Integrative Approach to Risk Factors in Simple Chronic Obstructive Airway Diseases of the Lung or Associated with Metabolic Syndrome-Analysis and Prediction.

We conducted an epidemiological non-interventional cross-sectional and case-control study from 1 January 2023 until 26 May 2023 in Oltenia region, southwestern Romania. Throughout the research, 160 consecutive patients were included from two different clinical departments (1-Pneumology; 2-Diabetes and Nutritional Diseases). Subjects were voluntary adult individuals of any gender who expressed their written consent. The clinical data of the patients were correlated with the exposure to behavioral risk factors (diet, lifestyle, exposure to pollutants) to identify some negative implications that could be corrected to improve the quality of life of patients with simple chronic obstructive airway diseases of the lung or associated with metabolic syndrome (MS). In the first group of patients with respiratory diseases, there was a higher degree of exposure to toxic substances (43.75%) compared to the second group of patients with diabetes (18.75%); it is also noticeable that in the first group, there were noticeably fewer individuals who have never smoked (25%) compared to the second group (50%). Respiratory function impairment was observed to be more severe in overweight individuals. In the group of patients with known lung diseases, a positive correlation was noted between the presence of MS and respiratory dysfunctions of greater severity. Additionally, potential exacerbating factors affecting lung function, such as direct exposure to toxins and smoking, were considered. Potential secondary factors exacerbating respiratory dysfunction were considered by correlating biochemical parameters with dietary habits. These included reduced consumption of vegetables, inadequate hydration, and increased intake of sweets and products high in saturated or trans fats (commonly found in junk food), primarily due to their potential contribution to excess weight. Compared to patients without MS, the severity of the pulmonary function impairment correlated with the number of criteria met for MS and, independently, with an increase in weight.

Interactions between Cognitive, Affective, and Respiratory Profiles in Chronic Respiratory Disorders: A Cluster Analysis Approach.

This study conducted at Leamna Pulmonology Hospital investigated the interrelations among cognitive, affective, and respiratory variables within a cohort of 100 patients diagnosed with chronic respiratory conditions, utilizing sophisticated machine learning-based clustering techniques. Spanning from October 2022 to February 2023, hospitalized individuals confirmed to have asthma or COPD underwent extensive evaluations using standardized instruments such as the mMRC scale, the CAT test, and spirometry. Complementary cognitive and affective assessments were performed employing the MMSE, MoCA, and the Hamilton Anxiety and Depression Scale, furnishing a holistic view of patient health statuses. The analysis delineated three distinct clusters: Moderate Cognitive Respiratory, Severe Cognitive Respiratory, and Stable Cognitive Respiratory, each characterized by unique profiles that underscore the necessity for tailored therapeutic strategies. These clusters exhibited significant correlations between the severity of respiratory symptoms and their effects on cognitive and affective conditions. The results highlight the benefits of an integrated treatment approach for COPD and asthma, which is personalized based on the intricate patterns identified through clustering. Such a strategy promises to enhance the management of these diseases, potentially elevating the quality of life and everyday functionality of the patients. These findings advocate for treatment customization according to the specific interplays among cognitive, affective, and respiratory dimensions, presenting substantial prospects for clinical advancement and pioneering new avenues for research in the domain of chronic respiratory disease management.

Assessment of Dietary and Lifestyle Quality among the Romanian Population in the Post-Pandemic Period.

BACKGROUND: The pandemic caused by the SARS-CoV-2 virus demonstrated the importance of prevention through a healthy diet and lifestyle, the most vulnerable people being those with severe chronic conditions, those who are overweight, and those with an unbalanced immune system. This study aims to examine the nutritional status and lifestyle behaviors of the Romanian population. METHODS: The evaluation of the eating habits and lifestyle of the Romanian population in the post-pandemic period was carried out based on a cross-sectional observational study with the help of a questionnaire. RESULTS: A total of 4704 valid answers were registered (3136 female and 1568 male respondents). Among the respondents, most of them belong to the young population, 2892 between the ages of 18 and 40, i.e., 61.5%. Most male respondents are overweight (1400) and obese (780). Most respondents indicated a tendency to consume 1-2 meals per day irregularly (p = 0.617). Only 974 respondents adopted a healthy diet, and 578 a healthy lifestyle. CONCLUSIONS: The present study reports low adherence to a healthy diet (20.7%) and healthy lifestyle (12.28%), especially among the young population (<30 years). In the current context, it reports a reduced tendency to consume vegetables and fruits among the population, below the daily average recommended by the nutrition guidelines, a tendency towards sedentary behavior, and even deficient hydration of some of the respondents; these negative aspects can create a long-term series of nutritional and psycho-emotional imbalances. Our results evidence that complex surveys among the population are regularly required to investigate nutritional or lifestyle deficiencies; moreover, it could be helpful in further educational measures in nutrition, food, and environmental safety.

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.