The effect of cyberchondria on anxiety, depression and quality of life during COVID-19: the mediational role of obsessive-compulsive symptoms and Internet addiction

Since the global pandemic of the coronavirus disease 2019 (COVID-19), online health information-seeking behaviors have notably increased. Cyberchondria can be a vulnerability factor for the worsening of anxiety-depressive symptoms and quality of life. The current study aims to understand the predictive effect of cyberchondria on health anxiety, anxiety, depression and quality of life considering the mediating effect of obsessive-compulsive symptoms and Internet addiction and the moderating effect of COVID anxiety. 572 Italian participants (66% female; Mean age = 34; SD = 15) took part in a cross-sectional online survey involving CSS-12, MOCQ-R, IAT, SHAI, HADS, WHOQoL-BREF and CAS. Mediation and moderation analyses were conducted. Obsessive-compulsive symptoms and Internet addiction were found to partially mediate the cyberchondria-health anxiety and the cyberchondria-anxiety links and to totally mediate the cyberchondria-depression and the cyberchondria-quality of life links. COVID anxiety was found to moderate the relationship between cyberchondria and anxiety. The findings suggest that compulsivity may have a key role in the explanation of the underlying mechanisms of cyberchondria. Healthcare practitioners should provide additional support for individuals with cyberchondria. As such, cyberchondria is a contributing factor to the exacerbation of anxiety-depressive disorders and may impact on the quality of life

The Impact of COVID-19 Pandemic on searching for health-related information and cyberchondria on the general population in Italy

Objectives: The Internet has become one of the most common sources people use to search for health-related information, a behavior rapidly increased during the novel Coronavirus disease 2019 (COVID-19). The present study aimed to investigate behavioral patterns in the online health-related searches and Cyberchondria (CYB) during the COVID-19 pandemic time, in order to explore socio-demographic and psychopathological factors related to CYB. Methods: During the third wave of the COVID-19 pandemic in Italy, a cross-sectional online survey collected the main socio-demographic variables and habits related to Internet use of 572 participants. CYB was measured by the Cyberchondria Severity Scale-Short Version and different psychopathological factors were measured by specific questionnaires: the Coronavirus Anxiety Scale, the Hospital Anxiety and Depression Scale, the Short Health Anxiety Inventory, the Meta-Cognitions about Health Questionnaire, the Internet Addiction Test, the Maudsley Obsessional-Compulsive Questionnaire-Short Version, the Rosenberg's Self-Esteem Scale, and the WHO Quality of Life-BREF. Descriptives, non-parametric ANOVAs, and Spearman correlations were performed. Results: In the present sample, the Internet was the main source participants used to search for health-related information and nearly one-third increased this habit during the pandemic. Higher expression of CYB emerged in females, in younger participants, in students, and in those suffering from a physical/psychiatric illness. CYB showed a positive correlation with different phenomenology of anxiety (i.e., anxiety about COVID-19, health anxiety, general anxiety, metacognitive believes about anxiety) and with depression, obsessive-compulsive symptoms, and problematic usage of the Internet. Conversely, quality of life and self-esteem showed a negative correlation with CYB. Conclusion: During the COVID-19 pandemic, the use of the Internet for health-related information and CYB contribute to the psychological stress affecting individuals and society. Delineating subjects more vulnerable to CYB and associated psychopathological factors will help to elaborate operational indications for prevention and psychological support

Intrahepatic cholestasis of pregnancy after ovarian hyperstimulation syndrome with wild-type ABCB4 gene: a peculiar case and literature review

Background: Intrahepatic cholestasis of pregnancy (ICP) in the first trimester occurring after ovarian hyperstimulation syndrome (OHSS) is a rare condition and few cases are reported in the literature. Hyperestrogenism may explain this problem in genetically predisposed women. The objective of this article is to report one of these rare cases and offer an overview of the other published cases. Case presentation: We report a case of severe OHSS followed by ICP in the first trimester. The patient was admitted to the intensive care unit and was treated according to the guidelines for the management of OHSS. Moreover, the patient also received ursodeoxycholic acid for ICP, which brought to an improvement of her clinical conditions. The pregnancy continued without other complications until the 36th week of gestation, when the patient developed ICP in the third trimester and underwent cesarean section for increased bile acid levels and cardiotocographic (CTG) pathologic alterations. The newborn was a healthy baby weighing 2500 gr. We also reviewed other case reports published by other authors about this clinical condition. We present what is, to our knowledge, the first case of ICP developed in the first trimester of pregnancy after OHSS in which genetic polymorphisms of ABCB4 (MDR3) have been investigated. Conclusions: ICP in the first trimester might be induced by elevated serum estrogen levels after OHSS in genetically predisposed women. In these women, it might be useful to check for genetic polymorphisms to know if they have a predisposition for ICP recurrence in the third trimester of pregnancy

Interleukin-17A (IL-17A): A silent amplifier of COVID-19

One of the hallmarks of COVID-19 is the cytokine storm that provokes primarily pneumonia followed by systemic inflammation. Emerging evidence has identified a potential link between elevated interleukin-17A (IL-17A) levels and disease severity and progression. Considering that per se, IL-17A can activate several inflammatory pathways, it is plausible to hypothesize an involvement of this cytokine in COVID-19 clinical outcomes. Thus, IL-17A could represent a marker of disease progression and/or a target to develop therapeutic strategies. This hypothesis paper aims to propose this "unique" cytokine as a silent amplifier of the COVID-19 immune response and (potentially) related therapy

Using Two-Step Cluster Analysis and Latent Class Cluster Analysis to Classify the Cognitive Heterogeneity of Cross-Diagnostic Psychiatric Inpatients

The heterogeneity of cognitive profiles among psychiatric patients has been reported to carry significant clinical information. However, how to best characterize such cognitive heterogeneity is still a matter of debate. Despite being well suited for clinical data, cluster analysis techniques, like the Two-Step and the Latent Class, received little to no attention in the literature. The present study aimed to test the validity of the cluster solutions obtained with Two-Step and Latent Class cluster analysis on the cognitive profile of a cross-diagnostic sample of 387 psychiatric inpatients. Two-Step and Latent Class cluster analysis produced similar and reliable solutions. The overall results reported that it is possible to group all psychiatric inpatients into Low and High Cognitive Profiles, with a higher degree of cognitive heterogeneity in schizophrenia and bipolar disorder patients than in depressive disorders and personality disorder patients

Role of evaluating tumor‑infiltrating lymphocytes, programmed death‑1 ligand 1 and mismatch repair proteins expression in malignant mesothelioma

The tumor immune microenvironment (TME) and immune checkpoints have been reported to serve a role in the pathogenesis of malignant mesothelioma (MM) and treatment outcome. Additionally, mismatch Repair (MMR) deficiency appears to enhance the response to checkpoints blockade in several tumors. The aim of the present study was to analyze programmed death‑1 ligand 1 (PD‑L1) expression in MM and to characterize the TME. This could help to understand the immune response, and evaluate its prognostic and predictive values. We also investigated MMR protein expression. We retrospectively analyzed 55 mesotheliomas to determine PD‑L1, CD4+, CD8+, mutL homolog 1 (MLH1), mutS homolog 2 (MSH2), mutS homolog 6 (MSH6) and PMS1 homolog 2, mismatch repair system component (PMS2) expression. We used an immunoscore (1+, 2+ and 3+) to evaluate tumor‑infiltrating lymphocytes (TILs). TILs were observed in all but two samples (53/55); the majority had an immunoscore 1+ (30/53), while 2+/3+ was reported for 23/53 samples. A predominance of CD8+ was highlighted in 8 cases (15%). PD‑L1 expression of ≥1% on tumor cells was displayed in 40 cases; in 9 of these, ≥50% expression was reported. Of note, alterations in MMR staining was not observed. In addition, survival analysis revealed that epithelioid subtype was associated with better prognosis. We observed a trend towards poorer prognosis for ≥50% PD‑L1 expression on tumor cells, lower immunoscore (1+) and CD8+ TIL predominance. The present study highlighted the importance of exploring the TME and the standardization of PD‑L1 assessment guidelines to apply in the field of immunotherapy

A first look into radiomics application in testicular imaging: A systematic review

The aim of this systematic review was to evaluate the state of the art of radiomics in testicular imaging by assessing the quality of radiomic workflow using the Radiomics Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). A systematic literature search was performed to find potentially relevant articles on the applications of radiomics in testicular imaging, and 6 final articles were extracted. The mean RQS was 11,33 ± 3,88 resulting in a percentage of 31,48% ± 10,78%. Regarding QUADAS-2 criteria, no relevant biases were found in the included papers in the patient selection, index test, reference standard criteria and flow-and-timing domain. In conclusion, despite the publication of promising studies, radiomic research on testicular imaging is in its very beginning and still hindered by methodological limitations, and the potential applications of radiomics for this field are still largely unexplored

APOLLO 11 Project, Consortium in Advanced Lung Cancer Patients Treated With Innovative Therapies: Integration of Real-World Data and Translational Research

Introduction: Despite several therapeutic efforts, lung cancer remains a highly lethal disease. Novel therapeutic approaches encompass immune-checkpoint inhibitors, targeted therapeutics and antibody-drug conjugates, with different results. Several studies have been aimed at identifying biomarkers able to predict benefit from these therapies and create a prediction model of response, despite this there is a lack of information to help clinicians in the choice of therapy for lung cancer patients with advanced disease. This is primarily due to the complexity of lung cancer biology, where a single or few biomarkers are not sufficient to provide enough predictive capability to explain biologic differences; other reasons include the paucity of data collected by single studies performed in heterogeneous unmatched cohorts and the methodology of analysis. In fact, classical statistical methods are unable to analyze and integrate the magnitude of information from multiple biological and clinical sources (eg, genomics, transcriptomics, and radiomics). Methods and objectives: APOLLO11 is an Italian multicentre, observational study involving patients with a diagnosis of advanced lung cancer (NSCLC and SCLC) treated with innovative therapies. Retrospective and prospective collection of multiomic data, such as tissue- (eg, for genomic, transcriptomic analysis) and blood-based biologic material (eg, ctDNA, PBMC), in addition to clinical and radiological data (eg, for radiomic analysis) will be collected. The overall aim of the project is to build a consortium integrating different datasets and a virtual biobank from participating Italian lung cancer centers. To face with the large amount of data provided, AI and ML techniques will be applied will be applied to manage this large dataset in an effort to build an R-Model, integrating retrospective and prospective population-based data. The ultimate goal is to create a tool able to help physicians and patients to make treatment decisions. Conclusion: APOLLO11 aims to propose a breakthrough approach in lung cancer research, replacing the old, monocentric viewpoint towards a multicomprehensive, multiomic, multicenter model. Multicenter cancer datasets incorporating common virtual biobank and new methodologic approaches including artificial intelligence, machine learning up to deep learning is the road to the future in oncology launched by this project