Efficacy of acupuncture in refractory irritable bowel syndrome patients: a randomized controlled trial.

Previous studies have confirmed that acupuncture for irritable bowel syndrome (IBS) provided an additional benefit over usual care alone. Therefore, we performed a multicenter, randomized, sham-controlled trial to assess the efficacy and safety of acupuncture versus sham acupuncture for refractory IBS in patients in the context of conventional treatments. Patients in the acupuncture and sham acupuncture groups received real or sham acupuncture treatment in 3 sessions per week for a total of 12 sessions. The primary outcome was a change in the IBS-Symptom Severity Scale (IBS-SSS) score from baseline to week 4. A total of 521 participants were screened, and 170 patients (85 patients per group) were enrolled and included in the intention-to-treat analysis. Baseline characteristics were comparable across the two groups. From baseline to 4 weeks, the IBS-SSS total score decreased by 140.0 (95% CI: 126.0 to 153.9) in the acupuncture group and 64.4 (95% CI: 50.4 to 78.3) in the sham acupuncture group. The between-group difference was 75.6 (95% CI: 55.8 to 95.4). Acupuncture efficacy was maintained during the 4-week follow-up period. There were no serious adverse events. In conclusion, acupuncture provided benefits when combined with treatment as usual, providing more options for the treatment of refractory IBS.

Radiomics-based analysis of dynamic contrast-enhanced magnetic resonance image: A prediction nomogram for lymphovascular invasion in breast cancer.

OBJECTIVE: To develop and validate a nomogram for quantitively predicting lymphovascular invasion (LVI) of breast cancer (BC) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and morphological features. METHODS: We retrospectively divided 238 patients with BC into training and validation cohorts. Radiomic features from DCE-MRI were subdivided into A1 and A2, representing the first and second post-contrast images respectively. We utilized the minimal redundancy maximal relevance filter to extract radiomic features, then we employed the least absolute shrinkage and selection operator regression to screen these features and calculate individualized radiomics score (Rad score). Through the application of multivariate logistic regression, we built a prediction nomogram that integrated DCE-MRI radiomics and MR morphological features (MR-MF). The diagnostic capabilities were evaluated by comparing C-indices and calibration curves. RESULTS: The diagnostic efficiency of the A1/A2 radiomics model surpassed that of the A1 and A2 alone. Furthermore, we incorporated the MR-MF (diffusion-weighted imaging rim sign, peritumoral edema) and optimized Radiomics into a hybrid nomogram. The C-indices for the training and validation cohorts were 0.868 (95% CI: 0.839-0.898) and 0.847 (95% CI: 0.787-0.907), respectively, indicating a good level of discrimination. Moreover, the calibration plots demonstrated excellent agreement in the training and validation cohorts, confirming the effectiveness of the calibration. CONCLUSION: This nomogram combined MR-MF and A1/A2 Radiomics has the potential to preoperatively predict LVI in patients with BC.

Pea protein-inulin conjugate prepared by atmospheric pressure plasma jet combined with glycosylation: structure and emulsifying properties.

Pea protein is one of plant proteins with high nutritional value, but its lower solubility and poor emulsifying properties limit its application in food industry. Based on wet-heating glycosylation of pea protein and inulin, effects of discharge power of atmospheric pressure plasma jet (APPJ) on structure, solubility, and emulsifying ability of pea protein-inulin glycosylation conjugate were explored. Results indicated that the APPJ discharge power did not affect the primary structure of pea protein. However, changes in secondary and spatial structure of pea protein were observed. When APPJ discharge power was 600 W, the solubility of glycosylation conjugate was 75.0% and the emulsifying stability index was 98.9 min, which increased by 14.85 and 21.95% than that of only glycosylation sample, respectively. These findings could provide technical support for APPJ treatment combination with glycosylation to enhance the physicochemical properties of plant-based proteins.

Computer-aided molecular design and optimization of potent inhibitors disrupting APC‒Asef interaction.

Colorectal cancer (CRC) is the second leading cause of cancer mortality worldwide. At initial diagnosis, approximately 20% of patients are diagnosed with metastatic CRC (mCRC). Although the APC‒Asef interaction is a well-established target for mCRC therapy, the discovery and development of effective and safe drugs for mCRC patients remains an urgent and challenging endeavor. In this study, we identified a novel structural scaffold based on MAI inhibitors, the first-in-class APC‒Asef inhibitors we reported previously. ONIOM model-driven optimizations of the N-terminal cap and experimental evaluations of inhibitory activity were performed, and 24-fold greater potency was obtained with the best inhibitor compared to the parental compound. In addition, the cocrystal structure validated that the two-layer π‒π stacking interactions were essential for inhibitor stabilization in the bound state. Furthermore, in vitro and in vivo studies have demonstrated that novel inhibitors suppressed lung metastasis in CRC by disrupting the APC‒Asef interaction. These results provide an intrinsic structural basis to further explore drug-like molecules for APC‒Asef-mediated CRC therapy.

Automated classification of cellular expression in multiplexed imaging data with Nimbus.

Multiplexed imaging offers a powerful approach to characterize the spatial topography of tissues in both health and disease. To analyze such data, the specific combination of markers that are present in each cell must be enumerated to enable accurate phenotyping, a process that often relies on unsupervised clustering. We constructed the Pan-Multiplex (Pan-M) dataset containing 197 million distinct annotations of marker expression across 15 different cell types. We used Pan-M to create Nimbus, a deep learning model to predict marker positivity from multiplexed image data. Nimbus is a pre-trained model that uses the underlying images to classify marker expression across distinct cell types, from different tissues, acquired using different microscope platforms, without requiring any retraining. We demonstrate that Nimbus predictions capture the underlying staining patterns of the full diversity of markers present in Pan-M. We then show how Nimbus predictions can be integrated with downstream clustering algorithms to robustly identify cell subtypes in image data. We have open-sourced Nimbus and Pan-M to enable community use at https://github.com/angelolab/Nimbus-Inference.

Animal models of studying the pathogenesis of multi-organ tissue damage in lupus.

Moderate-to-severe systemic lupus erythematosus (SLE) is characterized by extensive autoantibody deposition and persistent autoinflammation. As the existing animal models are limited in accurately reproducing the pathological characteristics of human SLE, we introduced a novel animal model simulating multi-organ autoinflammation through intra-organ injections. The model closely mimicked key features of SLE, including IgG deposition, inflammation, and tissue damage. The model could be used to assess the roles of IgG, immune cells, cytokines, and Fc gamma receptor (FcγR) in the pathogenesis of autoinflammation. The results obtained from this model could be confirmed by lupus MRL/lpr mice. The review suggested that the diagnostic criteria should be reconsidered to incorporate IgG deposition in tissues and highlighted the limitations of current T-cell and B-cell-focused treatments. To summarize, the IgG deposition model can be used to investigate the pathogenesis and treatment of multi-organ tissue damage associated with SLE.

Identification of Microorganism in Infected Wounds by Positively Charged Selective Sensor Array and Deep Learning Algorithm.

Microorganism are ubiquitous and intimately connected with human health and disease management. The accurate and fast identification of pathogenic microorganisms is especially important for diagnosing infections. Herein, three tetraphenylethylene derivatives (S-TDs: TBN, TPN, and TPI) featuring different cationic groups, charge numbers, emission wavelengths, and hydrophobicities were successfully synthesized. Benefiting from distinct cell wall binding properties, S-TDs were collectively utilized to create a sensor array capable of imaging various microorganisms through their characteristic fluorescent signatures. Furthermore, the interaction mechanism between S-TDs and different microorganisms was explored by calculating the binding energy between S-TDs and cell membrane/wall constituents, including phospholipid bilayer and peptidoglycan. Using a combination of the fluorescence sensor array and a deep learning model of residual network (ResNet), readily differentiation of Gram-negative bacteria (G-), Gram-positive bacteria (G+), fungi, and their mixtures was achieved. Specifically, by extensive training of two ResNet models with large quantities of images data from 14 kinds of microorganism stained with S-TDs, identification of microorganism was achieved at high-level accuracy: over 92.8% for both Gram species and antibiotic-resistant species, with 90.35% accuracy for the detection of mixed microorganism in infected wound. This novel method provides a rapid and accurate method for microbial classification, potentially aiding in the diagnosis and treatment of infectious diseases.

Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning.

Image-based spatial transcriptomics methods enable transcriptome-scale gene expression measurements with spatial information but require complex, manually tuned analysis pipelines. We present Polaris, an analysis pipeline for image-based spatial transcriptomics that combines deep-learning models for cell segmentation and spot detection with a probabilistic gene decoder to quantify single-cell gene expression accurately. Polaris offers a unifying, turnkey solution for analyzing spatial transcriptomics data from multiplexed error-robust FISH (MERFISH), sequential fluorescence in situ hybridization (seqFISH), or in situ RNA sequencing (ISS) experiments. Polaris is available through the DeepCell software library (https://github.com/vanvalenlab/deepcell-spots) and https://www.deepcell.org.

Polyhedral Oligomeric Sesquioxane Cross-Linked Chitosan-Based Multi-Effective Aerogel Preparation and Its Water-Driven Recovery Mechanism.

The use of environmentally friendly and non-toxic biomass-based interfacial solar water evaporators has been widely reported as a method for water purification in recent years. However, the poor stability of the water transport layer made from biomass materials and its susceptibility to deformation when exposed to harsh environments limit its practical application. To address this issue, water-driven recovery aerogel (PCS) was prepared by cross-linking epoxy-based polyhedral oligomeric silsesquioxane (EP-POSS) epoxy groups with chitosan (CS) amino groups. The results demonstrate that PCS exhibits excellent water-driven recovery performance, regaining its original volume within a very short time (1.9 s) after strong compression (ε > 80%). Moreover, PCS has a water absorption rate of 2.67 mm s-1 and exhibits an excellent water absorption capacity of 22.09 g g-1 even after ten cycles of absorption-removal. Furthermore, a photothermal evaporator (PCH) was prepared by loading the top layer with hydrothermally reacted tannins (HAs) and Zn2+ complexes. The results indicate that PCH achieves an impressive evaporation rate of 1.89 kg m-2 h-1 under one sun illumination. Additionally, due to the antimicrobial properties of Zn2+, PCH shows inhibitory effects against Staphylococcus aureus and Escherichia coli, thereby extending the application of solar water evaporators to include antimicrobial purification in natural waters.

VvERF117 positively regulates grape cold tolerance through direct regulation of the antioxidative gene BAS1.

Cold stress significantly threatens grape quality, yield, and geographical distribution. Although ethylene-responsive factors (ERFs) are recognized for their pivotal roles in cold stress, the regulatory mechanisms of many ERFs contributing to tolerance remain unclear. In this study, we identified the cold-responsive gene VvERF117 and elucidated its positive regulatory function in cold tolerance. VvERF117 exhibits transcriptional activity and localizes to the nucleus. VvERF117 overexpression improved cold tolerance in transgenic Arabidopsis, grape calli, and grape leaves, whereas VvERF117 silencing increased cold sensitivity in grape calli and leaves. Furthermore, VvERF117 overexpression remarkably upregulated the expression of several stress-related genes. Importantly, BAS1, encoding a 2-Cys peroxidase (POD), was confirmed as a direct target gene of VvERF117. Meanwhile, compared to the wild-type, POD activity and H2O2 content were remarkably increased and decreased in VvERF117-overexpressing grape calli and leaves, respectively. Conversely, VvERF117 silencing displayed the opposite trend in grape calli and leaves under cold stress. These findings indicate that VvERF117 plays a positive role in cold resistance by, at least in part, enhancing antioxidant capacity through regulating the POD-encoding gene VvBAS1, leading to effective mitigation of reactive oxygen species.

Does Serum Uric Acid to Creatinine Ratio Predict Mortality Risk in Patients With Heart Failure?

BACKGROUND: Previous studies have established a positive correlation between serum uric acid to creatinine (SUA/Cr) ratio and cardiovascular disease, but the relationship between SUA/Cr ratio and the prognosis of heart failure (HF) remains unknown. This study investigated the potential of SUA/Cr ratio as a prognostic predictor for patients with HF. METHODS: This single-center prospective cohort study enrolled 2,122 patients with HF between March 2013 and June 2017. All patients were divided into 3 groups according to SUA/Cr ratio tertiles and were followed up with until December 31, 2022. The association between SUA/Cr ratio and the prognosis of HF was assessed using the Cox proportional hazards model. RESULTS: The mean (SD) age and mean (SD) SUA/Cr ratio of the study cohort (66% male) were 59.3 (14.7) years and 4.71 (2.09), respectively. During a median follow-up period of 15 months (range, 11-26 months), 390 end-point events were observed. Prognosis analysis revealed that a high SUA/Cr ratio was associated with an increased mortality risk of HF (hazard ratio, 1.62 [95% CI, 1.26-2.09]; P < .001) compared with the SUA/Cr ratio in the lowest tertile. After adjusting for covariates, the hazard ratio for mortality risk of HF was 1.71 (95% CI, 1.23-2.37; P = .001). Subgroup analysis showed that mortality risk increased in direct proportion with the SUA/Cr ratio in female patients, patients with a history of hypertension and ß-blocker use, and patients with UA levels below 428 µmol/L and creatinine levels less than 97 mg/dL. Stratification by age; by history of diabetes, hyperlipidemia, and smoking; and by level of fasting plasma glucose, however, had no obvious effect on the association between SUA/Cr ratio and HF prognosis. Patients with higher SUA/Cr ratios had reduced left ventricular ejection fraction and increased left ventricular end-diastolic diameter. CONCLUSION: A high SUA/Cr ratio was an independent risk factor for the mortality risk of HF.

Sirtuin 1, as a potential prognosis marker in renal clear cell carcinoma, regulates lipid metabolism and immune infiltration.

Renal clear cell carcinoma (ccRCC) is a malignancy with a dismal prognosis, caused by the buildup of fat and glycogen. Sirtuin 1 (Sirt1) is a deacetylase that regulates lipid metabolism. In this study, we collected tumor and paracancer tissues from 386 ccRCC patients and followed their prognosis over an extended time period. The expression of Sirt1 in these tissues was assessed using immunohistochemistry, and LinkedOmics database analysis identified differentially expressed genes associated with Sirt1. The survival curve was generated using the Kaplan-Meier method, and immune infiltration was analyzed using the Tumor Immune Estimation Resource (TIMER) web tool. Our findings revealed that Sirt1 was expressed in tumor tissues, but not in normal tissues, and its high expression was associated with a worse prognosis. Furthermore, we observed a positive correlation between high Sirt1 expression and perirenal fat invasion and necrosis, leading to poorer survival outcomes. We established a nomogram to predict prognosis, and a correlation was observed with immune infiltration. In conclusion, our results suggest that high Sirt1 expression is associated with lipid metabolism disorder and immune infiltration, ultimately contributing to a dismal prognosis in ccRCC.

Deep learning on tertiary lymphoid structures in hematoxylin-eosin predicts cancer prognosis and immunotherapy response.

Tertiary lymphoid structures (TLSs) have been associated with favorable immunotherapy responses and prognosis in various cancers. Despite their significance, their quantification using multiplex immunohistochemistry (mIHC) staining of T and B lymphocytes remains labor-intensive, limiting its clinical utility. To address this challenge, we curated a dataset from matched mIHC and H&E whole-slide images (WSIs) and developed a deep learning model for automated segmentation of TLSs. The model achieved Dice coefficients of 0.91 on the internal test set and 0.866 on the external validation set, along with intersection over union (IoU) scores of 0.819 and 0.787, respectively. The TLS ratio, defined as the segmented TLS area over the total tissue area, correlated with B lymphocyte levels and the expression of CXCL13, a chemokine associated with TLS formation, in 6140 patients spanning 16 tumor types from The Cancer Genome Atlas (TCGA). The prognostic models for overall survival indicated that the inclusion of the TLS ratio with TNM staging significantly enhanced the models' discriminative ability, outperforming the traditional models that solely incorporated TNM staging, in 10 out of 15 TCGA tumor types. Furthermore, when applied to biopsied treatment-naïve tumor samples, higher TLS ratios predicted a positive immunotherapy response across multiple cohorts, including specific therapies for esophageal squamous cell carcinoma, non-small cell lung cancer, and stomach adenocarcinoma. In conclusion, our deep learning-based approach offers an automated and reproducible method for TLS segmentation and quantification, highlighting its potential in predicting immunotherapy response and informing cancer prognosis.

Progress in single-cell multimodal sequencing and multi-omics data integration.

With the rapid advance of single-cell sequencing technology, cell heterogeneity in various biological processes was dissected at different omics levels. However, single-cell mono-omics results in fragmentation of information and could not provide complete cell states. In the past several years, a variety of single-cell multimodal omics technologies have been developed to jointly profile multiple molecular modalities, including genome, transcriptome, epigenome, and proteome, from the same single cell. With the availability of single-cell multimodal omics data, we can simultaneously investigate the effects of genomic mutation or epigenetic modification on transcription and translation, and reveal the potential mechanisms underlying disease pathogenesis. Driven by the massive single-cell omics data, the integration method of single-cell multi-omics data has rapidly developed. Integration of the massive multi-omics single-cell data in public databases in the future will make it possible to construct a cell atlas of multi-omics, enabling us to comprehensively understand cell state and gene regulation at single-cell resolution. In this review, we summarized the experimental methods for single-cell multimodal omics data and computational methods for multi-omics data integration. We also discussed the future development of this field.

Single cell analyses of cancer cells identified two regulatorily and functionally distinct categories in differentially expressed genes among tumor subclones.

To explore the feature of cancer cells and tumor subclones, we analyzed 101,065 single-cell transcriptomes from 12 colorectal cancer (CRC) patients and 92 single cell genomes from one of these patients. We found cancer cells, endothelial cells and stromal cells in tumor tissue expressed much more genes and had stronger cell-cell interactions than their counterparts in normal tissue. We identified copy number variations (CNVs) in each cancer cell and found correlation between gene copy number and expression level in cancer cells at single cell resolution. Analysis of tumor subclones inferred by CNVs showed accumulation of mutations in each tumor subclone along lineage trajectories. We found differentially expressed genes (DEGs) between tumor subclones had two populations: DEGCNV and DEGreg. DEGCNV, showing high CNV-expression correlation and whose expression differences depend on the differences of CNV level, enriched in housekeeping genes and cell adhesion associated genes. DEGreg, showing low CNV-expression correlation and mainly in low CNV variation regions and regions without CNVs, enriched in cytokine signaling genes. Furthermore, cell-cell communication analyses showed that DEGCNV tends to involve in cell-cell contact while DEGreg tends to involve in secreted signaling, which further support that DEGCNV and DEGreg are two regulatorily and functionally distinct categories.

Microbiological impact of long-term wine grape cultivation on soil organic carbon in desert ecosystems: a study on rhizosphere and bulk sandy soils.

The improvement of nutrients in soil is essential for using deserts and decertified ecosystems and promoting sustainable agriculture. Grapevines are suitable crops for desert soils as they can adapt to harsh environments and effectively impact soil nutrients; however, the mechanisms underlying this remain unclear. This study explored the impact of the different duration(3, 6, and 10 years) of grape cultivation on soil organic carbon, physicochemical properties, enzyme activities, microbial communities, and carbon cycle pathways in both rhizosphere and bulk soils. Partial least squares path modeling was used to further reveal how these factors contributed to soil nutrient improvement. Our findings indicate that after long-term grape cultivation six years, soil organic carbon, total nitrogen, total phosphorus, microbial biomass carbon and nitrogen, and enzyme activities has significantly increased in both rhizosphere and bulk soils but microbial diversity decreased in bulk soil. According to the microbial community assembly analysis, we found that stochastic processes, particularly homogenizing dispersal, were dominant in both soils. Bacteria are more sensitive to environmental changes than fungi. In the bulk soil, long-term grape cultivation leads to a reduction in ecological niches and an increase in salinity, resulting in a decrease in soil microbial diversity. Soil enzymes play an important role in increasing soil organic matter in bulk soil by decomposing plant litters, while fungi play an important role in increasing soil organic matter in the rhizosphere, possibly by decomposing fine roots and producing mycelia. Our findings enhance understanding of the mechanisms of soil organic carbon improvement under long-term grape cultivation and suggest that grapes are suitable crops for restoring desert ecosystems.

DeepRisk network: an AI-based tool for digital pathology signature and treatment responsiveness of gastric cancer using whole-slide images.

BACKGROUND: Digital histopathology provides valuable information for clinical decision-making. We hypothesized that a deep risk network (DeepRisk) based on digital pathology signature (DPS) derived from whole-slide images could improve the prognostic value of the tumor, node, and metastasis (TNM) staging system and offer chemotherapeutic benefits for gastric cancer (GC). METHODS: DeepRisk is a multi-scale, attention-based learning model developed on 1120 GCs in the Zhongshan dataset and validated with two external datasets. Then, we assessed its association with prognosis and treatment response. The multi-omics analysis and multiplex Immunohistochemistry were conducted to evaluate the potential pathogenesis and spatial immune contexture underlying DPS. RESULTS: Multivariate analysis indicated that the DPS was an independent prognosticator with a better C-index (0.84 for overall survival and 0.71 for disease-free survival). Patients with low-DPS after neoadjuvant chemotherapy responded favorably to treatment. Spatial analysis indicated that exhausted immune clusters and increased infiltration of CD11b+CD11c+ immune cells were present at the invasive margin of high-DPS group. Multi-omics data from the Cancer Genome Atlas-Stomach adenocarcinoma (TCGA-STAD) hint at the relevance of DPS to myeloid derived suppressor cells infiltration and immune suppression. CONCLUSION: DeepRisk network is a reliable tool that enhances prognostic value of TNM staging and aid in precise treatment, providing insights into the underlying pathogenic mechanisms.

Roles of SP/KLF transcription factors in odontoblast differentiation: From development to diseases.

OBJECTIVES: A zinc-finger transcription factor family comprising specificity proteins (SPs) and Krüppel-like factor proteins (KLFs) plays an important role in dentin development and regeneration. However, a systematic regulatory network involving SPs/KLFs in odontoblast differentiation has not yet been described. This review examined the expression patterns of SP/KLF gene family members and their current known functions and mechanisms in odontoblast differentiation, and discussed prospective research directions for further exploration of mechanisms involving the SP/KLF gene family in dentin development. MATERIALS AND METHODS: Relevant literature on SP/KLF gene family members and dentin development was acquired from PubMed and Web of Science. RESULTS: We discuss the expression patterns, functions, and related mechanisms of eight members of the SP/KLF gene family in dentin development and genetic disorders with dental problems. We also summarize current knowledge about their complementary or synergistic actions. Finally, we propose future research directions for investigating the mechanisms of dentin development. CONCLUSIONS: The SP/KLF gene family plays a vital role in tooth development. Studying the complex complementary or synergistic interactions between SPs/KLFs is helpful for understanding the process of odontoblast differentiation. Applications of single-cell and spatial multi-omics may provide a more complete investigation of the mechanism involved in dentin development.

Nuclear ribonucleoprotein RALY downregulates foot-and-mouth disease virus replication but antagonized by viral 3C protease.

The internal ribosome entry site (IRES) element constitutes a cis-acting RNA regulatory sequence that recruits the ribosomal initiation complex in a cap-independent manner, assisted by various RNA-binding proteins and IRES trans-acting factors. Foot-and-mouth disease virus (FMDV) contains a functional IRES element and takes advantage of this element to subvert host translation machinery. Our study identified a novel mechanism wherein RALY, a member of the heterogeneous nuclear ribonucleoproteins (hnRNP) family belonging to RNA-binding proteins, binds to the domain 3 of FMDV IRES via its RNA recognition motif residue. This interaction results in the downregulation of FMDV replication by inhibiting IRES-driven translation. Furthermore, our findings reveal that the inhibitory effect exerted by RALY on FMDV replication is not attributed to the FMDV IRES-mediated assembly of translation initiation complexes but rather to the impediment of 80S ribosome complex formation after binding with 40S ribosomes. Conversely, 3Cpro of FMDV counteracts RALY-mediated inhibition by the ubiquitin-proteasome pathway. Therefore, these results indicate that RALY, as a novel critical IRES-binding protein, inhibits FMDV replication by blocking the formation of 80S ribosome, providing a deeper understanding of how viruses recruit and manipulate host factors. IMPORTANCE: The translation of FMDV genomic RNA driven by IRES element is a crucial step for virus infections. Many host proteins are hijacked to regulate FMDV IRES-dependent translation, but the regulatory mechanism remains unknown. Here, we report for the first time that cellular RALY specifically interacts with the IRES of FMDV and negatively regulates viral replication by blocking 80S ribosome assembly on FMDV IRES. Conversely, RALY-mediated inhibition is antagonized by the viral 3C protease by the ubiquitin-proteasome pathway. These results would facilitate further understanding of virus-host interactions and translational control during viral infection.