Elements Linked to the Development of Gastrointestinal Symptoms inside Patients Hospitalized using Covid19

This pilot study aimed to test the feasibility of providing varenicline in combination with nicotine replacement therapy (NRT) and motivational interviewing (MI) to adult male smokers attending a clinic in a hostel for homeless people.
A single group pre- and post-treatment (12weeks following intervention commencement) design with embedded process evaluation (at weekly counselling and fortnightly safety check-ins). Participants were 20 male smokers attending a health clinic within a homelessness service in Sydney, Australia, between December 2019 and March 2020. Participants set a target quit date 7-days post intervention commencement. Adverse events, self-reported abstinence, cigarettes per day, treatment adherence and acceptability of the study interventions were assessed 12weeks post intervention commencement. Abstinence was biochemically verified. Results are complete cases.
Retention was 65% at 12-weeks post-intervention commencement (n=13). No related adverse events were reported. Three participanettes smoked-per-day.Cyberchondria is a clinical entity of excessive and repetitive online health-related searches, associated with health anxiety, obsessive-compulsive symptoms and intolerance of uncertainty. Its relationships with depressive and somatic symptoms have not yet received much attention. The purpose of this study was to examine the individual and comparative effects of several psychopathology constructs on the severity of cyberchondria. Through an online platform, participants (N = 749) completed specific self-report measures assessing the severity of cyberchondria, anxiety, intolerance of uncertainty, depressive, somatic, and obsessive-compulsive symptoms. Standard and hierarchical multiple regression analyses were used to assess how well the independent variables influenced the levels of cyberchondria, before and after controlling for age, education, and sex. When measures of all constructs were included in the analysis, all were significant predictors of cyberchondria levels, except for anxiety. Health anxiety made the strongest contribution. When age, education and sex were controlled for, all measures except for anxiety were also significant predictors of cyberchondria severity. Our study confirms that health anxiety, obsessive-compulsive symptoms and intolerance of uncertainty are all associated with cyberchondria severity, with health anxiety making the strongest unique contribution. Depression and somatic symptoms also predicted cyberchondria severity. These findings have important implications for research and clinical practice.
Youth mental health disorders are strong predictors of adult mental health disorders. Early identification of mental health disorders in youth is important as it could aid early intervention and prevention. In a disorder agnostic manner, we aimed to identify influential psychopathology symptoms that could impact mental health in youth.
This study sampled 6063 participants from the Philadelphia Neurodevelopmental Cohort and comprised of youth of ages 12-21 years. A mixed graphical model was used to estimate the network structure of 115 symptoms corresponding to 16 psychopathology domains. Dizocilpine Importance of individual symptoms in the network were assessed using node influence measures such as strength centrality and predictability.
The generated network had stronger associations between symptoms within a psychopathological domain; overall had no negative associations. A conduct disorder symptom eliciting threatening others and a depression symptom - persistent sadness or depressed mood - had the greatest strength centralities (β = 2.85). Fear of traveling in a car and compulsively going in and out a door had the largest predictability (classification accuracy=0.99). Conduct disorder, depression, and obsessive compulsive disorder symptoms generally had the largest strength centralities. Suicidal thoughts had the largest bridge strength centrality (β = 2.85). Subgroup networks revealed that network structure differed by socioeconomic status (low versus high, p=0.04) and network connectivity patterns differed by sex (p=0.01), but not for age or race.
Psychopathology symptom networks offer insights that could be leveraged for early identification, intervention, and possibly prevention of mental health disorders.
Psychopathology symptom networks offer insights that could be leveraged for early identification, intervention, and possibly prevention of mental health disorders.
Bipolar disorder (BD) is a chronic mood disorder characterized by recurrent episodes of mania or hypomania and depression, expressed by changes in energy levels and behavior. However, most of relapse studies use evidence-based approaches with statistical methods. With the advance of the precision medicine this study aims to use machine learning (ML) approaches as a possible predictor in depressive relapses in BD.
Four accepted and well used ML algorithms (Support Vector Machines, Random Forests, Naïve Bayes, and Multilayer Perceptron) were applied to the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) dataset in a cohort of 800 patients (507 patients presented depressive relapse and 293 did not), who became euthymic during the study and were followed for one year.
The ML algorithms presented reasonable performance in the prediction task, ranging from 61 to 80% in the F-measure. The Random Forest algorithm obtained a higher average of performance (Relapse Group 68%; No Relapse Group 74%). The three most important mood symptoms observed in the relapse visit (Random Forest) were interest; depression mood and energy.
Social and psychological parameters such as marital status, social support system, personality traits, might be an important predictor in depressive relapses, although we did not compute this data in our study.
Our findings indicate that applying precision medicine models by means of machine learning in BD studies could be feasible as a sensible approach to better support medical decision-making in the BD treatment and prevention of future relapses.
Our findings indicate that applying precision medicine models by means of machine learning in BD studies could be feasible as a sensible approach to better support medical decision-making in the BD treatment and prevention of future relapses.
The death of a child is a highly traumatic event and often leads to mental health problems, including posttraumatic stress disorder (PTSD). Previous studies have focused on overall PTSD after the loss of an only child; however, little attention has been given to PTSD at the symptom level. This study aims to identify the network structure of PTSD symptoms in bereaved parents who have lost their only child, known as Shidu parents in Chinese society.
A cross-sectional study enrolled 385 bereaved individuals who had lost an only child across 10 cities in China from November 2016 to July 2017. PTSD symptoms were measured by the PTSD Checklist for DSM-5 (PCL-5). Network analysis was implemented by using the R packages qgraph and bootnet.
The PTSD network revealed that diminished interest, exaggerated startle, irritability/anger, and nightmares were the most central symptoms. The strongest connections emerged between the symptoms of recurrent thoughts and nightmares, irritability/anger and reckless/self-destructive behavior, and hypervigilance and exaggerated startle.
We utilized cross-sectional data, and it is therefore not possible to infer the evolution of the symptom network over time. In addition, participants were limited to parents who had lost an only child, and the findings of this study must be interpreted with caution.
The current study provides further clarity regarding how PTSD symptoms relate to each other in bereaved parents who have lost an only child. Symptoms with high centrality and connectedness may be viable targets for intervention in bereaved parents who have lost an only child.
The current study provides further clarity regarding how PTSD symptoms relate to each other in bereaved parents who have lost an only child. Symptoms with high centrality and connectedness may be viable targets for intervention in bereaved parents who have lost an only child.
Evidence concerning the impact of COVID-19-related stress exposure on prenatal attachment in pregnant women is unknown. In this study we sought to assess the effect of psychological distress and risk perception of COVID-19 on prenatal attachment in a Italian sample of pregnant women.
1179 pregnant women completed an anonymous online survey and self-report questionnaires measuring socio-demographic and obstetric characteristics, psychological distress (STAI Form Y-1-2 and BDI-II), prenatal attachment (PAI) and risk perception of COVID-19. Data were collected from March 2020 to April 2020 referring to the national lockdown period.
After adjusting for the socio-demographic and obstetric factors in the multivariable analysis, we found out the state anxiety was shown to be a significant predictor (p<0001) of prenatal attachment. Moreover, the COVID-19-risk perception positively moderate the relationship between trait anxiety and prenatal attachment (p=0008), indicating that when COVID-19-risk perception ihe mother's mental health during pandemics, to safeguard maternal and infant mental health.
Neuroimaging studies have revealed abnormal cortical folding pattern and disruptive functional connectivity in major depressive disorder (MDD). Combining structure and function in the same population may further our understanding of the neuropathological mechanisms of MDD.
Sixty-two patients with MDD and 61 healthy controls (HCs) underwent structural and resting-state functional magnetic resonance imaging (MRI). Group differences in the cortical folding (measured by local gyrification index (LGI)) were analyzed in FreeSurfer. Taking the brain regions with significant group differences in LGI as seed regions, the resting-state functional connectivity analysis was further conducted to explore the corresponding functional connectivity alterations.
Comparing with HCs, patients with MDD showed significantly decreased LGI in the right fusiform gyrus (cohen's d=0.70). In the seed-based functional connectivity analysis, we found that compared with HCs, patients with MDD showed decreased functional connections between the right fusiform gyrus with sensorimotor areas (precentral and postcentral gyrus) (cohen's d=1.32) and right superior temporal gyrus (cohen's d=0.94).
Main limitations are the relatively small sample size and the cross-sectional study design.
Decreased LGI in the right fusiform gyrus, as well as decreased functional connectivity between the right fusiform gyrus and the sensorimotor area and right superior temporal gyrus, appears to play a role in the pathophysiology of MDD.
Decreased LGI in the right fusiform gyrus, as well as decreased functional connectivity between the right fusiform gyrus and the sensorimotor area and right superior temporal gyrus, appears to play a role in the pathophysiology of MDD.
Bipolar disorder (BD) is often accompanied by trait-related cognitive impairments, but it is unclear which neurocircuitry abnormalities give rise to these impairments and whether neurocircuitry differences are exacerbated with illness progression. This longitudinal fMRI study of recently diagnosed BD patients investigates whether aberrant working memory (WM) related activity in the cognitive control network is accentuated by new affective episodes.
Forty-seven recently diagnosed BD patients in full or partial remission and 38 healthy controls were assessed with neurocognitive tests and fMRI during the performance of a verbal n-back WM task at baseline and follow-up (15.4 months in average).
Patients showed WM-related hypo-activity in dorsal prefrontal cortex (dPFC) and impaired cognitive function within attention and psychomotor speed, WM and executive function, and verbal learning and memory compared to controls at baseline. During the follow-up period, 26 patients experienced at least one affective episode (BD+), while 21 remained in remission (BD-).