The mental health of Australians bereaved during the first two years of the COVID-19 pandemic: a latent class analysis

BACKGROUND

The COVID-19 pandemic disrupted many areas of life, including culturally accepted practices at end-of-life care, funeral rites, and access to social, community, and professional support. This survey investigated the mental health outcomes of Australians bereaved during this time to determine how these factors might have impacted bereavement outcomes.

METHODS

An online survey indexing pandemic and bereavement experiences, levels of grief, depression, anxiety, and health, work, and social impairment. Latent class analysis (LCA) was used to identify groups of individuals who shared similar symptom patterns. Multinomial regressions identified pandemic-related, loss-related, and sociodemographic correlates of class membership.

RESULTS

1911 Australian adults completed the survey. The LCA identified four classes: low symptoms (46.8%), grief (17.3%), depression/anxiety (17.7%), and grief/depression/anxiety (18.2%). The latter group reported the highest levels of health, work, and social impairment. The death of a child or partner and an inability to care for the deceased due to COVID-19 public health measures were correlated with grief symptoms (with or without depression and anxiety). Preparedness for the person's death and levels of pandemic-related loneliness and social isolation differentiated all four classes. Unemployment was associated with depression/anxiety (with or without grief).

CONCLUSIONS

COVID-19 had profound impacts for the way we lived and died, with effects that are likely to ricochet through society into the foreseeable future. These lessons learned must inform policymakers and healthcare professionals to improve bereavement care and ensure preparedness during and following future predicted pandemics to prevent negative impacts.

[Automated cephalometric landmark identification and location based on convolutional neural network]

Objective: To develop an automated landmark location system applicable to the case of landmark missing. Methods: Four and eighty-one lateral cephalograms, which contained 240 males and 241 females, with an average age of (24.5±5.6) years, taken from January 2015 to January 2021 in the Department of Orthodontics, Capital Medical University School of Stomatology, and met the inclusion criteria were collected. Five postgraduate orthodontic students were the annotators to manually locate 61 possible landmarks in 481 lateral cephalograms. Two assistant professors in the department as reviewers performed calibration. Two professors as arbitrators, made final decision. Data sets were established (341 were used as training set, 40 as validation set, and 100 as test set). In this paper, an automatic landmarks identification and location model based on convolutional neural networks (CNN), CephaNET, was developed. The model was trained by feeding the original image into the feature extraction module and convolutional pose machine (CPM) module to locate landmarks with high accuracy using deep supervision. Training set was enhanced to 1 684 images by histogram equalization, cropping, and adjustment of brightness. The model was trained to compare the Gaussian heat maps output from the network with the set threshold to identify landmark missing cases. Test set of 100 lateral cephalograms was used to test the accuracy of the model. The evaluation criteria used were success detection rate of missing landmark, mean radial error (MRE) and success detection rate (SDR) in the range of 2.0, 2.5, 3.0, 3.5 and 4.0 mm. Results: The model identified and located 61 commonly used landmarks in 0.13 seconds on average. It had an average accuracy of 93.5% in identifying missing landmarks. The MRE of our testing set was (1.19±0.91) mm. SDR of 2.0, 2.5, 3.0, 3.5 and 4.0 mm were 85.4%, 90.2%, 93.5%, 95.4%, 97.0% respectively. Conclusions: The model proposed in this paper could adapt to the absence of landmark in lateral cephalograms and locate 61 commonly used landmarks with high accuracy to meet the requirements of different cephalometric analysis methods.

Treatment Tolerance and Toxicity in Elderly Oropharyngeal Cancer Patients and Implication on Outcomes

PURPOSE/OBJECTIVE(S)

To investigate the tolerance level and toxicity for standard of care treatment for oropharyngeal cancer (OP) in elderly patients and their impact on outcomes.

MATERIALS/METHODS

Using our in-house head and neck cancer database, we looked for non-metastatic OP cases that received definitive treatment between 1/2009-6/2020. All patients received either definitive radiation therapy (RT) +/- concomitant systemic therapy (ST), or surgery followed by adjuvant RT or RT-ST. For the elderly (age at diagnosis ≥65 years) and young (<65 years) patients, we compared treatment package time (TPT) (time from surgery to RT conclusion) for adjuvant RT, total RT duration and unplanned RT interruptions. ST details and dose/protocol modifications were also compared. We evaluated worst grade of pain and mucositis, hospitalization for non-hydration causes and febrile neutropenia (FN) during RT. Feeding tube (FT) use and weight loss were compared. The independent effect of these indicators on locoregional (LRFS), distant (DRFS) recurrence free and overall (OS) survival was assessed using multivariate analyses (MVA).

RESULTS

A cohort of 326 patients was included: 36% elderly (n = 118) and 64% young (n = 208), with no differences in AJCC stage distribution (8th), treatment received and HPV status (HPV+ve: 73% vs 74.6%; p = 0.86). In 23.6 % who received adjuvant RT, median TPT was 86 (range 72-128) and 81 (65-137) days for elderly vs young (p = 0.27); whereas in the definitive RT cases 76.4%, total RT duration was 49 days for both age groups. Overall, prescribed RT course was not completed in 4% and unplanned RT interruptions occurred in 22.8% and both were non-significant between age groups. Among the 261 patients that received ST, elderly utilized more cetuximab (26 vs 12%; p = 0.007). For those who received cisplatin, 20% of elderly had cumulative dose <200 mg/m2 compared to 6% among the younger age group (p = 0.006); and cisplatin was changed to carboplatin or cetuximab in 18% vs 8% (p = 0.019). Delayed/cancelled cycles and dose reductions were similar. There were more hospitalizations (47% vs 27%; p<0.001) and a trend for more FN (9% vs 3%; p = 0.09) with older age, while worst pain and mucositis was similar. FTs were used more in elderly patients (64% vs 50%; p = 0.02), for a median of 97 vs 64 days (p = 0.31); of which 19.5% vs 11% (p = 0.28) were inserted before RT start. However, % weight loss was non-significant. On MVA, longer RT duration, FT use and hospitalizations predicted worse LRFS and DRFS; and they were prognostic for OS in addition to TPT >90 days (p<0.05 for all). Nevertheless, elderly vs young had non-significant 3-year LRFS (91% vs 90% and 67% vs 69%), DRFS (86% vs 90% and 79% vs 81%) & OS (85% vs 81% and 39% vs 52%) for HPV+ve and HPV-ve respectively (p>0.05).

CONCLUSION

Elderly patients with OP need more multi-disciplinary supportive care when receiving RT and concurrent ST. However, survival outcomes are equivalent to younger patients. Ongoing studies should enroll more elderly candidates and stratify endpoints with age.

Sexual Orientation and Gender Identity (SOGI) Data Collection: Opportunities to Advance Best Clinical Practices for LGBTQ+ Patients in Radiation Oncology

PURPOSE/OBJECTIVE(S)

A long-standing barrier to progress against health disparities is the lack of data regarding cancer risks, prevalence, treatment, and outcomes for sexual and gender minority (SGM) patients. Sexual orientation and gender identity (SOGI) data are not routinely collected by individual oncologists, cancer centers, or most non-federal hospital systems. Alarmingly high proportions of SGM patients report discrimination in healthcare or avoid routine care due to perceived lack of acceptance in the healthcare system. For these and other reasons, healthcare institutions must adopt practices that promote an inclusive environment for all patients including those self-identified from SGM groups. One strategy to achieve this aim is through SOGI data collection. The purpose of this study was to pilot new procedures and training for SOGI data collection, the aims of this project were to standardize the collection of SOGI data for all new patients referred to the Division of Radiation Oncology; promote clinical staff awareness of SGM health disparities and strategies for fostering an inclusive hospital environment; and to provide SGM patients and caregivers educational resources and support systems tailored to their needs.

MATERIALS/METHODS

We designed a Quality Improvement program for collecting SOGI data, which was approved by our institution's QIAB. Patient access specialists (PAS) were trained to collect SOGI data from newly registered patients and enter the data into the electronic health record. Radiation Oncology staff completed surveys before and after SOGI training to estimate its impact on the provision of patient care. A Fisher's exact test was utilized to evaluate associations between training and provider-reported outcomes.

RESULTS

Within a 3-week period starting in January 2023, two 1-hour interactive training sessions were offered to twenty-five PAS. Three 1-hour training sessions were offered to twenty-seven Radiation Oncology clinical staff. (1) Confidence for incorporating SOGI classifiers around patients improved from before training (52%, 13/25) to after training (100%, 17/17) among medical providers surveyed (odds ratio (OR) 32, 95% confidence interval (CI) 0.70-1493, p = 0.005). Use of SOGI data in clinical decision making increased from before training (9/25, 36%) to after training (100%, 17/17) among medical providers (OR 60.79, 95% CI 3.271-1130, p<0.0001). (2) A clinical pathway for SGM patients was developed to facilitate referral to our institution's SGM patient support group and distribution of patient education materials focused on sexual health.

CONCLUSION

Establishing standardized SOGI data collection can facilitate the provision of tailored resources and care that meets the needs of patients and staff in a large comprehensive cancer center. Specialized training for staff developed through this initiative helps foster an inclusive and welcoming environment that promotes the integration, visibility, and advancement of SGM cancer care at our institution.

Enabling early detection of knee osteoarthritis using diffusion-relaxation correlation spectrum imaging

AIM

To present a technique that enables detection of early stage OA of the knee using diffusion-relaxation correlation spectrum imaging (DR-CSI).

MATERIALS AND METHODS

Fifty-five early osteoarthritis patients (OA, Kellgren-Lawrence [KL] score 1 to 2; mean age, 56.4 years) and 49 healthy volunteers (mean age, 56.7 years) were underwent magnetic resonance imaging (MRI) with T2-mapping and DR-CSI techniques. Maps of mean apparent diffusion coefficient (ADC), T2 relaxation time and volume fraction Vi for DR-CSI compartment i (A, B, C, D) sensitivity, specificity, and positive and negative likelihood ratio (PLR, NLR) were assessed to determine the diagnostic accuracy for detection of early-stage degeneration of knee articular cartilage. The structural abnormalities of articular cartilage were evaluated using modified Whole-Organ MR Imaging Scores (WORMS).

RESULTS

All intra- and interobserver agreements for DR-CSI compartment volume fractions and modified WORMS of cartilage were excellent. Early OA versus the controls had higher V_C, lower V_A and V_B (p<0.001), but comparable V_D (p>0.05). V_A, V_B and V_C had a moderate association with WORMS. No significant correlation was identified between V_D and WORMS. V_C had better ability than V_A,V_B, V_D, T2 and ADC to discriminate early OA patients from healthy controls (area under the curve, 0.898). Sensitivity, specificity, PLR, and NLR of V_C with a cut-off value of 29.9% were 81.8% (95% confidence interval [CI], 69.1-90.9%), 95.9% (86-99.5%), 20.05% (5.13-78.34%), and 0.19% (0.11-0.33%).

CONCLUSIONS

DR-CSI compartment volume fractions may be sensitive indicators for detecting early-stage degeneration in knee articular cartilage.

[Automated diagnostic classification with lateral cephalograms based on deep learning network model]

Objective: To establish a comprehensive diagnostic classification model of lateral cephalograms based on artificial intelligence (AI) to provide reference for orthodontic diagnosis. Methods: A total of 2 894 lateral cephalograms were collected in Department of Orthodontics, Capital Medical University School of Stomatology from January 2015 to December 2021 to construct a data set, including 1 351 males and 1 543 females with a mean age of (26.4± 7.4) years. Firstly, 2 orthodontists (with 5 and 8 years of orthodontic experience, respectively) performed manual annotation and calculated measurement for primary classification, and then 2 senior orthodontists (with more than 20 years of orthodontic experience) verified the 8 diagnostic classifications including skeletal and dental indices. The data were randomly divided into training, validation, and test sets in the ratio of 7∶2∶1. The open source DenseNet121 was used to construct the model. The performance of the model was evaluated by classification accuracy, precision rate, sensitivity, specificity and area under the curve (AUC). Visualization of model regions of interest through class activation heatmaps. Results: The automatic classification model of lateral cephalograms was successfully established. It took 0.012 s on average to make 8 diagnoses on a lateral cephalogram. The accuracy of 5 classifications was 80%-90%, including sagittal and vertical skeletal facial pattern, mandibular growth, inclination of upper incisors, and protrusion of lower incisors. The acuracy rate of 3 classifications was 70%-80%, including maxillary growth, inclination of lower incisors and protrusion of upper incisors. The average AUC of each classification was ≥0.90. The class activation heat map of successfully classified lateral cephalograms showed that the AI model activation regions were distributed in the relevant structural regions. Conclusions: In this study, an automatic classification model for lateral cephalograms was established based on the DenseNet121 to achieve rapid classification of eight commonly used clinical diagnostic items.

[Research on multi-class orthodontic image recognition system based on deep learning network model]

Objective: To develop a multi-classification orthodontic image recognition system using the SqueezeNet deep learning model for automatic classification of orthodontic image data. Methods: A total of 35 000 clinical orthodontic images were collected in the Department of Orthodontics, Capital Medical University School of Stomatology, from October to November 2020 and June to July 2021. The images were from 490 orthodontic patients with a male-to-female ratio of 49∶51 and the age range of 4 to 45 years. After data cleaning based on inclusion and exclusion criteria, the final image dataset included 17 453 face images (frontal, smiling, 90° right, 90° left, 45° right, and 45° left), 8 026 intraoral images [frontal occlusion, right occlusion, left occlusion, upper occlusal view (original and flipped), lower occlusal view (original and flipped) and coverage of occlusal relationship], 4 115 X-ray images [lateral skull X-ray from the left side, lateral skull X-ray from the right side, frontal skull X-ray, cone-beam CT (CBCT), and wrist bone X-ray] and 684 other non-orthodontic images. A labeling team composed of orthodontic doctoral students, associate professors, and professors used image labeling tools to classify the orthodontic images into 20 categories, including 6 face image categories, 8 intraoral image categories, 5 X-ray image categories, and other images. The data for each label were randomly divided into training, validation, and testing sets in an 8∶1∶1 ratio using the random function in the Python programming language. The improved SqueezeNet deep learning model was used for training, and 13 000 natural images from the ImageNet open-source dataset were used as additional non-orthodontic images for algorithm optimization of anomaly data processing. A multi-classification orthodontic image recognition system based on deep learning models was constructed. The accuracy of the orthodontic image classification was evaluated using precision, recall, F1 score, and confusion matrix based on the prediction results of the test set. The reliability of the model's image classification judgment logic was verified using the gradient-weighted class activation mapping (Grad-CAM) method to generate heat maps. Results: After data cleaning and labeling, a total of 30 278 orthodontic images were included in the dataset. The test set classification results showed that the precision, recall, and F1 scores of most classification labels were 100%, with only 5 misclassified images out of 3 047, resulting in a system accuracy of 99.84%(3 042/3 047). The precision of anomaly data processing was 100% (10 500/10 500). The heat map showed that the judgment basis of the SqueezeNet deep learning model in the image classification process was basically consistent with that of humans. Conclusions: This study developed a multi-classification orthodontic image recognition system for automatic classification of 20 types of orthodontic images based on the improved SqueezeNet deep learning model. The system exhibitted good accuracy in orthodontic image classification.

The effect of microRNA-663b in the inhibition of interleukin-1-induced nucleus pulposus cell apoptosis and inflammatory response

The aim of this study was to explore the role and pathological mechanism of microRNA-663b in interleukin-1beta (IL-1β)-induced inflammation and apoptosis of nucleus pulposus cells. First, the best concentration and time to construct the nucleus pulposus cell inflammation model was screen out. Overexpression or inhibition of miR-663b expression was performed by adding microRNA-663b mimic or microRNA-663b inhibitor. 293T cells were transfected according to experimental requirements. The luciferase activity of each group was detected to analyze the targeted regulation of microRNA-663b on interleukin-1 receptor (IL1R1). Compared with the mimic negative control (NC) group, the expression of inflammatory factors in the microRNA-663b overexpression group was inhibited (P<0.05), and the expression of type 2 collagen and polysaccharide protein increased (P<0.05), and the apoptosis of nucleus pulposus cells was inhibited (P<0.01), and the number of TUNEL-positive cells decreased significantly (P<0.01), and the microRNA and protein expression of IL1R1, the ratio of P-P65/P65 and phospho-nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha (P-IκBα)/nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha (IκBα) protein expression were significantly decreased (P<0.05). The expression of inflammatory factors in the miR-663b inhibitor group was significantly higher than that in the inhibitor NC group (P<0.01), and the expression of type 2 collagen and polysaccharide protein was significantly decreased (P<0.01), and the number of apoptosis cells and TUNEL staining positive cells increased (p<0.01). The expression of IL1R1 gene and protein was significantly increased (P<0.01). The ratio of P-P65/P65 and P-IκBα/IκBα protein expression increased (P<0.05). IL1R1 is a downstream target gene of microRNA-663b. MicroRNA-663b may down-regulate the expression of IL1R1 at the transcriptional level by targeting IL1R1, inhibit the inflammatory response of nucleus pulposus cells, and slow down the degeneration of nucleus pulposus cells.

The effect of microRNA-663b in the inhibition of interleukin-1-induced nucleus pulposus cell apoptosis and inflammatory response

The aim of this study was to explore the role and pathological mechanism of microRNA-663b in interleukin-1beta (IL-1β)-induced inflammation and apoptosis of nucleus pulposus cells. First, the best concentration and time to construct the nucleus pulposus cell inflammation model was screen out. Overexpression or inhibition of miR-663b expression was performed by adding microRNA-663b mimic or microRNA-663b inhibitor. 293T cells were transfected according to experimental requirements. The luciferase activity of each group was detected to analyze the targeted regulation of microRNA-663b on interleukin-1 receptor (IL1R1). Compared with the mimic negative control (NC) group, the expression of inflammatory factors in the microRNA-663b overexpression group was inhibited (P<0.05), and the expression of type 2 collagen and polysaccharide protein increased (P<0.05), and the apoptosis of nucleus pulposus cells was inhibited (P<0.01), and the number of TUNEL-positive cells decreased significantly (P<0.01), and the microRNA and protein expression of IL1R1, the ratio of P-P65/P65 and phospho-nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha (P-IκBα)/nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha (IκBα) protein expression were significantly decreased (P<0.05). The expression of inflammatory factors in the miR-663b inhibitor group was significantly higher than that in the inhibitor NC group (P<0.01), and the expression of type 2 collagen and polysaccharide protein was significantly decreased (P<0.01), and the number of apoptosis cells and TUNEL staining positive cells increased (p<0.01). The expression of IL1R1 gene and protein was significantly increased (P<0.01). The ratio of P-P65/P65 and P-IκBα/IκBα protein expression increased (P<0.05). IL1R1 is a downstream target gene of microRNA-663b. MicroRNA-663b may down-regulate the expression of IL1R1 at the transcriptional level by targeting IL1R1, inhibit the inflammatory response of nucleus pulposus cells, and slow down the degeneration of nucleus pulposus cells.

P10.09.B Retroelement expression in glioma tumors exhibits subtype specific patterns

Background

There are ~3 million transposable elements in the human genome constituting about 42% of all basepairs. Retroelements (REs) are ~90% of the transposable elements present in the human genome. Active REs are considered highly mutagenic and have been implicated in multiple steps of cancer development and progression, as well as in neurologic diseases. RE activity has functional effects on the genome, including the maintenance of centromere and telomere integrity, and deleterious gene expression. Previous studies have shown that certain families of RE (HERVK, L1, Alu) are expressed in gliomas, however, their specific role as arbitrators of oncogenesis or promoters of the innate anti-tumor immune response remains uncertain. Moreover, it has been shown that a soluble form of PD-L1 (sPD-L1) that blocks its inhibitory activity is produced by exaptation of an intronic endogenous retroelement (LINE-2A) in the gene encoding PD-L1, highlighting the importance of REs as potential therapeutic targets. In this analysis we aim to identify the unique patterns of RE expression across major subtypes of glioma.

Material and Methods

We conducted a differential expression analysis of 49 RE families using RNA-seq data measured in glioma tumors from The Cancer Genome Atlas (TCGA). RE counts were produced using the software REDiscoverTE. Pairwise comparisons between glioma subtypes (defined by WHO2021) were done using in 625 tumor samples adjusting for age, sex and race.

Results

10 of the 49 considered RE families exhibited significantly different (false discovery rate, FDR, &lt;0.05) expression in at least one glioma subtype. Alu(Fold change, FC=1.5), RNA(FC=11.3), PiggyBac(FC=1.6), rRNA(FC=5.23) and Dong-R4(FC=1.8) were overexpressed in IDH-wildtype glioblastoma while Gypsy(FC=0.4) and CRP1(FC=0.26) were decreased in expression. scRNA (FC=2.7) were overexpressed in IDH-mutant oligodendroglioma compared to glioblastoma while Dong-R4 (FC = 0.53) showed decreased expression. LTR (FC=2.02) and tRNA-Deu (FC=1.46), showed increased expression in IDH-wildtype diffuse astrocytomas compared to IDH-mutant, 1p/19q-codeleted oligodendrogliomas while Gypsy (FC =0.41) showed decreased expression.

Conclusion

We have shown that expression of certain RE families within gliomas have subtype-specific patterns. While it is well established that RE expression is dysregulated in cancer, our analysis is the first at exploring a wide range of retroelements in the context of glioma by subtype. Given the important role of REs in transcriptional control, genomic instability, chromosomal rearrangements, and oncogenic activation, the identification of individual families and specific REs in glioma holds an intrinsic value to potential biomarkers and immunotherapy targets.

Automatic 3-Dimensional Cephalometric Landmarking via Deep Learning

The increasing use of 3-dimensional (3D) imaging by orthodontists and maxillofacial surgeons to assess complex dentofacial deformities and plan orthognathic surgeries implies a critical need for 3D cephalometric analysis. Although promising methods were suggested to localize 3D landmarks automatically, concerns about robustness and generalizability restrain their clinical use. Consequently, highly trained operators remain needed to perform manual landmarking. In this retrospective diagnostic study, we aimed to train and evaluate a deep learning (DL) pipeline based on SpatialConfiguration-Net for automatic localization of 3D cephalometric landmarks on computed tomography (CT) scans. A retrospective sample of consecutive presurgical CT scans was randomly distributed between a training/validation set (n = 160) and a test set (n = 38). The reference data consisted of 33 landmarks, manually localized once by 1 operator(n = 178) or twice by 3 operators (n = 20, test set only). After inference on the test set, 1 CT scan showed "very low" confidence level predictions; we excluded it from the overall analysis but still assessed and discussed the corresponding results. The model performance was evaluated by comparing the predictions with the reference data; the outcome set included localization accuracy, cephalometric measurements, and comparison to manual landmarking reproducibility. On the hold-out test set, the mean localization error was 1.0 ± 1.3 mm, while success detection rates for 2.0, 2.5, and 3.0 mm were 90.4%, 93.6%, and 95.4%, respectively. Mean errors were -0.3 ± 1.3° and -0.1 ± 0.7 mm for angular and linear measurements, respectively. When compared to manual reproducibility, the measurements were within the Bland-Altman 95% limits of agreement for 91.9% and 71.8% of skeletal and dentoalveolar variables, respectively. To conclude, while our DL method still requires improvement, it provided highly accurate 3D landmark localization on a challenging test set, with a reliability for skeletal evaluation on par with what clinicians obtain.

A Combined sEMG and Accelerometer System for Monitoring Functional Activity in Stroke

Remote monitoring of physical activity using bodyworn sensors provides an alternative to assessment of functional independence by subjective, paper-based questionnaires. This study investigated the classification accuracy of a combined surface electromyographic (sEMG) and accelerometer (ACC) sensor system for monitoring activities of daily living in patients with stroke. sEMG and ACC data were recorded from 10 hemi paretic patients while they carried out a sequence of 11 activities of daily living (Identification tasks), and 10 activities used to evaluate misclassification errors (non-Identification tasks). The sEMG and ACC sensor data were analyzed using a multilayered neural network and an adaptive neuro-fuzzy inference system to identify the minimal sensor configuration needed to accurately classify the identification tasks, with a minimal number of misclassifications from the non-Identification tasks. The results demonstrated that the highest sensitivity and specificity for the identification tasks was achieved using a subset of 4 ACC sensors and adjacent sEMG sensors located on both upper arms, one forearm, and one thigh, respectively. This configuration resulted in a mean sensitivity of 95.0 %, and a mean specificity of 99.7 % for the identification tasks, and a mean misclassification error of < 10% for the non-Identification tasks. The findings support the feasibility of a hybrid sEMG and ACC wearable sensor system for automatic recognition of motor tasks used to assess functional independence in patients with stroke.

Evaluation of an accelerated 3D modulated flip-angle technique in refocused imaging with an extended echo-train sequence with compressed sensing for imaging of the knee: comparison with routine 2D MRI sequences

AIM

To accelerate the acquisition of high-resolution magnetic resonance imaging (MRI) by using the three-dimensional (3D) matrix sequence with compressed sensing and to compare it with conventional two-dimensional (2D) proton-density (PD) and fast spin-echo (FSE) sequences.

MATERIALS AND METHODS

3D matrix, 2D FSE, and PD sequences were acquired from 68 participants using 3 T magnetic resonance imaging (MRI). Two radiologists scored image quality independently on a four-point scale. The structural similarity index (SSIM), and signal- (SNRs) and contrast-to-noise ratios (CNRs) of different anatomical structures of the knee were assessed and compared between sequences using Wilcoxon signed-rank tests and Cohen's kappa.

RESULTS

The median acquisition time reduction was 44.5%. There was a substantial to perfect agreement for the rating between the 3D matrix FSE and 2D FSE or PD sequences when evaluating cartilage, subchondral bone, and ligaments (κ=0.783-872, p>0.05). The mean SSIM values between the 3D matrix FSE and 2D FSE, and between the 3D matrix PD and 2D PD sequences was 0.994 and 0.971, respectively, which are acceptable. No significant differences were found in SNR between the 3D matrix FSE and 2D FSE, and between the 3D matrix PD and 2D PD sequences, even though the SNR appeared to be higher on routine 2D sequences. The CNR of subchondral bone-meniscus, subchondral bone-joint fluid, and meniscus-joint fluid did not differentiate significantly between the 3D matrix sequence and routine 2D sequences.

CONCLUSIONS

3D matrix reduced the acquisition time in routine clinical knee MRI without the loss in image quality, SNR, and CNR.

Differences in the clinical characteristics and long-term outcome of peripartum tako-tsubo cardiomyopathy and peripartum cardiomyopathy

Background

Some peripartum-associated cardiomyopathy patients present similarly to those of tako-tsubo cardiomyopathy (TCM), little is known about the clinical course of peripartum TCM.

Purpose

To know clinicial characteristics and outcomes of peripartum TCM, in comparison with peripartum cardiomyopathy (PPCM)

Methods and results

Of 31 pregnancy-associated cardiomyopathy patients in a tertiary hospital, 10 cases of peripartum TCM and 21 cases of PPCM were found. Maternal near-missed death was significantly more common in the peripartum TCM group than in the PPCM group (100.0% vs. 76.2%, p=0.030). Complete recovery was observed with all peripartum TCM cases, while 23.8% of the PPCM cases had residual left ventricle (LV) dysfunction. LV ejection fraction greater than 30% on the initial echocardiogram independently predicted early echocardiographic recovery of left ventricular systolic function (odds ratio 331.33, 95% confidence interval 3.87–28402.60, p=0.011). There was no difference between the two groups in terms of the rate of adverse clinical events at 3 years of follow-up (PPCM group: 26.3% [5/19] vs. TCM group: 33.3% [3/9], p=0.750).

Conclusions

One-third of pregnancy-associated cardiomyopathy patients had peripartum TCM. With contemporary supportive care, both PPCM and peripartum TCM patients had a low mortality rate and excellent long-term outcomes.

Kaplan-Meier survival curves for death,

Funding Acknowledgement

Type of funding source: None

Detection and quantification of Aflatoxin B1 in corn and corn-grown soils in the district of Anuradhapura, Sri Lanka

Aflatoxin B1 contamination adversely affects human health by impairing long-term physical and cognitive development. Several crops have been associated with aflatoxin B1 contamination and corn is one of them. In the Anuradhapura district of the North Central Province of Sri Lanka, corn is one of the main agricultural produce. Due to poor farming practices in this area, it is possible that aflatoxin B1 is somehow transported from soil to the corn ears. This study was carried out to detect and quantify aflatoxin B1 in corn and corn-grown soils in Anuradhapura. Corn (n = 60) and corn-grown soil (n = 60) samples were randomly collected from 20 minor-scale corn-grown fields with three random replicates. Each sample was prepared for the measurement of aflatoxin B1 levels using the Enzyme-Linked Immunosorbent Assay (ELISA). Though 20 ppb is considered as the poisonous or deleterious level for corn consumption, there were toxin contaminations of up to 60–70 ppb in the corn kernel, while majority of soil had 350–400 ppb of aflatoxin B1 levels. Fifteen corn samples had exceeded the acceptable level while 22 samples were free of aflatoxin B1 and 23 samples were within the acceptable level. The results showed that the presence of aflatoxin B1 in corn is not habitually distributed throughout Anuradhapura district and it increased with the soil aflatoxin B1 concentration. It appears that there is a relationship between corn kernel and corn-grown soil aflatoxin B1 levels.

0995 Nightcap For School-nights: Association Between Milk Intake And Sleep Duration In First-graders

Introduction

Insufficient sleep has been identified as an obesity risk factor due to mechanistic pathways contributing to higher carbohydrate intake, including in children. Dietary intake of macronutrients, such as fats and protein found in milk, may serve as a modifiable risk factor for adequate sleep. We hypothesize that milk intake among a sample of urban first-graders may be associated with sleep duration.

Methods

Cross-sectional analysis of parent reports of an adapted version of the Child Sleep Health Questionnaire (CSHQ) and Block Dietary Data Systems Food Frequency Questionnaire (FFQ) were analyzed among a sample of 837 Black children in Brooklyn, New York. Summary scores were created for milk type. Milk intake was classified by fat content: whole milk and 2% categorized as high-fat, and 1% and skim as low-fat. Independent t-test, correlations and regression analysis to identify associations between parent reports of child’s sleep duration and milk intake were conducted.

Results

On average, children were 7.3 + 0.6 years old and 52% female. Nearly 57% of parents were immigrants. Children’s mean BMI was 17.27, approximately at the 85th BMI percentile according to CDC index-for-age percentiles. On average, FFQ data reported children consumed high-fat milk 6 days a week. Linear variable regression analysis between high-fat milk intake and sufficient sleep were significant (β =, 0.090, p &lt; 0.05). BMI was significantly associated with high-fat milk intake (β= 0.17, p&lt;0.05). However, high-fat milk intake was not significantly associated with (in)sufficient sleep, after controlling for BMI, sex and age. No difference was reported between immigrant parents and U.S. born parents.

Conclusion

Plausibly, high-fat milk is contributing to satiety and longer sleep duration. Future studies should include more comprehensive measurement of milk consumption (i.e. time of day and volume) to consider possible effects on children’s sleep. Actigraphy measures and sleep diaries should also be considered.

Support

Bezos Grant and Community Service Plan grant.

0580 Habitual Sleep Pattern, Anxiety and Depression are Predictive of Excessive Daytime Sleepiness in a Large-Scale Clinical Samples of Obstructive Sleep Apnea

Introduction

Excessive daytime sleepiness (EDS) is a common symptom that patients with obstructive sleep apnea (OSA) seek medical attention for. Prevalence ranged from 20% to 60%. Previous studies reported factors associated with EDS included age, body mass index (BMI), depression, and OSA severity. In most studies, the sample size was small, participants having specific co-morbidities, and the definitions of EDS was heterogeneous. Moreover, the association between anxiety, depression, habitual sleep pattern, and EDS has not been widely studied. Therefore, the present study aims to investigate the prevalence of EDS and associated factors, especially anxiety, depression, and habitual sleep pattern, in a large-scale clinical sample.

Methods

Data was prospectively collected from 8,081 adult patients who underwent initial overnight polysomnography (PSG) for the first time were referred from 2009 to 2016. Patients with total recording time less than 240 minutes and missing data were excluded. Data collected include demographics, anthropometrics, co-morbidities, and self-reported habitual sleep patterns. Subjective sleepiness was assessed by the Epworth Sleepiness Scale (ESS) with EDS defined as ESS ≥10. Anxiety and depression were assessed by the Hospital Anxiety and Depression Scale (HADS). The stepwise forward Logistic regression was used to identified predictors for EDS.

Results

In 5,780 (82.6%) patients with OSA (apnea-hypopnea index, AHI≥5/h), mean age was 63.9±0.2 yr, BMI was 27.7±0.1, and ESS was 10.4±0.1 and 80.5% were male. Prevalence of EDS in all OSA patients was 55.1% where the patients with severe OSA had higher prevalence (59.8%) than that in mild (49.5%) and moderate OSA (51.8%). Anxiety (OR: 2.036, 95% CI:1.153-1.502), depression (OR: 1.159, 95% CI:1.01-1.33), and short sleep (&lt;6hr/night) (OR:1.316, 95% CI:1.32,1.70) were top three risk factors for EDS. Other risk factors for EDS included AHI, arousal index, % total sleep time with SpO2&lt;90%, %REM, smoking while hypnotic use and long sleep (≥ 8hr/night) were associated with lower risk.

Conclusion

Anxiety, depression, short sleep are predictive and OSA severity are predictive of EDS while long sleep was associated with lower risk.

Support

National Taiwan University (NTU-EPR-104R8951-1; 105R8951-1; 106R880301), Center of electronics technology integration (NTU-107L900502, 108L900502) by the Ministry of Education in Taiwan and MediaTek Inc (201802034RIND).

Prophylactic low-molecular-weight heparin administration protected against severe acute pancreatitis partially by VEGF/Flt-1 signaling in a rat model

BACKGROUND

The present study was aimed to explore the effects and the underlying mechanism of prophylactic low-molecular-weight heparin (LMWH) treatment on taurocholate-induced severe acute pancreatitis (SAP) in a rat model.

METHODS

Rat SAP model was induced by injection of 4% sodium taurocholate into the pancreatic duct. LMWH was applied half an hour before the induction of pancreatitis at the dose of 200 IU/kg subcutaneous injection. The rats were euthanized at 1 h, 6 h, and 12 h after taurocholate-induced SAP. The inflammatory and oxidative response markers were assessed. And the vascular endothelial growth factor (VEGF) and Fms-related tyrosine kinase 1 (Flt-1) expression were evaluated by immunohistochemistry (IHC) and western blot methods.

RESULTS

The expression of inflammatory and oxidative response markers increased after induction of SAP. IHC and western blot results showed the VEGF and Flt-1 expression were increased in SAP group. Prophylactic LMWH administration reduced the inflammatory and oxidative response markers expression and decreased the expression of VEGF and Flt-1.

CONCLUSIONS

This study suggested that prophylactic LMWH treatment mitigated the severity of pancreatitis in rat SAP model by anti-inflammation and oxidative response. The underlying mechanism may result from downregulating VEGF/Flt-1 signaling of LMWH in SAP rat model.