Browse Articles

SHIP-AGE: Frailty, renal function, and multi-component primary care in rural Mecklenburg-West Pomerania (MV-FIT)- study protocol

Background. Chronic kidney disease (CKD) is a leading risk factor for cardiovascular disease and all-cause mortality among older adults. Mecklenburg-West Pomerania has the highest CKD prevalence in Germany and Europe, however, its impact on frailty prevention strategies in primary care remains poorly understood. The SHIP-AGE/MV-FIT study aims to investigate the role of CKD in frailty incidence. Methods. The SHIP-AGE/MV-FIT cohort is a prospective, longitudinal, population-based observational study targeting individuals ≥ 65 years with mGFR >30 mL/min. The cohort will consist of approximately 820 elderly participants who will be monitored over a three-year period. They will undergo a comprehensive, multi-factorial geriatric assessment, along with a structured monitoring and management program aimed at preventing frailty. The program incorporates evidence-based, multi-component care, including physical activity, medication review, nutritional optimization, and fall prevention strategies. Discussion. SHIP-AGE/MV-FIT will clarify CKD’s role in frailty progression and identify mechanisms underlying frailty and pre-frailty. Additionally, the study aims to implement and evaluate multi-component healthcare strategies for frailty and fall prevention, assess patient adherence and quality of life, and explore elderly individuals’ experiences with primary care interventions. By integrating SHIP-AGE data with findings from the SHIP (Study of Health in Pomerania) cohorts in our region, this research will contribute to evidence-based strategies for maintaining health, independence, and well-being in aging populations, particularly in rural primary care settings, such as Mecklenburg-West Pomerania.

BanglaNewsClassifier: A machine learning approach for news classification in Bangla Newspapers using hybrid stacking classifiers

Bangla news floods the web, and the need for smarter and more efficient classification techniques is greater than ever. Previous studies mostly focused on traditional models, overlooking the potential of hybrid techniques to handle the ever-growing complex dataset and its linguistic patterns in Bangla to achieve higher accuracy. Addressing the challenge, this study presents a comprehensive approach to classify Bangla news articles into eight distinct categories using various machine learning and deep learning techniques. The use of traditional machine learning algorithms, deep learning architectures, and hybrid models, including novel stacking classifiers, was a part of our experiment. This study utilized a dataset of 118,404 Bangla news articles, applying rigorous feature extraction techniques including TF-IDF vectorization and word2Vec embeddings. Our best-performing model, a stacking meta-classifier combining bidirectional long short-term memory and support vector machine, achieved a remarkable 94% accuracy, leaving all basic models’ performance behind. Also, we provided an in-depth analysis of model performances, including confusion matrices, ROC curves, and error analysis, offering insights into the strengths and limitations of each approach. This research contributes significantly to the field of Bangla natural language processing and demonstrates the efficacy of ensemble methods and deep learning in news classification for low-resource languages.

Unraveling job demand-control-support patterns and job stressors as predictors: Cross-sectional latent profile and network analysis among Italian hospital workers

The Job Demand-Control-Support (JDCS) model postulates that patterns of job demand, job control, and social support lead to eight job types that are associated with well-being and health. This study employed latent profile analysis (LPA) to identify JDCS profiles among Italian hospital workers (n = 1464) and examined the predictive roles of role clarity and negative relationships at work on profile membership. Furthermore, adopting a network perspective, this study explored the interrelationships among JDCS factors within each identified profile. The LPA results revealed four profiles: isolated prisoner, moderate strain, low strain, and participatory leader. In addition, role clarity increased the likelihood of being included in the low-strain, moderate-strain, and participatory leader profiles. In contrast, negative relationships at work increased the risk of being included in the isolated prisoner profile. Finally, the results of network analysis revealed that networks differed across profiles in terms of density (interconnections between nodes) and edge strength (magnitude of relationships between nodes). Our study extends previous JDCS research by highlighting that researchers should consider empirically identified profiles rather than theoretically defined subgroups. The implications for stress theory, future research, and practice are also discussed.

Investigation of locomotive syndrome improvement by total hip arthroplasty in patients with hip osteoarthritis: A before-after comparative study focusing on 25-question geriatric locomotive function scale

Background The 25-Question Geriatric Locomotive Function Scale (GLFS-25) is one of the tests used to assess the risk of locomotive syndrome (LS). It is a comprehensive tool for measuring LS improvement after total hip arthroplasty (THA) and provides beneficial information for rehabilitation after THA. The primary objective of this study was to clarify LS improvement in patients with hip osteoarthritis (OA) who have undergone unilateral primary THA using GLFS-25. A secondary objective was to identify the impact of THA on each specific GLFS-25 item for optimizing functional recovery. Methods The participants of this study were 273 patients who underwent primary THA for hip OA. LS was evaluated using the GLFS-25, stand-up test, and two-step test before receiving THA and three months after THA. Results Before THA, items rated as “moderately difficult” (score ≥2) in GLFS-25 included pain-related Q3 and Q4, activities of daily living (ADL)-related Q12, Q13, Q15, and Q18, and social function-related Q21 and Q23. At three months after THA, these subjective symptoms showed significant improvement. Further analysis of the relationship between these subjective symptom improvements and LS improvement revealed that all items, except pain-related Q3, were significantly associated with LS improvement. Conclusions Patients experienced not only severe hip pain and physical discomfort but also significant difficulties with activities of daily living (ADL) and social participation before THA. LS improvement after THA was strongly associated with improvements in the subjective symptoms of ADL and social functioning. Based on these findings, rehabilitation strategies that focus on enhancing mobility, improving ADL and social engagement, and optimizing gait function after THA are crucial for further supporting LS recovery.

Evaluation of data driven low-rank matrix factorization for accelerated solutions of the Vlasov equation

Low-rank methods have shown success in accelerating simulations of a collisionless plasma described by the Vlasov equation, but still rely on computationally costly linear algebra every time step. We propose a data-driven factorization method using artificial neural networks, specifically with convolutional layer architecture, that trains on existing simulation data. At inference time, the model outputs a low-rank decomposition of the distribution field of the charged particles, and we demonstrate that this step is faster than the standard linear algebra technique. Numerical experiments show that the method achieves comparable reconstruction accuracy for interpolation tasks, generalizing to unseen test data in a manner beyond just memorizing training data; patterns in factorization also inherently followed the same numerical trend as those within algebraic methods (e.g., truncated singular-value decomposition). However, when training on the first 70% of a time-series data and testing on the remaining 30%, the method fails to meaningfully extrapolate. Despite this limiting result, the technique may have benefits for simulations in a statistical steady-state or otherwise showing temporal stability. These results suggest that while the model offers a computationally efficient alternative for datasets with temporal stability, its current formulation is best suited for interpolation rather than for predicting future states in time-evolving systems. This study thus lays the groundwork for further refinement of neural network-based approaches to low-rank matrix factorization in high-dimensional plasma simulations.

A web-based workplace exercise intervention among office workers with spinal pain: Protocol of a mixed methods study

Introduction Musculoskeletal disorders are a major cause of disability worldwide, significantly impacting office workers due to prolonged sitting and lack of movement. Implementing therapeutic exercise interventions in the workplace has been identified as a feasible and cost-effective strategy to address spinal pain. However, understanding workers’ perspectives and workplace barriers is essential for designing effective interventions. This study aims to develop and evaluate a web-based workplace intervention with active breaks to reduce spinal pain among office workers. Methods This study follows a sequential exploratory mixed-methods design. The qualitative phase will use semi-structured interviews with office workers to explore their experiences with spinal pain, active breaks, and perceived barriers to implementation. These findings will inform the development of a six-week web-based therapeutic exercise intervention, which will be evaluated through a two-arm cluster randomised controlled trial. The trial will compare an intervention group performing structured active breaks during work hours with a control group maintaining their usual routine. Primary outcomes include pain intensity (Visual Analogue Scale), spinal dysfunction (Spine Functional Index), and adherence to the program. Secondary outcomes include quality of life (EQ-5D-5L) and exercise motivation (Behavioural Regulation in Exercise Questionnaire-2). Statistical analyses will compare within- and between-group differences to assess the intervention’s effectiveness. Discussion Web-based interventions can enhance adherence to active breaks and provide an accessible, cost-effective solution for spinal pain management in sedentary workplaces. By adopting a mixed-methods approach, this study will generate valuable insights into implementing workplace exercise interventions, taking into account workers’ expectations, workplace context, and adherence factors. Findings may inform future interventions aimed at managing musculoskeletal disorders in office workers. Trial registration ClinicalTrials.gov NCT05571124

Gestational weight gain and its determinants among pregnant women attending antenatal care at West Shawa Hospitals, Oromia, Ethiopia

Background Gestational Weight Gain (GWG) is a crucial factor influencing maternal and neonatal health outcomes. Identifying the determinants of GWG can help develop targeted interventions to improve pregnancy outcomes. Objective This study aimed to assess the magnitude of gestational weight gain and identify its determinants among pregnant women attending antenatal care (ANC) services at West Shoa Hospital, Ethiopia, in 2024. Methodology A bidirectional cohort study was conducted among 885 pregnant women attending antenatal care (ANC) services at West Shoa Hospitals, Ethiopia, before 12 weeks of gestation. Data were collected through face-to-face interviews using the CesPro application and document review. The determinants of GWG were analyzed using an ordinal logistic regression model, assuming the proportional odd assumptions. The Brant test was used to determine whether the parallel assumption was held. The STATA “ologit” command was used for ordinal regression, and the “brant” test was applied to verify the validity of the model. Odds ratios (OR) with 95% confidence intervals (CI) were estimated, and statistical significance was declared at p < 0.05. Results Approximately 69% of pregnant women experienced insufficient weight gain, 26% had adequate weight gain, and 5% had excessive weight gain during pregnancy. Pre-pregnancy Body Mass Index was a significant determinant of gestational weight gain. Compared to underweight women, overweight women had 10.58 times higher odds (95% CI: 5.24–21.37) of being in a higher weight gain category, while obese women had 10.64 times higher odds (95% CI: 1.87–60.57) of achieving normal or excessive gestational weight gain. Partner education significantly influenced gestational weight gain, with those who could only read and write having 0.22 times lower odds (95% CI: 0.05–0.98) of excessive weight gain compared to those with higher education. Maternal occupation also played a role, as daily laborers had 0.26 times lower odds (95% CI: 0.08–0.87) of adequate weight gain than employed women. The normal hemoglobin category was associated with increased odds of being in a higher weight gain category (adequate or excessive) compared to a lower category, with an odds ratio (OR) of 1.04 (95% CI: 1.01–1.08). Conversely, alcohol consumption was associated with lower odds of being in a higher weight gain category, with an OR of 0.49 (95% CI: 0.25–0.99), suggesting that alcohol drinkers had lower odds of experiencing normal or excessive weight gain compared to non-drinkers. Conclusion A significant proportion of pregnant women experienced inadequate gestational weight gain. Pre-pregnancy BMI, partner’s educational status, maternal occupation, hemoglobin levels, and alcohol consumption were key determinants of gestational weight gain. These findings highlight the need for targeted nutritional counseling and lifestyle interventions to promote optimal weight gain during pregnancy.

Hepatoprotective activity of raspberry ketone against streptozotocin-induced type 2 diabetes in male rats

Type 1 diabetes encompasses a spectrum of metabolic disorders marked by insulin deficiency, resulting in elevated blood glucose levels, commonly referred to as hyperglycemia. This persistent condition often precipitates lipid profile abnormalities, causing cholesterol alterations, low-and high-density lipoproteins, and triglycerides. The liver is particularly vulnerable to increased oxidative stress and inflammatory responses, which activate the transcription of pro-apoptotic genes and ultimately contribute to hepatocyte damage. This study analyzed the potential therapeutic role of raspberry ketone (RK), a natural antioxidant with antiapoptotic and anti-inflammatory properties, in male albino rats with induced type 2 diabetes. Fifty rats were equally divided into five groups: control, rats orally administered 200 mg /kg Body Weight (BW) RK for 5 days, diabetic rats intramuscularly injected once with 60 mg/kg BW streptozotocin, streptozotocin-induced diabetic rats orally administered 200 mg/kg BW RK for 5 days, and streptozotocin-induced diabetic rats orally administered 100 mg/kg metformin. Streptozotocin treatment significantly affected blood biochemical parameters, lipid profiles, oxidative stress markers, immunotoxicity biomarkers, and DNA damage biomarkers. Conversely, RK efficiently ameliorated the toxic effects of streptozotocin on the liver by reducing the pathological and biochemical changes associated with diabetes through its antioxidant and anti-inflammatory properties. Therefore, incorporating RK into the diet of diabetic patients can help prevent hepatocyte damage associated with diabetes. In conclusion, oral administration of RK exerts hepatoprotective effects by offering antioxidant, antiapoptotic, and anti-inflammatory properties against streptozotocin-induced type 1 diabetes in male rats.

LC-MS profiling and antioxidant, antifungal, and anticancer potentials of Tunisian Allium sativum L. extracts

Despite Garlic’s (Allium. sativum) long-standing reputation for therapeutic properties, comprehensive studies on Tunisian garlic are lacking. This study aims to evaluate different Tunisian A. sativum extracts rich in bioactive compounds (phenolic acids, flavonoids, and vitamins), exploring their potential bioactivities (antifungal, antioxidant, and cytotoxic). A. sativum samples underwent hexane, ethyl acetate, methanol, and water-based extractions. LC-MS quantification assessed bioactive compounds. Antioxidant activity was determined via the DPPH assay, antifungal effects were evaluated against Aspergillus spp., and cytotoxic effects were assessed using the MTT assay on U266 human multiple myeloma and MDA-MB-231 metastatic breast cancer cell lines. The aqueous extract exhibited the highest phenolic acid content (96.25 mg/kg fw) and the most water-soluble vitamins (14.69 mg/kg fw). In contrast, the methanol extract was richest in flavonoids, while the ethyl acetate extract had the highest concentration of fat-soluble vitamins (20.21 mg/kg fw). Both aqueous and methanolic extracts demonstrated potent antioxidant activity. The aqueous extract exhibited the strongest antifungal activity (MIC: 1.5 mg/mL for A. flavus and 3 mg/mL for A. niger). Furthermore, the ethyl acetate extract showed remarkable cytotoxic effects against cancer cell lines, indicating its potential as an effective agent against metastatic breast cancer and refractory multiple myeloma. A. sativum emerges as a functional food source with antioxidant, antifungal, and cytotoxic activities, particularly against multiple myeloma. While this study provides a strong foundation for further exploration, additional research is needed to identify active compounds, elucidate mechanisms, and assess therapeutic potential.

Composite GDP nowcasting using macroeconomic variables and electricity data

Accurate and timely forecasting of the gross domestic product (GDP), known as “nowcasting”, is crucial for macroeconomic regulation. In this paper, we propose a composite GDP nowcasting model that combines predictions from a dynamic factor model using mixed-frequency macroeconomic indicators and a regression model using real-time electricity data. In the regression model, we introduce changes in electricity capacity, in addition to electricity consumption, as a new predictor to reflect expectations for future electricity demand. Such a nowcasting model not only leverages the correlation between GDP and other macroeconomic indicators, but also utilizes the information contained in electricity data, which is closely related to production. We perform nowcasting for year-on-year quarterly GDP growth rates using data from Fujian Province, China. The results demonstrate that the proposed composite nowcasting model effectively reduces forecast errors compared to the dynamic factor model and the regression model.

Reverse T3 in patients with hypothyroidism on different thyroid hormone replacement

Background Reverse T3 (rT3) is a biologically inactive form of T3 (triiodothyronine), a thyroid hormone, that is created by peripheral 5 deiodination of T4 (thyroxine) by type 1 and type 3 deiodinase enzymes (D1 and D3 respectively) and may block T3 binding to the thyroid hormone receptor. Approximately 15% of patients on L-T4 replacement therapy with a normalized thyroid-stimulating hormone (TSH) report experience continued fatigue and other hypothyroid symptoms; therefore, efforts are needed to understand why this occurs and how it can be corrected. Decades ago, endocrinologists realized that in patients with severe illnesses, rT3 is typically high and T3 is typically low; this was termed “euthyroid sick syndrome”. More recently, functional medicine and other doctors, have argued that high rT3 is detrimental and can block T3 from binding to the thyroid hormone receptor. Due to the lack of peer-reviewed publications on this topic, functional medicine doctors continue to rely heavily on rT3 levels to treat patients that may have no other laboratory findings of hypothyroidism and often prescribe L-T3-only preparations to patients in an effort to lower rT3. Methods The initial rT3 measurements done by liquid chromatography/tandem mass spectrometry (LC/MS-MS) were retrospectively analyzed from the initial blood tests in 976 consecutive patients, with symptoms of fatigue and treated for hypothyroidism, in a private Endocrinology practice. TSH, free T3 and free T4 were measured by electrochemiluminescence immunoassay (ECLIA). The upper limit of normal rT3 (24.1 ng/dL) was used as a cut-off for results above the normal range. Results The number of patients with rT3 levels above normal range varied significantly with the type of thyroid hormone replacement prescribed. The highest rate of an elevated rT3 was 20.9% (29/139) in patients taking T4 alone. Nine% (31/345) of patients not taking thyroid hormone replacement had elevated rT3. Patients on all types of L-T4 treatment had higher rT3 levels than those not on L-T4 treatment (p < 0.00001) and they also had a higher percentage of rT3 levels above the cutoff of 24.1 ng/dL (p < 0.00001). Linear regression analysis showed rT3 levels correlated with free T4 and free T3 levels and inversely with log TSH levels. Conclusions This study found elevated rT3 levels in patients with symptoms of fatigue on various thyroid hormone replacements with the highest levels of rT3 in those taking L-T4 replacement alone and the lowest levels of rT3 in those on preparations that contained L-T3 alone.

Modeling predictors of behavior of American consumers suffering from food intolerances or food allergies

Food intolerances, allergies, and celiac disease cause hypersensitive reactions to certain foods, collectively termed adverse reactions to food. This study aims to identify predictors of purchasing intentions and behaviors among individuals with such reactions, using three behavioral models: the Theory of Planned Behavior, the Health Belief Model, and Protection Motivation Theory. The authors created a new, comprehensive model by integrating these theories, combining their strengths to provide a more robust framework. The survey, conducted via the Forthright platform using the Computer-Assisted Web Interview (CAWI) method, involved 1,088 respondents. This article is the first to explore predictors of consumer intentions and behaviors regarding food products suitable for individuals with intolerances or allergies, offering new insights into consumer decision-making models.

Impact of on-field repeated sprint training on aerobic fitness and anaerobic performance in football athletes: A matched-pair design

Background Repeated sprint ability (RSA) is essential for football performance, especially in maintaining high-intensity efforts throughout a match. Repeated sprint training (RST) improves both aerobic and anaerobic capacities; however, its effects on players in different positional roles remain underexplored, particularly with regard to tailored conditioning protocols. Objective This study assessed the impact of on-field RST on physical performance metrics, including aerobic capacity (VO₂max), sprint speed (10m DASH), vertical jump height (VJH), and power output, with a focus on positional differences among forwards, defenders, and goalkeepers. Methods Forty male football players (aged 18–25 years) were purposively sampled and matched by position before allocation into experimental (RST) and control groups. The experimental group completed a structured four-week RST program, while the control group continued routine football training involving technical, tactical, and endurance drills. Pre- and post-intervention assessments included the Cooper Test (aerobic capacity), VO₂max, VJH, power output, and 10m DASH. Results After 4 weeks of RST, significant mprovements were observed in the experimental group. VO2max increased by 4.4 ml/kg/min (95% CI: 2.9 to 6.0; p < 0.001, d = 1.31), and 10m sprint time decreased by 0.32 seconds (95% CI: -0.45 to -0.19; p < 0.001, d = 1.36) in forwards. VJH improved significantly (p < 0.001) among defenders (Δ = 3.44 cm, 95% CI: 1.76 to 5.12, d = 1.06), while power improvements were most notable in defenders (Δ = 43.44W, 95% CI: 28.62 to 58.26, d = 1.00). Goalkeepers showed modest, non-significant improvements. Significant positional differences were identified for VJH and power output (p < 0.001). Conclusion RST significantly enhanced physical performance metrics, particularly for forwards and defenders. The findings emphasize the importance of positional specificity in training programs to optimize football performance.

Cycloparaazine, a full-azine carbon nanoring

Bioenergetic stress potentiates antimicrobial resistance and persistence

Eliminating The Need for Sequential Confirmation of Response in Multiple Myeloma

Disease response and progression assessment in multiple myeloma (MM) is based on various measurements of monoclonal protein (serum and urine protein electrophoresis, serum free light chain (FLC, and or quantitative immunoglobulins). Currently, the IMWG consensus response criteria require two sequential assessments of any one marker made at any time before confirmation of disease progression and the institution of any new therapy. However, this can be cumbersome in clinical trials. Herein, we hypothesized that if two markers meet the progression criteria simultaneously, a repeat of either will not be necessary for confirmation. We retrospectively studied all sequential patients with myeloma enrolled in clinical trials at Mayo Clinic. We identified 583 episodes of confirmed progression in our study. Among the 583 progression episodes, nearly 70% (sensitivity of the simultaneous criteria) met the two simultaneous variable criteria at the first testing, indicating progression. Conversely, among 413 patients who met progression criteria by two simultaneous values, 98% (specificity of the simultaneous criteria) of patients subsequently had confirmed progression by sequential values. In summary, for patients with two disease burden markers meeting the simultaneous progression criteria, sequential assessment either one for confirmation may not be necessary to determine disease progression.

40 Years of colloidal nanocrystals in JCP

Hydrogen diffusion in the confinement between graphene and Ni(111): Full-dimensional simulation of nuclear quantum effects

The temperature-dependent diffusion of hydrogen on a Ni(111) surface and in the confinement between Ni(111) and an adsorbed graphene sheet [Gr/Ni(111)] is studied by ring polymer molecular dynamics (RPMD) simulations on neural network potentials. Static periodic density-functional theory calculations reveal weakened bonding of hydrogen and higher diffusion barriers in the confinement. Furthermore, local density of hydrogen atoms has a significant influence on their shape and properties. For a hydrogen density of 0.25 ML, the graphene sheet switches to the weaker bound van der Waals configuration, resulting in a broad confinement with similar properties as the clean metal surface. For a hydrogen density of 0.04 ML, the graphene behaves like a carpet and bends up locally around the hydrogen atom. This presses the hydrogen atom to the surface, resulting in lower intercalation energy and a higher diffusion barrier. The RPMD simulations were used to quantify the effect of temperature and nuclear quantum effects on the diffusion. For 0.25 ML hydrogen coverage, the diffusion coefficients are similar to the clean surface, with a crossover temperature to the deep-tunneling regime of ∼100 K, whereas for 0.04 ML, diffusion at low temperatures is significantly decreased. At temperatures above 200 K, on the other hand, diffusion is more similar for both hydrogen coverages due to a more flexible graphene sheet. This study reveals that two-dimensional confinements adapt to their content, and full-dimensional simulations with the inclusion of nuclear quantum effects can greatly enhance our understanding of them, needed for their targeted usage as storage media or catalysts.

Chirped-pulse Fourier transform microwave spectrum of the HCl–DCl heterodimer

Hydrohalic acid dimers provide a fundamental opportunity to study hydrogen bond rearrangement dynamics at a high level of detail. The (HCl)2 and (DCl)2 homodimers do not have a pure rotational spectrum due to rapid geared tunneling motions that interchange the role of the hydrogen bond donor and acceptor. In this work, we report the pure rotational spectrum of HCl–DCl, which has a preference for deuterium in the hydrogen bond donor position. However, the quadrupole coupling constants indicate a significant amount of geared tunneling, consistent with significant zero-point wavefunction amplitude in the less stable DCl–HCl configuration. A comparison of experimental results with previously published wavefunction calculations based on model and ab initio potentials is consistent with a picture in which about 14% of the probability density distribution is located in the less stable well.

Ion dynamics in hexagonal boron nitride ionogel electrolytes

Ionogel electrolytes incorporating exfoliated hexagonal boron nitride (hBN) nanoplatelets are promising materials for next-generation energy storage systems. However, detailed understanding of their ion transport properties at the molecular level remains limited. This study employs diffusion and relaxation nuclear magnetic resonance (NMR) techniques, including fast-field cycling (FFC) NMR, to investigate the dynamics of ionic species in hBN-ionogels. By spanning a broad frequency range from 30 kHz using FFC NMR to high-field NMR (500–800 MHz), we reveal distinct relaxation mechanisms governing ion dynamics in ionogels with and without lithium salts. Our results highlight the role of hBN in modulating molecular rotation and translational motion, significantly affecting 1H and 19F relaxation profiles. The presence of Li+ alters the dynamic behavior in ionogels, enhancing anion mobility at the interface. Notably, 7Li relaxation reveals strong interactions with the hBN surface that cannot be detected by diffusion NMR. These findings underscore the importance of spanning a broad frequency range in NMR studies of ionogels and provide critical insights into optimizing their design as novel electrolytes.