ObjectiveThis study aims to explore the potential of different orders of magnitude single-nucleotide polymorphism (SNP) locus combinations for predicting distant kinship relationships. A high-density SNP locus set was constructed, and a comprehensive assessment of its inference capability was conducted. MethodsFirstly, we selected three commercial chip panels, CGA (Chinese genotyping array, Illumina), GSA (Global screening array, Illumina), Affy (23MF_V2 high-density SNP array, Affymetrix) and merged them after quality control, forming a high-density SNP locus panel(1 180 k). Secondly, we selected 161 samples and collected their peripheral blood samples by using whole-genome sequencing technology. Within this sample population, the levels of kinship relationships fully covered the range from level 1 to level 9, and the number of kinship pairs at each level was consistently maintained at over 50 pairs. From 161 samples data of whole-genome sequencing, the 1 180 k locus set was extracted, which is referred to as the high-density SNP locus set in the following text. The kinship inference was conducted using the identity-by-descent (IBD) algorithm with the selected optimal parameters. To comprehensively evaluate the performance of the high-density SNP locus set in kinship inference, we compared it with the three commercial chip panels, the intersection of these three chip loci, and the control sets constructed by randomly reducing the number of the high-density SNP locus set. Based on the changes in the IBD lengths, as well as the dynamic trends in prediction accuracy, we conducted a scientific assessment of the kinship inference capability of the high-density SNP locus set. ResultsAfter screening, a set of 1 184 334 autosomal SNPs was obtained. During the process of screening the optimal IBD length threshold, the result revealed that 0 cM, 1 cM, and 2 cM all demonstrated good applicability. However, to avoid the issue of a large amount of redundant information caused by setting a too low IBD length threshold, this study ultimately selected 2 cM as the optimal threshold. Compared with the average results of three chip panels, the high-density SNP locus set increased the total IBD length and the average IBD length across levels 1-9; the accuracy of the confidence interval for level 8 was 70.97%, which represented a 3.50% improvement; the average confidence interval accuracy for levels 1-8 was 91.39%, representing a 1.00% increase; and the false negative rates at levels 8 and 9 were reduced by 2.42% and 6.76%, respectively. The system efficacy of the high-density SNP locus set for kinship inference of first to eighth degree relationships reached 98.91%. Through random reduction of the high-density SNP locus set results, it is found that increasing the number of SNPs with the panel, the detection efficiency of IBD length showed a significant upward trend. At the same time, the overall trend in the accuracy of kinship relationship prediction as well as the confidence interval accuracy also indicated that both metrics steadily increased with the addition of more loci. ConclusionThe results show that the high-density SNPs panel significantly enhances the efficacy of distant kinship inference, accurately covering kinship degrees, with the average confidence interval accuracy for first to eighth degree relationships stably above 90%. The study finds that increasing the number of SNPs panel can improve the ability to predict distant kinship.

Alzheimer's disease(AD)is the most common neurodegenerative disorder worldwide,characterized primarily by cognitive impairment and memory dysfunction.Its pathological mechanisms involve the toxic accumulation of amyloid β-protein(Aβ),hyperphosphorylation of Tau protein leading to neurofibrillary tangles,mitochondrial dysfunction,synaptic impairment,cholinergic system dysfunction,neuroinflammation,and oxidative stress.Current clinical treatments for AD include acetylcholinesterase inhibitors and N-methyl-D-aspartate antagonists,which can improve cognitive function but fail to slow disease progression and often have side effects.Research indicates that traditional Chinese medicine(TCM)may regulate the phosphatidylinositol 3-kinase/protein kinase B(PI3K/AKT)signaling pathway,promoting neuronal survival,inhibiting neuroinflammation,reducing oxidative stress,preventing apoptosis,and decreasing Aβ deposition,thus improving the symptoms of AD.This review summarizes the mechanisms by which individual TCM components,extracts,and formulas may regulate the PI3K/AKT pathway in the treatment of AD,with the aim of providing a theoretical foundation for the application of TCM in AD therapy.

Alzheimer's disease(AD)is the most common neurodegenerative disorder worldwide,characterized primarily by cognitive impairment and memory dysfunction.Its pathological mechanisms involve the toxic accumulation of amyloid β-protein(Aβ),hyperphosphorylation of Tau protein leading to neurofibrillary tangles,mitochondrial dysfunction,synaptic impairment,cholinergic system dysfunction,neuroinflammation,and oxidative stress.Current clinical treatments for AD include acetylcholinesterase inhibitors and N-methyl-D-aspartate antagonists,which can improve cognitive function but fail to slow disease progression and often have side effects.Research indicates that traditional Chinese medicine(TCM)may regulate the phosphatidylinositol 3-kinase/protein kinase B(PI3K/AKT)signaling pathway,promoting neuronal survival,inhibiting neuroinflammation,reducing oxidative stress,preventing apoptosis,and decreasing Aβ deposition,thus improving the symptoms of AD.This review summarizes the mechanisms by which individual TCM components,extracts,and formulas may regulate the PI3K/AKT pathway in the treatment of AD,with the aim of providing a theoretical foundation for the application of TCM in AD therapy.

Dysregulated RNA splicing is a well-recognized characteristic of colorectal cancer (CRC); however, its intricacies remain obscure, partly due to challenges in profiling full-length transcript variants at the single-cell level. Here, we employ high-depth long-read scRNA-seq to define the full-length transcriptome of colorectal epithelial cells in 12 CRC patients, revealing extensive isoform diversities and splicing alterations. Cancer cells exhibited increased transcript complexity, with widespread 3'-UTR shortening and reduced intron retention. Distinct splicing regulation patterns were observed between intrinsic-consensus molecular subtypes (iCMS), with iCMS3 displaying even higher splicing factor activities and more pronounced 3'-UTR shortening. Furthermore, we revealed substantial shifts in isoform usage that result in alterations of protein sequences from the same gene with distinct carcinogenic effects during tumorigenesis of CRC. Allele-specific expression analysis revealed dominant mutant allele expression in key oncogenes and tumor suppressors. Moreover, mutated PPIG was linked to widespread splicing dysregulation, and functional validation experiments confirmed its critical role in modulating RNA splicing and tumor-associated processes. Our findings highlight the transcriptomic plasticity in CRC and suggest novel candidate targets for splicing-based therapeutic strategies.
Humans ; Colorectal Neoplasms/metabolism* ; RNA Isoforms/metabolism* ; Single-Cell Analysis ; RNA Splicing ; Gene Expression Regulation, Neoplastic ; RNA, Neoplasm/metabolism* ; Transcriptome

This study investigated the effects and action mechanism of tannins from Galla chinensis cream(TGCC) on the skin wound of rat tail. Male Sprague Dawley(SD) rats were randomly divided into a control group, model group, model+low-dose TGCC(50 mg per rat) group, model+high-dose TGCC group(100 mg per rat), and model+TGC+FAK inhibitor(Y15) cream(100 mg+10 mg per rat) group, with 10 rats in each group. After the rat tail skin injury model was successfully constructed, in the treatment group, corresponding drugs were applied to the wound surface, while in the control and model groups, the same amount of cream base as the TGCC group was applied by the same method. Then, sterile gauze was wrapped around the wound edge, and these operations were performed three times a day for 28 consecutive days. The wound healing status at the third, seventh, eleventh, fourteenth, twenty-first, and twenty-eighth days was recorded, and the wound healing rate and healing time were calculated. On the day after the last dose of medication, rat serum and tail skin wound tissue were collected for analyzing the activities of serum alanine aminotransferase(ALT), aspartate aminotransferase(AST), creatinine(CREA), urea, reactive oxygen species(ROS), interferon gamma(IFN-γ), interleukin(IL)-1β, IL-6, IL-4, IL-10, tumor necrosis factor(TNF)-α, as well as catalase(CAT), glutathione(GSH), lactate dehydrogenase(LDH), malondialdehyde(MDA), myeloperoxidase(MPO), superoxide dismutase(SOD), total antioxidant capacity(T-AOC), platelet endothelial cell adhesion molecule-1(CD31), and leukocyte differentiation antigen 34(CD34) in the wound tissue of rat tail skin. Hematoxylin-eosin, Masson, and sirius red staining were used to observe the morphological changes in the wound tissue of rat tail skin. The thickness of the epidermis, the number of fibroblasts and blood vessels, and the contents of collagen fibers, typeⅠ collagen(COLⅠ), and COLⅢ were calculated. The mRNA expressions of keratin 10(KRT10), KRT14, vascular endothelial growth factor(VEGF), fibroblast growth factor(FGF), epidermal growth factor(EGF), CD31, CD34, matrix metallopeptidase-2(MMP-2), MMP-9, COLⅠ, COLⅢ, desmin, fibroblast specific protein 1(FSP1), IFN-γ, IL-1β, TNF-α, IL-4, IL-6, and IL-10 in skin wound tissue were determined by quantitative real-time polymerase chain reaction(PCR). Western blot was utilized to detect the protein expressions of KRT10, KRT14, VEGF, FGF, EGF, MMP-2, MMP-9, COLⅠ, COLⅢ, desmin, FSP1, focal adhesion kinase(FAK), phosphorylated focal adhesion kinase(p-FAK), phosphatidylin-ositol-3-kinase(PI3K), phosphorylated phosphatidylin-ositol-3-kinase(p-PI3K), protein kinase B(Akt), phosphorylated protein kinase B(p-Akt), mammalian target of rapamycin(mTOR), and phosphorylated mammalian target of rapamycin(p-mTOR). The results manifest that TGCC can dramatically elevate the healing rate of rat tail wounds and shorten wound healing time. Besides, it can reduce serum ROS levels, the contents of MDA, MPO, and LDH in the rat skin wound tissue, as well as the serum IFN-γ, IL-1β, IL-6, and TNF-α levels and the mRNA expression levels of IFN-γ, IL-1β, IL-6, and TNF-α in the skin wound tissue. It can elevate the activities of CAT, GSH, SOD, and T-AOC in wound tissue, the IL-4 and IL-10 contents in serum, and the mRNA expressions of IL-4 and IL-10 in the wound tissue. In addition, TGGC can inhibit inflammatory cell infiltration and increase the epidermal thickness, counts of fibroblasts and blood vessels, and contents of collagen fibers, COLⅠ, and COLⅢ. Besides, TGCC can elevate the mRNA and protein expressions of epidermal differentiation markers(KRT10 and KRT14), endothelial cell markers(CD31 and CD34), angiogenesis and fibroblast proliferation, differentiation markers(VEGF, FGF, EGF, COLⅠ, COLⅢ, desmin, and FSP1), reduce the mRNA and protein expressions of gelatinases(MMP-2 and MMP-9), and increase protein expressions of p-FAK, p-PI3K, p-Akt, p-mTOR, as well as ratios of p-FAK/FAK, p-PI3K/PI3K, p-Akt/Akt, and p-mTOR/mTOR. These results suggest that TGCC can significantly facilitate skin wound healing, and its mechanism may be related to the activation of the FAK/PI3K/Akt/mTOR signaling pathway, inhibition of inflammatory cell infiltration in skin wound tissue, elevation of epidermal thickness, counts of fibroblasts and vessels, and contents of collagen fiber, COLⅠ, and COLⅢ, and reduction of MMP-2 and MMP-9 expressions, thus accelerating wound healing.
Animals ; Male ; Wound Healing/drug effects* ; Rats ; Rats, Sprague-Dawley ; Signal Transduction/drug effects* ; TOR Serine-Threonine Kinases/genetics* ; Phosphatidylinositol 3-Kinases/genetics* ; Skin/metabolism* ; Proto-Oncogene Proteins c-akt/genetics* ; Tannins/pharmacology* ; Humans ; Drugs, Chinese Herbal/administration & dosage* ; Focal Adhesion Kinase 1/genetics*

Objective:To develop habitat radiomics models to predict early treatment responses to the hepatic arterial infusion chemotherapy (HAIC) combined with targeted therapy or immunotherapy in advanced hepatocellular carcinoma (HCC) patients, and to guide clinical diagnosis and treatment.Methods:From October 2021 to Decemeber 2023, at Army Characteristic Medical Center of PLA (Chongqing Daping Hospital) and the First Affiliated Hospital of Chongqing Medical University, 94 patients with advanced HCC who received HAIC combined with targeted therapy or immunotherapy were retrospectively enrolled. According to the treatment results, the patients were divided into response group and non-response group. Univariate and multivariate logistic regression were performed to analyze the clinical data of the patients. Based on contrast-enhanced CT images, tumor habitats were delineated and habitat features were extracted with k-means clustering, and the imaging features of arterial and venous phases were also extracted. The least absolute shrinkage and selection operator (LASSO) was used for dimensionality reduction. Feature selection was performed using LASSO to reduce dimensions, and then the selected features were further refined through stepwise logistic regression analysis.Binary logistic regression models were conducted to develop the habitat radiomics model, arterial phase radiomics model (APRM), venous phase radiomics model (VPRM), clinical data model, as well as the combination of radiomics model and clinical data model to predict early treatment (after 2 treatment cycles) response. Receiver operating characteristic curves (ROC) were plotted, and model performance was evaluated by the area under the curve (AUC), calibration curves, and decision curve. The models were validated through Bootstrap methods (1 000 times). DeLong test was used to compare AUC values.Results:The results of cluster analysis identified 3 characteristic habitats in HCC imaging: low-, medium-, and high-enhancement tumor habitats. The proportion of high-enhancement habitats was higher than that in the non-response group. A predictive model was established based on the proportions of these 3 habitats. Based on the proportion of low-, medium-, and high-enhancement habitats within the tumor, a habitat radiomics model was constructed. After LASSO selection and logistic regression analysis, 3 arterial phase and 3 venous phase radiomic features were selected to build the APRM and VPRM, respectively. Logistic regression analysis identified the following factors for the clinical data model: comorbidities ( OR=0.275, P=0.031), maximum tumor diameter ( OR=1.149, P=0.019), red blood cell count ( OR=0.463, P=0.022), alpha fetoprotein >400 μg/L ( OR=3.452, P=0.017), and tyrosine kinase inhibitor therapy ( OR=3.072, P=0.048). Among the single predictive model′s comparison, the AUC of habitat radiomics model was 0.860 (95% confidence interval(95% CI): 0.789 to 0.932), while those of the APRM、VPRM and clinical data model were 0.850 (95% CI: 0.773 to 0.926), 0.855 (95% CI: 0.782 to 0.928), and 0.774 (95% CI: 0.681 to 0.867), respectively, and there were no statistically significant among these models (all P>0.05). Among the combination models, the AUC of the habitat rediomic-clinical data combination model was 0.881 (95% CI: 0.814 to 0.947); the AUC of arterial phase rediomic-clinical data combination model was 0.897 (95% CI: 0.833 to 0.961); and the AUC of venous phase rediomic-clinical data combination model was 0.888 (95% CI: 0.826 to 0.951), but there were no statistically significant among the 3 models (all P>0.05). The calibration curve showed that the habitat rediomic-clinical data combination model had the most accurate predictive probability. Internal validation showed that the AUC of habitat rediomic-clinical data combination model was 0.848 (95% CI: 0.772 to 0.922), and the predictive performance was better than that of the clinical-data model (0.733 (95% CI: 0.670 to 0.863)). Conclusion:The habitat radiomics model based on enhanced CT can effectively predict early treatment responses to the HAIC combined with targeted therapy or immunotherapy in advanced HCC patients, which provides theoretical basis for individualized treatment in advanced HCC.

OBJECTIVE: Observational studies have found associations between inflammatory bowel disease (IBD) and the risk of dementia, including Alzheimer's dementia (AD) and vascular dementia (VD); however, these findings are inconsistent. It remains unclear whether these associations are causal. METHODS: We conducted a meta-analysis by systematically searching for observational studies on the association between IBD and dementia. Mendelian randomization (MR) analysis based on summary genome-wide association studies (GWASs) was performed. Genetic correlation and Bayesian co-localization analyses were used to provide robust genetic evidence. RESULTS: Ten observational studies involving 80,565,688 participants were included in this meta-analysis. IBD was significantly associated with dementia (risk ratio [ RR] =1.36, 95% CI = 1.04-1.78; I ² = 84.8%) and VD ( RR = 2.60, 95% CI = 1.18-5.70; only one study), but not with AD ( RR = 2.00, 95% CI = 0.96-4.13; I ² = 99.8%). MR analyses did not supported significant causal associations of IBD with dementia (dementia: odds ratio [ OR] = 1.01, 95% CI = 0.98-1.03; AD: OR = 0.98, 95% CI = 0.95-1.01; VD: OR = 1.02, 95% CI = 0.97-1.07). In addition, genetic correlation and co-localization analyses did not reveal any genetic associations between IBD and dementia. CONCLUSION Our study did not provide genetic evidence for a causal association between IBD and dementia risk. The increased risk of dementia observed in observational studies may be attributed to unobserved confounding factors or detection bias.
Humans ; Mendelian Randomization Analysis ; Inflammatory Bowel Diseases/complications* ; Dementia/etiology* ; Observational Studies as Topic ; Genome-Wide Association Study

Objective:To investigate and summarize the imaging and pathological features of non-acute occlusion following flow diverter (FD) implantation in animal models.Methods:Four experimental pigs (experimental group) that experienced non-acute occlusion (occlusion time exceeding 24 hours) within the FD stent implanted in the common carotid artery, and 19 pigs (control group) that did not experience stent occlusion during the same period were involved. Using an interventional approach under digital subtraction angiography (DSA), the 4 occluded FD lumens were mechanically opened. Optical coherence tomography (OCT), intravascular ultrasound (IVUS) and histopathological examinations were performed to evaluate the intraluminal composition and characteristics of the occlusive tissues. These findings were compared with non-occluded FD stents to summarize the imaging and pathological changes within the occluded FD lumen.Results:The occlusion times of the FD stents in the 4 experimental pigs were 16 weeks, 20 weeks, 20 weeks, and 24 weeks postoperatively. All occluded stents were successfully recanalized under DSA, with a technical success rate of 4/4. Among the 19 non-occluded FD stents, OCT and IVUS revealed uniform (16 stents) or non-uniform (3 stents) neointimal coverage of the stent struts, presenting as homogeneous high/slightly high signal intensity or medium echogenicity. Histopathological examination indicated that the neointima was primarily composed of smooth muscle cells and a small amount of fibrous connective tissues. In contrast, the 4 occluded FD stents demonstrated excessive neointimal proliferation and plaque formation, leading to luminal loss, as shown by OCT and IVUS. The occlusion tissues predominantly presented as homogeneous high signal intensity with weak attenuation (fibrous plaques) on OCT, with some regions showing blurred low signal intensity and strong attenuation (lipid plaques). IVUS presented homogeneous echogenicity (fibrous plaques) and hypoechogenic zones (lipid plaques). Histopathological examination showed that the occlusion tissues mainly consisted of smooth muscle cells, fibrous connective tissues, and lipids, accompanied by numerous foam cells and a minor presence of inflammatory cells.Conclusions:Histopathological examinations confirm that non-acute occlusion of FD is mainly caused by excessive hyperplasia of intima along with the formation of fibrous plaques and lipid plaques. OCT and IVUS have typical finding in imaging that can assist in determining the cause of stent occlusion as well as the lesion's nature, thereby providing crucial guidance for subsequent clinical treatment and drug selection.

Objective:To develop habitat radiomics models to predict early treatment responses to the hepatic arterial infusion chemotherapy (HAIC) combined with targeted therapy or immunotherapy in advanced hepatocellular carcinoma (HCC) patients, and to guide clinical diagnosis and treatment.Methods:From October 2021 to Decemeber 2023, at Army Characteristic Medical Center of PLA (Chongqing Daping Hospital) and the First Affiliated Hospital of Chongqing Medical University, 94 patients with advanced HCC who received HAIC combined with targeted therapy or immunotherapy were retrospectively enrolled. According to the treatment results, the patients were divided into response group and non-response group. Univariate and multivariate logistic regression were performed to analyze the clinical data of the patients. Based on contrast-enhanced CT images, tumor habitats were delineated and habitat features were extracted with k-means clustering, and the imaging features of arterial and venous phases were also extracted. The least absolute shrinkage and selection operator (LASSO) was used for dimensionality reduction. Feature selection was performed using LASSO to reduce dimensions, and then the selected features were further refined through stepwise logistic regression analysis.Binary logistic regression models were conducted to develop the habitat radiomics model, arterial phase radiomics model (APRM), venous phase radiomics model (VPRM), clinical data model, as well as the combination of radiomics model and clinical data model to predict early treatment (after 2 treatment cycles) response. Receiver operating characteristic curves (ROC) were plotted, and model performance was evaluated by the area under the curve (AUC), calibration curves, and decision curve. The models were validated through Bootstrap methods (1 000 times). DeLong test was used to compare AUC values.Results:The results of cluster analysis identified 3 characteristic habitats in HCC imaging: low-, medium-, and high-enhancement tumor habitats. The proportion of high-enhancement habitats was higher than that in the non-response group. A predictive model was established based on the proportions of these 3 habitats. Based on the proportion of low-, medium-, and high-enhancement habitats within the tumor, a habitat radiomics model was constructed. After LASSO selection and logistic regression analysis, 3 arterial phase and 3 venous phase radiomic features were selected to build the APRM and VPRM, respectively. Logistic regression analysis identified the following factors for the clinical data model: comorbidities ( OR=0.275, P=0.031), maximum tumor diameter ( OR=1.149, P=0.019), red blood cell count ( OR=0.463, P=0.022), alpha fetoprotein >400 μg/L ( OR=3.452, P=0.017), and tyrosine kinase inhibitor therapy ( OR=3.072, P=0.048). Among the single predictive model′s comparison, the AUC of habitat radiomics model was 0.860 (95% confidence interval(95% CI): 0.789 to 0.932), while those of the APRM、VPRM and clinical data model were 0.850 (95% CI: 0.773 to 0.926), 0.855 (95% CI: 0.782 to 0.928), and 0.774 (95% CI: 0.681 to 0.867), respectively, and there were no statistically significant among these models (all P>0.05). Among the combination models, the AUC of the habitat rediomic-clinical data combination model was 0.881 (95% CI: 0.814 to 0.947); the AUC of arterial phase rediomic-clinical data combination model was 0.897 (95% CI: 0.833 to 0.961); and the AUC of venous phase rediomic-clinical data combination model was 0.888 (95% CI: 0.826 to 0.951), but there were no statistically significant among the 3 models (all P>0.05). The calibration curve showed that the habitat rediomic-clinical data combination model had the most accurate predictive probability. Internal validation showed that the AUC of habitat rediomic-clinical data combination model was 0.848 (95% CI: 0.772 to 0.922), and the predictive performance was better than that of the clinical-data model (0.733 (95% CI: 0.670 to 0.863)). Conclusion:The habitat radiomics model based on enhanced CT can effectively predict early treatment responses to the HAIC combined with targeted therapy or immunotherapy in advanced HCC patients, which provides theoretical basis for individualized treatment in advanced HCC.