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Comparison of SS-EPI DWI and one-minute TGSE-BLADE DWI for diagnosis of acute infarction
Abstract The efficacy of 2D turbo gradient- and spin-echo diffusion-weighted imaging with non-Cartesian BLADE trajectory (TGSE-BLADE DWI) has not been well studied for acute stroke due to its long acquisition time. This study was performed to compare distortion, artifacts and image quality between single-shot echo planar imaging (SS-EPI) DWI and TGSE-BLADE DWI with acquisition time reduced to 1 min by simultaneous multi-slice (SMS) imaging, and to evaluate the diagnostic performance of TGSE-BLADE DWI for acute infarctions. Total 104 patients with a past history of stroke or symptoms suspicious for acute infarction or who had undergone surgery for brain tumor within two days were prospectively enrolled. Ten lesions in 9 patients were diagnosed as acute or subacute infarction and were detectable only in TGSE-BLADE DWI but not in SS-EPI DWI. Scores for geometric distortion, susceptibility artifacts, overall image quality, lesion conspicuity and diagnostic confidence were lower for SS-EPI DWI than TGSE-BLADE DWI ( p ≤ .001). Distortion was significantly worse in SS-EPI DWI than TGSE-BLADE DWI ( p < .001). SNR of centrum semiovale was significantly higher in SS-EPI DWI than TGSE-BLADE DWI ( p < .001). One-minute TGSE-BLADE DWI showed better image quality than SS-EPI DWI in terms of distortion and artifacts, and higher diagnostic performance for acute infarctions.
Triglyceride-glucose index as a superior marker of insulin resistance for predicting long-term major adverse cardiovascular events following coronary artery bypass grafting in China
The Omicron variant BA.2.86.1 of SARS- CoV-2 demonstrates an altered interaction network and dynamic features to enhance the interaction with the hACE2
Common molecular profile of multiple structurally distinct warfare arsenicals in causing cutaneous chemical vesicant injury
Abstract Skin exposure to arsenicals such as lewisite and phenylarsine oxide leads to severe cutaneous damage. Here, we characterized the molecular pathogenesis of skin injury caused by additionally structurally distinct warfare arsenicals including diphenylchlorarsine (DPCA), diphenylcyanoarsine (DPCYA), diethylchloroarsine (DECA). Cutaneous exposure to DPCA/DPCYA showed marked increase in skin erythema and edema at 6 and 24 h followed by scar formation at 72 h, while DECA did not produce such visual injuries in mouse skin. Clinical observations showed significant increase in Draize score and skin bi-fold thickness in a time-dependent manner. DPCA or DPCYA-exposed skin histology revealed highly inflamed hypodermal areas with infiltrated immune cells at 6 and 24 h, however, epidermal cell necrosis was seen at 72 h. Significantly high number of macrophage infiltration observed at 6 h, whereas peak neutrophil infiltration occurred at 72 h. Number of micro-blisters also increased. However, these effects were nonsignificant following topical DECA exposure. RT-PCR confirmed augmented inflammatory responses in the skin challenged with both DPCA/DPCYA, which accompanied increased ROS and unfolded protein response (UPR) signaling. DECA also increased ROS with changes in UPR. Disrupted tight (Yap/ZO-1) and adherens (Yap/α-Catenin) junction proteins underlie time-dependent apoptotic cell death of epidermal keratinocytes. Thus, these studies identify arsenicals-manifested signaling pathways similar to those of lewisite.
Reduced low-density lipoprotein cholesterol levels are associated with increased risk of gestational diabetes mellitus in Chinese women
Abstract Lipid levels in women with gestational diabetes mellitus (GDM) have been extensively studied, whether low-density lipoprotein cholesterol (LDL-C) is a risk factor for GDM development remains unclear. This study aimed to investigate the correlation between serum LDL-C levels and the risk of GDM. A case–control study was conducted. Glycolipid metabolic and oxidative stress indicators were measured in 696 women with GDM and 1048 healthy pregnant women. Serum LDL-C levels were significantly lower in the GDM group than in the control group (P < 0.001). Subgroup analysis indicated that reduced LDL-C levels were associated with an increased risk of GDM after adjusting for differences in maternal age, pre-pregnancy body mass index (BMI), gestational age at sampling, fasting glucose and insulin levels, and homeostatic model assessment of insulin resistance (odds ratio [OR] 1.372, 95% confidence interval [CI] 1.050–1.794, P = 0.021 for medium-LDL-C subgroup; OR 1.672, 95% CI 1.219–2.294, P = 0.001 for low-LDL-C subgroup). The risk of GDM decreased by 17.6% per 1 mmol/L increase in LDL-C level (OR 0.824, 95% CI 0.733–0.926, P = 0.001). Furthermore, apolipoprotein (apo) A1 and high-density lipoprotein cholesterol (HDL-C) levels were lower, whereas pre-pregnancy and delivery BMI, triglyceride (TG)/HDL-C ratios, and second-trimester fasting glucose levels were higher in the low-LDL-C GDM subgroup than those in the high- and/or medium-LDL-C GDM subgroups (P < 0.05). ApoA1 and HDL-C levels were lower but TG/HDL-C ratios were higher in the medium-LDL-C GDM subgroup than those in the high-LDL-C GDM subgroup (P < 0.05). We concluded that reduced LDL-C levels were associated with an elevated risk of GDM in the study population. Low LDL-C levels correlated with increased BMI and unfavorable TG, HDL, and glucose metabolism.
Identification of UBE2N as a biomarker of Alzheimer’s disease by combining WGCNA with machine learning algorithms
Unraveling patterns and drivers of saurophagy in South American lizards
The effect of broadcast struvite fertilization on element soil content and microbial activity changes in winter wheat cultivation in southwest Poland
Pore compaction and crack evolution of shale rock under high-speed impact loading and different confining pressures
On the automated radiosynthesis of pharmaceutical grade [68Ga]Ga-Pentixafor, its pre-clinical evaluation, clinical application and radiation dosimetry aspects
Feasibility study of texture-based machine learning approach for early detection of neonatal jaundice
Exploration of stagnation-point flow of Reiner–Rivlin fluid originating from the stretched cylinder for the transmission of the energy and matter
A new cut-off value of FRAX tools as an osteoporosis screening tool for Thai geriatric population
Abstract Identifying osteoporosis in geriatric populations is essential for fragility fracture prevention. While dual-energy X-ray absorptiometry (DXA) remains the gold standard for diagnosing osteoporosis, its availability and cost for mass screening are limited. This study aims to determine an effective fracture risk assessment tool (FRAX) cut-off value for screening osteoporosis in the Thai geriatric population. The demographic data, FRAX hip fracture (HF), major osteoporotic fracture (MOF), and Bone mineral density (BMD) of community-dwelling Thai adults aged ≥ 60 years, conducted between March 2021 to August 2022 were analyzed. Osteoporosis is defined as a BMD T-score ≤ − 2.5. The accuracy of FRAX in identifying osteoporosis was assessed using the area under the receiver operating characteristic curve (AUC). Among 2991 participants (average age 69.2 ± 6.5 years), the discriminative ability was acceptable for both FRAX hip fracture (HF) (AUC = 0.75) and major osteoporotic fracture (MOF) (AUC = 0.72). A cut-off value of 1.5 for FRAX HF and 4.5 for FRAX MOF demonstrated excellent sensitivity (90.4%) and a high negative predictive value (89.7%) in osteoporosis detection. This study identifies FRAX cut-off values that can effectively screen for high-risk osteoporosis in the Thai geriatric population and suggests that FRAX could be a valuable tool for initial osteoporosis screening in Thai seniors.
Improving Malaria diagnosis through interpretable customized CNNs architectures
Abstract Malaria, which is spread via female Anopheles mosquitoes and is brought on by the Plasmodium parasite, persists as a serious illness, especially in areas with a high mosquito density. Traditional detection techniques, like examining blood samples with a microscope, tend to be labor-intensive, unreliable and necessitate specialized individuals. To address these challenges, we employed several customized convolutional neural networks (CNNs), including Parallel convolutional neural network (PCNN), Soft Attention Parallel Convolutional Neural Networks (SPCNN), and Soft Attention after Functional Block Parallel Convolutional Neural Networks (SFPCNN), to improve the effectiveness of malaria diagnosis. Among these, the SPCNN emerged as the most successful model, outperforming all other models in evaluation metrics. The SPCNN achieved a precision of 99.38 $$\pm$$ 0.21%, recall of 99.37 $$\pm$$ 0.21%, F1 score of 99.37 $$\pm$$ 0.21%, accuracy of 99.37 ± 0.30%, and an area under the receiver operating characteristic curve (AUC) of 99.95 ± 0.01%, demonstrating its robustness in detecting malaria parasites. Furthermore, we employed various transfer learning (TL) algorithms, including VGG16, ResNet152, MobileNetV3Small, EfficientNetB6, EfficientNetB7, DenseNet201, Vision Transformer (ViT), Data-efficient Image Transformer (DeiT), ImageIntern, and Swin Transformer (versions v1 and v2). The proposed SPCNN model surpassed all these TL methods in every evaluation measure. The SPCNN model, with 2.207 million parameters and a size of 26 MB, is more complex than PCNN but simpler than SFPCNN. Despite this, SPCNN exhibited the fastest testing times (0.00252 s), making it more computationally efficient than both PCNN and SFPCNN. We assessed model interpretability using feature activation maps, Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP) visualizations for all three architectures, illustrating why SPCNN outperformed the others. The findings from our experiments show a significant improvement in malaria parasite diagnosis. The proposed approach outperforms traditional manual microscopy in terms of both accuracy and speed. This study highlights the importance of utilizing cutting-edge technologies to develop robust and effective diagnostic tools for malaria prevention.
Multiple respiratory assessment and thresholds for noninvasive ventilation in adult patients with spinal muscular atrophy
Clinical and pathological risk factors for postencephalitic epilepsy after herpes simplex virus-1 encephalitis in children
Evaluating the impact of self myofascial release and traditional recovery strategies on volleyball athletes using thermal imaging and biochemical assessments
Impacts of land use change on carbon storage in the Guangxi Beibu Gulf Economic Zone based on the PLUS-InVEST model
Measurements of face mask’s capability to block ionizing radiation
Abstract Experts suggest wearing a face mask during a radiation emergency if it is impossible to get inside immediately and high-level protective respirators are unavailable. This study quantitatively investigated seven face mask materials’ ability to block radioactive alpha and beta radiation. Rayon fiber, pure cotton, paper fiber, polyester fiber, nonwoven fiber, advanced nonwoven fiber, and N95 were examined. The results suggest that the abovementioned mask materials can block more than 90% of alpha particles. Rayon fiber, polyester fiber, and N95 can block almost all radioactive alpha particles. On the other hand, the measurements suggest that all tested materials could not effectively block beta particles. Polyester fiber and N95 block more than 10% of beta particles, which outperform other mask materials. In addition, the results imply that the electret fiber might help block beta particles. This study suggests that wearing a relatively thick polyester or N95 mask may be a better choice than wearing a thin nonwoven mask to prevent inhaling alpha and beta particles during a radiation emergency.