Boosted Enzyme Activity via Encapsulation within Metal-Organic Frameworks with Pores Matching Enzyme Size and Shape.

A novel and versatile approach called "physical imprinting" is introduced to modulate enzyme conformation using mesoporous materials, addressing challenges in achieving improved enzyme activity and stability. Metal-organic frameworks with tailored mesopores, precisely matching enzyme size and shape, are synthesized. Remarkably, enzymes encapsulated within these customized mesopores exhibit over 1670% relative activity compared to free enzymes, maintaining outstanding efficiency even under harsh conditions such as heat, exposure to organic solvents, wide-ranging pH extremes from acidic to alkaline, and exposure to a digestion cocktail. After 18 consecutive cycles of use, the immobilized enzymes retain 80% of their initial activity. Additionally, the encapsulated enzymes exhibit a substantial increase in catalytic efficiency, with a 14.1-fold enhancement in kcat/KM compared to native enzymes. This enhancement is among the highest reported for immobilized enzymes. The improved enzyme activity and stability are corroborated by solid-state UV-vis, electron paramagnetic resonance, Fourier-transform infrared spectroscopy, and solid-state NMR spectroscopy. The findings not only offer valuable insights into the crucial role of size and shape complementarity within confined microenvironments but also establish a new pathway for developing solid carriers capable of enhancing enzyme activity and stability.

The Diagnostic Accuracy of Transcranial Color-Coded Doppler Ultrasound Technique in Stratifying Intracranial Cerebral Artery Stenoses in Cerebrovascular Disease Patients: A Systematic Review and Meta-Analysis.

The early and accurate stratification of intracranial cerebral artery stenosis (ICAS) is critical to inform treatment management and enhance the prognostic outcomes in patients with cerebrovascular disease (CVD). Digital subtraction angiography (DSA) is an invasive and expensive procedure but is the gold standard for the diagnosis of ICAS. Over recent years, transcranial color-coded Doppler ultrasound (TCCD) has been suggested to be a useful imaging method for accurately diagnosing ICAS. However, the diagnostic accuracy of TCCD in stratifying ICASs among patients with CVD remains unclear. Therefore, this systematic review and meta-analysis aimed at evaluating the diagnostic accuracy of TCCD in the stratification of intracranial steno-occlusions among CVD patients. A total of six databases-Embase, CINAHL, Medline, PubMed, Google Scholar, and Web of Science (core collection)-were searched for studies that assessed the diagnostic accuracy of TCCD in stratifying ICASs. The meta-analysis was performed using Meta-DiSc 1.4. The Quality Assessment of Diagnostic Accuracy Studies tool version 2 (QUADAS-2) assessed the risk of bias. Eighteen studies met all of the eligibility criteria. TCCD exhibited a high pooled diagnostic accuracy in stratifying intracranial steno-occlusions in patients presenting with CVD when compared to DSA as a reference standard (sensitivity = 90%; specificity = 87%; AUC = 97%). Additionally, the ultrasound parameters peak systolic velocity (PSV) and mean flow velocity (MFV) yielded a comparable diagnostic accuracy of "AUC = 0.96". In conclusion, TCCD could be a noble, safe, and accurate alternative imaging technique to DSA that can provide useful diagnostic information in stratifying intracranial steno-occlusions in patients presenting with CVD. TCCD should be considered in clinical cases where access to DSA is limited.

Association of renal elasticity evaluated by real-time shear wave elastography with renal fibrosis in patients with chronic kidney disease.

OBJECTIVE: Renal fibrosis is a final common pathological hallmark in the progression of chronic kidney disease (CKD). Non-invasive evaluation of renal fibrosis by mapping renal stiffness obtained by shear wave elastography (SWE) may facilitate the clinical therapeutic regimen for CKD patients. METHODS: A cohort of 162 patients diagnosed with CKD, who underwent renal biopsy, was prospectively and consecutively recruited between April 2019 and December 2021. The assessment of renal cortex stiffness was performed using SWE imaging. The patients were classified into different groups based on pathological renal fibrosis (mild group: n = 74; moderate-to-severe group: n = 88). Binary logistic regression model and generalized additive model were conducted to investigate the association of renal elasticity with renal fibrosis. RESULTS: Compared with the mildly impaired group, the moderate-to-severe group showed a significant decline in renal elasticity (P < .001). In the fully adjusted model, each 10 kPa drop in renal elasticity was associated with a 3.5-fold increment in the risk of moderate-to-severe renal fibrosis (fully adjusted odds ratio, 4.54; 95% CI, 2.41-8.57). Particularly, participants in the lowest elasticity group (≤29.92 kPa) had a 20-fold increased chance of moderate-to-severe renal fibrosis than those in the group with highest elasticity (≥37.93 kPa). An inverse linear association was observed between renal elasticity increment and moderate-to-severe renal fibrosis risk. CONCLUSION: There is a negative linear association between increased renal elasticity and moderate-to-severe renal fibrosis risk among CKD patients. Patients with diminished renal stiffness have a higher risk of moderate-to-severe renal fibrosis. ADVANCES IN KNOWLEDGE: CKD patients with reduced renal stiffness have a higher likelihood of moderate-to-severe renal fibrosis.

Interpretable machine learning model integrating clinical and elastosonographic features to detect renal fibrosis in Asian patients with chronic kidney disease.

BACKGROUND: Non-invasive renal fibrosis assessment is critical for tailoring personalized decision-making and managing follow-up in patients with chronic kidney disease (CKD). We aimed to exploit machine learning algorithms using clinical and elastosonographic features to distinguish moderate-severe fibrosis from mild fibrosis among CKD patients. METHODS: A total of 162 patients with CKD who underwent shear wave elastography examinations and renal biopsies at our institution were prospectively enrolled. Four classifiers using machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and K-Nearest Neighbor (KNN), which integrated elastosonographic features and clinical characteristics, were established to differentiate moderate-severe renal fibrosis from mild forms. The area under the receiver operating characteristic curve (AUC) and average precision were employed to compare the performance of constructed models, and the SHapley Additive exPlanations (SHAP) strategy was used to visualize and interpret the model output. RESULTS: The XGBoost model outperformed the other developed machine learning models, demonstrating optimal diagnostic performance in both the primary (AUC = 0.97, 95% confidence level (CI) 0.94-0.99; average precision = 0.97, 95% CI 0.97-0.98) and five-fold cross-validation (AUC = 0.85, 95% CI 0.73-0.98; average precision = 0.90, 95% CI 0.86-0.93) datasets. The SHAP approach provided visual interpretation for XGBoost, highlighting the features' impact on the diagnostic process, wherein the estimated glomerular filtration rate provided the largest contribution to the model output, followed by the elastic modulus, then renal length, renal resistive index, and hypertension. CONCLUSION: This study proposed an XGBoost model for distinguishing moderate-severe renal fibrosis from mild forms in CKD patients, which could be used to assist clinicians in decision-making and follow-up strategies. Moreover, the SHAP algorithm makes it feasible to visualize and interpret the feature processing and diagnostic processes of the model output.

Integrating shear wave elastography and estimated glomerular filtration rate to enhance diagnostic strategy for renal fibrosis assessment in chronic kidney disease.

Background: Assessing renal fibrosis non-invasively in patients with chronic kidney disease (CKD) remains a considerable clinical challenge. This study aimed to investigate the diagnostic efficacy of different approaches that combine shear wave elastography (SWE) and estimated glomerular filtration rate (eGFR) in distinguishing between mild fibrosis and moderate-to-severe fibrosis in CKD patients. Methods: In this prospective study, 162 patients underwent renal SWE examinations and renal biopsies. Using SWE, the right renal cortex stiffness was measured, and the corresponding SWE value was recorded. Four diagnostic patterns were used to combine eGFR and SWE value: in isolation, in series, in parallel, and in integration. The receiver operating characteristic (ROC) curve was established, and the area under the ROC curve (AUC) was calculated to quantify diagnostic performance. Sensitivity, specificity, and accuracy were computed. Results: The eGFR demonstrated sensitivity of 68.2% and specificity of 83.8%, whereas the SWE value displayed sensitivity of 84.1% and specificity of 62.2%, yielding a similar AUC (78.2% and 77.8%, respectively). Combining in series improved specificity to 97.3%, superior to other diagnostic patterns (all P values <0.01), but compromised sensitivity to 58.0%. When combined in parallel, the sensitivity increased to 94.3%, exceeding any other strategies (all P values <0.05), but the specificity dropped to 48.7%. The integrated strategy, incorporating eGFR with SWE value via the logistic regression algorithm, exhibited an AUC of 85.8%, outperforming all existing approaches (all P values <0.01), with balanced sensitivity, specificity, and accuracy of 86.4%, 74.3%, and 80.9%, respectively. Conclusions: Using an integrated strategy to combine eGFR and SWE value could improve diagnostic performance in distinguishing between mild renal fibrosis and moderate-to-severe renal fibrosis in patients with CKD, thereby helping clinicians perform a more accurate clinical diagnosis.

Robust Oxidase-Mimetic Supramolecular Nanocatalyst for Lignin Biodegradation.

Enzymatic catalysis presents an eco-friendly, energy-efficient method for lignin degradation. However, challenges arise due to the inherent incompatibility between enzymes and native lignin. In this work, we introduce a supramolecular catalyst composed of fluorenyl-modified amino acids and Cu2+, designed based on the aromatic stacking of the fluorenyl group, which can operate in ionic liquid environments suitable for the dissolution of native lignin. Amino acids and halide anions of ionic liquids shape the copper site's coordination sphere, showcasing remarkable catechol oxidase-mimetic activity. The catalyst exhibits thermophilic property, and maintains oxidative activity up to 75 °C, which allows the catalyzed degradation of the as-dissolved native lignin with high efficiency even without assistance of the electron mediator. In contrast, at this condition, the native copper-dependent oxidase completely lost its activity. This catalyst with superior stability and activity offer promise for sustainable lignin valorization through biocatalytic routes compatible with ionic liquid pretreatment, addressing limitations in native enzymes for industrially relevant conditions.

A Comparative Study of Transcranial Color-Coded Doppler (TCCD) and Transcranial Doppler (TCD) Ultrasonography Techniques in Assessing the Intracranial Cerebral Arteries Haemodynamics.

Cerebrovascular disease (CVD) poses a major public health and socio-economic burden worldwide due to its high morbidity and mortality rates. Accurate assessment of cerebral arteries' haemodynamic plays a crucial role in the diagnosis and treatment management of CVD. The study compared a non-imaging transcranial Doppler ultrasound (TCD) and transcranial color-coded Doppler ultrasound (with (cTCCD) and without (ncTCCD)) angle correction in quantifying middle cerebral arteries (MCAs) haemodynamic parameters. A cross-sectional study involving 50 healthy adults aged ≥ 18 years was conducted. The bilateral MCAs were insonated via three trans-temporal windows (TTWs-anterior, middle, and posterior) using TCD, cTCCD, and ncTCCD techniques. The MCA peak systolic velocity (PSV) and mean flow velocity (MFV) were recorded at proximal and distal imaging depths that could be visualised on TCCD with a detectable spectral waveform. A total of 152 measurements were recorded in 41 (82%) subjects with at least one-sided open TTW across the three techniques. The mean PSVs measured using TCD, ncTCCD, and cTCCD were 83 ± 18 cm/s, 81 ± 19 cm/s, and 93 ± 21 cm/s, respectively. There was no significant difference in PSV between TCD and ncTCCD (bias = 2 cm/s, p = 1.000), whereas cTCCD yielded a significantly higher PSV than TCD and ncTCCD (bias = -10 cm/s, p < 0.001; bias = -12 cm/s, p ≤ 0.001, respectively). The bias in MFV between TCD and ncTCCD techniques was (bias = -0.5 cm/s; p = 1.000), whereas cTCCD demonstrated a higher MFV compared to TCD and ncTCCD (bias = -8 cm/s, p < 0.001; bias = -8 cm/s, p ≤ 0.001, respectively). TCCD is a practically applicable imaging technique in assessing MCA blood flow velocities. cTCCD is more accurate and tends to give higher MCA blood flow velocities than non-imaging TCD and ncTCCD techniques. ncTCCD is comparable to non-imaging TCD and should be considered in clinical cases where using both TCD and TCCD measurements is needed.

Nomogram Analysis Based on Clinical and Sonographic Characteristics for the Assessment of Postpartum Stress Urinary Incontinence.

OBJECTIVES: We aimed to develop and validate a nomogram integrating clinical and sonographic characteristics for the individualized SUI risk evaluation in the early postpartum stage. METHODS: This was a prospective cross-sectional study. From June 2020 to September 2022, singleton primiparas who underwent TPUS examination at 6-8 weeks postpartum were recruited. They were divided into the training and validation cohorts at a ratio of 8:2 according to the temporal split. All subjects were interviewed before TPUS examination. Univariate and multivariate logistic analyses were performed to develop three models: the clinical, sonographic, and combined models. The ROC curve was plotted to evaluate model discrimination ability. Finally, the combined model was selected to establish the nomogram. The nomogram's discrimination, calibration, and clinical usefulness were evaluated in the training and validation cohorts. RESULTS: The performance of the combined model was better than that of the clinical and sonographic models. Six predictors (BMI, delivery mode, lateral episiotomy, SUI during pregnancy, cystocele, and bladder neck funneling) remained in the combined model. The nomogram based on the combined model had good discrimination with AUCs of 0.848 (95% CI: 0.796-0.900) and 0.872 (95% CI: 0.789-0.955) in the training and validation cohorts, respectively, and the calibration curve showed good efficiency in assessing postpartum SUI. Decision curve analysis showed that the nomogram was clinically useful. CONCLUSIONS: The nomogram based on clinical and sonographic characteristics showed good efficiency in assessing postpartum SUI risk and can be a convenient and reliable tool for individual SUI risk assessment.

Ultrasound-based radiomics analysis in the assessment of renal fibrosis in patients with chronic kidney disease.

PURPOSE: Assessment of renal fibrosis non-invasively in chronic kidney disease (CKD) patients is still a clinical challenge. In this study, we aimed to establish a radiomics model integrating radiomics features derived from ultrasound (US) images with clinical characteristics for the assessment of renal fibrosis severity in CKD patients. METHODS: A total of 160 patients with CKD who underwent kidney biopsy and renal US examination were prospectively enrolled. Patients were classified into the mild or moderate-severe fibrosis group based on pathology results. Radiomics features were extracted from the US images, and a radiomics signature was constructed using the maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) regression algorithms. Multivariable logistic regression was employed to construct the radiomics model, which incorporated the radiomics signature and the selected clinical variables. The established model was evaluated for discrimination, calibration, and clinical utility in the derivation cohort and internal cross-validation (CV) analysis, respectively. RESULTS: The radiomics signature, consisting of nine identified fibrosis-related features, achieved moderate discriminatory ability with an area under the receiver operating characteristic curve (AUC) of 0.72 (95% confidence interval (CI) 0.64-0.79). By combining the radiomics signature with significant clinical risk factors, the radiomics model showed satisfactory discrimination performance, yielding an AUC of 0.85 (95% CI 0.79-0.91) in the derivation cohort and a mean AUC of 0.84 (95% CI 0.77-0.92) in the internal CV analysis. It also demonstrated fine accuracy via the calibration curve. Furthermore, the decision curve analysis indicated that the model was clinically useful. CONCLUSION: The proposed radiomics model showed favorable performance in determining the individualized risk of moderate-severe renal fibrosis in patients with CKD, which may facilitate more effective clinical decision-making.

Using elastography-based multilayer perceptron model to evaluate renal fibrosis in chronic kidney disease.

BACKGROUND: Given its progressive deterioration in the clinical course, noninvasive assessment and risk stratification for the severity of renal fibrosis in chronic kidney disease (CKD) are required. We aimed to develop and validate an end-to-end multilayer perceptron (MLP) model for assessing renal fibrosis in CKD patients based on real-time two-dimensional shear wave elastography (2D-SWE) and clinical variables. METHODS: From April 2019 to December 2021, a total of 162 patients with CKD who underwent a kidney biopsy and 2D-SWE examination were included in this single-center, cross-sectional, and prospective clinical study. 2D-SWE was performed to measure the right renal cortex stiffness, and the corresponding elastic values were recorded. Patients were categorized into two groups according to their histopathological results: mild and moderate-severe renal fibrosis. The patients were randomly divided into a training cohort (n = 114) or a test cohort (n = 48). The MLP classifier using a machine learning algorithm was used to construct a diagnostic model incorporating elastic values with clinical features. Discrimination, calibration, and clinical utility were used to appraise the performance of the established MLP model in the training and test sets, respectively. RESULTS: The developed MLP model demonstrated good calibration and discrimination in both the training [area under the receiver operating characteristic curve (AUC) = 0.93; 95% confidence interval (CI) = 0.88 to 0.98] and test cohorts [AUC = 0.86; 95% CI = 0.75 to 0.97]. A decision curve analysis and a clinical impact curve also showed that the MLP model had a positive clinical impact and relatively few negative effects. CONCLUSIONS: The proposed MLP model exhibited the satisfactory performance in identifying the individualized risk of moderate-severe renal fibrosis in patients with CKD, which is potentially helpful for clinical management and treatment decision-making.

Assessment of Renal Fibrosis in Patients With Chronic Kidney Disease Using Shear Wave Elastography and Clinical Features: A Random Forest Approach.

OBJECTIVE: Renal fibrosis is the common pathological hallmark of chronic kidney disease (CKD) progression. In this study, a random forest (RF) classifier based on 2-D shear wave elastography (SWE) and clinical features for the differential severity of renal fibrosis in patients with CKD is proposed. METHODS: A total of 162 patients diagnosed with CKD who underwent 2-D SWE and renal biopsy were prospectively enrolled from April 2019 to December 2021 and then randomized into training (n = 114) and validation (n = 48) cohorts at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression and recursive feature elimination for support vector machines (SVM-RFE) algorithm were employed to select renal fibrosis-related features from clinical information and elastosonographic findings. An RF model was subsequently constructed using the aforementioned informative parameters in the training cohort and evaluated in terms of discrimination, calibration and clinical utility in both cohorts. RESULTS: The LASSO and SVM-RFE analyses revealed that age, sex, blood urea nitrogen, renal resistive index, hypertension and the 2D-SWE value were independent risk variables associated with renal fibrosis severity. The established RF model incorporating these six variables exhibited fine discrimination in both the derivation (area under the curve [AUC]: 0.84, 95% confidence interval [CI]: 0.76-0.91) and validation (AUC: 0.88, 95% CI: 0.77-0.98) cohorts. Moreover, the calibration curve revealed satisfactory predictive accuracy, and the decision curve analysis revealed a significant clinical net benefit. CONCLUSION: The developed RF model, via a combination of the 2-D SWE value and clinical information, indicated satisfactory diagnostic performance and clinical practicality toward differentiating moderate-severe from mild renal fibrosis, which may provide critical insight into risk stratification for patients with CKD.

Development and Deployment of a Novel Diagnostic Tool Based on Conventional Ultrasound for Fibrosis Assessment in Chronic Kidney Disease.

RATIONALE AND OBJECTIVES: Accurate identification of risk information about fibrosis severity is crucial for clinical decision-making and clinical management of patients with chronic kidney disease (CKD). This study aimed to develop an ultrasound (US)-derived computer-aided diagnosis tool for identifying CKD patients at high risk of developing moderate-severe renal fibrosis, in order to optimize treatment regimens and follow-up strategies. MATERIALS AND METHODS: A total of 162 CKD patients undergoing renal biopsies and US examinations were prospectively enrolled and randomly divided into training (n = 114) and validation (n = 48) cohorts. A multivariate logistic regression approach was employed to develop the diagnostic tool named S-CKD for differentiating moderate-severe renal fibrosis from mild one in the training cohort by integrating the significant variables, which were screened out from demographic characteristics and conventional US features via the least absolute shrinkage and selection operator regression algorithm. The S-CKD was then deployed as both an online web-based and an offline document-based, easy-to-use auxiliary device. In both the training and validation cohorts, the S-CKD's diagnostic performance was evaluated through discrimination and calibration. The clinical benefit of using S-CKD was revealed by decision curve analysis (DCA) and clinical impact curves. RESULTS: The proposed S-CKD achieved an area under the receiver operating characteristic curve of 0.84 (95% confidence interval (CI): 0.77-0.91) and 0.81 (95% CI: 0.68-0.94) in the training and validation cohorts, respectively, indicating satisfactory diagnosis performance. Results of the calibration curves showed that S-CKD has excellent predictive accuracy (Hosmer-Lemeshow test: training cohort, p = 0.497; validation cohort, p = 0.205). The DCA and clinical impact curves exhibited a high clinical application value of the S-CKD at a wide range of risk probabilities. CONCLUSION: The S-CKD tool developed in this study is capable of discriminating between mild and moderate-severe renal fibrosis in patients with CKD and achieving promising clinical benefits, which may aid clinicians in personalizing medical decision-making and follow-up arrangement.

Solid-Phase Screening and Synthesis of Molecularly Imprinted Nanoparticles for Selective Recognition and Detection of Brain Natriuretic Peptide.

A new recognition method is explored for the rapid detection of B-type natriuretic peptide (BNP) based on the rational design and solid-phase synthesis of molecularly imprinted nanoparticles (nanoMIP) encapsulated with carbon dots. The nanosized magnetic template is first prepared by attaching the epitope of BNP on amino-functionalized magnetic carriers. High-dilution polymerization of monomers in the presence of magnetic template generates lightly crosslinked imprinted nanoparticles. To obtain the optimal MIP formulation, a new combinatorial screening approach is developed by a competitive fluorescence assay using the magnetic template. The resultant nanoMIP exhibits high affinity and selectivity toward BNP with an equilibrium dissociation constant (KD ) of ≈10-11  m. The proposed assay allows fast BNP detection within ≈7 min with a linear range of its concentration from 0.25 to 5000 pg mL-1 and a limit of detection of 0.208 pg mL-1 (S/N = 3). To demonstrate its practicability in clinical diagnosis, unknown real serum samples from 160 individuals are analyzed and the relative standard deviation is less than 4.43%. Compared with the routine electrochemiluminescence detection method that is widely used in hospital, the relative error is less than 4.98% and the correlation coefficient is 0.994.

Exceptional Electron-Rich Heteroaromatic Pentacycle for Ultralow Band Gap Conjugated Polymers and Photothermal Therapy.

Stable redox-active conjugated molecules with exceptional electron-donating abilities are key components for the design and synthesis of ultralow band gap conjugated polymers. While hallmark electron-rich examples such as pentacene derivatives have been thoroughly explored, their poor air stability has hampered their broad incorporation into conjugated polymers for practical applications. Herein, we describe the synthesis of the electron-rich, fused pentacyclic pyrazino[2,3-b:5,6-b']diindolizine (PDIz) motif and detail its optical and redox behavior. The PDIz ring system exhibits a lower oxidation potential and a reduced optical band gap than the isoelectronic pentacene while retaining greater air stability in both solution and the solid state. The enhanced stability and electron density, together with readily installed solubilizing groups and polymerization handles, allow for the use of the PDIz motif in the synthesis of a series of conjugated polymers with band gaps as small as 0.71 eV. The tunable absorbance throughout the biologically relevant near-infrared I and II regions enables the use of these PDIz-based polymers as efficient photothermal therapeutic reagents for laser ablation of cancer cells.

Isometric Covalent Triazine Framework-Derived Porous Carbons as Metal-Free Electrocatalysts for the Oxygen Reduction Reaction.

Covalent triazine frameworks (CTFs) and their derivative N-doped carbons have attracted much attention for application in energy conversion and storage. However, previous studies have mainly focused on developing new building blocks and optimizing synthetic conditions. The use of isometric building blocks to control the porous structure and to fundamentally understand structure-property relationships have rarely been reported. In this work, two isometric building blocks are used to produce isometric CTFs with controllable pore geometries. The as-prepared CTF with nonplanar hexagonal rings demonstrates higher surface area, larger pore volume, and richer N content than the planar CTF. After pyrolysis, nonplanar porous CTF-derived N-doped carbons exhibit admirable catalytic activity for oxygen reduction in alkaline media (half-wave potential: 0.86 V; Tafel slope: 65 mV dec-1 ), owing to their larger pore volume and the abundance of pyridinic and graphitic N species. When assembled into a zinc-air battery, the as-made electrocatalysts show high capacities of up to 651 mAh g-1 and excellent durability.

A nomogram based on shear wave elastography for assessment of renal fibrosis in patients with chronic kidney disease.

BACKGROUND: Non-invasive evaluation of renal fibrosis is still challenging. This study aimed to establish a nomogram based on shear wave elastography (SWE) and clinical features for the assessment of the severity of renal fibrosis in patients with chronic kidney disease (CKD). METHODS: One hundred and sixty-two patients with CKD who underwent kidney biopsy and SWE examination were prospectively enrolled between April 2019 and December 2021. Patients were classified into mildly or moderately-severely impaired group based on pathology results. All patients were randomly divided into a training (n = 113) or validation cohort (n = 49). Least absolute shrinkage and selection operator (LASSO) algorithm was used for data dimensionality reduction and feature selection. Then, a diagnostic nomogram incorporating the selected features was constructed using multivariable logistic regression analysis. Nomogram performance was evaluated for discrimination, calibration, and clinical utility in training and validation cohorts. RESULTS: The established SWE nomogram, which integrated SWE value, hypertension, and estimated glomerular filtration rate, showed fine calibration and discrimination in both training (area under the receiver operator characteristic curve (AUC) = 0.94; 95% confidence interval (CI) 0.89-0.98) and validation cohorts (AUC = 0.84; 95% CI 0.71-0.96). Significant improvement in net reclassification and integrated discrimination indicated that the SWE value is a valuable biomarker to assess moderate-severe renal impairment. Furthermore, decision curve analysis revealed that the SWE nomogram has clinical value. CONCLUSION: The proposed SWE nomogram showed favorable performance in determining individualized risk of moderate-severe renal pathological impairment in patients with CKD, which will help to facilitate clinical decision-making.

Estrogen Suppresses Cytokines Release in cc4821 Neisseria meningitidis Infection via TLR4 and ERß-p38-MAPK Pathway.

Estrogen has long been known to possess immune-modulatory effects in diseases, and multiple pathological conditions show great sex disparities. However, the impact of estrogen in Neisseria meningitidis infection has not been determined. The present study aimed to investigate the role of estrogen in N. meningitidis infection and the molecular mechanism. We selected 35 N. meningitidis isolates representing different clonal complexes (cc), serogroups, and isolation sources to infect the HBMEC cell line. Results showed that the expression of estrogen receptor (ER) ß in N. meningitidis-infected cells was downregulated compared with that in normal cells. The expression of ERß induced by invasive isolates was lower than that in carriers. Serogroup C isolates induced the lowest expression of ERß compared with serogroup A and B isolates. We used four cc4821 N. meningitidis isolates to infect two kinds of host cells (human brain microvascular endothelial cells and meningeal epithelial cells). The results showed that 17 ß-estradiol (E2) could inhibit the release of inflammatory factors interleukin (IL)-6, IL-8, and tumor necrosis factor-α after N. meningitidis infection via TLR4. E2 could inhibit the activation of the p38-MAPK signal pathway induced by N. meningitidis infection through binding to ERß, and significantly inhibit the release of inflammatory factors in N. meningitidis-infected host cells. This study demonstrated that estrogen plays a protective role in N. meningitidis infection. ERß is potentially associated with the release of inflammatory cytokines in N. meningitidis infection, which sheds light on a possible therapeutic strategy for the treatment of invasive diseases caused by N. meningitidis.