Frequency, severity, and risk factors related to sexual dysfunction in Chinese women with T2D

Background: The aim of the present study was to assess the frequency and severity of female sexual dysfunction (FSD) in those with T2D (T2D) compared with non-diabetic controls. In addition, risk factors for FSD were analyzed. Methods: Sexual dysfunction, measured using the Female Sexual Function Index (FSFI), was evaluated using a questionnaire in 184 women with T2D and 146 non-diabetic controls at three study sites in China. In the T2D group, FSD was examined by education level, correlations between FSD and other variables were analyzed, and risk factors were studied. Results: The frequency of FSD in the T2D group was 75.0%, much higher than in the control group (56.2%; P = 0.001). The severity of FSD in the T2D group was 17.84 ± 8.47 (mean ± SD), significantly lower than in the control group (21.14 ± 8.08; P = 0.001). In patients with T2D, being older (P = 0.001), taking oral antidiabetic medications (P = 0.013), and having diabetic neuropathy (P = 0.036) were risk factors for FSD. Conclusions: The rate of FSD is high in China and, as seen in the literature, more severe in diabetics than non-diabetics. Being older, taking oral antidiabetic medications, and diabetic neuropathy are risk factors for FSD

MetaGPT: Meta Programming for Multi-Agent Collaborative Framework

Recently, remarkable progress has been made in automated task-solving through the use of multi-agents driven by large language models (LLMs). However, existing works primarily focuses on simple tasks lacking exploration and investigation in complicated tasks mainly due to the hallucination problem. This kind of hallucination gets amplified infinitely as multiple intelligent agents interact with each other, resulting in failures when tackling complicated problems.Therefore, we introduce MetaGPT, an innovative framework that infuses effective human workflows as a meta programming approach into LLM-driven multi-agent collaboration. In particular, MetaGPT first encodes Standardized Operating Procedures (SOPs) into prompts, fostering structured coordination. And then, it further mandates modular outputs, bestowing agents with domain expertise paralleling human professionals to validate outputs and reduce compounded errors. In this way, MetaGPT leverages the assembly line work model to assign diverse roles to various agents, thus establishing a framework that can effectively and cohesively deconstruct complex multi-agent collaborative problems. Our experiments conducted on collaborative software engineering tasks illustrate MetaGPT's capability in producing comprehensive solutions with higher coherence relative to existing conversational and chat-based multi-agent systems. This underscores the potential of incorporating human domain knowledge into multi-agents, thus opening up novel avenues for grappling with intricate real-world challenges. The GitHub repository of this project is made publicly available on: https://github.com/geekan/MetaGP

A nanozyme tag enabled chemiluminescence imaging immunoassay for multiplexed cytokine monitoring

We report a new concept of a chemiluminescence imaging nanozyme immunoassay (CINIA), in which nanozymes are exploited as catalytic tags for simultaneous multiplex detection of cytokines. The CINIA provides a novel and universal nanozyme-labeled multiplex immunoassay strategy for high-throughput detection of relevant biomarkers and further disease diagnosis

A nanozyme tag enabled chemiluminescence imaging immunoassay for multiplexed cytokine monitoring

We report a new concept of a chemiluminescence imaging nanozyme immunoassay (CINIA), in which nanozymes are exploited as catalytic tags for simultaneous multiplex detection of cytokines. The CINIA provides a novel and universal nanozyme-labeled multiplex immunoassay strategy for high-throughput detection of relevant biomarkers and further disease diagnosis

Development and validation of a nomogram model for predicting unfavorable functional outcomes in ischemic stroke patients after acute phase

IntroductionPrediction of post-stroke functional outcome is important for personalized rehabilitation treatment, we aimed to develop an effective nomogram for predicting long-term unfavorable functional outcomes in ischemic stroke patients after acute phase.MethodsWe retrospectively analyzed clinical data, rehabilitation data, and longitudinal follow-up data from ischemic stroke patients who underwent early rehabilitation at multiple centers in China. An unfavorable functional outcome was defined as a modified Rankin Scale (mRS) score of 3–6 at 90 days after onset. Patients were randomly allocated to either a training or test cohort in a ratio of 4:1. Univariate and multivariate logistic regression analyses were used to identify the predictors for the development of a predictive nomogram. The area under the receiver operating characteristic curve (AUC) was used to evaluate predictive ability in both the training and test cohorts.ResultsA total of 856 patients (training cohort: n = 684; test cohort: n = 172) were included in this study. Among them, 518 patients experienced unfavorable outcomes 90 days after ischemic stroke. Trial of ORG 10172 in Acute Stroke Treatment classification (p = 0.024), antihypertensive agents use [odds ratio (OR) = 1.86; p = 0.041], 15-day Barthel Index score (OR = 0.930; p < 0.001) and 15-day mRS score (OR = 13.494; p < 0.001) were selected as predictors for the unfavorable outcome nomogram. The nomogram model showed good predictive performance in both the training (AUC = 0.950) and test cohorts (AUC = 0.942).ConclusionThe constructed nomogram model could be a practical tool for predicting unfavorable functional outcomes in ischemic stroke patients underwent early rehabilitation after acute phase

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

What and Where the Themes Dominate in Image

The image captioning is to describe an image with natural language as human, which has benefited from the advances in deep neural network and achieved substantial progress in performance. However, the perspective of human description to scene has not been fully considered in this task recently. Actually, the human description to scene is tightly related to the endogenous knowledge and the exogenous salient objects simultaneously, which implies that the content in the description is confined to the known salient objects. Inspired by this observation, this paper proposes a novel framework, which explicitly applies the known salient objects in image captioning. Under this framework, the known salient objects are served as the themes to guide the description generation. According to the property of the known salient object, a theme is composed of two components: its endogenous concept (what) and the exogenous spatial attention feature (where). Specifically, the prediction of each word is dominated by the concept and spatial attention feature of the corresponding theme in the process of caption prediction. Moreover, we introduce a novel learning method of Distinctive Learning (DL) to get more specificity of generated captions like human descriptions. It formulates two constraints in the theme learning process to encourage distinctiveness between different images. Particularly, reinforcement learning is introduced into the framework to address the exposure bias problem between the training and the testing modes. Extensive experiments on the COCO and Flickr30K datasets achieve superior results when compared with the state-of-the-art methods