Development and validation of a predictive model for vertebral fracture risk in osteoporosis patients.

OBJECTIVE: This study aimed to develop and validate a predictive model for osteoporotic vertebral fractures (OVFs) risk by integrating demographic, bone mineral density (BMD), CT imaging, and deep learning radiomics features from CT images. METHODS: A total of 169 osteoporosis-diagnosed patients from three hospitals were randomly split into OVFs (n = 77) and Non-OVFs (n = 92) groups for training (n = 135) and test (n = 34). Demographic data, BMD, and CT imaging details were collected. Deep transfer learning (DTL) using ResNet-50 and radiomics features were fused, with the best model chosen via logistic regression. Cox proportional hazards models identified clinical factors. Three models were constructed: clinical, radiomics-DTL, and fusion (clinical-radiomics-DTL). Performance was assessed using AUC, C-index, Kaplan-Meier, and calibration curves. The best model was depicted as a nomogram, and clinical utility was evaluated using decision curve analysis (DCA). RESULTS: BMD, CT values of paravertebral muscles (PVM), and paravertebral muscles' cross-sectional area (CSA) significantly differed between OVFs and Non-OVFs groups (P < 0.05). No significant differences were found between training and test cohort. Multivariate Cox models identified BMD, CT values of PVM, and CSAPS reduction as independent OVFs risk factors (P < 0.05). The fusion model exhibited the highest predictive performance (C-index: 0.839 in training, 0.795 in test). DCA confirmed the nomogram's utility in OVFs risk prediction. CONCLUSION: This study presents a robust predictive model for OVFs risk, integrating BMD, CT data, and radiomics-DTL features, offering high sensitivity and specificity. The model's visualizations can inform OVFs prevention and treatment strategies.

Effects of short-term exposure to Pomacea canaliculata secretions on Limnodrilus hoffmeisteri and Propsilocerus akamusi: A study based on behavior, intestinal microbiota, and antioxidant system.

Pomacea canaliculata is one of the most notorious invasive aquatic snail, capable of influencing various aquatic organisms through their secretions. Limnodrilus hoffmeisteri and Propsilocerus akamusi are the most prevalent and powerful bioturbators in aquatic ecosystems. However, the mechanism of P. canaliculata's secretions affecting bioturbators remains unknown. This study aimed to investigate the effects of P. canaliculata's secretion on L. hoffmeisteri and P. akamusi. L. hoffmeisteri and P. akamusi were treated for 24 h with P. canaliculata and the native species Bellamya aeruginosa secretions at different densities (1 or 20). The migration numbers and aggregation rate of L. hoffmeisteri indicated that P. canaliculata secretion caused L. hoffmeisteri to become alert and migrate away from the nucleus community, resulting in poor population identification, especially at high concentrations. Moreover, the antioxidant enzymatic activity, lipid peroxidation, intestinal microbial diversity, and composition of the two bioturbators were analyzed. Superoxide dismutase (SOD) activity and malondialdehyde (MDA) concentration were elevated following P. canaliculata secretion treatment, indicating oxidative damage. Furthermore, the composition and diversity of intestinal microbiota of L. hoffmeisteri and P. akamusi were changed. The abundance of functional microbiota decreased, and pathogenic bacteria such as Aeromonas became dominant in the intestines of both bioturbators. The current research evaluates the effects of P. canaliculata secretion on the behavior, oxidative stress, and intestinal microbial composition and diversity of two bioturbators, providing new insights into the assessment of post-invaded ecosystems.

Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images.

Purpose: To develop and validate a deep learning radiomics (DLR) model that uses X-ray images to predict the classification of osteoporotic vertebral fractures (OVFs). Material and methods: The study encompassed a cohort of 942 patients, involving examinations of 1076 vertebrae through X-ray, CT, and MRI across three distinct hospitals. The OVFs were categorized as class 0, 1, or 2 based on the Assessment System of Thoracolumbar Osteoporotic Fracture. The dataset was divided randomly into four distinct subsets: a training set comprising 712 samples, an internal validation set with 178 samples, an external validation set containing 111 samples, and a prospective validation set consisting of 75 samples. The ResNet-50 architectural model was used to implement deep transfer learning (DTL), undergoing -pre-training separately on the RadImageNet and ImageNet datasets. Features from DTL and radiomics were extracted and integrated using X-ray images. The optimal fusion feature model was identified through least absolute shrinkage and selection operator logistic regression. Evaluation of the predictive capabilities for OVFs classification involved eight machine learning models, assessed through receiver operating characteristic curves employing the "One-vs-Rest" strategy. The Delong test was applied to compare the predictive performance of the superior RadImageNet model against the ImageNet model. Results: Following pre-training separately on RadImageNet and ImageNet datasets, feature selection and fusion yielded 17 and 12 fusion features, respectively. Logistic regression emerged as the optimal machine learning algorithm for both DLR models. Across the training set, internal validation set, external validation set, and prospective validation set, the macro-average Area Under the Curve (AUC) based on the RadImageNet dataset surpassed those based on the ImageNet dataset, with statistically significant differences observed (P<0.05). Utilizing the binary "One-vs-Rest" strategy, the model based on the RadImageNet dataset demonstrated superior efficacy in predicting Class 0, achieving an AUC of 0.969 and accuracy of 0.863. Predicting Class 1 yielded an AUC of 0.945 and accuracy of 0.875, while for Class 2, the AUC and accuracy were 0.809 and 0.692, respectively. Conclusion: The DLR model, based on the RadImageNet dataset, outperformed the ImageNet model in predicting the classification of OVFs, with generalizability confirmed in the prospective validation set.

Profiling the selected hotspots for ear traits in two maize-teosinte populations.

KEY MESSAGE: Eight selected hotspots related to ear traits were identified from two maize-teosinte populations. Throughout the history of maize cultivation, ear-related traits have been selected. However, little is known about the specific genes involved in shaping these traits from their origins in the wild progenitor, teosinte, to the characteristics observed in modern maize. In this study, five ear traits (kernel row number [KRN], ear length [EL], kernel number per row [KNR], cob diameter [CD], and ear diameter [ED]) were investigated, and eight quantitative trait loci (QTL) hotspots were identified in two maize-teosinte populations. Notably, our findings revealed a significant enrichment of genes showing a selection signature and expressed in the ear in qbdCD1.1, qbdCD5.1, qbpCD2.1, qbdED1.1, qbpEL1.1, qbpEL5.1, qbdKNR1.1, and qbdKNR10.1, suggesting that these eight QTL are selected hotspots involved in shaping the maize ear. By combining the results of the QTL analysis with data from previous genome-wide association study (GWAS) involving two natural panels, we identified eight candidate selected genes related to KRN, KNR, CD, and ED. Among these, considering their expression pattern and sequence variation, Zm00001d025111, encoding a WD40/YVTN protein, was proposed as a positive regulator of KNR. This study presents a framework for understanding the genomic distribution of selected loci crucial in determining ear-related traits.

Decoding the gene regulatory network of endosperm differentiation in maize.

The persistent cereal endosperm constitutes the majority of the grain volume. Dissecting the gene regulatory network underlying cereal endosperm development will facilitate yield and quality improvement of cereal crops. Here, we use single-cell transcriptomics to analyze the developing maize (Zea mays) endosperm during cell differentiation. After obtaining transcriptomic data from 17,022 single cells, we identify 12 cell clusters corresponding to five endosperm cell types and revealing complex transcriptional heterogeneity. We delineate the temporal gene-expression pattern from 6 to 7 days after pollination. We profile the genomic DNA-binding sites of 161 transcription factors differentially expressed between cell clusters and constructed a gene regulatory network by combining the single-cell transcriptomic data with the direct DNA-binding profiles, identifying 181 regulons containing genes encoding transcription factors along with their high-confidence targets, Furthermore, we map the regulons to endosperm cell clusters, identify cell-cluster-specific essential regulators, and experimentally validated three predicted key regulators. This study provides a framework for understanding cereal endosperm development and function at single-cell resolution.

Constructing a Deep Learning Radiomics Model Based on X-ray Images and Clinical Data for Predicting and Distinguishing Acute and Chronic Osteoporotic Vertebral Fractures: A Multicenter Study.

RATIONALE AND OBJECTIVES: To construct and validate a deep learning radiomics (DLR) model based on X-ray images for predicting and distinguishing acute and chronic osteoporotic vertebral fractures (OVFs). METHODS: A total of 942 cases (1076 vertebral bodies) with both vertebral X-ray examination and MRI scans were included in this study from three hospitals. They were divided into a training cohort (n = 712), an internal validation cohort (n = 178), an external validation cohort (n = 111), and a prospective validation cohort (n = 75). The ResNet-50 model architecture was used for deep transfer learning (DTL), with pre-training performed on RadImageNet and ImageNet datasets. DTL features and radiomics features were extracted from lateral X-ray images of OVFs patients and fused together. A logistic regression model with the least absolute shrinkage and selection operator was established, with MRI showing bone marrow edema as the gold standard for acute OVFs. The performance of the model was evaluated using receiver operating characteristic curves. Eight machine learning classification models were evaluated for their ability to distinguish between acute and chronic OVFs. The Nomogram was constructed by combining clinical baseline data to achieve visualized classification assessment. The predictive performance of the best RadImageNet model and ImageNet model was compared using the Delong test. The clinical value of the Nomogram was evaluated using decision curve analysis (DCA). RESULTS: Pre-training resulted in 34 and 39 fused features after feature selection and fusion. The most effective machine learning algorithm in both DLR models was Light Gradient Boosting Machine. Using the Delong test, the area under the curve (AUC) for distinguishing between acute and chronic OVFs in the training cohort was 0.979 and 0.972 for the RadImageNet and ImageNet models, respectively, with no statistically significant difference between them (P = 0.235). In the internal validation cohort, external validation cohort, and prospective validation cohort, the AUCs for the two models were 0.967 vs 0.629, 0.886 vs 0.817, and 0.933 vs 0.661, respectively, with statistically significant differences in all comparisons (P < 0.05). The deep learning radiomics nomogram (DLRN) was constructed by combining the predictive model of RadImageNet with clinical baseline features, resulting in AUCs of 0.981, 0.974, 0.895, and 0.902 in the training cohort, internal validation cohort, external validation cohort, and prospective validation cohort, respectively. Using the Delong test, the AUCs for the fused feature model and the DLRN in the training cohort were 0.979 and 0.981, respectively, with no statistically significant difference between them (P = 0.169). In the internal validation cohort, external validation cohort, and prospective validation cohort, the AUCs for the two models were 0.967 vs 0.974, 0.886 vs 0.895, and 0.933 vs 0.902, respectively, with statistically significant differences in all comparisons (P < 0.05). The Nomogram showed a slight improvement in predictive performance in the internal and external validation cohort, but a slight decrease in the prospective validation cohort (0.933 vs 0.902). DCA showed that the Nomogram provided more benefits to patients compared to the DLR models. CONCLUSION: Compared to the ImageNet model, the RadImageNet model has higher diagnostic value in distinguishing between acute and chronic OVFs. Furthermore, the diagnostic performance of the model is further improved when combined with clinical baseline features to construct the Nomogram.

Patterns of de novo metastasis and survival outcomes by age in breast cancer patients: a SEER population-based study.

Background: The role of age in metastatic disease, including breast cancer, remains obscure. This study was conducted to determine the role of age in patients with de novo metastatic breast cancer. Methods: Breast cancer patients diagnosed with distant metastases between 2010 and 2019 were retrieved from the Surveillance, Epidemiology, and End Results database. Comparisons were performed between young (aged ≤ 40 years), middle-aged (41-60 years), older (61-80 years), and the oldest old (> 80 years) patients. Adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) were estimated using multivariate Cox proportional hazard models. Survival analysis was performed by the Kaplan-Meier method. Results: This study included 24155 (4.4% of all patients) de novo metastatic breast cancer patients. The number of young, middle-aged, older, and the oldest old patients were 195 (8.3%), 9397 (38.9%), 10224 (42.3%), and 2539 (10.5%), respectively. The 5-year OS rate was highest in the young (42.1%), followed by middle-aged (34.8%), older (28.3%), and the oldest old patients (11.8%). Multivariable Cox regression analysis showed that middle-aged (aHR, 1.18; 95% CI, 1.10-1.27), older (aHR, 1.42; 95% CI, 1.32-1.52), and the oldest old patients (aHR, 2.15; 95% CI, 1.98-2.33) had worse OS than young patients. Consistently, middle-aged (aHR, 1.16; 95% CI, 1.08-1.25), older (aHR, 1.32; 95% CI, 1.23-1.43), and the oldest old patients (aHR, 1.86; 95% CI, 1.71-2.03) had worse BCSS than young patients. Conclusion: This study provided clear evidence that de novo metastatic breast cancer had an age-specific pattern. Age was an independent risk factor for mortality in patients with de novo metastatic breast cancer.

Excretory/secretory products from Trichinella spiralis adult worms ameliorate myocardial infarction by inducing M2 macrophage polarization in a mouse model.

BACKGROUND: Ischemia-induced inflammatory response is the main pathological mechanism of myocardial infarction (MI)-caused heart tissue injury. It has been known that helminths and worm-derived proteins are capable of modulating host immune response to suppress excessive inflammation as a survival strategy. Excretory/secretory products from Trichinella spiralis adult worms (Ts-AES) have been shown to ameliorate inflammation-related diseases. In this study, Ts-AES were used to treat mice with MI to determine its therapeutic effect on reducing MI-induced heart inflammation and the immunological mechanism involved in the treatment. METHODS: The MI model was established by the ligation of the left anterior descending coronary artery, followed by the treatment of Ts-AES by intraperitoneal injection. The therapeutic effect of Ts-AES on MI was evaluated by measuring the heart/body weight ratio, cardiac systolic and diastolic functions, histopathological change in affected heart tissue and observing the 28-day survival rate. The effect of Ts-AES on mouse macrophage polarization was determined by stimulating mouse bone marrow macrophages in vitro with Ts-AES, and the macrophage phenotype was determined by flow cytometry. The protective effect of Ts-AES-regulated macrophage polarization on hypoxic cardiomyocytes was determined by in vitro co-culturing Ts-AES-induced mouse bone marrow macrophages with hypoxic cardiomyocytes and cardiomyocyte apoptosis determined by flow cytometry. RESULTS: We observed that treatment with Ts-AES significantly improved cardiac function and ventricular remodeling, reduced pathological damage and mortality in mice with MI, associated with decreased pro-inflammatory cytokine levels, increased regulatory cytokine expression and promoted macrophage polarization from M1 to M2 type in MI mice. Ts-AES-induced M2 macrophage polarization also reduced apoptosis of hypoxic cardiomyocytes in vitro. CONCLUSIONS: Our results demonstrate that Ts-AES ameliorates MI in mice by promoting the polarization of macrophages toward the M2 type. Ts-AES is a potential pharmaceutical agent for the treatment of MI and other inflammation-related diseases.

Effect of Low-Concentration Atropine Eye Drops in Controlling the Progression of Myopia in Children: A One- and Two-Year Follow-Up Study.

PURPOSE: Atropine eye drops have been shown to slow the progression of myopia, but there has been limited research on the effectiveness of 0.05% atropine in treating myopia. This study aimed to investigate the safety and efficacy of 0.05% atropine eye drops in controlling myopia in children. METHODS: The study included 424 participants aged 6 to 12 years between January 1, 2015, and January 1, 2021. Of these, 213 were randomly assigned to the 0.05% atropine group and 211 to the placebo group. The cycloplegic spherical equivalent (SE), axial length (AL), corneal curvature (K), and anterior chamber depth (ACD) were measured using IOLMaster. The lens power and corneal astigmatism were also determined. The changes in ocular biometric parameters were compared between the two groups, and the contributions of ocular characteristics to SE progression were calculated and compared. RESULTS: Over a 12-month period, the changes in spherical equivalent were -0.03 ± 0.28 and -0.32 ± 0.14 in the atropine and placebo groups, respectively (P = .01). The changes in axial length were 0.06 ± 0.11 and 0.17 ± 0.12, respectively (P = .01). At 18 and 24 months, there were significant differences in axial length and spherical equivalent between the atropine and placebo groups. Multiple regression models accounting for changes in AL, K, and lens magnification explained 87.23% and 98.32% of SE changes in the atropine and placebo groups, respectively. At 1 year (p = .01) and 2 years (p = .03), there were significant differences in photophobia between the atropine and placebo groups. CONCLUSIONS: This two-year follow-up study demonstrates that 0.05% atropine eye drops are safe and effective in preventing the development of myopia in school-aged children.

Fangchinoline inhibits the PEDV replication in intestinal epithelial cells via autophagic flux suppression.

Animal and human health are severely threatened by coronaviruses. The enteropathogenic coronavirus, porcine epidemic diarrhea virus (PEDV), is highly contagious, leading to porcine epidemic diarrhea (PED), which causes large economic losses in the world's swine industry. Piglets are not protected from emerging PEDV variants; therefore, new antiviral measures for PED control are urgently required. Herein, the anti-PEDV effects and potential mechanisms of fangchinoline (Fan) were investigated. Fan dose-dependently inhibited a PEDV infection at 24 h post-infection (EC50 value = 0.67 µM). We found that Fan mainly affected the PEDV replication phase but also inhibited PEDV at the attachment and internalization stages of the viral life cycle. Mechanistically, Fan blocked the autophagic flux in PEDV-infected cells by regulating the expression of autophagy-related proteins and changing PEDV virus particles. In summary, Fan inhibits PEDV infection by blocking the autophagic flux in cells. Our findings will help develop new strategies to prevent and treat PEDV infection.

Differentiation of acute and chronic vertebral compression fractures using conventional CT based on deep transfer learning features and hand-crafted radiomics features.

BACKGROUND: We evaluated the diagnostic efficacy of deep learning radiomics (DLR) and hand-crafted radiomics (HCR) features in differentiating acute and chronic vertebral compression fractures (VCFs). METHODS: A total of 365 patients with VCFs were retrospectively analysed based on their computed tomography (CT) scan data. All patients completed MRI examination within 2 weeks. There were 315 acute VCFs and 205 chronic VCFs. Deep transfer learning (DTL) features and HCR features were extracted from CT images of patients with VCFs using DLR and traditional radiomics, respectively, and feature fusion was performed to establish the least absolute shrinkage and selection operator. The MRI display of vertebral bone marrow oedema was used as the gold standard for acute VCF, and the model performance was evaluated using the receiver operating characteristic (ROC).To separately evaluate the effectiveness of DLR, traditional radiomics and feature fusion in the differential diagnosis of acute and chronic VCFs, we constructed a nomogram based on the clinical baseline data to visualize the classification evaluation. The predictive power of each model was compared using the Delong test, and the clinical value of the nomogram was evaluated using decision curve analysis (DCA). RESULTS: Fifty DTL features were obtained from DLR, 41 HCR features were obtained from traditional radiomics, and 77 features fusion were obtained after feature screening and fusion of the two. The area under the curve (AUC) of the DLR model in the training cohort and test cohort were 0.992 (95% confidence interval (CI), 0.983-0.999) and 0.871 (95% CI, 0.805-0.938), respectively. While the AUCs of the conventional radiomics model in the training cohort and test cohort were 0.973 (95% CI, 0.955-0.990) and 0.854 (95% CI, 0.773-0.934), respectively. The AUCs of the features fusion model in the training cohort and test cohort were 0.997 (95% CI, 0.994-0.999) and 0.915 (95% CI, 0.855-0.974), respectively. The AUCs of nomogram constructed by the features fusion in combination with clinical baseline data were 0.998 (95% CI, 0.996-0.999) and 0.946 (95% CI, 0.906-0.987) in the training cohort and test cohort, respectively. The Delong test showed that the differences between the features fusion model and the nomogram in the training cohort and the test cohort were not statistically significant (P values were 0.794 and 0.668, respectively), and the differences in the other prediction models in the training cohort and the test cohort were statistically significant (P < 0.05). DCA showed that the nomogram had high clinical value. CONCLUSION: The features fusion model can be used for the differential diagnosis of acute and chronic VCFs, and its differential diagnosis ability is improved when compared with that when either radiomics is used alone. At the same time, the nomogram has a high predictive value for acute and chronic VCFs and can be a potential decision-making tool to assist clinicians, especially when a patient is unable to undergo spinal MRI examination.

Lactate is useful for the efficient replication of porcine epidemic diarrhea virus in cell culture.

Porcine epidemic diarrhea virus (PEDV) is a deadly pathogen infecting pig herds, and has caused significant economic losses around the world. Vaccination remains the most effective way of keeping the PEDV epidemic under control. Previous studies have shown that the host metabolism has a significant impact on viral replication. In this study, we have demonstrated that two substrates of metabolic pathway, glucose and glutamine, play a key role in PEDV replication. Interestingly, the boosting effect of these compounds toward viral replication appeared to be dose-independent. Furthermore, we found that lactate, which is a downstream metabolite, promotes PEDV replication, even when added in excess to the cell culture medium. Moreover, the role of lactate in promoting PEDV was independent of the genotype of PEDV and the multiplicity of infection (MOI). Our findings suggest that lactate is a promising candidate for use as a cell culture additive for promoting PEDV replication. It could improve the efficiency of vaccine production and provide the basis for designing novel antiviral strategies.

ZmLBD5, a class-II LBD gene, negatively regulates drought tolerance by impairing abscisic acid synthesis.

Lateral organ boundaries domain (LBD) proteins are plant-specific transcription factors. Class-I LBD genes have been widely demonstrated to play pivotal roles in organ development; however, knowledge on class-II genes remains limited. Here, we report that ZmLBD5, a class-II LBD gene, is involved in the regulation of maize (Zea mays) growth and the drought response by affecting gibberellin (GA) and abscisic acid (ABA) synthesis. ZmLBD5 is mainly involved in regulation of the TPS-KS-GA2ox gene module, which is comprised of key enzyme-encoding genes involved in GA and ABA biosynthesis. ABA insufficiency increases stomatal density and aperture in overexpression plants and causes a drought-sensitive phenotype by promoting water transpiration. Increased GA1 levels promotes seedling growth in overexpression plants. Accordingly, CRISPR/Cas9 knockout lbd5 seedlings are dwarf but drought-tolerant. Moreover, lbd5 has a higher grain yield under drought stress conditions and shows no penalty in well-watered conditions compared to the wild type. On the whole, ZmLBD5 is a negative regulator of maize drought tolerance, and it is a potentially useful target for drought resistance breeding.

ABA-inducible DEEPER ROOTING 1 improves adaptation of maize to water deficiency.

Root architecture remodelling is critical for forage moisture in water-limited soil. DEEPER ROOTING 1 (DRO1) in Oryza, Arabidopsis, and Prunus has been reported to improve drought avoidance by promoting roots to grow downward and acquire water from deeper soil. In the present study, we found that ZmDRO1 responded more strongly to abscisic acid (ABA)/drought induction in Zea mays ssp. mexicana, an ancestral species of cultivated maize, than in B73. It was proposed that this is one of the reasons why Zea mays ssp. mexicana has a more noticeable change in the downward direction angle of the root and fewer biomass penalties under water-deficient conditions. Thus, a robust, synthetic ABA/drought-inducible promoter was used to control the expression of ZmDRO1B73 in Arabidopsis and cultivated maize for drought-resistant breeding. Interestingly, ABA-inducible ZmDRO1 promoted a larger downward root angle and improved grain yield by more than 40% under water-limited conditions. Collectively, these results revealed that different responses to ABA/drought induction of ZmDRO1 confer different drought avoidance abilities, and we demonstrated the application of ZmDRO1 via an ABA-inducible strategy to alter the root architecture of modern maize to improve drought adaptation in the field.

The associations between serum high-density lipoprotein cholesterol levels and malignant behavior in pancreatic neuroendocrine neoplasms.

BACKGROUND: The role of serum high-density lipoprotein cholesterol (HDL-c) in tumorigenesis are observed in several endocrine-related cancers. However, its role in pancreatic neuroendocrine neoplasms (PNENs) has not been understood. In the current study, the relationship between HDL-c levels and malignant behavior in PNENs was explored. METHODS: One hundred ninety-seven patients with histopathology confirmed PNENs were included. PNENs were divided into three grades (G1, G2 and G3) as 2017 WHO classification based on ki67 index and mitosis count. The demographic data, clinical information, tumor morphological and pathological features (organs invasion, lymph node metastasis, vascular invasion and perineural invasion), and serum tumor biomarkers were collected. The relationships between HDL-c levels and malignant behaviors in PNENs were analyzed using logistic regression analysis. Models were also developed for the identification of high grade PNENs. RESULTS: The levels of serum HDL-c in G2/G3 tumor were significantly lower than that in G1 tumor (P = 0.031). However, no such difference was found between G3 and G1/G2. The proportions of low HDL-c (≤ 0.9 mmol/L) were higher in high-grade PNENs (G2/G3 or G3) than those in low-grade (G1 or G1/G2) (29.0 vs 15.2%, P = 0.032; 37.0 vs 20.5%, P = 0.023). The risk of G2/G3 tumors in patients with high serum HDL-c levels was decreased (odds ratio (OR) = 0.35, 95% confidence interval (CI): 0.12-0.99). Similarly, the risk of G3 PNENs increased in patients with low HDL-c levels (OR = 2.51, 95%CI:1.12-5.60). HDL-c level was also associated with a high ki67 index (> 55%) (OR = 0.10, 95%CI: 0.02-0.51) and neuroendocrine carcinoma G3 (OR = 0.21, 95%CI: 0.06-0.80). The area under the curve (AUC) of HDL-c + tumor size + age was 0.85 (95% CI: 0.79-0.91) in identifying G2/G3 PNENs, and HDL-c (> 0.9 mmol/L) + tumor size + age had an AUC of 0.77 (95% CI: 0.70-0.84) in identifying G3 PNENs. HDL-c level was associated with lymph node metastasis (OR = 0.24, 95%CI:0.08-0.99). CONCLUSION: Serum HDL-c levels were significantly associated with malignant behaviors in PNENs, in particular to tumor grade and lymph node metastasis.

ZmLBD5 Increases Drought Sensitivity by Suppressing ROS Accumulation in Arabidopsis.

Drought stress is known to significantly limit crop growth and productivity. Lateral organ boundary domain (LBD) transcription factors-particularly class-I members-play essential roles in plant development and biotic stress. However, little information is available on class-II LBD genes related to abiotic stress in maize. Here, we cloned a maize class-II LBD transcription factor, ZmLBD5, and identified its function in drought stress. Transient expression, transactivation, and dimerization assays demonstrated that ZmLBD5 was localized in the nucleus, without transactivation, and could form a homodimer or heterodimer. Promoter analysis demonstrated that multiple drought-stress-related and ABA response cis-acting elements are present in the promoter region of ZmLBD5. Overexpression of ZmLBD5 in Arabidopsis promotes plant growth under normal conditions, and suppresses drought tolerance under drought conditions. Furthermore, the overexpression of ZmLBD5 increased the water loss rate, stomatal number, and stomatal apertures. DAB and NBT staining demonstrated that the reactive oxygen species (ROS) decreased in ZmLBD5-overexpressed Arabidopsis. A physiological index assay also revealed that SOD and POD activities in ZmLBD5-overexpressed Arabidopsis were higher than those in wild-type Arabidopsis. These results revealed the role of ZmLBD5 in drought stress by regulating ROS levels.

Increased Drug Resistance and Biofilm Formation Ability in ST34-Type Salmonella Typhimurium Exhibiting Multicellular Behavior in China.

Salmonella Typhimurium is an important food-borne pathogen. In this paper, multicellular behavior and associated characteristics of S. Typhimurium isolated from human and animal source food were studied. All the S. Typhimurium strains exhibiting multicellular behavior (100%) belonged to the ST34 type. In addition, most of the ST34-type multicellular behavior S. Typhimurium strains had a human origin (69.11%) and 98% of the ST34-type multicellular behavior strains exhibited strong biofilm formation capacity, which was much higher than that of non-multicellular behavior strains (7%, P < 0.01). Antibiotic resistance in ST34-type multicellular behavior strains was significantly higher than in strains with non-multicellular behavior for most conventional drugs (P < 0.05); notably, Polymyxin B (8%) and Imipenem (1%) resistances were also observed in the ST34-type strains. Furthermore, all the ST34-type multicellular behavior strains (100%) exhibited Multiple Drug Resistance (resistance to ≥3antibiotics), which was much higher than that of the non-multicellular behavior strains (P < 0.05). Consistent with the drug-resistant phenotype, the carrying rates of most drug-resistant genes in ST34-type multicellular behavior strains were higher than that those in non-multicellular behavior strains (P < 0.05). Therefore, this study revealed the emergence of a prevalent ST34-type multicellular behavior S. Typhimurium strains with increased biofilm formation ability and drug resistance rate, which poses a threat to public health safety, and highlights the need for comprehensive monitoring of the strains.

qMrdd2, a novel quantitative resistance locus for maize rough dwarf disease.

BACKGROUND: Maize rough dwarf disease (MRDD), a widespread disease caused by four pathogenic viruses, severely reduces maize yield and grain quality. Resistance against MRDD is a complex trait that controlled by many quantitative trait loci (QTL) and easily influenced by environmental conditions. So far, many studies have reported numbers of resistant QTL, however, only one QTL have been cloned, so it is especially important to map and clone more genes that confer resistance to MRDD. RESULTS: In the study, a major quantitative trait locus (QTL) qMrdd2, which confers resistance to MRDD, was identified and fine mapped. qMrdd2, located on chromosome 2, was consistently identified in a 15-Mb interval between the simple sequence repeat (SSR) markers D184 and D1600 by using a recombinant inbred line (RIL) population derived from a cross between resistant ("80007") and susceptible ("80044") inbred lines. Using a recombinant-derived progeny test strategy, qMrdd2 was delineated to an interval of 577 kb flanked by markers N31 and N42. We further demonstrated that qMrdd2 is an incompletely dominant resistance locus for MRDD that reduced the disease severity index by 20.4%. CONCLUSIONS: A major resistance QTL (qMrdd2) have been identified and successfully refined into 577 kb region. This locus will be valuable for improving maize variety resistance to MRDD via marker-assisted selection (MAS).

Functional Supramolecular Gels Based on the Hierarchical Assembly of Porphyrins and Phthalocyanines.

Supramolecular gels containing porphyrins and phthalocyanines motifs are attracting increased interests in a wide range of research areas. Based on the supramolecular gels systems, porphyrin or phthalocyanines can form assemblies with plentiful nanostructures, dynamic, and stimuli-responsive properties. And these π-conjugated molecular building blocks also afford supramolecular gels with many new features, depending on their photochemical and electrochemical characteristics. As one of the most characteristic models, the supramolecular chirality of these soft matters was investigated. Notably, the application of supramolecular gels containing porphyrins and phthalocyanines has been developed in the field of catalysis, molecular sensing, biological imaging, drug delivery and photodynamic therapy. And some photoelectric devices were also fabricated depending on the gelation of porphyrins or phthalocyanines. This paper presents an overview of the progress achieved in this issue along with some perspectives for further advances.

Screening and development of DNA aptamers as capture probes for colorimetric detection of patulin.

Patulin (PAT) is a kind of mycotoxin that has serious harmful impacts on both food quality and human health. A high-affinity ssDNA aptamer that specifically binds to patulin was generated using systemic evolution of ligands by exponential enrichment (SELEX) assisted by graphene oxide (GO). After 15 rounds of positive and negative selection, a highly enriched ssDNA pool was sequenced and the representative sequences were subjected to binding assays to evaluate their affinity and specificity. Of the eight aptamer candidates tested, the sequence PAT-11 bound to patulin with high affinity and excellent selectivity with a dissociation constant (Kd) of 21.83 ± 5.022 nM. The selected aptamer, PAT-11, was subsequently used as a recognition element to develop a detection method for patulin based on an enzyme-chromogenic substrate system. The colorimetric aptasensor exhibited a linear range from 50 to 2500 pg mL(-1), and the limit of detection was found to be 48 pg mL(-1). The results indicated that GO-SELEX technology was appropriate for the screening of aptamers against small-molecule toxins, offering a promising application for aptamer-based biosensors.