Clinicopathological characteristics of gastric cancer patients with dermatomyositis and analysis of perioperative management: a case series study

BackgroundThis study aimed to investigate the clinical characteristics of gastric cancer (GC) patients with dermatomyositis (DM) and summarize the perioperative outcomes.MethodsThe clinical and pathological data of five patients diagnosed with co-occurring DM and GC (DM-GC group) were retrospectively analyzed, who were admitted to the Department of Gastrointestinal Surgery at Ren ji Hospital, Shanghai Jiao Tong University, between January 2012 and April 2023. Their data were compared with 618 GC patients (GC-1 group) from September 2016 to August 2017 and 35 GC patients who were meticulously screened from 14,580 GC cases from January 2012 and April 2023. The matching criteria included identical gender, age, tumor location, TNM stage, and surgical procedure (7 GC patients were matched for each DM-GC patient).ResultsAnalysis indicated that the DM-GC group comprised four female and one male patient. The female proportion was significantly higher (P = 0.032) than that of GC-1 group. In DM-GC group, four DM patients were diagnosed as GC within 12 months. One DM patients was diagnosed as GC within 15 months. Among them, four patients presented with varying degrees of skin rashes, muscle weakness while one patient had elevated CK levels as the typical symptom. Similarly, the preoperative tumor markers (CA-199 and CA-125) in the DM-GC group were significantly higher than normal levels (CA-199: 100 vs. 28.6%, P = 0.002; CA-125: 40 vs. 2.9%, P = 0.003) compared to GC-2 group. Moreover, postoperative complication incidence and the length of hospital stay were significantly higher in the DM-GC than GC-2 group [complication rate: 40 vs. 8.6%, P = 0.047; hospital stay: 15 days (range: 9–28) vs. 9 days (range: 8–10), P = 0.021].ConclusionGC Patients with dermatomyositis are more prone to experience postoperative complications and longer hospital stay

Surface functionalization of vertical graphene significantly enhances the energy storage capability for symmetric supercapacitors

Vertical graphene (VG) sheets, which consist of few-layer graphene vertically aligned on the substrate with three dimensionally interconnected porous network, make them become one of the most promising energy storage electrodes, especially for SCs. Nevertheless, the intrinsic hydrophobic nature of pristine VG sheets severely limited its application in aqueous SCs. Here, electrochemical oxidation strategy is adopted to increase the hydrophilicity of VG sheets by introducing oxygen functional groups so that the aqueous electrolyte can fully be in contact with the VG sheets to improve charge storage performance. Our work demonstrated that the introduction of oxygen functional groups not only greatly improved the hydrophilicity but also generated a pseudo capacitance to increase the specific capacitance. The resulting capacitance of electrochemically oxidized VG for 7 min (denoted as EOVG-7) exhibited three orders of magnitude higher (1605 mF/cm²) compared to pristine VG sheets. Through assembled two EOVG-7 electrodes, a symmetric supercapacitor demonstrated high specific capacitance of 307.5 mF/cm², high energy density of 138.3 μWh/cm2 as well as excellent cyclic stability (84% capacitance retention after 10000 cycles). This strategy provides a promising way for designing and engineering carbon-based aqueous supercapacitors with high performance

Orchestrating the Development Lifecycle of Machine Learning-based IoT Applications: A Taxonomy and Survey

Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock the potential of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services. Hence, orchestrating ML pipelines that encompass model training and implication involved in the holistic development lifecycle of an IoT application often leads to complex system integration. This article provides a comprehensive and systematic survey of the development lifecycle of ML-based IoT applications. We outline the core roadmap and taxonomy and subsequently assess and compare existing standard techniques used at individual stages