Intracerebral hemorrhage‐ (ICH‐) induced secondary brain injury (SBI) is a very complex pathophysiological process. However, the molecular mechanisms and drug targets of SBI are highly intricate and still elusive, yet a clear understanding is crucial for the treatment of SBI. In the current study, we aimed to confirm that nuclear factor‐E2‐related factor 2 (Nrf2)/Optineurin‐ (OPTN‐) mediated mitophagy alleviated SBI by inhibiting nucleotide‐binding oligomerization domain‐like receptor pyrin domain‐containing 3 (NLRP3) inflammasome activation based on the isobaric tag for relative and absolute quantization (iTRAQ) quantification proteomics. Human ICH brain specimens were collected for iTRAQ‐based proteomics analysis. Male Nrf2 wild‐type (WT) and knockout (KO) mice were employed to establish ICH murine models. The survival rate, hematoma volume, neurofunctional outcomes, blood‐brain barrier (BBB) permeability, brain edema, spatial neuronal death, NLRP3 inflammasome, inflammatory response, mitochondrial function, and mitophagy level were evaluated after ICH. The iTRAQ quantification analysis showed that the differentially expressed proteins (DEPs), Nrf2 and NLRP3, were closely associated with the initiation and development of SBI after ICH. The Nrf2 KO mice had a significantly lower survival rate, bigger hematoma volume, worse neurological deficits, and increased BBB disruption, brain edema, and neuronal death when compared with the Nrf2 WT mice after ICH. Furthermore, Nrf2 KO enhanced NLRP3 inflammasome activation and neuroinflammation as evidenced by the NF‐κB activation and various proinflammatory cytokine releases following ICH. Moreover, Nrf2 could interact with and modulate the mitophagy receptor OPTN, further mediating mitophagy to remove dysfunctional mitochondria after ICH. Furthermore, OPTN small interfering RNA (siRNA) increased the NLRP3 inflammasome activation by downregulating mitophagy level and enhancing mitochondrial damage in the Nrf2 WT mice after ICH. Together, our data indicated that Nrf2/OPTN inhibited NLRP3 inflammasome activation, possibly via modulating mitophagy, therefore alleviating SBI after ICH.

Background: Thromboprophylaxis has been determined to be safe, effective and cost-effective for hospitalised patients at venous thromboembolism (VTE) risk. However, Chinese medical institutions have not yet fully used or improperly used thromboprophylaxis. The effectiveness of information technology applied to thromboprophylaxis in hospitalised patients has been proved in many retrospective studies, lacking of prospective research evidence. Methods: All hospitalised patients aged >18 years not discharged within 24 hours from 1 September 2020 to 31 May 2021 were prospectively enrolled. Patients were randomly assigned to the control (9890 patients) or intervention group (9895 patients). The control group implemented conventional VTE prevention programmes; the intervention group implemented an Artificial Intelligence Clinical Assistant Decision Support System (AI-CDSS) on the basis of conventional prevention. Intergroup demographics, disease status, hospital length of stay (LOS), VTE risk assessment and VTE prophylaxis were compared using the χ² test, Fisher’s exact test, t-test or Wilcoxon rank-sum test. Univariate and multivariate logistic regressions were used to explore the risk factor of VTE. Results: The control and intervention groups had similar baseline characteristics. The mean age was 58.32±15.41 years, and mean LOS was 7.82±7.07 days. In total, 5027 (25.40%) and 2707 (13.67%) patients were assessed as having intermediate-to-high VTE risk and high bleeding risk, respectively. The incidence of hospital-associated VTE (HA-VTE) was 0.38%, of which 86.84% had deep vein thrombosis. Compared with the control group, the incidence of HA-VTE decreased by 46.00%, mechanical prophylaxis rate increased by 24.00% and intensity of drug use increased by 9.72% in the intervention group. However, AI-CDSS use did not increase the number of clinical diagnostic tests, prophylaxis rate or appropriate prophylaxis rate. Conclusions: Thromboprophylaxis is inadequate in hospitalised patients with VTE risk. The role of AI-CDSS in VTE risk management is unknown and needs further in-depth study. Trial registration number: ChiCTR2000035452.

Background: Artificial intelligence technology is widely used in the medical industry. Our retrospective study evaluated the effectiveness of an AI-CDSS in improving the incidence of hospital-related VTE and the impact of anticoagulant drug use. Methods: This study collected relevant data on adult patients over 18 years of age who are not discharged 24 hours, from January to July 2019 and from January to July 2020, the VTE high-risk department of Ruijin Hospital. Before and after using AI-CDSS, the incidence of hospital-related VTE and using anticoagulants were analyzed. Results: Between January to July 2019 and January to July 2020, 3,565 and 4,423 adult patients over 18 years old were hospitalized in our hospital and were designed as a control group and intervention group, respectively (7,988 in total). Both groups had similar baseline characteristics. There were 4,716 (59.03%) male patients, the mean age was 60.43±13.09 years, and the mean stay was 7.56±7.76 days. More than half of the patients (4,605, 57.58%) came from the respiratory. VTE events during hospitalization occurred in 41 patients; overall, 5.13/1,000 (41 episodes in 7,988 patients). Compared with the control group, before implementing AI-CDSS, the rate of VTE during hospitalization was reduced from 5.89/1,000 (21 episodes in 3,565 patients) to 4.75/1,000 patients (20 episodes in 4,423 patients) (relative reduction of 19.35%) in the intervention group. The use rate of anticoagulant drugs was increased from 19.97% (712/3,565) in the control group to 22.88% (1,012/4,423) in intervention group [P<0.01, odds ratio (OR): 1.19, 95 percent confidence interval (95% CI) (1.07–1.32)], (relative 14.57% increase). Poisson’s regression results showed that department, age ≥75 years [OR: 3.09, 95% Cl (1.45–6.33)], duration of hospitalization [OR: 1.04, 95% CI (1.03–1.05)], heart failure [OR: 5.13, 95% CI (1.74–13.54)] and renal failure [OR: 3.60, 95% CI (0.90–11.34)] were high-risk factors for VTE events. Conclusions: Implementing AI-CDSS can help clinicians identify hospitalized patients at increased VTE risk, take effective preventive measures, and improve clinicians’ compliance with the American College of Chest Physicians (ACCP) guidelines.