A radiomics-based model for the outcome prediction in COVID-19 positive patients through deep learning with both longitudinal chest x-ray and chest computed tomography images

The coronavirus disease 2019 (COVID-19) pandemic had a major impact on global health and was associated with millions of deaths worldwide. During the pandemic, imaging characteristics of chest X-ray (CXR) and chest computed tomography (CT) played an important role in the screening, diagnosis and monitoring the disease progression. Various studies suggested that quantitative image analysis methods including artificial intelligence and radiomics can greatly boost the value of imaging in the management of COVID-19. However, few studies have explored the use of longitudinal multi-modal medical images with varying visit intervals for outcome prediction in COVID-19 patients. This study aims to explore the potential of longitudinal multimodal radiomics in predicting the outcome of COVID-19 patients by integrating both CXR and CT images with variable visit intervals through deep learning. 2274 patients who underwent CXR and/or CT scans during disease progression were selected for this study. Of these, 946 patients were treated at the University of Pennsylvania Health System (UPHS) and the remaining 1328 patients were acquired at Stony Brook University (SBU) and curated by the Medical Imaging and Data Resource Center (MIDRC). 532 radiomic features were extracted with the Cancer Imaging Phenomics Toolkit (CaPTk) from the lung regions in CXR and CT images at all visits. We employed two commonly used deep learning algorithms to analyze the longitudinal multimodal features, and evaluated the prediction results based on the area under the receiver operating characteristic curve (AUC). Our models achieved testing AUC scores of 0.816 and 0.836, respectively, for the prediction of mortality. © 2023 SPIE.

Research on the classification of CT images of COVID-19 by pre-training model based on self-attention mechanism

Nowadays, with the outbreak of COVID-19, the prevention and treatment of COVID-19 has gradually become the focus of social disease prevention, and most patients are also more concerned about the symptoms. COVID-19 has symptoms similar to the common cold, and it cannot be diagnosed based on the symptoms shown by the patient, so it is necessary to observe medical images of the lungs to finally determine whether they are COVID-19 positive. As the number of patients with symptoms similar to pneumonia increases, more and more medical images of the lungs need to be generated. At the same time, the number of physicians at this stage is far from meeting the needs of patients, resulting in patients unable to detect and understand their own conditions in time. In this regard, we have performed image augmentation, data cleaning, and designed a deep learning classification network based on the data set of COVID-19 lung medical images. accurate classification judgment. The network can achieve 95.76% classification accuracy for this task through a new fine-tuning method and hyperparameter tuning we designed, which has higher accuracy and less training time than the classic convolutional neural network model. © 2023 SPIE.

Non-Hodgkin's lymphoma complicated with human coronavirus HKU1 pneumonia: A case report and literature review.

Objective: To analyze the clinical manifestations, diagnostic methods and treatment process of the patients with non-Hodgkin's lymphoma complicated with human coronavirus(HCoV)-HKU1 pneumonia and improve the clinical medical staff's awareness of the disease, and to reduce the occurrence of clinical adverse events. Method(s): The clinical data of a patient with non-Hodgkin's lymphoma complicated with HCoV-HKU1 pneumonia with hot flashes and night sweats, dry cough and dry throat as the main clinical features who were hospitalized in the hospital in January 2021 were analyzed, and the relevant literatures were reviewed and the clinical manifestations and diagnosis of HCoV-HKU1 were analyzed. Result(s): The female patient was admitted to the hospital due to diagnosed non-Hodgkin's lymphoma for more than 2 months. The physical examination results showed Karnofsky score was 90 points;there was no palpable enlargement of systemic superfical lymph nodes;mild tenderness in the right lower abdomen, no rebound tenderness, and slightly thicker breath sounds in both lungs were found, and a few moist rales were heard in both lower lungs. The chest CT results showed diffuse exudative foci in both lungs, and the number of white blood cells in the urine analysis was 158 muL-1;next generation sequencing technique(NGS) was used the detect the bronchoalveolar lavage fluid, and HCoV-HKU1 pneumonia was diagnosed. At admission, the patient had symptoms such as dull pain in the right lower abdomen, nighttime cough, and night sweats;antiviral treatment with oseltamivir was ineffective. After treatment with Compound Sulfamethoxazole Tablets and Lianhua Qingwen Granules, the respiratory symptoms of the patient disappeared. The re-examination chest CT results showed the exudation was absorbed. Conclusion(s): The clinical symptoms of the patients with non-Hodgkin's lymphoma complicated with HCoV-HKU1 pneumonia are non-specific. When the diffuse shadow changes in the lungs are found in clinic, and the new coronavirus nucleic acid test is negative, attention should still be paid to the possibility of other HCoV infections. The NGS can efficiently screen the infectious pathogens, which is beneficial to guide the diagnosis and treatment of pulmonary infectious diseases more accurately.Copyright © 2023 Jilin University Press. All rights reserved.

Impact of the new corona virus (SARS-CoV-2) pandemic on climateric women's health: websurvey

The COVID-19 pandemic has impacted society: causing the collapse of health systems around the world, and also had a significant impact on the economy, personal care, mental health and the quality of life of the population. Few studies have been done about pandemic and the climacteric population, and the impact on quality of life and health. Our objective was to Investigate changes in the health and health care of climacteric women residing in Brazil during the pandemic period. Cross-sectional study with climacteric women aged between 40 and 70 years, residing in Brazil. The evaluation was carried out using a Google Docs electronic form with questions related to sociodemographic, clinical, gynecological data, treatments, access to health services and consultations, as well as changes in behavior. The Menopause Rating Scale - MRS was applied to assess climacteric symptoms, validated for Portuguese. Result(s): 419 women answered the questionnaire. More than 45% were between 51 and 60 years of age, 56.6% being married and residing in Brazilian capitals. 60% of participants reported weight gain during the pandemic. 50.8% of participants reported a decrease in the weekly practice of physical activity More than 80% reported worsening mental health during this period, and 66.1% had a change in their sleep pattern. More than half reported having difficulty accessing gynecological consultations. Women living in capital cities reported a greater increase in alcohol consumption (p=0.002). Food intake increased for 54.9%;the category of civil servant was associated with a significant increase in consumption in relation to other professions (p=0.038). Women whose family incomes changed during the pandemic had a higher prevalence of weight gain (p=0.033) and also had a higher occurrence of changes in sleep quality (72.6% vs. 61.5%;p=0.018). Women with a high school education had a higher occurrence of alterations in personal and health care outcomes (p<0.001). Conclusion(s): We observed an important reduction in the health care of climacteric women during the pandemic period. Changes in life habits, such as increased food consumption and reduced physical activity, were quite prevalent. There was a deterioration in mental health, with a high prevalence of anxiety symptoms and changes in sleep quality. Despite the attenuation of the pandemic, attention should be given to the health care of this population, as the changes may have repercussions for many years.Copyright © 2023

HarDNet and Dual-Code Attention Mechanism based model for medical images segmentation

During the formation of medical images, they are easily disturbed by factors such as acquisition devices and tissue backgrounds, causing problems such as blurred image backgrounds and difficulty in differentiation. In this paper, we combine the HarDNet module and the multi-coding attention mechanism module to optimize the two stages of encoding and decoding to improve the model segmentation performance. In the encoding stage, the HarDNet module extracts medical image feature information to improve the segmentation network operation speed. In the decoding stage, the multi-coding attention module is used to extract both the position feature information and channel feature information of the image to improve the model segmentation effect. Finally, to improve the segmentation accuracy of small targets, the use of Cross Entropy and Dice combination function is proposed as the loss function of this algorithm. The algorithm has experimented on three different types of medical datasets, Kvasir-SEG, ISIC2018, and COVID-19CT. The values of JS were 0.7189, 0.7702, 0.9895, ACC were 0.8964, 0.9491, 0.9965, SENS were 0.7634, 0.8204, 0.9976, PRE were 0.9214, 0.9504, 0.9931. The experimental results showed that the model proposed in this paper achieved excellent segmentation results in all the above evaluation indexes, which can effectively assist doctors to diagnose related diseases quickly and improve the speed of diagnosis and patients’quality of life. Author

Preoperative evaluation and timing of surgery for adult patients after covid-19 infection: Recommendations of peking union medical college hospital (2023).

With the adjustment of China's epidemic prevention and control guidelines regarding coronavirus disease of 2019(COVID-19), the preoperative evaluation and timing of surgery for patients after COVID-19 infection have become the focus of attention for both healthcare workers and patients. Based on the latest study and related clinical experience, Peking Union Medical College Hospital (PUMCH) has therefore compiled this multidisciplinary, evidence-based recommendation for concise, individualized, and practical preoperative evaluation and timing of surgery for patients after COVID-19 infection. The recommendations emphasize patients' COVID-19 infection history, the severity of symptoms, and medical/physiologic recovery status during preoperative evaluation. The determination of appropriate length of time between recovery from COVID-19 and surgery/procedure should take into account of patients' underlying health conditions, the severity of the COVID-19 infection course, and the types of surgery and anesthesia scheduled, to minimize postoperative complications. The recommendations are intended to aid healthcare workers in evaluating these patients, scheduling them for the optimal timing of surgery, and optimizing perioperative management and postoperative recovery.Copyright © 2023, Peking Union Medical College Hospital. All rights reserved.

Feasibility and outcomes of psychotherapeutic group therapy for patients with fatigue and emotional and cognitive disturbances secondary to COVID-19 disease.

About 10 % of all symptomatic COVID-19 patients suffer from long-lasting health complaints. Fatigue, cognitive and emotional disorders are the most frequent neuropsychiatric symptoms. Evidence-based therapies for these post-covid impairments are still lacking. Here, we examined the feasibility of a newly developed group-therapy program for patients with fatigue, emotional and cognitive disorders following COVID-19. 24 patients with ICD-10 diagnosis of F06.8 and U0.09 participated in the group therapy on average 13 month after their acute COVID-19 infection. Before and after the group therapy they underwent a comprehensive clinical and neuropsychological assessment. The group therapy was held online and consisted of 8 weekly sessions with psychotherapeutic and psychoeducational elements regarding fatigue and pacing, mindfulness, psychiatric disorders, cognition as well as physical activity after COVID-19. Participation in the group was high with an average of 7.25 of 8 visited sessions. Mean overall group satisfaction was 7.78 out of 10 points. Patients improved in their self-reported fatigue, daily living skills, depression and subjective cognitive abilities as well as in their objective performance in neuropsychological tests of attention during the study time. The newly developed group therapy program for patients with fatigue and emotional and cognitive disorders following an infection with SARS-CoV-2 was well accepted and evaluated and is feasible in an online setting. Copyright © 2023. Thieme. All rights reserved.

Ferritin - novel uses of a well-known marker in paediatrics

Serum ferritin is one of the most widely used laboratory tests and is associated with both iron deficiency and iron overload. Currently, more and more attention is paid to the involvement of ferritin in processes other than iron metabolism. Low serum ferritin is unanimously associated with iron deficiency, while elevated serum ferritin may be a consequence of various medical conditions such as iron overload, an inflammatory process, SARS-CoV-2, organ failure, cancer, and endocrine disorders, including metabolic syndrome. We present a review of the literature on the role of ferritin in a variety of less obvious disease states in children.Copyright © 2023 Termedia Publishing House Ltd.. All rights reserved.

Increased diagnosis of diabetes in secondary care: Is Covid-19 the only reason?

Aim: To evaluate the prevalence of new diabetes in secondary care during the second wave of the Covid-19 pandemic. Method(s): Data were collected prospectively for patients presenting to the hospital with new diagnosis of diabetes from December 2020 to May 2021. It included demographics, risk factors, presenting glucose, other investigations and treatment. Result(s): In the six-month study period, 31 patients were diagnosed with new diabetes. Thus far, approximately 13 patients have been identified to have type 1 diabetes and the average age was 37 years. Everyone was discharged with insulin except one patient. Prior to the pandemic in the year 2019, only 17 patients were diagnosed with diabetes in the hospital. Conclusion(s): The lockdown led to a reduction in physical activity and varied diet which may have contributed to weight gain;worsening insulin resistance. It is plausible that severe acute respiratory syndrome coronavirus 2 (SARS-CoV- 2) could trigger autoimmune type 1 diabetes or accelerate its presentation. Together with a hesitancy for patients to seek medical attention and reduced access to face-to- face primary care consultations, this may have contributed to the increased presentation of diabetes-related emergencies and reduction in symptomatic hyperglycaemia. Various studies found patients with pre-existing diabetes have a worse outcome if they develop Covid-19. Overall, during the pandemic, physical and mental health worsened, pre-disposing to medical conditions and impacting self-management of health and disease. We predict the increase in new diagnoses of diabetes in secondary care is multifactorial due to the effects of the pandemic rather than Covid-19 infection solely.

Covid-19 disruption, resilience and industrial policy: the automotive sector in the West Midlands

The Covid-19 pandemic and Brexit have focused attention on the resilience of key sectors and firms. This paper explores the financial resilience of the 50 largest automotive firms in the West Midlands region of the UK in their response to disruption and economic shocks. The findings demonstrate that 22 firms are at high risk due to poor current liquidity ratios, with Coventry and Birmingham emerging as locations most susceptible to firm closures. High-risk firms include key flagship original equipment manufacturers operating at the downstream end of supply chains. If these firms were to fail, there would be a significant destructive impact on both the industry and the local economy. We assert an effective subnational industrial policy is required in order to support economic resilience in regions such as the West Midlands where a few firms account for a disproportionate share of employment and value-added.

Forecasting COVID-19 Pandemic using Prophet, LSTM, hybrid GRU-LSTM, CNN-LSTM, Bi-LSTM and Stacked-LSTM for India

The COVID-19 Pandemic has been around for four years and remains a health concern for everyone. Although things are somewhat returning to normal, increased incidence of COVID-19 cases in some regions of the world (such as China, Japan, France, South Korea, etc.) has bred worry and anxiety in world, including India. The scientific community, which includes governmental organizations and healthcare facilities, was eager to learn how the COVID-19 Pandemic would develop. The current work makes an attempt to address this question by employing cutting-edge machine learning and Deep Learning algorithms to anticipate the daily incidence of COVID-19 for India over the course of the next six months. For the purpose famous timeseries algorithms were implemented including LSTM, Bi-Directional LSTM and Stacked LSTM and Prophet. Owing to success of hybrid algorithms in specific problem domains- the present study also focuses on such algorithms like GRU-LSTM, CNN-LSTM and LSTM with Attention. All these models have been trained on timeseries dataset of COVID-19 for India and performance metrics are recorded. Of all the models, the simplistic algorithms have performed better than complex and hybrid ones. Owing to this best result was obtained with Prophet, Bidirectional LSTM and Vanilla LSTM. The forecast reveals flat nature of COVID-19 case load for India in future six months. . © 2023 IEEE.

Complex presentations in people with diabetes: Does HbA1c help or hinder?

HbA1c measurement is widely used for diagnosis/ management/remission of diabetes with international schemes certifying comparability. A) 75 year-old Chinese female with type 2 diabetes was admitted in April 2020 with Covid-19 and diabetic ketoacidosis. Glucose was 35mmol/l and HbA1c 150mmol/mol with previous HbA1c of 45mmol/mol on metformin and alogliptin. She was treated for ketoacidosis and once-daily Lantus introduced along with supportive management of viral illness. B) 68 year-old Afro-Caribbean with type 2 diabetes on metformin before admission, presented with new onset, jerky ballistic movements of high amplitude in right arm, 10-15 movements every 5 min. Admission glucose was >33mmol/l, ketones 1.8mmol/l and HbA1c >217mmol/ mol. Hemichorea-hemiballism, a hyperglycaemia related movement was diagnosed and insulin commenced. Glucose decreased to 8-20mmol/ l, reaching 5-15mmol/ l by time of discharge. Ballistic movements resolved when glycaemic control improved with HbA1c 169mmol/mol, 25 days after discharge. C) Several days before admission, a female with diabetes over 20 years required attention from paramedics on four occasions for hypoglycaemia. Months beforehand metformin was replaced by gliclazide due to chronic kidney disease with HbA1c 50mmol/mol, and she was transfused six weeks before admission for microcytic anaemia. Gliclazide was discontinued and her diet modified which prevented further hypoglycaemic episodes. Variant haemoglobin, beta-thalassaemia which can overestimate glycaemia;undetected by HbA1c HPLC method, invalidated HbA1c as did the blood transfusion. These cases highlight that inadequate understanding of HbA1c can lead to acute presentations of dysglycaemia. As HbA1c accuracy can be affected by multiple factors, clinical assessment and triangulation are key to the management of such patients.

E-research in early psychosis: Methodological recommendations for ethics and data security

Aims: The COVID-19 pandemic compelled replacement in traditional research practices (paper-pencil questionnaire) to technology-driven practices (online surveys). Such methods may be effective in reaching larger samples, geographically harder-to-reach populations, reduce recruitment costs, increase cost and time efficiency of recruitment. Despite these advantages, concerns about privacy and confidentiality, sample bias, data quality such as inaccurate responses, duplicate survey completion, and fraudster activity or bots prevail. We aim to provide researchers and reviewers with a series of recommendations for effectively executing and evaluating data collection via online platforms. Method(s): A rapid literature review was conducted and best practices and strategies to mitigate problems with e-research data collection were collated in summer 2021. Based on study needs, these strategies were applied in an on-going e-research in early psychosis intervention services with multiple stakeholder groups across Canada. Result(s): The results were categorized and prioritized based on strategy effectiveness (most, moderate, least) and at three implementation stages (before, during, and after recruitment). An 11-step data quality checklist was adapted and implemented in consultation and approval from institutional research ethics board thus ensured ethical acceptability. Key strategies include not sharing the full survey link publicly, collecting and checking paradata, attention check questions, and so forth. Conclusion(s): Given their unique strengths, the challenges of internetbased research and data collection should not deter researchers from using such approaches. Further, our study provides concrete evidence-based practices and insights for advancing ethical and highquality e-research, taking into account specific considerations associated with early psychosis settings.

Virtual educational therapy: A case study of clinical supports and advocacy

A case study is a research approach that is used to generate an in-depth, multifaceted understanding of a complex issue in its real-life context. It is both time- and space-bound and is useful to explore, describe, and explain phenomena. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences, including education. Many master's programs employ the case study methodology as the basis for the culminating project. The case study methodology is especially relevant to advancing "younger disciplines" such as educational therapy. Many do not understand the training and difference in approaches between an Educational Therapist and a tutor, so publishing case studies is crucial. This book presents a board-certified educational therapist's year-long case study of clinical supports and advocacy for a student with learning disabilities who is attending school remotely during the COVID-19 pandemic. With online and blended learning, now the norm in K-12 education, educational therapists need new models of intervention, treatment, and relationship-building for their child-age clients. The book offers detailed single-case research focused on a middle-school student who is learning virtually while challenged with attention-deficit/hyperactivity disorder as well as visual and verbal memory issues, but who is nonetheless found ineligible for special education services. Across eight chapters, the book describes the neuropsychological principles, research-based techniques, personal interactions, clinical approaches, and advocacy efforts that led to a vulnerable student's significant gains in academic skills and outcomes. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

A breath of fresh air

Submission content Introduction: This is a story about the day I wheeled a patient outside. I know, it sounds somewhat underwhelming. But little did I know that this short trip down a hospital corridor and beyond the entrance foyer would mark a profound shift in perspective both for me and my patient, which I hope will influence me for the rest of my career. Main Body: "Paul" was in his 50s and severely afflicted by COVID-19, resulting in a protracted ICU admission with a slow and arduous ventilator wean. Throughout his time on the unit, Paul had seen no daylight;no view of the outside world. He was struggling to make progress and was becoming exasperated. His deteriorating mood in turn affected his sleep, which further undermined his progress. Due to COVID-19, visiting was not permitted and Paul's cuffed tracheostomy meant that he couldn't speak to his family. One day, witnessing Paul's psychological decline, I asked him if he fancied a trip outside. Despite initial reluctance, he eventually gave in to some gentle persuasion from the staff nurse, with whom he had developed a close bond. So there we went;Paul, his nurse and me. And as we wheeled his bed through the door into open air, Paul's whole demeanour suddenly changed. He appeared as though the weight of the world had been lifted from his shoulders and his face lit up with awe, a tear emerging in the corner of his eye. In that moment he rediscovered life. Not as a hospital patient, but as a person. Watching the world go by, he remembered what it was like to be a member of the human race, not the subject of endless tests and treatments. He tasted freedom. Conclusion(s): Awakened by his experience of the forgotten outside world, when we eventually returned to the ICU Paul was an entirely different man. To Paul, the trip outside symbolised progress. After weeks of frustration and despair, he finally had a purpose;a motivation to get better. Meanwhile, I was having my own quiet realisation. I now understood what it truly meant to deliver holistic care. It can become all too easy to focus on the clinical aspects;to obsess about the numbers. But in fact, often what matter most to patients are the 'little things', to which no amount of medication is the solution. I now try to consider during my daily review: what matters to this patient? How are they feeling? What are they thinking? What else can I do to help their psychological recovery? And as for me personally? Having witnessed Paul's reaction to the outside world, I suddenly became aware of how little attention I normally pay to the world around me. How little I appreciate the simple ability to walk outside, and the fundamental things we take for granted. Now, when I'm feeling annoyed or frustrated about something trivial, I stop and think of Paul. I then thank my lucky stars for what I have to be grateful for. Ultrasound Ninja.

A mask-guided attention deep learning model for COVID-19 diagnosis based on an integrated CT scan images database

The global extent of COVID-19 mutations and the consequent depletion of hospital resources highlighted the necessity of effective computer-assisted medical diagnosis. COVID-19 detection mediated by deep learning models can help diagnose this highly contagious disease and lower infectivity and mortality rates. Computed tomography (CT) is the preferred imaging modality for building automatic COVID-19 screening and diagnosis models. It is well-known that the training set size significantly impacts the performance and generalization of deep learning models. However, accessing a large dataset of CT scan images from an emerging disease like COVID-19 is challenging. Therefore, data efficiency becomes a significant factor in choosing a learning model. To this end, we present a multi-task learning approach, namely, a mask-guided attention (MGA) classifier, to improve the generalization and data efficiency of COVID-19 classification on lung CT scan images. The novelty of this method is compensating for the scarcity of data by employing more supervision with lesion masks, increasing the sensitivity of the model to COVID-19 manifestations, and helping both generalization and classification performance. Our proposed model achieves better overall performance than the single-task (without MGA module) baseline and state-of-the-art models, as measured by various popular metrics.

Relationship between attentional Bias, anxiety, depression and post-traumatic stress disorder in primary and middle school students

Objective: To investigate the relationship between negative attentional bias and post-traumatic stress disorder(PTSD) in the context of higher depression and anxiety symptoms after the outbreak of COVID-19, so as to provide scientific basis for mental health education in primary and secondary schools. Methods: From March to April 2021, a total of 708 students from primary school and junior high school (grade 6 through grade 9) in Beijing, Shanxi, Hunan, Shandong, Hebei, Hubei of China were selected. The Children's Revised Impact of Event Scale(CRIES), the Attention to Positive and Negative Information Scale (APNI)and Depression, Anxiety and Stress Scale-21(DASS-21) were used in a questionnaire survey. Results: A total of 242 students were diagnosed with PTSD, and the detection rate was 34.2%. The scores of intrusion and high arousal of boys(7.92+or-5.33, 8.60+or-5.41) were lower than those of girls(8.72+or-4.85, 9.50+or-4.76), and the difference was statistically significant (t=-2.04, -2.32, P < 0.05). There were statistically significant differences of negative attention bias, CRIES score, intrusion, debarb and high arousal among primary and middle school students of different grades (F=3.57, 5.99, 4.45, 4.60, 7.40, P < 0.05). Negative attention bias, anxiety, depression and post-traumatic stress symptoms were significantly positively correlated (r=0.27-0.84, P < 0.05). Logistic regression analysis showed that anxiety (OR=1.13, 95%CI=1.06-1.20) and negative attention bias (OR=1.10, 95%CI=1.07-1.12) were positively associated with PTSD symptoms in primary and middle school students(P < 0.01). Conclusion: Anxiety and depressive symptoms show impacts on negative attention bias and might exacerbate the symptoms of post-traumatic stress disorder. Therefore, emotional adjustments can help reduce the post-traumatic stress response in the post-epidemic period.

An Improved COVID-19 Lung X-Ray Image Classification Algorithm Based on ConvNeXt Network

Aiming at the new coronavirus that appeared in 2019, which has caused a large number of infected patients worldwide due to its high contagiousness, in order to detect the source of infection in time and cut off the chain of transmission, we developed a new Chest X-ray (CXR) image classification algorithm with high accuracy, simple operation and fast processing for COVID-19. The algorithm is based on ConvNeXt pure convolutional neural network, we adjusted the network structure and loss function, added some new Data Augmentation methods and introduced attention mechanism. Compared with other classical convolutional neural network classification algorithms such as AlexNet, ResNet-34, ResNet-50, ResNet-101, ConvNeXt-tiny, ConvNeXt-small and ConvNeXt-base, the improved algorithm has better performance on COVID dataset.

Possibilities of low-dose radiation therapy in the treatment of intrapulmonary injuries caused by COVID-19 infection.

This paper examines the relevance of the use of a single irradiation of lungs in treatment of pneumonia caused by a new coronavirus infection. Clinical observations are presented that demonstrate perspectives in the treatment of this disease. Patients with severe pneumonia who were prescribed LD-RT (low-dose radiation therapy) at a dose of 0.5-1.5 Gy showed shorter recovery times and no complications. This method of treatment has shown its effectiveness in a number of studies from different countries, predicting success and economic benefits in its further use and study. A literature search containing information on relevant studies was carried out in PubMed, EMBASE, Web of Science and Google Scholar systems. Attention was focused on full-text articles given their general availability in a pandemic.Copyright © 2021 VIDAR Publishing House. All right reserved.

Associations between adverse childhood experiences and conspiracy endorsement - the mediating role of epistemic trust and personality functioning: A representative study during the COVID-19 pandemic

Aim: Conspiracy endorsement has gained much attention in the context of the COVID-19 pandemic, as it constitutes a major public health challenge that is associated with reduced adherence to preventative measures. However, little is known about the developmental backdrops and personality characteristics that render an individual prone to conspiracy endorsement. There is a growing body of evidence implying a detrimental role of adverse childhood experiences (ACEs) - a highly prevalent burden - in the development of epistemic trust and personality functioning. This study aimed to investigate the association between ACEs and conspiracy endorsement as well as the mediating role of epistemic trust and personality functioning. Method(s): Analyses are based on cross-sectional representative data of the German population collected during the COVID-19 pandemic (N = 2501). Structural equation modelling (SEM) with personality functioning (OPD-SQS) and epistemic trust (ETMCQ) as mediators of the association between ACEs and conspiracy endorsement were conducted. Result(s): In total, 20.4% (n = 508) of all participants endorsed conspiracies. There was a significant association between ACEs and conspiracy endorsement (beta = 0.25, p < 0.001;explained variance 6%). The variance of conspiracy endorsement increased to 19% after adding epistemic trust and personality functioning as mediators (beta = 0.12, p < 0.001), indicating a partial mediation and direct prediction from these mediators. Fit indices demonstrated a good model fit. Conclusion(s): Evidence on the far-reaching and detrimental effects of early childhood adversities are further increased by demonstrating an association between ACEs and conspiracy endorsement. Our findings contribute to a deeper understanding of the underlying mechanisms by including epistemic trust and personality functioning.Copyright © 2023