Fetal MRI Analysis of Corpus Callosal Abnormalities: Classification, and Associated Anomalies.

BACKGROUND: Corpus callosal abnormalities (CCA) are midline developmental brain malformations and are usually associated with a wide spectrum of other neurological and non-neurological abnormalities. The study aims to highlight the diagnostic role of fetal MRI to characterize heterogeneous corpus callosal abnormalities using the latest classification system. It also helps to identify associated anomalies, which have prognostic implications for the postnatal outcome. METHODS: In this study, retrospective data from antenatal women who underwent fetal MRI between January 2014 and July 2023 at Rush University Medical Center were evaluated for CCA and classified based on structural morphology. Patients were further assessed for associated neurological and non-neurological anomalies. RESULTS: The most frequent class of CCA was complete agenesis (79.1%), followed by hypoplasia (12.5%), dysplasia (4.2%), and hypoplasia with dysplasia (4.2%). Among them, 17% had isolated CCA, while the majority (83%) had complex forms of CCA associated with other CNS and non-CNS anomalies. Out of the complex CCA cases, 58% were associated with other CNS anomalies, while 8% were associated with non-CNS anomalies. 17% of cases had both. CONCLUSION: The use of fetal MRI is valuable in the classification of abnormalities of the corpus callosum after the confirmation of a suspected diagnosis on prenatal ultrasound. This technique is an invaluable method for distinguishing between isolated and complex forms of CCA, especially in cases of apparent isolated CCA. The use of diffusion-weighted imaging or diffusion tensor imaging in fetal neuroimaging is expected to provide further insights into white matter abnormalities in fetuses diagnosed with CCA in the future.

Automatic Localization of the Pons and Vermis on Fetal Brain MR Imaging Using a U-Net Deep Learning Model.

BACKGROUND AND PURPOSE: An MRI of the fetus can enhance the identification of perinatal developmental disorders, which improves the accuracy of ultrasound. Manual MRI measurements require training, time, and intra-variability concerns. Pediatric neuroradiologists are also in short supply. Our purpose was developing a deep learning model and pipeline for automatically identifying anatomic landmarks on the pons and vermis in fetal brain MR imaging and suggesting suitable images for measuring the pons and vermis. MATERIALS AND METHODS: We retrospectively used 55 pregnant patients who underwent fetal brain MR imaging with a HASTE protocol. Pediatric neuroradiologists selected them for landmark annotation on sagittal single-shot T2-weighted images, and the clinically reliable method was used as the criterion standard for the measurement of the pons and vermis. A U-Net-based deep learning model was developed to automatically identify fetal brain anatomic landmarks, including the 2 anterior-posterior landmarks of the pons and 2 anterior-posterior and 2 superior-inferior landmarks of the vermis. Four-fold cross-validation was performed to test the accuracy of the model using randomly divided and sorted gestational age-divided data sets. A confidence score of model prediction was generated for each testing case. RESULTS: Overall, 85% of the testing results showed a ≥90% confidence, with a mean error of <2.22 mm, providing overall better estimation results with fewer errors and higher confidence scores. The anterior and posterior pons and anterior vermis showed better estimation (which means fewer errors in landmark localization) and accuracy and a higher confidence level than other landmarks. We also developed a graphic user interface for clinical use. CONCLUSIONS: This deep learning-facilitated pipeline practically shortens the time spent on selecting good-quality fetal brain images and performing anatomic measurements for radiologists.

Automatic Ventriculomegaly Detection in Fetal Brain MRI: A Step-by-Step Deep Learning Model for Novel 2D-3D Linear Measurements.

In this study, we developed an automated workflow using a deep learning model (DL) to measure the lateral ventricle linearly in fetal brain MRI, which are subsequently classified into normal or ventriculomegaly, defined as a diameter wider than 10 mm at the level of the thalamus and choroid plexus. To accomplish this, we first trained a UNet-based deep learning model to segment the brain of a fetus into seven different tissue categories using a public dataset (FeTA 2022) consisting of fetal T2-weighted images. Then, an automatic workflow was developed to perform lateral ventricle measurement at the level of the thalamus and choroid plexus. The test dataset included 22 cases of normal and abnormal T2-weighted fetal brain MRIs. Measurements performed by our AI model were compared with manual measurements performed by a general radiologist and a neuroradiologist. The AI model correctly classified 95% of fetal brain MRI cases into normal or ventriculomegaly. It could measure the lateral ventricle diameter in 95% of cases with less than a 1.7 mm error. The average difference between measurements was 0.90 mm in AI vs. general radiologists and 0.82 mm in AI vs. neuroradiologists, which are comparable to the difference between the two radiologists, 0.51 mm. In addition, the AI model also enabled the researchers to create 3D-reconstructed images, which better represent real anatomy than 2D images. When a manual measurement is performed, it could also provide both the right and left ventricles in just one cut, instead of two. The measurement difference between the general radiologist and the algorithm (p = 0.9827), and between the neuroradiologist and the algorithm (p = 0.2378), was not statistically significant. In contrast, the difference between general radiologists vs. neuroradiologists was statistically significant (p = 0.0043). To the best of our knowledge, this is the first study that performs 2D linear measurement of ventriculomegaly with a 3D model based on an artificial intelligence approach. The paper presents a step-by-step approach for designing an AI model based on several radiological criteria. Overall, this study showed that AI can automatically calculate the lateral ventricle in fetal brain MRIs and accurately classify them as abnormal or normal.

Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging.

Central nervous system abnormalities in fetuses are fairly common, happening in 0.1% to 0.2% of live births and in 3% to 6% of stillbirths. So initial detection and categorization of fetal Brain abnormalities are critical. Manually detecting and segmenting fetal brain magnetic resonance imaging (MRI) could be time-consuming, and susceptible to interpreter experience. Artificial intelligence (AI) algorithms and machine learning approaches have a high potential for assisting in the early detection of these problems, improving the diagnosis process and follow-up procedures. The use of AI and machine learning techniques in fetal brain MRI was the subject of this narrative review paper. Using AI, anatomic fetal brain MRI processing has investigated models to predict specific landmarks and segmentation automatically. All gestation age weeks (17-38 wk) and different AI models (mainly Convolutional Neural Network and U-Net) have been used. Some models' accuracy achieved 95% and more. AI could help preprocess and post-process fetal images and reconstruct images. Also, AI can be used for gestational age prediction (with one-week accuracy), fetal brain extraction, fetal brain segmentation, and placenta detection. Some fetal brain linear measurements, such as Cerebral and Bone Biparietal Diameter, have been suggested. Classification of brain pathology was studied using diagonal quadratic discriminates analysis, K-nearest neighbor, random forest, naive Bayes, and radial basis function neural network classifiers. Deep learning methods will become more powerful as more large-scale, labeled datasets become available. Having shared fetal brain MRI datasets is crucial because there aren not many fetal brain pictures available. Also, physicians should be aware of AI's function in fetal brain MRI, particularly neuroradiologists, general radiologists, and perinatologists.

The incidence and characteristics of chronic pain and fatigue after 12 months later admitting with COVID-19; The Post- COVID 19 syndrome.

INTRODUCTION: This study aimed to evaluate chronic pain and fatigue in patients 12 months after hospitalization for Covid-19. METHODS: We studied the COVID-19 patients discharged from Hospital, March 10 and April 20, 2020. RESULTS: A total of 157 patients were included in this study. Forty-three patients (27.4%) complained of chronic fatigue and muscle weakness in the last six months. The visual analog fatigue scale (VAFS) score of 3.84 ± 1.48 was obtained. Forty patients (25.5%) were suspected of Chronic Fatigue Syndrome (CFS).Twenty-four patients (15.3%) had severe chronic pain or exacerbation of previous chronic pain, most of which were reported in the lower back (70.8%) and lower extremities (66.7%). Pain intensity had a mean score of 2.33 ± 0.87 and was mainly described as "muscle cramps," "persistent dull pain," and "boring and numbing." In women, chronic pain and fatigue, extended hospital stays, ICU admission, and depressed mood were common than in men.For these pain and fatigue, 37% used nonsteroidal anti-inflammatory drugs, and 16.3% used antidepressants. Only one person had applied for physiotherapy, and none of the patients had received psychotherapy. CONCLUSION: Fatigue and chronic pain in patients recovering from COVID-19 are common complications, even after 12 months of illness.

Immunohistochemical Expression Pattern of MLH1, MSH2, MSH6, and PMS2 in Tumor Specimen of Iranian Gastric Carcinoma Patients.

BACKGROUND: Gastric cancer is the third leading cause of cancer-related death. Determining molecular and histopathologic tumor features, which may contribute to the development or progression of gastric cancer, can improve the prognosis. Expression patterns of DNA repair proteins such as MLH1, MSH2, MSH6, and PMS2 that are associated with microsatellite instability (MSI) are some of the markers that are useful in predicting the prognosis of gastric cancer. PURPOSE: The purpose was to determine the immunohistochemical expression pattern of MLH1, MSH2, MSH6, and PMS2 in tumor specimens of Iranian gastric carcinoma patients. METHODS: In this prospective cohort, 186 consecutive patients with gastric cancer, attending Taleghani Hospital, were enrolled. The immunohistochemical expression patterns of MLH1, MSH2, MSH6, and PMS2 in tumor specimens among them were determined. RESULTS: The results of this study demonstrated that 91.4% of our gastric cancer patients were negative for MSI, and 8.6% of them were MSI positive. The positive MSI was seen in 5.9% and 15.7% of male and female subjects, respectively, with a significant difference (P = 0.043). The other variables were not related to MSI results (P > 0.05). CONCLUSION: According to the obtained results, the expression of MLH1, MSH2, MSH6, and PMS2 in tumor specimens is positive in 8.6% of the total Iranian gastric cancer sample size, which is mainly positive in female subjects. However, it is not related to the location and stage of the tumor.

Focal nodular hyperplasia in a 14-year-old child: A case report.

BACKGROUND: Focal nodular hyperplasia (FNH) is a benign rare liver neoplasm in children and includes only 2% of all pediatric liver tumors. Here we reported the case of a 14-year-old girl with vague flank pain who was managed conservatively. CASE PRESENTATION: Our case is a 14-year-old child (female), with a 5 cm diameter lesion in the right lobe of the liver in CT scan, and histologic findings compatible with FNH. A solid mass lobulated contour, intense enhancement with a hypodense central area, possibly indicative of central scar, was seen. Despite her mild flank pain we did not insist on surgical resection and managed her conservatively. Her pain resolved 2 weeks later and an imaging follow-up with ultrasound 6 months later showed no increase in size or numbers. CONCLUSION: FNH is an uncommon mass lesion in children. Our patient had mild symptomatic severity, and several guidelines recommend surgical treatment in this condition, but our team performed conservative and medical treatment for her and got the desired result. Therefore, the combination of these factors raises the importance of introducing the case. According to FNH's nature, stability, complications, and evaluation of pain are essential to avoid unnecessary surgeries.

Survey of Mast Cell Density in Transitional Cell Carcinoma.

BACKGROUND & OBJECTIVE: Transitional cell carcinoma (TCC) is the world's seventh most common tumor and forms more than 90% of urinary bladder tumors. Invasive tumors are associated with poor prognosis, even with surgical treatment and chemotherapy. Some studies have found that an increase in the number of mast cells in TCC is related to the tumor grade and its aggressiveness. This study investigated the relationship between mast cell density (MCD) and features of TCC (tumor stage, grade, prognosis, and recurrence). METHODS: Fifty-one cases with TCC were selected, and MCD was determined by immunohistochemistry (IHC) and Giemsa staining. Mortality rate and tumor recurrence were recorded. RESULTS: The MCD mean was higher in high-grade tumors than in low-grade tumors (in IHC method: 9.127 vs 5.296; in Giemsa method: 5.512 vs 2.608). Also, the MCD mean in dead patients was higher than in survived patients (in IHC method: 11.390 vs 6.211; in Giemsa method: 7.460 vs 3.35). Patients with tumor recurrence showed a higher MCD mean than those without recurrence (in IHC method: 9.395 vs 5.475; in Giemsa method: 5.715 vs 2.931). CONCLUSION: Using mast cell tryptase and Giemsa, MCD may be associated with a positive correlation with tumor grade in TCC. Correlations between MCD, recurrence, prognosis, and tumor stage are probably caused by the effect of tumor grade (all with P<0.05).

Nanomedicine for COVID-19: the role of nanotechnology in the treatment and diagnosis of COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the recent outbreak of coronavirus 2019 (COVID-19). Although nearly two decades have passed since the emergence of pandemics such as SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV), no effective drug against the CoV family has yet been approved, so there is a need to find newer therapeutic targets. Currently, simultaneous research across the globe is being performed to discover efficient vaccines or drugs, including both conventional therapies used to treat previous similar diseases and emerging therapies like nanomedicine. Nanomedicine has already proven its value through its application drug delivery and nanosensors in other diseases. Nanomedicine and its components can play an important role in various stages of prevention, diagnosis, treatment, vaccination, and research related to COVID-19. Nano-based antimicrobial technology can be integrated into personal equipment for the greater safety of healthcare workers and people. Various nanomaterials such as quantum dots can be used as biosensors to diagnose COVID-19. Nanotechnology offers benefits from the use of nanosystems, such as liposomes, polymeric and lipid nanoparticles, metallic nanoparticles, and micelles, for drug encapsulation, and facilitates the improvement of pharmacological drug properties. Antiviral functions for nanoparticles can target the binding, entry, replication, and budding of COVID-19. The toxicity-related inorganic nanoparticles are one of the limiting factors of its use that should be further investigated and modified. In this review, we are going to discuss nanomedicine options for COVID-19 management, similar applications for related viral diseases, and their gap of knowledge.

Diagnostic value of high-frequency ultrasound in carpal tunnel syndrome during pregnancy: A case-control study.

Background: Carpal tunnel syndrome (CTS) is the most prevalent entrapment syndrome in the upper limbs, for which pregnancy is a known risk factor. CTS diagnosis is confirmed via nerve conduction studies (NCSs), which sometimes is expensive, and the electrical stimulation makes it an unpleasant diagnostic modality, especially for pregnant subjects. Recently, high-frequency ultrasonography (HF-USG) is known as a diagnostic method. This study is concerned with determining the diagnostic value of this modality for CTS among pregnant women. Methods: This cross-sectional case-control study was conducted with 40 CTS cases and 40 matched controls. The HF-USG of wrists was performed bilaterally on all participants with a focus on the median nerve cross-sectional area (MNCSA) at the carpal tunnel (CT) inlet. Results: Mean MNCSA was statistically different between the CTS group (11.71 ± 1.86 mm2, range: 8 to 18 mm2) and the control group (6.75 ± 1.38 mm2, range: 4 to 11 mm2) (P < 0.001). The receiver operating characteristic (ROC) curve was drawn, and the cross-sectional area (CSA) cut-off point of 8.5 mm2 showed sensitivity and specificity of 98% and 93%, respectively. The positive predictive value (PPV) and the negative predictive value (NPV) were 95% and 98%, respectively, with the mentioned point as the diagnostic threshold. Conclusion: HF-USG of the median nerve can be utilized as a preferable alternative to NCS (the current gold standard diagnostic method) in pregnant women, due to its convenience and lower cost, or at least, it can be used as a screening tool among pregnant women with suspicious symptoms.