Category: Statistics

Front Psychol. 2024 Nov 20;15:1431861. doi: 10.3389/fpsyg.2024.1431861. eCollection 2024.

ABSTRACT

PURPOSE: As future doctors, medical students’ prosocial behaviors may affect the relationship between doctors and patients. This study aims to explore the effects of trait mindfulness on prosocial behaviors, as well as the mediating role of psychological capital and the moderating role of moral identity among medical students.

METHODS: A cross-sectional survey was conducted between July and October 2023 across four medical colleges in China, using cluster random sampling. The questionnaire included general demographic information, the Prosocial Tendencies Measurement Scale, the Five-Facet Mindfulness Questionnaire, the Psychological Capital Questionnaire, and the Moral Identity Scale. The SPSS 25.0 and PROCESS v3.4 macro were used for descriptive statistics, correlation analysis, and mediation and moderation analyses.

RESULTS: A total of 2,285 samples were included. The analyses showed that prosocial behavior was positively correlated with trait mindfulness, psychological capital, and moral identity (r = 0.293, 0.444, and 0.528, p < 0.01); trait mindfulness predicts prosocial behavior (β = 0.292, 95% CI [0.253, 0.332]); and psychological capital played a partial mediation role between trait mindfulness and prosocial behaviors (β = 0.413, 95% CI [0.368, 0.459]). Furthermore, moral identity played the moderating roles between trait mindfulness and prosocial behavior (β = 0.049, 95% CI [0.011, 0.087]) and between PsyCap and prosocial behavior (β = 0.062, 95% CI [0.032, 0.092]).

CONCLUSION: Trait mindfulness, psychological capital, and moral identity are conducive to the development of medical students’ prosocial behavior. These findings provide evidence for the cultivation of prosocial behaviors and for the development of mental health courses, which should be tailored to medical students.

PMID:39635702 | PMC:PMC11614594 | DOI:10.3389/fpsyg.2024.1431861

Nanophotonics. 2021 Dec 22;11(11):2483-2505. doi: 10.1515/nanoph-2021-0660. eCollection 2022 Jun.

ABSTRACT

Nanophotonics inverse design is a rapidly expanding research field whose goal is to focus users on defining complex, high-level optical functionalities while leveraging machines to search for the required material and geometry configurations in sub-wavelength structures. The journey of inverse design begins with traditional optimization tools such as topology optimization and heuristics methods, including simulated annealing, swarm optimization, and genetic algorithms. Recently, the blossoming of deep learning in various areas of data-driven science and engineering has begun to permeate nanophotonics inverse design intensely. This review discusses state-of-the-art optimizations methods, deep learning, and more recent hybrid techniques, analyzing the advantages, challenges, and perspectives of inverse design both as a science and an engineering.

PMID:39635678 | PMC:PMC11502023 | DOI:10.1515/nanoph-2021-0660

Nanophotonics. 2023 Oct 13;12(21):4109-4116. doi: 10.1515/nanoph-2023-0549. eCollection 2023 Oct.

ABSTRACT

Chaotic semiconductor lasers have been widely investigated for high-speed random bit generation, which is applied for the generation of cryptographic keys for classical and quantum cryptography systems. Here, we propose and demonstrate a self-chaotic microlaser with enhanced chaotic bandwidth for high-speed random bit generation. By designing tri-mode interaction in a deformed square microcavity laser, we realize a self-chaotic laser caused by two-mode internal interaction, and achieve an enhanced chaotic standard bandwidth due to the photon-photon resonance effect by introducing the third mode. Moreover, 500 Gb/s random bit generation is realized and the randomness is verified by the NIST SP 800-22 statistics test. Our demonstration promises the applications of microlasers in secure communication, chaos radar, and optical reservoir computing, and also provides a platform for the investigations of multimode nonlinear laser dynamics.

PMID:39635643 | PMC:PMC11502037 | DOI:10.1515/nanoph-2023-0549

IEEE Trans Med Robot Bionics. 2024 May;6(2):632-642. doi: 10.1109/tmrb.2024.3369894. Epub 2024 Feb 26.

ABSTRACT

Devices for functional resistance training (FRT) during walking are often configured to resist the knee or both the hip and knee joints. Adding resistance to the hip in addition to the knee should alter the effects of training; however, these configurations have not been directly compared. We examined how FRT during walking differs during the knee or hip and knee conditions. Fourteen non-disabled individuals received FRT during treadmill walking with a device configured to provide a viscous resistance to the knee or the hip and knee during separate visits. Between these configurations, we compared gait kinetics, muscle activation, kinematic aftereffects, peripheral fatigue, and corticospinal excitability. Adding resistance to the hip increased hip flexion moment and concentric power during the swing phase. However, this did not result in significant differences in muscle activation, aftereffects, peripheral fatigue, or corticospinal excitability between the configurations. Instead, both configurations produced similar changes in these variables. These results indicate that, aside from kinetics, walking with resistance at the hip and knee was not different from resisting the knee in the acute setting. However, further research is needed to determine if long-term training with resistance at the hip induces differential effects than resisting the knee alone.

PMID:39635626 | PMC:PMC11612632 | DOI:10.1109/tmrb.2024.3369894

Front Med (Lausanne). 2024 Nov 20;11:1497309. doi: 10.3389/fmed.2024.1497309. eCollection 2024.

ABSTRACT

BACKGROUND: Osteosarcoma and chondrosarcoma are common malignant bone tumors, and accurate differentiation between these two tumors is crucial for treatment strategies and prognosis assessment. However, traditional radiological methods face diagnostic challenges due to the similarity in imaging between the two.

METHODS: Clinical CT images and pathological data of 76 patients confirmed by pathology from January 2018 to January 2024 were retrospectively collected from Guizhou Medical University Affiliated Hospital and Guizhou Medical University Second Affiliated Hospital. A total of 788 radiomic features, including shape, texture, and first-order statistics, were extracted in this study. Six machine learning models, including Random Forest (RF), Extra Trees (ET), AdaBoost, Gradient Boosting Tree (GB), Linear Discriminant Analysis (LDA), and XGBoost (XGB), were trained and validated. Additionally, the importance of features and the interpretability of the models were evaluated through SHAP value analysis.

RESULTS: The RF model performed best in distinguishing between these two tumor types, with an AUC value close to perfect at 1.00. The ET and AdaBoost models also demonstrated high performance, with AUC values of 0.98 and 0.93, respectively. SHAP value analysis revealed significant influences of wavelet-transformed GLCM and First Order features on model predictions, further enhancing diagnostic interpretability.

CONCLUSION: This study confirms the effectiveness of combining machine learning with radiomic features in improving the accuracy and interpretability of osteosarcoma and chondrosarcoma diagnosis. The excellent performance of the RF model is particularly suitable for complex imaging data processing, providing valuable insights for the future.

PMID:39635595 | PMC:PMC11614641 | DOI:10.3389/fmed.2024.1497309

Front Med (Lausanne). 2024 Nov 20;11:1442519. doi: 10.3389/fmed.2024.1442519. eCollection 2024.

ABSTRACT

INTRODUCTION: Mucosal deficiency is one of the most challenging conditions in patients with acute severe ulcerative colitis (ASUC). Intravenous corticosteroids (CS) are the first-line treatment, with infliximab (IFX) used as a rescue therapy. However, the efficacy remains unsatisfactory. We investigated whether CS combined with IFX as first-line therapy would improve outcomes in patients with ASUC with mucosal deficiency.

METHODS: A retrospective study was performed at a tertiary inflammatory bowel disease center. The primary outcomes included clinical remission, endoscopic improvement, and endoscopic remission at week 14. The secondary outcomes included the colectomy rate within 90 days and durable clinical remission.

RESULTS: A total of 43 patients with ASUC with mucosal deficiency were included in the analysis (25 in the CS combined with the IFX group and 18 in the CS sequential IFX group). At week 14, endoscopic improvement was observed in 21 of 25 patients (84.0%) receiving the CS combined with the IFX regimen, compared to 9 of 18 (50.0%) patients receiving the CS sequential IFX regimen (p = 0.017). Durable clinical remission rates were significantly higher in the combined group than in the sequential group (85.7% vs. 35.7%, p = 0.004). There was no statistically significant difference between the two groups in terms of clinical and endoscopic remission at week 14 or colectomy rate within 90 days. Multivariate analysis confirmed that the CS combined with the IFX regimen was an independent predictive factor for a higher endoscopic improvement rate at week 14 (odds ratio (OR) 8.428, 95%confidence interval (CI) 1.539-46.153, p = 0.014) and a higher durable clinical remission rate (OR 10.800, 95%CI 2.095-55.666, p = 0.004).

CONCLUSION: CS combined with IFX as first-line therapy may be an effective induction strategy in patients with ASUC with mucosal deficiency. Further large-scale, multicenter prospective studies are needed.

PMID:39635590 | PMC:PMC11614599 | DOI:10.3389/fmed.2024.1442519

Front Med (Lausanne). 2024 Nov 20;11:1434166. doi: 10.3389/fmed.2024.1434166. eCollection 2024.

ABSTRACT

BACKGROUND: Acute exacerbation of chronic obstructive pulmonary disease (COPD) poses a significant public health challenge globally, resulting in considerable health and economic burden. To date, there has been insufficient research in Ethiopia regarding poor treatment outcomes associated with these acute exacerbations.

OBJECTIVE: This study aims to assess the poor treatment outcomes of acute exacerbations of chronic obstructive pulmonary disease and identify the associated factors among admitted patients in East Gojjam in 2023.

DESIGN: An institutional-based cross-sectional study design was employed.

METHODS: The institutional-based cross-sectional study was conducted from 7 April 2023 to 7 May 2023, involving 384 participants selected through simple random sampling. Data were extracted from patient charts and registers. Data entry was performed using EpiData, and the analysis was conducted using IBM SPSS Statistics version 26 software. Binary logistic regression analysis was used to identify the association between dependent and independent variables. Variables with a p-value of <0.25 in the bivariable logistic regression analysis were considered candidates for multivariable logistic regression. Variables with a p-value of <0.05 were considered statistically significant.

RESULTS: Out of a total of 346 patients, 99 (28.6%) (95% CI, 23.9-33.3) developed poor treatment outcomes following exacerbations of chronic obstructive pulmonary diseases. Poor treatment outcomes were significantly associated with the following variables: age 65 or older (AOR = 3.9; 95% CI: 1.57-9.71), presence of comorbidities (AOR = 2.6; 95% CI: 1.287-5.20), a hospital stay longer than 7 days (AOR = 3.9; 95% CI: 1.97-7.70), and low oxygen saturation (<88%) (AOR = 9.0; 95% CI: 4.43-18.34).

CONCLUSION: Approximately one-third of the patients treated for acute exacerbations of chronic obstructive pulmonary disease at the Debre Markos Comprehensive Specialized Hospital experienced poor treatment outcomes. There is a significant association between poor treatment outcomes of acute exacerbation of chronic obstructive pulmonary disease and age ≥ 65 years, having comorbidities, prolonged hospital stay, and low oxygen saturation.

PMID:39635589 | PMC:PMC11615673 | DOI:10.3389/fmed.2024.1434166

Nanophotonics. 2022 Jun 15;11(14):3299-3306. doi: 10.1515/nanoph-2022-0160. eCollection 2022 Jul.

ABSTRACT

The possibility of using weak optical signals to perform sensing of delicate samples constitutes one of the main goals of quantum photonic sensing. Furthermore, the nanoscale confinement of electromagnetic near fields in photonic platforms through surface plasmon polaritons has motivated the development of highly sensitive quantum plasmonic sensors. Despite the enormous potential of plasmonic platforms for sensing, this class of sensors is ultimately limited by the quantum statistical fluctuations of surface plasmons. Indeed, the fluctuations of the electromagnetic field severely limit the performance of quantum plasmonic sensing platforms in which delicate samples are characterized using weak near-field signals. Furthermore, the inherent losses associated with plasmonic fields levy additional constraints that challenge the realization of sensitivities beyond the shot-noise limit. Here, we introduce a protocol for quantum plasmonic sensing based on the conditional detection of plasmons. We demonstrate that the conditional detection of plasmonic fields, via plasmon subtraction, provides a new degree of freedom to control quantum fluctuations of plasmonic fields. This mechanism enables improvement of the signal-to-noise ratio of photonic sensors relying on plasmonic signals that are comparable to their associated field fluctuations. Consequently, the possibility of using weak plasmonic signals to sense delicate samples, while preserving the sample properties, has important implications for molecule sensing, and chemical detection.

PMID:39635548 | PMC:PMC11501117 | DOI:10.1515/nanoph-2022-0160

Front Immunol. 2024 Nov 20;15:1487078. doi: 10.3389/fimmu.2024.1487078. eCollection 2024.

ABSTRACT

OBJECTIVE: In China, lung cancer ranks first in both incidence and mortality among all malignant tumors. Non-small cell lung cancer (NSCLC) constitutes the vast majority of cases, accounting for 80% to 85% of cases. Immune checkpoint inhibitors (ICIs), either as monotherapies or combined with other treatments, have become the standard first-line therapy for NSCLC patients. This study aimed to establish a nomogram model for NSCLC patients receiving immunotherapy incorporating demographic information, clinical characteristics, and laboratory indicators.

METHODS: From January 1, 2019, to December 31, 2022, a prospective longitudinal cohort study involving 1321 patients with NSCLC undergoing immunotherapy was conducted at Chongqing University Cancer Hospital. Clinical and pathological characteristics, as well as follow-up data, were collected and analyzed. To explore prognostic factors affecting overall survival (OS), a Cox regression model was used to test the significance of various variables. Independent prognostic indicators were identified through multivariate analysis and then used to construct a nomogram prediction model. To validate the accuracy and practicality of this model, the concordance index (C-index), area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to assess the predictive performance of the nomogram.

RESULT: In the final model, 11 variables from the training cohort were identified as independent risk factors for patients with NSCLC: age, KPS score, BMI, diabetes, targeted therapy, Hb, WBC, LDH, CRP, PLR, and LMR. The C-index for OS in the training cohort was 0.717 (95% CI, 0.689-0.745) and 0.704 (95% CI, 0.660-0.750) in the validation cohort. Calibration curves for survival probability showed good concordance between the nomogram predictions and actual observations. The AUCs for 1-year, 2-year, and 3-year OS in the training cohort were 0.724, 0.764, and 0.79, respectively, and 0.725, 0.736, and 0.818 in the validation cohort. DCA demonstrated that the nomogram model had a greater overall net benefit.

CONCLUSION: A prognostic model for OS in NSCLC patients receiving immunotherapy was established, providing a simple and reliable tool for predicting patient survival (https://icisnsclc.shinyapps.io/DynNomapp/). This model offers valuable guidance for clinicians in making treatment decisions and recommendations.

PMID:39635526 | PMC:PMC11614804 | DOI:10.3389/fimmu.2024.1487078

Front Immunol. 2024 Nov 20;15:1439971. doi: 10.3389/fimmu.2024.1439971. eCollection 2024.

ABSTRACT

BACKGROUND: The inflammatory macrophage response contributes to severe Ebola virus disease, with liver and lung injury in humans.

OBJECTIVE: We sought to further define the activation status of hepatic and pulmonary macrophage populations in Ebola virus disease.

METHODS: We compared liver and lung tissue from terminal Ebola virus (EBOV)-infected and uninfected control cynomolgus macaques challenged via the conjunctival route. Gene and protein expression was quantified using the nCounter and GeoMx Digital Spatial Profiling platforms. Macrophage phenotypes were further quantified by digital pathology analysis.

RESULTS: Hepatic macrophages in the EBOV-infected group demonstrated a mixed inflammatory/non-inflammatory profile, with upregulation of CD163 protein expression, associated with macrophage activation syndrome. Hepatic macrophages also showed differential expression of gene sets related to monocyte/macrophage differentiation, antigen presentation, and T cell activation, which were associated with decreased MHC-II allele expression. Moreover, hepatic macrophages had enriched expression of genes and proteins targetable with known immunomodulatory therapeutics, including S100A9, IDO1, and CTLA-4. No statistically significant differences in M1/M2 gene expression were observed in hepatic macrophages compared to controls. The significant changes that occurred in both the liver and lung were more pronounced in the liver.

CONCLUSION: These data demonstrate that hepatic macrophages in terminal conjunctivally challenged cynomolgus macaques may express a unique inflammatory profile compared to other macaque models and that macrophage-related pharmacologically druggable targets are expressed in both the liver and the lung in Ebola virus disease.

PMID:39635525 | PMC:PMC11615675 | DOI:10.3389/fimmu.2024.1439971