Advances in serum thyroid hormone levels and seizures.

Epilepsy, a common neurological disorder, is characterized by paroxysmal, short-term, repetitive, and stereotypical features, significantly impacting patients' quality of life. Currently, the pathogenesis of epilepsy remains incompletely understood. Changes in neuronal excitability, imbalances in glutamate and gamma-aminobutyric acid (GABA) levels, alterations in the activity of GABA receptors, and dysfunction of GABA receptors are considered closely related to its occurrence. Thyroid hormones, vital for human growth and development, also play a crucial role in the nervous system. They mediate oxidative stress, influence reactive oxygen species production, affect mitochondrial function and neuronal excitability, and modulate glutamate and GABA levels. Also, they combine with thyroid hormone receptors and exert genomic effects by regulating the expression of numerous genes. However, once there are defects in thyroid hormone signaling, these defects may lead to severe neurodevelopmental disorders that are associated with an increased frequency of seizures. The impact of antiseizure medications (ASMs) on serum thyroid hormone levels, particularly traditional ASMs, has been extensively studied. It is reported that conventional ASMs such as phenobarbital, phenytoin sodium, carbamazepine, and valproate sodium were more likely to induce subclinical hypothyroidism (elevated TSH with normal FT4) or isolated hypothyroidism (decreased FT4 with normal TSH). However, the new ASMs, such as levetiracetam, have no effect on thyroid hormone levels. Together, seizures not only affect thyroid hormone levels, but abnormal thyroid hormone levels can also influence seizures. However, the precise mechanism underlying the interaction between serum thyroid hormone levels and seizures remains unclear. This review aims to explore the relationship between thyroid hormone levels and seizures, along with the underlying mechanisms.

A tiny sample rapid visual detection technology for imidacloprid resistance in Aphis gossypii by CRISPR/Cas12a.

Insecticide resistance monitoring is essential for guiding chemical pest control and resistance management policies. Currently, rapid and effective technology for monitoring the resistance of tiny insects in the field is absent. Aphis gossypii Glover is a typical tiny insect, and one of the most frequently reported insecticide-resistant pests. In this study, we established a novel CRISPR/Cas12a-based rapid visual detection approach for detecting the V62I and R81T mutations in the ß1 subunit of the nAChR in A. gossypii, to reflect target-site resistance to imidacloprid. Based on the nAChR ß1 subunit gene in A. gossypii, the V62I/R81T-specific RPA primers and crRNAs were designed, and the ratio of 10 µM/2 µM/10 µM for ssDNA/Cas12a/crRNA was selected as the optimal dosage for the CRISPR reaction, ensuring that Cas12a only accurately recognizes imidacloprid-resistance templates. Our data show that the field populations of resistant insects possessing V62I and R81T mutations to imidacloprid can be accurately identified within one hour using the RPA-CRISPR/Cas12a detection approach under visible blue light at 440-460 nm. The protocol for RPA-CRISPR detection necessitates a single less than 2 mm specimen of A. gossypii tissues to perform RPA-CRISPR detection, and the process only requires a container at 37 °C and a portable blue light at 440-460 nm. Our research represents the first application of RPA-CRISPR technology in insecticide resistance detection, offers a new method for the resistance monitoring of A. gossypii or other tiny insects, helps delay the development of resistance to imidacloprid, improves the sustainability of chemical control, and provides theoretical guidance for managing pest resistance.

High-Accuracy Maize Disease Detection Based on Attention Generative Adversarial Network and Few-Shot Learning.

This study addresses the problem of maize disease detection in agricultural production, proposing a high-accuracy detection method based on Attention Generative Adversarial Network (Attention-GAN) and few-shot learning. The method introduces an attention mechanism, enabling the model to focus more on the significant parts of the image, thereby enhancing model performance. Concurrently, data augmentation is performed through Generative Adversarial Network (GAN) to generate more training samples, overcoming the difficulties of few-shot learning. Experimental results demonstrate that this method surpasses other baseline models in accuracy, recall, and mean average precision (mAP), achieving 0.97, 0.92, and 0.95, respectively. These results validate the high accuracy and stability of the method in handling maize disease detection tasks. This research provides a new approach to solving the problem of few samples in practical applications and offers valuable references for subsequent research, contributing to the advancement of agricultural informatization and intelligence.

High-Precision Tomato Disease Detection Using NanoSegmenter Based on Transformer and Lightweighting.

With the rapid development of artificial intelligence and deep learning technologies, their applications in the field of agriculture, particularly in plant disease detection, have become increasingly extensive. This study focuses on the high-precision detection of tomato diseases, which is of paramount importance for agricultural economic benefits and food safety. To achieve this aim, a tomato disease image dataset was first constructed, and a NanoSegmenter model based on the Transformer structure was proposed. Additionally, lightweight technologies, such as the inverted bottleneck technique, quantization, and sparse attention mechanism, were introduced to optimize the model's performance and computational efficiency. The experimental results demonstrated excellent performance of the model in tomato disease detection tasks, achieving a precision of 0.98, a recall of 0.97, and an mIoU of 0.95, while the computational efficiency reached an inference speed of 37 FPS. In summary, this study provides an effective solution for high-precision detection of tomato diseases and offers insights and references for future research.

Porcine deltacoronavirus and its prevalence in China: a review of epidemiology, evolution, and vaccine development.

Porcine deltacoronavirus (PDCoV) is one of the most important enteropathogenic pathogens, and it causes enormous economic losses to the global commercial pork industry. PDCoV was initially reported in Hong Kong (China) in 2012 and subsequently emerged in swine herds with diarrhea in Ohio (USA) in 2014. Since then, it has spread to Canada, South Korea, mainland China, and several Southeast Asian countries. Information about the epidemiology, evolution, prevention, and control of PDCoV and its prevalence in China has not been comprehensively reported, especially in the last five years. This review is an update of current information on the general characteristics, epidemiology, geographical distribution, and evolutionary relationships, and the status of PDCoV vaccine development, focusing on the prevalence of PDCoV in China and vaccine research in particular. Together, this information will provide us with a greater understanding of PDCoV infection and will be helpful for establishing new strategies for controlling this virus worldwide.

Sufu negatively regulates both initiations of centrosome duplication and DNA replication.

Centrosome duplication and DNA replication are two pivotal events that higher eukaryotic cells use to initiate proliferation. While DNA replication is initiated through origin licensing, centrosome duplication starts with cartwheel assembly and is partly controlled by CP110. However, the upstream coordinator for both events has been, until now, a mystery. Here, we report that suppressor of fused protein (Sufu), a negative regulator of the Hedgehog (Hh) pathway playing a significant role in restricting the trafficking and function of glioma-related (Gli) proteins, acts as an upstream switch by facilitating CP110 phosphorylation by CDK2, promoting intranuclear Cdt1 degradation and excluding prereplication complex (pre-RC) components from chromosomes, independent of its canonical function in the Hh pathway. We found that Sufu localizes to both the centrosome and the nucleus and that knockout of Sufu induces abnormalities including centrosome amplification, increased nuclear size, multipolar spindle formation, and polyploidy. Serum stimulation promotes the elimination of Sufu from the centrosome by vesicle release at the ciliary tip and from the nucleus via protein degradation, which allows centrosome duplication and DNA replication to proceed. Collectively, this work reveals a mechanism through which Sufu negatively regulates the G1-S transition.

Association of Serum FAM19A5 with Cognitive Impairment in Vascular Dementia.

OBJECTIVE: Family with sequence similarity 19 member A5 (FAM19A5), a novel chemokine-like peptide, is a secreted protein mainly expressed in the brain. FAM19A5 was recently found to be involved in a variety of neurological diseases; however, its correlation with vascular dementia (VaD) remains unclear. The aim of the study is to explore the association between serum FAM19A5 and cognitive impairment in subjects with VaD. METHOD: 136 VaD subjects and 81 normal controls were recruited in the study. Their demographic and clinical baseline data were collected on admission. All subjects received Mini-Mental State Examination (MMSE) evaluation, which was used to test their cognitive functions. A sandwich enzyme-linked immunosorbent assay (ELISA) was applied to detect the serum levels of FAM19A5. RESULTS: No significant differences were found between the two groups regarding the demographic and clinical baseline data (p > 0.05). The serum FAM19A5 levels were significantly higher compared to normal controls (p < 0.001). The Spearman correlation analysis indicated that serum FAM19A5 levels and MMSE scores have a significant negative correlation in VaD patients (r = -0.414, <0.001). Further multiple regression analysis indicated that serum FAM19A5 levels were independent risk predictors for cognitive functions in VaD (ß = 0.419, p = 0.031). CONCLUSION: The serum FAM19A5 level of VaD patients is significantly increased, which may serve as a biomarker to predict cognitive function of VaD.