The transcriptome of Icerya aegyptiaca (Hemiptera: Monophlebidae) and comparison with neococcoids reveal genetic clues of evolution in the scale insects.

BACKGROUND: Scale insects are worldwide sap-sucking parasites, which can be distinguished into neococcoids and non-neococcoids. Neococcoids are monophyletic with a peculiar reproductive system, paternal genome elimination (PGE). Different with neococcoids, Iceryini, a tribe in non-neococcoids including several damaging pests, has abdominal spiracles, compound eyes in males, relatively abundant wax, unique hermaphrodite system, and specific symbionts. However, the current studies on the gene resources and genomic mechanism of scale insects are mainly limited in the neococcoids, and lacked of comparison in an evolution frame. RESULT: We sequenced and de novo assembled a transcriptome of Icerya aegyptiaca (Douglas), a worldwide pest of Iceryini, and used it as representative of non-neococcoids to compare with the genomes or transcriptomes of other six species from different families of neococcoids. We found that the genes under positive selection or negative selection intensification (simplified as "selected genes" below) in I. aegyptiaca included those related to neurogenesis and development, especially eye development. Some genes related to fatty acid biosynthesis were unique in its transcriptome with relatively high expression and not detected in neococcoids. These results may indicate a potential link to the unique structures and abundant wax of I. aegyptiaca compared with neococcoids. Meanwhile, genes related to DNA repair, mitosis, spindle, cytokinesis and oogenesis, were included in the selected genes in I. aegyptiaca, which is possibly associated with cell division and germ cell formation of the hermaphrodite system. Chromatin-related process were enriched from selected genes in neococcoids, along with some mitosis-related genes also detected, which may be related to their unique PGE system. Moreover, in neococcoid species, male-biased genes tend to undergo negative selection relaxation under the PGE system. We also found that the candidate horizontally transferred genes (HTGs) in the scale insects mainly derived from bacteria and fungi. bioD and bioB, the two biotin-synthesizing HTGs were exclusively found in the scale insects and neococcoids, respectively, which possibly show potential demand changes in the symbiotic relationships. CONCLUSION: Our study reports the first I. aegyptiaca transcriptome and provides preliminary insights for the genetic change of structures, reproductive systems and symbiont relationships at an evolutionary aspect. This will provide a basis for further research and control of scale insects.

Sulfur and nitrogen co-doped magnetic biochar coupled with hydroxylamine for high-efficiency of persulfate activation and mechanism study.

Biochar has recently become a central issue in advanced oxidation processes (AOPs) based on peroxydisulfate (PDS) activation. However, the PDS activation by biochar must be improved. In this study, S, N co-doped magnetic biochar (IBC) was prepared by a simple impregnation-pyrolysis method using Eichhornia crassipes stems with inherent sulfur and nitrogen as the raw materials for biochar. The reductant hydroxylamine (HA) was employed to further enhance PDS activation by the IBC for organic pollutant degradation. Incorporating HA in PDS activation over IBC significantly improved its compatibility with complex water, catalytic degradation, stability performance, and mineralization rate of organic pollutants. The outstanding performance of the HA/PDS/IBC system for organic degradation was due to the increased free radicals SO4·-, O2·-, and non-radical 1O2 generated, as well as the electrons transferred between IBC, PDS, and organic pollutants, which were verified by electron paramagnetic resonance (EPR) detection and electrochemical characterizations. Furthermore, HA-enhanced Fe(III)/Fe(II) cycling, surface functional groups, and S and N doping contributed to the generation of reactive oxygen species (ROS). Moreover, the toxicity assessment indicated that the toxicity of the degradation intermediates decreased. Therefore, this research proposes a new insight into the enhanced degradation of pollutants by increasing PDS activation using biochar-based catalytic materials.

[The effects and mechanisms of interleukin-22 and interferon-γ on epithelial-mesenchymal transition of pleural mesothelial cells].

OBJECTIVE: To investigate the effects of interleukin (IL)-22 and interferon (IFN)-γ on epithelial-mesenchymal transition (EMT) of pleural mesothelial cells, and to explore the relevant signal transduction pathways, in tuberculous pleural effusion. METHODS: Twenty-two patients (12 males and 10 females, age range 22-64 years, mean 38.4 years) with tuberculous pleurisy hospitalized in department of respiratory medicine of the First People's Hospital of Foshan were recruited from July 2013 to June 2014. Diagnosis was confirmed by pathology. Freshly isolated pleural mesothelial cells (PMCs) from tuberculous pleural effusion were cultured either in medium alone as control, or stimulated with IL-22 and/or IFN-γ, and the phosphorylated signal transducers and activators of transcription (STAT) signalings in PMCs were determined by flowcytometry. In some experiments, STAT inhibitors were added into coculture with IL-22 and/or IFN-γ, and then morphological changes of PMCs were observed, and the expressions of epithelial markers such as cytokeratin-8 and E-cadherin as well as mesenchymal markers such as vimentin and α-Smooth muscle actin (SMA) were also determined by flow cytometry. RESULTS: As compared with the control group, IFN-γ induced epithelial-mesenchymal transition of PMCs via a STAT1 pathway as evidenced by down-regulation of cytokeratin-8 [(43.8 ± 2.8)% vs (14.3 ± 1.5)%,t=8.1, P<0.05] and E-cadherin [(43.8 ± 1.9)% vs (13.4 ± 1.2)%, t=9.7, P<0.05], and by up-regulation of vimentin [(41.2 ± 2.4)% vs (70.6 ± 3.6)%,t=8.5, P<0.05] and α-SMA [(55.8 ± 2.0)% vs (80.6 ± 2.9)%,t=7.2, P<0.05]. IL-22 not only maintained the epithelial property of PMCs, but also reverted IFN-γ-induced EMT [cytokeratin-8 (62.4 ± 3.1)%, E-cadherin (46.5 ± 3.6)%, vimentin (36.7 ± 2.8)%, and α-SMA (35.2 ± 2.5)% in 'IFN-γ + IL-22' group, all P<0.05 as compared with those of IFN-γ group]. Whereas addition of STAT3 inhibitor significantly abrogated such anti-EMT effect of IL-22 on PMCs [cytokeratin-8 (16.7 ± 0.7)%, E-cadherin (14.4 ± 0.9)%, vimentin (67.9 ± 2.5)%, and α-SMA (79.2 ± 5.7)% in 'IFN-γ + IL-22 + STAT3 inhibitor' group, all P<0.05 as compared with those of 'IFN-γ + IL-22' group]. CONCLUSION: The IL-22-STAT3 signal pathway could revert IFN-γ-induced EMT of PMCs, and might play a protective role in anti-pleural fibrosis in tuberculous pleurisy.

Progress in the research of immunotherapy­related hyperprogression (Review).

Immunotherapy has become an effective method for the treatment of a variety of malignant tumors. However, with the development of immunotherapy, the phenomenon of hyperprogression in patients with cancer has gradually attracted attention. Hyperprogression refers to a condition in which the progression of a disease during treatment of a patient with cancer is suddenly accelerated. To date, no reliable marker has been found to predict accelerated tumor growth during immune checkpoint inhibitor (ICI) treatment. The aim the present study was to summarize the definition of hyperprogression and the difference between hyperprogression and pseudocytosis, and investigate the potential mechanisms of hyperprogression including clinical characteristics, potential molecular markers and the immune microenvironment. The effect of macrophage-related different types and factors on tumors in the immune microenvironment was analyzed, and the findings may be used to determine the future directions of research in hyperprogression.

Novel indocyanine green-loaded photothermal nanoparticles targeting TRPV1 for thermal ablation treatment of severe murine asthma induced by ovalbumin and lipopolysaccharide.

To identify a replacement strategy for bronchial thermoplasty (BT) with non-invasive and free-of-severe side effect is urgently needed in the clinic for severe asthma treatment. In this study, PLGA-PEG@ICG@TRPV1 pAb (PIT) photothermal nanoparticles targeting bronchial TRPV1 were designed for photothermal therapy (PTT) against severe murine asthma induced by ovalbumin and lipopolysaccharide. PIT was formulated with a polyethylene glycol (PEG)-grafted poly (lactic-co-glycolic) acid (PLGA) coating as a skeleton structure to encapsulate indocyanine green (ICG) and was conjugated to the polyclonal antibody against transient receptor potential vanilloid 1 (TRPV1 pAb). The results revealed that PIT held good druggability due to its electronegativity and small diameter. PIT demonstrated great photothermal effects both in vivo and in vitro and exhibited good ability to target TRPV1 in vitro because of its selective cell uptake and specific cell toxicity toward TRPV1-overexpressing cells. The PIT treatment effectively reduced asthma symptoms in mice. This is evident from improvements in expiratory airflow limitation, significant decreases in inflammatory cell infiltration in the airways, and increases in goblet cell and columnar epithelial cell proliferation. In conclusion, PIT alleviates severe murine asthma symptoms through a combination of TRPV1 targeting and photothermal effects.

Supplementation of diets with bovine colostrum influences immune function in dogs.

While the need for colostrum in neonates is well established, the systemic effect of feeding bovine colostrum (BC) to adult humans is gaining increasing attention. However, no systematic studies evaluating the immunomodulatory effect of BC in dogs have been reported. The aim of the present study was to evaluate the immunomodulatory effect of dietary supplementation of BC in dogs. The study was conducted in two phases: pre-test (8 weeks) and test (40 weeks), with twenty-four dogs (mean age 2.5 years) randomised into two groups. In the 'pre-test' phase, both groups were fed a nutritionally complete diet. At the end of the 'pre-test' phase, all dogs received a canine distemper virus (CDV) vaccine, and dogs in the 'test group' were switched to a diet supplemented with 0.1% spray-dried BC. Response to the CDV vaccine was evaluated by measuring vaccine-specific plasma IgG levels. Gut-associated lymphoid tissue response was assessed by measuring faecal IgA levels. Gut microbiota were evaluated by the temporal temperature gel electrophoresis methodology. Dogs fed the BC-supplemented diet demonstrated a significantly higher vaccine response and higher levels of faecal IgA when compared with the control group. Supplementing diets with BC also resulted in significantly increased gut microbiota diversity and stability in the test group. In conclusion, diets supplemented with BC significantly influence immune response in dogs.

Interrelationship of circulating matrix metalloproteinase-9, TNF-α, and OPG/RANK/RANKL systems in COPD patients with osteoporosis.

Previous studies have shown that matrix metalloproteinase-9 (MMP-9) and its cognate inhibitor TIMP-1, inflammatory cytokine TNF-α, and the OPG/RANK/RANKL system may each play individual roles in the pathogenesis of osteoporosis in patients with COPD. In the present study, we investigated the interrelationships of these factors in male COPD patients with and without osteoporosis. The serum levels of MMP-9, MMP-9/TIMP-1 ratio, TNF-α, RANKL, OPG, and the RANKL/OPG ratio were higher in COPD patients with osteoporosis than in individuals with normal or low bone mineral density (BMD) (N = 30, all P < 0.05 or < 0.01). The lung function FEV1%Pre and the BMD of the lumbar spine and femoral neck were found to be negatively correlated with MMP-9 serum level (r = -0.36, P < 0.05, r = -0.58, P < 0.001, and r = -0.62, P < 0.01, respectively), RANKL serum level (r = -0.21, P < 0.05, and r = -0.25, P < 0.05, and r = -0.26, P < 0.05, respectively), and RANKL/OPG ratio (r = -0.23, P < 0.05, r = -0.33, P < 0.05, and r = -0.38, P < 0.05, respectively). However, they had no correlation with TIMP-1, TNF-α, OPG, or RANK. The MMP-9 serum level was found to be positively correlated with TNF-α level (r = 0.35, P < 0.05) and RANKL/OPG ratio (r = 0.27, P < 0.05) but not associated with RANKL. These results suggest that MMP-9, TNF-α, and the OPG/RANK/RANKL system may be closely interrelated and may play interactive roles in pathogenesis of osteoporosis in COPD.

Discovery of a novel circulation route of free-living Serratiasymbiotica mediated by predatory ladybird beetles.

Horizontal transmission of bacteria to varied hosts can maintain and even expand microbial niches. We previously found that the aphid gut bacterium Serratia symbiotica strain SsMj can be transmitted to ladybird beetles via predation, but whether the predator is a new host, a reservoir or a dead end of this bacterium is unknown. This study aims to provide a clear picture of SsMj circulation from aphids to plants and predators. We first found that SsMj in aphids and ladybirds was abundantly distributed not only in digestive tracts but also in droppings. We found no evidence for vertical transmission of SsMj to aphid offspring. Instead, we showed that it could be transmitted to conspecific aphids by sharing the same plant or contacting honeydews. The key finding of this study is that SsMj was transmitted from aphids to ladybirds through predation, while ladybirds could also transfer SsMj back to aphids, possibly through feces. Together, this evidence suggests that SsMj is able to survive in the digestive tracts and droppings of insects and to expand its host range with plants and predators as reservoirs.

Artificial Intelligence-Assisted Left Ventricular Diastolic Function Assessment and Grading: Multiview Versus Single View.

BACKGROUND: Clinical assessment and grading of left ventricular diastolic function (LVDF) requires quantification of multiple echocardiographic parameters interpreted according to established guidelines, which depends on experienced clinicians and is time consuming. The aim of this study was to develop an artificial intelligence (AI)-assisted system to facilitate the clinical assessment of LVDF. METHODS: In total, 1,304 studies (33,404 images) were used to develop a view classification model to select six specific views required for LVDF assessment. A total of 2,238 studies (16,794 two-dimensional [2D] images and 2,198 Doppler images) to develop 2D and Doppler segmentation models, respectively, to quantify key metrics of diastolic function. We used 2,150 studies with definite LVDF labels determined by two experts to train single-view classification models by AI interpretation of strain metrics or video. The accuracy and efficiency of these models were tested in an external data set of 388 prospective studies. RESULTS: The view classification model identified views required for LVDF assessment with good sensitivity (>0.9), and view segmentation models successfully outlined key regions of these views with intersection over union > 0.8 in the internal validation data set. In the external test data set of 388 cases, AI quantification of 2D and Doppler images showed narrow limits of agreement compared with the two experts (e.g., left ventricular ejection fraction, -12.02% to 9.17%; E/e' ratio, -3.04 to 2.67). These metrics were used to detect LV diastolic dysfunction (DD) and grade DD with accuracy of 0.9 and 0.92, respectively. Concerning the single-view method, the overall accuracy of DD detection was 0.83 and 0.75 by strain-based and video-based models, and the accuracy of DD grading was 0.85 and 0.8, respectively. These models could achieve diagnosis and grading of LVDD in a few seconds, greatly saving time and labor. CONCLUSION: AI models successfully achieved LVDF assessment and grading that compared favorably with human experts reading according to guideline-based algorithms. Moreover, when Doppler variables were missing, AI models could provide assessment by interpreting 2D strain metrics or videos from a single view. These models have the potential to save labor and cost and to facilitate work flow of clinical LVDF assessment.

Echocardiography-based AI for detection and quantification of atrial septal defect.

Objectives: We developed and tested a deep learning (DL) framework applicable to color Doppler echocardiography for automatic detection and quantification of atrial septal defects (ASDs). Background: Color Doppler echocardiography is the most commonly used non-invasive imaging tool for detection of ASDs. While prior studies have used DL to detect the presence of ASDs from standard 2D echocardiographic views, no study has yet reported automatic interpretation of color Doppler videos for detection and quantification of ASD. Methods: A total of 821 examinations from two tertiary care hospitals were collected as the training and external testing dataset. We developed DL models to automatically process color Doppler echocardiograms, including view selection, ASD detection and identification of the endpoints of the atrial septum and of the defect to quantify the size of defect and the residual rim. Results: The view selection model achieved an average accuracy of 99% in identifying four standard views required for evaluating ASD. In the external testing dataset, the ASD detection model achieved an area under the curve (AUC) of 0.92 with 88% sensitivity and 89% specificity. The final model automatically measured the size of defect and residual rim, with the mean biases of 1.9 mm and 2.2 mm, respectively. Conclusion: We demonstrated the feasibility of using a deep learning model for automated detection and quantification of ASD from color Doppler echocardiography. This model has the potential to improve the accuracy and efficiency of using color Doppler in clinical practice for screening and quantification of ASDs, that are required for clinical decision making.

A background parenchymal enhancement quantification framework of breast magnetic resonance imaging.

Background: Background parenchymal enhancement (BPE) is defined as the enhanced proportion of normal fibroglandular tissue on enhanced magnetic resonance imaging. BPE shows promise as a quantitative imaging biomarker (QIB). However, the lack of consensus among radiologists in their semi-quantitative grading of BPE limits its clinical utility. Methods: The main objective of this study was to develop a BPE quantification model according to clinical expertise, with the BPE integral being used as a QIB to incorporate both the volume and intensity of the enhancement metrics. The model was applied to 2,786 cases to compare our quantitative results with radiologists' semi-quantitative BPE grading to evaluate the effectiveness of using the BPE integral as a QIB for analyzing BPE. Comparisons between multiple groups of nonnormally distributed BPE integrals were performed using the Kruskal-Wallis test. Results: Our study found a considerable degree of concordance between our BPE quantitative integral and radiologists' semi-quantitative assessments. Specifically, our research results revealed significant variability in BPE integral attained through the BPE quantification framework among all semi-quantitative BPE grading groups labeled by experienced radiologists, including mild-moderate (P<0.001), mild-marked (P<0.001), and moderate-marked (P<0.001). Furthermore, there was an apparent correlation between BPE integral and BPE grades, with marked BPE displaying the highest BPE integral, followed by moderate BPE, with mild BPE exhibiting the lowest BPE integral value. Conclusions: The study developed and implemented a BPE quantification framework, which incorporated both the volume and intensity of enhancement and which could serve as a QIB for BPE.

Clinical diagnosis and etiology of patients with Chlamydia psittaci pneumonia based on metagenomic next-generation sequencing.

The incidence of severe Chlamydia psittaci (C. psittaci) pneumonia and coinfections is increasing. Early detection of this condition is needed to prevent negative outcomes, along with detailed descriptions of its associated clinical characteristics. Our study contributes by undertaking etiological analysis of patients with C. psittaci pneumonia based on metagenomic next-generation sequencing (mNGS). A retrospective analysis of 30 patients with C. psittaci pneumonia was undertaken and confirmed by mNGS or polymerase chain reaction (PCR). Clinical manifestations of the severe and non-severe C. psittaci pneumonia groups were compared for clinical reference. Etiological analyses were also performed to comprehensively understand pathogeny and coinfection with other respiratory pathogens in C. psittaci patients. The absolute value of lymphocytes (LYM) in the severe group was lower than in the non-severe group. At the same time, neutrophil-to-lymphocyte ratio (NLR), procalcitonin (PCT), alanine aminotransferase (ALT), D-II polymer, brain natriuretic peptide (BNP), myoglobin (MYO), and cardiac troponin I (cTnI) were significantly higher (P < 0.05) in the severe group. mNGS has a broader pathogen spectrum and can more sensitively detect C. psittaci and other low-abundance pathogens with a higher positive detection rate (100%, 13/13 vs. 46%, 6/13, P <0.05) than conventional culture methods. mNGS detected the following dominant species associated with C. psittaci in patients: bacteria (53.2%, 39% gram-positive, 61% gram-negative), fungi (12.9%), and viruses (33.9%). A total of 73.3% (11/15) of patients had suspected coinfections, with a coinfection rate of 91.7% (11/12) in the severe group. No coinfection or death occurred in the non-severe group. Prognosis in the severe group was poor, with a mortality rate of 27.3% (3/11) for patients with coinfection. Eight of 11 patients with coinfections (72.7%) recovered. In conclusion, the clinical symptoms of severe C. psittaci pneumonia manifested as abnormal inflammatory indicators, impaired liver function, myocardial injury, coagulation, and relatively low immune responses. The higher proportion of patients with coinfections in our study supports the use of mNGS for comprehensive early detection of respiratory infections in patients with C. psittaci pneumonia. Simultaneous early identification of coinfections would further improve the clinical treatment of these patients.

Cardiac Adaptation to Prolonged High Altitude Migration Assessed by Speckle Tracking Echocardiography.

Objective: Exposure to high altitudes represents physiological stress that leads to significant changes in cardiovascular properties. However, long-term cardiovascular adaptions to high altitude migration of lowlanders have not been described. Accordingly, we measured changes in cardiovascular properties following prolonged hypoxic exposure in acclimatized Han migrants and Tibetans. Methods: Echocardiographic features of recently adapted Han migrant (3-12 months, n = 64) and highly adapted Han migrant (5-10 years, n = 71) residence in Tibet (4,300 m) using speckle tracking echocardiography were compared to those of age-matched native Tibetans (n = 75) and Han lowlanders living at 1,400 m (n = 60). Results: Short-term acclimatized migrants showed increased estimated pulmonary artery systolic pressure (PASP) (32.6 ± 5.1 mmHg vs. 21.1 ± 4.2 mmHg, p < 0.05), enlarged right ventricles (RVs), and decreased fractional area change (FAC) with decreased RV longitudinal strain (-20 ± 2.8% vs. -25.5 ± 3.9%, p < 0.05). While left ventricular ejection fraction (LVEF) was preserved, LV diameter (41.7 ± 3.1 mm vs. 49.7 ± 4.8 mm, p < 0.05) and LV longitudinal strain (-18.8 ± 3.2% vs. -22.9 ± 3.3%, p < 0.05) decreased. Compared with recent migrants, longer-term migrants had recovered RV structure and functions with slightly improved RV and LV longitudinal strain, though still lower than lowlander controls; LV size remained small with increased mass index (68.3 ± 12.7 vs. 59.3 ± 9.6, p < 0.05). In contrast, native Tibetans had slightly increased PASP (26.1 ± 3.4 mmHg vs. 21.1 ± 4.2 mmHg, p < 0.05) with minimally altered cardiac deformation compared to lowlanders. Conclusion: Right ventricular systolic function is impaired in recent (<1 year) migrants to high altitudes but improved during the long-term dwelling. LV remodeling persists in long-term migrants (>5 years) but without impairment of LV systolic or diastolic function. In contrast, cardiac size, structure, and function of native Tibetans are more similar to those of lowland dwelling Hans.

Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review.

Background: Coronary artery disease (CAD) is a progressive disease of the blood vessels supplying the heart, which leads to coronary artery stenosis or obstruction and is life-threatening. Early diagnosis of CAD is essential for timely intervention. Imaging tests are widely used in diagnosing CAD, and artificial intelligence (AI) technology is used to shed light on the development of new imaging diagnostic markers. Objective: We aim to investigate and summarize how AI algorithms are used in the development of diagnostic models of CAD with imaging markers. Methods: This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guideline. Eligible articles were searched in PubMed and Embase. Based on the predefined included criteria, articles on coronary heart disease were selected for this scoping review. Data extraction was independently conducted by two reviewers, and a narrative synthesis approach was used in the analysis. Results: A total of 46 articles were included in the scoping review. The most common types of imaging methods complemented by AI included single-photon emission computed tomography (15/46, 32.6%) and coronary computed tomography angiography (15/46, 32.6%). Deep learning (DL) (41/46, 89.2%) algorithms were used more often than machine learning algorithms (5/46, 10.8%). The models yielded good model performance in terms of accuracy, sensitivity, specificity, and AUC. However, most of the primary studies used a relatively small sample (n < 500) in model development, and only few studies (4/46, 8.7%) carried out external validation of the AI model. Conclusion: As non-invasive diagnostic methods, imaging markers integrated with AI have exhibited considerable potential in the diagnosis of CAD. External validation of model performance and evaluation of clinical use aid in the confirmation of the added value of markers in practice. Systematic review registration: [https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022306638], identifier [CRD42022306638].

Elevated Fasting Blood Glucose Levels Are Associated with Worse Clinical Outcomes in COVID-19 Patients Than in Pneumonia Patients with Bacterial Infections.

Aims: We investigate how fasting blood glucose (FBG) levels affect the clinical severity in coronavirus disease 2019 (COVID-19) patients, pneumonia patients with sole bacterial infection, and pneumonia patients with concurrent bacterial and fungal infections. Methods: We enrolled 2761 COVID-19 patients, 1686 pneumonia patients with bacterial infections, and 2035 pneumonia patients with concurrent infections. We used multivariate logistic regression analysis to assess the associations between FBG levels and clinical severity. Results: FBG levels in COVID-19 patients were significantly higher than in other pneumonia patients during hospitalisation and at discharge (all p < 0.05). Among COVID-19 patients, the odds ratios of acute respiratory distress syndrome (ARDS), respiratory failure (RF), acute hepatitis/liver failure (AH/LF), length of stay, and intensive care unit (ICU) admission were 12.80 (95% CI, 4.80−37.96), 5.72 (2.95−11.06), 2.60 (1.20−5.32), 1.42 (1.26−1.59), and 5.16 (3.26−8.17) times higher in the FBG ≥7.0 mmol/L group than in FBG < 6.1 mmol/L group, respectively. The odds ratios of RF, AH/LF, length of stay, and ICU admission were increased to a lesser extent in pneumonia patients with sole bacterial infection (3.70 [2.21−6.29]; 1.56 [1.17−2.07]; 0.98 [0.88−1.11]; 2.06 [1.26−3.36], respectively). The odds ratios of ARDS, RF, AH/LF, length of stay, and ICU admission were increased to a lesser extent in pneumonia patients with concurrent infections (3.04 [0.36−6.41]; 2.31 [1.76−3.05]; 1.21 [0.97−1.52]; 1.02 [0.93−1.13]; 1.72 [1.19−2.50], respectively). Among COVID-19 patients, the incidence rate of ICU admission on day 21 in the FBG ≥ 7.0 mmol/L group was six times higher than in the FBG < 6.1 mmol/L group (12.30% vs. 2.21%, p < 0.001). Among other pneumonia patients, the incidence rate of ICU admission on day 21 was only two times higher. Conclusions: Elevated FBG levels at admission predict subsequent clinical severity in all pneumonia patients regardless of the underlying pathogens, but COVID-19 patients are more sensitive to FBG levels, and suffer more severe clinical complications than other pneumonia patients.

Echocardiography-based AI detection of regional wall motion abnormalities and quantification of cardiac function in myocardial infarction.

Objective: To compare the performance of a newly developed deep learning (DL) framework for automatic detection of regional wall motion abnormalities (RWMAs) for patients presenting with the suspicion of myocardial infarction from echocardiograms obtained with portable bedside equipment versus standard equipment. Background: Bedside echocardiography is increasingly used by emergency department setting for rapid triage of patients presenting with chest pain. However, compared to images obtained with standard equipment, lower image quality from bedside equipment can lead to improper diagnosis. To overcome these limitations, we developed an automatic workflow to process echocardiograms, including view selection, segmentation, detection of RWMAs and quantification of cardiac function that was trained and validated on image obtained from bedside and standard equipment. Methods: We collected 4,142 examinations from one hospital as training and internal testing dataset and 2,811 examinations from other hospital as the external test dataset. For data pre-processing, we adopted DL model to automatically recognize three apical views and segment the left ventricle. Detection of RWMAs was achieved with 3D convolutional neural networks (CNN). Finally, DL model automatically measured the size of cardiac chambers and left ventricular ejection fraction. Results: The view selection model identified the three apical views with an average accuracy of 96%. The segmentation model provided good agreement with manual segmentation, achieving an average Dice of 0.89. In the internal test dataset, the model detected RWMAs with AUC of 0.91 and 0.88 respectively for standard and bedside ultrasound. In the external test dataset, the AUC were 0.90 and 0.85. The automatic cardiac function measurements agreed with echocardiographic report values (e. g., mean bias is 4% for left ventricular ejection fraction). Conclusion: We present a fully automated echocardiography pipeline applicable to both standard and bedside ultrasound with various functions, including view selection, quality control, segmentation, detection of the region of wall motion abnormalities and quantification of cardiac function.

A Deep Learning Model System for Diagnosis and Management of Adnexal Masses.

Appropriate clinical management of adnexal masses requires a detailed diagnosis. We retrospectively collected ultrasound images of 1559 cases from the first Center of Chinese PLA General Hospital and developed a fully automatic deep learning (DL) model system to diagnose adnexal masses. The DL system contained five models: a detector, a mass segmentor, a papillary segmentor, a type classifier, and a pathological subtype classifier. To test the DL system, 462 cases from another two hospitals were recruited. The DL system identified benign, borderline, and malignant tumors with macro-F1 scores that varied from 0.684 to 0.791, a benefit to preventing both delayed and overextensive treatment. The macro-F1 scores of the pathological subtype classifier to categorize the benign masses varied from 0.714 to 0.831. The detailed classification can inform clinicians of the corresponding complications of each pathological subtype of benign tumors. The distinguishment between borderline and malignant tumors and inflammation from other subtypes of benign tumors need further study. The accuracy and sensitivity of the DL system were comparable to that of the expert and intermediate sonographers and exceeded that of the junior sonographer.

Automated Analysis of Doppler Echocardiographic Videos as a Screening Tool for Valvular Heart Diseases.

OBJECTIVES: This study sought to develop a deep learning (DL) framework to automatically analyze echocardiographic videos for the presence of valvular heart diseases (VHDs). BACKGROUND: Although advances in DL have been applied to the interpretation of echocardiograms, such techniques have not been reported for interpretation of color Doppler videos for diagnosing VHDs. METHODS: The authors developed a 3-stage DL framework for automatic screening of echocardiographic videos for mitral stenosis (MS), mitral regurgitation (MR), aortic stenosis (AS), and aortic regurgitation (AR) that classifies echocardiographic views, detects the presence of VHDs, and, when present, quantifies key metrics related to VHD severities. The algorithm was trained (n = 1,335), validated (n = 311), and tested (n = 434) using retrospectively selected studies from 5 hospitals. A prospectively collected set of 1,374 consecutive echocardiograms served as a real-world test data set. RESULTS: Disease classification accuracy was high, with areas under the curve of 0.99 (95% CI: 0.97-0.99) for MS; 0.88 (95% CI: 0.86-0.90) for MR; 0.97 (95% CI: 0.95-0.99) for AS; and 0.90 (95% CI: 0.88-0.92) for AR in the prospective test data set. The limits of agreement (LOA) between the DL algorithm and physician estimates of metrics of valve lesion severities compared to the LOAs between 2 experienced physicians spanned from -0.60 to 0.77 cm2 vs -0.48 to 0.44 cm2 for MV area; from -0.27 to 0.25 vs -0.23 to 0.08 for MR jet area/left atrial area; from -0.86 to 0.52 m/s vs -0.48 to 0.54 m/s for peak aortic valve blood flow velocity (Vmax); from -10.6 to 9.5 mm Hg vs -10.2 to 4.9 mm Hg for average peak aortic valve gradient; and from -0.39 to 0.32 vs -0.31 to 0.32 for AR jet width/left ventricular outflow tract diameter. CONCLUSIONS: The proposed deep learning algorithm has the potential to automate and increase efficiency of the clinical workflow for screening echocardiographic images for the presence of VHDs and for quantifying metrics of disease severity.

Deep neural network-based detection and segmentation of intracranial aneurysms on 3D rotational DSA.

OBJECTIVE: Accurate diagnosis and measurement of intracranial aneurysms are challenging. This study aimed to develop a 3D convolutional neural network (CNN) model to detect and segment intracranial aneurysms (IA) on 3D rotational DSA (3D-RA) images. METHODS: 3D-RA images were collected and annotated by 5 neuroradiologists. The annotated images were then divided into three datasets: training, validation, and test. A 3D Dense-UNet-like CNN (3D-Dense-UNet) segmentation algorithm was constructed and trained using the training dataset. Diagnostic performance to detect aneurysms and segmentation accuracy was assessed for the final model on the test dataset using the free-response receiver operating characteristic (FROC). Finally, the CNN-inferred maximum diameter was compared against expert measurements by Pearson's correlation and Bland-Altman limits of agreement (LOA). RESULTS: A total of 451 patients with 3D-RA images were split into n = 347/41/63 training/validation/test datasets, respectively. For aneurysm detection, observed FROC analysis showed that the model managed to attain a sensitivity of 0.710 at 0.159 false positives (FP)/case, and 0.986 at 1.49 FP/case. The proposed method had good agreement with reference manual aneurysmal maximum diameter measurements (8.3 ± 4.3 mm vs. 7.8 ± 4.8 mm), with a correlation coefficient r = 0.77, small bias of 0.24 mm, and LOA of -6.2 to 5.71 mm. 37.0% and 77% of diameter measurements were within ±1 mm and ±2.5 mm of expert measurements. CONCLUSIONS: A 3D-Dense-UNet model can detect and segment aneurysms with relatively high accuracy using 3D-RA images. The automatically measured maximum diameter has potential clinical application value.

Applicability of Transthoracic Contrast Echocardiography for the Diagnosis and Treatment of Idiopathic Pulmonary Arteriovenous Malformations.

Objective: To explore the preferred test to screen for pulmonary arteriovenous malformations (PAVMs) and to predict the probability of interventional embolization. Methods: We performed a retrospective observational study evaluating patients with idiopathic PAVMs from 2009 to 2019. After clinical evaluation, a total of 105 patients were studied, including 71 patients with positive digital subtraction pulmonary angiography (DSPA) findings and 34 with negative DSPA findings. The following patient data were assessed: blood test, chest radiograph, transthoracic contrast echocardiography (TTCE), and DSPA findings. Results: The majority of patients with idiopathic PAVMs were female (66.2% with positive DSPA findings). We found a good κ-coefficient of 0.77 with strong consistency for inter observer agreement concerning the pulmonary right-to-left shunt (RLS) grade on TTCE, which was superior to conventional chest radiographs. The positive predictive value (PPV) of the radiographic features for PAVMs on DSPA was 0.83 (95% CI 0.64-1.0) and 0.44 for the possibility of embolization (95% CI 0.19-0.70). The PPV of the shunt grade of PAVMs on DSPA was 0.14 (95% CI 0.01-0.29) for grade 1, 0.74 (95% CI 0.60-0.88) for grade 2, and 0.97 (95% CI 0.92-1.0) for grade 3. The PPVs of pulmonary shunt grades 2 and 3 on TTCE for the possibility of embolization for PAVMs were 0.21 (95% CI, 0.05-0.36) and 0.87 (95% CI, 0.79-0.99), respectively. Conclusion: TTCE is the preferred screening test for PAVMs. The pulmonary RLS grade on TTCE not only identifies the likelihood of PAVMs but also predicts the probability for embolization.