904 research outputs found
X-ray image analysis for dental disease: A deep learning approach using EfficientNets
No full textDental cavities are a highly common persistent dental problem that impacts populations across different age groups on a global scale. It is crucial to get a dental issue diagnosed as early as possible and with as much accuracy as possible to treat it efficiently and prevent any related issues. If a dental infection is not treated, it will eventually grow and cause tooth loss. Dental X-ray images are crucial and beneficial in the diagnostic process of dental diseases for dentists. By applying Deep Learning (DL) techniques to dental X-ray images, dental experts can efficiently and precisely etect dental conditions, including dental cavities, fillings and implants. The objective of this research is to assess the performance of DL-based methods for dental disease detection via panoramic radiographs. In this study, we evaluated the performance of all of the EfficientNet variants (e.g., EfficientNets B0-B7) to determine which one is the most effective model for detecting dental disease. Moreover, we utilized the Borderline Synthetic Minority Oversampling Technique (SMOTE) to cope with the issue related to the minority classes contained in the dataset. To assess the efficacy of the model, various metrics are employed, including recall, accuracy, precision, loss, and F1-score. As a result, the performance of the EfficientNet-B5 model was superior to that of the other EfficientNet models. The EfficientNet-B5 model achieved the following values for its metrics: F1-score, accuracy, recall, AUC, and precision: 98.37%, 98.32%, 98.32%, 99.21%, and 98.32%, respectively. The accuracy rates for the EfficientNet-B0, EfficientNet-B1, EfficientNet-B2, EfficientNet-B3, EfficientNet-B4, EfficientNet-B6, and EfficientNet-B7, are 91.59%, 94.12%, 93.28%, 85.71%, 94.96%, 96.64% and 90.76%, respectively. The results indicated that the EfficientNet-B5 model performs better than other EfficientNet classifiers, which supports dental professionals significantly in the recognition of dental diseases
Polyamines: natural and engineered abiotic and biotic stress tolerance in plants
No full textAbstract not availableSyed Sarfraz Hussain, Muhammad Ali, Maqbool Ahmad, Kadambot H.M. Siddiqu
IDD-Net: A Deep Learning Approach for Early Detection of Dental Diseases Using X-Ray Imaging
No full textEarly detection of dental diseases such as cavities, periodontitis, and periapical infections is crucial for effective management and prevention, as these conditions can lead to severe complications if left untreated. However, traditional diagnostic methods are often manual, time consuming, and heavily reliant on expert judgment, which can introduce variability and delay in diagnosis. To address these critical challenges, we propose IDD-Net (Identification of Dental Disease Network), a novel deep learning-based model designed for the automatic detection of dental diseases using panoramic X-ray images. The proposed framework leverages Convolutional Neural Networks (CNN) to enhance the accuracy and efficiency of dental condition classification, thereby significantly improving the diagnostic process. In our comprehensive evaluation, IDD-Net’s performance is rigorously compared to four state-of-the-art deep learning models: AlexNet, InceptionResNet-V2, Xception, and MobileNet-V2. To tackle the issue of class imbalance, we employ the Synthetic Minority Over-sampling Technique with Tomek links (SMOTE Tomek), ensuring a balanced sample distribution that enhances model training. Experimental results showcase IDDNet’s exceptional performance, achieving a 99.97% AUC, 98.99% accuracy, 98.24% recall, 98.99% precision, and a 98.97% F1-score, thus outperforming benchmark classifiers. These findings underscore the transformative potential of IDD-Net as a reliable and efficient tool for assisting dental and medical professionals in the early detection of dental diseases. By streamlining the diagnostic process, IDD-Net not only improves patient outcomes but also has the potential to reshape standard practices in dental care, paving the way for more proactive and preventive
approaches in oral health management
Repurposing FDA approved drugs against monkeypox virus DNA dependent RNA polymerase: virtual screening, normal mode analysis and molecular dynamics simulation studies
No full textZoonotic monkeypox disease, caused by the double-stranded DNA monkeypox virus, has become a global concern. Due to the absence of a specific small molecule drug for the disease, this report aims to identify potential inhibitor drugs for monkeypox. This study explores a drug repurposing strategy using virtual screening to evaluate 1615 FDA approved drugs against the monkeypox virus DNA dependent RNA polymerase subunit A6R. Normal mode analysis and molecular dynamics simulation assessed the flexibility and stability of the target protein in complex with the top screened drugs. The analysis identified Nilotinib (ZINC000006716957), Conivaptan (ZINC000012503187), and Ponatinib (ZINC000036701290) as the most potential RNA polymerase inhibitors with binding energies of - 7.5 kcal/mol. These drugs mainly established hydrogen bonds and hydrophobic interactions with the protein active sites, including LEU95, LEU90, PRO96, MET110, and VAL113, and residues nearby. Normal mode analysis and molecular dynamics simulation confirmed the stability of interactions between the top drugs and the protein. In conclusion, we have discovered promising drugs that can potentially control the monkeypox virus and should be further explored through experimental assays and clinical trials to assess their actual activity against the disease. The findings of this study could lay the foundation for screening repurposed compounds as possible antiviral treatments against various highly pathogenic viruses
Nanocrystals formation and intense green emission in thermally annealed AlN:Ho films for microlaser cavities and photonic applications
No full textPlasma magnetron sputtered thin films of AlN:Ho deposited on flat silicon substrates and optical fiber were characterized and analyzed for structural changes after thermal annealing at 1173 K for 40 min, by atomic force microscopy (AFM). The films grown, at liquid nitrogen temperature, on silicon substrates were amorphous while those deposited around optical fiber were crystalline. The films were also investigated for any change in the luminescence when thermal activation was performed for 40 min in a nitrogen atmosphere. The AFM analysis identified the existence of crystalline structures in parts of the films after thermal annealing. The x-ray diffraction could not provide those results. The films around optical fiber were crystalline even deposited at liquid nitrogen temperature. Clearly, amorphous films are hard to achieve on smaller substrate size. Direct observation of green emission is possible with naked eye, when the thermally annealed films are studied under cathodoluminescence. The green emission occurs at 549 nm as a result from (5)S(2) -> (5)I(8) transition in Ho(3+) that enhanced with thermal activation, making it a very useful candidate for photonic and optical devices applications. (C) 2010 American Institute of Physics. [doi:10.1063/1.3478770
Modeling of sorption enhanced steam methane reforming in an adiabatic packed bed reactor using various CO₂ sorbents
No full textA 1-D heterogeneous model of sorption-enhanced steam methane reforming (SE-SMR) process in a packed bed reactor consisting of nickel catalyst well mixed with CO₂ sorbent particles is investigated for three types of common sorbents. The performance of SE-SMR process is studied under low medium pressure conditions (3 – 11 bar) to find the optimum operating conditions. Optimal CaO sorption corresponding to 82% CH₄ conversion and 85% H₂ purity is found at 900 K, 3 bar, 3.5 kgm⁻²s⁻¹ and S/C of 3.0. In contrast, lithium zirconate (LZC) and hydrotalcite (HTC) sorbents exhibited best sorptions under the operating conditions of 773 K, 5 bar and S/C of 3 with CH₄ conversion of 91.3% and 55.2%, and H₂ purity of 94.1% and 77.8% respectively. In these conditions, the CH₄ conversion increased by 114%, 111% and 67% compared to the conventional SMR for the processes enhanced by HTC, LZC and CaO sorption respectively
STUDY REGARDING RENAL FAILURE IN KALA PATHAR POISONING
No full textDr. Abdul Basit Maqbool*, Dr. Abdullah Sarfraz Cheema, Dr. Muhammad Ahma
In situ tailoring the morphology of In(OH)(3) nanostructures via surfactants during anodization and their transformation into In2O3 nanoparticles
No full textThe present work reports the effect of various surfactants on the morphology of In(OH)(3) nanostructures prepared via anodization. In-sheets were anodized in an environmentally benign electrolyte containing a small quantity of CTAB, CTAC, and PDDA surfactants at room temperature. The produced nanostructures were characterized using XRD, HRTEM, SAED, and EDAX. The morphology of indium hydroxide (In(OH)(3)) nanostructures was successfully tailored in situ with the help of surfactants in 1 M KCl aqueous electrolyte. XRD results confirmed the formation of In(OH)(3) and indium oxyhydroxide (InOOH) nanostructures in the pristine form which were transformed into single-phase cubic In2O3 nanoparticles (NPs) after calcination. HRTEM analyses showed that the morphology and size of the In(OH)(3) nanostructures can be tuned to form nanorods, nanosheets and nanostrips using different surfactants. The results revealed that CTAC and PDDA surfactants have a profound effect on the morphology of In(OH)(3) nanostructure compared to CTAB due to the higher concentration of Cl- ion. The possible mechanism of surfactants effect on the morphology is proposed. Furthermore, annealing converted the In(OH)(3) nanostructures into spherical In2O3 NPs with uniform and homogeneous size. We anticipate that the morphology of other metal-oxides nanostructure can be tuned using this simple, facile and rapid technique. In2O3 NPs prepared without and with CTAB surfactant were further explored for the non-enzymatic detection of hydrogen peroxide (H2O2). Electrochemical measurements showed enhanced electrocatalytic performance with fast electron transfer (similar to 2s) between the redox centers of H2O2 and electrode surface. The In2O3 NPs prepared using CTAB/Au electrode exhibited about 4-fold increase in sensitivity compared to the bare Au electrode. The biosensor also demonstrated good reproducibility, higher selectivity, and increased shelf life.
Factors Affecting Cotton Production in Pakistan:Empirical Evidence from Multan District
No full textThis paper attempts to examine the factors affecting cotton production in Multan region using primary source of data. A sample of 60 small farmers, 25 medium and 15 large farmers was randomly selected from two Tehsils namely Multan and Shujabad of district Multan. The Cobb-Douglas Production Function is employed to assess the effects of various inputs like cultivation, seed and sowing, irrigation, fertilizer, plant protection, inter-culturing / hoeing and labour cost on cotton yield. The results depicted that seed, fertilizer and irrigation were found scarce commodity for all category of farmers in district Multan. The Cobb-Douglas Production Function results revealed that the coefficients for cultivation (0.113) and seed (0.103) were found statistically significant at 1 percent level. The Cost-Benefit Ratio for the large farmers was found higher (1.41) than that of small (1.22) and medium (1.24) farmers. There is a dire need to ensure the availability of these scarce inputs by both public and private sectors as these inputs were major requirement of the cotton crop.Cotton; Cobb- Douglas Production Function; Cost Benefit Ratio; Marginal Value Product; Allocate Efficiency of Critical Inputs; Multan District; Pakistan
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