Meticulous research for design of plasmonics sensors for cancer detection and food contaminants analysis via machine learning and artificial intelligence.

Cancer is one of the leading causes of death worldwide, making early detection and accurate diagnosis critical for effective treatment and improved patient outcomes. In recent years, machine learning (ML) has emerged as a powerful tool for cancer detection, enabling the development of innovative algorithms that can analyze vast amounts of data and provide accurate predictions. This review paper aims to provide a comprehensive overview of the various ML algorithms and techniques employed for cancer detection, highlighting recent advancements, challenges, and future directions in this field. The main challenge is finding a safe, auditable and reliable analysis method for fundamental scientific publication. Food contaminant analysis is a process of testing food products to identify and quantify the presence of harmful substances or contaminants. These substances can include bacteria, viruses, toxins, pesticides, heavy metals, allergens, and other chemical residues. Machine learning (ML) and artificial intelligence (A.I) proposed as a promising method that possesses excellent potential to extract information with high validity that may be overlooked with conventional analysis techniques and for its capability in a wide range of investigations. A.I technology used in meta-optics can develop optical devices and systems to a higher level in future. Furthermore (M.L.) and (A.I.) play key roles as a health Approach for nano materials NMs safety assessment in environment and human health research. Beside, benefits of ML in design of plasmonic sensors for different applications with improved resolution and detection are convinced.

Comparison of the Effect of Dialectical Behavior Therapy, Mindfulness Based Cognitive Therapy and Positive Psychotherapy on Perceived Stress and Quality of Life in Patients with Irritable Bowel Syndrome: a Pilot Randomized Controlled Trial.

This study aimed to compare dialectical behavior therapy (DBT), mindfulness based cognitive therapy (MBCT) and positive psychotherapy (PPT) effects on perceived stress (PS) and quality of life (QOL) among patients with irritable bowel syndrome (IBS). Seventy six eligible patients with a Rome- IV diagnosis were randomly allocated in DBT (n = 18), MBCT (n = 20), PPT (n = 18), and control groups (n = 20). All the patients were evaluated for PS by perceived stress scale (PSS) and QOL by irritable bowel syndrome quality of life (IBS-QOL) on the studied groups at the time of their inclusion in the study and 8 weeks after it. Each of the intervention groups took part in 8 group sessions. Conversely, the control group were evaluated without any intervention. 46 female and 27 male in 4 groups completed the study. The results showed significant differences between the groups based on the variables of the PSS and IBS-QOL (p < 0.05). In addition, levels of PS were significantly lower for the MBCT intervention compared with the other groups; besides, the significant effects of the QOL variables represented the higher scores of the PPT compared to the treatment groups. The interventions could not be generalized to other samples. Some other limitations included the lack of conducting a follow-up plan. This study provides initial evidence that MBCT and PPT are more effective than other treatment groups on PS decrease and QOL improvement among patients with IBS, respectively.

VEGF delivery by smart polymeric PNIPAM nanoparticles affects both osteogenic and angiogenic capacities of human bone marrow stem cells.

OBJECTIVE: Bone tissue engineering (BTE) faces a major challenge with cell viability after implantation of a construct due to lack of functional vasculature within the implant. Human bone marrow derived mesenchymal stem cells (hBMSCs) have the potential to undergo transdifferentiation towards an endothelial cell phenotype, which may be appropriate for BTE in conjunction with the appropriate scaffolds and microenvironment. HYPOTHESIS AND METHODS: We hypothesized that slow delivery of vascular endothelial growth factor (VEGF) by using nanoparticles in combination with osteogenic stimuli might enhance both osteogenic and angiogenic differentiation of angiogenic primed hBMSCs cultured in an osteogenic microenvironment. Therefore, we developed a new strategy to enhance vascularization in BTE in vitro by synthesis of smart temperature sensitive poly(N­isopropylacrylamide) (PNIPAM) nanoparticles. We used PNIPAM nanoparticles loaded with collagen to investigate their ability to deliver VEGF for both angiogenic and osteogenic differentiation. RESULTS: We used the free radical polymerization technique to synthesize PNIPAM nanoparticles, which had particle sizes of approximately 100 nm at 37 °C and LCST of 30-32 °C. The cumulative VEGF release after 72 h for VEGF loaded PNIPAM (VEGF-PNIPAM) nanoparticles was 70%; for VEGF-PNIPAM loaded collagen hydrogels, it was 23%, which indicated slower release of VEGF in the VEGF-PNIPAM loaded collagen system. Immunocytochemistry (ICC) and inverted microscope visualization confirmed endothelial differentiation and capillary-like tube formation in the osteogenic culture medium after 14 days. Quantitative real-time polymerase chain reaction (QRT-PCR) also confirmed expressions of collagen type I (Col I), runt-related transcription factor 2 (RUNX2), and osteocalcin (OCN) osteogenic markers along with expressions of platelet-endothelial cell adhesion molecule-1 (CD31), von Willebrand factor (vWF), and kinase insert domain receptor (KDR) angiogenic markers. Our data clearly showed that VEGF released from PNIPAM nanoparticles and VEGF-PNIPAM loaded collagen hydrogel could significantly contribute to the quality of engineered bone tissue.