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ObjectivesThis study was conducted in order to establish and validate a radiomics model for predicting lymph node (LN) metastasis of intrahepatic cholangiocarcinoma (IHC) and to determine its prognostic value.MethodsFor this retrospective study, a radiomics model was developed in a primary cohort of 103 IHC patients who underwent curative-intent resection and lymphadenectomy. Radiomics features were extracted from arterial phase computed tomography (CT) scans. A radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator (LASSO) method. Multivariate logistic regression analysis was adopted to establish a radiomics model incorporating radiomics signature and other independent predictors. Model performance was determined by its discrimination, calibration, and clinical usefulness. The model was internally validated in 52 consecutive patients.ResultsThe radiomics signature comprised eight LN-status–related features and showed significant association with LN metastasis in both cohorts (p < 0.001). A radiomics nomogram that incorporates radiomics signature and CA 19-9 level showed good calibration and discrimination in the primary cohort (AUC 0.8462) and validation cohort (AUC 0.8921). Promisingly, the radiomics nomogram yielded an AUC of 0.9224 in the CT-reported LN-negative subgroup. Decision curve analysis confirmed the clinical utility of this nomogram. High risk for metastasis portended significantly lower overall and recurrence-free survival than low risk for metastasis (both p < 0.001). The radiomics nomogram was an independent preoperative predictor of overall and recurrence-free survival.ConclusionsOur radiomics model provided a robust diagnostic tool for prediction of LN metastasis, especially in CT-reported LN-negative IHC patients, that may facilitate clinical decision-making.Key Points• The radiomics nomogram showed good performance for prediction of LN metastasis in IHC patients, particularly in the CT-reported LN-negative subgroup.• Prognosis of high-risk patients remains dismal after curative-intent resection.• The radiomics model may facilitate clinical decision-making and define patient subsets benefiting most from surgery.

Carbon dioxide (CO2) is the major carbonaceous component of many planetary atmospheres, which includes the Earth throughout its history. Carbon fixation chemistry—which reduces CO2 to organics, utilizing hydrogen as the stoichiometric reductant—usually requires high pressures and temperatures, and the yields of products of potential use to nascent biology are low. Here we demonstrate an efficient ultraviolet photoredox chemistry between CO2 and sulfite that generates organics and sulfate. The chemistry is initiated by electron photodetachment from sulfite to give sulfite radicals and hydrated electrons, which reduce CO2 to its radical anion. A network of reactions that generates citrate, malate, succinate and tartrate by irradiation of glycolate in the presence of sulfite was also revealed. The simplicity of this carboxysulfitic chemistry and the widespread occurrence and abundance of its feedstocks suggest that it could have readily taken place on the surfaces of rocky planets. The availability of the carboxylate products on early Earth could have driven the development of central carbon metabolism before the advent of biological CO2 fixation.Carbon dioxide is a substantial component of many planetary atmospheres, but reduction of carbon dioxide requires conditions and substrates that are rare on planetary surfaces. Now, the reduction of carbon dioxide to organic species with biological relevance has been photochemically coupled to the oxidation of sulfite, suggesting that prebiotic carbon fixation could take place on the surfaces of rocky planets.

Over the past decade, artificial intelligence (AI) has contributed substantially to the resolution of various medical problems, including cancer. Deep learning (DL), a subfield of AI, is characterized by its ability to perform automated feature extraction and has great power in the assimilation and evaluation of large amounts of complicated data. On the basis of a large quantity of medical data and novel computational technologies, AI, especially DL, has been applied in various aspects of oncology research and has the potential to enhance cancer diagnosis and treatment. These applications range from early cancer detection, diagnosis, classification and grading, molecular characterization of tumors, prediction of patient outcomes and treatment responses, personalized treatment, automatic radiotherapy workflows, novel anti‐cancer drug discovery, and clinical trials. In this review, we introduced the general principle of AI, summarized major areas of its application for cancer diagnosis and treatment, and discussed its future directions and remaining challenges. As the adoption of AI in clinical use is increasing, we anticipate the arrival of AI‐powered cancer care. This review introduced the general principle of AI, summarize major areas of its application for cancer diagnosis, treatment, and precision medicine and discuss its future directions and remaining challenges.

Background This study aimed to investigate the effect of systemic inflammation, assessed by high sensitivity C‐reactive protein (hs‐CRP) levels, on prediabetes progression and regression in middle‐aged and older adults based on the China Health and Retirement Longitudinal Study (CHARLS). Methods Participants with prediabetes from CHARLS were followed up 4 years later with blood samples collected for measuring fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c). The level of hs‐CRP was assessed at baseline and categorized into tertiles (low, middle, and high groups). Prediabetes at baseline and follow‐up was defined primarily according to the American Diabetes Association (ADA) criteria. Logistic regression models were used to estimate the odds ratios (ORs) and confidence intervals (CIs). We also performed stratified analyses according to age, gender, BMI, the presence of hypertension, and the disease history of heart disease and dyslipidemia and sensitivity analyses excluding a subset of participants with incomplete data. Results Of the 2,874 prediabetes included at baseline, 834 participants remained as having prediabetes, 146 progressed to diabetes, and 1,894 regressed to normoglycemia based on ADA criteria with a 4 year follow‐up. After multivariate logistics regression analysis, prediabetes with middle (0.67–1.62 mg/L) and high (>1.62 mg/L) hs‐CRP levels had an increased incidence of progressing to diabetes compared with prediabetes with low hs‐CRP levels (<0.67 mg/L; OR = 1.846, 95%CI: 1.129–3.018; and OR = 1.632, 95%CI: 0.985–2.703, respectively), and the incidence of regressing to normoglycemia decreased (OR = 0.793, 95%CI: 0.645–0.975; and OR = 0.769, 95%CI: 0.623–0.978, respectively). Stratified analyses and sensitivity analyses showed consistent results. Conclusions Low levels of hs‐CRP are associated with a high incidence of regression from prediabetes to normoglycemia and reduced odds of progression to diabetes. This study found that low levels of hs‐CRP are associated with a high incidence of regression from prediabetes to normoglycemia and reduced odds of progression to diabetes. Subgroup analyses showed a stronger association between hs‐CRP and type 2 diabetes in women than in men. Thus, prediabetes may need to be closely monitored in middle‐aged and older women with elevated hs‐CRP. In addition, whether a lower cutoff value for hs‐CRP elevation in women should be proposed is a question worthy of future research.

Life is an out-of-equilibrium system sustained by a continuous supply of energy. In extant biology, the generation of the primary energy currency, adenosine 5′-triphosphate and its use in the synthesis of biomolecules require enzymes. Before their emergence, alternative energy sources, perhaps assisted by simple catalysts, must have mediated the activation of carboxylates and phosphates for condensation reactions. Here, we show that the chemical energy inherent to isonitriles can be harnessed to activate nucleoside phosphates and carboxylic acids through catalysis by acid and 4,5-dicyanoimidazole under mild aqueous conditions. Simultaneous activation of carboxylates and phosphates provides multiple pathways for the generation of reactive intermediates, including mixed carboxylic acid–phosphoric acid anhydrides, for the synthesis of peptidyl–RNAs, peptides, RNA oligomers and primordial phospholipids. Our results indicate that unified prebiotic activation chemistry could have enabled the joining of building blocks in aqueous solution from a common pool and enabled the progression of a system towards higher complexity, foreshadowing today’s encapsulated peptide–nucleic acid system.Life requires a constant supply of energy, but the energy sources that drove the transition from prebiotic chemistry to biochemistry on the early Earth are unknown. Now, a potentially prebiotic chemical activating reagent has been shown to enable the synthesis, in aqueous conditions and catalysed by small molecules, of peptides, peptidyl–RNAs, RNA oligomers and primordial phospholipids.

This paper proposes a remote sensing image compression and encryption algorithm based on block compressive sensing and multiple S-boxes that utilize a novel hyperchaotic system. Specifically, given that remote sensing images are large-sized, we develop a block compression–encryption algorithm comprising our novel hyperchaotic system combined with block compressive sensing. The proposed hyperchaotic system involves a two-dimensional Logistic coupling Cubic map that couples Logistic map and Cubic map. Compared to existing chaotic systems, the proposed has better ergodicity, randomness, and hyperchaotic characteristics. Moreover, to improve plaintext correlation, the block built-in scrambling and S-box substitution combine the plaintext-related weight sequence in the encryption stage, with each image block using different S-boxes to enhance the algorithm’s randomness. The experimental results highlight that the proposed algorithm affords a good encryption effect, reconstruction accuracy, and anti-attack ability, especially in resisting cropping attacks.

Although previous neuroimaging evidence has confirmed the brain functional disturbances in thyroid‐associated ophthalmopathy (TAO), the dynamic characteristics of brain activity and functional connectivity (FC) in TAO were rarely concerned. The present study aims to investigate the alterations of temporal variability of brain activity and FC in TAO using resting‐state functional magnetic resonance imaging (rs‐fMRI). Forty‐seven TAO patients and 30 age‐, gender‐, education‐, and handedness‐matched healthy controls (HCs) were enrolled and underwent rs‐fMRI scanning. The dynamic amplitude of low‐frequency fluctuation (dALFF) was first calculated using a sliding window approach to characterize the temporal variability of brain activity. Based on the dALFF results, seed‐based dynamic functional connectivity (dFC) analysis was performed to identify the temporal variability of efficient communication between brain regions in TAO. Additionally, correlations between dALFF and dFC and the clinical indicators were analyzed. Compared with HCs, TAO patients displayed decreased dALFF in the left superior occipital gyrus (SOG) and cuneus (CUN), while showing increased dALFF in the left triangular part of inferior frontal gyrus (IFGtriang), insula (INS), orbital part of inferior frontal gyrus (ORBinf), superior temporal gyrus (STG) and temporal pole of superior temporal gyrus (TPOsup). Furthermore, TAO patients exhibited decreased dFC between the left STG and the right middle occipital gyrus (MOG), as well as decreased dFC between the left TPOsup and the right calcarine fissure and surrounding cortex (CAL) and MOG. Correlation analyses showed that the altered dALFF in the left SOG/CUN was positively related to visual acuity (r = .409, p = .004), as well as the score of QoL for visual functioning (r = .375, p = .009). TAO patients developed abnormal temporal variability of brain activity in areas related to vision, emotion, and cognition, as well as reduced temporal variability of FC associated with vision deficits. These findings provided additional insights into the neurobiological mechanisms of TAO. We explored the spatiotemporal alterations of both brain activity and connectivity in thyroid‐associated ophthalmopathy (TAO) by using dynamic amplitude of low‐frequency fluctuation and dynamic functional connectivity, respectively. We also identified correlations between the abnormal dynamic brain activity in the left occipital area and the visual deficits in TAO patients.

Predicting the number of new suspected or confirmed cases of novel coronavirus disease 2019 (COVID-19) is crucial in the prevention and control of the COVID-19 outbreak. Social media search indexes (SMSI) for dry cough, fever, chest distress, coronavirus, and pneumonia were collected from 31 December 2019 to 9 February 2020. The new suspected cases of COVID-19 data were collected from 20 January 2020 to 9 February 2020. We used the lagged series of SMSI to predict new suspected COVID-19 case numbers during this period. To avoid overfitting, five methods, namely subset selection, forward selection, lasso regression, ridge regression, and elastic net, were used to estimate coefficients. We selected the optimal method to predict new suspected COVID-19 case numbers from 20 January 2020 to 9 February 2020. We further validated the optimal method for new confirmed cases of COVID-19 from 31 December 2019 to 17 February 2020. The new suspected COVID-19 case numbers correlated significantly with the lagged series of SMSI. SMSI could be detected 6–9 days earlier than new suspected cases of COVID-19. The optimal method was the subset selection method, which had the lowest estimation error and a moderate number of predictors. The subset selection method also significantly correlated with the new confirmed COVID-19 cases after validation. SMSI findings on lag day 10 were significantly correlated with new confirmed COVID-19 cases. SMSI could be a significant predictor of the number of COVID-19 infections. SMSI could be an effective early predictor, which would enable governments’ health departments to locate potential and high-risk outbreak areas.

Tibet’s ancient topography and its role in climatic and biotic evolution remain speculative due to a paucity of quantitative surface-height measurements through time and space, and sparse fossil records. However, newly discovered fossils from a present elevation of ∼4,850 m in central Tibet improve substantially our knowledge of the ancient Tibetan environment. The 70 plant fossil taxa so far recovered include the first occurrences of several modern Asian lineages and represent a Middle Eocene (∼47 Mya) humid subtropical ecosystem. The fossils not only record the diverse composition of the ancient Tibetan biota, but also allow us to constrain the Middle Eocene land surface height in central Tibet to ∼1,500 ± 900 m, and quantify the prevailing thermal and hydrological regime. This “Shangri-La”–like ecosystem experienced monsoon seasonality with a mean annual temperature of ∼19 °C, and frosts were rare. It contained few Gondwanan taxa, yet was compositionally similar to contemporaneous floras in both North America and Europe. Our discovery quantifies a key part of Tibetan Paleogene topography and climate, and highlights the importance of Tibet in regard to the origin of modern Asian plant species and the evolution of global biodiversity.

Spinal cord injury (SCI) is a catastrophic injury to the central nervous system (CNS) that can lead to sensory and motor dysfunction, which seriously affects patients' quality of life and imposes a major economic burden on society. The pathological process of SCI is divided into primary and secondary injury, and secondary injury is a cascade of amplified responses triggered by the primary injury. Due to the complexity of the pathological mechanisms of SCI, there is no clear and effective treatment strategy in clinical practice. Exosomes, which are extracellular vesicles of endoplasmic origin with a diameter of 30–150 nm, play a critical role in intercellular communication and have become an ideal vehicle for drug delivery. A growing body of evidence suggests that exosomes have great potential for repairing SCI. In this review, we introduce exosome preparation, functions, and administration routes. In addition, we summarize the effect and mechanism by which various exosomes repair SCI and review the efficacy of exosomes in combination with other strategies to repair SCI. Finally, the challenges and prospects of the use of exosomes to repair SCI are described.