Analysis of spatiotemporal pattern and quantification of gastrointestinal slow waves caused by anticholinergic drugs

Postoperative Gastrointestinal Dysfunction After Neuromuscular Blockade Reversal With Sugammadex Versus Cholinesterase Inhibitors in Patients Undergoing Gastrointestinal Surgery: A Systematic Review and Meta-Analysis

BACKGROUND

Postoperative gastrointestinal dysfunction (POGD) commonly occurs following gastrointestinal (GI) surgery and is associated with specific anesthetic agents. Cholinesterase inhibitors employed for reversing neuromuscular blockade have been implicated in development of POGD. Sugammadex, a novel reversal agent, is linked with reduced POGD. However, there is a lack of comprehensive comparative review between these agents regarding their impact on POGD following GI surgery. This study aims to systematically review the effects of sugammadex on POGD compared to cholinesterase inhibitors following GI surgery.

METHODS

MEDLINE, EMBASE, and CENTRAL were searched as of July 2022 to identify articles comparing sugammadex with cholinesterase inhibitors in patients undergoing gastrointestinal surgery, specifically in relation to POGD. Secondary endpoints included length of hospital stay, readmission rates, pulmonary complications, and postoperative morbidity.

RESULTS

From 198 citations, 2 randomized controlled trials (RCTs) and 3 retrospective cohorts with 717 patients receiving sugammadex and 812 patients receiving cholinesterase inhibitors were included. Significantly lower rates of prolonged postoperative ileus (OR .44, 95% CI .25-.77, P < .05, I2 = 56%, low certainty evidence) was observed with sugammadex. No significant difference in any other outcome was observed. Narrative review of readmission data demonstrated no significant difference.

CONCLUSION

The use of sugammadex following gastrointestinal surgery is associated with significantly lower rates of prolonged postoperative ileus compared to cholinesterase inhibitors. However, these do not translate into a significant reduction in length of stay, morbidity, or postoperative nausea and vomiting. Results are limited by the numer of studies included and missing data, more robust RCTs are needed before recommendations can be made.

Feature recognition in multiple CNNs using sEMG images from a prototype comfort test

OBJECTIVE

Deep learning-based CNN networks have recently been investigated to solve the problem of body posture recognition based on surface electromyographic signals (sEMG). Influenced by these studies, to develop a combined approach of sEMG and CNNs in the study of human-product interactions and the impact of body comfort, and to compare the advantages and disadvantages of various CNNs networks.

METHODS

In this study, sEMG measurements were carried out by building a prototype usability experiment, and the data were divided into four categories, with two types of datasets: training and testing. Four CNNs, LeNet-5, VGGNet-11, InceptionNet V4, and DenseNet, were used for the recognition of sEMG images.

RESULTS

DenseNet is another type of convolutional neural network with deep layers, which has a unique advantage over other algorithms. unique advantages over other algorithms. DenseNet has fewer layers and better accuracy than InceptionNet V4, but not only does it bypass enhanced feature reuse, but its network is easier to train and has some regularization effects, while also mitigating the problems of gradient disappearance and model degradation.

CONCLUSION

These findings could lead to a more appropriate CNN model and a useful tool for developing comfort judgments of surface EMG signals, furthering the development of products that come into contact with the human body without the need for routine retraining.

Trihexyphenidyl Alters Its Host's Metabolism, Neurobehavioral Patterns, and Gut Microbiome Feedback Loop-The Modulating Role of Anacyclus pyrethrum

Trihexyphenidyl (THP)-a synthetic anticholinergic medication used to manage parkinsonism and extrapyramidal symptoms-has gained significant clinical recognition. However, there is a critical gap in understanding its withdrawal effects. This study investigates the intricate interplay between gut microbiota and oxidative stress during THP withdrawal. Furthermore, it explores the therapeutic potential of Anacyclus pyrethrum (AEAP) for alleviating the associated adverse effects. This comprehensive research combines behavioral tests, biochemical analysis, gut microbiome assessment utilizing matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS), and oxidative stress measures. The results reveal that the chronic administration of THP leads to severe withdrawal syndrome, marked by heightened anxiety, depressive-like behaviors, increased cortisol levels, elevated oxidative stress, and gut dysbiosis. However, the administration of AEAP alongside THP shows a significant capacity to mitigate these deleterious effects. Co-treatment and post-treatment with AEAP increased bacterial density and diversity, promoting the proliferation of beneficial bacteria associated with improved gut health. Furthermore, AEAP administration reduced cortisol levels and exhibited potent antioxidant properties, effectively countering the THP-induced oxidative damage. This study highlights the withdrawal effects of THP and underscores the therapeutic potential of AEAP for managing these symptoms. The findings reveal its promising effects in alleviating behavioral and biochemical impairments, reducing oxidative stress, and restoring gut microbiota, which could significantly impact the clinical management of THP withdrawal and potentially extend to other substance withdrawal scenarios.

Classification of precancerous lesions based on fusion of multiple hierarchical features

PURPOSE

To investigate an identification method for precancerous gastric cancer based on the fusion of superficial features and deep features of gastroscopic images. The purpose of this study is to make most use of superficial features and deep features to provide clinicians with clinical decision support to assist the diagnosis of precancerous gastric diseases and reduce the workload of doctors.

METHODS

According to the nature of gastroscopic images, 75-dimensional shallow features were manually designed, including histogram features, texture features and high-order features of the image; then, based on the constructed convolutional neural networks such as ResNet and GoogLeNet, before the output layer. A fully connected layer is added as the deep feature of the image. In order to ensure consistent feature weights, the number of neurons in the fully connected layer is designed to be 75 dimensions. Therefore, the superficial and deep features of the image are concatenated, and a machine learning classifier is used to identify gastric polyps, there are three types of gastric precancerous diseases such as gastric polyps, gastric ulcers and gastric erosions.

RESULTS

A dataset with 420 images was collected for each disease, and divided into a training set and a test set with a ratio of 5:1, and then based on the dataset, three methods, such as traditional machine learning, deep learning, and feature fusion, were used respectively. For model training and testing of traditional machine learning and feature fusion, SVM, RF and BP neural network are used as the classification results of the classifier. For deep learning, the GoogLeNet, ResNet, and ResNeXt were implemented. The test results of the model on the test set show that the recognition accuracy of the proposed feature fusion method reaches (SVM: 85.18%; RF: 83.42%; BPNN: 85.18%), which is better than the traditional machine learning method (SVM: 80.17%; RF: 82.37%; BPNN: 84.12%) and the deep learning method (GoogLeNet: 82.54%; ResNet-18: 81.67%; ResNet-50: 81.67%; ResNeXt-50: 82.11%), which proves that this method has obvious advantages.

CONCLUSION

This study provides a new strategy for the identification of precancerous gastric cancer, improving the efficiency and accuracy of precancerous gastric cancer identification, and hopes to provide substantial practical help for the identification of gastric precancerous diseases.

A deep learning based framework for the classification of multi- class capsule gastroscope image in gastroenterologic diagnosis

Purpose: The purpose of this paper is to develop a method to automatic classify capsule gastroscope image into three categories to prevent high-risk factors for carcinogenesis, such as atrophic gastritis (AG). The purpose of this research work is to develop a deep learning framework based on transfer learning to classify capsule gastroscope image into three categories: normal gastroscopic image, chronic erosive gastritis images, and ulcer gastric image. Method: In this research work, we proposed deep learning framework based on transfer learning to classify capsule gastroscope image into three categories: normal gastroscopic image, chronic erosive gastritis images, and ulcer gastric image. We used VGG- 16, ResNet-50, and Inception V3 pre-trained models, fine-tuned them and adjust hyperparameters according to our classification problem. Results: A dataset containing 380 images was collected for each capsule gastroscope image category, and divided into training set and test set in a ratio of 70%, and 30% respectively, and then based on the dataset, three methods, including as VGG- 16, ResNet-50, and Inception v3 are used. We achieved highest accuracy of 94.80% by using VGG- 16 to diagnose and classify capsule gastroscopic images into three categories: normal gastroscopic image, chronic erosive gastritis images, and ulcer gastric image. Our proposed approach classified capsule gastroscope image with respectable specificity and accuracy. Conclusion: The primary technique and industry standard for diagnosing and treating numerous stomach problems is gastroscopy. Capsule gastroscope is a new screening tool for gastric diseases. However, a number of elements, including image quality of capsule endoscopy, the doctors' experience and fatigue, limit its effectiveness. Early identification is necessary for high-risk factors for carcinogenesis, such as atrophic gastritis (AG). Our suggested framework will help prevent incorrect diagnoses brought on by low image quality, individual experience, and inadequate gastroscopy inspection coverage, among other factors. As a result, the suggested approach will raise the standard of gastroscopy. Deep learning has great potential in gastritis image classification for assisting with achieving accurate diagnoses after endoscopic procedures.

Transcription factor NFE2L3 promotes the proliferation of esophageal squamous cell carcinoma cells and causes radiotherapy resistance by regulating IL-6

OBJECTIVE

To scrutinize the impact of overexpression and interference of NFE2L3 on radiosensitivity of esophageal squamous cell carcinoma cells (ESCC) and its downstream mechanism and to assess whether NFE2L3 expression alters in vivo radiosensitivity of ESCC by developing a subcutaneous tumor model in mice.

METHODS

Through RNA-Seq, we compared the differentially expressed genes between the ECA-109R cell line and its parent ECA-109 cell line. The differentially expressed genes were selected and verified by qRT-PCR. Transfection of ESCC cell lines with NFE2L3 inhibitor or mimic lentivirus constructs was done to study the activity of NFE2L3. To assess the effect of NFE2L3 on cellular growth and proliferation, clonogenic survival assay, EdU incorporation assay, and CCK-8 assay were done after irradiation. To probe how many irradiated DNA double-strand breaks were produced, the corresponding intensity of γ-H2AX foci were detected by immunofluorescence. Apoptotic cells were assayed by flow cytometry assay after irradiation; To investigate the downstream genes of NFE2L3, we knocked NFE2L3, and RNA-Seq was used to find out the downstream genes. qRT-PCR and western blot ensued to score associated protein profiles. The in vivo ESCC cell radiosensitivity was scrutinized by nude mouse xenograft models.

RESULTS

The differential genes between ECA-109R cells and its parent ECA-109 cells were compared by qRT-PCR to unveil a significant increase in NFE2L3 expression. Functional analysis indicated that NFE2L3 increased radioresistance in ESCC cells. Then, through high-throughput sequencing and bioinformatics analysis, IL-6 was found to be a hub gene that played a role downstream of NFE2L3 and was verified by qRT-PCR, western blot, and double luciferase reporter gene experiment. NFE2L3 could regulate ESCC cell radiosensitivity via the IL-6-STAT3 signaling pathway, and downregulation of IL-6 expression could reverse the effects of highly expressed NFE2L3. In vivo tumor xenograft experiments confirmed that NFE2L3 affects the sensitivity to radiation therapy.

CONCLUSION

NFE2L3 can affect the radiosensitivity of ESCC cells through IL-6 transcription and IL-6/STAT3 signaling pathway. This makes NFE2L3 a putative target to regulate ESCC cell radiosensitivity.

Influence of Acetylcholine Esterase Inhibitors and Memantine, Clinically Approved for Alzheimer's Dementia Treatment, on Intestinal Properties of the Mouse

Four drugs are currently approved for the treatment of Alzheimer’s disease (AD) by the FDA. Three of these drugs—donepezil, rivastigmine, and galantamine—belong to the class of acetylcholine esterase inhibitors. Memantine, a NMDA receptor antagonist, represents the fourth and a combination of donepezil and memantine the fifth treatment option. Recently, the gut and its habitants, its microbiome, came into focus of AD research and added another important factor to therapeutic considerations. While the first data provide evidence that AD patients might carry an altered microbiome, the influence of administered drugs on gut properties and commensals have been largely ignored so far. However, the occurrence of digestive side effects with these drugs and the knowledge that cholinergic transmission is crucial for several gut functions enforces the question if, and how, this medication influences the gastrointestinal system and its microbial stocking. Here, we investigated aspects such as microbial viability, colonic propulsion, and properties of enteric neurons, affected by assumed intestinal concentration of the four drugs using the mouse as a model organism. All ex vivo administered drugs revealed no direct effect on fecal bacteria viability and only a high dosage of memantine resulted in reduced biofilm formation of E. coli. Memantine was additionally the only compound that elevated calcium influx in enteric neurons, while all acetylcholine esterase inhibitors significantly reduced esterase activity in colonic tissue specimen and prolonged propulsion time. Both, acetylcholine esterase inhibitors and memantine, had no effect on general viability and neurite outgrowth of enteric neurons. In sum, our findings indicate that all AD symptomatic drugs have the potential to affect distinct intestinal functions and with this—directly or indirectly—microbial commensals.