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  Background & Objectives : Cortical spreading depression (SD), a self-propagating depolarization of neuroglial cells, is believed to play a role in different neurological disorders including epilepsy and migraine aura. A brief period of excitation heralds SD which is immediately followed by nerve cell depression and later by prolonged excitation. The aim of the present study was to investigate relationship between SD and late phase of excitability and seizure burst activity inlateral amygdale of rat.

  Methods: Male Wistar rats with 250-350 gram body weight were used. We usedamygdale slices taken from Wistar rats. SD was induced by KCl. After superfusion of these slices with sub-epileptic concentration of bicuculline for 45 min, the induction of SD in the lateral amygdale resulted in presence of interictal and ictalepileptiform field potentials.

  Results: After initiation of SD in lateral amygdale, glutamate receptors (NMDA, AMPA) antagonists as well as K⁺ and Ca⁺⁺ channels blockers were able to decrease the amplitude of excitatory postsynaptic potentials.

  Conclusion: The results imply a possible role for SD in temporal lobe epilepsy in predisposed neural tissue with increased excitation or decreased inhibition. The study of the late phase of SD excitability may help us to understand the mechanism of SD action in associated neurological disorders. This finding may improve the therapeutic strategies for treatment of epilepsy.

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Background & objectives: The purpose of this study was to determine the bacterial bioaerosols in indoor air of Ardabil universities and to investigate the factors affecting their concentration in Ardabil city.
Methods: Air sampling was performed using Andersen single-stage sampler at a flow rate of 28.3 liters per minute and a respiratory range of 10 min. In this study, trypticase soy agar containing cycloheximide antibiotic was used for bacterial culture. Biochemical tests such as DNase, catalase, oxidase, coagulase, bile esculin hydrolysis test, urease, citrate test, antibiotic resistance to novobiocin, bacitracin and optochin were used for identification and differentiation of isolates.
Result: The mean bacterial concentration in Ardabil university of medical sciences in the open air, outside the college, the halls, the classrooms and the staff rooms  was 18, 88.4, 76.6, 77.4 CFU/m³ , respectively. The concentration of bacteria in Islamic Azad university of Ardabil in the open air, outside the college, the halls, the classrooms and the staff rooms was 103, 97, 124, 132 CFU/m³, respectively. The dominant species of bacteria in indoor air were Staphylococcus aureus, Staphylococcus epidermidis, Actinomycetes and Bacillus, respectively.
Conclusion: According to the results of this study, it is found that the concentration of bacterial bioaerosols in indoor air is not more than standard, but the abundance of bacterial species can cause lung, intestinal and other diseases in academic personnel, staff and students in the long-term.

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Immunotherapy has emerged as a promising and effective approach in cancer treatment by stimulating the body’s immune system to target and eliminate malignant cells. Despite its significant therapeutic potential, several challenges remain, including accurate patient selection, identification of appropriate therapeutic targets, and the minimization of adverse effects.
Artificial intelligence (AI) plays a critical role in addressing these challenges by analyzing complex genomic, proteomic, and clinical datasets. Machine learning and deep learning algorithms can accurately identify patients likely to respond to immunotherapy, enabling the development of personalized treatment plans while avoiding unnecessary interventions in low-response individuals.
A key application of AI is predicting the efficacy of immune checkpoint inhibitors such as PD-1 and CTLA-4. By integrating medical imaging and genomic data, AI models can forecast treatment outcomes, enhance diagnostic precision, and reduce healthcare costs. Furthermore, AI is increasingly used in drug development, where it simulates novel molecular structures and predicts their therapeutic efficacy, thereby accelerating drug discovery and lowering development expenses. AI also contributes to identifying and managing side effects, improving the safety profile of immunotherapy.
Nevertheless, the implementation of AI in oncology is not without limitations. These include the need for high-quality, annotated datasets, algorithmic interpretability, and ethical concerns such as data privacy, algorithm transparency, and psychological impacts of extensive genetic testing, excessive diagnostic testing, potential treatment discrimination, and unclear legal responsibilities.
This article concludes that with robust data infrastructure and the advancement of interpretable AI models, the full potential of AI in cancer immunotherapy can be realized. This synergy promises a major leap toward precision medicine and a brighter future in cancer care.