Occurrence of microplastics in the headwaters of Yellow River on the Tibetan Plateau: Source analysis and ecological risk assessment.

The widespread occurrence of Microplastics (MPs) has aroused increasing concerns. However, the fate of MPs in remote areas remains poorly understood. Here, the spatial distribution, potential sources, and environmental risks of MPs were analyzed in the headstream of the Yellow River on the eastern Tibetan Plateau. The average MP abundances are (464.3 ± 200.9) items /m3 and (63.6 ± 34.7) items /kg in the water and sediment, respectively, with both majority polymer is polypropylene (PP) (water: 28.7 %; sediment: 25.2 %). The structural equation modeling and conditional fragmentation model were used in this study to analyze the source and impact factors of riverine MPs. According to the models, MPs were influenced by water quality parameters and anthropogenic activities. Furthermore, the source analysis through MP characteristics and statistical analysis showed that the main sources of MPs include domestic sewage, plastic waste disposal, and the use of agricultural plastic film. Moreover, the differences in MP sources along the river were distinguished by the conditional fragmentation model. The potential ecological risks of MPs were evaluated, resulting in relatively medium-to-low levels. Our findings will serve as important references for improving the understanding of the plateau environmental impacts of MP distribution in the headwaters of large rivers.

Convergence analysis of sparse TSK fuzzy systems based on spectral Dai-Yuan conjugate gradient and application to high-dimensional feature selection.

Dealing with high-dimensional problems has always been a key and challenging issue in the field of fuzzy systems. Traditional Takagi-Sugeno-Kang (TSK) fuzzy systems face the challenges of the curse of dimensionality and computational complexity when applied to high-dimensional data. To overcome these challenges, this paper proposes a novel approach for optimizing TSK fuzzy systems by integrating the spectral Dai-Yuan conjugate gradient (SDYCG) algorithm and the smoothing group L0 regularization technique. This method aims to address the challenges faced by TSK fuzzy systems in handling high-dimensional problems. The smoothing group L0 regularization technique is employed to introduce sparsity, select relevant features, and improve the generalization ability of the model. The SDYCG algorithm effectively accelerates convergence and enhances the learning performance of the network. Furthermore, we prove the weak convergence and strong convergence of the new algorithm under the strong Wolfe criterion, which means that the gradient norm of the error function with respect to the weight vector converges to zero, and the weight sequence approaches a fixed point.

Single-nucleus chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry.

Single-nucleus analysis allows robust cell-type classification and helps to establish relationships between chromatin accessibility and cell-type-specific gene expression. Here, using samples from 92 women of several genetic ancestries, we developed a comprehensive chromatin accessibility and gene expression atlas of the breast tissue. Integrated analysis revealed ten distinct cell types, including three major epithelial subtypes (luminal hormone sensing, luminal adaptive secretory precursor (LASP) and basal-myoepithelial), two endothelial and adipocyte subtypes, fibroblasts, T cells, and macrophages. In addition to the known cell identity genes FOXA1 (luminal hormone sensing), EHF and ELF5 (LASP), TP63 and KRT14 (basal-myoepithelial), epithelial subtypes displayed several uncharacterized markers and inferred gene regulatory networks. By integrating breast epithelial cell gene expression signatures with spatial transcriptomics, we identified gene expression and signaling differences between lobular and ductal epithelial cells and age-associated changes in signaling networks. LASP cells and fibroblasts showed genetic ancestry-dependent variability. An estrogen receptor-positive subpopulation of LASP cells with alveolar progenitor cell state was enriched in women of Indigenous American ancestry. Fibroblasts from breast tissues of women of African and European ancestry clustered differently, with accompanying gene expression differences. Collectively, these data provide a vital resource for further exploring genetic ancestry-dependent variability in healthy breast biology.

Sequential action-induced invariant representation for reinforcement learning.

How to accurately learn task-relevant state representations from high-dimensional observations with visual distractions is a realistic and challenging problem in visual reinforcement learning. Recently, unsupervised representation learning methods based on bisimulation metrics, contrast, prediction, and reconstruction have shown the ability for task-relevant information extraction. However, due to the lack of appropriate mechanisms for the extraction of task information in the prediction, contrast, and reconstruction-related approaches and the limitations of bisimulation-related methods in domains with sparse rewards, it is still difficult for these methods to be effectively extended to environments with distractions. To alleviate these problems, in the paper, the action sequences, which contain task-intensive signals, are incorporated into representation learning. Specifically, we propose a Sequential Action-induced invariant Representation (SAR) method, which decouples the controlled part (i.e., task-relevant information) and the uncontrolled part (i.e., task-irrelevant information) in noisy observations through sequential actions, thereby extracting effective representations related to decision tasks. To achieve it, the characteristic function of the action sequence's probability distribution is modeled to specifically optimize the state encoder. We conduct extensive experiments on the distracting DeepMind Control suite while achieving the best performance over strong baselines. We also demonstrate the effectiveness of our method at disregarding task-irrelevant information by applying SAR to real-world CARLA-based autonomous driving with natural distractions. Finally, we provide the analysis results of generalization drawn from the generalization decay and t-SNE visualization. Code and demo videos are available at https://github.com/DMU-XMU/SAR.git.

Integrated Single-Cell Multiomic Profiling of Caudate Nucleus Suggests Key Mechanisms in Alcohol Use Disorder.

Alcohol use disorder (AUD) is likely associated with complex transcriptional alterations in addiction-relevant brain regions. We characterize AUD-associated differences in cell type-specific gene expression and chromatin accessibility in the caudate nucleus by conducting a single-nucleus RNA-seq assay and a single-nucleus RNA-seq + ATAC-seq (multiome) assay on caudate tissue from 143 human postmortem brains (74 with AUD). We identified 17 cell types. AUD was associated with a higher proportion of microglia in an activated state and more astrocytes in a reactive state. There was widespread evidence for differentially expressed genes across cell types with the most identified in oligodendrocytes and astrocytes, including genes involved in immune response and synaptic regulation, many of which appeared to be regulated in part by JUND and OLIG2 . Microglia-astrocyte communication via interleukin-1 beta, and microglia-astrocyte-oligodendrocyte interaction via transforming growth factor beta 1 were increased in individuals with AUD. Expression quantitative trait loci analysis revealed potential driver genes of AUD, including ADAL, that may protect against AUD in medium spiny neurons and interneurons. This work provides a thorough profile of the effects of AUD in the human brain and identifies several promising genes for further study.

Targeting Ovarian Cancer Stem Cells by Dual Inhibition of the Long Noncoding RNA HOTAIR and Lysine Methyltransferase EZH2.

Persistence of cancer stem cells (CSCs) is believed to contribute to resistance to platinum-based chemotherapy and disease relapse in ovarian cancer, the fifth leading cause of cancer-related death among US women. HOXC transcript antisense RNA (HOTAIR) is a long noncoding RNA (lncRNA) overexpressed in high-grade serous ovarian cancer and linked to chemoresistance. However, HOTAIR impacts chromatin dynamics in ovarian CSCs and how this oncogenic lncRNA contributes to drug resistant disease are incompletely understood. Here we generated HOTAIR knock-out (KO) high-grade serous ovarian cancer cell lines using paired CRISPR guide RNA design to investigate the function of HOTAIR. We show loss of HOTAIR function re-sensitized ovarian cancer cells to platinum treatment and decreased the population of ovarian CSCs. Furthermore, HOTAIR KO inhibited development of stemness-related phenotypes, including spheroid formation ability, as well as expression of key stemness-associated genes ALDH1A1, NOTCH3, SOX9, and PROM1. HOTAIR KO altered both the cellular transcriptome and chromatin accessibility landscape of multiple oncogenic-associated genes and pathways, including the NF-kB pathway. HOTAIR functions as an oncogene by recruiting enhancer of zeste 2 (EZH2) to catalyze H3K27 tri-methylation to suppress downstream tumor suppressor genes, and it was of interest to inhibit both HOTAIR and EZH2. In vivo, combining a HOTAIR inhibitor with an EZH2 inhibitor and platinum chemotherapy decreased tumor formation and increased survival. These results suggest a key role for HOTAIR in ovarian CSCs and malignant potential. Targeting HOTAIR in combination with epigenetic therapies may represents therapeutic strategy to ameliorate ovarian cancer progression and resistance to platinum-based chemotherapy.

Genome-wide meta-analyses of cross substance use disorders in European, African, and Latino ancestry populations.

Genetic risks for substance use disorders (SUDs) are due to both SUD-specific and SUD-shared genes. We performed the largest multivariate analyses to date to search for SUD-shared genes using samples of European (EA), African (AA), and Latino (LA) ancestries. By focusing on variants having cross-SUD and cross-ancestry concordant effects, we identified 45 loci. Through gene-based analyses, gene mapping, and gene prioritization, we identified 250 SUD-shared genes. These genes are highly expressed in amygdala, cortex, hippocampus, hypothalamus, and thalamus, primarily in neuronal cells. Cross-SUD concordant variants explained ~ 50% of the heritability of each SUD in EA. The top 5% individuals having the highest polygenic scores were approximately twice as likely to have SUDs as others in EA and LA. Polygenic scores had higher predictability in females than in males in EA. Using real-world data, we identified five drugs targeting identified SUD-shared genes that may be repurposed to treat SUDs.

TRIM59 deficiency promotes M1 macrophage activation and inhibits colorectal cancer through the STAT1 signaling pathway.

Tumor-associated macrophages play a crucial role in the tumor microenvironment. Tripartite motif 59 (TRIM59), a member of the tripartite motif (TRIM) family, is known to be associated with immunological diseases and macrophage activation. The functional and molecular mechanisms by which TRIM59 affects the occurrence and development of colorectal cancer (CRC) through macrophages are still not well understood. To address this, we generated macrophage-specific TRIM59 conditional knockout mice and utilized these mice to establish colitis-associated cancer and MC38 transplanted CRC models for further investigation. We found that the deficiency of TRIM59 in macrophages inhibited colorectal tumorigenesis in mice. This tumor-suppressive effect was achieved by promoting the activation of M1 macrophages via STAT1 signaling pathway. Further mechanistic studies revealed that TRIM59 could regulate macrophage polarization by ubiquitinating and degrading STAT1. These findings provide evidence that TRIM59 deficiency promotes M1 macrophage activation and inhibits CRC through the STAT1 signaling pathway, suggesting that the TRIM59/STAT1 signaling pathway may be a promising target for CRC.

Metaproteomics-informed stoichiometric modeling reveals the responses of wetland microbial communities to oxygen and sulfate exposure.

Climate changes significantly impact greenhouse gas emissions from wetland soil. Specifically, wetland soil may be exposed to oxygen (O2) during droughts, or to sulfate (SO42-) as a result of sea level rise. How these stressors - separately and together - impact microbial food webs driving carbon cycling in the wetlands is still not understood. To investigate this, we integrated geochemical analysis, proteogenomics, and stoichiometric modeling to characterize the impact of elevated SO42- and O2 levels on microbial methane (CH4) and carbon dioxide (CO2) emissions. The results uncovered the adaptive responses of this community to changes in SO42- and O2 availability and identified altered microbial guilds and metabolic processes driving CH4 and CO2 emissions. Elevated SO42- reduced CH4 emissions, with hydrogenotrophic methanogenesis more suppressed than acetoclastic. Elevated O2 shifted the greenhouse gas emissions from CH4 to CO2. The metabolic effects of combined SO42- and O2 exposures on CH4 and CO2 emissions were similar to those of O2 exposure alone. The reduction in CH4 emission by increased SO42- and O2 was much greater than the concomitant increase in CO2 emission. Thus, greater SO42- and O2 exposure in wetlands is expected to reduce the aggregate warming effect of CH4 and CO2. Metaproteomics and stoichiometric modeling revealed a unique subnetwork involving carbon metabolism that converts lactate and SO42- to produce acetate, H2S, and CO2 when SO42- is elevated under oxic conditions. This study provides greater quantitative resolution of key metabolic processes necessary for the prediction of CH4 and CO2 emissions from wetlands under future climate scenarios.

In silico generation and augmentation of regulatory variants from massively parallel reporter assay using conditional variational autoencoder.

Predicting the functional consequences of genetic variants in non-coding regions is a challenging problem. Massively parallel reporter assays (MPRAs), which are an in vitro high-throughput method, can simultaneously test thousands of variants by evaluating the existence of allele specific regulatory activity. Nevertheless, the identified labelled variants by MPRAs, which shows differential allelic regulatory effects on the gene expression are usually limited to the scale of hundreds, limiting their potential to be used as the training set for achieving a robust genome-wide prediction. To address the limitation, we propose a deep generative model, MpraVAE, to in silico generate and augment the training sample size of labelled variants. By benchmarking on several MPRA datasets, we demonstrate that MpraVAE significantly improves the prediction performance for MPRA regulatory variants compared to the baseline method, conventional data augmentation approaches as well as existing variant scoring methods. Taking autoimmune diseases as one example, we apply MpraVAE to perform a genome-wide prediction of regulatory variants and find that predicted regulatory variants are more enriched than background variants in enhancers, active histone marks, open chromatin regions in immune-related cell types, and chromatin states associated with promoter, enhancer activity and binding sites of cMyC and Pol II that regulate gene expression. Importantly, predicted regulatory variants are found to link immune-related genes by leveraging chromatin loop and accessible chromatin, demonstrating the importance of MpraVAE in genetic and gene discovery for complex traits.

Dynamic Single Cell Transcriptomics Defines Kidney FGF23/KL Bioactivity and Novel Segment-Specific Inflammatory Targets.

FGF23 via its coreceptor αKlotho (KL) provides critical control of phosphate metabolism, which is altered in rare and very common syndromes, however the spatial-temporal mechanisms dictating renal FGF23 functions remain poorly understood. Thus, developing approaches to modify specific FGF23-dictated pathways has proven problematic. Herein, wild type mice were injected with rFGF23 for 1, 4 and 12h and renal FGF23 bioactivity was determined at single cell resolution. Computational analysis identified distinct epithelial, endothelial, stromal, and immune cell clusters, with differential expressional analysis uniquely tracking FGF23 bioactivity at each time point. FGF23 actions were sex independent but critically relied upon constitutive KL expression mapped within proximal tubule (S1-S3) and distal tubule (DCT/CNT) cell sub-populations. Temporal KL-dependent FGF23 responses drove unique and transient cellular identities, including genes in key MAPK- and vitamin D-metabolic pathways via early- (AP-1-related) and late-phase (EIF2 signaling) transcriptional regulons. Combining ATACseq/RNAseq data from a cell line stably expressing KL with the in vivo scRNAseq pinpointed genomic accessibility changes in MAPK-dependent genes, including the identification of FGF23-dependent EGR1 distal enhancers. Finally, we isolated unexpected crosstalk between FGF23-mediated MAPK signaling and pro-inflammatory TNF receptor activation via NF-κB, which blocked FGF23 bioactivity in vitro and in vivo . Collectively, our findings have uncovered novel pathways at the single cell level that likely influence FGF23-dependent disease mechanisms. Translational statement: Inflammation and elevated FGF23 in chronic kidney disease (CKD) are both associated with poor patient outcomes and mortality. However, the links between these manifestations and the effects of inflammation on FGF23-mediated mineral metabolism within specific nephron segments remain unclear. Herein, we isolated an inflammatory pathway driven by TNF/NF-κB associated with regulating FGF23 bioactivity. The findings from this study could be important in designing future therapeutic approaches for chronic mineral diseases, including potential combination therapies or early intervention strategies. We also suggest that further studies could explore these pathways at the single cell level in CKD models, as well as test translation of our findings to interactions of chronic inflammation and elevated FGF23 in human CKD kidney datasets.

Towards AI-Driven Healthcare: Systematic Optimization, Linguistic Analysis, and Clinicians' Evaluation of Large Language Models for Smoking Cessation Interventions.

Creating intervention messages for smoking cessation is a labor-intensive process. Advances in Large Language Models (LLMs) offer a promising alternative for automated message generation. Two critical questions remain: 1) How to optimize LLMs to mimic human expert writing, and 2) Do LLM-generated messages meet clinical standards? We systematically examined the message generation and evaluation processes through three studies investigating prompt engineering (Study 1), decoding optimization (Study 2), and expert review (Study 3). We employed computational linguistic analysis in LLM assessment and established a comprehensive evaluation framework, incorporating automated metrics, linguistic attributes, and expert evaluations. Certified tobacco treatment specialists assessed the quality, accuracy, credibility, and persuasiveness of LLM-generated messages, using expert-written messages as the benchmark. Results indicate that larger LLMs, including ChatGPT, OPT-13B, and OPT-30B, can effectively emulate expert writing to generate well-written, accurate, and persuasive messages, thereby demonstrating the capability of LLMs in augmenting clinical practices of smoking cessation interventions.

Enhanced performance of thermally activated delayed fluorescent light emitting diodes by optimized host polarity.

The interaction between the intrinsic polarity of the host material and the TADF guest material affects charge injection and transport, exciton formation, charge recombination, and emission mechanisms. Therefore, understanding and controlling the interaction between the intrinsic polarity of the host material and the TADF guest material is very important to realize efficient TADF-OLED devices. This study investigated the molecular interaction between different polar host materials and a thermally activated delayed fluorescence material (DMAc-PPM). It has been found that interaction between the host and guest (π-π stacking interaction, multiple CH/π contacts) greatly influence the molecular transition dipole moment orientation of the guest. And the OLED devices based on the strong polar host (DPEPO) exhibited the highest EQEmax and lowest luminescence intensity, while devices using the weaker polar hosts mCP and CBP achieved higher luminance and lower EQEmax. Then, the strong polar host DPEPO was mixed with the weaker polar hosts CBP and mCP, respectively. The devices prepared based on the mixed-host DPEPO: mCP showed a 2.2 times improvement in EQEmax from 6.3% to 20.1% compared to the single-host mCP. The devices prepared based on the mixed-host DPEPO: CBP showed a 3.1 times improvement in luminance intensity from 1023 cd/m2 to 4236 cd/m2 compared to the single host of DPEPO. This suggests that optimizing the polarity of host materials has the potential to enhance the performance of solution prepared OLED devices.

Systematic proteomics analysis revealed different expression of laminin interaction proteins in breast cancer: lower in luminal subtype and higher in claudin-low subtype.

Background: Breast cancer is a major public health concern. Proteomics enables identification of proteins with aberrant properties. Here, we identified proteins with abnormal expression levels in breast cancer tissues and systematically analyzed and validated the data to locate potential diagnostic and therapeutic targets. Methods: Protein expression level in breast cancer tissues and para-carcinoma tissues were detected by Isobaric Tags for Relative and Absolute Quantification (iTRAQ) technology and further screened through Gene Expression Profiling Interactive Analysis (GEPIA) database. Cellular components, protein domain and Reactome pathway analysis were performed to screen functional targets. Abnormal expression levels of functional targets were validated by Oncomine database, quantitative real time polymerase chain reaction (qRT-PCR) and proteomics detection. Protein correlation analysis was performed to explain the abnormal expression levels of potential targets in breast cancer. Results: Overall, 207 and 207 proteins were up- and down-regulated, respectively, in breast cancer tissues, and approximately 50% were also detected in the GEPIA database. The overlapping proteins were mainly extracellular proteins containing epidermal growth factor-like domain in leukocyte adhesion molecule (EGF-Lam) domain and enriched in laminin interaction pathway. Moreover, the downregulated laminin interaction proteins could be functional targets, which were also validated through Oncomine-Richardson and Oncomine-Curtis database. However, the lower expression level of laminin interaction proteins only fit for luminal breast cancer cells with no or low metastasis ability because the proteins achieved higher expression level in more invasive claudin-low breast cancer cells. In addition, when compared with corresponding in situ carcinoma tissues, above-mentioned proteins also showed higher expression levels in invasive carcinoma tissues. Finally, we have revealed the negative correlation between the laminin interaction proteins and the claudins. Conclusions: The laminin interaction protein, especially for laminins with ß1 and γ1 subunits and their integrin receptors with α1 and α6 subunits, showed lower expression levels in luminal breast cancer with no or lower metastatic ability, but showed higher expression levels in claudin-low breast cancer with higher metastatic ability; and their higher expression could be related to the low claudin expression.

Oxygen tension-dependent variability in the cancer cell kinome impacts signaling pathways and response to targeted therapies.

Most cells in solid tumors are exposed to oxygen levels between 0.5% and 5%. We developed an approach that allows collection, processing, and evaluation of cancer and non-cancer cells under physioxia, while preventing exposure to ambient air. This aided comparison of baseline and drug-induced changes in signaling pathways under physioxia and ambient oxygen. Using tumor cells from transgenic models of breast cancer and cells from breast tissues of clinically breast cancer-free women, we demonstrate oxygen-dependent differences in cell preference for epidermal growth factor receptor (EGFR) or platelet-derived growth factor receptor beta (PDGFRß) signaling. Physioxia caused PDGFRß-mediated activation of AKT and extracellular regulated kinase (ERK) that reduced sensitivity to EGFR and phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) inhibition and maintained PDGFRß+ epithelial-mesenchymal hybrid cells with potential cancer stem cell (CSC) properties. Cells in ambient air displayed differential EGFR activation and were more sensitive to targeted therapies. Our data emphasize the importance of oxygen considerations in preclinical cancer research to identify effective drug targets and develop combination therapy regimens.

In-Silico and in vitro Studies Revealed that Rosmarinic Acid Inhibited Methanogenesis via Regulating Composition and Function of Rumen Microbiota.

Inhibition of methyl-coenzyme M reductase can suppress the activity of ruminal methanogens, thereby reducing enteric methane emissions of ruminants. However, developing specific and environmentally friendly inhibitors is a challenging endeavor. To identify a natural and effective methane inhibitor that specifically targets methyl-coenzyme M reductase, molecular docking technology was employed to screen a library of phytogenic compounds. A total of 52 candidate compounds were obtained through molecular docking technique. Rosmarinic acid (RA) was one of the compounds that could traverse a narrow channel and bind to the active sites of methyl-coenzyme M reductase, with a calculated binding free energy of -9.355 kcal/mol. Furthermore, the effects of rosmarinic acid supplementation on methane production, rumen fermentation, and the microorganism's community in dairy cows were investigated through in vitro rumen fermentation simulations according to a random design. Supplementation of RA resulted in a 15% decrease in methane production compared with the control. In addition, RA increased the molar proportion of acetate and propionate, whereas the sum of acetate and butyrate divided by propionate was decreased. At the bacterial level, the relative abundance of Rikenellaceae RC9 gut group, Christensenellaceae R7 group, Candidatus Saccharimonas, Desulfovibrio, and Lachnospiraceae FE2018 group decreased with RA supplementation. Conversely, the addition of RA significantly increased the relative abundance of DNF00809 (a genus from Eggerthellaceae), Denitrobacterium, an unclassified genus from Eggerthellaceae, an unclassified genus from Bacteroidales, and an unclassified genus from Atopobiaceae. At the archaeal level, the relative abundance of Methanobrevibacter decreased, while that of Methanosphaera increased with the RA supplementation. These findings suggested that RA has the potential to be used as a novel natural additive for inhibiting ruminal methane production.

The correlation between serum calcium levels and prognosis in patients with severe acute osteomyelitis.

Objective: To explore the relationship between serum calcium levels and the prognosis of severe acute osteomyelitis, and to assess the effectiveness of calcium levels in prognostic evaluation. Methods: Relevant patient records of individuals diagnosed with severe acute osteomyelitis were obtained for this retrospective study from the Medical Information Mart for Intensive Care (MIMIC-IV). The study aimed to assess the impact of different indicators on prognosis by utilizing COX regression analysis. To enhance prognostic prediction for critically ill patients, a nomogram was developed. The discriminatory capacity of the nomogram was evaluated using the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve, in addition to the calibration curve. Result: The study analyzed a total of 1,133 cases of severe acute osteomyelitis, divided into the survivor group (1,025 cases) and the non-survivor group (108 cases). Significant differences were observed between the two groups in terms of age, hypertension, sepsis, renal injury, and various laboratory indicators, including WBC, PLT, Ca2+, CRP, hemoglobin, albumin, and creatinine (P<0.05). However, no significant differences were found in race, gender, marital status, detection of wound microbiota, blood sugar, lactate, and ALP levels. A multivariate COX proportional hazards model was constructed using age, hypertension, sepsis, Ca2+, creatinine, albumin, and hemoglobin as variables. The results revealed that hypertension and sepsis had a significant impact on survival time (HR=0.514, 95% CI 0.339-0.779, P=0.002; HR=1.696, 95% CI 1.056-2.723, P=0.029). Age, hemoglobin, Ca2+, albumin, and creatinine also showed significant effects on survival time (P<0.05). However, no statistically significant impact on survival time was observed for the other variables (P>0.05). To predict the survival time, a nomogram was developed using the aforementioned indicators and achieved an AUC of 0.841. The accuracy of the nomogram was further confirmed by the ROC curve and calibration curve. Conclusion: According to the findings, this study establishes that a reduction in serum calcium levels serves as a distinct and standalone predictor of mortality among individuals diagnosed with severe acute osteomyelitis during their stay in the Intensive Care Unit (ICU) within a span of two years.

Fabrication of lignosulfonate-derived porous carbon via pH-tunable self-assembly strategy for efficient atrazine removal.

Herein, a straightforward protocol was developed for the one-pot synthesis of N-doped lignosulfonate-derived carbons (NLDCs) with a tunable porous structure using natural amino acids-templated self-assembly strategy. Specifically, histidine was employed as a template reagent, leading to the preparation of 10-NLDC-21 with remarkable characteristics, including the large specific surface area (SBET = 1844.5 m2/g), pore volume (Vmes = 1.22 cm3/g) and efficient adsorption for atrazine (ATZ) removal. The adsorption behavior of ATZ by NLDCs followed the Langmuir and pseudo-second-order models, suggesting a monolayer chemisorption nature of ATZ adsorption with the maximum adsorption capacity reached up to 265.77 mg/g. Furthermore, NLDCs exhibited excellent environmental adaptability and recycling performance. The robust affinity could be attributed to multi-interactions including pore filling, electrostatic attraction, hydrogen bonding and π-π stacking between the adsorbents and ATZ molecules. This approach offers a practical method for exploring innovative bio-carbon materials for sewage treatment.

Elastic stable intramedullary nail combined with Kirschner wire (E-K) technique for treating pediatric distal tibial diaphyseal metaphyseal junction (DTDMJ) fractures.

Objective: Elastic stable intramedullary nail (ESIN) is a commonly used method for treating diaphyseal fractures of the tibia, but its application in Distal Tibial Diaphyseal Metaphyseal Junction (DTDMJ) fractures has been a subject of controversy. This study aims to evaluate the clinical efficacy of the Elastic stable intramedullary nail-Kirschner wire (E-K) technique in treating pediatric DTDMJ fractures, providing better clinical decision-making for clinicians in diagnosing and treating such fractures. Methods: We conducted a retrospective analysis of patients aged 3-9 years who received treatment at our hospital from January 2019-January 2021 for distal tibial diaphyseal metaphyseal junction (DTDMJ) fractures. Based on their surgical procedures, they were categorized into the Elastic Stable Intramedullary Nail-Kirschner wire group (E-K) and the ESIN group. Demographic data, surgical duration, clinical outcomes, complications, and imaging data were recorded. Results: The study included a total of 57 patients, with 24 cases in the E-K group and 33 cases in the ESIN group. There were 30 males and 27 females. The average age was (6.25 ± 1.59) years in the E-K group and (6.27 ± 1.48) years in the ESIN group. There were no significant differences between the two groups in terms of gender, age, weight, time from injury to surgery, follow-up time, side of injury, associated injuries, nail site infection, deep infection, and nail removal time (P > 0.05). Neither group experienced nonunion or refracture. The E-K group exhibited significantly lower coronal and sagittal plane angular values at the final follow-up compared to the ESIN group (P < 0.001). In the E-K group, the final follow-up coronal plane angle was 2.67 (1.09)°, while in the ESIN group, it was 6.55 (2.05)°. The final follow-up sagittal plane angle was 3.12 (1.54)° in the E-K group and 7.58 (1.48)° in the ESIN group. Both groups showed good alignment in the initial postoperative x-rays, with no statistically significant differences. However, during clinical healing, the ESIN group exhibited significant displacement, whereas the E-K group had minimal displacement, demonstrating a significant statistical difference (P < 0.001). There was a statistically significant difference in the AOFAS joint function assessment between the two groups (P = 0.027). Conclusion: The E-K technique is a viable option for treating DTDMJ fractures in pediatric patients, with well-established clinical efficacy. Its advantages include a straightforward surgical procedure, safety, and a low incidence of severe complications.