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Glioblastoma (GBM) is the most common and malignant type of primary brain tumor. Even after surgery and chemoradiotherapy, residual GBM cells can infiltrate the healthy brain parenchyma to form secondary tumors. To mitigate GBM recurrence, we recently developed an injectable hydrogel that can be crosslinked in the resection cavity to attract, collect, and ablate residual GBM cells. We previously optimized a thiol-Michael addition hydrogel for physical, chemical, and biological compatibility with the GBM microenvironment and demonstrated CXCL12-mediated chemotaxis can attract and entrap GBM cells into this hydrogel. In this study, we synthesize hydrogels under conditions mimicking GBM resection cavities and assess feasibility of histotripsy to ablate hydrogel-encapsulated cells. The results showed the hydrogel synthesis was bio-orthogonal, not shear-thinning, and can be scaled up for injection into GBM resection mimics in vitro. Experiments also demonstrated ultrasound imaging can distinguish the synthetic hydrogel from healthy porcine brain tissue. Finally, a 500 kHz transducer applied focused ultrasound treatment to the synthetic hydrogels, with results demonstrating precise histotripsy bubble clouds could be sustained in order to uniformly ablate red blood cells encapsulated by the hydrogel for homogeneous, mechanical fractionation of the entrapped cells. Overall, this hydrogel is a promising platform for biomaterials-based GBM treatment.

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PURPOSE: Cancer treatments often become ineffective because of acquired drug resistance. To characterize changes in breast cancer cells accompanying development of resistance to inhibitors of the oncogenic transcription factor, FOXM1, we investigated the suppression of cell death pathways, especially ferroptosis, in FOXM1 inhibitor-resistant cells. We also explored whether ferroptosis activators can synergize with FOXM1 inhibitors and can overcome FOXM1 inhibitor resistance. METHODS: In estrogen receptor-positive and triple-negative breast cancer cells treated with FOXM1 inhibitor NB73 and ferroptosis activators dihydroartemisinin and JKE1674, alone and in combination, we measured suppression of cell viability, motility, and colony formation, and monitored changes in gene and protein pathway expressions and mitochondrial integrity. RESULTS: Growth suppression of breast cancer cells by FOXM1 inhibitors is accompanied by increased cell death and alterations in mitochondrial morphology and metabolic activity. Low doses of FOXM1 inhibitor strongly synergize with ferroptosis inducers to reduce cell viability, migration, colony formation, and expression of proliferation-related genes, and increase intracellular Fe+2 and lipid peroxidation, markers of ferroptosis. Acquired resistance to FOXM1 inhibition is associated with increased expression of cancer stem-cell markers and proteins that repress ferroptosis, enabling cell survival and drug resistance. Notably, resistant cells are still sensitive to growth suppression by low doses of ferroptosis activators, effectively overcoming the acquired resistance. CONCLUSION: Delineating changes in viability and cell death pathways that can overcome drug resistance should be helpful in determining approaches that might best prevent or reverse resistance to therapeutic targeting of FOXM1 and ultimately improve patient clinical outcomes.

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Spectroscopic techniques generate one-dimensional spectra with distinct peaks and specific widths in the frequency domain. These features act as unique identities for material characteristics. Deep neural networks (DNNs) has recently been considered a powerful tool for automatically categorizing experimental spectra data by supervised classification to evaluate material characteristics. However, most existing work assumes balanced spectral data among various classes in the training data, contrary to actual experiments, where the spectral data is usually imbalanced. The imbalanced training data deteriorates the supervised classification performance, hindering understanding of the phase behavior, specifically, sol-gel transition (gelation) of soft materials and glycomaterials. To address this issue, this paper applies a novel data augmentation method based on a generative adversarial network (GAN) proposed by the authors in their prior work. To demonstrate the effectiveness of the proposed method, the actual imbalanced spectral data from Pluronic F-127 hydrogel and Alpha-Cyclodextrin hydrogel are used to classify the phases of data. Specifically, our approach improves 8.8%, 6.4%, and 6.2% of the performance of the existing data augmentation methods regarding the classifier's F-score, Precision, and Recall on average, respectively. Specifically, our method consists of three DNNs: the generator, discriminator, and classifier. The method generates samples that are not only authentic but emphasize the differentiation between material characteristics to provide balanced training data, improving the classification results. Based on these validated results, we expect the method's broader applications in addressing imbalanced measurement data across diverse domains in materials science and chemical engineering.

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Polysaccharide-based hydrogels are promising for many biomedical applications including drug delivery, wound healing, and tissue engineering. We illustrate herein self-healing, injectable, fast-gelling hydrogels prepared from multi-reducing end polysaccharides, recently introduced by the Edgar group. Simple condensation of reducing ends from multi-reducing end alginate (M-Alg) with amines from polyethylene imine (PEI) in water affords a dynamic, hydrophilic polysaccharide network. Trace amounts of acetic acid can accelerate the gelation time from hours to seconds. The fast-gelation behavior is driven by rapid Schiff base formation and strong ionic interactions induced by acetic acid. A cantilever rheometer enables real-time monitoring of changes in viscoelastic properties during hydrogel formation. The reversible nature of these crosslinks (imine bonds, ionic interactions) provides a hydrogel with low toxicity in cell studies as well as self-healing and injectable properties. Therefore, the self-healing, injectable, and fast-gelling M-Alg/PEI hydrogel holds substantial promise for biomedical, agricultural, controlled release, and other applications.

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False results and time delay are longstanding challenges in biosensing. While classification models and deep learning may provide new opportunities for improving biosensor performance, such as measurement confidence and speed, it remains a challenge to ensure that predictions are explainable and consistent with domain knowledge. Here, we show that consistency of deep learning classification model predictions with domain knowledge in biosensing can be achieved by cost function supervision and enables rapid and accurate biosensing using the biosensor dynamic response. The impact and utility of the methodology were validated by rapid and accurate quantification of microRNA (let-7a) across the nanomolar (nM) to femtomolar (fM) concentration range using the dynamic response of cantilever biosensors. Data augmentation and cost function supervision based on the consistency of model predictions and experimental observations with the theory of surface-based biosensors improved the F1 score, precision, and recall of a recurrent neural network (RNN) classifier by an average of 13.8%. The theory-guided RNN (TGRNN) classifier enabled quantification of target analyte concentration and false results with an average prediction accuracy, precision, and recall of 98.5% using the initial transient or entire dynamic response, which is indicative of high prediction accuracy and low probability of false-negative and false-positive results. Classification scores were used to establish new relationships among biosensor performance characteristics (e.g., measurement confidence) and design parameters (e.g., inputs and hyperparameters of classification models and data acquisition parameters) that may be used for characterizing biosensor performance.

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Here, we provide a new methodology for reducing false results and time delay of biosensors, which are barriers to industrial, healthcare, military, and consumer applications. We show that integrating machine learning with domain knowledge in biosensing can complement and improve the biosensor accuracy and speed relative to the performance achieved by traditional regression analysis of a standard curve based on the biosensor steady-state response. The methodology was validated by rapid and accurate quantification of microRNA across the nanomolar to femtomolar range using the dynamic response of cantilever biosensors. Theory-guided feature engineering improved the performance and efficiency of several classification models relative to the performance achieved using traditional feature engineering methods (TSFRESH). In addition to the entire dynamic response, the technique enabled rapid and accurate quantification of the target analyte concentration and false-positive and false-negative results using the initial transient response, thereby reducing the required data acquisition time (i.e., time delay). We show that model explainability can be achieved by combining theory-guided feature engineering and feature importance analysis. The performance of multiple classifiers using both TSFRESH- and theory-based features from the biosensor's initial transient response was similar to that achieved using the entire dynamic response with data augmentation. We also show that the methodology can guide design of experiments for high-performance biosensing applications, specifically, the selection of data acquisition parameters (e.g., time) based on potential application-dependent performance thresholds. This work provides an example of the opportunities for improving biosensor performance, such as reducing biosensor false results and time delay, using explainable machine learning models supervised by domain knowledge in biosensing.

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Women rapidly progress from recreational cocaine use to dependence, consume greater quantities of cocaine, experience more positive subjective effects of cocaine and have higher incidences of relapse during abstinence. These effects have been replicated in animal models of cocaine addiction and indicate an enhanced sensitivity and therefore, vulnerability of females to cocaine addiction. Furthermore, it has been demonstrated that estradiol (E2) is a key mediator of the aforementioned effects of cocaine in women and female animals. However, studies identifying the influence of E2 on cocaine-associated reward and its underlying neurobiological mechanisms are lacking. Here, we further explored the influence of E2 on cocaine conditioned place preference in female rats. We show that E2 mediates cocaine-conditioned reward by potentiating cocaine-context associations. In addition, the E2-mediated increases in cocaine-induced CPP are associated with increased activation of ERK1/2 and mTOR proteins in the nucleus accumbens, dorsal striatum, and ventral tegmental area. To assess the involvement of ERK1/2 and mTOR in E2-mediated enhanced cocaine-CPP, we inhibited ERK1/2 and/or mTOR activity during cocaine-conditioning and before CPP-test. Inhibition of ERK1/2 during conditioning blocked cocaine-CPP in females, inhibition mTOR was without effect, and inhibiting ERK1/2 and mTOR before CPP-test blocked cocaine-CPP. In conclusion, we have established that E2 enhances cocaine-conditioned reward by potentiating cocaine-context associations formed during conditioning. Additionally, activation of ERK1/2 during cocaine-conditioning is necessary for the potentiation of cocaine-conditioned reward by E2. SIGNIFICANCE STATEMENT: Studies characterizing the molecular substrates underlying the effects of E2 during the formation of cocaine-context associations are virtually unknown. In this study, we established the influence of E2 during the formation of cocaine-CPP and characterized the role of ERK1/2 and mTOR activity on this effect within significant nodes of the reward pathway. The elucidation of the role of E2 in cocaine-induced intracellular signaling fills a significant gap in our knowledge regarding the mechanisms by which E2 affects intracellular signaling pathways to indicate the motivational salience of a stimulus. These data are crucial to our understanding of how fluctuating hormone levels can render females increasing sensitive to the rewarding effects of cocaine.

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The Materials Genome Initiative (MGI) seeks to accelerate the discovery and engineering of advanced materials via high-throughput experimentation (HTE), which is a challenging task, given the common trade-off between design for optimal processability vs performance. Here, we report a HTE method based on automated formulation, synthesis, and multiproperty characterization of bulk soft materials in well plate formats that enables accelerated engineering of functional composite hydrogels with optimized properties for processability and performance. The method facilitates rapid high-throughput screening of hydrogel composition-property relations for multiple properties in well plate formats. The feasibility and utility of the method were demonstrated by application to several functional composite hydrogel systems, including alginate/poly(N-isopropylacrylamide) (PNIPAM) and poly(ethylene glycol) dimethacrylate (PEGDMA)/poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) hydrogels. The HTE method was leveraged to identify formulations of conductive PEGDMA/PEDOT:PSS composite hydrogels for optimized performance and processability in three-dimensional (3D) printing. This work provides an advance in experimental methods based on automated dispensing, mixing, and sensing for the accelerated engineering of soft functional materials.

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PURPOSE: Few targeted treatment options currently exist for patients with advanced, often recurrent breast cancers, both triple-negative breast cancer (TNBC) and hormone receptor-positive breast cancer. Forkhead box M1 (FOXM1) is an oncogenic transcription factor that drives all cancer hallmarks in all subtypes of breast cancer. We previously developed small-molecule inhibitors of FOXM1 and to further exploit their potential as anti-proliferative agents, we investigated combining FOXM1 inhibitors with drugs currently used in the treatment of breast and other cancers and assessed the potential for enhanced inhibition of breast cancer. METHODS: FOXM1 inhibitors alone and in combination with other cancer therapy drugs were assessed for their effects on suppression of cell viability and cell cycle progression, induction of apoptosis and caspase 3/7 activity, and changes in related gene expressions. Synergistic, additive, or antagonistic interactions were evaluated using ZIP (zero interaction potency) synergy scores and the Chou-Talalay interaction combination index. RESULTS: The FOXM1 inhibitors displayed synergistic inhibition of proliferation, enhanced G2/M cell cycle arrest, and increased apoptosis and caspase 3/7 activity and associated changes in gene expression when combined with several drugs across different pharmacological classes. We found especially strong enhanced effectiveness of FOXM1 inhibitors in combination with drugs in the proteasome inhibitor class for ER-positive and TNBC cells and with CDK4/6 inhibitors (Palbociclib, Abemaciclib, and Ribociclib) in ER-positive cells. CONCLUSION: The findings suggest that the combination of FOXM1 inhibitors with several other drugs might enable dose reduction in both agents and provide enhanced efficacy in treatment of breast cancer.

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Thyroid dysfunction is known to be associated with obesity, but the reliability of this relationship is easily affected by drug treatment, age, and subclinical hypothyroidism (SCH) with no apparent symptoms. Our research aims to compare obese and overweight BMI ranges with SCH and without SCH in a large sample of young, first-episode and drug-naïve (FEDN) patients with major depressive disorder (MDD), which has received little systemic investigation. A total of 1289 FEDN MDD young outpatients were recruited for this study. Serum thyroid function and lipid level parameters were measured; HAMD and PANSS scales were used to assess patients' depression and positive symptoms. A self-administered questionnaire collected other clinical and demographic data. The prevalence of SCH in FEDN MDD young patients was 58.26%. Compared to patients without SCH, the patients with SCH had a more prolonged illness duration, higher BMI levels, increased prevalence of overweight and obesity, higher HAMD score and PANSS-positive symptom scores, higher levels of TG, TC, LDL-C, and lower levels of HDL-C. Further logistic regression indicated that overweight BMI, obese BMI, illness duration, HAMD score, HDL-C, and TC were significantly associated with SCH. Our results indicate that obesity and overweight may be associated with SCH in young, FEDN MDD patients. The importance of regular thyroid function assessment in young FEDN MDD patients with high BMI should be taken into account.

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The federal government is funding a sea change in health care by investing in interventions targeting social determinants of health, which are significant contributors to illness and health inequity. This funding power has encouraged states, professional and accreditation organizations, health care entities, and providers to focus heavily on social determinants. We examine how this shift in focus affects clinical practice in the fields of oncology and emergency medicine, and highlight potential areas of reform.

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A simple sugar mixture transfers functional components to surfaces with intricate geometry.

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Benzodiazepine withdrawal is a widespread problem with potentially severe and deadly consequences. Currently, the only medications available for treating benzodiazepine withdrawal are short-acting and long-acting benzodiazepines. Identifying other drugs to help in treating benzodiazepine withdrawal is necessary. Gabapentin, an anxiolytic drug that is also used off-label to treat alcohol withdrawal, is a potential candidate for modulating benzodiazepine withdrawal. Using electronic records from a large inpatient psychiatric facility, a retrospective study of 172 patients presenting with benzodiazepine withdrawal was conducted to determine if the coincidental use of gabapentin for other medical conditions was associated with better outcomes of benzodiazepine withdrawal (N=57 gabapentin, N=115 no gabapentin). The primary outcomes were hospital length of stay and total amount of benzodiazepines given (lorazepam milligram equivalent). In this retrospective analysis of electronic medical record data, the patients experiencing benzodiazepine withdrawal who received gabapentin as an adjunct to the use of benzodiazepines were administered a smaller amount of benzodiazepines and had a shorter length of hospital stay relative to the comparison group who did not receive adjunctive gabapentin. These results suggest the potential use of gabapentin as an adjunct to the use of benzodiazepines for treating benzodiazepine withdrawal. The limitations of this study included a small sample size and variability in medication management strategies across the sample.

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INTRODUCTION: Chondrosarcoma, although relatively uncommon, represents a significant percentage of primary osseous tumors. Nonetheless, there are few large-cohort, longitudinal studies of long-term survival and treatment outcomes of chondrosarcoma patients and none using the National Cancer Database (NCDB). METHODS: Chondrosarcoma patients were identified from the 2004-2015 NCDB datasets and divided on three primary tumor sites: appendicular, axial, and other. Demographic, treatment, and long-term survival data were determined for each group. Multivariate Cox analysis and Kaplan-Meier survival curves were generated to assess long-term survival over time for each. RESULTS: In total, 5,329 chondrosarcoma patients were identified, of which 2,686 were appendicular and 1,616 were axial. Survival was higher among the appendicular cohort than axial at 1-year, 5-year, and 10-year (89.52%, 75.76%, and 65.24%, respectively). Multivariate Cox analysis identified patients in the appendicular cohort to have significantly greater likelihood of death with increasing age category, distant metastases at presentation, and male sex (p<0.001 for each). Best outcomes for seen for those undergoing surgical treatment (p<0.001). Patients in the axial cohort were with increased likelihood of death with increasing age category and distant metastases (p<0.001), while surgical treatment with or without radiation were associated with a significant decrease (p<0.001). Kaplan-Meier survival analysis showed worst survival for the axial cohort (p<0.001) and patients with distant metastases at presentation (p<0.001). Survival was not significantly different between older (2004-2007) and more recent years (2012-2016) (p = 0.742). CONCLUSIONS: For both appendicular and axial chondrosarcomas, surgical treatment remains the mainstay of treatment due to its continued superiority for the long-term survival of patients, although advancements in survival over the last decade have been insignificant. Presence of distant metastases and axial involvement are significant, poor prognostic factors perhaps because of difficulty in surgical excision or extent of disease.

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INTRODUCTION: Previous studies about osteosarcoma patient characteristics, management, and outcomes have limited patient numbers, combine varied tumor types, and/or are older studies. METHODS: Patients with osteosarcoma from the 2004 to 2015 National Cancer Database data sets were separated into axial, appendicular, and other. Demographic and treatment data as well as 1-, 5-, and 10-year survival were determined for each group. A multivariate Cox analysis of patient variables with the likelihood of death was performed, and the Kaplan Meier survival curves were generated. RESULTS: Four thousand four hundred thirty patients with osteosarcoma (3,435 appendicular, 810 axial, and 185 other) showed survival at 1-year, 5-year, and 10-year and was highest among the appendicular cohort (91.17%, 64.43%, and 58.58%, respectively). No change in survival was seen over the periods studied. The likelihood of death was greater with increasing age category, distant metastases, and treatment with radiation alone but less with appendicular primary site, treatment with surgery alone, or surgery plus chemotherapy. DISCUSSION: Despite advances in tumor management, surgical excision remains the best predictor of survival for osteosarcomas. No difference was observed in patient survival from 2004 to 2015 and, as would be expected, distant metastases were a poor prognostic sign, as was increasing age, male sex, and axial location.

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Autotomy, self-mutilation of a denervated limb, is common in animals after peripheral nerve injury (PNI) and is a reliable proxy for neuropathic pain in humans. Understanding the occurrence and treatment of autotomy remains challenging. The objective of this study was to investigate the occurrence of autotomy in nude and Wistar rats and evaluate the differences in macrophage activation and fiber sensitization contributing to the understanding of autotomy behavior. Autotomy in nude and Wistar rats was observed and evaluated 6 and 12 weeks after sciatic nerve repair surgery. The numbers of macrophages and the types of neurons in the dorsal root ganglion (DRG) between the two groups were compared by immunofluorescence studies. Immunostaining of T cells in the DRG was also assessed. Nude rats engaged in autotomy with less frequency than Wistar rats. Autotomy symptoms were also relatively less severe in nude rats. Immunofluorescence studies revealed increased macrophage accumulation and activation in the DRG of Wistar rats. The percentage of NF200+ neurons was higher at 6 and 12 weeks in Wistar rats compared to nude rats, but the percentage of CGRP+ neurons did not differ between two groups. Additionally, macrophages were concentrated around NF200-labeled A fibers. At 6 and 12 weeks following PNI, CD4+ T cells were not found in the DRG of the two groups. The accumulation and activation of macrophages in the DRG may account for the increased frequency and severity of autotomy in Wistar rats. Our results also suggest that A fiber neurons in the DRG play an important role in autotomy.

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Management of diabetes-related foot ulceration (DFU) includes pressure offloading resulting in a period of reduced activity. The metabolic effects of this are unknown. This study aims to investigate changes in bone mineral density (BMD) and body composition 12 weeks after hospitalisation for DFU. A longitudinal, prospective, observational study of 22 people hospitalised for DFU was conducted. Total body, lumbar spine, hip and forearm BMD, and total lean and fat mass were measured by dual-energy X-ray absorptiometry (DXA) during and 12 weeks after hospitalisation for DFU. Significant losses in total hip BMD of the ipsilateral limb (- 1.7%, p < 0.001), total hip BMD of the contralateral limb (- 1.4%, p = 0.005), femoral neck BMD of the ipsilateral limb (- 2.8%, p < 0.001) and femoral neck BMD of the contralateral limb (- 2.2%, p = 0.008) were observed after 12 weeks. Lumbar spine and forearm BMD were unchanged. HbA1c improved from 75 mmol/mol (9.2%) to 64 mmol/mol (8.0%) (p = 0.002). No significant changes to lean and fat mass were demonstrated. Total hip and femoral neck BMD decreased bilaterally 12 weeks after hospitalisation for DFU. Future research is required to confirm the persistence and clinical implications of these losses.

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