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Nanostructured Lipid Carriers (NLC) are nano-sized colloidal drug delivery system that contains a lipid mixture consisting of both solid and liquid lipids in their core. This Lipid-Based Nanosystem is introduced as a biocompatible, non-toxic, and safe nano-drug delivery system as compared to polymeric or metallic nanoparticles. Due to its safety, stability, and high drug loading capacity compared to other lipid-based nanocarriers, NLC gained the attention of researchers to formulate safe and effective drug carriers. The ability to increase drug solubility and permeability while encapsulating the drug in a lipidic shell makes them an ideal carrier for drug delivery through difficult-to-achieve routes. Surface modification of NLC and the use of various additives result in drug targeting and increased residence time. With such qualities, NLCs can be used to treat a variety of diseases such as cancer, infections, neurodegenerative diseases, hypertension, diabetes, and pain management. This review focuses on the recent developments being made to deliver the drugs and genes through different routes via these nanocarriers. Here, we also discuss about historical background, structure, types of NLC and commonly employed techniques for manufacturing lipid-based nanocarriers.

There are two distinct forms of Newton’s second law of motion (1686) i.e. original or Principia’s form of (change in motion is proportional to impressed force, F = KdV) and textbook form (rate of change of momentum is proportional to impressed force, F =ma). Newton neither gave acceleration nor F =ma, it is mentioned by IOP England, publications of the American Institute of Physics, etc. Truesdell has inconsistently pointed out in 1960 that Euler had given F =ma in 1752, but the truth is that Euler had given F=2ma in the said paper. Euler had also given various equations such as F =ma/n, F =2ma, F =ma/2g, F =ma etc.; but these are ignored by Truesdell. The exceptionally useful equation F =ma was given by Euler in 1775, and then succeeding scientists inconsistently tried to show that F =ma follows from the original form of the law. Consequently, some arbitrary assumptions are made, original form, F =KdV; and the fact that Euler gave F =ma are not mentioned in the standard textbooks. For comparison, Newton’s first law and third law (Reaction =-Action) are the same in the Principia and textbooks. In the existing literature, F =ma is obtained from Principia’s definition of NSLM, by replacing ‘change in motion’ equal to ‘rate of change of momentum’, but motion is not ascribed to any units and dimensions. If the original definition of Newton’s law is changed in a postulatory way i.e. ‘change in motion’ is replaced by ‘rate of change in momentum’ and ‘proportionality’ by ‘equality’; then F=ma is obtained from a modified equation without any arbitrary assumption. In 1893, Rouse Ball randomly altered Newton’s second law as a change in momentum [per unit time] is always proportional to the impressed force.

Wireless Sensor Networks (WSN) is an increasingly growing field, due to its enormous applications. In WSNs, energy conservation is the most important design challenge. In WSNs, unequal clustering can be classified as the best data transmission method that saves energy, where the size of the cluster changes in proportion to the cluster head’s (CH’s) distance from the base station (BS), so as to prevent energy holes/hot-spots from being formed. We have developed GA-UCR in this paper, a “Genetic Algorithm based Unequal Clustering and Routing Protocol for Wireless Sensor Networks”. For CH election, genetic algorithm (GA) has been utilized with three fitness functions- remaining energy of CH nodes, distance between CH and BS/sink, and inter-cluster separation. For inter-cluster multi-hopping, to route the data towards BS, again GA is utilized due to the NP-Hard nature of the problem, with three fitness functions-residual/remaining energy of next hop nodes, CH to next hop node distance and number of hops. Simulation outcomes and analysis show that with reference to energy consumption, network lifetime and scalability, the proposed algorithm exceeds the existing algorithms such as Direct propagation, LEACH, TL-LEACH, GCA, EAERP and GAECH.

In this article, we propose a novel routing algorithm for wireless sensor network, which achieves uniform energy depletion across all the nodes and thus leading to prolonged network lifetime. The proposed algorithm, divides the Region of Interest into virtual zones, each having some designated cluster head nodes. In the entire process, a node can either be a part of a cluster or it may remain as an independent entity. A non-cluster member transmits its data to next hop node using IRP-Intelligent Routing Process (based on the trade-off between the residual energy of itself as well as its neighbor, and the required energy to transmit packets to its neighbor). If on the transmission path, some cluster member is elected as a next hop, it rejects IRP and transmits the packets to cluster head, which later forwards them to sink (adopting multihop communication among cluster heads). Routing is not solely performed using clusters, rather they aid the overall routing process, hence this protocol is named as Cluster Aided Multipath Routing (CAMP). CAMP has been compared with various sensor network routing protocols, viz., LEACH, PEGASIS, DIRECT TRANSMISSION, CEED, and CBMR. It is found that the proposed algorithm outperformed them in network lifetime, energy consumption and coverage ratio.

Clustering-based networks play a vital role in efficient utilization of energy consumption of each sensor node (SN) in wireless sensor networks (WSNs). Furthermore, firstly, prolonged network’s lifetime is observed as the key factor to analyze the protocol’s efficiency. However, in critical applications, i.e., military surveillance, environmental monitoring and structural health monitoring, stability region is also an important aspect for consideration. This provides reliability of data from each SN in the network. On the other hand, once a SN dies at any region, we are not able to sense that region which leaves the region vulnerable from detection of events. With this reason, it is highly important for an energy efficient protocol to provide good stability region with prolonged network lifetime. Secondly, a protocol should be intelligent enough to handle homogeneous as well as heterogeneous nodes efficiently in the network (i.e., homogeneous and heterogeneous WSNs) because once the network executes, a homogeneous WSN is also transformed in heterogeneous WSN. This is because of different radio communication features, occurrence of random events or morphological attributes of the network field. optimized-HEED protocols are one of the most recent clustering-based algorithms which improved the various shortcomings of classical protocol, i.e., HEED and provided far efficient results in terms of energy consumption, load balancing and network lifetime. However, these demonstrated their efficiency for homogeneous WSN only. In this paper, we extend the optimized-HEED protocols for heterogeneous WSNs model on the basis of varying levels of node heterogeneity (in terms of energy), i.e., 1-level, 2-level, 3-level and multi-level, and propose these as heterogeneous optimized-HEED (Hetero-OHEED) protocols. Simulation results confirm that by increasing the level of node’s heterogeneity, stability region of each Hetero-OHEED protocol enhances extremely with prolonged network lifetime. These provide a rich solution in designing of efficient protocols for those applications, where stability region and network lifetime require equal importance.

Poultry ( Gallus domesticus ) farming plays an important role as an income generating enterprise in a developing country like Nepal, contributing more than 4% to the national Gross Domestic Product (GDP). Newcastle Disease (ND) is a major poultry disease affecting both commercial and backyard poultry production worldwide. There were more than 90 reported ND outbreaks in Nepal in 2018 with over 74,986 birds being affected. ND is responsible for over 7% of total poultry mortality in the country. Recent outbreaks of ND in 2021 affected many farms throughout Nepal and caused massive loss in poultry production. ND is caused by a single-stranded ribonucleic acid (RNA) virus that presents very similar clinical symptoms as Influenza A (commonly known as bird flu) adding much complexity to clinical disease identification and intervention. We conducted a nationwide ND and Influenza A (IA) prevalence study, collecting samples from representative commercial and backyard poultry farms from across the major poultry production hubs of Nepal. We used both serological and molecular assessments to determine disease exposure history and identification of strains of ND Virus (NDV). Of the 40 commercial farms tested, both NDV (n = 28, 70%) and IAV (n = 11, 27.5%) antibodies were detected in majority of the samples. In the backyard farms (n = 36), sero-prevalence of NDV and IAV were 17.5% (n = 7) and 7.5% (n = 3) respectively. Genotype II NDV was present in most of the commercial farms, which was likely due to live vaccine usage. We detected never reported Genotype I NDV in two backyard farm samples. Our investigation into 2021 ND outbreak implicated Genotype VII.2 NDV strain as the causative pathogen. Additionally, we developed a Tablet formulation of the thermostable I2-NDV vaccine (Ranigoldunga ™ ) and assessed its efficacy on various (mixed) breeds of chicken ( Gallus domesticus ). Ranigoldunga ™ demonstrated an overall efficacy >85% with a stability of 30 days at room temperature (25°C). The intraocularly administered vaccine was highly effective in preventing ND, including Genotype VII.2 NDV strain.

Meiosis produces haploid gametes through a succession of chromosomal events, including pairing, synapsis, and recombination. Mechanisms that orchestrate these events remain poorly understood. We found that the SUMO (small ubiquitin-like modifier)–modification and ubiquitin-proteasome systems regulate the major events of meiotic prophase in mouse. Interdependent localization of SUMO, ubiquitin, and proteasomes along chromosome axes was mediated largely by RNF212 and HEI10, two E3 ligases that are also essential for crossover recombination. RNF212-dependent SUMO conjugation effected a checkpointlike process that stalls recombination by rendering the turnover of a subset of recombination factors dependent on HEI10-mediated ubiquitylation. We propose that SUMO conjugation establishes a precondition for designating crossover sites via selective protein stabilization. Thus, meiotic chromosome axes are hubs for regulated proteolysis via SUMO-dependent control of the ubiquitin-proteasome system.

Purpose: To compare the accuracy in astigmatism reduction by using IOLM 700 steep total keratometry (TK) axis, Berdahl and Hardten astigmatism fix, and Barrett Rx formula following misaligned toric intraocular lens (IOL). Methods: Ten patients with residual refractive astigmatism due to misalignment following toric IOL implantation were included in this retrospective study. They were analyzed at days 4, 7/8, and 10/11 following primary cataract surgery on the platform of Berdahl and Hardten astigmatism fix, Barrett Rx formula, and IOLM 700 to determine the optimum axis of repositioning, and underwent IOL realignment on the steep TK axis of IOLM 700 assisted by the Callisto eye. The final outcome parameters were subjective refraction and orientation of toric IOL assessed 22 ± 1 days following repositioning surgery. These parameters were fed in the Barrett Rx formula and its vector analysis graph was utilized to determine the predicted ideal axis with the least residual astigmatism and the estimated residual astigmatism if the toric IOL was realigned according to the axis suggested by Berdahl and Hardten astigmatism fix and Barrett Rx formula. Results: Realigning the toric IOL on IOLM 700 steep TK axis along with the Callisto eye reduces the residual refractive astigmatism significantly (P = 0.003) from 2.00 ± 0.78 D to 0.18 ± 0.12 D (90.5 ± 7.6%) in comparison to the estimated 0.57 ± 0.31 D (68.4 ± 21.9%) by Berdahl and Hardten astigmatism fix and 0.61 ± 0.33 D (66.4 ± 23.5%) by Barrett Rx formula. Conclusion: Realigning the misaligned toric IOL on the IOLM 700 steep TK axis gives a better reduction in the residual refractive astigmatism in comparison to Berdahl and Hardten astigmatism fix and Barrett Rx formula.