Mitochondrial-derived peptides (MDPs), including humanin, MOTS-c, and small humanin-like peptides (SHLPs), have emerged as promising therapeutic candidates for neurodegenerative diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), and Huntington's disease (HD). This review systematically evaluates current literature retrieved from databases including PubMed, Scopus, and Web of Science using keywords such as "mitochondrial-derived peptides," "neurodegeneration," "humanin," "MOTS-c," and "SHLPs." Studies were included based on their relevance to mitochondrial function, oxidative stress, neuroprotection, and anti-inflammatory mechanisms in AD, PD, and HD models. Despite growing interest, current research remains limited in understanding the precise molecular pathways. Our review highlights their role in mitigating disease-specific pathologies such as Amyloid-beta (Aβ) toxicity in AD, dopaminergic neuron loss in PD, and mutant huntingtin aggregation in HD while also emphasizing their potential to attenuate oxidative stress and neuroinflammation. By identifying critical knowledge gaps, particularly in the areas of molecular mechanisms of MDPs in neuroprotection, targeted delivery, and clinical translation, this review provides a comprehensive framework to guide future investigations.

Cloud is very effective technology, which works over the Internet provides services such as servers, storage, networking, workstations, virtual environment etc. This technology supports the distributed environment with different servers to complete the tasks. On demand of these services, the way to balance the heavy load and allow scheduling the task plays vital role. To attain enormous performance in virtual environment, many algorithms to handle those are already developed. We can implement virtualization on operating systems to balance the work load and attain better overall performance. Based on the availability of resources, we can avail more than one container to fasten the execution completion. Incoming tasks should be schedule to all available servers in equal manner based on resource availability is called load balancing. Among multiple containers, the incoming requests are assigned to available one is called task scheduling. In this paper, Improved Grey Wolf Optimization (GWO) is implemented for load balancing and SJF is implemented for task scheduling, through which we can obtain maximum throughput with minimal makespan.

Cloud computing is an innovative evolving technology provides supreme computing power to the users in cloud environment over the Internet. Requirements of end users are getting better service at low cost and Service Providers requirements are attain maximum gain with minimal overhead. Load Balancing in Virtual Machine plays a vital role to achieve better performance. Request for Virtual Machine and submission of tasks is purely dynamic, which is based on client demand for task execution. In traditional task scheduling method, the scheduling is not optimized. In this paper, two different Meta heuristic scheduling algorithms are implemented for better scheduling of tasks. Many optimization algorithms such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to handle the load balance in dynamic environment. In this paper, different LB optimized techniques and conventional algorithm results are compared and analyze by its metrics results.

Thiol-modified aptamer oligonucleotides containing gold nanoparticles (G@NPs) bound to glycated hemoglobin (HbA1c) with high affinity in whole blood samples. Aptamer thiol groups enhanced the stability of aptamers adsorbed on the surface of G@NPs. A detection strategy was used to visually confirm the presence of HbA1c and the absorbance of the colored compounds at selected wavelengths. Modified G@NPs are used in many colorimetric detection strategies due to their high extinction coefficient and strongly distance-dependent optical properties. In the present study, as the target analyte reports a method to prepare gold nanoparticle-bound purple aptamer aggregates that rapidly collapse into bluish red dispersed nanoparticles upon binding of HbA1c. HbA1c was detected in unpretreated whole blood after dilution at pH 7.4 and with UV-VIS double beam spectrophotometer with a lower limit of detection (LOD) (0.1 μM) and a much broader linear detection range (0.1 μM–100 μM). This current colorimetric method provides a rapid (7 min), accurate and high determination of whole blood HbA1c with minimal interference from uric acid, bovine serum albumin, hemoglobin, and glucose. The lower detection limit and broader linearity of aptamer-bound G@NPs hybrids improve the accuracy and precision of HbA1c measurements.