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We propose to exploit such properties for biosensing and neural interfaces.Although previous studies have shown that the host immune response is crucial in determining clinical outcomes in COVID-19 patients, the association between host immune signatures and COVID-19 patient outcomes remains unclear. Based on the enrichment levels of 11 immune signatures (eight immune-inciting and three immune-inhibiting signatures) in leukocytes of 100 COVID-19 patients, we identified three COVID-19 subtypes Im-C1, Im-C2, and Im-C3, by clustering analysis. Im-C1 had the lowest immune-inciting signatures and high immune-inhibiting signatures. Im-C2 had medium immune-inciting signatures and high immune-inhibiting signatures. Im-C3 had the highest immune-inciting signatures while the lowest immune-inhibiting signatures. Im-C3 and Im-C1 displayed the best and worst clinical outcomes, respectively, suggesting that antiviral immune responses alleviated the severity of COVID-19 patients. We further demonstrated that the adaptive immune response had a stronger impact on COVID-19 outcomes than the innate immune response. The patients in Im-C3 were younger than those in Im-C1, indicating that younger persons have stronger antiviral immune responses than older persons. Nevertheless, we did not observe a significant association between sex and immune responses in COVID-19 patients. In addition, we found that the type II IFN response signature was an adverse prognostic factor for COVID-19. Our identification of COVID-19 immune subtypes has potential clinical implications for the management of COVID-19 patients.Lupus cystitis (LC) is a rare manifestation of SLE, the diagnosis of LC is challenging, especially in the absence of other systemic manifestations or the obvious disease activity index of SLE, further cystoscopy and bladder biopsy are crucial. The symptoms of cystitis could be controlled with high-dose GC, but in the process of GC reduction, the condition repeated. Here we report one case of refractory LC treated with Belimumab. The case is a 45-year-old female patient who had SLE and presented with urinary urgency, frequency and pain for more than 3 years. Laboratory assays revealed high ANA, reduced complement 3 level, proteinuria, significantly elevated Leukocyte esterase and leukocyte in urine, with the negative urinary culture of bacteria and mycoplasma, meanwhile, the cystoscopy and bladder biopsy showed interstitial inflammation. Confirming the diagnosis of refractory SLE-LC, recommended-dose Belimumab (10 mg/kg fortnightly for 3 times, monthly for 6 times) was administered, resulting in the normalization of urinary activity and significant reductions in leukocyte counts and protein levels of urine. No lupus cystitis relapse or SLE activity occurred during 10 months of follow-up. ALLN manufacturer Our case confirmed the efficacy and good follow-up outcomes of Belimumab treatment for refractory SLE-LC.

Adolescents are at increased risk of developing major depressive disorder (MDD) than many other age groups. Although the neural correlates of MDD in adults have been studied prospectively, such adolescent depression studies are mainly cross-sectional. We extracted data regarding the relationship between cortical thickness and later development of adolescent MDD from a national community study that uses an accelerated longitudinal design to examine the psychological, environmental, and neural differences related to drinking and brain development.

692 subjects (age 12-21 years; 50% female) without a history of MDD were assessed with structural neuroimaging at baseline. We compared those 101 subjects who transitioned to MDD by 1-year follow-up to those who remained non-depressed over the same time period. FreeSurfer’s autosegmentation process estimated vertex-wide cortical thicknesses and its Query, Design, Estimate, Contrast (Qdec) application investigated cortical thickness between those who later developed MDD and those who remained without MDD (Monte Carlo corrected for multiple comparisons, vertex-wise cluster threshold of 1.3, p<0.01).

Those who transitioned in the next year to MDD had, at baseline, thinner cortices in the superior frontal cortex, precentral and postcentral regions, and superior temporal cortex, above and beyond effects attributable to age and sex. No cortical thickness sex differences or sex-by-depression interactions were observed.

A larger sample size could improve statistical power and future investigations will be needed to confirm our results.

Thinner cortices over frontal and temporal regions may be linked to enhanced vulnerability for future depression during the adolescent-young adulthood transition.

Thinner cortices over frontal and temporal regions may be linked to enhanced vulnerability for future depression during the adolescent-young adulthood transition.

Selective serotonin reuptake inhibitors (SSRIs) are suggested as the first-line treatment for patients with major depressive disorder (MDD), but the remission rate is unsatisfactory. We aimed to establish machine learning models and explore variables available at baseline to predict the 8-week outcome among patients taking SSRIs.

Data from 400 patients were used to build machine learnings. The last observation carried forward approach was used to determine the remitter/non-remitter status of the patients at week 8. Using least absolute shrinkage and selection operator (LASSO) to select features, we built 4 different machine learning algorithms including gradient boosting decision tree, support vector machine (SVM), random forests, and logistic regression with five-fold cross-validation. Then, we adopted Shapley additive explanations (SHAP) values to interpret the model output.

The remission rate is 67.8%. We obtained 78 features from the baseline characteristics, including 25 sociodemographic characteristics, 31 clinical features, 15 psychological traits and 7 neurocognitive functions, and 13 of these features were selected to establish SVM. The accuracy of the SVM prediction is 74.49%, reaching an average area under the curve of 0.734±0.043. The sensitivity is 0.899±0.038 with a positive predictive value of 0.776±0.028. The specificity is 0.422±0.091 with a negative predictive value of 0.674±0.086. According to the SHAP values, neurocognitive functions and anxiety and hypochondriasis symptoms were important predictors.

Our study supports the utilization of machine learning approaches with inexpensive and highly accessible variables to accurately predict the 8-week treatment outcome of SSRIs in patients with MDD.

Our study supports the utilization of machine learning approaches with inexpensive and highly accessible variables to accurately predict the 8-week treatment outcome of SSRIs in patients with MDD.