Inferring Fluid Dynamics via Inverse Rendering

Humans have a strong intuitive understanding of physical processes such as fluid falling by just a glimpse of such a scene picture, i.e., quickly derived from our immersive visual experiences in memory. This work achieves such a photo-to-fluid-dynamics reconstruction functionality learned from unannotated videos, without any supervision of ground-truth fluid dynamics. In a nutshell, a differentiable Euler simulator modeled with a ConvNet-based pressure projection solver, is integrated with a volumetric renderer, supporting end-to-end/coherent differentiable dynamic simulation and rendering. By endowing each sampled point with a fluid volume value, we derive a NeRF-like differentiable renderer dedicated from fluid data; and thanks to this volume-augmented representation, fluid dynamics could be inversely inferred from the error signal between the rendered result and ground-truth video frame (i.e., inverse rendering). Experiments on our generated Fluid Fall datasets and DPI Dam Break dataset are conducted to demonstrate both effectiveness and generalization ability of our method

Clinical characteristics and outcomes in patients with traumatic brain injury in China: a prospective, multicentre, longitudinal, observational study

Background Large-scale studies are required to better characterise traumatic brain injury (TBI) and to identify the most effective treatment approaches for TBI. However, evidence is scarce and mostly originates from high-income countries. We aimed to describe the existing care for patients with TBI and the outcomes in China. Methods The Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) China registry is a prospective, multicentre, longitudinal, observational study done in 56 neurosurgical centres across China. We collected data of patients who were admitted to hospital with a clinical diagnosis of TBI and an indication for CT. Patients who were discharged directly from the emergency room were excluded. The primary endpoint was survival on discharge. Prognostic analyses were applied to identify predictors of mortality. Variations in mortality were compared between centres and provinces within China. Mortality was compared with expected mortality, estimated using the CRASH basic model. This study was registered with ClinicalTrials.gov, NCT02210221. Findings From Dec 22, 2014, to Aug 1, 2017, 13 627 patients with TBI from 56 centres were enrolled in the registry. Data from 13 138 patients from 52 hospitals in 22 provinces of China were analysed. Most patients were male (9782 [74%]), with a median age of 48 years (IQR 33–61). The median Glasgow Coma Scale (GCS) score was 13 (IQR 9–15), and the leading cause of injury was road-traffic incident (6548 [50%]). Overall, 637 (5%) patients died, including 552 (20%) patients with severe TBI. Age, GCS score, injury severity score, pupillary light reflex, CT findings (compressed basal cistern and midline shift ≥5 mm), presence of hypoxia, systemic hypotension, altitude higher than >500 m, and GDP per capita were significantly associated with survival in all patients with TBI. Variation in mortality existed between centres and regions. The expected 14-day mortality was 1116 (13%), but 544 (7%) deaths within 14 days were observed (observed to expected ratio 0·49 [95% CI 0·45–0·53]). Interpretation The results show differences in mortality between centres and regions across China, which indicates potential for identifying best practices through comparative effectiveness research. The risk factors identified in prognostic analyses might contribute to developing benchmarks for assessing quality of care. Funding None

Right median nerve electrical stimulation for acute traumatic coma (the Asia Coma Electrical Stimulation trial): study protocol for a randomised controlled trial

Abstract Background Traumatic brain injury (TBI) has become the most common cause of death and disability in persons between 15 and 30 years of age, and about 10–15% of patients affected by TBI will end up in a coma. Coma caused by TBI presents a significant challenge to neuroscientists. Right median nerve electrical stimulation has been reported as a simple, inexpensive, non-invasive technique to speed recovery and improve outcomes for traumatic comatose patients. Methods/design This multicentre, prospective, randomised (1:1) controlled trial aims to demonstrate the efficacy and safety of electrical right median nerve stimulation (RMNS) in both accelerating emergence from coma and promoting long-term outcomes. This trial aims to enrol 380 TBI comatose patients to partake in either an electrical stimulation group or a non-stimulation group. Patients assigned to the stimulation group will receive RMNS in addition to standard treatment at an amplitude of 15–20 mA with a pulse width of 300 μs at 40 Hz ON for 20 s and OFF for 40 s. The electrical treatment will last for 8 h per day for 2 weeks. The primary endpoint will be the percentage of patients regaining consciousness 6 months after injury. The secondary endpoints will be Extended Glasgow Outcome Scale, Coma Recovery Scale-Revised and Disability Rating Scale scores at 28 days, 3 months and 6 months after injury; Glasgow Coma Scale, Glasgow Coma Scale Motor Part and Full Outline of Unresponsiveness scale scores on day 1 and day 7 after enrolment and 28 days, 3 months and 6 months after injury; duration of unconsciousness and mechanical ventilation; length of intensive care unit and hospital stays; and incidence of adverse events. Discussion Right median nerve electrical stimulation has been used as a safe, inexpensive, non-invasive therapy for neuroresuscitation of coma patients for more than two decades, yet no trial has robustly proven the efficacy and safety of this treatment. The Asia Coma Electrical Stimulation (ACES) trial has the following novel features compared with other major RMNS trials: (1) the ACES trial is an Asian multicentre randomised controlled trial; (2) RMNS therapy starts at an early stage 7–14 days after the injury; and (3) various assessment scales are used to evaluate the condition of patients. We hope the ACES trial will lead to optimal use of right median nerve electrical treatment. Trial registration ClinicalTrials.gov, NCT02645578 . Registered on 23 December 2015

The preventive effect of dexmedetomidine on paroxysmal sympathetic hyperactivity in severe traumatic brain injury patients who have undergone surgery: a retrospective study

Background Paroxysmal sympathetic hyperactivity (PSH) results and aggravates in secondary brain injury, which seriously affects the prognosis of severe traumatic brain injury patients. Although several studies have focused on the treatment of PSH, few have concentrated on its prevention. Methods Ninety post-operation (post-op) severe traumatic brain injury (sTBI) patients admitted from October 2014 to April 2016 were chosen to participate in this study. Fifty of the post-op sTBI patients were sedated with dexmedetomidine and were referred as the “dexmedetomidine group” (admitted from May 2015 to April 2016). The other 40 patients (admitted from October 2014 to May 2015) received other sedations and were referred as the “control group.” The two groups were then compared based on their PSH scores and the scores and ratios of those patients who met the criteria of “probable,” “possible” and “unlikely” using the PSH assessment measure (PSH-AM) designed by Baguley et al. (2014). The durations of the neurosurgery intensive care unit (NICU) and hospital stays and the Glasgow outcome scale (GOS) values for the two groups were also compared to evaluate the therapeutic effects and the patients’ prognosis. Results The overall PSH score for the dexmedetomidine group was 5.26 ± 4.66, compared with 8.58 ± 8.09 for the control group. The difference between the two groups’ PSH scores was significant (P = 0.017). The score of the patients who met the criterion of “probable” was 18.33 ± 1.53 in the dexmedetomidine group and 22.63 ± 2.97 in the control group, and the difference was statistically significant (P = 0.045). The ratio of patients who were classified as “unlikely” between the two groups was statistically significant (P = 0.028); that is, 42 (84%) in the dexmedetomidine group and 25 (62.5%) in the control group. The differences in NICU, hospital stays and GOS values between the two groups were not significant. Conclusion Dexmedetomidine has a preventive effect on PSH in sTBI patients who have undergone surgery

Additional file 1: of Right median nerve electrical stimulation for acute traumatic coma (the Asia Coma Electrical Stimulation trial): study protocol for a randomised controlled trial

SPIRIT 2013 checklist: recommended items to address in a clinical trial protocol and related documents. (PDF 77 kb

Mortality Prediction in Severe Traumatic Brain Injury Using Traditional and Machine Learning Algorithms

Prognostic prediction of traumatic brain injury (TBI) in patients is crucial in clinical decision and health care policy making. This study aimed to develop and validate prediction models for in-hospital mortality after severe traumatic brain injury (sTBI). We developed and validated logistic regression (LR), LASSO regression, and machine learning (ML) algorithms including support vector machines (SVM) and XGBoost models. Fifty-four candidate predictors were included. Model performance was expressed in terms of discrimination (C-statistic) and calibration (intercept and slope). For model development, 2804 patients with sTBI in the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) China Registry study were included. External validation was performed in 1113 patients with sTBI in the CENTER-TBI European Registry study. XGBoost achieved high discrimination in mortality prediction, and it outperformed logistic and LASSO regression. The XGBoost model established in this study also outperformed prediction models currently available, including the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) core and International Mission for Prognosis and Analysis of Clinical Trials (CRASH) basic models. When including 54 variables, XGBoost and SVM reached C-statistics of 0.87 (95% confidence interval [CI]: 0.81-0.92) and 0.85 (95% CI: 0.79-0.90) at internal validation, and 0.88 (95% CI: 0.87-0.88) and 0.86 (95% CI: 0.85-0.87) at external validation, respectively. A simplified version of XGBoost and SVM using 26 variables selected by recursive feature elimination (RFE) reached C-statistics of 0.87 (95% CI: 0.82-0.92) and 0.86 (95% CI: 0.80-0.91) at internal validation, and 0.87 (95% CI: 0.87-0.88) and 0.87 (95% CI: 0.86-0.87) at external validation, respectively. However, when the number of variables included decreased, the difference between ML and LR diminished. All the prediction models can be accessed via a web-based calculator. Glasgow Coma Scale (GCS) score, age, pupillary light reflex, Injury Severity Score (ISS) for brain region, and the presence of acute subdural hematoma were the five strongest predictors for mortality prediction. The study showed that ML techniques such as XGBoost may capture information hidden in demographic and clinical predictors of patients with sTBI and yield more precise predictions compared with LR approaches

The Herbal Medicine () for Sleep Quality Improvements: A Systematic Review and Meta-analysis

Background: Sleep disturbances are common and bothersome among cancer and noncancer populations. Suanzaoren ( Ziziphi Spinosae Semen ) is commonly used to improve sleep, yet its efficacy and safety are unclear. Methods: We systematically searched PubMed, Cochrane Library, and EMBASE from inception through October 5, 2021, to identify randomized trials of Suanzaoren . We included randomized trials comparing Suanzaoren to placebo, medications, cognitive behavioral therapy (CBT), or usual care for improving sleep outcomes in cancer and noncancer patients with insomnia or sleep disturbance. We performed a risk of bias analysis following Cochrane guidelines. Depending on heterogeneity, we pooled studies with similar comparators using fixed- and random-effects models. Results: We included participants with insomnia disorder (N = 785) or sleep disturbance (N = 120) from 9 trials. Compared with placebo, Suanzaoren led to significant subjective sleep quality improvements in participants with insomnia and patients with sleep disturbance combined (standard mean difference −0.58, 95% CI −1.04, −0.11; P  < .01); Compared with benzodiazepines or CBT, Suanzaoren was associated with a significant decrease in insomnia severity (mean difference −2.68 points, 95% CI −5.50, −0.22; P  = .03) at 4 weeks in the general population and cancer patients. The long-term effects of Suanzaoren were mixed among trials. Suanzaoren did not increase the incidence of major adverse events. The placebo-controlled studies had a low risk of bias. Conclusion: Suanzaoren is associated with short-term patient-reported sleep quality improvements among individuals with insomnia or sleep disturbance. Due to the small sample size and variable study quality, the clinical benefits and harms of Suanzaoren , particularly in the long term, should be further assessed in a sufficiently powered randomized trial. Registration: PROSPERO CRD4202128194