Search Results (1,749)

This review explores the biofilm architecture and drug resistance of Enterococcus faecalis in clinical and environmental settings. The biofilm in E. faecalis is a heterogeneous, three-dimensional, mushroom-like or multilayered structure, characteristically forming diplococci or short chains interspersed with water channels for nutrient exchange and waste removal. Exopolysaccharides, proteins, lipids, and extracellular DNA create a protective matrix. Persister cells within the biofilm contribute to antibiotic resistance and survival. The heterogeneous architecture of the E. faecalis biofilm contains both dense clusters and loosely packed regions that vary in thickness, ranging from 10 to 100 µm, depending on the environmental conditions. The pathogenicity of the E. faecalis biofilm is mediated through complex interactions between genes and virulence factors such as DNA release, cytolysin, pili, secreted antigen A, and microbial surface components that recognize adhesive matrix molecules, often involving a key protein called enterococcal surface protein (Esp). Clinically, it is implicated in a range of nosocomial infections, including urinary tract infections, endocarditis, and surgical wound infections. The biofilm serves as a nidus for bacterial dissemination and as a reservoir for antimicrobial resistance. The effectiveness of first-line antibiotics (ampicillin, vancomycin, and aminoglycosides) is diminished due to reduced penetration, altered metabolism, increased tolerance, and intrinsic and acquired resistance. Alternative strategies for biofilm disruption, such as combination therapy (ampicillin with aminoglycosides), as well as newer approaches, including antimicrobial peptides, quorum-sensing inhibitors, and biofilm-disrupting agents (DNase or dispersin B), are also being explored to improve treatment outcomes. Environmentally, E. faecalis biofilms contribute to contamination in water systems, food production facilities, and healthcare environments. They persist in harsh conditions, facilitating the spread of multidrug-resistant strains and increasing the risk of transmission to humans and animals. Therefore, understanding the biofilm architecture and drug resistance is essential for developing effective strategies to mitigate their clinical and environmental impact. Full article

A multi-step method introduced by Raziyeh and Masoud for solving nonlinear systems with convergence order five has been considered in this paper. The convergence of the method was studied using Taylor series expansion, which requires the function to be six times differentiable. However, our convergence study does not depend on the Taylor series. We use the derivative of F up to two only in our convergence analysis, which is presented in a more general Banach space setting. Semi-local analysis is also discussed, which was not given in earlier studies. Unlike in earlier studies (where two sets of assumptions were used), we used the same set of assumptions for semi-local analysis and local convergence analysis. We discussed the dynamics of the method and also gave some numerical examples to illustrate theoretical findings. Full article

Network connectivity and area coverage are the most important aspects in the applications of wireless sensor networks (WSNs). The resource and energy constraints of sensor nodes, operational conditions, and network size pose challenges to the optimal coverage of targets in the region of interest (ROI). The main idea is to achieve maximum area coverage and connectivity with strategic deployment and the minimal number of sensor nodes. This work addresses the problem of network area coverage in randomly distributed WSNs and provides an efficient deployment strategy using an enhanced version of cuckoo search optimization (ECSO). The “sequential update evaluation” mechanism is used to mitigate the dependency among dimensions and provide highly accurate solutions, particularly during the local search phase. During the preference random walk phase of conventional CSO, particle swarm optimization (PSO) with adaptive inertia weights is defined to accelerate the local search capabilities. The “opposition-based learning (OBL)” strategy is applied to ensure high-quality initial solutions that help to enhance the balance between exploration and exploitation. By considering the opposite of current solutions to expand the search space, we achieve higher convergence speed and population diversity. The performance of ECSO-OBL is evaluated using eight benchmark functions, and the results of three cases are compared with the existing methods. The proposed method enhances network coverage with a non-uniform distribution of sensor nodes and attempts to cover the whole ROI with a minimal number of sensor nodes. In a WSN with a 100 m² area, we achieved a maximum coverage rate of 98.45% and algorithm convergence in 143 iterations, and the execution time was limited to 2.85 s. The simulation results of various cases prove the higher efficiency of the ECSO-OBL method in terms of network coverage and connectivity in WSNs compared with existing state-of-the-art works. Full article

Background/Objectives: Additive manufacturing (AM) technology has emerged as an innovative approach in dentistry. Recently, manufacturers have developed permanent resins engineered explicitly for the fabrication of definitive prostheses using AM techniques. This systematic review evaluated the mechanical and physical properties of 3D-printed permanent resins in comparison to milled resins and hybrid ceramics for the fabrication of indirect dental restorations. Methods: Three electronic databases—Scopus, Web of Science, and PubMed—were searched for English-language articles. Two independent researchers conducted study selection, data extraction, quality assessment, and the evaluation of the certainty of evidence. In vitro studies assessing the mechanical and physical properties of the permanent resins were included in this review. Results: A total of 1779 articles were identified through electronic databases. Following full-text screening and eligibility assessment, 13 studies published between 2023 and 2024 were included in this qualitative review. The investigated outcomes included physical properties (surface roughness, color changes, water sorption/solubility) and mechanical properties (flexural strength, elastic modulus, microhardness). Conclusions: Three-dimensionally printed permanent resins show promising potential for fabricating indirect dental restorations. However, the current evidence regarding their mechanical and physical properties remain limited and inconsistent, mainly due to variability in study methodologies. Full article

Introduction: Congestive heart failure (CHF), a complex clinical syndrome characterized by the heart’s inability to pump blood effectively due to structural or functional impairments, is a growing public health concern, with profound implications for patients’ physical and emotional well-being. In India, the burden of CHF is rising due to aging demographics and increasing prevalence of lifestyle-related risk factors. Among the subtypes of CHF, heart failure with preserved ejection fraction (HFpEF), i.e., heart failure with left ventricular ejection fraction of ≥50% with evidence of spontaneous or provokable increased left ventricular filling pressure, and heart failure with reduced ejection fraction (HFrEF), i.e., heart failure with left ventricular ejection fraction of 40% or less and is accompanied by progressive left ventricular dilatation and adverse cardiac remodeling, may present differing impacts on health-related quality of life (HRQoL), i.e., an individual’s or a group’s perceived physical and mental health over time, yet comparative data remains limited. This study assesses HRQoL among CHF patients using the Minnesota Living with Heart Failure Questionnaire (MLHFQ), one of the most widely used health-related quality of life questionnaires for patients with heart failure based on physical and emotional dimensions and identifies sociodemographic and clinical variables influencing these outcomes. Methods: A cross-sectional analytical study was conducted among 233 CHF patients receiving inpatient and outpatient care at the Department of Cardiology at a quaternary care teaching hospital in coastal Karnataka in India. Participants were enrolled using convenience sampling. HRQoL was evaluated through the MLHFQ, while sociodemographic and clinical characteristics were recorded via a structured proforma. Statistical analyses included descriptive measures, independent t-test, Spearman’s correlation and stepwise multivariable linear regression to identify associations and predictors. Results: The mean HRQoL score was 56.5 ± 6.05, reflecting a moderate to high symptom burden. Patients with HFpEF reported significantly worse HRQoL (mean score: 61.4 ± 3.94) than those with HFrEF (52.9 ± 4.64; p < 0.001, Cohen’s d = 1.95). A significant positive correlation was observed between HRQoL scores and age (r = 0.428; p < 0.001), indicating that older individuals experienced a higher burden of symptoms. HRQoL also varied significantly across NYHA functional classes (χ² = 69.9, p < 0.001, ε² = 0.301) and employment groups (χ² = 17.0, p < 0.001), with further differences noted by education level, gender and marital status (p < 0.05). Multivariable linear regression identified age (B = 0.311, p < 0.001) and gender (B = –4.591, p < 0.001) as significant predictors of poorer HRQoL. Discussion: The findings indicate that patients with HFpEF experience significantly poorer HRQoL than those with HFrEF. Older adults and female patients reported greater symptom burden, underscoring the importance of demographic-sensitive care approaches. These results highlight the need for routine integration of HRQoL assessment into clinical practice and the development of comprehensive, personalized interventions addressing both physical and emotional health dimensions, especially for vulnerable subgroups. Conclusions: CHF patients, especially those with HFpEF, face reduced HRQoL. Key factors include age, gender, education, employment, marital status, and NYHA class, underscoring the need for patient-centered care. Full article

This study involved the fabrication of poly (vinyl alcohol) (PVA) nanocomposite films using the solution-casting process, which incorporated strontium-coated silver oxide (Sr-Ag₂O) nanoparticles generated by a plant-extract assisted method. Various characterization techniques, such as XRD, SEM, TEM, UV, and FTIR, showed the formation and uniform distribution of Sr-Ag₂O nanoparticles in the PVA film, which are biocompatible nanocomposite films. The presence of hydroxyl groups leads to appreciable mixing and interaction between the Sr-Ag₂O nanoparticles and the PVA polymer. Mechanical and thermal results suggest enhanced tensile strength and increased thermal stability. In addition, the sample of PVA/Sr-Ag₂O (1.94/0.06 wt. ratio) nanocomposite film showed decreased hydrophilicity, lower hemolysis, non-toxicity, and appreciable cell migration activity, with nearly 19.95% cell migration compared to the standard drug, and the presence of Sr-Ag₂O nanoparticles favored the adhesion and spreading of cells, which triggered the reduction in the gaps. These research findings suggest that PVA/Sr-Ag₂O nanocomposite films with good mechanical, antimicrobial, non-toxic, and biocompatible properties could be applied in biological wound-healing applications. Full article

Background/Objectives: A nanostructured membrane of polycaprolactone (a synthetic polymer) was synthesized using an electrospinning technique aiming to enhance its hydrophilicity and rate of degradation by surface modification via aminolysis. Since polycaprolactone nanofibrous films are naturally hydrophobic and with slow degradation, which restricts their use in biological systems, amino groups were added to the fiber surface using the aminolysis technique, greatly increasing the wettability of the membranes. Methods: Polycaprolactone nanofibrous membranes were synthesized via the electrospinning technique and surface modification by aminolysis. Trypsin, pepsin, and pancreatin were conjugated onto the aminolyzed PNF surface to further strengthen biocompatibility by enhancing the hydrophilicity, porosity, and biodegradation rate. SEM, FTIR, EDX, and liquid displacement method were performed to investigate proteolytic efficiency and morphological and physical characteristics such as hydrophilicity, porosity, and degradation rates. Results: Enzyme activity tests, which showed a zone of clearance, validated the successful enzyme conjugation and stability over a wide range of pH and temperatures. Scanning electron microscopy (SEM) confirms the smooth morphology of nanofibers with diameters ranging from 150 to 950 nm. Fourier transform infrared spectroscopy (FTIR) revealed the presence of O–H, C–O, C=O, C–N, C–H, and O–H functional groups. Energy-dispersive X-ray (EDX) elemental analysis indicates the presence of carbon, oxygen, and nitrogen atoms owing to the presence of peptide and amide bonds. The liquid displacement technique and contact angle proved that Pepsin-PNFs possess notably increased porosity (88.50% ± 0.31%) and hydrophilicity (57.6° ± 2.3 (L), 57.9° ± 2.5 (R)), respectively. Pancreatin-PNFs demonstrated enhanced enzyme activity and degradation rate on day 28 (34.61%). Conclusions: These enzyme-conjugated PNFs thus show improvements in physicochemical properties, making them ideal candidates for various biomedical applications. Future studies must aim for optimization of enzyme conjugation and in vitro and in vivo performance to investigate the versatility of these scaffolds. Full article

This study presents a multi-modal investigation into the wear behavior of bio-based epoxy composites reinforced with multi-walled carbon nanotubes (MWCNTs) at 0–0.75 wt%. A Taguchi L16 orthogonal array was employed to systematically assess the influence of MWCNT content, load (20–50 N), and sliding speed (1–2.5 m/s) on wear rate (WR), coefficient of friction (COF), and surface roughness (Ra). Statistical analysis revealed that MWCNT content contributed up to 85.35% to wear reduction, with 0.5 wt% identified as the optimal reinforcement level, achieving the lowest WR (3.1 mm³/N·m) and Ra (0.7 µm). Complementary morphological characterization via SEM and AFM confirmed microstructural improvements at optimal loading and identified degradation features (ploughing, agglomeration) at 0 wt% and 0.75 wt%. Regression models (R² > 0.95) effectively captured the nonlinear wear response, while a Random Forest model trained on GLCM-derived image features (e.g., correlation, entropy) yielded WR prediction accuracy of R² ≈ 0.93. Key image-based predictors were found to correlate strongly with measured tribological metrics, validating the integration of surface texture analysis into predictive modeling. This integrated framework combining experimental design, mathematical modeling, and image-based machine learning offers a robust pathway for designing high-performance, sustainable nanocomposites with data-driven diagnostics for wear prediction. Full article

Background/Objectives: Fatty acids, the building blocks of lipids, contribute to numerous crucial life processes and are implicated in numerous disease pathologies. Circulating fatty acids can be extracted/trans-esterified to their respective methyl ester forms and quantified from a variety of biological samples. This study aims to identify quantifiable fatty acids (through alkali trans-esterification) in human circulation, assess the correlation of the detectable fatty acid methyl esters (FAMEs) compounds between whole blood, serum and plasma matrices and propose the most ideal matrix for quantification of FAMEs. Methods: This anonymised study was carried out in a tertiary hospital after obtaining ethical approval and involved analysis of residual fasting whole blood, serum and plasma samples obtained from 20 apparently healthy subjects attending the routine health check services at the study centre. Fatty acids were converted to its methyl ester form by methanolic KOH trans-esterification and subjected to GCMS analysis. Paired t test, Pearsons’s correlation, linear regression and Bland Altman test were employed to assess the agreeability between matrices. Results: 9 out of 37 FAME compounds were detected in all three matrices. Strong correlations and statistically significant regression equations were obtained for the 9 compounds between plasma and serum matrices. Undecanoate, pentadecanoate, linolenate, and palmitate levels were lowest in plasma, while stearate, heptadecanoate levels were highest in whole blood. Myristate was highest in serum, dodecanoate was highest in plasma while docosahexanoate was found to be comparable in all three matrices. Methyl ester forms of dodeconate, myristate, pentadecanoate, palmitate, heptadecanoate, stearate, and linolenate were observed in higher concentrations in plasma when compared to serum. Conclusions: The current study shows similar & correlating FAME concentrations between serum and plasma matrix; however, whole blood FAME concentrations appear significantly different. Plasma serves as the most ideal matrix for detection and quantification of circulating fatty acids. Full article

Snakebite remains the most neglected tropical disease globally, with India experiencing the highest rates of mortality and morbidity. While most envenomation cases in India are attributed to the ‘big four’ snakes, research has predominantly focused on Russell’s viper (Daboia russelii), spectacled cobra (Naja naja), and common krait (Bungarus caeruleus), leading to a considerable gap in our understanding of saw-scaled viper (Echis carinatus carinatus) venoms. For instance, the venom gland transcriptome and inter- and intra-population venom variation in E. c. carinatus have largely remained uninvestigated. A single study to date has assessed the effectiveness of commercial antivenoms against this species under in vivo conditions. To address these crucial knowledge gaps, we conducted a detailed investigation of E. c. carinatus venom and reported the first venom gland transcriptome. A proteotranscriptomic evaluation revealed snake venom metalloproteinases, C-type lectins, L-amino acid oxidases, phospholipase A₂s, and snake venom serine proteases as the major toxins. Moreover, we assessed the intra-population venom variation in this species using an array of biochemical analyses. Finally, we determined the venom toxicity and the neutralising efficacy of a commercial antivenom using a murine model of snake envenoming. Our results provide a thorough molecular and functional profile of E. c. carinatus venom. Full article

Background/Objectives:Titanium implant surface modifications enhance osseointegration and prevent microbial colonization, improving implant longevity. Antimicrobial coatings, particularly cerium- and bismuth-doped hydroxyapatite (CeHAp and BiHAp), have gained attention for reducing infection-related complications. This study evaluates the antimicrobial activity of CeHAp and BiHAp coatings on CpTi compared to untreated CpTi in artificial saliva at pH levels of 4.5, 6.5, and 8. Methods: Antibacterial efficacy against Staphylococcus aureus (S. aureus), Escherichia coli (E. coli), and Candida albicans (C. albicans) was assessed using the broth dilution method. Titanium rods coated with test compounds were incubated in inoculated nutrient broth, and microbial inhibition was determined via optical density at 600 nm. A statistical analysis was performed using the Kruskal–Wallis ANOVA test, the median and Interquartile Range were determined for the variables, and a Dwass–Steel–Critchlow–Fligner intergroup pairwise comparison was conducted. Results: The results showed that both the CeHAp and BiHAp coatings demonstrated significant antimicrobial activity against S. aureus (OD = 0.01) at pH 6.5, which was more pronounced than the activity observed against E. coli (OD = 0.05), with the difference being statistically significant (p = 0.001). The least antimicrobial activity was observed against C. albicans (0.21) at pH 8 (p = 0.001). Conclusion: These findings highlight the pH-dependent effectiveness of BiHAp and CeHAp coatings in inhibiting microbial growth. Their application on titanium implants may enhance antimicrobial properties, contributing to improved dental implant success and broader biomedical applications. Full article

Background/Objectives: Dental cavity preparation is a critical procedure in restorative dentistry that involves the removal of decayed tissue while preserving a healthy tooth structure. Excessive stress during tooth preparation leads to enamel cracking, dentin damage, and long term compressive pulp health. This study employed finite element analysis (FEA) to investigate the stress distribution in dental structures during cavity preparation using round diamond burs of varying diameters and depths of cut (DOC). Methods: A three-dimensional human maxillary first molar was generated from computed tomography (CT) scan data using 3D Slicer, Fusion 360, and ANSYS Space Claim 2024 R-2. Finite element analysis (FEA) was conducted using ANSYS Workbench 2024. Round diamond burs with diameters of 1, 2, and 3 mm were modeled. Cutting simulations were performed for DOC of 1 mm and 2 mm. The burs were treated as rigid bodies, whereas the dental structures were modeled as deformable bodies using the Cowper–Symonds model. Results: The simulations revealed that larger bur diameters and deeper cuts led to higher stress magnitudes, particularly in the enamel and dentin. The maximum von Mises stress was reached at 136.98 MPa, and dentin 140.33 MPa. Smaller burs (≤2 mm) and lower depths of cut (≤1 mm) produced lower stress values and were optimal for minimizing dental structural damage. Pulpal stress remained low but showed an increasing trend with increased DOC and bur size. Conclusions: This study provides clinically relevant guidance for reducing mechanical damage during cavity preparation by recommending the use of smaller burs and controlled cutting depths. The originality of this study lies in its integration of CT-based anatomy with dynamic FEA modeling, enabling a realistic simulation of tool–tissue interaction in dentistry. These insights can inform bur selection, cutting protocols, and future experimental validations. Full article

This paper focuses on the influence of torsional loading on the vibration-based unbalance fault diagnosis technology under variable-speed conditions. The coupled flexural–torsional nonstationary governing equations of motion are obtained and solved numerically. Taking the short-time chirp Fourier transform from the acceleration signal, which is determined from the numerical solutions, the influence of variable loading on the magnitude of the fundamental rotational harmonic—a diagnostic feature for conventional unbalance diagnosis technology—as well as its speed-invariant version for novel unbalance diagnosis technology is assessed. Numerical assessment shows that despite the stationary conditions, where the first rotational harmonic magnitude is independent from the torsional load, the conventional unbalance technology depends on the variable torsional load. However, the novel speed-invariant diagnostic technology is independent of the variable torsional load. The dependency of the conventional unbalance fault diagnosis technology on the variable torsional load and the independency of the novel speed-invariant unbalance diagnostic technology on the variable loading are justified by performing thorough experimental investigations on a variable-speed wind turbine with a permissible level of unbalance. Full article

Major depressive disorder is a mental illness characterized by persistent sadness or loss of interest that affects a person’s daily life. Early detection of this disorder is crucial for providing timely and effective treatment. Neuroimaging modalities, namely, functional magnetic resonance imaging, can be used to identify changes in brain regions related to major depressive disorder. In this study, regional homogeneity images, one of the derivative of functional magnetic resonance imaging is employed to detect major depressive disorder using the proposed feature/sample evolving voting ensemble approach. A total of 2380 subjects consisting of 1104 healthy controls and 1276 patients with major depressive disorder from Rest-meta-MDD consortium are studied. Regional homogeneity features from 90 regions are extracted using automated anatomical labeling template. These regional homogeneity features are then fed as an input to the proposed feature/sample selective evolving voting ensemble for classification. The proposed approach achieves an accuracy of 91.93%, and discriminative features obtained from the classifier are used to identify brain regions which may be responsible for major depressive disorder. A total of nine brain regions, namely, left superior temporal gyrus, left postcentral gyrus, left anterior cingulate gyrus, right inferior parietal lobule, right superior medial frontal gyrus, left lingual gyrus, right putamen, left fusiform gyrus, and left middle temporal gyrus, are identified. This study clearly indicates that these brain regions play a critical role in detecting major depressive disorder. Full article

This study introduces a novel reflective–formative hierarchical model specification for the classic push–pull tourist motivation construct, aligning its measurement with the theoretical distinction between intrinsic “push” drives and external “pull” attributes. Unlike the traditional reflective-reflective structuring of tourist motivation we defied the higher order factors (novelty, knowledge and facilities as formative. Using partial least squares structural equation modeling (PLS-SEM) on a purposive sample of 319 international tourists, we empirically validate the reflective–formative (reflective first-order, formative second-order) model. The reflective–formative model showed a superior fit and predictive power: it explained substantially more variance in key outcome constructs (social motives (R² = 53.60) and self-actualization (R² = 23.10)) than the traditional reflective–reflective specification (social motives (R² = 49.30) and self-actualization (R² = 21.70)), which is consistent with best-practice guidelines for theoretically grounded models. In contrast, the incorrectly specified reflective–reflective model showed stronger effects between unrelated constructs, supporting concerns that choosing the wrong type of measurement model can lead to incorrect conclusions. By reconciling the push–pull theory with measurement design, this work’s main contributions are a theoretically justified reflective–formative model for tourist motivation, and evidence of its empirical benefits. These findings highlight a methodological innovation in motivation modeling and underscore that modeling push–pull motives formatively yields more accurate insights for theory and practice. Full article