Immune Responses of Healthy Pregnant Women following an Elective Cesarean Section: Effects of Anesthetic Procedures.

A weakened immune system and more inflammatory cytokines being released are possible effects of the surgical stress that a cesarean section induces. This kind of reaction, in addition to the altered reaction to catecholamines, has the potential to significantly affect the immune system of the mother and the patients' general postoperative course. This prospective study compared the plasma levels of catecholamines and cytokines in healthy pregnant patients having cesarean sections under spinal anesthesia versus general anesthesia. A total of 30 pregnant women undergoing elective cesarean sections were divided into two groups: 15 who received general anesthesia (GA) and 15 who received spinal anesthesia (SA). Blood samples were collected from all subjects before anesthesia induction (pre-OP), 6 h postoperatively (6 h post-OP), and 12 h (12 h post-OP), to measure levels of tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), IL-8, IL-4, IL-10, norepinephrine (NE), and epinephrine (EPI). When we compared the two groups, we discovered that only IL-6 and IL-4 had significantly higher levels pre-OP, whereas all studied cytokines exhibited an increase in the GA versus SA group at 6 and 12 h post-OP. In the case of catecholamines, we discovered that serum levels are positively related with pro-inflammatory or anti-inflammatory cytokines, depending on the time of day and type of anesthetic drugs. Compared to SA, GA has a more consistent effect on the inflammatory response and catecholamine levels. The findings of this study confirm that the type of anesthesia can alter postoperative immunomodulation to various degrees via changes in cytokine and catecholamine production. SA could be a preferable choice for cesarean section because it is an anesthetic method that reduces perioperative stress and allows for less opioid administration, impacting cytokine production with proper immunomodulation.

Expression of Calcitonin Gene-Related Peptide and Calcitonin Receptor-like Receptor in Colorectal Adenocarcinoma.

Colorectal cancer is one of the most widespread types of cancer that still causes many deaths worldwide. The development of new diagnostic and prognostic markers, as well as new therapeutic methods, is necessary. The calcitonin gene-related peptide (CGRP) neuropeptide alongside its receptor calcitonin receptor-like receptor (CRLR) could represent future biomarkers and a potential therapeutic target. Increased levels of CGRP have been demonstrated in thyroid, prostate, lung, and breast cancers and may also have a role in colorectal cancer. At the tumor level, it acts through different mechanisms, such as the angiogenesis, migration, and proliferation of tumor cells. The aim of this study was to measure the level of CGRP in colorectal cancer patients' serum by enzyme-linked immunosorbent assay (ELISA) and determine the level of CGRP and CRLR at the tumor level after histopathological (HP) and immunohistochemical (IHC) analysis, and then to correlate them with the TNM stage and with different tumoral characteristics. A total of 54 patients with newly diagnosed colorectal adenocarcinoma were evaluated. We showed that serum levels of CGRP, as well as CGRP and CRLR tumor level expression, correlate with the TNM stage, with local tumor extension, the presence of lymph node metastasis, and distant metastasis, and also with the tumor differentiation degree. CGRP is present in colorectal cancer from the incipient TNM stage, with levels increasing with the stage, and can be used as a diagnostic and prognostic marker and may also represent a potentially new therapeutic target.

Intraplacental Villous Artery Doppler can Improve the Ability to Predict Placenta-Mediated Disease.

OBJECTIVE: Evaluation of Intraplacental Villous Artery Doppler (IPVA) as a predictive factor compared to umbilical artery (UA) Doppler in placenta-mediated disease (PMD). METHODS: This prospective study included a group of 106 pregnant women, of which 76 patients constituted the PMD group: preeclampsia (PE) and small for gestational age (SGA), and 30 pregnant women constituted the control group. IPVA and UA Doppler evaluation was performed in 2 pregnancy periods: 20.0-23.6 weeks, and 28.0-32.6 weeks of gestation. RESULTS: From the study of maternal characteristics and risk factors for the presented pathology, we found that no studied risk factor was statistically involved in the evolution toward PMD during pregnancy. In the control group, we noticed a decrease in IPVA PI and RI, along with an increase in gestational age, while in the PMD group, these indices increased. Both in the 2nd and the 3rd trimester, we had a significant statistical difference between the two groups (p<0.001). Regarding the degree of prediction of the changes that occurred at this level, we found a good statistical correlation. A higher degree of positive predictability is noted, for IPVA-PI, but also for UA-PI, but with better sensitivity (72.27%) for UA PI in the 2nd trimester. CONCLUSIONS: We can conclude that both Doppler measurements, IPVA and UA can be used to evaluate and detect pregnancy complications that belong to PMD, preeclampsia, and/or fetal growth restriction.

Tumor Area Highlighting Using T2WI, ADC Map, and DWI Sequence Fusion on bpMRI Images for Better Prostate Cancer Diagnosis.

Prostate cancer is the second most common cancer in men worldwide. The results obtained in magnetic resonance imaging examinations are used to decide the indication, type, and location of a prostate biopsy and contribute information about the characterization or aggressiveness of detected cancers, including tumor progression over time. This study proposes a method to highlight prostate lesions with a high and very high risk of being malignant by overlaying a T2-weighted image, apparent diffusion coefficient map, and diffusion-weighted image sequences using 204 pairs of slices from 80 examined patients. It was reviewed by two radiologists who segmented suspicious lesions and labeled them according to the prostate imaging-reporting and data system (PI-RADS) score. Both radiologists found the algorithm to be useful as a "first opinion", and they gave an average score on the quality of the highlight of 9.2 and 9.3, with an agreement of 0.96.

An Automated Method for Classifying Liver Lesions in Contrast-Enhanced Ultrasound Imaging Based on Deep Learning Algorithms.

BACKGROUND: Contrast-enhanced ultrasound (CEUS) is an important imaging modality in the diagnosis of liver tumors. By using contrast agent, a more detailed image is obtained. Time-intensity curves (TIC) can be extracted using a specialized software, and then the signal can be analyzed for further investigations. METHODS: The purpose of the study was to build an automated method for extracting TICs and classifying liver lesions in CEUS liver investigations. The cohort contained 50 anonymized video investigations from 49 patients. Besides the CEUS investigations, clinical data from the patients were provided. A method comprising three modules was proposed. The first module, a lesion segmentation deep learning (DL) model, handled the prediction of masks frame-by-frame (region of interest). The second module performed dilation on the mask, and after applying colormap to the image, it extracted the TIC and the parameters from the TIC (area under the curve, time to peak, mean transit time, and maximum intensity). The third module, a feed-forward neural network, predicted the final diagnosis. It was trained on the TIC parameters extracted by the second model, together with other data: gender, age, hepatitis history, and cirrhosis history. RESULTS: For the feed-forward classifier, five classes were chosen: hepatocarcinoma, metastasis, other malignant lesions, hemangioma, and other benign lesions. Being a multiclass classifier, appropriate performance metrics were observed: categorical accuracy, F1 micro, F1 macro, and Matthews correlation coefficient. The results showed that due to class imbalance, in some cases, the classifier was not able to predict with high accuracy a specific lesion from the minority classes. However, on the majority classes, the classifier can predict the lesion type with high accuracy. CONCLUSIONS: The main goal of the study was to develop an automated method of classifying liver lesions in CEUS video investigations. Being modular, the system can be a useful tool for gastroenterologists or medical students: either as a second opinion system or a tool to automatically extract TICs.

The Role of Neutrophil to Lymphocyte Ratio in the Assessment and Rehabilitation of Knee Osteoarthritis Patients.

Osteoarthritis (OA) is considered to be a real problem for many people. The last decade is characterized through an increased interest in using a non-specific, simply and readily available marker of inflammation-neutrophil to lymphocyte ratio (NLR)-to predict various chronic diseases (gastrointestinal and colorectal cancers, lung cancer, cardiovascular events, sarcoidosis, arthritis). The aim of our study is to establish the correlation between NLR and other parameters of clinical and functional status in KOA patients and to compare the NLR values before and after rehabilitation program. 90 patients, aged 40 to 82 years, diagnosed with mild (8 patients), moderate (70 patients) and severe (12 patients) KOA, in accordance with Kellgren and Lawrence score. Statistical assessment showed different values for the erythrocyte sedimentation (ESR) rate at 1-hour, Visual Analogue Scale (VAS), and Lequesne index in the studied group. NLR regression was significant for ESR at 1 and 2 hours. As an independent diagnostic marker, NLR has limited value, however it can be considered an inexpensive additional biomarker for the diagnosis of KOA and for monitoring the rehabilitation program.

Deep Learning Algorithms in the Automatic Segmentation of Liver Lesions in Ultrasound Investigations.

BACKGROUND: The ultrasound is one of the most used medical imaging investigations worldwide. It is non-invasive and effective in assessing liver tumors or other types of parenchymal changes. METHODS: The aim of the study was to build a deep learning model for image segmentation in ultrasound video investigations. The dataset used in the study was provided by the University of Medicine and Pharmacy Craiova, Romania and contained 50 video examinations from 49 patients. The mean age of the patients in the cohort was 69.57. Regarding presence of a subjacent liver disease, 36.73% had liver cirrhosis and 16.32% had chronic viral hepatitis (5 patients: chronic hepatitis C and 3 patients: chronic hepatitis B). Frames were extracted and cropped from each examination and an expert gastroenterologist labelled the lesions in each frame. After labelling, the labels were exported as binary images. A deep learning segmentation model (U-Net) was trained with focal Tversky loss as a loss function. Two models were obtained with two different sets of parameters for the loss function. The performance metrics observed were intersection over union and recall and precision. RESULTS: Analyzing the intersection over union metric, the first segmentation model obtained performed better compared to the second model: 0.8392 (model 1) vs. 0.7990 (model 2). The inference time for both models was between 32.15 milliseconds and 77.59 milliseconds. CONCLUSIONS: Two segmentation models were obtained in the study. The models performed similarly during training and validation. However, one model was trained to focus on hard-to-predict labels. The proposed segmentation models can represent a first step in automatically extracting time-intensity curves from CEUS examinations.

Assessing Putative Markers of Colorectal Cancer Stem Cells: From Colonoscopy to Gene Expression Profiling.

Cancer stem cells (CSCs) are proposed to be involved in colorectal cancer (CRC) initiation, growth, and metastasis. The aim of our pilot study was to assess possible correlations between the clinicopathological characteristics of CRC patients and CSCs gene expression patterns, in order to provide insight into new methods for patient stratification and targeted therapeutic strategies. Our study involved 60 CRC patients, and the following three specific CSC genes were targeted: PROM1/CD133, ALCAM/CD166 and HCAM /CD44. Data are presented as relative mRNA expression of target genes to GAPDH. The expression of total CD133 and CD166 was assessed in paired samples of CRC tumors and adjacent tissue, while CD44 was assessed in similar samples. The qRT-PCR analysis detected all three targeted genes to different extents, in both normal and tumor tissue. In nine cases (15.69%), total CD133 had a higher expression in tumor tissue, whilst in 28 cases (47.06%) the expression was higher in non-malignant peritumor tissue. The total CD166 expression was increased in tumor tissue compared with paired non-invaded peritumor samples in eight cases (13.73%), whilst in eight cases (13.73%) the expression was higher in non-malignant peritumor tissue. Total CD44 expression was higher in tumor tissue compared with paired non-invaded peritumor samples in 47 cases (78.95%). In the remaining cases the difference between paired samples was biologically insignificant. In conclusion, our study suggests that qRT-PCR is feasible in assessing the gene expression profiles of CSCs from CRC, and a promising pathway to be followed for determining how often a person needs screening by colonoscopy and at which age to start. This could improve CRC diagnosis and early patient stratification, and open the way for new oncologic treatment development.

Nodular and Micronodular Basal Cell Carcinoma Subtypes Are Different Tumors Based on Their Morphological Architecture and Their Interaction with the Surrounding Stroma.

Basal cell carcinoma (BCC) is the most frequent cancer of the skin and comprises low-risk and high-risk subtypes. We selected a low-risk subtype, namely, nodular (N), and a high-risk subtype, namely, micronodular (MN), with the aim to identify differences between them using a classical morphometric approach through a gray-level co-occurrence matrix and histogram analysis, as well as an approach based on deep learning semantic segmentation. From whole-slide images, pathologists selected 216 N and 201 MN BCC images. The two groups were then manually segmented and compared based on four morphological areas: center of the BCC islands (tumor, T), peripheral palisading of the BCC islands (touching tumor, TT), peritumoral cleft (PC) and surrounding stroma (S). We found that the TT pattern varied the least, while the PC pattern varied the most between the two subtypes. The combination of two distinct analysis approaches yielded fresh insights into the characterization of BCC, and thus, we were able to describe two different morphological patterns for the T component of the two subtypes.

IBD Monitor: Romanian National Mobile Application for Inflammatory Bowel Disease Personalized Treatment and Monitoring.

BACKGROUND: In the last 30 years, we have seen an increase in the incidence of inflammatory bowel disease (IBD). Most cases are diagnosed in the 2nd and 3rd decades of life, a population group that is most familiar with the latest innovations in technology. Patients want to obtain more information about their disease and have complete control over the pathology, while reducing physical meetings with their doctor. Starting from these ideas, the present study aimed to develop a mobile application (app) to support IBD patients on symptoms/events reporting and on treatment administration monitoring. METHODS: A multidisciplinary team was created to document and develop the app requirements and design its functionality. The app was beta-tested by several IBD patients. Their feedback was used to further refine the app. RESULTS: We developed connected apps for both smartphones and smartwatches, with dedicated sections for event reporting and medication administration reminders/reporting. CONCLUSIONS: The development of apps dedicated to IBD patients is still in early progress. By creating this app, we aim to improve the evolution and compliance of IBD patients and to obtain new information that will have a beneficial impact on the management of these patients and open the door for personalized medicine.

Deep Learning: a Promising Method for Histological Class Prediction of Breast Tumors in Mammography.

The objective of the study was to determine if the pathology depicted on a mammogram is either benign or malignant (ductal or non-ductal carcinoma) using deep learning and artificial intelligence techniques. A total of 559 patients underwent breast ultrasound, mammography, and ultrasound-guided breast biopsy. Based on the histopathological results, the patients were divided into three categories: benign, ductal carcinomas, and non-ductal carcinomas. The mammograms in the cranio-caudal view underwent pre-processing and segmentation. Given the large variability of the areola, an algorithm was used to remove it and the adjacent skin. Therefore, patients with breast lesions close to the skin were removed. The remaining breast image was resized on the Y axis to a square image and then resized to 512 × 512 pixels. A variable square of 322,622 pixels was searched inside every image to identify the lesion. Each image was rotated with no information loss. For data augmentation, each image was rotated 360 times and a crop of 227 × 227 pixels was saved, resulting in a total of 201,240 images. The reason why our images were cropped at this size is because the deep learning algorithm transfer learning used from AlexNet network has an input image size of 227 × 227. The mean accuracy was 95.8344% ± 6.3720% and mean AUC 0.9910% ± 0.0366%, computed on 100 runs of the algorithm. Based on the results, the proposed solution can be used as a non-invasive and highly accurate computer-aided system based on deep learning that can classify breast lesions based on changes identified on mammograms in the cranio-caudal view.