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Coherent optics has profoundly impacted diverse applications ranging from communications, LiDAR to quantum computations. However, developing coherent systems in integrated photonics comes at great expense in hardware integration and energy efficiency. Here we demonstrate a high-coherence parallelization strategy for advanced integrated coherent systems at minimal cost. By using a self-injection locked microcomb to injection lock distributed feedback lasers, we achieve a record high on-chip gain of 60 dB with no degradation in coherence. This strategy enables highly coherent channels with linewidths down to 10 Hz and power over 20 dBm. The overall electrical-to-optical efficiency reaches 19%, comparable to that of advanced semiconductor lasers. This method supports a silicon photonic communication link with an unprecedented data rate beyond 60 Tbit/s and reduces phase-related DSP consumption by 99.99999% compared to traditional III-V laser pump schemes. This work paves the way for realizing scalable, high-performance coherent integrated photonic systems, potentially benefiting numerous applications. Researchers demonstrate the high-coherence parallelization in integrated photonics. Their high-coherence, high-power, multiwavelength light source drives a silicon photonic link with a 60 Tbit/s data rate and significantly reduces digital signal processing consumption. [ABSTRACT FROM AUTHOR]

Background: Radiomics provided opportunities to quantify the tumor phenotype non-invasively. This study extracted contrast-enhanced computed tomography (CECT) radiomic signatures and evaluated clinical features of bone metastasis in non-small-cell lung cancer (NSCLC). With the combination of the revealed radiomics and clinical features, the predictive modeling on bone metastasis in NSCLC was established. Methods: A total of 318 patients with NSCLC at the Tianjin Medical University Cancer Institute & Hospital was enrolled between January 2009 and December 2019, which included a feature-learning cohort (n = 223) and a validation cohort (n = 95). We trained a radiomics model in 318 CECT images from feature-learning cohort to extract the radiomics features of bone metastasis in NSCLC. The Kruskal-Wallis and the least absolute shrinkage and selection operator regression (LASSO) were used to select bone metastasis-related features and construct the CT radiomics score (Rad-score). Multivariate logistic regression was performed with the combination of the Rad-score and clinical data. A predictive nomogram was subsequently developed. Results: Radiomics models using CECT scans were significant on bone metastasis prediction in NSCLC. Model performance was enhanced with each information into the model. The radiomics nomogram achieved an AUC of 0.745 (95% confidence interval [CI]: 0.68,0.80) on predicting bone metastasis in the training set and an AUC of 0.808(95% confidence interval [CI]: 0.71,0.88) in the validation set. Conclusion: The revealed invisible image features were of significance on guiding bone metastasis prediction in NSCLC. Based on the combination of the image features and clinical characteristics, the predictive nomogram was established. Such nomogram can be used for the auxiliary screening of bone metastasis in NSCLC. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Peritoneal recurrence is the predominant pattern of recurrence in advanced ovarian cancer (AOC) and portends a dismal prognosis. Accurate prediction of peritoneal recurrence and disease-free survival (DFS) is crucial to identify patients who might benefit from intensive treatment. We aimed to develop a predictive model for peritoneal recurrence and prognosis in AOC., Methods: In this retrospective multi-institution study of 515 patients, an end-to-end multi-task convolutional neural network (MCNN) comprising a segmentation convolutional neural network (CNN) and a classification CNN was developed and tested using preoperative CT images, and MCNN-score was generated to indicate the peritoneal recurrence and DFS status in patients with AOC. We evaluated the accuracy of the model for automatic segmentation and predict prognosis., Results: The MCNN achieved promising segmentation performances with a mean Dice coefficient of 84.3% (range: 78.8%-87.0%). The MCNN was able to predict peritoneal recurrence in the training (AUC 0.87; 95% CI 0.82-0.90), internal test (0.88; 0.85-0.92), and external test set (0.82; 0.78-0.86). Similarly, MCNN demonstrated consistently high accuracy in predicting recurrence, with an AUC of 0.85; 95% CI 0.82-0.88, 0.83; 95% CI 0.80-0.86, and 0.85; 95% CI 0.83-0.88. For patients with a high MCNN-score of recurrence, it was associated with poorer DFS with P < 0.0001 and hazard ratios of 0.1964 (95% CI: 0.1439-0.2680), 0.3249 (95% CI: 0.1896-0.5565), and 0.3458 (95% CI: 0.2582-0.4632)., Conclusion: The MCNN approach demonstrated high performance in predicting peritoneal recurrence and DFS in patients with AOC., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Cerebral ischemia/reperfusion causes high disability, recurrence, and mortality. Ischemic stroke is a powerful stimulus that triggers significant microglia activation. Ginsenoside Rb1 (GS-Rb1) has been demonstrated to have neuroprotective effects in the central nervous system. In this study, the effects of GS-Rb1 against cerebral ischemia/reperfusion were explored. A mouse model of middle cerebral artery occlusion (MCAO) was used to mimic the cerebral ischemia/reperfusion. Mice in MCAO + GS-Rb1 groups received 5, 10, or 20 mg/kg GS-Rb1 through intraperitoneal injection. Modified neurological severity scoring (mNSS) showed neurological function, while the open field test tested the anxiety-like behaviors. Cognitive impairment was evaluated by Morris water maze. Protein levels were evaluated by ELISA and Western blot and mRNA levels were analyzed by qRT-PCR. When compared to the MCAO mice, mice in the MCAO + GS-Rb1 group had significantly lower mNSS scores and less brain water content. GS-Rb1 alleviated both cognitive impairment and anxiety and inhibited microglial activation in the cerebral ischemia/reperfusion model. GS-Rb1 enhanced M2-type microglia polarization while inhibiting M1-type microglia polarization. In summary, we observed that GS-Rb1 had neuro-protective effects in a cerebral ischemia/reperfusion mouse model through regulating the microglia polarization.