The impact of antenatal cluster management on maternal delivery and postpartum rehabilitation.

OBJECTIVE: Pregnancy care can improve maternal pregnancy outcomes. Cluster nursing, an evidence-based, patient-centered model, enhances pregnancy care, can provide patients with high-quality nursing services, has been widely used in clinical practice in recent years. However, most previous studies evaluated cluster nursing program only for a single clinical scenario. In this study, we developed and implemented a antenatal cluster care program for various prenatal issues faced by puerpera to analyze its application effect. METHODS: This is a historical before and after control study. 89 expectant mothers who had their prenatal information files registered in the outpatient department of a grade III, level A hospital from June 2020 to September 2021 were finally enrolled in observation group, and received prenatal cluster management. Another set of 89 expectant mothers from January 2019 to December 2019 were included in the control group and received traditional routine prenatal management. The effect of cluster nursing management on maternal delivery and postpartum rehabilitation was evaluated and compared between the two groups. RESULTS: Compared with the control group, the observation group had a significantly higher natural delivery rate, better neonatal prognosis, higher rates of exclusive breastfeeding, lower incidence of postpartum complications, shorter postpartum hospital stay, better postpartum health status, and higher satisfaction with nursing services. Compared with before intervention, the SAS and SDS scores of the observation group showed significant improvement after intervention. CONCLUSION: Antenatal cluster care is beneficial to improve maternal and neonatal outcomes, and can have positive effects on natural pregnancy and breastfeeding, while improving the multimedia health education ability of medical care and emphasizing the importance of social support.

Composition and influencing factors of hospitalization expenses for epilepsy patients based on path analysis.

OBJECTIVE: This study aimed to understand the composition and influencing factors of epilepsy patients' hospitalization expenses, thus providing a reference for reducing the disease burden of epilepsy patients in low- and middle-income developing countries. METHODS: A total of 4206 hospitalized cases of epilepsy from 2018 to 2020 were collected. Descriptive statistics were used to understand the patient cost composition, path analysis was used to understand the direct and indirect factors of hospitalization expenses. RESULTS: From 2018 to 2020, the average hospitalization expenses for epilepsy patients was 4,299.93 RMB yuan, and the average length of stay was 2.47 days. The highest proportion of hospitalization expenses was diagnosis costs (> 50%), followed by comprehensive medical service costs and drug costs. In terms of the total effect coefficient, the major factors affecting the hospitalization expenses were length of stay (0.880), emergency admission(0.463), and the comorbidities and complications(> 0.250). Hospital length of stay, discharge mode(death) and number of hospitalizations(2 times) affect hospitalization expenses through direct effect. Long-term hospitalization (> 30 days), admission routes(emergency), the comorbidities and complications, presence of drug allergy, and age also affect hospitalization expenses through indirect effects. CONCLUSION: Diagnosis costs and length of stay are important factors affecting the medical expenses of epilepsy inpatients. In general, the quality control of the hospital is good, but it still needs to standardize the diagnosis and treatment behavior of medical staff through the clinical path.

Benzo-fused BOPAM Fluorophores: Synthesis, Post-functionalization, Photophysical Properties, and Acid Sensing Applications.

A novel category of asymmetric boron chromophores with the attachment of two BF2 moieties denoted as BOPAM, has been successfully synthesized via a one-pot three-step reaction starting from N-phenylbenzothioamide. This synthetic route results in the production of [a] and [b]benzo-fused BOPAMs along with post-functionalization of the [a]benzo-fused BOPAMs. The photophysical properties of these compounds have been systematically investigated through steady-state absorption and fluorescence emission measurements in solvents at both ambient and cryogenic temperatures, as well as in the solid state. Computational methods have been employed to elucidate the emissive characteristics of the benzo-fused BOPAMs, revealing distinctive photophysical attributes, including solvent-dependent fluorescence intensity. Remarkably, certain BOPAM derivatives exhibit noteworthy photophysical phenomena, such as the induction of off-on fluorescence emission under specific solvent conditions and the manifestation of intermolecular charge transfer states in solid-state matrices. Through post-functionalization strategies involving the introduction of electron-donating groups onto the [a]benzo-fused BOPAM scaffold, an intramolecular charge transfer (ICT) pathway is activated, leading to substantial fluorescence quenching via non-radiative decay processes. Notably, one [a]benzo-fused BOPAM variant exhibits a pronounced fluorescence enhancement upon exposure to acidic conditions, thereby underscoring its potential utility in pH-sensing applications.

Multiview representation learning for identification of novel cancer genes and their causative biological mechanisms.

Tumorigenesis arises from the dysfunction of cancer genes, leading to uncontrolled cell proliferation through various mechanisms. Establishing a complete cancer gene catalogue will make precision oncology possible. Although existing methods based on graph neural networks (GNN) are effective in identifying cancer genes, they fall short in effectively integrating data from multiple views and interpreting predictive outcomes. To address these shortcomings, an interpretable representation learning framework IMVRL-GCN is proposed to capture both shared and specific representations from multiview data, offering significant insights into the identification of cancer genes. Experimental results demonstrate that IMVRL-GCN outperforms state-of-the-art cancer gene identification methods and several baselines. Furthermore, IMVRL-GCN is employed to identify a total of 74 high-confidence novel cancer genes, and multiview data analysis highlights the pivotal roles of shared, mutation-specific, and structure-specific representations in discriminating distinctive cancer genes. Exploration of the mechanisms behind their discriminative capabilities suggests that shared representations are strongly associated with gene functions, while mutation-specific and structure-specific representations are linked to mutagenic propensity and functional synergy, respectively. Finally, our in-depth analyses of these candidates suggest potential insights for individualized treatments: afatinib could counteract many mutation-driven risks, and targeting interactions with cancer gene SRC is a reasonable strategy to mitigate interaction-induced risks for NR3C1, RXRA, HNF4A, and SP1.

Temporally controlled multistep division of DNA droplets for dynamic artificial cells.

Synthetic droplets mimicking bio-soft matter droplets formed via liquid-liquid phase separation (LLPS) in living cells have recently been employed in nanobiotechnology for artificial cells, molecular robotics, molecular computing, etc. Temporally controlling the dynamics of synthetic droplets is essential for developing such bio-inspired systems because living systems maintain their functions based on the temporally controlled dynamics of biomolecular reactions and assemblies. This paper reports the temporal control of DNA-based LLPS droplets (DNA droplets). We demonstrate the timing-controlled division of DNA droplets via time-delayed division triggers regulated by chemical reactions. Controlling the release order of multiple division triggers results in order control of the multistep droplet division, i.e., pathway-controlled division in a reaction landscape. Finally, we apply the timing-controlled division into a molecular computing element to compare microRNA concentrations. We believe that temporal control of DNA droplets will promote the design of dynamic artificial cells/molecular robots and sophisticated biomedical applications.

Correlation analysis of mental toughness, family social support, and anxiety of nursing staff.

OBJECTIVE: This study aims to explore the impact of family social support affects anxiety levels and mental toughness among nursing staff, and to identify the pathways of how mental toughness develops. METHODS: We selected 256 nursing staff from the Third People's Hospital of Chengdu using a convenience sampling method. Participants completed a questionnaire assessing family social support, anxiety level and mental toughness of the nursing staff. The questionnaires included the general information questionnaire, Perceived Social Support Scale (PSSS), Self-Rating Anxiety Scale (SAS), and the Connor-Davidson resilience scale (CD-RISC). Then, we analyzed the correlation between nursing staff' family social support, anxiety symptoms and mental toughness by using Pearson correlation. Finally, we analyzed the effect of family social support on mental toughness and anxiety levels by using linear regression, and analyzed the path of family social support and psychological toughness on anxiety symptoms by using structural equation modeling. RESULTS: We finally collected 246 valid questionnaires with a valid recovery rate of 96.09%. 116 (47.15%) nursing staff reported a moderate level of family social support, with a mean PSSS score of (58.98 ± 7.64). Anxiety risk was identified in 43.39% of participants, with a mean SAS score of 50.47 ± 10.96. In terms of mental toughness, 104 (42.28%) nursing staff exhibited a low level of mental toughness, and 116 (47.15%) demonstrated moderate level of mental toughness with CD-RISC score of (58.23 ± 10.12). Correlation analyses revealed a strong negative correlation between the family social support, mental toughness and their anxiety (r = -0.586, -0.516, respectively), and a strong positive correlation between family social support and mental toughness (r = 0.571). Regression analysis showed that family social support was a significant negative predictor for anxiety (ß = -0.841, t = -9.488), but a significant positive predictor for mental toughness (ß = 0.756, t = 11.669). Mediation analysis indicated that mental toughness mediated 26.28% of the relationship between family social support and anxiety levels. CONCLUSION: Family social support can significantly reduce anxiety levels in nursing staff directly, as well as indirectly by increasing mental toughness.

Myotis bat STING attenuates aging-related inflammation in female mice.

Bats, notable as the only flying mammals, serve as natural reservoir hosts for various highly pathogenic viruses in humans (e.g., SARS-CoV and Ebola virus). Furthermore, bats exhibit an unparalleled longevity among mammals relative to their size, particularly the Myotis bats, which can live up to 40 years. However, the mechanisms underlying these distinctive traits remain incompletely understood. In our prior research, we demonstrated that bats exhibit dampened STING-interferon activation, potentially conferring upon them the capacity to mitigate virus- or aging-induced inflammation. To substantiate this hypothesis, we established the first in vivo bat-mouse model for aging studies by integrating Myotis davidii bat STING ( MdSTING) into the mouse genome. We monitored the genotypes of these mice and performed a longitudinal comparative transcriptomic analysis on MdSTING and wild-type mice over a 3-year aging process. Blood transcriptomic analysis indicated a reduction in aging-related inflammation in female MdSTING mice, as evidenced by significantly lower levels of pro-inflammatory cytokines and chemokines, immunopathology, and neutrophil recruitment in aged female MdSTING mice compared to aged wild-type mice in vivo. These results indicated that MdSTING knock-in attenuates the aging-related inflammatory response and may also improve the healthspan in mice in a sex-dependent manner. Although the underlying mechanism awaits further study, this research has critical implications for bat longevity research, potentially contributing to our comprehension of healthy aging in humans.

Magnetic resonance imaging-based prognostic model for subsequent distant metastasis in patients with ipsilateral breast tumor recurrence following breast-conserving surgery.

Background: Ipsilateral breast tumor recurrence (IBTR) following breast-conserving surgery (BCS) has been considered a risk factor for distant metastasis (DM). Limited data are available regarding the subsequent outcomes after IBTR. Therefore, this study aimed to determine the clinical course after IBTR and develop a magnetic resonance imaging (MRI)-based predictive model for subsequent DM. Methods: We retrospectively extracted quantitative features from MRI to construct a radiomics cohort, with all eligible patients undergoing preoperative MRI at time of primary tumor and IBTR between 2010 and 2018. Multivariate Cox analysis was performed to identify factors associated with DM. Three models were constructed using different sets of clinicopathological, qualitative, and quantitative MRI features and compared. Additionally, Kaplan-Meier analysis was performed to assess the prognostic value of the optimal model. Results: Among the 183 patients who experienced IBTR, 47 who underwent MRI for both primary and recurrent tumors were enrolled. Multivariate analysis demonstrated that the independent prognostic factors were human epidermal growth factor receptor 2 (HER2) status [hazard ratio (HR) =5.40] and background parenchymal enhancement (BPE) (HR =7.94) (all P values <0.01). Furthermore, four quantitative MRI features of recurrent tumors were selected through the least absolute shrinkage and selection operator (LASSO) method. The combined model exhibited superior performance [concordance index (C-index) 0.77] compared to the clinicoradiological model (C-index 0.71; P=0.006) and radiomics model (C-index 0.70; and P=0.01). Furthermore, the combined model successfully categorized patients into low- and high-risk subgroups with distinct prognoses (P<0.001). Conclusions: The clinicopathological and MRI features of IBTR were associated with secondary events following surgery. Additionally, the MRI-based combined model exhibited the highest predictive efficacy. These findings could be helpful in risk stratification and tailoring follow-up strategies in patients with IBTR.

Efficacy of rituximab-containing regimens used as first-line and rescue therapy for giant cell hepatitis with autoimmune hemolytic anemia a retrospective study.

OBJECTIVE: To evaluate the efficacy of rituximab (RTX)-containing therapy as first-line as well as rescue treatment for giant cell hepatitis with autoimmune hemolytic anemia (GCH-AHA). METHODS: This retrospective study recruited patients diagnosed with GCH-AHA and treated with conventional immunosuppressor regimens consisting of prednisone or RTX-containing regimes consisting of RTX and prednisone, with or without another immunosuppressor. The primary outcomes were the complete remission (CR) rate and time-period required for CR. The secondary outcomes included relapses and adverse events. RESULTS: Twenty patients (8 females and 12 males; age range 1-26 months), 15 receiving conventional regimens and 5 receiving RTX-containing regimens, were included. The CR rates were 73.3 % (11/15) and 100 % (5/5) in the conventional and RTX-containing groups, respectively. The time-period required for CR was significantly shorter in the RTX-containing group than in the conventional group (6 (3-8) versus 14 (5-25) months, P = 0.015). Relapses occurred in 30.8 % (4/13) of patients in the conventional group; all achieved CR after adding RTX. Relapses occurred in 40.0 % (2/5) of patients in the RTX-containing group; both achieved CR after adding intravenous immune globulins or tacrolimus. Transient low immunoglobulin and infections were recorded in both groups. Treatment withdrawal was achieved in 73.3 % (11/15) and 60.0 % (3/5) of patients receiving conventional and RTX-containing regimens after 36 (2-101) and 22 (4-41) months, respectively. Two patients in conventional group died of disease progression and infection. CONCLUSIONS: RTX-containing first-line therapy achieves CR of GCH-AHA more quickly than the conventional therapy. RTX is efficacious when added to rescue therapy.

Large-scale foundation model on single-cell transcriptomics.

Large pretrained models have become foundation models leading to breakthroughs in natural language processing and related fields. Developing foundation models for deciphering the 'languages' of cells and facilitating biomedical research is promising yet challenging. Here we developed a large pretrained model scFoundation, also named 'xTrimoscFoundationα', with 100 million parameters covering about 20,000 genes, pretrained on over 50 million human single-cell transcriptomic profiles. scFoundation is a large-scale model in terms of the size of trainable parameters, dimensionality of genes and volume of training data. Its asymmetric transformer-like architecture and pretraining task design empower effectively capturing complex context relations among genes in a variety of cell types and states. Experiments showed its merit as a foundation model that achieved state-of-the-art performances in a diverse array of single-cell analysis tasks such as gene expression enhancement, tissue drug response prediction, single-cell drug response classification, single-cell perturbation prediction, cell type annotation and gene module inference.

Predicting Regional Recurrence and Prognosis in Stereotactic Body Radiation Therapy-Treated Clinical Stage I Non-small Cell Lung Cancer Using a Radiomics Model Constructed With Surgical Data.

PURPOSE: Risk stratification of regional recurrence (RR) is clinically important in the design of adjuvant treatment and surveillance strategies in patients with clinical stage I non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT). This study aimed to develop a radiomics model predicting occult lymph node metastasis (OLNM) using surgical data and apply it to the prediction of RR in SBRT-treated early-stage NSCLC patients. METHODS AND MATERIALS: Patients with clinical stage I NSCLC who underwent curative surgery with systematic lymph node dissection from January 2013 to December 2018 (the training cohort) and from January 2019 to December 2020 (the validation cohort) were included. A preoperative computed tomography-based radiomics model, a clinical feature model, and a fusion model predicting OLNM were constructed. The performance of the 3 models was quantified and compared in the training and validation cohorts. Subsequently, the radiomics model was used to predict RR in a cohort of consecutive SBRT-treated early-stage NSCLC patients from 2 academic medical centers. RESULTS: A total of 769 patients were included. Eight computed tomography features were identified in the radiomics model, achieving areas under the curves of 0.85 (95% CI, 0.81-0.89) and 0.83 (95% CI, 0.80-0.88) in the training and validation cohorts, respectively. Nevertheless, adding clinical features did not improve the performance of the radiomics model. With a median follow-up of 40.0 (95% CI, 35.2-44.8) months, 32 of the 213 patients in the SBRT cohort developed RR and those in the high-risk group based on the radiomics model had a higher cumulative incidence of RR (P < .001) and shorter regional recurrence-free survival (P = .02), progression-free survival (P = .004) and overall survival (P = .006) than those in the low-risk group. CONCLUSIONS: The radiomics model based on pathologically confirmed data effectively identified patients with OLNM, which may be useful in the risk stratification among SBRT-treated patients with clinical stage I NSCLC.

Development and validation of peritumoral vascular and intratumoral radiomics to predict pathologic complete responses to neoadjuvant chemotherapy in patients with triple-negative breast cancer.

BACKGROUND: To develop and validate a peritumoral vascular and intratumoral radiomics model to improve pretreatment predictions for pathologic complete responses (pCRs) to neoadjuvant chemoradiotherapy (NAC) in patients with triple-negative breast cancer (TNBC). METHODS: A total of 282 TNBC patients (93 in the primary cohort, 113 in the validation cohort, and 76 in The Cancer Imaging Archive [TCIA] cohort) were retrospectively included. The peritumoral vasculature on the maximum intensity projection (MIP) from pretreatment DCE-MRI was segmented by a Hessian matrix-based filter and then edited by a radiologist. Radiomics features were extracted from the tumor and peritumoral vasculature of the MIP images. The LASSO method was used for feature selection, and the k-nearest neighbor (k-NN) classifier was trained and validated to build a predictive model. The diagnostic performance was assessed using the ROC analysis. RESULTS: One hundred of the 282 patient (35.5%) with TNBC achieved pCRs after NAC. In predicting pCRs, the combined peritumoral vascular and intratumoral model (fusion model) yields a maximum AUC of 0.82 (95% confidence interval [CI]: 0.75, 0.88) in the primary cohort, a maximum AUC of 0.67 (95% CI: 0.57, 0.76) in the internal validation cohort, and a maximum AUC of 0.65 (95% CI: 0.52, 0.78) in TCIA cohort. The fusion model showed improved performance over the intratumoral model and the peritumoral vascular model, but not significantly (p > 0.05). CONCLUSION: This study suggested that combined peritumoral vascular and intratumoral radiomics model could provide a non-invasive tool to enable prediction of pCR in TNBC patients treated with NAC.

Res-TransNet: A Hybrid deep Learning Network for Predicting Pathological Subtypes of lung Adenocarcinoma in CT Images.

This study aims to develop a CT-based hybrid deep learning network to predict pathological subtypes of early-stage lung adenocarcinoma by integrating residual network (ResNet) with Vision Transformer (ViT). A total of 1411 pathologically confirmed ground-glass nodules (GGNs) retrospectively collected from two centers were used as internal and external validation sets for model development. 3D ResNet and ViT were applied to investigate two deep learning frameworks to classify three subtypes of lung adenocarcinoma namely invasive adenocarcinoma (IAC), minimally invasive adenocarcinoma and adenocarcinoma in situ, respectively. To further improve the model performance, four Res-TransNet based models were proposed by integrating ResNet and ViT with different ensemble learning strategies. Two classification tasks involving predicting IAC from Non-IAC (Task1) and classifying three subtypes (Task2) were designed and conducted in this study. For Task 1, the optimal Res-TransNet model yielded area under the receiver operating characteristic curve (AUC) values of 0.986 and 0.933 on internal and external validation sets, which were significantly higher than that of ResNet and ViT models (p < 0.05). For Task 2, the optimal fusion model generated the accuracy and weighted F1 score of 68.3% and 66.1% on the external validation set. The experimental results demonstrate that Res-TransNet can significantly increase the classification performance compared with the two basic models and have the potential to assist radiologists in precision diagnosis.

Magnetic resonance imaging-based radiomics analysis for prediction of treatment response to neoadjuvant chemoradiotherapy and clinical outcome in patients with locally advanced rectal cancer: A large multicentric and validated study.

Our study investigated whether magnetic resonance imaging (MRI)-based radiomics features could predict good response (GR) to neoadjuvant chemoradiotherapy (nCRT) and clinical outcome in patients with locally advanced rectal cancer (LARC). Radiomics features were extracted from the T2 weighted (T2W) and Apparent diffusion coefficient (ADC) images of 1070 LARC patients retrospectively and prospectively recruited from three hospitals. To create radiomic models for GR prediction, three classifications were utilized. The radiomic model with the best performance was integrated with important clinical MRI features to create the combined model. Finally, two clinical MRI features and ten radiomic features were chosen for GR prediction. The combined model, constructed with the tumor size, MR-detected extramural venous invasion, and radiomic signature generated by Support Vector Machine (SVM), showed promising discrimination of GR, with area under the curves of 0.799 (95% CI, 0.760-0.838), 0.797 (95% CI, 0.733-0.860), 0.754 (95% CI, 0.678-0.829), and 0.727 (95% CI, 0.641-0.813) in the training and three validation datasets, respectively. Decision curve analysis verified the clinical usefulness. Furthermore, according to Kaplan-Meier curves, patients with a high likelihood of GR as determined by the combined model had better disease-free survival than those with a low probability. This radiomics model was developed based on large-sample size, multicenter datasets, and prospective validation with high radiomics quality score, and also had clinical utility.

Predicting Prognosis of Phyllodes Tumors Using a Mammography- and Magnetic Resonance Imaging-Based Radiomics Model: A Preliminary Study.

PURPOSE: To investigate whether a radiomics model based on mammography (MG) and magnetic resonance imaging (MRI) can be used to predict disease-free survival (DFS) after phyllodes tumor (PT) surgery. METHOD: About 131 PT patients who underwent MG and MRI before surgery between January 2010 and December 2020 were retrospectively enrolled, including 15 patients with recurrence and metastasis and 116 without recurrence. 884 and 3138 radiomic features were extracted from MG and MR images, respectively. Then, multiple radiomics models were established to predict the recurrence risk of the patients by applying a support vector machine classifier. The area under the ROC curve (AUC) was calculated to evaluate model performance. After dividing the patients into high- and low-risk groups based on the predicted radiomics scores, survival analysis was conducted to compare differences between the groups. RESULTS: In total, 3 MG-related and 5 MRI-related radiomic models were established; the prediction performance of the T1WI feature fusion model was the best, with an AUC value of 0.93. After combining the features of MG and MRI, the AUC increased to 0.95. Furthermore, the MG, MRI and all-image radiomic models had statistically significant differences in survival between the high- and low-risk groups (P < .001). All-image radiomics model showed higher survival performance than the MG and MRI radiomics models alone. CONCLUSIONS: Radiomics features based on preoperative MG and MR images can predict DFS after PT surgery, and the prediction score of the image radiomics model can be used as a potential indicator of recurrence risk.

Parabacteroides distasonis-Derived Outer Membrane Vesicles Enhance Antitumor Immunity Against Colon Tumors by Modulating CXCL10 and CD8+ T Cells.

Purpose: Given the potent immunostimulatory effects of bacterial outer membrane vesicles (OMVs) and the significant anti-colon tumor properties of Parabacteroides distasonis (Pd), this study aimed to elucidate the role and potential mechanisms of Pd-derived OMVs (Pd-OMVs) against colon cancer. Methods: This study isolated and purified Pd-OMVs from Pd cultures and assessed their characteristics. The effects of Pd-OMVs on CT26 cell uptake, proliferation, and invasion were investigated in vitro. In vivo, a CT26 colon tumor model was used to investigate the anti-colon tumor effects and underlying mechanisms of Pd-OMVs. Finally, we evaluated the biosafety of Pd-OMVs. Results: Purified Pd-OMVs had a uniform cup-shaped structure with an average size of 165.5 nm and a zeta potential of approximately -9.56 mV, and their proteins were associated with pathways related to immunity and apoptosis. In vitro experiments demonstrated that CT26 cells internalized the Pd-OMVs, resulting in a significant decrease in their proliferation and invasion abilities. Further in vivo studies confirmed the accumulation of Pd-OMVs in tumor tissues, which significantly inhibited the growth of colon tumors. Mechanistically, Pd-OMVs increased the expression of CXCL10, promoting infiltration of CD8+ T cells into tumor tissues and expression of pro-inflammatory factors TNF-α, IL-1ß, and IL-6. Notably, Pd-OMVs demonstrated a high level of biosafety. Conclusion: This paper elucidates that Pd-OMVs can exert significant anti-colon tumor effects by upregulating the expression of the chemokine CXCL10, thereby increasing the infiltration of CD8+ T cells into tumors and enhancing antitumor immune responses. This suggests that Pd-OMVs may be developed as a novel nanoscale potent immunostimulant with great potential for application in tumor immunotherapy. As well as developed as a novel nano-delivery carrier for combination with other antitumor drugs.

Multiple-component covalent organic frameworks for simultaneous extraction and determination of multitarget pollutants in sea foods.

Persistent organic pollutants (POPs), such as perfluoroalkyl and polyfluoroalkyl substances (PFASs), polychlorinated biphenyls (PCBs), and bisphenols (BPs), have been raising global concerns due to their toxic effects on environment and human health. The monitoring of residues of POPs in seafood is crucial for assessing the accumulation of these contaminants in the study area and mitigating potential risks to human health. However, the diversity and complexity of POPs in seafood present significant challenges for their simultaneous detection. Here, a novel multi-component fluoro-functionalized covalent organic framework (OH-F-COF) was designed as SPE adsorbent for simultaneous extraction POPs. On this basis, the recognition and adsorption mechanisms were investigated by molecular simulation. Due to multiple interactions and large specific surface area, OH-F-COF displayed satisfactory coextraction performance for PFASs, PCBs, and BPs. Under optimized conditions, the OH-F-COF sorbent was employed in a strategy of simultaneous extraction and stepwise elution (SESE), in combination with HPLC-MS/MS and GC-MS method, to effectively determined POPs in seafood collected from coastal areas of China. The method obtained low detection limits for BPs (0.0037 -0.0089 ng/g), PFASs (0.0038 -0.0207 ng/g), and PCBs (0.2308 -0.2499 ng/g), respectively. This approach provided new research ideas for analyzing and controlling multitarget POPs in seafood. ENVIRONMENTAL IMPLICATIONS: Persistent organic pollutants (POPs), such as perfluoroalkyl and polyfluoroalkyl substances (PFASs), polychlorinated biphenyls (PCBs), and bisphenols (BPs), have caused serious hazards to human health and ecosystems. Hence, there is a need to develop a quantitative method that can rapidly detect POPs in environmental and food samples. Herein, a novel multi-component fluorine-functionalized covalent organic skeletons (OH-F-COF) were prepared at room temperature, and served as adsorbent for POPs. The SESE-SPE strategy combined with chromatographic techniques was used to achieve a rapid detection of POPs in sea foods from the coastal provinces of China. This method provides a valuable tool for analyzing POPs in environmental and food samples.

Identification of Chronic Pancreatitis Associated microRNAs and Genes for the Diagnosis of Pancreatic Cancer.

OBJECTIVE: The timely identification of both malignant and nonmalignant pancreatic lesions has the potential to significantly enhance prognosis and implement risk management strategies across various levels. microRNAs (miRs) and their corresponding targets play a crucial role in the development of pancreatic lesions and can serve as valuable diagnostic and therapeutic targets. The objective of our study was to investigate potential diagnostic markers that can effectively differentiate between malignant and nonmalignant pancreatic lesions. METHODS: Gene Expression Omnibus (GEO) database with GSE24279 dataset was utilized to screen differentially expressed miRNAs (DEMs). We utilized the TargetScanHuman database to predict the target genes associated with hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p. Furthermore, a cohort comprising healthy individuals (n = 52), chronic pancreatitis (CP; n = 34), and pancreatic adenocarcinoma (PAAD; n = 53) patients was recruited to ascertain the levels of plasma markers. RESULTS: We identified 3 miRNAs (hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p) and 2 proteins (PCDH1 and AMN) as potential diagnostic markers for distinguishing between CP and PAAD. The area under the curve (AUC) values for all markers exceeded .800. Notably, a combination of plasma PCDH1 and AMN demonstrated excellent diagnostic performance (AUC = .921; 95% CI: .866-.977; sensitivity = .792; specificity = .941) in discriminating between CP and PAAD. In addition, the model of hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p yielded an AUC of .928, sensitivity of .830, and specificity of .912, respectively. CONCLUSION: Plasma levels of miRNAs (hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p) and their corresponding targets (PCDH1 and AMN) hold promise as potential biomarkers for predicting PAAD in patients with CP.

Construction of dual-chiral covalent organic frameworks for enantioselective separation.

Developing novel chiral stationary phases (CSPs) with versatility is of great importance in enantiomer separation. This study fabricated a dual-chiral covalent organic framework (PA-CA COF) via successive post-synthetic modifications. The chiral trans-1,2-cyclohexanediamine (CA) and (D)-penicillamine (PA) groups were periodically aligned within nanochannels of the COF, allowing selective recognition of enantiomers through intermolecular interactions. It can be a versatile high-performance liquid chromatography (HPLC) CSP for separating a wide range of enantiomers, including chiral pharmaceutical intermediates and chiral drugs. With separation performance comparable to commercial chiral columns and even greater versatility, the PA-CA COF@SiO2 column held promise for practical applications. Chiral separation results combined with molecular simulation indicated that the mixed mode of PA and CA resulted in the broad separation capability of PA-CA COF. The introduction of the dual-chiral COFs concept opens up a new avenue for chiral recognition and separation, holding great potential for practical enantiomer separation.

Small Molecules Promote the Rapid Generation of Dental Epithelial Cells from Human-Induced Pluripotent Stem Cells.

Human-induced pluripotent stem cells (hiPSCs) offer a promising source for generating dental epithelial (DE) cells. Whereas the existing differentiation protocols were time-consuming and relied heavily on growth factors, herein, we developed a three-step protocol to convert hiPSCs into DE cells in 8 days. In the first phase, hiPSCs were differentiated into non-neural ectoderm using SU5402 (an FGF signaling inhibitor). The second phase involved differentiating non-neural ectoderm into pan-placodal ectoderm and simultaneously inducing the formation of oral ectoderm (OE) using LDN193189 (a BMP signaling inhibitor) and purmorphamine (a SHH signaling activator). In the final phase, OE cells were differentiated into DE through the application of Purmorphamine, XAV939 (a WNT signaling inhibitor), and BMP4. qRT-PCR and immunostaining were performed to examine the expression of lineage-specific markers. ARS staining was performed to evaluate the formation of the mineralization nodule. The expression of PITX2, SP6, and AMBN, the emergence of mineralization nodules, and the enhanced expression of AMBN and AMELX in spheroid culture implied the generation of DE cells. This study delineates the developmental signaling pathways and uses small molecules to streamline the induction of hiPSCs into DE cells. Our findings present a simplified and quicker method for generating DE cells, contributing valuable insights for dental regeneration and dental disease research.