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National Institute of Mental Health, National Institute for Neurological Disorders and Stroke, National Institute on Drug Abuse, National Science Foundation.

National Institute of Mental Health, National Institute for Neurological Disorders and Stroke, National Institute on Drug Abuse, National Science Foundation.

Tumor-infiltrating lymphocytes (TILs) are clinically significant in triple-negative breast cancer (TNBC). Although a standardized methodology for visual TILs assessment (VTA) exists, it has several inherent limitations. We established a deep learning-based computational TIL assessment (CTA) method broadly following VTA guideline and compared it with VTA for TNBC to determine the prognostic value of the CTA and a reasonable CTA workflow for clinical practice.

We trained three deep neural networks for nuclei segmentation, nuclei classification and necrosis classification to establish a CTA workflow. The automatic TIL (aTIL) score generated was compared with manual TIL (mTIL) scores provided by three pathologists in an Asian (n=184) and a Caucasian (n=117) TNBC cohort to evaluate scoring concordance and prognostic value.

The intraclass correlations (ICCs) between aTILs and mTILs varied from 0.40 to 0.70 in two cohorts. Multivariate Cox proportional hazards analysis revealed that the aTIL score was associated with disease free survival (DFS) in both cohorts, as either a continuous [hazard ratio (HR)=0.96, 95% CI 0.94-0.99] or dichotomous variable (HR=0.29, 95% CI 0.12-0.72). A higher C-index was observed in a composite mTIL/aTIL three-tier stratification model than in the dichotomous model, using either mTILs or aTILs alone.

The current study provides a useful tool for stromal TIL assessment and prognosis evaluation for patients with TNBC. A workflow integrating both VTA and CTA may aid pathologists in performing risk management and decision-making tasks.

National Natural Science Foundation of China, Guangdong Medical Research Foundation, Guangdong Natural Science Foundation.

National Natural Science Foundation of China, Guangdong Medical Research Foundation, Guangdong Natural Science Foundation.

Induction of autoantigen-specific Treg cells that suppresstissue-specific autoimmunity without compromising beneficial immune responses is the holy-grail for immunotherapy to autoimmune diseases.

In a model of experimentalautoimmune uveitis (EAU) that mimics human uveitis, ocular inflammation was induced by immunization with retinal antigen interphotoreceptor retinoid-binding protein (IRBP). Mice were given intraperitoneal injection of αCD4 antibody (Ab) after the onset of disease, followed by administration of IRBP. EAU was evaluated clinically and functionally. Splenocytes, CD4

CD25

and CD4

CD25

T cells were sorted and cultured with IRBP or αCD3 Ab. T cell proliferation and cytokine production were assessed.

The experimental approach resulted in remission of ocular inflammation and rescue of visual function in mice with established EAU. Mechanistically, the therapeutic effect was mediated by induction of antigen-specific Treg cells that inhibited IRBP-driven Th17 response in TGF-β and IL-10 dependent fashion. Importantly, the Ab-mediated immune tolerance could be achieved in EAU mice by administration of retinal autoantigens, arrestin but not limited to IRBP only, in an antigen-nonspecific bystander manner. Further, these EAU-suppressed tolerized mice did not compromise their anti-tumor T immunity in melanoma model.

We successfully addressed a specific immunotherapy of EAU by in vivo induction of autoantigen-specific Treg cells without compromising host overall T cell immunity, which should have potential implication for patients with autoimmune uveitis.

This study was supported by the Natural Science Foundation of Guangdong Province and the Fundamental Research Fund of the State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center.

This study was supported by the Natural Science Foundation of Guangdong Province and the Fundamental Research Fund of the State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center.

Machine learning (ML) and artificial intelligence are emerging as important components of precision medicine that enhance diagnosis and risk stratification. Risk stratification tools for hypertrophic cardiomyopathy (HCM) exist, but they are based on traditional statistical methods. The aim was to develop a novel machine learning risk stratification tool for the prediction of 5-year risk in HCM. The goal was to determine if its predictive accuracy is higher than the accuracy of the state-of-the-art tools.

Data from a total of 2302 patients were used. The data were comprised of demographic characteristics, genetic data, clinical investigations, medications, and disease-related events. Four classification models were applied to model the risk level, and their decisions were explained using the SHAP (SHapley Additive exPlanations) method. Unwanted cardiac events were defined as sustained ventricular tachycardia occurrence (VT), heart failure (HF), ICD activation, sudden cardiac death (SCD), cardiac death, and all-cause death.

The proposed machine learning approach outperformed the similar existing risk-stratification models for SCD, cardiac death, and all-cause death risk-stratification it achieved higher AUC by 17%, 9%, and 1%, respectively. The boosted trees achieved the best performing AUC of 0.82. The resulting model most accurately predicts VT, HF, and ICD with AUCs of 0.90, 0.88, and 0.87, respectively.

The proposed risk-stratification model demonstrates high accuracy in predicting events in patients with hypertrophic cardiomyopathy. The use of a machine-learning risk stratification model may improve patient management, clinical practice, and outcomes in general.

The proposed risk-stratification model demonstrates high accuracy in predicting events in patients with hypertrophic cardiomyopathy. The use of a machine-learning risk stratification model may improve patient management, clinical practice, and outcomes in general.Local fiber orientation distributions (FODs) can be computed from diffusion magnetic resonance imaging (dMRI). The accuracy and ability of FODs to resolve complex fiber configurations benefits from acquisition protocols that sample a high number of gradient directions, a high maximum b-value, and multiple b-values. However, acquisition time and scanners that follow these standards are limited in clinical settings, often resulting in dMRI acquired at a single shell (single b-value). In this work, we learn improved FODs from clinically acquired dMRI. We evaluate patch-based 3D convolutional neural networks (CNNs) on their ability to regress multi-shell FODs from single-shell FODs, using constrained spherical deconvolution (CSD). We evaluate U-Net and High-Resolution Network (HighResNet) 3D CNN architectures on data from the Human Connectome Project and an in-house dataset. check details We evaluate how well each CNN can resolve FODs 1) when training and testing on datasets with the same dMRI acquisition protocol; 2) when testing on a dataset with a different dMRI acquisition protocol than used to train the CNN; and 3) when testing on a dataset with a fewer number of gradient directions than used to train the CNN. This work is a step towards more accurate FOD estimation in time- and resource-limited clinical environments.Alternatives to nasopharyngeal sampling are needed to increase capacity for SARS-CoV-2 testing. Among 275 participants, we piloted the collection of nasal mid-turbinate swabs amenable to self-testing, including polyester flocked swabs as well as 3D-printed plastic lattice swabs, placed into viral transport media or an RNA stabilization agent. Flocked nasal swabs identified 104/121 individuals who were PCR-positive for SARS-CoV-2 by nasopharyngeal sampling (sensitivity 87%, 95% CI 79-92%), missing those with low viral load (105 viral copies/mL.

Attention deficit hyperactivity disorder (ADHD) is a widely recognized mental health problem in developed countries but remains under-investigated in developing settings. This study examines the prevalence, correlates, and consequences of ADHD symptoms among elementary school students in rural China.

Cross-sectional data were collected from 6,719 students across 120 rural primary schools in China on ADHD symptoms, demographic characteristics, and academic performance in reading and math. ADHD symptoms were evaluated using the caregiver-reported ADHD Rating Scale-IV.

The prevalence of ADHD symptoms was 7.5% in our sample. Male students, students in lower grade levels, and students with lower cognitive ability showed a significantly higher prevalence of ADHD symptoms (ORs=2.56, 2.06, and 1.84, respectively; p<0.05). Left-behind children showed a significantly lower prevalence of ADHD symptoms than did children who were living with their parents (OR=0.74, p < 0.05). Adjusted regressions show that stuchallenges. Taken together, the findings indicate a need for educators and policymakers in rural China to develop programs to reduce risk and support students with ADHD symptoms.As a proinflammatory cytokine of the interleukin-1 (IL-1) family, IL-18 plays important roles in host protection against bacterial, viral, and fungal infection. We cloned the open reading frame of snakehead (Channa argus) IL-18 (shIL-18) and found that it contained 609 base pairs and encoded 202 amino acid residues. The shIL-18 included a conserved IL-1-like family signature and two potential IL-1β-converting enzyme cutting sites; one was conserved in all analyzed IL-18s, but the other was unique to shIL-18. Unlike other IL-18s, shIL-18 also contained a predicted signal peptide. In this study, shIL-18 was constitutively expressed in all tested tissues, and its expression was induced by Aeromonas schubertii and Nocardia seriolae in the head kidney and spleen in vivo and by lipoteichoic acid, lipopolysaccharides, and polyinosinic-polycytidylic acid in head kidney leukocytes in vitro. Moreover, recombinant shIL-18 upregulated the expression of interferon-γ, IL-1β, and tumor necrosis factor-α1 and -α2 and promoted the proliferation of leukocytes. Taken together, these results showed that IL-18 played crucial roles in host defense against bacterial infection in fish, as it does in mammals.Interferon regulatory factor 8 (IRF8), also known as interferon consensus sequence-binding protein (ICSBP), is a negative regulatory factor of interferon (IFN) and plays an important role in cell differentiation and innate immunity in mammals. In recent years, some irf8 homologous genes have been cloned and confirmed to take part in innate immune response in fish, but the mechanism still remains unclear. In this paper, a grass carp (Ctenopharyngodon idella) irf8 gene (Ciirf8) was cloned and characterized. The deduced protein (CiIRF8) possesses a highly conserved N-terminal DNA binding domain but a less well-conserved C-terminal IRF association domain (IAD). Ciirf8 was widely expressed in all tested tissues of grass carp and up-regulated following poly(IC) stimulation. Ciirf8 expression was also up-regulated in CIK cells upon treatment with poly(IC). To explore the molecular mechanism of how fish IRF8 regulates ifn1 expression, the similarities and differences of grass carp IRF8 and IRF2 were compared and contrasted.