Advancing presurgical non-invasive molecular subgroup prediction in medulloblastoma using artificial intelligence and MRI signatures.

Global investigation of medulloblastoma has been hindered by the widespread inaccessibility of molecular subgroup testing and paucity of data. To bridge this gap, we established an international molecularly characterized database encompassing 934 medulloblastoma patients from thirteen centers across China and the United States. We demonstrate how image-based machine learning strategies have the potential to create an alternative pathway for non-invasive, presurgical, and low-cost molecular subgroup prediction in the clinical management of medulloblastoma. Our robust validation strategies-including cross-validation, external validation, and consecutive validation-demonstrate the model's efficacy as a generalizable molecular diagnosis classifier. The detailed analysis of MRI characteristics replenishes the understanding of medulloblastoma through a nuanced radiographic lens. Additionally, comparisons between East Asia and North America subsets highlight critical management implications. We made this comprehensive dataset, which includes MRI signatures, clinicopathological features, treatment variables, and survival data, publicly available to advance global medulloblastoma research.

Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging.

Cardiac magnetic resonance imaging (CMR) is the gold standard for cardiac function assessment and plays a crucial role in diagnosing cardiovascular disease (CVD). However, its widespread application has been limited by the heavy resource burden of CMR interpretation. Here, to address this challenge, we developed and validated computerized CMR interpretation for screening and diagnosis of 11 types of CVD in 9,719 patients. We propose a two-stage paradigm consisting of noninvasive cine-based CVD screening followed by cine and late gadolinium enhancement-based diagnosis. The screening and diagnostic models achieved high performance (area under the curve of 0.988 ± 0.3% and 0.991 ± 0.0%, respectively) in both internal and external datasets. Furthermore, the diagnostic model outperformed cardiologists in diagnosing pulmonary arterial hypertension, demonstrating the ability of artificial intelligence-enabled CMR to detect previously unidentified CMR features. This proof-of-concept study holds the potential to substantially advance the efficiency and scalability of CMR interpretation, thereby improving CVD screening and diagnosis.

The role of magnesium in cardiac arrest.

Cardiac arrest is a leading cause of death globally. Only 25.8% of in-hospital and 33.5% of out-of-hospital individuals who achieve spontaneous circulation following cardiac arrest survive to leave the hospital. Respiratory failure and acute coronary syndrome are the two most common etiologies of cardiac arrest. Effort has been made to improve the outcomes of individuals resuscitated from cardiac arrest. Magnesium is an ion that is critical to the function of all cells and organs. It is often overlooked in everyday clinical practice. At present, there have only been a small number of reviews discussing the role of magnesium in cardiac arrest. In this review, for the first time, we provide a comprehensive overview of magnesium research in cardiac arrest focusing on the effects of magnesium on the occurrence and prognosis of cardiac arrest, as well as in the two main diseases causing cardiac arrest, respiratory failure and acute coronary syndrome. The current findings support the view that magnesium disorder is associated with increased risk of cardiac arrest as well as respiratory failure and acute coronary syndrome.

Identifying low-dimensional trajectories of mechanically-ventilated patient systems: Empirical phenotypes of joint patient+care processes to enhance temporal analysis in ARDS research.

Refined management of mechanically ventilation is an obvious target for improving patient outcomes, but is impeded by the nature of data for study and hypothesis generation. The connections between clinical outcomes and temporal development of iatrogenic injuries current lung-protective ventilator settings remain poorly understood. Analysis of lung-ventilator system (LVS) evolution at relevant timescales is frustrated by data volume and multiple sources of heterogeneity. This work motivates, presents, and validates a computational pipeline for resolving LVS systems into the joint evolution of data-conditioned model parameters and ventilator information. Applied to individuals, the workflow yields a concise low-dimensional representation of LVS behavior expressed in phenotypic breath waveforms suitable for analysis. The effectiveness of this approach is demonstrated through application to multi-day observational series of 35 patients. Individual patient analyses reveal multiple types of patient-oriented dynamics and breath behavior to expose the complexity of LVS evolution; less than 10% of phenotype changes related to ventilator settings changes. Dynamics are shown to including both stable and unstable phenotype transitions as well as both discrete and continuous changes unrelated to ventilator settings. At a cohort scale, 721 phenotypes constructed from individual data are condensed into a set of 16 groups that empirically organize around certain settings (positive end-expository pressure and ventilator mode) and structurally similar pressure-volume loop characterizations. Individual and cohort scale phenotypes, which may be refined by hypothesis-specific constructions, provide a common framework for ongoing temporal analysis and investigation of LVS dynamics.

Turn-On Protein Switches for Controlling Actin Binding in Cells.

Within a shared cytoplasm, filamentous actin (F-actin) plays numerous and critical roles across the cell body. Cells rely on actin-binding proteins (ABPs) to organize F-actin and to integrate its polymeric characteristics into diverse cellular processes. Yet, the multitude of ABPs that engage with and shape F-actin make studying a single ABP's influence on cellular activities a significant challenge. Moreover, without a means of manipulating actin-binding subcellularly, harnessing the F-actin cytoskeleton for synthetic biology purposes remains elusive. Here, we describe a suite of designed proteins, Controllable Actin-binding Switch Tools (CASTs), whose actin-binding behavior can be controlled with external stimuli. CASTs were developed that respond to different external inputs, providing options for turn-on kinetics and enabling orthogonality. Being genetically encoded, we show that CASTs can be inserted into native protein sequences to control F-actin association locally and engineered into new structures to control cell and tissue shape and behavior.

A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation.

OBJECTIVE: Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR). METHODS: A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation. RESULTS: The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83%±27%. CONCLUSION: The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.

A Novel Strategy for the Synthesis of High Stability of Luminescent Zero Dimensional-Two Dimensional CsPbBr3 Quantum Dot/1,4-bis(4-methylstyryl)benzene Nanoplate Heterostructures at an Atmospheric Condition.

Perovskite quantum dots (QDs), emerging with excellent bright-green photoluminescence (PL) and a large absorption coefficient, are of great potential for the fabrication of light sources in underwater optical wireless communication systems. However, the instability caused by low formation energy and abundant surface traps is still a major concern for perovskite-based light sources in underwater conditions. Herein, we propose ultra-stable zero dimensional-two dimensional (0D-2D) CsPbBr3 QD/1,4-bis(4-methylstyryl)benzene (p-MSB) nanoplate (NP) heterostructures synthesized via a facile approach at room temperature in air. CsPbBr3 QDs can naturally nucleate on the p-MSB NP toluene solution, and the radiative combination is drastically intensified owing to the electron transfer within the typical type-II heterostructures, leading to a sharply increased PLQY of the heterostructure thin films up to 200% compared with the pristine sample. The passivation of defects within CsPbBr3 QDs can be effectively realized with the existence of p-MSB NPs, and thus the obviously improved PL is steadily witnessed in an ambient atmosphere and thermal environment. Meanwhile, the enhanced humidity stability and a peak EQE of 9.67% suggests a synergetic strategy for concurrently addressing the knotty problems on unsatisfied luminous efficiency and stability of perovskites for high-performance green-emitting optoelectronic devices in underwater applications.

Deciphering the Functional Long Non-Coding RNAs Derived from MicroRNA Loci.

Albeit the majority of eukaryotic genomes can be pervasively transcribed to a diverse population of lncRNAs and various subtypes of lncRNA are discovered. However, the genome-wide study of miRNA-derived lncRNAs is still lacking. Here, it is reported that over 800 miRNA gene-originated lncRNAs (molncRNAs) are generated from miRNA loci. One of them, molnc-301b from miR-301b and miR-130b, functions as an "RNA decoy" to facilitate dissociation of the chromatin remodeling protein SMARCA5 from chromatin and thereby sequester transcription and mRNA translation. Specifically, molnc-301b attenuates erythropoiesis by mitigating the transcription of erythropoietic and translation-associated genes, such as GATA1 and FOS. In addition, a useful and powerful CRISPR screen platform to characterize the biological functions of molncRNAs at large-scale and single-cell levels is established and 29 functional molncRNAs in hematopoietic cells are identified. Collectively, the focus is on miRNA-derived lncRNAs, deciphering their landscape during normal hematopoiesis, and comprehensively evaluating their potential roles.

A methodology of phenotyping ICU patients from EHR data: high-fidelity, personalized, and interpretable phenotypes estimation.

Objective: Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR). Methods: A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation. Results: The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83% ± 27%. Conclusion: The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.

AI Transformers for Radiation Dose Reduction in Serial Whole-Body PET Scans.

Purpose: To develop a deep learning approach that enables ultra-low-dose, 1% of the standard clinical dosage (3 MBq/kg), ultrafast whole-body PET reconstruction in cancer imaging. Materials and Methods: In this Health Insurance Portability and Accountability Act-compliant study, serial fluorine 18-labeled fluorodeoxyglucose PET/MRI scans of pediatric patients with lymphoma were retrospectively collected from two cross-continental medical centers between July 2015 and March 2020. Global similarity between baseline and follow-up scans was used to develop Masked-LMCTrans, a longitudinal multimodality coattentional convolutional neural network (CNN) transformer that provides interaction and joint reasoning between serial PET/MRI scans from the same patient. Image quality of the reconstructed ultra-low-dose PET was evaluated in comparison with a simulated standard 1% PET image. The performance of Masked-LMCTrans was compared with that of CNNs with pure convolution operations (classic U-Net family), and the effect of different CNN encoders on feature representation was assessed. Statistical differences in the structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and visual information fidelity (VIF) were assessed by two-sample testing with the Wilcoxon signed rank t test. Results: The study included 21 patients (mean age, 15 years ± 7 [SD]; 12 female) in the primary cohort and 10 patients (mean age, 13 years ± 4; six female) in the external test cohort. Masked-LMCTrans-reconstructed follow-up PET images demonstrated significantly less noise and more detailed structure compared with simulated 1% extremely ultra-low-dose PET images. SSIM, PSNR, and VIF were significantly higher for Masked-LMCTrans-reconstructed PET (P < .001), with improvements of 15.8%, 23.4%, and 186%, respectively. Conclusion: Masked-LMCTrans achieved high image quality reconstruction of 1% low-dose whole-body PET images.Keywords: Pediatrics, PET, Convolutional Neural Network (CNN), Dose Reduction Supplemental material is available for this article. © RSNA, 2023.

Myricetin improves pathological changes in 3×Tg-AD mice by regulating the mitochondria-NLRP3 inflammasome-microglia channel by targeting P38 MAPK signaling pathway.

BACKGROUND: Alzheimer's disease (AD) represents the common neurodegenerative disease featured by the manifestations of cognitive impairment and memory loss. AD could be alleviated with medication and improving quality of life. Clinical treatment of AD is mainly aimed at improving the cognitive function of patients. Donepezil, memantine and galantamine are commonly used drug. But they could only relieve AD, not cure it. Therefore, new treatment strategies focusing on AD pathogenesis are of great significance and value. Myricetin (Myr) is a natural flavonoid extracted from Myrica rubra. And it shows different bioactivities, such as anti-inflammation, antioxidation as well as central nervous system (CNS) activities. Nonetheless, its associated mechanism in treating AD remains unknown. PURPOSE: Here we focused on investigating Myr's effect on treating AD and exploring if its protection on the nervous system activity was associated with specifically inhibiting P38 MAPK signaling pathway while regulating mitochondria-NLRP3 inflammasome-microglia. STUDY DESIGN AND METHODS: This work utilized triple transgenic mice (3 × Tg-AD) as AD models and Aß25-35 was used to induce BV2 cells to build an in vitro AD model. Behavioristics, pathology and related inflammatory factors were examined. Molecular mechanisms are investigated by western-blot, immunofluorescence staining, CETSA, molecular docking, network pharmacology. RESULTS: According to our findings, Myr could remarkably improve memory loss, spatial learning ability, Aß plaque deposition, neuronal and synaptic damage in 3 × Tg-AD mice through specifically inhibiting P38 MAPK pathway activation while restraining microglial hyperactivation. Furthermore, Myr promoted the transformation of microglial phenotype, restored the mitochondrial fission-fusion balance, facilitated mitochondrial biogenesis, and restrained NLRP3 inflammasome activation and neuroinflammation. For the in-vitro experiments, P38 agonist dehydrocorydaline (DHC) was utilized to confirm the key regulatory role of P38 MAPK signaling pathway on the mitochondria-NLRP3 inflammasome-microglia channel. CONCLUSIONS: Our results revealed the therapeutic efficacy of Myr in experimental AD, and implied that the associated mechanism is possibly associated with inhibiting tmitochondrial dysfunction, activating NLRP3 inflammasome, and neuroinflammation which was mediated by P38 MAPK pathway. Myr is the drug candidate in AD therapy via targeting P38 MAPK pathway.

In-silico evaluation of an artificial pancreas achieving automatic glycemic control in patients with type 1 diabetes.

Artificial pancreas (AP) is a useful tool for maintaining the blood glucose (BG) of patients with type 1 diabetes (T1D) within the euglycemic range. An intelligent controller has been developed based on general predictive control (GPC) for AP. This controller exhibits good performance with the UVA/Padova T1D mellitus simulator approved by the US Food and Drug Administration. In this work, the GPC controller was further evaluated under strict conditions, including a pump with noise and error, a CGM sensor with noise and error, a high carbohydrate intake, and a large population of 100 in-silico subjects. Test results showed that the subjects are in high risk for hypoglycemia. Thus, an insulin on board (IOB) calculator, as well as an adaptive control weighting parameter (AW) strategy, was introduced. The percentage of time spent in euglycemic range of the in-silico subjects was 86.0% ± 5.8%, and the patient group had a low risk of hypoglycemia with the GPC+IOB+AW controller. Moreover, the proposed AW strategy is more effective in hypoglycemia prevention and does not require any personalized data compared with the IOB calculator. Thus, the proposed controller realized an automatic control of the BG of patients with T1D without meal announcements and complex user interaction.

Cucumber glossy fruit 1 (CsGLF1) encodes the zinc finger protein 6 that regulates fruit glossiness by enhancing cuticular wax biosynthesis.

Cucumber glossiness is an important visual quality trait that affects consumer choice. Accumulating evidence suggests that glossy trait is associated with cuticular wax accumulation. However, the molecular genetic mechanism controlling cucumber glossiness remains largely unknown. Here, we report the map-based cloning and functional characterization of CsGLF1, a locus that determines the glossy trait in cucumber. CsGLF1 encodes a homolog of the Cys2His2-like fold group (C2H2) -type zinc finger protein 6 (ZFP6) and its deletion leads to glossier pericarp and decreased cuticular wax accumulation. Consistently, transcriptomic analysis demonstrated that a group of wax biosynthetic genes were downregulated when CsZFP6 was absent. Further, transient expression assay revealed that CsZFP6 acted as a transcription activator of cuticular wax biosynthetic genes. Taken together, our findings demonstrated a novel regulator of fruit glossiness, which will provide new insights into regulatory mechanism of fruit glossiness in cucumber.

Low-count whole-body PET/MRI restoration: an evaluation of dose reduction spectrum and five state-of-the-art artificial intelligence models.

PURPOSE: To provide a holistic and complete comparison of the five most advanced AI models in the augmentation of low-dose 18F-FDG PET data over the entire dose reduction spectrum. METHODS: In this multicenter study, five AI models were investigated for restoring low-count whole-body PET/MRI, covering convolutional benchmarks - U-Net, enhanced deep super-resolution network (EDSR), generative adversarial network (GAN) - and the most cutting-edge image reconstruction transformer models in computer vision to date - Swin transformer image restoration network (SwinIR) and EDSR-ViT (vision transformer). The models were evaluated against six groups of count levels representing the simulated 75%, 50%, 25%, 12.5%, 6.25%, and 1% (extremely ultra-low-count) of the clinical standard 3 MBq/kg 18F-FDG dose. The comparisons were performed upon two independent cohorts - (1) a primary cohort from Stanford University and (2) a cross-continental external validation cohort from Tübingen University - in order to ensure the findings are generalizable. A total of 476 original count and simulated low-count whole-body PET/MRI scans were incorporated into this analysis. RESULTS: For low-count PET restoration on the primary cohort, the mean structural similarity index (SSIM) scores for dose 6.25% were 0.898 (95% CI, 0.887-0.910) for EDSR, 0.893 (0.881-0.905) for EDSR-ViT, 0.873 (0.859-0.887) for GAN, 0.885 (0.873-0.898) for U-Net, and 0.910 (0.900-0.920) for SwinIR. In continuation, SwinIR and U-Net's performances were also discreetly evaluated at each simulated radiotracer dose levels. Using the primary Stanford cohort, the mean diagnostic image quality (DIQ; 5-point Likert scale) scores of SwinIR restoration were 5 (SD, 0) for dose 75%, 4.50 (0.535) for dose 50%, 3.75 (0.463) for dose 25%, 3.25 (0.463) for dose 12.5%, 4 (0.926) for dose 6.25%, and 2.5 (0.534) for dose 1%. CONCLUSION: Compared to low-count PET images, with near-to or nondiagnostic images at higher dose reduction levels (up to 6.25%), both SwinIR and U-Net significantly improve the diagnostic quality of PET images. A radiotracer dose reduction to 1% of the current clinical standard radiotracer dose is out of scope for current AI techniques.

High Performance 0D ZnO Quantum Dot/2D (PEA)2PbI4 Nanosheet Hybrid Photodetectors Fabricated via a Facile Antisolvent Method.

Two-dimensional (2D) organic-inorganic perovskites have great potential for the fabrication of next-generation photodetectors owing to their outstanding optoelectronic features, but their utilization has encountered a bottleneck in anisotropic carrier transportation induced by the unfavorable continuity of the thin films. We propose a facile approach for the fabrication of 0D ZnO quantum dot (QD)/2D (PEA)2PbI4 nanosheet hybrid photodetectors under the atmospheric conditions associated with the ZnO QD chloroform antisolvent. Profiting from the antisolvent, the uniform morphology of the perovskite thin films is obtained owing to the significantly accelerated nucleation site formation and grain growth rates, and ZnO QDs homogeneously decorate the surface of (PEA)2PbI4 nanosheets, which spontaneously passivate the defects on perovskites and enhance the carrier separation by the well-matched band structure. By varying the ZnO QD concentration, the Ion/Ioff ratio of the photodetectors radically elevates from 78.3 to 1040, and a 12-fold increase in the normalized detectivity is simultaneously observed. In addition, the agglomeration of perovskite grains is governed by the annealing temperature, and the photodetector fabricated at a relatively low temperature of 120 °C exhibits excellent stability after a 50-cycle test in the air condition without any encapsulation.

Predicting embryonic aneuploidy rate in IVF patients using whole-exome sequencing.

Infertility is a major reproductive health issue that affects about 12% of women of reproductive age in the United States. Aneuploidy in eggs accounts for a significant proportion of early miscarriage and in vitro fertilization failure. Recent studies have shown that genetic variants in several genes affect chromosome segregation fidelity and predispose women to a higher incidence of egg aneuploidy. However, the exact genetic causes of aneuploid egg production remain unclear, making it difficult to diagnose infertility based on individual genetic variants in mother's genome. In this study, we evaluated machine learning-based classifiers for predicting the embryonic aneuploidy risk in female IVF patients using whole-exome sequencing data. Using two exome datasets, we obtained an area under the receiver operating curve of 0.77 and 0.68, respectively. High precision could be traded off for high specificity in classifying patients by selecting different prediction score cutoffs. For example, a strict prediction score cutoff of 0.7 identified 29% of patients as high-risk with 94% precision. In addition, we identified MCM5, FGGY, and DDX60L as potential aneuploidy risk genes that contribute the most to the predictive power of the model. These candidate genes and their molecular interaction partners are enriched for meiotic-related gene ontology categories and pathways, such as microtubule organizing center and DNA recombination. In summary, we demonstrate that sequencing data can be mined to predict patients' aneuploidy risk thus improving clinical diagnosis. The candidate genes and pathways we identified are promising targets for future aneuploidy studies.

Dynamics of Detection of Anti-SARS-CoV-2 IgG/IgM and SARS-CoV-2 RNA in Respiratory Tract Specimens in 48 COVID-19 Patients.

BACKGROUND: The rapid spread of pneumonia caused by SARS-CoV-2 has seriously threatened people. In this study, we detected the expression of anti-SARS-CoV-2 IgG/IgM and respiratory tract SARS-CoV-2 RNA in patients with COVID-19 and explored the correlation and clinical significance between SARS-CoV-2 antibody and respiratory SARS-CoV-2 RNA. METHODS: From March 5, 2020 to April 28, 2020, 48 cases with COVID-19 diagnosed in Beijing Xiaotangshan Hospital were enrolled. SARS-CoV-2 RNAs were detected by real-time fluorescence RT-PCR method. Serum SARS-CoV-2 IgG/IgM antibodies were determined by colloidal gold immunochromatography. The statistical analysis was performed using chi-squared test. RESULTS: In all the patients, SARS-CoV-2 RNA among 270 upper respiratory tract (nasal or throat swabs) samples, 71 lower respiratory tract (sputum) samples, and anti-SARS-CoV-2 IgM/IgG antibodies in 123 serum samples were detected during the hospitalization period. The positive rate of anti-SARS-CoV-2 IgG was significantly higher than that of anti-SARS-CoV-2 IgM within the first week after symptom onset (p < 0.05). The positive rate of anti-SARS-CoV-2 IgG was also significantly higher than that of anti-SARS-CoV-2 IgM during day 8 - 30 after symptom onset (p < 0.01). The positive rate of SARS-CoV-2 RNA in the lower respiratory tract specimens (64.8%, 46/71) was significantly higher than that in the upper respiratory tract (46.7%, 126/270) (p < 0.05). The positive rate (100%, 4/4) of SARS-CoV-2 RNA detection in the lower respiratory tract specimens before IgG seroconversion was significantly higher than that of the positive rate (59.3%, 32/54) after IgG seroconversion (p < 0.01). The positive rate (72.2%, 57/79) of SARS-CoV-2 RNA detection in the upper respiratory tract specimens before IgG seroconversion was significantly higher than that of the positive rate (30.7%, 39/127) after IgG seroconversion (p < 0.01). CONCLUSIONS: Anti-SARS-CoV-2 IgG might be detected within the first week after symptom onset. The application of SARS-CoV-2 antibody (IgG/IgM) detection is important for the suspected cases of SARS-CoV-2 infection with negative SARS-CoV-2 RNA results. The positive rate of SARS-CoV-2 RNA detection in the lower respiratory tract specimens was significantly higher than that in the upper respiratory tract. Sputum detection is recommended for the detection of SARS-CoV-2 RNA. Using lower respiratory tract specimens may reduce the false negative PCR tests. The detection of SARS-CoV-2 RNA can be improved by investigating follow-up specimens over time.

Moderate vs. mild cases of overseas-imported COVID-19 in Beijing: a retrospective cohort study.

This study compared the differences in the clinical manifestations, treatment courses and clinical turnover between mild and moderate coronavirus disease 2019 (COVID-19). Clinical data of the patients with imported COVID-19 admitted to Beijing Xiaotangshan Designated Hospital between March 15 and April 30, 2020, were retrospectively analysed. A total of 53 COVID-19 patients were included, with 21 mild and 32 moderate cases. Compared with the mild group, the moderate group showed significant differences in breathing frequency, lymphocyte count, neutrophil percentage, neutrophil/lymphocyte ratio, procalcitonin, C-reactive protein, and dynamic erythrocyte sedimentation rate. In the moderate group, 87.5% exhibited ground-glass opacities, 14% exhibited consolidative opacities, 53.1% exhibited local lesions and 68.8% exhibited unilateral lesions. The proportion of patients who received antiviral or antibiotic treatment in the moderate group was higher than that in the mild group, and the number of cases that progressed to severe disease in the moderate group was also significantly higher (18.7% vs. 0%, p = 0.035). Compared with patients with mild COVID-19, those with moderate COVID-19 exhibited more noticeable inflammatory reactions, more severe pulmonary imaging manifestations and earlier expression of protective antibodies. The overall turnover of the moderate cases was poorer than that of the mild cases.

Artificial intelligence enables whole-body positron emission tomography scans with minimal radiation exposure.

PURPOSE: To generate diagnostic 18F-FDG PET images of pediatric cancer patients from ultra-low-dose 18F-FDG PET input images, using a novel artificial intelligence (AI) algorithm. METHODS: We used whole-body 18F-FDG-PET/MRI scans of 33 children and young adults with lymphoma (3-30 years) to develop a convolutional neural network (CNN), which combines inputs from simulated 6.25% ultra-low-dose 18F-FDG PET scans and simultaneously acquired MRI scans to produce a standard-dose 18F-FDG PET scan. The image quality of ultra-low-dose PET scans, AI-augmented PET scans, and clinical standard PET scans was evaluated by traditional metrics in computer vision and by expert radiologists and nuclear medicine physicians, using Wilcoxon signed-rank tests and weighted kappa statistics. RESULTS: The peak signal-to-noise ratio and structural similarity index were significantly higher, and the normalized root-mean-square error was significantly lower on the AI-reconstructed PET images compared to simulated 6.25% dose images (p < 0.001). Compared to the ground-truth standard-dose PET, SUVmax values of tumors and reference tissues were significantly higher on the simulated 6.25% ultra-low-dose PET scans as a result of image noise. After the CNN augmentation, the SUVmax values were recovered to values similar to the standard-dose PET. Quantitative measures of the readers' diagnostic confidence demonstrated significantly higher agreement between standard clinical scans and AI-reconstructed PET scans (kappa = 0.942) than 6.25% dose scans (kappa = 0.650). CONCLUSIONS: Our CNN model could generate simulated clinical standard 18F-FDG PET images from ultra-low-dose inputs, while maintaining clinically relevant information in terms of diagnostic accuracy and quantitative SUV measurements.

Comparison of the clinical manifestations between different age groups of patients with overseas imported COVID-19.

The current study investigated the clinical manifestations and outcomes of different age groups of patients with overseas imported COVID-19. In total, 53 COVID-19 patients admitted to the designated Beijing Xiaotangshan Hospital between March 16 and April 15 of 2020 were included. Based on the percentage of disease aggravation during hospital stay according to CT, the patients were divided into two groups: ≤40 years (group A; n = 41) and >40 years (group B; n = 12). The demographic data, epidemiological history, disease courses, potential complications, clinical symptoms, lab indices, chest CT outcomes, treatment protocols and turnovers of the two groups were compared. According to clinical typing, compared with group A, group B had a significantly greater proportion of the common type of COVID-19 (P<0.05) and greater comorbidity of type 2 diabetes (P<0.001). The two groups presented significantly different lab indices. Group B showed significantly more frequent CT abnormalities, with greater proportions of multiple lesions and bilateral lung involvement (P<0.05). During hospitalization, group B had a greater proportion of disease aggravation according to CT (P<0.01). Compared with group A, group B received a significantly greater proportion of antiviral therapy and presented a significantly greater occurrence of adverse drug reactions (P<0.05). The two groups did not significantly differ in time from admission to clinical symptom improvement or from disease onset to negative outcomes according to nucleic acid testing, the appearance of IgG or the appearance of IgM. They also did not significantly differ in length of stay. Older imported COVID-19 patients, particularly those with type 2 diabetes, showed a broader pulmonary extent and faster development of the disease, more severe pathogenetic conditions and a greater risk of developing a critically severe type. Increased attention should be given to this population in clinical practice.