The Relationship between Estimated Glomerular Filtration Rate and Diabetic Retinopathy

Diabetic retinopathy (DR) is the leading cause of visual impairment and blindness in working-aged people. Several studies have suggested that glomerular filtration rate (GFR) was correlated with DR. This is a hospital-based study and the aim of it was to examine the relationship between the GFR and DR in patients with type 2 diabetes mellitus (T2DM). We used CKD-EPI equation to estimate GFR and SPSS 19.0 and EmpowerStats software to assess their relationship. Among the 1613 participants (aged 54.75 ± 12.19 years), 550 (34.1%) patients suffered from DR. The multivariate analysis revealed that the risk factors for DR include age (P<0.001, OR = 0.940), duration of diabetes (P<0.001, OR = 1.163), hemoglobin A1c (P=0.007, OR = 1.224), systolic blood pressure (P<0.001, OR = 1.032), diastolic blood pressure (P=0.007, OR = 0.953), high density lipoprotein cholesterol (P=0.024, OR = 3.884), and eGFR (P=0.010, OR = 0.973). Through stratified analysis and saturation effect analysis, our data suggests that eGFR of 99.4 mL/min or lower might imply the early stage of DR in diabetic patients. Thus, the evaluation of eGFR has clinical significance for the early diagnosis of DR

Coevolutionary fuzzy attribute order reduction with complete attribute-value space tree

Since big data sets are structurally complex, high-dimensional, and their attributes exhibit some redundant and irrelevant information, the selection, evaluation, and combination of those large-scale attributes pose huge challenges to traditional methods. Fuzzy rough sets have emerged as a powerful vehicle to deal with uncertain and fuzzy attributes in big data problems that involve a very large number of variables to be analyzed in a very short time. In order to further overcome the inefficiency of traditional algorithms in the uncertain and fuzzy big data, in this paper we present a new coevolutionary fuzzy attribute order reduction algorithm (CFAOR) based on a complete attribute-value space tree. A complete attribute-value space tree model of decision table is designed in the attribute space to adaptively prune and optimize the attribute order tree. The fuzzy similarity of multimodality attributes can be extracted to satisfy the needs of users with the better convergence speed and classification performance. Then, the decision rule sets generate a series of rule chains to form an efficient cascade attribute order reduction and classification with a rough entropy threshold. Finally, the performance of CFAOR is assessed with a set of benchmark problems that contain complex high dimensional datasets with noise. The experimental results demonstrate that CFAOR can achieve the higher average computational efficiency and classification accuracy, compared with the state-of-the-art methods. Furthermore, CFAOR is applied to extract different tissues surfaces of dynamical changing infant cerebral cortex and it achieves a satisfying consistency with those of medical experts, which shows its potential significance for the disorder prediction of infant cerebrum

FedTP: Federated Learning by Transformer Personalization

Federated learning is an emerging learning paradigm where multiple clients collaboratively train a machine learning model in a privacy-preserving manner. Personalized federated learning extends this paradigm to overcome heterogeneity across clients by learning personalized models. Recently, there have been some initial attempts to apply Transformers to federated learning. However, the impacts of federated learning algorithms on self-attention have not yet been studied. This paper investigates this relationship and reveals that federated averaging algorithms actually have a negative impact on self-attention where there is data heterogeneity. These impacts limit the capabilities of the Transformer model in federated learning settings. Based on this, we propose FedTP, a novel Transformer-based federated learning framework that learns personalized self-attention for each client while aggregating the other parameters among the clients. Instead of using a vanilla personalization mechanism that maintains personalized self-attention layers of each client locally, we develop a learn-to-personalize mechanism to further encourage the cooperation among clients and to increase the scablability and generalization of FedTP. Specifically, the learn-to-personalize is realized by learning a hypernetwork on the server that outputs the personalized projection matrices of self-attention layers to generate client-wise queries, keys and values. Furthermore, we present the generalization bound for FedTP with the learn-to-personalize mechanism. Notably, FedTP offers a convenient environment for performing a range of image and language tasks using the same federated network architecture - all of which benefit from Transformer personalization. Extensive experiments verify that FedTP with the learn-to-personalize mechanism yields state-of-the-art performance in non-IID scenarios. Our code is available online

Identifying Ketamine Responses in Treatment-Resistant Depression Using a Wearable Forehead EEG

This study explores the responses to ketamine in patients with treatment-resistant depression (TRD) using a wearable forehead electroencephalography (EEG) device. We recruited fifty-five outpatients with TRD who were randomised into three approximately equal-sized groups (A: 0.5 mg/kg ketamine; B: 0.2 mg/kg ketamine; and C: normal saline) under double-blind conditions. The ketamine responses were measured by EEG signals and Hamilton Depression Rating Scale (HDRS) scores. At baseline, responders showed a significantly weaker EEG theta power than did non- responders (p < 0.05). Responders exhibited a higher EEG alpha power but lower EEG alpha asymmetry and theta cordance at post-treatment than at baseline (p < 0.05). Furthermore, our baseline EEG predictor classified responders and non-responders with 81.3 +- 9.5% accuracy, 82.1 +- 8.6% sensitivity and 91.9 +- 7.4% specificity. In conclusion, the rapid antidepressant effects of mixed doses of ketamine are associated with prefrontal EEG power, asymmetry and cordance at baseline and early post-treatment changes. The prefrontal EEG patterns at baseline may account for recognising ketamine effects in advance. Our randomised, double- blind, placebo-controlled study provides information regarding clinical impacts on the potential targets underlying baseline identification and early changes from the effects of ketamine in patients with TRD.Comment: This revised article is submitting to IEEE TBM

Global research landscape and trends of papillary thyroid cancer therapy: a bibliometric analysis

BackgroundPapillary thyroid cancer (PTC) is the most common endocrine malignancy worldwide. The treatment of PTC has attracted extensive attention and discussion from the public and scholars. However, no article has systematically assessed the related literature. Therefore, we conducted a bibliometric and knowledge map analysis to reveal the dynamic scientific developments in the PTC therapy field.MethodsWe retrieved publications related to PTC therapy from the Web of Scientific Core Collection (WoSCC) on May 1, 2023. The bibliometric package in R software, VOSviewer and CiteSpace software were used to analyze countries/regions, institutions, journals, authors, references, and keywords. Then, we systematized and summarized the research landscape, global trends and hot topics of research.ResultsThis bibliometric analysis spanned from 2012 to 2022 and involved 18,501 authors affiliated with 3,426 institutions across 87 countries/regions, resulting in the publication of 3,954 papers in 860 academic journals. Notably, the number of publications and citations related to PTC therapy research has exhibited a steady increase over the past decade. China and the United States have emerged as leading contributors in terms of publication count, with the United States also being the most cited country. Furthermore, among the top 10 institutions with the highest number of published papers, half were located in China. Among the journals, Thyroid is ranked first in terms of total publications and citations. The most productive individual author was Miyauchi Akira. While previous research primarily focused on surgery and radioactive iodine therapy, the increasing emphasis on health awareness and advancements in medical technology have led to the emergence of active surveillance, thermal ablation, and genomic analysis as prominent areas of research.ConclusionIn conclusion, this comprehensive and quantitative bibliometric analysis elucidates the research trends and hotspots within PTC therapy, drawing from a substantial body of publications. This study provides valuable insights into the historical and current landscape of PTC therapy research while also offering guidance for future research directions. This study serves as a valuable resource for researchers and practitioners seeking new avenues of exploration in the field

A Hierarchical Meta-model for Multi-Class Mental Task Based Brain-Computer Interfaces

In the last few years, many research works have been suggested on Brain- Computer Interface (BCI), which assists severely physically disabled persons to communicate directly with the help of electroencephalogram (EEG) signal, generated by the thought process of the brain. Thought generation inside the brain is a dynamic process, and plenty thoughts occur within a small time window. Thus, there is a need for a BCI device that can distinguish these various ideas simultaneously. In this research work, our previous binary-class mental task classication has been extended to the multi-class mental task problem. The present work proposed a novel feature construction scheme for multi mental task classication. In the proposed method, features ar