Early Detection of Oral Potentially Malignant Disorders: A Review on Prospective Screening Methods with Regard to Global Challenges.

Oral cancer is a cancer type that is widely prevalent in low-and middle-income countries with a high mortality rate, and poor quality of life for patients after treatment. Early treatment of cancer increases patient survival, improves quality of life and results in less morbidity and a better prognosis. To reach this goal, early detection of malignancies using technologies that can be used in remote and low resource areas is desirable. Such technologies should be affordable, accurate, and easy to use and interpret. This review surveys different technologies that have the potentials of implementation in primary health and general dental practice, considering global perspectives and with a focus on the population in India, where oral cancer is highly prevalent. The technologies reviewed include both sample-based methods, such as saliva and blood analysis and brush biopsy, and more direct screening of the oral cavity including fluorescence, Raman techniques, and optical coherence tomography. Digitalisation, followed by automated artificial intelligence based analysis, are key elements in facilitating wide access to these technologies, to non-specialist personnel and in rural areas, increasing quality and objectivity of the analysis while simultaneously reducing the labour and need for highly trained specialists.

Autoimmune Encephalitis Due to COVID-19 in a Young Patient.

Autoimmune encephalitis is an inflammatory condition caused by different factors, including viral infections, diagnosed after ruling out other causes of encephalitis. The current study reported novel autoimmune encephalitis in an 11-year-old girl who presented with seizures, cognitive dysfunction, and neurological impairments. During the admission, the researchers observed high levels of anti-N-methyl-D-aspartate receptor (NMDAR) antibodies in the cerebrospinal fluid (CSF). Besides, she had positive anti-COVID-19IgG. Therefore, the diagnosis of COVID-19-induced autoimmune encephalitis was specific. The patient received anti-epileptic, anti-viral drugs, IVIG, and rituximab and was discharged with remission. The case diagnosis was made by anti-NMDAR antibodies, which highlights the importance of this diagnostic tool. Similar cases have been reported earlier, but the point of this case was her younger age compared to the previous cases and her developing neurological deficit before COVID-19 presentations.

Five-Year Survival and Complications of Thoracic Endovascular Aortic Repair (TEVAR): A Single Tertiary Center Registry for All-Comers Patients.

INTRODUCTION: Studies on the mid and long-term outcomes of thoracic endovascular aortic repair (TEVAR) are scarce. This study aimed to investigate the long-term outcomes of TEVAR for all-comers patients with various types of thoracic aortic pathologies, predominantly thoracic aortic dissections and thoracic aortic aneurysms. METHODS: Between January 2005 and December 2015, 123 consecutive patients with thoracic aortic pathologies underwent TEVAR. The patients were divided into groups based on aortic pathologies (dissections or aneurysms) and the type of procedure (hybrid TEVAR or non-hybrid TEVAR). Mortality, complications, and reintervention rates were compared between these four groups. The Kaplan-Meier curve and the Cox regression analysis were employed to estimate survivals and factors associated with 5-year mortality. RESULTS: One hundred twenty-three consecutive patients with various aortic pathologies undergoing TEVAR were evaluated. Hypertension (79.7 %) and smoking (39 %) were the most common comorbidities in the study population. The patients were followed up for a mean duration of 58.1 ± 7.1 months to evaluate the postprocedural surveillance. Most complications occurred in the dissection and hybrid groups; however, these differences were nonsignificant. The overall 5-year surveillance and reintervention rates were 65 % and 12.1 %, respectively. A significant difference existed in 5-year survival between the patients with aneurysmal and aortic dissection pathologies based on the log-rank test (P = 0.01). On the other hand, no significant differences were observed in 5-year survival between the patients who underwent hybrid and non-hybrid TEVAR. The Cox regression analysis confirmed that chronic kidney disease, previous coronary artery bypass grafting, age >65 years, platelet counts <150∗ 103/µL, aneurysmal aortic pathology, and ejection fraction ≤ 50 % were predictors of 5-year mortality. CONCLUSIONS: The long-term outcomes of TEVAR for various aortic pathologies disclosed that this procedure was well-tolerated in a group of patients with high comorbid conditions. The five-year survival rate of TEVAR for thoracic aortic dissections was more favorable than that for thoracic aortic aneurysms, but there were no significant differences between hybrid and non-hybrid procedures regarding the mortality rate.

5-ALA fluorescence in randomly selected pediatric brain tumors assessed by spectroscopy and surgical microscope.

PURPOSE: Fluorescence-guided surgery applying 5-aminolevulinic acid (5-ALA) in high-grade gliomas is an established method in adults. In children, results have so far been ambiguous. The aim of this study was to investigate 5-ALA-induced fluorescence in pediatric brain tumors by using the surgical microscope and a spectroscopic hand-held probe. METHODS: Fourteen randomly selected children (age 4-17) with newly MRI-verified brain tumors were included. No selection was based on the suspected diagnosis prior to surgery. All patients received 5-ALA (20 mg /kg) either orally or via a gastric tube prior to surgery. Intratumoral fluorescence was detected with the microscope and the probe. Moreover, fluorescence in the skin of the forearm was measured. Histopathology samples revealed seven low-grade gliomas, four medulloblastomas, one diffuse intrinsic pontine glioma, one glioblastoma and one atypical meningioma. Blood samples were analyzed, and potential clinical side effects were monitored. RESULTS: Microscopically, vague fluorescence was visible in two patients. Intratumoral fluorescence could be detected in five patients with the probe, including the two patients with vague microscopic fluorescence. Three of the oldest children had PpIX fluorescence in the skin. Nine children did not show any fluorescence in the tumor or in the skin. No clinical side effects or laboratory adverse events were observed. CONCLUSION: Fluorescence could not be used to guide surgery in this study, neither with the surgical microscope nor with the hand-held probe. In nine children, no fluorescence was discerned and children with noticeable fluorescence were all older than nine years. 5-ALA was considered safe to apply in children.

Diseased thyroid tissue classification in OCT images using deep learning: Towards surgical decision support.

Intraoperative guidance tools for thyroid surgery based on optical coherence tomography (OCT) could aid distinguish between normal and diseased tissue. However, OCT images are difficult to interpret, thus, real-time automatic analysis could support the clinical decision-making. In this study, several deep learning models were investigated for thyroid disease classification on 2D and 3D OCT data obtained from ex vivo specimens of 22 patients undergoing surgery and diagnosed with several thyroid pathologies. Additionally, two open-access datasets were used to evaluate the custom models. On the thyroid dataset, the best performance was achieved by the 3D vision transformer model with a Matthew's correlation coefficient (MCC) of 0.79 (accuracy = 0.90) for the normal-versus-abnormal classification. On the open-access datasets, the custom models achieved the best performance (MCC > 0.88, accuracy > 0.96). Results obtained for the normal-versus-abnormal classification suggest OCT, complemented with deep learning-based analysis, as a tool for real-time automatic diseased tissue identification in thyroid surgery.

Effect of parenting intervention through "Care for Child Development Guideline" on early child development and behaviors: a randomized controlled trial.

BACKGROUND: Several studies showed that parenting intervention programs play a core component in early child development. Considering the limited healthcare resources in developing countries, group-session intervention based on care for child development (CCD) guideline might be cost-effective. METHODS: This randomized controlled trial was conducted at an outpatient public Pediatrics clinic in Isfahan, Iran. We included 210 pregnant women aged 18-45 years in their third trimester and followed their children for 18 months. The intervention group underwent 5 educational group sessions, each lasting for almost 45 minutes. The main outcomes were the children's development and socio-emotional behavior problems based on Bayley Scales of Infant and Toddler Development-III (BSID-III) at 12 months and the Children Behavior Checklist (CBCL) at 18 months. RESULTS: Overall, data of 181 children were included in the current study, including 80 in the intervention group and 101 controls. The adjusted median/mean differences between intervention and control groups using median/linear regression were not significant for all BSID-III domains except for median differences for cognitive score based on BSID-III (ß (SE): - 4.98(2.31), p:0.032) and mean differences for anxiety/depression score based on CBCL (ß (SE): - 2.54(1.27), p:0.046). CONCLUSION: In this study, parenting interventions through CCD group sessions were significantly effective on just one subscale of children's socio-emotional behavior domains based on CBCL and one domain of children's development based on BSID-III. There might be a ceiling or floor effects for the BSID-III and CBCL assessment, respectively, leaving little room for improvement as almost all children have achieved their full developmental potential in our study. TRIAL REGISTRATION: IRCT20190128042533N2, Date of registration: 16/01/2020, www.irct.ir.

Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images.

In the application of deep learning on optical coherence tomography (OCT) data, it is common to train classification networks using 2D images originating from volumetric data. Given the micrometer resolution of OCT systems, consecutive images are often very similar in both visible structures and noise. Thus, an inappropriate data split can result in overlap between the training and testing sets, with a large portion of the literature overlooking this aspect. In this study, the effect of improper dataset splitting on model evaluation is demonstrated for three classification tasks using three OCT open-access datasets extensively used, Kermany's and Srinivasan's ophthalmology datasets, and AIIMS breast tissue dataset. Results show that the classification performance is inflated by 0.07 up to 0.43 in terms of Matthews Correlation Coefficient (accuracy: 5% to 30%) for models tested on datasets with improper splitting, highlighting the considerable effect of dataset handling on model evaluation. This study intends to raise awareness on the importance of dataset splitting given the increased research interest in implementing deep learning on OCT data.

Reversible Cerebral Vasoconstriction Syndrome and Multisystem Inflammatory Syndrome in Children With COVID-19.

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) involves multiple organs and shows increased inflammatory markers. Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, several studies have reported the association between severe COVID-19 and MIS-C. Reversible cerebral vasoconstriction syndrome (RCVS) presents with thunderclap headaches and multifocal reversible vasoconstriction on imaging. RCVS is very rare in children. This article reports two cases of pediatric COVID-19 with severe MIS-C and clinical and imaging features indicative of RCVS. METHODS: Clinical, laboratory, and imaging data of the patients were reviewed. The diagnosis of RCVS was confirmed based on clinical symptomatology and brain magnetic resonance imaging findings. RESULTS: Two pediatric patients with clinical findings compatible with severe MIS-C and hemodynamic compromise presented to the hospital. During their hospitalization course, they developed thunderclap headaches and neurological deficits. Both were receiving vasoactive agents, intravenous immunoglobulin, and immunosuppressants. Imaging studies showed marked multifocal cerebral vasoconstriction in both cases and infarcts in one. The course and management of the patients will be presented. After controlling inflammation and elimination of triggers, both patients were ultimately symptom free upon discharge. Cerebral vasoconstriction had completely resolved on follow-up imaging. CONCLUSIONS: Although a variety of symptoms including headaches may be seen in pediatric COVID-19 patients with MIS-C, RCVS should be considered as a differential diagnosis in cases of thunderclap headache accompanied by neurological signs in these patients. Imaging findings and follow-up are also key in establishing the diagnosis.

Longitudinally extensive transverse myelitis as a sign of multisystem inflammatory syndrome following COVID-19 infection: A pediatric case report.

COVID-19 infection can cause inflammatory reactions that could involve several organs. In the pediatric population, Multi-System Inflammatory Syndrome in Children (MIS-C) has been reported as one of the consequences of COVID-19. We report a unique pediatric COVID-19 patient with MIS-C, associated with paralysis of the extremities. MRI showed abnormal signal in the cervical spinal cord compatible with transverse myelitis. Methylprednisolone and IVIG were administered, without significant symptom improvement. As a next step, Infliximab was tried for her, and she responded remarkably well to this treatment. Infliximab may be considered as a treatment option in COVID-19 patients with transverse myelitis.

Does Anatomical Contextual Information Improve 3D U-Net-Based Brain Tumor Segmentation?

Effective, robust, and automatic tools for brain tumor segmentation are needed for the extraction of information useful in treatment planning. Recently, convolutional neural networks have shown remarkable performance in the identification of tumor regions in magnetic resonance (MR) images. Context-aware artificial intelligence is an emerging concept for the development of deep learning applications for computer-aided medical image analysis. A large portion of the current research is devoted to the development of new network architectures to improve segmentation accuracy by using context-aware mechanisms. In this work, it is investigated whether or not the addition of contextual information from the brain anatomy in the form of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) masks and probability maps improves U-Net-based brain tumor segmentation. The BraTS2020 dataset was used to train and test two standard 3D U-Net (nnU-Net) models that, in addition to the conventional MR image modalities, used the anatomical contextual information as extra channels in the form of binary masks (CIM) or probability maps (CIP). For comparison, a baseline model (BLM) that only used the conventional MR image modalities was also trained. The impact of adding contextual information was investigated in terms of overall segmentation accuracy, model training time, domain generalization, and compensation for fewer MR modalities available for each subject. Median (mean) Dice scores of 90.2 (81.9), 90.2 (81.9), and 90.0 (82.1) were obtained on the official BraTS2020 validation dataset (125 subjects) for BLM, CIM, and CIP, respectively. Results show that there is no statistically significant difference when comparing Dice scores between the baseline model and the contextual information models (p > 0.05), even when comparing performances for high and low grade tumors independently. In a few low grade cases where improvement was seen, the number of false positives was reduced. Moreover, no improvements were found when considering model training time or domain generalization. Only in the case of compensation for fewer MR modalities available for each subject did the addition of anatomical contextual information significantly improve (p < 0.05) the segmentation of the whole tumor. In conclusion, there is no overall significant improvement in segmentation performance when using anatomical contextual information in the form of either binary WM, GM, and CSF masks or probability maps as extra channels.