Two-dimensional stretchable blazed wavelength-tunable grating based on PDMS.

This paper reports a two-dimensional stretchable blazed wavelength-tunable grating based on polydimethylsiloxane (PDMS). In the elastic range, stretch the grating along (Y-axis) and perpendicular to (X-axis) the grating line, fix the position of the +1st-order spot to maintain the grating period, and only change the groove angle to tune the blazed wavelength. By stretching the grating up to 20% of the Y-axis, and 5.2% of the X-axis, the groove angle is reduced by 1.33°, and the blazed wavelength of the first-order diffraction shifts toward the short-wave direction by 42.3 nm. The sensitivity of a spectrometer can be enhanced by tuning the blazed wavelength of the PDMS grating to the wavelength of the spectrum peak under observation in the bands from 460.8 nm to 503.1 nm.

Analytical design of a high-performing +1st order diffraction convex grating imaging spectrometer.

A novel concentric spectrometer having one convex grating and one concave mirror, working at ${+}{1}$+1st order diffraction, and with a small size, high resolution, and high diffraction efficiency, is proposed. It can simultaneously achieve high resolution and compactness by increasing the grating groove density. A compact spectrometer operating at a wavelength of 740-790 nm with an excellent imaging quality is designed. Its spectral resolution reaches 0.049 nm, and its diffraction efficiency improves by 27% compared to the conventional Offner spectrometer with convex grating working at ${-}{1}$-1st order diffraction. This is suitable for small, light, and low-cost atmospheric gas monitoring satellites.

Constructing a Predictive Model of Depression in Chemotherapy Patients with Non-Hodgkin's Lymphoma to Improve Medical Staffs' Psychiatric Care.

OBJECTIVES: Depression is highly prevalent in non-Hodgkin's lymphoma (NHL) patients undergoing chemotherapy. The social stress associated with malignancy induces neurovascular pathology promoting clinical levels of depressive symptomatology. The purpose of this study was to establish an effective depressive symptomatology risk prediction model to those patients. METHODS: This study included 238 NHL patients receiving chemotherapy, 80 of whom developed depressive symptomatology. Different types of variables (sociodemographic, medical, and psychosocial) were entered in the models. Three prediction models (support vector machine-recursive feature elimination model, random forest model, and nomogram prediction model based on logistic regression analysis) were compared in order to select the one with the best predictive power. The selected model was then evaluated using calibration plots, ROC curves, and C-index. The clinical utility of the nomogram was assessed by the decision curve analysis (DCA). RESULTS: The nomogram prediction has the most efficient predictive ability when 10 predictors are included (AUC = 0.938). A nomogram prediction model was constructed based on the logistic regression analysis with the best predictive accuracy. Sex, age, medical insurance, marital status, education level, per capita monthly household income, pathological stage, SSRS, PSQI, and QLQ-C30 were included in the nomogram. The C-index was 0.944, the AUC value was 0.972, and the calibration curve also showed the good predictive ability of the nomogram. The DCA curve suggested that the nomogram had a strong clinical utility. CONCLUSIONS: We constructed a depressive symptomatology risk prediction model for NHL chemotherapy patients with good predictive power and clinical utility.