Integration of caveolin-mediated cytosolic delivery and enzyme-responsive releasing of squalenoyl nanoparticles enhance the anti-cancer efficacy of chidamide in pancreatic cancer.

We explored the potential of overcoming the dense interstitial barrier in pancreatic cancer treatment by enhancing the uptake of hydrophilic chemotherapeutic drugs. In this study, we synthesized the squalenoyl-chidamide prodrug (SQ-CHI), linking lipophilic squalene (SQ) with the hydrophilic antitumor drug chidamide (CHI) through a trypsin-responsive bond. Self-assembled nanoparticles with sigma receptor-bound aminoethyl anisamide (AEAA) modification, forming AEAA-PEG-SQ-CHI NPs (A-C NPs, size 116.6 ± 0.4 nm), and reference nanoparticles without AEAA modification, forming mPEG-SQ-CHI NPs (M-C NPs, size 88.3 ± 0.3 nm), were prepared. A-C NPs exhibited significantly higher in vitro CHI release (74.7 %) in 0.5 % trypsin medium compared to release (20.2 %) in medium without trypsin. In vitro cell uptake assays revealed 3.6 and 2.3times higher permeation of A-C NPs into tumorspheres of PSN-1/HPSC or CFPAC-1/HPSC, respectively, compared to M-C NPs. Following intraperitoneal administration to subcutaneous tumor-bearing nude mice, the A-C NPs group demonstrated significant anti-pancreatic cancer efficacy, inducing cancer cell apoptosis and inhibiting proliferation in vivo. Mechanistic studies revealed that AEAA surface modification on nanoparticles promoted intracellular uptake through caveolin-mediated endocytosis. This nanoparticle system presents a novel therapeutic approach for pancreatic cancer treatment, offering a delivery strategy to enhance efficacy through improved tumor permeation, trypsin-responsive drug release, and specific cell surface receptor-mediated intracellular uptake.

Diverging death risks: Mortality as a corollary of economic, social, cultural and person capital.

Introduction: Diverging death risks are associated with a wide range of social factors, including not only education and income but also other economic and non-economic resources. The aim of this study was to assess the association of mortality risks with four types of resources: economic, social, cultural and person capital. Methods: We used data of 2,952 participants from the Disparities in the Netherlands survey and annual mortality data from Statistics Netherlands for the period 2014 to 2021. Economic capital was measured through education, income, occupation, home equity, and liquid assets. Social capital was measured by the strength of social ties, the size of the core discussion network, and access to people in resourceful positions; cultural capital by lifestyle, digital skills, and mastery of English, and person capital by self-rated health, impediments to climbing stairs, self-confidence, self-image, people's appearance, and body mass index. To accommodate the fact that each capital was derived from several indicators, we used Partial Least Squares (PLS) Cox Regression. Results: In multiple regression, higher economic, cultural, and person capital were associated with lower mortality (hazard ratio, 0.77; 95% confidence interval [CI, 0.65 to 0.90], 0.77 [0.64-0.93] and 0.80; [0.70-0.92]), adjusted for all capital measures and sex. Conclusion: The finding that more economic, cultural and person capital is associated with lower mortality provides empirical support for an approach that uses a broad spectrum of capital measures - hitherto rarely included simultaneously in epidemiological research - in order to understand diverging death risks. By integrating sociological concepts, cohort data, and epidemiological research methods, our study highlights the need for further research on the interplay between different forms of resources in shaping health inequalities. In designing public health interventions, we advocate the adoption of a multidimensional capital-based framework for tackling social disparities in mortality.

Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis.

Objective: The objective of this study was to investigate the effectiveness of a machine learning algorithm in diagnosing OSA in children based on clinical features that can be obtained in nonnocturnal and nonmedical environments. Patients and methods: This study was conducted at Beijing Children's Hospital from April 2018 to October 2019. The participants in this study were 2464 children aged 3-18 suspected of having OSA who underwent clinical data collection and polysomnography(PSG). Participants' data were randomly divided into a training set and a testing set at a ratio of 8:2. The elastic net algorithm was used for feature selection to simplify the model. Stratified 10-fold cross-validation was repeated five times to ensure the robustness of the results. Results: Feature selection using Elastic Net resulted in 47 features for AHI ≥5 and 31 features for AHI ≥10 being retained. The machine learning model using these selected features achieved an average AUC of 0.73 for AHI ≥5 and 0.78 for AHI ≥10 when tested externally, outperforming models based on PSG questionnaire features. Linear Discriminant Analysis using the selected features identified OSA with a sensitivity of 44% and specificity of 90%, providing a feasible clinical alternative to PSG for stratifying OSA severity. Conclusions: This study shows that a machine learning model based on children's clinical features effectively identifies OSA in children. Establishing a machine learning screening model based on the clinical features of the target population may be a feasible clinical alternative to nocturnal OSA sleep diagnosis.

Albumin conjugation promotes arsenic trioxide transport through alkaline phosphatase-associated transcytosis in MUC4 wildtype pancreatic cancer cells.

Pancreatic cancer (PC) has a poor prognosis due to chemotherapy resistance and unfavorable drug transportation. Albumin conjugates are commonly used as drug carriers to overcome these obstacles. However, membrane-bound glycoprotein mucin 4 (MUC4) has emerged as a promising biomarker among the genetic mutations affecting albumin conjugates therapeutic window. Human serum albumin-conjugated arsenic trioxide (HSA-ATO) has shown potential in treating solid tumors but is limited in PC therapy due to unclear targets and mechanisms. This study investigated the transport mechanisms and therapeutic efficacy of HSA-ATO in PC cells with different MUC4 mutation statuses. Results revealed improved penetration of ATO into PC tumors through conjugated with HSA. However, MUC4 mutation significantly affected treatment sensitivity and HSA-ATO uptake both in vitro and in vivo. Mutant MUC4 cells exhibited over ten times higher IC50 for HSA-ATO and approximately half the uptake compared to wildtype cells. Further research demonstrated that ALPL activation by HSA-ATO enhanced transcytosis in wildtype MUC4 PC cells but not in mutant MUC4 cells, leading to impaired uptake and weaker antitumor effects. Reprogramming the transport process holds potential for enhancing albumin conjugate efficacy in PC patients with different MUC4 mutation statuses, paving the way for stratified treatment using these delivery vehicles.

A Three-Dimensional Technique for The Visualization of Mitochondrial Ultrastructural Changes in Pancreatic Cancer Cells.

Understanding the dynamic features of the cell organelle ultrastructure, which is not only rich in unknown information but also sophisticated from a three-dimensional (3D) perspective, is critical for mechanistic studies. Electron microscopy (EM) offers good imaging depth and allows for the reconstruction of high-resolution image stacks to investigate the ultrastructural morphology of cellular organelles even at the nanometer scale; therefore, 3D reconstruction is gaining importance due to its incomparable advantages. Scanning electron microscopy (SEM) provides a high-throughput image acquisition technology that allows for reconstructing large structures in 3D from the same region of interest in consecutive slices. Therefore, the application of SEM in large-scale 3D reconstruction to restore the true 3D ultrastructure of organelles is becoming increasingly common. In this protocol, we suggest a combination of serial ultrathin section and 3D reconstruction techniques to study mitochondrial cristae in pancreatic cancer cells. The details of how these techniques are performed are described in this protocol in a step-by-step manner, including the osmium-thiocarbohydrazide-osmium (OTO) method, the serial ultrathin section imaging, and the visualization display.

Predicting specific mortality from laryngeal cancer based on competing risk model: a retrospective analysis based on the SEER database.

Background: Laryngeal carcinoma is one of the most common types of head and neck tumors. The mortality rate in patients with laryngeal cancer has not declined in recent years. Previous studies have shown that laryngeal cancer mortality is related to the extent of laryngeal cancer, the proportion of lymph node metastases, treatment modalities, and postoperative lifestyle habits. Thus, early identifying patients at high risk of laryngeal cancer-specific death is of great clinical importance. However, in the presence of competing risk, the existing survival models based on Cox proportional hazards model may be biased in estimating tumor-specific mortality. In this study, we developed and validated a nomogram based on competitive risk analysis for patients with laryngeal cancer. Methods: We used SEER*Stat (Version 4.6.1) software to identify patients in the Surveillance, Epidemiology, and End Results (SEER) database who were diagnosed with laryngeal cancer between 2000 and 2019 as study subjects. The collected data included demographic data, the primary site of laryngeal cancer, the histological type of tumor, tumor size, and other variables. After excluding cases with missing information, the entire cohort was randomly split into a training cohort and a validation cohort at a 7:3 ratio. The training cohort was used in building the model while the validation cohort was used to validate the model. Univariate and multivariate Fine&Gray regression analyses were used to screen statistically significant variables, and the model performance was measured by establishing a consistency index, receiver operating characteristic curve (ROC), and calibration curves. Results: After excluding cases with missing information, 3,805 patients (2,264 in the training cohort and 1,141 in the validation cohort) were included in the study and followed for a median of 16 months. A total of 411 died of laryngeal cancer, and 2,104 patients died from other causes. Among 3,805 patients, the vast majority was male (80.9%), and Caucasian (77.2%), and aged 60-80 years old (58.4%). Conclusions: Advanced age and keratinized SCC are risk factors for laryngeal cancer-specific death. These high-risk patients should be given more attention and closer monitoring in clinical practice.

Diagnosis of obstructive sleep apnea in children based on the XGBoost algorithm using nocturnal heart rate and blood oxygen feature.

PURPOSE: Obstructive sleep apnea (OSA) is a serious type of obstructive sleep-disordered breathing (SDB) that can cause a series of adverse effects on children's cardiovascular, growth, cognition, etc. The gold standard for diagnosis is polysomnography (PGS), which is used to assess the prevalence of OSA by obtaining the apnea-hypopnea index (AHI), but this diagnosis method is expensive and needs to be performed in a specialized laboratory, making it difficult to be of benefit to children with suspected OSA on a large scale. Our goal was to use a machine learning method to identify children with OSA of varying severity using data on children's nighttime heart rate and blood oxygen data. METHODS: This study included 3139 children who received diagnostic PSG with suspected OSA. Age, sex, BMI, 3 % oxygen depletion index (ODI), average nighttime heart rate and fastest heart rate were used as predictive features. Data sets were established with AHI ≥ 1, AHI ≥ 5, and AHI ≥ 10 as the diagnostic criteria for mild, moderate and severe OSA, and the samples of each data set were randomly divided into a training set and a test set at a ratio of 8:2. An OSA diagnostic model was established based on the XGBoost algorithm, and the ability of the machine learning model to diagnose OSA children with different severities was evaluated through different classification ability evaluation indicators. As a comparison, traditional classifier Logistic Regression was used to perform the same diagnostic task. The SHAP algorithm was used to evaluate the role of these features in the classification task. RESULTS: We established a diagnostic model of OSA in children based on the XGBoost algorithm. On the test set, the AUCs of the model for diagnosing mild, moderate, and severe OSA were 0.95, 0.88, and 0.88, respectively, and the classification accuracy was 90.45 %, 85.67 %, and 89.81 %, respectively, perform better than Logistic Regression classifiers. ODI is the most important feature in all classification tasks, and a higher fastest heart rate and ODI make the model tend to classify samples as positive. A high BMI value caused the model to tend to classify samples as positive in the mild and moderate classification tasks and as negative in the classification task with severe OSA. CONCLUSION: Using heart rate and blood oxygen data as the main features, a machine learning diagnostic model based on the XGBoost algorithm can accurately identify children with OSA at different severities. This diagnostic modality reduces the number of signals and the complexity of the diagnostic process compared to PSG, which could benefit children with suspected OSA who do not have the opportunity to receive a diagnostic PSG and provide a diagnostic priority reference for children awaiting a diagnostic PSG.

A capital-based approach to better understand health inequalities: Theoretical and empirical explorations.

Background: The persistence of health inequalities may be driven by differences in education and income, but also by other economic and non-economic factors. Our aim was to explore how the association between single-dimensional health and socioeconomic status (SES) changes when including health-related person capital, economic capital, social capital, cultural capital and attractiveness and personality capital. Methods: We used a capital-based approach to understand health inequalities. It presumes intertwined relationships between broadly measured health ('health-related person capital') and embodied resources ('attractiveness and personality capital') on the one hand, and ESC capital, i.e., economic, social, and cultural resources on the other. We used cross-sectional data on 152,592 participants from the Dutch Lifelines cohort study and estimated correlations using partial least squares structural equation modelling. Results: The correlation between SES and health-related person capital (r = 0.15) was stronger than the correlations between SES and single-dimensional health (physical and mental health; r = 0.12 and r = 0.04, respectively). ESC capital, combining economic, social and cultural capital, showed a correlation of 0.34 with health-related person capital. This was stronger than the correlation between health-related person capital and economic capital alone (r = 0.19). Lastly, the correlation between health-related person capital and ESC capital increased when health related, attractiveness and personality resources were combined into a single person capital construct (from r = 0.34 to r = 0.49). Conclusions: This exploratory study shows the empirical interconnectedness of various types of resources, and their potential role in the persistence of health inequalities. Our findings corroborate the idea of considering health as a multidimensional concept, and to extend conventional SES indicators to a broader measurement of economic and non-economic resources.

Multiple cross displacement amplification combined with nanoparticle-based lateral flow biosensor for rapid and sensitive detection of Epstein-Barr virus.

Introduction: Epstein-Barr virus (EBV) is a highly dangerous virus that is globally prevalent and closely linked to the development of nasopharyngeal cancer (NPC). Plasma EBV DNA analysis is an effective strategy for early detection, prognostication and monitoring of treatment response of NPC. Methods: Here, we present a novel molecular diagnostic technique termed EBV-MCDA-LFB, which integrates multiple cross displacement amplification (MCDA) with nanoparticle-based lateral flow (LFB) to enable simple, rapid and specific detection of EBV. In the EBV-MCDA-LFB system, a set of 10 primers was designed for rapidly amplifying the highly conserved tandem repeat BamHI-W region of the EBV genome. Subsequently, the LFB facilitate direct assay reading, eliminating the use of extra instruments and reagents. Results: The outcomes showed that the 65°C within 40 minutes was the optimal reaction setting for the EBV-MCDA system. The sensitivity of EBV-MCDA-LFB assay reached 7 copies per reaction when using EBV recombinant plasmid, and it showed 100% specificity without any cross-reactivity with other pathogens. The feasibility of the EBV-MCDA-LFB method for EBV detection was successfully validated by 49 clinical plasma samples. The complete detection process, consisting of rapid template extraction (15 minutes), MCDA reaction (65°C for 40 minutes), and LFB result reading (2 minutes), can be finalized within a 60-minutes duration. Discussion: EBV-MCDA-LFB assay designed here is a fast, extremely sensitive and specific technique for detecting EBV in field and at the point-of-care (PoC), which is especially beneficial for countries and regions with a high prevalence of the disease and limited economic resources.

SymMap database and TMNP algorithm reveal Huanggui Tongqiao granules for Allergic rhinitis through IFN-mediated neuroimmuno-modulation.

Allergic rhinitis (AR) is a series of reactions to allergen mediated by immunoglobulin E (IgE) and is one of the most common allergic diseases that affects children. Traditional Chinese Medicine, due to its diverse regulatory functions, may offer new strategies for AR therapy. Huanggui Tongqiao Granules (HTG) is a Chinese formula consisting of twelve herbs and has long been prescribed for patients with AR. The aim of this study is to determine the possible targets and action mechanisms of HTG for the AR treatment. SymMap database and TMNP algorithm were employed to show that interferon-gamma (IFN-gamma), acting as a molecular link between immunity and neural circuits, is the involved key target. The enrichment of immune and virus-related signaling pathways indicated the neuroimmunomodulatory potential of HTG. Then, AR mouse model was established by ovalbumin (OVA) challenge and was used to verify the therapeutic effects of HTG in vivo. HTG significantly relieved AR symptoms and nasal mucosal inflammation, reduced OVA-specific IgE levels and balanced IFN-gamma/IL-4 ratio. Moreover, transcriptional profile based on clinical data presented that blood cell-specific IFN-gamma co-expressed gene module (BIM) was underexpressed in AR patients, further validating the potential of IFN-gamma as target for AR. Collectively, these findings suggest that HTG could be a promising candidate drug for AR.

Increases in symptoms of depression and anxiety in adults during the initial phases of the COVID-19 pandemic are limited to those with less resources: Results from the Lifelines Cohort Study.

BACKGROUND: The COVID-19 pandemic may have a differential impact on mental health based on an individual's capital, i.e. resources available to maintain and enhance health. We assessed trajectories of depression and anxiety symptoms, and their association with different elements of capital. METHODS: Data on 65,854 individuals (mean baseline age = 50·4 (SD = 12·0) years) from the Lifelines COVID-19 cohort were used. Baseline mental health symptoms were on average measured 4.7 (SD = 1·1) years before the first COVID-19 measurement wave, and subsequent waves were (bi)weekly (March 30─August 05, 2020). Mental health symptom trajectories were estimated using a two-part Latent Class Growth Analysis. Class membership was predicted by economic (education, income, and occupation) and person capital (neuroticism, poor health condition, and obesity) FINDINGS: Most individuals were unlikely to report symptoms of depression (80·6%) or anxiety (75·9%), but stable-high classes were identified for both conditions (1·6% and 6·7%, respectively). The stable-high depression class saw the greatest increase in symptoms after COVID, and the stable-high anxiety class reported an increase in the probability of reporting symptoms after COVID. At the first COVID-measurement, the mean number of symptoms increased compared to baseline (depression:4·7 vs 4·1; anxiety:4·3 vs 4·2); the probability of reporting symptoms also increased (depression:0·96 vs 0·65; anxiety:0·92 vs 0·70). Membership in these classes was generally predicted by less capital, especially person capital; odds ratios for person capital ranged from 1·10-2·22 for depression and 1·08-1·51 for anxiety. INTERPRETATION: A minority of individuals, possessing less capital, reported an increase in symptoms of depression or anxiety after COVID. FUNDING: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

KAT6A and ENL Form an Epigenetic Transcriptional Control Module to Drive Critical Leukemogenic Gene-Expression Programs.

Epigenetic programs are dysregulated in acute myeloid leukemia (AML) and help enforce an oncogenic state of differentiation arrest. To identify key epigenetic regulators of AML cell fate, we performed a differentiation-focused CRISPR screen in AML cells. This screen identified the histone acetyltransferase KAT6A as a novel regulator of myeloid differentiation that drives critical leukemogenic gene-expression programs. We show that KAT6A is the initiator of a newly described transcriptional control module in which KAT6A-catalyzed promoter H3K9ac is bound by the acetyl-lysine reader ENL, which in turn cooperates with a network of chromatin factors to induce transcriptional elongation. Inhibition of KAT6A has strong anti-AML phenotypes in vitro and in vivo, suggesting that KAT6A small-molecule inhibitors could be of high therapeutic interest for mono-therapy or combinatorial differentiation-based treatment of AML. SIGNIFICANCE: AML is a poor-prognosis disease characterized by differentiation blockade. Through a cell-fate CRISPR screen, we identified KAT6A as a novel regulator of AML cell differentiation. Mechanistically, KAT6A cooperates with ENL in a "writer-reader" epigenetic transcriptional control module. These results uncover a new epigenetic dependency and therapeutic opportunity in AML. This article is highlighted in the In This Issue feature, p. 587.

Current Status of Tissue Clearing and the Path Forward in Neuroscience.

Due to the complexity and limited availability of human brain tissues, for decades, pathologists have sought to maximize information gained from individual samples, based on which (patho)physiological processes could be inferred. Recently, new understandings of chemical and physical properties of biological tissues and multiple chemical profiling have given rise to the development of scalable tissue clearing methods allowing superior optical clearing of across-the-scale samples. In the past decade, tissue clearing techniques, molecular labeling methods, advanced laser scanning microscopes, and data visualization and analysis have become commonplace. Combined, they have made 3D visualization of brain tissues with unprecedented resolution and depth widely accessible. To facilitate further advancements and applications, here we provide a critical appraisal of these techniques. We propose a classification system of current tissue clearing and expansion methods that allows users to judge the applicability of individual ones to their questions, followed by a review of the current progress in molecular labeling, optical imaging, and data processing to demonstrate the whole 3D imaging pipeline based on tissue clearing and downstream techniques for visualizing the brain. We also raise the path forward of tissue-clearing-based imaging technology, that is, integrating with state-of-the-art techniques, such as multiplexing protein imaging, in situ signal amplification, RNA detection and sequencing, super-resolution imaging techniques, multiomics studies, and deep learning, for drawing the complete atlas of the human brain and building a 3D pathology platform for central nervous system disorders.

Adulthood Socioeconomic Position and Type 2 Diabetes Mellitus-A Comparison of Education, Occupation, Income, and Material Deprivation: The Maastricht Study.

In an effort to better quantify the impact of adulthood socioeconomic circumstances on prediabetes and type 2 diabetes (T2DM), we set out to examine the relative importance of four adulthood socioeconomic indicators. Using cross-sectional data from The Maastricht Study on 2011 middle-aged older men and women, our findings indicate that low educational level (OR = 1.81, 95% CI = 1.24-2.64), low occupational level (OR = 1.42, 95% CI = 0.98-2.05), and material deprivation (OR = 1.78, 95% CI = 1.33-2.38) were independently associated with T2DM. Low income (OR = 1.28, 95% CI = 0.88-1.87) was the strongest, albeit not significant, SEP (socioeconomic position) correlate of prediabetes. This association confirms SEP as a multifaceted concept and indicates the need to measure SEP accordingly. In order to tackle the social gradient in prediabetes and T2DM, one should, therefore, address multiple SEP indicators and their possible pathways.