Black phosphorus consists of stacked layers of phosphorene, a two-dimensional semiconductor with promising device characteristics. We report the realization of a widely tunable band gap in few-layer black phosphorus doped with potassium using an in situ surface doping technique. Through band structure measurements and calculations, we demonstrate that a vertical electric field from dopants modulates the band gap, owing to the giant Stark effect, and tunes the material from a moderate-gap semiconductor to a band-inverted semimetal. At the critical field of this band inversion, the material becomes a Dirac semimetal with anisotropic dispersion, linear in armchair and quadratic in zigzag directions. The tunable band structure of black phosphorus may allow great flexibility in design and optimization of electronic and optoelectronic devices.
Thin flakes of black phosphorus (BP) are a two-dimensional (2D) semiconductor whose energy gap is predicted being sensitive to the number of layers and external perturbations. Very recently, it was found that a simple method of potassium (K) doping on the surface of BP closes its band gap completely, producing a Dirac semimetal state with a linear band dispersion in the armchair direction and a quadratic one in the zigzag direction. Here, based on first-principles density functional calculations, we predict that, beyond the critical K density of the gap closure, 2D massless Dirac Fermions (i.e., Dirac cones) emerge in K-doped few-layer BP, with linear band dispersions in all momentum directions, and the electronic states around Dirac points have chiral pseudospins and Berry's phase. These features are robust with respect to the spin-orbit interaction and may lead to graphene-like electronic transport properties with greater flexibility for potential device applications.
Background Recently, the triglyceride glucose (TyG) index has been considered a surrogate marker of insulin resistance which is a well-known pathogenic factor in nonalcoholic fatty liver disease (NAFLD). However, few studies have investigated the relationship between the TyG index and NAFLD. Thus, we investigated the relationship between the TyG index and NAFLD and the effectiveness of the TyG index compared with the homeostasis model assessment of insulin resistance (HOMA-IR) in identifying NAFLD in Korean adults. Methods Participants of 4,986 who underwent ultrasonography in a health promotion center were enrolled. The TyG index was calculated as ln [fasting triglycerides (mg/dL)×fasting glucose (mg/dL)/2], and HOMA-IR was estimated. NAFLD was diagnosed by ultrasonography. Results Significant differences were observed in metabolic parameters among the quartiles of the TyG index. The prevalence of NAFLD significantly increased with increment in the TyG index. After adjusting for multiple risk factors, a logistic regression analysis was performed. When the highest and lowest quartiles of the TyG index and HOMA-IR were compared, the odds ratios for the prevalence of NAFLD were 2.94 and 1.93 (95% confidence interval, 2.32 to 3.72 and 1.43 to 2.61; both P for trend <0.01), respectively. According to the receiver operating characteristic analysis, the TyG index was superior to HOMA-IR in predicting NAFLD. Conclusion The TyG index and prevalence of NAFLD were significantly related and the TyG index was superior to HOMA-IR in predicting NAFLD in Korean adults.
Background The application of deep learning to retinal photographs has yielded promising results in predicting age, sex, blood pressure, and haematological parameters. However, the broader applicability of retinal photograph-based deep learning for predicting other systemic biomarkers and the generalisability of this approach to various populations remains unexplored. MethodsWith use of 236 257 retinal photographs from seven diverse Asian and European cohorts (two health screening centres in South Korea, the Beijing Eye Study, three cohorts in the Singapore Epidemiology of Eye Diseases study, and the UK Biobank), we evaluated the capacities of 47 deep-learning algorithms to predict 47 systemic biomarkers as outcome variables, including demographic factors (age and sex); body composition measurements; blood pressure; haematological parameters; lipid profiles; biochemical measures; biomarkers related to liver function, thyroid function, kidney function, and inflammation; and diabetes. The standard neural network architecture of VGG16 was adopted for model development. Findings In addition to previously reported systemic biomarkers, we showed quantification of body composition indices (muscle mass, height, and bodyweight) and creatinine from retinal photographs. Body muscle mass could be predicted with an R² of 0•52 (95% CI 0•51-0•53) in the internal test set, and of 0•33 (0•30-0•35) in one external test set with muscle mass measurement available. The R² value for the prediction of height was 0•42 (0•40-0•43), of bodyweight was 0•36 (0•34-0•37), and of creatinine was 0•38 (0•37-0•40) in the internal test set. However, the performances were poorer in external test sets (with the lowest performance in the European cohort), with R² values ranging between 0•08 and 0•28 for height, 0•04 and 0•19 for bodyweight, and 0•01 and 0•26 for creatinine. Of the 47 systemic biomarkers, 37 could not be predicted well from retinal photographs via deep learning (R²≤0•14 across all external test sets).Interpretation Our work provides new insights into the potential use of retinal photographs to predict systemic biomarkers, including body composition indices and serum creatinine, using deep learning in populations with a similar ethnic background. Further evaluations are warranted to validate these findings and evaluate the clinical utility of these algorithms.
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