Breast percent density changes in digital mammography pre- and post-radiotherapy.

INTRODUCTION: Breast cancer (BC), the most frequently diagnosed malignancy among women worldwide, presents a public health challenge and affects mortality rates. Breast-conserving therapy (BCT) is a common treatment, but the risk from residual disease necessitates radiotherapy. Digital mammography monitors treatment response by identifying post-operative and radiotherapy tissue alterations, but accurate assessment of mammographic density remains a challenge. This study used OpenBreast to measure percent density (PD), offering insights into changes in mammographic density before and after BCT with radiation therapy. METHODS: This retrospective analysis included 92 female patients with BC who underwent BCT, chemotherapy, and radiotherapy, excluding those who received hormonal therapy or bilateral BCT. Percent/percentage density measurements were extracted using OpenBreast, an automated software that applies computational techniques to density analyses. Data were analysed at baseline, 3 months, and 15 months post-treatment using standardised mean difference (SMD) with Cohen's d, chi-square, and paired sample t-tests. The predictive power of PD changes for BC was measured based on the receiver operating characteristic (ROC) curve analysis. RESULTS: The mean age was 53.2 years. There were no significant differences in PD between the periods. Standardised mean difference analysis revealed no significant changes in the SMD for PD before treatment compared with 3- and 15-months post-treatment. Although PD increased numerically after radiotherapy, ROC analysis revealed optimal sensitivity at 15 months post-treatment for detecting changes in breast density. CONCLUSIONS: This study utilised an automated breast density segmentation tool to assess the changes in mammographic density before and after BC treatment. No significant differences in the density were observed during the short-term follow-up period. However, the results suggest that quantitative density assessment could be valuable for long-term monitoring of treatment effects. The study underscores the necessity for larger and longitudinal studies to accurately measure and validate the effectiveness of quantitative methods in clinical BC management.

Thalamic Alterations in Motor Neuron Diseases: A Systematic Review of MRI Findings.

BACKGROUND: Motor neuron diseases (MNDs) are progressive neurodegenerative disorders characterized by motor impairment and non-motor symptoms. The involvement of the thalamus in MNDs, especially in conditions such as amyotrophic lateral sclerosis (ALS), and its interaction with frontotemporal dementia (FTD), has garnered increasing research interest. This systematic review analyzed magnetic resonance imaging (MRI) studies that focused on thalamic alterations in MNDs to understand the significance of these changes and their correlation with clinical outcomes. METHODS: Following PRISMA 2020 guidelines, the PubMed and Scopus databases were searched from inception to June 2023 for studies related to MRI findings in the thalamus of patients with MNDs. Eligible studies included adult patients diagnosed with ALS or other forms of MND who underwent brain MRI, with outcomes related to thalamic alterations. Studies were evaluated for risk of bias using the Newcastle-Ottawa scale. RESULTS: A total of 52 studies (including 3009 MND patients and 2181 healthy controls) used various MRI techniques, including volumetric analysis, diffusion tensor imaging, and functional MRI, to measure thalamic volume, connectivity, and other alterations. This review confirmed significant thalamic changes in MNDs, such as atrophy and microstructural degradation, which are associated with disease severity, progression, and functional disability. Thalamic involvement varies across different MND subtypes and is influenced by the presence of cognitive impairment and mutations in genes including chromosome 9 open reading frame 72 (C9orf72). The synthesis of findings across studies indicates that thalamic pathology is a prevalent early biomarker of MNDs that contributes to motor and cognitive deficits. The thalamus is a promising target for monitoring as its dysfunction underpins a variety of clinical symptoms in MNDs. CONCLUSIONS: Thalamic alterations provide valuable insights into the pathophysiology and progression of MNDs. Multimodal MRI techniques are potent tools for detecting dynamic thalamic changes, indicating structural integrity, connectivity disruption, and metabolic activity.

Marker-controlled watershed algorithm and fuzzy C-means clustering machine learning: automated segmentation of glioblastoma from MRI images in a case series.

Introduction and importance: Automated segmentation of glioblastoma multiforme (GBM) from MRI images is crucial for accurate diagnosis and treatment planning. This paper presents a new and innovative approach for automating the segmentation of GBM from MRI images using the marker-controlled watershed segmentation (MCWS) algorithm. Case presentation and methods: The technique involves several image processing techniques, including adaptive thresholding, morphological filtering, gradient magnitude calculation, and regional maxima identification. The MCWS algorithm efficiently segments images based on local intensity structures using the watershed transform, and fuzzy c-means (FCM) clustering improves segmentation accuracy. The presented approach achieved improved segmentation accuracy in detecting and segmenting GBM tumours from axial T2-weighted (T2-w) MRI images, as demonstrated by the mean characteristics performance metrics for GBM segmentation (sensitivity: 0.9905, specificity: 0.9483, accuracy: 0.9508, precision: 0.5481, F_measure: 0.7052, and jaccard: 0.9340). Clinical discussion: The results of this study underline the importance of reliable and accurate image segmentation for effective diagnosis and treatment planning of GBM tumours. Conclusion: The MCWS technique provides an effective and efficient approach for the segmentation of challenging medical images.

Efficacy of a facial-aging web app on sun protection behaviors among primary school students in Iran: a randomized controlled trial.

BACKGROUND: Skin cancers resulting from excessive exposure to solar ultraviolet (UV) radiation are on the rise. This study aims to investigate the impact of facial-aging app intervention on promoting safe and healthy behaviors and its influence on reducing students' UV exposure. METHOD: Utilizing a Pretest-Posttest repeated-measures design, we developed a theory-guided web app on the WhatsApp platform, named the Sunshine and Skin Health app. This app allows users to visualize their altered faces in three stages of adolescence, middle age, and old age based on sun protection behavior. The intervention continued within WhatsApp, incorporating 27 health messages grounded in the PMT theory, eight educational files, and a skin cancer video clip. The primary outcome is the change in sun protection behavior between the two groups (intervention and control) immediately after the intervention (T2) and the secondary outcome is the change in sun protection behavior between the two groups at 3 months follow-up (T3). The data are analyzed in SPSS 22 and a significance level of 0.05 is considered. RESULTS: The results revealed no significant difference between the two groups before the intervention. However, in the intervention group, there were significant differences in the utilization of sunglasses, hats, and sunscreen in the last month, as well as sunscreen reapplication after washing their hands and face, both immediately after the intervention and at the 3-month follow-up, compared to the control group (P = 0.001). Furthermore, a significant intervention effect, time effect, and interaction effect between group and time were observed in behaviors related to using sunscreen in the last month and sunscreen reapplication after washing hands and face (P = 0.001). Specifically, the intervention group exhibited a significant difference from Time 1 to 2 and from Time 1 to 3 (p = 0.001), but no significant difference from Time 2 to 3. In contrast, the control group did not show any significant differences over time. CONCLUSIONS: This study indicated that the Facial-Aging web app can effectively encourage safe behaviors in sunlight. To ensure the maintenance and sustainability of these behaviors over the long term, it is crucial to consider implementing booster sessions. TRIAL REGISTRATION: Iranian Registry of Clinical Trials IRCT20200924048825N1. Registered prospectively on 8 February 2021.

Machine Learning Approaches to Identify Affected Brain Regions in Movement Disorders Using MRI Data: A Systematic Review and Diagnostic Meta-analysis.

BACKGROUND: Movement disorders such as Parkinson's disease are associated with structural and functional changes in specific brain regions. Advanced magnetic resonance imaging (MRI) techniques combined with machine learning (ML) are promising tools for identifying imaging biomarkers and patterns associated with these disorders. PURPOSE/HYPOTHESIS: We aimed to systematically identify the brain regions most commonly affected in movement disorders using ML approaches applied to structural and functional MRI data. We searched the PubMed and Scopus databases using relevant keywords up to June 2023 for studies that used ML approaches to detect brain regions associated with movement disorders using MRI data. STUDY TYPE: A systematic review and diagnostic meta-analysis. POPULATION/SUBJECTS: Sixty-seven studies with 6,285 patients were included. FIELD STRENGTH/SEQUENCE: Studies utilizing 1.5T or 3T MR scanners and the acquisition of diffusion tensor imaging (DTI), structural MRI (sMRI), functional MRI (fMRI), or a combination of these were included. ASSESSMENT: The authors independently assessed the study quality using the CLAIM and QUADAS-2 criteria and extracted data on diagnostic accuracy measures. STATISTICAL TESTS: Sensitivity, specificity, accuracy, and area under the curve were pooled using random-effects models. Q statistics and the I2 index were used to evaluate heterogeneity, and Begg's funnel plot was used to identify publication bias. RESULTS: sMRI showed the highest sensitivity (93%) and mixed modalities had the highest specificity (90%) for detecting regional abnormalities. sMRI had a 94% sensitivity for identifying subcortical changes. The support vector machine (93%) and logistic regression (91%) models exhibited high diagnostic accuracies. DATA CONCLUSION: The combination of advanced MR neuroimaging techniques and ML is a promising approach for identifying brain biomarkers and affected regions in movement disorders with subcortical structures frequently implicated. Structural MRI, in particular, showed strong performance. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Different types of mobile phone use while driving and influencing factors on intention and behavior: Insights from an expanded theory of planned behavior.

Mobile phone use while driving (MPUWD) is a significant concern due to its negative impact on road safety. This cross-sectional study aimed to assess different types of MPUWD and identify factors influencing intention and behavior among drivers in Zahedan. A total of 392 participants provided information on demographic and driving characteristics, as well as constructs derived from the Theory of Planned Behavior (TPB) augmented with moral norms. Findings revealed that a majority of drivers (86.4%) engaged in MPUWD, primarily involving calling, using applications, and texting. However, most participants did not intend to use their phones while driving in the following week. Multiple regression analysis identified age, receiving driving fines, subjective norm, and perceived behavior control as significant predictors of intention for MPUWD. Additionally, factors such as age, receiving driving fines, driving hours, car gearbox type, attitude, perceived behavior control, behavioral intention, and moral norm were significant predictors of actual MPUWD. Older participants demonstrated better behavior in various mobile phone activities while driving. Overall, the study emphasized that the main TPB constructs and moral norms have a greater influence compared to other variables in predicting MPUWD. Perceived behavioral control was the most important predictor of the intention and behavior of MPUWD. Understanding these factors can guide efforts to discourage MPUWD through targeted interventions and strategies to promote safer driving practices.

Purification, Molecular Docking and Cytotoxicity Evaluation of Bioactive Pentacyclic Polyhydroxylated Triterpenoids from Salvia urmiensis.

Triterpenoids, as one of the largest classes of naturally occurring secondary metabolites in higher plants, are of interest due to their high structural diversity and wide range of biological activities. In addition to several promising pharmacological activities such as antimicrobial, antiviral, antioxidant, anti-inflammatory and hepatoprotective effects, a large number of triterpenoids have revealed high potential for cancer therapy through their strong cytotoxicity on cancer cell lines and, also, low toxicity in normal cells. So, this study was aimed at discovering novel and potentially bioactive triterpenoids from the Salvia urmiensis species. For this, an ethyl acetate fraction of the acetone extract of the aerial parts of the plant was chromatographed to yield five novel polyhydroxylated triterpenoids (1: -5: ). Their structure was elucidated by extensive spectroscopic methods including 1D (1H, 13C, DEPT-Q) and 2D NMR (COSY, HSQC, HMBC, NOESY) experiments, as well as HRESIMS analysis. Cytotoxic activity of the purified compounds was also investigated by MTT assay against the MCF-7 cancer cell line. Furthermore, a molecular docking analysis was applied to evaluate the inhibition potential of the ligands against the nuclear factor kappa B (NF-κB) protein, which promotes tumor metastasis or affects gene expression in cancer disease. The 1ß,11ß,22α-trihydroxy-olean-12-ene-3-one (compound 4: ) indicated the best activity in both in vitro and in silico assays, with an IC50 value of 32 µM and a docking score value of - 3.976 kcal/mol, respectively.

A systematic review of brain metastases from lung cancer using magnetic resonance neuroimaging: Clinical and technical aspects.

INTRODUCTION: Brain metastases (BMs) are common in lung cancer (LC) and are associated with poor prognosis. Magnetic resonance imaging (MRI) plays a vital role in the detection, diagnosis and management of BMs. This review summarises recent advances in MRI techniques for BMs from LC. METHODS: This systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was conducted in three electronic databases: PubMed, Scopus and the Web of Science. The search was limited to studies published between January 2000 and March 2023. The quality of the included studies was evaluated using appropriate tools for different study designs. A narrative synthesis was carried out to describe the key findings of the included studies. RESULTS: Sixty-five studies were included. Standard MRI sequences such as T1-weighted (T1w), T2-weighted (T2w) and fluid-attenuated inversion recovery (FLAIR) were commonly used. Advanced techniques included perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and radiomics analysis. DWI and PWI parameters could distinguish tumour recurrence from radiation necrosis. Radiomics models predicted genetic mutations and the risk of BMs. Diagnostic accuracy was improved with deep learning (DL) approaches. Prognostic factors such as performance status and concurrent chemotherapy impacted survival. CONCLUSION: Advanced MRI techniques and specialised MRI methods have emerging roles in managing BMs from LC. PWI and DWI improve diagnostic accuracy in treated BMs. Radiomics and DL facilitate personalised prognosis and treatment. Magnetic resonance imaging plays a key role in the continuum of care for BMs of patients with LC, from screening to treatment monitoring.

Determinants of diabetes self-care activities in patients with type 2 diabetes based on self-determination theory.

AIMS: In type 2 diabetes control, self-management is an effective way to minimize the risk of developing complications and improve the quality of life. Self-determination theory (SDT) proposed a promising explanatory framework to predict self-regulated behavior which was particularly relevant for self-management. This study aimed to investigate whether SDT constructs can affect the self-management and controlling glycated hemoglobin A1c (HbA1c) levels in type 2 diabetic patients or not. METHODS: This cross-sectional study was conducted from August to December 2022 at two diabetes clinics in Zahedan. The study included 300 patients with type 2 diabetes. Data collection was done using a researcher-administrated questionnaire that included demographic data, diabetes self-care activities, and self-determination constructs including autonomous support, autonomous motivation, and perceived competence. RESULTS: The mean of the overall score of self-care activities was 34.62 ± 11.86 out of a maximum of 70. Patients in the fourth quarter (wealthiest) of the socioeconomic status had the highest mean self-care score (P = 0.003). There was a significant relationship between diet score with perceived competence (P = 0.009). Perceived competence (P<0.001) and controlled self-regulation (P<0.001) were the predictors of exercise score in diabetes patients. Independent self-regulation (P<0.001, r = 0.21) and overall self-regulation (P = 0.001, r = 0.19) were significantly related to blood-glucose testing score. There was a significant relationship between foot care score with perceived competence (P = 0.048, r = 0.11) and autonomous support (P = 0.013, r = 0.14). Multiple regression showed that exercise was the predictor of HbA1c (P = 0.014). CONCLUSION: Exercise is crucial for achieving good individualized glycemic control and reducing the risk of diabetes complications. The findings provided valuable insights into the determinants of self-care activities in patients with type 2 diabetes and underscore the need for interventions that address socioeconomic disparities, enhance perceived competence, and provide autonomy support to improve diabetes self-care.

Brain metastases from breast cancer using magnetic resonance imaging: A systematic review.

Despite improvements in imaging and treatment approaches, brain metastases (BMs) continue to be the primary cause of mortality and morbidity in about 20% of adult cancer patients. This research aimed to review the magnetic resonance imaging (MRI) and clinical characteristics of BMs resulting from breast cancer (BC). A systematic review of original research articles published from January 2000 to June 2023. We selected studies that reported MRI findings of BMs in BC patients. We excluded reviews, case reports, books/book chapters, animal studies and irrelevant records. We identified 24 studies that included 1580 BC patients with BMs. T1-weighted (T1-w) (pre- and postcontrast), T2-weighted (T2-w), fluid-attenuated inversion recovery (FLAIR) and T2*-weighted (T2*-w) was used to measure the lesion size, shape and area. In other studies, advanced structural techniques including diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI) and susceptibility-weighted imaging (SWI) were used to more precisely and sensitively evaluate the pathological area. Furthermore, functional and metabolic techniques like functional MRI (fMRI), magnetic resonance spectroscopy (MRS) and perfusion-weighted imaging (PWI) have also been utilised. The MRI findings of BMs varied depending on the MRI technique, the BC subtype, the lesion size and shape, the presence of haemorrhage or necrosis and the comparison with other brain tumours. Some MRI findings were associated with prognosis, recurrence or cognitive impairment in BC patients with BMs. MRI detects, characterises and monitors BMs from BC. Findings vary by MRI technique, BC subtype, lesion characteristics and comparison with other brain tumours. More research should validate emerging MRI techniques, determine the clinical implications of findings and explore the underlying mechanisms and biology of BMs from BC. MRI is a valuable tool for diagnosis, targeted therapy and studying BC metastasis.

Knowledge, attitudes, and practices regarding Crimean-Congo hemorrhagic fever in a high-prevalence suburban community, southeast of Iran.

Crimean Congo Hemorrhagic Fever (CCHF) is a zoonotic viral disease with a high mortality rate. The World Health Organization has classified it as a high-priority pathogen due to its severity. To manage this disease, knowledge, attitude, and practices (KAP) of the community play an important role. This study was conducted in a suburban area in southeastern Iran, an endemic region with a high occurrence of CCHF. A cross-sectional study was performed among 176 livestock farmers in Zahedan suburb, and data were collected through an interviewer-administered questionnaire. Single-factor and multi-factor analysis of variance was used to identify factors related to participants' knowledge, attitude, and risk of CCHF. All statistical analyses were conducted using SPSS 24, and a significance level of p ≤ 0.05 was adopted to determine statistical significance. Overall, the knowledge level about CCHF was moderate. Participants demonstrated a better understanding of high-risk occupations compared to the transmission routes and symptoms of CCHF. Conversely, their knowledge about the prevention and treatment of CCHF was found to be inadequate. The participants exhibited a good attitude towards CCHF prevention; most considered it a dangerous disease, believed in the effectiveness of preventive measures, and demonstrated the ability to address perceived obstacles. High-frequency engagement in high-risk behaviors related to CCHF was observed among the study participants. In the multi-factor model, the most critical factors associated with knowledge were age (p = 0.044), CCHF infection (p = 0.047), and CCHF education (p = 0.004). Income (p = 0.001), keeping livestock at home (p = 0.028), and receiving CCHF education (p = 0.012) were the most critical factors associated with attitude toward the risk of CCHF. The most important factors related to CCHF high-risk behavior were age (p = 0.045), gender (p = 0.028), and handling livestock (p < 0.001). These findings emphasize the need for maintaining health educational efforts on CCHF.

Prevalence and risk factors of pre-hypertension and hypertension among adults in Southeastern Iran: Findings from the baseline survey of the Zahedan adult cohort study.

Hypertension (HTN) stands as the leading modifiable risk factor for cardiovascular disease(CVD) and premature death globally. Understanding its prevalence and risk factors is essential for effective prevention and management of HTN. This study aimed to investigate the prevalence of Pre-hypertension (pre-HTN), HTN, and its risk factors in adults participating in the Zahedan adult cohort study (ZACS). This cross-sectional study used the baseline data of the ZACS. Ordinal logistic regression analysis was used to estimate crude and adjusted odds ratios (ORs) with 95% confidence intervals (CIs) for potential risk factors. Among the 10,016 participants in this study, 60.89% were women, with an average age of 50.44 ± 9.18 years. The prevalence of pre-HTN and HTN was 42.03% (men 45.44%, women 39.84%) and 18.47% (men 21.09%, women 16.79%), respectively. Being male, older age, having higher socioeconomic status (SES), being overweight and obese, having a family history of HTN, comorbidities such as diabetes and CVD, as well as abnormal blood lipid levels (triglycerides and HDL cholesterol) were the most significant predictors of pre-HTN and HTN. These findings highlight that more than half of the participants in this study exhibit pre-HTN or HTN, placing them at risk for CVD and stroke. Implementing comprehensive preventive strategies tailored to these identified risk factors is imperative to alleviate the disease burden, enhance disease management, and improve HTN treatment and control.

Obstructive sleep apnea and attention deficits: A systematic review of magnetic resonance imaging biomarkers and neuropsychological assessments.

BACKGROUND AND OBJECTIVE: Obstructive sleep apnea (OSA) is a common sleep disorder that causes intermittent hypoxia and sleep fragmentation, leading to attention impairment and other cognitive deficits. Magnetic resonance imaging (MRI) is a powerful modality that can reveal the structural and functional brain alterations associated with attention impairment in OSA patients. The objective of this systematic review is to identify and synthesize the evidence on MRI biomarkers and neuropsychological assessments of attention deficits in OSA patients. METHODS: We searched the Scopus and PubMed databases for studies that used MRI to measure biomarkers related to attention alteration in OSA patients and reported qualitative and quantitative data on the association between MRI biomarkers and attention outcomes. We also included studies that found an association between neuropsychological assessments and MRI findings in OSA patients with attention deficits. RESULTS: We included 19 studies that met our inclusion criteria and extracted the relevant data from each study. We categorized the studies into three groups based on the MRI modality and the cognitive domain they used: structural and diffusion tensor imaging MRI findings, functional, perfusion, and metabolic MRI findings, and neuropsychological assessment findings. CONCLUSIONS: We found that OSA is associated with structural, functional, and metabolic brain alterations in multiple regions and networks that are involved in attention processing. Treatment with continuous positive airway pressure can partially reverse some of the brain changes and improve cognitive function in some domains and in some studies. This review suggests that MRI techniques and neuropsychological assessments can be useful tools for monitoring the progression and response to treatment of OSA patients.

Antitumor activities of a novel fluorinated small molecule (A1) in CT26 colorectal cancer cells: molecular docking and in vitro studies.

Chemotherapeutic treatment of colorectal cancer (CRC) has not been satisfactory until now; therefore, the discovery of more efficient medications is of great significance. Based on available knowledge, the CXCL12/CXCR4 axis plays a significant role in tumorigenesis, and inhibition of CXCR4 chemokine receptor with AMD3100 is one of the most known therapeutic modalities in cancer therapy. Herein, N, N''-thiocarbonylbis(N'-(3,4-dimethylphenyl)-2,2,2-trifluoroacetimidamide) (A1) was synthesized as a potent CXCR4 inhibitor. A1 inhibitory activity was first evaluated employing Molecular Docking simulations in comparison with the most potent CXCR4 inhibitors. Then, the antiproliferative and cytotoxic effect of A1 on CT26 mouse CRC cells was investigated by MTT assay technique and compared with those of the control molecule, AMD3100. The impact of the target compounds IC50 on apoptosis, cell cycle arrest, and CXCR4 expression was determined by flow cytometry technique. Our finding demonstrated that A1 induces a cytotoxic effect on CT26 cells at 60 µg/mL concentration within 72 h and provokes cell apoptosis and G2/M cell cycle arrest in comparison with the untreated cells, while AMD3100 did not show a cytotoxic effect up to 800 µg/mL dose. The obtained results show that A1 (at a concentration of 40 µg/mL) significantly reduced the proliferation of CT26 cells treated with 100 ng/mL of CXCL12 in 72 h. Moreover, treatment with 60 µg/mL of A1 and 100 ng/mL of CXCL12 for 72 h significantly decreased the number of cells expressing the CXCR4 receptor compared to the control group treated with CXCL12. Eventually, the obtained results indicate that A1, as a dual-function fluorinated small molecule, may benefit CRC treatment through inhibition of CXCR4 and exert a cytotoxic effect on tumor cells.Communicated by Ramaswamy H. Sarma.

Automated segmentation of meningioma from contrast-enhanced T1-weighted MRI images in a case series using a marker-controlled watershed segmentation and fuzzy C-means clustering machine learning algorithm.

INTRODUCTION AND IMPORTANCE: Accurate segmentation of meningiomas from contrast-enhanced T1-weighted (CE T1-w) magnetic resonance imaging (MRI) is crucial for diagnosis and treatment planning. Manual segmentation is time-consuming and prone to variability. To evaluate an automated segmentation approach for meningiomas using marker-controlled watershed segmentation (MCWS) and fuzzy c-means (FCM) algorithms. CASE PRESENTATION AND METHODS: CE T1-w MRI of 3 female patients (aged 59, 44, 67 years) with right frontal meningiomas were analyzed. Images were converted to grayscale and preprocessed with Otsu's thresholding and FCM clustering. MCWS segmentation was performed. Segmentation accuracy was assessed by comparing automated segmentations to manual delineations. CLINICAL DISCUSSION: The approach successfully segmented meningiomas in all cases. Mean sensitivity was 0.8822, indicating accurate identification of tumors. Mean Dice similarity coefficient between Otsu's and FCM1 was 0.6599, suggesting good overlap between segmentation methods. CONCLUSION: The MCWS and FCM approach enables accurate automated segmentation of meningiomas from CE T1-w MRI. With further validation on larger datasets, this could provide an efficient tool to assist in delineating meningioma boundaries for clinical management.

Post-COVID-19 Vaccination CNS Magnetic Resonance Imaging Findings: A Systematic Review.

Objective: This systematic review aims to synthesize and analyze the available literature on central nervous system (CNS) magnetic resonance imaging (MRI) findings in individuals who have received COVID-19 vaccinations. Our objective is to enhance understanding of potential neurological side effects, inform clinical practice, and guide future research on the neurological implications of COVID-19 vaccination. Methods: In this systematic review, we conducted a comprehensive search in PubMed, Scopus, and Web of Science from January 2020 to April 2023, using terms related to COVID-19 vaccination and CNS MRI findings. We evaluated the quality of the study, extracted relevant data, and included 89 eligible studies that covered various vaccines, demographics of patients, symptoms, and MRI findings to provide a thorough understanding of SARS-CoV-2 vaccination-related CNS problems. Results: We investigated CNS MRI findings following COVID-19 vaccination across various vaccine types. Common diseases associated with post-vaccination CNS MRI findings included cerebral venous sinus thrombosis (CVST), vaccine-induced immune thrombotic thrombocytopenia (VITT), acute disseminated encephalomyelitis (ADEM), acute myelitis, autoimmune encephalitis (AE), and others. Patients presented with diverse onset symptoms and neurological manifestations. Abnormalities identified in CNS MRI findings included white matter (WM) hyperintensity. Our analysis offers a comprehensive overview of the current literature on post-vaccination CNS MRI findings. Discussion. We highlight a range of post-COVID-19 vaccination CNS MRI findings, including CVST, with a higher incidence in individuals receiving the ChAdOx1 (AstraZeneca) vaccine. Other notable observations include cases of ADEM, myelitis or transverse myelitis (TM), Guillain-Barré syndrome (GBS), and acute encephalopathy following COVID-19 vaccination. The incidence of these neurological complications is extremely rare, and the benefits of vaccination outweigh the risks. The reviewed studies were primarily case reports or case series, and thus large-scale epidemiological studies and controlled clinical trials are needed to better understand the underlying mechanisms and risk factors associated with these neurological complications following COVID-19 vaccination.

MRI findings in movement disorders and associated sleep disturbances.

BACKGROUND: One of the most useful tools for identifying sleep disturbances is neuroimaging, especially magnetic resonance imaging (MRI). This review research was to look at the role of MRI findings in movement disorders and sleep disturbances. METHODS: This review collects all MRI data on movement disorders and sleep disruptions. Between 2000 and 2022, PubMed and Google Scholar were utilized to find original English publications and reviews. According to the inclusion and exclusion criteria, around 100 publications were included. We only looked at research that explored MRI modality together with movement problems, sleep disorders, and brain area involvement. Most of the information focuses on movement irregularities and sleep interruptions. RESULTS: Movement disorders such as Parkinson's disease (PD), Huntington's disease (HD), neuromuscular diseases, rapid eye movement (REM) sleep behavior movement disorder (RBD), cerebellar movement disorders, and brainstem movement disorders are assessed using MRI-based neuroimaging techniques. Some of the brain areas were associated with disorders in movement abnormalities and related sleep disturbances. This review found that many people with mobility disorders also have sleep problems. Some brain areas' malfunctions may cause motor and sleep issues. CONCLUSION: Neuroimaging helps us understand the sleep difficulties associated with movement disorders by examining the structural and functional implications of movement disorders and sleep disturbances.

Photoaging and theory-based intervention to improve sun protection behaviors in students in Zahedan: study protocol for a randomized controlled trial.

BACKGROUND: Excessive exposure to solar ultraviolet (UV) radiation can cause skin cancer. Implementing new technologies and computational algorithms can potentially change the outlook for cancer prevention and facilitate early detection of melanoma, thereby reducing mortality. Mobile technology as a potential provider of health services in delivering health information and conducting interventions, especially in skin fields, where a significant part of diagnosis is based on visual examination, can be important. Evidence showed that constructs of protection motivation theory (PMT) were good predictors of practicing sun protection behaviors in students. This study will investigate whether mobile applications improve safe and healthy behaviors and affect students' reduced UV exposure. METHOD/DESIGN: This randomized controlled trial will be conducted on 320 students on 06/04/2022 in Zahedan. We created mobile applications (Sunshine and Skin Health and WhatsApp apps). Sunshine and Skin Health app allows users to see their changed faces in three stages of adolescence, middle age, and old age based on sun protection behavior. The WhatsApp app has 27 health messages based on PMT theory, eight educational files, and a skin cancer clip that will be sent through WhatsApp during a week. Randomization will be performed using a 1:1 (control: intervention) ratio. The primary endpoint is the group difference in sun-protective behaviors and PMT constructs immediately after the intervention. The secondary endpoint is the group difference in sun-protective behaviors and PMT constructs at a 3-month follow-up. The data will be analyzed in SPSS.22, and the significance level will be considered at 0.05. DISCUSSION: The present study examines the effectiveness of mobile applications in improving sun-protective behaviors. If this intervention enhances sun protection behaviors, it can prevent students' skin damage. TRIAL REGISTRATION: Iranian Registry of Clinical Trials IRCT20200924048825N1. Prospectively registered on 8 February 2021.

Neurological manifestation in COVID-19 disease with neuroimaging studies.

OBJECTIVE: Magnetic resonance imaging (MRI) of the brain or spine examines the findings as well as the time interval between the onset of symptoms and other adverse effects in coronavirus disease that first appeared in 2019 (COVID-19) patients. The goal of this study is to look at studies that use neuroimaging to look at neurological and neuroradiological symptoms in COVID-19 patients. METHODS: We try to put together all of the research on how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes neurological symptoms and cognitive-behavioral changes and give a full picture. RESULTS: We have categorized neuroimaging findings into subtitles such as: headache and dizziness; cerebrovascular complications after stroke; Intracerebral Hemorrhage (ICH); Cerebral Microbleeds (CMBs); encephalopathy; meningitis; encephalitis and myelitis; altered mental status (AMS) and delirium; seizure; neuropsychiatric symptoms; Guillain-Barre Syndrome (GBS) and its variants; smell and taste disorders; peripheral neuropathy; Mild Cognitive Impairment (MCI); and myopathy and myositis. CONCLUSION: In this review study, we talked about some MRI findings that show how COVID-19 affects the nervous system based on what we found.