Noncanonical PDK4 action alters mitochondrial dynamics to affect the cellular respiratory status.

Dynamic regulation of mitochondrial morphology provides cells with the flexibility required to adapt and respond to electron transport chain (ETC) toxins and mitochondrial DNA-linked disease mutations, yet the mechanisms underpinning the regulation of mitochondrial dynamics machinery by these stimuli is poorly understood. Here, we show that pyruvate dehydrogenase kinase 4 (PDK4) is genetically required for cells to undergo rapid mitochondrial fragmentation when challenged with ETC toxins. Moreover, PDK4 overexpression was sufficient to promote mitochondrial fission even in the absence of mitochondrial stress. Importantly, we observed that the PDK4-mediated regulation of mitochondrial fission was independent of its canonical function, i.e., inhibitory phosphorylation of the pyruvate dehydrogenase complex (PDC). Phosphoproteomic screen for PDK4 substrates, followed by nonphosphorylatable and phosphomimetic mutations of the PDK4 site revealed cytoplasmic GTPase, Septin 2 (SEPT2), as the key effector molecule that acts as a receptor for DRP1 in the outer mitochondrial membrane to promote mitochondrial fission. Conversely, inhibition of the PDK4-SEPT2 axis could restore the balance in mitochondrial dynamics and reinvigorates cellular respiration in mitochondrial fusion factor, mitofusin 2-deficient cells. Furthermore, PDK4-mediated mitochondrial reshaping limits mitochondrial bioenergetics and supports cancer cell growth. Our results identify the PDK4-SEPT2-DRP1 axis as a regulator of mitochondrial function at the interface between cellular bioenergetics and mitochondrial dynamics.

Prohibitin1 maintains mitochondrial quality in isoproterenol-induced cardiac hypertrophy in H9C2 cells.

BACKGROUND INFORMATION: Various types of stress initially induce a state of cardiac hypertrophy (CH) in the heart. But, persistent escalation of cardiac stress leads to progression from an adaptive physiological to a maladaptive pathological state. So, elucidating molecular mechanisms that can attenuate CH is imperative in developing cardiac therapies. Previously, we showed that Prohibitin1 (PHB1) has a protective role in CH-induced oxidative stress. Nevertheless, it is unclear how PHB1, a mitochondrial protein, has a protective role in CH. Therefore, we hypothesized that PHB1 maintains mitochondrial quality in CH. To test this hypothesis, we used Isoproterenol (ISO) to induce CH in H9C2 cells overexpressing PHB1 and elucidated mitochondrial quality control pathways. RESULTS: We found that overexpressing PHB1 attenuates ISO-induced CH and restores mitochondrial morphology in H9C2 cells. In addition, PHB1 blocks the pro-hypertrophic IGF1R/AKT pathway and restores the mitochondrial membrane polarization in ISO-treated cells. We observed that overexpressing PHB1 promotes mitochondrial biogenesis, improves mitochondrial respiratory capacity, and triggers mitophagy. CONCLUSION: We conclude that PHB1 maintains mitochondrial quality in ISO-induced CH in H9C2 cells. SIGNIFICANCE: Based on our results, we suggest that small molecules that induce PHB1 in cardiac cells may prove beneficial in developing cardiac therapies.

Nanoplastics Penetrate Human Bronchial Smooth Muscle and Small Airway Epithelial Cells and Affect Mitochondrial Metabolism.

Micro- and nanoplastic particles, including common forms like polyethylene and polystyrene, have been identified as relevant pollutants, potentially causing health problems in living organisms. The mechanisms at the cellular level largely remain to be elucidated. This study aims to visualize nanoplastics in bronchial smooth muscle (BSMC) and small airway epithelial cells (SAEC), and to assess the impact on mitochondrial metabolism. Healthy and asthmatic human BSMC and SAEC in vitro cultures were stimulated with polystyrene nanoplastics (PS-NPs) of 25 or 50 nm size, for 1 or 24 h. Live cell, label-free imaging by holotomography microscopy and mitochondrial respiration and glycolysis assessment were performed. Furthermore, 25 and 50 nm NPs were shown to penetrate SAEC, along with healthy and diseased BSMC, and they impaired bioenergetics and induce mitochondrial dysfunction compared to cells not treated with NPs, including changes in oxygen consumption rate and extracellular acidification rate. NPs pose a serious threat to human health by penetrating airway tissues and cells, and affecting both oxidative and glycolytic metabolism.

High-throughput method for Oxygen Consumption Rate measurement (OCR) in plant mitochondria.

BACKGROUND: Conventional methods to measure oxygen consumption, such as Clark-type electrodes, have limitations such as requiring a large amount of starting material. Moreover, commercially available kits for high-throughput methods are usually optimized for animal cells and mitochondria. Here, we present a novel method to measure the oxygen consumption rate using a high-throughput assay in isolated mitochondria of European beech seeds. To perform the measurements, we adapted the Agilent Seahorse XF Cell Mito Stress Test Kit protocol for measurements on plant mitochondria. RESULTS: The optimized protocol for OCR measurement of mitochondria isolated from beech seeds allowed the observation of storage period-dependent gradual decreases in non-phosphorylating respiration, phosphorylating respiration and maximal FCCP-stimulated respiration. The longer the seeds were stored, the greater the impairment of respiratory function. CONCLUSIONS: Thanks to this method it is possible to minimize the amount of plant material and conduct research to obtain information on the respiratory condition and activity of plant mitochondria, including the efficiency of oxidative phosphorylation and the maximum oxidative capacity of the respiratory chain. We demonstrated that the improved protocol is suitable for study of plant material.

Mitochondrial bioenergetic changes in systemic lupus erythematosus immune cell subsets: Contributions to pathogenesis and clinical applications.

The association of dysregulated metabolism in systemic lupus erythematosus (SLE) pathogenesis has prompted investigations into metabolic rewiring and the involvement of mitochondrial metabolism as a driver of disease through NLRP3 inflammasome activation, disruption of mitochondrial DNA maintenance, and pro-inflammatory cytokine release. The use of Agilent Seahorse Technology to gain functional in situ metabolic insights of selected cell types from SLE patients has identified key parameters that are dysregulated during disease. Mitochondrial functional assessments specifically can detect dysfunction through oxygen consumption rate (OCR), spare respiratory capacity, and maximal respiration measurements, which, when coupled with disease activity scores could show potential as markers of disease activity. CD4+ and CD8 + T cells have been assessed in this way and show that oxygen consumption rate, spare respiratory capacity, and maximal respiration are blunted in CD8 + T cells, with results not being as clear cut in CD4 + T cells. Additionally, glutamine, processed by mitochondrial substrate level phosphorylation is emerging as a key role player in the expansion and differentiation of Th1, Th17, ϒδ T cells, and plasmablasts. The role that circulating leukocytes play in acting as bioenergetic biomarkers of diseases such as diabetes suggests that this may also be a tool to detect preclinical SLE. Therefore, the metabolic characterization of immune cell subsets and the collection of metabolic data during interventions is also essential. The delineation of the metabolic tuning of immune cells in this way could lead to novel strategies in treating metabolically demanding processes characteristic of autoimmune diseases such as SLE.

Attenuation of PM2.5-induced alveolar epithelial cells and lung injury through regulation of mitochondrial fission and fusion.

BACKGROUND: Exposure to particulate matter (PM) with an aerodynamic diameter less than 2.5 µm (PM2.5) is a risk factor for developing pulmonary diseases and the worsening of ongoing disease. Mitochondrial fission and fusion are essential processes underlying mitochondrial homeostasis in health and disease. We examined the role of mitochondrial fission and fusion in PM2.5-induced alveolar epithelial cell damage and lung injury. Key genes in these processes include dystrophin-related protein 1 (DRP1) and optic atrophy 1 (OPA1) respectively. METHODS: Alveolar epithelial (A549) cells were treated with PM2.5 (32 µg/ml) in the presence and absence of Mdivi-1 (10µM, a DRP1 inhibitor) or BGP-15 (10µM, an OPA1 activator). Results were validated using DRP1-knockdown (KD) and OPA1-overexpression (OE). Mice were injected intraperitoneally with Mdivi-1 (20 mg/kg), BGP-15 (20 mg/kg) or distilled water (control) one hour before intranasal instillation of PM2.5 (7.8 mg/kg) or distilled water for two consecutive days. RESULTS: PM2.5 exposure of A549 cells caused oxidative stress, enhanced inflammation, necroptosis, mitophagy and mitochondrial dysfunction indicated by abnormal mitochondrial morphology, decreased mitochondrial membrane potential (ΔΨm), reduced mitochondrial respiration and disrupted mitochondrial fission and fusion. Regulating mitochondrial fission and fusion pharmacologically using Mdivi-1 and BGP-15 and genetically using DRP1-KD and OPA1-OE prevented PM2.5-induced celluar damage in A549 cells. Mdivi-1 and BGP-15 attenuated PM2.5-induced acute lung injury in mice. CONCLUSION: Increased mitochondrial fission and decreased mitochondrial fusion may underlie PM2.5-induced alveolar epithelial cell damage in vitro and lung injury in vivo.

Recognition of Arabic Air-Written Letters: Machine Learning, Convolutional Neural Networks, and Optical Character Recognition (OCR) Techniques.

Air writing is one of the essential fields that the world is turning to, which can benefit from the world of the metaverse, as well as the ease of communication between humans and machines. The research literature on air writing and its applications shows significant work in English and Chinese, while little research is conducted in other languages, such as Arabic. To fill this gap, we propose a hybrid model that combines feature extraction with deep learning models and then uses machine learning (ML) and optical character recognition (OCR) methods and applies grid and random search optimization algorithms to obtain the best model parameters and outcomes. Several machine learning methods (e.g., neural networks (NNs), random forest (RF), K-nearest neighbours (KNN), and support vector machine (SVM)) are applied to deep features extracted from deep convolutional neural networks (CNNs), such as VGG16, VGG19, and SqueezeNet. Our study uses the AHAWP dataset, which consists of diverse writing styles and hand sign variations, to train and evaluate the models. Prepossessing schemes are applied to improve data quality by reducing bias. Furthermore, OCR character (OCR) methods are integrated into our model to isolate individual letters from continuous air-written gestures and improve recognition results. The results of this study showed that the proposed model achieved the best accuracy of 88.8% using NN with VGG16.

A Smart Real-Time Parking Control and Monitoring System.

Smart parking is an artificial intelligence-based solution to solve the challenges of inefficient utilization of parking slots, wasting time, congestion producing high CO2 emission levels, inflexible payment methods, and protecting parked vehicles from theft and vandalism. Nothing is worse than parking congestion caused by drivers looking for open spaces. This is common in large parking lots, underground garages, and multi-story car parks, where visibility is limited and signage can be confusing or difficult to read, so drivers have no idea where available parking spaces are. In this paper, a smart real-time parking management system has been introduced. The developed system can deal with the aforementioned challenges by providing dynamic allocation for parking slots while taking into consideration the overall parking situation, providing a mechanism for booking a specific parking slot by using our Artificial Intelligence (AI)-based application, and providing a mechanism to ensure that the car is parked in its correct place. For the sake of providing cost flexibility, we have provided two technical solutions with cost varying. The first solution is developed based on a motion sensor and the second solution is based on a range-finder sensor. A plate detection and recognition system has been used to detect the vehicle's license plate by capturing the image using an IoT device. The system will recognize the extracted English alphabet and Hindu-Arabic Numerals. The proposed solution was built and field-tested to prove the applicability of the proposed smart parking solution. We have measured and analyzed keen data such as vehicle plate detection accuracy, vehicle plate recognition accuracy, transmission delay time, and processing delay time.

Dissecting Rubella Placental Infection in an In Vitro Trophoblast Model.

Vertical transmission of rubella virus (RuV) occurs at a high rate during the first trimester of pregnancy. The modes of vertical transmission including the response of trophoblasts to RuV are not well understood. Here, RuV-trophoblast interaction was studied in the BeWo trophoblast cell line. Analysis included early and late time-point kinetics of virus infection rate and the antiviral innate immune response at mRNA and protein level. BeWo characteristics were addressed through metabolic activity by extracellular flux analysis and syncytiotrophoblast formation through incubation with forskolin. We found that RuV infection of BeWo led to profuse type III interferon (IFN) production. Transfecting trophoblast cells with dsRNA analog induced an increase in the production of type I IFN-ß and type III IFNs; however, this did not occur in RuV-infected BeWo trophoblasts. IFN-ß and to a lesser extent type III IFN-λ1 were inhibitory to RuV. While no significant metabolic alteration was detected, RuV infection reduced the cell number in the monolayer culture in comparison to the mock control and resulted in detached and floating cells. Syncytia formation restricted RuV infection. The use of BeWo as a relevant cell culture model for infection of trophoblasts highlights cytopathogenicity in the absence of a type I IFN response as a pathogenic alteration by RuV.

Anticancer effect and apoptosis induction by azaflavanone derivative in human prostate cancer cells.

Polyphenols are naturally occurring organic compounds with varying structures represented by four major groups: flavonoids, phenolic acids, lignans and stilbenes. Several studies suggested that these secondary metabolites have health benefits due to its anti-tumorigenic effect. Therefore, substantial effort has been put forward to isolate and characterize these natural compounds and synthesize analogues that may serve as potential anti-cancer therapeutics. This present study is aimed at designing and synthesis of azaflavanone derivative and in understanding its mechanism of action in vitro and in vivo. Molecular docking studies predicted that the compound can potentially bind strongly to the Cyclin E1-Cdk2 complex which is a key mediator of the cell cycle progression indicating a biological interference in aggressive prostate cancer. Further downstream studies to understand its cytotoxicity and mechanism of action showed this azaflavanone derivative markedly inhibits viability of prostate cancer cells (DU145) showing an IC50 value of 0.4 µM compared to other cancer cells. The pharmacological ROS insult using the azaflavanone derivative increases the oxidative damage leading to high expression of apoptotic markers with increasing concentration. On compound treatment, the cells lose the metabolic flexibility accompanied by mitochondrial dysfunction leading to cell cycle arrest and apoptosis. Further, no compound mediated toxicity was observed in xenograft mouse model of prostate cancer at a concentration as high as 5 mg/kg. The tumor burden was reduced to 60% rendering the azaflavanone derivative a potential candidate in cancer therapeutics. Collectively, the compound triggers cell cycle arrest and ROS mediated oxidative stress sensitizing the cancerous cells towards apoptosis.

A Novel Steganography Method for Character-Level Text Image Based on Adversarial Attacks.

The Internet has become the main channel of information communication, which contains a large amount of secret information. Although network communication provides a convenient channel for human communication, there is also a risk of information leakage. Traditional image steganography algorithms use manually crafted steganographic algorithms or custom models for steganography, while our approach uses ordinary OCR models for information embedding and extraction. Even if our OCR models for steganography are intercepted, it is difficult to find their relevance to steganography. We propose a novel steganography method for character-level text images based on adversarial attacks. We exploit the complexity and uniqueness of neural network boundaries and use neural networks as a tool for information embedding and extraction. We use an adversarial attack to embed the steganographic information into the character region of the image. To avoid detection by other OCR models, we optimize the generation of the adversarial samples and use a verification model to filter the generated steganographic images, which, in turn, ensures that the embedded information can only be recognized by our local model. The decoupling experiments show that the strategies we adopt to weaken the transferability can reduce the possibility of other OCR models recognizing the embedded information while ensuring the success rate of information embedding. Meanwhile, the perturbations we add to embed the information are acceptable. Finally, we explored the impact of different parameters on the algorithm with the potential of our steganography algorithm through parameter selection experiments. We also verify the effectiveness of our validation model to select the best steganographic images. The experiments show that our algorithm can achieve a 100% information embedding rate and more than 95% steganography success rate under the set condition of 3 samples per group. In addition, our embedded information can be hardly detected by other OCR models.

WYSIWYG: IoT Device Identification Based on WebUI Login Pages.

With the improvement of intelligence and interconnection, Internet of Things (IoT) devices tend to become more vulnerable and exposed to many threats. Device identification is the foundation of many cybersecurity operations, such as asset management, vulnerability reaction, and situational awareness, which are important for enhancing the security of IoT devices. The more information sources and the more angles of view we have, the more precise identification results we obtain. This study proposes a novel and alternative method for IoT device identification, which introduces commonly available WebUI login pages with distinctive characteristics specific to vendors as the data source and uses an ensemble learning model based on a combination of Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) for device vendor identification and develops an Optical Character Recognition (OCR) based method for device type and model identification. The experimental results show that the ensemble learning model can achieve 99.1% accuracy and 99.5% F1-Score in the determination of whether a device is from a vendor that appeared in the training dataset, and if the answer is positive, 98% accuracy and 98.3% F1-Score in identifying which vendor it is from. The OCR-based method can identify fine-grained attributes of the device and achieve an accuracy of 99.46% in device model identification, which is higher than the results of the Shodan cyber search engine by a considerable margin of 11.39%.

A High-Precision Method for Segmentation and Recognition of Shopping Mall Plans.

Most studies on map segmentation and recognition are focused on architectural floor plans, while there are very few analyses of shopping mall plans. The objective of the work is to accurately segment and recognize the shopping mall plan, obtaining location and semantic information for each room via segmentation and recognition. This work can be used in other applications such as indoor robot navigation, building area and location analysis, and three-dimensional reconstruction. First, we identify and match the catalog of a mall floor plan to obtain matching text, and then we use the two-stage region growth method we proposed to segment the preprocessed floor plan. The room number is then obtained by sending each segmented room section to an OCR (optical character recognition) system for identification. Finally, the system retrieves the matching text to match the room number in order to obtain the room name, and outputs the needed room location and semantic information. It is considered a successful detection when a room region can be successfully segmented and identified. The proposed method is evaluated on a dataset including 1340 rooms. Experimental results show that the accuracy of room segmentation is 92.54%, and the accuracy of room recognition is 90.56%. The total detection accuracy is 83.81%.

Scale-Mark-Based Gauge Reading for Gauge Sensors in Real Environments with Light and Perspective Distortions.

Nowadays, many old analog gauges still require the use of manual gauge reading. It is a time-consuming, expensive, and error-prone process. A cost-effective solution for automatic gauge reading has become a very important research topic. Traditionally, different types of gauges have their own specific methods for gauge reading. This paper presents a systematized solution called SGR (Scale-mark-based Gauge Reading) to automatically read gauge values from different types of gauges. Since most gauges have scale marks (circular or in an arc), our SGR algorithm utilizes PCA (principal components analysis) to find the primary eigenvector of each scale mark. The intersection of these eigenvectors is extracted as the gauge center to ascertain the scale marks. Then, the endpoint of the gauge pointer is found to calculate the corresponding angles to the gauge's center. Using OCR (optical character recognition), the corresponding dial values can be extracted to match with their scale marks. Finally, the gauge reading value is obtained by using the linear interpolation of these angles. Our experiments use four videos in real environments with light and perspective distortions. The gauges in the video are first detected by YOLOv4 and the detected regions are clipped as the input images. The obtained results show that SGR can automatically and successfully read gauge values. The average error of SGR is nearly 0.1% for the normal environment. When the environment becomes abnormal with respect to light and perspective distortions, the average error of SGR is still less than 0.5%.

A Novel Memory and Time-Efficient ALPR System Based on YOLOv5.

With the rapid development of deep learning techniques, new innovative license plate recognition systems have gained considerable attention from researchers all over the world. These systems have numerous applications, such as law enforcement, parking lot management, toll terminals, traffic regulation, etc. At present, most of these systems rely heavily on high-end computing resources. This paper proposes a novel memory and time-efficient automatic license plate recognition (ALPR) system developed using YOLOv5. This approach is ideal for IoT devices that usually have less memory and processing power. Our approach incorporates two stages, i.e., using a custom transfer learned model for license plate detection and an LSTM-based OCR engine for recognition. The dataset that we used for this research was our dataset consisting of images from the Google open images dataset and the Indian License plate dataset. Along with training YOLOv5 models, we also trained YOLOv4 models on the same dataset to illustrate the size and performance-wise comparison. Our proposed ALPR system results in a 14 megabytes model with a mean average precision of 87.2% and 4.8 ms testing time on still images using Nvidia T4 GPU. The complete system with detection and recognition on the other hand takes about 85 milliseconds.

A Smart Visual Sensing Concept Involving Deep Learning for a Robust Optical Character Recognition under Hard Real-World Conditions.

In this study, we propose a new model for optical character recognition (OCR) based on both CNNs (convolutional neural networks) and RNNs (recurrent neural networks). The distortions affecting the document image can take different forms, such as blur (focus blur, motion blur, etc.), shadow, bad contrast, etc. Document-image distortions significantly decrease the performance of OCR systems, to the extent that they reach a performance close to zero. Therefore, a robust OCR model that performs robustly even under hard (distortion) conditions is still sorely needed. However, our comprehensive study in this paper shows that various related works can somewhat improve their respective OCR recognition performance of degraded document images (e.g., captured by smartphone cameras under different conditions and, thus, distorted by shadows, contrast, blur, etc.), but it is worth underscoring, that improved recognition is neither sufficient nor always satisfactory-especially in very harsh conditions. Therefore, in this paper, we suggest and develop a much better and fully different approach and model architecture, which significantly outperforms the aforementioned previous related works. Furthermore, a new dataset was gathered to show a series of different and well-representative real-world scenarios of hard distortion conditions. The new OCR model suggested performs in such a way that even document images (even from the hardest conditions) that were previously not recognizable by other OCR systems can be fully recognized with up to 97.5% accuracy/precision by our new deep-learning-based OCR model.

Anticancer Effect of Cathelicidin LL-37, Protegrin PG-1, Nerve Growth Factor NGF, and Temozolomide: Impact on the Mitochondrial Metabolism, Clonogenic Potential, and Migration of Human U251 Glioma Cells.

Glioblastoma (GBM) is one of the most aggressive and lethal malignancy of the central nervous system. Temozolomide is the standard of care for gliomas, frequently results in resistance to drug and tumor recurrence. Therefore, further research is required for the development of effective drugs in order to guarantee specific treatments to succeed. The aim of current study was to investigate the effects of nerve growth factor (NGF), human cathelicidin (LL-37), protegrin-1 (PG-1), and temozolomide on bioenergetic function of mitochondria, clonogenicity, and migration of human U251 glioma cells. Colony formation assay was used to test the ability of the glioma cells to form colonies in vitro. The U251 glioma cells migration was evaluated using wound-healing assay. To study the mitochondrial metabolism in glioma cells we measured oxygen consumption rates (OCR) and extracellular acidification rates (ECAR) using a Seahorse XF cell Mito stress test kit and Seahorse XF cell Glycolysis stress kit, respectively. We revealed that LL-37, NGF, and TMZ show strong anti-tumorigenic activity on GMB. LL-37 (4 µM), TMZ (155 µM), and NGF (7.55 × 10-3 µM) inhibited 43.9%-60.3%, 73.5%-81.3%, 66.2% the clonogenicity of glioma U251 cells for 1-2 days, respectively. LL-37 (4 µM), and NGF (7.55 × 10-3 µM) inhibited the migration of U251 glioma cells on the third and fourth days. TMZ also inhibited the migration of human glioma U251 cells over 1-3 days. In contrast, PG-1 (16 µM) stimulated the migration of U251 glioma cells on the second, fourth, and sixth days. Anti-mitogenic and anti-migration activities of NGF, LL-37, and TMZ maybe are relation to their capacity to reduce the basal OCR, ATP-synthetase, and maximal respiration of mitochondria in human glioma U251 cells. Glycolysis, glycolytic capacity and glycolytic spare in glioma U251 cells haven`t been changed under the effect of NGF, LL-37, PG-1, and TMZ in regard to control level. Thus, LL-37 and NGF inhibit migration and clonogenicity of U251 glioma cells, which may indicate that these compounds have anti-mitogenic and anti-migration effects on human glioma cells. The study of the mechanisms of these effects may contribute in the future to the use of NGF and LL-37 as therapeutic agents for gliomas.

Studies on the effects of bone marrow stem cells on mitochondrial function and the alleviation of ARDS.

Mesenchymal stem cells (MSCs) can alleviate acute respiratory distress syndrome (ARDS), but the mechanisms involved are unclear, especially about their specific effects on cellular mitochondrial respiratory function. Thirty mice were allocated into the Control, LPS, and LPS + Bone marrow mesenchymal stem cell (BMSC) group (n = 10/group). Mouse alveolar epithelial cells (MLE-12) and macrophage cells (RAW264.7) were divided into the same groups. Pathological variation, inflammation-related factors, reactive oxygen species (ROS), ATP levels, and oxygen consumption rate (OCR) were analyzed. Pathologic features of ARDS were observed in the LPS group and were significantly alleviated by BMSCs. The trend in inflammation-related factors among the three groups was the LPS group > LPS + BMSC group > Control group. In the MLE-12 co-culture system, IL-6 was increased in the LPS group but not significantly reduced in the LPS + BMSC group. In the RAW264.7 co-culture system, IL-1ß, TNF-α, and IL-10 levels were all increased in the LPS group, IL-1ß and TNF-α levels were reduced by BMSCs, while IL-10 level kept increasing. ROS and ATP levels were increased and decreased respectively in both MLE-12 and RAW264.7 cells in the LPS groups but reversed by BMSCs. Basal OCR, ATP-linked OCR, and maximal OCR were lower in the LPS groups. Impaired basal OCR and ATP-linked OCR in MLE-12 cells were partially restored by BMSCs, while impaired basal OCR and maximal OCR in RAW264.7 cells were restored by BMSCs. BMSCs improved the mitochondrial respiration dysfunction of macrophages and alveolar epithelial cells induced by LPS, alleviated lung tissue injury, and inflammatory response in a mouse model of ARDS.

Penehyclidine mitigates postoperative nausea and vomiting and intraoperative oculocardiac reflex in patients undergoing strabismus surgery: a prospective, randomized, double-blind comparison.

BACKGROUND: Postoperative nausea and vomiting (PONV) is one of the most frequent complications following strabismus surgery. Penehyclidine, an anticholinergic agent, is widely used as premedication. This study investigated the effect of preoperative penehyclidine on PONV in patients undergoing strabismus surgery. METHODS: In this prospective, randomized, double-blind study, patients scheduled for strabismus surgery under general anesthesia were randomly assigned to either penehyclidine (n = 114) or normal saline (n = 104) group. Penehyclidine was administrated immediately after anesthesia induction, and normal saline was substituted as control. PONV was investigated from 0 to 48 h after surgery. Intraoperative oculocardiac reflex (OCR) was also recorded. RESULTS: Compared with normal saline, penehyclidine significantly reduced PONV incidence (30.7% vs. 54.8%, P < 0.01) and mitigated PONV severity as indicated by severity scoring (P < 0.01). Compared with normal saline, penehyclidine also significantly reduced OCR incidence (57.9% vs. 77.9%, P < 0.01) and mitigated OCR severity, as indicated by the requirement for atropine rescue (77.3% vs. 90.1%, P < 0.05) and the maximum decrease of heart rate during OCR (23.1 ± 9.4 bpm vs. 27.3 ± 12.4 bpm, P < 0.05). The recovery course did not differ between groups. CONCLUSIONS: Penehyclidine administrated after anesthesia induction significantly reduced the incidence of PONV and alleviated intraoperative OCR in patients undergoing strabismus surgery. TRIAL REGISTRATION: ClinicalTrials.gov ( NCT04054479 ). Retrospectively registered August 13, 2019.

An Observation on Enhanced Extracellular Acidification and Lactate Production Induced by Inhibition of Lactate Dehydrogenase A.

The Warburg effect, representing enhanced glycolysis and lactate production in adequately oxygenated cancer cells, has been widely regarded to cause increased extracellular acidification. Converting pyruvate to lactate by lactate dehydrogenase A (LDHA) is the last step of glycolysis. Here, we report an interesting counterintuitive observation that inhibition of LDHA resulted in enhanced glycolysis in MDA-MB-231 breast cancer cells. The cells were treated with FX11 (7-benzyl-2,3-dihydroxy-6-methyl-4-propylnaphthalene-1-carboxylic acid), a specific LDHA inhibitor. Seahorse assay reported dose-dependent increases in both oxygen consumption rate (OCR) and extracellular acidification rate (ECAR). Independent biochemical measurements also confirmed the increase of lactate production under FX11 treatment. The reasons and mechanism of these observations of elevated ECAR and lactate production in the MDA-MB-231 breast cancer cells under FX11 treatment remain to be investigated.