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Young age has consistently correlated with lower adherence to pre-exposure prophylaxis (PrEP) in young men who have sex with men (YMSM). Digital medicine, a dynamic healthcare platform of wearable physiological sensors and mobile communication technology that can respond to medication nonadherence rapidly, has the potential in promoting PrEP adherence. We evaluated the feasibility and acceptability of Proteus Discover, a digital monitoring adherence system, to measure PrEP adherence and provide real-time feedback among cisgender YMSM and transgender women. One hundred HIV-negative young men and transgender women ages 16-24 years were enrolled in a 24-week randomized controlled crossover study to tenofovir disoproxil fumarate with emtricitabine (TDF/FTC) coencapsulated with Proteus Discover versus TDF/FTC standard-of-care. Participants in the 12-week Proteus Discover arm received weekly SMS text messages to promote pill taking based on Proteus Discover adherence data. Dried blood spots (DBS) were collected at 4-week intervals for tenofovir diphosphate (TFV-DP) in red blood cells as the referent and questionnaires were completed to assess acceptability, usability, and patterns of use. Linear mixed models analyzed the relationship between 30-day adherence measured by DBS and Proteus Discover. PrEP adherence was high overall. Adherence, as measured by DBS, was correlated with adherence as measured by Proteus Discover (p value = 0.03). Most participants reported that Proteus Discover helped them take their PrEP daily and that the system was easy to use. However, a majority (53.5%-60.5%) disagreed with the statement that wearing the patch was not an issue. There was an incremental increase in TFV-DP in DBS with adherence by Proteus Discover. More research is warranted to explore optimizing PrEP adherence for youth through real-time monitoring.

Brain ventricular and subcortical structures are heritable both in size and shape. Genetic influences on brain region size have been studied using conventional volumetric measures, but little is known about the genetic basis of ventricular and subcortical shapes. Here we developed pipelines to extract seven complementary shape measures for lateral ventricles, subcortical structures, and hippocampal subfields. Based on over 45,000 subjects in the UK Biobank and ABCD studies, 60 genetic loci were identified to be associated with brain shape features (P < 1.09 × 10-10), 19 of which were not detectable by volumetric measures of these brain structures. Ventricular and subcortical shape features were genetically related to cognitive functions, mental health traits, and multiple brain disorders, such as the attention-deficit/hyperactivity disorder. Vertex-based shape analysis was performed to precisely localize the brain regions with these shared genetic influences. Mendelian randomization suggests brain shape causally contributes to neurological and neuropsychiatric disorders, including Alzheimer’s disease and schizophrenia. Our results uncover the genetic architecture of brain shape for ventricular and subcortical structures and prioritize the genetic factors underlying disease-related shape variations.

Although pulsed dye laser (PDL) remains the gold standard for the treatment of port-wine stains (PWS), hematoporphyrin monomethyl ether photodynamic therapy (HMME-PDT) is another treatment modality that has been shown to be effective in the treatment of PWS. This study aimed to observe the clinical efficacy and therapeutic response of HMME-PDT in the treatment of pediatric Chinese patients with PWS and to analyze the association between the efficacy of therapy and the dermoscopic features of PWS.Pediatric patients with PWS and negative HMME skin test were enrolled between December 2017 and May 2021. Patients received an intravenous injection of 5 mg/kg HMME, and lesions were irradiated with 532-nm LED green light with a power density of 70-80 mW/cmA total of 216 pediatric patients (1-14 years) were recruited. Sixty-six patients had the pink type, while 150 had the purple type. After two HMME-PDT sessions, 55 patients showed excellent efficacy (25.46%), 77 patients showed good efficacy (35.65%), 69 patients showed fair efficacy (31.94%), and 15 patients showed no improvement (6.95%). Dotted and globular vessels were highly associated with excellent efficacy (41.82%); linear vessels were mainly associated with good efficacy (54.55%); reticular vessels were mainly associated with fair (55.07%) and mixed vessels were mainly associated with no improvement (26.66%).HMME-PDT is an effective and safe treatment for pediatric patients with PWS. Dotted and globular vessels as well as linear vessels showed better efficacy compared to the other dermoscopic patterns in patients with PWS. Dermoscopy can provide useful clinical information about treatment outcomes.

Alzheimer's disease (AD) is the common neurodegenerative disease in the center never system and the typical dementia in old people. The major pathological changes of AD are the accumulation of amyloid-β (Aβ) plaques, neurofibrillary tangles, loss of cholinergic neurons, inflammation and metabolism dysfunction. However, the molecular mechanism leading to AD pathogenesis is not clear. More and more studies reported that long non-coding RNAs (lncRNAs) play important roles in AD. In this review, we briefly introduce the recent research progress on lncRNAs in AD, including their regulation of clearance of the Aβ plaques, synaptic function, inflammation reaction and mitochondrial function, and thus providing the references for that lncRNAs can serve as a potential diagnostic biomarker and therapeutic target in AD.阿尔茨海默病(Alzheimer's disease, AD)是常见的神经退行性疾病,也是最为常见的一类老年痴呆类型。AD主要的病理变化包括淀粉样蛋白(amyloid-β, Aβ)沉积、tau蛋白过度磷酸化、胆碱能神经元的缺失、神经炎症以及代谢障碍等。然而,有关AD具体的发病机制和分子谱尚不清楚,这给AD的诊断和治疗带来很大的挑战。越来越多的研究发现,长链非编码RNA (long non-coding RNAs, lncRNAs)在AD发病机制中发挥着重要作用。本文就lncRNAs在AD的研究进展,从lncRNAs通过影响Aβ聚积、突触功能、炎症反应和线粒体功能等方面参与AD的发病来展开综述介绍,为AD早期诊断的生物标志物和药物治疗靶点提供新思路。.

This study reports the results of focus groups with school nurses and teachers from elementary, middle, and high schools to explore their perceptions of child and adolescent oral health. Participants included 14 school nurses and 15 teachers (83% female; 31% Hispanic; 21% White; 21% Asian; 14% African American; and 13% Others). Respondents were recruited from Los Angeles County schools and scheduled by school level for six one-hour focus groups using Zoom. Audio recordings were transcribed, reviewed, and saved with anonymization of speaker identities. NVivo software (QSR International, Melbourne, Australia) was used to facilitate content analysis and identify key themes. The nurses’ rate of “Oral Health Education” comments statistically exceeded that of teachers, while teachers had higher rates for “Parental Involvement” and “Mutual Perception” comments. “Need for Care” was perceived to be more prevalent in immigrants to the United States based on student behaviors and complaints. “Access to Care” was seen as primarily the nurses’ responsibilities. Strong relationships between community clinics and schools were viewed by some as integral to students achieving good oral health. The results suggest dimensions and questions important to item development for oral health surveys of children and parents to address screening, management, program assessment, and policy planning.

Objective To provide a basis for further optimizing the diagnosis and treatment strategies of severe and critical corona virus disease 2019 (COVID-19) by investigating and analyzing the epidemiological and clinical characteristics of the death cases. Methods The epidemiological and clinical characteristics of 47 death cases obtained from Huoshenshan Hospital in Whuhan, Hubei Province were retrospectively analyzed. Results All the patients developed initial symptoms in Wuhan. The time from onset to admission was (12.60±5.60) days. Most of them were male (68.09%) with non-nosocomial infection (91.49%), advanced age (>60 years, 89.36%). Over half of the cases (51.06%) reported a history of contact with suspected or confirmed patients, and comorbidity of chronic diseases (70.21%). Multiple organ dysfunction syndrome (MODS) occurred in 29 cases (61.70%) with heart failure (51.06%) and renal failure (36.17%). The main clinical symptoms included fever, fatigue, dyspnea and cough. At admission,most cases were severe (55.32%) or critical (42.55%), and the in-hospital survival was longer for the severe than for the critical (P=0.02). 76.59% of the patients received invasive mechanical ventilation, and they had a longer in-hospital survival than those with non-invasive mechanical ventilation (P

We investigated the use of a system with an ingestible sensor (Proteus Digital Health Feedback system) coencapsulated with antiretrovirals (ARVs) to measure real-time adherence. To assess the safety and impact, if any, coencapsulation might have on ARV concentrations, we evaluated the pharmacokinetics of ARVs coencapsulated with an ingestible sensor for eight commonly used fixed-dose combination ARVs: emtricitabine (FTC)/tenofovir disoproxil fumarate (TDF); FTC/tenofovir alafenamide (TAF); efavirenz (EFV)/FTC/TDF; abacavir (ABC)/lamivudine (3TC); dolutegravir (DTG)/ABC/3TC; rilpivirine (RPV)/TAF/FTC; elvitegravir (EVG)/cobicistat (COBI)/FTC/TAF; and bictegravir (BIC)/FTC/TAF. The steady-state apparent peak plasma concentration (Cmax) and area under the concentration-time curve (AUC) were determined from plasma concentrations measured at predose, 1, 2, 4, and 6 h postdose, and compared with literature values. A total of 49 unique patients on stable regimens for at least 12 weeks with undetectable viral loads were recruited. Cmax and AUC values were not statistically significantly different from literature values for all of the formulations except the Cmax of FTC/TDF, Cmax of BIC, and the Cmax of RPV. In a subsequent evaluation of FTC/TDF and BIC/FTC/TAF using a crossover design, the geometric mean ratio (GMR) between the coencapsulated and the unencapsulated formulations for FTC/TDF were the following: FTC, 84.6% (90% confidence interval [CI] 66.6–107.4) for AUC and 77.5% (60.1–99.9) for Cmax. For tenofovir (TFV), the GMR was 96.2% (90% CI 89.2–103.8) for AUC and 87.3% (64.2–118.7) for Cmax. The GMR for BIC (from the BIC/FTC/TAF formulation) was 98.0% (90% CI 84.5–113.5) for AUC and 89.9% (84.5–95.7) for Cmax. The observed deviation in FTC/TDF (Truvada) may be due to participant characteristics, fasted/fed conditions, and/or random variation and may warrant further investigations with a larger sample size. These findings provide assurance for use of coencapsulated ARVs for future HIV treatment-adherence research.

Functional magnetic resonance imaging (fMRI) has been widely used to identify brain regions linked to critical functions, such as language and vision, and to detect tumors, strokes, brain injuries, and diseases. It is now known that large sample sizes are necessary for fMRI studies to detect small effect sizes and produce reproducible results. Here we report a systematic association analysis of 647 traits with imaging features extracted from resting-state and task-evoked fMRI data of more than 40,000 UK Biobank participants. We used a parcellation-based approach to generate 64,620 functional connectivity measures to reveal fine-grained details about cerebral cortex functional architectures. The difference between functional organizations at rest and during task has been quantified, and we have prioritized important brain regions and networks associated with a variety of human traits and clinical outcomes. For example, depression was most strongly associated with decreased connectivity in the somatomotor network. We have made our results publicly available and developed a browser framework to facilitate exploration of brain function-trait association results (http://165.227.92.206/).

The present study aimed to compare the therapeutic effect of sodium/glucose cotransporter 2 (SGLT2) inhibitor and benazepril on diabetic nephropathy (DN) rats and provide a potential novel agent for the clinical treatment of DN. The DN model was established on rats. Animals were dosed orally with SGLT2 and benazepril daily for 4 weeks. The pathological state of renal tissues were evaluated using hematoxylin and eosin, Masson and periodic acid-Schiff staining. The change in the morphology of renal tissues was observed through transmission electron microscopy. Western blotting was utilized to determine the expression level of TGF-β, N-terminal fragment of the B-type natriuretic peptide precursor (NT-proBNP) and matrix metalloproteinase-9 (MMP-9). The expression level of endothelin 1 (ET-1), von Willebrand factor (vWF), collagen (col)-I and α smooth muscle actin (α-SMA) in renal tissues was visualized using immunohistochemical assay. Significant pathological changes in the glomerular basement membrane, mesangial membrane, renal tubules, lumen, renal interstitial region and renal tubular epithelial cells were observed in DN rats, accompanied by increased collagen fibers. SGLT2 inhibitor treatment demonstrated more alleviatory effects on the pathological changes of renal tissues compared with benazepril. Compared with control, TGF-β and NT-proBNP were upregulated in DN rats, accompanied by the downregulation of MMP-9, ET-1, vWF, col-I and α-SMA, which were markedly reversed by treatment with SGLT2 inhibitor and benazepril. Compared with benazepril, the effects of SGLT2 inhibitor on the expression level of TGF-β, NT-proBNP, MMP-9, ET-1, vWF, col-I and α-SMA were more significant. Overall, SGLT2 inhibitor demonstrated an increased therapeutic effect against DN rats compared with benazepril by regulating cytokines, renal fibrosis and extracellular matrix degradation.

Hematoporphyrin monomethyl ether-photodynamic therapy (HMME-PDT) has been showing promising results in the treatment of port-wine stains (PWSs). We evaluated the clinical efficacy and treatment response of HMME-PDT in adult Chinese patients with PWSs.A single-center retrospective study recruited adult PWS patients with negative HMME skin test results from December 2017 to May 2020. Patients received an intravenous injection of 5 mg/kg HMME and the lesions were exposed to 532 nm LED green light with an irradiation power density of 85-95 mW/cmA total of 72 patients aged between 18 and 55 years were recruited. There were 65 patients of the flat purple type, 5 of the hypertrophic type, and 2 of the nodular thickening type. Of the 65 patients, 7 showed excellent efficacy (10.77%), 13 patients indicated good efficacy (20.00%), 47 patients showed fair efficacy (64.62%), while 3 cases displayed no improvement (4.62%). All five patients of the purple and hypertrophic type showed fair efficacy (100%), and no improvement was observed in patients of the nodular thickening type (100%). Pain, pruritus, and a burning sensation were observed during treatment. Edema was noted on the treated areas post-treatment. No other obvious systemic adverse reactions were observed.HMME-PDT is an effective and safe treatment for adult patients with purple PWSs. Multiple HMME-PDT treatments can improve the response and cure rate.

Cardiovascular health interacts with cognitive and psychological health in complex ways. Yet, little is known about the phenotypic and genetic links of heart-brain systems. Using cardiac and brain magnetic resonance imaging (CMR and brain MRI) data from over 40,000 UK Biobank subjects, we developed detailed analyses of the structural and functional connections between the heart and the brain. CMR measures of the cardiovascular system were strongly correlated with brain basic morphometry, structural connectivity, and functional connectivity after controlling for body size and body mass index. The effects of cardiovascular risk factors on the brain were partially mediated by cardiac structures and functions. Using 82 CMR traits, genome-wide association study identified 80 CMR-associated genomic loci (P < 6.09 × 10-10), which were colocalized with a wide spectrum of heart and brain diseases. Genetic correlations were observed between CMR traits and brain-related complex traits and disorders, including schizophrenia, bipolar disorder, anorexia nervosa, stroke, cognitive function, and neuroticism. Our results reveal a strong heart-brain connection and the shared genetic influences at play, advancing a multi-organ perspective on human health and clinical outcomes.

Background: Dental caries is the most common chronic childhood infectious disease and is a serious public health problem affecting both developing and industrialized countries, yet it is preventable in most cases. This study evaluated the potential of screening for dental caries among children using a machine learning algorithm applied to parent perceptions of their child’s oral health assessed by survey. Methods: The sample consisted of 182 parents/caregivers and their children 2–7 years of age living in Los Angeles County. Random forest (a machine learning algorithm) was used to identify survey items that were predictors of active caries and caries experience. We applied a three-fold cross-validation method. A threshold was determined by maximizing the sum of sensitivity and specificity conditional on the sensitivity of at least 70%. The importance of survey items to classifying active caries and caries experience was measured using mean decreased Gini (MDG) and mean decreased accuracy (MDA) coefficients. Results: Survey items that were strong predictors of active caries included parent’s age (MDG = 0.84; MDA = 1.97), unmet needs (MDG = 0.71; MDA = 2.06) and the child being African American (MDG = 0.38; MDA = 1.92). Survey items that were strong predictors of caries experience included parent’s age (MDG = 2.97; MDA = 4.74), child had an oral health problem in the past 12 months (MDG = 2.20; MDA = 4.04) and child had a tooth that hurt (MDG = 1.65; MDA = 3.84). Conclusion: Our findings demonstrate the potential of screening for active caries and caries experience among children using surveys answered by their parents.

Near-range millimeter wave imaging techniques for persons have been widely used in public security field. To improve the throughput and security as well, a suspicious object detection method is designed for the Walk Through System we built. Simple Online and Realtime Tracking algorithm is applied based on the detection result of YOLOv5 in in each frame. The method can effectively detect objects at a video rate of 10Hz.

The human cerebral cortex plays a crucial role in brain functions. However, genetic influences on the human cortical functional organizations are not well understood. Using a parcellation-based approach with resting-state and task-evoked functional magnetic resonance imaging (fMRI) from 40,253 individuals, we identified 47 loci associated with functional areas and networks at rest, 15 of which also affected the functional connectivity during task performance. Heritability and locus-specific genetic effects patterns were observed across different brain functional areas and networks. Specific functional areas and networks were identified to share genetic influences with cognition, mental health, and major brain disorders (such as Alzheimer’s disease and schizophrenia). For example, in both resting and task fMRI, the APOE ε4 locus strongly associated with Alzheimer’s disease was particularly associated with the visual cortex in the secondary visual and default mode networks. In summary, by analyzing biobank-scale fMRI data in high-resolution brain parcellation, this study advances our understanding of the genetic determinants of cerebral cortex function and prioritizes genetically associated fMRI traits for clinical applications of specific brain disorders.

Brain regions communicate with each other via tracts of myelinated axons, commonly referred to as white matter. White matter microstructure can be measured in the living human brain using diffusion based magnetic resonance imaging (dMRI), and has been found to be altered in patients with neuropsychiatric disorders. Although under strong genetic control, few genetic variants influencing white matter microstructure have ever been identified. Here we identified common genetic variants influencing white matter microstructure using dMRI in 42,919 individuals (35,741 in the UK Biobank). The dMRIs were summarized into 215 white matter microstructure traits, including 105 measures from tract-specific functional principal component analysis. Genome-wide association analysis identified many novel white matter microstructure associated loci (P< 2.3 × 10−10). We identified shared genetic influences through genetic correlations between white matter tracts and 62 other complex traits, including stroke, neuropsychiatric disorders (e.g., ADHD, bipolar disorder, major depressive disorder, schizophrenia), cognition, neuroticism, chronotype, as well as non-brain traits. Common variants associated with white matter microstructure alter the function of regulatory elements in glial cells, particularly oligodendrocytes. White matter associated genes were enriched in pathways involved in brain disease pathogenesis, neurodevelopment process, and repair of white matter damage (P< 1.5 × 10−8). In summary, this large-scale tract-specific study provides a big step forward in understanding the genetic architecture of white matter and its genetic links to a wide spectrum of clinical outcomes.

Objectives: Evaluating children’s oral health status and treatment needs is challenging. We aim to build oral health assessment toolkits to predict Children’s Oral Health Status Index (COHSI) score and referral for treatment needs (RFTN) of oral health. Parent and Child toolkits consist of short-form survey items (12 for children and 8 for parents) with and without children’s demographic information (7 questions) to predict the child’s oral health status and need for treatment. Methods: Data were collected from 12 dental practices in Los Angeles County from 2015 to 2016. We predicted COHSI score and RFTN using random Bootstrap samples with manually introduced Gaussian noise together with machine learning algorithms, such as Extreme Gradient Boosting and Naive Bayesian algorithms (using R). The toolkits predicted the probability of treatment needs and the COHSI score with percentile (ranking). The performance of the toolkits was evaluated internally and externally by residual mean square error (RMSE), correlation, sensitivity and specificity. Results: The toolkits were developed based on survey responses from 545 families with children aged 2 to 17 y. The sensitivity and specificity for predicting RFTN were 93% and 49% respectively with the external data. The correlation(s) between predicted and clinically determined COHSI was 0.88 (and 0.91 for its percentile). The RMSEs of the COHSI toolkit were 4.2 for COHSI (and 1.3 for its percentile). Conclusions: Survey responses from children and their parents/guardians are predictive for clinical outcomes. The toolkits can be used by oral health programs at baseline among school populations. The toolkits can also be used to quantify differences between pre- and post-dental care program implementation. The toolkits’ predicted oral health scores can be used to stratify samples in oral health research. Knowledge Transfer Statement: This study creates the oral health toolkits that combine self- and proxy- reported short forms with children’s demographic characteristics to predict children’s oral health and treatment needs using Machine Learning algorithms. The toolkits can be used by oral health programs at baseline among school populations to quantify differences between pre and post dental care program implementation. The toolkits can also be used to stratify samples according to the treatment needs and oral health status.

Author(s): Marcus, Marvin; Xiong, Di; Wang, Yan; Maida, Carl A; Hays, Ron D; Coulter, Ian D; Spolsky, Vladimir W; Lee, Steve Y; Shen, Jie; Crall, James J; Liu, Honghu | Abstract: ObjectivesTo develop child- and parent-reported toolkits for active caries and caries experience in children and adolescents, ages 8-17.MethodsA sample of 398 child/parent dyads recruited from 12 dental practices in Los Angeles County completed a computer-assisted survey that assessed oral health perceptions. In addition, children received a dental examination that identified the presence or absence of active caries and caries experience. A Multiple Adaptive Regression Splines model was used to identify a subset of survey items associated with active caries and caries experience. The splines and coefficients were refined by generalized cross-validation. Sensitivity and specificity for both dependent variables were evaluated.ResultsEleven child self-reported items were identified that had sensitivity of 0.82 and specificity of 0.45 relative to active caries. Twelve parent-reported items had a sensitivity of 0.86 and specificity of 0.50. Seven child self-reported items had a sensitivity of 0.86 and specificity of 0.34, and 11 parent-reported items had a sensitivity of 0.86 and specificity of 0.47 for caries experience.ConclusionsThe survey items identified here are useful in distinguishing children with and without active caries and with and without caries experience. This research presents a path towards using children's and their parents' reports about oral health to screen for clinically determined caries and caries exposure. The items identified in this study can be useful when clinical information is unavailable.

In this paper, the spatial-variant property of inverse synthetic aperture radar (ISAR) imaging plane of space targets is exploited for phase unwrapping in bistatic interferometric ISAR (InISAR) imaging of space targets. We utilize the interferometric phases in different orbit arcs to obtain the correct fuzzy factor in a calculated set. The threshold for incorrect fuzzy factor excluding and the orbit arc selection strategy are derived for efficiency. Numerical simulations illustrate the effectiveness of the proposed method.

Multiband fusion imaging can effectively improve the range resolution of inverse synthetic aperture radar (ISAR) imaging. In this study, the block sparse Bayesian learning (BSBL) method is applied to multiband fusion imaging to achieve high-resolution ISAR imaging of a block-structured target. The BSBL method is suitable for the ISAR imaging of numerous and continuous scatterers because it considers the block structure characteristics of the signal. The validity of the proposed method is verified by the simulation and real-data experimental results.

Phase-coherence of signals in different sub-bands is a prerequisite for multiband fusion radar imaging. Here, a mutual-coherence compensation method based on valid dominant poles and phase autofocusing is proposed to compensate for the incoherent phases (ICPs) between sub-bands in multiband fusion inverse synthetic aperture radar (ISAR) imaging. In the proposed method, the linear ICP compensation and fixed ICP compensation are decoupled rather than the ICPs being compensated pulse by pulse as in traditional methods. In this method, the valid dominant poles that correspond to strong scatterers in the estimated poles of each sub-band are first extracted to estimate the linear ICP. Then, phase autofocusing technology based on the same rotation centre is applied to multiple pulses in each sub-band in the ISAR coherently processing time to jointly compensate for all the fixed ICPs at once. Furthermore, a unitary estimation of signal parameters via the rotational invariance techniques (U-ESPRIT) method, which can exploit considerable information from the measured data, is utilised to estimate the poles of the sub-bands to improve the accuracy of the estimation of the linear ICP between sub-bands. The effectiveness and robustness of the method were verified experimentally based on both numerical simulations and real data.

High micellar stability and well-performed drug loading and release are two conflicting factors for unimolecular micelles as an ideal drug delivery system. Achieving the formation of unimolecular micelles with short hydrophilic blocks is a challenging and promising approach to solve this bottleneck and limitation of current unimolecular micelle systems. In this work, dissipative particle dynamics (DPD) simulation is used to study the synergetic effect of crosslinking and electrostatic repulsion on stability of unimolecular micelles and to analyze the micro-mechanism and factors influencing this synergetic stabilization strategy. The strategy can generate unimolecular micelles with extremely high stability for various supramolecular polymers with short hydrophilic chains. Protonation of DEAEMA blocks leads to a large improvement in micellar hydrophilicity. The protonated middle layer further shrinks through crosslinking to produce the largest charge density, enlarging the electrostatic repulsion between colloidal particles. Additionally, the crosslinking and protonation treatment maximizes the extension degree of hydrophilic EO segments due to the increasing steric hindrance and poor compatibility between DEAHEMA and EO blocks. In this study, the relation between shrinkage degree of hydrophobic cores and stability of unimolecular micelles is first reported. The above-mentioned transition of micellar structures and properties results in the maximum degree of core shrinkage (Rg of MMA blocks) corresponding to the high stability of unimolecular micelles. Further study shows that the increasing cyclization degree, the mode of end cyclization, and the crosslinking and electrostatic repulsion of the middle layer all exert favorable effects on the stability of unimolecular micelles due to controlled shrinkage of hydrophobic cores.

Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian time series model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the log transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectories. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for cases and deaths in U.S. states. We evaluated the model by training it on progressively longer periods of the pandemic and computing its predictive accuracy over 21-day forecasts. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Colorado, and West Virginia. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course., Author summary COVID-19 models can be roughly classified as mathematical models that simulate disease within a population, including epidemiological compartmental models, or statistical curve-fitting models that fit a function to observed data and extrapolate forward into the future. Bridging this divide, we combine the strengths of curve-fitting statistical models and the structure of epidemiological models, by embedding a Bayesian velocity model and a machine learning algorithm (random forest) into the framework of a compartmental model. Fusing these models together exploits the particular strengths of each to glean as much information as possible from the currently available data. We identify the velocity of log cumulative cases as an excellent target for modeling and extrapolating COVID-19 case trajectories. We empirically evaluate the predictive performance of the model and provide predicted trajectories with credible intervals for cumulative confirmed case count, active confirmed infections and COVID-19 deaths for each of the 50 U.S. states. Combining sophisticated data analytic methods with proven epidemiological models offers an empirically grounded strategy for making realistic predictions and quantifying their uncertainty. These predictions indicate substantial variation in the COVID-19 trajectories of U.S. states.