Mitofusin 1 and 2 overexpression reduces AßO-mediated ER stress and apoptosis in N2a APPswe cells.

Alzheimer's disease (AD) is the most common neurodegenerative disorder, and amyloid beta oligomers (AßO), which are pathological markers of AD, are known to be highly toxic. AßO increase mitochondrial dysfunction, which is accompanied by a decrease in mitochondrial fusion. Although mitofusin (Mfn) 1 and Mfn2 are mitochondrial fusion proteins, Mfn2 is known to regulate endoplasmic reticulum (ER) function, as it is located in the ER. Several studies have shown that AßO exacerbates ER stress, however, the exact mechanism requires further elucidation. In this study, we used mouse neuroblastoma cells stably overexpressing the amyloid precursor protein (APP) with the Swedish mutation (N2a APPswe cells) to investigate the role of Mfn in ER stress. Our results revealed that  amyloid beta (Aß) caused cellular toxicity in N2a APPswe cells, upregulated ER stress-related proteins, and promoted ER expansion. The AßO-mediated ER stress was reduced when Mfn1 and Mfn2 were overexpressed. Moreover, Mfn1 and Mfn2 overexpressed resulted in reduced apoptosis of N2a APPswe cells. In conclusion, our results indicate that both Mfn1 and Mfn2 reduce ER stress and apoptosis. Our data provide a foundation for future studies on the roles of Mfn1 and Mfn2 in the molecular mechanisms underlying AßO-mediated ER stress and the pathogenesis of AD.

3D mobile regression vision transformer for collateral imaging in acute ischemic stroke.

PURPOSE: The accurate and timely assessment of the collateral perfusion status is crucial in the diagnosis and treatment of patients with acute ischemic stroke. Previous works have shown that collateral imaging, derived from CT angiography, MR perfusion, and MR angiography, aids in evaluating the collateral status. However, such methods are time-consuming and/or sub-optimal due to the nature of manual processing and heuristics. Recently, deep learning approaches have shown to be promising for generating collateral imaging. These, however, suffer from the computational complexity and cost. METHODS: In this study, we propose a mobile, lightweight deep regression neural network for collateral imaging in acute ischemic stroke, leveraging dynamic susceptibility contrast MR perfusion (DSC-MRP). Built based upon lightweight convolution and Transformer architectures, the proposed model manages the balance between the model complexity and performance. RESULTS: We evaluated the performance of the proposed model in generating the five-phase collateral maps, including arterial, capillary, early venous, late venous, and delayed phases, using DSC-MRP from 952 patients. In comparison with various deep learning models, the proposed method was superior to the competitors with similar complexity and was comparable to the competitors of high complexity. CONCLUSION: The results suggest that the proposed model is able to facilitate rapid and precise assessment of the collateral status of patients with acute ischemic stroke, leading to improved patient care and outcome.

Combination Therapy of Radiation and Hyperthermia, Focusing on the Synergistic Anti-Cancer Effects and Research Trends.

Despite significant therapeutic advances, the toxicity of conventional therapies remains a major obstacle to their application. Radiation therapy (RT) is an important component of cancer treatment. Therapeutic hyperthermia (HT) can be defined as the local heating of a tumor to 40-44 °C. Both RT and HT have the advantage of being able to induce and regulate oxidative stress. Here, we discuss the effects and mechanisms of RT and HT based on experimental research investigations and summarize the results by separating them into three phases. Phase (1): RT + HT is effective and does not provide clear mechanisms; phase (2): RT + HT induces apoptosis via oxygenation, DNA damage, and cell cycle arrest; phase (3): RT + HT improves immunological responses and activates immune cells. Overall, RT + HT is an effective cancer modality complementary to conventional therapy and stimulates the immune response, which has the potential to improve cancer treatments, including immunotherapy, in the future.

Clinical evaluation of a deep-learning model for automatic scoring of the Alberta stroke program early CT score on non-contrast CT.

BACKGROUND: Automated measurement of the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) can support clinical decision making. Based on a deep learning algorithm, we developed an automated ASPECTS scoring system (Heuron ASPECTS) and validated its performance in a prespecified clinical trial. METHODS: For model training, we used non-contrast computed tomography images of 487 patients with acute ischemic stroke (AIS). For the clinical trial, 326 patients (87 with AIS, 56 with other acute brain diseases, and 183 with no brain disease) were enrolled. The results of Heuron ASPECTS were compared with the consensus generated by two stroke experts using the Bland-Altman agreement. A mean difference of less than 0.35 and a maximum allowed difference of less than 3.8 were considered the primary outcome target. The sensitivity and specificity of the model for the 10 regions of interest and dichotomized ASPECTS were calculated. RESULTS: The Bland-Altman agreement had a mean difference of 0.03 [95% confidence interval (CI): -0.08 to 0.14], and the upper and lower limits of agreement were 2.80 [95% CI: 2.62 to 2.99] and -2.74 [95% CI: -2.92 to -2.55], respectively. For ASPECTS calculation, sensitivity and specificity to detect the early ischemic change for 10 ASPECTS regions were 62.78% [95% CI: 58.50 to 67.07] and 96.63% [95% CI: 96.18 to 97.09], respectively. Furthermore, in a dichotomized analysis (ASPECTS >4 vs. ≤4), the sensitivity and specificity were 94.01% [95% CI: 91.26 to 96.77] and 61.90% [95% CI: 47.22 to 76.59], respectively. CONCLUSIONS: The current trial results show that Heuron ASPECTS reliably measures the ASPECTS for use in clinical practice.

Retraction Note to: Analysis of loxoprofen in tablets, patches, and equine urine as tert-butyldimethylsilyl derivative by gas chromatography-mass spectrometry.

The authors have retracted this article [1] because after publication they became aware that the equine urine samples analysed for loxoprofen in this study were in fact equine plasma samples. Therefore the results and conclusions of this article cannot be relied upon. All authors agree to this retraction.

Analysis of loxoprofen in tablets, patches, and equine urine as tert-butyldimethylsilyl derivative by gas chromatography-mass spectrometry.

Loxoprofen is a non-steroidal anti-inflammatory drug of the 2-arylpropionic acid type, which has used to treat musculoskeletal disorders in the horse racing industry. However, it has also used illicitly to mask clinical signs of inflammation and pain in racehorses. Thus, its accurate analysis has become an important issue in horse doping laboratories. In this study, an analytical method of loxoprofen was developed as tert-butyldimethylsilyl (TBDMS) derivative by gas chromatography-mass spectrometry (GC-MS). Characteristic fragment ions of [M-15], [M-57], and [M-139] permitted the accurate and selective detection of loxoprofen. Under optimal conditions, this method showed good linearity (r ≥ 0.999) in the range of 10-500 ng/mL, repeatability (% relative standard deviation = 5.6-8.5), and accuracy (% relative error = - 0.3-0.9) with a detection limit of 1.0 ng. When applied to the analysis of loxoprofen in tablet and patch products, loxoprofen was positively identified as TBDMS derivative by GC-MS. The present method provided rapid and accurate determination of loxoprofen in patch and tablet products. Levels of loxoprofen were highest in equine urine at 0.5 and 1 h after oral administration with single dose (3 mg/kg) to three horses, and then rapidly reduced to below the lower limit of quantification at 24 h. Therefore, the present method will be useful for the pharmacokinetic study and doping tests for loxoprofen and other similar acidic drugs in horses.