Methods and participant characteristics in the Cancer Risk in Vegetarians Consortium: a cross-sectional analysis across 11 prospective studies.

BACKGROUND: The associations of vegetarian diets with risks for site-specific cancers have not been estimated reliably due to the low number of vegetarians in previous studies. Therefore, the Cancer Risk in Vegetarians Consortium was established. The aim is to describe and compare the baseline characteristics between non-vegetarian and vegetarian diet groups and between the collaborating studies. METHODS: We harmonised individual-level data from 11 prospective cohort studies from Western Europe, North America, South Asia and East Asia. Comparisons of food intakes, sociodemographic and lifestyle factors were made between diet groups and between cohorts using descriptive statistics. RESULTS: 2.3 million participants were included; 66% women and 34% men, with mean ages at recruitment of 57 (SD: 7.8) and 57 (8.6) years, respectively. There were 2.1 million meat eaters, 60,903 poultry eaters, 44,780 pescatarians, 81,165 vegetarians, and 14,167 vegans. Food intake differences between the diet groups varied across the cohorts; for example, fruit and vegetable intakes were generally higher in vegetarians than in meat eaters in all the cohorts except in China. BMI was generally lower in vegetarians, particularly vegans, except for the cohorts in India and China. In general, but with some exceptions, vegetarians were also more likely to be highly educated and physically active and less likely to smoke. In the available resurveys, stability of diet groups was high in all the cohorts except in China. CONCLUSIONS: Food intakes and lifestyle factors of both non-vegetarians and vegetarians varied markedly across the individual cohorts, which may be due to differences in both culture and socioeconomic status, as well as differences in questionnaire design. Therefore, care is needed in the interpretation of the impacts of vegetarian diets on cancer risk.

A Deep-Learning-Enabled Electrocardiogram and Chest X-Ray for Detecting Pulmonary Arterial Hypertension.

The diagnosis and treatment of pulmonary hypertension have changed dramatically through the re-defined diagnostic criteria and advanced drug development in the past decade. The application of Artificial Intelligence for the detection of elevated pulmonary arterial pressure (ePAP) was reported recently. Artificial Intelligence (AI) has demonstrated the capability to identify ePAP and its association with hospitalization due to heart failure when analyzing chest X-rays (CXR). An AI model based on electrocardiograms (ECG) has shown promise in not only detecting ePAP but also in predicting future risks related to cardiovascular mortality. We aimed to develop an AI model integrating ECG and CXR to detect ePAP and evaluate their performance. We developed a deep-learning model (DLM) using paired ECG and CXR to detect ePAP (systolic pulmonary artery pressure > 50 mmHg in transthoracic echocardiography). This model was further validated in a community hospital. Additionally, our DLM was evaluated for its ability to predict future occurrences of left ventricular dysfunction (LVD, ejection fraction < 35%) and cardiovascular mortality. The AUCs for detecting ePAP were as follows: 0.8261 with ECG (sensitivity 76.6%, specificity 74.5%), 0.8525 with CXR (sensitivity 82.8%, specificity 72.7%), and 0.8644 with a combination of both (sensitivity 78.6%, specificity 79.2%) in the internal dataset. In the external validation dataset, the AUCs for ePAP detection were 0.8348 with ECG, 0.8605 with CXR, and 0.8734 with the combination. Furthermore, using the combination of ECGs and CXR, the negative predictive value (NPV) was 98% in the internal dataset and 98.1% in the external dataset. Patients with ePAP detected by the DLM using combination had a higher risk of new-onset LVD with a hazard ratio (HR) of 4.51 (95% CI: 3.54-5.76) in the internal dataset and cardiovascular mortality with a HR of 6.08 (95% CI: 4.66-7.95). Similar results were seen in the external validation dataset. The DLM, integrating ECG and CXR, effectively detected ePAP with a strong NPV and forecasted future risks of developing LVD and cardiovascular mortality. This model has the potential to expedite the early identification of pulmonary hypertension in patients, prompting further evaluation through echocardiography and, when necessary, right heart catheterization (RHC), potentially resulting in enhanced cardiovascular outcomes.

Protocol for a feasibility randomized controlled trial of gentle yoga in older patients discharged from phase II cardiac rehabilitation.

Background: Physical activity (PA) is essential following an acute cardiac event. Cardiac rehabilitation (CR) is commonly prescribed, and PA after CR is recommended. Because of age-related changes in functional ability and multi-comorbidity, many older cardiac patients struggle to continue performing PA at home after CR. Depressive symptoms and anxiety are prevalent in cardiac patients and associated with poor self-care, including lack of daily PA. Yoga has been demonstrated to improve psychological and physical health outcomes in cardiac patients, but it is unknown whether yoga, modified for older CR patients - Gentle Yoga - is beneficial in managing psychological distress and maintaining PA following phase II CR. Our specific aims are to:1) determine the feasibility and acceptability of a modified gentle yoga intervention delivered via video conferencing for older cardiac patients; 2) compare, at 3-month follow-up, the effects and determine effect sizes of a gentle yoga intervention versus control on psychological health and physical health. Methods: We are conducting a 2-group (intervention versus control) randomized controlled pilot study. The intervention is a 12-week gentle yoga program delivered via video conference. Short-term effects will be evaluated at 3-month. Conclusion: This study is designed to be suited for older cardiac patients who would not have access to supervised PA opportunities after facility-based CR to enhance PA. This study will provide data about the feasibility and acceptability of the protocol for older cardiac patients and will offer effect sizes to determine sample size for a fully powered randomized controlled trial.

Artificial Intelligence-Enabled Electrocardiography Predicts Future Pacemaker Implantation and Adverse Cardiovascular Events.

Medical advances prolonging life have led to more permanent pacemaker implants. When pacemaker implantation (PMI) is commonly caused by sick sinus syndrome or conduction disorders, predicting PMI is challenging, as patients often experience related symptoms. This study was designed to create a deep learning model (DLM) for predicting future PMI from ECG data and assess its ability to predict future cardiovascular events. In this study, a DLM was trained on a dataset of 158,471 ECGs from 42,903 academic medical center patients, with additional validation involving 25,640 medical center patients and 26,538 community hospital patients. Primary analysis focused on predicting PMI within 90 days, while all-cause mortality, cardiovascular disease (CVD) mortality, and the development of various cardiovascular conditions were addressed with secondary analysis. The study's raw ECG DLM achieved area under the curve (AUC) values of 0.870, 0.878, and 0.883 for PMI prediction within 30, 60, and 90 days, respectively, along with sensitivities exceeding 82.0% and specificities over 81.9% in the internal validation. Significant ECG features included the PR interval, corrected QT interval, heart rate, QRS duration, P-wave axis, T-wave axis, and QRS complex axis. The AI-predicted PMI group had higher risks of PMI after 90 days (hazard ratio [HR]: 7.49, 95% CI: 5.40-10.39), all-cause mortality (HR: 1.91, 95% CI: 1.74-2.10), CVD mortality (HR: 3.53, 95% CI: 2.73-4.57), and new-onset adverse cardiovascular events. External validation confirmed the model's accuracy. Through ECG analyses, our AI DLM can alert clinicians and patients to the possibility of future PMI and related mortality and cardiovascular risks, aiding in timely patient intervention.

Enhancing the Central Venous Catheterization Competency of Medical Students through a Specialized Team and an Interactive Response System: A pre-post study.

Background: Central venous catheterization (CVC) is a critical clinical procedure. To avoid complications, possessing good knowledge regarding the CVC care bundle and skills for the proper insertion and maintenance of CVC are important. Objectives: To evaluate the effectiveness of an educational intervention and the use of an interactive response system in enhancing the CVC bundle care and insertion skills of medical students undergoing critical care medicine training. Materials and Methods: Sixth-year medical students (equivalent to fourth-year students in the United States) engaged in didactic lessons, interactive demonstrations, and simulator training facilitated by a CVC team comprising three thoracic and two vascular surgeons (all with a minimum 5 years of experience in central venous access) during their intensive care unit (ICU) rotation. Self-reported knowledge and confidence levels were assessed using pre-and posttests administered through the Zuvio App, an interactive response system. Results: A total of 60 students underwent the educational intervention, of which 54 completed the pretest and 40 completed the posttest. In the posttest, significant improvement was found in the CVC bundle care competency and understanding (P = 0.002), preprocedural preparation (P = 0.002), insertion procedures (P = 0.004), complications (P = 0.003), and insertion depth decisions (P = 0.001). Staff and students reported that assessment and interaction via the Zuvio App were valuable, practical, and feasible in a clinical setting, providing trainees with an individual competency portfolio of receiving precise medical education. Conclusions: Integrating the training provided by a specialized team with an interactive response system enhanced the knowledge and competency level in CVC insertion among medical students in this study.

Cordycepin Augments the Efficacy of Anti-PD1 against Colon Cancer.

Colon cancer has a poor clinical response to anti-PD1 therapy. This study aimed to evaluate the effect of cordycepin on the efficacy of anti-PD1 treatment in colon cancer. The viability of CT26 mouse colon carcinoma cells, cell-cycle progression, morphology, and the expression of mRNA and protein were assessed. A syngeneic animal model was established by implanting CT26 cells into BALB/c mice for in vivo experiments. Multi-parameter flow cytometry was used to analyze the splenic cell lineages and tumor microenvironment (TME). The in vitro data revealed that cordycepin, but not adenosine, inhibited CT26 cell viability. The protein, but not mRNA, expression levels of A2AR and A2BR were suppressed by cordycepin but not by adenosine in CT26 cells. The combination of cordycepin, but not adenosine, with anti-PD1 exhibited a greater tumor-inhibitory effect than anti-PD1 alone as well as inhibited the expression of A2AR and A2BR in splenic macrophages. In the TME, the combination of cordycepin and anti-PD1 increased the number of CD3+ T cells and neutrophils and decreased the number of natural killer (NK) cells. Overall, cordycepin augmented the antitumor effects of anti-PD1 against mouse colon carcinoma cells and inhibited the expression of the adenosine receptors A2AR and A2BR in splenic macrophages and intratumoral NK cells.

Differential Gene Expression in MRI-classified Glioblastoma.

Previous characterization of the genome and transcriptome of glioblastoma (GBM) has revealed molecular alterations that potentially drive GBM pathogenesis and heterogeneity 1-6 . These open-resources are evolving, such as The Cancer Genome Atlas (TCGA) and The Cancer Imaging Atlas (TCIA) at the National Institute of Health comprising a large cohort of molecular and MRI data. Yet, no report deciphers the link between molecular signatures and MRI-classified GBM. The necessity to re-form molecular and imaging data motivated our computational approach to integrate TCIA and TCGA datasets derived from GBM. We uncovered common and distinct molecular signatures across GBM patients and specific to tumor locations, respectively. Despite heterogeneity in GBM, the top 12 genes from our analysis highlights that the dysregulation of a subset of neurotransmitter receptor or transporter and synaptic activity is common across GBM patients. The coherent layer of imaging and molecular information would help us stratify precision neuro-oncology and treatment options in ways that are not possible through MRI or genomic data alone. Our findings provide molecular targets in the disrupted neurocircuit of GBM, suggesting imbalanced excitation and inhibition. Given the fact that GBM patients exhibit similar symptoms resembling patients with neurodegenerative diseases and seizures, our results supported the hypothesis-GBM in the context of neurological disorders beyond a solely cancerous disease.

Impact of proximity of left atrium to descending aorta on left inferior pulmonary vein triggers or drivers of atrial fibrillation: A risk score model.

BACKGROUND: Prior studies have investigated cardiac anatomy and clinical parameters as predictors for pulmonary vein and non-pulmonary vein triggers. OBJECTIVE: We aimed to assess the link between the descending aorta to left inferior pulmonary vein (Dao-LIPV) distance and the occurrence of triggers and drivers in atrial fibrillation (AF) ablation procedures. METHODS: Drug-refractory AF patients who underwent first-time index catheter ablation from January 2010 to December 2019 were retrospectively assembled. The Dao-LIPV distance was measured from preablation pulmonary vein computed tomography. Patients were assigned to groups on the basis of the presence of LIPV triggers or drivers. Multivariate logistic regression was used to identify risk factors. RESULTS: A total of 886 consecutive patients with drug-refractory AF were studied, and 63 (7.1%) patients were identified to have LIPV triggers or drivers. The Dao-LIPV distance had a better predictive performance (area under the curve, 0.70) compared with persistent AF (area under the curve, 0.57). Multivariate logistic regression analysis showed that Dao-LIPV distance ≤2.5 mm (odds ratio, 3.96; 95% CI, 2.15-7.29; P < .001) and persistent AF (odds ratio, 1.73; 95% CI, 1.02-2.94]; P = .044) were independent predictors for the presence of LIPV triggers or drivers. A risk score model was established to predict the probability of LIPV triggers or drivers with persistent AF (10.2%), Dao-LIPV distance ≤2.5 mm (11.4%), and both (15.0%). CONCLUSION: The proximity of the Dao-LIPV was correlated to the presence of LIPV triggers or drivers. We developed a risk score model indicating that persistent AF and Dao-LIPV distances ≤2.5 mm significantly increase the risk of LIPV triggers or drivers, aiding electrophysiologists in preparing for and performing catheter ablation more effectively.

Osteoporotic Precise Screening Using Chest Radiography and Artificial Neural Network: The OPSCAN Randomized Controlled Trial.

Background Diagnosing osteoporosis is challenging due to its often asymptomatic presentation, which highlights the importance of providing screening for high-risk populations. Purpose To evaluate the effectiveness of dual-energy x-ray absorptiometry (DXA) screening in high-risk patients with osteoporosis identified by an artificial intelligence (AI) model using chest radiographs. Materials and Methods This randomized controlled trial conducted at an academic medical center included participants 40 years of age or older who had undergone chest radiography between January and December 2022 without a history of DXA examination. High-risk participants identified with the AI-enabled chest radiographs were randomly allocated to either a screening group, which was offered fully reimbursed DXA examinations between January and June 2023, or a control group, which received usual care, defined as DXA examination by a physician or patient on their own initiative without AI intervention. A logistic regression was used to test the difference in the primary outcome, new-onset osteoporosis, between the screening and control groups. Results Of the 40 658 enrolled participants, 4912 (12.1%) were identified by the AI model as high risk, with 2456 assigned to the screening group (mean age, 71.8 years ± 11.5 [SD]; 1909 female) and 2456 assigned to the control group (mean age, 72.1 years ± 11.8; 1872 female). A total of 315 of 2456 (12.8%) participants in the screening group underwent fully reimbursed DXA, and 237 of 315 (75.2%) were identified with new-onset osteoporosis. After including DXA results by means of usual care in both screening and control groups, the screening group exhibited higher rates of osteoporosis detection (272 of 2456 [11.1%] vs 27 of 2456 [1.1%]; odds ratio [OR], 11.2 [95% CI: 7.5, 16.7]; P < .001) compared with the control group. The ORs of osteoporosis diagnosis were increased in screening group participants who did not meet formalized criteria for DXA compared with those who did (OR, 23.2 [95% CI: 10.2, 53.1] vs OR, 8.0 [95% CI: 5.0, 12.6]; interactive P = .03). Conclusion Providing DXA screening to a high-risk group identified with AI-enabled chest radiographs can effectively diagnose more patients with osteoporosis. Clinical trial registration no. NCT05721157 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Smith and Rothenberg in this issue.

Adapting the serious illness conversation guide for unhoused older adults: a rapid qualitative study.

BACKGROUND: Older adults experiencing homelessness (OAEH) age quickly and die earlier than their housed counterparts. Illness-related decisions are best guided by patients' values, but healthcare and homelessness service providers need support in facilitating these discussions. The Serious Illness Conversation Guide (SICG) is a communication tool to guide discussions but has not yet been adapted for OAEH. METHODS: We aimed to adapt the SICG for use with OAEH by nurses, social workers, and other homelessness service providers. We conducted semi-structured interviews with homelessness service providers and cognitive interviews with OAEH using the SICG. Service providers included nurses, social workers, or others working in homeless settings. OAEH were at least 50 years old and diagnosed with a serious illness. Interviews were conducted and audio recorded in shelters, transitional housing, a hospital, public spaces, and over Zoom. The research team reviewed transcripts, identifying common themes across transcripts and applying analytic notetaking. We summarized transcripts from each participant group, applying rapid qualitative analysis. For OAEH, data that referenced proposed adaptations or feedback about the SICG tool were grouped into two domains: "SICG interpretation" and "SICG feedback". For providers, we used domains from the Toolkit of Adaptation Approaches: "collaborative working", "team", "endorsement", "materials", "messages", and "delivery". Summaries were grouped into matrices to help visualize themes to inform adaptations. The adapted guide was then reviewed by expert palliative care clinicians for further refinement. RESULTS: The final sample included 11 OAEH (45% Black, 61 ± 7 years old) and 10 providers (80% White, 8.9 ± years practice). Adaptation themes included changing words and phrases to (1) increase transparency about the purpose of the conversation, (2) promote OAEH autonomy and empowerment, (3) align with nurses' and social workers' scope of practice regarding facilitating diagnostic and prognostic awareness, and (4) be sensitive to the realities of fragmented healthcare. Responses also revealed training and implementation considerations. CONCLUSIONS: The adapted SICG is a promising clinical tool to aid in the delivery of serious illness conversations with OAEH. Future research should use this updated guide for implementation planning. Additional adaptations may be dependent on specific settings where the SICG will be delivered.

An optimized ligation-mediated PCR method for chromosome walking and fusion gene chromosomal breakpoints identification.

Molecular techniques that recover unknown sequences next to a known sequence region have been widely applied in various molecular studies, such as chromosome walking, identification of the insertion site of transposon mutagenesis, fusion gene partner, and chromosomal breakpoints, as well as targeted sequencing library preparation. Although various techniques have been introduced for efficiency enhancement, searching for relevant single molecular event present in a large-sized genome remains challenging. Here, the optimized ligation-mediated polymerase chain reaction (PCR) method was developed and successfully identified chromosomal breakpoints far away from the exon of the new exon junction without the need for nested PCR. In addition to recovering unknown sequences next to a known sequence region, the high efficiency of the method could also improve the performance of targeted  next-generation sequencing (NGS).

Multimodal fusion for anticipating human decision performance.

Anticipating human decisions while performing complex tasks remains a formidable challenge. This study proposes a multimodal machine-learning approach that leverages image features and electroencephalography (EEG) data to predict human response correctness in a demanding visual searching task. Notably, we extract a novel set of image features pertaining to object relationships using the Segment Anything Model (SAM), which enhances prediction accuracy compared to traditional features. Additionally, our approach effectively utilizes a combination of EEG signals and image features to streamline the feature set required for the Random Forest Classifier (RFC) while maintaining high accuracy. The findings of this research hold substantial potential for developing advanced fault alert systems, particularly in critical decision-making environments such as the medical and defence sectors.

Impaired gut barrier integrity and reduced colonic expression of free fatty acid receptors in patients with Parkinson's disease.

BACKGROUND: Altered gut metabolites, especially short-chain fatty acids (SCFAs), in feces and plasma are observed in patients with Parkinson's disease (PD). OBJECTIVE: We aimed to investigate the colonic expression of two SCFA receptors, free fatty acid receptor (FFAR)2 and FFAR3, and gut barrier integrity in patients with PD and correlations with clinical severity. METHODS: In this retrospective study, colonic biopsy specimens were collected from 37 PD patients and 34 unaffected controls. Of this cohort, 31 participants (14 PD, 17 controls) underwent a series of colon biopsies. Colonic expression of FFAR2, FFAR3, and the tight junction marker ZO-1 were assayed by immunofluorescence staining. The You Only Look Once (version 8, YOLOv8) algorithm was used for automated detection and segmentation of immunostaining signal. PD motor function was assessed with the Movement Disorder Society (MDS)-Unified Parkinson's Disease Rating Scale (UPDRS), and constipation was assessed using Rome-IV criteria. RESULTS: Compared with controls, PD patients had significantly lower colonic expression of ZO-1 (p < 0.01) and FFAR2 (p = 0.01). On serial biopsy, colonic expression of FFAR2 and FFAR3 was reduced in the pre-motor stage before PD diagnosis (both p < 0.01). MDS-UPDRS motor scores did not correlate with colonic marker levels. Constipation severity negatively correlated with colonic ZO-1 levels (r = -0.49, p = 0.02). CONCLUSIONS: Colonic expression of ZO-1 and FFAR2 is lower in PD patients compared with unaffected controls, and FFAR2 and FFAR3 levels decline in the pre-motor stage of PD. Our findings implicate a leaky gut phenomenon in PD and reinforce that gut metabolites may contribute to the process of PD.

Discovery of Dual MER/AXL Kinase Inhibitors as Bifunctional Small Molecules for Inhibiting Tumor Growth and Enhancing Tumor Immune Microenvironment.

A series of bifunctional compounds have been discovered for their dual functionality as MER/AXL inhibitors and immune modulators. The furanopyrimidine scaffold, renowned for its suitability in kinase inhibitor discovery, offers at least three distinct pharmacophore access points. Insights from molecular modeling studies guided hit-to-lead optimization, which revealed that the 1,3-diketone side chain hybridized with furanopyrimidine scaffold that respectively combined amino-type substituent and 1H-pyrazol-4-yl substituent on the top and bottom of the aryl regions to produce 22 and 33, exhibiting potent antitumor activities in various syngeneic and xenograft models. More importantly, 33 demonstrated remarkable immune-modulating activity by upregulating the expression of total T-cells, cytotoxic CD8+ T-cells, and helper CD4+ T-cells in the spleen. These findings underscored the bifunctional capabilities of 33 (BPR5K230) with excellent oral bioavailability (F = 54.6%), inhibiting both MER and AXL while modulating the tumor microenvironment and highlighting its diverse applicability for further studies to advance its therapeutic potential.

Comprehensive clinical application analysis of artificial intelligence-enabled electrocardiograms for screening multiple valvular heart diseases.

BACKGROUND: Valvular heart disease (VHD) is becoming increasingly important to manage the risk of future complications. Electrocardiographic (ECG) changes may be related to multiple VHDs, and (AI)-enabled ECG has been able to detect some VHDs. We aimed to develop five deep learning models (DLMs) to identify aortic stenosis, aortic regurgitation, pulmonary regurgitation, tricuspid regurgitation, and mitral regurgitation. METHODS: Between 2010 and 2021, 77,047 patients with echocardiography and 12-lead ECG performed within 7 days were identified from an academic medical center to provide DLM development (122,728 ECGs), and internal validation (7,637 ECGs). Additional 11,800 patients from a community hospital were identified to external validation. The ECGs were classified as with or without moderate-to-severe VHDs according to transthoracic echocardiography (TTE) records, and we also collected the other echocardiographic data and follow-up TTE records to identify new-onset valvular heart diseases. RESULTS: AI-ECG adjusted for age and sex achieved areas under the curves (AUCs) of >0.84, >0.80, >0.77, >0.83, and >0.81 for detecting aortic stenosis, aortic regurgitation, pulmonary regurgitation, tricuspid regurgitation, and mitral regurgitation, respectively. Since predictions of each DLM shared similar components of ECG rhythms, the positive findings of each DLM were highly correlated with other valvular heart diseases. Of note, a total of 37.5-51.7% of false-positive predictions had at least one significant echocardiographic finding, which may lead to a significantly higher risk of future moderate-to-severe VHDs in patients with initially minimal-to-mild VHDs. CONCLUSION: AI-ECG may be used as a large-scale screening tool for detecting VHDs and a basis to undergo an echocardiography.

Signal-averaged electrocardiography as a noninvasive tool for evaluating the ventricular substrate in patients with nonischemic cardiomyopathy: reassessment of an old tool.

Introduction: Signal-averaged electrocardiography (SAECG) provides diagnostic and prognostic information regarding cardiac diseases. However, its value in other nonischemic cardiomyopathies (NICMs) remains unclear. This study aimed to investigate the role of SAECG in patients with NICM. Methods and results: This retrospective study included consecutive patients with NICM who underwent SAECG, biventricular substrate mapping, and ablation for ventricular arrhythmia (VA). Patients with baseline ventricular conduction disturbances were excluded. Patients who fulfilled at least one SAECG criterion were categorized into Group 1, and the other patients were categorized into Group 2. Baseline and ventricular substrate characteristics were compared between the two groups. The study included 58 patients (39 men, mean age 50.4 ± 15.5 years), with 34 and 24 patients in Groups 1 and 2, respectively. Epicardial mapping was performed in eight (23.5%) and six patients (25.0%) in Groups 1 and 2 (p = 0.897), respectively. Patients in Group 1 had a more extensive right ventricular (RV) low-voltage zone (LVZ) and scar area than those in Group 2. Group 1 had a larger epicardial LVZ than Group 2. Epicardial late potentials were more frequent in Group 1 than in Group 2. There were more arrhythmogenic foci within the RV outflow tract in Group 1 than in Group 2. There was no significant difference in long-term VA recurrence. Conclusion: In our NICM population, a positive SAECG was associated with a larger RV endocardial scar, epicardial scar/late potentials, and a higher incidence of arrhythmogenic foci in the RV outflow tract.

Investigating cartilage-related diseases by polarization-resolved second harmonic generation (P-SHG) imaging.

Establishing quantitative parameters for differentiating between healthy and diseased cartilage tissues by examining collagen fibril degradation patterns facilitates the understanding of tissue characteristics during disease progression. These findings could also complement existing clinical methods used to diagnose cartilage-related diseases. In this study, cartilage samples from normal, osteoarthritis (OA), and rheumatoid arthritis (RA) tissues were prepared and analyzed using polarization-resolved second harmonic generation (P-SHG) imaging and quantitative image texture analysis. The enhanced molecular contrast obtained from this approach is expected to aid in distinguishing between healthy and diseased cartilage tissues. P-SHG image analysis revealed distinct parameters in the cartilage samples, reflecting variations in collagen fibril arrangement and organization across different pathological states. Normal tissues exhibited distinct χ33/χ31 values compared with those of OA and RA, indicating collagen type transition and cartilage erosion with chondrocyte swelling, respectively. Compared with those of normal tissues, OA samples demonstrated a higher degree of linear polarization, suggesting increased tissue birefringence due to the deposition of type-I collagen in the extracellular matrix. The distribution of the planar orientation of collagen fibrils revealed a more directional orientation in the OA samples, associated with increased type-I collagen, while the RA samples exhibited a heterogeneous molecular orientation. This study revealed that the imaging technique, the quantitative analysis of the images, and the derived parameters presented in this study could be used as a reference for disease diagnostics, providing a clear understanding of collagen fibril degradation in cartilage.

Neck strength alone does not mitigate adverse associations of soccer heading with cognitive performance in adult amateur players.

OBJECTIVES: Soccer heading is adversely associated with neurocognitive performance, but whether greater neck strength or anthropometrics mitigates these outcomes is controversial. Here, we examine the effect of neck strength or anthropometrics on associations of soccer heading with neurocognitive outcomes in a large cohort of adult amateur players. METHODS: 380 adult amateur league soccer players underwent standardized measurement of neck strength (forward flexion, extension, left lateral flexion, right lateral flexion) and head/neck anthropometric measures (head circumference, neck length, neck circumference and neck volume). Participants were assessed for heading (HeadCount) and cognitive performance (Cogstate) on up to 7 visits over a period of two years. Principal components analysis (PCA) was performed on 8 neck strength and anthropometric measures. We used generalized estimating equations to test the moderation effect of each of the three PCs on 8 previously identified adverse associations of 2-week and 12-month heading estimates with cognitive performance (psychomotor speed, immediate verbal recall, verbal episodic memory, attention, working memory) and of unintentional head impacts on moderate to severe central nervous system symptoms. RESULTS: 3 principal components (PC's) account for 80% of the variance in the PCA. In men, PC1 represents head/neck anthropometric measures, PC2 represents neck strength measures, and PC3 represents the flexor/extensor (F/E) ratio. In women, PC1 represents neck strength, PC2 represents anthropometrics, and PC3 represents the F/E ratio. Of the 48 moderation effects tested, only one showed statistical significance after Bonferroni correction, which was not robust to extensive sensitivity analyses. CONCLUSION: Neither neck strength nor anthropometrics mitigate adverse associations of soccer heading with cognitive performance in adult amateur players.

Radiation Safety Assessment in Prostate Cancer Treatment: A Predictive Approach for I-125 Brachytherapy.

This study uses Monte Carlo simulation and experimental measurements to develop a predictive model for estimating the external dose rate associated with permanent radioactive source implantation in prostate cancer patients. The objective is to estimate the accuracy of the patient's external dose rate measurement. First, I-125 radioactive sources were implanted into Mylar window water phantoms to simulate the permanent implantation of these sources in patients. Water phantom experimental measurement was combined with Monte Carlo simulation to develop predictive equations, whose performance was verified against external clinical data. The model's accuracy in predicting the external dose rate in patients with permanently implanted I-125 radioactive sources was high (R2 = 0.999). A comparative analysis of the experimental measurements and the Monte Carlo simulations revealed that the maximum discrepancy between the measured and calculated values for the water phantom was less than 5.00%. The model is practical for radiation safety assessments, enabling the evaluation of radiation exposure risks to individuals around patients with permanently implanted I-125 radioactive sources.