The effect of swimming and silymarin on placental growth factor in pregnant rats exposed to cadmium

Background: Exercise and dietary supplements can partially mitigate the negative effects of cadmium. The present study aimed to investigate the effect of swimming and silymarin on placental growth factor (PLGF) in pregnant mice exposed to cadmium. Methods: Seventy-two 8-week-old pregnant Wistar rats (weighing 20 ± 200 g) were divided into 9 groups, with 8 rats in each group. Cadmium chloride at a dose of 400 mg/kg body weight was fed to rats by drinking a water solution. Silymarin (100 mg/kg body weight) was injected subcutaneously 3 times a week. The exercise program during pregnancy consisted of 60 minutes of swimming per day, conducted for 5 days a week. The microscopic sections of samples were taken 2 days after birth using the usual method of tissue sectioning. A 1-way analysis of variance (ANOVA) and Tukey post hoc test at the error level of 0.05 were used to analyze the data. Results: The PLGF index in the cadmium group showed a significant decrease (P < 0.001) compared to the cadmium + silymarin and cadmium + silymarin and swimming groups. However, swimming training alone had no effect on PLGF index (P = 0.162). Conclusion: Cadmium significantly reduced PLGF levels in neonatal lung tissue, and regular swimming endurance exercises and silymarin supplementation inhibited the effects of cadmium chloride

Original Article Antioxidant and Antiepileptic Activity of 1-[1-(3-Methoxyphenyl) (Tetralyl)] Piperidine as a New Derivative of Phencyclidine on Pentylentetrazole-Induced Kindling Mice

ABSTRAC

Association of physical activity with increased PI3K and Akt mRNA levels in adipose tissues of obese and non-obese adults

Abstract Phosphatidylinositol-3-kinase (PI3K)/Akt signaling pathway regulates glucose and lipid metabolism. We examined the association of PI3K and Akt expression in visceral (VAT) and subcutaneous adipose tissue (SAT) with daily physical activity (PA) in non-diabetic obese and non-obese adults. In this cross-sectional study, we included 105 obese (BMI ≥ 30 kg/m2) and 71 non-obese (BMI < 30 kg/m2) subjects (aged/ ≥ 18 years). PA was measured using a valid and reliable International Physical Activity Questionnaire(IPAQ)-long-form, and the metabolic equivalent of task(MET) was calculated. Real-time PCR was performed to analyze the mRNA relative expression. VAT PI3K expression had a lower level in obese compared to non-obese (P = 0.015), while its expression was higher in active individuals than inactive ones (P = 0.029). SAT PI3K expression was increased in active individuals compared to inactive ones (P = 0.031). There was a rise in VAT Akt expression in the actives compared to the inactive participants (P = 0.037) and in non-obese/active compared to non-obese/inactive individuals (P = 0.026). Obese individuals had a decreased expression level of SAT Akt compared to non-obsesses (P = 0.005). VAT PI3K was directly and significantly associated with PA in obsesses (β = 1.457, P = 0.015). Positive association between PI3K and PA suggests beneficial effects of PA for obese individuals that can be partly described by PI3K/Akt pathway acceleration in adipose tissue

Artificial Intelligence in Cancer Care: From Diagnosis to Prevention and Beyond

&lt;p&gt;Artificial Intelligence (AI) has made significant strides in revolutionizing cancer care, encompassing various aspects from diagnosis to prevention and beyond. With its ability to analyze vast amounts of data, recognize patterns, and make accurate predictions, AI has emerged as a powerful tool in the fight against cancer. This article explores the applications of AI in cancer care, highlighting its role in diagnosis, treatment decision-making, prevention, and ongoing management. In the realm of cancer diagnosis, AI has demonstrated remarkable potential. By processing patient data, including medical imaging, pathology reports, and genetic profiles, AI algorithms can assist in early detection and accurate diagnosis. Image recognition algorithms can analyze radiological images, such as mammograms or CT scans, to detect subtle abnormalities and assist radiologists in identifying potential tumors. AI can also aid pathologists in analyzing tissue samples, leading to more precise and efficient cancer diagnoses. AI's impact extends beyond diagnosis into treatment decision-making. The integration of AI algorithms with clinical data allows for personalized treatment approaches. By analyzing patient characteristics, disease stage, genetic markers, and treatment outcomes, AI can provide valuable insights to oncologists, aiding in treatment planning and predicting response to specific therapies. This can lead to more targeted and effective treatment strategies, improving patient outcomes and reducing unnecessary treatments and side effects. Furthermore, AI plays a crucial role in cancer prevention. By analyzing genetic and environmental risk factors, AI algorithms can identify individuals at higher risk of developing certain cancers. This enables targeted screening programs and early interventions, allowing for timely detection and prevention of cancer. Additionally, AI can analyze population-level data to identify trends and patterns, contributing to the development of public health strategies for cancer prevention and control. AI's involvement in cancer care goes beyond diagnosis and treatment, encompassing ongoing management and survivorship. AI-powered systems can monitor treatment response, track disease progression, and detect recurrence at an early stage. By continuously analyzing patient data, including imaging, laboratory results, and clinical assessments, AI algorithms can provide real-time insights, facilitating timely interventions and adjustments to treatment plans. This proactive approach to disease management improves patient outcomes and enhances quality of life.&lt;/p&gt