Sepsis remains a significant challenge for healthcare systems worldwide,despite advancements in diagnostic and therapeutic approaches that have mitigated its impact.The underlying pathophysiological mechanisms of sepsis are not yet fully understood.Autophagy,a cellular stress response,has been shown to play a critical role in the de-velopment of sepsis.Golgiphagy,a specific autophagic process targeting the Golgi apparatus,has been identified as a key factor influencing sepsis pathophysiology.This process operates through autophagy-related receptors and contributes to or-gan damage during sepsis.This review will explore the receptors associated with Golgiphagy,its role in sepsis progres-sion,and its effects on various organs,with the goal of informing treatment strategies for sepsis.

Identifying drug-drug interactions(DDIs)is essential to prevent adverse effects from polypharmacy.Although deep learning has advanced DDI identification,the gap between powerful models and their lack of clinical application and evaluation has hindered clinical benefits.Here,we developed a Multi-Dimensional Feature Fusion model named MDFF,which integrates one-dimensional simplified molec-ular input line entry system sequence features,two-dimensional molecular graph features,and three-dimensional geometric features to enhance drug representations for predicting DDIs.MDFF was trained and validated on two DDI datasets,evaluated across three distinct scenarios,and compared with advanced DDI prediction models using accuracy,precision,recall,area under the curve,and F1 score metrics.MDFF achieved state-of-the-art performance across all metrics.Ablation experiments showed that integrating multi-dimensional drug features yielded the best results.More importantly,we obtained adverse drug reaction reports uploaded by Xiangya Hospital of Central South University from 2021 to 2023 and used MDFF to identify potential adverse DDIs.Among 12 real-world adverse drug reaction reports,the predictions of 9 reports were supported by relevant evidence.Additionally,MDFF demon-strated the ability to explain adverse DDI mechanisms,providing insights into the mechanisms behind one specific report and highlighting its potential to assist practitioners in improving medical practice.

Identifying drug-drug interactions (DDIs) is essential to prevent adverse effects from polypharmacy. Although deep learning has advanced DDI identification, the gap between powerful models and their lack of clinical application and evaluation has hindered clinical benefits. Here, we developed a Multi-Dimensional Feature Fusion model named MDFF, which integrates one-dimensional simplified molecular input line entry system sequence features, two-dimensional molecular graph features, and three-dimensional geometric features to enhance drug representations for predicting DDIs. MDFF was trained and validated on two DDI datasets, evaluated across three distinct scenarios, and compared with advanced DDI prediction models using accuracy, precision, recall, area under the curve, and F1 score metrics. MDFF achieved state-of-the-art performance across all metrics. Ablation experiments showed that integrating multi-dimensional drug features yielded the best results. More importantly, we obtained adverse drug reaction reports uploaded by Xiangya Hospital of Central South University from 2021 to 2023 and used MDFF to identify potential adverse DDIs. Among 12 real-world adverse drug reaction reports, the predictions of 9 reports were supported by relevant evidence. Additionally, MDFF demonstrated the ability to explain adverse DDI mechanisms, providing insights into the mechanisms behind one specific report and highlighting its potential to assist practitioners in improving medical practice.

Sepsis remains a significant challenge for healthcare systems worldwide,despite advancements in diagnostic and therapeutic approaches that have mitigated its impact.The underlying pathophysiological mechanisms of sepsis are not yet fully understood.Autophagy,a cellular stress response,has been shown to play a critical role in the de-velopment of sepsis.Golgiphagy,a specific autophagic process targeting the Golgi apparatus,has been identified as a key factor influencing sepsis pathophysiology.This process operates through autophagy-related receptors and contributes to or-gan damage during sepsis.This review will explore the receptors associated with Golgiphagy,its role in sepsis progres-sion,and its effects on various organs,with the goal of informing treatment strategies for sepsis.

Objective To assess the effect of ascorbic acid/ferric chloride (AA/FeCl3) in attenuating cartilage damage in rats with osteoarthritis. Methods Thirty adult male Wistar rats with surgically induced osteoarthritis were randomized into 2 groups for treatment with intra-articular injection of saline (control group) or AA/FeCl3 mixture (AA group) once a week starting from the third week after the operation. At 6, 9, and 12 weeks after the operation, 5 rats from each group were sacrificed for observing subchondral bone changes on X-ray films and evaluation of cartilage degeneration in the right knee joints using safranin-O/Fast green staining and a modified OARSI scoring system. The degradation of the cartilage matrix was observed by immunohistochemical staining for type II collagen. Results X-ray examination in saline control group revealed the presence of osteophytes and narrowing of the joint space at 9 weeks, and the joint line disappeared at 12 weeks after the surgery; only slight irregularity of the articular surface was observed in the AA group at 9 and 12 weeks. OARSI scores were significantly lower in AA group than in the control group at 9 weeks (18.67 ± 0.67 vs 12.17 ± 2.75;P<0.05) and 12 weeks (20.11 ± 1.84 vs 13.77 ± 0.40;P<0.05) but not at 6 weeks after the surgery. The content of type 2 collagen in AA group was significantly higher than that in the control group at 6 weeks (0.36 ± 0.039 vs 0.49 ± 0.029;P<0.05) and 9 weeks after the surgery (0.25 ± 0.041 vs 0.38 ± 0.040;P<0.05). Conclusions Early intra-articular injection of AA/FeCl3 can effectively delay the progression of post-traumatic osteoarthritis in rats.

Objective To assess the effect of ascorbic acid/ferric chloride (AA/FeCl3) in attenuating cartilage damage in rats with osteoarthritis. Methods Thirty adult male Wistar rats with surgically induced osteoarthritis were randomized into 2 groups for treatment with intra-articular injection of saline (control group) or AA/FeCl3 mixture (AA group) once a week starting from the third week after the operation. At 6, 9, and 12 weeks after the operation, 5 rats from each group were sacrificed for observing subchondral bone changes on X-ray films and evaluation of cartilage degeneration in the right knee joints using safranin-O/Fast green staining and a modified OARSI scoring system. The degradation of the cartilage matrix was observed by immunohistochemical staining for type II collagen. Results X-ray examination in saline control group revealed the presence of osteophytes and narrowing of the joint space at 9 weeks, and the joint line disappeared at 12 weeks after the surgery; only slight irregularity of the articular surface was observed in the AA group at 9 and 12 weeks. OARSI scores were significantly lower in AA group than in the control group at 9 weeks (18.67 ± 0.67 vs 12.17 ± 2.75;P<0.05) and 12 weeks (20.11 ± 1.84 vs 13.77 ± 0.40;P<0.05) but not at 6 weeks after the surgery. The content of type 2 collagen in AA group was significantly higher than that in the control group at 6 weeks (0.36 ± 0.039 vs 0.49 ± 0.029;P<0.05) and 9 weeks after the surgery (0.25 ± 0.041 vs 0.38 ± 0.040;P<0.05). Conclusions Early intra-articular injection of AA/FeCl3 can effectively delay the progression of post-traumatic osteoarthritis in rats.

Objective:To establish a GC method for the determination of phenol and L-menthol in glycerin Zhiyang lotions. Meth-ods:A Zebron ZB-WAX(0. 32 mm × 30. 0 m,0. 50 μm) capillary column was used with an FID detector. The column temperature was 60℃, maintained for 1 min, and then raised to 160℃ at the rate of 8℃·min-1 , and maintained 10 minutes. The inlet tempera-ture was 180℃, the detector temperature was 300℃, and the carrier gas was nitrogen. Results:The linear range of phenol and L-men-thol was 0. 5-10. 0 mg·ml-1(r=0. 999 9) and 0. 25-5. 0 mg·ml-1(r=0. 999 9), respectively. The average recovery of phenol and L-menthol was 99. 01%(RSD=0. 90%,n=9)and 99. 70%(RSD=0. 98%,n=9), respectively. Conclusion: The method is sim-ple, accurate and reliable, and can be used to determine the concentration of phenol and L-menthol in glycerin Zhiyang lotions.