The prognostic effect of mechanical, ultrastructural, and ECM signatures in glioblastoma core and rim.

Glioblastoma (GBM) is a highly invasive, aggressive brain cancer that carries a median survival of 15 months and is resistant to standard therapeutics. Recent studies have demonstrated that intratumoral heterogeneity plays a critical role in promoting resistance by mediating tumor adaptation through microenvironmental cues. GBM can be separated into two distinct regions-a core and a rim, which are thought to drive specific aspects of tumor evolution. These differences in tumor progression are regulated by the diverse biomolecular and biophysical signals in these regions, but the acellular biophysical characteristics remain poorly described. This study investigates the mechanical and ultrastructural characteristics of the tumor extracellular matrix (ECM) in patient-matched GBM core and rim tissues. Seven patient-matched tumor core and rim samples and one non-neoplastic control were analyzed using atomic force microscopy, scanning electron microscopy, and immunofluorescence imaging to quantify mechanical, ultrastructural, and ECM composition changes. The results reveal significant differences in biophysical parameters between GBM core, rim, and non-neoplastic tissues. The GBM core is stiffer, denser, and is rich in ECM proteins hyaluronic acid and tenascin-C when compared to tumor rim and non-neoplastic tissues. These alterations are intimately related and have prognostic effect with stiff, dense tissue correlating with longer progression-free survival. These findings reveal new insights into the spatial heterogeneity of biophysical parameters in the GBM tumor microenvironment and identify a set of characteristics that may correlate with patient prognosis. In the long term, these characteristics may aid in the development of strategies to combat therapeutic resistance.

Pulmonary Fibrosis Diagnosis and Disease Progression Detected Via Hair Metabolome Analysis.

BACKGROUND: Fibrotic interstitial lung disease is often identified late due to non-specific symptoms, inadequate access to specialist care, and clinical unawareness precluding proper and timely treatment. Biopsy histological analysis is definitive but rarely performed due to its invasiveness. Diagnosis typically relies on high-resolution computed tomography, while disease progression is evaluated via frequent pulmonary function testing. This study tested the hypothesis that pulmonary fibrosis diagnosis and progression could be non-invasively and accurately evaluated from the hair metabolome, with the longer-term goal to minimize patient discomfort. METHODS: Hair specimens collected from pulmonary fibrosis patients (n = 56) and healthy subjects (n = 14) were processed for metabolite extraction using 2DLC/MS-MS, and data were analyzed via machine learning. Metabolomic data were used to train machine learning classification models tuned via a rigorous combination of cross validation, feature selection, and testing with a hold-out dataset to evaluate classifications of diseased vs. healthy subjects and stable vs. progressed disease. RESULTS: Prediction of pulmonary fibrosis vs. healthy achieved AUROCTRAIN = 0.888 (0.794-0.982) and AUROCTEST = 0.908, while prediction of stable vs. progressed disease achieved AUROCTRAIN = 0.833 (0.784 - 0.882) and AUROCTEST = 0. 799. Top metabolites for diagnosis included ornithine, 4-(methylnitrosamino)-1-3-pyridyl-N-oxide-1-butanol, Thr-Phe, desthiobiotin, and proline. Top metabolites for progression included azelaic acid, Thr-Phe, Ala-Tyr, indoleacetyl glutamic acid, and cytidine. CONCLUSION: This study provides novel evidence that pulmonary fibrosis diagnosis and progression may in principle be evaluated from the hair metabolome. Longer term, this approach may facilitate non-invasive and accurate detection and monitoring of fibrotic lung diseases.

Drug-loaded nanoparticles for cancer therapy: a high-throughput multicellular agent-based modeling study.

Interactions between biological systems and nanomaterials have become an important area of study due to the application of nanomaterials in medicine. In particular, the application of nanomaterials for cancer diagnosis or treatment presents a challenging opportunity due to the complex biology of this disease spanning multiple time and spatial scales. A system-level analysis would benefit from mathematical modeling and computational simulation to explore the interactions between anticancer drug-loaded nanoparticles (NPs), cells, and tissues, and the associated parameters driving this system and a patient's overall response. Although a number of models have explored these interactions in the past, few have focused on simulating individual cell-NP interactions. This study develops a multicellular agent-based model of cancer nanotherapy that simulates NP internalization, drug release within the cell cytoplasm, "inheritance" of NPs by daughter cells at cell division, cell pharmacodynamic response to the intracellular drug, and overall drug effect on tumor dynamics. A large-scale parallel computational framework is used to investigate the impact of pharmacokinetic design parameters (NP internalization rate, NP decay rate, anticancer drug release rate) and therapeutic strategies (NP doses and injection frequency) on the tumor dynamics. In particular, through the exploration of NP "inheritance" at cell division, the results indicate that cancer treatment may be improved when NPs are inherited at cell division for cytotoxic chemotherapy. Moreover, smaller dosage of cytostatic chemotherapy may also improve inhibition of tumor growth when cell division is not completely inhibited. This work suggests that slow delivery by "heritable" NPs can drive new dimensions of nanotherapy design for more sustained therapeutic response.

Spatially Resolved Microglia/Macrophages in Recurrent Glioblastomas Overexpress Fatty Acid Metabolism and Phagocytic Genes.

BACKGROUND: Glioblastoma (GBM) tumors are rich in tumor-associated microglia/macrophages. Changes associated with treatment in this specific cell population are poorly understood. Therefore, we studied changes in gene expression of tumor-associated microglia/macrophages (Iba1+) cells in de novo versus recurrent GBMs. METHODS: NanoString GeoMx® Digital Spatial Transcriptomic Profiling of microglia/macrophages (Iba1+) and glial cells (Gfap+) cells identified on tumor sections was performed on paired de novo and recurrent samples obtained from three IDH-wildtype GBM patients. The impact of differentially expressed genes on patient survival was evaluated using publicly available data. RESULTS: Unsupervised analyses of the NanoString GeoMx® Digital Spatial Profiling data revealed clustering based on the transcriptomic data from Iba1+ and Gfap+ cells. As expected, conventional differential gene expression and enrichment analyses revealed upregulation of immune-function-related genes in Iba1+ cells compared to Gfap+ cells. A focused differential gene expression analysis revealed upregulation of phagocytosis and fatty acid/lipid metabolism genes in Iba1+ cells in recurrent GBM samples compared to de novo GBM samples. Importantly, of these genes, the lipid metabolism gene PLD3 consistently correlated with survival in multiple different publicly available datasets. CONCLUSION: Tumor-associated microglia/macrophages in recurrent GBM overexpress genes involved in fatty acid/lipid metabolism. Further investigation is needed to fully delineate the role of PLD phospholipases in GBM progression.

Identification of Idiopathic Pulmonary Fibrosis and Prediction of Disease Severity via Machine Learning Analysis of Comprehensive Metabolic Panel and Complete Blood Count Data.

BACKGROUND: Diagnosis of idiopathic pulmonary fibrosis (IPF) typically relies on high-resolution computed tomography imaging (HRCT) or histopathology, while monitoring disease severity is done via frequent pulmonary function testing (PFT). More reliable and convenient methods of diagnosing fibrotic interstitial lung disease (ILD) type and monitoring severity would allow for early identification and enhance current therapeutic interventions. This study tested the hypothesis that a machine learning (ML) ensemble analysis of comprehensive metabolic panel (CMP) and complete blood count (CBC) data can accurately distinguish IPF from connective tissue disease ILD (CTD-ILD) and predict disease severity as seen with PFT. METHODS: Outpatient data with diagnosis of IPF or CTD-ILD (n = 103 visits by 53 patients) were analyzed via ML methodology to evaluate (1) IPF vs CTD-ILD diagnosis; (2) %predicted Diffusing Capacity of Lung for Carbon Monoxide (DLCO) moderate or mild vs severe; (3) %predicted Forced Vital Capacity (FVC) moderate or mild vs severe; and (4) %predicted FVC mild vs moderate or severe. RESULTS: ML methodology identified IPF from CTD-ILD with AUCTEST = 0.893, while PFT was classified as DLCO moderate or mild vs severe with AUCTEST = 0.749, FVC moderate or mild vs severe with AUCTEST = 0.741, and FVC mild vs moderate or severe with AUCTEST = 0.739. Key features included albumin, alanine transaminase, %lymphocytes, hemoglobin, %eosinophils, white blood cell count, %monocytes, and %neutrophils. CONCLUSION: Analysis of CMP and CBC data via proposed ML methodology offers the potential to distinguish IPF from CTD-ILD and predict severity on associated PFT with accuracy that meets or exceeds current clinical practice.

Anti-tumor effect of pH-sensitive drug-loaded nanoparticles optimized via an integrated computational/experimental approach.

The acidic pH of tumor tissue has been used to trigger drug release from nanoparticles. However, dynamic interactions between tumor pH and vascularity present challenges to optimize therapy to particular microenvironment conditions. Despite recent development of pH-sensitive nanomaterials that can accurately quantify drug release from nanoparticles, tailoring release to maximize tumor response remains elusive. This study hypothesizes that a computational modeling-based platform that simulates the heterogeneously vascularized tumor microenvironment can enable evaluation of the complex intra-tumoral dynamics involving nanoparticle transport and pH-dependent drug release, and predict optimal nanoparticle parameters to maximize the response. To this end, SPNCD nanoparticles comprising superparamagnetic cores of iron oxide (Fe3O4) and a poly(lactide-co-glycolide acid) shell loaded with doxorubicin (DOX) were fabricated. Drug release was measured in vitro as a function of pH. A 2D model of vascularized tumor growth was calibrated to experimental data and used to evaluate SPNCD effect as a function of drug release rate and tissue vascular heterogeneity. Simulations show that pH-dependent drug release from SPNCD delays tumor regrowth more than DOX alone across all levels of vascular heterogeneity, and that SPNCD significantly inhibit tumor radius over time compared to systemic DOX. The minimum tumor radius forecast by the model was comparable to previous in vivo SPNCD inhibition data. Sensitivity analyses of the SPNCD pH-dependent drug release rate indicate that slower rates are more inhibitory than faster rates. We conclude that an integrated computational and experimental approach enables tailoring drug release by pH-responsive nanomaterials to maximize the tumor response.

Disease diagnosis and severity classification in pulmonary fibrosis using carbonyl volatile organic compounds in exhaled breath.

BACKGROUND: Pathophysiological conditions underlying pulmonary fibrosis remain poorly understood. Exhaled breath volatile organic compounds (VOCs) have shown promise for lung disease diagnosis and classification. In particular, carbonyls are a byproduct of oxidative stress, associated with fibrosis in the lungs. To explore the potential of exhaled carbonyl VOCs to reflect underlying pathophysiological conditions in pulmonary fibrosis, this proof-of-concept study tested the hypothesis that volatile and low abundance carbonyl compounds could be linked to diagnosis and associated disease severity. METHODS: Exhaled breath samples were collected from outpatients with a diagnosis of Idiopathic Pulmonary Fibrosis (IPF) or Connective Tissue related Interstitial Lung Disease (CTD-ILD) with stable lung function for 3 months before enrollment, as measured by pulmonary function testing (PFT) DLCO (%), FVC (%) and FEV1 (%). A novel microreactor was used to capture carbonyl compounds in the breath as direct output products. A machine learning workflow was implemented with the captured carbonyl compounds as input features for classification of diagnosis and disease severity based on PFT (DLCO and FVC normal/mild vs. moderate/severe; FEV1 normal/mild/moderate vs. moderately severe/severe). RESULTS: The proposed approach classified diagnosis with AUROC=0.877 ± 0.047 in the validation subsets. The AUROC was 0.820 ± 0.064, 0.898 ± 0.040, and 0.873 ± 0.051 for disease severity based on DLCO, FEV1, and FVC measurements, respectively. Eleven key carbonyl VOCs were identified with the potential to differentiate diagnosis and to classify severity. CONCLUSIONS: Exhaled breath carbonyl compounds can be linked to pulmonary function and fibrotic ILD diagnosis, moving towards improved pathophysiological understanding of pulmonary fibrosis.

Rising role of 3D-printing in delivery of therapeutics for infectious disease.

Modern drug delivery to tackle infectious disease has drawn close to personalizing medicine for specific patient populations. Challenges include antibiotic-resistant infections, healthcare associated infections, and customizing treatments for local patient populations. Recently, 3D-printing has become a facilitator for the development of personalized pharmaceutic drug delivery systems. With a variety of manufacturing techniques, 3D-printing offers advantages in drug delivery development for controlled, fine-tuned release and platforms for different routes of administration. This review summarizes 3D-printing techniques in pharmaceutics and drug delivery focusing on treating infectious diseases, and discusses the influence of 3D-printing design considerations on drug delivery platforms targeting these diseases. Additionally, applications of 3D-printing in infectious diseases are summarized, with the goal to provide insight into how future delivery innovations may benefit from 3D-printing to address the global challenges in infectious disease.

A clinical marker-based modeling framework to preoperatively predict lymph node and vascular space involvement in endometrial cancer patients.

INTRODUCTION: Endometrial cancer (EC) has high mortality at advanced stages. Poor prognostic factors include grade 3 tumors, deep myometrial invasion, lymph node metastasis (LNM), and lymphovascular space invasion (LVSI). Preoperative knowledge of patients at higher risk of lymph node involvement, when such involvement is not suspected, would benefit surgery planning and patient prognosis. This study implements an ensemble machine learning approach that evaluates Cancer Antigen 125 (CA125) along with histologic type, preoperative grade, and age to predict LVSI, LNM and stage in EC patients. METHODS: A retrospective chart review spanning January 2000 to January 2015 at a regional hospital was performed. Women 18 years or older with a diagnosis of EC and preoperative or within one-week CA125 measurement were included (n = 842). An ensemble machine learning approach was implemented based on a stacked generalization technique to evaluate CA125 in combination with histologic type, preoperative grade, and age as predictors, and LVSI, LNM and disease stage as outcomes. RESULTS: The ensemble approach predicted LNM and LVSI in EC patients with AUROCTEST of 0.857 and 0.750, respectively, and predicted disease stage with AUROCTEST of 0.665. The approach achieved AUROCTEST for LVSI and LNM of 0.750 and 0.643 for grade 1 patients, and of 0.689 and 0.952 for grade 2 patients, respectively. CONCLUSION: An ensemble machine learning approach offers the potential to preoperatively predict LVSI, LNM and stage in EC patients with adequate accuracy based on CA125, histologic type, preoperative grade, and age.

Mesh and layered electrospun fiber architectures as vehicles for Lactobacillus acidophilus and Lactobacillus crispatus intended for vaginal delivery.

Bacterial vaginosis (BV) is a recurrent condition that affects millions of women worldwide. The use of probiotics is a promising alternative or an adjunct to traditional antibiotics for BV prevention and treatment. However, current administration regimens often require daily administration, thus contributing to low user adherence and recurrence. Here, electrospun fibers were designed to separately incorporate and sustain two lactic acid producing model organisms, Lactobacillus crispatus (L. crispatus) and Lactobacillus acidophilus (L. acidophilus). Fibers were made of polyethylene oxide and polylactic-co-glycolic acid in two different architectures, one with distinct layers and the other with co-spun components. Degradation of mesh and layered fibers was evaluated via mass loss and scanning electron microscopy. The results show that after 48 h and 6 days, cultures of mesh and layered fibers yielded as much as 108 and 109 CFU probiotic/mg fiber in total, respectively, with corresponding daily recovery on the order of 108 CFU/(mg·day). In addition, cultures of the fibers yielded lactic acid and caused a significant reduction in pH, indicating a high level of metabolic activity. The formulations did not affect vaginal keratinocyte viability or cell membrane integrity in vitro. Finally, mesh and layered probiotic fiber dosage forms demonstrated inhibition of Gardnerella, one of the most prevalent and abundant bacteria associated with BV, respectively resulting in 8- and 6.5-log decreases in Gardnerella viability in vitro after 24 h. This study provides initial proof of concept that mesh and layered electrospun fiber architectures developed as dissolving films may offer a viable alternative to daily probiotic administration.

Computational Modeling of Probiotic Recovery from 3D-Bioprinted Scaffolds for Localized Vaginal Application.

Lactobacilli, play a beneficial role in the female reproductive tract (FRT), regulating pH via lactic acid metabolism to help maintain a healthy environment. Bacterial vaginosis (BV) is characterized by a dysregulated flora in which anaerobes such as Gardnerella vaginalis (Gardnerella) create a less acidic environment. Current treatment focuses on antibiotic administration, including metronidazole, clindamycin, or tinidazole; however, lack of patient compliance as well as antibiotic resistance may contribute to 50% recurrence within a year. Recently, locally administered probiotics such as Lactobacillus crispatus (L. crispatus) have been evaluated as a prophylactic against recurrence. To mitigate the lack of patient compliance, sustained probiotic delivery has been proposed via 3D-bioprinted delivery vehicles. Successful delivery depends on a variety of vehicle fabrication parameters influencing timing and rate of probiotic recovery; detailed evaluation of these parameters would benefit from computational modeling complementary to experimental evaluation. This study implements a novel simulation platform to evaluate sustained delivery of probiotics from 3D-bioprinted scaffolds, taking into consideration bacterial lactic acid production and associated pH changes. The results show that the timing and rate of probiotic recovery can be realistically simulated based on fabrication parameters that affect scaffold degradation and probiotic survival. Longer term, the proposed approach could help personalize localized probiotic delivery to the FRT to advance women's health.

Rapid-dissolving electrospun nanofibers for intra-vaginal antibiotic or probiotic delivery.

The emergence of probiotics as an alternative and adjunct to antibiotic treatment for microbiological disturbances of the female genitourinary system requires innovative delivery platforms for vaginal applications. This study developed a new, rapid-dissolving form using electrospun polyethylene oxide (PEO) fibers for delivery of antibiotic metronidazole or probiotic Lactobacillus acidophilus, and performed evaluation in vitro and in vivo. Fibers did not generate overt pathophysiology or encourage Gardnerella growth in a mouse vaginal colonization model, inducing no alterations in vaginal mucosa at 24 hr post-administration. PEO-fibers incorporating metronidazole (100 µg MET/mg polymer) effectively prevented and treated Gardnerella infections (∼3- and 2.5-log reduction, respectively, 24 hr post treatment) when administered vaginally. Incorporation of live Lactobacillus acidophilus (107 CFU/mL) demonstrated viable probiotic delivery in vitro by PEO and polyvinyl alcohol (PVA) fibers to inhibit Gardnerella (108 CFU/mL) in bacterial co-cultures (9.9- and 7.0-log reduction, respectively, 24 hr post-inoculation), and in the presence of vaginal epithelial cells (6.9- and 8.0-log reduction, respectively, 16 hr post-inoculation). Administration of Lactobacillus acidophilus in PEO-fibers achieved vaginal colonization in mice similar to colonization observed with free Lactobacillus. acidophilus. These experiments provide proof-of-concept for rapid-dissolving electrospun fibers as a successful platform for intra-vaginal antibiotic or probiotic delivery.

Development and Characterization of Lactobacillus rhamnosus-Containing Bioprints for Application to Catheter-Associated Urinary Tract Infections.

Catheter-associated urinary tract infections (CAUTI) are a significant healthcare burden affecting millions of patients annually. CAUTI are characterized by infection of the bladder and pathogen colonization of the catheter surface, making them especially difficult to treat. Various catheter modifications have been employed to reduce pathogen colonization, including infusion of antibiotics and antimicrobial compounds, altering the surface architecture of the catheter, or coating it with nonpathogenic bacteria. Lactobacilli probiotics offer promise for a "bacterial interference" approach because they not only compete for adhesion to the catheter surface but also produce and secrete antimicrobial compounds effective against uropathogens. Three-dimensional (3D) bioprinting has enabled fabrication of well-defined, cell-laden architectures with tailored release of active agents, thereby offering a novel means for sustained probiotic delivery. Silicone has shown to be a promising biomaterial for catheter applications due to mechanical strength, biocompatibility, and its ability to mitigate encrustation on the catheter. Additionally, silicone, as a bioink, provides an optimum matrix for bioprinting lactobacilli. This study formulates and characterizes novel 3D-bioprinted Lactobacillus rhamnosus (L. rhamnosus)-containing silicone scaffolds for future urinary tract catheterization applications. Weight-to-weight (w/w) ratio of silicone/L. rhamnosus was bioprinted and cured with relative catheter dimensions in diameter. Scaffolds were analyzed in vitro for mechanical integrity, recovery of L. rhamnosus, antimicrobial production, and antibacterial effect against uropathogenic Escherichia coli, the leading cause of CAUTI. The results show that L. rhamnosus-containing scaffolds are capable of sustained recovery of live bacteria over 14 days, with sustained production of lactic acid and hydrogen peroxide. Through the use of 3D bioprinting, this study presents a potential alternative strategy to incorporate probiotics into urinary catheters, with the ultimate goal of preventing and treating CAUTI.

Metabolomic differentiation of tumor core versus edge in glioma.

OBJECTIVE: Gliomas exhibit high intratumor and interpatient heterogeneity. Recently, it has been shown that the microenvironment and phenotype differ significantly between the glioma core (inner) and edge (infiltrating) regions. This proof-of-concept study differentiates metabolic signatures associated with these regions, with the potential for prognosis and targeted therapy that could improve surgical outcomes. METHODS: Paired glioma core and infiltrating edge samples were obtained from 27 patients after craniotomy. Liquid-liquid metabolite extraction was performed on the samples and metabolomic data were obtained via 2D liquid chromatography-mass spectrometry/mass spectrometry. To gauge the potential of metabolomics to identify clinically relevant predictors of survival from tumor core versus edge tissues, a boosted generalized linear machine learning model was used to predict metabolomic profiles associated with O6-methylguanine DNA methyltransferase (MGMT) promoter methylation. RESULTS: A panel of 66 (of 168) metabolites was found to significantly differ between glioma core and edge regions (p ≤ 0.05). Top metabolites with significantly different relative abundances included DL-alanine, creatine, cystathionine, nicotinamide, and D-pantothenic acid. Significant metabolic pathways identified by quantitative enrichment analysis included glycerophospholipid metabolism; butanoate metabolism; cysteine and methionine metabolism; glycine, serine, alanine, and threonine metabolism; purine metabolism; nicotinate and nicotinamide metabolism; and pantothenate and coenzyme A biosynthesis. The machine learning model using 4 key metabolites each within core and edge tissue specimens predicted MGMT promoter methylation status, with AUROCEdge = 0.960 and AUROCCore = 0.941. Top metabolites associated with MGMT status in the core samples included hydroxyhexanoycarnitine, spermine, succinic anhydride, and pantothenic acid, and in the edge samples metabolites included 5-cytidine monophosphate, pantothenic acid, itaconic acid, and uridine. CONCLUSIONS: Key metabolic differences are identified between core and edge tissue in glioma and, furthermore, demonstrate the potential for machine learning to provide insight into potential prognostic and therapeutic targets.

Untangling the web of glioblastoma treatment resistance using a multi-omic and multidisciplinary approach.

Glioblastoma (GBM), the most common human brain tumor, has been notoriously resistant to treatment. As a result, the dismal overall survival of GBM patients has not changed over the past three decades. GBM has been stubbornly resistant to checkpoint inhibitor immunotherapies, which have been remarkably effective in the treatment of other tumors. It is clear that GBM resistance to therapy is multifactorial. Although therapeutic transport into brain tumors is inhibited by the blood brain barrier, there is evolving evidence that overcoming this barrier is not the predominant factor. GBMs generally have a low mutation burden, exist in an immunosuppressed environment and they are inherently resistant to immune stimulation, all of which contribute to treatment resistance. In this review, we evaluate the contribution of multi-omic approaches (genomic and metabolomic) along with analyzing immune cell populations and tumor biophysical characteristics to better understand and overcome GBM multifactorial resistance to treatment.

Release Kinetics of Metronidazole from 3D Printed Silicone Scaffolds for Sustained Application to the Female Reproductive Tract.

Sustained vaginal administration of antibiotics or probiotics has been proposed to improve treatment efficacy for bacterial vaginosis. 3D printing has shown promise for development of systems for local agent delivery. In contrast to oral ingestion, agent release kinetics can be fine-tuned by the 3D printing of specialized scaffold designs tailored for particular treatments while enhancing dosage effectiveness via localized sustained release. It has been challenging to establish scaffold properties as a function of fabrication parameters to obtain sustained release. In particular, the relationships between scaffold curing conditions, compressive strength, and drug release kinetics remain poorly understood. This study evaluates 3D printed scaffold formulation and feasibility to sustain the release of metronidazole, a commonly used antibiotic for BV. Cylindrical silicone scaffolds were printed and cured using three different conditions relevant to potential future incorporation of temperature-sensitive labile biologics. Compressive strength and drug release were monitored for 14d in simulated vaginal fluid to assess long-term effects of fabrication conditions on mechanical integrity and release kinetics. Scaffolds were mechanically evaluated to determine compressive and tensile strength, and elastic modulus. Release profiles were fitted to previous kinetic models to differentiate potential release mechanisms. The Higuchi, Korsmeyer-Peppas, and Peppas-Sahlin models best described the release, indicating similarity to release from insoluble or polymeric matrices. This study shows the feasibility of 3D printed silicone scaffolds to provide sustained metronidazole release over 14d, with compressive strength and drug release kinetics tuned by the fabrication parameters.

Formulation and characterization of pressure-assisted microsyringe 3D-printed scaffolds for controlled intravaginal antibiotic release.

Bacterial vaginosis (BV) is a highly recurrent vaginal condition linked with many health complications. Topical antibiotic treatments for BV are challenged with drug solubility in vaginal fluid, lack of convenience and user adherence to daily treatment protocols, among other factors. 3D-printed scaffolds can provide sustained antibiotic delivery to the female reproductive tract (FRT). Silicone vehicles have been shown to provide structural stability, flexibility, and biocompatibility, with favorable drug release kinetics. This study formulates and characterizes novel metronidazole-containing 3D-printed silicone scaffolds for eventual application to the FRT. Scaffolds were evaluated for degradation, swelling, compression, and metronidazole release in simulated vaginal fluid (SVF). Scaffolds retained high structural integrity and sustained release. Minimal mass loss (<6%) and swelling (<2%) were observed after 14 days in SVF, relative to initial post-cure measurements. Scaffolds cured for 24 hr (50 °C) demonstrated elastic behavior under 20% compression and 4.0 N load. Scaffolds cured for 4 hr (50 °C), followed by 72 hr (4 °C), demonstrated the highest, sustained, metronidazole release (4.0 and 27.0 µg/mg) after 24 hr and 14 days, respectively. Based upon daily release profiles, it was observed that the 24 hr timepoint had the greatest metronidazole release of 4.08 µg/mg for scaffolds cured at 4 hr at 50 °C followed by 72 hr at 4 °C. For all curing conditions, release of metronidazole after 1 and 7 days showed > 4.0-log reduction in Gardnerella concentration. Negligible cytotoxicity was observed in treated keratinocytes comparable to untreated cells, This study shows that pressure-assisted microsyringe 3D-printed silicone scaffolds may provide a versatile vehicle for sustained metronidazole delivery to the FRT.

Evaluation of stacked ensemble model performance to predict clinical outcomes: A COVID-19 study.

BACKGROUND: The application of machine learning (ML) to analyze clinical data with the goal to predict patient outcomes has garnered increasing attention. Ensemble learning has been used in conjunction with ML to improve predictive performance. Although stacked generalization (stacking), a type of heterogeneous ensemble of ML models, has emerged in clinical data analysis, it remains unclear how to define the best model combinations for strong predictive performance. This study develops a methodology to evaluate the performance of "base" learner models and their optimized combination using "meta" learner models in stacked ensembles to accurately assess performance in the context of clinical outcomes. METHODS: De-identified COVID-19 data was obtained from the University of Louisville Hospital, where a retrospective chart review was performed from March 2020 to November 2021. Three differently-sized subsets using features from the overall dataset were chosen to train and evaluate ensemble classification performance. The number of base learners chosen from several algorithm families coupled with a complementary meta learner was varied from a minimum of 2 to a maximum of 8. Predictive performance of these combinations was evaluated in terms of mortality and severe cardiac event outcomes using area-under-the-receiver-operating-characteristic (AUROC), F1, balanced accuracy, and kappa. RESULTS: The results highlight the potential to accurately predict clinical outcomes, such as severe cardiac events with COVID-19, from routinely acquired in-hospital patient data. Meta learners Generalized Linear Model (GLM), Multi-Layer Perceptron (MLP), and Partial Least Squares (PLS) had the highest AUROC for both outcomes, while K-Nearest Neighbors (KNN) had the lowest. Performance trended lower in the training set as the number of features increased, and exhibited less variance in both training and validation across all feature subsets as the number of base learners increased. CONCLUSION: This study offers a methodology to robustly evaluate ensemble ML performance when analyzing clinical data.

Lactobacillus crispatus-loaded electrospun fibers yield viable and metabolically active bacteria that kill Gardnerella in vitro.

Bacterial vaginosis (BV) is a common condition that affects one-third of women worldwide. BV is characterized by low levels of healthy lactobacilli and an overgrowth of common anaerobes such as Gardnerella. Antibiotics for BV are administered orally or vaginally; however, approximately half of those treated will experience recurrence within 6 months. Lactobacillus crispatus present at high levels has been associated with positive health outcomes. To address the high recurrence rates following BV treatment, beneficial bacteria have been considered as an alternative or adjunct modality. This study aimed to establish proof-of-concept for a new long-acting delivery vehicle for L. crispatus. Here, it is shown that polyethylene oxide (PEO) fibers loaded with L. crispatus can be electrospun with poly(lactic-co-glycolic acid) (PLGA) fibers (ratio 1:1), and that this construct later releases L. crispatus as metabolically viable bacteria capable of lactic acid production and anti-Gardnerella activity. Probiotic-containing fibers were serially cultured in MRS (deMan, Rogosa, Sharpe) broth with daily media replacement and found to yield viable L. crispatus for at least 7 days. Lactic acid levels and corresponding pH values generally corresponded with levels of L. crispatus cultured from the fibers and strongly support the conclusion that fibers yield viable L. crispatus that is metabolically active. Cultures of L. crispatus-loaded fibers limited the growth of Gardnerella in a dilution-dependent manner during in vitro assays in the presence of cultured vaginal epithelial cells, demonstrating bactericidal potential. Exposure of VK2/E6E7 cells to L. crispatus-loaded fibers resulted in minimal loss of viability relative to untreated cells. Altogether, these data provide proof-of-concept for electrospun fibers as a candidate delivery vehicle for application of vaginal probiotics in a long-acting form.