Predictors of Complicated Appendicitis with Evolution to Appendicular Peritonitis in Pediatric Patients

Background and Objecitves: Appendicitis is one of the most frequent surgical emergencies in pediatric surgery. Complicated appendicitis can evolve with appendicular peritonitis characterized by the diffusion of the pathological process to the peritoneal cavity, thus producing generalized or localized inflammation of the peritoneum. The capacity to anticipate the possibility of perforation in acute appendicitis can direct prompt management and lower morbidity. There is no specific symptom that could be used to anticipate complicated appendicitis, and diagnostic clues include a longer period of symptoms, diffuse peritoneal signs, high fever, elevated leukocytosis and CRP, hyponatremia, and high ESR. Imagistic methods, particularly US and CT, are useful but not sufficient. There are no traditional inflammation biomarkers able to predict the evolution of uncomplicated to complicated appendicitis alone, but the predictive capacity of novel biomarkers is being investigated. Materials and Methods: The present study represents a retrospective evaluation of children hospitalized between January 2021 and July 2022 in the Grigore Alexandrescu Clinical Emergency Hospital for Children with a diagnosis of acute appendicitis settled based on clinical characteristics, traditional and novel biomarkers, and ultrasonographic features. The children were subsequently grouped into two groups based on the existence of appendicular peritonitis on intraoperative inspection of the abdominal cavity. The aim of this report is to establish the predictors that may aid physicians in timely identifying pediatric patients diagnosed with acute appendicitis at risk for developing complicated appendicitis with evolution to appendicular peritonitis. Results: The neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte radio (PLR) are representative severity markers in infections. This report analyzes the benefit of these markers for distinguishing uncomplicated appendicitis from complicated appendicitis in pediatric patients. Conclusions: Our study suggests that a value of neutrophil-to-lymphocyte ratio greater than 8.39 is a reliable parameter to predict the evolution to appendicular peritonitis

Decision Support Algorithm Based on the Concentrations of Air Pollutants Visualization

As medical technologies are continuously evolving, consumer involvement in health is also increasing significantly. The integration of the Internet of Things (IoT) concept in the health domain may improve the quality of healthcare through the use of wearable sensors and the acquisition of vital and environmental parameters. Currently, there is significant progress in developing new approaches to provide medical care and maintain the safety of the life of the population remotely and around the clock. Despite the standards for emissions of harmful substances into the atmosphere established by the legislation of different countries, the level of pollutants in the air often exceeds the permissible limits, which is a danger not only for the population but also for the environment as a whole. To control the situation an Air Quality Index (AQI) was introduced. For today, many works discuss AQI, however, most of them are aimed rather at studying the methodologies for calculating the index and comparing air quality in certain regions of different countries, rather than creating a system that will not only calculate the index in real-time but also make it publicly available and understandable to the population. Therefore we would like to present a decision support algorithm for a solution called “Environmental Sensing to Act for a Better Quality of Life: Smart Health” with the primary goal of ensuring the transformation of raw environmental data collected by special sensors (data which typically require scientific interpretation) into a form that can be easily understood by the average user; this is achieved through the proposed algorithm. The obtained result is a system that increases the self-awareness and self-adaptability of people in environmental monitoring by offering easy to read and understand suggestions. The algorithm considers three types of parameters (concentration of PM10 (particulate matter), PM2.5, and NO2) and four risk levels for each of them. The technical implementation is presented in a step-like procedure and includes all the details (such as calculating the Air Quality Index—AQI, for each parameter). The results are presented in a front-end where the average user can observe the results of the measurements and the suggestions for decision support. This paper presents a supporting decision algorithm, highlights the basic concept that was used in the development process, and discusses the result of the implementation of the proposed solution

Decision Support Algorithm Based on the Concentrations of Air Pollutants Visualization

As medical technologies are continuously evolving, consumer involvement in health is also increasing significantly. The integration of the Internet of Things (IoT) concept in the health domain may improve the quality of healthcare through the use of wearable sensors and the acquisition of vital and environmental parameters. Currently, there is significant progress in developing new approaches to provide medical care and maintain the safety of the life of the population remotely and around the clock. Despite the standards for emissions of harmful substances into the atmosphere established by the legislation of different countries, the level of pollutants in the air often exceeds the permissible limits, which is a danger not only for the population but also for the environment as a whole. To control the situation an Air Quality Index (AQI) was introduced. For today, many works discuss AQI, however, most of them are aimed rather at studying the methodologies for calculating the index and comparing air quality in certain regions of different countries, rather than creating a system that will not only calculate the index in real-time but also make it publicly available and understandable to the population. Therefore we would like to present a decision support algorithm for a solution called “Environmental Sensing to Act for a Better Quality of Life: Smart Health” with the primary goal of ensuring the transformation of raw environmental data collected by special sensors (data which typically require scientific interpretation) into a form that can be easily understood by the average user; this is achieved through the proposed algorithm. The obtained result is a system that increases the self-awareness and self-adaptability of people in environmental monitoring by offering easy to read and understand suggestions. The algorithm considers three types of parameters (concentration of PM10 (particulate matter), PM2.5, and NO2) and four risk levels for each of them. The technical implementation is presented in a step-like procedure and includes all the details (such as calculating the Air Quality Index—AQI, for each parameter). The results are presented in a front-end where the average user can observe the results of the measurements and the suggestions for decision support. This paper presents a supporting decision algorithm, highlights the basic concept that was used in the development process, and discusses the result of the implementation of the proposed solution

Thermal Maturity and Kerogen Type of Badenian Dispersed Organic Matter from the Getic Depression, Romania

The aim of this study is to evaluate the thermal maturity of Upper Badenian (Middle Miocene) petroleum source rocks of the Getic Depression, Romania, and to characterize the dispersed organic matter using organic petrography associated with Rock-Eval pyrolysis. A total of 33 core samples of Upper Badenian source rocks from the central–southern part of Getic Depression was studied. The results show that most samples with values of total organic carbon (TOC) o%) ranging between 0.41% and 0.55%, and the values of Tmax between 409 °C and 443 °C. Optical microscopy with reflected white light and fluorescence blue light was used for identification of terrigenous macerals (vitrinite, liptinite as, resinite, cutinite, sporinite, and inertinite) associated with marine liptinite macerals (telalginite and lamalginite) showing yellow and bright–yellow epifluorescence

Bridging the Chemical Profile and Biological Activities of a New Variety of <i>Agastache foeniculum</i> (Pursh) Kuntze Extracts and Essential Oil

This study investigated the phytochemical content of alcoholic extracts and essential oil of a new variety of medicinal plants, Agastache foeniculum (Pursh), which Kuntze adapted for cultivation in Romania, namely “Aromat de Buzău”. The essential oil was investigated by GC-MS, while the identification and quantification of various compounds from alcoholic extracts were performed by HPLC-DAD. The total phenol and flavonoid contents of the extracts were evaluated by using standard phytochemical methods. The antioxidant activities of ethanol, methanol extracts, and essential oil of the plant were also assessed against 2,2′-diphenyl-1-picrylhydrazyl (DPPH•), 2,2′-azino-bis-(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS•+), and by ferric reducing power (FRAP) using spectroscopic methods. Cyclic voltammetry was used to evaluate the antioxidant capacity of the essential oil. The concentrations of phenolic compounds were higher in methanolic extract compared to ethanolic extract. A significant correlation was found between total phenol and total flavonoid contents (r = 0.9087). Significant high correlations were also found between the total phenolic compounds and the antioxidant activities of the extracts (r ≥ 0.8600, p A. foeniculum as a potential source of bioactive compounds and a good candidate for pharmaceutical plant-based products

Candidate proteomic biomarkers for non-alcoholic fatty liver disease (steatosis and non-alcoholic steatohepatitis) discovered with mass-spectrometry: a systematic review

<p><i>Context</i>: Non-alcoholic fatty liver disease (NAFLD) is characterized by lipid accumulation in the liver which is accompanied by a series of metabolic deregulations. There are sustained research efforts focusing upon biomarker discovery for NAFLD diagnosis and its prognosis in order investigate and follow-up patients as minimally invasive as possible.</p> <p><i>Objective</i>: The objective of this study is to critically review proteomic studies that used mass spectrometry techniques and summarize relevant proteomic NAFLD candidate biomarkers.</p> <p><i>Methods</i>: Medline and Embase databases were searched from inception to December 2014.</p> <p><i>Results</i>: A final number of 22 records were included that identified 251 candidate proteomic biomarkers. Thirty-three biomarkers were confirmed – 14 were found in liver samples, 21 in serum samples, and two from both serum and liver samples.</p> <p><i>Conclusion</i>: Some of the biomarkers identified have already been extensively studied regarding their diagnostic and prognostic capacity. However, there are also more potential biomarkers that still need to be addressed in future studies.</p

Environmental Performance of a Mixed Crop–Dairy Cattle Farm in Alexandria (Romania)

Agricultural specialization has increased considerably in Europe over the last decades, leading to the separation of crop and livestock production at both farm and regional levels. Such a transformation is often associated with higher environmental burdens due to excessive reliance on exogenous inputs and manure management issues. Reconnecting crop and livestock production via mixed farming systems (MFSs) could improve circularity and resilience, leading to reduced environmental impacts. The objective of this study was to evaluate the life cycle environmental performance of a commercial mixed crop–dairy cattle farm in Romania and to compare it against the corresponding specialized systems. The evaluation covered both dairy cattle production (milk and meat) and cash crops. Overall, the results show that the coupled system improves environmental performance by reducing the over-reliance on high-impact inputs like synthetic fertilizers and exogenous feed. The carbon footprint for the milk production of the studied system (1.17 kg CO2 eq.) per kg of fat- and protein-corrected milk (FPCM) was 10% lower than the mean value of common intensive milk production systems. The eutrophication impacts (2.52 × 10−4 kg P eq and 2.67 × 10−4 kg N eq./kg of FPCM) presented values of one order of magnitude less than their specialized counterparts. However, the impacts of the studied MFS, albeit lower than those for comparable specialized systems, still remain relatively high. In particular, methane emissions from enteric fermentation (0.54 kg CO2 eq./kg FPCM) were a major contributor to the carbon footprint. This highlighted the need to address the elevated emissions from enteric fermentation with better feed management, as well as improving and reinforcing the system’s self-sufficiency

mRNA COVID-19 Vaccine Reactogenicity among Healthcare Workers: Results from an Active Survey in a Pediatric Hospital from Bucharest, January&ndash;February 2021

In Romania, health and social workers were prioritized for COVID-19 vaccination. We aimed to describe the vaccine adverse events identified through an active survey (using an electronic questionnaire) conducted among the staff of a pediatric hospital from Bucharest, vaccinated with the mRNA Pfizer-BioNTech vaccine. Data on the frequency and duration of adverse events were collected and analyzed using Microsoft Excel, Epi Info, and MedCalc. The questionnaire was sent to 426 persons. The participation rate was 81.2% after 1st dose and 63.8% after the 2nd dose. Overall, 81.9% were women, median age 42 (IQR 32&ndash;50 years). A total of 48 respondents (14.8%) reported no adverse event after the 1st dose and 35 (14.1) after the 2nd dose. No anaphylaxis was reported. The most frequent adverse event was pain at injection site, being reported by 261 responders (80.3%) after 1st dose and 187 (75.1%) after 2nd dose. Fatigue and headache were reported significantly less frequently in our study compared with data provided by the vaccine manufacturer. The current study has shown higher local reactogenicity after the first dose of the vaccine and higher systemic reactogenicity after the second dose. This real-world knowledge of the reactogenicity and safety profile may increase the vaccine&rsquo;s acceptance rate among healthcare workers

Outcomes of Prospectively Followed Pregnancies in Rheumatoid Arthritis: A Multicenter Study from Romania

Women with rheumatoid arthritis (RA) may carry an increased risk of adverse pregnancy outcomes (APO). The aims of this study were to compare pregnancy outcomes in RA patients as compared to the general obstetric population (GOP) and to identify a risk profile in RA. A case-control study was conducted on 82 prospectively followed pregnancies in RA and 299 pregnancies from the GOP. The mean age at conception was 31.50 &plusmn; 4.5 years, with a mean disease duration of 8.96 &plusmn; 6.3 years. The frequency of APO in RA patients was 41.5%, 18.3% experienced spontaneous abortions, 11.0% underwent preterm deliveries, 7.3% had small for gestational age infants, 4.9% experienced intrauterine growth restriction, 1.2% experienced stillbirth, and 1.2% suffered from eclampsia. The risk of APO was correlated with a maternal age higher than 35 years (p = 0.028, OR = 5.59). The rate of planned pregnancies was 76.8%, and the subfertility rate was 4.9%. Disease activity improved every trimester, and approximately 20% experienced an improvement in the second trimester. Planned pregnancies and corticosteroids use (&le;10 mg daily) were protective factors for APO in RA pregnancies (p &lt; 0.001, OR = 0.12, p = 0.016, OR = 0.19, respectively). There was no significant association between APO and disease activity or DMARDs used before and during pregnancy. Regarding the comparison between the RA group and the controls, RA mothers were significantly older (p = 0.001), had shorter pregnancies (p &lt; 0.001), and had neonates with a lower birth weight (p &lt; 0.001)

Comparative Evaluation of the Dynamics of Animal Husbandry Air Pollutant Emissions Using an IoT Platform for Farms

One of the major challenges of animal husbandry, in addition to those related to the economic situation and the current energy crisis, is the major contribution of this sector to atmospheric pollution. Awareness of pollution sources and their permanent monitoring in order to ensure efficient management of the farm, with the aim of reducing emissions, is a mandatory issue, both at the macro level of the economic sector and at the micro level, specifically at the level of each individual farm. In this context, the acquisition of consistent environmental data from the level of each farm will constitute a beneficial action both for the decision-making system of the farm and for the elaboration or adjustment of strategies at the national level. The current paper proposes a case study of air pollutants in a cattle farm for different seasons (winter and summer) and the correlation between their variation and microclimate parameters. A further comparison is made between values estimated using the EMEP (European Monitoring and Evaluation Programme, 2019) methodology for air pollutant emission and values measured by sensors in a hybrid decision support platform for farms. Results show that interactions between microclimate and pollutant emissions exist and they can provide a model for the farm’s activities that the farmer can manage according to the results of the measurements