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.

ObjectiveTo evaluate the public health governance capacity in Jiangsu Province and provide an optimized pathway for the construction of a “strong, rich, beautiful, and high-quality” new Jiangsu. MethodsA total of 806 policy documents, 658 public information reports, and 148 research literatures related to public health governance capacity in Jiangsu Province from January 1995 to December 2023 were collected. The status of current public health goverance was assessed based on the evaluation criteria suitable for public health systems, and the strengths and the weaknesses of the system were identified. ResultsThe public health governance capability of Jiangsu Province was scored at 738.3 points, ranking 3rd nationally. Maternal health care and emergency response capacities achieved leading positions nationwide, both ranking 2nd. Jiangsu had exhibited a standardized guidance in the strategic level, a well-established management mechanism, an extensive coverage in information collection, and a scientifically established health targets setting. However, bottlenecks remained, including an unclear division of responsibilities across organizational departments, an insufficient public-health workforce, the absence of a stable growth mechanism for government funding investment, and difficulties in promptly identifying public needs. ConclusionJiangsu’s public-health system demonstrates leading nationally, yet several components remain underdeveloped. Future efforts should consolidate advantages while addressing weaknesses, further diversify content and forms, establish a stable funding increase mechanism, and clarify departmental functions, thereby providing solid health support for realizing the developmental goals of a “strong, rich, beautiful and high-quality” new Jiangsu.

ObjectiveTo systematically assess the public health governance capacity in Zhejiang Province, to conduct an in-depth analysis of its strengths and weaknesses, so as to provide scientific basis and strategic recommendations for further enhancement. MethodsA systematic collection of policy documents, public information reports, and research literature related to public health governance capacity in Zhejiang Province from 2002 to 2023 was conducted (encompassing a total of 1 263 policy documents, 138 pieces of information reports and 631 research articles). Based on the evaluation criteria suitable for public health systems previously developed by the research team, the basic status and magnitude of change in public health governance capacity in Zhejiang Province was evaluated. Additionally, normative gap analyses were employed to identify the strengths and weaknesses. ResultsZhejiang Province ranked 4th nationwide in terms of public health governance capacity with a score of 733.4 points (1 000.0-point maximum). The province has effectively implemented the principle of health first (scoring 698.5 points in the assessment of health-first strategy implementation) and attached sufficient importance to health-related goals (scoring 658.2 points in the scientific rationality of goal setting). However, the implementation of inter-departmental coordination and incentive mechanisms only scored 178.7 points, the feasibility of management and monitoring mechanisms scored even lower at only 144.0 points, and the coverage of incentive mechanisms scored 286.0 points. ConclusionZhejiang Province has effectively implemented its health first strategy and attached great importance to health targets, but still needs to strengthen cross-departmental coordination mechanisms and health-oriented incentives.

Background: and Purpose Accurately predicting long-term functional outcomes of basilar artery occlusion (BAO) remains challenging. We compared the predictive performance of the baseline, 24-hour, and 72-hour National Institutes of Health Stroke Scale (NIHSS) scores for 90-day BAO functional outcomes using the Acute Basilar Artery Occlusion: Endovascular Thrombectomy versus Standard Medical Treatment (ATTENTION) trial data. We identified the optimal assessment time point, determined treatment-specific NIHSS cutoff values, and explored the role of early neurological function in treatment effects. Methods: This retrospective post hoc analysis included 324 patients with acute BAO with baseline NIHSS scores ≥10 and complete NIHSS assessments at each time point. The primary outcome was a favorable 90-day functional outcome (modified Rankin Scale score, 0–3). Receiver operating characteristic curve analysis was used to assess the predictive ability of NIHSS scores. The optimal 72-hour NIHSS predictive cutoff values were determined for the endovascular treatment (EVT) and best medical management (BMM) subgroups. Results: The 72-hour NIHSS score showed the highest predictive accuracy for the primary outcome (area under the receiver operating characteristic curve [AUC]: 0.954), outperforming the 24-hour (AUC: 0.903) and baseline (AUC: 0.688) scores; its optimal predictive cut-off value was ≤11 in the EVT group (sensitivity: 85.6%, specificity: 92.9%, positive predictive value [PPV]: 91.8%, negative predictive value [NPV]: 87.4%) and ≤9 in the BMM group (sensitivity: 84.6%, specificity: 95.1%, PPV: 84.6%, NPV: 95.1%). Conclusions The 72-hour NIHSS score outperformed the baseline and 24-hour scores in predicting 90-day functional outcomes and mediating the effects of EVT. Treatment-specific 72-hour NIHSS cut-off values may guide early risk stratification and prognostic assessments.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

The PA-PB1 interface of the influenza polymerase is an attractive site for antiviral drug design. In this study, we designed and synthesized a mini-library of indazole-containing compounds based on rational structure-based design to target the PB1-binding interface on PA. Biological evaluation of these compounds through a viral yield reduction assay revealed that compounds 27 and 31 both had a low micromolar range of the half maximal effective concentration (EC₅₀) values against A/WSN/33 (H1N1) (8.03 μmol/L for 27; 14.6 μmol/L for 31), while the most potent candidate 24 had an EC₅₀ value of 690 nM. Compound 24 was effective against different influenza strains including a pandemic H1N1 strain and an influenza B strain. Mechanistic studies confirmed that compound 24 bound PA with a K _d which equals to 1.88 μmol/L and disrupted the binding of PB1 to PA. The compound also decreased the lung viral titre in mice. In summary, we have identified a potent anti-influenza candidate with potency comparable to existing drugs and is effective against different viral strains. The therapeutic options for influenza infection have been limited by the occurrence of antiviral resistance, owing to the high mutation rate of viral proteins targeted by available drugs. To alleviate the public health burden of this issue, novel anti-influenza drugs are desired. In this study, we present our discovery of a novel class of indazole-containing compounds which exhibited favourable potency against both influenza A and B viruses. The EC₅₀ of the most potent compounds were within low micromolar to nanomolar concentrations. Furthermore, we show that the mouse lung viral titre decreased due to treatment with compound 24. Thus our findings identify promising candidates for further development of anti-influenza drugs suitable for clinical use.

Poly(ADP-ribosyl)ation (PARylation) is a specific form of post-translational modification (PTM) predominantly triggered by the activation of poly-ADP-ribose polymerase 1 (PARP1). However, the role and mechanism of PARylation in the advancement of acute kidney injury (AKI) remain undetermined. Here, we demonstrated the significant upregulation of PARP1 and its associated PARylation in murine models of AKI, consistent with renal biopsy findings in patients with AKI. This elevation in PARP1 expression might be attributed to trimethylation of histone H3 lysine 4 (H3K4me3). Furthermore, a reduction in PARylation levels mitigated renal dysfunction in the AKI mouse models. Mechanistically, liquid chromatography-mass spectrometry indicated that PARylation mainly occurred in receptor for activated C kinase 1 (RACK1), thereby facilitating its subsequent phosphorylation. Moreover, the phosphorylation of RACK1 enhanced its dimerization and accelerated the ubiquitination-mediated hypoxia inducible factor-1α (HIF-1α) degradation, thereby exacerbating kidney injury. Additionally, we identified a PARP1 proteolysis-targeting chimera (PROTAC), A19, as a PARP1 degrader that demonstrated superior protective effects against renal injury compared with PJ34, a previously identified PARP1 inhibitor. Collectively, both genetic and drug-based inhibition of PARylation mitigated kidney injury, indicating that the PARylated RACK1/HIF-1α axis could be a promising therapeutic target for AKI treatment.