Delta-like ligand 3: A promising target against small cell lung cancer.

This commentary highlighted the current knowledge about novel DLL3-targeting agents for refractory small cell lung cancer.

PB-LNet: a model for predicting pathological subtypes of pulmonary nodules on CT images.

OBJECTIVE: To investigate the correlation between CT imaging features and pathological subtypes of pulmonary nodules and construct a prediction model using deep learning. METHODS: We collected information of patients with pulmonary nodules treated by surgery and the reference standard for diagnosis was post-operative pathology. After using elastic distortion for data augmentation, the CT images were divided into a training set, a validation set and a test set in a ratio of 6:2:2. We used PB-LNet to analyze the nodules in pre-operative CT and predict their pathological subtypes. Accuracy was used as the model evaluation index and Class Activation Map was applied to interpreting the results. Comparative experiments with other models were carried out to achieve the best results. Finally, images from the test set without data augmentation were analyzed to judge the clinical utility. RESULTS: Four hundred seventy-seven patients were included and the nodules were divided into six groups: benign lesions, precursor glandular lesions, minimally invasive adenocarcinoma, invasive adenocarcinoma Grade 1, Grade 2 and Grade 3. The accuracy of the test set was 0.84. Class Activation Map confirmed that PB-LNet classified the nodules mainly based on the lungs in CT images, which is in line with the actual situation in clinical practice. In comparative experiments, PB-LNet obtained the highest accuracy. Finally, 96 images from the test set without data augmentation were analyzed and the accuracy was 0.89. CONCLUSIONS: In classifying CT images of lung nodules into six categories based on pathological subtypes, PB-LNet demonstrates satisfactory accuracy without the need of delineating nodules, while the results are interpretable. A high level of accuracy was also obtained when validating on real data, therefore demonstrates its usefulness in clinical practice.

High Emission Efficiency and Thermal Stability in Zero-Dimensional Hybrid Zinc Halide as a Blue Light Emitter.

Exploring highly efficient blue-emissive lead-free halide materials is a significant and challenging objective in the study of luminescent materials. This study reports the synthesis of a new zero-dimensional (0D) hybrid zinc halide of [CYP]ZnBr4 (CYP = 1-cyclohexylpiperazine) containing an isolated [ZnBr4]2- tetrahedron. [CYP]ZnBr4 exhibits strong blue light emission with a high photoluminescence quantum yield (PLQY) of 79.22%, surpassing all previously reported 0D zinc halide counterparts. According to the theoretical and experimental studies, the blue light emission is attributed to intrinsic self-trapped excitons resulting from strong electron-phonon coupling and structural deformation. Importantly, [CYP]ZnBr4 demonstrates excellent structural and luminescence stability toward high temperatures (180 °C) over at least half a month. High luminescence efficiency and stability enable [CYP]ZnBr4 to be an efficient blue phosphor to fabricate white light-emitting diodes (LEDs), which produces high-quality white light with a color rendering index (CRI) of 93.1 and a correlated color temperature (CCT) of 5304 K, closely resembling natural sunlight. This white LED also exhibits consistent performance and stability across different drive currents, suggesting the potential for high-power optoelectronic applications. Overall, this study paves the way for the utilization of 0D hybrid halides in advanced solid-state lighting applications.

SMARCA4: Current status and future perspectives in non-small-cell lung cancer.

SMARCA4, also known as transcription activator, is an ATP-dependent catalytic subunit of SWI/SNF (SWItch/Sucrose NonFermentable) chromatin-remodeling complexes that participates in the regulation of chromatin structure and gene expression by supplying energy. As a tumor suppressor that has aberrant expression in ∼10% of non-small-cell lung cancers (NSCLCs), SMARCA4 possesses many biological functions, including regulating gene expression, differentiation and transcription. Furthermore, NSCLC patients with SMARCA4 alterations have a weak response to conventional chemotherapy and poor prognosis. Therefore, the mechanisms of SMARCA4 in NSCLC development urgently need to be explored to identify novel biomarkers and precise therapeutic strategies for this subtype. This review systematically describes the biological functions of SMARCA4 and its role in NSCLC development, metastasis, functional epigenetics and potential therapeutic approaches for NSCLCs with SMARCA4 alterations. Additionally, this paper explores the relationship and regulatory mechanisms shared by SMARCA4 and its mutually exclusive catalytic subunit SMARCA2. We aim to provide innovative treatment strategies and improve clinical outcomes for NSCLC patients with SMARCA4 alterations.

Omentin as an Independent Predictor of Metabolic Syndrome and Obesity Among Adolescents in Northeast China.

Purpose: We investigated the association of omentin with metabolic syndrome (MetS), MetS components, and obesity in adolescents. Methods: A total of 742 middle-school students from Liaoyang City were enrolled in this cross-sectional study using the stratified cluster sampling method. Clinical information and blood samples were collected, and serum omentin levels were measured using enzyme-linked immunosorbent assay. Results: Mean plasma omentin levels were lower in male than in female participants (88.25 (interquartile range 63.02-133.61) vs 99.46 (interquartile range 69.08-188.35) ng/L, P = 0.004). The participants were divided into four groups according to the quartile (Q) values of omentin from low to high. With increasing omentin levels from Q1 to Q4, the age of adolescents and the proportion of males gradually increased (P < 0.05), whereas the body mass index (BMI) (P < 0.05) and prevalence of MetS (P > 0.05) tended to decrease. Omentin levels were significantly and negatively correlated with waist circumference and BMI (correlation coefficients of -0.099 and -0.115, respectively). Regression analysis showed that omentin level was independently associated with the risk of MetS (Odds ratio, OR = 0.639, 95% confidence interval, CI (0.432, 0.945)), which was attributed to the association with central obesity (OR = 0.775, 95% CI (0.605, 0.993)) among MetS components. Increased omentin levels also indicated a reduced risk of obesity (OR = 0.700, 95% CI (0.563, 0.870)). Conclusion: Omentin is an independent predictor of MetS and obesity among adolescents in northeast China.

Genetic risk score to improve prediction and treatment in gestational diabetes mellitus.

Diabetes mellitus is a chronic disease caused by the interaction of genetics and the environment that can lead to chronic damage to many organ systems. Genome-wide association studies have identified accumulating single-nucleotide polymorphisms related to type 2 diabetes mellitus and gestational diabetes mellitus. Genetic risk score (GRS) has been utilized to evaluate the incidence risk to improve prediction and optimize treatments. This article reviews the research progress in the use of the GRS in diabetes mellitus in recent years and discusses future prospects.

Automatic pulmonary ground-glass opacity nodules detection and classification based on 3D neural network.

PURPOSE: Pulmonary ground-glass opacity (GGO) nodules are more likely to be malignant compared with solid solitary nodules. Due to indistinct boundaries of GGO nodules, the detection and diagnosis are challenging for doctors. Therefore, designing an automatic GGO nodule detection and classification scheme is significantly essential. METHODS: In this paper, we proposed a two-stage 3D GGO nodule detection and classification framework. First, we used a pretrained 3D U-Net to extract lung parenchyma. Second, we adapted the architecture of Mask region-based convolutional neural networks (RCNN) to handle 3D medical images. The 3D model was then applied to detect the locations of GGO nodules and classify lesions (benign or malignant). The class-balanced loss function was also used to balance the number of benign and malignant lesions. Finally, we employed a novel false positive elimination scheme called the feature-based weighted clustering (FWC) to promote the detection accuracy further. RESULTS: The experiments were conducted based on fivefold cross-validation with the imbalanced data set. Experimental results showed that the mean average precision could keep a high level (0.5182) in the phase of detection. Meanwhile, the false positive rate was effectively controlled, and the competition performance metric (CPM) reached 0.817 benefited from the FWC algorithm. The comparative statistical analyses with other deep learning methods also proved the effectiveness of our proposed method. CONCLUSIONS: We put forward an automatic pulmonary GGO nodules detection and classification framework based on deep learning. The proposed method locate and classify nodules accurately, which could be an effective tool to help doctors in clinical diagnoses.

Assessment of PAH degradation potential of native species from a coking plant through identifying of the beneficial bacterial community within the rhizosphere soil.

Understanding the mechanisms underlying plant-rhizobacteria interactions in field-contaminated soils is crucial for designing effective rhizoremediation strategies. This study aimed to test the ability of four native herb species to remove polycyclic aromatic hydrocarbons (PAHs) and to analyze their associated bacterial community structures and functional genes within the rhizosphere from the abandoned site of a former Shenyang coking plant in China; the bulk soil was collected as control. All four species removed PAHs, of which the rhizosphere of Kochia scoparia had the highest PAH removal rate (almost 30.2%). Although the composition of the bacterial community within the rhizosphere varied among plant species, all plant species could promote the growth of Sphingomonas, Pedomicrobium, Rhodoplanes, Blastoccus, Mycobacterium, Devosia, and Pseudomonas, and their relative abundance positively correlated with the removal rates of PAHs, soil moisture, and total carbon/total nitrogen in the rhizosphere. Moreover, the activities of 1-aminocyclopropane-1 -carboxylic deaminase gene and Gram-negative ring-hydroxylating dioxygenase gene significantly (P < 0.05) increased compared with those in the control, and these activities had a strong positive correlation with the removal rates of PAHs [r = 0.759 (P < 0.01) and 0.87 (P < 0.01), respectively]. The findings of this study indicated that PAHs were the main factor driving the composition of beneficial bacteria in PAH rhizodegradation, and the PAH rhizoremediation of native plants grown in coking plant can be controlled though altering soil properties.