nBEST: Deep-learning-based non-human primates Brain Extraction and Segmentation Toolbox across ages, sites and species.

Accurate processing and analysis of non-human primate (NHP) brain magnetic resonance imaging (MRI) serves an indispensable role in understanding brain evolution, development, aging, and diseases. Despite the accumulation of diverse NHP brain MRI datasets at various developmental stages and from various imaging sites/scanners, existing computational tools designed for human MRI typically perform poor on NHP data, due to huge differences in brain sizes, morphologies, and imaging appearances across species, sites, and ages, highlighting the imperative for NHP-specialized MRI processing tools. To address this issue, in this paper, we present a robust, generic, and fully automated computational pipeline, called non-human primates Brain Extraction and Segmentation Toolbox (nBEST), whose main functionality includes brain extraction, non-cerebrum removal, and tissue segmentation. Building on cutting-edge deep learning techniques by employing lifelong learning to flexibly integrate data from diverse NHP populations and innovatively constructing 3D U-NeXt architecture, nBEST can well handle structural NHP brain MR images from multi-species, multi-site, and multi-developmental-stage (from neonates to the elderly). We extensively validated nBEST based on, to our knowledge, the largest assemblage dataset in NHP brain studies, encompassing 1,469 scans with 11 species (e.g., rhesus macaques, cynomolgus macaques, chimpanzees, marmosets, squirrel monkeys, etc.) from 23 independent datasets. Compared to alternative tools, nBEST outperforms in precision, applicability, robustness, comprehensiveness, and generalizability, greatly benefiting downstream longitudinal, cross-sectional, and cross-species quantitative analyses. We have made nBEST an open-source toolbox (https://github.com/TaoZhong11/nBEST) and we are committed to its continual refinement through lifelong learning with incoming data to greatly contribute to the research field.

Associations of Dietary Anthocyanidins Intake with Bone Health in Children: A Cross-Sectional Study.

PURPOSE: Bone health and body composition share several common mechanisms like oxidative stress and inflammation. Anthocyanins have antioxidant and anti-inflammatory properties. We have reported that anthocyanins are associated with better body composition in children, but the associations with bone health have not been elucidated. We aimed to explore the association of anthocyanins with bone mineral content (BMC) and bone mineral density (BMD) at multiple sites in children. METHODS: In this cross-sectional study, 452 Chinese children aged 6-9 years were recruited. A validated 79-item food frequency questionnaire was used to collect dietary information. BMC and BMD at multiple sites (whole body; whole body excluding head, WBEH; limbs; arms; legs) were measured by dual-energy X-ray. RESULTS: Higher dietary intake of total anthocyanidins (per one standard deviation increase) was associated with a 1.28-13.6 g (1.31-1.60%, compared to median) higher BMC at all sites and a 3.61-6.96 mg (0.65-0.90%) higher BMD at the whole body, WBEH, and arm sites after controlling for a number of possible covariates. The results were similar and more pronounced for cyanidin, but not for delphinidin and peonidin. Higher dietary intake of cyanidin (per one standard deviation increase) was associated with a 1.33-15.4 g (1.48-1.68%) higher BMC at all sites and a 4.15-7.77 mg (0.66-1.00%) higher BMD at all sites except the legs. No statistically significant associations with BMC or BMD were found for dietary intake of delphinidin and peonidin. CONCLUSIONS: Higher dietary intake of total anthocyanidins and cyanidins were associated with higher BMC and BMD in Chinese children.

Aqueous Processable One-Dimensional Polypyrrole Nanostructured by Lignocellulose Nanofibril: A Conductive Interfacing Biomaterial.

One-dimensional (1D) nanomaterials of conductive polypyrrole (PPy) are competitive biomaterials for constructing bioelectronics to interface with biological systems. Synergistic synthesis using lignocellulose nanofibrils (LCNF) as a structural template in chemical oxidation of pyrrole with Fe(III) ions facilitates surface-confined polymerization of pyrrole on the nanofibril surface within a submicrometer- and micrometer-scale fibril length. It yields a core-shell nanocomposite of PPy@LCNF, wherein the surface of each individual fibril is coated with a thin nanoscale layer of PPy. A highly positive surface charge originating from protonated PPy gives this 1D nanomaterial a durable aqueous dispersity. The fibril-fibril entanglement in the PPy@LCNFs facilely supported versatile downstream processing, e.g., spray thin-coating on glass, flexible membranes with robust mechanics, or three-dimensional cryogels. A high electrical conductivity in the magnitude of several to 12 S·cm-1 was confirmed for the solid-form PPy@LCNFs. The PPy@LCNFs are electroactive and show potential cycling capacity, encompassing a large capacitance. Dynamic control of the doping/undoping process by applying an electric field combines electronic and ionic conductivity through the PPy@LCNFs. The low cytotoxicity of the material is confirmed in noncontact cell culture of human dermal fibroblasts. This study underpins the promises for this nanocomposite PPy@LCNF as a smart platform nanomaterial in constructing interfacing bioelectronics.

Cryptanalysis and Improvement of Several Identity-Based Authenticated and Pairing-Free Key Agreement Protocols for IoT Applications.

Internet of Things (IoT) applications have been increasingly developed. Authenticated key agreement (AKA) plays an essential role in secure communication in IoT applications. Without the PKI certificate and high time-complexity bilinear pairing operations, identity-based AKA (ID-AKA) protocols without pairings are more suitable for protecting the keys in IoT applications. In recent years, many pairing-free ID-AKA protocols have been proposed. Moreover, these protocols have some security flaws or relatively extensive computation and communication efficiency. Focusing on these problems, the security analyses of some recently proposed protocols have been provided first. We then proposed a family of eCK secure ID-AKA protocols without pairings to solve these security problems, which can be applied in IoT applications to guarantee communication security. Meanwhile, the security proofs of these proposed ID-AKA protocols are provided, which show they can hold provable eCK security. Some more efficient instantiations have been provided, which show the efficient performance of these proposed ID-AKA protocols. Moreover, comparisons with similar schemes have shown that these protocols have the least computation and communication efficiency at the same time.

CCT5 induces epithelial-mesenchymal transition to promote gastric cancer lymph node metastasis by activating the Wnt/ß-catenin signalling pathway.

BACKGROUND: Lymph node (LN) metastasis confers gastric cancer (GC) progression, poor survival and cancer-related death. Aberrant activation of Wnt/ß-catenin promotes epithelial-mesenchymal transition (EMT) and LN metastasis, whereas the constitutive activation mutation of Wnt/ß-catenin is rare in GC, suggesting that the underlying mechanisms enhancing Wnt/ß-catenin activation need to be further investigated and understood. METHODS: Bioinformatics analyses and immunohistochemistry (IHC) were used to identify and detect LN metastasis-related genes in GC. Cellular functional assays and footpad inoculation mouse model illustrate the biological function of CCT5. Co-immunoprecipitation assays, western blot and qPCR elucidate the interaction between CCT5 and E-cadherin, and the regulation on ß-catenin activity. RESULTS: CCT5 is upregulated in LN metastatic GCs and correlates with poor prognosis. In vitro assays prove that CCT5 markedly promotes GC cell proliferation, anti-anoikis, invasion and lymphatic tube formation. Moreover, CCT5 enhances xenograft GC growth and popliteal lymph node metastasis in vivo. Furthermore, CCT5 binds the cytoplasmic domain of E-cadherin and abrogates the interaction between E-cadherin and ß-catenin, thereby releasing ß-catenin to the nucleus and enhancing Wnt/ß-catenin signalling activity and EMT. CONCLUSION: CCT5 promotes GC progression and LN metastasis by enhancing wnt/ß-catenin activation, suggesting a great potential of CCT5 as a biomarker for GC diagnosis and therapy.

Incorporation of Complexation into a Coamorphous System Dramatically Enhances Dissolution and Eliminates Gelation of Amorphous Lurasidone Hydrochloride.

As a BCS II drug, the atypical antipsychotic agent lurasidone hydrochloride (LH) has low oral bioavailability mainly because of its poor aqueous solubility/dissolution. Unexpectedly, amorphous LH exhibited a much lower dissolution than that of its stable crystalline form arising from its gelation during the dissolution process. In the current study, a supramolecular coamorphous system of LH with l-cysteine hydrochloride (CYS) was prepared and characterized by powder X-ray diffraction and differential scanning calorimetry. Surprisingly, in comparison to crystalline and amorphous LH, such a coamorphous system dramatically enhanced solubility (at least ∼50-fold in the physiological pH range) and dissolution (∼1200-fold) of LH, and exhibited superior physical stability under long-term storage condition. More importantly, the coamorphous system was able to eliminate gelation of amorphous LH during dissolution. In order to further explore the mechanism of such improvement, the internal interactions of the coamorphous system in the solid state and in aqueous solution were investigated. Fourier transform infrared spectroscopy, Raman spectroscopy, and solid-state 13C NMR suggested that intermolecular hydrogen bonds formed between the nitrogen atom in the benzisothiazole ring of LH and the NH3+ group of CYS after coamorphization. A fluorescence quenching test with a Stern-Volmer plot and density functional theory modeling, phase-solubility study, and NMR test in D2O indicated that ground-state complexation occurred between LH and CYS in aqueous solution, which contributed to the solubility and dissolution enhancement of LH. The current study offers a promising strategy to overcome poor solubility/dissolution and be able to eliminate gelation of amorphous materials by coamorphization and complexation.

Postoperative glioma segmentation in CT image using deep feature fusion model guided by multi-sequence MRIs.

OBJECTIVES: Computed tomography (CT) and magnetic resonance imaging (MRI) are the most commonly selected methods for imaging gliomas. Clinically, radiotherapists always delineate the CT glioma region with reference to multi-modal MR image information. On this basis, we develop a deep feature fusion model (DFFM) guided by multi-sequence MRIs for postoperative glioma segmentation in CT images. METHODS: DFFM is a multi-sequence MRI-guided convolutional neural network (CNN) that iteratively learns the deep features from CT images and multi-sequence MR images simultaneously by utilizing a multi-channel CNN architecture, and then combines these two deep features together to produce the segmentation result. The whole network is optimized together via a standard back-propagation. A total of 59 CT and MRI datasets (T1/T2-weighted FLAIR, T1-weighted contrast-enhanced, T2-weighted) of postoperative gliomas as tumor grade II (n = 24), grade III (n = 18), or grade IV (n = 17) were included. Dice coefficient (DSC), precision, and recall were used to measure the overlap between automated segmentation results and manual segmentation. The Wilcoxon signed-rank test was used for statistical analysis. RESULTS: DFFM showed a significantly (p < 0.01) higher DSC of 0.836 than U-Net trained by single CT images and U-Net trained by stacking the CT and multi-sequence MR images, which yielded 0.713 DSC and 0.818 DSC, respectively. The precision values showed similar behavior as DSC. Moreover, DSC and precision values have no significant statistical difference (p > 0.01) with difference grades. CONCLUSIONS: DFFM enables the accurate automated segmentation of CT postoperative gliomas of profit guided by multi-sequence MR images and may thus improve and facilitate radiotherapy planning. KEY POINTS: • A fully automated deep learning method was developed to segment postoperative gliomas on CT images guided by multi-sequence MRIs. • CT and multi-sequence MR image integration allows for improvements in deep learning postoperative glioma segmentation method. • This deep feature fusion model produces reliable segmentation results and could be useful in delineating GTV in postoperative glioma radiotherapy planning.

Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning.

OBJECTIVE: Accurate detection and segmentation of organs at risks (OARs) in CT image is the key step for efficient planning of radiation therapy for nasopharyngeal carcinoma (NPC) treatment. We develop a fully automated deep-learning-based method (termed organs-at-risk detection and segmentation network (ODS net)) on CT images and investigate ODS net performance in automated detection and segmentation of OARs. METHODS: The ODS net consists of two convolutional neural networks (CNNs). The first CNN proposes organ bounding boxes along with their scores, and then a second CNN utilizes the proposed bounding boxes to predict segmentation masks for each organ. A total of 185 subjects were included in this study for statistical comparison. Sensitivity and specificity were performed to determine the performance of the detection and the Dice coefficient was used to quantitatively measure the overlap between automated segmentation results and manual segmentation. Paired samples t tests and analysis of variance were employed for statistical analysis. RESULTS: ODS net provides an accurate detection result with a sensitivity of 0.997 to 1 for most organs and a specificity of 0.983 to 0.999. Furthermore, segmentation results from ODS net correlated strongly with manual segmentation with a Dice coefficient of more than 0.85 in most organs. A significantly higher Dice coefficient for all organs together (p = 0.0003 < 0.01) was obtained in ODS net (0.861 ± 0.07) than in fully convolutional neural network (FCN) (0.8 ± 0.07). The Dice coefficients of each OAR did not differ significantly between different T-staging patients. CONCLUSION: The ODS net yielded accurate automated detection and segmentation of OARs in CT images and thereby may improve and facilitate radiotherapy planning for NPC. KEY POINTS: • A fully automated deep-learning method (ODS net) is developed to detect and segment OARs in clinical CT images. • This deep-learning-based framework produces reliable detection and segmentation results and thus can be useful in delineating OARs in NPC radiotherapy planning. • This deep-learning-based framework delineating a single image requires approximately 30 s, which is suitable for clinical workflows.

Nonlocal total variation based on symmetric Kullback-Leibler divergence for the ultrasound image despeckling.

BACKGROUND: Ultrasound imaging is safer than other imaging modalities, because it is noninvasive and nonradiative. Speckle noise degrades the quality of ultrasound images and has negative effects on visual perception and diagnostic operations. METHODS: In this paper, a nonlocal total variation (NLTV) method for ultrasonic speckle reduction is proposed. A spatiogram similarity measurement is introduced for the similarity calculation between image patches. It is based on symmetric Kullback-Leibler (KL) divergence and signal-dependent speckle model for log-compressed ultrasound images. Each patch is regarded as a spatiogram, and the spatial distribution of each bin of the spatiogram is regarded as a weighted Gamma distribution. The similarity between the corresponding bins of the two spatiograms is computed by the symmetric KL divergence. The Split-Bregman fast algorithm is then used to solve the adapted NLTV object function. Kolmogorov-Smirnov (KS) test is performed on synthetic noisy images and real ultrasound images. RESULTS: We validate our method on synthetic noisy images and clinical ultrasound images. Three measures are adopted for the quantitative evaluation of the despeckling performance: the signal-to-noise ratio (SNR), structural similarity index (SSIM), and natural image quality evaluator (NIQE). For synthetic noisy images, when the noise level increases, the proposed algorithm achieves slightly higher SNRS than that of the other two algorithms, and the SSIMS yielded by the proposed algorithm is obviously higher than that of the other two algorithms. For liver, IVUS and 3DUS images, the NIQE values are 8.25, 6.42 and 9.01, all of which are higher than that of the other two algorithms. CONCLUSIONS: The results of the experiments over synthetic and real ultrasound images demonstrate that the proposed method outperforms current state-of-the-art despeckling methods with respect to speckle reduction and tissue texture preservation.

A transformer-based multi-task deep learning model for simultaneous T-stage identification and segmentation of nasopharyngeal carcinoma.

Background: Accurate tumor target contouring and T staging are vital for precision radiation therapy in nasopharyngeal carcinoma (NPC). Identifying T-stage and contouring the Gross tumor volume (GTV) manually is a laborious and highly time-consuming process. Previous deep learning-based studies have mainly been focused on tumor segmentation, and few studies have specifically addressed the tumor staging of NPC. Objectives: To bridge this gap, we aim to devise a model that can simultaneously identify T-stage and perform accurate segmentation of GTV in NPC. Materials and methods: We have developed a transformer-based multi-task deep learning model that can perform two tasks simultaneously: delineating the tumor contour and identifying T-stage. Our retrospective study involved contrast-enhanced T1-weighted images (CE-T1WI) of 320 NPC patients (T-stage: T1-T4) collected between 2017 and 2020 at our institution, which were randomly allocated into three cohorts for three-fold cross-validations, and conducted the external validation using an independent test set. We evaluated the predictive performance using the area under the receiver operating characteristic curve (ROC-AUC) and accuracy (ACC), with a 95% confidence interval (CI), and the contouring performance using the Dice similarity coefficient (DSC) and average surface distance (ASD). Results: Our multi-task model exhibited sound performance in GTV contouring (median DSC: 0.74; ASD: 0.97 mm) and T staging (AUC: 0.85, 95% CI: 0.82-0.87) across 320 patients. In early T category tumors, the model achieved a median DSC of 0.74 and ASD of 0.98 mm, while in advanced T category tumors, it reached a median DSC of 0.74 and ASD of 0.96 mm. The accuracy of automated T staging was 76% (126 of 166) for early stages (T1-T2) and 64% (99 of 154) for advanced stages (T3-T4). Moreover, experimental results show that our multi-task model outperformed the other single-task models. Conclusions: This study emphasized the potential of multi-task model for simultaneously delineating the tumor contour and identifying T-stage. The multi-task model harnesses the synergy between these interrelated learning tasks, leading to improvements in the performance of both tasks. The performance demonstrates the potential of our work for delineating the tumor contour and identifying T-stage and suggests that it can be a practical tool for supporting clinical precision radiation therapy.

Cross-Domain Mutual-Assistance Learning Framework for Fully Automated Diagnosis of Primary Tumor in Nasopharyngeal Carcinoma.

Accurate T-staging of nasopharyngeal carcinoma (NPC) holds paramount importance in guiding treatment decisions and prognosticating outcomes for distinct risk groups. Regrettably, the landscape of deep learning-based techniques for T-staging in NPC remains sparse, and existing methodologies often exhibit suboptimal performance due to their neglect of crucial domain-specific knowledge pertinent to primary tumor diagnosis. To address these issues, we propose a new cross-domain mutual-assistance learning framework for fully automated diagnosis of primary tumor using H&N MR images. Specifically, we tackle primary tumor diagnosis task with the convolutional neural network consisting of a 3D cross-domain knowledge perception network (CKP net) for excavated cross-domain-invariant features emphasizing tumor intensity variations and internal tumor heterogeneity, and a multi-domain mutual-information sharing fusion network (M2SF net), comprising a dual-pathway domain-specific representation module and a mutual information fusion module, for intelligently gauging and amalgamating multi-domain, multi-scale T-stage diagnosis-oriented features. The proposed 3D cross-domain mutual-assistance learning framework not only embraces task-specific multi-domain diagnostic knowledge but also automates the entire process of primary tumor diagnosis. We evaluate our model on an internal and an external MR images dataset in a three-fold cross-validation paradigm. Exhaustive experimental results demonstrate that our method outperforms the state-of-the-art algorithms, and obtains promising performance for tumor segmentation and T-staging. These findings underscore its potential for clinical application, offering valuable assistance to clinicians in treatment decision-making and prognostication for various risk groups.

A brain subcortical segmentation tool based on anatomy attentional fusion network for developing macaques.

Magnetic Resonance Imaging (MRI) plays a pivotal role in the accurate measurement of brain subcortical structures in macaques, which is crucial for unraveling the complexities of brain structure and function, thereby enhancing our understanding of neurodegenerative diseases and brain development. However, due to significant differences in brain size, structure, and imaging characteristics between humans and macaques, computational tools developed for human neuroimaging studies often encounter obstacles when applied to macaques. In this context, we propose an Anatomy Attentional Fusion Network (AAF-Net), which integrates multimodal MRI data with anatomical constraints in a multi-scale framework to address the challenges posed by the dynamic development, regional heterogeneity, and age-related size variations of the juvenile macaque brain, thus achieving precise subcortical segmentation. Specifically, we generate a Signed Distance Map (SDM) based on the initial rough segmentation of the subcortical region by a network as an anatomical constraint, providing comprehensive information on positions, structures, and morphology. Then we construct AAF-Net to fully fuse the SDM anatomical constraints and multimodal images for refined segmentation. To thoroughly evaluate the performance of our proposed tool, over 700 macaque MRIs from 19 datasets were used in this study. Specifically, we employed two manually labeled longitudinal macaque datasets to develop the tool and complete four-fold cross-validations. Furthermore, we incorporated various external datasets to demonstrate the proposed tool's generalization capabilities and promise in brain development research. We have made this tool available as an open-source resource at https://github.com/TaoZhong11/Macaque_subcortical_segmentation for direct application.

PHF14 enhances DNA methylation of SMAD7 gene to promote TGF-ß-driven lung adenocarcinoma metastasis.

Aberrant activation of TGF-ß signaling plays a pivotal role in cancer metastasis and progression. However, molecular mechanisms underlying the dysregulation of TGF-ß pathway remain to be understood. Here, we found that SMAD7, a direct downstream transcriptional target and also a key antagonist of TGF-ß signaling, is transcriptionally suppressed in lung adenocarcinoma (LAD) due to DNA hypermethylation. We further identified that PHF14 binds DNMT3B and serves as a DNA CpG motif reader, recruiting DNMT3B to the SMAD7 gene locus, resulting in DNA methylation and transcriptional suppression of SMAD7. Our in vitro and in vivo experiments showed that PHF14 promotes metastasis through binding DNMT3B to suppress SMAD7 expression. Moreover, our data revealed that PHF14 expression correlates with lowered SMAD7 level and shorter survival of LAD patients, and importantly that SMAD7 methylation level of circulating tumor DNA (ctDNA) can potentially be used for prognosis prediction. Together, our present study illustrates a new epigenetic mechanism, mediated by PHF14 and DNMT3B, in the regulation of SMAD7 transcription and TGF-ß-driven LAD metastasis, and suggests potential opportunities for LAD prognosis.

Dietary Restriction and Rapamycin Affect Brain Aging in Mice by Attenuating Age-Related DNA Methylation Changes.

The fact that dietary restriction (DR) and long-term rapamycin treatment (RALL) can ameliorate the aging process has been reported by many researchers. As the interface between external and genetic factors, epigenetic modification such as DNA methylation may have latent effects on the aging rate at the molecular level. To understand the mechanism behind the impacts of dietary restriction and rapamycin on aging, DNA methylation and gene expression changes were measured in the hippocampi of different-aged mice. Examining the single-base resolution of DNA methylation, we discovered that both dietary restriction and rapamycin treatment can maintain DNA methylation in a younger state compared to normal-aged mice. Through functional enrichment analysis of genes in which DNA methylation or gene expression can be affected by DR/RALL, we found that DR/RALL may retard aging through a relationship in which DNA methylation and gene expression work together not only in the same gene but also in the same biological process. This study is instructive for understanding the maintenance of DNA methylation by DR/RALL in the aging process, as well as the role of DR and RALL in the amelioration of aging.

Graph-enhanced U-Net for semi-supervised segmentation of pancreas from abdomen CT scan.

Objective. Accurate segmentation of the pancreas from abdomen CT scans is highly desired for diagnosis and treatment follow-up of pancreatic diseases. However, the task is challenged by large anatomical variations, low soft-tissue contrast, and the difficulty in acquiring a large set of annotated volumetric images for training. To overcome these problems, we propose a new segmentation network and a semi-supervised learning framework to alleviate the lack of annotated images and improve the accuracy of segmentation.Approach.In this paper, we propose a novel graph-enhanced pancreas segmentation network (GEPS-Net), and incorporate it into a semi-supervised learning framework based on iterative uncertainty-guided pseudo-label refinement. Our GEPS-Net plugs a graph enhancement module on top of the CNN-based U-Net to focus on the spatial relationship information. For semi-supervised learning, we introduce an iterative uncertainty-guided refinement process to update pseudo labels by removing low-quality and incorrect regions.Main results.Our method was evaluated by a public dataset with four-fold cross-validation and achieved the DC of 84.22%, improving 5.78% compared to the baseline. Further, the overall performance of our proposed method was the best compared with other semi-supervised methods trained with only 6 or 12 labeled volumes.Significance.The proposed method improved the segmentation performance of the pancreas in CT images under the semi-supervised setting. It will assist doctors in early screening and making accurate diagnoses as well as adaptive radiotherapy.

Low-dose cadmium exposure promotes osteoclastogenesis by enhancing autophagy via inhibiting the mTOR/p70S6K1 signaling pathway.

Cadmium (Cd)-induced bone damage may be mediated through activating osteoclastogenesis. However, the underlying mechanism is unknown. The purpose of this study was to explore the effect and possible mechanism of CdCl2-induced osteoclastogenesis in RAW264.7 cells. We found that a low concentration of CdCl2 (0.025 and 0.050 µM) did not affect the viability of RAW264.7 cells, but promoted osteoclastogenesis. A low concentration of CdCl2 increased the mRNA and protein expression of osteoclastogenesis-related genes. TRAP staining and transmission electron microscopy (TEM) also demonstrated that CdCl2 promoted osteoclastogenesis. A low concentration of CdCl2 upregulated the levels of LC3-II and Beclin-1, and decreased p62 expression. TEM showed relatively abundant autophagic vacuoles (autophagosomes) after CdCl2 exposure. A low concentration of CdCl2 downregulated the expression levels of Mtor and p70S6K1, and the relative protein expression ratios of p-mTOR/mTOR and p-p70S6K1/p70S6K1. When cells were treated with the autophagy inhibitor chloroquine (CQ) or mTOR activator MHY1485 combined with CdCl2, the expressions of osteoclastogenesis related-genes were decreased and autophagy was attenuated compared with cells treated with CdCl2 alone. Deficiencies in autophagosomes and osteoclasts were also observed. Taken together, the results indicate that a low concentration of CdCl2 promotes osteoclastogenesis by enhancing autophagy via inhibiting the mTOR/p70S6K1 signaling pathway.

The Relationship between Dietary Pattern and Bone Mass in School-Age Children.

Early bone accrual significantly influences adult bone health and osteoporosis incidence. We aimed to investigate the relationship between dietary patterns (DPs), bone mineral content (BMC) and bone mineral density (BMD) in school-age children in China. Children aged six-nine years (n = 465) were enrolled in this cross-sectional study. DPs were identified by principal component factor analysis. Total body (TB) and total body less head (TBLH) BMC and BMD were measured using dual-energy X-ray absorptiometry. Five DPs were identified. After adjustment for covariates, multiple linear regression analysis showed that the "fruit-milk-eggs" dietary pattern was positively associated with TB (ß = 10.480; 95% CI: 2.190, 18.770) and TBLH (ß = 5.577; 95% CI: 0.214, 10.941) BMC, the "animal organs-refined cereals" pattern was associated with low TB BMC (ß = -10.305; 95% CI: -18.433, -2.176), TBLH BMC (ß = -6.346; 95% CI: -11.596, -1.096), TB BMD (ß = -0.006; 95% CI: -0.011, -0.001) and TBLH BMD (ß = -0.004; 95% CI: -0.007, -0.001). In conclusion, our study recommends home or school meals should be rich in fruit, milk, eggs with a moderate amount of vegetables, coarse grains and meat to promote bone development for school-age children.

The Prognostic Impact of Lymph Node Dissection on Primary Tumor Resection for Stage IV Non-Small Cell Lung Cancer: A Population-Based Study.

Objective: Selected patients with stage IV non-small cell lung cancer (NSCLC) who underwent primary tumor resection have witnessed a survival benefit. Whether additional lymph node dissection (LND) would result in a better effect remain unknown. We investigated the prognostic impact of LND on patients with stage IV NSCLC who received primary tumor resection (PTR). Methods: Patients with stage IV NSCLC who underwent PTR were identified from the Surveillance, Epidemiology, and End Results database from 2004 to 2016. Propensity-score matching was performed to minimize the confounding effect, and lung cancer-specific survival (CSS) and overall survival (OS) were compared after matching. Multivariable Cox regression was used to identify prognostic factors and to adjust for covariates in subgroup analysis. The effect of the number of lymph nodes examined on the CSS was evaluated by repeating the Cox analysis in a binary method. Results: A total of 4,114 patients with stage IV NSCLC who receive surgery met our criteria, of which 2,622 (63.73%) underwent LND and 628 patients were identified 1:1 in LND and non-LND groups after matching. Compared with the non-LND group, the LND group had a longer CSS (median: 23 vs. 16 months, p < 0.001) and OS (median: 21 vs. 15 months, p < 0.001). Multivariable regression showed that LND was independently associated with favorable CCS [hazard ratio (HR) = 0.78, 95% confidence interval (CI) 0.69-0.89, P < 0.001] and OS (HR = 0.79, 95% CI 0.70-0.89, P < 0.001). Subgroup analysis suggested that LND is an independent favorable predictor to survival in the surgical patients who were older age (>60 years old), female, T3-4, N0, and M1a stage and those who underwent sublobar resection. In addition, a statistically significant CCS benefit was associated with an increasing number of lymph nodes examined through 25 lymph nodes. Conclusions: LND with a certain range of lymph nodes number examined was associated with improved survival for patients with stage IV NSCLC who received primary tumor resection. The results may have implications for guidelines on lymph nodes management in selective advanced NSCLC for surgery.

A multi-perspective information aggregation network for automatedT-staging detection of nasopharyngeal carcinoma.

AccurateT-staging is important when planning personalized radiotherapy. However,T-staging via manual slice-by-slice inspection is time-consuming while tumor sizes and shapes are heterogeneous, and junior physicians find such inspection challenging. With inspiration from oncological diagnostics, we developed a multi-perspective aggregation network that incorporated various diagnosis-oriented knowledge which allowed automated nasopharyngeal carcinomaT-staging detection (TSD Net). Specifically, our TSD Net was designed in multi-branch architecture, which can capture tumor size and shape information (basic knowledge), strongly correlated contextual features, and associations between the tumor and surrounding tissues. We defined the association between the tumor and surrounding tissues by a signed distance map which can embed points and tumor contours in higher-dimensional spaces, yielding valuable information regarding the locations of tissue associations. TSD Net finally outputs aT1-T4 stage prediction by aggregating data from the three branches. We evaluated TSD Net by using the T1-weighted contrast-enhanced magnetic resonance imaging database of 320 patients in a three-fold cross-validation manner. The results show that the proposed method achieves a mean area under the curve (AUC) as high as 87.95%. We also compared our method to traditional classifiers and a deep learning-based method. Our TSD Net is efficient and accurate and outperforms other methods.

Concomitant pulmonary and thyroid tumors identified by FDG PET/CT and immunohistochemical techniques.

BACKGROUND: The exact diagnosis of double primary papillary adenocarcinoma of thyroid and lung is even rarer, to our knowledge no report in the literature by [¹8F]-2-fluoro-2-deoxy-D-glucose-positron emission tomography/X-ray CT(FDG PET/CT) with surgical specimens immunohistochemistry(IHC). We report a patient with abnormal FDG PET/CT in thyroid and lung, this unusual presentation may lead to misdiagnosis without surgical specimens IHC. CASE PRESENTATION: A 56-year-old man with coughing three months. FDG PET/CT was performed, and resection specimens of lung and thyroid were detected by hematoxylin eosin staining (HE) and IHC. PET/CT: lung tumor SUVmax: 3.69, delay: 5.17; and thyroid tumor SUVmax 19.97. HE reveal papillary adenocarcinoma, but histological differentiation of primary pulmonary adenocarcinoma from metastatic adenocarcinoma is sometimes difficult because of their phenotypic similarities. So IHC was performed, the IHC of lung tumor: cytokeratin 20 (CK20)⁻, thyroglobulin(Tg)⁻, cytokeratin7(CK7)+, thyroid transcription factor-1 (TTF-1)+; thyroid tumor: CK7+, TTF-1+, thyroglobulin+, CK20⁻. Therefore, the final diagnosis was double primary adenocarcinomas of thyroid and lung. CONCLUSION: FDG PET/CT has preliminary diagnostic capacity of multiple primary tumors; the final diagnosis should be adopted for specimens after tumor-specific markers IHC to obtain. Consequently, effective therapeutic approaches can be designed and conducted.