In Situ Forming, Enzyme-Responsive Peptoid-Peptide Hydrogels: An Advanced Long-Acting Injectable Drug Delivery System.

Long-acting drug delivery systems are promising platforms to improve patient adherence to medication by delivering drugs over sustained periods and removing the need for patients to comply with oral regimens. This research paper provides a proof-of-concept for the development of a new optimized in situ forming injectable depot based on a tetrabenzylamine-tetraglycine-d-lysine-O-phospho-d-tyrosine peptoid-D-peptide formulation ((NPhe)4GGGGk(AZT)y(p)-OH). The chemical versatility of the peptoid-peptide motif allows low-molecular-weight drugs to be precisely and covalently conjugated. After subcutaneous injection, a hydrogel depot forms from the solubilized peptoid-peptide-drug formulation in response to phosphatase enzymes present within the skin space. This system is able to deliver clinically relevant concentrations of a model drug, the antiretroviral zidovudine (AZT), for 35 days in Sprague-Dawley rats. Oscillatory rheology demonstrated that hydrogel formation began within ∼30 s, an important characteristic of in situ systems for reducing initial drug bursts. Gel formation continued for up to ∼90 min. Small-angle neutron scattering data reveal narrow-radius fibers (∼0.78-1.8 nm) that closely fit formation via a flexible cylinder elliptical model. The inclusion of non-native peptoid monomers and D-variant amino acids confers protease resistance, enabling enhanced biostability to be demonstrated in vitro. Drug release proceeds via hydrolysis of an ester linkage under physiological conditions, releasing the drug in an unmodified form and further reducing the initial drug burst. Subcutaneous administration of (NPhe)4GGGGk(AZT)y(p)-OH to Sprague-Dawley rats resulted in zidovudine blood plasma concentrations within the 90% maximal inhibitory concentration (IC90) range (30-130 ng mL-1) for 35 days.

Responses of vascular plant fine roots and associated microbial communities to whole-ecosystem warming and elevated CO2 in northern peatlands.

Warming and elevated CO2 (eCO2) are expected to facilitate vascular plant encroachment in peatlands. The rhizosphere, where microbial activity is fueled by root turnover and exudates, plays a crucial role in biogeochemical cycling, and will likely at least partially dictate the response of the belowground carbon cycle to climate changes. We leveraged the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment, to explore the effects of a whole-ecosystem warming gradient (+0°C to 9°C) and eCO2 on vascular plant fine roots and their associated microbes. We combined trait-based approaches with the profiling of fungal and prokaryote communities in plant roots and rhizospheres, through amplicon sequencing. Warming promoted self-reliance for resource uptake in trees and shrubs, while saprophytic fungi and putative chemoorganoheterotrophic bacteria utilizing plant-derived carbon substrates were favored in the root zone. Conversely, eCO2 promoted associations between trees and ectomycorrhizal fungi. Trees mostly associated with short-distance exploration-type fungi that preferentially use labile soil N. Additionally, eCO2 decreased the relative abundance of saprotrophs in tree roots. Our results indicate that plant fine-root trait variation is a crucial mechanism through which vascular plants in peatlands respond to climate change via their influence on microbial communities that regulate biogeochemical cycles.

When Being Bad Feels Good: A Systematic Review of the Relationship Between Positive Emotion and Antisocial Behavior in Children and Adolescents.

Antisocial behavior in childhood and adolescence is associated with poor family and peer relationships, and a higher risk of mental and physical health problems in adulthood, as well as criminality. Emotions play a central role in children's moral development, but most research has focused on negative emotions (e.g., shame and guilt), in relation to childhood antisocial behavior. Research in adult populations indicates that positive emotions experienced in anticipation of, during, and after antisocial acts may play an important role in the development and maintenance of antisocial behavior. Consequently, this systematic review aimed to investigate the relationship between positive emotion and antisocial behavior in children and adolescents. A systematic search in five databases was conducted, yielding 52 studies that used different methodological approaches, samples, designs and methods to examine this association. Results provide support for a positive relationship between positive emotion and antisocial behavior across community, forensic and clinical samples. This link appeared to be stronger for younger children, boys, and for children high in social dominance, callous-unemotional or sensation-seeking traits. Results suggested that positive affect may act in concert with negative emotion, cognitive, personality and motivational processes, as well as peer influences to determine the initiation and maintenance of antisocial behavior. This review presents directions for future research and discusses the implications of findings for prevention and intervention programs for youth with antisocial behavior.

Diagnosis and Treatment of Presumed Naltrexone-XR-precipitated Opioid Withdrawal in a Patient Chronically Treated With Buprenorphine-XR: A Case Report.

ABSTRACT: Naltrexone, buprenorphine, and methadone are Food and Drug Administration-approved medications for the treatment of opioid use disorder in the United States. Naltrexone, an opioid antagonist, can precipitate opioid withdrawal if administered too quickly after the use of full or partial opioid agonists for those with either dependence or use disorder. We describe a case of severe precipitated opioid withdrawal syndrome after reported buprenorphine extended-release (XR) administration, despite the patient having been stable on buprenorphine-XR for several years, with no missed doses or recent opioid use. Naltrexone levels were sent and helped to diagnose suspected inadvertent naltrexone-XR administration in this patient, which was likely the etiology of his precipitated opioid withdrawal syndrome. We suggest the use of high-dose buprenorphine, as well as adjunctive medications including benzodiazepines, as a treatment strategy for naltrexone-XR precipitated withdrawal in the setting of chronic buprenorphine-XR treatment.

Dual stimuli-responsive delivery system for self-regulated colon-targeted delivery of poorly water-soluble drugs.

Inflammatory bowel disease (IBD) are chronic inflammatory conditions which cause significant patient morbidity. Local drug delivery to the colon can improve treatment efficacy and reduce side effects associated with IBD treatment. Smart drug delivery systems are designed to regulate the release of therapeutic agents at the desired site of action. pH-responsive drug carriers have been previously utilised for improved oral drug delivery beyond stomach harsh conditions. Additionally, the colon possesses a diverse microbiome secreting bioactive molecules e.g., enzymes, that can be exploited for targeted drug delivery. We herein synthesised and characterised a 2-hydroxyethyl methacrylate and methacrylic acid copolymer, crosslinked with an azobenzyl crosslinker, that displayed pH- and enzyme-responsive properties. The swelling and drug release from hydrogel were analysed in pH 1.2, 6.5 and 7.4 buffers, and in the presence of rat caecal matter using metronidazole and mesalamine as model BCS Class I and IV drugs, respectively. Swelling studies displayed pH-responsive swelling behaviour, where swelling was maximum at pH 7.4 and minimum at pH 1.2 (69 % versus 32 %). Consequently, drug release was limited in gastric and small intestinal conditions but increased significantly when exposed to colonic conditions containing caecal matter. This system displays promising capacity for achieving colon-targeted drug delivery with enhanced dissolution of poorly water-soluble drugs for local treatment of IBD and other colon-targeted therapies.

Multimodal MALDI imaging mass spectrometry for improved diagnosis of melanoma.

Imaging mass spectrometry (IMS) provides promising avenues to augment histopathological investigation with rich spatio-molecular information. We have previously developed a classification model to differentiate melanoma from nevi lesions based on IMS protein data, a task that is challenging solely by histopathologic evaluation. Most IMS-focused studies collect microscopy in tandem with IMS data, but this microscopy data is generally omitted in downstream data analysis. Microscopy, nevertheless, forms the basis for traditional histopathology and thus contains invaluable morphological information. In this work, we developed a multimodal classification pipeline that uses deep learning, in the form of a pre-trained artificial neural network, to extract the meaningful morphological features from histopathological images, and combine it with the IMS data. To test whether this deep learning-based classification strategy can improve on our previous results in classification of melanocytic neoplasia, we utilized MALDI IMS data with collected serial H&E stained sections for 331 patients, and compared this multimodal classification pipeline to classifiers using either exclusively microscopy or IMS data. The multimodal pipeline achieved the best performance, with ROC-AUCs of 0.968 vs. 0.938 vs. 0.931 for the multimodal, unimodal microscopy and unimodal IMS pipelines respectively. Due to the use of a pre-trained network to perform the morphological feature extraction, this pipeline does not require any training on large amounts of microscopy data. As such, this framework can be readily applied to improve classification performance in other experimental settings where microscopy data is acquired in tandem with IMS experiments.

Automated vs. manual coding of neuroimaging reports via natural language processing, using the international classification of diseases, tenth revision.

Objective: Natural language processing (NLP) can generate diagnoses codes from imaging reports. Meanwhile, the International Classification of Diseases (ICD-10) codes are the United States' standard for billing/coding, which enable tracking disease burden and outcomes. This cross-sectional study aimed to test feasibility of an NLP algorithm's performance and comparison to radiologists' and physicians' manual coding. Methods: Three neuroradiologists and one non-radiologist physician reviewers manually coded a randomly-selected pool of 200 craniospinal CT and MRI reports from a pool of >10,000. The NLP algorithm (Radnosis, VEEV, Inc., Minneapolis, MN) subdivided each report's Impression into "phrases", with multiple ICD-10 matches for each phrase. Only viewing the Impression, the physician reviewers selected the single best ICD-10 code for each phrase. Codes selected by the physicians and algorithm were compared for agreement. Results: The algorithm extracted the reports' Impressions into 645 phrases, each having ranked ICD-10 matches. Regarding the reviewers' selected codes, pairwise agreement was unreliable (Krippendorff α = 0.39-0.63). Using unanimous reviewer agreement as "ground truth", the algorithm's sensitivity/specificity/F2 for top 5 codes was 0.88/0.80/0.83, and for the single best code was 0.67/0.82/0.67. The engine tabulated "pertinent negatives" as negative codes for stated findings (e.g. "no intracranial hemorrhage"). The engine's matching was more specific for shorter than full-length ICD-10 codes (p = 0.00582x10-3). Conclusions: Manual coding by physician reviewers has significant variability and is time-consuming, while the NLP algorithm's top 5 diagnosis codes are relatively accurate. This preliminary work demonstrates the feasibility and potential for generating codes with reliability and consistency. Future works may include correlating diagnosis codes with clinical encounter codes to evaluate imaging's impact on, and relevance to care.