Ice sheet and precession controlled subarctic Pacific productivity and upwelling over the last 550,000 years.

The polar oceans play a vital role in regulating atmospheric CO2 concentrations (pCO2) during the Pleistocene glacial cycles. However, despite being the largest modern reservoir of respired carbon, the impact of the subarctic Pacific remains poorly understood due to limited records. Here, we present high-resolution, 230Th-normalized export productivity records from the subarctic northwestern Pacific covering the last five glacial cycles. Our records display pronounced, glacial-interglacial cyclicity superimposed with precessional-driven variability, with warm interglacial climate and high boreal summer insolation providing favorable conditions to sustain upwelling of nutrient-rich subsurface waters and hence increased export productivity. Our transient model simulations consistently show that ice sheets and to a lesser degree, precession are the main drivers that control the strength and latitudinal position of the westerlies. Enhanced upwelling of nutrient/carbon-rich water caused by the intensification and poleward migration of the northern westerlies during warmer climate intervals would have led to the release of previously sequestered CO2 from the subarctic Pacific to the atmosphere. Our results also highlight the significant role of the subarctic Pacific in modulating pCO2 changes during the Pleistocene climate cycles, especially on precession timescale ( ~ 20 kyr).

Iron Metabolic Biomarkers and the Mortality Risk in the General Population: A Nationwide Population-Based Cohort Study.

Context: Iron is an essential element in the human body and plays a critical role in many physiological and cellular processes. However, the association between iron status and the risk of all-cause or cause-specific mortality has not been well-investigated. And it is unclear whether the association between iron metabolic biomarkers and the risk of mortality differs between people with and without diabetes mellitus (DM). Objective: This work aimed to investigate associations between iron metabolic biomarkers and all-cause and cause-specific mortality risk in the general population, and heterogeneities in the associations among population with and without DM.. Methods: A total of 29 166 adults from the National Health and Nutrition Examination Survey (NHANES) III and NHANES 1999 to 2010 were included, with linkage to the National Death Index to December 31, 2019. Cox proportional-hazard models and Fine-Gray subdistribution hazard models were used to estimate associations between iron metabolic biomarkers and outcomes. Results: During a median follow-up of 18.83 years, 9378 deaths were observed, including 3420 cardiovascular disease (CVD) deaths and 1969 cancer deaths. A significant linear association between serum ferritin (SF) and all-cause mortality was observed among the overall population and those without DM. J-shaped associations between transferrin saturation (TSAT) and all-cause and CVD mortality were observed among all populations. In the overall population, compared to the first quartile (Q1) group, the adjusted hazard ratio (HR) (95% CI) for all-cause mortality was 1.07 (1.00-1.15), 1.05 (0.98-1.12), 1.13 (1.05-1.21) in Q2, Q3, and Q4 groups for SF, while the HR was 0.94 (0.88-0.99), 0.92 (0.86-0.97), and 0.93 (0.88-0.99) for TSAT. In individuals without DM, the adjusted HR of the Q4 of SF were 1.19 (1.03-1.37) for CVD mortality and 1.25 (1.05-1.48) for cancer mortality. In individuals with DM, the adjusted HRs of the Q4 of TSAT were 0.76 (0.62-0.93) for CVD mortality and 1.47 (1.07-2.03) for cancer mortality. Conclusion: Iron metabolism abnormalities increase mortality risk in the general population. The associations of iron status with mortality were significantly different between individuals with and without DM, which indicated tailored strategies for iron homeostasis are needed.

Networks of Nerve Fibers, and Blood and Lymphatic Vessels in the Mouse Auricle: The Structural Basis of Ear Acupuncture.

Objective: Ear acupuncture, as a system for treating and preventing diseases through stimulation of points on the auricle, has been systematically introduced during the last 60 years. Although the auricular cartography was described somatotopically as an inverted fetus by Paul Nogier, MD, the underlying mechanism of auricular stimulation remains unclear. The aim of this research was to gain an understanding of the structural basis of auricular stimulation, as well as showing the distribution of the nerve fibers, and the blood and lymphatic vessels. Materials and Methods: The distribution of nerve fibers, and blood and lymphatic vessels was examined in whole-mount auricular skins of mice by combining the biomarkers protein gene product 9.5, cluster of differentiation 31, and lymphatic-vessel endothelial hyaluronan receptor-1 following tissue-clearing treatment with multiple immunofluorescent staining. Results: The labeled nerve fibers, and the blood and lymphatic vessels were distributed extensively in the inner and outer parts of the auricular skin. Auricular nerves aligning with blood vessels ran from the basal region to the peripheral region and crossed over lymphatic vessels, thus forming the neural, vascular, and lymphatic networks. Conclusions: As these are important tissue components of auricular skin, this result implies that the auricular nerve fibers, and blood and lymphatic vessels may coordinate with each other to respond directly to auricular stimulation.

Improving radiomic modeling for the identification of symptomatic carotid atherosclerotic plaques using deep learning-based 3D super-resolution CT angiography.

Rationale and objectives: Radiomic models based on normal-resolution (NR) computed tomography angiography (CTA) images can fail to distinguish between symptomatic and asymptomatic carotid atherosclerotic plaques. This study aimed to explore the effectiveness of a deep learning-based three-dimensional super-resolution (SR) CTA radiomic model for improved identification of symptomatic carotid atherosclerotic plaques. Materials and methods: A total of 193 patients with carotid atherosclerotic plaques were retrospectively enrolled and allocated into either a symptomatic (n = 123) or an asymptomatic (n = 70) groups. SR CTA images were derived from NR CTA images using deep learning-based three-dimensional SR technology. Handcrafted radiomic features were extracted from both the SR and NR CTA images and three risk models were developed based on manually measured quantitative CTA characteristics and NR and SR radiomic features. Model performances were assessed via receiver operating characteristic, calibration, and decision curve analyses. Results: The SR model exhibited the optimal performance (area under the curve [AUC] 0.820, accuracy 0.802, sensitivity 0.854, F1 score 0.847) in the testing cohort, outperforming the other two models. The calibration curve analyses and Hosmer-Lemeshow test demonstrated that the SR model exhibited the best goodness of fit, and decision curve analysis revealed that SR model had the highest clinical value and potential patient benefits. Conclusions: Deep learning-based three-dimensional SR technology could improve the CTA-based radiomic models in identifying symptomatic carotid plaques, potentially providing more accurate and valuable information to guide clinical decision-making to reduce the risk of ischemic stroke.

A genomic association study revealing subphenotypes of childhood steroid-sensitive nephrotic syndrome in a larger genomic sequencing cohort.

Dissecting the genetic components that contribute to the two main subphenotypes of steroid-sensitive nephrotic syndrome (SSNS) using genome-wide association studies (GWAS) strategy is important for understanding the disease. We conducted a multicenter cohort study (360 patients and 1835 controls) combined with a GWAS strategy to identify susceptibility variants associated with the following two subphenotypes of SSNS: steroid-sensitive nephrotic syndrome without relapse (SSNSWR, 181 patients) and steroid-dependent/frequent relapse nephrotic syndrome (SDNS/FRNS, 179 patients). The distribution of two single-nucleotide polymorphisms (SNPs) in ANKRD36 and ALPG was significant between SSNSWR and healthy controls, and that of two SNPs in GAD1 and HLA-DQA1 was significant between SDNS/FRNS and healthy controls. Interestingly, rs1047989 in HLA-DQA1 was a candidate locus for SDNS/FRNS but not for SSNSWR. No significant SNPs were observed between SSNSWR and SDNS/FRNS. Meanwhile, chromosome 2:171713702 in GAD1 was associated with a greater steroid dose (>0.75 mg/kg/d) upon relapse to first remission in patients with SDNS/FRNS (odds ratio = 3.14; 95% confidence interval, 0.97-9.87; P = 0.034). rs117014418 in APOL4 was significantly associated with a decrease in eGFR of greater than 20% compared with the baseline in SDNS/FRNS patients (P = 0.0001). Protein-protein intersection network construction suggested that HLA-DQA1 and HLA-DQB1 function together through GSDMA. Thus, SSNSWR belongs to non-HLA region-dependent nephropathy, and the HLA-DQA/DQB region is likely strongly associated with disease relapse, especially in SDNS/FRNS. The study provides a novel approach for the GWAS strategy of SSNS and contributes to our understanding of the pathological mechanisms of SSNSWR and SDNS/FRNS.

Radiomics-based discrimination of coronary chronic total occlusion and subtotal occlusion on coronary computed tomography angiography.

OBJECTIVES: Differentiating chronic total occlusion (CTO) from subtotal occlusion (SO) is often difficult to make from coronary computed tomography angiography (CCTA). We developed a CCTA-based radiomics model to differentiate CTO and SO. METHODS: A total of 66 patients with SO underwent CCTA before invasive angiography and were matched to 66 patients with CTO. Comprehensive imaging analysis was conducted for all lesioned vessels, involving the automatic identification of the lumen within the occluded segment and extraction of 1,904 radiomics features. Radiomics models were then constructed to assess the discriminative value of these features in distinguishing CTO from SO. External validation of the model was performed using data from another medical center. RESULTS: Compared to SO patients, CTO patients had more blunt stumps (internal: 53/66 (80.3%) vs. 39/66 (59.1%); external: 36/50 (72.0%) vs. 20/50 (40.0%), both p < 0.01), longer lesion length (internal: median length 15.4 mm[IQR: 10.4-22.3 mm] vs. 8.7 mm[IQR: 4.9-12.6 mm]; external:11.8 mm[IQR: 6.1-23.4 mm] vs. 6.2 mm[IQR: 3.5-9.1 mm]; both p < 0.001). Sixteen unique radiomics features were identified after the least absolute shrinkage and selection operator regression. When added to the combined model including imaging features, radiomics features provided increased value for distinguishing CTO from SO (AUC, internal: 0.772 vs. 0.846; p = 0.023; external: 0.718 vs. 0.781, p = 0.146). CONCLUSIONS: The occluded segment vessels of CTO and SO have different radiomics signatures. The combined application of radiomics features and imaging features based on CCTA extraction can enhance diagnostic confidence.

Fully automated deep learning system for osteoporosis screening using chest computed tomography images.

Background: Osteoporosis, a disease stemming from bone metabolism irregularities, affects approximately 200 million people worldwide. Timely detection of osteoporosis is pivotal in grappling with this public health challenge. Deep learning (DL), emerging as a promising methodology in the field of medical imaging, holds considerable potential for the assessment of bone mineral density (BMD). This study aimed to propose an automated DL framework for BMD assessment that integrates localization, segmentation, and ternary classification using various dominant convolutional neural networks (CNNs). Methods: In this retrospective study, a cohort of 2,274 patients underwent chest computed tomography (CT) was enrolled from January 2022 to June 2023 for the development of the integrated DL system. The study unfolded in 2 phases. Initially, 1,025 patients were selected based on specific criteria to develop an automated segmentation model, utilizing 2 VB-Net networks. Subsequently, a distinct cohort of 902 patients was employed for the development and testing of classification models for BMD assessment. Then, 3 distinct DL network architectures, specifically DenseNet, ResNet-18, and ResNet-50, were applied to formulate the 3-classification BMD assessment model. The performance of both phases was evaluated using an independent test set consisting of 347 individuals. Segmentation performance was evaluated using the Dice similarity coefficient; classification performance was appraised using the receiver operating characteristic (ROC) curve. Furthermore, metrics such as the area under the curve (AUC), accuracy, and precision were meticulously calculated. Results: In the first stage, the automatic segmentation model demonstrated excellent segmentation performance, with mean Dice surpassing 0.93 in the independent test set. In the second stage, both the DenseNet and ResNet-18 demonstrated excellent diagnostic performance in detecting bone status. For osteoporosis, and osteopenia, the AUCs were as follows: DenseNet achieved 0.94 [95% confidence interval (CI): 0.91-0.97], and 0.91 (95% CI: 0.87-0.94), respectively; ResNet-18 attained 0.96 (95% CI: 0.92-0.98), and 0.91 (95% CI: 0.87-0.94), respectively. However, the ResNet-50 model exhibited suboptimal diagnostic performance for osteopenia, with an AUC value of only 0.76 (95% CI: 0.69-0.80). Alterations in tube voltage had a more pronounced impact on the performance of the DenseNet. In the independent test set with tube voltage at 100 kVp images, the accuracy and precision of DenseNet decreased on average by approximately 14.29% and 18.82%, respectively, whereas the accuracy and precision of ResNet-18 decreased by about 8.33% and 7.14%, respectively. Conclusions: The state-of-the-art DL framework model offers an effective and efficient approach for opportunistic osteoporosis screening using chest CT, without incurring additional costs or radiation exposure.

Projection Stereolithography 3D Printing High-Conductive Hydrogel for Flexible Passive Wireless Sensing.

Hydrogel-based electronics have inherent similarities to biological tissues and hold potential for wearable applications. However, low conductivity, poor stretchability, nonpersonalizability, and uncontrollable dehydration during use limit their further development. In this study, projection stereolithography 3D printing high-conductive hydrogel for flexible passive wireless sensing is reported. The prepared photocurable silver-based hydrogel is rapidly planarized into antenna shapes on substrates using surface projection stereolithography. After partial dehydration, silver flakes within the circuits form sufficient conductive pathways to achieve high conductivity (387 S cm-1). By sealing the circuits to prevent further dehydration, the resistance remains stable when tensile strain is less than 100% for at least 30 days. Besides, the sealing materials provide versatile functionalities, such as stretchability and shape memory property. Customized flexible radio frequency identification tags are fabricated by integrating with commercial chips to complete the accurate recognition of eye movement, realizing passive wireless sensing.

Induction of autism-related behavior in male mice by early-life vitamin D deficiency: association with disruption of the gut microbial composition and homeostasis.

Vitamin D deficiency (VDD) during early life emerges as a potential risk factor for autism spectrum disorder (ASD). Individuals with autism commonly exhibit lower vitamin D (VD) levels compared to the general population, and VD deficiency is prevalent during pregnancy and lactation. Moreover, gastrointestinal comorbidity, prevalent in ASD patients, correlates closely with disruptions in the gut microbiota and altered intestinal permeability. Therefore, it is fascinating and significant to explore the effects of maternal VD deficiency during pregnancy and lactation on the maturation of the gut microbiota of the offspring and its relevance to autism spectrum disorders. In this study, we established maternal pregnancy and lactation VD-deficient mouse models, employed shotgun macrogenomic sequencing to unveil alterations in the gut microbiome of offspring mice, and observed autism-related behaviours. Furthermore, fecal microbial transplantation (FMT) reversed repetitive and anxious behaviours and alleviated social deficits in offspring mice by modulating the gut microbiota and increasing short-chain fatty acid levels in the cecum, along with influencing the concentrations of claudin-1 and occludin in the colon. Our findings confirm that VDD during pregnancy and lactation is a risk factor for autism in the offspring, with disturbances in the structure and function of the offspring's gut microbiota contributing at least part of the effect. The study emphasises the importance of nutrition and gut health early in life. Simultaneously, this study further demonstrates the effect of VDD on ASD and provides potential ideas for early prevention and intervention of ASD.

Prediction of transformation in the histopathological growth pattern of colorectal liver metastases after chemotherapy using CT-based radiomics.

Chemotherapy alters the prognostic biomarker histopathological growth pattern (HGP) phenotype in colorectal liver metastases (CRLMs) patients. We aimed to develop a CT-based radiomics model to predict the transformation of the HGP phenotype after chemotherapy. This study included 181 patients with 298 CRLMs who underwent preoperative contrast-enhanced CT followed by partial hepatectomy between January 2007 and July 2022 at two institutions. HGPs were categorized as pure desmoplastic HGP (pdHGP) or non-pdHGP. The samples were allocated to training, internal validation, and external validation cohorts comprising 153, 65, and 29 CRLMs, respectively. Radiomics analysis was performed on pre-enhanced, arterial phase, portal venous phase (PVP), and fused images. The model was used to predict prechemotherapy HGPs in 112 CRLMs, and HGP transformation was analysed by comparing these findings with postchemotherapy HGPs determined pathologically. The prevalence of pdHGP was 19.8% (23/116) and 45.8% (70/153) in chemonaïve and postchemotherapy patients, respectively (P < 0.001). The PVP radiomics signature showed good performance in distinguishing pdHGP from non-pdHGPs (AUCs of 0.906, 0.877, and 0.805 in the training, internal validation, and external validation cohorts, respectively). The prevalence of prechemotherapy pdHGP predicted by the radiomics model was 33.0% (37/112), and the prevalence of postchemotherapy pdHGP according to the pathological analysis was 47.3% (53/112; P = 0.029). The transformation of HGP was bidirectional, with 15.2% (17/112) of CRLMs transforming from prechemotherapy pdHGP to postchemotherapy non-pdHGP and 30.4% (34/112) transforming from prechemotherapy non-pdHGP to postchemotherapy pdHGP (P = 0.005). CT-based radiomics method can be used to effectively predict the HGP transformation in chemotherapy-treated CRLM patients, thereby providing a basis for treatment decisions.

Single-cell analysis with childhood and adult systemic lupus erythematosus.

Patients with systemic lupus erythematosus (SLE), a heterogeneous and chronic autoimmune disease, exhibit unique changes in the complex composition and transcriptional signatures of peripheral blood mononuclear cells (PBMCs). While the mechanism of pathogenesis for both childhood-onset SLE (cSLE) and adult-onset SLE (aSLE) remains unclear, cSLE patients are considered more unpredictable and dangerous than aSLE patients. In this study, we analysed single-cell RNA sequencing data (scRNA-seq) to profile the PBMC clusters of cSLE/aSLE patients and matched healthy donors and compared the PBMC composition and transcriptional variations between the two groups. Our analysis revealed that the PBMC composition and transcriptional variations in cSLE patients were similar to those in aSLE patients. Comparative single-cell transcriptome analysis between healthy donors and SLE patients revealed IFITM3, ISG15, IFI16 and LY6E as potential therapeutic targets for both aSLE and cSLE patients. Additionally, we observed that the percentage of pre-B cells (CD34-) was increased in cSLE patients, while the percentage of neutrophil cells was upregulated in aSLE patients. Notably, we found decreased expression of TPM2 in cSLE patients, and similarly, TMEM150B, IQSEC2, CHN2, LRP8 and USP46 were significantly downregulated in neutrophil cells from aSLE patients. Overall, our study highlights the differences in complex PBMC composition and transcriptional profiles between cSLE and aSLE patients, providing potential biomarkers that could aid in diagnosing SLE.

Effect evaluation of competency-based education (CBE) combined with multi-disciplinary team (MDT) teaching mode in respiratory rehabilitation nursing teaching：A randomized controlled trial.

AIM: To explore the application effect of competency-based education (CBE) combined with multi-disciplinary team (MDT) teaching mode in respiratory rehabilitation nursing teaching. BACKGROUND: Respiratory rehabilitation, as an important non drug treatment for chronic respiratory disease, started late in China, with low implementation rate in hospitals and insufficient knowledge of nursing staff. Therefore, it is urgent to cultivate high professional level nurses with strong professional ability. DESIGN: A randomized controlled trial. METHODS: We selected nurses from the respiratory and critical care department at a Grade III, Level A hospital in Beijing, who participated in the respiratory rehabilitation training program between March 2020 and August 2022, as our study participants. Participants were grouped using a random number method. Nurses who participated in the study from March 2020 to May 2021 were set as the control group, and the nurses who participated in the study from June 2021 to August 2022 were set as the test group. The control group used traditional teaching methods, and the test group used the CBE combined MDT teaching model. At the end of the test, the theory, operating skills, satisfaction and core competence were assessed. RESULTS: After training, the theoretical examination score of the test group (93.71 ± 1.94) was higher than that of the control group (92.37 ± 1.92), the operational examination score of the test group (93.11 ± 2.12) was higher than that of the control group (91.61 ± 1.93), the overall teaching satisfaction of the test group (4.45 ± 0.50) was higher than that of the control group (4.13 ± 0.57), and the total score of the core competence of the test group (148.73 ± 7.08) was higher than that of the control group (141.02 ± 6.41). The difference between the two groups was statistically significant (P<0.05). CONCLUSION: CBE combined with MDT teaching mode has a good effect in Pulmonary and Critical Care Medicine (PCCM) respiratory rehabilitation nursing teaching.

Artificial intelligence to predict T4 stage of pancreatic ductal adenocarcinoma using CT imaging.

BACKGROUND: The accurate assessment of T4 stage of pancreatic ductal adenocarcinoma (PDAC) has consistently presented a considerable difficulty for radiologists. This study aimed to develop and validate an automated artificial intelligence (AI) pipeline for the prediction of T4 stage of PDAC using contrast-enhanced CT imaging. METHODS: The data were obtained retrospectively from consecutive patients with surgically resected and pathologically proved PDAC at two institutions between July 2017 and June 2022. Initially, a deep learning (DL) model was developed to segment PDAC. Subsequently, radiomics features were extracted from the automatically segmented region of interest (ROI), which encompassed both the tumor region and a 3 mm surrounding area, to construct a predictive model for determining T4 stage of PDAC. The assessment of the models' performance involved the calculation of the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS: The study encompassed a cohort of 509 PDAC patients, with a median age of 62 years (interquartile range: 55-67). The proportion of patients in T4 stage within the model was 16.9%. The model achieved an AUC of 0.849 (95% CI: 0.753-0.940), a sensitivity of 0.875, and a specificity of 0.728 in predicting T4 stage of PDAC. The performance of the model was determined to be comparable to that of two experienced abdominal radiologists (AUCs: 0.849 vs. 0.834 and 0.857). CONCLUSION: The automated AI pipeline utilizing tumor and peritumor-related radiomics features demonstrated comparable performance to that of senior abdominal radiologists in predicting T4 stage of PDAC.

Multiple interface coupling on natural tourmaline enables high-efficiency removal of antibiotic: Superior property and mechanism.

Reasonably designing highly active, environmentally friendly, and cost-effective catalysts for efficient elimination of pollutants from water is desirable but challenging. Herein, an efficient heterogeneous photo-Fenton catalyst tourmaline (TM)/tungsten oxide (WO3-x) (named TW10) containing tungsten/boron/iron (W/B/Fe) synergistic active centers and 90% of cheap natural tourmaline (TM) mineral rich in Fe and B elements. The TW10 catalyst can quickly activate peroxymonosulfate (PMS) to generate massive active free radicals, which may induce the rapid and efficient degradation of tetracycline (TC). The TW10/PMS/Visible light system can effectively degrade up to 98.7% of tetracycline (TC) in actual waters (i.e. seawater, Yellow River, and Yangtze River water), and the catalytic degradation rates reach 1.65, 5.569, and 2.38 times higher than those of TM, WO3-x, and commercial P25 (Degussa, Germany), respectively. In addition, the catalyst can be recycled and reused multiple times. Electron spin resonance spectroscopy (EPR), X-ray photoelectron spectroscopy (XPS), and liquid chromatograph-mass spectrometer (LC-MS) analyses confirm that the synergistic catalytic effect of W/B/Fe sites on the TW10 catalyst accelerates the electron transfer between Fe(II) and Fe(III), as well as between W(V) and W(VI), and thus promotes the rapid degradation of TC. The catalytic reaction mechanism and degradation pathway of TC were explored. This work provides a feasible route for the design and development of new eco-friendly and efficient catalyst.

The role and impact of abnormal vitamin levels in autism spectrum disorders.

The prevalence of autism spectrum disorder (ASD), a neurodevelopmental disorder with a predominance of social behavioral disorders, has increased dramatically in various countries in recent decades. The interplay between genetic and environmental factors is believed to underlie ASD pathogenesis. Recent analyses have shown that abnormal vitamin levels in early life are associated with an increased risk of autism. As essential substances for growth and development, vitamins have been shown to have significant benefits for the nervous and immune systems. However, it is unknown whether certain vitamin types influence the emergence or manifestation of ASD symptoms. Several studies have focused on vitamin levels in children with autism, and neurotypical children have provided different insights into the types of vitamins and their intake. Here, we review the mechanisms and significance of several vitamins (A, B, C, D, E, and K) that are closely associated with the development of ASD in order to prevent, mitigate, and treat ASD. Efforts have been made to discover and develop new indicators for nutritional assessment of children with ASD to play a greater role in the early detection of ASD and therapeutic remission after diagnosis.

Inhibition Effect of Okanin Toward Human Cytochrome P450 3A4 and 2D6 with Multi-spectroscopic Studies and Molecular Docking.

Okanin, a major flavonoid of a popular herb tea, Coreopsis tinctoria Nutt., showed strong inhibition on CYP3A4 and CYP2D6. The strong interaction between okanin and CYPs were determined by enzyme kinetics, multispectral technique and molecular docking. The inhibition type of two enzymes, CYP3A4 and CYP2D6, by okanin are mixed and non-competitive inhibition type, respectively. The IC50 values and the binding constant of okanin to CYP3A4 can be deduced that the interaction was stronger than that of CYP2D6. The Conformations of CYP3A4 and CYP2D6 were changed by okanin. The evidence from fluorescence measurement along with molecular docking verified that these two CYPs were bound with okanin by hydrogen bonds and hydrophobic forces. Our investigation suggested that okanin may lead to interactions between herb and drug by inhibiting CYP3A4 and CYP2D6 activities, thus its consumption should be taken with caution.

Prediction of macrotrabecular-massive hepatocellular carcinoma by using MR-based models and their prognostic implications.

PURPOSE: To evaluate the efficacy of MRI-based radiomics and clinical models in predicting MTM-HCC. Additionally, to investigate the ability of the radiomics model designed for MTM-HCC identification in predicting disease-free survival (DFS) in patients with HCC. METHODS: A total of 336 patients who underwent oncological resection for HCC between June 2007 and March 2021 were included. 127 patients in Cohort1 were used for MTM-HCC identification, and 209 patients in Cohort2 for prognostic analyses. Radiomics analysis was performed using volumes of interest of HCC delineated on pre-operative MRI images. Radiomics and clinical models were developed using Random Forest algorithm in Cohort1 and a radiomics probability (RP) of MTM-HCC was obtained from the radiomics model. Based on the RP, patients in Cohort2 were divided into a RAD-MTM-HCC (RAD-M) group and a RAD-non-MTM-HCC (RAD-nM) group. Univariate and multivariate Cox regression analyses were employed to identify the independent predictors for DFS of patients in Cohort2. Kaplan-Meier curves were used to compare the DFS between different groups pf patients based on the predictors. RESULTS: The radiomics model for identifying MTM-HCC showed AUCs of 0.916 (95% CI: 0.858-0.960) and 0.833 (95% CI: 0.675-0.935), and the clinical model showed AUCs of 0.760 (95% CI: 0.669-0.836) and 0.704 (95% CI: 0.532-0.843) in the respective training and validation sets. Furthermore, the radiomics biomarker RP, portal or hepatic vein tumor thrombus, irregular rim-like arterial phase hyperenhancement (IRE) and AFP were independent predictors of DFS in patients with HCC. The DFS of RAD-nM group was significantly higher than that of the RAD-M group (p < .001). CONCLUSION: MR-based clinical and radiomic models have the potential to accurately diagnose MTM-HCC. Moreover, the radiomics signature designed to identify MTM-HCC also can be used to predict prognosis in patients with HCC, realizing the diagnostic and prognostic aims at the same time.

Interactions studies of CYP2D6 with quercetin and hyperoside by spectral analysis and molecular dynamics simulations.

Some ingredients from herbal medicine can significantly affect the activity of CYP2D6, thus leading to serious interactions between herbs and drugs. Quercetin and hyperoside are active ingredients widely found in vegetables, fruits, and herbal medicines. Quercetin and hyperoside have many biological activities. In this work, the characteristic bindings of CYP2D6 with quercetin/hyperoside are revealed by multi-spectroscopy analysis, molecular docking, and molecular dynamics simulations. The fluorescence of CYP2D6 is statically quenched by quercetin and hyperoside. The binding constant (Ka ) values of CYP2D6-quercetin/hyperoside range from 104 L mol-1 , which indicates that these two flavonoids bind moderately to CYP2D6. Meanwhile, quercetin has a stronger quenching ability to CYP2D6 than that of hyperoside. The secondary structure of CYP2D6 is obviously changed by binding with quercetin/hyperoside. The docking results reveal that the quercetin/hyperoside enters the active site of CYP2D6 near heme and binds to CYP2D6 by hydrogen bonds and van der Waals forces. The molecular dynamics simulation results indicate that the binding of quercetin/hyperoside can stabilize the two complexes, enhance the flexibility of CYP2D6 backbone atoms, and make a more unfolded and looser structure of CYP2D6.

A 4D Printed Adhesive, Thermo-Contractile, and Degradable Hydrogel for Diabetic Wound Healing.

Chronic wound healing remains a substantial clinical challenge. Current treatments are often either prohibitively expensive or insufficient in meeting the various requirements needed for effective diabetic wound healing. A 4D printing multifunctional hydrogel dressing is reported here, which aligns perfectly with wounds owning various complex shapes and depths, promoting both wound closure and tissue regeneration. The hydrogel is prepared via digital light process (DLP) 3D printing of the mixture containing N-isopropylacrylamide (NIPAm), curcumin-loaded Pluronic F127 micelles (Cur-PF127), and poly(ethylene glycol) diacrylate-dopamine (PEGDA575-Do), a degradable crosslinker. The use of PEGDA575-Do ensures tissue adhesion and degradability, and cur-PF127 serves as an antibacterial agent. Moreover, the thermo-responsive mainchains (i.e., polymerized NIPAm) enables the activation of wound contraction by body temperature. The features of the prepared hydrogel, including robust tissue adhesion, temperature-responsive contraction, effective hemostasis, spectral antibacterial, biocompatibility, biodegradability, and inflammation regulation, contribute to accelerating diabetic wound healing in Methicillin-resistant Staphylococcus aureus (MRSA)-infected full-thickness skin defect diabetic rat models and liver injury mouse models, highlighting the potential of this customizable, mechanobiological, and inflammation-regulatory dressing to expedite wound healing in various clinical settings.

Ozonated autohemotherapy combined with pulsed radiofrequency in the treatment of thoracic postherpetic neuralgia in older adults: a retrospective study.

Postherpetic neuralgia (PHN) seriously affects the quality of life of the elderly population. This study aimed to evaluate the efficacy of ozonated autohemotherapy (O3-AHT) combined with pulsed radiofrequency (PRF) in the treatment of thoracic PHN in older adults. The medical records of patients with thoracic PHN aged 65 years and older from June 2018 until March 2021 in Shengli Oilfield Central Hospital were reviewed. They were assigned into two groups: PRF alone (PRF group, n = 107) and PRF combined with O3-AHT (PRF + O3-AHT group, n = 109). Visual Analogue Scale for pain was evaluated at pre-treatment, 1 day, 1, 3 and 6 months after treatment. Quality of life and sleep quality were assessed using Short-Form 36 Health Survey and Athens Insomnia Scale at pre-treatment and 6 months post-treatment, respectively. The median age of patients in the PRF and PRF + O3-AHT groups were 69 (67-73) years and 68 (67-72) years, respectively. The former included 62 females and the latter included 51 females. Compared with pre-treatment, the Visual Analogue Scale scores of two groups declined at post-treatment. Patients in the PRF + O3-AHT group showed obviously lower Visual Analogue Scale scores compared with those in the PRF group at 1, 3, and 6 months after treatment and they had earlier withdrawal time for drugs. However, dizziness, tachycardia, sleepiness, and nausea were presented after combination therapy. These symptoms resolved spontaneously after a period of rest. Additionally, O3-AHT combined with PRF was associated with a significant decrease in the Athens Insomnia Scale score and with a significant improvement in every dimension of the Short-Form 36 Health Survey. To conclude, O3-AHT combined with PRF is an effective way to relieve thoracic PHN in older patients.