Category: Statistics

Photodiagnosis Photodyn Ther. 2023 Dec 23:103943. doi: 10.1016/j.pdpdt.2023.103943. Online ahead of print.

ABSTRACT

AIMS: Bleached enamel reversal using antioxidants sodium ascorbate (SA), Green tea extract (GTE), Grape seed extract (GSE), Curcumin photosensitizer (CP) and Er: YAG laser on the adhesive strength and marginal leakage of composite material bonded to the bleached enamel surface.

MATERIALS AND METHODS: Enamel surface of hundred and twenty sound human first premolar teeth was cleansed using pumice and bleached with 35% hydrogen peroxide. The samples were randomly divided into 5 groups based on the antioxidants used. n=20 Group 1 (Control): No antioxidant agent, Group 2: 10% SA solution, Group 3: 6.5% GSE, Group 4: 5% GTE, Group 5: Er: YAG laser and Group 6: CP. Following reversal, the composite was built and cured for 40 seconds. All the specimens were stored in distilled water at room temperature for 1 day. Microleakage, SBS, and failure mode were analyzed. Kolmogorov-Smirnov test, one-way analysis of variance, and Tukey’s multiple post hoc test were used to analyze the data statistically.

RESULTS: Group 2 (SA) (20.11 ±5.79 nm) exhibited minimum value of microleakage and highest SBS (10.22 ± 1.62 MPa). Whereas, Group 1 (No antioxidant agent) displayed maximum scores of marginal leakage (28.11±8.89 nm) and lowest SBS (7.02 ± 1.22 MPa).

CONCLUSION: CP, GTE and GSE can be used as a potential alternative to the commonly used SA solution to reverse the negative impact of bleaching on the enamel surface. The use of reversal agents CP, GTE and GSE improves bond values with a decrease in microleakage scores However, future studies are still warranted to conclude the outcomes of the existing study.

PMID:38145770 | DOI:10.1016/j.pdpdt.2023.103943

Bioresour Technol. 2023 Dec 23:130252. doi: 10.1016/j.biortech.2023.130252. Online ahead of print.

ABSTRACT

The escalating demand for long-chain polyunsaturated fatty acids (PUFAs) due to their vital health effects has deepened the exploration of sustainable sources. Thraustochytrium sp. stands out as a promising platform for omega-3 and 6 PUFA production. This research strategically optimizes key parameters: temperature, salinity, pH, and G:Y:P ratio and the optimized conditions for maximum biomass, total lipid, and DHA enhancement were 28 °C, 50 %, 6, and 10:1:2 respectively. Process optimization enhanced 32.30 and 31.92 % biomass (9.88 g/L) and lipid (6.57 g/L) yield. Notably, DHA concentration experienced a substantial rise of 69.91 % (1.63 g/L), accompanied by notable increases in EPA and DPA by 82.69 % and 31.47 %, respectively. MANOVA analysis underscored the statistical significance of the optimization process (p < 0.01), with all environmental factors significantly influencing biomass and lipid data (p < 0.05), particularly impacting DHA production. Thraustochytrium sp. can be a potential source of commercial DHA production with the fine-tuning of these key process parameters.

PMID:38145766 | DOI:10.1016/j.biortech.2023.130252

Environ Res. 2023 Dec 23:117995. doi: 10.1016/j.envres.2023.117995. Online ahead of print.

ABSTRACT

BACKGROUND: The increasing problem of bacterial resistance, particularly with quinolone-resistant Escherichia coli (QnR eco) poses a serious global health issue.

METHODS: We collected data on QnR eco resistance rates and detection frequencies from 2014 to 2021 via the China Antimicrobial Resistance Surveillance System, complemented by meteorological and socioeconomic data from the China Statistical Yearbook and the China Meteorological Data Service Centre (CMDC). Comprehensive nonparametric testing and multivariate regression models were used in the analysis.

RESULT: Our analysis revealed significant regional differences in QnR eco resistance and detection rates across China. Along the Hu Huanyong Line, resistance rates varied markedly: 49.35 in the northwest, 54.40 on the line, and 52.30 in the southeast (P = 0.001). Detection rates also showed significant geographical variation, with notable differences between regions (P < 0.001). Climate types influenced these rates, with significant variability observed across different climates (P < 0.001). Our predictive model for resistance rates, integrating climate and healthcare factors, explained 64.1% of the variance (adjusted R-squared = 0.641). For detection rates, the model accounted for 19.2% of the variance, highlighting the impact of environmental and healthcare influences.

CONCLUSION: The study found higher resistance rates in warmer, monsoon climates and areas with more public health facilities, but lower rates in cooler, mountainous, or continental climates with more rainfall. This highlights the strong impact of climate on antibiotic resistance. Meanwhile, the predictive model effectively forecasts these resistance rates using China’s diverse climate data. This is crucial for public health strategies and helps policymakers and healthcare practitioners tailor their approaches to antibiotic resistance based on local environmental conditions. These insights emphasize the importance of considering regional climates in managing antibiotic resistance.

PMID:38145731 | DOI:10.1016/j.envres.2023.117995

Acta Psychol (Amst). 2023 Dec 23;242:104122. doi: 10.1016/j.actpsy.2023.104122. Online ahead of print.

ABSTRACT

BACKGROUND: Culturally appropriate mental health care is essential in remote Australia. However, while associated with the development of an effective therapeutic alliance, current literature insufficiently reports the retention and psychotherapy outcomes of Indigenous adults. We aimed to describe the characteristics and retention of clients attending the Far North Mental Health and Wellbeing Service (FNS).

METHODS: We conducted a retrospective cross-sectional study on clients who received one or more psychotherapy consultations between 1st July 2019 and 31st December 2020. Population, entrance, and treatment characteristics were described, with retention compared between the major cultural groups. Entrance characteristics comprised referral pathway and reason for presentation and were investigated as alternative predictors of client retention.

FINDINGS: There were 186 non-Indigenous (68.3 % female) and 174 Indigenous (62.6 % female) clients, with a median number of 3.0 consultations (IQR 2.0-5.3). Indigenous status did not significantly predict retention. Referral pathway significantly predicted the number of consultations (Wald X²(6) = 17.67, p = .0071) and immediate discontinuation (Wald X²(6) = 12.94, p = .044), with self-referred clients having the highest retention. Initial presentation reason significantly predicted the number of consultations (Wald X²(5) = 13.83, p = .017), with clients with potential health hazards related to socioeconomic and psychosocial circumstances having the lowest retention. Significantly more Indigenous clients presented for this reason (20.1 % vs 4.3 %).

INTERPRETATION: Comparable retention of Indigenous clients suggests cultural appropriateness of the psychotherapy being delivered by the FNS. Services might use the described therapeutic approach as a guide for culturally appropriate care.

PMID:38145592 | DOI:10.1016/j.actpsy.2023.104122

Accid Anal Prev. 2023 Dec 24;196:107433. doi: 10.1016/j.aap.2023.107433. Online ahead of print.

ABSTRACT

Driving behavior is considered as the primary crash influencing factor, whereas studies claimed that over 90% crashes were attributed by behavior features. Therefore, unveil pre-crash driving behavior features is of great importance for crash prevention. Previous studies have established the correlations between features such as vehicle speed, speed variability, and the probability of crash occurrences, but these analyses have concluded inconsistent results. This is due to the varying operating characteristics among roadway facilities, where given the same driving behavior statistical features, the corresponding traffic states are not identical. In this study, a behavioral entropy index was proposed to address the abovementioned issue. First, through comparing the individual driving behavior with the group distribution, behavioral entropy index was calculated to quantify the abnormality of driving behavior. Then, crash classification models were established by comparing the behavioral entropy prior to crash events and normal driving conditions. The empirical analyses have been conducted based on 1,634,770 naturalistic driving trajectories and 1027 crash events. And models have been carried out for urban roadway sections, urban intersections, and highway sections separately. The results showed that utilizing the behavior entropy instead of the statistical features could enhance the crash classification accuracy by 11.3%. And common pre-crash features of increased behavioral entropy were identified. Moreover, the speed coefficient of variation (QCV) entropy was concluded as the most influencing factor, which can be used for real-time driving risk monitoring and enables individual-level hazard mitigation.

PMID:38145588 | DOI:10.1016/j.aap.2023.107433

IEEE Trans Med Imaging. 2023 Dec 25;PP. doi: 10.1109/TMI.2023.3347258. Online ahead of print.

ABSTRACT

Low-dose computed tomography (LDCT) helps to reduce radiation risks in CT scanning while maintaining image quality, which involves a consistent pursuit of lower incident rays and higher reconstruction performance. Although deep learning approaches have achieved encouraging success in LDCT reconstruction, most of them treat the task as a general inverse problem in either the image domain or the dual (sinogram and image) domains. Such frameworks have not considered the original noise generation of the projection data and suffer from limited performance improvement for the LDCT task. In this paper, we propose a novel reconstruction model based on noise-generating and imaging mechanism in full-domain, which fully considers the statistical properties of intrinsic noises in LDCT and prior information in sinogram and image domains. To solve the model, we propose an optimization algorithm based on the proximal gradient technique. Specifically, we derive the approximate solutions of the integer programming problem on the projection data theoretically. Instead of hand-crafting the sinogram and image regularizers, we propose to unroll the optimization algorithm to be a deep network. The network implicitly learns the proximal operators of sinogram and image regularizers with two deep neural networks, providing a more interpretable and effective reconstruction procedure. Numerical results demonstrate our proposed method improvements of > 2.9 dB in peak signal to noise ratio, > 1.4% promotion in structural similarity metric, and > 9 HU decrements in root mean square error over current state-of-the-art LDCT methods.

PMID:38145543 | DOI:10.1109/TMI.2023.3347258

IEEE Trans Vis Comput Graph. 2023 Dec 25;PP. doi: 10.1109/TVCG.2023.3346641. Online ahead of print.

ABSTRACT

Currently, growing data sources and long-running algorithms impede user attention and interaction with visual analytics applications. Progressive visualization (PV) and visual analytics (PVA) alleviate this problem by allowing immediate feedback and interaction with large datasets and complex computations, avoiding waiting for complete results by using partial results improving with time. Yet, creating a progressive visualization requires more effort than a regular visualization but also opens up new possibilities, such as steering the computations towards more relevant parts of the data, thus saving computational resources. However, there is currently no comprehensive overview of the design space for progressive visualization systems. We surveyed the related work of PV and derived a new taxonomy for progressive visualizations by systematically categorizing all PV publications that included visualizations with progressive features. Progressive visualizations can be categorized by well-known visualization taxonomies, but we also found that progressive visualizations can be distinguished by the way they manage their data processing, data domain, and visual update. Furthermore, we identified key properties such as uncertainty, steering, visual stability, and real-time processing that are significantly different with progressive applications. We also collected evaluation methodologies reported by the publications and conclude with statistical findings, research gaps, and open challenges. A continuously updated visual browser of the survey data is available at visualsurvey.net/pva.

PMID:38145517 | DOI:10.1109/TVCG.2023.3346641

JMIR Pediatr Parent. 2023 Dec 25;6:e53933. doi: 10.2196/53933.

ABSTRACT

BACKGROUND: Stress and anxiety during pregnancy are extremely prevalent and are associated with numerous poor outcomes, among the most serious of which are increased rates of preterm birth and low birth weight infants. Research supports that while in-person mindfulness training is effective in reducing pregnancy stress and anxiety, there are barriers limiting accessibility.

OBJECTIVE: The aim of this paper is to determine if mindfulness meditation training with the Headspace app is effective for stress and anxiety reduction during pregnancy.

METHODS: A longitudinal, single-arm trial was implemented with 20 pregnant women who were instructed to practice meditation via the Headspace app twice per day during the month-long trial. Validated scales were used to measure participant’s levels of stress and anxiety pre- and postintervention. Physiological measures reflective of stress (heart rate variability and sleep) were collected via the Oura Ring.

RESULTS: Statistically significant reductions were found in self-reported levels of stress (P=.005), anxiety (P=.01), and pregnancy anxiety (P<.0001). Hierarchical linear modeling revealed a statistically significant reduction in the physiological data reflective of stress in 1 of 6 heart rate variability metrics, the low-frequency power band, which decreased by 13% (P=.006). A total of 65% of study participants (n=13) reported their sleep improved during the trial, and 95% (n=19) stated that learning mindfulness helped with other aspects of their lives. Participant retention was 100%, with 65% of participants (n=13) completing about two-thirds of the intervention, and 50% of participants (n=10) completing ≥95%.

CONCLUSIONS: This study found evidence to support the Headspace app as an effective intervention to aid in stress and anxiety reduction during pregnancy.

PMID:38145479 | DOI:10.2196/53933

Hippocampus. 2023 Dec 25. doi: 10.1002/hipo.23592. Online ahead of print.

ABSTRACT

In this study, we aimed to understand the contributions of hippocampal anteroposterior subregions (head, body, tail) and subfields (cornu ammonis 1-3 [CA1-3], dentate gyrus [DG], and subiculum [Sub]) and encoding strategies to the age-related verbal memory decline. Healthy participants were administered the California Verbal Learning Test-II to evaluate verbal memory performance and encoding strategies and underwent 4.7 T magnetic resonance imaging brain scan with subsequent hippocampal subregions and subfields manual segmentation. While total hippocampal volume was not associated with verbal memory performance, we found the volumes of the posterior hippocampus (body) and Sub showed significant effects on verbal memory performance. Additionally, the age-related volume decline in hippocampal body volume contributed to lower use of semantic clustering, resulting in lower verbal memory performance. The effect of Sub on verbal memory was statistically independent of encoding strategies. While total CA1-3 and DG volumes did not show direct or indirect effects on verbal memory, exploratory analyses with DG and CA1-3 volumes within the hippocampal body subregion suggested an indirect effect of age-related volumetric reduction on verbal memory performance through semantic clustering. As semantic clustering is sensitive to age-related hippocampal volumetric decline but not to the direct effect of age, further investigation of mechanisms supporting semantic clustering can have implications for early detection of cognitive impairments and decline.

PMID:38145465 | DOI:10.1002/hipo.23592

Scand J Prim Health Care. 2023 Dec 25:1-11. doi: 10.1080/02813432.2023.2296119. Online ahead of print.

ABSTRACT

OBJECTIVE: To explore the Nordic municipal health and care services’ ability to promote principal goals within care for older people during the COVID-19 pandemic.

DESIGN AND SETTING: Two surveys were conducted among managers of municipal health care services for older people in Denmark, Finland, Norway and Sweden; the first around 6 months into the pandemic (survey 1), and the second around 12 months later (survey 2). Data were analysed through descriptive statistics, and multiple regression (OLS).

SUBJECTS: 1470 (survey 1, 2020) and 745 (survey 2, 2021) managers. 32% in home care, 51% in nursing homes, 17% combined.

RESULTS: In all countries the pandemic seems to have had more negative impact on eldercare services’ ability to promote an active and social life, than on the ability to promote or enhance older people’s mental and physical health. The regression analysis indicates that different factors influence the ability to promote these goals. Managers within nursing homes reported reduced ability to promote mental and physical health and an active social life to a significantly lower degree than managers of home care. The effect of three prevention strategies (lock down, testing, and/or organisational change), were explored. Organisational change (reorganize staff and practice, restrict use of substitutes) tended to impact the units’ ability to promote a social life in a positive direction, while lock down (areas, buffets etc) tended to impact both the ability to promote mental/physical health and a social life in a negative direction.

CONCLUSION: Measures that can improve opportunities for an active and social life during a pandemic should have high priority, particularily within home care.

PMID:38145400 | DOI:10.1080/02813432.2023.2296119