Jeppesenbuck8967

Exploratory analyses found relationships between worse and more variable mood (sadness, anger, and impulsivity) with worse and more variable sleep that evening (efficiency, WASO, and sleep onset time). The sample size was modest, fairly homogenous, and included mainly euthymic persons with BD. EMA-based assessments of mood and sleep are correlated with validated scale scores and provide novel insight into intra-individual variability. Further work on the complex two-way interactions between sleep and mood is needed to better understand how to improve outcomes in BD.Post-traumatic stress disorder (PTSD) develops after an exposure to a life-threatening event and is characterized by intrusive memories. According to memory reconsolidation theory retrieval of memory under certain conditions leads to its labilization and subsequent re-storage which could be disrupted by drugs. Propranolol has been the most commonly investigated drug for memory reconsolidation therapy in clinical trials. Intervention with propranolol have shown mixed results in PTSD patients with some studies showing improvement in symptoms while other failing to replicate these findings. We conducted a systematic review and meta-analysis to determine the efficacy of trauma memory disruption by propranolol on PTSD symptoms and physiological responses in PTSD patients. 3224 publications were assessed for eligibility. Seven studies on effects of propranolol on PTSD symptoms and 3 studies on effects of propranolol on physiological responses were incorporated in the meta-analyses. find more Overall, results indicate that propranolol did not show a beneficial effect on PTSD symptoms (standardized mean difference 1.29; 95% CI = -2.16 - 0.17). Similarly, propranolol did not influence skin conductance (standardized mean difference 0.77; 95% CI = -1.85 - 0.31) or EMG response (standardized mean difference 0.16; 95% CI = -0.65 - 0.33). However, propranolol significantly reduced heart rate after trauma memory recall compared to placebo (standardized mean difference 0.67; 95% CI = -1.27 to -0.07). This study finds a lack of evidence for the efficacy of propranolol on traumatic memory disruption, in PTSD patients, to recommend its routine clinical use. However, a high level of heterogeneity, variation in propranolol dosage and inadequate sample sizes mean that these findings require cautious interpretation.A magnetic hybrid material based on the use of the mixed-ligand Metal-Organic Framework (MOF) PUM198 is proposed for the magnetic dispersive micro solid-phase extraction (MD-μSPE) of the 16 polycyclic aromatic hydrocarbons (PAHs) included in the US-EPA priority pollutants list. PUM198 is a thermally robust MOF characterized by a doubly interpenetrated microporous framework in which Zn2+ ions and carboxylate groups define 2D planes that are pillared by a bis-pyridine-bis amide ligand containing a biphenyl scaffold. PUM198 revealed to be ideal to adsorb PAHs efficiently through non-covalent interactions. A Plackett-Burman Design followed by a Central Composite Design and the multicriteria method of the desirability functions were applied to find the optimal conditions for the extraction of the investigated PAHs, resulting in a reduced solvent consumption, i.e., 50 μL of solvent per extraction for 5 mL of sample, approximatively 3-20 times lower than those reported in previous studies, thus satisfying the principles of green analytical chemistry. Method validation proved the reliability of the method for the determination of PAHs at trace level, obtaining detection limits in the 6.7-27 ng/L range, good precision with RSDs% lower than 19% and recovery rates in the 99 (±13)-126 (±8)% range near the quantitation limit. Finally, the applicability of the method was demonstrated by analyzing underground water samples taken from contaminated sites.In the present work, a method for the determination of parabens and bisphenol A in urine samples has been developed. For this purpose, a novel hierarchical mesoporous silica doped with β-cyclodextrin was developed and used as a sorbent for preconcentration and clean-up step, before analyte determination by liquid chromatography coupled to mass spectrometry detector. Disordered silica materials were also synthesized for comparison purposes. All materials were characterized by electron microscopy, X-ray diffraction, porosimetry, nuclear magnetic resonance, thermogravimetric analysis, elemental CNH analysis, and confocal microscopy, and the attachment of cyclodextrins has been proved as well as their uniform distribution in the resulting material. After the optimization of several protocol parameters, good analytical features were achieved, including recoveries in the range of 96-109% for all analytes, as well as relative standard deviations between 8 and 24%. Also, limits of quantification in the range of 0.003-0.19 µg L-1 were obtained in all cases. The developed method was applied to the determination of parabens and bisphenol A in real urine samples in comparison with a reference method using C18 cartridges, including the correction with creatinine content. Target analytes were detected in all analysed samples, with being BPA the most detected compound.A novel automated method was developed to test short-chain aldehyde emissions using a chamber and a flow-cell coupled with a dynamic solid-phase microextraction (SPME) on-fiber derivatization technique. Calibration curves of short-chain aldehydes were developed for quantitation of target analytes, including formaldehyde, acetaldehyde, propionaldehyde, and acrolein emitted from the chamber outlet. The linearity range for the aldehydes was 0.8 to 2130.0 µg/m3. The limits of quantitation (LOQ) for various aldehydes ranged from ∼0.2 - 1.9 µg/m3. By using this method, emission rate curves are measured with an automated system. Compared with a traditional 2,4-dinitrophenyl hydrazine (DNPH)-high performance liquid chromatography (HPLC) method for aldehyde emission measurement, this method provided an automated technique to study the emission of short-chain aldehydes from multiple samples in one experiment. Furthermore, by using dynamic SPME coupled to an on-fiber derivatization technique, the repeatability and sensitivity of the method is comparable and even better than traditional techniques.The knowledge of compounds stability in the process of sample preparation for analysis and during analysis itself helps assess the accuracy and precision of estimating their concentration in tested samples. The present paper shows that a significant amount of CBD present in the blood/plasma sample analyzed by means of GC transforms in the hot GC injector not only to 9α-hydroxyhexahydrocannabinol, 8-hydroxy-iso-hexahydrocannabinol, delta-9-tetrahydrocannabinol, Δ8-tetrahydrocannabinol, and cannabinol but also to the trifluoroacetic esters of Δ9-THC and Δ8-THC, when trifuoroacetic acid is used as protein precipitation agent. The amount of those newly revealed CBD transformation products depends on the GC injector temperature and on the extrahent type when extracts of the supernatants centrifuged from human plasma samples are analyzed after their preliminary protein precipitation by trifuoroacetic acid. Although trifuoroacetic acid as a protein precipitating agent has many disadvantages, it is quite often used for this purpose due to its very high protein precipitation efficiency. The results presented in the study demonstrate why the use of trifuoroacetic acid for plasma samples deproteinization should be avoided when CBD is determined by GC.

To identify the challenges and opportunities for rolling out a bespoke model of group antenatal care called Pregnancy Circles (PC) within the National Health Service what kind of support and training is needed and what adaptations are appropriate, including during a pandemic when face-to-face interaction is limited.

Exploratory qualitative study (online focus group). Study co-designed with midwives. Data analysed thematically using an ecological model to synthesise.

Five maternity services within the National Health Service.

Seven midwives who facilitated PCs. Three senior midwives with implementation experience participated in the co-design process.

Three themes operating across the ecological model were identified 'Implementing innovation', 'Philosophy of care' and 'Resource management'. Tensions were identified between group care's focus on relationships and professional autonomy, and concepts of efficiency within the NHS's market model of care. Midwives found protected time, training and ongoingtation of group care in the NHS requires senior leadership and expertise in change management, protected time for training and delivery of the model, and funding for equipment. Training and ongoing support, are vital for sustainability and quality control. There is potential for online delivery and integrating group care with continuity models.

Implementation of group care in the NHS requires senior leadership and expertise in change management, protected time for training and delivery of the model, and funding for equipment. Training and ongoing support, are vital for sustainability and quality control. There is potential for online delivery and integrating group care with continuity models.The paper proposes a graph-theoretical approach to auscultation, bringing out the potential of graph features in classifying the bioacoustics signals. The complex network analysis of the bioacoustics signals - vesicular (VE) and bronchial (BR) breath sound - of 48 healthy persons are carried out for understanding the airflow dynamics during respiration. The VE and BR are classified by the machine learning techniques extracting the graph features - the number of edges (E), graph density (D), transitivity (T), degree centrality (Dcg) and eigenvector centrality (Ecg). The higher value of E, D, and T in BR indicates the temporally correlated airflow through the wider tracheobronchial tract resulting in sustained high-intense low-frequencies. The frequency spread and high-frequencies in VE, arising due to the less correlated airflow through the narrow segmental bronchi and lobar, appears as a lower value for E, D, and T. The lower values of Dcg and Ecg justify the inferences from the spectral and other graph parameters. The study proposes a methodology in remote auscultation that can be employed in the current scenario of COVID-19.

Chronic lymphocytic leukemia (CLL) is one of the most common types of leukemia in the western world which affects mainly the elderly population. Progress of the disease is very heterogeneous both in terms of necessity of treatment and life expectancy. The current scoring system for prognostic evaluation of patients with CLL is called CLL-IPI and predicts the general progress of the disease but is not a measure or a decision aid for the necessity of treatment. Due to the heterogeneous behavior of CLL it is important to develop tools that will identify if and when patients will necessitate treatment for CLL. Recently, Machine Learning (ML) has spread to many public health fields including diagnosis and prognosis of diseases.

Existing machine learning methods for CLL treatment prediction rely on expensive tests, such as genetic tests, rendering them useless in peripheral or low-resource clinics such as those in developing countries. We aim to develop a model for predicting whether a patient will need treatment for CLL within two years of diagnosis using a machine learning model based on only on demographic data and routine laboratory tests.