Design of a Thermally Stable Heat-Resistant (Al2O3+Al3Ti)/Al Composite with Excellent High-Temperature Mechanical Properties: Multimechanism Synergistic Strengthening Strategy.

The effectiveness of the room-temperature strengthening strategy for aluminum (Al) is compromised at increased temperatures due to grain and precipitate phase coarsening. Overcoming the heightened activity of grain boundaries and dislocations poses a significant challenge in enhancing the high-temperature strength through traditional precipitation strengthening. This study presents novel strengthening strategies that integrate intergranular reinforcements, intragranular reinforcements, refined grain, and stacking faults within an (Al2O3+Al3Ti)/Al composite prepared using sol-gel and powder metallurgy technology. Excellent high-temperature tensile properties are achieved; also, a remarkable fatigue performance at increased temperatures that surpasses those of other existing Al alloys and composites is revealed. These superior characteristics can be attributed to its exceptionally stable microstructure and the synergistic strengthening mechanisms mentioned above. This work offers new insights into designing and fabricating thermally stable Al matrix composites for high-temperature applications.

Novel Strategy for Human Deep Vein Thrombosis Diagnosis Based on Metabolomics and Stacking Machine Learning.

Deep vein thrombosis (DVT) is a serious health issue that often leads to considerable morbidity and mortality. Diagnosis of DVT in a clinical setting, however, presents considerable challenges. The fusion of metabolomics techniques and machine learning methods has led to high diagnostic and prognostic accuracy for various pathological conditions. This study explored the synergistic potential of dual-platform metabolomics (specifically, gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS)) to expand the detection of metabolites and improve the precision of DVT diagnosis. Sixty-one differential metabolites were identified in serum from DVT patients: 22 from GC-MS and 39 from LC-MS. Among these, five key metabolites were highlighted by SHapley Additive exPlanations (SHAP)-guided feature engineering and then used to develop a stacking diagnostic model. Additionally, a user-friendly interface application system was developed to streamline and automate the application of the diagnostic model, enhancing its practicality and accessibility for clinical use. This work showed that the integration of dual-platform metabolomics with a stacking machine learning model enables faster and more accurate diagnosis of DVT in clinical environments.

Oxolinic Acid Generated Green Fluorescence Based on a Terbium-Functionalized Covalent Organic Framework.

Oxolinic acid (OXO), a classic environmental contaminant, has a terrible detrimental effect on human health. The exploration of efficient strategies to detect and detecting OXO has remarkable significance. Herein, we reported a novel terbium(III)-functionalized covalent organic framework (Bpy-DhBt-COF@Tb3+) by fixing Tb3+ on the bipyridine-connecting COF (Bpy-DhBt-COF) as a turn-on fluorescent switch toward OXO for the first time. In this platform, Tb3+ acts as the specific recognition units for OXO and the response signal, while Bpy-DhBt-COF acts as the safehaven for Tb3+. Once introducing OXO to Bpy-DhBt-COF@Tb3+, OXO can instead water molecules coordinate with Tb3+ and sensitize Tb3+ instantly, thereby producing a significant fluorescence signal. Profiting from the excellent porosity of Bpy-DhBt-COF@Tb3+, it can obtain optimal response toward OXO only within 10 s with an ultrasensitive detection limit of 12.5 nM. Furthermore, Bpy-DhBt-COF@Tb3+ displayed outstanding selectivity toward OXO than other general quinolones. Based on these, a Tb3+-based COF was explored for the first time for the turn-on fluorescence detection of an OXO with rapid response, high sensitivity, and outstanding selectivity. In this work, we not only exhibit the attractive performance of Tb3+-functionalized COF to detect OXO but also propose a prospect strategy for creating other fluorescent sensors for multiple targets.

Novel ratio-expressions of genes enables estimation of wound age in contused skeletal muscle.

Given that combination with multiple biomarkers may well raise the predictive value of wound age, it appears critically essential to identify new features under the limited cost. For this purpose, the present study explored whether the gene expression ratios provide unique time information as an additional indicator for wound age estimation not requiring the detection of new biomarkers and allowing full use of the available data. The expression levels of four wound-healing genes (Arid5a, Ier3, Stom, and Lcp1) were detected by real-time polymerase chain reaction, and a total of six expression ratios were calculated among these four genes. The results showed that the expression levels of four genes and six ratios of expression changed time-dependent during wound repair. The six expression ratios provided additional temporal information, distinct from the four genes analyzed separately by principal component analysis. The overall performance metrics for cross-validation and external validation of four typical prediction models were improved when six ratios of expression were added as additional input variables. Overall, expression ratios among genes provide temporal information and have excellent potential as predictive markers for wound age estimation. Combining the expression levels of genes with ratio-expression of genes may allow for more accurate estimates of the time of injury.

Development of an anisotropic polarizable model for the all-atom AMOEBA force field.

For planar and rigid π-conjugated molecular systems, electrostatic and inductive interactions are pivotal in governing molecular packing structures and electron polarization energies. These electrostatic interactions typically exhibit an anisotropic nature within π-conjugated systems. In this study, we utilize the atoms in molecules (AIM) theory in conjunction with linear response theory to decompose molecular polarizability into distributed atomic polarizability tensors. On the basis of atomic polarizability tensors, we extended an anisotropic polarizable model into the AMOEBA polarizable force field. Both anisotropic and isotropic polarizable models in combination with various density functional theory (DFT)-derived atomic multipoles were applied to optimize the experimental crystals of naphthalene and anthracene. Furthermore, these two types of electrostatic models, coupled with the evolutionary algorithm USPEX program, are utilized to predict the crystal structures of oligoacenes. Our findings demonstrate that the anisotropic polarizable model exhibits superior performance in crystal refinement and crystal structure prediction. This enriched anisotropic polarizable model is seamlessly integrated into the AMOEBA polarizable force field and readily applicable within our modified Tinker program.

[Chemical constituents from Alangium chinense subsp. pauciflorum].

Chemical constituents of 70% ethanol extract of Alangium chinense subsp. pauciflorum were investigated. The 70% ethanol extract of A. chinense subsp. pauciflorum was isolated and purified by D-101 macroporous resins, silica gel, Sephadex LH-20 and other methods. As a result, nineteen compounds were isolated and identified as 4-cyclohexene-1α,2α,3α-triol-1-O-ß-D-glucoside(1), 1ß,4α,6α,13-tetrahydroxy-eudesm-11(12)-ene(2), sucrose(3), 1'-O-benzyl-α-L-rhamnopyranosyl-(1″→6')-ß-D-glucopyranoside(4), bis(2-ethylhexyl)benzene-1,2-dicarboxylate(5),(Z)-10-heneicosenoic acid(6), di-O-methylcrenati(7), methyl-α-D-fructofuranoside(8), ß-daucosterol(9), syringic acid(10), vanillicacid(11), octacosanol(12), isoarborinol(13), 2,7-dihydroxy-6-methyl-4-(1-methylethyl)-1-naphthalenecarboxylate(14),vanillin(15), coniferyl aldehyde(16), 9(11)-dehydroergosterolperoxide(17), 5α,8α-epidioxy-(22E,24R)-ergosta-6,22-dien-3ß-ol(18), ß-sitosterol(19), respectively. Compounds 1 and 2 were new compounds, compounds 5-11, 13, 15-18 were isolated from Alangium for the first time.The anti-inflammatory activity of compourd 1 was determinded by the LPS-induced RAW264.7 macrophage inflammation model. The results showed that the new compound 1 has a certain inhibitory effect on LPS-induced NO production of RAW264.7 cells, and the inhibitory rate was 54.57%.

Combined metabolomics and machine learning algorithms to explore metabolic biomarkers for diagnosis of acute myocardial ischemia.

Acute myocardial ischemia (AMI) remains the leading cause of death worldwide, and the post-mortem diagnosis of AMI represents a current challenge for both clinical and forensic pathologists. In the present study, the untargeted metabolomics based on ultra-performance liquid chromatography combined with high-resolution mass spectrometry was applied to analyze serum metabolic signatures from AMI in a rat model (n = 10 per group). A total of 28 endogenous metabolites in serum were significantly altered in AMI group relative to control and sham groups. A set of machine learning algorithms, namely gradient tree boosting (GTB), support vector machine (SVM), random forest (RF), logistic regression (LR), and multilayer perceptron (MLP) models, was used to screen the more valuable metabolites from 28 metabolites to optimize the biomarker panel. The results showed that classification accuracy and performance of MLP model were better than other algorithms when the metabolites consisting of L-threonic acid, N-acetyl-L-cysteine, CMPF, glycocholic acid, L-tyrosine, cholic acid, and glycoursodeoxycholic acid. Finally, 17 blood samples from autopsy cases were applied to validate the classification model's value in human samples. The MLP model constructed based on rat dataset achieved accuracy of 88.23%, and ROC of 0.89 for predicting AMI type II in autopsy cases of sudden cardiac death. The results demonstrated that MLP model based on 7 molecular biomarkers had a good diagnostic performance for both AMI rats and autopsy-based blood samples. Thus, the combination of metabolomics and machine learning algorithms provides a novel strategy for AMI diagnosis.

A novel method for determining postmortem interval based on the metabolomics of multiple organs combined with ensemble learning techniques.

Determining postmortem interval (PMI) is one of the most challenging and essential endeavors in forensic science. Developments in PMI estimation can take advantage of machine learning techniques. Currently, applying an algorithm to obtain information on multiple organs and conducting joint analysis to accurately estimate PMI are still in the early stages. This study aimed to establish a multi-organ stacking model that estimates PMI by analyzing differential compounds of four organs in rats. In a total of 140 rats, skeletal muscle, liver, lung, and kidney tissue samples were collected at each time point after death. Ultra-performance liquid chromatography coupled with high-resolution mass spectrometry was used to determine the compound profiles of the samples. The original data were preprocessed using multivariate statistical analysis to determine discriminant compounds. In addition, three interrelated and increasingly complex patterns (single organ optimal model, single organ stacking model, multi-organ stacking model) were established to estimate PMI. The accuracy and generalized area under the receiver operating characteristic curve of the multi-organ stacking model were the highest at 93% and 0.96, respectively. Only 1 of the 14 external validation samples was misclassified by the multi-organ stacking model. The results demonstrate that the application of the multi-organ combination to the stacking algorithm is a potential forensic tool for the accurate estimation of PMI.

Release Behavior of Liquid Crystal Monomers from Waste Smartphone Screens: Occurrence, Distribution, and Mechanistic Modeling.

Liquid crystal display (LCD) screens can release many organic pollutants into the indoor environment, including liquid crystal monomers (LCMs), which have been proposed as a novel class of emerging pollutants. Knowing the release pathways and mechanisms of LCMs from various components of LCD screens is important to accurately assess the LCM release and reveal their environmental transport behavior and fate in the ambient environment. A total of 47, 43, and 33 out of 64 target LCMs were detected in three disassembled parts of waste smartphone screens, including the LCM layer (LL), light guide plate (LGP), and screen protector (SP), respectively. Correlation analysis confirmed LL was the source of LCMs detected in LGP and SP. The emission factors of LCMs from waste screen, SP, and LGP parts were estimated as 2.38 × 10-3, 1.36 × 10-3, and 1.02 × 10-3, respectively. A mechanism model was developed to describe the release behaviors of LCMs from waste screens, where three characteristics parameters of released LCMs, including average mass proportion (AP), predicted subcooled vapor pressures (PL), and octanol-air partitioning coefficients (Koa), involving coexistence of absorption and adsorption mechanisms, could control the diffusion-partitioning. The released LCMs in LGP could reach diffusion-partition equilibrium more quickly than those in SP, indicating that LCM release could be mainly governed through SP diffusions.

Identification of Transformation Products of Organic UV Filters by Photooxidation and Their Differential Estrogenicity Assessment.

Organic ultraviolet filters (OUVFs) are extensively released into aquatic environments, where they undergo complex phototransformation. However, there is little knowledge regarding their transformation products (TPs) and associated endocrine disruption potentials. In the present study, we characterized the chemical and toxicological profiles of TPs for two common OUVFs, oxybenzone (BP3) and ethylhexyl methoxycinnamate (EHMC), by photooxidation under environmentally relevant conditions. It is hypothesized that TPs of the tested OUVFs will show varied estrogenicity at different reaction times. High-resolution liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) identified 17 TPs of 7 m/z for BP-3 and 13 TPs of 8 m/z for EHMC at confidence levels ≤2. Five novel TPs of 2 m/z were reported for the first time with structure-diagnostic MS/MS spectra. Estrogenicity assessment using the MCF-7-luc cell line showed discrepant estrogenic activities exhibited by OUVF-TPs over time. Specifically, BP3-TPs exhibited significantly greater estrogenicity than the parent at several reaction times, whereas EHMC-TPs displayed fluctuating estrogenicity with a declining trend. Correlation analysis coupled with molecular docking simulations further suggested several TPs of BP3 as potential endocrine disruptive compounds. These findings underscore the necessity of considering mixtures during chemical testing and risk assessment and highlight the potentially greater risks associated with post-transformation cocktails.

Postmortem Interval Estimation Using Protein Chip Technology Combined with Multivariate Analysis Methods.

OBJECTIVES: To estimate postmortem interval (PMI) by analyzing the protein changes in skeletal muscle tissues with the protein chip technology combined with multivariate analysis methods. METHODS: Rats were sacrificed for cervical dislocation and placed at 16 ℃. Water-soluble proteins in skeletal muscles were extracted at 10 time points (0 d, 1 d, 2 d, 3 d, 4 d, 5 d, 6 d, 7 d, 8 d and 9 d) after death. Protein expression profile data with relative molecular mass of 14 000-230 000 were obtained. Principal component analysis (PCA) and orthogonal partial least squares (OPLS) were used for data analysis. Fisher discriminant model and back propagation (BP) neural network model were constructed to classify and preliminarily estimate the PMI. In addition, the protein expression profiles data of human skeletal muscles at different time points after death were collected, and the relationship between them and PMI was analyzed by heat map and cluster analysis. RESULTS: The protein peak of rat skeletal muscle changed with PMI. The result of PCA combined with OPLS discriminant analysis showed statistical significance in groups with different time points (P<0.05) except 6 d, 7 d and 8 d after death. By Fisher discriminant analysis, the accuracy of internal cross-validation was 71.4% and the accuracy of external validation was 66.7%. The BP neural network model classification and preliminary estimation results showed the accuracy of internal cross-validation was 98.2%, and the accuracy of external validation was 95.8%. There was a significant difference in protein expression between 4 d and 25 h after death by the cluster analysis of the human skeletal muscle samples. CONCLUSIONS: The protein chip technology can quickly, accurately and repeatedly obtain water-soluble protein expression profiles in rats' and human skeletal muscles with the relative molecular mass of 14 000-230 000 at different time points postmortem. The establishment of multiple PMI estimation models based on multivariate analysis can provide a new idea and method for PMI estimation.

3D matrix promotes cell dedifferentiation into colorectal cancer stem cells via integrin/cytoskeleton/glycolysis signaling.

The potential for tumor occurrence triggered by cancer stem cells (CSCs) has emerged as a significant challenge for human colorectal cancer therapy. However, the underlying mechanism of CSC development remains controversial. Our study provided evidence that the bulk of tumor cells could dedifferentiate to CSCs and reacquire CSC-like phenotypes if cultured in the presence of extracellular matrix reagents, such as Matrigel and fibrin gels. In these 3D gels, CD133- colorectal cancer cells can regain tumorigenic potential and stem-like phenotypes. Mechanistically, the 3D extracellular matrix could mediate cytoskeletal F-actin bundling through biomechanical force associated receptors integrin ß1 (ITGB1), contributing to the release of E3 ligase tripartite motif protein 11 (TRIM11) from cytoskeleton and degradation of the glycolytic rate-limiting enzyme phosphofructokinase (PFK). Consequently, PFK inhibition resulted in enhanced glycolysis and upregulation of hypoxia-inducible factor 1 (HIF1α), thereby promoting the reprogramming of stem cell transcription factors and facilitating tumor progression in patients. This study provided novel insights into the role of the extracellular matrix in the regulation of CSC dedifferentiation in a cytoskeleton/glycolysis-dependent manner.

Psychometric Properties of the Chinese Warwick-Edinburgh Mental Well-being Scale in Medical Staff: Cross-sectional Study.

BACKGROUND: Worldwide, mental well-being is a critical issue for public health, especially among medical staff; it affects professionalism, efficiency, quality of care delivery, and overall quality of life. Nevertheless, assessing mental well-being is a complex problem. OBJECTIVE: This study aimed to evaluate the psychometric properties of the Chinese-language version of the 14-item Warwick-Edinburgh Mental Well-being Scale (WEMWBS) in medical staff recruited mainly from 6 hospitals in China and provide a reliable measurement of positive mental well-being. METHODS: A cross-sectional online survey was conducted of medical staff from 15 provinces in China from May 15 to July 15, 2020. Confirmatory factor analysis (CFA) was conducted to test the structure of the Chinese WEMWBS. The Spearman correlations of the Chinese WEMWBS with the 5-item World Health Organization Well-Being Index (WHO-5) were used to evaluate convergent validity. The Cronbach α and split-half reliability (λ) represented internal consistency. A graded response model was adopted for an item response theory (IRT) analysis. We report discrimination, difficulty, item characteristic curves (ICCs), and item information curves (IICs). ICCs and IICs were used to estimate reliability and validity based on the IRT analysis. RESULTS: A total of 572 participants from 15 provinces in China finished the Chinese WEMWBS. The CFA showed that the 1D model was satisfactory and internal consistency reliability was excellent, with α=.965 and λ=0.947, while the item-scale correlation coefficients ranged from r=0.727 to r=0.900. The correlation coefficient between the Chinese WEMWBS and the WHO-5 was significant, at r=0.746. The average variance extraction value was 0.656, and the composite reliability value was 0.964, with good aggregation validity. The discrimination of the Chinese WEMWBS items ranged from 2.026 to 5.098. The ICCs illustrated that the orders of the category thresholds for the 14 items were satisfactory. CONCLUSIONS: The Chinese WEMWBS showed good psychometric properties and can measure well-being in medical staff.

Regulation of Tyrosinase Gene Expression by Retinoic Acid Pathway in the Pacific Oyster Crassostrea gigas.

Retinoic acid (RA) plays important roles in various biological processes in animals. RA signaling is mediated by two types of nuclear receptors, namely retinoic acid receptor (RAR) and retinoid x receptor (RXR), which regulate gene expression by binding to retinoic acid response elements (RAREs) in the promoters of target genes. Here, we explored the effect of all-trans retinoic acid (ATRA) on the Pacific oyster Crassostera gigas at the transcriptome level. A total of 586 differentially expressed genes (DEGs) were identified in C. gigas upon ATRA treatment, with 309 upregulated and 277 downregulated genes. Bioinformatic analysis revealed that ATRA affects the development, metabolism, reproduction, and immunity of C. gigas. Four tyrosinase genes, including Tyr-6 (LOC105331209), Tyr-9 (LOC105346503), Tyr-20 (LOC105330910), and Tyr-12 (LOC105320007), were upregulated by ATRA according to the transcriptome data and these results were verified by real-time quantitative polymerase chain reaction (RT-qPCR) analysis. In addition, increased expression of Tyr (a melanin-related TYR gene in C. gigas) and Tyr-2 were detected after ATRA treatment. The yeast one-hybrid assay revealed the DNA-binding activity of the RA receptors CgRAR and CgRXR, and the interaction of CgRAR with RARE present in the Tyr-2 promoter. These results provide evidence for the further studies on the role of ATRA and the mechanism of RA receptors in mollusks.

Application of Linear Regression Model of Gpnmb Gene in Rat Injury Time Estimation.

OBJECTIVES: To investigate the effects of injury time, postmortem interval (PMI) and postmortem storage temperature on mRNA expression of glycoprotein non-metastatic melanoma protein B (Gpnmb), and to establish a linear regression model between Gpnmb mRNA expression and injury time, to provide aimed at providing potential indexes for injury time estimation. METHODS: Test group SD rats were anesthetized and subjected to blunt contusion and randomly divided into 0 h, 4 h, 8 h, 12 h, 16 h, 20 h and 24 h groups after injury, with 18 rats in each group. After cervical dislocation, 6 rats in each group were collected and stored at 0 ℃, 16 ℃ and 26 ℃, respectively. The muscle tissue samples of quadriceps femoris injury were collected at 0 h, 12 h and 24 h postmortem at the same temperature. The grouping method and treatment method of the rats in the validation group were the same as above. The expression of Gpnmb mRNA in rat skeletal muscle was detected by RT-qPCR. The Pearson correlation coefficient was used to evaluate the correlation between Gpnmb mRNA expression and injury time, PMI, and postmortem storage temperature. SPSS 25.0 software was used to construct a linear regression model, and the validation group data was used for the back-substitution test. RESULTS: The expression of Gpnmb mRNA continued to increase with the prolongation of injury time, and the expression level was highly correlated with injury time (P<0.05), but had little correlation with PMI and postmortem storage temperature (P>0.05). The linear regression equation between injury time (y) and Gpnmb mRNA relative expression (x) was y=0.611 x+4.489. The back-substitution test proved that the prediction of the model was accurate. CONCLUSIONS: The expression of Gpnmb mRNA is almost not affected by the PMI and postmortem storage temperature, but is mainly related to the time of injury. Therefore, a linear regression model can be established to infer the time of injury.

Inhibitor of apoptosis-stimulating p53 protein protects against inflammatory bowel disease in mice models by inhibiting the nuclear factor kappa B signaling.

Drugs and therapies available for the treatment of inflammatory bowel disease (IBD) are not satisfactory. Our previous study has established the inhibitor of apoptosis-stimulating p53 protein (iASPP) as an oncogenic regulator in colorectal cancer by forming a regulatory axis or feedback loop with miR-124, p53, or p63. As iASPP could target and inhibit nuclear factor kappa B (NF-κB) activation, in this study the role and mechanism of iASPP in IBD was investigated. The aberrant up-regulation of iASPP in IBD was subsequently confirmed, based on online data sets, clinical sample examinations and 2,4,6-trinitrobenzene sulfonic acid (TNBS)- and dextran sulfate sodium (DSS)-induced colitis mice models. TNBS or DSS stimulation successfully induced colon shortness, body weight loss, mice colon oxidative stress and inflammation. In both types of colitis mice models, iASPP over-expression improved, whereas iASPP knockdown aggravated TNBS or DSS stimulation-caused colon shortness, body weight loss and mice colon oxidative stress and inflammation. Meanwhile, in both types of colitis mice models, iASPP over-expression inhibited p65 phosphorylation and decreased the levels of tumor necrosis factor (TNF)-α, interleukin (IL)-1ß, IL-6, C-X-C motif chemokine ligand (CXCL)1 and CXCL2 in mice colons, whereas iASPP knockdown exerted opposite effects.

Anti-anemia drug FG4592 retards the AKI-to-CKD transition by improving vascular regeneration and antioxidative capability.

Acute kidney injury (AKI) is a known risk factor for the development of chronic kidney disease (CKD), with no satisfactory strategy to prevent the progression of AKI to CKD. Damage to the renal vascular system and subsequent hypoxia are common contributors to both AKI and CKD. Hypoxia-inducible factor (HIF) is reported to protect the kidney from acute ischemic damage and a novel HIF stabilizer, FG4592 (Roxadustat), has become available in the clinic as an anti-anemia drug. However, the role of FG4592 in the AKI-to-CKD transition remains elusive. In the present study, we investigated the role of FG4592 in the AKI-to-CKD transition induced by unilateral kidney ischemia-reperfusion (UIR). The results showed that FG4592, given to mice 3 days after UIR, markedly alleviated kidney fibrosis and enhanced renal vascular regeneration, possibly via activating the HIF-1α/vascular endothelial growth factor A (VEGFA)/VEGF receptor 1 (VEGFR1) signaling pathway and driving the expression of the endogenous antioxidant superoxide dismutase 2 (SOD2). In accordance with the improved renal vascular regeneration and redox balance, the metabolic disorders of the UIR mice kidneys were also attenuated by treatment with FG4592. However, the inflammatory response in the UIR kidneys was not affected significantly by FG4592. Importantly, in the kidneys of CKD patients, we also observed enhanced HIF-1α expression which was positively correlated with the renal levels of VEGFA and SOD2. Together, these findings demonstrated the therapeutic effect of the anti-anemia drug FG4592 in preventing the AKI-to-CKD transition related to ischemia and the redox imbalance.

Regulation of laryngeal squamous cell cancer progression by the lncRNA RP11-159K7.2/miR-206/DNMT3A axis.

Long non-coding RNAs (lncRNAs), which are longer than 200 nt, have been proved to play a role in promoting or inhibiting cancer progression. The following study investigated the role and underlying mechanisms of lncRNA RP11-159K7.2 in laryngeal squamous cell carcinoma (LSCC) progression. Briefly, in situ hybridization (ISH) and real-time quantitative PCR (RT-qPCR) showed higher expression of RP11-159K7.2 in LSCC tissues and cell lines. Patients with low expression level of RP11-159K7.2 lived longer compared to those with high expression of RP11-159K7.2 (χ2  = 39.111, ***P < 0.001). Multivariate Cox regression analysis suggested that lncRNA RP11-159K7.2 was an independent prognostic factor for LSCC patients (HR = 2.961, ***P < 0.001). Furthermore, to investigate the potential involvement of RP11-159K7.2 in the development of LSCC, we knocked out the expression of endogenous RP11-159K7.2 in TU-212 cells and AMC-HN-8 cells via CRISPR/Cas9 double vector lentiviral system. RP11-159K7.2 knockout decreased LSCC cell growth and invasion both in vitro and in vivo. Mechanically, we found that RP11-159K7.2 could positively regulate the expression of DNMT3A by sponging miR-206. In addition, a feedback loop was also discovered between DNMT3A and miR-206. To sum up, these findings suggest that lncRNA RP11-159K7.2 could be used as a potential biomarker for prognosis and treatment of LSCC.

Recent Development on Controlled Synthesis of Metal Sulfides Hollow Nanostructures via Hard Template Engaged Strategy: A Mini-Review.

In this mini-review, we highlighted the recent progresses in the controlled synthesis of metal sulfides hollow nanostructures via hard template technique. After a brief introduction about the formation mechanism of the inorganic hollow nanostructures via hard template technique, the discussions primarily focused on the emerging development of metal sulfides hollow nanostructures. Various synthetic strategies were summarized concerning the use of the hard template engaged strategies to fabricate various metal sulfides hollow nanostructures, such as hydrothermal method, solvothermal method, ion-exchange, sulfidation or calcination etc. Finally, the perspectives and summaries have been presented to demonstrate that a facile synthetic technique would be widely used to fabricate metal sulfides hollow nanostructures with multi-shells and components.

Novel insights into wound age estimation: combined with "up, no change, or down" system and cosine similarity in python environment.

Wound age estimation is a complex, multifactorial issue. It is considered to have great practical significance that combining multi-biomarkers and multi-methods for injury time estimation. We optimized our earlier "up, no change, or down" model by adding data on the expression levels of mRNAs encoding ABHD2, MAD2L2, and ARID5A, and we converted the relative quantitative expression levels of seven genes into a vector rather than a color model. We used Python to derive the cosine similarity (CS) between a test set and the vector matrix; the highest similarity most accurately reflected the injury time. For the optimized model, the internal and external verifications were approximately 0.71 and 0.66, respectively. The good double-blinded results indicated that the model was stable and reliable. In summary, we used a vector matrix and cosine similarities derived by Python to mine the levels of genes expressed in contused skeletal muscle. We are the first to combine several biomarkers and methods for wound age estimation.