Occurrence of acute oesophageal necrosis (black oesophagus) in a single tertiary centre

Acute oesophageal necrosis (AON) is a rare condition characterised by the endoscopic finding of diffuse, circumferential, black mucosal pigmentation of the oesophagus, which typically stops at the gastro-oesophageal junction. This observational study aimed to assess the occurrence, clinical characteristics and outcomes of AON in a consecutive endoscopic cohort in a single tertiary university centre. A retrospective analysis of endoscopic data of upper gastrointestinal endoscopy (UGE) was carried out from 2008 to 2018. Out of 25,970 UGE, 16 patients (0.06%) had AON; 75.0% were men with a median age of 75 years. Almost all patients underwent diagnosis during emergency UGE performed for gastrointestinal bleeding, but one patient was diagnosed during elective UGE for persistent vomiting and diarrhoea. All patients reported one or more pre-existing comorbidities and concomitant acute events. Two patients had AON as the first presentation of Zollinger-Ellison syndrome (ZES). One patient developed an oesophageal stenosis, and another patient presented a relapse of AON. Mortality was 50%, but no patient died as a direct consequence of AON. AON is a rare cause of gastrointestinal bleeding diagnosed mainly during emergency UGE. Our study showed that ZES might manifest with this critical presentation, and endoscopists must be aware of this evidence

Occurrence of gastric cancer and carcinoids in atrophic gastritis during prospective long-term follow up

Objective. Atrophic gastritis (AG) is a risk condition for gastric cancer and type I gastric carcinoids. Recent studies assessing the overall risk of gastric cancer and carcinoids in AG at long-term follow up are lacking. This study aimed to investigate in a prospective cohort of AG patients the occurrence of gastric cancer and carcinoids at long-term follow up. Methods. A total of 200 AG patients from a prospective cohort (67% female, median age 55 years) with a follow up of 7.5 (range: 4-23.4) years were included. Inclusion criteria were presence of AG and at least one follow-up gastroscopy with biopsies at ≥4 years after AG diagnosis. Follow-up gastroscopies at 4-year intervals were performed. Results. Overall, 22 gastric neoplastic lesions were detected (crude incidence 11%). Gastric cancer was diagnosed in four patients at a median follow up of 7.2 years (crude incidence 2%). Eleven type I gastric carcinoids were detected at a median follow up of 5.1 years (crude incidence of 5.5%). In seven patients, six low-grade and one high-grade dysplasia were found. The annual incidence rate person-year were 0.25% (95% confidence interval [CI]: 0.067-0.63%), 0.43% (95% CI: 0.17-0.89%), and 0.68% (95% CI: 0.34-1.21%) for gastric cancer, dysplasia, and type I-gastric carcinoids, respectively. The incidence rates of gastric cancer and carcinoids were not different (p = 0.07). Conclusion. This study shows an annual incidence rate of 1.36% person-year for gastric neoplastic lesions in AG patients at long-term follow up. AG patients are similarly exposed to gastric cancer and type I gastric carcinoids

pyphysio: A physiological signal processing library for data science approaches in physiology

The lack of open-source tools for physiological signal processing hinders the development of standardized pipelines in physiology. Researchers usually must rely on commercial software that, by implementing black-box algorithms, undermines the control on the analysis and prevents the comparison of the results, ultimately affecting the scientific reproducibility. We introduce pyphysio as a step towards a data science approach oriented to compute physiological indicators, in particular of the Autonomic Nervous System activity. pyphysio serves as a basis for machine learning modules and it implements a suite of combinable algorithms for processing of signals from either by wearable or medical-grade quality devices. Keywords: Physiological signal processing, Psychophysiology, Autonomic indicators, Data science, Pytho

Improved time-resolved measurements of inorganic ions in particulate matter by PILS-IC integrated with a sample pre-concentration system

A particle-into-liquid sampler coupled with ion chromatograph (PILS-IC) for the on-line measurement of inorganic ions has been modified by the insertion of two ion-exchange pre-concentration cartridges that enrich the sample during the period of the IC analysis. The limits of detection of the modified instrument were 10-15 times lower and the time coverage 24 times higher (from 2 to 48 min per hour) than those of the original PILS-IC setup. The instrumental performance in terms of recovery and break-through volume from the cartridges was satisfactory. The modified PILS-IC was operated in comparison with a diffusion denuder line and with a high-resolution time-of-flight aerosol mass spectrometer (HR-TOF-AMS) during a short intensive measurement period organized in the framework of the European Monitoring and Evaluation Programme (EMEP), a co-operative program for monitoring and evaluation of the long-range transmission of the air pollutants in Europe. The instrument showed a quantitative response in agreement with the results of the diffusion lines, and an ability to trace fine concentration variations not so different from the performance of the much more complex HR-TOF-AMS. From the time patterns of the ion concentrations measured by the modified PILS-IC, it was possible to obtain useful information about the variations in the air quality and in the strength of the particulate matter sourc

I'm alone but not lonely. U-shaped pattern of self-perceived loneliness during the COVID-19 pandemic in the UK and Greece

Objectives: In the past months, many countries have adopted varying degrees of lockdown restrictions to control the spread of the COVID-19 virus. According to the existing literature, some consequences of lockdown restrictions on people's lives are beginning to emerge yet the evolution of such consequences in relation to the time spent in lockdown is understudied. To inform policies involving lockdown restrictions, this study adopted a data-driven Machine Learning approach to uncover the short-term time-related effects of lockdown on people's physical and mental health. // Study design: An online questionnaire was launched on 17 April 2020, distributed through convenience sampling and was self-completed by 2,276 people from 66 different countries. // Methods: Focusing on the UK sample (N = 325), 12 aggregated variables representing the participant's living environment, physical and mental health were used to train a RandomForest model to estimate the week of survey completion. // Results: Using an index of importance, Self-Perceived Loneliness was identified as the most influential variable for estimating the time spent in lockdown. A significant U-shaped curve emerged for loneliness levels, with lower scores reported by participants who took part in the study during the 6th lockdown week (p = 0.009). The same pattern was replicated in the Greek sample (N = 137) for week 4 (p = 0.012) and 6 (p = 0.009) of lockdown. // Conclusions: From the trained Machine Learning model and the subsequent statistical analysis, Self-Perceived Loneliness varied across time in lockdown in the UK and Greek populations, with lower symptoms reported during the 4th and 6th lockdown weeks. This supports the dissociation between social support and loneliness, and suggests that social support strategies could be effective even in times of social isolation

I'm alone but not lonely. U-shaped pattern of self-perceived loneliness during the COVID-19 pandemic in the UK and Greece.

OBJECTIVES: In the past months, many countries have adopted varying degrees of lockdown restrictions to control the spread of the COVID-19 virus. According to the existing literature, some consequences of lockdown restrictions on people's lives are beginning to emerge yet the evolution of such consequences in relation to the time spent in lockdown is understudied. To inform policies involving lockdown restrictions, this study adopted a data-driven Machine Learning approach to uncover the short-term time-related effects of lockdown on people's physical and mental health. STUDY DESIGN: An online questionnaire was launched on 17 April 2020, distributed through convenience sampling and was self-completed by 2,276 people from 66 different countries. METHODS: Focusing on the UK sample (N = 325), 12 aggregated variables representing the participant's living environment, physical and mental health were used to train a RandomForest model to estimate the week of survey completion. RESULTS: Using an index of importance, Self-Perceived Loneliness was identified as the most influential variable for estimating the time spent in lockdown. A significant U-shaped curve emerged for loneliness levels, with lower scores reported by participants who took part in the study during the 6th lockdown week (p = 0.009). The same pattern was replicated in the Greek sample (N = 137) for week 4 (p = 0.012) and 6 (p = 0.009) of lockdown. CONCLUSIONS: From the trained Machine Learning model and the subsequent statistical analysis, Self-Perceived Loneliness varied across time in lockdown in the UK and Greek populations, with lower symptoms reported during the 4th and 6th lockdown weeks. This supports the dissociation between social support and loneliness, and suggests that social support strategies could be effective even in times of social isolation

Self-perceived loneliness and depression during the Covid-19 pandemic: a two-wave replication study

The global Covid-19 pandemic has forced countries to impose strict lockdown restrictions and mandatory stay-at-home orders with varying impacts on individual's health. Combining a data-driven machine learning paradigm and a statistical approach, our previous paper documented a U-shaped pattern in levels of self-perceived loneliness in both the UK and Greek populations during the first lockdown (17 April to 17 July 2020). The current paper aimed to test the robustness of these results by focusing on data from the first and second lockdown waves in the UK. We tested a) the impact of the chosen model on the identification of the most time-sensitive variable in the period spent in lockdown. Two new machine learning models - namely, support vector regressor (SVR) and multiple linear regressor (MLR) were adopted to identify the most time-sensitive variable in the UK dataset from Wave 1 (n = 435). In the second part of the study, we tested b) whether the pattern of self-perceived loneliness found in the first UK national lockdown was generalisable to the second wave of the UK lockdown (17 October 2020 to 31 January 2021). To do so, data from Wave 2 of the UK lockdown (n = 263) was used to conduct a graphical inspection of the week-by-week distribution of self-perceived loneliness scores. In both SVR and MLR models, depressive symptoms resulted to be the most time-sensitive variable during the lockdown period. Statistical analysis of depressive symptoms by week of lockdown resulted in a U-shaped pattern between weeks 3 and 7 of Wave 1 of the UK national lockdown. Furthermore, although the sample size by week in Wave 2 was too small to have a meaningful statistical insight, a graphical U-shaped distribution between weeks 3 and 9 of lockdown was observed. Consistent with past studies, these preliminary results suggest that self-perceived loneliness and depressive symptoms may be two of the most relevant symptoms to address when imposing lockdown restrictions

The Interaction between Serotonin Transporter Allelic Variation and Maternal Care Modulates Instagram Sociability in a Sample of Singaporean Users

Human social interactions ensure recognition and approval from others, both in offline and online environments. This study applies a model from behavioral genetics on Instagram sociability to explore the impact of individual development on behavior on social networks. We hypothesize that sociable attitudes on Instagram resulted from an interaction between serotonin transporter gene alleles and the individual’s social relationship with caregivers. We assess the environmental and genetic components of 57 Instagram users. The self-report questionnaire Parental Bonding Instrument is adopted to determine the quality of parental bonding. The number of posts, followed users (“followings”), and followers are collected from Instagram as measures of online social activity. Additionally, the ratio between the number of followers and followings (“Social Desirability Index”) was calculated to estimate the asymmetry of each user’s social network. Finally, buccal mucosa cell samples were acquired, and the polymorphism rs25531 (T/T homozygotes vs. C-carriers) within the serotonin transporter gene was examined. In the preliminary analysis, we identified a gender effect on the number of followings. In addition, we specifically found a gene–environment interaction on the standardized Instagram “Social Desirability Index” in line with our predictions. Users with the genotype more sensitive to environmental influences (T/T homozygotes) showed a higher Instagram “Social Desirability Index” than nonsensitive ones (C-carriers) when they experienced positive maternal care. This result may contribute to understanding online social behavior from a gene*environment perspective

Alterations in Cortisol Profiles among Mothers of Children with ASD Related to Poor Child Sleep Quality

Caregivers of children with autism spectrum disorder (ASD) experience poorer sleep, but studies have not yet used objective measures to investigate how child and caregiver sleep affect each other. In this study, 29 mothers and their child with ASD aged between 6 and 16 years were recruited. Questionnaires measuring child autism, maternal depression, and maternal and child sleep quality were administered. Cortisol salivary samples were also obtained from the mothers over the course of a day. Results revealed that maternal depression is significantly correlated with their subjective sleep quality, sleep latency and daytime dysfunction. Child sleep quality was also found to be significantly correlated with ASD severity. In terms of maternal cortisol profiles, a significant number of mothers showed a flattened diurnal cortisol expression, and children of mothers with a flattened cortisol profile had significantly more sleep problems. Overall, results suggest that maternal and child sleep are affected by the child’s disability but also are mutually related. Future studies may consider employing measures such as actigraphy or somnography to quantify sleep quality and establish causal pathways between sleep, cortisol expression and caregiver and child outcomes. The present study has clinical implications in examining family sleep when considering treatment for ASD