Unadjusted Bivariate Two-Group Comparisons

Single-cell RNA sequencing reveals epithelial cells driving brain metastasis in lung adenocarcinoma

Brain metastases (BM) of lung adenocarcinoma (LUAD) are the most common intracranial malignancy leading to death. However, the cellular origins and drivers of BM from LUAD have not been clarified. Cellular composition was characterized by single-cell sequencing analysis of primary lung adenocarcinoma (pLUAD), BM and lymph node metastasis (LNM) samples in GSE131907. Our study briefly analyzed the tumor microenvironment (TME), focusing on the role of epithelial cells (ECs) in BM. We have discovered a population of brain metastasis-associated epithelial cells (BMAECs) expressing SPP1, SAA1, and CDKN2A, and it has been observed that this population is mainly composed of aneuploid cells from pLUAD, playing a crucial role in brain metastasis. Our study concluded that both LNM and BM in LUAD originated from pLUAD lesions, but there is currently insufficient evidence to prove a direct association between BM lesions and LNM lesions, which provides inspiration for further investigation of the TME in BM.

Role of monocytes and dendritic cells in cardiac reverse remodelling after cardiac resynchronization therapy

BACKGROUND AND AIMS

Monocytes and dendritic cells (DC) are both key inflammatory cells, with recognized effects on cardiac repair. However, there are distinct subsets of monocytes with potential for beneficial or detrimental effects on heart failure (HF) pathogenesis. The connection between reverse cardiac remodelling, the potential anti-inflammatory effect of cardiac resynchronization therapy (CRT) and monocytes and DC homeostasis in HF is far from being understood. We hypothesized that monocytes and DC play an important role in cardiac reverse remodelling and CRT response. Therefore, we aimed to assess the potential role of baseline peripheral levels of blood monocytes and DC subsets and their phenotypic and functional activity for CRT response, in HF patients. As a secondary objective, we aimed to evaluate the impact of CRT on peripheral blood monocytes and DC subsets, by comparing baseline and post CRT circulating levels and phenotypic and functional activity.

METHODS

Forty-one patients with advanced HF scheduled for CRT were included in this study. The quantification and phenotypic determination of classical (cMo), intermediate (iMo) and non-classical monocytes (ncMo), as well as of myeloid (mDC) and plasmacytoid DC (pDC) were performed by flow cytometry in a FACSCanto™II (BD) flow cytometer. The functional characterization of total monocytes and mDC was performed by flow cytometry in a FACSCalibur flow cytometer, after in vitro stimulation with lipopolysaccharide from Escherichia coli plus interferon (IFN)-γ, in the presence of Brefeldina A. Comparisons between the control and the patient group, and between responders and non-responders to CRT were performed.

RESULTS

Compared to the control group, HF population presented a significantly lower frequency of pDC at baseline and a higher proportion of monocytes and mDC producing IL-6 and IL-1β, both before and 6-months after CRT (T6). There was a remarkable decrease of cMo and an increase of iMo after CRT, only in responders. The responder group also presented higher ncMo values at T6 compared to the non-responder group. Both responders and non-responders presented a decrease in the expression of CD86 in all monocyte and DC populations after CRT. Moreover, in non-responders, the increased frequency of IL-6-producing DC persisted after CRT.

CONCLUSION

Our study provides new knowledge about the possible contribution of pDC and monocytes subsets to cardiac reverse remodelling and response to CRT. Additionally, CRT is associated with a reduction on CD86 expression by monocytes and DC subsets and in their potential to produce pro-inflammatory cytokines, contributing, at least in part, for the well described anti-inflammatory effects of CRT in HF patients.

Artificial intelligence aided precise detection of local recurrence on MRI for nasopharyngeal carcinoma: a multicenter cohort study

Background

MRI is the routine examination to surveil the recurrence of nasopharyngeal carcinoma, but it has relatively lower sensitivity than PET/CT. We aimed to find if artificial intelligence (AI) could be competent pre-inspector for MRI radiologists and whether AI-aided MRI could perform better or even equal to PET/CT.

Methods

This multicenter study enrolled 6916 patients from five hospitals between September 2009 and October 2020. A 2.5D convolutional neural network diagnostic model and a nnU-Net contouring model were developed in the training and test cohorts and used to independently predict and visualize the recurrence of patients in the internal and external validation cohorts. We evaluated the area under the ROC curve (AUC) of AI and compared AI with MRI and PET/CT in sensitivity and specificity using the McNemar test. The prospective cohort was randomized into the AI and non-AI groups, and their sensitivity and specificity were compared using the Chi-square test.

Findings

The AI model achieved AUCs of 0.92 and 0.88 in the internal and external validation cohorts, corresponding to the sensitivity of 79.5% and 74.3% and specificity of 91.0% and 92.8%. It had comparable sensitivity to MRI (e.g., 74.3% vs. 74.7%, P = 0.89) but lower sensitivity than PET/CT (77.9% vs. 92.0%, P < 0.0001) at the same individual-specificities. The AI model achieved moderate precision with a median dice similarity coefficient of 0.67. AI-aided MRI improved specificity (92.5% vs. 85.0%, P = 0.034), equaled PET/CT in the internal validation subcohort, and increased sensitivity (81.9% vs. 70.8%, P = 0.021) in the external validation subcohort. In the prospective cohort of 1248 patients, the AI group had higher sensitivity than the non-AI group (78.6% vs. 67.3%, P = 0.23), albeit nonsignificant. In future randomized controlled trials, a sample size of 3943 patients in each arm would be required to demonstrate the statistically significant difference.

Interpretation

The AI model equaled MRI by expert radiologists, and AI-aided MRI by expert radiologists equaled PET/CT. A larger randomized controlled trial is warranted to demonstrate the AI's benefit sufficiently.

Funding

The Sun Yat-sen University Clinical Research 5010 Program (2015020), Guangdong Basic and Applied Basic Research Foundation (2022A1515110356), and Guangzhou Science and Technology Program (2023A04J1788).

Serum homocysteine level in newly diagnosed young patients with abnormal glucose tolerance

Background

Hyperhomocysteinemia is an emerging risk factor causing early-onset cardiovascular events. The objective of the study was to assess serum homocysteine levels in newly diagnosed young persons (age < 30 years) with abnormal glucose tolerance.

Methodology

This cross-sectional study included 40 young participants with newly diagnosed abnormal glucose tolerance (including prediabetes and diabetes mellitus) and an equal number of young persons with normal glucose tolerance (normal glucose tolerance vs prediabetes vs. diabetes mellitus-age (years): 25.0 (22.0, 28.0) vs 25.50 (21.50, 27.0) vs 28.0 (25.0, 29.0), median (interquartile range)). Glycemic status was diagnosed by American Diabetes Association, 2021 criteria. After taking clinical information, fasting blood was collected to measure homocysteine by chemiluminescent immunoassay.

Results

Homocysteine level was different across the spectrum of glycemic status (normal glucose tolerance vs. prediabetes vs. diabetes mellitus: 15.57 (13.89-20.71) vs 13.19 (11.40-18.11) vs 12.27 (9.97-14.42) µmol/L; median (interquartile range); p = 0.006). Serum homocysteine was significantly elevated in participants with normal glucose tolerance than diabetes mellitus (p = 0.006) but statistically similar in prediabetes in comparison to both normal glucose tolerance and diabetes mellitus (p = NS for both). Homocysteine level was above the normal level in 47.5% of participants with normal glucose tolerance and in 22.5% with abnormal glucose tolerance (p = 0.019).

Conclusions

Patients under 30 years with diabetes mellitus had lower homocysteine levels than those with normal glucose tolerance.

Reduced numbers of regulatory T cells in chronic heart failure seems not to be restored by cardiac resynchronization therapy

BACKGROUND

T cells have been implicated in the development and progression of inflammatory processes in chronic heart failure (CHF). Cardiac resynchronization therapy (CRT) has beneficial effects on symptoms and cardiac remodeling in CHF. However, its impact on the inflammatory immune response remains controversial. We aimed to study the impact of CRT on T cells in heart failure (HF) patients.

METHODS

Thirty-nine HF patients were evaluated before CRT (T0) and six months later (T6). Quantification of T cells, their subsets, and their functional characterization, after in vitro stimulation, were evaluated by flow cytometry.

RESULTS

T regulatory (Treg) cells were decreased in CHF patients (healthy group (HG): 1.08 ± 0.50 versus (heart failure patients (HFP)-T0: 0.69 ± 0.40, P = 0.022) and remaining diminished after CRT (HFP-T6: 0.61 ± 0.29, P = 0.003). Responders (R) to CRT presented a higher frequency of T cytotoxic (Tc) cells producing IL-2 at T0 compared with non-responders (NR) (R: 36.52 ± 12.55 versus NR: 24.71 ± 11.66, P = 0.006). After CRT, HF patients presented a higher percentage of Tc cells expressing TNF-α and IFN-γ (HG: 44.50 ± 16.62 versus R: 61.47 ± 20.54, P = 0.014; and HG: 40.62 ± 15.36 versus R: 52.39 ± 18.66, P = 0.049, respectively).

CONCLUSION

The dynamic of different functional T cell subpopulations is significantly altered in CHF, which results in an exacerbated pro-inflammatory response. Even after CRT, it seems that the inflammatory condition underlying CHF continues to evolve with the progression of the disease. This could be due, at least in part, to the inability to restore Treg cells levels.

TRIAL REGISTRATION

Observational and prospective study with no trial registration.

Age Related Functional Connectivity Signature Extraction Using Energy-Based Machine Learning Techniques

The study of brain connectivity plays an important role in understanding the functional organizations of the brain. It also helps to identify connectivity signatures that can be used for evaluating neural disorders and monitoring treatment efficacy. In this work, age-related changes in brain connectivity are studied to obtain aging signatures based on various modeling techniques. These include an energy-based machine learning technique to identify brain network interaction differences between two age groups with a large (30 years) age gap between them. Disconnectivity graphs and activation maps of the seven prominent resting-state networks (RSN) were obtained from functional MRI data of old and young adult subjects. Two-sample t-tests were performed on the local minimums with Bonferroni correction to control the family-wise error rate. These local minimums are connectivity states showing not only which brain regions but also how strong they are working together. They work as aging signatures that can be used to differentiate young and old groups. We found that the attention network's connectivity signature is a state with all the regions working together and young subjects have a stronger average connectivity among these regions. We have also found a common pattern between young and old subjects where the left and right brain regions of the frontal network are sometimes working separately instead of together. In summary, in this work, we combined machine learning and statistical approaches to extract connectivity signatures, which can be utilized to distinguish aging brains and monitor possible treatment efficacy.

Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases

The use of biomarkers as early warning systems in the evaluation of disease risk has increased markedly in the last decade. Biomarkers are indicators of typical biological processes, pathogenic processes, or pharmacological reactions to therapy. The application and identification of biomarkers in the medical and clinical fields have an enormous impact on society. In this review, we discuss the history, various definitions, classifications, characteristics, and discovery of biomarkers. Furthermore, the potential application of biomarkers in the diagnosis, prognosis, and treatment of various diseases over the last decade are reviewed. The present review aims to inspire readers to explore new avenues in biomarker research and development.

Dynamics of COVID-19 Lockdown on Blood Indices and Its Impact on Individuals' Immunological Health Status: A Cohort Study in Madinah, Saudi Arabia

Objective

The complete blood count (CBC) is an essential blood test that has been used for decades to assess individuals’ overall health status. This study aimed to investigate the contributions of lockdown conditions to individuals’ overall health status using blood indices as biological markers. During lockdown, people are limited to confined spaces, have access to limited nutritional supply options, experience increased stress, and are exposed to other environmental factors.

Methods

Our study’s target population included all outpatients who were severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-negative and requested CBC assessments as part of their routine health checks. A total of 2414 CBC results were included, covering a period from February 2019 to December 2020. The average of different blood indices during the COVID-19 lockdown was compared to the 10-month period preceding the lockdown.

Results

The average counts of RBCs, hemoglobin, and hematocrit showed a significant increase during the lockdown period, which lasted from May 2020 to September 2020. Reductions were observed for the RBC distribution width, total white blood cell count, platelets, and platelet distribution width.

Conclusion

Our findings suggested that the overall health status of individuals improved during the lockdown period in the short term, but health status might be adversely affected under these conditions of a longer period. Both RDW and PDW could be used as indicators for the overall health status when assessed against other blood indices.

Perineural Invasion Is a Strong Prognostic Factor but Not a Predictive Factor of Response to Adjuvant Chemotherapy in Node-Negative Colon Cancer

Purpose

To validate the prognostic value and evaluate the predictive value of response to adjuvant chemotherapy of perineural invasion (PNI) in node-negative colon cancer using the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) 18 tumor registry database.

Methods

Patients diagnosed with colon cancer from the SEER database between January 1, 2010 and December 31, 2015 were identified. Chi-square analysis was performed to evaluate different demographic and clinical features of patients between PNI-negative (PNI (-)) and PNI-positive (PNI (+)) groups. Univariate and multivariate Cox proportional hazard regression models were built to examine the relationship of demographic and clinical features and survival outcomes with the hazard ratios (HRs) and 95% confidence intervals (CIs).

Results

In total, 57,255 node-negative colon cancer patients were extracted from the SEER database. The receipt of chemotherapy was not an independent prognostic factor for CSS in T3 colon cancer with or without the presence of PNI (P >0.05). The receipt of chemotherapy was independently associated with 34.0% decreased risk of cancer-specific mortality compared with those without the receipt of chemotherapy in T4 colon cancer without the presence of PNI (HR = 0.660, 95%CI = 0.559-0.779, P <0.001); the receipt of chemotherapy was independently associated with 36.0% decreased risk of cancer-specific mortality compared with those without the receipt of chemotherapy in T4 colon cancer with the presence of PNI (HR = 0.640, 95%CI = 0.438-0.935, P = 0.021).

Conclusions

The present study demonstrated the poor prognosis of PNI (+) in both stage I and II colon cancer. However, the presence of PNI was not a predictive factor of response to adjuvant chemotherapy in node-negative colon cancer.

Midwifery Education Institutions in Italy Creation and Validation of Clinical Preceptors’ Assessment Tool: Students’ and Expert Midwives’ Views

Background: The aim of the study is to create and validate a midwifery preceptor’s evaluation form to be used by midwifery students. The International Confederation of Midwives recommends that clinical placements need to be supervised by a preceptor in order to be efficient for students who, in this way, gain competence and proper practice within the midwifery practical area. Methods: This is an observational multi-center transversal study and leads to the validation of an evaluation questionnaire. Methodically, the following steps were followed: literature review, focus group with midwifery students, meeting between expert midwives, creation of the preceptor’s assessment form, filling in of the forms by midwifery students and expert midwives, and validation of the form. The study was carried out in eight Italian universities and included eighty-eight midwifery students and eight midwives. Results and Conclusion: A midwifery preceptor’s assessment questionnaire was created made up of four attribute areas which, as a total, included 33 items. Cronbach’s alpha score was calculated after examining the forms filled in by students and expert midwives. An alpha score of 0.97–0.85 was obtained. The result was Pearson Correlation Coefficient r = 0.78.

The Effect of a Nature-Based Environmental Education Program on Children’s Environmental Attitudes and Behaviors: A Randomized Experiment with Primary Schools

The evidence about the effects of nature-based instruction on pro-environmentalism is uncompelling, mainly due to a lack of controlled experiments. This hinders causal claims and the provision of intervention guidelines. We present an experiment examining the impact of a nature-based environmental education (NBEE) program included in the school curriculum on children’s environmental attitudes (EA) and behaviors (EB). Children who followed the program through traditional instruction were used as a control group. Seven Spanish primary schools participated in the program and school classes were randomly assigned to the NBEE program (experimental group, N = 516) or the environmental education (EE) through traditional instruction (control group, N = 218). Our results indicate that children’s EA increased more in the NBEE group than in the control group. On average, EB remained virtually the same in both groups of children across time. In light of our findings, we encourage the promotion of a nature-based pedagogy in formal education to enhance children’s environmental attitudes.

Repeated Measures Designs and Analysis of Longitudinal Data: If at First You Do Not Succeed-Try, Try Again

Anesthesia, critical care, perioperative, and pain research often involves study designs in which the same outcome variable is repeatedly measured or observed over time on the same patients. Such repeatedly measured data are referred to as longitudinal data, and longitudinal study designs are commonly used to investigate changes in an outcome over time and to compare these changes among treatment groups. From a statistical perspective, longitudinal studies usually increase the precision of estimated treatment effects, thus increasing the power to detect such effects. Commonly used statistical techniques mostly assume independence of the observations or measurements. However, values repeatedly measured in the same individual will usually be more similar to each other than values of different individuals and ignoring the correlation between repeated measurements may lead to biased estimates as well as invalid P values and confidence intervals. Therefore, appropriate analysis of repeated-measures data requires specific statistical techniques. This tutorial reviews 3 classes of commonly used approaches for the analysis of longitudinal data. The first class uses summary statistics to condense the repeatedly measured information to a single number per subject, thus basically eliminating within-subject repeated measurements and allowing for a straightforward comparison of groups using standard statistical hypothesis tests. The second class is historically popular and comprises the repeated-measures analysis of variance type of analyses. However, strong assumptions that are seldom met in practice and low flexibility limit the usefulness of this approach. The third class comprises modern and flexible regression-based techniques that can be generalized to accommodate a wide range of outcome data including continuous, categorical, and count data. Such methods can be further divided into so-called “population-average statistical models” that focus on the specification of the mean response of the outcome estimated by generalized estimating equations, and “subject-specific models” that allow a full specification of the distribution of the outcome by using random effects to capture within-subject correlations. The choice as to which approach to choose partly depends on the aim of the research and the desired interpretation of the estimated effects (population-average versus subject-specific interpretation). This tutorial discusses aspects of the theoretical background for each technique, and with specific examples of studies published in Anesthesia & Analgesia, demonstrates how these techniques are used in practice.

Survival Analysis and Interpretation of Time-to-Event Data: The Tortoise and the Hare

Survival analysis, or more generally, time-to-event analysis, refers to a set of methods for analyzing the length of time until the occurrence of a well-defined end point of interest. A unique feature of survival data is that typically not all patients experience the event (eg, death) by the end of the observation period, so the actual survival times for some patients are unknown. This phenomenon, referred to as censoring, must be accounted for in the analysis to allow for valid inferences. Moreover, survival times are usually skewed, limiting the usefulness of analysis methods that assume a normal data distribution. As part of the ongoing series in Anesthesia & Analgesia, this tutorial reviews statistical methods for the appropriate analysis of time-to-event data, including nonparametric and semiparametric methods—specifically the Kaplan-Meier estimator, log-rank test, and Cox proportional hazards model. These methods are by far the most commonly used techniques for such data in medical literature. Illustrative examples from studies published in Anesthesia & Analgesia demonstrate how these techniques are used in practice. Full parametric models and models to deal with special circumstances, such as recurrent events models, competing risks models, and frailty models, are briefly discussed.

Systematic Review and Meta-analysis: Sometimes Bigger Is Indeed Better

Clinicians encounter an ever increasing and frequently overwhelming amount of information, even in a narrow scope or area of interest. Given this enormous amount of scientific information published every year, systematic reviews and meta-analyses have become indispensable methods for the evaluation of medical treatments and the delivery of evidence-based best practice. The present basic statistical tutorial thus focuses on the fundamentals of a systematic review and meta-analysis, against the backdrop of practicing evidence-based medicine. Even if properly performed, a single study is no more than tentative evidence, which needs to be confirmed by additional, independent research. A systematic review summarizes the existing, published research on a particular topic, in a well-described, methodical, rigorous, and reproducible (hence "systematic") manner. A systematic review typically includes a greater range of patients than any single study, thus strengthening the external validity or generalizability of its findings and the utility to the clinician seeking to practice evidence-based medicine. A systematic review often forms the basis for a concomitant meta-analysis, in which the results from the identified series of separate studies are aggregated and statistical pooling is performed. This allows for a single best estimate of the effect or association. A conjoint systematic review and meta-analysis can provide an estimate of therapeutic efficacy, prognosis, or diagnostic test accuracy. By aggregating and pooling the data derived from a systemic review, a well-done meta-analysis essentially increases the precision and the certainty of the statistical inference. The resulting single best estimate of effect or association facilitates clinical decision making and practicing evidence-based medicine. A well-designed systematic review and meta-analysis can provide valuable information for researchers, policymakers, and clinicians. However, there are many critical caveats in performing and interpreting them, and thus, like the individual research studies on which they are based, there are many ways in which meta-analyses can yield misleading information. Creators, reviewers, and consumers alike of systematic reviews and meta-analyses would thus be well-served to observe and mitigate their associated caveats and potential pitfalls.