Background: To investigate whether metformin monotherapy or adjunctive therapy improves the prognosis in patients with any type of cancer compared to non-metformin users (age ≥18). Methods: Databases (Medline, Embase, and the Cochrane Central Register of Controlled Trials) and clinical trial registries (ClinicalTrials.gov; the World Health Organization International Clinical Trials Registry Platform) were screened for randomized, controlled trials (RCT) reporting at least progression-free survival (PFS) and/or overall survival (OS). Main outcome measures included hazard ratios (HR), and combined HRs and 95% confidence intervals (CI) were calculated using random-effects models. Results: Of the 8419 records screened, 22 RCTs comprising 5943 participants were included. Pooled HRs were not statistically significant in both PFS (HR 0.97, 95% CI 0.82–1.15, I² = 50%) and OS (HR 0.98, 95% CI 0.86–1.13, I² = 33%) for patients with cancer between the metformin and control groups. Subgroup analyses demonstrated that metformin treatment was associated with a marginally significant improvement in PFS in reproductive system cancers (HR 0.86, 95% CI 0.74–1.00) and a significantly worse PFS in digestive system cancers (HR 1.45, 95% CI 1.03–2.04). The PFS or OS was observed consistently across maintenance dose, diabetes exclusion, median follow-up, risk of bias, and combined antitumoral therapies. Conclusion: Metformin treatment was not associated with cancer-related mortality in adults compared with placebo or no treatment. However, metformin implied beneficial effects in the PFS of the patients with reproductive system cancers but was related to a worse PFS in digestive system cancers. Systematic review registration: PROSPERO registration number CRD42022324672.

Brain-controlled wheelchairs are one of the most promising applications that can help people gain mobility after their normal interaction pathways have been compromised by neuromuscular diseases. The feasibility of using brain signals to control wheelchairs has been well demonstrated by healthy people in previous studies. However, most potential users of brain-controlled wheelchairs are people suffering from severe physical disabilities or who are in a “locked-in” state. To further validate the clinical practicability of our previously proposed P300-based brain-controlled wheelchair, in this study, 10 subjects with severe spinal cord injuries participated in three experiments and completed ten predefined tasks in each experiment. The average accuracy and information transfer rate (ITR) were 94.8% and 4.2 bits/min, respectively. Moreover, we evaluated the physiological and cognitive burdens experienced by these individuals before and after the experiments. There were no significant changes in vital signs during the experiment, indicating minimal physiological and cognitive burden. The patients’ average systolic blood pressure before and after the experiment was 113±13.7 mmHg and 114±11.9 mmHg, respectively (P = 0.122). The patients’ average heart rates before and after the experiment were 79±8.4/min and 79±8.2/min, respectively (P = 0.147). The average task load, measured by the National Aeronautics and Space Administration task load index, ranged from 10.0 to 25.5. The results suggest that the proposed P300-based brain-controlled wheelchair is safe and reliable; additionally, it does not significantly increase the patient’s physical and mental task burden, demonstrating its potential value in clinical applications. Our study promotes the development of a more practical brain-controlled wheelchair system.

Neural stem cell (NSC) has gained considerable attention in traumatic brain injury (TBI) treatment because of their ability to replenish dysfunctional neurons and stimulate endogenous neurorestorative processes. However, their therapeutic effects are hindered by the low cell retention rate after transplantation into the dynamic brain. In this study, we found cerebrospinal fluid (CSF) flow after TBI is an important factor associated with cell loss following NSC transplantation. Recently, several studies have shown that hydrogels could serve as a beneficial carrier for stem cell transplantation, which provides a solution to prevent CSF flow-induced cell loss after TBI. For this purpose, we evaluated three different hydrogel scaffolds and found the gelatin methacrylate (GelMA)/sodium alginate (Alg) (GelMA/Alg) hydrogel scaffold showed the best capabilities for NSC adherence, growth, and differentiation. Additionally, we detected that pre-differentiated NSCs, which were loaded on the GelMA/Alg hydrogel and cultured for 7 days in neuronal differentiation medium (NSC [7d]), had the highest cell retention rate after CSF impact. Next, the neuroprotective effects of the NSC-loaded GelMA/Alg hydrogel scaffold were evaluated in a rat model of TBI. NSC [7d]-loaded GelMA/Alg markedly decreased microglial activation and neuronal death in the acute phase, reduced tissue loss, alleviated astrogliosis, promoted neurogenesis, and improved neurological recovery in the chronic phase. In summary, we demonstrated that the integration with the GelMA/Alg and modification of NSC differentiation could inhibit the influence of CSF flow on transplanted NSCs, leading to increased number of retained NSCs and improved neuroprotective effects, providing a promising alternative for TBI treatment.

Endogenous neurogenesis could promote stroke recovery. Furthermore, anti-inflammatory phenotypical microglia (M2-microglia) could facilitate Neural Stem Cell (NSC)-mediated neurogenesis following Ischemic Stroke (IS). Nonetheless, the mechanisms through which M2 microglia influence NSC-mediated neurogenesis post-IS remain unclear. On the other hand, M2 microglia-derived small Extracellular Vesicles (M2-sEVs) could exert phenomenal biological effects and play significant roles in cell-to-cell interactions, highlighting their potential involvement in NSC-mediated neurogenesis post-IS, forming the basis of this study. M2-sEVs were first isolated from IL-4-stimulated microglia. For in vivo tests, M2-sEVs were intravenously injected into mice every day for 14 days after transient Middle Cerebral Artery Occlusion (tMCAO). Following that, the infarct volume and neurological function, as well as NSC proliferation in the Subventricular Zone and dentate gyrus, migration, and differentiation in the infarct area, were examined. For in vitro tests, M2-sEVs were administered to NSC subjected to Oxygen-Glucose Deprivation (OGD) and then reoxygenation, after which NSC proliferation and differentiation were assessed. Finally, M2-sEVs were subjected to microRNA sequencing to explore the regulatory mechanisms. Our findings revealed that M2-sEVs reduced the infarct volume and increased the neurological score in mice post-tMCAO. Furthermore, M2-sEV treatment promoted NSC proliferation and neuronal differentiation both in vivo and in vitro. Additionally, microRNA sequencing revealed miR-93-5p and miR-25-3p enrichment in M2-sEVs. Inhibitors of these miRNAs prevented TGFBR, PTEN, and FOXO3 downregulation in NSC, reversing M2-sEVs’ beneficial effects on neurogenesis and sensorimotor recovery. M2-sEVs increased NSC proliferation and neuronal differentiation, and protected against IS, at least partially, via delivering miR-25-3p and miR-93-5p to downregulate TGFBR, PTEN, and FOXO3 expression in NSC.

Electroencephalography (EEG) plays a crucial role in neuroscience research and clinical practice, but it remains limited by nonuniform data, noise, and difficulty in labeling. To address these challenges, we develop a pretraining framework named DMAE-EEG, a denoising masked autoencoder for mining generalizable spatiotemporal representation from massive unlabeled EEG. First, we propose a novel brain region topological heterogeneity (BRTH) division method to partition the nonuniform data into fixed patches based on neuroscientific priors. Second, we design a denoised pseudo-label generator (DPLG), which utilizes a denoising reconstruction pretext task to enable the learning of generalizable representations from massive unlabeled EEG, suppressing the influence of noise and artifacts. Furthermore, we utilize an asymmetric autoencoder with self-attention as the backbone in the proposed DMAE-EEG, which captures long-range spatiotemporal dependencies and interactions from unlabeled EEG data across 14 public datasets. The proposed DMAE-EEG is validated on both generative (signal quality enhancement) and discriminative tasks (motion intention recognition). In the quality enhancement, DMAE-EEG outperforms existing statistical methods with normalized mean squared error (nMSE) reduction of 27.78%–50.00% under corruption levels of 25%, 50%, and 75%, respectively. In motion intention recognition, DMAE-EEG achieves a relative improvement of 2.71%–6.14% in intrasession classification balanced accuracy across 2–6 class motor execution and imagery tasks, outperforming state-of-the-art methods. Overall, the results suggest that the pretraining framework DMAE-EEG can capture generalizable spatiotemporal representations from massive unlabeled EEG and enhance the knowledge transferability across sessions, subjects, and tasks in various downstream scenarios, advancing EEG-aided diagnosis and brain–computer communication and control, and other clinical practice.

Background: Progressive haemorrhagic injury after surgery in patients with traumatic brain injury often results in poor patient outcomes. This study aimed to develop and validate a practical predictive tool that can reliably estimate the risk of postoperative progressive haemorrhagic injury (PHI) in patients with traumatic brain injury (TBI). Methods: Data from 645 patients who underwent surgery for TBI between March 2018 and December 2020 were collected. The outcome was postoperative intracranial PHI, which was assessed on postoperative computed tomography. The least absolute shrinkage and selection operator (LASSO) regression model, univariate analysis, and Delphi method were applied to select the most relevant prognostic predictors. We combined conventional coagulation test (CCT) data, thromboelastography (TEG) variables, and several predictors to develop a predictive model using binary logistic regression and then presented the results as a nomogram. The predictive performance of the model was assessed with calibration and discrimination. Internal validation was assessed. Results: The signature, which consisted of 11 selected features, was significantly associated with intracranial PHI (p < 0.05, for both primary and validation cohorts). Predictors in the prediction nomogram included age, S-pressure, D-pressure, pulse, temperature, reaction time, PLT, prothrombin time, activated partial thromboplastin time, FIB, and kinetics values. The model showed good discrimination, with an area under the curve of 0.8694 (95% CI, 0.8083–0.9304), and good calibration. Conclusion: This model is based on a nomogram incorporating CCT and TEG variables, which can be conveniently derived at hospital admission. It allows determination of this individual risk for postoperative intracranial PHI and will facilitate a timely intervention to improve outcomes.