Green Fluorescent Carbon Dots with Critically Controlled Surface States: Make Silk Shine via Feeding Silkworms.

Feeding silkworms with functional materials as additives to produce naturally modified silk is a facile, diverse, controllable, and environmentally friendly method with a low cost of time and investment. Among various additives, carbon dots (CDs) show unique advantages due to their excellent biocompatibility and fluorescence stability. Here, a new type of green fluorescent carbon dots (G-CDs) is synthesized with a high oil-water partition ratio of 147, a low isoelectric point of 5.16, an absolute quantum yield of 71%, and critically controlled surface states. After feeding with G-CDs, the silkworms weave light yellow cocoons whose green fluorescence is visible to the naked eye under UV light. The luminous silk is sewn onto the cloth to create striking patterns with beautiful fluorescence. Such G-CDs have no adverse effect on the survival rate and the life cycle of silkworms and enable their whole bodies to glow under UV light. Based on the strong fluorescence, chemical stability, and biological safety, G-CDs are found in the digestive tracts, silk glands, feces, cocoons, and even moth bodies. G-CDs accumulate in the posterior silk glands where fibroin protein is secreted, indicating its stronger combination with fibroin than sericin, which meets the requirements for practical applications.

Natalizumab reduces loss of gray matter and thalamic volume in patients with relapsing-remitting multiple sclerosis: A post hoc analysis from the randomized, placebo-controlled AFFIRM trial.

BACKGROUND: Loss of brain gray matter fractional volume predicts multiple sclerosis (MS) progression and is associated with worsening physical and cognitive symptoms. Within deep gray matter, thalamic damage is evident in early stages of MS and correlates with physical and cognitive impairment. Natalizumab is a highly effective treatment that reduces disease progression and the number of inflammatory lesions in patients with relapsing-remitting MS (RRMS). OBJECTIVE: To evaluate the effect of natalizumab on gray matter and thalamic atrophy. METHODS: A combination of deep learning-based image segmentation and data augmentation was applied to MRI data from the AFFIRM trial. RESULTS: This post hoc analysis identified a reduction of 64.3% (p = 0.0044) and 64.3% (p = 0.0030) in mean percentage gray matter volume loss from baseline at treatment years 1 and 2, respectively, in patients treated with natalizumab versus placebo. The reduction in thalamic fraction volume loss from baseline with natalizumab versus placebo was 57.0% at year 2 (p < 0.0001) and 41.2% at year 1 (p = 0.0147). Similar findings resulted from analyses of absolute gray matter and thalamic fraction volume loss. CONCLUSION: These analyses represent the first placebo-controlled evidence supporting a role for natalizumab treatment in mitigating gray matter and thalamic fraction atrophy among patients with RRMS. CLINICALTRIALS.GOV IDENTIFIER: NCT00027300URL: https://clinicaltrials.gov/ct2/show/NCT00027300.

Konectom™ cognitive processing speed test enables reliable remote, unsupervised cognitive assessment in people with multiple sclerosis: Exploring the use of substitution time as a novel digital outcome measure.

BACKGROUND: The Konectom™ smartphone-based cognitive processing speed (CPS) test is designed to assess processing speed and account for impact of visuomotor function on performance. OBJECTIVE: Evaluate reliability and validity of Konectom CPS Test, performed in clinic and remotely. METHODS: Data were collected from people with multiple sclerosis (PwMS) aged 18-64 years and healthy control participants (HC) matched for age, sex, and education. Remote test-retest reliability (intraclass correlation coefficients, ICC); correlation with established clinical measures (Spearman correlation coefficients); group analyses between cognitively impaired/unimpaired PwMS; and influence of age, sex, education, and upper limb motor function on CPS Test measures were assessed. RESULTS: Eighty PwMS and 66 HC participated. CPS Test measures from remote tests had good test-retest reliability (ICC of 0.67-0.87) and correlated with symbol digit modalities test (highest |ρ| = 0.80, p < 0.0001). Remote measures were stable (change from baseline < 5%) and correlated with MS disability (highest |ρ| = 0.39, p = 0.0004) measured by Expanded Disability Status Scale. CPS Test measures displayed sensitivity to cognitive impairment (highest d = 1.47). Demographics and motor function had the lowest impact on CPS Test substitution time, a measure accounting for visuomotor function. CONCLUSION: Konectom CPS Test measures provide valid, reliable remote measurements of cognitive processing speed in PwMS.

Esophago-pulmonary artery fistula caused by fish bone impaction.

We present a case of a 65-year-old male who experienced posterior sternal pain, accompanied by a week-long fever following the consumption of fish. Computed tomography (CT) examination revealed a fish bone in the middle esophageal, along with a small amount of gas in the mediastinum. A focal pseudoaneurysm formation was observed in the posterior wall of the left pulmonary artery trunk, accompanied by the presence of gas and septic emboli in the main trunk of the left pulmonary artery and some of its branches. Furthermore, distal pulmonary tissue infarction with associated infection was observed (Figure 1A-F). Clinical diagnosis: Esophago-pulmonary artery fistula caused by fish bone impaction. Reports of esophago-pulmonary artery fistulas without involvement of the trachea or bronchi are rare.

Immune cholangitis related to anti-programmed cell death and programmed cell death ligand agents for the treatment of intrahepatic cholangiocarcinoma.

Anti-programmed cell death (anti-PD1) and anti-programmed cell death ligand (anti-PDL1) agents represent a burgeoning field of immunotherapy with an expanding array of indications. In this report, we present the observation of a patient with intrahepatic cholangiocarcinoma exhibiting features of immune-related cholangitis.

Deep-Learning Models for Detection and Localization of Visible Clinically Significant Prostate Cancer on Multi-Parametric MRI.

BACKGROUND: Deep learning for diagnosing clinically significant prostate cancer (csPCa) is feasible but needs further evaluation in patients with prostate-specific antigen (PSA) levels of 4-10 ng/mL. PURPOSE: To explore diffusion-weighted imaging (DWI), alone and in combination with T2-weighted imaging (T2WI), for deep-learning-based models to detect and localize visible csPCa. STUDY TYPE: Retrospective. POPULATION: One thousand six hundred twenty-eight patients with systematic and cognitive-targeted biopsy-confirmation (1007 csPCa, 621 non-csPCa) were divided into model development (N = 1428) and hold-out test (N = 200) datasets. FIELD STRENGTH/SEQUENCE: DWI with diffusion-weighted single-shot gradient echo planar imaging sequence and T2WI with T2-weighted fast spin echo sequence at 3.0-T and 1.5-T. ASSESSMENT: The ground truth of csPCa was annotated by two radiologists in consensus. A diffusion model, DWI and apparent diffusion coefficient (ADC) as input, and a biparametric model (DWI, ADC, and T2WI as input) were trained based on U-Net. Three radiologists provided the PI-RADS (version 2.1) assessment. The performances were determined at the lesion, location, and the patient level. STATISTICAL TESTS: The performance was evaluated using the areas under the ROC curves (AUCs), sensitivity, specificity, and accuracy. A P value <0.05 was considered statistically significant. RESULTS: The lesion-level sensitivities of the diffusion model, the biparametric model, and the PI-RADS assessment were 89.0%, 85.3%, and 90.8% (P = 0.289-0.754). At the patient level, the diffusion model had significantly higher sensitivity than the biparametric model (96.0% vs. 90.0%), while there was no significant difference in specificity (77.0%. vs. 85.0%, P = 0.096). For location analysis, there were no significant differences in AUCs between the models (sextant-level, 0.895 vs. 0.893, P = 0.777; zone-level, 0.931 vs. 0.917, P = 0.282), and both models had significantly higher AUCs than the PI-RADS assessment (sextant-level, 0.734; zone-level, 0.863). DATA CONCLUSION: The diffusion model achieved the best performance in detecting and localizing csPCa in patients with PSA levels of 4-10 ng/mL. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

A Machine-Learning Derived Huntington's Disease Progression Model: Insights for Clinical Trial Design.

BACKGROUND: Applying machine-learning algorithms to large datasets such as those available in Huntington's disease offers the opportunity to discover hidden patterns, often not discernible to clinical observation. OBJECTIVES: To develop and validate a model of Huntington's disease progression using probabilistic machine learning methods. METHODS: Longitudinal data encompassing 2079 assessment measures from four observational studies (PREDICT-HD, REGISTRY, TRACK-HD, and Enroll-HD) were integrated and machine-learning methods (Bayesian latent-variable analysis and continuous-time hidden Markov models) were applied to develop a probabilistic model of disease progression. The model was validated using a separate Enroll-HD dataset and compared with existing clinical reference assessments (Unified Huntington's Disease Rating Scale [UHDRS] diagnostic confidence level, total functional capacity, and total motor scores) and CAG-age product. RESULTS: Nine disease states were discovered based on 44 motor, cognitive, and functional measures, which correlated with reference assessments. The validation set included 3158 participants (mean age, 48.4 years) of whom 61.5% had manifest disease. Analysis of transition times showed that "early-disease" states 1 and 2, which occur before motor diagnosis, lasted ~16 years. Increasing numbers of participants had motor onset during "transition" states 3 to 5, which collectively lasted ~10 years, and the "late-disease" states 6 to 9 also lasted ~10 years. The annual probability of conversion from one of the nine identified disease states to the next ranged from 5% to 27%. CONCLUSIONS: The natural history of Huntington's disease can be described by nine disease states of increasing severity. The ability to derive characteristics of disease states and probabilities for progression through these states will improve trial design and participant selection. © 2021 International Parkinson and Movement Disorder Society.

Effect of AI-assisted software on inter- and intra-observer variability for the X-ray bone age assessment of preschool children.

BACKGROUND: With the rapid development of deep learning algorithms and the rapid improvement of computer hardware in the past few years, AI-assisted diagnosis software for bone age has achieved good diagnostic performance. The purpose of this study was to investigate the effect of AI-assisted software on residents' inter-observer agreement and intra-observer reproducibility for the X-ray bone age assessment of preschool children. METHODS: This prospective study was approved by the Institutional Ethics Committee. Six board-certified residents interpreted 56 bone age radiographs ranging from 3 to 6 years with structured reporting by the modified TW3 method. The images were interpreted on two separate occasions, once with and once without the assistance of AI. After a washout period of 4 weeks, the radiographs were reevaluated by each resident in the same way. The reference bone age was the average bone age results of the three experts. Both TW3-RUS and TW3-Carpal were evaluated. The root mean squared error (RMSE), mean absolute difference (MAD) and bone age accuracy within 0.5 years and 1 year were used as metrics of accuracy. Interobserver agreement and intraobserver reproducibility were evaluated using intraclass correlation coefficients (ICCs). RESULTS: With the assistance of bone age AI software, the accuracy of residents' results improved significantly. For interobserver agreement comparison, the ICC results with AI assistance among 6 residents were higher than the results without AI assistance on the two separate occasions. For intraobserver reproducibility comparison, the ICC results with AI assistance were higher than results without AI assistance between the 1st reading and 2nd reading for each resident. CONCLUSIONS: For preschool children X-ray bone age assessment, in addition to improving diagnostic accuracy, bone age AI-assisted software can also increase interobserver agreement and intraobserver reproducibility. AI-assisted software can be an effective diagnostic tool for residents in actual clinical settings.

Effect of Temperature and Freezing on Human Adipose Tissue Material Properties Characterized by High-Rate Indentation: Puncture Testing.

The characterization of human subcutaneous adipose tissue (SAT) under high-rate loading is valuable for development of biofidelic finite element human body models (FE-HBMs) to predict seat belt-pelvis interaction and injury risk in vehicle crash simulations. While material characterization of SAT has been performed at 25 °C or 37 °C, the effect of temperature on mechanical properties of SAT under high-rate and large-deformation loading has not been investigated. Similarly, while freezing is the most common preservation technique for cadaveric specimens, the effect of freeze-thaw on the mechanical properties of SAT is also absent from the literature. Therefore, the aim of this study was to determine the effect of freezing and temperature on mechanical properties of human SAT. Fresh and previously frozen human SAT specimens were obtained and tested at 25 °C and 37 °C. High-rate indentation and puncture tests were performed, and indentation-puncture force-depth responses were obtained. While the chance of material failure was found to be different between temperatures and between fresh and previously frozen tissue, statistical analyses revealed that temperature and freezing did not change the shear modulus and failure characteristics of SAT. Therefore, the results of the current study indicated that SAT material properties characterized from either fresh or frozen tissue at either 25 °C or 37 °C could be used for enhancing the biofidelity of FE-HBMs.

Prediction of pelvic lymph node metastasis in prostate cancer using radiomics based on T2-weighted imaging. / 基于T2åŠ æƒæˆåƒçš„å½±åƒç»„å­¦åˆ—çº¿å›¾æ¨¡åž‹é¢„æµ‹å‰åˆ—è ºç™Œç›†è ”æ·‹å·´ç»“è½¬ç§».

OBJECTIVES: Pelvic lymph node metastasis (PLNM) is an important factor that affects the stage and prognosis of prostate cancer. Invasive extended pelvic lymph node dissection (ePLND) is the most effective method for clinically diagnosing PLNM. Accurate preoperative prediction of PLNM can reduce unnecessary ePLND. This study aims to investigate the clinical value of radiomics nomogram in predicting PLNM of prostate cancer based on T2-weighted imaging (T2WI). METHODS: Magnetic resonance (MR) data of 71 patients with prostate cancer who underwent ePLND from January 2017 to June 2021 in Peking University First Hospital were collected retrospectively. All patients were assigned into a training set (January 2017 to December 2020, n=56, containing 186 lymph nodes) and a test set (January 2021 to June 2021, n=15, containing 45 lymph nodes) according to the examination time of multiparametric magnetic resonance imaging (mpMRI). Two radiologists matched the dissected lymph nodes on MRI images, and manually annotated the region of interest (ROI). Based on the outlined ROI, 3 metastatic lymph node prediction models were established: Model 1 (only image features of T2WI), Model 2 (radiomics features based on random forest), and Model 3 (combination of the image and radiomics features). A nomogram was also established. The clinicopathologic characteristics of the patients were obtained from the medical records, including age, the Gleason score, the level of prostate-specific antigen (PSA), and clinical and pathological T stage. The preoperative radiological features of the pelvic lymph nodes (LNs) include size of LNs (the short and long diameters) and volume of LNs. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the 3 models and decision curve analysis (DCA) was used to evaluate the clinical benefits of the models. RESULTS: No significant differences were found between the training set and test set regarding age, Gleason scores, PSA level, and clinical and pathological T stage (all P>0.05). The differences in volume, short diameter and long diameter between metastatic and non-metastatic LNs were statistically significant in both training set and test set (all P<0.05). In multivariate regression analysis, the short diameter and marginal status of LNs were included in Model 1. Eighteen omics features were selected to construct Model 2. The signal distribution of LNs and Rad score were the significant risk factors for predicting metastasis of pelvic LNs in Model 3. The C-index of nomogram based on Model 3 reached 0.964, and the calibration curve showed that the model had high calibration degree. In the test set, the area under the curves of Model 1, 2, and 3 were 0.78, 0.93, and 0.96 respectively, Model 2 and Model 3 showed significantly higher diagnostic efficiency than Model 1 (Model 1 vs Model 2, P=0.019; Model 1 vs Model 3, P=0.020). There was no significant difference in the area under the curve between Model 2 and Model 3 (P=0.649). The DCA results of the 3 models showed that all models obtained higher net benefits than the PLNM-all or PLNM-none protocol in different ranges of threshold probabilities and Model 3 had the highest clinical benefit. CONCLUSIONS: The radiomics nomogram based on T2WI shows a good predictive efficacy for preoperative PLNM in patients with prostate cancer, which could be served as an imaging biomarker to optimize decision-making and adjust adjuvant treatments.

Automated segmentation of kidney and renal mass and automated detection of renal mass in CT urography using 3D U-Net-based deep convolutional neural network.

OBJECTIVES: To develop a 3D U-Net-based deep learning model for automated segmentation of kidney and renal mass, and detection of renal mass in corticomedullary phase of computed tomography urography (CTU). METHODS: Data on 882 kidneys obtained from CTU data of 441 patients with renal mass were used to learn and evaluate the deep learning model. The CTU data of 35 patients with small renal tumors (diameter ≤ 1.5 cm) were used for additional testing. The ground truth data for the kidney, renal tumor, and cyst were manually annotated on corticomedullary phase images of CTU. The proposed segmentation model for kidney and renal mass was constructed based on a 3D U-Net. The segmentation accuracy was evaluated through the Dice similarity coefficient (DSC). The volume of the maximum 3D volume of interest of renal tumor and cyst in the predicted segmentation by the model was used as an identification indicator, while the detection performance of the model was evaluated by the area under the receiver operation characteristic curve. RESULTS: The proposed model showed a high accuracy in segmentation of kidney and renal tumor, with average DSC of 0.973 and 0.844, respectively. It performed moderately in the renal cyst segmentation, with an average DSC of 0.536 in the test set. Also, this model showed good performance in detecting renal tumor and cyst. CONCLUSIONS: The proposed automated segmentation and detection model based on 3D U-Net shows promising results for the segmentation of kidney and renal tumor, and the detection of renal tumor and cyst. KEY POINTS: • The segmentation model based on 3D U-Net showed high accuracy in segmentation of kidney and renal neoplasm, and good detection performance of renal neoplasm and cyst in corticomedullary phase of CTU. • The segmentation model based on 3D U-Net is a fully automated aided diagnostic tool that could be used to reduce the workload of radiologists and improve the accuracy of diagnosis. • The segmentation model based on 3D U-Net would be helpful to provide quantitative information for diagnosis, treatment, surgical planning, etc.

Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images.

BACKGROUND: The 3D U-Net model has been proved to perform well in the automatic organ segmentation. The aim of this study is to evaluate the feasibility of the 3D U-Net algorithm for the automated detection and segmentation of lymph nodes (LNs) on pelvic diffusion-weighted imaging (DWI) images. METHODS: A total of 393 DWI images of patients suspected of having prostate cancer (PCa) between January 2019 and December 2020 were collected for model development. Seventy-seven DWI images from another group of PCa patients imaged between January 2021 and April 2021 were collected for temporal validation. Segmentation performance was assessed using the Dice score, positive predictive value (PPV), true positive rate (TPR), and volumetric similarity (VS), Hausdorff distance (HD), the Average distance (AVD), and the Mahalanobis distance (MHD) with manual annotation of pelvic LNs as the reference. The accuracy with which the suspicious metastatic LNs (short diameter > 0.8 cm) were detected was evaluated using the area under the curve (AUC) at the patient level, and the precision, recall, and F1-score were determined at the lesion level. The consistency of LN staging on an hold-out test dataset between the model and radiologist was assessed using Cohen's kappa coefficient. RESULTS: In the testing set used for model development, the Dice score, TPR, PPV, VS, HD, AVD and MHD values for the segmentation of suspicious LNs were 0.85, 0.82, 0.80, 0.86, 2.02 (mm), 2.01 (mm), and 1.54 (mm) respectively. The precision, recall, and F1-score for the detection of suspicious LNs were 0.97, 0.98 and 0.97, respectively. In the temporal validation dataset, the AUC of the model for identifying PCa patients with suspicious LNs was 0.963 (95% CI: 0.892-0.993). High consistency of LN staging (Kappa = 0.922) was achieved between the model and expert radiologist. CONCLUSION: The 3D U-Net algorithm can accurately detect and segment pelvic LNs based on DWI images.

In Vitro Mechanical Characterization and Modeling of Subcutaneous Adipose Tissue: A Comprehensive Review.

Mechanical models of adipose tissue are important for various medical applications including cosmetics, injuries, implantable drug delivery systems, plastic surgeries, biomechanical applications such as computational human body models for surgery simulation, and blunt impact trauma prediction. This article presents a comprehensive review of in vivo experimental approaches that aimed to characterize the mechanical properties of adipose tissue, and the resulting constitutive models and model parameters identified. In particular, this study examines the material behavior of adipose tissue, including its nonlinear stress-strain relationship, viscoelasticity, strain hardening and softening, rate-sensitivity, anisotropy, preconditioning, failure behavior, and temperature dependency.

Mechanism of indoleamine 2, 3-dioxygenase inhibiting cardiac allograft rejection in mice.

Indoleamine 2, 3-dioxygenase (IDO)-mediated regulation of tryptophan metabolism plays an important role in immune tolerance in transplantation, but it has not been elucidated which mechanism specifically induces the occurrence of immune tolerance. Our study revealed that IDO exerts immunosuppressive effects through two pathways in mouse heart transplantation, 'tryptophan depletion' and 'tryptophan metabolite accumulation'. The synergism between IDO+ DC and TC (tryptophan catabolic products) has stronger inhibitory effects on T lymphocyte proliferation and mouse heart transplant rejection than the two intervention factors alone, and significantly prolong the survival time of donor-derived transplanted skin. This work demonstrates that the combination of IDO+ DC and TC can induce immune tolerance to a greater extent, and reduce the rejection of transplanted organs.

SIRT1 mediates salidroside-elicited protective effects against MPP+ -induced apoptosis and oxidative stress in SH-SY5Y cells: involvement in suppressing MAPK pathways.

Parkinson's disease (PD) is a progressive neurodegenerative disease, leading to tremor, rigidity, bradykinesia, and gait impairment. Salidroside has been reported to exhibit antioxidative and neuroprotective properties in PD. However, the underlying neuroprotective mechanisms effects of salidroside are poorly understood. Recently, a growing body of evidences suggest that silent information regulator 1 (SIRT1) plays important roles in the pathophysiology of PD. Hence, the present study investigated the roles of SIRT1 in neuroprotective effect of salidroside against N-methyl-4-phenylpyridinium (MPP+ )-induced SH-SY5Y cell injury. Our findings revealed that salidroside attenuates MPP+ -induced neurotoxicity as evidenced by the increase in cell viability, and the decreases in the caspase-3 activity and apoptosis ratio. Simultaneously, salidroside pretreatment remarkably increased SIRT1 activity, SIRT1 mRNA and protein levels in MPP+ -treated SH-SY5Y cell. However, sirtinol, a SIRT1 activation inhibitor, significantly blocked the inhibitory effects of salidroside on MPP+ -induced cytotoxicity and apoptosis. In addition, salidroside abolished MPP+ -induced the production of reactive oxygen species (ROS), the up-regulation of NADPH oxidase 2 (NOX2) expression, the down-regulations of superoxide dismutase (SOD) activity and glutathione (GSH) level in SH-SY5Y cells, while these effects were also blocked by sirtinol. Finally, we found that the inhibition of salidroside on MPP+ -induced phosphorylation of p38, extracellular signal-regulated kinase (ERK) and c-Jun NH2-terminal kinase (JNK) were also reversed by sirtinol in SH-SY5Y cells. Taken together, these results indicated that SIRT1 contributes to the neuroprotection of salidroside against MPP+ -induced apoptosis and oxidative stress, in part through suppressing of mitogen-activated protein kinase (MAPK) pathways.

Bayesian inference with historical data-based informative priors improves detection of differentially expressed genes.

MOTIVATION: Modern high-throughput biotechnologies such as microarray are capable of producing a massive amount of information for each sample. However, in a typical high-throughput experiment, only limited number of samples were assayed, thus the classical 'large p, small n' problem. On the other hand, rapid propagation of these high-throughput technologies has resulted in a substantial collection of data, often carried out on the same platform and using the same protocol. It is highly desirable to utilize the existing data when performing analysis and inference on a new dataset. RESULTS: Utilizing existing data can be carried out in a straightforward fashion under the Bayesian framework in which the repository of historical data can be exploited to build informative priors and used in new data analysis. In this work, using microarray data, we investigate the feasibility and effectiveness of deriving informative priors from historical data and using them in the problem of detecting differentially expressed genes. Through simulation and real data analysis, we show that the proposed strategy significantly outperforms existing methods including the popular and state-of-the-art Bayesian hierarchical model-based approaches. Our work illustrates the feasibility and benefits of exploiting the increasingly available genomics big data in statistical inference and presents a promising practical strategy for dealing with the 'large p, small n' problem. AVAILABILITY AND IMPLEMENTATION: Our method is implemented in R package IPBT, which is freely available from https://github.com/benliemory/IPBT CONTACT: yuzhu@purdue.edu; zhaohui.qin@emory.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Study on AEMs with Excellent Comprehensive Performance Prepared by Covalently Cross-Linked p-Triphenyl with SEBS Remotely Grafted Piperidine Cations.

A series of SEBS-C6-PIP-yPTP (y = 0-15%) AEMs with good mechanical and chemical stability were prepared by combining the strong rigidity of p-triphenyl, good toughness of SEBS, and excellent stability of PIP cations. After the introduction of a p-triphenyl polymer into the main chain, a clear hydrophilic-hydrophobic phase separation structure was constructed within the membrane, forming a continuous and interconnected ion transport channel to improve ion transport efficiency. Moreover, the molecular chains of the cross-linked AEMs change from chain-like to network-like, and the tighter binding between each molecule increases the tensile strength. The special structure of the six-membered ring makes PIP have a significant constraint effect; when nucleophilic substitution and Hoffman elimination occur at the α and ß positions, the required transition state potential energy increases, making the reaction difficult to occur and improving the alkaline stability of the polymer membrane. The SEBS-C6-PIP-15%PTP membrane has the best mechanical properties (Ts = 38.79 MPa, Eb = 183.09% at 80 °C, 100% RH), the highest ion conductivity (102.02 mS. cm-1 at 80 °C), and the best alkaline stability (6.23% degradation at 80 °C in a 2 M NaOH solution for 1400 h). It can be seen that organic-organic covalent cross-linking is an effective means to improve the comprehensive performance of AEMs.

Recent advances in imaging and artificial intelligence (AI) for quantitative assessment of multiple myeloma.

Multiple myeloma (MM) is a malignant blood disease, but there have been significant improvements in the prognosis due to advancements in quantitative assessment and targeted therapy in recent years. The quantitative assessment of MM bone marrow infiltration and prognosis prediction is influenced by imaging and artificial intelligence (AI) quantitative parameters. At present, the primary imaging methods include computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). These methods are now crucial for diagnosing MM and evaluating myeloma cell infiltration, extramedullary disease, treatment effectiveness, and prognosis. Furthermore, the utilization of AI, specifically incorporating machine learning and radiomics, shows great potential in the field of diagnosing MM and distinguishing between MM and lytic metastases. This review discusses the advancements in imaging methods, including CT, MRI, and PET/CT, as well as AI for quantitatively assessing MM. We have summarized the key concepts, advantages, limitations, and diagnostic performance of each technology. Finally, we discussed the challenges related to clinical implementation and presented our views on advancing this field, with the aim of providing guidance for future research.

Large-scale screening of clinical assessments to distinguish between states in the Integrated HD Progression Model (IHDPM).

Background: Understanding the sensitivity and utility of clinical assessments across different HD stages is important for study/trial endpoint selection and clinical assessment development. The Integrated HD Progression Model (IHDPM) characterizes the complex symptom progression of HD and separates the disease into nine ordered disease states. Objective: To generate a temporal map of discriminatory clinical measures across the IHDPM states. Methods: We applied the IHDPM to all HD individuals in an integrated longitudinal HD dataset derived from four observational studies, obtaining disease state assignment for each study visit. Using large-scale screening, we estimated Cohen's effect sizes to rank the discriminative power of 2,472 clinical measures for separating observations in disease state pairs. Individual trajectories through IHDPM states were examined. Discriminative analyses were limited to individuals with observations in both states of the pairs compared (N = 3,790). Results: Discriminative clinical measures were heterogeneous across the HD life course. UHDRS items were frequently identified as the best state pair discriminators, with UHDRS Motor items - most notably TMS - showing the highest discriminatory power between the early-disease states and early post-transition period states. UHDRS functional items emerged as strong discriminators from the transition period and on. Cognitive assessments showed good discriminative power between all state pairs examined, excepting state 1 vs. 2. Several non-UHDRS assessments were also flagged as excellent state discriminators for specific disease phases (e.g., SF-12). For certain state pairs, single assessment items other than total/summary scores were highlighted as having excellent discriminative power. Conclusion: By providing ranked quantitative scores indicating discriminatory ability of thousands of clinical measures between specific pairs of IHDPM states, our results will aid clinical trial designers select the most effective outcome measures tailored to their study cohort. Our observations may also assist in the development of end points targeting specific phases in the disease life course, through providing specific conceptual foci.