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Alzheimer's disease (AD) is a neurodegenerative disease associated with senile plaques (SP) and neurofibrillary tangles (NFTs) in the brain. With aging of the population, AD has become the most common form of dementia. However, the mechanisms leading to AD are still under investigation, and there are currently no specific drugs for its treatment. Therefore, further study on the pathogenesis of AD to develop new drugs for AD treatment remains a top priority. Several studies have suggested that intracellular calcium homeostasis is dysregulated in AD, and this has been implicated in the deposition of amyloid β (Aβ), hyperphosphorylation of tau protein, abnormal synaptic plasticity, and apoptosis, all of which are involved in the occurrence and development of AD. In addition, some based on pathways linking calcium homeostasis and AD have achieved results in AD treatment. This review comprehensively explores the relationship between calcium homeostasis and the pathogenesis of AD to provide a theoretical basis for the future exploration of AD and the development of novel therapeutic drugs.

Objective BRAF V600E is the most common mutation found in thyroid cancer and is particularly associated with papillary thyroid carcinoma (PTC). Currently, genetic mutation detection relies on invasive procedures. This study aimed to extract radiomic features and utilize deep transfer learning (DTL) from ultrasound images to develop a noninvasive artificial intelligence model for identifying BRAF V600E mutations. Materials and methods Regions of interest (ROI) were manually annotated in the ultrasound images, and radiomic and DTL features were extracted. These were used in a joint DTL–radiomics (DTLR) model. Fourteen DTL models were employed, and feature selection was performed using the LASSO regression. Eight machine learning methods were used to construct predictive models. Model performance was primarily evaluated using area under the curve (AUC), accuracy, sensitivity and specificity. The interpretability of the model was visualized using gradient-weighted class activation maps (Grad-CAM). Results Sole reliance on radiomics for identification of BRAF V600E mutations had limited capability, but the optimal DTLR model, combined with ResNet152, effectively identified BRAF V600E mutations. In the validation set, the AUC, accuracy, sensitivity and specificity were 0.833, 80.6%, 76.2% and 81.7%, respectively. The AUC of the DTLR model was higher than that of the DTL and radiomics models. Visualization using the ResNet152-based DTLR model revealed its ability to capture and learn ultrasound image features related to BRAF V600E mutations. Conclusion The ResNet152-based DTLR model demonstrated significant value in identifying BRAF V600E mutations in patients with PTC using ultrasound images. Grad-CAM has the potential to objectively stratify BRAF mutations visually. The findings of this study require further collaboration among more centers and the inclusion of additional data for validation.

BRAF is the most common mutation found in thyroid cancer and is particularly associated with papillary thyroid carcinoma (PTC). Currently, genetic mutation detection relies on invasive procedures. This study aimed to extract radiomic features and utilize deep transfer learning (DTL) from ultrasound images to develop a noninvasive artificial intelligence model for identifying BRAF mutations. Regions of interest (ROI) were manually annotated in the ultrasound images, and radiomic and DTL features were extracted. These were used in a joint DTL-radiomics (DTLR) model. Fourteen DTL models were employed, and feature selection was performed using the LASSO regression. Eight machine learning methods were used to construct predictive models. Model performance was primarily evaluated using area under the curve (AUC), accuracy, sensitivity and specificity. The interpretability of the model was visualized using gradient-weighted class activation maps (Grad-CAM). Sole reliance on radiomics for identification of BRAF mutations had limited capability, but the optimal DTLR model, combined with ResNet152, effectively identified BRAF mutations. In the validation set, the AUC, accuracy, sensitivity and specificity were 0.833, 80.6%, 76.2% and 81.7%, respectively. The AUC of the DTLR model was higher than that of the DTL and radiomics models. Visualization using the ResNet152-based DTLR model revealed its ability to capture and learn ultrasound image features related to BRAF mutations. The ResNet152-based DTLR model demonstrated significant value in identifying BRAF mutations in patients with PTC using ultrasound images. Grad-CAM has the potential to objectively stratify BRAF mutations visually. The findings of this study require further collaboration among more centers and the inclusion of additional data for validation.

Global atmospheric CO2 concentrations, driven by anthropogenic emissions and natural carbon cycle dynamics, have emerged as a critical accelerator of climate change. However, due to the spatiotemporal heterogeneity of carbon sources and sinks, estimating CO2 flux remains highly uncertain. Accurately quantifying the contribution of various carbon sources and sinks to atmospheric CO2 concentration is essential for understanding the carbon cycle and global carbon balance. In this study, GEOS-Chem (version 13.2.1), driven by MERRA-2 meteorological data, was used to simulate monthly global CO2 concentrations from 2006 to 2010. The model was configured with a horizontal resolution of 2.5° longitude × 2.0° latitude and 47 vertical hybrid-sigma layers up to 0.01 hPa. To evaluate the impact of different emission sources and sinks, the “Inventory switching and replacing” approach was applied, designing a series of numerical experiments in which individual emission sources were selectively disabled. The contributions of eight major CO2 flux components, including fossil fuel combustion, biomass burning, balanced biosphere, net land exchange, aviation, shipping, ocean exchange, and chemical sources, were quantified by comparing the baseline simulation (BASE) with source-specific perturbation experiments (no_X). The results show that global CO2 concentration exhibits a spatial pattern with higher concentrations in the Northern Hemisphere and land areas, with East Asia, Southeast Asia, and eastern North America being high-concentration regions. The global average CO2 concentration increased by 1.8 ppm year−1 from 2006 to 2010, with China’s eastern region experiencing the highest growth rate of 3.0 ppm year−1. Fossil fuel combustion is identified as the largest CO2 emission source, followed by biomass burning, while oceans and land serve as significant CO2 sinks. The impact of carbon flux on atmospheric CO2 concentration is primarily determined by the spatial distribution of emissions, with higher flux intensities in industrialized and biomass-burning regions leading to more pronounced local concentration increases. Conversely, areas with strong carbon sinks, such as forests and oceans, exhibit lower net CO2 accumulation.

Previous studies demonstrated that elevated brain natriuretic peptide (BNP) level is associated with adverse clinical outcomes of acute cerebral infarction (ACI). Researchers hypothesized that BNP might be a potential neuroprotective factor against cerebral ischemia because of the antagonistic effect of the natriuretic peptide system on the renin-angiotensin system and regulation of cardiovascular homeostasis. However, whether decreasing the BNP level can improve the prognosis of ACI has not been studied yet. The main effect of sacubitril/valsartan is to enhance the natriuretic peptide system. We investigated whether the intervention of plasma BNP levels with sacubitril/valsartan could improve the prognosis of patients with ACI. In a randomized, controlled, parallel-group trial of patients with ACI within 48 hours of symptom onset and need for antihypertensive therapy, patients have randomized within 24 hours to sacubitril/valsartan 200mg once daily (the intervention group) or to conventional medical medication (the control group). The primary outcome was a change in plasma BNP levels before and after sacubitril/valsartan administration. The secondary outcomes included plasma levels of brain-derived neurotrophic factor (BDNF), Corin and neprilysin (NEP) before and after medication, the modified Rankin scale, and the National Institutes of Health Stroke Scale (at onset, at discharge, 30 days, and 90 days after discharge). We evaluated 80 eligible patients admitted to the Stroke Center of Lianyungang Second People's Hospital between 1st May, 2021 and 31st June, 2022. Except for 28 patients excluded before randomization and 14 patients who did not meet the criteria or dropped out or lost to follow-up during the trial, the remaining 38 patients (intervention group: 17, control group: 21) had well-balanced baseline features. In this trial, we found that plasma BNP levels (P = 0.003) decreased and NEP levels (P = 0.006) increased in enrolled patients after treatment with sacubitril/valsartan. There were no differences in plasma BDNF and Corin levels between the two groups. Furthermore, no difference in functional prognosis was observed between the two groups (all P values>0.05). Sacubitril/valsartan reduced endogenous plasma BNP levels in patients with ACI and did not affect their short-term prognosis.

This study investigated the effects of four selenium fertilizers (nano-Se, EDTA-chelated Se, organic Se, and microbial Se) at three concentrations (50, 25, and 12.5 mg·L−1) on garlic (Allium sativum L. cv. ‘Xusuan 918’) through foliar application during critical growth stages. Comprehensive evaluation combining agronomic traits, yield components, nutritional quality (soluble sugars, vitamin C), and selenium accumulation revealed distinct fertilizer-specific responses. Organic Se at 50 mg·L−1 (O1) maximized vegetative growth (21.83% increased plant spread), while 25 mg·L−1 microbial Se (M2) showed optimal yield enhancement (10.04% higher bulb dry weight vs. CK). Notably, 50 mg·L−1 nano-Se (N1) achieved simultaneous improvement in nutritional quality and selenium biofortification (29-fold bulb Se enrichment). Principal component analysis integrated with membership function method identified N1 treatment (D-value = 0.571) as the most effective protocol for selenium-enriched garlic production, demonstrating the importance of fertilizer selection for crop-specific selenium management strategies.

In this study, a garlic DNA fingerprinting system based on single nucleotide polymorphisms (SNPs) was developed using representative cultivated garlic accessions. SNP markers were identified based on reduced-representation genome sequencing (GBS) data from 77 garlic accessions. A total of 26,701,817 raw SNPs were detected and subsequently filtered to obtain 7,006 high-quality candidate SNP loci. Among these, KASP primers were successfully designed for 4,297 loci, corresponding to an assay design success rate of 61.3%. Of these candidate loci, 30 generated reliable KASP genotyping results, from which 13 high-quality core SNP markers were finally selected through stepwise screening based on genotyping quality, marker polymorphism, and discriminatory ability. The average minor allele frequency (MAF), expected heterozygosity (He), and polymorphism information content (PIC) of these core markers were 0.235, 0.281, and 0.227, respectively. Based on these 13 core SNP markers, an SNP-based DNA fingerprinting system for garlic was established. Genotyping analysis of the 77 accessions showed that 73 could be effectively distinguished, whereas 4 accessions remained unresolved. Overall, this core SNP marker set showed effective discriminatory power for most garlic accessions and provides a practical basis for germplasm identification, variety protection, and molecular breeding in garlic.

The prevalence of cerebral small vessel disease (CSVD) is increasing due to the accelerating global aging process, resulting in a substantial burden on all countries, as cognitive dysfunction associated with CSVD is also on the rise. Clock genes have a significant impact on cognitive decline and dementia. Furthermore, the pattern of DNA methylation in clock genes is strongly associated with cognitive impairment. Thus, the aim of this study was to explore the connection between DNA promoter methylation of PER1 and CRY1 and cognitive dysfunction in patients with CSVD. We recruited patients with CSVD admitted to the Geriatrics Department of the Lianyungang Second People's Hospital between March 2021 and June 2022. Based on their Mini-Mental State Examination score, patients were categorized into two groups: 65 cases with cognitive dysfunction and 36 cases with normal cognitive function. Clinical data, 24-h ambulatory blood pressure monitoring parameters, and CSVD total load scores were collected. Moreover, we employed methylation-specific PCR to analyze the peripheral blood promoter methylation levels of clock genes PER1 and CRY1 in all CSVD patients who were enrolled. Finally, we used binary logistic regression models to assess the association between the promoter methylation of clock genes (PER1 and CRY1) and cognitive dysfunction in patients with CSVD. (1) A total of 101 individuals with CSVD were included in this study. There were no statistical differences between the two groups in baseline clinical data except MMSE and AD8 scores. (2) After B/H correction, the promoter methylation rate of PER1 was higher in the cognitive dysfunction group than that in the normal group, and the difference was statistically significant (adjusted < 0.001). (3) There was no significant correlation between the promoter methylation rates of PER1 and CRY1 in peripheral blood and circadian rhythm of blood pressure (  > 0.05). (4) Binary logistic regression models showed that the influence of promoter methylation of PER1 and CRY1 on cognitive dysfunction were statistically significant in Model 1 ( < 0.001; = 0.025), and it still existed after adjusting for confounding factors in Model 2. Patients with the promoter methylation of PER1 gene ( = 16.565, 95% , 4.057-67.628; < 0.001) and the promoter methylation of CRY1 gene ( = 6.017, 95% , 1.290-28.069; = 0.022) were at greater risk of cognitive dysfunction compared with those with unmethylated promoters of corresponding genes in Model 2. The promoter methylation rate of PER1 gene was higher in the cognitive dysfunction group among CSVD patients. And the hypermethylation of the promoters of clock genes PER1 and CRY1 may be involved in affecting cognitive dysfunction in patients with CSVD.

This study aims to elucidate the mechanism by which guanidinoacetic acid (GAA) reduces abdominal fat deposition in broilers, focusing on the regulatory role of microRNAs (miRNAs). Excessive fat accumulation in the abdomen is a common issue in meat-type chickens as it reduces feed efficiency and compromises meat quality. By performing small RNA (sRNA) sequencing and analysis on broilers from GAA-treated and control groups, a total of 46 differentially expressed miRNAs were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that these miRNAs were significantly enriched in pathways related to lipid metabolism. Further investigation revealed that GAA likely reduces fat deposition and promotes lipid oxidation by up-regulating gga-miR-103-3p and gga-miR-107-3p, thereby inhibiting their target genes, stearoyl-CoA desaturase (SCD) and transmembrane protein 35B (TMEM35B). These findings provide a theoretical foundation for the application of GAA to improve growth performance and meat quality in broilers.

Background and purpose Cerebral small vessel disease (CSVD) is a common cause of stroke and senile vascular cognitive impairment, imposing a heavy burden on public health care systems worldwide. Hypertension and 24-hour blood pressure variability (BPV), known to be significant risk factors for cognitive dysfunction, have been found to be associated with cognitive function in CSVD patients in previous studies. However, as a derived part of BPV, there are few studies on the relationship between circadian rhythm of blood pressure and cognitive dysfunction in CSVD patients, and the relationship between them is still unclear. Thus, this study aimed to investigate whether the disturbance of circadian rhythm of blood pressure can affect the cognitive function of patients with CSVD. Methods A total of 383 CSVD patients hospitalized in the Geriatrics Department of the Lianyungang Second People’s Hospital between May 2018 and June 2022 were enrolled in this study. The clinical information and parameters of 24-hour ambulatory blood pressure monitoring were compared between the cognitive dysfunction group ( n  = 224) and the normal group ( n  = 159). Finally, a binary logistic regression model was used to assess the relationship between circadian rhythm of blood pressure and cognitive dysfunction in patients with CSVD. Results (1) Patients in the cognitive dysfunction group were older, had lower blood pressure on admission, and had a greater number of previous cardiovascular and cerebrovascular diseases ( P  < 0.05). (2) More patients in the cognitive dysfunction group had circadian rhythm abnormalities in blood pressure, especially the non-dipper and reverse-dipper types ( P  < 0.001). (3) In the elderly, there was a statistical difference in the circadian rhythm of blood pressure between the cognitive dysfunction group and the normal group, but this phenomenon did not exist in the middle-aged. (4) Binary logistic regression analysis showed that after adjusting for confounding factors, the risk of cognitive dysfunction in CSVD patients with non-dipper type was 4.052 times higher than that of dipper type (95% CI, 1.782–9.211; P  = 0.001), and reverse-dipper type was 8.002 times higher than those with dipper type (95% CI, 3.367–19.017; P <0.001). Conclusions The disturbance of circadian rhythm of blood pressure may affect the cognitive function of patients with CSVD, and the risk of cognitive dysfunction in non-dipper and reverse-dipper types are higher.