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SummaryBackgroundDetecting microsatellite instability (MSI) in colorectal cancer is crucial for clinical decision making, as it identifies patients with differential treatment response and prognosis. Universal MSI testing is recommended, but many patients remain untested. A critical need exists for broadly accessible, cost-efficient tools to aid patient selection for testing. Here, we investigate the potential of a deep learning-based system for automated MSI prediction directly from haematoxylin and eosin (H&E)-stained whole-slide images (WSIs). MethodsOur deep learning model (MSINet) was developed using 100 H&E-stained WSIs (50 with microsatellite stability [MSS] and 50 with MSI) scanned at 40× magnification, each from a patient randomly selected in a class-balanced manner from the pool of 343 patients who underwent primary colorectal cancer resection at Stanford University Medical Center (Stanford, CA, USA; internal dataset) between Jan 1, 2015, and Dec 31, 2017. We internally validated the model on a holdout test set (15 H&E-stained WSIs from 15 patients; seven cases with MSS and eight with MSI) and externally validated the model on 484 H&E-stained WSIs (402 cases with MSS and 77 with MSI; 479 patients) from The Cancer Genome Atlas, containing WSIs scanned at 40× and 20× magnification. Performance was primarily evaluated using the sensitivity, specificity, negative predictive value (NPV), and area under the receiver operating characteristic curve (AUROC). We compared the model's performance with that of five gastrointestinal pathologists on a class-balanced, randomly selected subset of 40× magnification WSIs from the external dataset (20 with MSS and 20 with MSI). FindingsThe MSINet model achieved an AUROC of 0·931 (95% CI 0·771–1·000) on the holdout test set from the internal dataset and 0·779 (0·720–0·838) on the external dataset. On the external dataset, using a sensitivity-weighted operating point, the model achieved an NPV of 93·7% (95% CI 90·3–96·2), sensitivity of 76·0% (64·8–85·1), and specificity of 66·6% (61·8–71·2). On the reader experiment (40 cases), the model achieved an AUROC of 0·865 (95% CI 0·735–0·995). The mean AUROC performance of the five pathologists was 0·605 (95% CI 0·453–0·757). InterpretationOur deep learning model exceeded the performance of experienced gastrointestinal pathologists at predicting MSI on H&E-stained WSIs. Within the current universal MSI testing paradigm, such a model might contribute value as an automated screening tool to triage patients for confirmatory testing, potentially reducing the number of tested patients, thereby resulting in substantial test-related labour and cost savings. FundingStanford Cancer Institute and Stanford Departments of Pathology and Biomedical Data Science.

Since the outbreak of COVID-19, many COVID-19 research studies have proposed different models for predicting the trend of COVID-19. Among them, the prediction model based on mathematical epidemiology (SIR) is the most widely used, but most of these models are adapted in special situations based on various assumptions. In this study, a general adapted time-window based SIR prediction model is proposed, which is characterized by introducing a time window mechanism for dynamic data analysis and using machine learning method predicts the basic reproduction number and the exponential growth rate of the epidemic. We analyzed COVID-19 data from February to July 2020 in seven countries–––China, South Korea, Italy, Spain, Brazil, Germany and France, and the numerical results showed that the framework can effectively measure the real-time changes of the parameters during the epidemic, and error rate of predicting the number of COVID-19 infections in a single day is within 5%.

The glycocalyx is a layer of villus-like structure covering the luminal surface of vascular endothelial cells. Damage to the glycocalyx has been proven linked to the development of many diseases. However, the factors that promote damage to the glycocalyx are not fully elaborated. This review summarizes factors leading to the reduction of the glycocalyx in detail, including inflammatory factors, ischemia-reperfusion, oxidative stress, lipids, glucose, high sodium, female sex hormones and others. Additionally, the mechanisms underlying its degradation are discussed. To better prevent and treat related diseases induced by glycocalyx degradation, it is a meaningful measure to avoid these factors.

The problem of direction-of-arrival (DOA) estimation is investigated for co-prime array, where the co-prime array consists of two uniform sparse linear subarrays with extended inter-element spacing. For each sparse subarray, true DOAs are mapped into several equivalent angles impinging on the traditional uniform linear array with half-wavelength spacing. Then, by applying the estimation of signal parameters via rotational invariance technique (ESPRIT), the equivalent DOAs are estimated, and the candidate DOAs are recovered according to the relationship among equivalent and true DOAs. Finally, the true DOAs are estimated by combining the results of the two subarrays. The proposed method achieves a better complexity–performance tradeoff as compared to other existing methods.

Dear Editor, Regarding the recent study published in Critical Care on the role of haptoglobin in acute kidney injury in critically ill adults with ARDS and therapy with VV ECMO [1], we would like to explore the association between serum haptoglobin levels and clinical outcomes of patients in septic shock. Cell-free hemoglobin and its degradation component heme contribute to multiorgan failure and worse clinical outcomes of septic shock [3]. The role of cell-free hemoglobin and haptoglobin in acute kidney injury in critically ill adults with ARDS and therapy with VV ECMO.

Background Since the coronavirus disease 2019 (COVID-19) pandemic outbreak, the incidence of mental health problems in perinatal women has been high, and particularly prominent in China which was the first country affected by COVID-19. This paper aims to investigate the current situation and the related factors of maternal coping difficulties after discharge during COVID-19. Methods General information questionnaires (the Perinatal Maternal Health Literacy Scale, Postpartum Social Support Scale and Post-Discharge Coping Difficulty Scale-New Mother Form) were used to investigate 226 puerperal women in the third week of puerperium. The influencing factors were analyzed by single factor analysis, correlation and multiple linear regression. Results The total score of coping difficulties after discharge was 48.92 ± 12.05. At the third week after delivery, the scores of health literacy and social support were 21.34 ± 5.18 and 47.96 ± 12.71. There were negative correlations among health literacy, social support and coping difficulties after discharge ( r  = -0.34, r  = -0.38, P  < 0.001). Primipara, family income, health literacy and social support were the main factors influencing maternal coping difficulties after discharge. Conclusion During the COVID-19 pandemic, puerperal women in a low- and middle-income city had moderate coping difficulties after discharge and were affected by many factors. To meet the different needs of parturients and improve their psychological coping ability, medical staff should perform adequate assessment of social resources relevant to parturients and their families when they are discharged, so they can smoothly adapt to the role of mothers.

The tensile behavior, serrated flow, and dynamic strain aging of Fe-(20 to 24)Mn-(0.4 to 0.6)C twinning-induced plasticity (TWIP) steel have been investigated. A mathematical approach to analyze the DSA and PLC band parameters has been developed. For Fe-(20 to 24)Mn-(0.4 to 0.6)C TWIP steel with a theoretical ordering index (TOI) between 0.1 and 0.3, DSA can occur at the very beginning of plastic deformation and provide serrations during work hardening, while for TOI less than 0.1 the occurrence of DSA is delayed and twinning-dominant work hardening remains relatively smooth. The critical strain for the onset of DSA and PLC bands in Fe-Mn-C TWIP steels decreases as C content increases, while the numbers of serrations and bands increase. As Mn content increases, the critical strain for DSA and PLC band varies irregularly, but the numbers of serrations and bands increase. For Fe-(20 to 24)Mn-(0.4 to 0.6)C TWIP steel with grain size of about 10 to 20 μm, the twinning-induced work hardening rate is about 2.5 to 3.0 GPa, while the DSA-dominant hardening rate is about 2.0 GPa on average. With increasing engineering strain from 0.01 to 0.55 at an applied strain rate of 0.001s−1, the cycle time for PLC bands in Fe-Mn-C TWIP steel increases from 6.5 to 162 seconds, while the band velocity decreases from 4.5 to 0.5 mm s−1, and the band strain increases from 0.005 to 0.08. Increasing applied strain rate leads to a linear increase of band velocity despite composition differences. In addition, the influence of the Mn and C content on the tensile properties of Fe-Mn-C TWIP steel has been also studied. As C content increases, the yield strength and tensile strength of Fe-Mn-C TWIP steel increase, but the total elongation variation against C content is dependent on Mn content. As Mn content increases, the yield strength and tensile strength decrease, while the total elongation increases, despite C content. Taking both tensile properties and serrated flow behavior into consideration, Fe-22Mn-0.4C TWIP steel shows excellent mechanical performance with a high product of tensile strength and total elongation and a slightly serrated stress–strain response. To suppress the negative effect of DSA in Fe-Mn-C TWIP steels on the stability of tensile behavior, a TOI lower than 0.1 is strongly suggested.

Photoelectrochemical catalysis is an attractive way to provide direct hydrogen production from solar energy. However, solar conversion efficiencies are hindered by the fact that light harvesting has so far been of limited efficiency in the near-infrared region as compared to that in the visible and ultraviolet regions. Here we introduce near-infrared-active photoanodes that feature lattice-matched morphological hetero-nanostructures, a strategy that improves energy conversion efficiency by increasing light-harvesting spectral range and charge separation efficiency simultaneously. Specifically, we demonstrate a near-infrared-active morphological heterojunction comprised of BiSeTe ternary alloy nanotubes and ultrathin nanosheets. The heterojunction’s hierarchical nanostructure separates charges at the lattice-matched interface of the two morphological components, preventing further carrier recombination. As a result, the photoanodes achieve an incident photon-to-current conversion efficiency of 36% at 800 nm in an electrolyte solution containing hole scavengers without a co-catalyst. The solar conversion efficiencies of photoelectrochemical catalysis are hindered by the light harvesting range. Here, the authors use near-infrared-active photoanodes that feature lattice-matched morphological hetero-nanostructures to realize efficient photoelectrochemical hydrogen production.

This paper, based on the node insertion algorithm for NURBS curves, explicitly defines the method for calculating the node coordinates and weights of subdivided element, without altering the geometric properties and parameter distribution of the RANCF elements. However, the diversity in the definition of RANCF elements can lead to uncontrollable subdivision in parameter space. To address the distortion in the subdivision process of non-uniformly parameterized elements, this paper introduces a distribution density function for the element parameter points and establishes a calculation method for the parameter space subdivision nodes corresponding to the arc length in physical space. This method enables precise subdivision of differently parameterized arc elements in the physical space. Ultimately, guided by numerical computation results, the subdivision criteria for RANCF elements were determined. The results indicate that local refinement in regions with denser parameter point distribution can both ensure computational accuracy and improve computational efficiency.

Background Hypervirulent Klebsiella pneumoniae (hvKP) is emerging around the Asian-Pacific region and it is the major cause of the community-acquired pyogenic liver abscesses. Multidrug-resistant hypervirulent Klebsiella pneumoniae (MDR-hvKP) isolates were reported in France, China and Taiwan. However, the international-ally agreed definition for hvKP and the virulence level of hvKP are not clear. Results In this study, 56 hvKP isolates were collected from March 2008 to June 2012 and investigated by string test, capsule serotyping, multilocus sequence typing (MLST), virulence gene detection and serum resistance assay. Among the 56  K. pneumoniae isolates, 64.3% had the hypermucoviscosity phenotype, meanwhile, 64.3% were the K1 serotype and 19.6% were the K2 serotype. Within the K1 serotype, 94.4% were ST23, and within the K2 serotype, ST65, ST86 and ST375 accounted for the same percentage 27.3%. The serum resistance showed statistically normal distribution. According to the 50% lethal dose of Galleria. mellonella infection model, hvKP isolates were divided into high virulence level group and moderate virulence level group. The ability of each method evaluating the virulence level of hvKP was assessed using the area under the receiver operating characteristic curve. Conclusions K1 ST23 K. pneumoniae was the most prevalent clone of the hvKP. However, K1 ST23 K. pneumoniae was the dominant clone in the moderate virulence level group. MLST was a relatively reliable evaluation method to discriminate the virulence level of hvKP in our study.