Research Progress in the Treatment of Complications and Sequelae of COVID-19

With the improvement in the understanding of COVID-19 and the widespread vaccination of COVID-19 vaccines in various countries, the epidemic will be brought under control soon. However, multiple viruses could result in the post-viral syndrome, which is also common among patients with COVID-19. Therefore, the long-term consequences and the corresponding treatment of COVID-19 should be the focus in the post-epidemic era. In this review, we summarize the therapeutic strategies for the complications and sequelae of eight major systems caused by COVID-19, including respiratory system, cardiovascular system, neurological system, digestive system, urinary system, endocrine system, reproductive system and skeletal complication. In addition, we also sorted out the side effects reported in the vaccine trials. The purpose of this article is to remind people of possible complications and sequelae of COVID-19 and provide robust guidance on the treatment. It is extremely important to conduct long-term observational prognosis research on a larger scale, so as to have a comprehensive understanding of the impact of the SARS-CoV-2 on the human body and reduce complications to the greatest extent.

A Customisable Cell Line Model of Glioblastoma

<p><b>Glioblastoma is an extremely malignant brain tumour with one of the lowest survival rates of all cancers. Current treatments do very little to alter this prognosis. Research into new therapies and the biology of glioblastoma has made scarce progress over the past decades. This is partly due to the combination of the tumour’s heterogeneity, and the inability of the current animal models to accurately depict this. This project was a pilot study into the development and characterisation of a novel cell line model of glioblastoma, which could be transplanted into immune competent mice, in order to study the disease.</b></p> <p>An immortalised C57BL/6 astrocyte cell line, with an EGFP transgene, was used as the base to add glioblastoma specific mutations. To produce a ‘classical-like’ glioblastoma model, a knockout in Pten was induced, onto which two separate lines the human oncogenes, EGFRVIII and RAS V12, were stably expressed. ‘Secondary-like’ models were created with a knockout of P53, and the stable transfection of IDH1R132H.</p> <p>The ‘classical-like’ cell lines were assessed for how well they mimicked a classical glioblastoma. The Pten knockout cell line showed an increased proliferative and metabolic rate compared with the astrocytes and a significant increase in clonogenicity. The addition of RAS V12 to the cells showed an increased migratory capacity; and the Pten + EGFRVIII cell line had a tendency towards an increased proliferation. The ‘secondary-like’ cell lines were assessed for their survival-related phenotypes. The P53 cell line showed a decreased proliferative rate, but with an increased metabolic rate and clonogenic ability. The introduction of the IDH1 mutant protein resulted in a decreased rate of G2 arrest in response to ionising radiation.</p> <p>These cell lines recapitulated what is seen in human glioblastoma tumours and show potential as a transplantation model. Future research will investigate the tumorigenicity of these cell lines.</p>

A Customisable Cell Line Model of Glioblastoma

<p><b>Glioblastoma is an extremely malignant brain tumour with one of the lowest survival rates of all cancers. Current treatments do very little to alter this prognosis. Research into new therapies and the biology of glioblastoma has made scarce progress over the past decades. This is partly due to the combination of the tumour’s heterogeneity, and the inability of the current animal models to accurately depict this. This project was a pilot study into the development and characterisation of a novel cell line model of glioblastoma, which could be transplanted into immune competent mice, in order to study the disease.</b></p> <p>An immortalised C57BL/6 astrocyte cell line, with an EGFP transgene, was used as the base to add glioblastoma specific mutations. To produce a ‘classical-like’ glioblastoma model, a knockout in Pten was induced, onto which two separate lines the human oncogenes, EGFRVIII and RAS V12, were stably expressed. ‘Secondary-like’ models were created with a knockout of P53, and the stable transfection of IDH1R132H.</p> <p>The ‘classical-like’ cell lines were assessed for how well they mimicked a classical glioblastoma. The Pten knockout cell line showed an increased proliferative and metabolic rate compared with the astrocytes and a significant increase in clonogenicity. The addition of RAS V12 to the cells showed an increased migratory capacity; and the Pten + EGFRVIII cell line had a tendency towards an increased proliferation. The ‘secondary-like’ cell lines were assessed for their survival-related phenotypes. The P53 cell line showed a decreased proliferative rate, but with an increased metabolic rate and clonogenic ability. The introduction of the IDH1 mutant protein resulted in a decreased rate of G2 arrest in response to ionising radiation.</p> <p>These cell lines recapitulated what is seen in human glioblastoma tumours and show potential as a transplantation model. Future research will investigate the tumorigenicity of these cell lines.</p>

Predicting survival in patients with advanced disease

Prognostication is an important part of care planning. It is also an essential skill to assist patients and their families in care and life planning. Every day, clinicians depend on their experience, knowledge, current research, and models to make prognostic estimates, which are the foundation of many clinical decisions. This chapter reviews current research and understanding about how clinicians formulate and utilize prognostic estimates in clinical settings. Palliative care clinicians must understand prognosis research since it informs individual clinical decisions as well as research design, policy development, and service composition. While no prognostic model is universal, this chapter will prepare clinicians to make the best use of the existing data in prognosis science. Limitations of prognostic methods and the future of predictive analytics will also be explored.

Apathy after stroke: Diagnosis, mechanisms, consequences, and treatment

Apathy is a reduction in goal-directed activity in the cognitive, behavioral, emotional, or social domains of a patient’s life and occurs in one out of three patients after stroke. Despite this, apathy is clinically under-recognized and poorly understood. This overview provides a contemporary introduction to apathy in stroke for researchers and practitioners, covering topics including diagnosis, neurobiological mechanisms, associated consequences, and potential treatments for apathy. Apathy is often misdiagnosed as other post-stroke conditions such as depression. Accurate differential diagnosis of apathy, which manifests as reductions in initiative, and depression, which manifests as negative emotionality, is important as it informs prognosis. Research on the neurobiology of apathy suggests that there are few consistent associations between stroke lesion location and the development of apathy. These may be resolved by adopting a network neuroscience approach, which models apathy as a pathology arising from structural or functional damage to brain networks underlying motivated behavior. Importantly, networks can be affected by physiological changes related to stroke, including the acute infarct but also diaschisis and neurodegeneration. Aside from neurobiological changes, apathy is also associated with other negative outcome measures such as functional disability, cognitive impairment, and emotional distress, suggesting that apathy is indicative of a worse prognosis following stroke. Unfortunately, high-quality trials aimed at treating apathy are scarce. Antidepressants may have limited effects on apathy. Acetylcholine and dopamine pharmacotherapy, behavioral interventions, and transcranial magnetic stimulation may be more promising avenues for treatment.

Multifactorial 10-Year Prior Diagnosis Prediction Model of Dementia

Dementia is a neurodegenerative disorder that affects the older adult population. To date, no cure or treatment to change its course is available. Since changes in the brains of affected individuals could be evidenced as early as 10 years before the onset of symptoms, prognosis research should consider this time frame. This study investigates a broad decision tree multifactorial approach for the prediction of dementia, considering 75 variables regarding demographic, social, lifestyle, medical history, biochemical tests, physical examination, psychological assessment and health instruments. Previous work on dementia prognoses with machine learning did not consider a broad range of factors in a large time frame. The proposed approach investigated predictive factors for dementia and possible prognostic subgroups. This study used data from the ongoing multipurpose Swedish National Study on Aging and Care, consisting of 726 subjects (91 presented dementia diagnosis in 10 years). The proposed approach achieved an AUC of 0.745 and Recall of 0.722 for the 10-year prognosis of dementia. Most of the variables selected by the tree are related to modifiable risk factors; physical strength was important across all ages. Also, there was a lack of variables related to health instruments routinely used for the dementia diagnosis.

Machine learning in prognosis research

Machine learning offers an alternative to the methods for prognosis research in large and complex datasets and for delivering dynamic models of prognosis. Machine learning foregrounds the capacity to learn from large and complex data about the pathways, predictors, and trajectories of health outcomes in individuals. This reflects wider societal drives for data-driven modelling embedded and automated within powerful computers to analyse large amounts of data. Machine learning derives algorithms that can learn from data and can allow the data full freedom, for example, to follow a pragmatic approach in developing a prognostic model. Rather than choosing factors for model development in advance, machine learning allows the data to reveal which features are important for which predictions. This chapter introduces key machine learning concepts relevant to each of the four prognosis research types, explains where it may enhance prognosis research, and highlights challenges.

Electronic healthcare records and prognosis research

Electronic healthcare record (EHR) data, collected during the daily business of patient consultations and treatments, offer huge opportunities to expand the range and scale of prognosis research, in particular because of the real-time and continuous recording of potential prognostic factors and health-related events, and the amount of data and individuals involved. However, with these opportunities come challenges related to the size and complexity of EHR data. This chapter provides an overview of these issues.