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Abstract While ultrasonography effectively diagnoses Hashimoto’s thyroiditis (HT), exploring its transcriptomic landscape could reveal valuable insights into disease mechanisms. This study aimed to identify HT-associated RNA signatures and investigate their potential for enhanced molecular characterization. Samples comprising 31 HT patients and 30 healthy controls underwent RNA sequencing of peripheral blood. Differential expression analysis identified transcriptomic features, which were integrated using multi-omics factor analysis. Pathway enrichment, co-expression, and regulatory network analyses were performed. A novel machine-learning model was developed for HT molecular characterization using stacking techniques. HT patients exhibited increased thyroid volume, elevated tissue hardness, and higher antibody levels despite being in the early subclinical stage. Analysis identified 79 HT-associated transcriptomic features (3 mRNA, 6 miRNA, 64 lncRNA, 6 circRNA). Co-expression (77 nodes, 266 edges) and regulatory (18 nodes, 45 edges) networks revealed significant hub genes and modules associated with HT. Enrichment analysis highlighted dysregulation in immune system, cell adhesion and migration, and RNA/protein regulation pathways. The novel stacking-model achieved 95% accuracy and 97% AUC for HT molecular characterization. This study demonstrates the value of transcriptome analysis in uncovering HT-associated signatures, providing insights into molecular changes and potentially guiding future research on disease mechanisms and therapeutic strategies.

Abstract Background Hashimoto's thyroiditis (HT) is an autoimmune disorder with unclear molecular mechanisms. While current diagnosis is well-established, understanding of the gut-thyroid axis in HT remains limited. This study aimed to uncover novel molecular signatures in HT by integrating gut metagenome and host transcriptome data (miRNA/mRNA), potentially elucidating disease pathogenesis and identifying new therapeutic targets. Methods We recruited 31 early HT patients and 30 healthy controls in a two-stage study (discovery and validation). Blood and fecal samples underwent RNA and metagenomic sequencing, respectively. Integrative analysis included differential expression, weighted correlation network, correlation and random forest analyses. Regression models and ROC curve analysis were used to evaluate the significance of identified molecular signatures in HT. Results Integrative analysis revealed subtle changes in gut microbiota diversity and composition in early HT, increased abundance of Bacillota_A and Spirochaetota at the phylum level, and significant differences in 24 genera and 67 species. Ecological network analysis indicated an imbalance in the gut microbiota with reduced inhibitory interactions against pathogenic genera in HT. Functional analysis showed changes in infection- and immune-related pathways. Three characteristic species (Salaquimonas_sp002400845, Clostridium_AI_sp002297865, and Enterocloster_citroniae) were identified as most relevant to HT. Analysis of miRNA and mRNA expression profiles uncovered pathways related to immune response, inflammation, infection, metabolism, proliferation, and thyroid cancer in HT. Based on correlations with HT and interactions between them, six characteristic RNAs (hsa-miR-548aq-3p, hsa-miR-374a-5p, GADD45A, IRS2, SMAD6, WWTR1) were identified. Furthermore, our study uncovered significant gut microbiota-host transcriptome interactions in HT, revealing enrichment in metabolic, immune, and cancer-related pathways, particularly with strong associations among those 9 key molecular signatures. The validation stage confirmed improved HT classification accuracy by combining these signatures (AUC = 0.95, ACC = 0.85), suggesting their potential significance in understanding HT pathogenesis. Conclusion Our study reveals novel molecular signatures linking gut microbiome and host transcriptome in HT, providing new insights into the disease pathogenesis. These findings not only enhance our understanding of the gut-thyroid axis but also suggest potential new directions for therapeutic interventions in HT.

BackgroundContrast-enhanced ultrasound (CEUS) shows potential for the differential diagnosis of breast lesions in general, but its effectiveness remains unclear for the differential diagnosis of lesions highly suspicious for breast cancers.ObjectiveThis study aimed to evaluate the diagnostic value of CEUS in differentiating pathological subtypes of suspicious breast lesions defined as category 4 of US-BI-RADS.MethodsThe dataset of 150 breast lesions was prospectively collected from 150 patients who underwent routine ultrasound and CEUS examination and were highly suspected of having breast cancers. All lesions were pathologically confirmed by US-guided needle biopsy and surgery. The qualitative features and the quantitative parameters of CEUS of these breast lesions were analyzed. The CEUS and biopsy examinations were performed after informed consent.ResultsIn the qualitative features, crab clam-like enhancement, the presence of more than two enhanced vessels within lesions, and surrounding enriched vessels inserting into lesions were able to differentiate atypical fibroadenomas (FIB) and mass-like non-puerperal mastitis (NPM) from invasive ductal carcinomas (IDC) and ductal carcinomas in situ (DCIS) (p 0.05). The logistic regression analysis results showed that heterogeneous perfusion, crab clam-like enhancement, and partial_ IMAX were independent risk factors for benign and malignant breast lesions (p 0.05).ConclusionSome qualitative risk features of CEUS can distinguish malignant breast lesions from NPM and atypical FIB with a high score of US-BI-RADS, aiding physicians to reduce the misdiagnosis of suspicious breast lesions in clinical practice.

Background: Fluoroquinolones, including ciprofloxacin, levofloxacin, and moxifloxacin, are extensively employed as broad-spectrum antibacterial agents. However, their use is discouraged during pregnancy due to potential adverse events (AEs). The aim of this study is to systematically investigate the association between fluoroquinolones (specifically ciprofloxacin, levofloxacin, and moxifloxacin) and AEs related to pregnancy, as well as their potential impact on congenital disorders. Methods: A disproportionality analysis was conducted utilizing FDA Adverse Event Reporting System (FAERS) data spanning from the first quarter of 2004 to September 2023. The objective was to identify potential AEs signatures associated with fluoroquinolones through conducting reporting odds ratios (RORs) and Bayesian confidence propagation neural networks (BCPNN). Assessing the potential risk of pregnancy-associated AEs involved comparing each fluoroquinolone with all other medications. Additionally, in-depth comparative analyses were carried out between various fluoroquinolones and a reference drug (azithromycin). Results: A total of 1159 cases were identified, involving AEs related to pregnancy and congenital disorders. Obvious disproportionate association of abortion spontaneous and other nine AEs was identified for fluoroquinolone during gestation. Upon comparison with all the other drugs, ciprofloxacin exhibited an elevated risk of spontaneous abortion, non-site specific bone disorders congenital and 10 other significant signals. Levofloxacin demonstrated an increased risk of congenital tongue disorders and three other significant signals. Moxifloxacin displayed a noteworthy signal indicating multiple congenital cardiac abnormalities. Conclusions: We present compelling evidence regarding pregnancy-related AEs and congenital disorders linked to fluoroquinolones. Considering perinatal and genotoxicity aspects, we explore whether levofloxacin or moxifloxacin might be preferable when fluoroquinolones are deemed necessary to balance the benefits of pregnant women and fetuses.

Drug combinations have emerged as a promising therapeutic approach in cancer treatment, aimed at overcoming drug resistance and improving the efficacy of monotherapy regimens. However, identifying effective drug combinations has traditionally been time-consuming and often dependent on chance discoveries. Therefore, there is an urgent need to explore alternative strategies to support experimental research. In this study, we propose network-based prediction models to identify potential drug combinations for 11 types of cancer. Our approach involves extracting 55,299 associations from literature and constructing human protein interactomes for each cancer type. To predict drug combinations, we measure the proximity of drug-drug relationships within the network and employ a correlation clustering framework to detect functional communities. Finally, we identify 61,754 drug combinations. Furthermore, we analyze the network configurations specific to different cancer types and identify 30 key genes and 21 pathways. The performance of these models is subsequently assessed through in vitro assays, which exhibit a significant level of agreement. These findings represent a valuable contribution to the development of network-based drug combination design strategies, presenting potential solutions to overcome drug resistance and enhance cancer treatment outcomes.

Abstract African ancestry is a significant risk factor for prostate cancer and advanced disease. Yet, genetic studies have largely been conducted outside the context of Sub-Saharan Africa, identifying 278 common risk variants contributing to a multiethnic polygenic risk score, with rare variants focused on a panel of roughly 20 pathogenic genes. Based on this knowledge, we are unable to determine polygenic risk or differentiate prostate cancer status interrogating whole genome data for 113 Black South African men. To further assess for potentially functional common and rare variant associations, here we interrogate 247,780 exomic variants for 798 Black South African men using a case versus control or aggressive versus non-aggressive study design. Notable genes of interest include HCP5, RFX6 and H3C1 for risk, and MKI67 and KLF5 for aggressive disease. Our study highlights the need for further inclusion across the African diaspora to establish African-relevant risk models aimed at reducing prostate cancer health disparities.

Background In papillary thyroid carcinoma (PTC) patients with Hashimoto’s thyroiditis (HT), preoperative ultrasonography frequently reveals the presence of enlarged lymph nodes in the central neck region. These nodes pose a diagnostic challenge due to their potential resemblance to metastatic lymph nodes, thereby impacting the surgical decision-making process for clinicians in terms of determining the appropriate surgical extent. Methods Logistic regression analysis was conducted to identify independent risk factors associated with central lymph node metastasis (CLNM) in PTC patients with HT. Then a prediction model was developed and visualized using a nomogram. The stability of the model was assessed using ten-fold cross-validation. The performance of the model was further evaluated through the use of ROC curve, calibration curve, and decision curve analysis. Results A total of 376 HT PTC patients were included in this study, comprising 162 patients with CLNM and 214 patients without CLNM. The results of the multivariate logistic regression analysis revealed that age, Tg-Ab level, tumor size, punctate echogenic foci, and blood flow grade were identified as independent risk factors associated with the development of CLNM in HT PTC. The area under the curve (AUC) of this model was 0.76 (95% CI [0.71–0.80]). The sensitivity, specificity, accuracy, and positive predictive value of the model were determined to be 88%, 51%, 67%, and 57%, respectively. Conclusions The proposed clinic-ultrasound-based nomogram in this study demonstrated a favorable performance in predicting CLNM in HT PTCs. This predictive tool has the potential to assist clinicians in making well-informed decisions regarding the appropriate extent of surgical intervention for patients.

Background and purpose: Objective assessment of delivered radiotherapy (RT) to thoracic organs requires fast and accurate deformable dose mapping. The aim of this study was to implement and evaluate an artificial intelligence (AI) deformable image registration (DIR) and organ segmentation-based AI dose mapping (AIDA) applied to the esophagus and the heart. Materials and methods: AIDA metrics were calculated for 72 locally advanced non-small cell lung cancer patients treated with concurrent chemo-RT to 60 Gy in 2 Gy fractions in an automated pipeline. The pipeline steps were: (i) automated rigid alignment and cropping of planning CT to week 1 and week 2 cone-beam CT (CBCT) field-of-views, (ii) AI segmentation on CBCTs, and (iii) AI-DIR-based dose mapping to compute dose metrics. AIDA dose metrics were compared to the planned dose and manual contour dose mapping (manual DA). Results: AIDA required ∼2 min/patient. Esophagus and heart segmentations were generated with a mean Dice similarity coefficient (DSC) of 0.80±0.15 and 0.94±0.05, a Hausdorff distance at 95th percentile (HD95) of 3.9±3.4 mm and 14.1±8.3 mm, respectively. AIDA heart dose was significantly lower than the planned heart dose (p = 0.04). Larger dose deviations (>=1Gy) were more frequently observed between AIDA and the planned dose (N = 26) than with manual DA (N = 6). Conclusions: Rapid estimation of RT dose to thoracic tissues from CBCT is feasible with AIDA. AIDA-derived metrics and segmentations were similar to manual DA, thus motivating the use of AIDA for RT applications.

Abstract Background African ancestry is a significant risk factor for advanced prostate cancer (PCa). Mortality rates in sub-Saharan Africa are 2.5-fold greater than global averages. However, the region has largely been excluded from the benefits of whole genome interrogation studies. Additionally, while structural variation (SV) is highly prevalent, PCa genomic studies are still biased towards small variant interrogation. Methods Using whole genome sequencing and best practice workflows, we performed a comprehensive analysis of SVs for 180 (predominantly Gleason score ≥ 8) prostate tumours derived from 115 African, 61 European and four ancestrally admixed patients. We investigated the landscape and relationship of somatic SVs in driving ethnic disparity (African versus European), with a focus on African men from southern Africa. Results Duplication events showed the greatest ethnic disparity, with a 1.6- (relative frequency) to 2.5-fold (count) increase in African-derived tumours. Furthermore, we found duplication events to be associated with CDK12 inactivation and MYC copy number gain, and deletion events associated with SPOP mutation. Overall, African-derived tumours were 2-fold more likely to present with a hyper-SV subtype. In addition to hyper-duplication and deletion subtypes, we describe a new hyper-translocation subtype. While we confirm a lower TMPRSS2-ERG fusion-positive rate in tumours from African cases (10% versus 33%), novel African-specific PCa ETS family member and TMPRSS2 fusion partners were identified, including LINC01525, FBXO7, GTF3C2, NTNG1 and YPEL5. Notably, we found 74 somatic SV hotspots impacting 18 new candidate driver genes, with CADM2, LSAMP, PTPRD, PDE4D and PACRG having therapeutic implications for African patients. Conclusions In this first African-inclusive SV study for high-risk PCa, we demonstrate the power of SV interrogation for the identification of novel subtypes, oncogenic drivers and therapeutic targets. Identifying a novel spectrum of SVs in tumours derived from African patients provides a mechanism that may contribute, at least in part, to the observed ethnic disparity in advanced PCa presentation in men of African ancestry.

Abstract Background Whether Hashimoto’s thyroiditis (HT) affects the lymph node metastasis of papillary thyroid carcinoma (PTC) remains uncertain. The diagnostic criteria for HT differed in previous studies. Our study focused on analysing the influence of HT on PTC lymph node metastasis (LNM) with stringent diagnostic criteria for HT. Methods A total of 444 patients diagnosed with PTC from 2019 to 2020 were enrolled and divided into two groups: HT group and non-HT group. Diagnostic criteria of HT were as follows: thyroid peroxidase antibody (+) and postoperative histopathology of Hashimoto’s disease. Results There was no significant difference in the LNM rate between HT group and non-HT group. Patients in the HT group had fewer numbers of metastatic LNs and lower metastatic LNs ratio in central region. In the HT group, age

Background and Purpose: Reducing trismus in radiotherapy for head and neck cancer (HNC) is important. Automated deep learning (DL) segmentation and automated planning was used to introduce new and rarely segmented masticatory structures to study if trismus risk could be decreased. Materials and Methods: Auto-segmentation was based on purpose-built DL, and automated planning used our in-house system, ECHO. Treatment plans for ten HNC patients, treated with 2 Gy × 35 fractions, were optimized (ECHO0). Six manually segmented OARs were replaced with DL auto-segmentations and the plans re-optimized (ECHO1). In a third set of plans, mean doses for auto-segmented ipsilateral masseter and medial pterygoid (MIMean, MPIMean), derived from a trismus risk model, were implemented as dose-volume objectives (ECHO2). Clinical dose-volume criteria were compared between the two scenarios (ECHO0 vs. ECHO1; ECHO1 vs. ECHO2; Wilcoxon signed-rank test; significance: p

Background and purpose: Magnetic resonance (MR) only radiation therapy for prostate treatment provides superior contrast for defining targets and organs-at-risk (OARs). This study aims to develop a deep learning model to leverage this advantage to automate the contouring process. Materials and methods: Six structures (bladder, rectum, urethra, penile bulb, rectal spacer, prostate and seminal vesicles) were contoured and reviewed by a radiation oncologist on axial T2-weighted MR image sets from 50 patients, which constituted expert delineations. The data was split into a 40/10 training and validation set to train a two-dimensional fully convolutional neural network, DeepLabV3+, using transfer learning. The T2-weighted image sets were pre-processed to 2D false color images to leverage pre-trained (from natural images) convolutional layers’ weights. Independent testing was performed on an additional 50 patient’s MR scans. Performance comparison was done against a U-Net deep learning method. Algorithms were evaluated using volumetric Dice similarity coefficient (VDSC) and surface Dice similarity coefficient (SDSC). Results: When comparing VDSC, DeepLabV3+ significantly outperformed U-Net for all structures except urethra (P

Abstract Background Endoscopic carpal tunnel release (ECTR) has been gradually adopted for the treatment of severe carpal tunnel syndrome (CTS). However, perioperative assessment of neuroanatomical parameters of median nerve, which are important determinant of median nerve recovery, has rarely been reported. This one-year prospective study aimed to investigate the natural history of the neuroanatomical morphology of the median nerve after ECTR in severe CTS patients by high-frequency ultrasonography and assess the ability of neuroanatomical measures to quantify morphological recovery of the median nerve after ECTR. Methods This study recruited 31 patients (44 wrists) with a definitive diagnosis of severe CTS and underwent ECTR operation. The edema length (EL) of median nerve from the inlet of the carpal tunnel to the proximal wrist was detected on long axis imaging plane and the anteroposterior diameter (D) and cross-sectional area (CSA) at the inlet of the carpal tunnel on short axis imaging plane were detected by high frequency ultrasound. All these metrics were detected at 3 days before surgery and at the 2nd week, 4th week, 3rd month, 6th month and 12th month after surgery separately. Results There was no significant difference of each parameter between the 2-week postoperative (1.914 ± 0.598 cm in EL, 0.258 ± 0.039 cm in D and 0.138 ± 0.015 cm2 in CSA) and 3-days preoperative time points (P-EL =0.250; P-D = 0.125; P-CSA =0.712). From the fourth week to the third month after surgery, the parameters quickly improved. The EL (0.715 ± 0.209 cm), D (0.225 ± 0.017 cm) and CSA (0.117 ± 0.012 cm2) at the 3- month postoperative time points were more reduced than at the fourth week after surgery (P-EL

We use magnetotransport in dual-gated magnetic topological insulator heterostructures to map out a phase diagram of the topological Hall and quantum anomalous Hall effects as a function of the chemical potential (primarily determined by the back gate voltage) and the asymmetric potential (primarily determined by the top gate voltage). A theoretical model that includes both surface states and valence band quantum well states allows the evaluation of the variation of the Dzyaloshinskii-Moriya interaction and carrier density with gate voltages. The qualitative agreement between experiment and theory provides strong evidence for the existence of a topological Hall effect in the system studied, opening up a route for understanding and manipulating chiral magnetic spin textures in real space.

Background B-mode ultrasound is one of the most commonly used imaging techniques for evaluating thyroid nodules due to its noninvasive property and excellent performance in terms of discriminating between benign and malignant nodules. However, the accuracy of differential diagnosis strongly depends on the experience of ultrasonographers. In addition to B-mode ultrasound, the elastic mode and contrast-enhanced mode have shown complimentary value in the diagnosis of thyroid nodules. The combination of multiple modes in ultrasonic techniques may effectively undermine diagnostic subjectiveness and improve accuracy. In this study, we evaluated the diagnostic value of combining the three ultrasonic modes for differentiating thyroid cancers. Methods In this retrospective study, we analyzed a total of 196 thyroid nodules with suspected malignancies from 185 patients who gave informed consent. Xi’an Jiaotong University granted ethical approval (No. 2018200) to carry out the study within its facilities. All the patients received ultrasonic examinations with the B mode, elastic mode and contrast-enhanced mode, followed by histopathological confirmation by fine-need aspiration or surgery. A predictive multivariate logistic regression model was selected to integrate the variety of data obtained from the three modes. Results The combination of three ultrasonic techniques for differentiating malignant from benign thyroid nodules showed the highest diagnostic accuracy of 0.985 compared to the B mode alone (0.841) and the two-mode combination. The accuracy of the B mode combined with the elastic technique was 0.954, and the accuracy of the B mode combined with the contrast-enhanced technique was 0.960. Discussion Multimode ultrasonic techniques should be recommended to patients with suspected malignant thyroid nodules in routine clinical practice.

We propose a novel one-stage object detection network, called adaptively dense feature pyramid network (ADFPNet), to detect objects cross various scales. The proposed network is developed on single shot multibox detector (SSD) framework with a new proposed ADFP module, which is consisted of two components: a dense multi scales and receptive fields block (DMSRB) and an adaptively feature calibration block (AFCB). Specifically, DMSRB block extracts rich semantic information in a dense way through atrous convolutions with different atrous rates to extract dense features in multi scales and receptive fields; the AFCB block calibrate the dense features to retain features contributing more and depress features contributing less. The extensive experiments have been conducted on VOC 2007, VOC 2012, and MS COCO dataset to evaluate our method. In particular, we achieve the new state of the art accuracy with the mAP of 82.5 on VOC 2007 test set and the mAP of 36.4 on COCO test-dev set using a simple VGG-16 backbone. When testing with a lower resolution (300 × 300), we achieve an mAP of 81.1 on VOC 2007 test set with an FPS of 62.5 on an NVIDIA 1080ti GPU, which meets the requirement for real-time detection.

A major rise in the prevalence and influence of colorectal cancer (CRC) leads to substantially increasing healthcare costs and even death. It is widely accepted that early detection and removal of colonic polyps can prevent CRC. Detection of colonic polyps in colonoscopy videos is problematic because of complex environment of colon and various shapes of polyps. Currently, researchers indicate feasibility of Convolutional Neural Network (CNN)-based detection of polyps but better feature extractors are needed to improve detection performance. In this paper, we investigated the potential of the single shot detector (SSD) framework for detecting polyps in colonoscopy videos. SSD is a one-stage method, which uses a feed-forward CNN to produce a collection of fixed-size bounding boxes for each object from different feature maps. Three different feature extractors, including ResNet50, VGG16, and InceptionV3 were assessed. Multi-scale feature maps integrated into SSD were designed for ResNet50 and InceptionV3, respectively. We validated this method on the 2015 MICCAI polyp detection challenge datasets, compared it with teams attended the challenge, YOLOV3 and two-stage method, Faster-RCNN. Our results demonstrated that the proposed method surpassed all the teams in MICCAI challenge and YOLOV3 and was comparable with two-stage method. Especially in detection speed aspect, our proposed method outperformed all the methods, met real-time application requirement. Meanwhile, we also indicated that among all the feature extractors, InceptionV3 obtained the best result of precision and recall. In conclusion, SSD- based method achieved excellent detection performance in polyp detection and can potentially improve diagnostic accuracy and efficiency.

When a ferromagnet is placed in contact with a superconductor owing to incompatible spin order, the Cooper pairs from the superconductor cannot survive more than 1 or 2 nm inside the ferromagnet. This is confirmed in the measurements of ferromagnetic nickel (Ni) nanowires contacted by superconducting niobium (Nb) leads. However, when a 3 nm thick copper oxide (CuO) buffer layer made by exposing an evaporated or a sputtered 3 nm Cu film to air is inserted between the Nb electrodes and the Ni wire, the spatial extent of the superconducting proximity range is dramatically increased from 2 to a few tens of nanometers. A scanning transmission electron microscope study confirms the formation of a 3 nm thick CuO layer when an evaporated Cu film is exposed to air. Magnetization measurements of such a 3 nm CuO film on a SiO_{2}/Si substrate and on Nb/SiO_{2}/Si show clear evidence of ferromagnetism. One way to understand the long-range proximity effect in the Ni nanowire is that the CuO buffer layer with ferromagnetism facilitates the conversion of singlet superconductivity in Nb into triplet supercurrent along the Ni nanowires.

Abstract. T-cell acute lymphoblastic leukemia (T-ALL) is a heterogeneous group of hematological tumors composed of distinct subtypes that vary in their genetic abnormalities. In the past decade, large-scale genomic analysis has shed new light on providing potentially important oncogenic or tumor suppressive candidates involved in the disease progression. Following in silico analysis, functional studies are usually performed to vigorously investigate the biological roles of candidate genes. For this purpose, animal models faithfully recapitulating the human disease are widely applied to decipher the mechanism underlying T-cell transformation. Conversely, an increased understanding of T-ALL biology, including identification of oncogene NOTCH1, TAL1 and MYC as well as tumor suppressor phosphatase and tensin homolog (PTEN), has significantly improved the development of T-ALL animal models. These progresses have opened opportunities for development of new therapeutic strategy to benefit T-ALL patients. In this review, we particularly summarize the mouse and zebrafish models used in T-ALL research and also the most recent advances from these in vivo studies.

Abstract Previous studies have shown that uterine leiomyomas (UL) are benign tumours with contributions from environmental and genetic factors. We aimed to replicate two initial significant genetic factors, TNRC6B and BET1L, in a Han Chinese population. A total of 2,055 study subjects were recruited, and 55 SNPs mapped to TNRC6B and BET1L were selected and genotyped in samples from these subjects. Genetic associations were analysed at both the single marker and haplotype levels. Associations between targeted SNPs and relevant clinical features of UL were analysed in case only samples. Functional consequences of significant SNPs were analysed by bioinformatics tools. Two SNPs, rs2280543 from BET1L (χ2 = 18.3, OR = 0.64, P = 1.87 × 10−5) and rs12484776 from TNRC6B (χ2 = 19.7, OR = 1.40, P = 8.91 × 10−6), were identified as significantly associated with the disease status of UL. Rs2280543 was significantly associated with the number of fibroid nodes (P = 0.0007), while rs12484776 was significantly associated with node size (χ2 = 54.88, P = 3.44 × 10−11). Both SNPs were a significant eQTL for their genes. In this study, we have shown that both BET1L and TNRC6B contributed to the risk of UL in Chinese women. Significant SNPs from BET1L and TNRC6B were also identified as significantly associated with the number of fibroid nodes and the size of the node, respectively.

Most tumor cells exhibit obligatory demands for essential amino acids (EAAs), but the regulatory mechanisms whereby tumor cells take up EAAs and EAAs promote malignant transformation remain to be determined. Here, we show that oncogenic MYC, solute carrier family (SLC) 7 member 5 (SLC7A5), and SLC43A1 constitute a feedforward activation loop to promote EAA transport and tumorigenesis. MYC selectively activates Slc7a5 and Slc43a1 transcription through direct binding to specific E box elements within both genes, enabling effective EAA import. Elevated EAAs, in turn, stimulate Myc mRNA translation, in part through attenuation of the GCN2-eIF2α-ATF4 amino acid stress response pathway, leading to MYC-dependent transcriptional amplification. SLC7A5/SLC43A1 depletion inhibits MYC expression, metabolic reprogramming, and tumor cell growth in vitro and in vivo. These findings thus reveal a MYC-SLC7A5/SLC43A1 signaling circuit that underlies EAA metabolism, MYC deregulation, and tumorigenesis.

T-cell acute lymphoblastic leukemias (T-ALLs) are aggressive and heterogeneous hematologic tumors resulting from the malignant transformation of T-cell progenitors. The major challenges in the treatments of T-ALL are dose-limiting toxicities of chemotherapeutics and drug resistance. Despite important progress in deciphering the genomic landscape of T-ALL, translation of these findings into effective targeted therapies remains largely unsuccessful. New targeted agents with significant antileukemic efficacy and less toxicity are in urgent need. We herein report that the expression of WEE1, a nuclear tyrosine kinase involved in cell cycle G2-M checkpoint signaling, is significantly elevated in T-ALL. Mechanistically, oncogenic MYC directly binds to the WEE1 promoter and activates its transcription. T-ALL cells particularly rely on the elevated WEE1 for cell viability. Pharmacological inhibition of WEE1 elicits global metabolic reprogramming which results in a marked suppression of aerobic glycolysis in T-ALL cells, leading to an increased dependency on glutaminolysis for cell survival. As such, dual targeting of WEE1 and glutaminase (GLS1) induces synergistic lethality in multiple T-ALL cell lines and shows great efficacy in T-ALL patient-derived xenografts. These findings provide mechanistic insights in the regulation of WEE1 kinase in T-ALL and suggest an additional vulnerability during WEE1 inhibitor treatments. In aggregate, we highlight a promising combination strategy of dual inhibition of cell cycle kinase and metabolic enzymes for T-ALL therapeutics.

In order to achieve transmission safety,nodes longer life and higher operation efficiency for the hierar-chical wireless sensor network,a shared secret key management scheme for WSN based on LEACH protocol was proposed.The scheme has good integrity and timeliness,dynamic key safety,node security independence and key survival,and also has the ability to resist the man-in-the-middle attack,DDoS attacks and so on.Furthermore,en-ergy consumption is lower to distribute shared key,and efficiency is higher to operate network.

An amendment to this paper has been published and can be accessed via the original article.

The aim of this study was to investigate the diagnostic values of conventional ultrasound and contrast-enhanced ultrasound (CEUS) in benign and malignant thyroid nodules with calcification. Conventional ultrasound and CEUS were performed in 122 patients with thyroid nodules with calcification. The thyroid nodules were characterized as benign or malignant by pathological diagnosis. The sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accordance rate of the two imaging methods were determined. The area under the receiver operating characteristics curve (AUC) was used to assess the diagnostic values of the two imaging methods. In 122 cases of thyroid nodules with calcification, 73 benign nodules and 49 malignant nodules were verified by pathological diagnosis. The sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accordance rate of conventional ultrasound were 50%, 77%, 59%, 69%, and 66%, respectively, and those of CEUS were 90%, 92%, 88%, 93%, and 91%, respectively. There were significant differences between the two imaging methods. AUCs of conventional ultrasound and CEUS were 0.628 ± 0.052 and 0.908 ± 0.031, suggesting low and high diagnostic values, respectively. CEUS has high diagnostic values, being significantly greater than those of conventional ultrasound, in differential diagnosis of benign and malignant thyroid nodules with calcification.