Polymeric Micellar Nanoparticles Enable Image-guided Drug Delivery in Solid Tumors.

We report the development of a nanotechnology to co-deliver chemocoxib A with a reactive oxygen species (ROS)-activatable and COX-2 targeted pro-fluorescent probe, fluorocoxib Q (FQ) enabling real time visualization of COX-2 and CA drug delivery into solid cancers, using a di-block PPS 135 - b -POEGA 17 copolymer, selected for its intrinsic responsiveness to elevated reactive oxygen species (ROS), a key trait of the tumor microenvironment. FQ and CA were synthesized independently, then co-encapsulated within micellar PPS 135 - b -POEGA 17 co-polymeric nanoparticles (FQ-CA-NPs), and were assessed for cargo concentration, hydrodynamic diameter, zeta potential, and ROS-dependent cargo release. The uptake of FQ-CA-NPs in mouse mammary cancer cells and cargo release was assessed by fluorescence microscopy. Intravenous delivery of FQ-CA-NPs to mice harboring orthotopic mammary tumors, followed by vital optimal imaging, was used to assess delivery to tumors in vivo . The CA-FQ-NPs exhibited a hydrodynamic diameter of 109.2 ± 4.1 nm and a zeta potential (σ) of -1.59 ± 0.3 mV. Fluorescence microscopy showed ROS-dependent cargo release by FQ-CA-NPs in 4T1 cells, decreasing growth of 4T1 breast cancer cells, but not affecting growth of primary human mammary epithelial cells (HMECs). NP-derived fluorescence was detected in mammary tumors, but not in healthy organs. Tumor LC-MS/MS analysis identified both CA (2.38 nmol/g tumor tissue) and FQ (0.115 nmol/g tumor tissue), confirming the FQ-mediated image guidance of CA delivery in solid tumors. Thus, co-encapsulation of FQ and CA into micellar nanoparticles (FQ-CA-NPs) enabled ROS-sensitive drug release and COX-2-targeted visualization of solid tumors.

Structural optimization of siRNA conjugates for albumin binding achieves effective MCL1-directed cancer therapy.

The high potential of siRNAs to silence oncogenic drivers remains largely untapped due to the challenges of tumor cell delivery. Here, divalent lipid-conjugated siRNAs are optimized for in situ binding to albumin to improve pharmacokinetics and tumor delivery. Systematic variation of the siRNA conjugate structure reveals that the location of the linker branching site dictates tendency toward albumin association versus self-assembly, while the lipid hydrophobicity and reversibility of albumin binding also contribute to siRNA intracellular delivery. The lead structure increases tumor siRNA accumulation 12-fold in orthotopic triple negative breast cancer (TNBC) tumors over the parent siRNA. This structure achieves approximately 80% silencing of the anti-apoptotic oncogene MCL1 and yields better survival outcomes in three TNBC models than an MCL-1 small molecule inhibitor. These studies provide new structure-function insights on siRNA-lipid conjugate structures that are intravenously injected, associate in situ with serum albumin, and improve pharmacokinetics and tumor treatment efficacy.

Predicting glaucoma progression using deep learning framework guided by generative algorithm.

Glaucoma is a slowly progressing optic neuropathy that may eventually lead to blindness. To help patients receive customized treatment, predicting how quickly the disease will progress is important. Structural assessment using optical coherence tomography (OCT) can be used to visualize glaucomatous optic nerve and retinal damage, while functional visual field (VF) tests can be used to measure the extent of vision loss. However, VF testing is patient-dependent and highly inconsistent, making it difficult to track glaucoma progression. In this work, we developed a multimodal deep learning model comprising a convolutional neural network (CNN) and a long short-term memory (LSTM) network, for glaucoma progression prediction. We used OCT images, VF values, demographic and clinical data of 86 glaucoma patients with five visits over 12 months. The proposed method was used to predict VF changes 12 months after the first visit by combining past multimodal inputs with synthesized future images generated using generative adversarial network (GAN). The patients were classified into two classes based on their VF mean deviation (MD) decline: slow progressors (< 3 dB) and fast progressors (> 3 dB). We showed that our generative model-based novel approach can achieve the best AUC of 0.83 for predicting the progression 6 months earlier. Further, the use of synthetic future images enabled the model to accurately predict the vision loss even earlier (9 months earlier) with an AUC of 0.81, compared to using only structural (AUC = 0.68) or only functional measures (AUC = 0.72). This study provides valuable insights into the potential of using synthetic follow-up OCT images for early detection of glaucoma progression.

Immunotherapy in Biliary Tract Cancers: Current Standard-of-Care and Emerging Strategies.

Biliary tract cancers (BTCs), comprising intrahepatic, perihilar, and distal cholangiocarcinoma as well as gallbladder adenocarcinoma, continue to be challenging to manage. Conventional chemotherapy regimens for advanced disease are limited in both options and benefits, and more effective perioperative regimens are also needed. Over the last decade, immunotherapy has had a profound impact on the management of many solid tumor types, particularly in using immune checkpoint inhibition to enable a tumor-directed T cell response. Immunotherapy administered on its own has had limited utility in BTCs, in part due to a hostile immune microenvironment and the relative infrequency of biomarker-based tumor-agnostic indications for immunotherapy. However, immunotherapy in conjunction with chemotherapy, molecularly targeted therapies, and/or anti-angiogenic therapies has gained traction, supported by evidence that these agents can impart favorable immunomodulatory effects on the tumor microenvironment. The TOPAZ-1 trial led to the first BTC-specific immunotherapy approval, establishing the combination of durvalumab with gemcitabine and cisplatin as the preferred first-line treatment for advanced or metastatic disease. Recently, the KEYNOTE-966 trial showed positive results for the combination of pembrolizumab with gemcitabine and cisplatin in the same setting, adding further evidence for the addition of immune checkpoint inhibition to the standard chemotherapy backbone. Meanwhile, advances in the molecular profiling of BTCs has contributed to the recent proliferation of molecularly targeted therapeutics for the subset of BTCs harboring alterations in IDH1, FGFR2, MAP kinase signaling, HER2, and beyond, and there has been great interest in investigating combinations of these agents with immunotherapy. Emerging immunotherapy strategies beyond immune checkpoint inhibition are also being studied in BTCs, and these include immunostimulatory receptor agonists, Wnt signaling modulators, adoptive cell therapy, and cancer vaccines. A large number of trials are underway to explore promising new combinations and immune-targeted strategies, offering opportunities to expand the role of immunotherapy in BTC management in the near future.

Albumin-binding RNAi Conjugate for Carrier Free Treatment of Arthritis.

Osteoarthritis (OA) and rheumatoid arthritis (RA) are joint diseases that are associated with pain and lost quality of life. No disease modifying OA drugs are currently available. RA treatments are better established but are not always effective and can cause immune suppression. Here, an MMP13-selective siRNA conjugate was developed that, when delivered intravenously, docks onto endogenous albumin and promotes preferential accumulation in articular cartilage and synovia of OA and RA joints. MMP13 expression was diminished upon intravenous delivery of MMP13 siRNA conjugates, consequently decreasing multiple histological and molecular markers of disease severity, while also reducing clinical manifestations such as swelling (RA) and joint pressure sensitivity (RA and OA). Importantly, MMP13 silencing provided more comprehensive OA treatment efficacy than standard of care (steroids) or experimental MMP inhibitors. These data demonstrate the utility of albumin 'hitchhiking' for drug delivery to arthritic joints, and establish the therapeutic utility of systemically delivered anti-MMP13 siRNA conjugates in OA and RA. Editorial summary: Lipophilic siRNA conjugates optimized for albumin binding and "hitchhiking" can be leveraged to achieve preferential delivery to and gene silencing activity within arthritic joints. Chemical stabilization of the lipophilic siRNA enables intravenous siRNA delivery without lipid or polymer encapsulation. Using siRNA sequences targeting MMP13, a key driver of arthritis-related inflammation, albumin hitchhiking siRNA diminished MMP13, inflammation, and manifestations of osteoarthritis and rheumatoid arthritis at molecular, histological, and clinical levels, consistently outperforming clinical standards of care and small molecule MMP antagonists.

Core polymer optimization of ternary siRNA nanoparticles enhances in vivo safety, pharmacokinetics, and tumor gene silencing.

Gene silencing with siRNA nanoparticles (si-NPs) is promising but still clinically unrealized for inhibition of tumor driver genes. Ternary si-NPs containing siRNA, a single block NP core-forming polymer poly[(2-(dimethylamino)ethyl methacrylate)-co-(butyl methacrylate)] (DMAEMA-co-BMA, 50B), and an NP surface-forming diblock polymer 20 kDa poly(ethylene glycol)-block-50B (20kPEG-50B) have the potential to improve silencing activity in tumors due to the participation of both 50B and 20kPEG-50B in siRNA electrostatic loading and endosome disruptive activity. Functionally, single block 50B provides more potent endosomolytic activity, while 20kPEG-50B colloidally stabilizes the si-NPs. Here, we systematically explored the role of the molecular weight (MW) of the core polymer and of the core:surface polymer ratio on ternary si-NP performance. A library of ternary si-NPs was formulated with variation in the MW of the 50B polymer and in the ratio of the core and surface forming polymeric components. Increasing 50B core polymer MW and ratio improved si-NP in vitro gene silencing potency, endosome disruptive activity, and stability, but these features also correlated with cytotoxicity. Concomitant optimization of 50B size and ratio resulted in the identification of lead ternary si-NPs 50B4-DP100, 50B8-DP100, and 50B12-DP25, with potent activity and minimal toxicity. Following intravenous treatment in vivo, all lead si-NPs displayed negligible toxicological effects and enhanced pharmacokinetics and tumor gene silencing relative to more canonical binary si-NPs. Critically, a single 1 mg/kg intravenous injection of 50B8-DP100 si-NPs silenced the tumor driver gene Rictor at the protein level by 80% in an orthotopic breast tumor model. 50B8-DP100 si-NPs delivering siRictor were assessed for therapeutic efficacy in an orthotopic HCC70 mammary tumor model. This formulation significantly inhibited tumor growth compared to siControl-NP treatment. 50B8-DP100 si-NPs were also evaluated for safety and were well-tolerated following a multi-dose treatment scheme. This work provides new insight on ternary si-NP structure-function relationships and identifies core polymer optimization strategies that can yield safe si-NP formulations with potent oncogene silencing.

Structural Optimization of siRNA Conjugates for Albumin Binding Achieves Effective MCL1-Targeted Cancer Therapy.

The high potential for therapeutic application of siRNAs to silence traditionally undruggable oncogenic drivers remains largely untapped due to the challenges of tumor cell delivery. Here, siRNAs were optimized for in situ binding to albumin through C18 lipid modifications to improve pharmacokinetics and tumor delivery. Systematic variation of siRNA conjugates revealed a lead structure with divalent C18 lipids each linked through three repeats of hexaethylene glycol connected by phosphorothioate bonds. Importantly, we discovered that locating the branch site of the divalent lipid structure proximally (adjacent to the RNA) rather than at a more distal site (after the linker segment) promotes association with albumin, while minimizing self-assembly and lipoprotein association. Comparison to higher albumin affinity (diacid) lipid variants and siRNA directly conjugated to albumin underscored the importance of conjugate hydrophobicity and reversibility of albumin binding for siRNA delivery and bioactivity in tumors. The lead conjugate increased tumor siRNA accumulation 12-fold in orthotopic mouse models of triple negative breast cancer over the parent siRNA. When applied for silencing of the anti-apoptotic oncogene MCL-1, this structure achieved approximately 80% MCL1 silencing in orthotopic breast tumors. Furthermore, application of the lead conjugate structure to target MCL1 yielded better survival outcomes in three independent, orthotopic, triple negative breast cancer models than an MCL1 small molecule inhibitor. These studies provide new structure-function insights on optimally leveraging siRNA-lipid conjugate structures that associate in situ with plasma albumin for molecular-targeted cancer therapy.

The Burden of Proof studies: assessing the evidence of risk.

Exposure to risks throughout life results in a wide variety of outcomes. Objectively judging the relative impact of these risks on personal and population health is fundamental to individual survival and societal prosperity. Existing mechanisms to quantify and rank the magnitude of these myriad effects and the uncertainty in their estimation are largely subjective, leaving room for interpretation that can fuel academic controversy and add to confusion when communicating risk. We present a new suite of meta-analyses-termed the Burden of Proof studies-designed specifically to help evaluate these methodological issues objectively and quantitatively. Through this data-driven approach that complements existing systems, including GRADE and Cochrane Reviews, we aim to aggregate evidence across multiple studies and enable a quantitative comparison of risk-outcome pairs. We introduce the burden of proof risk function (BPRF), which estimates the level of risk closest to the null hypothesis that is consistent with available data. Here we illustrate the BPRF methodology for the evaluation of four exemplar risk-outcome pairs: smoking and lung cancer, systolic blood pressure and ischemic heart disease, vegetable consumption and ischemic heart disease, and unprocessed red meat consumption and ischemic heart disease. The strength of evidence for each relationship is assessed by computing and summarizing the BPRF, and then translating the summary to a simple star rating. The Burden of Proof methodology provides a consistent way to understand, evaluate and summarize evidence of risk across different risk-outcome pairs, and informs risk analysis conducted as part of the Global Burden of Diseases, Injuries, and Risk Factors Study.

Automatic Artifact Detection Algorithm in Fetal MRI.

Fetal MR imaging is subject to artifacts including motion, chemical shift, and radiofrequency artifacts. Currently, such artifacts are detected by the MRI operator, a process which is subjective, time consuming, and prone to errors. We propose a novel algorithm, RISE-Net, that can consistently, automatically, and objectively detect artifacts in 3D fetal MRI. It makes use of a CNN ensemble approach where the first CNN aims to identify and classify any artifacts in the image, and the second CNN uses regression to determine the severity of the detected artifacts. The main mechanism in RISE-Net is the stacked Residual, Inception, Squeeze and Excitation (RISE) blocks. This classification network achieved an accuracy of 90.34% and a F1 score of 90.39% and outperformed other state-of-the-art architectures, such as VGG-16, Inception, ResNet-50, ReNet-Inception, SE-ResNet, and SE-Inception. The severity regression network had an MSE of 0.083 across all classes. The presented algorithm facilitates rapid and accurate fetal MRI quality assurance that can be implemented into clinical use.

Discovery of a Redox-Activatable Chemical Probe for Detection of Cyclooxygenase-2 in Cells and Animals.

Cyclooxygenase-2 (COX-2) expression is up-regulated in inflammatory tissues and many premalignant and malignant tumors. Assessment of COX-2 protein in vivo, therefore, promises to be a powerful strategy to distinguish pathologic cells from normal cells in a complex disease setting. Herein, we report the first redox-activatable COX-2 probe, fluorocoxib Q (FQ), for in vivo molecular imaging of pathogenesis. FQ inhibits COX-2 selectively in purified enzyme and cell-based assays. FQ exhibits extremely low fluorescence and displays time- and concentration-dependent fluorescence enhancement upon exposure to a redox environment. FQ enters the cells freely and binds to the COX-2 enzyme. FQ exhibits high circulation half-life and metabolic stability sufficient for target site accumulation and demonstrates COX-2-targeted uptake and retention in cancer cells and pathologic tissues. Once taken up, it undergoes redox-mediated transformation into a fluorescent compound fluorocoxib Q-H that results in high signal-to-noise contrast and differentiates pathologic tissues from non-pathologic tissues for real-time in vivo imaging.

Fetal Organ Anomaly Classification Network for Identifying Organ Anomalies in Fetal MRI.

Rapid development in Magnetic Resonance Imaging (MRI) has played a key role in prenatal diagnosis over the last few years. Deep learning (DL) architectures can facilitate the process of anomaly detection and affected-organ classification, making diagnosis more accurate and observer-independent. We propose a novel DL image classification architecture, Fetal Organ Anomaly Classification Network (FOAC-Net), which uses squeeze-and-excitation (SE) and naïve inception (NI) modules to automatically identify anomalies in fetal organs. This architecture can identify normal fetal anatomy, as well as detect anomalies present in the (1) brain, (2) spinal cord, and (3) heart. In this retrospective study, we included fetal 3-dimensional (3D) SSFP sequences of 36 participants. We classified the images on a slice-by-slice basis. FOAC-Net achieved a classification accuracy of 85.06, 85.27, 89.29, and 82.20% when predicting brain anomalies, no anomalies (normal), spinal cord anomalies, and heart anomalies, respectively. In a comparison study, FOAC-Net outperformed other state-of-the-art classification architectures in terms of class-average F1 and accuracy. This work aims to develop a novel classification architecture identifying the affected organs in fetal MRI.

Postpartum Length of Stay and Hospital Readmission Before and During the Coronavirus Disease 2019 (COVID-19) Pandemic.

OBJECTIVE: To compare postpartum hospitalization length of stay (LOS) and hospital readmission among obstetric patients before (March 2017-February 2020; prepandemic) and during the coronavirus disease 2019 (COVID-19) pandemic (March 2020-February 2021). METHODS: We conducted a retrospective cohort study, using Epic Systems' Cosmos research platform, of obstetric patients who delivered between March 1, 2017, and February 28, 2021, at 20-44 weeks of gestation and were discharged within 7 days of delivery. The primary outcome was short postpartum hospitalization LOS (less than two midnights for vaginal births and less than three midnights for cesarean births) and secondary outcome was hospital readmission within 6 weeks of postpartum hospitalization discharge. Analyses compared outcomes before and during the pandemic using standardized differences and Bayesian logistic mixed-effects models, among all births and stratified by mode of delivery. RESULTS: Of the 994,268 obstetric patients in the study cohort, 742,113 (74.6%) delivered prepandemic and 252,155 (25.4%) delivered during the COVID-19 pandemic. During the COVID-19 pandemic, the percentage of short postpartum hospitalizations increased among all births (28.7-44.5%), vaginal births (25.4-39.5%), and cesarean births (35.3-55.1%), which was consistent with the adjusted analysis (all births: adjusted odds ratio [aOR] 2.35, 99% credible interval 2.32-2.39; vaginal births: aOR 2.14, 99% credible interval 2.11-2.18; cesarean births aOR 2.90, 99% credible interval 2.83-2.98). Although short postpartum hospitalizations were more common during the COVID-19 pandemic, there was no change in readmission in the unadjusted (1.4% vs 1.6%, standardized difference=0.009) or adjusted (aOR 1.02, 99% credible interval 0.97-1.08) analyses for all births or when stratified by mode of delivery. CONCLUSION: Short postpartum hospitalization LOS was significantly more common during the COVID-19 pandemic for obstetric patients with no change in hospital readmissions within 6 weeks of postpartum hospitalization discharge. The COVID-19 pandemic created a natural experiment, suggesting shorter postpartum hospitalization may be reasonable for patients who are self-identified or health care professional-identified as appropriate for discharge.

Birth Hospital Length of Stay and Rehospitalization During COVID-19.

OBJECTIVES: To determine if birth hospitalization length of stay (LOS) and infant rehospitalization changed during the coronavirus disease 2019 (COVID-19) era among healthy, term infants. METHODS: Retrospective cohort study using Epic's Cosmos data from 35 health systems of term infants discharged ≤5 days of birth. Short birth hospitalization LOS (vaginal birth <2 midnights; cesarean birth <3 midnights) and, secondarily, infant rehospitalization ≤7 days after birth hospitalization discharge were compared between the COVID-19 (March 1 to August 31, 2020) and prepandemic eras (March 1 to August 31, 2017, 2018, 2019). Mixed-effects models were used to estimate adjusted odds ratios (aORs) comparing the eras. RESULTS: Among 202 385 infants (57 110 from the COVID-19 era), short birth hospitalization LOS increased from 28.5% to 43.0% for all births (vaginal: 25.6% to 39.3%, cesarean: 40.1% to 61.0%) during the pandemic and persisted after multivariable adjustment (all: aOR 2.30, 95% confidence interval [CI] 2.25-2.36; vaginal: aOR 2.12, 95% CI 2.06-2.18; cesarean: aOR 3.01, 95% CI 2.87-3.15). Despite shorter LOS, infant rehospitalizations decreased slightly during the pandemic (1.2% to 1.1%); results were similar in adjusted analysis (all: aOR 0.83, 95% CI 0.76-0.92; vaginal: aOR 0.82, 95% CI 0.74-0.91; cesarean: aOR 0.87, 95% CI 0.69-1.10). There was no change in the proportion of rehospitalization diagnoses between eras. CONCLUSIONS: Short infant LOS was 51% more common in the COVID-19 era, yet infant rehospitalization within a week did not increase. This natural experiment suggests shorter birth hospitalization LOS among family- and clinician-selected, healthy term infants may be safe with respect to infant rehospitalization, although examination of additional outcomes is needed.

Medical Augmentation (Med-Aug) for Optimal Data Augmentation in Medical Deep Learning Networks.

Deep learning (DL) algorithms have become an increasingly popular choice for image classification and segmentation tasks; however, their range of applications can be limited. Their limitation stems from them requiring ample data to achieve high performance and adequate generalizability. In the case of clinical imaging data, images are not always available in large quantities. This issue can be alleviated by using data augmentation (DA) techniques. The choice of DA is important because poor selection can possibly hinder the performance of a DL algorithm. We propose a DA policy search algorithm that offers an extended set of transformations that accommodate the variations in biomedical imaging datasets. The algorithm makes use of the efficient and high-dimensional optimizer Bi-Population Covariance Matrix Adaptation Evolution Strategy (BIPOP-CMA-ES) and returns an optimal DA policy based on any input imaging dataset and a DL algorithm. Our proposed algorithm, Medical Augmentation (Med-Aug), can be implemented by other researchers in related medical DL applications to improve their model's performance. Furthermore, we present our found optimal DA policies for a variety of medical datasets and popular segmentation networks for other researchers to use in related tasks.

Changes in body mass index, obesity, and overweight in Southern Africa development countries, 1990 to 2019: Findings from the Global Burden of Disease, Injuries, and Risk Factors Study.

BACKGROUND: High body mass index (BMI) is associated with stroke, ischemic heart disease (IHD), and type 2 diabetes mellitus (T2DM). An epidemiological analysis of the prevalence of high BMI, stroke, IHD, and T2DM was conducted for 16 Southern Africa Development Community (SADC) using Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study data. METHODS: GBD obtained data from vital registration, verbal autopsy, and ICD codes. Prevalence of high BMI (≥25 kg/m2), stroke, IHD, and T2DM attributed to high BMI were calculated. Cause of Death Ensemble Model and Spatiotemporal Gaussian regression was used to estimate mortality due to stroke, IHD, and T2DM attributable to high BMI. RESULTS: Obesity in adult females increased 1.54-fold from 12.0% (uncertainty interval [UI]: 11.5-12.4) to 18.5% (17.9-19.0), whereas in adult males, obesity nearly doubled from 4.5 (4.3-4.8) to 8.8 (8.5-9.2). In children, obesity more than doubled in both sexes, and overweight increased by 27.4% in girls and by 37.4% in boys. Mean BMI increased by 0.7 from 22.4 (21.6-23.1) to 23.1 (22.3-24.0) in adult males, and by 1.0 from 23.8 (22.9-24.7) to 24.8 (23.8-25.8) in adult females. South Africa 44.7 (42.5-46.8), Swaziland 33.9 (31.7-36.0) and Lesotho 31.6 (29.8-33.5) had the highest prevalence of obesity in 2019. The corresponding prevalence in males for the three countries were 19.1 (17.5-20.7), 19.3 (17.7-20.8), and 9.2 (8.4-10.1), respectively. The DRC and Madagascar had the least prevalence of adult obesity, from 5.6 (4.8-6.4) and 7.0 (6.1-7.9), respectively in females in 2019, and in males from 4.9 (4.3-5.4) in the DRC to 3.9 (3.4-4.4) in Madagascar. CONCLUSIONS: The prevalence of high BMI is high in SADC. Obesity more than doubled in adults and nearly doubled in children. The 2019 mean BMI for adult females in seven countries exceeded 25 kg/m2. SADC countries are unlikely to meet UN2030 SDG targets. Prevalence of high BMI should be studied locally to help reduce morbidity.

Coronavirus Disease 2019 (COVID-19) Pandemic and Pregnancy Outcomes in a U.S. Population.

OBJECTIVE: To examine whether the coronavirus disease 2019 (COVID-19) pandemic altered risk of adverse pregnancy-related outcomes and whether there were differences by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection status among pregnant women. METHODS: In this retrospective cohort study using Epic's Cosmos research platform, women who delivered during the pandemic (March-December 2020) were compared with those who delivered prepandemic (matched months 2017-2019). Within the pandemic epoch, those who tested positive for SARS-CoV-2 infection were compared with those with negative test results or no SARS-CoV-2 diagnosis. Comparisons were performed using standardized differences, with a value greater than 0.1 indicating meaningful differences between groups. RESULTS: Among 838,489 women (225,225 who delivered during the pandemic), baseline characteristics were similar between epochs. There were no significant differences in adverse pregnancy outcomes between epochs (standardized difference<0.10). In the pandemic epoch, 108,067 (48.0%) women had SARS-CoV-2 testing available; of those, 7,432 (6.9%) had positive test results. Compared with women classified as negative for SARS-CoV-2 infection, those who tested positive for SARS-CoV-2 infection were less likely to be non-Hispanic White or Asian or to reside in the Midwest and more likely to be Hispanic, have public insurance, be obese, and reside in the South or in high social vulnerability ZIP codes. There were no significant differences in the frequency of preterm birth (8.5% vs 7.6%, standardized difference=0.032), stillbirth (0.4% vs 0.4%, standardized difference=-0.002), small for gestational age (6.4% vs 6.5%, standardized difference=-0.002), large for gestational age (7.7% vs 7.7%, standardized difference=-0.001), hypertensive disorders of pregnancy (16.3% vs 15.8%, standardized difference=0.014), placental abruption (0.5% vs 0.4%, standardized difference=0.007), cesarean birth (31.2% vs 29.4%, standardized difference=0.039), or postpartum hemorrhage (3.4% vs 3.1%, standardized difference=0.019) between those who tested positive for SARS-CoV-2 infection and those classified as testing negative. CONCLUSION: In a geographically diverse U.S. cohort, the frequency of adverse pregnancy-related outcomes did not differ between those delivering before compared with during the pandemic, nor between those classified as positive compared with negative for SARS-CoV-2 infection during pregnancy.

Cross Attention Squeeze Excitation Network (CASE-Net) for Whole Body Fetal MRI Segmentation.

Segmentation of the fetus from 2-dimensional (2D) magnetic resonance imaging (MRI) can aid radiologists with clinical decision making for disease diagnosis. Machine learning can facilitate this process of automatic segmentation, making diagnosis more accurate and user independent. We propose a deep learning (DL) framework for 2D fetal MRI segmentation using a Cross Attention Squeeze Excitation Network (CASE-Net) for research and clinical applications. CASE-Net is an end-to-end segmentation architecture with relevant modules that are evidence based. The goal of CASE-Net is to emphasize localization of contextual information that is relevant in biomedical segmentation, by combining attention mechanisms with squeeze-and-excitation (SE) blocks. This is a retrospective study with 34 patients. Our experiments have shown that our proposed CASE-Net achieved the highest segmentation Dice score of 87.36%, outperforming other competitive segmentation architectures.

A Toxicokinetic Framework and Analysis Tool for Interpreting Organisation for Economic Co-operation and Development Guideline 305 Dietary Bioaccumulation Tests.

The Organisation for Economic Co-operation and Development guideline 305 for bioaccumulation testing in fish includes the option to conduct a dietary test for assessing a chemical's bioaccumulation behavior. However, the one-compartment toxicokinetic model that is used in the guidelines to analyze the results from dietary bioaccumulation tests is not consistent with the current state of the science, experimental practices, and information needs for bioaccumulation and risk assessment. The present study presents 1) a 2-compartment toxicokinetic modeling framework for describing the bioaccumulation of neutral hydrophobic organic chemicals in fish and 2) an associated toxicokinetic analysis tool (absorption, distribution, metabolism, and excretion [ADME] B calculator) for the analysis and interpretation of dietary bioaccumulation test data from OECD-305 dietary tests. The model framework and ADME-B calculator are illustrated by analysis of fish dietary bioaccumulation test data for 238 substances representing different structural classes and susceptibilities to biotransformation. The ADME of the chemicals is determined from dietary bioaccumulation tests and bioconcentration factors, biomagnification factors, and somatic and intestinal biotransformation rates. The 2-compartment fish toxicokinetic model can account for the effect of the exposure pathway on bioaccumulation, which the one-compartment model cannot. This insight is important for applying a weight-of-evidence approach to bioaccumulation assessment where information from aqueous and dietary test endpoints can be integrated to improve the evaluation of a chemical's bioaccumulation potential. Environ Toxicol Chem 2019;39:171-188. © 2019 SETAC.