Oxidative stress promotes oral carcinogenesis via Thbs1-mediated M1-like tumor-associated macrophages polarization.

Although oxidative stress is closely associated with tumor invasion and metastasis, its' exact role and mechanism in the initial stage of oral cancer remain ambiguous. Glutamine uptake mediated by alanine-serine-cysteine transporter 2 (ASCT2) participates in glutathione synthesis to resolve oxidative stress. Currently, we firstly found that ASCT2 deletion caused oxidative stress in oral mucosa and promoted oral carcinogenesis induced by 4-Nitroquinoline-1-oxide (4-NQO) using transgenic mice of ASCT2 knockout in oral epithelium. Subsequently, we identified an upregulated gene Thbs1 linked to macrophage infiltration by mRNA sequencing and immunohistochemistry. Importantly, multiplex immunohistochemistry showed M1-like tumor-associated macrophages (TAMs) were enriched in cancerous area. Mechanically, targeted ASCT2 effectively curbed glutamine uptake and caused intracellular reactive oxygen species (ROS) accumulation, which upregulated Thbs1 in oral keratinocytes and then activated p38, Akt and SAPK/JNK signaling to polarize M1-like TAMs via exosome-transferred pathway. Moreover, we demonstrated M1-like TAMs promoted malignant progression of oral squamous cell carcinoma (OSCC) both in vitro and in vivo by a DOK transformed cell line induced by 4-NQO. All these results establish that oxidative stress triggered by ASCT2 deletion promotes oral carcinogenesis through Thbs1-mediated M1 polarization, and indicate that restore redox homeostasis is a new approach to prevent malignant progression of oral potentially malignant disorders.

Screening of gastric cancer diagnostic biomarkers in the homologous recombination signaling pathway and assessment of their clinical and radiomic correlations.

BACKGROUND: Homologous recombination plays a vital role in the occurrence and drug resistance of gastric cancer. This study aimed to screen new gastric cancer diagnostic biomarkers in the homologous recombination pathway and then used radiomic features to construct a prediction model of biomarker expression to guide the selection of chemotherapy regimens. METHODS: Gastric cancer transcriptome data were downloaded from The Cancer Genome Atlas database. Machine learning methods were used to screen for diagnostic biomarkers of gastric cancer and validate them experimentally. Computed Tomography image data of gastric cancer patients and corresponding clinical data were downloaded from The Cancer Imaging Archive and our imaging centre, and then the Computed Tomography images were subjected to feature extraction, and biomarker expression prediction models were constructed to analyze the correlation between the biomarker radiomics scores and clinicopathological features. RESULTS: We screened RAD51D and XRCC2 in the homologous recombination pathway as biomarkers for gastric cancer diagnosis by machine learning, and the expression of RAD51D and XRCC2 was significantly positively correlated with pathological T stage, N stage, and TNM stage. Homologous recombination pathway blockade inhibits gastric cancer cell proliferation, promotes apoptosis, and reduces the sensitivity of gastric cancer cells to chemotherapeutic drugs. Our predictive RAD51D and XRCC2 expression models were constructed using radiomics features, and all the models had high accuracy. In the external validation cohort, the predictive models still had decent accuracy. Moreover, the radiomics scores of RAD51D and XRCC2 were also significantly positively correlated with the pathologic T, N, and TNM stages. CONCLUSIONS: The gastric cancer diagnostic biomarkers RAD51D and XRCC2 that we screened can, to a certain extent, reflect the expression status of genes through radiomic characteristics, which is of certain significance in guiding the selection of chemotherapy regimens for gastric cancer patients.

Comparative assessment of the capability of machine learning-based radiomic models for predicting omental metastasis in locally advanced gastric cancer.

The study aims to investigate the predictive capability of machine learning algorithms for omental metastasis in locally advanced gastric cancer (LAGC) and to compare the performance metrics of various machine learning predictive models. A retrospective collection of 478 pathologically confirmed LAGC patients was undertaken, encompassing both clinical features and arterial phase computed tomography images. Radiomic features were extracted using 3D Slicer software. Clinical and radiomic features were further filtered through lasso regression. Selected clinical and radiomic features were used to construct omental metastasis predictive models using support vector machine (SVM), decision tree (DT), random forest (RF), K-nearest neighbors (KNN), and logistic regression (LR). The models' performance metrics included accuracy, area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In the training cohort, the RF predictive model surpassed LR, SVM, DT, and KNN in terms of accuracy, AUC, sensitivity, specificity, PPV, and NPV. Compared to the other four predictive models, the RF model significantly improved PPV. In the test cohort, all five machine learning predictive models exhibited lower PPVs. The DT model demonstrated the most significant variation in performance metrics relative to the other models, with a sensitivity of 0.231 and specificity of 0.990. The LR-based predictive model had the lowest PPV at 0.210, compared to the other four models. In the external validation cohort, the performance metrics of the predictive models were generally consistent with those in the test cohort. The LR-based model for predicting omental metastasis exhibited a lower PPV. Among the machine learning algorithms, the RF predictive model demonstrated higher accuracy and improved PPV relative to LR, SVM, KNN, and DT models.

Metal-organic cages for gas adsorption and separation.

The unique high surface area and tunable cavity size endow metal-organic cages (MOCs) with superior performance and broad application in gas adsorption and separation. Over the past three decades, for instance, numerous MOCs have been widely explored in adsorbing diverse types of gas including energy gases, greenhouse gases, toxic gases, noble gases, etc. To gain a better understanding of the structure-performance relationships, great endeavors have been devoted to ligand design, metal node regulation, active metal site construction, cavity size adjustment, and function-oriented ligand modification, thus opening up routes toward rationally designed MOCs with enhanced capabilities. Focusing on the unveiled structure-performance relationships of MOCs towards target gas molecules, this review consists of two parts, gas adsorption and gas separation, which are discussed separately. Each part discusses the cage assembly process, gas adsorption strategies, host-guest chemistry, and adsorption properties. Finally, we briefly overviewed the challenges and future directions in the rational development of MOC-based sorbents for application in challenging gas adsorption and separation, including the development of high adsorption capacity MOCs oriented by adsorbability and the development of highly selective adsorption MOCs oriented by separation performance.

Interactions between Balantidium ctenopharyngodoni and microbiota reveal its low pathogenicity in the hindgut of grass carp.

BACKGROUND: Hosts, parasites, and microbiota interact with each other, forming a complex ecosystem. Alterations to the microbial structure have been observed in various enteric parasitic infections (e.g. parasitic protists and helminths). Interestingly, some parasites are associated with healthy gut microbiota linked to the intestinal eubiosis state. So the changes in bacteria and metabolites induced by parasite infection may offer benefits to the host, including protection from other parasitesand promotion of intestinal health. The only ciliate known to inhabit the hindgut of grass carp, Balantidium ctenopharyngodoni, does not cause obvious damage to the intestinal mucosa. To date, its impact on intestinal microbiota composition remains unknown. In this study, we investigated the microbial composition in the hindgut of grass carp infected with B. ctenopharyngodoni, as well as the changes of metabolites in intestinal contents resulting from infection. RESULTS: Colonization by B. ctenopharyngodoni was associated with an increase in bacterial diversity, a higher relative abundance of Clostridium, and a lower abundance of Enterobacteriaceae. The family Aeromonadaceae and the genus Citrobacter had significantly lower relative abundance in infected fish. Additionally, grass carp infected with B. ctenopharyngodoni exhibited a significant increase in creatine content in the hindgut. This suggested that the presence of B. ctenopharyngodoni may improve intestinal health through changes in microbiota and metabolites. CONCLUSIONS: We found that grass carp infected with B. ctenopharyngodoni exhibit a healthy microbiota with an increased bacterial diversity. The results suggested that B. ctenopharyngodoni reshaped the composition of hindgut microbiota similarly to other protists with low pathogenicity. The shifts in the microbiota and metabolites during the colonization and proliferation of B. ctenopharyngodoni indicated that it may provide positive effects in the hindgut of grass carp.

[Retracted] The therapeutic effect of artesunate on rosacea through the inhibition of the JAK/STAT signaling pathway.

Following the publication of this paper, it was drawn to the Editor's attention by a concerned reader that the GAPDH control western blotting data shown in Fig. 4A were strikingly similar to data appearing in different form in another article written by different authors at different research institutes. Owing to the fact that the contentious data in the above article had already been published prior to its submission to Molecular Medicine Reports, the Editor has decided that this paper should be retracted from the Journal. The authors were asked for an explanation to account for these concerns, but the Editorial Office did not receive a reply. The Editor apologizes to the readership for any inconvenience caused. [Molecular Medicine Reports 17: 8385­8390, 2018; DOI: 10.3892/mmr.2018.8887].

Alterations of gut microbiota and short-chain fatty acids induced by Balantidium polyvacuolum in the hindgut of Xenocyprinae fishes providing new insights into the relationship among protozoa, gut microbiota and host.

Introduction: Parasitic ciliates are protozoans with a global distribution. Along with the gut microbiota, they have formed a micro-ecosystem that affects the host's nutrition, metabolism, and immunity. The interactions and relationships among the three components of this microecosystem (protozoa, gut microbiota, and host) remain only partially understood. Xenocypris fish and the unique ciliate Balantidium polyvacuolum in its hindgut are good materials to study the interplay. Methods: In this study, 16S rRNA gene amplicon sequencing and short-chain fatty acids (SCFAs) identification were used. Network was also constructed to understand their relationships. Results: We found that the gut microbiota of B. polyvacuolum-infected X. davidi and X. argentea had higher diversity, richness, and evenness than uninfected ones. B. polyvacuolum could lead to an increase of Fusobacterium and Chloroflexi in both X. davidi and X. argentea, while significantly increase the abundance of genera Romboutsia and Clostridium in X. argentea. Besides, B. polyvacuolum could significantly increase the content of total SCFAs and acetic acid in X. davidi and increase the concentrations of propionic, isobutyric and butanoic acids in X. argentea. Furthermore, correlation analyses showed that B. polyvacuolum may alter SCFAs by affecting key SCFAs-producing bacteria such as Clostridium and Cetobacterium. Discussion: This study greatly expands our understanding of relationships among B. polyvacuolum, gut microbiota and host Xenocypris fish, which sheds new insights into the mechanism of interaction among protozoa, gut microbiota and host.

Tailored Catalysis Inducing Exceptionally Fire-Safe and Mechanically Reinforced Epoxy at An Ultralow Loading.

An unconventional P/N/Si-free fire safety of epoxy at an ultralow loading with a significantly improved mechanical robustness and toughness via a mere nanocomposite technique is a great challenge. To achieve the goal, a proof of concept is proposed associated with a hierarchical manipulation of catalysis-tailored FexSy ultrathin nanosheets on organic-layered double hydroxide (LDH-DBS@FexSy) toward the formation of porous piling structure via a self-sacrificing conversion of metal-organic framework. A sufficient characterization certified the targeted architecture and composition. A P/N/Si-free ultralow loading of 2 wt % LDH-DBS@FexSy (i.e., 0.6 wt % FexSy) imparted epoxy with UL-94 V-0 rating, a 36.1% reduction of peak heat release rate, as well as a pronounced fire-protection feature. A systematic contrastive investigation evidenced a time-dependent fire-shielding effect induced by a featured catalysis-tailored ultrafast charring behavior at the interface of epoxy and LDH nanosheets. Intriguingly, the tensile strength, impact strength, and flexural strength were simultaneously enhanced by 62.2, 185.4, and 62.9%, respectively, with a 0.6 wt % incorporation of FexSy hierarchy on the basis of a "root-soil"-inspired interfacial "interlocking" structure. In perspective, an integrated manipulation of an interface catalysis-tailored ultrafast charring and hierarchical "interlocking" construction offer an effective balance of the fire safety, mechanical robustness, and toughness of polymers.

Hub gene identification and molecular subtype construction for Helicobacter pylori in gastric cancer via machine learning methods and NMF algorithm.

Helicobacter pylori (HP) is a gram-negative and spiral-shaped bacterium colonizing the human stomach and has been recognized as the risk factor of gastritis, peptic ulcer disease, and gastric cancer (GC). Moreover, it was recently identified as a class I carcinogen, which affects the occurrence and progression of GC via inducing various oncogenic pathways. Therefore, identifying the HP-related key genes is crucial for understanding the oncogenic mechanisms and improving the outcomes of GC patients. We retrieved the list of HP-related gene sets from the Molecular Signatures Database. Based on the HP-related genes, unsupervised non-negative matrix factorization (NMF) clustering method was conducted to stratify TCGA-STAD, GSE15459, GSE84433 samples into two clusters with distinct clinical outcomes and immune infiltration characterization. Subsequently, two machine learning (ML) strategies, including support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF), were employed to determine twelve hub HP-related genes. Beyond that, receiver operating characteristic and Kaplan-Meier curves further confirmed the diagnostic value and prognostic significance of hub genes. Finally, expression of HP-related hub genes was tested by qRT-PCR array and immunohistochemical images. Additionally, functional pathway enrichment analysis indicated that these hub genes were implicated in the genesis and progression of GC by activating or inhibiting the classical cancer-associated pathways, such as epithelial-mesenchymal transition, cell cycle, apoptosis, RAS/MAPK, etc. In the present study, we constructed a novel HP-related tumor classification in different datasets, and screened out twelve hub genes via performing the ML algorithms, which may contribute to the molecular diagnosis and personalized therapy of GC.

Exploring Cancer Dependency Map genes and immune subtypes in colon cancer, in which TIGD1 contributes to colon cancer progression.

BACKGROUND: Tumour-dependent genes identified in CRISPR-Cas9 screens have been widely reported in Cancer Dependency Maps (CDMs). CDM-derived tumour-dependent genes play an important role in tumorigenesis and progression; however, they have not been investigated in colon cancer (CC). METHODS: CDM genes overexpressed in CC were identified from the TCGA-COAD dataset and CDM platform. A CDM signature and prognostic nomogram were constructed by Lasso Cox regression and multivariate Cox analyses. A weighted correlation network analysis (WGCNA) and consensus clustering were used to define coexpressed genes with CDM risk scores and to determine two new immune subtypes. A comprehensive investigation was performed between the two subtypes and immune regulation, the immune microenvironment and the impact of immunotherapy. RESULTS: First, 1304 overexpressed CDM genes were identified. Then, a CDM signature with five cancer-dependent genes (MMS19, NOP14, POLRMT, SNAPC5 and TIGD1) and a prognostic nomogram were constructed, and they demonstrated robust predictive performance and a close relationship with clinical characteristics in different CC datasets. Patients with high CDM risk scores showed worse survival outcome and weaker response to chemotherapy. Additionally, TIGD1 genes were oncogenes that affected the CC cell cycle, according to cell functional experiments that involved the suppression of the TIGD1 gene. Furthermore, WGCNA and consensus clustering were used to define coexpressed genes with CDM risk scores and to determine two new immune subtypes. Finally, systematic investigations were conducted with the relationship between the CDM subtypes and immune regulation. CONCLUSIONS: This study constructed a CDM signature consisting of five risk genes that predict survival in CC patients. In addition, the immune subtypes provided valuable insights into immunotherapy for CC patients. TIGD1, as an oncogene, is independent prognostic factors for CC, and contributes to CC progression.

Development and validation of a CT radiomics and clinical feature model to predict omental metastases for locally advanced gastric cancer.

""We employed radiomics and clinical features to develop and validate a preoperative prediction model to estimate the omental metastases status of locally advanced gastric cancer (LAGC). A total of 460 patients (training cohort, n = 250; test cohort, n = 106; validation cohort, n = 104) with LAGC who were confirmed T3/T4 stage by postoperative pathology were continuously collected retrospectively, including clinical data and preoperative arterial phase computed tomography images (APCT). Dedicated radiomics prototype software was used to segment the lesions and extract features from the preoperative APCT images. The least absolute shrinkage and selection operator (LASSO) regression was used to select the extracted radiomics features, and a radiomics score model was constructed. Finally, a prediction model of omental metastases status and a nomogram were constructed combining the radiomics scores and selected clinical features. An area under the curve (AUC) of the receiver operating characteristic curve (ROC) was used to validate the capability of the prediction model and nomogram in the training cohort. Calibration curves and decision curve analysis (DCA) were used to evaluate the prediction model and nomogram. The prediction model was internally validated by the test cohort. In addition, 104 patients from another hospital's clinical and imaging data were gathered for external validation. In the training cohort, the combined prediction (CP) model (AUC 0.871, 95% CI 0.798-0.945) of the radiomics scores combined with the clinical features, compared with clinical features prediction (CFP) model (AUC 0.795, 95% CI 0.710-0.879) and radiomics scores prediction (RSP) model (AUC 0.805, 95% CI 0.730-0.879), had the better predictive ability. The Hosmer-Lemeshow test of the CP model showed that the prediction model did not deviate from the perfect fitting (p = 0.893). In the DCA, the clinical net benefit of the CP model was higher than that of the CFP model and RSP model. In the test and validation cohorts, the AUC values of the CP model were 0.836 (95% CI 0.726-0.945) and 0.779 (95% CI 0.634-0.923), respectively. The preoperative APCT-based clinical-radiomics nomogram showed good performance in predicting omental metastases status in LAGC, which may contribute to clinical decision-making.

Multiomics characteristics and immunotherapeutic potential of EZH2 in pan-cancer.

Enhancer of zeste homolog 2 (EZH2) is a significant epigenetic regulator that plays a critical role in the development and progression of cancer. However, the multiomics features and immunological effects of EZH2 in pan-cancer remain unclear. Transcriptome and clinical raw data of pan-cancer samples were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and subsequent data analyses were conducted by using R software (version 4.1.0). Furthermore, numerous bioinformatics analysis databases also reapplied to comprehensively explore and elucidate the oncogenic mechanism and therapeutic potential of EZH2 from pan-cancer insight. Finally, quantitative reverse transcription polymerase chain reaction and immunohistochemical assays were performed to verify the differential expression of EZH2 gene in various cancers at the mRNA and protein levels. EZH2 was widely expressed in multiple normal and tumor tissues, predominantly located in the nucleoplasm. Compared with matched normal tissues, EZH2 was aberrantly expressed in most cancers either at the mRNA or protein level, which might be caused by genetic mutations, DNA methylation, and protein phosphorylation. Additionally, EZH2 expression was correlated with clinical prognosis, and its up-regulation usually indicated poor survival outcomes in cancer patients. Subsequent analysis revealed that EZH2 could promote tumor immune evasion through T-cell dysfunction and T-cell exclusion. Furthermore, expression of EZH2 exhibited a strong correlation with several immunotherapy-associated responses (i.e., immune checkpoint molecules, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR) status, and neoantigens), suggesting that EZH2 appeared to be a novel target for evaluating the therapeutic efficacy of immunotherapy.

Deep learning model for diagnosing early gastric cancer using preoperative computed tomography images.

Background: Early gastric cancer (EGC) is defined as a lesion restricted to the mucosa or submucosa, independent of size or evidence of regional lymph node metastases. Although computed tomography (CT) is the main technique for determining the stage of gastric cancer (GC), the accuracy of CT for determining tumor invasion of EGC was still unsatisfactory by radiologists. In this research, we attempted to construct an AI model to discriminate EGC in portal venous phase CT images. Methods: We retrospectively collected 658 GC patients from the first affiliated hospital of Nanchang university, and divided them into training and internal validation cohorts with a ratio of 8:2. As the external validation cohort, 93 GC patients were recruited from the second affiliated hospital of Soochow university. We developed several prediction models based on various convolutional neural networks, and compared their predictive performance. Results: The deep learning model based on the ResNet101 neural network represented sufficient discrimination of EGC. In two validation cohorts, the areas under the curves (AUCs) for the receiver operating characteristic (ROC) curves were 0.993 (95% CI: 0.984-1.000) and 0.968 (95% CI: 0.935-1.000), respectively, and the accuracy was 0.946 and 0.914. Additionally, the deep learning model can also differentiate between mucosa and submucosa tumors of EGC. Conclusions: These results suggested that deep learning classifiers have the potential to be used as a screening tool for EGC, which is crucial in the individualized treatment of EGC patients.

Development and validation of a predictive model combining clinical, radiomics, and deep transfer learning features for lymph node metastasis in early gastric cancer.

Background: This study aims to develop and validate a predictive model combining deep transfer learning, radiomics, and clinical features for lymph node metastasis (LNM) in early gastric cancer (EGC). Materials and methods: This study retrospectively collected 555 patients with EGC, and randomly divided them into two cohorts with a ratio of 7:3 (training cohort, n = 388; internal validation cohort, n = 167). A total of 79 patients with EGC collected from the Second Affiliated Hospital of Soochow University were used as external validation cohort. Pre-trained deep learning networks were used to extract deep transfer learning (DTL) features, and radiomics features were extracted based on hand-crafted features. We employed the Spearman rank correlation test and least absolute shrinkage and selection operator regression for feature selection from the combined features of clinical, radiomics, and DTL features, and then, machine learning classification models including support vector machine, K-nearest neighbor, random decision forests (RF), and XGBoost were trained, and their performance by determining the area under the curve (AUC) were compared. Results: We constructed eight pre-trained transfer learning networks and extracted DTL features, respectively. The results showed that 1,048 DTL features extracted based on the pre-trained Resnet152 network combined in the predictive model had the best performance in discriminating the LNM status of EGC, with an AUC of 0.901 (95% CI: 0.847-0.956) and 0.915 (95% CI: 0.850-0.981) in the internal validation and external validation cohorts, respectively. Conclusion: We first utilized comprehensive multidimensional data based on deep transfer learning, radiomics, and clinical features with a good predictive ability for discriminating the LNM status in EGC, which could provide favorable information when choosing therapy options for individuals with EGC.

CT-based radiomic nomogram for preoperative prediction of DNA mismatch repair deficiency in gastric cancer.

Background: DNA mismatch repair (MMR) deficiency has attracted considerable attention as a predictor of the immunotherapy efficacy of solid tumors, including gastric cancer. We aimed to develop and validate a computed tomography (CT)-based radiomic nomogram for the preoperative prediction of MMR deficiency in gastric cancer (GC). Methods: In this retrospective analysis, 225 and 91 GC patients from two distinct hospital cohorts were included. Cohort 1 was randomly divided into a training cohort (n = 176) and an internal validation cohort (n = 76), whereas cohort 2 was considered an external validation cohort. Based on repeatable radiomic features, a radiomic signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. We employed multivariable logistic regression analysis to build a radiomics-based model based on radiomic features and preoperative clinical characteristics. Furthermore, this prediction model was presented as a radiomic nomogram, which was evaluated in the training, internal validation, and external validation cohorts. Results: The radiomic signature composed of 15 robust features showed a significant association with MMR protein status in the training, internal validation, and external validation cohorts (both P-values <0.001). A radiomic nomogram incorporating a radiomic signature and two clinical characteristics (age and CT-reported N stage) represented good discrimination in the training cohort with an AUC of 0.902 (95% CI: 0.853-0.951), in the internal validation cohort with an AUC of 0.972 (95% CI: 0.945-1.000) and in the external validation cohort with an AUC of 0.891 (95% CI: 0.825-0.958). Conclusion: The CT-based radiomic nomogram showed good performance for preoperative prediction of MMR protein status in GC. Furthermore, this model was a noninvasive tool to predict MMR protein status and guide neoadjuvant therapy.

In Vitro Assessment of Berberine against Ichthyophthirius multifiliis in Goldfish.

Ichthyophthirius multifiliis is a pathogenic ciliate parasite, which infects almost all freshwater teleost fish and leads to significant economic losses. The present study aimed to evaluate the acute toxicity of berberine to the free-living stages of I. multifiliis, that is, theronts and tomonts. Our results indicated that 99.30% of I. multifiliis theronts were killed by a concentration of 15 mg/L berberine during the 4 h exposure time, while berberine had no effect on protomonts. Nevertheless, berberine at a concentration of 5 mg/L could effectively reduce the release of theronts from tomonts treated for 4 h. Additionally, according to the transmission electron microscopy results, berberine at 15 mg/L could strongly change the shape of protomonts, destroy their organelles, and significantly decrease the number of ribosomes. The median lethal concentration (LC50) of berberine for goldfish at 96 h was 528.44 mg/L, which was almost 67 times the median effective concentration (EC50) of berberine for killing theronts (7.86 mg/L). The results demonstrated that berberine could be an effective and safe potential parasiticide for killing I. multifiliis.

Effects of Routine Health Care Combined with Oral Vitamin D on Linear Growth in 5-Year-Old Children.

Objective: The aim is to evaluate the effects of routine health care combined with oral vitamin D on linear growth in 5-year-old children. Materials and Methods: The 5-year-old children who received routine health care in Shiyan Maternal and Child Health Care Hospital from January 2019 to January 2021 were retrospectively analyzed. They were divided into the supplement group and the non-supplement group according to whether or not they received oral vitamin D, and reasons for not taking oral vitamin D and its influence on children's linear growth were analyzed. Results: A total of 368 children were enrolled, including 228 children in the supplement group, accounting for 61.96%. The analysis of the influencing factors of vitamin D supplementation showed that the proportion of children with well-educated parents and living in cities and towns was higher (all P < 0.05). Comparing the general situation of children in the two groups, it was found that the height, weight, and head circumference of children in the supplement group were notably higher than those in the non-supplement group (all P < 0.05). At age 4 and 5, the height of the supplement group was significantly higher than that of the non-supplement group (all P < 0.001). Linear analysis showed that the relationship between height and age in the supplement group was Y (height, cm) = 10.07 × X (age, years) + 61.18, while that in the non-supplement group was Y (height, cm) = 8.296 × X (age, years) + 62.81, with significant difference (all P < 0.05). Serum 25-hydroxyvitamin D concentration in the supplement group was significantly higher than that in the non-supplement group, and the proportion of children ≥75 nmol/L was evidently higher than that in the supplement group (all P < 0.05). In addition, the incidence of vitamin D-related hypercalcemia in the supplement group was significantly higher than that in the non-supplement group (all P < 0.05). Conclusion: There are still more children without vitamin D supplement, which is closely related to their parents' education background and place of residence. Additionally, vitamin D supplementation can promote growth and improve 25-hydroxyvitamin D levels in children, but with the risk of related complications.

Intensive Health Care plus Vitamin D Administration Benefits the Growth and Development of Young Children and Reduces the Incidence of Nutritional Disorders.

This study was intended to assess the effect of intensive health care plus vitamin D administration on the growth, development, and nutritional status of young children. Totally, 131 young children who were admitted to Shiyan Maternal and Child Health Care Hospital from January 2020 to January 2021 were included and assigned via the random number table method at a ratio of 1 : 1 : 1 to receive either vitamin D administration (vitamin D group, n = 42), intensive health care (IHC) (IHC group, n = 44), or vitamin D administration plus intensive health care (combination group, n = 45). All children received a normal diet and routine care. After the intervention, all children showed robust improvement in their height, weight, neuropsychological development, and nutritional status, in which the combination therapy was associated with better outcomes in terms of physical development, neuropsychological development, and nutritional status, and a higher serum 25-hydroxyvitamin D3 (25-(OH)D3) level of the children versus monotherapy. Children receiving combined therapy had a significantly lower incidence of nutritional disorders than those receiving single therapy. Intensive health care plus vitamin D benefits the growth and development of young children and reduces the incidence of nutritional disorders in children.

A Necroptosis-Related lncRNA-Based Signature to Predict Prognosis and Probe Molecular Characteristics of Stomach Adenocarcinoma.

Background: As a caspase-independent type of cell death, necroptosis plays a significant role in the initiation, and progression of gastric cancer (GC). Numerous studies have confirmed that long non-coding RNAs (lncRNAs) are closely related to the prognosis of patients with GC. However, the relationship between necroptosis and lncRNAs in GC remains unclear. Methods: The molecular profiling data (RNA-sequencing and somatic mutation data) and clinical information of patients with stomach adenocarcinoma (STAD) were retrieved from The Cancer Genome Atlas (TCGA) database. Pearson correlation analysis was conducted to identify the necroptosis-related lncRNAs (NRLs). Subsequently, univariate Cox regression and LASSO-Cox regression were conducted to establish a 12-NRLs signature in the training set and validate it in the testing set. Finally, the prognostic power of the 12-NRLs signature was appraised via survival analysis, nomogram, Cox regression, clinicopathological characteristics correlation analysis, and the receiver operating characteristic (ROC) curve. Furthermore, correlations between the signature risk score (RS) and immune cell infiltration, immune checkpoint molecules, somatic gene mutations, and anticancer drug sensitivity were analyzed. Results: In the present study, a 12-NRLs signature comprising REPIN1-AS1, UBL7-AS1, LINC00460, LINC02773, CHROMR, LINC01094, FLNB-AS1, ITFG1-AS1, LASTR, PINK1-AS, LINC01638, and PVT1 was developed to improve the prognosis prediction of STAD patients. Unsupervised methods, including principal component analysis and t-distributed stochastic neighbor embedding, confirmed the capability of the present signature to separate samples with RS. Kaplan-Meier and ROC curves revealed that the signature had an acceptable predictive potency in the TCGA training and testing sets. Cox regression and stratified survival analysis indicated that the 12-NRLs signature were risk factors independent of various clinical parameters. Additionally, immune cell infiltration, immune checkpoint molecules, somatic gene mutations, and half-inhibitory concentration differed significantly among different risk subtypes, which implied that the signature could assess the clinical efficacy of chemotherapy and immunotherapy. Conclusion: This 12-NRLs risk signature may help assess the prognosis and molecular features of patients with STAD and improve treatment modalities, thus can be further applied clinically.

RNF114 Silencing Inhibits the Proliferation and Metastasis of Gastric Cancer.

RNF114 (E3 ubiquitin ligase RING finger protein 114) was first identified as a zinc-binding protein that promotes psoriasis development; however, its role in gastric cancer is still unclear. We explored the relationship between RNF114 and gastric cancer using bioinformatics and molecular biology techniques. The results showed that RNF114 was highly expressed in gastric cancer and negatively correlated with the patient's prognosis. Functional assays suggested that RNF114 silencing suppressed the proliferation and metastasis of gastric cancer cells to a certain extent. Further studies showed that RNF114 expression was potentially targeted by miR-218-5p and methylation modification, and mediated downstream EGR1 (early growth response 1) degradation by the ubiquitylation approach. Together, the present results highlight the detrimental effects of RNF114 overexpression in gastric cancer and contribute to a better understanding of the mechanisms underlying RNF114 functionality.