Bon Sante (Good Health): Factors Influencing PrEP Use Among Haitians/Haitian Americans.

BACKGROUND: The HIV/AIDS epidemic has disproportionately affected Black individuals in the USA, and this health disparity has increased over time. Despite the effectiveness of pre-exposure prophylaxis (PrEP) as a prevention tool for HIV, there are disparities in its use, and uptake of this intervention remains low among racial and ethnic minorities, including Haitians/Haitian Americans. In this study, factors influencing PrEP use among Haitians/Haitian Americans in Miami, FL, are explored to provide necessary data to address disparities. METHODS: The research team collaborated with local organizations to recruit 30 individuals (Haitians/Haitian Americans) between February 4 and October 1, 2021, and conducted semi-structured interviews. All interviews were audio-recorded and transcribed, and NVivo® was used to analyze the transcripts for emergent themes. RESULTS: The study sample comprised 30 adults of Haitian descent in Miami, FL (50% female, approximately 67% with a high school education or more, mean age = 43.7 ± 13 years, and 74.2% born in Haiti). Four primary themes emerged from the analysis: (1) limited PrEP awareness, (2) underutilization of PrEP, (3) inadequate discussion of HIV prevention strategies, and (4) PrEP delivery encompassing barriers and facilitators for PrEP delivery and promotion strategies. CONCLUSION: This study indicated that there is a critical need to increase Haitians/Haitian Americans' knowledge regarding PrEP. Health communication interventions tailored specifically for Haitians/Haitian Americans that target stigma, attitudes toward HIV, and risk perception may be significant in increasing PrEP in this population.

A systematic review on the use of artificial intelligence in gynecologic imaging - Background, state of the art, and future directions.

OBJECTIVE: Machine learning, deep learning, and artificial intelligence (AI) are terms that have made their way into nearly all areas of medicine. In the case of medical imaging, these methods have become the state of the art in nearly all areas from image reconstruction to image processing and automated analysis. In contrast to other areas, such as brain and breast imaging, the impacts of AI have not been as strongly felt in gynecologic imaging. In this review article, we: (i) provide a background of clinically relevant AI concepts, (ii) describe methods and approaches in computer vision, and (iii) highlight prior work related to image classification tasks utilizing AI approaches in gynecologic imaging. DATA SOURCES: A comprehensive search of several databases from each database's inception to March 18th, 2021, English language, was conducted. The databases included Ovid MEDLINE(R) and Epub Ahead of Print, In-Process & Other Non-Indexed Citations, and Daily, Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials, and Ovid Cochrane Database of Systematic Reviews and ClinicalTrials.gov. METHODS OF STUDY SELECTION: We performed an extensive literature review with 61 articles curated by three reviewers and subsequent sorting by specialists using specific inclusion and exclusion criteria. TABULATION, INTEGRATION, AND RESULTS: We summarize the literature grouped by each of the three most common gynecologic malignancies: endometrial, cervical, and ovarian. For each, a brief introduction encapsulating the AI methods, imaging modalities, and clinical parameters in the selected articles is presented. We conclude with a discussion of current developments, trends and limitations, and suggest directions for future study. CONCLUSION: This review article should prove useful for collaborative teams performing research studies targeted at the incorporation of radiological imaging and AI methods into gynecological clinical practice.

Unenhanced MRI of the Abdomen and Pelvis in the Comprehensive Evaluation of Acute Atraumatic Abdominal Pain in Children.

OBJECTIVE. The purpose of this study is to show the utility of rapid unenhanced MRI in the comprehensive assessment of acute atraumatic abdominal pain in children, including appendicitis and alternate diagnoses, and to review the MRI features of common acute abdominal and pelvic conditions in a large, single-institution cohort. CONCLUSION. Rapid unenhanced MRI is an excellent option for the initial, comprehensive evaluation of acute abdominal emergencies in pediatric patients because it can diagnose the full range of presenting abnormalities, including causes of abdominal pain warranting surgical and nonsurgical management.

Physician Burnout in a Rural Kansas Community.

INTRODUCTION: Physician wellness and burnout are topics of intense discussion and study, however, less is known about rural physician burnout. The aim of this study was to assess levels of physician burnout in the rural Kansas community of Salina. METHODS: An electronic, confidential survey was conducted among 145 physicians with active privileges at the local health center and/or surgical center. The survey included demographic features, practice characteristics, and the abbreviated Maslach Burnout Inventory™ (aMBI). In addition, survey participants were invited to provide free-text responses to questions concerning specific causes of burnout and mechanisms to combat feelings of burnout. RESULTS: Of 145 invited, 76 physicians completed the survey. Thirty-six respondents self-identified as primary care physicians, 22 as subspecialists, and 18 as surgeons. aMBI scores for emotional exhaustion (EE), depersonalization (D) and personal accomplishment (PA) ranged from 0 to 18. The mean EE score was 8.4 (SD = 4.9), mean D score was 4.8 (SD = 3.9), and mean PA score was 15.2 (SD = 2.8). Using tertiles, physician burnout (i.e., those in the first tertile) for EE was 39% (30/76), D was 34% (26/76), and PA was 41% (31/75); 22% of physicians surveyed scored high on both EE and D as measured by tertiles, suggestive of more serious burnout. No significant differences in aMBI scores were observed for demographic features or practice characteristics; physicians who worked with medical students had higher PA scores. Contributing to burnout were demands of documentation and difficult patient encounters, while true time away might ameliorate rural physician burnout. CONCLUSIONS: As measured by aMBI constructs, burnout is prevalent among the responding rural physicians practicing in the Salina community.

Utilizing magnetization transfer imaging to investigate tissue remodeling in a murine model of autosomal dominant polycystic kidney disease.

PURPOSE: Noninvasive imaging techniques that quantify renal tissue composition are needed to more accurately ascertain prognosis and monitor disease progression in polycystic kidney disease (PKD). Given the success of magnetization transfer (MT) imaging to characterize various tissue remodeling pathologies, it was tested on a murine model of autosomal dominant PKD. METHODS: C57Bl/6 Pkd1 R3277C mice at 9, 12, and 15 months were imaged with a 16.4T MR imaging system. Images were acquired without and with RF saturation in order to calculate MT ratio (MTR) maps. Following imaging, the mice were euthanized and kidney sections were analyzed for cystic and fibrotic indices, which were compared with statistical parameters of the MTR maps. RESULTS: The MTR-derived mean, median, 25th percentile, skewness, and kurtosis were all closely related to indices of renal pathology, including kidney weight/body weight, cystic index, and percent of remaining parenchyma. The correlation between MTR and histology-derived cystic and fibrotic changes was R(2) = 0.84 and R(2) = 0.70, respectively. CONCLUSION: MT imaging provides a new, noninvasive means of measuring tissue remodeling PKD changes and may be better suited for characterizing renal impairment compared with conventional MR techniques.

Automatic total kidney volume measurement on follow-up magnetic resonance images to facilitate monitoring of autosomal dominant polycystic kidney disease progression.

BACKGROUND: Renal imaging examinations provide high-resolution information about the anatomic structure of the kidneys and are used to measure total kidney volume (TKV) in autosomal dominant polycystic kidney disease (ADPKD) patients. TKV has become the gold-standard image biomarker for ADPKD progression at early stages of the disease and is used in clinical trials to characterize treatment efficacy. Automated methods to segment the kidneys and measure TKV are desirable because of the long time requirement for manual approaches such as stereology or planimetry tracings. However, ADPKD kidney segmentation is complicated by a number of factors, including irregular kidney shapes and variable tissue signal at the kidney borders. METHODS: We describe an image processing approach that overcomes these problems by using a baseline segmentation initialization to provide automatic segmentation of follow-up scans obtained years apart. We validated our approach using 20 patients with complete baseline and follow-up T1-weighted magnetic resonance images. Both manual tracing and stereology were used to calculate TKV, with two observers performing manual tracings and one observer performing repeat tracings. Linear correlation and Bland-Altman analysis were performed to compare the different approaches. RESULTS: Our automated approach measured TKV at a level of accuracy (mean difference ± standard error = 0.99 ± 0.79%) on par with both intraobserver (0.77 ± 0.46%) and interobserver variability (1.34 ± 0.70%) of manual tracings. All approaches had excellent agreement and compared favorably with ground-truth manual tracing with interobserver, stereological and automated approaches having 95% confidence intervals ∼ ± 100 mL. CONCLUSIONS: Our method enables fast, cost-effective and reproducible quantification of ADPKD progression that will facilitate and lower the costs of clinical trials in ADPKD and other disorders requiring accurate, longitudinal kidney quantification. In addition, it will hasten the routine use of TKV as a prognostic biomarker in ADPKD.

Semi-automated segmentation of pre-operative low grade gliomas in magnetic resonance imaging.

BACKGROUND: Segmentation of pre-operative low-grade gliomas (LGGs) from magnetic resonance imaging is a crucial step for studying imaging biomarkers. However, segmentation of LGGs is particularly challenging because they rarely enhance after gadolinium administration. Like other gliomas, they have irregular tumor shape, heterogeneous composition, ill-defined tumor boundaries, and limited number of image types. To overcome these challenges we propose a semi-automated segmentation method that relies only on T2-weighted (T2W) and optionally post-contrast T1-weighted (T1W) images. METHODS: First, the user draws a region-of-interest (ROI) that completely encloses the tumor and some normal tissue. Second, a normal brain atlas and post-contrast T1W images are registered to T2W images. Third, the posterior probability of each pixel/voxel belonging to normal and abnormal tissues is calculated based on information derived from the atlas and ROI. Finally, geodesic active contours use the probability map of the tumor to shrink the ROI until optimal tumor boundaries are found. This method was validated against the true segmentation (TS) of 30 LGG patients for both 2D (1 slice) and 3D. The TS was obtained from manual segmentations of three experts using the Simultaneous Truth and Performance Level Estimation (STAPLE) software. Dice and Jaccard indices and other descriptive statistics were computed for the proposed method, as well as the experts' segmentation versus the TS. We also tested the method with the BraTS datasets, which supply expert segmentations. RESULTS AND DISCUSSION: For 2D segmentation vs. TS, the mean Dice index was 0.90 ± 0.06 (standard deviation), sensitivity was 0.92, and specificity was 0.99. For 3D segmentation vs. TS, the mean Dice index was 0.89 ± 0.06, sensitivity was 0.91, and specificity was 0.99. The automated results are comparable with the experts' manual segmentation results. CONCLUSIONS: We present an accurate, robust, efficient, and reproducible segmentation method for pre-operative LGGs.

scikit-image: image processing in Python.

scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal Modified BSD open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image. More information can be found on the project homepage, http://scikit-image.org.

Supervised segmentation of polycystic kidneys: a new application for stereology data.

Stereology is a volume estimation method, typically applied to diagnostic imaging examinations in population studies where planimetry is too time-consuming (Chapman et al. Kidney Int 64:1035-1045, 2003), to obtain quantitative measurements (Nyengaard J Am Soc Nephrol 10:1100-1123, 1999, Michel and Cruz-Orive J Microsc 150:117-136, 1988) of certain structures or organs. However, true segmentation is required in order to perform advanced analysis of the tissues. This paper describes a novel method for segmentation of region(s) of interest using stereology data as prior information. The result is an efficient segmentation method for structures that cannot be easily segmented using other methods.

Cortical and subcortical atrophy in Alzheimer disease: parallel atrophy of thalamus and hippocampus.

Brain atrophy is a key imaging hallmark of Alzheimer disease (AD). In this study, we carried out an integrative evaluation of AD-related atrophy. Twelve patients with AD and 13 healthy controls were enrolled. We conducted a cross-sectional analysis of total brain tissue volumes with SIENAX. Localized gray matter atrophy was identified with optimized voxel-wise morphometry (FSL-VBM), and subcortical atrophy was evaluated by active shape model implemented in FMRIB's Integrated Registration Segmentation Toolkit. SIENAX analysis demonstrated total brain atrophy in AD patients; voxel-based morphometry analysis showed atrophy in the bilateral mediotemporal regions and in the posterior brain regions. In addition, regarding the diminished volumes of thalami and hippocampi in AD patients, subsequent vertex analysis of the segmented structures indicated shrinkage of the bilateral anterior thalami and the left medial hippocampus. Interestingly, the volume of the thalami and hippocampi were highly correlated with the volume of the thalami and amygdalae on both sides in AD patients, but not in healthy controls. This complex structural information proved useful in the detailed interpretation of AD-related neurodegenerative process, as the multilevel approach showed both global and local atrophy on cortical and subcortical levels. Most importantly, our results raise the possibility that subcortical structure atrophy is not independent in AD patients.

The characterisation of mammalian tissue with 2D relaxation methods.

The potential of two-dimensional (2D) relaxometry for characterising mammalian tissue is explored on samples of liver, kidney (cortex, medulla and ureter) as well as cartilage. Significant differences are found between the T(1)-T(2) spectra of healthy and diseased human cartilage which suggests that 2D relaxometry could have potential use in clinical diagnosis. The effect of reducing the recovery delay on the T(1)-T(2) spectrum is explored to try to identify the optimum balance between speed and accuracy.

Multisliced ultrafast 2D relaxometry.

By associating each slice in a spatially homogeneous sample with a different inversion-recovery delay time, multislice methods are used to reduce the acquisition times of 2D inversion-recovery T(1)-T(2) relaxation spectra to just a few minutes. The increased speed comes at the expense of reduced signal/noise and this is reflected most noticeably in shifts in the component longitudinal relaxation times. Nevertheless, the major features of the 2D relaxation spectra are reproduced.

MRICOM-MRI COntrast Modelling using 2D T1-T2 correlation spectra and relaxation signatures.

Image contrast is calculated by inputting experimental 2D T(1)-T(2) relaxation spectra into the ODIN software interface. The method involves characterising a magnetic resonance imaging pulse sequence with a "relaxation signature" which describes the sensitivity of the sequence to relaxation and is independent of sample parameters. Maximising (or minimising) the overlap between the experimental 2D T(1)-T(2) relaxation spectra and the relaxation signature can then be used to maximise image contrast. The concept is illustrated using relaxation signatures for the echo planar imaging and Turbo spin-echo imaging sequences, together with in-vitro 2D T(1)-T(2) spectra for liver and cartilage.