Automated molecular structure segmentation from documents using ChemSAM.

Chemical structure segmentation constitutes a pivotal task in cheminformatics, involving the extraction and abstraction of structural information of chemical compounds from text-based sources, including patents and scientific articles. This study introduces a deep learning approach to chemical structure segmentation, employing a Vision Transformer (ViT) to discern the structural patterns of chemical compounds from their graphical representations. The Chemistry-Segment Anything Model (ChemSAM) achieves state-of-the-art results on publicly available benchmark datasets and real-world tasks, underscoring its effectiveness in accurately segmenting chemical structures from text-based sources. Moreover, this deep learning-based approach obviates the need for handcrafted features and demonstrates robustness against variations in image quality and style. During the detection phase, a ViT-based encoder-decoder model is used to identify and locate chemical structure depictions on the input page. This model generates masks to ascertain whether each pixel belongs to a chemical structure, thereby offering a pixel-level classification and indicating the presence or absence of chemical structures at each position. Subsequently, the generated masks are clustered based on their connectivity, and each mask cluster is updated to encapsulate a single structure in the post-processing workflow. This two-step process facilitates the effective automatic extraction of chemical structure depictions from documents. By utilizing the deep learning approach described herein, it is demonstrated that effective performance on low-resolution and densely arranged molecular structural layouts in journal articles and patents is achievable.

Unraveling the molecular mechanisms driving enhanced invasion capability of extravillous trophoblast cells: a comprehensive review.

Precise extravillous trophoblast (EVT) invasion is crucial for successful placentation and pregnancy. This review focuses on elucidating the mechanisms that promote heightened EVT invasion. We comprehensively summarize the pivotal roles of hormones, angiogenesis, hypoxia, stress, the extracellular matrix microenvironment, epithelial-to-mesenchymal transition (EMT), immunity, inflammation, programmed cell death, epigenetic modifications, and microbiota in facilitating EVT invasion. The molecular mechanisms underlying enhanced EVT invasion may provide valuable insights into potential pathogenic mechanisms associated with diseases characterized by excessive invasion, such as the placenta accreta spectrum (PAS), thereby offering novel perspectives for managing pregnancy complications related to deficient EVT invasion.

A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks.

The powerful combination of large-scale drug-related interaction networks and deep learning provides new opportunities for accelerating the process of drug discovery. However, chemical structures that play an important role in drug properties and high-order relations that involve a greater number of nodes are not tackled in current biomedical networks. In this study, we present a general hypergraph learning framework, which introduces Drug-Substructures relationship into Molecular interaction Networks to construct the micro-to-macro drug centric heterogeneous network (DSMN), and develop a multi-branches HyperGraph learning model, called HGDrug, for Drug multi-task predictions. HGDrug achieves highly accurate and robust predictions on 4 benchmark tasks (drug-drug, drug-target, drug-disease, and drug-side-effect interactions), outperforming 8 state-of-the-art task specific models and 6 general-purpose conventional models. Experiments analysis verifies the effectiveness and rationality of the HGDrug model architecture as well as the multi-branches setup, and demonstrates that HGDrug is able to capture the relations between drugs associated with the same functional groups. In addition, our proposed drug-substructure interaction networks can help improve the performance of existing network models for drug-related prediction tasks.

A meta-analysis of treatment for early-stage cervical cancer: open versus minimally invasive radical trachelectomy.

BACKGROUND: In previous systematic reviews, meta-analysis was lacking, resulting in the statistical difference between the data of different surgeries being impossible to judge. This meta-analysis aims to contrast the fertility results and cancer outcomes between open and minimally invasive surgery. METHOD: We systematically searched databases including PubMed, Embase, Cochrane, and Scopus to collect studies that included open and minimally invasive radical trachelectomy. A random-effect model calculated the weighted average difference of each primary outcome via Review Manager V.5.4. RESULT: Eight studies (1369 patients) were incorporated into our study. For fertility results, the Open group excels MIS group in pregnancies-Third trimester delivery [OR = 2.68; 95% CI (1.29, 5.59); P = 0.008]. Nevertheless, there is no statistical difference in clinical pregnancy, miscarriage, and second-trimester rate. Concerning cancer outcomes, no difference was detected in the overall survival [OR = 1.56; 95% CI (0.70, 3.45); P = 0.27] and recurrence [OR = 0.63; 95% CI (0.35, 1.12); P = 0.12]. Concerning surgery-related outcomes, the comprehensive effects revealed that the estimated blood loss of the Open group was higher than that of the MIS group[MD = 139.40; 95% CI (79.05, 199.75); P < 0.0001]. However, there was no difference between the postoperative complication rate in the two groups [OR = 1.52; 95% CI (0.89, 2.60); P = 0.12]. CONCLUSION: This meta-analysis suggested that the fertility result of the Open group may be better than the MIS group, while the MIS group has better surgery-related outcomes. Owing to the poor cases of our study, a more robust conclusion requires more relevant articles in the future. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42022352999.

An extensive benchmark study on biomedical text generation and mining with ChatGPT.

MOTIVATION: In recent years, the development of natural language process (NLP) technologies and deep learning hardware has led to significant improvement in large language models (LLMs). The ChatGPT, the state-of-the-art LLM built on GPT-3.5 and GPT-4, shows excellent capabilities in general language understanding and reasoning. Researchers also tested the GPTs on a variety of NLP-related tasks and benchmarks and got excellent results. With exciting performance on daily chat, researchers began to explore the capacity of ChatGPT on expertise that requires professional education for human and we are interested in the biomedical domain. RESULTS: To evaluate the performance of ChatGPT on biomedical-related tasks, this article presents a comprehensive benchmark study on the use of ChatGPT for biomedical corpus, including article abstracts, clinical trials description, biomedical questions, and so on. Typical NLP tasks like named entity recognization, relation extraction, sentence similarity, question and answering, and document classification are included. Overall, ChatGPT got a BLURB score of 58.50 while the state-of-the-art model had a score of 84.30. Through a series of experiments, we demonstrated the effectiveness and versatility of ChatGPT in biomedical text understanding, reasoning and generation, and the limitation of ChatGPT build on GPT-3.5. AVAILABILITY AND IMPLEMENTATION: All the datasets are available from BLURB benchmark https://microsoft.github.io/BLURB/index.html. The prompts are described in the article.

Effects of high manganese-cultivated seedlings on cadmium uptake by various rice (Oryza sativa L.) genotypes.

Cadmium (Cd) contamination in paddy soil threatens rice growth and food safety, enriching manganese (Mn) in rice seedlings is expected to reduce Cd uptake by rice. The effects of 250 µM Mn-treated seedlings on reducing Cd uptake of four rice genotypes (WYJ21, ZJY1578, HHZ, and HLYSM) planted in 0.61 mg kg-1 Cd-contaminated soil, were studied through the hydroponic and pot experiments. The results showed that the ZJY1578 seedling had the highest Mn level (459 µg plant-1), followed by WYJ21 (309 µg plant-1), and less Mn accumulated in the other genotypes. The relative expression of OsNramp5 (natural resistance-associated macrophage protein) was reduced by 42.7 % in ZJY1578 but increased by 23.3 % in HLYSM. The expressions of OsIRT1 (iron-regulated transporter-like protein) were reduced by 24.0-56.0 % in the four genotypes, with the highest reduction in ZJY1578. Consequently, a greater reduction of Cd occurred in ZJY1578 than that in the other genotypes, i.e., the root and shoot Cd at the tillering were reduced by 27.8 % and 48.5 %, respectively. At the mature stage, total Cd amount and distribution in the shoot and brown rice were also greatly reduced in ZJY1578, but the inhibitory effects were weakened compared to the tillering stage. This study found various responses of Cd uptake and transporters to Mn-treated seedlings among rice genotypes, thus resulting in various Cd reductions. In the future, the microscopic transport processes of Cd within rice should be explored to deeply explain the genotypic variation.

Reclamation of tidal flats to paddy soils reshuffles the soil microbiomes along a 53-year reclamation chronosequence: Evidence from assembly processes, co-occurrence patterns and multifunctionality.

Coastal soil microbiomes play a key role in coastal ecosystem functioning and are intensely threatened by land reclamation. However, the impacts of coastal reclamation on soil microbial communities, particularly on their assembly processes, co-occurrence patterns, and the multiple soil functions they support, remain poorly understood. This impedes our capability to comprehensively evaluate the impacts of coastal reclamation on soil microbiomes and to restore coastal ecosystem functions degraded by reclamation. Here, we investigated the temporal dynamics of bacterial and fungal communities, community assembly processes, co-occurrence patterns, and ecosystem multifunctionality along a 53-year chronosequence of paddy soil following reclamation from tidal flats. Reclamation of tidal flats to paddy soils resulted in decreased ß-diversity, increased homogeneous selection, and decreased network complexity and robustness of both bacterial and fungal communities, but caused contrasting α-diversity response patterns of them. Reclamation of tidal flats to paddy soils also decreased the multifunctionality of coastal ecosystems, which was largely associated with the fungal network complexity and α-diversity. Collectively, this work demonstrates that coastal reclamation strongly reshaped the soil microbiomes at the level of assembly mechanisms, interaction patterns, and functionality level, and highlights that soil fungal community complexity should be considered as a key factor in restoring coastal ecosystem functions deteriorated by land reclamation.

PCSK9 promotes T helper 1 and T helper 17 cell differentiation by activating the nuclear factor-κB pathway in ankylosing spondylitis.

OBJECTIVE: Our previous study reveals that proprotein convertase subtilisin/kexin type 9 (PCSK9) is positively related to inflammatory markers, T helper (Th)-17 cells, and treatment response in ankylosing spondylitis (AS) patients. Subsequently, this study aimed to explore the effect of PCSK9 on Th cell differentiation and its potential molecular mechanism in AS. METHODS: Serum PCSK9 was determined by enzyme-linked immunosorbent assay in 20 AS patients and 20 healthy controls (HCs). Then naïve CD4+ T cells were isolated from AS patients and infected with PCSK9 overexpression or knockdown adenovirus followed by polarization assay. Afterward, PMA (an NF-κB activator) was administrated. RESULTS: PCSK9 was increased in AS patients compared to HCs (p < .001), and it was positively related to Th1 cells (p = .050) and Th17 cells (p = .039) in AS patients. PCSK9 overexpression increased the CD4+ IFN-γ+ cells (p < .05), CD4+ IL-17A+ cells (p < .01), IFN-γ (p < .01), and IL-17A (p < .01), while it exhibited no effect on CD4+ IL-4+ cells or IL-4 (both p > .05); its knockdown displayed the opposite function on them. Moreover, PCSK9 overexpression upregulated the p-NF-κB p65/NF-κB p65 (p < .01), while it had no effect on p-ERK/ERK or p-JNK/JNK (both p > .05); its knockdown decreased p-NF-κB p65/NF-κB p65 (p < .01) and p-JNK/JNK (p < .05). Then, PMA upregulates p-NF-κB p65/NF-κB p65 (p < .001) and increased CD4+ IFN-γ+ cells, CD4+ IL-17A+ cells, IFN-γ, and IL-17A (all p < .01), also it alleviated the effect of PCSK9 knockdown on NF-κB inhibition and Th cell differentiation (all p < .01). CONCLUSION: PCSK9 enhances Th1 and Th17 cell differentiation in an NF-κB-dependent manner in AS, while further validation is necessary.

Pharmacophoric-constrained heterogeneous graph transformer model for molecular property prediction.

Informative representation of molecules is a crucial prerequisite in AI-driven drug design and discovery. Pharmacophore information including functional groups and chemical reactions can indicate molecular properties, which have not been fully exploited by prior atom-based molecular graph representation. To obtain a more informative representation of molecules for better molecule property prediction, we propose the Pharmacophoric-constrained Heterogeneous Graph Transformer (PharmHGT). We design a pharmacophoric-constrained multi-views molecular representation graph, enabling PharmHGT to extract vital chemical information from functional substructures and chemical reactions. With a carefully designed pharmacophoric-constrained multi-view molecular representation graph, PharmHGT can learn more chemical information from molecular functional substructures and chemical reaction information. Extensive downstream experiments prove that PharmHGT achieves remarkably superior performance over the state-of-the-art models the performance of our model is up to 1.55% in ROC-AUC and 0.272 in RMSE higher than the best baseline model) on molecular properties prediction. The ablation study and case study show that our proposed molecular graph representation method and heterogeneous graph transformer model can better capture the pharmacophoric structure and chemical information features. Further visualization studies also indicated a better representation capacity achieved by our model.

[Analysis of the positive rate of otitis media in 1-12 years old children based on brain MRI].

Objective:To investigate the positive imaging rate of otitis media in children aged 1-12 years by analyzing the positive rate of otitis media suggested by cranial magnetic resonance imaging（MRI） images in children. Methods:By collecting the brain MRI images of children aged 1-12 in Department of Otolaryngology, Jinan children's Hospital from January 2014 to December 2020, the overall incidence of otitis media and mastoiditis was firstly determined, and then it was divided into 12 age groups according to age, each age group was split into boy and girl groups according to gender, each group was divided into left, right and bilateral groups, with the changes of otitis media and mastoiditis in the scanning field as the positive standard statistical analysis of the results. Results:Among 12 439 children in the study, 1321 cases were diagnosed with tympanitis, with a positive rate of 10.62%. Among them, 892 patients were male, with a positive rate of 67.52%, and 429 cases were female, with a positive rate of 32.48%. The positive rate of the male was higher than that of female children, 1.84 times higher than that of female children. The positive momentum in male and female children was negatively correlated with age （P<0.01）. The favorable rates of male children in the left ear, right ear, and both ears were higher than those in female children of the same age（P<0.05）. There was no difference in the positive rate of the left and right ear in children with tympanitis （P=0.76）. Conclusion:Craniocerebral MRI examination in children with tympanitis can clarify the inflammation of the middle ear cavity and the effusion in the mastoid air chamber. The positive rate of children at two years old showed a steep decline, which may be due to the acceleration of mastoid gasification, the change of tympanic environment, the increase of air chamber in the tympanic room, the evolution of air pressure could offset the negative pressure caused by poor Eustachian tube function, to reduce the incidence of tympanitis.

Volatile fatty acids changed the microbial community during feammox in coastal saline-alkaline paddy soil.

In order to indicate the effect of volatile fatty acids (VFAs) on the characteristics of feammox and dissimilatory iron reducing bacteria (DIRB) in paddy soils, different VFAs were selected with paddy soils for anaerobic cultivation. Five treatments were set up, respectively, only adding N and both adding N and C (formate + NH4+ (Fo-N), acetate + NH4+ (Ac-N), propionate + NH4+ (Pr-N), and butyrate + NH4+ (Bu-N)) treatments. The concentration of Fe(II), Fe(III), NH4+, and VFAs was assessed within 45 d, and the bacterial community was determined after cultivation. The oxidation rates of NH4+ were the highest in N treatment, while it was the lowest in Fo-N treatment. Under the four C treatments, the consumption of NH4+ and Fe(III) was the fastest in Pr-N treatment, which was consumed by 31.2% and 76.3%, respectively. Different VFAs selected for distinct DIRB. Compared with N treatment, Ac-N and Bu-N treatment increased the relative abundance of DIRB, such as Geobacter and Clostridia, which increased the consumption of VFAs during incubation. Overall, VFAs, especially formate, could promote Fe(III) reduction and compete with the feammox process for the electron acceptors to decrease the feammox reaction, and prohibited soil NH4+ loss. Therefore, VFAs, which was released from organic fertilizer, could reduce NH4+ loss in feammox process of saline-alkaline paddy soils.

Serum PCSK9 is positively correlated with disease activity and Th17 cells, while its short-term decline during treatment reflects desirable outcomes in ankylosing spondylitis patients.

OBJECTIVE: Proprotein convertase subtilisin/kexin type 9 (PCSK9) participates in the autoimmune disease pathology by regulating T helper (Th) cell differentiation, NF-κB pathway, toll-like receptor 4, etc. This study intended to investigate the association of serum PCSK9 with disease activity, Th cells, and treatment response in ankylosing spondylitis (AS) patients. METHODS: Eighty-nine active AS patients were enrolled in this multicenter, prospective study. Serum was collected from AS patients at week (W)0, W4, W8, and W12, as well as from 20 osteoarthritis patients and 20 healthy controls after enrollment to detect PCSK9 by ELISA. Based on the ASAS40 response at W12, AS patients were classified as responders and non-responders. RESULTS: PCSK9 was increased in AS patients versus healthy controls (P < 0.001) and osteoarthritis patients (P = 0.006). In AS patients, PCSK9 was positively linked with C-reactive protein (CRP) (P = 0.003) and ASDAS-CRP (P = 0.017), but not with other clinical properties (P > 0.05). Besides, PCSK9 was negatively correlated with interleukin-4 (P = 0.034), positively associated with Th17 cells (P = 0.005) and interleukin-17A (P = 0.014), but did not relate to Th1 cells, interferon-γ, or Th2 cells (all P > 0.05). Additionally, PCSK9 was decreased from W0 to W12 in general AS patients (P < 0.001) and responders (P < 0.001) but remained unchanged in non-responders (P = 0.129). Moreover, PCSK9 was lower at W4 (P = 0.045), W8 (P = 0.008), and W12 (P = 0.004) in responders versus non-responders. Furthermore, the treatment options did not affect the PCSK9 level (P > 0.05). CONCLUSION: Serum PCSK9 is positively associated with disease activity and Th17 cells, while its short-term decline reflects desirable treatment response in AS patients.

Pre-Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding.

The latest biological findings observe that the motionless "lock-and-key" theory is not generally applicable and that changes in atomic sites and binding pose can provide important information for understanding drug binding. However, the computational expenditure limits the growth of protein trajectory-related studies, thus hindering the possibility of supervised learning. A spatial-temporal pre-training method based on the modified equivariant graph matching networks, dubbed ProtMD which has two specially designed self-supervised learning tasks: atom-level prompt-based denoising generative task and conformation-level snapshot ordering task to seize the flexibility information inside molecular dynamics (MD) trajectories with very fine temporal resolutions is presented. The ProtMD can grant the encoder network the capacity to capture the time-dependent geometric mobility of conformations along MD trajectories. Two downstream tasks are chosen to verify the effectiveness of ProtMD through linear detection and task-specific fine-tuning. A huge improvement from current state-of-the-art methods, with a decrease of 4.3% in root mean square error for the binding affinity problem and an average increase of 13.8% in the area under receiver operating characteristic curve and the area under the precision-recall curve for the ligand efficacy problem is observed. The results demonstrate a strong correlation between the magnitude of conformation's motion in the 3D space and the strength with which the ligand binds with its receptor.

Antibiotics sulfamethoxazole alter nitrous oxide production and pathways in estuarine sediments: Evidenced by the N15-O18 isotopes tracing.

Estuarine antibiotic residues are profoundly impacting microbial nitrogen (N) cycling and associated N2O production, but the response of N2O production pathways to antibiotics remains poorly understood. Here, 15N-18O labeling technique combined with molecular methods were used to investigate the impacts of sulfamethoxazole on the contribution of ammonia oxidation (nitrifier nitrification, nitrifier denitrification, and nitrification-coupled denitrification) and heterotrophic denitrification (HD) to N2O production in estuarine sediments. Results showed that environmental concentration of sulfamethoxazole (4 ng/g) promoted the total N2O production by 17.1% through nitrifier denitrification. Environmentally relevant (40-4000 ng/g) and irrelevant (40,000 ng/g) concentration of sulfamethoxazole drove nitrification denitrification to gradually lose the dominant role in total N2O production and ammonia oxidation-derived N2O, replaced by HD and nitrifier nitrification, while total N2O production were inhibited. Furthermore, when HD dominated the total N2O production, the HD-derived N2O increased by 63.6% with sulfamethoxazole concentration reaching 40,000 ng/g. The mechanistic investigation further showed that nitrifying bacteria were more susceptible to sulfamethoxazole than nitrifying archaea and denitrifiers. The increased expression of nirS gene carried by non-dominant denitrifiers improved the ratio of nirS:nosZ and hence increased HD-derived N2O under high sulfamethoxazole stresses. Overall, our results provide a comprehensive view into how antibiotics regulate N2O production and its pathways in estuarine sediments.

Risk assessment of heavy metals in a typical mangrove ecosystem - A case study of Shankou Mangrove National Natural Reserve, southern China.

Mangroves bear enormous ecosystem value, while the ecosystems are facing increasing environmental pressures. In this study, 73 samples of soil sediments in mangroves, paddy fields, grasslands, forests, and shrimp ponds were collected from Shankou Mangrove National Nature Reserve (SKMNNR), Guangxi Zhuang Autonomous Region, China. The pollution status and ecological risks of heavy metal elements of Cr, Ni, Cu, Zn, As, Cd, Pb, V, and Co were determined using the enrichment factor (EF), geoaccumulaton index (Igeo), and potential ecological risk index (PERI). The average concentration is shown to be substantially lower than the background value. In general, the Igeo values indicated that the pollution conditions of different land use types in SKMNNR are relatively minor. Most of the PERI values were at the moderate level. This study demonstrates that the current status of sediment quality in SKMNNR is relatively good, and the pollution level is relatively low. Large-scale coastal aquaculture development and industrial expansion should not no longer be permitted there.

Metagenomics highlights the impact of climate and human activities on antibiotic resistance genes in China's estuaries.

Estuarine environments faced with contaminations from coastal zones and the inland are vital sinks of antibiotic resistance genes (ARGs). However, little is known about the temporal-spatial pattern of ARGs and its predominant constraints in estuarine environments. Here, we leveraged metagenomics to investigate ARG profiles from 16 China's estuaries across 6 climate zones in dry and wet seasons, and disentangled their relationships with environmental constraints. Our results revealed that ARG abundance, richness, and diversity in dry season were higher than those in wet season, and ARG abundance exhibited an increasing trend with latitude. The prevalence of ARGs was significantly driven by human activities, mobile gene elements, microbial communities, antibiotic residuals, physicochemical properties, and climatic variables. Among which, climatic variables and human activities ranked the most important factors, contributing 44% and 36% of the total variance of observed ARGs, respectively. The most important climatic variable shaping ARGs is temperature, where increasing temperature is associated with decreased ARGs. Our results highlight that the prevalence of ARGs in estuarine environments would be co-driven by anthropogenic activities and climate, and suggest the dynamics of ARGs under future changing climate and socioeconomic development.

The temporal response of dissolved heavy metals to landscape indices in the Le'an river, China.

Heavy metals in watersheds are a serious concern due to their toxicity, abundance, and persistence in the environment, especially in mining areas. Source analyses and exploration of other related factors are one of the most important methods to help with effective prevention and control of heavy metal pollution in watersheds. In this study, the concentrations of Cr, Co, Ni, Cu, Zn, As, Cd, Sb, Ba and Pb were measured in the Le'an River, and PCA (principal component analysis) and APCS-MLR (absolute principal component scores - multivariate linear regression) methods were used to identify the sources of the dissolved heavy metals. Additionally, a CA (correlation analysis) method was used to explore the correlations between landscape indices and concentrations of heavy metals. Results show that the main sources for these dissolved heavy metals are mining activities, fertilizers, pesticides, and natural sources. Specific results of PCA and APCS-MLR suggest that Cu, Zn, Cd, Ba are mainly related to mining activities, Cr and Pb are due to fertilizers and pesticides, and Co and Ni are mainly due to natural sources. Correlations between landscapes and heavy metals revealed significant temporal variations, with the strongest responses of dissolved heavy metals to landscape indices appearing in December and March. The propensity of positive or negative responses of the heavy metals to landscape indices are determined by the sources, and their temporal variations may be related to the seasonal changes of rainfall and plant metabolism.

Amelioration of Alzheimer's disease pathology by mitophagy inducers identified via machine learning and a cross-species workflow.

A reduced removal of dysfunctional mitochondria is common to aging and age-related neurodegenerative pathologies such as Alzheimer's disease (AD). Strategies for treating such impaired mitophagy would benefit from the identification of mitophagy modulators. Here we report the combined use of unsupervised machine learning (involving vector representations of molecular structures, pharmacophore fingerprinting and conformer fingerprinting) and a cross-species approach for the screening and experimental validation of new mitophagy-inducing compounds. From a library of naturally occurring compounds, the workflow allowed us to identify 18 small molecules, and among them two potent mitophagy inducers (Kaempferol and Rhapontigenin). In nematode and rodent models of AD, we show that both mitophagy inducers increased the survival and functionality of glutamatergic and cholinergic neurons, abrogated amyloid-ß and tau pathologies, and improved the animals' memory. Our findings suggest the existence of a conserved mechanism of memory loss across the AD models, this mechanism being mediated by defective mitophagy. The computational-experimental screening and validation workflow might help uncover potent mitophagy modulators that stimulate neuronal health and brain homeostasis.

Robust weakly supervised learning for COVID-19 recognition using multi-center CT images.

The world is currently experiencing an ongoing pandemic of an infectious disease named coronavirus disease 2019 (i.e., COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Computed Tomography (CT) plays an important role in assessing the severity of the infection and can also be used to identify those symptomatic and asymptomatic COVID-19 carriers. With a surge of the cumulative number of COVID-19 patients, radiologists are increasingly stressed to examine the CT scans manually. Therefore, an automated 3D CT scan recognition tool is highly in demand since the manual analysis is time-consuming for radiologists and their fatigue can cause possible misjudgment. However, due to various technical specifications of CT scanners located in different hospitals, the appearance of CT images can be significantly different leading to the failure of many automated image recognition approaches. The multi-domain shift problem for the multi-center and multi-scanner studies is therefore nontrivial that is also crucial for a dependable recognition and critical for reproducible and objective diagnosis and prognosis. In this paper, we proposed a COVID-19 CT scan recognition model namely coronavirus information fusion and diagnosis network (CIFD-Net) that can efficiently handle the multi-domain shift problem via a new robust weakly supervised learning paradigm. Our model can resolve the problem of different appearance in CT scan images reliably and efficiently while attaining higher accuracy compared to other state-of-the-art methods.

Can Clinical Symptoms and Laboratory Results Predict CT Abnormality? Initial Findings Using Novel Machine Learning Techniques in Children With COVID-19 Infections.

The rapid spread of coronavirus 2019 disease (COVID-19) has manifested a global public health crisis, and chest CT has been proven to be a powerful tool for screening, triage, evaluation and prognosis in COVID-19 patients. However, CT is not only costly but also associated with an increased incidence of cancer, in particular for children. This study will question whether clinical symptoms and laboratory results can predict the CT outcomes for the pediatric patients with positive RT-PCR testing results in order to determine the necessity of CT for such a vulnerable group. Clinical data were collected from 244 consecutive pediatric patients (16 years of age and under) treated at Wuhan Children's Hospital with positive RT-PCR testing, and the chest CT were performed within 3 days of clinical data collection, from January 21 to March 8, 2020. This study was approved by the local ethics committee of Wuhan Children's Hospital. Advanced decision tree based machine learning models were developed for the prediction of CT outcomes. Results have shown that age, lymphocyte, neutrophils, ferritin and C-reactive protein are the most related clinical indicators for predicting CT outcomes for pediatric patients with positive RT-PCR testing. Our decision support system has managed to achieve an AUC of 0.84 with 0.82 accuracy and 0.84 sensitivity for predicting CT outcomes. Our model can effectively predict CT outcomes, and our findings have indicated that the use of CT should be reconsidered for pediatric patients, as it may not be indispensable.