Recyclable and mechanically tough nanocellulose reinforced natural rubber composite conductive elastomers for flexible multifunctional sensor.

The development of flexible wearable multifunctional electronics has gained great attention in the field of human motion monitoring. However, developing mechanically tough, highly stretchable, and recyclable composite conductive materials for application in multifunctional sensors remained great challenges. In this work, a mechanically tough, highly stretchable, and recyclable composite conductive elastomer with the dynamic physical-chemical dual-crosslinking network was fabricated by the combination of multiple hydrogen bonds and dynamic ester bonds. To prepare the proposed composite elastomers, the polyaniline-modified carboxylate cellulose nanocrystals (C-CNC@PANI) were used as both conductive filler to yield high conductivity of 15.08 mS/m, and mechanical reinforcement to construct the dynamic dual-crosslinking network with epoxidized natural rubber latex to realize the high mechanical strength (8.65 MPa) and toughness (29.57 MJ/m3). Meanwhile, the construction of dynamic dual-crosslinking network endowed the elastomer with satisfactory recyclability. Based on these features, the composite conductive elastomers were used as strain sensors, and electrode material for assembling flexible and recyclable self-powered sensors for monitoring human motions. Importantly, the composite conductive elastomers maintained reliable sensing and energy harvesting performance even after multiple recycling process. This study provides a new strategy for the preparation of recyclable, mechanically tough composite conductive materials for wearable sensors.

From Simple to Complex Scenes: Learning Robust Feature Representations for Accurate Human Parsing.

Human parsing has attracted considerable research interest due to its broad potential applications in the computer vision community. In this paper, we explore several useful properties, including high-resolution representation, auxiliary guidance, and model robustness, which collectively contribute to a novel method for accurate human parsing in both simple and complex scenes. Starting from simple scenes: we propose the boundary-aware hybrid resolution network (BHRN), an advanced human parsing network. BHRN utilizes deconvolutional layers and multi-scale supervision to generate rich high-resolution representations. Additionally, it includes an edge perceiving branch designed to enhance the fineness of part boundaries. Building on BHRN, we construct a dual-task mutual learning (DTML) framework. It not only provides implicit guidance to assist the parser by incorporating boundary features, but also explicitly maintains the high-order consistency between the parsing prediction and the ground truth. Toward complex scenes: we develop a domain transform method to enhance the model robustness. By transforming the input space from the spatial domain to the polar harmonic Fourier moment domain, the mapping relationship to the output semantic space is highly stable. This transformation yields robust representations for both clean and corrupted data. When evaluated on standard benchmark datasets, our method achieves superior performance compared to state-of-the-art human parsing methods. Furthermore, our domain transform strategy significantly improves the robustness of DTML dramatically in most complex scenes.

In Situ Polymerization of Hydrogel Electrolyte on Electrodes Enabling the Flexible All-Hydrogel Supercapacitors with Low-Temperature Adaptability.

All-hydrogel supercapacitors are emerging as promising power sources for next-generation wearable electronics due to their intrinsic mechanical flexibility, eco-friendliness, and enhanced safety. However, the insufficient interfacial adhesion between the electrode and electrolyte and the frozen hydrogel matrices at subzero temperatures largely limit the practical applications of all-hydrogel supercapacitors. Here, an all-hydrogel supercapacitor is reported with robust interfacial contact and anti-freezing property, fabricated by in situ polymerizing hydrogel electrolyte onto hydrogel electrodes. The robust interfacial adhesion is developed by the synergistic effect of a tough hydrogel matrix and topological entanglements. Meanwhile, the incorporation of zinc chloride (ZnCl2 ) in the hydrogel electrolyte prevents the freezing of water solvents and endows the all-hydrogel supercapacitor with mechanical flexibility and fatigue resistance across a wide temperature range of 20 °C to -60 °C. Such all-hydrogel supercapacitor demonstrates satisfactory low-temperature electrochemical performance, delivering a high energy density of 11 mWh cm-2 and excellent cycling stability with a capacitance retention of 90% over 10000 cycles at -40 °C. Notably, the fabricated all-hydrogel supercapacitor can endure dynamic deformations and operate well under 2000 tension cycles even at -40 °C, without experiencing delamination and electrochemical failure. This work offers a promising strategy for flexible energy storage devices with low-temperature adaptability.

A multiple cross-linking strategy to develop an environment-friendly and water resistance wheat gluten protein wood adhesive.

Wheat gluten (WG) shows great promise to synthesize environment-friendly wood adhesives. However, their weak bonding strength and poor water resistance have limited its application in the commercial wood-based panel industry. In this study, a novel WG-based adhesive was developed by constructing a multiple cross-linking network generated by covalent and non-covalent bonds. The potential mechanism was revealed by FT-IR analysis. Furthermore, their surface morphology, thermal stability, viscosity, and residual rate of adhesives with different compositions were systematically characterized and compared. The results showed that the hydrogen bonding, reactions between amine groups and tannin, and ring opening reaction of epoxy, synergistically contributed to generate a highly crosslinked network. The wet/boil water strength of the plywood prepared from WG/tannin/ethylene imine polymer (PEI)-glycerol triglycidyl ether (GTE) adhesive with the addition of 15 % GTE could reach 1.21 MPa and 1.20 MPa, respectively, and a mildew resistance ability was observed. This study provides a facile strategy to fabricate high-performance plant protein-based adhesives with desirable water resistance for practical application.

Coupling of Adhesion and Anti-Freezing Properties in Hydrogel Electrolytes for Low-Temperature Aqueous-Based Hybrid Capacitors.

Solid-state zinc-ion capacitors are emerging as promising candidates for large-scale energy storage owing to improved safety, mechanical and thermal stability and easy-to-direct stacking. Hydrogel electrolytes are appealing solid-state electrolytes because of eco-friendliness, high conductivity and intrinsic flexibility. However, the electrolyte/electrode interfacial contact and anti-freezing properties of current hydrogel electrolytes are still challenging for practical applications of zinc-ion capacitors. Here, we report a class of hydrogel electrolytes that couple high interfacial adhesion and anti-freezing performance. The synergy of tough hydrogel matrix and chemical anchorage enables a well-adhered interface between hydrogel electrolyte and electrode. Meanwhile, the cooperative solvation of ZnCl2 and LiCl hybrid salts renders the hydrogel electrolyte high ionic conductivity and mechanical elasticity simultaneously at low temperatures. More significantly, the Zn||carbon nanotubes hybrid capacitor based on this hydrogel electrolyte exhibits low-temperature capacitive performance, delivering high-energy density of 39 Wh kg-1 at -60 °C with capacity retention of 98.7% over 10,000 cycles. With the benefits of the well-adhered electrolyte/electrode interface and the anti-freezing hydrogel electrolyte, the Zn/Li hybrid capacitor is able to accommodate dynamic deformations and function well under 1000 tension cycles even at -60 °C. This work provides a powerful strategy for enabling stable operation of low-temperature zinc-ion capacitors.

Association of chronic diseases with depression in the United States, NHANES 2007-2018.

Depression often coexists with many chronic diseases. However, previous studies mainly focused on the association between a single chronic disease or chronic diseases of the elderly and depression. This study included 26,177 adults aged more than 20 years old from the 2007-2018 National Health and Nutrition Examination Survey. Depression was determined by nine questions which were from the Patient Health Questionnaire. We used propensity score matching to reduce the influence of confounders between the depression and non-depression groups. A multivariate logistic regression model was used to analyze the relationship between various chronic diseases and the number of diseases and depression. The prevalence of depression in participants with chronic diseases was higher than that in participants without chronic diseases, 20.8% of participants with chronic bronchitis had depression. After matching and controlling sleep, insurance and smoking, the highest risk of depression (OR = 1.524; 95% CI: 1.162-2.001) was found in people with stroke, followed by arthritis (OR = 1.464; 95% CI: 1.275-1.681). The percentage of participants with two or more chronic diseases with depression and without depression was 68.9% and 51.9%, respectively. Participants with five or more chronic diseases had the highest risk of depression (OR = 3.653; 95% CI: 3.001-4.446). In conclusion, patients with chronic diseases are at higher risk for depression, especially those with multiple chronic diseases. This study suggested that we should pay more attention to the mental health of people with chronic diseases.

Network pharmacology-based strategy to investigate the effect and mechanism of α-solanine against glioma.

BACKGROUND: An anti-tumour activity has been demonstrated for α-solanine, a bioactive compound extracted from the traditional Chinese herb Solanum nigrum L. However, its efficacy in the treatment of gliomas and the underlying mechanisms remain unclear. The aim of this study was to investigate the inhibitory effects of α-solanine on glioma and elucidate its mechanisms and targets using network pharmacology, molecular docking, and molecular biology experiments. METHODS: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was utilized to predict the potential targets of α-solanine. GeneCards was used to gather glioma-related targets, and the STRING online database was used to analyze protein-protein interaction (PPI) networks for the shared targets. Hub genes were identified from the resulting PPI network and further investigated using Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Additionally, prognostic and gene set enrichment analyses (GSEA) were carried out to identify potential therapeutic targets and their underlying mechanisms of action in relation to the prognosis of gliomas. In vitro experiments were conducted to verify the findings from the network pharmacology analysis. RESULTS: A total of 289 α-solanine targets and 1149 glioma-related targets were screened, of which 78 were common targets. 11 hub genes were obtained, including SRC, HRAS, HSP90AA1, IGF1, MAPK1, MAPK14, KDR, STAT1, JAK2, MAP2K1, and IGF1R. The GO and KEGG pathway analyses unveiled that α-solanine was strongly associated with several signaling pathways, including positive regulation of MAP kinase activity and PI3K-Akt. Moreover, α-solanine (10 µM and 15 µM) inhibited the proliferation and migration but promoted the apoptosis of glioma cells. Finally, STAT1 was identified as a potential mediator of the effect of α-solanine on glioma prognosis. CONCLUSION: α-Solanine can inhibit the proliferation and migration of gliomas by regulating multiple targets and signalling pathways. These findings lay the foundation for the creation of innovative clinical anti-glioma agents.

Highly Conductive, Anti-Freezing Hemicellulose-Based Hydrogels Prepared via Deep Eutectic Solvents and Their Applications.

Hydrogels containing renewable resources, such as hemicellulose, have received a lot of attention owing to their softness and electrical conductivity which could be applied in soft devices and wearable equipment. However, traditional hemicellulose-based hydrogels generally exhibit poor electrical conductivity and suffer from freezing at lower temperatures owing to the presence of a lot of water. In this study, we dissolved hemicellulose by employing deep eutectic solvents (DESs), which were prepared by mixing choline chloride and imidazole. In addition, hemicellulose-based DES hydrogels were fabricated via photo-initiated reactions of acrylamide and hemicellulose with N, N'-Methylenebisacrylamide as a crosslinking agent. The produced hydrogels demonstrated high electrical conductivity and anti-freezing properties. The conductivity of the hydrogels was 2.13 S/m at room temperature and 1.97 S/m at -29 °C. The hydrogel's freezing point was measured by differential scanning calorimetry (DSC) to be -47.78 °C. Furthermore, the hemicellulose-based DES hydrogels can function as a dependable and sensitive strain sensor for monitoring a variety of human activities.

Diagnostic accuracy of optical coherence tomography for margin assessment in breast-conserving surgery: A systematic review and meta-analysis.

BACKGROUND: Breast cancer is the most common malignant tumor among women, and its incidence is increasing annually. At present, the results of the study on whether optical coherence tomography (OCT) can be used as an intraoperative margin assessment method for breast-conserving surgery (BCS) are inconsistent. We herein conducted this systematic review and meta-analysis to assess the diagnostic value of OCT in BCS. METHODS: PubMed, Web of Science, Cochrane Library, and Embase were used to search relevant studies published up to September 15, 2022. We used Review Manager 5.4, Meta-Disc 1.4, and STATA 16.0 for statistical analysis. RESULTS: The results displayed 18 studies with 782 patients included according to the inclusion and exclusion criteria. Meta-analysis showed the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and the area under the curve (AUC) of OCT in the margin assessment of BCS were 0.91 (95% CI 0.88-0.93), 0.88 (95% CI 0.83-0.92), 7.53 (95% CI 5.19-10.93), 0.11(95% CI 0.08-0.14), 70.37 (95% CI 39.78-124.47), and 0.94 (95% CI 0.92-0.96), respectively. CONCLUSIONS: OCT is a promising technique in intraoperative margin assessment of breast cancer patients.

Association between anemia and depression: results from NHANES 2005-2018 and mendelian randomization analyses.

BACKGROUND: The relationship between anemia and depression remains controversial. OBJECTIVE: To explore the association between anemia/hemoglobin and depression. METHODS: The data for our cross-sectional study were obtained from the National Health and Nutrition Examination Survey (NHANES) 2005-2018. Weighted multivariate logistic regression was performed to examine the association between anemia/hemoglobin and depression. Inverse variance weighted (IVW), weighted-median, and MR-Egger were used in MR analyses to assess the causal relationship between anemia/hemoglobin and depression. Heterogeneity and directional pleiotropy were assessed using the Cochrane Q test and Egger-intercept test, respectively. Sensitivity analysis was conducted by the leave-one-out approach. All analyses were carried out using IBM SPSS 24.0 and R version 4.2.2. RESULTS: A total of 29,391 NHANES participants were included in this study. After adjusting for all covariates, the association between anemia/hemoglobin and depression was not significant (P < 0.05). IVW estimates revealed that broad anemia had no significant effect on the risk of depression (OR = 1.00, 95% CI = 0.99-1.01, P = 0.432). Findings of weighted median and MR-Egger were consistent with those from IVW (weighted median: OR = 1.00, 95% CI = 0.99-1.02; P = 0.547; MR-Egger: OR = 1.01, 95% CI = 0.98-1.03, P = 0.605). The results of three MR Analyses methods also showed no causal association between hemoglobin and depression. CONCLUSIONS: Our findings do not support a causal association between anemia and depression. The association between hemoglobin concentration and depression was not statistically significant either.

Using machine learning algorithms to identify chronic heart disease: National Health and Nutrition Examination Survey 2011-2018.

OBJECTIVE: The number of heart disease patients is increasing. Establishing a risk assessment model for chronic heart disease (CHD) based on risk factors is beneficial for early diagnosis and timely treatment of high-risk populations. METHODS: Four machine learning models, including logistic regression, support vector machines (SVM), random forests, and extreme gradient boosting (XGBoost), were used to evaluate the CHD among 14 971 participants in the National Health and Nutrition Examination Survey from 2011 to 2018. The area under the receiver-operator curve (AUC) is the indicator that we evaluate the model. RESULTS: In four kinds of models, SVM has the best classification performance (AUC = 0.898), and the AUC value of logistic regression and random forest were 0.895 and 0.894, respectively. Although XGBoost performed the worst with an AUC value of 0.891. There was no significant difference among the four algorithms. In the importance analysis of variables, the three most important variables were taking low-dose aspirin, chest pain or discomfort, and total amount of dietary supplements taken. CONCLUSION: All four machine learning classifiers can identify the occurrence of CHD based on population survey data. We also determined the contribution of variables in the prediction, which can further explore their effectiveness in actual clinical data.

Strain-induced orientation facilitates the fabrication of highly stretchable and tough xylan-based hydrogel for strain sensors.

Stretchable and tough polysaccharide-based functional hydrogels have gained popularity for various applications. However, it still remains a great challenge to simultaneously own satisfactory stretchability and toughness, particularly when incorporating renewable xylan to offer sustainability. Herein, we describe a novel stretchable and tough xylan-based conductive hydrogel utilizing the natural feature of rosin derivative. The effect of different compositions on the mechanical properties and the physicochemical properties of corresponding xylan-based hydrogels were systematically investigated. Owing to the multiple non-covalent interactions among different components to dissipate energies and the strain-induced orientation of rosin derivative during the stretching, the highest tensile strength, strain, and toughness of xylan-based hydrogels could reach 0.34 MPa, 2098.4 %, and 3.79 ± 0.95 MJ/m3, respectively. Furthermore, by incorporating MXene as the conductive fillers, the strength and toughness of hydrogels were further enhanced to 0.51 MPa and 5.95 ± 1.19 MJ/m3. Finally, the synthesized xylan-based hydrogels were able to serve as a reliable and sensitive strain sensor to monitor the movements of human beings. This study provides new insights to develop stretchable and tough conductive xylan-based hydrogel, especially utilizing the natural feature of bio-based resources.

Skin-mimicking strategy to fabricate strong and highly conductive anti-freezing cellulose-based hydrogels as strain sensors.

Conductive hydrogels have attracted increasing attention for applications in wearable and flexible strain sensors. However, owing to their relatively weak strength, poor elasticity, and lack of anti-freezing ability, their applications have been limited. Herein, we present a skin-mimicking strategy to fabricate cellulose-enhanced, strong, elastic, highly conductive, and anti-freezing hydrogels. Self-assembly of cellulose to fabricate a cellulose skeleton is essential for realizing a skin-mimicking design. Furthermore, two methods, in situ polymerization and solvent replacement, were compared and investigated to incorporate conductive and anti-freezing components into hydrogels. Consequently, when the same ratio of glycerol and lithium chloride was used, the anti-freezing hydrogels prepared by in situ polymerization showed relatively higher strength (1.0 MPa), while the solvent-replaced hydrogels exhibited higher elastic recovery properties (94.6 %) and conductivity (4.5 S/m). In addition, their potential as strain sensors for monitoring human behavior was analyzed. Both hydrogels produced reliable signals and exhibited high sensitivity. This study provides a new horizon for the fabrication of strain sensors that can be applied in various environments.

Diagnostic performance of magnetic resonance imaging-based machine learning in Alzheimer's disease detection: a meta-analysis.

PURPOSE: Advanced machine learning (ML) algorithms can assist rapid medical image recognition and realize automatic, efficient, noninvasive, and convenient diagnosis. We aim to further evaluate the diagnostic performance of ML to distinguish patients with probable Alzheimer's disease (AD) from normal older adults based on structural magnetic resonance imaging (MRI). METHODS: The Medline, Embase, and Cochrane Library databases were searched for relevant literature published up until July 2021. We used the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to evaluate all included studies' quality and potential bias. Random-effects models were used to calculate pooled sensitivity and specificity, and the Deeks' test was used to assess publication bias. RESULTS: We included 24 models based on different brain features extracted by ML algorithms in 19 papers. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the summary receiver operating characteristic curve for ML in detecting AD were 0.85 (95%CI 0.81-0.89), 0.88 (95%CI 0.84-0.91), 7.15 (95%CI 5.40-9.47), 0.17 (95%CI 0.12-0.22), 43.34 (95%CI 26.89-69.84), and 0.93 (95%CI 0.91-0.95). CONCLUSION: ML using structural MRI data performed well in diagnosing probable AD patients and normal elderly. However, more high-quality, large-scale prospective studies are needed to further enhance the reliability and generalizability of ML for clinical applications before it can be introduced into clinical practice.

A skin-inspired biomimetic strategy to fabricate cellulose enhanced antibacterial hydrogels as strain sensors.

With the development of wearable devices, the fabrication of strong, tough, antibacterial, and conductive hydrogels for sensor applications is necessary but remains challenging. Here, a skin-inspired biomimetic strategy integrated with in-situ reduction has been proposed. The self-assembly of cellulose to generate a cellulose skeleton was essential to realize the biomimetic structural design. Furthermore, in-situ generation of silver nanoparticles on the skeleton was easily achieved by a heating process. This process not only offered the excellent antibacterial property to hydrogels, but also improved the mechanical properties of hydrogels due to the elimination of negative effect of silver nanoparticles aggregation. The highest tensile strength and toughness could reach 2.0 MPa and 11.95 MJ/m3, respectively. Moreover, a high detection range (up to 1300%) and sensitivity (gauge factor = 4.4) was observed as the strain sensors. This study provides a new horizon to fabricate strong, tough and functional hydrogels for various applications in the future.

Decoupling the electronic and geometric effects of Pt catalysts in selective hydrogenation reaction.

Decoupling the electronic and geometric effects has been a long cherished goal for heterogeneous catalysis due to their tangled relationship. Here, a novel orthogonal decomposition method is firstly proposed to settle this issue in p-chloronitrobenzene hydrogenation reaction on size- and shape-controlled Pt nanoparticles (NPs) carried on various supports. Results suggest Fermi levels of catalysts can be modulated by supports with varied work function (Wf). And the selectivity on Pt NPs of similar size and shape is linearly related with the Wf of support. Optimized Fermi levels of the catalysts with large Wf weaken the ability of Pt NPs to fill valence electrons into the antibonding orbital of C-Cl bond, finally suppressing the hydrodehalogenation side reaction. Foremost, the geometric effect is firstly spun off through orthogonal relation based on series of linear relationships over various sizes of Pt NPs reflecting the electronic effect. Moreover, separable nested double coordinate system is established to quantitatively evaluate the two effects.

Theabrownins Produced via Chemical Oxidation of Tea Polyphenols Inhibit Human Lung Cancer Cells in vivo and in vitro by Suppressing the PI3K/AKT/mTOR Pathway Activation and Promoting Autophagy.

During the fermentation of dark tea, theabrownins (TBs), carbohydrates, and other substances get irreversibly complex. Recent research on the biological activity of TBs is not based on free TBs. In the present study, some brown polyphenol oxidized polymers, the generalized TBs (TBs-C), were prepared via alkali oxidation from tea polyphenols (TP). We also investigated the inhibitory mechanism of TBs-C on non-small-cell-lung cancer (NSCLC). TBs-C demonstrated a stronger inhibition than TP on the NSCLC cell lines A549, H2030, HCC827, H1975, and PC9. Next, A549 and H2030 cell lines were selected as subjects to explore this mechanism. TBs-C was found to inhibit proliferation, promote apoptosis, and induce G1 cell-cycle arrest in the cells. In addition, TBs-C increased autophagic flux, which in turn promoted the death of lung cancer cells. Moreover, TBs-C suppressed the PI3K/AKT/mTOR pathway activation, promoted autophagy, and increased the expression of p21 downstream of AKT, which resulted in G1 cell-cycle arrest. In xenotransplanted NSCLC nude mice derived from A549 cells, TBs-C could significantly suppress tumor growth by inhibiting the PI3K/AKT/mTOR pathway without causing hepatotoxicity, brain toxicity, or nephrotoxicity. We believe that our present findings would facilitate advancement in the research and industrialization of TBs.

Risk of contralateral ovarian cancer in patients undergoing conservative surgery for ovarian cancer.

BACKGROUND: Among patients with ovarian cancer (OC), the risk of contralateral OC remains controversial and few studies have focused on the occurrence of contralateral OC after conservative surgery. METHODS: Basing on the Surveillance, Epidemiology, and End Results (SEER) database registered between 2000 and 2018, Logistic and Cox regressions were established to test the risk factors of contralateral OC. Kaplan-Meier mothed was used to calculate the cumulative risk curve for contralateral OC and compared using log-rank test. Furthermore, the frequency of contralateral OC and standardized incidence ratios (SIRs) were evaluated. RESULTS: 18807 patients were included, 69 patients developed contralateral OC. Logistic and Cox regressions showed patients diagnosed >50 years had lower risk of contralateral OC (Odds ratio [OR]:0.42, 95% confidence interval [CI]: 0.24-0.73; Hazard ratios [HR]:0.44, 95%CI:0.24-0.77). Patients with radical surgery had lower contralateral OC risk (OR:0.20, 95%CI: 0.11-0.36; HR: 0.17, 95%CI: 0.09-0.30). The SIR for contralateral OC was high in all patients (SIR: 2.37, 95%CI: 1.85-3.00) and highest if patients diagnosed <50 years with conservative surgery (SIR: 27.33, 95%CI: 19.86-36.69). However, the SIR for contralateral OC was low in patients diagnosed ≥50 years with radical surgery (SIR: 0.54, 95%CI: 0.26-1.00). No statistically significant SIRs were observed in patients diagnosed ≥50 years with conservative surgery and patients diagnosed <50 years with radical surgery. CONCLUSIONS: Our study provided some information for clinicians to assess the risk of contralateral OC and suggested young patients should not undergo hysterectomy to prevent contralateral OC. Moreover, clinical surveillance cannot be relaxed.

Value of Assessment of Different Neoplasias in the Adnexa in the Differential Diagnosis of Malignant Ovarian Tumor and Benign Ovarian Tumor: A Meta-analysis.

To evaluate the accuracy of the assessment of different neoplasias in the adnexa (ADNEX) model in the differential diagnosis of malignant and benign ovarian tumors, the optimal cutoff value and the accuracy in diagnosing ovarian tumors at different stages, PubMed, Web of Science and Cochrane Library databases were retrieved to search literature with per-patient analysis until publication of the last study in November 2021. STATA 14.1, Meta-Disc 1.4 and Revman software 5.3 were used in the performance of meta-analysis. To explore sources of heterogeneity, a subgroup analysis was conducted for the ADNEX model. The pooled sensitivity, specificity, diagnostic odds ratio, positive likelihood, negative likelihood ratio and area under the summary receiver operating characteristic curve were 0.91 (95% confidence interval [CI]: 0.89-0.93), 0.84 (95% CI: 0.80-0.88), 55.55 (95% CI: 40.47-76.26), 5.71 (95% CI: 4.49-7.26), 0.10 (95% CI: 0.08-0.13) and 0.94 (95% CI: 0.92-0.96) in differentiating benign and malignant ovarian tumors, respectively. The area under the curve in identifying benign, borderline, stage I and stages II-IV were 0.93, 0.73, 0.27 and 0.92. The ADNEX model had high diagnostic performance was influential in the diagnosis of benign and stage II-IV ovarian tumors.

Geometric resistant polar quaternion discrete Fourier transform and its application in color image zero-hiding.

As a typical frequency-domain analysis method, quaternion discrete Fourier transform (QDFT) has been widely used in information hiding in color images. However, due to the sensitivity of QDFT to geometric attacks, existing QDFT-based information hiding schemes have limited ability in resisting geometric attacks. In this study, a kind of novel geometrically resilient polar QDFT (PQDFT) is constructed and the properties of the proposed PQDFT are analyzed. Subsequently, a PQDFT-based color image zero-hiding scheme robust to geometric attacks is proposed for lossless copyright protection of color images, which experimentally shows reasonable resistance against geometric and common attacks, indicating better robustness compared with the existing QDFT-based information hiding schemes and other leading-edge zero-hiding schemes.