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Small cell lung cancer (SCLC) is a deadly neuroendocrine malignancy, notorious for its rapid tumor growth, early metastasis, and relatively cold immune environment. Only standard chemotherapies and a few immune checkpoint inhibitors have been approved for SCLC treatment, revealing an urgent need for novel therapeutic approaches. Moreover, SCLC has been recently recognized as a malignancy with high intratumoral and intertumoral heterogeneity, which explains the modest response rate in some patients and the early relapse. Molecular subtypes defined by the expression of lineage-specific transcription factors (ASCL1, NEUROD1, POU2F3, and, in some studies, YAP1) or immune-related genes display different degrees of neuroendocrine differentiation, immune cell infiltration, and response to treatment. Despite the complexity of this malignancy, a few biomarkers and targets have been identified and many promising drugs are currently undergoing clinical trials. In this review, we integrate the current progress on the genomic landscape of this shapeshifting malignancy, the characteristics and treatment vulnerabilities of each subtype, and promising drugs in clinical phases.

Loss-of-function mutations at the retinoblastoma (RB1) gene are associated with increased mortality, metastasis, and poor therapeutic outcome in several cancers, including osteosarcoma. However, the mechanism(s) through which RB1 loss worsens clinical outcome remains understudied. Ubiquitin-like with PHD and Ring Finger domains 1 (UHRF1) has been identified as a critical downstream effector of the RB/E2F signaling pathway that is overexpressed in various cancers. Here, we determined the role and regulatory mechanisms of UHRF1 in rendering osteosarcoma cells more aggressive. Higher UHRF1 expression correlated with malignancy in osteosarcoma cell lines, clinical samples, and genetically engineered mouse models. Gain- and loss-of-function assays revealed that UHRF1 has cell-intrinsic and extrinsic functions promoting cell proliferation, migration, invasion, angiogenesis, and metastasis. UHRF1 overexpression induced angiogenesis by suppressing AMPK activation and Semaphorin 3E (SEMA3E) expression. Further, UHRF1-mediated migration and metastasis resulted, at least in part, through altered expression of extracellular vesicles and their cargo, including urokinase-type plasminogen activator (uPA). Novel osteosarcoma genetically engineered mouse models confirmed that knocking out Uhrf1 considerably decreased metastasis and reversed the poorer survival associated with Rb1 loss. This presents a new mechanistic insight into RB1 loss-associated poor prognosis and novel oncogenic roles of UHRF1 in the regulation of angiogenesis and exosome secretion, both critical for osteosarcoma metastasis. This provides substantial support for targeting UHRF1 or its downstream effectors as novel therapeutic options to improve current treatment for osteosarcoma.

Retinoblastoma is an aggressive childhood cancer of the developing retina that initiates by biallelic RB1 gene inactivation. Tumor progression in retinoblastoma is driven by epigenetics, as retinoblastoma genomes are stable, but the mechanism(s) that drive these epigenetic changes remain unknown. Lymphoid-specific helicase (HELLS) protein is an epigenetic modifier directly regulated by the RB/E2F pathway. In this study, we used novel genetically engineered mouse models to investigate the role of HELLS during retinal development and tumorigenesis. Our results indicate that Hells-null retinal progenitor cells divide, undergo cell-fate specification, and give rise to fully laminated retinae with minor bipolar cells defects, but normal retinal function. Despite the apparent nonessential role of HELLS in retinal development, failure to transcriptionally repress Hells during retinal terminal differentiation due to retinoblastoma (RB) family loss significantly contributes to retinal tumorigenesis. Loss of HELLS drastically reduced ectopic division of differentiating cells in Rb1/p107-null retinae, significantly decreased the incidence of retinoblastoma, delayed tumor progression, and increased overall survival. Despite its role in heterochromatin formation, we found no evidence that Hells loss directly affected chromatin accessibility in the retina but functioned as transcriptional co-activator of E2F3, decreasing expression of cell cycle genes. We propose that HELLS is a critical downstream mediator of E2F-dependent ectopic proliferation in RB-null retinae. Together with the nontoxic effect of HELLS loss in the developing retina, our results suggest that HELLS and its downstream pathways could serve as potential therapeutic targets for retinoblastoma.

Network embedding’s goal is to learn the low-dimensional node feature representation in the network. The learned features are used in various network analysis tasks, such as node classification, link prediction, community detection and recommendation, etc. The existing network embedding methods do not make full use of high-order structure information in social networks. Moreover, the correlation between structure information and node attribute information is not considered. The effect of these methods applied in the social network is not ideal. A social network embedding method combining loop-free path and attributes network embedding (LFNE) is proposed to solve these problems. The high-order structural similarity of nodes is calculated first based on the loop-free path between nodes to eliminate the influence of loop path and large-degree nodes on node structure similarity. This algorithm makes the network embedding method better integrate the high-order social network structure information. Then the node attributes similarity is calculated by combining the loop-free path similarity measurement index between nodes, and the correlation between social network structure information and attribute information is fully utilized to eliminate the noise in attribute information. Finally, the node structure similarity and attribute similarity are fused and applied to learning the low-dimensional feature representation of nodes in the stacked denoising autoencoder. Comparison of experiments with representative algorithms in recent years on three social network datasets shows that the LFNE algorithm can achieve relatively significant results in node classification and link prediction with better network embedding performance.

The BL17B beamline at the Shanghai Synchrotron Radiation Facility was first designed as a versatile high‐throughput protein crystallography beamline and one of five beamlines affiliated to the National Facility for Protein Science in Shanghai. It was officially opened to users in July 2015. As a bending magnet beamline, BL17B has the advantages of high photon flux, brightness, energy resolution and continuous adjustable energy between 5 and 23 keV. The experimental station excels in crystal screening and structure determination, providing cost‐effective routine experimental services to numerous users. Given the interdisciplinary and green energy research demands, BL17B beamline has undergone optimization, expanded its range of experimental methods and enhanced sample environments for a more user‐friendly testing mode. These methods include single‐crystal X‐ray diffraction, powder crystal X‐ray diffraction, wide‐angle X‐ray scattering, grazing‐incidence wide‐angle X‐ray scattering (GIWAXS), and fully scattered atom pair distribution function analysis, covering structure detection from crystalline to amorphous states. This paper primarily presents the performance of the BL17B beamline and the application of the GIWAXS methodology at the beamline in the field of perovskite materials. [ABSTRACT FROM AUTHOR]

Duke Kunshan University (DKU), a new liberal arts and science university in China, with Duke University and Wuhan University as academic partners, is an experiment in twenty-first-century characteristics and global challenges. DKU's undergraduate degree program features an innovative, integrated, and interdisciplinary curriculum focused on cross-disciplinary challenges and research experiences. Its interdisciplinary research centers and graduate programs help attract international faculty and students, while its residential setting promotes diverse, rich, and meaningful interactions beyond the classroom. The shared vision for DKU among students, parents, faculty, staff, partner universities, and other stakeholders helps ensure the university's success. And the phenomenal achievements of the first cohort of graduates from the inaugural class of 2022 is evidence that the DKU experiment is working, despite many challenges. [ABSTRACT FROM AUTHOR]

Background The aim of this study was to investigate serum neopterin levels in patients with acute pancreatitis (AP) and its predictive value for mortality in cases of severe acute pancreatitis (SAP).

Adversarial attacks against speaker recognition models have attracted widespread attention and posed a serious threat to speaker recognition systems' security in recent years. A voiceprint adversarial sample detection model e_Xception is proposed to solve the problems of excessive parameter size and poor robustness of existing voiceprint adversarial sample detection methods. Xception is taken as the backbone network and embeds efficient channel attention (ECA) modules to fully extract speech features. A lightweight network model e_halfXception is designed to reduce parameters' number while still maintaining high accuracy by reasonably reducing the width of the network model. Finally, a high-frequency masked speech data enhancement strategy HF-Mask is proposed to improve the model's generalization. Experimental results demonstrate that high accuracy is achieved in the detection of six adversarial samples, FGSM, BIM, PGD, MI-FGSM, C&W and FAKEBOB, outperforming other detection methods, and the robustness of the model is investigated unknown attack algorithms, unknown target models, and unknown perturbation degrees, validating the model's generalization. [ABSTRACT FROM AUTHOR]

Research has been conducted on murine class alpha gluthione S-transferase subunit types A1 and A2. The relationship between residues 207, 216, and 221 and the catalytic activity of murine class alpha gluthione S-transferase subunit types A1 and A2 toward (+)-anti-7,8-dihydroxy-9,10-oxy-7,8,9,10-tetrahydrobenzo[a]pyrene have been investigated and discussed.