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Abstract Cuproptosis is a newly discovered form of programmed cell death significantly depending on the transport efficacy of copper (Cu) ionophores. However, existing Cu ionophores, primarily small molecules with a short blood half‐life, face challenges in transporting enough amounts of Cu ions into tumor cells. This work describes the construction of carrier‐free nanoparticles (Ce6@Cu NPs), which self‐assembled by the coordination of Cu2+ with the sonosensitizer chlorin e6 (Ce6), facilitating sonodynamic‐triggered combination of cuproptosis and ferroptosis. Ce6@Cu NPs internalized by U87MG cells induce a sonodynamic effect and glutathione (GSH) depletion capability, promoting lipid peroxidation and eventually inducing ferroptosis. Furthermore, Cu+ concentration in tumor cells significantly increases as Cu2+ reacts with reductive GSH, resulting in the downregulation of ferredoxin‐1 and lipoyl synthase. This induces the oligomerization of lipoylated dihydrolipoamide S‐acetyltransferase, causing proteotoxic stress and irreversible cuproptosis. Ce6@Cu NPs possess a satisfactory ability to penetrate the blood‐brain barrier, resulting in significant accumulation in orthotopic U87MG‐Luc glioblastoma. The sonodynamic‐triggered combination of ferroptosis and cuproptosis in the tumor by Ce6@Cu NPs is evidenced both in vitro and in vivo with minimal side effects. This work represents a promising tumor therapeutic strategy combining ferroptosis and cuproptosis, potentially inspiring further research in developing logical and effective cancer therapies based on cuproptosis.

Abstract Stratification of spontaneous intracerebral hemorrhage (sICH) patients without cerebral herniation at admission, to determine the subgroups may be suffered from poor outcomes or benefit from surgery, is important for following treatment decision. The aim of this study was to establish and verify a de novo nomogram predictive model for long-term survival in sICH patients without cerebral herniation at admission. This study recruited sICH patients from our prospectively maintained ICH patient database (RIS-MIS-ICH, ClinicalTrials.gov Identifier: NCT03862729) between January 2015 and October 2019. All eligible patients were randomly classified into a training cohort and a validation cohort according to the ratio of 7:3. The baseline variables and long-term survival outcomes were collected. And the long-term survival information of all the enrolled sICH patients, including the occurrence of death and overall survival. Follow-up time was defined as the time from the onset to death of the patient or the last clinical visit. The nomogram predictive model was established based on the independent risk factors at admission for long-term survival after hemorrhage. The concordance index (C-index) and ROC curve were used to evaluate the accuracy of the predictive model. Discrimination and calibration were used to validate the nomogram in both the training cohort and the validation cohort. A total of 692 eligible sICH patients were enrolled. During the average follow-up time of 41.77 ± 0.85 months, a total of 178 (25.7%) patients died. The Cox Proportional Hazard Models showed that age (HR 1.055, 95% CI 1.038–1.071, P

Abstract Semi‐supervised learning aims to boost the model performance by large amounts of unlabelled data, thereby reducing the overheads of labelling. For joint pedestrian and face detection in real‐world scenarios, the existing semi‐supervised object detection methods rarely focus on the category relevance between samples, resulting in unsatisfied classification performance. And it is not effective for existing semi‐supervised methods to integrate the categories from two datasets to obtain an ensemble network. In this work, a novel approach aiming to fully utilise category‐relevant information is proposed. Firstly, the multi‐teacher distillation for decoupling pedestrian and face categories are introduced to eliminate category unfairness in distillation process. Second, a coupled attention module embedded in classification head of the student network is proposed to better grasp the relevance of different categories from teachers and guide distillation. Moreover, the constraint loss is designed for stabilising the training process and better converging, so as to tailor a versatile student. The experimental results on the CrowdHuman and WiderFace benchmarks demonstrate the superiority of the approach over the state‐of‐the‐art methods.

Abstract Previous genome-wide association studies (GWAS) reported that the allele C of rs945270 of the kinectin 1 gene (KTN1) most significantly increased the gray matter volume (GMV) of the putamen and modestly regulated the risk for attention deficit hyperactivity disorder (ADHD). On the other hand, ADHD is known to be associated with a reduction in subcortical and cortical GMVs. Here, we examined the interrelationships of the GMVs, rs945270 alleles, and ADHD symptom scores in the same cohort of children. With data of rs945270 genotypes, GMVs of 118 brain regions, and ADHD symptom scores of 3372 boys and 3129 girls of the Adolescent Brain Cognition Development project, we employed linear regression analyses to examine the pairwise correlations adjusted for the third of the three traits and other relevant covariates, and examine their mediation effects. We found that the major allele C of rs945270 modestly increased risk for ADHD in males only when controlling for the confounding effects of the GMV of any one of the 118 cerebral regions (0.026 ≤ p ≤ 0.059: Top two: left and right putamen). This allele also significantly increased putamen GMV in males alone (left p = 2.8 × 10−5, and right p = 9.4 × 10−5; α = 2.1 × 10−4) and modestly increased other subcortical and cortical GMVs in both sexes (α

BackgroundNeurological disorders are a major and increasing global health challenge, which accounts for a substantial portion of the disease burden worldwide. The aim of this systematic analysis is to present the most comprehensive and up-to-date estimates of disease burden, epidemiological trends, and attributable risk factors of neurological disorders at global, regional, and national levels.MethodsWe extracted data of 18 neurological disorders from the Global Burden of Disease 2019 study database. The burden of neurological disorders was measured using the incidence, prevalence, mortality, and disability-adjusted life years (DALYs), and further described according to age, sex, year, geographical location and socio-demographic Index (SDI). All estimates were presented with corresponding 95% uncertainty intervals (UIs).FindingsGlobally, in 2019, there were nearly 10 million deaths and 349 million DALYs due to neurological disorders. Among the 18 neurological disorders, stroke was the biggest contributor to DALYs (143232.18 [95%UI 133095.81-153241.82] in thousands) and deaths (6552.72 [95%UI 5995.20-7015.14] in thousands), followed by neonatal encephalopathy due to birth asphyxia and trauma. From 1990 to 2019, the DALYs of neurological diseases belonging to the communicable, maternal, neonatal and nutritional categories showed a sharp decrease, while Alzheimer's disease and other dementias and Parkinson's disease showed a large increase. Neurological disorders exhibited different profiles in different regions and age groups. A significant correlation between the SDI and the age-standardized DALY rates was also found except for Alzheimer's disease and other dementias. In addition, risk factors such as high systolic blood pressure, low birth weight and short gestation period, and metabolic risk contribute significantly to neurological disorders.InterpretationThe overall burden of neurological disorders has increased from 1990 to 2019, especially for non-communicable neurological disorders. The substantial variations of burden across regions emphasize the need for region-specific interventional strategies and allocation of resources based on priorities.

BackgroundEarly hematoma growth is associated with poor functional outcomes in patients with intracerebral hemorrhage (ICH). We aimed to explore whether quantitative hematoma heterogeneity in non-contrast computed tomography (NCCT) can predict early hematoma growth.MethodsWe used data from the Risk Stratification and Minimally Invasive Surgery in Acute Intracerebral Hemorrhage (Risa-MIS-ICH) trial. Our study included patients with ICH with a time to baseline NCCT 6 mL or a hematoma volume increase of 33% compared with the baseline NCCT.ResultsA total of 158 patients were enrolled in the study, of which 31 (19.6%) had hematoma growth. The multivariate logistic regression analysis revealed that time to initial baseline CT (P = 0.040, odds ratio [OR]: 0.824, 95 % confidence interval [CI]: 0.686–0.991), “heterogeneous” in the density category (P = 0.027, odds ratio [OR]: 5.950, 95 % confidence interval [CI]: 1.228–28.828), and CV of hematoma HUs (P = 0.018, OR: 1.301, 95 % CI: 1.047–1.617) were independent predictors of hematoma growth. By evaluating the receiver operating characteristic curve, the CV of hematoma HUs (AUC = 0.750) has a superior predictive value for hematoma growth than for heterogeneous density (AUC = 0.638). The CV of hematoma HUs had an 18% cutoff, with a specificity of 81.9 % and a sensitivity of 58.1 %.ConclusionThe CV of hematoma HUs can serve as a quantitative hematoma heterogeneity index that predicts hematoma growth in patients with early ICH independently.

Background and Purpose: The treatment of patients with intracerebral hemorrhage along with moderate hematoma and without cerebral hernia is controversial. This study aimed to explore risk factors and establish prediction models for early deterioration and poor prognosis.Methods: We screened patients from the prospective intracerebral hemorrhage (ICH) registration database (RIS-MIS-ICH, ClinicalTrials.gov Identifier: NCT03862729). The enrolled patients had no brain hernia at admission, with a hematoma volume of more than 20 ml. All patients were initially treated by conservative methods and followed up ≥ 1 year. A decline of Glasgow Coma Scale (GCS) more than 2 or conversion to surgery within 72 h after admission was defined as early deterioration. Modified Rankin Scale (mRS) ≥ 4 at 1 year after stroke was defined as poor prognosis. The independent risk factors of early deterioration and poor prognosis were determined by univariate and multivariate regression analysis. The prediction models were established based on the weight of the independent risk factors. The accuracy and value of models were tested by the receiver operating characteristic (ROC) curve.Results: After screening 632 patients with ICH, a total of 123 legal patients were included. According to statistical analysis, admission GCS (OR, 1.43; 95% CI, 1.18–1.74; P < 0.001) and hematoma volume (OR, 0.9; 95% CI, 0.84–0.97; P = 0.003) were the independent risk factors for early deterioration. Hematoma location (OR, 0.027; 95% CI, 0.004–0.17; P < 0.001) and hematoma volume (OR, 1.09; 95% CI, 1.03–1.15; P < 0.001) were the independent risk factors for poor prognosis, and island sign had a trend toward significance (OR, 0.5; 95% CI, 0.16-1.57; P = 0.051). The admission GCS and hematoma volume score were combined for an early deterioration prediction model with a score from 2 to 5. ROC curve showed an area under the curve (AUC) was 0.778 and cut-off point was 3.5. Combining the score of hematoma volume, island sign, and hematoma location, a long-term prognosis prediction model was established with a score from 2 to 6. ROC curve showed AUC was 0.792 and cutoff point was 4.5.Conclusions: The novel early deterioration and long-term prognosis prediction models are simple, objective, and accurate for patients with ICH along with a hematoma volume of more than 20 ml.

Objective: Neuroendoscopic treatment is an alternative therapeutic strategy for the treatment of septate chronic subdural hematoma (sCSDH). However, the safety and efficacy of this strategy remain controversial. We compared the clinical outcomes of neuroendoscopic treatment with those of standard (large bone flap) craniotomy for sCSDH reported in our center. Furthermore, the safety and efficacy of the neuroendoscopic treatment procedure for sCSDH were evaluated.Methods: We retrospectively collected the clinical data of 43 patients (37 men and six women) with sCSDH who underwent either neuroendoscopic treatment or standard (large bone flap) craniotomy, such as sex, age, smoking, drinking, medical history, use of antiplatelet drugs, postoperative complications, sCSDH recurrence, length of hospital stay, and postoperative hospital stay. We recorded the surgical procedures and the neurological function recovery prior to surgery and 6 months following the surgical treatment.Results: The enrolled patients were categorized into neuroendoscopic treatment (n = 23) and standard (large bone flap) craniotomy (n = 20) groups. There were no differences in sex, age, smoking, drinking, medical history, antiplatelet drug use, postoperative complications, and sCSDH recurrence between the two groups (p > 0.05). However, the patients in neuroendoscopic treatment group had a shorter length of total hospital stay and postoperative hospital stay as compared with the standard craniotomy group (total hospital stay: 5.26 ± 1.89 vs. 8.15 ± 1.04 days, p < 0.001; postoperative hospital stay: 4.47 ± 1.95 vs. 7.96 ± 0.97 days, p < 0.001). The imaging and Modified Rankin Scale at the 6-month follow-up were satisfactory, and no sCSDH recurrence was reported in the two groups.Conclusions: The findings of this study indicate that neuroendoscopic treatment is safe and effective for sCSDH; it is minimally invasive and could be clinically utilized.

Abstract Background Although acute kidney injury (AKI) is a known risk factor for adverse clinical outcomes in patients with spontaneous intracerebral haemorrhage (SICH), little is known about the predisposing factors that contribute to renal failure and short-term prognosis in the setting of SICH already complicated by AKI. In this study, we aimed to identify the renal failure factors in SICH patents with AKI. Methods Five hundred forty-three patients with SICH complicated by differential severities of AKI who were admitted to the First Affiliated Hospital of Fujian Medical University from January 2016 to December 2018 were retrospectively studied. Logistic regression and receiver operator characteristic (ROC) curve analysis were performed to determine the best predictive and discriminative variables. Multivariate Cox regression analysis was performed to identify prognostic factors for renal recovery. Results In the multivariable adjusted model, we found that hypernatremia, metabolic acidosis, elevated serum creatine kinase, hyperuricaemia, proteinuria, and the use of colloids and diuretics were all independent risk factors for the occurrence of stage 3 AKI in SICH patients. The area under the curve analysis indicated that hypernatremia and hyperuricaemia were predictive factors for stage 3 AKI, and the combination of these two parameters increased their predictability for stage 3 AKI. Kaplan-Meier survival curves revealed that the renal recovery rate in SICH patients with stages 1 and 2 AKI was significantly higher than that in SICH patients with stage 3 AKI. Multivariate Cox regression analysis suggested that hypernatremia and the occurrence of stage 3 AKI are predictors for poor short-term renal recovery. Conclusions These findings illustrate that hypernatremia and hyperuricaemia represent potential risk factors for the occurrence of stage 3 AKI in SICH patients. Those patients with hypernatremia and stage 3 AKI were associated with a poor short-term prognosis in renal recovery.

Background and Purpose: Perihematomal edema (PHE) is associated with poor functional outcomes after intracerebral hemorrhage (ICH). Early identification of risk factors associated with PHE growth may allow for targeted therapeutic interventions.Methods: We used data contained in the risk stratification and minimally invasive surgery in acute intracerebral hemorrhage (Risa-MIS-ICH) patients: a prospective multicenter cohort study. Patients' clinical, laboratory, and radiological data within 24 h of admission were obtained from their medical records. The absolute increase in PHE volume from baseline to day 3 was defined as iPHE volume. Poor outcome was defined as modified Rankin Scale (mRS) of 4 to 6 at 90 days. Binary logistic regression was used to assess the relationship between iPHE volume and poor outcome. The receiver operating characteristic curve was used to find the best cutoff. Linear regression was used to identify variables associated with iPHE volume (ClinicalTrials.gov Identifier: NCT03862729).Results: One hundred ninety-seven patients were included in this study. iPHE volume was significantly associated with poor outcome [P = 0.003, odds ratio (OR) 1.049, 95% confidence interval (CI) 1.016–1.082] after adjustment for hematoma volume. The best cutoff point of iPHE volume was 7.98 mL with a specificity of 71.4% and a sensitivity of 47.5%. Diabetes mellitus (P = 0.043, β = 7.66 95% CI 0.26–15.07), black hole sign (P = 0.002, β = 18.93 95% CI 6.84–31.02), and initial ICH volume (P = 0.018, β = 0.20 95% CI 0.03–0.37) were significantly associated with iPHE volume. After adjusting for hematoma expansion, the black hole sign could still independently predict the increase of PHE (P < 0.001, β = 21.62 95% CI 10.10–33.15).Conclusions: An increase of PHE volume >7.98 mL from baseline to day 3 may lead to poor outcome. Patients with diabetes mellitus, black hole sign, and large initial hematoma volume result in more PHE growth, which should garner attention in the treatment.

Human–object interaction (HOI) is a human-centered object detection task that aims to identify the interactions between persons and objects in an image. Previous end-to-end methods have used the attention mechanism of a transformer to spontaneously identify the associations between persons and objects in an image, which effectively improved detection accuracy; however, a transformer can increase computational demands and slow down detection processes. In addition, the end-to-end method can result in asymmetry between foreground and background information. The foreground data may be significantly less than the background data, while the latter consumes more computational resources without significantly improving detection accuracy. Therefore, we proposed an input-controlled transformer, “ratio-transformer” to solve an HOI task, which could not only limit the amount of information in the input transformer by setting a sampling ratio, but also significantly reduced the computational demands while ensuring detection accuracy. The ratio-transformer consisted of a sampling module and a transformer network. The sampling module divided the input feature map into foreground versus background features. The irrelevant background features were a pooling sampler, which were then fused with the foreground features as input data for the transformer. As a result, the valid data input into the Transformer network remained constant, while irrelevant information was significantly reduced, which maintained the foreground and background information symmetry. The proposed network was able to learn the feature information of the target itself and the association features between persons and objects so it could query to obtain the complete HOI interaction triplet. The experiments on the VCOCO dataset showed that the proposed method reduced the computational demand of the transformer by 57% without any loss of accuracy, as compared to other current HOI methods.

Forest fire detection from videos or images is vital to forest firefighting. Most deep learning based approaches rely on converging image loss, which ignores the content from different fire scenes. In fact, complex content of images always has higher entropy. From this perspective, we propose a novel feature entropy guided neural network for forest fire detection, which is used to balance the content complexity of different training samples. Specifically, a larger weight is given to the feature of the sample with a high entropy source when calculating the classification loss. In addition, we also propose a color attention neural network, which mainly consists of several repeated multiple-blocks of color-attention modules (MCM). Each MCM module can extract the color feature information of fire adequately. The experimental results show that the performance of our proposed method outperforms the state-of-the-art methods.

Chaotification can be employed to weaken or eliminate the feature of line spectra of waterborne noise. The efficiency of this method lies on the use of small control. The analysis reveals that the critical control gain depends on the stiffness of vibration isolation systems. Thus, an isolation raft system based on quasi-zero-stiffness (QZS) property is proposed for line spectrum chaotification. A nonlinear time-delay controller is derived accordingly. Comparative analysis shows that the new approach allows much smaller control, and the intensity of line spectra is further reduced. Numerical simulations also indicate other advantages with the introduction of QZS system into chaotification.