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Investigation of alpha-glucosidase inhibition activity of Artabotrys sumatranus leaf extract using metabolomics, machine learning and molecular docking analysis

One way to treat diabetes mellitus type II is by using α-glucosidase inhibitor, that will slow down the postprandial glucose intake. Metabolomics analysis of Artabotrys sumatranus leaf extract was used in this research to predict the active compounds as α-glucosidase inhibitors from this extract. Both multivariate statistical analysis and machine learning approaches were used to improve the confidence of the predictions. After performance comparisons with other machine learning methods, random forest was chosen to make predictive model for the activity of the extract samples. Feature importance analysis (using random feature permutation and Shapley score calculation) was used to identify the predicted active compound as the important features that influenced the activity prediction of the extract samples. The combined analysis of multivariate statistical analysis and machine learning predicted 9 active compounds, where 6 of them were identified as mangiferin, neomangiferin, norisocorydine, apigenin-7-O-galactopyranoside, lirioferine, and 15,16-dihydrotanshinone I. The activities of norisocorydine, apigenin-7-O-galactopyranoside, and lirioferine as α-glucosidase inhibitors have not yet reported before. Molecular docking simulation, both to 3A4A (α-glucosidase enzyme from Saccharomyces cerevisiae, usually used in bioassay test) and 3TOP (a part of α-glucosidase enzyme in human gut) showed strong to very strong binding of the identified predicted active compounds to both receptors, with exception of neomangiferin which only showed strong binding to 3TOP receptor. Isolation based on bioassay guided fractionation further verified the metabolomics prediction by succeeding to isolate mangiferin from the extract, which showed strong α-glucosidase activity when subjected to bioassay test. The correlation analysis also showed a possibility of 3 groups in the predicted active compounds, which might be related to the biosynthesis pathway (need further research for verification). Another result from correlation analysis was that in general the α-glucosidase inhibition activity in the extract had strong correlation to antioxidant activity, which was also reflected in the predicted active compounds. Only one predicted compound had very low positive correlation to antioxidant activity.

An overview of healthcare-associated Candida auris outbreaks in Ministry of Health hospitals–Saudi Arabia 2020–2022; Retrospective multicentric study

Background Candida auris (C. auris) is an emerging fungus pathogen associated with nosocomial infections that is seen as a serious global health issue. Aim To describe the epidemiology and features of hospital-acquired Candida auris outbreaks in the Ministry of Health hospitals (MOH). Methods A three-year (2020–2022) retrospective analysis of Candida auris outbreaks in the Saudi MOH hospitals. A total of 45 hospitals were involved, with 511 cases of Candida auris. The data collected from the General Directorate of Infection Prevention and Control (GDIPC) platform comprises all patients’ data for C. auris instances, whether infected or colonized. Results Out of the 511 Candida auris recruited cases, 291 (56.9%) were infected, whereas 220 (43.1%) were colonized. 32.9% of cases were above the age of 65, and 68.9% were male. The majority of cases were admitted in the ICU unit (95.5%). Approximately 18.8% of cases were diabetes, and 18.5% were hypertensive. Central lines were discovered in 37.7% of the cases. Approximately 85.9% of cases had Foley catheters, 68.5% were on ventilators, and 53.8% had a tracheostomy. The crude mortality rate was particularly high (41.5%) among the study cases. It was non-significantly higher among infected individuals (44.7%) than colonized patients (37.3%) at (p value = 0.093). The prevalence of hypertension (p = 0.001), DM (p = 0.003), and peripheral line insertion (p = 0.004) was significantly higher among colonized patients. While the presence of COVID-19 (p = 0.001), and central line insertion (p = 0.001) was significantly higher among infected patients. Conclusion C. auris is a new pathogen that causes hospital outbreaks. Strict adherence to infection prevention and control criteria established by the Centre for Disease Control (CDC) and GDPIC has significantly contained and reduced the spread of these outbreaks. One-month retrospective surveillance before diagnosing the index case and a prospective surveillance strategy for at least three months is highly recommended.

Correction: Will R&D make investors more tolerant? Analysis based on the performance forecast of Chinese listed companies

Monocyte-cancer cell fusion is mediated by phosphatidylserine—CD36 receptor interaction and induced by ionizing radiation

Emerging evidence suggests that fusion of cancer cells with leucocytes, such as macrophages, plays a significant role in cancer metastasis and results in tumor hybrid cells that acquire resistance to chemo- and radiation therapy. However, the precise mechanisms behind the leukocyte-cancer cell fusion remain unclear. The present in vitro study explores the presence of fusion between the monocyte cell line (THP-1) and the breast cancer cell line (MCF-7) in relation to the expression of CD36 and phosphatidylserine with and without treatment of these cells with ionizing radiation. The study reveals that spontaneous THP-1/MCF-7 cell fusion increases significantly from 2.8% to 6% after irradiation. The interaction between CD36 and phosphatidylserine plays a pivotal role in THP-1/MCF-7 cell fusion, as inhibiting this interaction using anti-CD36 antibodies significantly reduces cell fusion. While irradiation leads to a dose-dependent escalation in phosphatidylserine expression in MCF-7 cells, it does not impact the expression of CD36 in either THP-1 or MCF-7 cells. To the best of our knowledge, this is the first study to demonstrate the involvement of the CD36-phosphatidylserine interaction in the fusion between monocytes and cancer cells, shedding light on a novel explanatory mechanism for the roles of CD36 and phosphatidylserine in tumor progression.

Unravelling the complexity of the relationship between social support sources and loneliness: A mixed-methods study with older adults

Loneliness is an increasingly significant social and public health issue in contemporary societies. The available evidence suggests that social support is one of the key psychosocial processes for the reduction and prevention of loneliness. This study investigated the role played by sources of social support in the experience of social and emotional loneliness, identifying seven sources of support split between family (spouse/partner, children, grandchildren, siblings) and non-family (friends, neighbours). The study population comprised people aged 65 years and over living in Spain, with a partner (without cohabiting children), alone or in a nursing home. A mixed-methods approach was used, combining data from a survey involving 887 participants (quantitative phase) and data from semi-structured interviews with 30 older adults (qualitative phase). The relationship between the various sources and loneliness was analysed using structural equation modelling (SEM) for the survey data and thematic analysis for the qualitative information. The results from both phases of the study suggest different association dynamics between sources of social support and the social and emotional dimensions of loneliness. Lower levels of emotional loneliness were related to support from the following sources: spouse, children, grandchildren, siblings and friends. Lower levels of social loneliness were related to support from the following sources: spouse, grandchildren, siblings and friends. In contrast, greater levels of emotional loneliness were related to support from neighbours and greater levels of social loneliness were related to support from children. The findings of this study contribute to a better understanding of the association between social support and loneliness and suggest that interventions aimed at reducing loneliness could be more effectively targeted by considering the specific effects of support derived from different sources.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.