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Prevalence and risk factors of hypertension among clients seeking care at Selected Healthcare Facilities in Kenya
Background Hypertension remains one of the primary risk factors for cardiovascular disease and the leading cause of mortality worldwide. According to the World Health Organization (WHO), as estimated 1.28 billion adults suffer from hypertension worldwide and approximately half are unaware of the problem. This study aimed to explore the prevalence of hypertension, and the association of sociodemographic, behavioral, and physiological factors related to hypertension in patients seeking care at the different healthcare facilities in Kenya. Methodology We carried out a cross-sectional survey study between April 2023 and July 2023. The general adult public visiting the outpatient clinics were recruited from 8 healthcare facilities in Kenya. Summary statistics were presented as medians and interquartile ranges for continuous data and frequencies and percentages for categorical data. The non-parametric Kruskal–Wallis test was used to compare the continuous variables and Fisher’s exact test was used to compare the categorical variables between group associations. Results A total of 1444 clients were recruited and included in the analysis. The median age of participants was 47.0 years, 54.3% were females, 75.1% were married and 42.9% reported living in the rural areas. The prevalence of hypertension was found in 29.4% of clients, of which 48.5% lacked awareness of their diagnosis. Of the patients who knew of their diagnosis (n = 412), 53.1% did not achieve blood pressure control as defined by Joint National Committee on prevention, detection, evaluation, and treatment of high blood pressure. Of note, 39.1% of participants with hypertension were from faith-based health facilities, 31.5% were from public institution and 29.4% were from private institutions (p < 0.001). In rural areas, faith-based facilities are the dominant care providers. Type of facility, age, gender, education, marital status, body mass index and residence were associated with hypertension (p < 0.05). Conclusion The study highlights a significant burden of hypertension among adults attending outpatient clinics in Kenya. Nearly half of hypertensive individuals were unaware of their condition, and among those diagnosed, more than half did not achieve target blood pressure levels, indicating gaps in screening, awareness, and management. These findings emphasize the need for targeted interventions, including improved screening, awareness campaigns, and enhanced treatment strategies to improve hypertension control in diverse healthcare settings.
Pathogenic characteristics of an unencapsulated Streptococcus suis serotype 31 strain isolated from a patient in Thailand
Validation of the human disharmony loop: Pectoralis minor tenotomy significantly reduces pain and improves function in historically challenging patients who meet reproducible and explicit diagnostic criteria
Background/Objectives Patients commonly present with a mix of intractable shoulder pain, persistent impingement/loss of shoulder motion, neck pain, headaches, and distal neuropathy. These patients are notoriously resistant to surgical and non-surgical treatments. Previously we proposed the Human Disharmony Loop (HDL) as a model that anatomically explains these symptoms and diagnostically predicts successful response to surgical intervention. The purpose of this study is to validate positive surgical treatment outcomes in patients diagnosed in the HDL via retrospective chart review. We hypothesized that pectoralis minor release would reliably decrease pain and occipital headaches and increase shoulder motion for patients who met diagnostic criteria for the HDL. Methods Patients diagnosed with the HDL and treated with pectoralis minor release at two separate institutions with at least 6-month follow-up were included. Diagnosis was based on explicit anatomic and symptomatic criteria: coracoid tenderness, scapular protraction, and at least one end symptom. Neuropathy was tested using the scratch-collapse test. Outcomes included pain scores, clinical neuropathic lesions, rotator cuff impingement signs, shoulder range of motion, and complications. Results 115 patients were included. Average age was 48. 37% were male. 89% of patients who received a preoperative subcoracoid injection reported a significant decrease in presenting symptoms. 6 months after PM release, median VAS pain scores decreased from 8 to 2. Occipital headaches decreased from 66% to 6%. Rotator cuff impingement decreased from 87% to 10%. Median shoulder abduction increased from 90 to 180 degrees. Neuropathy decreased at the following locations: scalenes 57–2%, suprascapular 51–0%, quadrilateral 81–5%, radial 60–11%, cubital 31–25%, carpal 53–25%. 25% of patients required secondary distal neurolysis. Complications remained low at 3% (3 seroma, 1 wound dehiscence). Conclusions Patients diagnosed with the Human Disharmony Loop exhibit a dramatic clinical improvement following pectoralis minor release. A medial coracoid pectoralis minor block injection can aid in diagnosis but does not rule-out the syndrome. Patients showed significant reductions in shoulder pain, headaches, concomitant neuropathic lesions and improved shoulder range of motion. Patients should be counseled that some may need secondary neurolysis for residual neuropathy.
Three value-based factors predict perceived difficulty in moral decision-making
Short term hemodynamic effects of atrial fibrillation in a closed-loop human cardiac-baroreflex system
Atrial fibrillation (AF) remains the leading cardiac cause of stroke and AF-related death rate in the United States has been increasing for over twenty years. While the effect of standalone AF on heart rate is well established, there is a lack of clarity on its impact on other critical hemodynamic metrics. This is ostensibly due to interaction with other common comorbidities, especially hypertension. In addition, AF has a complex relationship with the state of the baroreflex. Evidence indicates that baroreflex sensitivity (BRS), the ability of the intrinsic cardiac control system to initiate parasympathetic response, is suppressed during AF. Therefore, a proper assessment of the hemodynamic impact of AF must take the state of the baroreflex into consideration. In this paper, we present a lumped parameter model of the human cardiovascular-baroreflex system that adequately translates AF-induced electrophysiological changes to measurable hemodynamic effects. We consider the stochastic effects of the electrical disruption in the sinus node, the absence of atrial contraction and BRS suppression. Our model provides insight into the impact of standalone AF on key benchmarks: heart rate, arterial pressure and stroke volume, under varying degrees of BRS suppression. In addition, the development of a tractable mathematical model is essential for the in-silico evaluation of emerging neuromodulation therapies for AF. Our model predictions are in agreement with published clinical data and suggest that high blood pressure during standalone AF is strongly dependent on the extent of damage to the baroreflex, which may explain conflicting reports of AF-related hypertension and normotension.
Fabrication of a PVA-encapsulated MCC/S–VO2 composite via melt intercalation for efficient fixed-bed adsorption of methylene blue
Abstract In this study, a novel multifunctional composite comprising polyvinyl alcohol (PVA), microcrystalline cellulose (MCC), sulfur, and vanadium dioxide (VO 2 ) was successfully synthesized through a solvent-free melt intercalation method. The process involved dispersing MCC/S–VO 2 material within a PVA matrix in the molten state, enabling homogeneous mixing and effective interfacial integration, and subsequently calcining the mixture at 300 °C. Calcination preserved the monoclinic phase of VO 2 and enhanced the composite’s porosity through thermal decomposition of PVA and MCC, resulting in uniformly distributed active phases within black, granular adsorbents. X-ray diffraction (XRD) confirmed the sustained integrity of the crystalline monoclinic VO 2 phase. X-ray photoelectron spectroscopy (XPS) detected the elements carbon, oxygen, sulfur, and vanadium, along with carbonized polyvinyl alcohol (PVA), all exhibiting distinct spectral characteristics that point to strong interfacial interactions within the composite. Scanning and transmission electron microscopy (SEM and TEM) revealed a porous, layered architecture with VO 2 nanosheets uniformly distributed throughout the carbon-rich matrix. Continuous fixed-bed column experiments were conducted under varying operational parameters, including bed heights, initial methylene blue (MB) concentrations, and flow rates. The composite demonstrated exceptional adsorption efficiency, achieving a peak capacity of 47.7 mg g −1 under optimal conditions (bed height: 0.5 cm, MB concentration: 20 mg L −1 , flow rate: 1 mL min −1 , column diameter: 1 cm). Breakthrough curve analysis confirmed the validity of the BDST model for performance prediction (R 2 > 0.9), whereas the Thomas and Yoon–Nelson models exhibited lower correlation coefficients (R 2 < 0.9), suggesting reduced applicability under the tested parameters. Notably, the adsorbent retained over 83.6% of its initial adsorption capacity after four regeneration cycles, underscoring its structural resilience and recyclability. The adsorption mechanism for MB onto the synthesized composite was attributed to π–π interactions, hydrogen bonding, and electron donation from the amine groups of MB to vanadium sites. This study, for the first time, demonstrates the use of solid PVA in a solvent-free melt intercalation process to fabricate polymer–inorganic hybrid adsorbents, offering a novel and eco-friendly strategy for advanced wastewater treatment.
The impact of industrial digitalization on the urban-rural income gap
Along with the rapid development of the global digital economy, China is experiencing profound transformations in industrial digitization. These transformations may significantly affect the urban-rural income gap. Using panel data from 30 Chinese provinces from 2012 to 2022, this paper empirically examined the impact of industrial digitalization on the urban-rural income gap based on a fixed-effects model. The findings reveal that the development of industrial digitalization in China widens the urban-rural income gap. Mechanism analysis indicates that industrial digitalization increases software business revenue and employment in the information services sector, thereby expanding the urban-rural income gap; additionally, industrial digitalization widens the income gap between urban migrants and rural migrant populations, further increasing the overall urban-rural income disparity. Heterogeneity analysis demonstrates that in the eastern region, industrial digitalization significantly enlarges the urban-rural income gap, whereas its effects are not significant in the central and western regions. The conclusions of this study provide empirical support and policy insights for China in advancing industrial digitalization and promoting common prosperity.
Circulating CD24/Siglec-10 biomarkers predict post-resuscitation outcomes in a cardiac arrest cohort
Abstract CD24 can bind to its receptor, Siglec-10, and suppress immune responses induced by tissue damage. We aimed to study serum biomarkers in CD24/Siglec-10 axis and explore their associations with the survival and neurological prognosis of patients after return of spontaneous circulation (ROSC) following cardiac arrest. A prospective cohort study was performed. Eligible patients with ROSC were enrolled. Clinical information and serum levels of sCD24, sSiglec-10, sialic acid, tumor necrosis factor (TNF-α), interleukin-6 (IL-6), high mobility group protein 1 (HMGB1), neuron specific enolase (NSE), and neuraminidase activity were collected on days 1, 3 and 7 after ROSC. The 28-day survival and neurological outcome were documented. The study included 104 ROSC patients, with 30 and 74 in survivor and non-survivor groups, respectively. The levels of all biomarkers, except for sSiglec-10, sialic acid, and neuraminidases on day 1, were significantly higher in non-survivor than survivor group. Serum sCD24 level was positively correlated with sialic acid, HMGB1, TNF-α, IL-6, NSE, neuraminidases activity, and APACHE II score. Multivariate logistic regression analysis showed that serum sCD24 level was independently associated with 28-day poor neurological prognosis and mortality after ROSC. Multiple serum biomarkers within the CD24/Siglec-10 axis were significantly elevated following ROSC. Elevated serum sCD24 level emerged as a predictor of both 28-day poor neurological prognosis and all-cause mortality in patients after cardiac arrest. Further large-scale studies are warranted to validate these findings.
Evaluating voter perceptions of political party similarity: A mixed-method study of party positions in Taiwan
This study examines voter perceptions of political party similarity using data from a validated online survey conducted in Taiwan. It primarily collects qualitative data through open-ended questions, complemented by Multiple Correspondence Analysis (MCA) and feature matching techniques. The findings reveal that party competition in Taiwan is multidimensional, extending beyond traditional blue-green and unification-independence divides. Notably, local Taiwanese issues and social concerns have become increasingly prominent among emerging third parties. Feature matching results show that 22.53% of respondents clearly distinguish the Taiwan People’s Party (TPP), while 11.42% identify the New Power Party (NPP), differentiating it from the pan-green camp as part of the emerging third force. Taiwan’s unique political context, shaped by democratization, cross-strait tensions, and the rise of influential third parties, provides valuable insights for comparative politics. The study offers an analytical framework for understanding party system evolution in emerging democracies and deepens our grasp of how identity politics and diverse political engagement transform political competition. This framework enables scholars to systematically capture complex voter perceptions in multi-party systems and facilitates comparative analysis across political environments marked by identity-based polarization and increasing party plurality.
Integration of SuperCam based chemical imaging and clustering to correlate geochemistry and mineralogy in heterogeneous samples
Abstract Peridotites are ultramafic rocks whose mineralogical diversity makes them suitable case studies for testing analytical methodologies. In this study, a peridotite sample from the Ronda massif (Málaga, Spain) was examined demonstrating a practical methodology for distinguishing geochemically distinct regions within complex lithologies. The sample was analyzed using a multi-instrumental approach combining laser-induced breakdown spectroscopy (LIBS), micro-energy dispersive X-ray fluorescence (µ-EDXRF), and Raman spectroscopy, all of them integrated in the Perseverance rover payload. LIBS and µ-EDXRF were used to assess elemental composition and spatial distribution across the sample, whereas Raman spectroscopy confirmed the presence of mineral phases such as, olivine, pyroxenes, and chromiferous spinels in specific regions. Elemental LIBS ratios such as Mg# and Cr# further supported the identification of compositional variations related to mineral phases. Spectral LIBS data were processed using k-means clustering to segment geochemical zones and detect spatial trends. This integrated spectroscopic and statistical approach enables interpretation of ultramafic rock complexity and offers an efficient framework for planetary exploration under mission constraints.
Small-scale experimental and numerical simulation of blasting in jointed rock-like materials under varied joint and explosive conditions
This study focuses on the blasting failure of rock-like materials, aiming to investigate the effect of the joint spatial characteristics and explosive parameters. Rock mass blasting is complex, and the influence of factors like lithology, rock structure, and explosive characteristics needs consideration. In similar model tests, concrete casting molds are used to prepare nine groups of joint models with different joint quantities, angles, and distances. Mixed emulsion explosives are used for blasting, and the crack expansion process and blasting fragmentation properties are analyzed. In numerical simulations, ANSYS/LS-DYNA is employed with carefully selected material parameters. The results show that the blasting effect of rock mass first increases and then decreases with joint width, increases with joint number and spacing, and first increases and then decreases with joint inclination angle. The consistency between the similar model tests and simulation results validates the conclusions of this study, providing theoretical support and practical guidance for optimizing the blasting excavation parameters of the jointed rock mass. This research offers a scientific decision-making basis for dynamic adjustments of blast schemes, safety risk pre-control, and construction efficiency optimization in engineering management.
Generative AI-assisted clinical interviewing of mental health
Abstract The standard assessment of mental health typically involves clinical interviews conducted by highly trained clinicians. While effective, this approach faces substantial limitations, including high costs, high clinician workload, variability in expertise, and a lack of standardization. Recent progress in large language models (LLMs) offer a promising avenue to address these limitations by simulating clinician-administered interviews through AI-powered systems. However, few studies have rigorously validated such tools. In this study, we used TalkToAlba to develop and evaluat an AI assistant designed to conduct clinical interviews aligned with DSM-5 criteria. Participants ( N = 303) included individuals with self-reported clinician-diagnosed mental health disorders, namely, major depressive disorder (MDD), generalized anxiety disorder (GAD), obsessive-compulsive disorder (OCD), post-traumatic stress disorder (PTSD), attention-deficit/hyperactivity disorder (ADD/ADHD), autism spectrum disorder (ASD), eating disorders (ED), substance use disorder (SUD), and bipolar disorder (BD)—alongside healthy controls. The AI assistant conducted diagnostic interviews and assessed the likelihood of each disorder, while another AI system analyzed interview transcripts to verify diagnostic criteria and generate comprehensive justifications for its conclusions. The results showed that the AI-powered clinical interview achieved higher agreement (i.e., Cohen’s Kappa), sensitivity, and specificity in identifying self-reported, clinician-diagnosed disorders compared to established rating scales. It also exhibited significantly lower co-dependencies between diagnostic categories. Additionally, most participants rated the AI-powered interview as highly empathic, relevant, understanding, and supportive. These findings suggest that AI-powered clinical interviews can serve as accurate, standardized, and person-centered tools for assessing common mental disorders. Their scalability, low cost, and positive user experience position them as a valuable complement to traditional diagnostic methods, with potential for widespread application in mental health care delivery.
Gene editing of the thioester reductase step in the biosynthesis of lysergic acid amides
Ergot alkaloids derived from lysergic acid are important in agriculture, as food and feed contaminants, and in medicine, as the foundation of several pharmaceuticals. The fungus Metarhizium brunneum makes several lysergic acid amides, with lysergic acid α-hydroxyethylamide (LAH) being produced in by far the highest concentration. The multifunctional enzyme lysergyl peptide synthetase 3 (Lps3) has multiple domains that play important roles in lysergic acid amide synthesis. We hypothesized a role for the reductase domain of Lps3 in liberating LAH from an enzyme-bound precursor and tested this hypothesis with CRISPR/Cas9-based gene editing experiments. We transformed M. brunneum with a Cas9/single guide RNA complex and a donor DNA that replaced the tyrosine at the active site of the reductase domain of Lps3 with a phenylalanine. Sanger sequencing of edited and wild-type genes demonstrated successful editing of the reductase domain without non-target mutations in Lps3. High performance liquid chromatography of the edited strain showed a significant reduction of LAH and accumulation of the precursor lysergic acid. The phenotype was similar when the edited allele of lpsC was in a wild-type background or in backgrounds with late pathway genes easO or easP knocked out, except no LAH was detectable when the edit was in the easO knockout background. The data demonstrate that the reductase domain plays a key role or roles in formation of LAH. The abundant lysergic acid accumulating in the mutants, as opposed to later pathway intermediates in LAH biosynthesis (such as lysergyl-alanine), indicated severe debilitation of Lps3. The data indicate a requirement for the reductase domain of Lps3 in synthesis of lysergic acid amides and demonstrate the feasibility of the CRISPR/Cas9-based approach for editing genes in Metarhizium species.
AI-Driven intrusion detection and prevention systems to safeguard 6G networks from cyber threats
Abstract Sixth-generation (6G) wireless networks, which boast previously unheard-of capacity, reliability, and efficiency, are projected to begin testing and implementation as early as 2030. To meet the demands of new applications, the emphasis is currently on developing 6G networks. The advent of 6G presents additional difficulties, especially in intrusion detection, where sophisticated attacks call for cutting-edge security measures. This research proposes a novel technique using a machine learning algorithm in a 6G network cyber-attack monitoring and intrusion detection system. Here, the 6G network has been monitored, and intrusion detection for cyberattack using blockchain federated Gaussian multi-agent Q-encoder neural networks (BFGMAQENN). Then, the 6G network has been optimized using whale swarm binary wolf optimization (WSBWO). The experimental analysis has been carried out for various cyberattack datasets regarding detection accuracy, data integrity, scalability, communication overhead, and network efficiency. The proposed model attained detection accuracy of 97%, data integrity of 94%, scalability of 93%, communication overhead of 60%, and network efficiency of 98%.
DCAF-GAN: Enhancing historical landscape restoration with dual-branch feature extraction and attention fusion
Historical landscape restoration has become a crucial area of research in cultural heritage preservation, and with the advancement of digital technologies, effectively restoring damaged historical images has become a critical challenge. Traditional restoration methods face difficulties in handling large occlusions, complex structural features, and maintaining high fidelity in restored images. Existing deep learning methods often focus on restoring a single feature, making it difficult to achieve high-quality reconstruction of both texture and structure. To address these challenges, we propose DCAF-GAN, a novel deep learning model that effectively restores both fine textures and global structures in damaged historical landscapes through a dual-branch encoder and a channel attention-guided fusion module. Experimental results show that DCAF-GAN achieves a PSNR of 29.12 and SSIM of 0.867 on the StreetView dataset, and a PSNR of 28.6 and SSIM of 0.854 on the Places2 dataset, significantly outperforming other models. These results demonstrate that DCAF-GAN not only provides high-quality restorations but also maintains computational efficiency. DCAF-GAN offers a promising solution for the digital preservation and restoration of cultural heritage, with significant potential for further applications.
Association between mediterranean diet during pregnancy and gestational weight gain: a prospective cohort study
Cardiovascular disease detection: A hybrid machine learning-AI framework for personalized diagnosis and risk assessment
Cardiovascular disease (CVD) is considered the number one killer disease in the world, underlining the importance of the application of more accurate diagnostic and therapeutic tools. Traditional screening procedures usually do not provide identification and guidance based on individual peculiarities that might result in less than beneficial results. This study seeks to create a hybrid computational framework that synergistically integrates a Support Vector Machine (SVM) classifier, a Particle Swarm Optimization (PSO) algorithm for hyperparameter tuning, and an AI-based interpretation module (SHapley Additive exPlanations, SHAP) to enable early diagnosis and risk assessment beyond various profiling of patients. A mathematical model was developed to provide the framework to deal with the diagnostic complexity of cardiovascular disease. Machine learning (ML) and AI techniques are then used to improve clinical decision-making. The proposed framework employs a variety of forms of patient data, namely electronic health records, medical images, and genomic data, to construct patient models. It utilizes advanced algorithms to enable accurate disease prognosis, identify high-risk individuals for early intervention, and facilitate personalized treatment strategies. This approach will help to eliminate the expense of ineffective therapies, shorten delays in care, and eventually improve patient outcomes and quality of life. Preliminary results on the MIMIC-III clinical database (v1.4) showed that the proposed framework performs better than previous methods by achieving higher accuracy 98.4%, precision 97.5%, recall 96.4%, F1 score 96.9%, and AUC-ROC 97.35%. Moreover, the sensitivity 96.4%, specificity 98.7%, and a low negative likelihood ratio (0.036) of the proposed framework demonstrate its ability and power in identifying high- and low-risk patients. The hybrid ML-AI framework provides an improved way for early detection of cardiovascular disease, which helps in personalizing treatments for patients. It also enables healthcare delivery through its combined predictive power to improve healthcare service.
Comprehensive analysis of coagulation-associated gene signature in bladder cancer diagnosis, prognosis, and immunotherapy
A stochastic wideband propagation-graph channel model for plants-affected drones air-to-ground mmWave communications
Unmanned aerial vehicles (UAVs) are experiencing extensive worldwide application across various fields, particularly in outdoor scenarios that often involve vegetation. A comprehensive understanding of the air-to-ground (A2G) wireless link channel and fading characteristics is crucial for the deployment and optimization of communication systems. In this paper, we propose a A2G wideband wireless channel model that integrates line-of-sight (LoS), reflection, and scattering propagation mechanisms at typical millimeter-wave (mmWave) bands based on the stochastic propagation-graph model. First, a geometric fractal tree modeling method is introduced to represent a single tree, the size and distribution of trees are modeled based on the stochastic theory. The propagation-graph (PG) model is then employed to simulate the A2G channel impulse response (CIR) at 28 GHz, taking into account maximal propagation delay constraints. On this basis, we investigate the spatial cross-correlation function (CCF) at different positions and effects of different UAV heights and circular movement radii on delay spread. Additionally, we study key small-scale statistical channel properties, including the power delay profile (PDP), power angular profile (PAP), and Doppler power spectrum density (DPSD). Our simulation results demonstrate that vegetation significantly impacts channel dispersion in spatial-temporal-frequency domains, and our model effectively captures the non-stationarity of UAV A2G channels.