155 articles were found through a database search (1971-2022), adhering to these inclusion criteria: individuals (18-65, all genders), involved in the criminal justice system, using substances, consuming licit/illicit psychoactive substances, and without unrelated psychopathology, and who were either in treatment programs or under judicial intervention. A subset of 110 articles underwent further review, with breakdown as follows: 57 articles from Academic Search Complete, 28 from PsycINFO, 10 from Academic Search Ultimate, 7 from Sociology Source Ultimate, 4 from Business Source Complete, 2 from Criminal Justice Abstracts, and 2 from PsycARTICLES; these figures were supplemented by manual searches. The analysis of these studies led to the selection of 23 articles, as they met the requirements of the research question; these articles constitute the final sample in this review. The results affirm that the criminal justice system's treatment approach effectively reduces recidivism and/or drug use, effectively addressing the criminogenic impact of imprisonment. see more In conclusion, interventions prioritizing therapeutic interventions should be selected, although there are still shortcomings in evaluating, monitoring, and scientifically publishing the effectiveness of this treatment for this population.
Models of the brain developed from human induced pluripotent stem cells (iPSCs) show potential to improve our grasp of the neurotoxic impact of drug use. Nonetheless, the extent to which these models accurately reflect the underlying genomic structure, cellular processes, and drug-induced modifications still needs to be definitively determined. Returning a list of sentences, each unique and structurally different, as per this JSON schema: list[sentence], new.
To gain a more comprehensive understanding of the ways to protect or reverse molecular changes resulting from substance use disorders, models of drug exposure are required.
Employing induced pluripotent stem cells derived from cultured postmortem human skin fibroblasts, a novel neural progenitor cells and neurons model was developed, which was then directly compared to isogenic brain tissue from the source individual. To assess the maturation of cellular models along the differentiation pathway from stem cells to neurons, we applied RNA-based cell-type and maturity deconvolution analyses, and DNA methylation epigenetic clocks trained on adult and fetal human tissues. This model's utility for understanding substance use disorders was assessed by comparing the gene expression profiles of morphine- and cocaine-treated neurons, respectively, to those found in postmortem brain tissue from patients with Opioid Use Disorder (OUD) and Cocaine Use Disorder (CUD).
Each human subject (N=2, each with two clones) shows that frontal cortex epigenetic age corresponds with skin fibroblast age, closely resembling the donor's chronological age. Stem cell derivation from fibroblasts effectively resets the epigenetic clock to an embryonic age. Progressive cell maturation occurs as stem cells differentiate into neural progenitor cells and neurons.
Measurement of DNA methylation and RNA gene expression profiles reveals critical details. Opioid overdose victims' neurons, when subjected to morphine treatment, displayed alterations in gene expression patterns comparable to those previously seen in individuals with opioid use disorder.
Opioid use is known to dysregulate the immediate early gene EGR1, evidenced by differential expression patterns in brain tissue.
Our approach involves the generation of an iPSC model from human postmortem fibroblasts. This model allows for a direct comparison with its matched isogenic brain tissue and can be utilized to simulate perturbagen exposure, analogous to that seen in opioid use disorder. Future explorations involving postmortem-derived brain cellular models, including the notable example of cerebral organoids, will serve as invaluable tools in understanding the mechanisms behind drug-induced modifications to the brain.
The following describes an iPSC model generated from human post-mortem fibroblasts. This model is directly comparable to corresponding isogenic brain tissue and is suitable for modeling perturbagen exposures, like those associated with opioid use disorder. Subsequent research incorporating postmortem brain cellular models, such as cerebral organoids, and analogous systems, can serve as a valuable resource for understanding the mechanisms of drug-induced cerebral changes.
The process of identifying psychiatric disorders hinges largely on the evaluation of the patient's displayed signs and symptoms. Deep learning models employing binary classification have been developed to potentially improve diagnosis, yet their implementation in clinical practice has been hampered by the varied presentations of the disorders involved. Our proposed normative model leverages the capabilities of autoencoders.
We leveraged resting-state functional magnetic resonance imaging (rs-fMRI) data from healthy controls to train our autoencoder model. Subsequently, to determine how each patient's functional brain networks (FBNs) connectivity deviated from typical patterns in schizophrenia (SCZ), bipolar disorder (BD), and attention-deficit hyperactivity disorder (ADHD), the model was applied. The FSL software library was employed for rs-fMRI data processing, involving both independent component analysis and dual regression. The correlation coefficients, calculated using Pearson's method, for the blood oxygen level-dependent (BOLD) time series of all functional brain networks (FBNs) were determined, and a subject-specific correlation matrix was created for each participant.
The basal ganglia network's functional connectivity demonstrates a critical role in the neuropathological processes of bipolar disorder and schizophrenia; its contribution in ADHD, however, is less demonstrable. Besides this, the unusual connectivity pattern between the basal ganglia network and the language network is more indicative of BD. In schizophrenia (SCZ), the significant connectivity lies in the relationship between the higher visual network and the right executive control network; however, in attention-deficit/hyperactivity disorder (ADHD), the connectivity between the anterior salience network and the precuneus networks is more critical. The results reveal the model's capacity to distinguish functional connectivity patterns, which are specific to different psychiatric disorders, as supported by the existing research. see more The two independent SCZ patient groups exhibited a congruency in their abnormal connectivity patterns, signifying the wide applicability of the presented normative model. Despite group-level disparities, closer analysis at the individual level revealed the fallacy of these observations, underscoring the significant heterogeneity of psychiatric disorders. These research results imply that a precision medicine methodology, zeroing in on the unique functional network alterations of each patient, could potentially prove more effective than the common practice of classifying patients into groups based on diagnosis.
Functional connectivity within the basal ganglia network is significantly implicated in the neurological underpinnings of bipolar disorder and schizophrenia, contrasting with its seemingly lesser role in attention-deficit/hyperactivity disorder. see more Besides this, the aberrant connectivity observed between the basal ganglia and the language networks is more strongly associated with BD. The connectivity pattern between the higher visual network and right executive control network, and the connectivity pattern between the anterior salience network and the precuneus networks, are highly relevant in SCZ and ADHD, respectively. The proposed model, in agreement with the literature, successfully identified functional connectivity patterns particular to different psychiatric disorders. The two independent groups of schizophrenia (SCZ) patients exhibited similar atypical connectivity patterns, thereby demonstrating the broader applicability of the presented normative model. However, the observed group-level discrepancies proved inconsequential when analyzed at the individual level, signifying a substantial heterogeneity within psychiatric disorders. It is implied by these results that a medical strategy tailored to the precise functional network changes of each patient, as opposed to a general grouping of diagnoses, could be a more effective choice.
Throughout an individual's lifetime, the co-occurrence of self-harm and aggression signifies dual harm. Determining if dual harm is a unique clinical condition requires a more thorough assessment of the available evidence. Through a systematic review, this research sought to identify if psychological factors uniquely predict dual harm, compared to separate occurrences of self-harm, aggression, or no harmful behaviors. A secondary objective was to rigorously evaluate the existing body of research.
The review, utilizing databases such as PsycINFO, PubMed, CINAHL, and EThOS on September 27, 2022, identified 31 eligible papers, accounting for a collective 15094 individuals. A narrative synthesis was performed following the use of an adapted version of the Agency for Healthcare Research and Quality instrument for assessing the risk of bias.
The studies evaluated the comparative mental health, personality, and emotional attributes of individuals within the various behavioral groupings. We observed tenuous support for dual harm as a distinct construct, exhibiting unique psychological traits. Our study, in contrast, proposes that psychological risk factors, associated with self-harm and aggression, combine to produce a dual harm.
The dual harm literature, as critically appraised, revealed numerous limitations. The clinical significance of the presented data and recommendations for future research are given.
A comprehensive study, referenced as CRD42020197323 and found at https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=197323, examines a pertinent area of research.
Herein is a review of the study registered with the identifier CRD42020197323. Additional details can be found at: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=197323.