Efficient federated learning for distributed neuroimaging data.

Recent advancements in neuroimaging have led to greater data sharing among the scientific community. However, institutions frequently maintain control over their data, citing concerns related to research culture, privacy, and accountability. This creates a demand for innovative tools capable of analyzing amalgamated datasets without the need to transfer actual data between entities. To address this challenge, we propose a decentralized sparse federated learning (FL) strategy. This approach emphasizes local training of sparse models to facilitate efficient communication within such frameworks. By capitalizing on model sparsity and selectively sharing parameters between client sites during the training phase, our method significantly lowers communication overheads. This advantage becomes increasingly pronounced when dealing with larger models and accommodating the diverse resource capabilities of various sites. We demonstrate the effectiveness of our approach through the application to the Adolescent Brain Cognitive Development (ABCD) dataset.

An Audiovisual Correlation Matching Method Based on Fine-Grained Emotion and Feature Fusion.

Most existing intelligent editing tools for music and video rely on the cross-modal matching technology of the affective consistency or the similarity of feature representations. However, these methods are not fully applicable to complex audiovisual matching scenarios, resulting in low matching accuracy and suboptimal audience perceptual effects due to ambiguous matching rules and associated factors. To address these limitations, this paper focuses on both the similarity and integration of affective distribution for the artistic audiovisual works of movie and television video and music. Based on the rich emotional perception elements, we propose a hybrid matching model based on feature canonical correlation analysis (CCA) and fine-grained affective similarity. The model refines KCCA fusion features by analyzing both matched and unmatched music-video pairs. Subsequently, the model employs XGBoost to predict relevance and to compute similarity by considering fine-grained affective semantic distance as well as affective factor distance. Ultimately, the matching prediction values are obtained through weight allocation. Experimental results on a self-built dataset demonstrate that the proposed affective matching model balances feature parameters and affective semantic cognitions, yielding relatively high prediction accuracy and better subjective experience of audiovisual association. This paper is crucial for exploring the affective association mechanisms of audiovisual objects from a sensory perspective and improving related intelligent tools, thereby offering a novel technical approach to retrieval and matching in music-video editing.

Livestock sector can threaten planetary boundaries without regionally differentiated strategies.

The livestock sector represents major challenges to safeguarding environmental integrity. This study comprehensively analyzes ten environmental footprints of the livestock sector from 1995 to 2022, with projections until 2030, and juxtaposes them with the planetary boundaries. We quantify greenhouse gas emissions, land use, water use, particulate matter formation, and biochemical flows associated with the livestock sector. Our findings indicate that the livestock sector alone poses a significant challenge to planetary boundaries and has the potential to threaten several of these boundaries by 2030. Scenario modeling shows that a "one-size-fits-all" strategy for all countries can be suboptimal. Conversely, a region-specific strategy that requires developed regions to align meat consumption with the Eat-Lancet diet while developing regions focus on improvement of production efficiency is optimal for reducing livestock's global environmental footprints. These findings highlight the need for targeted policy measures and regional strategies to effectively mitigate the environmental impacts of the livestock sector and ensure sustainable practices.

Sparassis latifolia polysaccharide alleviated lipid metabolism abnormalities in kidney of lead-exposed mice by regulating oxidative stress-mediated inflammation and autophagy based on multi-omics.

Lead is a common environmental pollutant which can accumulate in the kidney and cause renal injury. However, regulatory effects and mechanisms of Sparassis latifolia polysaccharide (SLP) on lipid metabolism abnormality in kidney exposed to lead are not clarified. In this study, mice were used to construct an animal model to observe the histopathological changes in kidney, measure lead content, damage indicators, differentially expressed metabolites (DEMs) and genes (DEGs) in key signaling pathways that cause lipid metabolism abnormalities based on lipidomics and transcriptomics, which were later validated using qPCR and western blotting. Co-treatment of Pb and N-acetylcysteine (NAC) were used to verify the link between SLP and oxidative stress. Our results indicated that treatment with SLP identified 276 DEMs (including metabolism of glycerophospholipid, sphingolipid, glycerolipid and fatty acid) and 177 DEGs (including genes related to oxidative stress, inflammation, autophagy and lipid metabolism). Notably, regulatory effects of SLP on abnormal lipid metabolism in kidney were mainly associated with oxidative stress, inflammation and autophagy; SLP could regulate abnormal lipid metabolism in kidney by reducing oxidative stress and affecting its downstream-regulated autophagy and inflammatory to alleviate renal injury caused by lead exposure. This study provides a theoretical basis for SLP intervention in lead injury.

[Soil Cadmium Prediction and Health Risk Assessment of an Oasis on the Eastern Edge of the Tarim Basin Based on Feature Optimization and Machine Learning].

Soil heavy metal pollution poses a serious threat to food security, human health, and soil ecosystems. Based on 644 soil samples collected from a typical oasis located at the eastern margin of the Tarim Basin, a series of models, namely, multiple linear regression （LR）, neural network （BP）, random forest （RF）, support vector machine （SVM）, and radial basis function （RBF）, were built to predict the soil heavy metal content. The optimal prediction result was obtained and utilized to analyze the spatial distribution features of heavy metal contamination and relevant health risks. The outcomes demonstrated that： ① The average Cd content in the study area was 0.14 mg·kg-1, which was 1.17 times the soil background value of Xinjiang, making it the primary factor of soil heavy metal contamination in the area. Additionally, the carcinogenicity risk coefficients of Cd for both adults and children were less than 10-4, indicating that there were no significant long-term health risks for humans in the area. ② The estimation accuracies of the five inversion models were compared, and the validation set of the RF model had an R2 value of 0.763 7, which was the highest among the five models. Additionally, the RMSE, MAE, and MBE of the RF model were the smallest among the five models. Therefore, the predicted values of the RF model were most consistent with the measured values of the soil Cd content. The predicted map of soil Cd distribution derived from the RF model coincided best with the interpolation map. ③ The RF model outperformed the other four models in predicting health risks associated with the soil Cd element for both adults and children, resulting in better prediction results. Comparatively, the predicted values of the LR model in the validation set varied greatly, leading to unreliable results. It was demonstrated that the RF was the best model for predicting soil Cd content and evaluating health risks in the study area, considering its superior generalization capability and anti-overfitting ability.

An Optimal Design Method for Lightweight Heating Film of Anisotropic Heat Conduction Substrate Based on Surrogate Model.

In space missions, heating films are crucial for uniformly heating onboard equipment for precise temperature control. This study develops an optimization method using surrogate models for lightweight anisotropic substrate thermal conductive heating films, meeting the requirements of uniform heating in thermal control for space applications. A feedforward neural network optimized by particle swarm optimization (PSO) was employed to create a surrogate model, mapping design parameters to the temperature uniformity of the heating film. This model served as the basis for applying the NSGA-II algorithm to quickly optimize both temperature uniformity and lightweight characteristics. In this study, the PSO-BP surrogate model was trained using heating film thermal simulation data, and the surrogate model demonstrated an accurate prediction of the mean square error (MSE) of the predicted temperature difference within 0.0168 s. The maximum temperature difference in the optimal model is 1.188 ℃, which is 30.5 times lower than before optimization, and the equivalent density is only increased by 3.9%. In summary, this optimization design method effectively captures the relationships among various parameters and optimization objectives. Its superior computational accuracy and design efficiency offer significant advantages in the design of devices such as heating films.

Signal interference between drugs and metabolites in LC-ESI-MS quantitative analysis and its evaluation strategy.

Liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS) is a widely utilized technique for in vivo pharmaceutical analysis. Ionization interference within electrospray ion source, occurring between drugs and metabolites, can lead to signal variations, potentially compromising quantitative accuracy. Currently, method validation often overlooks this type of signal interference, which may result in systematic errors in quantitative results without matrix-matched calibration. In this study, we conducted an investigation using ten different groups of drugs and their corresponding metabolites across three LC-ESI-MS systems to assess the prevalence of signal interference. Such interferences can potentially cause or enhance nonlinearity in the calibration curves of drugs and metabolites, thereby altering the relationship between analyte response and concentration for quantification. Finally, we established an evaluation scheme through a step-by-step dilution assay and employed three resolution methods: chromatographic separation, dilution, and stable labeled isotope internal standards correction. The above strategies were integrated into the method establishment process to improve quantitative accuracy.

Astrocytes modulate brain phosphate homeostasis via polarized distribution of phosphate uptake transporter PiT2 and exporter XPR1.

Aberrant inorganic phosphate (Pi) homeostasis causes brain calcification and aggravates neurodegeneration, but the underlying mechanism remains unclear. Here, we found that primary familial brain calcification (PFBC)-associated Pi transporter genes Pit2 and Xpr1 were highly expressed in astrocytes, with importer PiT2 distributed over the entire astrocyte processes and exporter XPR1 localized to astrocyte end-feet on blood vessels. This polarized PiT2 and XPR1 distribution endowed astrocyte with Pi transport capacity competent for brain Pi homeostasis, which was disrupted in mice with astrocyte-specific knockout (KO) of either Pit2 or Xpr1. Moreover, we found that Pi uptake by PiT2, and its facilitation by PFBC-associated galactosidase MYORG, were required for the high Pi transport capacity of astrocytes. Finally, brain calcification was suppressed by astrocyte-specific PiT2 re-expression in Pit2-KO mice. Thus, astrocyte-mediated Pi transport is pivotal for brain Pi homeostasis, and elevating astrocytic Pi transporter function represents a potential therapeutic strategy for reducing brain calcification.

Association of the PCSK6 rs1531817(C/A) polymorphism with the prognosis and coronary stenosis in premature myocardial infarction patients: a prospective cohort study.

BACKGROUND: Proprotein convertase subtilisins/kexin 6 (PCSK6) polymorphisms have been shown to be associated with atherosclerosis progression. This research aimed to evaluate the relationship of PCSK6 rs1531817 polymorphisms with coronary stenosis and the prognosis in premature myocardial infarction (PMI) patients. METHODS: This prospective cohort analysis consecutively included 605 PMI patients who performed emergency percutaneous coronary intervention (PCI) at Tianjin Chest Hospital sequentially between January 2017 and August 2022, with major adverse cardiovascular events (MACEs) as the outcome. Analyses assessed the relationships among PCSK6 rs1531817 polymorphism, Gensini score (GS), triple vessel disease (TVD), and MACEs. RESULTS: 92 (16.8%) patients experienced MACEs with an average follow-up of 25.7 months. Logistic analysis revealed that the PCSK6 rs1531817 CA + AA genotype was an independent protective factor against high GS and TVD. Cox analysis revealed that the PCSK6 rs1531817 CA + AA genotype was an independent protective factor against MACEs. The mediation effect results showed that apolipoprotein A1/apolipoprotein B (ApoA1/ApoB) partially mediated the association between PCSK6 rs1531817 polymorphism and coronary stenosis and that total cholesterol/high-density lipoprotein (TC/HDL) and TVD partially and in parallel mediated the association between the PCSK6 rs1531817 polymorphism and MACEs. CONCLUSION: Patients with the PCSK6 CA + AA genotype have milder coronary stenosis and a better long-term prognosis; according to the mediation model, ApoA1/ApoB and TC/HDL partially mediate. These results may provide a new perspective on clinical therapeutic strategy for anti-atherosclerosis and improved prognosis in PMI patients.

An artificial intelligence platform for the screening and managing of strabismus.

OBJECTIVES: Considering the escalating incidence of strabismus and its consequential jeopardy to binocular vision, there is an imperative demand for expeditious and precise screening methods. This study was to develop an artificial intelligence (AI) platform in the form of an applet that facilitates the screening and management of strabismus on any mobile device. METHODS: The Visual Transformer (VIT_16_224) was developed using primary gaze photos from two datasets covering different ages. The AI model was evaluated by 5-fold cross-validation set and tested on an independent test set. The diagnostic performance of the AI model was assessed by calculating the Accuracy, Precision, Specificity, Sensitivity, F1-Score and Area Under the Curve (AUC). RESULTS: A total of 6194 photos with corneal light-reflection (with 2938 Exotropia, 1415 Esotropia, 739 Vertical Deviation and 1562 Orthotropy) were included. In the internal validation set, the AI model achieved an Accuracy of 0.980, Precision of 0.941, Specificity of 0.979, Sensitivity of 0.958, F1-Score of 0.951 and AUC of 0.994. In the independent test set, the AI model achieved an Accuracy of 0.967, Precision of 0.980, Specificity of 0.970, Sensitivity of 0.960, F1-Score of 0.975 and AUC of 0.993. CONCLUSIONS: Our study presents an advanced AI model for strabismus screening which integrates electronic archives for comprehensive patient histories. Additionally, it includes a patient-physician interaction module for streamlined communication. This innovative platform offers a complete solution for strabismus care, from screening to long-term follow-up, advancing ophthalmology through AI technology for improved patient outcomes and eye care quality.

Effect of electroacupuncture pre-conditioning on the expression rhythm of core clock gene Bmal1 in uterine tissue of controlled hyperstimulation rats. / ç”µé’ˆé¢„å¤„ç†å¯¹ä¿ƒæŽ’åµå¤§é¼ å­å®«ç»„ç»‡æ ¸å¿ƒæ—¶é’ŸåŸºå› Bmal1表达节律的影响.

OBJECTIVES: To observe the effect of electroacupuncture (EA) pre-conditioning on the expression rhythm of clock gene Bmal1 in the uterine tissue of rats with controlled ovarian hyperstimulation(COH), so as to explore its mechanisms underlying improvement of the endometrial receptivity of ovarian superovulation during implantation. METHODS: Seventy-two female SD rats with typical estrous cycles were randomly divided into normal control, model and EA pre-conditioning (pre-EA) groups, with 24 rats in each group. The COH model was established by giving the rats with pregnant mare serum gonadotropin (PMSG) and human chorionic gonadotropin (HCG) by intraperitoneal injection. The rats of the pre-EA group received EA stimulation (1 Hz/5 Hz, a tolerable strength) of "Guanyuan"(CV4) and "Sanyinjiao"(SP6) for 15 min each time, once daily (at 21：00 every day). After successive EA intervention during the first two estrous cycles, the modeling began in the third estrus cycle and the EA intervention was continued till the end of modeling, followed by raising the rats with superovulation induction and male rats undergoing vasoligation in one cage (1∶1). The rats during the estrum in the normal control group or those of the model group at the end of modeling were raised together with the male rats undergoing vasoligation in one cage. On the 5th day (04：00 AM) after raising in one cage, the rats in the three groups were sacrificed in six batches every 4 hours, with 4 rats in each group in each batch. The H.E. staining was used for revealing alterations of the endometrial thickness, number of glands and blood vessels and tissue histology, and ELISA employed to ascertain the contents of estradiol (E2) and progesterone (Pg) in serum. The expression rhythm of core clock gene Bmal1 ［In the present study, Zeitgeber time (ZT) is an artificially set laboratory time, i.e., ZT7 (07：00) is light on and ZT19 (19：00) is light off.］ and the expression of endometrial HoxA10 and leukemia inhibitory factor (LIF) mRNAs were detected by quantitative real-time PCR. The Western blot was employed to detect the expression levels of HoxA10 and LIF proteins. RESULTS: Findings of the clock gene Bmal1 level showed that the expression peak was at ZT12 and the valley value at ZT20 in the normal control group, and that of the peak value was at ZT20 and valley value at ZT12 in the model group, while in the pre-EA group, the peak value was at ZT8, and the valley value at ZT4. The difference of Bmal1 levels among the three groups was most significant at ZT12 (12：00), therefore, the tissue samples were taken at ZT12 in this study for comparison of the levels of different indexes among the 3 groups. Compared with the control group, the endometrial thickness, number of glands and blood vessels, HoxA10 and LIF mRNAs and proteins were significantly down-regulated (P<0.01, P<0.05), and contents of serum E2 and Pg were considerably up-regulated in the model group (P<0.01, P<0.05). Relevant to the model group, the pre-EA group had an apparent increase in the endometrial thickness, number of glands and blood vessels, and expression levels of HoxA10 and LIF mRNAs and proteins (P<0.05, P<0.01), and a marked decrease in the serum Pg (P<0.05). At the ZT12 (12：00 noon), compared with the normal control group, the mRNA level of Bmal1 was significantly decreased in the model group (P<0.01)；and compared with the model group, the level of Bmal1 mRNA was significantly increased in the pre-EA group (P<0.05). In addition, at the node of ZT16, the mRNA level of Bmal1 was significantly decreased in the model group in comparison with the normal control group (P<0.01). CONCLUSIONS: EA preconditioning can improve the endometrial receptivity during the implantation window period in rats with COH, which may be related to its functions in regulating the expression of clock gene Bmal1 in the uterine tissue and in correcting the disturbance of clock gene rhythm.

Cognitive and psychiatric relevance of dynamic functional connectivity states in a large (N > 10,000) children population.

Children's brains dynamically adapt to the stimuli from the internal state and the external environment, allowing for changes in cognitive and mental behavior. In this work, we performed a large-scale analysis of dynamic functional connectivity (DFC) in children aged 9~11 years, investigating how brain dynamics relate to cognitive performance and mental health at an early age. A hybrid independent component analysis framework was applied to the Adolescent Brain Cognitive Development (ABCD) data containing 10,988 children. We combined a sliding-window approach with k-means clustering to identify five brain states with distinct DFC patterns. Interestingly, the occurrence of a strongly connected state with the most within-network synchrony and the anticorrelations between networks, especially between the sensory networks and between the cerebellum and other networks, was negatively correlated with cognitive performance and positively correlated with dimensional psychopathology in children. Meanwhile, opposite relationships were observed for a DFC state showing integration of sensory networks and antagonism between default-mode and sensorimotor networks but weak segregation of the cerebellum. The mediation analysis further showed that attention problems mediated the effect of DFC states on cognitive performance. This investigation unveils the neurological underpinnings of DFC states, which suggests that tracking the transient dynamic connectivity may help to characterize cognitive and mental problems in children and guide people to provide early intervention to buffer adverse influences.

Screening and Analysis of Core Genes for Osteoporosis Based on Bioinformatics Analysis and Machine Learning Algorithms.

Objective: This study aimed to identify osteoporosis-related core genes using bioinformatics analysis and machine learning algorithms. Methods: mRNA expression profiles of osteoporosis patients were obtained from the Gene Expression Profiles (GEO) database, with GEO35958 and GEO84500 used as training sets, and GEO35957 and GSE56116 as validation sets. Differential gene expression analysis was performed using the R software "limma" package. A weighted gene co-expression network analysis (WGCNA) was conducted to identify key modules and modular genes of osteoporosis. Kyoto Gene and Genome Encyclopedia (KEGG), Gene Ontology (GO), and gene set enrichment analysis (GSEA) were performed on the differentially expressed genes. LASSO, SVM-RFE, and RF machine learning algorithms were used to screen for core genes, which were subsequently validated in the validation set. Predicted microRNAs (miRNAs) from the core genes were also analyzed, and differential miRNAs were validated using quantitative real-time PCR (qPCR) experiments. Results: A total of 1280 differentially expressed genes were identified. A disease key module and 215 module key genes were identified by WGCNA. Three core genes (ADAMTS5, COL10A1, KIAA0040) were screened by machine learning algorithms, and COL10A1 had high diagnostic value for osteoporosis. Four core miRNAs (has-miR-148a-3p, has-miR-195-3p, has-miR-148b-3p, has-miR-4531) were found by intersecting predicted miRNAs with differential miRNAs from the dataset (GSE64433, GSE74209). The qPCR experiments validated that the expression of has-miR-195-3p, has-miR-148b-3p, and has-miR-4531 was significantly increased in osteoporosis patients. Conclusion: This study demonstrated the utility of bioinformatics analysis and machine learning algorithms in identifying core genes associated with osteoporosis.

Inter-modality source coupling: a fully-automated whole-brain data-driven structure-function fingerprint shows replicable links to reading in a large-scale (N∼8K) analysis.

OBJECTIVE: Both structural and functional brain changes have been individually associated with developing cognitive processes such as reading. However, there is limited research about the combined influence of resting-state functional and structural magnetic resonance imaging (rs-fMRI and sMRI) features in reading development, which could provide insights into the interplay between brain structure and function in shaping cognitive growth. We propose a method called inter-modality source coupling (IMSC) to study the coupling between the rs-fMRI and sMRI and its relationship to reading ability in school-age children. METHODS: This approach is applied to baseline data from four thousand participants (9-11 years) and replicated in a second group. Our analysis focused on the relationship of IMSC to overall reading score. RESULTS: Our findings indicate that higher reading ability was linked with increased function-structure coupling among higher-level cortical regions, particularly those links between the inferior parietal lobule and inferior frontal areas, and conversely, lower reading ability was associated with enhanced function-structure coupling among the fusiform and lingual gyrus. Our study found evidence of spatial correspondence between the data indicating an interplay between brain structure and function in our participants. CONCLUSION: Our approach revealed a linked pattern of whole brain structure to the corresponding functional connectivity pattern that correlated with reading ability. This novel IMSC analysis method provides a new approach to study the multimodal relationship between brain function and structure. SIGNIFICANCE: These findings have interesting implications for understanding the multimodal complexity underlying the development of the neural basis for reading ability in school-aged children.

Preservation of Organic Carbon Associated with Iron on Continental Shelves Influenced by Hydrodynamic Processes.

Understanding the environmental fate of organic carbon associated with iron (OC-Fe) is critically important for investigating OC preservation in aquatic systems. Here, we first investigate 13C and 14C isotopes of OC-Fe within grain size-fractionated sediments retrieved from the East China Sea and estimate their sources and reactivities of OC-Fe through isotope-mixing models and thermal pyrolysis approaches in order to reveal the fate of OC-Fe on continental shelves influenced by hydrodynamic processes. Our results show that the OC-Fe proportion in total OC (fOC-Fe) in the sortable silt fractions (20-63 µm) is the highest among three grain size fractions, likely suggesting that hydrodynamics may enhance the iron protection on OC. In addition, Δ14COC-Fe values fall within the range of from -358.73 to -64.03‰, and both Δ14COC-Fe values and ancient OC-Fe% exhibit strong positive linear relationships with fOC-Fe. This emphasized that the hydrodynamic processes may cause the ancient OC to be tightly associated with Fe, accompanying OC-Fe aging. Our findings shed new light on the preservation of OC-Fe in marginal seas to advance the recognition of carbon "rusty sinks" in seafloor sediments.

Association between the oral microbiome and brain resting state connectivity in schizophrenia.

Recent microbiome-brain axis findings have shown evidence of the modulation of microbiome community as an environmental mediator in brain function and psychiatric illness. This work is focused on the role of the microbiome in understanding a rarely investigated environmental involvement in schizophrenia (SZ), especially in relation to brain circuit dysfunction. We leveraged high throughput microbial 16s rRNA sequencing and functional neuroimaging techniques to enable the delineation of microbiome-brain network links in SZ. N = 213 SZ and healthy control subjects were assessed for the oral microbiome. Among them, 139 subjects were scanned by resting-state functional magnetic resonance imaging (rsfMRI) to derive brain functional connectivity. We found a significant microbiome compositional shift in SZ beta diversity (weighted UniFrac distance, p = 6 × 10-3; Bray-Curtis distance p = 0.021). Fourteen microbial species involving pro-inflammatory and neurotransmitter signaling and H2S production, showed significant abundance alterations in SZ. Multivariate analysis revealed one pair of microbial and functional connectivity components showing a significant correlation of 0.46. Thirty five percent of microbial species and 87.8 % of brain functional network connectivity from each component also showed significant differences between SZ and healthy controls with strong performance in classifying SZ from healthy controls, with an area under curve (AUC) = 0.84 and 0.87, respectively. The results suggest a potential link between oral microbiome dysbiosis and brain functional connectivity alteration in relation to SZ, possibly through immunological and neurotransmitter signaling pathways and the hypothalamic-pituitary-adrenal axis, supporting for future work in characterizing the role of oral microbiome in mediating effects on SZ brain functional activity.

[Predictive value of a risk prediction model guided by the ratio of respiratory rate to diaphragmatic thickening fraction for the timing of noninvasive-invasive mechanical ventilation transition in patients with acute exacerbation of chronic obstructive pulmonary disease].

OBJECTIVE: To evaluate the predictive value of a risk prediction model guided by the ratio of respiratory rate to diaphragm thickening fraction (RR/DTF) for noninvasive-invasive mechanical ventilation transition timing in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD), through ultrasound evaluation of diaphragm movement indicators. METHODS: Sixty-four patients diagnosed with AECOPD and undergoing non-invasive ventilation (NIV), who were admitted to the department of critical care medicine of the First Affiliated Hospital of Jinzhou Medical University from January 2022 to July 2023 were enrolled. They were divided into NIV successful group and NIV failure group based on the outcome of NIV within 24 hours. Clinical indicators such as RR/DTF, diaphragmatic excursion (DE), tidal volume (VT), respiratory rate (RR), pH value, partial pressure of carbon dioxide (PaCO2), and sputum excretion disorder were compared between the two groups after 2 hours of NIV. The factors influencing NIV failure were included in binary Logistic regression analysis, and an RR/DTF oriented risk prediction model was established. Receiver operator characteristic curve (ROC curve) analysis was used to assess the predictive value of this model for the timing of noninvasive-invasive mechanical ventilation transition in AECOPD patients. RESULTS: Among 64 patients with AECOPD, with 43 in the NIV successful group and 21 in the NIV failure group. There were no statistically significant differences in baseline data such as age, gender, body mass index (BMI), oxygenation index (P/F), smoking history, and acute physiological and chronic health evaluation II (APACHE II) between the two groups of patients, indicating comparability. Compared to the NIV successful group, the NIV failure group showed a significantly increase in RR/DTF, RR, PaCO2, and sputum retention, while VT and DE were significantly decreased [RR/DTF (%): 1.00±0.18 vs. 0.89±0.22, RR (bpm): 21.64±3.13 vs. 19.62±2.98, PaCO2 (mmHg, 1 mmHg ≈ 0.133 kPa): 70.82±8.82 vs. 65.29±9.47, sputum retention: 57.1% vs. 30.2%, VT (mL): 308.09±14.89 vs. 324.48±23.82, DE (mm): 19.91±2.94 vs. 22.05±3.30, all P < 0.05]. Binary Logistic regression analysis showed that RR/DTF [odds ratio (OR) = 147.989, 95% confidence interval (95%CI) was 3.321-595.412, P = 0.010], RR (OR = 1.296, 95%CI was 1.006-1.670, P = 0.045), VT (OR = 0.966, 95%CI was 0.935-0.999, P = 0.044), PaCO2 (OR = 1.086, 95%CI was 1.006~1.173, P = 0.035), and sputum retention (OR = 4.533, 95%CI was 1.025-20.049, P = 0.046) were independent risk factors for predicting NIV failure in AECOPD patients. ROC curve analysis showed that the area under the curve (AUC) of 0.713 with a 95%CI of 0.587-0.839 (P = 0.005). The sensitivity was 72.73%, the specificity was 88.10%, the Youden index was 0.394, and the optimal cut-off value was 0.87. CONCLUSIONS: The RR/DTF risk prediction model has good predictive value for the timing of noninvasive-invasive mechanical ventilation transition in AECOPD patients.

Cross-continental environmental and genome-wide association study on children and adolescent anxiety and depression.

Anxiety and depression in children and adolescents warrant special attention as a public health concern given their devastating and long-term effects on development and mental health. Multiple factors, ranging from genetic vulnerabilities to environmental stressors, influence the risk for the disorders. This study aimed to understand how environmental factors and genomics affect children and adolescents anxiety and depression across three cohorts: Adolescent Brain and Cognitive Development Study (US, age of 9-10; N=11,875), Consortium on Vulnerability to Externalizing Disorders and Addictions (INDIA, age of 6-17; N=4,326) and IMAGEN (EUROPE, age of 14; N=1888). We performed data harmonization and identified the environmental impact on anxiety/depression using a linear mixed-effect model, recursive feature elimination regression, and the LASSO regression model. Subsequently, genome-wide association analyses with consideration of significant environmental factors were performed for all three cohorts by mega-analysis and meta-analysis, followed by functional annotations. The results showed that multiple environmental factors contributed to the risk of anxiety and depression during development, where early life stress and school support index had the most significant and consistent impact across all three cohorts. In both meta, and mega-analysis, SNP rs79878474 in chr11p15 emerged as a particularly promising candidate associated with anxiety and depression, despite not reaching genomic significance. Gene set analysis on the common genes mapped from top promising SNPs of both meta and mega analyses found significant enrichment in regions of chr11p15 and chr3q26, in the function of potassium channels and insulin secretion, in particular Kv3, Kir-6.2, SUR potassium channels encoded by the KCNC1, KCNJ11, and ABCCC8 genes respectively, in chr11p15. Tissue enrichment analysis showed significant enrichment in the small intestine, and a trend of enrichment in the cerebellum. Our findings provide evidences of consistent environmental impact from early life stress and school support index on anxiety and depression during development and also highlight the genetic association between mutations in potassium channels, which support the stress-depression connection via hypothalamic-pituitary-adrenal axis, along with the potential modulating role of potassium channels.

Stability-Enhanced Cisplatin Gold Nanoparticles As Therapeutic Anticancer Agents.

Using dendron chemistry, we developed stability enhanced, carboxylate surface modified (negatively charged dendron) AuNPs (Au-NCD). Since the carboxylate surface of Au-NCD is optimal for complexation with cisplatin (Pt) moieties, we further synthesized Pt loaded Au-NCD (Au-NCD/Pt) to serve as potential therapeutic anticancer agents. The size distribution, zeta potential and surface plasmon resonance of both Au-NCDs and Au-NCD/Pt were characterized via dynamic light scattering, scanning transmission electron microscopy and ultraviolet-visible spectrophotometry. Surface chemistry, Pt uptake, and Pt release were evaluated using inductively coupled plasma-mass spectrometry and X-ray photoelectron spectroscopy. Colloidal stability in physiological media over a wide pH range (1 to 13) and shelf-life stability (up to 6 months) were also assessed. Finally, the cytotoxicity of both Au-NCD and Au-NCD/Pt to Chinese hamster ovary cells (CHO K1; as a normal cell line) and to human lung epithelial cells (A549; as a cancer cell line) were evaluated. The results of these physicochemical and functional cytotoxicity studies with Au-NCD/Pt demonstrated that the particles exhibited superlative colloidal stability, cisplatin uptake and in vitro anticancer activity despite low amounts of Pt release from the conjugate.

Application of microwave digestion for complete dissolution of igneous silicate rock samples: A simple and quick sample preparation procedure.

In the area of geochemical analyses of rock solutions, achieving a complete sample dissolution is a fundamental prerequisite for obtaining accurate, precise and reliable analytical results. The challenge posed by the presence of resistant minerals such as zircon, rutile, corundum, spinel, tourmaline, beryl, chromite, and cassiterite in different silicate rocks is a well-recognized challenge in geological studies. These minerals, due to their resilient nature, demand additional efforts to ensure complete dissolution during sample preparation. The prevailing conventional sample digestion methods require several days of laboratory work and the handling of large amounts of multiple types of acids, which also increase sample blanks. Until recently, there was a widely held belief that microwave-assisted digestion, where microwave radiation is transformed to heat, faced limitations in achieving complete dissolution of refractory minerals. This prevailing opinion led to skepticism about the applicability of microwave-assisted digestion for sample preparation of e.g. igneous rock samples containing these minerals. This study introduces a novel, universal and quick closed-vessel (pressurized) high-temperature microwave-assisted digestion method appropriate for dissolution of all major types of igneous silicate rock samples, including rocks containing refractory minerals. This streamlined and expeditious procedure, comprising three steps, requires only a total time of ∼9 h. The method proves its versatility by successfully dissolving both, mafic igneous samples (e.g., basalt) with low-content of resistant minerals, and felsic igneous samples (e.g., granite) with relatively high-content of resistant minerals. To validate the reliability of this procedure, 36 trace elements were analyzed: Li, Be, Sc, V, Cr, Co, Ni, Cu, Zn, Ga, Rb, Sr, Y, Zr, Nb, Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Hf, Ta, Pb, Th and U in several geological Certified Reference Materials (CRMs). The CRMs including basalts JB-3, BCR-2, BHVO-2; andesites JA-2, AGV-2; granodiorite GSP-2; granite JG-2 and alkaline granite MGL-OShBO, were digested and analyzed using triple quadrupole Inductively Coupled-Plasma-Mass Spectrometer (ICP-QQQ). The results of the analysis demonstrate remarkable consistency, closely aligning with both certified and literature values.