Browse Articles
Discover research articles across all indexed journals
Zoning optimization of ecosystem service value and landscape ecological risk in typical karst areas in southwest China
Clinical application of CNV-seq for diagnosing abnormal brain development in children
Diabetes-related distress and associated factors among adult diabetes mellitus patients attending public hospitals in Gedio zone, southern Ethiopia: Mediation analysis
Background Individuals with diabetes might experience distress due to their treatment regimen, interactions with physicians, interpersonal relationships, and emotional well-being. This may lead to lower self-efficacy, which would impair self-treatment adherence and result in poor glycemic control. Understanding and addressing gaps may help individuals with diabetes improve their glycemic control. Therefore, this study aimed to determine the magnitude of diabetes-related distress and associated factors among adult diabetes mellitus patients attending public hospitals in the Gedio zone, Southern Ethiopia. Method An institutional-based cross-sectional study was conducted from April to May 2024 at four public hospitals in the Gedio zone. Study participants who met the inclusion criteria were selected using systematic random sampling techniques. Data were collected using the Diabetes Distress Scale (DDS-17). Both bivariable and multivariable ordinal logistic regression were applied. In multi-variable ordinal logistic regression, a P value < 0.05 significant level was used to identify factors of diabetic-related distress. Mediation analyses were conducted using the bootstrapping technique. A variable showing a significant indirect effect was considered as a mediator. Results In this study, a total of 506 adult patients with diabetes participated. The overall prevalence for moderate and high levels of diabetes-related distress was 47.04% (95% CI;42.6–51.5) and 41.5% (95% CI; 37.2–45.9), respectively. Variables like never had planned physical activity (adjusted odd ratio, aOR=3.09;95%CI = 1.034–9.28; P = 0.04), poor social support (aOR=3.44;95%CI = 1.42–8.33;P < 0.006), complications (aOR=1.79; 95%CI = 1.13–2.84; P < 0.013) and high blood pressure (aOR= 1.85; 95%CI = 1.12–3.05; P = 0.016) were factors for diabetes related distress. Depression was identified as a partial mediator of the relation between social support and diabetes related distress. Conclusion Diabetes-related distress was highly prevalent in diabetes patients. Healthcare providers need to address this by integrating psycho-social care with collaborative medical care.
Characteristics of vicarious touch reports in a general population
Investigating visual search mechanisms and enhancing the diagnostic potential of the trail making test using eTMT
Impact of dietary salicylates on angiogenic factors and biochemical parameters in a rat model of preeclampsia
Background The pathophysiology of preeclampsia involves impaired cytotrophoblastic invasion, placental ischemia, inflammation, and angiogenic imbalance. Prophylactic low-dose aspirin can reduce the risk of preeclampsia and fetal growth restriction in high-risk women. This study evaluated the effect of dietary salicylates on the development of preeclampsia in rats treated with L-NAME (NG-nitro-L-arginine-methyl ester). Methodology Pregnant Sprague-Dawley rats were randomly assigned to six groups and treated with dietary salicylates at two dose levels (1 and 10 mg/kg diet) or aspirin (doses adjusted to dietary salicylates). Preeclampsia was induced by administering L-NAME in drinking water from gestational days 6–19. Results Neither dietary salicylates nor aspirin, at either dose, affected blood pressure in L-NAME-treated rats. The lower dose of dietary salicylates significantly reduced urinary albumin levels. Both interventions prevented an increase in the sFlt/PLGF ratio and mitigated histopathological placental changes in preeclamptic rats. The higher dose of aspirin reduced placental VEGFR2 protein levels. Conclusion Dietary salicylate supplementation does not provide clear preventive effects against preeclampsia.
Antifungal efficacy of pyrolysis-derived bio-oil aqueous phases from Tropical plant biomass against Candida auris
Permissiveness of American bison to infection with severe acute respiratory syndrome coronavirus-2
Abstract The COVID-19 pandemic represents one of the most significant public health events of the last century. As with other coronaviruses (SARS, MERS) the role of animals is of intense interest. Believed to have originated in bats, the role of other animals in the epidemiology of the SARS-CoV-2 pandemic is still unclear, as is the range of susceptible hosts. American bison were intranasally infected with SARS-CoV-2 and monitored for seroconversion and the presence of viral RNA in oronasal secretions and feces. Although clinical signs were not seen, permissiveness of bison to infection with SARS-CoV-2 was manifest by seroconversion, the presence of viral RNA in oronasal secretions, persistence of viral RNA in lymphoid tissue, and viral associated interstitial pneumonia. Retrospective sequencing of the inoculum revealed a common in vitro adaptation in the furin cleavage site of the spike protein that may have reduced in vivo viral fitness. As such, we cannot exclude the possibility that use of an isolate with an intact furin cleavage motif would more efficiently infect bison.
Alzheimer’s disease classification using a hybrid deep learning approach with multi-layer U-net segmentation and XAI driven analysis
Alzheimer’s disease (AD) is a neurodegenerative illness causing a significant decrease in cognitive function, and early, accurate diagnosis is of great therapeutic and diagnostic value. Currently, there is promising potential for applying various types of artificial intelligence techniques, such as enhanced models of deep learning, for classifying Alzheimer’s disease. Therefore, this study proposes an Outline of deep learning to classify Alzheimer’s disease with segmentation using the Multi-Layer U-Net and a hybrid classification approach combining multi-scale EfficientNet with SVM. The proposed methodology consists of a four-phase process: (1) Whole brain segmentation, (2) Gray matter segmentation using multi-layer U-Net segmentation, (3) Feature extraction using Multi-Scale Efficient Net with SVM for classification, and (4) XAI (explainable AI) techniques by integrating Saliency Map Quantitative Analysis for increased clinical trustworthiness and model interpretability. It is found that the experiment results provide promising classification performance for three classes – Alzheimer’s Disease (AD), Mild Cognitive Impairment (MCI) and Cognitively Normal (CN) with an overall accuracy of 97.78% ± 0.54%, precision of 97.18% ± 1.14% (AD), 97.78% ± 0.29% (CN) and 97.03% ± 1.10% (MCI), recall of 97.90% ± 0.77% (AD), 97.49% ± 1.34% (CN) and 97.25% ± 0.99% (MCI), and F1 score of 97.74% ± 0.63% (AD), 97.78% ± 0.79% (CN), and 97.54% ± 0.69%(MCI). The results obtained underscore the elegance of the proposed approach in correctly classifying Alzheimer’s disease stages. Future work will evaluate the model on publicly accessible Alzheimer’s disease MRI datasets and incorporate advanced XAI techniques for increased interpretability and diagnostic reliability. The work focuses on Human Health.
Assessment of brain penetration and tumor accumulation of niraparib and olaparib: insights from multimodal imaging in preclinical models
Abstract One of the most significant challenges in developing new treatments for primary and metastatic brain tumors is the requirement for crossing the blood-brain barrier (BBB). By examining the brain penetration and spatial distribution of drug candidates in animal models, we can gain valuable insights into their potential for clinical success. In this study, we used multimodal imaging alongside complementary analytical methods to assess the brain distribution of two clinically advanced poly(ADP-ribose) polymerase (PARP) inhibitors—niraparib and olaparib—across three preclinical models: healthy brain (nonhuman primates (NHPs)), primary brain tumors (mouse glioblastoma), and metastatic brain tumors (mouse breast cancer metastasis). The results suggest that niraparib has a superior ability (relative to olaparib) to penetrate the BBB, distribute widely throughout the brain, and accumulate in brain tumor lesions. These findings support further studies of niraparib as a potential treatment for tumors in the human brain.
Experience of special care patients receiving dental rehabilitation under general anesthesia at King Saud University Dental University Hospital
Robustness of inferences in risk and time experiments to lifecycle asset integration
Experiment participants can engage in unobservable asset integration, mentally incorporating non-experimental “field” resources into an ostensibly controlled scenario. We extend asset integration to include lifecycle intertemporal tradeoffs. Our model shows that interference from purely exogenous field resources can be controlled with a simple wealth-level adjustment. Highly substitutable endogenous resources, by contrast, require modeling the entire experiment-field interaction. We examine the practical implications using three classic experiments on risk and time. As interference worsens, experimental decisions tend to exhibit an attenuation toward less risk aversion and more patience. This occurs reliably with household-scale resources, but pocket-change amounts can also cause problems.
A novel hybrid mathematical deep learning technique for early warning of flashover in composite insulators
Dispersion patterns of spontaneous coal combustion fumes in deep open-pit mines effects of wind direction and speed
Creep model of roadway surrounding rock considering hardening effect and damage effect
The influence of confining pressure on creep characteristics was analyzed by carrying out creep tests of rocks under different confining pressures. A new creep model of roadway surrounding rock considering the combined influence of hardening effect and damage effect is established by introducing hardening function and damage variable. Combined with the yield function and plastic potential function, the one-dimensional sandstone creep model is extended to the three-dimensional sandstone creep model. Finally, the correctness of the model is verified by experimental data. The results show that the creep model curve of sandstone considering hardening and damage effects is in good agreement with the test curve. Only under the action of failure load, the model curve deviates from the test curve. But on the whole, the correlation coefficient between the two is above 0.90. This shows that the established model is correct. It also comprehensively reflects the common influence mechanism of hardening effect and damage effect of rock in the whole process of creep deformation. Compared with the Nishihara model, the model curve established in this paper better describes the creep curve with accelerated creep characteristics. At the same time, it also better describes the variation of decay creep and stable creep in creep behavior. When studying the influence of hardening effect and damage effect on rock creep behavior, it is necessary to comprehensively consider the combined effects of various influencing factors.In particular, the creep characteristics of different stages of accelerated creep, decay creep and stable creep are studied. Therefore, the established model can better simulate the characteristic behavior of different stages in the creep process of rock. It well reflects the influence of hardening and damage characteristics on the creep behavior of rock.
Void discovery inside Esztramos Hill using muographic methods
Development and evaluation of a novel capillary blood collection method for decentralized therapeutic drug monitoring using the True Dose kit
Abstract Interindividual variability in epirubicin exposure limits the efficacy of standard chemotherapy dosing based on body surface area (BSA) in breast cancer. This study evaluated a novel liquid-based capillary microsampling technology, True Dose®, to enable decentralized therapeutic drug monitoring (TDM). It integrates internal standards (IS) at the blood collection point, enabling the immediate precipitation of proteins and stabilization of analytes. Analytical validation using liquid chromatography-tandem mass spectrometry (LC-MS/MS) showed that epirubicin signal responses from capillary samples corresponded with those from conventional venous samples, achieving R² values of ≥ 0.99. Intra-assay coefficient of variation (CV%) improved over time, decreasing from up to 18.6% at T0 to ≤ 11% from Day 3 onward, consistent with time-dependent matrix equilibration and improved extraction efficiency. Pre- and post-activation stability studies confirmed analyte integrity for up to 14 days at ambient temperatures. Hematocrit levels ranging from 7 to 18 g/dL showed no more than 17% signal variation, confirming matrix stability. In a clinical substudy involving 4 early-stage breast cancer patients, capillary-collected samples (n = 4 Cap-TD) yielded epirubicin/doxorubicin ratios that differed by no more than 15% from venous-derived samples; interpretation is limited by the small sample size. The findings support the feasibility of the True Dose® system as a method for decentralized TDM of anthracycline (epirubicin) chemotherapy. The technology demonstrated analyte stability under room temperature storage for up to 14 days, time-dependent improvement in analytical precision, and minimal hematocrit bias, warranting further investigation in a broader clinical setting. Clinical trial identification: EudraCT 2017-000641-44 (registered 27 Feb 2017); EUCT 2024-514818-12-00 (CTIS, transitioned from EudraCT).
User acceptability profiles of vaginal treatments among women with genitourinary syndrome of menopause
Background This study aimed to identify patient segments of acceptability to vaginal dosage forms in women with the genitourinary syndrome of menopause and to identify the most important predictors and mediators of each segment using latent segment analysis. Methods A cross-sectional study included 351 peri- and postmenopausal women from two tertiary care hospitals in the United Arab Emirates. To select the best model, we ran a finite-mixture partial least squares segmentation (FIMIX-PLS). The number of resulting segments was used to run a partial least squares-predicted-oriented segmentation to assign cases to segments and maximize the segment-specific explained variance (R2) across all groups. Multi-group analysis was then performed to examine whether differences between segments were significant. Analyses were performed using the SmartPLS Software version 4.9, Results A two-segment model was identified (Entropy > 0.8, Corrected-Akaike’s Information Criterion = 1977.11; Bayesian Information Criterion = 1960.11), which indicated adequate and well-separated segments. The first and second segments had 179 and 171 of the cases, respectively, which were considered substantive representations of both segments. The model resulted in a weighted average R2 greater than that of the original sample, indicating better predictive relevance of the model. The multi-group analysis showed that the differences between the two segments were significant. Being on vaginal treatment was observed to be the variable that drove the partition of the segments. Perceived effectiveness for those who were not on treatment (segment 1) and those who were (segment 2) was predicted by affective attitude and intervention coherence, respectively, Conclusion The acceptability of vaginal treatments differs between experienced and anticipated users. Considering patient-related factors and previous treatment experiences can serve as a benchmark to improve patient acceptability of treatment.