Requirement of CRMP2 Phosphorylation in Neuronal Migration of Developing Mouse Cerebral Cortex and Hippocampus and Redundant Roles of CRMP1 and CRMP4.

The mammalian cerebral cortex is characterized by a 6-layer structure, and proper neuronal migration is critical for its formation. Cyclin-dependent kinase 5 (Cdk5) has been shown to be a critical kinase for neuronal migration. Several Cdk5 substrates have been suggested to be involved in ordered neuronal migration. However, in vivo loss-of-function studies on the function of Cdk5 phosphorylation substrates in neuronal migration in the developing cerebral cortex have not been reported. In this study, we demonstrated that Cdk5-mediated phosphorylation of collapsing mediator protein (CRMP) 2 is critical for neuronal migration in the developing cerebral cortex with redundant functions of CRMP1 and CRMP4. The cerebral cortices of triple-mutant CRMP1 knock-out (KO); CRMP2 knock-in (KI)/KI; and CRMP4 KO mice showed disturbed positioning of layers II-V neurons in the cerebral cortex. Further experiments using bromodeoxyuridine birthdate-labeling and in utero electroporation implicated radial migration defects in cortical neurons. Ectopic neurons were detected around the CA1 region and dentate gyrus in CRMP1 KO; CRMP2 KI/KI; and CRMP4 KO mice. These results suggest the importance of CRMP2 phosphorylation by Cdk5 and redundancy of CRMP1 and CRMP4 in proper neuronal migration in the developing cerebral cortex and hippocampus.

The factors related to failure of Enhanced Recovery after Surgery (ERAS) in colon cancer surgery.

PURPOSE: Enhanced Recovery after Surgery has been proven effective for patients with gastrointestinal cancer. But radical enhanced recovery could also lead to adverse clinical outcomes. Compared with reports on the estimation of successful implementation of enhanced recovery, studies on risk factors of enhanced recovery failure are still lacking. METHODS: A retrospective analysis was carried out on 102 patients in ERAS who underwent elective colon cancer surgery. This study included 102 patients with colon cancer between 2015 and 2019, defining enhanced recovery failure as postoperative length of stay over 10 days, stay in ICU over 24 h after surgery, reoperation, death, or unplanned readmission within 30 days after surgery. Univariate and multivariate analyses were performed to explore potential risk factors of failure. RESULTS: Aged ≥ 75, open operation, number of drainage tube over 1, re-urethral catheterization, and Clavien-Dindo grade over 2 were associated with ERAS failure, according to univariate analysis. Multivariate analysis showed that age ≥ 75 [OR 7.231; P = 0.009]; open operation (OR 3.599; P = 0.021); and number of drainage tube over 1 (OR 3.202; P = 0.020) were independent risk factors for ERAS failure. CONCLUSIONS: We found age ≥ 75, open operation, and number of drainage tube over 1 are independent risk factors associated with ERAS failure after colon cancer surgery.

An attention-based multi-modal MRI fusion model for major depressive disorder diagnosis.

Objective.Major depressive disorder (MDD) is one of the biggest threats to human mental health. MDD is characterized by aberrant changes in both structure and function of the brain. Although recent studies have developed some deep learning models based on multi-modal magnetic resonance imaging (MRI) for MDD diagnosis, the latent associations between deep features derived from different modalities were largely unexplored by previous studies, which we hypothesized may have potential benefits in improving the diagnostic accuracy of MDD.Approach.In this study, we proposed a novel deep learning model that fused both structural MRI (sMRI) and resting-state MRI (rs-fMRI) data to enhance the diagnosis of MDD by capturing the interactions between deep features extracted from different modalities. Specifically, we first employed a brain function encoder (BFE) and a brain structure encoder (BSE) to extract the deep features from fMRI and sMRI, respectively. Then, we designed a function and structure co-attention fusion (FSCF) module that captured inter-modal interactions and adaptively fused multi-modal deep features for MDD diagnosis.Main results.This model was evaluated on a large cohort and achieved a high classification accuracy of 75.2% for MDD diagnosis. Moreover, the attention distribution of the FSCF module assigned higher attention weights to structural features than functional features for diagnosing MDD.Significance.The high classification accuracy highlights the effectiveness and potential clinical of the proposed model.

Comparison of Single-Incision and Conventional Laparoscopic Sleeve Gastrectomy for Morbid Obesity: a Meta-Analysis.

BACKGROUND: Single-incision laparoscopic sleeve gastrectomy (SILSG) has been proposed as an alternative to conventional laparoscopic sleeve gastrectomy (CLSG) in obese patients. This study aims to compare the surgical outcomes of these two techniques. METHODS: A meta-analysis of existing literature obtained through a systematic literature search in the PubMed, EMBASE, and Cochrane Library CENTRAL databases from 2009 to 2019 was conducted. RESULTS: Eleven articles including 1168 patients were analyzed. Patients in the SILSG group reported greater satisfaction with cosmetic scar outcomes than those in the CLSG group (SMD = 2.47, 95% CI = 1.10 to 3.83, P = 0.00). There was no significant difference between the SILSG group and the CLSG group regarding operative time, intraoperative estimated blood loss, conversion rate, intraoperative complications, length of hospital stay, postoperative analgesia, postoperative complications, excess weight loss (EWL), and improvements in comorbidities (P > 0.05). CONCLUSIONS: Compared to CLSG, SILSG resulted in improved cosmetic satisfaction and showed no disadvantages in terms of surgical outcomes; thus, SILSG can serve as an alternative to CLSG for obese patients. Nonetheless, high-quality randomized controlled trials (RCTs) with large study populations and long follow-up periods are needed.

Flexible 64 × 64 Pixel AMOLED Displays Driven by Uniform Carbon Nanotube Thin-Film Transistors.

Carbon nanotube (CNT) thin-film transistors are expected to be promising for use in flexible electronics including flexible and transparent integrated circuits and in wearable chemical and physical sensors and for driving the circuits of flexible display panels. However, current devices based on CNT channels suffer from poor performance uniformity and low manufacturing yield; therefore, they are still far from being practical. This is usually caused by nonuniform deposition of the semiconducting CNTs and the rough surface of flexible substrates. Here, we report CNT thin-film transistors (TFTs) driving a flexible 64 × 64 pixel active matrix light-emitting diode display (AMOLED) by improving the formation of uniform CNT films and developing a new pretreatment technique for flexible substrates. The achieved AMOLED has uniform brightness and a high yield of 99.93% in its 4096 pixels. More than 8000 TFTs with high-purity semiconducting CNTs as the channel material show an average on-off current ratio of ∼107 and a carrier mobility of 16 cm2 V-1 s-1. The standard deviations of the on-state current and the carrier mobility are 4.1 and 6.5%, respectively. Our result shows that the panel driven by high-purity semiconducting CNTs is a promising strategy for the development of next-generation flexible, large-area displays.