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HOMA-IR, an independent predictor of advanced liver fibrosis in metabolic-dysfunction associated fatty liver disease: a cross-sectional study in Egyptian patients
Abstract While metabolic dysfunction-associated fatty liver disease (MAFLD) includes the homeostatic model assessment for insulin resistance (HOMA-IR) as one of the criteria to define metabolic dysregulation, the newly proposed metabolic dysfunction-associated steatotic liver disease (MASLD) has removed this criterion. We investigated whether the HOMA-IR can serve as an independent predictive marker for significant fibrosis in subjects with MAFLD. This is a cross-sectional multicenter study. We enrolled a total of 364 patients diagnosed with MAFLD. We conducted a multiple logistic regression analysis to assess the relationship between HOMA-IR and advanced stages of liver fibrosis (F ≥ 2), as assessed by the FIB-4 score and liver stiffness measurement (LSM). Each unit increase in insulin resistance, as measured by HOMA-IR, was associated with a 16% higher likelihood of displaying significant fibrosis, as determined by a non-invasive scoring test, regardless of diabetes or BMI status. HOMA-IR was independently associated with significant fibrosis in non-diabetic (OR: 1.14, 95% CI: 1.07–1.21, P < 0.001) and diabetic (OR: 1.03, 95% CI: 1.00–1.06, P = 0.03) patients. Moreover, significant fibrosis in lean was independently linked to HOMA-IR (OR: 1.06, 95% CI: 1.01–1.12, P = 0.03) and non-lean (OR: 1.04, 95% CI: 1.02–1.07, P < 0.001) patients. Insulin resistance measured by HOMA-IR should be assessed in patients with MAFLD as a key factor of disease progression and incorporated into the disease diagnostic criteria.
Green synthesized zinc oxide nanoparticles with lemongrass essential oil for the development of an effective herbal mouthwash
Deep learning-driven IoT solution for smart tomato farming
Abstract The rising food demand and challenges with respect to the climate have made precision agriculture (PA) vital for sustainable crop production. This study presents an IoT-based smart greenhouse platform tailored for tomato farming, integrating environmental sensing and deep learning. The system employs ESP32-based wireless sensors to collect real-time data on soil moisture, temperature, and humidity; this data is transmitted to a cloud dashboard (ThingsBoard) for remote monitoring. A Raspberry Pi equipped with a Pi Camera and a YOLOv8 model classifies tomato ripeness stages—green, half-ripened, and fully ripened—using real greenhouse images. Model optimizations, including quantization, pruning, and TensorRT, improved inference speed by 35% while maintaining 52.8% classification accuracy during our initial stage of the project. Energy profiling revealed daily consumption of 8.91 Wh for the ESP32 sensors and 78 Wh for the Raspberry Pi. This prototype demonstrates real-time monitoring, high model precision, and practical energy insights, paving the way for multi-node scalability and edge AI enhancements. Future work will explore incorporating Edge TPU for faster on-device processing, LoRa for low-power, long-distance data transfer, and automated control of irrigation and ventilation systems to realize a fully autonomous smart greenhouse.
Comparing visual and acoustic detectability of two coastal cetacean species off Sindhudurg, India, to better inform integrated survey protocol
Identification of SUMOylation modifiers involved in lung adenocarcinoma progression and Osimertinib resistance by integrated bioinformatics analysis
Construction and application of a self-care behavior intervention scheme for chronic heart failure patients based on interaction model of client health behavior
An eleven level single source switched capacitor boost inverter with reduced component stress and limited inrush current
Development and validation of a machine learning-based survival prediction model for Asian glioblastoma patients using the SEER database and Chinese data
Epidemiology and surgical management of the lower lumbar spinal metastases in China: a multicenter retrospective study
Utilization of valorised wood bark waste as an adsorbent for the uptake of Pb(II) ions
Synthesis, crystal structure, DFT calculations, in-vitro and in-silico studies of novel chromone-isoxazoline conjugates as antibacterial and anti-inflammatory agents
Microbiome characteristics associated with lymph node metastasis in laryngeal squamous cell carcinoma
Abstract Lymph node (LN) metastasis is a key prognostic factor in laryngeal squamous cell carcinoma (LSCC). Emerging evidence implicates the role of the microbiome in cancer progression. This study aimed to investigate the microbial features associated with lymph node metastasis in LSCC and their potential for patient stratification. Using 16 S rRNA gene sequencing, we characterized the microbiome of tumor tissues, adjacent normal tissues, lymph nodes, and oral rinses from 108 LSCC patients, including 36 with (LN+) and 72 without (LN-) cervical LN metastasis. Microbial functional potential was predicted using PICRUSt2. Based on repeated stratified 3 cross-validation, random forest models were used to identify metastasis-associated genera. Significant microbial differences were observed between LN + and LN- tumor tissues, with Ralstonia enriched in LN + tumors and Fusobacterium more abundant in LN- cases. All genera detected in lymph nodes were also found in tumor tissues. Functional predictions revealed enrichment of lipid biosynthesis, energy metabolism, and cell wall synthesis pathways in LN + patients, particularly in tumor and oral rinse samples, with low intra-group variability. Classifiers based on tumor, lymph node, and oral microbiota demonstrated the ability to distinguish LN + from LN- patients. The lymph node-derived classifier achieved an accuracy of 84.31% (95% confidence interval [CI]: 81.76% − 86.85%), followed by the tumor-based model (AUC = 84.11%, 95% CI: 81.75% − 86.46%) and oral rinse classifier (AUC = 79.88%, 95% CI: 77.09% − 83.11%). A tumor-specific 17 genera panel showed a discriminative efficacy of 84.11% (95% CI: 81.75% − 86.46%) in tumor tissues. These findings suggest that microbiome alterations may be associated with lymph node metastasis in LSCC. In addition, the oral microbiome showed potential as a non-invasive tool for occult lymph node metastasis detection. However, these results are preliminary and require validation in larger, independent cohorts.
A model based on preoperative nutrition-inflammation score for predicting mucocutaneous separation after enterostomy in colorectal cancer patients
Notch4 is essential for the maintenance of vascular homeostasis in the young adult pituitary posterior lobes
Abstract Notch signaling is critical for vascular development. However, the functions vary significantly depending on organs and the developmental stage. The adult vasculature is less susceptible to Notch signaling inhibition. Vessels in the adult pituitary posterior lobes maintain characteristics of immature vessels, such as sensitivity to VEGF. Here, we show that Notch4 deficiency caused reduction of vascular density, vascular branch length and the number of branching points and the larger radius vessels in the adult pituitary posterior lobes using the automated vascular image analysis method. Upregulation of NR2F2 by Notch4 loss induced arteriovenous identity deficiency. Notch4 has been reported to have dual roles on the regulation of RBP-J-mediated transcription. DAPT is a γ-secretase inhibitor, which inhibits Notch signaling. DAPT-treated adult pituitary posterior lobes showed similar vascular phenotypes to that of Notch4 knockout mice except for the length of affected larger radius vessels, suggesting the importance of RBP-J-mediated transcription in vascular maintenance of the adult pituitary posterior lobes. These data indicate that the maintenance of adult pituitary posterior lobe vasculature requires Notch signaling.
Computational modeling of surface energy effects on linear and nonlinear frequencies in different crystalline orientations of anodic aluminum micro-beams
Simulation of organic light-emitting diode-based inkjet printing using a piezoelectric fluid structural interaction model
Evaluation of microbiological criteria, planktonic communities and trophic state of groundwater resources in Siwa Oasis, Western Desert, Egypt
Abstract Due to their small size and the extreme environmental conditions in hyper-arid regions, wells are highly susceptible to disturbances. Western Desert wells are scarcely studied concerning their ecological status and the environmental factors affecting their aquatic communities. This study aimed to assess groundwater trophic states based on the microbial, phytoplankton and zooplankton composition in three drains and seventeen wells. Principal component analysis, based on physical and chemical parameters, classified the drains and wells into three groups differentiated as; (1) highly mineralized and eutrophic sites with an average conductivity of 12.01 mS/cm. (2) minimally mineralized and eutrophic sites with mean conductivity of 2.14 mS/cm, and (3) an intermediate group with mean conductivity of 7.77 mS/cm. The microbial load exceeded the recommended limits. Fecal coliforms, fecal streptococci and nitrogen-fixing bacteria revealing bacterial contamination in these wells. The studied drains and wells poorly characterized by planktonic species richness. The phytoplankton and zooplankton indices suggested that the ecological status was generally good, with mesotrophic levels. Both phytoplankton and zooplankton communities indicated a minimal disturbance in the drains and wells of Siwa Oasis, while the microbial characterization revealed that these ecosystems are vulnerable and affected by the human activities. This multidisciplinary approach of our study provides new ecological insights into groundwater systems in arid regions like Siwa Oasis. Our work offers baseline ecological data, identifies early warning indicators of water quality degradation, and contributes to a deeper understanding of the interaction between biological communities and groundwater quality.