Is Staged Surgery Always Necessary for Schatzker Type IV-VI Tibial Plateau Fractures? A Comparison Study.

AIMS: This study aims to compare the outcomes of immediate (followed by closed-incision negative-pressure therapy use) versus delayed ORIF in patients with Schatzker type IV-VI TPFs. PATIENTS AND METHODS: A prospective study of patients undergoing ORIF between January 2018 and December 2019 was performed. The inclusion criteria were patients (>18 years) with a closed fracture sent to the emergency room (ER) within 24 h of injury. All the patients underwent preoperative image evaluation. Two senior orthopedic trauma surgeons evaluated the soft tissue condition in the ER by 5P's of the compartment syndrome, judging the timing of the operation of definitive ORIF. Group 1 (n = 16) received delayed ORIF. Group 2 (n = 16) received immediate ORIF and ciNPT use. Patient follow-up occurred after 2 and 6 weeks and 3, 6, and 12 months after surgery. The assessments included the time to definitive fixation, the length of hospital stay, the time to bone union, surgical site complications, and reoperation within 12 months. A universal goniometer was used to measure the postoperative 3 m, 6 m, and 12 m ROM. RESULTS: The patient demographics were similar between the groups (p > 0.05). Group 2 displayed significantly a shorter time to definitive fixation (5.94 ± 2.02 vs. 0.61 ± 0.28, p < 0.0001) and hospital stay (14.90 ± 8/78 vs. 10.30 ± 6.78, p = 0.0016). No significant difference was observed in the time to bone union, surgical site complication incidence, and reoperation rates (p > 0.05). Flexion and flexion-extension knee ROM were demonstrated to be significantly improved in Group 2, 3, 6, and 12 months postoperatively (p < 0.0001). CONCLUSIONS: In this study, early ORIF and ciNPT use resulted in a shorter hospital length of stay, a reduced time to early active motion of the knee, and improved knee ROM. These results suggest that early ORIF with ciNPT for Schatzker type IV-VI TPFs is safe and effective in some patients. However, further research to confirm these findings across larger and more diverse populations is needed.

Fluorescent probes for targeting the Golgi apparatus: design strategies and applications.

The Golgi apparatus is an essential organelle constructed by the stacking of flattened vesicles, that is widely distributed in eukaryotic cells and is dynamically regulated during cell cycles. It is a central station which is responsible for collecting, processing, sorting, transporting, and secreting some important proteins/enzymes from the endoplasmic reticulum to intra- and extra-cellular destinations. Golgi-specific fluorescent probes provide powerful non-invasive tools for the real-time and in situ visualization of the temporal and spatial fluctuations of bioactive species. Over recent years, more and more Golgi-targeting probes have been developed, which are essential for the evaluation of diseases including cancer. However, when compared with systems that target other important organelles (e.g. lysosomes and mitochondria), Golgi-targeting strategies are still in their infancy, therefore it is important to develop more Golgi-targeting probes. This review systematically summarizes the currently reported Golgi-specific fluorescent probes, and highlights the design strategies, mechanisms, and biological uses of these probes, we have structured the review based on the different targeting groups. In addition, we highlight the future challenges and opportunities in the development of Golgi-specific imaging agents and therapeutic systems.

Discovery of Novel Spike Inhibitors against SARS-CoV-2 Infection.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is responsible for the current coronavirus disease pandemic. With the rapid evolution of variant strains, finding effective spike protein inhibitors is a logical and critical priority. Angiotensin-converting enzyme 2 (ACE2) has been identified as the functional receptor for SARS-CoV-2 viral entry, and thus related therapeutic approaches associated with the spike protein-ACE2 interaction show a high degree of feasibility for inhibiting viral infection. Our computer-aided drug design (CADD) method meticulously analyzed more than 260,000 compound records from the United States National Cancer Institute (NCI) database, to identify potential spike inhibitors. The spike protein receptor-binding domain (RBD) was chosen as the target protein for our virtual screening process. In cell-based validation, SARS-CoV-2 pseudovirus carrying a reporter gene was utilized to screen for effective compounds. Ultimately, compounds C2, C8, and C10 demonstrated significant antiviral activity against SARS-CoV-2, with estimated EC50 values of 8.8 µM, 6.7 µM, and 7.6 µM, respectively. Using the above compounds as templates, ten derivatives were generated and robust bioassay results revealed that C8.2 (EC50 = 5.9 µM) exhibited the strongest antiviral efficacy. Compounds C8.2 also displayed inhibitory activity against the Omicron variant, with an EC50 of 9.3 µM. Thus, the CADD method successfully discovered lead compounds binding to the spike protein RBD that are capable of inhibiting viral infection.

EfficientNet-based machine learning architecture for sleep apnea identification in clinical single-lead ECG signal data sets.

OBJECTIVE: Our objective was to create a machine learning architecture capable of identifying obstructive sleep apnea (OSA) patterns in single-lead electrocardiography (ECG) signals, exhibiting exceptional performance when utilized in clinical data sets. METHODS: We conducted our research using a data set consisting of 1656 patients, representing a diverse demographic, from the sleep center of China Medical University Hospital. To detect apnea ECG segments and extract apnea features, we utilized the EfficientNet and some of its layers, respectively. Furthermore, we compared various training and data preprocessing techniques to enhance the model's prediction, such as setting class and sample weights or employing overlapping and regular slicing. Finally, we tested our approach against other literature on the Apnea-ECG database. RESULTS: Our research found that the EfficientNet model achieved the best apnea segment detection using overlapping slicing and sample-weight settings, with an AUC of 0.917 and an accuracy of 0.855. For patient screening with AHI > 30, we combined the trained model with XGBoost, leading to an AUC of 0.975 and an accuracy of 0.928. Additional tests using PhysioNet data showed that our model is comparable in performance to existing models regarding its ability to screen OSA levels. CONCLUSIONS: Our suggested architecture, coupled with training and preprocessing techniques, showed admirable performance with a diverse demographic dataset, bringing us closer to practical implementation in OSA diagnosis. Trial registration The data for this study were collected retrospectively from the China Medical University Hospital in Taiwan with approval from the institutional review board CMUH109-REC3-018.

In silico identification of a novel Cdc2-like kinase 2 (CLK2) inhibitor in triple negative breast cancer.

Dysregulation of RNA splicing processes is intricately linked to tumorigenesis in various cancers, especially breast cancer. Cdc2-like kinase 2 (CLK2), an oncogenic RNA-splicing kinase pivotal in breast cancer, plays a significant role, particularly in the context of triple-negative breast cancer (TNBC), a subtype marked by substantial medical challenges due to its low survival rates. In this study, we employed a structure-based virtual screening (SBVS) method to identify potential CLK2 inhibitors with novel chemical structures for treating TNBC. Compound 670551 emerged as a novel CLK2 inhibitor with a 50% inhibitory concentration (IC50) value of 619.7 nM. Importantly, Compound 670551 exhibited high selectivity for CLK2 over other protein kinases. Functionally, this compound significantly reduced the survival and proliferation of TNBC cells. Results from a cell-based assay demonstrated that this inhibitor led to a decrease in RNA splicing proteins, such as SRSF4 and SRSF6, resulting in cell apoptosis. In summary, we identified a novel CLK2 inhibitor as a promising potential treatment for TNBC therapy.

An ensemble machine learning model generates a focused screening library for the identification of CDK8 inhibitors.

The identification of an effective inhibitor is an important starting step in drug development. Unfortunately, many issues such as the characterization of protein binding sites, the screening library, materials for assays, etc., make drug screening a difficult proposition. As the size of screening libraries increases, more resources will be inefficiently consumed. Thus, new strategies are needed to preprocess and focus a screening library towards a targeted protein. Herein, we report an ensemble machine learning (ML) model to generate a CDK8-focused screening library. The ensemble model consists of six different algorithms optimized for CDK8 inhibitor classification. The models were trained using a CDK8-specific fragment library along with molecules containing CDK8 activity. The optimized ensemble model processed a commercial library containing 1.6 million molecules. This resulted in a CDK8-focused screening library containing 1,672 molecules, a reduction of more than 99.90%. The CDK8-focused library was then subjected to molecular docking, and 25 candidate compounds were selected. Enzymatic assays confirmed six CDK8 inhibitors, with one compound producing an IC50 value of ≤100 nM. Analysis of the ensemble ML model reveals the role of the CDK8 fragment library during training. Structural analysis of molecules reveals the hit compounds to be structurally novel CDK8 inhibitors. Together, the results highlight a pipeline for curating a focused library for a specific protein target, such as CDK8.

Comparative analysis of machine learning algorithms for Alzheimer's disease classification using EEG signals and genetic information.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairments, and behavioral changes. The presence of abnormal beta-amyloid plaques and tau protein tangles in the brain is known to be associated with AD. However, current limitations of imaging technology hinder the direct detection of these substances. Consequently, researchers are exploring alternative approaches, such as indirect assessments involving monitoring brain signals, cognitive decline levels, and blood biomarkers. Recent studies have highlighted the potential of integrating genetic information into these approaches to enhance early detection and diagnosis, offering a more comprehensive understanding of AD pathology beyond the constraints of existing imaging methods. Our study utilized electroencephalography (EEG) signals, genotypes, and polygenic risk scores (PRSs) as features for machine learning models. We compared the performance of gradient boosting (XGB), random forest (RF), and support vector machine (SVM) to determine the optimal model. Statistical analysis revealed significant correlations between EEG signals and clinical manifestations, demonstrating the ability to distinguish the complexity of AD from other diseases by using genetic information. By integrating EEG with genetic data in an SVM model, we achieved exceptional classification performance, with an accuracy of 0.920 and an area under the curve of 0.916. This study presents a novel approach of utilizing real-time EEG data and genetic background information for multimodal machine learning. The experimental results validate the effectiveness of this concept, providing deeper insights into the actual condition of patients with AD and overcoming the limitations associated with single-oriented data.

Deep learning assists in acute leukemia detection and cell classification via flow cytometry using the acute leukemia orientation tube.

This study aimed to evaluate the sensitivity of AI in screening acute leukemia and its capability to classify either physiological or pathological cells. Utilizing an acute leukemia orientation tube (ALOT), one of the protocols of Euroflow, flow cytometry efficiently identifies various forms of acute leukemia. However, the analysis of flow cytometry can be time-consuming work. This retrospective study included 241 patients who underwent flow cytometry examination using ALOT between 2017 and 2022. The collected flow cytometry data were used to train an artificial intelligence using deep learning. The trained AI demonstrated a 94.6% sensitivity in detecting acute myeloid leukemia (AML) patients and a 98.2% sensitivity for B-lymphoblastic leukemia (B-ALL) patients. The sensitivities of physiological cells were at least 80%, with variable performance for pathological cells. In conclusion, the AI, trained with ResNet-50 and EverFlow, shows promising results in identifying patients with AML and B-ALL, as well as classifying physiological cells.

Dehydrative alkynylation of 3-hydroxyisoindolinones with terminal alkynes for the synthesis of 3-alkynylated 3,3-disubstituted isoindolinones.

A brand-new procedure for the synthesis of 3-alkynylated 3,3-disubstituted isoindolinones has been disclosed via a HOTf or Fe(OTf)3-catalyzed dehydrative alkynylation of 3-hydroxyisoindolinones with terminal alkynes. Aryl, alkenyl and alkyl terminal alkynes are suitable to couple with a broad range of 3-hydroxyisoindolinones to afford the desired products in moderate to good yields. This protocol features the use of an inexpensive catalyst, mild reaction conditions, broad substrate scope and easy elaboration of the products.

A novel HDAC8 inhibitor H7E exerts retinoprotective effects against glaucomatous injury via ameliorating aberrant Müller glia activation and oxidative stress.

Glaucoma is considered a neurodegenerative disease characterized by progressive visual field defects that may lead to blindness. Although controlling intraocular pressure (IOP) is the mainstay of glaucoma treatment, some glaucoma patients have unmet needs due to unclear pathogenic mechanisms. Recently, there has been growing evidence that neuroinflammation is a potential target for the development of novel antiglaucoma agents. In this study, we investigated the protective effects and cellular mechanisms of H7E, a novel small molecule inhibits HDAC8, using in vitro and in vivo glaucoma-like models. Importantly, H7E mitigated extracellular MMP-9 activity and MCP-1 levels in glutamate- or S100B-stimulated reactive Müller glia. In addition, H7E inhibited the upregulation of inflammation- and proliferation-related signaling pathways, particularly the ERK and JNK MAPK pathways. Under conditions of oxidative damage, H7E prevents retinal cell death and reduces extracellular glutamate released from stressed Müller glia. In a mouse model of NMDA-induced retinal degeneration, H7E alleviated functional and structural defects within the inner retina as assessed by electroretinography and optical coherence tomography. Our results demonstrated that the newly identified compound H7E protects against glaucoma damage by specifically targeting HDAC8 activity in the retina. This protective effect is attributed to the inhibition of Müller glial activation and the prevention of retinal cell death caused by oxidative stress.

Utility of polygenic scores across diverse diseases in a hospital cohort for predictive modeling.

Polygenic scores estimate genetic susceptibility to diseases. We systematically calculated polygenic scores across 457 phenotypes using genotyping array data from China Medical University Hospital. Logistic regression models assessed polygenic scores' ability to predict disease traits. The polygenic score model with the highest accuracy, based on maximal area under the receiver operating characteristic curve (AUC), is provided on the GeneAnaBase website of the hospital. Our findings indicate 49 phenotypes with AUC greater than 0.6, predominantly linked to endocrine and metabolic diseases. Notably, hyperplasia of the prostate exhibited the highest disease prediction ability (P value = 1.01 × 10-19, AUC = 0.874), highlighting the potential of these polygenic scores in preventive medicine and diagnosis. This study offers a comprehensive evaluation of polygenic scores performance across diverse human traits, identifying promising applications for precision medicine and personalized healthcare, thereby inspiring further research and development in this field.

Predicting ischemic stroke patients' prognosis changes using machine learning in a nationwide stroke registry.

Accurately predicting the prognosis of ischemic stroke patients after discharge is crucial for physicians to plan for long-term health care. Although previous studies have demonstrated that machine learning (ML) shows reasonably accurate stroke outcome predictions with limited datasets, to identify specific clinical features associated with prognosis changes after stroke that could aid physicians and patients in devising improved recovery care plans have been challenging. This study aimed to overcome these gaps by utilizing a large national stroke registry database to assess various prediction models that estimate how patients' prognosis changes over time with associated clinical factors. To properly evaluate the best predictive approaches currently available and avoid prejudice, this study employed three different prognosis prediction models including a statistical logistic regression model, commonly used clinical-based scores, and a latest high-performance ML-based XGBoost model. The study revealed that the XGBoost model outperformed other two traditional models, achieving an AUROC of 0.929 in predicting the prognosis changes of stroke patients followed for 3 months. In addition, the XGBoost model maintained remarkably high precision even when using only selected 20 most relevant clinical features compared to full clinical datasets used in the study. These selected features closely correlated with significant changes in clinical outcomes for stroke patients and showed to be effective for predicting prognosis changes after discharge, allowing physicians to make optimal decisions regarding their patients' recovery.

Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic images.

Automated coronary angiography assessment requires precise vessel segmentation, a task complicated by uneven contrast filling and background noise. Our research introduces an ensemble U-Net model, SE-RegUNet, designed to accurately segment coronary vessels using 100 labeled angiographies from angiographic images. SE-RegUNet incorporates RegNet encoders and squeeze-and-excitation blocks to enhance feature extraction. A dual-phase image preprocessing strategy further improves the model's performance, employing unsharp masking and contrast-limited adaptive histogram equalization. Following fivefold cross-validation and Ranger21 optimization, the SE-RegUNet 4GF model emerged as the most effective, evidenced by performance metrics such as a Dice score of 0.72 and an accuracy of 0.97. Its potential for real-world application is highlighted by its ability to process images at 41.6 frames per second. External validation on the DCA1 dataset demonstrated the model's consistent robustness, achieving a Dice score of 0.76 and an accuracy of 0.97. The SE-RegUNet 4GF model's precision in segmenting blood vessels in coronary angiographies showcases its remarkable efficiency and accuracy. However, further development and clinical testing are necessary before it can be routinely implemented in medical practice.

Lactoferrin as a therapeutic agent for attenuating hepatic stellate cell activation in thioacetamide-induced liver fibrosis.

Liver fibrosis is a chronic liver disease caused by prolonged liver injuries. Excessive accumulation of extracellular matrix replaces the damaged hepatocytes, leading to fibrous scar formation and fibrosis induction. Lactoferrin (LF) is a glycoprotein with a conserved, monomeric signal polypeptide chain, exhibiting diverse physiological functions, including antioxidant, anti-inflammatory, antibacterial, antifungal, antiviral, and antitumoral activities. Previous study has shown LF's protective role against chemically-induced liver fibrosis in rats. However, the mechanisms of LF in liver fibrosis are still unclear. In this study, we investigated LF's mechanisms in thioacetamide (TAA)-induced liver fibrosis in rats and TGF-ß1-treated HSC-T6 cells. Using ultrasonic imaging, H&E, Masson's, and Sirius Red staining, we demonstrated LF's ability to improve liver tissue damage and fibrosis induced by TAA. LF reduced the levels of ALT, AST, and hydroxyproline in TAA-treated liver tissues, while increasing catalase levels. Additionally, LF treatment decreased mRNA expression of inflammatory factors such as Il-1ß and Icam-1, as well as fibrogenic factors including α-Sma, Collagen I, and Ctgf in TAA-treated liver tissues. Furthermore, LF reduced TAA-induced ROS production and cell death in FL83B cells, and decreased α-SMA, Collagen I, and p-Smad2/3 productions in TGF-ß1-treated HSC-T6 cells. Our study highlights LF's ability to ameliorate TAA-induced hepatocyte damage, oxidative stress, and liver fibrosis in rats, potentially through its inhibitory effect on HSC activation. These findings suggest LF's potential as a therapeutic agent for protecting against liver injuries and fibrosis.

Impact of the implementation of the Intelligent Antimicrobial System (iAMS) on clinical outcomes among patients with bacteraemia caused by methicillin-resistant Staphylococcus aureus.

OBJECTIVES: This study aimed to investigate the clinical impact of the Intelligent Antimicrobial System (iAMS) on patients with bacteraemia due to methicillin-resistant (MRSA) and methicillin-susceptible Staphylococcus aureus (MSSA). METHODS: A total of 1008 patients with suspected SA infection were enrolled before and after the implementation of iAMS. Among them, 252 with bacteraemia caused by SA, including 118 in the iAMS and 134 in the non-iAMS groups, were evaluated. RESULTS: The iAMS group exhibited a 5.2% (from 55.2% to 50.0%; P = 0.96) increase in the 1-year survival rate. For patients with MRSA and MSSA compared to the non-iAMS group, the 1-year survival rate increased by 17.6% (from 70.9% to 53.3%; P = 0.41) and 7.0% (from 52.3% to 45.3%; P = 0.57), respectively, both surpassing the rate of the non-iAMS group. The iAMS intervention resulted in a higher long-term survival rate (from 70.9% to 52.3%; P = 0.984) for MRSA patients than for MSSA patients. MRSA patients experienced a reduced length of hospital stay (from 23.3% to 35.6%; P = 0.038), and the 45-day discharge rate increased by 20.4% (P = 0.064). Furthermore, the intervention resulted in a significant 97.3% relative decrease in near miss medication incidents reported by pharmacists (P = 0.013). CONCLUSIONS: Implementation of iAMS platform improved long-term survival rates, discharge rates, hospitalization days, and medical cost (although no significant differences were observed) among patients with MRSA bacteraemia. Additionally, it demonstrated significant benefits in ensuring drug safety.

USP24-i-101 targeting of USP24 activates autophagy to inhibit drug resistance acquired during cancer therapy.

Drug resistance in cancer therapy is the major reason for poor prognosis. Addressing this clinically unmet issue is important and urgent. In this study, we found that targeting USP24 by the specific USP24 inhibitors, USP24-i and its analogues, dramatically activated autophagy in the interphase and mitotic periods of lung cancer cells by inhibiting E2F4 and TRAF6, respectively. USP24 functional knockout, USP24C1695A, or targeting USP24 by USP24-i-101 inhibited drug resistance and activated autophagy in gefitinib-induced drug-resistant mice with doxycycline-induced EGFRL858R lung cancer, but this effect was abolished after inhibition of autophagy, indicating that targeting USP24-mediated induction of autophagy is required for inhibition of drug resistance. Genomic instability and PD-L1 levels were increased in drug resistant lung cancer cells and were inhibited by USP24-i-101 treatment or knockdown of USP24. In addition, inhibition of autophagy by bafilomycin-A1 significantly abolished the effect of USP24-i-101 on maintaining genomic integrity, decreasing PD-L1 and inhibiting drug resistance acquired in chemotherapy or targeted therapy. In summary, an increase in the expression of USP24 in cancer cells is beneficial for the induction of drug resistance and targeting USP24 by USP24-i-101 optimized from USP24-i inhibits drug resistance acquired during cancer therapy by increasing PD-L1 protein degradation and genomic stability in an autophagy induction-dependent manner.

Comparing the Long-Term Cardiovascular Outcomes of Lumbo-Peritoneal, Ventriculo-Peritoneal, or Non-Shunting Treatment after Idiopathic Normal Pressure Hydrocephalus: A Nationwide Retrospective Cohort Study.

Purpose: With advances in medical technology, the average lifespan has increased, leading to a growing significance of idiopathic normal pressure hydrocephalus (iNPH), particularly in the elderly population. Most patients with iNPH have been treated either with ventriculo-peritoneal shunts (VPS) or conservative measures. However, lumbo-peritoneal shunts (LPS) have emerged as an alternative treatment option for iNPH in recent decades, extensive research still lacks comparing outcomes with LPS to those with VPS or non-surgical treatment. The aim of the resent study is to disclose the long-term therapeutic outcomes of LPS, VPS, and non-shunting in patients with iNPH. Methods: We used the National Health Insurance Research Database in Taiwan to assess the long-term outcomes of these treatment options. We enrolled 5,537 iNPH patients who received shunting surgery, of which 5,254 were VPS and 283 were LPS. To compare the difference between each group, matching was conducted by propensity score matching using a 1:1 ratio based on LPS patients. Primary outcomes included death and major adverse cardiovascular events (MACEs) Results: Our findings show that VPS resulted in significantly more MACEs than non-surgical treatment (Odds ratio: 1.83, 95% confidence interval: 1.16-2.90). In addition, both VPS and LPS groups had significantly lower overall mortality rates than non-shunting group. Moreover, LPS had lower overall mortality but similar MACEs rates to VPS. Conclusions: Based on these findings, we propose that the LPS is preferable to the VPS, and surgical treatment should be considered the primary choice over conservative treatment unless contraindications are present.

Aldo-keto reductase family 1 member A1 (AKR1A1) exerts a protective function in alcohol-associated liver disease by reducing 4-HNE accumulation and p53 activation.

BACKGROUND: The development of alcohol-associated liver disease (ALD) is influenced by the amount and duration of alcohol consumption. The resulting liver damage can range from reversible stages, such as steatosis, steatohepatitis and alcoholic fibrosis, to the advanced and irreversible stage of cirrhosis. Aldo-keto reductase family 1 member A1 (AKR1A1) is a member of the aldo-keto reductase family that catalyzes the reduction of aldehyde groups to their corresponding alcohols in an NADPH-dependent manner. AKR1A1 was found to be downregulated in patients diagnosed with ALD. This study aims to interpret the protective effects of AKR1A1 on the development of ALD. METHODS: A 5% alcohol-fed (AF) Akr1a1 knockout (Akr1a1-/-) mouse model and an AML12 hepatocyte model were used. The effects of AKR1A1 on liver function, inflammation, oxidative stress, lipid accumulation, and fibrosis were assessed by ELISA, western blotting, RT‒PCR, and a variety of histological staining methods in AF-induced wild-type (WT) and Akr1a1-/- mice compared to control liquid diet-fed (PF) WT and Akr1a1-/- mice. RESULTS: The results demonstrated that AF-WT mice expressed higher levels of AKR1A1 than WT mice fed a control diet, and they did not show any noticeable liver steatosis. However, AF-Akr1a1-/- mice displayed a lower survival rate and more severe liver injury than AF-WT mice, as demonstrated by increased proinflammatory cytokines, oxidative stress, lipid accumulation, fibrosis, and reduced antioxidant enzymes in their livers. Additionally, elevated levels of 4-HNE and p53 phosphorylation were observed in AF-Akr1a1-/- mice, suggesting that the loss of AKR1A1 led to increased 4-HNE accumulation and subsequent activation of p53, which contributed to the progression of ALD. Furthermore, in AML12 hepatocytes, Akr1a1 knockdown aggravated oxidative stress and steatosis induced by palmitic acid/oleic acid (P/O) inflammation induced by lipopolysaccharide (LPS), and fibrosis induced by TGF-ß1. CONCLUSIONS: This loss-of-function study suggests that AKR1A1 plays a liver-protective role during chronic alcohol consumption by reducing the accumulation of 4-HNE and inhibiting 4-HNE-mediated p53 activation.

ERK/STAT3 activation through CCL17/CCR4 axis-mediated type 2 cytokine-involved signaling pathways in Th2 cells regulates cutaneous drug reactions.

Cutaneous drug reactions (CDRs) are common drug-induced allergic reactions that cause severe consequences in HIV/AIDS patients. The CCL17/CCR4 axis is involved in the immune mechanism of allergic diseases, but its role in the CDRs has not been determined. Here, we aimed to determine the role of the CCL17/CCR4 axis and the underlying mechanism involved in CDRs. In this study, the serum cytokine levels in patients with CDR and healthy controls were measured. The CCL17-triggered allergic profile was screened via a PCR array. Apoptosis of keratinocytes cocultured with CCL17-stimulated Th2 cells was analyzed by flow cytometry. An NVP-induced rat CDR model was established, and dynamic inflammatory factor levels and Th2 cells in the peripheral blood of the rats were measured. Rat skin lesions and signaling pathways in Th2 cells were also analyzed. We showed that the serum CCL17 level was significantly upregulated in CDR patients (P = 0.0077), and the Th2 cell subgroup was also significantly elevated in the CDR rats. The CCL17/CCR4 axis induces Th2 cells to release IL-4 and IL-13 via the ERK/STAT3 pathway. The CCR4 antagonist compound 47 can alleviate rash symptoms resulting from NVP-induced drug eruption, Th2 cell subgroup, IL-4, and IL-13 and inhibit keratinocyte apoptosis. Taken together, these findings indicate that the CCL17/CCR4 axis mediates CDR via the ERK/STAT3 pathway in Th2 cells and type 2 cytokine-induced keratinocyte apoptosis.

Effect of electroacupuncture at Neiguan (PC6) at different time points on myocardial ischemia reperfusion arrhythmia in rats.

OBJECTIVE: To observe the effects of electroacupuncture at Neiguan (PC6) at different time points on reperfusion arrhythmia (RA) after myocardial ischemia and reperfusion in rats, and to investigate the correlation of this protective effect with nerve growth factor (NGF), tyrosine kinase A (TrkA), tyrosine hydroxylase (TH), and norepinephrine (NE). METHODS：A total of 72 Sprague-Dawley male rats were randomly divided into six groups (n = 12 rats/group): normal group (Norm), sham operation group (Sham), ischemia reperfusion group (I/R), pre-ischemic electroacupuncture group (EAI), pre-reperfusion electroacupuncture group (EAII), post-reperfusion electroacupuncture group (EAIII). The myocardial ischemia-reperfusion injury (MIRI) model was induced by occlusion of left anterior descending coronary artery for 20 min followed by reperfusion for 40 min in rats. With no intervention in the Norm group and only threading without ligation in the Sham group. Electroacupuncture pre-treatment at 20 min/d for 7 d before ligation in the EAⅠ group, 20 min of electroacupuncture before reperfusion in the EAII group and 20 min of electroacupuncture after reperfusion in the EAIII group. The electrocardiogram (ECG) of each group was recorded throughout the whole process, and the success of the MIRI model was determined based on the changs of J-point and T-wave in the ECG. The arrhythmia score was used to record premature ventricular contractions, ventricular tachycardia and ventricular fibrillation during the reperfusion period to assess the reperfusion induced arrhythmias. The expression levels of NGF, TrkA, TH protein were measured by Western blot. Moreover, the expression levels of plasma and myocardial NE levels were detected by enzyme linked immunosorbent assay. RESULTS: The differences between Norm group and Sham group were not statistically significant in all indexes. Arrhythmia score, myocardial NGF, TrkA, TH, and NE expression were significantly higher in the I/R group compared with the Sham group. Arrhythmia score, myocardial NGF, TrkA, TH, and NE expression were significantly lower in each EA group compared with the I/R group. CONCLUSION: Electroacupuncture at Neiguan (PC6) at different time points can reduce the incidence and severity of reperfusion arrhythmias in rats. This protective effect is related to electroacupuncture regulating NGF, TrkA, TH, NE expression and reducing sympathetic hyperactivation.