Hourglass cesarean scar: A neglected external niche in association with the internal niche

Tahermanesh, Kobra; Hanjani, Soheil; Mostafavi, Seyed Reza Saadat; Shahriyari, Roya; Anvari-Yazdi, Abbas Fazel; Karimi-Zarchi, Mojgan; Samimi, Mansooreh; Karimi, Anita; Allahqoli, Leila; Alkatout, İbrahim

doi:10.1002/ijgo.14052

Cited by 2 publications

(2 citation statements)

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“…Increased expression of miR-362-5p as oncomiR through GADD45a down regulation and decreased expression of miR-320a as tumour suppressor through PI3K/AKT and NF-kb pathway are involved in the migration and invasion pathway ( 43 ). In this way, increasing the expression of miR-320a by inhibiting the phosphorylation of mentioned pathways leads to weakening the oncogenic function of bcr/abl ( 44 ).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of MicroRNA as Minimal Residual Disease in Leukemia: Diagnostic and Prognostic Approach: A Review

Amiri,

Sabernia,

Abouali

et al. 2023

ijph

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Various factors are effective in the development of minimal residual disease (MRD), one of which is MicroRNAsmiRNAs miRNAs and their dysfunction in gene expression have influential role in the pathogenesis of leukemia. Nowadays, treatments that lead to the suppression or replacement of miRNAs have been developed. Focusing on the role of miRNAs in managing the treatment of leukemia, in this review article we have investigated the miRNAs and signaling pathways involved in the process of apoptosis and cell proliferation, as well as miRNAs with oncogenic function in malignant leukemia cells. Among the studied miRNAs, miR-99a, and miR-181a play an essential role in apoptosis, proliferation and oncogenesis via AKT, MAPK, RAS, and mTOR signaling pathways. miR-223 and miR-125a affect apoptosis and oncogenesis via Wnt/B-catenin, PTEN/PI3K, and STAT5/AKT/ERK/Src signaling pathways. miR-100 also affects both apoptosis and oncogenesis; it acts via IGF1 and mTOR signaling pathways.

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Section: Introductionmentioning

confidence: 99%

Evaluation of MicroRNA as Minimal Residual Disease in Leukemia: Diagnostic and Prognostic Approach: A Review

Amiri,

Sabernia,

Abouali

et al. 2023

ijph

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“…[26], change over the course of the COVID-19 contamination, blood tests can provide physicians with data about the diagnosis of COVID-19 [12]. Consequently, blood tests may additionally supply a potentially precious instrument for the quick screening of infected patients and compensate for the deficiency of RT-PCR and CT scans by providing an initial step of detection [27].…”

Section: Introductionmentioning

confidence: 99%

A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques

2023

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Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), causing a disease called COVID-19, is a class of acute respiratory syndrome that has considerably affected the global economy and healthcare system. This virus is diagnosed using a traditional technique known as the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, RT-PCR customarily outputs a lot of false-negative and incorrect results. Current works indicate that COVID-19 can also be diagnosed using imaging resolutions, including CT scans, X-rays, and blood tests. Nevertheless, X-rays and CT scans cannot always be used for patient screening because of high costs, radiation doses, and an insufficient number of devices. Therefore, there is a requirement for a less expensive and faster diagnostic model to recognize the positive and negative cases of COVID-19. Blood tests are easily performed and cost less than RT-PCR and imaging tests. Since biochemical parameters in routine blood tests vary during the COVID-19 infection, they may supply physicians with exact information about the diagnosis of COVID-19. This study reviewed some newly emerging artificial intelligence (AI)-based methods to diagnose COVID-19 using routine blood tests. We gathered information about research resources and inspected 92 articles that were carefully chosen from a variety of publishers, such as IEEE, Springer, Elsevier, and MDPI. Then, these 92 studies are classified into two tables which contain articles that use machine Learning and deep Learning models to diagnose COVID-19 while using routine blood test datasets. In these studies, for diagnosing COVID-19, Random Forest and logistic regression are the most widely used machine learning methods and the most widely used performance metrics are accuracy, sensitivity, specificity, and AUC. Finally, we conclude by discussing and analyzing these studies which use machine learning and deep learning models and routine blood test datasets for COVID-19 detection. This survey can be the starting point for a novice-/beginner-level researcher to perform on COVID-19 classification.

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Hourglass cesarean scar: A neglected external niche in association with the internal niche

Abstract: The present study reports a case of uterine ‘hourglass' cesarean scar—the presence of an ‘external niche’ in association with the internal niche at the lower uterine segment, caused by poor uterine wound healing.

Cited by 2 publications

References 4 publications

Evaluation of MicroRNA as Minimal Residual Disease in Leukemia: Diagnostic and Prognostic Approach: A Review

Evaluation of MicroRNA as Minimal Residual Disease in Leukemia: Diagnostic and Prognostic Approach: A Review

A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques

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