Pulmonary light chain deposition disease is a rare entity characterized by immunoglobulin deposition within the lung parenchyma with pathologic features distinct from pulmonary amyloidosis. Here, the authors present the clinical presentation, associations, and radiologic features of pulmonary light chain deposition disease in a series of 4 patients as well as discuss the distinctions from amyloidosis. The present case series highlights the frequent presence of both cysts and nodules at CT. Clinically, lymphoma and/or autoimmune disease are often associated.

BACKGROUND: Research on the effects of physical exercise on canine body composition is limited. The aim of this study was to investigate the effects of a physical exercise programme on bodyweight, body condition score (BCS) and chest, abdominal and thigh circumferences in dogs. Twenty-one healthy dogs of different breeds exercised together with their owners during an eight-week programme consisting of jogging and strength exercises. Standardised measurements were performed in triplicates with a measuring tape on standing dogs. Chest circumference was measured at three anatomical locations, abdomen at two and thigh at one. Data on bodyweight, BCS (9-point scale) and circumferences were analysed with mixed model repeated measures analyses to evaluate changes after the programme and effects of target distance.
RESULTS: Seven dog owners choose a target distance of 2 km and 14 owners choose 5-10 km. Mean BCS decreased (P = 0.007) after the programme (5.1 ± 0.9 vs. 4.7 ± 0.6) but there was no effect of target distance. Almost all chest and abdominal circumference measurements decreased (P ≤ 0.007) with the 2 km group driving the reduction in chest circumference and the 5-10 km group driving the reduction in abdominal circumference. In contrast, thigh circumference (28.8 ± 0.4 vs. 30.2 ± 0.4) increased (P = 0.007) while bodyweight was maintained. There were positive correlations between BCS and abdominal/chest ratios before and after the programme (Pearson correlation; R square ≤ 0.43, P ≤ 0.0012) but the mean ratio remained constant.
CONCLUSIONS: Results indicated a redistribution between total body fat and muscle mass in body composition of normal weight to slightly overweight dogs after the physical exercise programme. The use of bodyweight alone was not a reliable evaluation method to complement the BCS assessment. However, repeated measurements of chest, abdominal and thigh circumference might aid in the assessment of body composition in dogs performing physical exercise. Further research should include a control group and objective evaluations of total body fat and lean mass, in order to investigate the effectiveness of physical exercise as a freestanding method for decreasing BCS and increasing muscle mass in overweight dogs.

Sustained injury from factors such as hypoxia, infection, or physical damage may provoke improper tissue repair and the anomalous deposition of connective tissue that causes fibrosis. This phenomenon may take place in any organ, ultimately leading to their dysfunction and eventual failure. Tissue fibrosis has also been found to be central in both the process of carcinogenesis and cancer progression. Thus, its prompt diagnosis and regular monitoring is necessary for implementing effective disease-modifying interventions aiming to reduce mortality and improve overall quality of life. While significant research has been conducted on these subjects, a comprehensive understanding of how their relationship manifests through modern imaging techniques remains to be established. This work intends to provide a comprehensive overview of imaging technologies relevant to the detection of fibrosis affecting thoracic organs as well as to explore potential future advancements in this field.

暂无摘要。

Electrical impedance tomography (EIT) is a non-radiation, non-invasive visual diagnostic technique. In order to improve the imaging resolution and the removing artifacts capability of the reconstruction algorithms for electrical impedance imaging in human-chest models, the HMANN algorithm was proposed using the Hadamard product to optimize multilayer artificial neural networks (MANN). The reconstructed images of the HMANN algorithm were compared with those of the generalized vector sampled pattern matching (GVSPM) algorithm, truncated singular value decomposition (TSVD) algorithm, backpropagation (BP) neural network algorithm, and traditional MANN algorithm. The simulation results showed that the correlation coefficient of the reconstructed images obtained by the HMANN algorithm was increased by 17.30% in the circular cross-section models compared with the MANN algorithm. It was increased by 13.98% in the lung cross-section models. In the lung cross-section models, some of the correlation coefficients obtained by the HMANN algorithm would decrease. Nevertheless, the HMANN algorithm retained the image information of the MANN algorithm in all models, and the HMANN algorithm had fewer artifacts in the reconstructed images. The distinguishability between the objects and the background was better compared with the traditional MANN algorithm. The algorithm could improve the correlation coefficient of the reconstructed images, and effectively remove the artifacts, which provides a new direction to effectively improve the quality of the reconstructed images for EIT.
电阻抗成像（EIT）是一种无辐射、非侵入式的可视化诊断技术。为提高胸部电阻抗成像技术重建算法的成像分辨率和去伪影能力，本研究提出了一种利用Hadamard product优化多层神经网络（MANN）的HMANN算法。将HMANN算法的重建图像与广义矢量模式匹配（GVSPM）算法、截断奇异值分解（TSVD）算法、反向传播（BP）神经网络算法和传统MANN算法的重建图像进行对比，仿真结果表明：相对于MANN算法，HMANN算法重建图像的相关系数在圆截面模型中可以提高17.30%，在肺截面模型中可以提高13.98%。虽然肺截面模型中HMANN算法重建图像的部分相关系数会有所下降，但在所有模型中，HMANN算法保留了MANN算法的图像信息，同时HMANN算法重建图像的伪影更少，检测目标与背景的可识别度比传统MANN算法高。本研究可以提升重建图像的相关系数，有效去除重建图像的伪影，为EIT成像技术提供了一种有效提升重建图像质量的新思路。.

BACKGROUND: Ankylosing spondylitis is chronic progressive disease, which decrease functions of musculoskeletal system including chest area. Those changes influences respiratory mechanics, worsen conditions of proper ventilation of lungs.
OBJECTIVE: Rating of functional and respiratory parameters and dependence between them at patients with ankylosing spondylitis.
METHODS: The study included 45 patients with diagnosed ankylosing spondylitis. Chest and upper limbs mobility, resting spinal curvature alignment were assessed, and respiratory parameters were measured in a plethysmographic chamber JAGGER MasterScreen Body.
RESULTS: Ankylosing spondylitis patients had lower respiratory parameters especially sReff, and FRC. Restriction of chest and upper limbs mobility was also demonstrated. Forward head extension was observed based on the occipital wall test. Correlations between functional parameters and correlations between functional and respiratory parameters were shown, in particular MIP, MEP, sReff, Rtot, TLC, ERV.
CONCLUSIONS: The study confirmed a decrease in functional and respiratory parameters in the examined patients with ankylosing spondylitis compared to the applicable standards. A significant relationship was found between functional parameters in the upper body and respiratory parameters, which worsen with increasing thoracic dysfunction. The obtained results indicate the directions of therapy that should be taken into account to improve respiratory parameters and reduce respiratory dysfunction in these patients. Chest-focused physiotherapy appears to be an important element in improving function in patients with ankylosing spondylitis.

The purpose of the study was to assess the performance of readers in diagnosing thoracic anomalies on standard chest radiographs (CXRs) with and without a deep-learning-based AI tool (Rayvolve) and to evaluate the standalone performance of Rayvolve in detecting thoracic pathologies on CXRs. This retrospective multicentric study was conducted in two phases. In phase 1, nine readers independently reviewed 900 CXRs from imaging group A and identified thoracic abnormalities with and without AI assistance. A consensus from three radiologists served as the ground truth. In phase 2, the standalone performance of Rayvolve was evaluated on 1500 CXRs from imaging group B. The average values of AUC across the readers significantly increased by 15.94%, with AI-assisted reading compared to unaided reading (0.88 ± 0.01 vs. 0.759 ± 0.07, p < 0.001). The time taken to read the CXRs decreased significantly, by 35.81% with AI assistance. The average values of sensitivity and specificity across the readers increased significantly by 11.44% and 2.95% with AI-assisted reading compared to unaided reading (0.857 ± 0.02 vs. 0.769 ± 0.02 and 0.974 ± 0.01 vs. 0.946 ± 0.01, p < 0.001). From the standalone perspective, the AI model achieved an average sensitivity, specificity, PPV, and NPV of 0.964, 0.844, 0.757, and 0.9798. The speed and performance of the readers improved significantly with AI assistance.

Digital health technologies (DHTs) are increasingly being adopted in clinical trials, as they enable objective evaluations of health parameters in free-living environments. Although lumbar accelerometers notably provide reliable gait parameters, embedding accelerometers in chest devices, already used for vital signs monitoring, could capture a more comprehensive picture of participants\' wellbeing, while reducing the burden of multiple devices. Here we assess the validity of gait parameters measured from a chest accelerometer. Twenty healthy adults (13 females, mean ± sd age: 33.9 ± 9.1 years) instrumented with lumbar and chest accelerometers underwent in-lab and outside-lab walking tasks, while monitored with reference devices (an instrumented mat, and a 6-accelerometers set). Gait parameters were extracted from chest and lumbar accelerometers using our open-source Scikit Digital Health gait (SKDH-gait) algorithm, and compared against reference values via Bland-Altman plots, Pearson\'s correlation, and intraclass correlation coefficient. Mixed effects regression models were performed to investigate the effect of device, task, and their interaction. Gait parameters derived from chest and lumbar accelerometers showed no significant difference and excellent agreement across all tasks, as well as good-to-excellent agreement and strong correlation against reference values, thus supporting the deployment of a single multimodal chest device in clinical trials, to simultaneously measure gait and vital signs.Trial Registration: The study was reviewed and approved by the Advarra IRB (protocol number: Pro00043100).

UNASSIGNED: To evaluate and compare magnetic resonance imaging (MRI) sequences that could potentially be used in the diagnosis of coronavirus disease 2019 (COVID-19).
UNASSIGNED: Included in the study were 42 patient who underwent thorax computed tomography (CT) for COVID-19 pneumonia and thorax MRI for any reason within 24 h after CT. The T2-weighted fast spin echo periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) (T2W-FSE-P), fast imaging employing steady-state acquisition, T2 fat-saturated FSE, axial T1 liver acquisition with volume acceleration (LAVA) and single-shot FSE images were compared in terms of their ability to show COVID-19 findings.
UNASSIGNED: The mean age of the patients was 47.2 ± 24 years. Of the patients, 22 were male (52.4%) and 20 (47.6%) were female. The interobserver intraclass coefficient (ICC) for the image quality score was the highest in the T2W-FSE-P sequence and lowest in the T1 LAVA sequence. All of the lesion-based evaluations of the interobserver agreement were statistically significant, with the kappa value varying between 0.798 and 0.998.
UNASSIGNED: All 5 sequences evaluated in the study were successful in showing the parenchymal findings of COVID-19. Since the T2W-FSE-P sequence had the best scores in both interobserver agreement and ICC for the image quality score, it was considered that it can be included in thorax MRI examinations to assist the diagnosis of COVID-19.

UNASSIGNED: X-ray Computed Tomography dose levels have been varying among modalities and scanning body regions due to the absence of incessant routine follow-up. Thus, the study aimed to compute the dose index discrepancies in Ethiopia for the most recurring scan protocols (head, chest, abdomen, and pelvis).
UNASSIGNED: A purposive sampling method was employed to select the hospitals due to the rare existence of functional CT scanners in Ethiopia. From the selected hospitals, a total of 1,385 (249 heads, 804 chests, 132 abdomens, and 200 pelvis) were collected in terms of standard dose metric values in the period of December 2019-March 2020. Patients\' DLP was computed into mean value using IBM SPSS Statistics 20 software. From the mean DLP, we can compute the effective dose.
UNASSIGNED: Patients\' dose level disparity was observed in this study though it is below the ICRP standard level for all body regions except for pelvis DLP (593.37 mGy-cm) at Black Lion. The dose level for the head and chest are computed within the recommended level at all hospitals. Effective doses for the pelvis at four hospitals (Teklehaimanot, Black Lion, ALERT, Paul\'s, and Ayder hospitals) were computed as 6.45, 8.90, 5.08, 6.54, and 6.84 mSv respectively, and the effective doses for abdomen at Ayder Hospital was obtained to be 8.90 mSv, which is above the recommended value.
UNASSIGNED: X-ray CT scanners are somewhat properly functioning although some sort of justification and optimization for pelvis and abdomen examinations are strongly recommended to implement as low as reasonably achievable principle.