Anti-PD-L1/TGF-βR fusion protein (SHR-1701) overcomes disrupted lymphocyte recovery-induced resistance to PD-1/PD-L1 inhibitors in lung cancer

Background Second-generation programmed cell death-protein 1/programmed death-ligand 1 (PD-1/PD-L1) inhibitors, such as bintrafusp alfa (M7824), SHR-1701, and YM101, have been developed to simultaneously block PD-1/PD-L1 and transforming growth factor-beta/transforming growth factor-beta receptor (TGF-β/TGF-βR). Consequently, it is necessary to identify predictive factors of lung cancer patients who are not only resistant to PD-1/PD-L1 inhibitors but also sensitive to bifunctional drugs. The purpose of this study was to search for such predictors. Methods Multivariable Cox regression was used to study the association between the clinical outcome of treatment with PD-1/PD-L1 inhibitors and lymphocyte recovery after lymphopenia in lung cancer patients. Murine CMT167 lung cancer cells were engineered to express the firefly luciferase gene and implanted orthotopically in the lung of syngeneic mice. Bioluminescence imaging, flow cytometry, and immunohistochemistry were employed to determine response to immunotherapy and function of tumor-infiltrating immune cells. Results For lung cancer patients treated with anti-PD-1/PD-L1 antibodies, poor lymphocyte recovery was associated with a shorter progression-free survival (PFS; P < 0.001), an accumulation of regulatory T cells (Tregs), and an elimination of CD8+ T cells in the peripheral blood. Levels of CD8+ T cells and Treg cells were also imbalanced in the tumors and peripheral immune organs of mice with poor lymphocyte recovery after chemotherapy. Moreover, these mice failed to respond to anti-PD-1 antibodies but remained sensitive to the anti-PD-L1/TGF-βR fusion protein (SHR-1701). Consistently, SHR-1701 but not anti-PD-1 antibodies, markedly enhanced IFN-γ production and Ki-67 expression in peripheral CD8+ T cells from patients with impaired lymphocyte recovery. Conclusions Lung cancer patients with poor lymphocyte recovery and suffering from persistent lymphopenia after previous chemotherapy are resistant to anti-PD-1/PD-L1 antibodies but might be sensitive to second-generation agents such as SHR-1701.publishedVersio

Local Diffusion Homogeneity Provides Supplementary Information in T2DM-Related WM Microstructural Abnormality Detection

Objectives: We aimed to investigate whether an inter-voxel diffusivity metric (local diffusion homogeneity, LDH), can provide supplementary information to traditional intra-voxel metrics (i.e., fractional anisotropy, FA) in white matter (WM) abnormality detection for type 2 diabetes mellitus (T2DM).Methods: Diffusion tensor imaging was acquired from 34 T2DM patients and 32 healthy controls. Voxel-based group-difference comparisons based on LDH and FA, as well as the association between the diffusion metrics and T2DM risk factors [i.e., body mass index (BMI) and systolic blood pressure (SBP)], were conducted, with age, gender and education level controlled.Results: Compared to the controls, T2DM patients had higher LDH in the pons and left temporal pole, as well as lower FA in the left superior corona radiation (p &lt; 0.05, corrected). In T2DM, there were several overlapping WM areas associated with BMI as revealed by both LDH and FA, including right temporal lobe and left inferior parietal lobe; but the unique areas revealed only by using LDH included left inferior temporal lobe, right supramarginal gyrus, left pre- and post-central gyrus (at the semiovale center), and right superior radiation. Overlapping WM areas that associated with SBP were found with both LDH and FA, including right temporal pole, bilateral orbitofrontal area (rectus gyrus), the media cingulum bundle, and the right cerebellum crus I. However, the unique areas revealed only by LDH included right inferior temporal lobe, right inferior occipital lobe, and splenium of corpus callosum.Conclusion: Inter- and intra-voxel diffusivity metrics may have different sensitivity in the detection of T2DM-related WM abnormality. We suggested that LDH could provide supplementary information and reveal additional underlying brain changes due to diabetes

Convolutional Neural Networks for Classification of T2DM Cognitive Impairment Based on Whole Brain Structural Features

PurposeCognitive impairment is generally found in individuals with type 2 diabetes mellitus (T2DM). Although they may not have visible symptoms of cognitive impairment in the early stages of the disorder, they are considered to be at high risk. Therefore, the classification of these patients is important for preventing the progression of cognitive impairment.MethodsIn this study, a convolutional neural network was used to construct a model for classifying 107 T2DM patients with and without cognitive impairment based on T1-weighted structural MRI. The Montreal cognitive assessment score served as an index of the cognitive status of the patients.ResultsThe classifier could identify T2DM-related cognitive decline with a classification accuracy of 84.85% and achieved an area under the curve of 92.65%.ConclusionsThe model can help clinicians analyze and predict cognitive impairment in patients and enable early treatment

Movement Protein Pns6 of Rice dwarf phytoreovirus Has Both ATPase and RNA Binding Activities

Cell-to-cell movement is essential for plant viruses to systemically infect host plants. Plant viruses encode movement proteins (MP) to facilitate such movement. Unlike the well-characterized MPs of DNA viruses and single-stranded RNA (ssRNA) viruses, knowledge of the functional mechanisms of MPs encoded by double-stranded RNA (dsRNA) viruses is very limited. In particular, many studied MPs of DNA and ssRNA viruses bind non-specifically ssRNAs, leading to models in which ribonucleoprotein complexes (RNPs) move from cell to cell. Thus, it will be of special interest to determine whether MPs of dsRNA viruses interact with genomic dsRNAs or their derivative sRNAs. To this end, we studied the biochemical functions of MP Pns6 of Rice dwarf phytoreovirus (RDV), a member of Phytoreovirus that contains a 12-segmented dsRNA genome. We report here that Pns6 binds both dsRNAs and ssRNAs. Intriguingly, Pns6 exhibits non-sequence specificity for dsRNA but shows preference for ssRNA sequences derived from the conserved genomic 5′- and 3′- terminal consensus sequences of RDV. Furthermore, Pns6 exhibits magnesium-dependent ATPase activities. Mutagenesis identified the RNA binding and ATPase activity sites of Pns6 at the N- and C-termini, respectively. Our results uncovered the novel property of a viral MP in differentially recognizing dsRNA and ssRNA and establish a biochemical basis to enable further studies on the mechanisms of dsRNA viral MP functions

An open science resource for establishing reliability and reproducibility in functional connectomics

Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals’ resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included

Development and application of random forest regression soft sensor model for treating domestic wastewater in a sequencing batch reactor

Abstract Small-scale distributed water treatment equipment such as sequencing batch reactor (SBR) is widely used in the field of rural domestic sewage treatment because of its advantages of rapid installation and construction, low operation cost and strong adaptability. However, due to the characteristics of non-linearity and hysteresis in SBR process, it is difficult to construct the simulation model of wastewater treatment. In this study, a methodology was developed using artificial intelligence and automatic control system that can save energy corresponding to reduce carbon emissions. The methodology leverages random forest model to determine a suitable soft sensor for the prediction of COD trends. This study uses pH and temperature sensors as premises for COD sensors. In the proposed method, data were pre-processed into 12 input variables and top 7 variables were selected as the variables of the optimized model. Cycle ended by the artificial intelligence and automatic control system instead of by fixed time control that was an uncontrolled scenario. In 12 test cases, percentage of COD removal is about 91. 075% while 24. 25% time or energy was saved from an average perspective. This proposed soft sensor selection methodology can be applied in field of rural domestic sewage treatment with advantages of time and energy saving. Time-saving results in increasing treatment capacity and energy-saving represents low carbon technology. The proposed methodology provides a framework for investigating ways to reduce costs associated with data collection by replacing costly and unreliable sensors with affordable and reliable alternatives. By adopting this approach, energy conservation can be maintained while meeting emission standards

Recent Advances in Nanoparticle-Based Optical Sensors for Detection of Pesticide Residues in Soil

The excessive and unreasonable use of pesticides has adversely affected the environment and human health. The soil, one of the most critical natural resources supporting human survival and development, accumulates large amounts of pesticide residues. Compared to traditional spectrophotometry analytical methods, nanoparticle-based sensors stand out for their simplicity of operation as well as their high sensitivity and low detection limits. In this review, we focus primarily on the functions that various nanoparticles have and how they can be used to detect various pesticide residues in soil. A detailed discussion was conducted on the properties of nanoparticles, including their color changeability, Raman enhancement, fluorescence enhancement and quenching, and catalysis. We have also systematically reviewed the methodology for detecting insecticides, herbicides, and fungicides in soil by using nanoparticles