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This article discusses a research paper published by researchers from Kerman University of Medical Sciences in Iran. The paper explores the historical perspective of diagnosing death in Persian medicine, highlighting the evolution of diagnostic criteria and the contributions of key scholars. Medieval Persian scholars, such as Qutb al-Din al-Shirazi, played a crucial role in this discourse, providing comprehensive diagnostic criteria for death in traditional Persian medicine. The research provides insights into the evolving understanding of death in Persian medicine and the intricate methods used for its diagnosis. [Extracted from the article]

A study conducted by the University of Tehran in Iran focuses on the scheduling of operating rooms (OR) in hospitals. The study emphasizes the importance of accurate scheduling in enhancing the overall performance of hospitals. The research proposes a multi-objective mixed-integer programming model for daily OR scheduling, taking into account factors such as setup time, surgical staff performance, and compatibility. The study also develops a constraint programming method to solve real-world instances of the scheduling problem. The research has been peer-reviewed and published in the International Journal of Systems Science Operations & Logistics. [Extracted from the article]

A recent study conducted by the University of London explores the integration of 3D printing technology in maxillofacial surgery in Iran. The study highlights the significant advancements this technology offers in terms of precision and efficiency in surgical procedures. However, the research also identifies barriers such as technological access, cost, and skill acquisition that need to be addressed for the full potential of 3D printing to be realized. The study emphasizes the importance of targeted educational programs, policy support, and infrastructure development to overcome these obstacles and fully leverage the benefits of this innovative technology in maxillofacial surgery. [Extracted from the article]

A recent study conducted at Zabol University of Medical Sciences in Iran has explored the use of green-synthesized Co doped NiO nanoparticles for the treatment of breast cancer. The researchers used the aqueous extract of Salvadora persica to synthesize the nanoparticles, which were then analyzed and confirmed through various techniques. The results showed that the cobalt-doped nanoparticles exhibited higher cytotoxic activity against human breast cancer cells compared to pure nanoparticles. The researchers concluded that these synthesized nanoparticles have potential applications in biological implementations and cancer treatment. [Extracted from the article]

A study conducted by researchers from Shiraz University in Fars, Iran, explores methods to improve the performance of Adaboost, a machine learning algorithm, in the classification of schizophrenic patients based on EEG signals. The researchers propose three novel schemes to identify and eliminate corrupted samples during the training process. The study compares these methods to the standard Adaboost algorithm using benchmark datasets and EEG signals from schizophrenic patients and control subjects. The results suggest that the proposed techniques enhance the performance of Adaboost and increase its robustness against noisy samples. [Extracted from the article]

A report from Hakim Sabzevari University in Iran discusses research on mind-body therapy and its effects on heart rate variability (HRV) during meditation. The study proposes a computational framework using recurrent neural networks (RNN) based on long short-term memory (LSTM) to classify HRV differences before and during meditation. The results show that the proposed approach successfully categorized the heart rate signals into two groups with a high accuracy of 99.25%. The research suggests that this approach can provide valuable insights into the cardiovascular system and physiological functions during meditation. [Extracted from the article]

Researchers at Sahand University of Technology in Tabriz, Iran have developed a novel automated approach to classify normal and abnormal heart sounds using machine learning techniques. The researchers partitioned heart sounds into four segments and extracted time-frequency and time-statistical features. They employed particle Swarm optimization (PSO) and Sequential Forward Feature Selection (SFFS) for efficient feature selection. The proposed method achieved superior accuracy, sensitivity, and specificity in distinguishing normal from abnormal heart sounds on the Physionet database. These findings outperformed previously established methods evaluated on the Physionet 2016 challenge database. [Extracted from the article]

A systematic review conducted by researchers at Kerman University of Medical Sciences in Iran examined the use of rodenticides in controlling zoonotic cutaneous leishmaniasis (ZCL) in the country. The review analyzed 13 studies that used zinc phosphide and Klerat mixed with wheat grains as rodenticides in five provinces of Iran. The findings showed that the implementation of rodent control programs significantly reduced the incidence rate of ZCL. This study provides valuable insights into the trends and effectiveness of rodent control programs in reducing ZCL in Iran over the past 50 years. [Extracted from the article]