The circadian clock plays a critical role in regulating various physiological processes, including gene expression, metabolic regulation, immune response, and the sleep-wake cycle in living organisms. Post-translational modifications (PTMs) are crucial regulatory mechanisms to maintain the precise oscillation of the circadian clock. By modulating the stability, activity, cell localization and protein-protein interactions of core clock proteins, PTMs enable these proteins to respond dynamically to environmental and intracellular changes, thereby sustaining the periodic oscillations of the circadian clock. Different types of PTMs exert their effects through distincting molecular mechanisms, collectively ensuring the proper function of the circadian system. This review systematically summarized several major types of PTMs, including phosphorylation, acetylation, ubiquitination, SUMOylation and oxidative modification, and overviewed their roles in regulating the core clock proteins and the associated pathways, with the goals of providing a theoretical foundation for the deeper understanding of clock mechanisms and the treatment of diseases associated with circadian disruption.
Protein Processing, Post-Translational/physiology* ; Circadian Rhythm/physiology* ; Humans ; Animals ; CLOCK Proteins/physiology* ; Circadian Clocks/physiology* ; Phosphorylation ; Acetylation ; Ubiquitination ; Sumoylation

Nitrofurantoin(NFT)is a nitrofuran antibiotic commonly used as a veterinary drug to treat bacterial infections in animals.However,due to the low solubility and bioaccumulation properties,NFT is prone to leave excessive residues in animal-derived foods and water systems,posing serious threats to human health and ecosystems.Therefore,there is an urgent need to develop an efficient and rapid detection method for NFT.In this work,nitrogen-doped carbon nanomaterials with unique bowl-like structures(N-CNBs)were synthesized via a hydrothermal-carbonization method.The morphology,surface structure,and specific surface area of N-CNBs were characterized using transmission electron microscopy(TEM),scanning electron microscopy(SEM),and X-ray photoelectron spectroscopy(XPS).The N-CNB modified glassy carbon electrode(N-CNB/GCE)was prepared,and the electrochemical test revealed that the N-CNB/GCE exhibited higher conductivity and larger electrochemical active surface area compared to bare GCE and nitrogen-doped hollow carbon nanosphere-modified electrode(N-HCNS/GCE).Additionally,the N-CNB/GCE demonstrated superior electrocatalytic activity toward NFT.An NFT electrochemical sensor was constructed based on N-CNB/GCE.The detection conditions of the sensor were optimized,and differential pulse voltammetry(DPV)was employed for NFT detection under optimal experimental conditions.The established NFT electrochemical sensor had a wide linear range of 0.4-500 μmol/L,a low detection limit(S/N=3)of 0.015 μmol/L and high selectivity,with excellent stability and reproducibility.The practical feasibility of this sensor was confirmed by analysis of NFT in milk and tap water samples,with spiked recoveries ranging from 94.2%to 108.9%.

OBJECTIVE: Hepatocellular carcinoma (HCC) is sensitive to ferroptosis, a new form of programmed cell death that occurs in most tumor types. However, the mechanism through which ferroptosis modulates HCC remains unclear. This study aimed to investigate the oncogenic role and prognostic value of FANCD2 and provide novel insights into the prognostic assessment and prediction of immunotherapy. METHODS: Using clinicopathological parameters and bioinformatic techniques, we comprehensively examined the expression of FANCD2 macroscopically and microcosmically. We conducted univariate and multivariate Cox regression analyses to identify the prognostic value of FANCD2 in HCC and elucidated the detailed molecular mechanisms underlying the involvement of FANCD2 in oncogenesis by promoting iron-related death. RESULTS: FANCD2 was significantly upregulated in digestive system cancers with abundant immune infiltration. As an independent risk factor for HCC, a high FANCD2 expression level was associated with poor clinical outcomes and response to immune checkpoint blockade. Gene set enrichment analysis revealed that FANCD2 was mainly involved in the cell cycle and CYP450 metabolism. CONCLUSION To the best of our knowledge, this is the first study to comprehensively elucidate the oncogenic role of FANCD2. FANCD2 has a tumor-promoting aspect in the digestive system and acts as an independent risk factor in HCC; hence, it has recognized value for predicting tumor aggressiveness and prognosis and may be a potential biomarker for poor responsiveness to immunotherapy.
Humans ; Carcinoma, Hepatocellular/diagnosis* ; Liver Neoplasms/diagnosis* ; Immunotherapy ; Fanconi Anemia Complementation Group D2 Protein/metabolism* ; Prognosis ; Male ; Female ; Middle Aged ; Biomarkers, Tumor/metabolism*

Aim To investigate the effect of p-AMPK activity on autophagy in different subtypes of MDA-MB-231(triple-negative breast cancer cells)and MCF-7(estrogen receptor-positive cells)and its regulatory mechanism.Methods MDA-MB-231 cells were trea-ted with EBSS,Baf-A1,and EBSS+Baf-A1 for four hours,and MCF-7 cells for eight hours.The effects of autophagy on cell proliferation and apoptosis were ob-served,mitochondrial morphology was examined,and the expression of autophagy markers LC3B,P62,LAMP1,TOM20,AMPK,p-AMPK,ULK1,and Bec-lin1/VPS34 proteins was detected.The autophagy pathway was validated by inhibiting AMPK activity.Results Breast cancer cells underwent autophagy af-ter starvation induction(EBSS),with inconsistent au-tophagy processes observed in different subtypes of breast cancer cells.Autophagy inhibited cell prolifera-tion.In MDA-MB-231 cells,autophagy led to an in-crease in p-AMPK levels and a decrease in ULK1 lev-els,initiating autophagy through p-AMPK activation of ULK1.In MCF-7 cells,both p-AMPK and ULK1 levels decreased after autophagy,suggesting that autophagy might not be mediated by p-AMPK activation.Conclu-sions MDA-MB-231 cells primarily initiate autophagy by directly activating ULK1 by p-AMPK,independent of the MTOR pathway.In MCF-7 cells autophagy might be triggered by inhibiting MTOR through AMPK activity or directly activating MTOR through other up-stream factors.Regulating p-AMPK activity based on the autophagy pathways in different cell subtypes could enable more precise targeting and treatment of different types of breast cancer.

Aim To investigate the effect of p-AMPK activity on autophagy in different subtypes of MDA-MB-231(triple-negative breast cancer cells)and MCF-7(estrogen receptor-positive cells)and its regulatory mechanism.Methods MDA-MB-231 cells were trea-ted with EBSS,Baf-A1,and EBSS+Baf-A1 for four hours,and MCF-7 cells for eight hours.The effects of autophagy on cell proliferation and apoptosis were ob-served,mitochondrial morphology was examined,and the expression of autophagy markers LC3B,P62,LAMP1,TOM20,AMPK,p-AMPK,ULK1,and Bec-lin1/VPS34 proteins was detected.The autophagy pathway was validated by inhibiting AMPK activity.Results Breast cancer cells underwent autophagy af-ter starvation induction(EBSS),with inconsistent au-tophagy processes observed in different subtypes of breast cancer cells.Autophagy inhibited cell prolifera-tion.In MDA-MB-231 cells,autophagy led to an in-crease in p-AMPK levels and a decrease in ULK1 lev-els,initiating autophagy through p-AMPK activation of ULK1.In MCF-7 cells,both p-AMPK and ULK1 levels decreased after autophagy,suggesting that autophagy might not be mediated by p-AMPK activation.Conclu-sions MDA-MB-231 cells primarily initiate autophagy by directly activating ULK1 by p-AMPK,independent of the MTOR pathway.In MCF-7 cells autophagy might be triggered by inhibiting MTOR through AMPK activity or directly activating MTOR through other up-stream factors.Regulating p-AMPK activity based on the autophagy pathways in different cell subtypes could enable more precise targeting and treatment of different types of breast cancer.

Objective:To explore the role of NK cells in allogeneic hematopoietic stem cell micro-transplantation(MST)in the treatment of patients with acute myeloid leukemia(AML).Methods:Data from 93 AML patients treated with MST at our center from 2013-2018 were retrospectively analyzed.The induction regimen was anthracycline and cytarabine combined with peripheral blood stem cells transplantation mobilization by granulocyte colony stimulating factor(GPBSC),followed by 2-4 courses of intensive treatment with medium to high doses of cytarabine combined with GPBSC after achieving complete remission(CR).The therapeutic effects of one and two courses of MST induction therapy on 42 patients who did not reach CR before transplantation were evaluated.Cox proportional hazards regression analysis was used to analyze the impact of donor NK cell dose and KIR genotype,including KIR ligand mismatch,2DS1,haplotype,and HLA-Cw ligands on survival prognosis of patients.Results:Forty-two patients received MST induction therapy,and the CR rate was 57.1％after 1 course and 73.7％after 2 courses.Multivariate analysis showed that,medium and high doses of NK cells was significantly associated with improved disease-free survival(DFS)of patients(HR=0.27,P=0.005;HR=0.21,P=0.001),and high doses of NK cells was significantly associated with improved overall survival(OS)of patients(HR=0.15,P=0.000).Donor 2DS1 positive significantly increases OS of patients(HR=0.25,P=0.011).For high-risk patients under 60 years old,patients of the donor-recipient KIR ligand mismatch group had longer DFS compared to the nonmismatch group(P=0.036);donor 2DS1 positive significantly prolonged OS of patients(P=0.009).Conclusion:NK cell dose,KIR ligand mismatch and 2DS1 influence the therapeutic effect of MST,improve the survival of AML patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.