Type 1 diabetes and environment . . . and other stories

A linked ecological analysis of environmental and demographic variables identified several factors, including poor air quality, outdoor light at night, and higher population density that were negatively associated with the incidence of diabetes (Diabetologia doi:10.1007/s00125-020-05087-7). A case-control study using a database of people known to have autoimmune disease raises anxiety about central nervous system inflammatory events (JAMA Neurol doi:10.1001/jamaneurol.2020.1162). A history of exposure to TNF inhibitors carried a threefold increase in risk both of demyelinating diseases, such as multiple sclerosis and optic neuritis, and of non-demyelinating conditions, such as encephalitis, neurosarcoidosis, and vasculitis.

The Korean Society for Neuro-Oncology (KSNO) Guideline for the Management of Brain Tumor Patients During the Crisis Period: A Consensus Recommendation Using the Delphi Method (Version 2023.1).

BACKGROUND: During the coronavirus disease 2019 (COVID-19) pandemic, the need for appropriate treatment guidelines for patients with brain tumors was indispensable due to the lack and limitations of medical resources. Thus, the Korean Society for Neuro-Oncology (KSNO), a multidisciplinary academic society, has undertaken efforts to develop a guideline that is tailored to the domestic situation and that can be used in similar crisis situations in the future. METHODS: The KSNO Guideline Working Group was composed of 22 multidisciplinary experts on neuro-oncology in Korea. In order to reach consensus among the experts, the Delphi method was used to build up the final recommendations. RESULTS: All participating experts completed the series of surveys, and the results of final survey were used to draft the current consensus recommendations. Priority levels of surgery and radiotherapy during crises were proposed using appropriate time window-based criteria for management outcome. The highest priority for surgery is assigned to patients who are life-threatening or have a risk of significant impact on a patient's prognosis unless immediate intervention is given within 24-48 hours. As for the radiotherapy, patients who are at risk of compromising their overall survival or neurological status within 4-6 weeks are assigned to the highest priority. Curative-intent chemotherapy has the highest priority, followed by neoadjuvant/adjuvant and palliative chemotherapy during a crisis period. Telemedicine should be actively considered as a management tool for brain tumor patients during the mass infection crises such as the COVID-19 pandemic. CONCLUSION: It is crucial that adequate medical care for patients with brain tumors is maintained and provided, even during times of crisis. This guideline will serve as a valuable resource, assisting in the delivery of treatment to brain tumor patients in the event of any future crisis.

An Efficient Deep Learning based Approach for the Detection of Brain Tumors

Deep learning has stretched out its roots even more in our daily lives. As a society, we are witnessing small changes in lifestyle such as self-driving cars, Google Assistant, Netflix recommendations, and spam email detection. Similarly, deep learning is also evolving in healthcare, and today many doctors often use it more comfortably. Using deep learning models we can detect severe brain tumors with the help of MRI scans, in fact in the Covid era, deep learning evolved majorly to detect the disease with the help of Lung X-Rays. Magnetic Resonance Imaging (MRI) is used when a person has a brain tumor to detect it. Brain tumors can fall into any category, and MRI scans of these millions of people are needed to determine if they have the disease and if so, which category they belong to. Determining the type of brain tumor can be a rigid task and deep learning models play an important role here. For the proposed deep learning model, we have implemented convolution neural networks (CNN) through which our model has achieved a testing accuracy of 96.5%. Also, along with this, the libraries of Keras and Tensorflow have been explored by the authors in this research. © 2022 IEEE.

Dyadic versus individual delivery of a yoga program for family caregivers of glioma patients undergoing radiotherapy: Results of a 3-arm randomized controlled trial.

BACKGROUND: Despite their significant distress, supportive care interventions for caregivers of glioma patients are generally lacking. And, whether caregivers are more likely to benefit from interventions targeting patient-caregiver dyads or caregivers individually is unknown. This pilot randomized controlled trial compared the feasibility and preliminary efficacy of a dyadic yoga (DY) versus an individual caregiver yoga (CY) intervention as a supportive care strategy for family caregivers. METHODS: Patient-caregiver dyads were randomized to a DY, CY or usual care (UC) arm. DY and CY interventions were delivered over 15 sessions. Caregivers completed assessments of their depressive symptoms, quality of life (QOL), and caregiving reactions at baseline, 6 weeks, and 12 weeks, and a subset completed qualitative interviews at 12 weeks. RESULTS: With a consent rate of 63%, 67 dyads were randomized. Attendance in the DY was higher than in the CY group (session means, DY = 12.23, CY = 9.00; p = 0.06). Caregivers (79% female; 78% non-Hispanic White; mean age, 53 years) reported significantly more subjective benefit in the CY arm than in the DY arm (d = 2.1; p < .01), which was consistent with the qualitative assessment. There were medium effect sizes for improved mental QOL (d = 0.46) and financial burden (d = 0.53) in favor of the CY over the UC group. Caregivers in the CY group reported more caregiving esteem (d = 0.56) and less health decline (d = 0.60) than those in the DY group. CONCLUSION: Individual rather than dyadic delivery may be a superior supportive care approach for this vulnerable caregiver population. A larger, adequately powered efficacy trial is warranted.

Multiclass Classification of Disease Using CNN and SVM of Medical Imaging

This paper proposed a model that deals with automatic prediction of the disease given the medical imaging. While most of the existing models deals with predicting disease in one part of the body either brain, heart or lungs, this paper focuses on three different organs brain, chest, and knee for better understanding the real word challenge where problems do not include crisp classification but the multiclass classification. For simplicity this paper focuses on just determining whether that organ is affected with the disease or not and future work can be done by further expanding the model for multiple disease detection of that organ. We have used CNN for multiclass image classification to determine the input medical image is brain, chest or knee and then SVM is used for binary classification to determine whether that input image is detected with the disease or not. Three different datasets from Kaggle are used: Brain Tumor MRI Dataset, COVID-19 Chest X-ray Image Dataset and Knee Osteoarthritis Dataset with KL Grading. Images from these datasets are used to make fourth datasets for training and testing the CNN for the prediction of the three different organs and after that output will be the input of respective SVM classifier based on the output result and predict the weather it is diagnostic with the disease or not. The proposed model can be employed as an effective and efficient method to detect different human diseases associated with different parts of the body without explicitly giving the input that it belongs to that part. For the transparency this model displays the accuracy of prediction made for the input image. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Don’t ignore the splinter haemorrhage . . . and other stories

[...]brain tumours after radiotherapy Radiotherapy is sometimes given after surgery for the treatment of pituitary adenomas and craniopharyngiomas, despite the risk of inducing a second brain tumour. Treatment of degenerative meniscal tears Although arthroscopic meniscectomy is frequently performed in people with degenerative meniscal tears, little evidence exists to suggest long term benefit. A randomised controlled trial from the Netherlands finds that self-reported knee function five years after treatment was no better in those receiving surgery than in those randomly assigned to exercise based physical therapy (JAMA Netw Open doi:10.1001/jamanetworkopen.2022.20394).

Transfer Learning from Healthy to Unhealthy Patients for the Automated Classification of Functional Brain Networks in fMRI

Functional Magnetic Resonance Imaging (fMRI) is an essential tool for the pre-surgical planning of brain tumor removal, which allows the identification of functional brain networks to preserve the patient’s neurological functions. One fMRI technique used to identify the functional brain network is the resting-state-fMRI (rs-fMRI). This technique is not routinely available because of the necessity to have an expert reviewer who can manually identify each functional network. The lack of sufficient unhealthy data has so far hindered a data-driven approach based on machine learning tools for full automation of this clinical task. In this article, we investigate the possibility of such an approach via the transfer learning method from healthy control data to unhealthy patient data to boost the detection of functional brain networks in rs-fMRI data. The end-to-end deep learning model implemented in this article distinguishes seven principal functional brain networks using fMRI images. The best performance of a 75% correct recognition rate is obtained from the proposed deep learning architecture, which shows its superiority over other machine learning algorithms that were equally tested for this classification task. Based on this best reference model, we demonstrate the possibility of boosting the results of our algorithm with transfer learning from healthy patients to unhealthy patients. This application of the transfer learning technique opens interesting possibilities because healthy control subjects can be easily enrolled for fMRI data acquisition since it is non-invasive. Consequently, this process helps to compensate for the usual small cohort of unhealthy patient data. This transfer learning approach could be extended to other medical imaging modalities and pathology.

Computer Vision-Based Prognostic Modelling of COVID-19 from Medical Imaging

COVID19 is a respiratory illness that is extremely infectious and is spreading at an alarming rate at the moment. Chest radiography images play an important part in the automated diagnosis of COVID19, which is accomplished via the use of several machine learning approaches. This chapter examines prognostic models for COVID-19 patients’ survival prediction based on clinical data and lung/lesion radiometric characteristics retrieved from chest imaging. While it seems that there are various early indicators of prognosis, we will discuss prognostic models or scoring systems that are useful exclusively to individuals who have received confirmation of their cancer diagnosis. A summary of some of the research work and strategies based on machine learning and computer vision that have been applied for the identification of COVID19 have been presented in this chapter. Some strategies based on pre-processing, segmentation, handmade features, deep features, and classification have been discussed, as well as some other techniques. Apart from that, a few relevant datasets have been provided, along with a few research gaps and challenges in the respective sector that have been identified, all of which will be useful for future study efforts. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Forthcoming Articles

B Neurological and neurophysiological changes associated with SARS-CoV-2 infection: New observations, new mechanisms b Muhammed Ali Haidar, Hussam Jourdi, Zeinab Haj Hassan, OHanes Ashekyan, Manal Fardoun, Mark Wehbe, Ghassan Dbaibo, Hassan Zaraket, Ali Eid, & Firas Kobeissy B Circular RNAs in the brain: A possible role in memory?. [Extracted from the article] Copyright of Neuroscientist is the property of Sage Publications Inc. 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.)

A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network

Brain tumor is a severe cancer and a life-threatening disease. Thus, early detection is crucial in the process of treatment. Recent progress in the field of deep learning has contributed enormously to the health industry medical diagnosis. Convolutional neural networks (CNNs) have been intensively used as a deep learning approach to detect brain tumors using MRI images. Due to the limited dataset, deep learning algorithms and CNNs should be improved to be more efficient. Thus, one of the most known techniques used to improve model performance is Data Augmentation. This paper presents a detailed review of various CNN architectures and highlights the characteristics of particular models such as ResNet, AlexNet, and VGG. After that, we provide an efficient method for detecting brain tumors using magnetic resonance imaging (MRI) datasets based on CNN and data augmentation. Evaluation metrics values of the proposed solution prove that it succeeded in being a contribution to previous studies in terms of both deep architectural design and high detection success.

Development of Deep Neural Network Algorithm for Identification of Cerebral Microstructural Changes in Brain Tumor for Post-COVID-19 Patients

The year 2019 brought the once in hundred years’ experience for the whole world. COVID-19 pandemic shaken almost all segments of everyone’s life and scientists all over the world are engaged in saving our existence. As there is a need of capturing microstructural changes like tumor boundary pixel level shifts and/or growth, deep learning can be a very promising to identify the pixel level changes occurred in brain MR images. The multi-layer execution using CNN architecture is possible, but there is a need for fast convolution and de-convolution with lowered strides. Conventional methods can provide acceptable results, but to identify the microstructural changes in (COVID-19 patient) MR image, accuracy and visibility at pixel level need to be very precise. Hence, this paper presents the methodology for analysis of pre- and post-COVID-19 brain tumor microstructures by means of development of novel CNNPostCoV deep learning algorithm. Proposed research uses IIARD-19 and IIARD-20 dataset of COVID-19 patient. Algorithm framed with convolution neural network architecture which provides better performance of dice score, sensitivity, and PPV parameters. Paper also presents the training and validation analysis for HGG, LGG, and combined dataset of multi-modal brain tumors. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COV-BT Ire study: safety and efficacy of the BNT162b2 mRNA COVID-19 vaccine in patients with brain tumors.

BACKGROUND: The BNT162b2 vaccine conferred 95% protection against COVID-19 in people aged 16 years or older. OBJECTIVE: The aim of this observational study was to evaluate safety and efficacy of vaccine in patients affected by primary brain tumor (PBT). METHODS: We proposed COVID-19 vaccine to all patients affected by PBT followed by Neuroncology Unit of National Cancer Institute Regina Elena. RESULTS: 102 patients received the first dose, 100 the second, and 73 patients received the booster dose. After first dose, we observed one patient with fever and severe fatigue, while after the second one, we recorded adverse events in ten patients. No correlation was observed between adverse events and comorbidities. CONCLUSIONS: The COVID-19 vaccine is safe and well tolerated in PBT patients.

Introduction of a multidisciplinary neuro-oncology long-term follow-up clinic at GOSH during the covid-19 pandemic: experience of the past 18 months

BackgroundThe number of children and young people (CYP) surviving brain tumours is increasing annually, with 5-year survival having nearly doubled over the past 40 years. However, more than two-thirds of these survivors have multiple long-term co-morbidities (‘late effects’) resulting from their tumour and/or treatment(s) necessitating lifelong follow-up from multiple professionals.ObjectiveWe sought to establish a collaborative multidisciplinary ‘one-stop shop’ long-term follow-up clinic for CYP who were >5 years from the end of treatment of a brain tumour to reduce the need for multiple hospital appointments and to provide holistic assessment and supportive care.ResultsThe first clinic began in January 2020 as a collaborative effort between healthcare professionals in the neuro-oncology, endocrinology, psychology, neuropsychology, physiotherapy and occupational therapy departments. Clinics ran once to twice-monthly throughout the year, and apart from a 6-month period during the Covid-19 pandemic, all appointments were carried out face to face. Over 18 months, a total of 61 patients have been seen in 111 appointments. Apart from neuro-oncology, CYP also had endocrinology (97%), neuropsychology (65%), physiotherapy/occupational therapy (55%) and psychology (53%) needs. Other specialties not represented in the clinic who were still involved in the care of these CYP included ophthalmology (57%), audiology (30%), neurology (22%) and CAMHS (22%). Feedback from CYP and their families thus far has been overwhelmingly positive, with all leaving feeling very satisfied or satisfied. Major issues include continued funding, the lack of clinic room space, and the appropriateness of the whole team seeing some CYP at the same time.ConclusionsFew models of similar multidisciplinary neuro-oncology long-term follow-up clinics exist in the UK, and streamlined funding of such services is sorely needed, despite the recognition from both families and professionals about their utility.

Quality of Life in Brain Tumor Patients and Their Relatives Heavily Depends on Social Support Factors during the COVID-19 Pandemic.

The COVID-19 pandemic is associated with significant morbidity, mortality, and restrictions on everyday life worldwide. This may be especially challenging for brain tumor patients given increased vulnerability due to their pre-existing condition. Here, we aimed to investigate the quality of life (QoL) in brain tumor patients and relatives in this setting. Over twelve weeks during the first wave of the pandemic (04-07/2020), brain tumor patients and their families from two large German tertiary care centers were asked to complete weekly questionnaires for anxiety, depression, distress, and well-being. Information regarding social support and living conditions was also collected. One hundred participants (63 patients, 37 relatives) completed 729 questionnaires over the course of the study. Compared to relatives, patients showed more depressive symptoms (p < 0.001) and reduced well-being (p = 0.013). While acceptance of lockdown measures decreased over time, QoL remained stable. QoL measures between patients and their families were weakly or moderately correlated. The number of social contacts was strongly associated with QoL. Age, living conditions, ongoing therapy, employment, and physical activity were other predictors. QoL is correlated between patients and their families and heavily depends on social support factors, indicating the need to focus on the entire family and their social situation for QoL interventions during the pandemic.

Managing children with brain tumors during the COVID-19 era: Don't stop the care!

The COVID-19 pandemic has substantially stressed health care systems globally, subsequently reducing cancer care services and delaying treatments. Pediatric populations infected by COVID-19 have shown mild clinical symptoms compared to adults, perhaps due to decreased susceptibility. Several scientific societies and governments have released information on the management of patients with cancer, wherein they warn against exposure to SARS-CoV-2 infection and suggest continuing treatment. To determine the best diagnostic and therapeutic approach, multidisciplinary tumor boards should convene regularly, including through conference calls and telematics platforms. A prompt diagnostic workup may reduce children's suffering and prevent loss of confidence in the health care system among parents. Moreover, ensuring adequate support and information regarding measures for preventing SARS-CoV-2 infection in pediatric patients and their families is essential for avoiding panic and excessive stress, allowing early reporting of any suspected symptoms of cancer and, in turn, facilitating early diagnosis and prompt modulation of treatment.

Overview of prognostic factors in adult gliomas.

Gliomas represent the majority of malignant central nervous system tumors, with the most aggressive subtype, glioblastoma, accounting for almost 57% of this entity. Type of glioma and its incidence can vary depending on the age of presentation. In turn, outcomes can vary significantly based on the actual type of glioma (histologically and molecularly) and age of the patient, as well as various tumor specific factors such as size, location, comorbidities, etc. In the last decade we have been able to identify key molecular features that have provided us with greater insight into the behavior of these tumors, but the spectrum of treatment options remains limited. In addition, ultimate causes of death in patients with gliomas are variable and stochastic in nature. Given these complicated factors, prognostication for gliomas remains extremely difficult. This review aims to discuss prognostication in low grade versus high grade gliomas, variability in treatment of these tumors, clinical features of poor prognosis, and differences in prognostic understanding between patients, caregivers, and providers. We will also make some general recommendations where appropriate on how to approach this subject from a palliative care perspective.