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Topical amlodipine-loaded solid lipid nanoparticles for enhanced burn wound healing: A repurposed approach

Burn wounds are a complicated process with ongoing psychological and physical issues for the affected individuals. Wound healing consists of multifactorial molecular mechanisms and interactions involving; inflammation, proliferation, angiogenesis, and matrix remodeling. Amlodipine (ADB), widely used in cardiovascular disorders, demonstrated antioxidant and anti-inflammatory effects in some non-cardiovascular studies. It was reported that amlodipine is capable of promoting the healing process by regulation of collagen production, extracellular matrix, re-epithelialization and wound healing through its vasodilation and angiogenic activity. The objective of the current study is to appraise the wound healing capacity of amlodipine-loaded SLN (ADB-SLN) integrated into a hydrogel. The in-vitro characterization revealed that the optimized formulation was nanometric (190.4 ± 1.6 nm) with sufficiently high entrapment efficiency (88 % ± 1.4) and sustained ADB release (85.45 ± 4.45 % after 12 h). Furthermore, in-vivo evaluation was conducted on second-degree burns induced in male Sprague-Dawley rats. ADB-SLN gel revealed a high wound contraction rate and a significant improvement in skin regeneration and inflammatory biomarkers levels, confirming its efficiency in enhancing wound healing compared to other tested and commercial formulations. To conclude, the present findings proved that ADB-SLN integrated hydrogel offers a promising novel therapy for burn wound healing with a maximum therapeutic value.

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Exploring RoBERTa model for cross-domain suggestion detection in online reviews

Detecting suggestions in online review requires contextual understanding of review text, which is an important real-world application of natural language processing. Given the disparate text domains found in product reviews, a common strategy involves fine-tuning bidirectional encoder representations from transformers (BERT) models using reviews from various domains. However, there hasn't been an empirical examination of how BERT models behave across different domains in tasks related to detecting suggestion sentences from online reviews. In this study, we explore BERT models for suggestion classification that have been fine-tuned using single-domain and cross-domain Amazon review datasets. Our results indicate that while single-domain models achieved slightly better performance within their respective domains compared to cross-domain models, the latter outperformed single-domain models when evaluated on cross-domain data. This was also observed for single-domain data not used for fine-tuning the single-domain model and on average across all tests. Although fine-tuning single-domain models can lead to minor accuracy improvements, employing multi-domain models that perform well across domains can help in cold start problems and reduce annotation costs.

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Uterine Cancer: A Nine-year Review from a Tertiary Hospital in Tamil Nadu

Background: With increasing uterine cancer in developing nations, there is a need for timely determination of the diagnosis, prognosis, and management options to reduce morbidity and mortality. Objective: To analyze the socio-demographic, etio-pathological features and management of uterine cancer and evaluate its correlation with grading/staging in our population. Methods: This retrospective descriptive study analyzed data from 97 histologically proven uterine cancer cases. Age, parity, symptoms, co-morbidities, body mass index (BMI), ultrasound features, histopathology type, stage and grade of the tumor, type of hysterectomy done, complications and mortality were analysed. Statistical analysis was done using ANOVA and chi-square test, and a p-value<0.05 indicated statistical significance. Results: The mean age of diagnosis was 57.91 years, and the mean BMI was 29.32 Kg/m2. Majority of the patients were multiparous (42.27%), and only 10% were nulliparous. The disease was detected at an earlier age in nulliparous and obese women. Diabetes and hypertension were found in 75.25%. Most of the patients were detected with stage I cancer (80.6%). Patients diagnosed with uterine cancer on biopsy were treated with total abdominal hysterectomy with bilateral salpingo-oophorectomy andbilateral pelvic lymph node dissection (55.8%). Over 36% of patients received postop radiotherapy and/or chemotherapy. 21% patients were lost to follow-up and 12.37% died. Also, 24 cases had postoperative complications (wound infection). Conclusion: Uterine cancer is common among obese women with diabetes and hypertension. In nulliparous and the obese, the cancer was detected at an earlier age. Most of our patients had stage 1 disease, and 90% was endometroid cancer. The study highlights the importance of endometrial sampling before hysterectomy in perimenopausal women to avoid suboptimal surgery in patients diagnosed with uterine cancer after a simple hysterectomy.

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A Topological Distance Between Multi-Fields Based on Multi-Dimensional Persistence Diagrams.

The problem of computing topological distance between two scalar fields based on Reeb graphs or contour trees has been studied and applied successfully to various problems in topological shape matching, data analysis, and visualization. However, generalizing such results for computing distance measures between two multi-fields based on their Reeb spaces is still in its infancy. Towards this, in the current article we propose a technique to compute an effective distance measure between two multi-fields by computing a novel multi-dimensional persistence diagram (MDPD) corresponding to each of the (quantized) Reeb spaces. First, we construct a multi-dimensional Reeb graph (MDRG), which is a hierarchical decomposition of the Reeb space into a collection of Reeb graphs. The MDPD corresponding to each MDRG is then computed based on the persistence diagrams of the component Reeb graphs of the MDRG. Our distance measure extends the Wasserstein distance between two persistence diagrams of Reeb graphs to MDPDs of MDRGs. We prove that the proposed measure is a pseudo-metric and satisfies a stability property. Effectiveness of the proposed distance measure has been demonstrated in (i) shape retrieval contest data - SHREC 2010 and (ii) Pt-CO bond detection data from computational chemistry. Experimental results show that the proposed distance measure based on the Reeb spaces has more discriminating power in clustering the shapes and detecting the formation of a stable Pt-CO bond as compared to the similar measures between Reeb graphs.

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