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Overconfidence in nutritional knowledge is linked to unnecessary gluten-free consumption
Assembly and breakage of head-to-head double hexamer reveals mpox virus E5-catalyzed DNA unwinding initiation
Diagnostic performance of microvascular flow imaging for noninvasive assessment of liver fibrosis in chronic liver disease
Background and aims Chronic liver disease (CLD) represents a significant global health challenge necessitating the evaluation of liver fibrosis. This study aimed to evaluate the diagnostic performance of microvascular flow (MV-flow) imaging in evaluating liver fibrosis and compare it with other non-invasive tools. Methods Between July 2020 and June 2022, we prospectively enrolled patients scheduled for liver biopsy, concurrently measuring MV-flow imaging, transient elastography (TE), and two-dimensional shear wave elastography (2D-SWE) as part of the assessment process. We evaluated the diagnostic performance of MV-flow imaging, 2D-SWE, and TE based on histologic staging of liver fibrosis using the area under the receiver operating characteristic curve (AUROC), and calculated the optimal cut-off value. Results A total of 89 participants were included. Non-alcoholic fatty liver disease was the most common etiology of CLD (32.6%). The liver fibrosis stage distribution was as follows: stage 0 (11.2%), stage 1 (31.5%), stage 2 (25.8%), stage 3 (13.5%), and stage 4 (18.0%). The MV-flow scoring system’s cut-off values and AUROCs for predicting stage 2, stage 3, and cirrhosis were 2.1 (0.836), 2.5 (0.955), and 2.9 (0.942), respectively. The MV-flow scoring system’s performance in predicting advanced fibrosis (stage 3) was comparable to TE ( p = 0.170) and 2D-SWE ( p = 0.456). MV-flow imaging misclassified 9.0% of patients in predicting advanced fibrosis. A sequential combination of 2D-SWE and MV-flow imaging, following the specified cut-off, minimized the risk of missing advanced fibrosis to 1.2%. Conclusion MV-flow imaging is an effective tool for predicting liver fibrosis stage. Integrating MV-flow imaging with 2D-SWE can enhance the assessment of liver fibrosis in patients with CLD.
Pig models reveal the interplay between fatty acids and cytokines in skeletal muscle
Evolution and transmission dynamics of wild poliovirus in Pakistan and Afghanistan (2012-2023)
Abstract Despite concerted global vaccination efforts, wild poliovirus remains endemic in two countries in 2024, Pakistan and Afghanistan. This study uses phylogeographic analysis of poliovirus genetic and epidemiological data from clinical and wastewater surveillance to identify the causes of poliovirus persistence and routes of spread over the last decade (2012 to 2023). Poliovirus genetic diversity declined after 2020, with one of two major genetic clusters dying out, and recent detections are now closely related genetically. High-risk and hard-to-access regions have sustained polio transmission over the past decade, even when interrupted elsewhere. Karachi, one of the most densely populated cities globally, has acted as a hub for the amplification and spread of poliovirus to other regions, many of which we show to be dead-end for onwards transmission despite frequent virus detection. Phylogenetic analysis has long been central to the polio surveillance network, and advancing the approaches used can provide critical epidemiological insights to accelerate eradication efforts.
Model parameterization of robotic systems through the bio-inspired optimization
The accurate modeling of dynamic systems, particularly robotic ones, is crucial in the industry. It enables simulation-based approaches that facilitate various tasks without requiring the physical system, thereby reducing risks and costs. These approaches range from model-in-the-loop (MiL), where a simulated model of the real plant is used for controller design, to hardware-in-the-loop (HiL), which provides more realistic simulations on specialized real-time hardware. Among these, MiL is widely adopted due to its simplicity and effectiveness in developing control strategies. However, to fully leverage the advantages of MiL, developing a robust and accurate system model parameterization methodology is essential. This methodology should be adaptable to a wide range of applications, adopt a holistic approach, and balance the cost-benefit trade-offs in model characteristics. Achieving this, however, introduces additional challenges related to system complexity and the inherent properties of the model. To address these challenges, this work proposes a model parameterization approach for robotic systems using bio-inspired optimization to develop accurate and practical models for system design. The approach formulates an optimization problem to determine the dynamic model parameters of a robot, ensuring its behavior closely resembles that of the real system. Due to the complexity of this problem, bio-inspired optimization techniques are particularly well-suited. The proposed method is validated using a theoretical, non-conservative model of a three-degree-of-freedom serial robot. The dynamic parameters of its three links were identified to effectively generalize the real system. To solve the optimization problem, three bio-inspired algorithms were employed: the genetic algorithm, particle swarm optimization, and differential evolution. The optimal parameterization obtained for the robot model demonstrated the effectiveness of the proposed approach in a MiL simulation environment, achieving an overall correlation of 0.9019 in the experiments. This correlation highlights the model’s ability to predict the robot’s behavior accurately. Additionally, the methodology’s efficacy was further validated in another electromechanical system, the reaction force-sensing series elastic actuator, yielding a correlation of 0.8379 in the resulting model.
Optimal allocation of STATCOM for multi-objective ORPD problem on thermal wind solar hydro scheduling using driving training based optimization
Abstract On IEEE 30, 57, 118 & 300-bus experimental networks, this work aims to solve the optimal reactive power dispatch (ORPD) problem. Initially, the conventional network is countered, and subsequently, renewable energy sources (RESs) such as wind power (WP), solar photovoltaic (PV) sources, and hydro power (HP) are combined with the traditional network. This study examines both single and multiple type objective functions (OFs). The Objectives include lowering active power loss (APL), lowering aggregated voltage deviation (AVD), lowering the voltage stability index (VSI), lowering reactive power loss and concurrently lowering AVD, APL & VSI. There are five test modules that comprise a total of 30 cases. Cases 5-8 and 13-30 are being conducted using STATCOM in conjunction with the test setup. The Driving Training Based Optimization (DTBO) method has been used to achieve the goals, and its performance has been compared to that of other optimization algorithms that have been reported in recent ORPD studies. Both stable load demand and uncertain changing load demand scenarios are included in the study. Appropriate probability density functions (PDF) are employed to estimate the uncertain WP, PV source, HP, and load demand. Uncertain scenarios with variable load demand, wind speed (WS), solar irradiance (SI), and water flow rate (WFR) are created using Monte Carlo simulations (MCS). Based on a range of studied cases, the experiment results show that the DTBO has a significantly stronger ability to solve ORPD challenges than the optimization methods discovered in the most recent ORPD literature. The usage of STATCOM improves power network performance for the ORPD issue, which is another significant finding. From simulation results it has been observed that for IEEE 30 bus the average power loss (APL) is 4.5086 MW, utilizing STATCOM the APL is reduced by 5.3% MW, with integrating renewable sources the APL is reduced 41%, and for both STATCOM and renewable sources (RESs) system it decreases to 43.6%. Hence, STATCOM and RES help to reduce the power losses using DTBO approach. Furthermore, average voltage deviation (AVD) improved by 97.4 % with incorporating STATCOM-RESs. Voltage stability index (VSI) improved by 26.9% with scheduling STATCOM and renewable sources (RESs). For the multi-objective situation APL & AVD both simultatiously improved to 5.0701(MW) & 0.1221 (p.u.), respectively, with incorporating STATCOM and RESs using DTBO. Voltage deviation converges at 40 iterations for with STATCOM but for without STATCOM it takes 80 iterations to converge. Similarly for voltage stability index with STATCOM converge 4 iterations earlier rather than without STATCOM system. Again for large scale IEEE 57 bus system The DTBO approach incorporating STATCOM and RESs provided optimal results. So, for IEEE 30, 57, 118 & 300 bus systems DTBO proves its superiority and robustness satisfactorily. From simulation results it has been observed that for IEEE 30 bus the average power loss (APL) is 4.5086 MW, utilizing STATCOM the APL is reduced by 5.3% MW, with integrating renewable sources the APL is reduced 41%, and for both STATCOM and renewable sources (RESs) system it decreases to 43.6%. Hence, STATCOM and RES help to reduce the power losses using DTBO approach. Furthermore, average voltage deviation (AVD) improved by 97.4 % with incorporating STATCOM-RESs. Voltage stability index (VSI) improved by 26.9% with scheduling STATCOM and renewable sources (RESs). For the multi-objective situation APL & AVD both simultatiously improved to 5.0701(MW) & 0.1221 (p.u.), respectively, with incorporating STATCOM and RESs using DTBO. Voltage deviation converge at 40 iterations for with STATCOM but for without STATCOM it takes 80 iterations to converge. So, for IEEE 30, 57, 118 & 300 bus systems DTBO proof its superiority and robustness satisfactorily.
Carbon trade biases and the emerging mesoscale structure of the European Emissions Trading System network
MAGNET-seq: A tandem PCR and hybrid capture method for enhanced target enrichment
Hybrid-capture based target enrichment and multiplex PCR methods enhance sequencing efficiency by focusing on specific genomic regions, while struggling to enrich tens of regions spanning hundreds to thousands of base pairs. We developed MAGNET-seq (Multiplex Amplification and tarGeted eNrichment of sElecTed sequences), a streamlined method that integrates targeted multiplex PCR with hybrid capture. We evaluated its performance using two primer sets: a Drug Resistance Targeting Primers with 43 targets and a Reference Primers set with 7 targets, including clinically relevant mutations such as EGFR c.2369C > T (p.T790M) and KRAS c.35G > T (p.G12C). Using a set of 43 target primers, MAGNET-seq demonstrated higher on-target ratios (average 86.2%) compared to standard targeted multiplex PCR (average 2.2%). Furthermore, MAGNET-seq with 7 target primers showed concordant variant allele frequencies (VAF) in low-VAF (≤ 1%) reference cell-free DNA (cfDNA) samples (0.05% to 1%), supporting its reproducibility. This approach provides a simplified and cost-efficient solution for targeted sequencing, particularly well-suited for applications that require detection of low-allele frequency variants such as somatic cancer mutations.
Machine learning models for predicting severe acute kidney injury in patients with sepsis-induced myocardial injury
Unveiling ongoing biogeochemical dynamics of CDOM from surface to deep ocean
Spatial autocorrelation of environmental factors influencing dengue outbreaks using Moran’s I: A study from Nepal (2020–2023)
Background Dengue fever, a mosquito-borne viral infection caused by the dengue virus, has become a significant global public health concern, especially in tropical and subtropical regions. Nepal, with its diverse geography and climate, has witnessed a rapid escalation in dengue cases in recent years, with the highest number of cases and fatalities reported in 2022. Objectives This study analyzed the spatial distribution of dengue in Nepal from 2020 to 2023, using Moran’s I spatial statistics to explore the relationship between environmental factors (such as vegetation indices, land surface temperature and precipitation) and dengue incidence. Methods By utilizing Geographic Information System (GIS) and spatial analysis techniques, the study seeks to identify high-incidence clusters and examine environmental factors contributing to the spread of dengue. Results This study examined dengue incidence in Nepal from 2020 to 2023, uncovering significant variations in disease patterns and their environmental correlations. Dengue cases peaked in 2022 (Moran’s I; 0.634, P-value; 0.001) before declining in 2023 (Moran’s I; 0.144, P-value; 0.036), likely due to targeted public health interventions. Spatial analysis revealed no significant patterns in 2020 (Moran’s I; −0.004, P-value; 0.288) and 2021 (Moran’s I; 0.006, P-value; 0.186), however, a focused spatial distribution emerged in 2022 and 2023. Environmental factors showed evolving relationships with dengue transmission: NDVI and LST showed negative correlations in 2020–2021, while NDWI and precipitation shifted from negative to positive correlations over the study period. Conclusion The findings showed significant spatial clustering of dengue cases in urban areas with correlations between higher precipitation and increased dengue incidence. These results highlighted the importance of adaptive public health strategies that account for environmental factors.
Distribution characteristics of microbial aerosols and optimization of protective methods during ultrasonic scaling procedure
Perinatal serotonin signalling dynamically influences the development of cortical GABAergic circuits with consequences for lifelong sensory encoding
Abstract Serotonin plays a prominent role in neurodevelopment, regulating processes from cell division to synaptic connectivity. Clinical studies suggest that alterations in serotonin signalling such as genetic polymorphisms or antidepressant exposure during pregnancy are risk factors for neurodevelopmental disorders. However, an understanding of how dysfunctional neuromodulation alters systems level activity over neocortical development is lacking. Here, we use a longitudinal imaging approach to investigate how genetics, pharmacology, and aversive experience disrupt state-dependent serotonin signalling with pathological consequences for sensory processing. We find that all three factors lead to increased neocortical serotonin levels during the initial postnatal period. Genetic deletion of the serotonin transporter or antidepressant dosing results in a switch from hypo- to hyper-cortical activity that arises as a consequence of altered cortical GABAergic microcircuitry. However, the trajectories of these manipulations differ with postnatal exposure to antidepressants having effects on adult sensory encoding. The latter is not seen in the genetic model despite a similar early phenotype, and a distinct influence of maternal genotype on the development of supragranular layers. These results reveal the dynamics and critical nature of serotonin signalling during perinatal life; pharmacological targeting of which can have profound life-long consequences for cognitive development of the offspring.
Associations between serum lipid levels and the macular retinal volumes in patients with diabetes
Purpose Macular retinal volumes can be measured by optical coherence tomography (OCT). However, the underlying pathogenesis was obscure. We compared the OCT and serum lipid data in participants with or without diabetes mellitus (DM) and diabetic retinopathy (DR) to explore the interpretation of the OCT data. Methods Data for eye examinations and blood tests in 41 eyes of 41 participants (23 men; mean age 49.1 ± 8.3) were analyzed. Eyes without macular lesions were included. Results Mean macular retinal volumes of ganglion cell layer (GCL) (P = 0.023) and neural retinal layers (NRL) including layers from internal to external limiting membranes (P = 0.013) were smaller in the DM without DR group than in the control group. Mean serum malondialdehyde-modified low-density lipoprotein (MDA-LDL) levels were higher in the DM without DR (P = 0.046) and with DR (P = 0.021) groups than in the control group. Serum low-density lipoprotein cholesterol (LDLC) levels showed a negative correlation with GCL volume (P = 0.005), and trends of negative correlations with retinal nerve fiber layer (RNFL) (P = 0.060) and NRL volumes (P = 0.051) in the control group. However, in the DM with DR group, LDLC levels showed significant positive correlations with RNFL (P = 0.002), GCL (P = 0.034), and NRL (P = 0.002) volumes. The DR group also showed positive correlations between MDA-LDL levels and RNFL (P < 0.001) and NRL (P = 0.006) volumes. Conclusions Macular retinal volumes may decrease owing to DM and elevated serum lipid levels. However, the volume may increase as serum lipid levels elevate after DR development. Further studies are warranted to understand the pathogenesis.
Predicting breast self-examination awareness in Sub-Saharan Africa using machine learning
Epithelial GREMLIN1 disrupts intestinal epithelial-mesenchymal crosstalk to induce a wnt-dependent ectopic stem cell niche through stromal remodelling
Abstract In homeostasis, counterbalanced morphogen signalling gradients along the vertical axis of the intestinal mucosa regulate the fate and function of epithelial and stromal cell compartments. Here, we use a disease-positioned mouse and human tissue to explore the consequences of pathological BMP signalling dysregulation on epithelial-mesenchymal interaction. Aberrant pan-epithelial expression of the secreted BMP antagonist Grem1 results in ectopic crypt formation, with lineage tracing demonstrating the presence of Lgr5(−) stem/progenitor cells. Isolated epithelial cell Grem1 expression has no effect on individual cell fate, indicating an intercompartmental impact of mucosal-wide BMP antagonism. Treatment with an anti-Grem1 antibody abrogates the polyposis phenotype, and triangulation of specific pathway inhibitors defines a pathological sequence of events, with Wnt-ligand-dependent ectopic stem cell niches forming through stromal remodelling following BMP disruption. These data support an emerging co-evolutionary model of intestinal cell compartmentalisation based on bidirectional regulation of epithelial-mesenchymal cell fate and function.
Development of a patient/proxy-reported instrument for pediatric antibiotic-associated diarrhea
Objective To develop and validate a patient/proxy-reported measure of the incidence and severity of pediatric antibiotic-associated diarrhea (PAAD) in inpatient and outpatient settings. Methods A patient advisory group, consisting of five parents and two children, was engaged as a research partner. Instrument items were developed from three sources: relevant items from two previously validated instruments; relevant constructs from a newly developed core outcome measurement set; and outcomes identified by parents and clinicians as being the most important. In a prospective observational study, children (birth to 17 years old) newly prescribed antibiotics or on antibiotics for ≤ 7 days, were included and assessed at the time of presentation and daily thereafter until two weeks after antibiotic therapy was completed. Internal consistency and convergent validity of the instrument were examined. Results Of 78 patients who agreed to participate and met the eligibility criteria, 30(38%) were lost to follow-up; Data from the remaining 48 were analyzed. By applying four different definitions of diarrhea, we found a broad range of incidence risks of PAAD (27%−83%). PAAD was more likely to develop in younger age groups (≤ 3 years old). Cronbach’s α for the severity scale was less than 0.7. A high correlation was found between the PAAD severity score and numerical rating score of diarrhea severity reported by parents (r > 0.5). Conclusion The PAAD instrument is the first designed to measure the incidence and severity of PAAD. The instrument has content and construct validity. For reliability analyses of the severity scale, larger studies are required.