Strategies for modeling aging and age-related diseases.

The ability to reprogram patient-derived-somatic cells to IPSCs (Induced Pluripotent Stem Cells) has led to a better understanding of aging and age-related diseases like Parkinson's, and Alzheimer's. The established patient-derived disease models mimic disease pathology and can be used to design drugs for aging and age-related diseases. However, the age and genetic mutations of the donor cells, the employed reprogramming, and the differentiation protocol might often pose challenges in establishing an appropriate disease model. In this review, we will focus on the various strategies for the successful reprogramming and differentiation of patient-derived cells to disease models for aging and age-related diseases, emphasizing the accuracy in the recapitulation of disease pathology and ways to overcome the limitations of its potential application in cell replacement therapy and drug development.

Ant Colony Optimization-Enabled CNN Deep Learning Technique for Accurate Detection of Cervical Cancer.

Cancer is characterized by abnormal cell growth and proliferation, which are both diagnostic indicators of the disease. When cancerous cells enter one organ, there is a risk that they may spread to adjacent tissues and eventually to other organs. Cancer of the cervix of the uterus often initially manifests itself in the uterine cervix, which is located at the very bottom of the uterus. Both the growth and death of cervical cells are characteristic features of this condition. False-negative results provide a significant moral dilemma since they may cause women to get an incorrect diagnosis of cancer, which in turn can result in the woman's premature death from the disease. False-positive results do not raise any significant ethical concerns; but they do require a patient to go through an expensive and time-consuming treatment process, and they also cause the patient to experience tension and anxiety that is not warranted. In order to detect cervical cancer in its earliest stages in women, a screening procedure known as a Pap test is often performed. This article describes a technique for improving images using Brightness Preserving Dynamic Fuzzy Histogram Equalization. To individual components and find the right area of interest, the fuzzy c-means approach is applied. The images are segmented using the fuzzy c-means method to find the right area of interest. The feature selection algorithm is the ACO algorithm. Following that, categorization is carried out utilizing the CNN, MLP, and ANN algorithms.

Non-obstructive intracellular nanolasers.

Molecular dyes, plasmonic nanoparticles and colloidal quantum dots are widely used in biomedical optics. Their operation is usually governed by spontaneous processes, which results in broad spectral features and limited signal-to-noise ratio, thus restricting opportunities for spectral multiplexing and sensing. Lasers provide the ultimate spectral definition and background suppression, and their integration with cells has recently been demonstrated. However, laser size and threshold remain problematic. Here, we report on the design, high-throughput fabrication and intracellular integration of semiconductor nanodisk lasers. By exploiting the large optical gain and high refractive index of GaInP/AlGaInP quantum wells, we obtain lasers with volumes 1000-fold smaller than the eukaryotic nucleus (Vlaser < 0.1 µm3), lasing thresholds 500-fold below the pulse energies typically used in two-photon microscopy (Eth ≈ 0.13 pJ), and excellent spectral stability (<50 pm wavelength shift). Multiplexed labeling with these lasers allows cell-tracking through micro-pores, thus providing a powerful tool to study cell migration and cancer invasion.