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Association between cardiometabolic index and albuminuria: Evidence from NHANES 2017–2020
Introduction Albuminuria is a crucial marker of kidney damage and serves as an early indicator of the risk for chronic kidney disease (CKD). Recent studies have suggested that the cardiometabolic index (CMI), could be valuable for screening renal insufficiency. However, the relationship between CMI and albuminuria remains underexplored. Therefore, the aim of this study was to investigate the association between CMI and albuminuria, with the goal of providing new insights for the clinical diagnosis, assessment, and early intervention of kidney disease. Methods The National Health and Nutrition Examination Survey (NHANES) for the period between 2017–2020 provided the data for this cross-sectional investigation. Triglyceride (TG) (mmol/L)/High density lipid-cholesterol (HDL-C) (mmol/L) × Waist height ratio (WHtR) was the formula used for calculating CMI. Using multifactorial logistic regression, the independent connection between albuminuria and CMI was investigated. The threshold effect was determined by means of a two-stage linear regression model. Additionally, subgroup analysis and interaction tests were carried out. Results A total of 3,339 participants were included, and 12.38% of them had albuminuria. As the CMI quartiles grew (quartile 1: 7.78%, quartile 2: 13.43%, quartile 3: 12.93%, quartile 4: 17.01%), so did the probability of albuminuria. The results of adjusted model 3 showed that a greater probability of albuminuria prevalence was strongly correlated with CMI (OR = 2.26, 95% CI: 1.58–3.23). This association held true for all subgroups (all P for trend > 0.05). Furthermore, with a two-stage linear regression model with an inflection point of 0.92, we discovered a nonlinear relationship between CMI and albuminuria. Conclusions Our findings indicate that CMI levels are significantly associated with the risk of albuminuria prevalence, suggesting that CMI could serve as a valuable biomarker for assessing the risk of albuminuria.
Pressure-induced amorphous-amorphous transition in pre-densified silica glass: an experimental exploration of the potential energy landscape
Fusion of motion smoothing algorithm and motion segmentation algorithm for human animation generation
In the field of human animation generation, the existing technology is often limited by the dependence on large-scale data sets, and it is difficult to capture subtle dynamic changes when processing motion transitions, resulting in insufficient animation fluency and realism. In order to improve the naturalness and diversity of human animation generation, a method combining motion smoothing algorithm and motion segmentation algorithm is proposed. Firstly, the tree-level model based on human skeleton topology and bidirectional unbiased Kalman filter are used for noise reduction pre-processing of motion data to improve the accuracy of motion capture. Then, combining the discriminant analysis algorithm based on sparse reconstruction and the multi-scale temporal association segmentation algorithm, the key motion segments of the behavior pattern change are identified adaptively. The experimental results show that the accuracy of the proposed algorithm reaches 0.96 in coarse-grained segmentation and 0.91 in fine-grained segmentation, and the segmentation time is 15 seconds on average, which significantly exceeds the prior art. In addition, the algorithm shows superior results in color fidelity, detail representation, motion fluency, frame-to-frame coherence, overall animation consistency, action authenticity, and character expressiveness, and the average user satisfaction is above 0.85. The research not only enhances the naturalness and diversity of human body animation, but also provides a new impetus for technological advances in computer graphics, virtual reality and augmented reality.
Cost-effectiveness of open vs. laparoscopic gastrectomy for locally advanced gastric cancer in China
Policy-mix and SME innovation: Evidence from China
The question of whether and how innovation policy can effectively influence innovation in small and medium-sized enterprises (SMEs) has received limited attention in academic research. This study takes a first step towards filling this gap by examining how innovation policy and policy mixes influence innovation in SMEs. This paper takes China’s National Equities Exchange and Quotations (NEEQ) listed enterprises from 2011 to 2020 as the research sample, and uses the Multi-Level Treatment Effect (MLTE) model to investigate the actual impact of different innovation policies on Small and Medium-sized Enterprise innovation and the heterogeneity of policy effects from the perspective of substantive and strategic innovation. It is found that innovation policies can obviously improve the innovation of SMEs, in particular the substantive innovation, and the effect of policy-mix in stimulating SME innovation is stronger than that of single innovation policy. SMEs that show “strong motivation” and “high ability” in innovation are more likely to be favored by relevant government agencies, and have a greater probability of becoming the implementation targets of innovation policies. As far as single innovation policies are concerned, government subsidy is better than tax incentive for high-tech SMEs, while tax incentive has a stronger role in promoting innovation than government subsidy for non-high-tech SMEs. By illuminating these differentiated impacts and the conditions under which innovation policies are most effective, this work not only advances our fundamental understanding of policy-driven innovation ecosystems but also offers actionable guidance to policymakers seeking to optimize the allocation of support to foster transformative innovation in the SME sector.
The abscisic acid signaling negative regulator OsPP2C68 confers drought and salinity tolerance to rice
Poor sleep patterns are associated with the prevalence of benign prostatic hyperplasia in US aged 40 and older: A cross-sectional study based on NHANES
Background The connection between sleep patterns (sleep duration, trouble sleeping and sleep disorders) and benign prostatic hyperplasia, commonly referred to as BPH, is not yet clear. Our aim is to investigate the impact of sleep patterns on BPH risk in US men aged 40 and older. Methods We performed an observational analysis using data from NHANES 2005–2008 on males aged 40 and up, including a total of 2,555 participants. After accounting for confounding variables, we applied weighted multivariable logistic regression to assess the relationship between sleep patterns and BPH risk according to the complex multi-stage sampling design of NHANES. Results In this study, 11.79% of the 2,555 American participants aged over 40 reported to have BPH. after adjusting for confounding variables, multivariable logistic regression analysis showed that short sleep duration, compared to healthy sleep duration (7–9 hours), was linked to a significantly higher risk of BPH (OR: 1.92, 95% CI: 1.42–2.41). Trouble sleeping and sleep disorder were also strongly associated with BPH. Moreover, there appears to be a stronger association among those with poor sleep patterns (OR: 2.07, 95% CI: 1.46–2.91). Conclusion Poor sleep patterns in men over 40 in the U.S. is significantly linked to a higher incidence of benign prostatic hyperplasia.
Precipitation isotopes and monsoon dynamics in the core monsoon zone of India
Responses of soil labile organic carbon stocks and the carbon pool management index to different vegetation restoration types in the Danxia landform region of southwest China
Soil organic carbon (SOC) is an important index for evaluating soil quality in the process of ecological restoration. It plays an important role in increasing soil carbon storage, improving soil texture, and promoting plant growth. Nevertheless, dating the variation in SOC and labile SOC fractions (LOCFs) during ecological restoration processes has not been sufficiently elucidated. To enrich our comprehension of the responses of SOC and its labile fractions to different vegetation restoration types, five vegetation restoration types were selected in the Danxia landform region of southwest China, namely, shrub (SH), bamboo forest (BF), Chinese fir forest (CFF), evergreen broad-leaved forest (EBF), and mixed coniferous-broadleaf forest (MCBF). The concentrations and stocks of SOC and LOCFs, such as dissolved organic carbon (DOC), microbial biomass organic carbon (MBC), and easily oxidizable organic carbon (EOC), and the carbon pool management index (CPMI) were investigated. Results showed that the different vegetation restoration types significantly influenced SOC stocks (P < 0.05), and the concentrations and stocks of SOC and LOCFs decreased with increasing soil depth in different vegetation types, except for MBC in BF and CFF. Additionally, BF and EBF had significantly higher total SOC stocks (92.75 t ha − 1 and 60.13 t ha − 1) compared with the three other vegetation types (26.18–47.48 t ha-1) at a depth of 0–30 cm. The largest total DOC stock was observed in EBF, while BF and EBF had significantly higher MBC and EOC stocks than SH, CFF, and MCBF (P < 0.05). Compared with SH, the CPMI increased by 49.7%, 32.9%, and 35.2% in BF, CFF, and EBF, respectively, except for the MCBF. SOC and LOCFs were closely related to soil physicochemical properties, and total nitrogen, total phosphorus and moisture had a pronounced effect on them. However, higher SOC and LOCFs stocks, and CPMI were observed in BF and EBF than in the other vegetation types. This result suggests that the two plant types exhibited better ability to sequester carbon than the other vegetation types. Overall, vegetation restoration promoted the accumulation of both SOC and its fractions, the results of which varied among the different vegetation types.
Study on the key factors of public participation in low carbon city construction and willingness to pay
Measurement accuracy and cutoffs for predicting primary aldosteronism diagnosis using Lumipulse® for renin and aldosterone measurements
Objectives Primary aldosteronism (PA) diagnosis is a multistep process that begins with screening based on the plasma aldosterone-to-renin ratio (ARR). As cut-off values depend on the methods used, the aim of this study was 1) to validate the performances of new aldosterone and renin chemiluminescent immuno-assays using the Lumipulse® analyzer (Fujirebio®) and then 2) to define the criteria to distinguish PA patients from those with essential hypertension (EH). Methods This monocentric retrospective study included 297 patients admitted to Georges Pompidou European Hospital, France, between January 2021 and December 2022 for the assessment of hypertension etiology. We evaluated analytical performances and compared Lumipulse® results for plasma renin (PRC) and plasma aldosterone (PAC) concentrations (n = 196) as well as 24-hour urine aldosterone excretion (24UA) (n = 201) to the reference methods (PRC: Liaison XL® Diasorin®; PAC and 24UA: LC-MS/MS). Receiver operating characteristic curve analysis was used to define the cut-off values for ARR, PAC, and 24UA to distinguish between PA and EH patients. Results Analytical characteristics were in accordance with requested performances. Our results showed excellent correlations between Lumipulse® and our reference methods. We propose cut-off values of 35 pmol/mIU as the ARR, 260 pmol/L for PAC, and 49 nmol/24h for 24UA for predicting PA diagnosis. Conclusions Lumipulse® (Fujirebio®) is a reliable alternative for measuring PRC, PAC, and 24UA for predicting PA diagnosis with the use of corresponding cut-off values for each criterion.
First record of Cinara todocola (Hemiptera: Aphididae) on endangered Christmas tree in South Korea: morphology, biology, and global invasion potential
Wastewater surveillance overcomes socio-economic limitations of laboratory-based surveillance when monitoring disease transmission: The South African experience during the COVID-19 pandemic
Wastewater and environmental surveillance has been promoted as a communicable disease surveillance tool because it overcomes inherent biases in laboratory-based communicable disease surveillance. Yet, little empirical evidence exists to support this notion, and it remains largely an intuitive, though highly plausible hypothesis. Our interdisciplinary study uses WES data to show evidence for underreporting of SARS-CoV-2 in the context of measurable and statistically significant associations between economic conditions and SARS-CoV-2 incidence and testing rates. We obtained geolocated, anonymised, laboratory-confirmed SARS-CoV-2 cases, wastewater SARS-CoV-2 viral load data and socio-demographic data for Gauteng Province, South Africa. We spatially located all data to create a single dataset for sewershed catchments served by two large wastewater treatment plants. We conducted epidemiological, persons infected and principal component analysis to explore the relationships between variables. Overall, we demonstrate the co-contributory influences of socio-economic indicators on access to SARS-CoV-2 testing and cumulative incidence, thus reflecting that apparent incidence rates mirror access to testing and socioeconomic considerations rather than true disease epidemiology. These analyses demonstrate how WES provides valuable information to contextualise and interpret laboratory-based epidemiological data. Whilst it is useful to have these associations established for SARS-CoV-2, the implications beyond SARS-CoV-2 are legion for two reasons, namely that biases inherent in clinical surveillance are broadly applicable across pathogens and all pathogens infecting humans will find their way into wastewater albeit in varying quantities. WES should be implemented to strengthen surveillance systems, especially where economic inequalities limit interpretability of conventional surveillance data.
Development of weighted residual RNN model with hybrid heuristic algorithm for movement recognition framework in ambient assisted living
Oseltamivir-induced hepatotoxicity: A retrospective analysis of the FDA adverse event reporting system
Assessing the potential for oseltamivir-induced liver damage is essential to ensure its safe administration. The aim of this study was to examine the association between hepatotoxicity and oseltamivir use and to describe the features of oseltamivir-induced hepatotoxicity. Data were obtained from the Adverse Event Reporting System of the US Food and Drug Administration (FAERS). Disproportionality and proportionality analyses were performed to evaluate the safety profile of oseltamivir-related hepatotoxicity and the occurrence of hepatotoxicity-related adverse events across sex and age groups. The FAERS recorded 20,340,254 adverse event reports between 2004 and 2023, of which 16,960,996 reports were included in the analysis. We identified 14 types of oseltamivir-related adverse events that were hepatotoxic and showed positive signals. The most frequently reported adverse event was abnormal hepatic function (n = 54), and the most severe adverse event was fulminant hepatitis. Compared with that for male individuals, the reporting odds ratio (ROR) was 0.5 for female individuals; and for male individuals, the ROR, compared with that for female individuals, was 4.19. The median time to hepatotoxic adverse events, excluding mixed liver injury, was < 5 days. Oseltamivir can cause liver toxicity, which is influenced by sex and age. Liver function tests and monitoring for signs of liver disease are crucial when using oseltamivir.
Using machine learning to explore the efficacy of administrative variables in prediction of subjective-wellbeing outcomes in New Zealand
Chinese medical named entity recognition utilizing entity association and gate context awareness
Recognizing medical named entities is a crucial aspect of applying deep learning in the medical domain. Automated methods for identifying specific entities from medical literature or other texts can enhance the efficiency and accuracy of information processing, elevate medical service quality, and aid clinical decision-making. Nonetheless, current methods exhibit limitations in contextual awareness and insufficient consideration of contextual relevance and interactions between entities. In this study, we initially encode medical text inputs using the Chinese pre-trained RoBERTa-wwm-ext model to extract comprehensive contextual features and semantic information. Subsequently, we employ recurrent neural networks in conjunction with the multi-head attention mechanism as the primary gating structure for parallel processing and capturing inter-entity dependencies. Finally, we leverage conditional random fields in combination with the cross-entropy loss function to enhance entity recognition accuracy and ensure label sequence consistency. Extensive experiments conducted on datasets including MCSCSet and CMeEE demonstrate that the proposed model attains F1 scores of 91.90% and 64.36% on the respective datasets, outperforming other related models. These findings confirm the efficacy of our method for recognizing named entities in Chinese medical texts.
Urban and rural disparities in stroke prediction using machine learning among Chinese older adults
Effects of dry cupping on exercise, autonomic activity and sleep in baseball players during preseason and in-season conditioning
Background Cupping therapy has been shown to alleviate muscle fatigue, sustain exercise capacity, enhance post-exercise recovery of autonomic activity, and improves sleep quality. However, variations in athletes’ training intensity, competition pressure, and fatigue levels throughout a sports season remain underexplored. Few studies have investigated whether the health benefits of cupping differ across various phases of a sports season. This study aimed to examine the effects of short-term cupping on athletes during preseason conditioning (PSC) and in-season conditioning (ISC). Methods Forty university baseball players were recruited and randomly assigned to either the cupping (dry cupping at − 400 mmHg) or sham (dry cupping at − 100 mmHg) group. Cupping was applied to the upper back and shoulders for 15 minutes, twice a week for 8 consecutive weeks (4 weeks each during PSC and ISC). Results Cupping had no significant effect on upper-extremity function during either PSC or ISC. Exercise tests during PSC and ISC revealed no postintervention changes in peak power, peak oxygen consumption, or anaerobic threshold. However, cupping during PSC improved postexercise recovery of low-frequency power (LF; P = .013; a component of heart rate variability) and that during ISC improved recovery of the LF/high-frequency power ratio (P = .004) and LF% (P = .037). Additionally, cupping during PSC notably enhanced daytime function, as measured by the Pittsburgh sleep quality index (P = .026). Conclusions The benefits of cupping therapy vary between PSC and ISC. Cupping during PSC and ISC notably improved the postexercise recovery of autonomic and sympathetic activities, respectively. However, improvements in sleep quality were only observed during PSC.