Design of Deep-Ultraviolet Zero-Order Waveplate Materials by Rational Assembly of [AlO2F4] and [SO4] Groups.

Zero-order waveplates are widely used in the manufacture of laser polarizer waves, which are important in polarimetry and the laser industry. However, there are still challenges in designing deep-ultraviolet (DUV) waveplate materials that satisfy large band gaps and small optical anisotropy simultaneously. Herein, three cases of aluminum sulfate fluorides: Na2AlSO4F3, Li4NH4Al(SO4)2F4, and Li6K3Al(SO4)4F4, with novel [AlSO4F3] layers or isolated [AlS2O8F4] trimers were designed and synthesized by the rational assembly of [AlO2F4] and [SO4] groups through a hydrothermal method. Experiments and theoretical calculations imply that these three possess short cutoff edges (λ < 200 nm) and small birefringence (0.0014-0.0076 @ 1064 nm), which fulfils the prerequisite for potential DUV zero-order waveplate materials. This work extends the exploration of DUV zero-order waveplate materials to the aluminum sulfate fluoride systems.

Gut microbiota impacts bone via Bacteroides vulgatus-valeric acid-related pathways.

Although the gut microbiota has been reported to influence osteoporosis risk, the individual species involved, and underlying mechanisms, remain largely unknown. We performed integrative analyses in a Chinese cohort of peri-/post-menopausal women with metagenomics/targeted metabolomics/whole-genome sequencing to identify novel microbiome-related biomarkers for bone health. Bacteroides vulgatus was found to be negatively associated with bone mineral density (BMD), which was validated in US white people. Serum valeric acid (VA), a microbiota derived metabolite, was positively associated with BMD and causally downregulated by B. vulgatus. Ovariectomized mice fed B. vulgatus demonstrated increased bone resorption and poorer bone micro-structure, while those fed VA demonstrated reduced bone resorption and better bone micro-structure. VA suppressed RELA protein production (pro-inflammatory), and enhanced IL10 mRNA expression (anti-inflammatory), leading to suppressed maturation of osteoclast-like cells and enhanced maturation of osteoblasts in vitro. The findings suggest that B. vulgatus and VA may represent promising targets for osteoporosis prevention/treatment.

Favorable impact of a novel endoscopic auxiliary system (NEAS) on fluoroscopy-guided lithotripsy for difficult bile duct stones: A pilot study.

OBJECTIVES: Laser lithotripsy under fluoroscopic guidance is difficult to perform and risky due to its invisibility. In this study we aimed to investigate the efficacy and safety of a novel endoscopic auxiliary system (NEAS)-assisted lithotripsy under fluoroscopy for treating difficult common bile duct (CBD) stones. METHODS: Patients with difficult CBD stones who were treated with NEAS-assisted laser lithotripsy (NEAS group) or conventional mechanical lithotripsy (ML) under fluoroscopy (ML group) were retrospectively evaluated. The primary outcome was the complete stone clearance rate and the secondary outcomes included operation time, complications, and medical cost. RESULTS: Seventeen patients were treated with NEAS-assisted laser lithotripsy and 144 patients underwent ML. Using the propensity score matching analysis, 17 pairs of cases treated with NEAS-assisted lithotripsy and ML were included. Patients in the NEAS group showed a higher stone clearance rate than the ML group (94.1% vs 58.8%, P = 0.039), as well as shorter operation time (41.9 min vs 49.4 min, P < 0.001) and lower medical cost (USD 4607 vs USD 5014, P < 0.001). There was no significant difference in the complication rate between the two groups (5.9% vs 17.6%, P = 0.601). CONCLUSION: NEAS-assisted fluoroscopy-guided laser lithotripsy is feasible and safe, which may be a promising technique in fluoroscopy-guided laser lithotripsy for difficult CBD stones.

First Principles Study of the Photoelectric Properties of Alkaline Earth Metal (Be/Mg/Ca/Sr/Ba)-Doped Monolayers of MoS2.

The energy band structure, density of states, and optical properties of monolayers of MoS2 doped with alkaline earth metals (Be/Mg/Ca/Sr/Ba) are systematically studied based on first principles. The results indicate that all the doped systems have a great potential to be formed and structurally stable. In comparison to monolayer MoS2, doping alkaline earth metals results in lattice distortions in the doped system. Therefore, the recombination of photogenerated hole-electron pairs is suppressed effectively. Simultaneously, the introduction of dopants reduces the band gap of the systems while creating impurity levels. Hence, the likelihood of electron transfer from the valence to the conduction band is enhanced, which means a reduction in the energy required for such a transfer. Moreover, doping monolayer MoS2 with alkaline earth metals increases the static dielectric constant and enhances its polarizability. Notably, the Sr-MoS2 system exhibits the highest value of static permittivity, demonstrating the strongest polarization capability. The doped systems exhibit a red-shifted absorption spectrum in the low-energy region. Consequently, the Be/Mg/Ca-MoS2 systems demonstrate superior visible absorption properties and a favorable band gap, indicating their potential as photo-catalysts for water splitting.

The design of highly conductive and stretchable polymer conductors with low-load nanoparticles.

Highly conductive and stretchable polymer conductors fabricated from conductive fillers and stretchable polymers are urgently needed in flexible electronics, implants, soft robotics, etc. However, polymer conductors encounter the conductivity-stretchability dilemma, in which high-load fillers needed for high conductivity always result in the stiffness of materials. Herein, we propose a new design of highly conductive and stretchable polymer conductors with low-load nanoparticles (NPs). The design is achieved by the self-assembly of surface-modified NPs to efficiently form robust conductive pathways. We employ computer simulations to elucidate the self-assembly of the NPs in the polymer matrices under equilibrium and tensile states. The conductive pathways retain 100% percolation probability even though the loading of the NPs is lowered to ∼2% volume. When the tensile strain reaches 400%, the percolation probability of the ∼2% NP system is still greater than 25%. The theoretical prediction suggests a way for advancing flexible conductive materials.

Comparison of different noncontrast computed tomographic markers for predicting early perihematomal edema expansion in patients with intracerebral hemorrhage.

OBJECTIVES: Noncontrast computed tomography (NCCT) imaging markers are associated with early perihematomal edema (PHE) growth. The aim of this study was to compare the predictive value of different NCCT markers in predicting early PHE expansion. METHODS: ICH patients who underwent baseline CT scan within 6 h of symptoms onset and follow-up CT scan within 36 h between July 2011 and March 2017 were included in this study. The predictive value of hypodensity, satellite sign, heterogeneous density, irregular shape, blend sign, black hole sign, island sign and expansion-prone hematoma for early perihematomal edema expansion were assessed, separately. RESULTS: 214 patients were included in our final analysis. After adjusting for ICH characteristics, hypodensity, blend sign, island sign and expansion-prone hematoma are still predictors of early perihematomal edema expansion in multivariable logistics regression analysis (all P < 0.05). The area under the receiver operating characteristic (ROC) curve of expansion-prone hematoma was significantly larger than the area under the ROC curve of hypodensity, blend sign and island sign in predicting PHE expansion (P = 0.003, P < 0.001 and P = 0.002, respectively). CONCLUSION: Compared with single NCCT imaging markers, expansion-prone hematoma seems to be optimal predictor for early PHE expansion than any single NCCT imaging marker.

Prediction of Early Perihematomal Edema Expansion Based on Noncontrast Computed Tomography Radiomics and Machine Learning in Intracerebral Hemorrhage.

OBJECTIVES: To investigate the predictive value of noncontrast computed tomography (NCCT) models based on radiomics features and machine learning for early perihematomal edema (PHE) expansion in patients with spontaneous intracerebral hemorrhage (ICH). METHODS: We retrospectively reviewed NCCT data from 214 patients with spontaneous ICH. All radiomics features were extracted from volume of interest of hematomas on admission scans. A total of 8 machine learning methods were applied for constructing models in the training and the test set. Receiver operating characteristic analysis and the areas under the curve were used to evaluate the predictive value. RESULTS: A total of 23 features were finally selected to establish models of early PHE expansion after feature screening. Patients were randomly assigned into training (n = 171) and test (n = 43) sets. The accuracy, sensitivity, and specificity in the test set were 72.1%, 90.0%, and 66.7% for the support vector machine model; 79.1%, 70.0%, and 84.4% for the k-nearest neighbor model; 88.4%, 90.0%, and 87.9% for the logistic regression model; 74.4%, 90.0%, and 69.7% for the extra tree model; 74.4%, 90.0%, and 69.7% for the extreme gradient boosting model; 83.7%, 100%, and 78.8% for the multilayer perceptron (MLP) model; 72.1%, 100%, and 65.6% for the light gradient boosting machine model; and 60.5%, 90.0%, and 53.1% for the random forest model, respectively. CONCLUSIONS: The MLP model seemed to be the best model for prediction of PHE expansion in patients with ICH. NCCT models based on radiomics features and machine learning could predict early PHE expansion and improve the discrimination of identify spontaneous intracerebral hemorrhage patients at risk of early PHE expansion.

A combined non-enhanced CT radiomics and clinical variable machine learning model for differentiating benign and malignant sub-centimeter pulmonary solid nodules.

BACKGROUND: Radiomics has been used to predict pulmonary nodule (PN) malignancy. However, most of the studies focused on pulmonary ground-glass nodules. The use of computed tomography (CT) radiomics in pulmonary solid nodules, particularly sub-centimeter solid nodules, is rare. PURPOSE: This study aims to develop a radiomics model based on non-enhanced CT images that can distinguish between benign and malignant sub-centimeter pulmonary solid nodules (SPSNs, <1 cm). METHODS: The clinical and CT data of 180 SPSNs confirmed by pathology were analyzed retrospectively. All SPSNs were divided into two groups: training set (n = 144) and testing set (n = 36). From non-enhanced chest CT images, over 1000 radiomics features were extracted. Radiomics feature selection was performed using the analysis of variance and principal component analysis. The selected radiomics features were fed into a support vector machine (SVM) to develop a radiomics model. The clinical and CT characteristics were used to develop a clinical model. Associating non-enhanced CT radiomics features with clinical factors were used to develop a combined model using SVM. The performance was evaluated using the area under the receiver-operating characteristic curve (AUC). RESULTS: The radiomics model performed well in distinguishing between benign and malignant SPSNs, with an AUC of 0.913 (95% confidence interval [CI], 0.862-0.954) in the training set and an AUC of 0.877 (95% CI, 0.817-0.924) in the testing set. The combined model outperformed the clinical and radiomics models with an AUC of 0.940 (95% CI, 0.906-0.969) in the training set and an AUC of 0.903 (95% CI, 0.857-0.944) in the testing set. CONCLUSIONS: Radiomics features based on non-enhanced CT images can be used to differentiate SPSNs. The combined model, which included radiomics and clinical factors, had the best discrimination power between benign and malignant SPSNs.

Orthodontic and Orthognathic Treatment Combined withSurgically Assisted Rapid Maxillary Expansion in an Adult Patient with a Hyperdivergent Skeletal Pattern.

In this case study, we report the successful treatment of a 35-year-old woman with a hyperdivergent skeletal pattern, open bite and severe transverse deficiency, exhibiting a skeletal Class III malocclusion. The treatment plan included 3D correction of these issues with surgically assisted rapid maxillary expansion (SARME) to solve the transverse deficiency, presurgical orthodontic treatment including aligning and levelling of the teeth in both arches, LeFort I osteotomy and bilateral sagittal split ramus osteotomy, and postsurgical correction of malocclusion. Orthodontic treatment was performed with labial brackets, and the patient achieved satisfactory occlusion and a significantly improved facial profile. Retention at the 1-year follow-up showed stable occlusion and arch forms with a harmonious facial profile.

Stratification of malignant renal neoplasms from cystic renal lesions using deep learning and radiomics features based on a stacking ensemble CT machine learning algorithm.

Using nephrographic phase CT images combined with pathology diagnosis, we aim to develop and validate a fusion feature-based stacking ensemble machine learning model to distinguish malignant renal neoplasms from cystic renal lesions (CRLs). This retrospective research includes 166 individuals with CRLs for model training and 47 individuals with CRLs in another institution for model testing. Histopathology results are adopted as diagnosis criterion. Nephrographic phase CT scans are selected to build the fusion feature-based machine learning algorithms. The pretrained 3D-ResNet50 CNN model and radiomics methods are selected to extract deep features and radiomics features, respectively. Fivefold cross-validated least absolute shrinkage and selection operator (LASSO) regression methods are adopted to identify the most discriminative candidate features in the development cohort. Intraclass correlation coefficients and interclass correlation coefficients are employed to evaluate feature's reproducibility. Pearson correlation coefficients for normal distribution features and Spearman's rank correlation coefficients for non-normal distribution features are used to eliminate redundant features. After that, stacking ensemble machine learning models are developed in the training cohort. The area under the receiver operator characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) are adopted in the testing cohort to evaluate the performance of each model. The stacking ensemble machine learning algorithm reached excellent diagnostic performance in the testing dataset. The calibration plot shows good stability when using the stacking ensemble model. Net benefits presented by DCA are higher than the Bosniak 2019 version classification when employing any machine learning algorithm. The fusion feature-based machine learning algorithm accurately distinguishes malignant renal neoplasms from CRLs, which outperformed the Bosniak 2019 version classification, and proves to be more applicable for clinical decision-making.

The nitrate uptake and growth of wheat were more inhibited under single-layer graphene oxide stress compared to multi-layer graphene oxide.

Although the phytotoxicity of graphene-based materials has been investigated extensively, the effects of different graphene-based materials on nutrient uptake in plants remain unclear. Here, we analyzed the differences in phytotoxicity between single-layer graphene oxide (sGO) and multi-layer graphene oxide (mGO) by analyzing the growth status and nitrate (NO3-) accumulation in wheat plants at 0, 100, 200, 400, and 800 mg L-1 graphene oxide supply. Both sGO and mGO displayed concentration-dependent inhibitory effects on biomass, root length, number of lateral roots, and nitrogen (N) nutrient status. Treatment with 400 mg L-1 sGO caused 0.9-, 1.3-, and 1-fold higher reductions in NO3--N, assimilated N, and total N concentrations in roots, respectively, than mGO treatment. Analysis of root oxidative stress and in situ NO3- uptake revealed that sGO caused more significant damage to the root tip and a lower NO3- net influx rate than mGO. In addition, the expression of NO3- transporter (NRT) genes in roots, including NRT1.5, NRT2.1, NRT2.2, NRT2.3, and NRT2.4, under sGO treatment were lower than those under mGO treatment. Overall, sGO treatment induced a more severe inhibitory effect on root growth and NO3- uptake and accumulation than mGO treatment, accompanied by significant suppression of the expression of NRTs in sGO-treated roots. This study provides a physiological and molecular basis for studying the phytotoxic effects of various sizes of graphene oxide.

3D island sign on computed tomography predicts early perihematomal edema expansion and poor outcome in patients with intracerebral hemorrhage.

BACKGROUND AND PURPOSE: To determine the prognostics significance of the computed tomography (CT) 3D island sign for predicting early perihematomal edema (PHE) expansion and poor functional outcome in patients presenting with intracerebral hemorrhage (ICH). METHODS: Between July 2011 and March 2017, patients with intracerebral hemorrhage who had undergone baseline CT within 6 h after ICH symptom onsets and follow-up CT in our hospital were included. Two different readers independently assessed the presence of 3D island sign on admission CT scan of each patient. Multivariable logistic regression analysis was used to analyze association between 3D island sign and early perihematomal edema expansion and poor functional outcome, separately. RESULTS: A total of 214 patients who met the inclusion criteria were included in our study, 3D island sign was observed in 60 patients (28.0 %) on admission CT scan. The multivariate logistic regression analysis demonstrated that baseline hematoma volume, time to baseline and follow-up CT scans and the presence of 3D island sign were predictors of early PHE expansion. After adjusting for age, baseline hematoma and edema volume, time to baseline and follow-up CT scans, GCS on admission, presence of intraventricular hemorrhage (IVH) and systolic blood pressure, the 3D island sign was an independently imaging marker for poor outcome (OR, 2.803; 95 % confidence interval, 1.189-6.609; P = 0.018). CONCLUSION: The 3D island sign in patients with intracerebral hemorrhage was a reliable predictor for early perihematomal edema expansion and poor functional outcome. It may serve as a potential therapeutic target for intervention.

Comparison of Different Machine Models Based on Multi-Phase Computed Tomography Radiomic Analysis to Differentiate Parotid Basal Cell Adenoma From Pleomorphic Adenoma.

Objective: This study explored the value of different radiomic models based on multiphase computed tomography in differentiating parotid pleomorphic adenoma (PA) and basal cell tumor (BCA) concerning the predominant phase and the optimal radiomic model. Methods: This study enrolled 173 patients with pathologically confirmed parotid tumors (training cohort: n=121; testing cohort: n=52). Radiomic features were extracted from the nonenhanced, arterial, venous, and delayed phases CT images. After dimensionality reduction and screening, logistic regression (LR), K-nearest neighbor (KNN) and support vector machine (SVM) were applied to develop radiomic models. The optimal radiomic model was selected by using ROC curve analysis. Univariate and multivariable logistic regression was performed to analyze clinical-radiological characteristics and to identify variables for developing a clinical model. A combined model was constructed by integrating clinical and radiomic features. Model performances were assessed by ROC curve analysis. Results: A total of 1036 radiomic features were extracted from each phase of CT images. Sixteen radiomic features were considered valuable by dimensionality reduction and screening. Among radiomic models, the SVM model of the arterial and delayed phases showed superior predictive efficiency and robustness (AUC, training cohort: 0.822, 0.838; testing cohort: 0.752, 0.751). The discriminatory capability of the combined model was the best (AUC, training cohort: 0.885; testing cohort: 0.834). Conclusions: The diagnostic performance of the arterial and delayed phases contributed more than other phases. However, the combined model demonstrated excellent ability to distinguish BCA from PA, which may provide a non-invasive and efficient method for clinical decision-making.

The degradation of allyl isothiocyanate and its impact on methane production from anaerobic co-digestion of kitchen waste and waste activated sludge.

Producing methane from anaerobic co-digestion of kitchen waste and waste activated sludge has been widely implemented in real-world situations. However, the fate and impact of allyl isothiocyanate (AITC), a main active component in cruciferous vegetables, in the anaerobic co-digestion has never been documented. This study therefore aims to provide such supports. Experimental results exhibited that AITC was degraded completely by microorganisms and served as a substrate to produce methane. As AITC increased from 0 to 60 mg/L, the maximum methane production decreased from 285.1 to 35.8 mL/g VS, and the optimum digestion time was also prolonged. The mechanism study demonstrated that AITC induced cell apoptosis by modifying the physicochemical properties of cell membrane, which resulted in inhibitions to the procedure of anaerobic co-digestion. The high-throughput sequencing showed that AITC enriched the microorganism for degradation of complex organic compounds such as Bacillus, but lessened anaerobes involved in hydrolysis, acidogenesis, and methanogenesis.

Deep Learning-Based Heart Sound Analysis for Left Ventricular Diastolic Dysfunction Diagnosis.

The aggravation of left ventricular diastolic dysfunction (LVDD) could lead to ventricular remodeling, wall stiffness, reduced compliance, and progression to heart failure with a preserved ejection fraction. A non-invasive method based on convolutional neural networks (CNN) and heart sounds (HS) is presented for the early diagnosis of LVDD in this paper. A deep convolutional generative adversarial networks (DCGAN) model-based data augmentation (DA) method was proposed to expand a HS database of LVDD for model training. Firstly, the preprocessing of HS signals was performed using the improved wavelet denoising method. Secondly, the logistic regression based hidden semi-Markov model was utilized to segment HS signals, which were subsequently converted into spectrograms for DA using the short-time Fourier transform (STFT). Finally, the proposed method was compared with VGG-16, VGG-19, ResNet-18, ResNet-50, DenseNet-121, and AlexNet in terms of performance for LVDD diagnosis. The result shows that the proposed method has a reasonable performance with an accuracy of 0.987, a sensitivity of 0.986, and a specificity of 0.988, which proves the effectiveness of HS analysis for the early diagnosis of LVDD and demonstrates that the DCGAN-based DA method could effectively augment HS data.

Significance of intra-nodular vessel sign in differentiating benign and malignant pulmonary ground-glass nodules.

BACKGROUND: The presence of pulmonary vessels inside ground-glass nodules (GGNs) of different nature is a very common occurrence. This study aimed to reveal the significance of pulmonary vessels displayed in GGNs in their diagnosis and differential diagnosis. RESULTS: A total of 149 malignant and 130 benign GGNs confirmed by postoperative pathological examination were retrospectively enrolled in this study. There were significant differences in size, shape, nodule-lung interface, pleural traction, lobulation, and spiculation (each p < 0.05) between benign and malignant GGNs. Compared with benign GGNs, intra-nodular vessels were more common in malignant GGNs (67.79% vs. 54.62%, p = 0.024), while the vascular categories were similar (p = 0.663). After adjusting the nodule size and the distance between the nodule center and adjacent pleura [radius-distance ratio, RDR], the occurrences of internal vessels between them were similar. The number of intra-nodular vessels was positively correlated with nodular diameter and RDR. Vascular changes were more common in malignant than benign GGNs (52.48% vs. 18.31%, p < 0.0001), which mainly manifested as distortion and/or dilation of pulmonary veins (61.19%). The occurrence rate, number, and changes of internal vessels had no significant differences among all the pre-invasive and invasive lesions (each p > 0.05). CONCLUSIONS: The incidence of internal vessels in GGNs is mainly related to their size and the distance between nodule and pleura rather than the pathological nature. However, GGNs with dilated or distorted internal vessels, especially pulmonary veins, have a higher possibility of malignancy.

Revealing the mechanisms of Triclosan affecting of methane production from waste activated sludge.

Triclosan (TCS), as an antimicrobial agent, is considered as a representative emerging contaminant and was frequently detected in excess sludge. This study investigated the effect of TCS on activate wastewater sludge (WAS) digestion through laboratory methane production experiment. It was concluded that TCS had a tendency to restrain methane production from sludge with its exposure level increasing. The results displayed that the yields of final maximum cumulative methane production were similar about 108.4 mL/g VSS at TCS level lower 200 mg TCS/kg TSS, while the values were approximately 95.2 mL/g VSS with TCS level over 550 mg TCS/kg TSS. Although TCS could be degraded, its intermediates in this study had no effect on sludge digestion. In addition, TCS at higher levels had seriously negative effect on the solubilization, hydrolysis, acidification, and methanogenesis processes. Microbial community was further analyzed to understand the TCS's effect on digestion system from a micro perspective.

Theory of relaxor-ferroelectricity.

Relaxor-ferroelectrics are fascinating and useful materials, but the mechanism of relaxor-ferroelectricity has been puzzling the scientific community for more than 65 years. Here, a theory of relaxor-ferroelectricity is presented based on 3-dimensional-extended-random-site-Ising-model along with Glauber-dynamics of pseudospins. We propose a new mean-field of pseudospin-strings to solve this kinetic model. The theoretical results show that, with decreasing pseudospin concentration, there are evolutions from normal-ferroelectrics to relaxor-ferroelectrics to paraelectrics, especially indicating by the crossovers from, (a) the sharp to diffuse change at the phase-transition temperature to disappearance in the whole temperature range of order-parameter, and (b) the power-law to Vogel-Fulcher-law to Arrhenius-relation of the average relaxation time. Particularly, the calculated local-order-parameter of the relaxor-ferroelectrics gives the polar-nano-regions appearing far above the diffuse-phase-transition and shows the quasi-fractal characteristic near and below the transition temperature. We also provide a new mechanism of Burns-transformation which stems from not only the polar-nano-regions but also the correlation-function between pseudospins, and put forward a definition of the canonical relaxor-ferroelectrics. The theory accounts for the main facts of relaxor-ferroelectricity, and in addition gives a good quantitative agreement with the experimental results of the order-parameter, specific-heat, high-frequency permittivity, and Burns-transformation of lead magnesium niobate, the canonical relaxor-ferroelectric.

Intraventricular Hemorrhage Growth: Definition, Prevalence and Association with Hematoma Expansion and Prognosis.

BACKGROUND/OBJECTIVES: The objective of this study is to propose a definition of intraventricular hemorrhage (IVH) growth and to investigate whether IVH growth is associated with ICH expansion and functional outcome. METHODS: We performed a prospective observational study of ICH patients between July 2011 and March 2017 in a tertiary hospital. Patients were included if they had a baseline CT scan within 6 h after onset of symptoms and a follow-up CT within 36 h. IVH growth was defined as either any newly occurring intraventricular bleeding on follow-up CT scan in patients without baseline IVH or an increase in IVH volume ≥ 1 mL on follow-up CT scan in patients with initial IVH. Poor outcome was defined as modified Rankin Scale score of 3-6 at 90 days. The association between IVH growth and functional outcome was assessed by using multivariable logistic regression analysis. RESULTS: IVH growth was observed in 59 (19.5%) of 303 patients. Patients with IVH growth had larger baseline hematoma volume, higher NIHSS score and lower GCS score than those without. Of 44 patients who had concurrent IVH growth and hematoma growth, 41 (93.2%) had poor functional outcome at 3-month follow-up. IVH growth (adjusted OR 4.15, 95% CI 1.31-13.20; P = 0.016) was an independent predictor of poor functional outcome (mRS 3-6) at 3 months in multivariable analysis. CONCLUSION: IVH growth is not uncommon and independently predicts poor outcome in ICH patients. It may serve as a promising therapeutic target for intervention.