Effects of DPTQ, a novel positive allosteric modulator of the dopamine D1 receptor, on spontaneous eye blink rate and spatial working memory in the nonhuman primate.

RATIONALE: Dopamine (DA) signaling through the D1 receptor has been shown to be integral to multiple aspects of cognition, including the core process of working memory. The discovery of positive allosteric modulators (PAMs) of the D1 receptor has enabled treatment modalities that may have alternative benefits to orthosteric D1 agonists arising from a synergism of action with functional D1 receptor signaling. OBJECTIVES: To investigate this potential, we have studied the effects of the novel D1 PAM DPTQ on a spatial delayed response working memory task in the rhesus monkey. Initial studies indicated that DPTQ binds to primate D1R with high affinity and selectivity and elevates spontaneous eye blink rate in rhesus monkeys in a dose-dependent manner consistent with plasma ligand exposures and central D1activation. RESULTS: Based on those results, DPTQ was tested at 2.5 mg/kg IM in the working memory task. No acute effect was observed 1 h after dosing, but performance was impaired 48 h later. Remarkably, this deficit was immediately followed by a significant enhancement in cognition over the next 3 days. In a second experiment in which DPTQ was administered on days 1 and 5, the early impairment was smaller and did not reach statistical significance, but statistically significant enhancement of performance was observed over the following week. Lower doses of 0.1 and 1.0 mg/kg were also capable of producing this protracted enhancement without inducing any transient impairment. CONCLUSIONS: DPTQ exemplifies a class of D1PAMs that may be capable of providing long-term improvements in working memory.

Simultaneous thermoosmotic and thermoelectric responses in nanoconfined electrolyte solutions: Effects of nanopore structures and membrane properties.

HYPOTHESIS: Nanofluidic systems provide an emerging and efficient platform for thermoelectric conversion and fluid pumping with low-grade heat energy. As a basis of their performance enhancement, the effects of the structures and properties of the nanofluidic systems on the thermoelectric response (TER) and the thermoosmotic response (TOR) are yet to be explored. METHODS: The simultaneous TER and TOR of electrolyte solutions in nanofluidic membrane pores on which an axial temperature gradient is exerted are investigated numerically and semi-analytically. A semi-analytical model is developed with the consideration of finite membrane thermal conductivity and the reservoir/entrance effect. FINDINGS: The increase in the access resistance due to the nanopore-reservoir interfaces accounts for the decrease of short circuit current at the low concentration regime. The decrease in the thermal conductivity ratio can enhance the TER and TOR. The maximum power density occurring at the nanopore radius twice the Debye length ranges from several to dozens of mW K-2 m-2 and is an order of magnitude higher than typical thermo-supercapacitors. The surface charge polarity can heavily affect the sign and magnitude of the short-circuit current, the Seebeck coefficient and the open-circuit thermoosmotic coefficient, but has less effect on the short-circuit thermoosmotic coefficient. Furthermore, the membrane thickness makes different impacts on TER and TOR for zero and finite membrane thermal conductivity.

Integrating additional factors into the TNM staging for cutaneous melanoma by machine learning.

BACKGROUND: Integrating additional factors into the TNM staging system is needed for more accurate risk classification and survival prediction for patients with cutaneous melanoma. In the present study, we introduce machine learning as a novel tool that incorporates additional prognostic factors to improve the current TNM staging system. METHODS AND FINDINGS: Cancer-specific survival data for cutaneous melanoma with at least a 5 years follow-up were extracted from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute and split into the training set (40,781 cases) and validation set (5,390 cases). Five factors were studied: the primary tumor (T), regional lymph nodes (N), distant metastasis (M), age (A), and sex (S). The Ensemble Algorithm for Clustering Cancer Data (EACCD) was applied to the training set to generate prognostic groups. Utilizing only T, N, and M, a basic prognostic system was built where patients were stratified into 10 prognostic groups with well-separated survival curves, similar to 10 AJCC stages. These 10 groups had a significantly higher accuracy in survival prediction than 10 stages (C-index = 0.7682 vs 0.7643; increase in C-index = 0.0039, 95% CI = (0.0032, 0.0047); p-value = 7.2×10-23). Nevertheless, a positive association remained between the EACCD grouping and the AJCC staging (Spearman's rank correlation coefficient = 0.8316; p-value = 4.5×10-13). With additional information from A and S, a more advanced prognostic system was established using the training data that stratified patients into 10 groups and further improved the prediction accuracy (C-index = 0.7865 vs 0.7643; increase in C-index = 0.0222, 95% CI = (0.0191, 0.0254); p-value = 8.8×10-43). Both internal validation using the training set and temporal validation using the validation set showed good stratification and a high predictive accuracy of the prognostic systems. CONCLUSIONS: The EACCD allows additional factors to be integrated into the TNM to create a prognostic system that improves patient stratification and survival prediction for cutaneous melanoma. This integration separates favorable from unfavorable clinical outcomes for patients and improves both cohort selection for clinical trials and treatment management.

Targeted mass spectrometry quantification of total soy protein residues from commercially processed ingredients for food allergen management.

Soybean is one of the most commonly allergenic foods in the U.S. However, the variety of commercial soy ingredients used in the food industry makes soybean a challenging allergen to detect and quantify. The processing methods used to produce soy-derived ingredients result in protein modifications that often substantially impact detection and quantification with commonly used antibody-based methods. This study aimed to develop a mass spectrometry (MS)-based method capable of quantifying commercially processed soy ingredients in food matrices. A quantification strategy using external standards with internal calibrants was developed and evaluated, resulting in the ultimate use of a matrix-independent standard curve of non-roasted soy flour with milk proteins as carrier proteins. The method performance was evaluated by quantifying six soy-derived ingredients in incurred food matrices using three quantification strategies. Out of the twelve ingredient-matrix combinations with 10 ppm incurred total soy protein, eight had maximum recoveries between 60 and 120% using the full standard curve strategy. Other quantification strategies may be useful for internal quality control and interlaboratory calibrations. Compared with three commercial ELISA kits, the MS method showed a substantial advantage in quantifying the highly processed soy proteins in food matrices. SIGNIFICANCE: The ability to quantify undeclared soy protein in food products regardless of the soy ingredient source is essential for food allergen management, risk assessment, and regulatory enforcement. The MS-based method described here is able to reliably quantify six different soy-derived ingredients incurred in a model processed food. When compared with existing commercial ELISA methods, the MS method is much less affected by matrices and ingredient types, indicating its wider applicability to a range of soy-derived ingredients and processed products.

Utility of Screening Chest Radiographs in Patients with Asymptomatic or Minimally Symptomatic COVID-19 in Singapore.

Background Singapore saw an escalation of coronavirus disease 2019 (COVID-19) cases from fewer than 4000 in April 2020 to more than 40 000 in June 2020, with most of these cases attributed to spread within shared facilities housing foreign workers. Appropriate triage and escalation of clinical care are crucial for this patient group managed in community care facilities (CCFs). Purpose To evaluate the imaging guideline recommendations for COVID-19 from the Fleischner Society and to analyze the clinical utility of screening chest radiography for asymptomatic or minimally symptomatic patients with COVID-19. Materials and Methods In this retrospective study, patients with reverse-transcription polymerase chain reaction-confirmed COVID-19 who were admitted to a designated CCF for continuation of their treatment during May 3-31, 2020, were identified. Upon admission, patients aged 36 years and older without any baseline chest images underwent chest radiography. All chest radiographs and clinical outcomes of patients, including those who were subsequently transferred to acute hospitals for escalation of care, were reviewed. Key proportions of patients with findings of pulmonary infection and those requiring further inpatient treatment were calculated, and 95% binomial proportion CIs were obtained using the Clopper-Pearson method. Results The study included 5621 patients. All patients were men (100%; 5621 of 5621), and the mean patient age was 37 years ± 8 (range, 17-60 years). A total of 1964 chest radiographs were obtained, of which normal images accounted for 98.0% (1925 of 1964 radiographs) and findings of pulmonary infection represented 2.0% (39 of 1964 radiographs). Only 0.2% of patients (four of 1964) with findings of pulmonary infection at chest radiography (all of whom were symptomatic) required supplemental oxygenation and inpatient treatment. None of the asymptomatic patients with findings of pulmonary infection required supplemental oxygenation, and they received only symptomatic treatment. Conclusion In accordance with Fleischner Society recommendations, screening chest radiography is not indicated in patients with coronavirus disease 2019 who are aged 17-60 years with mild or no symptoms unless there is risk of clinical deterioration. © RSNA, 2021 See also the editorial by Schaefer-Prokop and Prokop in this issue.

Miller's monkey updated: Communicative efficiency and the statistics of words in natural language.

Is language designed for communicative and functional efficiency? G. K. Zipf famously argued that shorter words are more frequent because they are easier to use, thereby resulting in the statistical law that bears his name. Yet, G. A. Miller showed that even a monkey randomly typing at a keyboard, and intermittently striking the space bar, would generate "words" with similar statistical properties. Recent quantitative analyses of human language lexicons (Piantadosi et al., 2012) have revived Zipf's functionalist hypothesis. Ambiguous words tend to be short, frequent, and easy to articulate in language production. Such statistical findings are commonly interpreted as evidence for pressure for efficiency, as the context of language use often provides cues to overcome lexical ambiguity. In this study, we update Miller's monkey thought experiment to incorporate empirically motivated phonological and semantic constraints on the creation of words. We claim that the appearance of communicative efficiency is a spandrel (Gould & Lewontin, 1979), as lexicons formed without the context of language use or reference to communication or efficiency exhibit comparable statistical properties. Furthermore, the updated monkey model provides a good fit for the growth trajectory of English as recorded in the Oxford English Dictionary. Focusing on the history of English words since 1900, we show that lexicons resulting from the monkey model provide a better embodiment of communicative efficiency than the actual lexicon of English. We conclude by arguing for the need to go beyond correlational statistics and to seek direct evidence for the mechanisms that underlie principles of language design.

Parallel Reaction Monitoring Mass Spectrometry Method for Detection of Both Casein and Whey Milk Allergens from a Baked Food Matrix.

Milk allergy is among the most common food allergies present in early childhood, which in some cases may persist into adulthood as well. Proteins belonging to both casein and whey fractions of milk can trigger an allergic response in susceptible individuals. Milk is present as an ingredient in many foods, and it can also be present as casein- or whey-enriched milk-derived ingredients. As whey proteins are more susceptible to thermal processing than caseins, conventional methods often posed a challenge in accurate detection of whey allergens, particularly from a processed complex food matrix. In this study, a targeted mass spectrometry method has been developed to detect the presence of both casein and whey allergens from thermally processed foods. A pool of 19 candidate peptides representing four casein proteins and two whey proteins was identified using a discovery-driven target selection approach from various milk-derived ingredients. These target peptides were evaluated by parallel reaction monitoring of baked cookie samples containing known amounts of nonfat dry milk (NFDM). The presence of milk could be detected from baked cookies incurred with NFDM at levels as low as 1 ppm using seven peptides representing α-, ß-, and κ-casein proteins and three peptides representing a whey protein, ß-lactoglobulin, by this consensus PRM method.

Designing an Adhesive Pillar Shape with Deep Learning-Based Optimization.

Over the past decades, significant effort has been made to improve the adhesive properties of adhesive pillars, by searching for pillar shapes with optimized interfacial stress distribution. However, the shape optimizations in the previous studies are conducted by considering specific pillar forms with a few parameters, hence with limited design space. In this study, we present a framework to find a free-form optimized adhesive pillar shape out of extensive design space. We generate 200 000 different shapes of adhesive pillars based on the Bézier curve with a few control points by considering two distinct edge shapes, sharp and truncated edges, to account for the limitation in the realistic manufacturing resolution. The resulting interfacial stress distributions from numerical simulations are used to train deep neural networks for each edge type. Our deep learning model shows greater than 99% classification accuracy on a limited data set with orders of magnitude speedup in computation time compared to finite element analyses. On the basis of the trained neural network, we conduct genetic optimization by maximizing a fitness function that prefers the uniform interfacial stress distribution with neither stress peak nor singularity. The optimized adhesive pillar shape is composed of smoothly mixed convex and concave parts and shows improved uniformity in the interfacial stress distribution. Our study also demonstrates that the deep learning can be used for nonparametric curve optimization task with diverse fitness function.

Performance test methods for near-infrared fluorescence imaging.

PURPOSE: Near-infrared fluorescence (NIRF) imaging using exogenous contrast has gained much attention as a technique for enhancing visualization of vasculature using untargeted agents, as well as for the detection and localization of cancer with targeted agents. In order to address the emerging need for standardization of NIRF imaging technologies, it is necessary to identify the best practices suitable for objective, quantitative testing of key image quality characteristics. Toward the development of a battery of test methods that are rigorous yet applicable to a wide variety of devices, we have evaluated techniques for phantom design, measurement, and calculation of specific performance metrics. METHODS: Using a NIRF imaging system for indocyanine green imaging, providing excitation at 780 nm and detection above 830 nm, we explored methods to evaluate uniformity, field of view, spectral crosstalk, spatial resolution, depth of field, sensitivity, linearity, and penetration depth. These measurements were performed using fluorophore-doped multiwell plate and high turbidity planar phantoms, as well as a 3D-printed multichannel phantom and a USAF 1951 resolution target. RESULTS AND CONCLUSIONS: Based on a wide range of approaches described in medical and fluorescence imaging literature, we have developed and demonstrated a cohesive battery of test methods for evaluation of fluorescence image quality in wide-field imagers. We also propose a number of key metrics that can facilitate direct, quantitative comparison of device performance. These methods have the potential to facilitate more uniform evaluation and inter-comparison of clinical and preclinical imaging systems than is typically achieved, with the long-term goal of establishing international standards for fluorescence image quality assessment.

How to Make the Most out of Very Little.

I review the problem of referential ambiguity that arises when children learn the meanings of words, along with a number of models that have been proposed to solve it. I then provide a formal analysis of why a resource-limited model that retains very few meaning hypotheses may be more effective than "big data" models that keep track of all word-meaning associations.

Synthesis and Pharmacological Characterization of 2-(2,6-Dichlorophenyl)-1-((1S,3R)-5-(3-hydroxy-3-methylbutyl)-3-(hydroxymethyl)-1-methyl-3,4-dihydroisoquinolin-2(1H)-yl)ethan-1-one (LY3154207), a Potent, Subtype Selective, and Orally Available Positive Allosteric Modulator of the Human Dopamine D1 Receptor.

Clinical development of catechol-based orthosteric agonists of the dopamine D1 receptor has thus far been unsuccessful due to multiple challenges. To address these issues, we identified LY3154207 (3) as a novel, potent, and subtype selective human D1 positive allosteric modulator (PAM) with minimal allosteric agonist activity. Conformational studies showed LY3154207 adopts an unusual boat conformation, and a binding pose with the human D1 receptor was proposed based on this observation. In contrast to orthosteric agonists, LY3154207 showed a distinct pharmacological profile without a bell-shaped dose-response relationship or tachyphylaxis in preclinical models. Identification of a crystalline form of free LY3154207 from the discovery lots was not successful. Instead, a novel cocrystal form with superior solubility was discovered and determined to be suitable for development. This cocrystal form was advanced to clinical development as a potential first-in-class D1 PAM and is now in phase 2 studies for Lewy body dementia.

Creating Prognostic Systems for Well-Differentiated Thyroid Cancer Using Machine Learning.

Updates to staging models are needed to reflect a greater understanding of tumor behavior and clinical outcomes for well-differentiated thyroid carcinomas. We used a machine learning algorithm and disease-specific survival data of differentiated thyroid carcinoma from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute to integrate clinical factors to improve prognostic accuracy. The concordance statistic (C-index) was used to cut dendrograms resulting from the learning process to generate prognostic groups. We created one computational prognostic model (7 prognostic groups with C-index = 0.8583) based on tumor size (T), regional lymph nodes (N), status of distant metastasis (M), and age to mirror the contemporary American Joint Committee on Cancer (AJCC) staging system (C-index = 0.8387). We showed that adding histologic type (papillary and follicular) improved the survival prediction of the model. We also showed that 55 is the best cutoff of age in the model, consistent with the changes from the most recent 8th edition staging manual from AJCC. The demonstrated approach has the potential to create prognostic systems permitting data driven and real time analysis that can aid decision-making in patient management and prognostication.

The Discovery of LML134, a Histamine H3 Receptor Inverse Agonist for the Clinical Treatment of Excessive Sleep Disorders.

Histamine H3 receptor (H3R) inverse agonists that have been in clinical trials for the treatment of excessive sleep disorders, have been plagued with insomnia as a mechanism-based side effect. We focused on the identification of compounds that achieve high receptor occupancy within a short time, followed by rapid disengagement from the receptor, a target profile that could provide therapeutic benefits without the undesired side effect of insomnia. This article describes the optimization work that led to the discovery of 1-(1-methyl-6-oxo-1,6-dihydropyridazin-3-yl)piperidin-4-yl 4-cyclobutylpiperazine-1-carboxylate (18 b, LML134).

Depression in neurodegenerative diseases: Common mechanisms and current treatment options.

Major depressive disorder (MDD) is a highly prevalent psychiatric disorder and a major cause of disability worldwide. This neurological condition is commonly associated with neurodegenerative diseases such as Alzheimer's disease (AD), Parkinson's disease (PD) and Huntington's disease (HD), and has a significant impact on the increasing burden of these neuropathologies. Over the past decades, some of the pathophysiological and molecular mechanisms that contribute to these diseases have been elucidated and these findings indicate that, despite presenting distinct features, there are several similarities between the neurobiological alterations that lead to MDD and neurodegeneration in AD, PD, and HD. For instance, disturbances in monoaminergic transmission and the hypothalamic-pituitary-adrenal (HPA) axis, increased oxidative and neuroinflammatory events, and impaired trophic support are thought to contribute to neuronal atrophy and death in all these diseases. In addition, neuroimaging findings have helped elucidate the structural and functional changes implicated in the relationship between depression and neurodegeneration, thus establishing a neuroanatomical signature to explain, at least in part, the comorbidity between MDD and AD, PD, and HD. The present review summarizes these findings and the current evidence regarding the effectiveness of common antidepressant therapies for the treatment of MDD in patients with these neurodegenerative diseases. This population is particularly vulnerable to the drawdowns of conventional antidepressant therapy (namely inadequate response and high risk of side effects), and the development of emerging therapeutic approaches to treat MDD in patients with AD, PD, and HD is thus of paramount importance to improve the quality of life of these individuals.

Detection of Six Commercially Processed Soy Ingredients in an Incurred Food Matrix Using Parallel Reaction Monitoring.

Soybeans are one of the major allergenic foods in many countries. Soybeans are commonly processed into different types of soy ingredients to achieve the desired properties. The processing, however, may affect the protein profiles and protein structure, thus affecting the detection of soy proteins. Mass spectrometry (MS) is a potential alternative to the traditional immunoassays for the detection of soy-derived ingredients in foods. This study aims to develop a liquid chromatography-tandem MS method that uniformly detects different types of soy-derived ingredients. Target peptides applicable to the detection of six commercial soy ingredients were identified based on the results of MS label-free quantification and a set of selection criteria. The results indicated that soy ingredient processing can result in different protein profiles. A total of six soy ingredients were then individually incurred into cookie matrices at different levels. Sample preparation methods were optimized, and a distinct improvement in peptide performance was observed after optimization. Cookies and dough incurred with different soy ingredients at 100 ppm total soy protein showed a similar level of peptide recovery (90% mean signal relative to unroasted soy flour), demonstrating the ability of the MS method to detect processed soy ingredients in a uniform manner.

Intralesional Cidofovir for Treatment of Acyclovir-Resistant Laryngeal Herpes Manifesting as Supraglottic Mass.

INTRODUCTION:: Laryngopharyngeal herpes simplex virus infection is rare and presents typically in the supraglottis. Findings on presentation can range from small mucosal lesions to fungating obstructive masses mimicking neoplasm. Laryngopharyngeal herpes is a medically treated disease. OBJECTIVES:: Identify potential treatment in cases that are refractory to antiviral medications. METHODS:: Individual case with treatment adapted from other case report. CASE PRESENTATION:: We report a case of bulky, obstructive supraglottic and glottic herpes virus laryngitis that presented with dysphonia, dysphagia, and airway complaints resistant to acyclovir analogues that was treated effectively with intralesional cidofovir injection. CONCLUSIONS:: Our promising initial response suggests a potential novel treatment for this unusual condition.

Intracellular Binding Site for a Positive Allosteric Modulator of the Dopamine D1 Receptor.

The binding site for DETQ [2-(2,6-dichlorophenyl)-1-((1S,3R)-3-(hydroxymethyl)-5-(2-hydroxypropan-2-yl)-1-methyl-3,4-dihydroisoquinolin-2(1H)-yl)ethan-1-one], a positive allosteric modulator (PAM) of the dopamine D1 receptor, was identified and compared with the binding site for CID 2886111 [N-(6-tert-butyl-3-carbamoyl-4,5,6,7-tetrahydro-1-benzothiophen-2-yl)pyridine-4-carboxamide], a reference D1 PAM. From D1/D5 chimeras, the site responsible for potentiation by DETQ of the increase in cAMP in response to dopamine was narrowed down to the N-terminal intracellular quadrant of the receptor; arginine-130 in intracellular loop 2 (IC2) was then identified as a critical amino acid based on a human/rat species difference. Confirming the importance of IC2, a ß2-adrenergic receptor construct in which the IC2 region was replaced with its D1 counterpart gained the ability to respond to DETQ. A homology model was built from the agonist-state ß2-receptor structure, and DETQ was found to dock to a cleft created by IC2 and adjacent portions of transmembrane helices 3 and 4 (TM3 and TM4). When residues modeled as pointing into the cleft were mutated to alanine, large reductions in the potency of DETQ were found for Val119 and Trp123 (flanking the conserved DRY sequence in TM3), Arg130 (located in IC2), and Leu143 (TM4). The D1/D5 difference was found to reside in Ala139; changing this residue to methionine as in the D5 receptor reduced the potency of DETQ by approximately 1000-fold. None of these mutations affected the activity of CID 2886111, indicating that it binds to a different allosteric site. When combined, DETQ and CID 2886111 elicited a supra-additive response in the absence of dopamine, implying that both PAMs can bind to the D1 receptor simultaneously.