Impact of age on the rotenone-induced sporadic Parkinson's disease model using Drosophila melanogaster

Rotenone, a naturally occurring toxin, has been used to induce sporadic Parkinson's disease (PD) in Drosophila melanogaster for decades. However, the age of flies varies considerably between studies in this model. To investigate the impact of age on the rotenone-induced PD model, we collected male flies at the age of 1, 5, 7, and 10 days post-eclosion, respectively. Then, flies were immediately exposed to a feeding medium supplemented with 250 μM rotenone for seven days. The motor ability of Drosophila was detected by negative geotaxis assay, and the number of dopamine (DA) neurons and tyrosine hydroxylase (TH) expression levels were evaluated. The results showed that both the motor deficits and mortality increased with age. The flies older than five days showed typical PD features, including the loss of DA neurons, decreased TH expression levels, and decreased locomotive ability. However, 1-day-old flies displayed an unstable motor deficit and little TH expression changes after seven days of rotenone exposure. Lastly, after 7 days of exposure to rotenone, the death rate of flies rapidly increased with increasing starting age. The death rates of 1-, 5-, 7-, and 10-days old flies were 10.0%, 22.8%, 41.5%, and 50.4%, respectively. The findings of this study suggest that age is a crucial factor impacting the Drosophila PD model. This information provides a reference for the age selection to use this model for future studies.

A sex- and site-specific relationship between body mass index and osteoarthritis: evidence from observational and genetic analyses

OBJECTIVE

We primarily aimed to investigate whether there are phenotypic and genetic links underlying body mass index (BMI) and overall osteoarthritis (OA). We then intended to explore whether the relationships differ across sexes and sites.

METHOD

We first evaluated the phenotypic association between BMI and overall OA using data from the UK Biobank. We then investigated the genetic relationship leveraging summary statistics of the hitherto largest genome-wide association studies performed for BMI and overall OA. Finally, we repeated all analyses in a sex- (female, male) and site- (knee, hip, spine) specific manner.

RESULTS

Observational analysis suggested an increased hazard of diagnosed OA per 5 kg/m² increment in BMI (hazard ratio = 1.38, 95% confidence interval (CI) = 1.37-1.39). A positive overall genetic correlation was observed for BMI and OA (r_g = 0.43, P = 4.72 × 10^-133), corroborated by 11 significant local signals. Cross-trait meta-analysis identified 34 pleiotropic loci shared between BMI and OA, of which seven were novel. Transcriptome-wide association study revealed 29 shared gene-tissue pairs, targeting nervous, digestive, and exo/endocrine systems. Mendelian randomization demonstrated a robust BMI-OA causal relationship (odds ratio = 1.47, 95% CI = 1.42-1.52). A similar pattern of effects was observed in sex- and site-specific analyses, with BMI affecting OA comparably in both sexes and most strongly in the knee.

CONCLUSION

Our work demonstrates an intrinsic relationship underlying BMI and overall OA, reflected by a pronounced phenotypic association, significant biological pleiotropy, and a putative causal link. Stratified analysis further reveals that the effects are distinct across sites and comparable across sexes.

A Probabilistic Formulation for Meta-Weight-Net

In the last decade, deep neural networks (DNNs) have become dominant tools for various of supervised learning tasks, especially classification. However, it is demonstrated that they can easily overfit to training set biases, such as label noise and class imbalance. Example reweighting algorithms are simple and effective solutions against this issue, but most of them require manually specifying the weighting functions as well as additional hyperparameters. Recently, a meta-learning-based method Meta-Weight-Net (MW-Net) has been proposed to automatically learn the weighting function parameterized by an MLP via additional unbiased metadata, which significantly improves the robustness of prior arts. The method, however, is proposed in a deterministic manner, and short of intrinsic statistical support. In this work, we propose a probabilistic formulation for MW-Net, probabilistic MW-Net (PMW-Net) in short, which treats the weighting function in a probabilistic way, and can include the original MW-Net as a special case. By this probabilistic formulation, additional randomness is introduced while the flexibility of the weighting function can be further controlled during learning. Our experimental results on both synthetic and real datasets show that the proposed method improves the performance of the original MW-Net. Besides, the proposed PMW-Net can also be further extended to fully Bayesian models, to improve their robustness.

High Electron Mobility Hot-Exciton Induced Delayed Fluorescent Organic Semiconductors

The development of high mobility emissive organic semiconductors is of great significance for the fabrication of miniaturized optoelectronic devices, such as organic light emitting transistors. However, great challenge exists in designing key materials, especially those who integrates triplet exciton utilization ability. Herein, dinaphthylanthracene diimides (DNADIs), with 2,6-extended anthracene donor, and 3'- or 4'-substituted naphthalene monoimide acceptors were designed and synthesized. By introducing acceptor-donor-acceptor structure, both materials show high electron mobility. Moreover, by fine-tuning of substitution sites, good integration with high solid state photoluminescence quantum yield of 26 %, high electron mobility of 0.02 cm²  V^-1  s^-1 , and the feature of hot-exciton induced delayed fluorescence were obtained in 4'-DNADI. This work opens a new avenue for developing high electron mobility emissive organic semiconductors with efficient utilization of triplet excitons.

Improving cooperativity of transcription activators by oligomerization domains in mammalian cells

Cooperative activation is critical for the applications of synthetic biology in mammalian cells. In this study, we have developed cooperative transcription factor by fusing oligomerization domain in mammalian cells. Firstly, we demonstrated that two oligomerized domains (CI434 and CI) successfully improved transcription factor cooperativity in bacterial cells but failed to increase cooperativity in mammalian cells, possibly because the additional mammalian activation domain disrupted their oligomerization capability. Therefore, we chose a different type of oligomerized domain (CarHC), whose ability to oligomerize is not dependent on its C-terminal domains, to fuse with a transcription factor (RpaR) and activation domain (VTR3), forming a potential cooperative transcription activator RpaR-CarH-VTR3 for mammalian regulatory systems. Compared with RpaR-VTR3, the cooperativity of RpaR-CarH-VTR3 was significantly improved with higher Hill coefficient and a narrower input range in the inducible switch system in mammalian cells. Moreover, a mathematical model based on statistical mechanics model was developed and the simulation results supported the hypothesis that the tetramer of the CarH domain in mammalian cells was the reason for the cooperative capacity of RpaR-CarH-VTR3.

Global issues in obstetric anaesthesia: perspectives from South Africa, Japan, China, Latin America and North America

South Africa is classified as a low- and middle-income country, with a complex mixture of resource-rich and resource-limited settings. In the major referral hospitals, the necessary skill level exists for the management of complex challenges. However, this contrasts with the frequently-inadequate skill levels of anaesthesia practitioners in resource-limited environments. In Japan, obstetricians administer anaesthesia for 40% of caesarean deliveries and 80% of labour analgesia. Centralisation of delivery facilities is now occurring and it is expected that obstetric anaesthesiologists will be available 24 h a day in centralised facilities in the future. In China, improvements in women's reproductive, maternal, neonatal, child, and adolescent health are critical government policies. Obstetric anaesthesia, especially labour analgesia, has received unprecedented attention. Chinese obstetric anaesthesiologists are passionate about clinical research, focusing on efficacy, safety, and topical issues. The Latin-American region has different landscapes, people, languages, and cultures, and is one of the world's regions with the most inequality. There are large gaps in research, knowledge, and health services, and the World Federation of Societies of Anaesthesiologists is committed to working with governmental and non-governmental organisations to improve patient care and access to safe anaesthesia. Anaesthesia workforce challenges, exacerbated by coronavirus disease 2019, beset North American healthcare. Pre-existing struggles by governments and decision-makers to improve health care access remain, partly due to unfamiliarity with the role of the anaesthesiologist. In addition to weaknesses in work environments and dated standards of work culture, the work-life balance demanded by new generations of anaesthesiologists must be acknowledged.

Beam Energy Dependence of Fifth- and Sixth-Order Net-Proton Number Fluctuations in Au+Au Collisions at RHIC

We report the beam energy and collision centrality dependence of fifth and sixth order cumulants (C_{5}, C_{6}) and factorial cumulants (κ_{5}, κ_{6}) of net-proton and proton number distributions, from center-of-mass energy (sqrt[s_{NN}]) 3 GeV to 200 GeV Au+Au collisions at RHIC. Cumulant ratios of net-proton (taken as proxy for net-baryon) distributions generally follow the hierarchy expected from QCD thermodynamics, except for the case of collisions at 3 GeV. The measured values of C_{6}/C_{2} for 0%-40% centrality collisions show progressively negative trend with decreasing energy, while it is positive for the lowest energy studied. These observed negative signs are consistent with QCD calculations (for baryon chemical potential, μ_{B}≤110  MeV) which contains the crossover transition range. In addition, for energies above 7.7 GeV, the measured proton κ_{n}, within uncertainties, does not support the two-component (Poisson+binomial) shape of proton number distributions that would be expected from a first-order phase transition. Taken in combination, the hyperorder proton number fluctuations suggest that the structure of QCD matter at high baryon density, μ_{B}∼750  MeV at sqrt[s_{NN}]=3  GeV is starkly different from those at vanishing μ_{B}∼24  MeV at sqrt[s_{NN}]=200  GeV and higher collision energies.

Semiparametric Efficient G-estimation with Invalid Instrumental Variables

The instrumental variable method is widely used in the health and social sciences for identification and estimation of causal effects in the presence of potential unmeasured confounding. In order to improve efficiency, multiple instruments are routinely used, leading to concerns about bias due to possible violation of the instrumental variable assumptions. To address this concern, we introduce a new class of G-estimators that are guaranteed to remain consistent and asymptotically normal for the causal effect of interest provided that a set of at least γ out of K candidate instruments are valid, for γ≤K set by the analyst ex ante, without necessarily knowing the identity of the valid and invalid instruments. We provide formal semiparametric efficiency theory supporting our results. Both simulation studies and applications to the UK Biobank data demonstrate the superior empirical performance of our estimators compared to competing methods.

[Retracted] Metformin exerts anticancer effects through the inhibition of the Sonic hedgehog signaling pathway in breast cancer

Following the publication of this paper, it was drawn to the Editor's attention by concerned readers that several of the cellular images shown in Figs. 6A and 8A, the scratch‑wound assay images shown in Fig. 5A, the western blotting data in Figs. 2C and 7A and the Matrigel invasion assays in Fig. 5C were strikingly similar to data appearing in different form in other articles by different authors. Owing to the fact that the contentious data in the above article had already been published elsewhere, or were already under consideration for publication, prior to its submission to International Journal of Molecular Medicine, the Editor has decided that this paper should be retracted from the Journal. The authors were asked for an explanation to account for these concerns, but the Editorial Office did not receive a reply. The Editor apologizes to the readership for any inconvenience caused. [International Journal of Molecular Medicine 36: 204‑214, 2015; DOI: 10.3892/ijmm.2015.2217].

Stress analysis of self-tightness metal sealing against ultrahigh pressure medium

Stress is one of the most important factors in metal-to-metal sealing. In this paper, two methods (theoretical and empirical) were adopted to calculate the normal stress of the brass sealing surfaces against different ultrahigh pressure liquid. The theoretical formula was derived in terms of force balance, and the empirical formula was obtained by polynomial curve fitting, which the fitted data were from simulated results; besides, the results calculated using the empirical formula agree well with the results by theoretical formula. Meanwhile, the equivalent stresses of the brass seal, normal stress and contact stress on the brass seal surfaces were simulated by finite element method, and the simulated results indicated these stresses are increased with the increase of liquid pressure, and the maximum stresses always appear on the tip of the brass seal.

Precision Machine Learning

We explore unique considerations involved in fitting machine learning (ML) models to data with very high precision, as is often required for science applications. We empirically compare various function approximation methods and study how they scale with increasing parameters and data. We find that neural networks (NNs) can often outperform classical approximation methods on high-dimensional examples, by (we hypothesize) auto-discovering and exploiting modular structures therein. However, neural networks trained with common optimizers are less powerful for low-dimensional cases, which motivates us to study the unique properties of neural network loss landscapes and the corresponding optimization challenges that arise in the high precision regime. To address the optimization issue in low dimensions, we develop training tricks which enable us to train neural networks to extremely low loss, close to the limits allowed by numerical precision.

Development and multicenter validation of a novel radiomics-based model for identifying eosinophilic chronic rhinosinusitis with nasal polyps

BACKGROUND

Reliable noninvasive methods are needed to identify endotypes of chronic rhinosinusitis with nasal polyps (CRSwNP) to facilitate personalized therapy. Previous computed tomography (CT) scoring system has limited and inconsistent performance in identifying eosinophilic CRSwNP. We aimed to develop and validate a radiomics-based model to identify eosinophilic CRSwNP.

METHODS

Surgical patients with CRSwNP were recruited from Tongji Hospital and randomly divided into training (n = 232) and internal validation cohort (n = 61). Patients from two additional hospitals served as external validation cohort-1 (n = 84) and cohort-2 (n = 54), respectively. Data were collected from October 2013 to May 2021. Eosinophilic CRSwNP was determined by histological criterion. The least absolute shrinkage and selection operator and the logistic regression (LR) algorithm were used to develop a radiomics model. Univariate and multivariate LR were employed to build models based on CT scores, clinical characteristics, and the combination of radiological and clinical characteristics. Model performance was evaluated by assessing discrimination, calibration, and clinical utility.

RESULTS

The radiomics model based on 10 radiomic features achieved an area under the curve (AUC) of 0.815 in the training cohort, significantly better than the CT score model based on ethmoid-to-maxillary sinus score ratio with an AUC of 0.655. The combination of radiomic features and blood eosinophil count had a further improved performance, achieving an AUC of 0.903. The performance of these models was confirmed in all validation cohorts with satisfying predictive calibration and clinical application value.

CONCLUSIONS

A CT radiomics-based model is promising to identify eosinophilic CRSwNP. This radiomics-based method may provide novel insights in solving other clinical concerns, such as guiding personalized treatment and predicting prognosis in patients with CRSwNP.

DANet: Semi-supervised differentiated auxiliaries guided network for video action recognition

Video Action Recognition (ViAR) aims to identify the category of the human action observed in a given video. With the advent of Deep Learning (DL) techniques, noticeable performance breakthroughs have been achieved in this study. However, the success of most existing DL-based ViAR methods heavily relies on the existence of a large amount of annotated data, i.e., videos with corresponding action categories. In practice, obtaining such a desired number of annotations is often difficult due to expensive labeling costs, which may lead to significant performance degradation for these methods. To address this issue, we propose an end-to-end semi-supervised Differentiated Auxiliary guided Network (DANet) to best use a few annotated videos. Except for the common supervised learning on a few annotated videos, the DANet also involves the knowledge of multiple pre-trained auxiliary networks to optimize the ViAR network in a self-supervised way on the unannotated data by removing the annotations. Considering the tight connection between video action recognition and classical static image-based visual tasks, the abundant knowledge from the pre-trained static image-based models can be used for training the ViAR model. Specifically, the DANet is a two-branch architecture, which includes a target branch of the ViAR network, and an auxiliary branch of multiple auxiliary networks (i.e., referring to diverse off-the-shelf models of relevant image tasks). Given a limited number of annotated videos, we train the target ViAR network end-to-end in a semi-supervised way, namely, with both the supervised cross-entropy loss on annotated videos, and the per-auxiliary weighted self-supervised contrastive losses on the same videos but without using annotations. Besides, we further explore different weighted guidance of the auxiliary networks to the ViAR network to better reflect different relationships between the image-based models and the ViAR model. Finally, we conduct extensive experiments on several popular action recognition benchmarks in comparison with existing state-of-the-art methods, and the experimental results demonstrate the superiority of DANet over most of the compared methods. In particular, the DANet obviously suppresses state-of-the-art ViAR methods even with very fewer annotated videos.

Identification and expression pattern analysis of PtCarA and PtCarB genes in Populus trichocarpa under different nitrogen treatments

Carbamoyl phosphate synthetase (CPS) catalyses the synthesis of ammonia carbamoyl phosphate (CP), which plays a key role in the biosynthesis of arginine and pyrimidine nucleotides. There are two subunits of the CPS enzyme in Populus trichocarpa, CarA (small subunit) and CarB (large subunit). Only when they coexist can CPS catalyse synthesis of CP. However, it is not clear how CPS responds to nitrogen (N) to affect arginine and pyrimidine nucleotide biosynthesis. In this study, bioinformatics methods were used to analyse the expression patterns of genes encoding CarA and CarB, and qRT-PCR and RNA-seq were used to investigate their molecular responses under different N concentrations. Phylogenetic analysis revealed that the phylogenetic trees of CarA and CarB had similar topologies. qRT-PCR showed that the PtCarA and PtCarB genes were regulated by N, while their N-regulated patterns differed in different tissues. The expression patterns of PtCarA and PtCarB show a significant positive correlation according to qRT-PCR and RNA-seq. The analysis of promoter cis-acting elements showed that the promoter regions of PtCarA1, PtCarA2 and PtCarB contained some identical cis-acting elements. According to analysis of the phylogenetic tree, expression patterns and promoter elements, we speculate that there might be coevolution among PtCarA1, PtCarA2 and PtCarB. This study provides valuable information for further understanding the function of CPS in poplar, especially for N response, and provides new ideas for studying the evolution of gene families related to heteromultimers.

Protein PDK4 Interacts with HMGCS2 to Facilitate High Glucoseinduced Myocardial Injuries

OBJECTIVES

As a distinct type of cardiomyopathy, diabetic cardiomyopathy (DCM) is featured as diastolic or systolic cardiac dysfunction in diabetic patients. In order to broaden the understanding of molecular mechanisms in DCM, we intended to explore the mechanism of the interaction between PDK4 protein and Hmgcs2 in high glucose (HG)-induced myocardial damage.

METHODS

PDK4 and Hmgcs2 expression in the myocardium of diabetes mellitus (DM) model rats and HG-incubated cardiomyocyte line H9C2 was analyzed by western blot analysis. Echocardiography and TUNEL assay were utilized for respective assessment of cardiac structure and function and cardiomyocyte apoptosis in DM rats after silencing PDK4 or/and Hmgcs2. In vitro, the impact of PDK4 and Hmgcs2 on HG-induced cardiomyocyte injuries was identified with cell counting kit-8 and flow cytometry assays, along with detection of LDH release, caspase-3/7 activities, and reactive oxygen species (ROS) and malondialdehyde (MDA) levels. Moreover, a coimmunoprecipitation assay was utilized to test the interaction between PDK4 and Hmgcs2.

RESULTS

Both PDK4 and Hmgcs2 were highly expressed in the myocardial tissues of DM rats. Mechanistically, PDK4 interacted with Hmgcs2 to upregulate Hmgcs2 expression in HG-induced H9C2 cells. Silencing PDK4 improved cardiac function and reduced cardiomyocyte apoptosis in DM rats. In HG-induced H9C2 cells, PDK4 or Hmgcs2 silencing enhanced cell viability and reduced LDH release, caspase-3/7 activities, cell apoptosis, and ROS and MDA levels, and these trends were further promoted by the simultaneous silencing of PDK4 and Hmgcs2.

CONCLUSION

In summary, the silencing of PDK4 and Hmgcs2 alleviated HG-induced myocardial injuries through their interaction.

The temperature of internet: Internet use and depression of the elderly in China

Introduction

Depression has become one of the most prevalent mental illnesses affecting the elderly in aging countries, i. e., in countries of the world whose population is slowly aging. It has become an important topic for scientists and policymakers to analyze how best to improve the elderly's mental health and save them from depression. The aim of this paper was to investigate whether and to what extent internet use may affect depression in the elderly. The heterogeneous effects of internet use on the elderly's depression across age, gender, and occupation were also investigated.

Methods

The data used in the present study were gathered from the China Health and Retirement Longitudinal Study that was conducted in 2018. The propensity score matching technique and the endogenous switch regression model were employed in this study to address potential endogeneity caused by both observed and unobserved factors.

Results

The results of the present study show that the elderly who are relatively young, male, well educated, live in an urban area, or have a small family are more likely to use the internet. The elderly who have healthy eyes or good eyesight, those who are not employed in the agricultural sector, or those who are retired, and those who are not eligible to receive any subsistence allowance or drink wine have a higher probability of using the internet. We also find that internet use significantly reduces the elderly's depression status by 3.370 points, which is roughly equivalent to a reduction of 37.19%. Heterogeneity analysis on internet use reveals that the health effect is particularly effective for agricultural workers, female, or the older elderly.

Conclusion

The results of the present study highlight the significant welfare effects brought about by the development of internet infrastructure. To improve the mental health of the elderly, the government should encourage them to adopt the internet. In particular, the needs of the elderly who are older, female, or have agricultural work should be paid more attention to motivate them to use the internet more to alleviate depression.

AI-ASSISTED METHODS FOR ASSESSING AFFECT AND BEHAVIORAL SYMPTOMS IN DEMENTIA: A SYSTEMATIC REVIEW

Negative affect and neurobehavioral symptoms occur in most people with dementia and significantly impact their health outcomes and sense of wellbeing. Detecting these symptoms in this population is challenging due to associated cognitive impairment and communication difficulties. Innovative technology and artificial intelligence (AI)-assisted tools are emerging for assessing affect and neurobehavioral symptoms in individuals with dementia. This review synthesizes research evidence to identify existing AI-assisted measurement tools and evaluate their accuracy in assessing affect and symptoms in people with mild cognitive impairment and dementia. PubMed, CINAHL, Scopus, and Web of Science databases were searched. Eight articles were identified. Multiple machine learning (ML) models were developed to assess affect, apathy, anxiety, depression, agitation, and wandering. One ML model detected positive and negative affect via facial expression with an overall accuracy of 86%. One ML model detected apathy based on speech and achieved an area under curve (AUC) accuracy of 0.77–0.88. Another speech-based ML model, based on paralinguistic markers, predicted apathy, anxiety, and depression by ≥0.3 points. Another model detected wandering based on activity monitoring data and showed 98% sensitivity and specificity. Furthermore, multiple ML models were developed to detect agitation using multi-modal sensors with AUC ranging from 0.50–0.82. Findings suggest that AI-assisted tools are a promising approach to detecting affect and neurobehavioral symptoms, yet the evidence is limited. More research is needed to develop comprehensive, accurate models to detect neurobehavior symptoms. The results have significant implications for supporting research and clinical practice to promote quality of care for people with dementia.

Fluid shear stress-induced down-regulation of miR-146a-5p inhibits osteoblast apoptosis via targeting SMAD4

Fluid shear stress (FSS) plays an important role in osteoblast apoptosis. However, the role of miRNA in osteoblast apoptosis under FSS and possible molecular mechanisms remain unknown. Our aim of the study was to explore whether miR-146a-5p regulates osteoblast apoptosis under FSS and its molecular mechanisms. FSS could down-regulate the expression of miR-146a-5p in MC3T3-E1 cells. We confirm that up-regulation of miR-146a-5p promotes osteoblasts apoptosis and down-regulation of miR-146a-5p inhibits osteoblasts apoptosis. We further demonstrated that FSS inhibits osteoblast apoptosis by down-regulated miR-146a-5p. Dual-luciferase reporter assay validated that SMAD4 is a direct target gene of miR-146a-5p. In addition, mimic-146a-5p suppressed FSS-induced up-regulation of SMAD4 protein levels, which suggests that FSS elevated SMAD4 protein expression levels via regulation miR-146a-5p. Further investigations showed that SMAD4 could inhibit osteoblast apoptosis. We demonstrated that miR-146a-5p regulates osteoblast apoptosis via targeting SMAD4. Taken together, our present study showed that FSS-induced down-regulation miR-146a-5p inhibits osteoblast apoptosis via target SMAD4. These findings may provide novel mechanisms for FSS to inhibit osteoblast apoptosis, and also may provide a potential therapeutic target for osteoporosis.

Collision-System and Beam-Energy Dependence of Anisotropic Flow Fluctuations

Elliptic flow measurements from two-, four-, and six-particle correlations are used to investigate flow fluctuations in collisions of U+U at sqrt[s_{NN}]=193  GeV, Cu+Au at sqrt[s_{NN}]=200  GeV and Au+Au spanning the range sqrt[s_{NN}]=11.5-200  GeV. The measurements show a strong dependence of the flow fluctuations on collision centrality, a modest dependence on system size, and very little if any, dependence on particle species and beam energy. The results, when compared to similar LHC measurements, viscous hydrodynamic calculations, and trento model eccentricities, indicate that initial-state-driven fluctuations predominate the flow fluctuations generated in the collisions studied.