Tag: nevin manimala

Quant Imaging Med Surg. 2023 May 1;13(5):3088-3103. doi: 10.21037/qims-22-1008. Epub 2023 Mar 13.

ABSTRACT

BACKGROUND: Recent reports have shown the potential for deep learning (DL) models to automatically segment of Couinaud liver segments and future liver remnant (FLR) for liver resections. However, these studies have mainly focused on the development of the models. Existing reports lack adequate validation of these models in diverse liver conditions and thorough evaluation using clinical cases. This study thus aimed to develop and perform a spatial external validation of a DL model for the automated segmentation of Couinaud liver segments and FLR using computed tomography (CT) in various liver conditions and to apply the model prior to major hepatectomy.

METHODS: This retrospective study developed a 3-dimensional (3D) U-Net model for the automated segmentation of Couinaud liver segments and FLR on contrast-enhanced portovenous phase (PVP) CT scans. Images were obtained from 170 patients from January 2018 to March 2019. First, radiologists annotated the Couinaud segmentations. Then, a 3D U-Net model was trained in Peking University First Hospital (n=170) and tested in Peking University Shenzhen Hospital (n=178) in cases with various liver conditions (n=146) and in candidates for major hepatectomy (n=32). The segmentation accuracy was evaluated using the dice similarity coefficient (DSC). Quantitative volumetry to evaluate the resectability was compared between manual and automated segmentation.

RESULTS: The DSC in the test data sets 1 and 2 for segments I to VIII was 0.93±0.01, 0.94±0.01, 0.93±0.01, 0.93±0.01, 0.94±0.00, 0.95±0.00, 0.95±0.00, and 0.95±0.00, respectively. The mean automated FLR and FLR% assessments were 493.51±284.77 mL and 38.53%±19.38%, respectively. The mean manual FLR and FLR% assessments were 500.92±284.38 mL and 38.35%±19.14%, respectively, in test data sets 1 and 2. For test data set 1, when automated segmentation of the FLR% was used, 106, 23, 146, and 57 cases were categorized as candidates for a virtual major hepatectomy of types 1, 2, 3, and 4, respectively; however, when manual segmentation of the FLR% was used, 107, 23, 146, and 57 cases were categorized as candidates for a virtual major hepatectomy of types 1, 2, 3, and 4, respectively. For test data set 2, all cases were categorized as candidates for major hepatectomy when automated and manual segmentation of the FLR% was used. No significant differences in FLR assessment (P=0.50; U=185,545), FLR% assessment (P=0.82; U=188,337), or the indications for major hepatectomy were noted between automated and manual segmentation (McNemar test statistic 0.00; P>0.99).

CONCLUSIONS: The DL model could be used to fully automate the segmentation of Couinaud liver segments and FLR with CT prior to major hepatectomy in an accurate and clinically practicable manner.

PMID:37179921 | PMC:PMC10167444 | DOI:10.21037/qims-22-1008

Quant Imaging Med Surg. 2023 May 1;13(5):2933-2940. doi: 10.21037/qims-22-186. Epub 2023 Mar 9.

ABSTRACT

BACKGROUND: Coronary computed tomography angiography (CTA) has been increasingly used to identify the degree of coronary artery stenosis and plaque lesions in vessels. This study evaluated the feasibility of using high-definition (HD) scanning with high-level deep learning image reconstruction (DLIR-H) to improve the image quality and spatial resolution when imaging calcified plaques and stents in coronary CTA as compared to the standard definition (SD) reconstruction mode with adaptive statistical iterative reconstruction-V (ASIR-V).

METHODS: A total of 34 patients (age 63.3±10.9 years; 55.88% female) with calcified plaques and/or stents who underwent coronary CTA in HD-mode were included in this study. Images were reconstructed with SD-ASIR-V, HD-ASIR-V, and HD-DLIR-H. Subjective image quality with image noise and clarity of vessels, calcifications, and stented lumens was evaluated by 2 radiologists using a 5-point scale. The kappa (κ) test was used to analyze the interobserver agreement. Objective image quality with image noise, signal-to-noise-ratio (SNR), and contrast-to-noise-ratio (CNR) was measured and compared. Image spatial resolution and beam-hardening artifacts (BHAs) were also evaluated using the calcification diameter and CT numbers in 3 points along the stented lumen (inside, at the proximal and distal ends just outside stent).

RESULTS: There were 45 calcified plaques and 4 coronary stents. HD-DLIR-H images had the highest overall image quality score (4.50±0.63) with the lowest image noise (22.59±3.59 HU) and the highest SNR (18.30±4.88) and CNR (26.56±6.33), followed by SD-ASIR-V50% image quality score (4.06±2.49), image noise (35.02±8.09 HU), SNR (12.77±1.59), CNR(15.67±1.92) and HD-ASIR-V50% image quality score (3.90±0.64), image noise (57.7±12.03 HU), SNR (8.16±1.86), CNR (10.01±2.39). HD-DLIR-H images also had the smallest calcification diameter measurement (2.36±1.58 mm), followed by HD-ASIR-V50% (3.46±2.07 mm) and SD-ASIR-V50% (4.06±2.49 mm). HD-DLIR-H images had the closest CT value measurements for the 3 points along the stented lumen, indicating much less BHA. Interobserver agreement on the image quality assessment was good to excellent (HD-DLIR-H: κ value =0.783; HD-ASIR-V50%: κ value =0.789; SD-ASIR-V50%: κ value =0.671).

CONCLUSIONS: Coronary CTA with HD scan mode and DLIR-H significantly improves the spatial resolution for displaying calcifications and in-stent lumens while simultaneously reducing image noise.

PMID:37179907 | PMC:PMC10167454 | DOI:10.21037/qims-22-186

Front Psychol. 2023 Apr 25;14:1164516. doi: 10.3389/fpsyg.2023.1164516. eCollection 2023.

ABSTRACT

In recent decades, there has been a massive incorporation of women into the labor market. However, the belief that certain jobs or business functions can be performed better by people of one gender than the other has not allowed for widespread changes in the business culture to achieve effective equality between women and men in companies. Examples of this are unequal access to employment, vertical and horizontal segregation in occupations, wage discrimination, problems in reconciling personal and professional life, or difficulties in accessing management positions in companies (glass ceiling). Other determinants of gender inequalities have been long working hours, as well as the presence of employees, characteristic of European business culture. The progress achieved to date began with the incorporation of women into the labor market under unequal conditions that soon called for the need to establish a regulatory framework to try to eradicate them. The legal status of women in Europe has undoubtedly improved as a result of the development of European regulations, which have been binding in the development of business policies in the Member States and have succeeded in modifying the organizational climate through proposals such as the development of Equality Plans or salary audits. Examples of the most recent legislative initiatives of the European Union on equality that affect business practices are Directive 2022/2041/EC on adequate minimum wages in the European Union or Directive 2022/2381/EC on a better gender balance among directors of listed companies. This study attempts to systematize the changes in the legislation on effective equality between men and women in business and to analyze its effect on organizational culture through the information available in the statistics on gender equality-mainly from the European Union-which gather quantitative and qualitative information on the adaptation of business culture to the new legal framework and the overcoming of gender stereotypes that have been guiding business management in the last decade.

PMID:37179899 | PMC:PMC10167931 | DOI:10.3389/fpsyg.2023.1164516

Front Psychol. 2023 Apr 27;14:1157823. doi: 10.3389/fpsyg.2023.1157823. eCollection 2023.

ABSTRACT

OBJECTIVE: While several initiatives, including monetary rewards and performance system reform, are used to inspire medical staff, none are fully effective. We sought to describe the intrinsic drive of medical staff and identify elements that improve work enthusiasm by increasing internal motivation.

METHODS: A cross-sectional study was conducted in which 2,975 employee representatives from 22 municipal hospitals in Beijing, China were interviewed using a self-made intrinsic motivation scale for medical staff which includes the achievement motivation, self-efficacy, conscientiousness, gratitude level and perceived organizational support. The Kruskal-Wallis analysis of variance and multiple linear regression methods were used to investigate the level of intrinsic motivation and identify any influencing factors. The correlation between employee drive and turnover intention was determined using Spearman rank correlation analysis and Kendall’s tau b rank correlation coefficient.

RESULTS: A total of 2,293 valid answers were obtained, with a valid recovery rate of 77.1%. There were statistically significant differences in intrinsic motivation and its five dimensions by marital status, political status, profession, service year, monthly income, number of working hours per week, and turnover intention (p < 0.05). Being divorced, a CPC member, in the nursing profession, and having a higher monthly income had a positive impact on intrinsic motivation while working a high hours per week had a negative effect. Higher work drive was associated with lower turnover intention. The correlation coefficients of intrinsic drive and its five dimensions with turnover intention ranged from 0.265 to 0.522 (p < 0.001).

CONCLUSION: Sociodemographic factors and work environment influenced the intrinsic motivation of medical staff. There was a correlation between work drive and turnover intention which indicated that stimulating the intrinsic drive of employees may help to increase staff retention.

PMID:37179890 | PMC:PMC10172483 | DOI:10.3389/fpsyg.2023.1157823

J Interprof Care. 2023 May 13:1-8. doi: 10.1080/13561820.2023.2213718. Online ahead of print.

ABSTRACT

Interprofessional education (IPE) has increasingly been delivered online using synchronous and asynchronous mediums; however, little research focuses on facilitation strategies in the synchronous environment. We explored whether the perceived strategies used by facilitators in online synchronous IPE facilitation are similar to those used in face-to-face and online asynchronous IPE facilitation, and if the strategies are used to a similar degree online in both synchronous and asynchronous environments. Following completion of an online IPE course, students and facilitators were invited to complete an anonymous questionnaire exploring their perceptions of the facilitation strategies used in their synchronous and asynchronous IPE experiences. Responses were received from 118 students and 21 facilitators. Descriptive statistics indicate that students and facilitators perceived facilitation strategies are being used in online synchronous settings that have previously been recognized in asynchronous and face-to-face IPE settings. This included strategies linked to communicating about the design/organization of the experience, providing direct instruction, facilitating and encouraging interprofessional interaction, and contextualizing IPE. Wilcoxon Signed-Rank tests demonstrated a perception that these strategies were being used to a greater extent in the synchronous than asynchronous environment. This knowledge is useful to further refine the training of online IPE facilitators in both synchronous and asynchronous settings.

PMID:37179457 | DOI:10.1080/13561820.2023.2213718

Med Sci Monit. 2023 May 14;29:e940599. doi: 10.12659/MSM.940599.

ABSTRACT

BACKGROUND Obturation is a crucial step in dentistry, involving the filling and sealing of a prepared root canal with sealer and core material to prevent bacterial invasion and ensure successful treatment. This study employed scanning electron microscopy to compare the effectiveness of 3 obturation techniques – single-cone technique (SCT), cold lateral compaction technique (CLCT), and continuous-wave technique (CWT) – in sealing dentin to recently introduced root canal bioceramic sealer in 30 extracted mandibular second premolars. The goal was to determine the optimal technique for minimizing gaps at the sealer-dentin interface. MATERIAL AND METHODS Thirty premolars were divided into 3 groups (n=10 each) based on obturation technique: SCT, CLCT, and CWT. All groups used CeraSeal bioceramic root canal sealer. Root samples were sectioned at apical, middle, and coronal thirds, and high-resolution scanning electron microscopy was performed to measure marginal/internal gaps. Data were analyzed using one-way ANOVA and Tukey range test, with P≤0.05 considered statistically significant. RESULTS CWT showed fewer voids at all levels, with non-significant differences within a technique. Among the techniques, SCT had the largest mean gaps at all levels [apical (5.43±0.16), middle (5.28±0.20), coronal (5.73±0.24)], while CWT had the smallest mean gaps [apical (3.02±0.19), middle (2.95±0.14), coronal (2.76±0.15)]. The differences in means between techniques were statistically significant (P≤0.05). CONCLUSIONS CWT obturation with CeraSeal root canal sealer produces fewer marginal gaps at the sealer-dentin interface.

PMID:37179453 | DOI:10.12659/MSM.940599

Sci Rep. 2023 May 13;13(1):7797. doi: 10.1038/s41598-023-34765-w.

ABSTRACT

Fecal samples from participants aged 60-80 were collected and sequenced by a high-throughput second-generation sequencer to explore the structural composition of gut microbiota in elderly patients with hepatocellular carcinoma(HCC). Comparison of gut microbiota between patients with hepatocellular carcinoma and healthy controls, α diversity and β diversity were statistically different. At the genus level, compared with the normal group, the abundance of A Blautia, Fusicatenibacter, Anaerostipes, Lachnospiraceae_ND3007_group, CAG-56, Eggerthella, Lachnospiraceae_FCS020_group and Olsenella were decreased significantly in the LC group. In contrast, the abundance of Escherichia-Shigella, Fusobacterium, Megasphaera, Veillonella, Tyzzerella_4, Prevotella_2 and Cronobacter increased significantly. The KEGG and COG pathway analyses showed that the dysbiosis of gut bacteria in primary liver carcinoma is associated with several pathways, including amino acid metabolism, replication and repair, nucleotide metabolism, cell motility, cell growth and death, and transcription. Age is negatively associated with the abundance of Bifidobacterium. Lachnospiraceae_ ND3007_ group, [Eubacterium]_hallii_group, Blautia, Fuscatenibacter and Anaerostipes are negatively correlated with ALT, AST and GGT levels (p < 0.05), respectively. Alpha-fetoprotein (AFP) is positively associated with the abundance of Erysipelatoclostridium, Magasphaera, Prevotella 2, Escherichia-Shigella, Streptococcus and [Eubacterium]_eligens_group (p < 0.05), respectively. A random forest model showed that the genera Eggerthella, Anaerostipes, and Lachnospiraceae_ ND3007_ group demonstrated the best predictive capacity. The area under the Receiver Operating Characteristic Curve of Eggerthella, Anaerostipes and Lachnospiraceae_ ND3007_ group are 0.791, 0.766 and 0.730, respectively. These data are derived from the first known gut microbiome study in elderly patients with hepatocellular carcinoma. Potentially, specific microbiota can be used as a characteristic index for screening, diagnosis, and prognosis of gut microbiota changes in elderly patients with hepatocellular carcinoma and even as a therapeutic clinical target.

PMID:37179446 | DOI:10.1038/s41598-023-34765-w

Sci Rep. 2023 May 13;13(1):7779. doi: 10.1038/s41598-023-34906-1.

ABSTRACT

Ethiopia has been challenged by the growing magnitude of diabetes in general and type-2 diabetes in particular. Knowledge extraction from stored dataset can be an important base for better decision on diabetes rapid diagnosis, suggestive on prediction for early intervention. Thus, this study was addressed these problem by application of supervised machine learning algorithms for classification and prediction of type 2 diabetes disease status and might provide context-specific information to program planners and policy makers so that, priority will be given to the more affected groups. To apply supervised machine learning algorithms; compare these algorithms and select the best algorithm based on their performance for classification and prediction of type-2 diabetic disease status (positive or negative) in public hospitals of Afar regional state, Northeastern Ethiopia. This study was conducted at Afar regional state from February to June of 2021. Decision tree; pruned J 48, Artificial neural network, K-nearest neighbor, Support vector machine, Binary logistic regression, Random forest, and Naïve Bayes supervised machine learning algorithms were applied using secondary data from the medical database record review. A total of 2239 sample Dataset diagnosed for diabetes from 2012 to April 22/2020 (1523 with type-2 diabetes and 716 without type-2 diabetes) was checked for its completeness prior to analysis. For all algorithms, WEKA3.7 tool was used for analysis purposes. Moreover, all algorithms were compared based on their correctly classification rate, kappa statistics, confusion matrix, area under the curve, sensitivity, and specificity. From the seven major supervised machine learning algorithms, the best classification and prediction results were obtained from random forest [correctly classified rate (93.8%), kappa statistics (0.85), sensitivity (0.98), area under the curve (0.97) and confusion matrix (out of 454 actual positive prediction for 446)] which was followed by decision tree pruned J 48 [correctly classified rate (91.8%), kappa statistics (0.80), sensitivity (0.96), area under the curve (0.91) and confusion matrices (out of 454 actual positive prediction for 438)] and k-nearest neighbor [correctly classified rate (89.8%), kappa statistics (0.76), sensitivity (0.92), area under the curve (0.88) and confusion matrices (out of 454 actual positive prediction for 421)]. Random forest, Decision tree pruned J48 and k-nearest neighbor algorithms have better classification and prediction performance for classifying and predicting type-2 diabetes disease status. Therefore, based on this performance, random forest algorithm can be judged as suggestive and supportive for clinicians at the time of type-2 diabetes diagnosis.

PMID:37179444 | DOI:10.1038/s41598-023-34906-1

Br J Cancer. 2023 May 13. doi: 10.1038/s41416-023-02290-2. Online ahead of print.

ABSTRACT

BACKGROUND: We sought to assess the influences of sleep duration, sleep adequacy, and daytime sleepiness on survival outcomes among Stage III colon cancer patients.

METHODS: We conducted a prospective observational study of 1175 Stage III colon cancer patients enrolled in the CALGB/SWOG 80702 randomised adjuvant chemotherapy trial who completed a self-reported questionnaire on dietary and lifestyle habits 14-16 months post-randomisation. The primary endpoint was disease-free survival (DFS), and secondary was overall survival (OS). Multivariate analyses were adjusted for baseline sociodemographic, clinical, dietary and lifestyle factors.

RESULTS: Patients sleeping ≥9 h-relative to 7 h-experienced a worse hazard ratio (HR) of 1.62 (95% confidence interval (CI), 1.01-2.58) for DFS. In addition, those sleeping the least (≤5 h) or the most (≥ 9 h) experienced worse HRs for OS of 2.14 (95% CI, 1.14-4.03) and 2.34 (95% CI, 1.26-4.33), respectively. Self-reported sleep adequacy and daytime sleepiness showed no significant correlations with outcomes.

CONCLUSIONS: Among resected Stage III colon cancer patients who received uniform treatment and follow-up within a nationwide randomised clinical trial, very long and very short sleep durations were significantly associated with increased mortality. Interventions targeting optimising sleep health among indicated colon cancer patients may be an important method by which more comprehensive care can be delivered.

TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01150045.

PMID:37179438 | DOI:10.1038/s41416-023-02290-2

Sci Rep. 2023 May 13;13(1):7788. doi: 10.1038/s41598-023-35044-4.

ABSTRACT

The social ecological model provides a comprehensive framework for understanding the multiple-level determinants of physical activity. This study explores the significant individual, social, and environmental variables and their interactions in relation to physical activity in middle-aged and older adults in Taiwan. A cross-sectional study design was implemented. Healthy middle-aged and older adults were recruited (n = 697) through face-to-face and online surveys. The data collected comprised self-efficacy, social support, neighbourhood environment, and demographic characteristics. Hierarchical regression was used for statistical analysis. Self-rated health (B = 74.74, p < .001; B = 101.45, p = .022) and self-efficacy (B = 17.93, p < .001; B = 14.95, p = .020) were the significant individual variables in both middle-aged and older adults. Neighbourhood environment (B = 6.90, p = .015) and the interaction between self-efficacy and neighbourhood environment (B = 1.56, p = .009) were significant in middle-aged adults. Self-efficacy was the most significant predictor for all participants, with the positive correlations of neighbourhood environment arising only for middle-aged adults with high self-efficacy. Policy making or project design should consider multilevel factors in order to facilitate their physical activity.

PMID:37179430 | DOI:10.1038/s41598-023-35044-4