Stability-Indicating TLC-Densitometric and HPLC Methods for Simultaneous Determination of Teneligliptin and Pioglitazone in Pharmaceutical Dosage Forms with Eco-Friendly Assessment.

The combination of teneligliptin hydrobromide hydrate and pioglitazone hydrochloride in pharmaceutical formulations has improved type 2 diabetes management. Two chromatographic methods TLC-densitometry and RP-HPLC were developed for simultaneous quantification of teneligliptin hydrobromide hydrate and pioglitazone hydrochloride in pharmaceutical formulations, ensuring accuracy and stability assessment. The TLC method uses a mobile phase of methanol, toluene, ethyl acetate and triethylamine (1:7:2:0.1, v/v/v/v) on TLC silica gel plates, scanned at 268 nm. The RP-HPLC method employs isocratic elution with acetonitrile and sodium acetate buffer (adjust pH 3.6 with glacial acetic acid, 60:40 v/v) on a shimpack C18 column (250 × 4.6 mm i.d., 5 µm), detected at 235 nm. Both methods offer high accuracy and reliability, making them valuable for pharmaceutical quality control. Additionally, an environmental impact assessment was conducted using eco-scale, Analytical Greenness Metric Approach, Green Analytical Procedure Index, and national environmental method index to evaluate solvent consumption, waste generation and energy usage. Statistical comparisons (t-tests and F-tests) validate the outcomes of both methods, ensuring their effectiveness in drug formulation analysis. These methods can enhance pharmaceutical quality control while fulfilling environmental responsibilities.

Attack detection and mitigation scheme through novel authentication model enabled optimized neural network in smart healthcare.

The Internet of Things (IoT) have become an important part of human in day-to-day life as it permits accesses and manages data flawlessly, the security of data in cloud storage is of great concern in healthcare applications. This paper proposes a secure authentication to protect the sensible data related to healthcare through IoT network. Initially, the Electrocardiography (ECG) signal from the patients are stored in the cloud in encrypted form, where the proposed modified Elliptic-curve Diffie-Hellman (ECDH) encryption is applied to ensure secure access to the stored data to be used for the analysis of arrhythmia. The obtained data for the arrhythmia diagnosis is subjected to classify the attack using the neural network (NN). The weights of the NN are tuned using the proposed hybrid tempest brain optimization algorithm, which integrates the characteristic features of collaborative search agents and the hybrid search agents. The proposed method obtained the values of 95%, 7150, and 111 of detection accuracy, number of genuine users, and information loss of the respectively, which shows the superiority of the proposed method in attack detection and mitigation.