bluuebunny/crossref_metadata_2025 huggingface dataset:
Detection of Emerging Pollutants Using Aptamer-Based Biosensors: Recent Advances, Challenges, and Outlook
Authors: Flores-Contreras Elda A. , González-González Reyna Berenice , González-González Everardo , Melchor-Martínez Elda M. , Parra-Saldívar Roberto , Iqbal Hafiz M. N.
Published: November 2022

The synergistic potentialities of innovative materials that include aptamers have opened new paradigms in biosensing platforms for high-throughput monitoring systems. The available nucleobase functional moieties in aptamers offer exclusive features for bioanalytical sensing applications. In this context, compared to various in-practice biological recognition elements, the utilization of aptamers in detection platforms results in an extensive range of advantages in terms of design flexibility, stability, and sensitivity, among other attributes. Thus, the utilization of aptamers-based biosensing platforms is extensively anticipated to meet unaddressed challenges of various in-practice and standard analytical and sensing techniques. Furthermore, the superior characteristics of aptasensors have led to their applicability in the detection of harmful pollutants present in ever-increasing concentrations in different environmental matrices and water bodies, seeking to achieve simple and real-time monitoring. Considering the above-mentioned critiques and notable functional attributes of aptamers, herein, we reviewed aptamers as a fascinating interface to design, develop, and deploy a new generation of monitoring systems to aid modern bioanalytical sensing applications. Moreover, this review aims to summarize the most recent advances in the development and application of aptasensors for the detection of various emerging pollutants (EPs), e.g., pharmaceutical, and personal care products (PPCPs), endocrine-disrupting chemicals (EDCs), pesticides and other agricultural-related compounds, and toxic heavy elements. In addition, the limitations and current challenges are also reviewed, considering the technical constraints and complexity of the environmental samples.
Glymphatic Dysfunction Mediates the Influence of White Matter Hyperintensities on Episodic Memory in Cerebral Small Vessel Disease
Authors: Ke Zhihong , Mo Yuting , Li Jiangnan , Yang Dan , Huang Lili , Yang Zhiyuan , Qin Ruomeng , Mao Chenglu , Lv Weiping , Huang Yanan , Hu Zheqi , Zhang Bing , Xu Yun
Published: November 2022

Glymphatic dysfunction has been linked to cognitive decline in several neurodegenerative diseases. In cerebral small vessel disease (CSVD), the mechanism of white matter hyperintensities (WMH)-related cognitive impairment (CI) is still under investigation. The diffusion tensor image (DTI) analysis along the perivascular space (ALPS) method has been considered to be a reliable parameter to evaluate glymphatic function. Therefore, we applied the ALPS-index to determine the influence of glymphatic function on CI in CSVD. In total, 137 CSVD patients (normal cognitive group, mild CI group, and dementia group) and 52 normal controls were included in this study. The ALPS-index was calculated based on the DTI. Correlation analyses and mediation analysis were conducted to examine the relationship between glymphatic function and cognition. Remarkable differences in the ALPS-index were observed between subjects with and without CI. The ALPS-index was negatively correlated with age, WMH volume, and general cognitive function in all CSVD patients. In the mild CI group, the ALPS-index was independently positively related to episodic memory, and mediated the relationship between WMH volume and episodic memory. In conclusion, the ALPS-index is a potential marker for early recognition of CI in CSVD. Glymphatic dysfunction mediates the relationship between WMH and CI in CSVD.
Validation and Normative Data on the Verbal Fluency Test in a Peruvian Population Ranging from Pediatric to Elderly Individuals
Authors: Zegarra-Valdivia Jonathan Adrián , Chino Brenda , Paredes Manrique Carmen Noelia
Published: November 2022

In neuropsychological evaluation, verbal fluency is a crucial measure of cognitive function, but this measure requires standardized and normative data for use. The present study aimed to obtain validation and normative data for the verbal fluency task in the Peruvian population, with participants ranging from 6 to 94 years and varying in age, educational level, and sex. We recruited 2602 healthy individuals and used linear regression analysis to determine the effect of age, sex, and educational level. We also evaluated internal consistency between categories and phonological tasks with Cronbach’s alpha and Pearson’s correlation analysis and calculated test-retest reliability after three months. We found significant effects of age, educational level, and sex on phonological and semantic fluency. Participants with more than 12 years of education had the highest scores overall. Regarding age, middle-aged participants (between 31 and 40 years old) had the highest scores; scores gradually decreased outside of this age range. Regarding sex, men performed better than women. These results will increase the ability of clinicians to precisely determine the degree to which verbal fluency is affected in patients of different ages and educational levels.
Acute Effects of High-Frequency Insular Stimulation on Interictal Epileptiform Discharge Rates in Patients with Refractory Epilepsy
Authors: Tran Thi Phuoc Yen , Dionne Antoine , Toffa Denahin , Bergeron David , Obaid Sami , Robert Manon , Bouthillier Alain , Assi Elie Bou , Nguyen Dang Khoa
Published: November 2022

Rationale: Deep brain stimulation (DBS) of several sites, such as the thalamus, has been shown to reduce seizure frequency and interictal epileptiform activity in patients with refractory epilepsy. Recent findings have demonstrated that the insula is part of the ‘rich club’ of highly connected brain regions. This pilot study investigated short-term effects of high-frequency (HF) insular DBS on interictal epileptiform discharge (IED) rate in patients with refractory epilepsy. Methods: Six patients with drug-resistant epilepsy undergoing an intracranial electroencephalographic study received two sets of 10 min continuous 150 Hz HF-DBS of the insula. For each patient, epileptiform activity was analyzed for a total of 80 min, starting 20 min prior to stimulation set 1 (S1), and ending 20 min after stimulation set 2 (S2). All IEDs were identified and classified according to their anatomic localization by a board-certified epileptologist. The IED rate during the 20 min preceding S1 served as a baseline for comparison with IED rate during S1, S2 and post-stimulation periods. Results: HF-DBS of the anterior insula (aINS) was performed in a patient with an aINS epileptic focus (patient 1). HF-DBS of the posterior insula (pINS) was performed in two patients with a pINS epileptic focus (patients 2 and 4), in one patient with an aINS focus (patient 3), and in two non-insular patients (patients 5 and 6). The total IED (irrespective of their location) rate significantly decreased (p < 0.01) in two patients (patients 1 and 2) during the stimulation period, whereas it significantly increased (p < 0.01) in one patient (patient 6); there was no change in the other three patients. Looking at subsets of spike localization, HF-DBS of the aINS significantly reduced aINS and orbitofrontal IEDs in patient 1 (p < 0.01), while HF-DBS of the pINS had an effect on pINS IEDs (p < 0.01) in both patients with a pINS focus; there was no significant effect of HF-DBS of the insula on IEDs in temporal or other frontal regions. Conclusion: Short-term HF-DBS of the insula had heterogeneous effects on the IED rate. Further work is required to examine factors underlying these heterogeneous effects, such as stimulation frequency, location of IEDs and subregions of the insula stimulated.
Review of Vibration Assessment Methods for Steel-Timber Composite Floors
Authors: Cheraghi-Shirazi Najmeh , Crews Keith , Malek Sardar
Published: November 2022

Human comfort is recognized as an essential serviceability requirement for timber floors. Although several standards and design criteria are available for designing steel and concrete floors, there is no consensus among researchers on the applicability of such design methods to timber composite floors. Adding steel to timber floors is intended to create long spans, however, vibration is still a major challenge in achieving longer spans. To highlight the extent of this issue, a comprehensive search in the literature was conducted. The most common vibration criteria that may be used to assess the performance of steel-timber composite floors under human-induced vibrations were reviewed. For lightweight composite floors, the 1 kN deflection limit was found to be the most suitable vibration limit based on a wide range of subjective evaluation studies. For composite floors comprising steel and heavier timber subfloors, the relevance of 1 kN deflection criterion and other criteria suggested in the literature are questionable due to the lack of subjective evaluation studies. In the advent of advanced computing and data analysis, conducting detailed numerical analysis validated by accurate on-site measurements is recommended. Special attentions should be given to accurate estimation of connection stiffness and damping ratio according to the findings of this study.
Canonical WNT Signaling Activated by WNT7B Contributes to L-HBs-Mediated Sorafenib Resistance in Hepatocellular Carcinoma by Inhibiting Mitophagy
Authors: Liu Li-Juan , Lv Zhao , Xue Xing , Xing Zhong-Yuan , Zhu Fan
Published: November 2022

Hepatocellular carcinoma (HCC) is the third leading cause of cancer death globally, with hepatitis B virus (HBV) infection accounting for over half of all cases. HBV leads to the development of HCC according to a body of literature. Our previous research and other studies also suggest that HBV causes chemotherapeutic treatment resistance, however, the mechanism is uncertain. The WNT family, which encodes secreted signaling molecules, has been linked to carcinogenesis in a variety of malignancies, including HCC. However, little is known regarding WNT7B, a WNT ligand, in the development of HCC and HBV-induced chemoresistance. In this study, the bioinformatics analysis and immunohistochemistry (IHC) staining of clinical samples revealed that WNT7B was overexpressed in HBV-associated HCC tissues versus nontumor liver tissues, which was related to HCC patient survival. Further study in vitro showed that WNT7B and its receptor frizzled-4 (FZD4) were upregulated in response to large hepatitis B surface antigens (L-HBs). L-HBs increased canonical WNT signaling in HCC cells through WNT7B/FZD4. According to functional experiments, WNT7B enhanced the cell proliferation and metastasis in HCC. In vivo and in vitro studies investigated whether L-HBs induced sorafenib resistance by WNT7B in HCC. Interestingly, L-HBs suppressed sorafenib-induced mitophagy by increasing WNT7B/CTNNB1 signaling, resulting in chemoresistance. The findings revealed that WNT7B could be a promising molecular therapeutic target as well as a predictor of sorafenib resistance in HBV-related HCC. The suppression of HBV structural proteins such as L-HBs may play a crucial role in systemic chemotherapy resistance in HBV-associated HCC.
Non-Invasive Biomarkers for Early Lung Cancer Detection
Authors: Saman Harman , Raza Afsheen , Patil Kalyani , Uddin Shahab , Crnogorac-Jurcevic Tatjana
Published: November 2022

Worldwide, lung cancer (LC) is the most common cause of cancer death, and any delay in the detection of new and relapsed disease serves as a major factor for a significant proportion of LC morbidity and mortality. Though invasive methods such as tissue biopsy are considered the gold standard for diagnosis and disease monitoring, they have several limitations. Therefore, there is an urgent need to identify and validate non-invasive biomarkers for the early diagnosis, prognosis, and treatment of lung cancer for improved patient management. Despite recent progress in the identification of non-invasive biomarkers, currently, there is a shortage of reliable and accessible biomarkers demonstrating high sensitivity and specificity for LC detection. In this review, we aim to cover the latest developments in the field, including the utility of biomarkers that are currently used in LC screening and diagnosis. We comment on their limitations and summarise the findings and developmental stages of potential molecular contenders such as microRNAs, circulating tumour DNA, and methylation markers. Furthermore, we summarise research challenges in the development of biomarkers used for screening purposes and the potential clinical applications of newly discovered biomarkers.
Natural Killer Cells in Chronic Lymphocytic Leukemia: Functional Impairment and Therapeutic Potential
Authors: Yano Max , Byrd John C. , Muthusamy Natarajan
Published: November 2022

Immunotherapy approaches have advanced rapidly in recent years. While the greatest therapeutic advances so far have been achieved with T cell therapies such as immune checkpoint blockade and CAR-T, recent advances in NK cell therapy have highlighted the therapeutic potential of these cells. Chronic lymphocytic leukemia (CLL), the most prevalent form of leukemia in Western countries, is a very immunosuppressive disease but still shows significant potential as a target of immunotherapy, including NK-based therapies. In addition to their antileukemia potential, NK cells are important immune effectors in the response to infections, which represent a major clinical concern for CLL patients. Here, we review the interactions between NK cells and CLL, describing functional changes and mechanisms of CLL-induced NK suppression, interactions with current therapeutic options, and the potential for therapeutic benefit using NK cell therapies.
Changes in Treatment Patterns and Costs for Lung Cancer Have Not Resulted in Relevant Improvements in Survival: A Population-Based Observational Study in Catalonia
Authors: Guarga Laura , Paco Noelia , Vela Emili , Clèries Montse , Corral Julieta , Delgadillo Joaquim , Pontes Caridad , Borràs Josep Maria
Published: November 2022

Objective: Few published studies have described multidisciplinary therapeutic strategies for lung cancer. This study aims to describe the different approaches used for treating lung cancer in Catalonia in 2014 and 2018 and to assess the associated cost and impact on patient survival. Methods: A retrospective observational cohort study using data of patients with lung cancer from health care registries in Catalonia was carried out. We analyzed change in treatment patterns, costs and survival according to the year of treatment initiation (2014 vs. 2018). The Kaplan–Meier method was used to estimate survival, with the follow-up until 2021. Results: From 2014 to 2018, the proportion of patients undergoing surgery increased and treatments for unresectable tumors decreased, mainly in younger patients. Immunotherapy increased by up to 9% by 2018. No differences in patient survival were observed within treatment patterns. The mean cost per patient in the first year of treatment increased from EUR 14,123 (standard deviation [SD] 4327) to EUR 14,550 (SD 3880) in surgical patients, from EUR 4655 (SD 3540) to EUR 5873 (SD 6455) in patients receiving curative radiotherapy and from EUR 4723 (SD 7003) to EUR 6458 (SD 10,116) in those treated for unresectable disease. Conclusions: From 2014 to 2018, surgical approaches increased in younger patients. The mean cost of treating patients increased, especially in pharmaceutical expenditure, mainly related to the use of several biomarker-targeted treatments. While no differences in overall patient survival were observed, it seems reasonable to expect improvements in this outcome in upcoming years as more patients receive innovative treatments.
Identification of a Small Molecule Inhibitor of Hyaluronan Synthesis, DDIT, Targeting Breast Cancer Cells
Authors: Karalis Theodoros , Shiau Andrew , Gahman Timothy , Skandalis Spyros , Heldin Carl-Henrik , Heldin Paraskevi
Published: November 2022

Breast cancer is a common cancer in women. Breast cancer cells synthesize large amounts of hyaluronan to assist their proliferation, survival, migration and invasion. Accumulation of hyaluronan and overexpression of its receptor CD44 and hyaluronidase TMEM2 in breast tumors correlate with tumor progression and reduced overall survival of patients. Currently, the only known small molecule inhibitor of hyaluronan synthesis is 4-methyl-umbelliferone (4-MU). Due to the importance of hyaluronan for breast cancer progression, our aim was to identify new, potent and chemically distinct inhibitors of its synthesis. Here, we report a new small molecule inhibitor of hyaluronan synthesis, the thymidine analog 5′-Deoxy-5′-(1,3-Diphenyl-2-Imidazolidinyl)-Thymidine (DDIT). This compound is more potent than 4-MU and displays significant anti-tumorigenic properties. Specifically, DDIT inhibits breast cancer cell proliferation, migration, invasion and cancer stem cell self-renewal by suppressing HAS-synthesized hyaluronan. DDIT appears as a promising lead compound for the development of inhibitors of hyaluronan synthesis with potential usefulness in breast cancer treatment.
Immune-Related Gene Signatures to Predict the Effectiveness of Chemoimmunotherapy in Triple-Negative Breast Cancer Using Exploratory Subgroup Discovery
Authors: Kholod Olha , Basket William I. , Mitchem Jonathan B. , Kaifi Jussuf T. , Hammer Richard D. , Papageorgiou Christos N. , Shyu Chi-Ren
Published: November 2022

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited therapeutic options. Although immunotherapy has shown potential in TNBC patients, clinical studies have only demonstrated a modest response. Therefore, the exploration of immunotherapy in combination with chemotherapy is warranted. In this project we identified immune-related gene signatures for TNBC patients that may explain differences in patients’ outcomes after anti-PD-L1+chemotherapy treatment. First, we ran the exploratory subgroup discovery algorithm on the TNBC dataset comprised of 422 patients across 24 studies. Secondly, we narrowed down the search to twelve homogenous subgroups based on tumor mutational burden (TMB, low or high), relapse status (disease-free or recurred), tumor cellularity (high, low and moderate), menopausal status (pre- or post) and tumor stage (I, II and III). For each subgroup we identified a union of the top 10% of genotypic patterns. Furthermore, we employed a multinomial regression model to predict significant genotypic patterns that would be linked to partial remission after anti-PD-L1+chemotherapy treatment. Finally, we uncovered distinct immune cell populations (T-cells, B-cells, Myeloid, NK-cells) for TNBC patients with various treatment outcomes. CD4-Tn-LEF1 and CD4-CXCL13 T-cells were linked to partial remission on anti-PD-L1+chemotherapy treatment. Our informatics pipeline may help to select better responders to chemoimmunotherapy, as well as pinpoint the underlying mechanisms of drug resistance in TNBC patients at single-cell resolution.
Type XXII Collagen Complements Fibrillar Collagens in the Serological Assessment of Tumor Fibrosis and the Outcome in Pancreatic Cancer
Authors: Madsen Emilie A. , Thorlacius-Ussing Jeppe , Nissen Neel I. , Jensen Christina , Chen Inna M. , Johansen Julia S. , Diab Hadi M. H. , Jørgensen Lars N. , Hansen Carsten P. , Karsdal Morten A. , Willumsen Nicholas
Published: November 2022

Circulating fragments of type III collagen, measured by PRO-C3, has shown promising results as a tumor fibrosis biomarker. However, the fibrotic tumor microenvironment consists of many other collagens with diverse functions and unexplored biomarker potential. One example hereof is type XXII collagen (COL22). In this study, we investigated the biomarker potential of COL22 by measuring this in serum. An ELISA, named PRO-C22, was developed and measured in two serum cohorts consisting of patients with various solid tumors (n = 220) and healthy subjects (n = 33) (Cohort 1), and patients with pancreatic ductal adenocarcinoma (PDAC) (n = 34), and healthy subjects (n = 20) (Cohort 2). In Cohort 1, PRO-C22 was elevated in the serum from patients with solid tumors, compared to healthy subjects (p < 0.01 to p < 0.0001), and the diagnostic accuracy (AUROC) ranged from 0.87 to 0.98, p < 0.0001. In Cohort 2, the high levels of PRO-C22, in patients with PDAC, were predictive of a worse overall survival (HR = 4.52, 95% CI 1.90–10.7, p = 0.0006) and this remained significant after adjusting for PRO-C3 (HR = 4.27, 95% CI 1.24–10.4, p = 0.0013). In conclusion, PRO-C22 has diagnostic biomarker potential in various solid tumor types and prognostic biomarker potential in PDAC. Furthermore, PRO-C22 complemented PRO-C3 in predicting mortality, suggesting an additive prognostic value when quantifying different collagens.
A Qualitative and Quantitative Analysis Strategy for Continuous Turbulent Gas Mixture Monitoring
Authors: Chen Yinsheng , Xia Wanyu , Chen Deyun , Zhang Tianyu , Song Tingting , Zhao Wenjie , Song Kai
Published: November 2022

Electronic noses are one of the predominant technological choices for gas mixture detection, but their application in real-world atmospheric environments still leaves several issues to be resolved. The key bottleneck is the effect of turbulence caused by the diffusion of gases in the atmosphere on the quantitative and qualitative analytical performance of the electronic nose. In light of this, this paper presents a quantitative and qualitative analysis strategy for gas mixture monitoring. This strategy adopts baseline manipulation of the raw sensor data to reduce drift interference, and then performs feature extraction on the multidimensional response signals of the MOS gas sensor array using principal component analysis (PCA). In order to improve gas mixture recognition accuracy, the whale optimization algorithm (WOA) is used to optimize the network structure of the long short-term memory (LSTM) model for turbulent gas mixture composition recognition. The least squares support vector machine (LSSVM) algorithm is adopted to implement turbulent gas mixture concentration prediction. This paper focuses on two aspects of hyper-parameter optimization for the development of an LSSVM with particle swarm optimization (PSO) and for improved training sample selection for the LSSVM which should subsequently increase the accuracy of concentration estimation. The effectiveness of the proposed strategy is evaluated with a dataset from a chemical sensor array exposed to turbulent gas mixtures. Experimental results revealed that the proposed strategy for turbulent gas mixtures has satisfactory outcomes for both qualitative gas composition recognition and quantitative gas concentration prediction.
Synthesis and Computational and X-ray Structure of 2, 3, 5-Triphenyl Tetrazolium, 5-Ethyl-5-phenylbarbituric Acid Salt
Authors: Bakheit Ahmed H. , Ghabbour Hazem A. , Hussain Hadayt , Al-Salahi Rashad , Ali Essam A. , Mostafa Gamal A. E.
Published: November 2022

The title compound triphenyl tetrazolium (TPT) of phenobarbital, 5-Ethyl-5-phenylbarbituric acid triphenyl tetrazolium salt (4) was prepared by the reaction of 5-Ethyl-5-phenyl-2,4,6(1H, 3H, 5H)-pyrimidinetrione, monosodium salt (1) with triphenyl tetrazolium chloride (3) in deionized water at an ambient temperature through a cation exchange reaction. Colorless crystals of compound four suitable for an X-ray structural analysis were obtained by slow evaporation from acetonitrile. Compound four had crystallized in the monoclinic space group, P21/c, with a = 15.3678 (9) Å, b = 12.2710 (7) Å, c = 21.8514 (13) Å, β = 109.867 (2)°, V = 3875.5 (4) Å3, and Z = 4. A Through density functional theory (DFT) calculations, the probable molecular association structure in the phenobarbitone -triphenyl tetrazolium solution was studied. With the 6-311G-(d,p) basis set, the gas phase features of the phenobarbital-triphenyl tetrazolium clusters with a phenobarbitone dimer and water molecules, including an optimum structure and intermolecular hydrogen bonding, were investigated in detail. In addition, the positions and strengths of the intermolecular hydrogen bond interactions between the phenobarbitone and triphenyl tetrazolium molecules were analyzed using atoms in molecule (AIM) analysis, reduced density gradient (RDG) methods, the XRD method, and the non-covalent interaction (NCI) index method. In addition, the molecular electrostatic potential (MEP) surfaces were analyzed to determine the electrophilic and nucleophilic centers.
STM/STS Study of the Density of States and Contrast Behavior at the Boundary between (7 × 7)N and (8 × 8) Structures in the SiN/Si(111) System
Authors: Mansurov Vladimir , Malin Timur , Teys Sergey , Atuchin Victor , Milakhin Denis , Zhuravlev Konstantin
Published: November 2022

The origin of the contrast appearing in STM images at the boundary between diverse ordered structures is studied using the example of two structures, (7 × 7)N and (8 × 8), formed in the system of a two-dimensional silicon nitride layer on the Si(111) surface during ammonia nitridation. A significant dependence of the contrast between these structures on the voltage applied to the tunnel gap was found and studied both experimentally and theoretically. Variations in the contrast were quantitatively studied in the range from −3 V to +3 V, and they were studied in more detail for the positive biases on the sample from +1 V to +2.5 V, where the contrast was changed more than 2 times. Within the one-dimensional Wentzel–Kramers–Brillouin (WKB) model for the tunnel current, a comparatively simple procedure is proposed for the correction of the experimental STS-spectra of differential conductivity to identify the adequate (feasible) density of electron states (DOS). It is shown that the (8 × 8) structure DOS corresponds to a graphene-like layer of silicon nitride structure. The proposed correction procedure of the empirical differential conductivity spectra measured by STS will be useful for the quantitative determination of the DOS of new two-dimensional materials and surface structures.
Two Cd(II)-Based MOFs Constructed from
Tris(3′-F-4′-carboxybiphenyl)amine: Synthesis, Crystal
Structure, Luminescence Sensing towards Nitrophenols
and Acetylacetone
Authors: Ru Jing , Shi Yixuan , Guo Qiang , Hu Boxuan , Li Leilei , Wang Yanlan , Ma Chunlin
Published: November 2022

Two new luminescent metal-organic frameworks (MOFs), namely [Cd4(TFBA)3(H2O)4]n (CdMOF-1) and [Cd(TFBA) (bipy)]n (CdMOF-2) (H3TFBA = tris(3′-F-4′-carboxybiphenyl)amine, 4,4′-bipy = 4,4’-bipyridine), have been prepared and successfully constructed via solvothermal conditions. Single-crystal structure analysis reveals that CdMOF-1 are two-dimensional network containing [Cd4O26] units. CdMOF-2 shows a non-interpenetration 3D microporous structure with a hexagon pore of 24.6 × 25.9 Å. Notably, CdMOF-2 exhibits turn off fluorescence behavior towards picric acid (TNP) and 4-Nitrophenol (PNP) with a limit of detection (LOD) value of 9.2 × 10−6 M and 1.81 × 10−5 M, respectively. Furthermore, CdMOF-2 shows obvious turn-on luminescence responses toward acetylacetone (Acac) with luminescence red-shift and a detection limit of 19.40 ppm. Additionally, the possible quenching or enhancing mechanism during the sensing process were evaluated by PXRD, UV-vis, and luminescence decay lifetime.
Optimal Choice as First-Line Therapy for Patients with Triple-Negative Breast Cancer: A Bayesian Network Meta-Analysis
Authors: Han Yiqun , Wang Jiayu , Wu Yun , Xu Hangcheng , Wang Yan , Xu Binghe
Published: November 2022

To identify the advantageous therapy as the first-line treatment for patients with triple-negative breast cancer (TNBC). Randomized controlled trials were searched for on Medline, Embase, ClinicalTrials.gov, and the Cochrane Library between January 2001 and December 2021. The primary endpoint was progression-free survival (PFS) and secondary endpoints were overall survival (OS) and treatment-related adverse events (TRAEs). A Bayesian framework was applied to facilitate indirect comparisons, of which the outcomes were presented using cumulative ranking curve (SUCRA) values, synthesized hazard ratio, risk ratio, and 95% credible interval. A total of 3140 patients were identified. Pooled results of PFS revealed that chemotherapy plus AKT inhibitors (AKTi) was likely the most effective therapy among enrolled therapies (SUCRA = 91.6%), of which the result remained consistent in comparative analysis for OS. In addition, no significant difference was detected between PD-1/PD-L1 antibodies in patients, whereas the PD-1 inhibitors (PD-1i) regimen was advantageous over PD-L1 inhibitor (PD-L1i) therapy for PD-L1 positive TNBC. Concerning TRAEs, an apparent heterogeneity associated with safety profiles were denoted among enrolled agents. Chemotherapy plus AKTi was the most effective therapy with comparable safety profiles. Chemotherapy plus the anti-PD-1 regimen was advantageous over the combination therapy based on the PD-L1 blockade.
Effect of Life-History Traits and Habitat Condition on Genetic Diversity between Invasive and Native Plant Populations
Authors: Hernández-Espinosa Raquel , González-Astorga Jorge , Rico Yessica , Gallego-Fernández Juan B.
Published: November 2022

Plant invasions have a huge impact on the health of ecosystems and human well-being. The invasion risk varies with the introduction pathway, the propagule pressure, and the genetic diversity of the founding population. We performed a systematic review and meta-analysis of 30 studies reporting the genetic diversity of 31 plant species in their invasive and native ranges. We evaluated if patterns of genetic diversity differ between ranges and whether these responses are influenced by life-history traits, hybridization, polyploidization, and habitat condition. We found that invasive populations had significantly lower genetic diversity and higher inbreeding than native populations. In fragmented and degraded habitats, the genetic diversity of invaders was lower, but inbreeding was not affected. Polyploid invaders with hybrid capacity also showed lower genetic diversity. Invasive herbs with vegetative propagation were more sensitive to the loss of genetic diversity and had higher levels of inbreeding. Our synthesis showed that the genetic response in the invaded range could result from historical processes, such as founder and bottleneck events. Traits such as selfing are more likely to preserve the signatures of founder events and influence the genetic diversity in invasive populations. Additionally, clonality seems to be the predominant reproduction system in the invaded range.
Biochemical and Mechanical Analysis of Occlusal and Proximal Carious Lesions
Authors: Al-Shareefi Sahar , Addie Ali , Al-Taee Lamis
Published: November 2022

A precise evaluation of caries excavation endpoint is essential in clinical and laboratory investigations. Caries invasion differentiates dentin into structurally altered layers. This study assessed these changes using Raman spectroscopy and Vickers microhardness. Ten permanent molars with occlusal and proximal carious lesions were assessed and compared at 130 points utilizing four Raman spectroscopic peaks: phosphate v1 at 960 cm−1, amide I (1650 cm−1), amide III (1235 cm−1) and the C-H bond of the pyrrolidine ring (1450 cm−1). The phosphate-to-amide I peak ratio and collagen integrity peak ratio (amide III: C-H bond) of carious zones were calculated and compared in both lesions. The former ratio was correlated to 130 Vickers microhardness indentations through lesions. The caries-infected dentin (CID) exhibited low phosphate peak, but higher amide I, III and C-H bond peaks than other zones in both lesions. The peaks in amide regions (I and III) varied in occlusal versus proximal lesions. A high correlation was found between mineral: matrix peak ratio and equivalent microhardness number within carious lesions, while the collagen integrity peak ratio was applied in proximal lesions only. Raman spectroscopy detected changes in the mineral and matrix contents within different carious zones and regions.
Left Ventricle Wall Motion Analysis with Real-Time MRI Feature Tracking in Heart Failure Patients: A Pilot Study
Authors: Li Yu (Yulee) , Craft Jason , Cheng Yang (Josh) , Gliganic Kathleen , Schapiro William , Cao Jie (Jane)
Published: November 2022

Volumetric measurements with cardiac magnetic resonance imaging (MRI) are effective for evaluating heart failure (HF) with systolic dysfunction that typically induces a lower ejection fraction (EF) than normal (<50%) while they are not sensitive to diastolic dysfunction in HF patients with preserved EF (≥50%). This work is to investigate whether HF evaluation with cardiac MRI can be improved with real-time MRI feature tracking. In a cardiac MRI study, we recruited 16 healthy volunteers, 8 HF patients with EF < 50% and 10 HF patients with preserved EF. Using real-time feature tracking, a cardiac MRI index, torsion correlation, was calculated which evaluated the correlation of torsional and radial wall motion in the left ventricle (LV) over a series of sequential cardiac cycles. The HF patients with preserved EF and the healthy volunteers presented significant difference in torsion correlation (one-way ANOVA, p < 0.001). In the scatter plots of EF against torsion correlation, the HF patients with EF < 50%, the HF patients with preserved EF and the healthy volunteers were well differentiated, indicating that real-time MRI feature tracking provided LV function assessment complementary to volumetric measurements. This study demonstrated the potential of cardiac MRI for evaluating both systolic and diastolic dysfunction in HF patients.
Deep Learning Approach in Image Diagnosis of Pseudomonas Keratitis
Authors: Kuo Ming-Tse , Hsu Benny Wei-Yun , Lin Yi Sheng , Fang Po-Chiung , Yu Hun-Ju , Hsiao Yu-Ting , Tseng Vincent S.
Published: November 2022

This investigation aimed to explore deep learning (DL) models’ potential for diagnosing Pseudomonas keratitis using external eye images. In the retrospective research, the images of bacterial keratitis (BK, n = 929), classified as Pseudomonas (n = 618) and non-Pseudomonas (n = 311) keratitis, were collected. Eight DL algorithms, including ResNet50, DenseNet121, ResNeXt50, SE-ResNet50, and EfficientNets B0 to B3, were adopted as backbone models to train and obtain the best ensemble 2-, 3-, 4-, and 5-DL models. Five-fold cross-validation was used to determine the ability of single and ensemble models to diagnose Pseudomonas keratitis. The EfficientNet B2 model had the highest accuracy (71.2%) of the eight single-DL models, while the best ensemble 4-DL model showed the highest accuracy (72.1%) among the ensemble models. However, no statistical difference was shown in the area under the receiver operating characteristic curve and diagnostic accuracy among these single-DL models and among the four best ensemble models. As a proof of concept, the DL approach, via external eye photos, could assist in identifying Pseudomonas keratitis from BK patients. All the best ensemble models can enhance the performance of constituent DL models in diagnosing Pseudomonas keratitis, but the enhancement effect appears to be limited.
Joint Efficient UAV Trajectory and Velocity Optimization for IoT Data Collection Using a New Projection Algorithm
Authors: Zheng Kuangyu , Ma Zimo , Zhao Mingyue , Zhou Zhuyang , Zhang Ziheng , Li Yifeng
Published: November 2022

Unmanned aerial vehicle (UAV)-assisted networking and communications are increasingly used in different applications, especially in the data collection of distributed Internet of Things (IoT) systems; its advantages include great flexibility and scalability. However, due to the UAV’s very limited battery capacity, the UAV energy efficiency has become a bottleneck for longer working time and larger area coverage. Therefore, it is critical to optimize the path and speed of the UAV with less energy consumption, while guaranteeing data collection under the workload and time requirements. In this paper, as a key finding, by analyzing the speed–power and the speed–energy relationships of UAVs, we found that there should be different speed selection strategies under different scenarios (i.e., fixed time or fixed distance), which can lead to much-improved energy efficiency. Moreover, we propose CirCo, a novel algorithm that jointly optimizes UAV trajectory and velocity for minimized energy consumption. CirCo is based on an original projection method, turning a 3D problem (GN locations and transmission ranges on the 2D plane, plus the minimum transmission time requirements on the temporal dimensions) into a 2D problem, which could help to directly find the feasible UAV crossing window, which greatly reduces the optimization complexity. Moreover, CirCo can classify the projected conditions to calculate the optimal path and speed schedule under each category, so that the energy consumption of each situation can be fine-regulated. The experiments demonstrate that CirCo can save as much as 54.3% of energy consumption and 62.9% of flight time over existing approaches.
Leveraging H3Africa Scholarly Publications for Technology-Enhanced Personalized Bioinformatics Education
Authors: Makolo Angela U. , Smile Olubukola , Ezekiel Kehinde B. , Destefano Antoinette M. , McCall Junell L. , Isokpehi Raphael D.
Published: November 2022

The Coronavirus Disease 2019 (COVID-19) pandemic has catalyzed the expectations for technology-enhanced interactions with personalized educational materials. Adjusting the content of educational materials to the geographical location of a learner is a customization feature of personalized education and is used to develop the interest of a learner in the content. The educational content of interest in this report is bioinformatics, in which the knowledge spans biological science and applied mathematics disciplines. The Human Heredity and Health in Africa (H3Africa) Initiative is a resource suitable for use when obtaining data and peer-reviewed scholarly articles, which are geographically relevant and focus on authentic problem solving in the human health domain. We developed a computerized platform of interactive visual representations of curated bioinformatics datasets from H3Africa projects, which also supports customization, individualization and adaptation features of personalized education. We obtained evidence for the positive effect size and acceptable usability of a visual analytics resource designed for the retrieval-based learning of facts on functional impacts of genomic sequence variants. We conclude that technology-enhanced personalized bioinformatics educational interventions have implications in (1) the meaningful learning of bioinformatics; (2) stimulating additional student interest in bioinformatics; and (2) improving the accessibility of bioinformatics education to non-bioinformaticians.
Color and Texture Analysis of Textiles Using Image Acquisition and Spectral Analysis in Calibrated Sphere Imaging System-I
Authors: Rout Nibedita , Baciu George , Pattanaik Priyabrata , Nakkeeran K. , Khandual Asimananda
Published: November 2022

Numerous imaging applications and analyses demand human perception, and color space transformation of device-dependent tri-band color interpretation (RGB) to device-independent CIE color space standards needs human intervention. The imaging acquisition environment, theoretical conversion errors, viewing geometry, well-defined illumination uniformity, and calibration protocols limit their precision and applicability. It is unfortunate that in most image processing applications, the spectral data are either unavailable or immeasurable. This study is based on developing a novel integrating sphere imaging system and experimentation with textiles’ controlled variation of texture and color. It proposes a simple calibration technique and describes how unique digital color signatures can be derived from calibrated RGB derivatives to extract the best features for color and texture. Additionally, an alter-ego of reflectance function, missing in the imaging domain, is suggested that could be helpful for visualization, identification, and application for qualitative and quantitative color-texture analysis. Our further investigation revealed promising colorimetric results while validating color characterization and different color combinations over three textures.
Furniture Image Classification Based on Depthwise Group Over-Parameterized Convolution
Authors: Ye Han , Zhu Xiaodong , Liu Chengyang , Yang Linlin , Wang Aili
Published: November 2022

In this paper, an improved VGG16 combined with depthwise group over-parameterized convolution (DGOVGG16) model is proposed to realize automatic furniture image classification. Firstly, depthwise over-parameterized convolution combined with group convolution is combined to construct depthwise group over-parameterized convolution, which is introduced to the VGG 16 model for reducing the number of parameters of the overall model while extracting more sufficient semantic features of furniture images. Then, this paper uses the ReLU activation function in the former part of the neural network to reduce the correlation between parameters and accelerate the weight update speed of the former part of the model. Meantime, the proposed model applies Leaky-ReLU activation function in the last layer to avoid the problem that some neurons do not update. Compared with the six furniture image classification methods based on MobileNetV2, AlexNet, ShuffleNetv2, GoogleNet, VGG 16 and GVGG16, the experimental results show the proposed DGOVGG16 with average accuracy (AA) of 95.51% has better classification performance.
A Survey of DDOS Attack Detection Techniques for IoT Systems Using BlockChain Technology
Authors: Khan Zulfiqar Ali , Namin Akbar Siami
Published: November 2022

The Internet of Things (IoT) is a network of sensors that helps collect data 24/7 without human intervention. However, the network may suffer from problems such as the low battery, heterogeneity, and connectivity issues due to the lack of standards. Even though these problems can cause several performance hiccups, security issues need immediate attention because hackers access vital personal and financial information and then misuse it. These security issues can allow hackers to hijack IoT devices and then use them to establish a Botnet to launch a Distributed Denial of Service (DDoS) attack. Blockchain technology can provide security to IoT devices by providing secure authentication using public keys. Similarly, Smart Contracts (SCs) can improve the performance of the IoT–blockchain network through automation. However, surveyed work shows that the blockchain and SCs do not provide foolproof security; sometimes, attackers defeat these security mechanisms and initiate DDoS attacks. Thus, developers and security software engineers must be aware of different techniques to detect DDoS attacks. In this survey paper, we highlight different techniques to detect DDoS attacks. The novelty of our work is to classify the DDoS detection techniques according to blockchain technology. As a result, researchers can enhance their systems by using blockchain-based support for detecting threats. In addition, we provide general information about the studied systems and their workings. However, we cannot neglect the recent surveys. To that end, we compare the state-of-the-art DDoS surveys based on their data collection techniques and the discussed DDoS attacks on the IoT subsystems. The study of different IoT subsystems tells us that DDoS attacks also impact other computing systems, such as SCs, networking devices, and power grids. Hence, our work briefly describes DDoS attacks and their impacts on the above subsystems and IoT. For instance, due to DDoS attacks, the targeted computing systems suffer delays which cause tremendous financial and utility losses to the subscribers. Hence, we discuss the impacts of DDoS attacks in the context of associated systems. Finally, we discuss Machine-Learning algorithms, performance metrics, and the underlying technology of IoT systems so that the readers can grasp the detection techniques and the attack vectors. Moreover, associated systems such as Software-Defined Networking (SDN) and Field-Programmable Gate Arrays (FPGA) are a source of good security enhancement for IoT Networks. Thus, we include a detailed discussion of future development encompassing all major IoT subsystems.
MULTforAD: Multimodal MRI Neuroimaging for Alzheimer’s Disease Detection Based on a 3D Convolution Model
Authors: Ismail Walaa N. , Rajeena P.P Fathimathul , Ali Mona A. S.
Published: November 2022

Alzheimer’s disease (AD) is a neurological disease that affects numerous people. The condition causes brain atrophy, which leads to memory loss, cognitive impairment, and death. In its early stages, Alzheimer’s disease is tricky to predict. Therefore, treatment provided at an early stage of AD is more effective and causes less damage than treatment at a later stage. Although AD is a common brain condition, it is difficult to recognize, and its classification requires a discriminative feature representation to separate similar brain patterns. Multimodal neuroimage information that combines multiple medical images can classify and diagnose AD more accurately and comprehensively. Magnetic resonance imaging (MRI) has been used for decades to assist physicians in diagnosing Alzheimer’s disease. Deep models have detected AD with high accuracy in computing-assisted imaging and diagnosis by minimizing the need for hand-crafted feature extraction from MRI images. This study proposes a multimodal image fusion method to fuse MRI neuroimages with a modular set of image preprocessing procedures to automatically fuse and convert Alzheimer’s disease neuroimaging initiative (ADNI) into the BIDS standard for classifying different MRI data of Alzheimer’s subjects from normal controls. Furthermore, a 3D convolutional neural network is used to learn generic features by capturing AlD biomarkers in the fused images, resulting in richer multimodal feature information. Finally, a conventional CNN with three classifiers, including Softmax, SVM, and RF, forecasts and classifies the extracted Alzheimer’s brain multimodal traits from a normal healthy brain. The findings reveal that the proposed method can efficiently predict AD progression by combining high-dimensional MRI characteristics from different public sources with an accuracy range from 88.7% to 99% and outperforming baseline models when applied to MRI-derived voxel features.