A tendency toward evening chronotype associates with less healthy diet among preschoolers: cross-sectional findings from the DAGIS study.

Study Objectives: Evidence suggests that adolescents and adults with a later chronotype have poorer sleep habits and are more susceptible to unhealthy behaviors, but little is known about these associations in younger children. The objective of the study was to (1) identify and compare individual chronotype tendencies among preschool-aged children and (2) investigate associations of sleep dimensions and chronotype with diet. Methods: Participants were 636 3-6 years old (mean ±â€…SD age: 4.74 ±â€…0.89 years, 49% girls) preschoolers from the cross-sectional Increased Health and Well-Being in Preschoolers (DAGIS) study in Finland. Sleep duration, sleep variability (in duration and midpoint), social jetlag, and midsleep on weekends adjusted for sleep debt (MSWEadj) were measured with 7-day actigraphy. Morning, intermediate, and evening chronotype tendencies were defined based on the lowest and highest 10th percentile cutoffs of MSWEadj. Food, energy, and macronutrient intake were assessed from 3-day records. Associations between sleep dimensions and diet were assessed with regression models. Results: MSWEadj was 1:13 ±â€…14 minutes for morning (n = 64), 2:25 ±â€…28 minutes for intermediate (n = 560), and 3:38 ±â€…15 minutes for evening (n = 64) chronotype tendency. Children with an evening chronotype tendency had greater social jetlag and sleep variability. Having an evening chronotype tendency was associated with higher added sugar, higher sugary food consumption, and lower vegetable consumption compared to intermediate tendency types. A later chronotype (MSWEadj) was associated with higher sugary food consumption, as well as lower vegetable and fiber intake. Sleep duration, social jetlag, and sleep variability were not associated with diet. Conclusions: Several less healthy sleep and diet behaviors were observed among children with later chronotypes. Future public health interventions aimed towards children would benefit from taking into account chronotype.

A preliminary study of modified inflatable mediastinoscopic and single-incision plus one-port laparoscopic esophagectomy.

Background: Esophageal malignancies have a high morbidity rate worldwide, and minimally invasive surgery has emerged as the primary approach for treating esophageal cancer. In recent years, there has been increasing discussion about the potential of employing inflatable mediastinoscopic and laparoscopic approaches as an option for esophagectomy. Building on the primary modification of the inflatable mediastinoscopic technique, we introduced a secondary modification to further minimize surgical trauma. Methods: We conducted a retrospective analysis of patients who underwent inflatable mediastinoscopy combined with laparoscopic esophagectomy at the Second Affiliated Hospital of Naval Medical University from March 2020 to March 2023. The patients were allocated to the following two groups: the traditional (primary modification) group, and the secondary modification group. Operation times, intraoperative bleeding, and postoperative complications were compared between the groups. Results: The procedure was successfully performed in all patients, and conversion to open surgery was not required in any case. There were no statistically significant differences in the surgical operation time, intraoperative bleeding, number of dissected lymph nodes, and rate of postoperative anastomotic leakage between the two groups. However, a statistically significant difference was observed in the length of the mobilized esophagus between the two groups. The mobilization of esophagus to the level of diaphragmatic hiatus via the cervical incision was successfully achieved in more patients in the secondary modification group than the primary modification group. Conclusions: Inflatable mediastinoscopy combined with single-incision plus one-port laparoscopic esophagectomy is a safe and effective surgical procedure. The use of a 5-mm flexible endoscope, ultra-long five-leaf forceps, and LigaSure Maryland forceps facilitates esophageal mobilization and lymph node dissection through a single cervical incision.

A Generalizable Causal-Invariance-Driven Segmentation Model for Peripancreatic Vessels.

Segmenting peripancreatic vessels in CT, including the superior mesenteric artery (SMA), the coeliac artery (CA), and the partial portal venous system (PPVS), is crucial for preoperative resectability analysis in pancreatic cancer. However, the clinical applicability of vessel segmentation methods is impeded by the low generalizability on multi-center data, mainly attributed to the wide variations in image appearance, namely the spurious correlation factor. Therefore, we propose a causal-invariance-driven generalizable segmentation model for peripancreatic vessels. It incorporates interventions at both image and feature levels to guide the model to capture causal information by enforcing consistency across datasets, thus enhancing the generalization performance. Specifically, firstly, a contrast-driven image intervention strategy is proposed to construct image-level interventions by generating images with various contrast-related appearances and seeking invariant causal features. Secondly, the feature intervention strategy is designed, where various patterns of feature bias across different centers are simulated to pursue invariant prediction. The proposed model achieved high DSC scores (79.69%, 82.62%, and 83.10%) for the three vessels on a cross-validation set containing 134 cases. Its generalizability was further confirmed on three independent test sets of 233 cases. Overall, the proposed method provides an accurate and generalizable segmentation model for peripancreatic vessels and offers a promising paradigm for increasing the generalizability of segmentation models from a causality perspective. Our source codes will be released at https://github.com/SJTUBME-QianLab/PC_VesselSeg.

Differential associations of fine and coarse particulate air pollution with cause-specific pneumonia mortality: A nationwide, individual-level, case-crossover study.

BACKGROUND: The connections between fine particulate matter (PM2.5) and coarse particulate matter (PM2.5-10) and daily mortality of viral pneumonia and bacterial pneumonia were unclear. OBJECTIVES: To distinguish the connections between PM2.5 and PM2.5-10 and daily mortality due to viral pneumonia and bacterial pneumonia. METHODS: Using a comprehensive national death registry encompassing all areas of mainland China, we conducted a case-crossover investigation from 2013 to 2019 at an individual level. Residential daily particle concentrations were evaluated using satellite-based models with a spatial resolution of 1 km. To analyze the data, we employed the conditional logistic regression model in conjunction with polynomial distributed lag models. RESULTS: We included 221,507 pneumonia deaths in China. Every interquartile range (IQR) elevation in concentrations of PM2.5 (lag 0-2 d, 37.6 µg/m3) was associated with higher magnitude of mortality for viral pneumonia (3.03%) than bacterial pneumonia (2.14%), whereas the difference was not significant (p-value for difference = 0.38). An IQR increase in concentrations of PM2.5-10 (lag 0-2 d, 28.4 µg/m3) was also linked to higher magnitude of mortality from viral pneumonia (3.06%) compared to bacterial pneumonia (2.31%), whereas the difference was not significant (p-value for difference = 0.52). After controlling for gaseous pollutants, their effects were all stable; however, with mutual adjustment, the associations of PM2.5 remained, and those of PM2.5-10 were no longer statistically significant. Greater magnitude of associations was noted in individuals aged 75 years and above, as well as during the cold season. CONCLUSION: This nationwide study presents compelling evidence that both PM2.5 and PM2.5-10 exposures could increase pneumonia mortality of viral and bacterial causes, highlighting the more robust effects of PM2.5 and somewhat higher sensitivity of viral pneumonia.

Hydrazone-linked covalent organic framework functionalized with cysteine as a fluorescence sensor and Exploration of paper chip for p-nitrophenol detection.

The large use and emission of p-nitrophenol (p-NP) seriously pollute the environment and endanger human health. In this work, a hydrazone-linked fluorescent covalent organic framework (BATHz-COF) was simply synthesized at room temperature and covalently linked N-acetyl-L-cysteine (NALC) via the "thiol-ene" click reaction, where carboxyl groups were introduced to improve dispersion and fluorescence intensity. As a rapid, good selectivity and reusability fluorescence sensor, the obtained COF-NALC has been used for quantitative analysis of p-NP predicated on the internal filtering effect (IFE). Under optimal conditions, COF-NALC enabled quantitative detection of p-NP with a linear range of 5-50 µM and the detection limit was 1.46 µM. The application of COF-NALC to the detection of p-NP in river water samples was successful, and the satisfactory recoveries were 98.0%-109.3%. Furthermore, the fluorescent COF paper chips constructed by in situ growth were combined with a smartphone to build a visual platform for the quick and real-time detection of p-NP, providing an excellent illustration for the development of intelligent fluorescence sensing in environmental analysis.

The relative contribution of PM2.5 components to the obstructive ventilatory dysfunction-insights from a large ventilatory function examination of 305,022 workers in southern China.

BACKGROUND: The new round of WHO/ILO Joint Estimates of the Work-related Burden of Disease assessment requires futher research to provide more evidence, especially on the health impact of ambient air pollution around the workplace. However, the evidence linking obstructive ventilatory dysfunction (OVD) to fine particulate matter (PM2.5) and its chemical components in workers is very limited. Evidence is even more scarce on the interactive effects between occupational factors and particle exposures. We aimed to fill these gaps based on a large ventilatory function examination of workers in southern China. METHODS: We conducted a cross-sectional study among 363,788 workers in southern China in 2020. The annual average concentration of PM2.5 and its components were evaluated around the workplace through validated spatiotemporal models. We used mixed-effect models to evaluate the risk of OVD related to PM2.5 and its components. Results were further stratified by basic characteristics and occupational factors. FINDINGS: Among the 305,022 workers, 119,936 were observed with OVD. We found for each interquartile range (IQR) increase in PM2.5 concentration, the risk of OVD increased by 27.8 (95 % confidence interval (CI): 26.5-29.2 %). The estimates were 10.9 % (95 %CI: 9.7-12.1 %), 15.8 % (95 %CI: 14.5-17.2 %), 2.6 % (95 %CI: 1.4-3.8 %), 17.1 % (95 %CI: 15.9-18.4 %), and 11 % (95 %CI: 9.9-12.2 %), respectively, for each IQR increment in sulfate, nitrate, ammonium salt, organic matter and black carbon. We observed greater effect estimates among females, younger workers, workers with a length of service of 24-45 months, and professional skill workers. Furthermore, it is particularly noteworthy that the noise-exposed workers, high-temperature-exposed workers, and less-dust-exposed workers were at a 5.7-68.2 % greater risk than others. INTERPRETATION: PM2.5 and its components were significantly associated with an increased risk of OVD, with stronger links among certain vulnerable subgroups.

Construction of multi-scale feature fusion segmentation model of MRI knee images based on dual attention mechanism weighted aggregation.

BACKGROUND: Early diagnosis of knee osteoarthritis is an important area of research in the field of clinical medicine. Due to the complexity in the MRI imaging sequences and the diverse structure of cartilage, there are many challenges in the segmentation of knee bone and cartilage. Relevant studies have conducted semantic fusion processing through splicing or summing forms, which results in reduced resolution and the accumulation of redundant information. OBJECTIVE: This study was envisaged to construct an MRI image segmentation model to improve the diagnostic efficiency and accuracy of different grade knee osteoarthritis by adopting the Dual Attention and Multi-scale Feature Fusion Segmentation network (DA-MFFSnet). METHODS: The feature information of different scales was fused through the Multi-scale Attention Downsample module to extract more accurate feature information, and the Global Attention Upsample module weighted lower-level feature information to reduce the loss of key information. RESULTS: The collected MRI knee images were screened and labeled, and the study results showed that the segmentation effect of DA-MFFSNet model was closer to that of the manually labeled images. The mean intersection over union, the dice similarity coefficient and the volumetric overlap error was 92.74%, 91.08% and 7.44%, respectively, and the accuracy of the differential diagnosis of knee osteoarthritis was 84.42%. CONCLUSIONS: The model exhibited better stability and classification effect. Our results indicated that the Dual Attention and Multi-scale Feature Fusion Segmentation model can improve the segmentation effect of MRI knee images in mild and medium knee osteoarthritis, thereby offering an important clinical value and improving the accuracy of the clinical diagnosis.

Comparing Efficacy of Regional Analgesia Techniques After Pediatric Inguinal Procedures.

No abstract.

Association Between Sleep Efficiency Variability and Cognition Among Older Adults: Cross-Sectional Accelerometer Study.

Background: Sleep efficiency is often used as a measure of sleep quality. Getting sufficiently high-quality sleep has been associated with better cognitive function among older adults; however, the relationship between day-to-day sleep quality variability and cognition has not been well-established. Objective: We aimed to determine the relationship between day-to-day sleep efficiency variability and cognitive function among older adults, using accelerometer data and 3 cognitive tests. Methods: We included older adults aged >65 years with at least 5 days of accelerometer wear time from the National Health and Nutrition Examination Survey (NHANES) who completed the Digit Symbol Substitution Test (DSST), the Consortium to Establish a Registry for Alzheimer's Disease Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). Sleep efficiency was derived using a data-driven machine learning algorithm. We examined associations between sleep efficiency variability and scores on each cognitive test adjusted for age, sex, education, household income, marital status, depressive symptoms, diabetes, smoking habits, alcohol consumption, arthritis, heart disease, prior heart attack, prior stroke, activities of daily living, and instrumental activities of daily living. Associations between average sleep efficiency and each cognitive test score were further examined for comparison purposes. Results: A total of 1074 older adults from the NHANES were included in this study. Older adults with low average sleep efficiency exhibited higher levels of sleep efficiency variability (Pearson r=-0.63). After adjusting for confounding factors, greater average sleep efficiency was associated with higher scores on the DSST (per 10% increase, ß=2.25, 95% CI 0.61 to 3.90) and AFT (per 10% increase, ß=.91, 95% CI 0.27 to 1.56). Greater sleep efficiency variability was univariably associated with worse cognitive function based on the DSST (per 10% increase, ß=-3.34, 95% CI -5.33 to -1.34), CERAD-WL (per 10% increase, ß=-1.00, 95% CI -1.79 to -0.21), and AFT (per 10% increase, ß=-1.02, 95% CI -1.68 to -0.36). In fully adjusted models, greater sleep efficiency variability remained associated with lower DSST (per 10% increase, ß=-2.01, 95% CI -3.62 to -0.40) and AFT (per 10% increase, ß=-.84, 95% CI -1.47 to -0.21) scores but not CERAD-WL (per 10% increase, ß=-.65, 95% CI -1.39 to 0.08) scores. Conclusions: Targeting consistency in sleep quality may be useful for interventions seeking to preserve cognitive function among older adults.

L-AP Alleviates Liver Injury in Septic Mice by Inhibiting Macrophage Activation via Suppressing NF-κB and NLRP3 Inflammasome/Caspase-1 Signal Pathways.

Liver injury and progressive liver failure are severe life-threatening complications in sepsis, further worsening the disease and leading to death. Macrophages and their mediated inflammatory cytokine storm are critical regulators in the occurrence and progression of liver injury in sepsis, for which effective treatments are still lacking. l-Ascorbic acid 6-palmitate (L-AP), a food additive, can inhibit neuroinflammation by modulating the phenotype of the microglia, but its pharmacological action in septic liver damage has not been fully explored. We aimed to investigate L-AP's antisepticemia action and the possible pharmacological mechanisms in attenuating septic liver damage by modulating macrophage function. We observed that L-AP treatment significantly increased survival in cecal ligation and puncture-induced WT mice and attenuated hepatic inflammatory injury, including the histopathology of the liver tissues, hepatocyte apoptosis, and the liver enzyme levels in plasma, which were comparable to NLRP3-deficiency in septic mice. L-AP supplementation significantly attenuated the excessive inflammatory response in hepatic tissues of septic mice in vivo and in cultured macrophages challenged by both LPS and ATP in vitro, by reducing the levels of NLRP3, pro-IL-1ß, and pro-IL-18 mRNA expression, as well as the levels of proteins for p-I-κB-α, p-NF-κB-p65, NLRP3, cleaved-caspase-1, IL-1ß, and IL-18. Additionally, it impaired the inflammasome ASC spot activation and reduced the inflammatory factor contents, including IL-1ß and IL-18 in plasma/cultured superannuants. It also prevented the infiltration/migration of macrophages and their M1-like inflammatory polarization while improving their M2-like polarization. Overall, our findings revealed that L-AP protected against sepsis by reducing macrophage activation and inflammatory cytokine production by suppressing their activation in NF-κB and NLRP3 inflammasome signal pathways in septic liver.

Ubiquitinome Analysis Uncovers Alterations in Synaptic Proteins and Glucose Metabolism Enzymes in the Hippocampi of Adolescent Mice Following Cold Exposure.

Cold exposure exerts negative effects on hippocampal nerve development in adolescent mice, but the underlying mechanisms are not fully understood. Given that ubiquitination is essential for neurodevelopmental processes, we attempted to investigate the effects of cold exposure on the hippocampus from the perspective of ubiquitination. By conducting a ubiquitinome analysis, we found that cold exposure caused changes in the ubiquitination levels of a variety of synaptic-associated proteins. We validated changes in postsynaptic density-95 (PSD-95) ubiquitination levels by immunoprecipitation, revealing reductions in both the K48 and K63 polyubiquitination levels of PSD-95. Golgi staining further demonstrated that cold exposure decreased the dendritic-spine density in the CA1 and CA3 regions of the hippocampus. Additionally, bioinformatics analysis revealed that differentially ubiquitinated proteins were enriched in the glycolytic, hypoxia-inducible factor-1 (HIF-1), and 5'-monophosphate (AMP)-activated protein kinase (AMPK) pathways. Protein expression analysis confirmed that cold exposure activated the mammalian target of rapamycin (mTOR)/HIF-1α pathway. We also observed suppression of pyruvate kinase M2 (PKM2) protein levels and the pyruvate kinase (PK) activity induced by cold exposure. Regarding oxidative phosphorylation, a dramatic decrease in mitochondrial respiratory-complex I activity was observed, along with reduced gene expression of the key subunits NADH: ubiquinone oxidoreductase core subunit V1 (Ndufv1) and Ndufv2. In summary, cold exposure negatively affects hippocampal neurodevelopment and causes abnormalities in energy homeostasis within the hippocampus.

Methylmercury induces inflammatory response and autophagy in microglia through the activation of NLRP3 inflammasome.

Methylmercury (MeHg) is a global environmental pollutant with neurotoxicity, which can easily crosses the blood-brain barrier and cause irreversible damage to the human central nervous system (CNS). CNS inflammation and autophagy are known to be involved in the pathology of neurodegenerative diseases. Meanwhile, MeHg has the potential to induce microglia-mediated neuroinflammation as well as autophagy. This study aims to further explore the exact molecular mechanism of MeHg neurotoxicity. We conducted in vitro studies using BV2 microglial cell from the central nervous system of mice. The role of inflammation and autophagy in the damage of BV2 cells induced by MeHg was determined by detecting cell viability, cell morphology and structure, reactive oxygen species (ROS), antioxidant function, inflammatory factors, autophagosomes, inflammation and autophagy-related proteins. We further investigated the relationship between the inflammatory response and autophagy induced by MeHg by inhibiting them separately. The results indicated that MeHg could invade cells, change cell structure, activate NOD-like receptor thermal protein domain associated protein 3 (NLRP3) inflammasome and autophagosome, release a large amount of inflammatory factors and trigger the inflammatory response and autophagy. It was also found that MeHg could disrupt the antioxidant function of cells. In addition, the inhibition of NLRP3 inflammasome alleviated both cellular inflammation and autophagy, while inhibition of autophagy increased cellular inflammation. Our current research suggests that MeHg might induce BV2 cytotoxicity through inflammatory response and autophagy, which may be mediated by the NLRP3 inflammasome activated by oxidative stress.

Multi-branch myocardial infarction detection and localization framework based on multi-instance learning and domain knowledge.

Objective. Myocardial infarction (MI) is a serious cardiovascular disease that can cause irreversible damage to the heart, making early identification and treatment crucial. However, automatic MI detection and localization from an electrocardiogram (ECG) remain challenging. In this study, we propose two models, MFB-SENET and MFB-DMIL, for MI detection and localization, respectively.Approach. The MFB-SENET model is designed to detect MI, while the MFB-DMIL model is designed to localize MI. The MI localization model employs a specialized attention mechanism to integrate multi-instance learning with domain knowledge. This approach incorporates handcrafted features and introduces a new loss function called lead-loss, to improve MI localization. Grad-CAM is employed to visualize the decision-making process.Main Results.The proposed method was evaluated on the PTB and PTB-XL databases. Under the inter-patient scheme, the accuracy of MI detection and localization on the PTB database reached 93.88% and 67.17%, respectively. The accuracy of MI detection and localization on the PTB-XL database were 94.89% and 85.83%, respectively.Significance. Our method achieved comparable or better performance than other state-of-the-art algorithms. The proposed method combined deep learning and medical domain knowledge, demonstrates effectiveness and reliability, holding promise as an efficient MI diagnostic tool to assist physicians in formulating accurate diagnoses.

Genetic variants, haplotype determination, and function of novel alleles of CYP2B6 in a Han Chinese population.

Amino acid variants in protein may result in deleterious effects on enzymatic activity. In this study we investigate the DNA variants on activity of CYP2B6 gene in a Chinese Han population for potential use in precision medicine. All exons in CYP2B6 gene from 1483 Chinese Han adults (Zhejiang province) were sequenced using Sanger sequencing. The effects of nonsynonymous variants on recombinant protein catalytic activity were investigated in vitro with Sf12 system. The haplotype of novel nonsynonymous variants with other single nucleotide variants in the same allele was determined using Nanopore sequencing. Of 38 alleles listed on the Pharmacogene Variation Consortium, we detected 7 previously reported alleles and 18 novel variants, of which 11 nonsynonymous variants showed lower catalytic activity (0.00-0.60) on bupropion compared to CYP2B6*1. Further, these 11 novel star-alleles (CYP2B6*39-49) were assigned by the Pharmacogene Variation Consortium, which may be valuable for pharmacogenetic research and personalized medicine.

Predicting particulate matter, nitrogen dioxide, and ozone across Great Britain with high spatiotemporal resolution based on random forest models.

In Great Britain, limited studies have employed machine learning methods to predict air pollution especially ozone (O3) with high spatiotemporal resolution. This study aimed to address this gap by developing random forest models for four key pollutants (fine and inhalable particulate matter [PM2.5 and PM10], nitrogen dioxide [NO2] and O3) by integrating multiple-source predictors at a daily level and 1-km resolution. The out-of-bag R2 (root mean squared error, RMSE) between predictions from models and measurements from monitoring stations in 2006-2013 was 0.85 (3.63 µg/m3) for PM2.5, 0.77 (6.00 µg/m3) for PM10, 0.85 (9.71 µg/m3) for NO2, and 0.85 (9.39 µg/m3) for maximum daily 8-h average (MDA8) O3 at daily level, and the predicting accuracy was higher at monthly and annual level. The high-resolution predictions captured characterized spatiotemporal patterns of the four pollutants. Higher concentrations of PM2.5, PM10, and NO2 were distributed in densely populated southern regions of Great Britain while O3 showed an inverse spatial pattern in general, which could not be fully depicted by monitoring stations. Therefore, predictions produced in this study could improve exposure assessment with less exposure misclassification and flexible exposure windows for future epidemiological studies to investigate the impact of air pollution across Great Britain.

A triple read-out visible biosensing platform based on multifunctional nanozyme and bipolar electrode for multi-mode detection and imaging of CEA.

In this paper, a proposal of closed bipolar electrode (BPE) and nanozyme based multi-mode biosensing platform is first presented. As a novel integrated chip, multi-mode-BPE (MMBPE) combines enzyme-linked immunoassay (ELISA), electrochemiluminescence (ECL), ECL imaging and light emitting diode (LED) imaging, enabling highly sensitive triple read-out visible detection of cancer embryonic antigen (CEA). The ECL probe Ab2@Au@Co3O4/CoFe2O4 hollow nanocubes (HNCs) with excellent peroxidase (POD) activity is introduced into the BPE cathode through immune adsorption. The Au@Co3O4/CoFe2O4 HNCs can increase the rate of hydrogen peroxide oxidation of TMB, thus promoting the reaction, and can be used for ELISA detection of CEA at different concentrations. The modification of the BPE sensing interface and reporting interface involved the introduction of the luminescent reagent Ru(bpy)32+ to the BPE anode. The decomposition rate of H2O2 increased under the catalytic action of Au@Co3O4/CoFe2O4 HNCs nanozyme, leading to an accelerated electron transfer rate in the MMBPE system and an enhanced ECL signal from Ru(bpy)32+. The LED imaging technology further provides a convenient and visible approach for CEA imaging in which no additional chemicals are needed. The integration of nanoenzymes as the catalytic core in MMBPE system provides impetus, while the combination of nanozymes with BPE expands the application of nanoenzymes in the field of biological analysis. The integration of intelligent chips with multiple modes of detection shows portable, miniaturized, and integrated excellent properties which meets the requirements of modern detection devices and thus offers a flexible approach for determination of nucleic acids, proteins, and cells.

Targeting ABCA1 via Extracellular Vesicle-Encapsulated Staurosporine as a Therapeutic Strategy to Enhance Radiosensitivity.

Cancer stem cells (CSCs) are essential for tumor initiation, recurrence, metastasis, and resistance. However, targeting CSCs as a therapeutic approach remains challenging. Here, a stemness signature based on 22-gene is developed to predict prognosis in esophageal squamous cell carcinoma (ESCC). Staurosporine (STS) is identified as a radioresistance suppressor by high-throughput screening of a library of 2131 natural compounds, leading to dramatically improved radiotherapy efficacy in subcutaneous tumor models. Mechanistically, STS inhibits cell proliferation through the mTOR/AKT signaling pathway and suppressed stemness by targeting ATP-binding cassette A1 (ABCA1), which is transcriptionally regulated by liver X receptor alpha (LXRα). STS can selectively bind to the nucleotide-binding domain (NBD) of ABCA1 and compete for ATP, blocking ABCA1-mediated drug efflux and facilitating intracellular accumulation of STS. Considering the cytotoxicity of STS, an extracellular vesicle-encapsulated STS system (EV-STS) is established for effective STS delivery. EV-STS shows remarkable tumor growth inhibition, even at half the dose of STS, with superior safety and efficacy. These findings indicate that ABCA1 may serve as a predictor of response to neoadjuvant chemotherapy and/or radiotherapy in ESCC patients. EV-STS has shown improved antitumor efficacy and low systemic toxicity, offering a promising therapeutic approach for ESCC.

Unveiling causal connections: Long-term particulate matter exposure and type 2 diabetes mellitus mortality in Southern China.

Evidence of the potential causal links between long-term exposure to particulate matters (PM, i.e., PM1, PM2.5, and PM1-2.5) and T2DM mortality based on large cohorts is limited. In contrast, the existing evidence usually suffers from inherent bias with the traditional association assessment. A prospective cohort of 580,757 participants in the southern region of China were recruited during 2009 and 2015 and followed up through December 2020. PM exposure at each residential address was estimated by linking to the well-established high-resolution simulation dataset. Hazard ratios (HRs) were calculated using time-varying marginal structural Cox models, an established causal inference approach, after adjusting for potential confounders. During follow-up, a total of 717 subjects died from T2DM. For every 1 µg/m3 increase in PM2.5, the adjusted HRs and 95% confidence interval (CI) for T2DM mortality was 1.036 (1.019-1.053). Similarly, for every 1 µg/m3 increase in PM1 and PM1-2.5, the adjusted HRs and 95% CIs were 1.032 (1.003-1.062) and 1.085 (1.054-1.116), respectively. Additionally, we observed a generally more pronounced impact among individuals with lower levels of education or lower residential greenness which as measured by the Normalized Difference Vegetation Index (NDVI). We identified substantial interactions between NDVI and PM1 (P-interaction = 0.003), NDVI and PM2.5 (P-interaction = 0.019), as well as education levels and PM1 (P-interaction = 0.049). The study emphasizes the need to consider environmental and socio-economic factors in strategies to reduce T2DM mortality. We found that PM1, PM2.5, and PM1-2.5 heighten the peril of T2DM mortality, with education and green space exposure roles in modifying it.