Classification and quantification of double superior vena cava evaluated by computed tomography imaging.

BACKGROUND: A double superior vena cava (DSVC) may cause technical difficulties in some cardiovascular procedures. However, no quantitative data exist to describe the morphological features of this anomaly. METHODS: From January 2015 to January 2019, the data of 128 consecutive patients diagnosed with DSVC on computed tomography (CT) images were retrospectively analyzed. We proposed an easy and rational method for DSVC classification based on the presence or absence of the left brachiocephalic vein (LBCV), the presence or absence of an anastomotic vein bridging the bilateral superior vena cava (SVC), and the drainage pattern of the left superior vena cava (LSVC). The following classifications were established: type I, LBVC absent, LSVC drainage into the right atrium via the coronary sinus; type II, LBCV present, LSVC drainage into the right atrium via the coronary sinus; type III, LBCV absent, LSVC drainage into the right atrium via the anastomosis; type IV, LBCV present, LSVC drainage into the right atrium via the anastomosis. The length, diameter, and area of the bilateral SVC and the coronary sinus were carefully measured across the 4 types. RESULTS: Type I was the most frequently occurring type (66 of 128, 51.6%), followed by type II (43 of 128, 33.6%), then type III (15 of 128, 11.7%), and type IV (4 of 128, 3.1%). The LSVC was significantly longer than the right SVC (RSVC) in all 4 types, and the diameters of the LSVC were significantly larger in types without the LBCV (i.e., types I and III) (P<0.0001 for all). Additionally, the diameter of the coronary sinus in types I and II was triple that in types III and IV (P<0.0001), which was thought to be due to increased venous blood reflux through the coronary sinus. CONCLUSIONS: The anatomical features of DSVC can be satisfactorily depicted on CT. The quantitative measurement of this anomaly by the reporting radiologists could assist clinicians to minimize the procedure-associated risks.

Mix-and-Interpolate: A Training Strategy to Deal With Source-Biased Medical Data.

Till March 31st, 2021, the coronavirus disease 2019 (COVID-19) had reportedly infected more than 127 million people and caused over 2.5 million deaths worldwide. Timely diagnosis of COVID-19 is crucial for management of individual patients as well as containment of the highly contagious disease. Having realized the clinical value of non-contrast chest computed tomography (CT) for diagnosis of COVID-19, deep learning (DL) based automated methods have been proposed to aid the radiologists in reading the huge quantities of CT exams as a result of the pandemic. In this work, we address an overlooked problem for training deep convolutional neural networks for COVID-19 classification using real-world multi-source data, namely, the data source bias problem. The data source bias problem refers to the situation in which certain sources of data comprise only a single class of data, and training with such source-biased data may make the DL models learn to distinguish data sources instead of COVID-19. To overcome this problem, we propose MIx-aNd-Interpolate (MINI), a conceptually simple, easy-to-implement, efficient yet effective training strategy. The proposed MINI approach generates volumes of the absent class by combining the samples collected from different hospitals, which enlarges the sample space of the original source-biased dataset. Experimental results on a large collection of real patient data (1,221 COVID-19 and 1,520 negative CT images, and the latter consisting of 786 community acquired pneumonia and 734 non-pneumonia) from eight hospitals and health institutions show that: 1) MINI can improve COVID-19 classification performance upon the baseline (which does not deal with the source bias), and 2) MINI is superior to competing methods in terms of the extent of improvement.

Accurate and efficient pulmonary CT imaging workflow for COVID-19 patients by the combination of intelligent guided robot and automatic positioning technology.

BACKGROUND: The ongoing coronavirus disease 2019 (COVID-19) pandemic has put radiologists at a higher risk of infection during the computer tomography (CT) examination for the patients. To help settling these problems, we adopted a remote-enabled and automated contactless imaging workflow for CT examination by the combination of intelligent guided robot and automatic positioning technology to reduce the potential exposure of radiologists to 2019 novel coronavirus (2019-nCoV) infection and to increase the examination efficiency, patient scanning accuracy and better image quality in chest CT imaging . METHODS: From February 10 to April 12, 2020, adult COVID-19 patients underwent chest CT examinations on a CT scanner using the same scan protocol except with the conventional imaging workflow (CW group) or an automatic contactless imaging workflow (AW group) in Wuhan Leishenshan Hospital (China) were retrospectively and prospectively enrolled in this study. The total examination time in two groups was recorded and compared. The patient compliance of breath holding, positioning accuracy, image noise and signal-to-noise ratio (SNR) were assessed by three experienced radiologists and compared between the two groups. RESULTS: Compared with the CW group, the total positioning time of the AW group was reduced ((118.0 ± 20.0) s vs. (129.0 ± 29.0) s, P = 0.001), the proportion of scanning accuracy was higher (98% vs. 93%), and the lung length had a significant difference ((0.90±1.24) cm vs. (1.16±1.49) cm, P = 0.009). For the lesions located in the pulmonary centrilobular and subpleural regions, the image noise in the AW group was significantly lower than that in the CW group (centrilobular region: (140.4 ± 78.6) HU vs. (153.8 ± 72.7) HU, P = 0.028; subpleural region: (140.6 ± 80.8) HU vs. (159.4 ± 82.7) HU, P = 0.010). For the lesions located in the peripheral, centrilobular and subpleural regions, SNR was significantly higher in the AW group than in the CW group (centrilobular region: 6.6 ± 4.3 vs. 4.9 ± 3.7, P = 0.006; subpleural region: 6.4 ± 4.4 vs. 4.8 ± 4.0, P < 0.001). CONCLUSIONS: The automatic contactless imaging workflow using intelligent guided robot and automatic positioning technology allows for reducing the examination time and improving the patient's compliance of breath holding, positioning accuracy and image quality in chest CT imaging.

Development of functional connectivity within and among the resting-state networks in anesthetized rhesus monkeys.

OBJECTIVE: The age-related changes in the resting-state networks (RSNs) exhibited temporally specific patterns in humans, and humans and rhesus monkeys have similar RSNs. We hypothesized that the RSNs in rhesus monkeys experienced similar developmental patterns as humans. METHODS: We acquired resting-state fMRI data from 62 rhesus monkeys, which were divided into childhood, adolescence, and early adulthood groups. Group independent component analysis (ICA) was used to identify monkey RSNs. We detected the between-group differences in the RSNs and static, dynamic, and effective functional network connections (FNCs) using one-way variance analysis (ANOVA) and post-hoc analysis. RESULTS: Eight rhesus RSNs were identified, including cerebellum (CN), left and right lateral visual (LVN and RVN), posterior default mode (pDMN), visuospatial (VSN), frontal (FN), salience (SN), and sensorimotor networks (SMN). In internal connections, the CN, SN, FN, and SMN mainly matured in early adulthood. The static FNCs associated with FN, SN, pDMN primarily experienced fast descending slow ascending type (U-shaped) developmental patterns for maturation, and the dynamic FNCs related to pDMN (RVN, CN, and SMN) and SMN (CN) were mature in early adulthood. The effective FNC results showed that the pDMN and VSN (stimulated), SN (inhibited), and FN (first inhibited then stimulated) chiefly matured in early adulthood. CONCLUSION: We identified eight monkey RSNs, which exhibited similar development patterns as humans. All the RSNs and FNCs in monkeys were not widely changed but fine-tuned. Our study clarified that the progressive synchronization, exploration, and regulation of cognitive RSNs within the pDMN, FN, SN, and VSN denoted potential maturation of the RSNs throughout development. We confirmed the development patterns of RSNs and FNCs would support the use of monkeys as a best animal model for human brain function.

A comparison between manual and artificial intelligence-based automatic positioning in CT imaging for COVID-19 patients.

OBJECTIVE: To analyze and compare the imaging workflow, radiation dose, and image quality for COVID-19 patients examined using either the conventional manual positioning (MP) method or an AI-based automatic positioning (AP) method. MATERIALS AND METHODS: One hundred twenty-seven adult COVID-19 patients underwent chest CT scans on a CT scanner using the same scan protocol except with the manual positioning (MP group) for the initial scan and an AI-based automatic positioning method (AP group) for the follow-up scan. Radiation dose, patient positioning time, and off-center distance of the two groups were recorded and compared. Image noise and signal-to-noise ratio (SNR) were assessed by three experienced radiologists and were compared between the two groups. RESULTS: The AP operation was successful for all patients in the AP group and reduced the total positioning time by 28% compared with the MP group. Compared with the MP group, the AP group had significantly less patient off-center distance (AP 1.56 cm ± 0.83 vs. MP 4.05 cm ± 2.40, p < 0.001) and higher proportion of positioning accuracy (AP 99% vs. MP 92%), resulting in 16% radiation dose reduction (AP 6.1 mSv ± 1.3 vs. MP 7.3 mSv ± 1.2, p < 0.001) and 9% image noise reduction in erector spinae and lower noise and higher SNR for lesions in the pulmonary peripheral areas. CONCLUSION: The AI-based automatic positioning and centering in CT imaging is a promising new technique for reducing radiation dose and optimizing imaging workflow and image quality in imaging the chest. KEY POINTS: • The AI-based automatic positioning (AP) operation was successful for all patients in our study. • AP method reduced the total positioning time by 28% compared with the manual positioning (MP). • AP method had less patient off-center distance and higher proportion of positioning accuracy than MP method, resulting in 16% radiation dose reduction and 9% image noise reduction in erector spinae.

Divergent Projection Patterns Revealed by Reconstruction of Individual Neurons in Orbitofrontal Cortex.

The orbitofrontal cortex (OFC) is involved in diverse brain functions via its extensive projections to multiple target regions. There is a growing understanding of the overall outputs of the OFC at the population level, but reports of the projection patterns of individual OFC neurons across different cortical layers remain rare. Here, by combining neuronal sparse and bright labeling with a whole-brain florescence imaging system (fMOST), we obtained an uninterrupted three-dimensional whole-brain dataset and achieved the full morphological reconstruction of 25 OFC pyramidal neurons. We compared the whole-brain projection targets of these individual OFC neurons in different cortical layers as well as in the same cortical layer. We found cortical layer-dependent projections characterized by divergent patterns for information delivery. Our study not only provides a structural basis for understanding the principles of laminar organizations in the OFC, but also provides clues for future functional and behavioral studies on OFC pyramidal neurons.

Features of family clusters of COVID-19 patients: A retrospective study.

BACKGROUND: To investigate and compare the clinical and imaging features among family members infected with COVID-19. METHODS: We retrospectively collected a total of 34 COVID-19 cases (15 male, 19 female, aged 48 ± 16 years, ranging from 10 to 81 years) from 13 families from January 17, 2020 through February 15, 2020. Patients were divided into two groups: Group 1 - part of the family members (first-generation) who had exposure history and others (second-generation) infected through them, and Group 2 - patients from the same family having identical exposure history. We collected clinical symptoms, laboratory findings, and high-resolution computed tomography (HRCT) features for each patient. Comparison tests were performed between the first- and second-generation patients in Group 1. RESULTS: In total there were 21 patients in Group 1 and 20 patients in Group 2. For Group 1, first-generation patients had significantly higher white blood cell count (6.5 × 109/L (interquartile range (IQR): 4.9-9.2 × 109/L) vs 4.5 × 109/L (IQR: 3.7-5.3 × 109/L); P = 0.0265), higher neutrophil count (4.9 × 109/L (IQR: 3.6-7.3 × 109/L) vs 2.9 × 109/L (IQR: 2.1-3.3 × 109/L); P = 0.0111), and higher severity scores on HRCT (3.9 ± 2.4 vs 2.0 ± 1.3, P = 0.0362) than the second-generation patients. Associated underlying diseases (odds ratio, 8.0, 95% confidence interval: 3.4-18.7, P = 0.0013) were significantly correlated with radiologic severity scores in second-generation patients. CONCLUSION: Analysis of the family cluster cases suggests that COVID-19 had no age or sex predominance. Secondarily infected patients in a family tended to develop milder illness, but this was not true for those with existing comorbidities.

A fatal outcome from SARS-CoV-2 infection: One case report of a young man with multiple organ damage.

The coronavirus disease 2019 (COVID-19) first emerged in Wuhan, China on December 2019 and has become a severe public health issue worldwide. A 36-year-old man was presented to the hospital staff with a fever that had already persisted for a three-day period, general weakness and diarrhea. He had no chronic diseases and was tested positive for COVID-19 with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleic acid. During his hospitalization, several abnormal indicators appeared in his laboratory tests, which implied systemic inflammation and multiple organ damage. A series of chest radiographs monitored the dynamic process of lung lesions, which could predict the clinical changes of the patient. His condition deteriorated rapidly, resulting in death due to acute respiratory distress syndrome (ARDS) on hospital day 13. The case indicates that inflammatory response may appear in people infected with SARS-CoV-2 and may lead to multiple organ damage (especially pancreatic damage). When a COVID-19 patient is entering into the critical stage, their condition could rapidly deteriorate.

High-refractive index of acrylate embedding resin clarifies mouse brain tissue.

Biological tissue transparency combined with light-sheet fluorescence microscopy is a useful method for studying the neural structure of biological tissues. The development of light-sheet fluorescence microscopy also promotes progress in biological tissue clearing methods. The current clarifying methods mostly use liquid reagent to denature protein or remove lipids first, to eliminate or reduce the scattering index or refractive index of the biological tissue. However, denaturing protein and removing lipids require complex procedures or an extended time period. Therefore, here we have developed acrylate resin with a high refractive index, which causes clearing of biological tissue directly after polymerization. This method can improve endogenous fluorescence retention by adjusting the pH value of the resin monomer.

Development of a neutral embedding resin for optical imaging of fluorescently labeled biological tissue.

Plastic embedding is widely applied in light microscopy analyses. Previous studies have shown that embedding agents and related techniques can greatly affect the quality of biological tissue embedding and fluorescent imaging. Specifically, it is difficult to preserve endogenous fluorescence using currently available acidic commercial embedding resins and related embedding techniques directly. Here, we developed a neutral embedding resin that improved the green fluorescent protein (GFP), yellow fluorescent protein (YFP), and DsRed fluorescent intensity without adjusting the pH value of monomers or reactivating fluorescence in lye. The embedding resin had a high degree of polymerization, and its fluorescence preservation ratios for GFP, YFP, and DsRed were 126.5%, 155.8%, and 218.4%, respectively.

Plastic embedding immunolabeled large-volume samples for three-dimensional high-resolution imaging.

High-resolution three-dimensional biomolecule distribution information of large samples is essential to understanding their biological structure and function. Here, we proposed a method combining large sample resin embedding with iDISCO immunofluorescence staining to acquire the profile of biomolecules with high spatial resolution. We evaluated the compatibility of plastic embedding with an iDISCO staining technique and found that the fluorophores and the neuronal fine structures could be well preserved in the Lowicryl HM20 resin, and that numerous antibodies and fluorescent tracers worked well upon Lowicryl HM20 resin embedding. Further, using fluorescence Micro-Optical sectioning tomography (fMOST) technology combined with ultra-thin slicing and imaging, we were able to image the immunolabeled large-volume tissues with high resolution.

Chemical reactivation of resin-embedded pHuji adds red for simultaneous two-color imaging with EGFP.

The pH-sensitive fluorescent proteins enabling chemical reactivation in resin are useful tools for fluorescence microimaging. EGFP or EYFP is good for such applications. For simultaneous two-color imaging, a suitable red fluorescent protein is an urgent need. Here a pH-sensitive red fluorescent protein, pHuji, is selected and verified to remain pH-sensitive in HM20 resin. We observe 183% fluorescence intensity of pHuji in resin-embeded mouse brain and 29.08-fold fluorescence intensity of reactivated pHuji compared to the quenched state. pHuji and EGFP can be quenched and chemically reactivated simultaneously in resin, thus enabling simultaneous two-color micro-optical sectioning tomography of resin-embedded mouse brain. This method may greatly facilitate the visualization of neuronal morphology and neural circuits to promote understanding of the structure and function of the brain.

Anterograde monosynaptic transneuronal tracers derived from herpes simplex virus 1 strain H129.

BACKGROUND: Herpes simplex virus type 1 strain 129 (H129) has represented a promising anterograde neuronal circuit tracing tool, which complements the existing retrograde tracers. However, the current H129 derived tracers are multisynaptic, neither bright enough to label the details of neurons nor capable of determining direct projection targets as monosynaptic tracer. METHODS: Based on the bacterial artificial chromosome of H129, we have generated a serial of recombinant viruses for neuronal circuit tracing. Among them, H129-G4 was obtained by inserting binary tandemly connected GFP cassettes into the H129 genome, and H129-ΔTK-tdT was obtained by deleting the thymidine kinase (TK) gene and adding tdTomato coding gene to the H129 genome. Then the obtained viral tracers were tested in vitro and in vivo for the tracing capacity. RESULTS: H129-G4 is capable of transmitting through multiple synapses, labeling the neurons by green florescent protein, and visualizing the morphological details of the labeled neurons. H129-ΔTK-tdT neither replicates nor spreads in neurons alone, but transmits to and labels the postsynaptic neurons with tdTomato in the presence of complementary expressed TK from a helper virus. H129-ΔTK-tdT is also capable to map the direct projectome of the specific neuron type in the given brain regions in Cre transgenic mice. In the tested brain regions where circuits are well known, the H129-ΔTK-tdT tracing patterns are consistent with the previous results. CONCLUSIONS: With the assistance of the helper virus complimentarily expressing TK, H129-ΔTK-tdT replicates in the initially infected neuron, transmits anterogradely through one synapse, and labeled the postsynaptic neurons with tdTomato. The H129-ΔTK-tdT anterograde monosynaptic tracing system offers a useful tool for mapping the direct output in neuronal circuitry. H129-G4 is an anterograde multisynaptic tracer with a labeling signal strong enough to display the details of neuron morphology.

Embedding and Chemical Reactivation of Green Fluorescent Protein in the Whole Mouse Brain for Optical Micro-Imaging.

Resin embedding has been widely applied to fixing biological tissues for sectioning and imaging, but has long been regarded as incompatible with green fluorescent protein (GFP) labeled sample because it reduces fluorescence. Recently, it has been reported that resin-embedded GFP-labeled brain tissue can be imaged with high resolution. In this protocol, we describe an optimized protocol for resin embedding and chemical reactivation of fluorescent protein labeled mouse brain, we have used mice as experiment model, but the protocol should be applied to other species. This method involves whole brain embedding and chemical reactivation of the fluorescent signal in resin-embedded tissue. The whole brain embedding process takes a total of 7 days. The duration of chemical reactivation is ~2 min for penetrating 4 µm below the surface in the resin-embedded brain. This protocol provides an efficient way to prepare fluorescent protein labeled sample for high-resolution optical imaging. This kind of sample was demonstrated to be imaged by various optical micro-imaging methods. Fine structures labeled with GFP across a whole brain can be detected.

High axial resolution imaging system for large volume tissues using combination of inclined selective plane illumination and mechanical sectioning.

To resolve fine structures of biological systems like neurons, it is required to realize microscopic imaging with sufficient spatial resolution in three dimensional systems. With regular optical imaging systems, high lateral resolution is accessible while high axial resolution is hard to achieve in a large volume. We introduce an imaging system for high 3D resolution fluorescence imaging of large volume tissues. Selective plane illumination was adopted to provide high axial resolution. A scientific CMOS working in sub-array mode kept the imaging area in the sample surface, which restrained the adverse effect of aberrations caused by inclined illumination. Plastic embedding and precise mechanical sectioning extended the axial range and eliminated distortion during the whole imaging process. The combination of these techniques enabled 3D high resolution imaging of large tissues. Fluorescent bead imaging showed resolutions of 0.59 µm, 0.47µm, and 0.59 µm in the x, y, and z directions, respectively. Data acquired from the volume sample of brain tissue demonstrated the applicability of this imaging system. Imaging of different depths showed uniform performance where details could be recognized in either the near-soma area or terminal area, and fine structures of neurons could be seen in both the xy and xz sections.