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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.
Does music counteract mental fatigue? A systematic review
Introduction Mental fatigue, a psychobiological state induced by prolonged and sustained cognitive tasks, impairs both cognitive and physical performance. Several studies have investigated strategies to counteract mental fatigue. However, potential health risks and contextual restrictions often limit these strategies, which hinder their practical application. Due to its noninvasive and portable nature, music has been proposed as a promising strategy to counteract mental fatigue. However, the effects of music on performance decrements vary with different music styles. Synthesizing studies that systematically report music style and its impact on counteracting performance decrements is crucial for theoretical and practical applications. Objectives This review aims to provide a comprehensive systematic analysis of different music styles in counteracting mental fatigue and their effects on performance decrements induced by mental fatigue. Additionally, the mechanisms by which music counteracts mental fatigue will be discussed. Methods A comprehensive search was conducted across five databases—Web of Science, PubMed, SCOPUS, SPORTDiscus via EBSCOhost, and the Psychological and Behavioral Sciences Collection via EBSCOhost—up to November 18, 2023. The selected studies focused solely on music interventions, with outcomes including subjective feelings of mental fatigue, physiological markers, and both cognitive and behavioral performance. Results Nine studies met the predetermined criteria for inclusion in this review. The types of music interventions that counteract mental fatigue include relaxing, exciting, and personal preference music, all of which were associated with decreased subjective feelings of mental fatigue and changes in objective physiological markers. Cognitive performance, particularly in inhibition and working memory tasks impaired by mental fatigue, was countered by both relaxing and exciting music. Exciting music was found to decrease reaction time more effectively than relaxing music in working memory tasks. The physiological marker of steady-state visually evoked potential-based brain-computer interface (SSVEP-BCI) amplitude increased, confirming that exciting music counteracts mental fatigue more effectively than relaxing music. Behavioral performance in tasks such as arm-pointing, the Yo-Yo intermittent test, and the 5 km time-trial, which were impaired by mental fatigue, were counteracted by personal preference music. Conclusion Relaxing music, exciting music, and personal preference music effectively counteract mental fatigue by reducing feelings of fatigue and mitigating performance decrements. Individuals engaged in mentally demanding tasks can effectively counteract concurrent or subsequent cognitive performance decrements by simultaneously listening to relaxing or exciting music without lyrics or by using music during recovery from mental fatigue. Exciting music is more effective than relaxing music in counteracting mental fatigue. Personal preference music is effective in counteracting behavioral performance decrements in motor control and endurance tasks. Mentally fatigued individuals could apply personal preference music to counteract subsequent motor control performance decrements or simultaneously listen to it to counteract endurance performance decrements. Future studies should specify and examine the effects of different music genres, tempos, and intensities in counteracting mental fatigue. Additionally, the role of music in counteracting mental fatigue in contexts such as work productivity, traffic accident risk, and sports requires further investigation, along with the underlying mechanisms.
The anthelmintic activity of the white wormwood (Artemisia herba Alba) against Haemonchus contortus in beef cattle
Networks of care for optimizing Primary Health Care Service Delivery in Ethiopia: Enhancing relational linkages and care coordination
Introduction Ethiopia has made notable progress in reducing maternal and perinatal mortality, yet challenges remain in meeting the 2030 Sustainable Development Goals. Persistent issues such as low service utilization, coupled with poor quality, fragmented care, and ineffective referral systems hinder progress. The "Improve Primary Health Care Service Delivery (IPHCSD)" project, implemented by JSI and Amref Health Africa since April 2022, seeks to address these gaps through a Networks of Care (NoCs) approach. This paper describes the lessons learned from implementing the NoCs approach to optimize primary health care in Ethiopia. Methods The project incorporates embedded implementation science, guided by the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework. Key implementation strategies co-designed included strengthening community engagement, establishing NoCs, and introducing quality improvement initiatives using the Model for Improvement. Routine program monitoring data, NoCs process evaluation, and facility service statistics were utilized for this study. Service statistics were analyzed using Student’s t-test and interrupted time-series analysis to compare maternal and child care outcomes before and after the NoCs intervention, with counterfactual estimates generated to assess the intervention’s impact. Qualitative data from key informant interviews were transcribed, coded, and analyzed to identify themes and patterns using Atlas.ti. Results The NoCs approach has significantly enhanced relational linkages between primary health care facilities and health care providers, fostering stronger collaboration and communication. This has fostered trust, improved care coordination, optimized primary health care performance, and increased health service utilization within woreda health systems. The interrupted time series analysis indicated that the rate of ANC 8+ visits was 29.8% per month higher than expected without the NoCs strategy (Coef: 2.39; p-value < 0.01) and an 18.4% increase in obstetric complications managed (Coef: 1.71; p-value = 0.050), with a 43% overall increase. Perinatal mortality decreased by 34%, from 31.3 to 20.1 per 1,000 births [t-test: 2.12; p-value: 0.040)]. Conclusion The NoCs approach in Ethiopia has proven effective in enhancing the relational elements, care coordination, and quality of primary health care services, leading to better maternal and child health outcomes. The findings expand the existing body of research on NoCs implementation best practices and further confirm that it provides a scalable model for strengthening health services in low-resource settings.
Aboveground and belowground trait coordination across twelve boreal forest tree species
AbstractThe existence of trait coordination in roots and leaves has recently been debated, with studies reaching opposing conclusions. Here, we assessed trait coordination across twelve boreal tree species. We show that there is only partial evidence for above-belowground coordination for “fast-slow” economic traits across boreal tree species, i.e., while N content in leaves and roots were positively correlated, as well as dry matter content, root dry matter content and leaf N had no significant relationship. For resource acquisition traits (i.e. related to light capture and nutrient uptake) we did not find strong evidence for trait coordination, as specific root length and specific leaf area were not positively correlated. We further show that site only explained between 0 and 7% of the total trait variation, while within-site variation contributed substantially to the total trait variation for a large number of traits (1.6–96%), and more so for morphological root traits than leaf traits. This likely influences the strength of above-belowground trait coordination found across species in our study. Understanding sources of trait variation and above-belowground trait relationships can contribute to improving global and regional C cycling models. However, fine-scale environmental variability should be accounted for given its importance for driving trait variation.
Disability and depression among stroke survivors attending rehabilitation facilities at three designated tertiary care hospitals in Bangladesh: A cross-sectional study
Background Poststroke depression (PSD) is a highly prevalent and serious mental health condition affecting a significant proportion of stroke survivors worldwide. While its exact causes remain under investigation, managing PSD presents a significant challenge. Aim This study aimed to evaluate the prevalence and predictors of depression among Bangladeshi stroke victims. Methods A cross-sectional study was carried out with 725 stroke victims who were receiving medical care at three designated tertiary care hospitals in Sylhet from January to December 2023. Depression and disability were measured using the Patient Health Questionnaire-9 and the Modified Rankin Scale. Logistic regression analysis was employed to examine the predictors linked to depression. Results According to the study, 80.8% of individuals had moderate to severe disability, and 58.1% of them experienced a moderate to severe level of depression. Individuals who had hemorrhagic stroke (AOR 1.31, 95% CI: 0.77–2.25), repeated episodes (AOR 3.41, 95% CI: 1.89–6.14), tobacco use (AOR 1.76, 95% CI: 1.16–2.67), or coexisting health conditions (AOR 1.68, 95% CI: 1.00–2.82) exhibited elevated levels of depression. Participants whose medical expenses covered by relatives or others were six times more likely to experience depressive symptoms (AOR 6.32, 95% CI: 1.61–24.76). Individuals who did not receive rehabilitation services had two times greater odds of being depressed (OR 1.85, 95% CI: 1.23–2.77, p = 0.003). Consequently, individuals with low functional status had eleven times greater levels of depression (AOR 11.03, 95% CI: 7.14–17.04). Conclusion More than half of the participants in this present study reported moderate to extreme levels of depression which is a serious health issue among Bangladeshi stroke survivors. Understanding the predictors of depression linked to stroke could enhance the effectiveness of therapeutic interventions for this condition. In addition, multidisciplinary teams should work collaboratively to address this serious issue.
Salvia pratensis L. extracts as potential eco-friendly herbicides for sustainable agricultural applications
Seizures elicited by transcorneal 6 Hz stimulation in developing rats
Seizures elicited by corneal 6-Hz stimulation are widely acknowledged as a model of temporal lobe seizures. Despite the intensive research in rodents, no studies hint at this model in developing animals. We focused on seven age groups of both male and female rats. Biphasic pulses with 0.3 ms duration and current intensities from 20 to 80 mA were applied transcorneally for 3 s to calculate threshold intensities for individual age groups. Threshold stimulation intensity necessary for elicitation of clonic seizures was highly age- and sex-dependent. The highest threshold was observed in the youngest (15-day-old) group then it decreased to the age of 25 days and increased again up to adulthood. The threshold current tended to be lower in females of all age groups. The incidence of convulsive seizures increased with stimulation intensity up to postnatal day 25 in either sex. In rats of 31 days old and older convulsions occurred irregularly regardless of the stimulation current and sex. For subsequent analysis, the animals were categorized into two groups: juveniles, aged 15 to 25 days, and adolescents/adults, aged 31 days and older. Our statistical analyses revealed an increased risk of convulsions after the stimulation with higher intensities in juvenile but not adolescent/adult rats. Females tended to be more sensitive to the stimulation with lower currents than males. Seizure severity was higher in females 18- to 25-day old compared to males of the same age and the seizure duration increased with stimulation intensities in juvenile but not adolescent/adult animals. The data extend the use of the rat 6 Hz model to immature animals and may be useful as a model of pediatric temporal lobe seizures.
Utilizing UAV and orthophoto data with bathymetric LiDAR in google earth engine for coastal cliff degradation assessment
Multi-feature fusion-based consumer perceived risk prediction and its interpretability study
E-commerce faces challenges such as content homogenization and high perceived risk among users. This paper aims to predict perceived risk in different contexts by analyzing review content and website information. Based on a dataset containing 262,752 online reviews, we employ the KeyBERT-TextCNN model to extract thematic features from the review content. Subsequently, we combine these thematic features with product and merchant characteristics. Using the PCA-K-medoids-XGBoost algorithm, we developed a predictive model for perceived risk. In the feature extraction phase, we identified 11 key features that influence perceived risk in online shopping. During the prediction phase, the model performs excellently across different sample types in the test set, achieving a precision (P) of 84%, a recall (R) of 86%, and an F1 score of 85%. Through the model’s interpretability analysis, we find that quality, functionality, and price are key features affecting perceived risk for electronic products. In the case of skincare products, skin safety is the most critical feature. Additionally, there are significant differences in feature characteristics between high-risk samples and normal samples.