Paediatric non-alcoholic fatty liver disease: impact on patients and mothers' quality of life

Background: Non-alcoholic fatty liver disease (NAFLD) is one of the causes of fatty liver in adults and is currently the primary form of chronic liver disease in children and adolescents. However, the psychological outcome (i.e. the behavioural problems that can in turn be related to psychiatric conditions, like anxiety and mood disorders, or lower quality of life) in children and adolescents suffering of NAFLD has not been extensively explored in the literature. Objectives: The present study aims at evaluating the emotional and behavioural profile in children suffering from NAFLD and the quality of life in their mothers. Patients and Methods: A total of 57 children (18 females/39 males) with NAFLD were compared to 39 age-matched control children (25 females/14 males). All participants were submitted to the following psychological tools to assess behavior, mood, and anxiety: the Multidimensional Anxiety Scale for Children (MASC), the Child Behavior Checklist (CBCL), and the Children's Depression Inventory (CDI). Moreover, the mothers of 40 NAFLD and 39 control children completed the World Health Organization Quality of Life-BREF (WHOQOL-BREF) questionnaire. Results: NAFLD children scored significantly higher as compared to control children in MASC (P = 0.001) and CDI total (P < 0.001) scales. The CBCL also revealed significantly higher scores for NAFLD children in total problems (P = 0.046), internalizing symptoms (P = 0.000) and somatic complaints (P < 0.001). The WHOQOL-BREF revealed significantly lower scores for the mothers of NAFLD children in the overall perception of the quality of life (P < 0.001), and in the "relationships" domain (P = 0.023). Conclusions: Increased emotional and behavioural problems were detected in children with NAFLD as compared to healthy control children, together with an overall decrease in their mothers' quality of life. These results support the idea that these patients may benefit from a psychological intervention, ideally involving both children and parents, whose quality of life is likely negatively affected by this disease

Sudden neck swelling with rash as late manifestation of COVID-19: a case report

Background: Although there are reports of otolaryngological symptoms and manifestations of CoronaVirus Disease 19 (COVID-19), there have been no documented cases of sudden neck swelling with rash in patients with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection described in literature. Case presentation: We report a case of a sudden neck swelling and rash likely due to late SARS-CoV-2 in a 64-year-old woman. The patient reported COVID-19 symptoms over the previous three weeks. Computed Tomography (CT) revealed a diffuse soft-tissue swelling and edema of subcutaneous tissue, hypodermis, and muscular and deep fascial planes. All the differential diagnoses were ruled out. Both the anamnestic history of the patient’s husband who had died of COVID-19 with and the collateral findings of pneumonia and esophageal wall edema suggested the association with COVID-19. This was confirmed by nasopharyngeal swab polymerase chain reaction. The patient was treated with lopinavir/ritonavir, hydroxychloroquine and piperacillin/tazobactam for 7 days. The neck swelling resolved in less than 24 h, while the erythema was still present up to two days later. The patient was discharged after seven days in good clinical condition and with a negative swab. Conclusion: Sudden neck swelling with rash may be a coincidental presentation, but, in the pandemic context, it is most likely a direct or indirect complication of COVID-19

Prevalence of pain in the departments of surgery and oncohematology of a paediatric hospital that has joined the project "Towards pain free hospital"

Background. Among hospitalized adults and children pain is undertreated. This study wants to assess the effectiveness of pain therapy in two departments of a large children's hospital. Materials and Methods. During a single day work three committees, administering a questionnaire to patients or parents, have evaluated the adherence to international recommendations (JCI and WHO) in the management of analgesic therapy. Patient demographics, prevalence and intensity (moderate and/or severe) of pain (during hospitalization, 24 hours before and at the time of the interview), analgesia (type, route, duration and frequency of administration) and Pain Management Index (=analgesic score-pain score) were recorded. Results. 75 patients participated in the study (age: 2 months up to 24 years, mean 7.8 ± 6). During hospitalization 43 children (57%) had no pain while 32 (43%) have experienced pain. 22 children (29 %) had pain 24 hours before and 12 (16%) at the time of the interview. The average value of the PMI was -0.8±1.3 with a minimum of -3 and a maximum of +2: 60% (19) of the children had a PMI less than 0 (undertreated pain) while 40% (13) had a value=or > 0. Out of 32 patients who needed an analgesic therapy 14 (44%) received an around-the-clock dosing, 8 (25%) an intermittent therapy and 10 (31%) no treatment.17 (77 %) were the single drug therapy and 5 (23%) the multimodal ones. Conclusion. The prevalence of pain in the two departments is high. The main cause is that knowledge is not still well translated into clinical practice

Extraction of video features for real-time detection of neonatal seizures

This paper presents a novel approach to the extraction of video features for real-time detection of neonatal seizures. In particular, after identification of a proper Region Of Interest (ROI) within the video frame, the broadening factor and the maximum distance between consecutive pairs of zeros of a properly extracted average differential luminosity signal are shown to be relevant features for a diagnosis. The ROI is selected by defining an area around the point where the maximum amplitude of the optical flow vector of that video frame sequence is observed. The located point is then tracked by an algorithm based on template matching and optical flow. The proposed approach allows to differentiate pathological movements (e.g., clonic and myoclonic seizures) from random ones. © 2011 IEEE

Video processing-based detection of neonatal seizures by trajectory features clustering

In this paper, we present a novel approach to early diagnosis, through a video processing-based approach, of the presence of neonatal seizures. In particular, image processing and gesture recognition techniques are first used to characterize typical gestures of neonatal seizures. More precisely, gesture trajectories are characterized by extracting some relevant features. In particular, selecting the point with the maximum amplitude of the optical flow vector of the video frame sequence, during a newborn movement, is selected and then tracked through an algorithm based on template matching and optical flow. The observed features are then clustered using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The proposed approach allows to efficiently differentiate pathological repetitive movements (e.g., clonic and subtle seizures) from random ones

Extraction of video features for real-time detection of neonatal seizures

This paper presents a novel approach to the extraction of video features for real-time detection of neonatal seizures. In particular, after identification of a proper Region Of Interest (ROI) within the video frame, the broadening factor and the maximum distance between consecutive pairs of zeros of a properly extracted average differential luminosity signal are shown to be relevant features for a diagnosis. The ROI is selected by defining an area around the point where the maximum amplitude of the optical flow vector of that video frame sequence is observed. The located point is then tracked by an algorithm based on template matching and optical flow. The proposed approach allows to differentiate pathological movements (e.g., clonic and myoclonic seizures) from random ones

Video processing-based detection of neonatal seizures by trajectory features clustering

In this paper, we present a novel approach to early diagnosis, through a video processing-based approach, of the presence of neonatal seizures. In particular, image processing and gesture recognition techniques are first used to characterize typical gestures of neonatal seizures. More precisely, gesture trajectories are characterized by extracting some relevant features. In particular, selecting the point with the maximum amplitude of the optical flow vector of the video frame sequence, during a newborn movement, is selected and then tracked through an algorithm based on template matching and optical flow. The observed features are then clustered using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The proposed approach allows to efficiently differentiate pathological repetitive movements (e.g., clonic and subtle seizures) from random ones. © 2012 IEEE

Maximum-likelihood detection of neonatal clonic seizures by video image processing

In this paper we consider the use of a well-known statistical method, namely Maximum-Likelihood Detection (MLD), to early diagnose, through a wire-free low-cost video processing-based approach, the presence of neonatal clonic seizures. Since clonic seizures are characterized by periodic movements of parts of the body (e.g., hands, legs), by evaluating the periodicity of the extracted signals it is possible to detect the presence of a clonic seizure. The proposed approach allows to differentiate clonic seizure-related movements from random ones. While we first consider a single-camera scenario, we then extend our approach to encompass the use of multiple sensors, such as several cameras or the Microsoft Kinect RBG-Depth sensor. In these cases, data fusion principles are considered to aggregate signals from multiple sensors

Maximum-likelihood detection of neonatal clonic seizures by video image processing

In this paper we consider the use of a well-known statistical method, namely Maximum-Likelihood Detection (MLD), to early diagnose, through a wire-free low-cost video processing-based approach, the presence of neonatal clonic seizures. Since clonic seizures are characterized by periodic movements of parts of the body (e.g., hands, legs), by evaluating the periodicity of the extracted signals it is possible to detect the presence of a clonic seizure. The proposed approach allows to differentiate clonic seizure-related movements from random ones. While we first consider a single-camera scenario, we then extend our approach to encompass the use of multiple sensors, such as several cameras or the Microsoft Kinect RBG-Depth sensor. In these cases, data fusion principles are considered to aggregate signals from multiple sensors. © 2014 IEEE