COMPARATIVE EVALUATION OF ORAL MICROBIOTA OF CHILDREN WITH AND WITHOUT EARLY CHILDHOOD CARIES BORN TO CARIES FREE MOTHERS

Objectives: To compare the level of mutans streptococci in children with and without Early childhood caries (ECC) born to caries free mothers.Methods: Twenty children aged between 3 and 6 years were selected depending on their caries experience, and the mother should be caries free inboth the groups. The children were divided into two groups. Group I had an active carious lesion and Group II were caries free. Saliva samples werecollected from the child and the mother in a sterile tube and bacterial culture was carried out to estimate the colony count.Results: There was a highly significant difference in the colony forming unit (CFU) between the 2 groups, indicating higher CFU in children with ECC.Conclusions: Even though there are higher chances of vertical transmission of MS from mother to their child, this study provides a new view thatmother alone is not a potential factor for mutans streptococci transmission to their child.Keywords: Mutans streptocci, Early childhood caries, Colony forming unit

An Optimistic-Robust Approach for Dynamic Positioning of Omnichannel Inventories

We introduce a new class of data-driven and distribution-free optimistic-robust bimodal inventory optimization (BIO) strategy to effectively allocate inventory across a retail chain to meet time-varying, uncertain omnichannel demand. While prior Robust optimization (RO) methods emphasize the downside, i.e., worst-case adversarial demand, BIO also considers the upside to remain resilient like RO while also reaping the rewards of improved average-case performance by overcoming the presence of endogenous outliers. This bimodal strategy is particularly valuable for balancing the tradeoff between lost sales at the store and the costs of cross-channel e-commerce fulfillment, which is at the core of our inventory optimization model. These factors are asymmetric due to the heterogenous behavior of the channels, with a bias towards the former in terms of lost-sales cost and a dependence on network effects for the latter. We provide structural insights about the BIO solution and how it can be tuned to achieve a preferred tradeoff between robustness and the average-case. Our experiments show that significant benefits can be achieved by rethinking traditional approaches to inventory management, which are siloed by channel and location. Using a real-world dataset from a large American omnichannel retail chain, a business value assessment during a peak period indicates over a 15% profitability gain for BIO over RO and other baselines while also preserving the (practical) worst case performance

Oral squamous cell carcinoma detection using EfficientNet on histopathological images

Introduction: Oral Squamous Cell Carcinoma (OSCC) poses a significant challenge in oncology due to the absence of precise diagnostic tools, leading to delays in identifying the condition. Current diagnostic methods for OSCC have limitations in accuracy and efficiency, highlighting the need for more reliable approaches. This study aims to explore the discriminative potential of histopathological images of oral epithelium and OSCC. By utilizing a database containing 1224 images from 230 patients, captured at varying magnifications and publicly available, a customized deep learning model based on EfficientNetB3 was developed. The model’s objective was to differentiate between normal epithelium and OSCC tissues by employing advanced techniques such as data augmentation, regularization, and optimization. Methods: The research utilized a histopathological imaging database for Oral Cancer analysis, incorporating 1224 images from 230 patients. These images, taken at various magnifications, formed the basis for training a specialized deep learning model built upon the EfficientNetB3 architecture. The model underwent training to distinguish between normal epithelium and OSCC tissues, employing sophisticated methodologies including data augmentation, regularization techniques, and optimization strategies. Results: The customized deep learning model achieved significant success, showcasing a remarkable 99% accuracy when tested on the dataset. This high accuracy underscores the model’s efficacy in effectively discerning between normal epithelium and OSCC tissues. Furthermore, the model exhibited impressive precision, recall, and F1-score metrics, reinforcing its potential as a robust diagnostic tool for OSCC. Discussion: This research demonstrates the promising potential of employing deep learning models to address the diagnostic challenges associated with OSCC. The model’s ability to achieve a 99% accuracy rate on the test dataset signifies a considerable leap forward in earlier and more accurate detection of OSCC. Leveraging advanced techniques in machine learning, such as data augmentation and optimization, has shown promising results in improving patient outcomes through timely and precise identification of OSCC

A Deep Learning Framework with an Intermediate Layer Using the Swarm Intelligence Optimizer for Diagnosing Oral Squamous Cell Carcinoma

One of the most prevalent cancers is oral squamous cell carcinoma, and preventing mortality from this disease primarily depends on early detection. Clinicians will greatly benefit from automated diagnostic techniques that analyze a patient’s histopathology images to identify abnormal oral lesions. A deep learning framework was designed with an intermediate layer between feature extraction layers and classification layers for classifying the histopathological images into two categories, namely, normal and oral squamous cell carcinoma. The intermediate layer is constructed using the proposed swarm intelligence technique called the Modified Gorilla Troops Optimizer. While there are many optimization algorithms used in the literature for feature selection, weight updating, and optimal parameter identification in deep learning models, this work focuses on using optimization algorithms as an intermediate layer to convert extracted features into features that are better suited for classification. Three datasets comprising 2784 normal and 3632 oral squamous cell carcinoma subjects are considered in this work. Three popular CNN architectures, namely, InceptionV2, MobileNetV3, and EfficientNetB3, are investigated as feature extraction layers. Two fully connected Neural Network layers, batch normalization, and dropout are used as classification layers. With the best accuracy of 0.89 among the examined feature extraction models, MobileNetV3 exhibits good performance. This accuracy is increased to 0.95 when the suggested Modified Gorilla Troops Optimizer is used as an intermediary layer

Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques

Prompt diagnostics and appropriate cancer therapy necessitate the use of gene expression databases. The integration of analytical methods can enhance detection precision by capturing intricate patterns and subtle connections in the data. This study proposes a diagnostic-integrated approach combining Empirical Bayes Harmonization (EBS), Jensen–Shannon Divergence (JSD), deep learning, and contour mathematics for cancer detection using gene expression data. EBS preprocesses the gene expression data, while JSD measures the distributional differences between cancerous and non-cancerous samples, providing invaluable insights into gene expression patterns. Deep learning (DL) models are employed for automatic deep feature extraction and to discern complex patterns from the data. Contour mathematics is applied to visualize decision boundaries and regions in the high-dimensional feature space. JSD imparts significant information to the deep learning model, directing it to concentrate on pertinent features associated with cancerous samples. Contour visualization elucidates the model’s decision-making process, bolstering interpretability. The amalgamation of JSD, deep learning, and contour mathematics in gene expression dataset analysis diagnostics presents a promising pathway for precise cancer detection. This method taps into the prowess of deep learning for feature extraction while employing JSD to pinpoint distributional differences and contour mathematics for visual elucidation. The outcomes underscore its potential as a formidable instrument for cancer detection, furnishing crucial insights for timely diagnostics and tailor-made treatment strategies

Functionalized Graphene-Incorporated Cupric Oxide Charge-Transport Layer for Enhanced Photoelectrochemical Performance and Hydrogen Evolution

The production of hydrogen (H2) through photoelectrochemical water splitting (PEC-WS) using renewable energy sources, particularly solar light, has been considered a promising solution for global energy and environmental challenges. In the field of hydrogen-scarce regions, metal oxide semiconductors have been extensively researched as photocathodes. For UV-visible light-driven PEC-WS, cupric oxide (CuO) has emerged as a suitable photocathode. However, the stability of the photocathode (CuO) against photo-corrosion is crucial in developing CuO-based PEC cells. This study reports a stable and effective CuO and graphene-incorporated (Gra-COOH) CuO nanocomposite photocathode through a sol-gel solution-based technique via spin coating. Incorporating graphene into the CuO nanocomposite photocathode resulted in higher stability and an increase in photocurrent compared to bare CuO photocathode electrodes. Compared to cuprous oxide (Cu2O), the CuO photocathode was more identical and thermally stable during PEC-WS due to its high oxidation number. Additionally, the CuO:Gra-COOH nanocomposite photocathode exhibited a H2 evolution of approximately 9.3 µmol, indicating its potential as a stable and effective photocathode for PEC-WS. The enhanced electrical properties of the CuO:Gra-COOH nanocomposite exemplify its potential for use as a charge-transport layer

Iodine as an efficient catalyst in ionic Diels–Alder reactions of α,β-unsaturated acetals

A variety of protected and unprotected ,-unsaturated aldehydes react with 1,3-dienes in the presence of I2 to give the corresponding cycloadducts

Baicalein, a Bioflavonoid, Prevents Cisplatin-Induced Acute Kidney Injury by Up-Regulating Antioxidant Defenses and Down-Regulating the MAPKs and NF-κB Pathways.

Acute renal failure is a serious complication of the anticancer drug cisplatin. The potential role of baicalein, a naturally occurring bioflavonoid on cisplatin-induced renal injury is unknown. Here, we assessed the effect of baicalein against a murine model of cisplatin-induced acute renal failure and investigated the underlying possible mechanisms. Renal function, kidney histology, inflammation, oxidative stress, renal mitochondrial function, proteins involved in apoptosis, nuclear translocation of Nrf2 and effects on intracellular signaling pathways such as MAPKs, and NF-κB were assessed. Pretreatment with baicalein ameliorated the cisplatin-induced renal oxidative stress, apoptosis and inflammation and improved kidney injury and function. Baicalein inhibited the cisplatin-induced expression of iNOS, TNF-α, IL-6 and mononuclear cell infiltration and concealed redox-sensitive transcription factor NF-κB activation via reduced DNA-binding activity, IκBα phosphorylation and p65 nuclear translocation in kidneys. Further studies demonstrated baicalein markedly attenuated cisplatin-induced p38 MAPK, ERK1/2 and JNK phosphorylation in kidneys. Baicalein also restored the renal antioxidants and increased the amount of total and nuclear accumulation of Nrf2 and downstream target protein, HO-1 in kidneys. Moreover, baicalein preserved mitochondrial respiratory enzyme activities and inhibited cisplatin-induced apoptosis by suppressing p53 expression, Bax/Bcl-2 imbalance, cytochrome c release and activation of caspase-9, caspase-3 and PARP. Our findings suggest that baicalein ameliorates cisplatin-induced renal damage through up-regulation of antioxidant defense mechanisms and down regulation of the MAPKs and NF-κB signaling pathways

Solar Fed 15 Level Cascaded Multilevel Inverter with ANFIS Control Strategy for Enhancement of Power Quality

Solar PV energy conversion systems suffer from degraded power quality issues because of harmonics in the system. In order to eliminate harmonics, this paper focuse on a solar-powered 15-level cascaded inverter. A number of controllers such as PI, Fuzzy Logic Neural Network and ANFIS are used in this study in order to manage the PWM frequency (Proportional Integral, Fuzzy Logic, NN and Adaptive Neuro Fuzzy Inference System, respectively). A PV-fed 15-level cascaded multilevel inverter for power quality improvement will be developed by using an ANFIS controller. The suggested ANFIS controller has a lower harmonic distortion than conventional controllers; hence the quality of the power improves. The PI, FL, ANN, and ANFIS controllers simulated results are analysed in MATLAB