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Potential risk factors associated with diabetic mellitus in patients with Febrile upper urinary tract calculi with infection
Purpose Patients with febrile upper urinary tract calculi with infection (FUUTCI) are prone to develop or have developed severe infection. This research aimed to evaluate the potential risk factors associated with diabetes mellitus (DM) in patients with FUUTCI. Materials and methods From September 2018 to December 2023, patients with FUUTCI admitted to our hospital were retrospectively studied. The patients were divided into a diabetic group (n=52) and a non-diabetic group (n=148), and the differences in demographics, etiology, infection indicators on admission, treatment, and outcome between the two groups were compared. Then regression analysis was performed for gender, stone location, occurrence of urinary sepsis, septic shock, use of pressors, bacterial multiresistance, positive fungal culture, and use of two or more antibiotics. Results Compared with non-diabetic patients (148 cases, 74.0%), diabetic patients (52 cases, 26.0%) had a higher prevalence of cardiovascular and cerebrovascular diseases (P=0.031), the rate of using two or more antibiotics (P=0.029), the positive rate of yeast culture (P=0.037), the procalcitonin value of admission or emergency (P=0.022). There was a significant difference in stone location (P=0.039). Regression analysis showed that DM was an independent risk factor for febrile urinary tract infection in patients with kidney stones compared to patients with ureteral stones (P=0.032). Conclusions In patients with FUUTCI, the risk factors associated with DM made treatment more complicated. In patients with FUUTCI and under the premise of active treatments, DM was not a risk factor for urosepsis, septic shock, use of vasoactive drugs, and infection of multi-drug resistant bacteria. Compared to patients with ureteral calculi, DM was an independent risk factor for febrile urinary tract infection in patients with kidney calculi.
Characterization of anti-canine CD20 antibody 4E1-7-B_f and comparison with commercially available anti-human CD20 antibodies
This study characterizes the previously reported anti-canine CD20 antibody 4E1-7-B_f and compares this with commercially available anti-human CD20 antibodies, rituximab and an obinutuzumab biosimilar. While the obinutuzumab biosimilar exhibited binding to canine CD20 in a CD20-transduced cell line, canine B-cell lymphoma cell line (CLBL-1/luc), and canine CD21 + B cells from healthy dogs, functional assays revealed the superiority of 4E1-7-B_f in antibody-dependent cellular cytotoxicity and complement-dependent cytotoxicity activities over those of the obinutuzumab biosimilar. Epitope analysis suggested an extracellular region on canine CD20 targeted by 4E1-7-B_f. Furthermore, the lipid raft localization of CD20 in CLBL-1/luc cells by treatment with 4E1-7-B_f classified this antibody as a type II anti-CD20 antibody which works with strong ADCC activity, similar to the obinutuzumab biosimilar, unlike rituximab, a type I anti-CD20 antibody, whose main action is CDC activity. These findings underscore the potential clinical utility of 4E1-7-B_f, emphasizing the specificity, potency, and therapeutic promise in canine lymphoma treatment.
Proteomic and phosphoproteomic analysis of rabies pathogenesis in the clinical canine brain and identification of a kinase inhibitor as a potential repurposed antiviral agent
Rabies is a fatal zoonosis caused by the rabies virus (RABV) that has afflicted humans for thousands of years. RABV infection leads to neurological symptoms and death; however, its pathogenesis in the brain is unclear, which complicates patient care. Given that no treatment exists for symptomatic cases, there is an urgent need for effective antiviral drugs. In this study, we aimed to investigate the pathogenic mechanism of RABV in the brain and screen for potential anti-RABV drugs. Protein samples were extracted from the brains of RABV-positive and RABV-negative dogs, and proteomic and phosphoproteomic analyses were conducted. The results showed that the synaptic vesicle cycle is critical to RABV pathogenesis. The kinases involved in the phosphorylation of proteins in the synaptic vesicle cycle were identified and examined as potential drug targets. Casein kinase 2 and protein kinase C were found to be key kinases for RABV replication, and five inhibitors of these enzymes were tested for their anti-RABV properties. Pretreating cells with the kinase inhibitor sunitinib significantly reduced the viral yield after RABV infection. Our findings suggest that RABV interferes with synaptic communication, which leads to rabies, and that inhibiting a vital kinase can reduce viral production. Hence, our findings have implications for the development of rabies treatment regimes.
Machine learning application to predict binding affinity between peptide containing non-canonical amino acids and HLA-A0201
Class Ι major histocompatibility complexes (MHC-Ι), encoded by the highly polymorphic HLA-A, HLA-B, and HLA-C genes in humans, are expressed on all nucleated cells. Both self and foreign proteins are processed to peptides of 8–10 amino acids, loaded into MHC-Ι, within the endoplasmic reticulum and then presented on the cell surface. Foreign peptides presented in this fashion activate CD8 + T cells and their immunogenicity correlates with their affinity for the MHC-Ι binding groove. Thus, predicting antigen binding affinity for MHC-Ι is a valuable tool for identifying potentially immunogenic antigens. While quite a few predictors for MHC-Ι binding exist, there are no currently available tools that can predict antigen/MHC-Ι binding affinity for antigens with explicitly labeled post-translational modifications or unusual/non-canonical amino acids (NCAAs). However, such modifications are increasingly recognized as critical mediators of peptide immunogenicity. In this work, we propose a machine learning application that quantifies the binding affinity of epitopes containing NCAAs to MHC-Ι and compares its performance with other commonly used regressors. Our model demonstrates robust performance, with 5-fold cross-validation yielding an R2 value of 0.477 and a root-mean-square error (RMSE) of 0.735, indicating strong predictive capability for peptides with NCAAs. This work provides a valuable tool for the computational design and optimization of peptides incorporating NCAAs, potentially accelerating the development of novel peptide-based therapeutics with enhanced properties and efficacy.
Needs assessment and preparedness of the primary health care network for scaling-up preventive tuberculosis treatment in 5 Brazilian capitals
This study aims to conduct a needs and preparedness assessment of public primary care units to scale up tuberculosis infection diagnosis and tuberculosis preventive treatment in 5 Brazilian capitals. This observational operational study was carried out across five Brazilian high tuberculosis-burden cities. Clinics with at least one monthly new tuberculosis case were included. Data on Purified Protein Derivative (PPD) storage, tuberculin skin testing (TST) and interferon-gamma release assay (IGRA) availability, personnel qualified for performing TST, radiological facilities and tuberculosis preventive treatment drug availability, were gathered between August 2023 and January 2024. Out of 285 clinics included, 78% (CI95%: 73%−82%) did not offer TST on-site, with only 28% (CI95%: 22%3%) having staff qualified to perform TST, and 35% (CI95%: 29%−40%) lacking dedicated refrigerators for PPD storage. Most (97%, CI95%: 94%−99%) clinics did not collect IGRA testing, with an average distance of 6.7 km (CI95%: 5%−7%) to IGRA labs and a turnaround time of 11.7 days (CI95%: 9%13%) for results. Most (87%, CI95%: 83%−91%) do not offer on-site radiological testing. The primary care network was unprepared to scaling up tuberculosis infection testing. Key issues include unavailability of TST mainly because of insufficient qualified personnel. Without accelerated qualification of staff for TST, scaling up tuberculosis preventive treatment will be impossible.
Harnessing hybrid perception on multi-scale features for hand-foot-mouth disease multi-region prediction based on Seq2Seq
Accurate prediction of Hand, Foot, and Mouth Disease (HFMD) is crucial for effective epidemic prevention and control. Existing prediction models often overlook the cross-regional transmission dynamics of HFMD, limiting their applicability to single regions. Furthermore, their ability to perceive spatio-temporal features holistically remains limited, hindering the precise modeling of epidemic trends. To address these limitations, a novel HFMD prediction model named Seq2Seq-HMF is proposed, which is based on the Sequence-to-Sequence(Seq2Seq) framework. This model leverages hybrid perception of multi-scale features. First, the model utilizes graph structure modeling for multi-regional epidemic-related features. Secondly, a novel Spatio-Temporal Parallel Encoding(STPE) Cell is designed; multiple STPE Cells constitute an encoder capable of hybrid perception across multi-scale spatio-temporal features. Within this encoder, graph-based feature representation and iterative convolution operations enable the capture of cumulative influence of neighboring regions across temporal and spatial dimensions, facilitating efficient extraction of spatio-temporal dependencies between multiple regions. Finally, the decoder incorporates a frequency-enhanced channel attention mechanism(FECAM) to improve the model’s comprehension of temporal correlations and periodic features, further refining prediction accuracy and multi-step forecasting capabilities. Experimental results, utilizing multi-regional data from Japan to predict HFMD cases one to four weeks ahead, demonstrate that our proposed Seq2Seq-HMF model outperforms baseline models. Additionally, the model performs well on single-region data from a city in southern China, confirming its strong generalization ability.
Depression, anxiety and change in eating habits during the COVID-19 pandemic in Brazilian university students
This cross-sectional study aims to evaluate the association between anxiety and depression with changes in the consumption of hyperpalatable foods and meal patterns in a sample of 771 Brazilian university students during the social isolation period in the COVID-19 pandemic. More than half of the subjects self-reported clinically significant symptoms of anxiety (53.8%) and depression (62.5%), with 47.6% having both. Most individuals who showed increased consumption of hyperpalatable foods were also part of the group that reported clinically significant symptoms of anxiety or depression. Statistical analysis was performed using exploratory structural equations. The latent variable “symptoms of anxiety and depression” was created using the anxiety and depression scores. Symptoms of anxiety and depression had a positive correlation with the increased consumption of hyperpalatable foods and meal substitution (standardized coefficient = 0.212), after analysing their total direct and indirect effects. It was concluded that higher scores of anxiety and depression negatively affects the eating habits of university students.
Effects of chemotherapy on skeletal muscle mitochondrial oxidative capacity using near-infrared spectroscopy (NIRS): Protocol paper for an observational mixed model repeated measures design in patients with breast and gynecological cancer
Mitochondrial dysfunction, a hallmark of metabolic disturbances in the skeletal muscle, has previously been studied in health participants using invasive muscle biopsy and/or time consuming, high-cost magnetic resonance spectroscopy. However, less is understood regarding mitochondrial dysfunction in patients with cancer. Near infrared spectroscopy (NIRS) is a non-invasive and cost-effective approach to assessing mitochondrial function of skeletal muscle by measuring oxygenated and deoxygenated hemoglobin and calculating the resulting tissue saturation index. NIRS has not yet been utilized to evaluate skeletal muscle change in cancer patients throughout chemotherapy. Therefore, we plan to conduct a single center clinical trial, using an observational mixed model repeated measures design to evaluate the change in mitochondrial oxidative capacity from baseline and throughout progression of an oncologist-prescribed chemotherapy regimen. Evaluation of mitochondrial function will be performed by taking real-time, NIRS in-situ measurements within the working muscle during stationary cycling exercise and subsequent recovery periods (e.g., “on” kinetics and “off” kinetics). We also plan to observe if there is a difference in mitochondrial oxidative capacity between different chemotherapy regimens across different patients. This pilot study will provide information on the feasibility of capturing on/off kinetics mitochondrial function data longitudinally using NIRS in patients newly diagnosed with breast or gynecological cancer, provide preliminary data for future extramural funding, as well as inform the scientific community of results through dissemination via conferences and peer-reviewed journal publications. This clinical trial has been registered with clinicaltrials.gov (Identifier: NCT006672497). Data collection started on July 26, 2021 and is ongoing through March 27, 2026. The authors confirm that all ongoing and related trials for this observation trial (no drug or intervention) are registered.
RETRACTED: Wind energy resource assessment based on joint wolf pack intelligent optimization algorithm
Wind energy is a clean and renewable energy source with great potential for development, but the intermittent and stochastic characteristics of wind speed have brought great challenges to the effective development and utilisation of wind energy resources, resulting in high development costs. Therefore, how to accurately assess the wind energy resources and effectively predict the wind speed has become a key issue to be solved in the current wind energy field. In view of this, the study proposes the Weibull model to model the wind speed data, and then introduces the wolf pack intelligent optimisation algorithm and improves it through the pollination mechanism to improve the accuracy of wind energy resource assessment. Secondly, considering the complexity and diversity of wind speed data characteristics, data decomposition technique, autoregressive moving average (ARIMA) model and cuckoo search algorithm are used to achieve data preprocessing, serial data modelling and hybrid prediction. The experimental results show that the Weibull model has good fitting accuracy for wind speed data, with residual sum of squares, RMSE, and average coefficient of determination of 0.05, 0.014, and 0.96, respectively, accurately reflecting the statistical characteristics of wind speed data. The wind speed prediction performance of the hybrid prediction model is good, with a maximum deviation of no more than 3% from the true value, which is significantly better than the compared VMD-ISOA-KELM model and CNN-BLSTM model, and its prediction error is relatively small. The hybrid prediction model has a smaller relative error value compared to a single algorithm, with a maximum value of less than 0.2. It has better prediction performance than the combination model, with a coefficient of determination approaching 1.0, a fitting accuracy of 0.994, a mean square error of 0.1947, a root mean square error of 0.3847, and an average absolute percentage error of 15.23%. And the research method can effectively evaluate the status of wind energy resources, with low time complexity at different data scales, taking no more than 5 seconds, and improving operational efficiency. This research method can provide strong technical support and reference basis for the development and utilisation of wind energy resources, and help to promote the sustainable development of wind energy industry.
Stories that bridge us: A mixed methods study to understand the impact of a hospital-wide storytelling event
Oral storytelling events for healthcare professionals are gaining in popularity, yet evaluation of these initiatives is scarce. We designed and assessed the impact of a hospital-wide storytelling event at an academic medical center in New England. This study was grounded in social constructivism, which posits that knowledge and collaborative meaning-making are socially constructed through interpersonal interactions and shared language. Stories were solicited from interdisciplinary staff on a theme, and six selected storytellers were paired with coaches. The hybrid in-person/virtual event was held in 2021. Attendees were invited to complete a post-event survey, as well as a semi-structured interview or written response. Storytellers were invited to reflect via a post-event focus group or written responses. Qualitative data were coded using a mixed inductive and deductive content analytic approach. Survey data were analyzed using descriptive statistics. The storytellers included representation from internal and emergency medicine, nursing, infrastructure project management, and research administration. The 155 attendees included 25 in-person/130 virtual. Qualitative data (nine participants) revealed that sharing stories fostered interpersonal connection and a sense of common humanity, enhanced by the storytellers’ vulnerability and diversity. Storytellers valued coaches’ emotional and creative support in co-creating stories with them. Lastly, the event was felt to strengthen the hospital community. These themes were echoed in the survey data (30 participants): > 75% of respondents indicated that the event helped them reflect on their values, connect with others, and access a sense of purpose. A multidisciplinary hospital-wide oral storytelling event is one way to enhance self-reflection, interpersonal connection, and a sense of community among healthcare professionals.
MAT-PointPillars: Enhanced PointPillars algorithm based on multi-scale attention mechanisms and transformer
Aiming at the problem that small and irregular detection targets such as cyclists have low detection accuracy and inaccurate recognition by existing 3D target detection algorithms, MAT-PointPillars (Multi-scale Attention and Transformer PointPillars), a 3D object detection algorithm, extends PointPillars with multi-scale vision Transformers and attention mechanisms. First, the algorithm employs pillar coding for semantic point cloud encoding and introduces an attention mechanism to refine the backbone’s upsampling process. Furthermore, the Transformer Encoder is introduced to improve the upsampling structure of the third stage of the backbone. On the KITTI dataset, our algorithm achieved 3D average detection accuracy (AP3D) of 81.15%, 62.02%, and 58.68% across three difficulty levels. Compared with the baseline model, the proposed algorithm improves AP3D by 2.44%, 1.19%, and 1.23% respectively. The real-time 3D object detection system is built based on ROS, and average running frames per second of the system is 22.63, which is higher than the sampling frequency of conventional LiDAR. By ensuring sufficient detection speed, the MAT-PointPillars algorithm can increase detection accuracy of cyclists in real-world scenarios.
Functional analysis within latent states: A novel framework for analysing functional time series data
Functional data analysis (FDA) enables modelling and interpretation of data represented as functions over a continuum like time, space, or frequency. This paper introduces the flawless analysis framework (FunctionaL Analysis Within LatEnt StateS), a nested FDA framework for analysing functional time series data. It provides comprehensive insights into the interplay between latent state characteristics, state occupancy dynamics, and functional attributes within states, while maintaining interpretability at each level. Applying flawless to functional time series of power spectral densities from electroencephalography (EEG) data from the Healthy Brain Network, we explore functional characteristics of resting state brain activity in n = 503 early adolescents aged 9 - 15 (X¯=11.5, SD = 1.7). We identify four functional latent states associated with variations in psychopathology and cognitive function. Bayesian regression models reveal important associations between the dynamics of latent state occupancy, functional traits within states, and relevant health measures. The integration of multiple FDA tools offers rich insights into functional and time-frequency characteristics of longitudinal data. For neuroscientific data this requires fewer assumptions about oscillatory peak frequencies, and captures more detailed frequency domain characteristics. flawless offers utility for novel and sophisticated insights into functional time series data across a range of areas for research and practice.
Survival benefit of radical prostatectomy in bone metastatic prostate cancer stratified by disease characteristics: A SEER-based retrospective analysis
Background The role of radical prostatectomy (RP) in patients with newly diagnosed bone-metastatic prostate cancer (PCa) remains insufficiently explored. Patients and methods Patients with newly diagnosed bone-metastatic PCa were retrospectively identified from the SEER-17 database and categorized into two groups based on local treatment: biopsy-only and RP. Notably, patients who had received radiotherapy were excluded due to the unavailability of radiotherapy target site details in the SEER database, which made it impossible to determine whether the radiotherapy was directed at metastatic lesions or the prostate. Kaplan-Meier methods were used to estimate cancer-specific survival (CSS) and overall survival (OS) between the two groups. Subgroup analyses stratified by T stage, N stage, PSA levels, and ISUP grade were conducted to assess the impact of disease characteristics on the efficacy of RP. A risk score incorporating these disease characteristics (T stage, N stage, PSA level, ISUP grade) was assigned to each patient, and risk-stratified subgroup analyses were performed to further evaluate the relationship between the efficacy of RP and overall disease characteristics. Results A total of 9,243 patients were included in this study, of whom 8,949 (96.8%) underwent biopsy alone and 294 (3.2%) underwent RP. Patients who underwent RP had better CSS (adjusted HR = 0.32, 95% CI: 0.23–0.44, p < 0.001; 5-year CSS rate: 83.0% vs. 44.5%) and OS (adjusted HR = 0.34, 95% CI: 0.26–0.45, p < 0.001; 5-year OS rate: 79.2% vs. 36.9%) compared with patients who underwent biopsy alone. The survival benefit persisted across all subgroups but were attenuated in patients with more advanced stage (T3 and N1) and higher grades of disease (PSA > 72.5 ng/ml and ISUP grade IV-V). Risk score analysis revealed diminishing benefits with increasing scores. Significant survival benefits were observed for scores 0–3 (all adjusted HR < 1, p < 0.05), whereas no survival differences were detected at the highest risk score (CSS: adjusted HR = 1.74, 95% CI: 0.54–5.65, p = 0.356; OS: adjusted HR = 1.56, 95% CI: 0.48–5.04, p = 0.456). Conclusion Survival benefits of RP in de novo bone metastatic prostate cancer are modulated by disease characteristics, with attenuated effects in advanced/high-grade disease. Risk-stratified patient selection is critical, and prospective studies are needed to validate optimal candidacy for RP.
Coping strategies of Ghanaian couples after unsuccessful infertility treatment: An exploratory qualitative study
Background Psychological distress and social burdens associated with infertility among couples have been well-documented. However, little is known about the specific coping strategies employed by couples in low-middle-income countries such as Ghana, in the aftermath of unsuccessful infertility treatment attempts. In this qualitative study, we explored specific coping strategies patients adopt to address psychological distress related to unsuccessful treatment for infertility. Methods A semi-structured interview approach was used to elicit qualitative responses from 18 fertility clients after unsuccessful treatment at four fertility clinics in Ghana. Thematic analysis (TA) was used to examine the coping strategies adopted by participants in response to psychological distress associated with infertility treatment failures. This allowed us to explore potential culturally specific coping strategies employed by participants in response to infertility-related psychological distress. Results The themes that emerged as coping strategies in response to infertility-related psychological distress were diversional activities, intrapersonal cognitive reframing, social isolation, familial support, religious coping, avoidance-focused coping strategies, seeking encouragement, and professional help. Conclusion The findings from this study indicate that coping strategies that involve isolating oneself may not provide lasting emotional relief for individuals experiencing infertility. Relational activities contribute positively to coping. This is relevant in helping health professionals in the management of infertility treatment failures, which may include setting up support groups of similar experiences to draw strength from each. Furthermore, the results underscore the need to integrate psychological interventions into the counseling of couples following an unsuccessful infertility treatment. The clinical and research implications of these findings are discussed.
High-accuracy spinal alignment monitoring using the head angle and visual distance in computer users
Study Design A Prospective Validation Study. Objectives To validate a novel, noninvasive method for estimating the spinal sagittal alignment during seated computer work, using the head angle (HA) and visual distance (VD) as primary parameters. Methods A 3D motion analysis system measured HA and VD in 21 healthy volunteers. The relationship between these parameters and spinal sagittal alignment, as determined by body surface markers, was investigated. To validate this method, radiographic measurements were taken in a separate group of 32 patients to confirm the link between body surface landmarks and actual spinal alignment. Additional variables, including gender, age, height, and weight, were incorporated into the model to improve accuracy. Results HA and VD showed significant correlations with spinal sagittal alignment, particularly for the cervical spine (C2-C7). Incorporating demographic factors further enhanced the predictive accuracy. Radiological validation confirmed that body surface marker-based measurements are closely aligned with standard radiographic indices widely used in spine surgery. Conclusions This study introduces a reliable and practical method for continuously monitoring spinal sagittal alignment in seated computer users. The approach demonstrates high accuracy, particularly for the cervical spine and holds promise for the development of posture-monitoring technologies to help prevent neck and back pain associated with prolonged computer use.
Prevalence, pathogenic bacteria, and risk factors associated with pediatric sepsis among under five children in a rural district hospital in Rwanda
Background Pediatric sepsis poses a significant health challenge in Sub-Saharan Africa, with limited data on prevalence and pathogen profiles. This study determined the prevalence of pediatric sepsis, identified bacterial pathogens, and evaluated associated risk factors among children aged 1–59 months at Butaro Hospital, Rwanda. Methods A cross-sectional study was conducted from March 2022 to December 2022. The study included 114 children aged 1–59 months with suspected sepsis admitted to the pediatric ward at Butaro Hospital. Blood cultures were analyzed, and risk factors assessed using multiple logistic regression. Data were analyzed using Stata 17. Results Of 114 enrolled children, 60.5% (n = 69) had positive blood cultures (95% CI: 51.2–69.1). Among these 69 children, the majority were females, 70.0% (n = 35) (95% CI: 53.7–81.3) and below 6 months 68.1% (n = 15) (95% CI: 45.3–84.7). Pathogenic bacteria identified were Coagulase-Negative Staphylococci (CNS), 59.4% (n = 41) and Staphylococcus aureus, 40.6% (n = 28). Female gender (AOR = 2.4, 95% CI: 1.0–5.4, p = 0.045) and leukocytosis (AOR = 6.0, 95% CI: 2.0–20.2, p = 0.003) were the risk factors for sepsis. Conclusions The study reveals a high prevalence of sepsis among children under-five, especially females and less than 6 months with female gender and diagnosis with leukocytosis being a significant risk factor. Diagnostic strategies should focus on WBC counts to better manage at-risk children. These single-center study results however may not be broadly representative due to regional and resource differences, but they offer valuable insights for improving pediatric care in similar low-resource settings.
Enhanced power density in solid oxide fuel cells using nickel-assisted gadolinium-doped ceria anodes
This study demonstrates the use of Gadolinium-doped ceria (GDC) (Ce₀.Gd₀.₂O₂) as the anode, BaNb₄MoO₂₀ (BNMO) as the electrolyte, and Lanthanum strontium cobalt oxide (LSCO) (La₀.Sr₀.₄CoO₃) as the cathode in the fabrication of a solid oxide fuel cell (SOFC). The synthesized nanocomposites were characterized using Fourier-transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) for structural analysis, and scanning electron microscopy (SEM) for surface morphology assessment. DC conductivity measurements revealed that LSCO exhibited a high conductivity of 5.2 S/cm, attributed to the efficient flow of electrons through the electrolyte, highlighting its potential as a promising cathode material. Nyquist plots displayed semi-circular arcs, which correspond to distinct electrochemical processes within the system. The diameter of these arcs reflects the charge transfer resistance, primarily due to grain boundary resistance, while the initial resistance preceding the arc is associated with the bulk properties of the electrolyte. Beyond the first semicircle, diffusion resistance increases with frequency as a result of electrode polarization. It was also observed that the cell voltage dropped in discrete steps when the current density reached 200 mA/cm2. Specifically, the voltage decreased from 0.75 V to 0.53 V at 500°C, and from 0.98 V to 0.73 V at 800°C, likely due to charge transfer resistance at the electrode-electrolyte interface. The power density curve indicated that the cell achieved power densities of approximately 0.094, 0.118, 0.146, and 0.184 W/cm2 at operating temperatures of 500, 600, 700, and 800°C, respectively, demonstrating favorable performance for an SOFC employing BNMO as the electrolyte.
Development and feasibility of a driving training program for Autistic student drivers
Driving licencing rates remain lower for autistic individuals capable of driving a motor vehicle, which can limit achieving independence in community mobility. However, there is limited autism-specific guidance in current driver training. The development and evaluation of the feasibility of an autism-specific Driving Training Program (DTP) intervention was conducted to improve the likelihood that autistic student drivers will safely and successfully learn to drive a motor vehicle and gain a driver’s licence. The DTP intervention was developed using a modified stepped approach for developing complex skills-based interventions. The Goals for Driving Education framework for explaining driving training behaviour modification formed the foundation of the intervention. A small-scale study was conducted using a single group pre-post-test design (n = 5), followed by semi-structured interviews and a survey (n = 12) to evaluate the feasibility of intervention components and participant acceptability. The driving performance of the autistic student drivers significantly improved, demonstrating the feasibility of the DTP intervention for training autistic student drivers to learn to drive. Participants also found the intervention acceptable, with program component refinement suggested. The DTP intervention is feasible for a larger randomised controlled trial after modifying highlighted program components.
Heavy metal distribution and ecological risk in surface sediments of the Bohai Sea
Heavy metal contamination in marine sediments poses significant ecological risks, particularly in semi-enclosed seas like the Bohai Sea, where limited water exchange exacerbates pollution retention. Heavy metals are persistent, bioaccumulative, and toxic, making their assessment crucial for environmental management. This study investigated the spatial distribution, seasonal dynamics, and potential ecological risks of heavy metal contamination in the central Bohai Sea, with an emphasis on regulatory interventions and anthropogenic influences. The annual average concentrations of Cu, Zn, Pb, Cd, Hg and As in surface sediments were 15.951, 32.556, 15.234, 0.250, 0.028 and 2.628 mg/kg, respectively, all below China’s Class I Marine Sediment Quality Standards. Seasonal variations revealed peak concentrations in August for Zn, Pb, Hg and As, likely driven by increased terrestrial inputs and hydrodynamic conditions. Cd exhibited the highest ecological risk, with a single-factor risk index exceeding 30 in May, followed by Hg, Pb, Cu, As and Zn. The comprehensive pollution index remained below 5 across all seasons, indicating overall low pollution levels. However, localized exceedances of Class I standards for Cu, Pb and Cd were observed, particularly in summer and autumn. Spatially, metal concentrations were higher near industrial and riverine discharge zones, with anthropogenic sources such as petrochemical industries, aquaculture, and urban runoff contributing significantly. This study highlighted seasonal and spatial heterogeneity in heavy metal contamination in the central Bohai Sea, emphasizing the influence of industrial activities and hydrodynamic processes. While overall pollution levels were low, the high ecological risk associated with Cd underscores the need for continued monitoring and targeted pollution control measures. Strengthening enforcement of industrial regulations, improving sediment management, and addressing seasonal fluctuations in pollutant inputs were critical for mitigating future risks. These findings provided a scientific foundation for sustainable marine environmental management and policy formulation in the Bohai Sea.
Predicting trajectories of the north star ambulatory assessment total score in Duchenne muscular dystrophy
The North Star Ambulatory Assessment (NSAA) is a widely used functional endpoint in drug development for ambulatory patients with Duchenne muscular dystrophy (DMD). Accurately predicting NSAA total score trajectories is important for designing randomized trials for novel therapies in DMD and for contextualizing outcomes, especially over longer-term follow-up (>18 months) when placebo-controlled studies are infeasible. We developed a prognostic model for NSAA total score trajectories over at most 5 years of follow-up for patients with DMD aged 4 to <16 years who were initially ambulatory and receiving corticosteroids but no other disease-modifying therapies. The model was based on longitudinal data from four natural history databases: UZ Leuven, PRO-DMD-01 (provided by CureDuchenne), the North Star Clinical Network, and iMDEX. Candidate predictors included age, height, weight, body mass index, steroid type and regime, NSAA total score, rise from floor velocity, and 10-meter walk/run velocity, as well as DMD genotype class, index year, and data source. Among N = 416 patients at baseline, mean age was 8.2 years, mean NSAA total score was 24, and 61% were receiving prednisone and 39% deflazacort, with the majority having been treated with daily corticosteroid regimens (69%) relative to other regimens (31%). Patients had an average of four NSAA assessments post-baseline during a median follow-up of 2.6 years (inter-quartile range 1.9 to 3.6 years). The best-fitting model in the full study sample explained 39% of the variation in NSAA total score changes, with prediction errors of ±3.6, 5.1, 5.9, 7.5, 9.5 NSAA units during follow-up years 1–5, respectively. The most important predictors were baseline age, NSAA, rise from floor velocity, and 10-meter walk/run velocity. In conclusion, trajectories of ambulatory motor function in DMD, as measured by the NSAA total score, can be well-predicted using readily available baseline characteristics. We discuss applications of these predictions to DMD drug development.