Search

In the medical domain, computer systems in digital healthcare have increased connectivity continuously and the DICOM Message Service Element (DIMSE) protocol has a critical role in exchanging biomedical imaging data among different digital healthcare systems. As the data communication technology is used to handle sensitive information such as patient information (e.g., patient's name, date of birth, and address) and medical images (e.g., ultrasound, X-ray, and MRI), it has emerged as a major target for security attacks. In this work, we study security concerns on the message exchange method used in the DIMSE protocol. It is important to know which DIMSE services are available on a given healthcare IT system to an adversary and we observe that the DIMSE protocol can be implemented in various ways across products, with each supporting different DIMSE services as well. We present DIMScern , a framework for discerning DIMSE services on remote medical devices. To show the effectiveness of DIMScern , we evaluate our framework on multiple DIMSE implementations, including commercial products and libraries, and identify the supported DIMSE services of them. We demonstrate that DIMScern successfully identifies medical services that are supported differently across 22 healthcare IT systems in a remote environment.

Over the past decade, deep neural networks have demonstrated significant success using the training scheme that involves mini-batch stochastic gradient descent on extensive datasets. Expanding upon this accomplishment, there has been a surge in research exploring the application of neural networks in other learning scenarios. One notable framework that has garnered significant attention is meta-learning. Often described as "learning to learn," meta-learning is a data-driven approach to optimize the learning algorithm. Other branches of interest are continual learning and online learning, both of which involve incrementally updating a model with streaming data. While these frameworks were initially developed independently, recent works have started investigating their combinations, proposing novel problem settings and learning algorithms. However, due to the elevated complexity and lack of unified terminology, discerning differences between the learning frameworks can be challenging even for experienced researchers. To facilitate a clear understanding, this paper provides a comprehensive survey that organizes various problem settings using consistent terminology and formal descriptions. By offering an overview of these learning paradigms, our work aims to foster further advancements in this promising area of research.

Herein, we report a comprehensive coordination study of the previously reported ligands cyclam , CB-cyclam , TMC , DMC , and CB-DMC and of their C -functional analogues, cyclam-E , CB-cyclam-E , TMC-E , DMC-E , and CB-DMC-E . This group of ligands includes cyclam , cross-bridged cyclams , their di- or tetramethylated derivatives, and the analogues bearing an additional hydroxyethyl group on one β-N position of the ring. The Cu(II) and Zn(II) complexes of these macrocycles have been highlighted previously for the biological interest, but the details of their structures in the solid state and in solution remained largely unexplored. In particular, we analyzed the impact that adding noncoordinating N -methyl and C -hydroxyethyl functionalities has in the structures of the complexes. All the Cu(II) and Zn(II) complexes were synthesized and investigated using single crystal X-ray diffraction and NMR, electronic absorption, and EPR spectroscopies, along with DFT studies. Dissociation kinetics experiments in acidic conditions and electrochemical studies were also performed. Special attention was paid to analyze the different configurations present in solution and in the solid state, as well as the impact of the C -appended hydroxyethyl group on the coordination behavior. Various ratios of the trans -I, trans -III, and cis -V configurations have been observed depending on the degree of N -methylation and the presence of the ethylene cross-bridge.

Herein, we aim to assess mortality risk prediction in peritoneal dialysis patients using machine-learning algorithms for proper prognosis prediction. A total of 1,730 peritoneal dialysis patients in the CRC for ESRD prospective cohort from 2008 to 2014 were enrolled in this study. Classification algorithms were used for prediction of N-year mortality including neural network. The survival hazard ratio was presented by machine-learning algorithms using survival statistics and was compared to conventional algorithms. A survival-tree algorithm presented the most accurate prediction model and outperformed a conventional method such as Cox regression (concordance index 0.769 vs 0.745). Among various survival decision-tree models, the modified Charlson Comorbidity index (mCCI) was selected as the best predictor of mortality. If peritoneal dialysis patients with high mCCI (>4) were aged ≥70.5 years old, the survival hazard ratio was predicted as 4.61 compared to the overall study population. Among the various algorithm using longitudinal data, the AUC value of logistic regression was augmented at 0.804. In addition, the deep neural network significantly improved performance to 0.841. We propose machine learning-based final model, mCCI and age were interrelated as notable risk factors for mortality in Korean peritoneal dialysis patients.

Aggregation of amyloid-β (Aβ) peptides is implicated in the development of Alzheimer's disease (AD), the most common type of dementia. Thus, numerous efforts to identify chemical tactics to control the aggregation pathways of Aβ peptides have been made. Among them, transition metal complexes as a class of chemical modulators against Aβ aggregation have been designed and utilized. Transition metal complexes are able to carry out a variety of chemistry with Aβ peptides (e.g., coordination chemistry and oxidative and proteolytic reactions for peptide modifications) based on their tunable characteristics, including the oxidation state of and coordination geometry around the metal center. This Viewpoint illustrates three strategies employing transition metal complexes toward modulation of Aβ aggregation pathways (i.e., oxidation and hydrolysis of Aβ as well as coordination to Aβ), along with some examples of such transition metal complexes. In addition, proposed mechanisms for three reactivities of transition metal complexes with Aβ peptides are discussed. Our greater understanding of how transition metal complexes have been engineered and used for alteration of Aβ aggregation could provide insight into the new discovery of chemical reagents against Aβ peptides found in AD.

Purpose: To determine impacts of psychological distress, gender role attitude, and housekeeping sharing on quality of life of gynecologic cancer survivors., Methods: Subjects completed questionnaires consisting of four measurements: FACT-G (ver. 4) for quality of life, anxiety and depression from SCL-90-R for psychological distress, gender role attitude, and housekeeping sharing. A total of 158 completed data sets were analyzed by descriptive statistics, independent t-test, ANOVA, Pearson's correlation coefficient, and multiple regression analysis using SPSS 21.0 program., Results: Quality of life was significantly and negatively correlated with psychological distress (r=-0.64, p < .001), but not with gender role attitude (r=-0.14, p=.820) or housekeeping sharing (r=0.08, p=.350). Psychological distress was significantly and positively correlated with gender role attitude (r=0.25, p < .010). Factors that significantly impacted quality of life were type of cancer (t=2.27, p=.025), number of treatment methods (t=2.58, p=.011), education level (t=3.33, p < .001), and psychological distress (t=-9.96, p < .001)., Conclusion: Nursing interventions that can relieve psychological distress need to be developed for gynecologic cancer survivors. Nurses need to put priority on ovarian cancer survivors who have low education level with multiple treatment methods when performing nursing interventions to improve the quality of life of gynecologic cancer survivors.

We address personalized image captioning, which generates a descriptive sentence for a user's image, accounting for prior knowledge such as her active vocabularies or writing style in her previous documents. As applications of personalized image captioning, we solve two post automation tasks in social networks: hashtag prediction and post generation. The hashtag prediction predicts a list of hashtags for an image, while the post generation creates a natural post text consisting of normal words, emojis, and even hashtags. We propose a novel personalized captioning model named Context Sequence Memory Network (CSMN). Its unique updates over existing memory networks include (i) exploiting memory as a repository for multiple types of context information, (ii) appending previously generated words into memory to capture long-term information, and (iii) adopting CNN memory structure to jointly represent nearby ordered memory slots for better context understanding. For evaluation, we collect a new dataset InstaPIC-1.1M, comprising 1.1M Instagram posts from 6.3K users. We further use the benchmark YFCC100M dataset to validate the generality of our approach. With quantitative evaluation and user studies via Amazon Mechanical Turk, we show that the three novel features of the CSMN help enhance the performance of personalized image captioning over state-of-the-art captioning models.

We propose an approach for retrieving a sequence of natural sentences for an image stream. Since general users often take a series of pictures on their experiences, much online visual information exists in the form of image streams, for which it would better take into consideration of the whole image stream to produce natural language descriptions. While almost all previous studies have dealt with the relation between a single image and a single natural sentence, our work extends both input and output dimension to a sequence of images and a sequence of sentences. For retrieving a coherent flow of multiple sentences for a photo stream, we propose a multimodal neural architecture called coherence recurrent convolutional network (CRCN), which consists of convolutional neural networks, bidirectional long short-term memory (LSTM) networks, and an entity-based local coherence model. Our approach directly learns from vast user-generated resource of blog posts as text-image parallel training data. We collect more than 22 K unique blog posts with 170 K associated images for the travel topics of NYC, Disneyland , Australia, and Hawaii. We demonstrate that our approach outperforms other state-of-the-art image captioning methods for text sequence generation, using both quantitative measures and user studies via Amazon Mechanical Turk.

Genetic mutations disrupting the structure and function of primary cilia cause various inherited retinal diseases in humans. Bardet-Biedl syndrome (BBS) is a genetically heterogeneous, pleiotropic ciliopathy characterized by retinal degeneration, obesity, postaxial polydactyly, intellectual disability, and genital and renal abnormalities. To gain insight into the mechanisms of retinal degeneration in BBS, we developed a congenital knockout mouse of Bbs8, as well as conditional mouse models in which function of the BBSome (a protein complex that mediates ciliary trafficking) can be temporally inactivated or restored. We demonstrate that BBS mutant mice have defects in retinal outer segment morphogenesis. We further demonstrate that removal of Bbs8 in adult mice affects photoreceptor function and disrupts the structural integrity of the outer segment. Notably, using a mouse model in which a gene trap inhibiting Bbs8 gene expression can be removed by an inducible FLP recombinase, we show that when BBS8 is restored in immature retinas with malformed outer segments, outer segment extension can resume normally and malformed outer segment discs are displaced distally by normal outer segment structures. Over time, the retinas of the rescued mice become morphologically and functionally normal, indicating that there is a window of plasticity when initial retinal outer segment morphogenesis defects can be ameliorated.

Accurate prediction of graft survival after kidney transplant is limited by the complexity and heterogeneity of risk factors influencing allograft survival. In this study, we applied machine learning methods, in combination with survival statistics, to build new prediction models of graft survival that included immunological factors, as well as known recipient and donor variables. Graft survival was estimated from a retrospective analysis of the data from a multicenter cohort of 3,117 kidney transplant recipients. We evaluated the predictive power of ensemble learning algorithms (survival decision tree, bagging, random forest, and ridge and lasso) and compared outcomes to those of conventional models (decision tree and Cox regression). Using a conventional decision tree model, the 3-month serum creatinine level post-transplant (cut-off, 1.65 mg/dl) predicted a graft failure rate of 77.8% (index of concordance, 0.71). Using a survival decision tree model increased the index of concordance to 0.80, with the episode of acute rejection during the first year post-transplant being associated with a 4.27-fold increase in the risk of graft failure. Our study revealed that early acute rejection in the first year is associated with a substantially increased risk of graft failure. Machine learning methods may provide versatile and feasible tools for forecasting graft survival.

Data regarding kidney transplantation (KT) and dialysis outcomes are rare in Asian populations. In the present study, we evaluated the clinical outcomes associated with KT using claims data from the Korean national public health insurance program. Among the 35,418 adult patients with incident dialysis treated between 2005 and 2008 in Korea, 1539 underwent KT. An optimal balanced risk set matching was attempted to compare the transplant group with the control group in terms of the overall survival and major adverse cardiac event-free survival. Before matching, the dialysis group was older and had more comorbidities. After matching, there were no differences in age, sex, dialysis modalities, or comorbidities. Patient survival was significantly better in the transplant group than in the matched control group (P < 0.001). In addition, the transplant group showed better major adverse cardiac event-free survival than the dialysis group (P < 0.001; hazard ratio, 0.49; 95% confidence interval, 0.32-0.75). Korean patients with incident dialysis who underwent long-term dialysis had significantly more cardiovascular events and higher all-cause mortality rates than those who underwent KT. Thus, KT should be more actively recommended in Korean populations.

Bardet Biedl syndrome (BBS) is a multisystem genetically heterogeneous ciliopathy that most commonly leads to obesity, photoreceptor degeneration, digit anomalies, genito-urinary abnormalities, as well as cognitive impairment with autism, among other features. Sequencing of a DNA sample from a 17-year-old female affected with BBS did not identify any mutation in the known BBS genes. Whole-genome sequencing identified a novel loss-of-function disease-causing homozygous mutation (K102*) in C8ORF37, a gene coding for a cilia protein. The proband was overweight (body mass index 29.1) with a slowly progressive rod-cone dystrophy, a mild learning difficulty, high myopia, three limb post-axial polydactyly, horseshoe kidney, abnormally positioned uterus and elevated liver enzymes. Mutations in C8ORF37 were previously associated with severe autosomal recessive retinal dystrophies (retinitis pigmentosa RP64 and cone-rod dystrophy CORD16) but not BBS. To elucidate the functional role of C8ORF37 in a vertebrate system, we performed gene knockdown in Danio rerio and assessed the cardinal features of BBS and visual function. Knockdown of c8orf37 resulted in impaired visual behavior and BBS-related phenotypes, specifically, defects in the formation of Kupffer's vesicle and delays in retrograde transport. Specificity of these phenotypes to BBS knockdown was shown with rescue experiments. Over-expression of human missense mutations in zebrafish also resulted in impaired visual behavior and BBS-related phenotypes. This is the first functional validation and association of C8ORF37 mutations with the BBS phenotype, which identifies BBS21. The zebrafish studies hereby show that C8ORF37 variants underlie clinically diagnosed BBS-related phenotypes as well as isolated retinal degeneration., (© The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Bardet-Biedl syndrome (BBS) is a well-known ciliopathy with mutations reported in 18 different genes. Most of the protein products of the BBS genes localize at or near the primary cilium and the centrosome. Near the centrosome, BBS proteins interact with centriolar satellite proteins, and the BBSome (a complex of seven BBS proteins) is believed to play a role in transporting ciliary membrane proteins. However, the precise mechanism by which BBSome ciliary trafficking activity is regulated is not fully understood. Here, we show that a centriolar satellite protein, AZI1 (also known as CEP131), interacts with the BBSome and regulates BBSome ciliary trafficking activity. Furthermore, we show that AZI1 interacts with the BBSome through BBS4. AZI1 is not involved in BBSome assembly, but accumulation of the BBSome in cilia is enhanced upon AZI1 depletion. Under conditions in which the BBSome does not normally enter cilia, such as in BBS3 or BBS5 depleted cells, knock down of AZI1 with siRNA restores BBSome trafficking to cilia. Finally, we show that azi1 knockdown in zebrafish embryos results in typical BBS phenotypes including Kupffer's vesicle abnormalities and melanosome transport delay. These findings associate AZI1 with the BBS pathway. Our findings provide further insight into the regulation of BBSome ciliary trafficking and identify AZI1 as a novel BBS candidate gene.

Objective: To investigate the mechanism underlying the anticancer effect of Artemisia species through the inhibition of cell growth and induction of apoptosis in breast carcinoma cells., Methods: To evaluate the anticancer activity of methanol extracts of eight Artemisia species (Artemisia stolonifera, Artemisia selengensis, Artemisia japonica, Artemisia Montana, Artemisia capillaris, Artemisia sylvatica, Artemisia keiskeana, and Artemisia scoparia), we first investigated the proliferation of estrogen receptor (ER)-positive MCF-7 breast carcinoma cells exposed to 5 or 200 g/mL for 72 h. Apoptosis induction was assessed by an Annexin V binding assay in cells exposed to extracts at a high concentration (200 g/mL). To verify the mechanism of apoptosis, ER expression and its related signaling was investigated using an immunoblot assay under the same conditions., Results: MCF-7 cells showed the strongest antiproliferative response to the tested extracts. However, a biphasic effect was observed: the extracts inhibited proliferation at high concentrations whereas they stimulated it at low ones. ER expression was similarly modulated by the extracts. However, all of the extracts induced apoptosis at a high concentration (200 g/mL). Compared to the control level, exposure to the extracts resulted in a remarkable increase in the shift of cell populations., Conclusion: The present study suggests that the tested Artemisia species exerted their anticancer effects through the induction of apoptosis via an ER-related pathway.

Bardet-Biedl Syndrome (BBS) is a pleiotropic and genetically heterozygous disorder caused independently by numerous genes (BBS1-BBS17). Seven highly conserved BBS proteins (BBS1, 2, 4, 5, 7, 8 and 9) form a complex known as the BBSome, which functions in ciliary membrane biogenesis. BBS7 is both a unique subunit of the BBSome and displays direct physical interaction with a second BBS complex, the BBS chaperonin complex. To examine the in vivo function of BBS7, we generated Bbs7 knockout mice. Bbs7(-/-) mice show similar phenotypes to other BBS gene mutant mice including retinal degeneration, obesity, ventriculomegaly and male infertility characterized by abnormal spermatozoa flagellar axonemes. Using tissues from Bbs7(-/-) mice, we show that BBS7 is required for BBSome formation, and that BBS7 and BBS2 depend on each other for protein stability. Although the BBSome serves as a coat complex for ciliary membrane proteins, BBS7 is not required for the localization of ciliary membrane proteins polycystin-1, polycystin-2, or bitter taste receptors, but absence of BBS7 leads to abnormal accumulation of the dopamine D1 receptor to the ciliary membrane, indicating that BBS7 is involved in specific membrane protein localization to cilia.

Objective: To investigate the anticancer, anti-inflammatory, and antiobesity activity of methanol extracts of eight distinct species: Artemisia Stolonifera (AST), Artemisia Selengensis (ASE), Artemisia Japonica, Artemisia Montana, Artemisia Capillaris (ACA), Artemisia Sylvatica (ASY), Artemisia Keiskeana (AKE), and Artemisia Scoparia (ASC) in vitro., Methods: Antiproliferative activity was investigated in human breast cancer estrogen receptor-a positive T47D and negative HS578T cell lines exposed to the extracts at various concentrations (5-200 mg/ mL) for 24, 48, and 72 h. For evaluating the anti-inflammatory activity of the extracts, inhibition of nitrite synthesis was investigated in lipopolysaccharide (LPS)-stimulated cultures of macrophages cells exposed to 10, 50, 100, and 200 mg/mL for 24 h. The antiobesity activity of the extracts was determined as triglyceride content and by a lipolysis assay in differentiated 3T3-L1 cells exposed to the extracts for 72 h at the same concentrations described above., Results: All extracts showed similar antiproliferative activity in a dose- and time-dependent manner in HS578T cells. Although extracts at lower concentrations and shorter times stimulated growth of T47D cells, the antiproliferative effects of the extracts on T47D cells at higher concentrations (> 100 mg/ mL) for 72 h were significantly greater than those of HS578T cells. In case of anti-inflammatory activity, some extracts (AST, ASE, ACA, and AKE) significantly reduced nitric oxide production at higher concentrations in the presence of LPS compared with that in control cells. Antiobesity activity was showed with reducing lipid accumulation significantly (> 50%) at concentrations above 100 mg/mL in most extracts (except AST and ACA). Additionally, AKE and ASC increased lipolysis by 11%-24% compared with that in the control., Conclusion: Artemisia spp. demonstrates potential as bioactive food supplements.

This paper presents the design of a system for a context-aware guidance service for hospital outpatients. Many patients have difficulties in knowing what to do and where to go due to the complexity of hospital-related processes and building structures. The intelligent hospital of the future will evolve in a smart way to automatically provide patients' smart mobile devices with relevant guidance information regarding their tasks, location, and process. In this study, we developed a context-aware-based hospital digital guidance system and evaluated ser satisfaction with respect to outpatient visits. From our pilot experiments with 15 outpatients, they were satisfied with the service. We believe that such a smart system would be a promising model for developing the intelligent hospitals of the future.

Nontypeable Haemophilus influenzae (NTHi) commonly causes otitis media, chronic bronchitis in emphysema, and early airway infections in cystic fibrosis. Long-term, low-dose azithromycin has been shown to improve clinical outcomes in chronic lung diseases, although the mechanism of action remains unclear. The inhibition of bacterial biofilms by azithromycin has been postulated to be one mechanism mediating these effects. We hypothesized that subinhibitory concentrations of azithromycin would affect NTHi biofilm formation. Laboratory strains of NTHi expressing green fluorescent protein and azithromycin-resistant clinical isolates were grown in flow-cell and static-culture biofilm models. Using a range of concentrations of azithromycin and gentamicin, we measured the degree to which these antibiotics inhibited biofilm formation and persistence. Large biofilms formed over 2 to 4 days in a flow cell, displaying complex structures, including towers and channels. Subinhibitory concentrations of azithromycin significantly decreased biomass and maximal thickness in both forming and established NTHi biofilms. In contrast, subinhibitory concentrations of gentamicin had no effect on biofilm formation. Furthermore, established NTHi biofilms became resistant to gentamicin at concentrations far above the MIC. Biofilm formation of highly resistant clinical NTHi isolates (azithromycin MIC of > 64 microg/ml) was similarly decreased at subinhibitory azithromycin concentrations. Clinically obtainable azithromycin concentrations inhibited biofilms in all but the most highly resistant isolates. These data show that subinhibitory concentrations of azithromycin have antibiofilm properties, provide mechanistic insights, and supply an additional rationale for the use of azithromycin in chronic biofilm infections involving H. influenzae.

Rationale: Nontypeable Haemophilus influenzae (NTHi) commonly infects patients with cystic fibrosis (CF), especially early in childhood. Bacteria biofilms are increasingly recognized as contributing to bacterial persistence and disease pathogenesis in CF., Objectives: This study investigated ability of NTHi to form biofilms and its impact on airway epithelia using in vivo and in vitro analyses., Methods: We evaluated bronchoalveolar lavage fluid from young patients with CF for evidence of NTHi biofilms. To further investigate the pathogenesis of NTHi in respiratory infections, we developed a novel in vitro coculture model of NTHi biofilm formation on polarized human airway epithelial cells grown at the air-liquid interface., Measurements and Main Results: In bronchoalveolar lavage fluid samples from young, asymptomatic patients with CF, we found morphologic evidence suggestive of NTHi biofilm formation. In addition, 10 clinical NTHi isolates from patients with CF formed biofilms on plastic surfaces. NTHi formed biofilms on the apical surface of cultured airway epithelia. These biofilms exhibited decreased susceptibility to antibiotics and were adherent to epithelial surfaces. Airway epithelial cells remained viable throughout 4 d of coculture, and responded to NTHi with nuclear factor-kappaB signaling, and increased chemokine and cytokine secretion., Conclusions: NTHi formed adherent biofilms on the apical surface airway epithelia with decreased susceptibility to antibiotics, and respiratory cells exhibited inflammatory and host defense responses-evidence of a dynamic host-pathogen interaction. The data presented here have implications both for understanding early CF lung disease pathogenesis and for the treatment of early, asymptomatic colonization of patients with CF with H. influenzae.