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BackgroundInsulin resistance (IR) is a pivotal pathogenic component of metabolic diseases. It is crucial to identify convenient and reliable indicators of insulin resistance for its early detection. This study aimed at assessing the predictive ability of seven novel obesity and lipid-related indices.MethodsA total of 5,847 female and 3,532 male healthy subjects were included in the study. The triglyceride glucose (TyG) index, TyG-body mass index (TyG-BMI), TyG-waist circumference (TyG-WC), lipid accumulation products (LAP), body roundness index (BRI), body adiposity index (BAI), and visceral adiposity index (VAI) were measured and calculated using the established formulae. IR was diagnosed using the homeostatic model assessment of insulin resistance (HOMA-IR) index over the third quantile.ResultsThe levels of all seven lipid-related indices were significantly higher in subjects with higher HOMA-IR values than in those with lower HOMA-IR values. These indices displayed moderate to high effectiveness [receiver operating characteristic (ROC) curve-area under the curve (AUC) > 0.6] in predicting IR. Among them, TyG-BMI (AUC: 0.729), LAP (AUC: 0.708), and TyG-WC (AUC: 0.698) showed the strongest association with HOMA-IR. In the female population, the AUC for TyG-BMI, LAP, and TyG-WC in predicting IR was 0.732, 0.705, and 0.718, respectively. Logistic regression analysis showed the optimal cut-off values of those indicators in predicting IR as follows: TyG-BMI: male subjects – 115.16 [odds ratio (OR) = 6.05, 95% CI: 5.09–7.19], female subjects – 101.58 (OR = 4.55, 95% CI: 4.00–5.16); LAP: male subjects – 25.99 (OR = 4.53, 95% CI: 3.82–5.38), female subjects – 16.11 (OR = 3.65, 95% CI: 3.22–4.14); and TyG-WC: male subjects – 409.43 (OR = 5.23, 95% CI: 4.48–6.24), female subjects – 342.48 (OR = 4.07, 95% CI: 3.59–4.61).ConclusionTyG-index-related parameters and LAP appear to be effective predictors of IR in the Chinese population. Specifically, TyG-BMI may be the most appropriate predictor of IR.

Coal tar pitch (CTP) has become an ideal choice in the preparation of anode precursors for lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs) because of its abundant carbon content, competitive pricing and adjustable structure properties. In this paper, sulfurized pitch-based carbon (SPC-800) was obtained by allowing CTP to react with sulfur at 350 °C and subsequently achieve carbonization at 800 °C. SPC-800 was more disordered and had a larger layer spacing than carbonized CTP (PC-800). Upon utilization as an anode for LIBs, SPC-800 possessed a higher reversible specific capacity (478.1 mAh g−1 at 0.1 A g−1), while utilization in SIBs displayed a capacity of 220.9 mAh g−1 at 20 mA g−1. This work is an important guide to the design of high-performance anodes suitable for use with both LIBs and SIBs.

Lake Erhai, a lake in the early stage of eutrophication, has been threatened by algal blooms (particularly the overproliferation of blue-green algae), which can have an impact on drinking water safety and the lake’s ecosystem. Understanding the governing factors of cyanobacterial blooms is critical for taking timely and effective action during this key eutrophication-transition period. However, long-term records of cyanobacterial bloom and its key dominating factors remain unclear. It is, therefore, essential to understand the bloom dynamics and the driving forces before any control strategies can be determined. We investigated the cyanobacterial phycocyanin concentration variability based on satellite observations from 2003 to 2019, by using the empirical orthogonal function analysis. We observed a decrease in the coverage of the dominant mode of variability in phycocyanin magnitudes compared to the period 2003 to 2011, with variations primarily occurring in the northern bays. The largest variability was identified to be predominant in July, and an apparent timing shift in variability was observed in December 2016 and 2017. The 95% quantile regression model indicated a distinct upper boundary response in cyanobacteria proliferation to the joint Total Nitrogen (TN) and Total Phosphorus (TP) concentrations, which occurred in summer from 2003 to 2011. An apparent response of cyanobacterial bloom to TP was observed during the winters from 2016 to 2019. Additionally, water level and TN: TP ratio played a central role in summer from 2003 to 2011, while from 2016 to 2019, TN: TP ratio was found to dominate in the summer months. In winter, air temperature turned out to be a significant modulating factor compared to water level. Our results suggest that implementing a phosphorus reduction strategy, while controlling TN: TP ratio and suitable water level manipulation, should be considered to ensure the sustainability of Lake Erhai, especially under a consistent global warming scenario.

PurposeThis study aimed to clarify the effect of early glucocorticoid (GC) application on achieving minimal manifestation (MM) status or better in the treatment of myasthenia gravis (MG) in the early clinical phase.MethodsA retrospective analysis was performed using data from 336 patients with MG who received GC therapy from January 2015 to September 2022 in the Zhengzhou University Henan Institute of Medical and Pharmaceutical Sciences Myasthenia Gravis Biobank (ZMB). Patients were divided into two groups: the early mono-GC group (treated with GC within 6 months of MG onset) and the delayed mono-GC group.ResultsKaplan–Meier analysis showed that the early mono-GC group achieved MM status earlier and more frequently than the delayed mono-GC group (log-rank test, p = 0.0082; hazard ratio [HR], 1.66; p = 0.011). The early mono-GC group had a lower maintenance oral GC dose than the delayed mono-GC group. In multivariate Cox regression analysis, early mono-GC (HR, 1.50; p = 0.043), early-onset MG (EOMG) (HR, 1.74; p = 0.034), and ocular MG (OMG) (HR, 1.90; p = 0.007) were associated with MM status or better. In conclusion, early mono-GC, EOMG, and OMG were positive predictors of treatment goals. In EOMG, OMG, and acetylcholine receptor antibody-positive MG (AChR-MG) subgroups, the maintenance oral GC doses in the early mono-GC group were significantly lower than the doses in the delayed mono-GC group (p < 0.05).ConclusionEarly intervention with GC led to better long-term outcomes and reduced the necessary maintenance dose of oral GC for patients with MG. EOMG and OMG were positive predictors of MM status or better with mono-GC.

Background: The prevalence of gestational diabetes mellitus (GDM) has been increasing globally, which has led to substantial implications for long-term maternal health including diabetes. The present study aimed to explore the rate of postpartum glucose screening (PGS) of women with GDM based on WeChat management and explore factors affecting the rate of PGS and impaired glucose regulation (IGR). Methods: In this prospective trial, GDM patients were enrolled in our WeChat platform groups. Demographic and medical data were collected at the baseline surveys and follow-up visits. GDM patients were managed throughout their pregnancies via the WeChat platform. We sent messages, involving multidisciplinary care, medical nutritional therapy, and glucose monitoring every three days in the chat groups, and reminded them to go to the hospital to complete PGS. Questionnaires about PGS via the WeChat platform were sent to those women who had delivered within 4 to 12 weeks postpartum. Answers to the questionnaires [understanding the necessity of postpartum oral glucose tolerance testing (OGTT) screening, and the results of 42-day postpartum OGTT screening, as well as the reasons for failing to finish postpartum screening, and the ways to get nutrition knowledge for GDM patients] were collected. Results: From 1 January 2016 to 31 August 2019, 490 participants were included in our WeChat groups, 375 of whom completed questionnaires. Among the 375 participants, 277 (73.9%) had completed post-partum 75 g OGTT, 202 (72.9%) had normal glucose levels, and 75 (27.1%) had impaired glucose regulation (IGR). Univariable logistic analysis and stepwise regression analysis demonstrated that a previous history of GDM and cesarean delivery were the two variables influencing PGS (odds ratio (OR): 0.44, 95% confidence interval (95% CI): 0.20–0.94; OR: 1.88, 95% CI: 1.04–3.39, respectively). Insulin treatment during pregnancy and cesarean delivery were found to have a significant association with postpartum IGR (OR: 3.74, 95% CI: 1.97–7.08; OR: 1.83, 95% CI: 1.02–3.28, respectively). Conclusions: The WeChat messaging platform may be a useful tool to promote postpartum OGTT screening in women with GDM. Women who failed to return for PGS were more likely to have prior GDM than those who returned for PGS. Women who had postpartum IGR were more likely to use insulin treatment during pregnancy and more likely to deliver by cesarean delivery compared to those who had normal postpartum glucose results. Clinical Trial Registration: The present study was registered at ClinicalTrials.gov Protocol Registration and Results System https://classic.clinicaltrials.gov/ (No. NCT02893072).

ObjectiveThe Global Leader Initiative on Malnutrition (GLIM) criteria have been recommended for malnutrition diagnosis recently, for which the first step is malnutrition risk screening with any validated tool. This study aims to investigate the incidence of nutritional risk and malnutrition in Crohn’s disease inpatients and compare the suitability of Nutritional Risk Screening 2002 (NRS-2002) and Malnutrition Universal Screening Tool (MUST) as the first-step screening tool for GLIM criteria.MethodsWe retrospectively analyzed the clinical data of Crohn’s disease inpatients in our hospital from August 2016 to December 2019. NRS-2002 and MUST were used for nutritional screening at the time of admission. GLIM and Patient Generated-Subjective Global Assessment (PG-SGA) were used for malnutrition assessment, respectively. Patients without nutritional risk screened by NRS-2002 but with malnutrition risk screened by MUST were especially screened out. The appendicular skeletal muscle mass index (ASMI), fat-free mass index (FFMI), body fat percent (BFP), and body cell mass (BCM) were measured by the Biospace Inbody S10 composition analyzer.ResultsA total of 146 Crohn’s disease patients were enrolled, of which 62.3 and 89.7% had nutritional or malnutrition risk according to NRS-2002 and MUST, respectively. The prevalence of malnutrition assessed by GLIM was 59.6% (87 cases) and 82.2% (120 cases) when NRS-2002 and MUST were used as the first step of GLIM respectively. Meanwhile, 99 patients (67.8%) had malnutrition when assessed by PG-SGA. There were 41 patients who were not at nutritional risk according to NRS-2002 but were at malnutrition risk determined by MUST. At last, 33 patients were GLIM-defined, and 16 patients were PG-SGA-defined malnutrition among the 41 patients.ConclusionThe nutritional risk or malnutrition is common in Crohn’s disease inpatients. It is recommended to use a variety of nutritional assessment tools for Crohn’s disease inpatients. MUST can be used as a good supplement for the patients with a score of NRS-2002 lower than 3 in order to decrease the miss rate of GLIM-defined malnutrition.

Preterm birth is a worldwide problem that affects infants throughout their lives significantly. Therefore, differentiating brain disorders, and further identifying and characterizing the corresponding biomarkers are key issues to investigate the effects of preterm birth, which facilitates the interventions for neuroprotection and improves outcomes of prematurity. Until now, many efforts have been made to study the effects of preterm birth; however, most of the studies merely focus on either functional or structural perspective. In addition, an effective framework not only jointly studies the brain function and structure at a group-level, but also retains the individual differences among the subjects. In this study, a novel dense individualized and common connectivity-based cortical landmarks (DICCCOL)-based multi-modality graph neural networks (DM-GNN) framework is proposed to differentiate preterm and term infant brains and characterize the corresponding biomarkers. This framework adopts the DICCCOL system as the initialized graph node of GNN for each subject, utilizing both functional and structural profiles and effectively retaining the individual differences. To be specific, functional magnetic resonance imaging (fMRI) of the brain provides the features for the graph nodes, and brain fiber connectivity is utilized as the structural representation of the graph edges. Self-attention graph pooling (SAGPOOL)-based GNN is then applied to jointly study the function and structure of the brain and identify the biomarkers. Our results successfully demonstrate that the proposed framework can effectively differentiate the preterm and term infant brains. Furthermore, the self-attention-based mechanism can accurately calculate the attention score and recognize the most significant biomarkers. In this study, not only 87.6% classification accuracy is observed for the developing Human Connectome Project (dHCP) dataset, but also distinguishing features are explored and extracted. Our study provides a novel and uniform framework to differentiate brain disorders and characterize the corresponding biomarkers.

Brain–computer interface (BCI) technology enables humans to interact with computers by collecting and decoding electroencephalogram (EEG) from the brain. For practical BCIs based on EEG, accurate recognition is crucial. However, existing methods often struggle to achieve a balance between accuracy and complexity. To overcome these challenges, we propose 1D convolutional neural networks with bidirectional recurrent attention unit network (1DCNN-BiRAU) based on a random segment recombination strategy (segment pool, SegPool). It has three main contributions. First, SegPool is proposed to increase training data diversity and reduce the impact of a single splicing method on model performance across different tasks. Second, it employs multiple 1D CNNs, including local and global models, to extract channel information with simplicity and efficiency. Third, BiRAU is introduced to learn temporal information and identify key features in time-series data, using forward–backward networks and an attention gate in the RAU. The experiments show that our model is effective and robust, achieving accuracy of 99.47% and 91.21% in binary classification at the individual and group levels, and 90.90% and 92.18% in four-category classification. Our model demonstrates promising results for recognizing human motor imagery and has the potential to be applied in practical scenarios such as brain–computer interfaces and neurological disorder diagnosis.

Background and Aims: To investigate the relationship among maternal demographic and clinical characteristics, gestational and postpartum oral glucose tolerance test (ppOGTT) results in patients with gestational diabetes mellitus (GDM).Methods: Patients with gestational diabetes mellitus from January 1, 2016, to August 31, 2019, were enrolled. General characteristics, dietary energy intake, pre-gestational body mass index (BMI), gestational oral glucose tolerance test (gOGTT), and 42 days ppOGTT results of all participants were collected. The relationships among maternal clinical characteristics, fasting glucose of gOGTT (gOGTT-FPG), 1 h postprandial glucose of gOGTT (gOGTT-1h PG), 2 h postprandial glucose of gOGTT (gOGTT-2h PG), and maternal postpartum glucose outcomes were evaluated.Results: A total of 156 patients with GDM were included in this study. Among them, 73.7% had inadequate daily total energy intake, an insufficient ratio of carbohydrates and protein, and an excessive fat ratio. Most of the patients (81.4%) were normal in their ppOGTT examination. Less than 20% of the patients (16.7%) were in the pre-diabetic situation, and 3 patients (1.9%) had diabetes. Pre-pregnancy BMI of patients with GDM was a risk factor for increased gOGTT-FPG levels. Those who were overweight before pregnancy had a greater risk for a higher gOGTT-FPG compared to those who had normal pre-pregnancy BMI (P = 0.021, odds ratio [OR] = 4.583). Abnormal gOGTT-2hPG was a risk factor for abnormal ppOGTT (P = 0.04). Those who had an elevated gOGTT-2hPG (≧8.5 mmol/L) had a 2.426 times higher risk for abnormal ppOGTT than those who had normal gOGTT-2hPG (

Objective: This study was to explore the role and necessity of muscle mass [fat-free mass index (FFMI) and appendicular skeletal muscle index (ASMI) measured by bioelectrical impedance analysis (BIA)] in nutritional status evaluation of patients with locally advanced (III, IVa) nasopharyngeal carcinoma (NPC).Methods: One hundred and thirty locally advanced NPC patients were recruited. Their nutritional status was assessed by albumin (ALB), body mass index (BMI), Nutritional Risk Screening 2002 (NRS 2002), Patient generated-Subjective Global Assessment (PG-SGA), and muscle mass. Consistency test and McNemar test were used to evaluate the consistency of muscle mass with ALB, BMI, NRS 2002, and PG-SGA, and correlation analysis was performed on muscle mass and PG-SGA or BMI.Results: 61/130 (46.9%) of the patients had nutritional risks according to NRS 2002, 68/130 (53.1%) of the patients had malnutrition according to PG-SGA assessment. FFMI and ASMI could determine the loss of muscle mass that cannot be detected by albumin (30.2 and 65.6%), BMI (28.0 and 35.3%), NRS 2002 (26.1 and 25.0%), and PG-SGA (18.6 and 55.6%). McNemar test showed that the malnutrition results assessed by FFMI and BMI were inconsistent (P

Asymptomatic individuals with coronavirus disease (COVID-19) have been identified via nucleic acid testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); however, the epidemiologic characteristics and viral shedding pattern of asymptomatic patients remain largely unknown. In this study, serological testing was applied when identifying nine asymptomatic cases of COVID-19 who showed persistent negative RT-PCR test results for SARS-CoV-2 nucleic acid and no symptoms of COVID-19. Two asymptomatic cases were presumed to be index patients who had cleared the virus when their close contacts developed symptoms of COVID-19. Three of the asymptomatic cases were local individuals who spontaneously recovered before their presumed index patients developed symptoms of COVID-19. This report presents the epidemiologic and clinical characteristics of asymptomatic individuals with SARS-CoV-2 infection that were undetected on RT-PCR tests in previous epidemiologic investigations probably due to the transient viral shedding duration.

Rosmarinus officinalis L. is a perennial evergreen subshrub of the family Lamiaceae in the class Dicotyledoneae. It is native to Europe and the Mediterranean coast of northern Africa and has broad development prospects in the food, medicinal, and chemical industries. Although the chemical composition of R. officinalis L. has been reported, the classifications and summary of the structure of its compounds are not sufficient. In this article, we summarize the chemical components of R. officinalis L. that have been reported over the past five years, including flavonoids, terpenoids, and phenylpropanoids, and their structure, and we introduce the applications of R. officinalis L., so as to provide a reference and scientific basis for the chemical composition analysis, structural identification, development and utilization of R. officinalis L..