Statistical significance: p value, 0.05 threshold, and applications to radiomics-reasons for a conservative approach

PEDRA-EFB0: colorectal cancer prognostication using deep learning with patch embeddings and dual residual attention

In computer-aided diagnosis systems, precise feature extraction from CT scans of colorectal cancer using deep learning is essential for effective prognosis. However, existing convolutional neural networks struggle to capture long-range dependencies and contextual information, resulting in incomplete CT feature extraction. To address this, the PEDRA-EFB0 architecture integrates patch embeddings and a dual residual attention mechanism for enhanced feature extraction and survival prediction in colorectal cancer CT scans. A patch embedding method processes CT scans into patches, creating positional features for global representation and guiding spatial attention computation. Additionally, a dual residual attention mechanism during the upsampling stage selectively combines local and global features, enhancing CT data utilization. Furthermore, this paper proposes a feature selection algorithm that combines autoencoders and entropy technology, encoding and compressing high-dimensional data to reduce redundant information and using entropy to assess the importance of features, thereby achieving precise feature selection. Experimental results indicate the PEDRA-EFB0 model outperforms traditional methods on colorectal cancer CT metrics, notably in C-index, BS, MCC, and AUC, enhancing survival prediction accuracy. Our code is freely available at https://github.com/smile0208z/PEDRA .

MIIVefa: An R Package for a New Type of Exploratory Factor Anaylysis Using Model-Implied Instrumental Variables

We present the R package MIIVefa, designed to implement the MIIV-EFA algorithm. This algorithm explores and identifies the underlying factor structure within a set of variables. The resulting model is not a typical exploratory factor analysis (EFA) model because some loadings are fixed to zero and it allows users to include hypothesized correlated errors such as might occur with longitudinal data. As such, it resembles a confirmatory factor analysis (CFA) model. But, unlike CFA, the MIIV-EFA algorithm determines the number of factors and the items that load on these factors directly from the data. We provide both simulation and empirical examples to illustrate the application of MIIVefa and discuss its benefits and limitations.

Preliminary evaluation of the effect of revised reimbursement scheme in outpatient hemodialysis for medical aid recipients in Korea

BACKGROUND

South Korea has universal health coverage guaranteeing equitable healthcare for all. However, equity issues have been raised regarding hemodialysis reimbursement for medical aid recipients with chronic kidney disease. Physicians and civic groups demanded a revision of the discriminatory policy, and in response, the Ministry of Health and Welfare amended the hemodialysis case payment scheme. This study aims to evaluate the effectiveness of the reform and detect any unintended policy outcomes.

METHODS

Data from the Health Insurance Review and Assessment Service of Korea was used. All subjects were patients with chronic kidney disease who received outpatient hemodialysis and medical aid from April 2017 to March 2022. The dzata was analyzed with descriptive statistics, and the generalized estimation equation was used to control for covariates and identify policy effects.

RESULTS

The reform of the case payment scheme in 2021 raised the compensation level per hemodialysis case, which was fixed for 7 years from 2014, by approximately 2,000 Korean won. There was no negative effect such as additional expenditure resulting from an unintentional increase in medical use.

CONCLUSION

A year has passed since the implementation of the outpatient hemodialysis rate system reform for medical aid recipients. Our results indicate that the reform has gone smoothly, and we anticipate continuous efforts by the government to guarantee universal health coverage to medical aid recipients. Through such consistent endeavors to correct the discriminatory aspects of policies, South Korea will achieve true universal health coverage.

Extraction and physiochemical characterization of micro-fibrillated cellulose based composite biofilm derived from Aegle marmelos fruit shells waste for packaging applications supported by in-silico docking studies

The utilization of waste shells from Aegle marmelos (bael) as a source of Micro fibrillated cellulose (MFC) was undertaken for applications in food packaging within the framework of this investigation. FTIR analysis of CAM and Raw Aegle marmelos shell powder (RAM) showed stretching of -OH and -CH groups at 3339 cm-1 and 2889 cm-1. Our results showed that crystallinity indices of CAM and RAM were 39.59 % and 18.89 %, respectively indicating a significant raise in crystallinity after extraction. FTIR analysis reveals the presence of hydroxyl groups (3306 cm-1) in all films. Exploration of the results indicated the development of covalent and H (hydrogen) bonds between CAM and Guar gum/Chitosan (GT), as discerned through FT-IR studies. X-ray diffraction unveiled in the amorphous feature of the films following CAM into GT composite film. Evaluation through FE-SEM exhibited a densely disordered network contributing to a heightened contact angle of the resultant film with an enhanced concentration of CAM. Cellulose Iβ and Chitosan showed stronger binding affinity of -7.3 kcal/mol, suggesting greater compatibility and stability. The antioxidant capacity of the films increased from 10.90 to 61.80 due to addition of CAM in the GT mixture where elevated concentrations demonstrated better scavenging activity.

Exploring the predictive value of pH in stratified mortality risk of NEC patients undergoing surgery: a retrospective study based on the PIC database

Background

Neonatal necrotizing enterocolitis (NEC) represents a severe gastrointestinal condition characterized by a high mortality rate, with a paucity of reliable prognostic biomarkers. The presence of an acidic pH, indicative of systemic acidosis and intestinal ischemia, has potential as a predictor of adverse outcomes. However, the relationship between pH levels and inflammatory markers, as well as its applicability across surgical and non-surgical patient subgroups, remains inadequately understood. Utilizing data from pediatric intensive care units, this study investigates the prognostic significance of pH in stratifying mortality risk in NEC and examines its association with variations in neutrophil and leukocyte counts.

Methods

Clinical and laboratory data of NEC patients were collected from pediatric critical care datasets. The population was stratified based on whether surgical treatment was performed. Each stratum was further divided into two groups: the mortality group and the discharge group. Intergroup comparisons and multivariate analyses were conducted to evaluate the predictive value of acidic pH levels for outcomes in NEC patients.

Results

A total of 124 NEC neonates were included, with a median age at admission of 9 days and a median weight of 2.34 kg. In both the non-surgical and surgical subgroups, neonates in the mortality group exhibited acidic pH levels. Multivariate regression analysis in the surgical group identified acidic pH as a risk factor for adverse outcomes. Among all NEC neonates, stratification based on pH levels revealed the highest mortality rate in the acidic pH group. A generalized linear regression model using pH as the dependent variable demonstrated that, in addition to conventional factors such as lactate and potassium (K+), increases in neutrophils and white blood cells (WBCs) also contributed to pH variations.

Conclusions

Acidic pH is closely associated with adverse outcomes in NEC neonates. Inflammation-related increases in WBC and neutrophils may reflect changes in pH levels in these patients.

Microbial metabolites associated in stool and left ventricle of heart failure patients revealed by meta-analysis

Heart Failure (HF) impacts approximately 64 million people globally. While overall incidence of HF is relatively stable across countries, the overall number of HF patients is increasing due to aging populations. Many articles examine the microbiome in HF, however, studies from humans have not been analyzed systematically. The aim of this meta-analysis is to bridge this gap by analyzing previously published data on human HF patients with untargeted metabolomics to understand whether microbially-mediated metabolites are consistently important for HF status. A systematic survey of the literature identified 708 articles discussing HF, the microbiome, and metabolomics. Of these, 82 were primary studies of HF patients, 61 studied human adults, 23 included an untargeted metabolomics measure, and 3 studies had data that was usable and publicly accessible. These studies include a GCMS study from stool, NMR of saliva and exhaled breath condensate, and LCMS from left ventricle of HF patients undergoing transplantation and unused donor hearts. Significant differences were observed from PCA between HF and controls for stool and left ventricle, but not saliva or EBC samples. OPLS-DA was conducted for stool and ventricle samples, and further revealed significant group differences. Univariate testing with FDR correction revealed 8 significant microbially-relevant metabolites (p < 0.005 after correction), most notably asparagine from left ventricle and 2-methylbutyryl carnitine from stool. Though there is much discussion of the microbiome in health outcomes in HF, there is limited research from human populations. Some microbial co-metabolites from both stool and heart were significantly associated with HF.

Incidental diagnosis and reporting rate of abdominal aortic aneurysms on lumbar spine magnetic resonance imaging

Background

Despite screening recommendations, many abdominal aortic aneurysms (AAAs) are incidentally imaged during various imaging studies but are frequently missed, especially when the imaging procedure is not specifically targeted to the aorta. The aims of this study were to conduct a retrospective analysis to assess the incidence of AAAs as incidental findings on lumbar spine magnetic resonance imaging (MRI) and to evaluate the detection rate of these findings reported by radiologists.

Methods

After ethics committee approval, this retrospective study included patients aged 55 years or older who underwent for any indication a lumbar spine 1.5-T MRI between January 2017 and June 2023 at the Unit of Radiology of the IRCCS (Research Hospital) Policlinico San Donato. Patients with a known history of AAA or prior AAA repair were excluded. The maximal aortic diameter of the abdominal aorta was measured on axial and sagittal T2-weighted images. A threshold of ≥30 mm was adopted to define the presence of an AAA. The Mann-Whitney U test was used to compare measurements between sexes and reporting status (reported or not reported). Bland-Altman analysis evaluated agreement between axial and sagittal measurements. Spearman's correlation assessed relationships between age, AAA detection, and measurement values.

Results

Out of 1,922 patients, 84 [4.4%, 95% confidence interval (CI): 3.5-5.3%] had an abdominal aorta diameter ≥30 mm, qualifying as AAAs. Only 26 (31.0%, 95% CI: 21.1-40.8%) of these AAAs were reported by radiologists. A significant difference in detection rates between sexes was observed (P<0.001), with males showing higher prevalence (8.4%, 95% CI: 6.4-10.3%) compared to females (1.7%, 95% CI: 0.9-2.4%). A significant difference was found also between the median maximal aortic diameter of 38 mm [interquartile range (IQR), 33-43 mm] for reported AAAs and 32 mm (IQR, 30-35 mm) for not reported AAAs (P=0.003). Bland-Altman analysis demonstrated good agreement between axial and sagittal measurements, showing also a strong correlation (ρ=0.93, P<0.001). Age correlated with the highest value between axial and sagittal measurements of AAAs (ρ=0.23, P<0.001) and detection rates (ρ=0.01, P<0.001), though the correlation was very weak.

Conclusions

This study identified a substantial incidence of AAAs on lumbar spine MRI scans, with a significant proportion of these cases going unreported by radiologists. Age and male sex were key factors influencing AAA prevalence, underscoring the need for targeted screening and management strategies that account for demographic differences.

Addressing Multiplicity in Retinal Sensitivity Analysis: An Alternative Approach to Assessing Gene Therapy Efficacy in Inherited Retinal Diseases

Purpose

The purpose of this study was to propose an alternative statistical approach that addresses the issue of multiplicity in microperimetry data analysis, offering a more balanced and sensitive measure of the efficacy of gene therapy in inherited retinal diseases (IRDs).

Methods

We analyzed microperimetry data from a phase II trial of AGTC-501 in patients with X-linked retinitis pigmentosa (XLRP). The Macular Integrity Assessment (MAIA; CenterVue, Padova, Italy) device was used to evaluate test-retest repeatability. A binomial model was used to calculate the probability of ≥7 decibel (dB) improvements due to chance alone across a 68-locus grid. We proposed an alternative approach to detect changes using a threshold of ≥7 loci with ≥7 dB mean improvement.

Results

Test-retest repeatability analysis showed a probability of < 5% for observing pointwise improvements ≥7 dB between 2 baseline visits. Applying the binomial distribution model, we found that the probability of observing improvements ≥7 dB in at least 7 unspecified loci purely by chance was 5.3%.

Conclusions

The proposed approach provides a balanced way to address multiplicity while maintaining reasonable statistical significance. Using ≥7 unspecified loci as the criterion for assessing sensitivity changes, offers a comprehensive assessment that can detect genuine treatment effects without being overly conservative.

Translational Relevance

This alternative statistical method has the potential to improve the evaluation of retinal sensitivity changes in gene therapy trials for IRDs, providing a more accurate measure of therapeutic efficacy and enhancing clinical decision making.

Postoperative Pain in Vitreoretinal Surgery With Combined Regional-General Anesthesia Versus General Anesthesia: A Systematic Review and Meta-Analysis

PURPOSE

To investigate postoperative pain variations in vitreoretinal surgeries conducted with combined regional-general anesthesia or general anesthesia.

DESIGN

Systematic review and meta-analysis.

METHODS

A systematic search of the PubMed, Embase, and Cochrane Library databases was conducted to identify relevant randomized controlled trials published before December 26, 2023. Studies comparing vitreoretinal surgeries conducted under combined anesthesia to general anesthesia were included, while studies using only regional anesthesia were excluded. The quality of the studies was assessed using the Cochrane Collaboration risk of bias tool, and the results are presented as odds ratios or standardized mean differences (SMDs) with 95% confidence intervals (CIs). I2 statistic was calculated to assess heterogeneity. The primary outcomes in our study included the proportions of patients needing as-needed postoperative analgesics, dosage of as-needed postoperative analgesia medications, and time to the first demand for as-needed postoperative analgesia. The secondary outcomes included postoperative pain scores at 0.5, 1, 2, 4, 6, 12, 24 hours (h), duration of surgery and anesthesia, and percentage of patients experiencing postoperative nausea and vomiting.

RESULTS

A total of 19 randomized controlled trials involving 1314 patients were analyzed. Combined anesthesia was associated with lower proportions of patients needing as-needed postoperative analgesics (odds ratio, 0.218 95% CI, 0.114-0.418, I2 = 55.2%), reduced dosage of as-needed postoperative analgesia medications (SMD, -1.429, 95% CI, -2.395 to -0.462, I2 = 90.4%), and longer time to the first demand for as-needed postoperative analgesia compared with general anesthesia alone (SMD, 2.650, 95% CI, 1.169-4.132, I2 = 96.5%). The effect of additional regional anesthesia lasted for 6 h (0.5 h SMD, -1.471, 95% CI, -2.498 to -0.444, I2 = 90.5%; 1 h, -1.507, -2.309 to -0.705, 92.7%; 2 h -1.487, -2.300 to -0.674, 93.2%; 4 h -1.052, -1.708 to -0.396, 89.5%; 6 h -1.053, -1.932 to -0.174, 93.4%; 12 h -0.286, -0.648 to 0.076, 57.1%; 24 h -0.297, -0.624 to 0.029, 64.0%). Combined anesthesia decreased postoperative nausea and vomiting risk without affecting the surgical or anesthesia duration.

CONCLUSIONS

Combined anesthesia demonstrated additional postoperative analgesic effects versus general anesthesia only. Further research is needed to validate the results of our study and to identify vision-threatening complications.

The GREENWATER study: patients' green sensitivity and potential recovery of injected contrast agents

OBJECTIVES

The environmental footprint of iodinated contrast agents (ICAs) and gadolinium-based contrast agents (GBCAs) is noteworthy. This study assesses: (1) patients' "green sensitivity" as measured by their acceptance in a sustainability study and (2) the resulting potential reduction of contrast residuals in wastewater.

MATERIALS AND METHODS

After ethical approval, participants scheduled for administration of ICAs or GBCAs for diagnostic purposes were enrolled in this prospective observational study from July 2022 to October 2023. They were asked to prolong their hospital stay by up to 60 min to collect their first urine in dedicated canisters, thereby measuring the recovery rates of ICAs and GBCAs as found/theoretical ratio of concentrations. Mann-Whitney U, χ2 tests, and multivariable regression analysis were used.

RESULTS

Patients scheduled for contrast-enhanced CT or MRI (n = 455) were screened; 422 (92.7%) accepted to participate. We enrolled 212 patients administered with ICAs and 210 administered with GBCAs. The median recovery rate was 51.2% (interquartile range 29.2-77.9%) for ICAs and 12.9% (9.0-19.3%) for GBCAs. At multivariable analysis, a significant effect of patient age (ICAs, p = 0.001; GBCAs, p = 0.014), urine volume (p < 0.001 for both), and time interval from contrast administration to urine collection (p < 0.001 for both) on recovery rates was found for both contrast agents; injected contrast volume (p = 0.046) and saline flushing usage (p = 0.008) showed a significant effect only for ICAs.

CONCLUSION

The high patient enrollment compliance (93%) and potential recovery rates of 51% (ICAs) and 13% (GBCAs) play in favor of sustainable practices in reducing the environmental footprint of contrast agents.

KEY POINTS

Question How many patients are willing to extend their stay in radiology by up to 60 min to help reduce the environmental impact of contrast agents? Findings Over 90% of screened patients agreed to extend their stay by up to 60 min and collect their urine in dedicated containers. Clinical relevance Patients demonstrated a high willingness to cooperate in reducing the environmental impact of contrast agents, allowing for a potential recovery of approximately 51% for iodinated and 13% for gadolinium-based contrast agents.

Optimizing Sensor Data Interpretation via Hybrid Parametric Bootstrapping

The Chalk River Laboratories (CRL) site in Ontario, Canada, has long been a hub for nuclear research, which has resulted in the accumulation of legacy nuclear waste, including radioactive materials such as uranium, plutonium, and other radionuclides. Effective management of this legacy requires precise contamination and risk assessments, with a particular focus on the concentration levels of fissile materials such as U235. These assessments are essential for maintaining nuclear criticality safety. This study estimates the upper bounds of U235 concentrations. We investigated the use of a hybrid parametric bootstrapping method and robust statistical techniques to analyze datasets with outliers, then compared these outcomes with those derived from nonparametric bootstrapping. This study underscores the significance of measuring U235 for ensuring safety, conducting environmental monitoring, and adhering to regulatory compliance requirements at nuclear legacy sites. We used publicly accessible U235 data from the Eastern Desert of Egypt to demonstrate the application of these statistical methods to small datasets, providing reliable upper limit estimates that are vital for remediation and decommissioning efforts. This method seeks to enhance the interpretation of sensor data, ultimately supporting safer nuclear waste management practices at legacy sites such as CRL.

MRI of pediatric ovarian masses: validation of the O-RADS framework

OBJECTIVE

The purpose of our study was to test the applicability and implications of using the O-RADS system, which is developed and validated on adults, to review MRI of ovarian masses among pediatric patients.

MATERIALS AND METHODS

We retrospectively reviewed consecutive MRI examinations from pediatric patients referred to imaging for suspected ovarian lesions, assessing them using the O-RADS framework. Malignancy frequencies among O-RADS classes were reviewed, and we appraised the potential for such approach to split patients into low (O-RADS 1, 2, and 3) and high risk (O-RADS 4 and 5). Multivariate analyses were conducted to review which clinical or imaging variables yielded a significant impact on malignancy, and a simplified reading framework was proposed accordingly.

RESULTS

109 female patients were included, with a median age of 13 years (IQR 11-15 years), 7 (7%) presenting with malignant lesions. Malignancy proportions were 0% (95% confidence Interval (CI) 0-35%) for the O-RADS 1 class, 0% (95% CI 0 - 5%) for the O-RADS 2 class, 0% (95% CI 0-14%) for the O-RADS 3 class, 50 (95% CI 1 - 99%) for the O-RADS 4 class, and 75% (95% CI 41-93%) for the O-RADS 5 class. The presence of peritoneal thickening or nodules (p < 0.001), lesion composition (p < 0.001), and absence of intralesional fat (p = 0.051) were individual predictors of malignancy, and the simplified reading framework proposed with such variables identified 11 likely malignant cases, detecting all 7 malignant lesions.

CONCLUSION

The O-RADS system may be applied to MRI performed in the pediatric population for ovarian masses, and a simplified reading framework based on O-RADS could also prove useful in such a setting.

KEY POINTS

Question The Ovarian-Adnexal Reporting and Data System (O-RADS) provides the risk of malignancy of ovarian masses among adults but has not been validated among pediatric patients. Findings Malignancy frequencies for O-RADS classes 1, 2, 3, and 4 were 0, 0, 50%, and 75%, indicating a good accuracy in lesion discrimination. Clinical relevance The Ovarian-Adnexal Reporting and Data System (O-RADS) can be effectively applied to MRI examinations in pediatric patients, enabling accurate classification of findings, with potential for score simplification in this age group.

A lethal disease of the European bison - posthitis is conditionally determined by its genomics

Posthitis is an incurable lethal disease of males of the European bison (Bison bonasus), regarded to be one of the major threats to the survival of the iconic species. Multiple attempts have been undertaken over the last 30 years to identify a source of infection and a primary pathogen. Studies indicated the disease could have a genetic background after tools developed for cattle (Bos taurus) revealed genomic regions that could be associated with its occurrence. In this study, we applied deep coverage targeted sequencing to 74 regions on 10 of the bison's chromosomes (1, 9, 12, 13, 15, 23, 25, 26, 29, and X) in search for species-specific single nucleotide polymorphism (SNP) markers that could help to explain the mechanism of the disease and be used to test for posthitis susceptibility. The association results were ranked based on p-values lower than 0.005 and odds ratios (OR) higher than 1. We obtained 30 SNP markers that met these requirements, all located on chromosome 25. However, none of the SNPs found in the study was significantly associated with posthitis occurrence after Bonferroni correction. The conditional nature of posthitis and 'false negative' sampling represent major difficulties in investigating this disease, and we recommend a complex genomic and environmental factors association assay that could eventually explain the puzzling etiology of posthitis and help mitigate this threat to the European bison.

Factors influencing nurses and nursing students' attitudes towards vaccinations: A cross-sectional study

BACKGROUND

Vaccination hesitancy remains the main obstacle to improving vaccination coverage. Influenza and COVID-19 vaccination coverage among healthcare professionals is essential. It is crucial to study the vaccination attitudes of healthcare professionals as they significantly influence the vaccination attitudes and behaviour of the rest of the population.

OBJECTIVE

The aim of this study was to describe the attitudes of Finnish nurses and nursing students towards COVID-19 and Influenza vaccination and explain what factors influence these attitudes.

DESIGN

A cross-sectional study.

SETTING AND PARTICIPANTS

A total of 1353 nurses from five hospital organisations and 580 nursing students from eight Universities of Applied Sciences participated in the survey. Participants were invited to complete the questionnaire through Webropol between March and September 2023.

METHODS

To collect data anonymously, a self-reported web-based questionnaire combining the Vaccination Attitude Examination (VAX) scale and Bergen's Social Media Addiction (BSMA) scale was used. K-means cluster analysis was performed to describe vaccination attitude profiles.

RESULTS

Four distinct vaccination attitude profiles were identified: Profile A - Confident Pro-Vaccine (n = 605) exhibited low hesitancy, with high confidence in vaccine safety and effectiveness; Profile B - Cautiously Pro-Vaccine (n = 764) showed moderate hesitancy, mainly concerned about unforeseen future effects; Profile C - Hesitant with Mistrust (n = 405) expressed high hesitancy, with significant worries about vaccine safety and mistrust in health authorities; and Profile D - Strongly Vaccine-Hesitant (n = 159) demonstrated very high hesitancy, marked by strong beliefs in potential long-term negative effects of vaccination. Significant differences in VAX-scale mean scores were found between the profiles, ranging from 1.27 for Profile A to 6.65 for Profile D. Overall, nursing students were more hesitant than practising nurses, with students being overrepresented in the more hesitant profiles. Clinical training in a COVID-19 unit was associated with more favourable vaccination attitudes among nursing students. The uptake of the full series of COVID-19 and annual Influenza vaccines was generally high in the sample (90.8 % and 87 %, respectively). However, a clear pattern between specific social media use and vaccination attitudes was not found. Major concerns related to vaccine hesitancy focused on the uncertainty of vaccines' long-term effects.

CONCLUSION

This study provides valuable insights into the complex nature of vaccination hesitancy among nurses and nursing students. These findings underscore the need for targeted interventions to address underlying concerns and promote vaccine acceptance within this demographic. In future research, it would be essential to gather more in-depth knowledge, particularly regarding nursing students' attitudes towards vaccination and the factors influencing them.

Simplifying Radiomics Workflow for Predicting Grade of Glioma: An Approach for Rapid and Reproducible Radiomics

Background

Radiomics with single Region of Interest (ROI) and single-sequence Magnetic Resonance Imaging (MRI) may facilitate the segmentation reproducibility and radiomics workflow due to a time-consuming and complicated delineation of that in multi-sequence MRI images.

Objective

This study aimed to evaluate the performance of the radiomics approach in grading glioma based on a single-ROI delineation as Gross Tumor Volume (GTV) in a single - sequence as contrast-enhanced T1-weighted MRI.

Material and Methods

This retrospective study was conducted on contrast-enhanced T1 weighted (CE T1W) MRI images of 60 grade II and 60 grade III glioma patients. The GTV regions were manually delineated. Radiomics features were extracted per patient. The segmentation reproducibility of the robust features was evaluated in several repetitions of GTV delineation. Finally, a linear Support Vector Machine (SVM) assessed the classification performance of the robust features.

Results

Four significant robust features were selected for training the model (P-value<0.05). The average Intraclass Correlation Coefficient (ICC) of the four features was 0.96 in several repetitions of GTV delineation. The linear SVM model differentiated grades II and III of glioma with an Area Under the Curve (AUC) of 0.9 in the training group.

Conclusion

High predicting power for glioma grading can be achieved with radiomics analysis by a single-ROI delineated on a single-sequence MRI image (CE T1W). In addition, single-ROI segmentation can increase radiomics reproducibility.

Assessing Health Disparities in Digital Services and Technologies During and After the COVID-19 Pandemic: A Pooled Cross-Sectional Analysis Using HINTS Data

Introduction

Digital health techniques were adopted faster during COVID-19, but the gap remains. This study analyzes how the digital gap affected pandemic patient portal uptake during and after. Patient portals improve physician connections and patient health information access, increasing health outcomes. Digital divide-lack of internet, digital literacy, and SDOH-may inhibit fair adoption. One study found that the internet and IT affected people during and after the pandemic. Learn why this patient demographic uses online patient portals so much. Like finding demographic, socioeconomic, and behavioral characteristics that affect portal usage, specifically drivers and barriers for health-conscious patients. This project developed a health promotion model and implemented initiatives to increase patient portal use and reduce health inequities.

Methods

In 2020 and 2022, we used Health Information National Trends Survey (HINTS) data. Patient portal use dominated. Our cross-sectional study assessed patient portal use and various predictors. Age, marital status, sex, mental health, education, income, urban/rural (urbanity), family size, trust, social media use, chronic condition, and health app use predict. We handled survey data collection biases with weighted analyses. Data was computed using survey weights for population representation. Logistic regression and weighted descriptive statistics utilized STATA SE 16.1.

Results

Demographic, socioeconomic, and behavioral characteristics significantly affect online patient portal use. Portal usage is positively correlated with health app use, urbanity, higher income, sex, Asian/Native American race/ethnicity, and hospital trust. In addition, our data shows low adoption rates across the digital divide. Patients who trust others utilize patient portals, and previously established characteristics affect self-motivation for health improvement.

Conclusion

Access obstacles and behavioral factors affect online patient portal adoption. Addressing the digital divide is crucial, but trust and self-motivation may improve patient portal utilization. Adopting health IT can improve patient participation and access to care, supporting equitable health outcomes.

Diagnostic performance of an artificial intelligence model for the detection of pneumothorax at chest X-ray

PURPOSE

Pneumothorax (PTX) is a common clinical urgency, its diagnosis is usually performed on chest radiography (CXR), and it presents a setting where artificial intelligence (AI) methods could find terrain in aiding radiologists in facing increasing workloads. Hence, the purpose of our study was to test an AI system for the detection of PTX on CXR examinations, to review its diagnostic performance in such setting alongside that of reading radiologists.

METHOD

We retrospectively ran an AI system on CXR examinations of patients who were imaged for the suspicion of PTX, and who also underwent computed tomography (CT) within the same day, the latter being used as reference standard. The performance of the proposed AI system was compared to that of reading radiologists, obtained from CXR reports.

RESULTS

Overall, the AI system achieved an accuracy of 74 % (95%CI 68-79 %), with a sensitivity of 66 % (95%CI 59-73 %) and a specificity of 93 % (95%CI 85-97 %). Human readers displayed a comparable accuracy (77 %, 95%CI 71-82 %, p = 0.355), with higher sensitivity (73 %, 95%CI 66-79 %, p = 0.040), albeit lower specificity (85 %, 95%CI 75-91 %, p = 0.034). The performance of AI was influenced by patient positioning at CXR (p = 0.040).

CONCLUSIONS

The proposed tool could represent an aid to radiologists in detecting PTX, improving specificity. Further improvement with training on more challenging cases may pave the way for its use as a screening or standalone tool.

Suitable temperature indicator for adverse health impacts in sub-tropical cities: a case study in Hong Kong from 2010-2019

Heat-health warning systems and services are important preventive actions for extreme heat, however, global evidence differs on which temperature indicator is more informative for heat-health outcomes. We comprehensively assessed temperature predictors on their summer associations with adverse health impacts in a high-density subtropical city. Maximum, mean, and minimum temperatures were examined on their associations with non-cancer mortality and hospital admissions in Hong Kong during summer seasons 2010-2019 using Generalized Additive Models and Distributed Lag Non-linear Models. In summary, mean and minimum temperatures were identified as strong indicators for mortality, with a relative risk(RR) and 95% confidence interval(CI) of 1.037 (1.006-1.069) and 1.055 (1.019-1.092), respectively, at 95th percentile vs. optimal temperature. Additionally, minimum temperatures captured the effects of hospital admissions, RR1.009 (95%CI: 1.000- 1.018). In stratified analyses, significant associations were found for older adults, female sex, and respiratory-related outcomes. For comparison, there was no association between maximum temperature and health outcomes. With climate change and projected increase of night-time warming, the findings from this comprehensive assessment method are useful to strengthen heat prevention strategies and enhance heat-health warning systems. Other locations could refer to this comprehensive method to evaluate their heat risk, especially in highly urbanized environments and subtropical cities.

Turkish Adaptation of QLQ-SH22 Quality of Sexual Life Assessment Scale in Cancer Patients - Validity and Reliability Study

Objectives

Cancer or its treatment can have direct or indirect effects on sexual functions. Routine assessment of sexual functioning is essential in cancer patients to identify sexual problems and provide counseling to patients about these issues. This study aimed to investigate the validity and reliability of the Turkish version of the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire - Sexual Health 22 prepared for the assessment of sexual function in cancer patients.

Methods

The sample of the study consisted of 201 patients, aged between 18-75, who received inpatient or outpatient treatment and treatment follow-up in the medical oncology unit of two private hospitals in Istanbul, regardless of cancer type, treatment stage and form. Language equivalence has been achieved. The validity of the scale was evaluated by factor analysis and the suitability of the model was determined by Goodness of Fit Indices. For the reliability of the scale, internal consistency was tested with Cronbach's alpha value. For consistency, a relationship was sought between the data taken 3 weeks apart using the test-retest method and the Spearman Correlation Test.

Results

Goodness of Fit Indices; It was calculated as Comparative Fit Index (CFI)=0.98, Tucker-Lewis Index (TLI)=0.98 Goodness of Fit Index (GFI)=1.32, Root Mean Square Error of Approximation (RMSEA)=0.04, Standardized Root Mean Square Residual (SRMR)=0.088. All factor loadings were found statistically significant with p<0.0001. In the internal consistency analysis, Cronbach alpha values of 0.91, 0.79 and 0.88 were obtained for sexual satisfaction, sexual pain subscales and all questions, respectively, and all groups were found to be reliable. In the Spearman Correlation Test used for consistency analysis, all coefficients were calculated above 0.7 and strong relationships were observed between the data. It was concluded that the scale was consistent in all areas.

Conclusion

All data obtained from the Turkish version of the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire - Sexual Health 22 are valid and reliable.

When the Others Are Dangerous: Paranoid Presentations in Subclinical Forms of Personality Disorders

The discriminant validity of paranoid personality disorder has been recently questioned, and paranoid presentations are now conceived of as transdiagnostic features of personality disorders (PDs). However, empirical results are inconsistent. This study investigated the link between subclinical personality disorders (except paranoid PD) and paranoid presentations, exploring how the severity of personality functioning affects this relationship. Nonclinical participants (N = 270, females: n = 194; 71.9%) completed self-report measures of the constructs of interest. In multiple regression analyses, subclinical borderline PD was primarily related to a wide range of paranoid presentations. Moreover, the severity of personality functioning increased the strength of the association between subclinical BPD and severe paranoid presentations. Results suggest that, when exploring unique contributions, paranoid presentations are especially associated with subclinical forms of BPD and highlight the importance of considering personality dysfunction severity. Additionally, the findings demonstrate that paranoid presentations are a relatively transdiagnostic dimension.

Intraocular pressure and optical coherence tomography concerning visual field outcomes in "green" patients: An observational study

This study investigates the relationship between intraocular pressure (IOP), optical coherence tomography (OCT) parameters, and visual field (VF) outcomes in 'Green' patients-those with normal OCT findings but potential VF abnormalities. Understanding this relationship is crucial for improving early detection and management strategies for glaucoma, especially in patients who show functional loss despite normal structural findings on OCT. A cross-sectional study was conducted at Taipei City Hospital, Renai Branch, Taiwan, from July 1, 2015, to July 1, 2023. Participants were referred for suspected glaucoma and included based on normal OCT parameters ('green' coding) and completed VF tests. Patients with any ocular disease that could confound results were excluded. Logistic regression models were used to assess relationships between IOP, OCT parameters (rim area, disc area, retinal nerve fiber layer thickness, cup-to-disc ratios, and cup volume), and VF outcomes. Age, sex, and IOP status (normal or ocular hypertension) were also included in the analysis. All data were analyzed using Statistical Package for the Social Sciences version 23.0. Larger disc area was a significant predictor of VF abnormalities, with an adjusted OR of 3.72 (95% confidence interval [CI], 1.14-12.15). Neither normal IOP nor ocular hypertension significantly predicted VF loss (adjusted OR = 0.89; 95% CI, 0.27-2.96). Female sex was associated with a higher likelihood of VF abnormalities (adjusted OR = 5.68; 95% CI, 1.03-31.25). Other OCT parameters, including retinal nerve fiber layer thickness and cup-to-disc ratios, were not significantly associated with VF outcomes. Disc area plays a critical role in predicting VF abnormalities in "green" patients, suggesting the importance of integrating disc size into screening and monitoring protocols. These findings challenge the reliance on IOP alone for predicting VF loss and support the need for more comprehensive assessments. Future research should explore longitudinal studies to further assess the predictive value of disc area and investigate additional factors, such as vascular and biomechanical influences, that may contribute to VF deterioration in this population.

Assessment of forced retention efficiency in stormwater drainage systems

In this study, an analysis of hydraulic systems with forced retention is presented as an alternative to traditional stormwater retention methods for drainage systems. This study evaluates the hydraulic efficiency of three different geometries of forced-retention hydraulic systems and compares the results with the efficiency of a traditional single-chamber retention tank and a traditional linear system without retention under two operational scenarios. Given the identical capacities of the solutions studied, an evaluation procedure was proposed based on the system's resilience time, which was measured until a set level of stormwater accumulation was reached in the system. Additionally, a sensitivity analysis of the models was performed using statistical tools and machine learning. This also provides a basis for verifying the applicability of artificial neural networks in assessing the efficiency of such solutions. The results of the global sensitivity analysis indicated that the inflow rate and the permissible level of stormwater accumulation in the system were key factors affecting the hydraulic efficiency of forced retention facilities, with high value of impact strengths of 121.651 and 76.035, respectively. In the scenario with a lower accumulation level, forced-retention hydraulic systems, particularly ring-shaped solutions, exhibited higher efficiency, with the hydraulic efficiency coefficient reaching 5.500. In contrast, in the scenario allowing for a higher level of system fill, the efficiency and selection of the most effective solution were closely related to the hydraulic conditions, with the efficiency of the forced retention units, describing the ratio of the analysed retention unit capacity to a conventional retention tank, ranging from 0.300 to 1.140. Based on these findings, it was concluded that forced retention can serve as an alternative to classic network tanks, especially in urban areas with limited available space.

Correlation of Sarcopenia with Coronary Artery Disease Severity and Pericoronary Adipose Tissue Attenuation: A Coronary CT Study

OBJECTIVE

To investigate the association between sarcopenia, as appraised with CT-derived muscle metrics, and cardiovascular status, as assessed via coronary CT angiography (CCTA) using the Coronary Artery Disease-Reporting and Data System (CAD-RADS) and with pericoronary adipose tissue (pCAT) metrics.

METHODS

A retrospective observational study conducted on patients who underwent CCTA. The cross-sectional area (CSA) and attenuation values of the paravertebral muscles at the T8 level and the pectoralis major muscles at the T6 level were measured. The patient height was employed for the normalization of the skeletal muscle CSA. The pCAT attenuation around the coronary arteries was assessed, and the CAD severity was graded using the CAD-RADS reporting system. Regression analyses were performed to assess the impact of demographics, clinical factors, and CT variables on the CAD-RADS and pCAT.

RESULTS

A total of 220 patients were included (132 males, median age 65 years). Regression analyses showed the associations of CAD with age and sex (p < 0.001). Familiarity with CAD was related to the left anterior descending artery pCAT (p = 0.002) and circumflex artery pCAT (p = 0.018), whereas age was related to the left anterior descending artery pCAT (p = 0.032). Weak positive correlations were found between the lower muscle density and lower pCAT attenuation (ρ = 0.144-0.240, p < 0.039).

CONCLUSIONS

This study demonstrated weak associations between the sarcopenia indicators and the cardiovascular risk, as assessed by the CAD severity and pCAT inflammation. However, these correlations were not strong predictors of CAD severity, as age and traditional cardiovascular risk factors overshadowed the impact of sarcopenia in the cardiovascular risk assessment.

In silico exploration of 4(α-l-rhamnosyloxy)-benzyl isothiocyanate: A promising phytochemical-based drug discovery approach for combating multi-drug resistant Staphylococcus aureus

Multidrug-resistant (MDR) Staphylococcus aureus infections significantly threaten global health. With rising resistance to current antibiotics and limited solutions, the urgent discovery of new, effective, and affordable antibacterials with low toxicity is imperative to combat diverse MDR S. aureus strains. Hence, in this study, we introduce an in silico phytochemical-based approach for discovering novel antibacterial agents, underscoring the potential of computational approaches in therapeutic discovery. Glucomoringin Isothiocyanate (GMG-ITC) from Moringa oleifera Lam. is one of the phytochemical compounds with several biological activities, including antimicrobial, anti-inflammatory, and antioxidant activities, and is also effective against S. aureus. This study focuses on screening GMG-ITC as a potential drug candidate to combat MDR S. aureus infections through a molecular docking approach. Moreover, interaction amino acid analysis, in silico pharmacokinetics, compound target prediction, pathway enrichment analysis and molecular dynamics (MD) simulations were conducted for further investigation. Molecular docking and interaction analysis showed strong binding affinity towards S. aureus lipase, dihydrofolate reductase, and other MDR S. aureus proteins, including penicillin-binding protein 2a, MepR, D-Ala:D-Ala ligase, and RPP TetM, through hydrophilic and hydrophobic interactions. GMG-ITC also showed a strong binding affinity to cyclooxygenase-2 and FAD-dependent NAD(P)H oxidase, suggesting that it is a potential anti-inflammatory and antioxidant candidate that may eliminate inflammation and oxidative stress associated with S. aureus infections. MD simulations validated the stability of the GMG-ITC molecular interactions determined by molecular docking. In silico pharmacokinetic analysis highlights its potency as a drug candidate, showing strong absorption, distribution, and excretion properties in combination with low toxicity. It acts as an active protease and enzyme inhibitor with moderate activity against GPCR ligands, ion channels, nuclear receptor ligands, and kinases. Enrichment analysis further elucidated its involvement in important biological, molecular, and cellular functions with potential therapeutic applications in diseases like cancer, hepatitis B, and influenza. Results suggest that GMG-ITC is an effective antibacterial agent that could treat MDR S. aureus-associated infections.

Predictive and classification capabilities of the timed up and go as a physical performance measure in hip osteoarthritis: a retrospective study of 606 patients

INTRODUCTION

Hip osteoarthritis (OA) is a common joint pathology that significantly constrains functional capacity. Assessing the impact of hip OA on functionality is crucial for research and clinical practices. The study aimed to assess hip OA patients' functionality using the Timed Up and Go (TUG) test and to evaluate its diagnostic ability to differentiate between different grades of hip OA. We hypothesized that the severity of hip OA would impact the time required to complete the TUG test.

MATERIALS AND METHODS

Patients (Ν = 606) with unilateral, primary hip OA were selected from de-identified data and divided according to the radiographic Kellgren-Lawrence classification system (groups: Grade 2, Grade 3, and Grade 4). Groups' differences were assessed using the X2 test of independence and the one-way ANOVA model. Correlations between dependent and independent variables were assessed using Pearson's correlation coefficient (r). A receiver operating characteristic (ROC) analysis was conducted to assess the TUG test's ability to differentiate between the hip OA grades.

RESULTS

Statistically significant differences were found among the three groups in age, gender distribution, TUG test, and occasional cane use (all p-values < 0.001). The correlation analysis shows a significant and strong positive correlation between TUG performance time and hip OA grades (r = .78, p < .001). The adjusted odds ratios (OR) were: Grade2-3=(2.29[95%CI: 1.89, 2.77], p < .001) and Grade3-4=(1.47[95%CI: 1.34, 1.62], p < .001). The TUG cut-off points from the ROC analysis were: Grades 2-3 = 10.25 s, Grades 2-4 = 11.35 s, and Grades 3-4 = 12.8 s.

CONCLUSIONS

This study provides evidence that the duration of the TUG test significantly increased with the severity of the disease. TUG can offer real-time data on the management and progression of hip OA. Future studies should explore the correlation between hip OA and the TUG test, as understanding the relationship can influence treatment and patient outcomes.

Twenty-Five Years of Progress-Lessons Learned From JMIR Publications to Address Gender Parity in Digital Health Authorships: Bibliometric Analysis

BACKGROUND

Digital health research plays a vital role in advancing equitable health care. The diversity of research teams is thereby instrumental in capturing societal challenges, increasing productivity, and reducing bias in algorithms. Despite its importance, the gender distribution within digital health authorship remains largely unexplored.

OBJECTIVE

This study aimed to investigate the gender distribution among first and last authors in digital health research, thereby identifying predicting factors of female authorship.

METHODS

This bibliometric analysis examined the gender distribution across 59,980 publications from 1999 to 2023, spanning 42 digital health journals indexed in the Web of Science. To identify strategies ensuring equality in research, a detailed comparison of gender representation in JMIR journals was conducted within the field, as well as against a matched sample. Two-tailed Welch 2-sample t tests, Wilcoxon rank sum tests, and chi-square tests were used to assess differences. In addition, odds ratios were calculated to identify predictors of female authorship.

RESULTS

The analysis revealed that 37% of first authors and 30% of last authors in digital health were female. JMIR journals demonstrated a higher representation, with 49% of first authors and 38% of last authors being female, yielding odds ratios of 1.96 (95% CI 1.90-2.03; P<.001) and 1.78 (95% CI 1.71-1.84; P<.001), respectively. Since 2008, JMIR journals have consistently featured a greater proportion of female first authors than male counterparts. Other factors that predicted female authorship included having female authors in other relevant positions and gender discordance, given the higher rate of male last authors in the field.

CONCLUSIONS

There was an evident shift toward gender parity across publications in digital health, particularly from the publisher JMIR Publications. The specialized focus of its sister journals, equitable editorial policies, and transparency in the review process might contribute to these achievements. Further research is imperative to establish causality, enabling the replication of these successful strategies across other scientific fields to bridge the gender gap in digital health effectively.

Quantitative evaluation of factors influencing the 3 Hz repetitive nerve stimulation test in patients with amyotrophic lateral sclerosis

INTRODUCTION/AIMS

Previous studies have suggested that treatments targeting the neuromuscular junction (NMJ) may play a role in the treatment of amyotrophic lateral sclerosis (ALS). However, factors impacting repetitive nerve stimulation (RNS), a technique to evaluate NMJ function, have yet to be fully elucidated. We aimed to identify independent factors contributing to the decremental response of the accessory nerve and evaluated its value in ALS clinical practice.

METHODS

A total of 626 patients who were diagnosed with ALS and underwent 3 Hz RNS tests on the accessory nerve were enrolled. Data on their clinical and electrophysiological indicators were divided into a training set (collected from June 2016 to December 2022) and a test set (collected from January to August 2023). Stepwise regression was used in independent variable selection and model building.

RESULTS

Forty-two percent of patients had a decrement larger than 10% and 24% had a decrement larger than 15%. Onset age, sex, onset site, forced vital capacity (FVC) and motor unit potential (MUP) duration were independent factors contributing to the results of the RNS test. MUP duration had the greatest impact on decremental response, followed by FVC and onset age. The decremental response in females was larger than in males. Upper limb onset was found to contribute more to the decrement than lower limb or bulbar onset.

DISCUSSION

In patients with ALS, NMJ safety factor is reduced during re-innervation. Decremental response is affected by multiple factors, which needs to be considered in clinical trials targeting the NMJ in these patients.

Comparison of three sorbents for thin film solid phase microextraction of haloacetic acids from water

Water disinfection inevitably leads to disinfection byproduct formation, such as haloacetic acids. Many disinfection byproducts reportedly have adverse health effects and, in many instances, including four haloacetic acids, are classified as potential carcinogens. As the global awareness of these compounds increases, more regulatory bodies include certain disinfection byproduct groups in their regulations. Rugged, fast, and cheap analytical quantification methods are therefore crucial. In this paper, a thin film extraction method for haloacetic acids is outlined. Thin films were synthesized in-house using a spin coating procedure, which allowed for easy adjustment of the sorbent choice and film geometry. PDMS, Carboxen®, and HLB were of interest and their extraction potential for HAAs from spiked water was tested in three film variations. PDMS films impregnated with HLB or Carboxen® improved the extraction drastically compared to PDMS films. Specifically, HLB impregnated films achieved excellent extraction efficiencies for tri-substituted analytes (51% for BDCAA, 77% for CDBAA, and 92% TBAA), which are often present at extremely low concentrations in water. In addition to the extraction experiment, a computational model was applied to compare PDMS and HLB. Trends observed in the computational data reflected in the experimental results, showing the validity of the model and confirming that physisorption through hydrogen bonding was mainly responsible for successful extraction.

Exploring the Impact of Equity, Diversity, and Inclusion Initiatives Within a Canadian Nursing Program: A Pilot Study

Interventions that aim to address equity, diversity, and inclusion (EDI) within the health professions often strive to promote the retention, recruitment, and success of individuals from historically underrepresented groups, who often belong to the same groups experiencing underservicing in health care. A pilot study aimed to examine the impact of ongoing EDI initiatives at Dalhousie University in Nova Scotia, Canada by exploring sense of belonging and curricular inclusion/representation from the perspectives of enrolled students. Intersectionality Theory was operationalized by way of considering the relational and contextual nature of marginalization. Results showed differences in perceptions of impacted sense of belonging and curricular inclusion/representation of diverse groups between respondents in the underrepresented subgroup as compared to their overrepresented counterparts. Differences in underrepresented and overrepresented subgroups' perceptions of impacted sense of belonging and curricular inclusion/representation suggest a need for further research to better understand the impact of EDI interventions on nursing students.

Algal Biodiversity of Nine Megaliths in South-East Bulgaria

This paper presents the first data on the biodiversity of lithophytic algae from Bulgarian megaliths obtained after the application of the direct sampling method, subsequent cultivation, and processing by light microscopy. A rich algal flora was found: 90 species and 1 variety of 65 genera from Cyanoprokaryota/Cyanobacteria (29 species, 13 genera), Chlorophyta (40 species and 1 variety, 38 genera), Streptophyta (5 species, 1 genus), and Ochrophyta (16 species, 13 genera). Among them were the globally rare Pseudodictyochloris multinucleata (Chlorophyta), found for the first time in such lowland and warm habitats, and Scotiella tuberculata (Chlorophyta), for which this is the first finding in the country. Three of the recorded species are conservationally important. The low floristic similarity between the sites (0-33%) shows the diversity of the algal flora, with no common species found for all the megaliths studied. The most widespread were the strongly adaptive and competitive Stichococcus bacillaris, Apatococcus lobatus, and Chloroidium ellipsoidium (Chlorophyta). The correlations estimated between the species number and substrate temperature (18.1-49.6 °C) suggest the prospect of future research related to the impact of global warming. In addition, the study points to the safety aspects as it revealed species from nine potentially toxin-producing cyanoprokaryotic genera that could be harmful to visitors' health.

Deep transfer learning for detection of breast arterial calcifications on mammograms: a comparative study

INTRODUCTION

Breast arterial calcifications (BAC) are common incidental findings on routine mammograms, which have been suggested as a sex-specific biomarker of cardiovascular disease (CVD) risk. Previous work showed the efficacy of a pretrained convolutional network (CNN), VCG16, for automatic BAC detection. In this study, we further tested the method by a comparative analysis with other ten CNNs.

MATERIAL AND METHODS

Four-view standard mammography exams from 1,493 women were included in this retrospective study and labeled as BAC or non-BAC by experts. The comparative study was conducted using eleven pretrained convolutional networks (CNNs) with varying depths from five architectures including Xception, VGG, ResNetV2, MobileNet, and DenseNet, fine-tuned for the binary BAC classification task. Performance evaluation involved area under the receiver operating characteristics curve (AUC-ROC) analysis, F1-score (harmonic mean of precision and recall), and generalized gradient-weighted class activation mapping (Grad-CAM++) for visual explanations.

RESULTS

The dataset exhibited a BAC prevalence of 194/1,493 women (13.0%) and 581/5,972 images (9.7%). Among the retrained models, VGG, MobileNet, and DenseNet demonstrated the most promising results, achieving AUC-ROCs > 0.70 in both training and independent testing subsets. In terms of testing F1-score, VGG16 ranked first, higher than MobileNet (0.51) and VGG19 (0.46). Qualitative analysis showed that the Grad-CAM++ heatmaps generated by VGG16 consistently outperformed those produced by others, offering a finer-grained and discriminative localization of calcified regions within images.

CONCLUSION

Deep transfer learning showed promise in automated BAC detection on mammograms, where relatively shallow networks demonstrated superior performances requiring shorter training times and reduced resources.

RELEVANCE STATEMENT

Deep transfer learning is a promising approach to enhance reporting BAC on mammograms and facilitate developing efficient tools for cardiovascular risk stratification in women, leveraging large-scale mammographic screening programs.

KEY POINTS

• We tested different pretrained convolutional networks (CNNs) for BAC detection on mammograms. • VGG and MobileNet demonstrated promising performances, outperforming their deeper, more complex counterparts. • Visual explanations using Grad-CAM++ highlighted VGG16's superior performance in localizing BAC.

Interplay between drought and plant viruses co-infecting melon plants

Drought affects crops directly, and indirectly by affecting the activity of insect pests and the transmitted pathogens. Here, we established an experiment with well-watered or water-stressed melon plants, later single infected with either cucumber mosaic virus (CMV: non-persistent), or cucurbit aphid-borne yellow virus (CABYV: persistent), or both CMV and CABYV, and mock-inoculated control. We tested whether i) the relation between CMV and CABYV is additive, and ii) the relationship between water stress and virus infection is antagonistic, i.e., water stress primes plants for enhanced tolerance to virus infection. Water stress increased leaf greenness and temperature, and reduced leaf water potential, shoot biomass, stem dimensions, rate of flowering, CABYV symptom severity, and marketable fruit yield. Virus infection reduced leaf water potential transiently in single infected plants and persistently until harvest in double-infected plants. Double-virus infection caused the largest and synergistic reduction of marketable fruit yield. The relationship between water regime and virus treatment was additive in 12 out of 15 traits at harvest, with interactions for leaf water content, leaf:stem ratio, and fruit set. We conclude that both virus-virus relations in double infection and virus-drought relations cannot be generalized because they vary with virus, trait, and plant ontogeny.

Sample size calculation for data reliability and diagnostic performance: a go-to review

Sample size, namely the number of subjects that should be included in a study to reach the desired endpoint and statistical power, is a fundamental concept of scientific research. Indeed, sample size must be planned a priori, and tailored to the main endpoint of the study, to avoid including too many subjects, thus possibly exposing them to additional risks while also wasting time and resources, or too few subjects, failing to reach the desired purpose. We offer a simple, go-to review of methods for sample size calculation for studies concerning data reliability (repeatability/reproducibility) and diagnostic performance. For studies concerning data reliability, we considered Cohen's κ or intraclass correlation coefficient (ICC) for hypothesis testing, estimation of Cohen's κ or ICC, and Bland-Altman analyses. With regards to diagnostic performance, we considered accuracy or sensitivity/specificity versus reference standards, the comparison of diagnostic performances, and the comparisons of areas under the receiver operating characteristics curve. Finally, we considered the special cases of dropouts or retrospective case exclusions, multiple endpoints, lack of prior data estimates, and the selection of unusual thresholds for α and β errors. For the most frequent cases, we provide example of software freely available on the Internet.Relevance statement Sample size calculation is a fundamental factor influencing the quality of studies on repeatability/reproducibility and diagnostic performance in radiology.Key points• Sample size is a concept related to precision and statistical power.• It has ethical implications, especially when patients are exposed to risks.• Sample size should always be calculated before starting a study.• This review offers simple, go-to methods for sample size calculations.

Investigation of missense mutation-related type 1 diabetes mellitus through integrating genomic databases and bioinformatic approach

Though genes are already known to be responsible for type 1 diabetes mellitus (T1DM), the knowledge of missense mutation of that disease gene has still to be under covered. A genomic database and a bioinformatics-based approach are integrated in the present study in order to address this issue. Initially, nine variants associated with T1DM were retrieved from the GWAS catalogue. Different genomic algorithms such as PolyPhen2.0, SNPs and GTEx analyser programs were used to study the structural and functional effects of these mutations. Subsequently, SNPnexus was also employed to understand the effect of these mutations on the function of the expressed protein. Nine missense variants of T1DM were identified using the GWAS catalogue database. Among these nine SNPs, three were predicted to be related to the progression of T1DM disease by affecting the protein level. TYK2 gene variants with SNP rs34536443 were thought to have a probably damaging effect. Meanwhile, both COL4A3 and IFIH1 genes with SNPs rs55703767 and rs35667974, respectively, might alter protein function through a possibly damaging prediction. Among the variants of the three genes, the TYK2 gene with SNP rs34536443 had the strongest contribution in affecting the development of T1DM, with a score of 0.999. We sincerely hope that the results could be of immense importance in understanding the genetic basis of T1DM.

'Spin' in urology non-randomised studies comparing therapeutic interventions: a temporal analysis

OBJECTIVE

To determine the prevalence of 'spin' (i.e., reporting practices that distort the interpretation of results by positively reflecting negative findings or downplaying potential harms) strategies and level of spin in urological observational studies and whether the use of spin has changed over time.

MATERIALS AND METHODS

MEDLINE and Embase were searched to identify observational studies comparing therapeutic interventions in the top five urology journals and major urological subspecialty journals, published between 2000 and 2001, 2010 and 2011, and 2020 and 2021.

RESULTS

A total of 235 studies were included. Spin was identified in 81% of studies, with a median of two strategies per study. The most commonly used strategies were inadequate implication for clinical practice (30%), causal language or causal claim (29%), and use of linguistic spin (29%). Moderate to high levels of spin were found in 55% of conclusions. From 2000 to 2020, the average number of strategies used has significantly decreased each decade (H = 27.459, P < 0.001), and the median level of spin in conclusions was significantly lower in studies published in the 2020s and 2010s than in the 2000s (H = 11.649, P = 0.003).

CONCLUSIONS

Our results suggest that 81% of urological observational studies comparing therapeutic interventions contained spin. Over the past two decades, the use of spin has significantly declined, but this remains an area for improvement, with 70% of included studies published in the 2020s employing spin. Medical writing should scrupulously avoid words or phrases that are not supported by data in the manuscript.

Outcomes of Laparoscopic Suture Rectopexy Versus Laparoscopic Mesh Rectopexy: A Systematic Review and Meta-Analysis

The contemporary literature provides conflicting evidence regarding the precedence of laparoscopic mesh rectopexy over laparoscopic suture rectopexy for full-thickness rectal prolapse. This study aimed to compare the clinical outcomes of mesh and suture rectopexy to improve the surgical management of complete rectal prolapse. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed to extract studies based on mesh versus suture rectopexy and published from 2001 to 2023. The articles of interest were obtained from PubMed Central, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Journal Storage (JSTOR), Web of Science, Embase, Scopus, and the Cochrane Library. The primary outcomes included rectal prolapse recurrence, constipation improvement, and operative time. The secondary endpoints included the Cleveland Clinic Constipation Score, Cleveland Clinic Incontinence Score, intraoperative bleeding, hospital stay duration, mortality, overall postoperative complications, and surgical site infection. A statistically significant low recurrence of rectal prolapse (odds ratio: 0.41, 95% confidence interval (CI) 0.21-0.80; p=0.009) and longer mean operative duration (mean difference: 27.05, 95% CI 18.86-35.24; p<0.00001) were observed in patients with mesh rectopexy versus suture rectopexy. Both study groups, however, had no significant differences in constipation improvement and all secondary endpoints (all p>0.05). The laparoscopic mesh rectopexy was associated with a low postoperative rectal prolapse recurrence and a longer operative duration compared to laparoscopic suture rectopexy. Prospective randomized controlled trials should further evaluate mesh and suture rectopexy approaches for postoperative outcomes to inform the surgical management of complete rectal prolapse.

Effects of Interobserver Segmentation Variability and Intensity Discretization on MRI-Based Radiomic Feature Reproducibility of Lipoma and Atypical Lipomatous Tumor

Segmentation and image intensity discretization impact on radiomics workflow. The aim of this study is to investigate the influence of interobserver segmentation variability and intensity discretization methods on the reproducibility of MRI-based radiomic features in lipoma and atypical lipomatous tumor (ALT). Thirty patients with lipoma or ALT were retrospectively included. Three readers independently performed manual contour-focused segmentation on T1-weighted and T2-weighted sequences, including the whole tumor volume. Additionally, a marginal erosion was applied to segmentations to evaluate its influence on feature reproducibility. After image pre-processing, with included intensity discretization employing both fixed bin number and width approaches, 1106 radiomic features were extracted from each sequence. Intraclass correlation coefficient (ICC) 95% confidence interval lower bound ≥ 0.75 defined feature stability. In contour-focused vs. margin shrinkage segmentation, the rates of stable features extracted from T1-weighted and T2-weighted images ranged from 92.68 to 95.21% vs. 90.69 to 95.66% after fixed bin number discretization and from 95.75 to 97.65% vs. 95.39 to 96.47% after fixed bin width discretization, respectively, with no difference between the two segmentation approaches (p ≥ 0.175). Higher stable feature rates and higher feature ICC values were found when implementing discretization with fixed bin width compared to fixed bin number, regardless of the segmentation approach (p < 0.001). In conclusion, MRI radiomic features of lipoma and ALT are reproducible regardless of the segmentation approach and intensity discretization method, although a certain degree of interobserver variability highlights the need for a preliminary reliability analysis in future studies.

Praziquantel-related visual disorders among recipients in mass drug administration campaigns in schistosomiasis endemic settings: Systematic review and meta-analysis protocol

BACKGROUND

Hundreds of millions of doses of Praziquantel (PZQ) have been administered to persons with and without schistosomiasis living in schistosomiasis endemic settings, through the mass drug administration (MDA) strategy which started in the early 2000s. A recent publication suggested high risk of PZQ-related visual disorders, raising public health concerns. We aim to systematically synthesize evidence on the magnitude of PZQ-related visual disorders.

METHODS

We will search PubMed, Google Scholar, CINAHL, SCOPUS, CENTRAL and LILACS from 1977 (when the first human clinical trials on PZQ started) to 31st May 2024, with no language restrictions. The key search terms will include "Praziquantel", "PZQ", "visual disorder", "adverse events", "side effects", "blurry vision" and "visual impairment" together with alternative terms and synonyms. All the countries endemic for schistosomiasis will be included as search terms. We will also search HINARI, Africa Journals Online, Thesis Databases and Preprint Repositories. Where necessary, we will contact expert researchers working in the field of schistosomiasis, UNICEF/UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases (TDR), pharmaceutical industries, country-specific Food and Drug Authorities (FDAs) and the European Medicines Agency databases. We will search Conference Proceedings and reference lists of relevant studies for additional studies. At least two authors will independently select studies, extract data and assess risk of bias in the included studies. Any disagreements or discrepancies will be resolved through discussion between the reviewers. Heterogeneity will be explored graphically, and statistically using the I2-statistic. We will conduct random-effects meta-analysis when heterogeneity is appreciable, and express dichotomous outcomes (visual adverse events including excessive lacrimation, blurry vision and visual impairments) as risk ratio (RR) or Odds Ratio (OR) with their 95% confidence interval (CI). We will perform subgroup analysis to assess the impact of heterogeneity, and sensitivity analyses to test the robustness of the effect estimates. The overall level of evidence will be assessed using GRADE.

EXPECTED OUTCOMES

The present review expects to identify and categorize visual disorders occurring after administration of PZQ, alone or in combination with other drugs. By synthesizing the data from multiple studies, the review aims to present a quantitative assessment of the risk or odds of experiencing a visual disorder in different populations after ingesting PZQ. The review will also generate insights into whether PZQ in combination with other drugs are associated with increased odds of visual disorders and whether the occurrence of visual disorders correlates with dosage or treatment duration. Policymakers, public health experts and stakeholders could rely on the review findings to deliver context-sensitive preventive chemotherapy programs by adjusting drug combinations or dosing schedules to reduce risk of visual adverse effects in populations treated with PZQ. The review aims to identify gaps in the current evidence regarding visual disorders following PZQ administration in schistosomiasis endemic settings which can serve as the basis for future research on important but unanswered questions.

DISSEMINATION AND PROTOCOL REGISTRATION

The findings of this study will be disseminated through stakeholder forums, conferences, and peer-review publications. The review protocol has been registered in the International Prospective Register for Systematic Reviews (PROSPERO)- CRD42023417963.

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus

Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that heavily burdens healthcare providers worldwide due to the steady increment of patients yearly. Worryingly, diabetes affects not only the aging population but also children. It is prevalent to control this problem, as diabetes can lead to many health complications. As evolution happens, humankind starts integrating computer technology with the healthcare system. The utilization of artificial intelligence assists healthcare to be more efficient in diagnosing diabetes patients, better healthcare delivery, and more patient eccentric. Among the advanced data mining techniques in artificial intelligence, stacking is among the most prominent methods applied in the diabetes domain. Hence, this study opts to investigate the potential of stacking ensembles. The aim of this study is to reduce the high complexity inherent in stacking, as this problem contributes to longer training time and reduces the outliers in the diabetes data to improve the classification performance. In addressing this concern, a novel machine learning method called the Stacking Recursive Feature Elimination-Isolation Forest was introduced for diabetes prediction. The application of stacking with Recursive Feature Elimination is to design an efficient model for diabetes diagnosis while using fewer features as resources. This method also incorporates the utilization of Isolation Forest as an outlier removal method. The study uses accuracy, precision, recall, F1 measure, training time, and standard deviation metrics to identify the classification performances. The proposed method acquired an accuracy of 79.077% for PIMA Indians Diabetes and 97.446% for the Diabetes Prediction dataset, outperforming many existing methods and demonstrating effectiveness in the diabetes domain.

Approximation of van Genuchten Parameter Ranges from Hydraulic Conductivity Data

The use of retention function and relative conductivity function is essential for the calculation of flow in a variably saturated media using the Richards equation. A widely used mathematical model for this is the Mualem-van Genuchten model which requires the shape parameters α and n . These, however, are difficult to obtain. When data is scarce, α and n are often taken from literature and may deviate largely from actual values. The current study presents a novel mathematical model for the approximation of α and n and for the further estimation of realistic value ranges, which may be used as parameter space, for example, for the calibration of a numerical model. The model was developed for cases where data is scarce and only values of saturated hydraulic conductivity are available. It is based on a large data set from literature and it is demonstrated that the model estimates mean values from that data set with a good accuracy. In order to show the applicability of the model, a second data set has been compiled anew (provided as Supporting Information). The model is incorporated into the current version of the freeware computer program HYPAGS, which enables easy usage.

Exploring the Association between Elevated Anxiety Symptoms and Low Skeletal Muscle Mass among Asymptomatic Adults: A Population-Based Study in Republic of Korea

Individuals with mental health problems are at higher risk of musculoskeletal diseases. However, the association between low muscle mass (LMM) and anxiety symptoms remains uninvestigated. This cross-sectional study enrolled 174,262 adults (73,833 women, 100,429 men), aged 18 to 89, who completed the anxiety scale and body composition analyses. Using bio-electrical impedance analysis, skeletal muscle mass index (SMI) was calculated based on appendicular skeletal muscle mass (ASM) (kg)/height (m2). LMM was defined as SMI < 7.0 kg/m2 in men and <5.4 kg/m2 in women. Anxiety symptoms were screened using the Clinical Useful Anxiety Outcome Scale (CUXOS) with cut-off scores of 20, 30, and 40. Multivariable logistic regression analyses were performed. LMM prevalence was 20.17% in women, 3.86% in men (p < 0.001). The prevalence of anxiety symptoms in LMM group decreased from mild (CUXOS > 20: women, 32.74%, men, 21.17%) to moderate (CUXOS > 30: 13.34%, 7.32%), to severe anxiety symptoms (CUXOS > 40: 4.00%, 1.73%). In multivariable-adjusted models, LMM was associated with mild (aOR (95% confidence interval)), women, 1.13 (1.08-1.17); men, 1.17 (1.08-1.27)), moderate (1.17 (1.11-1.24); 1.35 (1.19-1.53) and severe anxiety symptoms (1.18 (1.07-1.3), 1.36 (1.06-1.74)), demonstrating an increased risk of ORs with escalating anxiety severity. LMM was independently associated with a higher prevalence of anxiety symptoms.

Telehealth Utilization and Associations in the United States During the Third Year of the COVID-19 Pandemic: Population-Based Survey Study in 2022

BACKGROUND

The COVID-19 pandemic rapidly changed the landscape of clinical practice in the United States; telehealth became an essential mode of health care delivery, yet many components of telehealth use remain unknown years after the disease's emergence.

OBJECTIVE

We aim to comprehensively assess telehealth use and its associated factors in the United States.

METHODS

This cross-sectional study used a nationally representative survey (Health Information National Trends Survey) administered to US adults (≥18 years) from March 2022 through November 2022. To assess telehealth adoption, perceptions of telehealth, satisfaction with telehealth, and the telehealth care purpose, we conducted weighted descriptive analyses. To identify the subpopulations with low adoption of telehealth, we developed a weighted multivariable logistic regression model.

RESULTS

Among a total of 6252 survey participants, 39.3% (2517/6252) reported telehealth use in the past 12 months (video: 1110/6252, 17.8%; audio: 876/6252, 11.6%). The most prominent reason for not using telehealth was due to telehealth providers failing to offer this option (2200/3529, 63%). The most common reason for respondents not using offered telehealth services was a preference for in-person care (527/578, 84.4%). Primary motivations to use telehealth were providers' recommendations (1716/2517, 72.7%) and convenience (1516/2517, 65.6%), mainly for acute minor illness (600/2397, 29.7%) and chronic condition management (583/2397, 21.4%), yet care purposes differed by age, race/ethnicity, and income. The satisfaction rate was predominately high, with no technical problems (1829/2517, 80.5%), comparable care quality to that of in-person care (1779/2517, 75%), and no privacy concerns (1958/2517, 83.7%). Younger individuals (odd ratios [ORs] 1.48-2.23; 18-64 years vs ≥75 years), women (OR 1.33, 95% CI 1.09-1.61), Hispanic individuals (OR 1.37, 95% CI 1.05-1.80; vs non-Hispanic White), those with more education (OR 1.72, 95% CI 1.03-2.87; at least a college graduate vs less than high school), unemployed individuals (OR 1.25, 95% CI 1.02-1.54), insured individuals (OR 1.83, 95% CI 1.25-2.69), or those with poor general health status (OR 1.66, 95% CI 1.30-2.13) had higher odds of using telehealth.

CONCLUSIONS

To our best knowledge, this is among the first studies to examine patient factors around telehealth use, including motivations to use, perceptions of, satisfaction with, and care purpose of telehealth, as well as sociodemographic factors associated with telehealth adoption using a nationally representative survey. The wide array of descriptive findings and identified associations will help providers and health systems understand the factors that drive patients toward or away from telehealth visits as the technology becomes more routinely available across the United States, providing future directions for telehealth use and telehealth research.

Health and health behaviours in adolescence as predictors of education and socioeconomic status in adulthood - a longitudinal study

BACKGROUND

The positive association of health with education level and socioeconomic status (SES) is well-established. Two theoretical frameworks have been delineated to understand main mechanisms leading to socioeconomic health inequalities: social causation and health selection but how these work in adolescence is poorly known. We studied if adolescent health and health behaviours predict higher education and higher SES in adulthood and if family background and school performance in adolescence explain these associations.

METHODS

Surveys on health and health behaviours were sent to representative samples of 12-18-year-old Finns in 1981-1997 every second year (response rate 77.8%, N = 55,682). The survey data were linked with the respondents' and their parents' socioeconomic data from the Finnish national registries. Both latent variables, namely, health (perceived health, health complaints, chronic disease), health-compromising behaviours (smoking status, drunkenness frequency), and family background (parents' occupation-based SES, education, family type) and variables directly measuring health-enhancing behaviours (toothbrushing, physical activity) and school performance were used to predict higher education and higher occupation-based SES at age 34. Logistic regression analysis and structural equation models (SEM) were used.

RESULTS

In logistic regression analyses, good health, health-enhancing behaviours, and lack of health-compromising behaviours were related to higher education and SES, also after controlling for family background and school performance. In the SEM analyses, good health, health-enhancing behaviours, and lack of health-compromising behaviours directly predicted higher SES and higher education, although the standardised coefficients were low (from 0.034 to 0.12). In all models, health, lack of health-compromising behaviours, and health-enhancing behaviours predicted school performance, which in turn, predicted the outcomes, suggesting indirect routes to these. Good socioeconomic prospects in terms of family background predicted good health, healthy behaviours, and good school performance in adolescence and higher SES and higher education in adulthood.

CONCLUSION

Health and health behaviours in adolescence predicted education and SES in adulthood. Even though the relationships were modest, they support the health selection hypotheses and emphasise the importance of adolescence for health inequalities during the life-course. Health and health behaviours were strongly associated with school performance and family background which together modified the paths from health and health behaviours to the outcomes.

MRI-derived extracellular volume as a biomarker of cancer therapy cardiotoxicity: systematic review and meta-analysis

OBJECTIVES

MRI-derived extracellular volume (ECV) allows characterization of myocardial changes before the onset of overt pathology, which may be caused by cancer therapy cardiotoxicity. Our purpose was to review studies exploring the role of MRI-derived ECV as an early cardiotoxicity biomarker to guide timely intervention.

MATERIALS AND METHODS

In April 2022, we performed a systematic search on EMBASE and PubMed for articles on MRI-derived ECV as a biomarker of cancer therapy cardiotoxicity. Two blinded researchers screened the retrieved articles, including those reporting ECV values at least 3 months from cardiotoxic treatment. Data extraction was performed for each article, including clinical and technical data, and ECV values. Pooled ECV was calculated using the random effects model and compared among different treatment regimens and among those who did or did not experience overt cardiac dysfunction. Meta-regression analyses were conducted to appraise which clinical or technical variables yielded a significant impact on ECV.

RESULTS

Overall, 19 studies were included. Study populations ranged from 9 to 236 patients, for a total of 1123 individuals, with an average age ranging from 12.5 to 74 years. Most studies included patients with breast or esophageal cancer, treated with anthracyclines and chest radiotherapy. Pooled ECV was 28.44% (95% confidence interval, CI, 26.85-30.03%) among subjects who had undergone cardiotoxic cancer therapy, versus 25.23% (95%CI 23.31-27.14%) among those who had not (p = .003).

CONCLUSION

A higher ECV in patients who underwent cardiotoxic treatment could imply subclinical changes in the myocardium, present even before overt cardiac pathology is detectable.

CLINICAL RELEVANCE STATEMENT

The ability to detect subclinical changes in the myocardium displayed by ECV suggests its use as an early biomarker of cancer therapy-related cardiotoxicity.

KEY POINTS

• Cardiotoxicity is a common adverse effect of cancer therapy; therefore, its prompt detection could improve patient outcomes. • Pooled MRI-derived myocardial extracellular volume was higher in patients who underwent cardiotoxic cancer therapy than in those who did not (28.44% versus 25.23%, p = .003). • MRI-derived myocardial extracellular volume represents a potential early biomarker of cancer therapy cardiotoxicity.

Farit NA. The Efficacy of Elonide Nasal Corticosteroids in Managing Allergic Rhinitis: A Randomized, Double-Blinded Trial

Background: Mometasone furoate nasal spray is efficacious in relieving allergic rhinitis symptoms. The objectives of this study were, firstly, to compare the efficacy of Elonide to Nasonex® and a placebo and secondly, to investigate the side effects of Elonide. Method: This was a prospective, single-centered, double blinded, randomized, placebo-controlled, non-inferiority trial. A total of 163 participants from the Otorhinolaryngology Clinic, Hospital Canselor Tuanku Muhriz (HCTM), were randomized into three treatment groups receiving Elonide (n = 56), Nasonex® (n = 54), and placebo (n = 53) nasal sprays using an online randomizer (Random.org). Treatment was administered for 4 weeks. The primary outcome measure was the Total Nasal Resistance (TNR), and the secondary outcomes were the Visual Analogue Score (VAS) and the Rhinoconjunctivitis Quality of Life Questionnaire (RQOLQ) score. Side effects were recorded. Results: There were significant improvements for all groups from baseline. The Elonide group had the greatest mean difference for all primary and secondary outcomes compared to Nasonex® and the placebo (0.77 ± 2.44 vs. 0.35 ± 1.16, p = 1.00 vs. 0.17 ± 0.82, p = 0.01). Elonide is non-inferior to Nasonex (p = 1.00) and superior to the placebo (p < 0.05). The highest side effects reported were for Nasonex (n = 14, 26%), followed by the placebo (n = 8, 16%) and Elonide (n = 6, 12%); headaches (n = 9, 17%) and sore throat (n = 9, 17%) were the most common. Conclusions: Elonide has similar efficacy to Nasonex® when compared to a placebo in the treatment of AR in adults. Elonide is safe and tolerable, with fewer side effects and no adverse side effects.

Trans-omic analysis reveals opposite metabolic dysregulation between feeding and fasting in liver associated with obesity

Dysregulation of liver metabolism associated with obesity during feeding and fasting leads to the breakdown of metabolic homeostasis. However, the underlying mechanism remains unknown. Here, we measured multi-omics data in the liver of wild-type and leptin-deficient obese (ob/ob) mice at ad libitum feeding and constructed a differential regulatory trans-omic network of metabolic reactions. We compared the trans-omic network at feeding with that at 16 h fasting constructed in our previous study. Intermediate metabolites in glycolytic and nucleotide metabolism decreased in ob/ob mice at feeding but increased at fasting. Allosteric regulation reversely shifted between feeding and fasting, generally showing activation at feeding while inhibition at fasting in ob/ob mice. Transcriptional regulation was similar between feeding and fasting, generally showing inhibiting transcription factor regulations and activating enzyme protein regulations in ob/ob mice. The opposite metabolic dysregulation between feeding and fasting characterizes breakdown of metabolic homeostasis associated with obesity.

BrainNet: a fusion assisted novel optimal framework of residual blocks and stacked autoencoders for multimodal brain tumor classification

A significant issue in computer-aided diagnosis (CAD) for medical applications is brain tumor classification. Radiologists could reliably detect tumors using machine learning algorithms without extensive surgery. However, a few important challenges arise, such as (i) the selection of the most important deep learning architecture for classification (ii) an expert in the field who can assess the output of deep learning models. These difficulties motivate us to propose an efficient and accurate system based on deep learning and evolutionary optimization for the classification of four types of brain modalities (t1 tumor, t1ce tumor, t2 tumor, and flair tumor) on a large-scale MRI database. Thus, a CNN architecture is modified based on domain knowledge and connected with an evolutionary optimization algorithm to select hyperparameters. In parallel, a Stack Encoder-Decoder network is designed with ten convolutional layers. The features of both models are extracted and optimized using an improved version of Grey Wolf with updated criteria of the Jaya algorithm. The improved version speeds up the learning process and improves the accuracy. Finally, the selected features are fused using a novel parallel pooling approach that is classified using machine learning and neural networks. Two datasets, BraTS2020 and BraTS2021, have been employed for the experimental tasks and obtained an improved average accuracy of 98% and a maximum single-classifier accuracy of 99%. Comparison is also conducted with several classifiers, techniques, and neural nets; the proposed method achieved improved performance.

Causal Influence of Linguistic Learning on Perceptual and Conceptual Processing: A Brain-Constrained Deep Neural Network Study of Proper Names and Category Terms

Language influences cognitive and conceptual processing, but the mechanisms through which such causal effects are realized in the human brain remain unknown. Here, we use a brain-constrained deep neural network model of category formation and symbol learning and analyze the emergent model's internal mechanisms at the neural circuit level. In one set of simulations, the network was presented with similar patterns of neural activity indexing instances of objects and actions belonging to the same categories. Biologically realistic Hebbian learning led to the formation of instance-specific neurons distributed across multiple areas of the network, and, in addition, to cell assembly circuits of "shared" neurons responding to all category instances-the network correlates of conceptual categories. In two separate sets of simulations, the network learned the same patterns together with symbols for individual instances ["proper names" (PN)] or symbols related to classes of instances sharing common features ["category terms" (CT)]. Learning CT remarkably increased the number of shared neurons in the network, thereby making category representations more robust while reducing the number of neurons of instance-specific ones. In contrast, proper name learning prevented a substantial reduction of instance-specific neurons and blocked the overgrowth of category general cells. Representational similarity analysis further confirmed that the neural activity patterns of category instances became more similar to each other after category-term learning, relative to both learning with PN and without any symbols. These network-based mechanisms for concepts, PN, and CT explain why and how symbol learning changes object perception and memory, as revealed by experimental studies.

Brain tumor classification from MRI scans: a framework of hybrid deep learning model with Bayesian optimization and quantum theory-based marine predator algorithm

Brain tumor classification is one of the most difficult tasks for clinical diagnosis and treatment in medical image analysis. Any errors that occur throughout the brain tumor diagnosis process may result in a shorter human life span. Nevertheless, most currently used techniques ignore certain features that have particular significance and relevance to the classification problem in favor of extracting and choosing deep significance features. One important area of research is the deep learning-based categorization of brain tumors using brain magnetic resonance imaging (MRI). This paper proposes an automated deep learning model and an optimal information fusion framework for classifying brain tumor from MRI images. The dataset used in this work was imbalanced, a key challenge for training selected networks. This imbalance in the training dataset impacts the performance of deep learning models because it causes the classifier performance to become biased in favor of the majority class. We designed a sparse autoencoder network to generate new images that resolve the problem of imbalance. After that, two pretrained neural networks were modified and the hyperparameters were initialized using Bayesian optimization, which was later utilized for the training process. After that, deep features were extracted from the global average pooling layer. The extracted features contain few irrelevant information; therefore, we proposed an improved Quantum Theory-based Marine Predator Optimization algorithm (QTbMPA). The proposed QTbMPA selects both networks' best features and finally fuses using a serial-based approach. The fused feature set is passed to neural network classifiers for the final classification. The proposed framework tested on an augmented Figshare dataset and an improved accuracy of 99.80%, a sensitivity rate of 99.83%, a false negative rate of 17%, and a precision rate of 99.83% is obtained. Comparison and ablation study show the improvement in the accuracy of this work.