Double-stapled anastomosis without “dog-ears” reduces the anastomotic leakage in laparoscopic anterior resection of rectal cancer: A prospective, randomized, controlled study

BackgroundAnastomotic leakage (AL) is a major cause of postoperative morbidity and mortality in the treatment of colorectal cancer. The aim of this study was to investigate whether the resection of “dog-ears” in laparoscopic anterior resection of rectal cancer (called modified double-stapling technique, MDST) could reduce the rate of AL in patients with middle and high rectal cancer, as compared with the conventional double-stapling technique (DST).MethodsThe clinical data of 232 patients with middle and high rectal cancer were prospectively collected from September 2015 to October 2018. They were randomly divided into the MDST group (n = 116) and the DST group (n = 116) and the data were prospectively analyzed. Morbidity and AL rate were compared between the two groups.ResultsPatient demographics, tumor size, and time of first flatus were similar between the two groups. No difference was observed in the operation time between the two groups. The AL rate was significantly lower in the MDST group than in the DST group (3.4 vs. 11.2%, p = 0.032). The age and anastomotic technique were the factors associated with AL according to the multivariate analysis. The location of the AL in the DST group was further investigated, revealing that AL was in the same place as the “dog-ears” (11/13, 84.6%).ConclusionsOur prospective comparative study demonstrated that MDST have a better short-term outcome in reducing AL compared with DST. Therefore, this technique could be an alternative approach to maximize the benefit of laparoscopic anterior resection on patients with middle and high rectal cancer. The “dog-ears” create stapled corners potentially ischemic, since they represent the area with high incidence of AL.(NCT:02770911

Design and evaluation of a rodent-specific focal transcranial magnetic stimulation coil with the custom shielding application in rats

Repetitive TMS has been used as an alternative treatment for various neurological disorders. However, most TMS mechanism studies in rodents have been based on the whole brain stimulation, the lack of rodent-specific focal TMS coils restricts the proper translation of human TMS protocols to animal models. In this study, we designed a new shielding device, which was made of high magnetic permeability material, to enhance the spatial focus of animal-use TMS coils. With the finite element method, we analyzed the electromagnetic field of the coil with and without the shielding device. Furthermore, to assess the shielding effect in rodents, we compared the c-fos expression, the ALFF and ReHo values in different groups following a 15 min 5 Hz rTMS paradigm. We found that a smaller focality with an identical core stimulation intensity was achieved in the shielding device. The 1 T magnetic field was reduced from 19.1 mm to 13 mm in diameter, and 7.5 to 5.6 mm in depth. However, the core magnetic field over 1.5 T was almost the same. Meanwhile, the area of electric field was reduced from 4.68 cm2 to 4.19 cm2, and 3.8 mm to 2.6 mm in depth. Similar to this biomimetic data, the c-fos expression, the ALFF and ReHo values showed more limited cortex activation with the use of the shielding device. However, compared to the rTMS group without the shielding application, more subcortical regions, like the striatum (CPu), the hippocampus, the thalamus, and the hypothalamus were also activated in the shielding group. This indicated that more deep stimulation may be achieved by the shielding device. Generally, compared with the commercial rodents’ TMS coil (15 mm in diameter), TMS coils with the shielding device achieved a better focality (~6 mm in diameter) by reducing at least 30% of the magnetic and electric field. This shielding device may provide a useful tool for further TMS studies in rodents, especially for more specific brain area stimulation

Clinical analysis of orthodontic traction of impacted upper incisors

Objective To study the effect of orthodontic traction on the roots and periodontal soft and hard tissues of buried obstructed upper incisors. Methods This study was reviewed and approved by the ethics committee, and informed consent was obtained from the patients. From January 2018 to December 2022, 40 patients who underwent orthodontic traction on impacted upper incisors were selected; those whose contralateral homonymous apical foramen was not developed were placed in group A (23 cases), and those whose contralateral homonymous apical foramen was developed were placed in group B (17 cases). Software was used to measure the root length of the impacted upper incisors in groups A and B on cone beam CT (CBCT) images before and after traction and compare the changes in alveolar bone (alveolar bone width, labral bone plate thickness, and horizontal height of alveolar bone) and keratinized gingival width between each impacted upper incisor and the corresponding contralateral tooth immediately and one year after traction. Results The root length of the impacted upper incisors increased after traction compared to before traction (P0.05), whereas the width of the alveolar bone at the completion of traction in group B did not reach that of the contralateral homonymous tooth, with a significant difference in width (P<0.05). Neither the labial bone plate height or width in group A or B reached that of the contralateral homonymous tooth after traction (P<0.05). The keratinized gingival width on the affected side was also significantly smaller than that on the contralateral side (P<0.05), but it was increased significantly in group A at the one-year follow-up visit (P<0.05). Conclusion Tooth traction is conducive to impacted upper incisor root growth, alveolar bone reconstruction and keratinized gingival growth but cannot produce complete symmetry with respect to the contralateral side

Resource Allocation in V2X Communications Based on Multi-Agent Reinforcement Learning with Attention Mechanism

In this paper, we study the joint optimization problem of the spectrum and power allocation for multiple vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) users in cellular vehicle-to-everything (C-V2X) communication, aiming to maximize the sum rate of V2I links while satisfying the low latency requirements of V2V links. However, channel state information (CSI) is hard to obtain accurately due to the mobility of vehicles. In addition, the effective sensing of state information among vehicles becomes difficult in an environment with complex and diverse information, which is detrimental to vehicles collaborating for resource allocation. Thus, we propose a framework of multi-agent deep reinforcement learning based on attention mechanism (AMARL) to improve the V2X communication performance. Specifically, for vehicle mobility, we model the problem as a multi-agent reinforcement learning process, where each V2V link is regarded an agent and all agents jointly intercommunicate with the environment. Each agent allocates spectrum and power through its deep Q network (DQN). To enhance effective intercommunication and the sense of collaboration among vehicles, we introduce an attention mechanism to focus on more relevant information, which in turn reduces the signaling overhead and optimizes their communication performance more explicitly. Experimental results show that the proposed AMARL-based approach can satisfy the requirements of a high rate for V2I links and low latency for V2V links. It also has an excellent adaptability to environmental change

Antidepressants as Autophagy Modulators for Cancer Therapy

Cancer is a major global public health problem with high morbidity. Depression is known to be a high-frequency complication of cancer diseases that decreases patients’ life quality and increases the mortality rate. Therefore, antidepressants are often used as a complementary treatment during cancer therapy. During recent decades, various studies have shown that the combination of antidepressants and anticancer drugs increases treatment efficiency. In recent years, further emerging evidence has suggested that the modulation of autophagy serves as one of the primary anticancer mechanisms for antidepressants to suppress tumor growth. In this review, we introduce the anticancer potential of antidepressants, including tricyclic antidepressants (TCAs), tetracyclic antidepressants (TeCAs), selective serotonin reuptake inhibitors (SSRIs), and serotonin-norepinephrine reuptake inhibitors (SNRIs). In particular, we focus on their autophagy-modulating mechanisms for regulating autophagosome formation and lysosomal degradation. We also discuss the prospect of repurposing antidepressants as anticancer agents. It is promising to repurpose antidepressants for cancer therapy in the future

A New Cable-Less Seismograph with Functions of Real-Time Data Transmitting and High-Precision Differential Self-Positioning

This study developed a new cable-less seismograph system, which can transmit seismic data in real-time and automatically perform high-precision differential self-positioning. Combining the ZigBee technology with the high-precision differential positioning module, this new seismograph system utilized the wireless personal area network (WPAN) and real-time kinematic (RTK) technologies to improve its on-site performances and to make the field quality control (QC) and self-positioning possible. With the advantages of low-cost, good scalability, and good compatibility, the proposed new cable-less seismograph system can improve the field working efficiency and data processing capability. It has potential applications in noise seismology and mobile seismic monitoring

DrugOOD: Out-of-Distribution Dataset Curator and Benchmark for AI-Aided Drug Discovery – a Focus on Affinity Prediction Problems with Noise Annotations

AI-aided drug discovery (AIDD) is gaining popularity due to its potential to make the search for new pharmaceuticals faster, less expensive, and more effective. Despite its extensive use in numerous fields (e.g., ADMET prediction, virtual screening), little research has been conducted on the out-of-distribution (OOD) learning problem with noise. We present DrugOOD, a systematic OOD dataset curator and benchmark for AIDD. Particularly, we focus on the drug-target binding affinity prediction problem, which involves both macromolecule (protein target) and small-molecule (drug compound). DrugOOD offers an automated dataset curator with user-friendly customization scripts, rich domain annotations aligned with biochemistry knowledge, realistic noise level annotations, and rigorous benchmarking of SOTA OOD algorithms, as opposed to only providing fixed datasets. Since the molecular data is often modeled as irregular graphs using graph neural network (GNN) backbones, DrugOOD also serves as a valuable testbed for graph OOD learning problems. Extensive empirical studies have revealed a significant performance gap between in-distribution and out-of-distribution experiments, emphasizing the need for the development of more effective schemes that permit OOD generalization under noise for AIDD