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In December 2020, the Centers for Disease Control and Prevention updated its treatment guidelines for gonococcal infection and, for the first time, recommended universal test-of-cure for all individuals treated for pharyngeal gonorrhea. After the release of these guidelines, data are lacking on rates of return for the test-of-cure, particularly in populations other than men who have sex with men.We analyzed the demographic characteristics, clinical characteristics, rate of return for the recommended test-of-cure, and percent positivity for Neisseria gonorrhoeae on repeat pharyngeal specimens at a local public health department in Durham, NC.Of 101 individuals treated for pharyngeal gonorrhea between March 2021 and April 2022, 54.5% were men, 71.2% Black or African American, and 58.4% between the ages of 20 and 29 years. Most identified as either women who have sex with men (38.6%), men who have sex with men (24.8%), or men who have sex with women (22.8%). Of these individuals, 41 (40.6%) returned for a test-of-cure, with LGBTQ+ individuals more likely to return than men who have sex with women and women who have sex with men. Of those who returned for the test-of-cure, 4.9% of pharyngeal samples were equivocal and 2.4% positive for N. gonorrhoeae by nucleic acid amplification testing, likely reflecting false-positive tests.Despite recommendations to perform a test-of-cure 7 to 14 days after treatment of pharyngeal gonorrhea, rates of return continue to be low. Alternative strategies should be investigated to increase test-of-cure rates.

Maternal immune dysregulation, caused by gestational psychological stress, infection, and other perturbations, results in altered offspring neurodevelopment and increases risk for psychiatric disorders. Prior work has found that multiple cytokines play critical roles in shaping offspring neurodevelopment after gestational stress, though how maternal psychological stress impacts maternal, placental, and fetal cytokine levels more broadly remains unclear. The purpose of the present study was to assess changes to IL-1β, IL-2, IL-4, IL-6, IL-10, IL-17A, IFNγ, and TNFα in a widely-used mouse prenatal restraint stress model. After repetitive restraint stress on gestational days 12-14, stressed dams had increased serum levels of IL-1β, IL-6, and IL-10. Embryonic day 14 IL-2 and IL-1β levels were decreased in prenatally stressed male fetal forebrain, while placental IL-2 was decreased by stress regardless of offspring sex. Placental and fetal forebrain IL-2 levels were negatively correlated. These data provide important insights into the immune changes that occur with prenatal restraint stress.

Many middle school students perform below grade-level standards in reading (National Center for Education Statistics, Washington, 2019), and recent observation studies demonstrate middle school teachers' limited use of reading comprehension practices within content area instruction (e.g., science and social studies; as reported by Greenleaf (in: Hinchman (ed) Adolescent literacies: A handbook of practice-based research, Guilford Press, 2017)). In this experimental pilot study, we aimed to boost middle schoolers' reading comprehension outcomes by providing schoolwide professional development (PD) on integrating reading comprehension practices within content instruction for English language arts, social studies, and science teachers. Six schools were matched into pairs and randomized to the schoolwide PD or a business-as-usual (BAU) condition. Content area teachers in schools assigned to the PD condition received distributed PD resources to support implementation, and coaching in one reading comprehension practice in the fall (i.e., get the gist) and one in the spring (i.e., asking and answering questions). Contrary to traditional PD, this PD was implemented across three content areas, was narrow in scope but long in duration (one practice per semester), focused on practices that could feasibly be integrated into content area instruction, and included ongoing coaching in content area teams. Students in schools assigned to the PD condition significantly outperformed those in the BaU condition on a measure of main idea generation (

Purpose: When healthcare students participate in international service-learning experiences, they are exposed to a culture that is different from their own. This exposure, accomplished through cultural and clinical activities, can help to develop their clinical skills and result in personal growth. However, the individual impact of clinical versus cultural activities on personal growth is poorly understood. This study examines the impact on personal growth of clinical activities and cultural activities during international service-learning in occupational therapy students. Methods: Qualitative interviews using a phenomenological approach were utilized to explore the lived experiences of American occupational therapy students who traveled to Morocco. Responses were coded and analyzed to identify general themes on the impact of different clinical and cultural activities. Qualities of impactful experiences were also identified. Results: Participants identified authentic clinical activities that elicited an emotional response and cultural activities that involved full cultural immersion as being most impactful for their personal growth. Conclusion/Recommendations: To support healthcare students’ personal growth, international service-learning should be designed to include authentic clinical activities that elicit emotion and reflection and opportunities for full immersion into the culture of the country abroad.

This study assessed the prevalence of prenatal smoking, factors associated with prenatal smoking, and its association with birth outcomes in a sample of pregnant women in Egypt. Pregnant women were recruited during their last trimester from antenatal clinics in Cairo from June 2015 to May 2016. Participants completed an interviewer-administered survey that assessed tobacco use and attitudes, and exhaled carbon monoxide (CO) was measured. Gestational age at delivery and offspring birth weight were collected via a postnatal phone interview. Two hundred pregnant women ages 16–37 years participated. More than a quarter (29.0%) of women reported smoking (cigarettes, hookah, or both) during their current pregnancy, and hookah was more popular than cigarettes. Most women who smoked prior to their current pregnancy either maintained their current smoking habits (46.6%) or switched from dual to hookah-only smoking (46.6%). Current smokers during pregnancy had a higher mean (±SD) exhaled CO level (2.97 ± 1.45 vs. 0.25 ± 0.60 ppm, p < 0.001) and had babies with a lower mean birth weight (2583 ± 300 vs. 2991 ± 478 g, p < 0.001) than non-smokers. Smokers during pregnancy had greater odds of premature birth and/or low birth weight babies compared to non-smokers. Dual cigarette-hookah smokers had the highest risk. Additional focused programs are required to prevent women of childbearing age from initiating tobacco use and empower women to stop tobacco use during the preconception and gestational periods.

People with type 1 diabetes (T1D) are at increased risk of developing low bone mineral density and fractures. Optimization of calcium intake is a key component of pediatric bone health care. Despite the known risk factors for impaired bone health in T1D and the known benefits of calcium on bone accrual, there are limited data describing calcium intake in youth with T1D. In this cross-sectional study, calcium intake was assessed in 238 youth with T1D. One third of study participants were found to have inadequate calcium intake. Female sex, especially during adolescence, and obesity were identified as specific risk factors for inadequate calcium intake. Given the known adverse effects of T1D on bone health, efforts to promote calcium intake in youth with T1D should be considered.

When a novel coronavirus disease (COVID-19) made major headlines in 2020, it further exposed an existing public health crisis related to inequities within our communities and health care delivery system. Throughout the COVID-19 pandemic, populations of color had higher infection and mortality rates, and even experienced greater disease severity compared to whites. Populations of color often bear the brunt of COVID-19 and other health inequities, due to the multifaceted relationship between systemic racism and the social determinants of health. As this relationship continues to perpetuate health inequities, the local health department is an agency that has the jurisdiction and responsibility to prevent disease and protect the health of the communities they serve. When equity is integrated into a health department’s operational infrastructure as a disease prevention strategy, it can elevate the agency’s response to public health emergencies. Collecting, reporting, and tracking demographic data that is necessary to identify inequities becomes a priority to facilitate a more robust public health response. The purpose of this paper is to present strategies of how a local health department operationalized equity in various stages of COVID-19 response and apply these methods to future public health emergencies to better serve vulnerable communities.

As machine learning becomes ubiquitous, the need to deploy models on real-time, embedded systems will become increasingly critical. This is especially true for deep learning solutions, whose large models pose interesting challenges for target architectures at the “edge” that are resource-constrained. The realization of machine learning, and deep learning, is being driven by the availability of specialized hardware, such as system-on-chip solutions, which provide some alleviation of constraints. Equally important, however, are the operating systems that run on this hardware, and specifically the ability to leverage commercial real-time operating systems which, unlike general purpose operating systems such as Linux, can provide the low-latency, deterministic execution required for embedded, and potentially safety-critical, applications at the edge. Despite this, studies considering the integration of real-time operating systems, specialized hardware, and machine learning/deep learning algorithms remain limited. In particular, better mechanisms for real-time scheduling in the context of machine learning applications will prove to be critical as these technologies move to the edge. In order to address some of these challenges, we present a resource management framework designed to provide a dynamic on-device approach to the allocation and scheduling of limited resources in a real-time processing environment. These types of mechanisms are necessary to support the deterministic behavior required by the control components contained in the edge nodes. To validate the effectiveness of our approach, we applied rigorous schedulability analysis to a large set of randomly generated simulated task sets and then verified the most time critical applications, such as the control tasks which maintained low-latency deterministic behavior even during off-nominal conditions. The practicality of our scheduling framework was demonstrated by integrating it into a commercial real-time operating system (VxWorks) then running a typical deep learning image processing application to perform simple object detection. The results indicate that our proposed resource management framework can be leveraged to facilitate integration of machine learning algorithms with real-time operating systems and embedded platforms, including widely-used, industry-standard real-time operating systems.

Individuals with autism spectrum disorder (ASD) are at a greater risk for challenging behavior than individuals with other developmental disabilities. An essential step in the treatment of these behaviors is the identification of the function of the behavior. In the current study, data were collected from a large database, in which supervising clinicians from a community-based behavioral health agency recorded the topography and function(s) of behaviors treated as a part of an individual’s behavior intervention plan. In a sample of 3216 individuals (mean age = 10.67, SD = 4.61) with ASD, the frequency of the most common challenging behaviors and the identified function of the behavior were examined. Stereotypy was the most commonly reported topography, followed by noncompliance and aggression. Overall, escape was the most commonly reported function of behavior. To further evaluate how clinicians operationally define these behaviors, a part-of-speech text analysis was conducted and found a high degree of overlap in the operational definitions of challenging behavior (i.e., aggression, disruption and tantrum; noncompliance and tantrum; obsessive behavior and stereotypy; self-injurious behavior and aggression). These data are discussed in further detail.

Background: We examined the association of adult lung transplant candidates’ self-reported affect with transplant-related outcomes, evaluating whether a positive (vs negative) frame of mind might be protective. Method: Consenting waitlisted candidates from 6 centers completed the questionnaires including the Positive and Negative Affect Schedule annually and posttransplant. Univariate logistic regression analysis was performed to determine the association of baseline affect with outcomes of death or delisting. Models were subsequently adjusted for age, marital status, and education. Results: Questionnaires were completed by 169 candidates (77.9% participation). Mean positive affect, negative affect, and positive-to-negative affect ratio (positivity ratio) were similar to expected norms. The scores of the questionnaire did not change significantly over time. Fifteen (8.9%) waitlisted participants died. Candidates who died while waiting had lower positivity ratios compared to those who survived (1.82 vs 2.45; P = .02). A more negative affect was associated with increased death on the waiting list (adjusted odds ratio [OR] 1.10; P = .021). Conversely, a higher positivity ratio was associated with decreased death while waiting (adjusted OR: 0.45; P = .027). Conclusion: Negative affect may represent a novel risk factor for death on the waitlist. Enhancing positive affect may represent a useful target for psychological optimization in lung transplant candidates.

Dystroglycan, an extracellular matrix receptor, has essential functions in various tissues. Loss of α‐dystroglycan‐laminin interaction due to defective glycosylation of α‐dystroglycan underlies a group of congenital muscular dystrophies often associated with brain malformations, referred to as dystroglycanopathies. The lack of isogenic human dystroglycanopathy cell models has limited our ability to test potential drugs in a human‐ and neural‐specific context. Here, we generated induced pluripotent stem cells (iPSCs) from a severe dystroglycanopathy patient with homozygous FKRP (fukutin‐related protein gene) mutation. We showed that CRISPR/Cas9‐mediated gene correction of FKRP restored glycosylation of α‐dystroglycan in iPSC‐derived cortical neurons, whereas targeted gene mutation of FKRP in wild‐type cells disrupted this glycosylation. In parallel, we screened 31,954 small molecule compounds using a mouse myoblast line for increased glycosylation of α‐dystroglycan. Using human FKRP‐iPSC‐derived neural cells for hit validation, we demonstrated that compound 4‐(4‐bromophenyl)‐6‐ethylsulfanyl‐2‐oxo‐3,4‐dihydro‐1H‐pyridine‐5‐carbonitrile (4BPPNit) significantly augmented glycosylation of α‐dystroglycan, in part through upregulation of LARGE1 glycosyltransferase gene expression. Together, isogenic human iPSC‐derived cells represent a valuable platform for facilitating dystroglycanopathy drug discovery and therapeutic development.

We leverage variational autoencoders to generate transient light curves of distant exoplanets and stars in order to demonstrate the efficacy of deep learning techniques for this class of data. The ability to generate accurate light curves with desirable characteristics becomes more and more necessary with the success of recent astronomical missions and upcoming missions and will be a key enabler of the development of future models and research. The first study of its type to date, our initial results indicate a promising new research direction worthy of further development.

[Image: see text] In humans, C–X–C chemokine receptor type 4 (CXCR4) is a protein that is encoded by the CXCR4 gene and binds the ligand CXCL12 (also known as SDF-1). The CXCR4–CXCL12 interaction in cancer elicits biological activities that result in tumor progression and has accordingly been the subject of significant investigation for detection and treatment of the disease. Peptidic antagonists have been labeled with a variety of radioisotopes for the detection of CXCR4, but the methodology utilizing small molecules has predominantly used radiometals. We report here the development of a (18)F-radiolabeled cyclam-based small molecule radioprobe, [(18)F]MCFB, for imaging CXCR4 expression. The IC(50) value of [(19)F]MCFB for CXCR4 was similar to that of AMD3465 (111.3 and 89.8 nM, respectively). In vitro binding assays show that the tracer depicted a differential CXCR4 expression, which was blocked in the presence of AMD3465, demonstrating the specificity of [(18)F]MCFB. Positron emission tomography (PET) imaging studies showed a distinct uptake of the radioprobe in lymphoma and breast cancer xenografts. High liver and kidney uptakes were seen with [(18)F]MCFB, leading us to further examine the basis of its pharmacokinetics in relation to the tracer’s cationic nature and thus the role of organic cation transporters (OCTs). Substrate competition following the intravenous injection of metformin led to a marked decrease in the urinary excretion of [(18)F]MCFB, with moderate changes observed in other organs, including the liver. Our results suggest involvement of OCTs in the renal elimination of the tracer. In conclusion, the (18)F-radiolabeled monocyclam, [(18)F]MCFB, has potential to detect tumor CXCR4 in nonhepatic tissues.

In humans, C–X–C chemokine receptor type 4 (CXCR4) is a protein that is encoded by the CXCR4 gene and binds the ligand CXCL12 (also known as SDF-1). The CXCR4–CXCL12 interaction in cancer elicits biological activities that result in tumor progression and has accordingly been the subject of significant investigation for detection and treatment of the disease. Peptidic antagonists have been labeled with a variety of radioisotopes for the detection of CXCR4, but the methodology utilizing small molecules has predominantly used radiometals. We report here the development of a 18F-radiolabeled cyclam-based small molecule radioprobe, [18F]MCFB, for imaging CXCR4 expression. The IC50 value of [19F]MCFB for CXCR4 was similar to that of AMD3465 (111.3 and 89.8 nM, respectively). In vitro binding assays show that the tracer depicted a differential CXCR4 expression, which was blocked in the presence of AMD3465, demonstrating the specificity of [18F]MCFB. Positron emission tomography (PET) imaging studies showed a distinct uptake of the radioprobe in lymphoma and breast cancer xenografts. High liver and kidney uptakes were seen with [18F]MCFB, leading us to further examine the basis of its pharmacokinetics in relation to the tracer’s cationic nature and thus the role of organic cation transporters (OCTs). Substrate competition following the intravenous injection of metformin led to a marked decrease in the urinary excretion of [18F]MCFB, with moderate changes observed in other organs, including the liver. Our results suggest involvement of OCTs in the renal elimination of the tracer. In conclusion, the 18F-radiolabeled monocyclam, [18F]MCFB, has potential to detect tumor CXCR4 in nonhepatic tissues.

Background Challenging behaviors are prevalent among individuals with autism spectrum disorder; however, research exploring the impact of challenging behaviors on treatment response is lacking. Objective The purpose of this study was to identify types of autism spectrum disorder based on engagement in different challenging behaviors and evaluate differences in treatment response between groups. Methods Retrospective data on challenging behaviors and treatment progress for 854 children with autism spectrum disorder were analyzed. Participants were clustered based on 8 observed challenging behaviors using k means, and multiple linear regression was performed to test interactions between skill mastery and treatment hours, cluster assignment, and gender. Results Seven clusters were identified, which demonstrated a single dominant challenging behavior. For some clusters, significant differences in treatment response were found. Specifically, a cluster characterized by low levels of stereotypy was found to have significantly higher levels of skill mastery than clusters characterized by self-injurious behavior and aggression (P Conclusions These findings have implications on the treatment of individuals with autism spectrum disorder. Self-injurious behavior and aggression were prevalent among participants with the worst treatment response, thus interventions targeting these challenging behaviors may be worth prioritizing. Furthermore, the use of unsupervised machine learning models to identify types of autism spectrum disorder shows promise.

This paper reports on Australian case study research in schools where principals completed the Principals as Literacy Leaders (PALL) program. The purpose was to gather data about the effects of PALL on principals’ leadership and the impact of interventions in Reading on teaching, student learning, and achievement. Data gathering included interviews with principals, leadership teams and teachers (parents in some schools), a student survey and examples, and reading progress data. Findings indicated that PALL had impacted positively on principals’ leadership and provided a framework to lead their teachers in ways that improved teaching practice, student engagement and learning.

Ample research has shown that intensive applied behavior analysis (ABA) treatment produces robust outcomes for individuals with autism spectrum disorder (ASD); however, little is known about the relationship between treatment intensity and treatment outcomes. The current study was designed to evaluate this relationship. Participants included 726 children, ages 1.5 to 12 years old, receiving community-based behavioral intervention services. Results indicated a strong relationship between treatment intensity and mastery of learning objectives, where higher treatment intensity predicted greater progress. Specifically, 35% of the variance in mastery of learning objectives was accounted for by treatment hours using standard linear regression, and 60% of variance was accounted for using artificial neural networks. These results add to the existing support for higher intensity treatment for children with ASD.

The delta isoform of phosphoinositide 3-kinase (PI3Kδ) regulates various lymphocyte functions. Considering the key pro-inflammatory role of IL-17A and IL-17F cytokines in psoriasis and spondyloarthritis (SpA), we investigated the potential of PI3Kδ blockade to suppress IL-17A, IL-17F and associated pro-inflammatory cytokines that could synergize with IL-17A and IL-17F. Using in vitro studies with primary human cells and ex vivo studies with inflamed target tissues, we assessed if seletalisib, a selective PI3Kδ inhibitor, suppresses cytokine production by T cells and innate-like lymphocytes, and if seletalisib modulates the inflammatory responses in stromal cell populations in psoriasis (human dermal fibroblasts (HDF)) and SpA (fibroblast-like synoviocytes (FLS)). In vitro, seletalisib inhibited the production of pro-inflammatory cytokines, including IL-17A and IL-17F, from peripheral blood mononuclear cells (PBMCs), T helper 17 (Th17) cells as well as γδ-T cells and mucosal-associated invariant T cells. This inhibition resulted in decreased inflammatory activation of HDF in co-culture systems. Seletalisib was also efficacious in inhibiting SpA PBMCs and synovial fluid mononuclear cells (SFMCs) from producing pro-inflammatory cytokines. Furthermore, supernatant derived from cultured seletalisib-treated Th17 cells showed reduced potency for activating inflammatory responses from cultured SpA FLS and decreased their osteogenic differentiation capacity. Finally, analysis of inflamed SpA synovial tissue biopsies revealed activation of the PI3K-Akt-mTOR pathway. We observed that ex vivo seletalisib treatment of inflamed synovial tissue reduced IL-17A and IL-17F expression. Collectively, inhibition of PI3Kδ reduces the production of pro-inflammatory cytokines from IL-17-producing adaptive and innate-like lymphocytes and thereby inhibits downstream inflammatory and tissue remodeling responses. PI3Kδ-targeting may therefore represent a novel therapeutic avenue for the treatment of IL-17-mediated chronic inflammatory diseases such as psoriasis and SpA.

© 2019 American Society of Human Genetics The occurrence of non-epileptic hyperkinetic movements in the context of developmental epileptic encephalopathies is an increasingly recognized phenomenon. Identification of causative mutations provides an important insight into common pathogenic mechanisms that cause both seizures and abnormal motor control. We report bi-allelic loss-of-function CACNA1B variants in six children from three unrelated families whose affected members present with a complex and progressive neurological syndrome. All affected individuals presented with epileptic encephalopathy, severe neurodevelopmental delay (often with regression), and a hyperkinetic movement disorder. Additional neurological features included postnatal microcephaly and hypotonia. Five children died in childhood or adolescence (mean age of death: 9 years), mainly as a result of secondary respiratory complications. CACNA1B encodes the pore-forming subunit of the pre-synaptic neuronal voltage-gated calcium channel Cav2.2/N-type, crucial for SNARE-mediated neurotransmission, particularly in the early postnatal period. Bi-allelic loss-of-function variants in CACNA1B are predicted to cause disruption of Ca2+ influx, leading to impaired synaptic neurotransmission. The resultant effect on neuronal function is likely to be important in the development of involuntary movements and epilepsy. Overall, our findings provide further evidence for the key role of Cav2.2 in normal human neurodevelopment.

Background and objective Autism spectrum disorder (ASD) is a heterogeneous disorder. Research has explored potential ASD subgroups with preliminary evidence supporting the existence of behaviorally and genetically distinct subgroups; however, research has yet to leverage machine learning to identify phenotypes on a scale large enough to robustly examine treatment response across such subgroups. The purpose of the present study was to apply Gaussian Mixture Models and Hierarchical Clustering to identify behavioral phenotypes of ASD and examine treatment response across the learned phenotypes. Materials and methods The present study included a sample of children with ASD (N = 2400), the largest of its kind to date. Unsupervised machine learning was applied to model ASD subgroups as well as their taxonomic relationships. Retrospective treatment data were available for a portion of the sample (n = 1034). Treatment response was examined within each subgroup via regression. Results The application of a Gaussian Mixture Model revealed 16 subgroups. Further examination of the subgroups through Hierarchical Agglomerative Clustering suggested 2 overlying behavioral phenotypes with unique deficit profiles each composed of subgroups that differed in severity of those deficits. Furthermore, differentiated response to treatment was found across subtypes, with a substantially higher amount of variance accounted for due to the homogenization effect of the clustering. Discussion The high amount of variance explained by the regression models indicates that clustering provides a basis for homogenization, and thus an opportunity to tailor treatment based on cluster memberships. These findings have significant implications on prognosis and targeted treatment of ASD, and pave the way for personalized intervention based on unsupervised machine learning.

We apply cluster analysis to a sample of 2,116 children with Autism Spectrum Disorder in order to identify patterns of challenging behaviors observed in home and centerbased clinical settings. The largest study of this type to date, and the first to employ machine learning, our results indicate that while the presence of multiple challenging behaviors is common, in most cases a dominant behavior emerges. Furthermore, the trend is also observed when we train our cluster models on the male and female samples separately. This work provides a basis for future studies to understand the relationship of challenging behavior profiles to learning outcomes, with the ultimate goal of providing personalized therapeutic interventions with maximum efficacy and minimum time and cost.

We apply a seasonal decomposition time series analysis to TravisTorrent data in order to examine growth trends and periodic behavior related to number of builds in a continuous integration environment. We apply our techniques at the macro level using the full TravisTorrent repository consisting of 1,283 projects, and at the micro level considering the Apache Drill project. Our results demonstrate strong seasonal behavior at both the large and small scale using an additive time series model. In addition to being able to accurately capture trend and periodicity in build data, our techniques are also able to accurately forecast the expected number of builds for a future time interval.