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Introduction: The amygdala is a limbic region of high value for understanding anxiety and its treatment. Dopamine D2 receptors (D2Rs) and oxytocin receptors (OXTRs) have both been shown to participate in modulating anxiety involving effects in the amygdala. The goal is to understand if D2R–OXTR heterocomplexes exist in the central amygdala and if, through enhancing allosteric receptor–receptor interactions, may enhance anxiolytic actions.Methods: The methods used involve the shock-probe burying test, the in situ proximity ligation assay (PLA), image acquisition and analysis, and the BRET2 assay. Bilateral cannulas were introduced into the amygdala, and the effects of the coadministration of oxytocin and the D2R-like agonist quinpirole into the amygdala were studied.Results: The combination treatment enhanced the anxiolytic effects compared to the single treatment. The D2R/D3R antagonist raclopride blocked the effects of the combination treatment of oxytocin and the D2R agonist, although oxytocin is regarded as a distinct modulator of fear-mediating anxiolytic effects. In situ PLA results indicate the existence of D2R–OXTR heteroreceptor complexes and/or the co-location of OXTR and D2R within the same cell membrane nanodomains in the central amygdala. With BRET2, evidence is given for the existence of D2R–OXTR heteromers in HEK293 cells upon co-transfection.Discussion: The enhanced behavioral effects observed upon co-treatment with OXTR and D2R agonists may reflect the existence of improved positive receptor–receptor interactions in the putative D2R–OXTR heterocomplexes in certain neuronal populations of the basolateral and central amygdala. The D2R–OXTR heterocomplex, especially upon agonist co-activation in the central amygdala, may open a new pharmacological venue for the treatment of anxiety.

Purpose: To identify clinical phenotypes and biomarkers for best mortality prediction considering age, symptoms and comorbidities in COVID-19 patients with chronic neurological diseases in intensive care units (ICUs). Subjects and Methods: Data included 1252 COVID-19 patients admitted to ICUs in Cuba between January and August 2021. A k-means algorithm based on unsupervised learning was used to identify clinical patterns related to symptoms, comorbidities and age. The Stable Sparse Classifiers procedure (SSC) was employed for predicting mortality. The classification performance was assessed using the area under the receiver operating curve (AUC). Results: Six phenotypes using a modified v-fold cross validation for the k-means algorithm were identified: phenotype class 1, mean age 72.3 years (ys)—hypertension and coronary artery disease, alongside typical COVID-19 symptoms; class 2, mean age 63 ys—asthma, cough and fever; class 3, mean age 74.5 ys—hypertension, diabetes and cough; class 4, mean age 67.8 ys—hypertension and no symptoms; class 5, mean age 53 ys—cough and no comorbidities; class 6, mean age 60 ys—without symptoms or comorbidities. The chronic neurological disease (CND) percentage was distributed in the six phenotypes, predominantly in phenotypes of classes 3 (24.72%) and 4 (35,39%); χ² (5) 11.0129 p = 0.051134. The cerebrovascular disease was concentrated in classes 3 and 4; χ² (5) = 36.63, p = 0.000001. The mortality rate totaled 325 (25.79%), of which 56 (17.23%) had chronic neurological diseases. The highest in-hospital mortality rates were found in phenotypes 1 (37.22%) and 3 (33.98%). The SSC revealed that a neurological symptom (ageusia), together with two neurological diseases (cerebrovascular disease and Parkinson’s disease), and in addition to ICU days, age and specific symptoms (fever, cough, dyspnea and chilliness) as well as particular comorbidities (hypertension, diabetes and asthma) indicated the best prediction performance (AUC = 0.67). Conclusions: The identification of clinical phenotypes and mortality biomarkers using practical variables and robust statistical methodologies make several noteworthy contributions to basic and experimental investigations for distinguishing the COVID-19 clinical spectrum and predicting mortality.

Introducción. La interrelación entre la inflamación, el estrés oxidativo y la muerte neuronal constituye un tema creciente de investigación sobre epilepsia del lóbulo temporal farmacorresistente (ELTF). Objetivo: profundizar en los mecanismos moleculares que participan en la fisiopatología de la ELTF correlacionando variables clínicas con inmunológicas, estrés oxidativo y muerte neuronal. Métodos. Por inmunocitoquímica fueron medidos los marcadores CD3, CD4, CD8, CD19, CD25, HLA-DR; por nefelometría: IgG, IgM, IgA y albúmina; por ensayos inmunoenzimáticos, IL-1β, IL-6, TNF-α y óxido nítrico; por Western blot: NFκB, JNK, 4-hidroxi-2-nonenal, 3-nitrotirosina; por espectrofotometría: malonildialdehído, productos avanzados de glicación (AGE), productos avanzados de oxidación proteínas, ácido úrico, superóxido dismutasa (SOD), glutatión y vitamina C; por inmunohistoquímica: Anexina V, TUNEL, sinaptofisina, caspasa 3, p53 y bcl-2; por HPLC, glutamato, y por autorradiografía: 5-HT, 5-HT1A, 5-HT7. Resultados. Se demuestra que las crisis son el substrato patogénico de los trastornos inmunológicos, inflamatorios y de estrés oxidativo en la ELTF. Las variables clínicas se relacionan con los estudios moleculares y de proteínas realizados y todos en su conjunto evidencian la muerte neuronal en la ELTF.