Comparative Case Study of Efficacy of Oral Bilastine Monotherapy to Concomittant Administration of Oral Bilastine with Probiotic in Patients with Allergic Rhinitis.

Introduction: Allergic rhinitis continues to pose a global burden in both adult and paediatric population and requires adequate medical therapy to avoid long term complications making it a challenging concern. This prompts the need to find a suitable and appropriate treatment regimen for this condition. Aim: To study the clinical efficacy of Oral Bilastine and Probiotics in patients with allergic rhinitis. To compare the effectiveness of Bilastine monotherapy to Oral Bilastine combined with Probiotics in patients with allergic rhinitis. Materials and Methods: Total of 100 patients were selected and randomized into two groups- Group 1 and Group 2 and were treated with Oral Bilastine 20 mg OD and Oral Bilastine 20 mg with Oral Probiotics OD (Lactobacillus fermentum & L. paracasei - 4 billion cells) respectively to maintain uniformity. Baseline scoring and follow up scoring after 6 weeks was done using the Total symptom score, Absolute eosinophil count and Nasal smear eosinophil count and were evaluated and documented as per their response to treatment. Results: A significant reduction was observed in the severity of symptoms especially nasal obstruction, sneezing in patients of both groups along with extra nasal symptoms, with average mean score in Group 1 being 7.72 before treatment that reduced to 3.66 and that of Group 2 from 9.32 to 5.02. The mean score of Group 2 was greater in both before and after treatment which is highly significant. (p < 0.001). Conclusion: Monotherapy with Oral Bilastine has proven in relieving allergic symptoms. Addition of Oral Probiotics along with Bilastine has shown to have synergistic effect in further improving overall symptoms in allergic rhinitis.

Multi-source transfer learning for facial emotion recognition using multivariate correlation analysis.

Deep learning techniques have proven to be effective in solving the facial emotion recognition (FER) problem. However, it demands a significant amount of supervision data which is often unavailable due to privacy and ethical concerns. In this paper, we present a novel approach for addressing the FER problem using multi-source transfer learning. The proposed method leverages the knowledge from multiple data sources of similar domains to inform the model on a related task. The approach involves the optimization of aggregate multivariate correlation among the source tasks trained on the source dataset, thus controlling the transfer of information to the target task. The hypothesis is validated on benchmark datasets for facial emotion recognition and image classification tasks, and the results demonstrate the effectiveness of the proposed method in capturing the group correlation among features, as well as being robust to negative transfer and performing well in few-shot multi-source adaptation. With respect to the state-of-the-art methods MCW and DECISION, our approach shows an improvement of 7% and [Formula: see text]15% respectively.

Efficient Skip Connections-Based Residual Network (ESRNet) for Brain Tumor Classification.

Brain tumors pose a complex and urgent challenge in medical diagnostics, requiring precise and timely classification due to their diverse characteristics and potentially life-threatening consequences. While existing deep learning (DL)-based brain tumor classification (BTC) models have shown significant progress, they encounter limitations like restricted depth, vanishing gradient issues, and difficulties in capturing intricate features. To address these challenges, this paper proposes an efficient skip connections-based residual network (ESRNet). leveraging the residual network (ResNet) with skip connections. ESRNet ensures smooth gradient flow during training, mitigating the vanishing gradient problem. Additionally, the ESRNet architecture includes multiple stages with increasing numbers of residual blocks for improved feature learning and pattern recognition. ESRNet utilizes residual blocks from the ResNet architecture, featuring skip connections that enable identity mapping. Through direct addition of the input tensor to the convolutional layer output within each block, skip connections preserve the gradient flow. This mechanism prevents vanishing gradients, ensuring effective information propagation across network layers during training. Furthermore, ESRNet integrates efficient downsampling techniques and stabilizing batch normalization layers, which collectively contribute to its robust and reliable performance. Extensive experimental results reveal that ESRNet significantly outperforms other approaches in terms of accuracy, sensitivity, specificity, F-score, and Kappa statistics, with median values of 99.62%, 99.68%, 99.89%, 99.47%, and 99.42%, respectively. Moreover, the achieved minimum performance metrics, including accuracy (99.34%), sensitivity (99.47%), specificity (99.79%), F-score (99.04%), and Kappa statistics (99.21%), underscore the exceptional effectiveness of ESRNet for BTC. Therefore, the proposed ESRNet showcases exceptional performance and efficiency in BTC, holding the potential to revolutionize clinical diagnosis and treatment planning.

An efficient one-pot synthesis of benzothiazolo-4ß-anilino-podophyllotoxin congeners: DNA topoisomerase-II inhibition and anticancer activity.

An efficient one-pot iodination methodology for the synthesis of benzothiazolo-4ß-anilino-podophyllotoxin (5a-h) and benzothiazolo-4ß-anilino-4-O-demethylepipodophyllotoxin (6a-h) congeners has been successfully developed by using zirconium tetrachloride/sodium iodide. Interestingly, this protocol demonstrates enhancement of stereoselectivity apart from the improvement in the yields in comparison to previous methods reported for such related podophyllotoxin derivatives. These compounds have been designed and synthesized using association strategy by coupling of 4ß-podophyllotoxin and 4ß-demethylepipodophyllotoxin with a variety of substituted aminoaryl benzothiazoles. Some of the representative compounds have been evaluated for their cytotoxicity against selected human cancer cell lines and DNA topoisomerase-II inhibition activity.

Synthesis of 4ß-carbamoyl epipodophyllotoxins as potential antitumour agents.

A series of new 4ß-carbamoyl epipodophyllotoxin analogues have been synthesized and evaluated for their anticancer activity against eleven cancer cell lines including Zr-75-1, MCF7, KB, Gurav, DWD, Colo 205, A-549, Hop62, PC3, SiHa and A-2780. Most of the compounds exhibited better growth-inhibition activities against tested cell lines than that of etoposide. Further, compounds 6g and 6i are also evaluated for their DNA topoisomerase-II (topo-II) inhibition activity and they exhibited significant inhibition of topo-II catalytic activity comparable to etoposide.

Synthesis of 4ß-N-polyaromatic substituted podophyllotoxins: DNA topoisomerase inhibition, anticancer and apoptosis-inducing activities.

A new class of 4ß-N-polyaromatic substituted podophyllotoxin congeners have been synthesized and evaluated for their DNA topoisomerase-II (topo-II) inhibition as well as anticancer potential in some human cancer cell lines. The ease of synthesis and interesting biological activities make the present series of polyaromatic-podophyllotoxin congeners as a promising new structure for the development of new anticancer agents based on podophyllotoxin scaffold.

An efficient and facile nitration of phenols with nitric acid/zinc chloride under ultrasonic conditions.

An efficient and facile nitration of phenols using nitric acid/zinc chloride under ultrasonic condition has exhibited significant chemo as well as regioselectivity. This study has been extended to other aromatic compounds like naphthalene and anthracene.