Bioinformatics Analysis Reveals E6 and E7 of HPV 16 Regulate Metabolic Reprogramming in Cervical Cancer, Head and Neck Cancer, and Colorectal Cancer through the PHD2-VHL-CUL2-ELOC-HIF-1α Axis.

Human papillomavirus 16 (HPV 16) infection is associated with several types of cancer, such as head and neck, cervical, anal, and penile cancer. Its oncogenic potential is due to the ability of the E6 and E7 oncoproteins to promote alterations associated with cell transformation. HPV 16 E6 and E7 oncoproteins increase metabolic reprogramming, one of the hallmarks of cancer, by increasing the stability of hypoxia-induced factor 1 α (HIF-1α) and consequently increasing the expression levels of their target genes. In this report, by bioinformatic analysis, we show the possible effect of HPV 16 oncoproteins E6 and E7 on metabolic reprogramming in cancer through the E6-E7-PHD2-VHL-CUL2-ELOC-HIF-1α axis. We proposed that E6 and E7 interact with VHL, CUL2, and ELOC in forming the E3 ubiquitin ligase complex that ubiquitinates HIF-1α for degradation via the proteasome. Based on the information found in the databases, it is proposed that E6 interacts with VHL by blocking its interaction with HIF-1α. On the other hand, E7 interacts with CUL2 and ELOC, preventing their binding to VHL and RBX1, respectively. Consequently, HIF-1α is stabilized and binds with HIF-1ß to form the active HIF1 complex that binds to hypoxia response elements (HREs), allowing the expression of genes related to energy metabolism. In addition, we suggest an effect of E6 and E7 at the level of PHD2, VHL, CUL2, and ELOC gene expression. Here, we propose some miRNAs targeting PHD2, VHL, CUL2, and ELOC mRNAs. The effect of E6 and E7 may be the non-hydroxylation and non-ubiquitination of HIF-1α, which may regulate metabolic processes involved in metabolic reprogramming in cancer upon stabilization, non-degradation, and translocation to the nucleus.

Theta burst stimulation for depression: a systematic review and network and pairwise meta-analysis.

In clinical practice, theta burst stimulation (TBS) presents as a more efficient and potentially more effective therapeutic modality than conventional repetitive transcranial magnetic stimulation (rTMS), as it allows for the delivery of more stimuli in less time and at similar intensities. To date, accelerated treatment plans according to various continuous (cTBS) and intermittent TBS (iTBS) protocols for depression have been proposed. To investigate which of the TBS protocols provided a favorable risk-benefit balance for individuals with depression, this systematic review and random-effects model network meta-analysis was conducted. The study outcomes included response rate (primary), depression symptom improvement, remission rate, all-cause discontinuation rate, incidence of switch to mania, and incidence of headache/discomfort at treatment site. In this meta-analysis, a total of 23 randomized controlled trials (n = 960, mean age = 41.88 years, with 60.78% females) were included. Approximately 69.57% of the trials included individuals with an exclusive diagnosis of major depressive disorder. The following six TBS protocols (target) were evaluated: cTBS (right-dorsolateral prefrontal cortex [R-DLPFC]), cTBS (R-DLPFC) + iTBS (left-DLPFC [L-DLPFC]), iTBS (L-DLPFC), iTBS (L-DLPFC) + iTBS (R-DLPFC), iTBS (left-dorsomedial prefrontal cortex) + iTBS (right-dorsomedial prefrontal cortex), and iTBS (occipital lobe). Compared to sham, cTBS (R-DLPFC) + iTBS (L-DLPFC), iTBS (L-DLPFC), and iTBS (occipital lobe) had a higher response rate (k = 23); cTBS (R-DLPFC) + iTBS (L-DLPFC) and iTBS (L-DLPFC) dominated in the depression symptom improvement (k = 23); and iTBS (L-DLPFC) had a higher remission rate (k = 15). No significant differences were found for all-cause discontinuation rate (k = 17), incidence of switch to mania (k = 7), and incidence of headache/discomfort at treatment site (k = 10) between any TBS protocols and sham. Thus, cTBS (R-DLPFC) + iTBS (L-DLPFC) and iTBS (L-DLPFC) demonstrate favorable risk-benefit balance for the treatment of depression.

Antimalarial Drug Combination Predictions Using the Machine Learning Synergy Predictor (MLSyPred©) tool.

PURPOSE: Antimalarial drug resistance is a global public health problem that leads to treatment failure. Synergistic drug combinations can improve treatment outcomes and delay the development of drug resistance. Here, we describe the implementation of a freely available computational tool, Machine Learning Synergy Predictor (MLSyPred©), to predict potential synergy in antimalarial drug combinations. METHODS: The MLSyPred© synergy prediction method extracts molecular fingerprints from the drugs' biochemical structures to use as features and also cleans and prepares the raw data. Five machine learning algorithms (Logistic Regression, Random Forest, Support vector machine, Ada Boost, and Gradient Boost) were implemented to build prediction models. Implementation and application of the MLSyPred© tool were tested using datasets from 1540 combinations of 79 drugs and compounds biologically evaluated in pairs for three strains of Plasmodium falciparum (3D7, HB3, and Dd2). RESULTS: The best prediction models were obtained using Logistic Regression for antimalarials with the strains Dd2 and HB3 (0.81 and 0.70 AUC, respectively) and Random Forest for antimalarials with 3D7 (0.69 AUC). The MLSyPred© tool yielded 45% precision for synergistically predicted antimalarial drug combinations that were annotated and biologically validated, thus confirming the functionality and applicability of the tool. CONCLUSION:  The MLSyPred© tool is freely available and represents a promising strategy for discovering potential synergistic drug combinations for further development as novel antimalarial therapies.

Motor performance and functional connectivity between the posterior cingulate cortex and supplementary motor cortex in bipolar and unipolar depression.

Although implicated in unsuccessful treatment, psychomotor deficits and their neurobiological underpinnings in bipolar (BD) and unipolar (UD) depression remain poorly investigated. Here, we hypothesized that motor performance deficits in depressed patients would relate to basal functional coupling of the hand primary motor cortex (M1) and the posterior cingulate cortex (PCC) with the supplementary motor area (SMA). We performed a longitudinal, naturalistic study in BD, UD and matched healthy controls comprising of two resting-state functional MRI measurements five weeks apart and accompanying assessments of motor performance using a finger tapping task (FTT). A subject-specific seed-based analysis describing functional connectivity between PCC-SMA as well as M1-SMA was conducted. The basal relationships with motor performance were investigated using linear regression models and all measures were compared across groups. Performance in FTT was impaired in BD in comparison to HC in both sessions. Behavioral performance across groups correlated significantly with resting state functional coupling of PCC-SMA, but not of M1-SMA regions. This relationship was partially reflected in a reduced PCC-SMA connectivity in BD vs HC in the second session. Exploratory evaluation of large-scale networks coupling (SMN-DMN) exhibited no correlation to motor performance. Our results shed new light on the association between the degree of disruption in the SMA-PCC anticorrelation and the level of motor impairment in BD.

Non-Random Enrichment of Single-Nucleotide Polymorphisms Associated with Clopidogrel Resistance within Risk Loci Linked to the Severity of Underlying Cardiovascular Diseases: The Role of Admixture.

Cardiovascular disease (CVD) is one of the leading causes of death in Puerto Rico, where clopidogrel is commonly prescribed to prevent ischemic events. Genetic contributors to both a poor clopidogrel response and the severity of CVD have been identified mainly in Europeans. However, the non-random enrichment of single-nucleotide polymorphisms (SNPs) associated with clopidogrel resistance within risk loci linked to underlying CVDs, and the role of admixture, have yet to be tested. This study aimed to assess the possible interaction between genetic biomarkers linked to CVDs and those associated with clopidogrel resistance among admixed Caribbean Hispanics. We identified 50 SNPs significantly associated with CVDs in previous genome-wide association studies (GWASs). These SNPs were combined with another ten SNPs related to clopidogrel resistance in Caribbean Hispanics. We developed Python scripts to determine whether SNPs related to CVDs are in close proximity to those associated with the clopidogrel response. The average and individual local ancestry (LAI) within each locus were inferred, and 60 random SNPs with their corresponding LAIs were generated for enrichment estimation purposes. Our results showed no CVD-linked SNPs in close proximity to those associated with the clopidogrel response among Caribbean Hispanics. Consequently, no genetic loci with a dual predictive role for the risk of CVD severity and clopidogrel resistance were found in this population. Native American ancestry was the most enriched within the risk loci linked to CVDs in this population. The non-random enrichment of disease susceptibility loci with drug-response SNPs is a new frontier in Precision Medicine that needs further attention.

Accelerated intermittent theta burst stimulation in major depressive disorder: A systematic review.

BACKGROUND: Major depressive disorder [MDD] is expected to be the leading cause of overall global burden of disease by the year 2030 [WHO]. Non-response to first line pharmacological and psychotherapeutic antidepressive treatments is substantial, with treatment-resistant depression [TRD] affecting approximately one third of depressed patients. There is an urgent need for rapid acting and effective treatments in this population. Repetitive Transcranial Magnetic Stimulation [rTMS] is an non-invasive treatment option for patients with MDD or TRD. Recent studies have proposed new paradigms of TMS, one paradigm is accelerated intermittent Theta Burst Stimulation [aiTBS]. OBJECTIVE: This systematic review assesses the efficacy, safety and tolerability of aiTBS in patients with MDD. METHODS: This review was registered with PROSPERO [ID number: 366556]. A systematic literature review was performed using Pubmed, Web of Science and PsycINFO. Case reports/series, open-label and randomized controlled trials [RCTs] were eligible for inclusion if they met the following criteria; full text publication available in English describing a form of aiTBS for MDD or TRD. aiTBS was defined as at least three iTBS treatments sessions per day, during at least four days for one week. RESULTS: 32 studies were identified describing aiTBS in MDD, 13 studies described overlapping samples. Six articles from five unique studies met eligibility criteria; two open-label studies and three RCTs [two double blind and one quadruple blind]. Response rates directly after treatment ranged from 20.0% to 86.4% and remission rates ranged from 10.0 to 86.4%. Four weeks after treatment response rates ranged from 0.0% to 66.7% and remission rates ranged from 0.0% to 57.1%. Three articles described a significant reduction in suicidality scores. aiTBS was well tolerated and safe, with no serious adverse events reported. CONCLUSIONS: aiTBS is a promising form of non-invasive brain stimulation [NIBS] with rapid antidepressant and antisuicidal effects in MDD. Additionally, aiTBS was well tolerated and safe. However, the included studies had small samples sizes and differed in frequency, intersession interval, neuro localization and stimulation intensity. Replication studies and larger RCTs are warranted to establish efficacy, safety and long term effects.

Subject-specific whole-brain parcellations of nodes and boundaries are modulated differently under 10 Hz rTMS.

Repetitive transcranial magnetic stimulation (rTMS) has gained considerable importance in the treatment of neuropsychiatric disorders, including major depression. However, it is not yet understood how rTMS alters brain's functional connectivity. Here we report changes in functional connectivity captured by resting state functional magnetic resonance imaging (rsfMRI) within the first hour after 10 Hz rTMS. We apply subject-specific parcellation schemes to detect changes (1) in network nodes, where the strongest functional connectivity of regions is observed, and (2) in network boundaries, where functional transitions between regions occur. We use support vector machine (SVM), a widely used machine learning algorithm that is robust and effective, for the classification and characterization of time intervals of changes in node and boundary maps. Our results reveal that changes in connectivity at the boundaries are slower and more complex than in those observed in the nodes, but of similar magnitude according to accuracy confidence intervals. These results were strongest in the posterior cingulate cortex and precuneus. As network boundaries are indeed under-investigated in comparison to nodes in connectomics research, our results highlight their contribution to functional adjustments to rTMS.

Brain connectivity in major depressive disorder: a precision component of treatment modalities?

Major depressive disorder (MDD) is a very prevalent mental disorder that imposes an enormous burden on individuals, society, and health care systems. Most patients benefit from commonly used treatment methods such as pharmacotherapy, psychotherapy, electroconvulsive therapy (ECT), and repetitive transcranial magnetic stimulation (rTMS). However, the clinical decision on which treatment method to use remains generally informed and the individual clinical response is difficult to predict. Most likely, a combination of neural variability and heterogeneity in MDD still impedes a full understanding of the disorder, as well as influences treatment success in many cases. With the help of neuroimaging methods like functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), the brain can be understood as a modular set of functional and structural networks. In recent years, many studies have investigated baseline connectivity biomarkers of treatment response and the connectivity changes after successful treatment. Here, we systematically review the literature and summarize findings from longitudinal interventional studies investigating the functional and structural connectivity in MDD. By compiling and discussing these findings, we recommend the scientific and clinical community to deepen the systematization of findings to pave the way for future systems neuroscience roadmaps that include brain connectivity parameters as a possible precision component of the clinical evaluation and therapeutic decision.

Accelerated intermittent theta burst stimulation for major depressive disorder or bipolar depression: A systematic review and meta-analysis.

We aimed to systematically evaluate the clinical efficacy and safety of accelerated intermittent theta burst stimulation (aiTBS) for patients with major depressive disorder (MDD) or bipolar depression (BD). A random-effects model was adopted to analyze the primary and secondary outcomes using the Review Manager, Version 5.3 software. This meta-analysis (MA) identified five double-blind randomized controlled trials (RCTs) comprising 239 MDD or BD patients with a major depressive episode. Active aiTBS overperformed sham stimulation in the study-defined response. This MA found preliminary evidence that active aiTBS resulted in a greater response in treating major depressive episodes in MDD or BD patients than sham stimulation.

Seroprevalence of IgG and Subclasses against the Nucleocapsid of SARS-CoV-2 in Health Workers.

BACKGROUND: The nucleocapsid protein of SARS-CoV-2 participates in viral replication, transcription, and assembly. Antibodies against this protein have been proposed for the epidemiological analysis of the seroprevalence of COVID-19 associated with natural infection by SARS-CoV-2. Health workers were one of the most exposed populations, and some had an asymptomatic form of the disease, so detecting IgG antibodies and subclasses against the N protein can help to reclassify their epidemiological status and obtain information about the effector mechanisms associated with viral elimination. METHODS: In this study, we analyzed 253 serum samples collected in 2021 and derived from health workers, and evaluated the presence of total IgG and subclasses against the N protein of SARS-CoV-2 by indirect ELISA. RESULTS: From the analyzed samples, 42.69% were positive to anti-N IgG antibodies. A correlation between COVID-19 asymptomatic infection and IgG antibodies was observed (p = 0.006). The detected subclasses were: IgG1 (82.4%), IgG2 (75.9%), IgG3 (42.6%), and IgG4 (72.6%). CONCLUSIONS: This work provides evidence about the high seroprevalence of total IgG and subclasses of anti-N and their relations with the asymptomatic infection of SARS-CoV-2 and related symptoms.

Concurrent validity and reliability of suicide risk assessment instruments: A meta-analysis of 20 instruments across 27 international cohorts.

OBJECTIVE: A major limitation of current suicide research is the lack of power to identify robust correlates of suicidal thoughts or behavior. Variation in suicide risk assessment instruments used across cohorts may represent a limitation to pooling data in international consortia. METHOD: Here, we examine this issue through two approaches: (a) an extensive literature search on the reliability and concurrent validity of the most commonly used instruments and (b) by pooling data (N ∼ 6,000 participants) from cohorts from the Enhancing NeuroImaging Genetics Through Meta-Analysis (ENIGMA) Major Depressive Disorder and ENIGMA-Suicidal Thoughts and Behaviour working groups, to assess the concurrent validity of instruments currently used for assessing suicidal thoughts or behavior. RESULTS: We observed moderate-to-high correlations between measures, consistent with the wide range (κ range: 0.15-0.97; r range: 0.21-0.94) reported in the literature. Two common multi-item instruments, the Columbia Suicide Severity Rating Scale and the Beck Scale for Suicidal Ideation were highly correlated with each other (r = 0.83). Sensitivity analyses identified sources of heterogeneity such as the time frame of the instrument and whether it relies on self-report or a clinical interview. Finally, construct-specific analyses suggest that suicide ideation items from common psychiatric questionnaires are most concordant with the suicide ideation construct of multi-item instruments. CONCLUSIONS: Our findings suggest that multi-item instruments provide valuable information on different aspects of suicidal thoughts or behavior but share a modest core factor with single suicidal ideation items. Retrospective, multisite collaborations including distinct instruments should be feasible provided they harmonize across instruments or focus on specific constructs of suicidality. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Estimating uncertainty in read-out patterns: Application to controls-based denoising and voxel-based morphometry patterns in neurodegenerative and neuropsychiatric diseases.

Quantifying pathology-related patterns in patient data with pattern expression score (PES) is a standard approach in medical image analysis. In order to estimate the PES error, we here propose to express the uncertainty contained in read-out patterns in terms of the expected squared Euclidean distance between the read-out pattern and the unknown "true" pattern (squared standard error of the read-out pattern, SE2 ). Using SE2 , we predicted and optimized the net benefit (NBe) of the recently suggested method controls-based denoising (CODE) by weighting patterns of nonpathological variance (NPV). Multi-center MRI (1192 patients with various neurodegenerative and neuropsychiatric diseases, 1832 healthy controls) were analysed with voxel-based morphometry. For each pathology, accounting for SE2 , NBe correctly predicted classification improvement and allowed to optimize NPV pattern weights. Using these weights, CODE improved classification performances in all but one analyses, for example, for prediction of conversion to Alzheimer's disease (AUC 0.81 vs. 0.75, p = .01), diagnosis of autism (AUC 0.66 vs. 0.60, p < .001), and of major depressive disorder (AUC 0.62 vs. 0.50, p = .03). We conclude that the degree of uncertainty in a read-out pattern should generally be reported in PES-based analyses and suggest using weighted CODE as a complement to PES-based analyses.

TET Enzymes and 5hmC Levels in Carcinogenesis and Progression of Breast Cancer: Potential Therapeutic Targets.

Breast Cancer (BC) was the most common female cancer in incidence and mortality worldwide in 2020. Similarly, BC was the top female cancer in the USA in 2022. Risk factors include earlier age at menarche, oral contraceptive use, hormone replacement therapy, high body mass index, and mutations in BRCA1/2 genes, among others. BC is classified into Luminal A, Luminal B, HER2-like, and Basal-like subtypes. These BC subtypes present differences in gene expression signatures, which can impact clinical behavior, treatment response, aggressiveness, metastasis, and survival of patients. Therefore, it is necessary to understand the epigenetic molecular mechanism of transcriptional regulation in BC, such as DNA demethylation. Ten-Eleven Translocation (TET) enzymes catalyze the oxidation of 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC) on DNA, which in turn inhibits or promotes the gene expression. Interestingly, the expression of TET enzymes as well as the levels of the 5hmC epigenetic mark are altered in several types of human cancers, including BC. Several studies have demonstrated that TET enzymes and 5hmC play a key role in the regulation of gene expression in BC, directly (dependent or independent of DNA de-methylation) or indirectly (via interaction with other proteins such as transcription factors). In this review, we describe our recent understanding of the regulatory and physiological function of the TET enzymes, as well as their potential role as biomarkers in BC biology.

A conceptual model to inform the design of healthcare simulations that promote errors as a catalyst for learning: A discussion paper.

BACKGROUND: Previous studies have demonstrated that students who are engaged in learning tasks and make errors before receiving instruction on how to complete them, achieve better learning outcomes than students who first receive instruction and then complete the learning activities with the aim of avoiding errors. Although simulation literature often refers to errors as learning opportunities, to date, there is limited understanding of how pedagogical approaches that promote learning from errors can guide the design of simulation-based learning in healthcare education. AIMS: To (a) present the Learning from Errors conceptual model; and (b) provide an example of how educators can use this model. DESIGN: The Learning from Errors model is drawn from critical elements of two pedagogical approaches, productive failure and error management training and pedagogical features of high-quality healthcare simulations. METHODS: We describe the Learning from Errors model, which emphasises the need for adopting pedagogical methods that explicitly use errors as learning opportunities and ultimately inform simulation design. We then illustrate the application of this model to a simulation example. RESULTS: The model includes the following elements: i) normalisation of errors, ii) challenging simulation scenarios, iii) self-directed learning, iv) collaborative teamwork and v) comparison with best practice. CONCLUSION: This discussion paper presents the Learning from Errors conceptual model, an evidence-based approach that can assist educators in the design of simulations that embrace errors as a catalyst for learning.

From Sensor Data to Educational Insights.

Technology is gradually becoming an integral part of learning at all levels of educational [...].

Transmission of SARS-CoV-2 in a primary school setting with and without public health measures using real-world contact data: A modelling study.

Background: Stringent public health measures have been shown to influence the transmission of SARS-CoV-2 within school environments. We investigated the potential transmission of SARS-CoV-2 in a primary school setting with and without public health measures, using fine-grained physical positioning traces captured before the COVID-19 pandemic. Methods: Approximately 172.63 million position data from 98 students and six teachers from an open-plan primary school were used to predict a potential transmission of SARS-CoV-2 in primary school settings. We first estimated the daily average number of contacts of students and teachers with an infected individual during the incubation period. We then used the Reed-Frost model to estimate the probability of transmission per contact for the SARS-CoV-2 Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron variant (B.1.1.529). Finally, we built a binomial distribution model to estimate the probability of onward transmission in schools with and without public health measures, including face masks and physical distancing. Results: An infectious student would have 49.1 (95% confidence interval (CI) = 46.1-52.1) contacts with their peers and 2.00 (95% CI = 1.82-2.18) contacts with teachers per day. An infectious teacher would have 47.6 (95% CI = 45.1-50.0) contacts with students and 1.70 (95% CI = 1.48-1.92) contacts with their colleague teachers per day. While the probability of onward SARS-CoV-2 transmission was relatively low for the Alpha and Delta variants, the risk increased for the Omicron variant, especially in the absence of public health measures. Onward teacher-to-student transmission (88.9%, 95% CI = 88.6%-89.1%) and teacher-to-teacher SARS-CoV-2 transmission (98.4%, 95% CI = 98.5%-98.6%) were significantly higher for the Omicron variant without public health measures in place. Conclusions: Our findings illustrate that, despite a lower frequency of close contacts, teacher-to-teacher close contacts demonstrated a higher risk of transmission per contact of SARS-CoV-2 compared to student-to-student close contacts. This was especially significant with the Omicron variant, with onward transmission more likely occurring from teacher index cases than student index cases. Public health measures (eg, face masks and physical distance) seem essential in reducing the risk of onward transmission within school environments.

Effect of Minocycline on Depressive Symptoms in Patients With Treatment-Resistant Depression: A Randomized Clinical Trial.

Importance: Insufficient treatment response and resulting chronicity constitute a major problem in depressive disorders. Remission rates range as low as 15% to 40% and treatment-resistant depression (TRD) is associated with low-grade inflammation, suggesting anti-inflammatory interventions as a rational treatment strategy. Minocycline, which inhibits microglial activation, represents a promising repurposing candidate in the treatment of TRD. Objective: To determine whether 6 weeks of minocycline as add-on to antidepressant treatment as usual can significantly reduce depressive symptoms in patients with TRD. Design, Setting, and Participants: The study was conducted in Germany and designed as a multicenter double-blind randomized clinical trial (RCT) of 200 mg/d minocycline treatment over a course of 6 weeks with a 6-month follow-up. Participants were recruited from January 2016 to August 2020 at 9 university hospitals that served as study sites. Key inclusion criteria were a diagnosis of major depressive disorder (according to Diagnostic and Statistical Manual of Mental Disorders [Fifth Edition] criteria), severity of depressive symptoms on the Hamilton Depression Rating Scale (HAMD-17) greater than or equal to 16 points, aged 18 to 75 years, body mass index 18 to 40, Clinical Global Impression Scale (CGI-S) greater than or equal to 4, failure to adequately respond to an initial antidepressant standard medication as per Massachusetts General Hospital Antidepressant Treatment History Questionnaire, and stable medication for at least 2 weeks. A total of 258 patients were screened, of whom 173 were randomized and 168 were included into the intention-to-treat population. Statistical analysis was performed from April to November 2020. Interventions: Participants were randomized (1:1) to receive adjunct minocycline (200 mg/d) or placebo for 6 weeks. Main Outcomes and Measures: Primary outcome measure was the change in Montgomery-Åsberg Depression Rating Scale (MADRS) score from baseline to week 6 analyzed by intention-to-treat mixed model repeated measures. Secondary outcome measures were response, remission, and various other clinical rating scales. Results: Of 173 eligible and randomized participants (84 randomized to minocycline and 89 randomized to placebo), 168 formed the intention-to-treat sample (79 [47.0%] were women, 89 [53.0%] were men, 159 [94.6%] were White, 9 [6.4%] were of other race and ethnicity, including Asian and unknown ethnicity), with 81 in the minocycline group and 87 in the placebo group. The mean (SD) age was 46.1 (13.1) years, and the mean (SD) MADRS score at baseline was 26.5 (5.0). There was no difference in rates of completion between the minocycline (83.3% [70 of 81]) and the placebo group (83.1% [74 of 87]). Minocycline treatment did not alter the course of depression severity compared with placebo as assessed by a decrease in MADRS scores over 6 weeks of treatment (1.46 [-1.04 to 3.96], P = .25). Minocycline treatment also exhibited no statistically significant effect on secondary outcomes. Conclusions and Relevance: In this large randomized clinical trial with minocycline at a dose of 200 mg/d added to antidepressant treatment as usual for 6 weeks, minocycline was well tolerated but not superior to placebo in reducing depressive symptoms in patients with TRD. The results of this RCT emphasize the unmet need for therapeutic approaches and predictive biomarkers in TRD. Trial Registration: EU Clinical Trials Register Number: EudraCT 2015-001456-29.

Suicidality and relief of depressive symptoms with intermittent theta burst stimulation in a sham-controlled randomized clinical trial.

OBJECTIVES: Suicidality is a serious public health problem and is closely associated with the severity of depression. In this work, we examined the effects of accelerated intermittent theta burst stimulation (iTBS) on suicidal status, risk factors for suicide, and severity of depressive symptoms in subjects with major depressive disorder (MDD). METHODS: We present data from a quadruple-blind (patient, care provider, investigator, rater) sham-controlled crossover randomized clinical trial. During a 6-week observation period, each participant underwent 2 weeks of stimulation - each week with 20 sessions of active or sham iTBS. A suicide score was created using a composite of individual items from Montgomery-Åsberg Depression Scale (MADRS), Hamilton Depression Scale, and Beck Depression Inventory. The severity of depression was determined by MADRS total scores. In addition, we used demographic and Columbia Suicidality Rating Scale information to assess suicide risk. RESULTS: Among 81 participants, we observed a significant reduction in suicidality and this change was positively correlated with a change in depressive symptoms. A significant difference between active and sham iTBS provided evidence for antidepressant effects. Higher changes in levels of anxiety and impulsiviness also correlated with larger changes in suicidality. CONCLUSIONS: As neither suicide nor other serious adverse events were evidenced, this intervention was a safe and viable procedure to reduce suicidality and severity of depressive symptoms. Moreover, we identified more pronounced anti-suicidal effects in those with higher risk profiles. Unlike MADRS, composite suicidal scores did not provide evidence of an effect between stimulation conditions in this crossover design study. Even so, based on our promising results, parallel and larger studies could contribute to a better characterization of the anti-suicidal placebo effect and the benefit of using iTBS against suicidal symptoms.

Regional gene expression patterns are associated with task-specific brain activation during reward and emotion processing measured with functional MRI.

The exploration of the spatial relationship between gene expression profiles and task-evoked response patterns known to be altered in neuropsychiatric disorders, for example depression, can guide the development of more targeted therapies. Here, we estimated the correlation between human transcriptome data and two different brain activation maps measured with functional magnetic resonance imaging (fMRI) in healthy subjects. Whole-brain activation patterns evoked during an emotional face recognition task were associated with topological mRNA expression of genes involved in cellular transport. In contrast, fMRI activation patterns related to the acceptance of monetary rewards were associated with genes implicated in cellular localization processes, metabolism, translation, and synapse regulation. An overlap of these genes with risk genes from major depressive disorder genome-wide association studies revealed the involvement of the master regulators TCF4 and PAX6 in emotion and reward processing. Overall, the identification of stable relationships between spatial gene expression profiles and fMRI data may reshape the prospects for imaging transcriptomics studies.