Non-coding RNA Generated following Lariat Debranching Mediates Targeting of AID to DNA.

Transcription through immunoglobulin switch (S) regions is essential for class switch recombination (CSR), but no molecular function of the transcripts has been described. Likewise, recruitment of activation-induced cytidine deaminase (AID) to S regions is critical for CSR; however, the underlying mechanism has not been fully elucidated. Here, we demonstrate that intronic switch RNA acts in trans to target AID to S region DNA. AID binds directly to switch RNA through G-quadruplexes formed by the RNA molecules. Disruption of this interaction by mutation of a key residue in the putative RNA-binding domain of AID impairs recruitment of AID to S region DNA, thereby abolishing CSR. Additionally, inhibition of RNA lariat processing leads to loss of AID localization to S regions and compromises CSR; both defects can be rescued by exogenous expression of switch transcripts in a sequence-specific manner. These studies uncover an RNA-mediated mechanism of targeting AID to DNA.

HimGNN: a novel hierarchical molecular graph representation learning framework for property prediction.

Accurate prediction of molecular properties is an important topic in drug discovery. Recent works have developed various representation schemes for molecular structures to capture different chemical information in molecules. The atom and motif can be viewed as hierarchical molecular structures that are widely used for learning molecular representations to predict chemical properties. Previous works have attempted to exploit both atom and motif to address the problem of information loss in single representation learning for various tasks. To further fuse such hierarchical information, the correspondence between learned chemical features from different molecular structures should be considered. Herein, we propose a novel framework for molecular property prediction, called hierarchical molecular graph neural networks (HimGNN). HimGNN learns hierarchical topology representations by applying graph neural networks on atom- and motif-based graphs. In order to boost the representational power of the motif feature, we design a Transformer-based local augmentation module to enrich motif features by introducing heterogeneous atom information in motif representation learning. Besides, we focus on the molecular hierarchical relationship and propose a simple yet effective rescaling module, called contextual self-rescaling, that adaptively recalibrates molecular representations by explicitly modelling interdependencies between atom and motif features. Extensive computational experiments demonstrate that HimGNN can achieve promising performances over state-of-the-art baselines on both classification and regression tasks in molecular property prediction.

Plant genetic effects on microbial hubs impact host fitness in repeated field trials.

Although complex interactions between hosts and microbial associates are increasingly well documented, we still know little about how and why hosts shape microbial communities in nature. In addition, host genetic effects on microbial communities vary widely depending on the environment, obscuring conclusions about which microbes are impacted and which plant functions are important. We characterized the leaf microbiota of 200 Arabidopsis thaliana genotypes in eight field experiments and detected consistent host effects on specific, broadly distributed microbial species (operational taxonomic unit [OTUs]). Host genetic effects disproportionately influenced central ecological hubs, with heritability of particular OTUs declining with their distance from the nearest hub within the microbial network. These host effects could reflect either OTUs preferentially associating with specific genotypes or differential microbial success within them. Host genetics associated with microbial hubs explained over 10% of the variation in lifetime seed production among host genotypes across sites and years. We successfully cultured one of these microbial hubs and demonstrated its growth-promoting effects on plants in sterile conditions. Finally, genome-wide association mapping identified many putatively causal genes with small effects on the relative abundance of microbial hubs across sites and years, and these genes were enriched for those involved in the synthesis of specialized metabolites, auxins, and the immune system. Using untargeted metabolomics, we corroborate the consistent association between variation in specialized metabolites and microbial hubs across field sites. Together, our results reveal that host genetic variation impacts the microbial communities in consistent ways across environments and that these effects contribute to fitness variation among host genotypes.

Phase I trial of apatinib and paclitaxel+oxaliplatin+5-FU/levoleucovorin for treatment-naïve advanced gastric cancer.

Chinese guidelines recommend POF (paclitaxel, oxaliplatin, and 5-FU/levoleucovorin) as first-line treatment for advanced gastric cancer (AGC). Apatinib can augment the antitumor effect of paclitaxel, oxaliplatin, or fluorouracil in preclinical studies of AGC. A phase I clinical trial was conducted to evaluate the anticancer activity and maximum tolerated dose (MTD) of apatinib plus POF in treatment-naïve patients with AGC and to establish a recommended phase II dose. Participants received escalating doses of daily oral apatinib (250, 375, 500, 625, 750, and 850 mg) plus POF every 2 weeks using a conventional "3 + 3" study design. Among 21 treated patients, one experienced a dose-limiting toxicity (grade 3 skin ulceration at 850 mg). No MTD was reached. Apatinib 750 mg plus POF was recommended for phase II study. The most common grade 3-4 adverse events (AEs) were neutropenia (33.3%), mucositis (14.3%), and hand-foot syndrome (14.3%). Median progression-free and overall survival were 10.4 months (95% CI: 6.3, 14.6) and 18.4 months (95% CI: 9.8, 28.2), respectively. Apatinib up to 850 mg coadministered with POF was well tolerated with manageable AEs. The safety and anticancer activity of this regimen warrants its further investigation as first-line treatment for AGC in a larger study.

PASS-ALL study of paediatric-inspired versus adult chemotherapy regimens on survival of high-risk Philadelphia-negative B-cell acute lymphoblastic leukaemia with allogeneic haematopoietic stem cell transplantation.

This PASS-ALL study was designed to explore the effect of paediatric-inspired versus adult chemotherapy regimens on survival of adolescents and young adults (AYA) with high-risk Philadelphia chromosome-negative B-cell acute lymphoblastic leukaemia (HR PH-ve B-cell ALL) eligible for allogeneic haematopoietic stem cell transplantation (allo-HSCT). The PASS-ALL study is a multicentre, observational cohort study, and 143 patients with HR B-cell PH-ve ALL were enrolled from five centres-77 patients allocated in the paediatric-inspired cohort and 66 in the adult cohort with comparable baseline characteristics. Of the 143 patients, 128 cases underwent allo-HSCT. Three-year leukaemia-free survival (LFS) in the paediatric-inspired cohort was 72.2% (95% CI 60.8%-83.6%) compared with 44.6% (95% CI 31.9%-57.3%; p = 0.001). Furthermore, time-to-positive minimal residual disease (TTP-MRD) post-HSCT was marked different, 3-year cumulative incidence of relapse was 25.9% (95% CI 15.8%-37.2%) in paediatric cohort and 45.4% (95% CI 40.0%-57.9%) in adult cohort (p = 0.026). Finally, the 3-year OS rate was 75.3% (95% CI 64.9%-85.7%) for the paediatric-inspired cohort and 64.1% (95% CI 51.8%-76.4%) for the adult cohort (p = 0.074). On a multivariate analysis, paediatric-inspired regimen is a predictive factor for LFS (HR = 2.540, 95% CI 1.327-4.862, p = 0.005). Collectively, our data suggest that paediatric-inspired chemotherapy pre-HSCT results in deeper and durable MRD response reduces relapse post-HSCT and improves survival in HR B-cell PH-ve ALL patients with allo-HSCT.

Expanding Multiple-Resonance Structure of a Double-Borylated Skeleton by Fusing with Indolocarbazole a Multiple-Resonance Donor for Narrowband Green Emission.

Double-borylated multiple-resonance (MR) skeletons are promising templates for high performance, while the chemical design space is relatively limited. Peripheral segments are often used to decorate/fuse MR skeletons and modulate the photophysics but they can also cause unwanted spectral broadening. Herein, a narrowband MR emitter ICzDBA by fusing an MR-featured donor segment indolocarbazole into a double-borylated MR skeleton is developed. In ICzDBA, the nitrogen atom located away from the core benzene ring can also contribute to the generation of the overall MR-featured distribution through the long-range conjugation effect, along with the other boron/nitrogen atoms on the phenyl center. Thus, ICzDBA in toluene displays a narrowband emission peaking at 507 nm with a full width at half maximum of merely 20 nm (0.09 eV). Moreover, organic light-emitting diode devices using ICzDBA emitter exhibit ultrapure green emission with Commission Internationale de l'Eclairage (CIE) coordinates of (0.27, 0.70) and a high external quantum efficiency of 32.5%. These results manifest the importance of MR characters of peripheral decorations/fusions in preserving the narrowband features of MR skeletons, which provides a solution for further expanding MR structures with well-maintained narrowband characters.

HMGB1-RAGE axis contributes to myocardial ischemia/reperfusion injury via regulation of cardiomyocyte autophagy and apoptosis in diabetic mice.

Patients with acute myocardial infarction complicated with diabetes are more likely to develop myocardial ischemia/reperfusion (I/R) injury (MI/RI) during reperfusion therapy. Both HMGB1 and RAGE play important roles in MI/RI. However, the specific mechanisms of HMGB1 associated with RAGE are not fully clarified in diabetic MI/RI. This study aimed to investigate whether the HMGB1-RAGE axis induces diabetic MI/RI via regulating autophagy and apoptosis. A db/db mouse model of MI/RI was established, where anti-HMGB1 antibody and RAGE inhibitor (FPS-ZM1) were respectively injected after 10 min of reperfusion. The results showed that treatment with anti-HMGB1 significantly reduced the infarct size, serum LDH, and CK-MB level. Similar situations also occurred in mice administrated with FPS-ZM1, though the HMGB1 level was unchanged. Then, we found that treatment with anti-HMGB1 or FPS-ZM1 performed the same effects in suppressing the autophagy and apoptosis, as reflected by the results of lower LAMP2 and LC3B levels, increased Bcl-2 level, reduced BAX and caspase-3 levels. Moreover, the Pink1/Parkin levels were also inhibited at the same time. Collectively, this study indicates that the HMGB1-RAGE axis aggravated diabetic MI/RI via apoptosis and Pink1/Parkin mediated autophagy pathways, and inhibition of HMGB1 or RAGE contributes to alleviating those adverse situations.

A heterogeneous network-based method with attentive meta-path extraction for predicting drug-target interactions.

Predicting drug-target interactions (DTIs) is crucial at many phases of drug discovery and repositioning. Many computational methods based on heterogeneous networks (HNs) have proved their potential to predict DTIs by capturing extensive biological knowledge and semantic information from meta-paths. However, existing methods manually customize meta-paths, which is overly dependent on some specific expertise. Such strategy heavily limits the scalability and flexibility of these models, and even affects their predictive performance. To alleviate this limitation, we propose a novel HN-based method with attentive meta-path extraction for DTI prediction, named HampDTI, which is capable of automatically extracting useful meta-paths through a learnable attention mechanism instead of pre-definition based on domain knowledge. Specifically, by scoring multi-hop connections across various relations in the HN with each relation assigned an attention weight, HampDTI constructs a new trainable graph structure, called meta-path graph. Such meta-path graph implicitly measures the importance of every possible meta-path between drugs and targets. To enable HampDTI to extract more diverse meta-paths, we adopt a multi-channel mechanism to generate multiple meta-path graphs. Then, a graph neural network is deployed on the generated meta-path graphs to yield the multi-channel embeddings of drugs and targets. Finally, HampDTI fuses all embeddings from different channels for predicting DTIs. The meta-path graphs are optimized along with the model training such that HampDTI can adaptively extract valuable meta-paths for DTI prediction. The experiments on benchmark datasets not only show the superiority of HampDTI in DTI prediction over several baseline methods, but also, more importantly, demonstrate the effectiveness of the model discovering important meta-paths.

GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction.

Predicting the response of a cancer cell line to a therapeutic drug is an important topic in modern oncology that can help personalized treatment for cancers. Although numerous machine learning methods have been developed for cancer drug response (CDR) prediction, integrating diverse information about cancer cell lines, drugs and their known responses still remains a great challenge. In this paper, we propose a graph neural network method with contrastive learning for CDR prediction. GraphCDR constructs a graph neural network based on multi-omics profiles of cancer cell lines, the chemical structure of drugs and known cancer cell line-drug responses for CDR prediction, while a contrastive learning task is presented as a regularizer within a multi-task learning paradigm to enhance the generalization ability. In the computational experiments, GraphCDR outperforms state-of-the-art methods under different experimental configurations, and the ablation study reveals the key components of GraphCDR: biological features, known cancer cell line-drug responses and contrastive learning are important for the high-accuracy CDR prediction. The experimental analyses imply the predictive power of GraphCDR and its potential value in guiding anti-cancer drug selection.

Unveiling the distinctive ossification patterns of diffuse idiopathic skeletal hyperostosis and ankylosing spondylitis using computed tomography three-dimensional reconstruction.

OBJECTIVE: To quantitatively analyze the morphological characteristics of osteophytes in DISH and syndesmophytes in AS, and summarize different ossification patterns to help identify the two diseases. Associated factors for new bone formation would be investigated. METHODS: Fifty patients with DISH and 50 age-, sex-, CT examination site- matched patients with AS were enrolled. Radiographic and clinical data were reviewed. Osteophytes (syndesmophytes) in front of each vertebral body and the corresponding intervertebral disc space were defined as vertebral osteophytes unit (VOU). The volume, angle and location (contralateral, ipsilateral, bilateral) of osteophytes in each VOU were measured and compared between DISH and AS groups. RESULTS: In each VOU, the volume and angle of osteophytes in DISH were significantly larger. The best osteophytes volume and angle cutoff value in predicting DISH was 0.59 cm3 and 40.15°. Contralateral, bilateral, ipsilateral osteophytes were recorded in 59.32%, 36.38%, 4.3% of assessed VOUs in patients with DISH and 64.78%, 29.31%, 5.91% in AS (p<0.001), respectively. As to ipsilateral osteophytes, the volume was inversely correlated with the center of the vertebral body to the center of the descending aorta (DISH: r = -0.45, p= 0.01; AS: r = -0.83, p<0.001). Advanced age, disease duration, smoking and overweight contribute to the progression of osteophytes and syndesmophytes. CONCLUSION: Morphological features of osteophytes are helpful to distinguish DISH with AS. Aortic pulsations inhibit or hinder new bone formation in both DISH and AS. Maintaining normal BMI could postpone osteophytes formation.

Cooperative correction method for distortion and dispersion of deflected field of view in Risley-prism bionic-human-eye imaging systems.

The Risley-prism imaging system (RPIS) is a powerful way to achieve bionic human eye imaging with great advantages on large field of view (FOV) and variable resolution imaging owing to the autonomous controlled deflection of light. But the imaging dispersion originating from nonlinear and uneven light deflection results in limited imaging wavelength that seriously hinders its application. The existing solutions for imaging dispersion mainly rely on the hardware, which generally has bulky structure and limited improvement on image. Besides, the existing image evaluation methods for dispersion are not suitable for RPIS due to inhomogeneous dispersion. Herein, this paper systematically analyzes the mechanism and characteristics of dispersion in the RPIS, and proposes a cooperative correction method for image distortion and dispersion of multiple-color imaging, achieving the elimination of distortion and dispersion simultaneously without changing the optical structure. A dispersion evaluation index based on Pearson's correlation coefficient (PCC) is also established, and the objectivity and validity of the index are proved by experiments. Furthermore, a kind of compact RPIS based on an RGB camera is built, and both indoor and outdoor experiments are conducted. The experimental results demonstrate that proposed algorithm has strong universality and robustness for various scenes and targets.

Dual-polarization defogging method based on frequency division and blind separation of polarization information.

The current advancements in image processing have led to significant progress in polarization defogging methods. However, most existing approaches are not suitable for scenes with targets exhibiting a high degree of polarization (DOP), as they rely on the assumption that the detected polarization information solely originates from the airlight. In this paper, a dual-polarization defogging method connecting frequency division and blind separation of polarization information is proposed. To extract the polarization component of direct transmission light from the detected polarized signal, blind separation of overlapped polarized information is performed in the low-frequency domain based on visual perception. Subsequently, after estimating airlight, a high-quality defogging image can be restored. Extensive experiments conducted on real-world scenes and comparative tests confirm the superior performance of our proposed method compared to other competitive methods, particularly in reconstructing objects with high DOP. This work provides a quantitative approach for estimating the contributions of polarization light from different sources and further expands the application range of polarimetric defogging imaging.

Bio-inspired foveal super-resolution method for multi-focal-length images based on local gradient constraints.

Most existing super-resolution (SR) imaging systems, inspired by the bionic compound eye, utilize image registration and reconstruction algorithms to overcome the angular resolution limitations of individual imaging systems. This article introduces a multi-aperture multi-focal-length imaging system and a multi-focal-length image super-resolution algorithm, mimicking the foveal imaging of the human eye. Experimental results demonstrate that with the proposed imaging system and an SR imaging algorithm inspired by the human visual system, the proposed method can enhance the spatial resolution of the foveal region by up to 4 × compared to the original acquired image. These findings validate the effectiveness of the proposed imaging system and computational imaging algorithm in enhancing image texture and spatial resolution.

Joint constraints of guided filtering based confidence and nonlocal sparse tensor for color polarization super-resolution imaging.

This paper introduces a camera-array-based super-resolution color polarization imaging system designed to simultaneously capture color and polarization information of a scene in a single shot. Existing snapshot color polarization imaging has a complex structure and limited generalizability, which are overcome by the proposed system. In addition, a novel reconstruction algorithm is designed to exploit the complementarity and correlation between the twelve channels in acquired color polarization images for simultaneous super-resolution (SR) imaging and denoising. We propose a confidence-guided SR reconstruction algorithm based on guided filtering to enhance the constraint capability of the observed data. Additionally, by introducing adaptive parameters, we effectively balance the data fidelity constraint and the regularization constraint of nonlocal sparse tensor. Simulations were conducted to compare the proposed system with a color polarization camera. The results show that color polarization images generated by the proposed system and algorithm outperform those obtained from the color polarization camera and the state-of-the-art color polarization demosaicking algorithms. Moreover, the proposed algorithm also outperforms state-of-the-art SR algorithms based on deep learning. To evaluate the applicability of the proposed imaging system and reconstruction algorithm in practice, a prototype was constructed for color polarization image acquisition. Compared with conventional acquisition, the proposed solution demonstrates a significant improvement in the reconstructed color polarization images.

Y4 RNA fragments from cardiosphere-derived cells ameliorate diabetic myocardial ischemia‒reperfusion injury by inhibiting protein kinase C ß-mediated macrophage polarization.

The specific pathophysiological pathways through which diabetes exacerbates myocardial ischemia/reperfusion (I/R) injury remain unclear; however, dysregulation of immune and inflammatory cells, potentially driven by abnormalities in their number and function due to diabetes, may play a significant role. In the present investigation, we simulated myocardial I/R injury by inducing ischemia through ligation of the left anterior descending coronary artery in mice for 40 min, followed by reperfusion for 24 h. Previous studies have indicated that protein kinase Cß (PKCß) is upregulated under hyperglycemic conditions and is implicated in the development of various diabetic complications. The Y4 RNA fragment is identified as the predominant small RNA component present in the extracellular vesicles of cardio sphere-derived cells (CDCs), exhibiting notable anti-inflammatory properties in the contexts of myocardial infarction and cardiac hypertrophy. Our investigation revealed that the administration of Y4 RNA into the ventricular cavity of db/db mice following myocardial I/R injury markedly enhanced cardiac function. Furthermore, Y4 RNA was observed to facilitate M2 macrophage polarization and interleukin-10 secretion through the suppression of PKCß activation. The mechanism by which Y4 RNA affects PKCß by regulating macrophage activation within the inflammatory environment involves the inhibition of ERK1/2 phosphorylation In our study, the role of PKCß in regulating macrophage polarization during myocardial I/R injury was investigated through the use of PKCß knockout mice. Our findings indicate that PKCß plays a crucial role in modulating the inflammatory response associated with macrophage activation in db/db mice experiencing myocardial I/R, with a notable exacerbation of this response observed upon significant upregulation of PKCß expression. In vitro studies further elucidated the protective mechanism by which Y4 RNA modulates the PKCß/ERK1/2 signaling pathway to induce M2 macrophage activation. Overall, our findings suggest that Y4 RNA plays an anti-inflammatory role in diabetic I/R injury, suggesting a novel therapeutic approach for managing myocardial I/R injury in diabetic individuals.

Molecular cloning and gene expression of acc2 from grass carp (Ctenopharyngodon idella) and the regulation of glucose metabolism by ACCs inhibitor.

BACKGROUND: Acetyl-CoA carboxylase (ACC) catalyzes the carboxylation of acetyl-CoA to malonyl-CoA. Malonyl-CoA, which plays a key role in regulating glucose and lipid metabolism, is not only a substrate for fatty acid synthesis but also an inhibitor of the oxidation pathway. ACC exists as two isoenzymes that are encoded by two different genes. ACC1 in grass carp (Ctenopharyngodon idellus) has been cloned and sequenced. However, studies on the cloning, tissue distribution, and function of ACC2 in grass carp were still rare. METHODS AND RESULTS: The full-length cDNA of acc2 was 8537 bp with a 7146 bp open reading frame encoding 2381 amino acids. ACC2 had a calculated molecular weight of 268.209 kDa and an isoelectric point of 5.85. ACC2 of the grass carp shared the closest relationship with that of the common carp (Sinocyclocheilus grahami). The expressions of acc1 and acc2 mRNA were detected in all examined tissues.  The expression level of acc1 was high in the brain and fat but absent in the midgut and hindgut. The expression level of acc2 in the kidney was significantly higher than in other tissues, followed by the heart, brain, muscle, and spleen. ACCs inhibitor significantly reduced the levels of glucose, malonyl-CoA, and triglyceride in hepatocytes. CONCLUSIONS: This study showed that the function of ACC2 was evolutionarily conserved from fish to mammals. ACCs inhibitor inhibited the biological activity of ACCs, and reduced fat accumulation in grass carp.

Fanconi Anemia Complementary Group A (FANCA) Facilitates the Occurrence and Progression of Liver Hepatocellular Carcinoma.

BACKGROUND: Liver hepatocellular carcinoma (LIHC) is a serious liver disease worldwide, and its pathogenesis is complicated. AIMS: This study investigated the potential role of FANCA in the advancement and prognosis of LIHC. METHODS: Public databases, quantitative reverse transcription polymerase chain reaction (qRT-PCR), western blot (WB) and immunohistochemistry (IHC) were employed to measure FANCA expression between tumor and normal samples. The relationship between FANCA expression and prognosis of LIHC patients were examined. Functional enrichment of FANCA-related genes was performed. Furthermore, univariate and multivariate analyses were conducted to determine the independent prognosis value of FANCA in LIHC. Finally, influence of FANCA knockout on the proliferation, migration, and invasion of HepG2 cell was validated with cloning formation, CCK8, and Transwell assays. RESULTS: Expression analysis presented that FANCA had high expression level in LIHC tissues and cells. Receiver operating characteristic (ROC) curve analysis showed that FANCA was of great diagnosis value in LIHC. Clinicopathological analysis revealed that FANCA was significantly greater expressed in the advanced stage than in the early stage of LIHC. Univariate, multivariate, and Kaplan-Meier survival analysis confirmed that high expression of FANCA was strongly associated with poor survival of LIHC patients. In addition, high level of FANCA in LIHC showed a negative association with immunoinfiltrated B cells, T cells, and stromal scores. Moreover, Knockout of FANCA significantly inhibited HepG2 cell proliferative activity, migration, and invasion ability. CONCLUSIONS: Our data revealed that high level of FANCA was closely associated with LIHC malignant progression, suggesting its potential utility as a diagnostic, predictive indicator, and therapeutic target.

Multispectral image defogging based on a wavelength-dependent extinction coefficient model in fog.

Most of the state-of-the-art defogging models presented in the literature assume that the attenuation coefficient of all spectral channels is constant, which inevitably leads to spectral distortion and information bias. To address this issue, this paper proposes a defogging method that takes into account the difference between the extinction coefficients of multispectral channels of light traveling through fog. Then the spatially distributed transmission map of each spectral channel is reconstructed to restore the fog-degraded images. The experimental results of various realistic complex scenes show that the proposed method has more outstanding advantages in restoring lost detail, compensating for degraded spectral information, and recognizing more targets hidden in uniform ground fog than state-of-the-art technologies. In addition, this work provides a method to characterize the intrinsic property of fog expressed as multispectral relative extinction coefficients, which act as a fundament for further reconstruction of multispectral information.

The ORF8 protein of SARS-CoV-2 mediates immune evasion through down-regulating MHC-Ι.

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global pandemic and has claimed over 2 million lives worldwide. Although the genetic sequences of SARS-CoV and SARS-CoV-2 have high homology, the clinical and pathological characteristics of COVID-19 differ significantly from those of SARS. How and whether SARS-CoV-2 evades (cellular) immune surveillance requires further elucidation. In this study, we show that SARS-CoV-2 infection leads to major histocompability complex class Ι (MHC-Ι) down-regulation both in vitro and in vivo. The viral protein encoded by open reading frame 8 (ORF8) of SARS-CoV-2, which shares the least homology with SARS-CoV among all viral proteins, directly interacts with MHC-Ι molecules and mediates their down-regulation. In ORF8-expressing cells, MHC-Ι molecules are selectively targeted for lysosomal degradation via autophagy. Thus, SARS-CoV-2-infected cells are much less sensitive to lysis by cytotoxic T lymphocytes. Because ORF8 protein impairs the antigen presentation system, inhibition of ORF8 could be a strategy to improve immune surveillance.

Intra-abdominal pressure and residual renal function decline in peritoneal dialysis: a threshold-based investigation.

BACKGROUND: The potential impact of elevated intra-abdominal pressure (IAP) on residual renal function (RRF) has not been determined. The objective of this study was to investigate the relationship between IAP and the rate of RRF decline in newly initiated peritoneal dialysis (PD) patients, and to identify the optimal IAP threshold value for delaying the deterioration of RRF. METHODS: A cohort of 62 newly initiated PD patients who completed both 6- and 12-month follow-up evaluations was obtained using the Durand method. A logistic regression model was used to identify variables associated with a rapid decline in RRF. Receiver operating characteristic (ROC) curves were generated to determine the optimal threshold value. Another retrospective cohort analysis was performed to validate the identified critical value. RESULTS: For each 1 cmH2O increase in IAP, the risk of a rapid decline in the RRF increased by a factor of 1.679. Subsequent analysis revealed that patients in the high IAP group had more significant decreases in residual renal estimated glomerular filtration rate (eGFR) (Z = -3.694, p < 0.001) and urine volume (Z = -3.121, p < 0.001) than did those in the non-high IAP group. Furthermore, an IAP ≥15.65 cmH2O was a robust discriminator for the prediction of the rate of RRF decline. CONCLUSION: Patients in the high IAP group experienced a more rapid decline in RRF. Additionally, an optimal critical pressure of 15.65 cmH2O was identified for predicting the rate of RRF decline. IAP, as one of the factors contributing to the rapid decline in RRF in the first year of PD, should be given due attention.