Short-term Monitoring Research Articles

Detection of Atrial Fibrillation Using Insertable Cardiac Monitors in Patients With Cryptogenic Stroke in Japan (the LOOK Study): Protocol for a Prospective Multicenter Observational Study

Paroxysmal atrial fibrillation (AF) is a probable cause of cryptogenic stroke (CS), and its detection and treatment are important for the secondary prevention of stroke. Insertable cardiac monitors (ICMs) are clinically effective in screening for AF and are superior to conventional short-term cardiac monitoring. Japanese guidelines for determining clinical indications for ICMs in CS are stricter than those in Western countries. Differences between Japanese and Western guidelines may impact the detection rate and prediction of AF via ICMs in patients with CS. Available data on Japanese patients are limited to small retrospective studies. Furthermore, additional information about AF detection, including the number of episodes, cumulative episode duration, anticoagulation initiation (type and dose of regimen and time of initiation), rate of catheter ablation, role of atrial cardiomyopathy, and stroke recurrence (time of recurrence and cause of the recurrent event), was not provided in the vast majority of previously published studies. In this study, we aim to identify the proportion and timing of AF detection and risk stratification criteria in patients with CS in real-world settings in Japan. This is a multicenter, prospective, observational study that aims to use ICMs to evaluate the proportion, timing, and characteristics of AF detection in patients diagnosed with CS. We will investigate the first detection of AF within the initial 6, 12, and 24 months of follow-up after ICM implantation. Patient characteristics, laboratory data, atrial cardiomyopathy markers, serial magnetic resonance imaging findings at baseline, 6, 12, and 24 months after ICM implantation, electrocardiogram readings, transesophageal echocardiography findings, cognitive status, stroke recurrence, and functional outcomes will be compared between patients with AF and patients without AF. Furthermore, we will obtain additional information regarding the number of AF episodes, duration of cumulative AF episodes, and time of anticoagulation initiation. Study recruitment began in February 2020, and thus far, 213 patients have provided written informed consent and are currently in the follow-up phase. The last recruited participant (May 2021) will have completed the 24-month follow-up in May 2023. The main results are expected to be submitted for publication in 2023. The findings of this study will help identify AF markers and generate a risk scoring system with a novel and superior screening algorithm for occult AF detection while identifying candidates for ICM implantation and aiding the development of diagnostic criteria for CS in Japan. UMIN Clinical Trial Registry UMIN000039809; https://tinyurl.com/3jaewe6a. DERR1-10.2196/39307.

Short-Term Load Monitoring of a Power System Based on Neural Network

In order to improve the accuracy of power load forecasting, this paper proposes a neural network-based short-term monitoring method. First, the original energy load signal is decomposed by the CEEMDAN algorithm to obtain several eigenmode function components and residual components; several eigenmode function components and residual functions are fed into the NARX neural network for computational purposes. The partial hypothesis is superimposed in the following part to obtain the final short-term forecast. According to the test results, the MAPE of the CEEMDAN-NARX model is 4.753%, 3.540%, and 0.343% lower than the SVM, RNN, and NARX models, respectively, and 3.741% and 2.682% lower than CEEMDAN-SVM and CEEMDAN-RNN, respectively. The MAPE and RMSE of the CEEMDAN-NARX model are 0.765% and 101.7 MW, respectively, which are 0.468% and 45.2 MW lower than NARX models, respectively. Compared to CEEMDAN-SVM, the MAPE of CEEMDAN-NARX and CEEMDAN-RNN decreased by 0.986% and 0.692%, respectively, and the RMSE of CEEMDAN-NARX decreased by 111.5 and 65.7 MW, respectively, compared to CEEMDAN-SVM. Conclusion is that the load forecasting model based on the combination of CEEMDAN algorithm and NARX neural network can effectively connect, reduce the negative impact of noise on forecasting results, and improve forecasting accuracy.

497 Does Physical Activity Improve Patient Outcomes Following Lumbar Microdiscectomy?

INTRODUCTION: Lumbar microdiscectomy is widely performed for disc herniation, however outcomes may be unsatisfactory in 10-40% patients and up to 12.5% require re-operation. Evidence regarding the effect of physical activity on post-operative outcomes is scant. The majority of existing studies rely on biased activity self-reporting or short-term monitoring. Duration of physical activity presents a tangible goal for patient counselling, can be measured using wearable technology, and can be targeted to improve outcomes. METHODS: 53 patients underwent lumbar microdiscectomy and wore an accelerometer for 30 days post-operatively, which measured activity duration (lying, sitting, standing, walking, cycling) and intensity. Patient-reported outcome scores (VASB, VASL, ODI, EQ-5D, SF-12) at 1, 3, 6, and 12 months, and activity data were analysed. The primary outcome (treatment success) was a predetermined reduction in VASB, VASL, and ODI. The relationship between treatment success and activity was assessed using the Mann-Whitney U test. Associations between outcomes and activity were investigated using MANOVA and Pearson Correlations. RESULTS: Longer standing time was associated with worse ODI scores at 6 months (p = .034). There was no other statistically significant association between treatment success and activity duration or intensity. MANOVA demonstrated that shorter sitting time was associated with less back pain at 3 (p = .01) and 12 months (p = .011). Univariate analysis revealed that longer walking time was associated with improved EQ-5D scores at 3 (p = .001) and 12 months (p = .034), and improved SF-12 physical scores at 3 months (p = .026). CONCLUSIONS: Shorter standing time was associated with less disability, shorter sitting time with less back pain, and longer walking time with improved physical wellbeing after lumbar microdiscectomy. Higher-powered studies are needed to further explore the relationship between physical activity and outcomes.

Low-Cost Online Partial Discharge Monitoring System for Power Transformers

The article presents in detail the construction of a low-cost, portable online PD monitoring system based on the acoustic emission (AE) technique. A highly sensitive piezoelectric transducer was used as the PD detector, whose frequency response characteristics were optimized to the frequency of AE waves generated by discharges in oil-paper insulation. The popular and inexpensive Teensy 3.2 development board featuring a 32-bit MK20DX256 microcontroller with the ARM Cortex-M4 core was used to count the AE pulses. The advantage of the system is its small dimensions and weight, easy and quick installation on the transformer tank, storage of measurement data on a memory card, battery power supply, and immediate readiness for operation without the need to configure. This system may contribute to promoting the idea of short-term (several days or weeks) PD monitoring, especially in developing countries where, with the dynamically growing demand for electricity, the need for inexpensive transformer diagnostics systems is also increasing. Another area of application is medium-power transformers (up to 100 MVA), where temporary PD monitoring using complex measurement systems requiring additional infrastructure (e.g., control cabinet, cable ducts for power supply, and data transmission) and qualified staff is economically unjustified.

Short-Term and Textbook Surgical Outcomes During the Implementation of a Robotic Gastrectomy Program

BackgroundWhether gastric cancer patients derive greater benefit from robotic gastrectomy (RG), or open gastrectomy (OG) is unknown. We initiated a RG program in 2018, with prospective short-term outcome monitoring to ensure safety. We hypothesized that the RG program for gastric cancer can be safely implemented with equivalent safety and oncological textbook outcomes (TOs) to conventional open gastrectomy (OG). MethodsThe study included patients who underwent curative-intent OG or RG for gastric adenocarcinoma between January 2018 and December 2021. TO metrics were negative surgical margins, ≥ 15 lymph nodes examined, no severe (Clavien–Dindo grade ≥ IIIa) postoperative complications, no reinterventions within 90 days after surgery, no ICU admission, no prolonged length of stay (LOS; > 10 days), no 90-day postoperative mortality, and no readmission within 90 days after surgery. Overall TO was achieved when all these metrics were met. ResultsOf 161 patients, 120 underwent OG, and 41 underwent RG. The two groups’ demographic and disease characteristics did not differ significantly. Compared with OG patients, RG patients had a longer median surgery time (348 vs. 282 min), smaller median blood loss volume (50 vs. 150 mL), lower mean prescribed opioid dose at discharge (12 vs. 45 mg), and shorter median LOS (4 vs. 7 days; all p < 0.001). The groups’ postoperative complication rates (10% vs. 17%) did not differ significantly (p = 0.283). The overall TO rate of the RG group (73%) was higher than that of the OG group (60%), but the difference was not significant (p = 0.131). ConclusionWe were able to implement the RG program safely, without compromising safety or oncological outcomes.

Magnetic activity variability from Hα line intensive monitoring of two F-type stars with a hot Jupiter, τ Bootis A and υ Andromedae A

Abstract We report the results of intensive monitoring of the variability in the Hα line for two F-type stars, τ Boo and υ And, over the last four years 2019–2022, in order to investigate their stellar magnetic activity. The 4 yr Hα line intensity data taken with the 1.88 m reflector at the Okayama Branch Office, Subaru Telescope, shows the existence of a possible ∼123 d magnetic activity cycle for τ Boo. The result of the Hα variability as another tracer of the magnetic activity on the chromosphere is consistent with previous studies of the Ca ii H&amp;K line and suggests that the magnetic activity cycle persists in τ Boo. For υ And, we suggest a quadratic long-term trend in the Hα variability. Meanwhile, the short-term monitoring shows no significant period corresponding to specific variations likely induced by their hot Jupiter in both cases (≈3.31 and 4.62 d, respectively). In this Hα observation, we could not find any signature of the star–planet magnetic interaction. It is speculated that the detected magnetic activity variability of the two F-type stars is related to the stellar intrinsic dynamo.

Modal Parameters Prediction and Damage Detection of Space Grid Structure under Environmental Effects Using Stacked Ensemble Learning

A stacked ensemble learning model is developed to predict the modal parameters of space grid steel structures under environmental effects. Potential damage is detected via statistical analysis of the prediction residuals. For this purpose, five standalone heterogeneous machine learning models were trained for predicting natural frequencies; each model used the principal components of the environmental data as input parameters. Next, a stacked ensemble learner was built using the outputs of the five standalone models as its inputs. Finally, a damage indicator combining the predicted residuals of multiple orders of natural frequencies is proposed and statistically analyzed for accurate damage detection. To verify the effectiveness of the proposed method, a space grid model was created in the field environment and measured for a period. Dynamic and environmental data were collected, such as ambient temperature, humidity, wind speed and direction, and structural surface temperature. An automated procedure of the covariance-driven stochastic subspace identification method was conducted to identify bulk mode. The environmental dependence of the natural frequencies, damping ratios, and vibration modes was analyzed. Then, the method was validated based on short-term monitoring data from the baseline health state and unknown future states. The results show that the natural frequencies and damping ratios of space grid structures fluctuate significantly on a daily basis due to environmental influences. Stacked ensemble learning utilizes predictions from multiple heterogeneous models to produce a better predictive model. The statistical analysis of the prediction residuals by ensemble learning effectively removes the environmental influences, allowing for timely structural damage detection.

Introduction of trees plants in nurseries of Northern Kazakhstan from of the China

The manuscript presents the results of monitoring during the introduction near the capital of Kazakhstan of trees plants imported from China and planted in the nursery of the Akkol State Forestry Enterprise in the northern part of the Akmola region. Fraxinus Bungeana, Fraxinus Americana, Populus Euphratica were used as the object of study in the experiment. The adaptive characteristics of the selected plants were compared according to various bioecological indicators. Taxation and biometric indicators of these trees amounted to 60.1; 47.7; 22.3 percent. In samples of tree leaves of different sizes, the level of photosynthetic activity was determined. This indicator among the studied species was the lowest in Populus Euphratica (0.63 µg/g), average in Fraxinus Americana (0.76±0.01) and high in Fraxinus Bungeana (0.80 µg/g). The resistance of trees plants to the new environment was assessed by the amount of accumulation of heavy metals in their leaf blades. An increased concentration of copper and iron accumulated American ash, Bunge ash. The content of cadmium, lead, zinc did not exceed the MPC. In general, the monitoring showed that different types of trees introduced in the vicinity of the city of Nur-Sultan in the north of the Akmola region show different adaptive responses. It is shown that heavy metals accumulate more intensively in ash leaves. Populus Euphratica turned out to be the most resistant to this ecological environmental factor, and Fraxinus Bungeana is the most sensitive among ash representatives. In general, the adaptation of trees introduced from abroad in the region of northern Kazakhstan, near the city of Nur-Sultan, is carried out in accordance with their biological characteristics. It has been observed that Populus Euphratica is more resistant and Fraxinus Bungeana is the most sensitive species when introduced. In general, based on our research, an algorithm for short-term monitoring of introduced trees has been developed to preserve the biodiversity of ornamental plants. Key words: China, tree plants, nurseries, Northern Kazakhstan, introduction, photosynthesis, heavy metals crops

Utility of short-term telemetry heart rhythm monitoring and CHA2DS2-VASc stratification in patients presenting with suspected cerebrovascular accident.

Inpatient telemetry heart rhythm monitoring overuse has been linked to higher healthcare costs. To evaluate if CHA2DS2-VASc score could be used to indicate if a patient admitted with possible cerebrovascular accident (CVA) or transient ischemic attack (TIA) requires inpatient telemetry monitoring. A total of 257 patients presenting with CVA or TIA and placed on telemetry monitoring were analyzed retrospectively. We investigated the utility of telemetry monitoring to diagnose atrial fibrillation/flutter and the CHA2DS2-VASc scoring tool to stratify the risk of having CVA/TIA in these patients. In our study population, 63 (24.5%) of the patients with CVA/TIA and telemetry monitoring were determined to have no ischemic neurologic event. Of the 194 (75.5) patients that had a confirmed CVA/TIA, only 6 (2.3%) had an arrhythmia detected during their inpatient telemetry monitoring period. Individuals with a confirmed CVA/TIA had a statistically significant higher CHA2DS2-VASc score compared to individuals without an ischemic event (3.59 vs 2.61, P < 0.001). Given the low percentage of inpatient arrhythmias identified, further research should focus on discretionary use of inpatient telemetry on higher risk patients to diagnose the arrhythmias commonly leading to CVA/TIA. A prospective study assessing event rate of CVA/TIA in patients with higher CHA2DS2-VASc score should be performed to validate the CHA2DS2-VASc score as a possible risk stratifying tool for patients at risk for CVA/TIA.

Long-term vs. short-term measurements in indoor Rn concentration monitoring: Establishing a procedure for assessing exposure potential (RnEP)

Radon (Rn-222 or Rn) is a naturally occurring chemical element that belongs to the progeny of U-238 released from soil and rocks into the atmosphere, whose impact on organisms is not negligible when it occurs inside confined spaces, such as mine shafts or low-ventilated buildings. The current methodologies for indoor radon assessment are based on passive and active detection. If passive detectors are mainly designed for long-term assessment, active detectors are electronic equipment that can allow on-time visualization of results and, therefore, faster decision-making on risk mitigation measures implementation. However, despite offering a prompt and informed decision, the uncertainty associated with active detector results may cast doubt on the quality of the measurements, which especially arises from the duration of the monitoring campaign. Since radon emanation from soils and rocks depends on a set of variables related to meteorological parameters and soil structure, the seasonal variation of these parameters impacts indoor radon concentration assessment, both on short-term and long-term measurements. Therefore, short-term monitoring campaigns lasting a month, a week, or a day may not produce a statistically representative description of indoor radon exposure for the monitored building. Accordingly, the study compares the reliability of short-term and long-term indoor radon measurements. It proposes a procedure for assessing the indoor Radon Exposure Potential (RnEP) based on in situ measurements through the analysis of the frequency of occurrence of results divided into three classes of potential exposure (low, moderate and high) to reduce occupants' radon risk exposure.

Current Situation of Agricultural Soil Pollution in Jiangsu Province: A Meta-Analysis

In recent years, heavy metal contamination in agricultural soils in Jiangsu Province has attracted more and more attention. However, most studies have been characterized by their small scale, few samples, and short-term monitoring. The overall status and temporal accumulation characteristics of heavy metals have not been fully reflected. Therefore, this paper attempted to provide a comprehensive analysis of the current status of heavy metals and provide accurate information for soil pollution management in Jiangsu Province. This paper collected available data in the literature (1993–2021) on heavy metal-polluted agricultural soils in Jiangsu Province. Based on these available data, the weighted mean values of each heavy metal were obtained by meta-analysis. Then, the ecological risks in soils were evaluated and spatiotemporal variations in the accumulation of heavy metals were explored. In addition, suggestions for pollution prevention and control were made by predicting future heavy metal concentrations. The results showed that Cd and Hg were the major polluting elements in Jiangsu Province. The spatial enrichment of heavy metals followed the pattern of southern &gt; northern &gt; central. Heavy metal concentrations in Nanjing, Suzhou, and Xuzhou should be paid special attention. The ecological risk level of heavy metals in agricultural soils in Jiangsu Province was high, predominantly contributed by Hg and Cd. The accumulation of most heavy metals gradually decreased after 2010, while the opposite happened with Cd. Jiangsu Province should continue to take active pollution control measures in order to maintain the decreasing trend of heavy metal concentrations in farmland soils. This study could provide a scientific and theoretical basis for the development of pollution control and soil remediation measures.

An Assessment of the Utility of Serum Fructosamine in the Diagnosis and Monitoring of Diabetes Mellitus.

Fructosamine (FA) has gained importance as a new biomarker for hyperglycemia in the past decade and may be of indispensable use in certain conditions where hemoglobin A1c (HbA1c) falls short of utility such as disorders of red blood cells, patients with rapid glycemic excursions requiring more short-term monitoring, pregnancy, chronic kidney disease, etc. Methods: The present study was a hospital-based observational cross-sectional study conducted in the Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), Nagpur, India. Serum HbA1c, FA, albumin-corrected fructosamine (AlbF), total protein-corrected FA (PrF), hemoglobin (Hb), and hematocrit (Hct) were estimated in 32 controls (Group I) and 32 cases of diabetes mellitus (DM) (Group II). The clinical data and lab results were presented as mean±SD/±standard error (SE) of the mean. Student's t-test and ANOVAwere used to compare various parameters between the groups. Pearson correlation analysis was performed to assess the correlation between different diagnostic parameters. The receiver operating characteristic (ROC) curve was plotted to assess the diagnostic significance and cut-off value for FA, AlbF, and PrF. The controls and cases were matched for age and gender distribution. Serum HbA1c (p<0.0001), serum FA (p<0.0001), fasting blood sugar (p=0.001), postprandial blood sugar (p<0.0001), random blood sugar (p=0.001), hematocrit (p=0.002), AlbF (p<0.0001), and PrF (p<0.0001) were found to be significantly higher in known diabetic subjects compared to controls. The casegroup was further subdivided into pre-diabetic and diabetic groups. On correlation analysis of HbA1c with various parameters, a moderate correlation of HbA1c was noted with FA (r=0.522, p<0.0001) and AlbF (r=0.375, p=0.002) in all subjects. Additionally, a moderate correlation of FA (r=0.479, p=0.033), AlbF (r=0.444, p=0.050), and PrF (r=0.441, p=0.065) with HbA1c was also found in subjects with diabetic range glycemia. No such correlation was noted in the pre-diabetic group. No significant correlation was noted between FA and its corrected values in any range of glycemia. None of the parameters assessing glycemia were found to be significantly affected by hemoglobin status. On ROC curve analysis, HbA1c was found to be the best parameter (area under the curve (AUC) =83%, p<0.0001) followed by AlbF (AUC= 80.5%, p<0.0001) and uncorrected FA (AUC=80.5%, p<0.0001) to diagnose DM. Serum FA should be considered a valid diagnostic biomarker and of indispensable use in special populations where HbA1c falls short of utility such aspatients with red blood cell disorders or those showing rapid glycemic excursions such as those on corticosteroid therapy or insulin therapy, etc. It exhibits additional advantages over HbA1c with respect to lower reagent cost and easy automation on any conventional laboratory instruments based on simple colorimetry.

Characteristics of east Australian demersal trawl elasmobranch bycatch as revealed by short-term latitudinal monitoring

Elasmobranchs are being depleted on a global scale, caused mainly by fisheries. Demersal trawling is a component of mortality but is often not assessed. This could pose risk to benthic/demersal elasmobranchs which are often endemic and therefore vulnerable to fisheries when species ranges are within (or mainly within) trawl footprints. Northern New South Wales (NSW) is an area with endemism but also an area with fisheries such as the ocean prawn trawl (OPT) (penaeid sector). The OPT may interact with elasmobranchs, but this has never been comprehensively studied. To identify high assessment-priority species, determine spatiotemporal stratification for designing future monitoring, and to report catch rates of individuals caught during a trip (i.e. form baseline), we implemented an observer programme (2017 to 2019). To test for stratification of assemblages, we used model-based multivariate analysis. On 435 trawl trips, observers identified elasmobranchs from ~54 species, 13 orders and 34 families from variable catches. Only 2 elasmobranchs were protected in NSW, ~7% qualified for conservation listing, and ~33 and ~17% were endemic and lifeboat (listed elsewhere) species, respectively. Models suggested common elasmobranch assemblages were significantly affected by all strata (geographic zone, season and depth). Elasmobranch catch rates were low compared to other taxonomic groups (e.g. teleost fish), with 2 species captured at &gt;10, 5 species at &gt;2, and the remaining species &lt;2 individuals per trip. The occurrence of endemism and spatiotemporal assemblage variation was explained by mesoscale climate transitions and oceanography. This study forms a timely baseline which can be used to assess the impact of the OPT on elasmobranchs in the future.

Is the level of serum lactate dehydrogenase a potential biomarker for glucose monitoring with type 2 diabetes mellitus?

Type 2 diabetes mellitus (T2DM) is a metabolic disorder due to defects in insulin secretion or insulin resistance leading to the dysfunction and damage of various organs. To improve the clinical evaluation of short-term blood glycemic variability monitoring, it is critical to identify another blood cell status and nutritional status biomarker that is less susceptible to interference. This study identifies the significance of serum lactate dehydrogenase (LDH) level among T2DM patients treated in outpatient clinics and investigates the relationship of LDH level with other variables. This study comprised 72 outpatients with T2DM over 20 years of age. Blood samples were collected followed by a hematological analysis of serum glycated albumin (GA), LDH, fasting blood glucose, glycosylated hemoglobin, C-peptide, and insulin antibodies (insulin Ab). Serum LDH level was significantly correlated with GA (p < 0.001), C-peptide (p = 0.04), insulin Ab (p = 0.03), and thyroid-stimulating hormone (TSH) levels (p = 0.04). Hence, we performed a linear regression analysis of hematological markers. GA (p < 0.001, r2 = 0.45) and insulin Ab (p < 0.001, r2 = 0.40) were significantly associated with LDH level. Then, we classified patients into low (<200 U/L) and high (≥200 U/L) serum LDH level groups, respectively. GA (p < 0.001), C-peptide (p = 0.001), and TSH (p = 0.03) showed significant differences in patients with high LDH levels compared with those in patients with low LDH levels. In conclusion, we suggested that LDH level was independent of long-term but associated with short-term blood glucose monitoring. The results indicated that changes in serum GA induced cell damage and the abnormal elevation of the serum level of LDH may occur simultaneously with glycemic variability. It has been reported that many biomarkers are being used to observe glucose variability in T2DM. However, LDH could provide a more convenient and faster evaluation of glycemic variability in T2DM.

59: PREDICTION MODEL TO IDENTIFY SEPSIS PATIENTS FOR MINIMALLY INVASIVE MANAGEMENT OUTSIDE THE ICU

Introduction: Patients with sepsis who need intensive care unit (ICU) admission for short-term monitoring (< 48 hr) with low-dose vasopressors could be candidates for minimally invasive sepsis (MIS) approach outside the ICU. We aimed to describe such patients and develop a prediction model to find candidates for MIS approach. Methods: In a secondary analysis of institutional data, we identified adults (≥18 yr) with septic shock admitted to the Mayo Clinic medical ICU from emergency. MIS approach candidates were with < 48 hr of ICU stay, without advanced respiratory support or renal replacement therapy and alive at ICU & hospital discharge. Comparison group consisted of septic shock patients with an ICU stay of >48 hr who didn’t need advanced respiratory support before ICU admission. Wilcoxon rank, Chi Square, and Fisher’s exact tests were used to compare baseline characteristics. Logistic regression and prediction profiler were used to select predictive variables and assign cut-offs. Using points assigned to variables based on OR and clinical rational, a nominal logistic regression model was created. Model discrimination & calibration were assessed by receiver operating characteristic (ROC) curves and Hosmer-Lemeshow (H-L) goodness-of-fit test. Results: Out of 1795 MICU admissions, 109 patients (6 %) met MIS approach criteria and 97 in comparison group. MIS group was younger with lower respiratory rate, temperature, lactate, creatinine, BUN, and WBCs (p < 0.05 for all). Predictive variables (age >65 yr, O2 flow >4 L/mint, temperature < 37℃, creatinine >1.6 mg/dL, lactate >3 mmol/L, WBCs >15x109, heart rate >100 beats/mint, and respiration rate >25 breaths/ mint) were translated into an 8-point score. Model discrimination yielded ROC: AUC of 80%. Model fit was confirmed by agreement between observed and predicted probability and was well calibrated (H-L p = 0.94). MIS score cut-off of 3 resulted in a predictive model OR of 0.15 (CI: 0.08–0.28) and a negative predictive value of 91% [(CI: 88.69-92.92) (using prevalence of MIS patients ~20%)] Conclusions: Study implies that a significant minority of sepsis patients can be managed outside the ICU. Once validated in an independent, prospective sample our prediction model can be used to identify candidates for minimally invasive management outside of ICU

The accuracy of 8-hour ambulatory blood pressure monitoring, adjusted to seasons.

Ambulatory blood pressure monitoring (ABPM) is considered the most reliable and accurate measurement of blood pressure (BP). However, the use of ABPM has some limitations, which make it difficult to complete for the entire 24 h. We aimed to establish in which part of the day BP measurements are in highest correlation with full ABPM (over 24 h) results. We performed a retrospective cross-sectional study which included 3113 full ABPM. Each ABPM was divided into 6- and 8-hour segments, and mean BP in each time segment was calculated. Linear mix models for describing BP by BP in each time segment were performed. A total of 3113 ABPM measurements carried out on 2676 patients (mean age 57.78 ± 14.74) were included in the study. Linear mix models demonstrated significant association between mean systolic blood pressure (SBP) and diastolic blood pressure (DBP) in full ABPM, and SBP and DBP between 2-10 PM, respectively (SBP: β = 0.902, p < 0.001; DBP: β = 0.839, p < 0.001), adjusted for gender, age, season, and relevant interactions. This section had higher coefficient correlations than other sections which were examined. The study findings indicate high correlation between BP between 2-10 PM, and BP in full-ABPM, by each season. This time segment may be ideal for short-term BP monitoring as an initial screening test and for patients who are unable to complete full ABPM. However, since this time segment does not include nighttime hours, there is a risk of underdiagnosis of non-dipper.

Short-term atmospheric pollution monitoring in a selection of European capitals

Air pollution is increasing in the context of uncontrolled emissions from human activities, mostly from industry, transportation and agriculture. Urban agglomerations concentrate most environmental pollution sources, given the high population density and their needs of transportation and energy, both of electrical and thermal types, along with huge quantities of wastes, whose improper management leads to heavy pollutant emissions in atmosphere, but also to pollutant leakage in waters and soil. In general, in each country, at least in Europe, the capital is the most populated city, which also concentrates important industrial sites, leading to poor air quality. This affects the population both on short and long term. Studies have shown that air pollution even induces chronic diseases, not only of respiratory-type, but also of the heart, up till reducing life expectancy. When dealing with urban air pollution, one must also consider the physical-geographical conditions, which may accentuate or, on the contrary, may diminish pollution through an intense air circulation, thus dispersing pollutants.A selection of European capitals was chosen for the study presented here, as related to their air quality which was monitored for short term within the peak of the recent pandemic, in order to have a view and even a reference of the expected reduced pollution status as compared to the normal urban life conditions. The chosen capitals were: Paris, Berlin, Bucharest and Skopje, which are well-spread geographically across Europe and also have different economic statuses, along with different landscape conditions and climate influences, making them appropriate for comparison, differences being expected.

Impact of Mobile Monitoring Network Design on Air Pollution Exposure Assessment Models.

Short-term mobile monitoring campaigns are increasingly used to assess long-term air pollution exposure in epidemiology. Little is known about how monitoring network design features, including the number of stops and sampling temporality, impacts exposure assessment models. We address this gap by leveraging an extensive mobile monitoring campaign conducted in the greater Seattle area over the course of a year during all days of the week and most hours. The campaign measured total particle number concentration (PNC; sheds light on ultrafine particulate (UFP) number concentration), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2). In Monte Carlo sampling of 7327 total stops (278 sites × 26 visits each), we restricted the number of sites and visits used to estimate annual averages. Predictions from the all-data campaign performed well, with cross-validated R2s of 0.51-0.77. We found similar model performances (85% of the all-data campaign R2) with ∼1000 to 3000 randomly selected stops for NO2, PNC, and BC, and ∼4000 to 5000 stops for PM2.5 and CO2. Campaigns with additional temporal restrictions (e.g., business hours, rush hours, weekdays, or fewer seasons) had reduced model performances and different spatial surfaces. Mobile monitoring campaigns wanting to assess long-term exposure should carefully consider their monitoring designs.

Delimiting radiation protection distance of underground uranium mining and metallurgy facilities: A case study in China

Delimiting radiation protection distance of uranium mining and metallurgy facilities is an important radiation protection approach to control the effective public dose caused by radon and radon progeny. Ventilation shafts are the main radon release paths of underground uranium mines. It is of great importance to research the diffusion regularities and influence range of the radon around the ventilation shaft. In this study, long-term and short-term radon accumulation monitoring approaches were adapted for onsite investigation. More than 520 sets of radon concentration were acquired. The survey results effectively revealed the distribution regularities of the radon concentration around the ventilation shaft with time, space, and working conditions. These results provide data for radiation protection in uranium mines. In addition, a radiation protection distance delimiting way was proposed for the investigated facility through effective public dose assessment.

A hydrodynamic model–based approach to assess sampling approaches for dissolved oxygen criteria in the Chesapeake Bay

Technological advances in water quality measurement systems have provided the potential to expand high-frequency observations into coastal monitoring programs. However, with limited resources for monitoring budgets in natural waters that exhibit high temporal and spatial variability in water quality, there is a need to identify the locations and time periods where these new technologies can be deployed for maximum efficacy. To advance the capacity to make quantitative and objective decisions on the selection of monitoring locations and sampling frequency, we combined high-resolution numerical model simulations and multi-frequency water quality measurements to conduct a power analysis comparing alternative sampling designs in the assessment of water quality in the Chesapeake Bay. Specifically, we evaluated candidate monitoring networks that deployed both conventional long-term fixed station monitoring in deep channel areas and short-term continuous monitoring technologies in near-shore, shallow areas to assess 30-day dissolved oxygen criteria in two Bay tributaries. We conducted a cumulative frequency diagrams analysis to quantify the accuracy of each monitoring scheme in evaluating compliance with respect to the model. We used a Monte Carlo simulation to incorporate the spatial and temporal uncertainty of criteria failure. We found that additional long-term biweekly channel and short-term continuous shallow sampling efforts can lead to statistically unbiased and improved assessments at local spatial extents (less than 0.2 proportion of the assessed water body), especially when additional sampling is added at stations representing hypoxic water areas. Stations that represented seaward regions of the tributaries were more valuable in maintaining unbiased assessments of dissolved oxygen criteria attainment. This analysis highlights the importance of statistical evaluation of ongoing monitoring programs and suggests an approach to identify efficient deployments of monitoring resources and to improve assessment of other water quality metrics in estuarine ecosystems.