A Femtosecond Electron-Based Versatile Microscopy for Visualizing Carrier Dynamics in Semiconductors Across Spatiotemporal and Energetic Domains.

Carrier dynamics detection in different dimensions (space, time, and energy) with high resolutions plays a pivotal role in the development of modern semiconductor devices, especially in low-dimensional, high-speed, and ultrasensitive devices. Here, a femtosecond electron-based versatile microscopy is reported that combines scanning ultrafast electron microscopy (SUEM) imaging and time-resolved cathodoluminescence (TRCL) detection, which allows for visualizing and decoupling different dynamic processes of carriers involved in surface and bulk in semiconductors with unprecedented spatiotemporal and energetic resolutions. The achieved spatial resolution is better than 10 nm, and the temporal resolutions for SUEM imaging and TRCL detection are ≈500 fs and ≈4.5 ps, respectively, representing state-of-the-art performance. To demonstrate its unique capability, the surface and bulk carrier dynamics involved in n-type gallium arsenide (GaAs) are directly tracked and distinguished. It is revealed, in real time and space, that hot carrier cooling, defect trapping, and interband-/defect-assisted radiative recombination in the energy domain result in ordinal super-diffusion, localization, and sub-diffusion of carriers at the surface, elucidating the crucial role of surface states on carrier dynamics. The study not only gives a comprehensive physical picture of carrier dynamics in GaAs, but also provides a powerful platform for exploring complex carrier dynamics in semiconductors for promoting their device performance.

Gold nanoparticles targeting the autophagy-lysosome system to combat the inflammation-compromised osteogenic potential of periodontal ligament stem cells: From mechanism to therapy.

Although substantial data indicate that the osteogenic potential of periodontal ligament stem cells (PDLSCs) is compromised under inflammatory conditions, the underlying mechanism remains largely unexplored. In this study, we found that both the autophagy levels and autophagic flux levels were decreased in PDLSCs incubated under inflammatory conditions (I-PDLSCs). Based on the increased expression of LC3 II (at an autophagy level) and decreased accumulation of LC3 II (at an autophagic flux level) in I-PDLSCs, we speculated that the disruption of I-PDLSC autophagy arose from dysfunction of the cellular autophagy-lysosome system. Subsequently, our hypothesis was demonstrated by inhibited autophagosome-lysosome fusion, damaged lysosomal function, and suppressed activation of transcription factor EB (TFEB, a master regulator of the autophagy-lysosome system) in I-PDLSCs and verified by TFEB overexpression in I-PDLSCs. We found that gold nanoparticle (Au NP) treatment rescued the osteogenic potential of I-PDLSCs by restoring the inflammation-compromised autophagy-lysosome system. In this context, Au NP ceased to be effective when TFEB was knocked down in PDLSCs. Our data demonstrate the crucial role of the autophagy-lysosome system in cellular osteogenesis under inflammatory conditions and suggest a new target for rescuing inflammation-induced cell dysfunction using nanomaterials to aid cell biology and tissue regeneration.

Model development to predict central lymph node metastasis in cN0 papillary thyroid microcarcinoma by machine learning.

Background: Whether prophylactic central lymph node dissection is necessary for cN0 papillary thyroid microcarcinoma (PTMC) patients remains highly debatable. Surgeons desperately need a way to help with surgical decision-making. While traditional predictive models can better explain changes in variables, machine learning (ML) models may have better predictive performance. This study aims to develop models for predicting the risk of central lymph node metastasis (CLNM) by utilizing ML algorithms. Methods: The clinical records of 1,121 patients with cN0 PTMC who underwent initial thyroid resection at our hospital between January 2014 and December 2018 were retrospectively retrieved. Univariate and multivariate analyses were performed to examine risk factors associated with CLNM. Six ML algorithms for predicting CLNM were established and internally validated. Indices including the area under the receiver operating characteristic (AUROC), sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated to test the performance of the model. Results: The results showed 33.5% (376 out of 1,121) of patients had CLNM. In multivariate logistic regression (LR) analyses, gender, age, tumor size, multifocal lesions, and extrathyroidal extension (ETE) were all independent predictors of CLNM. The AUROC predictive values of the six ML algorithms were between 0.664 and 0.794, with the random forest (RF) model performing the best with an AUROC of 0.794. Therefore, we used the RF model and uploaded the results to a web-based risk calculator to predict an individual's probability of CLNM (https://xijing-thyroid.shinyapps.io/ptmc_clnm). Conclusions: Developing predictive models of CLNM in cN0 PTMC patients using the ML algorithm is a feasible method. Our online risk calculator based on the RF model may be a useful tool for surgical decisions.

Iodine intake level and incidence of thyroid disease in adults in Shaanxi province: a cross-sectional study.

BACKGROUND: Exploring the relationship between adult iodine intake level and thyroid disease in Shaanxi area is of great significance for adult scientific iodine supplement and individual iodine supplement strategy. At present, the relationship between iodine and incidence of thyroid disease has not been determined. METHODS: This study was based on the clinical data of 1,159 patients from the Shaanxi Province aged over 18 years and diagnosed with thyroid-related diseases who were admitted to the Xijing Hospital from 2016 to 2020, and 182 provincial healthy volunteers aged over 18 years who agreed and signed informed consent for physical examination in 2020. The chi-square test and nonparametric test were used to investigate the relationship between iodine intake level and thyroid disease. RESULTS: (I) A total of 1,341 patients were enrolled and observed in this study. The median urinary iodine (MUI) was 233.20 µg/L. Compared with the control, group participants the urine iodine (UI) of those with hyperthyroidism, Hashimoto's thyroiditis (HT), papillary thyroid cancer (PTC), and benign nodules was significantly different (P<0.05). (II) The incidence of PTC was higher in women with excessive iodine intake and people aged ≥45 years (P<0.05). (III) There was no significant difference in urinary iodine (UI), age, gender, and other factors between benign nodules and PTC (P>0.05). CONCLUSIONS: The iodine intake level of adults in Shaanxi is high, which is related to hyperthyroidism, HT, benign nodules, thyroid cancer, and other diseases. There were 3 factors, including excessive iodine intake, age ≥45 years, and female gender, found to be associated with the development of PTC.