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Existing medical treatments for endometriosis-related pain are often ineffective, underscoring the need for new therapeutic strategies. In this study, we applied a computational drug repurposing pipeline to stratified and unstratified disease signatures based on endometrial gene expression data to identify potential therapeutics from existing drugs, based on expression reversal. Of 3,131 unique genes differentially expressed by at least one of six endometriosis signatures, only 308 (9.8%) were in common; however, 221 out of 299 drugs identified, (73.9%) were shared. We selected fenoprofen, an uncommonly prescribed NSAID that was the top therapeutic candidate for further investigation. When testing fenoprofen in an established rat model of endometriosis, fenoprofen successfully alleviated endometriosis-associated vaginal hyperalgesia, a surrogate marker for endometriosis-related pain. These findings validate fenoprofen as a therapeutic that could be utilized more frequently for endometriosis and suggest the utility of the aforementioned computational drug repurposing approach for endometriosis.

Although prematurity is the single largest cause of death in children under 5 years of age, the current definition of prematurity, based on gestational age, lacks the precision needed for guiding care decisions. Here, we propose a longitudinal risk assessment for adverse neonatal outcomes in newborns based on a deep learning model that uses electronic health records (EHRs) to predict a wide range of outcomes over a period starting shortly before conception and ending months after birth. By linking the EHRs of the Lucile Packard Children's Hospital and the Stanford Healthcare Adult Hospital, we developed a cohort of 22,104 mother-newborn dyads delivered between 2014 and 2018. Maternal and newborn EHRs were extracted and used to train a multi-input multitask deep learning model, featuring a long short-term memory neural network, to predict 24 different neonatal outcomes. An additional cohort of 10,250 mother-newborn dyads delivered at the same Stanford Hospitals from 2019 to September 2020 was used to validate the model. Areas under the receiver operating characteristic curve at delivery exceeded 0.9 for 10 of the 24 neonatal outcomes considered and were between 0.8 and 0.9 for 7 additional outcomes. Moreover, comprehensive association analysis identified multiple known associations between various maternal and neonatal features and specific neonatal outcomes. This study used linked EHRs from more than 30,000 mother-newborn dyads and would serve as a resource for the investigation and prediction of neonatal outcomes. An interactive website is available for independent investigators to leverage this unique dataset: https://maternal-child-health-associations.shinyapps.io/shiny_app/.

Drug repurposing requires distinguishing established drug class targets from novel molecule-specific mechanisms and rapidly derisking their therapeutic potential in a time-critical manner, particularly in a pandemic scenario. In response to the challenge to rapidly identify treatment options for COVID-19, several studies reported that statins, as a drug class, reduce mortality in these patients. However, it is unknown if different statins exhibit consistent function or may have varying therapeutic benefit. A Bayesian network tool was used to predict drugs that shift the host transcriptomic response to SARS-CoV-2 infection towards a healthy state. Drugs were predicted using 14 RNA-sequencing datasets from 72 autopsy tissues and 465 COVID-19 patient samples or from cultured human cells and organoids infected with SARS-CoV-2. Top drug predictions included statins, which were then assessed using electronic medical records containing over 4,000 COVID-19 patients on statins to determine mortality risk in patients prescribed specific statins versus untreated matched controls. The same drugs were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus. Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins, including atorvastatin, were predicted to be active in > 50% of analyses. Analysis of the clinical database revealed that reduced mortality risk was only observed in COVID-19 patients prescribed a subset of statins, including simvastatin and atorvastatin. In vitro testing of SARS-CoV-2 infected cells revealed simvastatin to be a potent direct inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells. Statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared drug target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and clinically evaluate non-obvious mechanisms and derisk and accelerate drug repurposing opportunities.

Selective plane illumination microscopy (SPIM), or light sheet microscopy, is a powerful imaging approach. However, access to and interfacing microscopes with microfluidics have remained challenging. Complex interfacing with microfluidics has limited the SPIM's utility for studying the hydrodynamics of freely moving multicellular organisms. We developed SPIM-Flow, an inexpensive light sheet platform that enables easy integration with microfluidics. We used SPIM-Flow to investigate the hydrodynamics of a freely moving Hydra polyp via particle tracking in millimeter-sized chambers. Initial experiments across multiple animals, feeding on a chip (Artemia franciscana nauplii used as food), and baseline behaviors (tentacle swaying, elongation, and bending) indicated the organisms' health inside the system. Fluidics were used to investigate Hydra's response to flow. The results suggested that the animals responded to an established flow by bending and swaying their tentacles in the flow direction. Finally, using SPIM-Flow in a proof-of-concept experiment, the shear stress required to detach an animal from a surface was demonstrated. Our results demonstrated SPIM-Flow's utility for investigating the hydrodynamics of freely moving animals.

This qualitative research study describes how a Midwest community college's implementation of an Scholarships in Science, Technology, Engineering, and Mathematics (SSTEM) program influences engineering identity development for its students with financial need. Using a phenomenological approach, the study finds that the program enables community college students to have greater financial freedom and an ability to focus on engineering identity. In addition, the SSTEM program enhances student connections with STEM faculty, program staff, and peers. The study highlights the need for creating spaces for engineering identity development, developing connections between faculty, staff, and students, and enhancing transfer connections through different experiences. Future research might look to longitudinal designs and investigate additional contexts, engineering disciplines, gender differences, and programmatic structures to add nuance to these findings. The study suggests that practitioners might frame SSTEM and engineering experiences as opportunities for financial freedom and identity development and make further enhancements to transfer connections to four-year institutional partners. In terms of policy, the study suggests that policymakers consider identity development experiences an important aspect of funding SSTEM programs while enhancing programmatic support services available to students and placing greater emphasis on the collaborative actions, planned activities, and power dynamics between two- and four-year institutions funded by the SSTEM program.

BackgroundIdentifying pregnancies at risk for preterm birth, one of the leading causes of worldwide infant mortality, has the potential to improve prenatal care. However, we lack broadly applicable methods to accurately predict preterm birth risk. The dense longitudinal information present in electronic health records (EHRs) is enabling scalable and cost-efficient risk modeling of many diseases, but EHR resources have been largely untapped in the study of pregnancy.MethodsHere, we apply machine learning to diverse data from EHRs with 35,282 deliveries to predict singleton preterm birth.ResultsWe find that machine learning models based on billing codes alone can predict preterm birth risk at various gestational ages (e.g., ROC-AUC = 0.75, PR-AUC = 0.40 at 28 weeks of gestation) and outperform comparable models trained using known risk factors (e.g., ROC-AUC = 0.65, PR-AUC = 0.25 at 28 weeks). Examining the patterns learned by the model reveals it stratifies deliveries into interpretable groups, including high-risk preterm birth subtypes enriched for distinct comorbidities. Our machine learning approach also predicts preterm birth subtypes (spontaneous vs. indicated), mode of delivery, and recurrent preterm birth. Finally, we demonstrate the portability of our approach by showing that the prediction models maintain their accuracy on a large, independent cohort (5978 deliveries) from a different healthcare system.ConclusionsBy leveraging rich phenotypic and genetic features derived from EHRs, we suggest that machine learning algorithms have great potential to improve medical care during pregnancy. However, further work is needed before these models can be applied in clinical settings.

Preterm labor (PTL) is the leading cause of neonatal morbidity and mortality worldwide. Whereas many studies have investigated the maternal immune responses that cause PTL, fetal immune cell activation has recently been raised as an important contributor to the pathogenesis of PTL. In this study, we analyzed lymphocyte receptor repertoires in maternal and cord blood from 14 term and 10 preterm deliveries, hypothesizing that the high prevalence of infection in patients with PTL may result in specific changes in the T cell and B cell repertoires. We analyzed TCR β-chain (TCR-β) and IgH diversity, CDR3 lengths, clonal sharing, and preferential usage of variable and joining gene segments. Both TCR-β and IgH repertoires had shorter CDR3s compared with those in maternal blood. In cord blood samples, we found that CDR3 lengths correlated with gestational age, with shorter CDR3s in preterm neonates suggesting a less developed repertoire. Preterm cord blood displayed preferential usage of a number of genes. In preterm pregnancies, we observed significantly higher prevalence of convergent clones between mother/baby pairs than in term pregnancies. Together, our results suggest the repertoire of preterm infants displays a combination of immature features and convergence with maternal TCR-β clones compared with that of term infants. The higher clonal convergence in PTL could represent mother and fetus both responding to a shared stimulus like an infection. These data provide a detailed analysis of the maternal-fetal immune repertoire in term and preterm patients and contribute to a better understanding of neonate immune repertoire development and potential changes associated with PTL.

The novel SARS-CoV-2 virus emerged in December 2019 and has few effective treatments. We applied a computational drug repositioning pipeline to SARS-CoV-2 differential gene expression signatures derived from publicly available data. We utilized three independent published studies to acquire or generate lists of differentially expressed genes between control and SARS-CoV-2-infected samples. Using a rank-based pattern matching strategy based on the Kolmogorov-Smirnov Statistic, the signatures were queried against drug profiles from Connectivity Map (CMap). We validated 16 of our top predicted hits in live SARS-CoV-2 antiviral assays in either Calu-3 or 293T-ACE2 cells. Validation experiments in human cell lines showed that 11 of the 16 compounds tested to date (including clofazimine, haloperidol and others) had measurable antiviral activity against SARS-CoV-2. These initial results are encouraging as we continue to work towards a further analysis of these predicted drugs as potential therapeutics for the treatment of COVID-19.

The severe respiratory illness due to SARS-CoV-2, the virus responsible for coronavirus disease 2019 (COVID-19), is triggered by an intense pro-inflammatory host response. Statins, prescribed primarily for lipid reduction, are known to have anti-inflammatory and immunomodulatory properties and have been associated with a reduced mortality rate among COVID-19 patients taking statins as reported in two recent retrospective studies. However, a meta-analysis that included nine studies showed that statin use did not improve in-hospital outcomes of those with COVID-19. In addition, concerns regarding the use of statins and an increase in COVID-19 infections have been raised, as statins may increase the expression of angiotensin-converting enzyme 2 (ACE2), the primary receptor for the SARS-CoV-2 virus. Our goal was to investigate the effect of statins in COVID-19 patients in a large, diverse patient population across the United States containing nearly 120,000 patients diagnosed with COVID-19. We used propensity score matching of demographics, comorbidities, and medication indication to compare statin-treated patients (N = 2,297) with matched controls (N = 4,594). We observed a small, but statistically significant, decrease in mortality among patients prescribed statins (16.1%) when compared with matched COVID-19-positive controls (18.0 to 20.6%). These results support previous evidence that statins do not increase COVID-19-related mortality and may, in fact, have a mitigating effect on severity of the disease reflected in a slight reduction in mortality. Mixed findings on effects of statins in COVID-19 patients reported in the literature should prompt prospective randomized controlled trials in order to define better who might be advantaged with respect to clinical outcomes.

A review of studies published between 2000 and 2017 was conducted to understand students' engineering identity and their financial need within the community college setting. The review found that research studies used a variety of frameworks and methodologies to investigate community college engineering identity and financial need. In addition, scholars focused on investigating engineering identity across disciplines, rather than engineering identity within distinct sub-disciplines. Overall, studies focused on the importance of engineering transfer support and engagement. The review of current literature implicates that additional work is needed on engineering identity in relation to community college students with financial need, particularly in terms of more diverse methods and sampling. This study also points to the need of support and services that can understand, engage, and support students from community colleges with financial need passed on their identities.

Few therapeutic methods exist for preventing preterm birth (PTB), or delivery before completing 37 weeks of gestation. In the US, progesterone (P4) supplementation is the only FDA-approved drug for use in preventing recurrent spontaneous PTB. However, P4 has limited effectiveness, working in only approximately one-third of cases. Computational drug repositioning leverages data on existing drugs to discover novel therapeutic uses. We used a rank-based pattern-matching strategy to compare the differential gene expression signature for PTB to differential gene expression drug profiles in the Connectivity Map database and assigned a reversal score to each PTB-drug pair. Eighty-three drugs, including P4, had significantly reversed differential gene expression compared with that found for PTB. Many of these compounds have been evaluated in the context of pregnancy, with 13 belonging to pregnancy category A or B - indicating no known risk in human pregnancy. We focused our validation efforts on lansoprazole, a proton-pump inhibitor, which has a strong reversal score and a good safety profile. We tested lansoprazole in an animal inflammation model using LPS, which showed a significant increase in fetal viability compared with LPS treatment alone. These promising results demonstrate the effectiveness of the computational drug repositioning pipeline to identify compounds that could be effective in preventing PTB.

Background: Sleep disturbance has a prevalence of 30–78% in patients with advanced cancer. While pharmacotherapy is common, randomized controlled studies (RCTs) investigating available agents are limited. This study examines the efficacy and safety of temazepam or melatonin versus placebo for sleep in advanced cancer. Methods: This is a multicenter, randomized, double-blind, placebo-controlled study of temazepam, melatonin prolonged release (PR) or placebo for insomnia in patients with advanced cancer, and an insomnia severity index (ISI) score of >11. Results: Twenty-one participants were randomized: nine to temazepam, eight to melatonin, and four to placebo. Baseline characteristics between groups were similar. The adjusted mean difference in day 8 ISI score versus placebo was −9.1 (95% confidence interval [CI] −17.5, 0.7, p = 0.04) for temazepam and −9.6 (95% CI −18,−1.2, p = 0.03) for melatonin PR. There was no improvement in global quality of life. Both agents were well tolerated. Conclusion: Temazepam and melatonin PR were associated with a clinically significant improvement in patient-reported insomnia severity compared with placebo. Findings need confirmation with larger patient numbers. [ABSTRACT FROM AUTHOR]

Extracting information from scanned business documents, while a necessary commercial task, continues to be mostly done manually, requiring significant human effort. Current solutions for automated document information extraction still have limited capabilities in regards to user-required customizability and extraction of dataset-specific information, leaving the area as a very active field of research. In this paper, we propose modifications and improvements to our previously developed custom pipeline for extracting and tabulating key-value pairs from commercial invoice documents. Our design changes and additions adapt the pipeline to a wider variety of document types and use cases, primarily through the implementation of dataset-specific configuration files that promote customizability along with new technical modules that address both general and dataset-specific complexities. We compare our pipeline's performance against current machine learning and commercial solutions on a real-world dataset, and demonstrate that it is able to extract a wider variety of fields while maintaining competitive or greater accuracies compared to the alternate solutions. [ABSTRACT FROM AUTHOR]

This is a prospective, open-label, proof-of-concept study of tofacitinib, a Janus kinase inhibitor, as a steroid-sparing therapy in corticosteroid-dependent pulmonary sarcoidosis. Five patients with corticosteroid-dependent pulmonary sarcoidosis were treated with tofacitinib 5 mg twice daily. The primary endpoint was a [greater than or equal to] 50% reduction in corticosteroids at week 16 with no worsening in pulmonary function or respiratory symptoms. 60% of patients (3/5) met the primary endpoint. One patient was lost to follow up prior to steroid taper, and another was withdrawn due to worsening of known neurosarcoidosis. The three patients who met the primary endpoint each tapered to [less than or equal to] 5 mg/day prednisone, respiratory symptoms improved, and spirometry remained stable. In this proof-of-concept study, the addition of a JAK-inhibitor allowed 60% of patients with pulmonary sarcoidosis to successfully taper corticosteroids. JAK-inhibitors are a promising therapy for pulmonary sarcoidosis, which require further investigation in randomized trials. Trial Registration clinicaltrials.gov NCT03793439; registered Jan 4, 2019., Author(s): Marcia A. Friedman [sup.1], Brian Le [sup.1], Janelle Stevens [sup.2], Julianna Desmarais [sup.1], Daniel Seifer [sup.1], Kimberly Ogle [sup.3], Dongseok Choi [sup.1] [sup.3] [sup.4] [sup.5], Christina A. Harrington [sup.6], [...]

Background: Opioid switching is common, however, conversion tables have limitations. Guidelines suggest postswitch dose reduction, yet, observations show opioid doses may increase postswitch. Objectives: To document the opioid conversion factor postswitch in cancer, and whether pain and adverse effect outcomes differ between switched opioid groups. Design/Setting: This multicenter prospective longitudinal study included people with advanced cancer in Australia. Clinical data (demographics, opioids) and validated instruments (pain, adverse effects) were collected twice, seven days apart. Results: Opioid switch resulted in dose increase (median oral morphine equivalent daily dose 90 mg [interquartile range {IQR} 45–184] to 150 mg [IQR 79–270]), reduced average pain (5.1 [standard deviation {SD} 1.7] to 3.8 [SD 1.6]), and reduced adverse effects. Hydromorphone dose increased 2.5 times (IQR 1.0–3.6) above the original conversion factor used. Conclusions: Opioid switching resulted in overall dose increase, particularly when switching to hydromorphone. Higher preswitch dosing may require higher dose conversion ratios. Dose reduction postswitch risks undertreatment and may not be always appropriate. [ABSTRACT FROM AUTHOR]