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Effect of coenzyme Q10 on tibial fracture resistance in nicotine-exposed rats

The study aimed to evaluate the potential protection against fractures of oral Q10 supplementation in the tibias of rats exposed to nicotine. Nicotine is known to negatively impact bone density and increase the risk of fractures, in addition to affecting other systems such as the gastrointestinal system, impairing its absorption capacity, negatively affecting bone health. To investigate this, eighty male rats were divided into four groups (n = 20) receiving either nicotine hemisulfate or saline solution (SS) for 28 days. Two daily subcutaneous applications were administered accordingly. Concurrently, vegetable glycerin and Q10 gavage began on day "0". SS: the animals in this group received two daily subcutaneous applications of sodium chloride solution during the entire trial period. 30 days after starting the SS applications subcutaneously, the animals received vegetable glycerin daily until the end of the experiment. SS-Q10: the animals received the SS protocol and daily supplementation with Q10 until the end of the experiment. NIC: The animals received the protocol for NIC and vegetable glycerin daily until the end of the experiment. NIC-Q10: The animals received the protocol for NIC and daily supplementation and Q10 until the end of the experiment. Euthanasia occurred at 7 and 28 days after the beginning the gavage. The tibiae collected were processed for morphometric, densitometric, mechanical, and microtomographic (micro-Ct) analysis. A complementary analysis of intestinal changes was performed. The groups that received Q10 showed slightly better results regarding the mechanical resistance and micro-Ct parameters and to intestinal histomorphometry, as compared with groups not supplemented with Q10. Thus, in rats, it can be concluded that coenzyme Q10 exhibited a protective property to the skeletal system and the gastrointestinal tract, even in the presence of nicotine.

Liver segmentation network based on detail enhancement and multi-scale feature fusion

Mathematical modelling approach to understanding the effect of Mg-rich synthetic gypsum used as fertilizer on growth of Hevea brasiliensis in acid soils

Knowledge of plant growth dynamics is essential where constraints such as COVID-19 lockdown restrictions have limited its field establishment. Thus, modeling can be used to predict plant performance where field planting/monitoring cannot be achieved. This study was conducted on the growth dynamics of rubber planted on two acid soils treated with either dolomitic limestone (GML), kieserite or Mg-rich synthetic gypsum (MRSG) to supply the Mg required by rubber seedlings. To understand the effect of applied treatments on the changes in rubber growth, data on plant height, stem diameter and biomass were regressed against months after transplanting (MAT) using the equation y = A/ (1+be-ct), and its derivative dydt=Abce−ct(1+be−ct)z was utilized for estimating the growth rate of the parameters. The dynamics in plant height, stem girth and plant biomass were modelled using an exponential function of y = Aebt and their rate of change was derived using dx/dy = Abebt. The experiment indicated that the logistic growth curve model expressed as y = A/ (1+be-ct), closely described the growth in terms of each parameter against months after transplanting. A high probability level (a = 0.0001) was recorded in the model for all the treatments in the study. The growth of rubber seedlings in the glasshouse was improved by MRSG treatment in the two studied soils (Ultisol and Oxisol), giving comparable results to other Mg fertilizer treatments. The plant performed better on the Ultisol compared to the Oxisol. The results indicate the potential of using MRSG to replace conventional Mg-fertilizers to sustain rubber seedling growth.

High-density multielectrode arrays bring cellular resolution to neuronal activity and network analyses of corticospinal motor neurons

Virtual screening and molecular dynamics simulations identify repurposed drugs as potent inhibitors of Histone deacetylase 1: Implication in cancer therapeutics

Epigenetic processes are the critical events in carcinogenesis. Histone modification plays a crucial role in gene expression regulation, where histone deacetylases (HDACs) are key players in epigenetic processes. Inhibiting HDACs has shown promise in modern cancer therapy. However, the non-selective nature and drug resistance of most HDAC inhibitors (HDACIs) limits their clinical use. This limitation prompts a search for isoform-selective and more effective inhibitors. Histone deacetylase 1 (HDAC1) is a member of the class I HDAC family and has emerged as a promising target in various diseases, including cancer and neurodegeneration. Drug repurposing has gained significant interest in identifying treatments for new targets, which involves finding new uses for existing drugs beyond their original medical indications. Here, we employed virtual screening of repurposed drugs from the DrugBank database to identify potential HDAC1 inhibitors. We conducted a series of analyses, including molecular docking, drug profiling, PASS evaluation, and interaction analysis. Molecular dynamics (MD) simulations and MM-PBSA analysis were also performed for 300 ns. Through these analyses, we pinpointed Alectinib, which exhibits a promising drug profile in PASS analysis and higher affinity and efficiency for HDAC1 than the reference inhibitor. MD simulations revealed that Alectinib stabilizes HDAC1 with minimal structural perturbations. The findings suggest that Alectinib holds promise as a therapeutic lead for HDAC1-associated carcinogenesis after required validation.

Fracture properties of porcine versus human thoracic aortas from tricuspid/bicuspid aortic valve patients via symmetry-constraint Compact Tension testing

AbstractAneurysm rupture is a life-threatening event, yet its underlying mechanisms remain largely unclear. This study investigated the fracture properties of the thoracic aneurysmatic aorta (TAA) using the symmetry-constraint Compact Tension (symconCT) test and compared results to native and enzymatic-treated porcine aortas’ tests. With age, the aortic stiffness increased, and tissues ruptured at lower fracture energy $$D$$. Patients with bicuspid aortic valves were more sensitive to age, had stronger aortas and required more $$D$$ than tricuspid valves individuals (peak load: axial loading 4.42 $$\pm$$ 1.56 N vs 2.51 $$\pm$$ 1.60 N; circumferential loading 5.76 $$\pm$$ 2.43 N vs 4.82 $$\pm$$ 1.49 N. Fracture energy: axial loading 1.92 $$\pm$$ 0.60 kJ m-2 vs 0.74 $$\pm$$ 0.50 kJ m-2; circumferential loading 2.12 $$\pm$$ 2.39 kJ m-2 vs 1.47 $$\pm$$ 0.91 kJ m-2). Collagen content partly explained the variability in $$D$$, especially in bicuspid cases. Besides the primary crack, TAAs and enzymatic-treated porcine aortas displayed diffuse and shear-dominated dissection and tearing. As human tissue tests resembled enzymatic-treated porcine aortas, microstructural degeneration, including elastin loss and collagen degeneration, seems to be the main cause of TAA wall weakening. Additionally, a tortuous crack developing during the symconCT test reflected intact fracture toughening mechanisms and might characterize a healthier aorta.

Speed-dependent changes in the arm swing during independent walking in individuals after stroke

Background Increasing one’s walking speed is an important goal in post-stroke gait rehabilitation. Insufficient arm swing in people post-stroke might limit their ability to propel the body forward and increase walking speed. Purpose To investigate the speed-dependent changes (and their contributing factors) in the arm swing of persons post-stroke. Material and methods Twenty-five persons post-stroke (53±12.1 years; 40.72±43.0 months post-stroke) walked on a treadmill at comfortable (0.83m/s) and fast (1.01m/s) speed. Shoulder and elbow kinematics were compared between conditions using Statistical Parametric Mapping (SPM) analysis, and discrete parameters using a Wilcoxon signed-rank test or an independent sample t-test. The relations between speed-dependent changes in shoulder and elbow range of motion and clinical and gait parameters were assessed using Spearman correlation coefficients. Results The non-paretic arm showed expected speed-dependent kinematic adaptations with increases in active range of motion for shoulder flexion (p<0.001), extension (p<0.05), abduction (p = 0.001), rotation (p = 0.004) and elbow flexion (p<0.001). The paretic arm only showed an increase in shoulder abduction and elbow flexion (both p<0.001). Persons post-stroke with a more impaired arm swing coordination pattern only showed speed-dependent adaptations for elbow flexion (p<0.001) at the paretic side during fast walking. In contrast, persons post-stroke with a normal arm swing coordination pattern presented with increases in active range of motion of the shoulder abduction and elbow flexion (both p<0.001) at the paretic side when walking fast. More upper limb impairment (r = -0.521, p<0.01) and a wider step width (r = 0.534, p<0.01) were related to a larger increase in mean elbow flexion during faster walking. Conclusions Persons post-stroke show different changes in arm swing kinematics at the paretic compared to the non-paretic side when increasing walking speed. The changes are related to the impairment level and stability during walking, indicating that therapeutic interventions aiming to increase walking speed by improving arm swing might need to target these factors.

Downscaling of ERA5 reanalysis land surface temperature based on attention mechanism and Google Earth Engine

Object detection in motion management scenarios based on deep learning

In athletes’ competitions and daily training, in order to further strengthen the athletes’ sports level, it is usually necessary to analyze the athletes’ sports actions at a specific moment, in which it is especially important to quickly and accurately identify the categories and positions of the athletes, sports equipment, field boundaries and other targets in the sports scene. However, the existing detection methods failed to achieve better detection results, and the analysis found that the reasons for this phenomenon mainly lie in the loss of temporal information, multi-targeting, target overlap, and coupling of regression and classification tasks, which makes it more difficult for these network models to adapt to the detection task in this scenario. Based on this, we propose for the first time a supervised object detection method for scenarios in the field of motion management. The main contributions of this method include: designing a TSM module that combines temporal offset operation and spatial convolution operation to enhance the network structure’s ability to capture temporal information in the motion scene; designing a deformable attention mechanism that enhances the feature extraction capability of individual target actions in the motion scene; designing a decoupling structure that decouples the regression task from the classification task; and using the above approach for object detection in motion management scenarios. The accuracy of target detection in this scenario is greatly. To evaluate the effectiveness of our designed network and proposed methodology, we conduct experiments on open-source datasets. The final comparison experiment shows that our proposed method outperforms all the other seven common target detection networks on the same dataset with a map_0.5 score of 92.298%. In the ablation experiments, the reduction of each module reduces the accuracy of detection. The two types of experiments prove that the proposed method is effective and can achieve better results when applied to motion management detection scenarios.

Characterization and evaluation of the immobilized laccase enzyme potential in dye degradation via one factor and response surface methodology approaches

Abstract In the current study, calcium alginate was used as a carrier for Agaricus bisporus CU13 laccase immobilization, with an immobilization yield of the entrapped laccase of 91.95%. Free and immobilized enzymes showed their best enzyme activity at 60 °C as an optimum temperature. Free laccase was nearly completely inactivated at high temperatures (70 and 80 °C C) after 30 min, whereas the immobilized form retained around 60 and 40% of its activity after 30 min at the same temperatures. The metal ion of MgSO4 showed the best impact on the Agaricus bisporus laccase activity with a relative activity of 113.1 and 106.8% at concentrations of 10 mM and 2.5 mM, respectively. The best Cibacron D-Blue SGL dye degradation by laccase enzyme was obtained at pH 6.0, 0.354 U of laccase, and 100 mg/L of dye using hydroxybenzotriazole (HBT; 1 mM) as a mediator. Optimization ramps obtained from the central composite design (CCD) approach indicate that the rate at which the laccase enzyme decolorizes dye has increased significantly when the concentrations of the enzyme, and HBT increased, whereas dye concentration decreased. Finally, the immobilized enzyme was found to be efficient in decolorizing Cibacron D-Blue SGL dye in the presence of HBT as a mediator for different cycles.

Reverse shoulder arthroplasty in revision surgery—Indications and results

Background The number of reverse shoulder arthroplasty (RSA) procedures performed worldwide has increased over the last 10 years, with a corresponding increase in revision shoulder arthroplasty (SRSA). SRSA is often used for post-traumatic revision surgery in cases of infections and failure of anatomical prostheses. Data on outcomes with specific detail for each indication for the prosthetic solution as a secondary treatment are scarce, and inhomogeneous. Methods The questionnaires were sent by mail to 65 patients who underwent SRSA between January 2014 and November 2023. Based on the indications for SRSA, patients were categorized into post-traumatic shoulder arthritis, humeral head necrosis, failed proximal humerus fractures, failed proximal humerus osteosynthesis, prostheses loosening, and infection groups. Results Of the 65 patients included in the study, 39 completed the questionnaire, and the mean follow-up duration was 44 months (range, 12–104 months). The Constant score ranged from 28 points for all 6 groups (range, 38–66). The post-infection group showed the highest results, with 66 points (range, 24–90) on the Constant score; followed by 26 points (range, 49–6) points on the DASH score; and 0.90 (range, 0.763–1) on the EQ-5D-5L. Failed proximal humerus fractures presented the lowest scores: 38 points (range, 22–63) on the Constant score; 51 points (range, 73–30) points on the DASH score; and 0.61 (range, -0.496–1) on the EQ-5D-5L. Conclusions No previous study has investigated the influence of indications on the clinical outcome of SRSA so circumstantial. In this study, the highest outcome scores were observed in the post-infection group, whereas the lowest scores were observed in the failed humerus fracture group. Our results underline the influence of the indication on the clinical outcome of SRSA.

Pain and the perception of space in fibromyalgia

Magnitude of telemedicine utilization and associated factors among health professionals working at selected public hospitals in Southern Ethiopia

Background Despite the immense potential of telemedicine, its implementation in Ethiopia and other developing nations has faced formidable challenges, leading to disappointingly low utilization rates. Therefore, this study sought to assess the magnitude and factors associated with telemedicine service practice among healthcare professionals in the pilot public hospitals of Sidama and Southern Nations Nationalities Peoples Regions. Methods Cross-sectional study was conducted from June 1–30, 2021 among randomly selected 407 health professionals working at Pilot Hospitals in Southern Ethiopia. A pretested and structured self-administered questionnaire was used to collect the socio-demographic, knowledge and attitude of Health Professionals towards telemedicine and health system-related data. Data were coded and entered using Epi-data version 4.6. and exported to SPSS version 20 for analysis. Bi-variable and multivariable binary logistic regression was done to identify factors associated with telemedicine utilization. A P-value<0.05 and Adjusted odds ratio (AOR) together with 95% Confidence Interval (CI) was used to declare statistical significance. The data were presented by tables, text and figures and charts. Results The study found that 34.6% (95% CI: 30–39.6%), 54.1% (95% CI: 49.6–59.2%), and 26% (95% CI: 21.6–30.2%) of the respondents have good knowledge, a positive attitude, and practiced telemedicine service, respectively. Age ≥ 36 years (AOR = 2.99, 95% CI: 1.18–7.60), being a medical doctor (AOR = 3.91, 95% CI 1.15–13.25), having good knowledge (AOR = 2.75, 95% CI 1.54–4.89), presence of an information sharing culture (AOR = 3.95, 95% CI 1.16–13.45), presence of a practicing platform (AOR = 3.01, 95% CI 1.06–8.53), and presence of government commitment (AOR = 2.52, 95% CI 1.09–5.82) were found to be significantly associated with telemedicine service utilization. Conclusion Despite positive attitudes, the adoption of telemedicine among healthcare professionals in the study area remains limited. Factors such as age, profession, knowledge, and cultural factors influence its uptake. To promote wider adoption and address challenges, governments should: implement comprehensive guidelines, training programs, and platforms for healthcare professionals to effectively utilize telemedicine technologies can accelerate healthcare delivery in the study area.

Point spread function estimation with computed wavefronts for deconvolution of hyperspectral imaging data

Abstract Hyperspectral imaging (HSI) systems acquire images with spectral information over a wide range of wavelengths but are often affected by chromatic and other optical aberrations that degrade image quality. Deconvolution algorithms can improve the spatial resolution of HSI systems, yet retrieving the point spread function (PSF) is a crucial and challenging step. To address this challenge, we have developed a method for PSF estimation in HSI systems based on computed wavefronts. The proposed technique optimizes an image quality metric by modifying the shape of a computed wavefront using Zernike polynomials and subsequently calculating the corresponding PSFs for input into a deconvolution algorithm. This enables noise-free PSF estimation for the deconvolution of HSI data, leading to significantly improved spatial resolution and spatial co-registration of spectral channels over the entire wavelength range.

Potential associations of selected polymorphic genetic variants with COVID-19 disease susceptibility and severity

In this study, we analyzed the potential associations of selected laboratory and anamnestic parameters, as well as 12 genetic polymorphisms (SNPs), with clinical COVID-19 occurrence and severity in 869 hospitalized patients. The SNPs analyzed by qPCR were selected based on population-wide genetic (GWAS) data previously indicating association with the severity of COVID-19, and additional SNPs that have been shown to be important in cellular processes were also examined. We confirmed the associations of COVID-19 with pre-existing diabetes and found an unexpected association between less severe disease and the loss of smell and taste. Regarding the genetic polymorphisms, a higher allele frequency of the LZTFL1 and IFNAR2 minor variants significantly correlated with greater COVID-19 disease susceptibility (hospitalization) and severity, and a similar tendency was observed for the RAVER1 and the MUC5B variants. Interestingly, the ATP2B4 minor haplotype, protecting against malaria, correlated with an increased disease susceptibility, while in diabetic patients disease susceptibility was lower in the presence of a reduced-function ABCG2 transporter variant. Our current results, which should be reinforced by larger studies, indicate that together with laboratory and anamnestic parameters, genetic polymorphisms may have predictive value for the clinical occurrence and severity of COVID-19.

Topographic location and connectivity to channel of earthquake- and rainfall-induced landslides in Loess Plateau area

miRNA-328-3p regulates ZO-1 expression and inhibits PEDV proliferation via the PLC-β1-PKC pathway

Porcine epidemic diarrhea virus (PEDV) is a significant pathogen affecting swine, causing severe economic losses worldwide. This study explores the regulatory role of miRNA-328-3p to ZO-1 expression and its impact on PEDV proliferation via the PLC-β1-PKC pathway in IPEC-J2 cells. We found that miRNA-328-3p can target ZO-1, influencing its expression and subsequently affecting the integrity of tight junctions in the cells. Overexpression of PLC-β1, combined with miRNA-328-3p silencing, enhanced ZO-1 expression, while PLC-β1 knockdown combined with miRNA-328-3p overexpression inhibited ZO-1 expression. Furthermore, PLC-β1 overexpression increased both viral genome expression and PEDV titers, whereas its silencing had the opposite effect. Notably, our data indicated a negative correlation between PLC-β1 and PKC expression, and PKC silencing attenuated the upregulatory effect of PLC-β1 on ZO-1. These findings suggest that PLC-β1 modulates ZO-1 expression through the PKC pathway, providing new insights into the molecular mechanisms of PEDV infection and potential therapeutic targets.

Transcriptomic profiling and machine learning reveal novel RNA signatures for enhanced molecular characterization of Hashimoto’s thyroiditis

Research on the mechanism by which digital transformation peer effects influence innovation performance in emerging industries: A case study of China’s photovoltaic industry

The exploration of digital transformation peer effects on the innovation performance of emerging industries is crucial for analyzing the underlying mechanisms of digital transformation, optimizing resource allocation among peer enterprises, and enhancing industrial competitiveness. This study empirically examines the influence of digital transformation peer effects on the innovation performance of the photovoltaic industry, using data from 150 photovoltaic companies listed in Shanghai and Shenzhen between 2011 and 2022. The study found that: (1) The digital transformation of the photovoltaic industry is influenced by regional and industry-specific peer effects. Regional peer effects in digital transformation have a positive impact on the innovation performance of the photovoltaic industry, while industry-specific peer effects exert a negative impact on innovation performance. Moreover, these effects exhibit dynamic persistence; (2) Further analysis of the transmission mechanism reveals that the digital transformation peer effect positively influences the innovation performance of the photovoltaic industry, primarily through the mediating role of enhanced absorptive capacity. Additionally, the level of marketization and executive tenure significantly moderate this relationship; (3) The study further investigates the photovoltaic industry within the context of subsidy policy implementation, firm types, and strategic pacing. The results indicate that the digital transformation peer effect on innovation performance is most pronounced for technology-intensive firms adopting an analytical strategy after the withdrawal of photovoltaic subsidies. For labor-intensive firms employing a defensive strategy, the peer effect is more significant before the withdrawal of subsidies. In contrast, the negative impact of industry-specific digital transformation peer effects on innovation performance is more evident in photovoltaic companies that pursue an offensive strategy; (4) The heterogeneity analysis reveals that the digital transformation peer effect on innovation performance is more significant for small-scale photovoltaic enterprises with state-owned property rights. In contrast, the peer effect negatively impacts innovation performance in large-scale photovoltaic enterprises. These findings provide theoretical insights and practical guidance for governments and enterprises in formulating digital transformation strategies for emerging industries.

Perennial disaster patterns in Central Europe since 2000 and implications for hospital preparedness planning – a cross-sectional analysis

AbstractThe goal of this analysis is to describe seasonal disaster patterns in Central Europe in order to raise awareness and improve hospital disaster planning and resilience, particularly during peak events. Hospitals are essential pillars of a country’s critical infrastructure, vital for sustaining healthcare services and supporting public well-being—a key issue of national security. Disaster planning for hospitals is crucial to ensure their functionality under special circumstances. But the impact of climate change and seasonal variations in the utilization of hospital services are raising challenges. Therefore, the knowledge of perennial disaster patterns could help strengthen the resilience of hospitals. We conducted a cross-sectional analysis of the Emergency Events Database EM-DAT for disasters in Central Europe (Germany, France, Denmark, The Netherlands, Belgium, Luxembourg, Switzerland, Austria, Czech Republic, and Poland) between January 2000 and December 2023. Time distribution of disasters, patterns and longitudinal trends, were analyzed to discuss impact on disaster preparedness in hospitals. Out of 474 events, 83% were associated with a natural hazard and only 80 events (17%) were of technological cause. While technological disasters were spread equally over the whole year, the vast majority of disasters related to natural hazards (n = 394), i.e. storms (n = 178, 45%), floods (n = 101, 26%), and extreme temperatures (n = 93, 24%) peaked during summer and winter months. Fewer disasters were registered during autumn and especially spring seasons. More than 50% of the technological disasters were categorized in the transport accident subgroup. Technological disasters were spread equally over the whole year. Looking at the three most common disaster types, extreme temperatures, floods, and storms are clearly dominating and cause over 90% of the disasters due to natural hazards in central Europe. Overall, the number of events per year fluctuates without a clear trend, only the technological events appear to become less frequent with 70% (n = 56) of the registered disasters occurring in the first half of the study period (2000–2011). An overlap of hospital admissions due to seasonal effects and catastrophic events, mainly triggered by disasters of natural cause in vulnerable periods may lead to a partial collapse of the health care system. To close knowledge gaps, future comprehensive data collection is vital for informed decision-making. Awareness and preparedness are key: an "all-hazards" approach to manage diverse, potentially simultaneous seasonal threats is often the most versatile strategy for hospital emergency planning.