. It is imperative to develop strategies for the early detection of disease. Positron emission tomography (PET) imaging utilizing [(18)F]fluorodeoxyglucose (FDG) may provide a non-invasive means of characterizing inflammatory activity within atherosclerotic plaque, thus serving as a surrogate biomarker for detecting vulnerable plaque. The aim of this review is to explore the rationale for performing FDG imaging, provide an overview into the mechanism of action, and summarize findings from the early application of FDG PET imaging in the clinical setting to evaluate vascular disease. Alternative imaging biomarkers and approaches are briefly discussed.

Eye Movements in Mild Traumatic Brain Injury: Clinical Challenges.

Mild traumatic brain injury (mTBI), also known as concussion, is a common injury which affects patients of all demographics. There is a global effort to accurately diagnose and identify patients at highest risk of prolonged symptom burden to facilitate appropriate rehabilitation efforts. Underreporting is common with large numbers not engaging with services, in addition to differences in treatment outcomes according to ethnicity, age, and gender. As patients recover, symptomology evolves which challenges rehabilitative efforts with no clear definition of 'recovered'. This review describes key areas in mTBI such as diagnostic challenges, epidemiology, prognosis, and pathophysiology which serves as an introduction to "Eye Movements in Mild Traumatic Brain Injury: Ocular Biomarkers."

Eye Movements in Mild Traumatic Brain Injury: Ocular Biomarkers.

Mild traumatic brain injury (mTBI, or concussion), results from direct and indirect trauma to the head (i.e. a closed injury of transmitted forces), with or without loss of consciousness. The current method of diagnosis is largely based on symptom assessment and clinical history. There is an urgent need to identify an objective biomarker which can not only detect injury, but inform prognosis and recovery. Ocular motor impairment is argued to be ubiquitous across mTBI subtypes and may serve as a valuable clinical biomarker with the recent advent of more affordable and portable eye tracking technology. Many groups have positively correlated the degree of ocular motor impairment to symptom severity with a minority attempting to validate these findings with diffusion tract imaging and functional MRI. However, numerous methodological issues limit the interpretation of results, preventing any singular ocular biomarker from prevailing. This review will comprehensively describe the anatomical susceptibility, clinical measurement, and current eye tracking literature surrounding saccades, smooth pursuit, vestibulo-ocular reflex, vergence, pupillary light reflex, and accommodation in mTBI.

Eye Movement Abnormalities in Glaucoma Patients: A Review.

Glaucoma is a common condition that relies on careful clinical assessment to diagnose and determine disease progression. There is growing evidence that glaucoma is associated not only with loss of retinal ganglion cells but also with degeneration of cortical and subcortical brain structures associated with vision and eye movements. The effect of glaucoma pathophysiology on eye movements is not well understood. In this review, we examine the evidence surrounding altered eye movements in glaucoma patients compared to healthy controls, with a focus on quantitative eye tracking studies measuring saccades, fixation, and optokinetic nystagmus in a range of visual tasks. The evidence suggests that glaucoma patients have alterations in several eye movement domains. Patients exhibit longer saccade latencies, which worsen with increasing glaucoma severity. Other saccadic abnormalities include lower saccade amplitude and velocity, and difficulty inhibiting reflexive saccades. Fixation is pathologically altered in glaucoma with reduced stability. Optokinetic nystagmus measures have also been shown to be abnormal. Complex visual tasks (eg reading, driving, and navigating obstacles), integrate these eye movements and result in behavioral adaptations. The review concludes with a summary of the evidence and recommendations for future research in this emerging field.

A window into eye movement dysfunction following mTBI: A scoping review of magnetic resonance imaging and eye tracking findings.

Mild traumatic brain injury (mTBI), commonly known as concussion, is a complex neurobehavioral phenomenon affecting six in 1000 people globally each year. Symptoms last between days and years as microstructural damage to axons and neurometabolic changes result in brain network disruption. There is no clinically available objective biomarker to diagnose the severity of injury or monitor recovery. However, emerging evidence suggests eye movement dysfunction (e.g., saccades and smooth pursuits) in patients with mTBI. Patients with a higher symptom burden and prolonged recovery time following injury may show higher degrees of eye movement dysfunction. Likewise, recent advances in magnetic resonance imaging (MRI) have revealed both white matter tract damage and functional network alterations in mTBI patients, which involve areas responsible for the ocular motor control. This scoping review is presented in three sections: Section 1 explores the anatomical control of eye movements to aid the reader with interpreting the discussion in subsequent sections. Section 2 examines the relationship between abnormal MRI findings and eye tracking after mTBI based on the available evidence. Finally, Section 3 communicates gaps in our knowledge about MRI and eye tracking, which should be addressed in order to substantiate this emerging field.

Modeling Amyloid Aggregation Kinetics: A Case Study with Sup35NM.

Understanding the aggregation mechanism of amyloid proteins, such as Sup35NM, is essential to understanding amyloid diseases. Significant recent work has focused on using the fluorescence of thioflavin T (ThT), which undergoes a red shift when bound to amyloid aggregates, to monitor amyloid fibril formation. In the present study, the progression of the total mass of aggregates during fibril formation is monitored for initial monomer concentrations in order to infer the relevant aggregation mechanisms. This workflow was implemented using the amyloid-forming fragment Sup35NM under different agitation conditions and for initial monomer concentrations spanning 2 orders of magnitude. The analysis suggests that primary nucleation, monomeric elongation, secondary nucleation, and fragmentation might all be relevant, but their relative importance could not be determined unambiguously, despite the large set of high-quality data. Discriminating between the fibril-generating processes is shown to require additional information, such as a fibril length distribution. Using Sup35NM as a case study, a framework for fitting the parameters of arbitrary amyloid aggregation kinetics is developed based on a population balance model (PBM), which resolves not only the total aggregate mass (monitored experimentally via ThT fluorescence) but the entire fibril length distribution over time. In addition to the rich new set of ThT fluorescence data, we have reanalyzed a previously published aggregate size distribution using this method. With the size distribution, it was determined that in the reanalyzed in vitro experiment, secondary nucleation generated significantly fewer new Sup35NM fibrils than fragmentation. The proposed strategy of applying the same PBM to a combination of kinetic data from fluorescence monitoring and experimental fibril length distributions will allow the inference of aggregation mechanisms with far greater confidence than fluorescence studies alone.