Recent publications
Pseudopregnancy is the development of signs of pregnancy in the absence of an embryo or fetus. The objectives
of this study were to characterize pseudopregnancy in dromedary camels, determine its prevalence in camel
farms and practice, identify associated risk factors, and describe its clinical and hormonal properties. The
prevalence of pseudopregnancy on 100 camel farms with 4264 total female camels was determined to be 2.07 %
(86/4264) overall, while the rate among infertile animals was 17.68 % (1385/7833). The genital tracts of 58
pseudopregnant female camels were examined, and their breeding histories were examined. Serum concentrations
of estradiol-17 β (E2), progesterone (P4), and prolactin (PRL) in these animals were assessed. Five cyclic
camels and five in early pregnancy were used as control subjects. Signs of pseudopregnancy included being
anestrous, refusing to mate, and adopting a stiffened posture—with the head held high and the tail cocked—when
approached by a male. Normal pregnancy-associated mammary and abdominal changes were absent. Risk
factors associated with pseudopregnancy included age (odds ratio [OR] = 21.63, P = 0.0001) and a history of
reproductive disorders (OR = 4.155, P = 0.042). Based on their P4 levels, the pseudopregnant camels were
classified as either typical (high P4, 16/58, 27.59 %) or atypical (low P4, 42/58, 72.41 %). The main clinical
findings in the camels with typical pseudopregnancies were a narrow/closed cervix (56.25 %), clinical endometritis
(CE; 43.75 %), and pyometra (31.25 %), while those with atypical pseudopregnancies exhibited CE (50
%) and luteinized follicles (43.1 %). The pregnant camels had significantly (P = 0.0001) higher serum P4
concentrations (2.44 ± 0.32 ng/mL) than the pseudopregnant (0.68 ± 0.12 ng/mL) and cyclic camels (0.16 ±
0.01 ng/mL). Serum E2 levels did not differ significantly among the pseudopregnant (43.2 ± 1.05 pg/mL),
pregnant (47.72 ± 4.06 pg/mL), and cyclic (40.72 ± 1.03 pg/mL) camels. The pregnant camels had a significantly
(P = 0.04) higher average serum PRL concentration (3.61 ± 0.45 ng/mL) than the pseudopregnant (2.77
± 0.12 ng/mL) and cyclic camels (2.18 ± 0.11). In conclusion, pseudopregnancy in camels exhibits the same
external signs that characterize pregnancy, but pseudopregnancy involves an absence of edema of the udder,
milk production, and high PRL levels. We propose a division of pseudopregnant camels into typical and atypical
classes, depending on whether they have high P4 levels. Pseudopregnancy is associated with a high incidence of
other reproductive disorders, particularly in older camels.
Aim
To assess and compare subclinical alterations in superficial capillary plexus vessel density (SCPVD) and retinal layers thickness in the macular region between individuals with type 2 diabetes mellitus (DM) and healthy controls.
Methods
Swept-source OCT images were obtained from 29 control subjects and 24 diabetic subjects. Macular thickness (MT), retinal nerve fiber layer (RNFL) thickness, and ganglion cell layer (GCL) thickness were measured in the central macula and four quadrants of macular region using a 6.0 × 6.0 mm radial macular scan centered on the fovea. OCTA acquisition included a 3.0 × 3.0 mm macular scan for the foveal avascular zone (FAZ) and a 4.5 × 4.5 mm macular scan for SCPVD. The FAZ was manually mapped at the SCP on OCTA images.
Results
In diabetic subjects, the superficial capillary plexus vessel density (SCPVD) was significantly lower in both the central (P = 0.04) and inferior (P = 0.01) regions compared to the control group. Additionally, diabetic patients showed a significant reduction in temporal macular thickness (MT) and thinning of the ganglion cell layer (GCL) in all three quadrants except in the central and inferior macula (P < 0.05). There was also significant thinning of the superior macular retinal nerve fiber layer (RNFL) in diabetics compared to controls (P = 0.02). While the foveal avascular zone (FAZ) was larger in diabetic subjects, this difference was not statistically significant (P = 0.78). Duration of diabetes has shown a significantly high positive correlation (r = 0.77, P < 0.01) with superior macular VD.
Conclusion
The findings of this study suggest that the diabetic macula experiences significant ganglion cell layer (GCL) thinning and reduced superficial capillary plexus (SCP) vascular density even before the onset of clinical retinopathy. Swept-source OCT proves to be an essential tool for detecting these early changes in diabetic patients.
This study evaluated the microleakage in zirconia crowns cemented with bioactive vs resin cements at two margin locations: cementum/dentin deep margin and composite-elevated margins. Standardized mesial box cavities were prepared in 30 molar teeth, with proximal cavosurface margins placed 1 mm below the cemento-enamel junction (CEJ) and restored with resin composite. The teeth were prepared for zirconia crowns, with mesial margins on the composite and distal margins on tooth structure 1 mm below the CEJ. Following digitization and zirconia crown fabrication, the specimens were randomly allocated into five groups based on the type of cement used: one multistep adhesive resin, one self-adhesive resin, one bioactive hybrid ionic resin, and two bioceramic cements. Microleakage was evaluated by measuring the percentage of dye penetration depth at the interfaces, with data analyzed using two-way ANOVA. The results revealed a significant interaction between cement type and margin location, with elevated margins exhibiting less leakage than deep ones across all cement types (p≤0.001). However, the effect of margin location on microleakage varied depending on the cement type, with variations in microleakage scores at each margin location ranging from statistically nonsignificant (p>0.05) to statistically significant (p≤0.05). Adhesive resin and hybrid bioactive cements significantly outperformed others in reducing microleakage at both margin interfaces.
A series of zinc manganese lithium titanate nanoparticles doped with cerium (Ce) was successfully prepared using the sol-gel technique. The study employed X-ray diffraction (XRD), transmission electron microscopy (TEM), diffuse reflectance, and dielectric spectroscopies to identify nanoparticles and investigate the crystalline structure, dielectric properties, and electrochemical behavior of Zn3Mn0.5Li0.2Ti4−xCexO12 with different cerium concentrations (x = 0.0, 0.2, 0.6, and 1 mol%). The spherical-nanoparticles were produced by the sol-gel technique and calcinated at 700 °C for 4 h. The optical properties of Z Zn3Mn0.5Li0.2Ti4O12 co-doped with CeO₂ were analyzed using diffuse reflectance spectroscopy. The variation in the absorption edge with different CeO₂ content indicates changes in the material’s band gap and electronic structure. The impact of Ce³⁺ on the dielectric properties was also investigated. The improvement in electrochemical performance is attributed to internal rearrangements within the Zn3Mn0.5Li0.2Ti4O12 nanostructure, driven by the presence of Ce³⁺ ions. The capacitance of Zn3Mn0.5Li0.2Ti4O12 ranges from 41.58 to 38.28 F·g⁻¹ with varying the Ce³⁺ concentration from 0 to 1 mol% at a scan rate of 10 mV·s⁻¹. Additionally, EIS highlights the potential of these nanoceramics for energy storage applications. These findings supply priceless insights into how Ce co-doping affects the suitability of these nanostructures for electronic devices, solar cells, and energy storage implementations.
Herein, the magnetic ZVCo-MIL-88 A(Fe)@β-CD composite was fabricated via post-synthetic decoration of MIL-88 A(Fe)@β-CD with ZVCo to produce a magnetic efficient adsorbent for Cr(VI) removal. The experimental findings denoted that the ZVCo decoration boosted the adsorption capability of MIL-88 A(Fe)@β-CD, where the adsorption % of Cr(VI) improved from 73.07 to 94.02% after its decoration with 10 wt% of ZVCo. Furthermore, the ZVCo decoration ameliorated the recycling feature of MIL-88 A(Fe)@β-CD since the removal % of Cr(VI) by MIL-88 A(Fe)@β-CD and ZVCo-MIL-88 A(Fe)@β-CD reached 27.24 and 84.98%, respectively. The optimization experiments of the Cr(VI) ions clarified that the higher adsorption % fulfilled 94.02% at pH = 3, using ZVCo-MIL-88 A(Fe)@β-CD dosage = 0.5 g/L, Cr(VI) concentration = 50 mg/L, and at room temperature. Notably, the concentration of the adsorbed Cr(VI) brings off the equilibrium stage within an hour, implying the fast adsorption property of ZVCo-MIL-88 A(Fe)@β-CD. The kinetic and isotherms assessments denoted the contribution of the physical and chemical adsorption pathways in adsorbing the Cr(VI) species onto ZVCo-MIL-88 A(Fe)@β-CD. In addition, the XPS spectra and zeta potential results supposed that the process inside the Cr(VI)/ZVCo-MIL-88 A(Fe)@β-CD system proceeded through reduction reaction, coordination bonds, electrostatic interactions, and pore-filling mechanisms.
Supplementary Information
The online version contains supplementary material available at 10.1038/s41598-025-88259-y.
In the era of widespread misinformation, detecting fake news has become a crucial challenge, particularly on social media platforms. This paper introduces an optimized approach for Fake News Detection, combining BERT and GloVe embeddings with Principal Component Analysis (PCA) and attention mechanisms, enriched by social and temporal features for more effective text representation. Leveraging the CIC Truth Seeker Dataset 2023, we applied SHAP for feature selection and interpretability, ensuring transparency in the model’s predictions. Our methodology achieved a remarkable accuracy of 99.9% using a Random Forest classifier, showcasing the efficacy of this optimized hybrid approach. The integration of interpretability techniques such as LIME and SHAP provides deeper insights into the model’s decisions, making it a reliable tool for combating misinformation. This novel approach offers a robust and transparent solution to the growing threat of fake news, contributing significantly to the integrity of online information and public discourse on platforms like Twitter X.
This study entails a new technique, based on machine learning algorithms, for predicting the strain-stress behavior of Nitinol alloys. The study utilizes Orange data mining software to determine if algorithms like Linear Regression, Random Forest, k-nearest neighbors, and decision tree are related to the effectiveness. The nitinol-based alloy, which has both shape memory and superelasticity, is used in a wide range of biomedical devices, aerospace constructions, and automated apparatus. Temperature variation is a main factor affecting the Nitinol behavior. It is therefore required to carry out an in-depth analysis of strain-stress patterns. Employing machine learning algorithms along with data mining tools allows the exact prediction of the Nitinol alloy performance under any given condition. The research is purposed to establish the relationship between temperature and mechanical strength of Nitinol by analyzing strain-stress curves that were obtained from tensile tests carried out at different temperatures. Looking at overall results, kNN has the highest accuracy of the models upon comparing MSE and RMSE, and has higher R² and, therefore stands as the model of high reliability. Results show that machine learning algorithms successfully forecast the mechanical responses of Nitinol under temperature variations, which assists in determining their formability properties and hence, advancing Nitinol applications in various fields. This research demonstrates machine learning uses in developing materials science and engineering knowledge by showing the ability to predict Nitinol alloy behavior, which includes strain-stress characteristics at low temperatures. Furthermore, the research's significance lies in addressing ethical considerations and practical issues, such as the necessity of implementing an organized approach to achieve environmental benefits.
Global climate change has intensified the search for renewable energy sources. Solar power is a cost-effective option for electricity generation. Accurate energy forecasting is crucial for efficient planning. While various techniques have been introduced for energy forecasting, transformer-based models are effective for capturing long-range dependencies in data. This study proposes N hours-ahead solar irradiance forecasting framework based on variational mode decomposition (VMD) for handling meteorological data and a modified temporal fusion transformer (TFT) for forecasting solar irradiance. The proposed model decomposes raw solar irradiance sequences into intrinsic mode functions (IMFs) using VMD and optimizes the TFT using a variable screening network and a gated recurrent unit (GRU)-based encoder–decoder. Our study specifically targets the 1-h as well as different forecasting horizons for solar irradiance. The resulting deep learning model offers insights, including the prioritization of solar irradiance subsequences and an analysis of various forecasting window sizes. An empirical study shows that our proposed method has achieved high performance compared to other time series models, such as artificial neural network (ANN), long short-term memory (LSTM), CNN–LSTM, CNN–LSTM with temporal attention (CNN–LSTM-t), transformer, and the original TFT model.
This paper is devoted to the problem of existence of positive weak solutions for a class of fractional Kirchhoff-type systems with multiple parameters. By using the sub-supersolution method and some analysis techniques, we prove a new existence result. To the best of our knowledge, our results are new in the study of fractional Kirchhoff-type systems.
This paper aims to provide new versions of some known inequalities by applying Caputo fractional derivatives. Ostrowski, Hermite-Hadamard and Ostrowski-Grüss-type inequalities are given. Generalized conditions of existing inequalities are analysed to get new inequalities. Special cases are discussed to prove the general nature of former inequalities.
This work investigates positive solutions for equations involving the anisotropic variable exponent operator, a mathematical framework critical in modeling nonstandard growth phenomena. By deriving new existence results, the study advances the understanding of nonlinear partial differential equations in anisotropic and variable exponent settings. These findings have significant implications for real-world applications, such as fluid dynamics, image processing, and material science, where variable growth conditions arise naturally. More specifically, the study employs methods from functional analysis to investigate the existence, nonexistence, and minimal solutions of certain anisotropic equations within the framework of generalized Sobolev spaces.
Objective
Academic stressors among adolescents, strongly associated with emotional disturbance, increase the chance of psychiatric problems, and their severity increases over time when emotional and educational issues remain unresolved. The present study is designed to investigate the impact of cognitive behavioural therapy (CBT) on procrastination, burnout, self-handicapping behaviour, test anxiety, and school functioning among adolescents facing academic problems.
Materials and methods
A total of 200 adolescents were enrolled for eligibility assessment; 129 participants met the eligibility criteria, and they were allocated to the experimental and waitlist control conditions. After the baseline assessment, participants were involved in the treatment condition, and after 6–8 follow-up sessions, they were referred for the post-assessment. We used different assessment measures to assess the outcome, i.e. General Procrastination Scale (GPS), Maslach Burnout Inventory-Student Survey (MBI-SS), Self-handicapping Scale (SHS), Test Anxiety Inventory (TAI), and Living up to Parental Expectation Scale (LPES). Repeated measures ANOVA was used to analyze the results.
Results
The current RCT findings suggest that CBT was found an effective treatment condition to address the emotional problems among adolescents. CBT significantly reduced the degree of procrastination {F (1, 63) = 25.01, p < .000, η² = 0.29} academic burnout {F (1, 63) = 11.08, p < .000, η² = 0.16}, test anxiety {F(1, 63) = 88.17, p < .000, η² = 0.59}, and self-handicapping {F (3, 56) = 10.17, p < .000, η² = 0.16} among adolescents. CBT also significantly helped the students to manage parental unrealistic expectations through providing relationship skills and training {F (3,56) = 546.46, p < .001, η² = 0.89). Further analysis reveals that counseling sessions substantially improved students’ academic performance and students functioning at school in term of attendance and punctuality.
Conclusion
It is concluded that CBT efficiently addressed emotional and academic problems (i.e. procrastination, burnout, test anxiety, and self-handicapping behavior), improved students’ functioning at school (i.e. attendance & and academic grades), and guided the students to manage unrealistic parental expectations.
Anti-cancer peptides (ACPs) represent promising candidates for cancer therapy because they can target cancer cells selectively while leaving healthy cells unaffected. ACPs offer a multifaceted approach to cancer treatment by combining targeted cytotoxicity, immune system activation, and the potential to overcome drug resistance. Their development is aided by computational tools that expedite the discovery of promising candidates. As a result, they have received significant attention and broadly studied by many researchers. Currently, numerous peptide-based drugs are undergoing evaluation in preclinical and clinical trials. Accurately identifying ACPs has become a major focus of research, leading to the construction of diverse methods for their detection in silico. These methods implemented different training/testing datasets, classifiers, feature engineering, and feature selection techniques. Thus, it is indispensable to highlight the strengths and weaknesses of current methods and provide insights to improve novel computational tools for identification of ACPs. To address this, we conducted a comprehensive investigation of 26 available existing methods for ACPs, examining their feature engineering methods, classification learning algorithms, performance validation parameters, and availability of web servers. Subsequently, we performed a thorough performance assessment to examine the robustness of these studies using different benchmark datasets. Based on our findings, we offer potential strategies for enhancing model performance and effectiveness.
This study aims to investigate the impact of multi-channel spiral twist extrusion (MCSTE) on the corrosion and degradation properties of biodegradable AZ31 (Mg-3Al-1Zn, wt.%) magnesium alloy. Square AZ31 billets were processed using route C-MCSTE (with a 180° rotation between passes) at 250°C and with a ram speed of 10 mm/min for up to 8 passes. The extrusion process was conducted via dies with twist angles of 30° and 40°. The microstructural changes and grain size distribution in the alloy were determined with a Scanning Electron Microscopy equipped with Electron Backscatter Diffraction. Electrochemical tests were conducted in a simulated body fluid to model the environment in which medical implants operate. The mechanical properties of the alloy were tested before and after processing using compression tests. The billets processed with a 30° twist angle demonstrated superior mechanical and corrosion resistance compared to those processed with a 40° die. A 66% reduction in grain size was found in billets processed for 4 passes using the 30°-die as compared to the as-annealed condition. Billets processed for 4 and 8 passes showed ultimate compressive strength improvements of 23% and 31%, respectively compared to the as-annealed condition. The 8-pass processed sample using the 30° twist ring die showed 76% improvement in the corrosion rate compared to the as-annealed state. Furthermore, billets processed for 4 passes showed corrosion resistance and ultimate compressive strength improvements of 108% and 23%; respectively compared to the as-annealed condition. These findings imply that the developed MCSTE process can be adopted for industrial use, especially in the manufacturing of biodegradable magnesium alloys for medical implants.
Proteolysis Targeting Chimeras (PROTACs) have revolutionized cancer therapy by offering a selective and innovative approach to degrade key oncogenic proteins associated with various malignancies. These hybrid molecules exploit the ubiquitin‐proteasome system, facilitating the degradation of target proteins through an event‐driven mechanism, thereby overcoming drug resistance and enhancing selectivity. With diverse targets including androgen receptors, BTK, estrogen receptors, BET proteins, and BRAF, PROTACs offer a versatile strategy for personalized cancer treatment. Advantages of PROTACs over traditional small molecule inhibitors include their ability to operate at lower concentrations, catalyzing the degradation of multiple proteins of interest with reduced cytotoxicity. Notably, PROTACs address challenges associated with traditionally “undruggable” targets, expanding the therapeutic landscape of cancer therapy. Ongoing preclinical and clinical studies highlight the transformative potential of PROTACs, with promising results in prostate, breast, lung, melanoma, and colorectal cancers. Despite their potential, challenges persist in optimizing physicochemical properties and enhancing bioavailability. Further research is needed to refine PROTAC design and address complexities in molecule development. Nevertheless, the development of oral androgen receptor PROTACs represents a significant milestone, demonstrating the feasibility and efficacy of this innovative therapeutic approach. This review provides a comprehensive overview of PROTACs in cancer therapy, emphasizing their mechanism of action, advantages, and challenges. As PROTAC research progresses, continued exploration in both preclinical and clinical settings will be crucial to unlocking their full therapeutic potential and shaping the future of personalized cancer treatment.
Burkholderia pseudomallei causes melioidosis, a deadly infection having high fatality rates (20–50%) and antibiotic resistance, however, there’s no effective drug or vaccine available. Trehalose is a vital sugar for B. pseudomallei which influences the pathogen resilience and pathogenicity. This proposed computational strategy focuses on developing novel drugs against Trehalose-6-phosphate Phosphatase (TPP) to combat infections. This study found three novel drugs from Asinex, Zinc, Chembridge, and Drugbank databases through a comprehensive structure-based virtual screening. The process screened the top three compounds: BDG_34042863, BDF_33738612, and DB00139 along with control (2-methyl-6-phenoxytetrahydro-2 H-pyran-3,4,5-triol) with a binding energy score of -8.8 kcal/mol, -8.4 kcal/mol, and − 7.7 kcal/mol, -6.4 kcal/mol respectively. In a molecular dynamics simulation, the Ligand-protein complexes demonstrated substantial non-covalent interactions as well as a stable docked intermolecular binding conformation. Throughout the MDS (molecular dynamic simulation) period, the studied compounds showed stable consistent interactions; there were no noticeable changes in the interactions or binding mode. The BDG_34042863, BDF_33738612, and DB00139 had a mean deviation of 4.04, 7.18, and 7.10 measured in Å, respectively. In addition, the simulation trajectories of complexes underwent MM/GBSA analysis, which revealed binding affinity scores of -33.39, -41.1, -49.16, and − 41.29 measured in kcal/mol for the control, BDG_34042863, BDF_33738612, and DB00139, respectively. According to DFT Analysis, BDF_33738612 showed the smallest energy gap (0.46 eV), indicating high reactivity, while DB00139 showed the largest energy gap (5.66 eV), illustrating good kinetic stability compared to the control. The compounds exhibit notable differences in reactivity and stability levels as their HOMO-1 to LUMO + 1 and HOMO-2 to LUMO + 2 orbitals have greater energy gaps, ranging from 5.06 eV to 6.69 eV and 5.66 eV to 7.09 eV, respectively. The compounds also had favorable pharmacokinetic characteristics and were categorized as druglike. Among the selected compounds, BDF_33738612 demonstrated the most promising findings followed by BDG_34042863 and DB00139. The compounds may be employed in an experimental study to examine their anti-TPP activity against B. pseudomallei.
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