The intelligent oxygen management system is a software designed with various algorithms to automatically titrate inhaled oxygen concentration according to specific patterns. This system can be integrated into various ventilator devices and used during assisted ventilation processes, aiming to maintain the patient's blood oxygen saturation within a target range. This paper employs a scoping review methodology, focusing on research related to intelligent oxygen management systems in neonatal intensive care units. It reviews the fundamental principles, application platforms, and clinical outcomes of these systems, providing a theoretical basis for clinical implementation.
Humans ; Intensive Care Units, Neonatal ; Infant, Newborn ; Oxygen/administration & dosage* ; Oxygen Inhalation Therapy/methods* ; Respiration, Artificial

Objective: To assess the role of dynamic contrast-enhanced (DCE)-MRI of the extraocular muscles (EOMs) for determining the activity of thyroid-associated ophthalmopathy (TAO) and treatment response to glucocorticoids (GCs). Materials and Methods: We prospectively enrolled 65 patients with TAO (41 active, 82 eyes; 24 inactive, 48 eyes). Twenty-two active patients completed the GC treatment and follow-up assessment, including 15 patients (30 eyes) and 7 patients (14 eyes), defined as responsive and unresponsive, respectively. Model-free (time to peak [TTP], area under the curve [AUC], and Slope max) and model-based (Ktrans , Kep, and Ve) parameters of EOMs in embedded simplified histogram analyses were calculated and compared between groups. Multivariable logistic regression analysis was used to identify the independent predictors. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the diagnostic performance. Results: Active patients exhibited significantly higher TTP at the 10th percentile (-10th), TTP-mean, and TTP at the 90th percentile (-90th); AUC-10th, AUC-mean, AUC-90th, and AUC-max; Ktrans -10th and Ktrans -mean; and Ve-10th, Ve-mean, Ve-90th, and Ve-max than inactive patients (P < 0.05). Responsive patients exhibited significantly lower TTP-min; higher Ktrans -mean and Ktrans -max; and higher Kep-10th, Kep-mean, and Kep-max than unresponsive patients (P < 0.05). TTP-mean and Ve-mean were independent variables for determining disease activity (P = 0.017 and 0.022, respectively). A combination of the two parameters could determine active TAO with moderate performance (AUROC = 0.687). TTP-min and Ktrans -mean were independent predictors of the response to GCs (P = 0.023 and 0.004, respectively), uniting which could determine the response to GCs with decent performance (AUROC = 0.821). Conclusion DCE-MRI-derived model-free and model-based parameters of EOMs can assist in the evaluation of TAO. In particular, TTP-mean and Ve-mean could be useful for determining the activity of TAO, whereas TTP-min and K trans -mean could be promising biomarkers for determining the response to GCs.

Objective: To assess the role of dynamic contrast-enhanced (DCE)-MRI of the extraocular muscles (EOMs) for determining the activity of thyroid-associated ophthalmopathy (TAO) and treatment response to glucocorticoids (GCs). Materials and Methods: We prospectively enrolled 65 patients with TAO (41 active, 82 eyes; 24 inactive, 48 eyes). Twenty-two active patients completed the GC treatment and follow-up assessment, including 15 patients (30 eyes) and 7 patients (14 eyes), defined as responsive and unresponsive, respectively. Model-free (time to peak [TTP], area under the curve [AUC], and Slope max) and model-based (Ktrans , Kep, and Ve) parameters of EOMs in embedded simplified histogram analyses were calculated and compared between groups. Multivariable logistic regression analysis was used to identify the independent predictors. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the diagnostic performance. Results: Active patients exhibited significantly higher TTP at the 10th percentile (-10th), TTP-mean, and TTP at the 90th percentile (-90th); AUC-10th, AUC-mean, AUC-90th, and AUC-max; Ktrans -10th and Ktrans -mean; and Ve-10th, Ve-mean, Ve-90th, and Ve-max than inactive patients (P < 0.05). Responsive patients exhibited significantly lower TTP-min; higher Ktrans -mean and Ktrans -max; and higher Kep-10th, Kep-mean, and Kep-max than unresponsive patients (P < 0.05). TTP-mean and Ve-mean were independent variables for determining disease activity (P = 0.017 and 0.022, respectively). A combination of the two parameters could determine active TAO with moderate performance (AUROC = 0.687). TTP-min and Ktrans -mean were independent predictors of the response to GCs (P = 0.023 and 0.004, respectively), uniting which could determine the response to GCs with decent performance (AUROC = 0.821). Conclusion DCE-MRI-derived model-free and model-based parameters of EOMs can assist in the evaluation of TAO. In particular, TTP-mean and Ve-mean could be useful for determining the activity of TAO, whereas TTP-min and K trans -mean could be promising biomarkers for determining the response to GCs.

Objective: To assess the role of dynamic contrast-enhanced (DCE)-MRI of the extraocular muscles (EOMs) for determining the activity of thyroid-associated ophthalmopathy (TAO) and treatment response to glucocorticoids (GCs). Materials and Methods: We prospectively enrolled 65 patients with TAO (41 active, 82 eyes; 24 inactive, 48 eyes). Twenty-two active patients completed the GC treatment and follow-up assessment, including 15 patients (30 eyes) and 7 patients (14 eyes), defined as responsive and unresponsive, respectively. Model-free (time to peak [TTP], area under the curve [AUC], and Slope max) and model-based (Ktrans , Kep, and Ve) parameters of EOMs in embedded simplified histogram analyses were calculated and compared between groups. Multivariable logistic regression analysis was used to identify the independent predictors. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the diagnostic performance. Results: Active patients exhibited significantly higher TTP at the 10th percentile (-10th), TTP-mean, and TTP at the 90th percentile (-90th); AUC-10th, AUC-mean, AUC-90th, and AUC-max; Ktrans -10th and Ktrans -mean; and Ve-10th, Ve-mean, Ve-90th, and Ve-max than inactive patients (P < 0.05). Responsive patients exhibited significantly lower TTP-min; higher Ktrans -mean and Ktrans -max; and higher Kep-10th, Kep-mean, and Kep-max than unresponsive patients (P < 0.05). TTP-mean and Ve-mean were independent variables for determining disease activity (P = 0.017 and 0.022, respectively). A combination of the two parameters could determine active TAO with moderate performance (AUROC = 0.687). TTP-min and Ktrans -mean were independent predictors of the response to GCs (P = 0.023 and 0.004, respectively), uniting which could determine the response to GCs with decent performance (AUROC = 0.821). Conclusion DCE-MRI-derived model-free and model-based parameters of EOMs can assist in the evaluation of TAO. In particular, TTP-mean and Ve-mean could be useful for determining the activity of TAO, whereas TTP-min and K trans -mean could be promising biomarkers for determining the response to GCs.

Objective: To assess the role of dynamic contrast-enhanced (DCE)-MRI of the extraocular muscles (EOMs) for determining the activity of thyroid-associated ophthalmopathy (TAO) and treatment response to glucocorticoids (GCs). Materials and Methods: We prospectively enrolled 65 patients with TAO (41 active, 82 eyes; 24 inactive, 48 eyes). Twenty-two active patients completed the GC treatment and follow-up assessment, including 15 patients (30 eyes) and 7 patients (14 eyes), defined as responsive and unresponsive, respectively. Model-free (time to peak [TTP], area under the curve [AUC], and Slope max) and model-based (Ktrans , Kep, and Ve) parameters of EOMs in embedded simplified histogram analyses were calculated and compared between groups. Multivariable logistic regression analysis was used to identify the independent predictors. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the diagnostic performance. Results: Active patients exhibited significantly higher TTP at the 10th percentile (-10th), TTP-mean, and TTP at the 90th percentile (-90th); AUC-10th, AUC-mean, AUC-90th, and AUC-max; Ktrans -10th and Ktrans -mean; and Ve-10th, Ve-mean, Ve-90th, and Ve-max than inactive patients (P < 0.05). Responsive patients exhibited significantly lower TTP-min; higher Ktrans -mean and Ktrans -max; and higher Kep-10th, Kep-mean, and Kep-max than unresponsive patients (P < 0.05). TTP-mean and Ve-mean were independent variables for determining disease activity (P = 0.017 and 0.022, respectively). A combination of the two parameters could determine active TAO with moderate performance (AUROC = 0.687). TTP-min and Ktrans -mean were independent predictors of the response to GCs (P = 0.023 and 0.004, respectively), uniting which could determine the response to GCs with decent performance (AUROC = 0.821). Conclusion DCE-MRI-derived model-free and model-based parameters of EOMs can assist in the evaluation of TAO. In particular, TTP-mean and Ve-mean could be useful for determining the activity of TAO, whereas TTP-min and K trans -mean could be promising biomarkers for determining the response to GCs.

Objective: To assess the role of dynamic contrast-enhanced (DCE)-MRI of the extraocular muscles (EOMs) for determining the activity of thyroid-associated ophthalmopathy (TAO) and treatment response to glucocorticoids (GCs). Materials and Methods: We prospectively enrolled 65 patients with TAO (41 active, 82 eyes; 24 inactive, 48 eyes). Twenty-two active patients completed the GC treatment and follow-up assessment, including 15 patients (30 eyes) and 7 patients (14 eyes), defined as responsive and unresponsive, respectively. Model-free (time to peak [TTP], area under the curve [AUC], and Slope max) and model-based (Ktrans , Kep, and Ve) parameters of EOMs in embedded simplified histogram analyses were calculated and compared between groups. Multivariable logistic regression analysis was used to identify the independent predictors. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the diagnostic performance. Results: Active patients exhibited significantly higher TTP at the 10th percentile (-10th), TTP-mean, and TTP at the 90th percentile (-90th); AUC-10th, AUC-mean, AUC-90th, and AUC-max; Ktrans -10th and Ktrans -mean; and Ve-10th, Ve-mean, Ve-90th, and Ve-max than inactive patients (P < 0.05). Responsive patients exhibited significantly lower TTP-min; higher Ktrans -mean and Ktrans -max; and higher Kep-10th, Kep-mean, and Kep-max than unresponsive patients (P < 0.05). TTP-mean and Ve-mean were independent variables for determining disease activity (P = 0.017 and 0.022, respectively). A combination of the two parameters could determine active TAO with moderate performance (AUROC = 0.687). TTP-min and Ktrans -mean were independent predictors of the response to GCs (P = 0.023 and 0.004, respectively), uniting which could determine the response to GCs with decent performance (AUROC = 0.821). Conclusion DCE-MRI-derived model-free and model-based parameters of EOMs can assist in the evaluation of TAO. In particular, TTP-mean and Ve-mean could be useful for determining the activity of TAO, whereas TTP-min and K trans -mean could be promising biomarkers for determining the response to GCs.

As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity. This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing. Its purpose is to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security. Concurrently, the principles of compliance with the law, patient privacy respect, patient interest protection, and safety and reliability are underscored. Key issues such as informed consent, data usage, intellectual property protection, conflict of interest, and benefit sharing are examined in depth. The enactment of this expert consensus is intended to foster the profound integration and sustainable advancement of artificial intelligence within the medical domain, while simultaneously ensuring that artificial intelligence adheres strictly to the relevant ethical norms and legal frameworks during the processing of medical data.
Artificial Intelligence/legislation & jurisprudence* ; Humans ; Consensus ; Computer Security/standards* ; Confidentiality/ethics* ; Informed Consent/ethics*

Animals ; SARS-CoV-2 ; Antibodies, Neutralizing ; Macaca mulatta ; COVID-19/prevention & control* ; Antibodies, Viral ; Vaccines

Aim To investigate the effect of phillygenin ( PHI) on lipopolysacchride ( LPS) and normal human plasma ( NHP) induced inflammatory injury on alveolar type II epithelial A549 cells and the related mechanism. Methods A549 cells were exposured to 1 mg • L

ObjectiveTo observe the effects of Hedysarum polysaccharides（HPS）on the signaling pathways of B-cell lymphoma 2 （Bcl-2）， cysteinyl aspartate-specific protease 3 （Caspase-3）， and Bcl-2-associated X protein （Bax） in Schwann cells（SCs）cultured in high glucose，and explore the possible mechanism of HPS against diabetic peripheral neuropathy（DPN）. MethodFour SD suckling mice aged 5-7 days were randomly divided into a normal group，a high-glucose group，an HPS + high-glucose group，and an α-lipoic acid（α-LA）+ high-glucose group. SCs were extracted from the sciatic nerve and cultured in a 37 ℃，5% CO₂ incubator. After the cells reached 80% confluence，Cell Counting Kit-8（CCK-8）was used to screen the experimental concentrations suitable for high glucose，HPS， and α-LA interventions. Western blot and Real-time polymerase chain reaction （Real-time PCR）were used to detect the protein and mRNA expression of Bcl-2，Bax，and Caspase-3. The apoptosis rate of SCs was detected by flow cytometry using Annexin V-fluorescein isothiocyanate （FITC）/propidium iodide （PI）. ResultAs revealed by Western blot and real-time PCR，compared with the normal group，the high-glucose group showed reduced protein and mRNA expression of Bcl-2 and increased protein and mRNA expression of Bax and Caspase-3（P<0.01）. Compared with the high-glucose group，the HPS + high-glucose group and the α-LA + high-glucose group showed increased protein and mRNA expression of Bcl-2 and decreased protein and mRNA expression of Bax and Caspase-3（P<0.01）. As displayed by the results of flow cytometry using Annexin V/PI， compared with the normal group，the high-glucose group showed increased apoptosis rate；compared with the high-glucose group，the HPS + high-glucose group and the α-LA + high-glucose group showed reduced apoptosis rate（P<0.01）. ConclusionHPS can alleviate the apoptotic response of SCs，and its mechanism may be related to the inhibition of the activation of the Bcl-2/Caspase-3 signaling pathway.