The Orphan Drug Act at 40: Legislative Triumph and the Challenges of Success.

Policy Points The Orphan Drug Act (ODA) was the result of patient advocacy and by many measures has been strikingly successful. However, approximately 95% of the more than 7,000 known rare diseases still have no US Food and Drug Administration-approved treatment. The ODA's success led to sustained criticism of high drug prices, often for products that have orphan drug indications. Critics misconstrue the ODA's intent and propose reducing its incentives instead of pursuing policies focused on addressing broader prescription drug price challenges that exist in both the orphan and nonorphan drug market. Patients and their families will continue to defend the purpose and integrity of the ODA and to drive investments into rare disease research and clinical development.

An aging-susceptible circadian rhythm controls cutaneous antiviral immunity.

Aged skin is prone to viral infections, but the mechanisms responsible for this immunosenescent immune risk are unclear. We observed that aged murine and human skin expressed reduced levels of antiviral proteins (AVPs) and circadian regulators, including Bmal1 and Clock. Bmal1 and Clock were found to control rhythmic AVP expression in skin, and such circadian control of AVPs was diminished by disruption of immune cell IL-27 signaling and deletion of Bmal1/Clock genes in mouse skin, as well as siRNA-mediated knockdown of CLOCK in human primary keratinocytes. We found that treatment with the circadian-enhancing agents nobiletin and SR8278 reduced infection of herpes simplex virus 1 in epidermal explants and human keratinocytes in a BMAL1/CLOCK-dependent manner. Circadian-enhancing treatment also reversed susceptibility of aging murine skin and human primary keratinocytes to viral infection. These findings reveal an evolutionarily conserved and age-sensitive circadian regulation of cutaneous antiviral immunity, underscoring circadian restoration as an antiviral strategy in aging populations.

Fully automated multi-grid cryoEM screening using Smart Leginon.

Single-particle cryo-electron microscopy (cryoEM) is a swiftly growing method for understanding protein structure. With increasing demand for high-throughput, high-resolution cryoEM services comes greater demand for rapid and automated cryoEM grid and sample screening. During screening, optimal grids and sample conditions are identified for subsequent high-resolution data collection. Screening is a major bottleneck for new cryoEM projects because grids must be optimized for several factors, including grid type, grid hole size, sample concentration, buffer conditions, ice thickness and particle behavior. Even for mature projects, multiple grids are commonly screened to select a subset for high-resolution data collection. Here, machine learning and novel purpose-built image-processing and microscope-handling algorithms are incorporated into the automated data-collection software Leginon, to provide an open-source solution for fully automated high-throughput grid screening. This new version, broadly called Smart Leginon, emulates the actions of an operator in identifying areas on the grid to explore as potentially useful for data collection. Smart Leginon Autoscreen sequentially loads and examines grids from an automated specimen-exchange system to provide completely unattended grid screening across a set of grids. Comparisons between a multi-grid autoscreen session and conventional manual screening by 5 expert microscope operators are presented. On average, Autoscreen reduces operator time from ∼6 h to <10 min and provides a percentage of suitable images for evaluation comparable to the best operator. The ability of Smart Leginon to target holes that are particularly difficult to identify is analyzed. Finally, the utility of Smart Leginon is illustrated with three real-world multi-grid user screening/collection sessions, demonstrating the efficiency and flexibility of the software package. The fully automated functionality of Smart Leginon significantly reduces the burden on operator screening time, improves the throughput of screening and recovers idle microscope time, thereby improving availability of cryoEM services.

Learning to automate cryo-electron microscopy data collection with Ptolemy.

Over the past decade, cryo-electron microscopy (cryoEM) has emerged as an important method for determining near-native, near-atomic resolution 3D structures of biological macromolecules. To meet the increasing demand for cryoEM, automated methods that improve throughput and efficiency of microscope operation are needed. Currently, the targeting algorithms provided by most data-collection software require time-consuming manual tuning of parameters for each grid, and, in some cases, operators must select targets completely manually. However, the development of fully automated targeting algorithms is non-trivial, because images often have low signal-to-noise ratios and optimal targeting strategies depend on a range of experimental parameters and macromolecule behaviors that vary between projects and collection sessions. To address this, Ptolemy provides a pipeline to automate low- and medium-magnification targeting using a suite of purpose-built computer vision and machine-learning algorithms, including mixture models, convolutional neural networks and U-Nets. Learned models in this pipeline are trained on a large set of images from real-world cryoEM data-collection sessions, labeled with locations selected by human operators. These models accurately detect and classify regions of interest in low- and medium-magnification images, and generalize to unseen sessions, as well as to images collected on different microscopes at another facility. This open-source, modular pipeline can be integrated with existing microscope control software to enable automation of cryoEM data collection and can serve as a foundation for future cryoEM automation software.

Smart data collection for CryoEM.

This report provides an overview of the discussions, presentations, and consensus thinking from the Workshop on Smart Data Collection for CryoEM held at the New York Structural Biology Center on April 6-7, 2022. The goal of the workshop was to address next generation data collection strategies that integrate machine learning and real-time processing into the workflow to reduce or eliminate the need for operator intervention.

Unsupervised Representation Learning for Proteochemometric Modeling.

In silico protein-ligand binding prediction is an ongoing area of research in computational chemistry and machine learning based drug discovery, as an accurate predictive model could greatly reduce the time and resources necessary for the detection and prioritization of possible drug candidates. Proteochemometric modeling (PCM) attempts to create an accurate model of the protein-ligand interaction space by combining explicit protein and ligand descriptors. This requires the creation of information-rich, uniform and computer interpretable representations of proteins and ligands. Previous studies in PCM modeling rely on pre-defined, handcrafted feature extraction methods, and many methods use protein descriptors that require alignment or are otherwise specific to a particular group of related proteins. However, recent advances in representation learning have shown that unsupervised machine learning can be used to generate embeddings that outperform complex, human-engineered representations. Several different embedding methods for proteins and molecules have been developed based on various language-modeling methods. Here, we demonstrate the utility of these unsupervised representations and compare three protein embeddings and two compound embeddings in a fair manner. We evaluate performance on various splits of a benchmark dataset, as well as on an internal dataset of protein-ligand binding activities and find that unsupervised-learned representations significantly outperform handcrafted representations.

The use of telemedicine in the care of the pediatric trauma patient.

Telemedicine is increasingly becoming an important part of the health care system as it has the potential to help deliver quality medical care to underserved areas. When implemented correctly, it can be a cost-effective way of expanding access to excellent medical care. However, because it is a relatively new and quickly changing field, there are multiple issues and challenges that need to be addressed. This article reviews the current literature on various modalities of telemedicine, evidence for teletrauma, and challenges and barriers related to telemedicine.

Consistent screening of admitted infants with head injuries reveals high rate of nonaccidental trauma.

PURPOSE: Implementation of a nonaccidental trauma (NAT) screening guideline for the evaluation of infants admitted with an unwitnessed head injury has eliminated screening disparities. This study sought to determine the overall NAT rate and key predictive factors using this guideline. METHODS: All infants screened via the guideline from 2008 to 2015 were retrospectively reviewed. The overall rate of NAT as determined by our child abuse team was determined. In addition, a logistic regression model was developed to evaluate potential predictors of increased risk of NAT. RESULTS: A total of 563 infants were screened with an overall rate of NAT of 25.6% (n=144). NAT screening was consistent across race and insurance status. By univariate analysis, patients with government insurance or no insurance had a significantly higher rate of NAT, but race was not a factor. Also NAT victims had significantly higher ISS. Skeletal survey showed high positive predictive value of 94%. When regression modeling was performed, ISS, abnormal skeletal survey and having public or no insurance were significantly correlated with NAT, while race showed no correlation. CONCLUSION: One quarter of infants admitted with a head injury not witnessed in a public situation were identified as the victims of NAT. The high rate of abuse among this population supports routine screening in order to avoid missing intentional injuries and preventing future injuries. Race is not a predictor of NAT, but insurance status, as a proxy for socioeconomic status, is correlated, and further investigation is needed. LEVEL OF EVIDENCE: III.

Nonaccidental Trauma in Pediatric Surgery.

This article presents an overview of nonaccidental trauma in children, including common clinical presentation, evaluation, and diagnosis.

Deletion of the glutamate receptor 5 subunit of kainate receptors affects the development of morphine tolerance.

Previous reports utilizing pharmacological antagonists implicate kainate receptor (KAR) activation in the development of morphine tolerance, dependence, conditioned place preference (CPP), and locomotor sensitization, but the role of glutamate receptor (GluR) 5-containing KAR in these effects remains unclear because of limited selectivity of the inhibitors employed. Therefore, we examined responses to systemic morphine treatment in mice expressing a constitutive deletion of GluR5 [GluR5 knockout (KO)]. Unlike wild-type (WT) littermates, GluR5 KO mice do not develop tolerance after repeated morphine administration by subcutaneous injection or via subcutaneous pellet implantation. In contrast, GluR5 KO mice do not differ from WT with respect to thermal or mechanical nociceptive thresholds, acute morphine antinociception, morphine disposition in the central nervous system (CNS), morphine physical dependence as revealed by naloxone-precipitated withdrawal or development of place preference and locomotor hyperresponsiveness after chronic morphine administration. It is surprising that continuous subcutaneous infusion of the GluR2/GluR5-preferring antagonist LY293558 [(3S,4aR,6R,8aR)-6-[2-(1(2)H-tetrazole-5-yl)ethyl]decahydroisoquinoline-3-carboxylic acid] decreased the number of naloxone-precipitated jumps to a similar extent in WT and GluR5 KO mice. We observed opioid-induced hypersensitivity in both groups during morphine withdrawal as demonstrated by equivalent reductions in thermal and mechanical thresholds; however, this hypersensitivity was not evident during continuous systemic morphine infusion. These data collectively indicate that KARs containing the GluR5 subunit contribute to the development of morphine tolerance without affecting nociceptive thresholds, morphine analgesia, or disposition in CNS of morphine and its metabolite morphine-3-glucuronide. In addition, constitutive deletion of GluR5 does not alter the morphine-induced increase in locomotor activity or the acquisition of morphine reward as measured by a CPP paradigm.