Five new flavan-4-ol glycosides jixueqiosides A-E (1-5) and two new flavan glycosides jixueqiosides F and G (6 and 7), along with twelve known flavan-4-ol glycosides (8-19), were isolated from the roots of Pronephrium penangianum. Comprehensive spectral analyses, X-ray single-crystal diffraction, and theoretical electronic circular dichroism (ECD) calculations established structures and absolute configurations. A single crystal structure of flavan-4-ol glycoside (14) was reported for the first time, while the characteristic ECD and NMR data for all isolated flavan-4-ol glycosides (1-5 , 8-19) were analyzed, establishing a set of empirical rules. Activity screening of these isolates showed that 8 and 9 could inhibit the proliferation of MDA-MB-231 and MCF-7 cells with IC₅₀ values of 7.93 ? 2.85 ?mol?L^-1 and 5.87 ? 1.58 ?mol?L^-1 (MDA-MB-231), and 2.21 ? 1.38 ?mol?L^-1 and 3.52 ? 1.55 ?mol?L^-1 (MCF-7), respectively. Western blotting and flow cytometry analyses demonstrated that 8 and 9 dose-dependently induced apoptosis in MDA-MB-231 cells by up-regulating BAX, activating caspase-3 and down-regulating BCL-2. Additionally, compound 8 affected autophagy-related proteins, increasing the ratio of LC3-II/LC3-I and Beclin-1 levels to inhibit MDA-MB-231 cell proliferation. Moreover, anti-inflammatory studies indicated that 2, 3, 7, 13, 14, and 18 moderately inhibited tumor necrosis factor-a (TNF-a), interleukin-6 (IL-6), and nitric oxide (NO) release.
Humans ; Plant Roots/chemistry* ; Glycosides/isolation & purification* ; Anti-Inflammatory Agents/isolation & purification* ; Flavonoids/isolation & purification* ; Cell Proliferation/drug effects* ; Antineoplastic Agents, Phytogenic/isolation & purification* ; Molecular Structure ; Apoptosis/drug effects* ; Cell Line, Tumor ; Tumor Necrosis Factor-alpha/immunology* ; Drugs, Chinese Herbal/pharmacology* ; Interleukin-6/immunology* ; Animals ; Mice

With the rapid advancement of synthetic biology, CRISPR-Cas systems have emerged as a powerful tool for gene editing, demonstrating significant potential in various fields, including medicine, agriculture, and industrial biotechnology. This review comprehensively summarizes the significant progress in applying artificial intelligence (AI) technologies to the design, mining, and modification of CRISPR-Cas systems. AI technologies, especially machine learning, have revolutionized sgRNA design by analyzing high-throughput sequencing data, thereby improving the editing efficiency and predicting off-target effects with high accuracy. Furthermore, this paper explores the role of AI in sgRNA design and evaluation, highlighting its contributions to the annotation and mining of CRISPR arrays and Cas proteins, as well as its potential for modifying key proteins involved in gene editing. These advancements have not only improved the efficiency and precision of gene editing but also expanded the horizons of genome engineering, paving the way for intelligent and precise genome editing.
CRISPR-Cas Systems/genetics* ; Artificial Intelligence ; Gene Editing/methods* ; RNA, Guide, CRISPR-Cas Systems/genetics* ; Machine Learning ; Humans ; Genetic Engineering/methods* ; Synthetic Biology

Mitophagy is a selective degradation process of damaged mitochondria in response to mitochondrial toxicity,which plays a crucial role in regulating mitochondrial quality and quantity.Abnormal mitophagy can cause or exacerbate mitochondrial dysfunction,which is closely associated with the pathogenesis of numerous diseases.Given that cochlear hair cells are highly sensitive to energy metabolism,proper regulation of mitophagy is essential for maintaining auditory function.Mitochondrial damage resulting from mitophagy dysfunction involves diverse pathophysiological mechanisms underlying sensorineural hearing loss.This review summarizes the latest progress on mitophagy and its role in the pathogenesis of sensorineural hearing loss,aiming to enhance our understanding of the involvement of mitophagy in auditory function and provide a theoretical basis for future research on targeted therapy for mitochondria.

Objective:To develop a deep learning model based on super-resolution endorectal ultrasound（ERUS）images for the preoperative prediction of perineural invasion（PNI）in patients with rectal cancer，thereby providing a reference for risk stratification and individualized treatment planning.Methods:A retrospective analysis was conducted on 382 patients with rectal cancer who underwent total mesorectal excision at Fujian Medical University Union Hospital between June 2019 and February 2024. Patients were randomly divided into a training set（ n=305）and a test set（ n=77）at a ratio of 8∶2，and further grouped into PNI-negative group and PNI-positive group subgroups based on pathological results. Super-resolution ultrasound images were generated from original ERUS images using a generative adversarial network（GAN）. Deep convolutional neural networks were developed based on features from intratumoral and peritumoral regions to identify the optimal region of interest（ROI）. The dSR5_ResNet18 and dSR5_ResNet50 models were constructed using the super-resolution images with a 5-pixel peritumoral extension. Representative clinical features were selected for subgroup analysis based on sample size and intergroup statistical differences between PNI-positive and PNI-negative patients. Forest plots were used to evaluate model applicability and robustness across subgroups. Results:The dSR5_ResNet18 model，built using super-resolution images of the tumor combined with a 5-pixel peritumoral region，achieved the best predictive performance，with an AUC of 0.867（95% CI=0.782 - 0.952）in the test set. Decision curve analysis demonstrated that the dSR5_ResNet18 model provided the greatest net clinical benefit. Forest plot analysis indicated strong generalizability of the models across subgroups such as pathological N stage，maximum lesion length，and lymph node enlargement，though relatively weaker performance was observed in the carcinoembryonic antigen（CEA）subgroup. Among all models，dSR5_ResNet18 exhibited the most consistent performance across subgroups，with the narrowest confidence intervals and highest robustness. Conclusions:The deep learning model incorporating ERUS-based super-resolution reconstruction demonstrated excellent performance in the preoperative prediction of PNI in rectal cancer. It offers significant advantages in image quality and generalizability，and may serve as a valuable tool to assist clinicians in formulating personalized treatment strategies.

Objective:To develop a comprehensive training curriculum to enhance the effective implementation of the national initiative promoting dementia care and prevention.Methods:The Delphi method was utilized in an expert consultation that included 44 participants.The initial draft of the training curriculum was developed based on the current state of dementia care and prevention.This draft was subsequently evaluated for its importance, feasibility, and ease of dissemination.Experts offered targeted modifications and additional recommendations.Results:The recovery rate of the expert consultation questionnaire was 95.5%, with a recovery validity rate of 90.9%.The expert authority coefficient was 0.91, and the Kendall's coordination coefficient( W)for expert scoring was 0.316, with a significance level of P<0.001.Four course modules were ultimately identified: the foundation of memory clinic work, the complete management practice skills, group counseling techniques for caregivers, and practical skills for caregivers.The importance of these modules was rated with a mean of 4.92 to 4.95, and the coefficient of variation ranged from 0.044 to 0.063.Each module had a mean value of 4.92 to 4.95, with a coefficient of variation of 0.044 to 0.063; the mean value for practicality was between 4.78 and 4.92, with a coefficient of variation of 0.055 to 0.098; and the mean value for ease of generalization ranged from 4.28 to 4.65, with a coefficient of variation from 0.140 to 0.203.The four modules comprised a total of 55 specific course content items, with the mean value for each item ranging from 4.76 to 5.00 and a coefficient of variation from 0.000 to 0.121.The mean value of usefulness assigned to each entry ranged from 4.55 to 4.98, with a coefficient of variation from 0.031 to 0.150.Additionally, the mean value for ease of propagation assigned to each entry ranged from 4.00 to 4.83, with a coefficient of variation from 0.091 to 0.245. Conclusions:The developed training curriculum, which comprises four course modules and 55 items, demonstrated consistently high levels of importance, practicality, and ease of dissemination.These findings indicate that the curriculum is well-aligned with national initiatives aimed at enhancing dementia care and prevention.

OBJECTIVES: To study the molecular mechanisms of LDH-loaded si-NEAT1 for regulating paclitaxel resistance and tumor-associated macrophage (TAM) polarization in breast cancer. METHODS: qRT-PCR and Western blotting were used to detect the expression of lncRNA NEAT1, miR-133b, and PD-L1 in breast cancer SKBR3 cells and paclitaxel-resistant SKBR3 cells (SKBR3-PR). The effects of transfection with si-NEAT1 and miR-133b mimics on MRP, MCRP and PD-L1 expressions and cell proliferation, migration and apoptosis were investigated using qRT-PCR, Western blotting, scratch and Transwell assays, and flow cytometry. Rescue experiments were conducted using si-NEAT1 and miR-133b inhibitor. Human THP-1 macrophages were cultured in the presence of conditioned media (CM) derived from SKBR3 and SKBR3-PR cells with or with si-NEAT1 transfection for comparison of IL-4-induced macrophage polarization by detecting the surface markers. LDH@si-NEAT1 nanocarriers were constructed, and their effects on MRP, MCRP and PD-L1 expressions and cell behaviors of the tumor cells were examined. THP-1 cells were treated with the CM from LDH@si-NEAT1-treated tumor cells, and the changes in their polarization were assessed. RESULTS: SKBR3-PR cells showered significantly upregulated NEAT1 and PD-L1 expressions and lowered miR-133b expression as compared with their parental cells. Transfection with si-NEAT1 and miR-133b mimics inhibited viability, promoted apoptosis and enhanced MRP and BCRP expressions in SKBR3-PR cells. NEAT1 knockdown obvious upregulated miR-133b and downregulated PD-L1, MRP and BCRP expressions. The CM from SKBR3-PR cells obviously promoted M2 polarization of THP-1 macrophages, which was significantly inhibited by CM from si-NEAT1-transfected cells. Treatment with LDH@si-NEAT1 effectively inhibited migration and invasion, promoted apoptosis, and reduced MRP, BCRP and PD-L1 expressions in the tumor cells. The CM from LDH@si-NEAT1-treated SKBR3-PR cells significantly downregulated Arg-1, CD163, IL-10, and PD-L1 and upregulated miR-133b expression in THP-1 macrophages. CONCLUSIONS LDH@si-NEAT1 reduces paclitaxel resistance of breast cancer cells and inhibits TAM polarization by targeting the miR-133b/PD-L1 axis.
Humans ; MicroRNAs/genetics* ; RNA, Long Noncoding/genetics* ; Paclitaxel/pharmacology* ; Breast Neoplasms/metabolism* ; Drug Resistance, Neoplasm ; B7-H1 Antigen/metabolism* ; Cell Line, Tumor ; Female ; Tumor-Associated Macrophages ; Apoptosis ; Cell Proliferation ; Macrophages ; Cell Movement

Objective To explore the mediating role of mindful attention and awareness in depressive symptoms and insomnia severity among patients with comorbid depression and insomnia.Methods A cross-sectional study was conducted,enrolling 267 patients with comorbid depression and insomnia who were treated in the outpatient Department of Medical Psychology of Second Affiliated Hospital of Army Medical University,from March to May 2024.Basic demographic and clinical data were collected using a general information questionnaire.Depressive symptom severity was measured via the Patient Health Questionnaire-9(PHQ-9),insomnia severity via the Insomnia Severity Index(ISI),perceived stress via the Perceived Stress Scale-10(PSS-10),and mindful attention and awareness via the Mindful Attention Awareness Scale(MAAS).Pearson correlation analysis was used to examine the correlations between depressive severity,insomnia severity,perceived stress,and mindful attention and awareness.Mediation analysis was performed using Process 4.1.Results The PHQ-9 score was(13.80±5.98)and the ISI score was(17.10±5.56)in the 267 patients.Pearson correlation analysis showed that depressive severity and insomnia severity were positively correlated with perceived stress(r=0.531,0.351,P<0.001)and negatively correlated with mindful attention and awareness(r=-0.373,-0.350,P<0.001).Mediation analysis using Process 4.1 indicated that the combined mediating effect of mindful attention and awareness and insomnia between perceived stress level and depressive level was 0.157,with a 95%confidence interval(CI)of 0.102～0.217,and the total mediating effect was significant(P<0.001).Conclusion Perceived stress directly positively affects depression and indirectly exacerbates depression through insomnia as a mediator,and mindful attention and awareness can weaken the promoting effect of perceived stress on insomnia.

Objective To investigate how tumor-associated macrophages(TAMs)in cervical cancer cells respond to AMPK through phenotypic transformation.Methods RT-qPCR and Western blot were used to detect AMPK expression in cervical cancer tissues and HeLa,SiHa and Caski cell lines.The AMPK knockout cell line of HeLa cells was generated using the siRNA technique.The malignant biological behavior of cancer cells mediated by the AMPK/CCL18 axis was investigated using CCK-8,scratch assay and Transwell assay.Cell conditioned medium from each group was collected,and cervical cancer cell conditioned medium was co-cultured with human mononuclear cells(THP-1)to assess THP-1 recruitment and the expression of macrophage typing related markers.The impact of AMPK on tumor proliferation was also evaluated in nude mice.Results QRT-PCR and Western blot analysis showed that the mRNA(1.78±0.30)and protein(1.71±0.33)expression levels of AMPK in cancer tissues were significantly higher than those in adjacent tissues(1.00±0.10,1.00±0.11),and the differences were statistically significant(t=5.966,4.990,all P<0.01).Compared with normal cervical epithelial cells(HUCEC),the mRNA and protein expression of AMPK were significantly increased in three cell lines(HeLa,SiHa,Caski),and the differences were statistically significant(F=5.958,3.457,all P<0.01).Compared with the NC group,knocking down AMPK inhibited the proliferation of cervical cancer cells(1.33±0.18 vs 0.82±0.25),migration(180.07±7.64 vs 126.98±29.00),and invasion(115.90±13.19 vs 68.61±12.87)in cervical cancer cells,with statistical significance(t=4.015,4.337,6.285,all P<0.01).The study found that THP-1 cells exhibited significant reductions in migration(49.34±3.91 vs 33.96±10.94),invasion(28.54±3.06 vs 19.54±6.16)and M2-type polarization(1.01±0.13 vs 0.55±0.11)when AMPK reduction was induced,and these differences were statistically significant(t=3.242,3.207,6.510,all P<0.01).In vivo experiments further supported these findings,showing that AMPK reduction led to significant decreases in tumor volume(721.56±93.70mm3 vs 520.02±47.54mm3)and weight(1.19±0.06g vs 0.86±0.20g),with statistically significant differences(t=4.289,3.582,P<0.01).Conclusion Through modulating M2 polarization in TAMs,the AMPK/CCL18 axis increases cervical cancer cells'proliferation,migration and invasion.AMPK/CCL18 inhibition could provide novel therapeutic targets and approaches for cervical cancer treatment.

Objective:To develop a comprehensive training curriculum to enhance the effective implementation of the national initiative promoting dementia care and prevention.Methods:The Delphi method was utilized in an expert consultation that included 44 participants.The initial draft of the training curriculum was developed based on the current state of dementia care and prevention.This draft was subsequently evaluated for its importance, feasibility, and ease of dissemination.Experts offered targeted modifications and additional recommendations.Results:The recovery rate of the expert consultation questionnaire was 95.5%, with a recovery validity rate of 90.9%.The expert authority coefficient was 0.91, and the Kendall's coordination coefficient( W)for expert scoring was 0.316, with a significance level of P<0.001.Four course modules were ultimately identified: the foundation of memory clinic work, the complete management practice skills, group counseling techniques for caregivers, and practical skills for caregivers.The importance of these modules was rated with a mean of 4.92 to 4.95, and the coefficient of variation ranged from 0.044 to 0.063.Each module had a mean value of 4.92 to 4.95, with a coefficient of variation of 0.044 to 0.063; the mean value for practicality was between 4.78 and 4.92, with a coefficient of variation of 0.055 to 0.098; and the mean value for ease of generalization ranged from 4.28 to 4.65, with a coefficient of variation from 0.140 to 0.203.The four modules comprised a total of 55 specific course content items, with the mean value for each item ranging from 4.76 to 5.00 and a coefficient of variation from 0.000 to 0.121.The mean value of usefulness assigned to each entry ranged from 4.55 to 4.98, with a coefficient of variation from 0.031 to 0.150.Additionally, the mean value for ease of propagation assigned to each entry ranged from 4.00 to 4.83, with a coefficient of variation from 0.091 to 0.245. Conclusions:The developed training curriculum, which comprises four course modules and 55 items, demonstrated consistently high levels of importance, practicality, and ease of dissemination.These findings indicate that the curriculum is well-aligned with national initiatives aimed at enhancing dementia care and prevention.

Objective:To develop a deep learning model based on super-resolution endorectal ultrasound（ERUS）images for the preoperative prediction of perineural invasion（PNI）in patients with rectal cancer，thereby providing a reference for risk stratification and individualized treatment planning.Methods:A retrospective analysis was conducted on 382 patients with rectal cancer who underwent total mesorectal excision at Fujian Medical University Union Hospital between June 2019 and February 2024. Patients were randomly divided into a training set（ n=305）and a test set（ n=77）at a ratio of 8∶2，and further grouped into PNI-negative group and PNI-positive group subgroups based on pathological results. Super-resolution ultrasound images were generated from original ERUS images using a generative adversarial network（GAN）. Deep convolutional neural networks were developed based on features from intratumoral and peritumoral regions to identify the optimal region of interest（ROI）. The dSR5_ResNet18 and dSR5_ResNet50 models were constructed using the super-resolution images with a 5-pixel peritumoral extension. Representative clinical features were selected for subgroup analysis based on sample size and intergroup statistical differences between PNI-positive and PNI-negative patients. Forest plots were used to evaluate model applicability and robustness across subgroups. Results:The dSR5_ResNet18 model，built using super-resolution images of the tumor combined with a 5-pixel peritumoral region，achieved the best predictive performance，with an AUC of 0.867（95% CI=0.782 - 0.952）in the test set. Decision curve analysis demonstrated that the dSR5_ResNet18 model provided the greatest net clinical benefit. Forest plot analysis indicated strong generalizability of the models across subgroups such as pathological N stage，maximum lesion length，and lymph node enlargement，though relatively weaker performance was observed in the carcinoembryonic antigen（CEA）subgroup. Among all models，dSR5_ResNet18 exhibited the most consistent performance across subgroups，with the narrowest confidence intervals and highest robustness. Conclusions:The deep learning model incorporating ERUS-based super-resolution reconstruction demonstrated excellent performance in the preoperative prediction of PNI in rectal cancer. It offers significant advantages in image quality and generalizability，and may serve as a valuable tool to assist clinicians in formulating personalized treatment strategies.