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Diabetes mellitus among HIV patients on ART at Woldia comprehensive specialized hospital, Northeast Ethiopia
Abstract Diabetes mellitus (DM) is an emerging comorbidity among people living with HIV receiving antiretroviral therapy (ART), potentially impacting treatment outcomes, including virologic failure. Identifying key determinants of DM in this population is crucial for improving patient care. A hospital-based cross-sectional study was conducted from January 01 to May 30, 2024 at Woldia Comprehensive Specialized Hospital, Ethiopia. A total of 253 HIV patients on ART for at least six months were randomly selected. Data were collected via structured questionnaires, clinical measurements, and medical record reviews. DM was diagnosed on the basis of a fasting blood glucose level ≥ 126 mg/dl or random plasma glucose level ≥ 200 mg/dl. Logistic regression models were employed to identify factors associated with DM, reporting adjusted odds ratios (AORs) with 95% confidence intervals (CIs). Statistical significance was set at p < 0.05. The prevalence of DM among the study participants was 9.9%. Compared with female patients, male patients had a significantly greater risk of developing DM (AOR = 4.29, 95% CI = 1.079–17.04). A family history of DM was associated with a nearly 11-fold increased risk (AOR = 10.65, 95% CI = 2.82–40.20). Overweight individuals (BMI > 25 kg/m²) had a nearly sixfold-fold greater risk of DM (AOR = 5.95, 95% CI = 1.56–22.65). Additionally, ART interruption and restarting of ART were significantly associated with increased DM risk (AOR = 6.11, 95% CI = 1.88–19.84). Approximately one in ten HIV patients on ART had DM. The significant factors included male sex, family history of DM, overweight status, and ART interruption. These findings highlight the need for routine metabolic screening, targeted interventions and continuous monitoring to mitigate DM risk and optimize HIV care.
SSDDPM: A single SAR image generation method based on denoising diffusion probabilistic model
Characterization and therapeutic potential of newly isolated bacteriophages targeting the most common Salmonella serovars in Europe
Unlocking terpenoid treasures of rhizome and leaf volatiles of Curcuma caesia Roxb through 1D and 2D GC × GC TOFMS analysis
MRI-guided biopsy reduces biochemical recurrence in prostate cancer patients undergoing radiation therapy: a single-center study from Thailand
Investigation of the effects of concentration and voltage on the physicochemical properties of Nylon 6 nanofiber membrane
Abstract The nanofibers of Nylon 6 at different concentrations 14 wt%, 16% wt%, and 18 wt% were fabricated using electrospinning method. The effect of concentration and voltage on the morphology of the nanofibers were assessed through the utilisation of FTIR, XRD, Raman spectroscopy, atomic force microscopy AFM, scanning electron microscopy SEM, and EDS techniques. A study on the Nylon 6 molecule using DFT (Density Functional Theory) was conducted. Nylon 6 optimized molecular structure, featuring an amine group and nitrogen atom for electrophilic reactions, exhibits high kinetic stability with a measured band gap energy of 6.60 eV and electronic chemical potential of -3.83. The diameter of the fibres increased from 76.91 to 81.98 nm when the concentration and voltage were further increased, with the respective weight percentages of 14 wt% and 18 wt%. This work reveals the influence of concentration and voltage on the physical and chemical characteristics of nylon-6 NFM and its quantum electronic properties.
The perceptual and biomechanical effects of scaling back exosuit assistance to changing task demands
Determination and application of soil heavy metal geochemical baseline in the southern region of Wushan County in the Yangtze River Basin, China
Mettl3 deficiency leads to impaired insulin secretion via regulating Ire1a of mature β-cells in mice
Noninvasive estimation of internal spinal alignment in patients with adolescent idiopathic scoliosis using PCdare and back shape asymmetry
Abstract Optical 3D surface scanning is used increasingly to assess spinal deformity in patients with adolescent idiopathic scoliosis (AIS), largely because it avoids additional radiation burden. However, such approaches generally underestimate the extent of the abnormality. Improving the accuracy of such estimates requires a deeper understanding of AIS and its effect on back shape. We present a unique platform with publicly available code that contains noninvasive and nonionizing approaches to estimating the Cobb angle of the primary curve, called primary Cobb angle (pCA) and internal spinal alignment (ISL) in patients with AIS. Our approaches use asymmetries of the back shape during upright standing, the Adam’s forward bending test, bending forward, and lateral bending. The results have revealed strong (0.75 [0.53, 0.87]) to excellent (0.91 [0.81, 0.96]) correlations [95% confidence interval] and a median pairwise absolute error (IQR) of 3.4° (6.8°) between the estimated pCAs and clinical gold-standard assessments in 30 patients. The correlations (IQR) between estimated shape of ISLs and their references were very strong (0.87 (0.24)) to excellent (0.94 (0.03)), and the median root mean square error (IQR) between estimated and reference ISL was 6.9 mm (3.3 mm). These results indicate confidence both in the use of 3D scanning using a “back-shape-to-spine” approach and in the establishment of optical 3D surface scanning approaches for scoliosis screening and monitoring in clinical practice.
Multi-user frequency selective beam steering by reconfigurable intelligent surfaces in the Ka-band
Abstract Reconfigurable intelligent surfaces (RIS) have been proposed to extend the coverage of wireless communication signals at mm-wave frequencies. Here, we designed and fabricated a reconfigurable intelligent surface (RIS) for multi-user frequency selective beam steering (MU-FSBS) that achieves higher channel capacity than traditional time division multiple access (TDMA) techniques in multi-user communication scenarios. MU-FSBS is enabled by a varactor diode-tuned RIS that allows to steer several beams in the Ka-band independently at the same time. For such targeted multi-frequency beam steering, we implemented a customized neural network-based machine learning architecture specifically designed to optimize the bias voltage patterns of the RIS. As an experimental demonstration, we first accomplished independent beam steering of normally incident beams at 27 GHz and 31 GHz to deflection angles between 10 $$^\circ$$ and 45 $$^\circ$$ in a defined plane. Secondly, we compared the achievable channel capacities of the proposed MU-FSBS approach with those of TDMA, finding an average increase of approximately 50% in channel capacity at a fixed SNR of 20 dB. At an SNR of 60 dB, MU-FSBS even demonstrated a remarkable 84% increase in channel capacity compared to TDMA.
Organic ligands and CO2 unlock the potential for energy relevant metals recovery and carbon mineralization from mafic rocks
Achieving 5% 13C nuclear spin hyperpolarization in high-purity diamond at room temperature and low magnetic field
LDHB silencing enhances the effects of radiotherapy by impairing nucleotide metabolism and promoting persistent DNA damage
Abstract Lung cancer is the leading cause of cancer-related deaths globally, with radiotherapy as a key treatment modality for inoperable cases. Lactate, once considered a by-product of anaerobic cellular metabolism, is now considered critical for cancer progression. Lactate dehydrogenase B (LDHB) converts lactate to pyruvate and supports mitochondrial metabolism. In this study, a re-analysis of our previous transcriptomic data revealed that LDHB silencing in the NSCLC cell lines A549 and H358 dysregulated 1789 genes, including gene sets associated with cell cycle and DNA repair pathways. LDHB silencing increased H2AX phosphorylation, a surrogate marker of DNA damage, and induced cell cycle arrest at the G1/S or G2/M checkpoint depending on the p53 status. Long-term LDHB silencing sensitized A549 cells to radiotherapy, resulting in increased DNA damage and genomic instability as evidenced by increased H2AX phosphorylation levels and micronuclei accumulation, respectively. The combination of LDHB silencing and radiotherapy increased protein levels of the senescence marker p21, accompanied by increased phosphorylation of Chk2, suggesting persistent DNA damage. Metabolomics analysis revealed that LDHB silencing decreased nucleotide metabolism, particularly purine and pyrimidine biosynthesis, in tumor xenografts. Nucleotide supplementation partially attenuated DNA damage caused by combined LDHB silencing and radiotherapy. These findings suggest that LDHB supports metabolic homeostasis and DNA damage repair in NSCLC, while its silencing enhances the effects of radiotherapy by impairing nucleotide metabolism and promoting persistent DNA damage.
Digital twin-assisted graph matching multi-task object detection method in complex traffic scenarios
Network pharmacology and AI in cancer research uncovering biomarkers and therapeutic targets for RALGDS mutations
Abstract The lack of target therapies is accountable for the higher mortality of various types of cancer. To address this issue, we selected a target mutated Kirsten rat sarcoma virus oncogene homologue, which plays a significant role in various cancers. Our study aims to identify selective biomarkers and develop diagnostic and therapeutic strategies for KRAS-associated genes using artificial intelligence. Initially, Genomic data, cancer epidemiology, proteomics network interactions, and omics enrichment were analyzed. Structured E-pharmacophore model aided in capturing the binding cavity using eraser algorithms and fabricating a new selective lead compound for the KRSA. The selective molecule was abridged inside the binding cavity and stability was validated through 100 ns molecular dynamics simulations. Epidemiological-neural network studies indicated KRAS mutations leads 40 types of cancer, exclusively pancreatic and colorectal cancers, with diploid and missense mutations as primary factors. Pathway analysis highlighted the involvement of the MAPK and RAS signaling pathways in cancer development and proteomics analysis identified RALGDS as a key protein. Protein-based pharmacophore analysis mapped the biologically active features such as donor, acceptor and aromatic ring with the designed ligands. The results of interaction interpretation illustrate that the amino acid Tyr566 formed an H-bond interaction with the amine group of the octyl ring system and 20 amino acids crafted to properly orient the molecule to fit inside the polar cavity of KRAS protein. The MMGBSA score of − 53.33 kcal/mol conformed to the well-configured binding with KRSA and realistic model simulation exposed the π–π, π–cationic and hydrophobic interactions stabilised the molecule inside the KRSA protein throughout 100 ns simulation. The study demonstrates the vitality of AI and network pharmacology to identify potential-target biomarkers for KRAS-associated genes, paving the way for improved cancer diagnostics and therapeutics.
SEPDNet: simple and effective PCB surface defect detection method
Synthesize multiple V/H directional beams for high altitude platform station based on deep-learning algorithm
Abstract This paper investigates the integration of High-Altitude Platform Stations (HAPS) with Deep Learning (DL) models to enhance coverage capabilities. Recognizing the inherent limitations of traditional HAPS coverage, which is typically confined to a circular area, this work proposes a novel approach utilizing a 60-element Concentric Circular Array (CCA) operating at 2.1 GHz. To dynamically generate multiple vertical/horizontal (V/H) directional beams, the system integrates a Deep Neural Network (DNN) with a modified version of the Gravitational Search Algorithm and Particle Swarm Optimization (MGSA-PSO) algorithm. This hybrid approach optimizes the feeding phases of the CCA elements, enabling the system to effectively cover diverse road paths. Furthermore, the study incorporates realistic scenarios by utilizing the Computer Simulation Technology-Microwave Studio Suite (CST) with the Earth Explorer (EE) user interface tool to model real-world road paths, including those traversing challenging terrains such as rugged deserts with mountain chains and forested areas.
Cue combination and individual differences during weight judgements using familiar and newly learned cues
Abstract Human perception is often characterised by efficient combination of sensory signals (cues). In recent studies, people could also improve precision via newly learned cues, with applications to enhance perception in healthy and clinical groups. However, it is unclear whether new cues can enhance manual object interactions. To study how new cues are used for object weight perception, people compared weights of containers. With haptic information plus the familiar visual cue of volume, participants showed precision improvements indicating cue combination. By contrast, a group of participants briefly trained with a novel visual cue to weight (line orientation) did not show improvements expected from combination. We then asked whether prolonged training (12 h) with the novel cue would promote combination, testing for significant precision gains individually in six participants. Half of participants showed combination benefits, but these were not clearly related to training, as some combined cues before training. Using an illusion analogous to the size-weight illusion, we also asked whether the novel cue would become an automatic predictor of weight: two participants were susceptible to the illusion. We conclude that weight perception is susceptible to some enhancement, but subject to training effects and individual differences that are not yet understood.