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Coronavirus disease 2019 (COVID-19) advances to affect every part of the globe and remains a challenge to the human race. Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) was shown to affect many organs and organ systems including the thyroid gland as these parts highly express angiotensin-converting enzyme 2 (ACE2) protein, which functions as a receptor for initially entering the virus into the cells. Furthermore, some categories of the population including older people and persons with comorbidities are prone to be more vulnerable to COVID-19 and its complications. Recent reports showed that SARS-CoV-2 infection could cause Graves’ disease (autoimmune hyperthyroidism) in post-COVID-19 patients. Factors that may boost the mortality risk of COVID-19 patients are not completely known yet and a clear perception of the group of vulnerable people is also essential. This review briefly summarizes the features of Graves’ disease such as symptoms, risk factors, including environmental, genetic, immunological, and other factors, associated disorders, and therapeutic options. It comprehensively describes the recent advances in SARS-CoV-2-induced Graves’ disease and the pivotal role of autoimmune factors in inducing the disease. The review also discusses the possible risks of SARS-CoV-2 infection and associated COVID-19 in people with hyperthyroidism. Furthermore, it explains thyroid disease and its association with the severity of COVID-19.

Graves' disease (GD) is an autoimmune disorder that results in hyperthyroidism, in which the immune system mistakenly targets the thyroid gland, causing it to produce excessive amounts of thyroid hormones. Genetic predisposition, environmental factors such as infections and stress, disruptions in the gut microbiome, excessive iodine intake, and epigenetic changes have all been implicated in the development of GD. The recent pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) posed a serious global health crisis. The emergence of COVID-19 vaccines has been pivotal in combating the viral infection and its spread. However, reports of rare adverse events, including the development of autoimmune disorders such as GD following vaccination, have raised concerns. Autoimmune factors play a critical role in the pathogenesis of GD, particularly through the production of autoantibodies targeting the thyroid gland. In this review, reported cases are critically analyzed to elucidate commonalities and potential triggers for the development of this autoimmune disorder, highlighting the vital role of autoimmune mechanisms in inducing GD. We also discuss the molecular mechanisms underlying vaccine-induced autoimmunity, including antigen presentation, bystander activation, molecular mimicry, and the induction of inflammatory factors following vaccination. Understanding these mechanisms in COVID-19 vaccine-induced GD could enhance patient care and guide vaccination policies.

Object detection in high-resolution aerial imagery is challenging due to scale changes, occlusion, clutter, and limited annotated datasets. While CNNs like YOLO and Faster R-CNN have progressed, they lack effective long-range dependency capture. We propose the CNN augmented detection transformer approach which we called CATR. In our quest, we compared the proposed framework with the transformer-based DETR and state-of-the-art CNNs on the DOTA dataset. DETR, with its end-to-end transformer and direct set predictions, streamlines the pipeline by removing anchor boxes and non-maximum suppression, improving robustness in cluttered aerial scenes. Our findings show DETR’s superior accuracy (72% mAP@0.5), outperforming CNNs by up to 13%. However, DETR has higher computational expense (86.3 GFLOPs) and slower speed (12 FPS). The proposed hybrid CNN-transformer architecture has a balanced accuracy and speed, exploiting CNN features with global attention for improved small object detection, augmented by the segmentation by CNN. This study confirms transformer models, especially when combined with CNN, are highly promising for complex aerial environments, offering a strong alternative to traditional CNNs by globally modeling context and occlusion. While efficiency improvements are ongoing, this research provides a valuable path for future geospatial applications, including remote sensing and disaster response.

Abstract Context Pediatric differentiated thyroid cancer (DTC) differs from adult DTC in its underlying genetics and clinicopathological features. In this report, we studied these aspects in 48 cases of pediatric DTC. Patients and Methods We used the comprehensive Oncomine Childhood Cancer Gene panel on Ion Torrent next-generation sequencing platform. We included 48 patients (37 girls and 11 boys) with pediatric DTC (median age 17 years; range, 5-18 years) and studied the association between these genetic alterations and the clinicopathological features and outcome. Results Of 48 tumors, 33 (69%) had somatic genetic alterations that were mutually exclusive except in one tumor. BRAFV600E and RET-PTC1 were the most common, occurring in 9 different tumors (19%) each. RET-PTC3 and ETV6-NTRK3 were the next most common, with each occurring in 4 different tumors (8%). Other genetic alterations including EML4-NTRK1, EML4-ALK, NRAS, KRAS, PTEN, and CREBBP occurred once each. There were no differences between those who had mutations and those without mutations with respect to age, sex, tumor multifocality, extrathyroidal extension, vascular invasion, lymph node or distant metastasis, and American Thyroid Association response to therapy status at the last follow-up visits. Similarly, none of these factors was different between those with fusion genes vs single-point mutations vs no mutations. Conclusions In pediatric DTC, fusion genes are more common than single-point mutations. The most common genetic alterations are RET-PTC1, BRAFV600E, RET-PTC3, and ETV6-NTRK3. Other alterations occur rarely. Genetic alterations do not correlate with the clinicopathological features or the outcome.

Early detection of forest fires is essential to limit ecological damage and economic loss. This study evaluates two lightweight convolutional models for binary fire recognition using a balanced dataset of 5121 annotated images spanning diverse environments and illumination conditions. The first model, Att-MobileNetV2, augments MobileNetV2 with a Convolutional Block Attention Module to prioritize informative spatial and channel responses. The second model, MobileNetV2-TL, adopts transfer learning by retaining pre-trained MobileNetV2 weights and training compact task-specific heads. On the held-out test set, Att-MobileNetV2 attains 99.61% accuracy with an F1-score of 99.70%, precision of 99.32%, and recall of 99.19%. MobileNetV2-TL achieves 98.42% accuracy, 98.43% F1-score, 98.42% precision, and 99.47% recall. Ablation results indicate that attention improves discriminability over the MobileNetV2 backbone, and attention heatmaps provide qualitative evidence of focus on flame regions. Comparisons with classical machine-learning pipelines (RFC, SVM) and CNN baselines (e.g., VGG16) under a unified preprocessing and training regimen show consistent improvements. Model size and computational load remain sufficiently low for real-time inference on resource-limited platforms, including UAVs and fixed cameras. The results indicate a favorable balance between accuracy and efficiency and point to practical deployment in continuous fire-monitoring settings.

Coronavirus disease-2019 (COVID-19) is asymptomatic in most cases, but it is impartible and fatal in fragile and elderly people. Heretofore, more than four million people succumbed to COVID-19, while it spreads to every part of the globe. Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) induces various dysfunctions in many vital organs including the thyroid by utilizing ACE2 as a receptor for cellular entry. Emerging reports clearly show the involvement of SARS-CoV-2 in diverse thyroid disorders. Thus, this review article aims to review comprehensively all the recent developments in SARS-CoV-2-induced pathogenesis of thyroid diseases. The review briefly summarizes the recent key findings on the mechanism of SARS-CoV-2 infection, the role of ACE2 receptor in viral entry, SARS-CoV-2-activated molecular signaling in host cells, ACE2 expression in the thyroid, cytokine storm, and its vital role in thyroid dysfunction and long-COVID in relation to thyroid and autoimmunity. Further, it extensively discusses rapidly evolving knowledge on the potential part of SARS-CoV-2 in emerging various thyroid dysfunctions during and post-COVID-19 conditions which include subacute thyroiditis, Graves' diseases, Hashimoto's thyroiditis, thyrotoxicosis, and other recent advances in further discerning the implications of this virus within thyroid dysfunction. Unraveling the pathophysiology of SARS-CoV-2-triggered thyroid dysfunctions may aid pertinent therapeutic options and management of these patients in both during and post-COVID-19 scenarios. Keywords: thyroid, COVID-19, hyperthyroidism, hypothyroidism, SARS-CoV-2, ACE2, inflammation, IL6, subacute thyroiditis, Graves' disease, Hashimoto's thyroiditis, thyrotoxicosis, p38/MAPK, thyroid cancer

In this paper, a neural adaptive fault-tolerant control scheme is proposed for the integrated attitude and position control of spacecraft proximity operations in the presence of unknown parameters, disturbances, and actuator faults. The proposed controller is made up of a relative attitude control law and a relative position control law. Both the relative attitude control law and relative position control law are designed by adopting the neural networks (NNs) to approximate the upper bound of the lumped unknowns. Benefiting from the indirect neural approximation, the proposed controller does not need any model information for feedback. In addition, only two adaptive parameters are required for the indirect neural approximation, and the online calculation burden of the proposed controller is therefore significantly reduced. Lyapunov analysis shows that the overall closed-loop system is ultimately uniformly bounded. The proposed controller can ensure the relative attitude, angular velocity, position, and velocity stabilize into the small neighborhoods around the origin. Lastly, the effectiveness and superior performance of the proposed control scheme are confirmed by a simulated example.

Giant prolactinomas are defined as pituitary adenomas (PAs) ≥ 4 cm with plasma prolactin level > 1000 ng/ml with no other co-secretory component. The reasons for development of giant prolactinomas are not clear but genetics play an important pathogenic mechanism in some PAs. In this report, we describe a middle-aged woman who incidentally was found to have an infiltrative giant prolactinoma involving the sellar, supra- and parasellar regions and occupying most of the middle fossae of the skull extending all the way to the occipital and upper cervical regions. Anteriorly, it extends to the sphenoid and parasellar sinuses, nasopharynx and nasal cavities. It was initially thought to be a nasopharyngeal cancer but biopsy from a protruding component from the right nostril showed that it was a prolactinoma. Prolactin level after several dilutions was extremely high at 277,500 ng/ml (normal range 3.4–24.1 ng/ml). Surprisingly, her pituitary function evaluation showed only mild central hypothyroidism [(FT4 11.4 pmol/l (normal range 12–22) and TSH 1.9 mU/l (normal range 0.27–4.2)] and hypogonadotropic hypogonadism (E2 47 pmol/l, LH 1.9 u/l, FSH 5.9 u/l). Cosyntropin stimulation test was normal suggesting normal pituitary adrenal axis but insulin-induced hypoglycaemia test was not performed. In retrospect, the patient reported chronic nasal congestion and snoring, headaches on/off and deterioration in her hearing and visual acuity over the last few years. She ascribed these symptoms to sinusitis and advancing age, respectively. Whole exome sequencing revealed a novel variant in CDH23 (NM_022124.6:c.2621C > A, p.Ala874Asp), a gene that has been previously reported to be associated with PAs. The patient was treated with small doses of cabergoline (0.5 mg twice weekly) and reported remarkable improvement in her symptoms. Radiological evaluation confirmed the significant response of this giant prolactinoma to cabergoline.

Abstract Over the past 3 decades, advances in the molecular genetics of thyroid cancer (TC) have been translated into diagnostic tests, prognostic markers, and therapeutic agents. The main drivers in differentiated TC pathogenesis are single-point mutations and gene fusions in components of the Mitogen-activated protein kinase (MAPK) and phosphoinositide-3-kinase-protein kinase B/Akt (PI3K/Akt) pathways. Other important genetic alterations in the more advanced types of TC include TERT promoter, TP53, EIF1AX, and epigenetic alterations. Using this knowledge, several molecular tests have been developed for cytologically indeterminate thyroid nodules. Currently, 3 commercially available tests are in use including a DNA/RNA-based test (ThyroSeq v.3), an RNA-based test (Afirma Gene Sequencing Classifier), and a hybrid DNA/miRNA test, ThyGeNEXT/ThyraMIR. These tests are mostly used to rule out malignancy in Bethesda III and IV thyroid nodules because they all have high sensitivities and negative predictive values. Their common use, predominantly in the United States, has resulted in a significant reduction in unnecessary thyroid surgeries for benign nodules. Some of these tests also provide information on the underlying molecular drivers of TC; this may support decision making in initial TC management planning, although this practice has not yet been widely adopted. More importantly, molecular testing is essential in patients with advanced disease before using specific mono-kinase inhibitors (eg, selpercatinib for RET-altered TC) because these drugs are ineffective in the absence of a specific molecular target. This mini-review discusses the utilization of molecular data in the clinical management of patients with thyroid nodules and TC in these different clinical situations.

In this paper, a single SiGe Tunnel FET is used to design a Leaky Integrate and Fire (LIF) neuron with significant improvement in area, energy and cost. SiGe Tunnel Field-Effect Transistor (FET) transfer characteristic with steep sub-threshold swing has been used to observe LIF neuronal characteristics. By employing calibrated simulation using Atlas 2D, we have verified that the TFET with LIF characteristics can effectively replicate neuron behavior without relying on external circuitry. The proposed LIF neuron, based on SiGe TFET, exhibits significantly reduced energy consumption, specifically 210 fJ per spike. This energy consumption is ≈ 215 times lower compared to previously reported single-device neurons in existing literature. Additionally, we have achieved an impressive recognition precision of 91.3 % for Modified National Institute of Standards and Technology (MNIST) images.