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Purpose: To retrospectively identify the relationships between both CT morphological features and histogram parameters with pulmonary metastasis in patients with colorectal cancer (CRC) and compare the efficacy of single-slice and whole-lesion histogram analysis.Methods: Our study enrolled 196 CRC patients with pulmonary nodules (136 in the training dataset and 60 in the validation dataset). Twenty morphological features of contrast-enhanced chest CT were evaluated. The regions of interests were delineated in single-slice and whole-tumor lesions, and 22 histogram parameters were extracted. Stepwise logistic regression analyses were applied to choose the independent factors of lung metastasis in the morphological features model, the single-slice histogram model and whole-lesion histogram model. The areas under the curve (AUC) was applied to quantify the predictive accuracy of each model. Finally, we built a morphological-histogram nomogram for pulmonary metastasis prediction.Results: The whole-lesion histogram analysis (AUC of 0.888 and 0.865 in the training and validation datasets, respectively) outperformed the single-slice histogram analysis (AUC of 0.872 and 0.819 in the training and validation datasets, respectively) and the CT morphological features model (AUC of 0.869 and 0.845 in the training and validation datasets, respectively). The morphological-histogram model, developed with significant morphological features and whole-lesion histogram parameters, achieved favorable discrimination in both the training dataset (AUC = 0.919) and validation dataset (AUC = 0.895), and good calibration.Conclusions: CT morphological features in combination with whole-lesion histogram parameters can be used to prognosticate pulmonary metastasis for patients with colorectal cancer.

Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases. In the last decade, studies on AD diagnosis has attached great significance to artificial intelligence-based diagnostic algorithms. Among the diverse modalities of imaging data, T1-weighted MR and FDG-PET are widely used for this task. In this paper, we propose a convolutional neural network (CNN) to integrate all the multi-modality information included in both T1-MR and FDG-PET images of the hippocampal area, for the diagnosis of AD. Different from the traditional machine learning algorithms, this method does not require manually extracted features, instead, it utilizes 3D image-processing CNNs to learn features for the diagnosis or prognosis of AD. To test the performance of the proposed network, we trained the classifier with paired T1-MR and FDG-PET images in the ADNI datasets, including 731 cognitively unimpaired (labeled as CN) subjects, 647 subjects with AD, 441 subjects with stable mild cognitive impairment (sMCI) and 326 subjects with progressive mild cognitive impairment (pMCI). We obtained higher accuracies of 90.10% for CN vs. AD task, 87.46% for CN vs. pMCI task, and 76.90% for sMCI vs. pMCI task. The proposed framework yields a state-of-the-art performance. Finally, the results have demonstrated that (1) segmentation is not a prerequisite when using a CNN for the classification, (2) the combination of two modality imaging data generates better results.

Articular cartilage is not a physiologically self‐renewing tissue. Injury of cartilage often progresses from the articular surface to the subchondral bone, leading to pathogenesis of tissue degenerative diseases, such as osteoarthritis. Therapies to treat cartilage defects using autologous chondrocyte‐based tissue engineering have been developed and used for more than 20 years; however, the challenge of chondrocyte expansion in vitro remains. A promising cell source, cartilage stem/progenitor cells (CSPCs), has attracted recent attention. Because their origin and identity are still unclear, the application potential of CSPCs is under active investigation. Here we have captured the emergence of a group of stem/progenitor cells derived from adult human chondrocytes, highlighted by dynamic changes in expression of the mature chondrocyte marker, COL2, and mesenchymal stromal/stem cell (MSC) marker, CD146. These cells are termed chondrocyte‐derived progenitor cells (CDPCs). The stem cell‐like potency and differentiation status of CDPCs were determined by physical and biochemical cues during culture. A low‐density, low‐glucose 2‐dimensional culture condition (2DLL) was critical for the emergence and proliferation enhancement of CDPCs. CDPCs showed similar phenotype as bone marrow mesenchymal stromal/stem cells but exhibited greater chondrogenic potential. Moreover, the 2DLL‐cultured CDPCs proved efficient in cartilage formation both in vitro and in vivo and in repairing large knee cartilage defects (6–13 cm2) in 15 patients. These findings suggest a phenotype conversion between chondrocytes and CDPCs and provide conditions that promote the conversion. These insights expand our understanding of cartilage biology and may enhance the success of chondrocyte‐based therapies. Significance Injury of cartilage, a non‐self‐repairing tissue, often progresses to pathogenesis of degenerative joint diseases, such as osteoarthritis. Although tissue‐derived stem cells have been shown to contribute to tissue renewal and homeostasis, the derivation, biological function, and application potential of stem/progenitor cells found in adult human articular cartilage are incompletely understood. This study reports the derivation of a population of cartilage stem/progenitor cells from fully differentiated chondrocytes under specific culture conditions, which have the potential to reassume their chondrocytic phenotype for efficient cartilage regeneration. These findings support the possibility of using in vitro amplified chondrocyte‐derived progenitor cells for joint cartilage repair.

Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia. Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making. A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making.

Both HDACs and Mnks play important role in translating multiple oncogenic signaling pathways during oncogenesis. As HDAC and Mnk are highly expressed in a variety of tumors; thus simultaneous inhibit HDAC and Mnk can increase the inhibition of tumor cell proliferation and provide a new way of inhibiting tumor growth. Based on the previous work and the merge pharmacophore method; we designed and synthesized a series of 4,6-disubstituted pyrido[3,2-d]pyrimidine derivatives as HDAC and Mnk dual inhibitors. Among them; compound A12 displayed good HDAC and Mnk inhibitory activity. In vitro antiproliferative assay; compound A12 exhibited the best antiproliferative activity against human prostate cancer PC-3 cells. Docking study revealed that the pyrido[3,2-d]pyrimidine framework and hydroxamic acid motif of compound A12 were essential for maintaining the activity of HDAC and Mnk. These result indicated that A12 was a potent Mnk /HDAC inhibitor and will be further researched.

Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer’s disease (AD). Identifying MCI subjects who are at high risk of converting to AD is crucial for effective treatments. In this study, a deep learning approach based on convolutional neural networks (CNN), is designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data. First, MRI images are prepared with age-correction and other processing. Second, local patches, which are assembled into 2.5 dimensions, are extracted from these images. Then, the patches from AD and normal controls (NC) are used to train a CNN to identify deep learning features of MCI subjects. After that, structural brain image features are mined with FreeSurfer to assist CNN. Finally, both types of features are fed into an extreme learning machine classifier to predict the AD conversion. The proposed approach is validated on the standardized MRI datasets from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project. This approach achieves an accuracy of 79.9% and an area under the receiver operating characteristic curve (AUC) of 86.1% in leave-one-out cross validations. Compared with other state-of-the-art methods, the proposed one outperforms others with higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity. Results demonstrate great potentials of the proposed CNN-based approach for the prediction of MCI-to-AD conversion with solely MRI data. Age correction and assisted structural brain image features can boost the prediction performance of CNN.

ABSTRACT Since the original report that Halomonas sp. strain GFAJ-1 was capable of using arsenic instead of phosphorus to sustain growth, additional studies have been conducted, and GFAJ-1 is now considered a highly arsenic-resistant but phosphorus-dependent bacterium. However, the mechanisms supporting the extreme arsenic resistance of the GFAJ-1 strain remain unknown. In this study, we show that GFAJ-1 has multiple distinct arsenic resistance mechanisms. It lacks the genes to reduce arsenate, which is the essential step in the well-characterized resistance mechanism of arsenate reduction coupled to arsenite extrusion. Instead, GFAJ-1 has two arsenic resistance operons, arsH1-acr3-2-arsH2 and mfs1-mfs2-gapdh, enabling tolerance to high levels of arsenate. mfs2 and gapdh encode proteins homologous to Pseudomonas aeruginosa ArsJ and glyceraldehyde-3-phosphate dehydrogenase (GAPDH), respectively, which constitute the equivalent of an As(V) efflux system to catalyze the transformation of inorganic arsenate to pentavalent organoarsenical 1-arseno-3-phosphoglycerate and its subsequent extrusion. Surprisingly, the arsH1-acr3-2-arsH2 operon seems to consist of typical arsenite resistance genes, but this operon is sufficient to confer both arsenite and arsenate resistance on Escherichia coli AW3110 even in the absence of arsenate reductase, suggesting a novel pathway of arsenic detoxification. The simultaneous occurrence of these two unusual detoxification mechanisms enables the adaptation of strain GFAJ-1 to the particularly arsenic-rich environment of Mono Lake. IMPORTANCE Halomonas sp. strain GFAJ-1 was previously reported to use arsenic as a substitute for phosphorus to sustain life under phosphate-limited conditions. Although this claim was later undermined by several groups, how GFAJ-1 can thrive in environments with high arsenic concentrations remains unclear. Here, we determined that this ability can be attributed to the possession of two arsenic detoxification operons, arsH1-acr3-2-arsH2 and mfs1-mfs2-gapdh. mfs2 and gapdh encode proteins homologous to ArsJ and GAPDH in Pseudomonas aeruginosa; these proteins create an arsenate efflux pathway to reduce cellular arsenate accumulation. Interestingly, the combination of acr3-2 with either arsH gene was sufficient to confer resistance to both arsenite and arsenate in E. coli AW3110, even in the absence of arsenate reductase, suggesting a new strategy for bacterial arsenic detoxification. This study concludes that the survival of GFAJ-1 in high arsenic concentrations is attributable to the cooccurrence of these two unusual arsenic detoxification mechanisms.

The amygdala plays a major role in the processing of physiologic and behavioral responses to stress and is characterized by gamma-aminobutyric acid (GABA)-mediated high inhibitory tone under resting state. Human and animal studies showed that stress lead to a hyperactivity of amygdala, which was accompanied by the removal of inhibitory control. However, the contribution of hyperactivity of amygdala to stress-induced neuropsychiatric diseases, such as anxiety and mood disorders, is still dubious. In this review, we will summarize stress-induced various structural and functional alterations in amygdala, including the GABA receptors expression, GABAergic transmission and synaptic plasticity. It may provide new insight on the neuropathologic and neurophysiological mechanisms of neuropsychiatric diseases.

Porphyromonas gingivalis (P. gingivalis) is a major periodontal pathogen that can induce an immune response leading to a destructive inflammatory process. During the inflammatory process, interleukin-12 (IL-12) is secreted, correlating with bacterial clearance by macrophages. Bacterial sialidase has recently been shown to influence the synthesis and modification of the macromolecules on its surface, and is associated with the interaction between bacteria and host cells. We have previously constructed a P. gingivalis sialidase gene mutant strain in P. gingivalis W83 (ΔPG0352) and found that ΔPG0352 showed less pathogenicity than the wild-type strain. In this study, U937-differentiated macrophages were stimulated by P. gingivalis W83, ΔPG0352, or PG0352 complemented strain (comΔPG0352). Transmission electron microscopy showed that P. gingivalis caused a loss of membrane integrity in macrophages and the intracellular bacteria were enclosed within endocytic vacuoles. The expression of both IL-12p35 and IL-12p40 genes and the levels of IL-12p70 were significantly higher in U937 stimulated by ΔPG0352 than in those with P. gingivalis W83 and comΔPG0352. In order to explain why ΔPG0352 induced more IL-12 in macrophages, immunofluorescence assays, PCR arrays, and gene silence or overexpression experiments were carried out. Immunofluorescence assays showed that ΔPG0352 induced lower expression of CR3 in macrophages. After CR3 was suppressed, there were no significant differences in the IL-12p70 levels between macrophages stimulated by P. gingivalis W83, ΔPG0352 or comΔPG0352. PCR array experiments showed that miR-21 and lncRNA GAS5 were differentially expressed between macrophages stimulated by P. gingivalis W83 and ΔPG0352, which had been identified by real-time PCR. The results of CR3 blocking and lncRNA GAS5 gene silence or overexpression showed that the difference in IL-12 levels between P. gingivalis W83 and ΔPG0352 groups was associated with CR3, lncRNA GAS5 and miR-21. Thus it can be concluded that the sialidase-deficient strain is more easily cleared by attenuating CR3 activation, reducing the inhibition of lncRNA GAS5, inducing less miR-21 and more IL-12 in macrophages. These results indicate that inhibiting the activity of sialidase in P. gingivalis will cause rapid clearing by macrophages.

Clinical decision support systems (CDSSs) hold potential for the differential diagnosis of neurodegenerative diseases. We developed a novel CDSS, the PredictND tool, designed for differential diagnosis of different types of dementia. It combines information obtained from multiple diagnostic tests such as neuropsychological tests, MRI and cerebrospinal fluid samples. Here we evaluated how the classifier used in it performs in differentiating between controls with subjective cognitive decline, dementia due to Alzheimer’s disease, vascular dementia, frontotemporal lobar degeneration and dementia with Lewy bodies. We used the multiclass Disease State Index classifier, which is the classifier used by the PredictND tool, to differentiate between controls and patients with the four different types of dementia. The multiclass Disease State Index classifier is an extension of a previously developed two-class Disease State Index classifier. As the two-class Disease State Index classifier, the multiclass Disease State Index classifier also offers a visualization of its decision making process, which makes it especially suitable for medical decision support where interpretability of the results is highly important. A subset of the Amsterdam Dementia cohort, consisting of 504 patients (age 65 ± 8 years, 44% females) with data from neuropsychological tests, cerebrospinal fluid samples and both automatic and visual MRI quantifications, was used for the evaluation. The Disease State Index classifier was highly accurate in separating the five classes from each other (balanced accuracy 82.3%). Accuracy was highest for vascular dementia and lowest for dementia with Lewy bodies. For the 50% of patients for which the classifier was most confident on the classification the balanced accuracy was 93.6%. Data-driven CDSSs can be of aid in differential diagnosis in clinical practice. The decision support system tested in this study was highly accurate in separating the different dementias and controls from each other. In addition to the predicted class, it also provides a confidence measure for the classification.

Background: Autophagy has been found to be involved in animal and cell models of atherosclerosis, but to date, it lacks general observation in human atherosclerotic plaques. Here, we investigated autophagy in smooth muscle cells (SMCs), endothelial cells (ECs), and macrophages in human atherosclerotic plaques via transmission electron microscopy (TEM), western blotting, and immunohistochemistry analysis. Methods: The histopathologic morphology of these plaques was observed via hematoxylin and eosin staining. The ultrastructural morphology of the SMCs, ECs, and macrophages in these plaques was observed via TEM. The localization of microtubule-associated protein 1 light chain 3 (MAP1-LC3), a relatively special maker of autophagy, in plaques was observed by double fluorescent immunochemistry and western blotting. Results: All of these human atherosclerotic plaques were considered advanced and unstable in histologically observation. By double fluorescent immunochemistry, the expression of LC3-II increased in the SMCs of the fibrous cap, the macrophages, and the microvascular ECs of the plaque shoulders. The protein level of LC3-II by western blotting significantly increased in plaques compared with normal controls. In addition, TEM observation of plaques revealed certain features of autophagy in SMCs, ECs, and macrophages including the formation of myelin figures, vacuolization, and the accumulation of inclusions in the cytosol. These results indicate that autophagy is activated in SMCs, ECs, and macrophages in human advanced atherosclerotic plaques. Conclusions: Our study is to demonstrate the existence of autophagy in human atherosclerotic plaques by different methods, which may contribute to the development of pharmacological approaches to stabilize vulnerable and rupture-prone lesions.

Hypertension tends to perpetuate in families and the heritability of hypertension is estimated to be around 20–60%. So far, the main proportion of this heritability has not been found by single-locus genome-wide association studies. Therefore, the current study explored gene-gene interactions that have the potential to partially fill in the missing heritability. A two-stage discovery-confirmatory analysis was carried out in the Framingham Heart Study cohorts. The first stage was an exhaustive pairwise search performed in 2320 early-onset hypertensive cases with matched normotensive controls from the offspring cohort. Then, identified gene-gene interactions were assessed in an independent set of 694 subjects from the original cohort. Four unique gene-gene interactions were found to be related to hypertension. Three detected genes were recognized by previous studies, and the other 5 loci/genes (MAN1A1, LMO3, NPAP1/SNRPN, DNAL4, and RNA5SP455/KRT8P5) were novel findings, which had no strong main effect on hypertension and could not be easily identified by single-locus genome-wide studies. Also, by including the identified gene-gene interactions, more variance was explained in hypertension. Overall, our study provides evidence that the genome-wide gene-gene interaction analysis has the possibility to identify new susceptibility genes, which can provide more insights into the genetic background of blood pressure regulation.

Our central hypothesis is that protection against HIV infection will be powerfully influenced by the magnitude and quality of the B cell response. Although sterilizing immunity, mediated by pre-formed abundant and potent antibodies is the ultimate goal for B cell-targeted HIV vaccine strategies, scenarios that fall short of this may still confer beneficial defenses against viremia and disease progression. We evaluated the impact of sub-sterilizing pre-existing neutralizing antibody on the B cell response to SHIV infection. Adult male rhesus macaques received passive transfer of a sub-sterilizing amount of polyclonal neutralizing immunoglobulin (Ig) purified from previously infected animals (SHIVIG) or control Ig prior to intra-rectal challenge with SHIVSF162P4 and extensive longitudinal sampling was performed. SHIVIG treated animals exhibited significantly reduced viral load and increased de novo Env-specific plasma antibody. Dysregulation of the B cell profile was grossly apparent soon after infection in untreated animals; exemplified by a ≈50% decrease in total B cells in the blood evident 2-3 weeks post-infection which was not apparent in SHIVIG treated animals. IgD+CD5+CD21+ B cells phenotypically similar to marginal zone-like B cells were highly sensitive to SHIV infection, becoming significantly decreased as early as 3 days post-infection in control animals, while being maintained in SHIVIG treated animals, and were highly correlated with the induction of Env-specific plasma antibody. These results suggest that B cell dysregulation during the early stages of infection likely contributes to suboptimal Env-specific B cell and antibody responses, and strategies that limit this dysregulation may enhance the host's ability to eliminate HIV.

The combination external-beam radiotherapy and high-dose-rate brachytherapy is a standard form of treatment for patients with locally advanced uterine cervical cancer. Personalized radiotherapy in cervical cancer requires efficient and accurate dose planning and assessment across these types of treatment. To achieve radiation dose assessment, accurate mapping of the dose distribution from HDR-BT onto EBRT is extremely important. However, few systems can achieve robust dose fusion and determine the accumulated dose distribution during the entire course of treatment. We have therefore developed a toolbox (FZUImageReg), which is a user-friendly dose fusion system based on hybrid image registration for radiation dose assessment in cervical cancer radiotherapy. The main part of the software consists of a collection of medical image registration algorithms and a modular design with a user-friendly interface, which allows users to quickly configure, test, monitor, and compare different registration methods for a specific application. Owing to the large deformation, the direct application of conventional state-of-the-art image registration methods is not sufficient for the accurate alignment of EBRT and HDR-BT images. To solve this problem, a multi-phase non-rigid registration method using local landmark-based free-form deformation is proposed for locally large deformation between EBRT and HDR-BT images, followed by intensity-based free-form deformation. With the transformation, the software also provides a dose mapping function according to the deformation field. The total dose distribution during the entire course of treatment can then be presented. Experimental results clearly show that the proposed system can achieve accurate registration between EBRT and HDR-BT images and provide radiation dose warping and fusion results for dose assessment in cervical cancer radiotherapy in terms of high accuracy and efficiency.

The cold roll-beating forming (CRBF) process is a particular cold plastic bulk forming technology for metals that is adequate for shaping the external teeth of important parts. The process parameters of the CRBF process were studied in this work to improve the process performance. Of the CRBF process characteristics, the forming forces, tooth profile angle, surface roughness, and forming efficiency were selected as the target indices to describe the process performance. Single tooth experimental tests of ASTM 1045 steel were conducted with different roll-beating modes, spindle rotation speeds, and feed speeds. Using analysis of variance (ANOVA) and regression analysis, the influence of the process parameters in each index was investigated, and regression models of each index were established. Then, the linear weighted sum method and compound entropy weight method were used to determine the process parameters for multi-objective optimization. The results show that the impact capacity and optimum value range of the process parameters vary in different indices, and that, to achieve the comprehensive optimum effect of a small forming force, high product quality, and high forming efficiency, the optimal process parameter combination is the up-beating mode, a spindle rotation speed of 801 r/min, and a feed speed of 960 mm/min.