Wpływ długotrwałego stosowania rozyglitazonu na zdarzenia sercowo-naczyniowe — przegląd systematyczny i metaanaliza

Rosiglitazone has been proposed as a treatment strategy for type 2 diabetes mellitus (T2DM), and it could provide robust glucose-lowering capability with risk of cardiovascular events. We thus performed a systematic review and meta-analysis of controlled trials to assess the effect of this treatment on glycaemic control and cardiovascular events in patients with T2DM. We systematically search PubMed, Embase, and the Cochrane Central Register of Controlled Trials comparing rosiglitazone to other anti-diabetic treatments. These studies included randomised controlled trials (RCTs), cohort studies, and case-control studies that had treatment with at least six months of follow-up in patients with T2DM. We aimed to evaluate the long-term effect on cardiovascular risk of rosiglitazone compared with a basal insulin drug. The main outcomes included myocardial infarction, heart failure, stroke, cardiovascular mortality, and all-cause mortality. We included 11RCTs and four observational studies involving 20,079 individuals with T2DM allocated to rosiglitazone and a similar number to comparison groups of which only five compared rosiglitazone with placebo and collected data on cardiovascular outcomes. Among patients with T2DM, rosiglitazone is associated with a significantly increased risk of heart failure, with little increased risk of myocardial infarction, without a significantly increased risk of stroke, cardiovascular mortality, and all-cause mortality compared with placebo or active controls. Alternative methods to reduce the uncertainty in long-term pragmatic evaluations, inclusion of rosiglitazone in factorial trials, publication of cardiovascular outcome data from adverse event reporting in trials of rosiglitazone and a cardiovascular endpoint trial of rosiglitazone among people without diabetes.   Rozyglitazon został zaproponowany jako strategia leczenia cukrzycy typu 2 (type 2 diabetes mellitus; T2DM). Ma on zdolność do silnego obniżenia stężenia glukozy z jednoczesnym ryzykiem wystąpienia zdarzeń sercowo-naczyniowych. Autorzy przeprowadzili przegląd systematyczny i metaanalizę kontrolowanych badań, aby ocenić wpływ leczenia rozyglitazonem na kontrolę glikemii i zdarzenia sercowo­-naczyniowe u pacjentów z cukrzycą typu 2. Systematycznie przeszukano bazy PubMed, Embase oraz Centralny Rejestr Badań z Grupą Kontrolną im. Cochrane’a (Cochrane Central Register of Controlled Trials), porównując rozyglitazon z innymi terapiami przeciwcukrzycowymi. Badania te obejmowały randomizowane badania kontrolowane, badania kohortowe oraz badania kliniczno-kontrolne, które obejmowały leczenie z co najmniej 6-miesięcznym okresem badań kontrolnych u pacjentów z cukrzycą typu 2. Celem była ocena długoterminowego wpływu rozyglitazonu na ryzyko sercowo-naczyniowe w porównaniu z podstawowym lekiem przeciwcukrzycowym. Główne zdarzenia obejmowały zawał serca, niewydolność serca, udar, śmiertelność z powodu chorób sercowo-naczyniowych oraz śmiertelność niezależ­nie od przyczyny. Uwzględniono 11 randomizowanych badań kontrolowanych i 4 badania obserwacyjne obejmujące 20 079 pacjentów z cukrzycą typu 2 przypisanych do rozyglitazonu i podobną liczbę w grupach porównawczych, w których tylko 5 badań porównywało rozyglitazon z placebo i gromadziło dane dotyczące zdarzeń sercowo-naczyniowych. Wśród pacjentów z cukrzycą typu 2 rozyglitazon jest powiązany ze znacznie zwiększonym ryzykiem niewydolności serca, z nieznacznie zwiększonym ryzykiem zawału serca, bez istotnie zwiększonego ryzyka udaru, śmiertelności z powodu chorób sercowo-naczyniowych i śmiertelności niezależnie od przyczyny w porównaniu z placebo lub aktywną grupą kontrolną. Alternatywne metody zmniejszania niepewności w długoterminowych ocenach pragmatycznych, włączanie rozyglitazonu do badań czynnikowych, publikacja danych dotyczących zdarzeń sercowo-naczyniowych z doniesień o zdarzeniach niepożądanych w badaniach dotyczących rozyglitazonu i próba z udziałem rozyglitazonu w kierunku zdarzeń sercowo-naczyniowych wśród osób bez cukrzycy

SSformer: A Lightweight Transformer for Semantic Segmentation

It is well believed that Transformer performs better in semantic segmentation compared to convolutional neural networks. Nevertheless, the original Vision Transformer may lack of inductive biases of local neighborhoods and possess a high time complexity. Recently, Swin Transformer sets a new record in various vision tasks by using hierarchical architecture and shifted windows while being more efficient. However, as Swin Transformer is specifically designed for image classification, it may achieve suboptimal performance on dense prediction-based segmentation task. Further, simply combing Swin Transformer with existing methods would lead to the boost of model size and parameters for the final segmentation model. In this paper, we rethink the Swin Transformer for semantic segmentation, and design a lightweight yet effective transformer model, called SSformer. In this model, considering the inherent hierarchical design of Swin Transformer, we propose a decoder to aggregate information from different layers, thus obtaining both local and global attentions. Experimental results show the proposed SSformer yields comparable mIoU performance with state-of-the-art models, while maintaining a smaller model size and lower compute

The Carboxyl-Terminal Amino Acids Render Pro-Human LC3B Migration Similar to Lipidated LC3B in SDS-PAGE

LC3 is widely used marker for macroautophagy assays. After translation pro-LC3 is processed by Atg4 to expose C-terminal glycine residue for downstream conjugation reactions to accomplish the conversion of LC3-I to LC3-II. SDS-PAGE based Western blot (Wb) is generally utilized to quantify LC3-II levels where the LC3-I band migrates slower than LC3-II. We found that pro-human LC3B migrated at similar rate as LC3B-II in SDS-PAGE. The carboxyl-terminal five amino acids, particularly Lysine122 and Leucine123 of human LC3B play a major role in the faster migration of unprocessed LC3B, rendering it indistinguishable from LC3B-II in Wb assays. The unique faster migration of unprocessed LC3B than LC3B-I is also revealed in mouse LC3B, rat LC3B and rat LC3 but not in human LC3C. Our findings for the first time define pro-LC3 migration patterns for LC3 family member from human, mouse and rat species in SDS-PAGE. These findings provide a reference for pro-LC3 band patterns when Atg4 function is inhibited. © 2013 Wang et al

Investigation of ultrasmall 1 x N AWG for SOI-Based AWG demodulation integration microsystem

Optoelectronic integration technologies based on silicon-on-insulator (SOI) can bring revolutionary change to on-chip arrayed waveguide grating (AWG) demodulation systems. In this study, we present several ultrasmall 1 x N AWGs for an SOI-based AWG demodulation integration microsystem of different scales. The core sizes of the fabricated AWGs are smaller than 400 x 600 μm2. Experimental results match the simulation results, indicating that AWGs have a good transmission spectrum of low crosstalk below -20 dB and low insertion loss below -6.5 dB. The fabricated AWGs can be perfectly applied to improve the integration level and performance of the SOI-based AWG demodulation integration microsystem

Adaptive Random Fourier Features Kernel LMS

We propose the adaptive random Fourier features Gaussian kernel LMS (ARFF-GKLMS). Like most kernel adaptive filters based on stochastic gradient descent, this algorithm uses a preset number of random Fourier features to save computation cost. However, as an extra flexibility, it can adapt the inherent kernel bandwidth in the random Fourier features in an online manner. This adaptation mechanism allows to alleviate the problem of selecting the kernel bandwidth beforehand for the benefit of an improved tracking in non-stationary circumstances. Simulation results confirm that the proposed algorithm achieves a performance improvement in terms of convergence rate, error at steady-state and tracking ability over other kernel adaptive filters with preset kernel bandwidth.Comment: 5 pages, 2 figure

UperFormer: A Multi-scale Transformer-based Decoder for Semantic Segmentation

While a large number of recent works on semantic segmentation focus on designing and incorporating a transformer-based encoder, much less attention and vigor have been devoted to transformer-based decoders. For such a task whose hallmark quest is pixel-accurate prediction, we argue that the decoder stage is just as crucial as that of the encoder in achieving superior segmentation performance, by disentangling and refining the high-level cues and working out object boundaries with pixel-level precision. In this paper, we propose a novel transformer-based decoder called UperFormer, which is plug-and-play for hierarchical encoders and attains high quality segmentation results regardless of encoder architecture. UperFormer is equipped with carefully designed multi-head skip attention units and novel upsampling operations. Multi-head skip attention is able to fuse multi-scale features from backbones with those in decoders. The upsampling operation, which incorporates feature from encoder, can be more friendly for object localization. It brings a 0.4% to 3.2% increase compared with traditional upsampling methods. By combining UperFormer with Swin Transformer (Swin-T), a fully transformer-based symmetric network is formed for semantic segmentation tasks. Extensive experiments show that our proposed approach is highly effective and computationally efficient. On Cityscapes dataset, we achieve state-of-the-art performance. On the more challenging ADE20K dataset, our best model yields a single-scale mIoU of 50.18, and a multi-scale mIoU of 51.8, which is on-par with the current state-of-art model, while we drastically cut the number of FLOPs by 53.5%. Our source code and models are publicly available at: https://github.com/shiwt03/UperForme

Efficient Bi-Level Optimization for Recommendation Denoising

The acquisition of explicit user feedback (e.g., ratings) in real-world recommender systems is often hindered by the need for active user involvement. To mitigate this issue, implicit feedback (e.g., clicks) generated during user browsing is exploited as a viable substitute. However, implicit feedback possesses a high degree of noise, which significantly undermines recommendation quality. While many methods have been proposed to address this issue by assigning varying weights to implicit feedback, two shortcomings persist: (1) the weight calculation in these methods is iteration-independent, without considering the influence of weights in previous iterations, and (2) the weight calculation often relies on prior knowledge, which may not always be readily available or universally applicable. To overcome these two limitations, we model recommendation denoising as a bi-level optimization problem. The inner optimization aims to derive an effective model for the recommendation, as well as guiding the weight determination, thereby eliminating the need for prior knowledge. The outer optimization leverages gradients of the inner optimization and adjusts the weights in a manner considering the impact of previous weights. To efficiently solve this bi-level optimization problem, we employ a weight generator to avoid the storage of weights and a one-step gradient-matching-based loss to significantly reduce computational time. The experimental results on three benchmark datasets demonstrate that our proposed approach outperforms both state-of-the-art general and denoising recommendation models. The code is available at https://github.com/CoderWZW/BOD.Comment: 11pages, 5 figures, 6 table

A Survey on Deep Multi-modal Learning for Body Language Recognition and Generation

Body language (BL) refers to the non-verbal communication expressed through physical movements, gestures, facial expressions, and postures. It is a form of communication that conveys information, emotions, attitudes, and intentions without the use of spoken or written words. It plays a crucial role in interpersonal interactions and can complement or even override verbal communication. Deep multi-modal learning techniques have shown promise in understanding and analyzing these diverse aspects of BL. The survey emphasizes their applications to BL generation and recognition. Several common BLs are considered i.e., Sign Language (SL), Cued Speech (CS), Co-speech (CoS), and Talking Head (TH), and we have conducted an analysis and established the connections among these four BL for the first time. Their generation and recognition often involve multi-modal approaches. Benchmark datasets for BL research are well collected and organized, along with the evaluation of SOTA methods on these datasets. The survey highlights challenges such as limited labeled data, multi-modal learning, and the need for domain adaptation to generalize models to unseen speakers or languages. Future research directions are presented, including exploring self-supervised learning techniques, integrating contextual information from other modalities, and exploiting large-scale pre-trained multi-modal models. In summary, this survey paper provides a comprehensive understanding of deep multi-modal learning for various BL generations and recognitions for the first time. By analyzing advancements, challenges, and future directions, it serves as a valuable resource for researchers and practitioners in advancing this field. n addition, we maintain a continuously updated paper list for deep multi-modal learning for BL recognition and generation: https://github.com/wentaoL86/awesome-body-language

A novel one-pot three-step synthesis of 2-(1-Benzofuran-2-yl)quinoline-3-carboxylic acid derivatives

A facile and efficient one-pot three-step procedure for the preparation of 2-(1-benzofuran-2-yl)quinoline-3-carboxylic acid derivatives is described, featuring three different synthetic transformations, namely Williamson ether synthesis, hydrolysis of an ester group at the quinoline ring C-3 position, and intramolecular electrophilic cyclization reaction between the aldehyde group of salicylaldehyde and the methylene at the quinoline ring C-2 position