Disease diagnosis in smart healthcare: Innovation, technologies and applications

To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer’s disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed

Exploring the effect of digital transformation on firms’ innovation performance

The influence of digital industry and firm digitization on enterprise innovation has emerged as a critical research topic. To assess the impact of digital transformation on enhancing innovation output, we propose a game model of two organisations investing in digital transformation, analyse the index of enterprise digitalization level with Python tools for text analysis, and employ a fixed effect model. The findings indicate that firm digitalization and the level of regional digital industry innovation can both promote firm innovation. However, the regional digital industry innovation level can have a negative moderating effect on the firm digitalization innovation effect. Furthermore, the impact of firm digitalization on innovation is more visible in digital-related service industries. In other industries, the regional digital industry innovation level has a greater impact on innovation promotion. Due to firms' free-riding tendency in technology adoption, this study shows that the higher the level of digital industrialization in the region where the firm is located, the lower the marginal innovation efficiency of the firm's digital investment. When the level of development of digital industrialization in the region where a firm is located is higher, the "competitive effect" improves the marginal innovation efficiency of firms in adjacent areas, implying that digital industrialization has a spatial spillover effect. The relevant robustness test further verifies the conclusion of the empirical analysis. As a result, the digital industry should be given more attention and financial support

Game analysis on general purpose technology cooperative R&D with fairness concern from the technology chain perspective

General purpose technologies (GPTs) are regarded as a major source of productivity advancement and economic growth. As a kind of platform technology, GPTs have strong knowledge spillovers, which causes a single subject to lack R&D motivation and adopt a wait-and-see strategy. Cooperation R&D is an effective mode choice for GPTs. For this, three models based on upstream-led, downstream-led and balanced power structures were constructed to study the cooperation R&D modes of GPTs and influencing factors from a technology chain perspective. This study aims to reveal the effects of fairness concerns and power structures on three models. This study also focuses on the roles of knowledge spillovers and government support. The results indicate that different power structures will lead to an unequal distribution of profits between firm U and firm D in the technology chain. The balanced power structure should be the preferred model. The profits of firms in the leading position are always higher than those of firms in the following position. In addition, fairness concerns negatively impact the performance of firms, which may improve the bargaining ability of firms in the following position, but this does not bring a sustainable benefit. Government support (e.g., knowledge and technology support and R&D subsidies) and knowledge spillovers are two key factors influencing the decisions and outcomes of the technology chain. When a firm's relative innovation contribution level is greater, its profits in the leading position are the highest, followed by those in the balanced power structure, and they are lowest in the following position. In contrast, profits under balanced power are the highest, and those in the following position are still the lowest. This study enables a theoretical understanding of how and why the R&D process of GPTs can be regarded as a technology chain. It also sheds light on the fact that the balance power structure model should be the preferred choice and that both fairness concerns and government support should be considered for improving the R&D efficiency of GPT cooperation R&D in practice

Sirtuin 1 inhibiting thiocyanates (S1th)-a new class of isotype selective inhibitors of NAD(+) dependent lysine deacetylases

Sirtuin 1 (Sirt1) is a NAD(+) dependent lysine deacetylase associated with the pathogenesis of various diseases including cancer. In many cancer types Sirt1 expression is increased and higher levels have been associated with metastasis and poor prognosis. However, it was also shown, that Sirt1 can have tumor suppressing properties and in some instances even a dual role for the same cancer type has been reported. Increased Sirt1 activity has been linked to extension of the life span of cells, respectively, organisms by promoting DNA repair processes and downregulation of tumor suppressor proteins. This may have the downside of enhancing tumor growth and metastasis. In mice embryonic fibroblasts depletion of Sirt1 was shown to decrease levels of the DNA damage sensor histone H2AX. Impairment of DNA repair mechanisms by Sirt1 can promote tumorigenesis but also lower chemoresistance toward DNA targeting therapies. Despite many biological studies, there is currently just one small molecule Sirt1 inhibitor in clinical trials. Selisistat (EX-527) reached phase III clinical trials for treatment of Huntington's Disease. New small molecule Sirt1 modulators are crucial for further investigation of the contradicting roles of Sirt1 in cancer. We tested a small library of commercially available compounds that were proposed by virtual screening and docking studies against Sirt1, 2 and 3. A thienopyrimidone featuring a phenyl thiocyanate moiety was found to selectively inhibit Sirt1 with an IC50 of 13 mu M. Structural analogs lacking the thiocyanate function did not show inhibition of Sirt1 revealing this group as key for the selectivity and affinity toward Sirt1. Further analogs with higher solubility were identified through iterative docking studies and in vitro testing. The most active compounds (down to 5 mu M IC50) were further studied in cells. The ratio of phosphorylated gamma H2AX to unmodified H2AX is lower when Sirt1 is depleted or inhibited. Our new Sirtuin 1 inhibiting thiocyanates (S1th) lead to similarly lowered gamma H2AX/H2AX ratios in mouse embryonic fibroblasts as Sirt1 knockout and treatment with the reference inhibitor EX-527. In addition to that we were able to show antiproliferative activity, inhibition of migration and colony forming as well as hyperacetylation of Sirt1 targets p53 and H3 by the S1th in cervical cancer cells (HeLa). These results reveal thiocyanates as a promising new class of selective Sirt1 inhibitors.Chemical Immunolog

9p21 loss confers a cold tumor immune microenvironment and primary resistance to immune checkpoint therapy

Immune checkpoint therapy (ICT) provides substantial clinical benefits to cancer patients, but a large proportion of cancers do not respond to ICT. To date, the genomic underpinnings of primary resistance to ICT remain elusive. Here, we performed immunogenomic analysis of data from TCGA and clinical trials of anti-PD-1/PD-L1 therapy, with a particular focus on homozygous deletion of 9p21.3 (9p21 loss), one of the most frequent genomic defects occurring in ~13% of all cancers. We demonstrate that 9p21 loss confers "cold" tumor-immune phenotypes, characterized by reduced abundance of tumor-infiltrating leukocytes (TILs), particularly, T/B/NK cells, altered spatial TILs patterns, diminished immune cell trafficking/activation, decreased rate of PD-L1 positivity, along with activation of immunosuppressive signaling. Notably, patients with 9p21 loss exhibited significantly lower response rates to ICT and worse outcomes, which were corroborated in eight ICT trials of >1,000 patients. Further, 9p21 loss synergizes with PD-L1/TMB for patient stratification. A "response score" was derived by incorporating 9p21 loss, PD-L1 expression and TMB levels in pre-treatment tumors, which outperforms PD-L1, TMB, and their combination in identifying patients with high likelihood of achieving sustained response from otherwise non-responders. Moreover, we describe potential druggable targets in 9p21-loss tumors, which could be exploited to design rational therapeutic interventions

Discriminant correlation analysis for feature level fusion with application to multimodal biometrics

In this paper, we present Discriminant Correlation Analysis (DCA), a feature level fusion technique that incorporates the class associations in correlation analysis of the feature sets. DCA performs an effective feature fusion by maximizing the pair-wise correlations across the two feature sets, and at the same time, eliminating the between-class correlations and restricting the correlations to be within classes. Our proposed method can be used in pattern recognition applications for fusing features extracted from multiple modalities or combining different feature vectors extracted from a single modality. It is noteworthy that DCA is the first technique that considers class structure in feature fusion. Moreover, it has a very low computational complexity and it can be employed in realtime applications. Multiple sets of experiments performed on various biometric databases show the effectiveness of our proposed method, which outperforms other state-of-the-art approaches

Machine Learning Based Two-Tier Security Mechanism for IoT Devices Against DDoS Attacks

IoT devices are becoming an increasingly important part of our everyday lives, and their worth is rising with each passing year. Because IoT devices capture and handle all of our personal and private data, they are a primary target for cyber attackers. Due to the limited processing power and memory capacity of IoT devices, it is challenging to apply complicated security algorithms. The development of a lightweight security mechanism for IoT devices is necessary. In this context, we create a two-tier security solution for Internet of Things devices that protects against DDoS attacks, the most well-known kind of cyber assault. The suggested solution makes extensive use of statistical technologies and machine learning techniques at several tiers to effectively recognise DDoS attacks. The suggested technique made advantage of active learning to determine the appropriate attributes for detecting DDoS attack traffic

Concomitant spine trauma in patients with traumatic brain injury: Patient characteristics and outcomes

Objective: Spine injury is highly prevalent in patients with poly-trauma, but data on the co-occurrence of spine trauma in patients with traumatic brain injury (TBI) are scarce. In this study, we used the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) database to assess the prevalence, characteristics, and outcomes of patients with TBI and a concurrent traumatic spinal injury (TSI). Methods: Data from the European multi-center CENTER-TBI study were analyzed. Adult patients with TBI (≥18 years) presenting with a concomitant, isolated TSI of at least serious severity (Abbreviated Injury Scale; AIS ≥3) were included. For outcome analysis, comparison groups of TBI patients with TSI and systemic injuries (non-isolated TSI) and without TSI were created using propensity score matching. Rates of mortality, unfavorable outcomes (Glasgow Outcome Scale Extended; GOSe < 5), and full recovery (GOSe 7–8) of all patients and separately for patients with only mild TBI (mTBI) were compared between groups at 6-month follow-up. Results: A total of 164 (4%) of the 4,254 CENTER-TBI core study patients suffered from a concomitant isolated TSI. The median age was 53 [interquartile range (IQR): 37–66] years and 71% of patients were men. mTBI was documented in 62% of cases, followed by severe TBI (26%), and spine injuries were mostly cervical (63%) or thoracic (31%). Surgical spine stabilization was performed in 19% of cases and 57% of patients were admitted to the ICU. Mortality at 6 months was 11% and only 36% of patients regained full recovery. There were no significant differences in the 6-month rates of mortality, unfavorable outcomes, or full recovery between TBI patients with and without concomitant isolated TSI. However, concomitant non-isolated TSI was associated with an unfavorable outcome and a higher mortality. In patients with mTBI, a negative association with full recovery could be observed for both concomitant isolated and non-isolated TSI. Conclusion: Rates of mortality, unfavorable outcomes, and full recovery in TBI patients with and without concomitant, isolated TSIs were comparable after 6 months. However, in patients with mTBI, concomitant TSI was a negative predictor for a full recovery. These findings might indicate that patients with moderate to severe TBI do not necessarily exhibit worse outcomes when having a concomitant TSI, whereas patients with mTBI might be more affected