Effectiveness and implementation of decentralized, community- and primary care-based strategies in promoting hepatitis B testing uptake: a systematic review and meta-analysis.

Background: Expanding chronic hepatitis B (CHB) testing through effective implementation strategies in primary- and community-care setting is crucial for elimination. Our study aimed to determine the effectiveness of all available strategies in the literature and evaluate their specifications and implementation outcomes, thereby informing future programming and policymaking. Methods: We conducted a systematic review and meta-analysis (PROSPERO CRD42023455781), searching Scopus, Embase, PubMed, and CINAHL databases up to June 05, 2024, for randomized controlled trials investigating primary- and community-care-based implementation strategies to promote CHB testing. Studies were screened against a priori eligibility criteria, and their data were extracted using a standardized protocol if included. ROB-2 was used to assess the risk of bias. Implementation strategies' components were characterized using the Behavior Change Wheel (BCW) framework. Random-effect models were applied to pool the effectiveness estimate by strategy. Mixed-effect meta-regression was employed to investigate if effectiveness varied by the number of strategy's BCW components. Findings: 7146 unique records were identified. 25 studies were eligible for the review, contributing 130,598 participants. 19 studies were included in the meta-analysis. No studies were conducted in low-and-middle-income countries. Implementation outcomes were reported in only ten studies (40%). Community-based strategies included lay health workers-led education (Pooled Risk Difference = 27.9% [95% Confidence Interval = 3.4-52.4], I2 = 99.3%) or crowdsourced education on social media (3.1% [-2.2 to 8.4], 0.0%). Primary care-based strategies consisted of electronic alert system (8.4% [3.7-13.1], 95.0%) and healthcare providers-led education (HCPs, 62.5% [53.1-71.9], 27.5%). The number of BCW-framework-driven strategy components showed a significant dose-response relationship with effectiveness. Interpretation: HCPs-led education stands out, and more enriched multicomponent strategies had better effectiveness. Future implementation strategies should consider critical contextual factors and policies to achieve a sustainable impact towards hepatitis B elimination targets. Funding: Tran Dolch Post-Doctoral Fellowship in Hepatology, Johns Hopkins University School of Medicine, Baltimore MD, USA.

Developing an Individualized Patient Decision Aid for Chronic Coronary Disease Based on the ISCHEMIA Trial: A Mixed-Methods Study.

BACKGROUND: Pursuing initial invasive or conservative management of chronic coronary disease (CCD) is a preference-sensitive decision that should include shared decision-making. Communicating the benefits of either approach is challenging, as individual patients rarely achieve the population-averaged outcomes reported in clinical trials. Our objective was to develop a patient decision aid (PDA) with patient-specific estimates of outcomes for initial invasive versus conservative management of CCD, based on the ISCHEMIA trial (International Study of Comparative Health Effectiveness With Medical and Invasive Approaches). METHODS: This was a multiphase mixed-methods study using focus groups of outpatients with CCD, caregivers, clinicians, and researchers. Focus groups were held in Kansas City, MO and New York City, NY between September 2021 and June 2022. Patients with CCD were included if they had a positive stress test within 1 year. Phase 1 focused on patient priorities for outcomes to guide treatment decisions. Phase 2 involved PDA development and refinement. Phase 3 involved further refinement and member checking. Key themes involving shared decision-making and treatment preferences were elicited from focus groups using a deductive approach to develop a PDA representing the outcomes most important to patients. RESULTS: Of 46 patient and caregiver participants, the mean age was 63.5 years, 53% were female, 61% were White, 24% were Black, and 9% were Hispanic. When deciding between treatments, participants valued shared decision-making but generally deferred decisions to clinicians. The outcomes most important to participants were survival and quality of life, followed by physical functioning and symptoms. To represent these outcomes, participants favored simple visualizations, such as a speedometer or health meter. When deciding between treatment options, participants preferred to use the PDA collaboratively with a clinician instead of as a stand-alone tool. CONCLUSIONS: Our novel, patient-centered approach to developing a PDA for CCD with patient-specific outcomes has the potential to rapidly translate clinical trial results to individual patients and support shared decision-making.

International Variation in Health Status Benefits in Patients Undergoing Initial Invasive Versus Conservative Management for Chronic Coronary Disease: Insights From the ISCHEMIA Trial.

BACKGROUND: The ISCHEMIA trial (International Study of Comparative Health Effectiveness with Medical and Invasive Approaches) demonstrated greater health status benefits with an initial invasive strategy, as compared with a conservative one, for patients with chronic coronary disease and moderate or severe ischemia. Whether these benefits vary globally is important to understand to support global adoption of the results. METHODS: We analyzed participants' disease-specific health status using the validated 7-item Seattle Angina Questionnaire (SAQ: >5-point differences are clinically important) at baseline and over 1-year follow-up across 37 countries in 6 international regions. The average effect of initial invasive versus conservative strategies on 1-year SAQ scores was estimated using Bayesian proportional odds regression and compared across regions. RESULTS: Considerable regional variation in baseline health status was observed among 4617 participants (mean age=64.4±9.5 years, 24% women), with the mean SAQ summary scores of 67.4±19.5 in Eastern Europe participants (17% of the total), 71.4±15.4 in Asia-Pacific (18%), 74.9±16.7 in Central and South America (10%), 75.5±19.5 in Western Europe (26%), and 78.6±19.2 in North America (28%). One-year improvements in SAQ scores were greater in regions with lower baseline scores with initial invasive management (17.7±20.9 in Eastern Europe and 11.4±19.3 in North America), but similar in the conservative arm. Adjusting for baseline SAQ scores, similar health status benefits of an initial invasive strategy on 1-year SAQ scores were observed (ranging from 2.38 points [95% CI, 0.04-4.50] in North America to 4.66 points [95% CI, 2.46-6.94] in Eastern Europe), with an 88.3% probability that the difference in benefit across regions was <5 points. CONCLUSIONS: In patients with chronic coronary disease and moderate or severe ischemia, initial invasive management was associated with a consistent health status benefit across regions, with modest regional variability, supporting the international generalizability of health status benefits from invasive management of chronic coronary disease. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01471522.

The triple combination of Remdesivir (GS-441524), Molnupiravir and Ribavirin is highly efficient in inhibiting coronavirus replication in human nasal airway epithelial cell cultures and in a hamster infection model.

The use of fixed dose-combinations of antivirals with different mechanisms of action has proven key in the successful treatment of infections with HIV and HCV. For the treatment of infections with SARS-CoV-2 and possible future epi-/pandemic coronaviruses, it will be important to explore the efficacy of combinations of different drugs, in particular to avoid resistance development, such as in patients with immunodeficiencies. This work explores the effect of a combination of 3 broad-spectrum antiviral nucleosides on the replication of coronaviruses. To that end, we made use of primary human airway epithelial cell (HAEC) cultures grown at the air-liquid interface that were infected with the beta coronavirus OC43. We found that the triple combination of GS-441524 (the parent nucleoside of remdesivir), molnupiravir and ribavirin resulted in a more pronounced antiviral efficacy than what could be expected from a purely additive antiviral effect. The potency of this triple combination was next tested in SARS-CoV-2 infected hamsters in a prophylactic setup. To that end, for each of the drugs, intentionally suboptimal or even ineffective doses were selected. Yet, in the lungs of all hamsters that received triple prophylactic therapy (but not in those that received the respective double combinations) no infectious virus was detectable. Our findings indicate that co-administration of approved drugs for the treatment of coronavirus infections should be further explored but also against other families of viruses with epidemic and pandemic potential for which no effective antiviral treatment is available.

Progressive auto-segmentation for cone-beam computed tomography-based online adaptive radiotherapy.

Background and purpose: Accurate and automated segmentation of targets and organs-at-risk (OARs) is crucial for the successful clinical application of online adaptive radiotherapy (ART). Current methods for cone-beam computed tomography (CBCT) auto-segmentation face challenges, resulting in segmentations often failing to reach clinical acceptability. Current approaches for CBCT auto-segmentation overlook the wealth of information available from initial planning and prior adaptive fractions that could enhance segmentation precision. Materials and methods: We introduce a novel framework that incorporates data from a patient's initial plan and previous adaptive fractions, harnessing this additional temporal context to significantly refine the segmentation accuracy for the current fraction's CBCT images. We present LSTM-UNet, an innovative architecture that integrates Long Short-Term Memory (LSTM) units into the skip connections of the traditional U-Net framework to retain information from previous fractions. The models underwent initial pre-training with simulated data followed by fine-tuning on a clinical dataset. Results: Our proposed model's segmentation predictions yield an average Dice similarity coefficient of 79% from 8 Head & Neck organs and targets, compared to 52% from a baseline model without prior knowledge and 78% from a baseline model with prior knowledge but no memory. Conclusions: Our proposed model excels beyond baseline segmentation frameworks by effectively utilizing information from prior fractions, thus reducing the effort of clinicians to revise the auto-segmentation results. Moreover, it works together with registration-based methods that offer better prior knowledge. Our model holds promise for integration into the online ART workflow, offering precise segmentation capabilities on synthetic CT images.

Superficial and Invasive Spread of High-grade Squamous Intraepithelial Lesion and Squamous Cell Carcinoma: A Case Report.

We present a case of extensive spread of high-grade squamous intraepithelial lesion (HSIL)/cervical intraepithelial neoplasia grade 3 (CIN3) with foci of invasive squamous cell carcinoma (SCC) in a premenopausal woman. Superficial spread of CIN3 and cervical SCC to the endometrium and/or fallopian tubes is rare, especially in countries with cervical cancer screening programs. Our case occurred during the COVID-19 pandemic, which may have been a major contributing factor to delayed detection and, consequently extensive spread.

Imidazolium-Based Sulfonating Agent to Control the Degree of Sulfonation of Aromatic Polymers and Enable Plastics-to-Electronics Upgrading.

The accumulation of plastic waste in the environment is a growing environmental, economic, and societal challenge. Plastic upgrading, the conversion of low-value polymers to high-value materials, could address this challenge. Among upgrading strategies, the sulfonation of aromatic polymers is a powerful approach to access high-value materials for a range of applications, such as ion-exchange resins and membranes, electronic materials, and pharmaceuticals. While many sulfonation methods have been reported, achieving high degrees of sulfonation while minimizing side reactions that lead to defects in the polymer chains remains challenging. Additionally, sulfonating agents are most often used in large excess, which prevents precise control over the degree of sulfonation of aromatic polymers and their functionality. Herein, we address these challenges using 1,3-disulfonic acid imidazolium chloride ([Dsim]Cl), a sulfonic acid-based ionic liquid, to sulfonate aromatic polymers and upgrade plastic waste to electronic materials. We show that stoichiometric [Dsim]Cl can effectively sulfonate model polystyrene up to 92% in high yields, with minimal defects and high regioselectivity for the para position. Owing to its high reactivity, the use of substoichiometric [Dsim]Cl uniquely allows for precise control over the degree of sulfonation of polystyrene. This approach is also applicable to a wide range of aromatic polymers, including waste plastic. To prove the utility of our approach, samples of poly(styrene sulfonate) (PSS), obtained from either partially sulfonated polystyrene or expanded polystyrene waste, are used as scaffolds for poly(3,4-ethylenedioxythiophene) (PEDOT) to form the ubiquitous conductive material PEDOT:PSS. PEDOT:PSS from plastic waste is subsequently integrated into organic electrochemical transistors (OECTs) or as a hole transport layer (HTL) in a hybrid solar cell and shows the same performance as commercial PEDOT:PSS. This imidazolium-mediated approach to precisely sulfonating aromatic polymers provides a pathway toward upgrading postconsumer plastic waste to high-value electronic materials.

Unmet supportive care needs of patients with colorectal cancer based on survival stage: A scoping review.

BACKGROUND: Colorectal cancer (CRC) has emerged as one of the most common cancers, with increasing survival rates globally. As patients with CRC experience diverse treatment effects corresponding to different survival stages, understanding their unmet needs based on the survival stage is critical to tailor supportive care with limited medical resources. AIM: This study aimed to understand the unmet needs of patients with CRC across survival stages. METHODS: This scoping review followed the 5-stage framework established by Arksey and O'Malley. Five online databases were searched with narrative synthesis performed after data extraction. RESULTS: Fifteen studies were identified for this review, with 12 focusing on the acute survival stage and three reporting on the extended survival stage. Ten studies used validated scales to assess unmet needs, with the Supportive Care Needs Survey being the most common scale. Unmet needs in patients with CRC demonstrate distinct patterns across survival stages. Most studies reported a higher prevalence of unmet needs during the extended survival stage compared to the acute survival stage. Unmet emotional needs predominate during the acute survival stage, whereas unmet physical needs become most prominent in the extended survival stage. LINKING EVIDENCE TO ACTION: Healthcare providers are encouraged to conduct assessments tailored to the specific survival stage, with particular emphasis on addressing unmet needs during the extended survival stage. The development of standardized scales is recommended to comprehensively assess the unmet needs of patients with CRC.

Can input reconstruction be used to directly estimate uncertainty of a dose prediction U-Net model?

BACKGROUND: The reliable and efficient estimation of uncertainty in artificial intelligence (AI) models poses an ongoing challenge in many fields such as radiation therapy. AI models are intended to automate manual steps involved in the treatment planning workflow. We focus in this study on dose prediction models that predict an optimal dose trade-off for each new patient for a specific treatment modality. They can guide physicians in the optimization, be part of automatic treatment plan generation or support decision in treatment indication. Most common uncertainty estimation methods are based on Bayesian approximations, like Monte Carlo dropout (MCDO) or Deep ensembling (DE). These two techniques, however, have a high inference time (i.e., require multiple inference passes) and might not work for detecting out-of-distribution (OOD) data (i.e., overlapping uncertainty estimate for in-distribution (ID) and OOD). PURPOSE: In this study, we present a direct uncertainty estimation method and apply it for a dose prediction U-Net architecture. It can be used to flag OOD data and give information on the quality of the dose prediction. METHODS: Our method consists in the addition of a branch decoding from the bottleneck which reconstructs the CT scan given as input. The input reconstruction error can be used as a surrogate of the model uncertainty. For the proof-of-concept, our method is applied to proton therapy dose prediction in head and neck cancer patients. A dataset of 60 oropharyngeal patients was used to train the network using a nested cross-validation approach with 11 folds (training: 50 patients, validation: 5 patients, test: 5 patients). For the OOD experiment, we used 10 extra patients with a different head and neck sub-location. Accuracy, time-gain, and OOD detection are analyzed for our method in this particular application and compared with the popular MCDO and DE. RESULTS: The additional branch did not reduce the accuracy of the dose prediction model. The median absolute error is close to zero for the target volumes and less than 1% of the dose prescription for organs at risk. Our input reconstruction method showed a higher Pearson correlation coefficient with the prediction error (0.620) than DE (0.447) and MCDO (between 0.599 and 0.612). Moreover, our method allows an easier identification of OOD (no overlap for ID and OOD data and a Z-score of 34.05). The uncertainty is estimated simultaneously to the regression task, therefore requires less time and computational resources. CONCLUSIONS: This study shows that the error in the CT scan reconstruction can be used as a surrogate of the uncertainty of the model. The Pearson correlation coefficient with the dose prediction error is slightly higher than state-of-the-art techniques. OOD data can be more easily detected and the uncertainty metric is computed simultaneously to the regression task, therefore faster than MCDO or DE. The code and pretrained model are available on the gitlab repository: https://gitlab.com/ai4miro/ct-reconstruction-for-uncertainty-quatification-of-hdunet.

Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation.

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of very large-scale computational discovery carried out through experimental validation remain scarce, especially for materials with product applicability. Here, we demonstrate how this vision became reality by combining state-of-the-art machine learning (ML) models and traditional physics-based models on cloud high-performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. By focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade's worth of collective knowledge in the field as a byproduct. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the NaxLi3-xYCl6 (0≤ x≤ 3) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials that are currently under experimental investigation could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. The showcased screening of millions of materials candidates highlights the transformative potential of advanced ML and HPC methodologies, propelling materials discovery into a new era of efficiency and innovation.

Association of Patient Reported Outcomes With Caregiver Burden in Older Patients With Advanced Heart Failure: Insights From the SUSTAIN-IT Study.

BACKGROUND: Caregivers of patients with advanced heart failure may experience burden in providing care, but whether changes in patient health status are associated with caregiver burden is unknown. METHODS: This observational study included older patients (60-80 years old) receiving advanced surgical heart failure therapies and their caregivers at 13 US sites. Patient health status was assessed using the 12-item Kansas City Cardiomyopathy Questionnaire (range, 0-100; higher scores are better). Caregiver burden was assessed using the Oberst Caregiving Burden Scale, which measures time on task (OCBS-time) and task difficulty (OCBS-difficulty; range, 1-5; lower scores are better). Measurements occurred before surgery and 12 months after in 3 advanced heart failure cohorts: patients receiving long-term left ventricular assist device support; heart transplantation with pretransplant left ventricular assist device support; and heart transplantation without pretransplant left ventricular assist device support. Multivariable linear regression was used to identify predictors of change in OCBS-time and OCBS-difficulty at 12 months. RESULTS: Of 162 caregivers, the mean age was 61.0±9.4 years, 139 (86%) were female, and 140 (86%) were the patient's spouse. At 12 months, 99 (61.1%) caregivers experienced improved OCBS-time, and 61 (37.7%) experienced improved OCBS-difficulty (versus no change or worse OCBS). A 10-point higher baseline 12-item Kansas City Cardiomyopathy Questionnaire predicted lower 12-month OCBS-time (ß=-0.09 [95% CI, -0.14 to -0.03]; P<0.001) and OCBS-difficulty (ß=-0.08 [95% CI, -0.12 to -0.05]; P<0.001). Each 10-point improvement in the 12-item Kansas City Cardiomyopathy Questionnaire predicted lower 12-month OCBS-time (ß=-0.07 [95% CI, -0.12 to -0.03]; P=0.002) and OCBS-difficulty (ß=-0.09 [95% CI, -0.12 to -0.06]; P<0.001). CONCLUSIONS: Among survivors at 12 months, baseline and change in patient health status were associated with subsequent caregiver time on task and task difficulty in dyads receiving advanced heart failure surgical therapies, highlighting the potential for serial 12-item Kansas City Cardiomyopathy Questionnaire assessments to identify caregivers at risk of increased burden. REGISTRATION: URL: https://www.clinicaltrials.gov; unique identifier: NCT02568930.

Electrochemical Imaging of Precisely-Defined Redox and Reactive Interfaces.

Understanding the diverse electrochemical reactions occurring at electrode-electrolyte interfaces (EEIs) is a critical challenge to developing more efficient energy conversion and storage technologies. Establishing a predictive molecular-level understanding of solid electrolyte interphases (SEIs) is challenging due to the presence of multiple intertwined chemical and electrochemical processes occurring at battery electrodes. Similarly, chemical conversions in reactive electrochemical systems are often influenced by the heterogeneous distribution of active sites, surface defects, and catalyst particle sizes. In this mini review, we highlight an emerging field of interfacial science that isolates the impact of specific chemical species by preparing precisely-defined EEIs and visualizing the reactivity of their individual components using single-entity characterization techniques. We highlight the broad applicability and versatility of these methods, along with current state-of-the-art instrumentation and future opportunities for these approaches to address key scientific challenges related to batteries, chemical separations, and fuel cells. We establish that controlled preparation of well-defined electrodes combined with single entity characterization will be crucial to filling key knowledge gaps and advancing the theories used to describe and predict chemical and physical processes occurring at EEIs and accelerating new materials discovery for energy applications.

The triple combination of Remdesivir (GS-441524), Molnupiravir and Ribavirin is highly efficient in inhibiting coronavirus replication in human nasal airway epithelial cell cultures and in a hamster infection model.

The use of fixed dose-combinations of antivirals with different mechanisms of action has proven a key in the successful treatment of infections with HIV and HCV. For the treatment of infections with SARS-CoV-2 and possible future epi-/pandemic coronaviruses, it will be important to explore the efficacy of combinations of different drugs, in particular to avoid resistance development, such as in patients with immunodeficiencies. As a first effort, we studied the antiviral potency of combinations of antivirals. To that end, we made use of primary human airway epithelial cell (HAEC) cultures grown at the air-liquid interface that were infected with the beta coronavirus OC43. We found that the triple combination of GS-441524 (parent nucleoside of remdesivir), molnupiravir, and ribavirin resulted in a more pronounced antiviral efficacy than what could be expected from a purely additive antiviral effect. The potency of this triple combination was next tested in SARS-CoV-2 infected hamsters. To that end, for each of the drugs, intentionally suboptimal or even ineffective doses were selected. Yet, in the lungs of all hamsters that received triple prophylactic therapy with suboptimal/inactive doses of GS-441524, molnupiravir, and ribavirin, no infectious virus was detectable. Our finding indicate that co-administration of approved drugs for the treatment of coronavirus infections should be further explored but also against other families of viruses with epidemic and pandemic potential for which no effective antiviral treatment is available.

Multimodal radiotherapy dose prediction using a multi-task deep learning model.

BACKGROUND: In radiation therapy (RT), accelerated partial breast irradiation (APBI) has emerged as an increasingly preferred treatment modality over conventional whole breast irradiation due to its targeted dose delivery and shorter course of treatment. APBI can be delivered through various modalities including Cobalt-60-based systems and linear accelerators with C-arm, O-ring, or robotic arm design. Each modality possesses distinct features, such as beam energy or the degrees of freedom in treatment planning, which influence their respective dose distributions. These modality-specific considerations emphasize the need for a quantitative approach in determining the optimal dose delivery modality on a patient-specific basis. However, manually generating treatment plans for each modality across every patient is time-consuming and clinically impractical. PURPOSE: We aim to develop an efficient and personalized approach for determining the optimal RT modality for APBI by training predictive models using two different deep learning-based convolutional neural networks. The baseline network performs a single-task (ST), predicting dose for a single modality. Our proposed multi-task (MT) network, which is capable of leveraging shared information among different tasks, can concurrently predict dose distributions for various RT modalities. Utilizing patient-specific input data, such as a patient's computed tomography (CT) scan and treatment protocol dosimetric goals, the MT model predicts patient-specific dose distributions across all trained modalities. These dose distributions provide patients and clinicians quantitative insights, facilitating informed and personalized modality comparison prior to treatment planning. METHODS: The dataset, comprising 28 APBI patients and their 92 treatment plans, was partitioned into training, validation, and test subsets. Eight patients were dedicated to the test subset, leaving 68 treatment plans across 20 patients to divide between the training and validation subsets. ST models were trained for each modality, and one MT model was trained to predict doses for all modalities simultaneously. Model performance was evaluated across the test dataset in terms of Mean Absolute Percent Error (MAPE). We conducted statistical analysis of model performance using the two-tailed Wilcoxon signed-rank test. RESULTS: Training times for five ST models ranged from 255 to 430 min per modality, totaling 1925 min, while the MT model required 2384 min. MT model prediction required an average of 1.82 s per patient, compared to ST model predictions at 0.93 s per modality. The MT model yielded MAPE of 1.1033 ± 0.3627% as opposed to the collective MAPE of 1.2386 ± 0.3872% from ST models, and the differences were statistically significant (p = 0.0003, 95% confidence interval = [-0.0865, -0.0712]). CONCLUSION: Our study highlights the potential benefits of a MT learning framework in predicting RT dose distributions across various modalities without notable compromises. This MT architecture approach offers several advantages, such as flexibility, scalability, and streamlined model management, making it an appealing solution for clinical deployment. With such a MT model, patients can make more informed treatment decisions, physicians gain more quantitative insight for pre-treatment decision-making, and clinics can better optimize resource allocation. With our proposed goal array and MT framework, we aim to expand this work to a site-agnostic dose prediction model, enhancing its generalizability and applicability.

From Bulk to Interface: Solvent Exchange Dynamics and Their Role in Ion Transport and the Interfacial Model of Rechargeable Magnesium Batteries.

Multivalent battery chemistries have been explored in response to the increasing demand for high-energy rechargeable batteries utilizing sustainable resources. Solvation structures of working cations have been recognized as a key component in the design of electrolytes; however, most structure-property correlations of metal ions in organic electrolytes usually build upon favorable static solvation structures, often overlooking solvent exchange dynamics. We here report the ion solvation structures and solvent exchange rates of magnesium electrolytes in various solvents by using multimodal nuclear magnetic resonance (NMR) analysis and molecular dynamics/density functional theory (MD/DFT) calculations. These magnesium solvation structures and solvent exchange dynamics are correlated to the combined effects of several physicochemical properties of the solvents. Moreover, Mg2+ transport and interfacial charge transfer efficiency are found to be closely correlated to the solvent exchange rate in the binary electrolytes where the solvent exchange is tunable by the fraction of diluent solvents. Our primary findings are (1) most battery-related solvents undergo ultraslow solvent exchange coordinating to Mg2+ (with time scales ranging from 0.5 µs to 5 ms), (2) the cation transport mechanism is a mixture of vehicular and structural diffusion even at the ultraslow exchange limit (with faster solvent exchange leading to faster cation transport), and (3) an interfacial model wherein organic-rich regions facilitate desolvation and inorganic regions promote Mg2+ transport is consistent with our NMR, electrochemistry, and cryogenic X-ray photoelectron spectroscopy (cryo-XPS) results. This observed ultraslow solvent exchange and its importance for ion transport and interfacial properties necessitate the judicious selection of solvents and informed design of electrolyte blends for multivalent electrolytes.

Axonal endoplasmic reticulum tubules control local translation via P180/RRBP1-mediated ribosome interactions.

Local mRNA translation in axons is critical for the spatiotemporal regulation of the axonal proteome. A wide variety of mRNAs are localized and translated in axons; however, how protein synthesis is regulated at specific subcellular sites in axons remains unclear. Here, we establish that the axonal endoplasmic reticulum (ER) supports axonal translation in developing rat hippocampal cultured neurons. Axonal ER tubule disruption impairs local translation and ribosome distribution. Using nanoscale resolution imaging, we find that ribosomes make frequent contacts with axonal ER tubules in a translation-dependent manner and are influenced by specific extrinsic cues. We identify P180/RRBP1 as an axonally distributed ribosome receptor that regulates local translation and binds to mRNAs enriched for axonal membrane proteins. Importantly, the impairment of axonal ER-ribosome interactions causes defects in axon morphology. Our results establish a role for the axonal ER in dynamically localizing mRNA translation, which is important for proper neuron development.

Deep learning based automatic segmentation of the Internal Pudendal Artery in definitive radiotherapy treatment planning of localized prostate cancer.

Background and purpose: Radiation-induced erectile dysfunction (RiED) commonly affects prostate cancer patients, prompting clinical trials across institutions to explore dose-sparing to internal-pudendal-arteries (IPA) for preserving sexual potency. IPA, challenging to segment, isn't conventionally considered an organ-at-risk (OAR). This study proposes a deep learning (DL) auto-segmentation model for IPA, using Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) or CT alone to accommodate varied clinical practices. Materials and methods: A total of 86 patients with CT and MRI images and noisy IPA labels were recruited in this study. We split the data into 42/14/30 for model training, testing, and a clinical observer study, respectively. There were three major innovations in this model: 1) we designed an architecture with squeeze-and-excite blocks and modality attention for effective feature extraction and production of accurate segmentation, 2) a novel loss function was used for training the model effectively with noisy labels, and 3) modality dropout strategy was used for making the model capable of segmentation in the absence of MRI. Results: Test dataset metrics were DSC 61.71 ± 7.7 %, ASD 2.5 ± .87 mm, and HD95 7.0 ± 2.3 mm. AI segmented contours showed dosimetric similarity to expert physician's contours. Observer study indicated higher scores for AI contours (mean = 3.7) compared to inexperienced physicians' contours (mean = 3.1). Inexperienced physicians improved scores to 3.7 when starting with AI contours. Conclusion: The proposed model achieved good quality IPA contours to improve uniformity of segmentation and to facilitate introduction of standardized IPA segmentation into clinical trials and practice.

American Society of Pain and Neuroscience Best Practice (ASPN) Guideline for the Treatment of Sacroiliac Disorders.

Clinical management of sacroiliac disease has proven challenging from both diagnostic and therapeutic perspectives. Although it is widely regarded as a common source of low back pain, little consensus exists on the appropriate clinical management of sacroiliac joint pain and dysfunction. Understanding the biomechanics, innervation, and function of this complex load bearing joint is critical to formulating appropriate treatment algorithms for SI joint disorders. ASPN has developed this comprehensive practice guideline to serve as a foundational reference on the appropriate management of SI joint disorders utilizing the best available evidence and serve as a foundational guide for the treatment of adult patients in the United States and globally.