Neurocritical care involves complex pathophysiological mechanisms, and its incidence is higher, injuries are more severe, and treatment is more challenging in high-altitude environments. This consensus, based on the latest domestic and international evidence-based medical data, establishes a standardized, goal-oriented framework for neurocritical care management applicable in high-altitude regions and nationwide. The consensus was developed following international standards for evidence quality assessment and underwent two rounds of Delphi expert consultation, resulting in 32 recommendation statements covering three parts: management systems, monitoring and assessment, and core strategies. Key updates include: advocating for the establishment of independent neurocritical care units and implementing precise tiered diagnosis and treatment based on the "Five Differences in Critical Care" concept; constructing a "trinity" multimodal brain monitoring system centered on cerebral blood flow, cerebral oxygenation, and brain function, emphasizing routine bedside transcranial Doppler ultrasound, cerebral oximetry, and continuous electroencephalography monitoring; shifting management strategies from mild hypothermia therapy to targeted temperature management, and defining the "446" target management pathway for the supercritical stage; emphasizing the assessment of static and dynamic cerebrovascular autoregulation functions through multimodal methods to achieve individualized optimal mean arterial pressure management; elevating cerebrospinal fluid management goals to the level of "glymphatic system" function maintenance; implementing a multidisciplinary collaborative, whole-process management model focusing on patients' long-term neurological functional outcomes; de-escalation criteria include multidimensional indicators such as recovery of brain structure, restoration of cerebrovascular autoregulation, improvement in cerebrospinal fluid dynamics, and reduction in biomarker levels; and integrating cutting-edge technologies like artificial intelligence into post-critical care management and rehabilitation planning. This consensus systematically integrates the entire process of neurocritical care management, reflecting the modern connotation of goal-oriented, dynamic, and multimodal integration in neurocritical care medicine. It aims to adapt to new trends such as deepening understanding of pathophysiological mechanisms, the integration of medicine and engineering, and the empowerment of artificial intelligence, thereby further advancing the discipline of critical care medicine.

Mastitis is one of the common and prevalent diseases in dairy cows,and the natural prod-uct daidzein is a kind of natural flavonoids with a wide range of pharmacological and anti-inflam-matory effects.However,the effect of daidzein on mastitis in dairy cows has not been reported.Therefore,in this study,we explored the effects of daidzein on LPS-induced inflammatory response and tight junction damage in dairy mammary epithelial cells Firstly,we pretreated the MAC-T cell line using different concentrations of daidzein,and it was clarified that daidzein below 200 μmol/L had no effect on the cell activity.Next,we examined the production of pro-inflammatory mediators in LPS-stimulated MAC-T cell lines using qRT-PCR,and clarified that daidzein could reduce the production of pro-inflammatory mediators in a concentration-dependent manner.Subsequently,af-ter the expression of Occludin,Claudin3 and ZO-1 was detected by immunofluorescence and West-ern Blot,it was clear that daidzein could alleviate MAC-T cell intercellular tight junction injury.Fi-nally,it was demonstrated that daidzein significantly inhibited LPS-induced activation of the NF-κB signaling pathway within MAC-T using network pharmacological analysis and Western Blot.The above results suggest that daidzein can inhibit LPS-induced inflammatory response and tight junc-tion damage in mammary epithelial cells of cows by suppressing the activation of NF-κB signaling pathway.The present study provides a theoretical basis for the alleviation of mastitis by natural products and further expands the pharmacological effects of daidzein.

Objective:To develop CNN-Dinov2, a deep learning-based automatic classification model for Gram-stained images, enabling rapid diagnosis of three prevalent Gram-negative bacilli in bloodstream infections: Escherichia coli ( E.coli), Klebsiella pneumoniae ( K.pneumoniae), and Pseudomonas aeruginosa ( P.aeruginosa). Methods:This evaluation study analyzed 1 425 Gram-stained microscopic images from patients with bloodstream infections at Houjie Hospital, in Dongguan City, collected between January 2023 and January 2024. The images, all positive for blood culture and identified as target strains, were categorized into Escherichia coli (419 images), Klebsiella pneumoniae (411 images), Pseudomonas aeruginosa (413 images), and other Gram-negative bacilli (182 images). They were randomly split into a training set (1 141 images), a validation set (141 images), and a test set (143 images) in an 8∶1∶1 ratio. A hybrid CNN-Dinov2 model was developed by integrating ResNet′s local feature extraction with Dinov2′s global pre-trained features, followed by a fully connected layer. The model was optimized by inputting the preprocessed images and adjusting parameters through loss calculation and backpropagation. AlexNet, Dinov2, and ResNet18 served as control models. The models′ classification performance was assessed using accuracy, precision, weighted F1 score, and recall rate, derived from the confusion matrix. The PR curve and AP value further evaluated each model′s classification capability across the four image categories. Results:The CNN-Dinov2 model achieved a training accuracy of 99.74%, a validation accuracy of 98.12%, and a validation loss of 0.070 6, demonstrating robust generalization without overfitting. Validation metrics revealed superior performance with an accuracy of 98.60%, precision of 98.65%, a weighted F1 score of 98.60%, and a recall rate of 98.60%, outperforming other models. The confusion matrix confirmed its strong classification capability, with the highest sum of diagonal values for identifying four types of bacteria. The macro average precision (AP) values under the precision-recall (PR) curves were all 1, indicating excellent discrimination across all categories. Overall, the CNN-Dinov2 model exhibited the best performance among the four models evaluated.Conclusion:This study successfully developed CNN-Dinov2, an automated classification model for Gram staining images. It offers valuable support for the rapid diagnosis of bloodstream infections caused by Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa, demonstrating practical utility.

Mastitis is one of the common and prevalent diseases in dairy cows,and the natural prod-uct daidzein is a kind of natural flavonoids with a wide range of pharmacological and anti-inflam-matory effects.However,the effect of daidzein on mastitis in dairy cows has not been reported.Therefore,in this study,we explored the effects of daidzein on LPS-induced inflammatory response and tight junction damage in dairy mammary epithelial cells Firstly,we pretreated the MAC-T cell line using different concentrations of daidzein,and it was clarified that daidzein below 200 μmol/L had no effect on the cell activity.Next,we examined the production of pro-inflammatory mediators in LPS-stimulated MAC-T cell lines using qRT-PCR,and clarified that daidzein could reduce the production of pro-inflammatory mediators in a concentration-dependent manner.Subsequently,af-ter the expression of Occludin,Claudin3 and ZO-1 was detected by immunofluorescence and West-ern Blot,it was clear that daidzein could alleviate MAC-T cell intercellular tight junction injury.Fi-nally,it was demonstrated that daidzein significantly inhibited LPS-induced activation of the NF-κB signaling pathway within MAC-T using network pharmacological analysis and Western Blot.The above results suggest that daidzein can inhibit LPS-induced inflammatory response and tight junc-tion damage in mammary epithelial cells of cows by suppressing the activation of NF-κB signaling pathway.The present study provides a theoretical basis for the alleviation of mastitis by natural products and further expands the pharmacological effects of daidzein.

The drug resistance of the carbapenem-resistant Enterobacteriaceae(CRE)strains was mainly induced by multiple approaches such as production of carbapenemases,increase of bacterial outer membrane permeability,activation of active efflux pump system,formation of biofilm and drug modifying mechanisms.Those mecha-nisms involve deletion,mutation,insertion and posttranscriptional modification of relevant encoding genes,which may affect the susceptibility of the CRE strains to antibiotics.At present,the conventional clinical thera-pies include the use of traditional antibiotics,either the one-drug use or combined use of drugs.The development of novel antibacterial therapy is under way.The epidemiological characteristics of CRE infections,drug resist-ance mechanisms,current and prospective treatment strategies for CRE infections(covering new application of the drugs in available,the novel drugs such as ceftazidime/avibactam,meropenem/vaborbactam and imipenem/rele-bactam)were deeply reviewed in this article,so as to provide reliable reference for clinical prevention,control and treatment of CRE infections.

The drug resistance of the carbapenem-resistant Enterobacteriaceae(CRE)strains was mainly induced by multiple approaches such as production of carbapenemases,increase of bacterial outer membrane permeability,activation of active efflux pump system,formation of biofilm and drug modifying mechanisms.Those mecha-nisms involve deletion,mutation,insertion and posttranscriptional modification of relevant encoding genes,which may affect the susceptibility of the CRE strains to antibiotics.At present,the conventional clinical thera-pies include the use of traditional antibiotics,either the one-drug use or combined use of drugs.The development of novel antibacterial therapy is under way.The epidemiological characteristics of CRE infections,drug resist-ance mechanisms,current and prospective treatment strategies for CRE infections(covering new application of the drugs in available,the novel drugs such as ceftazidime/avibactam,meropenem/vaborbactam and imipenem/rele-bactam)were deeply reviewed in this article,so as to provide reliable reference for clinical prevention,control and treatment of CRE infections.

Objective:To develop CNN-Dinov2, a deep learning-based automatic classification model for Gram-stained images, enabling rapid diagnosis of three prevalent Gram-negative bacilli in bloodstream infections: Escherichia coli ( E.coli), Klebsiella pneumoniae ( K.pneumoniae), and Pseudomonas aeruginosa ( P.aeruginosa). Methods:This evaluation study analyzed 1 425 Gram-stained microscopic images from patients with bloodstream infections at Houjie Hospital, in Dongguan City, collected between January 2023 and January 2024. The images, all positive for blood culture and identified as target strains, were categorized into Escherichia coli (419 images), Klebsiella pneumoniae (411 images), Pseudomonas aeruginosa (413 images), and other Gram-negative bacilli (182 images). They were randomly split into a training set (1 141 images), a validation set (141 images), and a test set (143 images) in an 8∶1∶1 ratio. A hybrid CNN-Dinov2 model was developed by integrating ResNet′s local feature extraction with Dinov2′s global pre-trained features, followed by a fully connected layer. The model was optimized by inputting the preprocessed images and adjusting parameters through loss calculation and backpropagation. AlexNet, Dinov2, and ResNet18 served as control models. The models′ classification performance was assessed using accuracy, precision, weighted F1 score, and recall rate, derived from the confusion matrix. The PR curve and AP value further evaluated each model′s classification capability across the four image categories. Results:The CNN-Dinov2 model achieved a training accuracy of 99.74%, a validation accuracy of 98.12%, and a validation loss of 0.070 6, demonstrating robust generalization without overfitting. Validation metrics revealed superior performance with an accuracy of 98.60%, precision of 98.65%, a weighted F1 score of 98.60%, and a recall rate of 98.60%, outperforming other models. The confusion matrix confirmed its strong classification capability, with the highest sum of diagonal values for identifying four types of bacteria. The macro average precision (AP) values under the precision-recall (PR) curves were all 1, indicating excellent discrimination across all categories. Overall, the CNN-Dinov2 model exhibited the best performance among the four models evaluated.Conclusion:This study successfully developed CNN-Dinov2, an automated classification model for Gram staining images. It offers valuable support for the rapid diagnosis of bloodstream infections caused by Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa, demonstrating practical utility.

【Objective】 To investigate the status quo of disease perception and partner support of erectile dysfunction (ED) patients, and to analyze their correlation. 【Methods】 With convenient sampling method, 220 ED patients in a first-class hospital in Taiyuan were surveyed with the short version of disease perception questionnaire (BIPQ) and partner support coping questionnaire (DCI). 【Results】 The BIPQ score was (41.90±7.33), and the DCI score was (116.79±20.37). Pearson correlation analysis showed that except life influence, treatment control and emotional influence, the mutual support dimension of partner support of ED patients was correlated with all dimensions of disease perception (r=-0.173, 0.151, -0.182, 0.163, 0.188,P<0.05).Except cognitive comprehension, the negative support dimension of partner support of ED patients was correlated with the dimensions and total score of disease perception (r=0.399, 0.185, -0.167, -0.306, 0.269, 0.445, 0.320, 0.357,P<0.05). 【Conclusion】 ED patients have negative disease perception, and their partners have better coping strategies. Medical staff may pay more attention to partner support so as to reduce patients’ negative perception of disease and promote recovery.

To investigate the genomic features and perform cluster analysis of Carbapenem-resistant Klebsiella pneumoniae (CRKP) to provide an experimental basis for guiding the prevention and treatment of CRKP infections.A retrospective case-cohort study was conducted on 19 non-redundant CRKP strains isolated from the Tenth Affiliated Hospital of Southern Medical University between January and June 2023. Whole genome sequencing (WGS) and multilocus sequence typing (MLST) were performed to compare genomic features and analyze the resistance genes and homology of the strains.The results showed that the 19 CRKP strains were isolated from 8 different clinical departments, mainly from respiratory specimens. The whole genome sequencing revealed that the genomic lengths of CRKP ranged from 4.90 to 5.85 Mbp, with contigs N50 values>20 kb for each genome. The median overall GC content was 57.0% (50.4%-57.1%). Comparative genomic analysis identified three regions with high genomic variability. WGS detected 32 resistance genes across 11 categories. All 19 strains carried carbapenem resistance genes ( blaKPC-2 and blaOXA-48), blaTEM-1B extended-spectrum β-lactamase resistance genes, qnrS1 quinolone resistance gene, and fosA fosfomycin resistance gene, with each strain carrying only one carbapenemase gene. The detection rate of blaKPC-2 was 94.7% (18/19). MLST identified three sequence types: ST11, ST437 and ST147, with ST11 being predominant (89.5%, 17/19). Clustering analysis based on acquired resistance genes revealed three clonal transmission patterns among strains 72 and 90, and strains 88, 84, 66 and 79.In conclusion, CRKP strains carry multiple resistance genes, and clustering analysis indicating that nosocomial clonal transmission is closely related to acquired resistance genes. The ST11- blaKPC-2 type strain is the predominant clone. Strengthened surveillance and effective control strategies are necessary to reduce nosocomial transmission of CRKP.

To investigate the genomic features and perform cluster analysis of Carbapenem-resistant Klebsiella pneumoniae (CRKP) to provide an experimental basis for guiding the prevention and treatment of CRKP infections.A retrospective case-cohort study was conducted on 19 non-redundant CRKP strains isolated from the Tenth Affiliated Hospital of Southern Medical University between January and June 2023. Whole genome sequencing (WGS) and multilocus sequence typing (MLST) were performed to compare genomic features and analyze the resistance genes and homology of the strains.The results showed that the 19 CRKP strains were isolated from 8 different clinical departments, mainly from respiratory specimens. The whole genome sequencing revealed that the genomic lengths of CRKP ranged from 4.90 to 5.85 Mbp, with contigs N50 values>20 kb for each genome. The median overall GC content was 57.0% (50.4%-57.1%). Comparative genomic analysis identified three regions with high genomic variability. WGS detected 32 resistance genes across 11 categories. All 19 strains carried carbapenem resistance genes ( blaKPC-2 and blaOXA-48), blaTEM-1B extended-spectrum β-lactamase resistance genes, qnrS1 quinolone resistance gene, and fosA fosfomycin resistance gene, with each strain carrying only one carbapenemase gene. The detection rate of blaKPC-2 was 94.7% (18/19). MLST identified three sequence types: ST11, ST437 and ST147, with ST11 being predominant (89.5%, 17/19). Clustering analysis based on acquired resistance genes revealed three clonal transmission patterns among strains 72 and 90, and strains 88, 84, 66 and 79.In conclusion, CRKP strains carry multiple resistance genes, and clustering analysis indicating that nosocomial clonal transmission is closely related to acquired resistance genes. The ST11- blaKPC-2 type strain is the predominant clone. Strengthened surveillance and effective control strategies are necessary to reduce nosocomial transmission of CRKP.