Assessing the associations of 1,400 blood metabolites with major depressive disorder: a Mendelian randomization study

Background and objectivesMajor Depressive Disorder (MDD) is one of the most prevalent and debilitating health conditions worldwide. Previous studies have reported a link between metabolic dysregulation and MDD. However, evidence for a causal relationship between blood metabolites and MDD is lacking.MethodsUsing a two-sample bidirectional Mendelian randomization analysis (MR), we assessed the causal relationship between 1,400 serum metabolites and Major Depressive Disorder (MDD). The Inverse Variance Weighted method (IVW) was employed to estimate the causal association between exposures and outcomes. Additionally, MR-Egger regression, weighted median, simple mode, and weighted mode methods were used as supplementary approaches for a comprehensive appraisal of the causality between blood metabolites and MDD. Pleiotropy and heterogeneity tests were also conducted. Lastly, the relevant metabolites were subjected to metabolite function analysis, and a reverse MR was implemented to explore the potential influence of MDD on these metabolites.ResultsAfter rigorous screening, we identified 34 known metabolites, 13 unknown metabolites, and 18 metabolite ratios associated with Major Depressive Disorder (MDD). Among all metabolites, 33 were found to have positive associations, and 32 had negative associations. The top five metabolites that increased the risk of MDD were the Arachidonate (20:4n6) to linoleate (18:2n6) ratio, LysoPE(18:0/0:0), N-acetyl-beta-alanine levels, Arachidonate (20:4n6) to oleate to vaccenate (18:1) ratio, Glutaminylglutamine, and Threonine to pyruvate ratio. Conversely, the top five metabolites that decreased the risk of MDD were N6-Acetyl-L-lysine, Oleoyl-linoleoyl-glycerol (18:1 to 18:2) [2] to linoleoyl-arachidonoyl-glycerol (18:2 to 20:4) [2] ratio, Methionine to phosphate ratio, Pregnanediol 3-O-glucuronide, and 6-Oxopiperidine-2-carboxylic acid. Metabolite function enrichment was primarily concentrated in pathways such as Bile Acid Biosynthesis (FDR=0.177), Glutathione Metabolism (FDR=0.177), Threonine, and 2-Oxobutanoate Degradation (FDR=0.177). In addition, enrichment was noted in pathways like Valine, Leucine, and Isoleucine Biosynthesis (p=0.04), as well as Ascorbate and Aldarate Metabolism (p=0.04).DiscussionWithin a pool of 1,400 blood metabolites, we identified 34 known metabolites and 13 unknown metabolites, as well as 18 metabolite ratios associated with Major Depressive Disorder (MDD). Additionally, three functionally enriched groups and two metabolic pathways were selected. The integration of genomics and metabolomics has provided significant insights for the screening and prevention of MDD

Transplantation of human villous trophoblasts preserves cardiac function in mice with acute myocardial infarction

Over the past decade, cell therapies have provided promising strategies for the treatment of ischaemic cardiomyopathy. Particularly, the beneficial effects of stem cells, including bone marrow stem cells (BMSCs), endothelial progenitor cells (EPCs), mesenchymal stem cells (MSCs), embryonic stem cells (ESCs), and induced pluripotent stem cells (iPSCs), have been demonstrated by substantial preclinical and clinical studies. Nevertheless stem cell therapy is not always safe and effective. Hence, there is an urgent need for alternative sources of cells to promote cardiac regeneration. Human villous trophoblasts (HVTs) play key roles in embryonic implantation and placentation. In this study, we show that HVTs can promote tube formation of human umbilical vein endothelial cells (HUVECs) on Matrigel and enhance the resistance of neonatal rat cardiomyocytes (NRCMs) to oxidative stress in vitro. Delivery of HVTs to ischaemic area of heart preserved cardiac function and reduced fibrosis in a mouse model of acute myocardial infarction (AMI). Histological analysis revealed that transplantation of HVTs promoted angiogenesis in AMI mouse hearts. In addition, our data indicate that HVTs exert their therapeutic benefit through paracrine mechanisms. Meanwhile, injection of HVTs to mouse hearts did not elicit severe immune response. Taken together, our study demonstrates HVT may be used as a source for cell therapy or a tool to study cell-derived soluble factors for AMI treatment

Development and testing of an image transformer for explainable autonomous driving systems

Purpose – Perception has been identified as the main cause underlying most autonomous vehicle related accidents. As the key technology in perception, deep learning (DL) based computer vision models are generally considered to be black boxes due to poor interpretability. These have exacerbated user distrust and further forestalled their widespread deployment in practical usage. This paper aims to develop explainable DL models for autonomous driving by jointly predicting potential driving actions with corresponding explanations. The explainable DL models can not only boost user trust in autonomy but also serve as a diagnostic approach to identify any model deficiencies or limitations during the system development phase. Design/methodology/approach – This paper proposes an explainable end-to-end autonomous driving system based on “Transformer,” a state-of-the-art self-attention (SA) based model. The model maps visual features from images collected by onboard cameras to guide potential driving actions with corresponding explanations, and aims to achieve soft attention over the image’s global features. Findings – The results demonstrate the efficacy of the proposed model as it exhibits superior performance (in terms of correct prediction of actions and explanations) compared to the benchmark model by a significant margin with much lower computational cost on a public data set (BDD-OIA). From the ablation studies, the proposed SA module also outperforms other attention mechanisms in feature fusion and can generate meaningful representations for downstream prediction. Originality/value – In the contexts of situational awareness and driver assistance, the proposed model can perform as a driving alarm system for both human-driven vehicles and autonomous vehicles because it is capable of quickly understanding/characterizing the environment and identifying any infeasible driving actions. In addition, the extra explanation head of the proposed model provides an extra channel for sanity checks to guarantee that the model learns the ideal causal relationships. This provision is critical in the development of autonomous systems

Effect of regional anesthesia on the postoperative delirium: A systematic review and meta-analysis of randomized controlled trials

ObjectivePostoperative delirium (POD) starts in the recovery room and occurs up to 5 days after surgery. However, the POD guidelines issued by the European Society of Anesthesiology (ESA) suggest that the effect of regional anesthesia on POD is controversial. This meta-analysis aims to investigate whether perioperative regional anesthesia reduced the incidence of POD.MethodsStandard Published randomized controlled trails (RCTs) were searched from bibliographic databases to identify all evidence that reported regional anesthesia assessing incident delirium following diverse surgeries. The primary outcome was the incidence of POD, and the secondary outcomes were POD scores, pain scores, and emergence time. The relative risk (RR) for dichotomous outcomes and the weighted or standardized mean difference (WMD, SMD) for continuous outcomes were estimated using a random-effects model.ResultsTwenty RCTs with 2110 randomized participants undergoing different surgeries were included. Meta-analysis showed that regional anesthesia was associated with less POD incidence compared to general anesthesia (total intravenous anesthesia (TIVA) or inhalation anesthesia) (relative risk (RR) = 0.62, 95% confidence interval (CI) = 0.45–0.85)). Subgroup analysis showed that the decrease in POD incidence was associated with a nerve block (0.46, 95% CI = 0.32–0.67) and regional-combined-general anesthesia (0.42, 95% CI = 0.29–0.60). Regional anesthesia significantly reduced POD incidence in the recovery room after pediatric surgeries (0.41, 95% CI = 0.29–0.56). Regional anesthesia also reduced the POD score (SMD −0.93, 95% CI = −1.55 to −0.31) and pain score (SMD −0.95, 95% CI = −1.72 to −0.81). There was no significant difference in emergence time between regional anesthesia and general anesthesia (WMD −1.40, 95% CI = −3.83 to 6.63).ConclusionsThere was a significant correlation between regional anesthesia and the decrease in POD incidence, POD score, and pain score

A Prospective Case-Control Study of Radial Extracorporeal Shock Wave Therapy for Spastic Plantar Flexor Muscles in Very Young Children With Cerebral Palsy

To assess the effects of radial extracorporeal shock wave therapy (rESWT) on plantar flexor muscle spasticity and gross motor function in very young patients with cerebral palsy (CP).The design was case-control study (level of evidence 3).The setting was the Department of Pediatric Neurology and Neurorehabilitation, First Hospital of Jilin University, Changchun, China.Those with a diagnosis of CP and spastic plantar flexor muscles were recruited between April 2014 and April 2015.According to the parents' decision, patients received 1 ESWT session per week for 3 months, with 1500 radial shock waves per ESWT session and leg with positive energy flux density of 0.03mJ/mm(2), combined with traditional conservative therapy (rESWT group) or traditional conservative therapy alone (control group).The Modified Ashworth Scale (MAS) (primary outcome measure) and passive range of motion (pROM) measurements were collected at baseline (BL), 1 month (M1), and 3 months (M3) after BL. The Gross Motor Function Measure (GMFM)-88 was collected at BL and M3.Sixty-six patients completed the final review at 3 months and were included in the study. Subjects ranged in age from 12 to 60 months (mean age 27.013.6 months;median age 22.0 months;33.3% female). For the rESWT group (n=34), mean MAS grades at BL, M1, and M3 were 2.6, 1.9, and 1.5 on the left side and 1.9, 1.7, and 1.2 on the right side. For the control group (n=32), mean MAS grades at BL, M1, and M3 were 2.5, 2.4, and 2.1 on the left side and 1.8, 1.8, and 1.5 on the right side. The within-subject effects timexside and timextreatment were statistically significant (P<0.01). Similar results were found for the improvement of mean pROM. GMFM-88 improved from BL to M3, but showed no statistically significant difference between the groups. There were no significant complications.This study demonstrates that the combination of rESWT and traditional conservative therapy is more effective than traditional conservative therapy alone in the treatment of spasticity in very young patients with CP

Performance Evaluation of Silicon Mach-Zehnder Modulator after Cosmic Radiation to Enable Small Satellite Laser Communication

To evaluate the performance change of Photonic integrated circuits after real cosmic radiation, silicon Mach-Zehnder Modulators were mounted on International Space Station on Low Earth Orbit for 6 months’ radiation harshness. Measured data of the device before and after radiation showed the permanent change of the effective index (around 10-3), the reduced carrier recombination lifetime, and the extended high speed bandwidth

Nanoarchitectonic Engineering of Thermal-Responsive Magnetic Nanorobot Collectives for Intracranial Aneurysm Therapy

Stent-assisted coiling is a main treatment modality for intracranial aneurysms (IAs) in clinics, but critical challenges remain to be overcome, such as exogenous implant-induced stenosis and reliance on antiplatelet agents. Herein, we report an endovascular approach for IA therapy without stent grafting or microcatheter shaping, enabled by active delivery of thrombin (Th) to target aneurysms using innovative phase-change material (PCM)-coated magnetite-thrombin (Fe3O4-Th@PCM) FTP nanorobots. The nanorobots are controlled by an integrated actuation system of dynamic torque-force hybrid magnetic fields. With robust intravascular navigation guided by real-time ultrasound imaging, nanorobotic collectives can effectively accumulate and retain in model aneurysms constructed in vivo, followed by controlled release of the encapsulated Th for rapid occlusion of the aneurysm upon melting the protective PCM (thermally responsive in a tunable manner) through focused magnetic hyperthermia. Complete and stable aneurysm embolization was confirmed by postoperative examination and 2-week postembolization follow-up using digital subtraction angiography (DSA), contrast-enhanced ultrasound (CEUS) and histological analysis. The safety of the embolization therapy was assessed through biocompatibility evaluation and histopathology assays. Our strategy, seamlessly integrating secure drug packaging, agile magnetic actuation and clinical interventional imaging, avoids possible exogenous implant rejection, circumvents cumbersome microcatheter shaping, and offers a promising option for IA therapy

Critical Temperature Prediction of Superconductors Based on Atomic Vectors and Deep Learning

In this paper, a hybrid neural network (HNN) that combines a convolutional neural network (CNN) and long short-term memory neural network (LSTM) is proposed to extract the high-level characteristics of materials for critical temperature (Tc) prediction of superconductors. Firstly, by obtaining 73,452 inorganic compounds from the Materials Project (MP) database and building an atomic environment matrix, we obtained a vector representation (atomic vector) of 87 atoms by singular value decomposition (SVD) of the atomic environment matrix. Then, the obtained atom vector was used to implement the coded representation of the superconductors in the order of the atoms in the chemical formula of the superconductor. The experimental results of the HNN model trained with 12,413 superconductors were compared with three benchmark neural network algorithms and multiple machine learning algorithms using two commonly used material characterization methods. The experimental results show that the HNN method proposed in this paper can eectively extract the characteristic relationships between the atoms of superconductors, and it has high accuracy in predicting the Tc