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Background Bipolar disorder is highly prevalent and consists of biphasic recurrent mood episodes of mania and depression, which translate into altered mood, sleep and activity alongside their physiological expressions. Aims The IdenTifying dIgital bioMarkers of illnEss activity and treatment response in BipolAr diSordEr with a novel wearable device (TIMEBASE) project aims to identify digital biomarkers of illness activity and treatment response in bipolar disorder. Method We designed a longitudinal observational study including 84 individuals. Group A comprises people with acute episode of mania (n = 12), depression (n = 12 with bipolar disorder and n = 12 with major depressive disorder (MDD)) and bipolar disorder with mixed features (n = 12). Physiological data will be recorded during 48 h with a research-grade wearable (Empatica E4) across four consecutive time points (acute, response, remission and episode recovery). Group B comprises 12 people with euthymic bipolar disorder and 12 with MDD, and group C comprises 12 healthy controls who will be recorded cross-sectionally. Psychopathological symptoms, disease severity, functioning and physical activity will be assessed with standardised psychometric scales. Physiological data will include acceleration, temperature, blood volume pulse, heart rate and electrodermal activity. Machine learning models will be developed to link physiological data to illness activity and treatment response. Generalisation performance will be tested in data from unseen patients. Results Recruitment is ongoing. Conclusions This project should contribute to understanding the pathophysiology of affective disorders. The potential digital biomarkers of illness activity and treatment response in bipolar disorder could be implemented in a real-world clinical setting for clinical monitoring and identification of prodromal symptoms. This would allow early intervention and prevention of affective relapses, as well as personalisation of treatment.

Abstract Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable in ecological physiological recordings thanks to recent advances in wearable technology. Therefore, near-continuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning (ML), could mitigate this problem, bringing MDs monitoring outside the clinician’s office. Previous works predict a single label, either the disease state or a psychometric scale total score. However, clinical practice suggests that the same label may underlie different symptom profiles, requiring specific treatments. Here we bridge this gap by proposing a new task: inferring all items in HDRS and YMRS, the two most widely used standardized scales for assessing MDs symptoms, using physiological data from wearables. To that end, we develop a deep learning pipeline to score the symptoms of a large cohort of MD patients and show that agreement between predictions and assessments by an expert clinician is clinically significant (quadratic Cohen’s κ and macro-average F1 score both of 0.609). While doing so, we investigate several solutions to the ML challenges associated with this task, including multi-task learning, class imbalance, ordinal target variables, and subject-invariant representations. Lastly, we illustrate the importance of testing on out-of-distribution samples.

BackgroundPersonal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, is a promising paradigm to monitor mood disorders (MDs), a major determinant of the worldwide disease burden. However, collecting and annotating wearable data is resource intensive. Studies of this kind can thus typically afford to recruit only a few dozen patients. This constitutes one of the major obstacles to applying modern supervised machine learning techniques to MD detection. ObjectiveIn this paper, we overcame this data bottleneck and advanced the detection of acute MD episodes from wearables’ data on the back of recent advances in self-supervised learning (SSL). This approach leverages unlabeled data to learn representations during pretraining, subsequently exploited for a supervised task. MethodsWe collected open access data sets recording with the Empatica E4 wristband spanning different, unrelated to MD monitoring, personal sensing tasks—from emotion recognition in Super Mario players to stress detection in undergraduates—and devised a preprocessing pipeline performing on-/off-body detection, sleep/wake detection, segmentation, and (optionally) feature extraction. With 161 E4-recorded subjects, we introduced E4SelfLearning, the largest-to-date open access collection, and its preprocessing pipeline. We developed a novel E4-tailored transformer (E4mer) architecture, serving as the blueprint for both SSL and fully supervised learning; we assessed whether and under which conditions self-supervised pretraining led to an improvement over fully supervised baselines (ie, the fully supervised E4mer and pre–deep learning algorithms) in detecting acute MD episodes from recording segments taken in 64 (n=32, 50%, acute, n=32, 50%, stable) patients. ResultsSSL significantly outperformed fully supervised pipelines using either our novel E4mer or extreme gradient boosting (XGBoost): n=3353 (81.23%) against n=3110 (75.35%; E4mer) and n=2973 (72.02%; XGBoost) correctly classified recording segments from a total of 4128 segments. SSL performance was strongly associated with the specific surrogate task used for pretraining, as well as with unlabeled data availability. ConclusionsWe showed that SSL, a paradigm where a model is pretrained on unlabeled data with no need for human annotations before deployment on the supervised target task of interest, helps overcome the annotation bottleneck; the choice of the pretraining surrogate task and the size of unlabeled data for pretraining are key determinants of SSL success. We introduced E4mer, which can be used for SSL, and shared the E4SelfLearning collection, along with its preprocessing pipeline, which can foster and expedite future research into SSL for personal sensing.

Abstract After the introduction of all the speakers, the main aim of this workshop will be mentioned, which consists of identifying and highlighting those clinical, sociodemographic, environmental and other factors than might predict an increased risk of overall, depressive, manic or mixed relapses in bipolar disorder, which is crucial for the identification of high-risk individuals. Dr. Pacchiarotti will present main results from a systematic review performed recently by the work group aimed at collecting the available evidence regarding different factors that increase rates of mood recurrences or relapses for different polarities in bipolar disorder. Disclosure of Interest None Declared

Introduction With the rapid changes of attitude, investigation and legislation around cannabis and its subproducts in the Western world, there is a need to profoundly examine the consequences of its use in the general population and, specifically, in people affected by mental disorders. There is a clear relationship between cannabis use and psychosis, but there is also growing evidence of its relationship with manic episodes (Sideli et al, 2019). A systematic review published by the CANMAT Task Force in 2022 examined again the relationship between cannabis use and bipolar disorder (BD), establishing association with worsened course and functioning of BD in frequent users (Tourjman et al., 2023). On the other hand, some recent papers have highlighted the role of the endocannabinoid system (ECS) in BD, suggesting even possible beneficial effects, mainly through the CB2 receptor (Arjmand et al, 2019). Objectives To describe the impact of cannabis in the psychiatric readmission in BD and to approach the differences in course in cannabis users with regards to non-users. Methods We conducted a prospective cohort study including the patients admitted to our acute psychiatric unit with the diagnosis of manic or mixed episode during the period between 2015 and 2019 (including patients with one of both final diagnosis: BD or schizoaffective disorder). We established a follow-up of 3 years from the date of admission in which hospital readmissions are examined. Results The study, which included 309 patients, concluded that cannabis users were admitted and had the first episode at a younger age (p=0.005), a higher percentage of them did not have a previous diagnosis (p=0.026) nor a previous history of mental health issues (p=0.019) and it was more likely to be their first admission (p=0.011) and to suffer psychotic symptoms (p=0.002). As of treatment, the results were statistically significant regarding the fact that a lower proportion of patients had received previous psychiatric treatment (p=0.004) and previous electroconvulsive therapy (p=0.003). There was a higher chance of them being non-adherent with medication (p

Introduction Bipolar disorder (BD) is a chronic and recurrent disease characterized by acute mood episodes alternated with periods of euthymia. The available literature postulates that a biphasic dysregulation of mitochondrial bioenergetics might be observed in BD. Objectives We aimed to explore differences in in vivo mitochondrial respiration (1) intra-individually: longitudinally within patients during an acute mood episode of BD and after clinical remission, and (2) inter-individually: between patients with BD on depressive or manic episodes and healthy controls (HC). Methods Patients admitted to our acute psychiatric ward with a manic episode or bipolar depression were recruited. Different mitochondrial oxygen consumption rates (OCRs) were assessed during the acute episode (T0) and after clinical remission (T1) in one million of peripheral blood mononuclear cells (PBMC): Routine, Leak, ETC and Rox. They were measured as picomoles of oxygen per million cells (pmol O2/million). This experiment was also conducted in HC. High-resolution respirometry was performed at 37°C by polarographic oxygen sensors in a two-chamber Oxygraph-2k system. Manic and depressive symptoms were assessed using standardized psychometric scales. Oxygen consumption capacity was compared (1) intra-individually, during acute episodes and after clinical remission, and (2) inter-individually, during acute manic and depressive episodes, and in HC. Statistical analyses were performed with SPSS, GraphPad and R Statistics. Results 20 patients with BD (15 manic, 5 depressed) and 10 HC were included. A significant increase in the maximal oxygen consumption capacity (ETC) was observed in clinical remission (27.4 ± 17.4) compared to the acute episodes (21.1 ± 11.7, p = 0.001), which remained significant after subtracting Rox from the other rates (p = 0.001). At T1, patients admitted with a manic episode tended to show higher mean ETC (31.2 ± 18.7) compared with T0 (24.1 ± 12.0, p = 0.074); the tendency persisted after Rox subtraction (p = 0.076). Patients admitted with a depressive episode also showed higher ETC means in T1 (16.3 ± 3.8) compared to T0 (12.1 ± 3.4), but there were not significant differences (p = 0.231). When HC, manic and depressive patients at T0 were compared between them, significant differences were observed in ETC (H =8.5; p =0.014) and Rox (H =13.8; p = 0.001). After Rox deduction, differences in ETC remained (H =11.7; p = 0.003). Individuals with bipolar depression showed lower ETC rates (12.1 ± 3.4) than those with a manic episode (24.1 ± 12.0; t = -3.5, p = 0.003), which was also found after Rox deduction (p = 0.001). Conclusions In both manic and depressive episodes in BD, mitochondrial respiration might be reduced and increase after clinical remission. Further studies with larger samples will allow to confirm these results and also to identify potential mitochondrial state-dependent biomarkers. Disclosure of Interest A. Giménez-Palomo Grant / Research support from: AGP is supported by a Rio Hortega 2021 grant (CM21/00094) from the Spanish Ministry of Health financed by ISCIII and cofinanced by Fondo Social Europeo Plus (FSE+)., M. Guitart-Mampel: None Declared, A. Meseguer: None Declared, M. Valentí: None Declared, L. Bracco: None Declared, H. Andreu: None Declared, E. Vieta: None Declared, G. Garrabou: None Declared, I. Pacchiarotti: None Declared

The present contribution bears on the documentation and description of a few unusual sounds, i.e. double labial-velar articulations, in a number of Bantu zone C varieties belonging to the so-called “Sakata cluster” in the southwestern Democratic Republic of Congo (DRC). These phonemes, often considered typical of a linguistic area known as the “Macro-Sudan Belt”, are considerably more common in southern Central Africa than previously thought. The case of the Sakata varieties at hand represents one of particular interest considering the wide array of labial-velar articulations they present. First, we provide a spectral analysis of the data available to us, discussing the question of whether some of the sounds documented here should be described as labial-velar fricatives. Second, we proceed to review well-established models of sound change to test them against our data, with special focus on kiNgingele. We conclude by proposing that the presence of labial-velars in Sakata is part of a broader set of “uncommon” linguistic features present in northwestern Bantu: this, in turn, might point to the fact that the languages of the region went through stages of greater phonological diversity than suggested by today’s relative homogeneity. Sakata labial-velars may just be one trace of this diversity. Keywords: phonetic documentation, articulatory phonology, Bantu languages, acoustics, sound change

Due to the extreme scarcity of archaeological and historical data very little is known about the introduction of Southeast Asian crops such as banana, sugarcane, taro and greater yam in Africa and the role they played in the subsistence and lifeways of ancestral African communities. Therefore, we closely examine in this article comparative lexical data as a source to reconstruct the history of sugarcane in West-Central Africa. We focus more specifically on one branch of the Bantu language family, i.e., West-Coastal Bantu, in conjunction with data from Bantu languages spoken in the Congo rainforest and further south. We argue that despite their shared origins, sugarcane and bananas were not introduced in Africa as part of one single Southeast Asian package. Sugarcane made its way through West-Central together with crops of American origin such as maize, cassava, peanut, common bean, and (sweet) potato as part of the so-called “Columbian Exchange”, i.e., not earlier than the sixteenth century CE, while the ancestry of bananas in the Congo rainforest area likely goes back to the Early Iron Age, i.e., about 2,500 years ago.

BackgroundAerobic capacity has shown to predict physical and mental health-related quality of life in bipolar disorder (BD). However, the correlation between exercise respiratory capacity and mitochondrial function remains understudied. We aimed to assess longitudinally intra-individual differences in these factors during mood episodes and remission in BD.MethodsThis study included eight BD patients admitted to an acute psychiatric unit. Incremental cardiopulmonary exercise test (CPET) was conducted during acute episodes (T0), followed by constant work rate cycle ergometry (CWRCE) to evaluate endurance time, oxygen uptake at peak exercise (VO2peak) and at the anaerobic threshold. The second test was repeated during remission (T1). Mitochondrial respiration rates were assessed at T0 and T1 in peripheral blood mononuclear cells.ResultsEndurance time, VO2peak, and anaerobic threshold oxygen consumption showed no significant variations between T0 and T1. Basal oxygen consumption at T1 tended to inversely correlate with maximal mitochondrial respiratory capacity (r=-0.690, p=0.058), and VO2peak during exercise at T1 inversely correlated with basal and minimum mitochondrial respiration (r=-0.810, p=0.015; r=-0.786, p=0.021, respectively).ConclusionsOur preliminary data showed that lower basal oxygen consumption may be linked to greater mitochondrial respiratory capacity, and maximum oxygen uptake during the exercise task was associated with lower basal mitochondrial respiration, suggesting that lower oxygen requirements could be associated with greater mitochondrial capacity. These findings should be replicated in larger samples stratified for manic and depressive states.

Equine hepacivirus (EqHV, Flaviviridae, hepacivirus) is a small, enveloped RNA virus generally causing sub-clinical hepatitis with occasional fatalities. EqHV is reported in equids worldwide, but for Italy data are limited. To address this, a survey study was set up to estimate prevalence at a national level and among different production categories (equestrian; competition; work and meat; reproduction) and national macro-regions (North, Central, South, and Islands). Data obtained testing 1801 horse serum samples by Real-Time RT PCR were compared within the categories and regions. The NS3 fragment of the PCR-positive samples was sequenced by Sanger protocol for phylogenetic and mutational analysis. The tertiary structure of the NS3 protein was also assessed. The estimated national prevalence was 4.27% [1.97–6.59, 95% CI] and no statistical differences were detected among production categories and macro-regions. The phylogenesis confirmed the distribution in Italy of the three known EqHV subtypes, also suggesting a possible fourth sub-type that, however, requires further confirmation. Mutational profiles that could also affect the NS3 binding affinity to the viral RNA were detected. The present paper demonstrates that EqHV should be included in diagnostic protocols when investigating causes of hepatitis, and in quality control protocols for blood derived products due to its parental transmission.

Background: Equine influenza (EI) is a highly contagious viral disease of equids characterized by pyrexia and respiratory signs. Like other influenza A viruses, antigenic drift or shift could lead to a vaccine-induced immunity breakdown if vaccine strains are not updated. The aim of this study was to genetically characterize EIV strains circulating in Italy, detected in PCR-positive samples collected from suspected cases, especially in the absence of formal active surveillance. Methods: Between February and April 2019, blood samples and nasal swabs collected from each of the 20 symptomatic horses from North and Central Italy were submitted to the National Reference Centre for Equine Diseases in Italy to confirm preliminary analysis performed by other laboratories. Results: None of the sera analysed using haemagglutination inhibition and single radial haemolysis presented a predominant serological reactivity pattern for any antigen employed. All nasal swabs were positive with IAV RRT-PCR. Only one strain, isolated in an embryonated chicken egg from a sample collected from a horse of a stable located in Brescia, Lombardy, was identified as H3N8 Florida lineage clade 1 (FC1). In the constructed phylogenetic trees, this strain is located within the FC1, together with the virus isolated in France in 2018 (MK501761). Conclusions: This study reports the first detection of H3N8 FC1 in Italy, highlighting the importance of monitoring circulating EIV strains to verify the vaccine composition appropriateness for maximum efficacy.

European brown hare syndrome (EBHS) is a highly contagious and fatal viral disease, mainly affecting European brown hares (Lepus europaeus). The etiological agent, EBHS virus (EBHSV), belongs to the Lagovirus genus within the Caliciviridae family. The Italian hare (Lepus corsicanus) is endemic to Central-Southern Italy and Sicily and is classified as a vulnerable species. L. corsicanus is known to be susceptible to EBHS, but virological data available is scarce due to the few cases detected so far. In this study, we describe the occurrence of EBHS in two free-ranging L. corsicanus, found dead in a protected area of Central Italy. The two hares were identified as L. corsicanus using phenotypic criteria and confirmed through mitochondrial DNA analysis. Distinctive EBHS gross lesions were observed at necropsy and confirmed by subsequent histological examination. EBHSV was detected in the livers of the two animals initially using an antigen detection ELISA, followed by an EBHSV-specific reverse transcription-PCR, thus confirming the viral infection as the probable cause of death. The EBHS viruses detected in the two hares were identical, as based on blast analysis performed for the VP60 sequences and showed 98.86% nucleotide identity and 100% amino acid identity with strain EBHSV/GER-BY/EI97.L03477/2019, isolated in Germany in 2019. Phylogenetic analysis places our virus in group B, which includes strains that emerged after the mid-1980s. This study supports previous reports of EBHS in L. corsicanus and further expands the knowledge of the pathological and virological characteristics of the etiological agent. The ability of EBHSV to cause a fatal disease in the Italian hare represents a serious threat to the conservation of this vulnerable species, especially in populations kept in enclosed protected areas.

Chronic pain is an enormous public health concern, and its treatment is still an unmet medical need. Starting from data highlighting the promising effects of some nonsteroidal anti-inflammatory drugs in combination with gabapentin in pain treatment, we sought to combine ketoprofen lysine salt (KLS) and gabapentin to obtain an effective multimodal therapeutic approach for chronic pain. Using relevant in vitro models, we first demonstrated that KLS and gabapentin have supra-additive effects in modulating key pathways in neuropathic pain and gastric mucosal damage. To leverage these supra-additive effects, we then chemically combined the two drugs via co-crystallization to yield a new compound, a ternary drug-drug co-crystal of ketoprofen, lysine and gabapentin (KLS-GABA co-crystal). Physicochemical, biodistribution and pharmacokinetic studies showed that within the co-crystal, ketoprofen reaches an increased gastrointestinal solubility and permeability, as well as a higher systemic exposure in vivo compared to KLS alone or in combination with gabapentin, while both the constituent drugs have increased central nervous system permeation. These unique characteristics led to striking, synergistic anti-nociceptive and anti-inflammatory effects of KLS-GABA co-crystal, as well as significantly reduced spinal neuroinflammation, in translational inflammatory and neuropathic pain rat models, suggesting that the synergistic therapeutic effects of the constituent drugs are further boosted by the co-crystallization. Notably, while strengthening the therapeutic effects of ketoprofen, KLS-GABA co-crystal showed remarkable gastrointestinal tolerability in both inflammatory and chronic neuropathic pain rat models. In conclusion, these results allow us to propose KLS-GABA co-crystal as a new drug candidate with high potential clinical benefit–to–risk ratio for chronic pain treatment.

BackgroundDepressive and manic episodes within bipolar disorder (BD) and major depressive disorder (MDD) involve altered mood, sleep, and activity, alongside physiological alterations wearables can capture. ObjectiveFirstly, we explored whether physiological wearable data could predict (aim 1) the severity of an acute affective episode at the intra-individual level and (aim 2) the polarity of an acute affective episode and euthymia among different individuals. Secondarily, we explored which physiological data were related to prior predictions, generalization across patients, and associations between affective symptoms and physiological data. MethodsWe conducted a prospective exploratory observational study including patients with BD and MDD on acute affective episodes (manic, depressed, and mixed) whose physiological data were recorded using a research-grade wearable (Empatica E4) across 3 consecutive time points (acute, response, and remission of episode). Euthymic patients and healthy controls were recorded during a single session (approximately 48 h). Manic and depressive symptoms were assessed using standardized psychometric scales. Physiological wearable data included the following channels: acceleration (ACC), skin temperature, blood volume pulse, heart rate (HR), and electrodermal activity (EDA). Invalid physiological data were removed using a rule-based filter, and channels were time aligned at 1-second time units and segmented at window lengths of 32 seconds, as best-performing parameters. We developed deep learning predictive models, assessed the channels’ individual contribution using permutation feature importance analysis, and computed physiological data to psychometric scales’ items normalized mutual information (NMI). We present a novel, fully automated method for the preprocessing and analysis of physiological data from a research-grade wearable device, including a viable supervised learning pipeline for time-series analyses. ResultsOverall, 35 sessions (1512 hours) from 12 patients (manic, depressed, mixed, and euthymic) and 7 healthy controls (mean age 39.7, SD 12.6 years; 6/19, 32% female) were analyzed. The severity of mood episodes was predicted with moderate (62%-85%) accuracies (aim 1), and their polarity with moderate (70%) accuracy (aim 2). The most relevant features for the former tasks were ACC, EDA, and HR. There was a fair agreement in feature importance across classification tasks (Kendall W=0.383). Generalization of the former models on unseen patients was of overall low accuracy, except for the intra-individual models. ACC was associated with “increased motor activity” (NMI>0.55), “insomnia” (NMI=0.6), and “motor inhibition” (NMI=0.75). EDA was associated with “aggressive behavior” (NMI=1.0) and “psychic anxiety” (NMI=0.52). ConclusionsPhysiological data from wearables show potential to identify mood episodes and specific symptoms of mania and depression quantitatively, both in BD and MDD. Motor activity and stress-related physiological data (EDA and HR) stand out as potential digital biomarkers for predicting mania and depression, respectively. These findings represent a promising pathway toward personalized psychiatry, in which physiological wearable data could allow the early identification and intervention of mood episodes.

Introduction Current evidence has hypothesized the involvement of mitochondrial dysfunction during the acute episodes of BD compared to symptomatic remission. So far, no studies have compared mitochondrial and bioenergetic functions both in-vivo (respiratory parameters) and ex-vivo (cellular respiration) in different phases of the disease in the same individuals. Objectives This multidisciplinary pilot study aims at assessing bioenergetic and mitochondrial intra-individual differences between manic and euthymic states. Methods Four patients with a manic episode admitted to our acute psychiatric ward were recruited. Bioenergetic parameters were measured at admission (T0) and after symptomatic remission (T1). At admission (T0) and before discharge (T1), HAMD and YMRS total scores were obtained. For the assessment of cellular respiration, polymorphonuclear cells were obtained by a Ficoll density gradient centrifugation procedure. To determine oxygen consumption (at T0 and T1), a million of living peripheral blood mononuclear cells (PBMC) were used. High-resolution respirometry was performed at 37°C by polarographic oxygen sensors in a two-chamber Oxygraph-2k system. Specific oxygen uptakes (Routine: basal oxygen consumption; Proton Leak: oxygen consumption not coupled to ATP synthesis; and ETC: maximal capacity of the electron transport chain) rates were obtained using mitochondrial chain inhibitors and uncouplers. Oxygen consumption was normalized for protein concentration. Results are expressed as picomoles of oxygen per millilitre (pmol O2/s*μg prot). Also, a constant work rate exercise test was performed on a cycle ergometer and basal and effort respiratory variables were measured. Statistical analysis was performed with the SPSS v. 25.0 and GraphPad. Results were expressed as means and SD. Nonparametric tests (Mann–Whitney, Pearson) were used to determine differences (significant at p value