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.

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.

Smart nanosystems: Bio-inspired technologies that interact with the host environment.

Nanoparticle technologies intended for human administration must be designed to interact with, and ideally leverage, a living host environment. Here, we describe smart nanosystems classified in two categories: (i) those that sense the host environment and respond and (ii) those that first prime the host environment to interact with engineered nanoparticles. Smart nanosystems have the potential to produce personalized diagnostic and therapeutic schema by using the local environment to drive material behavior and ultimately improve human health.

Comparison of Modular PEG Incorporation Strategies for Stabilization of Peptide-siRNA Nanocomplexes.

Nanoparticulate systems have shown great promise in overcoming the considerable trafficking barriers associated with systemic nucleic acid delivery, which must be addressed to unlock the full potential of technologies such as RNAi and gene editing in vivo. In addition to mediating the cytoplasmic delivery of nucleic cargo and shielding it from nuclease degradation and immunostimulation, nucleic-acid-containing nanomaterials delivered intravenously must also be stable in the bloodstream after administration to avoid toxicity and off-target delivery. To this end, the hydrophilic molecule polyethylene glycol (PEG) has been deployed in many different nanoparticle systems to prevent aggregation and recognition by the reticuloendothelial system. However, the optimal strategy for incorporating PEG into self-assembled nucleic acid delivery systems to obtain nanoparticle stability while retaining important functions such as receptor targeting and cargo activity remains unclear. In this work, we develop substantially improved formulations of tumor-penetrating nanocomplexes (TPNs), targeted self-assembled nanoparticles formulated with peptide carriers and siRNA that have been shown to mitigate tumor burden in an orthotopic model of ovarian cancer. We specifically sought to tailor TPNs for intravenous delivery by systematically comparing formulations with three different classes of modular PEG incorporation (namely PEG graft polymers, PEG lipids, and PEGylated peptide), each synthesized using straightforward bioconjugation techniques. We found that the addition of PEG lipids or PEGylated peptide carriers led to the formation of small and stable nanoparticles, but only nanoparticles formulated with PEGylated peptide carriers retained substantial activity in a gene silencing assay. In vivo, this formulation significantly decreased accumulation in off-target organs and improved initial availability in circulation compared to results from the original non-PEGylated particles. Thus, from among a set of candidate strategies, we identified TPNs with admixed PEGylated peptide carriers as the optimal formulation for systemic administration of siRNA on the basis of their performance in a battery of physicochemical and biological assays. Moreover, this optimized formulation confers pharmacologic advantages that may enable further translational development of tumor-penetrating nanocomplexes, highlighting the preclinical value of comparing formulation strategies and the relevance of this systematic approach for the development of other self-assembled nanomaterials.

In Vivo Biotransformation Rates of Organic Chemicals in Fish: Relationship with Bioconcentration and Biomagnification Factors.

In vivo dietary bioaccumulation experiments for 85 hydrophobic organic substances were conducted to derive the in vivo gastrointestinal biotransformation rates, somatic biotransformation rates, bioconcentration factors (BCF), and biomagnification factors (BMF) for improving methods for bioaccumulation assessment and to develop an in vivo biotransformation rate database for QSAR development and in vitro to in vivo biotransformation rate extrapolation. The capacity of chemicals to be biotransformed in fish was found to be highly dependent on the route of exposure. Somatic biotransformation was the dominant pathway for most chemicals absorbed via the respiratory route. Intestinal biotransformation was the dominant metabolic pathway for most chemicals absorbed via the diet. For substances not biotransformed or transformed exclusively in the body of the fish, the BCF and BMF appeared to be closely correlated. For substances subject to intestinal biotransformation, the same correlation did not apply. We conclude that intestinal biotransformation and bioavailability in water can modulate the relationship between the BCF and BMF. This study also supports a fairly simple rule of thumb that may be useful in the interpretation of dietary bioaccumulation tests; i.e., chemicals with a BMFL of <1 tend to exhibit BCFs based on either the freely dissolved (BCFWW,fd) or the total concentration (BCFWW,t) of the chemical in the water that is less than 5000.

The Wisconsin Assessment of the Social and Built Environment (WASABE): a multi-dimensional objective audit instrument for examining neighborhood effects on health.

BACKGROUND: Growing evidence suggests that mixed methods approaches to measuring neighborhood effects on health are needed. The Wisconsin Assessment of the Social and Built Environment (WASABE) is an objective audit tool designed as an addition to a statewide household-based health examination survey, the Survey of the Health of Wisconsin (SHOW), to objectively measure participant's neighborhoods. METHODS: This paper describes the development and implementation of the WASABE and examines the instrument's ability to capture a range of social and built environment features in urban and rural communities. A systematic literature review and formative research were used to create the tool. Inter-rater reliability parameters across items were calculated. Prevalence and density of features were estimated for strata formed according to several sociodemographic and urbanicity factors. RESULTS: The tool is highly reliable with over 81% of 115 derived items having percent agreement above 95%. It captured variance in neighborhood features in for a diverse sample of SHOW participants. Sidewalk density in neighborhoods surrounding households of participants living at less than 100% of the poverty level was 67% (95% confidence interval, 55-80%) compared to 34% (25-44%) for those living at greater than 400% of the poverty level. Walking and biking trails were present in 29% (19-39%) of participant buffer in urban areas compared to only 7% (2-12%) in rural communities. Significant environmental differences were also observed for white versus non-white, high versus low income, and college graduates versus individuals with lower level of education. CONCLUSIONS: The WASABE has strong inter-rater reliability and validity properties. It builds on previous work to provide a rigorous and standardized method for systematically gathering objective built and social environmental data in a number of geographic settings. Findings illustrate the complex milieu of built environment features found in participants neighborhoods and have relevance for future research, policy, and community engagement purposes.

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.

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.

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.

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.

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.

Poromatosis in pregnancy: a case of 8 eruptive poromas in the third trimester.

The poroid family of neoplasms includes hidroacanthoma simplex, eccrine poroma, dermal duct tumor, and poroid hidradenoma. These benign adnexal neoplasms are derived from the eccrine or apocrine sweat ducts or glands. Poroid neoplasms, including poromas, have been reported during pregnancy and have been hypothesized to be hormonally influenced. Poromatosis, the occurrence of multiple poromas, rarely has been reported in association with hidrotic ectodermal dysplasia, prior radiation therapy, and non-Hodgkin lymphoma occurring after chemotherapy. We report a case of eruptive poromatosis in pregnancy with 8 poromas occurring in the third trimester, further supporting the hypothesis of a hormonal association in the etiology of this neoplasm.

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.

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.

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.