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Background: X-Linked Moesin-Associated Immune Deficiency (X-MAID) is a rare severe combined immunodeficiency (SCID) subtype that can present at any age due to its variability. Depending on severity, patients demonstrate failure to thrive, recurrent bacterial and viral infections, and increased susceptibility to varicella zoster. It has been characterized by marked lymphopenia with hypogammaglobulinemia and impaired T-cell migration and proliferation. Case Presentation: This is a report of a Cuban 7-year-old male with poor weight gain and facial dysmorphia. He had a history of recurrent bacterial gastrointestinal infections and pneumonia beginning at 4 months of age. He additionally had 4-6 upper respiratory tract and ear infections annually. While still living in Cuba, he was admitted for a profound EBV infection in the setting of significant leukopenia. A bone marrow biopsy confirmed no malignancy. After he moved to the United States, his laboratory work-up revealed marked leukopenia with low absolute neutrophil and lymphocyte count with low T and B cells, very low immunoglobulin levels IgG, IgA, and IgM, and poor vaccination responses to streptococcus pneumonia, varicella zoster, and SARS-CoV-2. Genetic testing revealed a missense pathogenic variant c.511C>T (p.Arg171Trp) in the moesin (MSN) gene associated with X-MAID. He was managed with Bactrim and acyclovir prophylaxis, and immunoglobulin replacement therapy, and considered for hematopoietic stem cell transplantation. Discussion(s): Diagnosis of X-MAID should be considered in patients with recurrent infections and profound lymphopenia. As with SCID, early diagnosis and intervention is of utmost importance to prevent morbidity and mortality. This case demonstrates the importance of genetic testing in identifying this disease as it may prompt an immunologist to consider HSCT if conservative management is suboptimal. In the current literature, HSCT appears promising, but the long-term outcomes have yet to be described.Copyright © 2023 Elsevier Inc.
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The COVID-19 pandemic has caused substantial damage to global health. Even though three years have passed, the world continues to struggle with the virus. Concerns are growing about the impact of COVID-19 on the mental health of infected individuals, who are more likely to experience depression, which can have long-lasting consequences for both the affected individuals and the world. Detection and intervention at an early stage can reduce the risk of depression in COVID-19 patients. In this paper, we investigated the relationship between COVID-19 infection and depression through social media analysis. Firstly, we managed a dataset of COVID-19 patients that contains information about their social media activity both before and after infection. Secondly, We conducted an extensive analysis of this dataset to investigate the characteristic of COVID-19 patients with a higher risk of depression. Thirdly, we proposed a deep neural network for early prediction of depression risk. This model considers daily mood swings as a psychiatric signal and incorporates textual and emotional characteristics via knowledge distillation. Experimental results demonstrate that our proposed framework outperforms baselines in detecting depression risk, with an AUROC of 0.9317 and an AUPRC of 0.8116. Our model has the potential to enable public health organizations to initiate prompt intervention with high-risk patients. © 2023 ACM.
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Aims: During the pandemic, youth were particularly vulnerable to experiencing financial hardship, education and employment disruption, and mental health impacts. Ensuring governments and services are prepared to support youth during future outbreaks or novel pandemics should be a key priority. This work aimed to explore youth experiences during COVID-19 and gather youth opinions on government responses to inform planning, policy, and decision-making for future pandemics. Method(s): Youth (ages 15-25) from Ireland and two provinces in Canada (British Columbia and Ontario) were interviewed at three time points during the COVID-19 pandemic. A thematic analysis was conducted using an inductive approach. This research was primarily youth-led and developed. Result(s): Across all three time points, youth experienced mental health and service uptake challenges, with mixed views on pandemic response. Opportunities for personal and societal growth were identified, with desire for incorporating youth voices into governmental decision making processes. Youth offered recommendations for effectively communicating accurate information, prevention of misinformation, and expressed needs regarding service accessibility throughout the pandemic and beyond. Conclusion(s): This work provides insights into the opinions of young people on government and information sharing during the progression of the COVID-19 pandemic. Recommendations were developed to ensure youth are consulted and represented in future pandemics.
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Aims: Trauma is particularly prevalent amongst Early Intervention (EI) patients and is associated with adverse clinical and prognostic outcomes. To determine the feasibility of a large-scale randomized controlled trial (RCT) of an 'EMDR for psychosis' intervention for trauma survivors with active psychotic symptoms supported by EI services, we conducted a single-blind RCT comparing 16 sessions of EMDRp + TAU versus TAU only. Method(s): EMDRp therapy and trial assessments were completed both in-person and remotely during the COVID-19 pandemic, and key feasibility outcomes (recruitment & retention, therapy attendance/ engagement, adherence to EMPRp treatment protocol, and the 'promise of efficacy' of EMDRp on relevant clinical outcomes) were examined at 6- and 12-month post-randomization assessments. Results and Conclusion(s): 60 participants (100% of the recruitment target) received TAU or EMDR + TAU. The feasibility criteria examined in this trial were fully met, and EMDRp was associated with promising signals of efficacy on a range of valuable post-treatment outcomes, including improved psychotic symptoms (PANSS), subjective recovery (QPR), post-traumatic symptoms (PCL-5;ITQ), depression (PHQ-9), anxiety (GAD-7) and general health status (EQ-5D-VAS) at the 6-month assessment. Signals of efficacy at 12-month were less pronounced, but remained robust for trauma symptoms and general health status. The findings will be discussed with relevance to future clinical trials of trauma-focused therapy in clients with early psychosis, and the provision of more tailored trauma therapies for EI service users.
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The pandemic took its toll on memory clinics with many forced to close or scale back their services. This led to lengthening waits for dementia assessment and left many without a diagnosis.
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Ionizable amino lipids are a major constituent of the lipid nanoparticles for delivering nucleic acid therapeutics (e.g., DLin-MC3-DMA in ONPATTRO , ALC-0315 in Comirnaty , SM-102 in Spikevax ). Scarcity of lipids that are suitable for cell therapy, vaccination, and gene therapies continue to be a problem in advancing many potential diagnostic/therapeutic/vaccine candidates to the clinic. Herein, we describe the development of novel ionizable lipids to be used as functional excipients for designing vehicles for nucleic acid therapeutics/vaccines in vivo or ex vivo use in cell therapy applications. We first studied the transfection efficiency (TE) of LNP-based mRNA formulations of these ionizable lipid candidates in primary human T cells and established a workflow for engineering of primary immune T cells. We then adapted this workflow towards bioengineering of CAR constructs to T cells towards non-viral CAR T therapy. Lipids were also tested in rodents for vaccine applications using self-amplifying RNA (saRNA) encoding various antigens. We have then evaluated various ionizable lipid candidates and their biodistribution along with the mRNA/DNA translation exploration using various LNP compositions. Further, using ionizable lipids from the library, we have shown gene editing of various targets in rodents. We believe that these studies will pave the path to the advancement in nucleic acid based therapeutics and vaccines, or cell gene therapy agents for early diagnosis and detection of cancer, and for targeted genomic medicines towards cancer treatment and diagnosis.
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In 2020 and 2021, there has been extensive scholarly and popular discussion about children's learning loss due to COVID-19 and its related school closures. This conversation generally overlooks the voices of young children. This study, set in a US context where children spent a year or more attending school exclusively remotely, reports from interviews with 10 second-graders about how they conceive of loss related to COVID-19 and particularly what it might mean to lose learning. The study finds that the children have extensive ideas about what it means to lose something tangible or intangible, and that their theories about loss are based in well-understood personal experience. It also shows how children use loss narratives to make sense of sociopolitical events and concepts in the world around them, and it offers the possibility that the upheaval wrought by COVID-19 has helped some children become quite emotionally aware and able not only to tolerate but also adaptively defend against difficult feelings. The article emphasizes the importance of foregrounding children's ideas and voices in making sense of their educational experiences. [ FROM AUTHOR] Copyright of Contemporary Issues in Early Childhood is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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The United States faces an unprecedented mental health crisis, with youth and young adults at the center. Even before the COVID-19 pandemic, nearly 50 percent of college students reported at least one mental health concern. The COVID-19 pandemic notably exacerbated these issues and underscored the urgent need to identify and implement ways to ameliorate the youth mental health crisis. In 2021, the National Academies of Sciences, Engineering, and Medicine called on the field of higher education to address growing concerns about student mental health by identifying and elevating emerging and promising approaches that offer a more holistic way to support students' mental health. Serving as the main entry point for more than 40 percent of students seeking a postsecondary degree, community colleges represent a tremendous and untapped opportunity to better address mental health in the United States, particularly for students who have been traditionally underserved (e.g., students of color, first-generation students, and low-income students). However, community colleges have limited evidence and guidance to inform the implementation of multilevel, holistic approaches to support students with varying mental health needs. To address this knowledge gap, this report shares a descriptive study of eight community colleges at the forefront of implementing multilevel approaches (a combination of prevention, early intervention, and treatment services) to support student mental health, as well as key facilitators for and barriers to their success. [For "How Community Colleges Can Support Student Mental Health Needs. Research Brief. RB-A2552-1," see ED627489.]
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The United States faces an unprecedented mental health crisis, with youth and young adults at the center. Even before the coronavirus disease 2019 (COVID-19) pandemic, nearly 50 percent of college students reported at least one mental health concern. Without adequate mental health support, college students, including those at community colleges, may be at risk for a variety of academic and nonacademic consequences that negatively affect their overall well-being, including lower college completion rates, higher rates of substance use, and lower lifetime earning potential. This research brief describes a study examining eight community colleges from across the United States which found that, although the institutions did offer mental health services, most lacked a clear organizing framework for those efforts, and that financial challenges limited the support offered to students. The research also highlighted the importance of community college leaders explicitly prioritizing student mental health, as well as broad staff buy-in to the effort. [For the full report, "Supporting the Mental Health Needs of Community College Students. Research Report. RR-A2552-1," see ED627480.]
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The objective: to assess risk factors for tuberculosis relapse during the COVID-19 pandemic. Results. During the retrospective study, medical documents of patients treated for tuberculosis in 2020-2022 were analyzed. 140 patients above 18 years old with confirmed tuberculosis were included: newly diagnosed tuberculosis – 50 patients, early relapses – 50 patients, and late relapses – 40 patients. At the first stage, significant factors for relapse development were identified (ANOVA): diabetes (F=19402,8;p=0,000000), substances abuse (F=547,6;p=0,000000), alcohol abuse (F=149,7;p=0,000000), MDR MTB (F=107,8;p=0,000000), HIV infection (F=72,4;p=0,000000), imprisonment (F=49,5;p=0,000000), chronic respiratory diseases (F=47,1;p=0,000000), smoking (F=29,0;p=0,000000), and social status (F=28,9;p= 0,000000). At the second stage (cluster analysis), factors implementing the risks of development of early or late relapses were identified. As well as before the COVID-19 pandemic, social, medical and biological risk factors play a significant role in the development of tuberculosis relapses;history of COVID-19 was not one of the risk factors;and stratification of the risk factors made it possible to identify factors that contribute to development of both early and late relapses and to determine preventive measures. © 2023 New Terra Publishing House. All rights reserved.
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IntroductionFamilies with young children who face economic and related adversities are the most likely group to miss out on the advantages of regular sustained participation in high quality early childhood education and care. In Australia, there are an estimated 11% of children assessed by teachers to have two or more developmental vulnerabilities and many of these children are living in economically disadvantaged contexts. Government policy in Australia aspires to provide universal access to Early Childhood Education and Care (ECEC) services to support children's outcomes and ensure workforce participation, but policy falls short of ensuring all families can take up high quality early childhood education and care. Government responses to the Covid crisis saw significant changes to the ECEC policy and funding mechanisms. It is timely therefore to reflect on the level of 'competence' in the Australian ECEC systems. Coined this term to refer to a system that is sustainable, inclusive, and effective for all families. MethodsUsing a Delphi methodology, we coalesced the insights of high-level stakeholders who have expertise in delivering services to families experiencing adversities and noted points of consensus and of divergence among these stakeholders. We have taken up the challenge of considering the Australian system from the point of view of families who typically find services hard to use. Results and ConclusionWe put forward a model that frames the characteristics of services that can inclusively engage with families - Approachable, Acceptable, Affordable, Accessible and Appropriate. We argue that more needs to be known about appropriateness and what effective pedagogy looks like on the ground for families and children.
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This dissertation research study aimed to determine the predictors of early literacy skills in kindergarteners with dyslexia familial risk. The home literacy environment, preschool attendance, and parental self-efficacy are known predictors of early literacy skills, specifically letter naming knowledge, in typically developing children. Letter naming knowledge is an early literacy skill that is important to future reading achievement and outcomes. Letter naming knowledge is also a powerful pre-literacy predictor of dyslexia. Dyslexia commonly manifest as difficulties in acquiring basic reading skills and is highly heritable. The population for this study was primary caregivers of kindergarteners with a first degree relative with a diagnosis of dyslexia. Participants completed the Home Literacy Checklist, Tool to Measure Parenting Self-Efficacy via an online survey with questions regarding preschool attendance, letter naming knowledge skills, and the presence of a diagnosis of dyslexia among first degree relatives. SPSS Statistics (Version 28) was used to run descriptive statistics. The sample was comprised of 12 primary caregivers. This sample size was not appropriate for the chosen analysis of multiple regression. Using exploratory data analysis, the data was organized and summarized. Due to an inadequate sample size caused by a low response rate, no conclusions could be drawn from this data. The research questions were not answered and there were no findings. COVID-19 restrictions impacted research sites and the level of participation, with participants being less available, school closures, and mandatory quarantines, creating a difficult atmosphere for completion of this research study. As a result, the lack of data during the COVID-19 pandemic and subsequent lockdowns led to several revisions of the research plan. The revisions involved eliminating the need for standardized test scores, using primary caregiver report to determine the level of difficulty of letter naming knowledge of the kindergarten student, and using social media to recruit participants. Recommendations for further research were comprised of ways to increase the pool of participants by expanding inclusion criteria, eliminating the use of standardized test scores, considering the length of the survey, and the use of social media along with an incentive. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
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To sustain early childhood intervention services during the Covid-19 pandemic, professionals, families, and communities had to move their support services to a digital distance. This posed particular challenges for all involved, as early childhood intervention is based on face-to-face contact in its basic design. However, as digital solutions were tested, the opportunities offered by supplementing face-to-face contacts with digital offerings also became apparent. Copyright © 2023 Walter de Gruyter GmbH, Berlin/Boston 2023.
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The menopause is a stage in the life cycle that affects all women. Managing perimenopausal and postmenopausal health is therefore a key issue for all healthcare professionals, not just gynecologists. The curriculum should include terminology and definitions, assessment, diagnosis and evidence-based management strategies. Healthcare professionals should be aware that women have different perceptions and experiences of the menopause which may be determined by: age and type of menopause, pre-existing health conditions, disability, employment and adverse childhood events. Specialist services may be required for some. These include women with chronic disease, premature ovarian insufficiency or early menopause or pre-existing health conditions and disability, as well as transgender and gender-nonconforming people. The COVID-19 pandemic has changed the mode of delivering healthcare from face-to-face only to include virtual consultations. Teaching now needs to include both types of consultations. A holistic approach is required and teaching should be provided by an accredited expert. Rees M, Abernethy K, Bachmann G, et al. The essential menopause curriculum for healthcare professionals: A European Menopause and Andropause Society (EMAS) position statement. Maturitas. 2022;158:70-77. doi: https://doi.org/10.1016/j.maturitas.2021.12.001Copyright © 2023
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Background: The coronavirus disease 2019 (COVID-19) pandemic and its associated morbidities are a great global concern. Diabetes mellitus (DM) is associated with adverse clinical outcomes and high mortality in patients with COVID-19. Objective(s): This study examined the frequency of BM, newly diagnosed hyperglycemia, and their impacts on hospitalized patients with COVID-19. Method(s): This retrospective study examined 810 medical records of PCR-confirmed COVID-19 patients admitted to Razi Hospital, Ahvaz, Iran. The clinical presentations, severity, and impacts of COVID-19 were compared between patients with and without DM. Disease severity was determined based on the NEWS2 scoring system. Result(s): This study included 810 medical records of COVID-19 patients, of whom 326 had pre-existing DM, and 484 were non-DM. The rates of diabetes and newly diagnosed hyperglycemia were 40.2% and 11.2%, respectively. The most common underlying diseases were hypertension (35.3%), ischemic heart disease (17.9%), and chronic kidney disease (11.9%), which were higher in people with diabetes than non-diabetics. The rate of acute kidney injury was higher in patients with diabetes than in non-diabetics (30.7% vs. 19.2%;P < 0.001) and in patients with severe COVID-19 than in those whose disease was not severe (27.8% vs. 21.5%;P = 0.04). The rates of severe COVID-19 (46.3% vs. 34.7%;P = 0.093), ICU admission (40.7% vs. 27.4%;P = 0.012), and mortality (18.5% vs. 10.5%;P = 0.079) were higher in patients with newly diagnosed hyperglycemia than in euglycemic patients. Conclusion(s): This study showed that COVID-19 infection is linked with newly diagnosed hyperglycemia and pre-existing DM, both associated with severe COVID-19, more need for ICU admission, and mortality.Copyright © 2023, Author(s).
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Background: The Early Youth Engagement (EYE) project co-developed the first engagement-focussed intervention derived from the perspectives of young people and their families in Early Intervention in Psychosis services. Patient and Public Involvement (PPI) in the EYE-2 study aimed to ensure that the voices of people experiencing first episode psychosis, and carers, were embedded in the refinement, delivery and evaluation of the Early Youth Engagement (EYE-2) approach across the UK. Method(s): The EYE-2 project used a multi-level PPI approach: Each of five sites had a PPI lead, trained and supported by senior PPI researchers. PPI leads convened local Lived Experience Advisory Panels (LEAPs), and co-delivered staff and researcher training. Practical challenges were identified and resolved. Results and Impacts: Tangible PPI impacts included: the co-produced revised booklets and EYE-2 website https://www.likemind.nhs.uk following workshops of over 40 people;the delivery of on average 52 peer-led intervention social groups at each site, allowing people to connect with peers with lived experience and build a sense of optimism, shared identity, interpersonal skills and problem-solving in a social environment, with activities driven by service users. Written outcomes included a PPI-led booklet on running peer-led social groups, a person-centred telephone interview for the HoNOS questionnaire for use during the Covid-19 pandemic, peer reviewed papers, blogs and video logs. Conclusion(s): PPI was integral to the delivery of the EYE-2 study;the multi-level approach facilitated a diversity of voices across different aspects of the study and ensured that people with lived experience shaped the intervention, research design and implementation.
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This study examined talk by parents about the early years transitions of their children (n = 7) in the context of parental non‐standard working hours and Finnish early childhood education and care (ECEC) services. Parents were interviewed at three time points: when their child was aged one, four, five or six years (a total of 21 interviews). The third interview was conducted during the COVID‐19 pandemic. This article focuses on the children's ECEC transitions and the interpretative frames used by parents when talking about their work and childcare. The frames used by the parents to discuss the children's transitions were stabilising the children's lives, balancing between staying at home and attending ECEC and adjusting to norms and rules. The diversity of families' experiences and their children's transitions during the early years should be considered when developing family policy and ECEC services. [ FROM AUTHOR] Copyright of International Journal of Social Welfare is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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We know that teacher support during independent reading can greatly impact the growth and development of beginning readers. However, the rapid conversion to online learning in the 2020-2021 school year required teachers to rethink and retool traditional strategies to meet the needs of their online learners. This teaching and learning guide explores the use of a digital reading check-in strategy to support beginning readers as they read independently on digital platforms. We explain how this strategy was implemented successfully in a first-grade classroom and how it can be employed with any digital reading platform used in classrooms today.
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A chronic respiratory disease known as pneumonia can be devastating if it is not identified and treated in a timely manner. For successful treatment and better patient outcomes, pneumonia must be identified early and properly classified. Deep learning has recently demonstrated considerable promise in the area of medical imaging and has successfully applied for a few image-based diagnosis tasks, including the identification and classification of pneumonia. Pneumonia is a respiratory illness that produces pleural effusion (a condition in which fluids flood the lungs). COVID-19 is becoming the major cause of the global rise in pneumonia cases. Early detection of this disease provides curative therapy and increases the likelihood of survival. CXR (Chest X-ray) imaging is a common method of detecting and diagnosing pneumonia. Examining chest X-rays is a difficult undertaking that often results in variances and inaccuracies. In this study, we created an automatic pneumonia diagnosis method, also known as a CAD (Computer-Aided Diagnosis), which may significantly reduce the time and cost of collecting CXR imaging data. This paper uses deep learning which has the potential to revolutionize in the area of medical imaging and has shown promising results in the detection and classification of pneumonia. Further research and development in this area is needed to improve the accuracy and reliability of these models and make them more accessible to healthcare providers. These models can provide fast and accurate results, with high sensitivity and specificity in identifying pneumonia in chest X-rays. © 2023 IEEE.
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Over the past few years, millions of people around the world have developed thoracic ailments. MRI, CT scan, reverse transcription, and other methods are among those used to detect thoracic disorders. These procedures demand medical knowledge and are exceedingly pricy and delicate. An alternate and more widely used method to diagnose diseases of the chest is X-ray imaging. The goal of this study was to increase detection precision in order to develop a computationally assisted diagnostic tool. Different diseases can be identified by combining radiological imaging with various artificial intelligence application approaches. In this study, transfer learning (TL) and capsule neural network techniques are used to propose a method for the automatic detection of various thoracic illnesses utilizing digitized chest X-ray pictures of suspected patients. Four public databases were combined to build a dataset for this purpose. Three pre trained convolutional neural networks (CNNs) were utilized in TL with augmentation as a preprocessing technique to train and evaluate the model. Pneumonia, COVID19, normal, and TB (Tb) were the four class classifiers used to train the network to categorize. © 2023 IEEE.