All cells are equipped with intricate signaling networks to meet the energy demands and respond to the nutrient availability in the body. AMP-activated protein kinase (AMPK) is among the most potent regulators of cellular energy balance. Under ATP -deprived conditions, AMPK phosphorylates substrates and affects various biological processes, such as lipid/glucose metabolism and protein synthesis. These actions further affect the cell growth, death, and functions, altering the cellular outcomes in energy-restricted environments. AMPK plays vital roles in maintaining good health. AMPK dysfunction is observed in various chronic diseases, making it a promising target for preventing and alleviating such diseases. Herein, we highlight the different AMPK functions, especially in allergy, aging, and cancer, to facilitate the development of new therapeutic approaches in the future.

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. Materials and Methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which weredetermined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The timerequired to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually,compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Conclusion Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculatethese measurements, the efficiency of diagnosis and treatment may be improved.

Glutathione (GSH), the main cellular antioxidant, dynamically influences tumor growth, metastasis, and resistance to therapy in the tumor microenvironment (TME), which comprises cancer cells, immune cells, stromal cells, and non-cellular components, including the extracellular matrix, metabolites, hypoxia, and acidity. Cancer stem cells (CSCs) and T cells are minor but significant cell subsets of the TME. GSH dynamics influences the fate of CSCs and T cells.Here, we explored GSH dynamics in CSCs and T cells within the TME, as well as therapeutic approaches that could target these dynamics.

Objective: Serotonin concentration is associated with suicide in patients with major depressive disorder. Loudness dependence of auditory-evoked potentials (LDAEPs), a representative neurophysiological indicator, is related to serotonin activity. Therefore, this study aimed to investigate the relationship between LDAEPs and suicidal ideation, suicide attempts, and the severity of depression. Methods: We evaluated the scalp N1, P2, and N1/P2 LDAEPs along with standardized low-resolution brain electromagnetic tomography (sLORETA)-localized N1, P2, and N1/P2 LDAEPs of 221 patients with major depressive disorder.The demographic and clinical data of the patients, including data on suicidal ideation and previous suicide attempts, were obtained from clinical interviews and medical records. The severity of depression was assessed using the Beck Depression Inventory and Hamilton Depression Rating Scale, whereas suicidal ideation was evaluated using the Beck Scale for Suicidal Ideation (BSS). Results: The total BSS score was associated with low N1/P2 LDAEP (p = 0.045), whereas P2 sLORETA-LDAEP was associated with lower previous suicide attempts (p = 0.043). In addition, suicide attempt was correlated with an elevated P2 left sLORETA-LDAEP (coefficient = 4.638, p = 0.038). Conclusion These findings suggest that suicidal ideation is associated with decreased LDAEP, whereas suicide attempt is associated with increased LDAEP.

Apolipoprotein E (apoE) plays a role in various physiological functions including lipid transport, synaptic plasticity, and immune modulation.Epidemiological studies suggest that the apoE4 allele increases the risk of post-traumatic sequelae. This study was performed to investigate regionspecific effects of the apoE4 isoform on post-traumatic neurodegeneration. Two focal brain injuries were introduced separately in the motor cortex and hippocampus of apoE4 knock-in, apoE3 knock-in, apoE knockout, and wild-type (WT) mice. Western blotting showed that the expression levels of pre-synaptic and post-synaptic markers at the recovery stage were lower in the hippocampal injury core in apoE4 mice, compared with apoE3 and WT mice. Fast glial activation (determined by immunohistochemistry with glial fibrillary acidic protein, ionized calcium binding adaptor molecule 1, and cluster of differentiation 45 antibodies) was characteristic of apoE4 mice with hippocampal injury penumbra. apoE4-specific changes were not observed after cortical injury. The intensity of microglial activation in the hippocampus was inversely correlated with the volume of injury reduction on sequential magnetic resonance imaging examinations, when validated using matched samples. These findings indicate that the effects of the interaction between apoE4 and focal brain damage are specific to the hippocampus. Manipulation of inflammatory cell responses could be beneficial for reducing post-traumatic hippocampal neurodegeneration in apoE4 carriers.

Apolipoprotein E (apoE) plays a role in various physiological functions including lipid transport, synaptic plasticity, and immune modulation.Epidemiological studies suggest that the apoE4 allele increases the risk of post-traumatic sequelae. This study was performed to investigate regionspecific effects of the apoE4 isoform on post-traumatic neurodegeneration. Two focal brain injuries were introduced separately in the motor cortex and hippocampus of apoE4 knock-in, apoE3 knock-in, apoE knockout, and wild-type (WT) mice. Western blotting showed that the expression levels of pre-synaptic and post-synaptic markers at the recovery stage were lower in the hippocampal injury core in apoE4 mice, compared with apoE3 and WT mice. Fast glial activation (determined by immunohistochemistry with glial fibrillary acidic protein, ionized calcium binding adaptor molecule 1, and cluster of differentiation 45 antibodies) was characteristic of apoE4 mice with hippocampal injury penumbra. apoE4-specific changes were not observed after cortical injury. The intensity of microglial activation in the hippocampus was inversely correlated with the volume of injury reduction on sequential magnetic resonance imaging examinations, when validated using matched samples. These findings indicate that the effects of the interaction between apoE4 and focal brain damage are specific to the hippocampus. Manipulation of inflammatory cell responses could be beneficial for reducing post-traumatic hippocampal neurodegeneration in apoE4 carriers.

Background: The predictors of poor prognosis in patients with coronavirus disease 2019 (COVID-19) using computed tomography (CT) have not been investigated in a large cohort.Therefore, the purpose of this study was to investigate the adverse initial CT features to predict poor prognosis in COVID-19. Methods: From February to April 2020, 281 COVID-19 patients who underwent CT at the time of admission were included. We divided the patients into the severe and non-severe disease groups. The severe group included patients with severe pneumonia or critical events.Intensive care unit admission or death were the critical events in this study. We compared the clinical and CT findings between the severe and non-severe groups and investigated the prognostic factors and critical events of the severe group using the regression analysis. Results: Among the 281 patients, 36 (12.8%) patients were in the severe group and 245 (87.2%) patients were in the non-severe group. Critical events occurred in 10 patients (3.6%).In the severe group, patients showed significantly more pneumonia with consolidation, crazy-paving appearance, pleural effusion, and higher CT scores than those in the non-severe group (all, p < 0.05). In the multivariate regression, pleural effusion (odds ratio [OR], 8.96;95% confidence interval [CI], 1.81–44.42; p = 0.007), CT score > 5 (OR, 3.70; 95% CI, 1.44– 9.53; p = 0.007), old age (> 77 years, OR, 9.96; 95% CI, 3.78–26.28; p < 0.001), and elevated C-reactive protein (OR, 4.15; 95% CI, 1.62–10.6; p = 0.003) were significant prognostic factors of severe disease. CT score > 5 (OR, 7.29; 95% CI, 1.37–38.68; p = 0.020), pleural effusion (OR, 5.67; 95% CI, 1.04–30.8; p = 0.045) and old age (OR, 8.6; 95% CI, 1.80–41.0; p = 0.007) were also significant predictors of critical events. Conclusion Pleural effusion and the extent of pneumonia on initial CT scans are associated with poor prognosis in patients with COVID-19.

In recent years, artificial intelligence (AI) technologies have greatly advanced and become a reality in many areas of our daily lives. In the health care field, numerous efforts are being made to implement the AI technology for practical medical treatments. With the rapid developments in machine learning algorithms and improvements in hardware performances, the AI technology is expected to play an important role in effectively analyzing and utilizing extensive amounts of health and medical data. However, the AI technology has various unique characteristics that are different from the existing health care technologies. Subsequently, there are a number of areas that need to be supplemented within the current health care system for the AI to be utilized more effectively and frequently in health care. In addition, the number of medical practitioners and public that accept AI in the health care is still low;moreover, there are various concerns regarding the safety and reliability of AI technologyimplementations. Therefore, this paper aims to introduce the current research and application status of AI technology in health care and discuss the issues that need to be resolved.

This study aimed to determine whether the curing times of Xtra Power and High Power modes of high-power light emitting diode (LED) curing light are sufficient for polymerization of resin sealants. The specimens were prepared and their microhardness values were measured and compared with those of specimens polymerized under conventional LED curing light.The filled sealant polymerized for 8 seconds in the High Power mode and for 3 seconds in the Xtra Power mode showed significantly lower microhardness than the control specimen (p = 0.000). The unfilled sealant polymerized for 8, 12 seconds in the High Power mode and for 6 seconds in the Xtra Power mode showed significantly lower microhardness than the control specimen (p = 0.000).The results of this study suggest that the short curing time with the Xtra Power and High Power modes of highpower LED curing light are not sufficient for adequate polymerization of sealants under specific conditions, taking into account the curing times and the type of sealant.
Polymerization ; Polymers