Deep learning and minimally invasive inflammatory activity assessment: a proof-of-concept study for development and score correlation of a panendoscopy convolutional network.

Background: Capsule endoscopy (CE) is a valuable tool for assessing inflammation in patients with Crohn's disease (CD). The current standard for evaluating inflammation are validated scores (and clinical laboratory values) like Lewis score (LS), Capsule Endoscopy Crohn's Disease Activity Index (CECDAI), and ELIAKIM. Recent advances in artificial intelligence (AI) have made it possible to automatically select the most relevant frames in CE. Objectives: In this proof-of-concept study, our objective was to develop an automated scoring system using CE images to objectively grade inflammation. Design: Pan-enteric CE videos (PillCam Crohn's) performed in CD patients between 09/2020 and 01/2023 were retrospectively reviewed and LS, CECDAI, and ELIAKIM scores were calculated. Methods: We developed a convolutional neural network-based automated score consisting of the percentage of positive frames selected by the algorithm (for small bowel and colon separately). We correlated clinical data and the validated scores with the artificial intelligence-generated score (AIS). Results: A total of 61 patients were included. The median LS was 225 (0-6006), CECDAI was 6 (0-33), ELIAKIM was 4 (0-38), and SB_AIS was 0.5659 (0-29.45). We found a strong correlation between SB_AIS and LS, CECDAI, and ELIAKIM scores (Spearman's r = 0.751, r = 0.707, r = 0.655, p = 0.001). We found a strong correlation between LS and ELIAKIM (r = 0.768, p = 0.001) and a very strong correlation between CECDAI and LS (r = 0.854, p = 0.001) and CECDAI and ELIAKIM scores (r = 0.827, p = 0.001). Conclusion: Our study showed that the AI-generated score had a strong correlation with validated scores indicating that it could serve as an objective and efficient method for evaluating inflammation in CD patients. As a preliminary study, our findings provide a promising basis for future refining of a CE score that may accurately correlate with prognostic factors and aid in the management and treatment of CD patients.

Artificial intelligence in Crohn's disease: the development of an automated score for disease activity evaluation This study introduces an innovative AI-based approach to evaluate Crohn's Disease. The AI system automatically analyzes images from capsule endoscopy, focusing on finding ulcers and erosions to measure disease activity. The research reveals a robust correlation between the AI-generated score assessing inflammation in the small bowel and traditional clinical scores. This suggests that the AI solution could be a quicker and more consistent way to evaluate Crohn's Disease, speeding up the evaluation process and reducing manual scoring variability. While promising, the study acknowledges limitations and emphasizes the need for further validation with larger groups of patients. Overall, it represents a crucial step toward integrating AI into gastroenterology, offering a glimpse into a future of more objective and personalized Crohn's Disease evaluation.

Artificial Intelligence and Panendoscopy-Automatic Detection of Clinically Relevant Lesions in Multibrand Device-Assisted Enteroscopy.

Device-assisted enteroscopy (DAE) is capable of evaluating the entire gastrointestinal tract, identifying multiple lesions. Nevertheless, DAE's diagnostic yield is suboptimal. Convolutional neural networks (CNN) are multi-layer architecture artificial intelligence models suitable for image analysis, but there is a lack of studies about their application in DAE. Our group aimed to develop a multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. In total, 338 exams performed in two specialized centers were retrospectively evaluated, with 152 single-balloon enteroscopies (Fujifilm®, Porto, Portugal), 172 double-balloon enteroscopies (Olympus®, Porto, Portugal) and 14 motorized spiral enteroscopies (Olympus®, Porto, Portugal); then, 40,655 images were divided in a training dataset (90% of the images, n = 36,599) and testing dataset (10% of the images, n = 4066) used to evaluate the model. The CNN's output was compared to an expert consensus classification. The model was evaluated by its sensitivity, specificity, positive (PPV) and negative predictive values (NPV), accuracy and area under the precision recall curve (AUC-PR). The CNN had an 88.9% sensitivity, 98.9% specificity, 95.8% PPV, 97.1% NPV, 96.8% accuracy and an AUC-PR of 0.97. Our group developed the first multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. The development of accurate deep learning models is of utmost importance for increasing the diagnostic yield of DAE-based panendoscopy.

Deep Learning for Automatic Diagnosis and Morphologic Characterization of Malignant Biliary Strictures Using Digital Cholangioscopy: A Multicentric Study.

Digital single-operator cholangioscopy (D-SOC) has enhanced the ability to diagnose indeterminate biliary strictures (BSs). Pilot studies using artificial intelligence (AI) models in D-SOC demonstrated promising results. Our group aimed to develop a convolutional neural network (CNN) for the identification and morphological characterization of malignant BSs in D-SOC. A total of 84,994 images from 129 D-SOC exams in two centers (Portugal and Spain) were used for developing the CNN. Each image was categorized as either a normal/benign finding or as malignant lesion (the latter dependent on histopathological results). Additionally, the CNN was evaluated for the detection of morphologic features, including tumor vessels and papillary projections. The complete dataset was divided into training and validation datasets. The model was evaluated through its sensitivity, specificity, positive and negative predictive values, accuracy and area under the receiver-operating characteristic and precision-recall curves (AUROC and AUPRC, respectively). The model achieved a 82.9% overall accuracy, 83.5% sensitivity and 82.4% specificity, with an AUROC and AUPRC of 0.92 and 0.93, respectively. The developed CNN successfully distinguished benign findings from malignant BSs. The development and application of AI tools to D-SOC has the potential to significantly augment the diagnostic yield of this exam for identifying malignant strictures.

Bladder Wall Stiffness after Cystectomy in Bladder Cancer Patients: A Preliminary Study.

Bladder cancer (BlCa), specifically urothelial carcinomas, is a heterogeneous disease that derives from the urothelial lining. Two main classes of BlCa are acknowledged: the non-muscle invasive BlCa and the muscle-invasive BlCa; the latter constituting an aggressive disease which invades locally and metastasizes systemically. Distinguishing the specific microenvironment that cancer cells experience between mucosa and muscularis propria layers can help elucidate how these cells acquire invasive capacities. In this work, we propose to measure the micromechanical properties of both mucosa and muscularis propria layers of the bladder wall of BlCa patients, using atomic force microscopy (AFM). To do that, two cross-sections of both the macroscopically normal urinary bladder wall and the bladder wall adjacent to the tumor were collected and immediately frozen, prior to AFM samples analysis. The respective "twin" formalin-fixed paraffin-embedded tissue fragments were processed and later evaluated for histopathological examination. H&E staining suggested that tumors promoted the development of muscle-like structures in the mucosa surrounding the neoplastic region. The average Young's modulus (cell stiffness) in tumor-adjacent specimens was significantly higher in the muscularis propria than in the mucosa. Similarly, the tumor-free specimens had significantly higher Young's moduli in the muscularis propria than in the urothelium. Young's moduli were higher in all layers of tumor-adjacent tissues when compared with tumor-free samples. Here we provide insights into the stiffness of the bladder wall layers, and we show that the presence of tumor in the surrounding mucosa leads to an alteration of its smooth muscle content. The quantitative assessment of stiffness range here presented provides essential data for future research on BlCa and for understanding how the biomechanical stimuli can modulate cancer cells' capacity to invade through the different bladder layers.

Artificial Intelligence and Capsule Endoscopy: Automatic Detection of Small Bowel Blood Content Using a Convolutional Neural Network.

Introduction: Capsule endoscopy has revolutionized the management of patients with obscure gastrointestinal bleeding. Nevertheless, reading capsule endoscopy images is time-consuming and prone to overlooking significant lesions, thus limiting its diagnostic yield. We aimed to create a deep learning algorithm for automatic detection of blood and hematic residues in the enteric lumen in capsule endoscopy exams. Methods: A convolutional neural network was developed based on a total pool of 22,095 capsule endoscopy images (13,510 images containing luminal blood and 8,585 of normal mucosa or other findings). A training dataset comprising 80% of the total pool of images was defined. The performance of the network was compared to a consensus classification provided by 2 specialists in capsule endoscopy. Subsequently, we evaluated the performance of the network using an independent validation dataset (20% of total image pool), calculating its sensitivity, specificity, accuracy, and precision. Results: Our convolutional neural network detected blood and hematic residues in the small bowel lumen with an accuracy and precision of 98.5 and 98.7%, respectively. The sensitivity and specificity were 98.6 and 98.9%, respectively. The analysis of the testing dataset was completed in 24 s (approximately 184 frames/s). Discussion/Conclusion: We have developed an artificial intelligence tool capable of effectively detecting luminal blood. The development of these tools may enhance the diagnostic accuracy of capsule endoscopy when evaluating patients presenting with obscure small bowel bleeding.

Introdução: A endoscopia por cápsula revolucionou a abordagem a doentes com hemorragia digestiva obscura. No entanto, a leitura de imagens de endoscopia por cápsula é morosa, havendo suscetibilidade para a perda de lesões significativas, limitando desta forma a sua eficácia diagnóstica. Este estudo visou a criação de um algoritmo de deep learning para deteção automática de sangue e resíduos hemáticos no lúmen entérico usando imagens de endoscopia por cápsula. Métodos: Foi desenvolvida uma rede neural convolucional com base num conjunto de 22,095 imagens de endoscopia de cápsula (13,510 imagens contendo sangue e 8,585 mucosa normal ou outros achados). Foi construído um grupo de imagens para treino, compreendendo 80% do total de imagens. O desempenho da rede foi comparado com a classificação consenso de dois especialistas em endoscopia por cápsula. Posteriormente, o desempenho da rede foi avaliado usando os restantes 20% de imagens. Foi calculada a sua sensibilidade, especificidade, exatidão e precisão. Resultados: O algoritmo detetou sangue e resíduos hemáticos no lúmen do intestino delgado com uma exatidão e precisão de 98.5% e 98.7%, respetivamente. A sensibilidade e especificidade foram 98.6% e 98.9%, respetivamente. A análise do conjunto de usado para teste da rede foi concluída em 24 segundos (aproximadamente 184 frames/s). Discussão/Conclusão: Foi desenvolvida uma ferramenta de inteligência artificial capaz de detetar efetivamente o sangue luminal. O desenvolvimento dessas ferramentas pode aumentar a precisão do diagnóstico da endoscopia por cápsula ao avaliar pacientes que apresentam sangramento obscuro do intestino delgado.

Performance of a Deep Learning System for Automatic Diagnosis of Protruding Lesions in Colon Capsule Endoscopy.

BACKGROUND: Colon capsule endoscopy (CCE) is an alternative for patients unwilling or with contraindications for conventional colonoscopy. Colorectal cancer screening may benefit greatly from widespread acceptance of a non-invasive tool such as CCE. However, reviewing CCE exams is a time-consuming process, with risk of overlooking important lesions. We aimed to develop an artificial intelligence (AI) algorithm using a convolutional neural network (CNN) architecture for automatic detection of colonic protruding lesions in CCE images. An anonymized database of CCE images collected from a total of 124 patients was used. This database included images of patients with colonic protruding lesions or patients with normal colonic mucosa or with other pathologic findings. A total of 5715 images were extracted for CNN development. Two image datasets were created and used for training and validation of the CNN. The AUROC for detection of protruding lesions was 0.99. The sensitivity, specificity, PPV and NPV were 90.0%, 99.1%, 98.6% and 93.2%, respectively. The overall accuracy of the network was 95.3%. The developed deep learning algorithm accurately detected protruding lesions in CCE images. The introduction of AI technology to CCE may increase its diagnostic accuracy and acceptance for screening of colorectal neoplasia.

Automatic detection of tumor vessels in indeterminate biliary strictures in digital single-operator cholangioscopy.

Background and study aims Indeterminate biliary strictures pose a significative clinical challenge. Dilated, irregular, and tortuous vessels, often described as tumor vessels, are frequently reported in biliary strictures with high malignancy potential during digital single-operator cholangioscopy (D-SOC). In recent years, the development of artificial intelligence (AI) algorithms for application to endoscopic practice has been intensely studied. We aimed to develop an AI algorithm for automatic detection of tumor vessels (TVs) in D-SOC images. Patients and methods A convolutional neural network (CNN) was developed. A total of 6475 images from 85 patients who underwent D-SOC (Spyglass, Boston Scientific, Marlborough, Massachusetts, United States) were included. Each frame was evaluated for the presence of TVs. The performance of the CNN was measured by calculating the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values. Results The sensitivity, specificity, positive predictive value, and negative predictive value were 99.3 %, 99.4 %, 99.6% and 98.7 %, respectively. The AUC was 1.00. Conclusions Our CNN was able to detect TVs with high accuracy. Development of AI algorithms may enhance the detection of macroscopic characteristics associated with high probability of biliary malignancy, thus optimizing the diagnostic workup of patients with indeterminate biliary strictures.

Biomechanical characterization of the small intestine to simulate gastrointestinal tract chyme propulsion.

Regular intestinal motility is essential to guarantee complete digestive function. The coordinative action and integrity of the smooth muscle layers in the small intestine's wall are critical for mixing and propelling the luminal content. However, some patients present gastrointestinal limitations which may negatively impact the normal motility of the intestine. These patients have altered mechanical and muscle properties that likely impact chyme propulsion and may pose a daily scenario for long-term complications. To better understand how mechanics affect chyme propulsion, the propulsive capability of the small intestine was examined during a peristaltic wave along the distal direction of the tract. It was assumed that such a wave works as an activation signal, inducing peristaltic contractions in a transversely isotropic hyperelastic model. In this work, the effect on the propulsion mechanics, from an impairment on the muscle contractile ability, typical from patients with systemic sclerosis, and the presence of sores resultant from ulcers was evaluated. The passive properties of the constitutive model were obtained from uniaxial tensile tests from a porcine small intestine, along with both longitudinal and circumferential directions. Our experiments show decreased stiffness in the circumferential direction. Our simulations show decreased propulsion forces in patients in systemic sclerosis and ulcer patients. As these patients may likely need medical intervention, establishing action concerning the impaired propulsion can help to ease the evaluation and treatment of future complications.

On the management of maternal pushing during the second stage of labor: a biomechanical study considering passive tissue fatigue damage accumulation.

BACKGROUND: During the second stage of labor, the maternal pelvic floor muscles undergo repetitive stretch loading as uterine contractions and strenuous maternal pushes combined to expel the fetus, and it is not uncommon that these muscles sustain a partial or complete rupture. It has recently been demonstrated that soft tissues, including the anterior cruciate ligament and connective tissue in sheep pelvic floor muscle, can accumulate damage under repetitive physiological (submaximal) loads. It is well known to material scientists that this damage accumulation can not only decrease tissue resistance to stretch but also result in a partial or complete structural failure. Thus, we wondered whether certain maternal pushing patterns (in terms of frequency and duration of each push) could increase the risk of excessive damage accumulation in the pelvic floor tissue, thereby inadvertently contributing to the development of pelvic floor muscle injury. OBJECTIVE: This study aimed to determine which labor management practices (spontaneous vs directed pushing) are less prone to accumulate damage in the pelvic floor muscles during the second stage of labor and find the optimum approach in terms of minimizing the risk of pelvic floor muscle injury. STUDY DESIGN: We developed a biomechanical model for the expulsive phase of the second stage of labor that includes the ability to measure the damage accumulation because of repetitive physiological submaximal loads. We performed 4 simulations of the second stage of labor, reflecting a directed pushing technique and 3 alternatives for spontaneous pushing. RESULTS: The finite element model predicted that the origin of the pubovisceral muscle accumulates the most damage and so it is the most likely place for a tear to develop. This result was independent of the pushing pattern. Performing 3 maternal pushes per contraction, with each push lasting 5 seconds, caused less damage and seemed the best approach. The directed pushing technique (3 pushes per contraction, with each push lasting 10 seconds) did not reduce the duration of the second stage of labor and caused higher damage accumulation. CONCLUSION: The frequency and duration of the maternal pushes influenced the damage accumulation in the passive tissues of the pelvic floor muscles, indicating that it can influence the prevalence of pelvic floor muscle injuries. Our results suggested that the maternal pushes should not last longer than 5 seconds and that the duration of active pushing is a better measurement than the total duration of the second stage of labor. Hopefully, this research will help to shed new light on the best practices needed to improve the experience of labor for women.

Deep learning and colon capsule endoscopy: automatic detection of blood and colonic mucosal lesions using a convolutional neural network.

Background and study aims Colon capsule endoscopy (CCE) is a minimally invasive alternative to conventional colonoscopy. However, CCE produces long videos, making its analysis time-consuming and prone to errors. Convolutional neural networks (CNN) are artificial intelligence (AI) algorithms with high performance levels in image analysis. We aimed to develop a deep learning model for automatic identification and differentiation of significant colonic mucosal lesions and blood in CCE images. Patients and methods A retrospective multicenter study including 124 CCE examinations was conducted for development of a CNN model, using a database of CCE images including anonymized images of patients with normal colon mucosa, several mucosal lesions (erosions, ulcers, vascular lesions and protruding lesions) and luminal blood. For CNN development, 9005 images (3,075 normal mucosa, 3,115 blood and 2,815 mucosal lesions) were ultimately extracted. Two image datasets were created and used for CNN training and validation. Results The mean (standard deviation) sensitivity and specificity of the CNN were 96.3 % (3.9 %) and 98.2 % (1.8 %) Mucosal lesions were detected with a sensitivity of 92.0 % and a specificity of 98.5 %. Blood was detected with a sensitivity and specificity of 97.2 % and 99.9 %, respectively. The algorithm was 99.2 % sensitive and 99.6 % specific in distinguishing blood from mucosal lesions. The CNN processed 65 frames per second. Conclusions This is the first CNN-based algorithm to accurately detect and distinguish colonic mucosal lesions and luminal blood in CCE images. AI may improve diagnostic and time efficiency of CCE exams, thus facilitating CCE adoption to routine clinical practice.

Artificial intelligence for automatic diagnosis of biliary stricture malignancy status in single-operator cholangioscopy: a pilot study.

BACKGROUND AND AIMS: The diagnosis and characterization of biliary strictures (BSs) is challenging. The introduction of digital single-operator cholangioscopy (DSOC) that allows direct visual inspection of the lesion and targeted biopsy sampling significantly improved the diagnostic yield in patients with indeterminate BSs. However, the diagnostic efficiency of DSOC remains suboptimal. Convolutional neural networks (CNNs) have shown great potential for the interpretation of medical images. We aimed to develop a CNN-based system for automatic detection of malignant BSs in DSOC images. METHODS: We developed, trained, and validated a CNN-based on DSOC images. Each frame was labeled as a normal/benign finding or as a malignant lesion if histopathologic evidence of biliary malignancy was available. The entire dataset was split for 5-fold cross-validation. In addition, the image dataset was split for constitution of training and validation datasets. The performance of the CNN was measured by calculating the area under the receiving operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values. RESULTS: A total of 11,855 images from 85 patients were included (9695 malignant strictures and 2160 benign findings). The model had an overall accuracy of 94.9%, sensitivity of 94.7%, specificity of 92.1%, and AUC of .988 in cross-validation analysis. The image processing speed of the CNN was 7 ms per frame. CONCLUSIONS: The developed deep learning algorithm accurately detected and differentiated malignant strictures from benign biliary conditions. The introduction of artificial intelligence algorithms to DSOC systems may significantly increase its diagnostic yield for malignant strictures.

Artificial intelligence and capsule endoscopy: automatic detection of vascular lesions using a convolutional neural network.

BACKGROUND: Capsule endoscopy (CE) is the first line for evaluation of patients with obscure gastrointestinal bleeding. A wide range of small intestinal vascular lesions with different hemorrhagic potential are frequently found in these patients. Nevertheless, reading CE exams is time-consuming and prone to errors. Convolutional neural networks (CNN) are artificial intelligence tools with high performance levels in image analysis. This study aimed to develop a CNN-based model for identification and differentiation of vascular lesions with distinct hemorrhagic potential in CE images. METHODS: The development of the CNN was based on a database of CE images. This database included images of normal small intestinal mucosa, red spots, and angiectasia/varices. The hemorrhagic risk was assessed by Saurin's classification. For CNN development, 11,588 images (9525 normal mucosa, 1026 red spots, and 1037 angiectasia/varices) were ultimately extracted. Two image datasets were created for CNN training and testing. RESULTS: The network was 91.8% sensitive and 95.9% specific for detection of vascular lesions, providing accurate predictions in 94.4% of cases. In particular, the CNN had a sensitivity and specificity of 97.1% and 95.3%, respectively, for detection of red spots. Detection of angiectasia/varices occurred with a sensitivity of 94.1% and a specificity of 95.1%. The CNN had a frame reading rate of 145 frames/sec. CONCLUSIONS: The developed algorithm is the first CNN-based model to accurately detect and distinguish enteric vascular lesions with different hemorrhagic risk. CNN-assisted CE reading may improve the diagnosis of these lesions and overall CE efficiency.

Automatic Identification of Papillary Projections in Indeterminate Biliary Strictures Using Digital Single-Operator Cholangioscopy.

INTRODUCTION: Characterization of biliary strictures is challenging. Papillary projections (PP) are often reported in biliary strictures with high malignancy potential during digital single-operator cholangioscopy. In recent years, the development of artificial intelligence (AI) algorithms for application to endoscopic practice has been intensely studied. We aimed to develop an AI algorithm for automatic detection of PP in digital single-operator cholangioscopy images. METHODS: A convolutional neural network (CNN) was developed. Each frame was evaluated for the presence of PP. The CNN's performance was measured by the area under the curve, sensitivity, specificity, and positive and negative predictive values. RESULTS: A total of 3,920 images from 85 patients were included. Our model had a sensitivity and specificity 99.7% and 97.1%, respectively. The area under the curve was 1.00. DISCUSSION: Our CNN was able to detect PP with high accuracy. Future development of AI tools may optimize the macroscopic characterization of biliary strictures.

Artificial intelligence and colon capsule endoscopy: automatic detection of blood in colon capsule endoscopy using a convolutional neural network.

Colon capsule endoscopy (CCE) is a minimally invasive alternative to conventional colonoscopy. Most studies on CCE focus on colorectal neoplasia detection. The development of automated tools may address some of the limitations of this diagnostic tool and widen its indications for different clinical settings. We developed an artificial intelligence model based on a convolutional neural network (CNN) for the automatic detection of blood content in CCE images. Training and validation datasets were constructed for the development and testing of the CNN. The CNN detected blood with a sensitivity, specificity, and positive and negative predictive values of 99.8 %, 93.2 %, 93.8 %, and 99.8 %, respectively. The area under the receiver operating characteristic curve for blood detection was 1.00. We developed a deep learning algorithm capable of accurately detecting blood or hematic residues within the lumen of the colon based on colon CCE images.

Dual-target compounds for Alzheimer's disease: Natural and synthetic AChE and BACE-1 dual-inhibitors and their structure-activity relationship (SAR).

Alzheimer's disease (AD) is a chronic neurodegenerative disease and represents the major cause of dementia worldwide. Currently, there are no available treatments capable to deliver disease-modifying effects, and the available drugs can only alleviate the symptoms. The exact pathology of AD is not yet fully understood and several hallmarks such as the presence of amyloid-ß (Aß) senile plaques, neurofibrillary tangles (NFTs) as well as the loss of cholinergic function have been associated to AD. Distinct pharmacological targets have been validated to address AD, with acetylcholinesterase (AChE) and ß-secretase-1 (BACE-1) being two of the most explored ones. A great deal of research has been devoted to the development of new AChE and BACE-1 effective inhibitors, tackled separately or in combination of both. The multi-factorial nature of AD conducted to the development of multi-target directed ligands (MTDLs), defined as single molecules capable to modulate more than one biological target, as an alternative approach to the old paradigm one-target one-drug. In this context, this review describes a collection of natural and synthetic compounds with dual-inhibitory properties towards both AChE and BACE-1 in the MTDLs context. Furthermore, this review also provides a critical comprehensive analysis of structure-activity relationships (SAR) of the synthetic compounds.

Gold nanoparticles reduce inflammation in cerebral microvessels of mice with sepsis.

BACKGROUND: Sepsis is an emergency medical condition that can lead to death and it is defined as a life-threatening organ dysfunction caused by immune dysregulation in response to an infection. It is considered the main killer in intensive care units. Sepsis associated-encephalopathy (SAE) is mostly caused by a sepsis-induced systemic inflammatory response. Studies report SAE in 14-63% of septic patients. Main SAE symptoms are not specific and usually include acute impairment of consciousness, delirium and/or coma, along with electroencephalogram (EEG) changes. For those who recover from sepsis and SAE, impaired cognitive function, mobility and quality of life are often observed months to years after hospital discharge, and there is no treatment available today to prevent that. Inflammation and oxidative stress are key players for the SAE pathophysiology. Gold nanoparticles have been demonstrated to own important anti-inflammatory properties. It was also reported 20 nm citrate-covered gold nanoparticles (cit-AuNP) reduce oxidative stress. In this context, we tested whether 20 nm cit-AuNP could alleviate the acute changes caused by sepsis in brain of mice, with focus on inflammation. Sepsis was induced in female C57BL/6 mice by cecal ligation and puncture (CLP), 20 nm cit-AuNP or saline were intravenously (IV) injected 2 h after induction of sepsis and experiments performed 6 h after induction. Intravital microscopy was used for leukocyte and platelet adhesion study in brain, blood brain barrier (BBB) permeability carried out by Evans blue assay, cytokines measured by ELISA and real time PCR, cell adhesion molecules (CAMs) by flow cytometry and immunohistochemistry, and transcription factors, by western blotting. RESULTS: 20 nm cit-AuNP treatment reduced leukocyte and platelet adhesion to cerebral blood vessels, prevented BBB failure, reduced TNF- concentration in brain, and ICAM-1 expression both in circulating polymorphonuclear (PMN) leukocytes and cerebral blood vessels of mice with sepsis. Furthermore, 20 nm cit-AuNP did not interfere with the antibiotic effect on the survival rate of mice with sepsis. CONCLUSIONS: Cit-AuNP showed important anti-inflammatory properties in the brain of mice with sepsis, being a potential candidate to be used as adjuvant drug along with antibiotics in the treatment of sepsis to avoid SAE.

On the mechanical response of the actomyosin cortex during cell indentations.

A mechanical model is presented to analyze the mechanics and dynamics of the cell cortex during indentation. We investigate the impact of active contraction on the cross-linked actin network for different probe sizes and indentation rates. The essential molecular mechanisms of filament stretching, cross-linking and motor activity, are represented by an active and viscous mechanical continuum. The filaments behave as worm-like chains linked either by passive rigid linkers or by myosin motors. In the first example, the effects of probe size and loading rate are evaluated using the model for an idealized rounded cell shape in which properties are based on the results of parallel-plate rheometry available in the literature. Extreme cases of probe size and indentation rate are taken into account. Afterward, AFM experiments were done by engaging smooth muscle cells with both sharp and spherical probes. By inverse analysis with finite element software, our simulations mimicking the experimental conditions show the model is capable of fitting the AFM data. The results provide spatiotemporal dependence on the size and rate of the mechanical stimuli. The model captures the general features of the cell response. It characterizes the actomyosin cortex as an active solid at short timescales and as a fluid at longer timescales by showing (1) higher levels of contraction in the zones of high curvature; (2) larger indentation forces as the probe size increases; and (3) increase in the apparent modulus with the indentation depth but no dependence on the rate of the mechanical stimuli. The methodology presented in this work can be used to address and predict microstructural dependence on the force generation of living cells, which can contribute to understanding the broad spectrum of results in cell experiments.