Adsorption Properties of Typical Lung Cancer Breath Gases on Ni-SWCNTs through Density Functional Theory

A lot of useful information is contained in the human breath gases, which makes it an effective way to diagnose diseases by detecting the typical breath gases. This work investigated the adsorption of typical lung cancer breath gases: benzene, styrene, isoprene, and 1-hexene onto the surface of intrinsic and Ni-doped single wall carbon nanotubes through density functional theory. Calculation results show that the typical lung cancer breath gases adsorb on intrinsic single wall carbon nanotubes surface by weak physisorption. Besides, the density of states changes little before and after typical lung cancer breath gases adsorption. Compared with single wall carbon nanotubes adsorption, single Ni atom doping significantly improves its adsorption properties to typical lung cancer breath gases by decreasing adsorption distance and increasing adsorption energy and charge transfer. The density of states presents different degrees of variation during the typical lung cancer breath gases adsorption, resulting in the specific change of conductivity of gas sensing material. Based on the different adsorption properties of Ni-SWCNTs to typical lung cancer breath gases, it provides an effective way to build a portable noninvasive portable device used to evaluate and diagnose lung cancer at early stage in time

Diagnostic Ultrasound Megahertz Signal Detection with Multi-bounce Laser Microphone

Diagnostic ultrasound is one of the most common imaging modalities in the medical imaging field. One critical component of this imaging technology is designing sensitive receivers for detecting megahertz ultrasound signals. Currently, piezoelectric transducers have been the most widely used detectors for ultrasound signal. However, newer imaging techniques, such as photoacoustic imaging, require broader detection bandwidth and more sensitive detectors in order to work with significantly weaker signal. Conventional piezoelectric transducers may be limited in their ability to detect the full frequency bandwidth with high sensitivity, due to restricted transducer size and material composition. Sensitive and broadband ultrasound signal detecting techniques are needed for the development of advanced ultrasound imaging. The multi-bounce laser microphone utilizes optical methods to detect the displacement of a gold-covered thin film diaphragm caused by the ultrasound signal pressure waves. This sensitive all-optical sensing technique would provide new opportunities for advanced ultrasound imaging as it is expected to achieve a higher signal-to-noise ratio (SNR) in a broader spectrum when compared with conventional transducers. Each laser beam bounce interrogating on the diaphragm would take a different optical path-length that reflects the displacement and be involved in accumulation of the ultimate phase difference between the reference and signal beams for the detector to recognize. The system was previously developed for detecting acoustic signatures generated by explosives below 10 kHz. To demonstrate its feasibility in biomedical imaging, experimental design in this work has applied this technique to detect ultrasound signals ranging from 100kHz to 1MHz. The results of the experiments conducted in this work prove that the multi-bounce laser microphone system is capable of detecting megahertz range ultrasound signal. As compared to the conventional fibre-optic hydrophone, the laser microphone system achieves a higher SNR as the number of bounces increases. In the test of detecting 500kHz ultrasound signal, the SNR provided by the fibre-optic hydrophone is 38dB while the one achieved by the13-bounce laser microphone is 54dB. This is a promising sensitivity enhancement with a number of potential applications for detecting signals of significantly lower power, that would otherwise be very difficult for conventional piezoelectric ultrasound transducers to detect

Penicillium marneffei-Stimulated Dendritic Cells Enhance HIV-1 Trans-Infection and Promote Viral Infection by Activating Primary CD4+ T Cells

Penicillium marneffei (P. marneffei) is considered an indicator pathogen of AIDS, and the endemicity and clinical features of P. marneffei have been described. While, how the co-infection of P. marneffei exacerbate deterioration of the immune response remains poorly understood. Here we isolated P. marneffei from the cutaneous lesions of AIDS patients and analyzed its effects on HIV-1-dendritic cells (DCs) interaction. We demonstrated that the monocyte-derived dendritic cells (MDDCs) could be activated by both thermally dimorphic forms of P. marneffei for significantly promoting HIV-1 trans-infection of CD4+ T cells, while these activated MDDCs were refractory to HIV-1 infection. Mechanistically, P. marneffei-activated MDDCs endocytosed large amounts of HIV-1 and sequestrated the internalized viruses into tetrapasnin CD81+ compartments potentially for proteolysis escaping. The activated MDDCs increased expression of intercellular adhesion molecule 1 and facilitated the formation of DC-T-cell conjunctions, where much more viruses were recruited. Moreover, we found that P. marneffei-stimulated MDDCs efficiently activated resting CD4+ T cells and induced more susceptible targets for viral infection. Our findings demonstrate that DC function and its interaction with HIV-1 have been modulated by opportunistic pathogens such as P. marneffei for viral dissemination and infection amplification, highlighting the importance of understanding DC-HIV-1 interaction for viral immunopathogenesis elucidation

Combined model of radiomics and clinical features for differentiating pneumonic-type mucinous adenocarcinoma from lobar pneumonia: An exploratory study

PurposeThe purpose of this study was to distinguish pneumonic-type mucinous adenocarcinoma (PTMA) from lobar pneumonia (LP) by pre-treatment CT radiological and clinical or radiological parameters.MethodsA total of 199 patients (patients diagnosed with LP = 138, patients diagnosed with PTMA = 61) were retrospectively evaluated and assigned to either the training cohort (n = 140) or the validation cohort (n = 59). Radiomics features were extracted from chest CT plain images. Multivariate logistic regression analysis was conducted to develop a radiomics model and a nomogram model, and their clinical utility was assessed. The performance of the constructed models was assessed with the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The clinical application value of the models was comprehensively evaluated using decision curve analysis (DCA).ResultsThe radiomics signature, consisting of 14 selected radiomics features, showed excellent performance in distinguishing between PTMA and LP, with an AUC of 0.90 (95% CI, 0.83–0.96) in the training cohort and 0.88 (95% CI, 0.79–0.97) in the validation cohort. A nomogram model was developed based on the radiomics signature and clinical features. It had a powerful discriminative ability, with the highest AUC values of 0.94 (95% CI, 0.90–0.98) and 0.91 (95% CI, 0.84–0.99) in the training cohort and validation cohort, respectively, which were significantly superior to the clinical model alone. There were no significant differences in calibration curves from Hosmer–Lemeshow tests between training and validation cohorts (p = 0.183 and p = 0.218), which indicated the good performance of the nomogram model. DCA indicated that the nomogram model exhibited better performance than the clinical model.ConclusionsThe nomogram model based on radiomics signatures of CT images and clinical risk factors could help to differentiate PTMA from LP, which can provide appropriate therapy decision support for clinicians, especially in situations where differential diagnosis is difficult

Pan-cancer analysis reveals potential of FAM110A as a prognostic and immunological biomarker in human cancer

BackgroundDespite great success, immunotherapy still faces many challenges in practical applications. It was previously found that family with sequence similarity 110 member A (FAM110A) participate in the regulation of the cell cycle and plays an oncogenic role in pancreatic cancer. However, the prognostic value of FAM110A in pan-cancer and its involvement in immune response remain unclear.MethodsThe Human Protein Atlas (HPA) database was used to detect the expression of FAM110A in human normal tissues, the Tumor Immune Estimation Resource (TIMER) and TIMER 2.0 databases were used to explore the association of FAM110A expression with immune checkpoint genes and immune infiltration, and the Gene Set Cancer Analysis (GSCA) database was used to explore the correlation between FAM110A expression and copy number variations (CNV) and methylation. The LinkedOmics database was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Statistical analysis and visualization of data from the The Cancer Genome Atlas (TCGA) or the Genotype–Tissue Expression (GTEx) databases were performed using the R software (version 3.6.3). Clinical samples were validated using immunohistochemistry.ResultsFAM110A expression was elevated in most tumor tissues compared with that in normal tissues. CNV and methylation were associated with abnormal FAM110A mRNA expression in tumor tissues. FAM110A affected prognosis and was associated with the expression of multiple immune checkpoint genes and abundance of tumor-infiltrating immune cells across multiple types of cancer, especially in liver hepatocellular carcinoma (LIHC). FAM110A-related genes were involved in multiple immune-related processes in LIHC.ConclusionFAM110A participates in regulating the immune infiltration and affecting the prognosis of patients in multiple cancers, especially in LIHC. FAM110A may serve as a prognostic and immunological biomarker for human cancer

The Association Between Diabetic Retinopathy and the Prevalence of Age-Related Macular Degeneration—The Kailuan Eye Study

This study aimed to investigate the prevalence of age-related macular degeneration (AMD) in patients with diabetes mellitus (DM) and diabetic retinopathy (DR) and analyze whether DR is a risk factor for AMD. This population-based epidemiological study included 14,440 people from the Kailuan Eye Study in 2016, of whom 1,618 were patients with type 2 DM aged over 50 years, and 409 had DM with DR. We analyzed whether there were differences in the prevalence of AMD between DM with DR and DM without DR, and conducted a hierarchical statistical analysis according to different stages of DR. Using variable regression analysis, we explored whether DR constituted a risk factor for AMD. In the DM population, the prevalence of wet AMD in patients with DM with and without DR was 0. 3 and 0.2%, respectively, with no significant difference (P = 0.607). Meanwhile, the prevalence of dry AMD in patients with DM with and without DR was 20.8 and 16.0%, respectively, with a significant difference. In the subgroup analysis of dry AMD, the prevalence of early, middle, and late dry AMD in DM with DR was 14.4, 5.9, and 0.5%, respectively. In DM without DR, the prevalence of early, middle, and late dry AMD was 10.5, 4.8, and 0.7%, respectively (P = 0.031). In the subgroup analysis of DR staging, statistical analysis could not be performed because of the limited number of patients with PDR. In the variable regression analysis of risk factors for dry AMD, after adjusting for age, sex, body mass index, hypertension, and dyslipidemia, DR constituted the risk factor for dry AMD. In conclusion, DM did not constitute a risk factor for AMD, and the prevalence of wet AMD and dry AMD in patients with DM and DR was higher than that in patients with DM without DR (among which dry AMD was statistically significant). Multivariate regression analysis confirmed that DR is an independent risk factor for dry AMD. Reasonable control of DM and slowing down the occurrence and development of DR may effectively reduce the prevalence of AMD in patients with DM