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Systematic review and meta‑analysis of teneligliptin for treatment of type 2 diabetes
Get PDFBackground and aim There are efcacy and safety concerns related to teneligliptin treatment. A systematic review of randomized controlled trials (RCTs) was undertaken to comprehensively profle the efcacy and safety of teneligliptin in the treatment of type 2 diabetes mellitus (T2DM). Methods Thirteen studies were chosen from a search of scientifc databases for RCTs using teneligliptin as a monotherapy or as an adjunct to other glycemic agents with pre-specifed inclusion criteria. We calculated weighted mean diferences (WMDs) and 95% confdence intervals (CIs) in each included trial and pooled the data using a random-efects model. Results Thirteen studies enrolled 2853 patients were identifed. Teneligliptin treatment was associated with weight gain (vs.placebo, weighted mean diference (WMD) 0.28 kg; 95% CI − 0.20–0.77 kg; I 2=86%; P=0.25). Compared to monotherapy, add on therapy with teneligliptin showed signifcant improvement in FPG mg/dl levels (WMD − 16.75 mg/dl; 95% CI − 19.38 to − 14.13 mg/dl), HOMA-β (WMD 7.91; 95% CI 5.38–10.45) and HOMA-IR (WMD − 0.27; 95% CI − 0.46 to − 0.07). The improvement in HbA1c was greater with monotherapy (WMD − 8.88 mmol/mol; 95% CI − 9.59 to − 8.08 mmol/mol).There was no signifcant risk of any hypoglycemia with teneligliptin compared to placebo (OR 0.84; 95% CI 0.44–1.60; I 2=0%; P=0.60). However, the risk was 1.84 times high when combined with other glycemic agents. The risk of cardiovascular events was comparable, regardless of treatment duration when compared to placebo or any other active comparator (OR 0.79; 95% CI 0.40–1.57; I 2=0%; P=0.50). [PROSPERO, CRD42022360785]. Conclusions Teneligliptin is an efective and safe therapeutic option for patients with T2DM, both as monotherapy and as add-on therapy. However, additional large-scale, high-quality, long-term follow-up clinical trials with diverse ethnic populations are required to confrm its long-term efcacy and safety
SYNTHESIS, CHARACTERIZATION AND ANTIMICROBIAL EVALUATION OF NOVEL BIS-HETERO CYCLIC DERIVATIVES
Get PDFThe present research outlines a series of bis-hetero cyclic derivatives (a1-6) synthesized from methyl-1-(2, 5-dioxopyrrolidin-1-yl) -6- methyl -2- oxo -4- phenyl -1, 2, 3, 4- tetrahydro pyrimidine -5- carboxylate treated with different aromatic aldehydes under acidic environment. The synthesized titled derivatives were confirmed by determination of physicochemical properties, by different spectral data and they were evaluated for in vitro antibacterial activity against Bacillus subtilis, Staphylococcus aureus, Escherichia coli and Pseudomonas aeruginosa and antifungal activity against Aspergillus niger and Candida albicans organisms at 25, 50, 100 µg mL-1 concentrations using streptomycin and fluconazole as reference standard drug respectively, through cup plate method. The in vitro antimicrobial assay results indicated that the derivatives a1, a2 and a3 showed significant antimicrobial activity, whereas the remaining derivatives showed moderate antimicrobial activities compared to the standard drugs. Further extension of this research to the cellular level is required to describe the mechanism of action, efficacy, and structural activity of these derivatives for antimicrobial activity
Exploring the structural, photophysical and optoelectronic properties of a diaryl heptanoid curcumin derivative and identification as a SARS-CoV-2 inhibitor
Get PDFDeveloping modifiable natural products those having antiviral activities against SARS-CoV-2 is a key research area which is popular in current scenario of COVID pandemic. A diaryl heptanoid curcumin and its derivatives are already presenting promising candidates for anti-viral drug development. We have synthesized single crystals of a dimethylamino derivative of natural curcumin and structural characterization was done by single crystal XRD analysis. Using steady-state absorption and emission spectra and guided by complimentary ab initio calculations, we unraveled the solvent effects on the photophysical properties of the dimethyl amino curcumin derivative. Chemical reactivity of the compound has investigated using frontier molecular orbitals and molecular electrostatic potential surface. High stability of the curcumin derivative in water environment has evaluated by Radial Distributions Functions (RDF) calculated via Molecular Dynamics (MD) simulations. The inhibitory activity of the title compound was evaluated by in silico methods and the stability of the protein-ligand complexes were studied using Molecular Dynamics simulations and MM-PBSA analysis. With this detailed study, we hope to motivate scientific community to develop new curcumin derivatives against SARS-CoV-2 virus
Design, docking, MD simulation and in-silco ADMET prediction studies of novel indolebased benzamides targeting estrogen receptor alfa positive for effective breast cancer therapy
Get PDFBreast cancer is one of the most common malignancies in women, afflicting millions of lives each year. Our current study suggests that the development of the most promising 7-substituted -1-(4-(piperidine-1-yl methoxy)benzyl)-1H-indole-3-carboxamide derivatives results in potent anticancer agents through in-silico investigations. The molecular docking was performed against estrogen receptor alpha (ER-α) positive (PDB ID: 3UUD) of breast cancer cells to anticipate the binding modes of the designed compounds and the likely mode of action. The interactions between the ligands and amino acid residues were thoroughly elucidated. The stability of the docked protein-ligand complexes was further confirmed by 100 ns molecular simulations methods. From in-silico studies, indole-based benzamides exhibited satisfactory physicochemical, drug-likeness and toxicity properties. To conclude, the most promising substituted benzamide analogs on the indole ring could serve as a possible modulator against ER-α positive breast cancer
Real-Time Emotion Recognition in Speech: A Machine Learning Perspective
Get PDFThis paper proposes a novel approach to voice to voice translation using recurrent neural networks (RNNs). Voice to voice translation is a challenging task that involves converting spoken words in one language to another language while retaining the speaker's voice characteristics. RNNs are a class of deep learning models that have shown promise in a variety of tasks involving the use of natural language, such as speech
recognition and machine translation. We present a RNN-based model that takes as input the audio signal in one language and produces the corresponding audio signal in the target language. We also introduce a new loss function that encourages the model to preserve the speaker's voice characteristics. We evaluate the proposed approach on a publicly available dataset and show that in terms of speaker similarity and translation accuracy, our model performs better than cutting-edge techniques. Our approach has potential applications in various
domains, including language learning, entertainment, and communicatio
Group Acceptance Sampling Plans for Resubmitted Lots Under Odd Generalized Exponential Log-Logistic Distribution
Get PDFIn this manuscript, we developed resubmitted lots with group acceptance sampling plan for the lifetime of the product follows the odd generalized exponential log logistic distribution introduced by Rosaiah et al. (2016c). The values of the design parameters of the proposed plan are obtained which are satisfying the both producer’s as well consumer’s risk by fixing the experiment termination time. An application of the proposed plan to the industry is presented and the Kolmogorov-Smirnov test was conducted. However, this plan provides reasonable fit for lifetime of items of ball bearings data. Finally, the advantage of the proposed plan reduces the sample size as compared with the ordinary group sampling scheme. An example is given to illustrate the methodology
Enumeration of doubly semi-equivelar maps on the Klein bottle
No full textAvertexv inamapM hastheface-sequence(pn1 1 .pn2 2 .....pnk
k ),if consecutive ni numbers of pi-gons are incident at v in the given cyclic order for 1 ≤ i ≤ k. A map is called semi-equivelar if the face-sequence of each vertex is same throughout the map. A doubly semi-equivelar map is a generalization of semi-equivelar map
which has precisely 2 distinct face-sequences. In this article, we determine all the types of doubly semi-equivelar maps of combinatorial curvature 0 on the Klein bottle. We present classification of doubly semi-equivelar maps on the Klein bottle and illustrate this classification for those doubly semi-equivelar maps which comprise of face-sequence pairs {(36),(33.42)} and {(33.42),(44)
Characteristic evaluation of concrete containing sugarcane bagasse ash as pozzolanic admixture
No full textThis study presents the influence of sugar cane bagasse ash (SCBA) as pozzolanic material on the microstructure, strength and durability properties of concrete. To enhance the pozzolanic properties of raw SCBA, it is incinerated at 6000C for
2 hours in muffle furnace at the rate of 100C/min and ball-milled for 240 minutes to increase its fineness more than cement. SCBA is pre-treated to remove adhered water molecules by heating to 1000C for 24 hours and then it is characterised by SEM/EDS, XRF, FTIR and TGA tests to assess the microstructural properties. In this study cement is replaced by SCBA with 5,10,15,20 and 25% by weight of cement to examine the mechanical and
durability properties of concrete. The optimum dosage of SCBA is determined based on various tests conducted on concrete and it is found to be 15%. The tests conducted are compressive strength, split tensile strength, sorptivity, and acid resistance. A maximum strength gain is observed with 15% replacement of
cement by SCBA with an increment of 14.8%, 20% and 18.2% for compressive strength at 28, 56 and 90 days, respectively, due to enhanced pozzolanic reactivity and improved microstructure of SCBA concrete and split strength is increased in the range of 16% to 18% over the reference concrete mix. The sorptivity of SCBA concrete is found to reduce by 24.4% compared to reference mix at 15% replacement. The mass loss, strength loss, and dimensional loss are studied on concrete samples based on acid resistance test. The SCBA blended concrete with 15% replacement of cement showed the least acid durability lost
factor which is 305.5 with sulphuric acid and 351.72 with hydrochloric acid
Enhanced Energy Efficient with a Trust Aware in MANET for Real-Time Applications
Get PDFMobile ad hoc networks (MANETs) are subjected to attack detection for transmitting and creating new messages or existing message modifications. The attacker on another node evaluates the forging activity in the message directly or indirectly. Every node sends short packets in a MANET
environment with its identifier, location on the map, and time through beacons. The attackers on the network broadcast the warning message using faked coordinates, providing the appearance of a network collision. Similarly, MANET degrades the channel utilization performance. Performance highly affects network performance through security algorithms. This paper
developed a trust management technique called Enhanced Beacon Trust Management with Hybrid Optimization (EBTM-Hyopt) for efficient cluster head selection and malicious node detection. It tries to build trust among connected nodes and may improve security by requiring every participating
node to develop and distribute genuine, accurate, and trustworthy material across the network. Specifically, optimized cluster head election is done periodically to reduce and balance the energy consumption to improve the lifetime
network. The cluster head election optimization is based on hybridizing Particle Swarm Optimization (PSO) and Gravitational Search Optimization Algorithm (GSOA) concepts to enable and ensure reliable routing. Simulation results show that the proposed EBTM-HYOPT outperforms the state-of-theart trust model in terms of 297.99 kbps of throughput, 46.34% of PDR, 13% of energy consumption, 165.6 kbps of packet loss, 67.49% of end-to-end delay, and 16.34% of packet lengt
Realtime Speech Translation using Recurrent Neural Networks
Get PDFThe vicinity of human-computerconnection, speech-centered emotion identification is a fast expanding discipline. Through voice signal analysis, it involves an automatic identification of human emotions. Numerous possible uses for this technology exist in industries like health care privacy, and entertainment. Emotion recognition often begins with the extraction of pertinent elements from speech signals, which are then classified into various emotional states using machine learning algorithms. This procedure faces a number of difficulties, including differences in speech patterns due to linguistic, cultural, and individual variables. However, the precision of emotion identification systems has substantially increased in recent years thanks to developments in deep learning algorithms including the accessibility of vast datasets. This study presents a rundown of the most recent advancements in speech-based emotion recognition, including applications, difficulties, and potential future approache