Optimization of the cavity length and pulse characterization based on germanene as a saturable absorber in an Er-doped fiber laser.

In this study, germanene-nanosheets (NSs) were synthesized by liquid-phase exfoliation, followed by an experimental investigation into the nonlinear saturable absorption characteristics and morphological structure of germanene. The germanene-NSs were employed as saturable absorbers, exhibiting saturation intensity and modulation depth values of 22.64M W/c m 2 and 4.48%, respectively. This demonstrated the feasibility of utilizing germanene-NSs passively mode-locked in an erbium-doped fiber laser (EDFL). By optimizing the cavity length, improvements in the output of EDFL characteristics were achieved, resulting in 883 fs pulses with a maximum average output power of 19.74 mW. The aforementioned experimental outcomes underscore the significant potential of germanene in the realms of ultrafast photonics and nonlinear optics.

Multimodality MRI-based radiomics approach to predict the posttreatment response of lung cancer brain metastases to gamma knife radiosurgery.

OBJECTIVES: To develop and validate a multimodality MRI-based radiomics approach to predicting the posttreatment response of lung cancer brain metastases (LCBM) to gamma knife radiosurgery (GKRS). METHODS: We retrospectively analyzed 213 lesions from 137 patients with LCBM who received GKRS between January 2017 and November 2020. The data were divided into a primary cohort (102 patients with 173 lesions) and an independent validation cohort (35 patients with 40 lesions) according to the time of treatment. Benefit result was defined using pretreatment and 3-month follow-up MRI images based on the Response Assessment in Neuro-Oncology Brain Metastases criteria. Valuable radiomics features were extracted from pretreatment multimodality MRI images using random forests. Prediction performance among the radiomics features of tumor core (RFTC) and radiomics features of peritumoral edema (RFPE) together was evaluated separately. Then, the random forest radiomics score and nomogram were developed through the primary cohort and evaluated through an independent validation cohort. Prediction performance was evaluated by ROC curve, calibration curve, and decision curve. RESULTS: Gender (p = 0.018), histological subtype (p = 0.009), epidermal growth factor receptor mutation (p = 0.034), and targeted drug treatment (p = 0.021) were significantly associated with posttreatment response. Adding RFPE to RFTC showed improved prediction performance than RFTC alone in primary cohort (AUC = 0.848 versus AUC = 0.750; p < 0.001). Finally, the radiomics nomogram had an AUC of 0.930, a C-index of 0.930 (specificity of 83.1%, sensitivity of 87.3%) in primary cohort, and an AUC of 0.852, a C-index of 0.848 (specificity of 84.2%, sensitivity of 76.2%) in validation cohort. CONCLUSIONS: Multimodality MRI-based radiomics models can predict the posttreatment response of LCBM to GKRS. KEY POINTS: • Among the selected radiomics features, texture features basically contributed the dominant force in prediction tasks (80%), especially gray-level co-occurrence matrix features (40%). • Adding RFPE to RFTC showed improved prediction performance than RFTC alone in primary cohort (AUC = 0.848 versus AUC = 0.750; p < 0.001). • The multimodality MRI-based radiomics nomogram showed high accuracy for distinguishing the posttreatment response of LCBM to GKRS (AUC = 0.930, in primary cohort; AUC = 0.852, in validation cohort).

Soliton phenomena in normal and anomalous dispersion regions in Er-doped mode-locked fiber lasers based on Cr2Si2Te6 saturable absorbers.

Investigations of optical solitons have always been a hot topic due to their important scientific research value. In recent years, ultrafast lasers based on two-dimensional materials such as saturable absorbers (SAs) have become the focus of optical soliton research. In this work, various soliton operations are demonstrated in Er-doped fiber lasers (EDFLs) based on ${{\rm Cr}_2}{{\rm Si}_2}{{\rm Te}_6}$ SAs. First, a low-threshold passively mode-locked EDFL with traditional soliton output is constructed, and the pump threshold is as low as 10.1 mW. Second, by adjusting the net dispersion of the cavity, stable dissipative soliton operation can also be obtained. Traditional soliton mode-locked operation with controllable Kelly sidebands from first order to fourth order is realized by adjusting the pump power in a double-ended pumped structure, and the SNR is as high as 55 dB. All results prove that ${{\rm Cr}_2}{{\rm Si}_2}{{\rm Te}_6}$ used as SA material has great potential and wide application prospects in investigating optical soliton operations in mode-locked fiber lasers with both normal and anomalous dispersion.

Third-order harmonic mode-locked and Q-switched Er-doped fiber laser based on a Cr2Ge2Te6 saturable absorber.

This paper reports the generation of fundamental solitons and third-order solitons in an erbium-doped fiber laser (EDFL) by a Cr2Ge2Te6-polyvinyl alcohol (CGT-PVA) saturable absorber (SA). Stable fundamental solitons at 1559.09 nm at a repetition frequency of 5.1 MHz were detected, and third-order solitons with a maximum output power of 6.807 mW and narrowest monopulse duration of 615.2 fs were obtained under a repetition frequency of 15.3 MHz by changing pump power. To the best of our knowledge, it is the first time to achieve a Q-switched pulse with a minimum pulse duration of 2.2 µs and maximum single pulse energy of 12.11 nJ in EDFL based on CGT-PVA SA after reducing the cavity length. Its repetition rate monotonically increased from 18.8 kHz to 61.8 kHz with a tuning range of about 43 kHz. The experimental results sufficiently demonstrate that CGT has enormous potential as an ultrafast photonics device.

Semi-supervised classification of fundus images combined with CNN and GCN.

PURPOSE: Diabetic retinopathy (DR) is one of the most serious complications of diabetes, which is a kind of fundus lesion with specific changes. Early diagnosis of DR can effectively reduce the visual damage caused by DR. Due to the variety and different morphology of DR lesions, automatic classification of fundus images in mass screening can greatly save clinicians' diagnosis time. To alleviate these problems, in this paper, we propose a novel framework-graph attentional convolutional neural network (GACNN). METHODS AND MATERIALS: The network consists of convolutional neural network (CNN) and graph convolutional network (GCN). The global and spatial features of fundus images are extracted by using CNN and GCN, and attention mechanism is introduced to enhance the adaptability of GCN to topology map. We adopt semi-supervised method for classification, which greatly improves the generalization ability of the network. RESULTS: In order to verify the effectiveness of the network, we conducted comparative experiments and ablation experiments. We use confusion matrix, precision, recall, kappa score, and accuracy as evaluation indexes. With the increase of the labeling rates, the classification accuracy is higher. Particularly, when the labeling rate is set to 100%, the classification accuracy of GACNN reaches 93.35%. Compared with DenseNet121, the accuracy rate is improved by 6.24%. CONCLUSIONS: Semi-supervised classification based on attention mechanism can effectively improve the classification performance of the model, and attain preferable results in classification indexes such as accuracy and recall. GACNN provides a feasible classification scheme for fundus images, which effectively reduces the screening human resources.

CT-Based Hand-crafted Radiomic Signatures Can Predict PD-L1 Expression Levels in Non-small Cell Lung Cancer: a Two-Center Study.

Here, we used pre-treatment CT images to develop and evaluate a radiomic signature that can predict the expression of programmed death ligand 1 (PD-L1) in non-small cell lung cancer (NSCLC). We then verified its predictive performance by cross-referencing its results with clinical characteristics. This two-center retrospective analysis included 125 patients with histologically confirmed NSCLC. A total of 1287 hand-crafted radiomic features were observed from manually determined tumor regions. Valuable features were then selected with a ridge regression-based recursive feature elimination approach. Machine learning-based prediction models were then built from this and compared each other. The final radiomic signature was built using logistic regression in the primary cohort, and then tested in a validation cohort. Finally, we compared the efficacy of the radiomic signature to the clinical model and the radiomic-clinical nomogram. Among the 125 patients, 89 were classified as having PD-L1 positive expression. However, there was no significant difference in PD-L1 expression levels determined by clinical characteristics (P = 0.109-0.955). Upon selecting 9 radiomic features, we found that the logistic regression-based prediction model performed the best (AUC = 0.96, P < 0.001). In the external cohort, our radiomic signature showed an AUC of 0.85, which outperformed both the clinical model (AUC = 0.38, P < 0.001) and the radiomics-nomogram model (AUC = 0.61, P < 0.001). Our CT-based hand-crafted radiomic signature model can effectively predict PD-L1 expression levels, providing a noninvasive means of better understanding PD-L1 expression in patients with NSCLC.

Is blockchain for Internet of Medical Things a panacea for COVID-19 pandemic?

The outbreak of the COVID-19 pandemic has deeply influenced the lifestyle of the general public and the healthcare system of the society. As a promising approach to address the emerging challenges caused by the epidemic of infectious diseases like COVID-19, Internet of Medical Things (IoMT) deployed in hospitals, clinics, and healthcare centers can save the diagnosis time and improve the efficiency of medical resources though privacy and security concerns of IoMT stall the wide adoption. In order to tackle the privacy, security, and interoperability issues of IoMT, we propose a framework of blockchain-enabled IoMT by introducing blockchain to incumbent IoMT systems. In this paper, we review the benefits of this architecture and illustrate the opportunities brought by blockchain-enabled IoMT. We also provide use cases of blockchain-enabled IoMT on fighting against the COVID-19 pandemic, including the prevention of infectious diseases, location sharing and contact tracing, and the supply chain of injectable medicines. We also outline future work in this area.

Nonlinear optical absorption properties of zirconium selenide in generating dark soliton and dark-bright soliton pairs.

Our work reports the preparation of zirconium selenide (ZrSe2)-polyvinyl alcohol (PVA) film-type saturable absorber (SA) and its nonlinear absorption performance in obtaining dark soliton and dark-bright soliton pairs in an Er-doped fiber (EDF) laser for the first time, to the best of our knowledge. The saturation intensity and modulation depth of the ZrSe2-PVA SA were ∼12.72MW/cm2 and 2.3%, respectively. Due to the modulation of the SA, under a pump power of 525.2 mW, stable dark soliton operation with an average output power of 9.75 mW, and a pulse repetition frequency of 20.84 MHz, a pulse width of 3.85 ns was attained successfully. By adjusting the state of the polarization controllers, dark-bright soliton pairs were also observed. To the best of our knowledge, this was the first demonstration focusing on the nonlinear optical absorption applications of ZrSe2 in obtaining dark soliton and dark-bright soliton pairs. Our results show that ZrSe2 is a good two-dimensional SA material for acting as an ultrafast optical device due to its suitable nonlinear optical absorption properties.

Nonlinear optical characteristics of ZrSe2 and its application for designing multi-wavelength mode-locked operations.

In our work, a ZrSe2-polyvinyl alcohol film-type saturable absorber (SA) with a modulation depth of 4.99% and a saturable intensity of 12.42MW/cm2 was successfully prepared and employed in mode-locked Er-doped fiber laser. The fiber laser can generate stable multi-wavelength mode-locked operations with a threshold power of 224 mW and a maximum average output power of 3.272 mW at the repetition rate of 3.38 MHz for the first time, to the best of our knowledge. Our experimental results fully prove that ZrSe2 nanosheets were efficient SA candidates for demonstrating multi-wavelength mode-locked operation fiber lasers due to their tunable absorption peak and excellent saturable absorption properties.

Discrimination of mediastinal metastatic lymph nodes in NSCLC based on radiomic features in different phases of CT imaging.

BACKGROUND: We aimed to develop radiomic models based on different phases of computed tomography (CT) imaging and to investigate the efficacy of models for diagnosing mediastinal metastatic lymph nodes (LNs) in non-small cell lung cancer (NSCLC). METHODS: Eighty-six NSCLC patients were enrolled in this study, and we selected 231 mediastinal LNs confirmed by pathology results as the subjects which were divided into training (n = 163) and validation cohorts (n = 68). The regions of interest (ROIs) were delineated on CT scans in the plain phase, arterial phase and venous phase, respectively. Radiomic features were extracted from the CT images in each phase. A least absolute shrinkage and selection operator (LASSO) algorithm was used to select features, and multivariate logistic regression analysis was used to build models. We constructed six models (orders 1-6) based on the radiomic features of the single- and dual-phase CT images. The performance of the radiomic model was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV). RESULTS: A total of 846 features were extracted from each ROI, and 10, 9, 5, 2, 2, and 9 features were chosen to develop models 1-6, respectively. All of the models showed excellent discrimination, with AUCs greater than 0.8. The plain CT radiomic model, model 1, yielded the highest AUC, specificity, accuracy and PPV, which were 0.926 and 0.925; 0.860 and 0.769; 0.871 and 0.882; and 0.906 and 0.870 in the training and validation sets, respectively. When the plain and venous phase CT radiomic features were combined with the arterial phase CT images, the sensitivity increased from 0.879 and 0.919 to 0.949 and 0979 and the NPV increased from 0.821 and 0.789 to 0.878 and 0.900 in the training group, respectively. CONCLUSIONS: All of the CT radiomic models based on different phases all showed high accuracy and precision for the diagnosis of LN metastasis (LNM) in NSCLC patients. When combined with arterial phase CT, the sensitivity and NPV of the model was be further improved.