Emergence of Artificial Intelligence and Machine Learning Models in Sudden Cardiac Arrest: A Comprehensive Review of Predictive Performance and Clinical Decision Support.

Sudden cardiac death/sudden cardiac arrest (SCD/SCA) is an increasingly prevalent cause of mortality globally, particularly in individuals with preexisting cardiac conditions. The ambiguous premortem warnings and the restricted interventional window related to SCD account for the complexity of the condition. Current reports suggest SCD to be accountable for 20% of all deaths hence accurately predicting SCD risk is an imminent concern. Traditional approaches for predicting SCA, particularly "track-and-trigger" warning systems have demonstrated considerable inadequacies, including low sensitivity, false alarms, decreased diagnostic liability, reliance on clinician involvement, and human errors. Artificial intelligence (AI) and machine learning (ML) models have demonstrated near-perfect accuracy in predicting SCA risk, allowing clinicians to intervene timely. Given the constraints of current diagnostics, exploring the benefits of AI and ML models in enhancing outcomes for SCA/SCD is imperative. This review article aims to investigate the efficacy of AI and ML models in predicting and managing SCD, particularly targeting accuracy in prediction.

Nucleic acid-responsive smart systems for controlled cargo delivery.

Stimulus-responsive delivery systems allow controlled, highly regulated, and efficient delivery of various cargos while minimizing side effects. Owing to the unique properties of nucleic acids, including the ability to adopt complex structures by base pairing, their easy synthesis, high specificity, shape memory, and configurability, they have been employed in autonomous molecular motors, logic circuits, reconfigurable nanoplatforms, and catalytic amplifiers. Moreover, the development of nucleic acid (NA)-responsive intelligent delivery vehicles is a rapidly growing field. These vehicles have attracted much attention in recent years due to their programmable, controllable, and reversible properties. In this work, we review several types of NA-responsive controlled delivery vehicles based on locks and keys, including DNA/RNA-responsive, aptamer-responsive, and CRISPR-responsive, and summarize their advantages and limitations.

Parameter Space and Potential for Biomarker Development in 25 Years of fMRI Drug Cue Reactivity: A Systematic Review.

Importance: In the last 25 years, functional magnetic resonance imaging drug cue reactivity (FDCR) studies have characterized some core aspects in the neurobiology of drug addiction. However, no FDCR-derived biomarkers have been approved for treatment development or clinical adoption. Traversing this translational gap requires a systematic assessment of the FDCR literature evidence, its heterogeneity, and an evaluation of possible clinical uses of FDCR-derived biomarkers. Objective: To summarize the state of the field of FDCR, assess their potential for biomarker development, and outline a clear process for biomarker qualification to guide future research and validation efforts. Evidence Review: The PubMed and Medline databases were searched for every original FDCR investigation published from database inception until December 2022. Collected data covered study design, participant characteristics, FDCR task design, and whether each study provided evidence that might potentially help develop susceptibility, diagnostic, response, prognostic, predictive, or severity biomarkers for 1 or more addictive disorders. Findings: There were 415 FDCR studies published between 1998 and 2022. Most focused on nicotine (122 [29.6%]), alcohol (120 [29.2%]), or cocaine (46 [11.1%]), and most used visual cues (354 [85.3%]). Together, these studies recruited 19 311 participants, including 13 812 individuals with past or current substance use disorders. Most studies could potentially support biomarker development, including diagnostic (143 [32.7%]), treatment response (141 [32.3%]), severity (84 [19.2%]), prognostic (30 [6.9%]), predictive (25 [5.7%]), monitoring (12 [2.7%]), and susceptibility (2 [0.5%]) biomarkers. A total of 155 interventional studies used FDCR, mostly to investigate pharmacological (67 [43.2%]) or cognitive/behavioral (51 [32.9%]) interventions; 141 studies used FDCR as a response measure, of which 125 (88.7%) reported significant interventional FDCR alterations; and 25 studies used FDCR as an intervention outcome predictor, with 24 (96%) finding significant associations between FDCR markers and treatment outcomes. Conclusions and Relevance: Based on this systematic review and the proposed biomarker development framework, there is a pathway for the development and regulatory qualification of FDCR-based biomarkers of addiction and recovery. Further validation could support the use of FDCR-derived measures, potentially accelerating treatment development and improving diagnostic, prognostic, and predictive clinical judgments.

A methodological checklist for fMRI drug cue reactivity studies: development and expert consensus.

Cue reactivity is one of the most frequently used paradigms in functional magnetic resonance imaging (fMRI) studies of substance use disorders (SUDs). Although there have been promising results elucidating the neurocognitive mechanisms of SUDs and SUD treatments, the interpretability and reproducibility of these studies is limited by incomplete reporting of participants' characteristics, task design, craving assessment, scanning preparation and analysis decisions in fMRI drug cue reactivity (FDCR) experiments. This hampers clinical translation, not least because systematic review and meta-analysis of published work are difficult. This consensus paper and Delphi study aims to outline the important methodological aspects of FDCR research, present structured recommendations for more comprehensive methods reporting and review the FDCR literature to assess the reporting of items that are deemed important. Forty-five FDCR scientists from around the world participated in this study. First, an initial checklist of items deemed important in FDCR studies was developed by several members of the Enhanced NeuroImaging Genetics through Meta-Analyses (ENIGMA) Addiction working group on the basis of a systematic review. Using a modified Delphi consensus method, all experts were asked to comment on, revise or add items to the initial checklist, and then to rate the importance of each item in subsequent rounds. The reporting status of the items in the final checklist was investigated in 108 recently published FDCR studies identified through a systematic review. By the final round, 38 items reached the consensus threshold and were classified under seven major categories: 'Participants' Characteristics', 'General fMRI Information', 'General Task Information', 'Cue Information', 'Craving Assessment Inside Scanner', 'Craving Assessment Outside Scanner' and 'Pre- and Post-Scanning Considerations'. The review of the 108 FDCR papers revealed significant gaps in the reporting of the items considered important by the experts. For instance, whereas items in the 'General fMRI Information' category were reported in 90.5% of the reviewed papers, items in the 'Pre- and Post-Scanning Considerations' category were reported by only 44.7% of reviewed FDCR studies. Considering the notable and sometimes unexpected gaps in the reporting of items deemed to be important by experts in any FDCR study, the protocols could benefit from the adoption of reporting standards. This checklist, a living document to be updated as the field and its methods advance, can help improve experimental design, reporting and the widespread understanding of the FDCR protocols. This checklist can also provide a sample for developing consensus statements for protocols in other areas of task-based fMRI.

Smart Strategies for Precise Delivery of CRISPR/Cas9 in Genome Editing.

The emergence of CRISPR/Cas technology has enabled scientists to precisely edit genomic DNA sequences. This approach can be used to modulate gene expression for the treatment of genetic disorders and incurable diseases such as cancer. This potent genome-editing tool is based on a single guide RNA (sgRNA) strand that recognizes the targeted DNA, plus a Cas nuclease protein for binding and processing the target. CRISPR/Cas has great potential for editing many genes in different types of cells and organisms both in vitro and in vivo. Despite these remarkable advances, the risk of off-target effects has hindered the translation of CRISPR/Cas technology into clinical applications. To overcome this hurdle, researchers have devised gene regulatory systems that can be controlled in a spatiotemporal manner, by designing special sgRNA, Cas, and CRISPR/Cas delivery vehicles that are responsive to different stimuli, such as temperature, light, magnetic fields, ultrasound (US), pH, redox, and enzymatic activity. These systems can even respond to dual or multiple stimuli simultaneously, thereby providing superior spatial and temporal control over CRISPR/Cas gene editing. Herein, we summarize the latest advances on smart sgRNA, Cas, and CRISPR/Cas nanocarriers, categorized according to their stimulus type (physical, chemical, or biological).

Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?

Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.

Alginate scaffolds improve functional recovery after spinal cord injury.

PURPOSE: In this systematic review and meta-analysis, the use of alginate for the repair of the damaged spinal cord was investigated. METHODS: After an extensive search of databases including MEDLINE, SCOPUS, EMBASE and Web of Science, an initial screening was performed based on inclusion and exclusion criteria. The full text of related articles was reviewed and data mining was performed. Data were analyzed by calculating the mean of ratios between treated and untreated groups using STATA software. Subgroup analysis was also performed due to heterogeneity. Articles were subjected to quality control and PRISMA guidelines were followed. RESULTS: Twelve studies and 17 experiments were included in the study. After SCI, alginate hydrogel had a moderate effect on motor function recovery (SMD = 0.64; 95% CI 0.28-1.00; p < 0.0001) and alginate scaffolds loaded with drugs, growth factors, or cells on the SCI group compared with untreated SCI animals showed has a strong effect in the treatment of SCI (SMD = 2.82; 95% CI 1.49-4.145; p < 0.0001). Treatment with drug/cell in combination with alginate was more strongly significant compared to the groups treated with drug/cell alone (SMD = 4.55; 95% CI 1.42-7.69; p < 0.0001). Alginate alone or in combination therapy when used as an implant, had a more significant effect than injection. CONCLUSION: These findings suggest that alginate is an efficient scaffold for functional recovery and even a much better scaffold for drug/cell delivery after SCI.

Intensive longitudinal characterization of multidimensional biobehavioral dynamics in laboratory rats.

Rats have been used as animal models for human diseases for more than a century, yet a systematic understanding of basal biobehavioral phenotypes of laboratory rats is still missing. In this study, we utilize wireless tracking technology and videography, collect and analyze more than 130 billion data points to fill this gap, and characterize the evolution of behavior and physiology of group-housed male and female rats (n = 114) of the most commonly used strains (Lister Hooded, Long-Evans, Sprague-Dawley, and Wistar) throughout their development. The resulting intensive longitudinal data suggest the existence of strain and sex differences and bi-stable developmental states. Under standard laboratory 12-h light/12-h dark conditions, our study found the presence of multiple oscillations such as circatidal-like rhythms in locomotor activity. The overall findings further suggest that frequent movement along cage walls or thigmotaxic activity may be a physical feature of motion in constrained spaces, critically affecting the interpretation of basal behavior of rats in cages.

Neurochemical underpinning of hemodynamic response to neuropsychiatric drugs: A meta- and cluster analysis of preclinical studies.

Functional magnetic resonance imaging (fMRI) is an extensively used method for the investigation of normal and pathological brain function. In particular, fMRI has been used to characterize spatiotemporal hemodynamic response to pharmacological challenges as a non-invasive readout of neuronal activity. However, the mechanisms underlying regional signal changes are yet unclear. In this study, we use a meta-analytic approach to converge data from microdialysis experiments with relative cerebral blood volume (rCBV) changes following acute administration of neuropsychiatric drugs in adult male rats. At whole-brain level, the functional response patterns show very weak correlation with neurochemical alterations, while for numerous brain areas a strong positive correlation with noradrenaline release exists. At a local scale of individual brain regions, the rCBV response to neurotransmitters is anatomically heterogeneous and, importantly, based on a complex interplay of different neurotransmitters that often exert opposing effects, thus providing a mechanism for regulating and fine tuning hemodynamic responses in specific regions.

No basal or drug-induced sex differences in striatal dopaminergic levels: a cluster and meta-analysis of rat microdialysis studies.

Sex differences in behavioural patterns of drug abuse and dependence have been hypothesized to be a consequence of sexual dimorphisms in brain pathways, particularly within the dopaminergic reward circuitry. Yet, how potential sex differences are manifested at a neurochemical level remains unclear. Here, we use a meta-analysis approach to investigate whether animal studies robustly indicate a different regulation of striatal dopamine transmission in males and females. Data from 39 microdialysis experiments on female rats (n = 676) were extracted and statistically compared with data from 1523 male rats. All drugs of abuse, independent of their molecular mechanisms of action, notably increase extracellular dopamine concentrations in the nucleus accumbens (NAc) and caudate putamen (CPu). No significant sex differences in basal levels or in dopaminergic response to drugs of abuse were found. However, basal dopamine levels in CPu (but not NAc) were significantly altered by ovariectomy. In conclusion, there are no sex-dependent differences in basal dopamine levels within the NAc and CPu. Previously reported sex differences in the CPu seem to be a result of ovariectomy and may only to a lesser, non-significant degree be attributed to a sexual duality.

Addiction Research Consortium: Losing and regaining control over drug intake (ReCoDe)-From trajectories to mechanisms and interventions.

One of the major risk factors for global death and disability is alcohol, tobacco, and illicit drug use. While there is increasing knowledge with respect to individual factors promoting the initiation and maintenance of substance use disorders (SUDs), disease trajectories involved in losing and regaining control over drug intake (ReCoDe) are still not well described. Our newly formed German Collaborative Research Centre (CRC) on ReCoDe has an interdisciplinary approach funded by the German Research Foundation (DFG) with a 12-year perspective. The main goals of our research consortium are (i) to identify triggers and modifying factors that longitudinally modulate the trajectories of losing and regaining control over drug consumption in real life, (ii) to study underlying behavioral, cognitive, and neurobiological mechanisms, and (iii) to implicate mechanism-based interventions. These goals will be achieved by: (i) using mobile health (m-health) tools to longitudinally monitor the effects of triggers (drug cues, stressors, and priming doses) and modify factors (eg, age, gender, physical activity, and cognitive control) on drug consumption patterns in real-life conditions and in animal models of addiction; (ii) the identification and computational modeling of key mechanisms mediating the effects of such triggers and modifying factors on goal-directed, habitual, and compulsive aspects of behavior from human studies and animal models; and (iii) developing and testing interventions that specifically target the underlying mechanisms for regaining control over drug intake.

Early detection and monitoring of cerebral ischemia using calcium-responsive MRI probes.

Cerebral ischemia is one of the leading causes of mortality and disability in infants and adults and its timely diagnosis is essential for an efficient treatment. We present a methodology for fast detection and real-time monitoring of fluctuations of calcium ions associated with focal ischemia using a molecular functional MRI approach. We used a dinuclear paramagnetic gadolinium(III) complex chelate that changes MR image contrast through its reversible interaction with extracellular calcium ions, while applying a remote transient middle cerebral artery occlusion as a model for ischemic stroke. Our method sensitively recognizes the onset and follows the dynamics of the ischemic core and penumbra with submillimeter spatial and second-scale temporal resolution, thus paving the way for noninvasive monitoring and development of targeted treatment strategies for cerebral ischemia.

Refined parcellation of the nervous system by algorithmic detection of hidden features within communities.

The nervous system can be represented as a multiscale network comprised by single cells or ensembles that are linked by physical or functional connections. Groups of morphologically and physiologically diverse neurons are wired as connectivity patterns with a certain degree of universality across species and individual variability. Thereby, community detection approaches are often used to characterize how neural units cluster into such densely interconnected groups. However, the communities may possess deeper structural features that remain undetected by current algorithms. We present a scheme for refined parcellation of neuronal networks, by identifying local integrator units (LU) that are contained in network communities. An LU is defined as a connected subnetwork in which all neuronal connections are constrained within this unit, and can be formed for instance by a set of interneurons. Our method uses the Louvain algorithm to detect communities and participation coefficients to discriminate local neurons from global hubs. The sensitivity of the algorithm for discovering LUs with respect to the choice of community detection algorithm and network parameters was tested by simulations of different synthetic networks. The appropriateness of the algorithm for real-world scenarios was demonstrated on weighted and binary Caenorhabditis elegans connectomes. The detected LUs are distinctly localized within the worm body and clearly define functional groups. This approach provides a robust, observer-independent parcellation strategy that is useful for functional structure confirmation and potentially contributes to the current efforts in quantitative whole-brain architectonics of different species as well as the analysis of functional connectivity networks.

Neural substrates of sexual arousal are not sex dependent.

Sexual arousal is a dynamical, highly coordinated neurophysiological process that is often induced by visual stimuli. Numerous studies have proposed that the cognitive processing stage of responding to sexual stimuli is the first stage, in which sex differences occur, and the divergence between men and women has been attributed to differences in the concerted activity of neural networks. The present comprehensive metaanalysis challenges this hypothesis and provides robust quantitative evidence that the neuronal circuitries activated by visual sexual stimuli are independent of biological sex. Sixty-one functional magnetic resonance imaging studies (1,850 individuals) that presented erotic visual stimuli to men and women of different sexual orientation were identified. Coordinate-based activation likelihood estimation was used to conduct metaanalyses. Sensitivity and clustering analyses of averaged neuronal response patterns were performed to investigate robustness of the findings. In contrast to neutral stimuli, sexual pictures and videos induce significant activations in brain regions, including insula, middle occipital, anterior cingulate and fusiform gyrus, amygdala, striatum, pulvinar, and substantia nigra. Cluster analysis suggests stimulus type as the most, and biological sex as the least, predictor for classification. Contrast analysis further shows no significant sex-specific differences within groups. Systematic review of sex differences in gray matter volume of brain regions associated with sexual arousal (3,723 adults) did not show any causal relationship between structural features and functional response to visual sexual stimuli. The neural basis of sexual arousal in humans is associated with sexual orientation yet, contrary to the widely accepted view, is not different between women and men.

Drinking Levels and Profiles of Alcohol Addicted Rats Predict Response to Nalmefene.

Background: Pharmacotherapeutic options supporting the treatment of alcohol dependence are recommended and available but underutilized, partly due to questions about efficacy. Nalmefene, a µ-opioid receptor antagonist and partial kappa receptor agonist, is recommended for reduction of alcohol consumption, but evidence about its effectiveness has been equivocal; identifying factors which predict response will help optimize treatment. Methods: The alcohol deprivation effect paradigm is a tightly controlled procedure comprising repeated deprivation and reintroduction phases, leading to increased preference for alcohol; reintroduction approximates relapse. Using a digital drinkometer system measuring high-resolution drinking behavior, we examined the effects of nalmefene on relapse drinking behavior in alcohol addicted rats. We also tested whether drinking behavior in the relapse phase prior to nalmefene administration predicted treatment response. We further examined whether longitudinal drinking behavior and locomotor activity predicted treatment response. Results: Our results showed that nalmefene (0.3 mg/kg) reduced relapse-like consumption significantly (∼20%) compared to vehicle on the first 2 days of alcohol reintroduction. Examining the first 6 h of a preceded treatment-free relapse episode revealed drinking patterns clustering the rats into responders (reduction of >40%, n = 17) and non-responders (reduction of <40%, n = 7) to subsequent nalmefene treatment. During the first 6 h of the preceding relapse phase, responders consumed more alcohol than non-responders; the amount of alcohol consumed during each drinking approach was larger but frequency of drinking did not differ. Longitudinal drinking behavior and locomotor activity did not significantly predict response. Conclusion: Our results suggest that nalmefene reduces alcohol intake during a relapse-like situation but effectiveness can differ greatly at the individual level. However, who responds may be informed by examining drinking profiles and rats that show high drinking levels prior to treatment are more likely to respond to nalmefene.

Quantitative evaluation of cue-induced reinstatement model for evidence-based experimental optimization.

Cue-induced reinstatement is a widely used model for investigating relapse of reward-seeking behavior with high face validity in relation to clinical observations. Yet, face validity is not sufficient to evaluate an animal model, and quantitative, evidence-based analysis is required to estimate the ultimate applicability of this paradigm. Furthermore, such analysis would allow an accurate and reproducible design of future experiments. Here, we conducted meta-analysis and cluster analysis to characterize the impact of cue type (visual, auditory, olfactory or combinations thereof), intensity (e.g. light frequency, sound volume and odor concentration), reward type (e.g. different drugs of abuse, sucrose and food pellets) and model parameters (e.g. housing condition, age, gender and strain of animals) on reinstatement levels. We selected 184 publications for meta-analysis based on inclusion criteria with a total number of 3889 rats. Our analysis suggested that the exact level of reinstatement depends on neither cue type, nor intensity nor on the type of reward. While all cues induced reinstatement to reward-seeking behavior, it is the model parameters, in particular, the housing conditions, age and strain, that defined the final reinstatement levels. In particular, single-housed, adolescent, Wistar or Lister Hooded rats showed significantly higher reinstatement than adult, Sparague-Dawley rats housed in groups. Our findings suggest that model parameters (for example, single housing) evoke stress-induced behaviors that affect reinstatement more than cue/reward factors.

Dopamine and opioid systems adaptation in alcoholism revisited: Convergent evidence from positron emission tomography and postmortem studies.

A major hypothesis in the addiction field suggests deficits in dopamine signaling during abstinence as a driving mechanism for the relapsing course of the disorder. Paradoxically, blockade of mu-opioid receptors (MORs) intended to suppress dopamine release and alcohol reward is a widely used treatment for preventing relapse in alcohol use disorder (AUD). To elucidate this apparent discrepancy, we systematically survey the literature on experimental studies in AUD subjects and animal models, which assessed striatal dopamine levels and D1, D2-like receptor, dopamine transporter and MOR via positron emission tomography (PET) and ex vivo receptor binding assays. The reported evidence indicates a changing dopaminergic signaling over time, which is associated with concomitant alterations in MOR, thus suggesting a highly dynamic regulation of the reward system during abstinence. Such a view can reconcile the various evidences from in vivo and postmortem studies, but makes developing an effective pharmacological intervention that specifically targets either dopamine receptors or the transporter system a daunting task.

Systemic neurotransmitter responses to clinically approved and experimental neuropsychiatric drugs.

Neuropsychiatric disorders are the third leading cause of global disease burden. Current pharmacological treatment for these disorders is inadequate, with often insufficient efficacy and undesirable side effects. One reason for this is that the links between molecular drug action and neurobehavioral drug effects are elusive. We use a big data approach from the neurotransmitter response patterns of 258 different neuropsychiatric drugs in rats to address this question. Data from experiments comprising 110,674 rats are presented in the Syphad database [ www.syphad.org ]. Chemoinformatics analyses of the neurotransmitter responses suggest a mismatch between the current classification of neuropsychiatric drugs and spatiotemporal neurostransmitter response patterns at the systems level. In contrast, predicted drug-target interactions reflect more appropriately brain region related neurotransmitter response. In conclusion the neurobiological mechanism of neuropsychiatric drugs are not well reflected by their current classification or their chemical similarity, but can be better captured by molecular drug-target interactions.

Alcohol reduces muscle fatigue through atomistic interactions with nicotinic receptors.

Alcohol consumption affects many organs and tissues, including skeletal muscle. However, the molecular mechanism of ethanol action on skeletal muscle remains unclear. Here, using molecular dynamics simulations and single channel recordings, we show that ethanol interacts with a negatively charged amino acid within an extracellular region of the neuromuscular nicotinic acetylcholine receptor (nAChR), thereby altering its global conformation and reducing the single channel current amplitude. Charge reversal of the negatively charged amino acid abolishes the nAChR-ethanol interaction. Moreover, using transgenic animals harboring the charge-reversal mutation, ex vivo measurements of muscle force production show that ethanol counters fatigue in wild type but not homozygous αE83K mutant animals. In accord, in vivo studies of motor coordination following ethanol administration reveal an approximately twofold improvement for wild type compared to homozygous mutant animals. Together, the converging results from molecular to animal studies suggest that ethanol counters muscle fatigue through its interaction with neuromuscular nAChRs.