Search Results (79)

Background/Objectives: Predicting diseases based on the gut microbiome pattern is still difficult because of compositional shortcomings, batch heterogeneity, and scanty modeling of inter-taxon interactions. This study introduces a Dysbiosis-Aware Multiset Transformer Framework called DysbioFormer, which predicts state diseases by recognizing patterns of microbes. Methods: The current methods are mainly based on flat abundance representations or fixed-order models which limit the capability of describing intricate interactions of communities and evolutionary structure. Results: DysbioFormer is a solution to these shortcomings, in which each sample of the microbiome is modeled as a permutation-invariant multiset of taxonomic tokens with compositional, phylogenetic, and harmonized cohort data. Stacked Set Attention Blocks are used to learn relational dependencies between taxa, whereas Pooling-by-Multihead-Attention is used to aggregate global disease-level embeddings and this is not based on sequence assumptions. The model has been tested on MicrobiomeHD, which consists of a wide variety of human gut microbiome samples at a variety of disease conditions and healthy controls. Experimental results demonstrate strong diagnostic performance, achieving an accuracy of 97%, an AUC of 0.97, and an F1-score of 96%, consistently outperforming classical machine learning models under identical evaluation protocols. Attention-derived signatures also can give interpretable connections among predictive results and disease-linked microbial taxa, enhancing biological plausibility. Conclusions: The suggested architecture enables scalable, cohort-agnostic microbial diagnostics, and provides a principled route to transforming the complex information of the microbiome into reliable clinical information. DysbioFormer creates a universal basis of future microbiome-based disease screening and precision health uses. Its design allows extending towards multi-omics integration, longitudinal studies, and decision-support infrastructure, supporting microbiome-informed translational medicine in a variety of clinical research settings. Full article

Parkinson’s disease (PD) is a complex neurodegenerative disorder influenced by age, genetic predispositions, and environmental exposures, with a growing global incidence. This review aims to summarize findings from ATSDR Toxicological Profiles, EPA Risk Assessments, and other sources of peer-reviewed literature to examine the potential associations between PD and select metals, pesticides, and chlorinated organic compounds. Additionally, it explores using computational toxicology methods to elucidate the interactions between specific chemicals, associated genes, and their possible roles in PD. A total of 29 substances were identified to be neurotoxic with direct or probable association with PD. Risk of disease onset or symptom exacerbation of PD has been linked to exposures to neurodegenerative metals, pesticides, chlorinated organic compounds, and other environmental toxicants, alongside intrinsic factors such as genetic predisposition and aging. Supporting evidence from neurotoxicological studies directly or possibly associated with PD are summarized in referenced toxicological profiles and EPA risk assessments. Genotoxic endpoints evaluated in exposure-induced neurodegeneration including oxidative stress, DNA strand breaks, mitochondrial dysfunction, impaired DNA repair, and telomere alterations may play a critical role in linking environmental exposures to PD pathogenesis. Although these endpoints represent imperative data gaps between environmental and genetic risk factors for PD, isolating individual substances may not be necessary for prevention, as many co-occur at contaminated sites or within certain occupations. Further research is needed to clarify causal relationships between environmental exposure and genotoxic endpoints seen in neurodegenerative processes that can also be seen in PD for consideration in the development of preventive and therapeutic strategies. Full article

Background: MicroRNAs (miRNAs) function as post-transcriptional gene expression regulators and influence the development and progression of several cancers, yet their roles in pediatric sarcomas remain poorly defined. Methods: RNA extracted from formalin-fixed paraffin-embedded tumor tissue scrolls of 108 pediatric tumors, including 32 osteosarcoma (OS), 26 Ewing’s sarcoma (EWS), and 50 rhabdomyosarcoma (RMS) cases, were analyzed for microRNA expression using the NanoString multiplex nCounter platform that yielded information on 827 human miRNAs. The expression of candidate miRNAs was validated with in situ hybridization (miRNA-ISH) and QuPath quantification on tissue microarray slides comprising an independent set of 48 OS, 17 EWS, and 104 RMS adult and pediatric cases collectively. Results: The differential expression analysis of nCounter data identified 23 miRNAs enriched in RMS, 33 in EWS, and 45 in OS (>3 fold change and p < 0.01). miR-206 was most strongly associated (>55 fold change, p < 1 × 10⁻⁹) with RMS and demonstrated the highest sensitivity and specificity in distinguishing RMS from EWS and OS; this finding was also confirmed by miRNA-ISH. A combined signature of differentially expressed miRNAs reliably separated alveolar from embryonal RMS. The expression of miR-9-5p in EWS and miR-140-5p in OS discriminated among the different tumors and correlated with adverse patient outcome. The nCounter assay exhibited greater sensitivity than miRNA-ISH in detecting miR-206 and miR-140-5p expression. Conclusions: Collectively, these findings demonstrate that distinct miRNA profiles can differentiate pediatric sarcoma types and subtypes and offer clinically relevant insights into tumor biology, prognosis, and potential diagnostic application. Full article

The need for sustainable development, coupled with the growth in industrialization, creates a complex environment in which businesses strive to achieve and maintain a competitive advantage. Information now forms a vital part of how firms perform in today’s globalized corporate world. This paper explores the impact of information systems on sustainable organizational operations. Furthermore, it observes how IT infrastructure and information security policy (ISP) play vital roles in the changing business environment. The importance of information security culture (ISC) as a mediator in developing the association between the independent and dependent variables is also investigated. Reviewing these categories’ interactions within the context of transitional economics is the main goal. To assess and predict the impact of ISs, ISP, ITI, and ISC on sustainable organizational performance (SOP), 214 businesses took part in a structured survey. For data cleaning and reliability analysis, SPSS software was used; for mediation analysis, the Preacher and Hayes approach was applied; and, for multiple linear regression analysis, Python was applied. The study is significant for developing countries in the role of IS for the effectiveness of IT governance and strategic integration. The findings indicate that organizational performance is substantially impacted by information security policy (ISP), IT infrastructure (ITI), and information security culture (ISC). Full article

In the era of big data, cloud, internet of things, virtual communities, and interconnected networks, the prominence of multiview data is undeniable. This type of data encapsulates diverse feature components across varying perspectives, each offering unique insights into the same underlying samples. Despite being sourced from diverse settings and domains, these data serve the common purpose of describing the same samples, establishing a significant interrelation among them. Thus, there arises a necessity for the development of multiview clustering methodologies capable of leveraging the wealth of information available across multiple views. This study introduces two novel weighted multiview k-means algorithms, W-MV-KM and weighted multiview k-means using L2 regularization, W-MV-KM-L2, designed specifically for clustering multiview data. These algorithms incorporate feature weights and view weights within the k-means (KM) framework. Our approach emphasizes a weighted multiview learning strategy, which assigns varying degrees of importance to individual views. We evaluate the clustering performance of our algorithms on seven diverse benchmark datasets spanning dermatology, textual, image, and digit domains. Through extensive experimentation and comparisons with existing methods, we showcase the superior effectiveness and utility of our newly introduced W-MV-KM-L2 algorithm. Full article

This study examines copper thin films under tensile stress and their shape and percentage change in electrical resistance (PCER) as a function of applied strain. Copper films of 100 and 200 nm thickness were sputtered onto polyethylene terephthalate (PET) substrates and were then sequentially stretched to examine how film thickness affects strain-induced morphological changes and electrical resistance. A scanning electron microscope (SEM) was used to track crack patterns, and electrical resistance was monitored throughout tensile testing. Thinner films (100 nm) had quick crack initiation and propagation, leading to an increase in PCER under strain, while thicker films (200 nm) had more gradual morphological and electrical resistance changes. This differential reaction demonstrates the importance of film thickness in mechanical deformation and strain sensitivity, which could affect the design of flexible electronic devices that require mechanical durability and reliable electrical performance. These findings will help to optimize film thickness for stretchable sensors and wearable electronics to balance strain sensitivity and morphological degradation. This study will help designers and users of sensors, stretchable electronics, and other devices that require mechanical durability and electrical performance to understand the relationship between mechanical deformation and electrical properties in thin films. This paper aligns with the ninth goal of the United Nations Sustainable Development Goals, specifically Target 9.5: Enhance Research and Upgrade Industrial Technologies. Full article

There is a shortage in the experimental research directly comparing the effectiveness of different nanoparticles in boosting asparaginase (ASNase) activity. This study assessed the impact of various nanoparticles on enhancing ASNase activity, stability, and anticancer effects through immobilization. Escherichia coli ASNase was immobilized on different nanoparticles, and its efficiency was measured. The research included analyzing the enzyme’s secondary structure, stability, activity at different temperatures, kinetic parameters, shelf life, and activity in blood serum. The anticancer efficacy was determined by measuring the IC₅₀. The study also investigated the anticancer mechanisms by examining the enzyme’s toxicity on cancer cells, focusing on apoptosis indicators like nuclear intensity, membrane permeability, mitochondrial membrane permeability, and cytochrome c release. Among the tested nanoparticles, nano chitosan yielded the best improvements. ASNase immobilized on nano chitosan reached 90% immobilization efficiency fastest among the studied nanoparticles, achieving this within 72 h, whereas other nanoparticles took 120 h. Immobilization modified ASNase’s secondary structure by increasing alpha helices and reducing random coils, with nanochitosan and magnetic iron oxide showing the most pronounced effects. Immobilized ASNase exhibited enhanced activity, stability across temperature (widest with nanochitosan, 25–65 °C), and a broader optimal pH range compared to the free enzyme, with a K_m of 1.227 mM and a V_max of 454.54 U/mg protein. Notably, the nano-chitosan-immobilized ASNase retained over 85% of its activity after 9 months of storage and maintained high activity in blood serum. This improved stability and activity translated into the highest anticancer activity (Lowest IC₅₀) and was more effective than doxorubicin in disrupting cancer cell structures. Full article

The mechanical and electrical deterioration of chromium (Cr) thin films sputtered onto polyethylene terephthalate (PET) substrates under tensile strain was studied. Understanding mechanical and electrical stability due to imposed strain is particularly important for device reliability, as the demand for flexible electronic devices increases. Cr thin films, widely spread across the field of electronic and sensor applications, face crack propagation with electrical degradation with tensile stress that can seriously compromise the performance. Accordingly, this study offers new findings on how Cr film thickness might influence crack formation and electrical resistance differently and also the general guidelines for flexible electronic component design with respect to long-term durability. Electrical resistances were measured while mechanically stretching 100- and 200 nm thin sheets. The study focused on crack development and propagation mechanisms in both film thicknesses and their effects on percentage change in electrical resistance (PCER). Scanning electronic microscopy (SEM) was used to characterize surface morphology and observe cracks as the strain rose. Early crack formation in 100 nm Cr films led to rapid PCER increases due to quick crack propagation and fast electrical degradation. Thicker 200 nm films, however, showed a more gradual PCER rise with fewer but deeper cracks, indicating a regulated strain response. Unlike the sharp PCER spike in 100 nm films, 200 nm samples were more variable, with three out of four showing a slight PCER decrease at the end, hinting at partial crack repair or conductive realignment before full failure. These results underscore the role of layer thickness in managing crack propagation and electrical stability, relevant for flexible electronics and strain sensors. This paper is aligned with the ninth goal of the United Nations Sustainable Development Goals, specifically Target 9.5: Enhance Research and Upgrade Industrial Technologies. Full article

Background: During the dissolution of amorphous solid dispersion (ASD) formulations, the drug load (DL) often impacts the release mechanism and the occurrence of loss of release (LoR). The ASD/water interfacial gel layer and its specific phase behavior in connection with DL strongly dictate the release mechanism and LoR of ASDs, as reported in the literature. Thermodynamically driven liquid-liquid phase separation (LLPS) and/or drug crystallization at the interface are the key phase transformations that drive LoR. Methods: In this study, a combination of Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) thermodynamic modeling and in silico molecular simulation was applied to investigate the release mechanism and the occurrence LoR of an ASD formulation consisting of ritonavir as the active pharmaceutical ingredient (API) and the polymer, polyvinylpyrrolidone-co-vinyl acetate (PVPVA64). A thermodynamically modeled ternary phase diagram of ritonavir (PVPVA64) and water was applied to predict DL-dependent LLPS in the ASD/water interfacial gel layer. Microscopic Erosion Time Testing (METT) was used to experimentally validate the phase diagram predictions. Additionally, in silico molecular simulation was applied to provide further insights into the phase separation, the release mechanism, and aggregation behavior on a molecular level. Results: Thermodynamic modeling, molecular simulation, and experimental results were consistent and complementary, providing evidence that ASD/water interactions and phase separation are essential factors driving the dissolution behavior and LoR at 40 wt% DL of the investigated ritonavir/PVPVA64 ASD system, consistent with previous studies. Conclusions: This study provides insights into the potential of blending thermodynamic modeling, molecular simulation, and experimental research to comprehensively understand ASD formulations. Such a combined approach can be leveraged as a computational framework to gain insights into the ASD dissolution mechanism, thereby facilitating in silico screening, designing, and optimization of formulations with the benefit of significantly reducing the number of experimental tests. Full article

Nanocomposites (NCs) consisting of 4%Ag/x%WO₃/TiO₂, with varied concentrations (x = 1, 3, 5, 7 wt.%) of WO₃, were successfully synthesized using the sol-gel process to examine their photocatalytic performance. The synthesized 4%Ag/x%WO₃/TiO₂ nanopowder was characterized using X-ray diffraction (XRD), Raman spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), UV–vis diffuse reflectance spectra (UV–vis DRS), photoluminescence (PL), and Brunauer–Emmett–Teller (BET) surface area analysis to elucidate its physicochemical properties. The photocatalytic evaluation revealed that the Ag/1%WO₃/TiO₂ nanocomposite exhibits 98% photoreduction efficiency for Cr(VI) after 2 h under visible light due to the impact of the plasmonic effect of Ag atoms. In addition, the Ag/4%WO₃/TiO₂ shows about 95% photooxidation efficiency for methylene blue (MB) dye after 4 h. Full article

Hi-C sequencing is a DNA-based next-generation sequencing method that preserves the 3D genome conformation and has shown promise in detecting genomic rearrangements in translational research studies. To evaluate Hi-C as a potential clinical diagnostic platform, analytical concordance with routine laboratory testing was assessed using primary pediatric leukemia and sarcoma specimens. Archived viable and non-viable frozen leukemic cells and formalin-fixed paraffin-embedded (FFPE) tumor specimens were analyzed. Pediatric acute myeloid leukemia (AML) and alveolar rhabdomyosarcoma (A-RMS) specimens with known genomic rearrangements were subjected to Hi-C to assess analytical concordance. Subsequently, a discovery cohort consisting of AML and acute lymphoblastic leukemia (ALL) cases without known genomic rearrangements based on prior clinical diagnostic testing was evaluated to determine whether Hi-C could detect rearrangements. Using a standard sequencing depth of 50 million raw read-pairs per sample, or approximately 5X raw genomic coverage, we observed 100% concordance between Hi-C and previous clinical cytogenetic and molecular testing. In the discovery cohort, a clinically relevant gene fusion was detected in 45% of leukemia cases (5/11). This study provides an institutional proof of principle evaluation of Hi-C sequencing to medical diagnostic testing as it identified several clinically relevant rearrangements, including those that were missed by current clinical testing workflows. Full article

Background: Inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn’s disease (CD), is a complex disease with increasing global incidence and prevalence. Although dairy consumption has been linked to various chronic diseases, its relationship with IBD remains uncertain. Additionally, there is a lack of data on this topic from Arab countries. This study aimed to investigate the association between dairy consumption and IBD through a case–control study among Arab populations, followed by a meta-analysis of available studies. Method: First, we used data from 158 UC patients, 244 CD patients, and 395 controls attending a polyclinic in Riyadh, Saudi Arabia. All participants were aged ≥ 18 years. Logistic regression was used to calculate the odds ratios (ORs) and 95% confidence intervals (95% CIs) of UC and CD for individuals who reported the highest versus the lowest frequencies of dairy consumption. Next, we conducted a meta-analysis, combining our results with those from other eligible studies after searching several databases. We used the I² statistics to examine statistical heterogeneity across studies and the regression test for funnel plot asymmetry to assess publication bias. Results: The case–control study showed a negative association between frequent dairy consumption and UC (OR (95% CI) = 0.64 (0.41, 1.00)) but not CD (OR (95% CI) = 0.97 (0.65, 1.45)). In the meta-analysis, the highest frequencies of dairy consumption were negatively associated with both UC and CD: ORs (95% CIs) = 0.82 (0.68, 0.98) and 0.72 (0.59, 0.87), respectively. A moderate heterogeneity across studies was noticed in the UC meta-analysis (I² = 59.58%) and the CD meta-analysis (I² = 41.16%). No publication bias was detected. Conclusions: Frequent dairy consumption could protect against the development of UC and CD, suggesting potential dietary recommendations in the context of IBD prevention. Full article

Few researches have explored the production of pharmaceuticals from aquatic plants. Therefore, this study explored, for the first time, the phytochemical composition and bioactivities of ten aquatic plants. Aquatic plant shoots from various Nile River canals were collected, dried, and ground for aqueous extract preparation. Phytochemical composition and antioxidant capacity were assessed using DPPH assays. Extracts were tested for antiparasitic, antibacterial, anti-biofilm, and anticancer activities through standard in vitro assays, measuring IC₅₀ values, and evaluating mechanisms of action, including cell viability and high-content screening assays. The results showed that the aquatic plants were rich in pharmaceutical compounds. The antioxidant capacity of these extracts exceeded that of vitamin C. The extracts showed promising antiparasitic activity against pathogens like Opisthorchis viverrini and Plasmodium falciparum, with IC₅₀ values between 0.7 and 2.5 µg/mL. They also demonstrated low MICs against various pathogenic bacteria, causing DNA damage, increased plasma membrane permeability, and 90% biofilm inhibition. In terms of anticancer activity, extracts were effective against a panel of cancer cell lines, with Ludwigia stolonifera exhibiting the highest efficacy. Its IC₅₀ ranged from 0.5 µg/mL for pancreatic, esophageal, and colon cancer cells to 1.5 µg/mL for gastric cancer cells. Overall, IC₅₀ values for all extracts were below 6 µg/mL, showing significant apoptotic activity, increased nuclear intensity, plasma membrane permeability, mitochondrial membrane permeability, and cytochrome c release, and outperforming doxorubicin. This study highlights the potential of aquatic plants as sources for new, safe, and effective drugs with strong antiparasitic, antibacterial, and anticancer properties. Full article

Neurotrophic tyrosine receptor kinase (NTRK) has been a remarkable therapeutic target for treating different malignancies, playing an essential role in oncogenic signaling pathways. Groundbreaking trials like NAVIGATE led to the approval of NTRK inhibitors by the Food and Drug Administration (FDA) to treat different malignancies, significantly impacting current oncology treatment. Accurate detection of NTRK gene fusion becomes very important for possible targeted therapy. Various methods to detect NTRK gene fusion have been applied widely based on sensitivity, specificity, and accessibility. The utility of different tests in clinical practice is discussed in this study by providing insights into their effectiveness in targeting patients who may benefit from therapy. Widespread use of NTRK inhibitors in different malignancies could remain limited due to resistance mechanisms that cause challenges to medication efficacy in addition to common side effects of the medications. This review provides a succinct overview of the application of NTRK inhibitors in various types of cancer by emphasizing the critical clinical significance of NTRK fusion gene detection. The discussion also provides a solid foundation for understanding the current challenges and potential changes for improving the efficacy of NTRK inhibitor therapy to treat different malignancies. Full article