In the current era of the digital world, the hash of any digital means considered as a footprint or fingerprint of any digital term but from the ancient era, human fingerprint considered as the most trustworthy criteria for identification and it also cannot be changed with time even up to the death of an individual. In the court of law, fingerprint-proof is undeniably the most dependable and acceptable evidence to date. Fingerprint designs are exclusive in each human and the chance of two individuals having identical fingerprints is an exceptional case about one in sixty-four thousand million also the fingerprint minutiae patterns of the undistinguishable twins are different, and the ridge pattern of each fingertip remain unchanged from birth to till death. Fingerprints can be divided into basic four categories i.e. Loop, whorl, arch, and composites, nevertheless, there are more than 100 interleaved ridge and valleys physiognomies, called Galton's details, in a single rolled fingerprint. Due to the immense potential of fingerprints as an effective method of identification, the present research paper tries to investigate the problem of blood group identification and analysis of diseases those arises with aging like hypertension, type 2-diabetes and arthritis from a fingerprint by analyzing their patterns correlation with blood group and age of an individual. The work has been driven by studies of anthropometry, biometric trademark, and pattern recognition proposing that it is possible to predict blood group using fingerprint map reading. Dermatoglyphics as a diagnostic aid used from ancient eras and now it is well established in number of diseases which have strong hereditary basis and is employed as a method for screening for abnormal anomalies. Apart from its use in predicting the diagnosis of disease; dermatoglyphics is also used in forensic medicine in individual identification, physical anthropology, human genetics and medicine. However, the Machine and Deep Learning techniques, if used for fingerprint minutiae patterns to be trained by Neural Network for blood group prediction and classification of common clinical diseases arises with aging based on lifestyle would be an unusual research work.
Objective: Tolterodine tartrate (tolterodine) is used for treating overactive bladder (OAB) with symptoms of urinary frequency, urgency and leakage. Tolterodine is an antimuscarinic (anticholinergic) agent. It works by blocking a chemical that causes contractions of the bladder. Present work involved development of a novel drug delivery system of tolterodine intended to be taken once daily.Methods: Extended release (ER) pellets of tolterodine were prepared and optimized for in vitro drug release. Subsequently, these pellets were filled into a suitable sized capsule. The resulting capsules were evaluated for in vitro drug release. Optimized formulation was subjected to accelerated stability studies for 3 mo and was evaluated for description, average weight, assay and drug release.Results: The optimized ER capsule exhibited similar dissolution profile as that of the reference listed drug (RLD), with approximately 45%, 75% and more than 80% release in 3 h, 5 h and 7 h respectively. Accelerated stability studies indicated good physical and chemical stability of the formulation.Conclusion: ER formulation of tolterodine was optimized and can be used as once a day dosage, reducing the frequency of administration when compared with the immediate release formulation. The developed formulation exhibited similar behavior as that of reference formulation Detrol LA marketed in the US.
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