Tag: pubmed

JAMA Netw Open. 2023 Jul 3;6(7):e2325158. doi: 10.1001/jamanetworkopen.2023.25158.

ABSTRACT

IMPORTANCE: Soft drink consumption is associated with weight gain in children and adolescents, but little is known about the association between soft drink consumption and prevalence of the overweight and obesity in adolescents.

OBJECTIVE: To investigate the association of soft drink consumption with overweight and obesity in adolescents enrolled in school (hereafter, school-going adolescents) using country-level and individual-level data.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used data from 3 cross-sectional studies including 107 countries and regions that participated in the Global School-Based Student Health Survey (2009-2017), the European Health Behavior in School-Aged Children study (2017-2018), and the US Youth Risk Behavior Survey (2019).

EXPOSURE: Daily soft drink consumption (consuming soft drinks 1 or more times per day or not).

MAIN OUTCOME AND MEASURE: Overweight and obesity defined by the World Health Organization Growth Reference Data.

RESULTS: Among the 107 countries and regions, 65 were low- and middle-income, and 42 were high-income countries and regions, with a total of 405 528 school-going adolescents (mean [SD] age, 14.2 [1.7] years; 196 147 [48.4%] males). The prevalence of overweight and obesity among adolescent students varied from 3.3% (95% CI, 2.6 to 4.1) in Cambodia to 64.0% (95% CI, 57.0 to 71.6) in Niue, and the prevalence of adolescent students consuming soft drinks 1 or more times per day varied from 3.3% (95% CI, 2.9 to 3.7) in Iceland to 79.6% (95% CI, 74.0 to 85.3) in Niue. There was a positive correlation between the prevalence of daily soft drink consumption and the prevalence of overweight and obesity (R, 0.44; P < .001). The pooled analysis using individual-level data also showed a statistically significant association between daily soft drink consumption and overweight and obesity (daily soft drink consumption vs nondaily soft drink consumption), with an odds ratio of 1.14 (95% CI, 1.08 to 1.21) among school-going adolescents.

CONCLUSIONS AND RELEVANCE: In this study of 107 countries and regions, the prevalence of daily consumption of soft drinks was associated with the prevalence of overweight and obesity among adolescent students. Our results, in conjunction with other evidence, suggest that reducing soft drink consumption should be a priority in combating adolescent overweight and obesity.

PMID:37486630 | DOI:10.1001/jamanetworkopen.2023.25158

Br J Radiol. 2023 Jul 24:20230292. doi: 10.1259/bjr.20230292. Online ahead of print.

ABSTRACT

Image reconstruction for positron emission tomography (PET) has been developed over many decades, with advances coming from improved modelling of the data statistics and improved modelling of the imaging physics. However, high noise and limited spatial resolution have remained issues in PET imaging, and state-of-the-art PET reconstruction has started to exploit other medical imaging modalities (such as MRI) to assist in noise reduction and enhancement of PET’s spatial resolution. Nonetheless, there is an ongoing drive towards not only improving image quality, but also reducing the injected radiation dose and reducing scanning times. While the arrival of new PET scanners (such as total body PET) is helping, there is always a need to improve reconstructed image quality due to the time and count limited imaging conditions. Artificial intelligence (AI) methods are now at the frontier of research for PET image reconstruction. While AI can learn the imaging physics as well as the noise in the data (when given sufficient examples), one of the most common uses of AI arises from exploiting databases of high-quality reference examples, to provide advanced noise compensation and resolution recovery. There are three main AI reconstruction approaches: i) direct data-driven AI methods which rely on supervised learning from reference data, ii) iterative (unrolled) methods which combine our physics and statistical models with AI learning from data, and iii) methods which exploit AI with our known models, but crucially can offer benefits even in the absence of any example training data whatsoever. This article reviews these methods, considering opportunities and challenges of AI for PET reconstruction.

PMID:37486607 | DOI:10.1259/bjr.20230292

Acta Biomed. 2023 Jul 24;94(S1):e2023205. doi: 10.23750/abm.v94iS1.14198.

ABSTRACT

Talar body fractures are uncommon fractures of the foot and its management results to be very hard due to retrograde vascularization and wide articular cartilage coverage of talar surface, which could easily lead to poor functional outcomes, avascular osteonecrosis and early post traumatic arthritis. We describe a case of displaced, vertical, talar body fracture in a 41-year-old patient treated with reduction and fixation by talar anteromedial approach coupled to medial malleolar osteotomy to better expose the fracture. Our literature review has found few studies, in addition with a low level of statistical evidence. We advocate for more studies with a bigger sample and with a design of randomized control trials.

PMID:37486598 | DOI:10.23750/abm.v94iS1.14198

Metabolomics. 2023 Jul 24;19(8):68. doi: 10.1007/s11306-023-02032-8.

ABSTRACT

INTRODUCTION: Lantana trifolia L. (Verbenaceae) is a shrubby plant. In folk medicine, its leaves are used in the form of infusions and syrups to treat angina, coughs, and colds; they are also applied as tranquilizer. Previous studies have reported the antimicrobial potential of the compounds present in L. trifolia leaves.

OBJECTIVES: To report the anti-Candida activities of the fractions obtained from the fruits and leaves of two L. trifolia specimens.

METHODS: The L. trifolia fractions were submitted to UFLC-DAD-(+)-ESI-MS/MS, and the data were analyzed by using multivariate statistical tools (PCA, PLS-DA) and spectral similarity analyses based on molecular networking, which aided dereplication of the bioactive compounds. Additionally, NMR analyses were performed to confirm the chemical structure of some of the major compounds in the fractions.

RESULTS: The ethyl acetate fractions presented MIC values lower than 100 µg mL^-1 against the three Candida strains evaluated herein (C. albicans, C. tropicalis, and C. glabrata). Fractions FrPo AcOEt, FrPe AcOEt, and FrPe nBut had MIC values of 1.46, 2.93, and 2.93 µg mL^-1 against C. glabrata, respectively. These values resembled the MIC value of amphotericin B, the positive control (0.5-1.0 µg mL^-1), against this same strain. Cytotoxicity was measured and used to calculate the selectivity index.

CONCLUSION: On the basis of our data, the most active fractions in the antifungal assay were more selective against C. glabrata than against non-infected cells. The analytical approach adopted here allowed us to annotate 29 compounds, nine of which were bioactive (PLS-DA results) and belong to the class of phenolic compounds.

PMID:37486581 | DOI:10.1007/s11306-023-02032-8

J Patient Rep Outcomes. 2023 Jul 24;7(1):77. doi: 10.1186/s41687-023-00616-5.

ABSTRACT

BACKGROUND: Longitudinal estimates of long COVID burden during Omicron remain limited. This study characterized long-term impacts of COVID-19 and booster vaccination on symptoms, Health-Related Quality of Life (HRQoL), and Work Productivity Activity Impairment (WPAI).

METHODS: Outpatients with ≥ 1 self-reported symptom and positive SARS-CoV-2 test at CVS Health United States test sites were recruited between 01/31 and 04/30/2022. Symptoms, EQ-5D and WPAI were collected via online surveys until 6 months following infection. Both observed and model-based estimates were analyzed. Effect sizes based on Cohen’s d quantified the magnitude of outcome changes over time, within and between vaccination groups. Mixed models for repeated measures were conducted for multivariable analyses, adjusting for covariates. Logistic regression assessed odds ratio (OR) of long COVID between vaccination groups.

RESULTS: At long COVID start (Week 4), 328 participants included 87 (27%) Boosted with BNT162b2, 86 (26%) with a BNT162b2 primary series (Primed), and 155 (47%) Unvaccinated. Mean age was 42.0 years, 73.8% were female, 26.5% had ≥ 1 comorbidity, 36.9% prior infection, and 39.6% reported ≥ 3 symptoms (mean: 3.1 symptoms). At Month 6, among 260 participants, Boosted reported a mean of 1.1 symptoms versus 3.4 and 2.8 in Unvaccinated and Primed, respectively (p < 0.001). Boosted had reduced risks of ≥ 3 symptoms versus Unvaccinated (observed: OR 0.22, 95% CI 0.10-0.47, p < 0.001; model-based: OR 0.36, 95% CI 0.15-0.87, p = 0.019) and Primed (observed: OR 0.29, 95% CI 0.13-0.67, p = 0.003; model-based: OR 0.59, 95% CI 0.21-1.65, p = 0.459). Results were consistent using ≥ 2 symptoms. Regarding HRQoL, among those with long COVID, Boosted had higher EQ-5D Utility Index (UI) than Unvaccinated (observed: 0.922 vs. 0.731, p = 0.014; model-based: 0.910 vs. 0.758, p-value = 0.038) and Primed (0.922 vs. 0.648, p = 0.014; model-based: 0.910 vs. 0.708, p-value = 0.008). Observed and model-based estimates for EQ-VAS and UI among Boosted were comparable with pre-COVID since Month 3. Subjects vaccinated generally reported better WPAI scores.

CONCLUSIONS: Long COVID negatively impacted HRQoL and WPAI. The BNT162b2 booster could have a beneficial effect in reducing the risk and burden of long COVID. Boosted participants reported fewer and less durable symptoms, which contributed to improve HRQoL and maintain WPAI levels. Limitations included self-reported data and small sample size for WPAI.

PMID:37486567 | DOI:10.1186/s41687-023-00616-5

Head Neck Pathol. 2023 Jul 24. doi: 10.1007/s12105-023-01570-w. Online ahead of print.

NO ABSTRACT

PMID:37486534 | DOI:10.1007/s12105-023-01570-w

Adv Exp Med Biol. 2023;1424:255-263. doi: 10.1007/978-3-031-31982-2_29.

ABSTRACT

The brain’s temperature measurements (T_B) in patients with severe brain damage are important, in order to offer the optimal treatment. The purpose of this research is the creation of mathematical models for the T_B‘s prediction, based on the temperatures in the bladder (T_BL), femoral artery (T_FA), ear canal (T_ΕC), and axilla (T_A), without the need for placement of intracranial catheter, contributing significantly to the research of the human thermoregulatory system.The research involved 18 patients (13 men and 5 women), who were hospitalized in the adult intensive care units (ICU) of Larissa’s two hospitals, with severe brain injury. An intracranial catheter with a thermistor was used to continuously measure T_B and other parameters. The T_B‘s measurements, and simultaneously one or more of T_BL, T_FA, T_EC, and T_A, were recorded every 1 h.To create T_B predicting models, the data of each measurement was separated into (a) model sample (measurements’ 80%) and (b) validation sample (measurements’ 20%). Multivariate linear regression analysis demonstrated that it is possible to predict brain’s temperature (PrT_B), using independent variables (R₂ was T_BL = 0.73, T_FA = 0.80, T_EC = 0.27, and T_A = 0.17, p < 0.05). Significant linear associations were found, statistically, and no difference in means between T_B and PrT_B of each prediction model. Also, the 95% limits of agreement and the percent coefficient of variation showed sufficient agreement between the T_B and PrT_B in each prediction model.In conclusion, brain’s temperature prediction models based on T_BL, T_FA, T_EC, and T_A were successful. Its determination contributes to the improvement of clinical decision-making.

PMID:37486502 | DOI:10.1007/978-3-031-31982-2_29

Adv Exp Med Biol. 2023;1424:233-240. doi: 10.1007/978-3-031-31982-2_26.

ABSTRACT

In an attempt to develop therapeutic agents to treat Alzheimer’s disease, a series of flavonoid analogues were collected, which already had established acetylcholinesterase (AChE) enzyme inhibition activity. For each molecule we also collected biological activity data (Ki). Then, 3D-QSAR (quantitative structure-activity relationship model) was developed which showed acceptable predictive and descriptive capability as represented by standard statistical parameters r2 and q2. This SAR data can explain the key descriptors which can be related to AChE inhibitory activity. Using the QSAR model, pharmacophores were developed based on which, virtual screening was done and a dataset was obtained which loaded as a prediction set to fit the developed QSAR model. Top 10 compounds fitting the QSAR model were subjected to molecular docking. CHEMBL1718051 was found to be the lead compound. This study is offering an example of a computationally-driven tool for prioritisation and discovery of probable AChE inhibitors. Further, in vivo and in vitro testing will show its therapeutic potential.

PMID:37486499 | DOI:10.1007/978-3-031-31982-2_26

Adv Exp Med Biol. 2023;1424:201-211. doi: 10.1007/978-3-031-31982-2_22.

ABSTRACT

Amyotrophic lateral sclerosis (ALS) is a late-onset fatal neurodegenerative disease characterized by progressive loss of the upper and lower motor neurons. There are currently limited approved drugs for the disorder, and for this reason the strategy of repositioning already approved therapeutics could exhibit a successful outcome. Herein, we used CMAP and L1000CDS² databases which include gene expression profiles datasets (genomic signatures) to identify potent compounds and classes of compounds which reverse disease’s signature which could in turn reverse its phenotype. ALS signature was obtained by comparing gene expression of muscle biopsy specimens between diseased and healthy individuals. Statistical analysis was conducted to explore differentially transcripts in patients’ samples. Then, the list of upregulated and downregulated genes was used to query both databases in order to determine molecules which downregulate the genes which are upregulated by ALS and vice versa. These compounds, based on their chemical structure along with known treatments, were clustered to reveal drugs with novel and potentially more effective mode of action with most of them predicted to affect pathways heavily involved in ALS. This evidence suggests that these compounds are strong candidates for moving to the next phase of the drug repurposing pipeline which is in vitro and in vivo experimental evaluation.

PMID:37486495 | DOI:10.1007/978-3-031-31982-2_22

Adv Exp Med Biol. 2023;1424:145-155. doi: 10.1007/978-3-031-31982-2_15.

ABSTRACT

Allergic contact dermatitis (ACD) is an inflammatory reaction affecting all age groups and both sexes. ACD is characterized by a delayed-type hypersensitivity reaction IV caused by skin contact with haptens. Chronic exposure typically leads to a decrease in erythema accompanied by lichenification (thickening and hardening of the skin) and persistent itching. The current study aims to investigate the patterns of contact sensitization in the Greek population using patch test data analysis. Patch test data from 240 patients (120 Males/120 Females) with allergic contact dermatitis were collected at the Laboratory for Patch Testing, National Reference Center for Occupational Dermatoses “Andreas Syggros” Hospital in Athens Greece. The contact allergic reactions were caused by ethylenediamine dihydrochloride 1%, thimerosal 0.5%, and methyldibromo-glutaronitrile 0.1% from the European baseline series of allergens; information was also collected for ICDRG evaluation, an extended MOAHLFA index and patient-reported outcomes (daily routine questionnaire). The chi-square test for independence and Spearman’s rank were used to evaluate the association and correlation, respectively, between patient characteristics and ACD-related factors. Multiple correspondence analysis (MCA), which is a data analysis approach, was used to find and depict underlying structures in the data collection for nominal categorical data. Statistically significant associations were found between the following pairs of characteristics: eczema triggers and gender and eczema triggers and hand dermatitis. The results from MCA showed that there is correlation between allergic contact dermatitis onset, allergens, and demographic variables.

PMID:37486488 | DOI:10.1007/978-3-031-31982-2_15