Ultrastructure of Synaptic Connectivity within Subregions of the Suprachiasmatic Nucleus Revealed by a Genetically Encoded Tag and Serial Blockface Electron Microscopy.

The hypothalamic suprachiasmatic nucleus (SCN) is the central circadian pacemaker in vertebrates. The SCN receives photic information exclusively through melanopsin-expressing retinal ganglion cells (mRGCs) to synchronize circadian rhythms with the environmental light cycles. The SCN is composed of two major peptidergic neuron types in the core and shell regions of the SCN. Determining how mRGCs interact with the network of synaptic connections onto and between SCN neurons is key to understand how light regulates the circadian clock and to elucidate the relevant local circuits within the SCN. To map these connections, we used a newly developed Cre-dependent electron microscopy (EM) reporter, APEX2, to label the mitochondria of mRGC axons. Serial blockface scanning electron microscopy was then used to resolve the fine 3D structure of mRGC axons and synaptic boutons in the SCN of a male mouse. The resulting maps reveal patterns of connectomic organization in the core and shell of the SCN. We show that these regions are composed of different neuronal subtypes and differ with regard to the pattern of mRGC input, as the shell receives denser mRGC synaptic input compared with the core. This finding challenges the present view that photic information coming directly from the retina is received primarily by the core region of the SCN.

Chatbot-assisted therapy for patients with methamphetamine use disorder: a preliminary randomized controlled trial.

Background: Methamphetamine (MA) use disorder is associated with a large public health burden. Despite the therapeutic effects of psychosocial interventions based on current evidence, finding an approach to retain patients in treatment remains a real-world challenge. The rapid development of mobile health (mHealth) systems suggests the potential to provide real-time personalized care at any time and from any location, minimize barriers to treatment, maximize use, and promote the dissemination of accessible therapeutic tools in at-risk populations. Our study aimed to investigate the feasibility and effectiveness of chatbots for the treatment of MA use disorder. Method: The inclusion criteria were (a) a diagnosis of MA use disorder as defined by the DSM-5, (b) age between 18 and 65 years, (c) no acute exacerbation of severe mental illness during the initial assessment, such as schizophrenia or bipolar I disorder, (d) willingness to participate in standard outpatient treatment for ≥ 6 months, and (e) an Android phone. Participants were randomly allocated to either a chatbot-assisted therapy via smartphone (CAT) group or a control group following simple randomization procedures (computerized random numbers) without blinding. All participants were followed up for 6 months. Treatment retention and monthly urine test results were analyzed as outcome measures. Participants' satisfaction with CAT was also assessed. Results: In total, 50 and 49 participants were allocated to the CAT and control groups, respectively. There were no significant differences in retention time between the two treatment groups (df = 1, p = 0.099). The CAT group had fewer MA-positive urine samples than the control group (19.5% vs. 29.6%, F = 9.116, p = 0.003). The proportion of MA-positive urine samples was positively correlated with the frequency of MA use (r = 0.323, p = 0.001), severity of MA use disorder (r = 0.364, p < 0.001), and polysubstance use (r = 0.212, p = 0.035), and negatively correlated with readiness to change (r = -0.330, p = 0.001). Totally 55 participants completed the study at the 6-month follow-up and 60% reported relative satisfaction. Conclusion: Participants in this study had favorable acceptance and generally positive outcomes, which indicates that chatbot is feasible for treating people who use MA.