Fibration of powdery materials.

Non-destructive processing of powders into macroscopic materials with a wealth of structural and functional possibilities has immeasurable scientific significance and application value, yet remains a challenge using conventional processing techniques. Here we developed a universal fibration method, using two-dimensional cellulose as a mediator, to process diverse powdered materials into micro-/nanofibres, which provides structural support to the particles and preserves their own specialties and architectures. It is found that the self-shrinking force drives the two-dimensional cellulose and supported particles to pucker and roll into fibres, a gentle process that prevents agglomeration and structural damage of the powder particles. We demonstrate over 120 fibre samples involving various powder guests, including elements, compounds, organics and hybrids in different morphologies, densities and particle sizes. Customized fibres with an adjustable diameter and guest content can be easily constructed into high-performance macromaterials with various geometries, creating a library of building blocks for different fields of applications. Our fibration strategy provides a universal, powerful and non-destructive pathway bridging primary particles and macroapplications.

Universal Spin Superconducting Diode Effect from Spin-Orbit Coupling.

We propose a universal spin superconducting diode effect (SDE) induced by spin-orbit coupling (SOC) in systems with spin-triplet correlations, where the critical spin supercurrents in opposite directions are unequal. By analysis from both the Ginzburg-Landau theory and energy band analysis, we show that the spin-↑↑ and spin-↓↓ Cooper pairs possess opposite phase gradients and opposite momenta from the SOC, which leads to the spin SDE. Two superconductors with SOC, a p-wave superconductor as a toy model and a practical superconducting nanowire, are numerically studied and they both exhibit spin SDE. In addition, our theory also provides a unified picture for both spin and charge SDEs.

Stomatin-like protein 2 promotes cell proliferation and survival under 5-Fluorouracil stress in hepatocellular carcinoma.

BACKGROUND: The crucial role of STOML2 in tumor progression has been documented recently in various cancers. Previous studies have shown that STOML2 promoted cancer cell proliferation, but the underlying mechanism is not fully illustrated. METHODS AND RESULTS: The expression and clinical relevance of STOML2 in pan-cancer was analyzed by TIMER2 web platform in pan-cancer. The prognostic significance of STOML2 in HCC was evaluated utilizing KM curve and a nomogram model. Signaling pathways associated with STOML2 expression were discovered by GSEA. CCK-8 assay was performed to evaluate the proliferative capacity of HCC cells after manipulating STOML2 expression. Flow cytometry was utilized to analyze cell cycle progression. Results indicated that increased STOML2 expression in HCC linked to unfavorable clinical outcomes. Cell cycle and cell division related terms were enriched under conditions of elevated STOML2 expression via GSEA analysis. A notable decrease in cell proliferation was observed in MHCC97H with STOML2 knocked-down, accompanied by G1-phase arrest, up-regulation of p21, down-regulation of CyclinD1 and its regulatory factor MYC, while STOML2 overexpression in Huh7 showed the opposite results. These results indicated that STOML2 was responsible for HCC proliferation by regulating the expression level of MYC/cyclin D1 and p21. Furthermore, an inverse correlation was found between STOML2 expression and 5-FU sensitivity. CONCLUSIONS: STOML2 promotes cell cycle progression in HCC which is associated with activation of MYC/CyclinD1/p21 pathway, and modulates the response of HCC to 5-FU.

Molecular Collapse States in Graphene/WSe_{2} Heterostructure Quantum Dots.

In relativistic physics, both atomic collapse in a heavy nucleus and Hawking radiation in a black hole are predicted to occur through the Klein tunneling process that couples particles and antiparticles. Recently, atomic collapse states (ACSs) were explicitly realized in graphene because of its relativistic Dirac excitation with a large "fine structure constant." However, the essential role of the Klein tunneling in the ACSs remains elusive in experiment. Here we systematically study the quasibound states in elliptical graphene quantum dots (GQDs) and two coupled circular GQDs. Bonding and antibonding molecular collapse states formed by two coupled ACSs are observed in both systems. Our experiments supported by theoretical calculations indicate that the antibonding state of the ACSs will change into a Klein-tunneling-induced quasibound state revealing deep connection between the ACSs and the Klein tunneling.

Realizing Valley-Polarized Energy Spectra in Bilayer Graphene Quantum Dots via Continuously Tunable Berry Phases.

The Berry phase plays an important role in determining many physical properties of quantum systems. However, tuning the energy spectrum of a quantum system via Berry phase is comparatively rare because the Berry phase is usually a fixed constant. Here, we report the realization of an unusual valley-polarized energy spectra via continuously tunable Berry phases in Bernal-stacked bilayer graphene quantum dots. In our experiment, the Berry phase of electron orbital states is continuously tuned from about π to 2π by perpendicular magnetic fields. When the Berry phase equals π or 2π, the electron states in the two inequivalent valleys are energetically degenerate. By altering the Berry phase to noninteger multiples of π, large and continuously tunable valley-polarized energy spectra are realized. Our result reveals the Berry phase's essential role in valleytronics and the observed valley splitting, on the order of 10 meV at a magnetic field of 1 T, is about 100 times larger than Zeeman splitting for spin, shedding light on graphene-based valleytronics.

Magnetic-Field-Tunable Valley-Contrasting Pseudomagnetic Confinement in Graphene.

Introducing quantum confinement has uncovered a rich set of interesting quantum phenomena and allows one to directly probe the physics of confined (quasi-)particles. In most experiments, however, an electrostatic potential is the only available method to generate quantum dots in a continuous system to confine (quasi-)particles. Here we demonstrate experimentally that inhomogeneous pseudomagnetic fields in strained graphene can introduce exotic quantum confinement of massless Dirac fermions. The pseudomagnetic fields have opposite directions in the two distinct valleys of graphene. By adding and tuning real magnetic fields, the total effective magnetic fields in the two valleys are imbalanced. By that we realized valley-contrasting spatial confinement, which lifts the valley degeneracy and results in field-tunable valley-polarized confined states in graphene. Our results provide a new avenue to manipulate the valley degree of freedom.

Transport Theory of Half-Quantized Hall Conductance in a Semimagnetic Topological Insulator.

Recently, a half-quantized Hall conductance (HQHC) plateau was experimentally observed in a semimagnetic topological insulator heterostructure. However, the heterostructure was metallic with a nonzero longitudinal conductance, which contradicts the common belief that quantized Hall conductance is usually observed in insulators. In this work, we systematically study the surface transport of a semimagnetic topological insulator with both gapped and gapless Dirac surfaces in the presence of dephasing process. In particular, we reveal that the HQHC is directly related to the half-quantized chiral current along the edge of a strongly dephasing metal. The Hall conductance keeps a half-quantized value for large dephasing strengths, while the longitudinal conductance varies with Fermi energies and dephasing strengths. Furthermore, we evaluate both the conductance and resistance as a function of the temperature, which is consistent with the experimental results. Our results not only provide the microscopic transport mechanism of the HQHC, but also are instructive for the probe of the HQHC in future experiments.

Research on a Fiber Optic Oxygen Sensor Based on All-Phase Fast Fourier Transform (apFFT) Phase Detection.

Fiber optic oxygen sensors based on fluorescence quenching play an important role in oxygen sensors. They have several advantages over other methods of oxygen sensing-they do not consume oxygen, have a short response time and are of high sensitivity. They are often used in special environments, such as hazardous environments and in vivo. In this paper, a new fiber optic oxygen sensor is introduced, which uses the all-phase fast Fourier transform (apFFT) algorithm, instead of the previous lock-in amplifier, for the phase detection of excitation light and fluorescence. The excitation and fluorescence frequency was 4 KHz, which was conducted between the oxygen-sensitive membrane and the photoelectric conversion module by the optical fiber and specially-designed optical path. The phase difference of the corresponding oxygen concentration was obtained by processing the corresponding electric signals of the excitation light and the fluorescence. At 0%, 5%, 15%, 21% and 50% oxygen concentrations, the experimental results showed that the apFFT had good linearity, precision and resolution-0.999°, 0.05° and 0.0001°, respectively-and the fiber optic oxygen sensor with apFFT had high stability. When the oxygen concentrations were 0%, 5%, 15%, 21% and 50%, the detection errors of the fiber optic oxygen sensor were 0.0447%, 0.1271%, 0.3801%, 1.3426% and 12.6316%, respectively. Therefore, the sensor that we designed has greater accuracy when measuring low oxygen concentrations, compared with high oxygen concentrations.

Cellulose-Based Hybrid Structural Material for Radiative Cooling.

Structural materials with excellent mechanical properties are vitally important for architectural application. However, the traditional structural materials with complex manufacturing processes cannot effectively regulate heat flow, causing a large impact on global energy consumption. Here, we processed a high-performance and inexpensive cooling structural material by bottom-up assembling delignified biomass cellulose fiber and inorganic microspheres into a 3D network bulk followed by a hot-pressing process; we constructed a cooling lignocellulosic bulk that exhibits strong mechanical strength more than eight times that of the pure wood fiber bulk and greater specific strength than the majority of structural materials. The cellulose acts as a photonic solar reflector and thermal emitter, enabling a material that can accomplish 24-h continuous cooling with an average dT of 6 and 8 °C during day and night, respectively. Combined with excellent fire-retardant and outdoor antibacterial performance, it will pave the way for the design of high-performance cooling structural materials.

Strong Electron Coupling of Ru and Vacancy-Rich Carbon Dots for Synergistically Enhanced Hydrogen Evolution Reaction.

The exploitation of ingenious strategies to improve the activity and stability of ruthenium (Ru) is crucial for the advancement of Ru-based electrocatalysts. Vacancy engineering is a typical strategy for modulating the catalytic activity of electrocatalysts. However, creating vacancies directly into pure metallic Ru is difficult because of the extremely stringent conditions required and will result in instability because the integrity of the crystal structure is destroyed. In response, a compromise tactic by introducing vacancies in a Ru composite structure is proposed, and vacancy-rich carbon dots coupled with Ru (Ru@CDs) are elaborately constructed. Specifically, the vacancy-rich carbon dots (CDs) serve as an excellent platform for anchoring and trapping Ru nanoparticles, thus restraining their agglomeration and growth. As expected, Ru@CDs exhibited excellent catalytic performance with a low overpotential of 30 mV at 10 mA cm-2 in 1 m KOH, a small Tafel slope of 22 mV decade-1 , and robust stability even after 10 000 cycles. The low overpotential is comparable to those of most previously reported Ru-based electrocatalysts. Additionally, spectroscopic characterizations and theoretical calculations demonstrate that the rich vacancies and the electron interactions between Ru and CDs synergistically lower the intermediate energy barrier and thereby maximize the activity of the Ru@CDs electrocatalyst.

Metabolic associated fatty liver disease increases coronavirus disease 2019 disease severity in nondiabetic patients.

BACKGROUND AND AIM: Coronavirus disease 2019 (COVID-19) has attracted increasing worldwide attention. While diabetes is known to aggravate COVID-19 severity, it is not known whether nondiabetic patients with metabolic dysfunction are also more prone to more severe disease. The association of metabolic associated fatty liver disease (MAFLD) with COVID-19 severity in nondiabetic patients was investigated here. METHODS: The study cohort comprised 65 patients with (i.e. cases) and 65 patients without MAFLD (i.e. controls). Each case was randomly matched with one control by sex (1:1) and age (±5 years). The association between the presence of MAFLD (as exposure) and COVID-19 severity (as the outcome) was assessed by binary logistic regression analysis. RESULTS: In nondiabetic patients with COVID-19, the presence of MAFLD was associated with a four-fold increased risk of severe COVID-19; the risk increased with increasing numbers of metabolic risk factors. The association with COVID-19 severity persisted after adjusting for age, sex, and coexisting morbid conditions. CONCLUSION: Health-care professionals caring for nondiabetic patients with COVID-19 should be cognizant of the increased likelihood of severe COVID-19 in patients with MAFLD.

Analysis of Risk Factors for Thromboembolic Events in 88 Patients with COVID-19 Pneumonia in Wuhan, China: A Retrospective Descriptive Report.

BACKGROUND Since the outbreak of COVID-19 in December 2019, there have been 96 623 laboratory-confirmed cases and 4784 deaths by December 29 in China. We aimed to analyze the risk factors and the incidence of thrombosis from patients with confirmed COVID-19 pneumonia. MATERIAL AND METHODS Eighty-eight inpatients with confirmed COVID-19 pneumonia were reported (31 critical cases, 33 severe cases, and 24 common cases). The thrombosis risk factor assessment, laboratory results, ultrasonographic findings, and prognoses of these patients were analyzed, and compared among groups with different severity. RESULTS Nineteen of the 88 cases developed DVT (12 critical cases, 7 severe cases, and no common cases). In addition, among the 18 patients who died, 5 were diagnosed with DVT. Positive correlations were observed between the increase in D-dimer level (≥5 µg/mL) and the severity of COVID-19 pneumonia (r=0.679, P<0.01), and between the high Padua score (≥4) and the severity (r=0.799, P<0.01). In addition, the CRP and LDH levels on admission had positive correlations with the severity of illness (CRP: r=0.522, P<0.01; LDH: r=0.600, P<0.01). A negative correlation was observed between the lymphocyte count on admission and the severity of illness (r=-0.523, P<0.01). There was also a negative correlation between the lymphocyte count on admission and mortality in critical patients (r=-0.499, P<0.01). Univariable logistic regression analysis showed that the occurrence of DVT was positively correlated with disease severity (crude odds ratio: 3.643, 95% CI: 1.218-10.896, P<0.05). CONCLUSIONS Our report illustrates that critically or severely ill patients have an associated high D-dimer value and high Padua score, and illustrates that a low threshold to screen for DVT may help improve detection of thromboembolism in these groups of patients, especially in asymptomatic patients. Our results suggest that early administration of prophylactic anticoagulant would benefit the prognosis of critical patients with COVID-19 pneumonia and would likely reduce thromboembolic rates.

Epidemiological features and viral shedding in children with SARS-CoV-2 infection.

A pandemic of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection broke out all over the world; however, epidemiological data and viral shedding in pediatric patients are limited. We conducted a retrospective, multicenter study, and followed-up with all children from the families with SARS-CoV-2 infected members in Zhejiang Province, China. All infections were confirmed by testing the SARS-CoV-2 RNA with real-time reverse transcription PCR method, and epidemiological data between children and adults in the same families were compared. Effect of antiviral therapy was evaluated observationally and fecal-viral excretion times among groups with different antiviral regiments were compared with Kaplan-Meier plot. By 29 February 2020, 1298 cases from 883 families were confirmed with SARS-CoV-2 infection and 314 of which were families with children. Incidence of infection in child close contacts was significantly lower than that in adult contacts (13.2% vs 21.2%). The mean age of 43 pediatric cases was 8.2 years and mean incubation period was 9.1 days. Forty (93.0%) were family clustering. Thirty-three children had coronavirus disease 2019 (20 pneumonia) with mild symptoms and 10 were asymptomatic. Fecal SARS-CoV-2 RNA detection was positive in 91.4% (32/35) cases and some children had viral excretion time over 70 days. Viral clearance time was not different among the groups treated with different antiviral regiments. No subsequent infection was observed in family contacts of fecal-viral-excreting children. Children have lower susceptibility of SARS-CoV-2 infection, longer incubation, and fecal-viral excretion time. Positive results of fecal SARS-CoV-2 RNA detection were not used as indication for hospitalization or quarantine.

Movable Valley Switch Driven by Berry Phase in Bilayer-Graphene Resonators.

Berry phase, the geometric phase accumulated over a closed loop in parameter space during an adiabatic cyclic evolution, has been demonstrated to play an important role in many quantum systems since its discovery. In gapped Bernal bilayer graphene, the Berry phase can be continuously tuned from zero to 2π, which offers a unique opportunity to explore the tunable Berry phase on physical phenomena. Here, we report experimental observation of Berry-phase-induced valley splitting and crossing in movable bilayer-graphene p-n junction resonators. In our experiment, the resonators are generated by combining the electric field of a scanning tunneling microscope tip with the gap of bilayer graphene. A perpendicular magnetic field changes the Berry phase of the confined bound states in the resonators from zero to 2π continuously and leads to the Berry phase difference for the two inequivalent valleys in the bilayer graphene. As a consequence, we observe giant valley splitting and unusual valley crossing of the lowest bound states. Our results indicate that the bilayer-graphene resonators can be used to manipulate the valley degree of freedom in valleytronics.

Metabolic-associated fatty liver disease is associated with severity of COVID-19.

The Corona Virus Disease 2019 (COVID-19) pandemic has attracted increasing worldwide attention. While metabolic-associated fatty liver disease (MAFLD) affects a quarter of world population, its impact on COVID-19 severity has not been characterized. We identified 55 MAFLD patients with COVID-19, who were 1:1 matched by age, sex and obesity status to non-aged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected patients without MAFLD. Our results demonstrate that in patients aged less than 60 years with COVID-19, MAFLD is associated with an approximately fourfold increase (adjusted odds ratio 4.07, 95% confidence interval 1.20-13.79, P = .02) in the probability for severe disease, after adjusting for confounders. Healthcare professionals caring for patients with COVID-19 need to be aware that there is a positive association between MAFLD and severe illness with COVID-19.

A predictive model and scoring system combining clinical and CT characteristics for the diagnosis of COVID-19.

OBJECTIVES: To develop a predictive model and scoring system to enhance the diagnostic efficiency for coronavirus disease 2019 (COVID-19). METHODS: From January 19 to February 6, 2020, 88 confirmed COVID-19 patients presenting with pneumonia and 80 non-COVID-19 patients suffering from pneumonia of other origins were retrospectively enrolled. Clinical data and laboratory results were collected. CT features and scores were evaluated at the segmental level according to the lesions' position, attenuation, and form. Scores were calculated based on the size of the pneumonia lesion, which graded at the range of 1 to 4. Air bronchogram, tree-in-bud sign, crazy-paving pattern, subpleural curvilinear line, bronchiectasis, air space, pleural effusion, and mediastinal and/or hilar lymphadenopathy were also evaluated. RESULTS: Multivariate logistic regression analysis showed that history of exposure (ß = 3.095, odds ratio (OR) = 22.088), leukocyte count (ß = - 1.495, OR = 0.224), number of segments with peripheral lesions (ß = 1.604, OR = 1.604), and crazy-paving pattern (ß = 2.836, OR = 2.836) were used for establishing the predictive model to identify COVID-19-positive patients (p < 0.05). In this model, values of area under curve (AUC) in the training and testing groups were 0.910 and 0.914, respectively (p < 0.001). A predicted score for COVID-19 (PSC-19) was calculated based on the predictive model by the following formula: PSC-19 = 2 × history of exposure (0-1 point) - 1 × leukocyte count (0-2 points) + 1 × peripheral lesions (0-1 point) + 2 × crazy-paving pattern (0-1 point), with an optimal cutoff point of 1 (sensitivity, 88.5%; specificity, 91.7%). CONCLUSIONS: Our predictive model and PSC-19 can be applied for identification of COVID-19-positive cases, assisting physicians and radiologists until receiving the results of reverse transcription-polymerase chain reaction (RT-PCR) tests. KEY POINTS: • Prediction of RT-PCR positivity is crucial for fast diagnosis of patients suspected of having coronavirus disease 2019 (COVID-19). • Typical CT manifestations are advantageous for diagnosing COVID-19 and differentiation of COVID-19 from other types of pneumonia. • A predictive model and scoring system combining both clinical and CT features were herein developed to enable high diagnostic efficiency for COVID-19.

Downregulation of DLGAP1-Antisense RNA 1 Alleviates Vascular Endothelial Cell Injury Via Activation of the Phosphoinositide 3-kinase/Akt Pathway Results from an Acute Limb Ischemia Rat Model.

OBJECTIVE: This study aimed to investigate the effect of long non-coding RNA (lncRNA) DLGAP1 antisense RNA 1 (DLGAP1-AS1) on vascular endothelial cell (VEC) injury via the phosphoinositide 3-kinase (PI3K)/Akt pathway in rat models of acute lower limb ischaemia-reperfusion (I/R). METHODS: Differentially expressed lncRNAs related to I/R were screened using the gene expression omnibus database. Acute lower limb I/R models were induced in male Wistar rats, in which the regulatory mechanisms of DLGAP1-AS1 silencing were analysed after the treatment of small interfering RNA (siRNA) against DLGAP1-AS1 or an inhibitor of the PI3K/Akt pathway. The relationship between DLGAP1-AS1 and the PI3K/Akt pathway was analysed. The levels of tumour necrosis factor (TNF)-α and vascular cell adhesion molecule-1 (VCAM-1), as well as malondialdehyde (MDA) concentration and creatine kinase (CK) activity, were measured. The number of circulating endothelial cells (CECs) and apoptosis of VECs were identified. RESULTS: Microarray based analysis indicated that DLGAP1-AS1 was highly expressed in I/R, which was further confirmed by detection of expression in rat models of acute lower limb I/R. Notably, the treatment of siRNA against DLGAP1-AS1 led to the activation of the PI3K/Akt pathway. In response to siRNA against DLGAP1-AS1, the levels of TNF-α and VCAM-1 were decreased, and MDA concentration and CK activity was downregulated. Reduced CEC numbers and suppressed VEC apoptosis were also observed. CONCLUSION: DLGAP1-AS1 silencing could further suppress the oxidative stress, exert an anti-apoptosis effect, and reduce inflammatory reaction, whereby VEC injury is alleviated by activation of the PI3K/Akt pathway in rats with acute lower limb I/R.

Epidemiological and clinical characteristics of three family clusters of COVID-19 transmitted by latent patients in China.

From 21 January 2020 to 9 February 2020, three family clusters involving 31 patients with coronavirus disease 2019 were identified in Wenzhou, China. The epidemiological and clinical characteristics of the family cluster patients were analysed and compared with those of 43 contemporaneous sporadic cases. The three index cases transmitted the infection to 28 family members 2-10 days before illness onset. Overall, 28 of the 41 sporadic cases and three of 31 patients in the family clusters came back from Wuhan (65.12 vs. 9.68%, P< 0.001). In terms of epidemiological characters and clinical symptoms, no significant differences were observed between the family cluster and sporadic cases. However, the lymphocyte counts of sporadic cases were significantly lower than those of family cluster cases ((1.32 ± 0.55) × 109/l vs. (1.63 ± 0.70) × 109/l, P = 0.037), and the proportion of hypoalbuminaemia was higher in sporadic cases (18/43, 41.86%) than in the family clusters (6/31, 19.35%) (P < 0.05). Within the family cluster, the second- and third-generation cases had milder clinical manifestations, without severe conditions, compared with the index and first-generation cases, indicating that the virulence gradually decreased following passage through generations within the family clusters. Close surveillance, timely recognition and isolation of the suspected or latent patient is crucial in preventing family cluster infection.

Clinical and radiographic characteristics, management and short-term outcomes of patients with COVID-19 in Wenzhou, China.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is an emerging viral disease. Here, we report the clinical features, management, and short-term outcomes of COVID-19 patients in Wenzhou, China, an area outside Wuhan. METHODS: Patients admitted to the Infectious Diseases Department of Ruian People's Hospital in Wenzhou, from January 21 to February 7, 2020, were recruited. Medical data on epidemiological history, demographics, clinical characteristics, laboratory tests, chest computerized tomography (CT) examination, treatment, and short-term outcomes were retrospectively reviewed. Blood biochemistry and routine tests were examined using standard methods and automatic machines. CT examination was performed several times during hospitalization as necessary. RESULTS: A total of 67 confirmed COVID-19 cases were diagnosed; 64 (95.4%) were common cases and three (4.5%) were severe cases. The most common symptoms at admission were fever (86.6%), cough (77.6%), productive cough (52.2%), chest distress (17.9%), and sore throat (11.9%), followed by diarrhea (7.4%), headache (7.4%), shortness of breath (6.0%), dizziness (4.5%), muscular soreness (4.5%), and running nose (4.5%). Thirty patients (47.8%) had increased C-reactive protein levels. The CT radiographs at admission showed abnormal findings in 54 (80.6%) patients. The patients were treated mainly by oxygen therapy and antiviral drugs. By March 3, 2020, all 67 patients completely recovered and had negative nucleic acid tests. The patients were discharged from the hospital and transferred to a medical observation isolation center for further observation. CONCLUSION: Cases of COVID-19 in Wenzhou are milder and have a better prognosis, compared to those in Wuhan. Timely and appropriate screening, diagnosis, and treatment are the key to achieve good outcomes.

Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning.

BACKGROUND: Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. METHODS: An integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with 27 confirmed general pneumonia patients from Ruian People's Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented based on ground-glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray-level co-occurrence matrix (GLCM) features, 15 gray-level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High-dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each feature was the average weights calculated by ReliefF in n times. Features with relevance larger than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using tenfold cross-validation. RESULTS AND CONCLUSIONS: The classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease.