Self-oscillating polymeric refrigerator with high energy efficiency.

Electrocaloric1,2 and electrostrictive3,4 effects concurrently exist in dielectric materials. Combining these two effects could achieve the lightweight, compact localized thermal management that is promised by electrocaloric refrigeration5. Despite a handful of numerical models and schematic presentations6,7, current electrocaloric refrigerators still rely on external accessories to drive the working bodies8-10 and hence result in a low device-level cooling power density and coefficient of performance (COP). Here we report an electrocaloric thin-film device that uses the electro-thermomechanical synergy provided by polymeric ferroelectrics. Under one-time a.c. electric stimulation, the device is thermally and mechanically cycled by the working body itself, resulting in an external-driver-free, self-cycling, soft refrigerator. The prototype offers a directly measured cooling power density of 6.5 W g-1 and a peak COP exceeding 58 under a zero temperature span. Being merely a 30-µm-thick polymer film, the device achieved a COP close to 24 under a 4 K temperature span in an open ambient environment (32% thermodynamic efficiency). Compared with passive cooling, the thin-film refrigerator could immediately induce an additional 17.5 K temperature drop against an electronic chip. The soft, polymeric refrigerator can sense, actuate and pump heat to provide automatic localized thermal management.

Lipidomic changes in a novel sepsis outcome-based analysis reveals potent pro-inflammatory and pro-resolving signaling lipids.

The purpose of this study was to investigate changes in the lipidome of patients with sepsis to identify signaling lipids associated with poor outcomes that could be linked to future therapies. Adult patients with sepsis were enrolled within 24h of sepsis recognition. Patients meeting Sepsis-3 criteria were enrolled from the emergency department or intensive care unit and blood samples were obtained. Clinical data were collected and outcomes of rapid recovery, chronic critical illness (CCI), or early death were adjudicated by clinicians. Lipidomic analysis was performed on two platforms, the Sciex™ 5500 device to perform a lipidomic screen of 1450 lipid species and a targeted signaling lipid panel using liquid-chromatography tandem mass spectrometry. For the lipidomic screen, there were 274 patients with sepsis: 192 with rapid recovery, 47 with CCI, and 35 with early deaths. CCI and early death patients were grouped together for analysis. Fatty acid (FA) 12:0 was decreased in CCI/early death, whereas FA 17:0 and 20:1 were elevated in CCI/early death, compared to rapid recovery patients. For the signaling lipid panel analysis, there were 262 patients with sepsis: 189 with rapid recovery, 45 with CCI, and 28 with early death. Pro-inflammatory signaling lipids from ω-6 poly-unsaturated fatty acids (PUFAs), including 15-hydroxyeicosatetraenoic (HETE), 12-HETE, and 11-HETE (oxidation products of arachidonic acid [AA]) were elevated in CCI/early death patients compared to rapid recovery. The pro-resolving lipid mediator from ω-3 PUFAs, 14(S)-hydroxy docosahexaenoic acid (14S-HDHA), was also elevated in CCI/early death compared to rapid recovery. Signaling lipids of the AA pathway were elevated in poor-outcome patients with sepsis and may serve as targets for future therapies.

LightNet: a novel lightweight convolutional network for brain tumor segmentation in healthcare.

Diagnosis, treatment planning, surveillance, and the monitoring of clinical trials for brain diseases all benefit greatly from neuroimaging-based tumor segmentation. Recently, Convolutional Neural Networks (CNNs) have demonstrated promising results in enhancing the efficiency of image-based brain tumor segmentation. Most current work on CNNs, however, is devoted to creating increasingly complicated convolution modules to improve performance, which in turn raises the computing cost of the model. This work proposes a simple and effective feed-forward CNN, LightNet (Light Network). Based on multi-path and multi-level, it replaces traditional convolutional methods with light operations, which reduces network parameters and redundant feature maps. In the up-sampling stage, a light channel attention module is added to achieve richer multi-scale and spatial semantic feature information extraction of brain tumor. The performance of the network is evaluated in the Multimodal Brain Tumor Segmentation Challenge (BraTS 2015) dataset, and results are presented here alongside other high-performing CNNs. Results show comparable accuracy with other methods but with increased efficiency, segmentation performance, and reduced redundancy and computational complexity. The result is a high-performing network with a balance between efficiency and accuracy, allowing, for example, better energy performance on mobile devices.

Inferring Cell-Cell Communications from Spatially Resolved Transcriptomics Data Using a Bayesian Tweedie Model.

Cellular communication through biochemical signaling is fundamental to every biological activity. Investigating cell signaling diffusions across cell types can further help understand biological mechanisms. In recent years, this has become an important research topic as single-cell sequencing technologies have matured. However, cell signaling activities are spatially constrained, and single-cell data cannot provide spatial information for each cell. This issue may cause a high false discovery rate, and using spatially resolved transcriptomics data is necessary. On the other hand, as far as we know, most existing methods focus on providing an ad hoc measurement to estimate intercellular communication instead of relying on a statistical model. It is undeniable that descriptive statistics are straightforward and accessible, but a suitable statistical model can provide more accurate and reliable inference. In this way, we propose a generalized linear regression model to infer cellular communications from spatially resolved transcriptomics data, especially spot-based data. Our BAyesian Tweedie modeling of COMmunications (BATCOM) method estimates the communication scores between cell types with the consideration of their corresponding distances. Due to the properties of the regression model, BATCOM naturally provides the direction of the communication between cell types and the interaction of ligands and receptors that other approaches cannot offer. We conduct simulation studies to assess the performance under different scenarios. We also employ BATCOM in a real-data application and compare it with other existing algorithms. In summary, our innovative model can fill gaps in the inference of cell-cell communication and provide a robust and straightforward result.

DHCR7 Expression Predicts Poor Outcomes and Mortality From Sepsis.

This is a study of lipid metabolic gene expression patterns to discover precision medicine for sepsis. OBJECTIVES: Sepsis patients experience poor outcomes including chronic critical illness (CCI) or early death (within 14 d). We investigated lipid metabolic gene expression differences by outcome to discover therapeutic targets. DESIGN SETTING AND PARTICITPANTS: Secondary analysis of samples from prospectively enrolled sepsis patients (first 24 hr) and a zebrafish endotoxemia model for drug discovery. Patients were enrolled from the emergency department or ICU at an urban teaching hospital. Enrollment samples from sepsis patients were analyzed. Clinical data and cholesterol levels were recorded. Leukocytes were processed for RNA sequencing and reverse transcriptase polymerase chain reaction. A lipopolysaccharide zebrafish endotoxemia model was used for confirmation of human transcriptomic findings and drug discovery. MAIN OUTCOMES AND MEASURES: The derivation cohort included 96 patients and controls (12 early death, 13 CCI, 51 rapid recovery, and 20 controls) and the validation cohort had 52 patients (6 early death, 8 CCI, and 38 rapid recovery). RESULTS: The cholesterol metabolism gene 7-dehydrocholesterol reductase (DHCR7) was significantly up-regulated in both derivation and validation cohorts in poor outcome sepsis compared with rapid recovery patients and in 90-day nonsurvivors (validation only) and validated using RT-qPCR analysis. Our zebrafish sepsis model showed up-regulation of dhcr7 and several of the same lipid genes up-regulated in poor outcome human sepsis (dhcr24, sqlea, cyp51, msmo1, and ldlra) compared with controls. We then tested six lipid-based drugs in the zebrafish endotoxemia model. Of these, only the Dhcr7 inhibitor AY9944 completely rescued zebrafish from lipopolysaccharide death in a model with 100% lethality. CONCLUSIONS: DHCR7, an important cholesterol metabolism gene, was up-regulated in poor outcome sepsis patients warranting external validation. This pathway may serve as a potential therapeutic target to improve sepsis outcomes.

DHCR7 Expression Predicts Poor Outcomes and Mortality from Sepsis.

Objective: Sepsis patients experience poor outcomes including chronic critical illness (CCI) or early death (within 14 days). We investigated lipid metabolic gene expression differences by outcome to discover therapeutic targets. Design: Secondary analysis of samples from prospectively enrolled sepsis patients and a zebrafish sepsis model for drug discovery. Setting: Emergency department or ICU at an urban teaching hospital. Patients: Sepsis patients presenting within 24 hours. Methods: Enrollment samples from sepsis patients were analyzed. Clinical data and cholesterol levels were recorded. Leukocytes were processed for RNA sequencing (RNA-seq) and reverse transcriptase polymerase chain reaction (RT-qPCR). A lipopolysaccharide (LPS) zebrafish sepsis model was used for confirmation of human transcriptomic findings and drug discovery. Measurements and Main Results: There were 96 samples in the derivation (76 sepsis, 20 controls) and 52 in the validation cohort (sepsis only). The cholesterol metabolism gene 7-Dehydrocholesterol Reductase ( DHCR7) was significantly upregulated in both derivation and validation cohorts in poor outcome sepsis compared to rapid recovery patients and in 90-day non-survivors (validation only) and validated using RT-qPCR analysis. Our zebrafish sepsis model showed upregulation of dhcr7 and several of the same lipid genes upregulated in poor outcome human sepsis (dhcr24, sqlea, cyp51, msmo1 , ldlra) compared to controls. We then tested six lipid-based drugs in the zebrafish sepsis model. Of these, only the Dhcr7 inhibitor AY9944 completely rescued zebrafish from LPS death in a model with 100% lethality. Conclusions: DHCR7, an important cholesterol metabolism gene, was upregulated in poor outcome sepsis patients warranting external validation. This pathway may serve as a potential therapeutic target to improve sepsis outcomes.

New Confidence Intervals for Relative Risk of Two Correlated Proportions.

Biomedical studies, such as clinical trials, often require the comparison of measurements from two correlated tests in which each unit of observation is associated with a binary outcome of interest via relative risk. The associated confidence interval is crucial because it provides an appreciation of the spectrum of possible values, allowing for a more robust interpretation of relative risk. Of the available confidence interval methods for relative risk, the asymptotic score interval is the most widely recommended for practical use. We propose a modified score interval for relative risk and we also extend an existing nonparametric U-statistic-based confidence interval to relative risk. In addition, we theoretically prove that the original asymptotic score interval is equivalent to the constrained maximum likelihood-based interval proposed by Nam and Blackwelder. Two clinically relevant oncology trials are used to demonstrate the real-world performance of our methods. The finite sample properties of the new approaches, the current standard of practice, and other alternatives are studied via extensive simulation studies. We show that, as the strength of correlation increases, when the sample size is not too large the new score-based intervals outperform the existing intervals in terms of coverage probability. Moreover, our results indicate that the new nonparametric interval provides the coverage that most consistently meets or exceeds the nominal coverage probability.

SAREV: A review on statistical analytics of single-cell RNA sequencing data.

Due to the development of next-generation RNA sequencing (NGS) technologies, there has been tremendous progress in research involving determining the role of genomics, transcriptomics and epigenomics in complex biological systems. However, scientists have realized that information obtained using earlier technology, frequently called 'bulk RNA-seq' data, provides information averaged across all the cells present in a tissue. Relatively newly developed single cell (scRNA-seq) technology allows us to provide transcriptomic information at a single-cell resolution. Nevertheless, these high-resolution data have their own complex natures and demand novel statistical data analysis methods to provide effective and highly accurate results on complex biological systems. In this review, we cover many such recently developed statistical methods for researchers wanting to pursue scRNA-seq statistical and computational research as well as scientific research about these existing methods and free software tools available for their generated data. This review is certainly not exhaustive due to page limitations. We have tried to cover the popular methods starting from quality control to the downstream analysis of finding differentially expressed genes and concluding with a brief description of network analysis.

Unraveling T Cell Responses for Long Term Protection of SARS-CoV-2 Infection.

Due to the COVID-19 pandemic, the global need for vaccines to prevent the disease is imperative. To date, several manufacturers have made efforts to develop vaccines against SARS-CoV-2. In spite of the success of developing many useful vaccines so far, it will be helpful for future vaccine designs, targetting long-term disease protection. For this, we need to know more details of the mechanism of T cell responses to SARS-CoV-2. In this study, we first detected pairwise differentially expressed genes among the healthy, mild, and severe COVID-19 groups of patients based on the expression of CD4+ T cells and CD8+ T cells, respectively. The CD4+ T cells dataset contains 6 mild COVID-19 patients, 8 severe COVID-19 patients, and 6 healthy donors, while the CD8+ T cells dataset has 15 mild COVID-19 patients, 22 severe COVID-19 patients, and 4 healthy donors. Furthermore, we utilized the deep learning algorithm to investigate the potential of differentially expressed genes in distinguishing different disease states. Finally, we built co-expression networks among those genes separately. For CD4+ T cells, we identified 6 modules for the healthy network, 4 modules for the mild network, and 1 module for the severe network; for CD8+ T cells, we detected 6 modules for the healthy network, 4 modules for the mild network, and 3 modules for the severe network. We also obtained hub genes for each module and evaluated the differential connectivity of each gene between pairs of networks constructed on different disease states. Summarizing the results, we find that the following genes TNF, CCL4, XCL1, and IFITM1 can be highly identified with SARS-CoV-2. It is interesting to see that IFITM1 has already been known to inhibit multiple infections with other enveloped viruses, including coronavirus. In addition, our networks show some specific patterns of connectivity among genes and some meaningful clusters related to COVID-19. The results might improve the insight of gene expression mechanisms associated with both CD4+ and CD8+ T cells, expand our understanding of COVID-19 and help develop vaccines with long-term protection.

Differences in the detection rates of depression and anxiety among middle school students in different time periods / 中国学校卫生

Objective@#To investigates the prevalence of depression and anxiety among middle school students at different times, and to provide a reference for mental health promotion among adolescents.@*Methods@#A total of 1 505 middle school students, selected from Chongqing in 2021 by using multi stage stratified random sampling method, were surveyed by using the Center for Epidemiologic Studies Depression Scale(CSE-D) and Zung Self Rating Anxiety Scale (SAS).@*Results@#At the beginning of the semester, 434(27.6%) and 601 students( 38.2 %) reported depressive and anxiety symptoms,respectively. While at the end of the semester showed that 463 cases (30.8%) were prone to depression, and 653 cases ( 43.4 %) reported depressive and anxiety symptoms,respectively. The detection rate of depressive symptoms at the end of the semester was significantly higher than that at the beginning of semester. (43.4%, 38.2 %, χ 2=8.55, P <0.05). Depressive of female students at the end of the semester( 52.8 %) was higher than that at the beginning of the semester(46.3%)( χ 2=6.19, P <0.05) The detection rate of anxiety symptoms in senior high school students at the end of the semester(44.9%) was significantly higher than that of at the beginning of the semester(28.6%) ( χ 2= 43.33 , P <0.01). No significant difference in anxiety symptoms boys and junior high school students between the beginning and the end of the semester ( χ 2=2.34, 0.71, P >0.05).@*Conclusion@#Substantial changes are observed in anxiety symptoms among female middle school students and depressive and anxiety symptoms among high school students in different periods of a semester. When investigating depression and anxiety of middle school students, time effect should be considered.

Flexible Nasal Endoscopic Procedures in Family Medicine: Indications and Effectiveness.

BACKGROUND AND OBJECTIVES: Most literature about flexible nasolaryngoscopy comes from specialty clinics, making it unclear if these indications can be effectively managed without referral. This study evaluated effectiveness of diagnosis and management of upper airway complaints, utilizing flexible nasal endoscopic procedures, in a family medicine center. METHODS: We performed retrospective chart review for all patients who had nasal endoscopy, nasopharyngoscopy, and nasolaryngoscopy performed at the University of Florida Family Medicine Center over 3 years (n=89) with 5 additional years of follow up. We used descriptive statistics to evaluate indications, diagnoses, effectiveness of management by family medicine, and referral rate. RESULTS: The most common primary indications were hoarseness (n=33, 37%), chronic cough (n=20, 22%), nasal obstruction (n=9, 10%), and unilateral ear dysfunction (n=6, 7%). The most common primary diagnoses found were allergic rhinitis/postnasal drip (n=41, 46%), laryngopharyngeal reflux (LPR)/gastroesophageal reflux disease (GERD; n=24, 27%), masses concerning for malignancy (n=4, 4.5%), true vocal cord (TVC) polyp (n=3, 3%), TVC nodules (n=3, 3%), and epistaxis (n=3, 3%). Of the four concerning masses, two were confirmed cancers (2%). In addition, there was one case (1%) of erythroleukoplakia with dysplasia of the TVC. Most patients had documented improvement with family medicine management (n=57, 64%) and another six (7%) had follow up without documentation of status and no need for referral. Thus, a total of 71% (n=64) did not require referral and 20% (n=18) needed specialist referral. CONCLUSIONS: Flexible nasal endoscopic procedures are effective in the care of patients in a family medicine residency center and can be safely performed and taught to residents.

Comprehensive characterization of a drug-resistance-related ceRNA network across 15 anti-cancer drug categories.

Cancer is still a major health problem around the world. The treatment failure of cancer has largely been attributed to drug resistance. Competitive endogenous RNAs (ceRNAs) are involved in various biological processes and thus influence the drug sensitivity of cancers. However, a comprehensive characterization of drug-sensitivity-related ceRNAs has not yet been performed. In the present study, we constructed 15 ceRNA networks across 15 anti-cancer drug categories, involving 217 long noncoding RNAs (lncRNAs), 158 microRNAs (miRNAs), and 1,389 protein coding genes (PCGs). We found that these ceRNAs were involved in hallmark processes such as "self-sufficiency in growth signals," "insensitivity to antigrowth signals," and so on. We then identified an intersection ceRNA network (ICN) across the 15 anti-cancer drug categories. We further identified interactions between genes in the ICN and clinically actionable genes (CAGs) by analyzing the co-expressions, protein-protein interactions, and transcription factor-target gene interactions. We found that certain genes in the ICN are correlated with CAGs. Finally, we found that genes in the ICN were aberrantly expressed in tumors, and some were associated with patient survival time and cancer stage.

Incidence risk of PD-1/PD-L1-related pneumonia and diarrhea in non-small cell lung cancer (NSCLC) patients: a systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: Programmed cell death-1 (PD-1) and programmed cell death ligand-1(PD-L1) inhibitor therapy have been approved for the treatment of many cancers, although their incidence of some side effects was high. We aim to fully investigate the incidence risk of PD-1/PD-L1 inhibitors-related pneumonia and diarrhea in NSCLC patients, as well as treatment-related deaths. METHODS: PubMed, Medline, Cochrane Library, and Clinical trials.gov databases were searched up to Sep 17, 2020, for clinical trials of PD-1 inhibitors and PD-L1 inhibitors in the treatment of NSCLC. Randomized controlled trials and their references were screened. RESULTS: Seventeen trials were included in our meta-analysis, including 11,363 patients. PD-1/PD-L1 inhibitors significantly increased the risk of developing all-grade and high-grade (grade ≥ 3) pneumonia (risk ratio [RR] = 2.28; 95% CI: 1.39-3.76; P < 0.01; RR = 2.38; 95% CI: 1.72-3.29; P < 0.01, respectively). The use of PD-1/PD-L1 inhibitor did not increase the risk of developing all-grade and high-grade diarrhea (RR = 0.79; 95% CI: 0.62-1.01; P = 0.06; RR = 0.96; 95% CI: 0.70-1.31; P = 0.78, respectively). There was no significant difference between the rate of death in PD-1 and PD-L1 inhibitors (P = 0.079). CONCLUSION: These data suggest that PD-1/PD-L1 inhibitors significantly increase the risk of all-grade and high-grade pneumonia in NSCLC patients and PD-1/PD-L1 monotherapy increases the risk of all-grade pneumonia in NSCLC patients compared to PD-1/PD-L1 inhibitor combination regimens. Physicians should pay more attention to NSCLC patients who treated with PD-1/PD-L1 inhibitors.

COVID-19: Reduced Lung Function and Increased Psycho-emotional Stress.

The COVID-19 outbreak causing reduced lung function and increased psycho-emotional stress in the community worldwide. Therefore, it is of interest to document such viral outbreak related emotional stress data in the community with known molecular and patho-physiological parameters of the affected individuals. We provide a concise, coherent, critical, precise, specific and direct narration of such events from a community research viewpoint using known molecular data in this editorial.

XPC inhibition rescues cisplatin resistance via the Akt/mTOR signaling pathway in A549/DDP lung adenocarcinoma cells.

Xeroderma pigmentosum, complementation group C (XPC) is an accessory recognition gene involved in the nucleotide excision repair (NER) pathway, which is activated during the initial DNA damage recognition stage. It participates in the regulation of DNA damage­induced proliferation and apoptosis. Emerging evidence demonstrates that upregulation of XPC increases the resistance of several tumor cell types to cytotoxic drugs. In addition, it can predict poor patient outcome for non­small cell lung cancer (NSCLC). However, the mechanisms linking upregulation of XPC and drug resistance in lung cancer are still unclear. In the present study, we aimed to confirm whether XPC was involved in the reversal of the cisplatin (DDP) resistance in drug­resistant A549/DDP lung adenocarcinoma cells. RT­PCR and western blot assays were used to examine XPC mRNA and protein expression levels. Cell viability was assessed by CCK­8 assay. The knockdown of XPC was achieved in A549/DDP cells using si­RNA, whereas cell proliferation and apoptosis were assessed by wound healing assay and flow cytometric analysis, respectively. The median inhibitory concentration (IC50) value of DDP was assessed by CCK­8 assay. Western blot assays were conducted for the examination of caspase­9/3, Bax and Bcl­2 protein levels, whereas the activation of the PI3K/Akt/mTOR signaling pathway was investigated in XPC­knockdown cells. High expression of XPC was noted in A549/DDP cells compared with that in A549 cells, which was associated with DDP resistance. XPC silencing significantly inhibited A549/DDP cell proliferation and increased the induction of apoptosis. In addition, XPC knockdown decreased the expression levels of the Akt/mTOR signaling proteins and the expression of their downstream mediator. The data of the present study revealed that XPC inhibition rescued DDP resistance in lung adenocarcinoma cells, which was dependent on the Akt/mTOR signaling pathway. Collectively, XPC may be considered a new strategy for curing DDP­resistant lung cancer and may improve the efficacy of conventional chemotherapy.

Pharmacometabolomics Identifies 3-Hydroxyadipic Acid, d-Galactose, Lysophosphatidylcholine (P-16:0), and Tetradecenoyl-l-Carnitine as Potential Predictive Indicators of Gemcitabine Efficacy in Pancreatic Cancer Patients.

Gemcitabine (GEM)-based chemotherapy is the standard regimen for the treatment of pancreatic cancer (PC). However, chemoresistance is a major challenge in PC treatment. Reliable biomarkers are urgently needed to predict the response to GEM-based therapies. GEM-sensitive (GEM-S) and GEM-resistant (GEM-R) pancreatic carcinoma xenograft models were established, and GEM monotherapy and GEM plus nanoparticle albumin-bound paclitaxel (nab-PTX) doublet therapy were administered to GEM-S/R tumor-bearing mice. Metabolomic mass spectrometry (MS) analysis of serum, liver, and tumor samples was performed using an ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometer. The results showed that both GEM monotherapy and combination therapy significantly inhibited the tumor growth in GEM-S subgroup. However, in the GEM-R subgroup, tumor growth was not significantly inhibited by GEM monotherapy, but was significantly suppressed by GEM combination therapy. Metabolic profiling analysis by hierarchical cluster analysis and partial least squares discriminant analysis showed that the differences in metabolites were most significant in serum of three types of samples in the GEM-S/R subgroups, regardless of the administration of GEM monotherapy or combination therapy. The differential metabolite analysis of serum samples revealed 38 and 26 differential metabolites between the GEM-R and GEM-S subgroups treated with GEM monotherapy or combination therapy, and four common discriminating metabolites were investigated: 3-hydroxyadipic acid, d-galactose, lysophosphatidylcholine (LysoPC) (P-16:0), and tetradecenoyl-l-carnitine. The relative amounts of the four metabolites changed significantly and consistently after GEM monotherapy or combination therapy. The levels of these four metabolites were significantly different in the GEM-S and GEM-R pancreatic carcinoma xenograft models; thus, these metabolites could be effective predictive indicators of the efficacy of chemotherapy in PC patients, regardless of the administration of GEM alone or GEM plus nab-PTX.

Two-channel autofluorescence analysis for oral cancer.

We created a two-channel autofluorescence test to detect oral cancer. The wavelengths 375 and 460 nm, with filters of 479 and 525 nm, were designed to excite and detect reduced-form nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD) autofluorescence. Patients with oral cancer or with precancerous lesions, and a control group with healthy oral mucosae, were enrolled. The lesion in the autofluorescent image was the region of interest. The average intensity and heterogeneity of the NADH and FAD were calculated. The redox ratio [(NADH)/(NADH + FAD)] was also computed. A quadratic discriminant analysis (QDA) was used to compute boundaries based on sensitivity and specificity. We analyzed 49 oral cancer lesions, 34 precancerous lesions, and 77 healthy oral mucosae. A boundary (sensitivity: 0.974 and specificity: 0.898) between the oral cancer lesions and healthy oral mucosae was validated. Oral cancer and precancerous lesions were also differentiated from healthy oral mucosae (sensitivity: 0.919 and specificity: 0.755). The two-channel autofluorescence detection device and analyses of the intensity and heterogeneity of NADH, and of FAD, and the redox ratio combined with a QDA classifier can differentiate oral cancer and precancerous lesions from healthy oral mucosae.

Long non-coding RNA NEAT1 regulates E2F3 expression by competitively binding to miR-377 in non-small cell lung cancer.

It has previously been demonstrated that the long non-coding RNA (lncRNA) nuclear enriched abundant transcript (NEAT)-1 is increased in multiple cancers and may be associated with cancer development. However, the function and mechanism of NEAT1 in non-small cell lung cancer (NSCLC) has not yet been fully elucidated. In the present study, the expression of NEAT1 in NSCLC was detected using quantitative polymerase chain reaction and association with survival was estimated. The effect of NEAT1 on proliferation was detected by growth curve and cell cycle analysis. Bioinformatics analysis was used to identify miRNAs that interact with NEAT1. Following this, a series of molecular biological techniques were used to verify the mechanism of NEAT1. The results indicated that NEAT1 was highly expressed in NSCLC, and high NEAT1 expression was associated with a shorter overall survival. NEAT1 promoted NSCLC cell growth and affected the cell cycle process in vitro. Furthermore, NEAT1 was observed to bind hsa-miR-377-3p, functioning as a competing endogenous RNA, which resulted in de-repression of its target gene E2F transcription factor 3 (E2F3). E2F3, as an oncogene, may promote NSCLC progression. These results suggested that NEAT1 may promote the development of NSCLC through the miR-377-3p-E2F3 pathway.

Vitamin E Reversed Apoptosis of Cardiomyocytes Induced by Exposure to High Dose Formaldehyde During Mice Pregnancy.

In this study, we investigated the protection effect of Vitamin E (Vit E) on formaldehyde (FA) exposure during pregnancy induced apoptosis of cardiomyocytes, and used an HL-1 cell line to confirmed the findings in vivo.Pregnant mice received different doses of FA (0.5 mg/kg, 1.0 mg/kg, 1.5 mg/kg, 0.1 µg Vit E, or 1.5 mg/kg + 0.1 µg Vit E). TUNEL staining was used to reveal the apoptosis in cardiomyocytes, and SOD, MDA, GSH, Livin, and Caspase-3 in cardiomyocytes were detected by ELISA, RT-PCR, and Western blot. For in vitro study, HL-1 cells were treated with vehicle, 5 µmol/L FA, 25 µmol/L FA, 50 µmol/L FA, 10 mg/L Vit. E, and 50 µmol/L FA+ 10 mg/L Vit E, respectively. CCK-8 assay and flow cytometry were used to evaluate cell vitality and apoptosis. A high dose of FA exposure led to cytotoxicity in both pregnant mice and offspring, as TUNEL staining revealed a significant apoptosis of cardiomyocytes, and the alternation in SOD, GSH, MDA, Livin, and Caspase-3 was found in cardiomyocytes. 0.1 µg Vit. E could reverse high doses of FA exposure induced apoptosis of cardiomyocytes in both pregnant mice and offspring. The in vitro study revealed that FA exposure induced a decrease of cell viability and increased cell apoptosis, as well as oxidative stress in HL-1 cells with alternation in SOD, GSH, MDA, Livin, and Caspase-3.This study revealed a high dose of FA induced oxidative stress and apoptosis of cardiomyocytes in both pregnant mice and offspring, and Vit E supplement during pregnancy reversed the systemic and myocardial toxicity of FA.

Role of tRNA selenocysteine 1 associated protein 1 in the proliferation and apoptosis of cardiomyocyte­like H9c2 cells.

Transfer RNA selenocysteine 1 associated protein 1 (Trnau1ap) serves an essential role in the synthesis of selenoproteins, which have critical functions in numerous biological processes. Selenium deficiency results in a variety of diseases, including cardiac disease. However, the mechanisms underlying myocardial injury induced by selenium deficiency remain unclear. The present study examined the effects of Trnau1ap under­ and overexpression in cardiomyocyte­like H9c2 cells, by transfection with small interfering RNA and an overexpression plasmid, respectively. Expression levels of glutathione peroxidase, thioredoxin reductase and selenoprotein K were decreased in Trnau1ap­underexpressing cells, and increased in Trnau1ap­overexpressing cells. Using MTT, proliferating cell nuclear antigen, annexin V and caspase­3 activity assays, it was demonstrated that reducing Trnau1ap expression levels inhibited the proliferation of H9c2 cells and induced apoptosis. Conversely, increasing Trnau1ap expression levels promoted cell growth. Western blot analysis revealed that the phosphoinositide 3­kinase/protein kinase B signaling pathway was activated in Trnau1ap­underexpressing cells. Furthermore, the apoptotic pathway was activated in these cells, evidenced by relatively greater expression levels of B­cell lymphoma (Bcl­2)­associated X protein and reduced expression levels of Bcl­2. Taken together, these findings suggest that Trnau1ap serves a key role in the proliferation and apoptosis of H9c2 cells. The present study provides insight into the underlying mechanisms of myocardial injury induced by selenium deficiency.