ObjectiveTo examine the effects of Chaihu Jia Longgu Muli Tang （CLMT）-containing serum on Neuro-2A cell damage when exposed to conditioned medium from lipopolysaccharide （LPS）-stimulated microglia （BV-2） and explore the underlying mechanism. MethodsBV-2 cells were activated by LPS at a concentration of 1 mg·L^-1 to generate conditioned medium. Concurrently， a blank control group， a model group and groups treated with varying concentrations of CLMT （5%， 10%， and 15%）， dexamethasone （2.5 μmol·L^-1）， stimulator of interferon genes （STING） agonist （MSA-2， 10 μmol·L^-1）， and STING inhibitor （STING-IN-4， 4 μmol·L^-1） were established. Neuro-2A cells were subsequently cultured in the conditioned medium from each treatment group. The viability of BV-2 cells following interventions was assessed by the cell counting kit-8 （CCK-8） assay. The levels of tumor necrosis factor-α （TNF-α）， interleukin-1β （IL-1β）， and interleukin-6 （IL-6） in the supernatant of Neuro-2A cells from each group were measured through enzyme linked immunosorbent assay （ELISA）. Additionally， the expression of tyrosine hydroxylase （TH） in Neuro-2A cells was evaluated through immunohistochemistry （IHC）， while the expression of phosphorylated alpha-synuclein （p-α-Syn） was assessed by immunofluorescence （IF） assay. The expression levels of STING， interferon regulatory factor 3 （IRF3）， phosphorylated interferon regulatory factor 3 （p-IRF3）， nuclear transcription factor-kappa B p65 （NF-κB p65）， and phosphorylated nuclear transcription factor-kappa B p65 （p-NF-κB p65） in Neuro-2A cells were determined by Western blot. ResultsIn comparison to the control group， both the model group and the MSA-2 group exhibited reduced viability of Neuro-2A cells （P<0.01）， elevated concentrations of TNF-α， IL-1β， and IL-6 in the cell supernatant （P<0.01）， reduced expression of TH （P<0.01）， increased expression of p-α-Syn （P<0.01）， and upregulated relative protein levels of STING， p-IRF3， and p-NF-κB p65 （P<0.01）. Compared with the model group， medium- and high-dose CLMT enhanced the viability of Neuro-2A cells （P<0.01）， lowered the levels of TNF-α， IL-1β， and IL-6 （P<0.01）， promoted TH expression， and suppressed p-α-Syn expression （P<0.01）. In the dexamethasone group， the levels of IL-6， TNF-α， and IL-1β were reduced （P<0.05， P<0.01）. In the STING-IN-4 group， the levels of TNF-α and IL-1β were decreased （P<0.01）. The expression of TH was increased in both the dexamethasone group and the STING-IN-4 group （P<0.01）， while the expression of p-α-Syn was reduced （P<0.01）. The relative protein levels of p-IRF3， STING， and p-NF-κB p65 were reduced in medium- and high-dose CLMT groups （P<0.05， P<0.01）. Similarly， the dexamethasone group exhibited decreases in the expression levels of STING， p-IRF3， and p-NF-κB p65 （P<0.01）. In the STING-IN-4 group， the expression levels of p-NF-κB p65， STING， and p-IRF3 were also diminished （P<0.05， P<0.01）. However， no statistically significant differences were observed in the expression of IRF3 or NF-κB p65 across the groups. ConclusionCLMT has the potential to suppress the activation of BV-2 microglial cells， consequently mitigating the inflammatory response induced by LPS and further preventing inflammation-mediated damage to Neuro-2A cells. The underlying mechanism is likely associated with the modulation of the STING/NF-κB signaling pathway， which results in reduced inflammation and confers neuroprotective effects.

Deep learning(DL)is a machine learning technique that emulates the human brain's functionality through multi-layered neural network models,enabling it to learn and extract features from data,thereby facilitating the automatic processing and learning of complex tasks.DL has achieved numerous significant breakthroughs in areas such as image recognition,speech recognition,and natural language processing,becoming one of the most promi-nent technologies in the field of artificial intelligence.With the rapid advancement of DL technology,its application in the medical field has yielded remarkable outcomes,offering new possibilities for the diagnosis and treatment of lumbar diseases.This review aims to elucidate the application and research progress of DL in diagnosing,planning surgeries,and predicting postoperative efficacy for lumbar spine diseases.

Deep learning(DL)is a machine learning technique that emulates the human brain's functionality through multi-layered neural network models,enabling it to learn and extract features from data,thereby facilitating the automatic processing and learning of complex tasks.DL has achieved numerous significant breakthroughs in areas such as image recognition,speech recognition,and natural language processing,becoming one of the most promi-nent technologies in the field of artificial intelligence.With the rapid advancement of DL technology,its application in the medical field has yielded remarkable outcomes,offering new possibilities for the diagnosis and treatment of lumbar diseases.This review aims to elucidate the application and research progress of DL in diagnosing,planning surgeries,and predicting postoperative efficacy for lumbar spine diseases.

Objective:To explore the current situation of clinical nurses' disaster nursing ability and its influencing factors, and to provide basis for carrying out clinical nurses' disaster nursing education and training.Methods:By convenient sampling method, 322 clinical nurses from Peking University Shenzhen Hospital in September 2020 were selected. The disaster nursing ability assessment tool and general information questionnaire were used to conduct a questionnaire survey to analyze the current situation of clinical nurses' disaster nursing ability and its main influencing factors.Results:The total score of disaster nursing ability of clinical nurses was (199.07±34.31) points, which was at the middle and lower level; the scores of each dimension from high to low were as follows: disaster preparedness score was (72.94±12.89) points, coping ability score was (80.00±13.82) points, disaster reduction/prevention capability score was (28.19±6.09) points, recovery/reconstruction ability score was (17.94±3.27) points. Regression analysis showed that the highest degree, clinical department, whether they have autonomously studied disaster nursing related courses after work, and whether they have participated in disaster rescue after work were the main influencing factors of clinical nurses' disaster nursing ability ( t values were -4.715-5.508, P<0.05). Conclusions:The overall level of disaster nursing ability of clinical nurses is at the middle and lower level. It should be combined with clinical nurses' work department, whether clinical nurses have disaster rescue experience and whether they have independently studied disaster nursing related courses, and targeted disaster nursing related training should be carried out to improve their disaster nursing knowledge and skill.