Developing Advanced AI Ecosystems to Enhance Diagnosis and Care for Patients with Depression.

Major Depressive Disorder (MDD) has a significant impact on the daily lives of those affected. This concept paper presents a project that aims at addressing MDD challenges through innovative therapy systems. The project consists of two use cases: a multimodal neurofeedback (NFB) therapy and an AI-based virtual therapy assistant (VTA). The multimodal NFB integrates EEG and fNIRS to comprehensively assess brain function. The goal is to develop an open-source NFB toolbox for EEG-fNIRS integration, augmented by the VTA for optimized efficacy. The VTA will be able to collect behavioral data, provide personalized feedback and support MDD patients in their daily lives. This project aims to improve depression treatment by bringing together digital therapy, AI and mobile apps to potentially improve outcomes and accessibility for people living with depression.

Influence of breast cancer risk factors on proliferation and DNA damage in human breast glandular tissues: role of intracellular estrogen levels, oxidative stress and estrogen biotransformation.

Breast cancer etiology is associated with both proliferation and DNA damage induced by estrogens. Breast cancer risk factors (BCRF) such as body mass index (BMI), smoking, and intake of estrogen-active drugs were recently shown to influence intratissue estrogen levels. Thus, the aim of the present study was to investigate the influence of BCRF on estrogen-induced proliferation and DNA damage in 41 well-characterized breast glandular tissues derived from women without breast cancer. Influence of intramammary estrogen levels and BCRF on estrogen receptor (ESR) activation, ESR-related proliferation (indicated by levels of marker transcripts), oxidative stress (indicated by levels of GCLC transcript and oxidative derivatives of cholesterol), and levels of transcripts encoding enzymes involved in estrogen biotransformation was identified by multiple linear regression models. Metabolic fluxes to adducts of estrogens with DNA (E-DNA) were assessed by a metabolic network model (MNM) which was validated by comparison of calculated fluxes with data on methoxylated and glucuronidated estrogens determined by GC- and UHPLC-MS/MS. Intratissue estrogen levels significantly influenced ESR activation and fluxes to E-DNA within the MNM. Likewise, all BCRF directly and/or indirectly influenced ESR activation, proliferation, and key flux constraints influencing E-DNA (i.e., levels of estrogens, CYP1B1, SULT1A1, SULT1A2, and GSTP1). However, no unambiguous total effect of BCRF on proliferation became apparent. Furthermore, BMI was the only BCRF to indeed influence fluxes to E-DNA (via congruent adverse influence on levels of estrogens, CYP1B1 and SULT1A2).

Influence of breast cancer risk factors and intramammary biotransformation on estrogen homeostasis in the human breast.

Understanding intramammary estrogen homeostasis constitutes the basis of understanding the role of lifestyle factors in breast cancer etiology. Thus, the aim of the present study was to identify variables influencing levels of the estrogens present in normal breast glandular and adipose tissues (GLT and ADT, i.e., 17ß-estradiol, estrone, estrone-3-sulfate, and 2-methoxy-estrone) by multiple linear regression models. Explanatory variables (exVARs) considered were (a) levels of metabolic precursors as well as levels of transcripts encoding proteins involved in estrogen (biotrans)formation, (b) data on breast cancer risk factors (i.e., body mass index, BMI, intake of estrogen-active drugs, and smoking) collected by questionnaire, and (c) tissue characteristics (i.e., mass percentage of oil, oil%, and lobule type of the GLT). Levels of estrogens in GLT and ADT were influenced by both extramammary production (menopausal status, intake of estrogen-active drugs, and BMI) thus showing that variables known to affect levels of circulating estrogens influence estrogen levels in breast tissues as well for the first time. Moreover, intratissue (biotrans)formation (by aromatase, hydroxysteroid-17beta-dehydrogenase 2, and beta-glucuronidase) influenced intratissue estrogen levels, as well. Distinct differences were observed between the exVARs exhibiting significant influence on (a) levels of specific estrogens and (b) the same dependent variables in GLT and ADT. Since oil% and lobule type of GLT influenced levels of some estrogens, these variables may be included in tissue characterization to prevent sample bias. In conclusion, evidence for the intracrine activity of the human breast supports biotransformation-based strategies for breast cancer prevention. The susceptibility of estrogen homeostasis to systemic and tissue-specific modulation renders both beneficial and adverse effects of further variables associated with lifestyle and the environment possible.

Qualitative and quantitative differences in estrogen biotransformation in human breast glandular and adipose tissues: implications for studies using mammary biospecimens.

Because of its assumed role in breast cancer etiology, estrogen biotransformation (and interaction of compounds therewith) has been investigated in human biospecimens for decades. However, little attention has been paid to the well-known fact that large inter-individual variations exist in the proportion of breast glandular (GLT) and adipose (ADT) tissues and less to adequate tissue characterization. To assess the relevance of this, the present study compares estrogen biotransformation in GLT and ADT. GLT and ADT were isolated from 47 reduction mammoplasty specimens derived from women without breast cancer and were characterized histologically and by their percentages of oil. Levels of 12 unconjugated and five conjugated estrogens were analyzed by GC- and UHPLC-MS/MS, respectively, and levels of 27 transcripts encoding proteins involved in estrogen biotransformation by Taqman® probe-based PCR. Unexpectedly, one-third of specimens provided neat GLT only after cryosection. Whereas 17ß-estradiol, estrone, and estrone-3-sulfate were detected in both tissues, estrone-3-glucuronide and 2-methoxy-estrone were detected predominately in GLT and ADT, respectively. Estrogen levels as well as ratios 17ß-estradiol/estrone and estrone-3-sulfate/estrone differed significantly between GLT and ADT, yet less than between individuals. Furthermore, estrogen levels in GLT and ADT correlated significantly with each other. In contrast, levels of most transcripts encoding enzymes involved in biotransformation differed more than between individuals and did not correlate between ADT and GLT. Thus, mixed breast tissues (and plasma) will not provide meaningful information on local estrogen biotransformation (and interaction of compounds therewith) whereas relative changes in 17ß-estradiol levels may be investigated in the more abundant ADT.

Novel insight in estrogen homeostasis and bioactivity in the ACI rat model of estrogen-induced mammary gland carcinogenesis.

Despite being widely used to investigate 17ß-estradiol (E2)-induced mammary gland (MG) carcinogenesis and prevention thereof, estrogen homeostasis and its significance in the female August Copenhagen Irish (ACI) rat model is unknown. Thus, levels of 12 estrogens including metabolites and conjugates were determined mass spectrometrically in 38 plasmas and 52 tissues exhibiting phenotypes ranging from normal to palpable tumor derived from a representative ACI study using two different diets. In tissues, 40 transcripts encoding proteins involved in estrogen (biotrans)formation, ESR1-mediated signaling, proliferation and oxidative stress were analyzed (TaqMan PCR). Influence of histo(patho)logic phenotypes and diet on estrogen and transcript levels was analyzed by 2-way ANOVA and explanatory variables influencing levels and bioactivity of estrogens in tissues were identified by multiple linear regression models. Estrogen profiles in tissue and plasma and the influence of Hsd17b1 levels on intra-tissue levels of E2 and E1 conclusively indicated intra-mammary formation of E2 in ACI tumors by HSD17B1-mediated conversion of E1. Proliferation in ACI tumors was influenced by Egfr, Igf1r, Hgf and Met levels. 2-MeO-E1, the only oxidative estrogen metabolite detected above 28-42 fmol/g, was predominately observed in hyperplastic tissues and intra-tissue conversion of E1 seemed to contribute to its levels. The association of the occurrence of 2-MeO-E1 with higher levels of oxidative stress observed in hyperplastic and tumor tissues remained equivocal. Thus, the present study provides mechanistic explanation for previous and future results observed in the ACI model.

Validity of stable isotope data in doping control: perspectives and proposals.

Δ(13)C and δ(13)C values of endogenous urinary steroids represent physiological random variables. Measurement uncertainty and biological scatter likewise contribute to the variances. The statistical distributions of negative controls are well investigated, but there is little knowledge about the corresponding distributions of steroid-users. For these reasons valid discrimination of steroid users from non-users by (13)C/(12)C analysis of endogenous steroids requires elaborate statistical treatment. Corresponding Bayesian approaches are presented following an introduction to the rationale. The use of mixture models appears appropriate. The distribution of routine data has been deconvolved and characterized accordingly. The mixture components, which presumably represent steroid users and non-users, exhibit considerable overlap. The validity of a given result depends on both the analytical uncertainty and the prior probability of doping offenses. Low analytical uncertainties but high prior probabilities facilitate valid detection of doping offenses. Two recommendations can be deduced. First, before starting an (13)C/(12)C analysis, any initial suspicion should be well-substantiated. This precludes use of permissive criteria derived from the steroid profile. Secondly, knowledge of relevant (13)C/(12)C distributions is required. This must cover representative numbers of authentic steroid users. Finally, it is desirable that the conditional probability for steroid administration rather than the measurement uncertainty is calculated and reported. This quantity possesses superior validity and it is largely independent of laboratory bias. The findings suggest and facilitate flexible handling of decision limits. Proposals for the evaluation of stable isotope data are presented.