The potential of baicalin to enhance neuroprotection and mitochondrial function in a human neuronal cell model.

Baicalin is a flavone glycoside derived from flowering plants belonging to the Scutellaria genus. Previous studies have reported baicalin's anti-inflammatory and neuroprotective properties in rodent models, indicating the potential of baicalin in neuropsychiatric disorders where alterations in numerous processes are observed. However, the extent of baicalin's therapeutic effects remains undetermined in a human cell model, more specifically, neuronal cells to mimic the brain environment in vitro. As a proof of concept, we treated C8-B4 cells (murine cell model) with three different doses of baicalin (0.1, 1 and 5 µM) and vehicle control (DMSO) for 24 h after liposaccharide-induced inflammation and measured the levels of TNF-α in the medium by ELISA. NT2-N cells (human neuronal-like cell model) underwent identical baicalin treatment, followed by RNA extraction, genome-wide mRNA expression profiles and gene set enrichment analysis (GSEA). We also performed neurite outgrowth assays and mitochondrial flux bioanalysis (Seahorse) in NT2-N cells. We found that in C8-B4 cells, baicalin at ≥ 1 µM exhibited anti-inflammatory effects, lowering TNF-α levels in the cell culture media. In NT2-N cells, baicalin positively affected neurite outgrowth and transcriptionally up-regulated genes in the tricarboxylic acid cycle and the glycolysis pathway. Similarly, Seahorse analysis showed increased oxygen consumption rate in baicalin-treated NT2-N cells, an indicator of enhanced mitochondrial function. Together, our findings have confirmed the neuroprotective and mitochondria enhancing effects of baicalin in human-neuronal like cells. Given the increased prominence of mitochondrial mechanisms in diverse neuropsychiatric disorders and the paucity of mitochondrial therapeutics, this suggests the potential therapeutic application of baicalin in human neuropsychiatric disorders where these processes are altered.

Network-based drug repurposing for schizophrenia.

Despite recent progress, the challenges in drug discovery for schizophrenia persist. However, computational drug repurposing has gained popularity as it leverages the wealth of expanding biomedical databases. Network analyses provide a comprehensive understanding of transcription factor (TF) regulatory effects through gene regulatory networks, which capture the interactions between TFs and target genes by integrating various lines of evidence. Using the PANDA algorithm, we examined the topological variances in TF-gene regulatory networks between individuals with schizophrenia and healthy controls. This algorithm incorporates binding motifs, protein interactions, and gene co-expression data. To identify these differences, we subtracted the edge weights of the healthy control network from those of the schizophrenia network. The resulting differential network was then analysed using the CLUEreg tool in the GRAND database. This tool employs differential network signatures to identify drugs that potentially target the gene signature associated with the disease. Our analysis utilised a large RNA-seq dataset comprising 532 post-mortem brain samples from the CommonMind project. We constructed co-expression gene regulatory networks for both schizophrenia cases and healthy control subjects, incorporating 15,831 genes and 413 overlapping TFs. Through drug repurposing, we identified 18 promising candidates for repurposing as potential treatments for schizophrenia. The analysis of TF-gene regulatory networks revealed that the TFs in schizophrenia predominantly regulate pathways associated with energy metabolism, immune response, cell adhesion, and thyroid hormone signalling. These pathways represent significant targets for therapeutic intervention. The identified drug repurposing candidates likely act through TF-targeted pathways. These promising candidates, particularly those with preclinical evidence such as rimonabant and kaempferol, warrant further investigation into their potential mechanisms of action and efficacy in alleviating the symptoms of schizophrenia.

Use of gene regulatory network analysis to repurpose drugs to treat bipolar disorder.

BACKGROUND: Bipolar disorder (BD) presents significant challenges in drug discovery, necessitating alternative approaches. Drug repurposing, leveraging computational techniques and expanding biomedical data, holds promise for identifying novel treatment strategies. METHODS: This study utilized gene regulatory networks (GRNs) to identify significant regulatory changes in BD, using network-based signatures for drug repurposing. Employing the PANDA algorithm, we investigated the variations in transcription factor-GRNs between individuals with BD and unaffected individuals, incorporating binding motifs, protein interactions, and gene co-expression data. The differences in edge weights between BD and controls were then used as differential network signatures to identify drugs potentially targeting the disease-associated gene signature, employing the CLUEreg tool in the GRAND database. RESULTS: Using a large RNA-seq dataset of 216 post-mortem brain samples from the CommonMind consortium, we constructed GRNs based on co-expression for individuals with BD and unaffected controls, involving 15,271 genes and 405 TFs. Our analysis highlighted significant influences of these TFs on immune response, energy metabolism, cell signalling, and cell adhesion pathways in the disorder. By employing drug repurposing, we identified 10 promising candidates potentially repurposed as BD treatments. LIMITATIONS: Non-drug-naïve transcriptomics data, bulk analysis of BD samples, potential bias of GRNs towards well-studied genes. CONCLUSIONS: Further investigation into repurposing candidates, especially those with preclinical evidence supporting their efficacy, like kaempferol and pramocaine, is warranted to understand their mechanisms of action and effectiveness in treating BD. Additionally, novel targets such as PARP1 and A2b offer opportunities for future research on their relevance to the disorder.

Effects of antipsychotic drugs on energy metabolism.

Schizophrenia (SCZ) is a complex neuropsychiatric disorder associated with altered bioenergetic pathways and mitochondrial dysfunction. Antipsychotic medications, both first and second-generation, are commonly prescribed to manage SCZ symptoms, but their direct impact on mitochondrial function remains poorly understood. In this study, we investigated the effects of commonly prescribed antipsychotics on bioenergetic pathways in cultured neurons. We examined the impact of risperidone, aripiprazole, amisulpride, and clozapine on gene expression, mitochondrial bioenergetic profile, and targeted metabolomics after 24-h treatment, using RNA-seq, Seahorse XF24 Flux Analyser, and gas chromatography-mass spectrometry (GC-MS), respectively. Risperidone treatment reduced the expression of genes involved in oxidative phosphorylation, the tricarboxylic acid cycle, and glycolysis pathways, and it showed a tendency to decrease basal mitochondrial respiration. Aripiprazole led to dose-dependent reductions in various mitochondrial function parameters without significantly affecting gene expression. Aripiprazole, amisulpride and clozapine treatment showed an effect on the tricarboxylic acid cycle metabolism, leading to more abundant metabolite levels. Antipsychotic drug effects on mitochondrial function in SCZ are multifaceted. While some drugs have greater effects on gene expression, others appear to exert their effects through enzymatic post-translational or allosteric modification of enzymatic activity. Understanding these effects is crucial for optimising treatment strategies for SCZ. Novel therapeutic interventions targeting energy metabolism by post-transcriptional pathways might be more effective as these can more directly and efficiently regulate energy production.

Use of a gene expression signature to identify trimetazidine for repurposing to treat bipolar depression.

OBJECTIVES: The aim of this study was to repurpose a drug for the treatment of bipolar depression. METHODS: A gene expression signature representing the overall transcriptomic effects of a cocktail of drugs widely prescribed to treat bipolar disorder was generated using human neuronal-like (NT2-N) cells. A compound library of 960 approved, off-patent drugs were then screened to identify those drugs that affect transcription most similar to the effects of the bipolar depression drug cocktail. For mechanistic studies, peripheral blood mononuclear cells were obtained from a healthy subject and reprogrammed into induced pluripotent stem cells, which were then differentiated into co-cultured neurons and astrocytes. Efficacy studies were conducted in two animal models of depressive-like behaviours (Flinders Sensitive Line rats and social isolation with chronic restraint stress rats). RESULTS: The screen identified trimetazidine as a potential drug for repurposing. Trimetazidine alters metabolic processes to increase ATP production, which is thought to be deficient in bipolar depression. We showed that trimetazidine increased mitochondrial respiration in cultured human neuronal-like cells. Transcriptomic analysis in induced pluripotent stem cell-derived neuron/astrocyte co-cultures suggested additional mechanisms of action via the focal adhesion and MAPK signalling pathways. In two different rodent models of depressive-like behaviours, trimetazidine exhibited antidepressant-like activity with reduced anhedonia and reduced immobility in the forced swim test. CONCLUSION: Collectively our data support the repurposing of trimetazidine for the treatment of bipolar depression.

Alteration patterns of peripheral concentrations of cytokines and associated inflammatory proteins in acute and chronic stages of schizophrenia: a systematic review and network meta-analysis.

BACKGROUND: Immune system dysfunction is considered to play an aetiological role in schizophrenia spectrum disorders, with substantial alterations in the concentrations of specific peripheral inflammatory proteins, such as cytokines. However, there are inconsistencies in the literature over which inflammatory proteins are altered throughout the course of illness. Through conducting a systematic review and network meta-analysis, this study aimed to investigate the patterns of alteration that peripheral inflammatory proteins undergo in both acute and chronic stages of schizophrenia spectrum disorders, relative to a healthy control population. METHODS: In this systematic review and meta-analysis, we searched PubMed, PsycINFO, EMBASE, CINAHL, and the Cochrane Central Register of Controlled Trials from inception to March 31, 2022, for published studies reporting peripheral inflammatory protein concentrations in cases of people with schizophrenia-spectrum disorders and healthy controls. Inclusion criteria were: (1) observational or experimental design; (2) a population consisting of adults diagnosed with schizophrenia-spectrum disorders with a specified indicator of acute or chronic stage of illness; (3) a comparable healthy control population without mental illness; (4) a study outcome measuring the peripheral protein concentration of a cytokine, associated inflammatory marker, or C-reactive protein. We excluded studies that did not measure cytokine proteins or associated biomarkers in blood. Mean and SDs of inflammatory marker concentrations were extracted directly from full-text publshed articles; articles that did not report data as results or supplementary results were excluded (ie, authors were not contacted) and grey literature and unpublished studies were not sought. Pairwise and network meta-analyses were done to measure the standardised mean difference in peripheral protein concentrations between three groups: individuals with acute schizophrenia-spectrum disorder, individuals with chronic schizophrenia-spectrum disorder, and healthy controls. This protocol was registered on PROSPERO, CRD42022320305. FINDINGS: Of 13 617 records identified in the database searches, 4492 duplicates were removed, 9125 were screened for eligibility, 8560 were excluded after title and abstract screening, and three were excluded due to limited access to the full-text article. 324 full-text articles were then excluded due to inappropriate outcomes, mixed or undefined schizophrenia cohorts, or duplicate study populations, five were removed due to concerns over data integrity, and 215 studies were included in the meta-analysis. 24 921 participants were included, with 13 952 adult cases of schizophrenia-spectrum disorder and 10 969 adult healthy controls (descriptive data for the entire cohort were not available for age, numbers of males and females, and ethnicity). Concentration of interleukin (IL)-1ß, IL-1 receptor antagonist (IL-1RA), soluble interleukin-2 receptor (sIL-2R), IL-6, IL-8, IL-10, tumour necrosis factor (TNF)-α, and C-reactive protein were consistently elevated in both individuals with acute schizophrenia-spectrum disorder and chronic schizophrenia-spectrum disorder, relative to healthy controls. IL-2 and interferon (IFN)-γ were significantly elevated in acute schizophrenia-spectrum disorder, while IL-4, IL-12, and IFN-γ were significantly decreased in chronic schizophrenia-spectrum disorder. Sensitivity and meta-regression analyses revealed that study quality and a majority of the evaluated methodological, demographic, and diagnostic factors had no significant impact on the observed results for most of the inflammatory markers. Specific exceptions to this included: methodological factors of assay source (for IL-2 and IL-8), assay validity (for IL-1ß), and study quality (for transforming growth factor-ß1); demographic factors of age (for IFN-γ, IL-4, and IL-12), sex (for IFN-γ and IL-12), smoking (for IL-4), and BMI (for IL-4); and diagnostic factors including diagnostic composition of schizophrenia-spectrum cohort (for IL-1ß IL-2, IL-6, and TNF-α), antipsychotic-free cases (for IL-4 and IL-1RA), illness duration (for IL-4), symptom severity (for IL-4), and subgroup composition (for IL-4). INTERPRETATION: Results suggest that people with schizophrenia-spectrum disorders have a baseline level of inflammatory protein alteration throughout the illness, as reflected by consistently elevated pro-inflammatory proteins, hypothesised here as trait markers (eg, IL-6), while those with acute psychotic illness might have superimposed immune activity with increased concentrations of hypothesised state markers (eg, IFN-γ). Further research is required to determine whether these peripheral alterations are reflected within the central nervous system. This research facilitates an entry point in understanding how clinically relevant inflammatory biomarkers might one day be useful to the diagnosis and prognostication of schizophrenia-spectrum disorders. FUNDING: None.

Adjunctive minocycline for major depressive disorder: A sub-study exploring peripheral immune-inflammatory markers and associated treatment response.

Background: Adjunctive minocycline shows promise in treating affective and psychotic disorders; however, the therapeutic mechanism remains unclear. Identifying relevant biomarkers may enhance the efficacy of novel adjunctive treatment candidates. We thus investigated the peripheral immune-inflammatory profile in a randomized controlled trial (RCT) of minocycline in major depressive disorder (MDD). Methods: This sub-study investigated serum samples from a RCT evaluating minocycline (200 mg/day, 12 weeks) in addition to treatment as usual for MDD (ACTRN12612000283875). Of the original sample (N = 71), serum assays were conducted in 47 participants (placebo n = 24; minocycline n = 23) targeting an array of 46 immune-inflammatory analytes including cytokines, chemokines, and acute-phase reactants. General estimating equations (GEE) were used to assess whether analyte concentration at baseline (effect modification) and change in analytes (change association) influenced change in Montgomery-Åsberg Depression Rating Scale (MADRS) score over time. The Benjamini-Hochberg approach was applied when adjusting for false discovery rates (FDR). Results: GEE models revealed several interaction effects. After adjusting for FDR several change association-models survived correction. However, no such models remained significant for effect modification. Three-way group × time × marker interactions were significant for complement C3 (B = -10.46, 95%CI [-16.832, -4.095], q = 0.019) and IL-1Ra (B = -9.008, 95%CI [-15.26, -2.751], q = 0.036). Two-way group × biomarker interactions were significant for ICAM-1/CD54 (B = -0.387, 95%CI [-0.513, -0.26], q < 0.001) and IL-8/CXCL8 (B = -4.586, 95%CI [-7.698, -1.475], q = 0.036) indicating that increases in the serum concentration of these analytes were associated with an improvement in MADRS scores in the minocycline group (compared with placebo). Conclusions: Change in complement C3, IL-1Ra, IL-8/CXCL8, and ICAM-1 may be associated with greater change in depressive scores following adjunctive minocycline treatment in MDD. Further investigations are needed to assess the utility of these biomarkers.

Metformin is Protective Against the Development of Mood Disorders.

INTRODUCTION: Mood disorders are a major cause of disability, and current treatment options are inadequate for reducing the burden on a global scale. The aim of this project was to identify drugs suitable for repurposing to treat mood disorders. METHODS: This mixed-method study utilized gene expression signature technology and pharmacoepidemiology to investigate drugs that may be suitable for repurposing to treat mood disorders. RESULTS: The transcriptional effects of a combination of drugs commonly used to treat mood disorders included regulation of the steroid and terpenoid backbone biosynthesis pathways, suggesting a mechanism involving cholesterol biosynthesis, and effects on the thyroid hormone signaling pathway. Connectivity Map analysis highlighted metformin, an FDA-approved treatment for type 2 diabetes, as a drug having global transcriptional effects similar to the mood disorder drug combination investigated. In a retrospective cohort study, we found evidence that metformin is protective against the onset of mood disorders. DISCUSSION: These results provide proof-of-principle of combining gene expression signature technology with pharmacoepidemiology to identify potential novel drugs for treating mood disorders. Importantly, metformin may have utility in the treatment of mood disorders, warranting future randomized controlled trials to test its efficacy.

Effects of Psychotropic Drugs on Ribosomal Genes and Protein Synthesis.

Altered protein synthesis has been implicated in the pathophysiology of several neuropsychiatric disorders, particularly schizophrenia. Ribosomes are the machinery responsible for protein synthesis. However, there remains little information on whether current psychotropic drugs affect ribosomes and contribute to their therapeutic effects. We treated human neuronal-like (NT2-N) cells with amisulpride (10 µM), aripiprazole (0.1 µM), clozapine (10 µM), lamotrigine (50 µM), lithium (2.5 mM), quetiapine (50 µM), risperidone (0.1 µM), valproate (0.5 mM) or vehicle control for 24 h. Transcriptomic and gene set enrichment analysis (GSEA) identified that the ribosomal pathway was altered by these drugs. We found that three of the eight drugs tested significantly decreased ribosomal gene expression, whilst one increased it. Most changes were observed in the components of cytosolic ribosomes and not mitochondrial ribosomes. Protein synthesis assays revealed that aripiprazole, clozapine and lithium all decreased protein synthesis. Several currently prescribed psychotropic drugs seem to impact ribosomal gene expression and protein synthesis. This suggests the possibility of using protein synthesis inhibitors as novel therapeutic agents for neuropsychiatric disorders.

Integrative Analyses of Transcriptomes to Explore Common Molecular Effects of Antipsychotic Drugs.

There is little understanding of the underlying molecular mechanism(s) involved in the clinical efficacy of antipsychotics for schizophrenia. This study integrated schizophrenia-associated transcriptional perturbations with antipsychotic-induced gene expression profiles to detect potentially relevant therapeutic targets shared by multiple antipsychotics. Human neuronal-like cells (NT2-N) were treated for 24 h with one of the following antipsychotic drugs: amisulpride, aripiprazole, clozapine, risperidone, or vehicle controls. Drug-induced gene expression patterns were compared to schizophrenia-associated transcriptional data in post-mortem brain tissues. Genes regulated by each of four antipsychotic drugs in the reverse direction to schizophrenia were identified as potential therapeutic-relevant genes. A total of 886 genes were reversely expressed between at least one drug treatment (versus vehicle) and schizophrenia (versus healthy control), in which 218 genes were commonly regulated by all four antipsychotic drugs. The most enriched biological pathways include Wnt signaling and action potential regulation. The protein-protein interaction (PPI) networks found two main clusters having schizophrenia expression quantitative trait loci (eQTL) genes such as PDCD10, ANK2, and AKT3, suggesting further investigation on these genes as potential novel treatment targets.

Brain derived neurotrophic factor in perioperative neurocognitive disorders: Current evidence and future directions.

An increase in the age of surgical patients as well as the volume of surgeries is associated with a rise in perioperative neurocognitive disorders. These disorders encompass acute delirium and longer-term cognitive dysfunctions. Brain derived neurotrophic factor (BDNF) is a neurotrophin that plays a dynamic role in a series of neurological functions including neuroplasticity, neurogenesis and synaptic regulation. Given the possible alterations to brain physiology in response to surgery, this review aims to explore the relationship between changes in central and peripheral BDNF concentrations and perioperative neurocognitive disorders. Higher levels of Brain tissue and blood BDNF have been associated with better cognitive function; however, the nature of the association between BDNF and delirium is uncertain. Preclinical models point to a significant depletion in BDNF expression and signalling within the brain post-operatively, while preliminary human studies demonstrate depletions in serum BDNF concentration after surgery. These findings suggest that the reduced BDNF concentrations may be associated with post-operative cognitive dysfunction. Thus, understanding the BDNF expression/signalling pathways may present a promising avenue for managing perioperative neurocognitive disorders symptoms. Nonetheless, given that results were primarily derived from preclinical models, it is critical for these findings to be validated in humans to confirm the relevance of this promising target.

Co-Expression Networks Unveiled Long Non-Coding RNAs as Molecular Targets of Drugs Used to Treat Bipolar Disorder.

Long non-coding RNAs (lncRNAs) may play a role in psychiatric diseases including bipolar disorder (BD). We investigated mRNA-lncRNA co-expression patterns in neuronal-like cells treated with widely prescribed BD medications. The aim was to unveil insights into the complex mechanisms of BD medications and highlight potential targets for new drug development. Human neuronal-like (NT2-N) cells were treated with either lamotrigine, lithium, quetiapine, valproate or vehicle for 24 h. Genome-wide mRNA expression was quantified for weighted gene co-expression network analysis (WGCNA) to correlate the expression levels of mRNAs with lncRNAs. Functional enrichment analysis and hub lncRNA identification was conducted on key co-expressed modules associated with the drug response. We constructed lncRNA-mRNA co-expression networks and identified key modules underlying these treatments, as well as their enriched biological functions. Processes enriched in key modules included synaptic vesicle cycle, endoplasmic reticulum-related functions and neurodevelopment. Several lncRNAs such as GAS6-AS1 and MIR100HG were highlighted as driver genes of key modules. Our study demonstrates the key role of lncRNAs in the mechanism(s) of action of BD drugs. Several lncRNAs have been suggested as major regulators of medication effects and are worthy of further investigation as novel drug targets to treat BD.

Common effects of bipolar disorder medications on expression quantitative trait loci genes.

The molecular mechanism(s) underpinning the clinical efficacy of the current drugs for bipolar disorder (BD) are largely unknown. This study evaluated the transcriptional perturbations potentially playing roles in the therapeutic efficacy of four commonly prescribed psychotropic drugs used to treat BD. NT2-N cells were treated with lamotrigine, lithium, quetiapine, valproate or vehicle control for 24 h. Genome-wide mRNA expression was quantified by RNA-sequencing. Incorporating drug-induced gene expression profiles with BD-associated transcriptional changes from post-mortem brains, we identified potential therapeutic-relevant genes associated with both drug treatments and BD pathophysiology and focused on expression quantitative trait loci (eQTL) genes with genome-wide association with BD. Each eQTL gene was ranked based on its potential role in the therapeutic effect across multiple drugs. The expression of highest-ranked eQTL genes were measured by RT-qPCR to confirm their transcriptional changes observed in RNA-seq. We found 775 genes for which at least 2 drugs reversed expression levels relative to the differential expression in post-mortem brains. Pathway analysis identified enriched biological processes highlighting mitochondrial and endoplasmic reticulum function. Differential expression of SRPK2 and CHDH was confirmed by RT-qPCR following multiple-dose treatments. We pinpointed potential genes involved in the beneficial effects of drugs used for BD and their main associated biological pathways. CHDH, which encodes a mitochondrial protein, had a significant dose-responsive downregulation following treatment with increasing doses of quetiapine and lamotrigine, which in combination with the enriched mitochondrial pathways suggests potential therapeutic roles and demand more studies on mitochondrial involvement in BD to identify novel treatment targets.

Naturally acquired antibody kinetics against Plasmodium vivax antigens in people from a low malaria transmission region in western Thailand.

BACKGROUND: Plasmodium vivax (P. vivax) is the dominant Plasmodium spp. causing the disease malaria in low-transmission regions outside of Africa. These regions often feature high proportions of asymptomatic patients with sub-microscopic parasitaemia and relapses. Naturally acquired antibody responses are induced after Plasmodium infection, providing partial protection against high parasitaemia and clinical episodes. However, previous work has failed to address the presence and maintenance of such antibody responses to P. vivax particularly in low-transmission regions. METHODS: We followed 34 patients in western Thailand after symptomatic P. vivax infections to monitor antibody kinetics over 9 months, during which no recurrent infections occurred. We assessed total IgG, IgG subclass and IgM levels to up to 52 P. vivax proteins every 2-4 weeks using a multiplexed Luminex® assay and identified protein-specific variation in antibody longevity. Mathematical modelling was used to generate the estimated half-life of antibodies, long-, and short-lived antibody-secreting cells. RESULTS: Generally, an increase in antibody level was observed within 1-week post symptomatic infection, followed by an exponential decay of different rates. We observed mostly IgG1 dominance and IgG3 sub-dominance in this population. IgM responses followed similar kinetic patterns to IgG, with some proteins unexpectedly inducing long-lived IgM responses. We also monitored antibody responses against 27 IgG-immunogenic antigens in 30 asymptomatic individuals from a similar region. Our results demonstrate that most antigens induced robust and long-lived total IgG responses following asymptomatic infections in the absence of (detected) boosting infections. CONCLUSIONS: Our work provides new insights into the development and maintenance of naturally acquired immunity to P. vivax and will guide the potential use of serology to indicate immune status and/or identify populations at risk.

Fatty Liver Index and Skeletal Muscle Density.

Accumulation of fat in the liver and skeletal muscle is associated with obesity and poor health outcomes. Liver steatosis is a characteristic of non-alcoholic fatty liver disease (NAFLD) and myosteatosis, of poor muscle quality in sarcopenia. In this study of 403 men (33-96 years), we investigated associations between the fatty liver index (FLI) and muscle density, as markers of fat accumulation in these organs. We also investigated associations between the FLI and parameters of sarcopenia, including DXA-derived appendicular lean mass (ALM) and handgrip strength by dynamometry. Muscle density was measured using pQCT at the radius and tibia. FLI was calculated from BMI, waist circumference, and levels of triglycerides and gamma-glutamyltransferase. There was a pattern of decreasing muscle density across increasing quartiles of FLI. After adjusting for age and lifestyle, mean radial muscle density in Q4 was 2.1% lower than Q1 (p < 0.001) and mean tibial muscle density was 1.8% lower in Q3 and 3.0% lower in Q4, compared to Q1 (p = 0.022 and < 0.001, respectively). After adjusting for age and sedentary lifestyle, participants in the highest FLI quartile were sixfold more likely to have sarcopenia. In conclusion, our results suggest that fat accumulation in the liver co-exists with fat infiltration into skeletal muscle.

Biprediction-Based Video Quality Enhancement via Learning.

Convolutional neural networks (CNNs)-based video quality enhancement generally employs optical flow for pixelwise motion estimation and compensation, followed by utilizing motion-compensated frames and jointly exploring the spatiotemporal correlation across frames to facilitate the enhancement. This method, called the optical-flow-based method (OPT), usually achieves high accuracy at the expense of high computational complexity. In this article, we develop a new framework, referred to as biprediction-based multiframe video enhancement (PMVE), to achieve a one-pass enhancement procedure. PMVE designs two networks, that is, the prediction network (Pred-net) and the frame-fusion network (FF-net), to implement the two steps of synthesization and fusion, respectively. Specifically, the Pred-net leverages frame pairs to synthesize the so-called virtual frames (VFs) for those low-quality frames (LFs) through biprediction. Afterward, the slowly fused FF-net takes the VFs as the input to extract the correlation across the VFs and the related LFs, to obtain an enhanced version of those LFs. Such a framework allows PMVE to leverage the cross-correlation between successive frames for enhancement, hence capable of achieving high accuracy performance. Meanwhile, PMVE effectively avoids the explicit operations of motion estimation and compensation, hence greatly reducing the complexity compared to OPT. The experimental results demonstrate that the peak signal-to-noise ratio (PSNR) performance of PMVE is fully on par with that of OPT while its computational complexity is only 1% of OPT. Compared with other state-of-the-art methods in the literature, PMVE is also confirmed to achieve superior performance in both objective quality and visual quality at a reasonable complexity level. For instance, PMVE can surpass its best counterpart method by up to 0.42 dB in PSNR.

Serology for Plasmodium vivax surveillance: A novel approach to accelerate towards elimination.

Plasmodium vivax is the most widespread causative agent of human malaria in the world. Despite the ongoing implementation of malaria control programs, the rate of case reduction has declined over the last 5 years. Hence, surveillance of malaria transmission should be in place to identify and monitor areas that require intensified malaria control interventions. Serological tools may offer additional insights into transmission intensity over parasite and entomological measures, especially as transmission levels decline. Antibodies can be detected in the host system for months to even years after parasite infections have been cleared from the blood, enabling malaria exposure history to be captured. Because the Plasmodium parasite expresses more than 5000 proteins, it is important to a) understand antibody longevity following infection and b) measure antibodies to more than one antigen in order to accurately inform on the exposure and/or immune status of populations. This review summarises current practices for surveillance of P. vivax malaria, the current state of research into serological exposure markers and their potential role for accelerating malaria elimination, and discusses further studies that need to be undertaken to see such technology implemented in malaria-endemic areas.

Biological Mechanism(s) Underpinning the Association between Antipsychotic Drugs and Weight Gain.

Weight gain and consequent metabolic alterations are common side-effects of many antipsychotic drugs. Interestingly, several studies have suggested that improvement in symptoms and adverse metabolic effects are correlated. We used next generation sequencing data from NT-2 (human neuronal) cells treated with aripiprazole, amisulpride, risperidone, quetiapine, clozapine, or vehicle control, and compared with the Pillinger P-score (ranked from 0 to 1, indicating greater increase in weight gain and related metabolic parameters) to identify the genes most associated with the drugs' propensity to cause weight gain. The top 500 genes ranked for their correlation with the drugs' propensity to cause weight gain were subjected to pathway analysis using DAVID (NIH). We further investigated transcription factors (TFs) that are more likely to regulate the genes involved in these processes using the prediction tool of key TFs from TRRUST. The results suggest an enrichment for genes involved in lipid biosynthesis and metabolism, which are of interest for mechanisms underpinning weight-gain. The list of genes involved in the lipid pathways that correlated with weight gain was enriched for genes transcriptionally regulated by SREBF1 and SREBF2. Furthermore, quetiapine significantly increased the expression of SREBF1 and SREBF2 in NT-2 cells. Our results suggest that the effects of these antipsychotic drugs on lipid metabolism may be mediated, at least in part, via regulation of SREBF1/SREBF2 expression, with evidence of a direct effect of quetiapine on the expression of SREBF1/2. The effects of antipsychotic drugs on lipid metabolism may influence white matter structure (therapeutic effect) and the risk of weight gain, lipid disturbances, and, consequently, metabolic syndrome (adverse effects). Understanding the different molecular effects of these drugs could inform a personalized medicine approach in treating patients with schizophrenia.

Baseline serum amino acid levels predict treatment response to augmentation with N-acetylcysteine (NAC) in a bipolar disorder randomised trial.

N-acetylcysteine (NAC) acts on glutamatergic and redox systems, two systems implicated in the pathophysiology of bipolar disorder (BD). This has led to the investigation of NAC as a potential candidate for the treatment of BD. The aim of this study was to investigate metabolomic markers to identify predictors of NAC response in a cohort of BD participants. This study is a secondary analysis of a 16-week, multi-site, randomized, double-blinded, parallel-group, placebo-controlled trial in BD participants with a current acute depressive episode. This study included trial participants who received either NAC 2000 mg/day, or placebo. Participants (NAC: n = 31, placebo: n = 29) were assessed at baseline and week 16 using the Montgomery Åsberg Depression Rating Scale (MADRS) and were dichotomised into "responders" (MADRS at week 16 < 50% of MADRS at baseline) and "non-responders" (MADRS at week 16 > 50% at baseline). Untargeted gas chromatography-mass spectrometry analysis was performed to analyse baseline levels of 68 serum metabolites. Of the nine metabolites that differentiated placebo and NAC groups, five were amino acids with lower levels in the NAC responder group compared with the NAC non-responders. Further analysis generated a predictive model of MADRS improvement including glycine, norleucine, threonine, proline, phenylalanine, tyrosine, glutamic acid, lysine and leucine (R2 = 0.853; adjusted R2 = 0.733). This prediction model predicted 85% of the variance in MADRS outcome after adjunctive treatment with NAC. BD participants with lower serum levels of free amino acids at baseline may be more likely to respond to adjunctive treatment with NAC.

Transcriptional Modulation of the Hippo Signaling Pathway by Drugs Used to Treat Bipolar Disorder and Schizophrenia.

Recent reports suggest a link between positive regulation of the Hippo pathway with bipolar disorder (BD), and the Hippo pathway is known to interact with multiple other signaling pathways previously associated with BD and other psychiatric disorders. In this study, neuronal-like NT2 cells were treated with amisulpride (10 µM), aripiprazole (0.1 µM), clozapine (10 µM), lamotrigine (50 µM), lithium (2.5 mM), quetiapine (50 µM), risperidone (0.1 µM), valproate (0.5 mM), or vehicle control for 24 h. Genome-wide mRNA expression was quantified and analyzed using gene set enrichment analysis (GSEA), with genes belonging to Hippo, Wnt, Notch, TGF- ß, and Hedgehog retrieved from the KEGG database. Five of the eight drugs downregulated the genes of the Hippo pathway and modulated several genes involved in the interacting pathways. We speculate that the regulation of these genes, especially by aripiprazole, clozapine, and quetiapine, results in a reduction of MAPK and NFκB pro-inflammatory signaling through modulation of Hippo, Wnt, and TGF-ß pathways. We also employed connectivity map analysis to identify compounds that act on these pathways in a similar manner to the known psychiatric drugs. Thirty-six compounds were identified. The presence of antidepressants and antipsychotics validates our approach and reveals possible new targets for drug repurposing.