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The mechanical reliability of a direct potable reuse (DPR) treatment train—consisting of ozone, biological activated carbon, microfiltration/ultrafiltration, reverse osmosis, and ultraviolet light with advanced oxidation—was evaluated using critical component analysis at the Demonstration Pure Water Facility in San Diego, Calif. Operators maintained logs of all mechanical issues that occurred over a yearlong test period; these were used to calculate several reliability metrics for the unit processes. Mechanical issues were also cross‐referenced with treatment performance data to determine whether any “critical” failures—i.e., those that impacted pathogen reduction performance—occurred. The results demonstrated that no critical failures occurred, though the communication systems experienced critical malfunctions. These malfunctions triggered an immediate system shutdown, mitigating the potential risk to public health. While several components experienced failures, malfunctions, and/or maintenance events, the processes maintained a high degree of availability, demonstrating that DPR facilities can be designed with a high degree of mechanical reliability.

COVID-19 pandemic led to major life changes. We assessed the psychological impact of COVID-19 on dental academics globally and on changes in their behaviors. We invited dental academics to complete a cross-sectional, online survey from March to May 2020. The survey was based on the Theory of Planned Behavior (TPB). The survey collected data on participants' stress levels (using the Impact of Event Scale), attitude (fears, and worries because of COVID-19 extracted by Principal Component Analysis (PCA), perceived control (resulting from training on public health emergencies), norms (country-level COVID-19 fatality rate), and personal and professional backgrounds. We used multilevel regression models to assess the association between the study outcome variables (frequent handwashing and avoidance of crowded places) and explanatory variables (stress, attitude, perceived control and norms). 1862 academics from 28 countries participated in the survey (response rate = 11.3%). Of those, 53.4% were female, 32.9% were <46 years old and 9.9% had severe stress. PCA extracted three main factors: fear of infection, worries because of professional responsibilities, and worries because of restricted mobility. These factors had significant dose-dependent association with stress and were significantly associated with more frequent handwashing by dental academics (B = 0.56, 0.33, and 0.34) and avoiding crowded places (B = 0.55, 0.30, and 0.28). Low country fatality rates were significantly associated with more handwashing (B = -2.82) and avoiding crowded places (B = -6.61). Training on public health emergencies was not significantly associated with behavior change (B = -0.01 and -0.11). COVID-19 had a considerable psychological impact on dental academics. There was a direct, dose-dependent association between change in behaviors and worries but no association between these changes and training on public health emergencies. More change in behaviors was associated with lower country COVID-19 fatality rates. Fears and stresses were associated with greater adoption of preventive measures against the pandemic.

Medical image segmentation is a critical component in clinical practice, facilitating accurate diagnosis, treatment planning, and disease monitoring. However, existing methods, often tailored to specific modalities or disease types, lack generalizability across the diverse spectrum of medical image segmentation tasks. Here we present MedSAM, a foundation model designed for bridging this gap by enabling universal medical image segmentation. The model is developed on a large-scale medical image dataset with 1,570,263 image-mask pairs, covering 10 imaging modalities and over 30 cancer types. We conduct a comprehensive evaluation on 86 internal validation tasks and 60 external validation tasks, demonstrating better accuracy and robustness than modality-wise specialist models. By delivering accurate and efficient segmentation across a wide spectrum of tasks, MedSAM holds significant potential to expedite the evolution of diagnostic tools and the personalization of treatment plans. Segmentation is an important fundamental task in medical image analysis. Here the authors show a deep learning model for efficient and accurate segmentation across a wide range of medical image modalities and anatomies.

Background Cardiac surgery–associated acute kidney injury (CSA-AKI) is a major complication that results in increased morbidity and mortality after cardiac surgery. Most established prediction models are limited to the analysis of nonlinear relationships and fail to fully consider intraoperative variables, which represent the acute response to surgery. Therefore, this study utilized an artificial intelligence–based machine learning approach thorough perioperative data-driven learning to predict CSA-AKI. Methods A total of 671 patients undergoing cardiac surgery from August 2016 to August 2018 were enrolled. AKI following cardiac surgery was defined according to criteria from Kidney Disease: Improving Global Outcomes (KDIGO). The variables used for analysis included demographic characteristics, clinical condition, preoperative biochemistry data, preoperative medication, and intraoperative variables such as time-series hemodynamic changes. The machine learning methods used included logistic regression, support vector machine (SVM), random forest (RF), extreme gradient boosting (XGboost), and ensemble (RF + XGboost). The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC). We also utilized SHapley Additive exPlanation (SHAP) values to explain the prediction model. Results Development of CSA-AKI was noted in 163 patients (24.3%) during the first postoperative week. Regarding the efficacy of the single model that most accurately predicted the outcome, RF exhibited the greatest AUC (0.839, 95% confidence interval [CI] 0.772–0.898), whereas the AUC (0.843, 95% CI 0.778–0.899) of ensemble model (RF + XGboost) was even greater than that of the RF model alone. The top 3 most influential features in the RF importance matrix plot were intraoperative urine output, units of packed red blood cells (pRBCs) transfused during surgery, and preoperative hemoglobin level. The SHAP summary plot was used to illustrate the positive or negative effects of the top 20 features attributed to the RF. We also used the SHAP dependence plot to explain how a single feature affects the output of the RF prediction model. Conclusions In this study, machine learning methods were successfully established to predict CSA-AKI, which determines risks following cardiac surgery, enabling the optimization of postoperative treatment strategies to minimize the postoperative complications following cardiac surgeries.

Introduction Much controversy exists on the effect of a fresh frozen plasma (FFP) transfusion on systemic inflammation and endothelial damage. Adverse effects of FFP have been well described, including acute lung injury. However, it is also suggested that a higher amount of FFP decreases mortality in trauma patients requiring a massive transfusion. Furthermore, FFP has an endothelial stabilizing effect in experimental models. We investigated the effect of fresh frozen plasma transfusion on systemic inflammation and endothelial condition. Methods A prospective predefined substudy of a randomized trial in coagulopathic non-bleeding critically ill patients receiving a prophylactic transfusion of FFP (12 ml/kg) prior to an invasive procedure. Levels of inflammatory cytokines and markers of endothelial condition were measured in paired samples of 33 patients before and after transfusion. The statistical tests used were paired t test or the Wilcoxon signed-rank test. Results At baseline, systemic cytokine levels were mildly elevated in critically ill patients. FFP transfusion resulted in a decrease of levels of TNF-α (from 11.3 to 2.3 pg/ml, P  = 0.01). Other cytokines were not affected. FFP also resulted in a decrease in systemic syndecan-1 levels (from 675 to 565 pg/ml, P  = 0.01) and a decrease in factor VIII levels (from 246 to 246%, P <0.01), suggestive of an improved endothelial condition. This was associated with an increase in ADAMTS13 levels (from 24 to 32%, P <0.01) and a concomitant decrease in von Willebrand factor (vWF) levels (from 474 to 423%, P <0.01). Conclusions A fixed dose of FFP transfusion in critically ill patients decreases syndecan-1 and factor VIII levels, suggesting a stabilized endothelial condition, possibly by increasing ADAMTS13, which is capable of cleaving vWF. Trial registrations Trialregister.nl NTR2262 , registered 26 March 2010 and Clinicaltrials.gov NCT01143909 , registered 14 June 2010.

Background Acute respiratory distress syndrome (ARDS) is etiologically and clinically a heterogeneous disease. Its diagnostic characteristics and subtype classification, and the application of these features to treatment, have been of considerable interest. Metabolomics is becoming important for identifying ARDS biology and distinguishing its subtypes. This study aimed to identify metabolites that could distinguish sepsis-induced ARDS patients from non-ARDS controls, using a targeted metabolomics approach, and to identify whether sepsis-induced direct and sepsis-induced indirect ARDS are metabolically distinct groups, and if so, confirm their metabolites and associated pathways. Methods This study retrospectively analyzed 54 samples of ARDS patients from a sepsis registry that was prospectively collected from March 2011 to February 2018, along with 30 non-ARDS controls. The cohort was divided into direct and indirect ARDS. Metabolite concentrations of five analyte classes (energy metabolism, free fatty acids, amino acids, phospholipids, sphingolipids) were measured using liquid chromatography–tandem mass spectrometry and gas chromatography–mass spectrometry by targeted metabolomics. Results In total, 186 metabolites were detected. Among them, 102 metabolites could differentiate sepsis-induced ARDS patients from the non-ARDS controls, while 14 metabolites could discriminate sepsis-induced ARDS subphenotypes. Using partial least-squares discriminant analysis, we showed that sepsis-induced ARDS patients were metabolically distinct from the non-ARDS controls. The main distinguishing metabolites were lysophosphatidylethanolamine (lysoPE) plasmalogen, PE plasmalogens, and phosphatidylcholines (PCs). Sepsis-induced direct and indirect ARDS were also metabolically distinct subgroups, with differences in lysoPCs. Glycerophospholipid and sphingolipid metabolism were the most significant metabolic pathways involved in sepsis-induced ARDS biology and in sepsis-induced direct/indirect ARDS, respectively. Conclusion Our study demonstrated a marked difference in metabolic patterns between sepsis-induced ARDS patients and non-ARDS controls, and between sepsis-induced direct and indirect ARDS subpheonotypes. The identified metabolites and pathways can provide clues relevant to the diagnosis and treatment of individuals with ARDS.

Multivariate statistical analysis, geostatistical techniques and structural equation modeling were used to determine the main factors and mechanisms controlling the spatial variation of groundwater quality in the Ain Azel plain, Algeria. Cluster analysis grouped the sampling wells into two statistically significant clusters based on similarities of groundwater quality characteristics. Principal component and factor analyses (PCA/ FA) revealed that two factors explained around 85 % of the total variance, which water-rock interaction and anthropogenic impact as the dominant factors affecting the groundwater quality. The distribution of factor score one represents high loading for EC, Ca, Mg, Na, K, and SO 4 in the western side and south eastern side of the plain, where water-rock interactions are dominate factors influence groundwater quality. Spatial distribution map of factor score 2 indicate that NO 3 , NO 2 , NH 4 , and COD show high concentration in central and southern side of the plain, where anthropogenic impact reduce groundwater quality. Further, one-way analysis of variance (one-way ANOVA) showed that the mean differences between cluster one and two show significantly differences for some water quality parameters including EC, Ca, Mg, Na, K, Cl, and SO 4 . Structural equation modeling (SEM) confirmed the finding of multivariate analysis. This study provides a new technique of confirming exploratory data analysis using SEM in groundwater quality.

Chronic obstructive pulmonary disease (COPD) exacerbations are associated with virus (mostly rhinovirus) and bacterial infections, but it is not known whether rhinovirus infections precipitate secondary bacterial infections. To investigate relationships between rhinovirus infection and bacterial infection and the role of antimicrobial peptides in COPD exacerbations. We infected subjects with moderate COPD and smokers and nonsmokers with normal lung function with rhinovirus. Induced sputum was collected before and repeatedly after rhinovirus infection and virus and bacterial loads measured with quantitative polymerase chain reaction and culture. The antimicrobial peptides secretory leukoprotease inhibitor (SLPI), elafin, pentraxin, LL-37, α-defensins and β-defensin-2, and the protease neutrophil elastase were measured in sputum supernatants. After rhinovirus infection, secondary bacterial infection was detected in 60% of subjects with COPD, 9.5% of smokers, and 10% of nonsmokers (P < 0.001). Sputum virus load peaked on Days 5-9 and bacterial load on Day 15. Sputum neutrophil elastase was significantly increased and SLPI and elafin significantly reduced after rhinovirus infection exclusively in subjects with COPD with secondary bacterial infections, and SLPI and elafin levels correlated inversely with bacterial load. Rhinovirus infections are frequently followed by secondary bacterial infections in COPD and cleavage of the antimicrobial peptides SLPI and elafin by virus-induced neutrophil elastase may precipitate these secondary bacterial infections. Therapy targeting neutrophil elastase or enhancing innate immunity may be useful novel therapies for prevention of secondary bacterial infections in virus-induced COPD exacerbations.

Air quality is a critical component of environmental health. Monitoring and analysis of particulate matter with a diameter of 2.5 micrometers or smaller (PM 2.5 ) plays a pivotal role in understanding air quality changes. This study focuses on the application of a new bandpass bootstrap approach, termed the Variable Bandpass Periodic Block Bootstrap (VBPBB), for analyzing time series data which provides modeled predictions of daily mean PM 2.5 concentrations over 16 years in Manhattan, New York, the United States. The VBPBB can be used to explore periodically correlated (PC) principal components for this daily mean PM 2.5 dataset. This method uses bandpass filters to isolate distinct PC components from datasets, removing unwanted interference including noise, and bootstraps the PC components. This preserves the PC structure and permits a better understanding of the periodic characteristics of time series data. The results of the VBPBB are compared against outcomes from alternative block bootstrapping techniques. The findings of this research indicate potential trends of elevated PM 2.5 levels, providing evidence of significant semi-annual, tri-annual, and weekly patterns missed by other methods.

PurposePremorbid conditions affect prognosis of acutely-ill aged patients. Several lines of evidence suggest geriatric syndromes need to be assessed but little is known on their relative effect on the 30-day survival after ICU admission. The primary aim of this study was to describe the prevalence of frailty, cognition decline and activity of daily life in addition to the presence of comorbidity and polypharmacy and to assess their influence on 30-day survival.MethodsProspective cohort study with 242 ICUs from 22 countries. Patients 80 years or above acutely admitted over a six months period to an ICU between May 2018 and May 2019 were included. In addition to common patients’ characteristics and disease severity, we collected information on specific geriatric syndromes as potential predictive factors for 30-day survival, frailty (Clinical Frailty scale) with a CFS > 4 defining frail patients, cognitive impairment (informant questionnaire on cognitive decline in the elderly (IQCODE) with IQCODE ≥ 3.5 defining cognitive decline, and disability (measured the activity of daily life with the Katz index) with ADL ≤ 4 defining disability. A Principal Component Analysis to identify co-linearity between geriatric syndromes was performed and from this a multivariable model was built with all geriatric information or only one: CFS, IQCODE or ADL. Akaike’s information criterion across imputations was used to evaluate the goodness of fit of our models.ResultsWe included 3920 patients with a median age of 84 years (IQR: 81–87), 53.3% males). 80% received at least one organ support. The median ICU length of stay was 3.88 days (IQR: 1.83–8). The ICU and 30-day survival were 72.5% and 61.2% respectively. The geriatric conditions were median (IQR): CFS: 4 (3–6); IQCODE: 3.19 (3–3.69); ADL: 6 (4–6); Comorbidity and Polypharmacy score (CPS): 10 (7–14). CFS, ADL and IQCODE were closely correlated. The multivariable analysis identified predictors of 1-month mortality (HR; 95% CI): Age (per 1 year increase): 1.02 (1.–1.03, p = 0.01), ICU admission diagnosis, sequential organ failure assessment score (SOFA) (per point): 1.15 (1.14–1.17, p < 0.0001) and CFS (per point): 1.1 (1.05–1.15, p < 0.001). CFS remained an independent factor after inclusion of life-sustaining treatment limitation in the model.ConclusionWe confirm that frailty assessment using the CFS is able to predict short-term mortality in elderly patients admitted to ICU. Other geriatric syndromes do not add improvement to the prediction model. Since CFS is easy to measure, it should be routinely collected for all elderly ICU patients in particular in connection to advance care plans, and should be used in decision making.