Assessment of Behavioral Risk Factors in Chronic Obstructive Airway Diseases of the Lung Associated with Metabolic Syndrome.

BACKGROUND: Diet and lifestyle play important roles in preventing and improving chronic diseases, and evaluating behavioral risk factors in these pathologies allows for efficient management. METHODS: A clinical study by screening biochemical parameters and pulmonary function was carried out to evaluate behavioral risk factors in obstructive pulmonary disease associated with metabolic syndrome. RESULTS: Of the total of 70 patients included in the clinical study, 46 were men and 24 were women (χ2 = 3.9, p = 0.168). Forty-eight patients presented at least three met risk criteria associated with the metabolic syndrome (19 women and 29 men). Regarding the assessment of lung function, only 7 of the patients presented normal spirometry values (χ2 = 75.28, p < 0.001), and the other 63 patients presented with ventilatory dysfunction; most (over 80%) declared that they were smokers or had smoked in the past (χ2 = 5.185, p = 0.075). In terms of body weight, 45 of the patients are overweight or obese, most of them declaring that they do not consume enough vegetable products, they consume large amounts of foods of animal origin (meat, milk, eggs) but also super processed foods (food products type of junk food), do not hydrate properly, and are predominantly sedentary people (54 of the patients do no physical activity at all; χ2 = 2.12, p = 0.713). CONCLUSION: From the statistical processing of the data, it is noted that insufficient hydration, low consumption of vegetables, increased consumption of hyper-caloric food products rich in additives, sedentary lifestyle, and smoking are the main disruptive behavioral factors that worsen the health status in lung disease associated with the metabolic syndrome. An important conclusion emerging from the study is that the imbalances that aggravate obstructive lung diseases are generated by unhealthy food and an unbalanced lifestyle.

Diagnostic Performance of an Artificial Intelligence Model Based on Contrast-Enhanced Ultrasound in Patients with Liver Lesions: A Comparative Study with Clinicians.

Contrast-enhanced ultrasound (CEUS) is widely used in the characterization of liver tumors; however, the evaluation of perfusion patterns using CEUS has a subjective character. This study aims to evaluate the accuracy of an automated method based on CEUS for classifying liver lesions and to compare its performance with that of two experienced clinicians. The system used for automatic classification is based on artificial intelligence (AI) algorithms. For an interpretation close to the clinical setting, both clinicians knew which patients were at high risk for hepatocellular carcinoma (HCC), but only one was aware of all the clinical data. In total, 49 patients with 59 liver tumors were included. For the benign and malignant classification, the AI model outperformed both clinicians in terms of specificity (100% vs. 93.33%); still, the sensitivity was lower (74% vs. 93.18% vs. 90.91%). In the second stage of multiclass diagnosis, the automatic model achieved a diagnostic accuracy of 69.93% for HCC and 89.15% for liver metastases. Readers demonstrated greater diagnostic accuracy for HCC (83.05% and 79.66%) and liver metastases (94.92% and 96.61%) compared to the AI system; however, both were experienced sonographers. The AI model could potentially assist and guide less-experienced clinicians to discriminate malignant from benign liver tumors with high accuracy and specificity.

Preoperative Immunocyte-Derived Ratios Predict Postoperative Recovery of Gastrointestinal Motility after Colorectal Cancer Surgery.

The aim of this study was to assess the role of immunocyte-derived ratios (IDRs), such as the systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR), as markers for the postoperative recovery of gastrointestinal function following colorectal cancer surgery. A retrospective analysis was conducted on a consecutive cohort of 260 patients who underwent radical colorectal cancer surgery within the timeframe spanning from January 2016 to December 2022. Data concerning the postoperative recovery of gastrointestinal function included the I-FEED score, time to pass flatus, toleration for liquids in the first 48 h, and the need for nasogastric tube reinsertion in the immediate postoperative period. A special emphasis was allocated towards the examination of IDRs and their interrelation with the postoperative gastrointestinal functional parameters. The I-FEED score exhibited a positive correlation with the NLR, SII, and PLR. The univariate analysis indicated that all IDRs, multiorgan resection, hemoglobin and protein levels, regional nodal extent of the tumor (N), and obesity significantly affected nasogastric tube reinsertion. The multivariate analysis showed that the SII and N1 stages were risk factors for nasogastric tube reinsertion after colorectal cancer surgery. The SII and multiorgan resection were the only classifiers that remained significant in the multivariable analysis for the toleration for liquids. In summation, certain preoperative IDRs, such as the SII, PLR, and NLR, may hold potential as predictive determinants for postoperative gastrointestinal functional recovery following colorectal cancer surgery.

The Impact of the COVID-19 Pandemic on Outcomes in Acute Pancreatitis: A Propensity Score Matched Study Comparing before and during the Pandemic.

We aimed to evaluate the outcomes and survival of patients with acute pancreatitis who shared the same clinical form, age, and sex before the pandemic, during the pandemic, and among those with confirmed COVID-19 infection upon hospital admission. This consideration used the sparse data in the existing literature on the influence of the pandemic and COVID-19 infection on patients with acute pancreatitis. To accomplish this, we conducted a multicentric, retrospective case-control study using propensity score matching with a 2:1 match of 28 patients with SARS-CoV-2 infection and acute pancreatitis, with 56 patients with acute pancreatitis pre-pandemic, and 56 patients with acute pancreatitis during the pandemic. The study outcome demonstrated a six-fold relative risk of death in patients with acute pancreatitis and SARS-CoV-2 infection compared to those with acute pancreatitis before the pandemic. Furthermore, restrictive measures implemented during the pandemic period led to a partial delay in the care of patients with acute pancreatitis, which likely resulted in an impairment of their immune state. This, in certain circumstances, resulted in a restriction of surgical treatment indications, leading to a three-fold relative risk of death in patients with acute pancreatitis during the pandemic compared to those with acute pancreatitis before the pandemic.

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.

Genomic Profiling - A Need for Clinical Decision? -Case Reports.

Cancer is still an important health issue worldwide due to increased incidence and mortality. Personalized medicine is the future of cancer treatment. Development in technology improved technical skills in DNA/RNA sequencing. NGS technology in solid-tumor samples can describe DNA or RNA analysis by including the entire genome to detect clinical relevant mutations. Genetic results may be considered having a dynamic impact because of heterogenous molecular alterations depending of time and treatment influence. We conducted a retrospective study of all NGS tests made in the last five years for the patients from 'Sf. Nectarie' Oncology Center, Craiova, Romania. We selected three relevant clinical cases where NGS analysis was performed and the results changed the perspective of the clinical decision. Our aim is to evaluate the importance of NGS results in clinical approach. Although medicine known an important development during the last decades, only a few patients can benefit of advanced personalized treatments. It is still hard to identify the alterations or gene mutations because of genetic tests are not easily available and only a small proportion of patients carries genetic alterations.

Probe - Based Confocal Endomicroscopy and Endoscopy in NBI Module - The Role in Clinical Decision - Case Reports.

Cancer remains a significant global cause of mortality, irrespective of a country's level of development. Among all cancer types, gastrointestinal cancers claim the highest number of lives annually. This disease predominantly affects individuals in their 6th to 8th decades of life. Unfortunately, diagnoses often occur during advanced stages of the illness, rendering chemotherapy less effective and offering a reserved prognosis. Conventional endoscopy, however, struggles to differentiate lesions based on their histological composition. Consequently, the management of patients largely depends on histopathological examinations, which can be time-consuming. Biopsy results are sometimes delayed, with precious weeks passing, particularly critical for patients with malignant lesions. Moreover, biopsies may yield inconclusive results if not precisely targeted, leading to potential mismanagement, unnecessary resections and burdensome pathology services. This series of cases underscores, as previous studies have, the value of modern endoscopic techniques in determining the appropriate therapeutic approach for each patient, an approach that ensures the highest quality of life.

Has COVID-19 Modified the Weight of Known Systemic Inflammation Indexes and the New Ones (MCVL and IIC) in the Assessment as Predictive Factors of Complications and Mortality in Acute Pancreatitis?

We aimed at evaluating the prognostic capacity of the inflammatory indices derived from routine complete blood cell counts in two groups of patients with acute pancreatitis from two different time periods, before and during the COVID-19 pandemic, when a high incidence of complications with surgical risk and mortality was found. Two new markers were introduced: the mean corpuscular volume to lymphocyte ratio (MCVL) and the cumulative inflammatory index (IIC), which were calculated at a baseline in the two groups of patients. Of the already established markers, none of them managed to effectively predict the complications with surgical risk and mortality, with a decrease of less than 50% in specificity in the peri-COVID group. The MCVL had the best prediction of complications with surgical risk in both the pre-COVID and peri-COVID groups, validated it as an independent factor by multivariate analysis. The IIC had the best prediction of mortality in both periods and was proven to be an independent factor by multivariate analysis. As the IIC predicted death best, we tested the occurrence of death and found that patients with PA who had an IIC > 12.12 presented a risk of death 4.08 times higher in the pre-COVID group and 3.33 times higher in the peri-COVID group. The new MCVL and IIC independent markers had a superior sensitivity and specificity in predicting surgical risk complications and, respectively, mortality in the group of patients with acute pancreatitis during the COVID-19 pandemic, which makes them widely applicable in populations with modified immune and inflammatory status. Conclusions: In patients with acute pancreatitis, MCVL has a significant predictive value regarding complications with surgical risk (abscess, necrosis, and pseudocyst), and the IIC has a significant predictive value for mortality.

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.

Diagnosis of lung cancer by flexible fiberoptic bronchoscopy: a descriptive study.

Flexible fiberoptic bronchoscopy (FFB) remains the most important minimally invasive method for the diagnosis of lung cancer (LC). We performed a retrospective study to assess the main endoscopic findings of malignant lung tumors in the large airways in a cohort of Romanian patients. The group consisted of 32 (84.21%) men and six (15.78%) women, with an average age of 64.63±6.07 years. The bronchoscopic examination allowed the detection and biopsy of 36 malignant lung tumors, and in two other cases, due to malignant atelectasis, the patients were sent to a Department of Thoracic Surgery, to perform the biopsy following the surgery. Histopathological (HP) examination revealed the presence of squamous cell carcinoma (SCC) in 19 (50%) patients, adenocarcinoma (ADC) in 11 (28.94%) patients and small cell lung cancer (SCLC) in eight (21.05%) patients. The macroscopic and microscopic analysis of the lung tumors showed that infiltrative forms were found in most cases (58.33%), followed by exophytic (mass) endobronchial lesions (22.22%) and mixed forms (19.44%). If most infiltrative forms were SCC (66.66%), the exophytic and mixed lesions were most frequently ADC (50% and 57.14%). The tumor lesions caused both malignant bronchial stenosis (57.89%) and malignant atelectasis (42.1%). The main mechanisms involved in bronchial malignant obstruction were endoluminal (50%), mixed (31.57%) and extraluminal (18.42%) mechanisms. In conclusion, FFB remains the main method of diagnosing LC in the large airways. The most common macroscopic appearance of lung tumors revealed by bronchoscopy was the infiltrative appearance. In half of our patients, the malignant bronchial obstruction was achieved by endoluminal mechanism. The most common pathological form found in our patients was the SCC, as described in half of the investigated patients.

Cerebrolysin Use in Patients with Liver Damage-A Translational Study.

The treatment of acute life-threatening events in patients suffering from chronic pathologies is problematic, as physicians need to consider multisystemic drug effects. Regarding Cerebrolysin, a Sonic Hedgehog signaling pathway amplifier and one of the few approved neurotrophic treatments for stroke patients, concerns of excessive Hedgehog pathway activation that could accelerate NAFLD progression to cirrhosis seem valid. We investigated stroke patients treated with Cerebrolysin that presented elevated levels of aspartate aminotransferase (AST) and/or alanine aminotransferase (ALT). We also investigated the efficiency of Cerebrolysin in reversing the neurogenesis inhibition within the hippocampus in a mouse model of NAFLD by evaluating behavior and histological outcomes. NeuN, BrdU and Iba1 positive signals in the cortex and hippocampus of the animals were also observed. Clinically, Cerebrolysin improved AST levels in a majority of stroke patients with hepatic damage. The same treatment in an experimental setup was able to reverse anxiety-like behavior in MCD mice, reducing their freezing time from 333.61 ± 21.81 s in MCD animals to 229.17 ± 26.28 in treated ones. The use of Cerebrolysin did not improve short-term memory nor rescued cell multiplication in the hippocampus after MCD food intake. Understanding the neuroprotective and neurotrophic effects that drugs have on NAFLD patients can significantly contribute to a suitable therapeutic approach.

Post-COVID-19 Syndrome Based on Disease Form and Associated Comorbidities.

(1) Background: SARS-CoV-2 has infected more than 97 million people worldwide and caused the death of more than 6 million. (2) Methods: Between 1 October and 31 December 2020, 764 patients diagnosed with SARS-CoV-2 infection were selected based on RT-PCR test results. The following parameters were noted: age, gender, origin, days of hospitalization, COVID-19 experienced form, radiographic imaging features, associated comorbidities, and recommended treatment at discharge. (3) Results: The mean age at the time of COVID-19 infection was 55.2 years for men and 55.3 years for women. There was a similar age distribution among patients, regardless of gender. There was a substantial difference between the average lengths of hospitalization and those with residual symptoms-most patients who reported symptoms after discharge had been admitted with moderately severe forms of illness. Fatigue was the main remaining symptom (36%). (4) Conclusions: In conclusion, to clarify the impact of SARS-CoV-2 infection on patients in the long term, further studies are needed to investigate the elements assessed. Well-designed recovery programs will be needed to effectively manage these patients, with multidisciplinary collaboration and a team of professionals involved in all aspects of post-COVID patient health.

Federated Learning Approach with Pre-Trained Deep Learning Models for COVID-19 Detection from Unsegmented CT images.

(1) Background: Coronavirus disease 2019 (COVID-19) is an infectious disease caused by SARS-CoV-2. Reverse transcription polymerase chain reaction (RT-PCR) remains the current gold standard for detecting SARS-CoV-2 infections in nasopharyngeal swabs. In Romania, the first reported patient to have contracted COVID-19 was officially declared on 26 February 2020. (2) Methods: This study proposes a federated learning approach with pre-trained deep learning models for COVID-19 detection. Three clients were locally deployed with their own dataset. The goal of the clients was to collaborate in order to obtain a global model without sharing samples from the dataset. The algorithm we developed was connected to our internal picture archiving and communication system and, after running backwards, it encountered chest CT changes suggestive for COVID-19 in a patient investigated in our medical imaging department on the 28 January 2020. (4) Conclusions: Based on our results, we recommend using an automated AI-assisted software in order to detect COVID-19 based on the lung imaging changes as an adjuvant diagnostic method to the current gold standard (RT-PCR) in order to greatly enhance the management of these patients and also limit the spread of the disease, not only to the general population but also to healthcare professionals.

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.

Perceived Stress in Hepatitis C Virus Infected Patients under the DAA-Based Therapy.

The Hepatitis C Virus (HCV) infection often associates medical and mental health conditions which lead to increased levels of distress. Our study aimed at assessing the level of perceived stress on a sample of 90 HCV infected patients treated with Direct-Acting Antiviral (DAA) agents for 12 weeks, and its possible correlations with clinical and evolutionary elements. The evaluation was conducted in three phases: before administration of the DAAs (BSL), at the End of the Treatment (EOT), and 24 weeks after the BSL (Sustained Viral Response­SVR). The perceived stress was measured using the Perceived Stress Scale (PSS). The efficiency of the DAA treatment reduced the levels of stress (98.99% moderate and high stress at BSL to 70.00% at SVR). It was observed, for the entire study period (BSL to SVR), that the decrease in the perceived stress severity was significantly associated with demographic items such as gender (p < 0.01), urban environment (p < 0.001), the age of the subjects (p < 0.05), and clinical data such as F4 degree of fibrosis (p = 0.001) and overweight or obesity class II (p < 0.01). The perceived stress is directly associated with the severity of the HCV infection, and it could be significantly lowered by an efficient therapeutic approach, as DAAs are nowadays.

Resource Management through Artificial Intelligence in Screening Programs-Key for the Successful Elimination of Hepatitis C.

BACKGROUND: The elimination of the Hepatitis C virus (HCV) will only be possible if rapid and efficient actions are taken. Artificial neural networks (ANNs) are computing systems based on the topology of the biological brain, containing connected artificial neurons that can be tasked with solving medical problems. AIM: We expanded the previously presented HCV micro-elimination project started in September 2020 that aimed to identify HCV infection through coordinated screening in asymptomatic populations and developed two ANN models able to identify at-risk subjects selected through a targeted questionnaire. MATERIAL AND METHOD: Our study included 14,042 screened participants from a southwestern region of Oltenia, Romania. Each participant completed a 12-item questionnaire along with anti-HCV antibody rapid testing. Hepatitis-C-positive subjects were linked to care and ultimately could receive antiviral treatment if they had detectable viremia. We built two ANNs, trained and tested on the dataset derived from the questionnaires and then used to identify patients in a similar, already existing dataset. RESULTS: We found 114 HCV-positive patients (81 females), resulting in an overall prevalence of 0.81%. We identified sharing personal hygiene items, receiving blood transfusions, having dental work or surgery and re-using hypodermic needles as significant risk factors. When used on an existing dataset of 15,140 persons (119 HCV cases), the first ANN models correctly identified 97 (81.51%) HCV-positive subjects through 13,401 tests, while the second ANN model identified 81 (68.06%) patients through only 5192 tests. CONCLUSIONS: The use of ANNs in selecting screening candidates may improve resource allocation and prioritize cases more prone to severe disease.

Depressive and Anxious Symptoms in Hepatitis C Virus Infected Patients Receiving DAA-Based Therapy.

Hepatitis C virus (HCV) represents the most important etiologic factor for advanced fibrosis/cirrhosis and hepatocellular carcinoma associated with a psychological dimension. Our study aims to assess, on a sample comprising of 90 HCV-infected subjects (96.67% F3-F4 METAVIR), the relationship between Direct-Acting Antiviral (DAA) therapies and the psychological effects of the liver disease, focused on the anxious and depressive symptoms. The comprehensive evaluation was done before starting the DAA treatment (BSL), after 12 weeks (End of Treatment-EOT), respectively after another 12 weeks (Sustained Viral Response-SVR). Presumable depressive and/or anxious symptoms were evaluated by Hospital Anxiety and Depression Scale (HADS). The reported depressive symptoms decreased from 21.11% (BSL) to 1.11% (SVR) (p < 0.00001), while the anxious ones dropped from 43.34% (BSL) to 4.44% (SVR) (p < 0.00001), without a clear evolutionary pattern. We identified no statistically significant interaction between comorbidities (anemia, CKD, obesity) over HADS scores evolution (p > 0.05), while the DAAs side-effects (fatigue, headache, pruritus) significantly influenced the anxious and depressive symptoms (p < 0.05). During and after the DAA-based therapy, patients with HCV infection presented a significantly reduced rate of the associated depressive and anxious relevant symptoms.

Micro-Elimination: Updated Pathway to Global Elimination of Hepatitis C in Small Communities and Industrial Settings during the COVID 19 Pandemic.

BACKGROUND: In response to the goal of the World Health Organisation to eliminate hepatitis C virus infections by 2030, Romania is striving for national elimination. An already successful micro-elimination project was expanded to test-and-treat specific populations and at-risk groups. The aim of this project was to identify the individuals with HCV infection in disadvantaged regions who do not have proper medical care access. MATERIALS AND METHODS: Our two-arm interventional cross-sectional study used rapid anti-HCV antibody testing on two population groups from the Romanian southwestern region of Oltenia, approached between September 2020 and May 2021. The first group consisted of predominantly over 40 years old individuals, recruited through five family doctors from two medium-sized towns (community lot-CL). We approached a second group, aged 18-65, through 11 medical offices of five large factories in the same region (industry lot, IL). A 12-items questionnaire was given to each participant, to determine risk factors and record demographic data. Eligible patients initiated antiviral therapy using direct-acting antivirals (DAAs). RESULTS: We enrolled 15,383 individuals between all 16 locations. The overall prevalence by antibody testing was 0.77% (119 cases). Of these, 57 subsequently received treatment with DAAs. We identified blood transfusions as a risk factor within the CL. Participants in the IL reported a relatively high risk for the following situations: sharing of personal hygiene belongings with another person, performing previous blood transfusions, dental interventions and previous surgery. CONCLUSIONS: In this global context, the use of micro-elimination allows interventions to be faster and more efficient. This is possible by targeting smaller and specific HCV risk groups.

Nanoparticle-functionalized dressings for the treatment of third-degree skin burns - histopathological and immunohistochemical study.

Skin burns are one of the most common injuries associated with increased morbidity and mortality, especially in the children and the elderlies. Severe burns, especially, result in a systemic immune and inflammatory response, which may reflect in multiple organ insufficiency, and a fast and effective local restorative process is essential for functionality recovering, as well as for interrupting the generalized systemic response. We have aimed here to assess the effect of different wound dressings in what it regards the morphology and clinical restoration after a skin burn. On a rat animal model, we have evaluated the macroscopic and histopathological features of controlled third degree skin burns in control animals versus treatments with local dressings of silver sulfadiazine (SDA) cream, simple gel (G), gel + silver nanoparticles (AgNPs) (G+NPS), gel + exosomes (G+EXO) and gel + AgNPs + exosomes (Gel+NPS+EXO), at 14 days and, respectively, 21 days after the lesion. Tissue fragments were harvested and processed for histopathology and immunohistochemistry. Immunofluorescence was utilized to evaluate the maturity of underlaying granulation tissue based on double stainings for smooth muscle actin (SMA) and cluster of differentiation 31 (CD31). Our study showed variability in what it regards the vessel density and immunoexpression of SMA between the treatments, and image analysis revealed that most SMA reduction and blood vessel density reduction in the maturing granulation tissue occurred for the G+NPS and G+NPS+EXO treatments. A complete re-epithelization was also observed for the G+NPS+EXO treatment. Overall, our results show that improved topic treatments promote faster re-epithelization and reparation of the dermis after skin burn lesions, providing thus an avenue for new treatments that aim both local recuperation and systemic infection prevention.

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.