Canadian Network for Mood and Anxiety Treatments (CANMAT) 2023 Update on Clinical Guidelines for Management of Major Depressive Disorder in Adults: Réseau canadien pour les traitements de l'humeur et de l'anxiété (CANMAT) 2023 : Mise à jour des lignes directrices cliniques pour la prise en charge du trouble dépressif majeur chez les adultes.

BACKGROUND: The Canadian Network for Mood and Anxiety Treatments (CANMAT) last published clinical guidelines for the management of major depressive disorder (MDD) in 2016. Owing to advances in the field, an update was needed to incorporate new evidence and provide new and revised recommendations for the assessment and management of MDD in adults. METHODS: CANMAT convened a guidelines editorial group comprised of academic clinicians and patient partners. A systematic literature review was conducted, focusing on systematic reviews and meta-analyses published since the 2016 guidelines. Recommendations were organized by lines of treatment, which were informed by CANMAT-defined levels of evidence and supplemented by clinical support (consisting of expert consensus on safety, tolerability, and feasibility). Drafts were revised based on review by patient partners, expert peer review, and a defined expert consensus process. RESULTS: The updated guidelines comprise eight primary topics, in a question-and-answer format, that map a patient care journey from assessment to selection of evidence-based treatments, prevention of recurrence, and strategies for inadequate response. The guidelines adopt a personalized care approach that emphasizes shared decision-making that reflects the values, preferences, and treatment history of the patient with MDD. Tables provide new and updated recommendations for psychological, pharmacological, lifestyle, complementary and alternative medicine, digital health, and neuromodulation treatments. Caveats and limitations of the evidence are highlighted. CONCLUSIONS: The CANMAT 2023 updated guidelines provide evidence-informed recommendations for the management of MDD, in a clinician-friendly format. These updated guidelines emphasize a collaborative, personalized, and systematic management approach that will help optimize outcomes for adults with MDD.

Engineering fibronectin-templated multi-component fibrillar extracellular matrices to modulate tissue-specific cell response.

Cells assemble fibronectin, the major extracellular matrix (ECM) protein, into fibrillar matrices, which serve as 3D architectural scaffolds to provide, together with other ECM proteins tissue-specific environments. Although recent approaches enable to bioengineer 3D fibrillar fibronectin matrices in vitro, it remains elusive how fibronectin can be co-assembled with other ECM proteins into complex 3D fibrillar matrices that recapitulate tissue-specific compositions and cellular responses. Here, we introduce the engineering of fibrillar fibronectin-templated 3D matrices that can be complemented with other ECM proteins, including vitronectin, collagen, and laminin to resemble ECM architectures observed in vivo. For the co-assembly of different ECM proteins, we employed their innate fibrillogenic mechanisms including shear forces, pH-dependent electrostatic interactions, or specific binding domains. Through recapitulating various tissue-specific ECM compositions and morphologies, the large scale multi-composite 3D fibrillar ECM matrices can guide fibroblast adhesion, 3D fibroblast tissue formation, or tissue morphogenesis of epithelial cells. In other examples, we customize multi-composite 3D fibrillar matrices to support the growth of signal propagating neuronal networks and of human brain organoids. We envision that these 3D fibrillar ECM matrices can be tailored in scale and composition to modulate tissue-specific responses across various biological length scales and systems, and thus to advance manyfold studies of cell biological systems.

Frequencies of CYP2C19 and CYP2D6 gene variants in a German inpatient sample with mood and anxiety disorders.

OBJECTIVES: Previous results demonstrated that CYP2D6 and CYP2C19 gene variants affect serum concentrations of antidepressants. We implemented a PGx service determining gene variants in CYP2D6 and CYP2C19 in our clinical routine care and report on our first patient cohort. METHODS: We analysed CYP2D6 and CYP2C19 allele, genotype, and phenotype frequencies, and actionable pharmacogenetic variants in this German psychiatric inpatient cohort. Two-tailed z-test was used to investigate for differences in CYP2D6 and CYP2C19 phenotypes and actionable/non-actionable genetic variant frequencies between our cohort and reference cohorts. RESULTS: Out of the 154 patients included, 44.8% of patients were classified as CYP2D6 normal metabolizer, 38.3% as intermediate metabolizers, 8.4% as poor metabolizers, and 2.6% as ultrarapid metabolizers. As for CYP2C19, 40.9% of patients were classified as normal metabolizers, 19.5% as intermediate metabolizers, 2.6% as poor metabolizers, 31.2% as rapid metabolizers, and 5.8% as ultrarapid metabolizers. Approximately, 80% of patients had at least one actionable PGx variant. CONCLUSION: There is a high prevalence of actionable PGx variants in psychiatric inpatients which may affect treatment response. Physicians should refer to PGx-informed dosing guidelines in carriers of these variants. Pre-emptive PGx testing in general may facilitate precision medicine also for other drugs metabolised by CYP2D6 and/or CYP2C19.

Light Color-Controlled pH-Adjustment of Aqueous Solutions Using Engineered Proteoliposomes.

Controlling the pH at the microliter scale can be useful for applications in research, medicine, and industry, and therefore represents a valuable application for synthetic biology and microfluidics. The presented vesicular system translates light of different colors into specific pH changes in the surrounding solution. It works with the two light-driven proton pumps bacteriorhodopsin and blue light-absorbing proteorhodopsin Med12, that are oriented in opposite directions in the lipid membrane. A computer-controlled measuring device implements a feedback loop for automatic adjustment and maintenance of a selected pH value. A pH range spanning more than two units can be established, providing fine temporal and pH resolution. As an application example, a pH-sensitive enzyme reaction is presented where the light color controls the reaction progress. In summary, light color-controlled pH-adjustment using engineered proteoliposomes opens new possibilities to control processes at the microliter scale in different contexts, such as in synthetic biology applications.

Mechanical stimulation and electrophysiological monitoring at subcellular resolution reveals differential mechanosensation of neurons within networks.

A growing consensus that the brain is a mechanosensitive organ is driving the need for tools that mechanically stimulate and simultaneously record the electrophysiological response of neurons within neuronal networks. Here we introduce a synchronized combination of atomic force microscopy, high-density microelectrode array and fluorescence microscopy to monitor neuronal networks and to mechanically characterize and stimulate individual neurons at piconewton force sensitivity and nanometre precision while monitoring their electrophysiological activity at subcellular spatial and millisecond temporal resolution. No correlation is found between mechanical stiffness and electrophysiological activity of neuronal compartments. Furthermore, spontaneously active neurons show exceptional functional resilience to static mechanical compression of their soma. However, application of fast transient (∼500 ms) mechanical stimuli to the neuronal soma can evoke action potentials, which depend on the anchoring of neuronal membrane and actin cytoskeleton. Neurons show higher responsivity, including bursts of action potentials, to slower transient mechanical stimuli (∼60 s). Moreover, transient and repetitive application of the same compression modulates the neuronal firing rate. Seemingly, neuronal networks can differentiate and respond to specific characteristics of mechanical stimulation. Ultimately, the developed multiparametric tool opens the door to explore manifold nanomechanobiological responses of neuronal systems and new ways of mechanical control.

Monitoring the mass, eigenfrequency, and quality factor of mammalian cells.

The regulation of mass is essential for the development and homeostasis of cells and multicellular organisms. However, cell mass is also tightly linked to cell mechanical properties, which depend on the time scales at which they are measured and change drastically at the cellular eigenfrequency. So far, it has not been possible to determine cell mass and eigenfrequency together. Here, we introduce microcantilevers oscillating in the Ångström range to monitor both fundamental physical properties of the cell. If the oscillation frequency is far below the cellular eigenfrequency, all cell compartments follow the cantilever motion, and the cell mass measurements are accurate. Yet, if the oscillating frequency approaches or lies above the cellular eigenfrequency, the mechanical response of the cell changes, and not all cellular components can follow the cantilever motions in phase. This energy loss caused by mechanical damping within the cell is described by the quality factor. We use these observations to examine living cells across externally applied mechanical frequency ranges and to measure their total mass, eigenfrequency, and quality factor. The three parameters open the door to better understand the mechanobiology of the cell and stimulate biotechnological and medical innovations.

A primer on the use of machine learning to distil knowledge from data in biological psychiatry.

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.

Assessing remission in major depressive disorder using a functional-structural data fusion pipeline: A CAN-BIND-1 study.

Neural network-level changes underlying symptom remission in major depressive disorder (MDD) are often studied from a single perspective. Multimodal approaches to assess neuropsychiatric disorders are evolving, as they offer richer information about brain networks. A FATCAT-awFC pipeline was developed to integrate a computationally intense data fusion method with a toolbox, to produce a faster and more intuitive pipeline for combining functional connectivity with structural connectivity (denoted as anatomically weighted functional connectivity (awFC)). Ninety-three participants from the Canadian Biomarker Integration Network for Depression study (CAN-BIND-1) were included. Patients with MDD were treated with 8 weeks of escitalopram and adjunctive aripiprazole for another 8 weeks. Between-group connectivity (SC, FC, awFC) comparisons contrasted remitters (REM) with non-remitters (NREM) at baseline and 8 weeks. Additionally, a longitudinal study analysis was performed to compare connectivity changes across time for REM, from baseline to week-8. Association between cognitive variables and connectivity were also assessed. REM were distinguished from NREM by lower awFC within the default mode, frontoparietal, and ventral attention networks. Compared to REM at baseline, REM at week-8 revealed increased awFC within the dorsal attention network and decreased awFC within the frontoparietal network. A medium effect size was observed for most results. AwFC in the frontoparietal network was associated with neurocognitive index and cognitive flexibility for the NREM group at week-8. In conclusion, the FATCAT-awFC pipeline has the benefit of providing insight on the 'full picture' of connectivity changes for REMs and NREMs while making for an easy intuitive approach.

Prevalence of Psychotropic Medication Use and Psychotropic Polypharmacy in Autistic Adults With or Without Intellectual Disability.

The aim of this study was to compare the rates of psychotropic medication use and psychotropic polypharmacy between autistic adults with and without intellectual disability (ID) and to examine factors associated with psychotropic medication use and psychotropic polypharmacy in autistic adults, stratified by the presence of ID. We conducted a retrospective medical chart review of outpatients with an autism diagnosis aged 18 years and older. The rates of psychotropic medication use and psychotropic polypharmacy were compared between autistic adults with and without ID. Subsequently, logistic regression analyses were performed to identify factors associated with psychotropic medication use and psychotropic polypharmacy in autistic adults with ID and those without ID, respectively. The rates of prevalence of psychotropic medication use and polypharmacy were significantly higher in participants with ID than those without ID (78.6% vs. 58.8% and 49.3% vs. 31.2%; p-values < 0.05). Age, gender, race, residence, presence of mood disorders, presence of schizophrenia, absence of anxiety disorder, number of psychiatric comorbidities, and presence of behaviors that challenge were significantly associated with these outcomes, depending on the presence/absence of ID. The need to optimize pharmacotherapy in autistic adults, stratifying by the presence of ID, is highlighted.

CYP2D6 Phenotype Influences Pharmacokinetic Parameters of Venlafaxine: Results from a Population Pharmacokinetic Model in Older Adults with Depression.

In this study, we aimed to improve upon a published population pharmacokinetic (PK) model for venlafaxine (VEN) in the treatment of depression in older adults, then investigate whether CYP2D6 metabolizer status affected model-estimated PK parameters of VEN and its active metabolite O-desmethylvenlafaxine. The model included 325 participants from a clinical trial in which older adults with depression were treated with open-label VEN (maximum 300 mg/day) for 12 weeks and plasma levels of VEN and O-desmethylvenlafaxine were assessed at weeks 4 and 12. We fitted a nonlinear mixed-effect PK model using NONMEM to estimate PK parameters for VEN and O-desmethylvenlafaxine adjusted for CYP2D6 metabolizer status and age. At both lower doses (up to 150 mg/day) and higher doses (up to 300 mg/day), CYP2D6 metabolizers impacted PK model-estimated VEN clearance, VEN exposure, and active moiety (VEN + O-desmethylvenlafaxine) exposure. Specifically, compared with CYP2D6 normal metabolizers, (i) CYP2D6 ultra-rapid metabolizers had higher VEN clearance; (ii) CYP2D6 intermediate metabolizers had lower VEN clearance; (iii) CYP2D6 poor metabolizers had lower VEN clearance, higher VEN exposure, and higher active moiety exposure. Overall, our study showed that including a pharmacogenetic factor in a population PK model could increase model fit, and this improved model demonstrated how CYP2D6 metabolizer status affected VEN-related PK parameters, highlighting the importance of genetic factors in personalized medicine.

Influence of CYP2C19, CYP2D6, and ABCB1 Gene Variants and Serum Levels of Escitalopram and Aripiprazole on Treatment-Emergent Sexual Dysfunction: A Canadian Biomarker Integration Network in Depression 1 (CAN-BIND 1) Study.

OBJECTIVES: Treatment-emergent sexual dysfunction is frequently reported by individuals with major depressive disorder (MDD) on antidepressants, which negatively impacts treatment adherence and efficacy. We investigated the association of polymorphisms in pharmacokinetic genes encoding cytochrome-P450 drug-metabolizing enzymes, CYP2C19 and CYP2D6, and the transmembrane efflux pump, P-glycoprotein (i.e., ABCB1), on treatment-emergent changes in sexual function (SF) and sexual satisfaction (SS) in the Canadian Biomarker Integration Network in Depression 1 (CAN-BIND-1) sample. METHODS: A total of 178 adults with MDD received treatment with escitalopram (ESC) from weeks 0-8 (Phase I). At week 8, nonresponders were augmented with aripiprazole (ARI) (i.e., ESC + ARI, n = 91), while responders continued ESC (i.e., ESC-Only, n = 80) from weeks 8-16 (Phase II). SF and SS were evaluated using the sex effects (SexFX) scale at weeks 0, 8, and 16. We assessed the primary outcomes, SF and SS change for weeks 0-8 and 8-16, using repeated measures mixed-effects models. RESULTS: In ESC-Only, CYP2C19 intermediate metabolizer (IM) + poor metabolizers (PMs) showed treatment-related improvements in sexual arousal, a subdomain of SF, from weeks 8-16, relative to CYP2C19 normal metabolizers (NMs) who showed a decline, F(2,54) = 8.00, p < 0.001, q = 0.048. Specifically, CYP2C19 IM + PMs reported less difficulty with having and sustaining vaginal lubrication in females and erection in males, compared to NMs. Furthermore, ESC-Only females with higher concentrations of ESC metabolite, S-desmethylcitalopram (S-DCT), and S-DCT/ESC ratio in serum demonstrated more decline in SF (r = -0.42, p = 0.004, q = 0.034) and SS (r = -0.43, p = 0.003, q = 0.034), respectively, which was not observed in males. ESC-Only females also demonstrated a trend for a correlation between S-DCT and sexual arousal change in the same direction (r = -0.39, p = 0.009, q = 0.052). CONCLUSIONS: CYP2C19 metabolizer phenotypes may be influencing changes in sexual arousal related to ESC monotherapy. Thus, preemptive genotyping of CYP2C19 may help to guide selection of treatment that circumvents selective serotonin reuptake inhibitor-related sexual dysfunction thereby improving outcomes for patients. Additionally, further research is warranted to clarify the role of S-DCT in the mechanisms underlying ESC-related changes in SF and SS. This CAN-BIND-1 study was registered on clinicaltrials.gov (Identifier: NCT01655706) on 27 July 2012.

Machine Learning Prediction of Quality of Life Improvement During Antidepressant Treatment of Patients With Major Depressive Disorder: A STAR*D and CAN-BIND-1 Report.

Background: Quality of life (QoL) is an important patient-centric outcome to evaluate in treatment of major depressive disorder (MDD). This work sought to investigate the performance of several machine learning methods to predict a return to normative QoL in patients with MDD after antidepressant treatment.Methods: Several binary classification algorithms were trained on data from the first 2 weeks of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study (n = 651, conducted from 2001 to 2006) to predict week 9 normative QoL (score ≥ 67, based on a community normative sample, on the Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form [Q-LES-Q-SF]) after treatment with citalopram. Internal validation was performed using a STAR*D holdout dataset, and external validation was performed using the Canadian Biomarker Integration Network in Depression-1 (CAN-BIND-1) dataset (n = 175, study conducted from 2012 to 2017) after treatment with escitalopram. Feature importance was calculated using SHapley Additive exPlanations (SHAP).Results: Random Forest performed most consistently on internal and external validation, with balanced accuracy (area under the receiver operator curve) of 71% (0.81) on the STAR*D dataset and 69% (0.75) on the CAN-BIND-1 dataset. Random Forest Classifiers trained on Q-LES-Q-SF and Quick Inventory of Depressive Symptomatology-Self-Rated variables had similar performance on both internal and external validation. Important predictive variables came from psychological, physical, and socioeconomic domains.Conclusions: Machine learning can predict normative QoL after antidepressant treatment with similar performance to that of prior work predicting depressive symptom response and remission. These results suggest that QoL outcomes in MDD patients can be predicted with simple patient-rated measures and provide a foundation to further improve performance and demonstrate clinical utility.Trial Registration: ClinicalTrials.gov identifiers NCT00021528 and NCT01655706.

Cross-disorder GWAS meta-analysis of endocannabinoid DNA variations in major depressive disorder, bipolar disorder, attention deficit hyperactivity disorder, autism spectrum disorder, and schizophrenia.

The endocannabinoid system (ECS) is implicated in multiple mental disorders. In this study, we explored DNA variations in the ECS across major depressive disorder (MDD), bipolar disorder, attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and schizophrenia by performing a cross-disorder genome-wide association study (GWAS) meta-analysis. We obtained six datasets from the Psychiatric Genomics Consortium containing GWAS summary statistics from European cohorts (284,023 cases and 508,515 controls). Effective sample size weighted meta-analysis was performed for 2241 single nucleotide polymorphisms (SNPs) pertaining to gene bodies of 33 endocannabinoid genes using METAL, where an overall z-statistic is calculated for each marker based on a weighted sum of individual statistics. Heterogeneity was examined with I2 and X2 tests. MAGMA gene-based analysis was also performed. We identified nine SNPs significantly associated with a change in risk of having a mental disorder. The lead SNP was rs12805732 (Gene: Diacylglycerol Lipase Alpha; DAGLA). Four SNPs had substantial heterogeneity (I2>60 %). DAGLA had the strongest association with disease risk in gene-based analysis. Our findings suggest that the ECS may be a shared pathway in mental disorders. Future studies validating these findings would contribute to the identification of biomarkers of disease risk across multiple mental disorders.

The emergence, implementation, and future growth of pharmacogenomics in psychiatry: a narrative review.

Psychotropic medication efficacy and tolerability are critical treatment issues faced by individuals with psychiatric disorders and their healthcare providers. For some people, it can take months to years of a trial-and-error process to identify a medication with the ideal efficacy and tolerability profile. Current strategies (e.g. clinical practice guidelines, treatment algorithms) for addressing this issue can be useful at the population level, but often fall short at the individual level. This is, in part, attributed to interindividual variation in genes that are involved in pharmacokinetic (i.e. absorption, distribution, metabolism, elimination) and pharmacodynamic (e.g. receptors, signaling pathways) processes that in large part, determine whether a medication will be efficacious or tolerable. A precision prescribing strategy know as pharmacogenomics (PGx) assesses these genomic variations, and uses it to inform selection and dosing of certain psychotropic medications. In this review, we describe the path that led to the emergence of PGx in psychiatry, the current evidence base and implementation status of PGx in the psychiatric clinic, and finally, the future growth potential of precision psychiatry via the convergence of the PGx-guided strategy with emerging technologies and approaches (i.e. pharmacoepigenomics, pharmacomicrobiomics, pharmacotranscriptomics, pharmacoproteomics, pharmacometabolomics) to personalize treatment of psychiatric disorders.

Developing an Electroencephalography-Based Model for Predicting Response to Antidepressant Medication.

Importance: Untreated depression is a growing public health concern, with patients often facing a prolonged trial-and-error process in search of effective treatment. Developing a predictive model for treatment response in clinical practice remains challenging. Objective: To establish a model based on electroencephalography (EEG) to predict response to 2 distinct selective serotonin reuptake inhibitor (SSRI) medications. Design, Setting, and Participants: This prognostic study developed a predictive model using EEG data collected between 2011 and 2017 from 2 independent cohorts of participants with depression: 1 from the first Canadian Biomarker Integration Network in Depression (CAN-BIND) group and the other from the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) consortium. Eligible participants included those aged 18 to 65 years who had a diagnosis of major depressive disorder. Data were analyzed from January to December 2022. Exposures: In an open-label trial, CAN-BIND participants received an 8-week treatment regimen of escitalopram treatment (10-20 mg), and EMBARC participants were randomized in a double-blind trial to receive an 8-week sertraline (50-200 mg) treatment or placebo treatment. Main Outcomes and Measures: The model's performance was estimated using balanced accuracy, specificity, and sensitivity metrics. The model used data from the CAN-BIND cohort for internal validation, and data from the treatment group of the EMBARC cohort for external validation. At week 8, response to treatment was defined as a 50% or greater reduction in the primary, clinician-rated scale of depression severity. Results: The CAN-BIND cohort included 125 participants (mean [SD] age, 36.4 [13.0] years; 78 [62.4%] women), and the EMBARC sertraline treatment group included 105 participants (mean [SD] age, 38.4 [13.8] years; 72 [68.6%] women). The model achieved a balanced accuracy of 64.2% (95% CI, 55.8%-72.6%), sensitivity of 66.1% (95% CI, 53.7%-78.5%), and specificity of 62.3% (95% CI, 50.1%-73.8%) during internal validation with CAN-BIND. During external validation with EMBARC, the model achieved a balanced accuracy of 63.7% (95% CI, 54.5%-72.8%), sensitivity of 58.8% (95% CI, 45.3%-72.3%), and specificity of 68.5% (95% CI, 56.1%-80.9%). Additionally, the balanced accuracy for the EMBARC placebo group (118 participants) was 48.7% (95% CI, 39.3%-58.0%), the sensitivity was 50.0% (95% CI, 35.2%-64.8%), and the specificity was 47.3% (95% CI, 35.9%-58.7%), suggesting the model's specificity in predicting SSRIs treatment response. Conclusions and Relevance: In this prognostic study, an EEG-based model was developed and validated in 2 independent cohorts. The model showed promising accuracy in predicting treatment response to 2 distinct SSRIs, suggesting potential applications for personalized depression treatment.

A standardized workflow for long-term longitudinal actigraphy data processing using one year of continuous actigraphy from the CAN-BIND Wellness Monitoring Study.

Monitoring sleep and activity through wearable devices such as wrist-worn actigraphs has the potential for long-term measurement in the individual's own environment. Long periods of data collection require a complex approach, including standardized pre-processing and data trimming, and robust algorithms to address non-wear and missing data. In this study, we used a data-driven approach to quality control, pre-processing and analysis of longitudinal actigraphy data collected over the course of 1 year in a sample of 95 participants. We implemented a data processing pipeline using open-source packages for longitudinal data thereby providing a framework for treating missing data patterns, non-wear scoring, sleep/wake scoring, and conducted a sensitivity analysis to demonstrate the impact of non-wear and missing data on the relationship between sleep variables and depressive symptoms. Compliance with actigraph wear decreased over time, with missing data proportion increasing from a mean of 4.8% in the first week to 23.6% at the end of the 12 months of data collection. Sensitivity analyses demonstrated the importance of defining a pre-processing threshold, as it substantially impacts the predictive value of variables on sleep-related outcomes. We developed a novel non-wear algorithm which outperformed several other algorithms and a capacitive wear sensor in quality control. These findings provide essential insight informing study design in digital health research.

The insertase YidC chaperones the polytopic membrane protein MelB inserting and folding simultaneously from both termini.

The insertion and folding of proteins into membranes is crucial for cell viability. Yet, the detailed contributions of insertases remain elusive. Here, we monitor how the insertase YidC guides the folding of the polytopic melibiose permease MelB into membranes. In vivo experiments using conditionally depleted E. coli strains show that MelB can insert in the absence of SecYEG if YidC resides in the cytoplasmic membrane. In vitro single-molecule force spectroscopy reveals that the MelB substrate itself forms two folding cores from which structural segments insert stepwise into the membrane. However, misfolding dominates, particularly in structural regions that interface the pseudo-symmetric α-helical domains of MelB. Here, YidC takes an important role in accelerating and chaperoning the stepwise insertion and folding process of both MelB folding cores. Our findings reveal a great flexibility of the chaperoning and insertase activity of YidC in the multifaceted folding processes of complex polytopic membrane proteins.

Psychiatric manifestations of Kleefstra syndrome: a case report.

Background: Kleefstra syndrome is a rare genetic condition, which affects at least 1 in 120,000 individuals who have a neurodevelopmental disorder, characterized by the core clinical phenotype of intellectual disability, hypotonia, severe speech delay, and distinct facial characteristics with additional clinical features including sleep disturbance, overweight, psychiatric disorders, and autism spectrum disorder. To date, a limited number of case reports of Kleefstra syndrome with psychiatric manifestations have been reported. Case presentation: We reported a case of a 35-year-old male diagnosed with Kleefstra syndrome, who also had diagnoses of autism spectrum disorder and moderate to severe intellectual disability. He exhibited various psychiatric manifestations, including temporarily manic-like symptoms, excessive eating/overweight, addictive/gambling behaviors, inappropriate and unsafe internet use, sleep disturbance, rigid routines, and behaviors that challenged in the form of meltdowns. These symptoms were eventually relatively successfully managed with a combination of non-pharmacological and pharmacological treatments. Conclusion: To our knowledge, there is only a limited number of case reports that detail patients with Kleefstra syndrome exhibiting various psychiatric manifestations. Our report adds further knowledge to the paucity of literature and highlights the effectiveness of a combination of non-pharmacological and pharmacological treatments for behavioral/psychiatric difficulties in Kleefstra syndrome.

Admission Clinical and EEG Features Associated With Mortality and Long-term Neurologic and Cognitive Outcomes in Pediatric Cerebral Malaria.

BACKGROUND AND OBJECTIVES: For children with cerebral malaria, mortality is high, and in survivors, long-term neurologic and cognitive dysfunctions are common. While specific clinical factors are associated with death or long-term neurocognitive morbidity in cerebral malaria, the association of EEG features with these outcomes, particularly neurocognitive outcomes, is less well characterized. METHODS: In this prospective cohort study of 149 children age 6 months to 12 years who survived cerebral malaria in Kampala, Uganda, we evaluated whether depth of coma, number of clinical seizures, or EEG features during hospitalization were associated with mortality during hospitalization, short-term and long-term neurologic deficits, or long-term cognitive outcomes (overall cognition, attention, memory) over the 2-year follow-up. RESULTS: Higher Blantyre or Glasgow Coma Scores (BCS and GCS, respectively), higher background voltage, and presence of normal reactivity on EEG were each associated with lower mortality. Among clinical and EEG features, the presence of >4 seizures on admission had the best combination of negative and positive predictive values for neurologic deficits in follow-up. In multivariable modeling of cognitive outcomes, the number of seizures and specific EEG features showed independent association with better outcomes. In children younger than 5 years throughout the study, seizure number and presence of vertex sharp waves were independently associated with better posthospitalization cognitive performance, faster dominant frequency with better attention, and higher average background voltage and faster dominant background frequency with better associative memory. In children younger than 5 years at CM episode but 5 years or older at cognitive testing, seizure number, background dominant frequency, and the presence of vertex sharp waves were each associated with changes in cognition, seizure number and variability with attention, and seizure number with working memory. DISCUSSION: In children with cerebral malaria, seizure number is strongly associated with the risk of long-term neurologic deficits, while seizure number and specific EEG features (average background voltage, dominant rhythm frequency, presence of vertex sharp waves, presence of variability) are independently associated with cognitive outcomes. Future studies should evaluate the predictive value of these findings.