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INTRODUCTION: The COVID-19 pandemic has highlighted significant racial and age-related health disparities. In response to pandemic-related restrictions, orthopedic surgery departments have expanded telemedicine use. We analyzed data from a tertiary care institute during the pandemic to understand potential racial and age-based disparities in access to care and telemedicine utilization. MATERIALS AND METHODS: Data on patient race and age, and numbers of telemedicine visits, in-person office visits, and types of telemedicine were extracted for time periods during and preceding the pandemic. We calculated odds ratios for visit occurrence and type across race and age groups. RESULTS: Patients ages 27–54 were 1.3 (95% confidence interval [CI] 1.1–1.4, p < 0.01) and 1.2 (95% CI 1.0–1.3, p < 0.05) times more likely to be seen than patients

Across the Global South, wildlife is an important source of nutrition and income, particularly for rural communities. While wildlife consumption provides a valuable source of nutrition, high levels of hunting can endanger wildlife species. In the Makira region of north-eastern Madagascar, human health is threatened by food insecurity and malnutrition, and spiny tenrecs are a crucial source of subsistence bushmeat with 91% of households consuming tenrecs at least once per year. Moreover, Makira’s human populations are increasing, resulting in forest decline and degradation. In order to inform conservation and public health efforts, we investigated the influence of hunting, habitat loss and forest degradation on spiny tenrec population dynamics in the Makira region. We collected data on tenrec population densities as well as hunting and consumption by humans in 51 communities. We used these data to conduct a population viability analysis for the two most commonly hunted spiny tenrecs (Tenrec ecaudatus and Setifer setosus). We found that current harvest rates for these species are generally sustainable in the Makira region, largely due to tenrecs’ extraordinary fecundity. However, even assuming no change in current hunting rates, tenrec populations could become threatened if fecundity were to decline or background mortality to increase, both possible scenarios under future trajectories of habitat loss and degradation. We find that any future increase in hunting-induced mortality rates for tenrecs are similarly unsustainable – and likely to occur as human populations expand and hunting areas decline. We conclude that there is a vanishing window of opportunity to sustainably manage tenrec hunting in northeastern Madagascar in order to avoid negative conservation and human health outcomes.

A surprising feature of the SARS-CoV-2 pandemic to date is the low burdens reported in sub-Saharan Africa (SSA) countries relative to other global regions. Potential explanations (e.g., warmer environments1, younger populations2–4) have yet to be framed within a comprehensive analysis accounting for factors that may offset the effects of climate and demography. Here, we synthesize factors hypothesized to shape the pace of this pandemic and its burden as it moves across SSA, encompassing demographic, comorbidity, climatic, healthcare and intervention capacity, and human mobility dimensions of risk. We find large scale diversity in probable drivers, such that outcomes are likely to be highly variable among SSA countries. While simulation shows that extensive climatic variation among SSA population centers has little effect on early outbreak trajectories, heterogeneity in connectivity is likely to play a large role in shaping the pace of viral spread. The prolonged, asynchronous outbreaks expected in weakly connected settings may result in extended stress to health systems. In addition, the observed variability in comorbidities and access to care will likely modulate the severity of infection: We show that even small shifts in the infection fatality ratio towards younger ages, which are likely in high risk settings, can eliminate the protective effect of younger populations. We highlight countries with elevated risk of ‘slow pace’, high burden outbreaks. Empirical data on the spatial extent of outbreaks within SSA countries, their patterns in severity over age, and the relationship between epidemic pace and health system disruptions are urgently needed to guide efforts to mitigate the high burden scenarios explored here.

Hypoxemia is a significant driver of mortality and poor clinical outcomes in conditions such as brain injury and cardiac arrest in critically ill patients, including COVID-19 patients. Given the host of negative clinical outcomes attributed to hypoxemia, identifying patients likely to experience hypoxemia would offer valuable opportunities for early and thus more effective intervention. We present SWIFT (SpO2 Waveform ICU Forecasting Technique), a deep learning model that predicts blood oxygen saturation (SpO2) waveforms 5 and 30 minutes in the future using only prior SpO2 values as inputs. When tested on novel data, SWIFT predicts more than 80% and 60% of hypoxemic events in critically ill and COVID-19 patients, respectively. SWIFT also predicts SpO2 waveforms with average MSE below .0007. SWIFT predicts both occurrence and magnitude of potential hypoxemic events 30 minutes in the future, allowing it to be used to inform clinical interventions, patient triaging, and optimal resource allocation. SWIFT may be used in clinical decision support systems to inform the management of critically ill patients during the COVID-19 pandemic and beyond., Author summary Hypoxemia, or loss of blood oxygen saturation, is a dangerous condition that drives morbidity and mortality in critically ill patients, including COVID-19 patients and patients with brain injury or cardiac arrest. The ability to identify hypoxemia before it occurs would expand the possibilities for effective clinical interventions. To this end, we present SWIFT (SpO2 Waveform ICU Forecasting Technique), a deep learning model that can predict blood oxygen saturation 5 and 30 minutes in the future in critically ill patients. In testing, SWIFT identified more than 80% and 60% of hypoxemic events in critically ill and COVID-19 patients, respectively. SWIFT can predict both the occurrence and magnitude of hypoxemic events, which provides clinical information that can help prevent hypoxemia in critically ill patients. SWIFT can be used in clinical decision support systems to improve the management of patients at risk for hypoxemia during the COVID-19 pandemic and beyond.

Child physical abuse is a leading cause of traumatic injury and death in children. In 2017, child abuse was responsible for 1688 fatalities in the United States, of 3.5 million children referred to Child Protection Services and 674,000 substantiated victims. While large referral hospitals maintain teams trained in Child Abuse Pediatrics, smaller community hospitals often do not have such dedicated resources to evaluate patients for potential abuse. Moreover, identification of abuse has a low margin of error, as false positive identifications lead to unwarranted separations, while false negatives allow dangerous situations to continue. This context makes the consistent detection of and response to abuse difficult, particularly given subtle signs in young, non-verbal patients. Here, we describe the development of artificial intelligence algorithms that use unstructured free-text in the electronic medical record—including notes from physicians, nurses, and social workers—to identify children who are suspected victims of physical abuse. Importantly, only the notes from time of first encounter (e.g.: birth, routine visit, sickness) to the last record before child protection team involvement were used. This allowed us to develop an algorithm using only information available prior to referral to the specialized child protection team. The study was performed in a multi-center referral pediatric hospital on patients screened for abuse within five different locations between 2015 and 2019. Of 1123 patients, 867 records were available after data cleaning and processing, and 55% were abuse-positive as determined by a multi-disciplinary team of clinical professionals. These electronic medical records were encoded with three natural language processing (NLP) algorithms—Bag of Words (BOW), Word Embeddings (WE), and Rules-Based (RB)—and used to train multiple neural network architectures. The BOW and WE encodings utilize the full free-text, while RB selects crucial phrases as identified by physicians. The best architecture was selected by average classification accuracy for the best performing model from each train-test split of a cross-validation experiment. Natural language processing coupled with neural networks detected cases of likely child abuse using only information available to clinicians prior to child protection team referral with average accuracy of 0.90±0.02 and average area under the receiver operator characteristic curve (ROC-AUC) 0.93±0.02 for the best performing Bag of Words models. The best performing rules-based models achieved average accuracy of 0.77±0.04 and average ROC-AUC 0.81±0.05, while a Word Embeddings strategy was severely limited by lack of representative embeddings. Importantly, the best performing model had a false positive rate of 8%, as compared to rates of 20% or higher in previously reported studies. This artificial intelligence approach can help screen patients for whom an abuse concern exists and streamline the identification of patients who may benefit from referral to a child protection team. Furthermore, this approach could be applied to develop computer-aided-diagnosis platforms for the challenging and often intractable problem of reliably identifying pediatric patients suffering from physical abuse.

A surprising feature of the SARS-CoV-2 pandemic to date is the low burdens reported in sub-Saharan Africa (SSA) countries relative to other global regions. Potential explanations (e.g., warmer environments1, younger populations2–4) have yet to be framed within a comprehensive analysis accounting for factors that may offset the effects of climate and demography. Here, we synthesize factors hypothesized to shape the pace of this pandemic and its burden as it moves across SSA, encompassing demographic, comorbidity, climatic, healthcare and intervention capacity, and human mobility dimensions of risk. We find large scale diversity in probable drivers, such that outcomes are likely to be highly variable among SSA countries. While simulation shows that extensive climatic variation among SSA population centers has little effect on early outbreak trajectories, heterogeneity in connectivity is likely to play a large role in shaping the pace of viral spread. The prolonged, asynchronous outbreaks expected in weakly connected settings may result in extended stress to health systems. In addition, the observed variability in comorbidities and access to care will likely modulate the severity of infection: We show that even small shifts in the infection fatality ratio towards younger ages, which are likely in high risk settings, can eliminate the protective effect of younger populations. We highlight countries with elevated risk of ‘slow pace’, high burden outbreaks. Empirical data on the spatial extent of outbreaks within SSA countries, their patterns in severity over age, and the relationship between epidemic pace and health system disruptions are urgently needed to guide efforts to mitigate the high burden scenarios explored here.

Self-assembly is a prominent phenomenon observed in nature. Inspired by this thermodynamically favorable approach, several natural and synthetic materials have been investigated to develop functional systems for various biomedical applications, including drug delivery. Furthermore, layered self-assembled systems provide added advantages of tunability and multifunctionality which are crucial for controlled and targeted drug release. Layer-by-layer (LbL) deposition has emerged as one of the most popular, well-established techniques for tailoring such layered self-assemblies. This review aims to provide a brief overview of drug delivery applications using LbL deposition, along with a discussion of associated scalability challenges, technological innovations to overcome them, and prospects for commercial translation of this versatile technique. Additionally, alternative self-assembly techniques such as metal-phenolic networks (MPNs) and Liesegang rings are also reviewed in the context of their recent utilization for controlled drug delivery. Blending the sophistication of these self-assembly phenomena with material science and technological advances can provide a powerful tool to develop smart drug carriers in a scalable manner.

Background— The accumulation of macrophages in inflamed atherosclerotic plaques has long been recognized. In an attempt to develop an imaging agent for detection of vulnerable plaques, we evaluated the feasibility of a liposomal-iodine nanoparticle contrast agent for computed tomography imaging of macrophage-rich atherosclerotic plaques in a mouse model. Methods and Results— Liposomal-iodine formulations varying in particle size and polyethylene glycol coating were fabricated and shown to stably encapsulate the iodine compound. In vitro uptake studies using optical and computed tomography imaging in the RAW 264.7 macrophage cell line identified the formulation that promoted maximal uptake. Dual-energy computed tomography imaging using this formulation in apolipoprotein E–deficient (ApoE −/− ) mice (n=8) and control C57BL/6 mice (n=6) followed by spectral decomposition of the dual-energy images enabled imaging of the liposomes localized in the plaque. Imaging cytometry confirmed the presence of liposomes in the plaque and their colocalization with a small fraction (≈2%) of the macrophages in the plaque. Conclusions— The results demonstrate the feasibility of imaging macrophage-rich atherosclerotic plaques using a liposomal-iodine nanoparticle contrast agent and dual-energy computed tomography.

Patients with advanced head and neck squamous cell carcinoma receiving chemotherapy have a poor prognosis partly due to normal tissue toxicity; therefore, development of a tumor-targeted drug delivery platform to minimize collateral toxicity is a goal of cancer nanomedicine. Aptamers can achieve this purpose. While conventional Systematic Evolution of Ligands by Exponential Enrichment (SELEX) screens aptamer-only libraries and conjugates them to delivery vehicles after selection, we hypothesized that specific delivery requires screening libraries with aptamer-nanoparticle conjugates. We designed a procedure called, “Conjugate-SELEX”, where liposomal nanoparticles (LNP) conjugated with aptamers is screened to identify aptamers that carried attached LNPs to the human head and neck squamous cell carcinoma cell cytosol. Aptamer-LNPs were simultaneously selected for a low affinity to human hepatocytes, minimizing hepatoxicity and LNP clearance. Post-SELEX Next Generation sequencing demonstrated convergence to a family of sequences with one base difference. Affinity pulldown and proteomics analysis identified the uptake-mediating surface receptor as the neuroblast differentiation-associated protein AHNAK (Desmoyokin), a ubiquitous intracellular protein expressed in certain epithelial cell types. Uptake studies with the lead aptamer-conjugates showed enhanced uptake and increased cytotoxicity induced by doxorubicin in cells treated with aptamer-conjugated LNPs over LNP controls. Conjugate-SELEX identifies aptamers capable of targeted cytosolic delivery of attached LNPs payload, while minimizing off-target delivery. The technique lends itself to identification of uptake-mediating surface receptors.

Introduction: Delivery of a nanoparticle payload to a specific cellular compartment is a prized goal of cancer nanomedicine. The well-known SELEX (systematic evolution of ligands by exponential enrichment) process is often used to identify aptamers that localize to target cellular compartments, but such aptamers cannot be guaranteed to traffic an attached payload nanoparticle to the target as well. We hypothesized that to find an aptamer capable of trafficking a nanoparticle to a specific cellular compartment, the screened library must consist of aptamer-nanoparticle conjugates. We therefore designed a screening procedure we call “Conjugate-SELEX”. In this process, the aptamer library is conjugated to liposomal nanoparticles, creating a nanoparticle-aptamer conjugate library for screening. At each round of Conjugate-SELEX, aptamers recovered from the desired cellular compartment are amplified, and used to create the conjugate library for the next round of Conjugate-SELEX. Negative screens against off-target cells (e.g. hepatocytes, unwanted uptake in which, causes many of the toxicities associated with nanoparticle therapeutics) facilitate the identification of an aptamer that efficiently transports payload into target cells while minimizing the uptake in off-target cells. Using fluorescently tagged particles, imaging the cells at each stage verifies the successful transport of the payload particles into the cytosol. Methods: We used conjugate-SELEX to screen for aptamers that carried an attached nanoparticle payload to the cytosol of UM-SCC-22A oral cancer cell line, while not being taken up by THLE-3 hepatocytes. IonTorrent sequencing of the remaining aptamers after each round demonstrated convergence to a small family of sequences with only one base difference between members. Mass spectrometric proteomics identified the surface receptor bound by these sequences as the neuroblast differentiation-associated protein AHNAK (desmoyokin) a ubiquitous intracellular protein with surface expression exclusively in certain epithelial cell types. Uptake studies with concertedly synthesized hit aptamer sequences showed enhanced uptake of targeted nanoparticles over controls, and increased cytotoxicity of doxorubicin in the nanoparticles over controls. Conclusions: Conjugate-SELEX is an excellent means of identifying aptamers with the ability to transport attached nanoparticle payloads to targeted cellular compartments, while minimizing off target effects. The technique also lends itself to the identification of surface receptors mediating the trafficking.[D.G., A.A., and N.V. contributed equally to this work.] Citation Format: Qingshan Mu, Akshaya Annapragada, Mayank Srivastava, Varatharasa Thiviyanathan, Xin Li, David Gorenstein, Annanth Annapragada, Nadarajah Vigneswaran. Conjugate-SELEX, a novel screening method, identifies aptamers that deliver payload to the cytosol of target cells. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3913.