Frequent social interactions are strongly linked to positive affect, longevity, and good health. Although there has been extensive research on changes in the size of social networks over time, little ...attention has been given to the development of contact frequency across the life span. In this cohort-sequential longitudinal study, we examined intraindividual changes in the frequency of social contact with family and nonfamily members, and potential moderators of these changes. The data come from the 1998, 2003, 2008, and 2013 waves of the German Socio-Economic Panel (SOEP) study (N = 36,716; age range: 17-85 years). Using latent growth curve analysis, we found that the frequency of in-person contact with family members remained relatively stable across the life span. In contrast, the frequency of visits to and from nonfamily members (neighbors, friends, and acquaintances) declined following a cubic trajectory and dropped below the frequency of family visits when respondents were in their mid-30s. Relationship status and gender had a slight effect on both of these relationship trajectories. Subjective current health status and employment status influenced the life span trajectory of nonfamily social contact only. Changes of residence and the birth of a child, both of which constitute major turning points in the life course, did not affect the life span trajectory of either family or nonfamily in-person contact. The findings are discussed here in the context of earlier findings and in relation to socioemotional selectivity and social convoy theory and the evolutionary life history approach.
Objectives This study investigated whether inhibiting late Na+ current by using ranolazine improved diastolic function in patients with heart failure with preserved ejection fraction (HFpEF). ...Background HFpEF accounts for >50% of all HF patients, but no specific treatment exists. Methods The RALI-DHF (RAnoLazIne for the Treatment of Diastolic Heart Failure) study was a prospective, randomized, double-blind, placebo-controlled small proof-of-concept study. Inclusion criteria were EF ≥45%, a mitral E-wave velocity/mitral annular velocity ratio (E/E′) >15 or N-terminal pro–B-type natriuretic peptide (NT-proBNP) concentration >220 pg/ml, a left ventricular end-diastolic pressure (LVEDP) ≥18 mm Hg, and time-constant of relaxation (tau) ≥50 ms. Patients were randomized to ranolazine (n = 12) or placebo (n = 8). Treatment consisted of intravenous infusion for 24 h, followed by oral treatment for 13 days. Results After 30 min of infusion, LVEDP (p = 0.04) and pulmonary capillary wedge pressure (p = 0.04) decreased in the ranolazine group but not in the placebo group. Mean pulmonary artery pressure showed a trend toward a decrease in the ranolazine group that was significant under pacing conditions at 120 beats/min (p = 0.02), but not for the placebo group. These changes occurred without changes in left ventricular end-systolic pressure or systemic or pulmonary resistance but in the presence of a small but significant decrease in cardiac output (p = 0.04). Relaxation parameters (e.g., tau, rate of decline of left ventricular pressure per minute dP/dtmin ) were unaltered. Echocardiographically, the E/E′ ratio did not significantly change after 22 h. After 14 days of treatment, no significant changes were observed in echocardiographic or cardiopulmonary exercise test parameters. There were no significant effects on NT-pro-BNP levels. Conclusions Results of this proof-of-concept study revealed that ranolazine improved measures of hemodynamics but that there was no improvement in relaxation parameters. (Ranolazine in Diastolic Heart Failure RALI-DHF; NCT01163734 )
Point-of-care lung ultrasound (LUS) is an attractive alternative to chest X-ray (CXR), but its diagnostic accuracy compared to CXR has not been well studied in coronavirus disease 2019 (COVID-19) ...patients. We conducted a prospective observational study to assess the correlation between LUS and CXR findings in COVID-19 patients. Ninety-six patients with a clinical diagnosis of COVID-19 underwent an LUS exam and CXR upon presentation. Physicians blinded to the CXR findings performed all LUS exams. Detection of pulmonary infiltrates by CXR versus LUS was compared between patients categorized as suspected or confirmed COVID-19 based on reverse transcriptase-polymerase chain reaction. Sensitivities and correlation by Kappa statistic were calculated between LUS and CXR. LUS detected pulmonary infiltrates more often than CXR in both suspected and confirmed COVID-19 subjects. The most common LUS abnormalities were discrete B-lines, confluent B-lines, and small subpleural consolidations. Most important, LUS detected unilateral or bilateral pulmonary infiltrates in 55% of subjects with a normal CXR. Substantial agreement was demonstrated between LUS and CXR for normal, unilateral or bilateral findings (Κ = 0.48 (95% CI 0.34 to 0.63)). In patients with suspected or confirmed COVID-19, LUS detected pulmonary infiltrates more often than CXR, including more than half of the patients with a normal CXR.
Weaning patients from mechanical ventilation (MV) is a critical and resource intensive process in the Intensive Care Unit (ICU) that impacts patient outcomes and healthcare expenses. Weaning methods ...vary widely among providers. Prolonged MV is associated with adverse events and higher healthcare expenses. Predicting weaning readiness is a non-trivial process in which the positive end-expiratory pressure (PEEP), a crucial component of MV, has potential to be indicative but has not yet been used as the target. We aimed to predict successful weaning from mechanical ventilation by targeting changes in the PEEP-level using a supervised machine learning model. This retrospective study included 12,153 mechanically ventilated patients from Medical Information Mart for Intensive Care (MIMIC-IV) and eICU collaborative research database (eICU-CRD). Two machine learning models (Extreme Gradient Boosting and Logistic Regression) were developed using a continuous PEEP reduction as target. The data is splitted into 80% as training set and 20% as test set. The model's predictive performance was reported using 95% confidence interval (CI), based on evaluation metrics such as area under the receiver operating characteristic (AUROC), area under the precision-recall curve (AUPRC), F1-Score, Recall, positive predictive value (PPV), and negative predictive value (NPV). The model's descriptive performance was reported as the variable ranking using SHAP (SHapley Additive exPlanations) algorithm. The best model achieved an AUROC of 0.84 (95% CI 0.83-0.85) and an AUPRC of 0.69 (95% CI 0.67-0.70) in predicting successful weaning based on the PEEP reduction. The model demonstrated a Recall of 0.85 (95% CI 0.84-0.86), F1-score of 0.86 (95% CI 0.85-0.87), PPV of 0.87 (95% CI 0.86-0.88), and NPV of 0.64 (95% CI 0.63-0.66). Most of the variables that SHAP algorithm ranked to be important correspond with clinical intuition, such as duration of MV, oxygen saturation (SaO2), PEEP, and Glasgow Coma Score (GCS) components. This study demonstrates the potential application of machine learning in predicting successful weaning from MV based on continuous PEEP reduction. The model's high PPV and moderate NPV suggest that it could be a useful tool to assist clinicians in making decisions regarding ventilator management.
Hypoxia is an important risk factor and indicator for the declining health of inpatients. Predicting future hypoxic events using machine learning is a prospective area of study to facilitate ...time-critical interventions to counter patient health deterioration.
This systematic review aims to summarize and compare previous efforts to predict hypoxic events in the hospital setting using machine learning with respect to their methodology, predictive performance, and assessed population.
A systematic literature search was performed using Web of Science, Ovid with Embase and MEDLINE, and Google Scholar. Studies that investigated hypoxia or hypoxemia of hospitalized patients using machine learning models were considered. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool.
After screening, a total of 12 papers were eligible for analysis, from which 32 models were extracted. The included studies showed a variety of population, methodology, and outcome definition. Comparability was further limited due to unclear or high risk of bias for most studies (10/12, 83%). The overall predictive performance ranged from moderate to high. Based on classification metrics, deep learning models performed similar to or outperformed conventional machine learning models within the same studies. Models using only prior peripheral oxygen saturation as a clinical variable showed better performance than models based on multiple variables, with most of these studies (2/3, 67%) using a long short-term memory algorithm.
Machine learning models provide the potential to accurately predict the occurrence of hypoxic events based on retrospective data. The heterogeneity of the studies and limited generalizability of their results highlight the need for further validation studies to assess their predictive performance.
Background/Objectives: During the COVID-19 pandemic and the burden on hospital resources, the rapid categorization of high-risk COVID-19 patients became essential, and lung ultrasound (LUS) emerged ...as an alternative to chest computed tomography, offering speed, non-ionizing, repeatable, and bedside assessments. Various LUS score systems have been used, yet there is no consensus on an optimal severity cut-off. We assessed the performance of a 12-zone LUS score to identify adult COVID-19 patients with severe lung involvement using oxygen saturation (SpO2)/fractional inspired oxygen (FiO2) ratio as a reference standard to define the best cut-off for predicting adverse outcomes. Methods: We conducted a single-centre prospective study (August 2020–April 2021) at Hospital del Mar, Barcelona, Spain. Upon admission to the general ward or intensive care unit (ICU), clinicians performed LUS in adult patients with confirmed COVID-19 pneumonia. Severe lung involvement was defined as a SpO2/FiO2 ratio <315. The LUS score ranged from 0 to 36 based on the aeration patterns. Results: 248 patients were included. The admission LUS score showed moderate performance in identifying a SpO2/FiO2 ratio <315 (area under the ROC curve: 0.71; 95%CI 0.64–0.77). After adjustment for COVID-19 risk factors, an admission LUS score ≥17 was associated with an increased risk of in-hospital death (OR 5.31; 95%CI: 1.38–20.4), ICU admission (OR 3.50; 95%CI: 1.37–8.94) and need for IMV (OR 3.31; 95%CI: 1.19–9.13). Conclusions: Although the admission LUS score had limited performance in identifying severe lung involvement, a cut-off ≥17 score was associated with an increased risk of adverse outcomes. and could play a role in the rapid categorization of COVID-19 pneumonia patients, anticipating the need for advanced care.
Cyanobacterial photosystem 2 and cytochrome
b
6
f complexes have been structurally resolved up to the molecular level while the adjustment of their function in response to environmental and ...intracellular parameters is based on various modifications of these complexes which have not yet been resolved in detail. This minireview summarizes recent results on two central modifications for each complex: (a) for the cytochrome
b
6
f complex the implication of PetP, a new subunit, and of three copies of PetC, the Rieske protein, for the fine-tuning of the photosynthetic electron transport is evaluated; (b) for photosystem 2, the heterogeneity of the D1 subunit and the role of subunit Psb27 is discussed in relation to stress response and the biogenesis/repair cycle. The presented “dynamic” models for both complexes should illustrate the need to complement structural by more extensive functional models which consider the flexibility of individual complexes in the physiological context – beyond structure.
Water binding to the Mn(4)O(5)Ca cluster of the oxygen-evolving complex (OEC) of Photosystem II (PSII) poised in the S(2) state was studied via H(2)(17)O- and (2)H(2)O-labeling and high-field ...electron paramagnetic resonance (EPR) spectroscopy. Hyperfine couplings of coordinating (17)O (I = 5/2) nuclei were detected using W-band (94 GHz) electron-electron double resonance (ELDOR) detected NMR and Davies/Mims electron-nuclear double resonance (ENDOR) techniques. Universal (15)N (I = ½) labeling was employed to clearly discriminate the (17)O hyperfine couplings that overlap with (14)N (I = 1) signals from the D1-His332 ligand of the OEC (Stich Biochemistry 2011, 50 (34), 7390-7404). Three classes of (17)O nuclei were identified: (i) one μ-oxo bridge; (ii) a terminal Mn-OH/OH(2) ligand; and (iii) Mn/Ca-H(2)O ligand(s). These assignments are based on (17)O model complex data, on comparison to the recent 1.9 Å resolution PSII crystal structure (Umena Nature 2011, 473, 55-60), on NH(3) perturbation of the (17)O signal envelope and density functional theory calculations. The relative orientation of the putative (17)O μ-oxo bridge hyperfine tensor to the (14)N((15)N) hyperfine tensor of the D1-His332 ligand suggests that the exchangeable μ-oxo bridge links the outer Mn to the Mn(3)O(3)Ca open-cuboidal unit (O4 and O5 in the Umena et al. structure). Comparison to literature data favors the Ca-linked O5 oxygen over the alternative assignment to O4. All (17)O signals were seen even after very short (≤15 s) incubations in H(2)(17)O suggesting that all exchange sites identified could represent bound substrate in the S(1) state including the μ-oxo bridge. (1)H/(2)H (I = ½, 1) ENDOR data performed at Q- (34 GHz) and W-bands complement the above findings. The relatively small (1)H/(2)H couplings observed require that all the μ-oxo bridges of the Mn(4)O(5)Ca cluster are deprotonated in the S(2) state. Together, these results further limit the possible substrate water-binding sites and modes within the OEC. This information restricts the number of possible reaction pathways for O-O bond formation, supporting an oxo/oxyl coupling mechanism in S(4).
The Effects of Climate Change on Mental Health Walinski, Annika; Sander, Julia; Gerlinger, Gabriel ...
Deutsches Ärzteblatt international,
02/2023, Letnik:
120, Številka:
8
Journal Article
Recenzirano
Odprti dostop
All over the world, climate change is exerting negative and complex effects on human living conditions and health. In this narrative review, we summarize the current global evidence regarding the ...effects of climate change on mental health.
A systematic literature search concerning the direct effects of acute extreme weather events (floods, storms, fires) and chronic stresses (heat, drought) due to climate change, as well as the indirect effects of climate change (food insecurity, migration), on the diagnoses of mental disorders, psychological distress, and psychiatric emergency admissions was carried out in PubMed and PsychInfo, and supplemented by expert selection. 1017 studies were identified, and 128 were included.
The heterogeneity of study methods does not permit any overall estimate of effect strength. The available evidence shows that traumatic experiences due to extreme weather events increase the risk of affective and anxiety disorders, especially the risk of post-traumatic stress disorder. Heat significantly increases the morbidity and mortality attributable to mental illness, as well as the frequency of psychiatric emergencies. Persistent stressors such as drought, food insecurity, and migration owing to climate change can also be major risk factors for mental illness.
The consequences of climate change are stress factors for mental health. Therefore, as global warming progresses, an increasing incidence and prevalence of mental illness is to be expected. Vulnerable groups, such as the (already) mentally ill, children, and adolescents, need to be protected. At the same time, there is a need for further systematic research on the mechanisms of action and effects of climate change on mental function.
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