Breast cancer is among the most common and fatal diseases for women, and no permanent treatment has been discovered. Thus, early detection is a crucial step to control and cure breast cancer that can save the lives of millions of women. For example, in 2020, more than 65% of breast cancer patients were diagnosed in early-stage of cancer, from whom all survived cancer. Although early detection is the most effective approach for cancer treatment, breast cancer screening conducted by radiologists is very expensive and time-consuming. More importantly, conventional methods of analyzing breast cancer images suffer from high false detection rates. Different breast cancer imaging modalities are used to extract and analyze the key features affecting diagnosis and treatment of breast cancer. These imaging modalities can be divided into subgroups such as mammograms, ultrasound, magnetic resonance imaging, histopathological images, or any combination of them. Radiologists or pathologists analyze images produced by these methods manually that leads to increase the risk of wrong decisions for cancer detection. Thus, utilization of new automatic methods to analyze all kinds of breast screening images to assist radiologists to interpret images is required. Recently, artificial intelligence (AI) has been widely utilized to automatically improve the early detection and treatment of different types of cancer, specifically breast cancer, thereby enhancing the survival chance of patients. Advances in AI algorithms, such as deep learning, and availability of datasets obtained from various imaging modalities have opened an opportunity to surpass limitations of current breast cancer analysis methods. In this article, we first review breast cancer imaging modalities, and their strengths and limitations. Then, we explore and summarize the most recent studies that employed AI in breast cancer detection using various breast imaging modalities. In addition, we report available datasets on the breast cancer imaging modalities which are important in developing AI-based algorithms and training deep learning models. In conclusion, this review paper tries to provide a comprehensive resource to help researchers working in breast cancer imaging analysis.

Gas embolism is a medical condition leading to the blockage of blood flow in the microvasculature by gas bubbles. While reported as rare, gas embolism often has devastating, fatal physiological consequences. Despite this acute importance, the genesis and evolution of air bubbles in blood vessels under different physiological conditions, such as blood viscosity and blood flow rate, is still understudied, largely because of difficult experimentation and in situ visualization. The objective of this work was to study the gas embolism phenomenon in vitro, using a microfluidic system that mimicked the architecture of microvasculature. The microfluidic systems comprised linear channels with two different air inlet types, namely, T- and Y-junctions with three different widths (20 µm, 40 µm, and 60 µm), and a 30 µm width honeycombed network with three bifurcation angles (30°, 60°, and 90°). Three synthetic liquids equivalent to 0%, 20%, and 46% hematocrit that mimicked the physiological blood viscosity and hematocrit concentrations were used. Our results show that: (i) 20 µm and 40 µm width channels had an elevated risk of gas embolism due to wide fluctuations in the total slug sizes; (ii) the resistance to the flow of air bubbles increased with the increase in the equivalent concentration of hematocrit; (iii) gas bubbles causing blockages and dampening of the flow velocity were frequently observed in 20 µm channels, and lastly (iv) increased risk of gas embolism was observed in the honeycomb architecture with 60° and 30° bifurcations. This work suggests that in vitro experimentation using microfluidic devices with microvascular tissue-like structures opens the possibility of studying this medical condition with high reproducibility and impacts the fact-based medical guidelines for preventing or mitigating iatrogenic occurrences.

1. Background and Objective: Spontaneous Intracranial Hemorrhage (ICH) is a catastrophic medical condition associated with significant morbidity and mortality. As the accumulation of unbound iron following ICH contributes to secondary brain injury, deferoxamine, an approved chelation drug, has become the center of attention. At the same time, its therapeutic remains a matter of dispute. This double-blinded Randomized Controlled Trial (RCT) aims to investigate the therapeutic potential of deferoxamine in neurological and radiological aspects. 2. Methods and Materials: The study enrolled 42 participants diagnosed with spontaneous ICH and confirmed by CT scan, randomly assigning them to either the deferoxamine treatment group or the placebo control group. While the control group received the routine treatment plus a placebo, the treatment group received routine treatment conjugated with 7.5 mg/kg of deferoxamine per hour intravenously over the first three days. The study compared hematoma and edema volume, GCS score, and mortality rates between the two groups. Our study employed rigorous randomization and blinding procedures to ensure unbiased results. 3. Results: There was a significant (p<0.05) improvement in patients' GCS up until the fourth day, but no significant difference was noted afterward. In addition, the volume of both edema and hematoma was significantly lower in the intervention group, as well as the length of stay, intubation requirement, and mortality. 4. Discussion: Deferoxamine administration can, at least in a short timeframe, improve neurological and radiological parameters.

Leadership performance by village leaders is essential to promote sustainable life among rural communities, especially fisheries community who living along coastal villages. Otherwise, previous studies found that performance issues among village’s leaders remain as serious problems, and need to find the best solution. This study was conducted to profile the job performance among village leaders based on demographic factors such as educational level, age and experiences as village leader. The data of this cross-sectional survey were collected by questionnaires on 300 respondents consist of members of village organization through stratified sampling’s technique, while the data was analysed by SPSS using items of mean, standard deviation, independent-sample t-test and anova. The finding shown that there were differences in job performance among village leaders on educational level, age and experiences. Interestingly, the finding told the best on job performance among village leaders are (i) the age between 41 to 50 years old; (iii) the experience between 11 to 20 years; and (iii) the higher educational level the higher job performance among them. This result can be using by government or any responsible parties to improve job performance among village leaders, especially for recruitment selection and for in-service training.

Among endocrine cancers, thyroid carcinoma is the most prevalent type of cancer. It's prevalence has been increased throughout the world during the last decades, which shows great regional variations. Considering the increasing prevalence and occurrence of thyroid cancer cases, in this study we: Among endocrine cancers, thyroid carcinoma is the most prevalent type of cancer. It's prevalence has been increased throughout the world during the last decades, which shows great regional variations. Considering the increasing prevalence and occurrence of thyroid cancer cases, in this study we decided to evaluate the clinical and pathological charactristics of this endocrine system cancer in the population of thyroid cancer patients reffered to imam Khomeini Hospital in Sari between March 2014 and March 2021. decided to evaluate the clinical and pathological charactristics of this endocrine system cancer in the population of thyroid cancer patients reffered to imam Khomeini Hospital in Sari between March 2014 and March 2021.

Aging associates with progressive loss of skeletal muscle function leading up to sarcopenia, a process characterized by impaired mobility and weakening of muscle strength. Molecular mechanisms underpinning sarcopenia are still poorly characterized. Since aging associates with profound epigenetic changes, epigenetic landscape alteration analysis in the skeletal muscle promises to highlight molecular mechanisms of age-associated sarcopenia. The study was conducted exploiting the short-lived turquoise killifish Nothobranchius furzeri (Nfu), a relatively new model for aging studies. The epigenetic analysis suggested for a less accessible and more condensed chromatin in old Nfu skeletal muscle. Specifically, an accumulation of heterochromatin regions was observed as consequence of increased levels of H3K27me3, HP1alpha, polycomb complex subunits and senescence associated heterochromatic foci (SAHFs). Consistently, euchromatin histone marks, including H3K9ac, decreased. The integrative bioinformatics analysis of RNASeq and ChIPSeq, related to skeletal muscle of Nfu at different ages, revealed a down-modulation of genes involved in cell cycle, differentiation and DNA repair and an up-regulation of inflammation and senescence genes. Undoubtedly, more studies are needed to disclose the detailed mechanisms, but this approach revealed an unprecedented specific features of Nfu skeletal muscle aging, potentially associated with sarcopenia onset and consequent impairment of swimming and mobility typical of old Nfu.