Dehnavi, Ali Zare

ABSTRACT Contrast‐enhanced ultrasound (CEUS) utilizes microbubble contrast agents to improve imaging clarity during surgeries, offering real‐time visualization of anatomy and perfusion without ionizing radiation. CEUS has demonstrated feasibility in congenital heart defect repairs for monitoring procedures and cerebral perfusion, in developmental dysplasia of hip surgeries for assessing hip perfusion, in neurosurgery for delineating tumor margins, and in urogenital surgeries for guiding interventions. Intraoperative CEUS improves precision, reduces reliance on postoperative imaging, and enhances safety. While it is a safe and effective tool for intraoperative decision‐making in pediatric surgeries, further studies are needed to expand its applications and standardize its use.

Background Early onset Parkinson's disease (EOPD), defined as Parkinson's disease (PD) diagnosed before age 50, often presents unique challenges compared to late-onset PD, particularly with regard to non-motor symptoms. Psychosis in EOPD is associated with increased functional impairment and may lead to a higher mortality risk. Objective Our study is aimed to determine the prevalence of psychosis in EOPD patients and its impact on all-cause mortality, along with examining the effects of antipsychotic medications and Selective Serotonin Reuptake Inhibitors (SSRIs) on mortality in EOPD patients. Methods Our retrospective cohort included EOPD patients diagnosed between 1990 and 2022 at Mayo Clinic, Rochester, Minnesota. Psychosis was defined using the National Institute of Neurological Disorders and Stroke/the National Institute of Mental Health (NINDS/NIMH) Work Group criteria. Cox proportional hazards models were used to analyze the association of psychosis and medications with mortality. Results Of 829 patients with EOPD, 158 (19.1%) developed psychosis at a median of 12.1 years after PD motor symptom onset. Psychosis was significantly associated with increased mortality in unadjusted (HR = 4.31, 95% CI: 2.59–7.18, p < 0.001) and adjusted (HR = 3.55, 95% CI: 2.10–6.01, p < 0.001) models. No significant difference in mortality risk was observed between patients treated with antipsychotics or SSRIs versus those who were not. Conclusions Psychosis is a possible complication in EOPD and is associated with a significant increase in all-cause mortality. The use of antipsychotics and SSRIs did not significantly alter the mortality risk in these patients. Further research is needed to understand the mechanisms driving this association and to develop tailored interventions.

Cannabis use disorder (CUD) is a growing public health concern, with rising prevalence and significant impact on individuals across age groups. This systematic review examines 24 studies investigating pharmacological and non-pharmacological interventions for CUD among adolescents (up to 17), young adults (18–24), and older adults (25–65). Database searches were conducted for randomized controlled trials of CUD interventions reporting outcomes such as cannabis use, abstinence, withdrawal symptoms, and treatment retention. For adolescents, interventions such as contingent rewards and family engagement have shown promise, while young adults benefit from technology-based platforms and peer support. In older adults, pharmacological adjuncts combined with counseling have shown promise in enhancing treatment outcomes. However, optimal treatment combinations remain uncertain, highlighting the need for further research. Addressing CUD requires tailored interventions that acknowledge developmental stages and challenges across the lifespan. Although promising interventions exist, further comparative effectiveness research is needed to delineate the most efficacious approaches.

Abstract This study aims to advance breast cancer (BC) subtype classification by employing machine learning algorithms to identify key diffusion parameters from apparent diffusion coefficient (ADC0-800) histogram, intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI). A total of 148 newly diagnosed BC patients were enrolled, confirmed through core needle biopsy, and subjected to histopathological analyses to assess key molecular markers such as estrogen and progesterone receptors (collectively termed hormone receptors), HER2, and ki67. These markers were then used to classify BC subtypes. Utilizing advanced post-processing techniques on multi-b-value MR Images, the study employed a diverse set of machine learning (ML) algorithms (supervised, unsupervised, and deep learning techniques) to quantitatively assess their diagnostic utility and subsequently identify algorithmically refined diffusion signatures. Machine learning algorithms demonstrated varying efficacies in the classification of BC subtypes. Key diffusion parameters were prioritized based on feature importance values from the ML models with the highest mean AUC and were further validated using group comparison tests and univariate logistic regression. In conclusion, our findings underscore the importance of tailored ML algorithms in classifying BC subtypes and advocate for a synergistic approach in personalized oncology and precision medicine.