Research advances in predicting the expansion of hypertensive intracerebral hemorrhage based on CT images: an overview.

Hematoma expansion (HE) is an important risk factor for death or poor prognosis in patients with hypertensive intracerebral hemorrhage (HICH). Accurately predicting the risk of HE in patients with HICH is of great clinical significance for timely intervention and improving patient prognosis. Many imaging signs reported in literatures showed the important clinical value for predicting HE. In recent years, the development of radiomics and artificial intelligence has provided new methods for HE prediction with high accuracy. Therefore, this article reviews the latest research progress in CT imaging, radiomics, and artificial intelligence of HE, in order to help identify high-risk patients for HE in clinical practice.

Automatic detection of cognitive impairment in patients with white matter hyperintensity and causal analysis of related factors using artificial intelligence of MRI.

PURPOSE: White matter hyperintensity (WMH) is a common feature of brain aging, often linked with cognitive decline and dementia. This study aimed to employ deep learning and radiomics to develop models for detecting cognitive impairment in WMH patients and to analyze the causal relationships among cognitive impairment and related factors. MATERIALS AND METHODS: A total of 79 WMH patients from hospital 1 were randomly divided into a training set (62 patients) and a testing set (17 patients). Additionally, 29 patients from hospital 2 were included as an independent testing set. All participants underwent formal neuropsychological assessments to determine cognitive status. Automated identification and segmentation of WMH were conducted using VB-net, with extraction of radiomics features from cortex, white matter, and nuclei. Four machine learning classifiers were trained on the training set and validated on the testing set to detect cognitive impairment. Model performances were evaluated and compared. Causal analyses were conducted among cortex, white matter, nuclei alterations, and cognitive impairment. RESULTS: Among the models, the logistic regression (LR) model based on white matter features demonstrated the highest performance, achieving an AUC of 0.819 in the external test dataset. Causal analyses indicated that age, education level, alterations in cortex, white matter, and nuclei were causal factors of cognitive impairment. CONCLUSION: The LR model based on white matter features exhibited high accuracy in detecting cognitive impairment in WMH patients. Furthermore, the possible causal relationships among alterations in cortex, white matter, nuclei, and cognitive impairment were elucidated.

Metagenomic Analyses Reveal Gut Microbial Profiles of Cnaphalocrocis medinalis Driven by the Infection of Baculovirus CnmeGV.

The composition of microbiota in the digestive tract gut is essential for insect physiology, homeostasis, and pathogen infection. Little is known about the interactions between microbiota load and oral infection with baculoviruses. CnmeGV is an obligative baculovirus to Cnaphalocrocis medinalis. We investigated the impact of CnmeGV infection on the structure of intestinal microbes of C. medinalis during the initial infection stage. The results revealed that the gut microbiota profiles were dynamically driven by pathogen infection of CnmeGV. The numbers of all the OTU counts were relatively higher at the early and later stages, while the microbial diversity significantly increased early but dropped sharply following the infection. The compositional abundance of domain bacteria Firmicutes developed substantially higher. The significantly enriched and depleted species can be divided into four groups at the species level. Fifteen of these species were ultimately predicted as the biomarkers of CnmeGV infection. CnmeGV infection induces significant enrichment of alterations in functional genes related to metabolism and the immune system, encompassing processes such as carbohydrate, amino acid, cofactor, and vitamin metabolism. Finally, the study may provide an in-depth analysis of the relationship between host microbiota, baculovirus infection, and pest control of C. medinalis.

Multilocular thymic cysts can be easily misdiagnosed as malignant tumor on computer tomography: A case report.

BACKGROUND: Multilocular thymic cyst (MTC) is a rare mediastinal lesion which is considered to occur in the process of acquired inflammation. It is usually characterized by well-defined cystic density and is filled with transparent liquid. CASE SUMMARY: We report on a 39-year-old male with a cystic-solid mass in the anterior mediastinum. Computer tomography (CT) imaging showed that the mass was irregular with unclear boundaries. After injection of contrast agent, there was a slight enhancement of stripes and nodules. According to CT findings, it was diagnosed as thymic cancer. CONCLUSION: After surgery, MTC accompanied by bleeding and infection was confirmed by pathological examination. The main lesson of this case was that malignant thymic tumor and MTC of the anterior mediastinum sometimes exhibit similar CT findings. Caution is necessary in clinical work to avoid misdiagnosis.

Predicting Hematoma Expansion and Prognosis in Cerebral Contusions: A Radiomics-Clinical Approach.

Hemorrhagic progression of contusion (HPC) often occurs early in cerebral contusions (CC) patients, significantly impacting their prognosis. It is vital to promptly assess HPC and predict outcomes for effective tailored interventions, thereby enhancing prognosis in CC patients. We utilized the Attention-3DUNet neural network to semi-automatically segment hematomas from computed tomography (CT) images of 452 CC patients, incorporating 695 hematomas. Subsequently, 1502 radiomic features were extracted from 358 hematomas in 261 patients. After a selection process, these features were used to calculate the radiomic signature (Radscore). The Radscore, along with clinical features such as medical history, physical examinations, laboratory results, and radiological findings, was employed to develop predictive models. For prognosis (discharge Glasgow Outcome Scale score), radiomic features of each hematoma were augmented and fused for correlation. We employed various machine learning methodologies to create both a combined model, integrating radiomics and clinical features, and a clinical-only model. Nomograms based on logistic regression were constructed to visually represent the predictive procedure, and external validation was performed on 170 patients from three additional centers. The results showed that for HPC, the combined model, incorporating hemoglobin levels, Rotterdam CT score of 3, multi-hematoma fuzzy sign, concurrent subdural hemorrhage, international normalized ratio, and Radscore, achieved area under the receiver operating characteristic curve (AUC) values of 0.848 and 0.836 in the test and external validation cohorts, respectively. The clinical model predicting prognosis, utilizing age, Abbreviated Injury Scale for the head, Glasgow Coma Scale Motor component, Glasgow Coma Scale Verbal component, albumin, and Radscore, attained AUC values of 0.846 and 0.803 in the test and external validation cohorts, respectively. Selected radiomic features indicated that irregularly shaped and highly heterogeneous hematomas increased the likelihood of HPC, while larger weighted axial lengths and lower densities of hematomas were associated with a higher risk of poor prognosis. Predictive models that combine radiomic and clinical features exhibit robust performance in forecasting HPC and the risk of poor prognosis in CC patients. Radiomic features complement clinical features in predicting HPC, although their ability to enhance the predictive accuracy of the clinical model for adverse prognosis is limited.

Semiparametric partial linear modeling of risk factors for ear infections: the Early Childhood Longitudinal Study.

The Early Childhood Longitudinal Study-Kindergarten Class of 2010-2011 (ECLS-K:2011) ascertained timing of ear infections within age specified intervals and parent's/caregiver's report of medically diagnosed hearing loss. In this nationally representative, school-based sample of children followed from kindergarten entry through fifth grade, academic performance in reading, mathematics, and science was assessed longitudinally. Prior investigations of this ECLS-K:2011 cohort showed that age has a non-linear, monotonically increasing functional relationship with academic performance. Because of this knowledge, a semiparametric partial linear model is proposed, in which the effect of age is modeled by an unknown monotonically increasing function along with other regression parameters. The parameters are estimated by a semiparametric maximum likelihood estimator. A test of a constant effect of age is also proposed. Simulation studies are conducted to evaluate the performance of the proposed method, as compared with the commonly used linear model; the former outperforms the latter based on several criteria. We then analyzed ECLS-K:2011 data to compare results of the partial linear parametric model estimation with that of classical linear regression models.

Automatic detection of mild cognitive impairment based on deep learning and radiomics of MR imaging.

Purpose: Early and rapid diagnosis of mild cognitive impairment (MCI) has important clinical value in improving the prognosis of Alzheimer's disease (AD). The hippocampus and parahippocampal gyrus play crucial roles in the occurrence of cognitive function decline. In this study, deep learning and radiomics techniques were used to automatically detect MCI from healthy controls (HCs). Method: This study included 115 MCI patients and 133 normal individuals with 3D-T1 weighted MR structural images from the ADNI database. The identification and segmentation of the hippocampus and parahippocampal gyrus were automatically performed with a VB-net, and radiomics features were extracted. Relief, Minimum Redundancy Maximum Correlation, Recursive Feature Elimination and the minimum absolute shrinkage and selection operator (LASSO) were used to reduce the dimensionality and select the optimal features. Five independent machine learning classifiers including Support Vector Machine (SVM), Random forest (RF), Logistic Regression (LR), Bagging Decision Tree (BDT), and Gaussian Process (GP) were trained on the training set, and validated on the testing set to detect the MCI. The Delong test was used to assess the performance of different models. Result: Our VB-net could automatically identify and segment the bilateral hippocampus and parahippocampal gyrus. After four steps of feature dimensionality reduction, the GP models based on combined features (11 features from the hippocampus, and 4 features from the parahippocampal gyrus) showed the best performance for the MCI and normal control subject discrimination. The AUC of the training set and test set were 0.954 (95% CI: 0.929-0.979) and 0.866 (95% CI: 0.757-0.976), respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high. Conclusion: The GP classifier based on 15 radiomics features of bilateral hippocampal and parahippocampal gyrus could detect MCI from normal controls with high accuracy based on conventional MR images. Our fully automatic model could rapidly process the MRI data and give results in 1 minute, which provided important clinical value in assisted diagnosis.

Characterization of a novel cell wall hydrolase CwlE involved in Bacillus thuringiensis subsp. israelensis mother cell lysis.

Cell wall hydrolases are ubiquitous among spore-form bacteria and essential for mother cell lysis. In this study, a novel cell wall hydrolase gene cwlE involved in mother cell lysis was characterized from Bacillus thuringiensis subsp. israelensis (Bti) strain Bt-59. cwlE was specifically expressed in Bti and located in the large plasmid carrying the insecticidal genes. The encoded CwlE protein consists of a MurNAc-LAA domain and two highly conserved catalytic residues (E26 and E151). The recombinant CwlE-His protein was able to digest the cell wall of Bti, indicating that CwlE is an N-acetylmuramoyl-L-alanine amidase. Transcriptional analysis indicated that cwlE began to express at the early stage of stationary phase and was controlled by SigE. Single mutation of cwlE gene delayed Bti mother cell lysis, while double mutation of cwlE and sigK completely blocked Bti mother cell lysis. After exposure to UV light to deactivate the crystal proteins, the level of decrease of insecticidal activity against mosquito larvae of Bt-59 (ΔcwlE-sigK) was less than that observed for Bt-59. This study elucidates the mechanism of Bti mother cell lysis and provides an effective strategy for mosquito control using Bt products with increased persistence.

CmHem, a hemolin-like gene identified from Cnaphalocrocis medinalis, involved in metamorphosis and baculovirus infection.

Background: As a member of the immunoglobulin superfamily, hemolins play a vital role in insect development and defense against pathogens. However, the innate immune response of hemolin to baculovirus infection varies among different insects. Methods and results: In this study, the hemolin-like gene from a Crambidae insect, Cnaphalocrocis medinalis, CmHem was cloned, and its role in insect development and baculovirus infection was analyzed. A 1,528 bp contig as potential hemolin-like gene of C. medinalis was reassembled from the transcriptome. Further, the complete hemolin sequence of C. medinalis (CmHem) was cloned and sequenced. The cDNA of CmHem was 1,515 bp in length and encoded 408 amino acids. The deduced amino acid of CmHem has relatively low identities (41.9-62.3%) to various insect hemolins. However, it contains four Ig domains similarity to other insect hemolins. The expression level of CmHem was the highest in eggs, followed by pupae and adults, and maintained a low expression level at larval stage. The synthesized siRNAs were injected into mature larvae, and the CmHem transcription decreased by 51.7%. Moreover, the abdominal somites of larvae became straightened, could not pupate normally, and then died. Infection with a baculovirus, C. medinalis granulovirus (CnmeGV), the expression levels of CmHem in the midgut and fat body of C. medinalis significantly increased at 12 and 24 h, respectively, and then soon returned to normal levels. Conclusions: Our results suggested that hemolin may be related to the metamorphosis of C. medinalis. Exposure to baculovirus induced the phased expression of hemolin gene in the midgut and fat body of C. medinalis, indicated that hemolin involved in the immune recognition of Crambidae insects to baculovirus.

Dual-energy Spectral CT Imaging of Primary Anorectal Malignant Melanoma: A Case Report.

BACKGROUND: Primary anorectal malignant melanoma (ARMM) is a rare tumor. It is often misdiagnosed as hemorrhoids, polyps or colorectal cancer due to the lack of specificity of their clinical symptoms and imaging manifestations. CASE PRESENTATION: In this study, we reported an 83-year-old female patient with ARMM. Computed tomography (CT) and Magnetic Resonance Imaging (MRI) showed uneven thickening of the intestinal wall about 7.0 cm from the anal margin, and no typical T1 high signal was seen on MRI. Dual-energy spectral CT showed that the effective atomic number (Zeff) of the tumor and the iodine concentration in the arterial phase (AP) and venous phase (VP) were different from other rectal malignancies reported in the previous literature. Sigmoidoscopy showed a large polypoid mass approximately 7.0 cm from the anal verge. Immunohistochemical staining showed that about 60% of Melan A and HMB-45 were positive, S-100 protein and Ki-67 were positive, and the pathological diagnosis was ARMM. CONCLUSION: This was the first dual-energy spectral CT imaging report of ARMM. The Zeff and iodine concentration in the arterial phase and venous phase could help distinguish between ARMM and other rectal malignancies.

Sedimentary Organic Facies Division and Hydrocarbon-Generation Potential of Source Rocks in Coal-Bearing Strata-A Case Study of the Upper Paleozoic in Huanghua Depression, Bohai Basin, China.

Sedimentary organic facies cover the formation, evolution, and spatial distribution characteristics of organic matter, and they are effective tools for oil and gas resource evaluation and basin prospect prediction. According to the basic organic rock composition of the sedimentary organic facies, combined with the sedimentary facies and organic matter geochemical characteristics of Carboniferous-Permian strata, the characteristics of organic facies and hydrocarbon-generation potential of Upper Paleozoic source rocks in Huanghua Depression are being discussed. The results show that source rocks of Taiyuan and Shanxi Formations in the study area were oil-prone, and the oil-generation potential of mudstone is greater than that of carbonaceous mudstone and coal. The organic facies in the study area can be divided into six types: (1) terrestrial forest organic facies; (2) shallow swamp forest organic facies; (3) deep swamp forest organic facies; (4) deep swamp reed organic facies; (5) flowing water swamp organic facies; and (6) open water organic facies. The Taiyuan Formation is mainly composed of flowing water swamp, deep swamp forest, and shallow swamp forest with a strong hydrocarbon-generation capacity, while the Shanxi Formation chiefly includes organic facies of the deep swamp forest and shallow swamp forest. The deep swamp reed sedimentary organic facies had the highest hydrocarbon-generation potential, while the terrestrial forest sedimentary organic facies had the worst hydrocarbon-generation potential. Coal had a certain oil-generating capacity but was weaker than that of mudstone. Compared with mudstone, coal had a stronger gas-generating capacity.

Early low blood MALT1 expression levels forecast better efficacy of PD­1 inhibitor­based treatment in patients with metastatic colorectal cancer.

Mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1) modulates colorectal cancer (CRC) malignant behaviors and tumor immune escape. The present study aimed to explore the association of MALT1 with treatment response and survival time among patients with metastatic CRC (mCRC) after programmed cell death protein-1 (PD-1) inhibitor-based treatment. MALT1 from the blood samples of 75 patients with unresectable mCRC receiving PD-1 inhibitor-based treatment at baseline and after 2-cycle treatment, as well as 20 healthy controls (HCs), was detected by reverse transcription-quantitative PCR. In the patients with mCRC, the objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS) and overall survival (OS) were calculated. MALT1 expression was elevated in patients with mCRC compared with that in HCs (P<0.001). In patients with mCRC, MALT1 expression was positively correlated with multiple (vs. single) metastasis (P=0.032) and peritoneum metastasis (P=0.029). MALT1 levels before treatment were decreased in ORR patients vs. non-ORR patients (P=0.043) and in DCR patients vs. non-DCR patients (P=0.007). Additionally, MALT1 expression was reduced after treatment compared with that before treatment (P<0.001). Meanwhile, MALT1 expression after treatment was notably decreased in ORR patients vs. non-ORR patients (P<0.001) and in DCR patients vs. non-DCR patients (P<0.001). Furthermore, a low MALT1 level before treatment was associated with longer PFS (P=0.030) and OS (P=0.025) times. Decreased MALT1 expression after treatment and a decline in MALT1 expression of >30% after treatment (ratio to MALT1 before treatment) (both P≤0.001) presented more significant associations with prolonged PFS and OS times. In conclusion, early low levels of blood MALT1 during therapy may predict an improved response to PD-1 inhibitor-based treatment and survival time in patients with mCRC.

Prediction of Ki-67 expression in gastrointestinal stromal tumors using radiomics of plain and multiphase contrast-enhanced CT.

OBJECTIVE: To study the value of radiomics models based on plain and multiphase contrast-enhanced CT to predict Ki-67 expression in gastrointestinal stromal tumors (GISTs). METHODS: A total of 215 patients with GISTs were retrospectively analyzed, including 150 patients in one hospital as the training set and 65 patients in another hospital as the external verification set. The tumor at the largest level of CT images was delineated as the region of interest (ROI). The maximum diameter of the ROI was defined as the tumor size. A total of 851 radiomics features were extracted from each ROI by 3D Slicer Radiomics. After dimensionality reduction, three machine learning classification algorithms including logistic regression (LR), random forest (RF), and support vector machine (SVM) were used for Ki-67 expression prediction. Using a multivariable logistic model, a nomogram was established to predict the expression of Ki-67 individually. RESULTS: Delong tests showed that the SVM models had the highest accuracy in the arterial phase (Z value 0.217-1.139) and venous phase (Z value 0.022-1.396). For the plain phase, LR and SVM models had the highest accuracy (Z value 0.874-1.824, 1.139-1.763). For the delayed phase, LR models had the highest accuracy (Z value 0.056-1.824). For the combined phase, RF models had the highest accuracy (Z value 0.232-1.978). There was no significant difference among the above models for KI-67 expression prediction (Z value 0.022-1.978). A nomogram was developed with a C-index of 0.913 (95% CI, 0.878 to 0.956). CONCLUSIONS: Radiomics of both plain and enhanced CT images could accurately predict the expression of Ki-67 in GIST. For patients who were not suitable to use contrast agents, plain scan could be used as an alternative. CLINICAL RELEVANCE STATEMENT: CT radiomics could accurately predict the expression of Ki-67 in GIST, which has a great clinical value in reflecting the proliferative activity of tumor cells and helping determine whether a patient is suitable for adjuvant therapy with imatinib. KEY POINTS: • Radiomics of both plain and enhanced CT images could accurately predict the expression of Ki-67 in GIST. • For patients who were not suitable to use contrast agents, plain scan could be used as an alternative. • A radiomics nomogram was developed to allow personalized preoperative evaluation with high accuracy.

Automatic differentiation of ruptured and unruptured intracranial aneurysms on computed tomography angiography based on deep learning and radiomics.

OBJECTIVES: Rupture of intracranial aneurysm is very dangerous, often leading to death and disability. In this study, deep learning and radiomics techniques were used to automatically detect and differentiate ruptured and unruptured intracranial aneurysms. MATERIALS AND METHODS: 363 ruptured aneurysms and 535 unruptured aneurysms from Hospital 1 were included in the training set. 63 ruptured aneurysms and 190 unruptured aneurysms from Hospital 2 were used for independent external testing. Aneurysm detection, segmentation and morphological features extraction were automatically performed with a 3-dimensional convolutional neural network (CNN). Radiomic features were additionally computed via pyradiomics package. After dimensionality reduction, three classification models including support vector machines (SVM), random forests (RF), and multi-layer perceptron (MLP) were established and evaluated via area under the curve (AUC) of receiver operating characteristics. Delong tests were used for the comparison of different models. RESULTS: The 3-dimensional CNN automatically detected, segmented aneurysms and calculated 21 morphological features for each aneurysm. The pyradiomics provided 14 radiomics features. After dimensionality reduction, 13 features were found associated with aneurysm rupture. The AUCs of SVM, RF and MLP on the training dataset and external testing dataset were 0.86, 0.85, 0.90 and 0.85, 0.88, 0.86, respectively, for the discrimination of ruptured and unruptured intracranial aneurysms. Delong tests showed that there was no significant difference among the three models. CONCLUSIONS: In this study, three classification models were established to distinguish ruptured and unruptured aneurysms accurately. The aneurysms segmentation and morphological measurements were performed automatically, which greatly improved the clinical efficiency. CLINICAL RELEVANCE STATEMENT: Our fully automatic models could rapidly process the CTA data and evaluate the status of aneurysms in one minute.

Progressive alteration of dynamic functional connectivity patterns in subcortical ischemic vascular cognitive impairment patients.

Alterations in the temporal evolution of brain states in the process of cognitive impairment aggravation due to subcortical ischemic vascular disease (SIVD) is not understood. The dynamic functional connectivity was investigated to identify the abnormal temporal properties of brain states associated with cognitive impairment caused by SIVD. Eighteen patients with subcortical ischemic vascular cognitive impairment with no dementia (SIVCIND), 19 dementia patients (SIVaD) and 26 normal controls were enrolled. We found that the occupancy rate and mean lifetime of brain states were associated with cognitive performance. SIVCIND had a higher occupancy rate and longer mean lifetime in weakly connected states than normal controls. SIVaD had similar but more extensive changes in the temporal properties of brain states. In addition, switching from weakly connected states to more strongly connected states was more difficult in SIVCIND and SIVaD patients than in normal controls, especially in SIVaD patients. The results revealed that not only the transition to but also maintenance in strongly connected states became increasingly difficult when SIVD-related cognitive impairment progressed into a more severe stage.

Automatic diagnosis of Parkinson's disease using artificial intelligence base on routine T1-weighted MRI.

Background: Parkinson's disease (PD) is the second most common neurodegenerative disease. An objective diagnosis method is urgently needed in clinical practice. In this study, deep learning and radiomics techniques were studied to automatically diagnose PD from healthy controls (HCs). Methods: 155 PD patients and 154 HCs were randomly divided into a training set (246 patients) and a testing set (63 patients). The brain subregions identification and segmentation were automatically performed with a VB-net, and radiomics features of billateral thalamus, caudatum, putamen and pallidum were extracted. Five independent machine learning classifiers [Support Vector Machine (SVM), Stochastic gradient descent (SGD), random forest (RF), quadratic discriminant analysis (QDA) and decision tree (DT)] were trained on the training set, and validated on the testing. Delong test was used to compare the performance of different models. Results: Our VB-net could automatically identify and segment the brain into 109 regions. 2,264 radiomics features were automatically extracted from the billateral thalamus, caudatum, putamen or pallidum of each patient. After four step of features dimensionality reduction, Delong tests showed that the SVM model based on combined features had the best performance, with AUCs of 0.988 (95% CI: 0.979 ~ 0.998, specificity = 91.1%, sensitivity =100%, accuracy = 89.4% and precision = 88.2%) and 0.976 (95% CI: 0.942 ~ 1.000, specificity = 100%, sensitivity = 87.1%, accuracy = 93.5% and precision = 88.6%) in the training set and testing set, respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high. Conclusion: The SVM model based on combined features could be used to diagnose PD with high accuracy. Our fully automatic model could rapidly process the MRI data and distinguish PD and HCs in one minute. It greatly improved the diagnostic efficiency and has a great potential value in clinical practice to help the early diagnosis of PD.

Efficacy and safety of camrelizumab plus apatinib compared to apatinib monotherapy as third-line or above therapy for metastatic colorectal cancer patients: A retrospective cohort study.

WHAT IS KNOWN AND OBJECTIVE: Programmed cell death protein-1 (PD-1) inhibitors synergize apatinib for anti-tumour effect by regulating tumour microenvironment, vascular endothelial growth factor, hypoxia condition, immune response, etc. This study aimed to investigate the treatment efficacy and safety of camrelizumab (PD-1 inhibitor) plus apatinib as third-line or above therapy in metastatic colorectal cancer (mCRC) patients. METHODS: Totally, 64 unresectable mCRC patients receiving camrelizumab plus apatinib (N = 31) and apatinib (N = 33) were retrospectively enrolled. RESULTS: Disease control rate (80.6% vs. 57.6%) (P = 0.047) was elevated in camrelizumab plus apatinib group compared to apatinib group; however, objective response rate (22.6% vs. 6.1%) (P = 0.078) only showed an increasing trend but did not achieve statistical significance. Besides, the median (95% confidence interval [CI]) progressive-free survival (PFS) and overall survival (OS) were 6.9 (3.7-10.1) and 11.5 (7.7-15.3) months in camrelizumab plus apatinib group; meanwhile, the median (95% CI) PFS and OS were 3.6 (1.7-5.5) and 6.7 (5.0-8.4) months in the apatinib group. Additionally, PFS (P = 0.017) and OS (P = 0.006) were prolonged in camrelizumab plus apatinib group compared with apatinib group, which was confirmed by further multivariate Cox's proportional hazards regression analysis (hazard ratio [HR] = 0.340, P < 0.001 for PFS; HR = 0.271, P < 0.001 for OS). The incidence of total, grade 1-2, and grade 3-4 adverse events did not differ between groups (all P > 0.05). CONCLUSION: Camrelizumab (PD-1 inhibitor) plus apatinib achieves a better treatment efficacy than apatinib as third-line or above therapy with a good safety profile in mCRC patients.

A Missense Variant in COMT Associated with Hearing Loss among Young Adults: The National Longitudinal Study of Adolescent to Adult Health (Add Health).

Hearing loss is a major public problem with a heritability of up to 70%. Catechol-O-methyltransferase (COMT) encodes an enzyme that is highly expressed in sensory hair cells of the inner ear. The association between COMT and hearing loss has not been reported previously in nationally representative population-based studies. A regression linear model was used to estimate associations between the allele/genotype of COMT and self-reported hearing loss based on 13,403 individuals from Wave IV of the Add Health study, a nationally representative sample of multiethnic U.S. young adults. The inverse variance-weighted effect magnitude was estimated using a genetic meta-analysis model. The "A" allele frequency of rs6480 (a missense variant in COMT) was 0.44. The prevalence of hearing loss was 7.9% for individuals with the "A" allele and 6.5% for those with the "G" allele. The "A" allele was significantly associated with increased hearing loss (p = 0.01). The prevalence of hearing loss was 6.0%, 7.2%, and 8.7% for individuals with GG, AG, and AA genotypes, respectively, which was consistent with a genetic additive model. The genotypic association model showed that rs4680 was significantly associated with increased hearing loss (p = 0.006). A missense variant of rs4680 in COMT was significantly associated with increased hearing loss among young adults in a multi-racial/ethnic U.S. population-based cohort.

Enhanced cleaner flotation behavior of non-metallic particles in waste printed circuit boards: From the perspective of particle size.

Flotation is an attractive method for separating the different components of waste printed circuit boards (WPCBs) due to its cleanliness and efficiency. Non-metallic particles (NMPs) with good floatability usually need to be floated, however, it is difficult to achieve complete removal. The effect of particle size on the flotation behavior of NMPs, which is usually ignored in previous studies, is concerned in this paper. Flotation tests and kinetic analysis were carried out to reveal the effect of reagent dosage on flotation characteristics of particles in narrow size fractions. As the fineness decreases, the particles are more likely to be floated. Equally, the finer the particle size, the lower the reagent dosage required to achieve the maximum recovery. For 1-0.5 mm and -0.045 mm, the maximum recovery increased from 42.16% (1500 g/t MIBC) to 97.31% (100 g/t MIBC). Therefore, the feasibility of reducing particle size by grinding to improve floatability was verified. The results show that the reduction of particle size can significantly promote its efficiency of being floated. After grinding treatment, -0.045 mm yields in each size fraction (1-0.5, 0.5-0.25, 0.25-0.125, 0.125-0.074, 0.074-0.045 mm) increased by 22.10%, 28.42%, 30.90%, 64.56%, 89.32%, resulting in an increase of 37.71%, 13.12%, 2.82%, 7.82% and 2.00% in maximum recovery, respectively. It is also proved that the particle size, rather than the resin content, has a more significant effect on the floatability of NMPs.