Addiction Research Consortium: Losing and regaining control over drug intake (ReCoDe)—From trajectories to mechanisms and interventions

One of the major risk factors for global death and disability is alcohol, tobacco, and illicit drug use. While there is increasing knowledge with respect to individual factors promoting the initiation and maintenance of substance use disorders (SUDs), disease trajectories involved in losing and regaining control over drug intake (ReCoDe) are still not well described. Our newly formed German Collaborative Research Centre (CRC) on ReCoDe has an interdisciplinary approach funded by the German Research Foundation (DFG) with a 12-year perspective. The main goals of our research consortium are (i) to identify triggers and modifying factors that longitudinally modulate the trajectories of losing and regaining control over drug consumption in real life, (ii) to study underlying behavioral, cognitive, and neurobiological mechanisms, and (iii) to implicate mechanism-based interventions. These goals will be achieved by: (i) using mobile health (m-health) tools to longitudinally monitor the effects of triggers (drug cues, stressors, and priming doses) and modify factors (eg, age, gender, physical activity, and cognitive control) on drug consumption patterns in real-life conditions and in animal models of addiction; (ii) the identification and computational modeling of key mechanisms mediating the effects of such triggers and modifying factors on goal-directed, habitual, and compulsive aspects of behavior from human studies and animal models; and (iii) developing and testing interventions that specifically target the underlying mechanisms for regaining control over drug intake

The ReCoDe addiction research consortium:Losing and regaining control over drug intake-Findings and future perspectives

Substance use disorders (SUDs) are seen as a continuum ranging from goal-directed and hedonic drug use to loss of control over drug intake with aversive consequences for mental and physical health and social functioning. The main goals of our interdisciplinary German collaborative research centre on Losing and Regaining Control over Drug Intake (ReCoDe) are (i) to study triggers (drug cues, stressors, drug priming) and modifying factors (age, gender, physical activity, cognitive functions, childhood adversity, social factors, such as loneliness and social contact/interaction) that longitudinally modulate the trajectories of losing and regaining control over drug consumption under real-life conditions. (ii) To study underlying behavioural, cognitive and neurobiological mechanisms of disease trajectories and drug-related behaviours and (iii) to provide non-invasive mechanism-based interventions. These goals are achieved by: (A) using innovative mHealth (mobile health) tools to longitudinally monitor the effects of triggers and modifying factors on drug consumption patterns in real life in a cohort of 900 patients with alcohol use disorder. This approach will be complemented by animal models of addiction with 24/7 automated behavioural monitoring across an entire disease trajectory; i.e. from a naïve state to a drug-taking state to an addiction or resilience-like state. (B) The identification and, if applicable, computational modelling of key molecular, neurobiological and psychological mechanisms (e.g., reduced cognitive flexibility) mediating the effects of such triggers and modifying factors on disease trajectories. (C) Developing and testing non-invasive interventions (e.g., Just-In-Time-Adaptive-Interventions (JITAIs), various non-invasive brain stimulations (NIBS), individualized physical activity) that specifically target the underlying mechanisms for regaining control over drug intake. Here, we will report on the most important results of the first funding period and outline our future research strategy.</p

Patterns of Alcohol Consumption Among Individuals With Alcohol Use Disorder During the COVID-19 Pandemic and Lockdowns in Germany

Importance Alcohol consumption (AC) leads to death and disability worldwide. Ongoing discussions on potential negative effects of the COVID-19 pandemic on AC need to be informed by real-world evidence. Objective To examine whether lockdown measures are associated with AC and consumption-related temporal and psychological within-person mechanisms. Design, Setting, and Participants This quantitative, intensive, longitudinal cohort study recruited 1743 participants from 3 sites from February 20, 2020, to February 28, 2021. Data were provided before and within the second lockdown of the COVID-19 pandemic in Germany: before lockdown (October 2 to November 1, 2020); light lockdown (November 2 to December 15, 2020); and hard lockdown (December 16, 2020, to February 28, 2021). Main Outcomes and Measures Daily ratings of AC (main outcome) captured during 3 lockdown phases (main variable) and temporal (weekends and holidays) and psychological (social isolation and drinking intention) correlates. Results Of the 1743 screened participants, 189 (119 [63.0%] male; median [IQR] age, 37 [27.5-52.0] years) with at least 2 alcohol use disorder (AUD) criteria according to the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) yet without the need for medically supervised alcohol withdrawal were included. These individuals provided 14 694 smartphone ratings from October 2020 through February 2021. Multilevel modeling revealed significantly higher AC (grams of alcohol per day) on weekend days vs weekdays (β = 11.39; 95% CI, 10.00-12.77; P < .001). Alcohol consumption was above the overall average on Christmas (β = 26.82; 95% CI, 21.87-31.77; P < .001) and New Year’s Eve (β = 66.88; 95% CI, 59.22-74.54; P < .001). During the hard lockdown, perceived social isolation was significantly higher (β = 0.12; 95% CI, 0.06-0.15; P < .001), but AC was significantly lower (β = −5.45; 95% CI, −8.00 to −2.90; P = .001). Independent of lockdown, intention to drink less alcohol was associated with lower AC (β = −11.10; 95% CI, −13.63 to −8.58; P < .001). Notably, differences in AC between weekend and weekdays decreased both during the hard lockdown (β = −6.14; 95% CI, −9.96 to −2.31; P = .002) and in participants with severe AUD (β = −6.26; 95% CI, −10.18 to −2.34; P = .002). Conclusions and Relevance This 5-month cohort study found no immediate negative associations of lockdown measures with overall AC. Rather, weekend-weekday and holiday AC patterns exceeded lockdown effects. Differences in AC between weekend days and weekdays evinced that weekend drinking cycles decreased as a function of AUD severity and lockdown measures, indicating a potential mechanism of losing and regaining control. This finding suggests that temporal patterns and drinking intention constitute promising targets for prevention and intervention, even in high-risk individuals

Image acquisition and planimetry systems to develop wounding techniques in 3D wound model

Wound healing represents a complex biological repair process. Established 2D monolayers and wounding techniques investigate cell migration, but do not represent coordinated multi-cellular systems. We aim to use wound surface area measurements obtained from image acquisition and planimetry systems to establish our wounding technique and in vitro organotypic tissue. These systems will be used in our future wound healing treatment studies to assess the rate of wound closure in response to wound healing treatment with light therapy (photobiomodulation). The image acquisition and planimetry systems were developed, calibrated, and verified to measure wound surface area in vitro. The system consists of a recording system (Sony DSC HX60, 20.4 M Pixel, 1/2.3″ CMOS sensor) and calibrated with 1mm scale paper. Macro photography with an optical zoom magnification of 2:1 achieves sufficient resolution to evaluate the 3mm wound size and healing growth. The camera system was leveled with an aluminum construction to ensure constant distance and orientation of the images. The JPG-format images were processed with a planimetry system in MATLAB. Edge detection enables definition of the wounded area. Wound area can be calculated with surface integrals. To separate the wounded area from the background, the image was filtered in several steps. Agar models, injured through several test persons with different levels of experience, were used as pilot data to test the planimetry software. These image acquisition and planimetry systems support the development of our wound healing research. The reproducibility of our wounding technique can be assessed by the variability in initial wound surface area. Also, wound healing treatment effects can be assessed by the change in rate of wound closure. These techniques represent the foundations of our wound model, wounding technique, and analysis systems in our ongoing studies in wound healing and therapy

Image acquisition and planimetry systems to develop wounding techniques in 3D wound model

Abstract Wound healing represents a complex biological repair process. Established 2D monolayers and wounding techniques investigate cell migration, but do not represent coordinated multi-cellular systems. We aim to use wound surface area measurements obtained from image acquisition and planimetry systems to establish our wounding technique and in vitro organotypic tissue. These systems will be used in our future wound healing treatment studies to assess the rate of wound closure in response to wound healing treatment with light therapy (photobiomodulation). The image acquisition and planimetry systems were developed, calibrated, and verified to measure wound surface area in vitro. The system consists of a recording system (Sony DSC HX60, 20.4 M Pixel, 1/2.3″ CMOS sensor) and calibrated with 1mm scale paper. Macro photography with an optical zoom magnification of 2:1 achieves sufficient resolution to evaluate the 3mm wound size and healing growth. The camera system was leveled with an aluminum construction to ensure constant distance and orientation of the images. The JPG-format images were processed with a planimetry system in MATLAB. Edge detection enables definition of the wounded area. Wound area can be calculated with surface integrals. To separate the wounded area from the background, the image was filtered in several steps. Agar models, injured through several test persons with different levels of experience, were used as pilot data to test the planimetry software. These image acquisition and planimetry systems support the development of our wound healing research. The reproducibility of our wounding technique can be assessed by the variability in initial wound surface area. Also, wound healing treatment effects can be assessed by the change in rate of wound closure. These techniques represent the foundations of our wound model, wounding technique, and analysis systems in our ongoing studies in wound healing and therapy.</jats:p

Proteomic Characterization of Prostate Cancer to Distinguish Nonmetastasizing and Metastasizing Primary Tumors and Lymph Node Metastases

Patients with metastatic prostate cancer (PCa) have a poorer prognosis than patients with organ-confined tumors. We strove to uncover the proteome signature of primary PCa and associated lymph node metastases (LNMs) in order to identify proteins that may indicate or potentially promote metastases formation. We performed a proteomic comparative profiling of PCa tissue from radical prostatectomy (RPE) of patients without nodal metastases or relapse at the time of surgical resection (n = 5) to PCa tissue from RPE of patients who suffered from nodal relapse (n = 5). For the latter group, we also included patient-matched tissue of the nodal metastases. All samples were formalin fixed and paraffin embedded. We identified and quantified more than 1200 proteins by liquid chromatography tandem mass spectrometry with subsequent label-free quantification. An increase of ribosomal or proteasomal proteins in LNM (compared to corresponding PCa) became apparent, while extracellular matrix components rather decreased. Immunohistochemistry (IHC) corroborated accumulation of poly-(ADP-ribose)-polymerase 1 and N-myc-downstream-regulated-gene 3, alpha/beta hydrolase domain-containing protein 11, and protein phosphatase slingshot homolog 3 in LNM. These findings strengthen the present interest in examining PARP inhibitors for the treatment of aggressive PCa. IHC also corroborated increased abundance of retinol dehydrogenase 11 in metastasized primary PCa compared to organ-confined PCa. Generally, metastasizing primary tumors were characterized by an enrichment of proteins involved in cellular lipid metabolic processes with concomitant decrease of cell adhesion proteins. This study highlights the usefulness of a combined proteomic-IHC approach to explore novel aspects in tumor biology. Our initial results open novel opportunities for follow-up studies

Development and Evaluation of MR-Based Radiogenomic Models to Differentiate Atypical Lipomatous Tumors from Lipomas

Background: The aim of this study was to develop and validate radiogenomic models to predict the MDM2 gene amplification status and differentiate between ALTs and lipomas on preoperative MR images. Methods: MR images were obtained in 257 patients diagnosed with ALTs (n = 65) or lipomas (n = 192) using histology and the MDM2 gene analysis as a reference standard. The protocols included T2-, T1-, and fat-suppressed contrast-enhanced T1-weighted sequences. Additionally, 50 patients were obtained from a different hospital for external testing. Radiomic features were selected using mRMR. Using repeated nested cross-validation, the machine-learning models were trained on radiomic features and demographic information. For comparison, the external test set was evaluated by three radiology residents and one attending radiologist. Results: A LASSO classifier trained on radiomic features from all sequences performed best, with an AUC of 0.88, 70% sensitivity, 81% specificity, and 76% accuracy. In comparison, the radiology residents achieved 60–70% accuracy, 55–80% sensitivity, and 63–77% specificity, while the attending radiologist achieved 90% accuracy, 96% sensitivity, and 87% specificity. Conclusion: A radiogenomic model combining features from multiple MR sequences showed the best performance in predicting the MDM2 gene amplification status. The model showed a higher accuracy compared to the radiology residents, though lower compared to the attending radiologist