Targeting mitochondria for ovarian aging: new insights into mechanisms and therapeutic potential.

Ovarian aging is a complex process characterized by a decline in oocyte quantity and quality, directly impacting fertility and overall well-being. Recent researches have identified mitochondria as pivotal players in the aging of ovaries, influencing various hallmarks and pathways governing this intricate process. In this review, we discuss the multifaceted role of mitochondria in determining ovarian fate, and outline the pivotal mechanisms through which mitochondria contribute to ovarian aging. Specifically, we emphasize the potential of targeting mitochondrial dysfunction through innovative therapeutic approaches, including antioxidants, metabolic improvement, biogenesis promotion, mitophagy enhancement, mitochondrial transfer, and traditional Chinese medicine. These strategies hold promise as effective means to mitigate age-related fertility decline and preserve ovarian health. Drawing insights from advanced researches in the field, this review provides a deeper understanding of the intricate interplay between mitochondrial function and ovarian aging, offering valuable perspectives for the development of novel therapeutic interventions aimed at preserving fertility and enhancing overall reproductive health.

Can transcranial photobiomodulation improve cognitive function in TBI patients? A systematic review.

Introduction: Transcranial photobiomodulation (tPBM) is a non-invasive neuromodulation technology which has become a promising therapy for treating many brain diseases. Although it has been confirmed in studies targeting neurological diseases including Alzheimer's and Parkinson's that tPBM can improve cognitive function, the effectiveness of interventions targeting TBI patients remains to be determined. This systematic review examines the cognitive outcomes of clinical trials concerning tPBM in the treatment of traumatic brain injury (TBI). Methods: We conducted a systematic literature review, following the PRISMA guidelines. The PubMed, Web of Science, Scopus, EMBASE, and Cochrane Library databases were searched before October 31, 2023. Results: The initial search retrieved 131 articles, and a total of 6 studies were finally included for full text-analysis after applying inclusion and exclusion criteria. Conclusion: Results showed improvements in cognition for patients with chronic TBI after tPBM intervention. The mechanism may be that tPBM increases the volume of total cortical gray matter (GM), subcortical GM, and thalamic, improves cerebral blood flow (CBF), functional connectivity (FC), and cerebral oxygenation, improving brain function. However, due to the significant heterogeneity in application, we cannot summarize the optimal parameters for tPBM treatment of TBI. In addition, there is currently a lack of RCT studies in this field. Therefore, given this encouraging but uncertain finding, it is necessary to conduct randomized controlled clinical trials to further determine the role of tPBM in cognitive rehabilitation of TBI patients.

Bacterial and fungal communities regulated directly and indirectly by tobacco-rape rotation promote tobacco production.

Tobacco continuous cropping is prevalent in intensive tobacco agriculture but often leads to microbial community imbalance, soil nutrient deficiency, and decreased crop productivity. While the tobacco-rape rotation has demonstrated significant benefits in increasing tobacco yield. Microorganisms play a crucial role in soil nutrient cycling and crop productivity. However, the internal mechanism of tobacco-rape rotation affecting tobacco yield through microbe-soil interaction is still unclear. In this study, two treatments, tobacco continuous cropping (TC) and tobacco-rape rotation (TR) were used to investigate how planting systems affect soil microbial diversity and community structure, and whether these changes subsequently affect crop yields. The results showed that compared with TC, TR significantly increased the Shannon index, Chao1 index, ACE index of bacteria and fungi, indicating increased microbial α-diversity. On the one hand, TR may directly affect the bacterial and fungal community structure due to the specificity of root morphology and root exudates in rape. Compared with TC, TR significantly increased the proportion of beneficial bacterial and fungal taxa while significantly reduced soil-borne pathogens. Additionally, TR enhanced the scale and complexity of microbial co-occurrence networks, promoting potential synergies between bacterial OTUs. On the other hand, TR indirectly changed microbial community composition by improving soil chemical properties and changing microbial life history strategies. Compared with TC, TR significantly increased the relative abundance of copiotrophs while reduced oligotrophs. Notably, TR significantly increased tobacco yield by 39.6% compared with TC. The relationships among yield, microbial community and soil chemical properties indicated that planting systems had the greatest total effect on tobacco yield, and the microbial community, particularly bacteria, had the greatest direct effect on tobacco yield. Our findings highlighted the potential of tobacco-rape rotation to increase yield by both directly and indirectly optimizing microbial community structure.

Emerging probing perspective of two-dimensional materials physics: terahertz emission spectroscopy.

Terahertz (THz) emission spectroscopy (TES) has emerged as a highly effective and versatile technique for investigating the photoelectric properties of diverse materials and nonlinear physical processes in the past few decades. Concurrently, research on two-dimensional (2D) materials has experienced substantial growth due to their atomically thin structures, exceptional mechanical and optoelectronic properties, and the potential for applications in flexible electronics, sensing, and nanoelectronics. Specifically, these materials offer advantages such as tunable bandgap, high carrier mobility, wideband optical absorption, and relatively short carrier lifetime. By applying TES to investigate the 2D materials, their interfaces and heterostructures, rich information about the interplay among photons, charges, phonons and spins can be unfolded, which provides fundamental understanding for future applications. Thus it is timely to review the nonlinear processes underlying THz emission in 2D materials including optical rectification, photon-drag, high-order harmonic generation and spin-to-charge conversion, showcasing the rich diversity of the TES employed to unravel the complex nature of these materials. Typical applications based on THz emissions, such as THz lasers, ultrafast imaging and biosensors, are also discussed. Step further, we analyzed the unique advantages of spintronic terahertz emitters and the future technological advancements in the development of new THz generation mechanisms leading to advanced THz sources characterized by wide bandwidth, high power and integration, suitable for industrial and commercial applications. The continuous advancement and integration of TES with the study of 2D materials and heterostructures promise to revolutionize research in different areas, including basic materials physics, novel optoelectronic devices, and chips for post-Moore's era.

Diagnostic performance of machine-learning algorithms for sepsis prediction: An updated meta-analysis.

BACKGROUND: Early identification of sepsis has been shown to significantly improve patient prognosis. OBJECTIVE: Therefore, the aim of this meta-analysis is to systematically evaluate the diagnostic efficacy of machine-learning algorithms for sepsis prediction. METHODS: Systematic searches were conducted in PubMed, Embase and Cochrane databases, covering literature up to December 2023. The keywords included machine learning, sepsis and prediction. After screening, data were extracted and analysed from studies meeting the inclusion criteria. Key evaluation metrics included sensitivity, specificity and the area under the curve (AUC) for diagnostic accuracy. RESULTS: The meta-analysis included a total of 21 studies with a data sample size of 4,158,941. Overall, the pooled sensitivity was 0.82 (95% confidence interval [CI] = 0.70-0.90; P< 0.001; I2=99.7%), the specificity was 0.91 (95% CI = 0.86-0.94; P< 0.001; I2= 99.9%), and the AUC was 0.94 (95% CI = 0.91-0.96). The subgroup analysis revealed that in the emergency department setting (6 studies), the pooled sensitivity was 0.79 (95% CI = 0.68-0.87; P< 0.001; I2= 99.6%), the specificity was 0.94 (95% CI 0.90-0.97; P< 0.001; I2= 99.9%), and the AUC was 0.94 (95% CI = 0.92-0.96). In the Intensive Care Unit setting (11 studies), the sensitivity was 0.91 (95% CI = 0.75-0.97; P< 0.001; I2= 98.3%), the specificity was 0.85 (95% CI = 0.75-0.92; P< 0.001; I2= 99.9%), and the AUC was 0.93 (95% CI = 0.91-0.95). Due to the limited number of studies in the in-hospital and mixed settings (n< 3), no pooled analysis was performed. CONCLUSION: Machine-learning algorithms have demonstrated excellent diagnostic accuracy in predicting the occurrence of sepsis, showing potential for clinical application.

Silk-based intelligent fibers and textiles: structures, properties, and applications.

Multifunctional fibers represent a cornerstone of human civilization, playing a pivotal role in numerous aspects of societal development. Natural biomaterials, in contrast to synthetic alternatives, offer environmental sustainability, biocompatibility, and biodegradability. Among these biomaterials, natural silk is favored in biomedical applications and smart fiber technology due to its accessibility, superior mechanical properties, diverse functional groups, controllable structure, and exceptional biocompatibility. This review delves into the intricate structure and properties of natural silk fibers and their extensive applications in biomedicine and smart fiber technology. It highlights the critical significance of silk fibers in the development of multifunctional materials, emphasizing their mechanical strength, biocompatibility, and biodegradability. A detailed analysis of the hierarchical structure of silk fibers elucidates how these structural features contribute to their unique properties. The review also encompasses the biomedical applications of silk fibers, including surgical sutures, tissue engineering, and drug delivery systems, along with recent advancements in smart fiber applications such as sensing, optical technologies, and energy storage. The enhancement of functional properties of silk fibers through chemical or physical modifications is discussed, suggesting broader high-end applications. Additionally, the review addresses current challenges and future directions in the application of silk fibers in biomedicine and smart fiber technologies, underscoring silk's potential in driving contemporary technological innovations. The versatility and sustainability of silk fibers position them as pivotal elements in contemporary materials science and technology, fostering the development of next-generation smart materials.

Recent research progress on tumour-specific responsive hydrogels.

Most existing hydrogels, even recently developed injectable hydrogels that undergo a reversible sol-gel phase transition in response to external stimuli, are designed to gel immediately before or after implantation/injection to prevent the free diffusion of materials and drugs; however, the property of immediate gelation leads to a very weak tumour-targeting ability, limiting their application in anticancer therapy. Therefore, the development of tumour-specific responsive hydrogels for anticancer therapy is imperative because tumour-specific responses improve their tumour-targeting efficacy, increase therapeutic effects, and decrease toxicity and side effects. In this review, we introduce the following three types of tumour-responsive hydrogels: (1) hydrogels that gel specifically at the tumour site; (2) hydrogels that decompose specifically at the tumour site; and (3) hydrogels that react specifically with tumours. For each type, their compositions, the mechanisms of tumour-specific responsiveness and their applications in anticancer treatment are comprehensively discussed.

Head-to-head comparison of [68Ga]Ga-FAPI-04 PET and [18F]FDG PET in the detection of cancer recurrence: a systematic review and meta-analysis.

Background: The comparative diagnostic performance of [68Ga]Ga-fibroblast activation protein inhibitors-04 {[68Ga]Ga-FAPI-04} positron emission tomography (PET) and fluorodeoxyglucose F 18 {[18F]FDG} PET in identifying cancer recurrence remains uncertain. The purpose of our study was to compare the diagnostic performance of [68Ga]Ga-FAPI-04 PET and [18F]FDG PET imaging in cancer recurrence. Methods: Up until March 1, 2024, we searched PubMed, Embase, and Web of Science for pertinent papers. Studies examining the diagnostic utility of [68Ga]Ga-FAPI-04 PET and [18F]FDG PET for cancer recurrence were included. Using a bivariate fixed-effect model and random-effect model, the pooled sensitivity and specificity for [68Ga]Ga-FAPI-04 PET and [18F]FDG PET were reported as estimates with 95% confidence intervals (CIs). The I2 statistic was used to evaluate the heterogeneity among the pooled studies. The included studies' quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) approach. Results: In all, 508 papers were found during the first search; ultimately, 12 studies totaling 224 patients were included. The pooled sensitivity of [68Ga]Ga-FAPI-04 PET and [18F]FDG PET for cancer recurrence were 0.97 (95% CI: 0.90-1.00) and 0.69 (95% CI: 0.60-0.77). The pooled sensitivity of [68Ga]Ga-FAPI-04 PET and [18F]FDG PET for gastrointestinal cancer recurrence were 1.00 (95% CI: 0.97-1.00) and 0.57 (95% CI: 0.42-0.74). The pooled specificity of [68Ga]Ga-FAPI-04 PET and [18F]FDG PET for gastrointestinal cancer recurrence were 0.66 (95% CI: 0.15-1.00) and 0.46 (95% CI, 0.00-1.00). Conclusions: Based on the previous studies, [68Ga]Ga-FAPI-04 PET shows higher sensitivity compared to [18F]FDG PET in detecting tumor recurrence, especially in detecting gastrointestinal cancer recurrence. [68Ga]Ga-FAPI-04 PET shows similar specificity compared to [18F]FDG PET in detecting gastrointestinal cancer recurrence. The detection results, however, came from investigations using modest sample numbers. In this matter, more extensive prospective study is required.

Hypoxia promotes histone H3K9 lactylation to enhance LAMC2 transcription in esophageal squamous cell carcinoma.

Hypoxia promotes tumorigenesis and lactate accumulation in esophageal squamous cell carcinoma (ESCC). Lactate can induce histone lysine lactylation (Kla, a recently identified histone marks) to regulate transcription. However, the functional consequence of histone Kla under hypoxia in ESCC remains to be explored. Here, we reveal that hypoxia facilitates histone H3K9la to enhance LAMC2 transcription for proliferation of ESCC. We found that global level of Kla was elevated under hypoxia, and thus identified the landscape of histone Kla in ESCC by quantitative proteomics. Furthermore, we show a significant increase of H3K9la level induced by hypoxia. Next, MNase ChIP-seq and RNA-seq analysis suggest that H3K9la is enriched at the promoter of cell junction genes. Finally, we demonstrate that the histone H3K9la facilitates the expression of LAMC2 for ESCC invasion by in vivo and in vitro experiments. Briefly, our study reveals a vital role of histone Kla triggered by hypoxia in cancer.

Construction and Validation of a Predictive Model for Coronary Artery Disease Using Extreme Gradient Boosting.

Purpose: Early recognition of coronary artery disease (CAD) could delay its progress and significantly reduce mortality. Sensitive, specific, cost-efficient and non-invasive indicators for assessing individual CAD risk in community population screening are urgently needed. Patients and Methods: 3112 patients with CAD and 3182 controls were recruited from three clinical centers in China, and differences in baseline and clinical characteristics were compared. For the discovery cohort, the least absolute shrinkage and selection operator (LASSO) regression was used to identify significant features and four machine learning algorithms (logistic regression, support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost)) were applied to construct models for CAD risk assessment, the receiver operating characteristics (ROC) curve and precision-recall (PR) curve were conducted to evaluate their predictive accuracy. The optimal model was interpreted by Shapley additive explanations (SHAP) analysis and assessed by the ROC curve, calibration curve, and decision curve analysis (DCA) and validated by two external cohorts. Results: Using LASSO filtration, all included variables were considered to be statistically significant. Four machine learning models were constructed based on these features and the results of ROC and PR curve implied that the XGBoost model exhibited the highest predictive performance, which yielded a high area of ROC curve (AUC) of 0.988 (95% CI: 0.986-0.991) to distinguish CAD patients from controls with a sensitivity of 94.6% and a specificity of 94.6%. The calibration curve showed that the predicted results were in good agreement with actual observations, and DCA exhibited a better net benefit across a wide range of threshold probabilities. External validation of the model also exhibited favorable discriminatory performance, with an AUC, sensitivity, and specificity of 0.953 (95% CI: 0.945-0.960), 89.9%, and 87.1% in the validation cohort, and 0.935 (95% CI: 0.915-0.955), 82.0%, and 90.3% in the replication cohort. Conclusion: Our model is highly informative for clinical practice and will be conducive to primary prevention and tailoring the precise management for CAD patients.

Mapping multimorbidity progression among 190 diseases.

BACKGROUND: Current clustering of multimorbidity based on the frequency of common disease combinations is inadequate. We estimated the causal relationships among prevalent diseases and mapped out the clusters of multimorbidity progression among them. METHODS: In this cohort study, we examined the progression of multimorbidity among 190 diseases among over 500,000 UK Biobank participants over 12.7 years of follow-up. Using a machine learning method for causal inference, we analyzed patterns of how diseases influenced and were influenced by others in females and males. We used clustering analysis and visualization algorithms to identify multimorbidity progress constellations. RESULTS: We show the top influential and influenced diseases largely overlap between sexes in chronic diseases, with sex-specific ones tending to be acute diseases. Patterns of diseases that influence and are influenced by other diseases also emerged (clustering significance Pau > 0.87), with the top influential diseases affecting many clusters and the top influenced diseases concentrating on a few, suggesting that complex mechanisms are at play for the diseases that increase the development of other diseases while share underlying causes exist among the diseases whose development are increased by others. Bi-directional multimorbidity progress presents substantial clustering tendencies both within and across International Classification Disease chapters, compared to uni-directional ones, which can inform future studies for developing cross-specialty strategies for multimorbidity. Finally, we identify 10 multimorbidity progress constellations for females and 9 for males (clustering stability, adjusted Rand index >0.75), showing interesting differences between sexes. CONCLUSION: Our findings could inform the future development of targeted interventions and provide an essential foundation for future studies seeking to improve the prevention and management of multimorbidity.

Mapping out clusters of diseases is crucial to addressing the rising challenge of co-occurrence of multiple diseases, known as multimorbidity. However, the current way of grouping diseases based on their associations isn't enough to understand how they develop over time. We've come up with a new approach to map out how groups of diseases progress together based on the strength of their causal relationships. By looking at how each disease affects the development of others, we can get a better understanding of how they form clusters. Our research goes beyond just showing which diseases occur together, and it's a step toward improving how we prevent and manage multiple health conditions in the future.

Adipose-derived stem cells and antibiotics: A novel synergistic approach for treating implant-related osteomyelitis.

Implant-related osteomyelitis poses a significant challenge in orthopedic practice, particularly due to the increasing prevalence of antibiotic-resistant infections and biofilm-associated complications. This article focused on exploring the potential of combination therapy with adipose-derived stem cells (ADSCs) and antibiotics to overcome these challenges, thereby enhancing treatment efficacy. A systematic synthesis of the results of recent in vivo studies, predominantly those using rat models, was performed. Studies that evaluated the effectiveness of ADSCs combined with antibiotics against common pathogens in implant-related osteomyelitis, particularly Staphylococcus aureus and methicillin-resistant Staphylococcus epidermidis, were selected. A significant reduction in symptoms such as swelling, abscess formation, and bacterial burden in the ADSCs + antibiotic-treated group was observed in all studies. In addition, microcomputed tomography revealed reduced osteolysis, indicating enhanced bone preservation. Furthermore, histological examination revealed improved tissue structure and altered immune response, signifying the dual role of ADSCs in enhancing antibiotic action and modulating the immune system. This review highlights the promising role of the concurrent use of ADSCs and antibiotics in the treatment of implant-related osteomyelitis. This novel therapeutic strategy has the potential to revolutionize the management of complex orthopedic infections, especially those resistant to conventional treatments. However, further research is required to translate the results of animal studies into clinical applications and to develop optimized treatment protocols for human use.

Mendelian randomization based on genome-wide association studies and expression quantitative trait loci, predicting gene targets for the complexity of osteoarthritis as well as the clinical prognosis of the condition.

Background: Osteoarthritis (OA) entails a prevalent chronic ailment, marked by the widespread involvement of entire joints. Prolonged low-grade synovial inflammation serves as the key instigator for a cascade of pathological alterations in the joints. Objective: The study seeks to explore potential therapeutic targets for OA and investigate the associated mechanistic pathways. Methods: Summary-level data for OA were downloaded from the genome-wide association studies (GWAS) database, expression quantitative trait loci (eQTL) data were acquired from the eQTLGen consortium, and synovial chip data for OA were obtained from the GEO database. Following the integration of data and subsequent Mendelian randomization analysis, differential analysis, and weighted gene co-expression network analysis (WGCNA) analysis, core genes that exhibit a significant causal relationship with OA traits were pinpointed. Subsequently, by employing three machine learning algorithms, additional identification of gene targets for the complexity of OA was achieved. Additionally, corresponding ROC curves and nomogram models were established for the assessment of clinical prognosis in patients. Finally, western blotting analysis and ELISA methodology were employed for the initial validation of marker genes and their linked pathways. Results: Twenty-two core genes with a significant causal relationship to OA traits were obtained. Through the application of distinct machine learning algorithms, MAT2A and RBM6 emerged as diagnostic marker genes. ROC curves and nomogram models were utilized for evaluating both the effectiveness of the two identified marker genes associated with OA in diagnosis. MAT2A governs the synthesis of SAM within synovial cells, thereby thwarting synovial fibrosis induced by the TGF-ß1-activated Smad3/4 signaling pathway. Conclusion: The first evidence that MAT2A and RBM6 serve as robust diagnostic for OA is presented in this study. MAT2A, through its involvement in regulating the synthesis of SAM, inhibits the activation of the TGF-ß1-induced Smad3/4 signaling pathway, thereby effectively averting the possibility of synovial fibrosis. Concurrently, the development of a prognostic risk model facilitates early OA diagnosis, functional recovery evaluation, and offers direction for further therapy.

The molecular binding sequence transformation of soil organic matter and biochar dissolved black carbon antagonizes the transport of 2,4,6-trichlorophenol.

Dissolved organic matter (DOM) and dissolved black carbon (DBC) are significant environmental factors that influence the transport of organic pollutants. However, the mechanisms by which their molecular diversity affects pollutant transport remain unclear. This study elucidates the molecular binding sequence and adsorption sites through which DOM/DBC compounds antagonize the transport of 2,4,6-trichlorophenol (TCP) using column experiments and modelling. DBC exhibits a high TCP adsorption rate (kn = 5.32 × 10-22 mol1-n∙Ln-1∙min-1) and conditional stability constant (logK = 5.19-5.74), indicating a strong binding affinity and antagonistic effect on TCP. This is attributed to the high relative content of lipid/protein compounds in DBC (25.65 % and 30.28 %, respectively). Moreover, the small molecule lipid compounds showed stronger TCP adsorption energy (Ead = -0.0071 eV/-0.0093 eV) in DOM/DBC, combined with two-dimensional correlation spectroscopy model found that DOM/DBC antagonized TCP transport in the environment through binding sequences that transformed from lipid/protein small molecule compounds to lignin/tannin compounds. This study used a multifaceted approach to comprehensively assess the impact of DOM/DBC on TCP transport. It reveals that the molecular diversity of DOM/DBC is a critical factor affecting pollutant transport, providing important insights into the environmental trend and ecological effects of pollutants.

Transcription factor NFYA inhibits ferroptosis in lung adenocarcinoma cells by regulating PEBP1.

BACKGROUND: Ferroptosis is an iron-dependent programmed cell death mediated by lipid peroxidation. The purpose was to explore the molecular mechanism by which phosphatidylethanolamine-binding protein 1 (PEBP1) regulates ferroptosis in lung adenocarcinoma (LUAD), hoping to identify novel therapeutic targets for LUAD. METHODS: The expression, enrichment pathways and upstream transcription factors of PEBP1 were analyzed using bioinformatics tools. Dual-luciferase reporter assay and chromatin immunoprecipitation (ChIP) experiments were conducted to validate the interaction and binding relationship between PEBP1 and the upstream transcription factor nuclear transcription factor Y subunit α (NFYA). Quantitative reverse transcription PCR (qRT-PCR) was conducted to measure the expression levels of PEBP1 and NFYA mRNA in LUAD cells. Cell viability was detected by cell counting kit-8 assay. In addition, levels of malondialdehyde (MDA), Fe2+, and lipid reactive oxygen species (ROS) were assessed to evaluate ferroptosis levels in LUAD cells. RESULTS: PEBP1 was downregulated and significantly enriched in the ferroptosis signaling pathway in LUAD. Overexpression of PEBP1 suppressed cell viability remarkably, while levels of MDA, Fe2+, and lipid ROS were increased. Conversely, knockdown of PEBP1 produced the opposite effects. The upstream transcription factor NFYA, predicted to be involved in the regulation of PEBP1, was also upregulated in LUAD. Dual-luciferase reporter assay, ChIP, and molecular experiments revealed that NFYA transcriptionally suppressed the expression of PEBP1, and overexpression of NFYA could reverse the effects caused by PEBP1 overexpression. CONCLUSION: PEBP1 regulated ferroptosis in LUAD, and the transcription factor NFYA inhibited ferroptosis in LUAD cells by transcriptionally downregulating PEBP1 expression.

Temporal variations in absorption and translocation of heavy metal(loid)s in pak choi (Brassica rapa L.) under open-field and greenhouse cultivation.

Elucidating the absorption and translocation of heavy metal(loid)s by common vegetables across different growth environments and stages is crucial for conducting accurate environmental risk assessments and for associated control. This study investigated temporal variations in the absorption and translocation capacities of pak choi (Brassica rapa L.) for As, Cd, Cr, Cu, Pb, and Zn in polluted soils during the plant growth cycle under greenhouse and open-field cultivation modes. Results showed high root metal(loid) bioconcentration factors and root-to-shoot translocation factors for Cd (0.25 and 1.44, respectively) and Zn (0.26 and 1.01), but low values for As (0.06 and 0.88) and Pb (0.06 and 0.87). The Cd concentration in the aerial edible parts peaked during the early slow growth period, whereas other heavy metal(loid)s peaked during the later stable maturity period. Root bioconcentration and root-to-shoot translocation factors did not significantly differ between cultivation modes. However, greenhouse cultivation exhibited lower average Cd and Zn concentrations in the edible parts and cumulative uptake amounts of most metal(loid)s than open-field cultivation during the typical harvest period spanning days 60 and 90. Short-term transitioning from open-field to greenhouse cultivation may reduce health risks associated with heavy metal(loid) intake via pak choi consumption. These findings facilitate sustainable agricultural practices and food safety management.

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OBJECTIVES: To develop effective measures to reduce antibiotic use duration in very low birth weight (VLBW) preterm infants in the neonatal intensive care unit through quality improvement methods. METHODS: The study population consisted of hospitalized VLBW preterm infants, with the percentage of hospitalization time during which antibiotics were used from November 2020 to June 2021 serving as the baseline. The specific quality improvement goal was to reduce the duration of antibiotic use. Factors affecting antibiotic use duration in preterm infants were analyzed using Pareto charts. Key drivers were identified, and specific interventions were formulated based on the stages of antibiotic use. Changes in the percentage of antibiotic use duration were monitored with run charts until the quality improvement target was achieved. RESULTS: From November 2020 to June 2021, the baseline antibiotic use duration percentage was 49%, with a quality improvement target to reduce this by 10% within 12 months. The Pareto analysis indicated that major factors influencing antibiotic duration included non-standard antibiotic use; delayed cessation of antibiotics when no infection evidence was present; prolonged central venous catheter placement; insufficient application of kangaroo care; and delayed progress in enteral nutrition. The interventions implemented included: (1) establishing sepsis evaluation and management standards; (2) educating medical staff on the rational use of antibiotics for preterm infants; (3) supervising the enforcement of antibiotic use standards during ward rounds; (4) for those without clear signs of infection and with negative blood cultures, discontinued the use of antibiotics 36 hours after initiation; (5) reducing the duration of central venous catheterization and parenteral nutrition to lower the risk of infection in preterm infants. The control chart showed that with continuous implementation of interventions, the percentage of antibiotic use duration was reduced from 49% to 32%, a statistically significant decrease. CONCLUSIONS: The application of quality improvement tools based on statistical principles and process control may significantly reduce the antibiotic use duration in VLBW preterm infants. Citation:Chinese Journal of Contemporary Pediatrics, 2024, 26(7): 736-742.

Aerobic mechanochemical reversible-deactivation radical polymerization.

Polymer materials suffer mechano-oxidative deterioration or degradation in the presence of molecular oxygen and mechanical forces. In contrast, aerobic biological activities combined with mechanical stimulus promote tissue regeneration and repair in various organs. A synthetic approach in which molecular oxygen and mechanical energy synergistically initiate polymerization will afford similar robustness in polymeric materials. Herein, aerobic mechanochemical reversible-deactivation radical polymerization was developed by the design of an organic mechano-labile initiator which converts oxygen into activators in response to ball milling, enabling the reaction to proceed in the air with low-energy input, operative simplicity, and the avoidance of potentially harmful organic solvents. In addition, this approach not only complements the existing methods to access well-defined polymers but also has been successfully employed for the controlled polymerization of (meth)acrylates, styrenic monomers and solid acrylamides as well as the synthesis of polymer/perovskite hybrids without solvent at room temperature which are inaccessible by other means.