Endothelial knockdown of the tumor suppressor, WWOX, increases inflammation in ventilator-induced lung injury.

Chronic cigarette smoke exposure decreases lung expression of WWOX which is known to protect the endothelial barrier during infectious models of acute respiratory distress syndrome (ARDS). Proteomic analysis of WWOX-silenced endothelial cells (ECs) was done using tandem mass tag mass spectrometry (TMT-MS). WWOX-silenced ECs as well as those isolated from endothelial cell Wwox knockout (EC Wwox KO) mice were subjected to cyclic stretch (18% elongation, 0.5 Hz, 4 h). Cellular lysates and media supernatant were harvested for assays of cellular signaling, protein expression, and cytokine release. These were repeated with dual silencing of WWOX and zyxin. Control and EC Wwox KO mice were subjected to high tidal volume ventilation. Bronchoalveolar lavage fluid and mouse lung tissue were harvested for cellular signaling, cytokine secretion, and histological assays. TMT-MS revealed upregulation of zyxin expression during WWOX knockdown which predicted a heightened inflammatory response to mechanical stretch. WWOX-silenced ECs and ECs isolated from EC Wwox mice displayed significantly increased cyclic stretch-mediated secretion of various cytokines (IL-6, KC/IL-8, IL-1ß, and MCP-1) relative to controls. This was associated with increased ERK and JNK phosphorylation but decreased p38 mitogen-activated kinases (MAPK) phosphorylation. EC Wwox KO mice subjected to VILI sustained a greater degree of injury than corresponding controls. Silencing of zyxin during WWOX knockdown abrogated stretch-induced increases in IL-8 secretion but not in IL-6. Loss of WWOX function in ECs is associated with a heightened inflammatory response during mechanical stretch that is associated with increased MAPK phosphorylation and appears, in part, to be dependent on the upregulation of zyxin.NEW & NOTEWORTHY Prior tobacco smoke exposure is associated with an increased risk of acute respiratory distress syndrome (ARDS) during critical illness. Our laboratory is investigating one of the gene expression changes that occurs in the lung following smoke exposure: WWOX downregulation. Here we describe changes in protein expression associated with WWOX knockdown and its influence on ventilator-induced ARDS in a mouse model.

Using time-series chest radiographs and laboratory data by machine learning for identifying pulmonary infection and colonization of Acinetobacter baumannii.

BACKGROUND: Accurately distinguishing between pulmonary infection and colonization in patients with Acinetobacter baumannii is of utmost importance to optimize treatment and prevent antibiotic abuse or inadequate therapy. An efficient automated sorting tool could prompt individualized interventions and enhance overall patient outcomes. This study aims to develop a robust machine learning classification model using a combination of time-series chest radiographs and laboratory data to accurately classify pulmonary status caused by Acinetobacter baumannii. METHODS: We proposed nested logistic regression models based on different time-series data to automatically classify the pulmonary status of patients with Acinetobacter baumannii. Advanced features were extracted from the time-series data of hospitalized patients, encompassing dynamic pneumonia indicators observed on chest radiographs and laboratory indicator values recorded at three specific time points. RESULTS: Data of 152 patients with Acinetobacter baumannii cultured from sputum or alveolar lavage fluid were retrospectively analyzed. Our model with multiple time-series data demonstrated a higher performance of AUC (0.850, with a 95% confidence interval of [0.638-0.873]), an accuracy of 0.761, a sensitivity of 0.833. The model, which only incorporated a single time point feature, achieved an AUC of 0.741. The influential model variables included difference in the chest radiograph pneumonia score. CONCLUSION: Dynamic assessment of time-series chest radiographs and laboratory data using machine learning allowed for accurate classification of colonization and infection with Acinetobacter baumannii. This demonstrates the potential to help clinicians provide individualized treatment through early detection.

Time-Series MR Images Identifying Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using a Deep Learning Approach.

BACKGROUND: Pathological complete response (pCR) is an essential criterion for adjusting follow-up treatment plans for patients with breast cancer (BC). The value of the visual geometry group and long short-term memory (VGG-LSTM) network using time-series dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for pCR identification in BC is unclear. PURPOSE: To identify pCR to neoadjuvant chemotherapy (NAC) using deep learning (DL) models based on the VGG-LSTM network. STUDY TYPE: Retrospective. POPULATION: Center A: 235 patients (47.7 ± 10.0 years) were divided 7:3 into training (n = 164) and validation set (n = 71). Center B: 150 patients (48.5 ± 10.4 years) were used as test set. FIELD STRENGTH/SEQUENCE: 3-T, T2-weighted spin-echo sequence imaging, and gradient echo DCE sequence imaging. ASSESSMENT: Patients underwent MRI examinations at three sequential time points: pretreatment, after three cycles of treatment, and prior to surgery, with tumor regions of interest manually delineated. Histopathology was the gold standard. We used VGG-LSTM network to establish seven DL models using time-series DCE-MR images: pre-NAC images (t0 model), early NAC images (t1 model), post-NAC images (t2 model), pre-NAC and early NAC images (t0 + t1 model), pre-NAC and post-NAC images (t0 + t2 model), pre-NAC, early NAC and post-NAC images (t0 + t1 + t2 model), and the optimal model combined with the clinical features and imaging features (combined model). The models were trained and optimized on the training and validation set, and tested on the test set. STATISTICAL TESTS: The DeLong, Student's t-test, Mann-Whitney U, Chi-squared, Fisher's exact, Hosmer-Lemeshow tests, decision curve analysis, and receiver operating characteristics analysis were performed. P < 0.05 was considered significant. RESULTS: Compared with the other six models, the combined model achieved the best performance in the test set yielding an AUC of 0.927. DATA CONCLUSION: The combined model that used time-series DCE-MR images, clinical features and imaging features shows promise for identifying pCR in BC. TECHNICAL EFFICACY: Stage 4.

Association of reduced cerebrospinal fluid NPTX2 levels with postoperative delirium in patients undergoing knee/hip replacement: a prospective cohort study.

BACKGROUND: Postoperative delirium (POD) is a common complication with poor prognosis in the elderly, but its mechanism has not been fully elucidated. There is evidence that the changes in synaptic activity in the brain are closely related to the occurrence of POD. And neuronal pentraxin 2 (NPTX2) can regulate synaptic activity in vivo. AIMS: This study aims to explore whether decreased NPTX2 levels affects POD and whether the cerebrospinal fluid (CSF) biomarkers of POD mediate this association. METHODS: In this prospective cohort study, we interviewed patients with knee/hip replacement 1 day before surgery to collect patient information and assess their cognitive function. CSF was extracted for measuring the CSF levels of NPTX2 and other POD biomarkers on the day of surgery. And postoperative follow-up visits were performed 1-7 days after surgery. RESULTS: Finally, 560 patients were included in the study. The patients were divided into POD group and NPOD (non-POD) group. The POD group had a median age of 80 years, a female proportion of 45%, a median BMI of 24.1 kg/m2, and a median years of education of 9 years. The Mann-Whitney U test showed that CSF NPTX2 levels were significantly lower in POD group, compared with the NPOD group (P < 0.05). Univariate binary logistic regression analysis showed that reduced CSF levels of NPTX2 protected against POD (crude OR = 0.994, 95% CI 0.993-0.995, P < 0.001). The receiver-operating characteristic (ROC) curve indicated that CSF NPTX2 level had high predictive value for POD. Mediation analyses showed that CSF T-tau (mediating proportion = 21%) and P-tau (mediating proportion = 29%) had significant mediating effects on the association between CSF NPTX2 and POD. CONCLUSION: CSF NPTX2 levels were associated with the occurrence of POD. Low CSF NPTX2 levels may be an independent protective factor for POD. CSF T-tau and P-tau could mediate the association between CSF NPTX2 and POD occurrence. CLINICAL TRIAL REGISTRATION: The trial registration number (TRN): ChiCTR2200064740, Date of Registration: 2022-10-15.

Self-Assembled and Wavelength-Tunable Quantum Dot Whispering-Gallery-Mode Lasers for Backlight Displays.

Thanks to the narrow line width and high brightness, colloidal quantum dot (CQD) lasers show promising applications in next-generation displays. However, CQD laser-based displays have yet to be demonstrated because of two challenges in integrating red, green, and blue (RGB) lasers: absorption from red CQDs deteriorates the optical gain of blue and green CQDs, and imbalanced white spectra lack blue lasing due to the high lasing threshold of blue CQDs. Herein, we introduce a facile surfactant-free self-assembly method to assemble RGB CQDs into high-quality whispering-gallery-mode (WGM) RGB lasers with close lasing thresholds among them. Moreover, these RGB lasers can lase nearly independently even when they are closely integrated, and they can construct an ultrawide color space whose color gamut is 105% of that of the BT.2020 standard. These combined strategies allow us to demonstrate the first full-color liquid crystal displays using CQD lasers as the backlight source.

Neoadjuvant docetaxel, epirubicin, and cyclophosphamide with or without metformin in breast cancer patients with metabolic abnormality: results from the randomized Phase II NeoMET trial.

PURPOSE: Breast cancer patients with metabolic syndrome (MetS) and its components show worse treatment responses to chemotherapy. Metformin is a widely used antidiabetic drug which also shows potential anticancer effect. This study aims to evaluate the efficacy, safety, and metabolic parameters change of metformin combined with docetaxel, epirubicin, and cyclophosphamide (TEC) in neoadjuvant treatment (NAT) for breast cancer patients with metabolic abnormality. METHODS: Eligible breast cancer patients were randomized to receive six cycles of TEC (docetaxel 75 mg/m2, epirubicin 75 mg/m2, and cyclophosphamide 500 mg/m2, d1, q3w) or TEC with metformin (TECM, TEC with oral metformin 850 mg once daily for the first cycle, then 850 mg twice daily for the following cycles). The primary end point was total pathological complete response (tpCR, ypTis/0N0) rate. RESULTS: Ninety-two patients were enrolled and randomized from October 2013 to December 2019: 88 patients were available for response and safety assessment. The tpCR rates were 12.5% (5/40) and 14.6% (7/48) in the TEC and TECM groups, respectively (P = 0.777). There was no difference in Ki67 decrease after NAT between two groups (P = 0.456). Toxicity profile were similar between two groups. No grade 3 or higher diarrhea were recorded. Total cholesterol (TC) and high-density lipoprotein cholesterol worsened after NAT in the TEC arm but remained stable in the TECM arm. The absolute increase of TC and low-density lipoprotein cholesterol (LDL-C) was significantly lower in the TECM group compared with the TEC group. After a median follow-up of 40.8 (4.7-70.8) months, no survival difference was observed between TEC and TECM groups (all P > 0.05). CONCLUSION: Adding metformin to TEC didn't increase pCR rate and disease outcome in breast cancer patients with metabolic abnormality. However, additional metformin treatment with chemotherapy would prevent TC and LDL-C increase after NAT. Trial Registration ClinicalTrials.gov Identifier: NCT01929811.

Root acid phosphatases and rhizobacteria synergistically enhance white lupin and rice phosphorus acquisition.

The rhizosheath is a belowground area that acts as a communication hub at the root-soil interface to promote water and nutrient acquisition. Certain crops, such as white lupin (Lupinus albus), acquire large amounts of phosphorus (P), owing partially to exudation of acid phosphatases (APases). Plant growth-promoting rhizobacteria also increase soil P availability. However, potential synergistic effects of root APases and rhizosheath-associated microbiota on P acquisition require further research. In this study, we investigated the roles of root purple APases (PAPs) and plant growth-promoting rhizobacteria in rhizosheath formation and P acquisition under conditions of soil drying (SD) and P treatment (+P: soil with P fertilizer; -P: soil without fertilizer). We expressed purple acid phosphatase12 (LaPAP12) in white lupin and rice (Oryza sativa) plants and analyzed the rhizosheath-associated microbiome. Increased or heterologous LaPAP12 expression promoted APase activity and rhizosheath formation, resulting in increased P acquisition mainly under SD-P conditions. It also increased the abundance of members of the genus Bacillus in the rhizosheath-associated microbial communities of white lupin and rice. We isolated a phosphate-solubilizing, auxin-producing Bacillus megaterium strain from the rhizosheath of white lupin and used this to inoculate white lupin and rice plants. Inoculation promoted rhizosheath formation and P acquisition, especially in plants with increased LaPAP12 expression and under SD-P conditions, suggesting a functional role of the bacteria in alleviating P deficit stress via rhizosheath formation. Together, our results suggest a synergistic enhancing effect of LaPAP12 and plant growth-promoting rhizobacteria on rhizosheath formation and P acquisition under SD-P conditions.

Hypoxia-responsive Immunostimulatory Nanomedicines Synergize with Checkpoint Blockade Immunotherapy for Potentiating Cancer Immunotherapy.

Inducing cell death while simultaneously enhancing antitumor immune responses is a promising therapeutic approach for multiple cancers. Celastrol (Cel) and 7-ethyl-10-hydroxycamptothecin (SN38) have contrasting physicochemical properties, but strong synergy in immunogenic cell death induction and anticancer activity. Herein, a hypoxia-sensitive nanosystem (CS@TAP) was designed to demonstrate effective immunotherapy for colorectal cancer by systemic delivery of an immunostimulatory chemotherapy combination. Furthermore, the combination of CS@TAP with anti-PD-L1 mAb (αPD-L1) exhibited a significant therapeutic benefit of delaying tumor growth and increased local doses of immunogenic signaling and T-cell infiltration, ultimately extending survival. We conclude that CS@TAP is an effective inducer of immunogenic cell death (ICD) in cancer immunotherapy. Therefore, this study provides an encouraging strategy to synergistically induce immunogenic cell death to enhance tumor cytotoxic T lymphocytes (CTLs) infiltration for anticancer immunotherapy.

Integrated Transcriptomic and Metabolomic Analyses Identify Critical Genes and Metabolites Associated with Seed Vigor of Common Wheat.

Seed aging is a common physiological phenomenon during storage which has a great impact on seed quality. An in-depth analysis of the physiological and molecular mechanisms of wheat seed aging is of great significance for cultivating high-vigor wheat varieties. This study reveals the physiological mechanisms of wheat seed aging in two cultivars differing in seed vigor, combining metabolome and transcriptome analyses. Differences between cultivars were examined based on metabolomic differential analysis. Artificial aging had a significant impact on the metabolism of wheat seeds. A total of 7470 (3641 upregulated and 3829 downregulated) DEGs were detected between non-aging HT and LT seeds; however, 10,648 (4506 up and 6142 down) were detected between the two cultivars after aging treatment. Eleven, eight, and four key metabolic-related gene families were identified in the glycolysis/gluconeogenesis and TCA cycle pathways, starch and sucrose metabolism pathways, and galactose metabolism pathways, respectively. In addition, 111 up-regulated transcription factor genes and 85 down-regulated transcription factor genes were identified in the LT 48h group. A total of 548 metabolites were detected across all samples. Cultivar comparisons between the non-aged groups and aged groups revealed 46 (30 upregulated and 16 downregulated) and 62 (38 upregulated and 24 downregulated) DIMs, respectively. Network analysis of the metabolites indicated that glucarate O-phosphoric acid, L-methionine sulfoxide, isocitric acid, and Gln-Gly might be the most crucial DIMs between HT and LT. The main related metabolites were enriched in pathways such as glyoxylate and dicarboxylate metabolism, biosynthesis of secondary metabolites, fatty acid degradation, etc. However, metabolites that exhibited differences between cultivars were mainly enriched in carbon metabolism, the TCA cycle, etc. Through combined metabolome and transcriptome analyses, it was found that artificial aging significantly affected glycolysis/gluconeogenesis, pyruvate metabolism, and glyoxylate and dicarboxylate metabolism, which involved key genes such as ACS, F16P2, and PPDK1. We thus speculate that these genes may be crucial in regulating physiological changes in seeds during artificial aging. In addition, an analysis of cultivar differences identified pathways related to amino acid and polypeptide metabolism, such as cysteine and methionine metabolism, glutathione metabolism, and amino sugar and nucleotide sugar metabolism, involving key genes such as BCAT3, CHI1, GAUT1, and GAUT4, which may play pivotal roles in vigor differences between cultivars.

Transcriptomic analysis reveals the dynamic changes of transcription factors during early development of chicken embryo.

BACKGROUND: The transition from fertilized egg to embryo in chicken requires activation of hundreds of genes that were mostly inactivated before fertilization, which is accompanied with various biological processes. Undoubtedly, transcription factors (TFs) play important roles in regulating the changes in gene expression pattern observed at early development. However, the contribution of TFs during early embryo development of chicken still remains largely unknown that need to be investigated. Therefore, an understanding of the development of vertebrates would be greatly facilitated by study of the dynamic changes in transcription factors during early chicken embryo. RESULTS: In the current study, we selected five early developmental stages in White Leghorn chicken, gallus gallus, for transcriptome analysis, cover 17,478 genes with about 807 million clean reads of RNA-sequencing. We have compared global gene expression patterns of consecutive stages and noted the differences. Comparative analysis of differentially expressed TFs (FDR < 0.05) profiles between neighboring developmental timepoints revealed significantly enriched biological categories associated with differentiation, development and morphogenesis. We also found that Zf-C2H2, Homeobox and bHLH were three dominant transcription factor families that appeared in early embryogenesis. More importantly, a TFs co-expression network was constructed and 16 critical TFs were identified. CONCLUSION: Our findings provide a comprehensive regulatory framework of TFs in chicken early embryo, revealing new insights into alterations of chicken embryonic TF expression and broadening better understanding of TF function in chicken embryogenesis.

Salivary microbiome in chronic kidney disease: what is its connection to diabetes, hypertension, and immunity?

BACKGROUND: The association between oral dysbiosis and chronic kidney disease (CKD) has gained increasing attention in recent years. Diabetes and hypertension are the most common conditions in CKD. However, a case-control study with matched confounding variables on the salivary microbiome in CKD and the influence of diabetes and hypertension on the microbiome has never been reported. METHODS: In our study, we compared the salivary microbiome profile between patients with CKD and healthy controls (HC) using 16S ribosomal DNA sequencing and examine its association with diabetes, hypertension, and immunity. RESULTS: We observed that the bacterial community was skewed in the saliva of CKD, with increased Lautropia and Pseudomonas, and decreased Actinomyces, Prevotella, Prevotella 7, and Trichococcus. No difference in the bacterial community between the CKD patients complicated with and without diabetes, and between those with and without hypertension. Prevotella 7 declined in CKD patients with/without hypertension with respect to HC, while Pseudomonas increased in CKD patients with/without hypertension. Pseudomonas was negatively associated with immunoglobin G in CKD patients. Both CKD patients with positive and negative antistreptolysin O had declined Prevotella 7 and Trichococcus compared to HC, whereas increased Pseudomonas. CONCLUSIONS: Our study identifies a distinct bacterial saliva microbiome in CKD patients characterized by alteration in composition. We unravel here that the co-occurrence diseases of diabetes and hypertension are not associated with specific bacterial alterations, suggesting that bacterial dysbiosis in saliva plays a role in renal damage regardless of the occurrence of diabetes and hypertension.

Predicting the molecular subtype of breast cancer and identifying interpretable imaging features using machine learning algorithms.

OBJECTIVES: To evaluate the performance of interpretable machine learning models in predicting breast cancer molecular subtypes. METHODS: We retrospectively enrolled 600 patients with invasive breast carcinoma between 2012 and 2019. The patients were randomly divided into a training (n = 450) and a testing (n = 150) set. The five constructed models were trained based on clinical characteristics and imaging features (mammography and ultrasonography). The model classification performances were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity. Shapley additive explanation (SHAP) technique was used to interpret the optimal model output. Then we choose the optimal model as the assisted model to evaluate the performance of another four radiologists in predicting the molecular subtype of breast cancer with or without model assistance, according to mammography and ultrasound images. RESULTS: The decision tree (DT) model performed the best in distinguishing triple-negative breast cancer (TNBC) from other breast cancer subtypes, yielding an AUC of 0.971; accuracy, 0.947; sensitivity, 0.905; and specificity, 0.941. The accuracy, sensitivity, and specificity of all radiologists in distinguishing TNBC from other molecular subtypes and Luminal breast cancer from other molecular subtypes have significantly improved with the assistance of DT model. In the diagnosis of TNBC versus other subtypes, the average sensitivity, average specificity, and average accuracy of less experienced and more experienced radiologists increased by 0.090, 0.125, 0.114, and 0.060, 0.090, 0.083, respectively. In the diagnosis of Luminal versus other subtypes, the average sensitivity, average specificity, and average accuracy of less experienced and more experienced radiologists increased by 0.084, 0.152, 0.159, and 0.020, 0.100, 0.048. CONCLUSIONS: This study established an interpretable machine learning model to differentiate between breast cancer molecular subtypes, providing additional values for radiologists. KEY POINTS: • Interpretable machine learning model (MLM) could help clinicians and radiologists differentiate between breast cancer molecular subtypes. • The Shapley additive explanations (SHAP) technique can select important features for predicting the molecular subtypes of breast cancer from a large number of imaging signs. • Machine learning model can assist radiologists to evaluate the molecular subtype of breast cancer to some extent.

A deep learning-machine learning fusion approach for the classification of benign, malignant, and intermediate bone tumors.

OBJECTIVES: To build and validate deep learning and machine learning fusion models to classify benign, malignant, and intermediate bone tumors based on patient clinical characteristics and conventional radiographs of the lesion. METHODS: In this retrospective study, data were collected with pathologically confirmed diagnoses of bone tumors between 2012 and 2019. Deep learning and machine learning fusion models were built to classify tumors as benign, malignant, or intermediate using conventional radiographs of the lesion and potentially relevant clinical data. Five radiologists compared diagnostic performance with and without the model. Diagnostic performance was evaluated using the area under the curve (AUC). RESULTS: A total of 643 patients' (median age, 21 years; interquartile range, 12-38 years; 244 women) 982 radiographs were included. In the test set, the binary category classification task, the radiological model of classification for benign/not benign, malignant/nonmalignant, and intermediate/not intermediate had AUCs of 0.846, 0.827, and 0.820, respectively; the fusion models had an AUC of 0.898, 0.894, and 0.865, respectively. In the three-category classification task, the radiological model achieved a macro average AUC of 0.813, and the fusion model had a macro average AUC of 0.872. In the observation test, the mean macro average AUC of all radiologists was 0.819. With the three-category classification fusion model support, the macro AUC improved by 0.026. CONCLUSION: We built, validated, and tested deep learning and machine learning models that classified bone tumors at a level comparable with that of senior radiologists. Model assistance may somewhat help radiologists' differential diagnoses of bone tumors. KEY POINTS: • The deep learning model can be used to classify benign, malignant, and intermediate bone tumors. • The machine learning model fusing information from radiographs and clinical characteristics can improve the classification capacity for bone tumors. • The diagnostic performance of the fusion model is comparable with that of senior radiologists and is potentially useful as a complement to radiologists in a bone tumor differential diagnosis.

Can cognition help predict suicide risk in patients with major depressive disorder? A machine learning study.

BACKGROUND: Previous studies suggest that deficits in cognition may increase the risk of suicide. Our study aims to develop a machine learning (ML) algorithm-based suicide risk prediction model using cognition in patients with major depressive disorder (MDD). METHODS: Participants comprised 52 depressed suicide attempters (DSA) and 61 depressed non-suicide attempters (DNS), and 98 healthy controls (HC). All participants were required to complete a series of questionnaires, the Suicide Stroop Task (SST) and the Iowa Gambling Task (IGT). The performance in IGT was analyzed using repeated measures ANOVA. ML with extreme gradient boosting (XGBoost) classification algorithm and locally explanatory techniques assessed performance and relative importance of characteristics for predicting suicide attempts. Prediction performances were compared with the area under the curve (AUC), decision curve analysis (DCA), and net reclassification improvement (NRI). RESULTS: DSA and DNS preferred to select the card from disadvantageous decks (decks "A" + "B") under risky situation (p = 0.023) and showed a significantly poorer learning effect during the IGT (F = 2.331, p = 0.019) compared with HC. Performance of XGBoost model based on demographic and clinical characteristics was compared with that of the model created after adding cognition data (AUC, 0.779 vs. 0.819, p > 0.05). The net benefit of model was improved and cognition resulted in continuous reclassification improvement with NRI of 5.3%. Several clinical dimensions were significant predictors in the XGBoost classification algorithm. LIMITATIONS: A limited sample size and failure to include sufficient suicide risk factors in the predictive model. CONCLUSION: This study demonstrate that cognitive deficits may serve as an important risk factor to predict suicide attempts in patients with MDD. Combined with other demographic characteristics and attributes drawn from clinical questionnaires, cognitive function can improve the predictive effectiveness of the ML model. Additionally, explanatory ML models can help clinicians detect specific risk factors for each suicide attempter within MDD patients. These findings may be helpful for clinicians to detect those at high risk of suicide attempts quickly and accurately, and help them make proactive treatment decisions.

Optical-Gain-based Sensing Using Inorganic-Ligand-Passivated Colloidal Quantum Dots.

Thanks to their extremely large surface-to-volume ratio, colloidal quantum dots are potential high-performance sensing materials. However, previous sensing works using their spontaneous emission suffer from low sensitivities. The absence of an amplification process and the presence of the steric hindrance of long-chain organic ligands are two possible causations. Herein we propose that these two issues can be circumvented by using the amplified spontaneous emission of colloidal quantum dots capped by short-chain inorganic ligands. To exemplify this concept, we performed humidity sensing and observed a ∼31 times enhancement in sensitivity. Meanwhile, we found that the amplified spontaneous emission threshold power was reduced by 34% in a high humidity environment. On the basis of our transient absorption measurements, we attribute these observations to the mitigation of ultrafast subpicosecond trapping processes, which are enabled by the absorption of water molecules.

Cigarette Smoke and Nicotine-Containing Electronic-Cigarette Vapor Downregulate Lung WWOX Expression, Which Is Associated with Increased Severity of Murine Acute Respiratory Distress Syndrome.

A history of chronic cigarette smoking is known to increase risk for acute respiratory distress syndrome (ARDS), but the corresponding risks associated with chronic e-cigarette use are largely unknown. The chromosomal fragile site gene, WWOX, is highly susceptible to genotoxic stress from environmental exposures and thus an interesting candidate gene for the study of exposure-related lung disease. Lungs harvested from current versus former/never-smokers exhibited a 47% decrease in WWOX mRNA levels. Exposure to nicotine-containing e-cigarette vapor resulted in an average 57% decrease in WWOX mRNA levels relative to vehicle-treated controls. In separate studies, endothelial (EC)-specific WWOX knockout (KO) versus WWOX flox control mice were examined under ARDS-producing conditions. EC WWOX KO mice exhibited significantly greater levels of vascular leak and histologic lung injury. ECs were isolated from digested lungs of untreated EC WWOX KO mice using sorting by flow cytometry for CD31+ CD45-cells. These were grown in culture, confirmed to be WWOX deficient by RT-PCR and Western blotting, and analyzed by electric cell impedance sensing as well as an FITC dextran transwell assay for their barrier properties during methicillin-resistant Staphylococcus aureus or LPS exposure. WWOX KO ECs demonstrated significantly greater declines in barrier function relative to cells from WWOX flox controls during either methicillin-resistant S. aureus or LPS treatment as measured by both electric cell impedance sensing and the transwell assay. The increased risk for ARDS observed in chronic smokers may be mechanistically linked, at least in part, to lung WWOX downregulation, and this phenomenon may also manifest in the near future in chronic users of e-cigarettes.

UCHL1, a deubiquitinating enzyme, regulates lung endothelial cell permeability in vitro and in vivo.

Increasing evidence suggests an important role for deubiquitinating enzymes (DUBs) in modulating a variety of biological functions and diseases. We previously identified the upregulation of the DUB ubiquitin carboxyl terminal hydrolase 1 (UCHL1) in murine ventilator-induced lung injury (VILI). However, the role of UCHL1 in modulating vascular permeability, a cardinal feature of acute lung injury (ALI) in general, remains unclear. We investigated the role of UCHL1 in pulmonary endothelial cell (EC) barrier function in vitro and in vivo and examined the effects of UCHL1 on VE-cadherin and claudin-5 regulation, important adherens and tight junctional components, respectively. Measurements of transendothelial electrical resistance confirmed decreased barrier enhancement induced by hepatocyte growth factor (HGF) and increased thrombin-induced permeability in both UCHL1-silenced ECs and in ECs pretreated with LDN-57444 (LDN), a pharmacological UCHL1 inhibitor. In addition, UCHL1 knockdown (siRNA) was associated with decreased expression of VE-cadherin and claudin-5, whereas silencing of the transcription factor FoxO1 restored claudin-5 levels. Finally, UCHL1 inhibition in vivo via LDN was associated with increased VILI in a murine model. These findings support a prominent functional role of UCHL1 in regulating lung vascular permeability via alterations in adherens and tight junctions and implicate UCHL1 as an important mediator of ALI.

A nomogram to predict the high-risk RS in HR+/HER2-breast cancer patients older than 50 years of age.

BACKGROUND: The 21-gene recurrence score (RS) testing can predict the prognosis for luminal breast cancer patients. Meanwhile, patients > 50 years with RS > 25 have improved survival with adjuvant chemotherapy. The current study aimed to develop a nomogram with routine parameters to predict RS. METHODS: We included patients diagnosed with hormone receptor (HR)-positive, human epidermal growth factor receptor-2 (HER2)-negative who underwent the 21-gene RS testing and aged > 50 years. The primary outcome was high-risk RS (> 25). Univariate and multivariate analyses were performed to identify significant predictors. A predictive nomogram based on logistic model was developed and evaluated with receiver operating characteristic (ROC) curves. The nomogram was internally validated for discrimination and calibration with bootstrapping method, and externally validated in another cohort. We then assessed the nomogram in different subgroups of patients and compared it with several published models. RESULTS: A total of 1100 patients were included. Five clinicopathological parameters were used as predictors of a high-risk RS, including tumor grade, histologic subtype, ER expression, PR expression, and Ki-67 index. The area under the curve (AUC) was 0.798 (95% CI 0.772-0.825) and optimism adjusted AUC was 0.794 (95% CI 0.781-0.822). External validation demonstrated an AUC value of 0.746 (95% CI 0.685-0.807), which had no significant difference with the training cohort (P = 0.124). Calibration plots indicated that the nomogram-predicted results were well fitted to the actual outcomes in both internal and external validation. The nomogram had better discriminate ability in patients who had tumors > 2 cm (AUC = 0.847, 95% CI 0.804-0.890). When compared with four other existing models, similar AUC was observed between our nomogram and the model constructed by discriminate Lee et al. CONCLUSIONS: We developed a user-friendly nomogram to predict the high-risk RS in luminal breast cancer patients who were older than 50 years of age, which could guide treatment decision making for those who have no access to the 21-gene RS testing.

Clinical characteristics and disease outcomes in ER+ breast cancer: a comparison between HER2+ patients treated with trastuzumab and HER2- patients.

BACKGROUND: Trastuzumab has changed the prognosis of HER2+ breast cancer. We aimed to investigate the prognosis of ER+/HER2+ patients treated with trastuzumab, thus to guide escalation endocrine treatment in ER+ breast cancer. METHODS: ER-positive early breast cancer patients operated at Ruijin Hospital between Jan. 2009 and Dec. 2017 were retrospectively included. Eligible patients were grouped as HER2-negative (HER2-neg) or HER2-positive with trastuzumab treatment (HER2-pos-T). Kaplan-Meier analysis and Cox proportional hazards model were used to compare the disease-free survival (DFS) and overall survival (OS) between these two groups. RESULTS: A total of 3761 patients were enrolled: 3313 in the HER2-neg group and 448 in the HER2-pos-T group. Patients in the HER2-pos-T group were associated with pre/peri-menopause, higher histological grade, LVI, higher Ki-67 level, lower ER and PR levels (all P <  0.05). At a median follow-up of 62 months, 443 DFS events and 191 deaths were observed. The estimated 5-year DFS rate was 89.7% in the HER2-neg group and 90.2% in the HER2-pos-T group (P = 0.185), respectively. Multivariable analysis demonstrated that patients in the HER2-pos-T group had a better DFS than patients in the HER2-neg group (HR 0.52, 95% CI: 0.37-0.73, P <  0.001). The estimated 5-year OS rates were 96.0% and 96.3% in the two groups, respectively (P = 0.133). Multivariate analysis found that HER2-pos-T group was still associated with significantly better OS compared with the HER2-neg group (HR 0.38, 95% CI: 0.22-0.67, P = 0.037). CONCLUSION: ER+/HER2+ breast cancer patients treated with trastuzumab were associated with superior outcome compared with ER+/HER2- patients, indicating HER2-positivity itself may not be an adverse factor for ER+ patients in the era of trastuzumab.

Efficacy of adjuvant chemotherapy stratified by age and the 21-gene recurrence score in estrogen receptor-positive breast cancer.

BACKGROUND: The 21-gene recurrence score (RS) can predict chemotherapy benefit in estrogen receptor-positive, human epidermal growth factor receptor-2-negative (ER+/HER2-) early breast cancer patients. Age would influence the interaction between RS and chemotherapy effect. The current study aimed to determine RS thresholds which were predictive of chemotherapy benefit in young and old women, respectively. METHODS: Patients diagnosed with pN0-1, ER+/HER2- breast cancer between 2009 and 2016 were retrospectively reviewed. Propensity score matching was performed according to chemotherapy usage. After stratifying patients with different cutoffs of age, the RS threshold indicating chemotherapy benefit in each age strata were determined by cox proportional hazard models. RESULTS: A total of 1227 patients were included. The median age was 58 years and the median RS was 24. After matching, the RS thresholds suggesting chemotherapy benefit varied with age. For patients ≤55 years, chemotherapy benefit was observed in those having RS > 25 (P = 0.03), with 4-year invasive disease-free survival (IDFS) of 97.0 and 89.3% in patients receiving chemotherapy or not. While patients derived no benefit from chemotherapy if they had RS ≤25 (P = 0.66, 4-year IDFS: 95.3% vs. 94.6%). For patients > 55 years, adjuvant chemotherapy was associated with better prognosis in those with RS > 36 (P = 0.014, 4-year IDFS: 94.7% vs. 76.2%), but not in those having RS ≤36 (P = 0.13, 4-year IDFS: 92.3% vs. 95.8%). CONCLUSIONS: Old patients need higher RS thresholds to demonstrate the chemotherapy benefit. Further efforts are warranted to investigate the association between age and predictive RS thresholds.