2D TiS2-Nanosheet-Coated Concave Gold Arrays with Triple-Coupled Resonances as Sensitive SERS Substrates.

Herein, a hybrid substrate for surface-enhanced Raman scattering (SERS) is fabricated, which couples localized surface plasmon resonance (LSPR), charge transfer (CT) resonance, and molecular resonance. Exfoliated 2D TiS2 nanosheets with semimetallic properties accelerate the CT with the tested analytes, inducing a remarkable chemical mechanism enhancement. In addition, the LSPR effect is coupled with a concave gold array located underneath the thin TiS2 nanosheet, providing a strong electromagnetic enhancement. The concave gold array is prepared by etching silicone nanospheres assembled on larger polystyrene nanospheres, followed by depositing a gold layer. The LSPR intensity near the gold layer can be adjusted by changing the layer thickness to couple the molecular and CT resonances, in order to maximize the SERS enhancement. The best SERS performance is recorded on TiS2-nanosheet-coated plasmonic substrates, with a detectable methylene blue concentration down to 10-13 m and an enhancement factor of 2.1 × 109 and this concentration is several orders of magnitude lower than that of the TiS2 nanosheet (10-11 m) and plasmonic substrates (10-9 m). The present hybrid substrate with triple-coupled resonance further shows significant advantages in the label-free monitoring of curcumin (a widely applied drug for treating multiple cancers and inflammations) in serum and urine.

Psychometric properties of the Chinese version of the empathy quotient among Chinese minority college students.

BACKGROUND: When the minority college students from the ethnic minority communities come to study in Chinese Han region, they encounter adapting difficulties of culture and socio-psychology, in which empathy plays a crucial role. Current instruments used to measure empathy have many limited effectiveness. The empathy quotient (EQ) scale which has been validated in many countries was explicitly designed for clinical applications and was intended to be sensitive to a lack of empathy. This study is to develop a complete Chinese version of the EQ scale and to assess its reliability and validity among Chinese minority college students in the Han Chinese region. METHODS: A total of 1638 Chinese minority college students in the Han region were selected and were randomly divided into two groups. One group of 818 students took part in the implementation of the exploratory factor analysis while the other group of 820 students participated in the confirmatory factor analysis. RESULTS: Twenty-nine items of the EQ were retained based on the factor analysis and four factors were extracted: self-awareness, cognitive empathy, social skills, and emotional reactivity, which can explain 51.793% of the total variance. The factors of the EQ scale were significantly correlated with each other, with the correlation coefficient ranging from 0.316 to 0.563. The coefficient of internal consistency (Cronbach's α) was 0.824 for the total scale and ranged from 0.640 to 0.818 for the subscales. Confirmatory factor analysis proved that the measured data fitted well with the hypothesized four-factor model. All of the items in the scale fitted the model well, and the point-measure correlation coefficient had acceptable consistency. CONCLUSIONS: The refined 29-item Chinese version of the EQ possesses good reliability and validity, and can be applied in assessing empathy among Chinese minority college students.

Rapid, label-free histopathological diagnosis of liver cancer based on Raman spectroscopy and deep learning.

Biopsy is the recommended standard for pathological diagnosis of liver carcinoma. However, this method usually requires sectioning and staining, and well-trained pathologists to interpret tissue images. Here, we utilize Raman spectroscopy to study human hepatic tissue samples, developing and validating a workflow for in vitro and intraoperative pathological diagnosis of liver cancer. We distinguish carcinoma tissues from adjacent non-tumour tissues in a rapid, non-disruptive, and label-free manner by using Raman spectroscopy combined with deep learning, which is validated by tissue metabolomics. This technique allows for detailed pathological identification of the cancer tissues, including subtype, differentiation grade, and tumour stage. 2D/3D Raman images of unprocessed human tissue slices with submicrometric resolution are also acquired based on visualization of molecular composition, which could assist in tumour boundary recognition and clinicopathologic diagnosis. Lastly, the potential for a portable handheld Raman system is illustrated during surgery for real-time intraoperative human liver cancer diagnosis.