Flexible Titanium Nitride/Germanium-Tin Photodetectors Based on Sub-Bandgap Absorption

We report an enhanced performance of flexible titanium nitride/germanium-tin (TiN/GeSn) photodetectors (PDs) with an extended photodetection range based on sub-bandgap absorption. Single-crystalline GeSn membranes transfer-printed on poly­(ethylene terephthalate) are integrated with plasmonic TiN to form a TiN/GeSn heterojunction. Formation of the heterojunction creates a Schottky contact between the TiN and GeSn. A Schottky barrier height of 0.49 eV extends the photodetection wavelength to 2530 nm and further enhances the light absorption capability within the detection range. In addition, finite-difference time-domain simulation proves that the integration of TiN and GeSn could enhance average absorption from 0.13 to 0.33 in the near-infrared (NIR) region (e.g., 1400–2000 nm) and more than 70% of light is absorbed in TiN. The responsivity of the fabricated TiN/GeSn PDs is increased from 30 to 148.5 mA W–1 at 1550 nm. There is also an ∼180 nm extension in the optical absorption wavelength of the flexible TiN/GeSn PD. The enhanced performance of the device is attributed to the absorption and separation of plasmonic hot carriers via TiN and the TiN/GeSn junction, respectively. The effect of external uniaxial strain is also investigated. A tensile strain of 0.3% could further increase the responsivity from 148.5 to 218 mA W–1, while it is decreased to 102 mA W–1 by 0.25% compressive strain. In addition, the devices maintain stable performance after multiple and long bending cycles. Our results provide a robust and cost-effective method to extend the NIR photodetection capability of flexible group IV PDs

Novel biomarker panel for the diagnosis and prognosis assessment of sepsis based on machine learning - Supplementary Tables

Background: The authors investigated a panel of novel biomarkers for diagnosis and prognosis assessment of sepsis usingmachine learning (ML) methods. Methods: Hematological parameters, liver function indices and inflammatory marker levels of 332 subjects were retrospectively analyzed. Results: The authors constructed sepsis diagnosis models and identified the random forest (RF) model to be the most optimal. Compared with PCT (procalcitonin) and CRP (C-reactive protein), the RF model identified sepsis patients at an earlier stage. The sepsis group had a mortality rate of 36.3%, and the RF model had greater predictive ability for the 30-day mortality risk of sepsis patients. Conclusion: The RF model facilitated the identification of sepsis patients and showed greater accuracy in predicting the 30-day mortality risk of sepsis patients.</p

Novel biomarker panel for the diagnosis and prognosis assessment of sepsis based on machine learning - Supplementary Figure 1

Background: The authors investigated a panel of novel biomarkers for diagnosis and prognosis assessment of sepsis usingmachine learning (ML) methods. Methods: Hematological parameters, liver function indices and inflammatory marker levels of 332 subjects were retrospectively analyzed. Results: The authors constructed sepsis diagnosis models and identified the random forest (RF) model to be the most optimal. Compared with PCT (procalcitonin) and CRP (C-reactive protein), the RF model identified sepsis patients at an earlier stage. The sepsis group had a mortality rate of 36.3%, and the RF model had greater predictive ability for the 30-day mortality risk of sepsis patients. Conclusion: The RF model facilitated the identification of sepsis patients and showed greater accuracy in predicting the 30-day mortality risk of sepsis patients.</p

C19orf66 colocalizes and interacts with ZIKV NS3.

(A) hNPC cells were transiently transfected with a plasmid encoding Flag-tagged ZIKV NS3 or Myc-tagged C19orf66, and 48 hours later, hNPC cells were co-stained with anti-Flag and anti-Myc antibodies as well as DAPI. The co-localization between C19orf66 and ZIKV NS3 is shown in yellow. The data are representative of three independent experiments. (B) 293FT cells co-transfected with plasmids encoding Flag-tagged NS3 and Myc-tagged C19orf66 were used for a co-IP assay. Cell lysates were precipitated with an anti-Myc antibody or control IgG, and the immunocomplexes were analyzed with a Flag antibody by Western blotting. (C) SNB19 cells were transfected with a plasmid encoding Flag-tagged NS3, followed by immunoprecipitation using anti-Flag antibody or control IgG. The immunocomplexes were analyzed with anti-C19orf66 antibody by Western blotting. (D) hNPC cells were infected with ZIKV at MOI of 1, and collected at 48 hours post infection, followed by immunoprecipitation using an anti-NS3 antibody or control IgG. The immunocomplexes were analyzed with anti- C19orf66 antibody by Western blotting. (E) hNPC cells were infected with ZIKV at an MOI of 1, and collected at 48 hours post infection, followed by immunoprecipitation using an anti-C19orf66 antibody or control IgG. The immunocomplexes were analyzed with an anti-NS2B or anti-NS3 antibody by Western blotting. (F) 293FT cells were co-transfected with plasmids encoding Myc-tagged C19orf66 and individual Flag-tagged truncated NS3 or full-length NS3. At 48 h post-transfection, cells were lysed, co-IP was performed by using an anti-Flag antibody, and the immunocomplexes were analyzed with an anti-Myc antibody by Western blotting.</p

MOESM3 of Zika virus antagonizes interferon response in patients and disrupts RIG-I–MAVS interaction through its CARD-TM domains

Additional file 3. Additional figures and table

C19orf66 influences ZIKV NS3 protein degradation through the lysosome pathway.

(A, B) 293FT cells were co-transfected with plasmids encoding Flag-tagged NS3 and Myc-tagged C19orf66, 36 hours later, the cells were treated with CQ (5 μM), NH4Cl (2 mM), MG132 (5 μM) or 3-MA (5 mM). Densitometry analysis of NS3 protein was performed, and the percentage of NS3 was calculated (the data were expressed as the means ± SDs from three repeat experiments, and comparisons between two groups were made by a two-tailed Student’s t test). (C) 293FT cells were transfected with an NS3-Flag, or C19orf66-Myc plasmids alone, or co-transfected with NS3- Flag and C19orf66-Myc plasmids for 24 h. (D) SNB19 cells were transfected with a specific C19orf66 siRNA or negative control (NC) for 24 h, infected with ZIKV at an MOI of 1 and harvested 48 hours post-infection. Lysosomes were isolated from cells while transfected with or without NS3 by the Lysosome Enrichment Kit. The lysosomal lysate was also analyzed by Western blotting analysis for C19orf66, NS3, and LAMP1 (a lysosome marker). (E, F) To confirm that the extract was the lysosomal fraction, we assessed the activity of acid phosphatase, which is a kind of lysosome marker enzyme (the results were expressed as the means ± SDs from three repeat experiments, and comparisons between two groups were made by a two-tailed Student’s t test). (G) hNPC cells were transfected with a plasmid encoding Flag-tagged NS3, with or without Myc C19orf66. The lysosomes, NS3, C19orf66 and nuclei were co-stained using LysoTracker (magenta), a Flag antibody (green), a Myc antibody (red) and DAPI (blue). The cells were analyzed using fluorescence microscopy.</p

ZIKV infection or IFN-β treatment induces C19orf66 expression.

hNPC cells were stimulated with IFN-β for 24 hours (h) at a concentration of 0, 1, or 10 ng/ml. And hNPC cells were infected with ZIKV at an MOI of 0.1, or 1 or without ZIKV. The mRNA and protein expressions of C19orf66 were detected by real time RT-PCR, and normalized to the expression of GAPDH in each sample (A, C) and Western blotting (B, D) respectively. The data shown in Fig 1A and 1C were expressed as the means ± SDs from three repeat experiments, and were analyzed by one-way ANOVA followed by Dunnett’s multiple comparison test. Ifnar1-/- mice were challenged with Zika virus (1x105 PFUs) via intraperitoneal injection for 6 days. The mRNA and protein expression levels of C19orf66 in the mouse testis, brain and spleen were detected by real time RT-PCR (E) and Western blotting (F). The data are representative of three independent experiments as seen in Fig 1B, 1D and 1F. The results shown in Fig 1E were expressed as the means and 95% confidence intervals (CIs), and the Mann-Whitney U test was applied for comparisons.</p

C19orf66 expression results in degradation of ZIKV NS3.

(A) Western blotting was used to analyze lysates from 293FT cells co-transfected with plasmids encoding ZIKV NS3-Flag and increasing amounts of Myc-C19orf66 (0, 0.1, 0.2, 0.4, 0.8 or 1.6 μg). (B) Densitometry analysis of NS3 protein was performed, the percentage of NS3 was calculated, and linear regression analysis was performed to show the relationship between the protein level of C19orf66 and ZIKV NS3 (R2 = 0.769, p(C) SNB19 cells were transfected with a specific C19orf66 siRNA or negative control at a final concentration of 50 nM. After 24 h of transfection, SNB19 cells were transfected a plasmid encoding Flag-tagged NS3, and were harvested at 24 hours. The expression levels of C19orf66, NS3-Flag or β-actin were analyzed by Western Blotting respectively. (D, E) 293FT cells were co-transfected with NS3-Flag plasmid and Myc-C19orf66 plasmid or vector plasmid, and 24 h later, the cells were treated with cycloheximide (CHX) (100 μg/ml) for 6, 12, 16, 20, or 24 h. The stability of NS3-Flag was analyzed by Western blotting. The protein levels of NS3 or C19orf66 were normalized to the level of β-actin using band intensity values. The half-life (t1/2) of NS3 co-transfected with Myc-C19orf66 was determined by linear regression analysis and calculated by the following formula, y = -0.024x+1.063 (R12 = 0.903, p122 = 0.1303, p2 = 0.1410). (F) Quantitative real time RT-PCR was used to analyze NS3 mRNA expression in 293FT cells 36 hours after co-transfection with NS3-Flag (1.6 μg) and with Myc-C19orf66 (1.6 μg) or control plasmid (the results were expressed as the means and 95% CIs from three repeat experiments, and comparisons were made by the Mann-Whitney U test). (G, H) Western blot analysis of lysates from 293FT cells co-transfected with C19orf66 and the NS3-deletion constructs, (G) protease domain (PD), or (H) helicase domain (HD).</p

C19orf66 restricts ZIKV infection in cells.

(A) hNPC cells were stably transduced with a retrovirus vector expressing C19orf66 or a vector control, infected with ZIKV at an MOI of 1, and then collected at 48 hours post infection. C19orf66 overexpression, the viral envelope protein of ZIKV and β-actin protein were verified by Western blotting analysis. The indicated cellular viral RNA (B) and supernatant viral RNA (C) levels were determined by using real time RT-PCR. The expression levels were normalized to the level of GAPDH. (D) C19orf66-overexpressing or control vector-transfected hNPC cells was infected with ZIKV at an MOI of 1, and then were collected at the indicated times. The number of cellular Zika virus RNA copies was determined by using quantitative real time RT-PCR. (E, F) The role of C19orf66 proteins in ZIKV infection was evaluated by using a plaque-forming assay. The results shown in Fig 2B, 2C and 2F were expressed as the means ± SDs from three repeat experiments, and comparisons were made by a two-tailed Student’s t test. (G) C19orf66-overexpressing hNPC cells and control cells were infected with ZIKV, and then stained with an anti- ZIKV E antibody as well as DAPI, and subsequently a secondary antibody conjugated to rhodamine was used to visualize the stained E proteins. (H) Samples were inspected by fluorescence microscope at a magnification of 200×, and the percentage of E protein-positive cells in six different fields was calculated (the results were expressed as the means and 95% confidence intervals (CIs), and the Mann-Whitney U test was applied for comparisons). (I) SNB19 cells were transfected with a specific C19orf66 siRNA or control at a final concentration of 50 nM. After 24 h of transfection, they were challenged with ZIKV at MOI of 1 and harvested 48 hours post infected. The expression levels of C19orf66, ZIKV E and β-actin were analyzed by Western blotting. Prior transfection of SNB19 cells with C19orf66 siRNA increased viral RNA levels in the cells (J) and supernatant (K). ZIKV RNA levels were measured by quantitative real time RT-PCR (the data shown in Fig 2J and 2K were expressed as the means ± SDs from three repeat experiments, and were analyzed by one-way ANOVA followed by Dunnett’s multiple comparison test). (L) SNB19 cells were transfected with a specific C19orf66 siRNA or negative control at a final concentration of 50 nM. After 24 h of transfection, they were challenged with ZIKV at an MOI of 1, and harvested at the indicated time points post-infection. The number of cellular Zika virus RNA copies was determined by using quantitative real time RT-PCR The data collected at each time point and shown in Fig 2D and 2L were expressed as the means ± SDs from three repeat experiments, and comparisons between two groups at each time point were made by a two-tailed Student’s t test.</p

MOESM2 of Zika virus antagonizes interferon response in patients and disrupts RIG-I–MAVS interaction through its CARD-TM domains

Additional file 2: Table S2. Functional categories of the 69 pre-defined antiviral ISGs in vivo