Serum creatinine/cystatin C ratio as a prognostic indicator for patients with colorectal cancer

BackgroundThis study aimed to explore the relationship between creatinine/cystatin C ratio and progression-free survival (PFS) and overall survival (OS) in colorectal cancer (CRC) patients undergoing surgical treatment.MethodsA retrospective analysis was conducted on 975 CRC patients who underwent surgical resection from January 2012 to 2015. Restricted three-sample curve to display the non-linear relationship between PFS/OS and creatinine-cystatin C ratio. Cox regression model and Kaplan-Meier method were used to evaluate the effect of the creatinine-cystatin C ratio on the survival of CRC patients. Prognostic variables with p-value ≤0.05 in multivariate analysis were used to construct prognostic nomograms. The receiver operator characteristic curve was used to compare the efficacy of prognostic nomograms and the traditional pathological stage.ResultsThere was a negative linear relationship between creatinine/cystatin C ratio and adverse PFS in CRC patients. Patients with low creatinine/cystatin C ratio had significantly lower PFS/OS than those with high creatinine/cystatin C ratio (PFS, 50.8% vs. 63.9%, p = 0.002; OS, 52.5% vs. 68.9%, p < 0.001). Multivariate analysis showed that low creatinine/cystatin C ratio was an independent risk factor for PFS (HR=1.286, 95%CI = 1.007–1.642, p=0.044) and OS (HR=1.410, 95%CI=1.087–1.829, p=0.010) of CRC patients. The creatinine/cystatin C ratio-based prognostic nomograms have good predictive performance, with a concordance index above 0.7, which can predict the 1–5-year prognosis.ConclusionCreatinine/cystatin C ratio may be an effective prognostic marker for predicting PFS and OS in CRC patients, aid in pathological staging, and along with tumour markers help in-depth prognostic stratification in CRC patients

Study of the B−→Λc+Λˉc−K−B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-} decay

The decay $B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-}$ is studied in proton-proton collisions at a center-of-mass energy of $\sqrt{s}=13$ TeV using data corresponding to an integrated luminosity of 5 $\mathrm{fb}^{-1}$ collected by the LHCb experiment. In the $\Lambda_{c}^+ K^{-}$ system, the $\Xi_{c}(2930)^{0}$ state observed at the BaBar and Belle experiments is resolved into two narrower states, $\Xi_{c}(2923)^{0}$ and $\Xi_{c}(2939)^{0}$, whose masses and widths are measured to be $$m(\Xi_{c}(2923)^{0}) = 2924.5 \pm 0.4 \pm 1.1 \,\mathrm{MeV}, \\ m(\Xi_{c}(2939)^{0}) = 2938.5 \pm 0.9 \pm 2.3 \,\mathrm{MeV}, \\ \Gamma(\Xi_{c}(2923)^{0}) = \phantom{000}4.8 \pm 0.9 \pm 1.5 \,\mathrm{MeV},\\ \Gamma(\Xi_{c}(2939)^{0}) = \phantom{00}11.0 \pm 1.9 \pm 7.5 \,\mathrm{MeV},$$ where the first uncertainties are statistical and the second systematic. The results are consistent with a previous LHCb measurement using a prompt $\Lambda_{c}^{+} K^{-}$ sample. Evidence of a new $\Xi_{c}(2880)^{0}$ state is found with a local significance of $3.8\,\sigma$, whose mass and width are measured to be $2881.8 \pm 3.1 \pm 8.5\,\mathrm{MeV}$ and $12.4 \pm 5.3 \pm 5.8 \,\mathrm{MeV}$, respectively. In addition, evidence of a new decay mode $\Xi_{c}(2790)^{0} \to \Lambda_{c}^{+} K^{-}$ is found with a significance of $3.7\,\sigma$. The relative branching fraction of $B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-}$ with respect to the $B^{-} \to D^{+} D^{-} K^{-}$ decay is measured to be $2.36 \pm 0.11 \pm 0.22 \pm 0.25$, where the first uncertainty is statistical, the second systematic and the third originates from the branching fractions of charm hadron decays.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-028.html (LHCb public pages

Multidifferential study of identified charged hadron distributions in ZZ-tagged jets in proton-proton collisions at s=\sqrt{s}=13 TeV

Jet fragmentation functions are measured for the first time in proton-proton collisions for charged pions, kaons, and protons within jets recoiling against a $Z$ boson. The charged-hadron distributions are studied longitudinally and transversely to the jet direction for jets with transverse momentum 20 $< p_{\textrm{T}} < 100$ GeV and in the pseudorapidity range $2.5 < \eta < 4$. The data sample was collected with the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 1.64 fb$^{-1}$. Triple differential distributions as a function of the hadron longitudinal momentum fraction, hadron transverse momentum, and jet transverse momentum are also measured for the first time. This helps constrain transverse-momentum-dependent fragmentation functions. Differences in the shapes and magnitudes of the measured distributions for the different hadron species provide insights into the hadronization process for jets predominantly initiated by light quarks.Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-013.html (LHCb public pages

MIL-CT: Multiple Instance Learning via a Cross-Scale Transformer for Enhanced Arterial Light Reflex Detection

One of the early manifestations of systemic atherosclerosis, which leads to blood circulation issues, is the enhanced arterial light reflex (EALR). Fundus images are commonly used for regular screening purposes to intervene and assess the severity of systemic atherosclerosis in a timely manner. However, there is a lack of automated methods that can meet the demands of large-scale population screening. Therefore, this study introduces a novel cross-scale transformer-based multi-instance learning method, named MIL-CT, for the detection of early arterial lesions (e.g., EALR) in fundus images. MIL-CT utilizes the cross-scale vision transformer to extract retinal features in a multi-granularity perceptual domain. It incorporates a multi-head cross-scale attention fusion module to enhance global perceptual capability and feature representation. By integrating information from different scales and minimizing information loss, the method significantly improves the performance of the EALR detection task. Furthermore, a multi-instance learning module is implemented to enable the model to better comprehend local details and features in fundus images, facilitating the classification of patch tokens related to retinal lesions. To effectively learn the features associated with retinal lesions, we utilize weights pre-trained on a large fundus image Kaggle dataset. Our validation and comparison experiments conducted on our collected EALR dataset demonstrate the effectiveness of the MIL-CT method in reducing generalization errors while maintaining efficient attention to retinal vascular details. Moreover, the method surpasses existing models in EALR detection, achieving an accuracy, precision, sensitivity, specificity, and F1 score of 97.62%, 97.63%, 97.05%, 96.48%, and 97.62%, respectively. These results exhibit the significant enhancement in diagnostic accuracy of fundus images brought about by the MIL-CT method. Thus, it holds potential for various applications, particularly in the early screening of cardiovascular diseases such as hypertension and atherosclerosis

CLRD: Collaborative Learning for Retinopathy Detection Using Fundus Images

Retinopathy, a prevalent disease causing visual impairment and sometimes blindness, affects many individuals in the population. Early detection and treatment of the disease can be facilitated by monitoring the retina using fundus imaging. Nonetheless, the limited availability of fundus images and the imbalanced datasets warrant the development of more precise and efficient algorithms to enhance diagnostic performance. This study presents a novel online knowledge distillation framework, called CLRD, which employs a collaborative learning approach for detecting retinopathy. By combining student models with varying scales and architectures, the CLRD framework extracts crucial pathological information from fundus images. The transfer of knowledge is accomplished by developing distortion information particular to fundus images, thereby enhancing model invariance. Our selection of student models includes the Transformer-based BEiT and the CNN-based ConvNeXt, which achieve accuracies of 98.77% and 96.88%, respectively. Furthermore, the proposed method has 5.69–23.13%, 5.37–23.73%, 5.74–23.17%, 11.24–45.21%, and 5.87–24.96% higher accuracy, precision, recall, specificity, and F1 score, respectively, compared to the advanced visual model. The results of our study indicate that the CLRD framework can effectively minimize generalization errors without compromising independent predictions made by student models, offering novel directions for further investigations into detecting retinopathy

The Origin, Function, Distribution, Quantification, and Research Advances of Extracellular DNA

In nature, DNA is ubiquitous, existing not only inside but also outside of the cells of organisms. Intracellular DNA (iDNA) plays an essential role in different stages of biological growth, and it is defined as the carrier of genetic information. In addition, extracellular DNA (eDNA) is not enclosed in living cells, accounting for a large proportion of total DNA in the environment. Both the lysis-dependent and lysis-independent pathways are involved in eDNA release, and the released DNA has diverse environmental functions. This review provides an insight into the origin as well as the multiple ecological functions of eDNA. Furthermore, the main research advancements of eDNA in the various ecological environments and the various model microorganisms are summarized. Furthermore, the major methods for eDNA extraction and quantification are evaluated

Table_1_Prognostic significance of sarcopenia diagnosed based on the anthropometric equation for progression-free survival and overall survival in patients with colorectal cancer.DOCX

BackgroundThe purpose of this study was to investigate the prognostic significance of sarcopenia diagnosed based on anthropometric equations for progression-free survival (PFS) and overall survival (OS) in patients with colorectal cancer (CRC).MethodsA total of 1,441 CRC patients who underwent surgical treatment between January 2012 and December 2016 were enrolled in this study. Sarcopenia was diagnosed according to validated anthropometric equations. The Kaplan–Meier method with the log-rank test was used to estimate the survival curve. Cox proportional hazards regression models with forward selection were used to evaluate risk factors affecting the prognosis of CRC patients. R package “survival” was used to build the prognostic nomograms to predict 1–5 years of PFS and OS in CRC patients. The concordance index (C-index) and calibration curve were used to evaluate the prognostic accuracy of the prognostic nomogram.ResultsTwo hundred and seventy-one patients (18.8%) were diagnosed with sarcopenia. Sarcopenia was significantly associated with advanced age, large tumor size, and high mortality. Compared with the non-sarcopenia patients, the PFS of sarcopenia patients was worse (5-year PFS, 48.34 vs. 58.80%, p = 0.003). Multivariate survival analysis showed that patients with sarcopenia had a higher risk (23.9%) of adverse PFS (HR, 1.239; 95%CI: 1.019–1.505, p = 0.031) than patients without sarcopenia. The OS of patients with sarcopenia was significantly worse than that of patients without sarcopenia (5-year OS: 50.92 vs. 61.62%, p = 0.001). In CRC patients, sarcopenia was independently associated with poor OS (HR: 1.273, 95%CI: 1.042–1.556, p ConclusionSarcopenia diagnosed based on anthropometric equations is an independent risk factor for PFS and OS in CRC patients.</p