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Chemoradiation-induced hearing loss (CRIHL) is one of the most devasting side effects for nasopharyngeal carcinoma (NPC) patients, which seriously affects survivors’ long-term quality of life. However, few studies have comprehensively characterized the risk factors for CRIHL. In this study, we found that age at diagnosis, tumor stage, and concurrent cisplatin dose were positively associated with chemoradiation-induced hearing loss. We performed a genome-wide association study (GWAS) in 777 NPC patients and identified rs1050851 (within the exon 2 of NFKBIA), a variant with a high deleteriousness score, to be significantly associated with hearing loss risk (HR = 5.46, 95% CI 2.93–10.18, P = 9.51 × 10–08). The risk genotype of rs1050851 was associated with higher NFKBIA expression, which was correlated with lower cellular tolerance to cisplatin. According to permutation-based enrichment analysis, the variants mapping to 149 hereditary deafness genes were significantly enriched among GWAS top signals, which indicated the genetic similarity between hereditary deafness and CRIHL. Pathway analysis suggested that synaptic signaling was involved in the development of CRIHL. Additionally, the risk score integrating genetic and clinical factors can predict the risk of hearing loss with a relatively good performance in the test set. Collectively, this study shed new light on the etiology of chemoradiation-induced hearing loss, which facilitates high-risk individuals’ identification for personalized prevention and treatment.

In the complex dynamic problems of practical engineering, the method of establishing dynamic equations and numerical analysis based on theories of mechanics is difficult to meet the needs of high dimension, multi-scale and high precision. In this paper, data-driven models of nonlinear dynamic systems are built through neural network. The numerical solution of state equations is used to simulate the experimental data as well as training data of neural network. The loss function with two coefficients is constructed according to the relationship between the data. Forward Euler method and the trained neural network are combined to predict the response data of different excitation frequencies. Then, whole data-driven modeling and substructure data-driven modeling are presented to model and analyse a five-degree-of-freedom duffing oscillator. By comparison, the prediction accuracy of substructure data-driven modeling is higher. Furthermore, the influence of loss function, hidden layer, training data and noise on the accuracy of the model is studied. The results show that the selection of training data and the number of hidden layers have a great impact on the prediction ability, and verify accuracy, generalization and robustness of the model. In order to improve the prediction accuracy of substructure data-driven modeling, adjustable learning rate is adopted in the optimizer. The effects of adding control parameters to the network input (APNI) and not adding control parameters to the network input (NAPNI) on the dynamic characteristics are considered respectively. The prediction accuracy of the model can be effectively improved by adding control parameters to the network input.

Gastric cancer is a gastrointestinal malignancy with high mortality and poor prognosis, and the molecular mechanism of gastric tumorigenesis remains unclear. TRIM44 has been reported to be involved in tumor development. However, the role of TRIM44 in tumor immunity is largely unknown.We analyzed TRIM44 expression in clinical gastric cancer tissues and normal tissues by using western blot, quantitative real-time PCR and bioinformatics analyses. We further investigated the involvement of TRIM44 in tumor immunity in vivo and found that it was dependent on extracellular matrix remodeling. We detected the interaction between TRIM44 and LOXL2 by using immunofluorescence staining and coimmunoprecipitation assays. We observed that TRIM44 mediates the stability of LOXL2 by ubiquitination assays.TRIM44 expression is high and is correlated with T-cell infiltration in gastric cancer. TRIM44 inhibits gastric tumorigenicity by regulating T-cell-mediated antitumor immunity and modulating the protein level of LOXL2. Mechanistically, TRIM44 directly binds to LOXL2 and affects the stability of LOXL2 to change extracellular matrix remodeling and influence tumor immunity.These findings demonstrate that TRIM44 regulates the stability of LOXL2 to remodel the tumor extracellular matrix to modulate tumor immunity in gastric cancer and that the TRIM44/LOXL2 complex is a promising biomarker for gastric cancer prognosis and might be a novel immunotherapy target.

We establish an infinite family of solutions in terms of elliptic functions of the lattice Boussinesq systems by setting up a direct linearisation scheme, which provides the solution structure for those equations in the elliptic case. The latter, which contains as main structural element a Cauchy kernel on the torus, is obtained from a dimensional reduction of the elliptic direct linearisation scheme of the lattice Kadomtsev–Petviashvili equation, which requires the introduction of a novel technical concept, namely the ‘elliptic cube root of unity’. Thus, in order to implement the reduction we define, more generally, the notion of elliptic$$N{\textrm{th}}$$ N th root of unity, and discuss some of its properties in connection with a special class of elliptic addition formulae. As a particular concrete application we present the class of elliptic multi-soliton solutions of the lattice Boussinesq systems.

In this study, we investigated the physiological responses of maize with different amylose content at seedlings to drought stress. For waxy maize (WMS) and normal maize (NMS), the decline of photosynthesis under drought stress (DS) was due to the stomatal limitation. DS increased the non-photochemical quenching coefficient (NPQ), whereas decreased the activities of peroxidase (POD) and the plant height (PH), compared with the plants under normal irrigation. The content of starch increased and decreased significantly upon moderate and severe drought stress, respectively. For high amylose maize (HAMSs), they showed stomatal limitation upon moderate stress, while non-stomatal limitation upon severe stress. The NPQ and POD showed contrary trend compared with WMS and NMS. DS significantly decreased the starch content and PH of them. The principal component analysis (PCA) showed HAMSs were more sensitive to drought than WMS and NMS. The GBSSIIa level of HAMSs was also lower than that of WMS and NMS. Therefore, we conclude that HAMSs respond to DS through redox regulation to avoid oxidative damage, whereas WMS and NMS by increasing starch biosynthesis, and the higher GBSSIIa level may produce more amylose, which could promote the growth of maize under drought effectively.

Chinese herbal slices (CHSs) are closely associated to microorganisms, whether they are endophytic or epiphytic in plants, or introduced during processing. In this study, the structures and predicted functions of microbial communities in 150 batches of samples from five types of CHSs were investigated by combining pure culture and 16 S rDNA amplicon sequencing. Bile-salt-tolerant gram-negative bacteria were detected in 56.0% of samples, andThe online version contains supplementary material available at 10.1007/s11756-022-01199-0.

Ship black carbon emissions have caused great harm to ecological environment. In order to estimate the black carbon emissions, thereby reducing the cost of black carbon experiments, here, we introduced four machine learning algorithms which are lasso regression, support vector machine, extreme gradient boosting, and artificial neural network to predict ship black carbon emissions. The prediction models were established with using the datasets acquired from similar marine engines under various steady-state conditions. The results show that SVM, XGB, and ANN have higher prediction accuracy than lasso regression, and the adjusted R

Background Plants accomplish multiple functions by the interrelationships between functional traits. Clarifying the complex relationships between plant traits would enable us to better understand how plants employ different strategies to adapt to the environment. Although increasing attention is being paid to plant traits, few studies focused on the adaptation to aridity through the relationship among multiple traits. We established plant trait networks (PTNs) to explore the interdependence of sixteen plant traits across drylands. Results Our results revealed significant differences in PTNs among different plant life-forms and different levels of aridity. Trait relationships for woody plants were weaker, but were more modularized than for herbs. Woody plants were more connected in economic traits, whereas herbs were more connected in structural traits to reduce damage caused by drought. Furthermore, the correlations between traits were tighter with higher edge density in semi-arid than in arid regions, suggesting that resource sharing and trait coordination are more advantageous under low drought conditions. Importantly, our results demonstrated that stem phosphorus concentration (SPC) was a hub trait correlated with other traits across drylands. Conclusions The results demonstrate that plants exhibited adaptations to the arid environment by adjusting trait modules through alternative strategies. PTNs provide a new insight into understanding the adaptation strategies of plants to drought stress based on the interdependence among plant functional traits.

Background There is growing recognition of natural history, complications, and outcomes of patients who develop non-acetaminophen (APAP) drug-induced acute liver failure (ALF). To clarify high-risk factors and develop a nomogram model to predict transplant-free survival (TFS) in patients with non-APAP drug-induced ALF. Methods Patients with non-APAP drug-induced ALF from 5 participating centers were retrospectively analyzed. The primary endpoint was 21-day TFS. Total sample size was 482 patients. Results Regarding causative agents, the most common implicated drugs were herbal and dietary supplements (HDS) (57.0%). The hepatocellular type (R ≥ 5) was the main liver injury pattern (69.0%). International normalized ratio, hepatic encephalopathy grades, the use of vasopressor, N-acetylcysteine, or artificial liver support system were associated with TFS and incorporated to construct a nomogram model (drug-induced acute liver failure-5, DIALF-5). The AUROC of DIALF-5 for 7-day, 21-day, 60-day, and 90-day TFS in the internal cohort were 0.886, 0.915, 0.920, and 0.912, respectively. Moreover, the AUROC of DIALF-5 for 21-day TFS had the highest AUROC, which was significantly higher than 0.725 of MELD and 0.519 of KCC (p p > 0.05). These results were successfully validated in the external cohort (147 patients). Conclusions Based on easily identifiable clinical data, the novel DIALF-5 model was developed to predict transplant-free survival in non-APAP drug-induced ALF, which was superior to KCC, MELD and had a similar prediction performance to ALFSG-PI but is more convenient, which can directly calculate TFS at multiple time points.

Precise and efficient ozone ($$\hbox {O}_{3}$$ O 3 ) concentration prediction is crucial for weather monitoring and environmental policymaking due to the harmful effects of high $$\hbox {O}_{3}$$ O 3 pollution levels on human health and ecosystems. However, the complexity of $$\hbox {O}_{3}$$ O 3 formation mechanisms in the troposphere presents a significant challenge in modeling $$\hbox {O}_{3}$$ O 3 accurately and quickly, especially in the absence of a process model. Data-driven machine-learning techniques have demonstrated promising performance in modeling air pollution, mainly when a process model is unavailable. This study evaluates the predictive performance of nineteen machine learning models for ozone pollution prediction. Specifically, we assess how incorporating features using Random Forest affects $$\hbox {O}_{3}$$ O 3 concentration prediction and investigate using time-lagged measurements to improve prediction accuracy. Air pollution and meteorological data collected at King Abdullah University of Science and Technology are used. Results show that dynamic models using time-lagged data outperform static and reduced machine learning models. Incorporating time-lagged data improves the accuracy of machine learning models by 300% and 200%, respectively, compared to static and reduced models, under RMSE metrics. And importantly, the best dynamic model with time-lagged information only requires 0.01 s, indicating its practical use. The Diebold-Mariano Test, a statistical test used to compare the forecasting accuracy of models, is also conducted.

Background Pork quality can directly affect customer purchase tendency and meat quality traits have become valuable in modern pork production. However, genetic improvement has been slow due to high phenotyping costs. In this study, whole genome sequence (WGS) data was used to evaluate the prediction accuracy of genomic best linear unbiased prediction (GBLUP) for meat quality in large-scale crossbred commercial pigs. Results We produced WGS data (18,695,907 SNPs and 2,106,902 INDELs exceed quality control) from 1,469 sequenced Duroc × (Landrace × Yorkshire) pigs and developed a reference panel for meat quality including meat color score, marbling score, L* (lightness), a* (redness), and b* (yellowness) of genomic prediction. The prediction accuracy was defined as the Pearson correlation coefficient between adjusted phenotypes and genomic estimated breeding values in the validation population. Using different marker density panels derived from WGS data, accuracy differed substantially among meat quality traits, varied from 0.08 to 0.47. Results showed that MultiBLUP outperform GBLUP and yielded accuracy increases ranging from 17.39% to 75%. We optimized the marker density and found medium- and high-density marker panels are beneficial for the estimation of heritability for meat quality. Moreover, we conducted genotype imputation from 50K chip to WGS level in the same population and found average concordance rate to exceed 95% and r2 = 0.81. Conclusions Overall, estimation of heritability for meat quality traits can benefit from the use of WGS data. This study showed the superiority of using WGS data to genetically improve pork quality in genomic prediction.

Epidemiological studies have demonstrated that the use of antidepressants is associated with a decreased risk of colorectal cancer (CRC); however, the mechanisms behind this association are yet unknown. Adrenergic system contributes to the stress-related tumor progression, with norepinephrine (NE) mainly secreted from adrenergic nerve fibers. Norepinephrine serotonin reuptake inhibitors are successfully used antidepressants. This study demonstrates that a widely used antidepressant venlafaxine (VEN) antagonizes NE-promoted colon cancer in vivo and in vitro. Bioinformatic analysis suggested that NE transporter (NET, SLC6A2), a target of VEN, was closely associated with the prognosis of clinical patients with CRC. In addition, the knockdown of NET antagonized the effect of NE. The NET-protein phosphatase 2 scaffold subunit alpha/phosphorylated Akt/vascular endothelial growth factor pathway partially mediates the antagonizing effect of VEN on NE’s actions in colon cancer cells. These were also confirmed by in vivo experiments. Our findings revealed for the first time that, in addition to its primary function as a transporter, NET also promotes NE-enhanced colon cancer cell proliferation, tumor angiogenesis, and tumor growth. This provides direct experimental and mechanistic evidence for the use of antidepressant VEN in the treatment of CRC and a therapeutic potential for repurposing existing drugs as an anti-cancer approach to improve the prognosis of patients with CRC.

Background Apatinib, a highly selective VEGFR2 inhibitor, significantly improved efficacy versus placebo as a third- and later-line treatment for advanced gastric cancer in phase 2 and 3 trials. This prospective, single-arm, multicenter phase IV AHEAD study was conducted to verify the safety and efficacy of apatinib in patients with advanced or metastatic gastric or gastroesophageal adenocarcinoma after at least two lines of systematic therapy in clinical practice settings. Methods Patients with advanced gastric cancer who had previously failed at least two lines of chemotherapy received oral apatinib until disease progression, death or unacceptable toxicity. The primary endpoint was safety. The secondary endpoints included objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS) and overall survival (OS). Adverse events were summarized by the incidence rate. Median OS and PFS were estimated using the Kaplan–Meier method. ORR, DCR, OS at 3 and 6 months, and PFS at 3 and 6 months were calculated, and their 95% CIs were estimated according to the Clopper-Pearson method. Results Between May 2015 and November 2019, a total of 2004 patients were enrolled, and 1999 patients who received at least one dose of apatinib were assessed for safety. In the safety population, 87.9% of patients experienced treatment-related adverse events (TRAEs), with the most common hypertension (45.2%), proteinuria (26.5%), and white blood cell count decreased (25.3%). Additionally, 51% of patients experienced grade ≥ 3 TRAEs. Fatal TRAEs occurred in 57 (2.9%) patients. No new safety concerns were reported. Among the 2004 patients included in the intention-to-treat population, the ORR was 4.4% (95% CI, 3.6–5.4%), and DCR was 35.8% (95% CI, 33.7–38.0%). The median PFS was 2.7 months (95% CI 2.2–2.8), and the median OS was 5.8 months (95% CI 5.4–6.1). Conclusions The findings in the AHEAD study confirmed the acceptable and manageable safety profile and clinical benefit of apatinib in patients with advanced gastric cancer as a third- or later-line of treatment. Trial registration This study was registered with ClinicalTrials.gov NCT02426034. Registration date was April 24, 2015.

Background Natronobacterium gregoryi Argonaute (NgAgo) was found to reduce mRNA without generating detectable DNA double-strand breaks in a couple of endogenous genes in zebrafish, suggesting its potential as a tool for gene knockdown. However, little is known about how it interacts with nucleic acid molecules to interfere with gene expression. Results In this study, we first confirmed that coinjection of NgAgo and gDNA downregulated target genes, generated gene-specific phenotypes and verified some factors (including 5’ phosphorylation, GC ratio, and target positions) of gDNAs affecting gene downregulation. Therein, the sense and antisense gDNAs were equally effective, suggesting that NgAgo possibly binds to DNA. NgAgo-VP64 with gDNAs targeting promoters upregulated the target genes, further providing evidence that NgAgo interacts with genomic DNA and controls gene transcription. Finally, we explain the downregulation of NgAgo/gDNA target genes by interference with the process of gene transcription, which differs from that of morpholino oligonucleotides. Conclusions The present study provides conclusions that NgAgo may target genomic DNA and that target positions and the gDNA GC ratio influence its regulation efficiency.

Aging results in deterioration of body functions and, ultimately, death. miRNAs contribute to the regulation of aging. The aim of this study was to explore the contribution of miRNAs to aging and senescence-related changes in gene expression. The expression changes of miRNAs in the blood of people and animal samples collected from different age subjects were examined using Affymetrix miRNA 4.0 microarray and qRT-PCR. MTT assay and flow cytometry were used to examine the effect of miR-23a on cell functions in WI-38 cells. The expression levels of 48 miRNAs, including miR-23a, miR-21, and miR-100, in the blood samples were higher in the middle-aged group than in the young or elderly group. Animal studies further suggested that the expression of miR-23a increased with age. In addition, upregulation of miR-23a dramatically suppressed the cell proliferation and arrested the WI-38 cell cycle in vitro. FOXO3a has been identified as a target gene of miR-23a. MiR-23a downregulated the expression of FOXO3a in WI-38 cells. MiRNAs have different expression levels in different age groups. miR-23a could suppress cell proliferation and arrest the cell cycle in WI-38 cells, which elucidated the mechanism through which miR-23a exerts pivotal role in WI-38 cells by targeting FOXO3a.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has elicited a worldwide pandemic since late 2019. There has been ~675 million confirmed coronavirus disease 2019 (COVID-19) cases, leading to more than 6.8 million deaths as of March 1, 2023. Five SARS-CoV-2 variants of concern (VOCs) were tracked as they emerged and were subsequently characterized. However, it is still difficult to predict the next dominant variant due to the rapid evolution of its spike (S) glycoprotein, which affects the binding activity between cellular receptor angiotensin-converting enzyme 2 (ACE2) and blocks the presenting epitope from humoral monoclonal antibody (mAb) recognition. Here, we established a robust mammalian cell-surface-display platform to study the interactions of S-ACE2 and S-mAb on a large scale. A lentivirus library of S variants was generated via in silico chip synthesis followed by site-directed saturation mutagenesis, after which the enriched candidates were acquired through single-cell fluorescence sorting and analyzed by third-generation DNA sequencing technologies. The mutational landscape provides a blueprint for understanding the key residues of the S protein binding affinity to ACE2 and mAb evasion. It was found that S205F, Y453F, Q493A, Q493M, Q498H, Q498Y, N501F, and N501T showed a 3–12-fold increase in infectivity, of which Y453F, Q493A, and Q498Y exhibited at least a 10-fold resistance to mAbs REGN10933, LY-CoV555, and REGN10987, respectively. These methods for mammalian cells may assist in the precise control of SARS-CoV-2 in the future.

Background Nicotine dependence is a significant public health issue, and understanding the factors associated with nicotine dependence in this population is crucial for developing effective interventions. This study examined the association between family functioning and nicotine dependence levels of smoking fathers based on the McMaster model of family functioning (MMFF), providing evidence for future interventions. Methods In this study, we selected fathers of first- to fifth-grade students from 10 pilot elementary schools in Qingdao whose families smoked. We used the Fagerstrom test to assess nicotine dependence and the Family Assessment Device to evaluate family functioning. We performed univariate analysis to compare differences among those with different levels of nicotine dependence, and we used an ordinal logistic regression analysis to investigate the influences related to nicotine dependence. Results This study included 874 smokers, with 78.5% having mild nicotine dependence, 11.7% having moderate dependence, and 9.84% having severe dependence. Univariate analysis showed that smokers with severe dependence had lower education levels, higher prevalence of chronic diseases, more frequent alcohol consumption, and poorer family functioning compared to those with mild to moderate dependence. Ordinal logistic regression analysis showed that poorer general functioning scores (OR = 1.087, 95% CI: 1.008–1.173, P = 0.030), poorer behavioral control (OR = 1.124, 95% CI: 1.026–1.232, P = 0.012), more quit attempts, frequent alcohol consumption, and longer smoking duration may be associated with a higher likelihood of developing severe nicotine dependence. The older age of starting smoking and higher education level may be associated with a lower likelihood of developing severe nicotine dependence. However, it is important to note that the cross-sectional nature of this study precludes the determination of causal relationships. Conclusions This study finds that heavy nicotine dependence in smoking fathers is associated with risky behaviors and demographics such as longer smoking duration and frequent alcohol consumption. Targeted smoking cessation interventions are crucial for this group, taking these specific factors into consideration. Family functioning, particularly general functioning and behavioral control, may also be linked to nicotine dependence, indicating the need for further research in this area.

Background In recent years, there have been breakthroughs in the preclinical research of respiratory diseases, such as organoids and organ tissue chip models, but they still cannot provide insight into human respiratory diseases well. Human lung slices model provides a promising in vitro model for the study of respiratory diseases because of its preservation of lung structure and major cell types. Methods Human lung slices were manually prepared from small pieces of lung tissues obtained from lung cancer patients subjected to lung surgery. To evaluate the suitability of this model for lung fibrosis research, lung slices were treated with CdCl2 (30 μM), TGF-β1 (1 ng/ml) or CdCl2 plus TGF-β1 for 3 days followed by toxicity assessment, gene expression analysis and histopathological observations. Results CdCl2 treatment resulted in a concentration-dependent toxicity profile evidenced by MTT assay as well as histopathological observations. In comparison with the untreated group, CdCl2 and TGF-β1 significantly induces MMP2 and MMP9 gene expression but not MMP1. Interestingly, CdCl2 plus TGF-β1 significantly induces the expression of MMP1 but not MMP2, MMP7 or MMP9. Microscopic observations reveal the pathogenesis of interstitial lung fibrosis in the lung slices of all groups; however, CdCl2 plus TGF-β1 treatment leads to a greater alveolar septa thickness and the formation of fibroblast foci-like pathological features. The lung slice model is in short of blood supply and the inflammatory/immune-responses are considered minimal. Conclusions The results are in favor of the hypothesis that idiopathic pulmonary fibrosis (IPF) is mediated by tissue damage and abnormal repair. Induction of MMP1 gene expression and fibroblast foci-like pathogenesis suggest that this model might represent an early stage of IPF.