Background Hypokalemia (LK) was associated with peritonitis in peritoneal dialysis (PD) patients, while the role of its degree and duration have not been fully established. Here, we conducted a retrospective cohort study to identify the relationships of LK degree and duration with peritonitis in PD patients. Methods A total of 602 PD patients in our department from Jan 1^st, 2009 to Dec 31^st, 2019 entered the last analysis. Data were collected from their medical records. Serum potassium (SK) levels, degree of hypokalemia, and duration of hypokalemia were analyzed with peritonitis. The time association of hypokalemia and peritonitis was also analyzed. Results There were totally 320 (53.7%) and 123 (20.7%) patients who had ever suffered from LK and serious hypokalemia (SLK) in the cohort. Only 6.82% and 0.5% patients had LK and SLK at baseline, while the incidence rose and kept in 25‐32% and 5.5‐8.2% after PD. Both LK (HR 1.437, 95% CI 1.014‐2.038, p=0.042) and SLK (HR 2.021, 95% CI 1.429‐2.857, p<0.001) did correlate to peritonitis after adjusted analyses, while only SLK remained the significance at each follow‐up point. The LK / SLK duration were 6 (3‐12) and 6 (3‐6) months, and only longer SLK duration correlated to peritonitis after adjusted analyses. After categorized, those LK duration more than 6 months and SLK duration more than 3 months presented significant association with peritonitis. Of the patients suffered from both hypokalemia and peritonitis, 70.4% patients’ LK times were earlier than peritonitis time, while most SLK times (62.7%) were later. SLK also correlated to combined endpoint. Conclusions Hypokalemia degree and duration were tightly associated with peritonitis. Hypokalemia might be a causal factor of peritonitis, while peritonitis might also aggravate hypokalemia. We should manage SK as much as possible and avoid hypokalemia, especially serious hypokalemia in clinic practice.

In recommender systems, measuring user similarity is essential for predicting a user's ratings on items. Most traditional works calculate the similarity based on historical ratings shared between two users, without considering the probability of users' different behaviors. To address this issue, our work designs a similarity measure based on the users' historical behavior probabilities. Based on the similarity method, a complex network of user relationships is modeled. The user degree and community information of the modeled network, as well as the number of shared ratings between users, are used with the proposed similarity measure to design a rating prediction algorithm for recommender systems. Experiments based on MovieLens and Netflix data sets show that this method is effective and can successfully improve the accuracy of rating predictions and reduce the number of neighbors required to achieve the optimal prediction accuracy. Our research shows that in a complex system, the relationship between users can be effectively measured by the users' historical behavior probability, providing a new perspective for future research on similarity measurement.

Given that everyone online is saturated with information, the theoretical significance of recommendation algorithms is evident in the fact that users need help finding products and content they care about. Collaborative filtering predicts a user's rating on an item by finding similar users that rated the item or similar items that were rated by the user, and using the selected similar neighbors to “collaboratively filter” the recommendation. In the process, selected neighbors are considered equally important despite their differences in popularity. Here, we explore a method of modeling recommender systems as networks that can be constructed by considering items as nodes and similarity between them as links. Our research shows that item centrality has a negative impact on the accuracy of rating predictions, which needs to be considered for better algorithm performance. Experiments show that collaborative filtering algorithms can be decentralized by our method and provide a better accuracy of rating prediction. Furthermore, the relationship between the prediction target and its neighbors can be further evaluated based on both their similarity and their centrality.