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Background: Benzodiazepines (BZDs) are readily available potent drugs that act as central depressants. These drugs are widely used, misused, and abused. For patients with BZD use disorder, the traditional sobriety monitoring method is periodic urine tests.

Methods: The utility of eye-scanning data related to non-convergence (the ability to cross eyes) collected using smartphones with the Previct Drugs app before and after ingestion of the BZD lorazepam for detecting BZD-driven effects was evaluated using data from 12 individuals from a historic clinical study (NCT05731999). Using a novel metric that represents the change in distance between irises when converging eyes, either in absolute terms (NCdiff) or individualized (NCdiffInd), classifiers were built using logistic regression.

Results: The ability to converge eyes is a strongly individual and acquired skill that is impaired after ingesting lorazepam. The maximum NCdiff for a BZD-sober individual may be smaller than the impaired NCdiff for another individual. Using the NCdiff measured in a sober condition after approximately 1 week of regular eye-scanning as the individual baseline to form NCdiffInd produced a highly functional classifier with an area under the curve (AUC) = 0.88, which was superior to a classifier based on NCdiff with an AUC = 0.79.

Conclusions: The loss of eye convergence induced by lorazepam is continuous, individual, and can be partial. Smartphone-based eye-scanning technology combined with a classifier adapted to the ability of eye convergence of individuals shows promising performance in detecting ingestion of lorazepam.

Background: It is known that illicit and prescribed drugs impact pupil size, eye movement and function. Still, comprehensive quantitative evaluations under known ambient light conditions are lacking, when smartphones are used for monitoring.

Methods: In this clinical study (NCT05731999), four medicinal products with addiction risks were administered to 48 subjects (18-70 years old, all with informed consent, 12 subjects per drug). Videos captured by smartphones at similar to 50 lux and similar to 500 lux documented the eye's reaction before and after controlled intake of single doses of oral oxycodone (20 mg), lorazepam (2 mg), lisdexamphetamine (70 mg) and inhaled cannabis flos (65 mg with 22% THC) over a 5-h test period. Data from three observational tests, non-convergence (NC, ability to cross the eyes), nystagmus (NY), and pupillary light reflex (PLR) were converted into 24 key features that represent different eye characteristics.

Results: Of the acquired data, 87-97% produced key features. At peak drug plasma concentration, oxycodone constricted pupils (p < 0.001); lorazepam induced non-convergence (p < 0.001); lisdexamphetamine induced dilated pupils (p < 0.001), irrespective of ambient light conditions. Inhaled cannabis induced miosis (p = 0.05 at similar to 50 lux, p = 0.10 at similar to 500 lux), a reduced light-induced amplitude (p = 0.003 at similar to 50 lux, p = 0.3 at similar to 500 lux) and redness of the sclerae (p = 0.14 at similar to 50 lux, p = 0.007 at similar to 500 lux). The drug effect lasted at least 5 h (p < 0.005) except for inhaled cannabis (2-3 h, p < 0.05).

Conclusion: The ocular response to oxycodone, lorazepam, lisdexamphetamine and cannabis, as measured under controlled light conditions using a smartphone-based assessment, demonstrated distinct and readily distinguishable patterns for each substance.

Background: Predictive eHealth tools will change the field of medicine, however long-term data is scarce. Here, we report findings on data collected over 6 years with an AI-based eHealth system for supporting the treatment of alcohol use disorder.

Methods: Since the deployment of Previct Alcohol, structured data has been archived in a data warehouse, currently comprising 505,641 patient days. The frequencies of relapse and caregiver-patient messaging over time was studied. The effects of both introducing an AI-driven relapse prediction tool and the COVID-19 pandemic were analyzed.

Results: The relapse frequency per patient day among Previct Alcohol users was 0.28 in 2016, 0.22 in 2020 and 0.25 in 2022 with no drastic change during COVID-19. When a relapse was predicted, the actual occurrence of relapse in the days immediately after was found to be above average. Additionally, there was a noticeable increase in caregiver interactions following these predictions. When caregivers were not informed of these predictions, the risk of relapse was found to be higher compared to when the prediction tool was actively being used. The prediction tool decreased the relapse risk by 9% for relapses that were of short duration and by 18% for relapses that lasted more than 3 days.

Conclusions: The eHealth system Previct Alcohol allows for high resolution measurements, enabling precise identifications of relapse patterns and follow up on individual and population-based alcohol use disorder treatment. eHealth relapse prediction aids the caregiver to act timely, which reduces, delays, and shortens relapses.

Objectives

There is an ongoing opioid crisis in the United States where the illicit and non-medical use of prescription opioids is associated with an increasing number of overdose deaths. Few studies have investigated opioid-induced effects on cell viability, and comparative studies are limited. Here, we examine the toxicity of six commonly used opioids: methadone, morphine, oxycodone, hydromorphone, ketobemidone, and fentanyl with respect to mitochondrial and membrane function in vitro.

Methods

The opioids were tested in four different cell cultures: primary cortical cell cultures, human neuroblastoma SH-SY5Y cells, and both differentiated and undifferentiated neuroblastoma/glioma hybrid NG108-15 cells. The mitochondrial activity was assessed using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and the membrane integrity was assessed by measuring the leakage of lactate dehydrogenase. To compare the different opioids, the toxic dose (TD₅₀) was calculated.

Results

The results displayed a similar trend of opioid-reduced cell viability in all four cell cultures. The most toxic opioid was methadone, followed by fentanyl, while morphine was overall ranked as the least toxic opioid displaying little to no negative impact on cell viability. The remaining opioids varied in rank between the different cell types.

Conclusion

This in vitro study highlights opioid-dependent variations in toxicity across all four tested cell types, with methadone emerging as the most potent opioid.

OBJECTIVES: Neuropeptide Y is associated with stress in animal and human laboratory studies. However, data from clinical studies are scarce and no clinical longitudinal studies have been published. The aim of this clinical study was to assess the possible association between changes in the levels of pain, depression, and stress measures, on the one hand, and plasma neuropeptide Y levels, on the other.

METHODS: Forty-four women with the fibromyalgia syndrome were exposed to a Cognitive Behavioral Therapy intervention. Levels of the plasma neuropeptide Y as well as pain, depression, and stress measures were obtained at the start and at the end of the intervention, and after a further six month follow-up. Based on these data, a before-and-after analysis was performed.

RESULTS: Almost all measures of pain, depression, and stress improved during the study; specifically, variables measuring life control (coping), depression, and stress-related time urgency improved significantly. Moreover, during the same time period, the mean plasma neuropeptide Y level was reduced from 93.2 ± 38.8 fmol/mL before the Cognitive Behavioral Therapy to 75.6 ± 42.9 fmol/mL (p<0.001) at the end of the study.

CONCLUSIONS: After exposure to a Cognitive Behavioral Therapy intervention, levels of most of the pain, depression, and stress measures improved, half of them significantly, as did the levels of neuropeptide Y. This circumstance indicates a possible functional relationship between pain-depression-stress and neuropeptide Y.

Purpose: eHealth systems allow efficient daily smartphone-based collection of self-reported data on mood, wellbeing, routines, and motivation; however, missing data is frequent. Within addictive disorders, missing data may reflect lack of motivation to stay sober. We hypothesize that qualitative questionnaire data contains valuable information, which after proper handling of missing data becomes more useful for practitioners.

Methods: Anonymized data from daily questionnaires containing 11 questions was collected with an eHealth system for 751 patients with alcohol use disorder (AUD). Two digital continuous biomarkers were composed from 9 wellbeing questions (WeBe-i) and from two questions representing motivation/self-confidence to remain sober (MotSC-i). To investigate possible loss of information in the process of composing the digital biomarkers, performance of neural networks to predict exacerbation events (relapse) in alcohol use disorder was compared.

Results: Long short-term memory (LSTM) neural networks predicted a coming exacerbation event 1-3 days (AUC 0.68-0.70) and 5-7 days (AUC 0.65-0.68) in advance on unseen patients. The predictive capability of digital biomarkers and raw questionnaire data was equal, indicating no loss of information. The transformation into digital biomarkers enable a continuous graphical display of each patient's clinical course and a combined interpretation of qualitative and quantitative aspects of recovery on a time scale.

Conclusion: By transforming questionnaire data with large proportion of missing data into continuous digital biomarkers, the information captured by questionnaires can be more easily used in clinical practice. Information, assessed by the capability to predict exacerbation events of AUD, is preserved when processing raw questionnaire data into digital biomarkers.

The illicit use of anabolic androgenic steroids (AAS) among adolescents and young adults is a major concern due to the unknown and unpredictable impact of AAS on the developing brain and the consequences of this on mental health, cognitive function and behaviour. The present study aimed to investigate the effects of supra-physiological doses of four structurally different AAS (testosterone, nandrolone, stanozolol and trenbolone) on neurite development and cell viability using an in vitro model of immature primary rat cortical cell cultures. A high-throughput screening image-based approach, measuring the neurite length and number of neurons, was used for the analysis of neurite outgrowth. In addition, cell viability and expression of the Tubb3 gene (encoding the protein beta-III tubulin) were investigated. Testosterone, nandrolone, and trenbolone elicited adverse effects on neurite outgrowth as deduced from an observed reduced neurite length per neuron. Trenbolone was the only AAS that reduced the cell viability as indicated by a decreased number of neurons and declined mitochondrial function. Moreover, trenbolone downregulated the Tubb3 mRNA expression. The adverse impact on neurite development was neither inhibited nor supressed by the selective androgen receptor (AR) antagonist, flutamide, suggesting that the observed effects result from another mechanism or mechanisms of action that are operating apart from AR activation. The results demonstrate a possible AAS-induced detrimental effect on neuronal development and regenerative functions. An impact on these events, that are essential mechanisms for maintaining normal brain function, could possibly contribute to behavioural alterations seen in AAS users.

Of painful conditions, somatic pain of acute nociceptive origin can be effectively managed clinically, while neuropathic pain of chronic neuropathy origin is difficult to control. For molecules involved in pain sensation, substance P (SP) is algesic, exacerbating painful sensation, while its amino-terminal fragment, heptapeptide SP(1-7), confers biological activities different from its full-length parent neuropeptide precursor. We previously demonstrated SP(1-7) interaction with pain processing to alleviate chronic pain. Here we evaluated SP(1-7) and its C-terminal amidated analogue SP(1-7) amide, together with SP and opioid agonist DAMGO. We tested mouse behaviors of both acute somatic pain in tail-flick latency assay, and neuropathic pain in sciatic nerve injury model of chronic constriction injury (CCI). DAMGO produced dose-dependent analgesia for somatic pain as expected, so did both SP(1-7) and its analogue SP(1-7) amide, while SP yielded the opposite effect of algesia, in a phenomenon we termed `contrintus', meaning 'opposite from within' to denote that two peptides of the same origin (SP and its metabolic fragment SP(1-7)) produced opposite effects. In CCI model, DAMGO showed a general reduction in allodynia sensitivity for both nerve-injured and normal paws, without selective effect for neuropathic pain, consistent with clinical observation that opioids are less effective for chronic neuropathic pain. On the other hand, both SP(1-7) and SP(1-7) amide displayed dose-dependent anti-allodynia effect that is selective for neuropathic pain. These findings suggest that SP(1-7) and its analogue may be useful for developing pharmaceuticals to treat neuropathic pain.

Aims: This study introduces new digital biomarkers to be used as precise, objective tools to measure and describe the clinical course of patients with alcohol use disorder (AUD).

Methods: An algorithm is outlined for the calculation of a new digital biomarker, the recovery and exacerbation index (REI), which describes the current trend in a patient's clinical course of AUD. A threshold applied to the REI identifies the starting point and the length of an exacerbation event (EE). The disease patterns and periodicity are described by the number, length, and distance between EEs. The algorithms were tested on data from patients from previous clinical trials (n = 51) and clinical practice (n = 1,717).

Results: Our study indicates that the digital biomarker-based description of the clinical course of AUD might be superior to the traditional self-reported relapse/remission concept and conventional biomarkers due to higher data quality (alcohol measured) and time resolution. We found that EEs and the REI introduce distinct tools to identify qualitative and quantitative differences in drinking patterns (drinks per drinking day, phosphatidyl ethanol levels, weekday and holiday patterns) and effect of treatment time.

Conclusions: This study indicates that the disease state-level, trend and periodicity-can be mathematically described and visualized with digital biomarkers, thereby improving knowledge about the clinical course of AUD and enabling clinical decision-making and adaptive care. The algorithms provide a basis for machine-learning-driven research that might also be applied for other disorders where daily data are available from digital health systems.

The important role of mitochondria in maintaining normal brain cell function has been demonstrated in several neurodegenerative diseases where mitochondrial dysfunction is a prominent feature. Accumulating evidence indicates that opioids may induce neuronal cell death and inhibit neurogenesis, two factors that are dependent on normal mitochondrial function. The aim of the present study was to examine the effects of morphine, methadone, and fentanyl on mitochondrial morphology. Cells from the neuroblastoma/glioma hybrid cell-line NG108-15 were seeded on 96-well cell culture plates and treated with MitoTracker™ for 30 min prior to opioid treatment. Morphine, methadone, and fentanyl were added at various concentrations and images of mitochondria were acquired every 30 min for four hours using a high-content imaging device. The morphological parameters total mitochondrial area, mitochondrial network, number of mitochondrial objects, and the mean area of mitochondrial objects were analyzed using automated image analysis. Methadone and fentanyl, but not morphine, decreased the mitochondrial network, the number of mitochondrial objects, and increased the mean area of mitochondrial objects. Both methadone and fentanyl altered mitochondrial morphology with no effects seen from morphine treatment. These data suggest that methadone and fentanyl disrupt mitochondrial morphology, which may contribute to neuronal cell death.