DEVELOPMENT AND IN VITRO–IN VIVO EVALUATION OF GASTRORETENTIVE FLOATING TABLETS OF AN ANTIRETROVIRAL AGENT RITONAVIR

Objective: The present research work concerns the development of the extended release of Ritonavir floating matrix tablets, designed to prolong the gastric residence time, increase the drug bioavailability, and diminish the side effects of irritating drugs. Methods: The floating tablets of Ritonavir were prepared by direct compression method using different grades of hydroxypropyl methylcellulose (HPMC), crospovidone, Polyox WSR 303, and sodium bicarbonate, as gas generating agent. Evaluation parameters and in vivo radiographic studies were conducted in suitable model. Results: Among all formulations, F21 was chosen as optimized formulation based on evaluation parameters such as floating lag time (33 s), total floating time (>24 h), and in vitro dissolution studies. From in vitro dissolution studies, the optimized formulation F21 and marketed product were shown 98.67% and 91.46±5.02% of drug release, respectively. The main appliance of medication discharge follows zero-order kinetics and non- Fickian transport by coupled diffusion and erosion. In vivo experiments maintained the potentials in extending the gastric residence time in the fasted state in beagle dogs. The mean gastric residence time of the optimized formulation found to be 330 min±40 in the stomach, where longer gastric residence time is an important condition for prolonged or controlled drug release and also for enhanced bioavailability. Conclusion: From in vitro and in vivo radiographic studies, Ritonavir floating tablets estimated to provide novel choice for harmless, inexpensive, and extended release for the effective management of AIDS

Gradient High Performance Liquid Chromatography method for determination of related substances in (7-{4-[4-(1-Benzothiophen-4-YL] Butoxy} Quinolin-2(1H)-one) dosage form

A sensitive HPLC method was developed and validated for the estimation of related substances in Brexpiprazole in drug Product. The developed method is found to be specific, reproducible, and stability indicating. Kromasil100-5 C18 (150x4.6mm), 5μ column was used and mobile phase consisted of mixture of phosphate buffer of pH5.2 and ACN in gradient program is used at a flow rate of 1.0mL/min at a wave length of 215 nm. The detector linearity was established from concentrations ranging from LOQ-150% of specification level with a correlation co-efficient of 0.999. The method was also validated for specificity, LOD, LOQ, accuracy, robustness, precision. The method is proved to be robust with respect to change in flow rate, pH, organic phase composition and column temperature. The proposed method is found to be sensitive, precise, rapid, reproducible, and offers good column life. Keywords: RP-HPLC; Stability indicating method; Brexpiprazole; validation

Decentralized Machine Learning based Energy Efficient Routing and Intrusion Detection in Unmanned Aerial Network (UAV)

Decentralized machine learning (FL) is a system that uses federated learning (FL). Without disclosing locally stored sensitive information, FL enables multiple clients to work together to solve conventional distributed ML problems coordinated by a central server. In order to classify FLs, this research relies heavily on machine learning and deep learning techniques. The next generation of wireless networks is anticipated to incorporate unmanned aerial vehicles (UAVs) like drones into both civilian and military applications. The use of artificial intelligence (AI), and more specifically machine learning (ML) methods, to enhance the intelligence of UAV networks is desirable and necessary for the aforementioned uses. Unfortunately, most existing FL paradigms are still centralized, with a singular entity accountable for network-wide ML model aggregation and fusion. This is inappropriate for UAV networks, which frequently feature unreliable nodes and connections, and provides a possible single point of failure. There are many challenges by using high mobility of UAVs, of loss of packet frequent and difficulties in the UAV between the weak links, which affect the reliability while delivering data. An earlier UAV failure is happened by the unbalanced conception of energy and lifetime of the network is decreased; this will accelerate consequently in the overall network. In this paper, we focused mainly on the technique of security while maintaining UAV network in surveillance context, all information collected from different kinds of sources. The trust policies are based on peer-to-peer information which is confirmed by UAV network. A pre-shared UAV list or used by asymmetric encryption security in the proposal system. The wrong information can be identified when the UAV the network is hijacked physically by using this proposed technique. To provide secure routing path by using Secure Location with Intrusion Detection System (SLIDS) and conservation of energy-based prediction of link breakage done by location-based energy efficient routing (LEER) for discovering path of degree connectivity.  Thus, the proposed novel architecture is named as Decentralized Federate Learning- Secure Location with Intrusion Detection System (DFL-SLIDS), which achieves 98% of routing overhead, 93% of end-to-end delay, 92% of energy efficiency, 86.4% of PDR and 97% of throughput

A Novel Cryptography-Based Multipath Routing Protocol for Wireless Communications

Communication in a heterogeneous, dynamic, low-power, and lossy network is dependable and seamless thanks to Mobile Ad-hoc Networks (MANETs). Low power and Lossy Networks (LLN) Routing Protocol (RPL) has been designed to make MANET routing more efficient. For different types of traffic, RPL routing can experience problems with packet transmission rates and latency. RPL is an optimal routing protocol for low power lossy networks (LLN) having the capacity to establish a path between resource constraints nodes by using standard objective functions: OF0 and MRHOF. The standard objective functions lead to a decrease in the network lifetime due to increasing the computations for establishing routing between nodes in the heterogeneous network (LLN) due to poor decision problems. Currently, conventional Mobile Ad-hoc Network (MANET) is subjected to different security issues. Weathering those storms would help if you struck a good speed-memory-storage equilibrium. This article presents a security algorithm for MANET networks that employ the Rapid Packet Loss (RPL) routing protocol. The constructed network uses optimization-based deep learning reinforcement learning for MANET route creation. An improved network security algorithm is applied after a route has been set up using (ClonQlearn). The suggested method relies on a lightweight encryption scheme that can be used for both encryption and decryption. The suggested security method uses Elliptic-curve cryptography (ClonQlearn+ECC) for a random key generation based on reinforcement learning (ClonQlearn). The simulation study showed that the proposed ClonQlearn+ECC method improved network performance over the status quo. Secure data transmission is demonstrated by the proposed ClonQlearn + ECC, which also improves network speed. The proposed ClonQlearn + ECC increased network efficiency by 8-10% in terms of packet delivery ratio, 7-13% in terms of throughput, 5-10% in terms of end-to-end delay, and 3-7% in terms of power usage variation

Secure Energy Aware Optimal Routing using Reinforcement Learning-based Decision-Making with a Hybrid Optimization Algorithm in MANET

Mobile ad hoc networks (MANETs) are wireless networks that are perfect for applications such as special outdoor events, communications in areas without wireless infrastructure, crises and natural disasters, and military activities because they do not require any preexisting network infrastructure and can be deployed quickly. Mobile ad hoc networks can be made to last longer through the use of clustering, which is one of the most effective uses of energy. Security is a key issue in the development of ad hoc networks. Many studies have been conducted on how to reduce the energy expenditure of the nodes in this network. The majority of these approaches might conserve energy and extend the life of the nodes. The major goal of this research is to develop an energy-aware, secure mechanism for MANETs. Secure Energy Aware Reinforcement Learning based Decision Making with Hybrid Optimization Algorithm (RL-DMHOA) is proposed for detecting the malicious node in the network. With the assistance of the optimization algorithm, data can be transferred more efficiently by choosing aggregation points that allow individual nodes to conserve power The optimum path is chosen by combining the Particle Swarm Optimization (PSO) and the Bat Algorithm (BA) to create a fitness function that maximizes across-cluster distance, delay, and node energy. Three state-of-the-art methods are compared to the suggested method on a variety of metrics. Throughput of 94.8 percent, average latency of 28.1 percent, malicious detection rate of 91.4 percent, packet delivery ratio of 92.4 percent, and network lifetime of 85.2 percent are all attained with the suggested RL-DMHOA approach

Recognition and Classification of Leaf Disease in Potato Plants

Farming is one of the most important lifelines of the country. A nation’s growth majorly depends on how advanced and effective their agricultural practices are in improving the crop yield. When a crop is grown many at times, farmers are unable to identify the health and wellbeing of the plant; they only recognize the problems when it becomes too late hence losing out on that year's expected yield. In this study, we have introduced a recognition and classification technique which is able to detect any ailments that the plant is suffering from at an early stage itself thus enabling the farmers to do the needful at a recoverable stage itself. To make the system as user-friendly as possible, we have provided a feature where the farmers are able to assess the health of the plant by providing a picture of the potato plants’ leaf

Isolation, identification and molecular characterization of Ralstonia solanacerum isolates collected from Southern Karnataka

Bacterial wilt caused by Ralstonia solanacearum, is the major threat to tomato cultivation in all tomato growing areas of Karnataka.  R. solanacearum was isolated from the infected host plants collected from different locations of southern Karnataka. The identity of the isolates was established using morphological, biochemical, and molecular analysis using species specific PCR primers. The race and biovar specificity of pathogen was determined through pathogenicity test on different host plants and the ability of isolates to use carbohydrates, respectively. Phylotype classification was done by phylotype specific multiplex PCR using phylotype specific primers. All the bacterial isolates showed the characteristic creamy white fluidal growth with pink centre on the Tetrazolium chloride medium. Further, the isolates amplified at 280 bp, which confirmed the identity of pathogen as Ralstonia solanacearum. Our results showed that all isolates belonged to Race 1 of the pathogen. Among different isolates obtained, four isolates each were identified to be Biovar III and Biovar IIIA, repectively, while two isolates were identified as Biovar IIIB. All the ten isolates were affiliated to Phylotype I of Ralstonia solanaceraum species complex. These findings may help in devising the management practices for bacterial wilt of tomato in southern Karnataka

Draft genome sequence of Sclerospora graminicola, the pearl millet downy mildew pathogen:Genome sequence of pearl millet downy mildew pathogen

Sclerospora graminicola pathogen is one of the most important biotic production constraints of pearl millet worldwide. We report a de novo whole genome assembly and analysis of pathotype 1. The draft genome assembly contained 299,901,251 bp with 65,404 genes. Pearl millet [Pennisetum glaucum (L.) R. Br.], is an important crop of the semi-arid and arid regions of the world. It is capable of growing in harsh and marginal environments with highest degree of tolerance to drought and heat among cereals (1). Downy mildew is the most devastating disease of pearl millet caused by Sclerospora graminicola (sacc. Schroet), particularly on genetically uniform hybrids. Estimated annual grain yield loss due to downy mildew is approximately 10?80 % (2-7). Pathotype 1 has been reported to be the highly virulent pathotype of Sclerospora graminicola in India (8). We report a de novo whole genome assembly and analysis of Sclerospora graminicola pathotype 1 from India. A susceptible pearl millet genotype Tift 23D2B1P1-P5 was used for obtaining single-zoospore isolates from the original oosporic sample. The library for whole genome sequencing was prepared according to the instructions by NEB ultra DNA library kit for Illumina (New England Biolabs, USA). The libraries were normalised, pooled and sequenced on Illumina HiSeq 2500 (Illumina Inc., San Diego, CA, USA) platform at 2 x100 bp length. Mate pair (MP) libraries were prepared using the Nextera mate pair library preparation kit (Illumina Inc., USA). 1 ?g of Genomic DNA was subject to tagmentation and was followed by strand displacement. Size selection tagmented/strand displaced DNA was carried out using AmpureXP beads. The libraries were validated using an Agilent Bioanalyser using DNA HS chip. The libraries were normalised, pooled and sequenced on Illumina MiSeq (Illumina Inc., USA) platform at 2 x300 bp length. The whole genome sequencing was performed by sequencing of 7.38 Gb with 73,889,924 paired end reads from paired end library, and 1.15 Gb with 3,851,788 reads from mate pair library generated from Illumina HiSeq2500 and Illumina MiSeq, respectively. The sequences were assembled using various assemblers like ABySS, MaSuRCA, Velvet, SOAPdenovo2, and ALLPATHS-LG. The assembly generated by MaSuRCA (9) algorithm was observed superior over other algorithms and hence used for scaffolding using SSPACE. Assembled draft genome sequence of S. graminicola pathotype 1 was 299,901,251 bp long, with a 47.2 % GC content consisting of 26,786 scaffolds with N50 of 17,909 bp with longest scaffold size of 238,843 bp. The overall coverage was 40X. The draft genome sequence was used for gene prediction using AUGUSTUS. The completeness of the assembly was investigated using CEGMA and revealed 92.74% proteins completely present and 95.56% proteins partially present, while BUSCO fungal dataset indicated 64.9% complete, 12.4% fragmented, 22.7% missing out of 290 BUSCO groups. A total of 52,285 predicted genes were annotated using BLASTX and 38,120 genes were observed with significant BLASTX match. Repetitive element analysis in the assembly revealed 8,196 simple repeats, 1,058 low complexity repeats and 5,562 dinucleotide to hexanucleotide microsatellite repeats.publishersversionPeer reviewe

Uso del láser en urgencias por periodontitis apical post tratamiento endodóntico

Introduction: Laser therapy and stimulation of the acupuncture points are anti-inflammatory and analgesic alternative treatments in dentistry.Objective: to describe the use of low power laser therapy in the emergency treatment of apical periodontitis after endodontic treatment during 2018.Methods: observational, descriptive, longitudinal, and prospective study of patients who attended emergency department at Guama Dentistry Clinic during 2018, Pinar del Río, presenting apical periodontitis after endodontic treatment; 86 patients participated in the study. Descriptive statistics was applied, respecting the bioethical principles.Results: female gender predominated (53,49 %), apical periodontitis after endodontic treatment was more prevalent in the age group 20-24 (30,23 %); 65,5 % of the patients presented remission and relief after the third treatment session. Only 2,33 % needed more than six treatment sessions.Conclusions: apical periodontitis after endodontic treatment is more common in women during the first half of the second decade of life. The treatment showed effectiveness from the first treatment sessions.Introducción: la terapia y estimulación con láser en puntos acupunturales constituyen alternativas de tratamiento antiinflamatorio y analgésico en estomatología.Objetivo: describir el uso de la terapia láser de baja potencia en el tratamiento de urgencias por periodontitis apical post tratamiento endodóntico durante el 2018.Método: estudio observacional, descriptivo, longitudinal y prospectivo en pacientes que acudieron a la consulta de urgencias de la Clínica Estomatológica “Guamá’’, municipio Pinar del Río, en el período durante el año 2018, por presentar periodontitis apical post tratamiento endodóntico. El universo estuvo constituido por 86 pacientes trabajándose con la totalidad. Se empleó estadística descriptiva y se siguieron los principios bioéticos.Resultados: predominó el sexo femenino (53,49 %), donde la periodontitis apical post tratamiento endodóntico se presentó en mayor cuantía en el grupo etario de 20 a 24 años de edad (30,23 %). El 65,5 % de los pacientes presentaron remisión y alivio tras la tercera sesión de tratamiento. Solo el 2,33 % necesitó más de seis sesiones de tratamiento.Conclusiones: la periodontitis apical post tratamiento endodóntico se presentan en mayor cuantía en las féminas, durante la primera mitad de la segunda década de vida. El tratamiento con terapia laser de baja frecuencia mostró efectividad desde las primeras sesiones de tratamiento