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The purpose of this study was to assess the feasibility of machine learning (ML) in predicting the risk of end-stage kidney disease (ESKD) from patients with chronic kidney disease (CKD). Data were obtained from a longitudinal CKD cohort. Predictor variables included patients’ baseline characteristics and routine blood test results. The outcome of interest was the presence or absence of ESKD by the end of 5 years. Missing data were imputed using multiple imputation. Five ML algorithms, including logistic regression, naïve Bayes, random forest, decision tree, and K-nearest neighbors were trained and tested using fivefold cross-validation. The performance of each model was compared to that of the Kidney Failure Risk Equation (KFRE). The dataset contained 748 CKD patients recruited between April 2006 and March 2008, with the follow-up time of 6.3 ± 2.3 years. ESKD was observed in 70 patients (9.4%). Three ML models, including the logistic regression, naïve Bayes and random forest, showed equivalent predictability and greater sensitivity compared to the KFRE. The KFRE had the highest accuracy, specificity, and precision. This study showed the feasibility of ML in evaluating the prognosis of CKD based on easily accessible features. Three ML models with adequate performance and sensitivity scores suggest a potential use for patient screenings. Future studies include external validation and improving the models with additional predictor variables.

Mental disorders account for a large and increasing health burden worldwide, as shown in the Global Burden of Diseases (GBD) Study 2010. Unpacking how this burden in children and adolescents varies with sex, geographical regions, and ethnicities and how it has changed in the last 3 decades are important to improve the existing public health policies and prevention strategies. The study was conducted using GBD 2019 database. The burden of children and adolescents’ (< 20 years old) mental disorders was displayed as prevalence, incidence, disability-adjusted life-years (DALYs), years of life lost, and years lived with disability globally between 1990 and 2019. The number of DALYs in children and adolescents diagnosed with mental disorders was 21.5 million (95% CI: 15.2–29.6 million) in 2019. From 1990 to 2019, the age-standardized rates of DALYs of mental disorders increased from 803.8 per 100,000 (95% CI: 567.7–1104.3 per 100,000) to 833.2 per 100,000 (95% CI: 589.0–1146.1 per 100,000) population. Over the past 30 years, there had been a huge increase in the number of individuals suffering from anxiety disorders, major depressive disorders, and conduct disorders including an alarming increase in the rate of eating disorders such as 24.3% in bulimia nervosa and 17.0% in anorexia nervosa. Globally, 8.8% of children and adolescents have been diagnosed with varieties of mental illnesses, accounting for a heavy disease burden on public health. Besides, the worldwide increasing rates of anxiety disorders, major depressive disorders, and eating disorders have brought considerable challenges to public health undertakings, for which further prevention and treatment countermeasures are urgently needed.

Molecular profiling of circulating extracellular vesicles (EVs) provides a promising noninvasive means to diagnose, monitor, and predict the course of metastatic breast cancer (MBC). However, the analysis of EV protein markers has been confounded by the presence of soluble protein counterparts in peripheral blood. Here we use a rapid, sensitive, and low-cost thermophoretic aptasensor (TAS) to profile cancer-associated protein profiles of plasma EVs without the interference of soluble proteins. We show that the EV signature (a weighted sum of eight EV protein markers) has a high accuracy (91.1 %) for discrimination of MBC, non-metastatic breast cancer (NMBC), and healthy donors (HD). For MBC patients undergoing therapies, the EV signature can accurately monitor the treatment response across the training, validation, and prospective cohorts, and serve as an independent prognostic factor for progression free survival in MBC patients. Together, this work highlights the potential clinical utility of EVs in management of MBC. A thermophoretic aptasensor can be used to profile cancer-associated proteins of extracellular vesicles (EVs) in patients’ plasma. Here, the authors use this technique to develop an EV-signature able to discriminate metastatic breast cancer, monitor treatment response, and predict patients’ progression-free survival.

HighlightsA facile method was developed to achieve visible light (green) and near infrared dual-mode afterglow emissions from carbon dots (CDs)-based materials at ambient conditions for the first time.We proposed a promising method in advanced information security applications through a special manner of readout.The as-developed method was confirmed to be applicable to many kinds of CDs for achieving or enhancing their afterglow performances.Near-infrared (NIR), particularly NIR-containing dual-/multi-mode afterglow, is very attractive in many fields of application, but it is still a great challenge to achieve such property of materials. Herein, we report a facile method to prepare green and NIR dual-mode afterglow of carbon dots (CDs) through in situ embedding o-CDs (being prepared from o-phenylenediamine) into cyanuric acid (CA) matrix (named o-CDs@CA). Further studies reveal that the green and NIR afterglows of o-CDs@CA originate from thermal activated delayed fluorescence (TADF) and room temperature phosphorescence (RTP) of o-CDs, respectively. In addition, the formation of covalent bonds between o-CDs and CA, and the presence of multiple fixation and rigid effects to the triplet states of o-CDs are confirmed to be critical for activating the observed dual-mode afterglow. Due to the shorter lifetime and insensitiveness to human vision of the NIR RTP of o-CDs@CA, it is completely covered by the green TADF during directly observing. The NIR RTP signal, however, can be readily captured if an optical filter (cut-off wavelength of 600 nm) being used. By utilizing these unique features, the applications of o-CDs@CA in anti-counterfeiting and information encryption have been demonstrated with great confidentiality. Finally, the as-developed method was confirmed to be applicable to many other kinds of CDs for achieving or enhancing their afterglow performances.

In China, rapid urbanization in recent decades has led to increasingly serious ecological and environmental problems, threatening sustainable development. Thus, a clear understanding of the relationship between urbanization and eco-environment is the basis for achieving regional sustainable development. However, despite the current global explosion of research interests on this topic, few studies have focused on ecologically fragile areas. To fill this gap, taking Aba Autonomous Prefecture in the eastern Qinghai-Tibet Plateau as a case study, we explored the relationship between urbanization and eco-environment from 2001 to 2018 using a coupled coordination degree model. The results show that the urbanization level and eco-environmental quality in Aba Prefecture achieved stable and continuous improvements from 0.202 to 0.428 and 0.372 to 0.422, respectively. Moreover, the coupling degree between them ranged from 0.524 to 0.652, indicating that the study area had transformed from uncoordinated development in the initial stage to transformation development in the final stage. Additionally, over the 18 years, the coordinated state of urbanization and eco-environment improved, with the coordinated level increasing from reluctant to moderate coordination after 2011. Lastly, we confirmed that urbanization in Aba Prefecture had an overall positive effect on the local eco-environment, although it gradually decreased as urbanization progressed. These findings have important implications for political decision-makers to achieve high-quality development in ecologically fragile areas.

Bisphenol A (BPA) is a monomer to produce polycarbonate plastics and can be released into the environment through human activities, leading to its accumulation in animals, plants and humans through direct contact or environmental exposure. Epidemiological studies have reported that BPA exposure is associated with metabolic disorders. The pancreas is an important endocrine organ and plays an important role in metabolic disorders. To explore the possible long-term effects of BPA exposure on neonatal health, bioinformatic methods were used to identify differentially expressed genes (DEGs) by comparing the neonatal pancreas after maternal exposure to BPA with the adult pancreas after direct exposure to BPA. Two datasets about BPA exposure and pancreatic abnormality, GSE82175 and GSE126297 in Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) were collected. Control (or BPA-exposed) offspring (maternal exposure) and Control (or BPA-exposed) adults (direct exposure) were defined as Control (or BPA) groups. The results showed that BPA disturbed the normal function of the pancreas in both offspring and adults, with offspring showing higher susceptibility to BPA than adults. Seventeen insulin secretion-related DEGs ( Stxbp5l , Fam3d , Mia3 , Igf1 , Hif1a , Aqp1 , Kif5b , Tiam1 , Map4k4 , Cyp51 , Pde1c , Rab3c , Arntl , Clock , Edn3 , Kcnb1 , and Krt20 ) in the BPA group were identified, and 15 regulator DEGs ( Zfp830 , 4931431B13Rik , Egr1 , Ddit4l , Cep55 , G530011O06Rik , Hspa1b , Hspa1a , Cox6a2 , Ibtk , Banf1 , Slc35b2 , Golt1b , Lrp8 , and Pttg1 ) with opposite expression trends and a regulator gene Cerkl with the similar expression trend in the Control and BPA groups were identified. Hif1α might be an important molecular target for pancreatic cancer caused by BPA exposure, and pregnancy is a critical window of susceptibility to BPA exposure.

Objective This study aimed to explore the association between the triglyceride glucose index (TyG) and the risk of in-hospital and one-year mortality in patients with chronic kidney disease (CKD) and cardiovascular disease (CAD) admitted to the intensive care unit (ICU). Methods The data for the study were taken from the Medical Information Mart for Intensive Care-IV database which contained over 50,000 ICU admissions from 2008 to 2019. The Boruta algorithm was used for feature selection. The study used univariable and multivariable logistic regression analysis, Cox regression analysis, and 3-knotted multivariate restricted cubic spline regression to evaluate the association between the TyG index and mortality risk. Results After applying inclusion and exclusion criteria, 639 CKD patients with CAD were included in the study with a median TyG index of 9.1 [8.6,9.5]. The TyG index was nonlinearly associated with in-hospital and one-year mortality risk in populations within the specified range. Conclusion This study shows that TyG is a predictor of one-year mortality and in-hospital mortality in ICU patients with CAD and CKD and inform the development of new interventions to improve outcomes. In the high-risk group, TyG might be a valuable tool for risk categorization and management. Further research is required to confirm these results and identify the mechanisms behind the link between TyG and mortality in CAD and CKD patients.

Triple-negative breast cancer (TNBC) is a highly metastatic and heterogeneous type of breast cancer with poor outcomes. Precise, non-invasive methods for diagnosis, monitoring and prognosis of TNBC are particularly challenging due to a paucity of TNBC biomarkers. Glycans on extracellular vesicles (EVs) hold the promise as valuable biomarkers, but conventional methods for glycan analysis are not feasible in clinical practice. Here, we report that a lectin-based thermophoretic assay (EVLET) streamlines vibrating membrane filtration (VMF) and thermophoretic amplification, allowing for rapid, sensitive, selective and cost-effective EV glycan profiling in TNBC plasma. A pilot cohort study shows that the EV glycan signature reaches 91% accuracy for TNBC detection and 96% accuracy for longitudinal monitoring of TNBC therapeutic response. Moreover, we demonstrate the potential of EV glycan signature for predicting TNBC progression. Our EVLET system lays the foundation for non-invasive cancer management by EV glycans. Triple-negative breast cancer (TNBC) lacks precise diagnostic and monitoring methods due to limited biomarkers. Here the authors develop a lectin-based thermophoretic assay (EVLET) that combines vibrating membrane filtration and thermophoretic amplification for efficient extracellular vesicle (EV) glycan profiling in the plasma of TNBC patients, enabling non-invasive cancer management by leveraging EV glycans.

The origin of homochirality in life is a fundamental mystery. Symmetry breaking and subsequent amplification of chiral bias are regarded as one of the underlying mechanisms. However, the selection and control of initial chiral bias in a spontaneous mirror symmetry breaking process remains a great challenge. Here we show experimental evidences that laminar chiral microvortices generated within asymmetric microchambers can lead to a hydrodynamic selection of initial chiral bias of supramolecular systems composed of exclusively achiral molecules within milliseconds. The self-assembled nuclei with the chirality sign affected by the shear force of enantiomorphic microvortices are subsequently amplified into almost absolutely chirality-controlled supramolecular gels or nanotubes. In contrast, turbulent vortices in stirring cuvettes fail to select the chirality of supramolecular gels. This study reveals that a laminar chiral microflow can induce enantioselection far from equilibrium, and provides an insight on the origin of natural homochirality. Symmetry breaking and chiral amplification are fundamental principles in chemistry and biology but the control of initial chiral bias remains a great challenge. Here the authors show that chiral microvortices can lead to a selection of initial chiral bias of supramolecular systems composed of achiral molecules.

Pleomorphic adenoma (PA), the most common benign salivary gland tumor, harbors unpredictable risks of recurrence and malignant transformation into carcinoma ex pleomorphic adenoma (CXPA), posing significant clinical challenges. To better delineate the tumor transformation trajectory, we performed single-cell RNA sequencing of normal salivary gland, primary PA, recurrent PA (rPA), and CXPA. Cell trajectory reconstruction, differential expression gene identification, and key gene network analysis were integrated to characterize molecular transitions and intercellular crosstalk driving PA recurrence and malignant transformation. Immunohistochemistry was used to validate key findings. GALNT13 + myoepithelial cells were identified as CXPA-specific malignant progenitors, delineating early malignant conversion. Concurrently, MIF + myoepithelial cells exhibited enhanced tissue-destructive capabilities. Fibroblasts enforced fibrotic restraint in primary PA and drove extracellular matrix degradation in CXPA. The tumor microenvironment exhibited stage-specific adaptations, with CXPA favoring pro-inflammatory MIF-CD74/CD44 signaling and rPA adopting immunosuppressive traits. Stromal reprogramming and immune-editing dynamics collectively orchestrated microenvironmental adaptation, linking cellular heterogeneity to clinical aggressiveness. This study provides the first comprehensive molecular atlas of PA-to-CXPA transformation, revealing malignant specialization of the myoepithelial subpopulation, fibroblast-mediated stromal reprogramming, and immune-editing driven microenvironmental adaptation. These findings provide a framework for precision stratification of the malignant potential of PA, while positioning microenvironmental intervention as a cornerstone of future clinical strategies.