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Validation of a biomarker-based medical product development tool or clinical test is an evidentiary process that must be tailored to the proposed use. Appropriate data and analyses are needed to demonstrate that the biomarker meets analytical and clinical performance criteria consistent with favorable benefit: risk balance.
Childhood abuse is a risk factor for poorer illness course in bipolar disorder, but the reasons why are unclear. Trait-like features such as affective instability and impulsivity could be part of the explanation. We aimed to examine whether childhood abuse was associated with clinical features of bipolar disorder, and whether associations were mediated by affective instability or impulsivity.
We analysed data from 923 people with bipolar I disorder recruited by the Bipolar Disorder Research Network. Adjusted associations between childhood abuse, affective instability and impulsivity and eight clinical variables were analysed. A path analysis examined the direct and indirect links between childhood abuse and clinical features with affective instability and impulsivity as mediators.
Affective instability significantly mediated the association between childhood abuse and earlier age of onset [effect estimate (θ)/standard error (SE): 2.49], number of depressive (θ/SE: 2.08) and manic episodes/illness year (θ/SE: 1.32), anxiety disorders (θ/SE: 1.98) and rapid cycling (θ/SE: 2.25). Impulsivity significantly mediated the association between childhood abuse and manic episodes/illness year (θ/SE: 1.79), anxiety disorders (θ/SE: 1.59), rapid cycling (θ/SE: 1.809), suicidal behaviour (θ/SE: 2.12) and substance misuse (θ/SE: 3.09). Measures of path analysis fit indicated an excellent fit to the data.
Affective instability and impulsivity are likely part of the mechanism of why childhood abuse increases risk of poorer clinical course in bipolar disorder, with each showing some selectivity in pathways. They are potential novel targets for intervention to improve outcome in bipolar disorder.
Despite knowing for many decades that depressive psychopathology is common in first-episode schizophrenia spectrum disorders (FES), there is limited knowledge regarding the extent and nature of such psychopathology (degree of comorbidity, caseness, severity) and its demographic, clinical, functional and treatment correlates. This study aimed to determine the pooled prevalence of depressive disorder and caseness, and the pooled mean severity of depressive symptoms, as well as the demographic, illness, functional and treatment correlates of depressive psychopathology in FES.
This systematic review, meta-analysis and meta-regression was prospectively registered (CRD42018084856) and conducted in accordance with PRISMA and MOOSE guidelines.
Forty studies comprising 4041 participants were included. The pooled prevalence of depressive disorder and caseness was 26.0% (seven samples, N = 855, 95% CI 22.1–30.3) and 43.9% (11 samples, N = 1312, 95% CI 30.3–58.4), respectively. The pooled mean percentage of maximum depressive symptom severity was 25.1 (38 samples, N = 3180, 95% CI 21.49–28.68). Correlates of depressive psychopathology were also found.
At least one-quarter of individuals with FES will experience, and therefore require treatment for, a full-threshold depressive disorder. Nearly half will experience levels of depressive symptoms that are severe enough to warrant diagnostic investigation and therefore clinical intervention – regardless of whether they actually fulfil diagnostic criteria for a depressive disorder. Depressive psychopathology is prominent in FES, manifesting not only as superimposed comorbidity, but also as an inextricable symptom domain.
Large-scale mass-sporting events are increasingly requiring greater prehospital event planning and preparation to address inherent event-associated medical conditions in addition to incidents that may be unexpected. The Bank of America Chicago Marathon (Chicago, Illinois USA) is one of the largest marathons in the world, and with the improvement of technology, the use of historical patient and event data, in conjunction with environmental conditions, can provide organizers and public safety officials a way to plan based on injury patterns and patient demands for care by predicting the placement and timing of needed medical support and resources.
During large-scale events, disaster planning and preparedness between event organizers, Emergency Medical Services (EMS), and local, state, and federal agencies is critical to ensure participant and public safety.
This study looked at the Bank of America Chicago Marathon, a significant endurance event, and took a unique approach of reviewing digital runner data retrospectively over a five-year period to establish patterns of medical demand geographically, temporally, and by the presenting diagnoses. Most medical complaints were musculoskeletal in nature; however, there were life-threatening conditions such as hyperthermia and cardiac incidents that highlight the need for detailed planning, coordination, and communication to ensure a safe and secure event.
The Chicago Marathon is one of the largest marathons in the world, and this study identified an equal number of participants requiring care on-course and at the finish line. Most medical complaints were musculoskeletal in nature; however, there were life-threatening conditions such as hyperthermia and cardiac incidents that highlight the need for detailed planning, multi-disciplined coordination, and communication to ensure a safe and secure event. As technology has evolved, the use, analysis, and implementation of historical digital data with various environmental conditions can provide organizers and public safety officials a map to plan injury patterns and patient demands by predicting the placement and timing of needed medical support, personnel, and resources.
Little is known about what motivates people to enroll in research registries. The purpose of this study is to identify facilitators of registry enrollment among diverse older adults.
Participants completed an 18-item Research Interest Assessment Tool. We used logistic regression analyses to examine responses across participants and by race and gender.
Participants (N=374) were 58% black, 76% women, with a mean age of 68.2 years. All participants were motivated to maintain their memory while aging. Facilitators of registry enrolled varied by both race and gender. Notably, blacks (estimate=0.71, p<0.0001) and women (estimate=0.32, p=0.03) were more willing to enroll in the registry due to home visits compared with whites and men, respectively.
Researchers must consider participant desire for maintaining memory while aging and home visits when designing culturally tailored registries.
Computational models of language acquisition often face evaluation issues associated with unsupervised machine learning approaches. These acquisition models are typically meant to capture how children solve language acquisition tasks without relying on explicit feedback, making them similar to other unsupervised learning models. Evaluation issues include uncertainty about the exact form of the target linguistic knowledge, which is exacerbated by a lack of empirical evidence about children's knowledge at different stages of development. Put simply, a model's output may be good enough even if it does not match adult knowledge because children's output at various stages of development also may not match adult knowledge. However, it is not easy to determine what counts as “good enough” model output. We consider this problem using the case study of speech segmentation modeling, where the acquisition task is to segment a fluent stream of speech into useful units like words. We focus on a particular Bayesian segmentation strategy previously shown to perform well on English, and discuss several options for assessing whether a segmentation model's output is good enough, including cross-linguistic utility, the presence of reasonable errors, and downstream evaluation. Our findings highlight the utility of considering multiple metrics for segmentation success, which is likely also true for language acquisition modeling more generally.
A core issue in machine learning is how to evaluate unsupervised learning approaches (von Luxburg, Williamson, & Guyon, 2011), since there is no a priori correct answer the way that there is for supervised learning approaches. Computational models of language acquisition commonly face this problem because they attempt to capture how children solve language acquisition tasks without explicit feedback, and so typically use unsupervised learning approaches. Moreover, evaluation is made more difficult by uncertainty about the exact nature of the target linguistic knowledge and a lack of empirical evidence about children's knowledge at specific stages in development. Given this, how do we know that a model's output is “good enough”? How should success be measured? To create informative cognitive models of acquisition that offer insight into how children acquire language, we should consider how to evaluate acquisition models appropriately (Pearl, 2014; Phillips, 2015; Phillips & Pearl, 2015b).
Pangolins are increasingly threatened by demand for their scales, which are used in traditional medicines, and for their meat, which is consumed as a luxury. As populations of Asian pangolins decline, the demand is shifting to the four species in Africa, where local cultural use may already pose some level of threat. During 2010−2015 a total of 65 pangolin-related seizures (surrendered and confiscated) were reported in Zimbabwe, with the annual number of confiscations increasing significantly over this period. Zimbabwean authorities have toughened their stance against this trade, and during January−June 2015 three-quarters of confiscations of pangolins (n = 12) resulted in the maximum jail sentence for at least one of the offenders in each case. At present there is no evidence that pangolins are being traded from Zimbabwe to China, and the increased enforcement may be key to ensuring Zimbabwe's pangolins are not threatened by the large-scale illegal trade witnessed in Asia.
Ensuring a consistent and systematic approach to the delivery of care for people with advanced disease is a priority for palliative care services worldwide. Many clinical tools are available to aid in this process; however, they are often used sporadically, and implementation of a routine set of clinical tools to guide care planning in the specialist palliative care sector in Australia has not been achieved. This study sought to recommend key clinical tools that may assist with the assessment and care planning of specialist palliative care provision for patients and family caregivers admitted to specialist palliative care settings (home, hospital, and hospice).
A mixed-methods sequential approach over four phases was employed, involving: (1) a palliative care sector survey, (2) a systematic literature review, (3) an appraisal of identified clinical tools, and (4) a focus group with an expert panel who critiqued and endorsed a final suite of clinical tools recommended for specialist palliative care.
Twelve tools with practical relevance were recommended for use across settings of care.
Significance of Results:
Palliative services should review current practices and seek to implement this recommended suite of tools to enhance assessment and guide care delivery across care settings. Subsequent evaluation should also occur.
In major depressive disorder (MDD), single nucleotide polymorphisms (SNPs) in monoaminergic genes may impact disease susceptibility, treatment response, and brain volume. The objective of this study was to examine the effect of such polymorphisms on hippocampal volume in patients with treatment-resistant MDD and healthy controls. Candidate gene risk alleles were hypothesised to be associated with reductions in hippocampal volume.
A total of 26 outpatients with treatment-resistant MDD and 27 matched healthy controls underwent magnetic resonance imaging and genotyping for six SNPs in monoaminergic genes [serotonin transporter (SLC6A4), norepinephrine transporter (SLC6A2), serotonin 1A and 2A receptors (HTR1A and HTR2A), catechol-O-methyltransferase (COMT), and brain-derived neurotrophic factor (BDNF)]. Hippocampal volume was estimated using an automated segmentation algorithm (FreeSurfer).
Hippocampal volume did not differ between patients and controls. Within the entire study sample irrespective of diagnosis, C allele-carriers for both the NET−182 T/C [rs2242446] and 5-HT1A−1019C/G [rs6295] polymorphisms had smaller hippocampal volumes relative to other genotypes. For the 5-HTTLPR (rs25531) polymorphism, there was a significant diagnosis by genotype interaction effect on hippocampal volume. Among patients only, homozygosity for the 5-HTTLPR short (S) allele was associated with smaller hippocampal volume. There was no association between the 5-HT2A, COMT, and BDNF SNPs and hippocampal volume.
The results indicate that the volume of the hippocampus may be influenced by serotonin- and norepinephrine-related gene polymorphisms. The NET and 5-HT1A polymorphisms appear to have similar effects on hippocampal volume in patients and controls while the 5-HTTLPR polymorphism differentially affects hippocampal volume in the presence of depression.
The extent to which observed differences in emotion processing and
regulation neural circuitry in adolescents with a history of suicide attempt
are paralleled by structural differences is unknown. We measured brain
cortical thickness and grey- and white-matter volumes in 100 adolescents: 28
with a history of suicide attempt and major depressive disorder (MDD); 31
with a history of MDD but no suicide attempt; and a healthy control group
(n = 41). The first group compared with controls showed
reduction in grey-matter volume in the right superior temporal gyrus (BA38),
a region important for social emotion processing.