Statistical Significance Tests Research Articles

Autogenic masking of allogenic inputs: Numerical modelling of signal preservation in deep-water fan strata

Variations in climate, sea-level and tectonic processes can be recorded in various ways in strata, and recognising and measuring such external signals preserved in strata is an important aspect of deciphering Earth history. Key questions are what type of signals are likely to be preserved; how input signals may be shredded, over-printed or otherwise reduced before preservation, by stochastic or autogenic processes; and how allocyclic products may be mimicked and masked by autogenic processes. A reduced-complexity numerical forward model of down-slope sediment erosion, transport and dispersive deposition of submarine fan strata is the simplest-possible formulation that produces reasonably realistic strata that can be robustly analysed for signal content across model realisations with a range of external forcing amplitudes and periods. Three different initial topographic surfaces that include increasing influence of random noise allow comparison of the signal preserved with different levels of random influence on the deterministic model. Spectral analysis, with rigorous testing for statistical significance, shows that the signal recorded depends strongly on the level of noise present in the underlying topography, that short-period high-amplitude allocyclic signals are the most likely to be preserved in this case, and that autogenic dynamics in the modelled system can effectively mask even long-period high-amplitude input signals because they have similar periodicities. Autogenic processes in this model can also produce 5:1 bundling of cyclical strata commonly assumed to be strong evidence for orbital forcing. This analysis suggests that we might be substantially underestimating the complexity involved in extracting a signal of external forcing from strata where any autogenic processes operate, to the extent that the two different effects may often be practically indistinguishable, indicating that a multiple hypothesis approach is important to account for the resulting uncertainty.

Effects of beetroot-pineapple juice on hypertensive elderly in Depok

The prevalence of hypertension, based on measurement results among the population aged ? 18 in Indonesia, increased by 8.3% from 2013 to 2018. Due to this rise, there is an evident need for treatments to decrease this rate. Introducing non-pharmacological therapies, such as beetroot and pineapple juice, maybe a viable solution. This study aimed to assess the difference in blood pressure before and after the consumption of beetroot and pineapple juice among elderly hypertensive patients in the Abadijaya Public Health Center region of Depok City. The research followed an experimental design, specifically a pretest-posttest one-group format. The study involved 15 elderly individuals aged between 60-80 years with blood pressure readings of ? 140/90 mmHg. They consumed 300 ml of the juice daily for seven consecutive days. Data analysis included the Shapiro-Wilk test for normality, the paired t-test, and the Wilcoxon test for statistical significance. The findings revealed a significant reduction in blood pressure after the consumption of beetroot and pineapple juice, with a p-value for systolic pressure at 0,000 and diastolic pressure at 0,020 (p<0,05). The average decrease in systolic blood pressure was 13,03 mmHg, and for diastolic blood pressure, it was 4,8 mmHg. There was a discernible difference in blood pressure measurements before and after juice consumption among the elderly hypertensive patients.

Association between Psychological Distress and Possible, Probable, and Definite Sleep Bruxism-A Comparison of Approved Diagnostic Procedures.

(1) Background: The relationship between sleep bruxism (SB) and psychological distress has been investigated in numerous studies and is heterogeneous. Different diagnostic procedures have been applied to determine SB. The aim of this study was to directly compare the association between psychological distress and SB diagnosed by different accepted methods. (2) Methods: Data of N = 45 subjects were analyzed, including group comparisons and correlation analyses. Following diagnostic methods for the determination of SB were used in one sample: self-report, clinical assessment, polysomnography with audio-video recording and a novel diagnostic sheet with analyzing software. Psychological distress was measured using the global severity index (GSI) of the Symptom Checklist-90-Standard (SCL-90-S). (3) Results: The GSI did not differ significantly between subjects with and without SB, regardless of the underlying diagnostic classification (p > 0.05). In-depth correlation analyses of self-report and clinical data revealed a weak-to-medium correlation with the GSI (r = 0.12-0.44). Due to non-normally distributed data, a test of statistical significance was not possible. Variables of instrumental methods such as the SB index (amount of SB activity per hour) of polysomnography (PSG) showed almost no correlation with psychological distress (r = -0.06-0.05). (4) Conclusions: Despite these limitations, the results provide an indication that the choice of diagnostic procedure may elucidate the variance in the correlation between SB and psychological distress.

Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric chest X-ray images.

Model initialization techniques are vital for improving the performance and reliability of deep learning models in medical computer vision applications. While much literature exists on non-medical images, the impacts on medical images, particularly chest X-rays (CXRs) are less understood. Addressing this gap, our study explores three deep model initialization techniques: Cold-start, Warm-start, and Shrink and Perturb start, focusing on adult and pediatric populations. We specifically focus on scenarios with periodically arriving data for training, thereby embracing the real-world scenarios of ongoing data influx and the need for model updates. We evaluate these models for generalizability against external adult and pediatric CXR datasets. We also propose novel ensemble methods: F-score-weighted Sequential Least-Squares Quadratic Programming (F-SLSQP) and Attention-Guided Ensembles with Learnable Fuzzy Softmax to aggregate weight parameters from multiple models to capitalize on their collective knowledge and complementary representations. We perform statistical significance tests with 95% confidence intervals and p-values to analyze model performance. Our evaluations indicate models initialized with ImageNet-pretrained weights demonstrate superior generalizability over randomly initialized counterparts, contradicting some findings for non-medical images. Notably, ImageNet-pretrained models exhibit consistent performance during internal and external testing across different training scenarios. Weight-level ensembles of these models show significantly higher recall (p<0.05) during testing compared to individual models. Thus, our study accentuates the benefits of ImageNet-pretrained weight initialization, especially when used with weight-level ensembles, for creating robust and generalizable deep learning solutions.

Appraisal of EnMAP hyperspectral imagery use in LULC mapping when combined with machine learning pixel-based classifiers

The recent availability of satellite hyperspectral imaging combined with the developments in the classification techniques have paved the way towards improving our ability to obtain information on the spatiotemporal distribution of land use/land cover (LULC) at improved accuracy. In this context, the present study aims at evaluating the combined use of the recently launched Environmental Mapping and Analysis Program (EnMAP) hyperspectral satellite mission with two powerful machine learning (ML) classifiers. In particular, the Support Vector Machines (SVM) and Random Forest (RF) are used synergistically with EnMAP imagery in performing LULC mapping at a typical Mediterranean setting located in northern Greece. Evaluation of the derived LULC maps is based on the computation of a series classification accuracy metrics. The McNemar's chi-square statistical significance testing was also computed to confirm the statistical significance of the differences between the classifiers. In overall, results showed that SVM slightly outperformed RF, exhibiting a higher overall accuracy, of 92.6% and 88.1%, respectively, whereas the statistical significance of the findings was also attested by the McNemar's statistical test results. To our knowledge, this study is one of the first published so far focusing on exploring the capabilities of ENMAP imagery when combined with different ML pixel-based classifiers in the context of LULC mapping. Our results, indeed provided useful insights on the potential of EnMAP datasets in deriving information on the spatiotemporal distribution of LULC at a highly fragmented Mediterranean landscape, evidencing the EnMAP promising potential in this field.

Assessment of public awareness about Semaglutide (Ozempic) use, safety, and adverse drug reaction in Saudi Arabia

Background: Obesity is a growing epidemic in Saudi Arabia, and Semaglutide has emerged as a promising pharmacotherapeutic option for weight management in obese patients. This study aims to assess public knowledge about Ozempic (semaglutide) use, safety, and adverse drug reactions in Saudi Arabia. Methods: We conducted a cross-sectional survey to collect data among the adult population in Saudi Arabia. Statistical tests of significance, including the chi-squared test and Spearman\&amp;apos;s rho correlation test, were employed to examine differences between groups and assess correlations between variables. Results: Of the 739 participants, 50.7% were females, and most participants had a bachelor&amp;apos;s degree level of education. Study respondents reported that they had heard of Ozempic, but only 244 (58%) knew its indication (Type 2 DM). The analysis showed that good knowledge was observed among the 31–40 and 18–30 age groups compared to other age groups (p = 0.020); no other sociodemographic variables showed a significant association with the knowledge level (p &amp;gt; 0.05).The questionnaire was distributed to a representative sample with a minimum of 385 participants among the Saudi population, with a 95% confidence level and a margin of error of 5%, through online platforms. Data were collected and analyzed using descriptive and inferential statistical methods. Conclusion: Semaglutide is a valuable medication for the treatment of type 2 diabetes and, under appropriate circumstances, for weight loss management. The study showed that the majority of the participants used it for weight loss management, and their awareness regarding its indications, contraindications, and adverse effects was not adequate.

Wingate normative-reference values for a large cohort of Canadian university students

Abstract Study aim: The Wingate Anaerobic Test evaluates anaerobic power and capacity. Used to assess performance, historically among athletic populations, its evaluative capacity for individuals of varying athletic abilities is limited by a lack of normative data based on large participant populations. This study developed Wingate normative-reference values based on a large-scale cohort that is representative of the Canadian university student population. Material and methods: Data were collected from 872 participants, aged 20 to 29 years (mean body mass index [BMI]: 24.44 kg/m2). Testing was completed on a cycle ergometer using a widely recognized protocol, with resistance set at 7.5% of participants’. An independent samples t-test was used to compared the means of dependent variables (i.e., peak power [PP], mean power [MP], and fatigue index fi) and test for statistical significance (p &lt; 0.05) between sexes, and Cohen’s d determined effect size. Results: Males had higher PP and MP (W and W · kg−1), whereas females exhibited lower FI (%). Statistically significant differences between sexes were observed for all variables. Conclusions: Collected data yielded normative-reference standards, including percentile rankings and performance classifications. These norms will allow for significant practical applications, including an effective method to assess anaerobic performance and health.

Geographic atrophy (GA) secondary to AMD: Online medical education can significantly improve ophthalmologists' knowledge and confidence on evidence‐based guidance for patient management

Aims/Purpose: To determine if online medical education for ophthalmologists (Ophth) could improve knowledge for GA disease impact, patient assessment and confidence in emerging therapies.Methods: Ophth participated in an online 30 min video, 3 expert roundtable discussion with accompanying slides. A repeated pairs pre−/post‐assessment study design was used, assessing three multiple choice knowledge and one confidence question. Statistical significance tests: McNemar's test for individual questions (5% significance level, p &lt; 0.05), paired sample t‐test for overall average correct responses and confidence rating; data collected 16 December 2022 to 22 March 2023. Test completers: 230.Results: At baseline, 3% Ophth answered all three questions correctly, increasing to 37% (p &lt; 0.01) post‐assessment. 58% Ophth reported improved confidence understanding the rationale for targeting the complement system with GA therapies. The activity significantly increased knowledge on impact of GA on time to loss of ability to drive (pre: 7%, post: 52%, p &lt; 0.001), on recall of detrimental lesion characteristics (pre: 40%, post: 67% p &lt; 0.01) as well as capability of automated OCT analysis algorithms to predict GA progression (pre: 51%, post: 69%, p &lt; 0.001). Only 24% Ophth polled currently document &gt;75% patients with advanced atrophic AMD with OCT/FAF imaging.Conclusions: Online medical education in the form of expert roundtable discussions with accompanying slides can significantly improve knowledge on assessing patients with GA using imaging, understand the rationale to treat and increase confidence in understanding the role of complement inhibition. Polling showed a lack in documenting patients for GA progression. Complementary education is warranted during the introduction of a novel therapeutic horizon in GA management in clinical practice to support evidence‐based patient selection guidance for emerging therapies.

Therapeutic Measures from Cardiovascular Damage using Statins, Acetylsalicylic Acid during the Abuse of Irrational Cycles of Anabolic Steroids in Bodybuilder Who Were Infected with the COVID-19

Introduction: We are increasingly witnessing the self-initiated, uncontrolled stacking of incompatible anabolic substances in various cycles in the young Balkan Recreational Bodybuilding population group, increasingly frequent acts of violence in our country and the emergence of a neglected and very silent epidemic that is taking on an increasingly aggressive course of illness and psychological behavior. Black market anabolic steroids are associated with many generally health problems and uncontrolled aggression behavior by abusers. Young unsolicited recreate bodybuilding are also associated with generalized atherosclerosis and potential increased high risk for heart disease that can structural and functional damage the cardiovascular system during muscle mass steroid cycles. Besides high calorie intake, elevating serum blood lipid cholesterol levels LDL lowering HDL levels, elevating, systolic and diastolic blood pressure take a certain participation in cardiovascular risk and therefor need a development of certain cardiovascular protection strategies for this avoiding medical supervision anabolic steroids abuser group which are not yet appropriate established. The aim of the short study was to estimate Cardiovascular Protective Strategy Measures on lipid levels, and blood pressure status in a young recreational bodybuilder with different ethnic groups who Abuse Anabolic Steroids during controversial and different mass Cycles with and without taking Polyunsaturated Fatty Acids and Acetylsalicylic Acid (ASA) and Statins. Subjects and methods: This study was conducted from beginning of January, 2022 till end of July, 2022. 140 subjects are included age 17-30 (74 male Recreational Bodybuilder Who Abuse Anabolic Steroids on Simvastatin’s 10mg and Acetylsalicylic Acid 150mg doses of (ASA) and 10 grams of Polyunsaturated Fatty Acids during mass Steroid Cycles and 66 impellers group of male Recreational Bodybuilder Who Abuse Anabolic Steroids consisting without consume Statins and Acetylsalicylic Acid (ASA) during mass Steroid Cycles). For the testing of statistical significance of differences between the exanimated groups non-parameter and parameter tests were used. The difference at a level of p&lt; (0,001) was statistically significant. Results: In all the tested subjects we investigated increasing lipid levels and blood pressure after six months of study. Analysis shows the statistically insignificant influence of antiplatelet therapy (150mg Acetylsalicylic acid) (ASA), combined with Simvastatin 10mg p&lt;(0,001) in steroid abuse subjects during irrational muscle mass steroid cycles. Conclusion: Lack of familiarity with combinations of anabolic substances, as well as the danger of oral applications of very harmful steroids from the former Soviet Union and German Democratic Republic (GDR), this study showed. Avoidance of physician supervision by recreate bodybuilders by deceiving of the athletic subjectivity look, seams appears almost always in gym. This cardiovascular protective measures have poor benefit appeared to be usefully blind preventing strategy of controlling blood lipid levels during muscle mass steroid cycles, with no guaranty of worsen cardiovascular condition by abusing anabolic steroids. The influence of the reflection of the COVID 19 virus on such dramatic therapeutic results in this population group of two groups cannot be ruled out for sure.

Risk assessment and spatio-temporal characteristics analysis of water bloom in three large-scale eutrophic reservoirs in Fujian Province, China

The occurrence of water blooms in reservoirs poses a serious threat to water safety, so the assessment of water bloom risk is crucial. In this study, we constructed a reservoir water bloom risk index (RWBRI) using the trophic state index (TSI), floating algae index (FAI), temperature, precipitation, and wind speed datasets, and estimated and validated the water bloom risk levels of three large-scale eutrophication reservoirs in Fujian Province, China, from 2019 to 2023 based on the Sentinel-2 MSI dataset at the pixel scale. The results showed that there is no positive correlation between the TSI and FAI parameters of the three reservoirs (the significance test coefficients for Shanzai Reservoir, Dongzhen Reservoir, and Shanmei Reservoir were −0.455**, −0.371**, and −0.049, respectively, and ** indicates that the reservoir passed the statistical significance test at the 0.01 significance level). The TSI of Shanmei Reservoir was less fluctuating and more stable than that of Shanzai Reservoir and Dongzhen Reservoir, but the average value of FAI was higher. The average value of water bloom risk in Shanzai Reservoir and Dongzhen Reservoirs is higher than Shanmei Reservoir (0.47, 0.46, and 0.41, respectively), but the trend of annual average changes indicates that the overall risk of Dongzhen Reservoir is decreasing, while the risk of Shanzai Reservoir and Shanmei Reservoir is increasing. Due to the influence of weather, the water bloom risk in spring and summer is higher than that in fall and winter in the three reservoirs, and the highest risk value usually occurs in summer. The high values in Shanzai Reservoir were mainly distributed in the two inlet channels in the northern and southwestern parts of the reservoir, the bank risk was higher than in the other areas of Dongzhen Reservoir, and the high value of the risk in Shanmei Reservoir was mainly distributed in the center of the reservoir. The RWBRI method can be used for water bloom risk identification in reservoirs with eutrophication trends by comparing it with four-hourly and monthly water quality level data.

A Regression Model of Dry Bulk Carriers’ Loading Time

Abstract Although optimization of Vessel Turnaround Time (VTT) is a well-known research problem and its importance has long been understood, research on dry bulk carriers VTT has no priority in the available literature. It is one of the initial motives of the author to write this paper. After a general theoretical introduction, this paper presents research on dry bulk carriers’ loading time, as a component with the dominant share in the total VTT. Through the research, a mathematical model of interdependencies between dry bulk carrier‘s loading time and selected independent variables is defined using a multiple linear regression model. Results of statistical significance tests confirmed that the best-fit regression model is the one which adequately describes the correlation between the dry bulk carrier‘s loading time and the following independent variables: cargo quantity (loaded), number of used cranes per vessel and loading process interruptions. The presented results establish important bases for the author’s further research in this field as well as reliable planning bases for cargo handling management processes at dry bulk cargo terminals where cargo loading/unloading to/from vessels is realized by gantry cranes and/or mobile harbour cranes.

The Belt and Road Initiative's Construction Projects Exert a Substantial Influence on Participating Countries' Economic Landscapes, Eliciting A Noteworthy Impact on their Financial Stability, Infrastructure Investments, and Fiscal Growth

The one belt one road initiative, spearheaded by China, stands as a monumental foreign policy endeavour designed to strengthen ties with 146 nations. The construction projects within this initiative wield a substantial influence on the economic landscapes of participating countries, significantly affecting financial stability, infrastructure investments, and fiscal expansion. Studies utilizing CO2 emissions as a function of financial data (FD), Foreign Direct Investment (FDI), Gross Domestic Product (GDP), squared Gross Domestic Product (GDP2), economic indicators (EC), and trade-related factors (TR) have demonstrated a statistically positive relationship between these variables. The study spans from 2014 to 2021, employing panel data regression techniques to analyze the impact of the One Belt One Road initiative on participating countries' economic landscapes. Utilizing dynamic seemingly unrelated regression (DSUR) panel estimation, the study assesses the initiative's impact on macroeconomic factors such as GDP, FDI, EPI, trade, and FDPS. Statistical significance tests, including p-values and F-statistics, are employed to gauge the significance of the relationship between these factors and the initiative. Null hypothesis testing is utilized to determine whether a variable Granger causes another variable, providing further insights into causal relationships. R2 values are employed to quantify the proportion of variance in the dependent variable attributable to the independent factors. These statistical methodologies offer a robust and empirical approach to comprehending the one belt one road initiative's profound impact on the economic landscapes of participating nations.

Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: a model evaluation study

Large language models (LLMs) such as GPT-4 hold great promise as transformative tools in health care, ranging from automating administrative tasks to augmenting clinical decision making. However, these models also pose a danger of perpetuating biases and delivering incorrect medical diagnoses, which can have a direct, harmful impact on medical care. We aimed to assess whether GPT-4 encodes racial and gender biases that impact its use in health care. Using the Azure OpenAI application interface, this model evaluation study tested whether GPT-4 encodes racial and gender biases and examined the impact of such biases on four potential applications of LLMs in the clinical domain-namely, medical education, diagnostic reasoning, clinical plan generation, and subjective patient assessment. We conducted experiments with prompts designed to resemble typical use of GPT-4 within clinical and medical education applications. We used clinical vignettes from NEJM Healer and from published research on implicit bias in health care. GPT-4 estimates of the demographic distribution of medical conditions were compared with true US prevalence estimates. Differential diagnosis and treatment planning were evaluated across demographic groups using standard statistical tests for significance between groups. We found that GPT-4 did not appropriately model the demographic diversity of medical conditions, consistently producing clinical vignettes that stereotype demographic presentations. The differential diagnoses created by GPT-4 for standardised clinical vignettes were more likely to include diagnoses that stereotype certain races, ethnicities, and genders. Assessment and plans created by the model showed significant association between demographic attributes and recommendations for more expensive procedures as well as differences in patient perception. Our findings highlight the urgent need for comprehensive and transparent bias assessments of LLM tools such as GPT-4 for intended use cases before they are integrated into clinical care. We discuss the potential sources of these biases and potential mitigation strategies before clinical implementation. Priscilla Chan and Mark Zuckerberg.

Enhancing Precision and Efficiency in Knee Arthroplasty: A Comparative Analysis of Computer-Assisted Measurements with a Novel Software Tool versus Manual Measurements for Lower Leg Geometry.

(1) Background: The aim of this prospective study was to evaluate measurement software in comparison with manual measurements using inter-observer and intra-observer variability on radiographs in the preoperative planning of total knee arthroplasty. (2) Methods: Two independent observers retrospectively measured the mechanical lateral proximal femoral angle (mLPFA), the mechanical lateral distal femoral angle (mLDFA), the joint line convergence angle (JLCA), the mechanical medial proximal tibial angle (mMPTA), the mechanical lateral distal tibial angle (mLDTA), the hip-knee angle or mechanical tibial-femoral axis angle (HKA), and the anatomical-mechanical angle (AMA) on 55 long-leg anteroposterior radiographs manually twice, followed by measurements using dedicated software. Variability between manual and computer-aided planning was assessed, and all measurements were performed a second time after 14 days in order to assess intra-observer variability. (3) Results: Concerning intra-observer variability, no statistically significant difference was observed regarding the software-based measurements. However, significant differences were noted concerning intra-observer variability when measuring the mLDFA and AMA manually. Testing for statistical significance regarding variability between manual and software-based measurements showed that the values varied strongly between manual and computer-aided measurements. Statistically significant differences were detected for mLPFA, mLDFA, mMPTA, and mLPTA on day 1, and mLPFA, mMPTA, and mLPTA on day 15, respectively. (4) Conclusions: Preoperative planning of leg axis angles and alignment using planning software showed less inter- and intra-observer variability in contrast to manual measurements, and results differed with respect to manual planning. We believe that the planning software is more reliable and faster, and we would recommend its use in clinical settings.

The Awareness of Sudden Infant Death Among Saudi Arabian Women in 2023.

Background Suddeninfantdeathsyndrome(SIDS) refers to the unexpected and unexplained death of a child under one year old. Thepathogenesis of SIDSremainsunclear. However, certain factors such asthe child'ssleeping position,sleeping on asoft mattress, and maternal smoking have been suggested to contribute to its occurrence. The objective of this studywasto evaluate the level of awarenessofSIDS among Saudi Arabian women in 2023. Methodology A cross-sectional study was conducted among 300 mothers in Saudi Arabia, over a period of three months, from June to August 2023, usingan online questionnairetogathersocio-demographic informationfrom mothersof infants younger than one year oldinSaudi Arabia,thesleeping practices oftheirinfants, andtheirknowledge aboutSIDSrisk factors.Datawerecoded using the Statistical Package for Social Sciences (SPSS) (IBM SPSS Statistics, Armonk, NY), and statistical significance tests were employed for data analysis.A p-value of<0.05 was considered significant. Results Among the 277 participating mothers, 44% were 31-40 years old,93%wereSaudi,60% were employed, 65% were nonsmokers, and 64%placedbabiesinasupine positionfor sleep.About37% ofmothers used a duvet for bedding during summer,compared to66%whouseda duvetduring winter, and 81% utilized a soft mattress coverin their children's beds.Additionally, 67% of mothers reportedthat their childrenuseda pacifier whilesleeping. More than half (54%) of mothers were aware of SIDSwith media as their primary source of information.Among those under 21 years old, 50% demonstrated a high level of awareness, compared to36%ofthose aged over 50. Conclusion Mostwomen in this study were found to be unaware of SIDS. Among those who were aware,themedia was the primary source of information. Higher educational attainment was associated with better understanding.

The Effect of some Soil Conditioners on the Readiness and Absorption of Nutrients

In the fall of 2022–2023, students from the Department of Plant Production at the Technical College / Musayyib, which is part of the Musayyib project area, conducted a potting experiment under the canopy of the college to investigate the impact of various soil conditioners on nutrient availability and uptake. The experiment followed a completely randomized block design (R.C.B.D.), with four replicates (1, 2, 3, 4 replicates) using four different soil conditioners (humic acid, agricultural sulfur, stucco and vermicompost waste). With 48 treatments across three replicates, we were able to compare means using the L.S.D. (0.05) test for statistical significance. The results showed that the readiness and absorption of nutritional elements were significantly affected by the overlap between the type of conditioners and the number of batches, that agricultural sulfur treatment was significantly superior compared to the rest of the treatments, and that the fourth batch was superior to the first.

Volume of the normal prostate gland in polish boys, aged 1–17 years: Based on transabdominal ultrasound – Prospective study

Until now, there are no established norms for prostate size in children. Prostate volume during development has been analyzed in small study groups. In diagnostic imaging, transabdominal ultrasound and magnetic resonance imaging are used. To establish prostate volume norms for individuals aged 1-17 years using transabdominal ultrasound. Between 2021 and 2023, transabdominal prostate ultrasound was performed on 482 Caucasian boys, aged 1-17 years, who were patients of the urology clinic. Normative data were based on results of 345 boys with normal lower urinary tract, urethral and penile structures. Patients with abnormal external genitalia, Prune Belly Syndrome, Myelomeningocele, chromosomal disorders, or prostate abnormalities found in ultrasound were excluded from the study. Patient eligibility was determined based on medical records and physical examinations. During ultrasound, height, anterior-posterior dimension, and width of prostate were assessed. Prostate volume was calculated using the ellipsoid formula VH x L(AP) x W x 0.523. Measurements were correlated with age, weight, and height. Results were analyzed using descriptive statistics, statistical significance tests for means, and correlation methods. After estimating preliminary results, taking into account the development periods, patients were divided into age groups: 1-4 years (n=70), 5-10 years (n=124), 11-12 years (n=43), 13-15 years (n=65), and 16-17 years (n=43). The table and nomogram shows prostate volumes based on age. Prostate size remains stable up to the age of 8. We noticed a transitional phase at the age of 8-11 years. A significant increase in volume occurs over 11 years of age. There was a statistically significant relationship between prostate size, age, height, and weight. Established norms can serve as a reference for prostate analysis in patients with defects of the genitourinary system. An interesting analysis would be a prostate size assessment in relation to stage of development on Tanner scale. Transabdominal ultrasound, being a non-invasive, painless, and readily accessible examination, allows assessment of prostate size even in boys aged 1 year old. A statistically significant relationship was found between prostate size, weight, and height. Norms for prostate size in boys were established according to age.

Network Modeling of Apoptosis-Related Genes Reveal a High-Risk Cluster in Multiple Myeloma

The plasma cell cancer multiple myeloma (MM) varies significantly in genomic characteristics, response to therapy, and long-term prognosis. Recent studies have shown the complexity of biological systems associated with poor outcomes in MM. One hallmark of cancer pathogenesis, applicable to MM, is the dysregulation of apoptotic cell death. Understanding the relationship between known drivers of MM and critical pathways in the apoptosis gene network is key towards identifying novel therapeutic targets. Recently, there has been a surge of interest in developing network-based approaches to study gene interactions and use machine learning tools to further elucidate useful biological factors. It is well-known that genes operate as network systems; however, interpretable computational methods are still limited. In this work, we combine a set of network analysis tools to provide novel biological insights into how the apoptosis network is dysregulated in MM. We identify a set of genes associated with poor outcomes in MM using both univariate and network-based approaches. Genomic and clinical data, including RNA-sequencing (RNA-seq) and copy number alteration (CNA), for 659 subjects were obtained from the Multiple Myeloma Research Foundation's CoMMpass (IA19) database. The apoptosis gene set is defined using the apoptosis hallmark pathway provided by the gene set enrichment analysis tool. The interactions between the given list of genes are provided via MetaCore. Univariate gene modeling is done using a Cox proportional hazards model. To determine the relationship between genes, we used Ollivier-Ricci curvature (ORC), a measure of network robustness. The ORC value between two genes incorporates information about both the two genes in question and their local neighborhoods, a concept critical for understanding the role a neighborhood of genes has on a given connection. Once edge information is computed, we used a uniform manifold approximation and projection (UMAP) to reduce the complexity of the dataset. The resulting projection is clustered using k-means clustering, and survival analysis of the given clusters is done using the Kaplan-Meier (KM) method. To understand which features are most informative, we used a random forest to predict the cluster labels using the RNA-seq data. After five-fold cross-validation, feature importance was computed using random forest's permutation importance methodology. All statistical significance testing was corrected for multiple comparisons via the Benjamini-Hochberg false discovery rate method with an alpha set to 0.05. Using RNA-seq data of genes associated with apoptosis, we identified nineteen apoptosis genes associated with poor prognosis in MM. Some of these genes, such as ANXA1 and WEE1 are known to have an effect in MM, while others, such as AVPR1A have yet to be studied extensively for their role in MM. When examining CNA data associated with apoptosis, a similar pattern emerged - nineteen genes were associated with poor prognosis. However, between the two key gene sets found by RNA-seq and CNA, only FAS appeared in both lists. FAS, Fas Cell Surface Death Receptor, is critical for triggering apoptosis. Half of the 37 genes identified using RNA-seq and CNA data are directly connected to the TP53 gene. While TP53 mutations are not a marker of the presence of MM itself, TP53 mutations have been shown to be associated with poorer outcomes. After reducing the complexity of the Ollivier-Ricci curvature analysis of the RNA-seq apoptosis network using UMAPs, the data revealed five clusters with KM curves for progression-free survival were significant (p&amp;lt;0.001). To identify the genes most impactful for separating these clusters, we trained a random forest model on the RNA-seq - note, it was the edge robustness values that were clustered - and evaluated its performance for predicted class membership. The model had an overall accuracy of approximately 78% with F1 scores for each class ranging from 61% to 87%. The top three features associated with the model include CTNNB1, RELA, and LEF1. Of note, CTNNB1 and RELA are directly interacted with the TP53 gene. Because these three genes discriminate between all five clusters, their expression levels may play a mediating effect on MM outcomes. Further investigation with external datasets is needed to validate these results.

Deep Learning Based Blood Abnormalities Detection As a Tool for Vexas Syndrome Screening

Introduction The VEXAS syndrome (vacuoles, E1 enzyme, X-linked, auto-inflammatory, somatic) described in 2020 caused by mutations of the UBA1 gene, displayed a large pleomorphic array of clinical and biological features. Nevertheless, these criteria do not allow to discriminate VEXAS from other myeloid malignancies on complete blood count + differential, notably due to the absence of peripheral blood characterization of the disease. This study aimed at singling out dysplastic features indicative of VEXAS among peripheral blood (PB) polymorphonuclears (PMN) from VEXAS patients compared to healthy patients and myelodysplastic (MDS) patients. As such task is tedious and subject to operator bias, a multicentric dataset has been used to design a deep learning algorithm for automatic detection of these features, finally tested on an external validation cohort. Patient, material and methods Patients written consents were obtained and five academic centers (number from 1 to 5) were involved in this 3 steps study. Firstly, 3 academic centers (number 1 to 3) enrolled 20 patients distributed as follow: 9 patients with UBA1 mut and 6 patients UBA1 wt with no other genetic mutation as well as 5 samples from MDS patients. A total of 25 PB smears and especially PMN images were gathered and screened for various abnormalities. Two independent morphologists blindly quantified PMN predefined characteristics. This dataset of 2,824 multilabelled PMN was evaluated and tested by a two-sample Wilcoxon Rank Sum test for statistical significance between UBA1 mut and the others. Secondly, in order to automate the detection of these significant features on PMN images, a convolutional neural network (CNN) was trained using a multicentric image dataset gathered from 4 academic centers (number 2 to 5). Patients were selected based on clinical and biological symptoms suggesting VEXAS syndrome, namely fever, skin lesions, chondrites, vasculitis and/or anemia. After evaluation of the UBA1 mutation status, this multicentric cohort was separated into confirmed VEXAS (n = 19) and UBA1 WT (n=20). A total of 6,615 annotated PMN images were collected from DI systems (Cellavision ®, Lund, Sweden) with 1 to 12 PB smears per patient, yielding a 48 UBA1 mut and 33 UBA1 wt smears. All patients were males, the median age was 74 year-old (IQR 67-77) and that of control patients 72 year- old (61-79). Finally, the CNN was evaluated on an external cohort from another academic hospital (center 1), including inflammatory patients, and patients screened for VEXAS syndrome for a total cohort of 15 patients and 1,887 images. Results Four specific abnomalities were observed in PMNs from VEXAS patients as compared with healthy or myelodysplastic controls. The automatic recognition of anomalies was then cast in a multilabel classification task, where each PMN image could be assigned to one or more labels during interference. In order to take into account this specificity, a CNN was trained, composed of different layers of convolutions and pooling (Figure1). The model was set-up to output an independent binary prediction for the presence or absence of each of the four anomalies of interest. Automatic detection of these 4 anomalies by the proposed model yielded area under the curve (AUC) of 0.827; 0.837; 0.927 and 0.947 (Figure 2). Regarding the general performance on the external validation cohort, the CNN achieved a Hamming loss of 0.141, and macro and micro F1 scores of 0.588 and 0.668 respectively. Discussion This study suggests that computer-assisted analysis of PB smears, focusing on suspected VEXAS cases, could provide valuable insights to determine which patients should undergo molecular testing. A deep-learning approach leveraging previously identified peripheral blood indicators and automatic analyzers is thus presented, which can help hematologists orient their suspicion before initiating further analysis.

XL1R-Net: Explainable AI-driven improved [formula omitted]-regularized deep neural architecture for NSCLC biomarker identification

Background and Objective:Non-small cell lung cancer (NSCLC) exhibits intrinsic molecular heterogeneity, primarily driven by the mutation of specific biomarkers. Identification of these biomarkers would assist not only in distinguishing NSCLC into its major subtypes — Adenocarcinoma and Squamous Cell Carcinoma, but also in developing targeted therapy. Medical practitioners use one or more types of omic data to identify these biomarkers, copy number variation (CNV) being one such type. CNV provides a measure of genomic instability, which is considered a hallmark of carcinoma. However, the CNV data has not received much attention for biomarker identification. This paper aims to identify biomarkers for NSCLC using CNV data. Methods:An eXplainable AI (XAI)-driven L1-regularized deep learning architecture, XL1R-Net, is proposed that introduces a novel modification of the standard L1-regularized gradient descent algorithm to arrive at an improved deep neural classifier for NSCLC subtyping. Further, XAI-based feature identification has been used to leverage the trained classifier to uncover a set of twenty NCSLC-relevant biomarkers. Results:The identified biomarkers are evaluated based on their classification performance and clinical relevance. Using Multilayer Perceptron (MLP)-based model, a classification accuracy of 84.95% using 10-fold cross-validation is achieved. Moreover, the statistical significance test on the classification performance also revealed the superiority of the MLP model over the competitive machine learning models. Further, the publicly available Drug–Gene Interaction Database reveals twelve of the identified biomarkers as potentially druggable. The K–M Plotter tool was used to verify eighteen of the identified biomarkers with a high probability of predicting NSCLC patients’ likelihood of survival. While nine of the identified biomarkers confirm the recent literature, five find mention in the OncoKB Gene List. Conclusion:A set of seven novel biomarkers that have not been reported in the literature could be investigated for their potential contribution towards NSCLC therapy. Given NSCLC’s genetic diversity, using only one omics data type may not adequately capture the tumor’s complexity. Multiomics data and its integration with other sources will be examined in the future to better understand NSCLC heterogeneity.