Research trends of sustainability and marketing research, 2010-2020: Topic modeling analysis.

In recent decades, rapid growth has been observed in the incorporation of sustainability into marketing. Accordingly, the contrasting relationships between them have been carefully studied to assess the relevance of one idea to the other and vice versa. In response to this change, scholars and practitioners have been tasked with exploring the trends in sustainability and marketing. Therefore, the purpose of this study is to investigate existing literatures on both sustainability and all levels of marketing, determine the research trends and provide implications of applying the trends for future research and practices. This research has investigated only the title, abstract, and keywords of 2147 articles that were published between 2010 and 2020 in SSCI or SCIE indexed journals by applying the topic modeling based on the Latent Dirichlet Allocation (LDA) model. The results show that the research trend has shifted from general sustainable concept to more environmental and industrial technology based on the empirical evidence of 14 latent topics of sustainability and marketing. This article aids in understanding the recent research trend in sustainability and marketing, and the findings will be a valuable resource for future scholars and practitioners. It contributes to both existing and future literatures by providing valuable insights from recent research trend in sustainability and marketing and by providing recommendations for future research avenue. Among other bibliometric review articles, this is the most up-to-date comprehensive and empirical article, providing overview of the research trend.

Mortality prediction of patients in intensive care units using machine learning algorithms based on electronic health records.

Improving predictive models for intensive care unit (ICU) inpatients requires a new strategy that periodically includes the latest clinical data and can be updated to reflect local characteristics. We extracted data from all adult patients admitted to the ICUs of two university hospitals with different characteristics from 2006 to 2020, and a total of 85,146 patients were included in this study. Machine learning algorithms were trained to predict in-hospital mortality. The predictive performance of conventional scoring models and machine learning algorithms was assessed by the area under the receiver operating characteristic curve (AUROC). The conventional scoring models had various predictive powers, with the SAPS III (AUROC 0.773 [0.766-0.779] for hospital S) and APACHE III (AUROC 0.803 [0.795-0.810] for hospital G) showing the highest AUROC among them. The best performing machine learning models achieved an AUROC of 0.977 (0.973-0.980) in hospital S and 0.955 (0.950-0.961) in hospital G. The use of ML models in conjunction with conventional scoring systems can provide more useful information for predicting the prognosis of critically ill patients. In this study, we suggest that the predictive model can be made more robust by training with the individual data of each hospital.

[Intussusception Caused by Colon Cancer in Pregnancy].

Intussusception is a common in pediatric age group. But it is rare in adults. And intussusception caused by tumor account for 1% of bowel obstructions in adult. Intussusception is an extremely rare cause of abdominal pain in pregnancy. In particular, cases of Intussusception due to colorectal cancer during pregnancy have never been reported in Korea. Our patient is a 34 years old woman who presented at 14 weeks of her second pregnancy. She presented with right lower abdominal discomfort and intermittent palpable mass which was usually spontaneously resolved. In the MRI study, pathologic asymmetric wall thickening was still noted and ileocolic intussusception was noted, and in colonoscopy, there was ulcerofungating mass around ileocecal valve which may be a leading point of intussusception. Biopsy was done. Pathologic finding was poorly differentiated adenocarcinoma. Under the patient agreement, we performed dilatation and curettage and laparoscopic right hemicolectomy and lymph node dissection. Now she is receiving a FOLFOX chemotherapy.

Sequence-based approach to finding functional lipases from microbial genome databases.

A sequence-based approach was used to retrieve functional lipases from microbial genome databases. Many novel genes assigned as putative lipases were tested using the criteria of the typical lipase sequence rule, based on a consensus sequence of a catalytic triad (Ser, Asp, His) and oxyanion hole sequence (HG). To obtain the lipase genes satisfying the sequence rule, PCR cloning was performed, while the lipase activities were tested using a tributyrin/tricaprylin plate and p-nitrophenyl caproate. Among nine putative lipases from four strains, five functional lipolytic proteins were obtained from Archaeoglobus fulgidus, Deinococcus radiodurans, and Agrobacterium tumefaciens. All five lipases exhibited a relatively low sequence similarity (less than 26.7%) with known lipases and turned out to belong to different lipase families. Accordingly, the current results indicate that the proposed strategic approach based on the microbial genome is an efficient and rapid method for finding novel and functional lipases.