Flower-like supramolecular self-assembly of phosphonic acid appended naphthalene diimide and melamine

Diverse supramolecular assemblies ranging from nanometres to micrometers of small aromatic π-conjugated functional molecules have attracted enormous research interest in light of their applications in optoelectronics, chemosensors, nanotechnology, biotechnology and biomedicines. Here we study the mechanism of the formation of a flower-shaped supramolecular structure of phosphonic acid appended naphthalene diimide with melamine. The flower-shaped assembly formation was visualised by scanning electron microscope (SEM) and transmission electron microscopy (TEM) imaging, furthermore, XRD and DLS used to determined mode of aggregation. Characteristically, phosphonic acid-substituted at imide position of NDIs possess two important properties resulting in the formation of controlled flower-like nanostructures: (i) the aromatic core of the NDI which is designed to optimize the dispersive interactions (π-π stacking and van der Waals interactions) between the cores within a construct and (ii) phosphonic acid of NDI interact with malamine through molecular recognition i.e. strong hydrogen-bonding (H-bonding). We believe such arrangements prevent crystallization and favour the directional growth of flower-like nanostructure in 3D fashion. These works demonstrate that complex self-assembly can indeed be attained through hierarchical non-covalent interactions of two components. Furthermore, flower-like structures built from molecular recognition by these molecules indicate their potential in other fields if combined with other chemical entities

FORMULATION DEVELOPMENT AND EVALUATION OF ORALLY DISINTEGRATING TABLET OF CHLORPHENERAMINE MALEATE BY SUBLIMATION TECHNIQUE

Objective: Chlorpheneramine maleate is a first-generation antihistamine drug used in the treatment of allergic conditions like rhinitis, urticaria, and cough cold, etc. In present work, the challenge has been made to develop an orally disintegrating tablet of chlorpheneramine maleate with an increase in bioavailability and patient compliance. Methods: The sublimation technique was used to prepare orally disintegrating tablets. Porous tablet prepared after sublimation of camphor at 60 °C in a hot air oven for 60 min. In the research work, 32full factorial design used to find out the effect of two variables like the amount of Crospovidone and Croscarmellose sodium. Results: All prepared formulations were analyzed for various parameters. DSC of pure drug and optimized formulation A (9) showed purity of sample and compatibility of all ingredients with each other. In FTIR study of pure drug and optimized formulation A (9) no major shifts were seen. An optimized formulation (A9) was found to have good hardness (3.2 kg/cm2), friability (<1%), disintegration time (26 s), % drug release (99.77 %) within 6 min. Conclusion: The result obtained showed that orally disintegrating tablet of chlorpheneramine maleate enhances dissolution rate, improves bioavailability which will improve patient compliance

Cuckoo Search-Driven Feature Selection for Decision Tree Modelling

Features are fundamental components of decision tree modeling, and their relevance, quality, and selection are crucial determinants of the model's effectiveness and performance. However, decision trees can be computationally expensive, requiring a significant amount of memory to store the trees and their associated data structures. To address this limitation, we present a novel approach that utilizes a Cuckoo Search-based feature selection algorithm to construct efficient and optimal decision trees. The Cuckoo Search algorithm, inspired by the behavior of cuckoo birds, is a powerful metaheuristic algorithm that effectively selects high-quality features and creates accurate decision trees in the subforest. We evaluate the proposed method on a variety of datasets from the standard UCI learning repository with different domains and sizes, and our results demonstrate that the algorithm creates optimal decision trees with high performance

A Review on Various Approach of Speech Recognition Technique

The Speech is most prominent & primary mode of Communication among of human being. The communication among human computer interaction is called human computer interface. Speech has potential of being important mode of interaction with computer. This paper gives an overview of major technological perspective and appreciation of the fundamental progress of speech recognition and also gives overview technique developed in each stage of speech recognition. This paper helps in choosing the technique along with their relative merits & demerits. A comparative study of different technique is done asperstages. After years of research and development the accuracy of automatic speech recognition remains one of the promising research challenges (eg. variation of the context, speakers, and environment). The design of Speech Recognition system requires careful attentions to the following issues: Definition of various types of speech classes, speech representation, feature extraction techniques, speech classifiers, and database and performance evaluation. The problems that are existing in ASR and the various techniques to solve these problems constructed by various research workers have been presented in a chronological order. Real-time speech recognition is a challenging task due to the variability of speech signals and the need for fast and accurate processing. Support Vector Machines (SVMs) are a popular machine learning technique that has been used for speech recognition tasks. In this paper, we present a real-time speech recognition system using SVM. The system is based on a feature extraction process that uses Mel-Frequency Cepstral Coefficients (MFCCs) to represent speech signals. The extracted features are then used as input to the SVM classifier, which is trained to recognize different speech signals. The proposed system was implemented using the Python programming language and the Scikit-learn machine learning library. The performance of the system was evaluated using a dataset of spoken digits. The results showed that the proposed system achieved high recognition accuracy and real-time performance, making it suitable for practical applications. Speech is a unique human characteristic used as a tool to communicate and express ideas. Automatic speech recognition (ASR) finds application in electronic devices that are too small to allow data entry via the commonly used input devices such as keyboards. Personal Digital Assistants (PDA) and cellular phones are such examples in which ASR plays an important role

Coagulation Markers as Predictive and Prognostic Factors in Carcinoma Breast Patients with Lymph Node Metastasis

  Objective: The purpose of this prospective observational study was to evaluate the predictive and prognostic value of coagulation markers in patients with lymph node metastases and cancer of the breast, as well as their associations with important histopathologic criteria. Methods: Between December 2020 and July 2022, 100 patients from the surgery department of a tertiary hospital were enrolled in the study. D-dimer, fibrinogen, and prothrombin time were assessed as coagulation indicators. Documented histopathologic characteristics included tumor grade, size, lymph node involvement, and estrogen receptor status. Chi-square tests, t-tests, Kaplan-Meier survival curves, and log-rank tests were all used in the statistical study. Results: Elevated D-dimer levels were significantly associated with higher tumor grade (p < 0.05) and lymph node involvement (p < 0.01). Elevated fibrinogen levels were linked to larger tumor size (p < 0.05). Abnormal coagulation markers were correlated with reduced disease-free survival (p < 0.001). Conclusion: In breast cancer patients with lymph node metastases, coagulation indicators have the potential to predict disease severity and prognoses. Together with established parameters, their clinical utility may result in more precise care and better patient outcome

Role of Neoadjuvant Paclitaxel Chemotherapy in Carcinoma Breast: A Prospective Study

  Objective: The purpose of this prospective study was to assess the value of neoadjuvant chemotherapy with paclitaxel in the treatment of breast cancer. Methods: There were 88 diagnosed breast cancer patients altogether, 44 in each of the two groups (paclitaxel group and control group). To verify eligibility, thorough clinical, radiological, and laboratory evaluations were made. The reduction of tumor size, pathological reactions, and safety profiles were evaluated. To compare results between groups, statistical tests were used during data processing. Results: At 12 and 24 weeks, the paclitaxel group showed significantly smaller tumor sizes than the control group. In the paclitaxel group, complete pathological responses were more common, indicating efficient tumor regression. The side effects of paclitaxel therapy were generally well-tolerated and controllable. Conclusion: In conclusion, patients with breast cancer showed encouraging improvements in histological responses and tumor size after neoadjuvant paclitaxel treatment. These results suggest the potential advantages of using paclitaxel in neoadjuvant therapy protocols, perhaps making breast-conserving surgery more feasible. In order to provide more individualized treatments, future research should investigate long-term outcomes and biomarkers indicative of paclitaxel sensitivit

Real Time Packet Classification and Analysis based on Bloom Filter for Longest Prefix Matching

Packet classification is an enabling function in network and security systems; hence, hardware-based solutions, such as TCAM (Ternary Content Addressable Memory), have been extensively adopted for high-performance systems. With the expeditious improvement of hardware architectures and burgeoning popularity of multi-core multi-threaded processors, decision-tree based packet classification algorithms such as HiCuts and HyperCuts are grabbing considerable attention, outstanding to their flexibility in satisfying miscellaneous industrial requirements for network and security systems. For high classification speed, these algorithms internally use decision trees, whose size increases exponentially with the ruleset size; consequently, they cannot be used with a large rulesets. However, these decision tree algorithms involve complicated heuristics for concluding the number of cuts and fields. Moreover, ?xed interval-based cutting not depicting the actual space that each rule covers is defeasible and terminates in a huge storage requirement. We propose a new packet classification that simultaneously supports high scalability and fast classification performance by using Bloom Filter. Bloom uses hash table as a data structure which is an efficient data structure for membership queries to avoid lookup in some subsets which contain no matching rules and to sustain high throughput by using Longest Prefix Matching (LPM) algorithm. Hash table data structure which improves the performance by providing better boundaries on the hash collisions and memory accesses per search. The proposed classification algorithm also shows good scalability, high classification speed, irrespective of the number of rules. Performance analysis results show that the proposed algorithm enables network and security systems to support heavy traffic in the most effective manner

Aspirin induced fixed drug eruptions: a case report

Fixed drug eruptions are common cutaneous adverse drug reactions, commonly caused by anticonvulsants, antibiotics and analgesics. Here, we report a case of a 27-year-old male of fixed drug eruptions due to Aspirin which was used in treatment of headache