Long COVID incidence in adults and children between 2020 and 2023: a real-world data study from the RECOVER Initiative.

Estimates of post-acute sequelae of SARS-CoV-2 infection (PASC) incidence, also known as Long COVID, have varied across studies and changed over time. We estimated PASC incidence among adult and pediatric populations in three nationwide research networks of electronic health records (EHR) participating in the RECOVER Initiative using different classification algorithms (computable phenotypes). Overall, 7% of children and 8.5%-26.4% of adults developed PASC, depending on computable phenotype used. Excess incidence among SARS-CoV-2 patients was 4% in children and ranged from 4-7% among adults, representing a lower-bound incidence estimation based on two control groups - contemporary COVID-19 negative and historical patients (2019). Temporal patterns were consistent across networks, with peaks associated with introduction of new viral variants. Our findings indicate that preventing and mitigating Long COVID remains a public health priority. Examining temporal patterns and risk factors of PASC incidence informs our understanding of etiology and can improve prevention and management.

Computer vision models enable mixed linear modeling to predict arbuscular mycorrhizal fungal colonization using fungal morphology.

The presence of Arbuscular Mycorrhizal Fungi (AMF) in vascular land plant roots is one of the most ancient of symbioses supporting nitrogen and phosphorus exchange for photosynthetically derived carbon. Here we provide a multi-scale modeling approach to predict AMF colonization of a worldwide crop from a Recombinant Inbred Line (RIL) population derived from Sorghum bicolor and S. propinquum. The high-throughput phenotyping methods of fungal structures here rely on a Mask Region-based Convolutional Neural Network (Mask R-CNN) in computer vision for pixel-wise fungal structure segmentations and mixed linear models to explore the relations of AMF colonization, root niche, and fungal structure allocation. Models proposed capture over 95% of the variation in AMF colonization as a function of root niche and relative abundance of fungal structures in each plant. Arbuscule allocation is a significant predictor of AMF colonization among sibling plants. Arbuscules and extraradical hyphae implicated in nutrient exchange predict highest AMF colonization in the top root section. Our work demonstrates that deep learning can be used by the community for the high-throughput phenotyping of AMF in plant roots. Mixed linear modeling provides a framework for testing hypotheses about AMF colonization phenotypes as a function of root niche and fungal structure allocations.

Species-specific responses of marine bacteria to environmental perturbation.

Environmental perturbations shape the structure and function of microbial communities. Oil spills are a major perturbation and resolving spills often requires active measures like dispersant application that can exacerbate the initial disturbance. Species-specific responses of microorganisms to oil and dispersant exposure during such perturbations remain largely unknown. We merged metatranscriptomic libraries with pangenomes to generate Core-Accessory Metatranscriptomes (CA-Metatranscriptomes) for two microbial hydrocarbon degraders that played important roles in the aftermath of the Deepwater Horizon oil spill. The Colwellia CA-Metatranscriptome illustrated pronounced dispersant-driven acceleration of core (~41%) and accessory gene (~59%) transcription, suggesting an opportunistic strategy. Marinobacter responded to oil exposure by expressing mainly accessory genes (~93%), suggesting an effective hydrocarbon-degrading lifestyle. The CA-Metatranscriptome approach offers a robust way to identify the underlying mechanisms of key microbial functions and highlights differences of specialist-vs-opportunistic responses to environmental disturbance.

External validity in distributed data networks.

PURPOSE: While much has been written about how distributed networks address internal validity, external validity is rarely discussed. We aimed to define key terms related to external validity, discuss how they relate to distributed networks, and identify how three networks (the US Food and Drug Administration's Sentinel System, the Canadian Network for Observational Drug Effect Studies [CNODES], and the National Patient Centered Clinical Research Network [PCORnet]) deal with external validity. METHODS: We define external validity, target populations, target validity, generalizability, and transportability and describe how each relates to distributed networks. We then describe Sentinel, CNODES, and PCORnet and how each approaches these concepts, including a sample case study. RESULTS: Each network approaches external validity differently. As its target population is US citizens and it includes only US data, Sentinel primarily worries about lack of external validity by not including some segments of the population. The fact that CNODES includes Canadian, United States, and United Kingdom data forces them to seriously consider whether the United States and United Kingdom data will be transportable to Canadian citizens when they meta-analyze database-specific estimates. PCORnet, with its focus on study-specific cohorts and pragmatic trials, conducts more case-by-case explorations of external validity for each new analytic data set it generates. CONCLUSIONS: There is no one-size-fits-all approach to external validity within distributed networks. With these networks and comparisons between their findings becoming a key part of pharmacoepidemiology, there is a need to adapt tools for improving external validity to the distributed network setting.

Virtually the Same? Evaluating the Effectiveness of Remote Undergraduate Research Experiences.

In-person undergraduate research experiences (UREs) promote students' integration into careers in life science research. In 2020, the COVID-19 pandemic prompted institutions hosting summer URE programs to offer them remotely, raising questions about whether undergraduates who participate in remote research can experience scientific integration and whether they might perceive doing research less favorably (i.e., not beneficial or too costly). To address these questions, we examined indicators of scientific integration and perceptions of the benefits and costs of doing research among students who participated in remote life science URE programs in Summer 2020. We found that students experienced gains in scientific self-efficacy pre- to post-URE, similar to results reported for in-person UREs. We also found that students experienced gains in scientific identity, graduate and career intentions, and perceptions of the benefits of doing research only if they started their remote UREs at lower levels on these variables. Collectively, students did not change in their perceptions of the costs of doing research despite the challenges of working remotely. Yet students who started with low cost perceptions increased in these perceptions. These findings indicate that remote UREs can support students' self-efficacy development, but may otherwise be limited in their potential to promote scientific integration.

The species coalescent indicates possible bat and pangolin origins of the COVID-19 pandemic.

A consensus species tree is reconstructed from 11 gene trees for human, bat, and pangolin beta coronaviruses from samples taken early in the pandemic (prior to April 1, 2020). Using coalescent theory, the shallow (short branches relative to the hosts) consensus species tree provides evidence of recent gene flow events between bat and pangolin beta coronaviruses predating the zoonotic transfer to humans. The consensus species tree was also used to reconstruct the ancestral sequence of human SARS-CoV-2, which was 2 nucleotides different from the Wuhan sequence. The time to most recent common ancestor was estimated to be Dec 8, 2019 with a bat origin. Some human, bat, and pangolin coronavirus lineages found in China are phylogenetically distinct, a rare example of a class II phylogeography pattern (Avise et al. in Ann Rev Eco Syst 18:489-422, 1987). The consensus species tree is a product of evolutionary factors, providing evidence of repeated zoonotic transfers between bat and pangolin as a reservoir for future zoonotic transfers to humans.

Tropical sharks feasting on and swimming through microplastics: First evidence from Malaysia.

Plastic can be degraded into microplastic (<5 mm) and has been polluting worldwide marine environment and negatively impact human health. Microplastics in marine organisms are still understudied in Malaysia, let alone from a subclass Elasmobranchii. Five tropical shark species (Carcharhinus dussumieri, Carcharhinus sorrah, Chiloscyllium hasseltii, Chiloscyllium punctatum, and Scoliodon laticaudus) were examined for the presence of microplastics. 74 sharks were sampled from the local wet market and 100 % of samples contained microplastics. A total of 2211 plastic particles were found in gastrointestinal tracts (GIT) and gills, where 29.88 ± 2.34 particles per shark (mean ± SEM). Black (40.07 %) and fiber (84.44 %) microplastics were the most dominant. Extracted microplastic sizes ranged from 0.007 mm to 4.992 mm. This study suggests that microplastic uptake is gender-related for some shark species. A subsample of microplastics (10 %) was used for polymer type identification, where polyester was recorded the highest (43.95 %).

The ACTIV-6 Stakeholder Advisory Committee: a model for virtual engagement in decentralized clinical trials.

Introduction: Engaging patients, caregivers, and other stakeholders to help guide the research process is a cornerstone of patient-centered research. Lived expertise may help ensure the relevance of research questions, promote practices that are satisfactory to research participants, improve transparency, and assist with disseminating findings. Methods: Traditionally engagement has been conducted face-to-face in the local communities in which research operates. Decentralized platform trials pose new challenges for the practice of engagement. We used a remote model for stakeholder engagement, relying on Zoom meetings and blog communications. Results: Here we describe the approach used for research partnership with patients, caregivers, and clinicians in the planning and oversight of the ACTIV-6 trial and the impact of this work. We also present suggestions for future remote engagement. Conclusions: The ACTIV-6 experience may inform proposed strategies for future engagement in decentralized trials.

Vancomycin Loading Doses and Nephrotoxicity on Medicine Teaching Services.

Background: Infectious Disease Society of America (IDSA) guidelines recommend the usage of a loading dose when using vancomycin for seriously ill patients. While the relationship between vancomycin and nephrotoxicity is the focus of many studies, few studies have examined the relationship between vancomycin loading doses and nephrotoxicity. Methods: We performed a retrospective cohort study examining vancomycin dosing for internal medicine teaching services' patients over the 2014-15 academic year at one academic medical center. We generated a list of all hospitalized patients aged 18-85 who received vancomycin and were admitted to a teaching service. Nephrotoxicity was determined by 7-day acute kidney injury (AKI) rate. Patients in the loading dose cohort were compared with those in the standard-dose cohort. Primary modeling used multivariable logistic regression with AKI as our outcome of interest. Results: Four hundred and thirty-eight patients were included for analysis. The loading dose (n = 365, 83%) and standard dosing (n = 73, 17%) cohorts were not significantly different regarding demographics, GFR, nephrotoxic drug exposure, total vancomycin received, trough levels, or comorbidities and were only significantly different regarding body mass index (BMI). The 7-day AKI rate was not significantly different between the two arms (6.3% in the standard dosing arm and 8.2% in the loading dose arm, p = 0.6). Conclusion: Few studies have examined the relationship between nephrotoxicity and vancomycin loading doses. The results of this study provide evidence that the use of loading doses is not significantly associated with increased 7-day AKI rate.

Uncovering in vivo biochemical patterns from time-series metabolic dynamics.

System biology relies on holistic biomolecule measurements, and untangling biochemical networks requires time-series metabolomics profiling. With current metabolomic approaches, time-series measurements can be taken for hundreds of metabolic features, which decode underlying metabolic regulation. Such a metabolomic dataset is untargeted with most features unannotated and inaccessible to statistical analysis and computational modeling. The high dimensionality of the metabolic space also causes mechanistic modeling to be rather cumbersome computationally. We implemented a faster exploratory workflow to visualize and extract chemical and biochemical dependencies. Time-series metabolic features (about 300 for each dataset) were extracted by Ridge Tracking-based Extract (RTExtract) on measurements from continuous in vivo monitoring of metabolism by NMR (CIVM-NMR) in Neurospora crassa under different conditions. The metabolic profiles were then smoothed and projected into lower dimensions, enabling a comparison of metabolic trends in the cultures. Next, we expanded incomplete metabolite annotation using a correlation network. Lastly, we uncovered meaningful metabolic clusters by estimating dependencies between smoothed metabolic profiles. We thus sidestepped the processes of time-consuming mechanistic modeling, difficult global optimization, and labor-intensive annotation. Multiple clusters guided insights into central energy metabolism and membrane synthesis. Dense connections with glucose 1-phosphate indicated its central position in metabolism in N. crassa. Our approach was benchmarked on simulated random network dynamics and provides a novel exploratory approach to analyzing high-dimensional metabolic dynamics.

Rare occurrence of Guillain-Barré syndrome after Moderna vaccine.

A Caucasian man in his 60s with a medical history significant for ruptured left middle cerebral artery aneurysm status post clipping 2005 with residual right eye blindness and right leg weakness with gait instability presented with loss of balance, weakness of his legs and fatigue for 3 days. No other antecedent event was identified other than receiving Moderna COVID-19 vaccine 4 weeks before the presentation and 3 days before symptom onset. CT head and CT angiogram of the head and neck were performed and demonstrated no acute intracranial bleeding and no vascular abnormalities. With the findings of diffuse hyporeflexia and cerebrospinal fluid showing albumino-cytological dissociation, Guillain-Barré syndrome was high on the differentials. Electromyogram showed evidence of demyelination. He was treated with intravenous immune globulin (IVIG) and was discharged to rehab with complete symptom resolution.

The macroscopic limit to synchronization of cellular clocks in single cells of Neurospora crassa.

We determined the macroscopic limit for phase synchronization of cellular clocks in an artificial tissue created by a "big chamber" microfluidic device to be about 150,000 cells or less. The dimensions of the microfluidic chamber allowed us to calculate an upper limit on the radius of a hypothesized quorum sensing signal molecule of 13.05 nm using a diffusion approximation for signal travel within the device. The use of a second microwell microfluidic device allowed the refinement of the macroscopic limit to a cell density of 2166 cells per fixed area of the device for phase synchronization. The measurement of averages over single cell trajectories in the microwell device supported a deterministic quorum sensing model identified by ensemble methods for clock phase synchronization. A strong inference framework was used to test the communication mechanism in phase synchronization of quorum sensing versus cell-to-cell contact, suggesting support for quorum sensing. Further evidence came from showing phase synchronization was density-dependent.

Characterizing the gene-environment interaction underlying natural morphological variation in Neurospora crassa conidiophores using high-throughput phenomics and transcriptomics.

Neurospora crassa propagates through dissemination of conidia, which develop through specialized structures called conidiophores. Recent work has identified striking variation in conidiophore morphology, using a wild population collection from Louisiana, United States of America to classify 3 distinct phenotypes: Wild-Type, Wrap, and Bulky. Little is known about the impact of these phenotypes on sporulation or germination later in the N. crassa life cycle, or about the genetic variation that underlies them. In this study, we show that conidiophore morphology likely affects colonization capacity of wild N. crassa isolates through both sporulation distance and germination on different carbon sources. We generated and crossed homokaryotic strains belonging to each phenotypic group to more robustly fit a model for and estimate heritability of the complex trait, conidiophore architecture. Our fitted model suggests at least 3 genes and 2 epistatic interactions contribute to conidiophore phenotype, which has an estimated heritability of 0.47. To uncover genes contributing to these phenotypes, we performed RNA-sequencing on mycelia and conidiophores of strains representing each of the 3 phenotypes. Our results show that the Bulky strain had a distinct transcriptional profile from that of Wild-Type and Wrap, exhibiting differential expression patterns in clock-controlled genes (ccgs), the conidiation-specific gene con-6, and genes implicated in metabolism and communication. Combined, these results present novel ecological impacts of and differential gene expression underlying natural conidiophore morphological variation, a complex trait that has not yet been thoroughly explored.

Empiric segmentation of high-risk patients: a structured literature review.

OBJECTIVES: Empiric segmentation is a rapidly growing, learning health system approach that uses large health care system data sets to identify groups of high-risk patients who may benefit from similar interventions. We aimed to review studies that used data-driven approaches to segment high-risk patient populations and describe how their designs and findings can inform health care leaders who are interested in applying similar techniques to their patient populations. STUDY DESIGN: Structured literature review. METHODS: We searched for original research articles published since 2000 that identified high-risk adult patient populations and applied data-driven analyses to segment the population. Two reviewers independently extracted study population source and criteria for high-risk designation, segmentation method, data types included, model selection criteria, and model results from the identified studies. RESULTS: Our search identified 224 articles, 12 of which met criteria for full review. Of these, 8 segmented high-risk patients and 4 segmented diagnoses without assigning patients to unique groups. Studies segmenting patients more often had clinically interpretable results. Common groups were defined by high prevalence of diabetes, cardiovascular disease, psychiatric conditions including substance use disorders, and neurologic disease (eg, stroke). Few studies incorporated patients' functional or social factors. Resulting patient and diagnosis clusters varied in ways closely linked to the model inputs, patient population inclusion criteria, and health care system context. CONCLUSIONS: Empiric segmentation can yield clinically relevant groups of patients with complex medical needs. Segmentation results are context dependent, suggesting the need for careful design and interpretation of segmentation models to ensure that results can inform clinical care and program design in the target setting.

"How Do We Do This at a Distance?!" A Descriptive Study of Remote Undergraduate Research Programs during COVID-19.

The COVID-19 pandemic shut down undergraduate research programs across the United States. A group of 23 colleges, universities, and research institutes hosted remote undergraduate research programs in the life sciences during Summer 2020. Given the unprecedented offering of remote programs, we carried out a study to describe and evaluate them. Using structured templates, we documented how programs were designed and implemented, including who participated. Through focus groups and surveys, we identified programmatic strengths and shortcomings as well as recommendations for improvements from students' perspectives. Strengths included the quality of mentorship, opportunities for learning and professional development, and a feeling of connection with a larger community. Weaknesses included limited cohort building, challenges with insufficient structure, and issues with technology. Although all programs had one or more activities related to diversity, equity, inclusion, and justice, these topics were largely absent from student reports even though programs coincided with a peak in national consciousness about racial inequities and structural racism. Our results provide evidence for designing remote Research Experiences for Undergraduates (REUs) that are experienced favorably by students. Our results also indicate that remote REUs are sufficiently positive to further investigate their affordances and constraints, including the potential to scale up offerings, with minimal concern about disenfranchising students.

Are we there yet? A machine learning architecture to predict organotropic metastases.

BACKGROUND & AIMS: Cancer metastasis into distant organs is an evolutionarily selective process. A better understanding of the driving forces endowing proliferative plasticity of tumor seeds in distant soils is required to develop and adapt better treatment systems for this lethal stage of the disease. To this end, we aimed to utilize transcript expression profiling features to predict the site-specific metastases of primary tumors and second, to identify the determinants of tissue specific progression. METHODS: We used statistical machine learning for transcript feature selection to optimize classification and built tree-based classifiers to predict tissue specific sites of metastatic progression. RESULTS: We developed a novel machine learning architecture that analyzes 33 types of RNA transcriptome profiles from The Cancer Genome Atlas (TCGA) database. Our classifier identifies the tumor type, derives synthetic instances of primary tumors metastasizing to distant organs and classifies the site-specific metastases in 16 types of cancers metastasizing to 12 locations. CONCLUSIONS: We have demonstrated that site specific metastatic progression is predictable using transcriptomic profiling data from primary tumors and that the overrepresented biological processes in tumors metastasizing to congruent distant loci are highly overlapping. These results indicate site-specific progression was organotropic and core features of biological signaling pathways are identifiable that may describe proliferative plasticity in distant soils.

An Interrupted Time-series Evaluation of the Association Between State Laws Mandating Prescriber Use of Prescription Drug Monitoring Programs and Discontinuation of Chronic Opioid Therapy in US Veterans.

BACKGROUND: Most states have recently passed laws requiring prescribers to use prescription drug monitoring programs (PDMPs) before prescribing opioid medications. The impact of these mandates on discontinuing chronic opioid therapy among Veterans managed in the Veterans Health Administration (VA) is unknown. We assess the association between the earliest of these laws and discontinuation of chronic opioid therapy in Veterans receiving VA health care. METHODS: We conducted a comparative interrupted time-series study in the 5 states mandating PDMP use before August 2013 (Ohio, West Virginia, Kentucky, New Mexico, and Tennessee), adjusting for trends in the 17 neighboring control states without such mandates. We modeled 25 months of prescribing for each state centered on the month the mandate became effective. We included Veterans prescribed long-term outpatient opioid therapy (305 of the preceding 365 d). Our outcomes were discontinuation of chronic opioid therapy (primary outcome) and the average daily quantity of opioids per Veteran over the following 6 months (secondary outcome). RESULTS: We included 250 monthly cohorts with 225,665 unique Veterans and 3.4 million Veteran-months. Baseline discontinuation rates before the PDMP mandates were 0.4%-2.7% per month. Kentucky saw a discontinuation increase of 1 absolute percentage point following its PDMP mandate which decreased over time. The other 4 states had no significant association between their mandates and change in opioid discontinuation. There was no evidence of decreasing opioid quantities following PDMP mandates. CONCLUSION: We did not find consistent evidence that state laws mandating provider PDMP use were associated with the discontinuation of chronic opioid therapy within the VA for the time period studied.

Interpretation of Near-Infrared Imaging in Acute and Chronic Wound Care.

Vascular assessment is a critical component of wound care. Current routine noninvasive vascular studies have limitations which can give a false sense of security of the presence of adequate perfusion for healing. Near-infrared imaging modalities can serve as an additional diagnostic assessment of wounds in which adequate perfusion is a concern. Correct interpretation of near-infrared images obtained is critical as subtleties that exist in the acute and chronic wound population goes beyond the interpretation that increased signal is consistent with adequate perfusion for healing. The objective of this paper is to educate providers on the correct interpretation of this point-of-care imaging modality in day-to-day wound-care practice to guide clinical decision-making for rapid wound resolution.

Wild Isolates of Neurospora crassa Reveal Three Conidiophore Architectural Phenotypes.

The vegetative life cycle in the model filamentous fungus, Neurospora crassa, relies on the development of conidiophores to produce new spores. Environmental, temporal, and genetic components of conidiophore development have been well characterized; however, little is known about their morphological variation. We explored conidiophore architectural variation in a natural population using a wild population collection of 21 strains from Louisiana, United States of America (USA). Our work reveals three novel architectural phenotypes, Wild Type, Bulky, and Wrap, and shows their maintenance throughout the duration of conidiophore development. Furthermore, we present a novel image-classifier using a convolutional neural network specifically developed to assign conidiophore architectural phenotypes in a high-throughput manner. To estimate an inheritance model for this discrete complex trait, crosses between strains of each phenotype were conducted, and conidiophores of subsequent progeny were characterized using the trained classifier. Our model suggests that conidiophore architecture is controlled by at least two genes and has a heritability of 0.23. Additionally, we quantified the number of conidia produced by each conidiophore type and their dispersion distance, suggesting that conidiophore architectural phenotype may impact N. crassa colonization capacity.

Vascular Assessment of the Lower Extremity with a Chronic Wound.

Peripheral arterial disease (PAD) affects many individuals worldwide and is associated with increased morbidity and mortality. Controversy exists on whether or not to screen asymptomatic patients. Further complicating this is that many patients with a chronic lower extremity wound are often asymptomatic. PAD and traditional noninvasive vascular studies may be inaccurate in providing a correct diagnosis. A review of current and novel vascular assessment modalities along with their benefits and limitations are presented here. A combination of these vascular assessments may help improve accuracy in diagnosis, providing timely care to those patients in need.