Ca2+ Depletion in the ER Causes Store-Operated Ca2+ Entry via the TRPC6 Channel in Mouse Brown Adipocytes

beta3-adrenergic activation causes Ca2+ release from the mitochondria and subsequent Ca2+ release from the endoplasmic reticulum (ER), evoking store-operated Ca2+ entry (SOCE) due to Ca2+ depletion from the ER in mouse brown adipocytes. In this study, we investigated how Ca2+ depletion from the ER elicits SOCE in mouse brown adipocytes using fluorometry of intracellular Ca2+ concentration ([Ca2+]i). The administration of cyclopiazonic acid (CPA), a reversible sarcoplasmic/endoplasmic reticulum calcium ATPase (SERCA) pump blocker in the ER, caused an increase in [Ca2+]i. Moreover, CPA induced SOCE was suppressed by the administration of a Ca2+ free Krebs solution and the transient receptor potential canonical 6 (TRPC6) selective blockers 2-APB, ML-9 and GsMTx-4 but not Pico145, which blocks TRPC1/4/5. Administration of TRPC6 channel agonist 1-oleoyl-2-acetyl-sn-glycerol (OAG) and flufenamic acid elicited Ca2+ entry. Moreover, our RT-PCR analyses detected mRNAs for TRPC6 in brown adipose tissues. In addition, western blot analyses showed the expression of the TRPC6 protein. Thus, TRPC6 is one of the Ca2+ pathways involved in SOCE. These modes of Ca2+ entry provide the basis for heat production via activation of Ca2+-dependent dehydrogenase and the expression of uncoupling protein 1 (UCP1). Enhancing thermogenic metabolism in brown adipocytes may serve as broad therapeutic utility to reduce obesity and metabolic syndrome.

Deep learning-based diffusion tensor image generation model: a proof-of-concept study

This study created an image-to-image translation model that synthesizes diffusion tensor images (DTI) from conventional diffusion weighted images, and validated the similarities between the original and synthetic DTI. Thirty-two healthy volunteers were prospectively recruited. DTI and DWI were obtained with six and three directions of the motion probing gradient (MPG), respectively. The identical imaging plane was paired for the image-to-image translation model that synthesized one direction of the MPG from DWI. This process was repeated six times in the respective MPG directions. Regions of interest (ROIs) in the lentiform nucleus, thalamus, posterior limb of the internal capsule, posterior thalamic radiation, and splenium of the corpus callosum were created and applied to maps derived from the original and synthetic DTI. The mean values and signal-to-noise ratio (SNR) of the original and synthetic maps for each ROI were compared. The Bland-Altman plot between the original and synthetic data was evaluated. Although the test dataset showed a larger standard deviation of all values and lower SNR in the synthetic data than in the original data, the Bland-Altman plots showed each plot localizing in a similar distribution. Synthetic DTI could be generated from conventional DWI with an image-to-image translation model.

Chest radiography as a biomarker of ageing: artificial intelligence-based, multi-institutional model development and validation in Japan

BACKGROUND

Chest radiographs are widely available and cost-effective; however, their usefulness as a biomarker of ageing using multi-institutional data remains underexplored. The aim of this study was to develop a biomarker of ageing from chest radiography and examine the correlation between the biomarker and diseases.

METHODS

In this retrospective, multi-institutional study, we trained, tuned, and externally tested an artificial intelligence (AI) model to estimate the age of healthy individuals using chest radiographs as a biomarker. For the biomarker modelling phase of the study, we used healthy chest radiographs consecutively collected between May 22, 2008, and Dec 28, 2021, from three institutions in Japan. Data from two institutions were used for training, tuning, and internal testing, and data from the third institution were used for external testing. To evaluate the performance of the AI model in estimating ages, we calculated the correlation coefficient, mean square error, root mean square error, and mean absolute error. The correlation investigation phase of the study included chest radiographs from individuals with a known disease that were consecutively collected between Jan 1, 2018, and Dec 31, 2021, from an additional two institutions in Japan. We investigated the odds ratios (ORs) for various diseases given the difference between the AI-estimated age and chronological age (ie, the difference-age).

FINDINGS

We included 101 296 chest radiographs from 70 248 participants across five institutions. In the biomarker modelling phase, the external test dataset from 3467 healthy participants included 8046 radiographs. Between the AI-estimated age and chronological age, the correlation coefficient was 0·95 (99% CI 0·95-0·95), the mean square error was 15·0 years (99% CI 14·0-15·0), the root mean square error was 3·8 years (99% CI 3·8-3·9), and the mean absolute error was 3·0 years (99% CI 3·0-3·1). In the correlation investigation phase, the external test datasets from 34 197 participants with a known disease included 34 197 radiographs. The ORs for difference-age were as follows: 1·04 (99% CI 1·04-1·05) for hypertension; 1·02 (1·01-1·03) for hyperuricaemia; 1·05 (1·03-1·06) for chronic obstructive pulmonary disease; 1·08 (1·06-1·09) for interstitial lung disease; 1·05 (1·03-1·06) for chronic renal failure; 1·04 (1·03-1·06) for atrial fibrillation; 1·03 (1·02-1·04) for osteoporosis; and 1·05 (1·03-1·06) for liver cirrhosis.

INTERPRETATION

The AI-estimated age using chest radiographs showed a strong correlation with chronological age in the healthy cohorts. Furthermore, in cohorts of individuals with known diseases, the difference between estimated age and chronological age correlated with various chronic diseases. The use of this biomarker might pave the way for enhanced risk stratification methodologies, individualised therapeutic interventions, and innovative early diagnostic and preventive approaches towards age-associated pathologies.

FUNDING

None.

TRANSLATION

For the Japanese translation of the abstract see Supplementary Materials section.

Rectangular tapered short stem excellently preserves proximal bone mineral density preservation than tapered wedge short stem

Fitmore stem is a rectangular, tapered, short, cementless stem. A characteristic feature of this stem is that it provides rotational stability due to the high medullary occupancy achieved by its rectangular cross-section and thick antero- posterior width. We aimed to investigate the differences in periprosthetic bone remodelling between a rectangular- tapered short stem and a short tapered-wedge stem. Eighty patients who underwent primary total hip arthroplasty using a rectangular-tapered short stem (Fitmore) or a short tapered-wedge stem (Tri-Lock BPS) were enrolled in this study. Bone mineral densities (BMDs) in the seven Gruen zones were evaluated using dual-energy X-ray absorptiometry at baseline, and at 6 and 24 months postoperatively. Peri-prosthetic BMD and clinical factors were assessed and compared. In addition, correlations between periprosthetic BMD changes and stem anteversion error were analyzed using Pearson's correlation coefficient in the two groups. A significantly better postoperative periprosthetic BMD change was found in zones 1 and 7 in the rectangular-tapered group. Additionally, no significant correlation was observed between stem anteversion error and periprosthetic BMD changes in the rectangular-tapered groups. However, in the tapered-wedge group, there were significant negative correlations between the stem anteversion error and BMD changes at 6 months and 24 months in zones 1 and 7. In the rectangular-tapered group, a significantly better postoperative periprosthetic BMD change was found particularly in the region proximal to the stem. Rectangular-tapered short stem can be more resistant to rotation due to higher medullary occupancy and may lead to better periprosthetic BMD than the tapered-wedge short stem, especially in the proximal region of the stem.

AI-based Virtual Synthesis of Methionine PET from Contrast-enhanced MRI: Development and External Validation Study

Background Carbon 11 (¹¹C)-methionine is a useful PET radiotracer for the management of patients with glioma, but radiation exposure and lack of molecular imaging facilities limit its use. Purpose To generate synthetic methionine PET images from contrast-enhanced (CE) MRI through an artificial intelligence (AI)-based image-to-image translation model and to compare its performance for grading and prognosis of gliomas with that of real PET. Materials and Methods An AI-based model to generate synthetic methionine PET images from CE MRI was developed and validated from patients who underwent both methionine PET and CE MRI at a university hospital from January 2007 to December 2018 (institutional data set). Pearson correlation coefficients for the maximum and mean tumor to background ratio (TBR_max and TBR_mean, respectively) of methionine uptake and the lesion volume between synthetic and real PET were calculated. Two additional open-source glioma databases of preoperative CE MRI without methionine PET were used as the external test set. Using the TBRs, the area under the receiver operating characteristic curve (AUC) for classifying high-grade and low-grade gliomas and overall survival were evaluated. Results The institutional data set included 362 patients (mean age, 49 years ± 19 [SD]; 195 female, 167 male; training, n = 294; validation, n = 34; test, n = 34). In the internal test set, Pearson correlation coefficients were 0.68 (95% CI: 0.47, 0.81), 0.76 (95% CI: 0.59, 0.86), and 0.92 (95% CI: 0.85, 0.95) for TBR_max, TBR_mean, and lesion volume, respectively. The external test set included 344 patients with gliomas (mean age, 53 years ± 15; 192 male, 152 female; high grade, n = 269). The AUC for TBR_max was 0.81 (95% CI: 0.75, 0.86) and the overall survival analysis showed a significant difference between the high (2-year survival rate, 27%) and low (2-year survival rate, 71%; P < .001) TBR_max groups. Conclusion The AI-based model-generated synthetic methionine PET images strongly correlated with real PET images and showed good performance for glioma grading and prognostication. Published under a CC BY 4.0 license. Supplemental material is available for this article.

Artificial intelligence-based model to classify cardiac functions from chest radiographs: a multi-institutional, retrospective model development and validation study

BACKGROUND

Chest radiography is a common and widely available examination. Although cardiovascular structures-such as cardiac shadows and vessels-are visible on chest radiographs, the ability of these radiographs to estimate cardiac function and valvular disease is poorly understood. Using datasets from multiple institutions, we aimed to develop and validate a deep-learning model to simultaneously detect valvular disease and cardiac functions from chest radiographs.

METHODS

In this model development and validation study, we trained, validated, and externally tested a deep learning-based model to classify left ventricular ejection fraction, tricuspid regurgitant velocity, mitral regurgitation, aortic stenosis, aortic regurgitation, mitral stenosis, tricuspid regurgitation, pulmonary regurgitation, and inferior vena cava dilation from chest radiographs. The chest radiographs and associated echocardiograms were collected from four institutions between April 1, 2013, and Dec 31, 2021: we used data from three sites (Osaka Metropolitan University Hospital, Osaka, Japan; Habikino Medical Center, Habikino, Japan; and Morimoto Hospital, Osaka, Japan) for training, validation, and internal testing, and data from one site (Kashiwara Municipal Hospital, Kashiwara, Japan) for external testing. We evaluated the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.

FINDINGS

We included 22 551 radiographs associated with 22 551 echocardiograms obtained from 16 946 patients. The external test dataset featured 3311 radiographs from 2617 patients with a mean age of 72 years [SD 15], of whom 49·8% were male and 50·2% were female. The AUCs, accuracy, sensitivity, and specificity for this dataset were 0·92 (95% CI 0·90-0·95), 86% (85-87), 82% (75-87), and 86% (85-88) for classifying the left ventricular ejection fraction at a 40% cutoff, 0·85 (0·83-0·87), 75% (73-76), 83% (80-87), and 73% (71-75) for classifying the tricuspid regurgitant velocity at a 2·8 m/s cutoff, 0·89 (0·86-0·92), 85% (84-86), 82% (76-87), and 85% (84-86) for classifying mitral regurgitation at the none-mild versus moderate-severe cutoff, 0·83 (0·78-0·88), 73% (71-74), 79% (69-87), and 72% (71-74) for classifying aortic stenosis, 0·83 (0·79-0·87), 68% (67-70), 88% (81-92), and 67% (66-69) for classifying aortic regurgitation, 0·86 (0·67-1·00), 90% (89-91), 83% (36-100), and 90% (89-91) for classifying mitral stenosis, 0·92 (0·89-0·94), 83% (82-85), 87% (83-91), and 83% (82-84) for classifying tricuspid regurgitation, 0·86 (0·82-0·90), 69% (68-71), 91% (84-95), and 68% (67-70) for classifying pulmonary regurgitation, and 0·85 (0·81-0·89), 86% (85-88), 73% (65-81), and 87% (86-88) for classifying inferior vena cava dilation.

INTERPRETATION

The deep learning-based model can accurately classify cardiac functions and valvular heart diseases using information from digital chest radiographs. This model can classify values typically obtained from echocardiography in a fraction of the time, with low system requirements and the potential to be continuously available in areas where echocardiography specialists are scarce or absent.

FUNDING

None.

Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis

BACKGROUND

Deep learning (DL), a subset of artificial intelligence (AI), has been applied to pneumothorax diagnosis to aid physician diagnosis, but no meta-analysis has been performed.

METHODS

A search of multiple electronic databases through September 2022 was performed to identify studies that applied DL for pneumothorax diagnosis using imaging. Meta-analysis via a hierarchical model to calculate the summary area under the curve (AUC) and pooled sensitivity and specificity for both DL and physicians was performed. Risk of bias was assessed using a modified Prediction Model Study Risk of Bias Assessment Tool.

RESULTS

In 56 of the 63 primary studies, pneumothorax was identified from chest radiography. The total AUC was 0.97 (95% CI 0.96-0.98) for both DL and physicians. The total pooled sensitivity was 84% (95% CI 79-89%) for DL and 85% (95% CI 73-92%) for physicians and the pooled specificity was 96% (95% CI 94-98%) for DL and 98% (95% CI 95-99%) for physicians. More than half of the original studies (57%) had a high risk of bias.

CONCLUSIONS

Our review found the diagnostic performance of DL models was similar to that of physicians, although the majority of studies had a high risk of bias. Further pneumothorax AI research is needed.

Deep learning-based screening tool for rotator cuff tears on shoulder radiography

BACKGROUND

Early diagnosis of rotator cuff tears is essential for appropriate and timely treatment. Although radiography is the most used technique in clinical practice, it is difficult to accurately rule out rotator cuff tears as an initial imaging diagnostic modality. Deep learning-based artificial intelligence has recently been applied in medicine, especially diagnostic imaging. This study aimed to develop a deep learning algorithm as a screening tool for rotator cuff tears based on radiography.

METHODS

We used 2803 shoulder radiographs of the true anteroposterior view to develop the deep learning algorithm. Radiographs were labeled 0 and 1 as intact or low-grade partial-thickness rotator cuff tears and high-grade partial or full-thickness rotator cuff tears, respectively. The diagnosis of rotator cuff tears was determined based on arthroscopic findings. The diagnostic performance of the deep learning algorithm was assessed by calculating the area under the curve (AUC), sensitivity, negative predictive value (NPV), and negative likelihood ratio (LR-) of test datasets with a cutoff value of expected high sensitivity determination based on validation datasets. Furthermore, the diagnostic performance for each rotator cuff tear size was evaluated.

RESULTS

The AUC, sensitivity, NPV, and LR- with expected high sensitivity determination were 0.82, 84/92 (91.3%), 102/110 (92.7%), and 0.16, respectively. The sensitivity, NPV, and LR- for full-thickness rotator cuff tears were 69/73 (94.5%), 102/106 (96.2%), and 0.10, respectively, while the diagnostic performance for partial-thickness rotator cuff tears was low at 15/19 (78.9%), NPV of 102/106 (96.2%) and LR- of 0.39.

CONCLUSIONS

Our algorithm had a high diagnostic performance for full-thickness rotator cuff tears. The deep learning algorithm based on shoulder radiography helps screen rotator cuff tears by setting an appropriate cutoff value.

LEVEL OF EVIDENCE

Level III: Diagnostic Study.

Lipopolysaccharide structure modulates cationic biocide susceptibility and crystalline biofilm formation in Proteus mirabilis

Chlorhexidine (CHD) is a cationic biocide used ubiquitously in healthcare settings. Proteus mirabilis, an important pathogen of the catheterized urinary tract, and isolates of this species are often described as “resistant” to CHD-containing products used for catheter infection control. To identify the mechanisms underlying reduced CHD susceptibility in P. mirabilis, we subjected the CHD tolerant clinical isolate RS47 to random transposon mutagenesis and screened for mutants with reduced CHD minimum inhibitory concentrations (MICs). One mutant recovered from these screens (designated RS47-2) exhibited ~ 8-fold reduction in CHD MIC. Complete genome sequencing of RS47-2 showed a single mini-Tn5 insert in the waaC gene involved in lipopolysaccharide (LPS) inner core biosynthesis. Phenotypic screening of RS47-2 revealed a significant increase in cell surface hydrophobicity and serum susceptibility compared to the wildtype, and confirmed defects in LPS production congruent with waaC inactivation. Disruption of waaC was also associated with increased susceptibility to a range of other cationic biocides but did not affect susceptibility to antibiotics tested. Complementation studies showed that repression of smvA efflux activity in RS47-2 further increased susceptibility to CHD and other cationic biocides, reducing CHD MICs to values comparable with the most CHD susceptible isolates characterized. The formation of crystalline biofilms and blockage of urethral catheters was also significantly attenuated in RS47-2. Taken together, these data show that aspects of LPS structure and upregulation of the smvA efflux system function in synergy to modulate susceptibility to CHD and other cationic biocides, and that LPS structure is also an important factor in P. mirabilis crystalline biofilm formation.

Deep Learning Model Based on Contrast-Enhanced Computed Tomography Imaging to Predict Postoperative Early Recurrence after the Curative Resection of a Solitary Hepatocellular Carcinoma

We aimed to develop the deep learning (DL) predictive model for postoperative early recurrence (within 2 years) of hepatocellular carcinoma (HCC) based on contrast-enhanced computed tomography (CECT) imaging. This study included 543 patients who underwent initial hepatectomy for HCC and were randomly classified into training, validation, and test datasets at a ratio of 8:1:1. Several clinical variables and arterial CECT images were used to create predictive models for early recurrence. Artificial intelligence models were implemented using convolutional neural networks and multilayer perceptron as a classifier. Furthermore, the Youden index was used to discriminate between high- and low-risk groups. The importance values of each explanatory variable for early recurrence were calculated using permutation importance. The DL predictive model for postoperative early recurrence was developed with the area under the curve values of 0.71 (test datasets) and 0.73 (validation datasets). Postoperative early recurrence incidences in the high- and low-risk groups were 73% and 30%, respectively (p = 0.0057). Permutation importance demonstrated that among the explanatory variables, the variable with the highest importance value was CECT imaging analysis. We developed a DL model to predict postoperative early HCC recurrence. DL-based analysis is effective for determining the treatment strategies in patients with HCC.

Formation and evolution of carbonaceous asteroid Ryugu: Direct evidence from returned samples

Samples of the carbonaceous asteroid Ryugu were brought to Earth by the Hayabusa2 spacecraft. We analyzed 17 Ryugu samples measuring 1 to 8 millimeters. Carbon dioxide-bearing water inclusions are present within a pyrrhotite crystal, indicating that Ryugu's parent asteroid formed in the outer Solar System. The samples contain low abundances of materials that formed at high temperatures, such as chondrules and calcium- and aluminum-rich inclusions. The samples are rich in phyllosilicates and carbonates, which formed through aqueous alteration reactions at low temperature, high pH, and water/rock ratios of <1 (by mass). Less altered fragments contain olivine, pyroxene, amorphous silicates, calcite, and phosphide. Numerical simulations, based on the mineralogical and physical properties of the samples, indicate that Ryugu's parent body formed ~2 million years after the beginning of Solar System formation.

Deep Learning-Based Time-to-Death Prediction Model for COVID-19 Patients Using Clinical Data and Chest Radiographs

Accurate estimation of mortality and time to death at admission for COVID-19 patients is important and several deep learning models have been created for this task. However, there are currently no prognostic models which use end-to-end deep learning to predict time to event for admitted COVID-19 patients using chest radiographs and clinical data. We retrospectively implemented a new artificial intelligence model combining DeepSurv (a multiple-perceptron implementation of the Cox proportional hazards model) and a convolutional neural network (CNN) using 1356 COVID-19 inpatients. For comparison, we also prepared DeepSurv only with clinical data, DeepSurv only with images (CNNSurv), and Cox proportional hazards models. Clinical data and chest radiographs at admission were used to estimate patient outcome (death or discharge) and duration to the outcome. The Harrel's concordance index (c-index) of the DeepSurv with CNN model was 0.82 (0.75-0.88) and this was significantly higher than the DeepSurv only with clinical data model (c-index = 0.77 (0.69-0.84), p = 0.011), CNNSurv (c-index = 0.70 (0.63-0.79), p = 0.001), and the Cox proportional hazards model (c-index = 0.71 (0.63-0.79), p = 0.001). These results suggest that the time-to-event prognosis model became more accurate when chest radiographs and clinical data were used together.

Multicenter registry of the Watchman left atrial appendage closure device for patients with atrial fibrillation in Japan: The TERMINATOR registry

Funding Acknowledgements

Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Boston Scientific Japan

Background

Transcatheter left atrial appendage closure (LAAC) provides an alternative to oral anticoagulation for thromboembolic risk reduction in patients with nonvalvular atrial fibrillation (AF). A meta-analysis of previous two randomized trials reported improved rates of hemorrhagic stroke, cardiovascular/unexplained death, and nonprocedural bleeding compared to warfarin (1). Recently, the next-generation LAAC device, the Watchman FLX system, became available, and showed a low incidence of adverse events and a high incidence of anatomic closure (2). This transcatheter stroke prevention has already been approved in Asian countries. However, there is little data of LAAC in Asian population.

Purpose

This study sought to assess efficacy and safety of LAAC for patients with nonvalvular AF in Asia.

Methods

The TERMINATOR (Transcatheter Modification of Left Atrial Appendage by Obliteration with Device) registry is a multicenter nonrandomized study in Japan. This enrolled patients who underwent LAAC in 23 Japanese institutions. The LAAC was indicated for patients with nonvalvular atrial fibrillation in whom oral anticoagulation is required, but who have a risk of bleeding (history of BARC type 3 bleeding or HAS-BLED score ≥3 points). Baseline patient and procedural characteristics and clinical outcomes were evaluated.

Results

A total of 729 patients were enrolled between September 2019 and November 2021. The mean age was 74.9±8.8 years and the mean CHA2DS2-VASc score was 4.7±1.5. The Watchman generation 2.5 and FLX system were used in 469 (64.3%) and 260 patients (35.7%), respectively. Procedural success was achieved in 722 patients (99.0%). In-hospital adverse events were as follows; 6 tamponades (0.8%), 3 pericardial effusion (0.4%), 2 device embolization (0.3%), no stroke (0%), and no death (0%). During follow-up, device-related thrombus and all-cause death were reported in 16 (2.2%) and 23 patients (3.2%), respectively.

Conclusions

LAAC with the Watchman system provides compatible efficacy and safety outcomes in Asian population.

Artificial intelligence-based model for COVID-19 prognosis incorporating chest radiographs and clinical data; a retrospective model development and validation study

OBJECTIVES

The purpose of this study was to develop an artificial intelligence-based model to prognosticate COVID-19 patients at admission by combining clinical data and chest radiographs.

METHODS

This retrospective study used the Stony Brook University COVID-19 dataset of 1384 inpatients. After exclusions, 1356 patients were randomly divided into training (1083) and test datasets (273). We implemented three artificial intelligence models, which classified mortality, ICU admission, or ventilation risk. Each model had three submodels with different inputs: clinical data, chest radiographs, and both. We showed the importance of the variables using SHapley Additive exPlanations (SHAP) values.

RESULTS

The mortality prediction model was best overall with area under the curve, sensitivity, specificity, and accuracy of 0.79 (0.72-0.86), 0.74 (0.68-0.79), 0.77 (0.61-0.88), and 0.74 (0.69-0.79) for the clinical data-based model; 0.77 (0.69-0.85), 0.67 (0.61-0.73), 0.81 (0.67-0.92), 0.70 (0.64-0.75) for the image-based model, and 0.86 (0.81-0.91), 0.76 (0.70-0.81), 0.77 (0.61-0.88), 0.76 (0.70-0.81) for the mixed model. The mixed model had the best performance (p value < 0.05). The radiographs ranked fourth for prognostication overall, and first of the inpatient tests assessed.

CONCLUSIONS

These results suggest that prognosis models become more accurate if AI-derived chest radiograph features and clinical data are used together.

ADVANCES IN KNOWLEDGE

This AI model evaluates chest radiographs together with clinical data in order to classify patients as having high or low mortality risk. This work shows that chest radiographs taken at admission have significant COVID-19 prognostic information compared to clinical data other than age and sex.

POS0522 ASSOCIATED FACTORS WITH PHYSICAL DYSFUNCTION OF ELDERLY-ONSET RHEUMATOID ARTHRITIS TREATED WITH A TREAT-TO-TARGET STRATEGY

Background

Achievement of normal physical function is an important outcome for older patients. Previous studies of younger cohorts showed that aging, comorbidities, and joint damage influenced the physical function of patients with RA who achieved clinical remission or low disease activity (LDA). We previously demonstrated that a treat-to-target (T2T) strategy for methotrexate (MTX)-naïve elderly-onset RA (EORA) was effective with an acceptable safety profile. It showed that 60.9% of 197 patients achieved HAQ Disability Index (HAQ-DI) ≤0.5 at three years by following the T2T strategy targeting LDA (1).

Objectives

We aimed to evaluate associated factors with HAQ-DI in the T2T strategy targeting LDA for patients with EORA during three-year observational period.

Methods

Treatment was adjusted to target LDA with conventional synthetic disease-modifying antirheumatic drugs (DMARDs), followed by biological DMARDs (bDMARDs) in 197 MTX-naïve EORA patients (mean age 74.9 years) with moderate-to-high disease activity. HAQ-DI was evaluated at week 0, 24, 52, 76, 104, 128, and 156. To evaluate associated factors with SDAI and HAQ-DI over the 36-month follow-up, Bayesian hierarchical logistic regression modeling was applied for 1067 periods from the 197 patients.

Results

At baseline, the enrolled 197 patients with EORA who had normal physical function (HAQ-DI ≤0.5) in 29.4%, HAQ-DI >0.5 and <1.5 in 36.5%, and HAQ-DI ≥1.5 in 33.0%, and the mean age (standard deviation [SD]) in each group was 72.7 (5.9), 74.8 (7.3), and 75.6 (6.7), respectively. Baseline SDAI increased in the group with higher HAQ-DI. The proportions of patients with each comorbidity and estimated creatinine clearance at baseline were not significantly different across the 3 groups.

In the multilevel logistic model, the association of MTX, bDMARDs, and GC use with changes in SDAI in each period was evaluated. Age, sex, and comorbidities (chronic lung disease, cardiovascular disease, history of malignancy, osteoporosis, history of serious infections, and osteoarthritis) were included as inter-individual factors. The model indicated that the use of bDMARDs was associated with a reduction of the SDAI (ΔSDAI: -9.75, SD 0.75, p<0.001), while neither MTX (ΔSDAI: -1.25, SD 1.13, p=0.270) nor GCs (ΔSDAI: -0.78, SD 0.88, p=0.372) was associated with changes in SDAI. Chronic lung diseases (ΔSDAI: 4.64, SD 1.44, p=0.001) and osteoporosis (ΔSDAI: 3.78, SD 1.46, p=0.001) at baseline were associated with the increment of SDAI.

The association of age, sex, the comorbidities, and MTX, bDMARDs, and GC use with physical function in each period was evaluated by the multilevel logistic model. The model indicated that older age (ΔHAQ-DI: 0.03, SD 0.01, p <0.001), chronic lung diseases (ΔHAQ-DI: 0.15, SD 0.10, p=0.001), and osteoporosis (ΔHAQ-DI: 0.30, SD 0.10, p=0.010) at baseline were associated with the increment of HAQ-DI. When the mean SDAI during the observation period was added to the model as an inter-individual factor, the associations of HAQ-DI with the chronic lung diseases and osteoporosis at baseline were not statistically significant.

Conclusion

These data indicate that bDMARDs had a central role in reducing disease activity in the T2T strategy targeting LDA in EORA patients. Chronic lung diseases and osteoporosis at baseline were associated with increase in disease activity and worsening of physical function. However, disease activity had a greater impact on physical function than the comorbidities at baseline.

References

[1]Sugihara T, et al. Rheumatology (Oxford). 2021;60(9):4252-4261

Disclosure of Interests

takahiko sugihara Speakers bureau: TS has received honoraria from Abbvie Japan Co., Ltd., AsahiKASEI Co., Ltd., Astellas Pharma Inc., Ayumi Pharmaceutical, Bristol Myers Squibb K.K., Chugai Pharmaceutical Co., Ltd., Eli Lilly Japan K.K., Mitsubishi-Tanabe Pharma Co., Ono Pharmaceutical, Pfizer Japan Inc., Takeda Pharmaceutical Co. Ltd., and UCB Japan Co. Ltd., Grant/research support from: TS has received research grants from AsahiKASEI Co., Ltd., Daiichi Sankyo., Chugai Pharmaceutical Co., Ltd., and Ono Pharmaceutical., Tatsuro Ishizaki: None declared, Hiroyuki Baba: None declared, Takumi Matsumoto: None declared, Kanae Kubo Speakers bureau: KK has received honoraria from Asahi KASEI, Astellas Pharma, Bristol Myers Squibb, Eisai, AbbVie GK, Boehringer Ingelheim, Daiichi-Sankyo, Chugai Pharmaceutical, Mitsubishi Tanabe Pharma and Nippon Shinyaku., Grant/research support from: KK has received research grants from Asahi KASEI, Mari Kamiya: None declared, Fumio Hirano: None declared, Tadashi Hosoya: None declared, Masayo Kojima Speakers bureau: MK has received speakers bureau from AbbVie, Astellas, Ayumi Pharma, Chugai, Eisai, Eli Lilly, Janssen, Ono Pharmaceutical, Pfizer, Tanabe-Mitsubishi, and Takeda Pharmaceutical Co., Ltd., Nobuyuki Miyasaka: None declared, Masayoshi Harigai Speakers bureau: MH has received speaker’s fee from AbbVie Japan GK, Ayumi Pharmaceutical Co., Boehringer Ingelheim Japan, Inc.,Bristol Myers Squibb Co., Ltd., Chugai Pharmaceutical Co., Ltd., Eisai Co., Ltd., Eli Lilly Japan K.K., GlaxoSmithKline K.K., Kissei Pharmaceutical Co., Ltd., Pfizer Japan Inc., Takeda Pharmaceutical Co., Ltd., and Teijin Pharma Ltd., Consultant of: MH is a consultant for AbbVie, Boehringer-ingelheim, Bristol Myers Squibb Co., Kissei Pharmaceutical Co.,Ltd. and Teijin Pharma., Grant/research support from: MH has received research grants from AbbVie Japan GK, Asahi Kasei Corp., Astellas Pharma Inc., Ayumi Pharmaceutical Co., Bristol Myers Squibb Co., Ltd., Chugai Pharmaceutical Co., Daiichi-Sankyo, Inc.,Eisai Co., Ltd., Kissei Pharmaceutical Co., Ltd., Mitsubishi Tanabe Pharma Co., Nippon Kayaku Co., Ltd., Sekiui Medical, Shionogi & Co., Ltd., Taisho Pharmaceutical Co., Ltd., Takeda Pharmaceutical Co., Ltd., and Teijin Pharma Ltd.

Artificial intelligence-based detection of atrial fibrillation from chest radiographs

OBJECTIVE

The purpose of this study was to develop an artificial intelligence (AI)-based model to detect features of atrial fibrillation (AF) on chest radiographs.

METHODS

This retrospective study included consecutively collected chest radiographs of patients who had echocardiography at our institution from July 2016 to May 2019. Eligible radiographs had been acquired within 30 days of the echocardiography. These radiographs were labeled as AF-positive or AF-negative based on the associated electronic medical records; then, each patient was randomly divided into training, validation, and test datasets in an 8:1:1 ratio. A deep learning-based model to classify radiographs as with or without AF was trained on the training dataset, tuned with the validation dataset, and evaluated with the test dataset.

RESULTS

The training dataset included 11,105 images (5637 patients; 3145 male, mean age ± standard deviation, 68 ± 14 years), the validation dataset included 1388 images (704 patients, 397 male, 67 ± 14 years), and the test dataset included 1375 images (706 patients, 395 male, 68 ± 15 years). Applying the model to the validation and test datasets gave a respective area under the curve of 0.81 (95% confidence interval, 0.78-0.85) and 0.80 (0.76-0.84), sensitivity of 0.76 (0.70-0.81) and 0.70 (0.64-0.76), specificity of 0.75 (0.72-0.77) and 0.74 (0.72-0.77), and accuracy of 0.75 (0.72-0.77) and 0.74 (0.71-0.76).

CONCLUSION

Our AI can identify AF on chest radiographs, which provides a new way for radiologists to infer AF.

KEY POINTS

• A deep learning-based model was trained to detect atrial fibrillation in chest radiographs, showing that there are indicators of atrial fibrillation visible even on static images. • The validation and test datasets each gave a solid performance with area under the curve, sensitivity, and specificity of 0.81, 0.76, and 0.75, respectively, for the validation dataset, and 0.80, 0.70, and 0.74, respectively, for the test dataset. • The saliency maps highlighted anatomical areas consistent with those reported for atrial fibrillation on chest radiographs, such as the atria.

Development and Validation of Artificial Intelligence-based Method for Diagnosis of Mitral Regurgitation from Chest Radiographs

Purpose

To develop an artificial intelligence-based model to detect mitral regurgitation on chest radiographs.

Materials and Methods

This retrospective study included echocardiographs and associated chest radiographs consecutively collected at a single institution between July 2016 and May 2019. Associated radiographs were those obtained within 30 days of echocardiography. These radiographs were labeled as positive or negative for mitral regurgitation on the basis of the echocardiographic reports and were divided into training, validation, and test datasets. An artificial intelligence model was developed by using the training dataset and was tuned by using the validation dataset. To evaluate the model, the area under the curve, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were assessed by using the test dataset.

Results

This study included a total of 10 367 images from 5270 patients. The training dataset included 8240 images (4216 patients), the validation dataset included 1073 images (527 patients), and the test dataset included 1054 images (527 patients). The area under the curve, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value in the test dataset were 0.80 (95% CI: 0.77, 0.82), 71% (95% CI: 67, 75), 74% (95% CI: 70, 77), 73% (95% CI: 70, 75), 68% (95% CI: 64, 72), and 77% (95% CI: 73, 80), respectively.

Conclusion

The developed deep learning-based artificial intelligence model may possibly differentiate patients with and without mitral regurgitation by using chest radiographs.Keywords: Computer-aided Diagnosis (CAD), Cardiac, Heart, Valves, Supervised Learning, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2022.

Deep learning-based detection of parathyroid adenoma by 99mTc-MIBI scintigraphy in patients with primary hyperparathyroidism

OBJECTIVE

It is important to detect parathyroid adenomas by parathyroid scintigraphy with 99m-technetium sestamibi (^99mTc-MIBI) before surgery. This study aimed to develop and validate deep learning (DL)-based models to detect parathyroid adenoma in patients with primary hyperparathyroidism, from parathyroid scintigrams with ^99mTc-MIBI.

METHODS

DL-based models for detecting parathyroid adenoma in early- and late-phase parathyroid scintigrams were, respectively, developed and evaluated. The training dataset used to train the models was collected from 192 patients (165 adenoma cases, mean age: 64 years ± 13, 145 women) and the validation dataset used to tune the models was collected from 45 patients (30 adenoma cases, mean age: 67 years ± 12, 37 women). The images were collected from patients who were pathologically diagnosed with parathyroid adenomas or in whom no lesions could be detected by either parathyroid scintigraphy or ultrasonography at our institution from June 2010 to March 2019. The models were tested on a dataset collected from 44 patients (30 adenoma cases, mean age: 67 years ± 12, 38 women) who took scintigraphy from April 2019 to March 2020. The models' lesion-based sensitivity and mean false positive indications per image (mFPI) were assessed with the test dataset.

RESULTS

The sensitivity was 82% [95% confidence interval 72-92%] with mFPI of 0.44 for the scintigrams of the early-phase model and 83% [73-92%] with mFPI of 0.31 for the scintigrams of the delayed-phase model in the test dataset, respectively.

CONCLUSIONS

The DL-based models were able to detect parathyroid adenomas with a high sensitivity using parathyroid scintigraphy with 99m-technetium sestamibi.

Artificial intelligence-based detection of aortic stenosis from chest radiographs

Aims

We aimed to develop models to detect aortic stenosis (AS) from chest radiographs-one of the most basic imaging tests-with artificial intelligence.

Methods and results

We used 10 433 retrospectively collected digital chest radiographs from 5638 patients to train, validate, and test three deep learning models. Chest radiographs were collected from patients who had also undergone echocardiography at a single institution between July 2016 and May 2019. These were labelled from the corresponding echocardiography assessments as AS-positive or AS-negative. The radiographs were separated on a patient basis into training [8327 images from 4512 patients, mean age 65 ±  (standard deviation) 15 years], validation (1041 images from 563 patients, mean age 65 ± 14 years), and test (1065 images from 563 patients, mean age 65 ± 14 years) datasets. The soft voting-based ensemble of the three developed models had the best overall performance for predicting AS with an area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of 0.83 (95% confidence interval 0.77-0.88), 0.78 (0.67-0.86), 0.71 (0.68-0.73), 0.71 (0.68-0.74), 0.18 (0.14-0.23), and 0.97 (0.96-0.98), respectively, in the validation dataset and 0.83 (0.78-0.88), 0.83 (0.74-0.90), 0.69 (0.66-0.72), 0.71 (0.68-0.73), 0.23 (0.19-0.28), and 0.97 (0.96-0.98), respectively, in the test dataset.

Conclusion

Deep learning models using chest radiographs have the potential to differentiate between radiographs of patients with and without AS.

Lay Summary

We created artificial intelligence (AI) models using deep learning to identify aortic stenosis (AS) from chest radiographs. Three AI models were developed and evaluated with 10 433 retrospectively collected radiographs and labelled from echocardiography reports. The ensemble AI model could detect AS in a test dataset with an area under the receiver operating characteristic curve of 0.83 (95% confidence interval 0.78-0.88). Since chest radiography is a cost-effective and widely available imaging test, our model can provide an additive resource for the detection of AS.

Artificial intelligence-supported lung cancer detection by multi-institutional readers with multi-vendor chest radiographs: a retrospective clinical validation study

Background

We investigated the performance improvement of physicians with varying levels of chest radiology experience when using a commercially available artificial intelligence (AI)-based computer-assisted detection (CAD) software to detect lung cancer nodules on chest radiographs from multiple vendors.

Methods

Chest radiographs and their corresponding chest CT were retrospectively collected from one institution between July 2017 and June 2018. Two author radiologists annotated pathologically proven lung cancer nodules on the chest radiographs while referencing CT. Eighteen readers (nine general physicians and nine radiologists) from nine institutions interpreted the chest radiographs. The readers interpreted the radiographs alone and then reinterpreted them referencing the CAD output. Suspected nodules were enclosed with a bounding box. These bounding boxes were judged correct if there was significant overlap with the ground truth, specifically, if the intersection over union was 0.3 or higher. The sensitivity, specificity, accuracy, PPV, and NPV of the readers’ assessments were calculated.

Results

In total, 312 chest radiographs were collected as a test dataset, including 59 malignant images (59 nodules of lung cancer) and 253 normal images. The model provided a modest boost to the reader’s sensitivity, particularly helping general physicians. The performance of general physicians was improved from 0.47 to 0.60 for sensitivity, from 0.96 to 0.97 for specificity, from 0.87 to 0.90 for accuracy, from 0.75 to 0.82 for PPV, and from 0.89 to 0.91 for NPV while the performance of radiologists was improved from 0.51 to 0.60 for sensitivity, from 0.96 to 0.96 for specificity, from 0.87 to 0.90 for accuracy, from 0.76 to 0.80 for PPV, and from 0.89 to 0.91 for NPV. The overall increase in the ratios of sensitivity, specificity, accuracy, PPV, and NPV were 1.22 (1.14–1.30), 1.00 (1.00–1.01), 1.03 (1.02–1.04), 1.07 (1.03–1.11), and 1.02 (1.01–1.03) by using the CAD, respectively.

Conclusion

The AI-based CAD was able to improve the ability of physicians to detect nodules of lung cancer in chest radiographs. The use of a CAD model can indicate regions physicians may have overlooked during their initial assessment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08847-9.

Pre-operative endovascular coil embolisation for chronic pulmonary aspergillosis

OBJECTIVE: To retrospectively evaluate the clinical outcomes of pre-operative endovascular coil embolisation (ECE) for chronic pulmonary aspergillosis (CPA).METHODS: We evaluated surgical patients with CPA between November 2016 and April 2020. Pre-operative ECE for CPA with severe adhesions was selectively performed to reduce intra-operative blood loss. ECE procedures, operative procedures, intra-operative blood loss and complications were evaluated.RESULTS: Twenty-eight patients (21 males and 7 females; median age: 55 years) were included in the study. Of the 28 patients, 8 (28.6%) underwent pre-operative ECE. Technical success rate in pre-operative ECE was 100%. The median time required for ECE procedures was 123 min. The median number of vessels embolised per procedure was 2.5. The median period between embolisation and surgery was 5 days. Major complications were observed in three patients (10.7%). There were no significant differences between patients with and without pre-operative ECE in operative time (284 vs. 365 min, respectively, P = 0.7602) and intra-operative blood loss (294 vs. 228 mL, respectively, P = 0.8987).CONCLUSIONS: Pre-operative ECE for CPA appears to be feasible and safe; however, its role in reducing intra-operative blood loss needs further investigation.

MHD mode identification by higher order singular value decomposition of C-2W Mirnov probe data

The C-2W device (also known as "Norman") at TAE Technologies has proven successful at generating stable, long-lived field-reversed configuration (FRC) plasmas with record temperatures. The largest Mirnov probe array in C-2W measures three components of the magnetic field just inside the vessel wall at 64 locations distributed approximately evenly in the cylindrical vessel's azimuthal and axial dimensions. This nearly rectangular array of probes creates a unique opportunity to apply higher order singular value decomposition (HOSVD) to efficiently analyze the external magnetic field data for the purposes of reconstructing the magnetohydrodynamic mode structures in the FRC. In the first application of this method for this purpose, HOSVD is shown to quickly and effectively detect and separate toroidal modes while indicating longitudinal dependence of mode phases and amplitudes, enhancing the coherence and utility of the vast quantity of data produced by this array. Analysis of the data from the entire array at once via HOSVD proves not only computationally more efficient than methods that separately analyze groups of probes at different axial locations but also leads to improved mode resolution at axial locations where these modes are weaker.

Diagnosing heart failure from chest X-ray images using deep learning

 

The development of deep learning technology has enabled machines to achieve high-level accuracy in interpreting medical images. While many previous studies have examined the detection of pulmonary nodules and cardiomegaly in chest X-rays using deep learning, the application of this technology to heart failure remains rare. In this study, we investigated the performance of a deep learning algorithm in terms of diagnosing heart failure using images obtained from chest X-rays. We used 952 chest X-ray images from a labeled database published by the National Institutes of Health. Two cardiologists respectively verified and relabeled these images, for a total of 260 “normal” and 378 “heart failure” images, and the remainder were discarded because they had been incorrectly labeled. In this study “heart failure” was defined as “cardiomegaly or congestion”, in a chest X-ray with cardiothoracic ratio (CTR) over 50% or radiographic presence of pulmonary edema. To enable the machine to extract a sufficient number of features from the images, we used the general machine learning approach called data augmentation and transfer learning. Owing mostly to this technique and the adequate relabeling process, we established a model to detect heart failure in chest X-ray by applying deep learning, and obtained an accuracy of 82%. Sensitivity and specificity to heart failure were 75% and 94.4%, respectively. Furthermore, heatmap imaging allowed us to visualize decisions made by the machine. The figure shows randomly selected examples of the prediction probabilities and heatmaps of the chest X-rays from the dataset. The original image is on the left and its heatmap is on the right, with its prediction probability written below. The red areas on the heatmaps show important regions, according to which the machine determined the classification. While some images with ambiguous radiolucency such as (e) and (f) were prone to be misdiagnosed by this model, most of the images like (a)–(d) were diagnosed correctly. Deep learning can thus help support the diagnosis of heart failure using chest X-ray images.

Heatmaps and probabilities of prediction

Funding Acknowledgement

Type of funding source: Public grant(s) – National budget only. Main funding source(s): JSPS KAKENHI

Determination of Lycopene Concentration in Fresh Tomatoes by Spectrophotometry: A Collaborative Study

BACKGROUND

Lycopene has been the object of considerable research attention recently, and the effects of the intake of lycopene, or of tomato products, have been studied in various ways. In Japan, interest in the health-promoting function of food components has increased.

OBJECTIVE

Developing a method to determine lycopene contents in tomato that meets the Japanese Agricultural Standard (JAS).

METHOD

In the proposed JAS method, the test sample consists of fresh tomatoes; a hexane-acetone mixture is utilized as the extraction solvent. A collaborative study was conducted to evaluate the interlaboratory performance of the method.

RESULTS

Ten laboratories participated and analyzed six test materials characterized by a lycopene content between 39 and 170 mg/kg as blind duplicates. After removing statistical outliers, RSDr ranged from 1.2 to 3.0% and RSDR ranged from 2.4 to 4.2%. The HorRat values were calculated and found to be in the 0.26-0.49 range.

CONCLUSIONS

The method for determining the lycopene content in tomato was evaluated by means of a collaborative study, and the reproducibility of this method was found to be acceptable.

HIGHLIGHTS

Intended for standardization in Japan, a method to determine lycopene content in tomato has been developed and shown to have acceptable precision in a collaborative study.

Effect of Biomechanical Environment on Degeneration of Meckel's Cartilage

During orofacial tissue development, the anterior and posterior regions of the Meckel's cartilage undergo mineralization, while the middle region undergoes degeneration. Despite the interesting and particular phenomena, the mechanisms that regulate the different fates of Meckel's cartilage, including the effects of biomechanical cues, are still unclear. Therefore, the purpose of this study was to systematically investigate the course of Meckel's cartilage during embryonic development from a biomechanical perspective. Histomorphological and biomechanical (stiffness) changes in the Meckel's cartilage were analyzed from embryonic day 12 to postnatal day 0. The results revealed remarkable changes in the morphology and size of chondrocytes, as well as the occurrence of chondrocyte burst in the vicinity of the mineralization site, an often-seen phenomenon preceding endochondral ossification. To understand the effect of biomechanical cues on Meckel's cartilage fate, a mechanically tuned 3-dimensional hydrogel culture system was used. At the anterior region, a moderately soft environment (10-kPa hydrogel) promoted chondrocyte burst and ossification. On the contrary, at the middle region, a more rigid environment (40-kPa hydrogel) enhanced cartilage degradation by inducing a higher expression of MMP-1 and MMP-13. These results indicate that differences in the biomechanical properties of the surrounding environment are essential factors that distinctly guide the mineralization and degradation of Meckel's cartilage and would be valuable tools for modulating in vitro cartilage and bone tissue engineering.

C-reactive protein : albumin ratio in patients with resectable intrahepatic cholangiocarcinoma

BACKGROUND

The C-reactive protein : albumin ratio (CAR) has been reported as a novel prognostic marker in several cancers. The aim of this study was to investigate the prognostic value of CAR in patients with intrahepatic cholangiocarcinoma (ICC).

METHODS

This was a single-centre retrospective study of patients who underwent surgery for ICC in a university hospital in Japan between 1998 and 2018. CAR, Glasgow Prognostic Score (GPS) and modified GPS (mGPS) were calculated. Their correlation with recurrence-free survival (RFS) and overall survival (OS) was analysed with Cox proportional hazards models.

RESULTS

Seventy-two patients were included in the study. Patients were divided into two groups according to the optimal CAR cut-off value of 0·02. CAR above 0·02 was associated with higher carbohydrate antigen 19-9 levels (20·5 versus 66·1 units/ml for CAR of 0·02 or less; P = 0·002), larger tumour size (3·2 versus 4·4 cm respectively; P = 0·031) and a higher rate of microvascular invasion (9 of 28 versus 25 of 44; P = 0·041). RFS and OS were shorter in patients with CAR above 0·02: hazard ratio (HR) 4·31 (95 per cent c.i. 2·02 to 10·63) and HR 4·80 (1·85 to 16·40) respectively. In multivariable analysis CAR above 0·02 was an independent prognostic factor of RFS (HR 3·29 (1·33 to 8·12); P < 0·001), but not OS.

CONCLUSIONS

CAR was associated with prognosis in patients who had hepatic resection for ICC.

Sign of Hard-X-Ray Pulsation from the γ-Ray Binary System LS 5039

To understand the nature of the brightest γ-ray binary system LS 5039, hard x-ray data of the object, taken with the Suzaku and NuSTAR observatories in 2007 and 2016, respectively, were analyzed. The two data sets jointly gave tentative evidence for a hard x-ray periodicity, with a period of ∼9  s and a period increase rate by ∼3×10^{-10}  s s^{-1}. Therefore, the compact object in LS 5039 is inferred to be a rotating neutron star, rather than a black hole. Furthermore, several lines of arguments suggest that this object has a magnetic field of several times ∼10^{10}  T, two orders of magnitude higher than those of typical neutron stars. The object is hence suggested to be a magnetar, which would be the first to be found in a binary. The results also suggest that the highly efficient particle acceleration process, known to be operating in LS 5039, emerges through interactions between dense stellar winds from the massive primary star, and ultrastrong magnetic fields of the magnetar.

Chest complications in immunocompromised patients without acquired immunodeficiency syndrome (AIDS): differentiation between infectious and non-infectious diseases using high-resolution CT findings

AIM

To differentiate between infectious and non-infectious diseases occurring in immunocompromised patients without acquired immunodeficiency syndrome (AIDS) using high-resolution computed tomography (HRCT).

MATERIALS AND METHODS

HRCT images of 555 patients with chest complications were reviewed retrospectively. Infectious diseases (n=341) included bacterial pneumonia (n=123), fungal infection (n=80), septic emboli (n=11), tuberculosis (n=15), pneumocystis pneumonia (n=101), and cytomegalovirus pneumonia (n=11), while non-infectious diseases (n=214) included drug toxicity (n=84), infiltration of underlying diseases (n=83), idiopathic pneumonia syndrome (n=34), diffuse alveolar haemorrhage (n=8), and pulmonary oedema (n=5). Lung parenchymal abnormalities were compared between the two groups using the χ² test and multiple logistic regression analysis.

RESULTS

The χ2 test results showed significant differences in many HRCT findings between the two groups. Multiple logistic regression analysis results indicated the presence of nodules with a halo and the absence of interlobular septal (ILS) thickening were the significant indicators that could differentiate infectious from non-infectious diseases. ILS thickening was generally less frequent among most infectious diseases and more frequent among most non-infectious diseases, with a good odds ratio (7.887, p<0.001). The sensitivity and accuracy for infectious diseases in the absence of ILS thickening were better (70% and 73%, respectively) than those of nodules with a halo (19% and 48%, respectively), while the specificity in the nodules with a halo was better (93%) than that of ILS thickening (78%).

CONCLUSIONS

The presence of nodules with a halo or the absence of ILS thickening tends to suggest infectious disease. Specifically, ILS thickening seems to be a more reliable indicator.

AB1050 CLINICAL IMPLICATIONS OF ULTRASONOGRAPHY (US) IN DIAGNOSIS AND MONITORING DISEASE ACTIVITY OF RELAPSING POLYCHONDRITIS (RP) AND COMPARATIVE INVESTIGATION BY US BETWEEN AURICLE OF RP, REPEATED TRAUMA, CELLULITIS AND HEALTHY SUBJECT

Objectives:

To assess the clinical implications of ultrasonography (US) in monitoring disease activity and diagnosis of relapsing polychondritis (RP).

Methods:

Firstly, auricular (n=5) and nasal (n=1) chondritis of six patients with RP were assessed by US before and after treatments. The relationship between US findings and serum markers were evaluated. Moreover, the comparisons of US findings between the auricle of patients with RP (n=5), repeated trauma (n=5), cellulitis (n=2) and healthy subjects (n=5) were also assessed.

Results:

US finding before treatment showed low-echoic swollen auricular and nasal cartilage with increased power Doppler signals (PDS) in all cases of RP. US findings corresponded to biopsy findings. After treatment, the swollen ear and nose completely resolved. Then, US findings also showed dramatic reductions in swollen cartilage with the decrease in PDS. Although serum markers completely improved, US finding remained in 1 of 6 cases, and this case showed flare due to PSL tapering. Finally, RP could be differentiated from the damage of repeated trauma and cellulitis by the presence or absence of PDS and subperichondrial serous effusion.

Conclusion:

US of auricular and nasal cartilage in RP possibly facilitates evaluation of auricular lesions and monitoring of disease activity, especially when we consider the treatment response and the timing of drug tapering.

Disclosure of Interests:

None declared

SAT0083 PREVALENCE OF DYSPHAGIA AND ASSOCIATED RISK FACTORS IN ELDERLY PATIENTS WITH RHEUMATOID ARTHRITIS

Background:

Dysphagia (swallowing disorder) is an important health concern among the elderly that is associated with a poor prognosis [1]. Rheumatic diseases such as dermatomyositis are thought to represent an important risk factor for dysphagia, but few studies have described the association between dysphagia and rheumatoid arthritis (RA), and details on the prevalence of dysphagia in RA patients is not known [2] [3].

Objectives:

The present study aimed to determine the prevalence of dysphagia and associated risk factors among elderly patients with rheumatoid arthritis.

Methods:

We conducted a cross-sectional study including 93 patients with RA and osteoarthritis (OA) over 65 years of age. OA patients were included in the study as healthy controls. Patients with a history of stroke, neuromuscular disease, or head and neck tumors were excluded from the study. From July to November 2019, the water swallowing test (WST) and repetitive saliva swallowing test (RSST) were performed to evaluate the presence or absence of dysphagia in the patients. We also checked oral conditions, hoarseness, temporomandibular joint symptoms, cervical range of motion limitations, and grip strength. In addition, interviews were conducted to investigate swallowing ability and aspiration history. We compared the prevalence of dysphagia between RA and OA patients and explored potential risk factors for dysphagia in RA patients using logistic regression models.

Results:

Our study subjects comprised 63 RA patients (mean age, 73.8 years; 86.5% female) and 30 OA patients (mean age, 75.8 years; 82.3% female). The WST and RSST revealed that RA patients had a significantly higher prevalence of dysphagia than OA patients (23.8% vs 6.7%, p<0.05). While RA patients with dysphagia (n=15) were significantly older and had a longer disease duration than the OA patients, we observed no difference in disease activity or administrated drugs. Of the RA patients with dysphagia, 60% reported no previous episodes of aspiration. Increasing age (odds ratio (OR) 3.21, 95% confidence interval (CI) 1.06-4.56), cervical range of motion limitations (OR 3.14, 95% CI 1.02-7.24), opening disorder of the jaw (OR 2.26, 95% CI 1.12-4.86), and decreased grip strength (OR 1.96, 95% CI 1.01-4.15) were identified as factors related to the presence of dysphagia. Coexistence of Sjogren’s syndrome did not significantly affect the prevalence of dysphagia.

Conclusion:

Dysphagia was more prevalent among RA patients than in OA patients, suggesting an association with temporomandibular involvement, cervical disorder, and muscle weakness. Subclinical dysphagia should be assessed and monitored carefully in the clinical course of elderly patients with RA.

References:

[1] KW Altman et al. Consequence of dysphagia in the hospitalized patient: impact on prognosis and hospital resources. Arch Otolaryngol Head Neck Surg. 2010 Aug; 136 (8):784-9.

[2] Gilheaney Ó et al. The Prevalence of Oropharyngeal Dysphagia in Adults Presenting with Temporomandibular Disorders Associated with Rheumatoid Arthritis: A Systematic Review and Meta-analysis. Dysphagia. 2017 Oct; 32 (5):587-600.

[3] Mugii N et al. Oropharyngeal Dysphagia in Dermatomyositis: Associations with Clinical and Laboratory Features Including Autoantibodies. PLoS One. 2016 May 11;11 (5):e0154746.

Disclosure of Interests:

None declared