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Targeting the glutamatergic system is posited as a potentially novel therapeutic strategy for psychotic disorders. While studies in subjects indicate that antipsychotic medication reduces brain glutamatergic measures, they were unable to disambiguate clinical changes from drug effects.
Aims
To address this, we investigated the effects of a dopamine D2 receptor partial agonist (aripiprazole) and a dopamine D2 receptor antagonist (amisulpride) on glutamatergic metabolites in the anterior cingulate cortex (ACC), striatum and thalamus in healthy controls.
Method
A double-blind, within-subject, cross-over, placebo-controlled study design with two arms (n = 25 per arm) was conducted. Healthy volunteers received either aripiprazole (up to 10 mg/day) for 7 days or amisulpride (up to 400 mg/day) and a corresponding period of placebo treatment in a pseudo-randomised order. Magnetic resonance spectroscopy (1H-MRS) was used to measure glutamatergic metabolite levels and was carried out at three different time points: baseline, after 1 week of drug and after 1 week of placebo. Values were analysed as a combined measure across the ACC, striatum and thalamus.
Results
Aripiprazole significantly increased glutamate + glutamine (Glx) levels compared with placebo (β = 0.55, 95% CI [0.15, 0.95], P = 0.007). At baseline, the mean Glx level was 8.14 institutional units (s.d. = 2.15); following aripiprazole treatment, the mean Glx level was 8.16 institutional units (s.d. = 2.40) compared with 7.61 institutional units (s.d. = 2.36) for placebo. This effect remained significant after adjusting for plasma parent and active metabolite drug levels. There was an observed increase with amisulpride that did not reach statistical significance.
Conclusions
One week of aripiprazole administration in healthy participants altered brain Glx levels as compared with placebo administration. These findings provide novel insights into the relationship between antipsychotic treatment and brain metabolites in a healthy participant cohort.
Objectives/Goals: Lung transplant is a life-saving surgery for patients with advanced lung diseases yet long-term survival remains poor. The clinical features and lung injury patterns of lung transplant recipients who die early versus those who survive longer term remain undefined. Here, we use cell-free DNA and rejection parameters to help elucidate this further. Methods/Study Population: Lung transplant candidacy prioritizes patients who have a high mortality risk within 2 years and will likely survive beyond 5 years. We stratified patients who died within 2 years of transplant as early death (n = 50) and those who survived past 5 years as long-term survivors (n = 53). Lung transplant recipients had serial blood collected as part of two prospective cohort studies. Cell-free DNA (cfDNA) was quantified using relative (% donor-derived cfDNA {%ddcfDNA}) and absolute (nuclear-derived {n-cfDNA}, mitochondrial-derived {mt-cfDNA}) measurements. As part of routine posttransplant clinical care, all patients underwent pulmonary function testing (PFT), surveillance bronchoscopy with bronchoalveolar lavage (BAL), transbronchial biopsy (TBBx), and donor-specific antibody testing (DSA). Results/Anticipated Results: Over the first 2 years after transplant, the number of episodes of antibody-mediated rejection (p) Discussion/Significance of Impact: Clinically, early-death patients perform worse on routine surveillance PFTs and experience a worse degree of CLAD. These patients also have higher levels of cfDNA as quantified by n-cfDNA and mt-cfDNA. These results provide preliminary evidence that early-death patients have worse allograft rejection, dysfunction, and molecular injury.
The Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) Cross-Trial Statistics Group gathered lessons learned from statisticians responsible for the design and analysis of the 11 ACTIV therapeutic master protocols to inform contemporary trial design as well as preparation for a future pandemic. The ACTIV master protocols were designed to rapidly assess what treatments might save lives, keep people out of the hospital, and help them feel better faster. Study teams initially worked without knowledge of the natural history of disease and thus without key information for design decisions. Moreover, the science of platform trial design was in its infancy. Here, we discuss the statistical design choices made and the adaptations forced by the changing pandemic context. Lessons around critical aspects of trial design are summarized, and recommendations are made for the organization of master protocols in the future.
High-fat diet (HFD) consumption during pregnancy can shape fetal brain development, increasing susceptibility to mental disorders. Nevertheless, the mechanisms underlying these negative outcomes remain unclear.
Objectives
We hypothesize that mHFD induces inflammation and oxidative stress (OS) in the fetal brain, disrupting excitatory/inhibitory (E/I) balance in the adult brain. This results in altered hypothalamic-pituitary-adrenal (HPA) axis reactivity, emotional regulation, and cognitive function. We tested the ability of N-acetyl-cysteine (NAC) - a powerful anti-oxidant and anti-inflammatory compound - to counteract mHFD effects.
Methods
Our mHFD model consists of female C57BL/6N mice fed either HFD (fat 58%, carbohydrate 25.5%, and protein 16.4%) or control diet (CD, fat 10.5%, carbohydrate 73.1% and protein 16.4%) before and during pregnancy (13 weeks). After 5 weeks on diets, half of them received NAC (1g/kg) for 8 weeks, until delivery.
Gene expression of Il-1b, Cd68, Tmem119, iNOS, and Arg1 was measured in fetal brains. Cognitive function and emotional phenotype were assessed in adult male and female offspring through the Morris Water Maze (MWM) and the Emergence test, respectively. HPA axis functionality was assessed by measuring plasma corticosterone levels by ELISA following acute stress. Gene expression of vesicular glutamate transporter 1 (Vglut1) and vesicular GABA transporter (Vgat) were assessed as markers of E/I balance.
Results
Exposure to mHFD induced inflammation and OS in the fetal brain of both sexes, by increasing Il-1b and iNOS/Arg1. Additionally, Cd68 and Tmem119 were specifically increased in females. In adulthood, mHFD reduced latency to emerge from the shelter in the Emergence test in both sexes. In females, mHFD impaired cognitive function, reducing time spent in the MWM target zone, and increased HPA reactivity in response to acute stress. Furthermore, mHFD decreased Vgat expression in both sexes, resulting in an imbalanced Vglut1/Vgat ratio towards excessive excitatory input. Maternal NAC supplementation rescued this imbalance.
Conclusions
Overall, these data show that mHFD increases inflammation and OS in fetal brains, with greater effects in female offspring, inducing alterations in the E/I neuronal balance with concomitant disruptions of the neuroendocrine system and the emotional and cognitive profiles during adulthood. The supplementation with NAC was effective in rescuing the E/I imbalance as well as the behavioral phenotype.
Bipolar disorder (BD) is characterised by heterogeneous phenotypic manifestations that may affect the achievement of a timely diagnosis delaying its therapeutic management. Increased circulating levels of pro-inflammatory cytokines and cortisol (CORT) have been observed in BD patients in addition to decreased levels of Brain-Derived-Neurotrophic Factor (BDNF) suggesting that the interaction among these mediators may play a role in the occurrence of affective episodes overall disrupting brain plasticity. However, knowledge on BD etiopathogenesis is still limited, including the causal relationship with inflammatory and neuroendocrine markers.
Objectives
To assess whether variations in peripheral neuroendocrine and inflammatory markers during acute phases of the disease and euthymia might predict the occurrence of affective episodes; to evaluate whether the interplay among these biomarkers might be exploited as a signature of BD.
Methods
We are currently recruiting BD patients during depressive or manic/hypomanic phases together with age- and sex-matched healthy controls (CTRLs). Complete blood count, pro-inflammatory, anti-inflammatory cytokines and BDNF will be assessed in serum; salivary cortisol awakening response test will be used to evaluate hypothalamic-pituitary-adrenal axis activity. MADRS, YMRS and HAM-A will be used to assess psychiatric symptoms, PSP and C-SSRS for global functioning and suicidal risk, IPSS and SRRS for stress levels and CIRS to evaluate physical comorbidities. All assessments will be carried out at the time of recruitment (T0) and after 3 (T1) and 6 (T2) months.
Results
Data have been so far collected on 28 BD patients (18 males, 10 females, age: 48.31±11.3) and 26 CTRLs (16 males, 10 females, age: 46.82±10.86). At T0, BD were characterised by a greater total number of white cells (7.83±1.86 BD vs. 6.78±1.87 CTRL, p<0.05), mean number of neutrophils (4.89±1.49 BD vs. 3.92±1.45 CTRL, p<0.05) and neutrophil/lymphocyte ratio (NLR) (2.52±1.1 BD vs. 1.9±0.69 CTRL, p<0.05). Moreover, BD patients showed overall a greater BMI (30.5±6.6 BD vs. 24.45±3.86 CTRL, p<.001). No difference was observed among groups with respect to sex and age.
Conclusions
Although preliminary, these results suggest that the active phases of BD are associated with a low-grade inflammatory state, potentially related to a different metabolic set-point in BD patients. Ultimately, this study will allow us to evaluate whether the presence of affective symptoms is correlated with fluctuations in the levels of inflammatory mediators, salivary cortisol and BDNF and to establish a reliable and highly predictive BD signature.
“Funded by: Bando Ricerca Indipendente ISS 2021-2023 to A. Berry project code ISS20-9286e4091f8e”
Chapter 5 gives an extended empirical example of the Benford agreement procedure for assessing the validity of social science data. The example uses country-level data collected and estimated by the Sea Around Us organization on the dollar values of reported and unreported fish landings from 2010 to 2016. We report Benford agreement analyses for the Sea Around Us data (1) by reporting status, (2) by decade, (3) for a large fishing region of 22 West African countries, and (4) foreach of the 22 individual countries in West Africa.
Chapter 4 begins with a discussion of the types and kinds of data most suitable for an analysis that uses the Benford probability distribution. Next we describe an R computer program – program Benford – designed to evaluate observed data for agreement with the Benford probability distribution; and we give an example of output from the program using a typical dataset. We then move to an overview of our workflow of Benford agreement analyses where we outline our process for assessing the validity of data using Benford agreement analyses. We end the chapter with a discussion of the concept of Benford validity, which we will employ in subsequent chapters.
Chapter 7 takes a closer look at some of the Sea Around Us fish-landings data that we assessed for Benford agreement in Chapter 5. We chose these data because of the mixed agreement findings among them: while the full dataset and several sets of subgroups indicated that the data exhibited Benford validity, when we analyzed West African countries individually, a number of them were found to have unacceptable Benford agreement and therefore problematic Benford validity. We present ways in which researchers can assess the impact of unacceptable Benford agreement on their analyses.
Chapter 3 describes and illustrates the Benford probability distribution. A brief summary of the origin and evolution of the Benford distribution is drawn and the development and assessment of various measures of goodness of fit between an empirical distribution and the Benford distribution are described and illustrated. These masures are Pearson’s chi-squared, Wilks’ likelihood-ratio, Hardy and Ramanujan’s partition theory, Fisher’s exact test, Kuiper’s measure, Tam Cho and Gaines’ d measure, Cohen’s w measure, and Nigrini’s MAD measure.
Chapter 6 provides a second empirical example of the Benford agreement procedure: here we analyze new daily COVID-19 cases at the US state level and at the global level across nations. Both the state-level and the global analyses consider time as a variable. Specifically we examine, (1) for the United States, new reports of COVID-19 between January 22, 2020 and November 16, 2021 at the state level, and (2) for the cross-national data, new reports of COVID-19 between February 24, 2020 and January 13, 2022. At the state level, we report Benford agreement analyses for (1) the full dataset, (2) cases grouped alphabetically, (3) cases grouped regionally, (4) cases grouped by days of the week, and (5) cases grouped by their governor’s party (Republican or Democratic). We then turn our Benford agreement analysis to global cross-national COVID-19 data to assess whether Benford agreement of COVID-19 varies across countries.
This chapter gives an overview of the remainder of the book. We first provide commonsense and social science examples of reliability and validity, two necessary conditions that data must posses to have trustworthy conclusions based upon it. We next introduce Benford’s law and offer a brief overview of other social science studies that have employed it to check the accuracy of their data. We then turn to an overview of our Benford agreement analysis procedure and introduce the concept of Benford validity. The chapter concludes with a plan for the remainder of the book.