Which Social Media Platforms Provide the Most Informative Data for Monitoring the Opioid Crisis?

Background and Aims: Social media can provide real-time insight into trends in substance use, addiction, and recovery. Prior studies have leveraged data from platforms such as Reddit and X (formerly Twitter), but evolving policies around data access have threatened their usability in opioid overdose surveillance systems. Here, we evaluate the potential of a broad set of platforms to detect emerging trends in the opioid crisis. Design: We identified 72 online platforms with a substantial global user base or prior citations in opioid-related research. We evaluated each platform's fit with our definition of social media, size of North American user base, and volume of opioid-related discourse. We created a shortlist of 11 platforms that met our criteria. We documented basic characteristics, volume and nature of opioid discussion, official policies regulating drug-related discussion, and data accessibility of shortlisted platforms. Setting: USA and Canada. Measurements: We quantified the volume of opioid discussion by number of platform-specific Google search hits for opioid terms. We captured informal language by including slang generated using a large language model. We report the number of opioid-related hits and proportion of opioid-related hits to hits for common nouns. Findings: We found that TikTok, YouTube, and Facebook have the most potential for use in opioid-related surveillance. TikTok and Facebook have the highest relative amount of drug-related discussions. Language on TikTok was predominantly informal. Many platforms offer data access tools for research, but changing company policies and user norms create instability. The demographics of users varies substantially across platforms. Conclusions: Social media data sources hold promise for detecting trends in opioid use, but researchers must consider the utility, accessibility, and stability of data on each platform. A strategy mixing several platforms may be required to cover all demographics suffering in the epidemic.

Improving the Generalizability and Performance of an Ultrasound Deep Learning Model Using Limited Multicenter Data for Lung Sliding Artifact Identification.

Deep learning (DL) models for medical image classification frequently struggle to generalize to data from outside institutions. Additional clinical data are also rarely collected to comprehensively assess and understand model performance amongst subgroups. Following the development of a single-center model to identify the lung sliding artifact on lung ultrasound (LUS), we pursued a validation strategy using external LUS data. As annotated LUS data are relatively scarce-compared to other medical imaging data-we adopted a novel technique to optimize the use of limited external data to improve model generalizability. Externally acquired LUS data from three tertiary care centers, totaling 641 clips from 238 patients, were used to assess the baseline generalizability of our lung sliding model. We then employed our novel Threshold-Aware Accumulative Fine-Tuning (TAAFT) method to fine-tune the baseline model and determine the minimum amount of data required to achieve predefined performance goals. A subgroup analysis was also performed and Grad-CAM++ explanations were examined. The final model was fine-tuned on one-third of the external dataset to achieve 0.917 sensitivity, 0.817 specificity, and 0.920 area under the receiver operator characteristic curve (AUC) on the external validation dataset, exceeding our predefined performance goals. Subgroup analyses identified LUS characteristics that most greatly challenged the model's performance. Grad-CAM++ saliency maps highlighted clinically relevant regions on M-mode images. We report a multicenter study that exploits limited available external data to improve the generalizability and performance of our lung sliding model while identifying poorly performing subgroups to inform future iterative improvements. This approach may contribute to efficiencies for DL researchers working with smaller quantities of external validation data.

Automated Real-Time Detection of Lung Sliding Using Artificial Intelligence: A Prospective Diagnostic Accuracy Study.

BACKGROUND: Rapid evaluation for pneumothorax is a common clinical priority. Although lung ultrasound (LUS) often is used to assess for pneumothorax, its diagnostic accuracy varies based on patient and provider factors. To enhance the performance of LUS for pulmonary pathologic features, artificial intelligence (AI)-assisted imaging has been adopted; however, the diagnostic accuracy of AI-assisted LUS (AI-LUS) deployed in real time to diagnose pneumothorax remains unknown. RESEARCH QUESTION: In patients with suspected pneumothorax, what is the real-time diagnostic accuracy of AI-LUS to recognize the absence of lung sliding? STUDY DESIGN AND METHODS: We performed a prospective AI-assisted diagnostic accuracy study of AI-LUS to recognize the absence of lung sliding in a convenience sample of patients with suspected pneumothorax. After calibrating the model parameters and imaging settings for bedside deployment, we prospectively evaluated its diagnostic accuracy for lung sliding compared with a reference standard of expert consensus. RESULTS: Two hundred forty-one lung sliding evaluations were derived from 62 patients. AI-LUS showed a sensitivity of 0.921 (95% CI, 0.792-0.973), specificity of 0.802 (95% CI, 0.735-0.856), area under the receiver operating characteristic curve of 0.885 (95% CI, 0.828-0.956), and accuracy of 0.824 (95% CI, 0.766-0.870) for the diagnosis of absent lung sliding. INTERPRETATION: In this study, real-time AI-LUS showed high sensitivity and moderate specificity to identify the absence of lung sliding. Further research to improve model performance and optimize the integration of AI-LUS into existing diagnostic pathways is warranted.

Promises and challenges in pharmacoepigenetics.

Pharmacogenetics, the study of how interindividual genetic differences affect drug response, does not explain all observed heritable variance in drug response. Epigenetic mechanisms, such as DNA methylation, and histone acetylation may account for some of the unexplained variances. Epigenetic mechanisms modulate gene expression and can be suitable drug targets and can impact the action of nonepigenetic drugs. Pharmacoepigenetics is the field that studies the relationship between epigenetic variability and drug response. Much of this research focuses on compounds targeting epigenetic mechanisms, called epigenetic drugs, which are used to treat cancers, immune disorders, and other diseases. Several studies also suggest an epigenetic role in classical drug response; however, we know little about this area. The amount of information correlating epigenetic biomarkers to molecular datasets has recently expanded due to technological advances, and novel computational approaches have emerged to better identify and predict epigenetic interactions. We propose that the relationship between epigenetics and classical drug response may be examined using data already available by (1) finding regions of epigenetic variance, (2) pinpointing key epigenetic biomarkers within these regions, and (3) mapping these biomarkers to a drug-response phenotype. This approach expands on existing knowledge to generate putative pharmacoepigenetic relationships, which can be tested experimentally. Epigenetic modifications are involved in disease and drug response. Therefore, understanding how epigenetic drivers impact the response to classical drugs is important for improving drug design and administration to better treat disease.

Determining the Effects of Pelleted Cranberry Vine Grains on the Ewe and Offspring during Pregnancy and Lactation.

When creating any new anti-parasitic interventions, it is important to evaluate their effects across all life stages. This study had three objectives, which were to evaluate the effect of feeding cranberry vine pellet (CVP) on (1) ewes' body weights and BCS during late gestation and lactation; (2) ewes' milk quality during lactation; and (3) lambs' body weight and growth parameters from birth to 65 days of age. Across two years, 41 Dorset ewes were fed either a 50% CVP or a matching control pellet (CON) from 104 ± 1.60 days of gestation for 62.8 ± 0.68 days of lactation. Measurements were collected from ewes (BW, BCS, and milk) and lambs (BW and body size). Milk from CVP ewes exhibited reduced milk fat and solids (p < 0.01) and increased concentrations of milk urea nitrogen (p = 0.02) when evaluated for the treatment-time. There was no significant difference in the BCS, protein, lamb BW, or growth measurements for treatment-time (p ≥ 0.05). Additional research that targets blood biochemistry and metabolic assessments is needed to fully determine the impact of this pellet on ewes and lambs.

Health Care Professionals' Perceptions About a Telehealth Model of Therapy After NICU Discharge.

The Baby Bridge program is an implementation strategy to improve access to in-person early therapy services following neonatal intensive care unit (NICU) discharge. The objective of this study was to evaluate acceptability of Baby Bridge telehealth services among health care providers. Interviews with health care providers were conducted, transcribed, and coded in NVivo. Deductive analysis was used to organize data into negative and positive comments, suggestions for optimization, and perceptions about the first visit. Next, a conventional approach was used to organize the data into themes. Telehealth was viewed as an acceptable, but not necessarily preferable, form of Baby Bridge delivery. Providers identified how telehealth may improve access to care, but with potential challenges in delivery. Suggestions for optimization of the Baby Bridge telehealth model were proposed. Identified themes included delivery model, family demographics, therapist and organizational characteristics, parent engagement, and therapy facilitation. These findings provide important insights to consider when transitioning from in-person therapy to telehealth.

The intrarenal renin-angiotensin system in hypertension: insights from mathematical modelling.

The renin-angiotensin system (RAS) plays a pivotal role in the maintenance of volume homeostasis and blood pressure. In addition to the well-studied systemic RAS, local RAS have been documented in various tissues, including the kidney. Given the role of the intrarenal RAS in the pathogenesis of hypertension, a role established via various pharmacologic and genetic studies, substantial efforts have been made to unravel the processes that govern intrarenal RAS activity. In particular, several mechanisms have been proposed to explain the rise in intrarenal angiotensin II (Ang II) that accompanies Ang II infusion, including increased angiotensin type 1 receptor (AT1R)-mediated uptake of Ang II and enhanced intrarenal Ang II production. However, experimentally isolating their contribution to the intrarenal accumulation of Ang II in Ang II-induced hypertension is challenging, given that they are fundamentally connected. Computational modelling is advantageous because the feedback underlying each mechanism can be removed and the effect on intrarenal Ang II can be studied. In this work, the mechanisms governing the intrarenal accumulation of Ang II during Ang II infusion experiments are delineated and the role of the intrarenal RAS in Ang II-induced hypertension is studied. To accomplish this, a compartmental ODE model of the systemic and intrarenal RAS is developed and Ang II infusion experiments are simulated. Simulations indicate that AT1R-mediated uptake of Ang II is the primary mechanism by which Ang II accumulates in the kidney during Ang II infusion. Enhanced local Ang II production is unnecessary. The results demonstrate the role of the intrarenal RAS in the pathogenesis of Ang II-induced hypertension and consequently, clinical hypertension associated with an overactive RAS.

Prospective Real-Time Validation of a Lung Ultrasound Deep Learning Model in the ICU.

OBJECTIVES: To evaluate the accuracy of a bedside, real-time deployment of a deep learning (DL) model capable of distinguishing between normal (A line pattern) and abnormal (B line pattern) lung parenchyma on lung ultrasound (LUS) in critically ill patients. DESIGN: Prospective, observational study evaluating the performance of a previously trained LUS DL model. Enrolled patients received a LUS examination with simultaneous DL model predictions using a portable device. Clip-level model predictions were analyzed and compared with blinded expert review for A versus B line pattern. Four prediction thresholding approaches were applied to maximize model sensitivity and specificity at bedside. SETTING: Academic ICU. PATIENTS: One-hundred critically ill patients admitted to ICU, receiving oxygen therapy, and eligible for respiratory imaging were included. Patients who were unstable or could not undergo an LUS examination were excluded. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 100 unique ICU patients (400 clips) were enrolled from two tertiary-care sites. Fifty-six patients were mechanically ventilated. When compared with gold standard expert annotation, the real-time inference yielded an accuracy of 95%, sensitivity of 93%, and specificity of 96% for identification of the B line pattern. Varying prediction thresholds showed that real-time modification of sensitivity and specificity according to clinical priorities is possible. CONCLUSIONS: A previously validated DL classification model performs equally well in real-time at the bedside when platformed on a portable device. As the first study to test the feasibility and performance of a DL classification model for LUS in a dedicated ICU environment, our results justify further inquiry into the impact of employing real-time automation of medical imaging into the care of the critically ill.

Targeting the Maladaptive Effects of Binge Drinking on Circadian Gene Expression.

Previous studies (1) support a role of circadian genes in regulating alcohol intake, and (2) reveal that harmful alcohol use alters circadian rhythms. However, there is minimal knowledge of the effects of chronic alcohol processes on rhythmic circadian gene expression across brain regions important for circadian biology and alcohol intake. Therefore, the present study sought to test the effects of chronic binge-like drinking on diurnal circadian gene expression patterns in the master circadian pacemaker (SCN), the ventral tegmental area (VTA), and the nucleus accumbens (NAc) in High Drinking in the Dark-1 (HDID-1) mice, a unique genetic risk model for drinking to intoxication. Consistent with earlier findings, we found that 8 weeks of binge-like drinking reduced the amplitude of several core circadian clock genes in the NAc and SCN, but not the VTA. To better inform the use of circadian-relevant pharmacotherapies in reducing harmful drinking and ameliorating alcohol's effects on circadian gene expression, we tested whether the casein kinase-1 inhibitor, PF-67046, or the phosphodiesterase type-4 (an upstream regulator of circadian signalling) inhibitor, apremilast, would reduce binge-like intake and mitigate circadian gene suppression. PF-67046 did not reduce intake but did have circadian gene effects. In contrast, apremilast reduced drinking, but had no effect on circadian expression patterns.

Enhancing Annotation Efficiency with Machine Learning: Automated Partitioning of a Lung Ultrasound Dataset by View.

BACKGROUND: Annotating large medical imaging datasets is an arduous and expensive task, especially when the datasets in question are not organized according to deep learning goals. Here, we propose a method that exploits the hierarchical organization of annotating tasks to optimize efficiency. METHODS: We trained a machine learning model to accurately distinguish between one of two classes of lung ultrasound (LUS) views using 2908 clips from a larger dataset. Partitioning the remaining dataset by view would reduce downstream labelling efforts by enabling annotators to focus on annotating pathological features specific to each view. RESULTS: In a sample view-specific annotation task, we found that automatically partitioning a 780-clip dataset by view saved 42 min of manual annotation time and resulted in 55±6 additional relevant labels per hour. CONCLUSIONS: Automatic partitioning of a LUS dataset by view significantly increases annotator efficiency, resulting in higher throughput relevant to the annotating task at hand. The strategy described in this work can be applied to other hierarchical annotation schemes.

Modelling female physiology from head to Toe: Impact of sex hormones, menstrual cycle, and pregnancy.

Men and women exhibit notable differences not only in the reproductive system and reproductive behaviors, but in many other organ and physiological systems as well. Notable examples include; the stress and immune systems, the anatomy of the brain, and the metabolic and cardiovascular functions. Furthermore, female physiology is affected by the menstrual cycle and by pregnancy and lactation. If we are to successfully develop effective sex-based therapies, we must attain a comprehensive understanding of the effects of sex hormones, the menstrual cycle, and pregnancy on physiological function. By analyzing experimental findings, mathematical modelling can play a major role in facilitating and contributing to advancing the understanding of sex differences in physiology and pathophysiology. In this review, we present a survey of existing sex-specific modelling studies of physiological systems, describe the impact of the menstrual cycle and pregnancy, and discuss future modelling opportunities.

High-affinity P2Y2 and low-affinity P2X7 receptor interaction modulates ATP-mediated calcium signaling in murine osteoblasts.

The P2 purinergic receptor family implicated in many physiological processes, including neurotransmission, mechanical adaptation and inflammation, consists of ATP-gated non-specific cation channels P2XRs and G-protein coupled receptors P2YRs. Different cells, including bone forming osteoblasts, express multiple P2 receptors; however, how P2X and P2Y receptors interact in generating cellular responses to various doses of [ATP] remains poorly understood. Using primary bone marrow and compact bone derived osteoblasts and BMP2-expressing C2C12 osteoblastic cells, we demonstrated conserved features in the P2-mediated Ca2+ responses to ATP, including a transition of Ca2+ response signatures from transient at low [ATP] to oscillatory at moderate [ATP], and back to transient at high [ATP], and a non-monotonic changes in the response magnitudes which exhibited two troughs at 10-4 and 10-2 M [ATP]. We identified P2Y2 and P2X7 receptors as predominantly contributing to these responses and constructed a mathematical model of P2Y2R-induced inositol trisphosphate (IP3) mediated Ca2+ release coupled to a Markov model of P2X7R dynamics to study this system. Model predictions were validated using parental and CRISPR/Cas9-generated P2Y2 and P2Y7 knockouts in osteoblastic C2C12-BMP cells. Activation of P2Y2 by progressively increasing [ATP] induced a transition from transient to oscillatory to transient Ca2+ responses due to the biphasic nature of IP3Rs and the interaction of SERCA pumps with IP3Rs. At high [ATP], activation of P2X7R modulated the response magnitudes through an interplay between the biphasic nature of IP3Rs and the desensitization kinetics of P2X7Rs. Moreover, we found that P2Y2 activity may alter the kinetics of P2X7 towards favouring naïve state activation. Finally, we demonstrated the functional consequences of lacking P2Y2 or P2X7 in osteoblast mechanotransduction. This study thus provides important insights into the biophysical mechanisms underlying ATP-dependent Ca2+ response signatures, which are important in mediating bone mechanoadaptation.

The dynein light chain 8 (LC8) binds predominantly "in-register" to a multivalent intrinsically disordered partner.

Dynein light chain 8 (LC8) interacts with intrinsically disordered proteins (IDPs) and influences a wide range of biological processes. It is becoming apparent that among the numerous IDPs that interact with LC8, many contain multiple LC8-binding sites. Although it is established that LC8 forms parallel IDP duplexes with some partners, such as nucleoporin Nup159 and dynein intermediate chain, the molecular details of these interactions and LC8's interactions with other diverse partners remain largely uncharacterized. LC8 dimers could bind in either a paired "in-register" or a heterogeneous off-register manner to any of the available sites on a multivalent partner. Here, using NMR chemical shift perturbation, analytical ultracentrifugation, and native electrospray ionization MS, we show that LC8 forms a predominantly in-register complex when bound to an IDP domain of the multivalent regulatory protein ASCIZ. Using saturation transfer difference NMR, we demonstrate that at substoichiometric LC8 concentrations, the IDP domain preferentially binds to one of the three LC8 recognition motifs. Further, the differential dynamic behavior for the three sites and the size of the fully bound complex confirmed an in-register complex. Dynamics measurements also revealed that coupling between sites depends on the linker length separating these sites. These results identify linker length and motif specificity as drivers of in-register binding in the multivalent LC8-IDP complex assembly and the degree of compositional and conformational heterogeneity as a promising emerging mechanism for tuning of binding and regulation.

Plant-Pathogenic Agrobacterium tumefaciens Strains Have Diverse Type VI Effector-Immunity Pairs and Vary in In-Planta Competitiveness.

The type VI secretion system (T6SS) is used by gram-negative bacteria to translocate effectors that can antagonize other bacterial cells. Models predict the variation in collections of effector and cognate immunity genes determine competitiveness and can affect the dynamics of populations and communities of bacteria. However, the outcomes of competition cannot be entirely explained by compatibility of effector-immunity (EI) pairs. Here, we characterized the diversity of T6SS loci of plant-pathogenic Agrobacterium tumefaciens and showed that factors other than EI pairs can impact interbacterial competition. All examined strains encode T6SS active in secretion and antagonism against Escherichia coli. The spectra of EI pairs as well as compositions of gene neighborhoods are diverse. Almost 30 in-planta competitions were tested between different genotypes of A. tumefaciens. Fifteen competitions between members of different species-level groups resulted in T6SS-dependent suppression in in-planta growth of prey genotypes. In contrast, ten competitions between members within species-level groups resulted in no significant effect on the growth of prey genotypes. One strain was an exceptional case and, despite encoding a functional T6SS and toxic effector protein, could not compromise the growth of the four tested prey genotypes. The data suggest T6SS-associated EI pairs can influence the competitiveness of strains of A. tumefaciens, but genetic features have a significant role on the efficacy of interbacterial antagonism.

The Agrobacterium Type VI Secretion System: A Contractile Nanomachine for Interbacterial Competition.

The bacterial type VI secretion system (T6SS) is a contractile nanomachine dedicated to delivering molecules out of bacterial cells. T6SS-encoding loci are in the genome sequences of many Gram-negative bacteria, and T6SS has been implicated in a plethora of roles. In the majority of cases, the T6SSs deliver effector proteins in a contact-dependent manner to antagonize other bacteria. Current models suggest that the effectors are deployed to influence social interactions in microbial communities. In this chapter, we describe the structure, function, and regulation of the T6SS and its effectors. We provide focus on the T6SS of Agrobacterium tumefaciens, the causative agent of crown gall disease, and relate the role of the T6SS to the ecology of A. tumefaciens.

Neuroimaging and Violence.

Violence is a serious public health issue across the world. This article assists clinicians in understanding the most up-to-date literature regarding structural and functional brain theories related to risk of violence. In this article, we review anatomic regions of the brain that have been implicated in violence and associated personality constructs associated with violence. We discuss different imaging techniques that have been used to uncover abnormal brain volume, associations, and functions throughout the brain in samples with violence history or risk. Finally, we discuss implications of these findings both for treatment considerations and future research directions.

Television's "crazy lady" trope: female psychopathic traits, teaching, and influence of popular culture.

OBJECTIVE: This article describes notable illustrations of female psychopathy on modern television to review various characters that will have utility in teaching students about female psychopathy in distinction to male psychopathy and to encourage consideration of the potential effects that viewing these countless examples may have on a generation of young women. METHODS: The authors use examples from soap operas, crime procedurals, reality television, fantasy, comedies, and young adult programs to illustrate gender differences in psychopathy and make specific teaching points. They also review the research literature related to popular culture's impact on behavior and gender roles. RESULTS: Gender differences in real-world psychopathy are mirrored in television portrayals. For example, female psychopaths, on TV and in reality, use sexual manipulation, demonstrate unstable emotions, and employ social aggression to achieve their ambitions. The examples of female psychopathic traits are prevalent on TV and easily accessible for teaching purposes. Research does give some support for a popular culture impact on behavior and gender roles. CONCLUSIONS: As compared to male psychopathy, female psychopathy is less recognized, and there are some notable differences in how the psychopathic traits manifest. Television provides myriad teaching examples that can highlight the gender distinctions such as use of sexual manipulation, emotional instability, and social aggression. Research suggests that the prevalence of "crazy ladies" on television may be negatively impacting gender stereotypes and normalizing bad behavior in young women.