Low-risk Category Research Articles

Age-dependent FRAX-based assessment and intervention thresholds for therapeutic decision making in osteoporosis in the Malaysian population.

We aimed to examine the role of age-dependent intervention thresholds (ITs) applied to the Fracture Risk Assessment (FRAX) tool in therapeutic decision making for osteoporosis in the Malaysian population. Data were collated from 1380 treatment-naïve postmenopausal women aged 40-85 years who underwent bone mineral density (BMD) measurements for clinical reasons. Age-dependent ITs, for both major osteoporotic fracture (MOF) and hip fracture (HF), were calculated considering a woman with a BMI of 25 kg/m2, aged between 40 and 85years, with a prior fragility fracture, sans other clinical risk factors. Those with fracture probabilities equal to or above upper assessment thresholds (UATs) were considered to have high fracture risk. Those below the lower assessment thresholds (LATs) were considered to have low fracture risk. The ITs of MOF and HF ranged from 0.7 to 18% and 0.2 to 8%, between 40 and 85years. The LATs of MOF ranged from 0.3 to 11%, while those of HF ranged from 0.1 to 5.2%. The UATs of MOF and HF were 0.8 to 21.6% and 0.2 to 9.6%, respectively. In this study, 24.8% women were in the high-risk category while 30.4% were in the low-risk category. Of the 44.8% (n=618) in the intermediate risk group, after recalculation of fracture risk with BMD input, 38.3% (237/618) were above the ITs while the rest (n=381, 61.7%) were below the ITs. Judged by the Youden Index, 11.5% MOF probability which was associated with a sensitivity of 0.62 and specificity of 0.83 and 4.0% HF probability associated with a sensitivity of 0.63 and a specificity 0.82 were found to be the most appropriate fixed ITs in this analysis. Less than half of the study population (44.8%) required BMD for osteoporosis management when age-specific assessment thresholds were applied. Therefore, in more than half, therapeutic decisions can be made without BMD based on these assessment thresholds.

Association between a low-risk COVID-19 extracorporeal membrane oxygenation criteria and mortality: A retrospective study.

Our study aimed to compare the outcomes of COVID-19 patients who met a low-risk inclusion criteria for veno-venous extra corporeal membrane oxygenation (VV ECMO) with those who did not meet criteria due to higher risk but were subsequently cannulated. This was a retrospective observational cohort study that included adult patients who were placed on VV ECMO for COVID-19 related acute respiratory distress syndrome (ARDS) at a tertiary care academic medical center. The primary outcome was the association between the low-risk criteria and mortality. The patients met the criteria if they met EOLIA severe ARDS criteria, no absolute contraindications (age > 60 years, BMI > 55 kg/m2, mechanical ventilation (MV) duration >7 days, irreversible neurologic damage, chronic lung disease, active malignancy, or advanced multiorgan dysfunction), and had three or less relative contraindications (age > 50 years, BMI > 45 kg/m2, comorbidities, MV duration > 4 days, acute kidney injury, receiving vasopressors, hospital LOS > 14 days, or COVID-19 diagnosis > 4 weeks). Sixty-five patients were included from March 2020 through March 2022. Patients were stratified into low-risk or high-risk categories. The median Sequential Organ Failure Assessment score was 7 and the median PaO2/FiO2 ratio was 44 at the time of ECMO cannulation. The in-hospital mortality was 47.8% in the low-risk group and 69.0% in the high-risk group (p = 0.096). There was not a statistically significant difference in survival between low-risk patients and high-risk patients; however, there was a trend toward higher survival in the lower-risk group.

Clinical Evaluation of an Artificial Intelligence-Based Decision Support System for the Diagnosis and American College of Radiology Thyroid Imaging Reporting and Data System Classification of Thyroid Nodules.

Background: This study aimed to evaluate the clinical impact of an artificial intelligence (AI)-based decision support system (DSS), Koios DS, on the analysis of ultrasound imaging and suspicious characteristics for thyroid nodule risk stratification. Methods: A retrospective ultrasound study was conducted on all thyroid nodules with histological findings from June 2021 to December 2022 in a thyroid nodule clinic. The diagnostic performance of ultrasound imaging was evaluated by six readers on the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) before and after the use of the AI-based DSS and by AI itself. Results: A total of 172 patients (83.1% women) with a mean age of 52.3 ± 15.3 years were evaluated. The mean maximum nodular diameter was 2.9 ± 1.2 cm, with 11.0% being differentiated thyroid carcinomas. Among the nodules initially classified as ACR TI-RADS 3 and 4, AI reclassified 81.4% and 24.5% into lower risk categories, respectively. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the readers and the AI-based DSS versus histological diagnosis. There was an increase in the area under the ROC curve (AUROC) after the use of AI (0.776 vs. 0.817, p < 0.001). The AI-based DSS improved the mean sensitivity (Sens) (82.3% vs. 86.5%) and specificity (Spe) (38.3% vs. 54.8%), produced a high negative predictive value (94.5% vs. 96.4%), and increased the positive predictive value (PPV) (14.0% vs. 16.1%) and diagnostic precision (43.0% vs. 49.3%). Based on the ACR TI-RADS score, there was significant improvement in interobserver agreement after the use of AI (r = 0.741 for ultrasound imaging alone vs. 0.981 for ultrasound imaging and the AI-based DSS, p < 0.001). Conclusions: The use of an AI-based DSS was associated with overall improvement in the diagnostic efficacy of ultrasound imaging, based on the AUROC, as well as an increase in Sens, Spe, negative and PPVs, and diagnostic accuracy. There was also a reduction in interobserver variability and an increase in the degree of concordance with the use of AI. AI reclassified more than half of the nodules with intermediate ACR TI-RADS scores into lower risk categories.

Abstract P283: Hispanic/Latino Ethnic Background and Genetic Ancestry in Relation to Atherosclerotic Cardiovascular Disease Risk Estimation: Insights From the Multi-Ethnic Study of Atherosclerosis (MESA)

Background: Hispanics are a highly admixed population. Pooled Cohort Equation (PCE) excluded them in its derivation. We analyzed Hispanic background and genetic ancestry in relation to atherosclerotic cardiovascular disease (ASCVD) risk. Methods: We included 966 Hispanics from Multi-Ethnic Study of Atherosclerosis (MESA), aged 44-75 years and free of diabetes/prior CVD. Statin users and those with LDL-C&gt;190 mg/dL were excluded. Continental genetic ancestry (West African [AA], European [EA]) was estimated by ADMIXTURE software. Predicted ASCVD events (myocardial infarction, stroke, coronary heart disease death) based on non-Hispanic White (NHW) and non-Hispanic Black (NHB) PCE versions, were compared with observed events based on 10-years Kaplan-Meier Estimates and presented as predicted-to-observed (P/O) ratios across Hispanic backgrounds and &gt;median genetic ancestry. Risk discrimination was assessed by Harrell’s C-statistic based on Cox models. Results: MESA Hispanics included 504 Mexicans [ME], 284 Caribbean [CA] and 178 Other Hispanics [OH]. At 10-years, there were 179 ASCVD events: 107 ME; 49 CA; 23 OH. PCE overestimates ASCVD risk (reflected by P/O ratio &gt;1.0). Using the NHW PCE, all Hispanics had a similar P/O ratio compared to NHW in the low-risk category, and lower P/O ratio compared to NHW in the high-risk category. The extent of overestimation varied across disaggregated Hispanic background (least in ME intermediate risk; most in CA high risk category). Higher P/O ratios were observed among Hispanics of &gt;median AA ancestry and lowest in Hispanics of &gt;median EA ancestry (Figure 1). Harrell’s C-statistic showed no statistically significant difference within groups. Conclusion: Disaggregating Hispanics by background and ancestry led to variability in PCE performance with greater overestimation of ASCVD risk for those of CA background or with &gt;median AA compared to those of ME background or &gt;median EA. The relationship between ethnicity, ancestry, and ASCVD risk in Hispanics should be further explored.

Abstract P278: 1-year Outcomes of A1c Reduction, Weight Loss, and Lowered QRISK3 Scores in Type 2 Diabetes Remission: Insights From an RCCT Leveraging Whole-Body Digital Twin Technology

Background and Aims: The effects of digital twin (DT) technology for remission of T2D on cardiovascular risk reduction is unknown. We evaluated the effectiveness of the DT to improve A1c and body weight and QRISK3 (QResearch Cardiovascular Disease Risk Algorithm 3), which is a prediction algorithm to estimate the 10-year risk for developing cardiovascular disease (CVD), in patients enrolled to achieve remission of T2DM. DT platform uses AI and Internet of Things, to integrate multi-dimensional data to give precision nutrition and health recommendations via the mobile app and by coaches. Materials and Methods: We evaluated the data from prospective cohort of 204 participants who had been on DT for 1 year. Remission was defined as A1C levels less than 6.5% without medication for over 3 months. Outcomes included the change in HbA1c, body weight and change in QRISK3 scores. The DT uses a machine learning algorithm to integrate clinical and sensor data to predict personal glucose response. Patients were connected to continuous glucose monitoring (CGM) throughout the study and self-recorded dietary intake using the mobile app. Results: The study participants had a mean diabetes duration of 3.7±2.6 years and a mean age of 44±8.2 years. Based on the ADA criteria, at 1 year, 73% (n=149/204) continued to be under remission. The mean QRISK3 at baseline was 12.6 (±10.7, 95% CI 11.2 to 14) which significantly reduced to 9.5 (±7.9, 95% CI 8.5 to 10.6) at 360 days, p=0.0009. A1c (%) at baseline was 8.9 (±1.8, 95% CI 8.6 to 9.1, minimum 5.5, maximum 16.2) which reduced to 6 (±0.6, 95% CI 5.9 to 6, minimum 4.7, maximum 9.2), p&lt;0.0001. Body weight (kg) at baseline was 78.3 (±14.2, 95% CI 76.4 to 80.2) which reduced to 70.7 (±12.8, 95% CI 69 to 72.4), &lt;0.0001. There was a significant correlation between the reduction in body weight and QRISK3 (Pearson r 0.24 95% CI 0.1 to 0.3, p=0.0004). However, the correlation between the reduction in A1c and QRISK3 (Pearson r 0.04, 95% CI -0.09 to 0.18, p=0.53 ns) was comparable. At baseline, there were 53% of participants who had low risk score (&lt;10), 28% as intermediate risk (10-20), 19% as high risk (&gt;20%) that at 1 year shifted to 67% as low risk, 23% as intermediate risk and 10% as high-risk scores. Conclusion: Incorporating the Digital Twin (DT) technology, which blends AI and IoT for tailored health guidance, our RCCT indicated significant 1-year advancements in A1c, weight, and QRISK3 scores among T2DM patients aiming for remission. The transformative potential of DT is evident, particularly in shifting a majority of participants towards lower cardiovascular risk categories.

Communicating the results of risk-based breast cancer screening through visualizations of risk: a participatory design approach

BackgroundRisk-based breast cancer (BC) screening raises new questions regarding information provision and risk communication. This study aimed to: 1) investigate women’s beliefs and knowledge (i.e., mental models) regarding BC risk and (risk-based) BC screening in view of implications for information development; 2) develop novel informational materials to communicate the screening result in risk-based BC screening, including risk visualizations of both quantitative and qualitative information, from a Human-Centered Design perspective.MethodsPhase 1: Interviews were conducted (n = 15, 40–50 years, 5 lower health literate) on women’s beliefs about BC risk and (risk-based) BC screening. Phase 2: In three participatory design sessions, women (n = 4–6 across sessions, 40–50 years, 2–3 lower health literate) made assignments and created and evaluated visualizations of risk information central to the screening result. Prototypes were evaluated in two additional sessions (n = 2, 54–62 years, 0–1 lower health literate). Phase 3: Experts (n = 5) and women (n = 9, 40–74 years) evaluated the resulting materials. Two other experts were consulted throughout the development process to ensure that the content of the information materials was accurate. Interviews were transcribed literally and analysed using qualitative thematic analysis, focusing on implications for information development. Notes, assignments and materials from the participatory design sessions were summarized and main themes were identified.ResultsWomen in both interviews and design sessions were positive about risk-based BC screening, especially because personal risk factors would be taken into account. However, they emphasized that the rationale of risk-based screening and classification into a risk category should be clearly stated and visualized, especially for higher- and lower-risk categories (which may cause anxiety or feelings of unfairness due to a lower screening frequency). Women wanted to know their personal risk, preferably visualized in an icon array, and wanted advice on risk reduction and breast self-examination. However, most risk factors were considered modifiable by women, and the risk factor breast density was not known, implying that information should emphasize that BC risk depends on multiple factors, including breast density.ConclusionsThe information materials, including risk visualizations of both quantitative and qualitative information, developed from a Human-Centered Design perspective and a mental model approach, were positively evaluated by the target group.

The cell death-related genes machine learning model for precise therapy and clinical drug selection in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the prevailing subtype of hepatocellular malignancy. While previous investigations have evidenced a robust link with programmed cell death (PCD) and tumorigenesis, a comprehensive inquiry targeting the relationship between multiple PCDs and HCC remains scant. Our aim was to develop a predictive model for different PCD patterns in order to investigate their impact on survival rates, prognosis and drug response rates in HCC patients. We performed functional annotation and pathway analysis on identified PCD-related genes (PCDRGs) using multiple bioinformatics tools. The prognostic value of these PCDRGs was verified through a dataset obtained from GEO. Consensus clustering analysis was utilized to elucidate the correlation between diverse PCD clusters and pertinent clinical characteristics. To comprehensively uncover the distinct PCD regulatory patterns, our analysis integrated gene expression profiling, immune cell infiltration and enrichment analysis. To predict survival differences in HCC patients, we established a PCD model. To enhance the clinical applicability for the model, we developed a highly accurate nomogram. To address the treatment of HCC, we identified several promising chemotherapeutic agents and novel targeted drugs. These drugs may be effective in treating HCC and could improve patient outcomes. To develop a cell death feature for HCC patients, we conducted an analysis of 12 different PCD mechanisms using eligible data obtained from public databases. Through this analysis, we were able to identify 1254 PCDRGs likely to contribute to cell death on HCC. Further analysis of 1254 PCDRGs identified 37 genes with prognostic value in HCC patients. These genes were then categorized into two PCD clusters A and B. The categorization was based on the expression patterns of the genes in the different clusters. Patients in PCD cluster B had better survival probabilities. This suggests that PCD mechanisms, as represented by the genes in cluster B, may have a protective effect against HCC progression. Furthermore, the expression of PCDRGs was significantly higher in PCD cluster A, indicating that this cluster may be more closely associated with PCD mechanisms. Furthermore, our observations indicate that patients exhibiting elevated tumour mutation burden (TMB) are at an augmented risk of mortality, in comparison to those displaying low TMB and low-risk statuses, who are more likely to experience prolonged survival. In addition, we have investigated the potential distinctions in the susceptibility of diverse risk cohorts towards emerging targeted therapies, designed for the treatment of HCC. Moreover, our investigation has shown that AZD2014, SB505124, LJI308 and OSI-207 show a greater efficacy in patients in the low-risk category. Conversely, for the high-risk group patients, PD173074, ZM447439 and CZC24832 exhibit a stronger response. Our findings suggest that the identification of risk groups and personalized treatment selection could lead to better clinical outcomes for patients with HCC. Furthermore, significant heterogeneity in clinical response to ICI therapy was observed among HCC patients with varying PCD expression patterns. This novel discovery underscores the prospective usefulness of these expression patterns as prognostic indicators for HCC patients and may aid in tailoring targeted treatment for those of distinct risk strata. Our investigation introduces a novel prognostic model for HCC that integrates diverse PCD expression patterns. This innovative model provides a novel approach for forecasting prognosis and assessing drug sensitivity in HCC patients, driving a more personalized and efficacious treatment paradigm, elevating clinical outcomes. Nonetheless, additional research endeavours are required to confirm the model's precision and assess its potential to inform clinical decision-making for HCC patients.

Deep learning model for personalized prediction of positive MRSA culture using time-series electronic health records

Methicillin-resistant Staphylococcus aureus (MRSA) poses significant morbidity and mortality in hospitals. Rapid, accurate risk stratification of MRSA is crucial for optimizing antibiotic therapy. Our study introduced a deep learning model, PyTorch_EHR, which leverages electronic health record (EHR) time-series data, including wide-variety patient specific data, to predict MRSA culture positivity within two weeks. 8,164 MRSA and 22,393 non-MRSA patient events from Memorial Hermann Hospital System, Houston, Texas are used for model development. PyTorch_EHR outperforms logistic regression (LR) and light gradient boost machine (LGBM) models in accuracy (AUROCPyTorch_EHR = 0.911, AUROCLR = 0.857, AUROCLGBM = 0.892). External validation with 393,713 patient events from the Medical Information Mart for Intensive Care (MIMIC)-IV dataset in Boston confirms its superior accuracy (AUROCPyTorch_EHR = 0.859, AUROCLR = 0.816, AUROCLGBM = 0.838). Our model effectively stratifies patients into high-, medium-, and low-risk categories, potentially optimizing antimicrobial therapy and reducing unnecessary MRSA-specific antimicrobials. This highlights the advantage of deep learning models in predicting MRSA positive cultures, surpassing traditional machine learning models and supporting clinicians’ judgments.

Guidelines for Winter Recreation near Wolverine Dens in Montane Western North America

Wolverine den in snowy areas with boulders or woody debris at or below tree line in montane western North America. They have naturally low reproductive rates, a fidelity to denning areas, and a sensitivity to human presence during denning. The goal was to synthesize existing ecological information for denning wolverine and identify risks from human presence in the categories of timing, distance, footprint, pattern of use, and frequency of use. The authors suggest commercial tenure holders and private users keep recreation in the low-risk category to minimize disturbance on denning females. Denning area surveys should be conducted prior to tenure application or renewals and dens can be identified by a concentration of tracks over more than three weeks from January 15 to May 15. Recreation should be restricted within a 5-km radius of confirmed dens during this window. Best practices include limiting the number of groups and concentrating movement on existing linear features as wolverine are sensitive to disturbance at a very low intensity of use and are at greatest risk when disturbances are dispersed and unpredictable.

Clinical and prognostic analysis of opsoclonus-myoclonus-ataxia syndrome in children

Objective: To summarize the clinical and prognostic features of children with opsoclonus-myoclonus-ataxia syndrome (OMAS). Methods: A total of 46 patients who met the diagnostic criteria of OMAS in the Department of Neurology, Beijing Children's Hospital from June 2015 to June 2023 were retrospectively analyzed. Centralized online consultations or telephone visits were conducted between June and August 2023. The data of the children during hospitalization and follow-up were collected, including clinical manifestations, assistant examination, treatment and prognosis. According to the presence or absence of tumor, the patients were divided into two groups. The chi-square test or Mann-Whitney U test was used to compare the differences between the two groups. Univariate Logistic regression was used to analyze the factors related to OMAS recurrence and prognosis. Results: There were 46 patients, with 25 males and the onset age of 1.5 (1.2, 2.4) years. Twenty-six (57%) patients were diagnosed with neuroblastoma during the course of the disease, and no patients were categorized into the high-risk group. A total of 36 patients (78%) were followed up for≥6 months, and all of them were treated with first-line therapy with glucocorticoids, gammaglobulin and (or) adrenocorticotrophic hormone. Among the 36 patients, 9 patients (25%) were treated with second-line therapy for ≥3 months, including rituximab or cyclophosphamide, and 17 patients (47%) received chemotherapy related to neuroblastoma. At the follow-up time of 4.2 (2.2, 5.5) years, 10 patients (28%) had relapsed of OMAS. The Mitchell and Pike OMS rating scale score at the final follow-up was 0.5 (0, 2.0). Seven patients (19%) were mildly cognitively behind their peers and 6 patients (17%) were severely behind. Only 1 patient had tumor recurrence during follow-up. The history of vaccination or infection before onset was more common in the non-tumor group than in the tumor group (55%(11/20) vs. 23%(6/26), χ²=4.95, P=0.026). Myoclonus occurred more frequently in the non-tumor group (40%(8/20) vs. 4%(1/26), χ²=7.23, P=0.007) as the onset symptom. Univariate Logistic regression analysis showed that the tumor group had less recurrence (OR=0.19 (0.04-0.93), P=0.041). The use of second-line therapy or chemotherapy within 6 months of the disease course had a better prognosis (OR=11.64 (1.27-106.72), P=0.030). Conclusions: OMAS in children mostly starts in early childhood, and about half are combined with neuroblastoma. Neuroblastoma in combination with OMAS usually has a low risk classification and good prognosis. When comparing patients with OMAS with and without tumors, the latter have a more common infection or vaccination triggers, and myoclonus, as the onset symptom, is more common. Early addition of second-line therapy is associated with better prognosis in OMAS.

Exploring prognostic microbiota markers in patients with endometrial carcinoma: Intratumoral insights

Endometrial cancer, a leading gynecological malignancy, is profoundly influenced by the uterine microbiota, a key factor in disease prognosis and treatment. Our study underscores the distinct microbial compositions in endometrial cancer compared to adjacent non-cancerous tissues, revealing a dominant presence of p_Actinobacteria in cancerous tissues as opposed to p_Firmicutes in surrounding areas. Through comprehensive analysis, we identified 485 unique microorganisms in cancer tissues, 26 of which correlate with patient prognosis. Employing univariate Cox regression and LASSO regression analyses, we devised a microbial risk scoring model, effectively stratifying patients into high and low-risk categories, thereby providing predictive insights into their overall survival. We further developed a nomogram that incorporates the microbial risk score along with age, grade, and clinical stage, significantly enhancing the accuracy of our clinical prediction model for endometrial cancer. Moreover, our study delves into the differential immune landscapes of high-risk and low-risk patients. The low-risk group displayed a higher prevalence of activated B cells and increased T cell co-stimulation, indicative of a robust immune response. Conversely, high-risk patients showed elevated tumor immune dysfunction and exclusion scores, suggesting less favorable outcomes in immunotherapy. Notably, the efficacy of IPS-CTLA4 and PD1/PD-L1/PD-L2 blockers was substantially higher in the low-risk group, pointing to a more responsive immunotherapeutic approach. In summary, our research elucidates the unique microbial patterns in endometrial cancer and adjacent tissues, and establishes both a microbial risk score model and a clinical prediction nomogram. These findings highlight the potential of uterine microbiota as a biomarker for customizing treatment strategies, enabling precise interventions for high-risk patients while preventing overtreatment in low-risk cases. This study emphasizes the microbiota's role in tailoring immunotherapy, offering a novel perspective in the treatment and prognosis of endometrial cancer. Significantly, our study's expansive sample analysis from the TCGA-UCEC cohort, employing linear discriminant analysis effect size methodology, not only validates but also enhances our understanding of the microbiota's role in endometrial cancer, paving the way for novel diagnostic and therapeutic approaches in its management.

The Relationship between Family Support and Suicide Ideation in College Students

Introduction: Suicide is the second leading cause of death among teenagers and early adults. Suicidal behavior is related to the family support provided. The aim of this research is to determine the relationship between family support and suicidal ideation among students at a university in Pekanbaru, Riau. Method: This research uses a descriptive correlation design using a cross-sectional approach. The sample consisted of 100 respondents taken using the accidental sampling technique. The measuring instruments used were the Perceived Social Support-Family Scale (PSS-Fa) questionnaire for family support and the Beck Scale for Suicide Ideation (BSS) for suicidal ideation. Data analysis used the chi-square test. Results: It is known that the majority of respondents have family support in the good and sufficient categories with the same number, namely 39 respondents (39.0%). For the variable suicidal ideation, the majority of respondents were in the low risk category totaling 52 respondents (52.0%), while the remainder were in the high risk category totaling 28 respondents (28.0%) and very high risk totaling 20 respondents (20 .0%). The results of statistical tests show that there is a relationship between family support and suicidal ideation in students (p value 0.021 α 0.05). Conclusion: Family support has a significant relationship with suicidal ideation.

Comprehensive environmental impact assessment and irrigation wastewater suitability of the Arab El-Madabegh wastewater treatment plant, ASSIUT CITY, EGYPT.

The presence of a wastewater treatment plant in the Arab El-Madabegh region, which discharges excessive amounts of raw effluent toward the nearby farming fields, is the area's main issue. Examining the harmful implications of raw effluent releases on groundwater quality, determining if treated wastewater effluent complies with regulations for discharge into the aquatic environment, and assessing irrigation appropriateness by the effluent are the main goals of this work. In order to accomplish these targets, twelve treated effluent samples from the Arab El-Madabegh wastewater treatment plant were gathered every two weeks starting in January 2012 and finishing in June 2012. They were tested to determine pH, Total Dissolved Solids (TDS), Total Suspended Solids (TSS), Temperature (Temp), Conductivity (EC), Turbidity (Turb.), Chemical Oxygen Demand (COD), Biological Oxygen Demand (BOD), Total Organic Carbon (TOC), NO3-, SO42-, Cl-, Ca2+, PO43-, HCO3-, Na+, Mg2+, and heavy metals such as (Fe, Mn, K+, Cr, Pb, Zn, Ni, Cu, and Cd). The outcomes revealed that all Egyptian and Food and Agricultural Organization (FAO) standards for unrestricted irrigation were met by the treated effluents, except for COD, which exceeded than the Egyptian allowed limit. The evaluation indices of the effluent's EC, SAR, PI, MR, and MH were in the low-risk category according to indicators of water quality for irrigation, nevertheless, The SSP and RSC both showed slightly higher values (67.9% and 2.76, respectively). As well, The average values of heavy metals in treated wastewater effluent were found to be below permitted limits, with the exception of lead and phosphate, which exceeded permissible limits in Egypt. The environmental sustainability (ecological friendliness) of reusing and recycling tertiary treated wastewater can be achieved in agriculture to reduce the adverse impacts on the aquatic environment.

Head-to-head comparison of American, European, and Asian TIRADSs in thyroid nodule assessment: systematic review and meta-analysis.

Ultrasound-based risk stratification systems (Thyroid Imaging Reporting and Data Systems (TIRADSs)) of thyroid nodules (TNs) have been implemented in clinical practice worldwide based on their high performance. However, it remains unexplored whether different TIRADSs perform uniformly across a range of TNs in routine practice. This issue is highly relevant today, given the ongoing international effort to establish a unified TIRADS (i.e. I-TIRADS), supported by the leading societies specializing in TNs. The study aimed to conduct a direct comparison among ACR-, EU-, and K-TIRADS in the distribution of TNs: (1) across the TIRADS categories, and (2) based on their estimated cancer risk. A search was conducted on PubMed and Embase until June 2023. Original studies that sequentially assessed TNs using TIRADSs, regardless of FNAC indication, were selected. General study characteristics and data on the distribution of TNs across TIRADSs were extracted. Seven studies, reporting a total of 41,332 TNs, were included in the analysis. The prevalence of ACR-TIRADS 1-2 was significantly higher than that of EU-TIRADS 2 and K-TIRADS 2, with no significant difference observed among intermediate- and high-risk categories of TIRADSs. According to malignancy risk estimation, K-TIRADS often classified TNs as having more severe risk, ACR-TIRADS as having moderate risk, and EU-TIRADS classified TNs as having lower risk. ACR-, EU-, and K-TIRADS assess TNs similarly across their categories, with slight differences in low-risk classifications. Despite this, focusing on cancer risk estimation, the three TIRADSs assess TNs differently. These findings should be considered as a prerequisite for developing the I-TIRADS.

Calcium Scoring on CT Coronary Angiography in Hypertensive Patients as a Criterion for the Prediction of Coronary Artery Disease

Introduction: Hypertension is the strongest independent predictor of Coronary Artery Disease (CAD) identified by Computed tomography of coronary arteries (CTCA). In this study, CTCA-assessed Coronary Calcium Scoring (CCS) was studied in hypertensive subjects referred for CTCA. Methods: After excluding TAVI and graft assessment patients, the individual electronic health records of 410 consecutive patients who underwent CTCA between July and November 2020, were reviewed with a mean age of 58.7 years. Risk factors were recorded including smoking (38%), hyperlipidaemia (33%), positive family history (22%), systemic hypertension (48%), diabetes mellitus (30%), and male gender (46%). Referral criteria, ethnicity, cardiac, and past medical history were recorded. Patients were stratified into four groups according to CAD severity: absent, mild, moderate, and severe disease, as seen on CTCA. The mean CCS for each CAD category was compared between hypertensive and non-hypertensive patients. Mean CCS were further compared according to the number of coronary arteries affected and the severity of CAD in each artery. Results: Out of all CTCA reports, 200 (48.8%) CCS were interpreted in the very low-risk category, 80 (19.5%) low risk, 58 (14.1%) moderate risk, 23 (5.6%) moderately high risk and 49 (12.0%) high risk. A significant difference in mean CCS and CAD severity was observed between mild, moderate, and severe CAD (p = 0.015 and p &lt; 0.001). Comparison of CCS between hypertensives and non-hypertensives, across the four CAD severity categories, revealed a significant difference in mean CCS in the severe CAD category (p = 0.03). There was no significant difference in the CCS between hypertensives with chest pain and hypertensives without chest pain. A higher number of affected coronary arteries was associated with a higher mean CCS and a significant difference in CCS was observed between hypertensive and non-hypertensive subjects for the number of arteries affected. Similar results were observed when comparing mean CCS in moderate-severely affected coronary arteries. Conclusion: Hypertensive patients with a high CCS were associated with a higher incidence of severe CAD independent of the presence of chest pain. These results suggest that the incorporation of CCS in the investigation of CAD on CT angiography may pose a powerful adjunct in proposing an alternative paradigm for the assessment of patients with hypertension, in the progress of coronary artery disease.

Constructing and Evaluating a Mitophagy-Related Gene Prognostic Model: Implications for Immune Landscape and Tumor Biology in Lung Adenocarcinoma.

Mitophagy, a conserved cellular mechanism, is crucial for cellular homeostasis through the selective clearance of impaired mitochondria. Its emerging role in cancer development has sparked interest, particularly in lung adenocarcinoma (LUAD). Our study aimed to construct a risk model based on mitophagy-related genes (MRGs) to predict survival outcomes, immune response, and chemotherapy sensitivity in LUAD patients. We mined the GeneCards database to identify MRGs and applied LASSO/Cox regression to formulate a prognostic model. Validation was performed using two independent Gene Expression Omnibus (GEO) cohorts. Patients were divided into high- and low-risk categories according to the median risk score. The high-risk group demonstrated significantly reduced survival. Multivariate Cox analysis confirmed the risk score as an independent predictor of prognosis, and a corresponding nomogram was developed to facilitate clinical assessments. Intriguingly, the risk score correlated with immune infiltration levels, oncogenic expression profiles, and sensitivity to anticancer agents. Enrichment analyses linked the risk score with key oncological pathways and biological processes. Within the model, MTERF3 emerged as a critical regulator of lung cancer progression. Functional studies indicated that the MTERF3 knockdown suppressed the lung cancer cell proliferation and migration, enhanced mitophagy, and increased the mitochondrial superoxide production. Our novel prognostic model, grounded in MRGs, promises to refine therapeutic strategies and prognostication in lung cancer management.

Prediction of a Multi-Gene Assay (Oncotype DX and Mammaprint) Recurrence Risk Group Using Machine Learning in Estrogen Receptor-Positive, HER2-Negative Breast Cancer-The BRAIN Study.

This study aimed to develop a machine learning-based prediction model for predicting multi-gene assay (MGA) risk categories. Patients with estrogen receptor-positive (ER+)/HER2- breast cancer who had undergone Oncotype DX (ODX) or MammaPrint (MMP) were used to develop the prediction model. The development cohort consisted of a total of 2565 patients including 2039 patients tested with ODX and 526 patients tested with MMP. The MMP risk prediction model utilized a single XGBoost model, and the ODX risk prediction model utilized combined LightGBM, CatBoost, and XGBoost models through soft voting. Additionally, the ensemble (MMP + ODX) model combining MMP and ODX utilized CatBoost and XGBoost through soft voting. Ten random samples, corresponding to 10% of the modeling dataset, were extracted, and cross-validation was performed to evaluate the accuracy on each validation set. The accuracy of our predictive models was 84.8% for MMP, 87.9% for ODX, and 86.8% for the ensemble model. In the ensemble cohort, the sensitivity, specificity, and precision for predicting the low-risk category were 0.91, 0.66, and 0.92, respectively. The prediction accuracy exceeded 90% in several subgroups, with the highest prediction accuracy of 95.7% in the subgroup that met Ki-67 <20 and HG 1~2 and premenopausal status. Our machine learning-based predictive model has the potential to complement existing MGAs in ER+/HER2- breast cancer.

Estimating Diabetes in Pregnant Women by FINDRISC

Background: Gestational diabetes is an increased sensitivity to carbohydrates that affects pregnant women. Pregnancy-related gestational diabetes typically manifests between 24 and 36 weeks of gestation and goes away at delivery. The potential that pregnant women with gestational diabetes may continue to have the disease even after giving delivery is not disproved, nevertheless..Objective: The purpose of this study is to use FINDRISC to describe the risk level of diabetes during pregnancy.Methods: 50 pregnant women who participated in this cross-sectional study served as responders. By responding to a survey, found at https://ee.kobotoolbox.org/x/7NcWHrV1, the research was completed. and then an evaluation of diabetes, called FINDRISC. FINDRISC is frequently used for early diabetes screening to estimate a person's risk of developing diabetes. The data was processed using SPSS 25.0 after the survey's respondents completed it.Results: Out of 50 respondents, it was determined that 29 people fall into the very low risk category, 15 people fall into the low risk category, and 6 people go into the suspected risk category because they had a FINDRISC score of nine or above. Out of the six individuals with a probable risk of developing diabetes, three (50%) had high blood sugar levels, four (67%) had excess BMT and obesity, abdomen circumference &gt;80 cm (100%), physical activity 4 days per week (67%) and had a family history (67%).Conclusion: Out of 50 respondents, 29 were classified as having a very low risk of developing diabetes, 15 as having a low risk, and 6 as having a suspected high risk of developing diabetes, according to the study's findings.

Utilization of the corticomedullary difference in magnetic resonance imaging-derived apparent diffusion coefficient for noninvasive assessment of chronic kidney disease in type 2 diabetes

BackgroundsAccumulating data demonstrated that the cortico-medullary difference in apparent diffusion coefficient (ΔADC) of diffusion-weighted magnetic resonance imaging (DWI) was a better correlation with kidney fibrosis, tubular atrophy progression, and a predictor of kidney function evolution in chronic kidney disease (CKD). ObjectivesWe aimed to assess the value of ΔADC in evaluating disease severity, differential diagnosis, and the prognostic risk stratification for patients with type 2 diabetes (T2D) and CKD. MethodsTotal 119 patients with T2D and CKD who underwent renal MRI were prospectively enrolled. Of them, 89 patients had performed kidney biopsy for pathological examination, including 38 patients with biopsy-proven diabetic kidney disease (DKD) and 51 patients with biopsy-proven non-diabetic kidney disease (NDKD) and Mix (DKD + NDKD). Clinicopathological characteristics were compared according to different ΔADC levels. Moreover, univariate and multivariate-linear regression analyses were performed to explore whether ΔADC was independently associated with estimated glomerular filtration rate (eGFR) and urinary albumin creatinine ratio (UACR). The diagnostic performance of ΔADC for discriminating DKD from NDKD + Mix was evaluated by receiver operating characteristic (ROC) analysis. In addition, an individual's 2- or 5-year risk probability of progressing to end-stage kidney disease (ESKD) was calculated by the kidney failure risk equation (KFRE). The effect of ΔADC on prognostic risk stratification was assessed. Additionally, net reclassification improvement (NRI) was used to evaluate the model performance. ResultsAll enrolled patients had a median ΔADC level of 86 (IQR 28, 155) × 10−6 mm2/s. ΔADC significantly decreased across the increasing staging of CKD (P < 0.001). Moreover, those with pathological-confirmed DKD has a significantly lower level of ΔADC than those with NDKD and Mix (P < 0.001). It showed that ΔADC was independently associated with eGFR (β = 1.058, 95% CI = [1.002,1.118], P = 0.042) and UACR (β = −3.862, 95% CI = [-7.360, -0.365], P = 0.031) at multivariate linear regression analyses. Besides, ΔADC achieved an AUC of 0.707 (71% sensitivity and 75% specificity) and AUC of 0.823 (94% sensitivity and 67% specificity) for discriminating DKD from NDKD + Mix and higher ESKD risk categories (≥50% at 5 years; ≥10% at 2 years) from lower risk categories (<50% at 5 years; <10% at 2 years). Accordingly, the optimal cutoff value of ΔADC for higher ESKD risk categories was 66 × 10−6 mm2/s, and the group with the low-cutoff level of ΔADC group was associated with 1.232 -fold (95% CI 1.086, 1.398) likelihood of higher ESKD risk categories as compared to the high-cutoff level of ΔADC group in the fully-adjusted model. Reclassification analyses confirmed that the final adjusted model improved NRI. ConclusionsΔADC was strongly associated with eGFR and UACR in patients with T2D and CKD. More importantly, baseline ΔADC was predictive of higher ESKD risk, independently of significant clinical confounding. Specifically, ΔADC <78 × 10−6 mm2/s and <66 × 10−6 mm2/s would help to identify T2D patients with the diagnosis of DKD and higher ESKD risk categories, respectively.

Treatment efficacy implications of incorrect utilization of tumor marker levels and risk group assignment according to the International Germ Cell Consensus Classification (IGCCCG).

505 Background: Patients withmetastatic germ cell tumors of the testis (GCTs) are risk-stratified according to the International Germ Cell Cancer Collaborative Group (IGCCCG) classification system for further therapy decision. This risk classification is based on metastatic load as well as tumor marker levels of AFP, HCG and LDH assessed pre-chemotherapy after orchiectomy treatment. An incorrect classification is possible when pre-orchiectomy marker levels are used, possibly resulting in over- or undertreatment of patients. Therefore, the aim was to investigate the potential frequency and clinical relevance of incorrect risk stratification using pre-orchiectomy tumor marker levels. Methods: Patients with metastasized nonseminomatous GCT (NSGCT)were included in a multicenter registry analysis which was conducted by investigators of the German Testicular Cancer Study Group (GTCSG). Based on the marker levels at different timepoints, IGCCCG risk groups were calculated. The agreement was tested using Cohen’s kappa. Results: A total of698 of 1983 (35%) patients were diagnosed with metastatic NSGCTs, and 546 (78%) had sufficient data for 232 follow-up data points. By using pre-orchiectomy tumor marker levels, 111 patients (20%) would have been incorrectly classified. Seventy-five patients (14%) were classified into a higher risk category, and 36 patients (6%) were classified into a lower risk category. Cohen’s kappa was 0.69 (p &lt; 0.001), showing a strong agreement between the use of both marker timepoints. The treatment of misclassified patients would have resulted in an overtreatment of 75 patients or undertreatment of 36 patients. Conclusions: The use of pre-orchiectomy tumor marker levels may lead to an incorrect risk classification and might subsequently lead to under- or overtreatment of patients. Part of these data were published in: Majewski et al. Cancer Med. 2023.