Ursodeoxycholic acid(UDCA)is a naturally occurring,low-toxicity,and hydrophilic bile acid(BA)in the human body that is converted by intestinal flora using primary BA.Solute carrier family 7 member 11(SLC7A11)functions to uptake extracellular cystine in exchange for glutamate,and is highly expressed in a variety of human cancers.Retroperitoneal liposarcoma(RLPS)refers to liposarcoma originating from the retroperitoneal area.Lipidomics analysis revealed that UDCA was one of the most significantly down-regulated metabolites in sera of RIPS patients compared with healthy subjects.The augmentation of UDCA concentration(≥25 μg/mL)demonstrated a suppressive effect on the proliferation of liposarcoma cells.[15N2]-cystine and[13Cs]-glutamine isotope tracing revealed that UDCA impairs cystine uptake and glutathione(GSH)synthesis.Mechanistically,UDCA binds to the cystine transporter SLC7A11 to inhibit cystine uptake and impair GSH de novo synthesis,leading to reactive oxygen species(ROS)accumulation and mitochondrial oxidative damage.Furthermore,UDCA can promote the anti-cancer effects of ferroptosis inducers(Erastin,RSL3),the murine double minute 2(MDM2)inhibitors(Nutlin 3a,RG7112),cyclin dependent kinase 4(CDK4)inhibitor(Abemaciclib),and glutaminase inhibitor(CB839).Together,UDCA functions as a cystine exchange factor that binds to SLC7A11 for antitumor activity,and SLC7A11 is not only a new transporter for BA but also a clinically applicable target for UDCA.More importantly,in combination with other antitumor chemotherapy or physiotherapy treatments,UDCA may provide effective and promising treatment strategies for RLPS or other types of tumors in a ROS-dependent manner.

Aim To design and synthesize a series of glycyrrhetinic acid derivatives by using glycyrrhetinic acid as the parent nucleus,screen their antitumor activ-ities,and investigate the in vitro and in vivo antitumor effects and mechanisms of the most active compound.Methods MTT assay was used to screen for the com-pound with the most potent antitumor activity.MTT as-say,wound healing assay,colony formation assay and Transwell migration assay were used to evaluate the effects of the compound on tumor cell viability and mi-gration.Flow cytometry was employed to assess the im-pact of the compound on tumor cell cycle progression and apoptosis.Western blot was conducted to verify the effects on the expression of pro-apoptotic proteins Bax,caspase-3 and cleaved caspase-3.A mouse model of hepatocellular carcinoma ascites tumor was estab-lished to examine the antitumor effects of the compound in vivo.Results Compound C22 was identified as having the most significant inhibitory effect on hepato-cellular carcinoma cells.C22 inhibited the viability and migration of hepatocellular carcinoma cells in a time and concentration-dependent manner.C22 upreg-ulated the expression of pro-apoptotic proteins Bax,caspase-3 and cleaved caspase-3 in hepatocellular car-cinoma cells,induced apoptosis,and arrested the cell cycle in the G0/G1 and S phases.C22 significantly re-duced the growth of mouse hepatocellular carcinoma as-cites tumors and prolonged survival.Conclusion Glycyrrhetinic acid derivative C22 significantly inhibits the viability and migration of hepatocellular carcinoma cells in vitro and in vivo,and induces cell cycle arrest and apoptosis.

Colorimetric analysis is a detection and quantification method based on observable color changes in response to analytes,which offers significant advantages including visually detectable signals,straightforward operation,rapid response,and low cost.Consequently,it plays a crucial role in a variety of fields.With increasingly diverse and complex application,colorimetric analysis requires continuous improvement in sensitivity,adaptability to diverse detection environments,and complex data handling capabilities.In recent years,the development of artificial intelligence technology,particularly within its core domain of machine learning(ML),has led to significant advancements in colorimetric analysis.The ML-assisted colorimetric analysis enables high-throughput and high-sensitivity detection,alongside automated analysis,thereby providing novel strategies to overcome the inherent limitations.This review categorized machine learning techniques and summarized their application in colorimetric analysis,introducing two fundamental categories of supervised learning,and unsupervised learning based on the division of core learning paradigms.The research progress of ML-assisted colorimetric analysis in the fields of environmental monitoring,biochemical detection,and food safety were summarized.Finally,the current challenges facing by this research area were analyzed and the research prospect of ML-assisted colorimetric analysis was outlined.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Oligodendrocyte lineage cells, including oligodendrocyte precursor cells (OPCs) and oligodendrocytes (OLs), are essential in establishing and maintaining brain circuits. Autophagy is a conserved process that keeps the quality of organelles and proteostasis. The role of autophagy in oligodendrocyte lineage cells remains unclear. The present study shows that autophagy is required to maintain the number of OPCs/OLs and myelin integrity during brain aging. Inactivation of autophagy in oligodendrocyte lineage cells increases the number of OPCs/OLs in the developing brain while exaggerating the loss of OPCs/OLs with brain aging. Inactivation of autophagy in oligodendrocyte lineage cells impairs the turnover of myelin basic protein (MBP). It causes MBP to accumulate in the cytoplasm as multimeric aggregates and fails to be incorporated into integral myelin, which is associated with attenuated endocytic recycling. Inactivation of autophagy in oligodendrocyte lineage cells impairs myelin integrity and causes demyelination. Thus, this study shows autophagy is required to maintain myelin quality during aging by controlling the turnover of myelin components.
Animals ; Autophagy/physiology* ; Oligodendroglia/metabolism* ; Myelin Sheath/physiology* ; Aging/pathology* ; Myelin Basic Protein/metabolism* ; Cell Lineage/physiology* ; Mice ; Oligodendrocyte Precursor Cells ; Mice, Inbred C57BL ; Brain/cytology* ; Cells, Cultured ; Cell Count

Ursodeoxycholic acid (UDCA) is a naturally occurring, low-toxicity, and hydrophilic bile acid (BA) in the human body that is converted by intestinal flora using primary BA. Solute carrier family 7 member 11 (SLC7A11) functions to uptake extracellular cystine in exchange for glutamate, and is highly expressed in a variety of human cancers. Retroperitoneal liposarcoma (RLPS) refers to liposarcoma originating from the retroperitoneal area. Lipidomics analysis revealed that UDCA was one of the most significantly downregulated metabolites in sera of RLPS patients compared with healthy subjects. The augmentation of UDCA concentration (≥25 μg/mL) demonstrated a suppressive effect on the proliferation of liposarcoma cells. [¹⁵N₂]-cystine and [¹³C₅]-glutamine isotope tracing revealed that UDCA impairs cystine uptake and glutathione (GSH) synthesis. Mechanistically, UDCA binds to the cystine transporter SLC7A11 to inhibit cystine uptake and impair GSH de novo synthesis, leading to reactive oxygen species (ROS) accumulation and mitochondrial oxidative damage. Furthermore, UDCA can promote the anti-cancer effects of ferroptosis inducers (Erastin, RSL3), the murine double minute 2 (MDM2) inhibitors (Nutlin 3a, RG7112), cyclin dependent kinase 4 (CDK4) inhibitor (Abemaciclib), and glutaminase inhibitor (CB839). Together, UDCA functions as a cystine exchange factor that binds to SLC7A11 for antitumor activity, and SLC7A11 is not only a new transporter for BA but also a clinically applicable target for UDCA. More importantly, in combination with other antitumor chemotherapy or physiotherapy treatments, UDCA may provide effective and promising treatment strategies for RLPS or other types of tumors in a ROS-dependent manner.