Finger Vein Template Protection with Directional Bloom Filter

Biometrics has become a widely accepted solution for secure user authentication. However, the use of biometric traits raises serious concerns about the protection of personal data and privacy. Traditional biometric systems are vulnerable to attacks due to the storage of original biometric data in the system. Because biometric data cannot be changed once it has been compromised, the use of a biometric system is limited by the security of its template. To protect biometric templates, this paper proposes the use of directional bloom filters as a cancellable biometric approach to transform the biometric data into a non-invertible template for user authentication purposes. Recently, Bloom filter has been used for template protection due to its efficiency with small template size, alignment invariance, and irreversibility. Directional Bloom Filter improves on the original bloom filter. It generates hash vectors with directional subblocks rather than only a single-column subblock in the original bloom filter. Besides, we make use of multiple fingers to generate a biometric template, which is termed multi-instance biometrics. It helps to improve the performance of the method by providing more information through the use of multiple fingers. The proposed method is tested on three public datasets and achieves an equal error rate (EER) as low as 5.28% in the stolen or constant key scenario. Analysis shows that the proposed method meets the four properties of biometric template protection. Doi: 10.28991/HIJ-2023-04-02-013 Full Text: PD

Contrast-Enhanced CT Density Predicts Response to Sunitinib Therapy in Metastatic Renal Cell Carcinoma Patients.

The first-line therapy in metastatic renal cell carcinoma (mRCC), sunitinib, exhibits an objective response rate of approximately 30%. Therapeutic alternatives such as other tyrosine kinase inhibitors, VEGF inhibitors, or mTOR inhibitors emphasize the clinical need to predict the patient's response to sunitinib therapy before treatment initiation. In this study, we evaluated the prognostic value of pretreatment portal venous phase contrast-enhanced computed tomography (CECT) mean tumor density on overall survival (OS), progression-free survival (PFS), and tumor growth in 63 sunitinib-treated mRCC patients. Higher pretreatment CECT tumor density was associated with longer PFS and OS [hazard ratio (HR)=0.968, P=.002, and HR=0.956, P=.001, respectively], and CECT density was inversely correlated with tumor growth (P=.010). Receiver operating characteristic analysis identified two CECT density cut-off values (63.67 HU, sensitivity 0.704, specificity 0.694; and 68.67 HU, sensitivity 0.593, specificity 0.806) which yielded subpopulations with significantly different PFS and OS (P<.001). Pretreatment CECT is therefore a promising noninvasive strategy for response prediction in sunitinib-treated mRCC patients, identifying patients who will derive maximum therapeutic benefit

Silicon-hydroxyapatite bioactive coatings (Si-HA) from diatomaceous earth and silica. Study of adhesion and proliferation of osteoblast-like cells

The aim of this study consisted on investigating the influence of silicon substituted hydroxyapatite (Si–HA) coatings over the human osteoblast-like cell line (SaOS-2) behaviour. Diatomaceous earth and silica, together with commercial hydroxyapatite were respectively the silicon and HA sources used to produce the Si–HA coatings. HA coatings with 0 wt% of silicon were used as control of the experiment. Pulsed laser deposition (PLD) was the selected technique to deposit the coatings. The Si–HA thin films were characterized by Fourier Transformed Infrared Spectroscopy (FTIR) demonstrating the efficient transfer of Si to the HA structure. The in vitro cell culture was established to assess the cell attachment, proliferation and osteoblastic activity respectively by, Scanning Electron Microscopy (SEM), DNA and alkaline phosphatase (ALP) quantification. The SEM analysis demonstrated a similar adhesion behaviour of the cells on the tested materials and the maintenance of the typical osteoblastic morphology along the time of culture. The Si–HA coatings did not evidence any type of cytotoxic behaviour when compared with HA coatings. Moreover, both the proliferation rate and osteoblastic activity results showed a slightly better performance on the Si–HA coatings from diatoms than on the Si–HA from silica.This work was supported by the UE-Interreg IIIA (SP1.P151/03) Proteus project and Xunta de Galicia ( Projects: 2006/12 and PGIDITO5PXIC30301PN)

Metabolic adaptation via regulated enzyme degradation in the pathogenic yeast Candida albicans

The virulence of Candida albicans is dependent upon fitness attributes as well as virulence factors. These attributes include robust stress responses and metabolic flexibility. The assimilation of carbon sources is important for growth and essential for the establishment of infections by C. albicans. Previous studies showed that the C. albicans ICL1 genes, which encode the glyoxylate cycle enzymes isocitratelyase are required for growth on non-fermentable carbon sources such as lactate and oleic acid and were repressed by 2% glucose. In contrast to S. cerevsiae, the enzyme CaIcl1 was not destabilised by glucose, resulting with its metabolite remaining at high levels. Further glucose addition has caused CaIcl1 to lose its signal and mechanisms that trigger destabilization in response to glucose. Another purpose of this study was to test the stability of the Icl1 enzyme in response to the dietary sugars, fructose, and galactose. In the present study, the ICL1 mRNAs expression was quantified using Quantitative Real Time PCR, whereby the stability of protein was measured and quantified using Western blot and phosphoimager, and the replacing and cloning of ICL1 ORF by gene recombination and ubiquitin binding was conducted via co-immuno-precipitation. Following an analogous experimental approach, the analysis was repeated using S. cerevisiaeas a control. Both galactose and fructose were found to trigger the degradation of the ICL1 transcript in C. albicans. The Icl1 enzyme was stable following galactose addition but was degraded in response to fructose. C. albicans Icl1 (CaIcl1) was also subjected to fructose-accelerated degradation when expressed in S. cerevisiae, indicating that, although it lacks a ubiquitination site, CaIcl1 is sensitive to fructose-accelerated protein degradation. The addition of an ubiquitination site to CaIcl1 resulted in this enzyme becoming sensitive to galactose-accelerated degradation and increases its rate of degradation in the presence of fructose. It can be concluded that ubiquitin-independent pathways of fructose-accelerated enzyme degradation exist in C. albicans

Depression, anxiety and stress among patients with dialysis and the association with quality of life

Studies addressing the nature of relationship between psychological symptoms and quality of life among dialysis patients in Malaysia are scarce. Hence, this study is intended to investigate the association between psychological symptoms such as depression, anxiety and stress on the quality of life in dialysis patients. A cross sectional multicentre study was conducted from May to October 2012 at 15 centres that provide haemodialysis and/or peritoneal dialysis. Apart from socio-demographic profile data collection, WHOQOL-BREF and DASS21 questionnaires were administered to study subjects. All three psychological symptoms had significant impact on quality of life domains of physical health, psychological health, social impact, perceived environment and overall quality of life. These findings suggest that subjects with symptoms of depression, anxiety and stress had poorer quality of life than those without, highlighting the negative impact of psychological symptoms

Design and testing of hydrophobic core/hydrophilic shell nano/micro particles for drug-eluting stent coating

In this study, we designed a novel drug-eluting coating for vascular implants consisting of a core coating of the anti-proliferative drug docetaxel (DTX) and a shell coating of the platelet glycoprotein IIb/IIIa receptor monoclonal antibody SZ-21. The core/shell structure was sprayed onto the surface of 316L stainless steel stents using a coaxial electrospray process with the aim of creating a coating that exhibited a differential release of the two drugs. The prepared stents displayed a uniform coating consisting of nano/micro particles. In vitro drug release experiments were performed, and we demonstrated that a biphasic mathematical model was capable of capturing the data, indicating that the release of the two drugs conformed to a diffusion-controlled release system. We demonstrated that our coating was capable of inhibiting the adhesion and activation of platelets, as well as the proliferation and migration of smooth muscle cells (SMCs), indicating its good biocompatibility and anti-proliferation qualities. In an in vivo porcine coronary artery model, the SZ-21/DTX drug-loaded hydrophobic core/hydrophilic shell particle coating stents were observed to promote re-endothelialization and inhibit neointimal hyperplasia. This core/shell particle-coated stent may serve as part of a new strategy for the differential release of different functional drugs to sequentially target thrombosis and in-stent restenosis during the vascular repair process and ensure rapid re-endothelialization in the field of cardiovascular disease

Identification of MCI individuals using structural and functional connectivity networks

Different imaging modalities provide essential complementary information that can be used to enhance our understanding of brain disorders. This study focuses on integrating multiple imaging modalities to identify individuals at risk for mild cognitive impairment (MCI). MCI, often an early stage of Alzheimer’s disease (AD), is difficult to diagnose due to its very mild or insignificant symptoms of cognitive impairment. Recent emergence of brain network analysis has made characterization of neurological disorders at a whole-brain connectivity level possible, thus providing new avenues for brain diseases classification. Employing multiple-kernel Support Vector Machines (SVMs), we attempt to integrate information from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) for improving classification performance. Our results indicate that the multimodality classification approach yields statistically significant improvement in accuracy over using each modality independently. The classification accuracy obtained by the proposed method is 96.3%, which is an increase of at least 7.4% from the single modality-based methods and the direct data fusion method. A cross-validation estimation of the generalization performance gives an area of 0.953 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. The multimodality classification approach hence allows more accurate early detection of brain abnormalities with greater sensitivity

Enriched white matter connectivity networks for accurate identification of MCI patients

Mild cognitive impairment (MCI), often a prodromal phase of Alzheimer’s disease (AD), is frequently considered to be good target for early diagnosis and therapeutic interventions of AD. Recent emergence of reliable network characterization techniques has made it possible to understand neurological disorders at a whole-brain connectivity level. Accordingly, we propose an effective network-based multivariate classification algorithm, using a collection of measures derived from white-matter (WM) connectivity networks, to accurately identify MCI patients from normal controls. An enriched description of WM connections, utilizing six physiological parameters, i.e., fiber count, fractional anisotropy (FA), mean diffusivity (MD), and principal diffusivities (λ1, λ2, λ3), results in six connectivity networks for each subject to account for the connection topology and the biophysical properties of the connections. Upon parcellating the brain into 90 regions-of-interest (ROIs), these properties can be quantified for each pair of regions with common traversing fibers. For building an MCI classifier, clustering coefficient of each ROI in relation to the remaining ROIs is extracted as feature for classification. These features are then ranked according to their Pearson correlation with respect to the clinical labels, and are further sieved to select the most discriminant subset of features using a SVM-based feature selection algorithm. Finally, support vector machines (SVMs) are trained using the selected subset of features. Classification accuracy was evaluated via leave-one-out cross-validation to ensure generalization of performance. The classification accuracy given by our enriched description of WM connections is 88.9%, which is an increase of at least 14.8% from that using simple WM connectivity description with any single physiological parameter. A cross-validation estimation of the generalization performance shows an area of 0.929 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. It was also found, based on the selected features, that portions of the prefrontal cortex, orbitofrontal cortex, parietal lobe and insula regions provided the most discriminant features for classification, in line with results reported in previous studies. Our MCI classification framework, especially the enriched description of WM connections, allows accurate early detection of brain abnormalities, which is of paramount importance for treatment management of potential AD patients