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Longitudinal progression of cost-related medication non-adherence among Medicare patients with diabetes at high risk of hospitalization: The role of dual eligibility

Objective Little is known about the longitudinal progression of cost-related medication non-adherence (CRN) among the high-need, high-cost diabetes population. We aim to document the longitudinal aspect of CRN among Medicare diabetes patients at high risk of hospitalization and the role of Medicare-Medicaid dual eligibility in CRN. Research design and methods 617 Medicare diabetes patients at high risk of hospitalization were followed up at 3-month intervals for a total of 16 surveys. Patients’ socio-demographic and health characteristics by dual eligibility were compared using Chi-square tests. The progression of CRN was documented using a Kaplan-Meier Survival Curve. A Cox Survival Regression analysis and a Generalized Estimating Equation (GEE) analysis were conducted to evaluate the adjusted hazard ratio (HR) and population-averaged effect of dual eligibility on CRN, controlling for socio-demographic and health characteristics. Results 303 patients (49.1%) reported dual eligibility, among whom 151 (49.8%) reported CRN; they were more likely to be under 65 (p < 0.01), had lower income (p < 0.01), were less likely to report cardiovascular disease (p = 0.05), and were less likely to report CRN (p < 0.01) compared to those who did not report dual eligibility. Those with dual eligibility had a lower hazard ratio (HR = 0.67, p < 0.01) and lower likelihood of reporting CRN (coefficient = −0.40, p < 0.01), and those with depression had higher hazard ratio (HR = 1.31, p = 0.03) and higher likelihood of reporting CRN (coefficient = 0.32, p < 0.01) in the Cox model and GEE, respectively. Conclusions While insurance coverage enables patients to overcome their major deficiency in income, many patients fall through the cracks as their disease progresses. Depression is a major risk factor for CRN. Health policy addressing CRN needs to be implemented in tandem with clinical intervention, targeting those at the increasing risk of CRN.

Mouse liver assembloids model periportal architecture and biliary fibrosis

Abstract Modelling liver disease in vitro requires systems that replicate disease progression1,2. Current tissue-derived organoids do not reproduce the complex cellular composition and tissue architecture observed in vivo3. Here, we describe a multicellular organoid system composed of adult hepatocytes, cholangiocytes and mesenchymal cells that recapitulates the architecture of the liver periportal region and, when manipulated, models aspects of cholestatic injury and biliary fibrosis. We first generate reproducible hepatocyte organoids with a functional bile canaliculi network that retain morphological features of in vivo tissue. By combining these with cholangiocytes and portal fibroblasts, we generate assembloids that mimic the cellular interactions of the periportal region. Assembloids are functional, consistently draining bile from bile canaliculi into the bile duct. Of note, manipulating the relative number of portal mesenchymal cells is sufficient to induce a fibrotic-like state, independently of an immune compartment. By generating chimeric assembloids of mutant and wild-type cells, or after gene knockdown, we show proof of concept that our system is amenable to investigating gene function and cell-autonomous mechanisms. Together, we demonstrate that liver assembloids represent a suitable in vitro system to study bile canaliculi formation, bile drainage and how different cell types contribute to cholestatic disease and biliary fibrosis in an all-in-one model.

Validation of the Multidimensional Sociosexual Orientation Inventory (SOI-M) in the Chilean population

We used the SOI-M Multidimensional Sociosexual Orientation Inventory developed by Jackson and Kirkpatrick for validation in the Chilean population. This 3-dimensional instrument measures sociosexuality in terms of short-term mating orientation, long-term mating orientation, and sociosexual behavior. This inventory allows us to capture the deployment of reproductive strategies in men and women simultaneously (that is, in the short and long-term). We tested the psychometric properties of the instrument on a final sample of 865 subjects (247 women and 616 men) aged between 18 and 55 years old (M = 23.56; SD = 5.52). First, with a subsample (N = 172), we performed an EFA to establish dimensionality. Then, with the remaining sample (N = 693) an ESEM and CFA. We performed an ESEM for confirmatory analysis, analyzed invariance by sex and relations with other variables. We verified the existence of three factors, maintaining the 20 original items. Then, we eliminated the redundant or cross-factor loading items, thus obtaining a reduced version of 15 items and three factors. We tested and verified the convergent and divergent validity of the instrument. Also, we tested invariance by sex, establishing that the SOI-M behaves invariantly. The SOI-M is a valid and reliable instrument to measure multidimensional sociosexuality in the Chilean population.

Colorimetry characteristics and color clustering of natural gem-quality spinel from Myanmar (Burma)

Color is the most critical factor in determining the value of gem-quality spinel. This paper examines the color mechanism and the colorimetric characteristics of spinel crystals under D65 and A light sources against nine neutral backgrounds. It also explores color clustering for over 400 spinel crystals in yellow, red, purple, and blue hues. Various analytical techniques were employed, including optical absorption spectroscopy, Energy-Dispersive X-ray Fluorescence analysis, a benchtop sphere spectrophotometer, the Munsell neutral value gray scale chart, and a standard illumination box. The study reveals distinct optical absorption peaks corresponding to different spinel colors, with these peaks being assigned to specific mechanisms based on previous research. Color analysis demonstrates that the chroma and hue of orange spinel crystals are primarily influenced by the red tone under both daylight and incandescent light. Additionally, red, and yellow tones significantly enhance the color of red spinel. For purple spinel, chroma shows a strong correlation with the a* value under both D65 and A light sources, while hue is easily influenced by the blue tone. In blue spinel, chroma is controlled by the green tone, and hue is affected by the blue tone. Lightness and chroma of all spinel colors increase significantly with the luminance factor of the Munsell neutral background, following a power function relationship with varying rates. However, the hue angle remains relatively unchanged, as gray backgrounds have minimal effect on hue. The colors of spinel crystals can be effectively predicted using calculated equations based on the luminance factor of the background under different light sources. The K-means clustering method is particularly effective for categorizing each spinel color into three distinct groups, which is crucial for developing a reliable color grading system for spinel.

Impacts of environment and forest type on the relationship between stand structure diversity and productivity in natural mixed forests

Forest productivity reflects forest growth quality and forms the basis for achieving forest service functions. The relationships between forest productivity and stand structure has been extensively studied, but it is still unclear whether environmental factors affect the relationship and how their relationships vary under the influence of stand type and environmental factors. A fixed monitoring dataset from 972 plots of natural mixed forests (including coniferous, broad-leaved, and coniferous and broad-leaved mixed forests) in Zhejiang Province was used. We analyzed the relationship between stand structure diversity (composition diversity and size differentiation diversity) and productivity in the different forest types and their influencing factors. Species richness, coefficient of the diameter at breast height (DBH) variation, and the DBH Shannon‒Wiener index were significantly positively correlated with productivity (P < 0.01). Environmental factors such as terrain and meteorology were associated with stand productivity and structure diversity. Considering the effects of environmental factors and stand density, it was evident that stand density was the primary direct factor influencing coniferous forest productivity (r∂ = 0.635), and terrain exerted a substantial indirect effect on productivity through stand density (r∂ = 0.202). In broad-leaved forests, topography (r∂ = −0.161), size differentiation diversity (r∂ = −0.519), and stand density (r∂ = 0.954) were the primary factors influencing productivity, with climate exerting a significant indirect effect via stand density and size differentiation diversity. In coniferous and broad-leaved mixed forests, stand density (r∂ = 0.862), size differentiation diversity (r∂ = −0.424), and composition diversity (r∂ = 0.260) were all significantly correlated with productivity. The effect of structure diversity on productivity in natural broad-leaved mixed forests in Zhejiang Province was modulated by environmental factors and stand density. Our research deepens understandings of the factors driving productivity in natural broad-leaved mixed forests and offers a theoretical foundation for sustainable development.

Privy by the Bay: Emerging hotspot analysis of 311 reports of human/animal waste near San Francisco Pit Stop locations, 2009–2022

Access to basic sanitation facilities is a significant challenge for homeless populations, and evidence on the effectiveness of interventions to address this issue are still limited. San Francisco, California, has a large population of people experiencing homelessness and limited public restrooms. To increase access to public restrooms among this population in the city, the Pit Stop Program launched in 2014, introducing clean and safe public restrooms to high-need areas. This study built upon prior Pit Stop Program evaluations by conducting an Emerging Hot Spot Analysis (EHSA) using ArcGIS Pro v3.1.1 of human/animal waste reports across San Francisco to the city’s 311 system using spatial and temporal characteristics, and examined the presence of these hotspots among San Francisco neighborhoods. We examined 5,940,667 reports to 311 in conjunction with Pit Stop and public restroom locations. Waste reports in San Francisco showed an upward trend from 2009 to 2022, reaching an all-time high in 2022. Pit Stop Program sites were generally concentrated in areas with the most waste reports, particularly the Tenderloin and Mission neighborhoods. Spatiotemporal hot spots were identified throughout the city. Locations containing Pit Stops were more likely to be classified as diminishing hot spots. There is evidence of improvement in the Tenderloin neighborhood, which has seen the longest and most concentrated Pit Stop intervention, however the effect appears to be small. The findings suggest the need for additional research and continued efforts to address sanitation issues for people experiencing homelessness.

Modeling hydrological processes and analyzing water balance utilizing remote sensing data and physical hydrological models in the Songnen Plain, China

Climate change and human activities have a substantial effect on the regional water cycle. An accurate simulation of the water cycle process in the Songnen Plain under the influence of climate change and human activities can aid in gaining a comprehensive understanding of the regional water cycle change pattern in a changing environment, which has significant scientific and practical value. Based on the MIKE SHE/MIKE 11 model, this study utilizes multi-source remote sensing data, measured hydrological data, and other basic data as data sources to simulate the water cycle process in the Songnen Plain over the past 40 years and analyze its pattern of change. The results show that groundwater level data derived from GRACE and GLDAS exhibits great accuracy in topographically varied regions and low accuracy in proximity to rivers. The estimated groundwater data and measured runoff data, integrated with the MIKE SHE/MIKE11 model, accurately replicate the changes in the water cycle within the Songnen Plain. Over the years, the Songnen Plain has experienced an average actual evapotranspiration of 421.61 mm, with an average rate of change of −0.36 mm/a. The average surface runoff has been 36.26 mm, with an average rate of change of −0.025 mm/a. The average groundwater level is 169.2 m, indicating a weak downward trend. The variations in the water balance of the Songnen Plain surplus and deficit throughout different time periods were 0.804 billion m³, 0.098 billion m³, −1.15 billion m³, and 0.645 billion m³, respectively. Regarding alterations in various land uses, water supply and demand are most pronounced in arid regions, where the water balance exhibits a trend of initial decline followed by an increase; paddy fields experienced a water deficit across different time periods, with the severity of the deficit intensifying; the water balance deficit of building land increases with economic growth; and the water balance of other land uses was contingent upon climatic conditions. This study provides novel ideas and methods for regional simulation of water cycle processes in regional water scarcity literature by innovatively integrating GRACE and GLDAS data with the MIKE SHE/MIKE 11 model.

Synthesis of bulk hexagonal diamond

Physics-informed extreme learning machine (PIELM) for consolidation around an expanded cylindrical cavity

This paper proposes a physics-informed extreme learning machine (PIELM) for analyzing consolidation immediately after cavity expansion. The deep neural networks in traditional physics-informed neural network (PINN) framework are substituted by the extreme learning machine (ELM) network with only one hidden layer. By using exact definition of stress invarients, the distribution of excess water pressure after cavity expansion is rigorously incorporated into PIELM framework as initial conditions. Then, a loss vector is obtained by combining governing equation, initial conditions and boundary conditions, and the ELM network can be directly trained by optimising the loss vector via the least squares method. It is found that: (i) the PIELM approach can provide accurate prediction for consolidation analysis after cavity expansion; and (ii) the dissipation of excess water pressure heavily relies on its initial distribution that is related to soil mechanical behaviour. This proposed approach can serve as an efficient tool to interpret consolidation coefficient from piezocone penetration tests (CPTU) with measured data.

Predicting in-hospital mortality in ICU patients with Coronary heart disease and diabetes mellitus using machine learning models

Background Coronary heart disease (CHD) and diabetes mellitus are highly prevalent in intensive care units (ICUs) and significantly contribute to high in-hospital mortality rates. Traditional risk stratification models often fail to capture the complex interactions among clinical variables, limiting their ability to accurately identify high-risk patients. Machine learning (ML) models, with their capacity to analyze large datasets and identify intricate patterns, provide a promising alternative for improving mortality prediction accuracy. Objective This study aims to develop and validate machine learning models for predicting in-hospital mortality in ICU patients with CHD and diabetes, and enhance model interpretability using SHapley Additive exPlanation (SHAP) values, thereby providing a more accurate and practical tool for clinicians. Methods We conducted a retrospective cohort study using data from the MIMIC-IV database, focusing on adult ICU patients with a primary diagnosis of CHD and diabetes. We extracted baseline characteristics, laboratory parameters, and clinical outcomes. The Boruta algorithm was employed for feature selection to identify variables significantly associated with in-hospital mortality, and 16 machine learning models, including logistic regression, random forest, gradient boosting, and neural networks, were developed and compared using receiver operating characteristic (ROC) curves and area under the curve (AUC) analysis. SHAP values were used to explain variable importance and enhance model interpretability. Results Our study included 2,213 patients, of whom 345 (15.6%) experienced in-hospital mortality. The Boruta algorithm identified 29 significant risk factors, and the top 13 variables were used for developing machine learning models. The gradient boosting classifier achieved the highest AUC of 0.8532, outperforming other models. SHAP analysis highlighted age, blood urea nitrogen, and pH as the most important predictors of mortality. SHAP waterfall plots provided detailed individualized risk assessments, demonstrating the model’s ability to identify high-risk subgroups effectively. Conclusions Machine learning models, especially the gradient boosting classifier, demonstrated superior performance in predicting in-hospital mortality in ICU patients with CHD and diabetes, outperforming traditional statistical methods. These models provide valuable insights for risk stratification and have the potential to improve clinical outcomes. Future work should focus on external validation and clinical implementation to further enhance their applicability and effectiveness in managing this high-risk population.

Early cellular events of osteomucosal healing in the tooth extraction socket

Healing after dentoalveolar trauma, such as tooth extraction, is unique to the oral cavity that involves osteomucosal healing – healing of soft and hard tissues at the same time – through a series of healing stages. The healing process of soft or hard tissues is well-documented previously; however, inter-dependency and cross-talks during the progression of their simultaneous healing processes remain unclear. In this study, we investigated spatial and temporal changes of epithelial, connective, and bone tissues, as well as the presence of osteoclasts, during the early stages of osteomucosal healing. We extracted the maxillary first molars in mice and examined the osteomucosal healing process daily for 7 days using histology, immunohistochemistry, and micro-computed tomography (microCT). Epithelial tissues closed progressively throughout 7 days. Collagen deposition began in the extraction sockets as early as day 2, forming a scaffold essential for both epithelial tissue closure and bone formation. Osteoclasts appeared on day 2, steadily increasing until day 5 and remained around the socket walls but not within the sockets. Woven bone formed rapidly around day 5, with significant mineralization observed by day 7. Notably, we identified elevated expression of RANKL throughout the process and a sharp increase in OPG near new bone on day 6. These findings demonstrated the sequential and coordinated mechanisms underlying early osteomucosal healing and provide novel insights into the critical early steps required for proper healing in the oral cavity.

Spectral efficiency and BER analysis of RNN based hybrid precoding for cell free massive MIMO under terahertz communication

In future wireless networks, integrating Terahertz (THz) communication with cell-free massive multiple-input multiple-output (CFMM) systems presents a promising approach to achieving high data rates and low latency. This paper investigates the use of recurrent neural network (RNN)-based hybrid precoding in CFMM systems operating in the THz band. The proposed method jointly designs analog and digital precoders to adapt to dynamic channel conditions and user mobility. However, THz communication is challenged by high path loss and sparse scattering, which complicate accurate channel estimation. To address this, the RNN is trained to predict optimal precoding weights by learning spatial and temporal channel patterns, thereby improving channel estimation and mitigating pilot contamination. Simulation results show that the proposed method achieves higher spectral efficiency and lower bit error rate (BER) than conventional techniques. Specifically, the RNN-based approach attains a spectral efficiency of 10 bps/Hz at a signal-to-noise ratio (SNR) of 30 dB, compared to 8.2 bps/Hz for minimum mean square error (MMSE) precoding. For 16-QAM, the RNN-based method achieves a BER of 10⁻⁶ at an SNR of 11 dB, while MMSE requires 12.5 dB to reach the same BER. Overall, the RNN-based hybrid precoding consistently outperforms traditional methods across various SNR levels, antenna configurations, and user densities, underscoring its potential in next-generation THz wireless systems.

Deciphering the genetic basis of grain iron and zinc content in wheat under heat and drought stress using GWAS

Wheat, a crucial food crop, is inherently deficient in essential micronutrients such as iron and zinc. Climate change exacerbates its vulnerability to abiotic stresses like drought and heat. Developing varieties that are both climate-resilient and nutrient-dense offers a sustainable approach. The objective of this study was to identify genomic regions linked to grain Fe content (GFeC), grain Zn content (GZnC) and thousand grain weight (TGW) traits in wheat grains subjected to heat and drought stress through genome-wide association studies (GWAS). A genetically diverse set of 280 wheat genotypes was assessed across three conditions: timely sown, late sown (heat stress), and restricted irrigation (drought stress) over two years. Variation in iron and zinc levels among genotypes was significant among the conditions, with moderate heritability. Through GWAS 37 significant MTAs across the conditions were identified. For thousand grain weight (TGW) 12 MTAs, for grain Fe content (GFeC) 14 MTAs, and for grain Zn content (GZnC) 11 MTAs were detected. Notably, four MTAs for GFeC two of which were specific to heat stress were located on chromosome 7A. Among these, AX-94432820 (LSIR_23) resides near a RING-H2 finger protein gene involved in metal-ion binding. Additionally, the stable SNP AX-94953068, also on 7A, is adjacent to TraesCS7A02G171600, a gene implicated in stress response. For GZnC, the stable SNP AX-95001849 (r2 = 12.89%) was significant under both TSIR and TSRI, it maps to a plasma membrane ATPase. Using multivariate analysis, MGIDI scores were calculated, identifying nine genotypes that excelled for all three traits and conditions: RAJ4546, UP3063, HD3334, DBW296, MP1368, DBW333, UP3058, DBW332, and BRW3863. These findings will support biofortification breeding of the nutri-rich wheat varieties.

Musa Paradisiaca derived intrinsically heteroatom doped carbon dots as antioxidant and controlled drug release behavior

This work presents a green, single-step hydrothermal synthesis of intrinsically nitrogen-doped Carbon dots (MCDs) derived from Musa paradisiaca, a low-cost and renewable biomass source. The eco-friendly synthesis avoids external dopants or harsh chemicals, offering a scalable and sustainable alternative to conventional multistep methods. The resulting MCDs, with an average particle size of 4.2 nm (TEM), display desirable surface functionalities (FTIR, XPS) and heteroatom doping. Optical characterization revealed a broad UV-vis absorption at 280 nm and strong blue photoluminescence at 440 nm. DLS and zeta potential measurements confirmed excellent colloidal stability. The MCDs demonstrated high antioxidant activity (>80% radical scavenging) and biocompatibility in cellular assays. Moreover, they enabled controlled drug release, underlining their promise as multifunctional nanocarriers. Given their green synthesis, stability, and performance, these MCDs are highly suitable for future biomedical and clinical applications, particularly in antioxidant therapy and targeted drug delivery.

The father’s singing voice may impact premature infants’ brain more than their mother’s: A NICU single-arm exploratory study protocol and preliminary data on a singing and EEG framework based on the fundamental frequency of voice and kinship

This article reports the protocol of a single-arm exploratory study investigating the impact of singing on the brain activity of premature infants in the Neonatal Intensive Care Unit (NICU). The study focuses on how the differentiation of voices, as defined by the fundamental frequency (F0) shaped by biological sex and kinship, influences neurophysiological responses when measured by electroencephalography (EEG). Premature infants, who are highly sensitive to auditory stimuli, may benefit from music-based interventions; however, there is limited understanding of how voice variations between male and female caregivers, and whether they are biologically related, affect brain activity. Our protocol outlines a structured intervention where infants are exposed to singing by four facilitators – a male and a female music therapist, the mother, and the father – and includes two singing stages: a sustained note and a lullaby, both interspersed with silent periods to allow for baseline measurements. EEG recordings track brain activity throughout these sessions, followed by quantitative EEG (qEEG) analysis and thorough statistical computations (e.g., mixed-effects models, spectral power analysis, and post-hoc tests) to explore how these auditory stimuli influence brain function. Preliminary data from five infants show that maternal singing elicits the highest delta spectral power in all measured conditions except during the ‘lullaby song’, where paternal singing elicits the highest effects followed by the male music therapist and then the mother. These early findings highlight the potential influence of parental voices, particularly the fathers’ voice, on neonatal brain development, while the detailed study protocol ensures rigor and replicability, providing a robust framework for future research. (clinicaltrials.gov unique identifier: NCT06398912).

A hybrid AI model integrating BKA-VMD and deep neural networks for industrial power load prediction

Accurate power load prediction is crucial for optimizing energy consumption and enhancing efficiency in industrial environments. However, the highly nonlinear and non-stationary nature of power load time series presents significant challenges. To address this, we propose a novel hybrid deep learning model that integrates optimized data decomposition with advanced sequence modeling to enhance feature extraction and temporal pattern learning. Specifically, Variational Mode Decomposition (VMD) optimized by the Black-Winged Kite Algorithm (BKA) extracts intrinsic mode functions, reducing noise and improving signal representation. The decomposed signals are processed by a hybrid neural network combining a One-Dimensional Convolutional Neural Network (1DCNN) for local feature extraction, a Bidirectional Temporal Convolutional Network (BiTCN) for long-range temporal dependencies, a Bidirectional Gated Recurrent Unit (BiGRU) for sequential pattern learning, and an attention mechanism to emphasize critical features. Extensive experiments, including comparisons with state-of-the-art models and ablation studies, validate our approach across three diverse industrial datasets. The results demonstrate that our model significantly outperforms existing methods, achieving lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The ablation study highlights the critical roles of the attention mechanism and the BiTCN-BiGRU combination in capturing complex temporal dependencies. These findings underscore the model’s robustness and adaptability for power load forecasting. Future research should focus on enhancing generalization and validating applicability across diverse industrial settings.

Optimal design of Halbach magnetized magnetic screw for wave energy converters based on KELM network optimized by weighted mean of vectors algorithm

This paper investigates the parameter optimization problem of Halbach magnetized magnetic screw (HMMS) for wave energy converter (WECs). A magnetic screw with improved Halbach magnetized PMs arrays is presented. To further enhance the thrust density of HMMS, a HMMS optimization model is proposed based on the kernel extreme learning machine (KELM) optimized by weIght meaN oF vectOrs (INFO) algorithm. The topology and working principle of HMMS are introduced. Based on comprehensive sensitivity analysis, the design space is stratified, and the nonlinear coupling relationships between parameters are addressed using the Kriging model to enhance the accuracy and efficiency of the optimization process. The INFO algorithm optimizes kernel parameters and regularization coefficients of KELM, which critically affect its output accuracy. By establishing the INFO-KELM optimization model, the final optimized structure is obtained. The FEA is utilized to assess the properties of the HMMS. Compared with the traditional radially magnetized screw, the thrust force of the optimized HMMS increased by 40.8%. Finally, a prototype is developed and platform tests are performed to validate the theoretical analysis results.

An investigation into the statistical precision attainable with a distribution-free method of constructing age-dependent reference centiles

The distribution-free approach to the construction of age-dependent reference centiles which has been originally published by this author in 1995 and applied since then in a multitude of large-scale studies has never been investigated from a sample-size planning perspective. In the present paper, this gap is filled using the precision criterion introduced by Jennen-Steinmetz and Wellek (2005) for the estimation of reference centiles for quantitative diagnostic markers being independent of other variables, and extended by Jennen-Steinmetz (2014) to the study of age-dependent markers. In the age-dependent case, that criterion does not admit an exact representation as a function of the sample size, even when interest is in estimating a one-sided reference limit. Hence, all sample-size results presented here are based on Monte Carlo simulation. The computations cover a broad range of conditional distributions of the marker at given age including both symmetric and positively skewed distributions. For the relationship between the conditional standard deviation and age, a linear function of different slopes was assumed. Except for the most extreme settings investigated, the sample sizes shown in the tables summarizing our numerical results do not exceed the order of magnitude which has been available for a recent, potentially very influential reference-value study of basic parameters making-up the normal fetal growth profile. Furthermore, our results suggest that in terms of sample-size requirements, the distribution-free approach of Wellek & Merz (1995) to the construction of age-dependent reference ranges is typically a good bit more efficient than reference-range determination by means of quantile regression.

Future Sequon Finder - A novel approach for predicting future N-linked glycosylation sequon locations on viral surface proteins

Influenza viruses are known to evade host immune responses by shielding vulnerable surface protein epitopes via N-linked glycosylation. A program titled Future Sequon Finder was developed to predict the locations in which glycan binding sites are most likely to emerge in future influenza hemagglutinin proteins. The predictive modeling approach considers how closely sites in currently circulating strains resemble glycosylation sequons at the nucleic acid level, the surface accessibility of those sites, and the mutation frequency of amino acids at those sites that would need to change to form a glycosylation sequon. The efficacy of this model is tested using historic human H1N1 and H3N2 influenza strains along with swine H1N1 strains. Through this analysis, it is revealed that glycosylation addition events in influenza hemagglutinin proteins are typically the result of single nucleotide mutation events. It is also demonstrated that site-specific mutation frequency and surface accessibility are powerful predictors of which sites will become glycosylated in human influenza viruses when considered with the genetic composition of the sites in question. Having been designed to incorporate these factors, the program successfully predicted almost every historic sequon addition event (28/30 in human IFVs, 14/15 in swine IFVs). For human strains, it also ranked the correct near-sequons highly among falsely predicted sequons based on site-specific mutation frequency. After demonstrating the model’s power with historical data, the program was used to predict future HA glycosylation sequon locations based on currently circulating human influenza viruses.

Plantar pressures and stabilometry effects of ischemic compression in Flexor digitorum brevis muscle Myofascial Trigger Point: A prepost study

Background Ischemic compression is a manual therapy that improves range of motion, pain and disability in Myofascial Pain Syndrome. Plantar foot pain is a common clinical entity that could be due to Flexor digitorum brevis trigger point. Effect on balance and plantar pressures after ischemic compression in Flexor digitorum brevis muscle trigger point have not been checked. Methods Eighteen subjects (aged 25.06 + /- 5.51 years) with bilateral Flexor digitorum brevis latent or active myofascial trigger points were recruited. Study design: pre-post study. We measured three static footprint and stabilometry variables before and after ischemic compression for 90 seconds at bilateral Flexor digitorum brevis Myofascial Trigger Point. A Shapiro-Wilk test was performed to check normality. Comparison of related measures was done by paired T-test or Wilcoxon Range Test depending on whether the distribution was normal or non-normal. Significant differences were considered with p-value <0.05. All statistics were calculated with a 95% confidence interval. Reliability was also assessed with an Intraclass correlation coefficient (ICC) and Standard error measured (SEM) calculation. Results Most variables have good to perfect reliability, with the exception of four variables which had moderate reliability and two variables which had only slight reliability. Reliable stabylometric variables included anteroposterior displacement of COP and surface with EO and EC. The footprint and stabilometry variables showed no significant differences after ischemic compression. Conclusions Ischemic compression in the Flexor digitorum brevis muscle showed no significant differences in plantar pressures and stabilometry. Other techniques like dry needling indicated worsened balance effects. More studies are required to check significant changes. The results are important because they demonstrate a technique to treat FDB MTrP without repercussions on plantar pressure or balance. NCT06509347 (clinicalTrials.gov) initial release 7/7/24 and last release 28/7/24.