Privacy-Preserving Federated Model Predicting Bipolar Transition in Patients With Depression:Prediction Model Development Study

BACKGROUND: Mood disorder has emerged as a serious concern for public health; in particular, bipolar disorder has a less favorable prognosis than depression. Although prompt recognition of depression conversion to bipolar disorder is needed, early prediction is challenging due to overlapping symptoms. Recently, there have been attempts to develop a prediction model by using federated learning. Federated learning in medical fields is a method for training multi-institutional machine learning models without patient-level data sharing. OBJECTIVE: This study aims to develop and validate a federated, differentially private multi-institutional bipolar transition prediction model. METHODS: This retrospective study enrolled patients diagnosed with the first depressive episode at 5 tertiary hospitals in South Korea. We developed models for predicting bipolar transition by using data from 17,631 patients in 4 institutions. Further, we used data from 4541 patients for external validation from 1 institution. We created standardized pipelines to extract large-scale clinical features from the 4 institutions without any code modification. Moreover, we performed feature selection in a federated environment for computational efficiency and applied differential privacy to gradient updates. Finally, we compared the federated and the 4 local models developed with each hospital's data on internal and external validation data sets. RESULTS: In the internal data set, 279 out of 17,631 patients showed bipolar disorder transition. In the external data set, 39 out of 4541 patients showed bipolar disorder transition. The average performance of the federated model in the internal test (area under the curve [AUC] 0.726) and external validation (AUC 0.719) data sets was higher than that of the other locally developed models (AUC 0.642-0.707 and AUC 0.642-0.699, respectively). In the federated model, classifications were driven by several predictors such as the Charlson index (low scores were associated with bipolar transition, which may be due to younger age), severe depression, anxiolytics, young age, and visiting months (the bipolar transition was associated with seasonality, especially during the spring and summer months). CONCLUSIONS: We developed and validated a differentially private federated model by using distributed multi-institutional psychiatric data with standardized pipelines in a real-world environment. The federated model performed better than models using local data only.</p

Unsupervised Episode Generation for Graph Meta-learning

In this paper, we investigate Unsupervised Episode Generation methods to solve Few-Shot Node-Classification (FSNC) problem via Meta-learning without labels. Dominant meta-learning methodologies for FSNC were developed under the existence of abundant labeled nodes for training, which however may not be possible to obtain in the real-world. Although few studies have been proposed to tackle the label-scarcity problem, they still rely on a limited amount of labeled data, which hinders the full utilization of the information of all nodes in a graph. Despite the effectiveness of Self-Supervised Learning (SSL) approaches on FSNC without labels, they mainly learn generic node embeddings without consideration on the downstream task to be solved, which may limit its performance. In this work, we propose unsupervised episode generation methods to benefit from their generalization ability for FSNC tasks while resolving label-scarcity problem. We first propose a method that utilizes graph augmentation to generate training episodes called g-UMTRA, which however has several drawbacks, i.e., 1) increased training time due to the computation of augmented features and 2) low applicability to existing baselines. Hence, we propose Neighbors as Queries (NaQ), which generates episodes from structural neighbors found by graph diffusion. Our proposed methods are model-agnostic, that is, they can be plugged into any existing graph meta-learning models, while not sacrificing much of their performance or sometimes even improving them. We provide theoretical insights to support why our unsupervised episode generation methodologies work, and extensive experimental results demonstrate the potential of our unsupervised episode generation methods for graph meta-learning towards FSNC problems.Comment: 11 pages, 9 figures, preprin

Public health measures on COVID-19 in North Korea: a quantitative analysis of media programmes in 2020–2022

Objectives Details regarding the management of COVID-19 in North Korea are unknown. The aim of this paper was to analyse media programmes in North Korea in order to understand public health measures and policies concerning COVID-19.Setting State-run news agency in North Korea.Primary and secondary outcome measures The classification of television programmes on COVID-19 broadcast in a state-run news agency, from January 2020 to May 2022, and public health measures introduced in the programmes.Results A total of 2671 programmes concerning COVID-19 were included in the study. These programmes provided detailed clinical guidelines to laypeople without medical expertise, including instructions for the usage of medication and preventive measures. An association between the media concern regarding COVID-19 and trade volume, as a proxy of border closure according to the concern of the authorities, provided hints to understand the priorities and aims of the authorities.Conclusions The research outcomes provided significant insights into the effort to understand an impaired healthcare system and prevalent drug abuse behaviours in North Korea. Findings from further studies on the recently collected data might suggest additional implications on the North Korean policies on COVID-19

Enhancing Mixing Performance in a Rotating Disk Mixing Chamber: A Quantitative Investigation of the Effect of Euler and Coriolis Forces

Lab-on-a-CD (LOCD) is gaining importance as a diagnostic platform due to being low-cost, easy-to-use, and portable. During LOCD usage, mixing and reaction are two processes that play an essential role in biochemical applications such as point-of-care diagnosis. In this paper, we numerically and experimentally investigate the effects of the Coriolis and Euler forces in the mixing chamber during the acceleration and deceleration of a rotating disk. The mixing performance is investigated under various conditions that have not been reported, such as rotational condition, chamber aspect ratio at a constant volume, and obstacle arrangement in the chamber. During disk acceleration and deceleration, the Euler force difference in the radial direction causes rotating flows, while the Coriolis force induces perpendicular vortices. Increasing the maximum rotational velocity improves the maximum rotational displacement, resulting in better mixing performance. A longer rotational period increases the interfacial area between solutions and enhances mixing. Mixing performance also improves when there is a substantial difference between Euler forces at the inner and outer radii. Furthermore, adding obstacles in the angular direction also passively promotes or inhibits mixing by configuration. This quantitative investigation provides valuable information for designing and developing high throughput and multiplexed point-of-care LOCDs

A Paradoxical Effect of Interleukin-32 Isoforms on Cancer

IL-32 plays a contradictory role such as tumor proliferation or suppressor in cancer development depending on the cancer type. In most cancers, it was found that the high expression of IL-32 was associated with more proliferative and progression of cancer. However, studying the isoforms of IL-32 cytokine has placed its paradoxical role into a wide range of functions based on its dominant isoform and surrounding environment. IL-32 beta, for example, was found mostly in different types of cancer and associated with cancer expansion. This observation is legitimate since cancer exhibits some hypoxic environment and IL-32 beta was known to be induced under hypoxic conditions. However, IL-32 theta interacts directly with protein kinase C-delta reducing NF-kappa B and STAT3 levels to inhibit epithelial-mesenchymal transition (EMT). This effect could explain the different functions of IL-32 isoforms in cancer. However, pro- or antitumor activity which is dependant on obesity, gender, and age as it relates to IL-32 has yet to be studied. Obesity-related IL-32 regulation indicated the role of IL-32 in cancer metabolism and inflammation. IL-32-specific direction in cancer therapy is difficult to conclude. In this review, we address that the paradoxical effect of IL-32 on cancer is attributed to the dominant isoform, cancer type, tumor microenvironment, and genetic background. IL-32 seems to have a contradictory role in cancer. However, investigating multiple IL-32 isoforms could explain this doubt and bring us closer to using them in therapy.N

Discovery of GSK3β Inhibitors through In Silico Prediction-and-Experiment Cycling Strategy, and Biological Evaluation

Direct inhibitors of glycogen synthase kinase 3β (GSK3β) have been investigated and reported for the past 20 years. In the search for novel scaffold inhibitors, 3000 compounds were selected through structure-based virtual screening (SBVS), and then high-throughput enzyme screening was performed. Among the active hit compounds, pyrazolo [1,5-a]pyrimidin-7-amine derivatives showed strong inhibitory potencies on the GSK3β enzyme and markedly activated Wnt signaling. The result of the molecular dynamics (MD) simulation, enhanced by the upper-wall restraint, was used as an advanced structural query for the SBVS. In this study, strong inhibitors designed to inhibit the GSK3β enzyme were discovered through SBVS. Our study provides structural insights into the binding mode of the inhibitors for further lead optimization